esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Non-Invasive Field Investigations
#### 2.2.2 ATP Bioluminescence Measurements  
The ATP (adenosine triphosphate), a key molecule in all living organisms that provides energy for cell metabolism with an amount that is related to biomass, was measured. Its measurement does not distinguish between the different types of microbes. ATP was measured in relative light units (RLU) using a bioluminometer (3 MT Clean-TraceTM NG Luminometer, 3M Italia Srl Pioltello (MI)) and its specific swabs (3M Clean-Trace Surface3 MT, 3M Italia Srl Pioltello (MI)) [38]. For each sampling point, a standardized analyzed area of 4 cm\({}^{2}\) was defined to obtain comparable data, and the read values were expressed as \(\text{RLU}/\text{cm}^{2}\). Measurements were conducted following preliminary microscopic observations, targeting areas exhibiting distinct surface alteration features--namely, salt efflorescence, red spots, white turf-like encrustations, pink discoloration, as well as dark green, light green, and black pigmentation (Table 1). A measurement was also performed on a control area, affected by salt efflorescence, that did not exhibit visible signs of biological colonization (Ss). The selected ATP measurement sites are shown in Figure 2c and Table 1 and are labelled as \"S\".  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \begin{tabular}{c} **Alteration** \\ **Phenomenology** \\ \end{tabular} & **Samples** & \begin{tabular}{c} **Atp Values** \\ **Rlu/Cm2** \\ \end{tabular} & **Isolate** & **Closest Relative(S)** & \begin{tabular}{c} **Accession** \\ **Number** \\ \end{tabular} & \begin{tabular}{c} **Identity** \\ **\%** \\ \end{tabular} \\ \hline \multirow{4}{*}{\begin{tabular}{c} Salt \\ efflorescence \\ \end{tabular} } & S\({}_{\text{4b}}\) & - & - & - & - & - \\ \cline{2-6}  & S\({}_{\text{Sb}}\) & - & - & - & - & - \\ \cline{2-6}  & S\({}_{\text{6b}}\) & 141 & - & - & - & - \\ \hline \multicolumn{2}{c}{Red spot} & S\({}_{\text{10}}\) & 238 & - & - & - & - \\ \hline \multirow{4}{*}{\begin{tabular}{c} White turf \\ \end{tabular} } & S\({}_{\text{2}}\) & 30,542 & A & \begin{tabular}{c} _Parengyodentium album_ \\ (Limber) C.C.T sang et al. \\ \end{tabular} & \begin{tabular}{c} MH860502.1 \\ \end{tabular} & 99.11 \\ \cline{2-6}  & S\({}_{\text{8}}\) & 8019 & \begin{tabular}{c} B \\ C \\ \end{tabular} & \
\begin{tabular}{c} (Limber) [PERSON] et al. \\ CBS:368.72 \\ \end{tabular} & MH860502.1 & 99.65 \\ \cline{2-6}  & &
\begin{tabular}{c} 99.82 \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 1: Alteration phenomenology, ATP values and biological identification: (-) analysis not performed; (n.i.) No evidence of isolation was observed.
# Spherical-Model-Based SLAM on Full-View Images for Indoor Environments
## 4 Method Overview
### Spherical Epipolar Search  
Suppose that the same point is observed by two spherical model cameras. Let the coordinates of the same point at the two camera coordinate systems be \(M_{1}\) and \(M_{2}\). The pose between the two cameras is represented by a rotation matrix, \(R\), and a translation vector, \(T\). According to the coplanar principle, we have  
\[M_{1}^{T}EM_{2}=0 \tag{10}\]  
where \(E=\left[T\right]_{-\times}R\) and  
\[\left[T\right]_{-\times}=\left[\begin{array}{ccc}0&-T_{z}&T_{y}\\ T_{z}&0&-T_{x}\\ -T_{y}&T_{x}&0\end{array}\right] \tag{11}\]  
By using the spherical projection in Equation (4), we have  
\[M_{1}^{T}EM_{2}=m_{1}^{T}Em_{2}=0 \tag{12}\]  
Given \(m_{1}\) is known, according to Equation (5), we represent Equation (12) as  
\[A\sin\theta_{2}\cos\varphi_{2}+B\sin\theta_{2}\sin\varphi_{2}+C\cos\theta_{2}=0 \tag{13}\]  
where  
\[\left[A,\ B,\ C\right]=m_{1}^{T}E \tag{14}\]  
In practice, we give an error range \(\Delta e>0\)  
\[\left|A\sin\theta_{2}\cos\varphi_{2}+B\sin\theta_{2}\sin\varphi_{2}+C\cos \theta_{2}\right|<\Delta e \tag{15}\]  
Equation (15) gives the constraint for the spherical epipolar search and identifies the corresponding feature point.
# Efficient and Effective Unsupervised Entity Alignment in Large Knowledge Graphs
## 5 Experiments
### Overall Results on DBP-15K  
We present the performance of various baselines on _DBP-15K_ in Table 2, where the results are sourced directly from OpenEA [55] or their original publications, except for those marked with \"*\", which are reproduced using their source code. The baselines denoted by \"+\" utilize pre-trained language models (such as LaBSE) to generate initial embeddings for entity names, while those indicated by \(\ddagger\) incorporate image information. We use **bold** formatting to highlight the best performance results among unsupervised methods, and underline formatting to denote the best performance results within each category of baselines. **Comparison with supervised methods. ** Our proposed method is initially compared to 26 supervised EA methods, which demonstrated superior performance across nearly all datasets. Specifically, when compared with all monitoring methods utilizing the relation structure, our method improves Hits@1 by 6.38% and 6.85% on _ZH-EN_ and _FR-EN_, respectively, although it shows slightly less effectiveness on _JA-EN_. Among these methods, RANM accounts for the heterogeneous information of KGs, while PEEA conducts 1-to-1 comparative searches; consequently, their training or comparison efficiency is lower than that of _MPGT-Align_. This indicates that _MPGT-Align_ remains highly competitive within this category of methods. In comparison to seven supervised methods with auxiliary information, _MPGT-Align_ achieves notable improvements on Hits@1 across the three datasets, increasing by 4.38%, 1.92%, and 7.05%, respectively. MMEA-cat [64] and GEEA [39] do not consider entity name information, but instead focus on image data, while GEEA also incorporates attribute information. However, the effects of these methods are not particularly remarkable. We attribute this success to two factors: (1) BERT's effective modeling capabilities in combining various textual inputs; and (2) the superiority of entity name modeling based on pre-trained language models over image-based modeling. Furthermore, _MPGT-Align_ outperforms these supervised techniques despite lacking training data. One primary reason for this advantage is that contrastive learning mechanisms generate more positive samples (each entity serves as its own positive sample). This further demonstrates that the InfoNCE loss function is well-suited for training EA tasks, and can effectively extract similar information among aligned entities in KGs. **Compared with unsupervised methods. ** In comparison to eight unsupervised methods, _MPGT-Align_ demonstrates superior performance; however, the improvement is not statistically significant. We attribute this primarily to our method's reliance on a straightforward relation structure and contrastive learning mechanism, similar to most unsupervised
## 3 Nanoparticles in EOR
### Zirconium Oxide  
Zirconium oxide, or zirconia (ZrO\({}_{2}\)) NPs are widely used in catalysis, ceramics, thermal coating, etc. [126]. [PERSON] et al. [136] applied in-house prepared ZrO\({}_{2}\) NPs dispersed in nonionic surfactant for EOR. They found that ZrO\({}_{2}\) NPs could change the wettability of the carbonate core from strong oil-wet to strong water-wet through the adsorption of NPs onto a carbonate surface confirmed by an SEM image. However, the wettability alteration process by ZrO\({}_{2}\) NPs (adsorption and growth) was a slow process, taking approximately two days. Recently, [PERSON] et al. [129] investigated the wettability alteration on carbonate core samples by means of ZrO\({}_{2}\) NP injection. The water contact angle decreased with increased concentration, salinity, and exposure times. Combining ZrO\({}_{2}\) NPs and a surfactant could achieve better results in wettability alteration for carbonate rock [137] because a cationic surfactant can form ion pairs with the cationic head and the acidic component of the crude, while NPs will develop a continuous wedge film around the liquid-solid surface. Moreover, interfacial tension between two fluids can be considerably reduced by ZrO\({}_{2}\)[138], and a higher adsorption energy is needed for the oil-water interface than the air-water interface.
## 2 Calculation Model of Drainage Radius of Single-Layer Gas Well
### Practical Application  
There is a gas well X1 in a gas reservoir, the production layer is s2, the effective gas layer thickness is \(8.2\) m, reservoir porosity is \(7.3\%\), initial gas saturation is \(70.87\%\), reservoir temperature is \(90.68\)\({}^{\circ}\)C and original reservoir pressure is \(20.956\) MPa. Well X1 undergoes multiple pressure recovery tests and flowing pressure tests. And then, these test data are used to plot the relationship of \(P/Z\) and \(G_{\text{P}}\). The relationships of \(P_{\text{R}}/Z_{\text{R}}\) and \(G_{\text{P}}\) and \(p_{\text{wf}}/Z_{\text{wf}}\) and \(G_{\text{P}}\) are shown in Figure 4. Table 3 compares the calculated results of original gas in place and the drainage radius for well X1 using the material balance method, flowing material balance method, and modified flowing material balance method. From Table 3, it can be observed that compared to the material balance method, the relative errors of original gas in place and drainage radius obtained by modified flowing material balance method is much smaller than that calculated by flowing material balance method. These results demonstrate that the calculation model of drainage radius derived from the proposed modified flowing material balance method has high precision and lower relative error.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Property**} & **Actual Value of Gas Reservoir Model** & \multicolumn{2}{c}{**Flowing Material Balance Method**} & \multicolumn{2}{c}{**Modified Flowing Material Balance Method**} \\ \cline{3-6}  & **Gas Reservoir Model** & **Value** & **Error (\%)** & **Value** & **Error (\%)** \\ \hline \(G_{\text{i}}\) (\(10^{8}\) m\({}^{3}\)) & 0.7581 & 0.5774 & 23.84 & 0.7403 & 2.35 \\ \hline \(r_{\text{e}}\) (m) & 500 & 436.4 & 12.72 & 494.1 & 1.18 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of calculation results for different methods.  
Figure 3: The relationship of pseudo-reservoir pressure and cumulative gas production.  
Calculation Model of Drainage Radius of Multi-Layer Commingled Gas Production Well and Its Application
# Graph Neural Networks for Routing Optimization: Challenges and Opportunities
###### Abstract  
In this paper, we explore the emerging role of graph neural networks (GNNs) in optimizing routing for next-generation communication networks. Traditional routing protocols, such as OSPF or the Dijkstra algorithm, often fall short in handling the complexity, scalability, and dynamic nature of modern network environments, including unmanned aerial vehicle (UAV), satellite, and 5G networks. By leveraging their ability to model network topologies and learn from complex interdependencies between nodes and links, GNNs offer a promising solution for distributed and scalable routing optimization. This paper provides a comprehensive review of the latest research on GNN-based routing methods, categorizing them into supervised learning for network modeling, supervised learning for routing optimization, and reinforcement learning for dynamic routing tasks. We also present a detailed analysis of existing datasets, tools, and benchmarking practices. Key challenges related to scalability, real-world deployment, explainability, and security are discussed, alongside future research directions that involve federated learning, self-supervised learning, and online learning techniques to further enhance GNN applicability. This study serves as the first comprehensive survey of GNNs for routing optimization, aiming to inspire further research and practical applications in future communication networks.  
graph neural networks; routing optimization; distributed learning; supervised learning; reinforcement learning; dynamic networks; network topology; future networks +
Footnote †: journal: _Sustainability_
# Phage-Phenotype Imaging of Myeloma Plasma Cells by Phage Display
Specifically, MM patients with high-grade angiogenesis generally show a lower number of CD45\({}^{+}\) PCs [17]. Specific biological probes, able to detect and identify specific immunophenotypes of PC, including those of myeloma, could support flow cytometry in the simultaneous evaluation of the presence/absence of molecular targets (surface or intracellular) associated with MM disease. In this context, phage display technology is a powerful tool for the identification of new ligands that specifically recognize targets of interest [18]. Phage-displayed random peptide libraries consist of billions of M13 bacteriophage (or simply phage) clones that expose random peptides on their capsid protein. From these combinatorial phage libraries, specific peptides able to recognize and selectively bind molecular targets on whole cells can be screened by the affinity selection technique of biopanning [19; 20]. M13 is a filamentous phage that measures 900 nm (length) and 6 nm (width). Its capsid consists of approximately 2700 copies of the major coat protein--namely, pVIII--capped on the two ends by 5 copies of 4 different minor coat proteins (pIX, pVII, pVI, and pIII) [21]. Consequently, M13 phage clones, engineered in PVIII, were considered advantageous in terms of their nanoscale size, high solubility, and multivalent binding sites compared with the antibodies. In fact, they have been successfully applied to several detection systems with fluorescent dyes of noble metal nanoparticles (such as gold, silver), silica-doped with quantum dots and a surface of micro-materials (such as magnetic beads) [22; 23; 24; 25; 26; 27; 28; 29; 30]. The aim of this study was to use an engineered phage as a probe for a phage-phenotype identification of plasma cells derived from an MM patient. Specifically, phage clones from the M13-pVIII-9 aa phage peptide library were directly labelled with isothiocyanate of fluorescein and used in recognition matrix tests of MM immunophenotype based on CD45, CD 56, CD38, and CD138.
## 2 Materials and Methods
### Accuracy Assessment  
A 10-fold cross-validation was employed to evaluate the performance of each evaluated predictive machine learning regression model, where the dataset was randomly partitioned into 10 subsets, with 9 subsets being used for training and 1 subset for testing [54]. This process was repeated 10 times, with each subset serving as the test set once. The model's performance was assessed using four metrics--coefficient of determination (R\({}^{2}\)), root mean squared error (RMSE), normalized root mean squared error (NRMSE), and mean absolute error (MAE). R\({}^{2}\) measured the proportion of variance in the dependent variable explained by the model, while RMSE and NRMSE provided an indication of the  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Machine Learning Algorithm**} & \multirow{2}{*}{**Abbreviation**} & \multicolumn{2}{c}{**Total Papers**} & \multirow{2}{*}{**Library**} & \multirow{2}{*}{**Reference**} \\  & & **Indexed in** & & \\  & & **WoSCC (-2023)** & & \\ \hline Random Forest & RF & 347 & randomForest & [27] \\ Cubist & CUB & 92 & Cubist & [28] \\ Multiple Linear & MLR & 85 & / & [25] \\ Regression & SVMs & 82 & kernlab & [29] \\ Support Vector Machines & ANNs & 72 & brnn & [30] \\  & & PLS & 61 & pls & [31] \\ Classification and & & CARTs & 40 & rpart & [32] \\ Regression Trees & & & & \\ Gradient Boosting & GBM & 35 & gbm & [33] \\ Machine & QRF & 31 & quantregForest & [34] \\ Quantile Random Forest & QRF & 31 & quantregForest & [35] \\ Extreme Gradient & XGB & 23 & xgboost & [35] \\ Boosting & K-Nearest Neighbors & KNN & 15 & / & [25] \\ Generalized Linear Model & GLM & 12 & / & [25] \\ Generalized Additive & & GAM & 11 & gam & [36] \\ Model & & & & \\ Least Absolute Shrinkage & LASSO & 9 & elasticnet & [37] \\ and Selection Operator & & Least Angle Regression & LAR & 2 & lars & [38] \\ \hline \hline \end{tabular} Algorithms with a non-indicated library were available in base R.  
\end{table}
Table 1: The machine learning regression algorithms used for the evaluation of digital SOC mapping in this study.  
model's accuracy, with lower values indicating a better fit. MAE, on the other hand, evaluated the model's ability to predict the actual values, with lower values indicating smaller absolute errors [55]. By using 10-fold cross-validation, the reliability and generalizability of the model's performance were ensured, as the results were averaged across multiple iterations, providing a robust estimate of the model's predictive capabilities [56].
# Inter-Unit Consistency and Validity of 10-Hz GNSS Units in Straight-Line Sprint Running
## 4 Discussion
### Peak Speed  
This study hypothesized that multiple GNSS units of the same system would be consistent and accurate in measuring peak speed during a maximal effort sprint. The results demonstrated _fair_ reliability among the eight GNSS units and that seven out of the eight units generally measured lower peak speeds than that video-based reference values (Table 3). The results are not in line with previous findings which suggested confidence in 10-Hz GNSS units being able to accurately measure consistent speeds and velocities [18]. The discrepancy in the peak speeds measured can be attributed to the compromises when measuring instantaneous velocities during great decelerations [38] and accelerations [28]. Hence, rapid changes in speed during the acceleration phase of the 45.72-m sprint in the present study could affect the accuracy of the GNSS units when measuring peak speeds. Higher accuracy and inter-unit reliability may be expected if GNSS units are applied to measure speed during a stable phase with small decelerations or accelerations.  
Compared with the video analysis which was used as the golden standard for the speed measurement, only one GNSS unit displayed statistically significant result (Unit 4, Table 3). It is worth noting that the effect sizes of the differences were medium to large across all units regardless of statistical significance. As the GNSS units tend to register lower peak speeds (7.25 (0.51) m/s) than video-based reference values (7.78 (0.90) m/s), such differences cannot be disregarded. Sport practitioners should keep in mind that GNSS readings may slightly underestimate peak speeds during high-speed running and interpret the results with consideration of the error window (SEM = 1.3 m/s).
# A Temperature-Robust Envelope Detector Receiving OOK-Modulated Signals for Low-Power Applications
## 4 Implementation Criteria
### Two Implemented Versions  
The proposed ED has been designed using an STMicroelectronics 90 nm CMOS technology with a 0.6 V supply voltage to receive 1 kbps OOK-modulated signals with a 433 MHz carrier frequency. Two different versions are provided in order to gain further insight into implementation criteria: version 1 with minimized effects of mismatch on temperature compensation, thus sensitivity, and version 2 with minimized \(C_{in}\) and area.
## 5 Results of QoE\({}_{i}\) Modeling and Discussion
### QoE\({}_{i}\) Estimation Model  
A weight value equal to 0.1 was assigned to each of the 10 identified user satisfaction indicators (D\({}_{1}\),...,D\({}_{10}\)). These indicators affect the increase in the overall level of _QoE\({}_{i}\)_ by multiplying their weights with a corresponding user rating. Negative indicators (C\({}_{1}\),..., C\({}_{5}\)) are associated with weight values equal to \(-\)0.2, which means that they affect the  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Mark**} & **Mean** & **Standard** & **Variance** & **Sum of** & **Median** & **Max** \\  & **(MOS)** & **Deviation** & **Variance** & **Squares** & **Min** & **Median** & **Max** \\ \hline D\({}_{1}\) & 3.32 & 1.01 & 1.03 & 1889 & 1 & 3 & 5 \\ D\({}_{2}\) & 3.17 & 0.99 & 0.99 & 1727 & 1 & 3 & 5 \\ D\({}_{3}\) & 3.71 & 0.96 & 0.91 & 2300 & 1 & 4 & 5 \\ D\({}_{4}\) & 3.00 & 1.02 & 1.04 & 1575 & 1 & 3 & 5 \\ Indicators of user & D\({}_{5}\) & 2.92 & 1.00 & 1.01 & 1499 & 1 & 3 & 5 \\ satisfaction & D\({}_{6}\) & 2.99 & 1.02 & 1.03 & 1568 & 1 & 3 & 5 \\ D\({}_{7}\) & 3.04 & 1.01 & 1.03 & 1616 & 1 & 3 & 5 \\ D\({}_{8}\) & 3.16 & 0.95 & 0.90 & 1697 & 1 & 3 & 5 \\ D\({}_{9}\) & 2.96 & 1.11 & 1.23 & 1569 & 1 & 3 & 5 \\ D\({}_{10}\) & 3.09 & 1.09 & 1.19 & 1674 & 1 & 3 & 5 \\ \hline \multirow{4}{*}{Indicators of user dissatisfaction (forms and measures of rigidity)} & C\({}_{1}\) & 2.83 & 1.05 & 1.09 & 1426 & 1 & 3 & 5 \\  & C\({}_{2}\) & 2.88 & 1.02 & 1.03 & 1462 & 1 & 3 & 5 \\ \cline{1-1}  & C\({}_{3}\) & 2.82 & 1.01 & 1.01 & 1402 & 1 & 3 & 5 \\ \cline{1-1}  & C\({}_{4}\) & 2.80 & 1.06 & 1.12 & 1408 & 1 & 3 & 5 \\ \cline{1-1}  & C\({}_{5}\) & 2.87 & 1.08 & 1.17 & 1472 & 1 & 3 & 5 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Descriptive statistics for indicators of user satisfaction and dissatisfaction.  
reduction of the \(QoE_{i}\) level. Based on the above, a mathematical formulation can be derived for estimating the user \(QoE_{i}\):  
\[QoE_{i}=0.1\sum_{j=1}^{10}D_{j}-0.2\sum_{k=1}^{5}C_{k} \tag{5}\]  
where \(D_{j}\) denotes the user's ratings of satisfaction indicators, and \(C_{k}\) denotes the ratings of dissatisfaction indicators. Therefore, the total level of user \(QoE_{i}\) is determined on a scale of real numbers from 0 to 5, noting that negative values of \(QoE_{i}\) are mapped to the value of 0. The arithmetic mean of individual, subjective \(QoE_{i}\) values calculated in this way, where \(i=1...157\), is equal to the \(QoE\) value for a group of 157 users and amounts to MOS = 0.503. If this MOS is rounded to an integer value, a score of 1 is obtained, which qualitatively represents a bad user \(QoE\) rating for the observed services and telecommunications operators.
# Secure Video Communication Using Multi-Equation Multi-Key Hybrid Cryptography
## 4 Experimentation
### Delay for Splitting File to Chunks  
This metric was used to monitor the amount of time that the program required to generate the video chunks from the video file. Implementing video encryption based on chunks is one of the most important contributions made by this study. As a result of this inquiry, the amount of processing time involved in the video processing module has been demonstrated. The chunk size is read by the FFMpeg utility that comes from the sender. The chunk is then divided into the nearest full frame, which ultimately results in a chunk size dictated by the user's input.  
Figure 4 depicts the time required by the video processing module to divide the video file into many chunks. This time is displayed in the form of a delay. The results demonstrate  
a straightforward and easy-to-understand correlation between the file size and the latency progression. According to the findings, the method does not result in an unanticipated increase in the required time. However, the generation of chunks is partly responsible for the overall delay. However, this is necessary to achieve a high level of security.
# Real-Time Webcam Heart-Rate and Variability Estimation with Clean Ground Truth for Evaluation +
## 3 Method
### Signal Filtering
#### 3.3.2 Wide and Narrow Band Filtering  
The strongest frequency component inside the resulting spectrum is then used to determine the passband range of a narrow-bandpass filter with a bandwidth of 0.47 Hz. This bandwidth has been chosen empirically and depends on the robustness of the subsequent peak detection algorithm to distinguish heat beat peaks from noise (higher the robustness, wider this bandwidth can be). This bandpass filter can either be realized via inverse FFT or a high order FIR filter (e.g., \(\sim\)50 th order Butterworth). The selected filter is then applied to the original extracted rPPG signal to produce noise-free BVP.