esa-sceva/satcom-chunk-collection huggingface dataset:
# A Survey on Adaptive Data Rate Optimization in LoRaWAN: Recent Solutions and Major Challenges
## 2 Technology Overview
### Adaptive Data Rate
fixed SF and BW sequence successively. An end node may use any set of transmission parameters to communicate with the gateway without handshaking. Message transmission from gateways to end nodes occurs on a programmable offset from the UL data rate in the first RW, and typically with the highly robust setting, the lowest data rate in the second RW. An end node notifies the gateway that it requires the use of ADR by configuring the ADR bit in the frame header. Once ADR is configured, the NS uses LinkADRreq, the MAC command that controls the end node's data rate and TP. The end node will respond with the LinkADRAns command to indicate acceptance or rejection of the new settings. The ADR algorithm comprises of an acknowledgement system which is devised to permit end nodes to intermittently verify that the NS received the UL message. If an ACK message is not received by the end node, the end node will switch to a lower data rate in an attempt to regain connectivity. The permutations of the transmission parameters produce a potential 6720 potential transmission settings of which the LinkADRreq command can only select from a subset of eight data rate settings and six transmission power settings [18]. Even though LoRaWAN stipulates a transmission parameter signalling scheme via the LinkADRreq command, there is no description available of how the communication should be handled. The specification does not state how the NS should instruct end nodes concerning adapting the data rate, when to change a setting, or the order in which the settings should be changed [22]. The NS is left with the responsibility to implement ADR. The end nodes also have the capability of managing the ADR transmission parameters using the ADR system that is nested on the end node side. This means that, the ADR scheme can run asynchronously at the NS side and the end node side. As stated in the LoRaWAN standard, there are two parameters that have been specified, namely ADR_ACK_LIMIT and ADR_ACK_DELAY. The default values for these parameters have been set to 64 and 32, respectively. For every UL packet that an end node transmits, ADR_ACK_CNT counter is increased by one. Once the ADR_ACK_CNT becomes equal to ADR_ACK_LIMIT without any DL response, the end node sets the ADRACKreq bit and waits for an ACK from the gateway for the subsequent ADR_ACK_DELAY UL packets. In the absence of an ACK ahead of ADR_ACK_DELAY UL message, the end node decreases the data rate, attempting to re-establish network connectivity. In accordance with the latest release, end nodes initially increase TP to secure connectivity. If that is inadequate, the end nodes then reduce the data rate as an element of the subsequent stage [26]. Figure 2 outlines the flow of the ADR scheme executed at the end node. Figure 2: Adaptive data rate flow [26]. SF: Spreading Factor.
## 2 Related Work  
### Pivot Translation  
In practice, it is almost impossible to train NMT models between arbitrary language pairs, because most language pairs have no parallel corpora, and training \(O(N^{2})\) models is resource-consuming. A simple but commonly used solution is pivot translation, given that most languages share a parallel corpus with a pivot language, such as English (due to its widespread global usage). Concretely, source-pivot (S-P) and pivot-target (P-T) models have been built, with the source sentence cascading through the S-P andP-T systems to generate the target sentence. Sometimes, more than one pivot language may be required in the translation route:  
\[S\to P_{1}\rightarrow\ldots\to P_{n}\to T \tag{1}\]  
where \(P_{1},\ldots,P_{n}\) are the pivot languages. Previous studies have proven pivot translation an effective and practical way for translation between unseen language pairs and can be applied to different architectures, such as statistical machine translation (SMT) [28; 29] and NMT [19; 30; 31]. Despite its convenience and practicability, pivot translation has two disadvantages: (a) translation error propagation in the cascaded pipeline, which is exacerbated when the translation route path increases and (b) an inference time and computation cost nearly double that of a direct translation model.
## 3 Results and Discussion
### Molecular Docking Analysis  
The concentrations of quercetin, gallic acid, protocatechuic acid, and epicatechin considerably increased after fermentation; therefore, they were subjected to a molecular docking analysis to evaluate their roles in skin-whitening and antiaging mechanisms. Table 3 lists the docking results of protocatechuic acid, epicatechin, gallic acid, and quercetin. The results revealed various degrees of theoretical binding affinities against the same enzyme. Quercetin exhibited the best binding energy (\(-\)6.8 kcal/mol) against tyrosinase. Epicate-chin exhibited the best binding energy against antiaging enzymes (collagenase, elastase,  
Figure 6: Effects of fermented _C. formosanum_ leaf extract on antityrosinase activity and melanin production in HEMn cells. Data are expressed as the means and standard deviations of three independent experiments.  
and hyaluronidase). Protocatechuric acid exhibited relatively low binding energies against the four types of tested enzymes compared with other phytochemical compounds. The docking interactions and docking complex of optimal phytochemical compounds against tested enzymes are presented in Figure 7. Quercetin and epicatechin formed various molecular forces when they bound to tested enzymes, which facilitated protein-ligand complex formation. Among the compounds, quercetin exhibited the best docking interactions against tyrosinase due to the formation of H bonds with Ser274 and Pro271, \(\pi\)-cation bonds with Lys463 and Lys239, and \(\pi\)-alkyl bonds with Pro488 and Leu268 (Figure 7A). Epicatechin exhibited the best docking interactions against collagenase, which were favored by the formation of H bonds with Asn287 and Tyr345, \(\pi\)-cation and \(\pi\)-anion bonds with Asp 340 and Lys291, and \(\pi\)-\(\pi\) interactions between Tyr337 and the aromatic ring of epicatechin (Figure 7B). Epicatechin exhibited the best docking interactions against elastase, which were favored by the formation of H bonds with Ser214 and \(\pi\) interactions with His57 (\(\pi\)-cation), Phe215 (\(\pi\)-\(\pi\)), Leu998 (\(\pi\)-sigma), and Val216 (\(\pi\)-alkyl) (Figure 7C). Epicatechin exhibited the best docking interactions against hyaluronidase, which were favored by the formation of H bonds with Gly187 and Asp297 and \(\pi\)-\(\pi\)-\(\pi\) and \(\pi\)-alkyl interactions with Tyr189 (Figure 7D). On the basis of these results, we believe that the significantly increased concentrations of quercetin and epicatechin after fermentation may contribute the most to the antityrosinase and antiaging activities of the CF leaf extract.
## 2 Monitoring Model Based on Statistical Process Control
### Fault Diagnosis Based on Nonlinear Contribution Plot  
In this paper, the contribution plot method was used to diagnose and analyze the statistical data. The total contribution of the \(i\)-th sample to the \(j\)-th variable is calculated as follows:  
\[\mathbf{contr}_{ij}^{SPE}=\big{(}\mathbf{x}_{ij}-\hat{\mathbf{x}}_{ij}\big{)}^{2} \tag{19}\]  
where \(\mathbf{x}_{ij}\) is the monitored value of the \(j\) variable at the \(i\) sample point, and \(\hat{\mathbf{x}}_{ij}\) is the predicted value of the \(j\) variable at the \(i\) sample point.  
In order to make the relative contribution graph more effective in dealing with a complex production process, the expectation factor is added to the traditional contribution graph, as follows:  
\[Rcontr_{ij}^{SPE}=\frac{contr_{ij}^{SPE}}{E\Big{[}contr_{ij}^{SPE}\Big{]}} \tag{20}\]  
The nonlinear contribution plot calculates the contribution of each variable of each sample point to the monitoring index so as to find out the main fault variables and complete the diagnosis of abnormal sample points in the monitoring process. The new input sample \(\mathbf{x}_{\text{new},r}\in\mathbf{R}^{1\times m}\) (\(r=1,2,\ldots,m\). used to distinguish different process variables) and the Gaussian radial basis kernel function are used in this paper; then the partial derivative of the kernel matrix \(\mathbf{K}_{\text{new}}\) of the new input point with respect to each variable is as follows:  
\[\mathbf{K}^{\prime}_{\text{new},i}=-\frac{1}{\sigma}(\mathbf{x}_{\text{new},r}-\mathbf{x} _{i,r})^{2}k(\mathbf{x}_{\text{new}},\mathbf{x}_{i}) \tag{21}\]  
where \(x_{i,r}\) is the \(i\) element of the \(r\) variable in the training sample, \(i=1,2,\ldots,n\). \(\sigma\) is constant.  
The partial derivative of the centralized kernel matrix \(\mathbf{K}_{\text{new}}\) with respect to each variable is as follows:  
\[\mathbf{\overline{K}}^{\prime}_{\text{new},i}=\mathbf{K}^{\prime}_{\text{new},i}-\mathbf{ K}^{\prime}_{\text{new},i}\mathbf{I}_{n} \tag{22}\]  
Using the latent variable matrix and kernel matrix calculated in the training model and monitoring model, the \(SPE\) contribution value of each variable is calculated as follows:  
\[contr_{r}^{SPE}=1-\frac{2}{n}\sum_{i=1}^{n}\mathbf{\overline{K}}^{\prime}_{\text{ new},i}-\mathbf{\overline{K}}^{\prime}_{\text{new}}\Big{(}2\mathbf{T}t_{\text{new}}^{ \text{T}}-\mathbf{U}\mathbf{T}^{\text{T}}\overline{\mathbf{K}}\mathbf{T}t_{\text{new}}^{\text {T}}\Big{)}+\mathbf{t}_{\text{new}}\mathbf{T}^{\text{T}}\overline{\mathbf{K}}\mathbf{T}\mathbf{U} ^{\text{T}}\overline{\mathbf{K}}^{\prime\text{T}}_{\text{new}}\,\mathsf{l} \tag{23}\]  
where \(\overline{\mathbf{K}}^{\prime}_{\text{new}}\) is the partial derivative kernel matrix composed of partial derivative vector \(\overline{\mathbf{K}}^{\prime}_{\text{new},i}\).
## 3 Results  
As shown by the analysis, the WC and SDS values were similar to those in the control group, being 1.1-1.3 s and 50%, respectively. Impact loads, however, showed significant changes (see Figure 3). Figure 2: Measured amplitudes (A) and phases (X) in the hip (left graph) and knee (right graph) goniograms. The magnitude of impact load at the beginning of the stance phase showed the typical dynamics on both sides; it increased with time after surgery. Impact loads were significantly reduced on both sides. Before surgery, impact loads on the intact and affected sides differed significantly from control. At the other time points, impact loads on both sides differed significantly from the respective values before surgery (p<0.05). WC duration data are presented in Figure 4.  
Figure 4: Walking cycle (WC) duration(s) for both sides at different time points, compared to the control group. Figure 3: Load amplitudes (g) for the affected and intact legs at different time points (before surgery, three months, and one year or more after ACL reconstruction), compared to the control group. One can see that the WC duration decreased with time after surgery. The changes were not statistically significant, but the trend was obvious. All the values were within normal ranges; however, as the WC duration decreased the walking speed increased, all other things being equal. The beginning of second double support phase (the parameter SDS) was also within the control range and showed no tendency to change. The observed changes in hip joint kinematics are presented in Table 2.  
Before ACL reconstruction, hip movement amplitudes on both sides (affected and unaffected) were significantly smaller than in control (p \(<\) 0.05). They remained so in the follow-up periods after the reconstruction. Meanwhile, extension amplitude (A2) increased on the affected side, and abduction-adduction amplitude increased, though insignificantly, on both sides. The moments of maximum flexion and extension (X1 and X2) significantly increased (delayed) at different time points of the study. For X2, the changes were significant compared to control in all study periods (p \(<\) 0.05). There were no significant changes in abduction-adduction and rotation movements. Knee joints showed characteristic changes associated with ACL injury and the subsequent functional restoration (Table 3). The amplitude of knee extension in single support phase was slightly increased and remained so at all times of the study, both on the affected and unaffected sides. The greatest changes were observed in the main amplitude of knee flexion in swing phase (A3). Before ACL reconstruction, the amplitude was significantly reduced on the affected side, by over 10 degrees versus control, and much less so on the intact side. Later on, the amplitude increased gradually, and after a year, it reached, on the operated side, the same values as in the control group. Similar, though less intensive, changes were seen on the intact side.
###### Abstract  
The mechanical properties of gas-containing coal and rock mass play important roles in controlling the occurrence and development of coal and gas outbursts. The gradual increase in mining depth will change the failure mechanism of gas-containing coal and rock mass. In order to further study the failure mechanism of gas-containing coal and rock mass, samples were taken from the gas-containing coal seam in the Wuhushan Coal Mine of the Wuhai Mining area of China. The mechanical parameters of coal samples during the failure process under different gas pressure conditions were measured and analyzed with the SAW-2000 rock mechanics testing machine, the gas-containing coal uniaxial compression device and inflation system. Meanwhile, the failure process and mechanical parameters of coal samples under different gas pressure were simulated by RFPA2D gas plate numerical simulation software. The results show that with increasing gas pressure in the coal there is decrease in Compressive strength, Elastic modulus, Strain, Peak strength and Bearing capacity and increase in Poisson's ratio. When the failure state appears in the coal, the cracks are longer and wider, more random cracks are generated, and the damage degree of the coal is greater. The numerical analysis' results are in good agreement with experimental results. The research results are applicable to the gas bearing coal with the same or similar gas geological conditions. The tests can be carried out repeatedly and reasonable results can be obtained according to the physical and mechanical parameters of the actual coal seam and the occurrence of gas. On this basis, physical experiments and numerical simulations of triaxial compression can also be carried out to further study the mechanical characteristics of deformation and the failure of gas-containing coal under gas pressure and provide technical support for revealing the mechanism of coal and gas outbursts.  
gas-containing coal; uniaxial compression; gas pressure; mechanical properties; damage Article  
Study on Mechanical Characteristics of Deformation and the Failure of Gas-Containing Coal in the Wuhai Mining Area of China under Different Gas Pressure Conditions [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] 1
## 2 Materials and Methods
### WRF-Chem Model  
The third version of the Weather Research and Forecasting model coupled to Chemistry (WRF-Chem V3, National Center for Atmospheric research, Boulder, CO, USA, 2011; [[https://ruc.noaa.gov/wrf/wrf-chem/](https://ruc.noaa.gov/wrf/wrf-chem/)]([https://ruc.noaa.gov/wrf/wrf-chem/](https://ruc.noaa.gov/wrf/wrf-chem/))) was used here. WRF-Chem is a non-hydrostatic limited-area numerical model, able to simulate both meteorological fields and dust particle transport and their reciprocal feedbacks. It is both a research and operational model, which simulates and predicts the atmospheric concentrations of different gases and particles, taking into consideration natural mechanisms and human activities (pollutants) that affect dust particles of different sizes, considering the ways they are emitted and deposited.
###### Abstract  
Electromagnetic interference (EMI) remains a difficult task in the design and operation of contemporary power electronic systems, especially in those applications where signal quality has a direct impact on the overall performance and efficiency. Conventional control schemes that have evolved to counteract the effects of EMI generally tend to have greater design complexity, greater error rates, poor control accuracy, and large amounts of harmonic distortion. In order to overcome these constraints, this paper introduces an intelligent and advanced control approach founded on the signal randomization principle. The suggested approach controls the switching activity of a DC-DC converter by dynamically tuned parameters like duty cycle, switching frequency, and signal modulation. A boost interleaved topology is utilized to maximize the current distribution and minimize ripple, and an innovative space vector-dithered sigma delta modulation (SV-DiSDM) scheme is proposed for cancelling harmonics via a digitalized control action. The used modulation scheme can effectively distribute the harmonic energy across a larger range of frequencies to largely eliminate EMI and boost the stability of the system. High-performance analysis is conducted by employing significant measures like total harmonic distortion (THD), switching frequency deviation, switching loss, and distortion product. Verification against conventional control models confirms the increased efficiency, less EMI, and greater signal integrity of the proposed method, and hence, it can be a viable alternative for EMI-aware power electronics applications.  
e +
Footnote †: journal: _arXiv preprint arXiv:1107.1138  
Article
# Analyzing the Economic Performance of a TLS-Based Structural Safety Diagnosis Process
## 2 Materials and Methods
### Challenges in Structural Safety Diagnosis  
Structural safety diagnosis is a key process for evaluating the structural soundness of buildings and infrastructure facilities to identify potential risk factors in advance and prepare appropriate maintenance measures [16]. Especially in old buildings, disaster-damaged facilities, such as those impacted by fire and earthquakes, and large factory facilities (e.g., pipe racks, conveyor belts, etc.), it is necessary to minimize facility safety and economic losses through rapid diagnosis [3; 17]. Most structural safety diagnostics evaluate the condition of a facility through a first visual inspection and a second detailed inspection [18; 19; 20]. Since the visual inspection process is highly dependent on the competence of the expert, there is high variability in the results depending on the skill and experience level of the inspector [18; 21]. In addition, in old facilities or disaster sites with a large survey area, it is difficult to survey all areas [22], and the risk of safety accidents when entering hazardous areas increases, which limits manpower input [3].  
Conventional methods require sample-based inspections, visual assessments, and, if necessary, partial dismantling inspections, resulting in long inspection periods and high labor requirements [23]. This increases the cost burden incurred during the maintenance phase and can lead to construction delays and additional costs when repairing fire damage or improving old facilities [24; 25]. In the case of old facilities, there are many cases where construction drawings or structural change history information are not properly managed [26]. This lack of data leads to frequent iterations and uncertainties during the inspection process and makes it difficult to reliably diagnose facilities in the long term [3; 22].  
Compared to labor-based methods, introducing digital-based hardware and software, such as TLS or BIM, and training the workforce requires significant upfront costs [27; 28]. Construction companies are hesitant to adopt new equipment or technologies due to high costs and uncertainty of payback [29; 30; 31], technical limitations [27], and cultural barriers in the construction industry [31; 32]. In addition, the manpower-based structural safety inspection process suffers from problems, such as manpower-centered inspection methods [33], long-term increases in manpower and duration [3], the inefficiency of information management [34], and a lack of economic validation [35]. In conclusion, the problems faced in structural safety diagnosis are economic efficiency and information uncertainty. In addition, the human-based visual inspection method is accompanied by labor input and long-term cost increase, so it is essential to verify the economic efficiency of the digital technologies that aim to replace or complement it.
# A Review of Image Reconstruction Algorithms for Diffuse Optical Tomography
## 3 Forward Process
### Radiative Transfer Equation
Image reconstruction employing RTE is particularly suitable for small measurement objects such as finger joints. Clinical applications for diagnosing rheumatoid arthritis have been reported [44; 45; 46]. Image reconstructions based on the RTE have been reported in fluorescent diffuse optical tomography (FDOT) [47], which reconstructs the concentrations of fluorescent probes [48; 49], and in quantitative photoacoustic tomography (QPAT) [50], which reconstructs the concentrations of exogenous and endogenous chromophores from photoacoustic pressure waves [51; 52; 53; 54]. FDOT and QPAT are used to image subjects that exist in shallow regions and in small objects, where DA is often invalid. Efficient and precise computational methods for RTE have been studied [20; 55].
## 3 Platform Subsystem Design
### Mechanical Subsystem  
An off-the-shelf 3U CubeSat frame composed of space-grade aluminum alloy was used for the structure of Light-1. The components of the Light-1 CubeSat can be seen in Figure 5 while the corresponding parts have been compiled in Table 1.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**No.** & **Component** \\ \hline
1 & CubeSat structure \\ \hline
2 & Payload controller \\ \hline
3 & Electric power system \\ \hline
4 & Onboard computer \\ \hline
5 & Solar panel Z+ PCB (GPS) \\ \hline
6 & Solar panel Z\(-\) PCB (S-band) \\ \hline
7 & Deployable solar panel \\ \hline
8 & GPS patch antenna \\ \hline
9 & S-band patch antenna \\ \hline
10 & Sun sensor \\ \hline \hline \end{tabular}
\end{table}
Table 1: Light-1 components.  
Figure 5: Light-1 assembly.  
All the components in Light-1 are connected to the mechanical structure via steel rods, while both payloads rest on brackets to support their weight. There are two solar panels on the \(+X\) and \(-X\) faces and one solar panel on the \(+Y\) face, which are connected to the structure with screws. Two deployable solar panels are connected with hinges. The UHF antenna and the deployable solar panels are connected to the EPS subsystem by a burn wire. Upon deployment, power will be delivered to the wire. Consequently, the wire will heat up and eventually cut, releasing the stowed antennas and solar panels. Each antenna is mounted on a spring-loaded hinge with 7075 grade aluminum housing. A 3 cm burn wire is then used to tie up the antenna rods. Light-1 power connection has two levels, 3.5 V and 5 V in which each component requires a different level. The OBC, UHF antenna, payload controller, GPS receiver, fine Sun sensors and both payloads require 3.5 V, while the magnetorquers and reaction wheels require 5 V. The S-band transceiver has a Vbat power connection because it requires 8.3 V.