esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
In this study, we present a hybrid GAN-ACO congestion control framework for WSNs aimed at enhancing energy efficiency, latency reduction, and reliability. Utilizing GANs for efficient cluster formation and ACO for adaptive routing, this approach balances load distribution while dynamically responding to congestion through pheromone-based path selection. The simulation results demonstrate that GAN-ACO consistently surpasses existing congestion control methods, such as FSFG, DRL-CaCC, and ACSRO, across key QoS metrics--particularly in terms of energy conservation, reduced latency, and higher reliability. This advantage is attributed to GANs' ability to create well-balanced clusters, minimizing redundant transmissions, while ACO optimally directs traffic to less congested paths, reducing overall delay. Despite these benefits, the computational intensity of GAN-ACO may limit its scalability in ultra-large WSN deployments, especially within resource-constrained settings. Future work could address this by introducing lightweight clustering techniques and integrating hierarchical routing to expand the framework's applicability across diverse WSN scenarios. Further, employing security enhancements, such as reinforcement learning for real-time adaptability and blockchain for data integrity, could fortify its resilience under high-risk conditions. Overall, GAN-ACO demonstrates robust performance and scalability, suggesting it as a viable solution for real-time, data-heavy applications in fields like environmental monitoring and smart cities, where prolonged network lifespan and QoS maintenance are critical. This framework effectively addresses key WSN congestion challenges, supporting reliable and efficient operation in modern IoT environments.  
Conceptualization, S.S.S. and B.A.; methodology, S.S.S.; software, S.S.S.; validation, S.S.S., B.A. and R.C.; formal analysis, S.S.S.; investigation, S.S.S.; resources, C.-R.C.; data curation, S.S.S.; writing--original draft preparation, S.S.S.; writing--review and editing, B.A., R.C. and C.-R.C.; visualization, S.S.S.; supervision, R.C.; project administration, R.C.; funding acquisition, R.C. and C.-R.C. All authors have read and agreed to the published version of the manuscript.  
This work was partially supported by the TrialsNet project, funded from the European Union's Horizon-JU-SNS-2022 Research and Innovation Programme under Grant Agreement No. 101095871.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.
## 5 Adjunctive Materials to Synthetic Bone Graft Substitutes (Growth Factors)
### Platelet-Rich Plasma (PRP)  
Platelet-rich plasma (PRP) is an autologous suspension of high concentration platelet-rich plasma (PRP) is a high concentration autologous suspension of platelets resulting from centrifugation of blood. The fold concentration of thrombocytes after preparation by a single- or double-centrifugation process ranged from 2- to 33-fold. Platelets are rich in many growth factors involved in different healing processes: platelet-derived growth factors (PDGF-aa, PDGF-ab and PDFG-bb isomers), transforming growth factor-beta (TGF-\(\beta\)), VEGF, interleukin-1, platelet-derived angiogenesis factor, platelet-derived endothelial growth factor, insulin-like growth factor (IGF-1), osteoclastin and osteepontin [8, 114]. Platelets are the first elements that release these growth factors at the fracture site, followed by macrophages, whose chemotaxis is stimulated by the factors released from the platelets. The above-mentioned growth factors are responsible for the osteoinductive property of PRP, promoting the angiogenesis, proliferation and differentiation of pluripotent mesenchymal cells on the lines of osteoblasts and chondrocytes [115]. There are several controversies regarding the clinical use of PRP: whether or not the platelets should be activated before application, the method of application (injection or direct application) and the possible combination with other elements that may stimulate bone healing (demineralized bone matrix--DBM; concentrated bone marrow aspirate--cBMA; bone allograft).  
Despite extensive research and widespread clinical use in various types of conditions, there are currently no data to certify the effectiveness of these preparations on accelerating bone healing; thus, the routine use of PRP for improving fracture healing rates and speed is not recommended.
# Depth Image Super Resolution Based on Edge-Guided Method
## 2 Proposed Method
### Edge Recovery Using Sparse Coding
#### 2.1.1 Sparse Dictionary Training
For LR dictionary \(\mathbf{A}_{l}\), the K-SVD dictionary training procedure [22] is applied to LR patches \(\left\{\mathbf{p}_{l}^{k}\right\}_{k}\), resulting in the dictionary \(\mathbf{A}_{l}\):  
\[\mathbf{A}_{l},\left\{\mathbf{q}^{k}\right\}=\underset{\mathbf{A}_{l},\left\{ \mathbf{q}^{k}\right\}}{\text{argmin}}\underset{k}{\sum}\left\|\mathbf{p}_{l}^ {k}-\mathbf{A}_{l}\mathbf{q}^{k}\right\|^{2}\quad s.t.\left\|\mathbf{q}^{k} \right\|_{0}\leq L \tag{5}\]A side product of this training is the sparse representation coefficients vectors \(\left\{\mathbf{q}^{k}\right\}_{k}\) that correspond to the training patches \(\left\{\mathbf{p}_{l}^{k}\right\}_{k}\). \(\left\|\right\|_{0}\) is the zero norm, and \(\left\|\mathbf{q}^{k}\right\|_{0}\) is used to count the nonzero entries of vector \(\mathbf{q}^{k}\). \(L\) is a constant that controls sparsity. The next step is the high-resolution dictionary construction. Recall that we assume that the HR patch \(\mathbf{p}_{h}^{k}\) can be approximated by \(\mathbf{p}_{h}^{k}=\mathbf{A}_{h}\mathbf{q}^{k}\). The dictionary \(\mathbf{A}_{h}\) is therefore sought such that this approximation is as exact as possible, i.e.,  
\[\mathbf{A}_{h}=\underset{\mathbf{A}_{h}}{\text{argmin}}\sum_{k}\left\|\mathbf{ p}_{h}^{k}-\mathbf{A}_{h}\mathbf{q}^{k}\right\|_{2}^{2}=\underset{\mathbf{A}_{h}}{ \text{argmin}}\|\mathbf{P}_{h}-\mathbf{A}_{h}\mathbf{Q}\|_{F}^{2} \tag{6}\]  
where the matrix \(\mathbf{P}_{h}\) is constructed with the HR training patches \(\left\{\mathbf{p}_{h}^{k}\right\}_{k}\) as its columns, and similarly, \(\mathbf{Q}\) contains \(\left\{\mathbf{q}^{k}\right\}_{k}\) as its columns (give that \(\mathbf{Q}\) has full row rank). \(\left\|\right\|_{F}\) is the Frobenius norm [28]. The solution of the least-squares problem is given by the following expression:  
\[\mathbf{A}_{h}=\mathbf{P}^{h}\mathbf{Q}^{T}\left(\mathbf{Q}\mathbf{Q}^{T} \right)^{-1} \tag{7}\]  
```
Input: A set of HR training images \(\left\{\mathbf{y}_{h}^{j}\right\}_{j}\) Output: LR-HR dictionary pairwise \(\left\{\mathbf{A}_{l},\mathbf{A}_{h}\right\}\) Step 1. Construct training set: use scale-down operator \(\mathbf{U}\) to construct LR images \(\left\{\mathbf{y}_{l}^{j}\right\}_{j}\) from HR training images \(\left\{\mathbf{y}_{h}^{j}\right\}_{j}\) and extract pairs of matching patches that form the training database \(\left\{\mathbf{p}_{h}^{k}\mathbf{p}_{l}^{k}\right\}_{k}\) from images \(\left\{\mathbf{y}_{h}^{j}\right\}_{j}\) and \(\left\{\mathbf{y}_{l}^{j}\right\}_{j}\). Step 2. LR dictionary training: apply K-SVD [22] dictionary training procedure to train LR patches \(\left\{\mathbf{p}_{l}^{k}\right\}_{k}\), resulting with LR dictionary \(\mathbf{A}_{l}\) and the sparse representation coefficients vectors \(\left\{\mathbf{q}^{k}\right\}_{k}\). Step 3. HR dictionary training: HR dictionary \(\mathbf{A}_{h}\) is trained using the sparse representation coefficients vectors \(\left\{\mathbf{q}^{k}\right\}_{k}\) to match corresponding LR one
```  
**Algorithm 1**
## 2 Distance Measurement Module Development
### Velocity Estimation  
This study develops the VDM and the DIAM to mount the drone as mission equipment. The driving vehicle information of each lane is obtained by hovering the drone at the outside the lane. Figure 7 shows the vehicle detection method using the drone. The drone LiDAR sensor radiates two measuring points to obtain information on the vehicle driving in the lane. \(H_{f}\). is the distance between the entrance measurement point and the LiDAR front sensor, \(H_{r}\) is the distance between the LiDAR rear sensor and the exit measurement point, \(\theta_{s}\) is the angle between the front/rear sensors and \(L\) is the distance between the two measurement points. The two LiDAR sensors are assigned to one lane and each sensor detects the vehicle by radiating each front/rear point. The distance (\(L\)) between the two measurement points is changed according to the flying height of the drone and the distance is calculated by Equation (1) [26].  
The distance between the two measuring points is expressed as follows:  
\[L=\sqrt{{H_{f}}^{2}+{H_{r}}^{2}-\left(2\;{H_{f}}\;{H_{r}}\;{cos\theta_{s}} \right)} \tag{1}\]  
From Equation (1), the driving vehicle velocity for each lane is expressed as follows:  
\[\text{V}=\frac{L}{\Delta t} \tag{2}\]  
Figure 7: Vehicle detection on lane.  
In Equation (2), V is the vehicle velocity and \(\Delta t\) is the measurement difference time between the LiDAR front sensor and the rear sensor at the two measuring points.  
Figure 8 shows the vehicle velocity algorithm. First of all, the LiDAR front sensor measures the distance of the entry point. The distance of the \(H_{f}\) changes when the vehicle driving through the entry measurement point. According to the change in the distance of \(H_{f}\), it is determined that the vehicle has passed the entrance measurement point and the time of the entry measurement point is recorded. The passing time is not recorded if there is no change in the distance of the \(H_{f}\). It records the passing time of the vehicle exit point if there is a change in \(H_{r}\) distance when the LiDAR rear sensor is radiating the exit point. The \(\Delta t\) is obtained using the passing time of the entrance measurement point of the LiDAR front sensor and the exit passing time of the LiDAR rear sensor. The passing vehicle count is determined by the change in \(H_{r}\) measurement distance.
# GPS/BDS RTK Positioning Based on Equivalence Principle Using Multiple Reference Stations
## Validation and Analysis
### Kinematic Experiment  
#### 3.2.1 Experiment Setup  
The kinematic experiment is conducted at Shandong University, Weihai, on November 18, 2018 (from 12:53 to 14:10 UTC). The rover receiver (Trimble NetR9) and antenna (TRM57971.00) are installed on the top of a car, driving on the city roads of Weihai, which is marked as TBR9 as shown in Figure 6. Two reference receivers (Hi-Target VNet) and antenna (HX-AT2300) are located on the rooftop of the Wentian building in Shandong University, while the third reference receiver (South NetS9) and antenna (TRM57971.00) are located on the rooftop of a civil building in Weihai. The three receivers are marked as SDWA, SDWB, and SOTH, respectively. The linear distance of SDWA and SOTH is approximately 6.3 km, and a total number of approximately 4621 epochs of data are collected with 1 s sampling interval and 10\({}^{\circ}\) elevation cut-off angle. The trajectory of the car is shown in Figure 7.  
In order to examine the performance of the proposed MBRTK, and compare the impacts of different satellite systems and reference receivers on the positioning results, six different schemes are conducted in this study. The differences between different schemes mainly focus on satellite systems and the number of reference receivers. The description of six positioning schemes is summarized in Table 4. Moreover, the reference coordinates of rover receiver can be calculated by Waypoint Inertial Explorer (IE) software 8.60 (NovAtel, Calgary, Alberta, Canada) for a forward-reverse model with three reference receivers [50], and the positioning errors are defined as the difference between positioning results and the reference coordinates. If the three-dimensional (3D) error of positioning is less than 10 cm, this epoch is defined to be successfully solved for the short baseline, and the availability of kinematic positioning is defined as the percentage of solved epochs of all epochs [38].  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Schemes**} & \multicolumn{3}{c}{**RMS/mm**} & \multicolumn{3}{c}{**Improvement**} & \multirow{2}{*}{**Number of Epochs**} & \multirow{2}{*}{**Success Rate**} \\ \cline{2-2} \cline{5-8}  & **N** & **E** & **U** & **N** & **E** & **U** & \\ \hline One & 2.8 & 2.3 & 7.3 & – & – & – & 2880 & 100\% \\ Two & 2.4 & 2.0 & 6.3 & 14\% & 13\% & 14\% & 2880 & 100\% \\ Three & 2.2 & 1.9 & 5.9 & 21\% & 17\% & 19\% & 2880 & 100\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Statistics of static experiment where RMS=root mean square.  
Figure 6: The scene of rover receiver (**a**) and the reference receivers (**b**) on the rooftop of the Wentian building.
## 4 Experimental Results and Analysis
### Example Result  
In the first experiment, single-look quad-polarization SAR images over Dalian were used to show the flow of the proposed method. The Pauli pseudocolor image of the data is shown in Figure 7a. We can observe that there is a bright red region in the center of the image. The region is the target port, in which the water is seriously interfered with by the surrounding man-made buildings and metal structures. The intermediate results of the proposed method are shown in Figure 7b-f. The image of the parameter PV is shown in Figure 7b. There is a red bright box in the water region of Figure 7b, which is the selected rectangle sampling region according to (8). The image of the parameter PRDV is shown in Figure 7c. Figure 7b shows that the PV of the water is much smaller than that of the land. Figure 7c shows that the PRDV parameter of the interference water is much larger than that of the normal water. We can also see that the difference of the PRDV parameter between the strong scattering region and the other regions in land is large. The water extraction result is shown in Figure 7d. Both the interference water and other normal water are well separated from the land. The interference water extraction result is shown in Figure 7e, where the extracted water pixels are drawn in gray, the detected interference water pixels are drawn in white, and the extracted ROI is marked with a red box. We can observe that the interference water is well separated from the normal water, but the extension of the extracted ROI is far away from the land of the target port. The ROI recognition result is shown in Figure 7f, where the final detected port is marked with a red box. We can see that the detected port is close to the true target port after the post-processing of the ROI.  
Figure 7: Port detection result of Dalian data: (**a**) the Pauli pseudocolor image; (**b**) the image of volume scattering power; (**c**) the image of the ratio of the double-bounce scattering power to the volume scattering power; (**d**) the water extraction result; (**e**) the interference water extraction result, where the extracted water pixels are drawn in gray, the detected interference water pixels are drawn in white, and the extracted ROI is marked with a red box; (**f**) the ROI recognition result, where the detected port is marked with a red box.
# A Connection Between the Kalman Filter and an Optimized LMS Algorithm for Bilinear Forms
## 8 Discussion  
In this paper, we have focused on the Kalman filter tailored for the identification of bilinear forms (KF-BF), together with its simplified version (SKF-BF). Also, we have developed an optimized version  
Figure 17: Normalized projection misalignment of the OLMS-BF and NLMS-BF (using different step-size parameters): (**Top**) identification of the temporal impulse response \(\mathbf{h}(n)\), (**Bottom**) identification of the spatial impulse response \(\mathbf{g}(n)\). The input signals are AR(1) processes, \(L=512\), and \(M=4\).  
Figure 18: Normalized misalignment of the OLMS-BF and NLMS-BF (using different step-size parameters). The input signals are AR(1) processes and \(M\L=2048\).  
of the LMS algorithm for bilinear forms, namely OLMS-BF. In addition, a comparison between the SKF-BF and OLMS-BF algorithms has been outlined, indicating strong similarities between these two solutions. In our framework, the bilinear term has been defined with respect to the impulse responses of the spatiotemporal model.  
The SKF-BF provides a reduced computational complexity as compared to KF-BF; the downside is that it has a slower convergence rate, more visible for correlated inputs. On the other hand, the SKF-BF and OLMS-BF algorithms perform very similarly. Experimental results also indicate that the algorithms tailored for bilinear forms outperform their regular counterparts (in such two-dimensional system identification scenarios), in terms of both convergence rate and tracking, as well as the steady-state misalignment. Adding to that, the reduced computational amount provided by the use of two shorter adaptive filters instead of a single (much longer) one, led us to conclude that the proposed algorithms could represent appealing solutions for the identification of bilinear forms.  
Conceptualization, L.-M.D.; Formal analysis, S.C.; Software, C.P.; Methodology, J.B.  
This research received no external funding.  
The authors declare no conflict of interest.
## 5 HUVEC Model Applications
### Cardiovascular and Metabolic Diseases
#### 5.1.2 Atherosclerosis  
Atherosclerosis is a chronic inflammatory disease characterized by the accumulation of low-density lipoproteins (LDL) and immune cells in the subendothelium, resulting in the formation of an atherosclerotic plaque. At early stages, morphological changes in the endothelium can be regulated by interaction with hemodynamic forces [2; 3; 111; 119]. The distribution of blood flow in different arterial regions produces shear stress (SS): High laminar and constant (LSS) and oscillatory and low (OSS). The first induces an atheoprotective phenotype in the EC, while the latter confers a pro-atherogenic phenotype [2; 5; 111; 120; 121]. The expression of atherosclerosis-related genes can be downregulated or upregulated, depending on the type of flow [120; 122; 123].  
[PERSON] et al. developed a system using the HUVEC model to study the effect of SS, since these cells can recruit different types of leukocytes in response to stimulation with tumor necrosis factor alpha (TNF-\(\alpha\)). An advantage of the system is the ability to grow multiple samples in parallel and expose the EC to different flow types [124].  
The ATP-Binding Cassette Transporter A1 (ABCA1) transporter is a transmembrane protein with flopase activity. This activity transfers phospholipid phosphatidylinositol (PS) from the inner to the outer membrane layer in response to activation stimuli or cellular stress. These mechanisms are essential for microvesicle (MV) biogenesis. [PERSON] et al. demonstrated that LSS decreases endothelial-derivedMV (EMV) formation in HUVEC. This decrease results from endogenous NO release and _ABCA1_ expression downregulation. OSS has an opposite effect, causing loss of phospholipid asymmetry in the membrane increasing EMV production. EMVs have been proposed as biomarkers of endothelial dysfunction, and high EMV plasmatic concentrations are associated with CVD [5].  
SS regulates the expression of different genes, for example, the HoxB9 gene. It has been reported that HoxB9 gene expression is induced by OSS in HUVEC [122]. Finally, HUVEC cultures have been used to study the pro- and anti-atherogenic effect of several molecules, such as arsenic and saikosaponin, in the early stages of atherosclerosis [125; 126].
# Leveraging IGOOSE-XGBoost for the Early Detection of Subclinical Mastitis in Dairy Cows
###### Abstract  
Subclinical mastitis in dairy cows poses a significant challenge to the dairy industry, leading to reduced milk yield, altered milk composition, compromised animal health, and substantial economic losses for dairy farmers. A model based on the XGBoost algorithm, optimized with an Improved GOOSE Optimization Algorithm (IGOOSE), is presented in this work as an innovative approach for predicting subclinical mastitis in order to overcome these problems. The Dairy Herd Improvement (DHI) records of 4154 cows served as the model's original foundation. A total of 3232 samples with 21 characteristics made up the final dataset, following extensive data cleaning and preprocessing. To overcome the shortcomings of the original GOOSE algorithm in intricate, high-dimensional problem spaces, three significant enhancements were made. First, an elite inverse strategy was implemented to improve population initialization, enhancing the algorithm's balance between global exploration and local exploitation. Second, an adaptive nonlinear control factor was added to increase the algorithm's stability and convergence speed. Lastly, a golden sine strategy was adopted to reduce the risk of premature convergence to suboptimal solutions. According to experimental results, the IGOOSE-XGBoost model works better than other models in predicting subclinical mastitistis, especially when it comes to recognizing somatic cell scores, which are important markers of the illness. This study provides a strong predictive framework for managing the health of dairy cows, allowing for the prompt identification and treatment of subclinical mastitis, which enhances the efficiency and quality of milk supply.  
subclinical mastitisti in cows; GOOSE optimization algorithm; XGBoost; machine learning; cow somatic cells 2025158763
## 2 Analysis of the FPLG System Working Process  
### Working Principle of the Power Generation System  
The free-piston linear internal combustion power generation system comprises an intake system, an FPLG prototype, a power conversion device, and a signal acquisition and control system, as shown in Figure 1. The FPLG prototype consists of two opposed free-piston engines and a linear motor, with the motor's mover connected to the pistons via a connecting rod. The system operates in two modes: cold start and stable power generation. During the cold start phase, the linear motor functions as a driver, using its moving part to move the pistons. This compresses the fuel-air mixture in the cylinders. Once ignition conditions are met, the engine ignites and transitions to stable power generation. Here, the linear motor acts as a generator.