esa-sceva/satcom-chunk-collection huggingface dataset:
# Quality of Tomato Juice as Influenced by Non-Thermal Air Plasma Treatment
## 3 Results and Discussion
### Effect of CAP on Physicochemical Properties
Dehydrogenase is regenerated by the dehydroroscorbate reductase enzyme, forming ascorbic acid. Cold plasma induces the hydrogenation of molecules, which can chemically transform dehydroosaccharide into ascorbic acid, reversing the decay mechanism and increasing vitamin C content [33]. Color is an important quality characteristic of juices and a major factor affecting the sensory perception and consumer acceptance of foods. Various processing methods are used not only to increase the palatability of fruit or vegetable juices but also to prolong their shelf life. Cold plasma processing is an interesting alternative to the traditional food processing and preservation methods, due to its limited effects on changes in quality attributes in the juices. The effect of CAP on the color of the tomato juice is shown in Table 2.  
Compared with the untreated juice, a significant decline in the brightness of the cold-plasma-treated samples was recorded on day 1 of the experiment. In turn, during storage (after 10 days), no statistically significant differences in the parameter L* values were observed in the samples exposed to cold plasma for 600 s, compared with the control product. The chromaticity index a* changed with the changing plasma treatment time. The longer the time of the tomato juice CAP treatment, the lower the values of this parameter. The storage time exerted an effect on this attribute. The chromaticity index b* changed after the cold plasma treatment on day 1. The CAP treatment was shown to reduce the yellowness parameter by approximately 32% in the juice treated for 600 s and the control product. On day 10, the chromaticity parameter b* was slightly lower than the values recorded on day 1 of the experiment. On the other hand, there was only a 1% difference in the values of this parameter in the control juice and the sample subjected to the longest cold plasma exposure (600 s). The total color difference \(\Delta E\) in the plasma-treated juices analyzed on day 1 was 2.65-2.94 (taking into account samples with extreme parameters), which indicates an average color deviation recognizable even by a non-expert. The total color difference was calculated for samples analyzed on day 10 as well. In the case of samples subjected to the 30 s cold plasma treatment, the \(\Delta E\) values were below 1, indicating an indiscernible deviation, and between 1 and 2 indicating slight differences recognizable by a person experienced in discriminating color nuances. Several studies reported various results of color changes after CAP treatment. For example, pomegranate juice became lighter after 7 min of plasma treatment with a total
# LoRaWAN Behaviour Analysis through Dataset Traffic Investigation
## 2 LoRaWAN in a Nutshell
### LoRa Physical Packet Structure  
In LoRa, there are two types of packets: downlink and uplink packets. Uplink are sent by end-devices to the NS relayed by one or many gateways. Uplink packets use the LoRa radio packet explicit mode in which the LoRa physical header (PHDR) plus a header CRC (PHDR_CRC) are included. The integrity of the payload is protected by a CRC.  
Figure 4: Join response message structure.  
Figure 3: Join request message structure.  
Figure 2: Over-The-Air Activation (OTAA) procedure flow graph.  
Each downlink message is sent by the Network Server to only one end-device and is relayed by a single gateway. Downlink messages use the radio packet explicit mode in which the LoRa physical header (PHDR) and a header CRC (PHDR_CRC) are included.  
Both uplink and downlink messages carry a physical payload that contains all the information needed to forward the message through the network. The physical payload is composed by a single-octet MAC header (MHDR), followed by a MAC payload, and ending with a 4-octet message integrity code (MIC). The structure of a LoRaWAN frame is shown in Figure 5.  
Inside the MAC payload there is the frame header (FHDR) that is the header of the packet at data link layer. The frame header is composed by device address (DevAddr) of 4 bytes, a frame control octet (FCtrl) of 1 byte, a 2-bytes frame counter (FCnt), and up to 15 bytes of frame options (FOpts) used to transport MAC commands.  
The FCnt is a kind of counter that allows the devices to keep track of the number of data frames sent uplink to the Network Server (FCntUp), and sent downlink from the Network Server to the device (FCntDown). Whenever an OTAA end-device successfully process a join procedure their frame counters is reset to 0 while the ABP devices have their FCnt initialized to 0 at fabrication ant it must never be reset to 0.
# Tensile Specim Circular Grid Pattern and AI-Based Strain Calculation Method
## 2 Theoretical Background
### Image Preprocessing  
Image-based strain measurement methods are divided into 2D and 3D methods, with 3D DIC methods generally being applied for experiments. In this study, 2D was used to simplify the tensile test; because the material was a thin sheet, the difference between 2D and 3D was negligible, so thickness direction strain was ignored. The tensile test was recorded on video and images for the training data were generated from the test video, with necessary data extracted by image preprocessing.  
To extract only single circles from the circular grid pattern in the video recorded during the tensile test, image binarization was applied. Image binarization [30], as shown in Figure 3, converts the image into two colors, 0 and 255, and was applied in this study to reduce noise. Image binarization generally sets pixels above a certain threshold to 0 and the others to 255. In this case, the generated images had white objects on a black background. Given image A and structural element B, dilation and erosion are defined as in Equation (2). In Equation (2), B\({}_{x}\) is the transformation of B by vector z, representing the symmetry of \(\hat{B}\).  
\[\begin{split} A\bigoplus B&=\left\{x|\left[\left( \hat{B}\right)_{z}\cap A\right]\
eq 0\right\}\\ A\odot B&=\left\{x|\left(B\right)_{z}\subseteq A \right\}\\ A\circ B&=\left(A\odot B\right)\bigoplus B\\ A\cdot B&=\left(A\bigoplus B\right)\odot B\\ \end{split} \tag{2}\]  
Figure 3: Image binarization.  
Figure 2: DIC measurement method.  
After reducing noise and extracting only circles through image binarization, the image size typically consists of approximately 20 \(\times\) 20 pixels. This size represents the pixel space where the circular pattern exists within a 1920 \(\times\) 1080 size when adjusting the camera position considering the elongation of the material during the tensile test. The circular pattern exists in a relatively localized area compared to the overall image size so, even when captured at high resolution during the tensile test, only low-resolution images can be obtained. The lower the image resolution, the fewer pixels are available to represent the deformation of the circle. For example, if a circle with a diameter of 10 mm is composed of pixels with sizes of 1 \(\times\) 1 mm and 2 \(\times\) 2 mm, when the circle deforms to 12 mm, the 1 \(\times\) 1 mm pixels would show 2 pixels, while the 2 \(\times\) 2 mm pixels would show 1 pixel, resulting in loss of deformation information. To overcome this issue with low-resolution images, super resolution was applied to obtain high-resolution images. Super resolution converts low-resolution images to high-resolution images through AI training, as shown in Figure 4[31].
# Impact of Wind on the Movement of the Load Carried by Rotary Crane
## 3 Equations of Motion for the Studied System
### Variability of the Area on Which Wind Power Has Influence  
In Equation (13), defining the impact of wind power, area \(A\), on which the force has influence, is often treated as a constant value resulting from the initial position of the load [12; 26]. The present paper includes the change of the area caused by both wind power and particular movements of crane's elements, as well as defines the parameter of the effective area \(A_{\mathit{eff}}\).  
In order to determine the parameter \(A_{\mathit{eff}}\), dependencies between the normal vectors of particular surfaces and the change of coordinates of three vertices of each analyzed surface were used.  
By using the coordinates of three points (vertices): \(P_{1}=(x_{1}\),\(y_{1}\),\(z_{1}\)), \(P_{2}=(x_{2}\),\(y_{2}\),\(z_{2})\), \(P_{3}=(x_{3}\),\(y_{3}\),\(z_{3})\), it is possible to determine the plane passing through these points, which may be presented by means of Equation [27]:  
\[\left|\begin{array}{ccc}X-x_{1}&Y-y_{1}&Z-z_{1}\\ x_{2}-x_{1}&y_{2}-y_{1}&z_{2}-z_{1}\\ x_{3}-x_{1}&y_{3}-y_{1}&z_{3}-z_{1}\end{array}\right|=0. \tag{16}\]  
Parameters of the general equation of this plane  
\[\alpha X+\beta Y+\gamma Z+\mu=0, \tag{17}\]  
constitute component elements of the normal vector  
\[\mathbf{n}_{1}=\left[\begin{array}{c}\alpha_{i}\\ \beta_{i}\\ \gamma_{i}\end{array}\right]. \tag{18}\]  
By determining the normal vector of the plane, where wind power has influence  
\[\mathbf{n}_{2}=\left[\begin{array}{c}1\\ 0\\ 0\end{array}\right], \tag{19}\]  
it is possible to determine the angle measure between the normal vectors \(\mathbf{n_{1}}\) and \(\mathbf{n_{2}}\).  
\[\cos\kappa_{i}=\frac{\mathbf{n}_{1}\circ\mathbf{n}_{2}}{|\mathbf{n}_{1}|| \mathbf{n}_{2}|}. \tag{20}\]  
Subsequently, the parameter of the effective area on which wind power has an impact may be depicted as  
\[A_{eff}=ab\cos(\kappa_{1})+ac\cos(\kappa_{2})+bc\cos(\kappa_{3}), \tag{21}\]  
where \(a\)--length, \(b\)--width, and \(c\)--height of the carried load, and \(\kappa_{1}\), \(\kappa_{2}\) as well as \(\kappa_{3}\) are the angles between the normal vectors of particular surfaces.
# Design and Implementation of DSLMS Algorithm Based Photoelectric Detection of Weak Signals
## 3 DSLMS Adaptive Filter
### Algorithm Performance Analysis
The DSLMS algorithm proposed in this paper uses time series correlation analysis to quantify the correlation of the error signal at two consecutive moments by calculating \(E[e(n)e(n-1)]^{2}\). Utilizing the non-correlated nature of noise, the method can effectively distinguish between noise and valuable signals in the signal. The DSLMS algorithm can identify noise variations and reduce their effects when adjusting the step size, significantly enhancing the adaptive filter's anti-interference capability. In addition, the algorithm employs a dynamic step-size adjustment mechanism that connects the step size to the correlation of the error signal, allowing the algorithm to automatically reduce the step size as it approaches the optimal solution in response to the reduction in error. Combining the DSLMS algorithm with the pair eliminator system and fine-tuning the segmented step-size factor, the methodology used in this study effectively improves the noise rejection capability. It balances the convergence speed with the system stability while ensuring the algorithm's performance.
## 5 Machine Learning Algorithms
### Text Emotion Recognition Model  
The model for analyzing emotions in Polish texts was developed using an artificially generated Chatbot dataset [30]. Its neural network architecture relied on LSTM recurrent networks, with the input layer incorporating embeddings derived from distribution models specifically designed for the Polish language. In NLP tasks, LSTMs are frequently combined with word embeddings, which enhances the model's capability to grasp and analyze the contextual and semantic relationships between words. Word embeddings serve as numerical representations that capture the semantic and syntactic associations between words in a dense vector space, enabling algorithms to better interpret word meaning and context. Common techniques for generating these embeddings, such as Word2 Vec or GloVe, are widely used in tasks like sentiment analysis, classification, and translation. For this model, the distribution models were generated using the Word2 Vec technique and trained on data from the NJKP (National Corpus of Polish) and Polish Wikipedia. Multiple models were applied, varying in type, architecture, and vector size, to optimize performance for the specific linguistic requirements of the task. The model for recognizing emotions in text is characterized by high efficiency. The model tested on generic data has an accuracy of about 96%, while in the case of data labeled by a human, the model's accuracy is 87%.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Classification Model** & **Training Datasets** & **Num. of Categories** & **Accuracy** \\ \hline Convolutional Neural Network & CREMA-D, RAVDESS, SAVEE, TESS & 7 & 71.00\% \\ \hline ResNet 18 & CREMA-D, IEMOCAP, & 6 & 67.62\% \\ ResNet 34 & RAVDESS, SAVEE, TESS & 6 & 68.15\% \\ ResNet 50 & & 4 & 75.76\% \\ ResNet 50 & & 5 & 68.86\% \\ ResNet 50 & & 6 & 62.18\% \\ \hline Stacked Time Distributed 2D CNN-LSTM & RAVDESS & 8 & 90.00\% \\ Stacked Time Distributed 2D CNN-Bidirectional LSTM with Attention & & 8 & 94.02\% \\ Parallel 2D CNN-Bidirectional LSTM with Attention & & 8 & 96.55\% \\ Parallel 2D CNN-Transformer Encoder & & 8 & 96.78\% \\ \hline \end{tabular}
\end{table}
Table 2: Comparison of neural network models for the emotion recognition problem using Mel spectrograms.
## 3 Classification of Systems
### Direction of Power Flows through the CVU  
CVUs are transmission units with seamlessly variable ratios and two shafts for power input and output. During normal driving situations, the power from the input(s) passes forward through the CVU. During either engine braking or regenerative braking, the power from the wheel(s) passes backward through the CVU to the input(s) within a limited time interval. In this paper, when analyzing the direction of power flows through the CVU, only normal driving situations are taken into account since the power passing through the CVU is continuous rather than intermittent. Additionally, the situations in which the power only transfers within the system, such as when the M/G starts the ICE through the CVU or when the ICE drives the M/G through the CVU for recharging, are ignored. In every driving mode, the power passing through the CVU to the wheels is unidirectional. The system is called a one-way manipulated system.  
Alternatively, if the input shaft and output shaft of the CVU can be interchanged in different driving modes, this kind of system is called a dual-way manipulated system, as the power passing through the CVU to the wheels is bidirectional. As shown in Figure 3, the power flows through the CVU from node A to node B in mode 1 and then from node B to node A in mode 2. Since the ratio coverage of the CVU is exploited twice, this feature can help increase the overall ratio coverage of the system by combining other transmission units such as clutches and gearsets.  
Figure 2: FPG and BPD of the TU/e system.  
In the BPD of a dual-way manipulated system, the CVU is at the center of a bidirectional bridge structure, as shown in Figure 4. This structure is required so that the two ends of the CVU can be connected to both the output and the input unit through connection lines. For hybrid systems with multiple input units, the two ends of the CVU can be connected to all of the inputs or to only one of them. Figure 5 shows a few examples of bidirectional bridge structures in different systems.
## 4 Experimental Validation
### WANARX Model  
The WANARX model was trained using the MATLAB \"System Identification Toolbox\". The available training dataset was normalized and arranged to create standard regressors with the structure reported below:  
\[\mathbf{x}_{k}=(y_{k-1},u_{k-1},u_{k-2})^{T} \tag{22}\]  
with \(n_{a}=1\), \(n_{b}=2\) and \(n_{k}=1\). The choice of model orders was made based on a combination of prior knowledge of the system and trial and error. A five-unit wavelet network was implemented, with the main parameters reported in Table 1. A FIT value of 89.2388% with the training dataset was observed. In Figure 4, a comparison between the measured and predicted output was reported as validation upon a testing dataset of \(M=128\) data points.
# Generative Aspect Sentiment Quad Prediction with Self-Inference Template
## 5 Results and Discussion
### Model Overfitting Analysis  
Due to the limited amount of data, the original model exhibits a significant overfitting issue, as shown in Figure 7. In all four datasets, the training set's loss steadily decreases, but the validation set's loss increases instead of decreasing. After applying our Self-Inference Template, a notable reduction in the validation set's loss is observed. Although there is still a subtle upward trend, it is considerably alleviated compared to the original model, indicating a significant reduction in overfitting.  
\begin{table}
\begin{tabular}{l c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{3}{c}{**Rest15**} & \multicolumn{3}{c}{**Rest16**} & \multicolumn{3}{c}{**ACOS\_Restaurant**} & \multicolumn{3}{c}{**ACOS\_Laptop**} \\  & **Pre** & **Rec** & **F1** & **Pre** & **Rec** & **F1** & **Pre** & **Rec** & **F1** & **Pre** & **Rec** & **F1** \\ \hline Paraphrase & 46.16 & 47.72 & 46.93 & 56.63 & 59.30 & 57.93 & 61.02 & 59.73 & 60.37 & 44.87 & 44.10 & 44.48 \\ Paraphrase+PT & 48.46 & 49.56 & 49.00 & 58.99 & 61.58 & 60.26 & 60.07 & 59.40 & 59.73 & 44.51 & 43.32 & 43.91 \\ Paraphrase+MT & 45.51 & 46.54 & 46.02 & 58.19 & 61.33 & 59.72 & 57.71 & 57.84 & 57.78 & 43.53 & 42.89 & 43.21 \\ Paraphrase+PM & 47.58 & 48.30 & 47.94 & 57.11 & 58.82 & 57.95 & 60.09 & 60.29 & 60.19 & 44.54 & 43.24 & 43.88 \\ \hline SIT & 47.89 & 50.13 & 48.98 & 58.98 & 61.60 & 60.26 & 63.13 & 63.49 & 63.31 & 44.38 & 44.61 & 44.49 \\ SIT+PT & 48.41 & 49.75 & 49.07 & **60.78** & **63.24** & **61.99** & **63.54** & **63.83** & **63.69** & 43.12 & 42.78 & 42.95 \\ SIT+MT & 47.93 & 49.50 & 48.70 & 58.30 & 60.96 & 59.60 & 61.79 & 63.27 & 62.51 & 44.46 & 44.35 & 44.41 \\ SIT+PM & **49.63** & **50.38** & **50.00** & 59.22 & 61.66 & 60.44 & 62.88 & 63.38 & 63.13 & **45.95** & **45.91** & **45.93** \\ \hline \hline \end{tabular}
\end{table}
Table 5: Results of ablation experiments for four datasets. The best results are in bold.
## 2 Materials and Methods
### Search Strategies and Selection Process  
Keyword searches were conducted in December 2022 and re-checked before finalizing the analysis in December 2024. Data collection focused on articles from PubMed, Embase, Web of Science, and Cochrane Library using a combination of search terms related to sleep quality (\"sleep quality\", \"sleep disturbance\", \"dyssomnia\" or \"sleep deprivation\"), obesity (\"obesity\", \"overweight\", or \"obese\"), and exercise (\"exercise\", \"physical activity\", \"training\", \"fitness\", \"resistance training\", \"endurance training\", \"running\", \"jogging\",\"yoga\", \"taichi\", \"cycling\", \"ergometer\" or \"bicycle\") (Table S1). Based on established selection criteria, interventions involving treatments other than exercise, such as drug administration, physical therapy, and medical device use, were excluded. Studies that did not specify exercise modalities, lacked a relevant control group or had insufficiently detailed data were excluded. Conference abstracts were excluded because of limited information, making them unsuitable for a thorough quality evaluation. Studies without full-text availability and review articles were also excluded.
## 1 Introduction
[PERSON] et al. used an electronic nose (EN), near-infrared spectroscopy (NIRS), and standard bioreactor probes to measure data to track the process of laboratory-scale yogurt fermentation. The sensor signals were fused by a cascade neural network. The results indicated that the fusion of online sensors with the selected analyzer can improve the monitoring and quality control of yogurt fermentation [25]. [PERSON] et al. verified the geographical origin of extra virgin olive oil by an electronic nose and UV-visible spectrophotometer combined with multivariate analysis. The paper indicated that objective instrument data related to two important sensory characteristics, oil color, and aroma, could provide complementary information [26]. [PERSON] et al. investigated the potential of visible fingerprints and physicochemical parameters and combined multivariate data analysis to classify extra virgin olive oil (EVOO) from different regions of Spain based on geographic sources. The results showed that after performing partial least squares discriminant analysis (PLS-DA) on the fusion matrix, the defined categories were perfectly distinguished, achieving 100% correct classification and a significant improvement in the overall prediction rate (92.5%) [27]. The application of fusion technology in the detection of water quality parameters is relatively limited. [PERSON] et al. proposed using UV/Vis spectral fusion turbidity data to detect COD, TSS, oil, etc., in Chinese restaurant wastewater and using Boosting IPW-PLS to establish a prediction model. The results indicated that the prediction model can effectively detect the water quality of wastewater, demonstrating the application prospects of data fusion technology for water quality detection [28]. [PERSON] et al. researched the fusion of UV absorption spectra with different optical paths to detect COD in wastewater and established a prediction model based on data fusion using PLS. The results showed that compared with the standard measurement method, the residual prediction deviation (RPD) of the model was 3.72, and the \(R_{pred}^{2}\) was 0.936, demonstrating excellent predictive performance [29]. According to the literature that has been reviewed, there are currently few research reports on using spectral and other factors of fusion techniques for COD detection. The above work indicates that detection methods based on multi-source data fusion have higher prediction accuracy for COD detection than single-spectral methods. Considering the complexity of the influence of environmental factors on water detection based on UV-Vis spectroscopy, this paper first presents the research conducted on the mechanism of the influence of environmental factors on UV-Vis spectroscopy. Then, research on multi-source data fusion algorithms is shown. Subsequently, research on multi-source data fusion modeling is presented. Finally, the COD prediction results of the model before and after the compensation of environmental factors are compared.
# Occlusion-Robust Multi-Target Tracking and Segmentation Framework with Mask Enhancement
## 5 Discussion  
False positive, false negative, and target identity drift are often encountered in traditional MOT algorithms. One of the main reasons for this situation is the occurrence of occlusion. In this paper, we propose an optimization method for the error problem  
Figure 4: The visualization of MOTS results in indoor and outdoor occluded scenarios. (**a**) Segment adaptation in outdoor crowded scenarios with static cameras. (**b**) Segment adaptation in indoor crowded scenarios with moving cameras.  
of trajectory association under occlusion scenarios with a mask-based MOTS framework, which achieves a better result for the multi-object tracking problem, as shown in Section 4.  
However, the proposed method also has certain limitations. Several challenges still remain in multi-object tracking research. Although our experiments demonstrate that the mask-enhanced network improves trajectory association in occluded scenarios, the current feature network does not utilize inter-object interaction. Since most occlusion scenarios result from object interactions, incorporating interaction information with a feature extraction network is a promising research direction. Additionally, our framework employs several manually designed hyperparameter thresholds. Current scene-adaptive parameter trackers lack designs for occluded scenarios. The development of parameter adaptation methods for mask-based MOTS frameworks remains an essential yet understudied research direction.