esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### Device Inspection Results

Table 3: Device inspection results. * Complementary angle to Programming angle: 47.489\({}^{\circ}\) (angular deviation: 0.006\({}^{\circ}\)) The measured programming angle of 42.505\({}^{\circ}\) and the complementary angle of 47.489\({}^{\circ}\) demonstrate an angular non-closure of merely 0.006\({}^{\circ}\). This minimal deviation confirms the device's exceptional capability to precisely orient the workpiece relative to the barrel cutter, which is essential for maintaining the correct programming angle throughout the machining process and achieving the desired surface geometry. Table 3 summarizes the device inspection results, providing a clear overview of the dimensional and angular precision achieved. The analysis of the obtained results indicates excellent conformity of the device with the designed specifications. As presented in this section and Table 3, the dimensional deviations of the surfaces are very small (e.g., surface flatness between 0.002 mm and 0.012 mm), suggesting precise manufacturing of the components and their correct assembly. Furthermore, the angular accuracy of the device is notably high, with an angular non-closure of only 0.006\({}^{\circ}\), demonstrating its ability to position the semi-finished product with the necessary precision for machining with barrel tools. The observed deviations can be attributed to inherent errors in the manufacturing process, measurement uncertainties, and material variations, but their magnitude is considered minimal, having a negligible impact on machining accuracy for the intended applications. Based on the results obtained, the following aspects can be stated:  
* The device positioned the workpiece with small dimensional deviations, indicating good accuracy in achieving the designed geometry. * The angular accuracy of the device was also high, with an angular non-closure of only 0.006\({}^{\circ}\). * The sources of deviations can be attributed to manufacturing errors, measurement errors, or material variations. * The impact of the deviations on machining accuracy is considered minimal, given their small values.
## 3 Method
### Landslide Hazard Identification Method and Construction of Interpretation Feature Library  
#### 3.3.1 Comprehensive Method for Landslide Hazard Identification  
Traditional landslide hazard identification methods typically rely solely on ground deformation data derived from InSAR, which frequently result in missed detections and false identifications, reducing accuracy and increasing the burden of field verification. To address these issues, this study integrates terrain features, proximity to rivers, optical remote-sensing interpretation, and InSAR deformation monitoring results to comprehensively extract landslide deformation information, thereby enhancing identification accuracy (Figure 5).  
The detailed methodology of the comprehensive landslide hazard identification approach is as follows: (1) based on the absolute values of surface deformation rates obtained through InSAR monitoring, deformation levels are categorized into four classes: 0-5 mm/a indicating low deformation, 5-15 mm/a indicating moderate deformation, 15-25 mm/a indicating significant deformation, and >25 mm/a indicating severe deformation. (2) To further enhance identification accuracy, an interpretation index system based on optical remote-sensing imagery is established, focusing on landslide characteristics such as accumulations at the front edge, tension cracks at the rear edge, notable subsided terraces or scarps, and negative terrain features markedly inconsistent with surrounding areas. (3) Considering the correlation between gravitational potential energy required for landslide occurrence and slope gradient, the study area slopes are classified into six categories: 0\({}^{\circ}\)\(\sim\)5\({}^{\circ}\), 5\({}^{\circ}\)\(\sim\)15\({}^{\circ}\), 15\({}^{\circ}\)\(\sim\)25\({}^{\circ}\), 25\({}^{\circ}\)\(\sim\)35\({}^{\circ}\), 35\({}^{\circ}\)\(\sim\)45\({}^{\circ}\), and >45\({}^{\circ}\). Additionally, accounting for significant water erosion impacts in loess regions, distances from river valleys are categorized into three intervals: 0-250 m, 250-500 m, and >500 m, each corresponding to varying probabilities and potential scales of landslide hazards. (4) Threatened objects, including residential buildings, major transportation routes, and engineering facilities, are integrated into the analysis to determine whether identified areas should be explicitly designated as landslide hazard zones. Based on these relationships, a comprehensive landslide hazard identification matrix model was constructed (Table 2).
## 2 Methodology
### Watermark Detection  
Watermark detection for copyright protection involves watermark extraction, the inverse process of watermark construction, and watermark inspection, the comparisons between watermark images extracted from the vector maps and watermark images stored in the copyright center.  
To extract watermarks, vector maps to be detected are preprocessed into individual blocks and both feature and non-feature vertices are obtained. Zero-watermarks \(Z_{n}\) and \(Z_{f}\) are obtained from the Intellectual Property Rights center and the grouping and transformation processes are performed to obtain the watermark secret keys \(Z_{n}^{\prime}\) and \(Z_{f}^{\prime}\), respectively. Following the same processes as watermark construction, both DTM and SVD are processed to extract the angle sequence \(A_{n}^{\prime}\) and the singular value binary table \(B_{f}^{\prime}\). The operators of exclusive disjunction (XOR) are performed between the watermark sequence \(Z_{n}^{\prime}\) and the angle sequence \(A_{n}^{\prime}\), and between the watermark sequence \(Z_{f}^{\prime}\) and the singular value binary table \(B_{f}^{\prime}\), respectively, such that encrypted watermark images are obtained according to the watermark sizes. The watermark image \(W^{\prime}\) for the vector maps to be detected can then be obtained from the encrypted watermark images after inverse operation of the Arnold scrambling.  
Watermark inspection needs to make comparisons between watermarks extracted and stored, whereas main indicators in this study used for assessment include watermark similarity, bit error rate and watermark capacity. The Normalized Correlation (\(NC\)) coefficients are used to assess the similarity between images [11] as follows:  
\[NC=\frac{\sum_{i,j}W_{i,j}*W^{\prime}_{i,j}}{\sqrt{\sum_{i,j}W^{2}_{i,j}}\sqrt{ \sum_{i,j}W^{\prime 2}_{i,j}}} \tag{9}\]  
where \(W_{i,j}\) and \(W^{\prime}_{i,j}\) are stored and extracted watermarks at the coordinates of \((i,j)\), respectively.  
The Bit Error Ratio (\(BER\)) is a quantitative metric to evaluate the specific error size of extracted watermark [54]:  
\[BER=\ \frac{100}{L}\sum_{i,j=0}^{L-1}\left\{\begin{array}{ll}0&W_{i,j}=W^{ \prime}_{i,j}\\ 1&W_{i,j}\
eq W^{\prime}_{i,j}\end{array}\right. \tag{10}\]  
where \(L\) is the bit length of the watermark information.  
The metrics of \(NC\) and \(BER\) are useful to assess the accuracy of the watermarks. \(NC\) ranges from 0 to 1, where high \(NC\) denotes strong similarity. When the vector maps are not altered, \(NC\) is 1 and \(BER\) is 0. If being attacked by different operations, the higher the \(NC\) is and/or the smaller the \(BER\) is, the easier the watermark can be recognized, and vice versa.  
Watermark capacity is also an important aspect to assess the ability of a watermarking scheme on protecting digital data. Given that vertex are the basic elements for vector maps, an indicator to watermark capacity is defined as the ratio between number of embeddable bits and number of vertices.
## 3 Methods
### KAN-BiLSTM Moisture Ratio Prediction Model
#### 3.4.1 KAN (Kolmogorov-Arnold Networks)  
The theoretical foundation of the KAN is based on the Kolmogorov-Arnold representation theorem, which states that any multivariate, continuous, and smooth function defined over a bounded domain can be decomposed into a finite sum of univariate continuous functions [15]. Building on this principle, KAN parameterizes the learning of high-dimensional functions as a process of adaptively combining univariate basis functions using polynomial transformations [25]. The structural innovation of KAN lies in its ability to learn univariate activation functions at the network's edge, introducing nonlinear mechanisms that enable effective modeling of complex functional relationships. This substantially enhances the network's capacity to represent temporal features.  
Theoretical analyses and empirical studies have demonstrated that KAN exhibits superior neural scaling properties compared to conventional architectures. This advantage arises primarily from two factors: (1) the replacement of parameterized weight matrices with analytically defined univariate basis functions that can be individually fine-tuned, and (2) the relaxation of constraints imposed by fixed activation functions, allowing for dynamic optimization of activation patterns.  
Formally, for an n-dimensional input-output system, KAN can be modeled as a function matrix that maps an n-dimensional input to an n-dimensional output [20]. A single KAN layer, as illustrated in Figure 7, can be expressed using Equation (11) and the matrix form in Equation (12) [19; 26]:  
\[x_{l+1,j}=\sum_{i=1}^{n_{l}}\tilde{x}_{l,j,i}=\sum_{i=1}^{n_{l}}\phi_{l,j,i}(x _{i,j}),\ j=1,\dots,n_{l+1} \tag{11}\]\[X_{l+1}=\begin{pmatrix}\phi_{l,1,1}(\cdot)&\dots&\phi_{l,1,n_{l}}(\cdot)\\ \phi_{l,2,1}(\cdot)&\dots&\phi_{l,2,n_{l}}(\cdot)\\ \vdots&\vdots&\vdots\\ \phi_{l,n_{l+1},1}(\cdot)&\dots&\phi_{l,n_{l+1},n_{l}}(\cdot)\\ \end{pmatrix}X_{l} \tag{12}\]  
Among them, \((l,\,i)\) represents the \(i\)th neuron in the \(l\) layer, and \(x_{l,i}\) represents the activation value of the \((l,\,i)\) layer neurons. The pre-activation value of \(\phi_{l,j,i}\) was \(x_{l,i}\), and the post-activation value of \(\phi_{l,j,i}\) was \(\tilde{x}_{l,j,i}\) = \(\phi_{l,j,i}(x_{l,j})\). KAN is generally composed of multiple KAN layers. When the input vector \(X\) is given, the output of KAN is [4]:  
\[KAN(X)=(\phi_{L-1}\circ\phi_{L-2}\circ\dots\circ\phi_{1}\circ\phi_{0})X \tag{13}\]  
In this equation, the intermediate matrix corresponds to the function matrix of the \(l\)-th KAN layer.  
In this study, the KAN layer functions as the feature extraction module within the KAN-BiLSTM model. By embedding KAN into the architecture, the impact on training outcomes was analyzed to evaluate its effectiveness in enhancing prediction performance.
## 4 Application Testing and Analysis
### Example Assessment Results  
The key indicator data for the Zhangcungou and Longtan slopes were input into the trained risk assessment model. Figure 9 displays the assessment results, demonstrating the model's capability to evaluate slope stability under varying conditions.  
In the figure, red represents the value of output result \(\theta_{i-A}\); orange represents the value of output result \(\theta_{i-B}\); yellow represents the value of output result \(\theta_{i-C}\); blue represents the value of output result \(\theta_{i-D}\); and the color bar at the bottom represents the risk level of the slope.  
The figure shows that the Zhangcungou slope was at the alert level on 12 September, which was attributed to continuous rainfall on the 10 th and 11 th days, leading to increased soil moisture content, decreased soil shear strength, and reduced slope stability. Heavy rainfall on 20 September further decreased stability in both the Zhangcungou and Longtan  
Figure 8: Key indicator data from 1 September to 30 September. (**a**) Zhangcungou data; (**b**) Longtan data.  
Figure 9: Results of risk level assessment from 1 September to 30 September. (**a**) Zhangcungou assessment results; (**b**) Longtan assessment results.  
slopes. Consequently, both slopes were upgraded by one risk level on the 21 st, owing to the lagged nature of the process of rainfall infiltration altering soil moisture content.  
The experimental results confirmed the validity and reliability of the hydropower station reservoir area slope landslide risk assessment model. Its risk assessment results were highly consistent with the actual situation, providing a long-term progressive change trend in geologic hazards for the safety management of hydropower station reservoir area slopes. This timely guidance on disaster prevention and mitigation is crucial for power station operations.
# A Survey on an Energy-Efficient and Energy-Balanced Routing Protocol for Wireless Sensor Networks
## 3 Terminologies Used in WSNs
### Average Network Energy  
The knowledge of average network energy is used by developers of WSNs to optimize the energy consumption by SNs. The average network energy at any given data transmission round can be expressed as:  
\[E_{av}=\frac{\sum\limits_{1}^{n}E_{re_{n}}}{n}, \tag{2}\]  
where \(n\) is the total number of SNs in the network. The \(E_{av}\) is widely used as a decision variable in designing energy-efficient and energy-balanced routing protocols for WSNs. In clustering-based routing protocols, the \(E_{av}\) is defined as the energy threshold in selecting the CHs for a particular data transmission phase in order to avoid network segregation. Some other energy-efficient and energy-balanced routing protocols use \(E_{av}\) or AE as the defined energy threshold in choosing the relay nodes towards the BS during that transmission. We studied and classified the different energy-efficient and energy-balanced routing protocol designs for WSNs that specify the AE as an input decision variable.
## 3 Results
### Effects of Catholyte Recirculation Rate
At 50 mL min\({}^{-1}\), the DO concentration decreased from \(0.9\pm 0.3\) mg L\({}^{-1}\) to \(0.3\pm 0.2\) mg L\({}^{-1}\), and it decreased from \(2.1\pm 0.6\) mg L\({}^{-1}\) to \(0.6\pm 0.3\) mg L\({}^{-1}\) at 200 mL min\({}^{-1}\). As the recirculation rate was further increased to 400 mL min\({}^{-1}\), the DO concentration decreased from \(4.5\pm 0.8\) mg L\({}^{-1}\) to \(1.5\pm 0.2\) mg L\({}^{-1}\) (Figure 3) This dissolved oxygen gradient was suitable for the creation of aerobic and anoxic microenvironments, which was an ideal habitat for nitrifiers and denitrifiers. In addition to power generation, nitrogen removal efficiency is also an important indicator for evaluating MES performance. Therefore, ammonium nitrogen removal and total nitrogen (TN) removal were also investigated at each recirculation rate. Ammonium nitrogen removal gradually increased from \(53.4\pm 1.6\%\) to \(89.1\pm 3.6\%\) as the recirculation rate increased from 50 mL min\({}^{-1}\) to 400 mL min\({}^{-1}\), thus decreasing the ammonium concentration to 4.5-17.6 mg L\({}^{-1}\) in the effluent. However, the TN removal decreased from \(79.7\pm 2.5\%\) to \(60.3\pm 2.8\%\) as the recirculation rate increased from 200 mL min\({}^{-1}\) to 400 mL min\({}^{-1}\), resulting in the accumulation of nitrate and nitrite in the concentration of \(11.6\pm 1.2\) at 400 mL min\({}^{-1}\). The reduction in TN removal at 400 mL min\({}^{-1}\) was probably because of the increased DO concentration at lower sampling point (Point C: \(2.2\pm 0.7\) mg L\({}^{-1}\), Point D: \(1.5\pm 0.2\) mg L\({}^{-1}\)), which would inhibit the denitrification process [32; 33]. Though a higher recirculation rate (400 mL min\({}^{-1}\)) could improve power generation, TN removal declined by 24.1% compared with that at 200 mL min\({}^{-1}\), and obviously more energy would be required to drive the higher circulation. Consequently, the TB-MES was operated at 200 mL min\({}^{-1}\) in the following tests.
# Ultrasound-Guided Procedures in Common Tendinopathies at the Elbow: From Image to Needle
## 3 Common Elbow Pathologies
### Distal Triceps Tendinopathy
#### 3.4.2 US Scanning and Guided Injection  
The triceps tendon (TT) evaluation can be performed with the patient positioned semi-supine on the examination bed. Using this positioning technique, the elbow rests on the patient's stomach, while the elbow is free for flexion/extension dynamic assessment. The dynamic evaluation is useful to assess for the TT continuity but also to rule out the presence of fluid/loose bodies in the olecranon fossa. For the TT assessment--the longitudinal view--the examiner can see the myotendinous junction and the TT insertion footprint on the olecranon (Figure 12a). The US features of TT tendinopathy comprise local hypoechogenicity, tendon thickening, hypervascularity, calcifications, and bony irregularities/spur (Figure 12b). Triceps tendon tendinopathy is rare; however, inflammation of the overlaying olecranon bursa is more common, particularly following trauma or in patients with inflammatory diseases. The way to aspirate olecranon bursitis should be decided with respect to local anatomy (e.g., septa, adjacent structures). Notably, US guidance provides clinicians freedom in all interventional procedures [88]. There is little evidence using US-guided injections in triceps tendinopathy. [PERSON] et al. [89] reported a case report of a patient with distal triceps partial tear treated with PRP injection and rehabilitation, resulting in pain-free activities of daily living and return to preinjury sports activity after four months. When injecting TT, a potentially vulnerable structure would be the ulnar nerve, which passes medially along the tendon. Using the lateral approach and the US guidance can reduce the risk of atrogenic injury to the nerve (Figure 13).  
Figure 12: Ultrasound images of the distal triceps tendon (TT): (**a**) normal longitudinal image of the TT; (**b**) US image that demonstrates a prominent spur (white arrow) at the olecranon’s TT insertion site.  
Figure 13: Ultrasound imaging and injection of the distal triceps tendinopathy: (**a**) the patient in the decubitus position with the shoulder internally rotated; (**b**) the same procedure as described before in detail—the needle is inserted from lateral to medial.
# Unmanned Aerial Vehicle Based Wireless Sensor Network for Marine-Coastal Environment Monitoring
## 3 SIMMA System Architecture
### SIMMA Graphic User Interface  
The GUI is a single page web application built with Angular 2 [53] that was designed specifically for its use in this project as a configuration and visualization tool. The selection of a web-based interface was sought out to avoid installing custom software on the client's equipment and, thus, reducing the compatibility issues amongst different operating systems, as well as providing a self-contained solution (Figure 12).  
To access its features, the control node must be connected through a network interface and afterwards, in a web explorer, access its predefined IP address. As the BBB provides connectivity through a Virtual Ethernet Port interface over USB, it is possible to connect this device through this serial port to any computer, simplifying its usage.  
Having accessed the interface, the user is able to configure a control node as a master or monitor. In master mode, the functionality of the GUI becomes far more complex, as it allows the user to not only configure the master node, but also the sensor nodes through interactions with the master. There are two modes of operation for the master node: manual and automatic. For configuration in a laboratory or a ground station, the manual mode must be selected, as it allows the user to set the LoRa RF settings of the master node and slave nodes (sensors and monitors), as well as the operation settings of the sensor nodes. Moreover, in this mode the user can manage the data from the sensor nodes, downloading or deleting them, and graphically visualize all the downloaded sensor data, as well as exporting them in several formats (xml, json, csv, or kml). The automatic mode is used when  
Figure 11: Delta Wing UAV used in the SIMMA project.  
the node is to be mounted on the UAV, having previously configured the rest of slave nodes with the same RF settings.  
When in monitor mode, the user is able to view the complete message log of the master node in real-time, including the localization of sensors nodes, network activities, and data extraction process. To be able to view a given master node, the monitor node must also be configured with the exact same LoRa RF configurations.
# Blind Recognition of Forward Error Correction Codes Based on Recurrent Neural Network
## 4 Simulation Results
### FEC Code Data Test Environment  
The experimental setup simulates generating 1 million samples, each of which is 10,000 in length and stored in int32. The SNR of the sample is evenly distributed between \(-10\) dB and \(10\) dB. The data set containing 70% of the samples was used as the training channel coding type recognition model and the data set containing 30% of the samples was used as the performance evaluation of the model. All training and testing are done in Keras using Nvidia 1080 Ti GPU. In the training process, we set learning rate as 0.002, if training loss has not reduced after every 10 epochs, set the study rate as 10% of the primary. Training network using batch size as 16 to train 200 epochs.
## 3 The Time-Varying Global Reliability Analysis Based on the HM-IMEM  
### The Traditional Maximum Entropy Method Based on the First four Moments  
The traditional maximum entropy method (MEM) uses the first four raw moments \(M_{Z,k}\) (\(k=0,1,\ldots,4\)) of the performance function \(Z\) as constraint conditions and the Shannonentropy \(H_{\rm s}\) of the \(Z\) as the objective function. The following optimization model can be established as [24]:  
\[\begin{split}\text{max}H_{\rm s}=\int_{-\infty}^{\infty}f_{Z}(z) \ln f_{Z}(z)dz\\ s.t.\left\{\begin{array}{l}\int_{-\infty}^{\infty}z^{k}f_{Z}(z )dz=M_{Z,k},k=1,2,3,4\\ \int_{-\infty}^{\infty}f_{Z}(z)dz=1\end{array}\right.\end{split} \tag{6}\]  
Based on the penalty function method [25; 26], Equation (6) can be converted to the unconstrained optimization problem, namely.  
\[L=-H_{\rm s}+\lambda_{0}\bigg{(}\int_{-\infty}^{\infty}f_{Z}(z)dz-1\bigg{)}+ \sum_{k=1}^{n}\lambda_{k}\bigg{[}\int_{-\infty}^{\infty}z^{k}f_{Z}(z)dz-M_{Z,k }\bigg{]} \tag{7}\]  
By solving the unconstrained optimization problem shown in Equation (7) with the help of the Newton method or quasi-Newton method [27; 28; 29], coefficients \(\lambda_{0}\), \(\lambda_{1}\), \(\cdots\), and \(\lambda_{4}\) can be obtained. Then, the maximum entropy probability density function (PDF)\(f_{Z}(z)\) and the failure probability \(P_{f}\) of the \(Z\) can be calculated as follows, respectively:  
\[f_{Z}(z)=\exp\left(\lambda_{0}-\sum_{k=1}^{4}\lambda_{k}z^{k}\right) \tag{8a}\] \[P_{f}=P(Z\leq 0)=\int_{-\infty}^{0}\exp\left(\lambda_{0}-\sum_{k=1}^{4} \lambda_{k}z^{k}\right)\text{d}z. \tag{8b}\]  
Although the traditional MEM is widely used in reliability analysis, there are still some issues with its accuracy and convergence. This is because the traditional MEM usually uses the dimension reduction method (DRM) for statistical moment estimation and only considers the first four moments as constraints [24]. This may lead to poor precision of the reliability index if the first four moments are not accurate enough or if the probability information is incomplete [30]. In addition, the traditional MEM usually determines the maximum entropy integral limits subjectively, which can result in numerical instability or even divergence of the MEM [31].  
In this regard, the generalized F-discrepancy (GF-discrepancy)-based point selection method [32], which can effectively control the estimation error, is introduced to estimate high-order statistical moments of the performance function based on Equations (6)-(8b). Meanwhile, the integral limits determination method and convergence criterion are introduced to rationally determine the integral limits and the order of the required high-order moments of the MEM, respectively. Furthermore, a new maximum entropy method is proposed noted as the high-order moments-based improved maximum entropy method (HM-IMEM). Finally, the HM-IMEM is applied to analyze the time-varying global reliability of the in-service transmission tower.