esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Results of Verification Test at the Factory  
As part of the system validation process in an industrial environment, a set of sensors was installed on cylindrical specimens of self-compacting concrete at the factory. These sensors were intended to monitor temperature and humidity evolution under real production conditions. Unfortunately, due to an unexpected electrical failure at the facility, the data acquisition system was interrupted, and the sensors were only able to record data for approximately one hour. As a result, the environmental monitoring test could not be completed.  
Despite this limitation, the cylindrical specimen containing the embedded sensors was preserved and later used to evaluate the potential influence of sensor placement on the mechanical properties of the concrete. This was carried out by comparing its performance in indirect tensile strength tests (Brazilian test) with that of similar specimens without sensors.  
The results are presented in Table 1. Specim P1, which contained embedded sensors, exhibited a tensile strength of 6 N/mm\({}^{2}\). In contrast, specimens P2 and P3, which did not contain sensors, reached values of 7.5 N/mm\({}^{2}\) and 9.6 N/mm\({}^{2}\), respectively. This represents a reduction of up to 37.5% in tensile strength for the instrumented specimen. Although the sample size is limited, this trend suggests that the presence and location of sensors may negatively affect the mechanical performance of the concrete, particularly in small specimens where the relative volume of the sensor is more significant. This observation is presented as preliminary and highlights the need for further investigation.  
Additionally, specimen P4 was tested under uniaxial compression and reached a compressive strength of 60.4 N/mm\({}^{2}\), serving as a reference for the concrete's overall mechanical performance.  
\begin{table}
\begin{tabular}{c c c} \hline  & **Brazilian Test (N/mm\({}^{2}\))** & **Compression Test (N/mm\({}^{2}\))** \\ \hline TestP1 (with sensors) & 6 & - \\ P2 & 7.5 & - \\ P3 & 9.6 & - \\ P4 & - & 60.4 \\ \hline \end{tabular} \({}^{\dagger}\) Values include indirect tensile strength from the Brazilian test and uniaxial compressive strength. Test P1 was performed with additional instrumentation (sensors), while tests P2 and P3 correspond only to indirect tension, and P4 to uniaxial compression.  
\end{table}
Table 1: Tests results \({}^{1}\).  
Figure 13: Temperature and humidity curves.  
Although only one specimen with embedded sensors was subjected to the Brazilian tensile strength test, the result provides preliminary evidence on the influence of sensor implantation in small-scale precast elements. This test was conducted under real industrial conditions, and its main objective was to assess the feasibility and limitations of embedded instrumentation in a less controlled setting. Despite its limited statistical value, this test helped identify potential interferences and informed the refinement of future experimental procedures.  
However, as can be seen in the image of Figure 14, the sensor does not significantly determine the fracture of the specimen.
## 2 Theories and Methods
### Regression Method  
In this paper, a back propagation neural network (BPNN) was chosen for joint angle regression. Prior to this, support vector machines (SVM) and random forest algorithms (RF) were also used to compare the accuracy of regression recognition. The results showed that the BPNN algorithm is relatively good. Because our research focuses on the effects of several psychological conditions, only the calculation results of the BPNN are reported in this paper. The artificial neural network is the most popular alternative method [20]. A BPNN is a multilayer feedforward network trained by error inverse propagation algorithm. It can approximate any nonlinear mapping relationship, and belongs to the global approximation method, so it has good generalization ability and good fault tolerance. The topology of the BPNN model includes an input layer, a hidden layer, and an output layer. The input layer node is \(i\), and its output \(x_{i}\) (\(i\) = 1, 2, 3,..., \(n\)) is transmitted to the second layer as a control variable value. Hidden layer node \(j\), input \(h_{j}\) and output \(O_{j}\) are:  
\[h_{i}=\sum_{i=1}^{n}\omega_{ij}x_{i}-\theta_{j}=\sum_{i=1}^{n+1}\omega_{ij}x_{i} \tag{5}\]  
\[O_{j}=f(h_{j})=\frac{1}{1+e^{-h_{j}}} \tag{6}\]  
where \(j\) = 1, 2,..., \(m\); \(\theta_{j}\) = \(\omega_{(n+1)/j}\); \(x_{n+1}\) = \(-1\). Output layer node \(k\), input \(h_{k}\) and output \(y_{k}\) are:  
\[h_{k}=\sum_{j=1}^{m}\omega_{jk}o_{j}-\theta_{j}=\sum_{j=1}^{m+1}\omega_{jk}o_{j} \tag{7}\]  
\[y_{k}=f(h_{k})=\frac{1}{1+e^{-h_{k}}} \tag{8}\]  
where \(k\) = 1, 2,..., \(l\); \(\theta_{k}\) = \(\omega_{(m+1)k}o_{m+1}\), \(o_{m+1}\) = \(-1\).  
In the network of this paper, the input layer contains 16 nodes, which represent features extracted from the sEMG signals of the four muscles. Data were trained with the Levenberg-Marquardt algorithm in a hidden layer, which the nodes had set to 20. The output layer consists of only one node which predicts the angle of the joint. Figure 1 shows the topology of BPNN. The recognition accuracy was verified by a 4-fold cross-validation method. The collected sample data were randomly divided into 4 groups, 3 groups were selected as the training set in turn, and 1 group was used as the test set, and the results were averaged 4 times.
# Gaze Estimation Based on a Multi-Stream Adaptive Feature Fusion Network
## 3 Proposed Method
### Eye Image Stream
When constructing the eye image feature extraction network, we observed that the left and right eye images are not only structurally symmetrical but also share correlations in feature representation. Therefore, we introduced a weight-sharing strategy for left and right eye feature extraction in the network to enhance the capability of extracting binocular features. This strategy leverages symmetry and correlation to improve feature consistency and complementarity, reducing model parameters, increasing training efficiency, and helping the network better learn the common characteristics of both eyes, thereby improving the accuracy of gaze estimation. In Section 4, within the experimental section, we evaluate the effectiveness of the eye image stream in extracting high-dimensional features. The experimental results indicate that while this stream significantly enhances feature extraction accuracy, it also reveals the potential risk of overfitting, which could negatively impact the performance of the gaze estimation task. To address this issue, we designed a fusion stream that integrates eye features with facial attribute information, enriching the network's feature representation and mitigating the overfitting problem caused by a single-stream approach.
## 3 Results and Discussion  
### Proximate Composition, \(pH\) Value, and Iron Content of Raw Chicken Liver  
The proximate composition of raw chicken liver has been determined as follows; moisture content 73.70 \(\pm\) 0.25%, protein content 20.52 \(\pm\) 0.63%, fat content 3.55 \(\pm\) 0.51%, and ash content 1.45 \(\pm\) 0.06%. In accordance with these results, the moisture content of raw chicken liver was determined to be 75.72% [39] and the ash content was detected to be 1.17% [40]. [PERSON] and [PERSON] [41] reported the moisture, protein, fat, and ash contents of raw chicken liver as 66.80%, 24.60%, 6.00%, and 1.40%, respectively.  
The pH value of raw chicken liver was determined to be 6.64 \(\pm\) 0.07. In a study, the pH of normally colored chicken livers (6.38) was significantly higher than that of abnormally colored chicken livers (6.18). The researchers reported that this was due to the fact that the chickens from which abnormally colored chicken livers were obtained were exposed to more stress, which caused glycolysis to accelerate, leading to a further decrease in pH [40]. It was observed that the pH values measured in normal-colored chicken livers in this study were closer to the pH values in the present study.  
The iron content of raw chicken liver was detected to be 47.03 \(\pm\) 4.43 \(\upmu\)g/g. The liver is a major systemic store of iron. Iron is bound by ferritin as a ferric complex in a protein shell. Ferritin is the major intracellular storage protein found in all cells, with the highest concentrations in liver, spleen, and bone marrow [42]. One study reported that the iron content of chicken liver was 69.933 \(\upmu\)g/g [40]. The reason why the iron content of chicken liver in this study was higher than that found in the present study is predicted to be due to differences in the feeding regimes of chickens.
# A Survey of Precision Formation Relative State Measurement Technology for Distributed Spacecraft
## 3 Analysis of the Characteristics of Relative State Measurement Technology
### Composite Relative Measurement Technology of Spacecraft Formation
#### 3.6.6 Vision + Laser Coupling Measurement Technology  
The visual + laser composite relative state measurement technology has strong robustness, so it is widely used in the measurement of cooperative targets in failure mode and the measurement of difficult non-cooperative targets. Laser measurements include laser ranging, laser tracking, and LiDAR. The laser tracker mainly provides visual reference values and assists the visual system in extracting specific features to complete the measurement [46]. Laser ranging is mainly used as an information source for image depth acquisition, to improve the overall algorithm accuracy and algorithm simplification [43]. The purpose of the measurement of a LiDAR point cloud and vision is to use the strong stability of the 3D laser's point cloud to make up for the poor light conditions for visual recognition. Vision sensors can compensate for the sparseness of laser point clouds and improve the overall measurement accuracy [71]. The process is shown in Figure 27. First, the stereo information of the target spacecraft is obtained via binocular camera reconstruction. The algorithm further combines the camera stereo-matching information and the radar point cloud to measure the target pose. Finally, the EKF filtering algorithm is used to obtain more accurate target spacecraft pose results. This improves the system robustness and expands the effective range.  
Figure 26: The schematic diagram of vision + inertial navigation + laser measurement.
# A Comprehensive Review and Benchmarking of Fairness-Aware Variants of Machine Learning Models
## 8 Results
### LAW Dataset Results  
Table 11 and Figure 7 provide a comprehensive comparison of fair machine learning algorithms evaluated on the LAW dataset. The results reveal important trade-offs between predictive performance and fairness across different models.  
Among all models, Prejudice Remover achieved the highest accuracy (\(0.9102\pm 0.0015\)), closely followed by Fairboost (\(0.9096\pm 0.0015\)) and Fairlearn variants--GB and LR--both reporting accuracies of 0.9081. While these top-performing models demonstrated strong predictive capability, their fairness characteristics varied. Notably, AdaFair delivered perfect fairness across all evaluated metrics (zero disparities), with only a slight reduction in accuracy (\(0.9017\pm 0.0000\)). This suggests AdaFair enforces strict fairness guarantees, even at the expense of minor performance loss.  
Models such as FairGBM and Fairlearn-GB/LR struck a commendable balance between accuracy and fairness. For instance, Fairlearn-GB reported low fairness disparities, including a statistical parity difference of 0.0096, a TPR difference of 0.0026, and an FPR difference of 0.0427. These results indicate reliable and equitable classification behavior, making such models highly suitable for fairness-sensitive domains.  
In contrast, while Prejudice Remover was the most accurate model, it also exhibited the highest false positive rate (FPR) disparity (0.1053), raising concerns about differential treatment in negative predictions across groups. Similarly, Fairboost, despite its strong accuracy, recorded a relatively higher FPR difference (0.0919), highlighting the potential fairness-performance trade-off, particularly along the FPR axis.  
The lower-performing models included Fair Decision Trees, which had the lowest accuracy (\(0.2928\pm 0.0145\)), making them unsuitable for practical deployment. NNB-based models (NNB-Parity and NNB-DF) achieved moderate accuracy (approximately 0.80-0.82), but exhibited higher disparities in fairness metrics, particularly in statistical parity and TPR difference, undermining their fairness reliability.  
In summary, Fairlearn-GB, FairGBM, and Fairlearn-LR emerged as the most balanced models, successfully combining high predictive performance with low fairness disparities. These models are strong candidates for deployment in legal or policy-sensitive contexts where equitable outcomes are critical. Meanwhile, AdaFair remains the most rigorous in fairness enforcement, suitable for applications demanding zero tolerance for group-level bias. Although Prejudice Remover and Fairboost offer the highest accuracy, their elevated FPR disparities necessitate careful consideration, especially in high-stakes scenarios.
## 4 Experiments
### Qualitative Analysis of DI-MDE Performance in Dynamic Scenarios
On a high-end NVIDIA RTX 2080 Ti GPU, DI-MDE achieves an average inference time of 38.2 ms per frame, translating to approximately 26 frames per second (FPS). In contrast, AdaBins runs at 45.6 ms per frame (22 FPS), and FC-CRFs takes 50.1 ms per frame (20 FPS). The faster running time of DI-MDE is critical for real-time applications such as autonomous driving and robotics, where timely depth estimation is crucial for safe navigation and interaction. On more resource-constrained devices, such as the NVIDIA Jetson TX2, DI-MDE maintains its performance edge, achieving an inference time of 61.9 ms per frame (16 FPS), compared to AdaBins at 78.3 ms per frame (13 FPS) and FC-CRFs at 85.7 ms per frame (11 FPS). This demonstrates the model's efficiency and its ability to perform well on edge devices, where real-time depth estimation is required but computational resources are limited.
## 4 SBS Energy Efficiency Management Decisions
### SBS State Admission and Correction Algorithm
Combine \(\mathbf{b}^{t-1}\) and \(\widetilde{b}_{new}^{t}\) to compute the new system performance objective function value \(Q\big{(}\widehat{\mathbf{p}}_{new}^{t^{*}},\widetilde{b}_{new}^{t}\big{)}\); if\(Q\big{(}\widehat{\mathbf{p}}_{new}^{t^{*}},\widetilde{b}_{new}^{t}\big{)}\leq Q \big{(}\widehat{\mathbf{p}}^{t^{*}},\widetilde{\mathbf{b}}^{t}\big{)}\) \(B_{om}^{t}=\widetilde{B}_{on}^{t}\), \(B_{stand}^{t}=\widetilde{B}_{stand}^{t}\), \(\mathbf{b}^{t}=\widetilde{b}_{new}^{t}\); else \(B_{om}^{t}\leftarrow\widetilde{B}_{om}^{t}-\{i\}\), \(B_{stand}^{t}\leftarrow\widetilde{B}_{stand}^{t}\cup\{i\}\) ; break; endif endwhile while\(B_{stand}^{t}\
eq\varnothing\)  The optimal load \(\mathbf{p}^{t^{*}}\) under \(B_{on}^{t}\) is calculated from Algorithm 1, and the system performance objective function value \(Q\big{(}\mathbf{p}^{t^{*}},\mathbf{b}^{t}\big{)}\) is calculated by combining the set \(\mathbf{b}^{t-1}\) of base station states in period \(t-1\) and the set \(\mathbf{b}^{t}\) of predicted states in period \(t\);  Select SBS \(i=\underset{j\in\widetilde{B}_{rand}^{t}}{argmax}T_{j}\), \(\widetilde{B}_{sleep}^{t}\leftarrow\widetilde{B}_{sleep}^{t}\cup\{i\}\), and at the same time adjust \(b_{i}^{t}\) in \(\mathbf{b}^{t}\) from \(b_{i}^{t}=0\) to \(b_{i}^{t}=-1\) to get the new \(\mathbf{b}_{new}^{t}\), \(B_{stand}^{t}\gets B_{stand}^{t}-\{i\}\) ;  Combine \(\mathbf{b}^{t-1}\) and \(\mathbf{b}_{new}^{t}\) to compute the new system performance objective function value \(Q\big{(}\mathbf{p}^{t^{*}},\mathbf{b}_{new}^{t}\big{)}\); if\(Q\big{(}\mathbf{p}^{t^{*}},\mathbf{b}_{new}^{t}\big{)}\leq Q\big{(}\mathbf{p}^{t^{*}},\mathbf{b} ^{t}\big{)}\) \(B_{super}^{t}=\widetilde{B}_{sleep}^{t}\), \(\mathbf{b}^{t}=\mathbf{b}_{new}^{t}\); else \(B_{sleep}^{t}\leftarrow\widetilde{B}_{sleep}^{t}-\{i\}\), \(B_{stand}^{t}\gets B_{stand}^{t}\cup\{i\}\) ; break; endif endwhile Output:\(B_{on}^{t}\), \(B_{stand}^{t}\), \(B_{sleep}^{t}\);
```  
**Algorithm 2** Joint optimization algorithm for SBS state control and user association (SSC-UA)
# Convolutional Neural Networks: A Roundup and Benchmark of Their Pooling Layer Variants
## 3 Putting the Methods to the Test
### Performance Evaluation  
For the performance comparison, we used the standard top 1 and top 5 testing accuracy (higher is better); for the computational complexity, we used the time required per epoch (lower is better), while also including three indicators, which can provide better insight into how well the details of the original image are maintained--for all three (higher values are better):  
**Root-Mean-Squared Contrast (RMSC)**[44], as defined in Formula (15) for a \(M\times N\) image:  
\[RMSC=\sqrt{\frac{1}{M\times N}\sum_{i=0}^{M-1}\sum_{j=0}^{N-1}{(x_{ij}-\overline {x})^{2}}} \tag{15}\]  
where  
\(x_{ij}:\): each pixel of the image;  
\(\overline{x}:\): \((\sum_{i=0}^{M-1}\sum_{j=0}^{N-1}{x_{ij}})/(M\times N)\).  
**Peak-Noise-to-Signal Ratio (PSNR)**[45], as defined in Formula (16) for a \(M\times N\) image:  
\[PSNR=20 log_{10}\bigg{(}\frac{MAX_{f}}{\sqrt{MSE}}\bigg{)} \tag{16}\]  
where:  
\(MSE:\): (Mean-Squared Error) \(=(\sum_{i=0}^{M-1}\sum_{j=0}^{N-1}{\|f(i,j)-g(i,j)\|^{2}})/(M\times N)\);  
\(f:\): the data of the original image;  
\(g:\): the data of the pooled image;  
\(MAX_{f}:\): the maximum signal value of the original image.  
**Structural Similarity Index (SSIM)**[46], which is defined by three combined metrics for luminance, contrast, and structure and can be simplified for two signals \(x,y\) in the form seen in Formula (17):  
\[SSIM(x,y)=\frac{(2\mu_{x}\mu_{y}+C_{1})(2\sigma_{xy}+C_{2})}{(\mu_{x}^{2}+\mu_ {y}^{2})(\sigma_{x}^{2}+\sigma_{y}^{2}+C_{2})} \tag{17}\]  
where:  
\(\mu_{x},\mu_{y}:\): the pixel mean \(\mu_{x}=\sum_{i=1}^{N}{x_{i}}/{N}\);  
\(\sigma_{x},\sigma_{y}:\): standard deviation \(\sigma_{x}=\sqrt{\sum_{i=1}^{N}{(x_{i}-\mu_{x})^{2}}/(N-1)}\);  
\(\sigma_{xy}:\): \(\sum_{i=1}^{N}{(x_{i}-\mu_{x})(y_{i}-\mu_{y})}/(N-1)\);  
\(C_{1}:\): \((k_{1}L)^{2}\);  
\(C_{2}:\): \((k_{2}L)^{2}\);  
\(L:\): the dynamic range of pixels, 255 for 8-bit grayscale images;  
\(k_{1}:\): A small constant <1, 0.01 used in the paper experiments;  
\(k_{2}:\): A small constant <1, 0.03 used in the paper experiments.  
All tests were performed using a PyTorch implementation of the methods, on an Nvidia GTX1080 GPU.
## 5 The Proposed Protocol
### System Initialization Phase  
My proposed protocol employs the ECC proposed by the National Institute of Standards and Technology (NIST) [36]. The system generates a curve over a finite field \(Fp\)\(Ep(a,\,b):\,y^{2}=x^{3}+ax+b(mod\,\,p)\) with the point \(G_{(x,\,y)}\). For simplicity, two coordinates \(x\) and \(y\) of \(G_{(x,\,y)}\) are ignored in the description of the protocol. \(S_{m}\) chooses a private key \(prk_{m}\) and computes its public key \(puk_{m}=prk_{m}.G\). Next, \(S_{m}\) registers with a certificate authority and has the certificate, signature, public key and private key validated. The same procedure is also conducted by \(S_{n}\).
# What Is a Causal Graph?
## 6 Pearlian DAG  
Consider the DAG of Figure 9.  
As it stands, this looks like a probabilistic DAG, representing purely conditional independence properties, such as \(C\perp\!\!\!\perp D\mid(A,B)\). [PERSON] [4], however, would endow it with additional causal meaning, using an interventional interpretation as in Section 4. He would regard it as asserting that, for any node, its conditional distribution, given its parents, would be the same in a purely observational setting and in any interventional setting that  
Figure 8: Instrumental variable: alternative augmented DAG.  
Figure 7: Instrumental variable: augmented DAG with error variables.  
Figure 9: Pearlian DAG.  
sets the values of some or all of the variables other than itself. For example, it requires the following:  
_The distribution of \(C\), given \((A,B)\), does not depend on whether \(A\) and \(B\) arose naturally or were set by external intervention._  
While this is a perfectly clear formal interpretation of Figure 9, it is problematic in that, if we are just presented with that DAG, we may not know whether it is meant to be interpreted as representing only probabilistic properties or it is supposed to be further endowed with [PERSON]'s causal semantics. This ambiguity can lead to confusion; a particular danger is to see, or construct, a probabilistic DAG, and then slide, unthinkingly, into giving it an unwarranted Pearlian causal interpretation.  
We can avoid this problem by explicit inclusion of regime indicators, one for each domain variable, as, for example, in Figure 10.  
Not only is this clearly not intended as a probabilistic DAG but the Pearlian causal semantics, which, in the case of Figure 9, require external specification, are now explicitly represented in the augmented DAG by moralisation. For example, Figure 10 represents the ECI \(C\perp\!\!\!\perp(F_{A},F_{B})\mid(A,B,F_{C},F_{D},F_{E})\). When \(F_{C}=c\
eq\emptyset\), \(C\) has a one-point distribution at \(c\) and this ECI holds trivially. But, for \(F_{C}=\emptyset\), we recover the property quoted in italics above (under any settings, idle or interventional, of \(F_{D}\) and \(F_{E}\)).  
We also note that an augmented Pearlian DAG can have no other Markov equivalent such DAG, since no arrow can be reversed without creating or destroying an immorality. In this sense, every arrow now carries causal meaning.  
However, just as we should not automatically interpret a regular DAG as Pearlian, so we should not unthinkingly simply augment it by adding an intervention indicator for each domain variable, which would have the same effect. We must consider carefully whether the many very strong properties embodied in any Pearlian or augmented DAG, relating probabilistic properties (parent-child distributions) across distinct regimes, are justifiable in the specific applied context we are modelling.