esa-sceva/satcom-chunk-collection huggingface dataset:
# An Updated Review of Smac Mimetics, LCL161, Birinapant, and GDC-0152 in Cancer Treatment
## 3 Smac Mimetics for Cancer Treatment  
The first Smac mimetic compound with eight amino acids was studied in 2000 [46]. Currently, eight Smac mimetics have been developed, and their anti-cancer potency has been evaluated in different preclinical and clinical studies. Smac mimetics are classified into two groups based on the number of Smac-mimicking moieties; for example, monovalent compounds contain one Smac-mimicking moiety (i.e., LCL161, AT-406 (Debio1143), GDC-0152, and GDC-0917 (CUDC-427)), and bivalent compounds are Smac-mimicking elements connected via a linker (i.e., birinapant (TL32711), BV6, and SM-164) (Figure 2) [23; 47; 48; 49; 50; 51; 52]. The anti-cancer potency of six of them has been elucidated in clinical trials. Bivalent compounds exhibit higher anti-cancer potency than monovalent compounds because the former possess better binding affinity with IAPs and higher potency to induce caspase-dependent apoptosis than the latter [53]. The anti-cancer potency of Smac mimetics is also dependent on their specificity to IAPs. For example, birinapant and AT406 preferentially target cIAP1 and cIAP2 rather than XIAP. LCL161 and GDC-0152 are pan-IAP inhibitors that have similar affinities to XIAP, cIAP1, and cIAP2 [23; 48; 49; 51]. Among these Smac mimetic compounds, LCL161, birinapant, and GDC-0152 are currently the most popular, in which their therapeutic effectiveness and the molecular mechanism of actions have been studied extensively in pre-clinical and clinical studies. Therefore, we summarize current LCL161, birinapant, and GDC-0152-related findings in the following sections.
# Interpretability Study of Gradient Information in Individual Travel Prediction
## 4 Experiment
### Benchmarking  
The construction of a mask matrix based on gradient information helps us explore the role of gradient information in reducing the information content of travel records. To quantify the impact of this information reduction on the model's predictive behavior, the following two evaluation metrics are proposed in this section:  
1. Jensen-Shannon Divergence (JS): The Jensen-Shannon divergence is a metric for assessing the difference between two probability distributions. The formula for  
Figure 5: Construction method of mask matrix.  
calculating the Jensen-Shannon divergence of the model's output distribution is as follows:  
\[JS(P\ |\ |\ Q)=\frac{1}{2}KL(P\ |\ |\ \ \frac{P+Q}{2})+\frac{1}{2}KL(Q\ |\ |\ \ \frac{P+Q}{2}) \tag{22}\]  
Here, \(P\) and \(Q\) represent the probability distributions predicted by the original model and the masked model, respectively. The KL (Kullback-Leibler divergence) is used to measure the degree of difference between two probability distributions, with the formula given below:  
\[KL(P||Q)=\sum_{x\in X}P(x)\ log\frac{P(x)}{Q(x)}\]  
Compared to the KL divergence, the JS divergence is symmetric and always returns a non-negative value. The JS divergence is zero if and only if \(P\) and \(Q\) are identical.
2. Predictive Accuracy Difference Ratio (PA): PA is a metric that measures the decline in prediction accuracy when the model faces masked data. The formula for calculating PA is as follows:  
\[PA=\frac{T_{original}}{T_{\text{total}}}-\frac{T_{masked}}{T_{\text{total}}} \tag{23}\]  
Here, \(T_{original}\) represents the number of correct predictions by the model on the original data, \(T_{masked}\) represents the number of correct predictions by the model on the masked data, and \(T_{\text{total}}\) represents the total number of samples. If the value of PA is larger, it indicates that the feature has a greater contribution to the model's prediction, or that the feature accounts for a larger amount of information in the model's prediction. It should be noted that we only apply masking to samples predicted as positive examples. Randomly selecting samples might include some that the model already predicts incorrectly, and these samples already represent the model's prediction errors.
## 3 Results and Discussion  
In the present work, different experiments were carried out to determine how different operating conditions affect the recovery of carbon fibers. The tests were carried out under both subcritical (temperature of 300 \({}^{\circ}\)C and pressures around 105-130 bar) and supercritical conditions (temperature of 400 \({}^{\circ}\)C and pressures around 240 bar).  
A temperature of 400 \({}^{\circ}\)C was chosen for the supercritical conditions to ensure a temperature above the critical point of water (374 \({}^{\circ}\)C), but away from instabilities around the critical point, and to avoid too high temperatures. Similarly, 300 \({}^{\circ}\)C was selected, as it is sufficiently lower than the critical point of water, but sufficiently high to achieve a reasonable DR but without reaching 100%, so that it would also allow us to study the effect or improvement that the use of catalysts can have.  
Other variables that were considered were the use of stirring, the specific surface (S.S.) of the composite samples (ratio between the surface of the samples and their masses), the reaction time (rt), the solvent/mass ratio of the composite, and the specific catalyst used in each case. The efficiency of the hydrothermal treatments applied was primarily determined as a function of the resulting resin degradation rate, DR (%).  
The primary goal of the process is the recovery of carbon fibers, as outlined in the introductory section. This is necessary from an environmental standpoint as well as from the perspective of the high economic and energetic value of virgin carbon fibers. Furthermore, it has been demonstrated that carbon fibers can be recovered while maintaining their mechanical properties. However, upon hydrolysis of the resins, they break down into numerous fragments, probably rendering them unsuitable for reuse in resins. Indeed, a complex mixture of products is generated in the aqueous phase, making the recovery of any particular compound in large quantities a challenging endeavor.  
The effects of most of the variables were studied by means of the AUTOCLAVE ENGINEERS subcritical reactor (\(\approx\)300 \({}^{\circ}\)C and 132 bar). The tests under supercritical conditions were performed in the PARR 4570 reactor (\(\approx\)400 \({}^{\circ}\)C and 243 bar). Table 1 shows the tests' operating conditions and results.
# Detection of Defects Using Spatial Variances of Guided-Wave Modes in Testing of Pipes
## 2 Background Theory  
Guided waves are dispersive and multimodal [2]. Dispersion causes the energy of a signal to spread out in space and time as it propagates [7]. Guided wave modes are categorized based on their displacement patterns (mode shapes) within the structure. Three main families of waves exist in pipes, which are longitudinal, torsional, and flexural. Longitudinal and torsional waves are axisymmetric waves, while flexural waves are non-axisymmetric. The popular nomenclature used for them are in the format of \(X(n,m)\), where \(X\) can be replaced by the letters \(L\) for longitudinal, \(T\) for torsional, and \(F\) for flexural waves; \(n\) shows the harmonic variations of displacement and stress around the circumference, and \(m\) represents the order of existence of the wave mode [28].  
Due to the sinusoidal and spatial variations of flexural waves, these coherent noises in the inspection can be reduced by adding the signals received from linearly spaced transducers around the circumference of the pipe. The resultant effect is the cancellation of non-axially symmetric flexural waves and the enhancement of axially symmetric torsional or longitudinal waves. However, cancellation is not ideal because of the imperfections of the real-world devices. These factors include the non-linear spacing of the transducer's placement, the variability of the transducer's frequency response, the number of transducers used, or the tolerances of the electronic test device. Furthermore, a scattering of an ideal axisymmetric wave with a non-axisymmetric feature would also result in the generation of other wave modes, which is commonly known as a wave mode conversion phenomenon [29; 30; 31; 32]. Thus, inspecting guided waves without the interference of these coherent noises is near impossible.  
For decades, the inspectors have been trained to interpret these resultant time-domain signals to extract the defect location. Nevertheless, the problem of detection can be defined in another way, i.e., the linearity and symmetry between the phases of the received waveforms. For better understating of this matter, in the following section, different conditions of waves arrived at spatially are demonstrated using a synthesized model.
## 5 Numerical Results
### Integration of EPON and WLAN
In this figure, the results are averaged over all three traffic types. We observe that the proposed aggregation techniques improve the mean throughput and to a lesser extent also the mean delay. Note that in Figs. 12 and 13 the 95% confidence intervals exhibit an error of less than 10% from the mean values.
## 2 Connection Options and Topology Optimization after Site Densification
### Link Budget and Fading Margin Calculations
In the planning phase, therefore, both the frequency band \(f\) and the antenna diameters should be carefully selected for a given hop length \(d\). Lower frequencies are more \"valuable\" and should be reserved for long-haul links. For simultaneously reducing the undesired radio interference [43] and increasing the \(FM\), preferably bigger dish diameters and the use of ATPC are recommended. Radio links using adaptive modulation have different \(P_{\text{RXth}}\) thresholds for the different modulation modes (Figure 9). The low modulation mode is active in the rainy minutes when \(FM\) compensates for the loss caused by rain. During sunny periods when the \(FM\) is available, the link can jump to elevated modulation modes; thus, more data can be transmitted [11; 31; 32; 33; 34]. For simplicity, Figure 9 shows quadrature amplitude modulation (QAM) only up to the 512 states [10]. State-of-the-art transceivers can use up to 4096-QAM to reach multi Gigabit/s bit rates as discussed in Section 3. Finally, the link is operational as long as its fade margin is greater than the losses caused by the rain and by the atmospheric attenuation (obstacle losses and interference are not considered). \[FM\ ^{[\text{dB}]}>A_{\text{r},0.01\%}{}^{[\text{dB}]}+A_{\text{a}} ^{[\text{dB}]}. \tag{11}\]  
Figure 8: Link budget and fading margin calculation.
## 1 Introduction
Therefore, for the evolution of the electron population, it seems advisable to solve the governing equations in a curvilinear system of coordinates defined by the local magnetic field directions. This type of mesh is known as Magnetic Field Aligned Mesh (MFAM). In the cases in which this magnetic field may be considered stationary (i.e., the magnetic field \(\vec{B}\) can be assumed irrotational in addition to solenoidal and stationary); the mesh whose element facets are locally aligned with the magnetic field directions is an input to the simulation and does not vary with the plasma flow development. An example of a typical axisymmetric simulation domain of a Hall Effect Thruster (HET) geometry is presented in Figure 1a. Figure 1b shows an example of MFAM and a cell detail is presented in Figure 1c. The MFAM venue has been utilized extensively both by the plasma fusion [19] and the plasma propulsion [20; 21] communities, and numerical diffusion has been documented in simulations for highly anisotropic systems [16; 22]. However, a number of difficulties still arise when using the MFAM, mainly in relation to low quality meshes, problematic boundary elements, calculation of gradients and the errors associated with them, as discussed by [PERSON] [23]. These difficulties have increased in modern plasma thruster designs with non-regular magnetic field topologies, which include: Singular points and cusp regions [24]. In the present work, efforts have been carried out to analyze the flow behavior and to justify using the MFAM as opposed to regular, structured, meshes. This analysis has been performed, following the discussion by [PERSON] [23], and tackling the issues of numerical diffusion in regular meshes and comparing it with the solutions obtained in MFAM. Different meshing strategies and problems arising from mesh quality and gradient reconstruction accuracy in magnetically aligned meshes are also addressed. Additionally, a benchmark test is presented showcasing the benefits of using MFAMs versus structured meshes in anisotropic plasmas. This manuscript is organized as follows: Section 2 explores the issue of numerical diffusion in a regular, structured, mesh for a typical diffusion problem in an anisotropic media. Section 3 is dedicated to meshing strategies for the MFAM and their effects on mesh quality. Section 4 addresses the necessity for gradient reconstruction in an unstructured mesh when solving for the electron fluid equations, proposes the Weighted Least Squares Face Interpolation (WLSQRFI) method as a suitable solution, and computes error estimates for its use. Finally, Section 5 presents benchmark results for the anisotropic diffusion problem in an MFAM versus a regular, structured, mesh and Section 6 concludes the article by reviewing the results presented.
# Dynamic Cooperative MAC Protocol for Navigation Carrier Ad Hoc Networks: A DiffServ-Based Approach
## 6 Performance Evaluation
### Simulation Results
#### 6.2.3 Performance Analysis in 2-Hop Optimal Relay Communication  
In this subsection, we present a numerical analysis for the performance with the increasing transmission range and the degrading link quality. The number of nodes is set to 70.  
Figure 11a,b depicts the impact of transmission distance on bit error ratio (BER) and throughput performance. It is observed that 802.11 EDCA, CoopMAC, and DDC-MAC have similar BER  
Figure 10: Throughput and average delay performances versus number of nodes in 1-hop OC mode. (**a**) Throughput in single-channel environment; (**b**) Throughput in multi-channel environment; (**c**) average delay in single-channel environment; (**d**) average delay in multi-channel environment.  
and throughput when the distance is closer than 150 km. When the distance exceeds 150 km, the performance of 802.11 EDCA and 802.11 DCF degrades sharply due to the deterioration of link quality and retransmission. When the distance is 400 km, the BER of 802.11 EDCA and 802.11 DCF all keep constant values to 95%, and their throughputs keep constant values to 530 kbps, while CoopMAC and DDC-MAC performs better than 802.11 DCF and 802.11 EDCA due to the benefits of the cooperative relay mechanism. Furthermore, DDC-MAC outperforms CoopMAC due to the optimal relay mechanism and the ACBS mechanism. When the transmission range is 400 km, the bit error ratios of CoopMAC and DDC-MAC keep constant values of 38.5% and 29.7%, and the throughputs are 4750 kbps and 6430 kbps, respectively.  
Figure 11c,d shows the PDR and AD performance of four protocols with environmental noise level varied from \(-\)94 dBm to \(-\)76 dBm. When the channel condition is good (_Noise \(<-\)90 dBm_), the PDR of four protocols is higher than 94%, the AD is lower than 0.1 s, and degrades slowly. When the noise level increases (_Noise \(>-\)90 dBm_), the performance of 802.11 EDCA and 802.11 DCF degrades sharply due to the deterioration of link quality. When _Noise \(=-\)75 dBm_, the PDR of 802.11 EDCA and 802.11 DCF is 21%, and the AD is 0.54 s and 0.72 s, respectively. CoopMAC and DDC-MAC outperforms 802.11 DCF and 802.11 EDCA due to the benefits from channel selection policy, and DDC-MAC outperforms CoopMAC due to the optimal relay mechanism and its adaptive antenna array. When _Noise \(=-\)75 dBm_, the PDRs of CoopMAC and DDC-MAC are 48.1% and 68.3%, and the AD is 0.33 s and 0.12 s, respectively.  
Figure 11: Impacts of transmission range and link quality on MAC performances with 2-hop optimal relay. (**a**) Transmission distance vs. bit error ratio; (**b**) transmission distance vs. throughput; (**c**) packet delivery ratio vs. link quality; (**d**) average delay vs. link quality.
# Forecasting Landslides via Three-Dimensional Discrete Element Modeling: Helong Landslide Case Study
## 4 Results and Discussions  
### Stability Analysis  
To determine the stability of the slope in its current state, we calculated the FOS. The calculation was done using the software Geo-Studio of 2007 version (GEOSLOPE international Ltd., Calgary, Alberta) and Figure 3b was selected as the section to be calculated. Four kinds of situations considering rainfall and an earthquake were set. From the investigation, we believe that the most likely method of slope failure is internal sliding in the upper loose soil, or upper loose soil sliding along the granite bedrock. The type of potential landslide is considered to be translation or rotation. Thus, the Morgenstern-Price method, which fully considers the force balance, as well as the force moment balance and can calculate  
Figure 7: Principle of a digital elevation model (DEM): (**a**) schematic of the force–displacement model between two particles and a (**b**) calculation flow chart.  
the slip surface of any shape, was applied to analyze the stability of the landslide [31]. For the rainfall condition, two water lines of different heights were set to represent the groundwater condition during heavy and light rainfalls. The shear strength of soil below the water table was set as the saturated shear strength, whereas the shear strength of the soil above the water table was set as the natural shear strength. According to the Seismic Ground Motion Parameters Zonation Map of China GB18306-2015, the seismic peak ground acceleration of the study area is 0.05 g, and the seismic load was calculated using the pseudo-static method. Other physical and mechanical parameters for calculation were obtained from Table 1.  
The results in Figure 8 show that when the slope is under natural conditions, the FOS was 1.856, indicating that the slope was stable without any other external loads. However, when the slope suffered a rainfall or an earthquake, the FOS value decreased sharply. The FOS of the slope was 1.506 under earthquake conditions, 1.318 when the water level was low, and 0.986 when the water level was high. When the slope suffered from a rainfall and an earthquake simultaneously, the FOS was only 1.075 in the low-water-level condition, and 0.832 in the high-water-level condition. The results show that the stability of the slope was sensitive to the level of the water table. According to the Technical Code for Building Slope Engineering (GB50330-2013) published by the Chinese Ministry of Housing and Urban-Rural Development, when the FOS of a slope is less than 1.35, the slope is considered unstable. Thus, forecasting the failure potential of the Helong landslide is vital.
# Load Frequency Optimal Active Disturbance Rejection Control of Hybrid Power System
## 3 The Proposed Control Strategy  
The proposed control strategy is a combination of the LQR and ADRC; the diagram of the controller is shown in Figure 3. In a power system interconnected by \(i\) regions, an \(i\)-th controller is necessary. Thus, the strategy includes an ADRC and LQR-based LFC strategy to adjust the frequency deviation and the ACE within each control region. Given a hybrid power system model, the LQR is employed to effectively manage the overall errors of the ACEs and frequency deviations within a specified range. Additionally, the ADRC is utilized as a precise controller for the ACEs and frequency deviations. In this diagram, the upper half part is the ADRC strategy, and the lower part is the revised LQR strategy. The details of the Tracking Differentiator (TD) and NFCL blocks are given in [20].
# A Survey on LoRaWAN Architecture, Protocol and Technologies
## 2 LoRa & LoRaWAN Technologies
### MAC Layer--LoRaWAN
#### 2.4.8 Adaptive Data Rate (ADR)  
The protocol supports a built-in Data Rate control mechanism which enables end-nodes to be configured with different data rates depending on the network condition. However, the protocol doesn't define an algorithm to control node transmission rates, the network server has to implement depending on the conditions.  
Aim of ADR to reduce frame transmission time since nodes closer to GWs can use lower SF values and have higher transmission rates that will reduce channel usage and energy consumption.  
ADR Process is initiated by \(LinkADRReq\) command given parameters in Figure 19.  
An ADR mechanism is illustrated by The Things Network (TTN is introduced in Section 3.2) [53]. The method is publicly available [54], and calculates the optimum data rate through 20 most recent UL measurements. It includes a frame counter, SNR and number of GWs that UP link received. When ADR is determined, the system starts a new measurement process to determine the next ADR value.  
ADR is an important feature in LoRaWAN networks to control coverage, interference and energy consumption. However, ADR hasn't been investigated fully by the researchers yet.
## 6 Flow Shop Scheduling--A Case Study
### Components of Pure GA
#### 6.2.2 Fitness Evaluation  
Once a solution representation is developed, developing the decoding procedure is vital to determine the fitness (makespan) of the corresponding schedule. The decoding is accomplished by a specific rule for assigning and sequencing the jobs guided by the permutation of the job in the chromosome under evaluation. In this paper, we considered an assignment rule and sequencing rule adopted from [50,54] by adjusting for a situation when lot streaming is not considered. The considered assignment rule takes care of: (i) the attached and detached setup time, (ii) release date of machines, (iii) the possibility of some jobs skipping some stages. If we consider job \(n\), which is allocated for a machine \(m\) in stage \(i\), then the completion time \(c_{n,i}\) is calculated for one of the following cases:  
**Case 1:**: If job \(n\) is considered the first job to be assigned to machine \(m\), and stage \(i\) is the first stage for this job \(n\); then, \(c_{n,i}\) becomes \(F_{m,i}+S_{m,i,n,0}+Q_{n}\cdot T_{n,m,i}\).
**Case 2:**: If job \(p\) is the last job assigned to machine \(m\) and \(i\) is the first stage to be visited for job \(n\), then \(c_{n,i}\) becomes \(c_{p,i}+S_{m,i,n,p}+Q_{n}\cdot T_{n,m,i}\).
**Case 3:**: If job \(n\) is the first job to be assigned to machine \(m\) and this job \(n\) has to visit stage \(l\) before visiting stage \(i\), then \(c_{n,i}\) becomes \(\max\{F_{m,i}+(1-A_{n,i})\cdot S_{m,i,n,0};\ \ c_{n,l}\}+Q_{n}\cdot T_{n,m,i}+A_{n,i} \cdot S_{m,i,n,0}\).
**Case 4:**: If job \(p\) is the last job to be assigned to machine \(m\), and job \(n\) has to visit stage \(l\) before visiting stage \(i\), then \(c_{n,i}\) becomes \(\max\{c_{p,i}+(1-A_{n,i})\cdot S_{m,i,n,p};\ \ c_{n,l}\}+Q_{n}\cdot T_{n,m,i}+A_{n,i} \cdot S_{m,i,n,p}\).  
In the assignment rule, we consider sequence-dependent setup time, the processing times of different units on parallel machines, machine release date, and machine eligibility to calculate each job's completion time in \(\pi\), visiting all the stages. Weadopted the assignment rule from the work presented in [54]. The basic steps are as follows:  
**Step 1:**: Decode the sizes of each job for the considered chromosome and obtain the permutation \(\pi\) from the chromosome (as shown in Figure 7). Initialize \(i=1\) and \(s=1\).
**Step 2:**: Set \(n=\pi(s)\).
**Step 3:**: If \(Q_{n}>0\), go to Step 4; otherwise, go to Step 7.
**Step 4:**: If \(B_{n,i}=1\), go to Step 5; otherwise, go to Step 6.
**Step 5:**: Assign job \(n\) to one of the available machines \(m\) in stage \(i\), where it will be completed at the earliest completion time \(c_{n,i}\).  
Figure 7: Solution representation.  
**Step 6:**: If \(i<I\), set \(i=i+1\) and go to Step 4; otherwise, go to Step 7.  
**Step 7:**: If \(s<N\), set \(s=s+1\) and go to Step 2; otherwise, go to Step 8.  
**Step 8:**: Calculate \(c_{max}=\max_{\gamma(n,i)}c_{n,i}\). Here, \(c_{max}\) defines the smallest makespan/completion time of all jobs processed in the system of considered chromosome.