esa-sceva/satcom-chunk-collection huggingface dataset:
# Triangular Mesh Surface Subdivision Based on Graph Neural Network
## 2 Related Work  
### Mesh Subdivision Algorithm  
Mesh subdivision algorithms based on neural networks are an emerging field of research, and they can be roughly divided into two categories: classical methods and neural subdivisions. There are similarities between the implementation strategies of classical mesh subdivision and neural subdivision, both of which can be divided into three parts: mesh upsampling, mesh vertex position adjustment, and subdivision iteration.  
Mesh upsampling: This step splits the original triangle into four smaller triangles by taking one point on each of the three sides of the original triangle and connecting them to form some new triangle faces. Here, both the LOOP subdivision and the neural subdivision use the midpoint upsampling method.  
Mesh vertex position adjustment: This step is the main difference between the classical and neural network methods. LOOP subdivision uses a fixed linear update strategy. The neural subdivision uses a data-driven method to realize a nonlinear vertex update scheme. Then, it outputs a three-dimensional displacement vector nf(x, y, z) for each set of 3D vertex coordinates pos(x, y, z) input into the neural network.  
Subdivision iteration: Mesh subdivision is implemented through multiple iterations and gradual subdivision.  
In general, each subdivision first adjusts the position of the original vertices of the input mesh, then upsamples to obtain new vertices, and then adjusts the position of the new vertices to obtain a subdivision mesh. Subsequently, the subdivision cycle is repeated until the termination condition is reached.  
The overall process of GNS is similar to the former, but, when adjusting the position of vertices, it does not distinguish between the old and new vertices. Instead, it upsamples the input coarse mesh M0 to obtain M1' and then adjusts the position of all vertices of the M1' mesh as a whole.
# Using Product Network Analysis to Optimize Product-to-Shelf Assignment Problems
## 4 Implementation and Experimental Results
### Environment and Data Description  
Figure 4 illustrates the layout of a simplified grocery store to demonstrate the feasibility of the proposed shelf space allocation method. There are 110 product items in the store, with each product item belonging to one of 11 categories such as meat, vegetable, fruit, dried food, etc. Products are assigned to a specific zone according to their category. The initial position (location) assignment for products and center coordinates of cells are respectively shown in Figure 4a,b.  
A transaction generator is developed to simulate customer shopping behavior. In this generator, four types of customers are modeled. Each with different purchasing probabilities for the various product categories (Table 1), and are assigned different weightings for transaction frequency (respectively 60%, 20%, 15%, and 5%). In this study, the total number of purchase transactions in the generator is set at 5000. Table 2 summarizes part of the simulated purchase transactions where a transaction contains T_ID (transaction identifier), C_ID (customer identifier), and purchased product items. Next, the purchase dataset will be respectively aggregated as a transaction-based dataset (TBD) and a customer-based dataset (CBD) according to unique T_ID or C_ID. After this process, the number of records in TBD is 5000 while the number of records in CBD is 1000.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Customer Type** & **Purchase Probability for All Categories** \\ \hline \multirow{3}{*}{Type I} & Meat (10\%), Vegetable (10\%), Fruit (10\%), Dehydrated Food (10\%), Dehydrated \\  & Drink (10\%), Seasonings (10\%), Cookie (10\%), Drink (10\%), Alcohol (10\%), Can (5\%), \\  & Kitchen Tool (5\%) \\ \hline \multirow{3}{*}{Type II} & Fruit (16\%), Dehydrated Food (16\%), Dehydrated Drink (8\%), Cookie (8\%), Drink (8\%), \\  & Alcohol (16\%), Can (8\%), Meat (30\%), Vegetable (30\%), Seasonings (30\%), \\  & Kitchen Tool (10\%) \\ \hline \multirow{3}{*}{Type III} & Dehydrated Food (20\%), Dehydrated Drink (20\%), Cookie (20\%), Drink (20\%), Meat (5\%), \\  & Vegetable (5\%), Fruit (4\%), Seasonings (3\%), Can (2\%), Kitchen Tool (1\%) \\ \hline \multirow{3}{*}{Type IV} & Alcohol (40\%), Kitchen Tool (40\%), Meat (2\%), Vegetable (2\%), Fruit (4\%), Dehydrated \\  & Food (1\%), Dehydrated Drink (2\%), Seasonings (1\%), Cookie (3\%), Drink (3\%), Can (3\%) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Four customer types and their purchase behavior.  
Figure 4: Simplified layout in the case study. (**a**) Initial product shelf assignment; (**b**) cell coordinates.
## 3 Results
### Vulcanization Characteristics  
Figure 8 shows the cure curves of the compounds containing resins in comparison with their controls for the CB-, CB/SI-, and SI-filled materials. Compared to their controls, the C9 and TP resin compounds show a minor influence on scorch time (ts\({}_{2}\)) and optimum cure time (t\({}_{90}\)) for all filler systems: see Table 6. Compared to the controls, the use of C9 and TP decreases the rheometer minimum torque S\({}_{\text{min}}\) in vulcanizates containing silica: CB/SI and SI.  
Figure 8: \(Cont.\)  
Figure 7: Tack measured as: (**a**) maximum forces; (**b**) separation distances of compounds containing resins and different filler systems in comparison to the controls.  
The effect of resins on the apparent crosslink density is shown in Figure 9 as derived from swelling experiments. It is resultant from the contribution of sulfur polymer-polymer crosslinks and from filler-rubber interaction. There is a slight increasing trend in going from CB towards SI, most likely because of the stronger chemical filler-rubber coupling for the latter in comparison with the physical interaction in the CB case. The addition of resins does not result in large apparent crosslink density differences for any of the systems.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Characteristics/Properties** & \multicolumn{2}{c}{**CB**} & \multicolumn{4}{c}{**CB/SI**} & \multicolumn{4}{c}{**SI**} \\ \hline  & **C** & **TP** & **C9** & **C** & **TP** & **C9** & **C** & **TP** & **C9** \\ \hline \hline Scorch time, \(\text{ts}_{2}\) (min) & 4.1 & 3.9 & 3.8 & 3.2 & 3.9 & 3.3 & 5.7 & 6.3 & 6.6 \\ \hline Optimum cure time,
## 4 Emerging Lightweight Security Approaches
### Symmetric Encryption--Lightweight AES Approaches
#### 4.1.3 Lightweight AES (LAES)  
Another lightweight AES (LAES) [78] proposal removes the MixColumns transformation and replaces it with a 128-bit permutation table. This permutation operation uses a fixed table that re-arranges the columns in the state matrix. MixColumns is a non-linear transformation on the columns of the state matrix, and the authors argue that the permutation operation is another non-linear operation, albeit less complex to implement in hardware than MixColumns. In this way, when using a 128-bit key, after the first AddRoundKey, the nine rounds include SubBytes, ShiftRows, _Permutation_, and AddRoundKey transformations (Figure 8).  
This variation in the AES round schedule aims to improve overall performance in terms of throughput, namely the number of bits processed per time. Performance efficiency is calculated by dividing the throughput by the area of the FPGA circuit.  
The benefits of the proposed LAES algorithm include improved operational frequency, increased throughput, reduced footprint area, and improved performance efficiency. As shown in Table 7, LAES can be considered as both a hardware improvement for low-resource devices and a lightweight block cipher with simpler rounds because it replaces the MixColumns transformation with a simpler non-linear operation.
# Networking of Multi-Robot Systems: Architectures and Requirements
## 4 Communication Issues and Requirements for MRS Systems
### The Networking Layers in the Nodes of the MRS System  
The networking layers at the nodes using R2R and R2I communication are shown in Figures 3 and 4 respectively. In Figure 3, the two nodes are either two BR, or a basic robot and a gateway robot. In Figure 4, the nodes consist of a gateway robot on one side and an access point (AP) to the infrastructure (or backbone) network and/or the Internet on the other side. In the first case (R2R communication), the shorter to medium range wireless protocols can be used. Such protocols include IEEE 802.15.4 (Zigbee), IEEE 802.11 a/b/g/n/ac (Wi-Fi), and IEEE 802.15.1 (Bluetooth). The first two cover the physical and data link layers, and Bluetooth covers the same layers as well as the network and transport layers. These protocols are also characterized with relatively lower energy consumption. In the second case (R2I communication), the medium to longer range wireless protocols can be used. Such protocols include IEEE 802.11 a/b/g/n/ac (Wi-Fi), IEEE 802.16 (WiMAX), Cellular, and Satellite. Above the underlying network protocols resides the Internet Protocol (IP) protocol at the network layer, the Transmission Control Protocol (TCP), User Datagram Protocol (UPDD), or Stream Control Transmission Protocol (SCTP) at the transport layer. These protocols are followed by the middleware layer, and the appropriate client and server protocols at the application layer.
## 3 Sensitivity Analysis of Penetration Velocity
Zone II-1 represents the second partitioned mean pressure area, exhibiting lower pressure compared with Zone I, with its minimum pressure being 10.3 times \(\sigma_{\text{m}}\). Zone II-2 constitutes the third partitioned mean pressure area, characterized by reduced pressure compared with Zone II-1, with its minimum pressure being 2.65 times \(\sigma_{\text{m}}\). Zone II-3, as the fourth partitioned mean pressure area, corresponds to the minimum pressure level, with its minimum pressure being 1.3 times \(\sigma_{\text{m}}\). In this study, the area outside Zone II-3 is not studied, where the interaction between the penetrator and the soil is ignored. Amongst the four different penetration velocities, the maximum pressure was 15.9 times that of \(\sigma_{\text{m}}\), the distribution pattern of mean pressure was generally consistent, and the size of Zone I also remained relatively consistent. When the velocity was 1.85 mm/s, the entire area of mean pressure was slightly larger. To investigate the influence of velocity on soil behavior, the performance of the soil heave at four different depths was analyzed (with normalized penetration depths for \(d_{\text{p}}/D=0.49\), 0.96, 1.43, and 1.89), as shown in Figure 8. The height of the soil heave, \(h_{\text{p}}\), exhibited a positive correlation with penetration depth. Furthermore, with increasing penetration velocity, the soil heave became more pronounced. The penetration velocity of 7.4 mm/s was four times that of 1.85 mm/s, and the \(h_{\text{p}}\) at different depths was approximately two times higher. Due to the balance between CEL computational efficiency and the characteristic of the penetration performance, the proper penetration velocity was set to 7.4 mm/s in subsequent analyses. On the one hand, higher penetration velocity causes more pronounced soil heave, facilitating the observation of soil flow, while the resistance on the penetrator structure and the mean pressure of the soil are not sensitive to penetration velocity. On the other hand, the CEL computational efficiency at a penetration velocity of 1.85 mm/s is four times that of 7.4 mm/s.
## 1 Introduction
To the best of the authors' knowledge, this effect has only been evaluated based on the measurement of free fatty acids (FFAs) and primarily only during the ripening of HPP-processed cheees and not during subsequent storage [15; 16; 17; 18; 19]. Furthermore, to the best of our knowledge, no previous studies have performed such in-depth scientific work on lipid fractions, and also no data are available on the impact of HPP on the free/non-esterified fatty acids profile (NEFAs) of raw ewes' milk cheees. The aim of the present work was, therefore, to study the possible effects of HPP on triglycerides, fatty acids (esterified and non-esterified), and conjugated linoleic acid (CLA) composition of raw ewes' milk PDO _Serra da Estrela_ cheese, immediately after processing and during subsequent storage under refrigeration for 15 months.
## 3 Spiral Rail Spike Positioning Method  
### Overall Process of Positioning Method  
The positioning method for spiral road nails includes three stages: image preprocessing, road nail edge detection, and center positioning of the road nail. In the image preprocessing stage, the detection area is enhanced by applying Contrast-Limited Adaptive Histogram Equalization (CLAHE) to limit contrast, which reduces the impact of lighting variations on subsequent positioning. In the edge detection stage, an improved Canny algorithm is proposed to extract the edge information of the image more effectively, aiming at the limitations of the traditional Canny algorithm in the face of abnormal spikes and its poor adaptability. In locating the center of the road stud, the gradient Hough transform is used to fit the circular features on the edge map, extract the road stud areas to be screened, and obtain the center coordinates of each region. At the same time, considering that non-railway spike circular interference objects such as ballast are easily misfitted as circles, a method of comparing shape features is adopted. Histogram of Oriented Gradients (HOG) feature vectors are extracted for the detected circular areas. Then, feature similarity  
Figure 2: The working principle of a robot.  
comparison is achieved by calculating the standard Euclidean distance (SED) between them and the HOG feature vectors of the template rail spike image. This process eliminates circular areas that do not match the characteristics of railway spikes, ultimately determining the areas of railway spikes and locating their centers. The overall algorithm flow is shown in Figure 3.
# A Low-Cost Secure IoT Mechanism for Monitoring and Controlling Polygeneration Microgrids
## 4 Results and Discussion
### Security Protection Analysis
#### 4.4.3 Denial of Service (DoS) Attacks  
Low Orbit Ion Cannon (LOIC) [41] was the mechanism used to strike the RPI3+. One of the DoS attacks requests 530,020 to the server from which 1239 failed in vulnerable environment. A high number in the failed parameter means that the server goes down and does not respond. To mitigate this attack, modifications in the host (.allow and.deny) files and Fail2 Ban (.local) rules were made.  
Figure 10: Pen-Test in the proposed scheme.  
Figure 9: Pen-Test in vulnerable environment.  
After those changes, a total of 44,045 requests were made to the server by the first attack, and only 10 failed. In addition, the request to the server stops when the failed goes to 10 because the Fail2 Ban detects and bans the invalid IP address. Then, another threat was carried out, since the Fail2 Ban had blocked the IP address, 0 requests were processed and 0 failed, as shown in Figure 11. This indicates the IP address is blocked and can no longer attack or attempt to access the RPI3+.  
The calculated failure rate of our system after some runs of DoS attacks in the vulnerable environment was 0.20% but the CPU was working at 100%. In the case of DDoS attacks, the system can shut down for an amount of time without the proposed scheme in this paper. The failure rate equation used was:  
\[F_{rate}=\frac{\sum_{i=1}^{n}F_{i}/R_{i}}{n}(100\%) \tag{1}\]  
where \(F\) is the failed parameter and \(R\) is the requested parameter of the LOIC software.
## 2 Data Details
### Dummy Variables for the West Region for Hours 3 AM, 11 AM, and 4 PM  
We follow a similar process as above for the West region, working with Table 2 and Figure 2 Panels A, B and C. From Table 2, for Hour 11 AM in the West region, the median price is USD 21.68. The minimum is only USD 0.01, while the maximum price is USD 555.09. During Hour 3 AM, the median, minimum, and maximum prices are USD 16.93, USD 0.01 and USD 161.02, respectively.  
Consider Figure 2 Panels A, B and C, which depict the time series of the price data for the West region at 11 AM, 4 PM, and 3 AM, respectively. Like in the North and Houston regions, dramatic swings in prices are apparent in these three plots. As noted previously, lagging prices at these time points will adversely impact the regression estimates of these event spikes on real-time pricing across the ERCOT.  
Based on a percentile analysis of the price data for Hours 11 AM and 4 PM in the West zone, we chose a cutoff value of USD 50 at the upper tail, since only roughly 2% of the data are above USD 50.  
A percentile analysis for the Hour 3 AM data yields cutoff values for the upper and lower tails of the price distribution. For the lower end, we chose a cutoff value of USD 1.00, and for the upper end, we choose a cutoff value of USD 32. Collectively, these account for roughly 2.5% of the data.  
**For Hours 11 AM and 4 PM in the West:**  
If 11 AM West Price >= $50, define _WPrice50_ = 1, else 0;  
If 4 PM West Price >= $50, define _WPrice50_ = 1, else 0.  
**For Hour 3 AM in the West:**  
If West Price <= $1, define _WPrice1_ = 1, else 0;  
If West Price >= $32, define _WPrice32_ = 1, else 0.
# A Machine Learning Method to Estimate Reference Evapotranspiration Using Soil Moisture Sensors
## Materials and Methods
### Remote Image System for the Estimation of the Water Balance  
The ultimate goal of the present research is to predict the actual values of the crop evapotranspiration, ETc, which is a part of the water balance equation [37].  
\[\Delta W=P+I-ET_{c}-D-R, \tag{5}\]  
where \(\Delta W\) is the water balance of the crop of interest in a given period, \(P\) is the rainfall, \(I\) is the irrigation, \(D\) is the drainage, and \(R\) is the surface runoff. According to the FAO-56 methodology [20], ETc can be calculated as the product of the reference evapotranspiration, ETo, and a crop coefficient, Kc, which is specific to the type of crop and its growth state. Therefore, the proposed method was designed to be integrated with the remote image capture system presented in Reference [39] to create a complete infrastructure for the computation of the water balance. Some sample views of this system are shown in Figure 4 for a crop of lettuce (_Lactuca sativa_ L) which was used as the crop of interest, while kikuyu grass was used as the reference crop for the estimation of ETo.  
The integration was done as follows: firstly, the meteorological data (daily temperature and rainfall), the soil moisture values and the irrigation of the reference crop, and the images of the crop of interest were captured with the corresponding remote modules and transmitted to the local coordinator node via XBee wireless connection [39]. A segmentation algorithm was applied to the images in the coordinator node to separate plants and background [38], as shown in Figure 4c, obtaining the percentage of green cover (PGC). Then, a regression model was applied to estimate Kc from the PGC, as defined in Reference [37]. Additionally, using the present method, ETo was calculated for the reference crop using the obtained data. Additionally, the estimation of ETc for the crop of interest was given by Kc x ETo. The resulting value was applied in Equation (5) to compute the daily water balance in the crop of interest and adopt the most adequate irrigation decisions.
Therefore, the decision was made to combine HPC technology with CNNs to classify weed species in an HPCC. Although there are already implemented HPCC architectures, such as Fugaku, SUMMIT, SIERRA, Sunway, Perlmutter, and others, which are within the top of the TOP500 list [16], as well as architectures existing in Mexico, including the LEO ATROX from the Data Analysis and Supercomputing Center (CADS) of the University of Guadalajara [17], the National Supercomputing Laboratory of Southeast Mexico (LNS) [18], the Laboratory National High-Performance Computing (LANCAD) [19], and the Galileo of the Autonomous University of Zacatecas (UAZ) [20], none of these were used. For this article, an HPCC was implemented with mid-range desktop computers, which, due to their characteristics, were useful for the distributed processing of CNNs. This decision was made as access to current HPC architectures has a high cost, requiring bureaucratic procedures to request the services they offer, which sometimes take a long response time [21]. This article aims to demonstrate that HPC technology can be accessed using idle or convenient computers to unify their processing and storage capabilities and use them for AI projects. For the validation of this HPCC, a weed classification process was carried out through the use of DIP and distributed DL; specifically, a CNN was applied to crop images. In addition, the processing time between a workstation and the proposed HPCC was also compared. For this, two pretrained CNN models were used, which were InceptionV3 and VGG16. We used the \"DeepWeeds\" dataset [22] to observe the behavior of the processing speed and the accuracy of the weed classification.
# An Enhanced YOLOv5 Model for Greenhouse Cucumber Fruit Recognition Based on Color Space Features
###### Abstract  
The identification of cucumber fruit is an essential procedure in automated harvesting in greenhouses. In order to enhance the identification ability of object detection models for cucumber fruit harvesting, an extended RGB image dataset (\(n=801\)) with 3943 positive and negative labels was constructed. Firstly, twelve channels in four color spaces (_RGB_, _YCbCr_, _HIS_, _La_*b_*) were compared through the Relief method to choose the channel with the highest weight. Secondly, the RGB image dataset was converted to the pseudo-color dataset of the chosen channel (_Cr_ channel) to pre-train the YOLOv5s model before formal training using the RGB image dataset. Based on this method, the YOLOv5s model was enhanced by the _Cr_ channel. The experimental results show that the cucumber fruit recognition precision of the enhanced YOLOv5s model was increased from 83.7% to 85.19%. Compared with the original YOLOv5s model, the average values of _AP_, _F1_, recall rate, and _mAP_ were increased by 8.03%, 7%, 8.7%, and 8%, respectively. In order to verify the applicability of the pre-training method, ablation experiments were conducted on SSD, Faster R-CNN, and four YOLOv5 versions (s, l, m, x), resulting in the accuracy increasing by 1.51%, 3.09%, 1.49%, 0.63%, 3.15%, and 2.43%, respectively. The results of this study indicate that the _Cr_ channel pre-training method is promising in enhancing cucumber fruit detection in a near-color background.  
deep learning; color space; Relief characteristic analysis; near color recognition Article