esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Case Analysis
### Water Conveyance Tunnel of Raw Water Project of Qingcaosha Water Source Area  
The raw water project of the Qingcaosha water source area will transfer the high-quality freshwater resources of the Qingcaosha reservoir to Shanghai. The water pipeline is mainly composed of the Changxing island water tunnel and the river-crossing tunnel. The project provides a large amount of raw water supply for Shanghai through the water conveyance tunnel. With a daily water supply of over 70,000 cubic meters and a beneficiary population of over 10 million, the regional water transfer efficiency is important. This project is the first time in China that a single-layer lining shield method is used to build a water conveyance tunnel. The tunnel across the river is 7.29 m long, the inner radius of the lining is 2.92 m, the outer radius is 3.4 m and the strength grade of segment concrete is C55 [26; 27]. With the calculation formula in Section 2, the maximum value of the internal force of the lining structure at half-water level, full-water level and pressurized water conveyance is calculated under the action of internal water pressure only, as shown in Table 1.  
According to the data in Table 1, the internal force of the lining of this water conveyance tunnel is small when pressureless water conveyance and the structural safety factor is high at this time. The maximum internal force of pressurized water conveyance is much larger than that of pressureless water conveyance. Under the action of 0.4 MPa water pressure, the maximum axial tensile force of the lining is 1220.2 kN and the maximum axial tensile stress is 2.54 MPa. Therefore, analyzing only the internal water pressure, the maximum water conveyance pressure that the water conveyance tunnel can bear is about 0.3 MPa, but considering that the external load can offset some of the internal water pressure, the actual water conveyance pressure that can be borne is larger than this value.
## 2 System Model
\begin{table}
\begin{tabular}{l l} \hline \hline
**Symbol** & **Description** \\ \hline \(n_{\text{T}}\) & Number of MIMO inputs \\ \(n_{\text{R}}\) & Number of MIMO outputs \\ \(\mu\) & MIMO input index, \(\mu=1,2,\ldots,n_{\text{T}}\) \\ \(\
u\) & MIMO output index, \(\
u=1,2,\ldots,n_{\text{R}}\) \\ \(s_{\mu}(k)\) & Input symbol sequence at \(\mu\)-th MIMO input \\ \(M_{\mu}\) & Modulation format index at \(\mu\)-th MIMO input \\ \(s_{\mu,m}\) & Gray coded input symbols \\ \(p_{\text{s},\mu}^{\text{(max)}}\) & Maximum transmit power at \(\mu\)-th MIMO input \\ \(p_{\mu}(k)\) & Pre-equalizer filter coefficients \\ \(b_{\mu}(k)\) & Pre-equalized symbol stream \\ \(L_{\text{p}}\) & Length of the pre-equalizer filter \\ \(N_{\mu}\) & Number of distinct pre-equalizer levels \\ \(h_{\
u\mu}(k)\) & Channel taps \\ \(w_{\mu}(k)\) & IM/DD noise mechanisms \\ \(r_{\
u}(k)\) & Received signal \\ \(f_{\
u\mu}(k)\) & Post-equalization filter \\ \(H\) & Frequency-selective channel matrix with \((n_{\text{R}}\times n_{\text{T}})\) dimensions \\ \(r\) & Received signal matrix with \((n_{\text{R}}\times 1)\) dimensions \\ \(b\) & Pre-equalized transmitted signal with \((n_{\text{T}}\times 1)\) dimensions \\ \(w\) & IM/DD noise mechanism with \((n_{\text{R}}\times 1)\) dimensions \\ \(P\) & Pre-equalizer with \((n_{\text{T}}\times n_{\text{T}})\) dimensions \\ \(s\) & Input signal with \((n_{\text{T}}\times 1)\) dimensions \\ \(F\) & Post-equalizer with \((n_{\text{R}}\times n_{\text{R}})\) dimensions \\ \(\theta_{\mu}\) & Noise-weighting factor \\ \(L_{t}\) & Length of the post-equalizer filter \\ \(P_{\text{n}}\) & Noise power \\ \(\varrho_{\mu}\) & Transmission quality \\ \(U_{\text{A}}\) & Half-vertical eye-opening \\ \(P_{\text{BER}}^{(\
u)}\) & BER at \(\mu\)-th MIMO input \\ erfc(\(\cdot\)) & Complimentary error operator \\ \(P_{\text{BER}}^{(s_{\mu\text{m}})}\) & Symbol-specific BER \\ \(S\) & Unitary matrix with \((n_{\text{R}}\times n_{\text{R}})\) dimensions \\ H & Conjugate transpose operator (Hermitian) \\ \(V\) & Eigen matrix with \((n_{\text{R}}\times n_{\text{T}})\) dimensions \\ \(D\) & Unitary matrix with \((n_{\text{T}}\times n_{\text{T}})\) dimensions \\ \(h_{m,i}^{(\mu)}\) & Hamming distance \\ \hline \hline \end{tabular}
\end{table} 
Table 1: List of symbols and their respective description. When traveling through the MMF channel, the transmitted signal is degraded due to ISI and ICI, originating from the channel. Afterwards, the IM/DD noise impairments \(w_{\mu}(k)\) further degrade the transmitted signal \(b_{\mu}(k)\)[26]. At the MIMO receiver, the post-processing of the received signal \(r_{\
u}(k)\) with \(\
u=1,2,\ldots,n_{\text{R}}\) is conducted using a post-equalizer \(f_{\
u\mu}(k)\). The residual interferences are mitigated with the help of an MMSE-based post-equalizer. When designing the post-equalizer, the MMSE principle is preferred over the ZF principle as the optimized coefficients of an MMSE equalizer contribute less to noise amplification.
## 2 Preliminary
### Basic Notations  
We assume a finite _alphabet_\(\Sigma\) for the symbols forming input strings throughout this paper. The set of all strings over \(\Sigma\) is denoted by \(\Sigma^{*}\), and \(\Sigma^{i}\) denotes the set of all strings of length just \(i\). The length of \(w\in\Sigma^{*}\) is denoted by \(|w|\), and the cardinality of a set \(C\) is similarly denoted by \(|C|\).  
Strings \(x\) and \(z\) are said to be a _prefix_ and _suffix_ of the string \(w=xyz\), respectively. Also, \(x,y,z\) are called _substrings_ of \(w\). The \(i\)th symbol of \(w\) is denoted by \(w[i]\). For integers \(i,j\) with \(1\leq i\leq j\leq|w|\), the substring of \(w\) from \(w[i]\) to \(w[j]\) is denoted by \(w[i,j]\).  
A _repetition_ is a string \(x^{k}\) for a symbol \(x\) and an integer \(k\geq 2\). A repetition \(w[i,j]=x^{k}\) is _maximal_ if \(w[i-1],w[i+1]\
eq x\). It is simply referred to by \(x^{+}\) if the length is unnecessary. Substrings \(w[i,j]\) and \(w[k,\ell]\) are _overlapping_ if \(i<k\leq j<\ell\). A string of length two is called a _pair_.
# Advanced Control and Fault Detection Strategies for District Heating and Cooling Systems--A Review
## 2 State of the Art Control Strategies for District Heating (DH) and District Cooling (DC) Systems
### DHC as an Enabling Infrastructure for Greater Renewable Sources Penetration
Concerning the solar fraction over the total thermal energy supply, 20% can be achieved without thermal energy storage (TES) installation, whereas a larger solar fraction can be accomplished with seasonal TES. Among the different technologies, water pit and borehole seasonal TES systems result in the most used in conjunction with SDH. Moreover, the installation of the seasonal TES system paves the way for more heat production flexibility and integration of large-scale heat pumps that improve the performance of the overall SDH system [23]. Further penetration of non-programmable renewable power generation can be also achieved thanks to the DHC systems, since excess electricity generated can be used directly or stored in the form of heat or cold in TES systems avoiding the curtailment of PV and wind production. Moreover, sector coupling by means of power-to-heat technologies, such as electric boilers and heat pumps, unlock remunerative balancing services for the power grid such as frequency regulation. Traditional DH/DC systems and 5 GDHC systems are conceived differently, opposing centralized with decentralized energy conversion systems [24]. Thus, in several cases, they exploit different control strategies that are presented in the next sections.
# Evaluation of Deep Learning-Based Automatic Floor Plan Analysis Technology: An AHP-Based Assessment
## 4 Results
### Application of Relative Weights for the Evaluation of Technology Value  
From the AHP analysis, relative weights were derived to assess the technology value of automatic floor plan technology. In this section, a scoring model is suggested to evaluate both manual and automatic approaches. The second survey was conducted to score both methods by assessing the technology value for each criterion and factor based on a 5-point scale. Consequently, except in terms of \"technical development phase\" and \"completeness of technology,\" the automatic technology had a higher score than the manual edit. This is because the automatic floor plan analysis only extracts the basic geometry of indoor information, whereas manual edits can extract indoor information as desired. The limitation of automation technology is that only the information that is shown in the floor plan image can be extracted, whereas the manual edit can add additional information from the floor plan image to acquire indoor information such as wall types and materials. Distinctiveness criteria had the largest difference among all criteria with scores of the following sub-factors, novelty (1.76), efficiency (1.52), and originality (1.35) ranking with the highest difference between manual and automatic methods in that order. \"Possibility of new technologies to emerge\" had the lowest difference (0.20) between manual and automatic methods. Figure 6 shows the distinctive differences in the scoring of each criterion between the manual and automatic approaches. The detailed score result of manual and automatic floor plan analysis technology is described in Table 7.  
The evaluation of creating indoor spatial information from floor plan images was derived by combining the score and relative weights derived from the AHP analysis. Table 8 presents the technology values of the manual and automatic approaches. The manual editing technology scored 58.38 out of 100 points, whereas automatic technology based on the deep learning algorithm scored 75 points. Efficiency has the highest importance among all factors, and received a higher score in the automatic approach. In the automatic approach, the score on the 5-point scale was 4.02, and the score for manual approach was 2.50. By applying relative weights, the automatic technology efficiency was 16.88 out of 21 (80%), whereas the manual approach exhibited 10.50 out of 21 (50%). The possibility of  
Figure 5: Radar diagram of relative weights of each criteria and factor. existence of similar technologies exhibited 12.03 out of 16 (75%) in the automatic approach, whereas the manual approach showed 9.79 out of 16 (61%). Except under commercialization potential, the manual approach had a higher value than the automatic approach. The biggest difference between the manual and automatic approaches was in novelty factor: manual having 2.66 out of 6 (44%) and automatic having 4.78 out of 6 (80%). Distinctiveness criteria generally had higher values in the automatic approach (79%) when compared with manual (48%).
## 3 Results and Discussion of CNN Models Classification
### Results for Resnet101 Model
#### 3.5.2 Selection of the Best CNN Model  
Based on the comparison, the VGG19 model stands out as the best among the three models evaluated. It not only achieved the highest average accuracy, but also had the lowest number of critical errors. The consistency of its performance across different folds further supports its robustness and reliability.  
In practical applications, the lower critical error rate of the VGG19 model is particularly advantageous, as it reduces the risk of misclassifications that could lead to costly errors in the manufacturing processes. Therefore, the VGG19 model is selected as the best model for the classification of drilled holes in melamine-faced chipboard.  
In conclusion, the VGG19 model demonstrated superior performance in terms of accuracy and critical error rate compared to the VGG16 and Resnet101 models. Its consistent performance across different folds and lower propensity for critical errors make it the most reliable choice for the classification task. Future work will focus on further refining the VGG19 model to enhance its accuracy and reduce misclassification rates even further.
# Underwater-Acoustic-OFDM Channel Estimation Based on Deep Learning and Data Augmentation
## 3 The UWA-OFDM-Channel-Estimation Framework Based on Deep Learning
### DL-Based-Estimated-UWA-OFDM Channel
Then, for each channel \(i\), we calculated the average value:  
\[z_{i}=\frac{1}{H\times W}\sum_{j=1}^{H}\sum_{k=1}^{W}f_{i,j,k}, \tag{11}\]  
Figure 6: A CNN-based-channel-estimation-network framework. Next, we introduced two learnable weight parameters, \(W_{1}\) and \(W_{2}\), mapped into the two representations \(u_{i}=\sigma(W_{1}\cdot z_{i})\) and \(v_{i}=\sigma(W_{2}\cdot z_{i})\), where \(\sigma\) was the ReLU activation function. The weighted channel representation \(\tilde{f}_{i}=u_{i}\cdot X_{i}\) was obtained by using \(u\) and \(v\) weighted for each channel, where \(\tilde{f}_{i}\) was a weighted representation of the channel. To ensure that the weighted channel representation and the original input had the same scale, the \(\tilde{f}_{i}\) were normalized as  
\[\tilde{f}_{i}=\frac{u_{i}\cdot f_{i}}{\sum\limits_{j=1}^{C}u_{j}\cdot f_{j}}. \tag{12}\]  
This ensured that the weighted channel representation summed to 1 after normalization. Finally, we multiplied the normalized weighted channel representation with the original input feature map, to obtain the final output:  
\[\tilde{H}=\tilde{f}\odot f, \tag{13}\]  
where \(\odot\) represented the multiplication by element. The last layer of CNN output comprised data close to the expected value and evaluated by the loss function. Finally, we used MSE and BER as the total loss function of the model, where BER prevented gradient decline when the MSE was small. They were, respectively, denoted as follows:  
\[\mathcal{L}_{MSE}=\frac{1}{N}{\sum\limits_{n}{{{(\tilde{H}_{n}-H_{n})}^{2}}}}, \tag{14}\]  
and  
\[\mathcal{L}_{BER}=\frac{1}{N}{\sum\limits_{n}{{{(\kappa{{(\hat{Y}_{n})}-X_{n})} ^{2}}}}}, \tag{15}\]  
where \(\tilde{H}_{n}\) and \(H_{n}\) represented the predictive channel information of the model and the corresponding ground-truth values, respectively, \(\hat{Y}_{n}\) and \(Y_{n}\) represented the estimated received information of the model and the corresponding ground-truth values, respectively, and \(\kappa(\cdot)\) represented the operation of hard decision making. Therefore, the total loss function was  
\[\mathcal{L}_{total}=\mathcal{L}_{MSE}+\mathcal{L}_{BER}. \tag{16}\]
# Attack Surface Score for Software Systems
## 4 Experimental Evaluation
### Experiment 1  
Two repositories, PR1 and PR2, were generated with nearly identical code size (LOC). PR1 contains code deliberately seeded with CWEs, while PR2 is derived from PR1 by remediating those vulnerabilities. PR1 includes 14 unique CWE instances, whereas PR2 retains only 2.  
The goal of this experiment is to verify that our analysis tool produces higher attack surface scores for the more vulnerable codebase.  
Configuration and Results  
The CWE distributions for PR1 and PR2 are listed in Table 9.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Impact Type** & **Integrity-Focused (Default)** & **Confidentiality-Focused** \\ \hline Read & 4 & 10 \\ Modify & 7 & 7 \\ Execute & 10 & 4 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Impact weights for different security contexts.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Security Context** & **ConsequenceScore** & **ImpactScore** & **DamagePotential** \\ \hline Integrity-focused & \(4+7=11\) & 7 & \(11\times 7=77\) \\ (default) & \(10+7=17\) & 7 & \(17\times 7=119\) \\ \hline \hline \end{tabular}
\end{table}
Table 8: Damage potential comparison for CWE-79.  
Table 10 provides a comparative overview of PR1 and PR2, including lines of code (LOC), technical debt, average analysis times, and normalized attack surface scores.  
Although PR1 contains seven times more CWE instances than PR2, suggesting a similar factor in score difference, the exact ratio is moderated by the individual damage potential and effort values for each issue.
# Evolution of Core Stability, Athletic Performance, and ACL Injury Risk across a Soccer Season
## 4 Discussion
### Core Stability and Athletic Performance  
Athletic performance mainly evolved significantly during pre-season training with a decrease in jump height and shooting accuracy and an improvement of sprinting time. In line with previous studies [25; 30; 31], soccer pre-season training improved sprinting. However, contrary to those studies, CMJ height significantly decreased. One hypothesis explaining this decrease in performance could be the accumulation of fatigue during pre-season training with the intensity of the first \"friendly\" games, in line with the findings of [PERSON] et al. [45] for futsal players.  
During the first part of the season (i.e., from START to MID), higher sprint times were observed. Previous studies showed heterogenous results about sprint times in the same period. [PERSON] et [PERSON] [31] demonstrated that sprinting was improved by specific sprint training during the first part of the season, while, without specific sprinting training, [PERSON] et al. [30] observed a decrease in sprinting. Thus, our results are in line with the aforementioned research. With the start of the championship matches, field training was more based on technico-tactical aspects, strength development, and the prevention program. Consequently, sprint training was not as present as in pre-season training, leading to worse sprinting performance. In addition, this result supports the absence of correlation between core stability and sprinting performance. Indeed, improvement of core strength (measured by CKCLEST) from PRE to MID and motor control (measured by LQ-YBT) from START to MID did not help to maintain or improve sprint times. Interestingly, core stability (measured in laboratory settings) and performance, when studied regardless of their seasonal evolution, seemed to be correlated, as sprinting and jumping performanceswere associated with better core stability [46]. Therefore, specific core stability training would have been more likely to lead to correlation between the evolutions of the different factors [24].  
Finally, during the latter half of the season, shooting speed was improved. This is a surprising result because we could hypothesize that due to the season fatigue, shooting speed would decrease. Indeed, during the second half of the season, fatigue has been reported to impact physiological parameters (e.g., VO\({}_{2}\)) and neuromuscular performance [25; 31]. This would also be in line [PERSON] et al. [47], who reported a decrease in shooting speed right after a specific fatiguing task. However, our performance results did not indicate any potential fatigue, as our athletic measurements remained stable. Therefore, this fatigue hypothesis is not supported by our data.
# Factors Affecting Trueness of Intraoral Scans: An Update
## 3 Results  
The present search strategy yielded 34 publications. After initial screening of the publications using titles, abstracts and keywords, 13 articles were considered within the inclusion criteria. After the application of exclusion criteria, a total of nine studies (6 in vitro and 3 in vivo) were finally identified (Figure 1) as eligible to be discussed [19; 20; 21; 22; 23; 24; 25; 26; 27]. All papers assessed were available on both databases used in this study. No unpublished studies were included in the analyses. In addition, none of the nine eligible studies presented any relevant selection, observational or confounding bias in relation to the analyses proposed by the present study. All studies included assessed IOS trueness by comparing 3D data from IOS meshes to reference scans taken with desktop or industrialized optical scanners. The characteristics of the studies included are summarized in Table 1. Studies on IOS of dental implants did not meet the inclusion criteria since the outcomes compared were not deviations or trueness of the actual 3D mesh acquired by the IOS, but of the final result on the linear and angular accuracy of the implants placed in the alveolar bone. The reason for this difference is that the purpose of performing IOS of scan bodies is to have not only its location in the dental arch but also its shape recognized by the software library of the same implant system. This has been considered predictable and differs from the outcomes related to the purpose of the present study. Figure 1: Flowchart of the systematic review. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  
In response to the constant technological development of IOS devices, this systematic review aimed at updating the knowledge on factors that still affect the trueness of current IOS technologies, as published in the last two years. Among the factors significantly affecting the IOS trueness of dental arches were the device, scan size, span length and disposition, operator's experience and rescans. In addition to these, preparation type and angles as well as presence of adjacent teeth were also found to significantly affect the IOS trueness of tooth preparations. This systematic review only analyzed articles assessing actual IOS trueness values of IOS systems. However, precision (reproducibility) values were also recorded since it shows how liable different scans of the same object performed by same operators could be. It was noted that trueness and precision values varied considerably among the included studies (Table 2).
# Data-Driven Calibration Estimation for Robust Remote Pulse-Oximetry
## 2 Materials and Methods
### Selection Criterion  
The SpO\({}_{2}\)-value corresponds to the signature vector that yields the best pulse signal quality (Equation (1)). To assess the pulse quality, we used the skewness of the pulse spectrum. The rationale behind the skewness metric is that the spectrum of a clean pulse is highly peaked (i.e., a high skewness), whereas a noisy signal has a clearly lower skewness. The quality metric, \(Q\), can be described as:  
\[Q=\frac{\frac{\frac{1}{N}\sum\limits_{f=1}^{N}\left(H_{P}(f)-\bar{H_{P}} \right)^{3}}{\left(\sqrt{\frac{1}{N}\sum\limits_{f=1}^{N}\left(H_{P}(f)-H_{P} \right)^{2}}\right)^{3}}}, \tag{8}\]  
where \(H_{P}\) denotes the frequency spectrum of pulse signal \(P\) (Equation (1)) and \(\bar{H_{P}}\) is the average of all spectral components of \(H_{P}\). The example for two different oxygen saturation levels visualized in Figure 3 shows clear, distinct optima (\(\alpha^{*}\),\(\beta^{*}\)) using skewness as quality metric, which shift as function of SpO\({}_{2}\). For each processing window, we stored the estimated pulse signature vector together with the corresponding value of the quality metric.