esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
These results suggest that A1 may promote a Th2 inflammatory response associated with allergy and asthma. In contrast, A2 appears to have a protective effect in this context [3; 16; 17]. Studies in rabbits have shown that A1 can promote the formation of atheroma plaques in the arteries, a key factor in the development of cardiovascular disease. It has been suggested that this effect may be mediated by \(\beta\)CM-7's ability to increase blood cholesterol levels. In contrast, A2 has been shown to have a small protective effect against the formation of these plaques [15; 18]. A multi-center study in rats and mice with type 1 diabetes found no significant difference in disease incidence between animals fed A1 and A2 [19]. This finding challenges the hypothesis that \(\beta\)CM-7 released during A1 digestion may contribute to the development of type 1 diabetes [20]. A1 has been found to induce lung inflammation in mice, whereas A2 does not [21]. This finding suggests that A1 may be involved in the development of respiratory diseases. These studies offer valuable insights, yet the applicability of findings from animal models to humans remains limited. Physiological and metabolic differences between species can significantly influence the response to A1 and A2 \(\beta\)-caseins. While animal research has provided a critical foundation for understanding the potential impacts of these milk proteins, focusing on human studies is essential to directly evaluate how genetic variations in \(\beta\)-casein affect health outcomes such as digestion, inflammation, and the risk of chronic diseases. It is known that not all individuals are affected by the peptide. A small subset of people seems to be more susceptible to the exposure and effects of \(\beta\)CM-7, which may trigger symptoms in various ways, either individually or in conjunction with other health conditions [22]. This review emphasizes the gut-neural axis effects of \(\beta\)CM-7 as a central research thread. By integrating evidence from gastrointestinal, immunological, and neurological studies, the manuscript aims to provide a cohesive perspective on how \(\beta\)CM-7 derived from A1 \(\beta\)-casein may influence health through interconnected digestive and neural pathways. Our aims were to critically analyze and synthesize human studies on the health effects of A1 and A2 \(\beta\)-casein proteins, with a particular emphasis on three key populations: children, who represent the most vulnerable group due to their developmental stage and sensitivity to dietary factors; athletes and individuals with high protein intake, for whom milk protein composition can significantly affect not only muscle recovery and performance but also digestive comfort; and middle aged adults, particularly regarding cardiovascular risks and inflammation-related conditions, as well as digestive disturbances associated with \(\beta\)-casein A1.
## 2 Materials and Methods
### Data Set and Non-Linear Mathematical Model
\begin{array}{l}b_{0}\sum\limits_{i=1}^{n}x_{0i}^{2}+b_{1}\sum\limits _{i=1}^{n}x_{0i}x_{1i}+b_{2}\sum\limits_{i=1}^{n}x_{0i}x_{2i}+b_{12}\sum\limits _{i=1}^{n}x_{0i}x_{1i}x_{2i}+b_{11}\sum\limits_{i=1}^{n}x_{0i}x_{1i}^{2}+b_{2 }\sum\limits_{i=1}^{n}x_{0i}x_{2i}^{2}=\sum\limits_{i=1}^{n}x_{0i}y_{i}\\ b_{0}\sum\limits_{i=1}^{n}x_{1i}x_{0i}+b_{1}\sum\limits_{i=1}^{n}x_{1i}^{2}+b_{ 2}\sum\limits_{i=1}^{n}x_{1i}x_{2i}+b_{12}\sum\limits_{i=1}^{n}x_{1i}^{2}x_{ 2i}+b_{11}\sum\limits_{i=1}^{n}x_{1i}^{3}+b_{22}\sum\limits_{i=1}^{n}x_{1i}x_ {2i}^{2}=\sum\limits_{i=1}^{n}x_{1i}y_{i}\\ b_{0}\sum\limits_{i=1}^{n}x_{2i}x_{0i}+b_{1}\sum\limits_{i=1}^{n}x_{2i}x_{1i}+b_ {2}\sum\limits_{i=1}^{n}x_{2i}^{2}+b_{12}\sum\limits_{i=1}^{n}x_{2i}^{2}x_{ 1i}+b_{11}\sum\limits_{i=1}^{n}x_{2i}x_{1i}^{2}+b_{22}\sum\limits_{i=1}^{n}x_{ 2i}^{3}=\sum\limits_{i=1}^{n}x_{2i}y_{i}\\ b_{0}\sum\limits_{i=1}^{n}x_{1i}x_{2i}x_{0i}+b_{1}\sum\limits_{i=1}^{n}x_{1i}^{ 2}x_{2i}+b_{2}\sum\limits_{i=1}^{n}x_{1i}x_{2i}^{2}+b_{12}\sum\limits_{i=1}^{ n}x_{1i}^{2}x_{2i}^{2}+b_{11}\sum\limits_{i=1}^{n}x_{1i}^{3}x_{2i}+b_{22}\sum \limits_{i=1}^{n}x_{1i}x_{2i}^{3}=\sum\limits_{i=1}^{n}x_{1i}x_{2i}y_{i}\\ b_{0}\sum\limits_{i=1}^{n}x_{1i}^{2}x_{1i}+b_{1}\sum\limits_{i=1}^{n}x_{1i}^{3}+b_ {2}\sum\limits_{i=1}^{n}x_{1i}^{2}x_{2i}+b_{12}\sum\limits_{i=1}^{n}x_{1i}^{3} x_{2i}+b_{11}\sum\limits_{i=1}^{n}x_{1i}^{4}+b_{22}\sum\limits_{i=1}^{n}x_{1i}^{2}x_{ 2i}^{2}=\sum\limits_{i=1}^{n}x_{1i}^{2}y_{i}\\ b_{0}\sum\limits_{i=1}^{n}x_{2i}^{2}x_{0i}+b_{1}\sum\limits_{i=1}^{n}x_{2i}^{2}x_{ 1i}+b_{2}\sum\limits_{i=1}^{n}x_{2i}^{3}+b_{12}\sum\limits_{i=1}^{n}x_{2i}^{3} x_{1i}+b_{11}\sum\limits_{i=1}^{n}x_{2i}^{2}x_{1i}^{2}+b_{22}\sum\limits_{i=1}^{n}x_{ 2i}^{4}=\sum\limits_{i=1}^{n}x_{2i}^{2}y_{i}\end{array} \right. \tag{6}\]  
This system allowed for the determination of statistical mathematical models that link the efficiency of lead and copper removal from the soil by two factors that influence the efficiency of the soil washing process: stirring time and S/L ratio. Models were developed in two variants corresponding to the two investigated concentrations of the washing solution--2% and 5% chitosan content. The experimental data set that served for developing the four mathematical models is summarized in Tables 1 and 2.
## 3 Results and Discussion
### Influence of Bends (R/D Ratio of 0.6) on the Explosion Wave Propagation  
Once the effect of bends on flame velocity and pressure rise were evaluated, the explosion wave propagation at 100 mbar was analysed. At this point, Al and MS dusts were selected owing to their higher explosion severity. Figure 7a,b show the wave propagation speed for bent and straight ducts. This was determined between positions 1 and 2 and 2 and 3, respectively (Figure 3). For Al dust, similar results were obtained for bent and straight ducts in the earlier stage. Nevertheless, between position 2 and 3, the bends caused the flame propagation velocity to build up considerably. It can be concluded that the first two bends had less influence on flame acceleration than the third bend, for Al dust. However, the opposite trend was observed for MS dust, where the first two bends had more influence on flame acceleration which was related to a higher increase in pressure in the first part of the duct. These results were in keeping with those described in Section 3.1, where the flame from the aluminium explosion went further, and an increase in speed was observed due to the turbulence created by the higher-pressure wave. In short, wave propagation was accelerated along the duct for both configurations of ducts and dusts. This occurred as a result of that explained before in Section 3.2.
# Z-OFDM: A New High-Performance Solution for Underwater Acoustic Communication
## 6 Conclusions  
To eliminate the adverse effects caused by the picket fence effect of FFT and improve the performance of underwater acoustic OFDM systems, this paper proposed using ZoomFFT to replace FFT in the modulator and demodulator of the OFDM system. Performance analysis through simulations with underwater acoustic channel models from Xiamen University, specifically Fuxian Lake and Wuyuan Bay, demonstrated that the Z-OFDM system significantly outperforms traditional FFT-based OFDM systems. Future research will focus on optimizing the selection of the refinement factor \(D\) and exploring the  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Feature/Metric** & **Z-OFDM** & **FFT-OFDM** \\ \hline Spectral Resolution & High (refined with Zoom-FFT) & Moderate (limited by FFT size) \\ \hline Channel Capacity & High & Low \\ \hline BER & Low, especially under interference & High \\ \hline SINR & High & Low \\ \hline Computational Complexity & Moderate (efficient spectral refinement) & High (requires larger FFT size for similar resolution) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison between Z-OFDM (\(D=2\)) and FFT-OFDM systems.  
application of spectrum refinement to other advanced Fourier Transform methods. Overall, Z-OFDM represents an advancement in underwater acoustic communication technology, offering improved performance and reliability.  
Conceptualization, H.S. and A.L.; methodology, H.S.; software, A.L. and J.C.; validation, A.L., H.S. and J.C.; formal analysis, A.L. and J.C.; investigation, A.L.; resources, H.H. and C.X.; data curation, H.S. and A.L; writing--original draft preparation, A.L.; writing--review and editing, H.S., A.L. and J.C.; visualization, A.L.; supervision, H.S.; project administration, H.S.; funding acquisition, H.S. All authors have read and agreed to the published version of the manuscript.  
This paper is supported by Guangxi Key Laboratory of Automatic Detecting Technology and Instruments (YQ23106), the Special Funds of the National Natural Science Foundation of China (Grant No. 62341304).  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.
## 2 Methods
### Simplified Coherent Averaging Method
#### 2.2.2 Simplified Coherent Averaging Method  
Although the coherent averaging method has its shortcomings, it can be used to extract an FECG from mixed abdomen ECG (AECG) signals. It is not used for extracting a perfect MECG, but for eliminating the MECG from the signal, so that the FECG can be revealed in the remaining signal. Assuming that the ECG signal is stationary for a short time and that the change of QRS width with instantaneous beats can be ignored, the simplified coherent averaging method is established as follows:1. Determine the R peak position of the MECG in the AECG signal and the corresponding RR intervals.
2. Take the R peak position as the midpoint, and take the maximum RR interval as the length. The data beyond the AECG signal are filled with zeroes.
3. Form the heart beat matrix with the truncated data segment. Calculate the column mean value.
4. Place the mean value repeatedly according to the R peak positions calculated in Step 1. Abandon the overlapped parts to obtain the coherent averaging MECG signal.  
When the MECG is extracted by the conventional coherent averaging method, the time domain is linearly scaled. As a result, the nonstationarity of time is restored to some extent, while the waveform amplitude only retains the average result, so the amplitude deviation caused by the nonstationarity is not recovered. However, the simplified coherence averaging method in this paper only extracts the stationary information of the MECG, and the nonstationary information is left in the remaining signals. Therefore, the useful information of the FECG will not be lost when the coherent averaging MECG is eliminated. Even if the QRS complexes of the FECG and MECG overlap, the information of the FECG will not be lost.
## 5 Result and Analysis
### GAM Ablation Comparison  
The experiment to evaluate the optimization effect of GAM was conducted on the MultiportGAM dataset and the PAMAP2 dataset, with GAM modules removed to compare the recognition performance of human movements. The comparison results on the MultiportGAM dataset are shown in Figure 14a, while the comparison results on the PAMAP2 dataset can be observed in Figure 14b.  
The experimental results showed that on the MultiportGAM dataset, compared to the original structure without adding GAM, GAM achieved an accuracy improvement of 9.38%, a recall improvement of 9.37%, and an F1-score improvement of 9.37%. On the PAMAP2 dataset, these three indicators increased by 7.15%, 7.17%, and 7.16%, respectively.  
To assess the generalization capability of the GAM module, we conducted a comparative analysis with other attention modules. These alternative attention modules were integrated into the residual blocks within the framework for experimentation, and the attention modules we compared included SE and CBAM. The MultiportGAM dataset and PAMAP2 dataset were employed as the dataset for our experiments. The experimental results are presented in Tables 5 and 6.  
It can be observed that compared to GAM, the application of CE and CBAM, two general attention mechanisms, in the hybrid method, results in an accuracy improvement of about 3% compared to the proposed framework without the application of attention mechanisms. However, there is still a gap of 3.4-5% compared to GAM-MLP, indicating that GAM has a higher adaptability to the proposed framework. This stems from the manual grouping module of GAM, which reduces errors between specified groups while adapting to the active features of the dataset.  
Thanks to GAM's ability to extract temporal features of sensor data and dynamically adjust the weights of accelerometer, gyroscope, and magnetometer data, GAM-MLP can better capture the associations between multiple sensors in different time periods, thereby improving the accuracy of action recognition.
# AetherGeo: A Spectral Analysis Interface for Geologic Mapping
## 4 Related Software Tools
### Sam  
Currently, SAM is the only algorithm available for classification based on specific spectral inputs. Considering a given layer (corresponding with raster spectral data) and a spectrum corresponding to a given endmember, the SAM algorithm attributes a value to each pixel based on the similarity between the pixel and the input spectrum. It is assumed that a higher similarity between both spectra indicates a higher likelihood of the endmember material being present. The SAM algorithm is described in Equation (1) [63]:  
\[\angle(\mathrm{x},\mathrm{y})=\arccos\frac{x\cdot y}{\|x\|\|y\|} \tag{1}\]  
where \(x\) and \(y\) are vectors composed of [\(x_{1}\), \(x_{2}\), \(\ldots\), \(x_{n}\)] and [\(y_{1}\), \(y_{2}\), \(\ldots\), \(y_{n}\)], respectively, and represent the array of reflectance values associated with a given pixel and the array of reflectance values associated with a given endmember.  
The usage of this algorithm for geological applications is common due to the reliability around light and angle deviations across the landscape [27; 64].  
The endmember used as input can be a spectrum from a spectral library, such as the United States Geological Survey (USGS) open-access library [28]. Another alternative is to generate a spectral library composed of the pure components present in a given image. This approach shows promising results since it is robust against sensor noise and deviations of the spectra [37].  
A typical workflow for classification after endmember extraction can be seen in Figure 8.  
The spectral dimensionality reduction step is not mandatory but is highly recommended, especially for hyperspectral data, due to what is known in the literature as the curse of dimensionality [35].  
Appendix B provides a practical example of this workflow with more optional steps regarding preprocessing and final data inspection. It shows how to use multiple functions to achieve classification based on an endmember extracted from the image, while starting only with a satellite image product without previous information or processing.
# Operational Optimisation of a Non-Recuperative 1-kWe Organic Rankine Cycle (ORC) Engine Prototype
## 3 Data Analysis
### Components Performance  
Thermodynamic losses arise from process-scale irreversibilities and component-scale energy losses, both of which can be determined through energy and exergy balances across the various components of the ORC system.
## 2 Materials and Methods
### Cell Culture Characterization  
Viability test:  
After washing the channels with PBS, a mix containing 1:200 propidium iodide (PL, Sigma-Aldrich), 1:200 calcein-AM (ThermoFisher Scientific) and 1:2500 hoechst 33342 (Invitrogen) in PBS was incubated for 20 min at 37 \({}^{\circ}\)C on the upper channels containing cells. Staining was observed with a fluorescence microscope (Leica microsystems SAS, Nanterre, France). The number of cells stained for PI (dead cells, PI\({}^{+}\)) or calcein-AM (live cells, C-AM\({}^{+}\)) was quantified using the \"Count Particles\" function in FIJI software(National Institute of Health). Viability was expressed in percentage as the number of viable cells (C-AM\({}^{+}\)) divided by the total number of cells (C-AM\({}^{+}\) + PI\({}^{+}\)) for each condition. Immunostaining:  
To characterize the cells, immunofluorescence staining of zonula occludens (ZO-1) and \(\beta\)-catenin junctions was performed directly in the device. Both upper and lower compartments were washed three times with DPBS. Cells were fixed by adding 3.2% paraformaldehyde (Electron Microscopy Sciences, Hatfield, PA, USA) in DPBS on upper channels for 10 min, and then permeabilized with 0.1% Triton X-100 (ThermoFisher Scientific) for 10 min. Rabbit-anti-ZO-1 (ref. 617300, Invitrogen) or rabbit anti-\(\beta\)-catenin (ref. ab32572, Abcam, Cambridge, UK) primary antibodies were incubated at 1:200 with 2% Bovine Serum Albumin (BSA) in DPBS for 1 h. A mix of 555 anti-rabbit IgG Alexa Fluor 555 secondary antibody (Invitrogen) at 1:500, Alexa Fluor 488 Phalloidin (Invitrogen) (labelling F-actin) at 1:500, and 1 \(\upmu\)g\(\cdot\)mL\({}^{-1}\) Hoechst 33342 (Invitrogen) (labelling nuclei) with 2% BSA in DPBS was incubated in the same channel for 1 h. Between each incubation, channels were washed three times with DPBS. The upper channel was filled with 95% glycerol (Invitrogen) in DPBS and kept at 4 \({}^{\circ}\)C until acquisition.  
For immunodetection, the spinning disk system Yokogawa CSU-W1 integrated in Metamorph software (Molecular Devices) was mounted to the camera port Nikon Ti-E inverted microscope. Objectives with working distance \(\geq\) 0.61 mm can be used to see the cells on this device, which were directly placed above the objective for detection through the baseplates of the device. A Z-dimension series of images were taken and processed using FIJI software. Images were taken with the same acquisition parameters for all conditions and no detectable signal was observed in the absence of primary antibodies.
# Reversible Data Hiding in Encrypted Color Halftone Images with High Capacity
## 3 Proposed Scheme
### Data Embedding
As for the third WPC embedding, we take image \(S_{R^{\prime}}\) as the cover image and copy it into two same images \(S^{\prime}_{R^{\prime}}\) and \(S^{\prime\prime}_{R^{\prime}}\). After the third embedding procedure, stego images \(T_{R^{\prime}}\) and \(T_{R^{\prime\prime}}\) are produced. 4. Finally, we combine four images \(T_{R^{\prime}}\), \(F_{R^{\prime\prime}}\), \(S_{R^{\prime\prime}}\), and \(T_{R^{\prime\prime}}\) into one image \(C_{R}\) using \(K_{d}\). As demonstrated in Figure 3, supposing that each image is \(2\times 2\) sized and the pixels are represented by different color, we stagger-stitch these pixels up-down and left-right. The reason why we adopt triple embedding is that it produces four stego images after embedding. Thus, the aspect ratio of the combined image is the same as the original image \(O\). The different aspect ratio of the combined image for different levels of embedding is listed in Table 1. The comparison result shows that triple embedding is suitable for image combination as it keeps the aspect ratio of the original image. For cover image \(G\) and \(B\), we perform the same embedding procedure to them, and then we have combined stego images \(C_{G}\) and \(C_{B}\). Finally, we merge the three channel images \(C_{R}\), \(C_{G}\), and \(C_{B}\) and then the stego image \(C\) is generated. The proposed data embedding procedure is summarized in Algorithm 1. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Embedding Levels** & **Number of Result Images** & **Aspect Ratio** \\ \hline Zero & 1 & \(N_{1}\):\(N_{2}\) \\ Once & 2 & \(2N_{1}\):\(N_{2}\) \\ Twice & 3 & \(3N_{1}\):\(N_{2}\) \\ Triple & 4 & \(N_{1}\):\(N_{2}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Aspect ratios resulting from different embedding levels. Figure 3: The demonstration of image combination.
# Improved RSSI Indoor Localization in IoT Systems with Machine Learning Algorithms
## 5 Results and Discussion
### Accuracy with Number of Vectors  
Figure 9 shows the relationship between the accuracy, number of samples, and vectors. When the number of samples increases, accuracy increases irrespective of the number of vectors. When the number of vectors increases, accuracy sees a significant reduction. We considered the linear kernel SVM in this experiment. Additionally, improving the classifier's performance did not always result in the addition of more support vectors. The trade-off between accuracy and the number of support vectors that we previously considered true did not always hold in this specific situation. If there were already more than a particular number of support vectors--in this case, about 110--then adding another support vector resulted in declining marginal returns.
# Detection of Induced GNSS Spoofing Using S-Curve-Bias
## 1 Introduction
These techniques, originally designed for multipath detection [32], were recently found to be useful to identify the deformation on the correlation function due to an intermediate spoofing attack. They generally have simple structures with low complexity, showing good feasibility. As in [33; 34; 35; 36], the ratio test metric is used as a measurement compared with predefined thresholds to judge the correlation distortion during the spoofing signals hauling process. However, when the signal's intervals are large, the two signals are not overlapping, and need to wait until distortion occurs. This means that the method is simple but has a large detection time. In the field of GNSS signal quality evaluation, there are a series of evaluation parameters for judging whether the signal quality is abnormal. Signal correlation domain analysis indexes have a correlation curve, correlation loss, S-curve-bias (SCB), etc. [37]. SCB is an index that can be used to describe the correlation distortion. Specially, SCB is used to describe the deviation between the highest peak and the symmetric point of the correlation curve. The code loop discriminator curve usually locks in the place where the phase is biased, causing SCB. The bias generally results from the influence of the channel transmission distortion and the nonlinear effects of power amplifiers or multipaths. The correlation peak of an induced spoofing attack is similar to such an abnormal signal. Thus SCB has the potential ability to detect spoofing. In this paper, by utilizing the dynamic feature of gradual adjustment process, an induced spoofing detection method is proposed based on SCB. The main work of this paper is briefly described as follows. In order to obtain the changing process of SCB, it is derived theoretically for induced spoofing. The theoretical results reflect the whole changing process of induced spoofing. It should be noted that multipath signals can also result in the SCB changing. However, the changing process of multipath is not generally similar with that of induced spoofing. From this point of view, the proposed algorithm is not sensitive to multipath signals in most cases. In addition, front-end filter of receiver may lead to significant SCB variations. However, since only dynamic changes in the SCB are relevant for our method, the the proposed algorithm is also robust to SCB due to the front-end filter. Then a metric is defined to detect induced spoofing. The performance of the proposed algorithm is evaluated with the Texas spoofing test battery (TEXBAT) [38], which has been used to evaluate performance of spoofing detection methods in many papers. In addition, the proposed algorithm is compared with another algorithm based on the ratio test metric, which utilizes the track loop's correlation distortion. The paper is organized as follows. The signal model is presented in Section 2. The proposed algorithm is derived in Section 3. Section 4 provides experimental results with the TEXBAT data sets. Conclusions are drawn in Section 5.