esa-sceva/satcom-chunk-collection huggingface dataset:
# Prediction and Comparison of In-Vehicle CO\({}_{2}\) Concentration Based on ARIMA and LSTM Models
## 3 Results
### Fitting Models and Prediction Result Analysis  
#### 3.2.1 ARIMA Model  
Using the ADF test to all datasets revealed a \(p\)-value exceeding 0.05, meaning that all datasets are non-stationary, meaning that the data required differencing. The ADF test is applied again after all data are first differenced, and the test results are shown in Table 7. The \(p\)-value is lower than the significance level of 0.05, and the t statistic is smaller than 1%, 5%, and 10%. The null hypothesis of non-stationarity in the time series should be rejected. It is indicated that the data after difference have been stabilized, and the mean and variance do not change with time, which meets the prerequisites for forecasting using the ARIMA model. Therefore, we set the order of difference d = 1 for all the datasets.  
Table 8 displays the AIC and BIC values corresponding to the optimal model hyperparameter combinations and the significance \(p\)-values after the Ljung-Box test. The \(p\)-values of the six models are greater than 0.05, indicating that the prediction residuals of the models conform to a sequence of random numbers and support the assumption that they are white noise.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Datasets**} & \multirow{2}{*}{
\begin{tabular}{c} **The Order of** \\ **Diffrence** \\ \end{tabular} } & \multirow{2}{*}{\(\mathbf{t}\)} & \multirow{2}{*}{\(\mathbf{p}\)**-Value**} & \multicolumn{3}{c}{**Critical Value**} \\ \cline{4-6}  & & & & **1\%** & **5\%** & **10\%** \\ \hline T1 & \(1\) & \(-3.717\) & 0.004 *** & \(-3.459\) & \(-2.874\) & \(-2.573\) \\ T2 & \(1\) & \(-6.516\) & 0.000 *** & \(-3.459\) & \(-2.874\) & \(-2.573\) \\ T3 & \(1\) & \(-15.166\) & 0.000 Figure 7 shows the Q-Q plots of predicted residuals for six optimal models. The \(x\)-axis in the Q-Q graph represents the quantile of the theoretical distribution, the \(y\)-axis represents the quantile of the model prediction residual, the red straight line is the normal distribution line, and the solid blue dot is the model prediction residual value. The closer the solid blue dot is to the red straight line, the more it conforms to the standard normal distribution. It can be seen from the figure that most of the solid blue dots are located on the red straight line, indicating that the predicted residual values of the best model obey the normal distribution. To conclude, the residuals satisfy the requirements of a white noise sequence and conform to a normal distribution. Therefore, the model is suitable for prediction.
## 2 Materials and Methods
### Materials
#### 2.1.2 Meteorological Data  
* NCEP-DOE Reanalysis II Data  
The MODIS GPP Collection 5.5 (MOD17A2, C5.5) data used in this paper implement National Center for Environmental Prediction-Department of Energy (NCEP-DOE) Reanalysis II data for direct meteorological inputs. For each flux tower site, six-hour data composed of air temperature (\(\mathrm{T_{air}}\), \({}^{\circ}\)C), downward shortwave radiation (SWRad, \(\mathrm{W\cdot m^{-2}}\)), surface pressure (Pres, Pa), and specific humidity (SH, kg\(\cdot\) kg\({}^{-1}\)) for 2003-2005 were downloaded from the Earth System Research Laboratory (ESRL) [38]. From these data, we obtained the daily average air temperature (\(\mathrm{T_{avg}}\), \({}^{\circ}\)C), daily minimum air temperature (\(\mathrm{T_{min}}\), \({}^{\circ}\)C), daily average VPD (\(\mathrm{VPD_{avg}}\), Pa), and daily total SWRad (\(\mathrm{MJ\cdot m^{-2}\cdot d^{-1}}\)) at a 1.9\({}^{\circ}\)\(\times\) 1.9\({}^{\circ}\) resolution. To interpolate the 1.9\({}^{\circ}\)\(\times\) 1.9\({}^{\circ}\) resolution NCEP-DOE data down to the 1-km MODIS pixel resolution, the daily data for each pixel were interpolated using a spatial nonlinear interpolation scheme following [PERSON] _et al._ (2005) [11].  
* Site Meteorological Data  
Site meteorological data (PAR, air temperature and VPD) during 2003-2005 were obtained directly from the flux towers or were derived from meteorological station data provided by the China Meteorological Administration (CMA) [39]. Based on the half-hourly flux tower measurements, which are composed of air temperature (T\({}_{\rm air}\), \({}^{\circ}\)C) and either PAR (\(\mu\)mol\(\cdot\) m\({}^{-2}\). s\({}^{-1}\)) or downward shortwave radiation (SWRad, W\(\cdot\) m\({}^{-2}\)), we obtained the daily T\({}_{\rm avg}\), T\({}_{\rm min}\) and PAR. In addition, the daily average e\({}_{\rm a}\) (_i.e._, the actual air vapor pressure, Pa) and daily average RH (_i.e._, relative humidity, %) were selected from the meteorological station datasets to calculate VPDavg [40]. The site meteorological data (T\({}_{\rm avg}\), T\({}_{\rm min}\), PAR and VPDavg) were then directly compared to the NCEP-DOE Reanalysis II data to evaluate how well the reanalysis datasets represented the local climatic conditions.
# A Survey of Detection and Mitigation for Fake Images on Social Media Platforms
## 4 Fake Image Detection Methods--Using Handcrafted Feature Set
### Forensic Features Based
#### 4.1.4 JPEG Compression
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**S. No**} & \multirow{2}{*}{**Author**} & **Detection Techniques** & **Dataset** & **Results** & **Pros** & **Cons** \\ \hline \multirow{2}{*}{1} & \multirow{2}{*}{[PERSON] et al. [44]} & Run-length run number & CASIA 1.0 & Accuracy 94.7\% & Works well in color and grayscale images & Not verified for any pre/post-processing over images \\  & & number & Columbia & 85\% & & Gives lower accuracy over real-world images, more realistic images \\ \hline \multirow{2}{*}{2} & \multirow{2}{*}{[PERSON] et al. [47]} & Markov features in the DWT and DCT domain & CASIA 1.0 & Accuracy 89.76\% & Works well for color and grayscale. & Gives lower accuracy over real-world images, more realistic images \\ \hline \multirow{2}{*}{3} & \multirow{2}{*}{[PERSON] and [PERSON] [48]} & Illumination-signature & DSO-1 & AUC & Good performance for images & Fails on images having sharp contrast skin-tones \\  & & using DRM & DSI-1 & 91.2\% & with faces & Not verified for color images. Not verified \\ \hline \multirow{2}{*}{4} & \multirow{2}{*}{[PERSON] et al. [49]} & \multirow{2}{*}{SVD + DCT + PCA} & \multirow{2}{*}{Columbia & Accuracy 80.79\% (No PCA) & Have excellent performance over grayscale images & Not verified with \\  & & & & 98.78\% (PCA) & & Pre/Post-processing \\ \hline \multirow{2}{*}{5} & \multirow{2}{*}{[PERSON] et al. [50]} & Markov features of DOCT domain & CASIA 1.0 & Accuracy 98.77\% & Can handle Gaussian blur and white Gaussian noise & Fails over small-size images \\  & & & CASIA 2.0 & 97.59\% & noise & \\ \hline \multirow{2}{*}{6} & \multirow{2}{*}{[PERSON] and [PERSON] [51]} & Machine learning—logistic regression & CASIA 1.0 & Accuracy 98.3\% & Can handle pre/post-processing & Fails when images are highly down-sampled \\  & & & CASIA 2.0 & 99.5\% & alterations & \\ \hline \multirow{2}{*}{7} & \multirow{2}{*}{[PERSON] and [PERSON] [54]} & Illumination reflectance and LBP & \multirow{2}{*}{CASIA 2.0} & Accuracy 94.59\% & Can handle down-sampling and resizing & Fails over small size images and blurred background \\ \hline \multirow{2}{*}{8} & \multirow{2}{*}{[PERSON] et al. [56]} & RFFT image frequency transform & \multirow{2}{*}{Columbia & Average Precision 0.918 & Can handle down-sampling and resizing & Fails over small-size images \\  & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of techniques used in splicing detection using forensic features. Quantization (DQ) effect, which is latent in the DCT coefficients. The technique uses SVM as a classifier. However, the method fails when the original image is not JPEG and if some other tampering has been made, like resampling or splicing. [PERSON] et al. [66] used a neural network with DCT for JPEG detection.
## 2 Description of the Method through a 1D Example  
Let us consider the 1D transient thermal problem depicted in Figure 1.  
The inverse problem can then now defined as: knowing the temperature evolution \(\theta(t)\) at \(z=0\), the question is to determine the position of the source(s) in the thickness, i.e., the vector \(\Omega\).  
In the paper, the following assumptions are made: (i) The source intensity is considered known; (ii) any geometry or shape of sources can be approximated by a sum of variable intensity source points; (iii) the material is considered orthotropic.  
In [11; 12], the authors tested different deterministic inversion methods, highlighting some crucial tricks:  
* For a regular thickness discretization, the number of reconstructable sources is directly linked to the Fourier coefficient \(Fo_{2}\). Indeed, an analysis of the Singular Value Decomposition (SVD) of the matrix \(\mathbf{A}\) demonstrates that the number of significant modes is limited and linked to the Fourier coefficient. Then, one could not reconstruct more sources than this number.
* It has also been shown that this limitation is linked to the shape of the temperature evolution at the surface depending on the depth of the source. For the deepest sources, their effect on the  
Figure 1: 1D direct problem.  
thermal evolution on the surface is very similar, and then, the inversion method sensitivity is not good enough to discriminate between them. The reconstruction is then trickier.
* The reconstruction is also very sensitive to the noise that appears in the temperature measurement at the surface. A Signal to Noise Ratio (SNR) of approximately 50 is sufficient to prevent the inversion method from reconstructing even the closest source to the surface.  
Then, to overcome the problem of noise sensitivity, the use of Bayesian inference to reconstruct the source(s) in the thickness is proposed.
## 3 Results and Discussion
### Quantification of Surfactant Retention by Nanoparticles  
Table 3 summarizes the experimental results obtained for the quantification of OADA retained in the nanoparticles and shows the effect of the sulfonate group content and the OADA concentration used in the polymerization medium on the retained surfactant percentage (Retention%) and on the surfactant content in the nanoparticles (\(Q_{\text{e}}\)).  
Figure 14: Influence of sulfonate groups on nanoparticles stabilization.  
Surprisingly, the use of greater amounts of OADA in the polymerization process did not result in an increase on the retained surfactant amount (\(Q_{\text{e}}\)) as could be expected when comparing with the results of the adsorption process as described in the literature [17; 32; 34]. Since OADA presents a double role in the nanoparticles' formation, its consumption may occur during the formation of a greater number of micelles and nuclei, producing smaller nanoparticles, as observed in Figure 9, and consequently resting less OADA molecules available to be immobilized or encapsulated in the nanocarriers. However, the larger amounts of surfactant retained were obtained by the 0 sulf20 and 12 sulf20 samples. This result corroborates the observations conducted through FTIR and IFT analysis. Besides, it is concluded that the surfactant content in the nanoparticles is determinant to obtain a low IFT system. The small nanoparticle size (preferably below 100 nm) and the presence of sulfonate groups are also relevant factors to produce an effective system.  
The results derived from the measurement of the surfactant retention capacity allowed to observe another relevant aspect that is related to the type of nucleation mechanism. Higher St-S amounts in the polymerization medium favor homogeneous particle nucleation [40], since anion-charged oligomers tend to be more soluble in water, resulting in more OADA molecules free in the medium, which are removed in the purification step. This would explain why the retained OADA in the 30 sulf20 SPSNP was the lowest observed.
# Research on Sentiment Classification of Online Travel Review Text
## 1 Introduction
* To address the problem of uneven sentiment distribution in online travel review texts, two types of sampling methods are compared and the most suitable online travel review text sampling method is identified. * This article introduces the online travel review text preprocessing method, Word2 vec-based text representation method, classification label acquisition method, and machine learning method-based sentiment classification method, thereby presenting the entire sentiment analysis process of online travel review texts. * TripAdvisor is a frequently used text source in sentiment analysis [47]; thus, by analyzing more than 20,000 review text datasets for four famous attractions in four countries derived from TripAdvisor, this paper validates the proposed method from a relatively extensive sample, which allows us to draw more reliable conclusions. Experimental results reveal that the method proposed in this paper is better suited to processing the sentiment classification of online travel review texts, and consequently provides a reference for related travel research. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Reference** & **Methods** & **Data** \\ \hline \multirow{3}{*}{[36]} & Word list-based methods, supervised learning methods & \multirow{3}{*}{TripAdvisor, Booking} \\  & (including k nearest neighbors, support vector machines & \\  & and naive Bayes) & \\  & Developed by the author & \\  & Leximancer & TripAdvisor \\  & Statistical Analysis & Expedia \\  & Deeply Moving, Pattern and SentiStrength & Survey data \\  & Developed by the author & TripAdvisor, Amazon \\  & AFINN & Airbnb \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The methods of sentiment analysis used in tourism research.
# A Design Method for Gammachirp Filterbank for Loudness Compensation in Hearing Aids
## 2 Design of Gammachirp Filterbank
Figure 2 provides a block diagram of the signal flow in the gammachirp filter. The implementation of the asymmetric filter in the gammachirp takes advantage of the method described in [31], and the IIR asymmetric compensation filter is defined as follows:  
\[H_{A}(f)\approx H_{\mathrm{C}}(z),z=e^{j2\pi f/f_{s}} \tag{6}\]  
\[H_{\mathrm{C}}(z)=\prod_{k=1}^{N}H_{\mathrm{C}k}(z) \tag{7}\]  
\[H_{\mathrm{C}k}(z)=\frac{(1-r_{k}e^{i\phi_{k}}z^{-1})(1-r_{k}e^{-j\phi_{k}}z^ {-1})}{(1-r_{k}e^{i\phi_{k}}z^{-1})(1-r_{k}e^{-j\phi_{k}}z^{-1})} \tag{8}\]  
The \(f_{s}\) is the sampling frequency, and \(N\) is the total number of responses in the cascade, which is usually taken as 4 [31]. The \(k\)-th pole and zero are defined with the absolute value \(r_{k}\):  
\[r_{k}=\exp(-p_{1}(p_{0}/p_{3})^{k-1}\cdot 2\pi bERB(f_{r})/f_{s}) \tag{9}\]  
and the phase of the pole \(\phi_{k}\), and zero \(\varphi_{k}\) is:  
\[\phi_{k}=\left\{ \begin{array}{cc}2\pi\{f_{r}+\Delta f_{r}\}/f_{s},f_{r}+ \Delta f_{r}\geq 0\\ 0,&\text{otherwise}\end{array} \right. \tag{10}\]  
Figure 2: Block diagram of the signal flow in the gammachirp filterbank. \[\varphi_{k}=\left\{ \begin{array}{l}2\pi\{f_{r}-\Delta f_{r}\}/f_{s},f_{r}-\Delta f _{r}\geq 0\\ 0,\hskip 28.452756 ptotherwise\end{array} \right. \tag{11}\]  
\[\Delta f_{r}=\left(p_{0}/p_{3}\right)^{k-1}\cdot p_{2}\cdot c\cdot bERB(f_{r}) \tag{12}\]  
where \(p_{0},p_{1},p_{2}\) and \(p_{3}\) are a set of positive coefficients related to the chirp factor. The final filterbank is composed of a series of gammachirp filters with various center frequencies, and the characteristic frequencies of the basilar membrane are usually selected as the center frequency of each subfilter [33; 34]. The center frequency of a filter in the filterbank can be calculated according to (13) [35]:  
\[\hat{f_{\rm i}}=\frac{1960\cdot i+1038.8}{26.28-i},i=1,2,3,\ldots \tag{13}\]  
where \(\hat{f_{\rm i}}\) represents the center frequency of the \(i\)th channel. Thus, a multichannel filterbank with an unequal width can be derived to match the sensitivity of human hearing.
# Binary and Multi-Class Malware Threads Classification
## 4 The Proposed Methods
The SFTA feature vector that was obtained has a \(1\times 21\) dimension. ```
Input: Visualized Malware Image. Output:\(1\times 21\) features vector dimension. 1. Open the malware image that was visualized. 2. Compute the SFTA using the Equation (1). 3. Twenty One features vector are produced. ```  
**Algorithm 2**Compute SFTA textures features. Algorithm 3 illustrates the application of a 2D Gabor filter to extract features. Equation (2) is used to obtain the mean squared energy and mean amplitude Gabor features. The feature vectors' dimensions are \(1\times 12\). ```
Input:\(1\times 12\) features vector dimension. 1. Open the malware image that was visualized. 2. Apply 2D Gabor filters to each image that has been converted. 3. Calculate the mean squared energy and mean amplitude Gabor features using the Equation (2). 4. Twelve features vector are produced. ```  
**Algorithm 3**Compute Gabor textures features. It is usually worthwhile to assess how good the chosen features are and how good the model is before we get started with the classification step. In general, features and models are regarded as being a decent representation when we are able to correctly categorize the malware families using the chosen features and classifiers. A review of the literature revealed that several studies using KNN, RF, NB, ELM, GDA, NN, and SVM showed improved accuracy findings. In this paper, we employed NB and GDA as useful methodologies for malware analysis. Step 3.1: **Naive Bayes (NB) Classifier**A probability-based classification technique called the Naive Bayes Algorithm counts the frequencies and permutations of values found in a dataset to create a set of likelihood. The top rated sample in the applicable class is included in Naive Bayes Classifier's system learning, which is based on test data [33]. Step 3.2: **Gaussian discriminant analysis (GDA) Classifier**  
A specific generative learning method called GDA [34] attempts to separately fit a Gaussian distribution to every class of data in order to produce the distribution of several classes [26].
# Unsupervised Learning Approach for Anomaly Detection in Industrial Control Systems
## 3 Proposed Method
### Data Preparation  
In this study, we used the HAI dataset to normalize and standardize the data in the initial stage. We also used PCA to select features and reduce dimensionality, and clustering techniques to identify groups of similar objects and identify groups of similar objects, in addition to basic preprocessing tasks. A clustering algorithm is a technique that groups similar data within a dataset based on their characteristics to form clusters. In this study, we removed the 'timestamp' information from the time series dataset before applying the clustering algorithm. To scale the data, we adjusted it to the range [0, 1] using the minimum and maximum values.
## 6 Ablation Experiments
### Analysis of Heatmaps from Ablation Experiments
The heatmap showed interruptions in the leakage representation at the joint bends, and false positive responses in the form of spot-like leakages appeared on the left side. (4) Heatmap Analysis of Image 4: Mixed-Type Leakage  
This case presents a mixed-type leakage scenario in the tunnel arch-waist region, characterized by irregular patch-shaped leakage with blurred boundaries and strong background textures from construction. The baseline SE-TransUNet model achieved an IoU of 0.95, and the leakage area was continuously highlighted in red and dark yellow in the heatmap, closely matching the actual leakage boundaries. After SE-Block components were removed, all variant models showed decreased IoU. In particular, removal of SE-Block3 led to a shrinkage in the high-confidence regions to light green, suggesting that  
Figure 11: The comparison results of Score-CAM heatmaps for the model after removing the SE module and ViT module. this module plays an important role in enhancing semantic features of area-type leakage through channel attention mechanisms, helping suppress background texture interference. Without SE-Block3, the model struggled to differentiate leakage from damp regions. The ViT-removed variant achieved an IoU of only 0.82, and its heatmap showed fragmented and dispersed yellow activations. In summary, SE-TransUNet exhibited favorable recognition performance across typical tunnel leakage scenarios, including area-type, linear, spot-type, and mixed-form leakages. Compared with ablation variants, its heatmaps provided more complete coverage of the leakage regions. The high-response areas (in red and yellow) showed greater alignment with the annotated leakage boundaries and exhibited stronger spatial continuity, allowing for a more accurate depiction of the spatial distribution characteristics of leakages in various forms.
## 3 Architecture
### Scheduling Workflow  
Taking the overall structure of IPCRSA shown in Figure 1, along with its various entities, as an example, this section describes the typical process of computing resource scheduling in IPCRSA, which is illustrated in Figure 5 below.  
Figure 4: Format of the computing resource request packet.  
Figure 5: Workflow of computing resource scheduling in IPCRSA.  
The Client generates a computing resource request packet based on the resource demand parameters the User provides.
2. The ICN name resolution system is used to query an SN within the L\({}_{3}\) level scheduling domain that has the identifier RSS_ID_L\({}_{3}\). The computing resource request packet is then randomly sent to any of these SNs, following the principle of proximity scheduling.
3. Through on-path packet matching, processing, and forwarding between SNs, a CRN that satisfies the User's demands and preferences can be identified.
4. The GN returns a scheduling success response packet to the Client.
5. The Client establishes a connection with the GN.
6. The User, through the GN, completes the deployment operation of the services on the target CRN.