esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experimental Setup
### Reducing Order of the IBIF Filter  
In order to reduce the complexity of the Kalman filter, we need to reduce the size of the matrices \(\mathbf{A}\) and \(\mathbf{C}\) in Equations (10) and (11). This is necessary due to the computational cost of Kalman filter in the multiplications of state-space matrices of size \(550\times 550\) when processing running speech. Since \(\mathbf{A}\) and \(\mathbf{C}\) depend on the length of the neck-skin impulse response \(h(n)\), the latter is truncated in the middle region and then windowed (Hanning function) to 350 points. This procedure seeks to maintain the performance of IBIF filter because most of the energy of the impulse response is concentrated in the middle section, while the extremes are considerably low in energy. As an example, Figure 7 shows a given \(h(n)\) in blue and the resulting truncated version in orange. The magnitude of the frequency response is shown in Figure 8.  
Figure 7: Neck-skin impulse response for a healthy female subject, full impulse (blue) and truncated version with a Hanning window (orange).
# A Hands-On Laboratory for Intelligent Control Courses
## 3 Project Description and Methodology  
The control loop is presented in Figure 1, which consists of the Direct Current (DC) motor, the power stage, the encoder and the Data Acquisition (DAQ) card. Furthermore, the control strategy to obtain the control requirements is fuzzy logic.  
\(\theta^{*}(s)\) is the reference of the angular position of the motor shaft, \(E(s)\) is the error, \(U_{c}(s)\) is the control signal sent to the DAQ, \(U_{PWM}(s)\) is the PWM signal in the form of voltage that is sent to the power stage, \(U_{M}(s)\) is the motor activation signal and \(\theta(s)\) is the current position of the motor shaft.  
The data acquisition card is the unit in charge of reading the position of the motor and sending the control signal for its correction if necessary. The process starts after reading a position byte from the encoder and sends it as feedback to the control loop to determine the error. The controller generates the control signal \(U_{C}\), which is converted to a PWM signal in the DAQ by \(U_{PWM}\). However, this last signal must be conditioned in the power stage and the signal \(U_{M}\) is sent to the motor. Table 2 describes with more detail the function of each one of the elements presented in the control loop. It is important to mention that the project consists of simple elements that are familiar to the students.  
The sequence required to successfully complete the project is described in Table 3. It is necessary to plan the practices sequentially since each of them is necessary for the final integration, where all the elements of the control system are joined.  
\begin{table}
\begin{tabular}{c p{284.5 pt}} \hline \hline
**Device** & **Description** \\ \hline Controller & A fuzzy controller works in a similar way to a conventional system: it accepts an input value, performs some calculations and generates an output value. \\ \hline DAQ & The DAQ takes the signal received from the encoder and performs its conditioning so that it can be compared with the reference position. Additionally, it converts the signal to the controller output and converts it to a PWM signal for the power stage. \\ \hline Power Stage & The power stage operates on the motor, in it is the H bridge that regulates the operation of the motor. Its operation is determined by the on or off of the different switches. \\ \hline Motor & The motor is the final action target. Through this element, the angular position of the shaft is regulated. \\ \hline Encoder & It allows coding the mechanical movement of the motor in different types of electrical impulses: binary digital, analog based on a wave, pulses, etc. In this way, the encoder is the interface between the motor and the controller. \\ \hline \hline \end{tabular}
\end{table}
Table 2: Description of the function of each device.  
Figure 1: Control loop proposed for the project.
# Extending Wireless Reschargeable Sensor Network Life without Full Knowledge
## 7 Exploration and Charging Algorithm
Line 7, computing the power needed to return home, is \(O(1)\) assuming the data is stored in a lookup table. The complexity of the Charge algorithm is \(O(nlog\ n)\), analysis given in the next paragraph. The execution time of all implementations of canVisitMoreNodes algorithm is \(O(1)\), and this will be analyzed later with each implementation. All other operations are \(O(1)\). This yields in a total execution time of \(O(1)+O(n)\times(O(nlog\ n)+O(1))=O(n^{2}log\ n)\). The time complexity of Algorithm 2 is \(O(nlog\ n)\). The first operation is a sort, and a quick sort would give \(O(nlog\ n)\). Lines 2-6 are each \(O(1)\). The _For_ loop at line 7 renders an \(O(n)\) execution time of everything inside the loop. All operations inside the for loop each take \(O(1)\). This renders the whole algorithm taking \(O(nlog\ n)+(O(n)\times O(1))=O(nlog\ n)\). The time complexity of all three implementations of canVisitMoreNodes is \(O(1)\). Detailed analysis of each one will be presented after each implementation. ```
1:\(\lambda\)
2:procedureCharge(\(vNodes,power\))
3:\(sNodes\gets sort(vNodes)\)\(\triangleright\) Sort visited nodes based on power
4:\(v\leftarrow empty\)\(\triangleright\) List of identified nodes to charge
5:\(v^{\prime}\leftarrow empty\)\(\triangleright\) List of candidate nodes to charge
6:\(C\gets 0\)\(\triangleright\) Total power used for charging
7:\(l\gets 0\)
8:for\(i\gets 1\)...sizeof\(sNodes-1\)do\(\triangleright\) Target power level to charge the nodes in \(v\)
9:\(sPower\leftarrow\) power level of \(i^{th}\) node in \(sNodes\)\(\triangleright\) Source node power level to consider
10:\(tPower\leftarrow\) power level of \(i^{th}+1\) node in \(sNodes\)\(\triangleright\) Next possible target power level
11:\(v^{\prime}\gets v+i^{th}\) node in \(sNodes\)\(\triangleright\) Add node to candidate list
12:neededPower\(=\lambda+\) ((tPower - sPower) \(\times\) (1 + sizeof \(v^{\prime}\)));\(\triangleright\) Power needed to use new target power level
13:if\(power\geq needPower\)then\(\triangleright\) Candidate list is feasible
14:\(v\leftarrow\)\(v^{\prime}\)
15:\(C=C+neededPower\)\(\triangleright\) Charging power update
16:\(I=tPower\)\(\triangleright\) Target power level update
17:else
18:return\(v,C,1\)\(\triangleright\) Early termination due to lack of power to charge the whole list
19:endif
20:endfor
21:return\(v,C,1\)
22:endprocedure
```  
**Algorithm 2** Find subset to charge. Determining the stopping point \(\gamma\) is done by implementing canVisitMoreNodes, where canVisitMoreNodes returns \(False\) once \(\gamma\) is reached. The next section addresses possible methods to implement canVisitMoreNodes.
# An Accelerated Maximum Flow Algorithm with Prediction Enhancement in Dynamic LEO Networks
## 3 System Model and Problem Formulation
### Satellite Network Failures  
The dynamic nature of LEO satellite networks is inherently susceptible to various disruptions, including satellite failures, link outages, and interference from cosmic events. These disruptions can significantly alter the network topology, necessitating a model that accurately reflects these changes. Key challenges include the intermittent disconnection of inter-satellite links (ISLs) due to satellite movement, orbital decay, or technical failures, as well as the limited lifespan of satellites. Additionally, other issues like beam pointing errors, insufficient channel capacity, and environmental factors like cosmic radiation further exacerbate the situation [37].  
To address these potential disruptions, we propose a failure model that simulates the disconnection of ISLs due to unexpected events, providing a more robust framework for dynamic network changes. The failure model incorporates a time-slotted system where each time slot represents a discrete interval during which the network's state can change due to these disruptions. For any ISL failure, we define a mapping function \(\varphi_{k:}\ L_{h}\to L_{h+1}\) that adjusts the set of available ISLs from time slot \(\tau_{h}\) to\(\tau_{h+1}\), reflecting the link disconnection. If an ISL \((v_{i},v_{j})\) fails in time slot \(\tau_{h}\), it is removed from the set of available ISLs, \(L_{h}\), **that is**, \(L_{h+1}=L_{h}\setminus\left\{\left(v_{i},v_{j}\right)\right\}\). The new graph \(G_{h+1}=(V,L_{h+1})\) represents the updated network, capturing the new set of available links.  
In response to unavailable links, the e-TEG graph adaptively adjusts the network topology and recalculates the maximum flow, ensuring that the flow optimization process remains accurate under changing conditions. Building upon the construction and updating techniques of the e-TEG model, Section 4 introduces the predictive acceleration algorithm, which optimizes flow calculations in the face of dynamic network changes.
###### Abstract  
The automatic segmentation technique for colorectal polyps in colonoscopy is considered critical for aiding physicians in real-time lesion identification and minimizing diagnostic errors such as false positives and missed lesions. Despite significant progress in existing research, accurate segmentation of colorectal polyps remains technically challenging due to persistent issues such as low contrast between polyps and mucosa, significant morphological heterogeneity, and susceptibility to imaging artifacts caused by bubbles in the colorectal lumen and poor lighting conditions. To address these limitations, this study proposed a novel pyramid vision transformer-based hierarchical path aggregation network (HPANet) for polyp segmentation. Specifically, firstly, the backward multi-scale feature fusion module (BMFM) was developed to enhance the ability of processing polyps with different scales. Secondly, the forward noise reduction module (FNRM) was designed to learn the texture features of the upper and lower layers to reduce the influence of noise such as bubbles. Finally, in order to solve the problem of boundary ambiguity caused by repeated up and down sampling, the boundary feature refinement module (BFRM) was developed to further refine the boundary. The proposed network was compared with several representative networks on five public polyp datasets. Experimental results show that the proposed network achieves better segmentation performance, especially on the Kvasir SEG dataset, where the mDice and mIoU coefficients reach 0.9204 and 0.8655.  
medical image segmentation; polyp segmentation; pyramid vision transformers; computer vision +
Footnote †: journal: _Algorithms_
# Relational Fisher Analysis: Dimensionality Reduction in Relational Data with Global Convergence +
## 4 Global Convergence of RFA and Extensions
### Kernel Extension  
In this subsection, we apply kernel trick to our RFA method and present the kernel RFA (KRFA) method, which can be used to nonlinear dimensionality reduction problems.  
The KRFA optimization problem is basically the same as Problem (3), except that the data point \(\mathbf{X}_{si}\) needs to be mapped to a reproducing kernel Hilbert space and obtain \(\phi(\mathbf{X}_{si})\), where \(\phi(\cdot)\) denotes the mapping function. In addition, the corresponding intrinsic and penalty graphs and the relational matrix should also be mapped to the reproducing kernel Hilbert space. The kernel function \(\mathbf{K}(\mathbf{X}_{si},\mathbf{X}_{sj})=\phi(\mathbf{X}_{si})^{T}\phi( \mathbf{X}_{sj})\).  
We suppose the projection matrix \(\mathbf{W}=\mathbf{\Phi}\Gamma\). We have \(\mathbf{W}^{T}\mathbf{\Phi}=\Gamma^{T}\mathbf{K}\).  
Normalizing and centering the data in the high-dimensional feature space, we substitute \(\mathbf{K}\) with  
\[\widehat{\mathbf{K}}=\mathbf{K}-\mathbf{1}_{N}\mathbf{K}-\mathbf{K}\mathbf{1 }_{N}+\mathbf{1}_{N}\mathbf{K}\mathbf{1}_{N}. \tag{20}\]  
In this way, the learning task of RFA is able to be described as  
\[\mathcal{L}_{\widehat{\mathbf{K}}}=\min_{\Gamma}\frac{\text{tr}(\Gamma^{T} \widehat{\mathbf{K}}\widetilde{\mathbf{L}}_{i}\widetilde{\mathbf{K}}^{T} \Gamma)}{\text{tr}(\Gamma^{T}\widehat{\mathbf{K}}\widetilde{\mathbf{L}}_{p} \widetilde{\mathbf{K}}^{T}\Gamma)}+\lambda\text{tr}(\Gamma^{T}\widehat{\mathbf{ K}}\widetilde{\mathbf{R}}\widetilde{\mathbf{K}}^{T}\Gamma), \tag{21}\]  
where \(\widetilde{\mathbf{L}}_{i}\), \(\widetilde{\mathbf{L}}_{p}\) and \(\widetilde{\mathbf{R}}\) are the intrinsic graph, the penalty graph and the relational matrix, respectively. The distance of the samples in the reproducing kernel Hilbert space can be calculated by  
\[D(x_{i},x_{j})=\sqrt{\mathbf{K}(x_{i},x_{i})+\mathbf{K}(x_{i},x_{j})-2\mathbf{ K}(x_{i},x_{j})}. \tag{22}\]With reference to the derivation and transformation procedure of RFA, KRFA can be eventually transformed into  
\[\tilde{\mathcal{L}}_{\mathbf{K}}=\min_{\mathbf{v}_{\mathbf{K}}^{\top}\mathbf{v}_{ \mathbf{K}}=\mathbf{I}_{t}}\frac{\text{tr}(\mathbf{V}_{\mathbf{K}}^{\top} \mathbf{\tilde{S}}_{\mathbf{F}}^{\top}\mathbf{V}_{\mathbf{K}})}{\text{tr}( \mathbf{V}_{\mathbf{K}}^{\top}\mathbf{\tilde{S}}_{\mathbf{F}}^{\top}\mathbf{V}_ {\mathbf{K}})}. \tag{23}\]  
As KRFA's optimization process is similar to RFA, we still use iterative methods to solve it.
# Explainable Machine Learning for Mapping Rainfall-Induced Landslide Thresholds in Italy
## 2 Study Area, Data, and Methodology
### Methods
#### 2.3.3 Machine Learning Models  
This study employed four machine learning models: XGBoost, RF, LightGBM, and LR. XGBoost is an efficient gradient boosting algorithm based on decision tree ensembles [45], incrementally constructing multiple decision trees to correct residual errors with built-in regularization and parallel computing capabilities. RF constructs multiple independent decision trees using bootstrap sampling and random feature selection to enhance diversity, reduce overfitting, and provide robust aggregated predictions through majority voting or averaging [46]. LightGBM optimizes computational efficiency through histogram-based feature binning and leaf-wise tree growth, prioritizing high-gain splits and efficiently handling categorical features without encoding [47]. LR is a statistical binary classification model that converts linear combinations of predictors into probabilities via a logistic function; it is favored for its simplicity, interpretability, and computational efficiency [48].  
The selected supervised learning algorithms span three core methodological categories: gradient boosting (XGBoost and LightGBM) for handling complex non-linear relationships, bagging-based ensemble (Random Forest) for robustness through bootstrap aggregation, and linear classification (Logistic Regression) as a statistical baseline. This diverse selection facilitates comprehensive performance comparison across distinct modeling approaches. Furthermore, hyperparameters for all machine learning models were optimized using GridSearchCV. The parameters and search spaces are detailed in Table 3.
###### Abstract  
Rapid and accurate detection of critical units is crucial for the security control of power systems, ensuring reliable and continuous operation. Inspired by the advantages of data-driven techniques, this paper proposes an integrated deep learning framework of dynamic security assessment, critical unit detection, and security control. In the proposed framework, a black-box deep learning model is utilized to evaluate the dynamic security of power systems. Then, the predictions of the model for specific operating conditions are interpreted by instance-level feature importance analysis. Furthermore, the critical units are detected by reasonable local interpretation, and the security control scheme is extracted with a sequential adjustment strategy according to the results of interpretation. The numerical simulations on the CEPRI36 benchmark system and the IEEE 118-bus system verified that our proposed framework is fast and accurate for specific operating conditions and, thereby, is a viable approach for online security control of power systems.  
dynamic security assessment; security control; deep learning; feature importance analysis; model interpretation
## 2 Materials and Methods
### Description of the Model
#### 2.1.1 Contact Forces  
Since the normal dynamics are neglected, the normal contact force corresponds only to the constant static load \(N\) of the vehicle's weight on one wheel. Tangential forces, on the contrary, which are characterized by rolling friction theory [27], increase linearly with respect to the relative sliding velocity at small creepages (creepage \(s\) is the sliding velocity normalized by the rolling velocity), due to the deformation of the interface during rolling [6]; at higher creepages, however, a saturated regime is reached, where the equivalent friction coefficient is equal to the Coulomb coefficient \(\mu_{0}\).  
The lateral creepage contains a quasi-static component that is approximately equal to the angle of attack \(\alpha\) and a dynamic component that depends on the wheel's vibrations. It is defined as:  
\[s=\frac{\Delta V_{y}+\dot{u}}{V_{x}}\approx\alpha+\frac{\dot{u}}{V_{x}}, \tag{1}\]  
Shen-Hedrick-Elkins' friction/creep law [28] is used to model the nonlinear frictional forces at the wheel-rail interface [25]. Furthermore, in the saturated regime, a decaying friction coefficient \(\mu\) with respect to creepage \(s\) is described by a function of the form \(\mu_{0}(1-\lambda e^{-\kappa/|s|})\), where \(\lambda\) is the parameter that governs the decay rate [14]. This feature is what the falling friction model mentioned in the introduction suggests is the cause of the instabilities associated with the curve squeal phenomenon.
## 1 Introduction
Remote sensing (RS) and geographic information system (GIS) are used for hydrological modeling and land-use change evaluation. Daily temperature and metrological variables are downscaled from daily GCM simulations to particular sites by using a statistical downscaling model (SDSM).
## 5 Application to Tensile Test Specim
### Results  
The results of the numerical simulations are summarized in Figure 11. Furthermore, a comparison of the numerical data with the experimental results is included. The diagrams on the left-hand side contain the stress-strain curve (black), taken from the data sheet [29], the experimental data (gray) (see Section 2), and the numerically obtained results (colored) for correlation lengths of \(l_{x}=5\,\mathrm{mm}\), \(l_{x}=10\,\mathrm{mm}\), and \(l_{x}=15\,\mathrm{mm}\) from top to bottom. The diagrams on the right hold again the stress-strain curve (black), taken from the data sheet [29] and the numerical data (gray). One dataset is highlighted. The corresponding stress-strain curve was based on a measurement length of \(50\,\mathrm{mm}\). For these specimens, the spatial distribution of the material properties was qualitatively investigated by deriving the results for three sections of \(15\,\mathrm{mm}\) each.  
Figure 10: Discretizations of cross-correlated homogeneous second-order Gaussian random fields to represent the spatially distributed material parameter \(\Lambda\) for different values of \(l_{x}\).  
Figure 11: Results of the numerical simulation in comparison with the experimentally obtained results and a stress–strain curve provided by the manufacturer [29].  
For a better comparability, Table 7 holds some additional information. For strain levels of 0.2%, 0.5%, 1.0%, 1.5%, and 2.0%, the mean stress, as well as the standard deviations were computed for the numerical and experimental data. For all numerical results, the stress refers to the horizontal component of the second Piola-Kirchhoff stress tensor.
# MoSe\({}_{2}\) with Ultra-Fine Pt Decoration for Efficient Photodegradation
###### Abstract  
Transition metal dichalcogenides are widely studied for their photocatalytic ability due to the adjustable bandgap, high carrier mobility and possibility of foreign-element doping. In this work, multilayer molybdenum diselenide (MoSe\({}_{2}\)) was decorated with ultra-fine Pt nanoparticles through the mild hydrothermal method. MoSe\({}_{2}\)-Pt nanocomposites were synthesized and showed good structural and chemical stabilities. The incorporation of Pt nanoparticles provides plenty of active sites for MoSe\({}_{2}\). The dominant Pt particle sizes are 1.8 nm, 1.8 nm, and 1.9 nm for the three synthesized samples, respectively. The mean crystal sizes of Pt (111) were calculated from X-ray diffraction patterns and we found that they were in accordance with the particle sizes. Both the particle sizes and mean crystal sizes are related to the synthesis conditions. X-ray photoelectron spectroscopy (XPS) characterizations revealed the formation of Se-Pt bonding. The relative contents of Pt-Se bonding were also calculated from XPS results, and they show the same trends as the optical absorption properties. Combining the XPS and optical absorption results, the effects of Se-Pt bonding during the photo-related process could be further confirmed. By degrading methylene blue (MB) under visible light, the synthesized nanocomposites proved promising for application in real-case degradation of organic pollutants. The sample synthesized with a moderate content of MoSe\({}_{2}\) exhibited the best photodegradation efficiency, which could be explained by the maximum Pt-Se contents. Based on the experimental findings, we proposed a possible photodegradation mechanism.  
MoSe\({}_{2}\); photodegradation; photocatalyst; two-dimensional materials +
Footnote †: journal: Applied Sciences
# Energy Consumption Analysis of ISO/IEC 29192-2 Standard Lightweight Ciphers
###### Abstract  
The Internet of Things (IoT) is a transformative technology that enables connection and communication between devices, systems, and objects using an existing network. This interconnectivity allows these objects to collect and analyze data, facilitating interaction in various environments. IoT spans areas such as homes, industries, cities, and even ecosystems, creating smart and responsive systems. In today's digital age, where protecting personal, industrial, and commercial information is critical, IoT technologies must ensure a high level of security. It is essential to employ secure communication protocols or applications to ensure that IoT devices with limited resources, such as embedded systems, can communicate safely. Due to the low processing and memory capacities of these devices, traditional encryption techniques like AES are often impractical. Consequently, numerous lightweight encryption techniques have been developed specifically for use in embedded systems. The first stage of this research focused on the CLEFIA, PRESENT, and LEA algorithms outlined in the ISO/IEC 29192-2:2019 standard. The second stage involved developing a test environment to run these algorithms on embedded systems, ensuring that they were compatible with the systems' constraints. The third stage focused on determining and analyzing the real-time energy consumption of these algorithms when implemented on embedded systems.  
energy consumption; ISO/IEC 29192-2; IoT security; lightweight cryptography +
Footnote †: journal: Applied Sciences