esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### ER Diltation, Vacuole Formation, and Autophagy as Markers of Oocyte Stress  
Our ultrastructural data revealed a significant change in the morphology of SER vesicles following exposure to Lindane, with notable dilation observed at both 1 uM and 10 uM concentrations. However, at the highest concentration (100 uM), this dilation was more pronounced and accompanied by vacuoles in the ooplasm. Given the critical role of the ER in calcium homeostasis, protein synthesis, and fertilization-related signaling,its structural disruption is indicative of ER stress [76; 77], affecting oocyte maturation in response to toxicants, as previous studies reported [78].  
In line with these findings, toxicological studies have increasingly demonstrated that exposure to OCPs such as Lindane may lead to alterations in ovarian function through complex molecular pathways. These compounds have been shown to induce oxidative stress, mitochondrial dysfunction, and ER stress, which synergistically activate intrinsic apoptotic cascades in oocytes and surrounding granulosa cells [65]. Lindane has been shown to upregulate pro-apoptotic proteins and cause ultrastructural alterations in both mitochondria and the ER, interfering with oocyte maturation and triggering follicular atresia as a result [58]. These mechanistic insights reinforce the hypothesis that environmental toxicants serve as pro-apoptotic agents within the ovarian microenvironment, contributing to infertility and early ovarian failure in exposed populations.  
In MII oocytes, vacuoles are rare and may indicate cytoplasmic immaturity or aging [74; 79]. Vacuolization is a common response to injury in mature oocytes, resulting from the swelling and merging of SER vesicles [80; 81]. Since Lindane affects calcium homeostasis, high concentrations likely induce ER stress, leading to an imbalance in intracellular calcium, causing dilated SER vesicles to turn into prominent vacuoles [82].  
Following exposure to the highest Lindane concentration (100 \(\upmu\)M), there was a significant increase in dense lamellar bodies and MVBs, identified as secondary lysosome-like structures, in the ooplasm of mouse oocytes [83; 84]. These structures function as autophagic vacuoles or autolysosomes, facilitating the breakdown of internal components and indicating enhanced autophagic activity due to cellular stress. Furthermore, the presence of extracellular debris in the perivitelline space further supports the occurrence of autophagic processes, highlighting degenerative changes linked to autophagic exocytosis and apoptosis [85; 86].
## 2 Design of Active Fault-Tolerant Control System  
### Architecture of MIMO-MFA Active Fault-Tolerant Control System Design  
In this paper, a MIMO-MFA active fault-tolerant control method is presented after the failure of a distributed electric drive vehicle system. Cooperative control of the drive and steering systems is used to ensure that the vehicle maintains the desired speed and does not deviate from the established path. The active fault-tolerant control framework of the 4 WID electric vehicle is illustrated in Figure 1. It is composed of the driver expectation model, MIMO-MFAC controller and controller plant.  
In the above, \(\omega_{(.)}{}^{*}(k+1)\) is the expected wheel speed, \(\omega_{r}{}^{*}(k+1)\) is the expected yaw rate, \(T_{(.)}(k)\) is the motor torque of the four wheels, \(\delta_{f}(k)\) is the front wheel angle, \(\mathbf{u}(k)\) is the system input, \(\mathbf{y}(k)\) is the system output, \(\mathbf{\varPhi}_{c}(k)\) is the pseudo-Jacobian matrix, \(\omega_{r}(k)\) is the actual yaw rate and \(\omega_{(.)}(k)\) is the actual wheel speed.  
In the framework of the driven desired model for tracking and controlling by means of the preview PID, according to the desired path and vehicle speed, the algorithm converts the target trajectory into the desired front wheel angles as the control parameter input. The architecture of the path-tracking controller is presented in Figure 2.  
In the above, \(\Delta y(k)\) is the lateral position deviation, \(\Delta\psi(k)\) is the heading angle deviation, \(\delta_{f}(k)\) is the front wheel angle, \(x(k)\) is the rear axis center longitudinal coordinate, \(y(k)\) is the rear-axis center lateral coordinate and \(\psi(k)\) is the heading angle.  
Figure 1: Architecture of MIMO-MFA active fault-tolerant control method.  
Figure 2: Architecture of path tracking controller.  
Then, it obtains the desired yaw rate according to the vehicle two-degrees-of-freedom differential Equations (1) and (2). As the driver expectation model is not the focus of this paper, we do not provide a detailed introduction here.  
\[(\mathrm{k}_{1}+\mathrm{k}_{2})\beta+\frac{1}{u}(ak_{1}-b\mathrm{k}_{2})\omega_{ r}-\mathrm{k}_{1}\delta_{f}=m(\dot{v}+u\omega_{r}) \tag{1}\]  
\[(a\mathrm{k}_{1}-b\mathrm{k}_{2})\beta+\frac{1}{u}(a^{2}\mathrm{k}_{1}+b^{2} \mathrm{k}_{2})\omega_{r}-a\mathrm{k}_{1}\delta_{f}=I_{z}\dot{\omega_{r}} \tag{2}\]  
where \(m\) is the vehicle mass, \(a\) is the front axle distance to the COG, \(b\) is the rear axle distance to the COG, \(\mathrm{k}_{1}\) is the front wheel total lateral stiffness, \(\mathrm{k}_{2}\) is the rear wheel total lateral stiffness, \(\beta\) is the centroid side angle, \(\delta_{f}\) is the front wheel angle, \(u\) is the centroid longitudinal speed, \(v\) is the centroid lateral speed and \(I_{z}\) is the vehicle moment of inertia around the \(Z\)-axis.
## 2 Related Works  
Satellite network security has advanced continuously through various studies. In this work, we systematically categorize prior research on satellite network security into three key areas for analysis and reference. On the basis of this categorization, we review prior studies, identify their limitations, and establish the research direction for our study.
## Abbreviations  
\begin{tabular}{l l} BMWK & Federal Ministry of Economic Affairs and Climate Action (in German) \\ CPC & Collective Pitch Control \\ DRC & Down-Regulation Control \\ MKS & Meter Kilogram Second \\ MPPT & Maximum Power Point Tracking \\ NPI & Nonlinear Proportional Integral \\ OTC & Optimal Torque Control \\ PI & Proportional Integral \\ PSFC & Power Signal Feedback Control \\
**Nomenclature** \\ _Parameters_ & \\ \(\beta_{0}\) & Pitch angle at the operating point, rad \\ \(C_{p}\) & Power coefficient, -- \\ \(C_{p,max}\) & Maximum value of the power coefficient, -- \\ \(K_{p}\), \(K_{i}\) & Gains of PI controller \\ \(K_{p1}\), \(K_{p2}\), \(K_{p3}\), \(K_{11}\), \(K_{12}\), \(K_{i3}\) & Gains of NPI controller \\ \(n_{x}\) & Gearbox ratio, -- \\ \(P_{ntal}\) & Rated power, MW \\ \(R\) & Rotor radius, m \\ \(T_{g,patel}\) & Rated generator torque, kg m\({}^{2}\) \\ \(v_{ci}\) & Cut-in value for the wind speed, m/s \\ \(v_{co}\) & Cut-out value for the wind speed, m/s \\ \(v_{p,patel}\) & Rated value for the wind speed, m/s \\ \(\lambda\) & Tip-speed ratio \\ \(\lambda\)* & Optimal tip-speed ratio \\ \(\eta\) & Efficiency \\ \(\rho_{a}\) & Density of air, kg/m\({}^{3}\) \\ \(\omega_{g,patel}\) & Rated value of the generator speed, rad/s \\ \(\omega_{gsp}\) & Set point for the generator speed, rad/s \\ _Variables_ & \\ \(\beta\) & Pitch angle, rad \\ \(\beta_{a}\) & Output of pitch actuators, rad \\ \(e\) & Control error \\ \(\lambda\) & Tip-speed ratio, -- \\ \(P\) & Power, MW \\ \(P_{n\!f}\) & Power reference, MW \\ \(t\) & Time \\ \(T_{ga}\) & Output of the torque controller, kg m\({}^{2}\) \\ \end{tabular}
# Dynamic Quality Monitoring System to Assess the Quality of Asphal Concrete Payement
## 4 Digital Construction Quality Evaluation Model for Asphalt Pavement Based on IPSO-RBF
### Decision Model of Pavement Quality Evaluation Based on IPSO-RBF  
With the aim of improving the efficiency and exactitude of the neural network algorithm, this study proposes a RBF network decision-making model based on an improved PSO algorithm. The PSO algorithm is utilized to train three key parameters of the RBF neural network, namely: The center value, width and output weight of hidden layer. Figure 7 provides the flow chart for the road digital construction quality evaluation model based on an IPSO-RBF neural network. The specific prediction steps are as follows:  
**Step 1.** Initialize sample data. The data are normalized, and the sample data are divided into the test set and training set.  
**Step 2.** Determine relative parameters of the PSO algorithm. This includes the number of particles, the maximum number of iterations and the initialization position and speed of particles.  
Figure 7: Flow chart of digital construction quality evaluation model for asphalt pavement based on IPSO-RBF.  
**Step 3.** Identify the number of hidden layer nodes in the RBF neural network by the trial-and-error method on the basis of Formula (16). At the same time, combined with the pavement digital construction quality evaluation index system, the number of nodes of input layer and output layer are determined.  
\[a=\frac{n_{r}+n_{c}}{2}\leq n_{y}\leq(n_{r}+n_{c})+10=b \tag{16}\]  
where \(n_{r}\), \(n_{c}\) and \(n_{y}\) are the number of input layers, output layers and hidden layer nodes of RBF neural network, respectively.  
**Step 4.** Optimize the relative parameters of the RBF neural network. The centre, width and the output weight of the hidden layer are used as particle information in search of the preferred parameter combination in the iterative process.  
**Step 5.** Calculate the fitness value of the current position of the particle. The MSE between the predicted value and the actual value is used as the fitness function, as shown in Formula (17). This approach can judge whether to update the optimal position of the current particle and the optimal position of the population particles in accordance with the fitness value of the current position.  
\[fitness=\frac{1}{n}\sum_{i=1}^{n}\left(y_{i}^{\prime}-y_{i}\right)^{2} \tag{17}\]  
where \(n\) is the number of samples. \({y_{i}}^{\prime}\) and \(y_{i}\) are the prediction value and actual value of the particle, respectively.  
**Step 6.** Renew the velocity and position information of particles according to Formulas (13) and (14). When the number of iterations exceeds the set maximum number of iterations, step 7 is performed; otherwise step 5 is reverted in order to repeat the iterative processes.  
**Step 7.** Calculate the MSE between the predicted value and the actual value of the pavement quality evaluation decision model based on IPSO-RBF to assess the exactness of the model.
# Knowledge Graph Recommendation Model Based on Feature Space Fusion
## 4 Experiments
### Performance Comparison  
The F1 and AUC were used as evaluation metrics in the comparative experiment, and the experimental results are shown in Table 3. In the table, the optimal value of the evaluation index is highlighted in bold. Through looking at the experimental results, it can be found that the KGRFSF proposed in this section has obtained the optimal value in each index of the two datasets by comparison. Specifically, compared with the suboptimal value of each index, the AUC of KGRFSF on the Last.FM dataset improved by two percentage points, and F1 improved by three percentage points. The AUC on the MovieLens-20 M dataset improved by 0.6 percentage points, and F1 improved by 0.2 percentage points. Further analysis of the experimental results can obtain the following conclusions:Among them, the embedding knowledge graph recommendation model CKE has the worst experimental results because CKE only considers the first-order association information of nodes in the embedding calculation and cannot effectively excavate the higher-order association information in the user-item interaction graph and knowledge graph. However, KGCN and KGAT consider the high-order neighborhood information of project nodes in KG and the importance of different neighbor nodes in aggregation. However, the two models do not consider the high-order neighborhood information of users in KG. CKAN considers the user's interest path on the KG and obtains the user's interest vector but fails to make full use of the interactive behavior information contained in UIG. However, KGRFSF deeply excavates behavioral features and uses the feature space fusion method to fuse behavioral features and content features, enriching the vector expressions of users and items and reducing the impact of \"noise\" caused by simple fusion of behavioral features and content features in the same space, thus achieving better recommendation results.
# Self-Adaptive Alternating Direction Method of Multipliers for Image Denoising
## 2 Related Work  
In recent years, image denoising has become a highly regarded application area in the field of artificial intelligence. In image processing, the Low-Rank Matrix Recovery (LRMR) technique has been successfully applied to remove noise from images, enhancing image quality and clarity [28; 29]. LRMR technology extends the concept of sparse representation of sample vectors to the low-rank scenario of matrices, becoming another important method for image denoising, following the path of compressive sensing (CS). In image denoising, LRMR decomposes the image data matrix into the sum of a low-rank matrix and a sparse noise matrix, then restores the low-rank matrix by solving a nuclear norm optimization problem, effectively eliminating noise from the image. Currently, LRMR mainly includes several common models such as Robust Principal Component Analysis (RPCA), Matrix Completion (MC), and Low-Rank Representation (LRR), all of which demonstrate good image denoising effects in various scenarios [30; 31].  
RPCA is a technique used to handle outlier values and noise in data, aiming to decompose the original data matrix into low-rank and sparse components. The core idea of RPCA assumes that the data matrix consists of a low-rank structure and a sparse noise component, recovering the original data by minimizing the difference between these two parts, thereby achieving denoising and outlier detection. RPCA exhibits strong robustness to outlier values and noise in data, effectively dealing with interfering factors, enhancing data accuracy and stability. It finds wide applications in fields such as image processing, video analysis, signal processing, and more. For example, in [32], [PERSON] et al. proposed a robust PCA method that relies on a nonconvex low-rank approximation and total variational regularization (TV) to address the image denoising problem, aiming to enhance the denoising performance. In [33], [PERSON] et al., proposed a workflow to reduce seismic traffic noise by utilizing the \(l_{p}\)-norm RPCA method. Although these algorithms perform well under specific common scenarios, in order to achieve real-time processing, it is necessary to accelerate and optimize the low-rank matrix recovery algorithm. This optimization involves reducing algorithm complexity, minimizing computational burden, and this is also one of the main insights of our research.
## 3 System Configurations and Modelling
### SUS and SDS Configurations
* _SE + DL/H&C configuration_ (Figure 5c): This configuration combines a single-effect AHT at the step-up thermal station with a double-lift AHP at the step-down thermal substation. This system produces hot water for the district network at a higher temperature (50 \({}^{\circ}\)C) than configuration (b) SE + SE/H&C (40 \({}^{\circ}\)C) for the same driving temperature. The evaporator E1 and absorber A2 are heat coupled as shown in Section 2.3. The evaporator E2 of the double-lift AHP produces chilled water at 6 \({}^{\circ}\)C for the DC network. Thus, the return water of the heat transport circuit could not be low enough to be used in the condenser of the AHT. Hence, the use of a cooling tower in the SUS is required. * _SE + 2 xSE/H&C configuration_ (Figure 5d): This configuration is a hybridization between configurations SE + SE/H (a) and SE + SE/H&C (b). At the step-down thermal substation, this configuration uses two single-effect AHP. This system configuration produces chilled water (as in the configuration SE + SE/H&C), but it also removes the cooling tower at the step-up thermal station (as in the configuration SE + SE/H&C). The evaporator of the first single-effect AHP (evaporator E1) is used to produce chilled water at 6 \({}^{\circ}\)C for the DC network and the evaporator of the other single-effect AHP is used to cool down the temperature of the return water of the primary circuit. Thus, the cooling tower is removed in this configuration and it simultaneously produces hot water for the DH network at 40 \({}^{\circ}\)C.
* _DA + DE/H&C configuration_ (Figure 5e): A double-absorption AHT is used at the step-up thermal station and a double-effect AHP at the thermal station. Since the technology of the heat transformer utilized in this configuration provides hot water for the transport network at a higher temperature, the double effect configuration can take advantage of this fact to increase the efficiency of the cooling production with respect to the single effect or the double lift configuration. This configuration produces hot water at 40 \({}^{\circ}\)C, and chilled water at 6 \({}^{\circ}\)C. Notice that a cooling tower is required in this case for both the SUS and the SDS. This configuration is similar to SE + SE/H&C (b) but using a double absorption instead of a single-effect heat transformer in the step-up station. The main benefit is the higher temperature available to drive the heat pump of the step-down absorption heat exchanger. * _DA + DL/H&C configuration_ (Figure 5f): In this case, a double-absorption AHT is also used instead of a single-effect AHT for the step-up station and a double-lift AHP is used in the step-down thermal substation. In this case, the system produces hot water at 60 \({}^{\circ}\)C, which is higher than that achieved by the configuration SE + DL/H&C (C) (50 \({}^{\circ}\)C) thanks to the double-absorption configuration. Chilled water is sent to the DC network at 6 \({}^{\circ}\)C. A cooling tower is required at the step-up thermal station.
# Performance Evaluation of TCP BBRv3 in Networks with Multiple Round Trip Times
## 3 BBR Operation Principles  
BBR is designed to maximize the delivery rate without building the standup queue, thus minimizing delay and converging to Kleinrock's optimal operating point. BBR estimates both the available bandwidth (\(BW\)) and the minimum round trip propagation time (\(RTT_{min}\)) on a transmission path, and it calculates the path's Bandwidth Delay Product (BDP) as  
\[BDP=BW*RTT_{min}. \tag{1}\]  
However, these measures cannot be obtained simultaneously due to the necessity of operating in different regions. Measuring \(RTT_{min}\) entails moving to the left from operational point (A), thereby reducing the transmission rate. Conversely, assessing the maximum available bandwidth requires temporarily overloading the network, thus shifting to the right of operational point (A). Probing for bandwidth increases the queue, whereas probing for minimum propagation time requires draining the queue.  
BBR spends most of its time transmitting at a constant, estimated delivery rate, although it periodically initiates bandwidth probing and refreshes the minimum Round Trip Time. When BBR probes for bandwidth, it increases the sending rate and immediately depletes any resulting queue. The BBR sender monitors the RTT with every reception of ACK; however, periodically, it enters the PROBE RTT phase, during which the transmission rate is substantially reduced to drain the queue. This mechanism guarantees the synchronization of the PROBE RTT phase across all long-lived BBR flows over time.  
The sending rate is determined purely by the bandwidth estimator (\(BW\)), while the \(RTT_{min}\) measure determines the calculated BDP, thus setting the inflight cap. The maximum inflight data are limited to 2*BDP, thus serving to accommodate delayed and aggregated ACKs [7]. Additionally, BBR maintains control variables, including the pacing_gain, the parameter used to multiply the observed delivery rate (\(BW\)), thus reflecting the current sending rate  
\[\texttt{sendingrate}=\texttt{pacing\_gain}*BW, \tag{2}\]  
and the windows increase factor (cwnd_gain), which regulates BBR inflight data volume to adhere to the desired limit.  
\[\texttt{current\,inflight}=\texttt{cwnd\_gain}*BW*RTT_{min}. \tag{3}\]
## 3 Results and Discussion
### Gpc  
Figure 3 shows the retention time results from the GPC analyses performed on the bio-based resins without and with lignin incorporation. Table 3 summarizes the resulting values of \(\mathrm{M_{n}}\), \(\mathrm{M_{w}}\), and polydispersity index (PDI).  
The differences in spectra obtained for BVE and BVEL suggest that the incorporation of lignin in the linear vinyl ester was overall successful, having produced a significant increase (20%) in the molecular weight of the lignin-containing prepolymer relative to the base prepolymer. The PDI obtained for the BVEL prepolymer was also higher (26%), indicating an increased heterogeneity of the polymer segments.
## 2 Materials and Methods  
The achira fiber is obtained during the process of extracting achira starch through a sieving procedure. The achira (_Canna edulis Ker_), also known as Sagu in the east of Cundi-namarca, is a plant of Andean origin cultivated in Colombia primarily for the extraction of starch from its rhizomes, which is used as a raw material for making traditional products, such as \"achira biscuits\", \"sagu bread\", and \"sponge cake\". Cundinamarca concentrates around 70% of these producers and 95% of the starch production [35].  
The starch extraction process involves a series of operations following the harvest of the achira rhizomes in the following order: collecting the rhizomes to prepare the suspension from which the starch is extracted, washing the rhizomes to achieve the highest degree of cleanliness, and preparing them for shredding. They are washed to remove impurities embedded in the rhizome's interstices. To release the starch present in the cells that form the rhizome, it is necessary to break them, which is mainly achieved through shredding, where the fibers are cut, breaking the cell walls to release the starch. Subsequently, sieving is carried out to separate the bran or fiber from the rhizome and the starch slurry. The result of sieving is separated from the starch through decantation. The obtained starch is washed to remove residues or impurities, and this process is repeated as many times as necessary until it is completely clean [36]. The main byproducts of the process are bran and \"mogolla\" or stain. The bran is obtained after separating the starch in the sieving operation, and it has a high crude fiber content (36%), a dry matter digestibility of 57%, a crude protein content of 3.6%, and low mineral contents [35]. This bran is the material that will be incorporated into the polymer matrices in the present research.  
In order to conduct this study, samples of achira fiber were obtained from industrial food waste. The equipment and methods used in the laboratory tests performed during this research were as follows:  
* TGA Test: TGA 550 Equipment, TA Instruments.
* DSC Test: DSC25, TA Instruments.
* FTIR Test: IRTracer-100, Shimadzu.
* Zeiss Stemi 305 Stereoscope.
* Moisture Balance PCE-MB 120C.  
Figure 1 shows the bran obtained from the extraction process of achira starch collected in situ and Figure 2 shows the achira fiber observed under a stereoscope at 45\(\times\) magnification.