esa-sceva/satcom-chunk-collection huggingface dataset:
# Implementation of Context Aware e-Health Environments Based on Social Sensor Networks
## 4 Characterization and Impact of Wireless Channel Behavior
Care, however, should be taken if transmission rate is potentially increased (_i.e._, by using new standards) or operational frequency is decreased. Finally, the comparison between measurements and simulations for the aforementioned six points is depicted in Figure 12. Measurement results have been obtained with the aid of an Agilent 9912 portable spectrum analyzer, coupled to short vertical monopoles, tuned in the center of the 2.4 GHz band, consistent with Bluetooth specifications given by the employed transceivers. The spectrum analyzer data and the RSSI obtained from the device are compared and two different data rates are also considered. Good agreement between simulation and measurement data can be seen, even in the case of a complex scenario, obtaining a mean error of 0.18 dB and a standard deviation of 3.24 dB. Figure 10: Comparison of Power Delay Profiles for both positions of the antenna (Transmitters 1 and 2): (**a**) Position 1 (green person in Figure 8); and (**b**) Position 2 (black person in Figure 8). The RSSI values usually have an error when they are compared with the spectrum analyzer caused by the kind of modulation used and therefore depending on the device used. In this case, an error of 10 dB is introduced and it is corrected in Figure 13. The existing error between corrected RSSI and the simulation is of 0.26 dB for 10 b/s and 0.07 dB for 100 b/s. The lower bit rate adjust better than the higher to the simulations power values, considering that the standard deviation in the first case is 4.1 dB and in the second case is 4.85 dB. The values that have been obtained are in all cases 15 dB above the sensitivity threshold of the employed transceivers, indicating that communication is feasible at any given location and position of the transceivers within the scenario under analysis, for the given  
Figure 11: Delay Spread estimation at a bi-dimensional plane at 1 m height for Position 1 (green person in Figure 8): (**a**) Transmitter 1 (Table); and (**b**) Transmitter 2 (Chest). Figure 12: Comparison among simulated, Received Signal Strength Indication (RSSI) and measured power values. transmitter to receiver linear radial. However, if location is changed, or if height is modified, sensitivity levels are not achieved. In the example scenario, the optimal configuration would be achieved with 3 transceivers (for the case of a relatively large room, as depicted in Figure 8), located in equidistant positions at a medium height (_i.e._, equivalent to chest height of the user standing in the scenario).
# Optimized Accelerated Over-Relaxation Method for Robust Signal Detection: A Metaheuristic Approach
## 3 Proposed Method for Signal Detection
### Proposed Novel PSO
#### 3.2.3 Modified Acceleration Coefficients
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Parameters** & **Novel PSO** & **Improved PSO** & **BPSO** & **WPSO** \\ \hline \multirow{2}{*}{\(w\)} & \(\left(1.2-\left\lfloor\frac{\delta_{300}-50}{100}\right\rfloor\right)\cdot\) & \(\left(1.2-\left\lfloor\frac{\delta_{300}-50}{150}\right\rfloor\right)\cdot\) & \multirow{2}{*}{1.2} & \multirow{2}{*}{\(0.9+0.3(\frac{100-t}{100})\)} \\  & \(e^{-\frac{t^{2}}{2(30\text{s}_{max})^{2}}}\) & \(e^{-\frac{t^{2}}{2(30\text{s}_{max})^{2}}}\) & & \\ \(c_{1}\) & \(2+0.5\cos(\frac{\pi t}{2})\) & \(2+0.5\sin\frac{\pi t}{2}\) & 2 & 2 \\ \(c_{2}\) & \(2-0.5\cos(\frac{\pi t}{2})\) & \(2-0.5\sin\frac{\pi t}{2}\) & 2 & 2 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Values of algorithm parameters. Figure 1: The acceleration coefficients curves of PSO algorithms. Figure 2: Novel PSO inertia weight and acceleration coefficients. ```
0:\(N\) (swarm size), \(D\) (dimension), \(t_{\max}\) (max iterations)
0: Optimal solution \(\mathbf{g}_{\text{best}}\)
1: Initialize \(g_{\text{NO}}\gets N\)
2:for each particle \(i=1\) to \(N\)do
3: Initialize position \(\mathbf{x}_{i}\) randomly within the search space
4: Initialize velocity \(\mathbf{v}_{i}\) randomly
5: Set \(\mathbf{p}_{\text{best},i}\leftarrow\mathbf{x}_{i}\)
6: Evaluate fitness \(f(\mathbf{x}_{i})\)
7:endfor
8: Set \(\mathbf{g}_{\text{best}}\leftarrow\arg\min_{i}f(\mathbf{p}_{\text{best},i})\)
9:for\(t=1\) to \(t_{\max}\)do
10:\(w_{\max}\gets 1.2-\left\lfloor\frac{g_{\text{NO}}-50}{150}\right\rfloor\)
11:\(w\gets w_{\max}\cdot e^{\frac{t}{2(0.5\max)^{2}}}\)
12:\(c_{1}\gets 2+0.5\cos(\frac{\pi t}{2})\)
13:\(c_{2}\gets 2-0.5\cos(\frac{\pi t}{2})\)
14:for each particle \(i=1\) to \(N\)do
15:for each dimension \(d=1\) to \(D\)do
16: Update velocity and position:
17:\(v_{il}(t+1)\gets wv_{il}(t)+r_{1}c_{1}(p_{\text{best},\text{id}}-x_{il}(t ))+r_{2}c_{2}(g_{\text{best},\text{d}}-x_{il}(t))\)
18:\(x_{id}(t+1)\gets x_{id}(t)+v_{id}(t+1)\)
19:endfor
20: Evaluate fitness \(f(\mathbf{x}_{i})\)
21:if\(f(\mathbf{x}_{i})<f(\mathbf{p}_{\text{best},i})\)then
22:\(\mathbf{p}_{\text{best},i}\leftarrow\mathbf{x}_{i}\)
23:if\(f(\mathbf{p}_{\text{best},i})<f(\mathbf{g}_{\text{best}})\)then
24:\(\mathbf{g}_{\text{best}}\leftarrow\mathbf{p}_{\text{best},i}\)
25:endif
26:endif
27:endfor
28:if stopping criterion is metthen
29:break
30:endif
31:endfor
32:return\(\mathbf{g}_{\text{best}}\) as the optimal solution
```  
**Algorithm 2** Novel Particle Swarm Optimization.
# An Access Control Model for Preventing Virtual Machine Hopping Attack
## 3 Preliminaries
### VM Hopping
#### 3.1.3 VM Hopping Defense  
At present, VM hopping defense is mainly solved by building healthier Hypervisors and designing more robust access control policies.  
(1) Build lightweight Hypervisor. In most computer systems, TCB (trusted computing base) is a combination of all the security devices that constitute a secure system. TCB, which provides security for the entire system, is highly reliable and is the basis for ensuring the safety of high-level application operations. However, the larger the TCB, the more code, the higher probability of vulnerability, and the harder it is to ensure its own credibility. Therefore, the design of lightweight Hypervisors should be as simple as possible. A lightweight Hypervisor, such as Trustvisor, Secvisor, and Cloudvisor, only retains the key feature and implements the other features in other virtualization layers. To our knowledge, ARCN, the latest lightweight Hypervisor, only has 25,000 lines of code.  
(2) Design access control policy [19]. Access control is a common technical means for system security and information security, ensuring the confidentiality and integrity of data from all aspects. In the field of virtualization, the security risks are mainly caused by illegal resource access, and the design of access control is aimed at the access rights of resources between the subject and the object. Therefore, the access control policy is used to solve the related problems in the virtualized domain. Due to the more complex state transitions and hazards of virtual machine hopping attacks, a set of access control policies should be designed specifically.  
In this paper, we assume the host system is trusted, and only concentrate on the access control policy of guest machines.
# Optimizing Crop Yield and Reducing Energy Consumption in Greenhouse Control Using PSO-MPC Algorithm
## 3 Methodology
### Biophysical Model  
The biophysical model structure that we used was based on the one proposed in [20]. The growth of crops through photosynthesis in a greenhouse is mainly dependent on canopy temperature, PAR, and \(CO_{2}\) concentration. The five-step process for determining the canopy photosynthesis rate is calculating the maximal rate of electron transport at 25 \({}^{\circ}\)C at the canopy level, determining the potential rate of electron transport, calculating the electron transport rate, determining the gross photosynthesis rate at the canopy level, and lastly calculating the net photosynthesis rate. The photosynthesisized carbohydrates are stored in a buffer, \(C_{Buf}\), whose outflow is affected by temperature. The buffer distributes the  
Figure 2: Comparisons of outdoor and simulated indoor climatic factors.  
carbohydrates (\(MC_{Bu/Fruit}\), \(MC_{Bu/Laf}\), \(MC_{Bu/Stem}\)) to the plant organs (\(C_{Fruit}\), \(C_{Laf}\), \(C_{Stem}\)). The model calculates fruit weight and numbers for each development stage and the portion of organ carbohydrates used for maintenance respiration. The harvested crop's dry matter is determined by integrating carbohydrate outflow from the last fruit's development stage.  
The state variables of the model, such as carbohydrate amount and dry matter, are described with differential equations that reflect the dynamics of these variables over time. These differential equations take into account the canopy photosynthesis and carbohydrate flow within the plant. By solving these differential equations, it is possible to predict the behavior of the plant over time, including its growth and development, and its response to different environmental conditions and management strategies. The specific dynamics of key state variables in this model are shown below:  
\[\dot{C}_{Buf} = MC_{AirBuf}-MC_{Bu/Fruit}-MC_{Bu/Laf}-MC_{Buf/Stem}-MC_{Bu/Air}\] \[\dot{C}_{Fruit\{j\}} = MC_{Buf/Fruit\{j\}}+MC_{Fruit\{j-1\} Fruit\{j\}}-MC_{Fruit\{j\} \ Fruit\{j+1\}}-MC_{FruitAir\{j\}}\] \[\dot{C}_{Laf} = MC_{Buf/Leaf}-MC_{LafAir}-MC_{LafHar}-MC_{FruitAir\{j\}} \tag{2}\] \[\dot{C}_{Stem} = MC_{Buf/Stem}-MC_{StemAir}\] \[DM_{Har} = \eta\cdot MC_{FruitHar}\]  
where \(C_{Buf}\), \(C_{Leaf}\), and \(C_{Stem}\) represent the carbohydrates in buffer, leaves, and stems, respectively, while \(C_{Fruit\{j\}}\) represents the carbohydrate at the j-th stage flower development stage. \(DM_{Har}\) represents the harvested dry matter. The detailed explanations of each term in (2) are presented in [20]. Carbohydrates for different organs and dry matter representing the crop yield can be estimated by solving these differential equations, which can then be used to estimate the crop yield.  
From the aforementioned greenhouse climate and biophysical models, the crop growth can be estimated based on outdoor meteorological and control parameters, which can be tuned optimally by our developed PSO-MPC control algorithm (details are in the next subsection) targeting for maximizing crop growth with a minimum energy cost.
## 4 Stability Analysis of Slopes Under Freeze-Thaw Cycles
### The Influence of Freeze-Thaw Cycles on the Slope Safety Factor  
The safety factor (F\({}_{\text{s}}\)) of the slope was analyzed using the strength reduction method. The results demonstrated a progressive reduction in stability: F\({}_{\text{s}}\) = 1.992 at 0 F-T cycles, 1.645 at 30 cycles, 1.223 at 60 cycles, and 1.105 at 90 cycles. A negative exponential correlation between the Fs and F-T cycle number (\(n\)) was identified, expressed as F\({}_{\text{s}}\) = 1.48 \(\exp(-n/91.89)+0.52\) (see Figure 11).  
Figure 10: Slices of the plastic zones of the slope for different numbers of freeze-thaw cycles. (**a**) F-T = 0; (**b**) F-T = 30; (**c**) F-T = 60; (**d**) F-T = 90.  
A progressive reduction in the safety margin and stability of the slope was observed with increasing F-T cycles.
# Global Power Dynamics in the Contemporary Space System
## 6 Systems Structure--New Balance of Power in Space
### Shift in Power Geographic Distribution  
A secondary significant characteristic of the contemporary space system structure is the transition in power distribution. This transition is not sudden, nor does it indicate a complete loss of power for traditional Western states. Instead, it is a gradual and persistent shift towards other countries, particularly China, which is gaining increasing influence in the global space system.  
While the United States and some European nations have maintained robust investments in space, other non-traditional space countries have come to recognize the significance and advantages (economic, geopolitical, and social) of investing in space. These newcomers have developed ambitious space policies, plans, projects, and investments aimed at reducing the gap with established space powers. This trend is evident in the substantial investments made by emerging economies, notably the BRICS nations, as well as by countries experiencing low economic growth.  
Despite the United States' continued dominance in space power, the emergence of new competitors, particularly China, has introduced a shift in the global balance of power. China's advancements in space technology have been particularly remarkable, posing a significant challenge to the existing order. While the United States continues to dominate global investment in space, its share of global spending has experienced a notable decline from 75% in 2000 to 59% in 2024, as an increasing number of countries engage in space-related activities [46].  
China has made significant advancements in the field of space technology and aspires to become the leading space power by the year 2045, coinciding with the centenary of the establishment of the People's Republic in 2049. China's space program is closely aligned with that of the United States, with government spending on space programs reaching nearly USD 14 billion in 2023, marking a significant milestone as it surpassed Russia for the first time in 2021 [88].
## 2 Computation of the Effective Length of Type-A Grounding System According to IEC 62305-3  
### Description of the Considered Grounding System  
As mentioned in the introductory section, the international standard [16] recommends the use of two types of grounding systems depending on the resistivity of the homogeneous soil in which the grounding system is buried. Concerning the soil, this is naturally an oversimplification since the soil is rarely homogeneous and more often heterogeneous in nature [19]. This heterogeneity can, in most cases, be approximated relatively accurately using horizontal layers of varying resistivity. In practice, the heterogeneity of most soils is adequately approximated using a two-layer earth model [20], only in some cases extending to a three-layer earth model. However, in this paper, we will concern ourselves with the homogeneous soil as prescribed in the standard [16].  
Regarding the grounding systems mentioned in the standard [16], in the scope of this paper we will observe the type-A grounding system intended for use in homogeneous soils of resistivity up to 3000 \(\Omega\)m, as stated in [16]. This is a type of radial earth electrode arrangement which comprises horizontal or vertical electrodes that do not form a closed loop. A minimum of two earth electrodes is prescribed, although this type of grounding system can have more. In this paper, we will limit our observations to the simplest arrangement consisting of two horizontal earth electrodes positioned one opposite another as depicted in Figure 1. The length \(\ell_{1}\) of each electrode is also prescribed in the standard [16] depending on the earth resistivity and the desired protection class. However, it is a well-known fact that during the dissipation of lightning current into the surrounding soil, only a part of the grounding grid arrangement contributes to this dissipation--i.e., the effective part of the grounding system. Therefore, it would be beneficial to ascertain the effective length of this type of grounding system when it is subjected to a lightning strike in order to optimize the usage of the number of grounding grid conductors that are installed. This is discussed in the following subsection.
## 5 Experimental Results and Analysis
### Comparison with State-of-the-Art Methods  
In order to verify the effectiveness of the proposed method, this paper compares it with other state-of-the-art methods. For the NTU RGB+D 60 dataset, experiments are conducted in both CS (cross-subject) protocols and CV (cross-view) protocols, and the experimental results are shown in Table 1. The methods used for comparison include handcraft-feature-based methods [32], RNN-based methods [33; 34; 35], CNN-based methods [36; 37; 10; 38], GCN-based methods [39; 40; 41], and the HGNN-based method [42; 43; 44; 45]. The experimental results show that the accuracy of our proposed method achieves 91.2% and 96.7% for action recognition in CS (cross-subject) protocols and CV (cross-view) protocols, respectively, and the accuracy of human action recognition is significantly improved, even compared with other hypergraph neural network-based methods. For the Kinetics-Skeleton dataset, the experimental results are shown in Table 1. The methods used for comparison include handcraft-feature-based methods [46], RNN-based methods [28], CNN-based methods [36], GCN-based methods [39; 40; 47; 30] and HGNN-based methods [42; 43; 44; 45]. The experimental results show that the accuracy of our proposed method is 39.1% and 61.3% in Top-1 and Top-5, respectively. Compared with other methods based on skeleton, our model achieves the state-of-the-art. From the above experimental results, it is easy to see that our proposed method can achieve a better performance by constructing hypergraphs from skeleton information in human actions. Thus, capturing higher-order correlations among skeletons can extract richer information, which can further improve the performance of action recognition.
This study proposes the K-prototypes clustering framework for comprehensive trip chain analysis, addressing two key research gaps: incomplete trip chain data acquisition and information loss in reconstruction. The methodology integrates three technical innovations: (1) mobile signaling data for complete origin-destination chain generation, (2) an entropy-weighted mechanism to balance numerical and categorical variable con tributions, and (3) the density peak theory for optimal cluster initialization. For feature selection, a novel hybrid approach combining [PERSON]'s V coefficient (for categorical associations) and Spearman's rank correlation (for ordinal relationships) is implemented to identify the most discriminative variables characterizing intercity travel behavior patterns. The Beijing-Tianjin-Hebei urban agglomeration, a nationally strategic region with functional integration and a Beijing-centered multi-tiered transportation network, serves as an empirical testbed. The framework is empirically validated using the Beijing-Tianjin-Hebei metropolitan region as a case study, analyzing anonymized mobile signaling data (2019-2021) across six major corridors. A 12-variable dataset is analyzed through silhouette coefficient optimization, revealing critical insights into multimodal transfer dynamics, individual behavioral variations, and transportation hub performance. These findings provide valuable implications for urban mobility optimization and sustainable development strategies [33; 34], demonstrating the framework's effectiveness in complex urban transportation analysis. The rest of the paper is organized as follows: Section 2 provides an overview of the study area, data collection, and methodology for generating intercity trip chains, including the extraction and processing of smartphone data to capture spatiotemporal mobility behaviors. To address the aforementioned research questions, Section 3 details the analytical framework, encompassing variable selection using the [PERSON]'s V-Spearman correlation coefficient method; determination of the optimal number of clusters via the Silhouette Coefficient; and the application of the K-prototypes algorithm for clustering mixed data types. Section 4 presents a discussion of the results, comparing temporal and spatial characteristics of intercity trip chains across different directions and years, while contextualizing the findings within the broader literature on urban mobility and transportation planning. Section 5 concludes the paper with highlights of the theoretical contributions.
# A Parcel Transportation and Delivery Mechanism for an Indoor Omnidirectional Robot
## 3 Design of the Transportation and Delivery Mechanism
### Design of the Transportation and Delivery Device  
The design of the transportation and delivery device of the robot was based on the information on the types of envelopes and parcels received. A parallelepiped container resembling a basket with a lid was designed to fit the parcels. As stated before, the dimensions of the container were defined to fit in the most frequently received parcels [53] while not jutting out from the radius defined by the robot base. The selected dimensions were 0.34 W m \(\times\) 0.12 L m \(\times\) 0.22 H m, which allowed the accommodation of 90% of the mail received (categories SE, LE, VSP, SP, and MP, Table 1). The design was developed based on the assumption that large envelopes (LEs) and medium-sized parcels (MPs) can be transported with the lid open, but the large (LPs) and very large parcels (VLPs) will not fit in the transportation basket and must be delivered manually by the staff of the building.  
Inspired by the [PERSON]'s razor [54], the new mechanism was composed of four basic parts: a basket, a lid, and two static parts fixed to the main body of the robot. Both the basket and the lid must be articulated in order to automatically load and unload parcels. One of the static parts allocates a servomotor that holds and rotates the lid (Figure 3), and the other static part allocates a servomotor that holds and rotates the basket (Figure 4). In this design, the servomotors used to rotate the lid and the basket are low-cost 8120 MG digital servos, which provide a rotation angle of up to 270\({}^{\circ}\) and a torque of 2.2 Nm at 8.4 V. This low-cost servomotor uses an internal digital controller that provides a very high torque while keeping a minimum standard size (0.04 m \(\times\) 0.02 m \(\times\) 0.04 m). Both servomotors are controlled by an electronic board providing two digital PWM signals. The cables of the mechanism are hidden inside the main body of the robot, while the electronic board controlling the servomotors is fitted under the piece that holds the basket. The reference positions of the servomotors controlling the lid and the basket must be calibrated during the assembly of the device. Figure 3a shows the transportation position of the lid. In this position, the servomotor does not block the lid, allowing its manual opening by the janitor to load a parcel inside the basket. Figure 3b shows the lid lifted 90\({}^{\circ}\) to allow basket rotation. Figure 4a shows the basket in the transportation position, and Figure 4b shows the basket starting a rotation. The basket does not require the application of any torque during the transportation of a parcel. The automatic unloading of a parcel requires the basket to rotate 130\({}^{\circ}\) to drop the parcel off at the delivery zone by the action of gravity. The adequacy of the servomotors used in the design will be validated experimentally.