esa-sceva/satcom-chunk-collection huggingface dataset:
# An IoT Transfer Learning-Based Service for the Health Status Monitoring of Grapevine
## 2 Materials and Methods
### Datasets
#### 2.1.2 Validation Data  
The validation of the models was performed in-field at the Chatzivarity winery in Goumenissa, Greece (Figure 2). The Chatzivarity State is an organic farm winery focused on the Greek varieties of Xinomavro, Negoska, Roditis, Malagousia, Muscat, and Assyrtico, while also cultivating the popular Sauvignon blanc, Merlot, Chardonnay, and Cabernet Sauvignon in a smaller scale.  
The three grape fields that the presented study focused on include the Xinomavro, Negoska, and Traminer varieties. These fields are visually presented in Figure 2, and they have grown on medium-textured sand and clay soils. Research was conducted in two fields of the winery. The first field (Field 1) is located adjacent to the estate's main building, where 30 rows in the east side are cultivated with the Xinomavro variety and the 54 rows in the west are cultivated with the Negoska variety. The second field (Field 2) is located 500 m to the north of the estate's main building and consists of the Traminer variety. The field survey for the validation of the developed models took place on 4 August 2022. During the  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Dataset Class** & **Instances** \\ \hline Healthy & 577 \\ Esca & 676 \\ Powdery mildew & 336 \\ Downy mildew & 96 \\ Nitrogen deficiency & 30 \\ Eriophyes vitis & 60 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The grapevine dataset classes by Eden Library with the number of images per class.  
Figure 1: Sample dataset images that belong to the grapevines subset from the Eden Library dataset.  
survey, both fields had visible symptoms of Esca, although Field 1 was in a better condition than Field 2.  
The visual examination of the disease's symptoms by the expert phytopathologists served as the foundation for the health status evaluation of the Chatzivarity case plants. The spots selected to validate the models were spots in a row that had both healthy and infected plants. Esca fungus has very discrete symptoms and is easily recognizable by a trained expert. Specifically, an interveinal \"striping\" is the foliar sign of Esca. The \"stripes\", which begin as yellow in white cultivars and crimson in red cultivars, dry out and turn necrotic.
## 3 Co-Optimization Model of EVs and PVs
### Objective Function  
Ignoring the carbon emissions caused by the loss of fluids (oil, coolant, and lubricating oil) for operation and maintenance when the EV runs, we take a scheduling cycle \(T\) as an example and construct an optimization model with the objective of minimizing carbon emission, which is shown in Equation (1).  
\[\text{min}C=\sum_{i=1}^{N}c_{ev.lifclass}^{i}+c_{ev,grid} \tag{1}\]  
where \(C\) is the total carbon emission during the charging process; \(N\) is the total number of EVs; \(c_{ev.lifclass}^{i}\) is the carbon emission equivalent to the battery life loss during the charging and discharging process of the \(i\)th EV; and \(c_{ev,grid}\) is the carbon emission when the EV cluster is charged directly from the power grid.  
By discretizing the time axis, dividing a scheduling cycle into \(n\) periods of length \(\Delta t\) (\(\Delta t=T/n\)), and freezing the time-varying EV charging and discharging power in the period of \(\Delta t\), Equation (1) can be decomposed into Equation (2).  
\[\text{min}C=\sum_{i=1}^{N}\sum_{k=1}^{T/\Delta t}c_{ev.lifclass}^{i}(k)+\sum_{ k=1}^{T/\Delta t}c_{ev.grid}(k) \tag{2}\]  
Thereinto,  
\[c_{ev.gird}(k)=\lambda_{grid}\left(\sum_{i=1}^{N}P_{ev}^{i}(k)-P_{ev.pv}(k) \right)\Delta t \tag{3}\]  
\[c_{ev.lifclass}^{i}(k)=\lambda_{hottery}/M^{i}\left(D^{i}(k),SOC^{i}(k)\right) \tag{4}\]  
where \(\lambda_{grid}\) is the carbon emission factor of grid power generation; \(P_{ev}^{i}(k)\) is the charging and discharging power of the EV in the \(k\) period, which is greater than 0 when charging and less than 0 when discharging (when \(P_{ev}^{i}(k)<0\), \(P_{ev}^{i}(k)=P_{ev.discharge}^{i}(k)\)); \(P_{ev.pv}(k)\) is the total power supplied to EVs from PV power plants in period \(k\); \(\lambda_{hottery}\) is the carbon emission coefficient of the EV power battery life cycle; and \(M\) is the battery cycle life (the total number of battery cycles), which is related to the discharge depth \(D\) and the SOC  
Figure 2: Schematic diagram of cooperative operation of EVs and greenhouse PVs.  
(state of charge) of the battery during discharge, i.e., \(1/M^{i}\) is the life loss of the battery in a single discharge cycle.
## 4 Design of the Controller
### Stability Analysis  
In order to verify the stability and the convergence of the position controller, for the position servo system, the Lyapunov function is written as: \(V_{i}=V_{2}+\frac{1}{2}{e_{3}}^{2}\), and results in Equation (36):  
\[\dot{V}_{i}=-k_{1}e_{1}^{2}-k_{2}e_{2}^{2}-k_{3}e_{3}^{2}+e_{2}\widetilde{ \theta}_{l2}+e_{3}\widetilde{\theta}_{i3}-e_{2}\widetilde{\widetilde{\alpha} }_{1}-e_{3}\widetilde{\widetilde{\alpha}}_{2}. \tag{36}\]  
Equation (37) shows the assumptions used here, and is written as:  
\[\left\{\begin{array}{l}k=\min\{k_{1},k_{2},k_{3}\}\\ \varepsilon=|\widetilde{\theta}_{i2}|+|\widetilde{\theta}_{i3}|+|\widetilde{ \alpha}_{1}|+|\widetilde{\alpha}_{2}|\end{array}\right., \tag{37}\]  
Based on Equations (36) and (37), Equation (38) is expressed as:  
\[\dot{V}_{i}\leq-k(e_{1}^{2}+e_{2}^{2}+e_{3}^{2})+|e_{2}||\widetilde{\theta}_{2 }|+|e_{3}||\widetilde{\theta}_{3}|+|e_{2}||\widetilde{\alpha}_{1}|+|e_{3}|| \widetilde{\widetilde{\alpha}}_{2}|\leq-2(k-1)V_{i}+\frac{1}{2}\varepsilon^{2}. \tag{38}\]  
When the inequality is solved, the non-negative Lyapunov function \(V_{i}\) is bounded by the condition given in the inequality, shown in Equation (39):  
\[V_{i}\leq[V_{i}(0)-\frac{\varepsilon^{2}}{4(k-1)}]e^{-2(k-1)t}+\frac{ \varepsilon^{2}}{4(k-1)}. \tag{39}\]  
When all the initial conditions satisfy the condition: \(V_{i}(0)\leq\varphi\), any positive number \(\varphi\) is chosen and \(k\geq 1+\frac{\varepsilon^{2}}{4\varphi}\), and then \(V_{i}(t)\leq\varphi\) is guaranteed for all \(t>0\). The tracking error can converge to a sufficiently small neighbor of zero, and the stability is proved. A similar process is applicable to the pressure controller and is not presented here.
# Block Recovery Rate-Based Unequal Error Protection for Three-Screen TV
## 2 Background
### UEP Research  
In Figure 5, once the encoder has decided the priority of a picture, the UEP and/or transmission scheduler can use the priority in both robust streaming and quality of service (QoS) handling. It applies different FEC overheads to pictures according to picture priority (PPn).  
There have been several research studies on FEC algorithms regarding the error propagation effect on video quality degradation in both base layer and enhancement layers [5; 6; 7; 8; 9; 13; 14]. In a study by [PERSON] et al. [5], a FEC assignment algorithm using the property of continuous packet losses is presented. Various UEP algorithms based on rate-distortion model have been presented for reducing video quality degradation from packet losses [9; 13]. Adaptive FEC assignment algorithms have been developed for  
Figure 4: Two encoding steps of Raptor codes. LT, Luby transform; LDPC, low-density parity-check.  
Figure 5: Unequal error protection with picture priority. PPn, picture priority.  
video streaming over best-effort networks [13; 14; 15]. To adapt time-varying channel environments, an optimal packet scheduling for rate-distortion was proposed [14; 15; 16; 17]. Also, a rate-distortion optimized scheduling algorithm was presented to adapt the streaming video over time-varying channels for minimizing the total distortion under the transmission rate constraint [18; 19]. There have been research studies on transmitting SVC video sequence safely over multicast channel or MIMO wireless systems using Raptor codes [20; 21; 22], research studies on the priority-based SVC or SHVC video streaming over multi-channel were introduced in [23; 24], and research studies on the UEP method with Raptor codes with SVC or HEVC temporal layer priorities have been presented in [25; 26; 27; 28; 29].
###### Abstract  
Phlorotannins are phenolic compounds existing in large amounts in Phaeophyta, with this amount differing according to the season and collection area. There are many pharmacological properties of phlorotannins, such as being antioxidant, antidiabetic, and anti-cancer. In this work, phlorotannins from the Phaeophyta _Sargassum linifolium_ were extracted, characterized, and identified, for use as an antioxidant and an anti-diabetic in a streptozotocin-induced diabetes rat model. Phlorotannins were characterized using ultraviolet (UV) and Fourier transform infrared (FT-IR) analysis,dimethoxy benzaldehyde assay (DMBA), and Folin-Ciocalteu assays. Groups of rats were tested as follows: normal control (negative control) (G1), normal rats treated with 60 mg/kg body weight of phlorotannins (G2), positive control diabetic rats injected with one dose of streptozotocin (G3), and a diabetic group treated with phlorotannins at 60 mg kg\({}^{-1}\) body weight (G4). The biochemical parameters were determined after 4 weeks of treatment. The results demonstrated that the extracted compound was a phlorotann, which had antioxidant properties. An _in vivo_ study confirmed that the glucose and insulin levels in G4 were relatively similar to those in the normal control G1. The glucosidase, alpha-amylase, glutathione, and catalase levels were 0.11 \(\pm\) 0.097, 420.5 \(\pm\) 13, 12.7 \(\pm\) 3, and 8.01 \(\pm\) 1.31 umol/min/g in G1, and 0.04 \(\pm\) 0.016, 184.75 \(\pm\) 55.24, 12.78 \(\pm\) 2.1, and 11.28 \(\pm\) 1.74 umol/min/g) in G4, respectively. There were no side effects in the kidney function of both G2 and G4, and the levels of cholesterol and triglyceride were also normal. The results demonstrated that phlorotannins have antioxidant properties _in vivo_ and that the diabetic rats had an activated AMPK expression. According to the histological analysis, phlorotannins improved the islet size and reversed necrotic and fibrotic alterations in the pancreas. The results of the present study suggest the use of phlorotannins derived from _Sargassum linifolium_ as an antioxidant and anti-diabetic for an _in vivo_ study. They could be used in developing medicinal preparations for treating diabetes and its related symptoms.  
phlorotannins; _Sargassum linifolium_; diabetes mellitus (DM); antioxidant; histopathological investigation 2023 by the authors. Licanese MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/)]([https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/))).
## 3 System Design and Implementation
### Personalized Medicine Care Assistance
#### 3.3.1 Personalized QR Barcode Generator  
Nowadays, most smart phones are come with a free barcode decoding application. This is beneficial for smart phone users as they can fully utilize it without any need for additional hardware devices and charges. In this implementation, a personalized QR (Quick-Response) barcode generator is designed and implemented as shown in Figure 10, to meet the needs of ubiquitous healthcare system's users, and provides a comprehensive healthcare services to the users. This personalized QR barcode generator is implemented in the C# programming language. Information such as patient name, patient ID, functions of the drugs, in-take instructions, dosage amount and expired date are included in the barcode for the patients' reference. Other than generate the barcode, a copy of the input details will be saved at the web server database for future reference. The QR barcode is adopted as it has larger and sufficient data capacity to encode all the information needed if compare to the 1 dimension parallel barcode. Alphanumeric data are encoded into QR barcode through encoding steps shown in Figure 11. To implement the personalized generator, QRode library [18], a.NET component is used to do the data encoding and generate a QR barcode. The QRCode library provides a function to encode the content into a QR code image which can be saved in JPEG, GIF, PNG or Bitmap formats, and also a function to decode a QR code image. The QR barcode generated can be printed out easily and can be attached to the medicine packs easily.  
Figure 11: Data encoding process.  
Figure 10: Personalized QR barcode generator in ubiquitous healthcare system.
## 1 Introduction
However, the filters inherently produce gray transition regions between solid and void regions. Recently, several projection schemes [22; 23; 24] have been proposed to alleviate the gray transition problem. In this study, we employ a threshold projection scheme expressed by a smoothed Heaviside function [25]. Finally, in order to deal with small numbers of target points along the F-D curve, we take into account the slope of the curve, as well as the values of the prescribed F-D curve, in order to adapt to the nonlinearities on the curve. Therefore, the slope is involved in the optimization problem as a constraint. The objective of this study is to find a structural layout that follows a target nonlinearity through density-based topology optimization. In research carried out previously, a relatively large number of target points were used [12]. In this study, however, we propose a method that guarantees convergence in the optimization design process with fewer target points. To do so, the slope defined at the corresponding target points on the force-displacement curve is set as the constraint in the optimization problem. This slope is related to the secant matrix. Assuming that the external force applied to the structure is concentrated in one place and considering the force-displacement curve for displacement at any degree of freedom, i.e., for single-input multiple-output (SIMO), the sensitivity of the slope to the design variables can be derived by direct differentiation of the inverse of the secant matrix with respect to the design variables. This study is organized as follows: In Section 2 the underlying theories are explained; in Section 2.1, we define the optimization problem and present the governing equations for a geometrically nonlinear finite element and sensitivity analysis for the objective function; in Section 2.2, we describe the density filter and parameterization for the energy interpolation; in Section 2.3, we describe concepts of the slope used in the optimization problem as a constraint and the sensitivity analysis; in Section 3, we demonstrate the performance of the proposed method using a thin plate; and in Section 4, we present the conclusions. This study dramatically improves the computational speed and the convergence of the optimization analysis by adding slope constraints as compared with similar researches conductedpreviously. In addition, the case studies in Section 3 were performed through in-house code using MATLAB. The topology optimization and optimal design in this study were basically constructed based on the 88 lines code [26].
# A Multitask-Based Transfer Framework for Cooperative Multi-Agent Reinforcement Learning
###### Abstract
Sometimes, learning multiple tasks simultaneously results in lower accuracy than learning only the target task, which is known as negative transfer. This problem is often attributed to the gradient conflicts among tasks. To alleviate the problem of model performance degradation caused by gradient conflicts between tasks during multitask training, MTT achieves conflict-free updates by ensuring a positive dot product between the final update and the gradient of each specific task, while maintaining a consistent optimization rate for all loss terms and dynamically adjusting the gradient magnitude according to the conflict level. Moreover, since each task in the framework has an independent observation embedding network and decision network, the framework shows strong adaptability to various reinforcement learning algorithms. The framework is capable of selectively integrating value-based reinforcement learning algorithms, including DQN [16] or policy-based approaches, like PPO [17], contingent upon the specific application context, thereby conferring the architecture with pronounced versatility. We evaluate the proposed method in two commonly used multi-agent environments, StarCraft II and Google Research Football. The experimental results show that our method outperforms the baseline methods in all tasks, and the model trained in StarCraft II can be effectively transferred to the Google Research Football environment, which fully verifies that our method can achieve efficient cross-domain transfer. The main contributions of this paper are as follows:1. We propose a two-stage multitask-based transfer framework, which is compatible with multiple reinforcement learning algorithms, covering value-based methods and policy-based methods. 2. During the training process, we extract the collaborative knowledge shared among multiple tasks based on a multitask training approach and then transfer it to new unseen tasks to promote their learning efficiency and teamwork effects. 3. To solve the conflict problem in the multitask learning process, we introduce a gradient processing method to ensure conflict-free updates during training. 4. We evaluate our framework in the two complex environments of StarCraft II and Google Research Football. The experimental results show that our method outperforms the baseline methods in all tasks and can achieve effective cross-domain transfer.
## 3 New Radio Link-Level Simulator  
New Radio link simulator software was used for all the experiments conducted and described in this paper. The simulator was developed in Telecom Italia laboratories with an engine implemented in MATLAB(r). The most critical blocks in terms of execution speed were, instead, implemented in C language and linked to the MATLAB engine via MEX. The purpose was to model a radio interface which was fully compliant with 3 GPP specification and to evaluate the link-level performances of 5G-based point-to-point communications. The simulator is a link-level simulator and targets scenarios with a single next-generation base station or gNodeB and a single UE for a SU-MIMO scenario where the UE can deploy an antenna array.  
The various blocks of the simulator provide a bit-level model of the 5G NR physical layer, as described in the 38 series of the 3 GPP specifications [37; 42; 43; 44]. The physical downlink shared channel (PDSCH) transmission chain can be represented as the block scheme of Figure 1, which also reflects the 5G NR simulator structure. The red blocks are those which exploit the C language to speed-up the computations.
# Arabic Text Clustering Using Self-Organizing Maps and Grey Wolf Optimization
## 2 Related Work
### Self-Organizing Maps
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Ref.** & **Model** & **Dataset** & **Purity** & **F1-Score** & **Precision** & **Recall** & **Accuracy** \\ \hline
[19] & K-Means & MSA & 93.3\% & 87.32\% & 87.13\% & 87.52\% & - \\
[21] & K-Means & Own corpus & - & & 93\% & 98\% & 88\% \\
[26] & K-Means + (PSO) & BBC, CNN, OSAC & 50\% & 47\% & 33\% & - & - \\
[5] & Brown clustering algorithm & Own corpus & 85\% & - & - & - & - \\
[25] & K-Means & Arabic tweets & 76.4\% & - & - & - & - \\
[23] & TF-IDF + BTO & Arabic tweets & - & - & - & - & - \\
[22] & K-Medious & Own corpus & - & 67\% & 60\% & 78\% & - \\
[2] & Markov + Fuzzy-C-Means + DBN & Own corpus & - & 91.02\% & 91.02\% & 90.9\% & - \\
[10] & Suffix Tree & Own corpus & - & 81.11\% & 80.3\% & 83.75\% & - \\
[11] & SOM & Own corpus & - & - & - & - & 93.4\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Arabic text clustering related work comparison. K-Means clustering algorithms [26; 39; 43]. These researchers mainly found that SOM are more effective than K-Means in clustering.
# YOLO-ESL: An Enhanced Pedestrian Recognition Network Based on YOLO
## 4 Experiment
### Comparative Experiments  
To validate the improved model, we conducted comparative tests with the YOLOv5-I, SSD, YOLOX-I, and YOLOv8 [31] networks, demonstrating that our YOLO-ESL network yields superior results. The dataset and experimental settings for the comparative experiments were consistent with those used in the ablation experiments, and the parameters for the networks in the comparative tests were maintained accordingly. Table 2. presents the results of these comparative experiments.  
Figure 8: The detection results of the YOLO-ESL network.  
Figure 7: Comparison of detection results between YOLOv7 and YOLO-ESL. (**a**) Detection results of YOLOv7. (**b**) Detection results of YOLO-ESL.  
As shown in Table 2, SSD achieves an mAP50 of 0.787 and an mAP50:95 of 0.457, while YOLOv5 shows corresponding values of 0.805 and 0.627. Although both SSD and YOLOv5 perform well at the IoU threshold of 0.5, YOLOv5 slightly outperforms SSD in mAP50 and significantly surpasses it in mAP50:95, indicating its superior performance under stricter IoU thresholds. With a GFLOPs of 60.9, SSD is well suited for lightweight applications.  
YOLOX delivers higher mAP50 and mAP50:95 values compared to YOLOv5, but this comes at the cost of higher computational demand, with a GFLOPs of 155.67. YOLOX sacrifices computational efficiency for improved detection accuracy. In contrast, YOLOv7 achieves an mAP50 of 0.907 and an mAP50:95 of 0.772, slightly lower than YOLOX in mAP50 by 0.2%, but reducing GFLOPs by 50.57 G, indicating superior computational efficiency.  
YOLOv8, with an mAP50 of 0.905 and an mAP50:95 of 0.789, shows comparable performance to YOLOv7 but with an increased GFLOPs of 165.4, reflecting higher computational complexity. While YOLOv8's FPS reaches 210, providing some improvements in real-time capability, it does not significantly surpass other models.  
Our proposed YOLO-ESL network demonstrates remarkable advantages across all metrics. Compared to YOLOv7, YOLO-ESL improves mAP50 by 3.0% and mAP50:95 by 2.9%, while reducing GFLOPs to 102.5, 53.17 G and 62.9 G lower than YOLOX and YOLOv8, respectively. Additionally, YOLO-ESL achieves an FPS of 217, surpassing YOLOv7's 208 and YOLOv8's 210, thus offering enhanced real-time processing capability.  
These results indicate that the YOLO-ESL model not only exceeds the performance of YOLOv7, YOLOX, and YOLOv8 in terms of accuracy, computational efficiency, and real-time capability, but also represents an optimal choice for applications demanding both high precision and real-time processing, such as autonomous driving and intelligent surveillance.