esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Problem Formulation
### Scenario Description  
We concentrate on the issue of multi-UAV cooperative searching and tracking for moving targets, as shown in Figure 1. The number and location of targets is unknown and the UAVs must therefore adopt a certain strategy to optimize the search and track process. To simplify the scenario and focus on the main problem, several assumptions are presented as follows:  
* We assume that multiple homogeneous fixed-wing UAVs are used to search multiple unknown moving targets. UAVs' flight is confined to a two-dimensional plane at a specific altitude and UAVs prevent collisions by stratifying the altitude. Targets wander randomly within the mission region;
* As shown in Figure 1, UAVs utilize the sensor to detect the targets below them. When a target falls in detection range of the sensor, UAVs can detect the target. But UAVs cannot distinguish the identities or indices of targets;
* Each UAV in our model possesses an adequate amount of memory and processing capacity. And UAVs cooperate with each other in a distributed framework. Each UAV can share information with neighboring UAVs in communication range and decides independently with its own cognitive information.
# FPGA Implementation of a Deep Learning Acceleration Core Architecture for Image Target Detection
## 3 Results and Discussion
### Deep Learning Acceleration Core Implementation and Verification  
This paper uses HLS to implement the calculation scheduling and data access core and Verilog HDL to implement the DSP computation matrix across the clock domain. The data loading kernel loads data from the DDR through the AXI bus and stores it in the local cache through the BRAM bus. The computation scheduler kernel interacts with the four BRAM local stores through the BRAM bus and sends the output data to the output cache through the AXI bus. The data storage kernel reads the data from the output cache and stores it in the DDR through the AXI bus. The PS section sends commands to each sub-module through the AXI4-LITE bus to control its operating status and inform the data storage address. The overall resource consumption of the deep learning acceleration core is shown in Table 3.  
Regarding lookup tables, the computation scheduling kernel consumes a large amount of data, mainly because its function is to remap the data addresses, output the data to the computation matrix, and compute the accumulation of the convolutional layers. The concurrency of the convolutional accumulation is the largest, requiring the sum of 256 fixed points in a pipeline, which consumes many lookup tables. On the one hand, the computation scheduling kernel needs to store many intermediate results. On the other hand, the cross-clock domain processing of the DSP computation matrix needs to use different clock domain triggers to avoid timing problems, which require many trigger cache data. In addition, the DSP consumption of this part is fixed and will not increase due to the parallelism of data computation. In addition to the intermediate data storage, the burst and outstanding modes of the AXI-MM bus require on-chip storage space to cache the data to be sent or received on the bus, which consumes some of the BRAM. In general, the overall resource consumption of the acceleration core architecture is less than 25% of the ZU15 EG platform used. In addition to the deep learning acceleration core, the hardware platform also requires a video input and output path. With the addition of these external modules, the overall resource consumption of the hardware platform is shown in Figure 17.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline  & **LUT** & **FF** & **DSP** & **BRAM** \\ \hline Computational scheduling kernel & 4739 & 33316 & 54 & 69.5 \\ Data Storage kernel & 5563 & 5868 & 21 & 8 \\ Data Loading kernel & 2619 & 3428 & 6 & 7.5 \\ Internal Storage & 732 & 100 & 8 & 0 \\ DSP Calculation Matrix & 4994 & 31139 & 128 & 0 \\ Acceleration core overall & 61634 & 73761 & 209 & 165 \\ Overall Occupancy & 18.0\% & 10.8\% & 5.9\% & 22.1\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Deep learning acceleration core overall resource consumption.
## 2 Materials and Methods
### Experiment Design and Configuration
#### 2.6.3 Evaluation Metrics  
The mean absolute error (MAE) defined in Equation (4) was used in our experiment to compare the model performance.  
\begin{table}
\begin{tabular}{c c c} \hline
**Backbone** & **Learning Rate** & **Batch Size** \\ \hline AlexNet & \(10^{-5}\) & 32 \\ VGG-16 & \(10^{-5}\) & 32 \\ ResNet-18 & \(10^{-3}\) & 32 \\ SqueezeNet & \(10^{-5}\) & 32 \\ GoogLeNet & \(10^{-3}\) & 32 \\ \hline \end{tabular}
\end{table}
Table 1: Hyperparameters of the developed models.  
\[\text{MAE}(\mathbf{y},\ \mathbf{x})=\sum_{i=1}^{n}\lvert y_{i}-\text{f}(x_{i})\rvert \tag{4}\]  
Figure 8: (**a**) Distribution of the original and unseen dataset; (**b**) distribution of the mixed datasets for training and testing the developed models.  
where \(n\) is the number of test samples, \(y_{i}\) is the expected value for the \(i\)th sample, \(x_{i}\) is the \(i\)th sample image in the given list of images (\(\mathbf{x}\)), and \(f(x_{i})\) is the output of a model for \(x_{i}\). In addition to reporting the overall MAE estimated based on all test samples, we report the mean absolute error over bin (MAEB), which is defined in Equation (5), in order to examine the model's predictive power across different HV ranges.  
\[\text{MAEB}(\mathbf{y},\ \mathbf{x})=\sum_{i=1}^{b}MAE(\mathbf{y}_{y},\mathbf{x}_{ b}) \tag{5}\]  
In Equation (5), \(\mathbf{y}_{b}\) refers to the expected HVs for all the samples that fall into bin \(b\) and \(\mathbf{x}_{b}\) refers to the corresponding list of images. Because the distribution of HVs in our dataset is a normal distribution, MAEB should be a better indicator of the model's predictive power than MAE.
## 5 Discussion
This latency, although tolerable in the current task pipeline, may hinder responsiveness in more time-critical scenarios such as dynamic obstacle avoidance, real-time multi-robot coordination, or high-speed harvesting in dense foliage. Third, the task scheduling mechanism is based on single-agent execution and does not yet account for distributed coordination or competition for shared resources, which could become critical in larger or multi-robot setups. These limitations highlight avenues for future improvement. A major priority is the integration of reinforcement learning techniques to enable dynamic policy adaptation across environments. Likewise, a hierarchical scheduling engine incorporating resource constraints and multi-robot coordination would expand the applicability to commercial-scale deployments. From an architectural standpoint, efforts are underway to reduce latency through edge computation and to optimize model synchronization rates based on task criticality. In summary, DigiHorticRobot contributes a significant advance in the design of agricultural digital twins by demonstrating a real-world implementation that closes the loop between digital planning and robotic execution. Its architecture supports semantic interoperability, predictive reasoning, and autonomous operation in structured greenhouse environments. Comparative analysis confirms that the system addresses core limitations identified in recent surveys and prototyping efforts. While challenges remain, the results presented lay a strong foundation for extending the approach to other crop types, operational contexts, and decision frameworks, thus advancing the vision of truly intelligent and sustainable horticultural automation.
## 3 Potential Security Implications of IoT in 6G Environment
### Network- or Device-Level Vulnerabilities
#### 3.1.2 Weak Device Authentication and Authorization  
IoT devices often rely on weak authentication mechanisms or shared credentials, making them susceptible to unauthorized access and impersonation attacks [5]. The expected increase in heterogeneous devices and autonomous systems in 6G will require the implementation of new, scalable authentication methods. Legacy devices often employ weak or compromised authentication protocols that can be easily exploited, which poses ongoing security risks [45].
## 3 Implementation  
In practice, the load data set is divided into training samples and testing samples for validation. To accomplish the PLF task, an integration scheme composed of the methods in Section 2 is proposed. The flowchart of the integration scheme is shown in Figure 6. As is seen in the flowchart, training samples are broken down into components by the method mentioned in Figure 2, and testing samples are extracted with the same indexes in the time domain and wavelet domain. Then, the Gaussian method in Equation (6) is employed to obtain the Gaussian distribution \(\mathrm{N}\sim\left(\mu_{r},\sigma_{r}^{2}\right)\) from the homogeneous components decomposed by the wavelet families, where \(\mu_{r}\) is treated as \(\hat{r}\) in IWELM model and \({\sigma_{r}}^{2}\) is the variance of Gaussian noise. An example for this Gaussian process can be seen in Figure 7.  
Figure 6: Flowchart of the proposed integration scheme.  
Figure 7: The product and convolution of Gaussian probability density functions (PDFs) on an exact load value.  
In the next step, all features \(\mathbf{F}\) decomposed by the involved wavelet family are gathered as the input of the IWELM model. Then, the model is trained with Algorithm 1 on training samples \((\mathbf{F},\mathbf{\dot{r}})\). \({\sigma_{\zeta}}^{2}\) is the variance of errors between \(\mathbf{\dot{r}}\) and training output, which is also deemed as the model uncertainty.  
Finally, based on the testing samples, the trained IWELM model is exploited to obtain the forecasting output \(\mathbf{o}\). The total uncertainty \({\sigma_{\zeta}}^{2}\) is the convolution of the \({\sigma_{r}}^{2}\) and \({\sigma_{\zeta}}^{2}\), and the forecasting output of the whole scheme is \(\mathbb{N}\sim\left({o,{\sigma_{\zeta}}^{2}}\right)\).
# A Novel Deep Learning Network Model for Extracting Lake Water Bodies from Remote Sensing Images
###### Abstract  
Extraction of lake water bodies from remote sensing images provides reliable data support for water resource management, environmental protection, natural disaster early warning, and scientific research, and helps to promote sustainable development, protect the ecological environment and human health. With reference to the classical encoding-decoding semantic segmentation network, we propose the network model R50A3-LWBENet for lake water body extraction from remote sensing images based on ResNet50 and three attention mechanisms. R50A3-LWBENet model uses ResNet50 for feature extraction, also known as encoding, and squeeze and excitation (SE) block is added to the residual module, which highlights the deeper features of the water body part of the feature map during the down-sampling process, and also takes into account the importance of the feature map channels, which can better capture the multiscale relationship between pixels. After the feature extraction is completed, the convolutional block attention module (CBAM) is added to give the model a global adaptive perception capability and pay more attention to the water body part of the image. The feature map is up-sampled using bilinear interpolation, and the features at different levels are fused, a process also known as decoding, to finalize the extraction of the lake water body. Compared with U-Net, AU-Net, RU-Net, ARU-Net, SER34 AUNet, and MU-Net, the R50A3-LWBENet model has the fastest convergence speed and the highest _MloU_ accuracy with a value of 97.6%, which is able to better combine global and local information, refine the edge contours of the lake's water body, and have stronger feature extraction capability and segmentation performance.  
remote sensing; water extraction; lake; semantic segmentation; attention mechanism; ResNet 2024, 14, 1344. [[https://doi.org/10.3390/app14041344](https://doi.org/10.3390/app14041344)]([https://doi.org/10.3390/app14041344](https://doi.org/10.3390/app14041344))
# Evaluation of Unfilled Sheath in Concrete Structures Using Response Waveform in Time Domain
## 3 Experiment Conditions
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Line\_No.**} & \multirow{2}{*}{
\begin{tabular}{c} X (mm) \\ \end{tabular} } & \multicolumn{5}{c}{Input Point\_No, (IP\_No)} \\ \cline{3-6}  & & **400** & **500** & **600** & **700** & **800** \\ \hline Line\_01 & 350 & IP\_01 & IP\_02 & IP\_03 & IP\_04 & IP\_05 \\ \hline Line\_02 & 400 & IP\_06 & IP\_07 & IP\_08 & IP\_09 & IP\_10 \\ \hline Line\_03 & 450 & IP\_11 & IP\_12 & IP\_13 & IP\_14 & IP\_15 \\ \hline Line\_04 & 500 & IP\_16 & IP\_17 & IP\_18 & IP\_19 & IP\_20 \\ \hline Line\_05 & 550 & IP\_21 & IP\_22 & IP\_23 & IP\_24 & IP\_25 \\ \hline Line\_06 & 600 & IP\_26 & IP\_27 & IP\_28 & IP\_29 & IP\_30 \\ \hline Line\_07 & 650 & IP\_31 & IP\_32 & IP\_33 & IP\_34 & IP\_35 \\ \hline Line\_08 & 700 & IP\_36 & IP\_37 & IP\_38 & IP\_39 & IP\_40 \\ \hline Line\_09 & 750 & IP\_41 & IP\_42 & IP\_43 & IP\_44 & IP\_45 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Location of the input points. Figure 12: Example of response waveform after normalization (Case_1: \(\varphi_{\text{S}}\) = 63 mm). In Specimens_1 and 2, the same measuring point is set on the backside. This changes the sheath from the measurement surface. The depth is as shown in Figure 8(b). Comparing these results, the influence of the depth of the sheath on the difference value is investigated. Table 4 summarizes the sheath size and depth for all measurements. DVA measurement conditions are determined on the basis of thickness measurement by IEW. Therefore, it is possible to evaluate by using DVA with the same measurement waves for IEW. Thus, in this study, the results of DVA and IEW are compared. Since IEW estimates the target thickness based on the dominant frequency of multiple reflections of elastic waves between the measurement and reflection surfaces, the measurement conditions should be set appropriately so that the multiple reflections are sufficiently formed. Table 5 shows the measurement conditions in this experiment. The input frequency is calculated from the contact time of the steel balls used in the experiment as in the following Equation:  
\[F_{inp}=\frac{1}{T_{c}}, \tag{11}\]  
where \(F_{inp}\) is the input frequency and \(T_{c}\) is the contact time, respectively. In addition, since 10% of the number of measurement data are used for pre-trigger, the number of valid data after normalization is about 3600. The number of calculated data for DVA can be determined arbitrarily from the number of measured data. To calculate the difference value, the number of data have to cover the time in which the reflected wave returns to the measurement point. In this study, we set the measurement conditions to satisfy the conditions. On the other hand, it should be carefully considered to make the number of data large because the influence of internal defects at a long distance is taken into account consequently. Therefore, the number of data is changed while the calculation of the difference value and its influence is examined as well.
# Sep-RefineNet: A Deinterleaving Method for Radar Signals Based on Semantic Segmentation
## 4 Experimental Results and Discussion
### Experimental Results
#### 4.2.1 Verify the Effectiveness of Sep-RefineNet
This leads to the fact that fewer effective elements on the diagonal in FCM and most of the upper triangular area is background elements. The FL allows the model to pay more attention to a small number of indistinguishable samples during the training, rather than updating the loss function indiscriminately. To verify the influence of different orders of self-correlation difference on the proposed algorithm when encoding FCM, the mean pixel accuracy and consumed time for test data of 5 th order, 10 th order, 15 th order, and 20 th order self-correlation difference were analyzed and compared in Table 4. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Difference Order** & **5 th** & **10 th** & **15 th** & **20 th** \\ \hline Mean pixel accuracy (\%) & 96.51 & 97.60 & 97.66 & 97.14 \\ Spend time (ms) & 9.33 & 11.52 & 17.86 & 29.50 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Results of self-correlation difference of orders on deinterleaving. Figure 11: Comparison results of the improved Sep-residual unit and focal loss that respectively replace the original residual unit and cross entropy. The red solid line is the result of SRU and FL. Blue dotted line, black dotted line, and green dotted line are the results of ORU and FL, SRU and CE, and ORU and CE, respectively. With the increase in the difference order in the FCM, the mean pixel accuracy is slightly improved, but the higher order will lead to a more serious harmonic influence. It leads to a decrease in deinterleaving accuracy. When reaching the 20 th order, the mean pixel accuracy of the prediction results will decrease, and the mean pixel accuracy of the 15 th order is 0.06% higher than that of the 10 th order, but the encoding processing time of the data has increased by 55.03%. Considering that the high order will bring serious harmonics, and due to the existence of the symmetrical structure, too high order is not conducive to the decoding process. In summary, the 10 th order difference is regarded as the parameter of FCM in the experiment. To verify the effectiveness of our proposed semantic segmentation network for FCM, the Sep-RefineNet is compared with U-Net [28], Deeplab-V2 [29], RefineNet [25] and PSPNet [30]. The same data are used for training and testing. It can be seen from Table 5 that the Sep-RefineNet has a higher mean pixel accuracy, which means that Sep-RefineNet has a better fitting ability for the FCM we built. Through multiple channels and multiple receptive fields, semantic features such as the texture and structure of FCM can be better segmented during convolution. However, the U-Net's network structure and the feature map fusion are relatively simple, and the large-scale hole convolution and pyramid hole pooling proposed in Deeplab-v2 and PSPNet for large target and background segmentation are not effective for FCM in this paper.
## 4 New Virtualization Solutions for Network Functions
### A Speed Focused Virtual Machine Manager: Firecracker  
Firecracker is an open-source, minimalist hypervisor written in Rust. It makes use of the Linux kernel-based virtual machine (KVM) to create and manage microVMs. Similar to Unikernels, Firecracker is built with minimal device emulation, which enables a faster startup time (150 microVMs per s [40]) and a reduced memory footprint for each microVM.  
In order to configure a microVM, users can interact with Firecracker through the application programming interface (API) thread, as shown in orange in Figure 7. A Firecracker instance runs in the user space and it is isolated by a jailer, consisting of a secure computing mode (seccomp), control groups (cgroups) and namespace policies. It also exposes a serial console and a reset controller to stop microVMs. Clients can use the rate limiter, via an API, to control network and storage resources and to configure bursts or specific bandwidth/operational limitations [40]. Host data are exposed to microVMs through file block devices. It is important to consider that Firecracker's minimalist approach leads to some limitations, such as the lack of filesystem sharing with the host, device hot-plug support and dynamic resizing of resources [41], which, however, should not be critical issues in the dynamic mMTC environments.  
Figure 6: Kata container nests a pod, encapsulating containers’ processes and namespaces, in a microVM for enhanced isolation.  
Figure 7: Firecracker enables the deployment of lightweight microVMs through a minimal virtual machine monitor.
# Enhancing Route Guidance with Integrated V2X Communication and Transportation Systems: A Review
## 3 Route Guidance Models
### Dynamic Routing Guidance
Yet, adjusting the step size would be of significant relevance and is expected to increase the computational efficiency and the quality of the model's results. Moreover, other studies have considered SO-DTA from an origin-based point of view [45; 51; 72; 73]. [PERSON] and Congduan [74] proposed an intelligent dynamic Route Guidance System (RGS) to tackle urban traffic congestion. Using reinforcement learning, the system calculates road weights based on real-time traffic predictions and optimizes Road Agents (RAs) through interaction with the traffic environment. It achieves significant reductions in average travel time, up to 27.2% in Map1 and 16.3% in Map2, and outperforms the DWkSP method in alleviating congestion. By combining DRL algorithms with route guidance, the system provides adaptive road condition predictions and excels in complex traffic scenarios. In this section, Dynamic Route Guidance, we explore strategies for providing real-time routing solutions by leveraging current traffic data and predictive algorithms. These methods aim to optimize traffic flow, reduce congestion, and enhance travel efficiency by dynamically updating routes based on changing traffic conditions where various approaches are discussed, highlighting their potential to address the nonlinear complexities of urban traffic systems. Building on this, the next section, Multi-Class Route Guidance, discusses approaches that account for diverse vehicle types and user behaviors, ensuring more tailored and effective route guidance solutions in heterogeneous traffic environments.
# A Review on Rainfall Measurement Based on Commercial Microwave Links in Wireless Cellular Networks
## 4 Signal Processing from RSL to Rainfall Map
### WAA Compensation  
When rainfall occurs, the radome will be covered with a layer of water film to absorb and scatter the signal, which is called wet antenna attenuation [62]. After the rain stops, the water droplets covering the antenna slowly evaporate over time, and so the WAA value gradually decreases until water droplets disappear. If WAA is not taken into account in the CML-based rainfall retrieval, the rain rate will be overestimated. WAA is related to radome material characteristics, temperature, and rain intensity, and WAA in the short link scenario is more significant. [PERSON] et al. [63] used eight short CMLs at 38 GHz frequency band to quantify WAA, and the experimental results confirm that WAA is closely related to rainfall intensity, and the WAA value is 1.5-2.0 dB in light rain (R \(<\) 2 mmh\({}^{-1}\)), 2.8-5.3 dB in rainstorm, and 6-9 dB during heavy rainstorm (R \(\approx\) 70-130 mmh\({}^{-1}\)). [PERSON] et al. [64] investigated the performance of six WAA models and the transportability of WAA model parameters among CMLs, which have different features. The results show that the WAA model derived from the rainfall intensity is better than constant WAA model and the time-dependent WAA model. Moreover, the rainfall-intensity-based WAA model does not depend on the frequency and link length, and so it can be reused by other CMLs, where the antenna characteristics are similar.