esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion
The weak point of InSAR, i.e., to distinguish a definite dip angle, even if it is possible to be discriminated through a better standard deviation, leads us to adopt the solution favoring the dip angle from seismology (Table 1). InSAR results showed a centroid source at a depth of \(\sim\)1 km (Table 1), shallow enough to induce 2.0 to 2.5 fringes of deformation in the C-band, even with the moderate magnitude of the earthquake. Until recently, InSAR in the Eastern Mediterranean could detect deformation of events as weak as M = 5.0, as in the case of the Gulpinar earthquake [43], located almost 3 km deeper than the event near Thiva. Depths of the mainshock obtained from location (0.9 km) and moment tensor inversion (1.0 km) are the same. As we have repeatedly mentioned, location depths are not highly accurate, due to the absence of local seismological stations, with an average error of 1.5 km. In any case, both seismological methods and geodesy converge on an uncommonly shallow moderate earthquake. In the city of La Habra (CA, USA), geodetic measurements, along with seismological observations, managed to identify a M\({}_{\rm w}\) = 5.1 event at a centroid depth of 2 km [44]. Very shallow moderate earthquakes have also been reported in France, M\({}_{\rm w}\) = 4.9, at a 1-km depth [45] and Ecuador, M\({}_{\rm w}\) = 5.0 at 1.5 km [46]. It has been shown that aftershock zones of dip-slip events in non-subduction environments are not characterized by a preferential up- or down-dip expansion [47]. In our case, we can assume that the mainshock occurred at a shallower depth and seismicity then expanded downdip. While our depth resolution and accuracy are incapable of forming a comprehensive and reliable view about the actual vertical distribution of seismicity, we infer that the aftershocks occur deeper than the mainshock, as they are located to the SW (i.e., down-dip). It is not possible to support this further, as aftershocks are not always caused by ruptures along the fault plane; instead, activation could occur in the damage zone or even further away [48]. Should the plane grossly defined by the aftershocks (i.e., an along-strike length of \(\sim\)5 km and down-dip width of \(\sim\)6 km) be activated as a whole, it could yield an event with a magnitude ranging between 5.0 [41] and 5.4 [42]. We note such magnitude estimations from possible rupture dimensions should be used cautiously due to their empirical nature [49], even though they are broadly used in seismotectonic [50] and seismic hazard [51; 52] research.
# Optimized Clustering Algorithms for Large Wireless Sensor Networks: A Review
## 3 Optimized Hierarchical Based Routing Protocols
### Genetic Algorithm  
The Genetic Algorithm (GA) is an adaptive heuristic approach based on biological genetic evolution for intelligent search and optimization. GA models the natural evolution by performing fitness tests on new structures to choose the best population [9]. With GA approaches, a population is made up of a group of chromosomes where a chromosome represents a complete solution to a relevant problem, and fitness shows the quality of a chromosome in function of concrete needs [28]. This kind of optimized algorithm is used for randomized search and optimization during routing of data. GA showed flexibility in solving dynamic problems and has been successfully applied within many NP-hard problems which include the clustering problem on WSN [7; 8; 20]. An example of the GA model is presented in Figure 4. Some clustering approaches based on GA are presented below.
## 1 Introduction
In the analysis of air traffic flows and delays, there is one part that can be predicted (deterministic) and another that cannot (stochastic). It is common to focus only on the deterministic part. However, when it comes to the aviation sector, non-predictable information (strikes, weather, etc.) is even more important than in other sectors. The study by [12] focuses on volatility (and on changes in it) by applying a stochastic modelling method to estimate future air traffic, delays and the cost of future delays in Germany to quantify risk and its significance for the delivery of cost-effective services. In this study, we propose negative binomial regression models as a way of estimating volatility in the number of flights in the FABEC area, which compromises the airspace of France, Benelux, Germany and Switzerland and is regarded as the core area of Europe. By estimating the necessary parameters, we can obtain the volatility in the number of flights depending on the days of the week and the hours. That is, we propose a method for reducing and using the uncertainty associated with the number of flights per hour so as to contribute to better planning of ANS. We use hourly traffic data for 2015-2019. As far as we know, this is a novel methodology that has never before been applied for estimating volatility in the aviation sector. By estimating volatility parameters, we are able to draw up simulations that reveal the full distribution of the number of flights for all FABEC countries. The distributions are very useful in understanding the likelihood and risk of the number of flights exceeding a given threshold number. The paper is structured as follows: Section 2 reviews the literature and presents the approach. Section 3 provides the data used for the analysis. Section 4 sets out the counting data model, which is a negative binomial regression model. Results and conclusions are given in Sections 5 and 6, respectively.
# Video over DSL with LDGM Codes for Interactive Applications
## 4 Findings
### Comparison across Channel Codes
The MPEG2-TS headers contain a 13-bit packet identifier, which can be employed to identify erasures. Multiple MPEG2-TS packets can be packed into RTP packets for core network transport. RTP packets also contain a sequence number in bits 16-31 of the header but up to eight MPEG-2 packets are typically contained in an RTP packet, limiting its header's use in detecting erasures. Notice also that RFC 6363 [41] describes a framework for transporting systematic codes in two RTP streams, similarly to the arrangement already shown in Figure 3.  
The coding rate, \(k/n\), was set to 90% with \(n=550\). In order to make the RS intra-packet coding rate the same for Pro-MPEG COP #3 and LDGM codes, the RS parameter m was set to 6, not 8 as is more usual. For RS coding, the packet stream was grouped 80 packets at a time. The Pro-MPEG COP #3 configuration was described in Section 2. From Figure 6, it is apparent that inter-packet RS [25] has the highest error-recovery property, which will also be higher than the standard intra-packet method. However, the industry standard Pro-MPEG COP #3 deteriorates sharply in its error recovery capability when the burst length increases. At packet burst lengths of 20 and 25 packets, LDGM provides better error recovery properties than Pro-MPEG COP #3. Moreover, the Pro-MPEG COP #3 latency budget is much longer in duration of time than LDGM because all packets in Pro-MPEG COP #3's interleaving matrix must first be assembled at the encoder (and likewise arrive at the decoder). Similarly, for RS coding there is a latency budget of \(k\) packets at the encoder for the intra-packet version and k.m for the evaluated inter-packet version. Moreover, as previously mentioned, when using LDGM, decoding can begin at an early stage when applying the belief-propagation algorithm. The computational complexity of the RS algorithm, according to Section 2, is much higher than that of LDGM.
## 6 Conclusions
**Nomenclature**  
**Variables**  
c & Specific heat of liquid water [kJ/kg K]  
C\({}_{\text{Electrolymer}}\) & Capital cost of the electrolyzer wind system [$]  
C\({}_{\text{Electricity}}\) & Cost of wind electricity [$]  
C\({}_{\text{f}}\) & Capacity Factor [%]  
C\({}_{\text{fuel}}\) & Cost of fuel [$]  
C\({}_{\text{I}}\) & Total investment cost of Wind Energy [$]  
C\({}_{\text{H2}}\) & Cost of hydrogen from geothermal energy [$]  
C\({}_{\text{Aspec}}\) & Average specific cost of wind turbine [$/KW]  
C\({}_{\text{OdeM}}\) & Cost operating and maintenance of Wind Turbine [$]  
C\({}_{\text{SU}A}\) & Capital cost of geothermal power plant [$]  
C\({}_{\text{u}}\) & Unit cost of electrolyzer [$/kW]  
C\({}_{\text{surf}}\) & Cost of construction [$]  
C\({}_{\text{WC}}\) & Completion well cost [$]  
d & Well Depth [$]  
Ec & Yearly cost of geothermal electricity [$0.1253/KWh and $0]  
I\({}_{\text{d}}\) & Interest rate [%]  
LCOE & Levelized cost of electricity [$0.1253/KWh and $1/kWh]  
LF & Load Factor [%]  
M\({}^{\prime}\)H\({}_{\text{2}}\) & Molar mass of hydrogen [kg / kmol]  
M\({}_{\text{H2}}\) & Amount of hydrogen produced [kg]  
M\({}_{\text{OM}}\) & Operation and maintenance cost of electrolyser-Geothermal power plant system [$0.1253/KWh and $2]  
Pr & Rated power of wind turbine [KW]  
t & Life span of the commercial wind turbine [years]  
T & Life time of the commercial electrolyser [years]  
T\({}_{\text{s}}\) & Reference temperature [$0.1253/KWh and $3]  
T\({}_{\text{0}}\) & Ambiant temperature [$0.1253/KWh and $4]  
\(W_{\text{act},\,electrolysis}\) & Minimum work required for an electrolyzer in non-ideal operation [kj/kg]  
W\({}_{\text{DC}}\) & Well drilling cost [$]  
W\({}_{\text{net}}\) & Steam Turbine net power output [kW]  
W\({}_{\text{rev},\,geo}\) & Maximum specific work [kj/kg]  
W\({}_{\text{rev},\,electrolysis}\) & Minimum work required for an electrolyzer in ideal operation [kj/kg]  
**Greek symbols**  
\(\alpha\) & Shear coefficient  
T\({}_{\text{electrolymer}}\) & Electrolyzer efficiency [%]  
I\({}_{\text{Iconv}}\) & Efficiency of rectifier [%]  
\(\Delta\)G\({}_{\text{electrolysis},\,\text{H2O}}\) & Change in the Gibbs function [kj/kmol]  
**Subscripts**  
0 & Dead state  
1,2,3 & State numbers, wind speed level  
a,b,c & Drilling Coefficients  
geo & Geothermal fluid  
H\({}_{\text{2}}\)O & Fresh Water  
H\({}_{\text{2}}\) & Hydrogen gas
## 4 Elastic Net Regression Analysis  
### Elastic Net Regression Setup  
When training and regressing the dimensionless model based on engineering data, selecting a suitable method is crucial. Due to challenges such as multicollinearity caused by issues like idle pushing, cutterhead jamming, and parameter regulation delays, along with the presence of anomalous data points, a regression method robust to these issues is required. Elastic net regression excels in handling multicollinearity while balancing variable selection and regularization. By combining L1 and L2 regularization, this method not only selects features but also retains some correlated yet less critical variables, ensuring that significant data is not overlooked. Its applicability and stability make it particularly suitable for dimensionless models predicting TBM cutterhead rotation speed, significantly enhancing the model's generalizability and predictive accuracy.  
To divide the dataset, a 0.2-0.8 split was applied, where 20% of the data was reserved for testing and 80% was used for training. The values of the L1 and L2 regularization parameters were determined using 10-fold cross-validation. This process involves randomly dividing the dataset into 10 subsets. In each iteration, one subset is used as the validation set, while the remaining nine subsets are used as the training set for model training and  
Figure 5: Comparison of torque data before and after low-pass filtering.  
evaluation. This process is repeated 10 times, with each subset serving as the validation set in turn. The average of the evaluation metrics across all iterations is used to assess model performance for the current hyperparameter combination. This approach identifies the optimal L1 and L2 parameters, enhancing the model's stability and predictive capability on unseen data.  
In this study, the combined parameter L1_ratio was employed to automatically balance the influence of L1 and L2 regularization. The optimal L1_ratio value was determined through cross-validation, ensuring that both regularization effects are effectively considered to achieve the best model performance.
## 3 Active Guidance Controller Design for IRWs Based on MARL
### MARL-Based Controller Design  
In the prototype vehicle shown in Figure 2, separate controllers are deployed for the IRWs located at the front and rear. As a two-axle vehicle travels on a track, both agents acquire dynamic state information about their respective IRWs through sensors. The controller's actions involve half of the torque differential exerted on the wheels on the same axle. Action outputs are augmented with Ornstein-Uhlenbeck (OU) noise to encourage the exploration of policies. The OU noise process, characterized by temporal correlation, mitigates the risk of extreme or sudden action changes and maintains a balance between exploration and exploitation [22]. The state feedback from the IRWs determines the expected reward, which the MARL algorithm attempts to maximize. The design of the MARL controller involves specifying critical elements such as the observation space, action space, reward function, and actor and critic networks, which are defined in this subsection.
## 5 Sensors
### GNSSs and IMUs  
GNSS (Global Navigation Satellite System) refers to satellite constellations that transmit signals from space to the Earth's surface, enabling precise geopositioning. Various GNSSs exist, including GPS (USA), Galileo (EU), GLONASS (Russia), and BeiDou (China). The accuracy of GNSS technologies varies: around 4 m for standard smartphones (expected to improve to sub-meter accuracy in the coming years); up to 2.5 cm in one minute with PPP (Precise Point Positioning); and centimeter-level accuracy or better with differential GNSSs, such as RTK (Real-Time Kinematic), which uses a ground-based station in addition to the mobile unit onboard the UGV [35; 36]. RTK devices specifically designed for installation on agricultural machinery for precision farming have been available for a long time and are standard equipment in new-generation tractors. When purchased aftermarket, they are often excessively expensive, largely due to their robust construction and high protection ratings (IP) against dust and liquid intrusion, which may not fully justify the cost. Fortunately, commercial edge solutions from the ICT sector are available today at significantly lower costs, often one order of magnitude cheaper [37; 38]. These devices go beyond simple plug-and-play sticks for basic GNSS signal streaming, offering fully flexible precise positioning and improved position-fix capabilities.  
IMUs (Inertial Measurement Units) provide measurements of acceleration, angular velocity, and, in some cases, magnetic fields. These data are used to determine the motion and orientation of an object, such as an entire UGV or the lens of an RGB camera directed at crops. Even if the costs of industrial-grade IMUs are wildly variable, the price of a reasonable model without a magnetometer for UGVs for field monitoring is in the range from EUR 100 to 1000. IMUs are often used in conjunction with GNSSs, LiDARs, and even RGB cameras within multi-sensor and data-fusion approaches [39; 40].
# Multi-Scale Twin Networks for Coastal Zone Change Detection in Remote Sensing Imagery
## 3 Principles and Methods
Step 3 (change result generation): The feature map \(\mathrm{F^{i(4)}}\) is first passed into the foreground attention module (FAM), which strengthens the network's ability to capture the foreground by analyzing the relationship between the background and foreground. This enables the change detection network to better integrate foreground-related contextual information and effectively delineate the boundaries of the change region, alleviating the imbalance problem. Finally, the feature map \(\mathrm{F^{(5)}}\) is fed into the classifier, and the predicted change map is generated using a thresholding technique.
# Random Access Performance of Distributed Sensors Attacked by Unknown Jammers
###### Abstract  
In this paper, we model and investigate the random access (RA) performance of sensor nodes (SN) in a wireless sensor network (WSN). In the WSN, a central head sensor (HS) collects the information from distributed SNs, and jammers disturb the information transmission primarily by generating interference. In this paper, two jamming attacks are considered: power and code jamming. Power jammers (if they are friendly jammers) generate noises and, as a result, degrade the quality of the signal from SNs. Power jamming is equally harmful to all the SNs that are accessing HS and simply induces denial of service (DoS) without any need to hack HS or SNs. On the other hand, code jammers mimic legitimate SNs by sending fake signals and thus need to know certain system parameters that are used by the legitimate SNs. As a result of code jamming, HS falsely allocates radio resources to SNs. The code jamming hence increases the failure probability in sending the information messages, as well as misleads the usage of radio resources. In this paper, we present the probabilities of successful preamble transmission with power ramping according to the jammer types and provide the resulting throughput and delay of information transmission by SNs, respectively. The effect of two jamming attacks on the RA performances is compared with numerical investigation. The results show that, compared to RA without jammers, power and code jamming degrade the throughput by up to 30.3% and 40.5%, respectively, while the delay performance by up to 40.1% and 65.6%, respectively.  
wireless sensor networks; random access channel (RACH); power jamming; code jamming +
Footnote †: journal: Sensors
# GraphMS:Drug Target Prediction Using Graph Representation Learning with Substructures
## 2 Related Work  
### DTI Prediction  
In recent years, drug-protein targeting prediction has been widely investigated. The molecular docking method, which takes the 3D structure of a given drug molecule and the target as input, is widely used to predict binding patterns and scores. Although molecular docking can provide visual interpretability, it takes time and is limited by the need to obtain the 3D structure of protein targets [14].  
Much effort has been devoted to developing machine learning methods for computational DTI prediction. [PERSON] et al. [15] extracted hidden characteristics of drugs and targets by integrating heterogeneous information and neighbor information. [PERSON] et al. [16] applied an SVM model to predict DTIs, based on chemical structure and enzyme reactions. [PERSON] et al. [2] developed an SVM framework for predicting DTIs, based on a bipartite local model, named BLM. [PERSON] et al. [17] extended this framework by combining BLM with a neighbor-based interaction-profile-inferring (NII) procedure (named BLM-NII), which is able to learn DTI features from neighbors.  
As the amount of data on drugs and protein targets has increased, algorithms from the field of deep learning have been used to predict DTIs. [PERSON] et al. [18] developed the deep belief network model, whose input is the fingerprint of the drug and composition of the protein sequence. [PERSON] et al. [19] used a stacked auto-encoder for representing learning, and developed other machine learning methods to predict DTIs.  
Recently, GRL has also been applied as an advanced method for identifying potential DTIs. The purpose of GRL is to encode the structural information into low-dimensional vectors and then quantify the graph. [PERSON] et al. [20] and [PERSON] et al. [21] proposed graph convolutional networks with attention mechanisms to model chemical structures  
Figure 1: Framework overview. The model contains five parts: (1) Perform feature integration on heterogeneous graph. (2) GCN encoder is designed to obtain the node-level representation. (3) Feature-level shuffle is taken on the node representation to generate negative samples. Then maximize the mutual information between the node–level representation and graph–level representation. (4) Partition the homogeneous graphs into k subgraphs. Shuffle nodes except the current subgraph and select k nodes randomly to obtain the corresponding negative subgraph. Then maximize the mutual information between the substructure–level representation and graph–level representation. (5) A decoder takes latent vectors as input and output the reconstructed drug-protein matrix.  
and demonstrated good interpretability. [PERSON] et al. [22] developed Att-GCN to predict drugs for both ordinary diseases and COVID-19.  
Our work solves the problem of retaining the substructure information of a graph. We also obtain an explanatory DTI prediction from low-dimensional representations.
# Research on Defect Detection of Bare Film in Landfills Based on a Temperature Spectrum Model
## 3 Defect Detection Experiment
### Test Analysis of Different Thicknesses of HDPE Film
As the film thickness increases, the thermal capacity increases. The HDPE film with a thickness of 0.20 cm takes longer to rise to the same temperature, while the thermal conductivity of the area of the defect does not change significantly. Therefore, the slower rise in temperature of the HDPE film with a thickness of 0.20 cm results in a later and blurrier appearance of the defect contour, and shrinkage occurs during the time period when the temperature difference characteristic is most significant. The time period with larger values of relative temperature difference is 11-18 min, and the largest value of temperature difference between the defective and intact areas is observed at t = 14 min, where the results are more pronounced. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Time/min**} & **Defect Areas Average** & **Complete Area** & **Temperature** \\  & **Temperature/\({}^{o}\)C** & **Average** & **Difference/\({}^{o}\)C** \\ \hline
0 & 32.6 & 31.9 & 0.7 \\
10 & 31.6 & 34.8 & \(-\)3.2 \\
11 & 32.5 & 35.6 & \(-\)3.1 \\
12 & 33.1 & 36.5 & \(-\)3.4 \\
13 & 33.7 & 37.5 & \(-\)3.8 \\
20 & 44.4 & 46.3 & \(-\)1.9 \\
25 & 47.3 & 49.0 & \(-\)1.7 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Temperature difference data at different times in the 0.15 cm HDPE film defective–complete area.
###### Abstract  
This paper analyzes and calculates the mechanical properties of composite tape spring structures during folding and bending and establishes a non-linear control equation for the folding and bending of composite laminate tape spring structures. Accurate expressions for folding and bending displacements are obtained. The influence of the cross-section's central angle and the composite tape spring's ply thickness on their mechanical properties are analyzed. Finite element numerical analysis is performed on [\(-\)45 45]s laminated composite tape springs, and the correctness of the theoretical derivation is proved by comparing the curvature radius-bending moment curve. Based on previous research, the mechanical properties of different tape spring materials and structures are compared, further studying the lightweight design of space deployment mechanisms. The results show that the steady-state bending moment performance of composite tape springs is excellent, with a 162.1% improvement in steady-state bending moment performance per unit mass compared to traditional metallic tape springs. Additionally, the critical bending moment performance of negative Poisson ratio honeycomb structure tape springs is also excellent, with a 62.3% improvement in steady-state bending moment performance per unit mass compared to traditional metallic tape springs.  
composite tape spring; folding moment; central angle of cross-section; ply thickness; critical bending moment; steady-state bending moment +
Footnote †: journal: Applied Sciences