esa-sceva/satcom-chunk-collection huggingface dataset:
# GreenLab, an IoT-Based Small-Scale Smart Greenhouse
## 2 Related Work
In autonomous systems, the solar energy produced is adjusted to the required energy. If the energy produced cannot meet the energy needs at a given time, an additional storage system (battery) is used. If the photovoltaic system is connected to other resources (diesel or wind generators), then it is called a hybrid photovoltaic system. Regarding photovoltaic systems for IoT-based greenhouses, several solutions were proposed in the literature [15; 16; 17]. The last solution includes also a mobile application with integrated cloud services for monitoring the photovoltaic system status. Other solutions, like the ones presented in [18; 19], are based on the concept of \"MIT's Personal Food Computer\" [20]. A brief comparison of the solutions presented in this section is available in Table 1, where the \"\(\check{\check{\vee}}\)\" symbol represents the presence of the feature named in each column, and \"X\" represents the absence of it. \begin{table}
\begin{tabular}{p{113.8 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Solution** & **IOT Connectivity** & **Mobile/Web Application** & **Green Energy** & **Irrigation System** & **Low Cost** & **Reference** \\ \hline A New IoT-Based Platform for Greenhouse Crop Production & \(\check{\check{\vee}}\) & \(\check{\check{\vee}}\) & \(\check{\chi}\) & \(\check{\check{\vee}}\) & \(\check{\chi}\) & [3] \\ \hline An Intelligent IoT-Based System Design for Controlling Temperature & \(\check{\check{\vee}}\) & \(\check{\chi}\) & \(\check{\check{\vee}}\) & \(\check{\chi}\) & \(\check{\chi}\) & [4] \\ \hline IoT-Based Smart Greenhouse Framework and Control Strategies for Sustainable Agriculture & \(\check{\check{\vee}}\) & \(\check{\check{\vee}}\) & \(\check{\check{\vee}}\) & \(\check{\check{\vee}}\) & \(\check{\chi}\) & [5] \\ \hline Application of Internet of Things (IoT) for Optimized Greenhouse Environments & \(\check{\check{\vee}}\) & \(\check{\chi}\) & \(\check{\check{\vee}}\) & \(\check{\check{\vee}}\) & \(\check{\check{\vee}}\) & [8] \\ \hline Smart Greenhouse and Plant Growth Control & \(\check{\check{\vee}}\) & \(\check{\check{\vee}}\) & \(\check{\chi}\) & \(\check{\check{\vee}}\) & \(\check{\check{\vee}}\) & [10] \\ \hline Greenhouse environmental control system based on SW-SVR & \(\check{\check{\vee}}\) & \(\check{\chi}\) & \(\check{\chi}\) & \(\check{\chi}\) & [11] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Solutions comparison.
# LoRaWAN Networking in Mobile Scenarios Using a WiFi Mesh of UAV Gateways
## 4 Simulation Model
### Firefighters Mobility Model  
The VSF algorithm adapts the topology of the UAV swarm according to the movements of GNs. To validate the performance of the developed algorithm, it is therefore necessary to assign a suitable mobility model to the GNs. Given the considered application, GNs must act similarly to firefighters during a wildfire operation. To the best of our knowledge, no mobility model has ever been created for this situation and therefore a new one has been developed based on information collected from [20] and [21]. An approximation of this behavior has been implemented. According to this model, each GN \(i\) is assigned to a team \(j\), with \(j=0,1,2..,N\). Teams can be placed anywhere in the simulation area, which is assumed to be an \(S\times S\) square. The whole area is divided vertically in \(N\) columns of equal width, one for each team, and each column is in turn split horizontally in \(R_{j}\) equal width rows, with \(R_{j}\) given by:  
\[R_{j}=\lfloor S/d_{rj}\rfloor, \tag{19}\]  
where \(d_{r}^{j}\) is the retreat distance of team \(j\). At this point, every team has an assigned vertical stripe with \(R_{j}\) cells. At \(t=0\), each team selects a random point in the furthest cell from the origin of the scenario in its assigned stripe and moves towards it with constant speed until the destination is reached. At this point, the team members start moving independently and randomly for a certain amount of time \(t_{rw}\) (deterministic or random) in a rectangular \(s_{j}^{x}\times s_{j}^{y}\) operation area. When time is over, teams select a new point in the second furthest cell and repeat the previously described process. Teams stop moving when the simulation is over or when they have visited all the cells in their column. This model tries to mimic the progress of a wildfire and the consequent retreat of firefighters to fallback areas where new attacks to the fire are attempted. A representation of the model is shown in Figure 3.
## 3 Experiments Results and Discussion
### Quantitative Comparison of Different Methods  
We evaluated WDTISeg with graph cuts, random walker, and DeepIGeoS (R-net). Table 1 presents a quantitative comparison of these methods on the testing data. All results are accepted after 8 interactions for the interactive segmentation method. Compared with the other three methods, the dice of WDTISeg (\(\lambda=0.5,\ \mathcal{L}_{\text{total}}\)) reached \(94.45\pm 3.26\) (%) after 8 interactions, which fully shows that our method can achieve a high segmentation accuracy with fewer interactions.  
Visual comparison results are shown in Figure 8. All interactive segmentation methods can attain a high accurate segmentation after enough interactions. However, results of graph cuts and random walker showed more rough edges. In contrast, our method was able to obtain a segmentation that fit more closely to the tumor margin. What is more, our WDTISeg only required simple point clicks, while graph cuts and GrabCut require more scribbles or a bounding box. The results of DeepIGeoS are more similar to that of our method, because we also used distance conversion to pass interaction information. However, it can be found that the segmentation result of our method was smoother at the tumor edge, especially at the lower right corner of case 4. This may benefit from the fact that we used a shape constraint loss to impose prior constraints on tumor shape.  
Figure 7: Dice rate on the training (left) and validation datasets (right) of different loss functions.
## 5 Case Study
### Planning Results  
The identification of the optimal cluster number is first implemented and the statistical results with the cluster number varying from 3 to 25 are shown in Figure 6.  
As shown in Figure 6, cluster number 19, which has the maximum stability degree in 40 trials, is determined as the optimal cluster number. Then, with the minimum sampling number \(t_{\text{min}}\) set as 3, the basic scenario set is divided into 19 clusters, while a total of 194 scenarios are generated for TNEP.  
By solving the proposed TNEP problem, the Pareto optimal front of planning results is obtained. In this paper, only the planning result with a wind curtailment percentage less than 5% is considered as a feasible planning scheme, thus ensuring a certain degree of wind power consumption. Figure 7 shows all feasible planning schemes from the Pareto front. It can be seen that the investment cost in each planning scheme ranges from 91.3 \(\times\) 10\({}^{5}\) $ to 165 \(\times\) 10\({}^{5}\) $, while the wind curtailment percentage decreases from 4.75% to 0%.  
Figure 5: Network topology of the modified IEEE RTS-24 system.  
Figure 6: Stability degree boxplot for cluster number analysis.  
To further investigate the impact of wind power distribution characteristics on TNEP, four feasible planning schemes, which have been marked in Figure 7, are selected for comparison. The detailed information is given in Table 1.  
As shown in Table 1, compared with the most robust planning scheme R1, the investment costs of the other three planning schemes are reduced by 12.06%, 24.97%, and 34.42%, respectively, whereas the wind curtailment percentages are only 0.15%, 0.40%, and 0.82%, respectively, of the expected wind power output. It can be observed that the investment cost of TNEP can be substantially reduced by limiting the wind power output, which only results in an extremely small wind curtailment. As a result, allowing an appropriate renewable curtailment can effectively improve the economic efficiency of TNEP for integrating renewable energy sources.  
The above analysis demonstrates that the proposed TNEP method can provide a robust planning scheme and other economic planning schemes simultaneously. Thus decision makers can make an effective trade-off between robustness and economic efficiency and choose the most suitable planning scheme based on actual situations.
###### Abstract  
UV-C irradiation is an innovative postharvest technique for increasing the safety of fruits and vegetables. This study investigated the effect of UV-C rays (UV-C1 = 0.26 KJ/m\({}^{2}\); UV-C2 = 0.40 KJ/m\({}^{2}\); UV-C3 = 0.67 KJ/m\({}^{2}\); and UV-C4 = 1.34 KJ/m\({}^{2}\)) on the preservation of the antioxidants, hardness, and color of fresh green asparagus during storage. UV-C1 and UV-C2 significantly maintained higher total phenolic content (10.6%), total flavonoid content (36%), rutin (14.3%), quercetin (27.03%), kaempfer-3-O-rutinoside (21.25%), and antioxidant activity (DPPH 7.5%). Over three weeks of storage, quercetin, ferulic acid, and kaempfer 3-O-rutinoside increased, while rutin and caffe acid decreased. Storage caused a significant change in the color and hardness of the control sample, but UV-C4 counteracted hardening for up to three weeks, and UV-C3 was the best dose for stabilizing color during storage. This study indicates that the choice of UV-C dose can be modulated based on the characteristics that are intended to be preserved in green asparagus, maintaining a balance between nutraceuticaltical and hedonic characteristics. To maintain the maximum level of nutraceuticaltical compounds over time, UV-C2 can be adopted, while to preserve texture and color, UV-C3 and UV-C4 are a better choice.  
green asparagus; UV radiation; storage; antioxidant activity; rutin; quercetin; hardness; color 2020
# Study on the Mechanism of Stress Sensitivity Changes in Ultra-Deep Carbonate Reservoirs
## 4 Example Application
### Stress Sensitivity Analysis of Ultra-Deep Carbonate Reservoirs  
According to the dual-porosity medium model that considers the initiation of pressure gradient and stress sensitivity, and includes formulas for both matrix and fracture stress sensitivity, we can see that in ultra-deep reservoirs consisting of fractured pore carbonate reservoirs, the degree of fracture permeability degradation in response to increased effective stress is more severe than the degradation seen in the matrix which is stress-sensitive, while showing a clear attenuation or decay characteristic. This effect is particularly pronounced near the bottom of the production wells, where the fracture permeability degradation is significantly greater than that seen in the matrix.  
Take the T74 reservoir of the Yijiangfang formation in the research area as an example to perform statistical analysis on the fracture development, establish a fracture model in Petrel, and perform numerical simulation of the fracture using Tnavigator software.  
Stress-sensitive settings in the simulator are as follows:  
\begin{tabular}{c} \hline \hline
**SATNUM = 1 (Matrix)** \\ \hline PERMX = ARRPERMX * E(\(-\)0.009324 * (1.465\(-\)0.324 InE)\(\Delta\)P) \\ PERMY = ARRPERMY * E(\(-\)0.009324 * (1.465\(-\)0.324 InE)\(\Delta\)P) \\ PERMZ = ARRPERMZ * E(\(-\)0.009324 * (1.465\(-\)0.324 InE)\(\Delta\)P) \\
**SATNUM = 2 (Fractured)** \\ PERMX = ARRPERMX * E(\(-\)0.125 * \(\Delta\)P/E) \\ PERMY = ARRPERMY * E(\(-\)0.125 * \(\Delta\)P/E) \\ PERMZ = ARRPERMZ * E(\(-\)0.125 * \(\Delta\)P/E) \\ \hline \hline \end{tabular}  
During actual development, it was found that the stress sensitivity of the carbonate reservoir mainly occurs at the bottom of the well. Comparative changes in the initial and stress-sensitive permeability of matrix-type carbonate reservoirs in the 15 th (bottom of the well) and 34 th (The oil-water interface) layers are illustrated in Figures 8 and 9. For the entire matrix-type carbonate reservoir, a reduction of approximately 1.6% in permeability occurred at a Young's modulus of 53.41 GPa. The lower rock stiffness may lead to fractures and plastic deformation that results in pore closure, and thus reduces the permeability of the rock (Figure 10).  
Through the analysis of the changing pressure and permeability of the reservoir's overall area (Figure 15), it has been found that the influence of stress changes on the overall performance of the reservoir exhibits apparent spatial distribution characteristics. The stress sensitivity exhibited particular emphasis in the regions near the bottom of the well, which may be related to the distribution and connectivity of crack networks. These observations hold significant value towards optimizing drilling layout and improving the recovery rate.  
Figure 12: Permeability under the influence of stress sensitivity in fractured carbonate reservoirs. Layer (**a**) = 15; (**b**) = 34.  
Figure 13: Permeability change curve of fractured carbonate reservoirs.
# Enhanced Fall Detection Using YOLOv7-W6-Pose for Real-Time Elderly Monitoring
## 3 Materials and Methods
### Pose Estimation and Fall Detection Algorithm  
#### 3.4.1 Pose Estimation with YOLOv7-W6-Pose  
The model processes each frame of the video to locate these points accurately, enabling the system to monitor the movements and postures of individuals continuously.  
To detect falls, an algorithm was developed that analyzes the positions of the identified key points. The algorithm works as follows:  
* _Position Analysis_: The algorithm tracks the relative positions of key body points, focusing particularly on the shoulders, torso, and feet. By continuously monitoring these positions, the algorithm can detect changes that may indicate a fall.
* _Distance Calculations_: It calculates the distances between these key points to assess the body's posture. For example, the algorithm measures the distance between the shoulders and the feet.
* _Fall Detection Criteria_: Significant changes in these measurements can suggest a fall. For instance, a noticeable drop in the positions of the shoulders relative to the feet indicates that the person has fallen.  
* _Thresholds and Conditions_: To accurately differentiate between normal activities and falls, the algorithm employs thresholds and conditions that have been fine-tuned using empirical data. This fine-tuning involved analyzing a dataset of various activities and fall events to determine the optimal threshold values for distances. These thresholds ensure that the algorithm can robustly identify falls without generating false alarms.  
Using a fixed threshold might not be effective because people have different body sizes. Setting a dynamic threshold based on the person's size and proportions provides a personalized threshold for each pose, enhancing the accuracy of fall detection. Our system integrates YOLOv7-W6-Pose and can automatically adjust thresholds by dynamically calculating body proportions, movement patterns, and key point positions based on the detected skeletons.  
* _Fine-Tuning and Optimization_: The algorithm was iteratively improved by testing it with real-world data and adjusting the thresholds and conditions based on the observed performance. This process ensured that the algorithm could reliably detect falls while minimizing false positives. By leveraging empirical data, the algorithm was calibrated to distinguish between typical daily activities and actual fall events accurately.  
The pose estimation provided by the YOLOv7-W6-Pose model, combined with the custom fall detection algorithm, forms a robust system for monitoring and detecting falls. The careful analysis of key point positions and the fine-tuning of detection criteria ensure high accuracy in identifying falls.
# On the Communication Performance of Airborne Distributed Coherent Radar
## 2 Materials and Methods
### Performance Analysis  
Based on the modeling of the topology of the communication system in Section 2, this section theoretically derives the analytical PDF of \(\gamma_{c}\) and \(\gamma_{b}\). To facilitate the derivation process, we first calculate the PDF of \(\sqrt{\gamma_{c}}\) and \(\sqrt{\gamma_{b}}\), and then consider the OP of the whole communication system.
## 3 Results and Discussion  
### Agarose Gel Electrophoresis Analysis  
Figure 1 shows the electrophoretic analysis of the samples collected at various times from the bead mill of Experiment 1 (Figure 1A) and Experiment 8 (Figure 1B) in Lines 1 to 7. Line 7 is a sample from a nonstop bead mill lysate at the end of 4 min. Line 8 shows the electrophoretic analysis of a lysate sample obtained by alkaline treatment. All the experiments performed (Table 1) had a similar electrophoretic behavior. The first band in Lines 1 to 7 indicates the presence of gDNA, the second band correspond to the open circular isoform, and the third and denser band indicates the presence of pDNA(sc). These results suggest that, as the milling time increases, more pDNA(sc) is released and the quality of the processed solution remains constant. When comparing these results with those of Line 8 of the alkaline treatment, the difference in RNA content is notable. The ribosomes probably remain intact during the bead milling. This assumption is based on one of the methods for the isolation of 70S ribosomes, where the prokaryotic cell begins with cell disruption with a French press at high pressure (16,000 pounds per square inch (psi)). In this methodology, the 70S ribosomes are recovered intact from the crude lysate [32]. In the bead mill, the cells are subjected to high stress produced by abrasion during rapid agitation with glass beads [21]. While in the alkaline treatment, the ribosomes are denatured by adding the lysis solution, and the RNA molecules are released. It is important to notice that, while mechanical lysis does not eliminate genomic DNA (Figure 1), neither proteins nor endotoxins as alkaline lysis does, since there is no precipitation step. This may imply further filtration operations to separate these contaminants.  
In general, the alkaline lysis is more acceptable than mechanical lysis due to the risk of pDNA degradation using the last one. It was noticed that the experimental design allowed for the exploration of disruption conditions where this effect was minimized, by comparing the results of Experiments 1 and 8 presented in Figure 1.
# A Hybrid CNN-LSTM Model for SMS Spam Detection in Arabic and English Messages
## 2 Related Work
proposed a processing method to normalize and expand text messages in order to improve the performance of classification algorithms when applied with these text messages. The proposed method is based on lexicography, semantic dictionaries and techniques of semantic analysis and disambiguation. The main idea was to standardize the words and create new attributes in order to expand the original text and reduce the factors that can degrade performance, such as redundancies and inconsistencies. In [21], the authors proposed a method for filtering SMS spam based on two data mining techniques: FP-growth and Naive Bayes. FP-growth algorithm is used to extract frequent itemset in text messages and Naive Bayes Classifier is used to classify the messages and filter those that are spam. The experimental evaluation that was performed by the authors on this approach showed an average accuracy of 98.5%. Concerning the detection of SMS spam in foreign languages, we only found a few works dealing with this problem. For example, in [26], the authors proposed a mobile-based system called \"SMSAssassin\" dedicated to filter SMS spam messages in India. This system is based on the application of Bayesian learning and Support Vector Machine techniques. For the experimental evaluation, the authors presented a new SMS Spam dataset collected from Indian users in the real-world. In another paper [27], the authors presented an analysis of different machine learning techniques for detecting SMS spam on a corpus of Indian messages. The authors used the UCI SMS spam dataset to which they added a set of Indian messages collected manually. For the experiments, they tested four simple algorithms: Multinomial Naive Bayes, Support Vector Machine, Random Forest, and Adaboost. The contribution of our paper, as compared to existing work, is the proposal of an efficient system for detecting SMS spam that can deal with both English and Arabic messages. To the best of our knowledge, the model that we propose in this paper is the first approach that uses a combination of the deep learning algorithms in order to classify Arabic short messages. In Table 1, we present a comparative summary on different works that are discussed in this section.
# Digitized Reconfigurable Metal Reflectarray Surfaces for Millimeter-Wave Beam-Engineering
## 3 Digitized Reconfigurable Metal Reflectarray  
The reflection phase of the rectangular groove element of a metal reflectarray antenna is generally designed to compensate the spatial phase delay from the feeder to that element, which is appropriately controlled by the manipulation of groove depth and the depth of each element is tuned by the phase matching condition by Equations (1) and (2). Afterwards, the ideally required reflection phase of the \(i\)-th groove, \(\phi_{i,o}\) is replaced with quantized phase states, \(\phi_{i,q}=q\times(\phi_{i,o}\) mod \(q)\), where \(q=2\pi/2^{n}\) and \(n\) is the number of quantized phase-shift bits. We use different subscripts to simply explain required -either \(o\), original or \(q\), quantized-phase of each element.  
Figure 4 shows the simulated radiation patterns of digitized reflectarray antennas when all required phases for high-gain antenna are quantized by the number of bits, \(n\). Increase the number of quantized bits higher than the gain of designed reflectarray antennas. The radiation characteristics of digitized metal reflectarray are comparable when the required phase of groove is quantized by four bits. This means that the 16-depth elements are sufficient for high-gain reflectarray. Moreover, the digitized metal reflectarray is easily constructed with only two bits in 1 dB-loss circumstance, which results in the reduction of the manufacturing time and cost [19]. Figure 5 shows the calculated four required phase patterns for digitized 20\({}^{\circ}\)-steered metal reflectarray antennas for non-digitized cases and three different bits.  
Figure 4: Simulated far-field patterns in H-plane of digitized reflectarray antennas when all required phases for high-gain antenna are quantized by the number of bits.  
Figure 5: Required phase patterns for 20\({}^{\circ}\)-steered digitized metal reflectarray antennas for a non-digitized case and three different bits, \(n\) = 1, 2, and 4.  
Figure 6 shows the simulated radiation patterns of digitized reflectarray antennas when a re-radiation field is directed at \(\theta\) = 20\({}^{\circ}\). The metal reflectarray antennas are constructed for five different bits from 1 to 4 bits and infinite bits. Infinite phase bit is continuous phase. The radiation patterns at working frequency are efficiently directed changing quantized bits in H-plane. The main lobe of radiation fields are well directed at \(\theta\) = 20\({}^{\circ}\) for all quantized bits. The maximum gains of the reflectarray antennas are rarely changed over two bits. The gain of 20\({}^{\circ}\)-steered antenna is 32.42 dB and half-power beam width is 2.44\({}^{\circ}\) in the H-plane. There is no dramatic change when the number of quantized bits decreases. Here, two-quantized-bits is sufficient to construct digitized metal reflectarray.