esa-sceva/satcom-chunk-collection huggingface dataset:
# Experimenting with Routing Protocols in the Data Center: An ns-3 Simulation Approach
## 4 Validation and Experimental Results
### Theoretical Analysis of BGP Behavior over Selected Scenarios
#### 4.3.2 Case Leaf Node Failure  
To analyze this case, let us consider what happens at the routing level when a leaf fails; this is analogous to a prefix that is no longer reachable, and \(k/2\) links down in the PoD of the failure. When a leaf is no longer reachable, its neighbors (\(k/2\) spines of the given PoD) will notify the fact with a BGP withdrawn that will spread throughout the fabric. In terms of packet count, this implies that each node in the fabric will send a BGP withdrawn out all of its interfaces. Similarly, two packets must be observed on each fabric link.  
The total number of links of a fat-tree multi-plane with \(R=1\) are \(k^{3}/2\) (before the leaf failure). As mentioned, \(k/2\) links go down after the failure, and consequently, if each link carries two packets, the total amount of BGP packets for a leaf failure is \(2\times(k^{3}/2-k/2)\) or equivalently  
\[k^{3}-k \tag{1}\]  
Note that this is a lower bound due to the following. When a Leaf node receives from a Spine the aforementioned withdrawal, its routing table still holds the reachability information for the given prefix using the rest of the spines, and therefore the leaf \"thinks\" it can still reach the prefix. This race condition can cause the leafs to send an BGP Update announcing the (now inexistent) routes to its corresponding spines in the PoD. Every announced route contains in its AS-PATH the ASN of the spine that receives it, and therefore is discarded by it. Thanks to the specific numbering of ASNs in the fat-tree, the inexisting route is no longer propagated, and the \"path hunting\" is stopped early. A simple way to find an expression that models that behavior is by adding one more packet for every spine-leaf link to the above expression (1). To determine the number of links, first we count the number of links inside a normal PoD, i.e., \(k/2\times k/2\) and multiply this by the amount of normal PoDs (\(k-1\)). Then, the number of links in the PoD of the failure is \(k/2\times(k/2-1)\). Consequently, the total amount of BGP packets that model this behavior is  
\[\frac{5k^{3}}{4}-\frac{3k}{2} \tag{2}\]  
Figure 5 compares packet growth as a function of \(k\) for the results of ns-3, Sibyl and the polynomials expressions for the Leaf Node failure scenario. Note that while Sibyl results follow polynomial (1), ns-3 DCE results follow both (1) and (2) alternatively. Regardless, the results are correct since both behavior may occur due to the nature of BGP and timing of control plane packets.  
Figure 4: Evolution of the results of ns-3 and Sibyl for the Spine failure scenario, in comparison with the evolution of the polynomial \(\frac{k^{4}}{4}-\frac{3k^{3}}{8}+\frac{5k^{2}}{4}-k\).
# Application of Compact Folded-Arms Square Open-Loop Resonator to Bandpass Filter Design
## 3 Microstrip Filter Design
### Resonator Dimension  
The folded-arms square open-loop resonator (FASOLR) is constructed to resonate at the proposed bandpass filter center frequency, \(f_{0}\), of 2.2 GHz. The substrate material employed in the design is the Rogers RT/Duroid 6010 LM with a relative permittivity, \(\varepsilon_{r}\), of 10.7, a loss tangent, \(\tan\delta\), of 0.0023, and a substrate thickness, \(h\), of 1.27 mm. The microstrip dimensions, the width (\(w\)) and the guided-wavelength (\(\lambda_{g}\)), are determined from [1] by means of Equations (5) and (6), respectively. The substrate material's effective permittivity or effective dielectric constant in Equation (6) is represented by \(\varepsilon_{e\!f}\), while the speed of light, \(c_{0}\), is a constant with a value of 3 \(\times\) 10\({}^{8}\) m/s. Using the technique reported in [1], the practical FASOLR dimensions were determined as shown in Figure 6.  
\[w=\frac{8h\,e^{A}}{e^{2A}-2}\ ;\ A=\frac{Z_{0}}{60}\sqrt{\frac{\varepsilon_{r}+1 }{2}}+\frac{\varepsilon_{r}-1}{\varepsilon_{r}+1}\bigg{[}0.23+\frac{0.11}{ \varepsilon_{r}}\bigg{]} \tag{5}\]  
\[\lambda_{g}=\frac{c_{0}}{f_{0}\sqrt{\varepsilon_{e\!f}}}\ ;\ \varepsilon_{e\!f}= \frac{\varepsilon_{r}+1}{2}+\frac{\varepsilon_{r}-1}{2}\bigg{(}1+12\frac{h}{ w}\bigg{)}^{-0.5} \tag{6}\]  
Figure 4: Fifth–order BPF circuit model, with equal LC resonators and inductor-only networks replacing J-inverters (\(L_{01}=3.6172\) nH, \(L_{12}=4.2989\) nH, \(L_{23}=5.8542\) nH).  
Figure 5: Fifth–order bandpass filter circuit model simulation results.
# The Design Value for Recycling End-of-Life Photovoltaic Panels
## 2 Challenges and Opportunities for Photovoltaic Energy
The bar chart shows two aspects: in a scenario of regular loss (PV panels operating up to 30 years), a dramatic increase of 60 million tons is expected by the end of 2050, and in an early loss scenario (PV panels failing before the age of 30), waste is expected to rise to 80 million tons by 2050 [19]. In general, a distinction can be made between low-value recycling solutions, which are aimed at the recovery and recycling of some fractions of the module's bill of materials, such as glassy and frame aluminium, and high-value recycling solutions, which make it possible to maximize the value of waste derived from end-of-life modules, recovering also the most valuable components in them. The latter are represented by silicon (contained in crystalline silicon modules), semiconductors used in thin-film panels, and silver, mainly used by crystalline silicon technologies. These materials are conceived to be enhanced and reinserted into new production processes. The recovery of the metal components of the panels is complex and requires additional treatments, which are expensive and have an impact on the environment, and the recovery of plastics is of little value. The major manufacturers of PV panels have experimented with different methods for the separation and recovery of various materials derived from PV panels at the end of their life. The various technologies that were investigated can be divided into types: physical treatments (shredding with separation by density or magnetism of metals), chemical treatments (acid/alkaline attack or dissolution with organic solvents) and thermal treatments (pyrolysis, incineration and melting of polymeric materials). To date, the total recycling of all components is not practiced. Numerous research projects underway (RESIELP recovery of silicon and other materials from EoL PV panels, PHOTORAMA, etc.) are moving in this direction. The most recent technologies make it possible to extract 99% of the high-value metals contained in photovoltaic solar panels (silver, silicon, copper and aluminum) and to reuse or return them to the supply chain [19]. This is fulfilled through an electrostatic separation process that separates small particles according to mass in a low-energy charged field. In  
Figure 7: Amount of waste produced by PV panels for 25 or 30 years of average durability. particular, a process of thermal and chemical separation (pyrolysis) enables the recovery of ultrapure silicon and other lost metals during the recycling of PV cells at their EoL. In the next section, the authors illustrate the proposed design methods for PV panel recycling, discussing the optimal materials, the best geometries and geometric proportions as well as the most convenient geometric tolerances in the couplings between the layers and the components that comprise the panel, to achieve longer durability of the PV panels and to attain the most current, efficient and effective solutions for recycling at their EoL.
# The Quest for the Best Explanation: Comparing Models and XAI Methods in Air Quality Modeling Tasks
###### Abstract  
Air quality (AQ) modeling is at the forefront of estimating pollution levels in areas where the spatial representativity is low. Large metropolitan areas in Asia such as Beijing face significant pollution issues due to rapid industrialization and urbanization. AQ nowcasting, especially in dense urban centers like Beijing, is crucial for public health and safety. One of the most popular and accurate modeling methodologies relies on black-box models that fail to explain the phenomena in an interpretable way. This study investigates the performance and interpretability of Explainable AI (XAI) applied with the eXtreme Gradient Boosting (XGBoost) algorithm employing the SHapley Additive exPlanations (SHAP) and the Local Interpretable Model-Agnostic Explanations (LIME) for PM\({}_{2.5}\) nowcasting. Using a SHAP-based technique for dimensionality reduction, we identified the features responsible for 95% of the target variance, allowing us to perform an effective feature selection with minimal impact on accuracy. In addition, the findings show that SHAP and LIME supported orthogonal insights: SHAP provided a view of the model performance at a high level, identifying interaction effects that are often overlooked using gain-based metrics such as feature importance; while LIME presented an enhanced overlook by justifying its local explanation, providing low-bias estimates of the environmental data values that affect predictions. Our evaluation set included 12 monitoring stations using temporal split methods with or without lagged-feature engineering approaches. Moreover, the evaluation showed that models retained a substantial degree of predictive power (R\({}^{2}\) > 0.93) even in a reduced complexity size. The findings provide evidence for deploying interpretable and performant AQ modeling tools where policy interventions cannot solely depend on predictive analytics tools. Overall, the findings demonstrate the large potential of directly incorporating explainability methods during model development for equal and more transparent modeling processes.  
XAI; air quality; nowcasting; XGBoost; SHAP; LIME 2025 15 7390
## 3 Preliminary and Model  
### Corruption in Vehicle Detection  
#### 3.1.1 Adverse Factors in Vehicle Detection  
In real-life situations, corruption can arise from a variety of factors, and we categorize these corruptions into four levels: weather, sensor damage, color change, and image transform. Based on real-world driving scenarios, we design 14 different corruption types. Specific introductions and details can be found in Appendix A.2.  
Weather-level corruption: Weather changes are commonly encountered during driving, which can significantly disrupt the input to the camera. For example. There are a number of factors that can affect the visual perception system, including rain, fog, snow, frost, low light, bright light, and other unusual circumstances, possibly leading to traffic accidents because autonomous driving automobiles are less aware of their surroundings. During imaging missions, raindrops, snowflakes, fog scattering, and dust can obscure detection objectives. Image acquisition units convert obscured scenes into vision signals mixed with external interferences, capturing the obscured scenes and affecting the detection capabilities of algorithms in the perception system, which can result in degraded performance or functional failure. In adverse weather conditions, 2D vehicle detectors trained using data collected in normal weather conditions may perform poorly. Considering common weather corruption, we study robustness under adverse weather, including snow, rain, fog, and frost. A visual simulation of weather conditions is created by applying image augmentation techniques.  
Sensor-level corruption: Sensors can introduce various corruption to captured data. Sensor vibration, lighting conditions, and reflective materials are all factors that can affect a sensor's performance. To simulate visual influence caused by motion or camera defects, we designed four practical sensor-level corruptions: Gaussian noise, speckle noise, zoom blur, and motion blur.  
Color-level corruption: Changes in brightness or color can also cause instability in vehicle detection results. So, we design three types of corruption caused by changes in brightness or color, including brightness, darkness, and saturation.  
Transform-level corruption: In addition to external environmental factors, image transform and loss of high-frequency information can occur during transmission and compression, such as JPEG compression and elastic transform. Furthermore, occlusion by obstacles significantly impacts vehicle detection results.  
Patch (occlusion) are used to simulate the impact of obstructions on vehicle detection. Whether the model can still accurately identify vehicles under various degrees of occlusion is vitally important.
# Region-Based Approach for Machining Time Improvement in Robot Surface Finishing
## 3 Materials and Methods
### Surface Subdivision and Machining Time Estimation
#### 3.3.4 Single Boundaries  
Each region is enveloped individually by its boundaries in Figure 2c, creating double boundaries. While effective for our previous analytical purposes, this configuration can leave unattended gaps that hinder the machining process, leading to unmachined regions on the workpiece. To address this, a single boundary was created between the existing double boundaries. In this approach, the distances between all the boundary points of each pair of adjacent region boundaries are calculated using the Euclidean distance metric.  
Let \(A\) and \(B\) represent matrices of boundary points from two different regions, where \(A\) and \(B\) are \(m\times n\) and \(p\times n\) matrices, respectively. Each row of \(A\) and \(B\) corresponds to the coordinates of a boundary point in an \(n\)-dimensional space. The Euclidian distance between any two points \(a_{i}\) from \(A\) and \(b_{j}\) from \(B\) is calculated as follows:  
\[d_{ij}=\sum_{k=1}^{n}\left(a_{ik}-b_{jk}\right)^{2} \tag{21}\]  
where \(d_{ij}\) is the distance between the \(i\)-th point in \(A\) and the \(j\)-th point in \(B\), and \(a_{ik}\) and \(b_{jk}\) are the \(k\)-th components of points \(a_{i}\) and \(b_{j}\), respectively. Pairs of boundary points whose distances fall below a specified threshold (adjacent regions) are identified as candidates for creating new boundary segments. Then, the midpoints between each pair of closely located boundary points are calculated and sorted based on their spatial coordinates to maintain a logical sequence:  
\[m_{ij}=\frac{a_{i}+b_{j}}{2} \tag{22}\]  
where \(m_{ij}\) represents the coordinate-wise average of the points \(a_{i}\) and \(b_{j}\). The midpoints \(m_{ij}\) are then smoothed using the Savitzky-Golay filter [44] to create a smoother transition between regions, as depicted in Figure 2c.
# Mass Collaboration and Learning: Opportunities, Challenges, and Influential Factors
## 4 Analysis of Affecting Factors
### Challenges  
* A major challenge nowadays concerns defining a mechanism for automatically and accurately evaluating the trustworthiness, reliability, validity, and credibility of large sets of co-created knowledge that are disseminated across multiple locations, delivered from time to time by unknown, non-pertinent, or even malicious agents.
* There is a global need to design a comprehensive and standard performance evaluation approach for mass collaborative learning that embraces a wide range of performance assessment methods, from rather traditional to highly novel and yet-untested approaches.  
* There have been few attempts to date to conceptualize an accepted model for knowledge production in mass collaboration.
# Development of Multi-Channel Seismic-Electrical Combined Rolling Coverage Measurement System
## 2 Design of the Combined Measurement System
### Design of Input and Output Control Circuit  
The channel input/output control circuit is shown in Figure 3. Each channel has two relays controlled by the MC1413 chip. The MC1413 operates at a high voltage and current, with the ability to sink a current of up to 500 mA and withstand a voltage of 50 V in the off state. It can also operate in parallel with high-load currents during output [28]. One of the relays, H, is used to control the power supply. Relay H is responsible for controlling the high-voltage power supply. After selecting a particular electrode, the MC1413 drives relay H to connect the electrode to the high-voltage power supply, allowing a high-voltage current to flow through the electrode to the ground. Relay L is used to control the measurement. After selecting a specific electrode, the MC1413 drives relay L to connect the electrode to the analog channel circuit, where the ADC in the analog channel measures the potential at the electrode contact point. Relays H and L will not be activated simultaneously, as this would cause a short circuit. The high-voltage power supply is adjustable, with a maximum voltage of 2000 V. The high-voltage power supply is used to input a high-voltage current into the ground to measure the apparent resistivity, polarization rate, phase difference, complex resistivity, and other data. As shown in Figure 4, during the time-domain electrical exploration process, the bridge converts the DC voltage into alternating waveforms of positive, zero, and negative voltages. As shown in Figure 5, during frequency-domain electrical exploration, the high-voltage bridge converts the DC voltage into square waveforms with frequencies ranging from 2\({}^{-6}\) Hz to 2\({}^{4}\) Hz. Additionally, the high-voltage bridge integrates protections against over-voltage, over-current, and other faults to ensure operator safety.  
When the instrument is used for seismic data acquisition (as shown in Figure 6), each channel's signal wire is connected to one output terminal of the geophone, while  
Figure 2: Overall architecture of the combined measurement system.  
the shielding layer is connected to the other terminal. In addition, the shielding layer is grounded at the instrument end. The seismic geophone used with the instrument has a coil resistance of 1800 \(\Omega\), a natural frequency of 10 Hz, and a sensitivity of 85.8 V/m/s. After selecting a particular channel, the MC1413 drives the relay L to connect the corresponding seismic geophone output to the analog channel circuit. The ADC in the analog channel then measures the output voltage waveform of the seismic geophone.  
Figure 4: Time-domain electrical method power supply waveform.  
Figure 5: Frequency-domain electrical method power supply waveform.  
Figure 3: The schematic diagram of the channel input/output control circuit during the electrical method data acquisition.
## 4 Design and Implementation of Proposed MPPT Algorithm
### Concept of Incremental Conductance (I & C)  
The concept behind the I& C algorithm is to effectively measure the derivative of SPV output power with output voltage [41]. The output power of solar-PV is given as,  
\[P_{SPV_{MPP}}=V_{SPV_{MPP}}\times I_{SPV_{MPP}} \tag{2}\]  
where, \(P_{SPV_{MPP}}\) is output maximum power of SPV, \(V_{SPV_{MPP}}\) is output voltage of SPV and \(I_{SPV_{MPP}}\) is output current of SPV. At the point of maximum power, the derivate of power with respect to voltage \((\frac{\partial P}{\partial V})\) is computed where slope of the curve \((\frac{\partial P}{\partial V})\) is zero.  
\[\frac{\partial P_{SPV}}{\partial V_{SPV}}=\frac{\partial(V_{SPV}\times I_{SPV })}{\partial V_{SPV}} \tag{3}\]  
\[\frac{\partial P_{SPV}}{\partial V_{SPV}}=V_{SPV}\times\frac{\partial I_{SPV }}{\partial V_{SPV}}+I_{SPV}=0 \tag{4}\]  
Figure 3: Single-diode equivalent circuit of Solar-PV system.  
Figure 2: Block diagram of the system under consideration.  
\[\frac{\partial I_{SPV}}{\partial V_{SPV}}=-\frac{I_{SPV}}{V_{SPV}}\approx\frac{ \Delta I}{\Delta V} \tag{5}\]  
where, \(\partial P_{SPV}\), \(\partial V_{SPV}\) and \(\partial I_{SPV}\) represent the changes associated with solar-PV power, voltage and current respectively. However, \(\Delta I\) and \(\Delta V\) are increments in the solar-PV current-voltage sequentially.  
The basic equations [42] of \(I\&C\) are derived using Power-Voltage curve of solar-PV. This algorithm is step by step explained in [43]. Equations of operating point are detailed below:  
* Operation point is at maximum power point (MPP) \[\frac{\partial P_{SPV}}{\partial V_{SPV}}=0,if\frac{I_{SPV}}{V_{SPV}}=-\frac{ \partial I_{SPV}}{\partial V_{SPV}}\] (6)
* Operation point is at left side of MPP \[\frac{\partial P_{SPV}}{\partial V_{SPV}}>0,if\frac{I_{SPV}}{V_{SPV}}>-\frac{ \partial I_{SPV}}{\partial V_{SPV}}\] (7)
* Operation point is at right side of MPP \[\frac{\partial P_{SPV}}{\partial V_{SPV}}<0,if\frac{I_{SPV}}{V_{SPV}}<-\frac{ \partial I_{SPV}}{\partial V_{SPV}}\] (8)
## 2 Principle of VMD
### Constrained Model Solving  
To overcome the aforementioned constraints, the constrained variational problem is decoupled by introducing a Lagrange multiplier \(\lambda(t)\) and second-order penalty factor \(\alpha\), leading to the formulation of a new augmented Lagrangian Optimization Model [16], which is expressed as follows:  
\[\begin{array}{l}L(\{u_{k}\},\{\omega_{k}\},\lambda)=\alpha\sum\limits_{k=1} ^{K}\left\|\left[\partial_{t}\left(\delta(t)+\frac{\mathrm{j}}{\pi t}\right) \otimes u_{k}(t)\right]\mathrm{e}^{-\mathrm{j}\omega_{k}t}\right\|_{2}^{2}\\ +\left\|f(t)-\sum\limits_{k=1}^{K}u_{k}(t)\right\|_{2}^{2}+\left\langle\lambda (t),f(t)-\sum\limits_{k=1}^{K}u_{k}(t)\right\rangle\end{array} \tag{2}\]  
The Alternating Direction Method of Multipliers (ADMM) is employed to solve the unconstrained problem. By integrating Fourier equidistant transformation, alternating optimization is applied to \(\lambda\), \(u_{k}\), and \(\omega_{k}\), as expressed below:  
\[\hat{u}_{k}^{n+1}(\omega)=\frac{f(\omega)-\sum\limits_{i<k}\hat{u}_{i}^{n+1}( \omega)+\frac{\hat{\lambda}^{n}(\omega)}{2}}{1+2\alpha\big{(}\omega-\omega_{k} ^{n}\big{)}^{2}} \tag{3}\]  
\[\omega_{k}^{n+1}=\frac{\int_{0}^{\infty}\omega\Big{|}\hat{u}_{k}^{n+1}(\omega) \Big{|}^{2}\mathrm{d}\omega}{\int_{0}^{\infty}\Big{|}\hat{u}_{k}^{n+1}(\omega) \Big{|}^{2}\mathrm{d}\omega} \tag{4}\]  
\[\hat{\lambda}^{n+1}(\omega)=\hat{\lambda}^{n}(\omega)+\tau\Bigg{(}f(\omega)- \sum\limits_{i<k}\hat{p}_{i}^{n+1}(\omega)\Bigg{)} \tag{5}\]  
Here, \(\tau\) represents the noise tolerance. \(\hat{u}_{k}^{n+1}(\omega)\) and \(\hat{\lambda}^{n+1}(\omega)\) are the Fourier transforms of \(u_{k}(t)\) and \(\lambda(t)\), respectively.  
In VMD, the modulus number K and the penalty factor \(\alpha\) are two important factors affecting the decomposition results, the determination of the K value determines the effective degree of information extraction, and \(\alpha\) affects the bandwidth of the IMF component, which determines whether there is signal loss and signal confusion. Therefore, the selection of VMD parameters is of great significance to the data decomposition, and the manual selection of parameters often does not obtain the best signal decomposition results, so this paper adopts the GWO algorithm [17] to optimize the parameters.  
GWO is inspired by the hunting and predatory activities of gray wolves, and its principle is to divide the wolf pack into four classes, A\({}_{1}\), A\({}_{2}\), A\({}_{3}\), and A\({}_{4}\), and to realize the renewal and iteration of the wolf pack by simulating the wolf pack's foraging and implementing the three behaviors of encircling, hunting, and attacking. The optimization process is shown in Figure 1, where H is the maximum number of iterations and h is the current number of iterations.