esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
For instance, tensor modeling is utilized in chemometrics to analyse spectroscopy [17], and is also applied to acquire two-dimensional direction of arrival (DOA) estimation by taking advantage of the Vandermonde structure in array manifold [18; 19]. For SAR imaging problems, tensor modeling is capable of exploiting the structured sparse distribution of the scatterers to gain more robustness. Because of the _curse of dimensionality_ in higher-order data processing [20; 21; 22], making use of the sparsity is becoming a research hotspot in recent years. In 2010, [PERSON] and [PERSON] applied tensor decomposition to propose a sparse reconstruction method, and derived the uniqueness condition of tensor decomposition [23]. [PERSON] et al. first introduced the Kronecker constraint into the construction of dictionary in 2011, and proposed the Kronecker-CS model [24]. In low rank tensor signal processing, [PERSON] et al. introduced the CS theorem into a two-step sparse signal reconstruction algorithm in 2012 [25]. The following year, [PERSON] and [PERSON] proposed two types of matching pursuit algorithms based on tensor model incorporating with CS method [26]. Although there has been little public research about combining sparse representation with tensor decomposition in SAR imaging field, potential advantages of this method with robustness and efficiency inspire us to apply it in solving the open problems. The application of higher-order CS in image processing, such as hyperspectral imaging [24; 27] provides the inspiration of this work, but this is still in the stage of preliminary study. We formulate the SAR echo signal as a Tucker model based on its Kronecker information and then construct a sparse reconstruction algorithm by utilizingthe structured sparsity of target scene. Both the theoretical analysis and numerical experiment verifications reveal that the proposed algorithm is superior to the existing methods, such as spatially variant apodization (SVA) [28; 29] and matching pursuit algorithms [30; 31; 32]. The following is the organization of other sections in this paper: Section 2 describes the received signal model of SAR imaging and its tensor modeling; Section 3 constructs the algorithm based on Kronecker constraint and multiway structured sparsity of the signal model; Section 4 provides several groups of numerical experiments to verify the proposed method, and compare with the existing methods in performance; Section 5 concludes this paper.
# A Novel Video Transmission Latency Measurement Method for Intelligent Cloud Computing
## 4 Evaluation
### Comparison of Proposed Methods with Existing Methods  
Based on the actual evaluation results, the advantages and disadvantages of the three measurement methods are listed, as shown in Table 2.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Number \(i\)**} & \multicolumn{2}{c}{**Network Port Extraction**} & \multirow{2}{*}{**Latency (ms)**} \\ \cline{2-3}  & \(\mathbf{T_{i}}\) & \(\mathbf{T_{r}}\) \\ \hline
1 & 78542 & 78542 & 0 \\
2 & 25078542 & 25078542 & 0 \\
3 & 50078965 & 51178795 & 43.9932 \\
4 & 75079107 & 9706464 & 43.9011 \\
5 & 100079433 & 103374265 & 43.8986 \\
6 & 125079707 & 129472713 & 43.9270 \\
7 & 150079966 & 155507059 & 43.9115 \\
8 & 175080285 & 181668404 & 43.8842 \\
9 & 200080505 & 207766329 & 43.9170 \\
10 & 225080814 & 233864295 & 43.9063 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Latency results in the First 10 s of video transmission.  
The measurement principle of Method 1 is the same as that of Method 3 and Method 3 uses out-of-band hash data and ieee1588 time synchronization to realize lossless remote measurement. The time value in the actual measurement uses a 32-bit counter that counts with a 25 MHz clock. Therefore, the minimum time unit of the timestamp in Method 1 and Method 3 is 40 ns. According to Equation (2), the maximum error between the arrival time of the reconstructed video and the arrival time of the original video due to \(\pm\)1 error is 80 ns and the accuracy is set as 0.1 us for statistical convenience. The measurement principle of Method 2 is similar to that of Reference [30]. Reference [30] measures time delay introduced by a video transmission system under test to the propagation of light from a light-emitting diode (LED) to a phototransistor (PT). The resistance of the PT decreases when the LED lights up in the displayed image. The sampling rate of PT is 2 kHz, yielding a precision of 0.5 ms. However, the measurement relies on the output of the display with a video camera, and it has limited precision of, e.g., 16.7 ms for a 60 Hz video camera. The actual precision of Reference [30] is subframe, the same as Method 2. The work in [26] uses an embedded barcode to measure the end-to-end delay of computer video chat applications. The precision of the method is subframe due to the influence of screen refresh and software execution speed. Reference [5] measures latency by waveform shift on an oscilloscope.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
\begin{tabular}{c} No content \\ distortion \\ \end{tabular} & Pre-encode & Yes & Per frame \\ \hline \hline \end{tabular}
\end{table}
Table 2: Pro and cons of 3 proposed methods.  
Figure 11: The delay results of method 3 in the form of web pages. The Chinese words in the picture are “unsafe”.  
The measurement accuracy can be guaranteed, but it is very inconvenient and cannot be measured online.
# Threat Analysis and Risk Assessment (TARA) Analysis of an Autonomous Emergency Braking (AEB) System
## 7 Suggested Improvements
### Compliance with ISO/SAE 21434 and ISO 24089 Standards
* **Collaborative Development of Security Protocols** Automotive manufacturers should facilitate collaboration between software developers, hardware engineers, and cybersecurity experts in the implementation of ISO standards during the whole lifecycle of the vehicle. The early involvement of cybersecurity professionals ensures the integration of security measures and the identification of vulnerabilities. Standardized protocols for data handling, access control, and encryption, such as RBAC, are important. Regular feedback loops and updates of protocols improve security and address emerging threats, enhancing risk mitigation. * **Training and Skill Development in Cybersecurity Standards** Manufacturers have to invest in target training in software, hardware, and cybersecurity teams regarding ISO/SAE 21434 and ISO 24089 [4; 5] Training on threat modeling, secure software development, incident response planning, and regular workshopswith current security practices or certifications are important. This is where collaboration on certifications with cybersecurity organizations validate the employees. This also creates a culture of compliance wherein proactive steps to improve the security posture of an organization are taken. * **Metrics and Benchmarking for Security Performance** Establishing KPIs is essential for assessing the effectiveness of cybersecurity practices in accordance with ISO standards. Manufacturers should define metrics that track security incidents, vulnerability remediation times, and employee training participation. Regular assessments enable benchmarking against industry standards, helping to identify gaps and areas for improvement. By utilizing these metrics, manufacturers can refine their cybersecurity strategies, enhancing compliance with ISO standards and fostering a resilient cybersecurity framework that adapts to the evolving automotive landscape. By applying these structured approaches to cybersecurity management, software update mechanisms, and ongoing compliance evaluation, the automotive sector can achieve consistent and thorough adherence to ISO/SAE 21434 and ISO 24089, thus building a robust foundation for secure AEB and other safety-critical automotive systems.
## 5 Transmission Model of Multipath QKD Network-Based Zero-Trust Relays
### Selection Phase: Mathematical Representation  
According to the illustrated model in Figure 5, in a zero-trust QKD relay network, the key negotiation between node \(A\) and node \(B\) occurs via multiple paths \(\{R_{1},R_{2},\ldots,R_{n}\}\). The network comprises \(n\) paths, each containing \(m_{i}\) nodes. Let \(b\) denote the number of public nodes in the paths.  
For a given path \(R_{i}\), the security state is represented by \(S_{i}\in\{0,1\}\), where \(S_{i}=1\) indicates the path is secure and \(S_{i}=0\) indicates the path is insecure. In the proposed algorithm, the paths dynamically change over time, ensuring improved security. The total probability of security of a given path \(R_{i}\) depends on the number of nodes \(m_{i}\) on that path \(R_{i}\), the trust levels \(p_{j}\) of the node \(j\) on that path, and the temporary penalties \(\delta(t)\) imposed for suspected compromise. Therefore, the adjusted security probability for the \(R_{i}\) path is defined as follows:  
\[P(S_{i})=\prod_{j=1}^{m}\max\{0,\big{(}p_{j}-\delta_{j}(t)\big{)}\},\quad 0 \leq\delta_{j}(t)<p_{j} \tag{4}\]  
where \(\delta_{j}(t)\) is the temporal penalty, defined as \(\delta_{j}(t)=\frac{W_{j}}{t^{k}},\quad 0\leq\delta_{j}(t)<p_{j},\quad k>0\), where \(W_{j}\) is the penalty weight based on historical observations of the node compromise, and \(t\) is the time. This time-dependent penalty reduces trust in the suspected nodes over time.  
In this paper, the combined security fraction is defined as \(P_{T}(A,B)\), where it describes the probability that a transmission with multipath key distribution is secure. The combined security fraction \(P_{T}(A,B)\) on the \(n\) paths is defined as follows:  
\[P_{T}(A,B)=\begin{cases}1-\prod_{i=1}^{n}(1-P(S_{i}))&\text{if at least one path is secure}\\ \prod_{i=1}^{n}(1-P(S_{i}))&\text{if all paths are insecure}\end{cases} \tag{5}\]  
If \(P_{T}(A,B)\geq P_{\text{min}}\), where \(P_{\text{min}}\) is the minimum acceptable security threshold, the transmission is deemed secure. Consequently, the distribution function of the transmission security state \(S(T)\) is defined as follows:  
\[S(T)=\begin{cases}1&P_{T}(A,B)\geq P_{\text{min}}\\ 0&P_{T}(A,B)<P_{\text{min}}\end{cases} \tag{6}\]
## 5 Experiments  
### Experimental Analyses of Monitoring Indicators  
In this section, the GPS L1 C/A code signal is tested, and the experimental scheme is shown in Figure 2. The GPS signals used in the experiment are generated by simulation, and the power ratio between GPS signals and the additional white Gaussian noise is \(-18\) dB. Broadband Gaussian interference covers the whole frequency range of GPS signals, and narrowband Gaussian interference only covers the frequency range with the center frequency of 3.563 MHz and the bandwidth of 80 KHz. The receiver bandwidth is 2 MHz.  
Figure 2: Experimental scheme of the interference monitoring.
## 4 Test Results and Analysis
### Strength Degradation  
Strength degradation is a process by which the load carrying capacity of a structural member decreases due to an increase in loading under constant displacement amplitude. \"The Code of Practice for Seismic Test Methods for Buildings\" (JGJ 101-2015) recommends that the strength degradation of structural members can be expressed in terms of a coefficient \(\lambda_{i}\) by using the following expression:  
\[\lambda_{i}=P_{j}^{i}/P_{j}^{i}, \tag{1}\]  
where \(P_{j}^{i}\) is the \(j\)th level of loading displacement (\(\Delta/\Delta y=j\)) and represents the peak point load of the \(i\)th loading cycle. Similarly, \(P_{j}^{i}\) is the \(j\)th level of loading displacement (\(\Delta/\Delta y=j\)) and represents the peak point load of the first loading cycle.  
To elucidate the variations in the load carrying capacity of the monolithic steel frame composite structure with increasing load cycles when using NEDCs, the variations in the strength degradation coefficient with loading level for all specimens are given in Figure 17.  
The figure shows that the strengths of specimens SF-2 and SF-1 in the PD and ND are extremely stable, with the strength of specimen SF-1 being stable until a displacement of 40 mm. For the same displacement magnitude, the maximum load value corresponding to the final cyclic loading and the load ratio corresponding to the first cyclic loading is above 0.88. This indicates that the specimens undergo less strength degradation and exhibit better workability under low-cyclic loading. The maximum load values corresponding to the second cycle for specimen SF-1 at the vertex displacements of 40 and 75 mm and for specimen SF-2 at the vertex displacement of 75 mm were significantly lower than those corresponding to the first cycle. This is due to the crush damage received at the bolt holes in the wall plate connectors during the second loading.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Specimens** & **Direction** & \(P_{y,t}\)**/**kN** & \(\Delta_{y,t}\)**/mm** & \(P_{mt,t}\)**/**kN** & \(\Delta_{m,t}\)**/mm** & \(P_{ft,t}\)**/**kN** & \(\Delta_{f,t}\)**/mm** \\ \hline SF-1 & (+) & 131.97 & 39.53 & 169.19 & 75.35 & 143.81 & 121.51 \\  & (\(-\)) & 110.71 & 42.41 & 141.94 & 90.35 & 120.65 & 120.34 \\ \hline SF-2 & (+) & 143.03 & 42.19 & 183.37 & 89.69 & 155.87 & 146.23 \\  & (\(-\)) & 123.61 & 43.37 & 158.47 & 90.68 & 134.69 & 145.21 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Characteristic points in the skeleton curves of SF-1 and SF-2.
# Text Classification of Patient Experience Comments in Saudi Dialect Using Deep Learning Techniques
## 3 Proposed Methodology
### Trained Models
#### 3.3.2 Bidirectional Gated Recurrent Unit  
The BiGRU is a variant of the GRU architecture, which is commonly used in NLP tasks, such as text classification and sequence labeling. It consists of two GRU layers, one processing the input sequence forward and the other processing it backward, allowing the network to capture context from both directions. The outputs from both layers are concatenated and fed into a dense layer for the final prediction. Using this deep learning architecture, we developed three different models with the intention of enhancing the performance.  
* **BiGRU with AraVec static word embeddings:** Following the same procedure used to build the BiLSTM model, AraVec pre-trained static word embeddings were used to vectorize the comments, which were then fed into a BiGRU model through an embedding layer, using the preset hyperparameter values listed in Table 14.
* **BiGRU with AraVec static word embeddings and hyperparameter tuning**: To obtain the hyperparameter combination that results in the best performance, we experimented with 30 random combinations of the BiGRU hyperparameters listed in Table 15. Table 16 provides the hyperparameter values for the best model found among the 30 trials in this experiment.
* BiGRU with PX-Vec static word embeddings: PX-Vec embeddings were used to represent the comments, then fed into a BiGRU model through an embedding layer. The hyperparameter values mentioned in Table 14 were used. Additionally, cross validation was applied in order to examine the reliability of our model.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Hyperparameter** & **Set of Values** \\ \hline Embedding layer & 300 \\ Bidirectional LSTM (activation = tanh) & 32 \\ Bidirectional LSTM (activation = tanh) & 32 \\ Bidirectional LSTM (activation = tanh) & 32 \\ Bidirectional LSTM (activation = linear) & 128 \\ Dropout & 0.2 \\ Dense (activation = sigmoid) & 25 \\ Optimizer & RMSprop \\ Recurrent dropout for LSTM & 0.4 \\ Loss & Binary Cross-entropy \\ Learning Rate & 0.001 \\ Epochs & 10 \\ Batch Size & 128 \\ \hline \hline \end{tabular}
\end{table}
Table 13: The structure of the best BiLSTM model found through hyperparameter tuning.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Hyperparameter** & **Value** \\ \hline Embedding layer & 300 \\ Bidirectional GRU (activation = linear) & 128 \\ Dropout & 0.2 \\ Bidirectional GRU (activation = linear) & 25 \\ Dropout & 0.2 \\ Dense (activation = sigmoid) & 25 \\ Recurrent dropout of GRU & 0 \\ Optimizer & Adam \\ Loss & Binary cross-entropy \\ Learning Rate & 0.001 \\ Epochs & 10 \\ Batch Size & 128 \\ \hline \hline \end{tabular}
\end{table}
Table 14: The BiGRU model structure and hyperparameter setup.
# Current State of Robotics in Hand Rehabilitation after Stroke: A Systematic Review
## 2 Materials and Methods
### Control Strategy of Hand Rehabilitation Robot  
The interaction control between the robot and the patient is a very important aspect in the research of hand rehabilitation robots, because the hand rehabilitation robot is interacting with the affected limb with impaired motor function, and the patient is the object with autonomous motor awareness. First, the interactive control creates a safe, comfortable, natural and active training environment for the patient, which prevents the patient's limbs from confronting the robot due to abnormal muscle activities, such as spasms and tremors and protects them from secondary injuries. Secondly, the interactive control will obtain the patient's active movement intention from the sensor signals and encourage the patient to actively participate in the movement to achieve the so-called active training, thus improving the rehabilitation effect. Depending on the signal used to obtain the active movement intention, the interaction control strategy between the robot and the patient can be basically divided into two categories: (1) control methods based on force signals; (2) control methods based on biomedical signals.
## 2 Background
As wireless communication systems using radio frequencies tend to require several mW when transmitting, reduced to \(\upmu\)W on average using a sleep cycle, and as modern batteries can generate at best 1 J/mm\({}^{3}\)--existing motes will have a life expectancy of around 14 h operation using a cubic mm battery [14]. Micro fuel-cells, whose development is being driven by the mobile phone industry [15], are predicted to store around 10 J/mm\({}^{3}\), increasing the mote's life tenfold [16]. It is reasonable to assume that advances in sensor, communication and processing design will reduce the power needed, however this still only provides a finite life expectancy. It is therefore likely that future networks will require some means of scavenging and storing energy from their surroundings. This may be in the form of vibration powered energy scavengers [17,18], thermogenerators [19] or surface mounted solar panels [20]. A number of excellent reviews, which discuss the issues involved in wireless sensor networks already exist, for example [21-23]. Examples of large scale deployments of the technology include the Smart Pebble, so called because it is the size of a typical piece of rock aggregate, which has been used to monitor chlorine levels in concrete structures [24]. The device is based on RFID technology and the chlorine sensor can be interrogated and powered remotely. Also, a joint venture between Purdue University, Notre Dame University and the EmNet Corporation has resulted in a wireless sensor network for a sewage system [25]. The system uses a citywide network of 105 manhole-mounted sensors and \"smart valves\" to automatically control storm runoff. The PipeNet project was designed to detect leaks in water pipe networks [26]. It was based on the Intel Mote Sensor Node which uses Bluetooth to transmit data from the various sensors. The system used 6 V, 12 Ah batteries which were shown to last around 55 days using the systems sleep mode when not transmitting. This can be compared to the Golden Gate project where Crossbow Mode hardware was used. In this case, the battery used supplied 18 Ah, but only lasted 23 days due to the high data transfer rates used [27]. In addition, the Intelligent Trench project makes use of radio-frequency identification (RFID) technology and GPS asset marking for locating buried utilities [28].
## 7 Conclusions and Limitations  
This study on the application of TL to cross-condition fault diagnosis of an aircraft ECS has demonstrated that TL-based algorithms, TCA and JDA, significantly improve the diagnosis accuracy of unlabelled ECS data under unknown operating conditions, compared to the non-TL approach. While the non-TL approach produces 63.72% average predictive accuracy across the four operating conditions, TCA improves the accuracy to 94.89%, and JDA achieves an average accuracy of 78.87%. Testing TCA and JDA on target domain data with different composition of cases than the source domain, TCA achieved average accuracy of 95.22%, and JDA has 77.47% accuracy over all scenarios. Contrary to the common belief that JDA achieves higher accuracy over TCA, the results above show that, for the dataset used, TCA is a more robust and accurate TL algorithm than JDA. This is because the accuracy of JDA is vulnerable to false pseudo label prediction during its iterative process, and the similarity of cases with different labels is likely to lead to such cases.  
Although unlabelled target domains are assumed, the accurate prediction of healthy cases from TCA enabled the conversion of nominal data to deviation data from healthy baselines, which is a common practice in fault diagnosis studies. Using deviation data, the predictive accuracy following the non-TL approach has improved to 72.70%, proving its value for cross-condition fault diagnosis problems. However, TCA remains the best strategy for this problem, owing to its ability to distinguish between overlapping classes using source domain knowledge.  
Regarding the real-life application of fault diagnosis on ECS, certain limitations of this study are worth considering. Firstly, imperfection in real ECS data may exceed the range considered in this study. For example, incorrect data collection in case of sensor malfunction would produce outliers in the dataset. How these outliers affect the TL-based fault diagnosis has not been discussed. Secondly, this paper has focused on single fault modes, but multiple faults are possible at the same time in real operations of ECS. Hence, advanced methods need to be developed to cover the possibility of multiple fault modes.  
Conceptualization, L.J., C.M.E. and I.K.J.; formal analysis, L.J., C
## 2 Materials and Methods
### Design of the DC-DC Controllers
#### 2.2.3 Signal Filtering  
The voltage and current values were measured in the solar panel with a SIGLENT SHS1062 oscilloscope and a sampling frequency of 20 kHz at the ADC acquisition points of the microinverter control card.  
As the discrete Fourier transform shows that the panel current has high-frequency noise (Figure 11), a Butterworth-type low-pass IIR filter was designed with a cutoff frequency of 115 Hz and order of 2 to eliminate the harmonics from the grid and guarantee high attenuation in high-frequency components. The filter is described by:  
\[\frac{U(z)}{E(z)}=k\frac{b_{0}+b_{1}z^{-1}+b_{2}z^{-2}}{1+a_{1}z^{-1}+a_{2}z^{ -2}}, \tag{43}\]  
where the gain \(k=52.25\times 10^{-6}\) and  
\[b_{0}=1,\quad b_{1}=2,\quad b_{2}=1,\] \[a_{0}=1,\quad a_{1}=-1.97945,\quad a_{2}=0.97966.\]  
Figure 10: 2P2Z controller with a double saturator.  
An eight-point-average filter was implemented for the panel voltage signal \(V_{pv}\) because considerable high-frequency magnitudes were not observed in the Fourier transform. The filter is described by:  
\[y(n)=\frac{1}{8}\sum_{k=0}^{7}x(n-k). \tag{44}\]