esa-sceva/satcom-chunk-collection huggingface dataset:
# Fisher Discriminative Sparse Representation Based on DBN for Fault Diagnosis of Complex System
## Appendix A Iterative Projective Method  
Since \(Q(X_{i})=r(A_{i},D,X_{i})+\lambda_{2}\Bigg{(}\left\|X_{i}-M_{i}\right\|_{F}^{2 }-\sum\limits_{j=1}^{c}\left\|M_{j}-M\right\|_{F}^{2}+\eta\|X_{i}\|_{F}^{2} \Bigg{)}\) is strictly convex to \(X_{i}\), \(2\tau\|X_{i}\|_{1}=\lambda_{1}\|X_{i}\|_{1}\) is the function convex to \(X_{i}\) and satisfying one-homogeneous constraints, we rewrote Equation (15) as:  
\[J_{(X_{i})}=\underset{X_{i}}{\operatorname{argmin}}\{Q(X_{i})+2\tau\|X_{i}\|_ {1}\}\]  
This can be represented as \(J_{(f)}=\underset{f}{\operatorname{argmin}}\{F(f)+2\tau J(f)\}\), in which \(J(f)=\|f\|_{1}\), \(\tau=\lambda_{1}/2\) and \(F(f)=r(A_{i},D,f)+\lambda_{2}\Bigg{(}\|f-M_{i}\|_{F}^{2}-\sum\limits_{j=1}^{c }\left\|M_{j}-M\right\|_{F}^{2}+\eta\|f\|_{F}^{2}\Bigg{)}\). This could be solved by the Iterative Projective Method [46]. Table 1 describes the Iterative Projection algorithm used to solve the minimization of Equation (20).  
To proof this calculation, we used two Lemmas: the Young-Fenchel duality, and sub-differential calculus.  
**Lemma 1.**_Young-Fenchel duality If \(J^{*}(g)\) is the conjugate function of \(J(f)\), Then \(g\in\partial J(f)\Leftrightarrow f\in\partial J*(g)\)._  
**Lemma 2.**_Sub-differential calculus If \(J(f)\) is one-homogeneous, which ensures \(J(\lambda f)=\lambda J(f)\), \(K=\partial J(0)\) is the set of sub-differential in 0. Then \(J*(f)\) is the representative function of \(K\)._  
Proof.: If \(f\) is the minimizer of objective function, then it satisfies \(0\in 2\tau\partial J(f)+\
abla F(f)\), And according to Lemma 1, \(f\in\partial J^{*}\left(-\frac{1}{2\tau}\
abla F(f)\right)\), Let \(g=f-\frac{1}{2}\
abla F(f)\), Then \(0\in\frac{1}{\tau}(g-f)-\frac{g}{\tau}+\frac{1}{\tau}\partial J^{*}\left( \frac{1}{\tau}(g-f)\right)\).  
The optimum solution of \(\min\left\{\frac{1}{2}\|w^{\prime}-\frac{g}{\tau}\|^{2}+\frac{1}{\tau}J^{*}(w^ {\prime})\right\}\) is \(w=\frac{1}{\tau}(g-f)\), since this optimization problem is to find out the closest point to \(\frac{g}{\tau}\) in \(K\), then we can get \(f^{p}=\left(I-\pi\frac{g}{\tau}K\right)\left(f^{p-1}-\frac{1}{2\sigma}\
abla F \big{(}f^{p-1}\big{)}\right)\).
# A Comprehensive Evaluation of Possible RNSS Signals in the S-Band for the KPS
## 6 Conclusions  
This paper presents signal performance analysis results for the possible S-band signals. In order to comprehensively evaluate the performance of the candidates, we considered the various signal performances. Each signal performance was analyzed using the FoM.  
The analysis results showed that the outstanding candidate signal was different depending on the signal performance of interest and the reception environments. When we assumed the reception environments given in Table 6 and a multipath delay of 51 m, BPSK(1), BOC\({}_{\text{s}}\)(5,2), and BPSK(\(n\)) (\(n=1,4,5,\) or 8) were superior for the spectral efficiency, ranging performance, and receiver complexity, respectively. The RF compatibility of the candidates was evaluated differently by the existing signal. As for the NavIC SPS and Globalstar, BOC\({}_{\text{c}}\)(4,4) was the best. Meanwhile, as for the NavIC RS signal, BOC\({}_{\text{s}}\)(3,1) was the best.  
These results clearly showed that there was a trade-off relationship between the signal performances and that a comprehensive evaluation of candidates was an essential process. For example, candidates with good RF compatibility with the NavIC SPS signal and ranging performance tended to have poor spectral efficiency and receiver complexity. It implied that if the ranging performance is the primary requirement of the designed signal, we need to accept the power loss due to the OOBE, as well as the high receiver implementation costs.  
The signal performance analysis methodology and results presented in this work can be used as a helpful reference for the S-band signal design for KPS. In future work, we will  
Figure 11: The AMSR for each candidate.  
Figure 10: The Gabor bandwidth and the RA of the MPEE for each candidate.  
conduct a detailed signal performance analysis considering the trade-off relationship and weight of each signal performance according to the purpose of the service.  
Conceptualization, K.H., S.L., M.Y. and J.-H.W.; methodology, K.H., S.L., M.Y. and J.-H.W.; software, K.H.; validation, K.H. and J.-H.W.; formal analysis, K.H.; investigation, K.H.; resources, J.-H.W.; data curation, K.H.; writing--original draft preparation, K.H.; writing--review and editing, J.-H.W.; visualization, [PERSON]; supervision, J.-H.W.; project administration, [PERSON]; funding acquisition, [PERSON]-[PERSON]. All authors have read and agreed to the published version of the manuscript.  
This work was supported by Electronics and Telecommunication Research Institute (ETRI) (21 ZH1100, Study on 3D Communication Technology for Hyper-Connectivity), and INHA UNIVER-SITY research grant.  
Not applicable.  
The authors declare no conflict of interest.  
The views and opinions expressed in this article are those of the authors and do not necessarily reflect the official policy or position of any agency of the government of South Korea.
# An Improved Q-Learning-Based Sensor-Scheduling Algorithm for Multi-Target Tracking
## 4 Detailed Description of the Proposed Algorithm
### Target Prioritizing
#### (3) Node Density  
Node density \(\rho\) indicates the distribution density of the nodes around the target. Owing to the fact that sensors are randomly deployed, there are areas where nodes are sparsely distributed in the network. When a target moves to this area, the system should give priority to it to ensure tracking reliability.
## 3 Results and Discussion
### Biofilm Assays on SSC  
Furthermore, S2\({}^{\text{BAC}}\) and S2-1 were used to produce biofilms on stainless steel coupons (SSC). When the SSC method was used to evaluate the antibiofilm activity of BAC, two approaches were used  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Strain**} & \multicolumn{3}{c}{**Metabolic Activity of Biofilm at BAC Concentrations (mg/L) 1**} \\ \cline{2-5}  & **0.0** & **1.25** & **2.5** & **5.0** \\ \hline S2-1 & 1.4837 \(\pm\) 0.4855 & 1.5688 \(\pm\) 0.6543 & 0 & 0 \\ S2\({}^{\text{BAC}}\) & 0.9494 \(\pm\) 0.3032 & 0.7381 \(\pm\) 0.2681 * & 1.3681 \(\pm\) 0.3844 ** & 0 \\ \hline \hline \end{tabular}  
* Biofilms were formed in groups of eight identical replicates, and the experiments were performed on two different occasions (\(n=16\)). The results were expressed as the mean \(\pm\) the standard deviation of metabolic activity. Reduced (*) or increased (**) activity in the presence of BAC differed significantly from controls without BAC (\(p<0.05\), Student’s \(t\)-test).  
\end{table}
Table 2: Effect of benzalkonium chloride (BAC) on the prevention of biofilm formation by two strains of _Listeria monocytogenes_. The metabolic activities (\(A_{570}\)–\(A_{600}\)) of biofilms were quantified after incubation for 24 h using the resazurin reduction assay.  
to test the effect of the presence of sub-inhibitory BAC: biofilm cell enumeration after incubation under different sub-inhibitory BAC concentrations (1.25 or 2.5 mg/L, 48 h), and biofilm logarithmic reduction after BAC exposure (1280 mg/L, 5 min).  
3.1 Biofilm Cell Enumeration after Incubation under Different Sub-Inhibitory BAC Concentrations (1.25 or 2.5 mg/L, 48 h)  
Biofilm-forming ability was determined by enumerating viable cells on culture plates. The results show that the presence of sub-inhibitory concentrations of BAC did not have a significant effect (\(p>0.05\)) on the number of viable cells in the biofilm (Figure 1a).  
The results concerning the biofilm-forming ability of the isolates on SSC suggested that biofilm growth tends to be more unaffected by sub-inhibitory concentrations of BAC than that on polystyrene. _L. monocytogenes_ strains may have diverse responses during their contact to abiotic surfaces. The type of contact surface may determine the biofilms' structure, therefore affecting the exposure of attached cells to stressful conditions [28].
## 4 Experimental Results  
### Dataset  
We used the DEAP dataset, which is widely used in emotion recognition research. It is available for download upon authorization from the homepage [41]. The DEAP dataset consists of two parts, namely the online ratings and the participant ratings. The online self-assessment contained the ratings of 120 1 min music videos by 14-16 volunteers with respect to arousal, valence, dominance, and liking the videos. Secondly, the other 32 participants watched a subset of 40 videos while recording various physiological signals; they also rated the videos in terms of arousal, valence, dominance, and liking from one to nine. The DEAP dataset contained various physiological signals, such as PPG, EMG, EEG, and EDA, and we only used the PPG signal. As arousal and valence were rated from one to nine and our purpose was a binary classification, we divided the arousal and valence classes according to the median value of five (low/high).  
We used the PPG data of DEAP with a length of \(20\times 40\times 8064\). Among the 32 participants, the PPG signals of only 20 participants were valid, and there were 8064 data points for the 1 min video clips (128 Hz). In the 1 min video, we divided the signal into 10 s and acquired six samples. Therefore, there were 4800 samples in all (\(20\times 40\times 6=4800\)).
# An Improved Winkler Foundation Modulus for a Beam in a Full Space
## 2 The Formula of the Winkler Foundation Modulus
### Beam Bending Response of the Beam on a Three-Dimensional Elastic Foundation
#### 2.2.2 The Response of the Foundation under a Single Cosine Load  
When considering a beam with a limited width, it is necessary to calculate the foundation response within the range of the beam's width. The average value of the y-distributed force \(q_{0}(y)\) of the double cosine load \(q(x,y)\) in the interval \([-\text{b},\text{b}]\) is calculated to obtain the single cosine load. As shown in Figure 4, the foundation is subjected to a single cosine load in the interval \([-\text{b},\text{b}]\), which can be written as follows:  
\[q_{s}(x,y)=q_{0}(y)\cos\lambda x \tag{28}\]  
where  
\[q_{0}(y)=\left\{\begin{array}{ll}0&y<-b\\ q_{0}&-b\leq y\leq b\\ 0&y>b\end{array}\right. \tag{29}\]  
It should be noted that the term \(q_{0}(y)\) can also be represented by the Fourier integral, as follows:  
\[q_{0}(y)=\frac{q_{0}}{\pi}\int_{0}^{\infty}\frac{2\sin(\omega b)\cos(\omega y )}{\omega}d\omega \tag{30}\]  
When a load by the expression in Equation (28) acts on the foundation, the displacement of the foundation should have a similar cosine expression, as follows:  
\[w_{s}(x,y)=w_{0}(y)\cos\lambda x \tag{31}\]  
Moreover, the concentrated load \(Q_{avg}\) and average displacement \(W_{avg}\) have the forms  
\[Q_{avg}=\int_{-b}^{b}q_{s}(x,y)dy \tag{32}\]  
\[W_{avg}=\frac{1}{2b}\int_{-b}^{b}w_{s}(x,y)dy \tag{33}\]  
Combining Equations (27)-(33), the relationship between force \(Q_{avg}\) and displacement \(W_{avg}\) can be obtained as follows:  
\[Q_{avg}=W_{avg}\frac{2E_{s}}{C(1-{\
u_{s}}^{2})}\beta\psi(\beta) \tag{34}\]  
where \(C=1.1\), from the research by [14]; \(\beta=b\lambda\); and  
\[\psi(x)=\frac{\pi}{2\int_{0}^{\pi/2}\frac{sin^{2}(xstant)}{x\cdot sint\cdot tan t }dt} \tag{35}\]
# Research Progress and Typical Case of Open-Pit to Underground Mining in China
## 2 Analysis of Key Technologies for the Transition from Open-Pit to Underground Mining
### Selection of Mining Methods and Technical Adaptability Analysis  
In open-pit to underground mining operations, the scientific selection of mining methods is critical for ensuring the safe and efficient transformation of mines as well as achieving optimal resource recovery and sustainable development [19]. Given the significant differences between open-pit and underground mining in terms of geological conditions, spatial structure, stress distribution, and operational methods, the technical adaptability of mining methods directly impacts resource recovery rates, mining efficiency, operational safety, and surface ecological effects [20]. Consequently, the systematic evaluation of method applicability, potential risks, and mitigation strategies holds substantial practical significance. Currently, the primary methods employed in open-pit to underground mining include the stope method (Figure 3a), caving method (Figure 3b,c), and backfilling method [21]. To further investigate the specific applications, advantages, and limitations of each mining method, a detailed analysis of the stope method, caving method, and backfilling method will be presented in the following sections.  
Figure 3: Schematic diagrams of common mining methods (adapted from [PERSON] [22]): (**a**) room and pillar method; (**b**) stope mining method; (**c**) caving mining method.  
Figure 2: Site diagram of the Xinqiao Mine development system. Source: Figure 2 adapted from [[[http://www.xqmcl.com/1](http://www.xqmcl.com/1)]([http://www.xqmcl.com/1](http://www.xqmcl.com/1)) (accessed on 8 July 2025).
# Implementing Smart City Technologies to Inspire Change in Consumer Energy Behaviour
## 5 Communication Solutions for Energy Services in Smart City
### Description of Communication Solutions
This network requires high level of service, very low latency (less than 10 ms) and stable high-speed data communication (10 Mbps-1 Gbps or more). Therefore, fibre optics, cellular networks and satellite networks are preferred. The used FAN/NAN (backhaul) networks have the same network necessities as WAN but with less coverage. These network protocols use cellular networks and fibre optic communication and connect the public services with the electrical distribution system. The HAN/BAN/IAN (access, distribution) networks have undergone many technological improvements and functionality, such as Wi-Fi and Bluetooth used in these types of networks.
# Onboard Processing in Satellite Communications Using AI Accelerators
## 2 Onboard AI Processing
### Onboard Applicability  
The applicability of the proposed use case to be implemented on board a satellite must be evaluated, considering the state of satellite payloads and their required onboard functionalities. This depends on the current level of technology. Some proposed cases (e.g., adaptive coding and modulation (ACM) optimization) require the onboard implementation of a complete satellite payload or a constellation. We propose a progressive scale of applicability scores that increase according to the complexity of the satellite or the regenerative level required to implement the proposed scenario. These levels can also correspond to the 4G/5G/6G radio access network (RAN) divisions and their functional groups: radio unit (RU), distributed unit (DU), and centralized unit (CU).
# Detecting Cover Crop End-Of-Season Using VEN\(\upmu\)S and Sentinel-2 Satellite Imagery
## 2 Materials
### Satellite and Ground Data
#### 2.2.3 Cover Crop Termination Records  
BARC maintains a comprehensive record of crop operations, including planting dates, harvest or termination dates, irrigation, and fertilization information for each field using the FarmLogic system [34]. Harvest or termination dates for cover crops and alfalfa in 2019 and 2020 were exported from the system. We selected 29 termination records (18 in 2019 and 11 in 2020) from 14 large fields (8 in 2019 and 6 in 2020) (>2 ha) for validation. Figure 3 shows the fields and field names used in the study. These cover crops include ryegrass, triticale/ryegrass mixture, orchard grass, ryegrass, as well as alfalfa. The termination dates spanned from 29 April to 6 September 6 2019, and from 9 April to 13 July in 2020. The alfalfa fields were harvested multiple times during the growing season. For example, field #5-9 was harvested four times in 2019 (7 May, 20 June, 27 July, and 6 September) and two times in  
Figure 2: Percent of clear VEN\(\mu\)S (**a**) and Sentinel-2 pixels (**b**) over BARC in 2019 and 2020. The cover crop termination periods are highlighted in light green.  
2020 (7 June and 13 July). We compared each harvest event to the termination date detected from the VEN\(\upmu\)S and Sentinel-2 NDVI time-series.
## 2 Proposed Method
### Label Classifier Based on Local Minimum Mean Discrepancy  
In the context of unsupervised domain adaptation, the absence of labels for target domain samples makes it impossible to access label information. Therefore, the first step is to leverage the labeled samples from the source domain to train the label classifier within the RASAN-W framework. This label classifier consists of two fully connected layers, employing the ReLU activation function in the first layer and the softmax activation function in the second layer. At the same time, the optimization objective of the label classifier includes the cross-entropy loss function, the LMMD loss function, and the pseudo-label loss function.
## 4 Proposed Model Design
### Comparison with Existing Models  
In this section, we compare the performance of the proposed model with the existing classification models recently published in [19; 20; 21] using the original benchmark dataset. For this comparison, all the models were implemented using a single machine. Listed in Table 8 are the results obtained by the proposed model and existing classification models. The outcomes indicated that our proposed model outperformed three existing predictors. Compared to the existing predictor (PreAIP), our proposed model exhibited the highest accuracy, 83.40%.  
Furthermore, a meaningful comparison should consider other measurement metrics such as sensitivity, specificity, and Mathews Correlation Coefficient (MCC) with the combination of accuracy. A high value of these metrics for a model shows that the model is more accurate and stable. From Table 8, we can observe that the proposed model achieved high values for these metrics compared to the existing mentioned models. For example, our proposed model had the most significant MCC values of 0.71, compared to 0.566 for the PreAIP. These results indicate the significance and stability of the proposed model compared to its counterpart prediction models.
# Thermal Fatigue Properties of Ultrasonically Bonded Copper Joints
## 2 Materials and Methods
### Observation of Microstructure  
The effect of the microstructure of ultrasonically bonded copper joints on their thermal fatigue properties was analyzed. Figure 3 shows the typical fracture surface of the C1020 joint. The copper-colored parts indicate the bonding interface; many bonding interfaces with needle-like structures are formed parallel to the direction of ultrasonic vibration. It is possible to observe each joint by cutting a cross-section perpendicular to the ultrasonic vibration. Microstructural analyses were performed using an optical microscope (OM) and scanning electron microscopy (SEM; JEOL Ltd., JSM-6500F, Tokyo, Japan and Hitachi High-Technologies Co., S-3000H, Tokyo, Japan) equipped with electron backscatter diffraction (EBSD). As for the EBSD analysis, image quality (IQ) and inverse  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Apparent CTE of the Substrate** & **Average Grain Size of the** \\  & **(10\({}^{-6}\)/K)** & **Substrate (\(\mu\)m)** \\ \hline C1020 substrate & 4.4 & 46.2 \\ C1940 substrate & 4.4 & 15.2 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Properties of copper substrates.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Chemical Composition (wt \%)**} & \begin{tabular}{c} **Tensile Strength** \\ **(N/mm\({}^{2}\))** \\ \end{tabular} & \
\begin{tabular}{c} **Vickers** \\ **Hardness (HV)** \\ \end{tabular} &
\begin{tabular}{c} **Linear Expansion** \\ **Coefficient (10\({}^{-6}\)/K)** \\ \end{tabular} \\ \hline C1020 & 99.96 min & - & - & - & 195–255 & 60 Max. & 17.7 \\ C1940 & 97.6 & 2.3 & 0.12 & 0.03 & 345–415 & 100–125 & 17.6 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Chemical composition and physical property values of the copper alloys used in this study.  
Figure 2: Orientation maps of (**a**) C1020 and (**b**) C1940 substrates.  
pole figure (IPF) maps, which represented the information of the diffraction intensity and the crystal orientation, were obtained near the bonding interface.
## 4 Results and Discussion  
### Organic Matter Content  
The total organic carbon (TOC) content, chloroform bitumen \"A\", and hydrocarbon generation potential are the primary parameters for evaluating the organic matter abundance of source rocks [38; 39; 40]. The Jurassic Tamulangou Formation source rocks in the Hongqi Depression are widely distributed and have substantial thicknesses, with dark mudstone layers generally ranging from 50 to 200 m. In the southern Shutenuover Depression, the cumulative thickness of dark mudstone exceeds 200 m, while in the northern Hongqi Ranch Depression, it surpasses 300 m [14]. In this study, a systematic collection and analysis of geochemical test data from 87 samples obtained from three key boreholes in the Hongqi Depression were conducted (Table 1). The TOC content of Jurassic Tamulangou Formation dark mudstones typically ranges from 0.098% to 4.27%, with an average variation between 0.14% and 2.91%. The average chloroform bitumen \"A\" content is 0.168%, and the average hydrocarbon generation potential (S\({}_{1}\) + S\({}_{2}\)) is 2.38 mg/g. The mean vitrinite reflectance (Ro) varies between 0.71% and 1.36%, indicating that the dark mudstones have generally entered the mature oil generation stage. Notably, the mean vitrinite reflectance of the dark mudstones in Well Hong-6, located in the central Shutenuover Depression, exceeds 1.3%, signifying a high-maturity oil generation stage. According to the organic matter abundance evaluation criteria for continental source rocks (SY/T5735-1995), the dark mudstones of the Jurassic Tamulangou Formation are classified as moderately productive source rocks with considerable hydrocarbon generation potential.
## 3 The Extended/Generalized Finite Element Methods (XFEM/GFEM)
### Displacement Approximation  
Using the enrichment functions of the previous subsection, the XFEM/GFEM displacement approximation can be obtained:  
\[\mathbf{u}\left(\mathbf{x}\right)=\underbrace{\sum_{I\in\mathcal{N}}N_{I} \left(\mathbf{x}\right)\mathbf{u}_{I}}_{\text{FE approximation}}+ \underbrace{\sum_{I\in\mathcal{N}^{I}}N_{I}\left(\mathbf{x}\right)H\left( \mathbf{x}\right)\mathbf{b}_{I}}_{\text{jump enriched part}}+\underbrace{ \sum_{T\in\mathcal{N}^{I}}\sum_{j}N_{T}\left(\mathbf{x}\right)F_{j}\left( \mathbf{x}\right)\mathbf{c}_{Tj}}_{\text{tip enriched part}} \tag{19}\]  
where \(\mathbf{b}_{I}\), \(\mathbf{c}_{Tj}\) are enriched degrees of freedom.  
The nodal sets of Equation (19) are defined as follows:  
\(\mathcal{N}\): is the set of all nodes in the FE mesh. \(\mathcal{N}^{j}\): is the set of jump enriched nodes. This nodal set includes all nodes whose support is split in two by the crack. \(\mathcal{N}^{t}\): is the set of tip enriched nodes. This nodal set includes all nodes whose support includes the crack front.  
The method resulting from the above approximation does not involve any modifications, for instance dealing with blending or conditioning issues, and is thus often referred to as the \"standard XFEM\".