esa-sceva/satcom-chunk-collection huggingface dataset:
# Router Activation Heuristics for Energy-Saving ECMP and Valiant Routing in Data Center Networks
## 4 Mathematical Models for Energy-Aware Routing Calculation
### Optimization Problems Formulation
#### 4.1.3 DEUnm Optimization Problem  
In the performance measures (1) and (7), it is assumed that the energy cost of traffic handling by each node is directly proportional to its capacity. Unfortunately, in practice, this is often a nonlinear function. Let us define variables \(g_{nl}\) and \(h_{nl}\):  
\[g_{nl}=\sum_{m\in M}w_{m}y_{lm}b_{ln}(1-d_{mn}), \tag{8}\]  
\[h_{nl}=\sum_{m\in M}w_{m}y_{lm}a_{ln}(1-s_{mn}), \tag{9}\]  
where \(g_{nl}\) and \(h_{nl}\) denote the total size of the flow transmitted by the link \(l\) and, respectively, sent by the node \(n\) and received by the node \(n\).  
Let the function \(E(n,z)\) determine the energy cost of receiving or sending flows of total size \(z\) by the \(n\)-th node. The energy-saving optimization problem for nonlinear energy consumption models can be formulated as follows:  
\[\min_{x_{n},y_{lm}}\left[f_{DEUnm}(x_{n},y_{lm})=\sum_{l\in L}\sum_{n\in N}(E(n,g_{nl}+E(n,h_{nl}))+\sum_{n\in N}x_{n}r_{n}\right], \tag{10}\]  
and solved subject to the constraints (2)-(6). The CPLEX mixed-integer problem (MIP) solver can be used to solve (1), (7), and (10) with quadratic \(E(n,z)\) problems. In the case of the performance measure (10) with nonlinear (nonquadratic) \(E(n,z)\), an appropriate nonlinear solver must be used.
## 3 Results  
### Selection of Studies  
The records from PubMed (n = 221), Scopus (n = 758), and Web of Science (n = 568), as well as the records identified through relevant reference lists (n = 8), were imported into Mendeley (version 1.19.8) (Mendeley, London, UK), upon which the duplicates were removed and the records were exported into Microsoft Excel software (Microsoft, Redmond, Washington, DC, USA) (n = 1182). Among 1182 articles, 1037 were excluded due to inappropriate content based on the title and/or abstract. The remaining 145 potential articles were read in a full text. Ninety-five articles of these 145 were excluded due to not meeting the eligibility criteria. Among the mentioned 95 articles, 49 were literature reviews, 9 were narrative reviews, 3 were protocols for systematic review, 1 was a topical review, 2 were intervention reviews, 1 included only tables (supplementary paper), 4 referred to surgical treatments, 2 referred to pharmacological treatment, 4 did not include AT, 8 were written in German, 3 were written in Spanish, 1 was written in Danish, and we were not able to obtain 8 articles for full-text assessment. Thus, a total of 50 systematic reviews and/or meta-analyses were included in the current umbrella review, among which the authors of 5 articles separately researched IAT and MAT, 4 articles researched only IAT, 16 articles researched only MAT, and the authors of 25 articles did not specify the researched type of AT. The flowchart of the study selection is shown in Figure 1.
## 2 Background
### Svm  
The support vector machine (SVM) algorithm dates to the 1960s and has since undergone several developments and refinements. The fundamental concepts of SVM were initially proposed by [PERSON] and [PERSON] as part of their work on pattern recognition and statistical learning theory [21; 22]. SVM is a powerful and versatile machine learning algorithm primarily used for classification tasks but can also be adapted for regression. It aims to find an optimal hyperplane in a high-dimensional space that maximally separates different classes of data points [23]. It can handle high-dimensional data effectively and is robust against overfitting. It works well with both linearly separable and nonlinearly separable data owing to the use of different kernel functions. SVMs also have a solid mathematical foundation, allowing for theoretical analysis and interpretation. SVMs can be effective in emotion classification tasks because they work well with high-dimensional data, such as EEG brainwave signals. It can handle both linear and nonlinear separations, making it versatile for different types of emotional patterns. The importance of SVMs lies in their ability to find a clear boundary between different emotions in EEG brainwave data. It can help identify the regions in the feature space where emotions are distinct, aiding in emotion classification [24]. The linear kernel is the simplest kernel used for SVM. The expression of the linear kernel function is as follows:  
\[K\big{(}x_{i},\ x_{j}\big{)}=\big{(}x_{i},\ x_{j}\big{)} \tag{1}\]  
where \(x_{i}\) and \(x_{j}\) denote data points and \(K\) denotes the kernel function [25].  
SVM is particularly effective in high-dimensional spaces, a common characteristic of EEG data. It constructs hyperplanes in multidimensional space to separate different classes, making it ideal for complex classification tasks. These capabilities motivated us to choose SVM as the machine learning model in our study.
## 4 Experimental Evaluation
### Wearable Textile Antenna Evaluation Scenarios  
The evaluation of a WTA is a more demanding and challenging task compared to the respective one of a conventional free-space antenna in terms of the measurements and experimental scenarios that need to be realized. Two general scenarios should be examined; when a WTA operates in free space and when the antenna is mounted onto a human body. Initially, it is useful to measure the WTA performance (gain, S11, e\({}_{\text{rad}}\)) in free space [58; 59; 60]. The free space measurements can be considered an ideal case as the effect of the lossy, complex, and inhomogeneous human body environment is not considered yet. Therefore, the free-space WTA performance is an optimum one. However, the human body can strongly impact the antenna performance due to its electromagnetic, structural, and environmental effects including absorption, depolarization, creeping waves, bending, crumpling, and wet conditions [61]. A WTA will mainly operate under the movement of a human body, and specific deformations on the antenna geometry are unavoidable. Therefore, structural conditions such as bending (e.g., antenna integration on a human arm) [62; 63; 64; 65; 66] and crumpling [67; 68] need to be experimentally evaluated. The WTA should be, also, measured under different environmental conditions such as high humidity to represent perspiration or exposure to water [69; 70]. Additionally, the WTA is considered to be part of the garment. Testing the durability of the textile antenna in terms of performance after washing and drying is thus required [71]. The effect of the surrounding or covering cloths should be examined, as well [72; 73]. Repeatability measurements in terms of the textile antenna performance should also be examined [74]. Furthermore, measurements on a real human body [75; 76] or on a human body phantom [77; 78] should be carried out. These antennas should be measured in terms of S11, near-field (if possible), and far-field parameters [79; 80; 81; 82; 83]. Furthermore, the Specific Absorption Rate (SAR) should be evaluated as well in terms of real applications and safety requirements.
# Measurement of Circadian Effectiveness in Lighting for Office Applications
Finally, preliminary evidence from literature suggests that the short-term history of light exposure affects the sensitivity of the circadian system to light [43, 88, 89], an important feature which so far is not covered by the CS metric definition. Despite this relatively long list of limitations, which certainly emphasizes the need for significant model improvements, the CS metric still provides the most accurate and complete available model of the spectral sensitivity of the human circadian system, with its physiological relevance having been validated in many field studies [84, 84, 90, 91, 92, 93, 94, 95, 96]. This previous research basically emphasizes the suitability and biological meaningfulness of the CS metric to be used in the present work for assessing the circadian effectiveness of the indoor lighting conditions in the office context, where the following thresholds can be established from literature [81, 83, 84, 90, 92, 93, 94, 96, 97]: For CS \(\geq 0.3\), representing a large circadian stimulus, positive effects on the office workers' sleep quality, mood, and stress perception are expected; for moderate stimuli with \(0.15<\text{CS}<0.3\) there are likely neither positive nor severe negative effects to be reported; for CS \(\leq 0.15\), an insufficient circadian stimulation must be concluded, which may lead to progressive circadian disruptions and severe negative effects on the office workers' well-being, sleep quality, and after-work recovery abilities.
# FBG-Based Monitoring of Geohazards: Current Status and Trends
## 3 Development of FBG-Based Geohazard Monitoring Systems
### Development of FBG Displacement Sensors
However, the measurement error is amplified with distance due to the computation method, which may limit the effective length of the deflectometer in field use. [PERSON] et al. reported the fabrication of a polyvinyl chloride (PVC) casing with FBG strain and temperature sensors adhered to the grooves of its surface [15] (Figure 4c). The integral method was utilized to calculate the displacements in different depths based on linear interpolation of strain distribution along the PVC casing. [PERSON] et al. successfully installed this inclinometer in nine boreholes for landslide monitoring in Wenzhou, China [50]. The slip surface determined by the monitoring results fit well with that calculated by limit equilibrium method. In Europe, [PERSON] et al. used a similar approach to monitor a sliding coal pit slope [21]. In a shaking table test, [PERSON] et al. employed the same method to calculate the lateral displacements of a small bar buried in soil [51]. The shortcomings of this inclinometer are that the boundary conditions have to be correctly specified and the measuring errors will accumulate during numerical integrations. As a very significant continuation of this work, a new generation of FBG-based in-place inclinometer was developed by [PERSON] et al. [49]. To make an inclinometer, the FBG-instrumented elastic bars were connected to stiff steel tubes equipped with sliding wheels. Then they are installed in a borehole through inclinometer casings. The FBG sensors can measure two parameters, i.e., the relative deflection and rotation angle of two ends of every bar. The displacement profile with respect to depth can be calculated using the superposition approach. As shown in Figure 4d, based on the classical indeterminate beam theory, the deformation of segments is coordinated with the casings and accumulated error in the displacement superposition process is efficiently avoided. Another benefit of this inclinometer is that the displacement measuring range can be enhanced using closely spaced sensing segments. Through field applications, the performance of this in-place inclinometer was verified.
# Real-Time Text-to-Cypher Query Generation with Large Language Models for Graph Databases
## 5 Implementation
### Demonstration  
This section provides a brief overview of the graphical user interface and application of the chatbot. Figure 3 shows the Gradio UI of the bot and the four possible communication scenarios. As the interface in 1 shows, the UI consists of three main components. In the center of the screen is the chat box for any located interaction with the bot. All user questions and any answers or help information from the bot are streamed into this. Above this, the available database descriptors are listed in a table with the data abbreviation and their short description. This should help the user to receive a quick overview of the available databases and their content. Below the chat is the input box, in which the user can ask and send questions to the bot. The first example shows the scenario in which the user asks a question that the chatbot cannot answer either from the chat history or with the help of a database. In the second exemplary chat process, an answer to the user's question is provided by a query to the database. Here, it can be observed as to how the chatbot first tells the user which database it uses to answer the question. It then presents the generated query and, after it has been executed, the number of results it has found. In this chat message, a dropdown is also added, which contains the raw JSON result of the database query. The last message of the bot displays the full text answer to the question. The third scenario shows the answering of a user request with the help of the chat history. For better visualization, in this simplified example, the same question was asked twice in a row, but in reality it would also be possible for the chatbot to refer to messages that are longer in the past. In this conversation, you can clearly see how the bot tells the user that it has decided to answer their question using history. It can also be seen here, that due to the rather low value of the temperature parameter of the _Chat LLM_, and the requirement in the prompt to adhere strictly to the history, the bot tries to deviate his answers as little as possible from the previous answers. The last scenario shows the error correction module in action. The user question asked in this example was \"How many stations are between Snoiarty St and Groitz Lane?\" Analogous to regular CQL generation, the user is again presented with the initially created cipher statement. However, as a timeout error occurs on the database when this query is executed, it is fixed within the error correction module, and the user is informed of the error and presented with the revised version. In the event that the error in the query could not be resolved in the first attempt, this message is presented to the user for each new version, as long as the loop is running. When the error has been fixed, the raw database results in the dropdown and the full text answer will be displayed again, identical to the second scenario.
# Model Test Study on a Shield Tunnel Adjacent to Pile in the Sub-Clay of a Coastal Area
## 4 Test Results
### Earth Pressure  
The earth pressure value caused by shield excavation was different under the different soil-pile positions. The change value of earth pressure at each position in the model test could be monitored through the micro pressure cell of No. 1-8. The radial pressure values of surrounding soil caused by shield tunneling at the different pile-tunnel locations under the working conditions of \(H/D=1\), 2 and 3 are shown in Figures 19-21.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Cover Span Ratio**} & \multicolumn{2}{c}{**Lining Grouping Support**} & \multirow{2}{*}{**Horizontal Position**} & \multicolumn{2}{c}{**Vertical Position**} \\ \cline{3-6}  & & **NO** & & **Yes** & **V = 0.5D** & **V = 0.0D** & **V = -0.5D** \\ \hline \multirow{3}{*}{\(H/D=1\)} & \multirow{3}{*}{/} & \multirow{3}{*}{1.006} & W = 1\(D\) & 0.951 & 0.928 & 0.878 \\  & & & W = 2\(D\) & 0.946 & 0.930 & 0.898 \\  & & & W = 3\(D\) & 0.990 & 0.954 & 0.888 \\ \hline \multirow{3}{*}{\(H/D=2\)} & \multirow{3}{*}{0.766} & W = 1\(D\) & 0.670 & 0.640 & 0.589 \\  & & & W = 2\(D\) & 0.665 & 0.618 & 0.583 \\  & & & W = 3\(D\) & 0.709 & 0.692 & 0.654 \\ \hline \multirow{3}{*}{\(H/D=3\)} & \multirow{3}{*}{0.482} & W = 1\(D\) & 0.611 & 0.598 & 0.562 \\  & & & W = 2\(D\) & 0.653 & 0.634 & 0.616 \\ \cline{1-1}  & & & W = 3\(D\) & 0.573 & 0.559 & 0.531 \\ \hline \end{tabular}
\end{table}
Table 9: Correction coefficient of settlement trough under all working conditions.  
Figure 19: The soil pressure at different position of piles when \(H/D=1\).  
Figure 20: The soil pressure at different position of piles when \(H/D=2\).  
The final state values of the radial earth pressure in the tunnel monitored by the orthogonal test of 27 groups showed that:  
1. With the increase in \(H/D\), the earth pressure increases, and the earth pressure around the tunnel tends to be uniform. When the pile exists, the earth pressure around the tunnel present an asymmetric distribution;
2. The existence of the pile increases the radial pressure of the nearby soil mass. When the transverse distance of the pile-tunnel was \(3D\), the influence on the earth pressure cell was small. The vertical influence range of the pile was 1 time the diameter of the pile from the bottom of the pile. When the pile bottom was located at the horizontal plane of the tunnel arch bottom, the influence on the earth pressure of the surrounding rock on the side of the pile was larger than the side of no-pile.
## 3 Methodology  
The methodology of this work is to expand the capabilities of currently proposed machine-learning receivers in OFDM systems by completing a Traditional Receiver's channel estimation, equalization, demodulation, and decoding, all with machine-learning models. Our approach is presented in Figure 1 and is organized into two separate blocks, DL-OFDM, utilized for all aspects from channel estimation through demodulation, and LDPC-GNN, which is utilized for rate matching and LDPC Decoding. Thus, in this paper, we explore a predominantly DL-driven architecture for resource-constrained devices, with potential applications ranging from IoT to mobile and vehicular communications.  
Our proposed method differs from other current published research efforts due to the inclusion of additional traditional receiver functions within the DL architecture. Current research focuses on improvements to specific sections of receiver performance, whereas our research aims to expand joint machine-learning capabilities within OFDM receivers. This change is outlined in Table 1.  
The evaluation method chosen for our architecture is the BER for each SNR, modulation scheme, and channel model. Since this DL architecture evaluates the performance of an LDPC channel decoder, using BER allows us to compare and contrast the output of the demodulation compared to the final output of the receiver. With this in mind, we perform BER evaluations on both DL-OFDM and LDPC-GNN models compared to their traditional  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Architecture**} & \begin{tabular}{c} **OFDM** \\ **Modulation** \\ \end{tabular} & \begin{tabular}{c} **Time Domain** \\ **Correction** \\ \end{tabular} & \
\begin{tabular}{c} **Channel** \\ **Estimation** \\ \end{tabular} & \multirow{2}{*}{**Demapping**} &
\begin{tabular}{c} **Channel** \\ **Decoding** \\ \end{tabular} \\ \hline
[8] & CNN & ✓ & ✓ & X & ✓ & X \\ \hline
[3] & Resnet & ✓ & X & ✓ & ✓ & X \\ \hline
[2] & DenseNet & X & ✓ & ✓ & ✓ & X \\ \hline
[7] & LSTM & X & ✓ & ✓ & ✓ & X \\ \hline
[9] & GNN & X & X & X & X & ✓ \\ \hline
[10] & GNN & X & X & X & X & ✓ \\ \hline Proposed Method & CNN and GNN & ✓ & X & ✓ & ✓ & ✓ \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of existing approaches.  
Figure 1: Overall architecture of the combined deep learning approach.  
receiver counterparts. This process was then completed with the DL architecture compared to a traditional receiver to show end-to-end performance gains.
# Review of Power System Resilience Concept, Assessment, and Enhancement Measures
## 5 Enhancement Measures for Power System Resilience
### Measures before an Event  
Some natural disasters such as hurricanes and tornadoes can be predicted [74]. Before the foreseeable occurrence of such disasters, conducting risk assessments on the system, determining appropriate topologies in advance, and formulating corresponding emergency response plans and disaster preparedness plans can help minimize losses and risks during system operations. Various machine learning approaches and advanced monitoring technologies can enhance the efficiency of the early decision-making process stage. For example, supervised or unsupervised learning approaches and Monte Carlo simulations can be applied to estimate the probability of system failures through statistical analysis. Particle swarm algorithms and mixed-integer linear algorithms can optimize response measures for power grid dispatch [11, 12, 75]. Advanced monitoring technologies such as situation awareness systems can facilitate the monitoring of constantly changing weather conditions, enabling the system to assess damages more efficiently and make timely decisions [71, 76, 77]. In addition, taking proactive control actions for distribution system reconfiguration and resource allocation before an event occurs can effectively enhance the resilience and economic efficiency of the power grid [78, 79].
## 4 Results and Discussion
### Classification  
In the next step, we compared the performance of three classifiers with the features selected in the previous step as input while the raw features were our baseline. Table 3 summarizes the performance of the various classifiers before and after feature selection. The accuracy value, root mean squared error (RMSE), mean absolute error (MAE), kappa statistic (KS) and the time taken to build the model for every classifier, arising from the 10-fold cross validation are given. Overall, accuracy levels after selection ranged between 94.186 and 100% depending on the classification method used and the dataset (Table 3).  
NB performed better on features selected using PCA and a graph-based approach whereby accuracy, MAE, kappa and time taken improved as compared to unfiltered features and RFE-selected features where there was no difference. This can be attributed to the working principle of RFE where the optimal number of features is not known _apriori_ (in advance) [58]. A Kruskal-Wallis test showed that there is no significant difference between the mean ranks of the groups (\(p<0.05\)), i.e., 20 iterations for each of the feature-selection methods.  
Figure 2: Network diagrams for the 2 datasets: figures on the left side represent the network before filtering while those on the right show the networks after filtering with maximal clique. On the filtered networks, different colors denote expression levels with red color showing features that were highly expressed.  
A study by [PERSON] and [PERSON] (2019) used five tumor types of gene expression cancer RNA-Seq data and, using Naive Bayes with 10-fold cross validation, achieved an accuracy of 98.7516%. This shows that NB accuracy levels will vary with the dataset being analyzed. In dataset GSE81089, which had a larger sample size of selected features, SMO and MLP achieved 100% accuracy when feature selection was performed prior to classification, and in fact, the PCA-selected features could be classified at 100% accuracy by all three classifiers. In the smaller dataset, SCLC dataset, accuracy levels were also lower. Notable is that a graph-based feature-selection approach gave the best classification results in the two datasets and also took the least time to execute. The time required to build the model improved after feature selection across the three classifiers though MLP required the longest duration and a graph-based approach the shortest (Table 3).
# Radiation-Hardened 16T SRAM Cell with Improved Read and Write Stability for Space Applications
## 3 Simulation Results and Analysis
### Write Stability Comparison  
Conventionally, the write ability of SRAM cells has been measured through static noise margin, similar to read operations. However, recent studies have mentioned that word line write trip voltage (WWTV) is a more reliable method to evaluate the writing ability of SRAM cells compared to write static noise margin (WSNM) [4]. As shown in Figure 8a, WTV is measured as the voltage difference between the word line voltage and VDD at the point where the storage nodes Q and QB intersect when the word line is activated. Therefore, cells with a longer WAT require more time to change the stored data, leading to a higher voltage rise at WL at the point where Q and QB intersect. Consequently, the proposed RHHS16T, having the shortest WAT among all comparison cells, shows the highest WWTV, as shown in Figure 8b.  
Figure 6: RSNM for different comparison cells with supply voltage variation.