esa-sceva/satcom-chunk-collection huggingface dataset:
# Variable Structure PID Controller for Satellite Attitude Control Considering Actuator Failure
## 3 Results
### Fault Tolerant Algorithm
: _Based on the stability analysis above, it could be found that the PD controller has an important property that, for any positive parameter \(k_{p}\) and \(k_{d}\), it is the key to solving the actuator failure issue. Noting that the fault situation is the output control torque being cut down, and it could be treated as that, the controller is generated by another positive control parameter. Hence, this method only effects the system performance parameters such as convergence rate and steady accuracy, but system stability could be ensured._  
**Remark 8**. : _The essence to solve actuator failure is a property of the PD controller. Global stability means that, even if part of the control torque is lost in the actuator, this condition could be treated, as a new group of control gain parameters can be implemented, and system stability could be ensured. For example, if the origin control parameters are \(k_{p}\) and \(k_{d}\), and when actuator failure occurs and its coupling matrix is \(\mathbf{\rho}\), it could be treated as if the new Lyapunov function is_  
\[V_{p}=\frac{1}{2}\mathbf{\omega}^{T}\mathbf{J}\mathbf{\omega}+2\mathbf{\rho}k_{p}(1-q_{0}) \tag{38}\]  
Its derivative is calculated, and the controller \(\mathbf{u}_{r}=-k_{d}\mathbf{\rho}\mathbf{\omega}-k_{p}\mathbf{\rho}\mathbf{q}_{\
u}\) is substituted (the original controller in (34) is the expected control torque, and \(\mathbf{u}_{r}\) is the actual torque produced by actuator); under actuator failure, it could be found that  
\[\dot{V}_{\rho}=-\mathbf{\omega}^{T}\mathbf{J}\dot{\mathbf{\omega}}-2\mathbf{\rho}k_{p}\dot{q} _{0}=-k_{d}\mathbf{\omega}^{T}\mathbf{\rho}\mathbf{\omega} \tag{39}\]  
Noting that the considered failure in this paper is the drop of control torque but not the positive and negative reverse, \(\mathbf{\rho}\) is a positive definite matrix, and \(\dot{V}_{\rho}\leq 0\) can be ensured. Hence, a system under this condition is still stable. It is worth noting that the proposed method is passive since the coupling matrix \(\mathbf{\rho}\) is unknown, and the new Lyapunov function is also unknown (but this does not change the controller since the gain factor \(\mathbf{\rho}\) is not used in controller design). The controller can only ensure system stability, but its performance cannot be ensured. **Remark 9**. : _The fault detection factor \(\delta\) can be affected by disturbances such as motor vibration, solar pressure, particle radiation, and so on, and this may cause the mis-judgement of actuator failure. Noting that the norm of these disturbances is at the level of \(10^{-4}\)-\(10^{-5}\) Nm [20; 21], which is far less than the torque drop in actuator failure (often \(10^{-2}\)-\(10^{-3}\) Nm), the disturbance would not affect the fault detection algorithm in the situation considered in this paper._
# Spam Review Detection Techniques: A Systematic Literature Review
## 3 Transformation
### Assessment of RQ2: What Methods Are Used to Solve the Problem of Spam Review Detection?
#### 3.2.1 Supervised Learning  
Supervised learning approaches used for spam review detection are commonly based on the classification methods [15]. In this learning technique, two datasets are required: training data and test data. Training data is utilized to train the classifier and afterwards test data is utilized to evaluate the performance of a classifier [28; 46]. Methods such as Support Vector Machine (SVM) and Naive Bayes (NB) have already shown great success in opinion mining.  
Figure 4: Taxonomy of Spam Review Detection Techniques.  
Researchers usually start by gathering and crawling the dataset. The next step is to prepare and pre-process the dataset according to the domain. Once the dataset is prepared then features are extracted from the dataset by using the feature engineering approach. The next step is to train the classifier by using training data. Finally, the performance of a classifier can be validated by using test data [13]. Table 12 shows the comparison of different supervised learning techniques used in the spam review detection works.
# Smartphone-Based Experimental Analysis of Rainfall Effects on LTE Signal Indicators
## 5 Data-Driven Analysis
### Variations in Measurements During Dry Period  
Before analyzing rain's impact on mobile signals, it is important to understand how the signals behave during dry periods, without the influence of rain. To achieve this, we examine signal features within both short- and long-term windows, referred to as small- and large-scale windows, respectively. Specifically, small-scale analysis focuses on variations within an hour, while large-scale analysis investigates variations over several days.
###### Abstract  
As a structural interference, spoofing is difficult to detect by the target receiver while the advent of a repeater makes the implementation of spoofing much easier. Most existing anti-spoofing methods are merely capable of detecting the spoofing, i.e., they cannot effectively remove counterfeit signals. Therefore, based on the similarities between multipath and spoofing, the feasibility of applying multipath mitigation methods to anti-spoofing is first analyzed in this paper. We then propose a novel algorithm based on maximum likelihood (ML) estimation to resolve this problem. The tracking channels with multi-correlators are constructed and a set of corresponding steps of detecting and removing the counterfeit signals is designed to ensure that the receiver locks the authentic signals in the presence of spoofing. Finally, the spoofing is successfully executed with a software receiver and the saved intermediate frequency (IF) signals, on this basis, the effectiveness of the proposed algorithm is verified by experiments.  
repeater; anti-spoofing; ML estimation; multi-correlators; detecting and removing the counterfeit signals +
Footnote †: journal: Sensors
## 4 Research Model and Hypothesizes
### Personal Innovativeness  
Many researchers have concluded that personal innovativeness can influence conception through the use of new technology [68]. The work of [69; 70] suggested that in the IT sense, personal innovativeness may play the role of a moderating variable on the factors which form the perception to adopt a specific technology, and they concluded that there is a need for further studies to examine the personal innovativeness as a moderator with different samples and IT innovations. [PERSON] and [PERSON] [20] found that people who are more innovative are likely to accept technology more quickly than others. This reinforces the fact that an innovative person is more capable of cultivating optimistic attitudes relative to others towards the expectations of using innovation. Thus, the effect of performance expectancy on behavioural intention is more visible among innovative individuals. Similarly, effort expectancy has greater effects on behavioural intention to use smart meter among innovative individuals compared to less innovative individuals. This has led us to the following hypotheses:  
**H7.**: _The user's personal innovativeness positively moderates the influence of performance expectancy on behavioural intention to use smart meter._  
**H8.**: _The user's personal innovativeness positively moderates the influence of effort expectancy on behavioural intention to use smart meter._A number of studies have shown that risk and uncertainty are related to any innovation [69; 71]. For example, [PERSON] and [PERSON] [20] believed that \"personal innovativeness characterizes the risk-taking tendency of some individuals and not others\". However, we believe, there are some risks when using smart meters, in particular with regard to user's personal data, which may be made available without permission to other parties. There is a greater likelihood that more knowledgeable users are more likely to face higher privacy threats in a way that will impact the perception of the service provider. Therefore, the impact of privacy on the behavioral intention of using the smart meter system tends to be more apparent to innovative people relative to less innovative people. Thus, the personal innovativeness moderator is hypothesized as follows:  
**H9.**_The user's personal innovativeness positively moderates the influence of privacy concern on behavioural intention to use smart meter._  
The current research model for investigating the moderating role of experience and personal innovativeness of smart meter acceptance (see Figure 2) is developed by incorporating the constructs from UTAUT2 and the constructs originating from our previous work, which examined the benefit factors [17], and explored the challenge factors related to smart meters acceptance [16].  
Figure 2: Descriptive analysis.
# A Cryptography-Powered Infrastructure to Ensure the Integrity of Robot Workflows
## 3 Compliance Architecture
### Dynamic View and Information Flow  
In the following subsection, we focus on the behaviour and information flow in our architecture. For that we assume that a client wants to execute a workflow queue on the execution entity and describe the resulting communication and the execution steps on each component.  
Figure 3 shows a sequence diagram which describes the information flow in our architecture. The communication can be split into two phases: (1) a preparation phase, where the client requests a signature for the workflow queue she intents to execute and (2) an execution phase where the workflow queue is sent to the workflow manager. Note, that as shown in Figure 1, the execution phase needs not be initiated immediately after the preparation phase, which means that there can be an arbitrary amount of time between them. Next, we describe the two phases in detail, the corresponding data structures as well as relation between them are depicted in Figure 4 below.  
Figure 4: Constructing a signed workflow description from a given workflow queue.  
Figure 3: The communication from the start of program design until execution. The extended execution entity includes the execution entity, the execution controller as well as the authorization handler. The box annotated with “opt” is an optional step, “alt” marks alternatives, one of which must be chosen.
# An Updated Review of Smac Mimetics, LCL161, Birinapant, and GDC-0152 in Cancer Treatment
## 1 Introduction  
Inhibitor of apoptosis proteins (IAPs), including cellular inhibitor of apoptosis protein 1 (cLAP1), cellular inhibitor of apoptosis protein 2 (cLAP2), melanoma inhibitor of apoptosis (ML-IAP/Livin), testis-specific inhibitor of apoptosis (Ts-IAP/ILP-2), neuronal apoptosis inhibitory protein (NAIP), X-linked inhibitor of apoptosis protein (XIAP), survivin, and BIR repeat containing ubiquitin-conjugating enzyme (BRUCE), are known for their anti-apoptotic effects [1, 2]. Members of the IAP family are characterized by the presence of the baculoviral IAP repeat (BIR) domain, which physically interacts with caspase proteins and inhibits the activity of caspases. BIR domains are grouped into two types, based on the presence or absence of the IAP binding motif (IBM) on the BIR domain [3]. Only type II BIR domain, which is with IBM, can interact with caspases. The BIR1-BIR2 linker of XIAP interacts with caspase-3 and -7 [3]. The BIR3 of XIAP inhibits the activity of caspase-9 by interacting with the N-terminal tetrapeptide of caspase-9 [4]. IAPs play important roles in mediating a variety of cellular processes, including apoptosis, mitosis, autophagy, and DNA damage in cancer cells [5, 6, 7, 8, 9, 10, 11, 12, 13, 14]. Therefore, the dysregulation of IAPs promotes tumorigenesis, metastasis, angiogenesis, and therapeutic resistance, including chemotherapy and radiotherapy [15, 16, 17, 18]. Currently, many IAP-targeting treatments, such as small-molecule inhibitors (i.e., ASTX660, Embelin, and YM155) [19, 20, 21], anti-sense oligonucleotides (i.e., LY2181308) [22], and Smac mimetic compounds (i.e., birinapant, LCL161, and GDC-0152) [23, 24, 25], have been developed. Despite the anti-cancer potency of birinapant, LCL161, and GDC-0152, they are still under investigation in preclinical and clinical studies, and these compounds have already received much attention in recent years. In this review, we mainly focus on describing the current development of LCL161,birinapant, and GDC-0152 as anti-cancer agents, and we discuss the potential of using these agents for the treatment of cancer in the future.
# Multi-UAV Area Coverage Based on Relative Localization: Algorithms and Optimal UAV Placement
###### Abstract  
This paper is concerned with relative localization-based optimal area coverage placement using multiple unmanned aerial vehicles (UAVs). It is assumed that only one of the UAVs has its global position information before performing the area coverage task and that ranging measurements can be obtained among the UAVs by using ultra-wide band (UWB) sensors. In this case, multi-UAV relative localization and cooperative coverage control have to be run simultaneously, which is a quite challenging task. In this paper, we propose a single-landmark-based relative localization algorithm, combined with a distributed coverage control law. At the same time, the optimal multi-UAV placement problem was formulated as a quadratic programming problem by compromising between optimal relative localization and optimal coverage control and was solved by using Sequential Quadratic Programming (SQP) algorithms. Simulation results show that our proposed method can guarantee that a team of UAVs can efficiently localize themselves in a cooperative manner and, at the same time, complete the area coverage task.  
coverage control; ultra-wide band (UWB) sensors; relative localization; unmanned aerial vehicles (UAVs) +
Footnote †: journal: _Sensors_  
**Citation**: [PERSON]; [PERSON]; [PERSON]; [PERSON] Multi-UAV Area Coverage Based on Relative Localization: Algorithms and Optimal UAV Placement. _Sensors_**2021**, _21_, _2400_[[https://doi.org/10.3300/s21072400](https://doi.org/10.3300/s21072400)]([https://doi.org/10.3300/s21072400](https://doi.org/10.3300/s21072400))
## 3 Results and Discussion
### Clinker Characterization
#### 3.2.3 Cl\({}^{-}\) Contents Analysis of Clinkers  
Based on the previous analysis of clinker XRD and f-CaO content, nine types of clinker calcined at 1450 \({}^{\circ}\)C were selected, with 5 wt% dihydrate gypsum added to produce cement, followed by the detection of Cl\({}^{-}\) content. Detecting Cl\({}^{-}\) in cement is crucial in the construction industry to ensure the safety and longevity of concrete structures. Excessive Cl\({}^{-}\) content can lead to the corrosion of reinforcing steel bars. As the use of alternative fuels increases, more chlorine is introduced compared to conventional fossil fuels. Consequently, it is essential to detect the Cl\({}^{-}\) content in cement. According to China's national standard GB/T 176-2017, the Cl\({}^{-}\) content in cement should not exceed 0.06 wt%.  
As shown in Figure 7, The Cl\({}^{-}\) content in cement produced with various biomass alternative fuels and clinker calcined at different substitution ratios ranges from 0.0021% to 0.0028%, which is significantly below China's national standard limit. This occurs because, on one hand, the basic oxides (K\({}_{2}\)O and Na\({}_{2}\)O) in the clinker react with chloride ions at elevated temperatures to form volatile KCl and NaCl. On the other hand, according to the study by [PERSON] et al., it was found that Cl\({}^{-}\) in cement raw meal reacts with oxides, such as SiO\({}_{2}\) and CaO, to form calcium chlorosilicate [Ca\({}_{3}\)(SiO\({}_{4}\))Cl\({}_{2}\)] [55].
# Research on Transmission Task Static Allocation Based on Intelligence Algorithm
## 2 Preliminaries
### Pso  
PSO is a group search optimization algorithm. The motion of each particle is determined by the value of the fitness function, and the \"direction\" and \"displacement\" of its motion are determined by the \"speed\" of each particle. Then, the particles iterate in the solution space according to the direction of the current optimal particle.  
In PSO, \(x\) represents the position of the particles, \(v\) represents the motion speed of the particles, and \(p\) represents the optimal position of the particles searched. PSO initializes a group of random particles and finds the optimal solution through iteration. In each iteration, the particle updates its position by tracking two optimal values. One optimal value is the optimal solution that the particle can find. This solution is called particle best; the other optimal value is the optimal solution found by the whole population at present, which is called the global best. Suppose that a population composed of \(K\) particles is searched in the D-dimensional solution space, where the position of the \(i\)th particle is expressed as a D-dimensional vector:  
\[X_{i}=\left(x_{i1},x_{i2},\ldots,x_{iD}\right)\,i=1,2,\ldots,K \tag{11}\]  
The motion speed of the \(i\)th particle is also a vector of the D-dimension:  
\[V_{i}=\left(v_{i1},v_{i2},\ldots,v_{iD}\right)\,i=1,2,\ldots,K \tag{12}\]  
The optimal position searched by the \(i\)th particle, namely, the particle best, is expressed as:  
\[Pbest_{i}=\left(p_{i1},p_{i2},\ldots,p_{iD}\right)\,i=1,2,\ldots,K \tag{13}\]  
The optimal position searched by the whole population, namely, the global best, is expressed as:  
\[Gbest=\left(g_{1},g_{2},\ldots,g_{D}\right) \tag{14}\]  
The update formula of speed and position is as follows:  
\[v_{id}=w*v_{id}+c_{1}r_{1}\left(p_{id}-x_{id}\right)+c_{2}r_{2}\left(g_{d}-x_ {id}\right),\ d=1,2,\ldots,D \tag{15}\]  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & **Explanation** \\ \hline \(Freq_{i}\) & Transmission frequency value of the task \\ _Task_ & Input data and transmission task sequence \\ \(Va_{ij}\) & Evaluation value of the transmission effect of the \(i\)th transmission \\  & equipment operating in the \(i\)th frequency band \\ _ValueMatrix_ & Transmission effect evaluation value matrix \\ \(Tr_{i}\) & Description of the \(i\)th transmitting equipment, including \(\left\langle TaFr,TaskNo\right\rangle\) \\ _Result_ & Output results, consisting \(n\) of \(Tr_{i}\).\(TaskNo\) ordered sequences \\ \hline \hline \end{tabular}
\end{table}
Table 1: TTSA parameter table.  
\[x_{\rm id}=x_{\rm id}+v_{\rm id},\ d=1,2,\ldots,D \tag{16}\]  
where \(c_{1}\) and \(c_{2}\) are the acceleration constant, \(r_{1}\) and \(r_{2}\) are uniform random numbers, and \(w\) is the inertia constant.
# Numerical Study of the Filtration Performance for Electrospun Nanofiber Membranes
## 3 Results and Discussion
### Model Parameter Validation and Results Analysis
#### 3.1.3 Comparison with Experimental Data  
To validate the reliability of the established 3D model, the simulated pressure drop was compared with experimental data and theoretical predictions using the Davies equation. As shown in Table 1, the relative error between the simulation results and experimental values is less than 8%, demonstrating the reliability of the established model. This consistency confirms that the numerical model accurately captures the key structural and flow characteristics of electrospun nanofiber membranes.  
Figure 5: Schematic diagram of the three-dimensional structure of the reconstructed fiber filter media.  
Moreover, the filtration efficiency of the model was evaluated using GeoDict's Filter-Dict module with the \"Caught on first touch\" model. The simulation results show that filtration efficiency increases with an increasing fiber diameter and solid volume fraction, which is consistent with experimental observations reported in the literature [18].
## 5 Implementation, Evaluation Details
### Metrics for Retinal Vessel Segmentation
#### 5.1.1 Mean Intersection over Union (Mean IOU)  
Mean Intersection over Union (Mean IOU), also known as the Jaccard Index, measures the overlap between the predicted segmentation mask and the ground truth mask. It is defined as the ratio of the intersection area to the union area of the predicted and ground truth masks. The Mean IOU is calculated as follows:  
\[\text{Mean IOU}=\frac{1}{N}\sum_{i=1}^{N}\frac{|A_{i}\cap B_{i}|}{|A_{i}\cup B _{i}|}\]  
where:  
* \(N\) is the total number of images.
* \(A_{i}\) is the predicted mask for the \(i\)-th image.
* \(B_{i}\) is the ground truth mask for the \(i\)-th image.
## 2 Basic Equations
The above equations may be stated in terms of these dimensionless representations of physical quantities in (19) as follows (the script has been neglected for its appropriateness):  
\[\frac{\partial^{2}u}{\partial x^{2}}+f_{1}\frac{\partial^{2}U}{\partial x^{2 }}-\frac{\partial\Theta^{s}}{\partial x}-f_{2}\frac{\partial\Theta^{f}}{ \partial x}=\frac{\partial^{2}u}{\partial t^{2}}+f_{3}\frac{\partial^{2}U}{ \partial t^{2}}, \tag{20}\]  
\[f_{1}\frac{\partial^{2}u}{\partial x^{2}}+f_{4}\frac{\partial^{2}U}{\partial x ^{2}}-f_{5}\frac{\partial\Theta^{s}}{\partial x}-f_{6}\frac{\partial\Theta^{ f}}{\partial x}=f_{7}\frac{\partial^{2}u}{\partial t^{2}}+f_{8}\frac{\partial^{2}U}{ \partial t^{2}}, \tag{21}\]  
\[\left[e_{s}+(1+e_{s}\tau^{s}_{\theta})\frac{\partial}{\partial t}+\tau^{s}_{ \theta}\frac{\partial^{2}}{\partial t^{2}}\right]\frac{\partial^{2}\Theta^{s }}{\partial x^{2}}=\left(1+\tau^{s}_{q}\frac{\partial}{\partial t}\right) \left(\frac{\partial^{2}\Theta^{s}}{\partial t^{2}}+f_{9}\frac{\partial^{2} \Theta^{f}}{\partial t^{2}}+f_{10}\frac{\partial^{3}u}{\partial t^{2}\partial x }+f_{11}\frac{\partial^{3}U}{\partial t^{2}\partial x}\right), \tag{22}\]  
\[\left[e_{f}+\left(1+e_{f}\tau^{f}_{\theta}\right)\frac{\partial}{\partial t}+ \tau^{f}_{\theta}\frac{\partial^{2}}{\partial t^{2}}\right]\frac{\partial^{2} \Theta^{f}}{\partial x^{2}}=\left(1+\tau^{f}_{q}\frac{\partial}{\partial t} \right)\left(f_{12}\frac{\partial^{2}\Theta^{s}}{\partial t^{2}}+f_{13}\frac{ \partial^{2}\Theta^{f}}{\partial t^{2}}+f_{14}\frac{\partial^{3}u}{\partial t ^{2}\partial x}+f_{15}\frac{\partial^{3}U}{\partial t^{2}\partial x}\right), \tag{23}\]  
\[\sigma_{xx}=\frac{\partial u}{\partial x}+f_{1}\frac{\partial U}{\partial x}- \Theta^{s}-f_{2}\Theta^{f}, \tag{24}\]  
\[\sigma=f_{1}\frac{\partial u}{\partial x}+f_{4}\frac{\partial U}{\partial x}- f_{5}\Theta^{s}-f_{6}\Theta^{f}, \tag{25}\]  
\[u(x,0)=U(x,y,0)=0,\frac{\partial u(x,0)}{\partial t}=\frac{\partial U(x,0)}{ \partial t}=0, \tag{26}\]  
\[\Theta^{s}(x,0)=\Theta^{f}(x,0)=0,\,\,\frac{\partial\Theta^{s}(x,0)}{ \partial t}=\frac{\partial\Theta^{f}(x,0)}{\partial t}=0, \tag{27}\]  
\[\Theta^{s}(0,t)=(1-\beta)\Theta_{o}H(t),\,\Theta^{f}(0,t)=\beta\Theta_{o}H(t), \tag{28}\]  
\[\sigma_{xx}(0,t)=0,\,\sigma(0,t)=0, \tag{29}\]  
where  
\(e_{s}=\frac{K^{s}_{s}}{\eta c^{2}K^{s}}\), \(e_{f}=\frac{K^{s}_{s}}{\eta c^{2}K^{s}}\), \(f_{1}=\frac{Q}{\lambda+2\mu}\), \(f_{2}=\frac{R_{12}}{R_{11}}\), \(f_{3}=\frac{\rho_{12}}{\rho_{11}}\), \(f_{4}=\frac{R}{\lambda+2\mu}\), \(f_{5}=\frac{R_{31}}{R_{11}}\), \(f_{6}=\frac{R_{22}}{R_{11}}\), \(f_{7}=\frac{\rho_{21}}{\rho_{11}}\), \(f_{8}=\frac{\rho_{22}}{\rho_{11}}\), \(f_{9}=\frac{F_{12}}{F_{11}}\), \(f_{10}=\frac{T_{0}R_{11}^{2}}{F_{11}(\lambda+2\mu)}\), \(f_{11}=\frac{T_{0}R_{11}R_{20}}{F_{11}(\lambda+2\mu)}\), \(f_{12}=\frac{F_{11}}{\eta K^{f}}\), \(f_{13}=\frac{F_{22}}{\eta K^{f}}\), \(f_{14}=\frac{T_{0}R_{11}R_{12}}{F_{11}(\lambda+2\mu)}\), \(f_{15}=\frac{T_{0}R_{11}R_{22}}{\eta K^{f}(\lambda+2\mu)}\).