esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Sharing State-of-Charge (SOC) Information among Grid-Forming Units without Communication
In this way, all grid-forming units can share the SOC of the grid-forming ESS. It is important to share the SOC information among grid-forming units because this information is incorporated into the system frequency in the proposed method, and the system frequency can be adjusted only by grid-forming units. By measuring the system frequency locally, grid-feeding DGs can adjust their output power to charge or discharge the ESS and hence maintain the SOC within a desired range. The details are discussed in the next section.
# Design and Experiment of Variable-Spray System Based on Deep Learning
## 2 Materials and Methods
### Semantic Segmentation Model for Wheat Weeds Based on DeepLab V3+  
#### 2.2.1 Data Acquisition  
The data acquisition interval was from 26 March to 1 April 2023. The collection site was the Smart Agriculture Demonstration Farm, Yiyang County, Luoyang City, Henan Province, China. A total of 620 images of wheat weeds, each with a size of 5184 \(\times\) 3450 pixels, was captured with the Canon EOS 60D camera at a distance of 50 cm from the vertical ground under different light intensities. To reduce the computational workload during model training, the image size was compressed to 512 \(\times\) 512 pixels.  
Each image was manually annotated via the LabelMe annotation tool (LabelMe, V4.5.13, Computer science and artificial intelligence laboratory, Massachusetts institute of technology, USA), with the resulting labels being formatted in JSON after annotation. The JSON files were then converted into the mask label image, where, as depicted in Figure 3b, red areas represented weeds and green areas signified wheat. Methods such as mirror flipping and brightness adjustment were used to increase the amount of training data and the generalization ability of the model. The enhanced sample data were allocated to a training set of 1500 images, a test set of 500 images, and a validation set of 500 images, according to a distribution ratio of 6:2:2.
# Enhancing Predictive Expert Method for Link Prediction in Heterogeneous Information Social Networks
## 5 Experiments
### Datasets  
We assessed our approach through experiments on three diverse heterogeneous information social network datasets. Each dataset was split into distinct training and testing sets, maintaining a fixed ratio \(\delta\). Further specifics are detailed in Table 1.  
The **Facebook** dataset [6] comprises a page-page network with concealed sites. Its nodes signify official pages, while the links denote reciprocal \"like\" relationships. This dataset encompasses four node types (politicians (P), governmental organizations (G), television shows (T), and companies (C)) and six link types.  
The **DBLP** dataset [7], a comprehensive computer science bibliography, was sourced from multiple publishers near the 2016 US elections. Our analysis centered on DBLP-4-Area, containing three node types (author (A), paper (P), and venue (V)) and two link types (paper-author (P-A) and paper-venue (P-V)).  
The **MovieLens** dataset [8] offers data on movies, actors, and directors. Our investigation focused on a specific subset with three node types (actor (A), director (D), and movie (M)) and two link types (movie-actor (M-A) and movie-director (M-D)).
## 3 Configuration of the System and Conditions
### Simulation Conditions  
To evaluate the effect of the selected estimators for robust data reconciliations, a number of conditions, including sensors with gross errors, were generated. In this work, 2000 conditions were generated in total in order to cover various combinations of the gross errors happening at different sensors. Two categories of errors were generated based on the standard condition:  
Gross errors: For each condition, the number of sensors with gross errors was decided by random at first, from only one sensor up to eight sensors. The sampling ratio for the number of errored sensors is shown in Figure 4. After deciding the number, corresponding sensor(s) with gross error were sampled among all sensors randomly. It should be highlighted that the generator power measurement was never considered when adding gross errors, as it was assumed to be more reliable than the thermal sensors. As for the selected sensors, their measurements were attached to gross errors, ranging between 3 to 10 times their uncertainties, whose values were sampled from a uniform random distribution. The directions of errors were also decided by random. In other words, for a measured parameter \(x_{i}\) with gross error, if its uncertainty was \(a_{i}\), then the gross error \(\delta x_{i}\) should satisfy the following random distribution:  
\[\delta x_{i}=\text{sgn}(S_{i})\cdot|X_{i}|,\quad S_{i}\,\sim\,U(-1,1),\,\,X_{i }\,\sim\,U(3a_{i},10a_{i}) \tag{12}\]  
where \(S_{i}\) and \(X_{i}\) are two random variables, sgn is the sign function, and \(U\) denotes a uniform distribution. Once \(\delta x_{i}\) was decided, it was then added to the value of \(x_{i}\) under the standard condition as the actual measurement result under such a condition.  
Random errors: As for the sensors not selected for gross error attachment in the previous process, their values were still added with random errors sampled within their uncertainties, based on their values under the standard condition (listed in Table 2). In other words, for a measured parameter \(x_{i}\), if its uncertainty was \(a_{i}\), then a random error  
Figure 4: Sampling ratio for the number of errored sensors.  
\(\delta x_{i}\sim U(-a_{i},a_{i})\), which was decided through a uniform distribution to avoid excessive errors, was generated and added to its value under the standard condition.
# Stratographic Division Method Based on the Improved YOLOv8
###### Abstract  
With the deepening of oilfield development, logging data proliferate, and their complexity makes manual stratigraphic division both difficult and time-consuming. Aimed at the current network model widely used to solve the problem of stratigraphic delineation, which has problems such as not considering the multi-scale features of logging curves and insufficient accuracy, the YOLOv8x target detection algorithm in deep learning is utilized to detect the target strata, which has the ability to characterize the multi-scale features and can improve the efficiency and accuracy of the division. In order to better localize and identify targets, this paper proposes a new stratigraphic automatic division method, YOLOv8x-CAMDP, which introduces a CA (Coordinate Attention) mechanism module into the original YOLOv8x model to improve the model's ability to identify stratigraphic interval boundaries. In addition, the CIOU loss function in the original YOLOv8x network model was replaced using the MDPIOU loss function to effectively improve the accuracy and efficiency of bounding box regression. Based on the logging data from the Xing 10 area pure oil zone, a thorough comparison of the YOLOv8x-CAMDP and YOLOv8x models' training results is presented. The YOLOv8x-CAMDP model achieves a mean Average Precision (mAP) value of 98.7%, outperforming the YOLOv8x model by one percentage point. Moreover, the YOLOv8x-CAMDP model demonstrates greater precision in boundary division for each stratigraphic interval. The application of the YOLOv8x-CAMDP model to project implementation achieved significant results in stratigraphic division, reduced workload, and optimized manual division. These results not only confirm the practical value of the YOLOv8x-CAMDP model but also demonstrate the prospect and potential of its wide application.  
deep learning; automated stratigraphic division; object detection; boundary division; YOLOv8x
## 4 Calculation and Results
### Execution of Lane-Change Distance \(S_{3}\)
The process parameters and calculation results for determining the decision sight distance of the exit within the right-turn ramp on the left at the interchange are tabulated in Table 5, utilizing the decision sight distance calculation model formula. The _Green Book_ does not specify an exact value for the decision sight distance. In contrast to the _Design Specification for Highway Alignment_, as depicted in Figure 15, it is evident that the specified sight distance values fall below the recommended values across all design vehicle speed levels. Given the unique nature of the right-turn ramp on the left, relying solely on these specified values for exit recognition of visual distance might pose challenges in meeting the visual distance requirements for drivers within the interchange exit section, potentially compromising diversion efficiency and safety. Therefore, it is necessary to consider augmenting the recognized visual distance based on an appropriate increase beyond the specified norms. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameters** & \multicolumn{3}{c}{**Freeway**} \\ \hline \(V\) (km/h) & 120 & 100 & 80 \\ \(S_{1}\) (m) & 106.7 & 88.9 & 71.1 \\ \(Q\) (pcu/h/ln) & 1650 & 1600 & 1500 \\ \(\lambda\) (pcu/s) & 0.458 & 0.444 & 0.417 \\ Widths of Traveled-way (m) & 3.75 & 3.75 & 3.75 \\ \(t_{c}\) (s) & 3.75 & 3.75 & 3.75 \\ Vehicle speed in the diversion area (km/h) & 95 & 85 & 75 \\ - & 107.5 & 92.5 & 77.5 \\ \(V\) (km/h) & 7.56 & 6.79 & 5.50 \\ \(t_{w}\) (s) & 225.8 & 174.5 & 118.4 \\ \(\mu\) & 0.10 & 0.12 & 0.13 \\ \(V_{d}\) (km/h) & 95 & 85 & 75 \\ \(a_{\text{max}}\) (m/s\({}^{2}\)) & 0.588 & 0.784 & 0.882 \\ \(\sigma_{\text{max}}\) (m/s\({}^{3}\)) & 0.6 & 0.6 & 0.6 \\ \(\tau_{\text{max}}\) & 3.5 & 3.5 & 3.5 \\ \(S_{3}\) (m) & 173.6 & 155.3 & 137.1 \\ Recommended values for decision sight & 510 & 420 & 330 \\ distance (m) & 460 (350) & 380 (290) & 300 (230) \\ Design Specification for Freeway Alignment & & & \\ decision sight distance (m) & 460 (350) & 380 (290) & 300 (230) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The parameters and recommended values for the decision sight distance calculation process. In conclusion, a comprehensive investigation of drivers' behavioral characteristics is imperative. This study aims to elucidate the rationale behind determining the identification of sight distance based on empirical data, with the overarching goal of enhancing the safety standards of interchanges.
# Edge Couplers in Silicon Photonic Integrated Circuits: A Review
## 5 Discussions and Perspectives
### Future Trends of Silicon Photonic Edge Couplers  
#### 5.4.1 Polarization and Mode (de)Multiplexing Technology  
It is easy to observe from Table 1 that the TE mode mostly performs better than the TM mode for a designated edge coupler, and nearly all related research works focus on the propagation and conversion of fundamental modes. As the high data transmission rate and large link capacity requirements increase, the tendency is to multiplex different polarization states and mode orders to carry more information in one particular device. Many studies have been conducted to design devices with low polarization-dependent-loss (PDL), and there are various approaches to improve the polarization insensitivity of edge couplers. For example, edge couplers with a square cross-section or based on multi-tip tapers have a square or enlarged effective cross-section area that can hold both TE and TM modes [28; 122]. Subwavelength gratings structures can be utilized to engineer effective refractive indexes, and it is feasible to realize the equal modal effective indexes of TE and TM modes with proper parameters [123]. Polarization (de)multiplexers can also be used in combination with conventional edge couplers to multiplex different polarization status and reduce polarization dependence [124]. Moreover, techniques of polarization manipulation and management are summarized in [125; 126]. As for mode multiplexing technology, broadening the taper width can support the existence of high-order modes [127], and a variety of mode (de)multiplexers have been proposed based on polarization splitters, directional couplers, and arrayed waveguide gratings [128; 129; 130; 131; 132].
# GSFedSec: Group Signature-Based Secure Aggregation for Privacy Preservation in Federated Learning
## 5 Performance Analysis
### Communication Overhead
#### 5.4.4 Message Size for [PERSON] et al. [23]  
For the analysis of this algorithm, we assumed that clients communicate with each other after 100 FL iterations. Hence, the total bandwidth at each 100-th round matched that in [5]. However, no communication occurred during the subsequent 99 rounds of training, and the bandwidth was saved for 99 rounds. Therefore, the average bandwidth for each round was 135,000/100 = 1350 bytes.  
Similarly, the communication costs for other related algorithms were computed; they are listed in Table 8 and Figure 8. The graph shows a large difference in the average packet size of our algorithm compared with other existing algorithms. This shows that our algorithm is considerably more efficient.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline Msg. & GID & MID & RAND & Payload & GS & TTL & TS \\ \hline Length & 16 & 16 & 128 & 1024 & 1539 & 8 & 32 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Message structure and sizes of fields (bits).
# ViT-Cap: A Novel Vision Transformer-Based Capsule Network Model for Finger Vein Recognition
## 4 Results
### Experiment 1: Preliminary Analysis  
We tested the models on four publicly available finger vein datasets, each of which was first divided into training and test sets. For the FV-USM dataset, we set the percentage of training data to test data at 8-4; for the SDUMDA dataset, we set the percentage of training data to test data at 5-1; for the MMCBNU dataset, the percentage of training data to test data was 6-4; for the HKPU dataset, the percentage of training data to test data was 8-4. Under the condition that the overall framework of the model remains unchanged, we used the method of controlling variables to set the number of encoder layers in the transformer module to one and two, the number of heads to 12 and 24, and the number of routing iterations in the capsule module to three and four, respectively. The number of training rounds of the model was unified to 300 epochs, and the accuracy, EER, and AUC values of the model under different parameters were obtained through experiments to evaluate the performance of the model. Table 2 shows the experimental results obtained under different parameters. The results in Table 2 clearly show that the performance of our proposed model was very good. On the four publicly available finger vein datasets, the average recognition accuracy of ViT-Cap reached 95.53%. On the FV-USM dataset, the ViT-Cap model achieved a minimum EER value of 0.28% and an AUC value of almost one. Through experimental analysis, when only the number of heads was changed, the results of the experiment using 24 heads were significantly better than that using only 12 heads. After completing the experiments of Table 2, we conducted an extreme experiment at the same time. We set the encoder layers, heads, and routing iterations to 8-24-6, respectively. The experimental results in Table 3 further show that when the performance of the model reached a certain level, changing the depth of the encoder layer and the number of routing iterations did not improve the recognition performance. For most deep learning models, increasing the depth of the model greatly improved the performance of the model. However, the framework of our ViT-Cap model determined that increasing the depth of the model did not greatly improve the overall performance of the model. We extracted the local feature information of the finger vein image through the encoder layer (layers of 1-2), which obtained the basic vein feature information, and then use the capsule network module to perform a deeper global vein feature information extraction of the finger vein features obtained by the encoder layer. The encoder layer and capsule network module complemented each other, enabling our model to encode dependencies between vein image features, thereby improving finger vein recognition performance. The setting of the number of routing iterations was essentially a hyperparameter. It was most reasonable to set routing iteration to three and four. If the parameters of routing iteration of the model were set too large, it greatly increased the training cost of the model.
###### Abstract  
It is difficult to determine unknown solar cell and photovoltaic (PV) module parameters owing to the nonlinearity of the characteristic current-voltage (I-V) curve. Despite this, precise parameter estimation is necessary due to the substantial effect parameters have on the efficacy of the PV system with respect to current and energy results. The problem's characteristics make the handling of algorithms susceptible to local optima and resource-intensive processing. To effectively extract PV model parameter values, an improved hybrid Sparrow Search Algorithm (SSA) with Exponential Distribution Optimization (EDO) based on the Differential Evolution (DE) technique and the bound-constraint modification procedure, called ISSAEDO, is presented in this article. The hybrid strategy utilizes EDO to improve global exploration and SSA to effectively explore the solution space, while DE facilitates local search to improve parameter estimations. The proposed method is compared to standard optimization methods using solar PV system data to demonstrate its effectiveness and speed in obtaining PV model parameters such as the single diode model (SDM) and the double diode model (DDM). The results indicate that the hybrid technique is a viable instrument for enhancing solar PV system design and performance analysis because it can predict PV model parameters accurately.  
Keywords:photovoltaic (PV) models; solar cell; Exponential Distribution Optimization (EDO); Sparrow Search Algorithm (SSA); differential evolution (DE) +
Footnote †: journal:
# Assessing Water Infiltration and Soil Water Repellency in Brazilian Atlantic Forest Soils
## 3 Results and Discussion  
### Soil Properties  
The 18 plots showed appreciable differences in soil texture. Sandy loam (i.e., P1M, P1D, R3U, R3M, R3D, and F5U) and sandy clay loam (i.e., P1U, R4S, R4M, R4D, F5M, and F5D) were the dominant soil textures among the plots, followed by clay loam (i.e., P2M, F6U, and F6M) and loam (i.e., P2U, P2D, and F6D). The soil texture of the plots is presented in the USDA textural triangle (Figure 3).  
As pointed out by our previous study [14], the OC at the pasture sites in the soil depth 0-0.10 m was similar to remnant forest, while restored forest sites presented the lowest OC values. The highest \(\rho_{\rm b}\) values were observed in the restored forest R4, where the exposure of the soil and trampling pressure during the land-use history was greater in comparison with restored forest R3. Forest soils were characterized by the lowest \(\rho_{\rm b}\) values, which can be related to the heterogeneous soil structure and higher soil macroporosity in this cover [51; 52]. At the time of sampling, the \(\theta_{\rm i}\) ranged from 0.12 to 0.32 cm\({}^{3}\) cm\({}^{-3}\) and the soil was significantly wetter in plots P1U, P2M, R4S, R4M, and F5I (Figure S1).  
Figure 3: Textural distribution of the 18 plots in the USDA textural triangle.
# I/Q Imbalance and Imperfect SIC on Two-Way Relay NOMA Systems
## 2 System Model
### Signal Model of Joint TX/RX Impaired by IQI
The received SINR for \(D_{1}\) to decode the signal \(s_{1}\) is given by:  
\[\Gamma_{D_{1}\to s_{1}}=\frac{{|{u_{t}}|}^{2}{|{u_{r}}|}^{2}\rho_{r}\gamma_{1} b_{1}}{\gamma_{1}\left({|{v_{t}^{*}}|}^{2}{|{u_{r}}|}^{2}\rho_{r}+{|{u_{t}}|}^{2}{|{u_{ r}}|}^{2}\rho_{r}b_{2}\right)+1}, \tag{14}\]  
where \(\rho_{r}=P_{r}/N_{0}\) represents the transmit SNR at the users, and the received SINR for \(D_{1}\) to decode \(s_{2}\) is given by:  
\[\Gamma_{D_{1}\to s_{2}}=\frac{{|{u_{t}}|}^{2}{|{u_{r}}|}^{2}\rho_{r}\gamma_{1} b_{2}}{\gamma_{1}\left({|{u_{t}}|}^{2}|\mu_{r}|}^{2}\rho_{r}b_{1}{+|{v_{t}^{*}}|}^{2} {|{u_{r}}|}^{2}\rho_{r}\right)+1}. \tag{15}\]  
The received SINR for \(D_{2}\) to decode the signal \(s_{1}\) is given by:  
\[\Gamma_{D_{2}\to s_{1}}=\frac{{|{u_{t}}|}^{2}{|{u_{r}}|}^{2}\rho_{r}\gamma_{2} b_{1}}{\gamma_{2}\left({|{u_{t}}|}^{2}|\mu_{r}|}^{2}\rho_{r}b_{2}{+|{v_{t}^{*}}|}^{2} {|{u_{r}}|}^{2}\rho_{r}\right)+1}. \tag{16}\]  
The PDF of the random variable \(\gamma_{i}\) is expressed as [39]:  
\[f_{\gamma_{i}}\left(x\right)=\frac{\left(K+1\right)e^{-K}}{\lambda_{i}}e^{- \frac{\left(K+1\right)}{\lambda_{i}}x}I_{0}\left(2\sqrt{\frac{K\left(K+1 \right)x}{\lambda_{i}}}\right), \tag{17}\]  
where \(\lambda_{i}\) is the mean value of \(\gamma_{i}\), \(i=1,2\). \(K\) is the Rician fading parameter, which represents the ratio of the power of the LoS component to the average power of the non-LoS component. \(I_{0}\left(\cdot\right)\) is the zeroth order modified Bessel function of the first kind. Applying the result of [39], the PDF of \(\lambda_{i}\) can be rewritten as follows:  
\[f_{\gamma_{i}}\left(x\right)=\bar{a}_{i}\sum_{l=0}^{\infty}\frac{\left(\bar{b} _{i}K\right)^{l}}{\left(l!\right)^{2}}x^{l}e^{-\bar{b}_{i}x}, \tag{18}\]  
where \(\bar{a}_{i}=\left(K+1\right)e^{-K}\lambda_{i}^{-1}\), \(\bar{b}_{i}=\left(K+1\right)\lambda_{i}^{-1}\) and \(I_{0}\left(x\right)=\sum_{l=0}^{\infty}x^{2l}2^{-2l}(l!)^{-2}\). Based on this, the CDF of \(\gamma_{i}\) is:  
\[\Gamma_{R\to s_{1}}=\frac{\left|\mu_{r}\right|^{2}\!\rho_{u}\gamma_{1}a_{1}}{ \left|\
u_{r}\right|^{2}\!\rho_{u}\gamma_{1}a_{1}+\left|A\right|^{2}\!\rho_{u }\gamma_{2}a_{2}+\left|A\right|^{2}}, \tag{10}\]0