esa-sceva/satcom-chunk-collection huggingface dataset:
# Simulation-Based Performance Analysis for a Novel AVS/RS Technology with Movable Lifts
## 3 The Novel AVS/RS/ML System  
### Description of the System  
In this paper, we propose a novel autonomous vehicle-based storage and retrieval system design with movable lifts (AVS/RS/ML) that could be utilized in high bay mini-load warehouses (see Figure 2). The proposed system is employed for storage (i.e., transport of totes with items from points of delivery to storage compartments) and retrieval operations (i.e., transport of totes with items from storage compartments to order-picking locations or directly to the production assembly line). Within this system, the autonomous vehicles (i.e., AGVs) can travel inside and outside of storage racks. AGVs are utilized for processing of storage and retrieval operations as well as for transport and distribution of totes throughout the production plant.  
According to that new system proposal, AGVs can make horizontal travel within an aisle floor and can be lifted vertically to reach upper levels of the storage racks by the help of MLs located in each aisle. To perform lifting process, AGVs attach themselves to the ML's lifting chains. For that, AGVs would be equipped with appropriate telescopic attachment (Figures 3-5) for automated manipulation with totes, retractable grippers for  
In the proposed AVS/RS/ML, there is no need to use expensive stacker cranes or complicated vertical lifting mechanisms together with aisle-captive shuttle vehicles. Those are relevant advantages compared to the existing autonomous vehicles or stacker cranes-based storage and retrieval systems. When we compare the introduced novel AVS/RS/ML with the recently developed system with AGVs [4], it is also a great advantage that AGVs lifted by MLs would not need to be equipped with their own lifting mechanisms and they do not consume electricity from their batteries during that lifting process. When AGVs are attached on the MLs, AGV batteries can also be charged by conductor bars and collectors attached along ML's lattice columns and on AGVs, respectively.
## 2 Related Work
### Uncertainty-Based Transfer Learning Technology  
In the realm of transfer learning, the uncertainty inherent in feature representations poses substantial challenges to the stability and robustness of general image classification models. Recent advancements in uncertainty-aware transfer learning seek to navigate these challenges by leveraging uncertainty to enhance model performance. Notably, [PERSON] et al. [25] explored the intricacies of transferability estimation within domain adaptation, introducing a non-intrusive method for unbiased transferability estimation. This approach utilizes uncertainty modeling within adversarial domain adaptation frameworks to refine the optimization process. Similarly, [PERSON] et al. [26] introduced an innovative uncertainty-induced methodology for assessing transferability. This technique employs uncertainty as a means to evaluate the channel-wise transferability of features from the source encoder in scenarios where source data and target labels are unavailable.  
Furthermore, the integration of uncertainty into transfer learning has seen promising applications within medical image analysis for clinical use cases. [PERSON] et al. [27] developed a deep uncertainty-aware transfer learning framework tailored for COVID-19 detection, employing convolutional neural networks (CNNs) to extract features from chest X-ray and computed tomography images. This framework incorporates epistemic uncertainty to identify regions of low confidence in the model's predictions. Moreover, [PERSON] et al. [28] crafted a deep learning architecture for the sequential analysis of cancer tumor progression using Cone Beam Computed Tomography (CBCT) images. This model incorporates an attention mechanism and provides uncertainty estimates for segmentation tasks, contributing to risk management in treatment planning and enhancing the model's calibration and reliability.  
Drawing inspiration from these pioneering works, our study introduces an uncertainty-guided source-free transfer learning approach. This model capitalizes on an uncertainty-guided mechanism to enable effective feature mapping from the source model to target data, thereby facilitating knowledge transfer in source-free domain adaptation tasks for medical image classification. Compared to the aforementioned works, this approach underscores the potential of integrating uncertainty into transfer learning frameworks to solve inter- and intra-domain imbalances. It progressively approximates the feature space of the target and source model's distributions through various inter- and intra-domain alignment strategies, improving adaptability and efficacy in domain adaptation scenarios.
# Intelligent Control System to Irrigate Orchids Based on Visual Recognition and 3D Positioning
## 2 System Design and Methodology  
The proposed irrigation system for the orchid seedlings consists of several modules, such as the visual recognition, the construction of the 3D coordinate system, the positioning of the watering point, and the control for the system process, as shown in Figures 2 and 3. The framework of YOLOv3 is used for the visual recognition to identify and frame the seedlings. The 3D coordinate system is established through the depth maps corresponding to the output images of YOLOv3 for localization of the seedlings in the 3D space. The desired watering point is the stem-root junction of the seedling, which can be positioned and watered accurately by the spraying nozzle. The system process control takes into account the sequential control of automatic watering process.  
Figure 3: Functional blocks of the proposed visual recognition system.  
Figure 2: Flow chart from object detection (YOLOv3) to automatic watering process.
## 3 Results
### Smartphone Applications for DFU
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Ref.** & **Year** & **Study Aim** & **Data Type** & **Model Used** & **Code** & **Training** & **Protocols** & **Evaluation Metrics** \\ \hline \multirow{4}{*}{[24]} & \multirow{4}{*}{2023} & Automatic foot & \multirow{4}{*}{Image} & \multirow{4}{*}{AFSegGAN} & Not & Learning rate: 0.002, & \multirow{4}{*}{\begin{tabular}{} \end{tabular} } & \multirow{4}{*}{\begin{tabular}{} \end{tabular} } & IoU: \\  & & & & & & steps = epochs & 1. AFSegGAN: 99.07\% \\  & & & & provided & batch size: 16 & \(\times\) batch per & 2. UNet:EffE: 85.01\% \\  & & & & & & epoch & 3. DeepLabV3*SE: \\ \hline \multirow{4}{*}{[71]} & \multirow{4}{*}{2024} & Enhance & ResNet50, & \multirow{4}{*}{\begin{tabular}{} \end{tabular} } & \multirow{4}{*}{\
\begin{tabular}{} \end{tabular} } & Learning rate: 0.001, & \multirow{4}{*}{
\begin{tabular}{} \end{tabular} } & Accuracy: \\  & & & & & batch size: & & ResNet50: 76\% \\  & & & & & 4 (ResNet50), & 8-fold cross- & ResNet50-GAN: 84\% \\  & & & & & 8 (ResNet50-GAN), & & F1 score: \\  & & & & & epochs: 100 & & ResNet50: 75\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: _Cont._learning model based on Faster R-CNN for object detection and Inception-ResNetV2 for feature extraction, with transfer learning from the MS COCO dataset. Researchers trained the model on 1775 annotated DFU images and deployed it on a cloud platform for real-time analysis. During the clinical evaluation, clinicians captured images using the app, and the cloud-based AI system returned predictions with an average response time of 5.9 s per case. The model demonstrated high sensitivity and specificity. Clinical validation revealed excellent inter-rater and intra-rater reliability ([PERSON]'s \(\alpha\) > 0.8), indicating strong agreement among clinicians. [PERSON] et al. [78] developed DFUCare, a deep learning-based platform for the automated detection, classification, and monitoring of DFUs using smartphone images. The system employs YOLOv5s for wound localization and InceptionResNetV2 for infection and ischemia classification, integrating CIELAB and YCbCr color space segmentation for enhanced wound analysis. Researchers trained and validated the system using two benchmark datasets: DFUC2020 [32] and DFUC2021 [34]. In testing, DFUCare achieved an F1 score of 0.80 and a mAP of 0.861 for wound localization. A pilot study at the Postgraduate Institute of Medical Education and Research, India, demonstrated strong agreement with physician assessments, supporting the system's clinical utility for remote wound monitoring. Table 6 summarizes recent studies, highlighting AI models, data types, and performance metrics used for AI-based smartphone applications for DFU.
## 3 Methodology  
This study proposes a decision support model for identifying and prioritizing the most effective CLP improvement strategies by integrating fuzzy MCDM methods with FCM. Fuzzy MCDM methods, such as fuzzy AHP and fuzzy TOPSIS, are operations research tools for ranking various parameters with respect to multiple criteria in complex decision-making problems [34]. In the problem of improvement strategy selection, fuzzy AHP is used to weight criteria, and fuzzy TOPSIS is used to rank the strategies based on the criteria. FCM is a causal cognition tool for modeling and simulating dynamic systems [35]. In this study, FCM is utilized to quantify the impact of strategies on CLP by modeling a CLP environment. Figure 1 shows a general view of the framework for selecting CLP improvement strategies, which includes three phases: identifying factors and strategies, ranking strategies, and quantifying strategies' impact on CLP.  
In the first phase, the key CLP factors are determined based on the study carried out by [PERSON] et al. [36]. Then, various CLP improvement strategies that correspond to key factors (i.e., strategies that could be used to address each factor) are identified through a comprehensive background review of the literature. In the second phase, the two fuzzy MCDM methods of fuzzy AHP and fuzzy TOPSIS are integrated and used to rank the identified improvement strategies by capturing experts' opinions of the importance of each strategy versus various criteria. In the third phase, an FCM model is developed based on the identified factors and top-ranked strategies for analyzing the effects of factors on each other and determining the impact of each strategy on CLP improvement. Finally, the  
Figure 1: Proposed framework to rank improvement strategies and quantify their impact on CLP.  
most effective CLP improvement strategies are prioritized, or ranked, according to their quantified impact on CLP. Two validation approaches, structural validity and behavioral validity, are used in this study to validate the developed decision support model. The structural validity approach is utilized to evaluate the list of model components (factors influencing CLP and CLP improvement strategies) and the relationships among them. Behavioral validity of the FCM model is evaluated using the extreme conditions test, as utilized by [PERSON] and [PERSON] [37], which compares the behavior of a developed model to the behavior of the real system under the same extreme conditions of input factors [38]. According to [PERSON] and [PERSON] [39], common validation tests such as the statistical hypothesis test are not suitable for FCM models, which simulate dynamic systems. Therefore, both utilized validation approaches compare the structure and behavior of the model with a real-world system empirically, using case studies and, theoretically, using the literature. An overview of the utilized case study is presented below.
# Connectivity Analysis in VANETs with Dynamic Ranges
## 4 Discussion
### General Observations
#### 4.4.1 Synchronized Mobility  
In this model, vehicles travel at the same speed and maintain a constant distance between each other, creating a predictable network topology. Under the synchronised mobility model with a lognormal path-loss model, signal strength decreases steadily with distance. This results in consistent network connectivity with minimal disruptions as long as vehicles remain within range. In the Rayleigh model, signal strength fluctuates rapidly due to multipath fading, leading to frequent link breakages and re-establishment even between nearby vehicles. The Nakagami-\(m\) model provides a middle ground between the lognormal and Rayleigh models, with network performance depending on the  
Figure 11: Connectivity probability for distributed transmission range. (**a**) Connectivity with Nakagami-\(m\) range. (**b**) Connectivity with Weibull range.  
parameter \(m\). Higher \(m\) values lead to more stable connections, while lower \(m\) values resemble Rayleigh fading. Finally, with the Weibull path-loss model, fading severity varies. At higher path-loss exponents \(C\), the network behaves similarly to the lognormal model with smooth degradation. Lower exponents result in behavior resembling Rayleigh fading, with frequent fluctuations.
## 3 Results and Analysis
### Mutual Influence between Adjacent Roadways
#### 3.3.2 Impact of Adjacent Roadway on the Stress Field around the Existing Roadway  
Based on the calculation method introduced above, we further calculated the peripheral hoop stress of the roadway of Stope 101 after the excavation of the adjacent roadway of Stope 102. The distribution curve is shown in Figure 9. Combined with Figure 7, it indicates that when the roadway of the adjacent slope is not excavated, the hoop stress at the sidewall monitoring points on both sides of the roadway is 14.5 MPa. After the excavation of the adjacent roadway, the stress distribution outside the roadway is no longer symmetrical. The hoop stress at the sidewall monitoring point on the side closer to the adjacent roadway increased to 15.7 MPa, an increase of 8.3%, while the hoop stress at the sidewall monitoring point on the side away from the neighboring roadway is 15.1 MPa, an increase of 4.1%. The stress values at the middle monitoring points of the roof and floor decreased by 4.1% and 6.9%, respectively. Figure 10 further presents a comparison of the stress distribution curves of Stope 101 before and after the excavation of the adjacent roadway. Overall, since the distance between the two opening centers is relatively large compared to the size of the roadway itself, the impact of the adjacent stope's roadway on the stress field around the existing roadway is very limited, and the mutual influence between the two can be essentially ignored.
## 2 Materials and Methods
### Photocatalytic Experiments with Green Nanoparticles  
The photocatalysis process was carried out in a borosilicate reactor exposed to ultraviolet radiation (365 nm) for a period of 60 to 420 min. Throughout the experiment, the temperature was kept constant at 25 \({}^{\circ}\)C, and a magnetic stirrer was used during this stage. Before exposure to ultraviolet radiation, the samples containing nanoparticles were kept in the dark and stirred at 80 rpm. The initial concentrations of the antibiotic were 10, 20, 30, and 40 mg L\({}^{-1}\), while the concentrations of nanoparticles were 1.0, 1.5, 2.0, 2.5, and 3.0 g L\({}^{-1}\). The pH of the sample was adjusted to 3.0, 6.0, and 9.0. At specific time intervals, 3 mL aliquots were collected and analyzed using a UV-6100 PC Double Beam spectrophotometer (Shanghai Instr., Shanghai, China) (231 nm).  
To determine the influence of hydroxyl radicals on the advanced oxidation of the antibiotic, isopropanol (2 mM) was used as the reagent. The reagent was added to the samples before the nanoparticles. After 300 min, an aliquot of the sample was subjected to antibiotic quantification using a spectrophotometer, as previously mentioned. The antibiotic concentration at this stage was 0.01 g L\({}^{-1}\), and the nanoparticle concentration was 1000 mg L\({}^{-1}\).  
The monitoring of photodegradation by-product generation was conducted through mass spectrometry analysis. A mass spectrometer, Impact II by Bruker Daltonics Corporation, Bremen, Germany, was used for this analysis. Samples were filtered and added to 1000 \(\mu\)L of HPLC-grade methanol. Compound identification was performed using ChEBI, GNPS, and PubChem.  
The energy required for the photodegradation process in this study was calculated using the electrical energy per order (EE/O) (Equation (1)):  
\[\mathrm{EE/O}=\frac{38.76\times\mathrm{P}}{\mathrm{k}\times\mathrm{V}} \tag{1}\]  
where P (kW) is the power of UV light, k (min\({}^{-1}\)) represents the rate constant for the pseudo-first order reaction, and V (L) is the volume of the sample [26,27].
## 2 Preliminaries
### Notation and Definitions  
For \(a\in\mathbb{R}\) and \(x\in\mathbb{R}^{n}\), let \(|a|\) and \(\|x\|=\sqrt{x^{\top}x}\) denote the absolute value and the Euclidean norm, respectively. Positive (resp. non-negative) real numbers are denoted\(\mathbb{R}_{+}\) (\(\mathbb{R}_{\geq 0}\)), and the maximum and minimum eigenvalues of a square matrix \(A\) is denoted \(\lambda_{\text{max}}(A)\), \(\lambda_{\text{min}}(A)\), respectively. The induced 2-norm of a matrix \(A\) is \(\|A\|=\sigma_{\text{max}}(A)\), where \(\sigma_{\text{max}}(A)\) is the largest singular value of \(A\). For square, real symmetric positive semidefinite matrices \(A\), \(\lambda_{\text{max}}(A)=\sigma_{\text{max}}(A)\). Any square matrix \(A\) can be written as the sum of a symmetric and skew symmetric part, \(A=\text{sym}(A)+\text{skew}(A)\), where \(\text{sym}(A)=(A+A^{\top})/2\) and \(\text{skew}(A)=(A-A^{\top})/2\). For a symmetric matrix \(A=A^{\top}\), we have the following inequality for quadratic forms: \(\lambda_{\text{min}}\|x\|^{2}\leq x^{\top}Ax\leq\lambda_{\text{max}}\|x\|^{2}\).  
For any \(u,v\in\mathbb{R}^{3}\), the matrix \(S(u)=-S^{\top}(u)\in\text{so}(3)\) is the skew-symmetric matrix such that \(S(u)v=u\times v\). From properties of the cross product we have \(S(u)v=-S(v)u\), \(S(u)u=0\) and \(u^{\top}S(v)u=0\), which implies that \(u^{\top}Au=u^{\top}\text{sym}(A)u\) for any square matrix \(A\).  
We make use of standard right-handed coordinate frames: \(\{n\}\), a local north-east-down tangent frame (assumed inertial), and \(\{b\}\), a body-fixed frame centered at the center of gravity of the UAV, with the \(x\)-axis in the longitudinal direction and the \(y\)-axis pointing towards the right wing.  
The three-dimensional special orthogonal group is the set of three-dimensional rotation matrices, given by  
\[\text{SO}(3)=\{R\in\mathbb{R}^{3\times 3}\colon R^{\top}R=I_{3},\det R=1\},\]  
where \(I_{3}\in\mathbb{R}^{3\times 3}\) is the identity matrix. The two-sphere \(\mathbb{S}^{2}\subset\mathbb{R}^{3}\) is defined by  
\[\mathbb{S}^{2}=\{x\in\mathbb{R}^{3}\colon\|x\|=1\}.\]  
The tangent space at a point \(x\in\mathbb{S}^{2}\) can be identified with the vectors that are orthogonal to \(x\):  
\[\text{T}_{x}\mathbb{S}^{2}=\{v\in\mathbb{R}^{3}\colon x^{\top}v=0\},\]  
and the normal space \(\text{N}_{x}\mathbb{S}^{2}\) is the set of vectors parallel to \(x\):  
\[\text{N}_{x}\mathbb{S}^{2}=\{w\in\mathbb{R}^{3}\colon w^{\top}v=0\text{ for all }v\in\text{T}_{x}\mathbb{S}^{2}\}.\]  
Define the orthogonal and parallel projections \(\Pi_{x}^{\perp}\colon\mathbb{R}^{3}\to\text{T}_{x}\mathbb{S}^{2}\) and \(\Pi_{x}^{\parallel}\colon\mathbb{R}^{3}\to\text{N}_{x}\mathbb{S}^{2}\) by  
\[\Pi_{x}^{\perp}=I_{3}-xx^{\top}=-S^{2}(x),\qquad\Pi_{x}^{\parallel}=xx^{\top}. \tag{1}\]  
Then, any vector \(v\in\mathbb{R}^{3}\) can be written as the sum \(v=\Pi_{x}^{\perp}v+\Pi_{x}^{\parallel}v\).
## 5 Open Research Issues and Challenges
### Security and Privacy  
Security and privacy issues are major concerns in every field. CRBANs enable the collection of critical data, and medical personnel use these to provide better services. However, the devices used present unique security and privacy challenges. Highly secured authentication and encryption mechanisms must be applied to prevent eavesdropping and other intrusions. Addressing security in these systems involves numerous difficulties. In CR networks, a node must carry out cognitive actions (such as learning, sensing and adapting) in its operating environment, which can be manipulated by attackers. CRBANs inherit most of the well-known security challenges from wireless sensor networks (WSNs). However, the typical characteristics of CRBANs, such as severe resource constraints and harsh environmental conditions, pose additional challenges for security and privacy support [32].
# An Improved Lax-Wendroff Scheme for Two-Dimensional Transient Thermal Simulation
## 2 Methods  
In this section, we present an LHOFD algorithm to numerically solve the 2D transient HCEs, and developed an IHOFD algorithm using stability analysis and an operator combination optimization strategy to improve the stability of the LHOFD algorithm. Then, based on the standard Taylor expansion and the Lagrange interpolation formula, we derive the formulas of general HOCFD coefficients and the truncation error coefficient as well as general HONFD coefficients and the truncation error coefficient of the 4 th-order spatial derivative. Finally, we derive a unified formula for general HOCFD and HONFD coefficients that can calculate the spatial derivative of any integer order.  
The two-dimensional transient thermal simulation is mainly implemented based on the two-dimensional transient HCEs. As a continuous, isotropic material, the two-dimensional transient HCEs can be rewritten as [1; 18]  
\[\frac{\partial T}{\partial t}=\alpha(\frac{\partial^{2}T}{\partial x^{2}}+ \frac{\partial^{2}T}{\partial y^{2}}) \tag{1}\]  
where \(\alpha=\
icefrac{{k}}{{\left\langle\rho c_{p}\right\rangle}}\) represents the thermal conductivity coefficient, \(\rho\) represents the density, \(c_{p}\) represents the specific heat capacity, \(k\) represents the thermal conductivity. The thermal parameters of the material are constant, including its density, specific heat ca pacity, and thermal conductivity. \(T=T(x,y,t)\) represents a two-dimensional transient temperature field.  
Equation (1), a continuous heat conduction problem, is transformed into a discrete time-evolving heat conduction problem by the computational domain meshing. In this paper, the explicit HOFD method discretizes the HCEs by the HOFD operators in the time-space domain.  
Based on the idea of the reference [5], this paper presents a Lax-Wendroff high-order finite difference (LHOFD) algorithm with the second-order temporal accuracy and 2\(M\)-order spatial accuracy to solve the numerical solution of the two-dimensional HCEs in Equation (1). An improved LHOFD (IHOFD) algorithm was developed, which significantly improved the stability of the LHOFD algorithm.