esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Application of the Proposed Method
### Case Study 2  
In this case study, \(F1=1.0,F2=3,F3=2\) are given. The topology obtained from Stage 1 is shown in Figure 7a, which resulted from the TEWS method. Similar to the previous case study, all compliances were observed to reduce simultaneously in this stage.  
After Stage 2, a new topology was obtained, which is shown in Figure 7b. The appearance of the trusses directed towards the upper right corner helps bear the compression loads in LC2 better. At the same time, the arrangement of these trusses made the tie bars bearing LC3 disappear or become smaller, worsening the performance of the structure under LC3.  
The evolution of compliances during Stage 2 is illustrated in Figure 8, where the compliance under LC2 was always selected as the objective function, explaining its importance compared to other loading conditions. The calculation results are listed in Table 2.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & \(c_{1}\) & \(c_{2}\) & \(c_{3}\) \\ \hline TEWS & 61.17191 & 52.23056 & **85.39076** \\ BCM & 76.93185 & **77.27681** & 77.27835 \\ \hline \hline \end{tabular} Bold font represents the maximum value.  
\end{table}
Table 1: Compliances using the traditional equally weighted sum (TEWS) method and bisection constraint method (BCM) in Case Study 1.  
Figure 8: Evolution of compliances in Case Study 2 during Stage 2.  
Figure 7: Topologies in Case Study 2: (**a**) after Stage 1 (traditional equally weighted Sum method); (**b**) after Stage 2.  
Unlike the previous case study, all compliances are very different from each other in the final topology generated in Stage 2. This is because the terminal condition given in Equation (16) was activated before the two largest compliances coincided. However, the most critical compliance (\(c_{2}\)) was reduced from 285.374 (TEWS) to 271.6586 (BCM), whilst the compliances under LC1 and LC3 remained smaller. Hence, the effectiveness of the proposed method was demonstrated in this case study.  
These two examples (Case Study 1 and Case Study 2) demonstrate that the solutions obtained using the TEWS method can be improved using the proposed method in terms of minimizing compliance despite the well-known TEWS method providing relatively good results. The proposed BCM method can be used for various TO problems considering multiple-loading conditions, including problems with different load correlations (load severities) between the given loading conditions. Thanks to the simple strategy of compliance constraining (relaxing) that allows for any possible reduction in the most critical compliance, the proposed method ensures the reliable yielding of the true solution of the min-max compliance formulation. Additionally, the effectiveness of convergence using two different terminal criteria is also demonstrated in these two examples, improving the stability of the proposed method.
# 5G Beyond 3 GPP Release 15 for Connected Automated Mobility in Cross-Border Contexts
## 2 5G Technologies beyond Release 15
### Nr Positioning  
Urban areas with tall buildings or regions with dense foliage require cm-precision positioning. mmWave technology can be exploited to complement global navigation satellite systems (GNSS), and to accurately measure vehicle position [18]. 3 GPP Rel-16 introduces native support for positioning on the NR carrier, applicable for mmWave frequencies [19].
# Design and Evaluation of Steganographic Channels in Fifth-Generation New Radio +
## 2 Background  
### Fifth-Generation Networks and Air Interface  
Digital mobile networks enable wireless, location-independent communication and data transfer between mobile devices, called user equipment (UE). The basic architecture consists of an access network and a core network. The so-called radio access network (RAN) interconnects several base stations, called gNodeBs (gNBs), that provide a radio interface for local signal transmission. The core network, on the other hand, is responsible for mobility management and acts as the central gateway for internal and external data transfer. The communication between the UE and the core network is divided into two planes: the user plane (UP) carries the user data traffic, while the control plane (CP) is used for signaling and control data. The wireless communication between gNBs and UE is performed over the air interface, called 5G New Radio, which comprises a protocol stack that implements OSI layers one to three. From a logical perspective, each protocol processes the data of the next higher and lower layers and transmits them over a logical communication link to the next responsible protocol layer. Figure 1 visualizes the complete 5G New Radio stack with all protocol layers and the channels between them. The mapping between the channels and protocols [3; 7] is shown for both the uplink (red) and downlink directions (blue).  
Layer 1, i.e., the physical layer, is responsible for the physical data transmission on the uplink and downlink channels. Layer 2 consists of the medium access control (MAC), Radio Link Control (RLC), Packet Data Convergence Protocol (PDCP), and Service Data Adaption Protocol (SDAP). MAC [8] and RLC [9] provide functions for the preparation of medium access and data transmission, whereas PDCP [10] merges the data of both the UP and CP. Confidentiality and integrity protection is also performed within the PDCP layer. SDAP [11] was introduced as a new protocol for the air interface. It adds quality-of-service (QoS) flows to meet the quality requirements of the communication link from the UE to the core network. The establishment, configuration, and management of the radio link is controlled by the Radio Resource Control (RRC) [12] on Layer 3. It ensures reachability of UE and initiates handovers to neighboring base stations. Additionally, the protection mechanisms for confidentiality and integrity in the RAN [3; 13] are activated and managed by the RRC.  
Figure 1: Logical channels in 5G New Radio for uplink (red) and downlink (blue) directions based on [3].
# Agriculture 5.0 in Colombia: Opportunities Through the Emerging 6G Network
## 4 Results and Discussion
### Current Colombian Agricultural Outlook
#### 4.3.5 Autonomous Aerial Systems for Smallholder Farming  
In Uraba (Antioquia), the Colombian Rural Development Agency (ADR) launched a large-scale precision agriculture program that introduced high-efficiency drones for plantain production in conflict-affected areas such as Turbo and San Pedro de Uraba. The initiative included technical training for 300 young farmers and aimed to improve sustainability by reducing agrochemical usage by up to 70%, while covering up to 21 hectares per hour per drone [69]. This case reflects the growing relevance of autonomous aerial systems as force multipliers for smallholder productivity and territorial peacebuilding. Under a 6G infrastructure, the performance and scalability of such solutions could be significantly enhanced. Massive Machine-Type Communications (mMTC) would enable simultaneous operation of numerous drones and ground sensors across fragmented terrains, while ultra-low latency (uRLLC) would support dynamic flight adjustments during spraying and monitoring. Moreover, Integrated Sensing and Communications (ISAC) would allow drones to collect agroclimatic and phytosanitary data during their routes, generating localized intelligence for early warnings and decision-making in crops such as plantain, cassava, and cocoa--without the need for permanent human supervision. To complement the previous case-specific domains, Table 9 outlines the projected impact of 6G across the entire food value chain--from production to consumption--highlighting potential transformations at each stage. This table has been adapted from the conceptual framework proposed by [18], which describes emerging technologies associated with 6G networks and their relevance for smart agriculture. In this version, the framework has been tailored to the Colombian context, incorporating local developments, tools, and ongoing national programs. It demonstrates how enabling technologies--such as AI, sensor networks, satellite imaging, and the IoT--are already being deployed in the country under various initiatives. While not yet operating under 6G infrastructure, these efforts reveal the foundational groundwork for 6G-driven innovations in yield forecasting, process automation, and agroclimatic decision-making.
# Software Integration of Power System Measurement Devices with AI Capabilities
## 2 Monitoring Devices  
As mentioned in the Introduction, there is a wide range of electrical monitoring devices on the market, each with different characteristics and performances. The quality of the monitoring system will depend on both the equipment it is composed of and its distribution over the grid. High-quality equipment will return more accurate and complete information than other low-end devices. Furthermore, depending on the characteristics of each device, it may be advisable to place it closer to or further away from the consumers or producers [3; 28; 29; 30]. For DERs such as PV plants, the placement and quality of the monitoring devices are crucial for real-time monitoring of energy production, consumption, and storage, enabling precise control and optimization to maintain grid stability and efficiency.  
Figure 1 shows an example of a typical distribution network, where PQMs are placed near the consumers, with PMUs at the beginning of each line and an SM at each consumer. This section explains what this equipment consists of and the reasons that justify its placement.
# Telescopic Network of Zhulong for Orbit Determination and Prediction of Space Objects
## 2 Data and Methods  
### Observation Data  
The Zhulong observation network is an integrated system of 15 cm telescopes, consisting of more than 9 telescope systems (Figure 1). It has been working steadily since February 2023. The telescope uses a camera and processor integration method to achieve remote control of the filming process, unattended operation, and automatic management of the entire work process based on environmental status information provided by meteorological sensors. This includes dome opening and closing, object search tracking, and recognition positioning. The design parameters are as follows:  
1. FoV: 6.5\({}^{\circ}\)\(\times\) 6.5\({}^{\circ}\);
2. Aperture: 150 mm;
3. Detection ability: 15 stars (under 20-star sky brightness);
4. Photometric accuracy: better than 0.3 magnitudes in star brightness.  
Under suitable observation conditions, LEO space objects can be tracked at a rate of 31 to 201 objects per day, with a sampling frequency of approximately 1 Hz and an average arc length of about 86 s.  
The direct data obtained through the telescope system are images, and continuous image information can be obtained through astronomical positioning technology to generate angle measurement data. Astronomical positioning refers to the process of providing a set of space object observation images, along with information about the observation time epoch and equipment pointing. It involves using star position estimation to determine the right ascension/declination or pitch/yaw of the celestial object at the moment of observation, thus forming a time series of angle measurements for the celestial object [27]. The core of astronomical positioning lies in how to correctly identify stars in the image and determine their location information, also known as star identification. Once the star identification is completed, the right ascension/declination of the space object can be determined using the right ascension/declination of the stars. It should be noted that the accuracy of the determined space object angle data ultimately depends on factors such as the accuracy of the image object's centroid position, the number and distribution of stars, the field of view, and the aspect ratio of the charge-coupled device (CCD) camera. The  
Figure 1: A telescope observation system in Zhulong.  
website not only provides image data for monitoring space objects but also provides their angle data.
# Ground Subsidence Analysis in Tianjin (China) Based on Sentinel-1A Data Using MT-InSAR Methods
## 2 Materials and Methods  
### Study Area  
Tianjin is situated 130 km southeast of Beijing and is on the western coast of the Bohai Sea (Figure 1). The city is located in the North China Plain (NCP), within the East Asia monsoon region, and is a typical city lacking surface and groundwater resources [3]. Tectonically, Tianjin is situated in a Meso-Cenozoic basin zone which was filled up by continental Tertiary and Quaternary deposits [4]. Considering the subsidence situation of Tianjin in recent years and the data availability, we choose the Tianjin urban area and its western suburbs as our study area. It covers an area of about 460 km\({}^{2}\) (Figure 1), including parts of urban areas (Hongqiao, Hebei, Heping and Nankai districts) and suburbs (Xiqing, Wuqing and Beichen districts).
# How to Isolate Non-Public Networks in B5G: A Review
## 6 Open Research Challenges
### Operation of NPNs with Isolation Requirements  
Operation becomes an important issue after deploying B5G NPNs to provide communication services to vertical industries, as who operates what part of the B5G NPNs needs to be specified. It should be clarified which entities are responsible for running the network  
Figure 5: Virtualization of CN using the UPF as an example.  
on a day-to-day basis and for managing the resources and services of the B5G NPNs [24; 34]. The entities could include enterprises using the NPNs and mobile network operators (MNOs) operating the B5G system. If the enterprise operates its NPN, management is undertaken independently from any other networks and called isolated operation. If the MNO operates a NPN, the management is part of the overall network operations and called a unified operation. For isolated operations, it is easy to configure and reconfigure an NPN according to dynamic states, such as traffic. However, continuous service is difficult when one terminal is out of its NPN's coverage, and the costs are relatively high [34]. For unified operations, continuous service can be provided at a low cost. However, reconfiguring the NPN is complex, and requests must be sent by the enterprise to the MNO based on the dynamic states, which need to be responded to and carried out by the MNO. Due to this, unified operations result in poor network flexibility and low data security [34].  
To address these issues, a joint operation between the enterprises and the MNOs is a better method that can ensure flexible and continuous services at a relatively low cost [34]. To achieve this, the demands and results of the operations need to be exchanged between the enterprises and the MNOs, for which data security and privacy are a serious challenge [35]. To solve this problem, federated learning, which contains a local model and a joint model, is a promising technology. Each enterprise or MNO can train its local model with the local data, and the results after training can be exchanged among enterprises and MNOs without sharing the private data [36]. The joint model can act as a global coordinator that is trained to obtain the global optimal management strategies with the received results of local models. With this potential research line, NPNs can be managed efficiently and securely through a joint operation based on federated learning.
## 4 Methodology and Testing Process
### Producing Datasets  
#### 4.1.1 Study on the Topology of Standard Nursing Units  
The development of nursing units can be traced back to the late 19 th and early 20 th centuries, when the Nightingale ward layout was proposed and evolved. Its primary characteristics included an open elongated ward configuration, centralized nursing functions at the ward's terminus, and an emphasis on improving ventilation, daylighting, and other environmental conditions within the ward spaces [31]. This layout served as a significant milestone in the spatial evolution of hospital architecture. Entering the 1930s to 1950s, air conditioning technology saw widespread application in buildings, significantly reducing the need for natural ventilation. Concurrently, the need to optimize workflow efficiency for medical staff became increasingly pressing. In 1955, the Nuffield Hospital Study Group published the \"Report on the Functions and Design of Hospitals\", recommending the centralized placement of nurse stations within the core area of nursing units [32]. Subsequently, this centralized layout paradigm gained widespread popularity, spawning various derivative forms such as double-corridor, single-corridor, and radial configurations. Early scholarly studies compared the nursing efficiency across these layout variations [33]. To further enhance nursing efficiency, decentralized nurse station layouts emerged and were implemented, proving beneficial in improving patient safety and mitigating nurse fatigue [34; 35; 36; 37; 38].  
In China, the development of modernized nursing units commenced in the late 1970s, predominantly adopting centralized nurse station layouts. These layouts evolved into single-corridor, double-corridor, and other composite forms. However, due to regulatory constraints and cultural norms, Chinese nursing units prioritized natural ventilation and daylighting, favoring south-facing patient rooms. The centralized nurse station layout remained predominant, while decentralized configurations were not widely adopted. In recent years, the single-corridor layout has gradually diminished due to its limited capacity and singular circulation organization. It has been supplanted by the main and sub-corridor typology, which ensured space for patient rooms on the south side while expanding the depth of medical support spaces to the north, thereby enhancing the auxiliary functions for medical care [39]. The nursing units of general hospitals typically feature two layout types: main and sub-corridor style and double-corridor style (Table 1). The primary differences between the two lie in the location of the vertical circulation, the accessibility of the corridors, and the depth of the north-side rooms. The double-corridor layout has a more complex functional topology, compared to the main and sub-corridor style.
## 3 Method
### Deformable Model Matching
#### 3.2.3 Optimization  
We use random hill climbing to solve the optimization problems in Equation (5). The probability distribution of Equation (5) consists of two relatively independent items. To make the results optimized and escape from the trap of local minimum, stepwise optimization is our strategy. For the first item, the probability of Equation (3) is obtained by matching 3D keypoints with each deformable model.  
Figure 4: Deformable representation of a 3D wireframe. Chair models of different shape parameters are generated by PCA. Numbers indicated in the figure are weighting parameters of the first three principal component directions, which are represented as \(S\).  
Qualitative results are shown in Figure 5. From the result, we can see that keypoints are fitted well with the deformable model through optimization.  
The optimization of the second item in Equation (5) is sensitive to the initial value of viewpoint parameters. We propose a method for determining the initial value by visible keypoints. Taking a car as an example, Figure 6 illustrates the detailed procedure.  
A discrimination mechanism is necessary for the objects that have different category than the priori models. For the objects whose category is out of our priori models, the deviation of deformable model projection and 2D keypoints would be larger compared with objects that have the same category with the corresponding deformable model. Consequently, a threshold is set for the deviation to judge whether it belongs to categories contained.  
A summary of the method is shown in Figure 7.  
Figure 5: Qualitative results of matching 3D keypoints with the deformable model. It can be seen that 3D keypoints and corresponding deformable models are well fitted after matching.  
Figure 6: Initial viewpoint determination method. The visibility of keypoints in the different viewpoints is counted as a dictionary. For an input point set, the initial viewpoint can be obtained by matching their visibility with the dictionary.  
Figure 7: The summary of the method. At first, keypoints are detected from the image. The 3D keypoints are then matched with each deformable model and 2D keypoints are matched with the projection of the deformable model. After the matching process, the optimal parameters of pose and shape are obtained from the best fit deformable model. Next step is to judge whether the target type belongs to the existing models according to the matching deviation. Finally, the object parameters are obtained.
## 4 Network and Service Modeling
### Services  
As far as service generation is concerned, each satellite takes images of the Earth while flying over determined regions, and we suppose, without loss of generality, that each image requires a single processing task to extract the information useful to the application. For this reason, from now on, the terms task and service will be used as synonyms. Image processing also reduces the data size. We represent the set of the \(N_{T}\) services generated in a cyclostationary period with:  
\[\Sigma=\{f^{h}\}_{h\in[0,\dots,N_{T}-1]} \tag{14}\]  
where each service \(f^{h}\) is represented by the tuple:  
\[f^{h}=\{f^{h}_{s};f^{h}_{0};f^{h}_{1};f^{h}_{1};f^{h}_{d}\} \tag{15}\]  
including the source node, the pre-processing size in Mb, the post-processing size in Mb, the service generation time cycle and the maximum number of time cycles within which the service shall reach the ground station, respectively. Finally, we assume that all images need the same processing task, which requires \(n_{cyc}\) CPU cycles for each bit to be processed.