esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Advanced Agricultural Approaches
### Vertical Farming (VF)  
Arable land is decreasing with the increase in the human population, urbanization, pollution, and soil erosion. The VF is the progressive technique of advanced agriculture, which allows one to grow the crops and plants in controlled conditions, and significantly, this efficiently decreases the consumption of many resources. VF is the most suitable agricultural approach in which a small patch of land is required to cultivate crops compared to traditional farming. This conventional farming technique needs several resources, not only for crops [65; 66]. Unfortunately, current farming practices, based on industrialized agriculture, are destroying soil health much more than natural reconstruction. Overall, it has been reported that the soil erosion rate is higher (10 to 40 times) than the formation rate of the soil. Recent measures used for farming cause a loss of healthy soil from the agricultural land, which is a major hazard to food production in the upcoming days. Besides, as we assume, about 70% of the freshwater is only utilized for the farming system, which will enhance the pressure on the existing water reservoirs or storage. VF is the solution for many limitations and challenges for land management and a shortage of freshwater.  
For this purpose, according to Mirai, a Japanese-based indoor farming tool provides data of about 25,000 square meters of Japanese agricultural farms. Notably, it utilizes the minimum level of irrigation water compared to the production of crops in outdoor conditions and 40% of the energy [67]. The aviation farm is the leader of VF, where the yield of agricultural products in New York increased by 390 times, while water consumption was reduced by 95% [68]. Carbon dioxide is one of the crucial factors in farming as compared to other aspects of agriculture. For the estimation of carbon dioxide, a new toolkit, which is a non-dispersive infrared (NDIR) carbon dioxide wireless sensor that participates in the complete control of VF [69; 70] connected to mobile devices and wireless communication technologies. These devices can automatically check the crops at each development phase.
# UAV Detection with Transfer Learning from Simulated Data of Laser Active Imaging
## 1 Introduction
Another study [15] applied transfer learning in the medical field, achieving glioma grading on conventional magnetic resonance images. These works mentioned above still need some labeled data in the target domain, while there is no dataset available for our application. Besides, it is also difficult to collect and construct such datasets under various scenarios. Therefore, we propose a method to simulate the imaging process of laser active illumination to generate synthesis images to construct the simulated dataset \(D_{sim}\) for the training of the proposed deep neural network. Much research has been conducted about UAV detection. [PERSON] et al. [16] used deep transfer learning to recognize targets in a UAV-to-ground situation, which is the opposite of our ground-to-UAV situation. [PERSON] et al. [17] trained a CNN to detect and recognize UAVs in a natural light scene, while we mainly want to detect UAVs at night. [PERSON] et al. [18] used a Doppler radar signal to detect small UAVs, while we want to use laser active illumination to detect UAVs. To the best of our knowledge, our work is the first attempt to detect small UAVs through deep transfer learning from simulated data in a laser active imaging field. Figure 1 shows the schematic illustration of the proposed method. Firstly, the model learns the general features in a large dataset of common objects in context (COCO) [19]. This step gives proper initial values to the network parameters. Then, the model learns the UAV features in the constructed simulated dataset. Finally, the trained model is applied to the real laser active imaging images to realize the high-precision detection of the UAVs. Our main contributions can be summarized as follows:  
1. A real-time UAV detection framework is established based on a CNN cooperating with transfer learning. To the best of our knowledge, this is the first study to analyze the problem of zero-shot object detection in the laser active imaging domain. 2. A dataset is constructed by simulating the process of laser active imaging. The knowledge learned from the simulated dataset is beneficial to UAV detection in real data. 3. We experimentally show that our algorithm can realize a high-precision UAV detection for our laser active imaging system, which proves the authenticity of the simulated data and the success of our solution. The remainder of this paper is as follows. In Section 2, we briefly introduce the related work. The simulation process is analyzed in Section 3. Then, the adopted algorithm and its improvement, as well as the experimental process, are described in Section 4. The analysis of the experimental results is in Section 5, and Section 6 summarizes and prospects our work.
# TCCCD: Triplet-Based Cross-Language Code Clone Detection
## 6 Experimental Results
### RQ1: Baselines' Effectiveness Comparison  
Under the same experimental environment and settings, we conducted experiments on several state-of-the-art baseline approaches, including CLCMiner [29], CLCDSA [15], and C4 [16], and compared them with our proposed approach. To evaluate the experimental results, the precision, recall, and F1-score were used as the effectiveness metrics.  
The experimental results are presented in Table 2, which clearly shows that our proposed approach outperformed the baseline methods in terms of the precision and F1-score, and CLCDSA tended to consider most code pairs as clones.  
Due to significant syntactic and structural differences between programming languages, CLCMiner failed to fully capture the similarity between cross-language code fragments using simple token sequences. Furthermore, the adaptation of the modification history and window algorithms in CLCMiner may suffer from inadequate adaptability to different programming languages, leading to suboptimal detection effectiveness.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Approach** & **Precision** & **Recall** & **F1** \\ \hline CLCMiner & 0.37 & 0.59 & 0.46 \\ CLCDSA & 0.53 & 0.6 & 0.67 \\ C4 & 0.93 & 0.90 & 0.92 \\
**TCCCD** & **0.96** & 0.91 & **0.93** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Effectiveness comparison of TCCCD and baselines.  
CLCDSA utilizes syntactical features and API documentation information to represent code snippets, which provides stronger expressive power and discrimination in code representation compared to the simple token sequence representation method used in CLCMiner.  
Compared to CLCMiner and CLCDSA, C4 demonstrated significant advancements in code representation and vector similarity contrastive learning. By leveraging the pre-trained model CodeBERT and contrastive learning, C4 can more comprehensively capture the semantic information of code in cross-language code clone detection, resulting in significant effectiveness in the precision and F1-score.  
Our approach clearly outperformed the baseline approaches, with an improvement in the F1-score from 0.92 to 0.93, precision from 0.94 to 0.96, and recall from 0.90 to 0.91.
## 2 Proposed SALDL Based on GCM
### Direct Deblurring Approach  
The predicted base layer is the approximation of the Gaussian-filtered original image, as shown in Equation (8). Therefore, conventional image deblurring methods can be considered to directly reconstruct the original image from the predicted base layer. Conventional image deblurring methods can restore missing details by removing the Gaussian blurring of the predicted base layer. Image deblurring problems [28] can be formulated as follows:  
\[\textit{arg}\,\min_{\mathbf{x}}\left\|\mathbf{x}^{(b)}-\mathbf{x}^{o}\otimes\mathbf{k}^{\delta }\right\|^{2}+\lambda\sum_{j=1}^{2}\left\|\mathbf{x}^{o}\otimes\mathbf{k}^{h,j}\right\| ^{\alpha} \tag{10}\]  
where \(\mathbf{k}^{h,j}\) indicates high-pass filters such as horizontal and vertical filters. \(\alpha\) controls the sparsity, and \(\lambda\) is a constant to weight the regularization term [28]. In general, the motion kernel \(\mathbf{k}^{\delta}\) in Equation (10) is unknown; however, a Gaussian filter \(\mathbf{k}^{\delta}\) can be used for the motion kernel based on the proposed GCM. Additionally, the motion kernel can be estimated directly from the base layer. This case corresponds to blind image deblurring. It appears that conventional image deblurring can yield good results. However, some issues exist. A comparison between Figures 2 and 4 shows that the predicted base layer differs from the blurred original image. In other words, textures and edges are missing, and noise is generated. In addition, the noises differed from the Gaussian random noise, which has been considered to solve the image deblurring problem. Therefore, conventional image deblurring methods are not suitable for restoring the original image from the predicted base layer.  
In another image deblurring approach, deep learning tools are used. More specifically, the DCNN can be trained to transform the predicted base layer to the original continuous image [39].  
\[\mathbf{x}^{(o)}=f_{\theta}^{DDN}(\mathbf{x}^{(b)}) \tag{11}\]  
where \(f_{\theta}^{DDN}\) denotes the direct deblurring network (DDN), and \(\mathbf{x}^{(o)}\) is the reconstructed continuous-tone image. Because the predicted base layer \(\mathbf{x}^{(b)}\) is the Gaussian-blurred version of the original image, \(f_{\theta}^{DDN}\) is regarded as a deblurring network. Because the predicted base layer has already lost some texture and sharpness, the input halftoned image \(\mathbf{x}^{i}\) can be used as additional information.
## 4 Result and Discussion
### Theoretical Contribution  
Figure 8 represents the comparison between the OEE and MTOVE over the three-month operation period. It shows the weekly average OEE calculation (availability \(\times\) performance \(\times\) quality) versus MTOVE (physical availability \(\times\) use of availability \(\times\) performance \(\times\) transportation quality) for three months. The graph illustrates that the weekly average OEE value was much higher than the MTOVE value obtained. Specifically, there were contrasting values for OEE and MTOVE; OEE obtained an average value of 54% and MTOVE obtained an average value of 25%. This happened because MTOVE accommodated truck utilization, which has a large impact on overall effectiveness results. However, the original OEE does not consider truck utilization. This UA was one of the factors that distinguished between the original OEE and the MTOVE concept.  
Measuring the effectiveness of coal transportation with the original OEE resulted in a gap with MTOVE. Notably, the use of availability affected the MTOVE value very much. The truck utilization measured by UA affected MTOVE and was not considered in the original OEE. Several studies have customized OEE to fit their industrial requirements. This research makes a strong theoretical contribution. The first contribution is the adaptation of the OEE metric to the mining domain, in this case to the coal-hauling process. The modification contributed to the identification of the six MTOVE metrics (physical availability, use of availability, productivity, fuel performance, coal losses, and quality of service). It was possible to measure all-important factors in hauling operations in one single index. The productivity performance allowed us to calculate the ratio of the actual productivity of the hauling process vs. the standard productivity. Fuel performance allowed us to calculate the ratio of the actual fuel consumption of the hauling process vs. the standard fuel consumption. With the coal losses factor, we can identify coal losses during coal transportation.  
Figure 7: Average MTOVE results.  
Finally, by measuring the transportation failure factor, we identified the number of trucks that failed to arrive at the stockpile. A deeper analysis will allow us to determine the detailed reason for the transport failure, whether because of a truck breakdown or truck accident. These measure the service level to the mining owner. The second theoretical contribution is increasing the scientific knowledge on metric development in the mining transportation sector through the Delphi study. Data collection and analysis were accomplished by the active participation of the researchers and the involvement of the mining experts and company management team in the implementation of the actual case. It closed the gap between theory and practice.
## 1 Introduction  
Atrial fibrillation (AF) is a condition affecting the normal sinus rhythm of the heart and it is strongly related to the risk of strokes [1]. In this context, it was proven that the hemodynamics of the left atrium (LA) and in particular the fluid dynamics characteristics of the left atrial appendage (LAA) play an important role in the stroke risk determination [2]. In fact, especially in non-valvular AF patients with contraindication to anticoagulation therapy, LAA is responsible for the formation of more than 90% of thrombi which can yield to cardioembolic events [3]. Presently, LAA occlusion is a diffused treatment strategy to reduce the migration of thrombi and the associated cardioembolic risk [4; 5]. This procedure is particularly challenging due to the LAA morphological and hemodynamics complexity [6].  
The morphology of the LAA is quite variable and it is commonly classified into four main shape classes: \"chicken wing\" (CW) (48%), \"cactus\" (CS) (30%), \"windsock\" (WS) (19%), and \"cauliflower\" (CF) (3%) [7]. In non-valvular AF conditions, the normal contractility of LAA is reduced, resulting in a high decreasing of velocity and, consequently, in a higher risk of clotting formation due to the hemostasis of the blood inside the LAA. Computational fluid dynamics (CFD) is turning out to be a useful instrument to evaluate LAA hemodynamic indicators such as velocity patterns, vorticity, wall shear stress and pressure [8; 9; 10]. The impact of AF on different LAA shapes hemodynamics was recently investigated in [11] highlighting the influence of LAA morphology on the blood velocity patterns although a quantitative assessment and correlation with geometrical features for the different shapes is missing. The changes in LAA position and morphology during cardiac cycle due to the LA motion are significant aspects to be considered in the LAA geometric evaluation [12]. Even if there are few studies in the literature that analyze LAA geometrical features [13; 14], to the best of our knowledge the variation of relevant LAA geometrical parameters have not been yet investigated. Hence, it is crucial to develop methods able to identify a correlation between morphological and flow dynamics parameters to prevent thrombi formation.  
This study aims to evaluate the dynamic of LAA geometrical indicators during cardiac cycle and the related hemodynamics by developing a computational model able to perform an accurate LA and LAA fluid dynamic analysis in order to investigate the correlation between LAA different shapes and stroke risk.
# Design and Optimization of Modular Solid Rocket Grain Matching Multi-Thrust Performance Curve
## 3 Results and Discussions
### Structural Strength Considerations  
Modular grain designs are particularly suitable for free-standing solid rocket motors, where the propellant grain is manufactured separately and later assembled into the combustion chamber. To ensure structural integrity during combustion, an inhibitor layer must be applied to the exterior of the propellant. This layer serves two critical functions: (1) combustion suppression: preventing unintended burning on the outer surface and (2) mechanical support: providing a rigid backing structure to withstand internal stresses.  
However, propellant-inhibitor debonding remains a major failure risk for modular grains, posing a significant practical challenge. To assess this risk, we performed a stress analysis on a modified slot grain configuration under 10 MPa pressure loading (from the result in Section 3.1). The finite element method (FEM) with a linear elastic material model is used. The elastic modulus of the propellant and inhibitor is 0.350 GPa and 0.280 GPa, respectively. The Poisson's ratio of the propellant and inhibitor are both 0.49. The simulation results (presented in Figure 19) reveal stress concentrations at the propellant-inhibitor interface, highlighting zones vulnerable to debonding during burning. The simulation results provide a basis for determining the minimum required bonding strength at the interface.
## 2 Materials and Methods
### The Design of the Kinematic Control Law  
In kinematic trajectory tracking control for a WIPR, the system can be simplified to a general two-wheeled non-complete mobile robot for trajectory tracking. The process involves utilizing a reference trajectory state vector \(q_{mr}=\left[x_{r},y_{r},\theta_{r}\right]^{T}\), an actual state vector \(q_{m}=\left[x,y,\theta\right]^{T}\), and a control objective designed to manage linear and angular velocities. The objective is to ensure that the actual robot travel trajectory aligns with the reference trajectory, even if the trajectory error \(q_{me}=\left[e_{x},e_{y},e_{\theta}\right]^{T}=[x_{r}-x,y_{r}-y,\theta_{r}- \theta]^{T}\) approaches zero. To meet the requirement expressed above, the control laws for \(v_{d}\) and \(w_{d}\) can be devised as Equation (8).  
\[\lim_{t\to\infty}\lVert q_{me}\rVert=0, \tag{8}\]  
The Lyapunov function is selected as Equation (9).  
\[V_{1}=\frac{1}{2}x_{e}^{2}+\frac{1}{2}y_{e}^{2}+1-\cos\theta_{e}, \tag{9}\]  
While the error persists, the value \(V_{1}\) remains greater than zero, thereby rendering the function positive definite. Equation (10) describes the derivative of \(V_{1}\).  
\[\dot{V}_{1}=e_{x}\dot{e}_{x}+e_{y}\dot{e}_{y}+\dot{e}_{\theta}\sin e_{\theta} \tag{10}\] \[=e_{x}\left(we_{y}-v+v_{r}\cos e_{\theta}\right)+e_{y}(-we_{x}+v_ {r}\sin e_{\theta})+(w_{r}-w)\sin e_{\theta}\] \[=-ve_{x}-w\sin e_{\theta}+e_{x}v_{r}\cos e_{\theta}+e_{y}v_{r}\sin e _{\theta}+w_{r}\sin e_{\theta}\leq 0,\]  
[PERSON]'s stability theorem [32] establishes that the system can achieve asymptotic stability if the function is negative definite, i.e., if \(\dot{V}_{1}\leq 0\). Accordingly, the sought control law is as follows (11).  
\[\left\{\begin{array}{l}v_{d}=v_{r}\cos e_{\theta}+k_{1}e_{x}\\ w_{d}=w_{r}+v_{r}e_{y}+k_{2}\sin e_{\theta}\end{array}\right., \tag{11}\]  
Both \(k_{1}\) and \(k_{2}\) are positive constants.  
Substituting the control law into \(\dot{V}_{1}\) gives the following equation.  
\[\dot{V}_{1}=-e_{x}(v_{r}\cos e_{\theta}+k_{1}e_{x})-\left(w_{r}+v_ {r}e_{y}+k_{2}\sin e_{\theta}\right)\sin e_{\theta}+e_{x}v_{r}\cos e_{\theta}+ e_{y}v_{r}\sin e_{\theta}+w_{r}\sin e_{\theta} \tag{12}\] \[=-e_{x}v_{r}\cos e_{\theta}+e_{x}v_{r}\cos e_{\theta}-k_{1}e_{x}^{ 2}-w_{r}\sin e_{\theta}+w_{r}\sin e_{\theta}-v_{r}e_{y}\sin e_{\theta}+e_{y}v_ {r}\sin e_{\theta}-k_{2}\sin_{e_{\theta}}^{2}\] \[=-k_{1}e_{x}^{2}-k_{2}\sin_{e_{\theta}}^{2}\leq 0.\]Therefore, \(\dot{V}_{1}\leq 0\) can be proven.  
So far, the desired velocity required for the design of the dynamical system is shown in Equation (11), and the velocity tracking problem of the dynamical system and the angle tracking problem of the pendulum will be solved next.
## 1 Introduction
In [19], it was experimentally demonstrated that the influence of the fibre link temperature on the recovered OCDMA auto-correlation could be mitigated by introducing an SOA as part of the OCDMA receiver. In [20], an SOA that was located at the transmission end was used to control the chirp of OCDMA code carriers to mitigate the effect of CD on the OCDMA auto-correlation width changes at room temperature. In this paper, to the best of our knowledge, we investigate for the first time the use of an SOA on a transmitter site to mitigate the effects of fibre temperature fluctuations that affect the received OCDMA auto-correlation that is recovered on the receiver site. We have experimentally shown, and by simulations, that a distorted OCDMA auto-correlation due to the temperature induced fibre dispersion (TD) can be corrected by manipulating the chirp of code carriers when traversing a biased SOA prior to entering the transmission link. In addition, the simultaneous effects of the varying values of group velocity dispersion (parameter \(\beta_{2}\)) and fibre link temperature on the OCDMA auto-correlation width changes were also investigated. We have shown that the impact of a changing \(\beta_{2}\) (0.03-0.06) ps\({}^{2}\)/nm, when the fibre link is exposed to 50 \({}^{\circ}\)C, can be continuously compensated by using the SOA controlling the code carriers' chirp. Our simulation results are in very good agreement with the experimental observations.
###### Abstract
Some geometrical changes were applied, including changes in the nozzle length, diffuser length, and diffuser-opening angle, and by replacing the conical nozzle with an ideal one and adding an exit flange. However, the best SR obtained was 1.67 in this study [13]. In [PERSON]'s study, the flow-field characteristics of increased speed in INVELOX system under different wind directions were studied. It was found that after considering the atmospheric boundary layer (ABL) wind profile, the SR of the system decreased. The best SR obtained in this proposed design was 1.9 [14]. A study conducted by [PERSON] et al. proposed several possible configurations for the INVELOX system. It was found that the lower cone and elbow sections and the presence of the duct with a constant section after the elbow had led to unsatisfactory results. In addition, the introduction of a constant section after the venturi section decreased system performance. Meanwhile, optimal performance could be obtained by changing the convergent-divergent diameter. From this study, the best SR obtained was 1.74, which was slightly lower than the first design [15]. Based on the reported previous work on the effects of the INVELOX design parameters, there is room to further improve its performance. In this research, a new omni-directional wind funnel is proposed by considering a few design parameters, such as the length of each guide vane, the number of guide vanes, the elbow diameter, the ratio of the diameter of the intake to the venturi (aiming to effectively direct the winds from multiple directions and amplify to an optimum wind speed range), and the channel to the coupled bluff splitter body and PVEH plate. It is expected to generate maximum energy output if an optimum vortex frequency is produced to match with the resonant frequency of the PVEH plate.
# Hybrid Encoding Scheme for AMBTC Compressed Images Using Ternary Representation Technique
## 3 Proposed Method
### Encoding of Flat Blocks  
The pixel values of a flat block \(I_{i}\) (i.e., \(b_{i}-a_{i}\leq\tau_{0}\)) are relatively close, and thus the bitmap plays an insignificant role in reconstructing the image block. Therefore, we omit the recording of the quantization value in addition to the bitmap, and use an 8-bit mean value \(\left(m_{i}\right)_{2}\) to represent the flat block, where:  
\[m_{i}=\text{round}\bigg{(}\frac{b_{i}+a_{i}}{2}\bigg{)} \tag{5}\]  
and \(\text{round}(x)\) is the function rounding \(x\) to the nearest integer.