esa-sceva/satcom-chunk-collection huggingface dataset:
# Automatic Detection System of Deteriorated PV Modules Using Drone with Thermal Camera
## 1 Introduction
The captured images, global positioning system (GPS) information, and drone status information are stored on an SD card connected to the mainboard. The saved information is later processed on an offline general-purpose computer. The proposed drone system can fly autonomously over an automatically planned flight path by our flight planning algorithm. As automatic hot-spot localization is one of the essential aspects of PV plant inspection, our system can estimate the exact GPS location of the faulty PV modules among thousands of PV modules. The main contributions of our study are as follows:  
1. We propose an automatic drone-based solution that can operate autonomously with minimal user intervention. 2. The proposed solution can detect and exactly locate the faulty PV modules in a large-scale PV power station. 3. Our approach was tested in a real environment on a large-scale 1-MW PV power station, demonstrating the practical applicability of our work. The remainder of the paper is organized as follows--Section 2 reviews related studies along with their limitations. The details about our hardware and software design are described in Section 3. Performance evaluation is presented in Section 4. The conclusions are covered in the last section of this paper.
## Main Results
### Active Rehabilitation Training Experiment  
As mentioned above, the rehabilitation experimental movements designed in this study were aimed at the degree of freedom (DoF) of flexion/extension of the elbow joint, that is, only the actuator at the elbow joint was used. The control block diagram of active rehabilitation training is shown in Figure 9. The perception module was used to obtain the relevant posture information and sEMG information required for motion recognition, and the feature values in the sliding window were preprocessed and extracted, and then input into the offline trained LSSVM model. The conversion of the control amount was input into the joint actuator, and the RBF sliding mode controller realized the position inner loop control while the impedance outer loop controller maintained the impedance relationship between the patient and the manipulator and realized the compliance control process based on the desired trajectory. The entire closed-loop rehabilitation training process completed the perception, decision-making and control steps in one control cycle, realizing active rehabilitation training for patients.  
Figure 8: Shoulder joint trajectory diagram.  
Figure 7: Elbow joint trajectory diagram.  
Due to the noise and sudden change in the position, the prediction results of the sEMG could not be applied directly to the actuator. Therefore, a fourth-order Butterworth filter was used to process the prediction results, where the low-pass cut-off frequency was set to \(f_{\text{c}}=10\) Hz, and the stepping position of the actuator was set to be no greater than 0.5R to avoid the generation of human-robot interaction forces due to large prediction values.  
The processed trajectory curve was relatively smooth, as shown in Figure 10. It can be observed that muscle instability of patients led to changes in the sEMG signals at the highest point, and further led to a decrease in the predicted trajectory within the range of the maximum prediction error \(\text{e}_{\text{max}}\), which was approximately equal to the mean absolute error (MAE). There was a certain control time delay in the active control process of the predicted expected trajectory points, but the overall tracking performance was good.  
The elbow joint torque and the human-robot interaction torque were obtained, as shown in Figure 11. Since the initial position was not completely vertical in the sagittal plane, the initial torque was not 0, and the whole process could basically reflect the movement changes. The human-robot interaction torque was maintained at about 0.2 Nm and there  
Figure 10: Comparison of predicted trajectory and processed trajectory.  
Figure 9: Active rehabilitation training control system.  
were slight fluctuations, which were due to the actual human-robot interaction process. In summary, the sliding mode adaptive impedance algorithm based on the sEMG recognition could accurately obtain the patient's motion intention and assist the patient in rehabilitation training, which has certain application value in the field of rehabilitation training.
## 1 Introduction  
In the construction of smart cities, Intelligent Transportation Systems (ITS) and the Internet of Vehicles (IoV) have become key technologies for improving traffic efficiency and driving safety [1]. The IoV leverages on-board units (OBUs) and wireless communication technologies to facilitate communication among vehicles and other entities. Based on specific application scenarios, it can be divided into two categories: vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) [2, 3]. The general entity of the system architecture of the IoV is shown in Figure 1, which contains vehicles, Roadside Units (RSUs), and Trusted Authority (TA). An OBU is a device mounted on a vehicle that is considered to have limited computing, storage, and communication capabilities; RSUs are curbside infrastructure with more computing and communication capabilities than OBUs; TA can provide the IoV with system initialization and entity registration as well as node authentication. Vehicles often exchange and share road information around them, such as road congestion, vehicle location, speed, etc., with other entities (such as other vehicles, RSUs). This information can help the vehicle dynamically judge the road traffic flow, thereby improving the experience of driver and traffic operation efficiency.  
However, due to the fact that the information exchange between vehicles and other vehicles or Roadside Units (RSUs) takes place over public channels, it is susceptible to different attacks, such as privacy breaches, impersonation attacks, ID/password guessing attacks, RSU hijacking attacks, and more. Particularly, with the extensive application of smart cards (SCs) in the authentication field, the attackers employ various hardware and software attack methods in their attempts to access the information stored within smart cards [4]. Therefore, designing a security authentication and key agreement scheme based on SCs is still a challenge.
## 4 Data Quality Control in TasMAN Using Fuzzy Rules
### Sensor Q4 QC Inputs
#### 4.1.2 Time since Last Maintenance  
Sensors will be affected, to a greater or lesser degree, by the environment in which they are deployed. Sensors exposed to excessive heat or dust for long periods of time may degrade in performance. This parameter is particularly important in a marine environment because of the presence of high salt concentrations and biofouling (algal growth, shellfish attachment, _etc._).  
Figure 2: Membership function for the parameter, time since last maintenance, for a number of sensors used in the TasMAN network.  
An example of this membership function, for one of our temperature sensors (EC-250) and the Seabird37 conductivity, temperature and pressure sensors, is shown in Figure 2. Here, S (small), M (medium) and L (large) refer to the error introduced in the sensor output by this parameter, time since last maintenance.  
The shape chosen for the membership functions reflects the observation that measurement quality from our marine sensors appears to plateau at various stages as it degrades. Figure 3 shows a conductivity time series where this plateau effect is prominent. The reason for the plateau behavior is not clear but may be a result of the growth of different organisms on the sensor (e.g., initially algae, followed later by shellfish).
# An Innovative Approach to Anomaly Detection in Communication Networks Using Multifractal Analysis
## 4 The Methodology of the Study on Network Traffic Anomaly Detection
The arrival of each new packet causes the victim to temporarily maintain a new connection to the open port for a specified period of time. After using all available ports, it stops responding to regular network traffic. The third DoS attack is ICMP DoS. It consists of flooding the victim with ping packets. The same number of packets is sent to the sender in response. Additionally, an attacker may use custom codes or send fragmented packets, which the recipient must reunite. This burdens both incoming and outgoing network channels, consuming considerable bandwidth and causing a denial of service. This attack floods the target system with ICMP packets, changing the source address. The second group of tested attacks includes brute force attacks. These attacks involve breaking the passwords or usernames of network resources or services using trial and error methods. Depending on the complexity, the process can take from a few seconds to many years. Secure Shell (SSH) and File Transfer Protocol (FTP) servers were installed on the PC1-Ubuntu host to perform tests. Then, using the hydra program available in Kali Linux, dictionary attacks were carried out by substituting passwords from [25] supplied with the system. The last group of tested attacks was a malware infection. Malware can identify different types of viruses, spyware, ransomware, rootkits, or Trojan horses. They consist of code developed by cybercriminals, designed to cause significant damage to data and systems or to gain unauthorized access to the network. A standard method of spreading malicious programs is to provide them in the form of a link or attachment in emails that require the user to click on the link or open a file to run malicious processes. Diamorphine and Reptile were used to simulate rootkit activity on the system. These tools can hide any processes and files. Additionally, a test malware file was downloaded from [26] and malware samples from [27].
# Feasibility of Obtaining Surface Layer Moisture Flux Using an IR Thermometer
## 3 Discussion of Statistical Analysis and Measurement Results  
### Paired t-Test for Controls (Kestrel and Sling Psychrometer) Assuming Equal Variances  
The experiment was conducted for three different surface conditions, namely grass, concrete and sand surfaces. Table 1 shows the results of the paired _t_-test analysis of the Kestrel and Sling Psychrometer control group dry- and wet-bulp temperature measurements for ultimate assessment of the IR Thermometer test group measurements. By applying \(p\) (T \(\leq\) t = 0.060) in the grassy area, the mean dry-bulp temperature values of 25.816 \({}^{\circ}\)C (Sdev, \(\pm\)2.930) and 24.521 \({}^{\circ}\)C (Sdev, \(\pm\)3.320), as measured by the Kestrel and Sling Psychrometer, respectively, are not significantly different for \(\alpha\)\(\leq\) 0.05. As further confirmation, the t-statistic value of 1.575 is not greater than the t-critical of 1.667 in Table 1. For concrete and sand surfaces, the values of \(p\) (T \(\leq\) t) are 0.214 and 0.342, respectively. This, in turn, suggests that the Kestrel mean dry-bulp temperature measured over concrete of 23.668 (\(\pm\)4.640) \({}^{\circ}\)C and the mean of 22.770 (\(\pm\)4.930) \({}^{\circ}\)C derived using the Sling Psychrometer are not significantly different. Likewise, the mean dry-bulp temperature measurements over sand with the Kestrel and Sling Psychrometer of 25.364 \({}^{\circ}\)C (\(\pm\)3.240) and 24.747 \({}^{\circ}\)C (\(\pm\)3.750), respectively, are statistically equivalent. In aggregate, these conclusions are confirmed by noting that the corresponding concrete/sand t-statistics values (0.797 and 0.412) are less than the t-critical thresholds (1.667 and 1.725) for both the Kestrel and Sling Psychrometer; their dry-bulp temperature mean measurements over concrete and sand are effectively the same (Table 1).  
In the case of wet-bulp temperature measurements, means of 18.910 \({}^{\circ}\)C (\(\pm\)2.710) versus 19.128 \({}^{\circ}\)C (\(\pm\)2.800), 18.378 \({}^{\circ}\)C (\(\pm\)3.500) versus 18.333 \({}^{\circ}\)C (\(\pm\)3.470) and 18.944 \({}^{\circ}\)C (\(\pm\)2.740) versus 18.763 \({}^{\circ}\)C (\(\pm\)2.960) are statistically the same with \(p\) (T \(\leq\) t) values 0.382, 0.478 and 0.441 at given \(\alpha\)\(\leq\) 0.05 for both the controls for all three surfaces. The calculated values for t-stat (\(-\)0.302, 0.055, and 0.149) are found to be lower in all three conditions compared to the t-critical (1.673, 1.667 and 1.725), which suggests that the means for wet-bulp temperatures are the same from these two controls (Table 1).
# Combating Fake News with Cryptography in Quantum Era with Post-Quantum Verifiable Image Proofs
## 9 Efficient Signature Verification in Post-Quantum VerITAS
### Proof System for the Lattice Hash
#### 9.3.4 ZeroCheck and Univariate SumCheck Relations  
Let \(\Omega_{m}:=\left\{1,\omega,\omega^{2},\ldots,\omega^{m-1}\right\}\subseteq F\) be the set of \(m\)-th roots of unity, and let \(d>m\) be a degree bound. The following two key relations are used in the proof system: ZeroCheck and Univariate SumCheck.  
The ZeroCheck relation \(R_{\text{ZC},m}\) is defined as:  
\[\begin{array}{ll}R_{\text{ZC},m}:=\left\{((pp,com_{u});(u,r))&:u\in F^{<d}[X],\\ \forall\omega\in\Omega_{m}:u(\omega)&=0,\\ commit(pp,u,r)&=com_{u}\right\}\end{array} \tag{16}\]  
This relation ensures that the polynomial \(u(X)\) evaluates to zero at all points in \(\Omega_{m}\), and that com_u is a valid commitment to \(u(X)\) using randomness \(r\).  
The Univariate SumCheck relation:  
\[\begin{array}{ll}R_{\text{SC},m}:=\left\{((pp,com_{u},s);(u,r)):u\in F^{<d} [X],\right.\\ \left.\begin{array}{c}\sum_{\omega\in\Omega_{m}}u(\omega)=s,\\ commit(pp,u,r)=com_{u}\right\}\end{array}\right.\end{array} \tag{17}\]  
This relation ensures that the sum of the polynomial \(u(X)\) evaluated at all points in \(\Omega_{m}\) equals \(s\), and that \(com_{u}\) is a valid commitment to \(u(X)\) using randomness \(r\)[21, 22].
# Life Cycle Assessment for the Production Phase of Nano-Silica-Modified Asphalt Mixtures
## 3 Methodology
### Goal and Scope Definition  
#### 3.1.1 Goal and System Boundaries  
The goal of this study is to assess the potential life-cycle environmental impacts resulting from modifying asphalt materials with nanomaterial (i.e., the environmental impacts of nano-silica-modified asphalt mixtures). Additionally, a comparison is made with the environmental impacts of unmodified asphalt mixture to provide a better understanding of the impact contribution of nanomaterials in asphalt materials to allow for informed decisions to be made. In other words, the extent to which the use of nano-silica-modified asphalt mixtures for asphalt concrete pavement is beneficial from the environmental perspective is evaluated.  
Two alternative case scenarios were examined. In CASE 1A, the environmental impact of nano-modified asphalt material was assessed. The use of nanomaterial (nano-silica), asphalt materials, and the production processes of asphalt mixtures were considered. Modification of bitumen with nanomaterial is depicted in Figure 2.  
In CASE 2A, the environmental impact of asphalt material production, excluding nanomaterial (conventional asphalt mixture), was assessed. The system boundaries which defines the unit process considered in the LCA studies [4] were limited to cover the following life cycle stages in this study: (1) raw materials extraction; (2) transportation of raw materials for a unit product manufacturing; (3) modification and production of asphalt materials in the plant. Transportation of asphalt materials  
Figure 2: Nano-modified bitumen (NMB) production.  
to the field, use, and the end-of-life were not included. The life cycle stages and key processes of nano-modified asphalt production in the plant are shown in Figure 3. The flow emissions and resource consumption (such as electricity and natural gas) for heating and the production processes were also included in the system boundaries.  
The results of this study will help practitioners in the asphalt concrete pavement industry to make informed decisions by considering the numerous benefits of nanomaterials (nano-silica) and the environmental impacts resulting from modifying asphalt mixtures with the nano-silica material.
## 4 Proposed Work
### Dynamic Sleep Scheduling  
To further reduce the energy consumption, we enable dynamic sleep scheduling in this phase. We propose a new Tsallis entropy enabled Bayesian probability (TE2 BP) algorithm to schedule the sensor nodes into sleep and active mode. The algorithm operates upon residual energy level, buffer factor and coverage expected rate metrics. The sleep scheduling slots are assigned to the sensor nodes by the corresponding cell headers. The involvement of sleep scheduling is to improve the energy efficiency in the network. The probability of a node being in active mode without considering the parameters is defined as \(P(A)\) and the probability of the node being in sleep node without considering the parameters is expressed as \(P(S)\). Let R be the sum of evidence of the values of residual energy, buffer factor and coverage expected rate. The probability of a node being in active mode with respect to the parameter evidence is represented as,  
\[P(A|R)=\frac{P(R|A)P(A)}{P(R|A)P(A)+P(R|S)P(S)} \tag{14}\]  
Similarly, the probability of a node being in sleep mode with respect to the parameter evidence is expressed as,  
\[P(S|R)=\frac{P(R|S)P(S)}{P(R|S)P(S)+P(R|A)P(A)} \tag{15}\]The threshold value for each metric is set by the Tsallis entropy and nodes having the values above the threshold value are set to active mode whereas other nodes are set to sleep mode which will be changed periodically. The entropy condition is expressed as,  
\[Ty(x_{i})=\frac{h}{y-1}\Bigg{(}1-\sum_{i}x_{i}^{y}\Bigg{)} \tag{16}\]  
In which, y is the entropy index and Ty is the threshold value. Through this method the nodes are scheduled into sleep and active mode in order to improve the energy efficiency of the network, thereby preventing the coverage holes.  
Table 3 presents the consumption of energy by every node in each mode which depicts that the energy consumption of each node in sleep mode is zero. Hence, the lifetime of the nodes in the network is further increased.
## 1 Introduction
The shared aperture cylinder cavity antenna array makes it possible to realize dual-CP radiation in a limited antenna aperture. The units of the proposed antenna array are excited by a feeding network consisting of both groove gap waveguides (GGWs) and ridge gap waveguides (RGWs). The modes and impedance transition of the feeding networks are carefully designed to guarantee the stability of the phase and amplitude distribution for each antenna unit. The antenna was built using stereo lithography appearance (SLA) 3D printing and metallization technology, which greatly decreases the difficulty and cost of the fabrication process [24; 25]. The number of array elements can be periodically expanded to conveniently construct a larger-scale antenna array. The paper is organized as follows. In Section 2, the design and optimization processes of the GWs; the dual band, dual-CP antenna unit; and the feeding network are discussed. Section 3 presents and discusses the simulated and measured results. Finally, the conclusion is drawn in Section 4.
To a certain extent, the pilot symbol power cost was reduced, as was the pilot symbol power consumption, and the noise interference was weakened. In this paper, to reduce the pilot cost and improve the accuracy of channel estimation, we treat the channel problem of estimating the DD domain as a sparse recovery problem and derive the sparse model expression from the input-output relationship of the DD domain. We provide a new sparse Bayesian framework based on the channel's sparse features, and the block successive majorization-minimization (SMM) technique is employed to handle the problem of the new prior structure. The simulation results show that, in comparison to existing reference approaches, our suggested method greatly reduces the noise interference in the situation of a low signal-to-noise ratio. The rest of the paper is organized as follows. The OTFS modulation system model is described in Section 2. The structured sparse Bayesian approach for channel estimation is presented in Section 3. The simulation results are reported in Section 4, and Section 5 concludes the paper.
## 3 Materials and Methods
### The Proposed End-to-End Solution Integrating CRF-RNN with CNNs for PCa Detection
#### 3.2.3 Experimental Setup and Implementation Details  
CNN performance might be particularly susceptible to two problems: (_i_) hyper-parameter settings and (_ii_) sensitivity to initialization values. Aiming at ensuring reliable and repeatable results, we tested several configurations and then trained multiple times.  
First, three partitions were created: training, validation, and testing, with 60%, 20% and 20% of the original dataset, respectively. For each architecture, twenty hyper-parameter configurations were randomly created; every configuration was repeatedly trained 30 times for 350 epochs or until the loss score (Binary Cross Entropy, BCE) did not improve over \(1\times 10^{-4}\) in the last 15 epochs. After the training, the model was evaluated with the test set. The configuration with the highest average AUROC value was considered the best of each given architecture. More specifically, the XmasNet-CRFpp architecture was trained through a two-step procedure: first, the XmasNet was trained; subsequently, using the weights of the best model, the mpMRI features were extracted to form an intermediary dataset. The CRF-RNN model was trained on this dataset, thus employing CRFs for postprocessing.  
The training was performed with a batch size of 6 for the state-of-the-art CNN architectures and of 1 for the hybrid CRF-RNN/CNN approach. In more detail, for the CNN architectures that do not integrate CRFs, a batch size of 6 was used, as it is the batch size value that showed a suitable trade-off between training time and performance. On the other hand, the hybrid CRF-XmasNet and XmasNet-CRFpp architectures, where the CRF was integrated within the CNN (as an end-to-end and as a postprocessing, respectively), used a batch size of 1, which was selected to avoid reaching the memory limits of the GPU (as also suggested in [31]).  
We employed a random sample algorithm, selecting one of several possible values for each hyper-parameter, as described in Table 4. Only high values for momentum \(m\) were considered in the grid search, based on good empirical results during prototyping when compared to lower values (e.g., \(m<0.9\)), as well as with the support of the literature [65].  
The proposed framework was developed in Keras [66] with TensorFlow backend [67]. Two computational platforms were used: the prototyping was conducted with a laptop equipped with 8 GB of RAM, an Nvidia 840M GPU and an Intel i7-4510U 2.00 GHz CPU; the training was performed on a remote server with 8 GB of RAM, an Nvidia 1080 ti GPU and an Intel i7-4790K 4.00 GHz CPU.