esa-sceva/satcom-chunk-collection huggingface dataset:
# Peak Age of Information Optimization in Cell-Free Massive Random Access Networks
## 1 Introduction
Subsequently, a global Particle Swarm Optimization (PSO) algorithm is proposed to dynamically adjust channel access probabilities for minimizing network-wide PAoI. However, since the global PSO algorithm requires full topology information and incurs high signaling overhead during iterative updates, we further design a local PSO algorithm that utilizes only neighboring AP topology information. Simulations demonstrate that the global PSO algorithm approaches theoretical optimality, while the local PSO algorithm maintains comparable performance without requiring global information, making it particularly suitable for large-scale access scenarios with wide-area coverage. The remainder of this paper is organized as follows: Section 2 establishes the system model for cell-free massive random access networks, including the logical grouping architecture and grant-free transmission mechanism. Section 3 derives the closed-form expressions of successful transmission probability and PAoI for both single-AP and multi-AP scenarios. Building on these analytical models, Section 4 proposes two particle swarm optimization algorithms: a global PSO requiring full topology information and a local PSO utilizing neighbor AP information, both designed to minimize network-wide PAoI by dynamically adjusting channel access probabilities. Section 5 validates the theoretical analysis through comprehensive simulations and demonstrates the effectiveness of the proposed algorithms. Finally, Section 6 concludes the paper.
# An In-Depth Analysis of Domain Adaptation in Computer and Robotic Vision
## 4 Conclusions
### Contributions and Significance of Cross-Domain Analysis  
This study's cross-domain analysis offers significant new knowledge around domain adaptation in computer and robotic vision. Our research shows the efficacy and usefulness of domain adaptation approaches by methodically assessing a variety of methodologies, models, and real-world implementations. Through a meticulous examination of performance metrics, we acquire a comprehensive understanding of the strengths and weaknesses inherent in various strategies. This comprehensive insight equips both researchers and practitioners with the information required to make informed and prudent choices.  
This research also shows how domain adaptation has a transformational effect on improving the adaptability, precision, and dependability of vision systems in real-world settings. Applications of domain adaptation in areas including agriculture, industrial automation, medical imaging, and autonomous driving demonstrate how it propels innovation and development across industries. Cross-domain analysis is significant because it can stimulate more study and advancement in domain adaptation. The problems and insights that have been discovered offer fresh avenues for investigation and chances to improve current methods. The research results may also be used to help create domain adaptation models that are more successful and efficient, advancing real-world applications and enhancing user experience.
# SAR Target Incremental Recognition Based on Hybrid Loss Function and Class-Bias Correction
## 4 Experiments and Discussions
### Experiment Setting  
The ResNet-18 [38] is used as the base network for experiments. Each old class is saved with \(k\) = 20 samples in a representative memory of old sample preservation. The procedure is implemented based on the deep learning framework PyTorch [35]. The training contains 120 Epochs, and the initial learning rate is set to 0.01. After the 40 th and 80 th Epochs, the learning rate is reduced to 0.001 and 0.0001, respectively. The model is trained by stochastic gradient descent throughout the incremental learning phase.  
The MSTAR dataset consists of 10 target classes, and the image size is 128 \(\times\) 128 pixels. The number of data for each training batch is set to 64. After random flipping and cropping, the dataset is used as input of the model. No additional data preprocessing is performed beyond that. The number of new classes for each experiment is set to be 1, 2, and 5 training classes, so 10, 5, and 2 incremental training phases will be performed, respectively. In each different incremental training phase, the 10 classes are arranged in a fixed random order. Experimentally all methods are trained in class increments on the available dataset at each phase. The generated models are evaluated based on test dataset consisting of all old and new classes. This paper follows a protocol for assessing incremental learning [8; 39], and the results of the experiments are reported as classification accuracy curves for each incremental batch. In addition, this paper creates three different random classification orders to run three experiments, and reports the average incremental accuracy and standard deviation.
## 2 Experiments and Methodology
### Variance-Covariance Fitting Model  
This study selects four functional models to fit the variance-covariance of the GNSS time series to investigate the stochastic effects of dispersion between the baselines. The variance-covariance fitting models are as follows:  
Gauss function:  
\[C(d)=C(0)e^{(-k^{2}d^{2})} \tag{11}\]  
Hirvonen covariance function:  
\[C(d)=\frac{C(0)}{1+k^{2}d^{2}} \tag{12}\]  
Exponential function:  
\[C(d)=C(0)e^{(-kd)} \tag{13}\]  
Polynomial function:  
\[C(d)=C(0)+k_{1}d+k_{2}d^{2}+k_{3}d^{3}+\cdots+k_{n}d^{n} \tag{14}\]  
where \(C(0)\) denotes the covariance between two signals with zero distance, \(k_{i}\) is a parameter to be determined and \(d^{i}\) is the length of the baseline between two points.  
The variance-covariance obtained by fitting the above equations is clearly based on the assumption of homogeneous and isotropic random field, in which the correlation between the two points is only related to the distance.
## 1 Introduction
It can not only be used in a single complex signal system but can also be used for signal estimation in multi-sinusoid communication systems when introducing the approach proposed in [27]. The rest of the paper is organized as follows. In Section 2, the deduction of the model expression is presented, and the estimation process is also described. In Section 3, a general MSE analysis of the new estimator is performed. The developed parameter design method is discussed in Section 4. In Section 5, a comparison between the new estimator and existing estimators is given by simulation. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Estimators** & **Merits/Demerits** & **Limits of Applicability** \\ \hline IpDFT with a cosine window [20] & Reduce the influence of spectral leakage by introducing a cosine window/increase the computation & Linear computation based on all DFT samples \\ \hline IpDTFT with a cosine window [21] & Reduce the influence of spectral leakage by introducing a cosine window/increase the computation, extra two DTFT computations & Almost two times CRLB \\ \hline e-FLLs/FLLs (frequency-domain linear least-squares) [22; 23] & Robust to harmonics/matrix computation for window, matrix inversion for linear least-squares & Amplitude and phase estimation \\ \hline QSE/HAQSE (hybrid A\&M and q-shift estimator) [24] & DFT-based, easily realizable, within \(\pm 0.003\) dB CRLB/at least extra four DTFT computations and several complex computations for the estimate & Edge effect \\ \hline Aboutanios and Mulgrew (A\&M)[25; 26] & DFT-based, easily realizable, 1.0147 times the asymptotic CRLB/extra four DTFT computations and several complex computations for the estimate & Edge effect \\ \hline Parabolic interpolation [27] & DFT-based, easily realizable, within \(\pm 0.526\) dB CRLB/extra three DTFT computations and several complex computations for the estimate & Limited accuracy \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Analysis of key estimators.
# The Clinical and Economic Value of Triclosan-Coated Surgical Sutures in Abdominal Surgery
## Materials and Methods
### Budget Impact Model
#### 2.2.1 Model Inputs  
The model included the following inputs: baseline SSI rate, SSI reduction, the additional cost of TCS over CS, the number of sutures used per procedure, and the cost of SSI. The baseline SSI rate was taken from an Italian trial that reported the SSI rate in 281 patients undergoing colorectal resection as equal to 10.6% [23]. SSI reduction, attributable to implementing TCS into clinical practice, was selected from the reviewed meta-analysis as previously described. The differential cost of sutures was assumed to be an additional \(\ell\)1 for TCS compared with CS. According to expert opinion, six absorbable sutures are required per abdominal procedure, with 600 sutures used when assuming that 100 procedures are performed annually. The cost of a single SSI episode was taken from an Italian study, published in 2000, that evaluated the hospital resources that are consumed to treat postoperative infections among patients with gastrointestinal cancer who underwent abdominal procedures [32]. The cost that was identified for each wound infection was inflated to 2019 costs according to the Italian Institute of Statistics Consumer Price Index of 1.296. An SSI cost--specific for an abdominal surgery and referred to the Italian environment--of \(\ell\)4,838 was therefore considered in this economic analysis. The cost breakdown was attributed to additional resource use (14%) and prolonged length of stay (LOS) (86%), as previously described [32].
# An Efficient Trajectory Planning Method for High-Speed Interception of Invasive Drones
## 3 Air Resistance Approximation and Motion Primitive Generation
### Motion Primitive Generation
#### 3.2.2 Cost Function  
The goal of the motion primitive in this article is to calculate a quadratic differentiable trajectory that guides a quadcopter from its initial state to a fully or partially defined terminal state in time \(T\), while minimizing the cost function:  
\[J_{\Sigma}=\frac{1}{T}{\int_{0}^{T}\|\mathbf{j}(t)\|^{2}dt} \tag{22}\]  
Rewriting (21), this cost function can be interpreted as the upper bound of the average value of the product of the inputs of the quadcopter system:  
\[f_{t}^{2}\|\mathbf{\omega}\|^{2}=\|\begin{bmatrix}\omega_{2}\\ -\omega_{1}\\ 0\end{bmatrix}\|^{2}=\|\begin{bmatrix}1&0&0\\ 0&1&0\\ 0&0&0\end{bmatrix}\mathbf{R}^{-1}\mathbf{j}\|\ \leq\|\mathbf{j}\|^{2} \tag{23}\]  
Thereby  
\[\frac{1}{T}{\int_{0}^{T}f_{t}^{2}(t)\|\mathbf{\omega}(t)\|^{2}dt}\leq J_{\Sigma}. \tag{24}\]  
This cost function has high computational efficiency and can be used to rank the input aggressiveness of multiple candidate feasible motion primitives, thereby filtering out trajectories that can achieve terminal translation constraint \(\sigma(T)\) with more reasonable thrust and body rate inputs.
# Node Deployment of Marine Monitoring Networks: A Multiobjective Optimization Scheme
## 1 Introduction
In the HMMN-EC, the underwater acoustic subnetwork consists of a number of battery-powered sensors with limited energy [13]--Autonomous Underwater Vehicles (AUVs) [14] and buoys [15] with acoustic receiving devices and RF transmitting devices; the sea-surface wireless subnetwork consists of unmanned ships which carry RF communication devices; and the air wireless subnetwork [16; 17] consists of Aerial Relay Nodes (ARNs), such as Unmanned Air Vehicles (UAVs) with RF communication equipment. Under the proposed network architecture, the node deployment problem is further investigated to achieve the minimum network cost while ensuring the maximum network lifetime. Specifically, a Multiobjective Optimization (MO) problem is formulated to minimize the costs and maximize the network lifetime by deploying different types of nodes in differenttiers of the network, considering the energy and capacity constraints of each node. The formulated optimization problem can be solved by Gorubi. As Gurobi does not work well when the network is scaled up, we propose a swarm-intelligent-based optimization approach to find the near-optimal solution of the formulated optimization problem. The main contributions are summarized as follows. (1) A novel integrated multitier hierarchical network architecture of MMN with support of edge computing is proposed, namely, HMMN-EC which integrates the UAN, the sea-surface wireless network with edge computing, and the air wireless network. (2) Based on the hierarchical network architecture, a multiobjective optimization framework is formulated to minimize the network deployment cost while maximizing the network lifetime by determining the deployment locations of network nodes, including ARNs, Edge Computing Nodes (ECNs), Sea-Surface Nodes (SSNs), and Underwater Relay Nodes (URNs), and the data transmission links between network nodes, subject to various constraints of the network topology, network connectivity, and the battery capacity. (3) An Ant-Colony-based Efficient Topology Optimization (AC-ETO) algorithm is presented to solve the formulated MO problem in various network scenarios of different numbers of nodes. (4) Extensive simulations are conducted to validate the performance of the proposed algorithm. The results show that the proposed algorithm approaches the optimal solution and outperforms some existing solutions. The rest of this paper is organized as follows. In Section 2, we review the related works about the deployment of MMN. Section 3 describes the network model and presents the problem formulation. In Section 4, an AC-ETO algorithm is proposed. Section 5 shows the numerical analysis, followed by concluding remarks in Section 6.
# A Review on the Prospects of Mobile Manipulators for Smart Maintenance of Railway Track
## 4 Robotic Maintenance Technology in Other Industries
### Mobile Manipulators for Inspection and Repair Tasks
#### 4.2.6 Other Applications of Mobile Manipulators  
Mobile manipulation is an issue of particular importance. Industry 4.0 uses modern technologies such as automation, digitalization, cyber-physical systems, and artificial intelligence to increase efficiency and productivity, while Industry 5.0 is planning to push the limit of modern technologies to create a human-centric industrial model with increased human-robot collaboration to increase resilience and sustainability. With dexterity and mobility features, mobile manipulators can help industries to achieve future demands by introducing Industry 5.0. Conceptual designs of mobile manipulators to assist helping humans in the industry surfaced in the research community in early 2000 [124; 125]. Various companies offer commercially available mobile manipulator platforms that can be reprogrammed for various use cases [126; 127]. As their versatility, accuracy, and environment perception improve alongside collaboration capacity, the areas of application are also expanding. Table 3 illustrates some of the diverse applications of mobile manipulators.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Application** & **Base Robot** & **Sensing Method** & **Key Technology and Contribution** \\ \hline
5G tower maintenance [128] & Aerial & Camera & Tower inspection and repair, a study of manipulator stability with wind disturbance \\ \hline Aerial drilling and screwing [129] & Aerial & Camera & An adaptive robust control system, real-time object detection \\ \hline Outdoor maintenance in industry [130] & Aerial & LiDAR, stereo camera, GNSS & Multidirectional thrust controller \\ \hline Cooperative payload delivery [131] & Aerial & RGB-D camera & Heavy load control, learning-based planning for aerial cooperation \\ \hline Lifting bars for assembly [132] & Aerial & External motion capture system & Multidirectional thrust control flying \\ \hline Window cleaning of building [133] & Magnetic climbing & Force sensors & Elastic actuators, passive obstacle avoidance \\ \hline Archive room patrolling [134] & Ground & Infrared camera, laser & Storage basket, monitor abnormal temperature \\ \hline Assembly task of space telescope [135] & Ground & Two cameras & Stretchable manipulator, assembly in space \\ \hline Indoor sign inspection [136] & Ground & Camera & Scissor mechanism for manipulator \\ \hline Dual robot payload delivery [137] & Ground & Multiple cameras & Extended payload carrying capability \\ \hline Penstock inspection [138] & Ground & Camera, laser & Wall climbing capability \\ \hline Water distribution pipe maintenance [139] & Ground & Four cameras & Navigation inside a pipe, contamination less rehabilitation (CLR) of pipe \\ \hline \hline \end{tabular}
\end{table}
Table 3: Different areas of application for mobile manipulators.
## 6 Conclusions  
The results which were obtained with this study have been compared with the similar ones in the literature.  
* [PERSON] et al. performed passive measurements in their studies, used the MA method, and classified five different types of mines at 85.8% [37]. In our novel study, 10 different types of explosives were classified with 95% performance.
* In the study of [PERSON] et al., active measurements were made the MA method was used, and the explosives were detected and visualized [29]. In our study, passive measurements were made, explosives were detected, and real time imaging was performed.
* [PERSON] et al. used a fluxgate sensor network in their study and reported that buried bodies having ferromagnetic content could be detected by passive measurements [33]. However, the position of the sensor network affected the results negatively. In our study, independent measurements without being effected by position were made with fluxgate sensors.
* In the previous studies [29; 33; 37] the measurement results changed depending on the sensor position, which made it difficult to make real-time measurements. By the measuring circuit developed in our study, position-independent measurements can be performed.  
Studies conducted with magnetic anomaly method for detection buried objects and explosives have still been continue. There are still many unknowns for the usage of this method in real-time measurements. It is thought that the studies to be conducted with different soil types, different geographical regions, and different classification methods will determine the future in this field.  
Conceptualization, S.G. and S.D. ; system design, S.G. and M.K; methodology, S.G., M.K. and S.D.; software, S.G. and M.K.; validation, S.G. and S.D.; supervision, S.G. and M.K.; writing, original draft, S.G. and M.K.; writing, review and editing, S.G., M.K. and S.D. All authors approved the final version.  
This research received no external funding.  
This study was supported by the Balikesir University Research Fund (project number 2016/103). [PERSON] and [PERSON] would like to thank to Turkish Academy of Sciences (TUBA) for partial support.  
The authors declare no conflict of interest.  
Figure 13: Steps for creating 32 \(\times\) 2 data matrices.
## 3 Results
### Comparison with the Literature  
Table 4 presents the related literature employing Polar Map images and DL methods for diagnosing CAD or measuring the agreement with human readers.  
The results of the study were consistent with the literature. There were studies reporting a similar or higher diagnostic efficiency. This discrepancy was caused by training data variations related to scale and class distribution.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Study** & **Input** & **Reference** & **Explainability** & **Results** \\ \hline \multirow{2}{*}{[26]} & \multirow{2}{*}{Polar Maps} & \multirow{2}{*}{Human reader} & \multirow{2}{*}{✓} & **Agreement**: 0.83 \\  & & & & **Sensitivity**: 0.47 \\  & & & & **Specificity**: 0.70 \\ \hline \multirow{2}{*}{[43]} & \multirow{2}{*}{Polar maps + cardiac risk factors} & \multirow{2}{*}{ICA} & \multirow{2}{*}{} & **Accuracy**: 0.857 \\ \hline \multirow{2}{*}{[22]} & \multirow{2}{*}{Polar Maps} & \multirow{2}{*}{ICA} & \multirow{2}{*}{✓} & **Sensitivity**: DL (0.82), SSS (0.75), \\  & & & U-TPD (0.77), and S-TPD (0.73) in men \\  & & & & U-TPD (0.71), SSS (0.71), U-TPD (0.7), and S-TPD (0.65) in women \\ \hline \multirow{2}{*}{[29]} & \multirow{2}{*}{Polar Maps} & \multirow{2}{*}{ICA} & \multirow{2}{*}{-} & **Accuracy**: 0.74 \\  & & & **Sensitivity**: 0.75. \\ \cline{1-1}  & & & **Specificity**: 0.73. \\ \cline{1-1}  & & & & Similar results with the experts \\ \cline{1-1}  & & & & Semi-Quantitative Analysis \\ \hline \hline \end{tabular}
\end{table}
Table 4: Performance metrics of the literature approaches.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Network** & **Accuracy** & **Sensitivity** & **Specificity** & **F-1 Score** \\ \hline VGG16 & 0.6770 & 0.7606 & 0.6117 & 0.6736 \\ VGG19 & 0.6955 & 0.7700 & 0.6374 & 0.6891 \\ ResNet152 & 0.6605 & 0.7465 & 0.5934 & 0.6584 \\ ResNet152V2 & 0.6605 & 0.7512 & 0.5897 & 0.6598 \\ InceptionV3 & 0.6543 & 0.7793 & 0.5568 & 0.6640 \\ InceptionResNetV2 & 0.6646 & 0.7793 & 0.5751 & 0.6707 \\ MobileNet & 0.6564 & 0.7653 & 0.5714 & 0.6613 \\ MobileNetV2 & 0.6584 & 0.7606 & 0.5788 & 0.6612 \\ DenseNet169 & 0.6091 & 0.7277 & 0.5165 & 0.6200 \\ DenseNet201 & 0.6296 & 0.7418 & 0.5421 & 0.6371 \\ NASNetMobile & 0.6379 & 0.7465 & 0.5531 & 0.6437 \\ EfficientNetB6 & 0.6667 & 0.7559 & 0.5971 & 0.6653 \\ EfficientNetB7 & 0.6564 & 0.7465 & 0.5861 & 0.6557 \\ EfficientNetV2B3 & 0.6111 & 0.6714 & 0.5641 & 0.6021 \\ ConvNeXtLarge & 0.6276 & 0.6526 & 0.6081 & 0.6057 \\ ConvNeXtXLarge & 0.6214 & 0.6479 & 0.6007 & 0.6000 \\ Xception & 0.6605 & 0.7465 & 0.5934 & 0.6584 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance metrics of CNN approaches with reference to ICA findings.
# Thermal Evaluation of a Hybrid Dryer with Solar and Geothermal Energy for Agroindustry Application
## 1 Introduction
### Hybrid Dryer Sizing
#### 1.1.4 Aerothermal Exchangers  
The operation of the aerothermal system is based on the circulation of an airflow through the heat exchanger buried 70 cm from the surface of the soil, which results in an increase in the temperature of the working fluid, taking advantage of the heat capacity or the soil's ability to store heat. Subsequently, the working fluid enters the solar collector by raising the air temperature, before finally, the hot air enters the drying chamber.  
For the design of the exchanger, it is necessary to determine the air outlet temperature as well as the maximum air flow flowing through the exchanger. This parameter will depend on the magnitude and characteristics of the diameter exchange surface, length, and number of tubes [13; 14]. In Figure 1, the hybrid dryer components are shown.
## 5 Simulation Results and Discussions  
### Simulation Setup and Evaluation Metrics  
#### 5.1.1 Simulation Setup  
The server used in this experiment was a Geforce RTX 2080 Ti GPU with 11G of RAM, the programming language was Python, and the programming framework was Pytorch. The experimental results were evaluated using Scikit-learn 1.0.2. The dataset used in the experiment was USTC-TFC2016 [26], with about 120,000 training samples. Detailed simulation parameters are shown in Table 3.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Dataset & USTC-TFC2016 \\ \hline Input data dimension & (28, 28, 1) \\ \hline Hidden feature dimension & 100 \\ \hline Environment & Python 3.10.4, Scikit-learn 1.1.0, \\ Torch 1.11.0, Numpy 1.22.3 \\ \hline Device & GeForce RTX 2080 Ti \\ \hline \multirow{4}{*}{Pretraining hyperparameters} & Optimizer & Adam \\ \cline{2-3}  & Weight\_decay & 0 \\ \cline{2-3}  & Batch size & 128 \\ \cline{2-3}  & Learning rate & 0.0001 \\ \cline{2-3}  & Epoch & 30 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Simulation environment and parameters.