esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Processes A and B Interdependence  
To show that the two processes are not independent, let us study the relationship between \(P_{S,B}(D)\) and \(P_{S,A}(D)\), at equal \(D\), i.e., the function \(P_{S,B}(D)=f(P_{S,A}(D))\). Figure 9a shows this relationship for Spino d'Adda. Each curved black line refers to a threshold, from 0 to 40 dB (41 curves). Similar curves are found for all the other sites (Appendix C). Notice that the \(D\) scale is reversed in both \(x\)- and \(y\)- axes shown in Figure 9; in other words, \(D\) decreases as \(P_{S,B}(D)\) and \(P_{S,A}(D)\) increase.  
These curves give the following information. Since the largest values of \(D\) exceeded give \(P_{S,B}(D)\approx P_{S,A}(D)\approx 0\), we can see that a large increase in \(P_{S,A}(D)\) does not correspond to a similar increase in \(P_{S,B}(D)\). In other words, few samples of the longest fades give a significant fraction of time but not a significant number of occurrences. For example, if \(P_{S,A}(D)\approx 0.4\) then \(P_{S,B}(D)\approx 0.1\).  
The approximate exponential nature of the relationships shown in Figure 9a can be modelled by the following equation, with \(x=P_{S,A}(D)\), \(y=P_{S,B}(D)\):  
\[y(x)=\mathrm{e}^{\beta x}(1-e^{-x})^{\alpha}/k \tag{3}\]  
Figure 9: \(P_{S,B}(D)\) (\(y\)-axis) versus \(P_{S,A}(D)\)\(x\)-axis, for thresholds from 0 to 40 dB, 80 GHz. The continuous red line is drawn from Equation (3); the continuous cyan line is drawn from Equation (5). (**a**) Spino d’Adda; (**b**) All sites.  
\[k=\mathrm{e}^{\beta}(1-e^{-1})^{\alpha} \tag{4}\]  
In Equations (3) and (4), \(\alpha=3.5385\), \(\beta=0.2958\) and the corresponding curve is the red line drawn in Figure 9. These numerical values have been determined by averaging the log values of \(\alpha\) and \(\beta\) of Equation (3) obtained by a best-fit analysis of each of the 41 curves. Notice that Equation (3) tends to represent an average value at small \(x\) and an upper bound at large \(x\).  
The cyan line drawn in Figure 9 is the hyperbolic cosine minus 1:  
\[y_{C}=\cosh(x)-1 \tag{5}\]  
Equation (5) practically gives a lower bound for all sites (Appendix C), quite precise in the range \(x=0\)\(\sim\)0.7. For \(x=0.7\)\(y_{C}=0.2552\).  
Figure 9b shows the curves of all sites and thresholds. We can see that Equation (3), with the values of \(\alpha\) and \(\beta\) found for Spino d'Adda, is a good estimate of the average value. In conclusion, Equations (3) and (4) describe useful overall relationships that link \(P_{S,B}(D)\) to \(P_{S,A}(D)\).  
The 45\({}^{\circ}\) line drawn in Figure 9 represents the curve linking \(P_{S,B}(D)\) and \(P_{S,A}(D)\) in the unrealistic case in which all fade durations were equal, namely when the two processes coincide. It is, however, a good reference line, as we show now by defining a uniformity index \(U\) in the next section.
# Integrated Photonics for IoT, RoF, and Distributed Fog-Cloud Computing: A Comprehensive Review
## 4 Photonic Implementations in Fog Computing Domains
### Transportation and Autonomous Systems  
Autonomous vehicles, such as smart cars, generate terabytes of data daily from LIDAR, GPS, and cameras. Relying solely on Cloud computing is insufficient for processing this data in real time. Fog computing provides the necessary local processing, storage, and decision-making capabilities at the network edge, enabling faster response times, which are critical for autonomous transportation [6; 22; 23]. These architecture requirements extend beyond cars to include ships, trains, trucks, buses, and drones. Integrated photonics provides compact, high-bandwidth interconnects between sensors, control units, and Fog nodes, facilitating real-time analytics.  
Example: Silicon photonic interconnects are being tested in vehicular gateways for the low-latency fusion of multi-sensor data. PIC-enabled RoF links support V2X communication [10].
## 3 Results and Discussion  
### Determination of the Model Parameters  
#### 3.1.1 Langmuir Isotherm  
For the simulation model, the most important parameters are isotherm and kinetic data of the biogas component, which are assumed to include only CH\({}_{4}\) and CO\({}_{2}\) in this study. The separation of mixed gas occurs due to the differences in selectivity of the adsorbent at the same pressure. Moreover, the difference in adsorption equilibrium and kinetics also affects the selectivity of mixed gas [21]. We used the Langmuir isotherm model to determine the adsorption equilibrium, expressed via Equation (2). The least square method in MATLAB(r) was used to determine the isotherm parameters (IP\({}_{i}\)) of CH\({}_{4}\) and CO\({}_{2}\) by minimizing the curve between experimental data from [PERSON] et al. [8] and the Langmuir isotherm equation.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter Name** & **Value** & **Unit** \\ \hline Adsorbent bulk solid density & 715 & kg/m\({}^{3}\) \\ Adsorbent particle radius & \(9\times 10^{-4}\) & m \\ Porosity & 0.33 & - \\ \hline \hline \end{tabular}
\end{table}
Table 2: Physical properties of CMS-3K.  
The function of the regression, which makes the solution closely match the experimental data, is expressed as follows [22]:  
\[\textit{MIN}\sum_{i}\left[F(x,x_{model,i})-y_{exp,i}\right]^{2}\text{ where }x=\left[ IP_{1},\ldots,IP_{4}\right] \tag{4}\]  
The adsorption equilibrium curves of CH\({}_{4}\) and CO\({}_{2}\) obtained by the Langmuir isotherm equation of CH\({}_{4}\) and CO\({}_{2}\) and fitted with experimental data are shown in Figure 3.  
The regressed curves lead to a slight underestimation of the CO\({}_{2}\) adsorption balance at pressures above 2.5 bar. The Langmuir isotherm parameters obtained for CH\({}_{4}\) and CO\({}_{2}\), corresponding to Type I (among the five types) as identified by [PERSON] [23], are summarized in Table 3. It should be noted that these values, to which the experimental data are available, have no physical meaning outside the temperature range (298-323 K).
# Temperature Distribution Characteristics of Concrete during Fire Occurrence in a Tunnel
## 2 Characteristics of Fire in a Tunnel
### Temperature Distribution Characteristics Due to Vehicle Fire in a Tunnel  
According to a report published by the Permanent International Association of Road Congresses on fire and smoke control in road tunnels [9], in the event of a fire in a tunnel, when air flows in through the tunnel entrance at 6 m/s, the temperature of the ceiling reaches approximately 400 \({}^{\circ}\)C from the spot of the fire to a point approximately 100 m from the tunnel exit [9]. According to Dutch regulations, when a fire occurs in a large tank lorry with a loading capacity of 50 m\({}^{3}\) or higher, the temperature rises to approximately 1400 \({}^{\circ}\)C.
###### Abstract  
Frame-wise steganalysis is a crucial task in low-bit-rate speech streams that can achieve active defense. However, there is no common theory on how to extract steganalysis features for frame-wise steganalysis. Moreover, existing frame-wise steganalysis methods cannot extract fine-grained steganalysis features. Therefore, in this paper, we propose a frame-wise steganalysis method based on mask-gating attention and bilinear codeword feature interaction mechanisms. First, this paper utilizes the mask-gating attention mechanism to dynamically learn the importance of the codewords. Second, the bilinear codeword feature interaction mechanism is used to capture an informative second-order codeword feature interaction pattern in a fine-grained way. Finally, multiple fully connected layers with a residual structure are utilized to capture higher-order codeword interaction features while preserving lower-order interaction features. The experimental results show that the performance of our method is better than that of the state-of-the-art frame-wise steganalysis method on large steganography datasets. The detection accuracy of our method is 74.46% on 1000K testing samples, whereas the detection accuracy of the state-of-the-art method is 72.32%.  
steganalysis; steganography; frame-wise; bilinear interaction; mask-gating attention
## 6 Conclusions
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline
**Analytical Point** & **S** & **Zn** & **Cu** & **Ni** & **Co** & **Fe** & **Total** & **Analytical Point** & **S** & **Zn** & **Cu** & **Ni** & **Co** & **Fe** & **Total** \\ \hline
10/1 & 34.38 & 0.07 & 34.75 & 0.08 & 0.07 & 30.6 & 99.95 & 5/1 & 34.31 & 0.02 & 34.6 & 0.14 & 0.19 & 30.46 & 99.72 \\
11/1 & 34.58 & 0.02 & 35.11 & 0.1 & 0.12 & 30.31 & 100.24 & 1/1 & 34.19 & 0.13 & 35.37 & 0.05 & 0.07 & 30.46 & 100.27 \\
2/1 & 34.28 & 0.02 & 34.37 & 0.25 & 0.17 & 30.68 & 99.77 & 2/1 & 34.22 & 0.01 & 35.12 & 0.04 & 0.15 & 30.27 & 99.81 \\
6/1 & 34.74 & 0.03 & 34.69 & 0.04 & 0.12 & 30.05 & 99.67 & 11/1 & 34.15 & 0.01 & 35.17 & 0.16 & 0.17 & 0.09 & 99.75 \\
7/1 & 34.54 & 0.06 & 34.05 & 0.39 & 0.55 & 30.05 & 99.64 & 3/1 & 34.27 & 0.12 & 34.64 & 0.33 & 0.14 & 30.71 & 100.21 \\
7/1 & 34.07 & 0.05 & 34.23 & 0.02 & 0.03 & 30.65 & 99.05 & 4/1 & 34.22 & 0.02 & 34.47 & 0.14 & 0.08 & 30.56 & 99.89 \\
8/1 & 34.15 & 0.1 & 34.37 & 0.02 & 0.05 & 30.5 & 99.19 & 1/1 & 34.2 & 0.05 & 34.61 & 0.42 & 0.16 & 30.44 & 99.88 \\
9/1 & 34.56 & 0.01 & 34.97 & 0.04 & 0.08 & 30.36 & 100.02 & 2/1 & 34.05 & 0.1 & 34.59 & 0.56 & 0.26 & 30.07 & 99.63 \\
1/1 & 33.87 & 0.13 & 34.38 & 0.07 & 0.06 & 31.35 & 99.86 & 4/1 & 33.99 & 0.055 & 34.21 & 0.06 & 30.47 & 0.04 & 30.87 \\
4/1 & 34.03 & 0.03 & 34.94 & 0.19 & 0.03 & 30.47 & 99.69 & 6/1 & 34.265 & 0.031 & 34.15 & 0.047 & 0.047 & 30.76 & 99.35 \\
5/1 & 34.13 & 0.11 & 35.22 & 0.07 & 0.07 & 30.4 & 100 & 8/1 & 34.443 & 0.114 & 34.55 & 0.01 & 0.07 & 30.41 & 99.67 \\
4/1 & 33.97 & 0.06 & 34.83 & 0.18 & 0.07 & 30.4 & 99.51 & 9/1 & 34.692 & 0.011 & 34.194 & 0.01 & 0.117 & 30.724 & 99.748 \\
5/1 & 34.01 & b.d.l. & 34.7 & 0.1 & 0.09 & 30.64 & 99.54 & 4/1 & 34.482 & 0.14 & 34.015 & 0.075 & 0.063 & 30.87 & 98.56 \\
6/1 & 34.17 & 0.13 & 34.63 & 0.08 & 0.05 & 30.22 & 99.28 & 3/1 & 34.523 & 0.125 & 34.495 & 0.049 & 0.065 & 30.407 & 99.664 \\
1/1 & 34.34 & b.d.l. & 34.03 & 0.05 & 31.18 & 99.94 & 3/1 & 34.698 & 0.104 & 34.425 & 0.018 & 0.058 & 30.417 & 99.947 \\
2/1 & 34.39 & 0.1 & 34.14 & 0.04 & 0.05 & 30.51 & 99.23 & 3/1 & 34.871 & b.d.l. & 34.03 & 0.042 & 30.755 & 99.938 \\
1/1 & 34.59 & 0.15 & 34.12 & 0.17 & 0.16 & 30.35 & 99.54 & 7/1 & 34.451 & b.d.l. & 33.651 & 0.074 & 0.053 & 30.864 & 99.903 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Chemical composition of cubanite from the Krzeminaka and Udryn Fe-T-V deposits in NE Folund identified via EMPA.
## 2 Materials and Methods
### Detection of Reducing Sugar by Dinitrosalicylic Acid Method (DNS)  
The concentration of reducing sugar in levan polymer was estimated quantitatively by 3-5 dinitrosalicylic acid (DNS) as follows, according to [10]: 0.1 g of levan powder was dissolved for 1 h by 0.1 N HCl at 100 \({}^{\circ}\)C. After levan hydrolysis, 1 mL of freshly prepared DNS was added to two test tubes; one contained 1 mL of hydrolyzed levan sample and the second contained 1 mL of distilled H\({}_{2}\)O as a blank. The tubes were put in a water bath container for 10 min, and then it was permitted to cool at ambient temperature. To obtain an appropriate dilution from the sample, 10 mL of distilled H\({}_{2}\)O were added to individual tubes, and the absorbance was measured spectrophotometrically at 540 nm. Using the standard glucose curve, the unknown concentration of the reducing sugar in the sample was obtained. A glucose standard solution with a concentration range of 100-600 mg mL\({}^{-1}\) was made in 0.05 M acetate buffer (pH 4.8).
## 2 Study Area, Dataset, and Methodology
### Meteorological and Human Activity Data Sources and Preprocessing  
To comprehensively analyze the driving factors of vegetation change, this study integrates temperature, precipitation, and nighttime light data, aligning these with NDVI data by resampling all datasets to a common spatial resolution and temporal scale. Specifically, NDVI data were derived from MODIS at a 1 km spatial resolution and aggregated monthly, while temperature and precipitation data from the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis v5 (ERA5-Land) dataset were interpolated to match the same spatial grid and time intervals using bilinear resampling. Nighttime light data, which included both the Defense Meteorological Satellite Program-Operational Line-scan System (DMSP-OLS) and the National Polar-orbiting Operational Environmental Satellite System Preparatory Project/Visible Infrared Imaging Radiometer Suite (NPP/VIIRS), were calibrated for intersensor consistency and subsequently aligned temporally through annual averaging. These steps ensure that all datasets are temporally synchronized and spatially  
Figure 1: Comprehensive map of the study area: (**a**) geographical location of the study area (the area indicated by the blue line), (**b**) temperature map for 2020, and (**c**) precipitation map for 2020.  
consistent, thereby minimizing biases and enhancing the reliability of correlation and trend analyses.
# Video Captioning Based on Channel Soft Attention and Semantic Reconstruct
## 1 Introduction
As mentioned above, a video captioning model based on channel soft attention and semantic reconstructor (CSA-SR) is proposed. It aims to fully use the feature maps of videos and the semantic information of videos. Our code is available at [[https://github.com/YiyongHuang/CSA-SR](https://github.com/YiyongHuang/CSA-SR)]([https://github.com/[PERSON]/CSA-SR](https://github.com/[PERSON]/CSA-SR)) accessed on 14 January 2021. The main contributions of this paper are summarized as follows:  
* A deep learning framework for video captioning is proposed, which optimizes the Sequential VLAD encoding by channel soft attention and leverages the semantic information by a semantic reconstructor. * An effective attention module is implemented, which exploits the temporal structure for each channel, where the \(c\)-th channel of input feature maps at \(t\)-th time step is the weighted sum of \(c\)-th channel of all feature maps. * A semantic reconstructor network is proposed which is composed of LSTM to assist the decoder to fully use the semantic information. * Experiment results on Microsoft Video Description (MSVD) dataset [12] and MSRVideo To Text (MSR-VTT) dataset [13] demonstrate the feasibility and effectiveness of the proposed method.
## 4 Experiments and Results
### Robustness Analysis
#### 4.3.4 Robustness of Element Attacks  
In the actual application operation of BIM data, the deletion and addition of elements are common types of attacks that BIM data may face. This paper establishes a mapping relationship between each element's coordinate value and the algorithm's watermark bit, allowing each element's watermark embedding process to be independent. Therefore, deleting or adding certain elements does not significantly affect the detection of watermark information of other watermark carriers. Moreover, the algorithm employs a voting mechanism to extract watermark information, enabling it to tolerate minor changes in figure elements that do not impact the final watermark sequence.  
However, BIM data may automatically update, leading to changes in the spatial location of adjacent elements due to the deletion or addition of elements, which can result in incorrect watermark extraction. To evaluate the algorithm's resistance to element deletion attacks, we conducted experiments with a 10% element deletion and addition interval and generated the corresponding BIM models shown in Figure 9. Table 7 and Figure 10 present the experimental results for different pixel deletion rates.  
Figure 8: Results of translation attacks.  
Figure 9: Partial model after element attack. (**a**) Deletion ratio: 40%. (**b**) Deletion ratio: 60%. (**c**) Addition ratio: 60%.  
Our analysis of Table 7 indicates that deleting and adding elements within a 10% range had no effect on extracting the watermark information from the three models. The watermark extraction of the office building remained unaffected by deletion attacks with 20% and 30% ratios, while the watermark information of the gymnasium and structure model displayed a small amount of noise but could still represent the copyright information completely. As the office building model had significantly more elements, even when subjected to 40% and 50% element deletion, the watermark information could still be extracted entirely. However, the gymnasium model and the structure model had fewer total elements, resulting in a lower NC value and slightly noisier watermark information compared to the office building. When 60% of the elements were deleted, the NC values of the three BIM models were higher than 0.90, except for the structure model under an attack of element addition. The overall analysis shows that the quality of the extracted watermark information from the BIM data decreased gradually with the increasing intensity of the element deletion and addition. However, the NC value of the watermark was higher than 0.89, significantly surpassing the threshold value of 0.75 required to display watermark information adequately. Therefore, this paper's algorithm exhibited strong robustness against element attacks, meeting the daily use needs.
## 4 Discussion
### Effect of Heat Treatment on Tuning the Stiffness and Damping Capacity of NiTi WMD
#### 4.3.2 The Mechanism behind the Tunable Resonant Frequency and Loss Factor  
The adjustable stiffness and damping capacity are contributed by the phase transformation damping of the NiTi alloy, which corresponds to the term \(\zeta\) (\(\Gamma\), \(\tau\), t) in the first-order and second-order SDOF systems.  
As for the NiTi wire itself, the stiffness is governed by the martensite start stress, which can be altered by the heat treatment temperature. Although the published results [23; 24] on how heat treatment affects transformation temperatures contradict with each other, it has been generally accepted that the plateau stress of pseudoelasticity decreases with increasing heat treatment temperatures [15]. Moreover, the rearrangement of cold-work dislocations and grain growth during the heat treatment also results in the softening effect of the NiTi wire, which may result in a lower resonant frequency and a higher loss factor.  
However, the conclusions above do not hold true for the NiTi WMDs in the present work because the coiled wires within WMD are heat-treated with restraint. Coiled wires are interlocked with each other due to Coulomb friction. In addition, fixtures also provide extra restraint for the WMDs during the heat treatment. Once the NiTi WMD is heated above A\({}_{\rm s}\), mechanical resistance generated from the intertwined wires and the fixtures prevents the WMD from recovering to its un-formed state. This process causes severer residual stress within the WMD after the heat treatment, which gives rise to stiffened WMD with a higher resonant frequency and a lower loss factor.  
Therefore, the tunable frequency and damping capacity of the heat-treated NiTi WMD is a combinatorial effect of the above two mechanisms, which governs the nonlinear term, \(\zeta(T,\tau,t)\). Although a comparison has been made between SS 304 and SE NiTi WMDs, the effect of phase transformation damping cannot be explicitly separated from the dynamic mechanical behavior of the WMD due to the limited quantity of tested samples. A phenomenological model describing the \(\zeta(T,\tau,t)\) will be numerically implemented in future work so that the dynamic mechanical behavior and vibration isolation performance of the heat-treated SE NiTi WMD can be conveniently predicted.
###### Abstract
Using a standard RGB camera to record facial video, extract the signal of the key area of the face and the background, and use the method of joint blind source separation (JBSS) [13; 14] to remove the common light component in the signal of the key area of the face and the background, and obtain the new face ROI signal, using ICA to extract the pulse signal from ROI signal, using the green channel signal to identify the pulse signal, using the FFT to calculate HR, this method removes illumination changes in order to enhance pulse information extraction and it also solves the problem of disordered output components after ICA, effectively reducing the impact of low-intensity ambient illumination and illumination changes [7; 15]. Recently, a mathematical model for rPPG measurement which was based on the optical and physiological considerations and the assumption of a single light source with a constant spectrum has been proposed [16]. The analysis in [16] shows that combining the models, e.g., plane-orthogonal-to-skin (POS) and chrominance-bass (CHROM) rPPG with different assumptions enables the construction of various algorithms to extract the pulse signal from a video. The above-mentioned rPPG methods and ICA-based BSS method takes a long time to converge, POS and CHROM need complex model operations, and focus on the analysis of signal-to-noise ratio without discussing the response time and accuracy of heart rate and HRV measurement. Avoiding the above-mentioned disadvantages, this paper provided a non-constant estimation method of HR and HRV with low-cost, fast response time, and better physiological parameters, using RSME and MAPE based on rPPG and ROI. A hybrid ICA and PSO algorithm is employed to speed up the convergence. Therefore, signal processing methods, i.e., waveform processing, BPF, detrended fluctuation analysis (DFA), and FFT, are used to eliminate noise signal. Finally, the single green light is obtained for estimating the values of HR and HRV. The rest of this paper is organized as follows. In the subsequent sections, Section 2 illustrates methodology and system description. Experimental results are obtained and described in Section 3. Section 4 discusses the findings of this study. Finally, conclusion is given in the last section.
## 3 Theory
### Gradient Boosting  
Gradient Boosting is a powerful ensemble learning technique that constructs a predictive model in a stage-wise fashion by sequentially optimizing the residuals of the previous models [33]. It is renowned for its ability to handle complex datasets and produce high-accuracy predictions across various domains. Unlike Random Forest, which builds independent trees in parallel, Gradient Boosting builds trees sequentially, with each subsequent tree aiming to correct the errors made by the previous ones. The prediction of a Gradient Boosting model can be expressed as follows [34]:  
\[y_{i,\ predicted}=\sum_{t=1}^{T}\gamma_{t}h_{t}(x_{i}) \tag{18}\]  
where \(y_{i,\ predicted}\) represents the predicted value for the \(i\)-th data point, \(T\) is the total number of trees in the ensemble, \(\gamma_{t}\) denotes the learning rate or shrinkage parameter associated with the \(t\)-th tree, and \(h_{t}(x_{i})\) represents the prediction of the \(t\)-th tree for the \(i\)-th data point. At each iteration, Gradient Boosting fits a new decision tree to the negative gradient of the loss function with respect to the current model's predictions. This process effectively minimizes the residual errors of the previous model, leading to a gradual improvementin prediction accuracy. The final prediction is obtained by summing the predictions of all individual trees, weighted by the corresponding learning rates [35].  
Gradient Boosting offers several advantages over other machine learning algorithms. It is capable of capturing intricate patterns and non-linear relationships within the data, making it suitable for complex regression tasks. Moreover, by iteratively minimizing the errors of preceding models, Gradient Boosting tends to produce highly accurate predictions, often outperforming other ensemble methods. However, Gradient Boosting is more sensitive to overfitting compared to Random Forest, necessitating careful tuning of hyperparameters such as the learning rate, tree depth, and regularization parameters. Additionally, the sequential nature of Gradient Boosting makes it less scalable for large datasets and computationally intensive tasks.