esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
### Spatiotemporal Variation in GPP, ET and WUE  
As shown in Figure 2, China's GPP, ET, and WUE all significantly increased during 2001-2020 (\(p<0.05\)), with average increase values of 6.70 g C m\({}^{-2}\) year\({}^{-1}\), 2.68 kg H\({}_{2}\)O m\({}^{-2}\) year\({}^{-1}\), and 0.007 g C H\({}_{2}\)O m\({}^{-2}\) year\({}^{-1}\), respectively. Furthermore, we investigated the changing characteristics of different climate zones, and the results revealed that the GPP, ET, and WUE of most climate zones in the study area significantly increased over the past 20 years, except for the WUE of the edge tropical regions, which did not show a significant trend. Notably, among the climate regions, the warm temperate semihumid region (V) presented the highest rates of increase in GPP, ET, and WUE.  
The spatial variation characteristics of GPP, ET, and WUE in terrestrial China over the past 20 years are shown in Figure 3, with approximately 50% of the regions exhibiting a significant increasing trend. The percentage of areas showing significant trends in GPP, ET, and WUE was 58.4%, 63.3%, and 48.2%, respectively, with nearly 91.4%, 98.8%, and 76.2% of these areas demonstrating significant increases. From a spatial perspective, the significantly increased areas are widely distributed in the central part of the study area, namely, the warm temperate semihumid region (V). In addition to the increasing trend, sporadic decreases are distributed in various climate regions.  
\begin{table}
\begin{tabular}{l l} \hline
**Pattern** & **Interaction** \\ \hline q(X 1 \(\cap\) X 2) \(<\) min(q(X 1), q(X 2)) & Nonlinear attenuation \\ min(q(X 1), q(X 2)) \(<\) q (X 1 \(\cap\) X 2) \(<\) max(q(X 1), q(X 2)) & Single-factor nonlinear attenuation \\ q(X 1 \(\cap\) X 1) \(>\) max(q(X 1), q(X 2)) & Dual-factor enhancement \\ q(X 1 \(\cap\) X 2) \(>\) q(X 1) \(+\) q(X 2) & Independence \\ q(X 1 \(\cap\) X 2) \(=\) q(X 1) \(+\) q(X 2) & Nonlinear enhancement \\ \hline \end{tabular}
\end{table}
Table 1: Judgment basis and interactive relationship for interactive detection [49].  
Figure 3: The spatial variation characteristics of GPP, ET, and WUE from 2001 to 2020 (**a–c**) (GPP: gross primary productivity, ET: evapotranspiration, WUE: water-use efficiency).  
Figure 2: Time variation characteristics of GPP, ET, and WUE from 2001 to 2020 (**a–c**) and their rates of variation in various climate zones (**d–f**). (GPP: gross primary productivity, ET: evapotranspiration, WUE: water–use efficiency) (The *** means \(p<0.001\), and ** means \(p<0.05\)).
# MT-CMVAD: A Multi-Modal Transformer Framework for Cross-Modal Video Anomaly Detection
## 3 Supposed Method
### Overview of Method
#### 3.1.3 Anomaly Scoring Mechanism  
The scoring module computes anomaly likelihood through dual-stream Transformer decoding: spatial decoders reconstruct original frame patches while temporal decoders predict subsequent frame features [31]. Reconstruction errors from both streams are aggregated using adaptive weighting, with larger deviations indicating higher anomaly probabilities. A contrastive learning strategy enhances discrimination by maximizing feature consistency between adjacent normal frames, combined with curriculum learning that progressively increases temporal context complexity from 16 to 64 frames during training.  
This unified architecture enables comprehensive modeling of normal video patterns through spatial attention mechanisms and temporal dependency modeling, where anomalies manifest as deviations in both spatial reconstruction fidelity and temporal evolution consistency.
## 3 Results and Discussion
### Spectral Features  
Spectra of all ROIs were extracted from VNIR hyperspectral images. The mean spectra of fillets of high-CL and low-CL groups are shown in Figure 4. The mean spectra of high-CL and low-CL fillets exhibited overall similar patterns, and curves in the visible spectral region (400-700 nm) showed low reflectance in the blue and green regions (about 400-550 nm) and high reflectance in red region (about 600-700 nm), which explains why broiler breast meat gets its red or pink color. However, the spectral profiles could also be distinguished from each other in several small reflectance differences. In spectral set of 400-1000 nm, the bands at 422 nm, 550 nm and 492 nm were related to deoxymyoglobin, and oxymyoglobin, and metmyoglobin, respectively [47]. Low-CL fillets exhibited higher reflectance signals at 550 nm and 492 nm than high-CL fillets, which indicated that the samples could be initially screened by meat color attributes (in accordance with the statistics in Table 1). Specifically, the bands at 980 nm and the weak valley at 762 nm were closely related to the presence of water in muscles, which could be attributed to the second and third overtone of O-H stretching mode of water, respectively [8; 30]. Only minor differences were observed in the moisture contents of the high-CL and low-CL muscles. As also indicated in previous study [8], the WHC is closely connected with the membrane and structural integrity of cells in the muscle tissue.
## 2 Rossler Chaotic and RCL-Shunted Josephson Junction System
### Lyapunov Stability of Systems  
Equation (7) may be the dynamical error system in the active control procedure. Therefore, the Lyapunov function is defined by the equation below.  
\[\mathbf{V}=\frac{1}{2}\Big{(}{s_{1}}{z_{a}}^{2}+{s_{2}}{z_{b}}^{2}+{s_{3}}{z_{c}}^{2} \Big{)} \tag{18}\]  
where \({s_{j=1,2,3}}\) are constant such that \(\dot{V}<0\).  
**Theorem 1**.: _The Lyapunov function in the active control procedure, which is used to transfer the RCLs-JJ system in Equation (2) to the quasi-Rossler system in Equation (13), can be defined by Equation (18)._  
**Proof.** Equation (18) should prove the first derivative is negative and the dynamic system is stable at the equilibrium (0, 0, 0). The first derivative of the Lyapunov function is shown below.  
\[\dot{V}=\big{(}{s_{1}}{\dot{z}_{a}}{z_{a}}+{s_{2}}{\dot{z}_{b}}{z_{b}}+{s_{3}}{ \dot{z}_{c}}{z_{c}}\big{)} \tag{19}\]  
Substituting Equation (12) into Equation (9), \(\Big{[}\ {\dot{z}_{a}}\ \ {\dot{z}_{b}}\ \ {\dot{z}_{c}}\ \Big{]}^{T}=\Big{[}\ -{z_{a}}\ \ - {z_{b}}\ \ -{z_{c}}\ \Big{]}^{T}\), and taking \({s_{1}}={s_{2}}={s_{3}}=1\), the following formula is found.  
\[\dot{V}=-\Big{(}{z_{a}}^{2}+{z_{b}}^{2}+{z_{c}}^{2}\Big{)}\leq 0 \tag{20}\]  
\(\square\)  
**Theorem 2**.: _Based on the \(\mathbf{u}^{(\mathbf{k})}\)in Equation (16), the Lyapunov function is defined in the iterative control stage to trace the trajectory of the [PERSON] system below._  
\[V=\frac{1}{2}\Big{(}{r_{1}}{e_{1}}^{2}+{r_{2}}{e_{2}}^{2}+{r_{3}}{e_{3}}^{2} \Big{)} \tag{21}\]  
**Proof.** Let \(\mathbf{u}^{(\mathbf{k}-\mathbf{1})}\) be defined as the formula below.  
\[\mathbf{u}^{(k-1)}=\mathbf{B}_{2}\Delta^{(k-1)}+\mathbf{M}\big{(}{x_{i}},{y_{i}}\big{)}\bm {G}(\mathbf{e}) \tag{22}\]  
Applying \(-\mathbf{u}^{(k-1)}\) to Equation (15), we obtain the error dynamics as \(\dot{\mathbf{e}}=\Big{[}\ -{z_{a}}\ \ \ -{z_{b}}\ \ \ -{z_{c}}\ \Big{]}^{T}\). Let \({z_{i=a,b,c}}={e_{j=1,2,3}}\) and \({r_{j=1,2,3}}\)= 1. The Lyapunov function is outlined below.  
\[\dot{V}=-\Big{(}{e_{1}}^{2}+{e_{2}}^{2}+{e_{3}}^{2}\Big{)}\leq\mathbf{0} \tag{23}\]  
This implies that Equation (15) employs the iterative learning control law, which is asymptotically stable at equilibrium.  
The Lyapunov function is generally a function of the trajectory error or the synchronization error. The error norm can be extended to be a cost function having the lowest cost when the trajectory between systems is close to each other [28; 29; 30]. The cost function in this work is defined by the formula below.  
\[C_{F}=\Big{|}\Big{|}{e_{1}}^{2}+{e_{2}}^{2}+{e_{3}}^{2}\Big{|}\Big{|} \tag{24}\]  
\(\square\)
## 4 Performance Evaluation
### Objective Quality Evaluation for Speech Enhancement
Figure 6: Comparison of the objective quality measures for seven different speech enhancement methods applied to the mismatched evaluation dataset: (**a**) perceptual evaluation of speech quality (PESQ) and (**b**) short-time objective intelligibility (STOI). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{5}{c}{**Noise**} \\ \cline{2-5}  & **babble** & **f16** & **buccabber2** & **factory2** & **Average** \\ \hline Noisy & 0.85 & 0.87 & 0.88 & 0.88 & 0.87 \\ SNMF & 0.84 & 0.88 & 0.87 & 0.88 & 0.87 \\ DRNN & 0.80 & 0.83 & 0.81 & 0.83 & 0.82 \\ SEGAN & 0.83 & 0.85 & 0.85 & 0.86 & 0.84 \\ U-Net & 0.82 & 0.85 & 0.84 & 0.85 & 0.84 \\ MTU-Net(spec) & 0.83 & 0.88 & 0.87 & 0.89 & 0.87 \\ MTU-Net(IBM) & 0.84 & 0.89 & 0.89 & 0.89 & 0.88 \\ MTU-Net(IRM) & 0.85 & 0.91 & 0.91 & 0.89 & 0.90 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of the short-time objective intelligibility (STOI) scores of seven different speech enhancement methods applied to the mismatched evaluation dataset.
## 2 IA for Two-Unit Series System with Three States
### Numerical Solution and Error Analysis
Same as the division in **STEP 1**, we apply the compound trapezoidal formula to \(\int_{0}^{t_{i}}A(u)Q^{\prime}(t_{i}-u)du\), therefore we can obtain  
\[\int_{0}^{t_{i}}A\left(u\right)Q^{\prime}\left(t-u\right)du\approx\dfrac{h}{2} \left[A\left(t_{0}\right)Q^{\prime}\left(t_{i}-t_{0}\right)+2\sum_{k=1}^{i-1} A\left(t_{k}\right)Q^{\prime}\left(t_{i}-t_{k}\right)+A\left(t_{i}\right)Q^{ \prime}\left(t_{i}-t_{i}\right)\right]. \tag{20}\]
**STEP 3:**: Bring \(\int_{0}^{t_{i}}A(u)Q^{\prime}(t_{i}-u)du\) into \(A\left(t\right)=e^{-\left(\lambda_{1}+\lambda_{2}\right)t}+\int_{0}^{t}A\left( u\right)Q^{\prime}\left(t-u\right)du\), then we can get  
\[A\left(t_{i}\right)\approx\tilde{A}\left(t_{i}\right)=F\left(t_{i}\right)+ \dfrac{h}{2}\left[A\left(t_{0}\right)Q^{\prime}\left(t_{i-0}\right)+2\sum_{k=1 }^{i-1}A\left(t_{k}\right)Q^{\prime}\left(t_{i-k}\right)+A\left(t_{i}\right)Q ^{\prime}\left(t_{i-i}\right)\right]. \tag{21}\]  
Then (21) can be rewritten as follows  
\[(I-\dfrac{h}{2}\tilde{Q})\tilde{A}=F, \tag{22}\]  
where  
\[\tilde{A}=\left[ \begin{array}{c}\tilde{A}\left(t_{0}\right)\\ \tilde{A}\left(t_{1}\right)\\ \tilde{A}\left(t_{2}\right)\\ \vdots\\ \tilde{A}\left(t_{0}\right)\end{array} \right],F=\left[ \begin{array}{c}F\left( t_{0}\right)\\ F\left(t_{1}\right)\\ F\left(t_{2}\right)\\ \vdots\\ F\left(t_{n}\right)\end{array} \right],\tilde{Q}=\left[ \begin{array}{ccc}0\\ Q^{\prime}\left(t_{1}\right)&Q^{\prime}\left(t_{0}\right)\\ Q^{\prime}\left(t_{2}\right)&2Q^{\prime}\left(t_{1}\right)&Q^{\prime}\left(t_{ 0}\right)\\ \vdots\\ Q^{\prime}\left(t_{n}\right)&2Q^{\prime}\left(t_{n-1}\right)&\cdots&2Q^{ \prime}\left(t_{1}\right)&Q^{\prime}\left(t_{0}\right)\end{array} \right]. \tag{23}\]  
According to (22), we can obtain the approximate value \(A(t_{i})\), and thus we can get the numerical solution of \(A(t)\). Error Analysis  
On one hand, according to the steps above and combined with Definition 2, Equation (8) can be rewritten as follows:  
\[A\left(t_{i}\right)=e^{-\left(\lambda_{1}+\lambda_{2}\right)t_{i}}+\frac{h}{2} \left[A\left(t_{0}\right)Q^{\prime}\left(t_{i}-t_{0}\right)+2\sum_{k=1}^{i-1}A \left(t_{k}\right)Q^{\prime}\left(t_{i}-t_{k}\right)+A\left(t_{i}\right)Q^{ \prime}\left(t_{i}-t_{i}\right)\right]+O\left(h^{2}\right). \tag{24}\]  
Then based on Lemma 2, the error of the compound _Simpson_ formula is \(O(h^{4})\), thus  
\[Q^{\prime}\left(t\right)=\tilde{Q}^{\prime}\left(t\right)+O\left(h^{4}\right), \tag{25}\]  
where \(\tilde{Q}^{\prime}\left(t\right)\) is the approximate value of \(Q^{\prime}\left(t\right)\).
## 3 Results and Discussion
### Antibacterial Effect
There was no significant difference (\(p>0.05\)) in the Enterobacteriaceae reduction compared with the control sample. The application of 0.25 wt% EOF by marinating showed a similar (\(p>0.05\)) antibacterial effect against Enterobacteriaceae (a microbial reduction of 4.85 log cfu/g) to 1.00 wt% EOF using the addition method (a microbial reduction of 3.76 log cfu/g). The additive effect of 0.50 and 1.00 wt% EOF by marinating showed a lower population of Enterobacteriaceae bacteria than the control sample on the first day and controlled coliform counts, which were not higher than 1 log cfu/g during storage. In addition, the bactericidal effect against the initial coliform load at 2.50 wt% EOF was evident using the methods of addition and marinating. It showed a bactericidal effect against Enterobacteriaceae until the end of storage. The results show a microbial reduction of 6.65 log cfu/g for the addition method at 2.50 wt% after grinding and 6.89 log cfu/g at 2.50 wt% for the marinating method before grinding (Table 1). The Enterobacteriaceae microorganisms included coliform bacteria, which are hygiene indicators, as well as _Escherichia coli_ and _Salmonella_ spp. which are foodborne pathogenic bacteria. The results show EOF exhibited excellent antimicrobial activity against Gram-negative and food-borne spoilage bacteria in food. [PERSON] et al. [9] found that adding oregano essential oil to minced sheep meat was more effective against the growth of _Salmonella enteritidis_ at a refrigerated temperature. This is in agreement with previous studies [20]. Per the EU regulation [43], _Salmonella_ spp. were not present in minced meat, whereas _E. coli_ were at 1.70-2.70 log cfu/g, and the aerobic colony count was 5.70-6.70 log cfu/g. The shelf-life of the minced meat was decided based on the TVC standard criterium (Table 1) due to this experiment using VRBG media for testing Enterobacteriaceaeae bacteria, which cannot identify _E. coli_ and _Salmonella_ spp. in minced meat. Considering EU regulations for acceptable-quality meat products, control samples and treated samples with 0.25 and 0.50 wt% EOF using the mixed method demonstrated TVC levels under the standard criterium on day 3. When the sample was treated with 1.00 and 2.50 wt%, the shelf-life of the ground beef was extended to 9 and 15 days, respectively. Marinating with 0.25, 0.50, 1.00, and 2.50 wt% EOF extended the minced meat storage life to 9, 9, 12, and 18 days, respectively. When comparing the effectiveness of the same concentration of EOF applied with marinating versus the addition method, the former exhibited greater antibacterial properties in preserving the minced meat.
# Patch-Wise Infrared and Visible Image Fusion Using Spatial Adaptive Weights
## 3 Proposed Method  
In this section, we first elaborate our proposed end-to-end deep learning based fusion network, then we discuss the formation of weight map. At last, we introduce the design of our new loss function.  
In our fusion network, we apply patchGAN [17] in the dual discriminator structure framework [12], as shown in Figure 1. Our model has one generator \(G\) and two discriminators \(D_{i}\) and \(D_{v}\). Given the infrared image \(i\) and visible image \(v\), the task of the generator is to generate a fused image which should be able to fool the discriminators. \(D_{\sigma}\) aims to distinguish the generated image from the visible image, while \(D_{i}\) is trained to discriminate between the original infrared image and the fused image. In general, the output of the discriminators is a scalar value that approximates the probability of the input from the source data rather than generated data \(G\).  
Inspired by the patchGAN [17], the generator as well as the discriminator is of the form convolution-BatchNorm-ReLu [12; 29; 30]. The generator's architecture is based on U-Net [31] with the auto-encoder system with skip connections. The discriminators used for our model are Markovian discriminators [17; 25], \"PatchGAN\", that only penalize structure at the scale of patches. This discriminator tries to signify if each \(N\times N\) patch in an image is real or fake. In vanilla GAN, given an input image, the output is a single probability of the image being real or fake, but here we get an \(N\times N\) array of output \(X\) where each \(X_{ij}\) represents the probability of each patch of an image being real or fake.  
We have observed that in infrared image, only the high activation region contains significant information. The complexity in IR-visible image fusion research lies in correctly extracting the information on thermal targets from the IR image, and trying to keep obvious background information of the visible image in the fusion image [32]. We intend to give more weight to the informative regions of IR. For this purpose, we generate an adaptive weight map \(W\) based on the IR image and utilize this weight map in the adversarial loss during training in a fashion which could preserve the informative region of the IR image. For the construction of the weight map \(W\), we take the IR image and apply average pooling. We can choose average pooling as well as max-pooling strategy to create the weight maps equal to the size of the output of the discriminator. The reason to choose average pooling is that the output of the average pooling can be considered as a smooth version of the input image. In this way, more weights can be given to high activation regions of the input infrared image. In contrast, max pooling can cause an abrupt change in weights of adjacent pixels. We have also compared these two strategies in ablation studies.The size of the weight map should match the size of the output of the discriminator.
# Transforming Customer Digital Footprints into Decision Enablers in Hospitality
## 5 Discussion
### Managerial Implications  
As practical implications of this study, the hotel industry could adopt the techniques to extract emotions and hotel aspects mentioned in online reviews, thereby creating a digital representation of customer satisfaction. The derived emotion-aspect profile will highlight the pain points of customers, which can be accounted for when improving services. This analysis can be directly linked to improving services to enhance the hotel aspects with limitations. Proper and timely management of responses and identifying sentiments expressed towards the hotel is crucial for hotel managers to disseminate positive eWOM to customers.  
Another significant implication of this study lies in the potential to conduct segmentation and benchmarking using data from digital sources. This capability can even extend to an individual level, where visitors' unique individualities and emotional characteristics can be derived based on their previous responses to other hotels over time. Identifying negative customers is crucial for effective response management, enabling proactive communication and action to mitigate negativity in online reviews towards the hotel. By distinguishing clusters of discontented customers or individuals with specific concerns, hotels can direct their efforts on resolving these issues and improving the overall customer experience. This strategic approach to customer segmentation facilitates resource allocation and prioritization, ensuring that developments are directed where they are most required.  
Furthermore, the self-adapting GSOM can be positioned as a valuable visualization tool to analyse data from multiple dimensions, providing insights that can be seamlessly integrated into current decision-making systems, thereby offering a competitive advantage to the hotel. Such analysis offers an opportunity for hotel managers to compare customer emotions towards competing hotels and take action to benchmark and enhance their position among similarly performing establishments. This approach facilitates the automatic detection of similarly performing hotels based on both aspects and emotions and as this is computationally less costly, integrating this into digital marketing strategies would be beneficial for continuous service improvement. Over time, hotels will be able to observe a shift in these profiles, thus identifying key characteristics that emerge over time.  
Therefore, the proposed approach offers the opportunity to fully leverage online reviews by supporting multiple analysis tasks using the derived representation of emotions and aspects mentioned in the reviews. This allows the hotel industry to tap into the wealth of digital data created on social media platforms to inform decision-making and strategy development. Consequently, the outcomes of this study can drive data-driven digital marketing strategies, as online reviews serve as a critical communication medium between customers and hotel managers.
## 2 Materials and Methods
### Anti-Loosening Coated Bolt Sorting Using CNN  
#### 2.3.1 Coated Bolt Sorting Using VGG16  
CNN, a deep learning method, features many layers, and the learning results depend on various structures. We used the VGGNet model, which has a simple structure and has proven to be amenable for use in many CNN-related studies. Depending on the number of layers, VGGNet is categorized as VGG11, VGG13, VGG16, or VGG19, with the structures shown in Figure 4. By repeatedly applying the convoluted layers with featured image filtering techniques and pooling the layers that reduce image data sizes, the image data input was vectorized as fully connected layers, which were then normalized via an activation function (Figure 4). The Sigmoid activation function, which is expressed in zeros and ones, is generally used for binary classification, whereas the softmax function, which is expressed in figures between zero and one, is used for multiple classification. However, inspired by the results reported by [PERSON] [12], we used softmax as the activation function for binary classification (Equation (1)) to sort defective from non-defective products. The probability of the product falling into either of these product categories (\(p_{1},p_{2}\)) is expressed between zero and one as the predicted value (\(\hat{y}\)).  
\[\text{softmax}(z)=\left[\frac{e^{z_{1}}}{\sum\limits_{j=1}^{2}e^{z_{j}}}\frac{ e^{z_{2}}}{\sum\limits_{j=1}^{2}e^{z_{j}}}\right]=[p_{1},p_{2}]=\hat{y} \tag{1}\]  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Attribute** & **Specification** \\ \hline Resolution & 600 LW/PH \\ Minimal illumination & 0.5 Lux \\ Focal length & 5–50 mm \\ Active array size & 3264 \(\times\) 2448 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Camera specifications.  
Figure 3: The data capture method.  
The learning process was executed using the VGG16 structure with the learning data obtained via the method outlined in Section 2.1 and 2.2. Python 3.7 and TensorFlow 2.0 were used to identify the parameters for producing the best data learning results. The results were obtained by executing Python scripts on an NVIDIA DGX STATION (Future Automotive Intelligent Electronics Core Technology Center, Cheonan, Korea). The batch size was 32 and the number of epochs was 100. For learning and verification, 80% of all the data were used. The learning accuracy and loss function are shown in Figure 5. The learning result exhibited an accuracy of 97%.