esa-sceva/satcom-chunk-collection huggingface dataset:
# A Comprehensive Review of Micro UAV Charging Techniques
## 7 Simultaneous Lightwave Information and Power Transfer (SLIPT)
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Reference** & **Name** & **Type** & \
\begin{tabular}{c} **Energy** \\ **Efficiency** \\ \end{tabular} &
\begin{tabular}{c} **Human** \\ **Intervention** \\ \end{tabular} & **Advantages** & **Drawbacks** \\ \hline
[121] & \begin{tabular}{c} UGV-assisted \\ WPT \\ \end{tabular} & Wireless & Medium & No & \
\begin{tabular}{c} On-demand \\ self-recharging No \\ human intervention \\ \end{tabular} &
\begin{tabular}{c} Complex \\ route/resource/landing \\ scheduling \\ \end{tabular} \\ \hline
[122] & \begin{tabular}{c} UAV-assisted \\ WPT \\ \end{tabular} & Wireless & Medium & No & \
\begin{tabular}{c} On-demand \\ self-recharging No \\ need to land \\ \end{tabular} &
\begin{tabular}{c} Hard to operate \\ autonomously Prone to aerial \\ collision \\ \end{tabular} \\ \hline
[123] & Stationary WPT & Wireless & Medium & No & \
\begin{tabular}{c} High charging \\ feasibility No human \\ intervention \\ \end{tabular} &
\begin{tabular}{c} Need additional flight \\ \end{tabular} \\ \hline
[124] & \begin{tabular}{c} RE-based \\ charging \\ \end{tabular} & \begin{tabular}{c} Harvesting \\ energy from \\ environment \\ \end{tabular} & Medium & No & \
\begin{tabular}{c} No need to land No \\ additional flight \\ \end{tabular} &
\begin{tabular}{c} Weather-dependent Limited \\ harvested energy Need \\ additional weight and size \\ \end{tabular} \\ \hline
[125] & Laser PB charging & Wireless & Low & Medium & \
\begin{tabular}{c} No need to land No \\ additional flight \\ \end{tabular} &
\begin{tabular}{c} High deployment cost Need \\ complete UAV motion \\ information \\ \end{tabular} \\ \hline
[126] & \begin{tabular}{c} Battery hot \\ swapping \\ \end{tabular} & \begin{tabular}{c} Swap \\ \end{tabular} & Very high & Medium & \
\begin{tabular}{c} Support multi-UAV \\ charging \\ \end{tabular} &
\begin{tabular}{c} High round-trip energy cost \\ Issues in autonomous \\ swapping \\ \end{tabular} \\ \hline
[127] & \begin{tabular}{c} CS-based \\ charging \\ \end{tabular} & Wired/wireless & High & Medium & \
\begin{tabular}{c} Support multi-UAV \\ charging \\ \end{tabular} &
\begin{tabular}{c} High round-trip energy cost \\ Low charging feasibility \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 5: A comparison of different UAV charging techniques in various applications. Figure 16: An illustration of A-RAN with SLIPT.
## 2 Theoretical Background and Problem Statement
### Barrier Lyapunov Function  
Within a system described by \(\gamma=D(\gamma)\), when the system incorporates a continuous and strictly positive function \(D(\gamma)\), defined on an open set \(\Omega\), the function \(V(\gamma)\) is characterized as differentiable, with its first derivatives maintaining continuity throughout the domain \(\Omega\). A notable feature of \(V(\gamma)\) is its asymptotic behavior: it diverges to infinity as \(\gamma\) means the periphery of \(\Omega\). Further, for any positive time t and any initial condition within \(\Omega\), specifically \(\gamma(0)\), the function \(V(\gamma)\), adheres to a strict upper bound, denoted by \(\overline{V}\). This ensures that \(V(\gamma)\) remains strictly positive and does not exceed \(\overline{V}\), thereby establishing \(\overline{V}>V(\gamma)>0\) across the domain.  
To facilitate this, the adoption of the following RBF is warranted:  
\[E=\frac{1}{2}\text{ln}\frac{a^{2}}{a^{2}-\gamma^{2}} \tag{8}\]  
where the parameters dictate that \(a\) must be greater than zero, with \(\gamma\) confined within the range of \(-a\) to a. Additionally, as the magnitude of \(\gamma\) approaches \(a\), E grows infinitely large.
## 4 Performance Analysis
### Data Augmentation Process  
To compare the effects of the MDA, we first sample the original data to obtain the pending data. Then, with the support of the MDA, the pending data are transformed into augmented data, and the original data are combined with the augmented data to create the synthetic data. As shown in Figure 2, the deep learning models can be fine-tuned on the original data and the synthetic data for a specific task, respectively.
## 3 Results
### Alterations in FC between WM Networks  
The results of repeated measures ANOVA showed that there was a significant interaction between group and stimulus condition (\(p<0.005\), blue links in Figure 3A). Compared with normal condition, the FC between the WM-frontotemporal network (WM-FTN) and the WM-precentral/postcentral network (WM-PCN) was significantly decreased in the PS group under cold condition. The differences between groups were not statistically significant (Figure 3B).
## 2 Materials and Methods
### Pilot Testing Design and Operation
#### 2.2.1 Injection and Extraction Operations  
The extraction pumping and injection flow rate scheme as designed was used in operating the pilot test, whereby extracted groundwater was fed manifolds and reinjected into the injection wells. By this method, injection and extraction rates were balanced and a hydraulic recirculation cell was established for each of the four pilot tests. The pilot test also included a shut-in period, during which recirculation was stopped and the test cell was allowed to incubate under ambient conditions. Based on the ambient hydraulic gradient in the upper aquifer and the shut-in duration, the injectate plume was not anticipated to migrate during the shut-in period.  
Hydrogeologic modeling conducted during the remedial design indicated that the average time of travel between adjacent injectors and extractor would be approximately 26 days at the 9.5 L per minute (lpm) (2.5 gallons per minute) recirculation flow rate [3]. As described earlier, the subsurface is heterogeneous, with interbedded layers and lenses of higher- and lower-permeability materials. These heterogeneities give rise to flow field anisotropy and preferential pathways between injector/extractor well sets. The conservative tracer (bromide) added along with the injectate indicated an earlier arrival travel time (presumably along preferential flow pathways) that was significantly less than 26 days (see Section 3.1 below). This result was taken into consideration in subsequent result interpretation for ERD for each of the pilot tests.  
This pilot test design allowed us to determine if this flow rate was appropriate for full-scale implementation. In addition, pilot test results were employed to indicate whether modification of the design flow rate schedule was necessary to achieve one or more of the following objectives: (i) adequate hydraulic control against plume spreading; (ii) improved efficiency of the treatment zone sweep (i.e., distribution, mixing and contact); (iii) improved in-situ treatment efficiency; and/or (iv) improved equipment and system operation and maintenance efficiency.
# Mathematical Modelling of the Effects of Plasma Treatment on the Diffusivity of Biofilm
## 2 Materials and Methods
### Biofilm Sterilization by Combinatorial Treatment of Plasma and CHX  
Biofilms were grown on titanium coupons (12.7 mm diameter and 3 mm thickness) in a CDC (Center for Disease Control and Prevention) biofilm bioreactor for 24 h in batch phase and then for 24 h under dynamic phase with agitation. Tryptic soy broth (TSB; 0.3% \(w/v\) for batch phase and 2% \(w/v\) for dynamic phase) in DI water was used for growing biofilms in the reactor at 37 \({}^{\circ}\)C for 48 h. An overnight culture of _Pseudomonas aeruginosa_ (PAO1) was adjusted to an optical density (OD600) equivalent to 10\({}^{8}\) CFU/mL. The standardized bacterial suspension was used to inoculate the reactor. For the entire 48 h biofilm growth, shear stress was produced by the baffle of biofilm reactor rotating at a speed of 130 rpm to avoid the presence of planktonic bacteria. After the selected growth time, the coupons were aseptically removed from the reactor and subjected to combinatorial treatment with jet plasma and CHX (1% \(v/v\) in DI water). Each coupon was first plasma treated for 15, 30 and 60 s. The plasma treated coupons were then treated with CHX for 5 and 15 s. For treatment with CHX, the plasma treated coupons were submerged in CHX solution in a 24 well plate and incubated for the indicated periods of time. After treatment with CHX, the antiseptic effect was halted by adding inactivating agent solution The inactivation agent consists of a solution of Tween 80 (30 g\(\cdot\)L\({}^{-1}\)), Saponine (30 g\(\cdot\)L\({}^{-1}\)), Histidine (1 g\(\cdot\)L\({}^{-1}\)), and Cysteine (1 g\(\cdot\)L\({}^{-1}\)) [37]. After combinatorial treatment, the treated coupons were suspended in a tube with phosphate buffer saline (PBS) and sonicated for 5 min in an ultrasonic bath with vortexing for 30 s to disrupt the biofilm and release the bacterial cells. The bacterial suspension was serially diluted and plated in triplicate on TSB agar. Plates were incubated at 37 \({}^{\circ}\)C for 24 h and colonies were counted.
## 1 Introduction
As the method is more applicable in practice and could produce a satisfactory accuracy, especially for the prediction of the extraction of rectangular mining area-induced subsidence, the method has been widely used in the prediction of underground coal mining-induced subsidence throughout the world, especially in China, and is also one of the recommended methods in the regulations of the Nation Coal Bureau [31]. However, because the difficulties of the accurate determination of the calculation area, and the complicated calculation of double integrals with an irregularly shaped integral domain, it is still a challenging problem to predict ground subsidence induced by the extraction of an irregularly shaped coal mining area based on PIM. Generally, the irregularly shaped mining area is divided into multiple rectangular sub-mining areas in order to calculate mining subsidence individually. However, this method necessitates modifying the prediction parameters for each sub-mining area, which is time-consuming. The division is subjective, and cannot approximate the in-situ mining area with the calculation area accurately, resulting in a worse agreement between the subsidence predictions and the observation ones [32]. In this paper, a new PIM-based method is proposed to predict ground subsidence induced by the extraction of irregularly shaped coal mining area, by making use of the Delaunay triangular mesh method and the Monte Carlo integration method. This method is usually applicable, since the Delaunay triangular mesh method could separate the arbitrarily shaped mining area and the calculation area with sufficient accuracy if the mesh size is small enough. The method is easily implemented because of the simplicity of the calculation of the double integral using the Monte Carlo method. The remainder of this paper is organized as follows. In the next section, the fundamentals of the PIM-based subsidence prediction are provided. Section 3 presents the details of the proposed subsidence prediction method. Section 4 evaluates comprehensively the proposed method, using two data sets, which are the simulation data and direct leveling-based subsidence observations. Section 5 concludes this paper.
# Insights into the Issue of Deploying a Private LoRaWAN
## 4 Experimental and Analytical Results
### Security Evaluation
#### 4.2.2 The State-of-the-Art Improvements  
In the following, we analyze the recent works and improvements to the LoRaWAN security approaches.  
[PERSON] and [PERSON] [70] propose a dual key-based activation scheme. Their proposal resolves the problem of key updates by using a dual key setup. Keys, that users share with the NS and the AS, are recomputed from previous keys and nonces by the AES encryption function. The proposal addresses such security requirements as authentication, message integrity, data confidentiality, and replay attack prevention. End node authentication is achieved by using the shared key with the NS and checking the CMAC value in the join phase.  
The public key infrastructure of LoRaWAN has several security issues related to key management and the join phase. For example, the paper [72] demonstrates that attackers may misuse the OTAA for a replay attack. The paper presents this attack and offers the countermeasure by adding a masking token. [PERSON] and [PERSON] [73] present a secure D2D link establishment scheme that consists of the SecureD2 DReq, and SecureD2 DAns messages exchanged between end nodes and the NS. The NS delivers security parameters to both nodes, so that both D2D nodes can securely establish cryptographic keys for protecting the D2D communication. A minor disadvantage is that the NS knows the encryption keys that are used between the nodes. In consequence, the NS has to be a trusted party. The work [75] presents a reputation system in order to select trustworthy nodes as proxies that are involved in the key derivation phase in order to improve the key robustness.
# Pet Fiber Reinforced Wet-Mix Shotcrete with Walnut Shell as Replaced Aggregate
## 3 Experimental Analysis
### Results of the First Series of Tests
#### 3.2.2 Density and Compressive Strength Relationship  
[PERSON] et al. [11] found that the fresh density was slightly higher than the hardened density and both showed a good relationship to each other. The difference of density between cast and sprayed mixes was not significant, except for few extreme cases [60]. Hence, in this article, fresh density of casting concrete with crushed walnut shell was recorded. It can be seen in Figure 8 that the average fresh density values ranged from 1850 to 2150 kg/m\({}^{3}\). According to the specification from ACI [61] and ASTM [62], the density of structural lightweight concrete should be less than 2000 kg/m\({}^{3}\). In addition, lightweight concrete is classified with a density ranging from 1314 kg/m\({}^{3}\) to 1777 kg/m\({}^{3}\)[11]. Thus, the range of density of specimens in these tests was close to the requirement of lightweight concrete.  
The compressive strength of concrete depends on density as one of the most important variables to consider in the design of concrete structures [63]. The relationship between compressive strength and density of concrete mixtures is plotted in Figure 8. A linear relationship between density and compressive strength can be observed in Equation (1), which obtained a coefficient of determination, \(R^{2}\), of 0.9782 for all specimens, and thus indicated high confidence for the relationship.  
\[y=11.5x+1644.6 \tag{1}\]  
where \(y\) is density (kg/m\({}^{3}\)) and \(x\) is the compressive strength (MPa) of casting concrete mixture.
###### Abstract  
The increasing need for scalable and efficient crop monitoring systems in industrial plant factories calls for image-based deep learning models that are both accurate and robust to domain variability. This study investigates the feasibility of CNN-based growth stage classification of butterhead lettuce (_Lacuca sativa_ L.) using two data types: raw images and images processed through GrabCut-Watershed segmentation. A ResNet50-based transfer learning model was trained and evaluated on each dataset, and cross-domain performance was assessed to understand generalization capability. Models trained and tested within the same domain achieved high accuracy (Model 1: 99.65%; Model 2: 97.75%). However, cross-domain evaluations revealed asymmetric performance degradation--Model 1-CDE (trained on raw images, tested on preprocessed images) achieved 82.77% accuracy, while Model 2-CDE (trained on preprocessed images, tested on raw images) dropped to 34.15%. Although GrabCut-Watershed offered clearer visual inputs, it limited the model's ability to generalize due to reduced contextual richness and oversimplification. In terms of inference efficiency, Model 2 recorded the fastest model-only inference time (0.037 s/image), but this excluded the segmentation step. In contrast, Model 1 achieved 0.055 s/image without any additional preprocessing, making it more viable for real-time deployment. Notably, Model 1-CDE combined the fastest inference speed (0.040 s/image) with stable cross-domain performance, while Model 2-CDE was both the slowest (0.053 s/image) and least accurate. Grad-CAM visualizations further confirmed that raw image-trained models consistently attended to meaningful plant structures, whereas segmentation-trained models often failed to localize correctly in cross-domain tests. These findings demonstrate that training with raw images yields more robust, generalizable, and deployable models. The study highlights the importance of domain consistency and preprocessing trade-offs in vision-based agricultural systems and lays the groundwork for lightweight, real-time AI applications in smart farming.  
commercial plant factory; convolutional neural network; image preprocessing; transfer learning; deep learning; GrabCut-Watershed segmentation; cross-testing; inference time
## 3 Research Method  
### Research Hypothesis Development  
#### 3.1.1 Relationship between Perceived Value and Customer Satisfaction  
The correlation between perceived value and customer satisfaction is fundamental for business success. A product or service that provides substantial value is more likely to satisfy customers. Satisfied customers, in turn, are more prone to becoming loyal, repeat buyers and brand advocates. Various studies across different industries have supported the connection between perceived value and satisfaction. [PERSON] et al. [49], [PERSON] et al. [30], [PERSON] et al. [50], and [PERSON] et al. [7] examined mobile instant messaging services, social applications, online shopping malls, and business-to-business services, respectively. These studies consistently found that higher perceived value leads to increased customer satisfaction. Therefore, this study proposes the following hypotheses to understand the relationship between perceived value dimensions and customer satisfaction in entertainment platforms:  
**H1:**: _Utilitarian value is positively related to customer satisfaction._
**H2:**: _Hedonic value is positively related to customer satisfaction._
**H3:**: _Social value is positively related to customer satisfaction._
applied sciences Article  
The Accuracy of Dynamic Sound Source Localization and Recognition Ability of Individual Head-Related Transfer Functions in Binaural Audio Systems with Head Tracking  
[PERSON] 1\({}^{\copyright}\), [PERSON] 2, [PERSON] 1,\({}^{\copyright}\), [PERSON] 2 and [PERSON] 1\({}^{\copyright}\)  
1 Faculty of Electrical Engineering and Computing, University of Zagreb, Unska 3, 10000 Zagreb, Croatia; [EMAIL_ADDRESS] (V.P.); [PERSON]K.J.)  
2 Department of Engineering Management, Universiteit Antwerpen, 2000 Antwerp, Belgium; [PERSON] (J.R.); [PERSON](H.P)  
* Correspondence: [EMAIL_ADDRESS]  
The use of audio systems that employ binaural synthesis with head tracking has become increasingly popular, particularly in virtual reality gaming systems. The binaural synthesis process uses the Head-Related Transfer Functions (HRTF) as an input required to assign the directions of arrival to sounds coming from virtual sound sources in the created virtual environments. Generic HRTFs are often used for this purpose to accommodate all potential listeners. The hypothesis of the research is that the use of individual HRTF in binaural synthesis instead of generic HRTF leads to improved accuracy and quality of virtual sound source localization, thus enhancing the user experience. A novel methodology is proposed that involves the use of dynamic virtual sound sources. In the experiments, the test participants were asked to determine the direction of a dynamic virtual sound source in both the horizontal and vertical planes using both generic and individual HRTFs. The gathered data are statistically analyzed, and the accuracy of localization is assessed with respect to the type of HRTF used. The individual HRTFs of the test participants are measured using a novel and efficient method that is accessible to a broad range of users.  
virtual sound source localization; acoustic signal processing; individual HRTF; binaural system with head tracking
# Comparative Study of Machine Learning Classifiers for Modelling Road Traffic Accidents
## 2 Literature Review
### Related Studies
The study evaluated models using an out-of-bag (OOB) estimate of error rate, mean square error (MSE) and RMSE. The RF method aims to reduce overfitting and is capable of improving model accuracy. Other performance evaluation methods used in different studies are precision, recall, f1 score, ROC curve, true positive rate (TPR), and false positive rate (FPR) [27; 30; 35; 36; 38]. The LDA approach was implemented by [32]. This study employed SVM, RF, LR, NB, and k-NN during the comparative analysis due, as mentioned, to their popularity and demonstrated capabilities in the literature. Furthermore, AdaBoost was considered even though there is no evidence of it tackling road accident problems. AdaBoost is perceived as improving the performance of weak classifiers. It can also handle image and text problems well. Overall, the literature review revealed that there is no such thing as a perfect method. Thus, with RTAs, the most appropriate approach to finding the best performing method is to continue constantly combining and comparing various methods.