esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Analysis of Results
#### 3.3.1 Characterization of Changes in the Effect of Mountain Topography on Wind Speeds  
Figure 10a is a wind speed cloud map. When the inlet wind speed is set at 5 m per second, the simulated maximum wind speed reaches 7.61 m per second, with a minimum wind speed of 0.008 m per second. When the inlet wind speed is increased to 10 m per second, the maximum wind speed rises to 15.82 m per second, and the minimum wind speed is 0.03 m per second, showing an increase in both maximum and minimum wind speeds compared to the scenario with an inlet wind speed of 5 m per second. When the inlet wind speed is further increased to 15 m per second, the maximum wind speed reaches 23.94 m per second, and the minimum wind speed is 0.04 m per second. At this point, the difference between the maximum and minimum wind speeds further widens. Regardless of how the inlet wind speed varies, the wind speed on the north side of the mountain range remains relatively high, gradually decreasing from the northwest to the southwest, with higher wind speeds at the mountaintop, and lower wind speeds on the leeward side of the mountain range.  
The wind speed streamline map in Figure 10b clearly shows the wind speed backflow phenomenon. As the airflow passes over the mountain range, a low-pressure area forms on the leeward side of the mountains. It is noteworthy that the greater the initial wind speed, the more pronounced this backflow effect becomes. The backflow wind speed decreases progressively as it blows towards the northern region beyond the mountains.  
At the same time, the wind speed movement around the mountain range is highly irregular. Local airflows have formed a swirling and chaotic pattern of flow, a characteristic that is vividly illustrated in the part circled in red in Figure 10b.  
To generate the wind speed data map shown in Figure 11, we meticulously collected data points from the southeast, northwest, north, and east directions within the study area. In this map, the central area is marked as 0; negative values correspond to the west and south directions, while positive values represent the north and east directions.  
Overall, the research findings indicate that, regardless of their initial intensities, the simulated wind speeds will exhibit a consistent movement pattern when encountering the mountain range. Moreover, the magnitude of the wind speed has a negligible impact on the overall trend.  
Figure 10: Wind speed cloud map of the Mesoscale Mountains region: (**a**) wind speed cloud map: (**b**) wind speed trace.
# Cluster Analysis of Pedestrian Mobile Channels in Measurements and Simulations
## 2 Cluster Tracking in Mobile Measurements  
In contrast to channels from a conventional MIMO system, massive MIMO systems give richer channel information [12; 13; 14]. Considering this, a virtual massive MIMO pedestrian mobile measurement campaign was performed to observe cluster evolution. To characterize the evolution process of clusters, which is greatly influential on cluster number in mobile channels, the tracking algorithm was considered. All details of the measurement setup, the data extraction and processing method, and data analysis are shown in the following.
## 5 Results and Discussion
### Voxel-Based Classification Results  
The discriminating ability of the proposed ALT-CNN-DENSE _Net_ and \(DETECT\)-_LC_ pipeline is assessed using two scenarios: NSCLC carcinoma subtypes classification (ADC, SCC and NOS) and lung cancer staging (I, II, IIIA and IIIB). The focus of this study is on ADC, SCC and NOS as they comprise 80% of the diagnosed subtypes [42] and are commonly available for all the study datasets. The described classifications are produced on NSCLC radiomics, NSCLC radiomics-genomics, and NSCLC radio-genomics datasets. Each dataset is split for training and testing purposes. The percentage split per class is around 55% for training, 15% for validation and around 30% for testing.  
An ablation study is conducted to signify the contribution of ALT-CNN-DENSE _Net_ in \(DETECT\)-_LC_ pipeline to the final classification output. Hence, the output of \(DETECT\)-_LC_ full pipeline is compared against our proposed radiomics preparatory stage (RPS) and off the shelf 3D ResNet-50 and Inception V3. The performance of ALT-CNN-DENSE _Net_ is compared with RESNET-50 and Inception V3 due the inherent similarities between them. An additional experiment for NSCLC-Radiomics dataset is conducted, where a set of slices are statically selected with predefined indices. The slices are selected at the middle of the CT series depending on the size of the series. This approach is used instead of the proposed radiomics unsupervised approach to determine the value of the preparatory stage.  
Figure 11: Elbow Method results using Distortion (on the **left**) and Inertia (on the **right**).  
Figure 10: Clustering Quality Evaluation using two quality evaluation measures (**a,b**). Clusters produced by modified kmeans variant, Agglomerative, Spectral and BIRCH clustering with varying number of clusters.  
The results of this experiment are reported as Static Selection (SS) + ALT-CNN-DENSE _Net_. NSCLC-Radiomics dataset is chosen for this experiment as it comprises the largest number of patients volumes and slices.
# Microcontroller Unit-Based Wireless Sensor Network Nodes: A Review
## 3 MCU-Based Hardware Platforms for Wireless Sensor Nodes
### Smart Cities and Automotive Applications
#### 3.3.2 Wireless Sensor Network Hardware for Automotive Applications  
The WSN node proposed by [31] was developed for intelligent automotive applications targeting public transportation. It focuses on monitoring mechanical and environmental stresses experienced by a travelling bus and its passengers. The node was developed around a Raspberry Pi 3 Bi+, which is equipped with a high-speed (1.4 GHz) processor (64-bit Cortex-A53) containing 1 GB RAM and up to 64 GB disk storage.  
Regarding the node's communication capabilities, the Huawei E3372 Megafon dongle was adopted as the communication module. It operates in the global system for mobile (GSM) communication (2G, 900 and 1900 MHz), universal mobile telecommunications system (UMTS) (3G, 900 and 2100 MHz), and long-term evolution (LTE) (4G, 800, 1800, 2100 and 2600 MHz) frequency bands. It is capable of operating at data rates from about 236.8 kbps to 150 Mbps. Furthermore, the node consists of on-board accelerometric, temperature and relative humidity sensors. The capacitive sensor (SNS-DH11) was chosen to monitor relative humidity readings varying from around 1% to 90%, with a precision of about \(\pm\)5%. The temperature sensor (Ad-001 Ds18b20) was selected to measure temperature readings. A 3-axis accelerometer ADXL-345 (SparaEE LLC, Placentia, CA, USA), based on the MMA7361 device, is utilized to detect mechanical stresses.  
Concerning the power supply of the node, the B1700526 EU (Yeeco, Germany) DC-DC converter is utilized to down-convert the 24 V supplied by the vehicle battery to the 5 V needed to power the Raspberry Pi. A second DC-DC converter (ZC104300 CPT) is also incorporated to supply 12 V to a monitor. The proposed node consumes approximately 0.08 kWh at peak. Since the node utilizes the vehicle's battery (~243 kWh) for power supply, it means that the energy efficiency of this node is not a critical factor.
# Reputation Consensus Mechanism for Blockchain Based on Information-Centric Networking
## 4 Design
### Reputation Model
#### 4.1.3 Reputation Output  
After implementing the correction and decay mechanisms, substituting into Equations (4) and (6), the node's reputation update formula is:  
\[R(n)=\begin{cases}R_{0}&\text{if }n=0,\\ e^{-\lambda T_{R}}\cdot R(n-1)+\frac{L}{1+e^{-k(n-n_{0})}}\cdot\Delta R(n)& \text{if }n>0.\end{cases} \tag{8}\]  
Based on the above reputation model, we designed a simplified simulation to analyze the convergence behavior of a single node under different conditions. The node's reputation value is set between 0 and 1. The simulation parameters used in the modeling process are shown in Table 2.  
In Figure 5, we detail the evolution of the reputation model under different parameter settings. The observations are as follows:  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Simulation Parameters** & **Value** \\ \hline Simulation tool & Python \\ \(\Delta R\) & 0.1 \\ \(L\) & 1 \\ \(k\) & 0.5 \\ \(n_{0}\) & 50 \\ \(R_{0}\) & 0.01–0.1 \\ Number of periods & 200 \\ \(\lambda\) & 0.106 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Simulation parameters.  
* **Logistic Model:** Without using the logistic correction model, the reputation value grows approximately linearly in the early stages, as shown in the figure. Assuming a reputation consensus threshold of 2/3, a system without the logistic correction model reaches the threshold in just 10 periods, whereas using the logistic model requires 62 periods. This design reduces the likelihood of nodes rapidly increasing their reputation through high initial values or early accumulation, thereby lowering the risk of Sybil attacks.
* **Midpoint \(n_{0}\) of the Logistic Function:** The parameter \(n_{0}\) determines the time at which the reputation value begins to grow rapidly. A larger \(n_{0}\) delays the onset of rapid reputation growth.
* **Initial Reputation Value \(R_{0}\):**\(R_{0}\) has a significant impact on the reputation value in the early stages of the model but has less influence in the later stages.
* **Slope \(k\) of the Logistic Function:** The value of \(k\) primarily affects the growth rate of the function. A smaller \(k\) results in slower reputation growth and a gentler curve. As \(k\) increases, the reputation value increases more rapidly in the mid-term, and the function curve becomes steeper.  
The above modeling and analysis reveal the impact of different parameters on the reputation model. Through graphical curves, we emphasize the importance of the logistic correction model and the reputation decay strategy in preventing Sybil attacks and reputation manipulation. Overall, the design of our reputation model effectively enhances the fairness and security of the system.  
In the next section, we will introduce the hybrid consensus mechanism design based on this reputation model.
## 3 Proposed Method
### Hand Region Extraction  
In this section, we describe the hand region extraction step, as shown in Figure 2. The hand region of interest and the center of the palm are first extracted from depth images provided by the Kinect V2 skeletal tracker, and then converted to binary images. The hand contours are extracted and described using a border-tracing algorithm.  
The depth images used to detect the hand based on the Kinect V2 skeletal tracker are shown in Figure 5a. These images are captured using a Microsoft Kinect V2 sensor, which estimates each of the user's body parts through input depth images and maps the learned body parts to the depth images through various user actions. In this manner, the camera can obtain skeleton-joint information for 25 joints, as shown in Figure 3, for example, hip, spine, head, shoulder, hand, foot, and thumb. Using the depth image obtained by the Kinect skeletal tracker, the hand region of interest (HRI) and the center of the palm are easily and effectively extracted.  
In order to remove noise from the hand region, the median filter and morphological processing [51] are applied. The results are then exported to the binary image, based on Kinect's depth signals with fixed thresholds.  
Once the binary images of the hand regions are detected, the hand contours are computed using the Moore-Neighbor algorithm [52]. This method is one of the most common algorithms used to extract the contours of objects (regions) from an image. At the end of this process, we obtain the  
Figure 1: Algorithm pipeline.  
Figure 2: Flowchart of hand region extraction.  
contour pixels of the hand as a boundary point set, \(P_{0},\ldots,P_{i-1},P_{i},\ldots,P_{n}\). These values are used to measure the curvature.
## 7 Hardware Complexity Optimization for MIMO  
This section focuses on the hardware complexity for MIMO equalization, which includes the FIR filter tap number for MIMO, single-stage architecture, and joint DSP for an MCF-based SDM. For a larger mode number \(m\), frequency-domain (FD)-RLS might provide a better performance pertaining to the conventional FD-LMS algorithm, for the convergence speed of FD-RLS is not strongly dependent on the input statistics. Until now, the main issue of the adaptive FD-RLS implementation is its relatively higher computational complexity, due to the recursive updating with a growing memory. In this section, a joint chromatic dispersion (CD) and DMGD optimization technique is proposed for a further reduction in the algorithmic complexity of the FD-RLS approach.
## 2 Related Work
### Active Networks  
In a conventional data communication network, routing components are passive, and routing decisions are made based only on packet header information. In contrast, active networks allow for the possibility of changes in real-time network operations, also allowing the possibility of performing network computing by using routing instructions and a user-defined process and by installing on-demand-based network services software. Active networks allow applications to adapt the infrastructure network. This paradigm improves the end-to-end performance of some types of network applications, through the delegation of the front-end implementation of tasks to network nodes [6]. Active networks also allow the ability to add computer power in the network. This scenario usually occurs within the same processor nodes but could also use general purpose processors that are externally connected as virtual routers to play the role of active co-processors, as an alternative to adding capacity to existing active network routers. A router-assistant to active nodes is presented by [PERSON] _et al._[7], which has the characteristics of transparency, IPv4 and IPv6 support, and complete control over layer 3 and above. An entirely middleware-based architecture is presented by [PERSON] _et al._[8], which addresses authentication, memory management, and interconnectivity problems that would otherwise be inherent and enables a highly functional multi-language interface for the deployment of dynamic protocols. Their results show the feasibility of an active network infrastructure implemented in middleware.
###### Abstract  
The stability of a backfill roof is critical to ensure safe production in underhand drift-and-fill mining. In this method, the basket weave drift layout is widely accepted. However, there is currently limited research on the displacement distribution and strength requirement of the backfill roof with this drift layout. To address the gap, a three-dimensional numerical model was established with FLAC3D, with the Maoping Lead-Zinc Mine as the engineering background, to investigate the stability of a backfill roof with the basket weave drift layout. In numerical simulations, an iterative search method was applied to determine the strength requirements of the backfill roof. The results show that the displacement of the backfill roof with the basket weave drift layout shows columnar or strip-like distribution patterns. The strength requirements (unconfined compressive strength) of the backfill roof in limited equilibrium for drift sizes of 3.5 m \(\times\) 3.0 m, 4.5 m \(\times\) 4.0 m, and 5.5 m \(\times\) 5.0 m were determined to be 1.34 MPa, 1.57 MPa, and 2.38 MPa, respectively. A comparison was also made between the strength requirement predicted by traditional analytical solutions and those obtained from numerical simulations, which shows that traditional solutions tend to yield conservative results. The method and analysis process employed in this study can provide valuable references for backfill strength design in mines using underhand drift-and-fill mining.  
backfill roof; strength requirement; underhand drift-and-fill mining; FLAC3D; analytical solutions 2020
# Crop Classification for Agricultural Applications in Hyperspectral Remote Sensing Images
## 4 Experimental Results and Analysis
### Crop-Wise Analysis
#### 4.5.2 Soya Crops  
The first crop used for the comparison is \"Soybean-no till\", class number 10 from the Indian Pines data. For this crop, the proposed method outperformed the state-of-the-art methods 3 DGSVM, CNN-MFL, SS3 FC, and WEDCT-MI with higher accuracy of 97.94%. The second crop used for the comparison is \"Soybean-min till\", class number 11 from Indian Pines data. For this crop, the proposed method outperformed the state-of-the-art methods 3 DGSVM, SS3 FC, and WEDCT-MI with higher accuracy of 99.35%. The third crop used for the comparison is \"Soybean-clean\", class number 12 from the Indian Pines data. For this crop, the proposed method outperformed the state-of-the-art methods 3 DGSVM, SS3 FC, and WEDCT-MI with higher accuracy of 95.97%. The method CNN-MFL had 95% accuracy, which is on par with the proposed method.
# Evaluation of Operator and Patient Doses after Irradiation with Handheld X-ray Devices
## 1 Introduction  
Radiographic imaging is an essential part of a modern dental examination. It has been determined that the diagnostic benefits of ionizing radiation far outweigh the risks to the patient; therefore, radiological evaluation has become routine during clinical examination [1]. The first handheld X-ray devices were designed for military use in the early 1990s of the last century. Apart from military areas, it has found wide use in dentistry-related situations such as temporary health clinics, patients with reduced mobility, nursing homes, autopsies, morques, and remote areas [2, 3]. There has been a recent increase in the marketing of handheld portable X-ray devices for intraoral radiography in general dental clinics. Handheld X-ray units challenge the concept of a \"controlled area\" as they are held by the operator. The distance from the body varies depending on how the device is held. Although the device has an integrated shield, the handheld X-ray system raises concerns about increased operator exposure due to X-ray leakage and backscatter radiation as the operator is in direct contact with the X-ray source [4, 5].  
Exposure to X-ray radiation poses a tangible risk to biological systems due to its energy transfer capabilities. Such energy can induce structural damage to critical biomolecules like DNA within the exposed cells, thereby elevating the risk of detrimental effects, including carcinogenesis. With the increasing ubiquity of radiographic procedures utilizing X-ray technology, concerns over radiation-induced malignancies have grown. Radiation safety protocols aim to mitigate these risks by regulating the utilization of ionizing radiation sources, including X-rays [6, 7, 8]. Adherence to guidelines set forth by the International Commission on Radiological Protection (ICRP) ensures that both practitioners and the general public are not subjected to radiation levels exceeding internationally recognizedsafety thresholds. Consequently, quantifying the radiation dose absorbed by both patients and especially healthcare personnel exposed to X-rays is of paramount importance. Recent advancements in the field have led to a heightened focus on continually monitoring and setting estimated dose limits during public X-ray exposures. This approach aims to ensure the optimal safeguarding of both patients and medical staff [9; 10].  
This study aimed to determine the amount of radiation dose to which selected critical organs and tissues on personnel's and patient's bodies are exposed after irradiation with handheld X-ray devices.
## 5 Proposed Framework  
This section presents our integrated ZTA and FL framework for secure O-RAN optimization. As shown in Figure 2, the solution addresses security, privacy, and performance challenges in 6G RAN environments.  
Figure 2 presents the integrated ZTA and FL (ZTA-FL) framework designed for O-RAN environments. The figure is organized into two main sections.  
The ZTA components are depicted across three hierarchical layers: the Service Management and Orchestration (SMO), the Non-Real-Time RAN Intelligent Controller (Non-RT RIC), and the Near-Real-Time (Near-RT RIC). Each layer integrates continuous authentication, runtime monitoring, and micro-segmentation mechanisms to enhance security.  
Figure 2: Integrated ZTA-FL framework for O-RAN.  
Different zones are defined as follows: the Core Zone (trusted components), the Controlled Zone (third-party applications under policy control), and the Quarantine Zone (isolated suspicious applications). The FL workflow is illustrated, showing multiple operators participating in collaborative training. Each operator trains a local model on its dataset and securely sends encrypted updates to a central aggregator. Privacy preservation mechanisms, such as noise injection, are applied at each node. The global model is then distributed back to the operators. Red shields mark critical protection points during data preprocessing and model aggregation and within training zones to ensure security throughout the FL process.
## 1 Introduction
Those methods are summarized as an integrated two-tier architecture modeling method based on a service layer and a network layer [22], the networking and modeling method of the SIN based on the hierarchical autonomous domain [5], the modeling method of the SIN architecture based on the distributed star-group [23], etc. The advantages of the above methods, including the complexity of structure of SIN, are fully considered. Therefore, the design of its architecture is carried out through stratification and chunking. However, those methods are poor regarding their versatility and portability. As a national infrastructure, the SIN provides information support and information guarantee for military and people's livelihoods. In fact, the size of the tasks and the focus of task requirements are different, and the differences in the provision of services by SIN are greater. For a specific task, if the whole SIN is involved in this task, it can lead to the following phenomena. One is that the delay of information transmission will be increased due to the large structure. The other is a huge waste of space resources due to the introduction of some non-essential components. Thus, it is necessary to consider the recombination of the SIN architecture. As a result, in the processes of modeling and designing the SIN architecture, we not only need to consider the versatility and portability of its architecture, but also the recombination of its architecture. Considering the versatility and portability of SIN architecture is for the overall situation. However, the reconfigurable SIN architecture is based on the whole situation, and is for specific tasks, that is, it is task-oriented. For the task-oriented requirements, the SIN architecture is rapidly reorganized for the specific task with the task as the traction. After the task is finished, it can realize the real-time release of the recombinant resources. Ultimately, the reconfigurable SIN provides the required functionality with minimal resources and smaller cost. As mentioned earlier, we have proposed a DaaC modeling idea that solves the problem of the poor versatility and portability of the SIN architecture. To overcome the problem of recombining the SIN architecture, and meeting the needs of the dynamic reorganization of the architecture according to specific task, we present a reconfigurable modeling method of task-oriented architecture for SIN based on DaaC in this paper. The remainder of the paper is organized as follows. Section 2 discusses the related work, including the concepts and features of SIN and the DaaC modeling idea. In Section 3, we build the reconfigurable model of the task-oriented architecture of SIN based on DaaC. Section 4 presents the verification of the instances, and we use a typical SIN as an example for the implementation. Section 5 discusses the verification results. Section 6 concludes the paper.