esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Proposed 3D-UWSN Design
### Data Gathering
Further, the objective is formulated as:  
\[J_{\pi}(\phi)=\mathbb{E}_{\xi_{t}\sim D\epsilon_{t}\sim\mathcal{N}}\big{[}log \pi_{\phi}\big{(}f_{\phi}(\epsilon_{t};S_{t})\big{|}S_{t}\big{)}-Q_{\phi}\big{(} S_{t},f_{\phi}(\epsilon_{t};S_{t})\big{)}\big{]} \tag{20}\]  
\(\epsilon_{t}\) is the given input noise vector, \(\pi_{\phi}\) is defined from \(f_{\phi}\) and \(D\) is the sample states and actions that are present in the prior iterations. From this, the approximate gradient \(\hat{\
abla}_{\phi}J_{\pi}(\phi)\) is expressed as:  
\[\hat{\
abla}_{\phi}J_{\pi}(\phi)=\
abla_{\phi}log\pi_{\phi}(A_{t}|S_{t})+ \big{(}\
abla_{A_{t}}log\pi_{\phi}(A_{t}|S_{t})-\
abla_{A_{t}}Q(S_{t},A_{t}) \big{)}\
abla_{\phi}f_{\phi}(\epsilon_{t};S_{t} \tag{21}\]  
The DACP predicts whether the AUV needs to move towards the next position of the CHs. After detecting the location from which the data have to be collected from the four CHs, the mid-point between them is determined and so the AUV can collect data from all the four CHs. The mid-points \(m_{1}\) and \(m_{2}\) of the two adjacent CHs are determined, and their mean value is the final position to which the AUV moves. When the \(CH_{a}\) and \(CH_{b}\) are adjacent to each other, the \(CH_{i}\) and \(CH_{j}\) are adjacent to each other so their mid-points \(m_{1}\) and \(m_{2}\) are expressed as:  
\[m_{1}=\bigg{(}\frac{x_{1(a)}+x_{2(b)}}{2},\frac{y_{1(a)}+y_{2(b)}}{2},\frac{z_ {1(a)}+z_{2(b)}}{2}\bigg{)} \tag{22}\]  
\[m_{2}=\bigg{(}\frac{x_{1(i)}+x_{2(j)}}{2},\frac{y_{1(i)}+y_{2(j)}}{2},\frac{z_ {1(i)}+z_{2(j)}}{2}\bigg{)} \tag{23}\]  
In this 3D-UWSN, the coordinates of \(CH_{a}\) and \(CH_{b}\) are \((x_{1(a)},\ y_{1(a)},z_{1(a)})\) and \(\Big{(}x_{2(b)},\ y_{2(b)},z_{2(b)}\Big{)}\); similarly, for \(CH_{I}\) and \(CH_{j}\), the coordinates are \((x_{1(i)},\ y_{1(i)},z_{1(i)})\) and \(\Big{(}x_{2(j)},\ y_{2(j)},z_{2(j)}\Big{)}\). Further, the moving position is predicted by:  
\[Mean=\frac{m_{1}+m_{2}}{2} \tag{24}\]  
The prediction of the mean value for the movement of the AUV leads to the collection of data from the four CHs at a time that limits the data collection delay, and, hence, the AUV reaches the CHs within the waiting time. The data collected from the members are delivered to the AUV during the time of arrival. Based on the predicted mid-point, the AUV is exactly positioned in between the four CHs at a one-hop distance and collects the data as shown in Figure 3. If the AUV's arrival to the next position is delayed, then the collected data are transmitted by the CH via inter-cluster routing. Figure 3: Movement of AUV in 3D-UWSN.
# Developments, Applications, and Innovations in Agricultural Sciences and Biotechnologies
## 5 Intercropping Systems and Resource Utilization
### Enhancing Photosynthetic Efficiency  
The study on jujube-cotton intercropping systems highlights the potential of agroforestry to optimize resource use and improve land productivity. By strategically configuring crop rows, the study demonstrates how intercropping balances competition and complementarity between crops.  
Maximized photosynthetic efficiency: Optimized row spacing minimizes competition for water and nutrients between crops, resulting in higher yields. In this study, a four-row cotton configuration was shown to optimize light capture and photosynthetic activity, leading to higher resource utilization efficiency.  
Efficient resource allocation: Intercropping reduced competition for light and nutrients between crops, improving overall system efficiency. The configuration ensures that both jujube and cotton plants receive adequate sunlight, enhancing photosynthetic activity.  
Sustainability benefits: The land equivalent ratio indicated that intercropping was more productive than monoculture systems, contributing to sustainable land use.
# Quality Monitoring for Laser Welding Based on High-Speed Photography and Support Vector Machine
## 2 Experimental Method  
### Experimental Details  
The experimental scheme shown in Figure 1 was set up. The laser source was a high-power disk laser (TruDisk 10003, TRUMPF, Ditzingen, Germany), with a constant laser power of 10 kW,which was used to produce a high-intensity laser beam. The beam diameter of the laser spot was 480 \(\upmu\)m, and the wavelength of the laser was 1030 nm. A MOTOMAN 6-axis robot (EA1400N, Yaskawa, Kitakyushu, Japan) was responsible for controlling the scanning track of the laser beam on the surface of specimen. The welding speed was 4.5 m/min. The shielding gas was argon in order to ensure the chemical stability of the welding surface environment, and the nozzle angle was 45\({}^{\circ}\). A high-speed camera system (Memrecam fx RX6, NAC, Tokyo, Japan), with complementary metal oxide semiconductor (CMOS) image sensors, was mounted to record the plume images. The frame rate was 2000 f/s, and the image resolution was 512 \(\times\) 512. An optical filter was placed in front of RX6 to filter unnecessary signals. The spectral band-pass filter setup on camera was 320-750 nm (measurement of UV and visible light induced plume and spatter). The specimen was a stainless steel 304 plate with dimensions of 119 mm \(\times\) 51 mm \(\times\) 20 mm. The intensity and the direction of the shielding gas remained unchanged during the welding process, and its impact on the state of plume was relatively stable. To simplify the model, the impact of shielding gas was not considered. Other parameters in the laser welding are shown in Table 1.
## 1 Introduction
It could also be that using ballistic (pulse-like) contractions (without sustaining the contraction after the peak force is reached), with an emphasis on fast force rise, would elicit an even higher RFD than \"fast-and-hard\" contractions. Recently, [PERSON] et al. [15] introduced the theoretical peak RFD, extrapolated from fast ballistic contractions of varying intensities (20-80% of the maximal voluntary force). Interestingly, this extrapolated value was higher than the actual peak RFD obtained during \"fast-and-hard\" contractions in females, while the opposite was the case in males [15]. Therefore, it could be that ballistics contractions would elicit an even higher RFD than \"fast-and-hard\" contractions, at least in females. The only study to analyze the difference in the RFD between \"hard-and-fast\" and \"fast-and-hard\" instructions, which considered the effect of sex, reported that the differences between instructions tended to be higher in females (\(\sim\)12%) than males (\(\sim\)8%). This indicated that females could be more sensitive to the instructions. [PERSON] and [PERSON] (2014) noted in their review paper that ballistic contractions in their laboratory elicited an \(\sim\)16% greater RFD compared to sustained contractions, but it was not clear whether the latter were \"hard-and-fast\" or \"fast-and-hard\" contractions. The purpose of this study was to compare the difference between \"fast-and-hard\" explosive sustained contractions (\"fast-and-hard\" instruction; referred to as explosive sustained contractions hereafter) and ballistic pulse-like contractions (referred to as ballistic contractions hereafter) on the RFD during isometric ankle and knee extension tasks in male and female participants. Based on the reported theoretical peak RFD extrapolated from submaximal ballistic contractions [15], we hypothesized that the ballistic contractions in our study will elicit a higher RFD than the sustained contractions. For the secondary aims, we checked the intrasession reliability of the RFD across different time intervals and the correlations between contraction types in the corresponding RFD variables. We hypothesized that the early RFD (50 ms) will show moderate reliability in both contraction types, whereas the late RFD (100-200 ms) will show good-to-excellent reliability [1]. Based on the studies comparing \"hard-and-fast\" to \"fast-and-hard\" contractions [10], we hypothesized that the correlations between contraction types will be moderate to strong (r = 0.4-0.9).
# ADSSD: Improved Single-Shot Detector with Attention Mechanism and Dilated Convolution
## 4 Experiments and Dataset
### Evaluation Criteria  
We used mean average precision (mAP) to evaluate the average accuracy of all categories and frames per second (FPS) to evaluate detection speed. The value of the AP is the area below the PR curve, where it is plotted based on the relationship between precision rate and recall rate. The AP is expressed as:  
\[AP=\int_{0}^{1}P(r)\mathrm{d}r \tag{7}\]  
where \(P(r)\) means the PR curve.  
The precision and recall are required for drawing the PR curve. They were formulated as follows:  
\[Precision=\frac{TP}{TP+FP} \tag{8}\]  
\[Recall=\frac{TP}{TP+FN} \tag{9}\]  
where \(TP\), \(FP\), and \(FN\) refer to the number of correctly predicted positive, incorrectly predicted positive, and incorrectly predicted negative samples.  
We calculate it in the same way by dividing the number of images by the total detection time.  
Based on the PASCAL VOC [24] dataset settings, we used AP, mAP, and FPS for evaluation.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Category** & **Trainval 12** & **Trainval 07** & **Test 07** \\ \hline Airplane & 670 & 238 & 204 \\ Bicycle & 552 & 243 & 239 \\ Bird & 765 & 330 & 282 \\ Boat & 508 & 181 & 172 \\ Bottle & 706 & 244 & 212 \\ Bus & 421 & 186 & 174 \\ Car & 1161 & 713 & 721 \\ Cat & 1080 & 337 & 322 \\ Chair & 1119 & 445 & 417 \\ Cow & 303 & 141 & 127 \\ Dining table & 538 & 200 & 190 \\ Dog & 1286 & 421 & 418 \\ Horse & 482 & 287 & 274 \\ Motorbike & 526 & 245 & 222 \\ Person & 4087 & 2008 & 2007 \\ Pottled plant & 527 & 245 & 224 \\ Sheep & 325 & 96 & 97 \\ Sofa & 507 & 229 & 223 \\ Train & 544 & 261 & 259 \\ Tv monitor & 575 & 256 & 259 \\ Total & 11,540 & 5011 & 4952 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The categories in the PASCAL VOC 2007 and the number of images. The PASCAL VOC 2012 has the same categories, but different numbers of images.
# Deep Reinforcement Learning-Based Deployment Method for Emergency Communication Network
## 5 Performance Evaluation
### Results and Analysis  
In this section, we first design multidimensional comparative experiments to benchmark our method against reinforcement learning algorithms and neural network architectures. The experimental framework comprehensively assesses performance through metrics such as cumulative reward curves and policy convergence speed, thereby validating the effectiveness of the proposed method. Subsequently, we conduct a systematic evaluation of model hyperparameters to reveal how key parameters influence the stability of emergency communication network.
## 4 Results and Discussion
### Velocity Profile
Furthermore, whenever there is a backflow in the divergent channel, the Hartmann number can be used to reduce the backflows. It controls the separation phenomena, resulting in a much smoother flow. The velocity behaves in a similar manner for a convergent channel and the increasing values of Ha. However, in this case, the change is slower as compared to the divergent  
Figure 3:1 Change in velocity for varying \(S\) (convergent channel). Figure 3:2 Change in velocity for varying \(S\) (divergent channel). channel. Furthermore, a stretching/converging channel has lower variations in velocity as compared to the shrinking channel (see Figure 7). To discuss the strength of nanoparticles on the velocity, Figures 8 and 9 are portrayed. A rise in the nanoparticle volume fraction reduces the velocity in a divergent channel. For the stretching of a divergent channel, again, a backflow is observed. However, when the channel is shrinking, there is no backflow. Again, a slightly higher value of the velocity can be observed for the copper nanoparticles than the silver nanoparticles. From all of this, one can easily say for a divergent channel that the higher volume fraction of the nanoparticles provides a low speed flow. On the other hand, a slow rise in the velocity for a convergent channel is evident: it keeps growing with the rising nanoparticle volume fraction. The influence of stretching and shrinking parameter \(S\) on the velocity profile is depicted in Figures 10 and 11. For a divergent channel, an upsurge in the velocity is witnessed. The stretching of a divergent channel marks a higher velocity near the walls as compared to the center of the channel. On the contrary, when a divergent channel is shrinking, the velocity decreases quite sharply near the walls, as shown in Figure 3:3 In Figure 11, the effects of the same parameter on a convergent channel are portrayed. It gives a clear picture of an accelerated flow near the walls for a stretching channel. On the contrary, a significant decrease in velocity is seen in a convergent channel with shrinking walls. For both of these cases, the changes are quite prominent near the walls of the channel. As we move towards the center, the effects get weaker and less influential.
# A Survey on Congestion Control for RPL-Based Wireless Sensor Networks
## 4 Congestion Control Schemes for RPL
### Schemes Forwarding Packets via a Single Parent  
We first summarize the congestion control schemes forwarding packets via a single parent. These RPL schemes use a parent node to forward packets towards the root until a new parent node needs to be chosen. Here, we also include the schemes which do not explicitly specify whether or not they forward packets through multiple parents. Table 4 at the end of this section summarizes the main feature, routing metrics, and evaluation tools and metrics for each scheme.
# MIU-Net: MIX-Attention and Inception U-Net for Histopathology Image Nuclei Segmentation
## 4 Experimental Setup  
### Datasets  
Kumar dataset comprised 30 tissues images, each of size 1000 \(\times\) 1000, containing 21,623 hand-annotated nuclear boundaries. Each 1000 \(\times\) 1000 image in this dataset was extracted from a separate whole slide image (WSI) (scanned at 40\(\times\)) of an individual patient downloaded from TCGA [27]. 40\(\times\) represents the magnification of the microscope. These images came from 18 different hospitals. The annotators were engineering students, and an expert pathologist examinated of annotation quality. Our annotations included both epithelial and stromal nuclei. For overlapping nuclei, we assigned each multi-nuclear pixel to the largest nucleus containing that pixel. All the images we used were RGB three-channel images.  
Because most existing datasets are annotated for nuclei instance segmentation. Since our task is the binary classification task of nucleus, we extract the black-and-white label map suitable for binary classification from the xml file.  
Correct and meticulous implementation of Pre-processing is very important for automatic diagnosis. Contrast Limited Adaptive Histogram Equalization(CLAHE) is used to improve contrast of the input images, rejecting noise [28]. Three channels of RGB images are divided into appropriately sized blocks, and then threshold is selected for equaliztion. It maps the pixel values of original photos to the new histogram in a balanced way. Gamma correction enhances the correction of gray areas in order to distinguish details of gray images. Vahadane [29] only normalize for brightness and coloring without damaging images texture part. Suitable Pre-processing methods will enhance the robustness of training model in Figure 4.  
[PERSON] dataset contains multiple organs. We selected the pathological image of the breast for testing. The Pre-processing of Vahadane lost mang nuclei edge and the intensity of contrast was insufficient, which brought a large amount of calculation consumption. The gamma correction method works better on the grayscale image. The advantage of CLAHE is that the contrast intensity is very high, and it has a good enhancement effect on marginal nuclei and independent large nuclei. So we choose CLAHE methods.  
Figure 3: Attention Module.
## 3 Results
### Impact of Satellite Data Spatial Resolution on Turbidity Retrieval
The resampling shows that 20-m imagery shows good agreement with the 2-m imagery in both sites: MAPE 8-10% with R\({}^{2}\) of 0.91 (Figures 14b and 15b). The agreement between the 250 and 2 m products is worse, especially for site 1, with R\({}^{2}\) of 0.50 and intercept of 120.5 NTU (Figure 14c). These results indicate that 250 m imagery would underestimate peak values, and overestimate the low values, especially in variable and highly turbid sites. The performance of the 250 m product in test site 2 is better, with an R\({}^{2}\) of 0.70 and an intercept of 2.32 NTU (Figure 15c).
# Production Optimization in a Grain Facility through Mixed-Integer Linear Programming +
## 5 A Real-World Case Study: Optimization in a Grain Processing Facility in Uruguay  
A specific real-world case study is considered to evaluate the proposed MILP model. The case study corresponds to the production planning of a company that processes and sells soybeans. The main details of the considered scenario are presented in this section.
## 2 Material and Methods
### Signal Preprocessing  
Preprocessing of ECG to suppress unwanted noise and artifacts, such as power line interference, base line wander, improper grounding effects, and movement artifact [19], is an important step to improve the quality of the signal. Preprocessing and extraction of ECG features of interest that are reliable/non-invasive remain a challenge and studies have reported different methods on this [20,21], with spectral filtering being the most common. The spectrum of ECG signal lies within 0.05-100 Hz; however, useful information is generally accepted to be between 0.5-45 Hz. In this study, a series of filters were used. The raw signal was high-pass filtered (second-order Butterworth) with a cut-off of 3 Hz to remove baseline drift, and a 45 Hz (second-order Butterworth) low-pass filter was employed to remove high-frequency noise. The ten-second window of the signal before and after filtering is shown in Figure 2. To visualize the signal, a time-axis magnified signal showing two consecutive R peaks is shown in Figure 3.  
Figure 1: Schematic block diagram.
# A Study on Assimilation of CYGNSS Wind Speed Data for Tropical Convection during 2018 January MJO
## Data and Methods  
### Data  
The main datasets used in this study include the Level 2 CYGNSS version 2.1 data, Global Precipitation Mission (GPM) Integrated Multi-satellite Retrievals for GPM (IMERG) data, and Level 2 ASCAT data. The CYGNSS Level 2 data is obtainable from the Physical Oceanography Distributed Active Archive Center (PODAAC) ([[https://podaac-tools.jpl.nasa.gov/drive/files/OceanWinds/cygns/L2/v2.1](https://podaac-tools.jpl.nasa.gov/drive/files/OceanWinds/cygns/L2/v2.1)]([https://podaac-tools.jpl.nasa.gov/drive/files/OceanWinds/cygns/L2/v2.1](https://podaac-tools.jpl.nasa.gov/drive/files/OceanWinds/cygns/L2/v2.1))). The GPM IMERG is a Level 3 gridded product for global rainfall estimates. Its spatial resolution is 0.1\({}^{\circ}\) and temporal resolution is 0.5 h. The IMERG product incorporates precipitation estimates from GPM and partner satellite microwave and infrared (IR) sensors. Rain gauge data was used in calibration of IMERG [42]. GPM IMERG rainfall data is available at [[https://pmm.nasa.gov/data-access/downloads/gpm](https://pmm.nasa.gov/data-access/downloads/gpm)]([https://pmm.nasa.gov/data-access/downloads/gpm](https://pmm.nasa.gov/data-access/downloads/gpm)). ASCAT is one of several instruments on the Meteorological Operational (MetOp) polar-orbiting satellite. It is an active microwave sensor designed to provide surface wind observations over the global oceans. The ASCAT ocean surface wind vector data has a resolution of 25 km and is available twice a day over the model domain. ASCAT data can be accessed from the PODAAC ([[https://podaac-tools.jpl.nasa.gov/drive/files/OceanWinds/ascat](https://podaac-tools.jpl.nasa.gov/drive/files/OceanWinds/ascat)]([https://podaac-tools.jpl.nasa.gov/drive/files/OceanWinds/ascat](https://podaac-tools.jpl.nasa.gov/drive/files/OceanWinds/ascat))).
# Asymmetric Quantum Multicast Network Coding: Asymmetric Optimal Cloning over Quantum Networks
## 3 Results
### \(1\to 2\) Asymmetric UQC Multicast Protocol
#### 3.1.1 \(1\to 2\) Quantum Multicast Protocol
 \tag{38}\]  
Since system \(G\) is effectively removed in Step 10, the unnormalized state on system \(CDEF\) after Step 10 can be written as  
\[ \begin{split}\sum_{j\
eq r}\beta_{j}|jj\rangle_{CD}\otimes(\cos \eta|j\rangle_{E}|r\rangle_{F}+\sin\eta|r\rangle_{E}|j\rangle_{F})\\ +\beta_{r}|rr\rangle_{CD}\otimes|00\rangle_{EF}.\end{split}  \tag{39}\]  
Then, the unnormalized state on \(CDEF\) after Step 11 can be written as  
\[ \begin{split}\sum_{j\
eq r}\beta_{j}|jj\rangle_{CD}\otimes(\cos \eta|j\rangle_{E}|r\rangle_{F}+\sin\eta|r\rangle_{E}|j\rangle_{F})\\ +\beta_{r}|rr\rangle_{CD}\otimes|rr\rangle_{EF}.\end{split}  \tag{40}\]  
In Step 12, after system \(CD\) is measured in the Fourier basis \(\{d^{-1/2}\cdot\sum_{x=0}^{d-1}\omega^{rx}|x\rangle\}_{p\in Z_{d}}\) and is discarded, the unnormalized state on \(EF\) for the measurement outcomes \(p_{1}\) and \(p_{2}\) can be written as  
\[ \begin{split}\sum_{j\
eq r}\beta_{j}\omega^{-j(p_{1}+p_{2})}(\cos \eta|j\rangle_{E}|r\rangle_{F}+\sin\eta|r\rangle_{E}|j\rangle_{F})\\ +\beta_{r}\omega^{-r(p_{1}+p_{2})}|rr\rangle_{EF}\end{split}  \tag{41}\]  
Hence, after applying \(Z^{p_{1}+p_{2}}\otimes Z^{p_{1}+p_{2}}\) on system \(EF\), the unnormalized state on \(EF\) becomes  
\[\omega^{r(p_{1}+p_{2})}\cdot\sum_{j=0,j\
eq r}^{d-1}\beta_{j}(\cos\eta|j \rangle_{E}|r\rangle_{F}+\sin\eta|r\rangle_{E}|j\rangle_{F})+\beta_{r}|rr \rangle_{EF}. \tag{42}\]  
This unnormalized state coincides with \(\left|\Psi_{2}^{(r)}\right\rangle\) defined by Equation (42) except a global phase. Since Equation (42) is the unnormalized state corresponding to the outcome \(r\) in Step 2, the final state of this protocol can be written as \(\sum_{r}\left|\Psi_{2}^{(r)}\right\rangle\left\langle\Psi_{2}^{(r)}\right|\). Hence, by Equation (32), the final states of protocol 2 on the target nodes \(t_{1}\) and \(t_{2}\) are \(1\to 2\) optimal asymmetric UQCs of the input state \(\left|\psi\right\rangle\). \(\Box\)
# A Data-Driven Deep Learning Framework for Prediction of Traffic Crashes at Road Intersections
## 5 Performance Evaluation
### Dataset  
In this paper, the evaluation of _RoadInTCP_ is conducted using a real-world transportation dataset from New York City spanning 2014 to 2016, including five categories of heterogeneous data: traffic crash records, Point-of-Interest (POI) data, taxi trip records, and weather data. A detailed statistical summary of these datasets is shown in Table 1. Notably, due to the unavailability of trip pickup and drop-off locations for taxis in thesecond half of 2016, we only focus on taxi trajectory records from the first half of that year. In the experiments, the dataset spanning from 2014 to 2016 is segmented into five parts, with each segment representing a half-yearly timeframe for evaluation. In this paper, we searched for all the data from 2010 to 2024, and found that only 2014 to the first half of 2016 had all five categories of data available. For other time periods, there has always been at least one type of data in lack, e.g., the unavailability of trip pickup and drop-off locations for taxis in the second half of 2016, and the unavailability of Point-of-Interest (POI) data in 2019. Moreover, we compared the OSM data from the past 8 years and found the changes in the road topology information used in this paper are minimal, with almost no alteration. Therefore, in the experiment, the dataset spanning from 2014 to 2016 is segmented into five parts.  
Specifically, the traffic crash records are chronologically segmented into five parts, reflecting the following distribution of records: 102,481 crashes occurred in the first half of 2014, followed by 123,114 crashes in the latter half. In 2015, there were 85,860 crashes in the first semester and 131,833 in the second. Lastly, for the first half of 2016, 91,625 traffic crashes were recorded. This stratification ensures a temporally consistent evaluation of _RoadIntTCP_ across varying conditions present within the provided datasets.