esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Proposed Method
### PDNet
#### 4.2.3 Detail of PDNet  
In order to avoid network failure caused by individual feature differences, the direction and location information were added to the image. These two aspects of multi-level features can be modeled based on the transformation of the coding module \(F_{pd}\). The established adjustment network (PDNet) consists of two key modules: a convolutional encoder network and an encoding module, taking Figure 6 below for relevant information. The former can be used to extract the characteristics of each abstract level during the operation. The latter module can transform the position and direction information to obtain multi-scale feature information, and set weights in the encoder network, which provides support for the adjustment of the position and direction.  
The \(F_{pd}\) calculation method used in this paper includes the weight parameter, which is obtained by calculating the translation and rotation parameters that are most similar to the template, combined with the multi-feature extraction detailed in Section 4.1 to make the network volume not too large. The similarity was calculated by determining the overlap between the classification result and the template. The pixel-level classification was performed by calculating the color classification that is close to a certain range, which is formulated as follows:  
\[D^{\prime}=\sqrt{\left(\frac{d_{c}}{m}\right)^{2}+\left(\frac{d_{s}}{S}\right) ^{2}} \tag{8}\]  
in which \(d_{c}\) means the color distance, \(d_{s}\) refer to the spatial distance, and \(m\) is a fixed constant. \(S=sqrt(N/K)\), where \(N\) represents pixels number, and K refers to presegmented number of pixel classes.  
Figure 6: PDNet network framework.
## 3 Graph to Metric Space Transformation
### Graph to Metric Space Algorithm
#### 3.1.3 Choice of \(\lambda\) and \(\phi(\sigma)()\)  
The choices in the above sub-modules play a crucial role in the graph to metric transformation algorithm to be used for community detection. The complex network is characterized by a small average diameter and a high clustering coefficient. Several studies on network structure analysis reveal that there are hub nodes and local nodes characterizing the interesting structure of the complex network. Suppose we have taken \(\phi=arccos\), \(\sigma_{CS}\) and constant \(\lambda\geq 0\). \(\lambda=0\) penalizes the effect of the direct edge in the metric and is suitable to extract communities from a highly dense graph. \(\lambda=1\) places a similar weight of the direct edge, and the common neighbor reduces the effect of the direct edge in the metric and is suitable to extract communities from a moderately dense graph. \(\lambda=2\) sets more importance for the direct edge than the common neighbor (this is the common case of available real networks). \(\lambda\geq 2\) penalizes the effect of the common neighbor in the metric and is suitable for extracting communities from a very sparse graph.  
The choice of \(\lambda\) depends on the data complexity for community detection (DCC) value (Section 4.5) of the input graph, _i.e._, whether it is sparse or dense, and its cluster structure.  
The algorithm for transforming a graph to the points of a metric space is given in Algorithm 1.  
**Theorem 1**.: \(M=(V,d)\) _constructed in the above Algorithm 1 is a metric space with respect to the metric \(d\), i.e.,:_  
The proof of the theorem is straight forward and satisfies the following metric properties:  
* \(d(v_{i},v_{j})\geq 0\) _and_ \(d(v_{i},v_{i})=0\)__
* \(d(v_{i},v_{j})=d(v_{j},v_{i})\)__
* \(d(v_{i},v_{j})\leq d(v_{i},v_{k})+d(v_{k},v_{j})\)__  
```
0:\(G=(V,E)\)
0:\(M=(V,d)\)
1:\(D^{\lambda}_{(n\times n)}=\begin{cases}0\ if\ i\
eq j\\ \lambda\geq 0\ if\ i=j\end{cases}\)
2:\(A=D^{\lambda}+E\)
3:for\(i=1\) to \(n\)do
4:for\(j=1\) to \(n\)do
5:\(d(v_{i},v_{j})=\phi(1-\frac{a_{i}\cdot a_{j}}{|a_{i}||a_{j}|})\), where \(v_{i},v_{j}\in V\) and \(a_{k}\) is the \(k\)-th row of \(A\) and \(\phi\) is an affine function.
6:endfor
7:endfor
8:return\(M=(V,d)\)
```  
**Algorithm 1** Mapping a graph into the metric space.
# Customer Churn Prediction Approach Based on LLM Embeddings and Logistic Regression
## 4 Materials and Methods
### Dataset  
#### 4.1.1 Data Description  
In this study, we utilized a publicly accessible labeled dataset from a telecommunications company [47]. This dataset comprises information on a total of 36,992 customers, divided into 16,980 non-churners and 20,012 churners. It includes 23 attributes that indicate whether the customer is categorized as a churner or a non-churner. This dataset is not structured; it includes numerical, categorical, date, and alphanumeric attributes that describe the customer's behavior, age, gender, and preferred offer type. It also indicates whether the customer has filed a complaint, along with the status of problem resolution and various other information. The dataset consists of approximately 60% categorical data, 30% numerical data, and 10% date and time data. The goal of constructing this dataset is to identify customers who are likely to churn based on the provided features, enabling the company to implement targeted retention strategies, such as offering special memberships to encourage them to remain loyal to their services.
## 7 Conclusions  
This paper presents the design of an OKID-based LQAT and the design of a functional observer to estimate the EID for unknown continuous-time square-non-square singular analog systems with all stable zeros and unknown matched-mismatched input and output disturbances. The proposed method significantly improves the tracking performance for systems subject to high-frequency disturbances. Additionally, an optimal OKID-based LQAT integrated with the modified functional observer-based EID estimator is newly proposed for unknown continuous-time singular systems.  
The advantages of the proposed method compared to other approaches are as follows: (i) it is capable of rejecting any kind of unknown state-dependent disturbances with a natural frequency smaller than the cut-off frequency; (ii) it does not require a priori information about the disturbances, such as their input matrices at the input or output terminals, rank conditions, differentiation of the measured outputs, or the number of independent signals in the unknown input or output disturbances; (iii) performing the inverse dynamics of the plant is not required; (iv) the system of interest does not need to be of square size; and (v) its implementation is quite simple, since it is a plug-in of an EID estimator that is used to improve any existing controller for a square-non-square servo system.  
For communication systems, delays might exist in the measurement outputs or control inputs, since the information is obtained through a communication channel with limited bandwidth. Thus, an extension of the proposed approach with control input constraints for some classes of unknown  
Figure 10: Control inputs for the OKID-based LQAT tracker with the proposed EID compensation for the non-square system with mismatched input and output disturbances.  
Figure 9: (**a**) Time responses of the proposed method and (**b**) errors of output \(e(t)=r(t)-y_{c}(t)\).  
time-delay large-scale interconnected linear systems, networked control systems, or non-linear systems will be considered as a future research topic.  
**Author Contributions:** Conceptualization and methodology, J.S.-H.T. and T.-J.S.; software and writing--original draft preparation, Y.-F.C. and C.-Y.C.; validation, S.-M.G.; formal analysis, L.-S.S.; writing--review and editing, J.I.C.; funding acquisition, J.S.-H.T. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by the Ministry of Science and Technology of the Republic of China (MOST 108-2221-E-006-213-MY3 and MOST 107-2221-E-006-203-MY2).  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Memory-Efficient Batching for Time Series Transformer Training: A Systematic Evaluation
## 5 Experimental Setup and Evaluation
### Results and Analysis
#### 5.3.1 Memory Optimization Evaluation
The batch size was 1000 samples and the dataset contained 500 time series features. The results of average memory usage measurement with varying sequence lengths and memory reduction percentages are displayed in Figure 10. Figure 10a displays the GPU memory usage to accommodate each batch of tensors with varying sequence lengths for the training process. The \(x\)-axis represents each dataset with an increasing batch size. The \(y\)-axis shows the memory usage measured in gigabytes (GB). The results are plotted on a logarithmic scale to help with visualization. The experimental results for the setting of varying sequence length are similar to the previous experiment of increasing the batch size. As the size (sequence length) of each sample increases, the memory usage for the baseline (labeled \"Original\") and our \"per-batch\" optimization variant (labeled \"Optimize\") increase linearly. However, the size of the raw data that was preloaded into GPU memory was exactly the same for the \"preload\" variant (labeled \"Optimize in memory\") resulting in a constant memory usage across all evaluated sequence lengths. Our optimizations reduced the memory usage by 94.5% and 99% for the \"preload\" and \"per-batch\" variants, respectively, as shown in Figure 10b.
# Backscatter Communications: Inception of the Battery-Free Era--A Comprehensive Survey
## 6 Efficient Power and Data Transfer in BackCom
### Scheduling and Resource Allocation
#### 6.3.4 Optimization
\begin{table}
\begin{tabular}{p{14.2 pt} p{142.3 pt}} \hline \hline
**Subsection** & **Ref.** & **Contribution** \\ \hline \hline \multirow{8}{*}{[95]} & **Energy Scheduling** \\  & [95] & \(\bullet\) Energy scheduling based on time division multiple access. \\  & [51] & \(\bullet\) Trade-off between successful transmission probability and the energy harvesting rate. \\  & **Data Scheduling** \\  & [139] & \(\bullet\) The discovery that data can be sent to gateways by changing impedance of antenna. \\  & **Resource Allocation** \\  & \(\bullet\) A resource allocation algorithm for energy efficiency maximization during data transmission. \\  & [101] & \(\bullet\) Investigation of three schemes for resource allocation based on time, throughput, and fairness. \\  & \(\bullet\) Discussion of energy-efficient resource allocation. \\  & **Time Allocation** \\ \multirow{2}{*}{**Scheduling and Resource Allocation**} & [95] & \(\bullet\) Optimal time allocation for full and half-duplex backscatter-assisted WPCN. \\  & [100] & \(\bullet\) Optimal time and energy allocation to enhance sum throughput. \\  & **Optimized time allocation for full and half-duplex backscatter-assisted WPCN.** \\  & **Cognitive Radio** \\  & [143] & \(\bullet\) A sum throughput optimal resource allocation (STORA) scheme. \\  & [148] & \(\bullet\) Relay selection and resource allocation in cognitive sensor networks. \\  & [150] & \(\bullet\) Optimization of allocated time in energy harvesting, transmission, and backscattering. \\  & \(\bullet\) An RF-powered cognitive radio network with Amb-BackCom. \\  & **Optimization** \\  & **Optimization** \\  & [127] & \(\bullet\) Optimization of channel training energy and energy allocation weights for energy beams. \\  & [139] & \(\bullet\) A state action reward state action (SARSA) algorithm for optimal policy. \\  & \(\bullet\) Performance optimization for cognitive radio networks with RF energy harvesting. \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Summary of BackCom scheduling and resource allocation.
## 6 Conclusions  
Due to the complexities and factors involved in disaster damage assessments, geospatial information drawn from multiple sources through an integrated approach at global and local levels should be used to support disaster relief, response, and recovery operations. To address challenges faced by disaster management agencies and to improve the use of existing information for disaster damage estimations, the following issues should be taken into account: (1) Produce more global datasets through inclusive and collaborative platforms to facilitate data access and update them periodically. At the same time, make full use of existing geo-information resources so that they add value through integrated information service platforms; (2) Develop methodologies specific to each disaster and validate them in conjunction with available data sources, damage assessment indicators, and guidelines for their transfer software tools for ease of use; (3) Shift the practice of generating 'information based on data' to developing 'knowledge based on information' through in-depth requirement analyses and solutions-oriented methodologies to generate the products needed for decision-making; (4) Develop standards related to datasets, methodologies, products, and platforms for the superior integration and utilization of geo-information to enhance disaster preparedness for an effective response, as spelled out under the Sendai Framework for Disaster Risk Reduction: 2015-2030; (5) Construct a geo-information integrated platform for information resource searching, use, and development at the global scale with data catalogs, metadata, and clearly specified policies; (6) Explore the potential and modalities of new advances and innovations such as big data, cloud computing, and environment modeling for superior disaster reduction applications [56; 57; 58].  
**Acknowledgments: This work was supported by the National Natural Science Foundation of China under Grant (41301591).**  
**Author Contributions:** [PERSON] collected and processed the data; [PERSON] extracted information; [PERSON] performed the data analysis; [PERSON] contributed to the discussion and made revisions; [PERSON] wrote the paper.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 3 5G Signal and Detection Performance Analysis
### Features of 5G Signal Physical Layer
#### 3.2.1 Cyclic Prefix (CP)  
CP-OFDM waveforms can be used for uplink and downlink transmission of 5G signals. After obtaining the OFDM modulation waveform, a segment of data at the end of each symbol is copied to the front end to obtain the CP-OFDM waveform, which is the cyclic prefix. The cyclic prefix prevents the previous symbol from falling into the range of this symbol due to multipath effects and does not destroy the orthogonality between subcarriers. When inter-symbol interference occurs, the presence of the cyclic prefix causes a cyclic shift in the signal, resulting in a phase shift in the demodulated symbols. The 5G system has flexible subcarrier settings and corresponding cyclic prefix types, as listed in Table 2[9], where \(\mu\) is the subcarrier parameter.
# Sporiasis Pathogenesis and Treatment
## 2 Pathogenesis
### Autoimmunity in Psoriasis  
Psoriasis shows clear autoimmune-related pathomechanisms. This very important area of research will allow for a deeper understanding of to which extent autoanti-specific T cells contribute to the development, chronification, and overall course of the disease.  
LL37 is one of two well-studied T cell autoantigens in psoriasis. CD4\({}^{+}\) and CD8\({}^{+}\) T cells specific for LL37 were found in two-thirds of patients with moderate to severe plaque psoriasis in a study. LL37-specific T cells produce IFN-\(\gamma\), and CD4\({}^{+}\) T cells produce IL-17, IL-21, and IL-22 as well. LL37-specific T cells can be found in lesional skin or in the blood, where they correlate with disease activity [77]. CD8\({}^{+}\) T cells activated through LL37 engage in epidermotropism, autoantigen recognition, and the further secretion of Th17 cytokines. The melanocytic protein ADAMTSL5 was found to be an HLA-C*06:02-restricted autoanti recognized by an autoreactive CD8\({}^{+}\) T cell TCR. This finding establishes melanocytes as autoimmune target cells, but does not exclude other cellular targets [78].  
Other autoanti candidates include lipid antigens generated by phospholipase A2 (PLA2) group IVD (PLA2G4D) and hair follicle-derived keratin 17 [79; 80]. Interestingly, keratin 17 exposure only lead to CD8+ T cell proliferation in patients with the HLA-Cw*0602 allele (see above) [81].
## 3 Fundamentals of the ED Process
#### Properties of the Receiver Operating Characteristic Curves  
The primary function of SU is to detect spectrum holes using a specific SS method. Methods for detecting PU signal transmission are thus critical for analysing the cognitive capabilities of SUs in CRNs. Two metrics are used to evaluate the SS quality, specifically false alarm probability and detection probability. The probability of a false alarm characterizes the case when the SU reports that a PU signal is present in an unoccupied spectrum. Additionally, the likelihood that SU will perform a correct assessment of the existence of PU when PU really occupies the frequency spectrum is defined as the detection probability.  
In this paper, ROC curves are used to present the performance of the ED approach. The ROC curve illustrates the relationship between the false alarm and the probability of detection (Figure 2). The precision of the ED process based on binary hypotheses \(H_{0}\) and \(H_{1}\) is comparable to that of a random guess. As shown in Figure 2, spaces above or below the curve presenting random guess processes represent the good or poor signal detection accuracy, respectively. If the line is near the random guess curve, the ED process is less precise and vice versa (Figure 2). Additionally, the area under the curve (AUC) is a dimension used to define precise signal detection. The greater the area under the curve, the more precise the signal detection is, and vice versa (Figure 2). The ROC-AUC model is being further used in this work as it represents the most commonly used model for analysing PU signal detection efficiency.
# Content-Based Approach for Improving Bloom Filter Efficiency
## 4 Experiments and Results
### Effect of Content Bits  
Here, we describe the influence of content bits on the quantity of false positives.  
First, we discuss the values of the parameters, following the settings in [7], as shown in Table 1. The content bits are considered in binary form. Later, we decrement by 1 as more content bits are required. Therefore,  
Figure 2 shows the reduction in false positives as more and more content bits are utilized. The content bits are shown along the x-axis and the false positives appear along the y-axis. In total, 1061 false positives are observed without using any of the content bits. Employing just two content bit results in a reduction of 54.38% (484 vs. 1061) in the false positives. Similarly, a 65.03% (371 vs. 1061) reduction in the false positives is seen while using three content bits. Moreover, a 69.84% (320 vs. 1061) reduction in the number is observed while using four bits. This reduction trend continues as more and more content bits are added. The maximum reduction of 79.83% (214 vs. 1061) is observed once seven content bits are employed.
# Experimental Demonstration of MmWave Vehicle-to-Vehicle Communications Using IEEE 802.11 ad
## 6 Discussion
**Funding: This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT (no. NRF-2017R1C1B1006607). This research was supported by the Gachon University Research Fund GCU-2018-0323 of 2018. Conflicts of Interest: The authors declare no conflict of interest. ** \begin{table}
\begin{tabular}{l c c} \hline \hline  & **Campus** & **City** \\ \hline Mean throughput (Mbps) & 177 & 194 \\ Mean disconnection (s) & 1.16 & 1.54 \\ Mean connection (s) & 1.45 & 2.38 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Experimental results on driving test. Figure 13: Average connection time while travelling through the campus and city.
# A Statistical Approach to Discovering Process Regime Shifts and Their Determinants
## 3 Methodology  
The proposed methodology sequentially addresses the three research questions stated in Section 1, i.e., revealing any present regime shifts in the process output data and identifying and statistically linking these shifts to the identified potential determinants.  
The methodology comprises several steps, including (1) translating different process views to a commonly agreed model. This translation is performed using a generally applicable Stochastic System and Process Modeling Framework [20], built upon the theoretical foundation of Bunge's Ontology [60; 61]. (2) Using this model, a statistical modeling basis is selected as the process output evaluation criterion in the regime change analysis. We note that this criterion may vary according to the output of the process being evaluated and the analyst's choice. (3) By using a minimal form of this basis (i.e., the statistical model with no explicit determinant variable included), statistical tests are performed to identify and date all regime shifts present in the data. (4) Process determinant variables are then sequentially introduced into the base model according to their change on the timeline and are statistically tested again for regime shifts. If an earlier regime shift disappears, it shows that the introduced determinant has a causal relationship with the regime shift. This step is repeated until all potential determinants are introduced and tested. (5) Lastly, the fitted model is analyzed for its significance or impact on the process output. A detailed account of all these steps is presented in the following sub-sections.
# Stabilization Methodology in Foundation Soils by ERT-3D Application in Estepona, South Spain
## 3 Case Study
### Analysis and Stabilisation Methodology  
According to the data--regulations, the state of the building, the undeveloped land surrounding the building, and the necessity of keeping the building stable (i.e., avoiding imminent collapse)--a simplified model was proposed, in which ERT-3D would be used, with subsequent processing of the reversal data via the RES2 DINV software [43], as seen in Figure 5. This analysis will consider the geotechnical data given in the previous sections.  
The developed methodology allowed the researchers to see the reality of the soil, as well as any possible cavities in the subsoil supporting the building's foundation. These cavities were filled with grout to consolidate the soil, and then, via contrast campaigns (ERT-3D), the researchers checked to determine if the filling worked, if it moved, etc. Thus, the proposed methodology provided a complete, three-dimensional analysis of the subsoil supporting the damaged foundation, where the differential settlements occurred. Later, along with this original geophysical technique, the researchers monitored, through successive contrast campaigns, the consolidation and underpinning of the subsoil with different passes of consolidation injections. The injection material depends on the lithological morphology of the soil to be consolidated. Due to the type of subsoil in this case, as well as the material's low cost and ease of use and control, this paper proposed injections of controlled cement grout [78; 79]. Synthetic resin is frequently used as an injection fluid, though it does not allow for high solicitation [52]. Three beneficial effects of injecting cement grout into the subsoil have been observed: (1) filling existing gaps, (2) soil compaction, and (3) interstitial water reduction and/or elimination.