esa-sceva/satcom-chunk-collection huggingface dataset:
# Method for Calculating of Lightning Impact Level on Overhead Transmission Line
## 2 Materials and Methods
### Study Area  
Let us apply the proposed method to some OHTLs of the Murmansk region of the Russian Federation. There are more than 270 lines with voltage classes 35, 110, 150 and 330 kV in this region. The Murmansk region is located in the northwestern part of Russia and has borders with Finland and Norway. The region is part of the Arctic zone of the Russian Federation and has a moderately cold climate with sufficiently low thunderstorm activity in the north and medium thunderstorm activity in the south. Therefore, two overhead lines were chosen to demonstrate the method: one of them is located in the north of the region, and the other is located in the south of the region.  
Lightning analysis in the Murmansk region was performed with the Blitzortung lightning detection network (LDN) [14]. This region was chosen due to its location in the Blitzortung LDN zone of reliable lightning detection, unlike other regions of the Russian Federation. There are more than 25 detectors within a 500 km radius. Additionally, we choose this LDN because we have easy access to the data and because we are participants in this project. The Blitzortung Network has been in operation since 2008, but reliable data for the Murmansk region has only been available since 2015, which is a limitation of thunderstorm research in that region using this LDN. Therefore, this work will use lightning data for the last five-year period from 2016 to 2020. The data set consists of 41,656 flashes to the ground between January 2016 and December 2020. Other research limitations when using Blitzortung network data are the detection of lightning into the ground only and the unavailability of lightning current information.  
To have an overview of thunderstorms in the studied region, the corresponding maps were constructed according to the methodology proposed in [5]. To construct a gridded dataset of ground flash density, the Murmansk region is divided into grids of 20 km \(\times\) 20 km cells. We have also converted the lightning and power lines data from the original World Geodetic System geographical coordinate system (WGS84) into the kilometer-based Universal Transverse Mercator (UTM) system to provide easier analysis of the data into the 20 km \(\times\) 20 km squares.
# A landslide Warning Method Based on K-Means-ResNet Fast Classification Model
## 2 Methodology  
### Time Series Decomposition  
Landslide displacement data and rainfall data are all time series data, and can be written as \(T=(t_{1},t_{2},\ldots,t_{t\_length})\).  
Landslide time series decomposition can obtain multiple unlabeled subsequences, providing a data basis for landslide data clustering. The landslide time series data are generally processed by the sliding window. Specifically, the time series is divided into multiple equal-length subsequences by a window with the length of \(w\) sliding sequentially over the series with a step of \(d\).
## 2 Materials and Methods
### Efficient-UNet Model for Multiorgan Segmentation
#### 2.3.1 Encoder Blocks  
The architecture consists of six blocks, known as MBConvBlocks (Mobile Inverted Residual Bottleneck blocks), each responsible for extracting and encoding features from the input data. These blocks progressively capture more complex and abstract information as we move through the encoder. The choice of different filter sizes in each encoder block allows capture of features at different spatial scales and capture of a range of details. This diversity in filter sizes enables the model to extract relevant information across multiple resolutions. The MBConvBlock can enhance automatic image segmentation for MRI images by offering several advantages. First, the depthwise convolution operation helps capture spatial details within the MRI images while preserving input channel information, making it particularly useful for identifying fine anatomical structures. Second, the pointwise convolution operation, or \(1~{}\times~{}1\) convolution, enables effective channel-wise combination, facilitating dimensionality reduction or expansion. This is vital in MRI image segmentation because it allows the model to focus on relevant features and reduce computational complexity. Finally, the expansion operation increases the model's representational capacity, allowing it to learn more complex features, which can be particularly beneficial for capturing nuanced organ boundaries. Overall, the MBConvBlock's design, which balances  
Figure 5: Overview of the used Efficient UNet network scheme. MBConvblocks: Mobile Inverted Residual Bottleneck blocks.  
accuracy and computational efficiency, can improve the segmentation process for images by efficiently capturing spatial information, reducing the parameter count, and enabling a more compact yet effective model representation. This makes it well-suited for accurate and efficient automatic organ segmentation in MRI images.
## 5 Conclusions  
This paper proposed a CSES optimization framework for AC/DC distribution systems with dual-time-scale coordination to address the challenges of RES integration. The research has successfully achieved its primary objective, with all key tasks outlined in the introduction being completed, including the establishment of the CSES framework, implementation of dual-time-scale coordination, and validation through case studies. The results conclusively demonstrate that the proposed approach effectively addresses the challenges of temporal-spatial imbalance and RES integration in AC/DC distribution systems. The main conclusions are as follows:  
1. The proposed CSES framework effectively leverages temporal-spatial complementarity across subnetworks, achieving significant improvements in RES utilization with rates of 99.31%, 88.10%, and 99.91% for AC1, AC2, and AC3 subnetworks, respectively, compared to 96.15%, 71.51%, and 92.47% under independent ESS operation.
2. The dual-time-scale coordination of CSES successfully reduces total operational costs by 16.51% compared to independent ESS operation and 3.31% compared to capacity-leasing CSES allocation while ensuring system-wide optimization.
3. The ADMM-based distributed algorithm achieves solution accuracy within 0.1% deviation of centralized optimization results while protecting privacy.  
While this study demonstrates the significant benefits of the proposed CSES framework, certain limitations define its current scope and offer avenues for future work. The current analysis focuses on optimal operational scheduling and does not include detailed dynamic stability or comprehensive reliability assessments of the system with CSES. Furthermore, while RES/load forecasts are used, extreme load imbalances or granular impacts of specific weather events on these forecasts are simplified. Lastly, detailed battery degradation models reflecting long-term impacts of CSES operational patterns and specific BMS-level protections beyond SOC limits were not incorporated. Addressing these aspects would further enhance the practical robustness and applicability of the proposed framework.  
Formal analysis, W.L. and Y.J.; supervision, Q.X. and H.J.; validation, Y.Z. and W.L.; writing--original draft, Y.Z.; writing--review and editing, Y.Z., Q.X. and W.L. All authors have read and agreed to the published version of the manuscript.  
This work was funded by Science and Technology Project of the State Grid Corporation of China (5700-202418337A-2-1-ZX).  
**Institutional Review Board Statement:** Not applicable.  
Figure 12: Relationship between objective values and the number of iterations. (**a**) AC subnetworks; (**b**) DC ring network.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available upon request from the corresponding author. The data are not publicly available due to privacy.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 11 Current Studies and Future Directions  
This section considers the current research in smart cities and the recent existing studies that consider key enabling technologies of smart cities. Moreover, this section provides future research directions for researchers dedicated to developing reliable communication networks for different smart city applications.
# Thermal Hydrolysis of Sewage Sludge: A Case Study of a WWTP in Burgos, Spain
## Abbreviations  
\(<>\) average value  
\(\%\)SC percentage of self-consumption  
\(\eta\) efficiency (\(\%\))  
\(\Cp\) specific heat (kJ/kg K)  
COD chemical oxygen demand (g/L)  
DL degree of loading (\(\%\))  
EP electric power (kW)  
F net annual cash flow (\(\xi\))  
h specific enthalpy (kJ/kg)  
HRT hydraulic retention time (h)HSE  
\begin{tabular}{l l} ki & injection coefficient \\ Io & initial capital investment (\(\xi\)) \\ IRR & internal rate of return (\%) \\ ITHP & intermediate thermal hydrolysis process \\ m & mass amount in batch cycle (kg) \\ M & mass (kg) \\ n & number of periods in economic analysis \\ N & number \\ NPV & net present value (\(\xi\)) \\ r & discount rate (\%) \\ Rg & gas universal constant (J/mol k) \\ SBP & specific biogas production (m\({}^{3}\)/t) \\ SCOD & soluble chemical oxygen demand (mg/L) \\ SRE & sludge recirculation exchanger \\ t & time (h) \\ T & temperature (\({}^{\circ}\)C, K) \\ T0 & ambient temperature \\ THP & thermal hydrolysis process \\ TP & thermal power \\ TS & total solid (kg) \\ V & volume (m\({}^{3}\)) \\ VS & volatile solid (kg) \\ VSR & volatile solid removal (\%) \\ WWTP & wastewater treatment plant \\ biogas-eng & biogas consumption of engines \\ biogas-rb & biogas consumption of the recovery boiler \\ biogas-st-gasom & biogas stored by gasometers \\ biogas-st & biogas stored \\ biogas-su-gasom & biogas supplied from gasometers \\ eg-useful & exhaust gases useful power recovered \\ eng & engine \\ eng-gross & gross energy \\ eng-net & engine net energy \\ fs & flash steam \\ eng-nom & engine at full load \\ fw & feeding water \\ in & inlet stream \\ inj & injection \\ ls & live steam \\ pg & process gases \\ pt & post-thickening section \\ rb & recovery boiler \\ out & outlet stream \\ thick & thickened sludge \\ total-losses & total losses of energy \\ \end{tabular}
# High Performance GAA SNWT with a Triangular Cross Section: Simulation and Experiments
## 2 Simulation  
### Device Parameters  
GAA SNWTs with three typical cross-sectional shapes, including triangles, squares, and circles, were simulated by TCAD tool Synopsys Sentaurus. The schematic structure of the device with three different cross-sectional shapes is shown in Figure 1. Considering the consistency in fabrication, the three nanowires were set as the same line width D. Some key process parameters were based on the requirements for a 5 nm technology node in IRDS 2017 [8].  
Owing to the excellent controllability of the channel potential in GAA SNWTs, the silicon film thickness or line width of nanowire can be relaxed to L\({}_{\text{g}}\)[9]. Therefore, the nanowire width of the GAA SNWTs was set to 16 nm according to IRDS 2017, and the gate length was set to the range of 12 to 28 nm. The other device parameters are listed in Table 1. The models we used in Sentaurus tools include Mobility (PhuMob &high field saturation & Enormal), Effective Intrinsic Density, Recombination (SRH(DopingDep)), and Quantum Potential model.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Device Parameter** & **Value** \\ \hline Gate length (nm) & 12–28 \\ Gate overlap length (nm) & 0 \\ Nanowire width (nm) & 16 \\ EOT (nm) & 0.67 \\ Channel doping concentration (cm\({}^{-3}\)) & \(1\times 10^{16}\) \\ Source/Drain extension doping concentration (cm\({}^{-3}\)) & \(5\times 10^{19}\) \\ Source/Drain doping concentration (cm\({}^{-3}\)) & \(1\times 10^{20}\) \\ Doping gradient (nm/dec) & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Device parameters used in the simulation.  
Figure 1: Schematic structure of GAA SNWTs with three cross-sectional shapes used in the simulation.
## Materials and Methods
### Procedure  
Gait data were recorded by a three-dimensional motion analysis system (Vicon(r), Oxford Metrics, Oxford, UK), according to the PlugInGait model (PGM) [13], to measure gait deviations of the pelvis and lower extremities with a sampling rate of 120 Hz. Thirteen reflective passive skin markers were placed on the subject's pelvis and lower limbs according to the PGM protocol. Heel and forefoot markers were also placed according to the PGM protocol [14]. A custom foot alignment device with a cross-hair laser, based on the novel device described by [PERSON] and [PERSON], was used to ensure proper placement of the lateral malleolus, heel and forefoot markers [15]. Participants walked barefoot at their own self-selected speed. Six gait cycles were chosen for further analysis.  
Lower limb X-rays for measuring anatomic LLD were performed either by a standing calibrated X-ray using a 2.54 cm metal ball and TraumaCad(r) software which provided the investigator with a full set of wizards, digital measurement tools and on-screen images [16] or by supine CT scanograms. Femur length was measured from the proximal aspect of the femoral head to the distal end of the femoral condyle; tibia length was measured from the tibia plateau to the tibia plafond. All X-ray measurements were performed by an experienced physician specialized in radiographic measurements. Each group was then divided into two additional subgroups according to their anatomic LLD findings (a longer right or left lower limb). The study was approved by the Medical Center's Ethics Committee.  
Clinical evaluation was performed by the main investigator encompassing lower limb joint range of motion (ROM) and muscle-tendon length by employing a plastic goniometer, manual muscle testing [17] and muscle tone assessment with the Modified Ashworth Scale [18], when relevant.
## 5 Results and Discussion
### RQ1: What State-of-the-Art Methods Exist for Optimizing LoRaWAN Energy Efficiency?
#### 5.1.4 State-of-the-Art Datasets and Simulators
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Author (s)** & **AI/ML Technique** & **Improvement** \\ \hline [PERSON] et al. [32] & DL ANN & Enhanced EE through optimal TP management \\ [PERSON] et al. [46] & K-Means Clustering & Improved network scalability while maintaining low \\ [PERSON] et al. [33] & GRU DL & 11\% increase in packet success ratio \\ [PERSON] and [PERSON] [34] & DNN & Improved SF management and packet success rates \\ [PERSON] et al. [35] & NN & Reduced energy consumption, 98\% localization accuracy \\ [PERSON] et al. [36] & DRL & 15\% improvement in throughput \\ [PERSON] et al. [37] & RL & 10\% improvement in DER \\ [PERSON] et al. [38] & DQN & Extended node lifespan by optimizing \\ [PERSON] et al. [41] & MARL & Optimized EE better than traditional ADR \\ [PERSON] et al. [31] & LSTM, DT, k-means & Predicted SF impacts with 3.5\% error rate \\ [PERSON] et al. [51] & ANN, PSO & Enhanced BER, spectral efficiency, \\ [PERSON] et al. [39] & Federated RL, Network Slicing & Optimized throughput by network slicing, \\ [PERSON] et al. [40] & Q-Learning, SARSA & achieved 3\% rejection rate \\ [PERSON] et al. [52] & ML-based CPLS models & 147\% increase in throughput \\ [PERSON] et al. [20] & ML multi-agent methodology & 30\% reduction in power consumption \\ [PERSON] et al. [43] & SVR, DNN & Extended battery life and reduced power consumption \\ [PERSON] et al. [30] & Supervised ML, RL & Ten times faster convergence in SF and TP allocation \\ [PERSON] et al. [47] & Q-Learning & 20\% improvement in PDR compared \\ [PERSON] et al. [48] & Ensemble Learning (kNN, RFR) & 16\% and 29\% improvements in mean \\ [PERSON] et al. [49] & DRL & and median error respectively \\ [PERSON] et al. [50] & RL, MAB & Consistently outperformed previous approaches \\ [PERSON] et al. [42] & ML Clustering & Significant decrease in energy consumption \\  & & with improved coverage \\ [PERSON] et al. [45] & K-Means Clustering & Improved network coverage probability by up to 5\% \\  & & and enhanced worst-case node \\  & & performance by 1.53 times \\ [PERSON] et al. [44] & ARIMA, ANN, SVM, RF, LSTM, & Improved forecasting of RSSI ARIMA with temperature \\  & & as regressor showed competitive performance \\  & & and significant accuracy. \\ [PERSON] et al. [25] & SVM and Decision Tree Classifier & Improved PDR and reduced collisions \\  & & by optimizing SF assignment \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Efficiency of AI and ML approaches.
# Physical Layer Definition for a Long-Haul HF Antarctica to Spain Radio Link
## 4 Physical Layer Tests Analysis
### Channel Error Performance Analysis  
The goal in this part of the study is to analyze the symbol error distribution and its impact on the received data for the Signaling modulation scheme.  
Direct-Sequence (DS) Signaling is a type of spread spectrum technique that consists in using a whole family of PN sequences as symbols [26] by means of a dictionary, so that each of them is associated to a number of bits. The analysis studies all the received data files, and afterwards, only the synchronized ones are taken into account to evaluate the results.  
The PN sequences used in this study are the so called Gold sequences [28] which are suitable codes for this type of modulation due to their trade-off between auto-correlation and cross-correlation properties [29]. Their cross-correlations are low-valued and almost invariant in the whole family. The demodulation method in the channel analysis is a matched filter, so in case of error, the other possible sequences are nearly equiprobable.  
The symbol error burst length is the figure of merit of this analysis, counting the number of consecutive errors that occur in the receiver as the burst length. The probability of appearance of each burst length will be represented in the symbol error burst probability. The first analysis conducted was for all the received files and, afterwards, the results for some selected files were evaluated (see Figure 10).  
Figure 10: PDF (Probability Density Function) of the error bursts length for the analysis of all files (blue) and for the analysis of the synchronization of the selected files (red).  
The results shown in Figure 10 correspond to 120 files of data received on February 2014 (5, 12, 16, 19 and 20). The analysis of all files (without selecting by synchronization) led us to the result of Symbol Error Rate \(SER=0.9686\) and \(BER=0.4989\), so we can conclude that there were a few data files properly demodulated. The results for the synchronization in the selected files (27 out of 120) show SER and BER values of 0.3157 and 0.1192 respectively. Although these values are high for a regular system, they are acceptable for our modem, designed with retransmission of data, and working in a 12,760 km hostile HF channel.  
Figure 10 shows the burst error length probability for both cases and both approach a negative exponential distribution. The most probable value for the burst length is 1, followed by 2 with approximately half the probability, and followed by 3 with a quarter the probability. The probability of having a number of consecutive errors equal or smaller than 3 is over 0.7, which means that almost all the errors appear in a value range from 1 to 3.  
The burst error length distribution for the selected files has no significant values for bursts longer than 10; the distribution for all the files has no probability of appearance for bursts longer than 80 symbols.  
This led us to the conclusion that most of the errors will appear alone or in a small groups, so that the communications system needs a channel code suitable for short bursts.
# Cotton Terry Textiles with Photo- and Bio-Activity in a Model Study and Real Conditions
###### Abstract  
The aim of the study was to assess the photocatalytic (decompose staining particles, K/S values, the color differences, CIE L*a*b* color) and antimicrobial properties of textiles modified with TiO\({}_{2}\) and ZnO nanoparticles (NPs) confirmed by X-ray diffraction, dynamic light scattering, SEM-EDX) in visible light conditions. The antimicrobial effectiveness of modified textiles under model conditions has been reported against 5 microorganisms: _Staphylococcus aureus_, _Escherichia coli_, _Bacillus subtilis_, _Candidida albicans_, _Aspergillus niger_ (AATCC Test Method 100-2004). In real conditions in bathrooms, significant biostatic activity was shown on the surface of the modified towels. The number of microorganisms decreased by 1-5 log to the level of 0-5 CFU/cm\({}^{2}\) in the case of bacteria: _Enterobacteriaceae_, _Enterococcus_, the coli group and _E. coli_, _Pseudomonas_. Statistically significant reduction of the total number of bacteria and fungi (by 1 log), and the concentration of gases (NO\({}_{2}\), CO\({}_{2}\), CO) in the air of bathrooms was determined. The removal or reduction of volatile organic compounds (VOCs) concentration (SPME-GC-MS analysis) in the air above the modified towels has also been determined. It was found that the lighting type (natural, artificial), time (1.5 and 7 h/day), air humidity (RH = 36-67%) and light intensity (81-167 lux) are important for the efficiency of photocatalysis. Textile materials modified with TiO\({}_{2}\) and ZnO NPs can be used as self-cleaning towels. They can also help purify air from microorganisms, VOCs and undesirable gases.  
Keywords:photocatalysis; self-cleaning materials; textiles; air quality; microorganisms; titanium dioxide; zinc oxide; volatile organic compounds +
Footnote †: journal: materials
# Research on Hard Rock Pillar Stability Prediction Based on SABO-LSSVM Model
## 3 Model Construction and Optimal Selection of Dataset Balancing Methods  
When LSSVM is applied to practical prediction cases, it is often combined with intelligent optimization algorithms to optimize the hyperparameters of the model itself in order to achieve better accuracy. In this study, we attempted to use the novel SABO algorithm for model optimization and applied it to the identification of stability states in mine pillars. Furthermore, to evaluate the optimization effects objectively and comprehensively, it is necessary to perform both horizontal and vertical model comparisons. In the horizontal comparison, the prediction results of the SABO-LSSVM model need to be compared with those of the initial models, such as LSSVM, SVM, and ELM, to assess whether the optimization algorithm effectively improves the model or has a negative impact. In addition, there  
Figure 5: Smote oversampling principle.  
Figure 6: Tomek Links principle.  
are various choices for optimization algorithms, and the effectiveness of optimizing the initial model is a criterion for selecting them. Therefore, in this study, in addition to applying SABO for optimization, we also selected classical and recent optimization methods as references. The classical methods included the genetic algorithm (GA) and particle swarm optimization (PSO), while the recent methods include grey wolf optimization (GWO), the whale optimization algorithm (WOA), and the Kepler optimization algorithm (KOA).