esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Phenolic Compounds Extraction from Renewable Resources within a Biorefinery Scheme
### Techniques for Phenolic Compounds Extraction from Agri-Food By-Products
#### 2.1.3 Enzyme Assisted Extraction (EAE)  
The use of enzymes in the agri-food sector is not something new; however, they are an option for extracting phenolic compounds since they can rupture the cell wall, thus increasing the analyte extraction. Pectinolytic enzymes are the most widely used to increase juice yield, especially in the juice industry. For their use in extracting phenolic compounds, parameters such as pH, time, temperature, and enzyme concentration need to be optimised for each substrate and process. One of the main drawbacks of EAE is the high cost of enzymes, which can represent a limitation for their implementation at the industrial level [65; 66; 67].  
Studies on the effect of enzymes in the extraction of phenolic compounds from grape marc have shown that tannase and pectinase increase the phenolic compounds' content and antioxidant activity; however, cellulase did not show any release of phenolic compounds, thus no improvement in the antioxidant capacity [68]. In a recent study [69], enzymatic extraction was applied to obtain non-extractable phenolic compounds (NEP) from sweet cherry pomace. Their results showed that enzymes positively affected the extraction of proanthocyanidin and phenolic compounds with high molecular weight. In addition, the enzyme extraction showed to extract up to four times more phenolic compounds than conventional methods.  
The emerging use of natural solvents and non-organic solvents used alone or in conjunction with non-conventional methods is moving forward in safer and sustainable processes, revealing promising results for the extraction of phenolic compounds. Although further research is needed to understand better their mechanisms, advantages, limitations, and industrial success, their implementation will certainly lead to more eco-friendly and effective extraction of phenolic compounds from by-products.
## 2 Related Work  
RIS-assisted networks are widely deployed in the 6G. In [6], the authors studied the use of RIS and multiple-input single-output (MISO) technologies. They proposed an alternating minimization approach to increase EE of the system under constraints of no beamforming and NLOS between transmitter (\(T_{x}\)) and receiver (\(R_{x}\)). Similarly, in [14], the authors demonstrated the use of a RIS-assisted MISO network to increase the sum rate. They optimized the continuous \(\Theta\) of RIS and beamformer of \(T_{x}\).  
The authors of [15] explored the use of RIS with single input single output (SISO) network assuming channel state information (CSI) and network achieved a higher signal-to-noise ratio (SNR). In [16], the authors also used RIS with MISO and maximized the signal-to-interference and noise ratio (SINR) of the network. In [17], the SNR of the network  
Figure 2: Evolution of industry from 1.0 to 5.0.  
was improved using RIS in the single cell. Furthermore, [PERSON] et al. proposed an alternating descent gradient algorithm to maximize the EE of the network. The algorithm showed a significant gain in the EE of the cell-free network.  
In [18], [PERSON] et al. applied deep reinforcement learning (DRL) to improve the EE. They showed that the EE improved up to 77% by increasing the number of RIS elements. Similarly, in [19], the authors proposed the Dinkelbach method to enhance the EE of the device-to-device (D2D) network. The algorithm showed significant improvements in EE of the D2D network. In [20], the authors used RIS surface to enhance the security of the physical layer in the 6G wireless network. The performance showed a significant improvement in security using RIS. Similarly, [PERSON] et al. in [21] optimized the power of the wireless network using RIS surface. The use of RIS in next-generation wireless networks is surveyed in [22].  
Moreover, [PERSON] et al. in [23] used an accelerated projected gradient algorithm to achieve higher EE and SE in a RIS-assisted multiple-input multiple-output (MIMO) network. Similarly, in [24], the authors used the power consumption method to enhance the data rate and EE of the network. In [25], the authors used an optimization algorithm to enhance the throughput and EE of the RIS-assisted network. Similarly, in [26], a Q-learning based approach is adopted to manage interference in the heterogeneous networks (HetNets). In [27], the authors surveyed the game-theoretical approaches for the virtualization of the cellular network.  
Table 1 summarizes the existing RIS-assisted networks. In this article, we propose an outer approximation algorithm (OAA) to maximize EE, SE, throughput and minimize the latency of the RIS-assisted network.
# Trajectory Tracking Control Strategy of 20-Ton Heavy-Duty AGV Considering Load Transfer
###### Abstract  
During the operation of outdoor heavy-duty Automated Guided Vehicle (AGV), the stability and safety of AGV are easily reduced due to load transfer. In order to solve this problem, a trajectory tracking control strategy considering load transfer is proposed to realize the trajectory tracking of AGV and the adaptive distribution of driving torque. The three-degree-of-freedom (3-DOF) kinematics model and pose error model of heavy-duty AGV vehicles are established. The lateral load transfer and longitudinal load transfer rules are analyzed. The vehicle trajectory tracking control strategy is composed of an improved model predictive controller (IMPC) and drive motor torque adaptive distribution controller considering load transfer. By optimizing the lateral acceleration of the vehicle body, the IMPC controller improves the problem of large driving force difference between the left and right sides of the wheel caused by the lateral transfer of the load and the problem of large wheel adhesion rate caused by the longitudinal transfer of the load is improved by the speed controller and the torque proportional distribution controller. The joint simulation platform of MATLAB/Simulink and CarSim is built to simulate and analyze the trajectory tracking of heavy-duty AGV under different pavement adhesion coefficients. The simulation results have shown that compared with the control strategy without considering load transfer, on the two types of pavements with different adhesion coefficients, the maximum lateral acceleration is reduced by 19.7%, and the maximum tire adhesion rate is reduced by 11.5%.  
heavy-duty AGV; improved MPC; load transfer; trajectory tracking; torque adaptive distribution controller +
Footnote †: journal: applied sciences  
Article
# Assessing the Security and Privacy of Baby Monitor Apps
## 4 Evaluation of the Privacy Policies
### User Access, Edit, and Deletion  
The GDPR grants users the right to access and seek a copy of their collected personal data on request (Article 15) [33], the right to rectification in order to correct and complete their personal data (Article 16), and the right to erasure also known as \"the right to be forgotten\" (Article 17) [34]. While users have the right to be informed about these control rights (Articles 13 and 14), two of the inspected apps did not provide such information. The Dormi app merely offers users the opportunity to contact them in case of queries or complaints about the collected data. Babyphone Mobile informs users of their rights regarding their stored personal data, i.e., the rights of access, rectification, and deletion in its general website privacy policy. This app offers users the ability to contact the developers of the app by email to exercise these rights.
## 2 Materials and Methods
### Model Evaluation  
To evaluate the performance differences among the GPTransformer, 1D-CNN, and GPTransCNN, comparative experiments were conducted using the coefficient of determination (R\({}^{2}\)), root mean square error (RMSE), and mean absolute error (MAE) [50,51] as evaluation metrics. The formulas are as follows:  
\[RMSE=\sqrt{\frac{1}{n}\sum_{i=1}^{n}(y_{i}-\hat{y}_{i})^{2}} \tag{9}\]  
\[R^{2}=1-\frac{\sum_{i=1}^{n}(y_{i}-\hat{y}_{i})^{2}}{\sum_{i=1}^{n}(y_{i}-\hat {y})^{2}} \tag{10}\]  
\[MAE=\frac{1}{n}\sum_{i=1}^{n}|y_{i}-\hat{y}_{i}| \tag{11}\]  
R\({}^{2}\) quantifies the proportion of variance in the data explained by the model, with values closer to 1 indicating a superior fit. RMSE and MAE assess the prediction errors, where lower values correspond to higher predictive accuracy. In formulas 9 to 11, \(y_{i}\) is the actual SOM content, \(\hat{y}_{i}\) is the predicted SOM content, \(\hat{y}\) is the mean SOM content, and \(n\) is the total number of samples.
# Survey of Dense Video Captioning: Techniques, Resources, and Future Perspectives
## 3 Techniques for Dense Video Captioning
### Transformer-Based Approaches  
#### 3.3.1 Knowledge-Enhanced Transformer Models  
Knowledge-enhanced transformer models are designed to integrate external knowledge into the video captioning process, aiming to enhance the diversity and accuracy of the generated captions. [PERSON] et al. [32] introduced the Text with Knowledge Graph Augmented Transformer (TextKG) for this purpose. In TextKG, both the external and internal streams utilize the self-attention module to model interactions among multi-modality information. Specifically, the external stream focuses on modeling interactions between knowledge graphs and video content, while the internal stream exploits multi-modal information within videos. This approach allows the model to leverage external knowledge effectively.  
Similarly, [PERSON] et al. [33] proposed a Center-enhanced Video Captioning model with Multimodal Semantic Alignment. This model integrates feature extraction and caption generation into a unified framework and introduces a cluster center enhancement module to capture key information within the multimodal representation. This facilitates more accurate caption generation. Both of these models, TextKG and the Center-enhanced Video Captioning model, effectively utilize external knowledge to overcome the limitations of relying solely on video-description pairs. By incorporating knowledge graphs and memory networks, they capture richer context, thereby enhancing the cognitive capability of the generated captions. However, the performance of these models is contingent upon the quality and coverage of the external knowledge sources they utilize.
# Markov-Modulated Poisson Process Modeling for Machine-to-Machine Heterogeneous Traffic
## 4 CHANAL Model and Performance Metrics
### Performance Metrics  
We assess the influence of H2H and M2M traffic using two performance metrics:  
\begin{table}
\begin{tabular}{l l c} \hline \hline
**Model Characteristics** & **CHANAL** & **CANAL** \\ \hline Heterogeneity traffic & Enabled & Disabled \\ Homogeneity traffic & Disabled & Enabled \\ Synchronization behavior & Enabled & Disabled \\ Real-time behavior & Disabled & Enabled \\ FIFO queuing & Enabled & Disabled \\ Random/standard queue & Disabled & Enabled \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison between CHANAL and CANAL models.  
Figure 4: Flow chart for CHANAL model.  
Service completion rate (scr): This measures how many requests are completed in a given amount of time and is derived from the average arrival requests and service rate for a certain type of traffic (for example, \(\text{scr}_{\text{(h)}}\) and \(\text{scr}_{\text{(m)}}\) indicate the service completion rate for H2H or M2M traffic, respectively [31]).
* Resource utilization (\(\text{ru}_{\text{(h)}}/\text{ru}_{\text{(m)}}\)) for H2H and M2M: This statistic, which compares the total number of resource blocks used in the network (c) to the number of utilized \(\text{rb}_{\text{(h)}}/\text{rb}_{\text{(m)}}\) in each state, indicates the likelihood that the system will be busy serving H2H/M2M arrivals.
# From Analogue to Digital Photogrammetry: Documentation of Padise Abbey in Two Different Time Stages
## 3 Re-Processing of Original Images
### Pre-Processing  
The digitized images had to be transformed to achieve the same position of the origin of the image coordinate system for all images. Fortunately, we had available a report from the original survey with calibrated coordinates of each fiducial mark. The coordinates were used as the destination coordinates in affine transformation. For transformation, affine transformation was considered as sufficient according to [21].  
After the transformation, the images had to be cropped by rectangular region to obtain the same size in pixels for each image. Transformation and cropping were carried out in software Autodesk Raster Design. The final resolution of the images was 11,717 \(\times\) 16,441 pixels.
## 4 Experiment
### Loss Function  
A combination of cross-entropy (21) and Dice loss functions (22) was used in our experiments. Cross-entropy loss is a common classification loss function that measures the difference between predicted results and ground truth annotations. In the computer visual field, it is commonly used to predict the class of an image, where each pixel is classified as either foreground or background. This loss function can help the model better distinguish between the foreground and background and reduce mis-segmentation.  
\[L_{CrossEntropyLoss}=-\sum_{x}\left(p(x)\log q(x)\right) \tag{21}\]  
The Dice loss function is a similarity-based loss function, as in Equation (22). It measures the degree of similarity between the predicted results and the true labeling. In medical image segmentation, the Dice loss function is often used for multi-classification problems, where each pixel is classified into categories. This loss function can help the model better preserve the shape and structural information of the target region and reduce over-smoothing.  
\[L_{DiceLoss}=1-2\times\frac{\sum_{i}P_{i}G_{i}}{\sum_{i}P_{i}+\sum_{i}G_{i}}. \tag{22}\]  
In this formula, \(P_{i}\) represents the probability value that the \(i\)-th element belongs to a certain class, and \(G_{i}\) represents the true label value of the \(i\)-th element. In our experiments, we combined the two loss functions with a weighted sum, as shown in Equation (23), where \(\lambda_{1}=0.4\) and \(\lambda_{2}=0.6\) represent the weights of cross-entropy and Dice losses, respectively. The combined loss function is defined as follows:  
\[L_{total-loss}=\lambda_{1}\times L_{CrossEntropyLoss}+\lambda_{2}\times L_{ DiceLoss}. \tag{23}\]
# Multi-Objective Optimized GPSR Intelligent Routing Protocol for UAV Clusters
## 2 Related Work
### Improved Routing Protocols Based on DRL  
In large-scale, highly dynamic unmanned clusters networks, reliable communication routing needs to meet the requirements of efficient energy utilization, low latency, node load balancing, etc. to achieve reliable data transmission. Since traditional single-objective optimization is difficult to comprehensively weigh various indicators that affect routing performance, such as link quality, latency, energy consumption, distance, and hole probability, multi-objective routing optimization problem design based on the above indicators can achieve the purpose of comprehensively improving network performance. In Table 1, this paper classifies and summarizes the characteristics of the above-mentioned improved geolocation routing protocols and intuitively shows the indicators covered and not covered in the design process of each routing protocol. In Table 2, this paper classifies and summarizes the advantages and disadvantages of the improved routing protocols based on RL and DL ideas when applied to large-scale, highly dynamic unmanned clusters.  
Currently, most UAV cluster routing protocols based on RL use the QL algorithm, but they have several shortcomings in UAV networks: (1) As the number of network nodes increases, the state and action spaces expand significantly, making it time-consuming to learn large Q-tables and difficult to ensure algorithm convergence, which hinders adaptation to large-scale network scenarios. (2) Most current routing protocols rarely consider multiple performance indicators such as distance, energy consumption, delay, and link quality, limiting their effectiveness in practical applications.  
In addition to RL techniques, routing protocols can explore the relationship between local network topology, geographic location, and link states using DL. However, further research is needed to address challenges such as designing appropriate deep network architectures, constructing relevant training datasets, and addressing potential overfitting issues.  
The DRL algorithm, developed from RL, combines DL's perception capabilities with RL's decision-making abilities. DRL-based dynamic routing algorithms can better perceive the changing multidimensional parameters of the environment, establish a mapping between environmental data and routing decisions, and are ideal for learning dynamic network topologies and traffic conditions [12]. This study uses an optimized DRL algorithm, Deep Double Q-Learning Network (DDQN), to design a Multi-Objective Optimized GPSR-based protocol, termed DDQN-MTGPSR.
## 3 5G RCS Message Filtering Models and Methods
### The HMM for RCS Message Short Text Fields Weighting
#### 3.2.3 HMM Training with the Observation Sequence and the Hidden State Sequence  
The HMMs are built for each short text field. While training, the HMM learns from the observation and hidden state pairs in the observation sequence and the hidden state sequence. Therefore, the proper hidden states should be labeled to each observations. In this case, the observation states are the word weights calculated based on words in the training set and the hidden states are labeled according to the sign of word weights. Observations with positive word weights are labeled as the ham state and, accordingly, observations with negative weights are labeled as the spam state. While training, the pairs of observation and hidden state feed the Baum-Welch algorithm (BM algorithm) [27]. It starts with initial parameters and repeats the expectation step (E-step) and the maximization step (M-step) until convergence, to find the optimal HMM parameters, such as the emission probability matrix and the state transition probability matrix. For efficient batch training, the observation sequences and hidden state sequences are connected together to feed the BM algorithm one after another.  
In terms of HMM, the HMM mentioned above \(\lambda\) can be defined as a 3-tuple:  
\[\lambda=(\pi,A,B), \tag{1}\]  
where \(\pi\) is the initial state distribution, \(A\) is the state transition probability matrix and \(B\) is the emission probability matrix.  
\(\pi\) is initialized as \(\pi_{0}=[0.5,0.5]\), which refers to the opportunity for starting from the spam state or the ham state.  
\(A\) indicates the transmission probability between the hidden states. In this case, because the ham and spam categories are used for short text field labeling, the HMM has two states, which are represented as \(S=\{s_{1},s_{2}\}=\{spam,ham\}\). Therefore, \(A\) is represented as \(A=(a_{ij})_{2\times 2}\), where \(a_{ij}=P(q_{t+1}=s_{j}|q_{t}=s_{i})\), for \(i,j=1,2\) and \(\sum\limits_{j=1}^{2}a_{ij}=1\), for \(i=1,2\). Before training, \(A\) is initialized as \(A_{0}=\begin{bmatrix}0.5&0.5\\ 0.5&0.5\end{bmatrix}\), supposing each state has the same opportunity to transfer to another including itself.  
\(B\) determines the probabilities of observations emitted by hidden states respectively. The matrix \(B_{0}\) with initial values is determined by the PDF parameter values of the spam and ham observations obtained in Section 3.2.2.  
While training, the HMM \(\lambda\) starts with its initial parameters \((\pi_{0},A_{0},B_{0})\). Then, it is optimized by the BM algorithm while being fed the training observation sequences and hidden state sequences. Finally, the optimal HMM parameters are derived. The HMMs for each short text field follow the same training process.  
The process of HMM training is shown in Figure 1.
## 2 Materials and Methods
### Manufacturing Samples, Equipment, and Bubble Configuration  
The film samples were taken based on the specifications of the products manufactured in the factory. The different samples varied in width (432 mm to 990 mm) and thickness (9.5 mm to 56.6 mm). These particular dimensional variations were used because of the technical capacity of the manufacturing process; they also reflect the variations in the factory's products. To ensure that the process was stable, the samples were taken at around 200 kg (approximately 3 h) after the film was calibrated.  
To manufacture the samples, only one mono-layer extrusion machine was used. The characteristics of the extruder are the following: Brand [PERSON] (Guarulhos, Brazil), 60 mm diameter screw with 1.5 m length (25:1 ratio), 125 mm diameter head, 1 mm die gap, and 150 mm diameter pull roller. The process temperatures were configured in the three screw-barrel zones (considering a variation of \(\pm\)7 \({}^{\circ}\)C) as follows: zone 1 at 180 \({}^{\circ}\)C, zone 2 at 185 \({}^{\circ}\)C, and zone 3 at 190 \({}^{\circ}\)C; and the die temperature at 195 \({}^{\circ}\)C [12].  
The configuration used was the configuration of \"high stalk\", because the main proportion of the material is 75% HDPE, a linear polymer. Figure 1 shows the difference between these two common configurations: \"in the pocket\" and \"high stalk\". Typically, to manufacture blown films of LDPE and LLDPE, the \"in the pocket\" configuration is used because it is a branched polymer, and for HDPE, the \"high-stalk\" configuration is used, within a certain range of values of neck height (normally from 7 to 9 times the die diameter) [9].
## 3 Methodology
### Dataset Description  
The proposed approach is validated using a publicly available dataset, i.e., the DREAMS database from the University of MONS-TCTS Laboratory ([PERSON], [PERSON]) and Universite Libre de Bruxelles - CHU de Charleroi Sleep Laboratory ([PERSON]) [41]. Details of this dataset are given in Table 1. It contains polysomnography (PSG) data or EEG signals of 16 female and 4 male human subjects for a whole night. The data was recorded using a 32 channel polygraph from subjects who were aged between 20 to 65 years. The EEG signals were sampled at a frequeny of 200 Hz. The sleep stages were annotated by experts following analysis of microevents, each of 30-second epoch, using the criteria defined by AASM. As mentioned earlier, data from 3 EEG channels (i.e., Fp2-A1, Cz2-A1, and O2-A1) is used in this study. Moreover, the dataset appears to be a little imbalanced in terms of gender. However, this bias in data is removed by grouping the subjects based upon their gender, i.e., the proposed methodology considers sleep stage detection individually for both genders, hence, the class imbalance in the dataset does not affect the classifier's performance. In addition, the same number of samples are selected from different sleep stages to create a well-balanced dataset.  
Figure 1: Block diagram of the proposed method.  
To the best of the authors' knowledge, very few studies that use data driven techniques have utilized this dataset. Therefore, to establish the robustness of the proposed approach, it is compared with eight different techniques while using the same dataset and experimental setup, i.e., (1) Raw EEG + ResNet, (2) PSD + CNN (5 layers [42]), (3) PSD + CNN (10 layers), (4) PSD + CNN (14 layers), (5) PSD + CNN (18 layers), (6) FE + Random Forest [43] (RF), (7) fast Fourier transform (FFT) + multilayered perceptron (MPC) [44], and (8) PSD + MPC.