esa-sceva/satcom-chunk-collection huggingface dataset:
# Game theory-based Routing for Wireless Sensor Networks: A Comparative Survey
## 3 Game Theory-Based Routing Protocols in WSNs
### Routing Protocols Based on Noncooperative Games
#### 3.2.2 Inter Cluster Routing Algorithm for a WSN
The designed game model in the protocol was used to decide if node B will act as the relay node for A. Technical strengths:  
* Leads to higher residual energy of the nodes. Technical weaknesses:
* The protocol is supposed to suffer from an energy hole problem. As CHs aggregate data to the sink node through cooperation, the CHs that are lying in the vicinity of the sink node will deplete energy faster;
* The authors of [39] claimed to reach Nash equilibrium point using certain conditions. Their calculations were not sufficient to prove their claim on finding the equilibrium point;
* The protocol only defines communication among the CHs. Communication between CHs and CMs should have been defined or assumed. Figure 4: Cooperative data transmission between two CHs in the protocol. Applications:
* It is challenging to specify any application for this protocol because it does not elaborate intra-cluster communication. Furthermore, knowing that the protocol may suffer from an energy hole problem, the network failure owing to nodes near the sink node dying early is inevitable. Improvement of this protocol before real time deployment is necessary. Probable future improvements:
* This kind of protocol, where data is aggregated by the CHs to the sink node in a cooperative manner, requires multiple sink nodes. The protocol can be redesigned by putting in effort to deploy multiple sink nodes to avoid energy hole problem and to support numerous sensor nodes participating in routing.
## 5 Overview of Experiments  
The proposed IV-SSIM metric was compared with 16 state-of-the-art quality metrics with publicly available implementations: PSNR\({}_{\rm Y}\) (PSNR of luma component), PSNR\({}_{\rm YUV}\) (weighted average of PSNR for three components with luma weight six times higher than the weights for both chroma components, as described in [42]), IV-PSNR [4], CS-PSNR [5], PSNR-HA and PSNR-HMA [6], VMAF [7], a pixel-based VIF [8], LPIPS [9], SFF [10], SSIM\({}_{\rm Y}\)[11] and its multiscale version, MS-SSIM [12], FSIM [13], MW-PSNR (morphological wavelet PSNR) [35], and MP-PSNR (morphological pyramid PSNR) [36]. Moreover, we have added an SSIM\({}_{\rm YUV}\) metric, calculated for all three color components and weighted in the same way as PSNR\({}_{\rm YUV}\).  
The effectiveness of the proposed IV-SSIM was evaluated in two experiments. In the first one (Section 6), the influence of different techniques of immersive video codingwas assessed. In the second experiment (Section 7), IV-SSIM was compared against the commonly used image quality assessment database--TID2013 [43]--in order to present its performance in non-immersive video applications.  
In each experiment, the metrics were compared using two commonly used correlation coefficients: PLCC (Pearson's linear correlation coefficient) and SROCC ([PERSON]'s rank-order correlation coefficient) [44].  
The calculation of PSNR\({}_{\rm Y}\), PSNR\({}_{\rm YUV}\), and IV-PSNR metrics was performed using the IV-PSNR software v4.0 [45] developed by the authors of the IV-PSNR metric [4]. The VMAF metric was calculated using the implementation provided by its authors [46]. Among all tested metrics, only these two software packages include a parallel variant of metric calculation, therefore, in Section 8 we provided results for both single- and multi-threaded computational time evaluation. All metrics were calculated on the same computer equipped with an AMD Ryzen 9 3900 XT processor (with 12 identical cores) in connection with 64 GiB of DDR4/3200 MT memory. The processor was operating in the default dynamic frequency setting; therefore, the core frequency for single-threaded loads was significantly higher than for multi-threaded variants.
# Coupled Spatiotemporal Characterization of Monsoon Cloud Cover and Vegetation Phenology
## Materials and Methods
### Spatiotemporal Characterization Methodology  
Analysis begins by applying the Principal Component (PC) transformation [41] to the NDVI image time series. The Principal Component transformation determines the dominant uncorrelated modes of variability (dimensions) that are present in the dataset. Each dimension is: (1) composed of a pair of associated spatial and temporal patterns, (2) uncorrelated with every other dimension, and (3) associated with some fraction of the total variance of the dataset. In the context of this work, the temporal patterns are referred to as Empirical Orthogonal Functions (EOFs) and the spatial patterns are referred to as Principal Components. The Principal Components can be conceptualized as spatial maps that depict the strength of each temporal EOF across the landscape.  
High-dimensional remote sensing data can be explored using the concept of a feature space [42], in which relationships among the spectral bands are visualized using sets of complementary scatterplots. However, a multi-year time series of daily observations results in a temporal feature space too voluminous to be efficiently explored by simple comparison of scatterplots. The Principal Component transform addresses this problem. Principal Component dimensions provide an alternative coordinate system with which to examine the temporal feature space. This alternative coordinate system is mathematically optimized in important ways (orthogonal dimensions, rank-ordered by variance). Each pixel's Principal Component value along any given dimension of this new coordinate system corresponds to the extent to which that mode of variability contributes to that pixel's time series. The ranked partition of variance can sometimes provide a basis for the segregation of deterministic and stochastic dimensions [19], in many cases condensing the data into a small (\(<10\)) number of physically meaningful patterns [23]. In such cases, the usable information that could potentially be obtained from pairwise examination of thousands of image time steps can be obtained by an investigation of a much smaller number of PC/EOF pairs.
## 3 Results and Discussion
### Life Cycle Assessment (LCA) of Both Asphalt Mixtures
#### 3.2.2 LCA Global Results for Asphalt Mixtures  
The purpose of the life cycle impact analysis is to use the results obtained in the inventory to assess the significance of potential environmental impacts, also providing information for the interpretation phase [75]. The modeling in the _SimaPro_ software of all data collected in the LCI allowed us to obtain the set of impacts generated by producing 1 ton of asphalt mixture in Stage A. The results of the LCA were translated into impact categories according to the core environmental impact assessment method EN 15804:2012+A2:2019/AC:2021 and are presented in Table 14 for the mixtures with NAs and RCAs. Table 14 provides a comprehensive overview of the core environmental impacts of AC14-NA and AC14-RCA in Stage A. While the results for the mixture with NAS offer valuable insights for understanding the environmental advantages of RCA incorporation in asphalt mixtures and will be used in the following subsection, this subsection predominantly concentrates on discussing the environmental impacts of the AC14-RCA mixture. Figures 9 and 10 visually represent how each raw material and process contributes to impact categories within Stages A. These visual aids clearly understand the specific environmental contributions and highlight the elements that significantly influence each impact category. A general analysis of Stage A's impacts (Figure 9) reveals that the most substantial contributions come from modules A1 and A3. It is primarily attributed to the production of bitumen, natural aggregate (NA) extraction, RCA processing, and heating of the aggregate and bitumen, as seen in Figure 10. \begin{table}
\begin{tabular}{l l l} \hline \hline \multicolumn{1}{c}{**Impact Category**} & \multicolumn{1}{c}{**Unit**} & \multicolumn{1}{c}{**RCA**} \\ \hline GWP—total & kg CO\({}_{2}\) eq. & 6.71 \\ GWP—fossil & kg CO\({}_{2}\) eq. & 6.71 \\ GWP—biogenic & kg CO\({}_{2}\) eq. & \(1.87\times 10^{-3}\) \\ GWP—luluc & kg CO\({}_{2}\) eq. & \(6.72\times 10^{4}\) \\ ODP & kg CFC 11 eq. & \(1.45\times 10^{-6}\) \\ AP & mol H\({}^{+}\) eq. & \(7.02\times 10^{-2}\) \\ EP—freshwater & kg P eq. & \(2.44\times 10^{-5}\) \\ EP—marine & kg N eq. & \(3.10\times 10^{-2}\) \\ EP—terrestrial & mol N eq. & \(3.40\times 10^{-1}\) \\ POCP & kg NMVOC eq. & \(9.34\times 10^{-2}\) \\ ADP—minerals \& metals & kg Sb eq. & \(1.03\times 10^{-5}\) \\ ADP—fossil fuels & MJ, net calorific value & \(9.23\times 10\) \\ WDP & m\({}^{3}\) world eq. deprived & \(1.24\times 10^{-1}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 13: Core environmental impacts of the RCA. Figure 10: Contribution of each input flow to the core environmental impacts of the AC14-RCA in Module A.
# Text Classification of Patient Experience Comments in Saudi Dialect Using Deep Learning Techniques
## 2 Related Work
### Bert-Based Arabic Text Classification  
BERT is a contextual word embedding that can be used to derive representations for textual data. This model has been shown to have high performance, and in this section we review some published work that utilizes Arabic pre-trained BERT models. In [PERSON] et al. [45], a classifier was developed to determine the classes for news articles using the Mowjaz data set. The classifier consists of three layers, with an input layer having the size of the hidden layers of AraBERT [46] and an output layer of size 10 (representing the number of labels). Additionally, two other classifiers were developed for comparison: TF-IDF with an SVM classifier and AraVec word embeddings with Bidirectional LSTM. In the testing set, the AraBERT classifier achieved the highest accuracy of 85.1% and outperformed the other classifiers. In their study, [PERSON] et al. [47] aimed to classify offensive and hate speech Arabic tweets. They utilized a data set of 7839 annotated tweets and conducted pre-processing before fine tuning the pre-trained AraBERT model. The output of AraBERT was then fed to two task-specific dense layers as part of multi-task learning (MTL), which classified whether the tweet was offensive and whether it contained hate speech. The MTL AraBERT model achieved a macro F1 score of 90.15% for the offensive class and 83.1% for hate speech.  
In another study, [PERSON] et al. [48] aimed to classify tweets as check worthy or not. They used a data set of 4,100,000 tweets to fine tune the AraBERT pre-trained embeddings with 12 BERT layers, followed by a hyperbolic tangent activation function to determine the probability distribution of tokens. The model was tested using different AraBERT versions, and the v0.2-base and v2-base models achieved accuracies of 68% and 69%, respectively. [PERSON] et al. [49] also employed AraBERT in their research in order to classify whether tweets are sarcastic or not. The authors used an ensemble approach with a hard-voting technique and AraBERT, resulting in an F1 score of 59% for the sarcastic class and an accuracy of 68%. [PERSON] et al. [50] conducted experiments with AraBERT v1 and v2 for multi-class and multi-label text classification of medical consultations obtained from Altibbi, comprising 578,000 consultations with unclear labels. The authors compared the performance of AraBERT + Bidirectional LSTM with Bidirectional LSTM using custom-built static word embeddings (AltibbiVec) and evaluated the performance using precision and recall. The best-performing model was AltibbiVec with BiLSTM, achieving a recall of 54.4%, precision of 26.8%, and F1 score of 35.46%. [PERSON] et al. [51] created a binary classifier to detect harmful social information using an imbalanced data set of over 10,000 tweets about COVID-19 provided by the NLP4 IF-2021 [52]. The authors utilized multiple classifiers and found that AraBERT-v2-tokenized achieved the best performance for Arabic, with a macro F1 score of 69.8%.
## 3 Results and Discussion
### ML Model Transferability Challenges and Solutions
[151] estimated SM across various land-cover types in Iran and found that training ML models on homogeneous regions--characterized by similar land cover, elevation, and physiographic factors--substantially improved model performance compared to training the models on the entire dataset at once. ML models often struggle to identify clear patterns when trained on large-scale datasets (having heterogeneous climate, soil, and land covers) as a whole. To address this, [PERSON] and [PERSON] [95] estimated multi-layer SM at a 1 km spatial resolution across the contiguous United States (CONUS) by training models on 11 distinct homogeneous clusters based on climate, land cover, vegetation, and soil types. The results showed that the models effectively captured SM variability across all sites, with most stations achieving RMSE below 0.04 cm\({}^{3}\)/cm\({}^{3}\). This approach indicates the effectiveness of training ML models on homogeneous regions to enhance their predictive performance.
## 2 Magnetorheological Suspension Structure Design
### Magnetorheological Fluid Design  
According to the direction of liquid flow in magnetorheological fluid and the direction of the external magnetic field, the working mode of magnetorheological fluid can be divided into extrusion, flow, shear, and mixing modes, as shown in Figure 1.  
The mode selected for investigation in this paper is the shear mode of magnetorheological fluid. In the presence of a magnetic field, the field passes vertically through the upper and lower plates. When one plate is fixed in place, the magnetorheological fluid flows between the two plates, causing the other plate to move parallel due to applied force. This results in a change in flow damping force of the magnetorheological fluid between the upper and lower plates.  
The relationship between the shear strain rate and shear stress of magnetorheological fluid is described as follows [12]:  
\[\tau=\eta\dot{\gamma} \tag{1}\]  
where \(\tau\)--shear stress of magnetorheological fluid;  
\(\eta\)--zero field viscosity;  
\(\dot{\gamma}\)--shear strain rate.  
The magnetorheological fluid medium utilized in this study is a type of mineral oil. The relationship between its magnetic induction strength and shear yield stress is illustrated in Figure 2.
## 2 Train Motion Constraints and Odometer  
### Motion Constraints Model  
Defining the train body frame as \(m\), the origin is the installation position of the MIMU on the carriage of the train. The \(x\), \(y\), and \(z\) axes point to the right, forward, and upward relative to the train body, respectively. Defining the MIMU frame as \(b\), the directions of each axis of the \(b\) frame should be consistent with the direction of each axis of the \(m\) frame to ensure that the installation angles are as small as possible.  
A high-speed train's wheels are pressed against the rails during running [22], and, assuming there is no side slip or jumping, the speeds of the \(x\) and \(z\) axes in the \(m\) frame are approximately zero. Ignoring the influence of the installation lever arm between the MIMU and the GNSS receiver, the constraint relationship is expressed as  
\[\left[\begin{array}{ccc}1&0&0\\ 0&0&1\end{array}\right]\mathbf{C}_{b}^{m}\mathbf{C}_{n}^{b}\mathbf{v}^{n}=\left[\begin{array} []{c}v_{x}^{m}\\ v_{z}^{m}\end{array}\right]\approx 0 \tag{1}\]  
where \(\mathbf{v}^{n}\) is the INS output speed in the \(n\) frame(local geographic coordinate frame), \(\mathbf{C}_{n}^{b}\) represents the rotation matrix from the \(n\) frame to the \(b\) frame; \(v_{x}^{m}\) and \(v_{z}^{m}\) are the speed of the train in the \(m\) frame; and \(\mathbf{C}_{b}^{m}\) is the conversion cosine matrix between the \(b\) frame and the \(m\) frame, which can be calculated from the installation angles \(\mathbf{a}=\left[\begin{array}{cc}a_{\theta}&a_{\gamma}&a_{\psi}\end{array} \right]^{T}\).  
Once the MIMU is fixed on the train, the errors of installation angles can be regarded as random constants with small angles. According to the conclusion reported in Reference [22], the roll installation angle has no effect on the motion constraints. Regardless of the influence of the roll installation angle error, the three-dimensional vector form of the remaining two installation angle errors is \(\delta\mathbf{a}=\left[\begin{array}{cc}\delta a_{\theta}&0&\delta a_{\psi} \end{array}\right]^{T}\).  
The estimated conversion matrix \(\widetilde{\mathbf{C}}_{b}^{m}\) can be expressed as  
\[\widetilde{\mathbf{C}}_{b}^{m}=(\mathbf{I}-[\delta\mathbf{a}\times])\mathbf{C}_{b}^{m}=\mathbf{C }_{b}^{m}-[\delta\mathbf{a}\times]\mathbf{C}_{b}^{m} \tag{2}\]  
The conversion matrix error \(\delta\mathbf{C}_{b}^{m}\) can be expressed as  
\[\delta\mathbf{C}_{b}^{m}=\widetilde{\mathbf{C}}_{b}^{m}-\mathbf{C}_{b}^{m}=-[\delta\mathbf{a} \times]\mathbf{C}_{b}^{m} \tag{3}\]
## 4 Conclusions  
The application of PEF led to a significant 9.4% reduction in processing time without compromising the olive oil's organoleptic and physicochemical characteristics. Both products derived from PEF and the traditional method were classified as extra virgin olive oil. Pre-treating with PEF also presents clear advantages at the industrial level compared to traditional processes since the malaxation time can be reduced to 33%, allowing considerable energy savings.  
In the studied production scale, the reduction in time resulted in a 12.3% increase in annual olive production and a subsequent rise of 12.3% in revenue and 36.8% in gross profit. Nonetheless, the improved economic margin in small production lines was not enough to offset the high upfront cost of PEF equipment. In those cases, renting should be considered, especially since production only occurs during a period of 3 months per year. Medium- to large-scale production lines can offset the initial investment, and therefore, careful consideration of scale is crucial when opting for the purchase or renting of PEF in olive oil production processes.  
PEF technology can be implemented in the olive oil industry with great economic returns. Therefore, more research should be conducted on higher production scales to confirm the study results.  
S.D.: Conceptualization, methodology, formal analysis, investigation, writing--original draft preparation, and writing--review and editing. E.P.-H.: formal analysis, investigation, writing--original draft preparation, and writing--review and editing. D.G.: methodology, investigation, writing--original draft, and writing--review and editing. D.R.: conceptualization, investigation, methodology, and writing--review and editing. L.R.: writing--review and editing. M.A.: conceptualization, methodology, investigation, and writing--review and editing. All authors have read and agreed to the published version of the manuscript.  
This work was financially supported by the projects INOVC+ (CENTRO 01 0246 FEDER 000044), WINBIO (POCI 01 0246 FEDER 1813 35), TAGUSVALLEY2030 IT (CENTRO 01 0246 FEDER000032), and TAGUSVALLEY2030 RHAq (CENTRO 04 3559 FSE 000143), under the European Social Fund from the European Union and managed by COMPETE 2020, CENTRO 2020, and PORTUGAL 2020.  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
**Acknowledgments:** The authors gratefully acknowledge Casa Anadia (Portugal) for providing the fruits of the olive and also the technical team at the EPDRA pilot plant for the valuable support provided in the development of the tests.  
**Conflicts of Interest:** Author [PERSON] was employed by the company EnergyPulse Systems. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## 3 Results
### Intrasubject Models
#### 3.1.2 DEMG Models  
Figure 5a shows the results distribution of the DEMG models trained on AR or ZC features with a maximum of two features (due to space limitation). Like the sEMG models, models trained without power features tended to have low \(R^{2}\), and the models trained with ZC and either MAV or RMS could not fit variance in data. Figure 5b shows the models trained on power features alone. RCNN models trained using RMS + WL features had a wide range of \(R^{2}\) depending on the selected muscles and subject. Similar to the sEMG models, the outlier in the results of the TA+PB muscles group is seen in Figure 5c.  
The best-performance DEMG model was RCNN (TA+SOL+GM+PB RMS+MAV) achieved (\(R^{2}\): 95.86% \(\pm\) 1.80%, NRMSE: 0.0504 \(\pm\) 0.0128). DEMG models had lower results than sEMG models. Therefore, they would not be used for the intersubject models.  
Figure 5: DEMG intrasubject models results. (**a**) boxplots illustrate the distribution of \(R^{2}\) values for models trained on various combinations of autoregressive model (AR) coefficients and zero crossing count (ZC) features. (**b**) illustrates the distribution of \(R^{2}\) values for models trained on Signal Power features: root mean square (RMS), mean absolute value (MAV), and waveform length (WL). (**c**) shows the distribution of \(R^{2}\) values for models for tibialis anterior (TA), soleus (SOL), gastrocnemius medialis (GM), and peroneus brevis (PB) muscles groups when using signal power features.
# Quantitative Analysis of Retinal Vascular Leakage in Retinal Vascular Using Machine Learning
## 2 Materials and Methods
### Automated Retinal Vascular Leakage Segmentation
#### 2.3.4 Automated Segmentation of Retinal Vascular Leakage  
Following retinal vascular segmentation, early and late FA images were normalized using a mean intensity of 32 and mean deviation of intensity of 16 in early images, and a mean intensity of 128 and mean deviation of intensity of 64 in late images, since the signal intensity was variable between images. After normalization of each image, early and late image registration was performed using k-nearest neighbors from key points detected by the \"KAZE\" algorithm [19]. Then, projective transformation was conducted for the early FA images by parameter estimation, using the random sample consensus (RANSAC) algorithm. If global matching is performed, misregistration occurs in the retinal peripheral area. The retinal region was divided into four regions with horizontally and vertically centered macula, and registration and projective transformation were performed in each region.  
Following registration and projective transformation, subtraction images were generated to extract retinal vascular leakage in the late-phase images. For the segmentation of the vascular leakage area, cluster analysis using the k-means method was used [20]. The vascular leakage area was classified into 10 clusters, and the cluster showing the highest intensity was defined as the vascular leakage area, and a binarized image was generated in white (=255) for the vascular leakage area and in black (=0) for the others. In late-phase FA images accompanied by a marked vascular leakage, leakage areas were classified into the top two clusters, because the leakage area was wide. In such FA images, many vascular regions were invisible due to high leakage. Therefore, the top two clusters were determined as leakage regions if the number of segmented vascular region pixels in the late-phase FA image was 50% lower than that of the early image.