esa-sceva/satcom-chunk-collection huggingface dataset:
# An Accuracy-Aware Energy-Efficient Multipath Routing Algorithm for WSNs
## 6 Evaluation
### The Effect of Accuracy Constraints on the Performance of the Algorithm
This is because a higher data transmission rate necessitates the delivery of a greater number of data packets. Consequently, additional paths need to be established to facilitate their transmission. Hence, the path intersection will increase, and the data aggregation at  
Figure 4: The effect of \(\Delta\) on AAL, PDR, and NRE under different data transmission rates when the adjacent node density is 20. (**a**) AAL performance, (**b**) PDR performance, (**c**) NRE performance. intersection nodes will affect the accuracy of the data received at the sink. Furthermore, at the same data transmission rate, increasing the accuracy constraint \(\Delta\) results in a greater number of established paths, further elevating the likelihood of path intersections and subsequent data aggregation. This, in turn, leads to an increase in data accuracy loss. Figure 4b shows the PDR performance under different data transmission rates. As we can see, the higher data transmission rate corresponds to lower PDR values, and \(\Delta=1\%\) exhibits the lowest PDR compared to other values of \(\Delta\). This is because higher transmission rates introduce a higher offered load on the network, leading to increased congestion and a subsequent reduction in PDR. Conversely, when the data transmission rate remains constant, PDR increases with the increasing \(\Delta\) values. This is because of the relaxation of accuracy constraints on the path, resulting in many available paths and subsequently improving the performance of data delivery. Figure 4c shows the NRE performance under different data transmission rates. It can be seen that a higher transmission rate brings a larger volume of data being transmitted that will consume more energy by the sensor nodes, resulting in lower NRE values. Additionally, smaller values of \(\Delta\) also correspond to lower NRE values. The reason for this is that smaller values of \(\Delta\) restrict the utilization of intersection nodes within the multipath, potentially requiring the transmission paths with more hops. Therefore, a greater number of nodes must participate in data transmission to consume more energy and consequently lower NRE values.
## 2 Related Work  
IoT-based smart city applications rely on secure wireless communications and require efficient techniques to detect malicious attacks and evaluate the trust of the IoT nodes. Trustful communication between different wireless nodes is an important area of research these days.  
In [20], the authors examined the trade-off between benefits and risks associated with trustworthy communication. They introduced an adaptive trust-based situation-aware access control framework, known as MATS, which incorporated semantic web technologies and game theory. The proposed framework used the dynamic and adaptive nature of the network, and its performance was evaluated through experimental results.  
In [21], the authors proposed a trust framework for IoTs and UAVs in the scenario of vehicular networks. The proposed framework implemented security and privacy measures to enhance the trust level of high-priority vehicle safety data. Additionally, the work proposed a trajectory optimization algorithm to establish a trustworthy agreement between nodes for data collection. The proposed framework improved the trust level of vehicle safety data by 33.34%.  
In [22], the authors proposed a framework named MapReduce to manage trust in big data. The proposed framework reduced data complexity and applied a trust mechanism using the bipartite matching algorithm. Furthermore, the work allocated trust-based time slots to the network users. Results showed a 94.7% improvement in searching the big data as compared to the exhaustive search technique.  
The authors in reference [23] addressed the necessity for reliable data in online networks, highlighting potential threats such as the creation of fraudulent accounts. They introduced a trust-aware framework designed to identify malicious users and utilized a data-balancing technique to pinpoint nodes causing disruptions in terms of frequent message sharing. Through extensive experiments, it was shown that the proposed scheme enhances data precision by 59.16% compared to baseline algorithms.  
In reference [24], the authors presented a trust-based attestation model to mitigate challenges arising from virtual networks in online real estate scenarios. The model uses rule-based decision making to recognize legitimate users of property data, thus establishing trust to foster reliable applications for authorized data users. The work also implemented the prototype of the proposed trust management system on an open-source MANO platform.  
Exploiting evidence theory, the authors in reference [25] proposed a decentralized service-oriented trust management model, concentrating on an IoT-based healthcare system. The work introduced a reward and punishment system where legitimate healthcare service providers are rewarded and punitive measures are applied to malicious users. The proposed system enabled trust in healthcare services and recommendations by providing security against on-off attacks, bad-mouthing, and good-mouthing simultaneously.
## 2 Proposed Circuit and Operating Modes
### Voltage Control with Non-Inverting Characteristics
The voltage across the non-conducting devices can be described as follows:  
\[V_{P(D_{1},D_{2})}=V_{s(P)} \tag{3}\]  
\[V_{P(D_{5},Q_{1})}=V_{o(P)} \tag{4}\]  
In the same voltage half-cycle, the turning on of the switching transistor \(Q_{4}\) ensures the continuous inductor current as non-conducting diode \(D_{5}\) that is connected in its series changes its operating state once the high-frequency PWM-controlled transistor \(Q_{2}\) changes its switching state from 'on' to 'off'. During a PWM interval, the transfer of power from the source to the inductor and then to output is shown in Figure 8c,d, respectively. The dynamic variation in the inductor voltage and capacitor current is computed by employing [PERSON]'s voltage law (KVL) for the inductor voltage and [PERSON]'s current law (KCL) for the capacitor current, respectively. The inductor voltage and the capacitor current during the turn-on PWM intervals for any value of the supplied voltage are expressed by  
\[L\frac{di_{id}}{dt}=kv_{s} \tag{5}\]  
\[C_{o}\frac{di_{c}}{dt}=-ki_{o} \tag{6}\]  
The inductor voltage and capacitor current during the turn-off PWM intervals for any value of the supplied voltage are as follows:  
\[L\frac{di_{id}}{dt}=(k-1)v_{o}+(1-k)v_{s} \tag{7}\]  
Figure 8:0The derivative values of inductor current and capacitor voltage in steady-state conditions are negligible; the right-hand sides of Equations (5)-(8) are added, respectively, to obtain the voltage and current transfer ratio. Figure 8:1  
Figure 8:2
## 4 Results
### PPP Time Transfer in Static Model with HAS Service
Firstly, the data from Table 3 clearly indicate that the GPS, Galileo, or Galileo/GPS integrated PPP  
Figure 7: The satellite number for GPS (G), Galileo (E), or Galileo/GPS (E/G) PPP time transfer. time transfer, facilitated by the Galileo HAS service in static model, is capable of achieving timing accuracy at the sub-nanosecond level (specifically, better than 0.5 ns). Secondly, a closer look reveals that the Galileo PPP time transfer exhibits slightly better precision (with an average STD value of 0.31 ns) compared to the GPS PPP time transfer (with an average STD value of 0.41 ns). This marginal improvement is likely the result of two primary factors: the use of hydrogen masers onboard Galileo satellites, which are known for their stability; and the superior accuracy of the Galileo satellite clock offset products provided by the Galileo HAS service when compared to those for GPS satellites [27]. Thirdly, the combined Galileo/GPS PPP time transfer (with an average STD value of 0.30 ns) demonstrates superior performance to the Galileo-only PPP time transfer. This enhancement in performance is mainly attributed to the increased reliability that comes with the fusion of two GNSS systems, effectively circumventing the issue of re-convergence that can be triggered by an insufficient number of satellites. This pattern is also discernible in the aforementioned figures, further validating our observations. When it comes to frequency stability (Table 4), the performance of GPS, Galileo, and the integrated Galileo/GPS PPP time transfer, all augmented by the Galileo HAS service, mirrors the previously discussed outcomes. The GPS PPP time transfer, as indicated by its modified Allan deviation (MDEV), performs the least effectively, with Galileo PPP exhibiting slightly better stability. However, the combined Galileo/GPS PPP time transfer stands out with the most impressive results. This indicates that the synergistic use of  
Figure 8: Galileo/GPS or Galileo PPP time transfer solutions in the static model. Galileo and GPS systems provides more robust frequency stability in the context of PPP time transfer. Regarding the frequency stability at the 960 s mark, the average frequency stabilities for GPS, Galileo, and the Galileo/GPS combined PPP in static mode are recorded as (5.80 \(\times\) 10\({}^{-14}\), 4.82 \(\times\) 10\({}^{-14}\), 4.07 \(\times\) 10\({}^{-14}\)), respectively. When considering the frequency stability at the 15,360 s interval, the average frequency stabilities for GPS, Galileo, and the Galileo/GPS combined PPP in static mode are determined to be (1.19 \(\times\) 10\({}^{-14}\), 1.13 \(\times\) 10\({}^{-14}\), 8.69 \(\times\) 10\({}^{-15}\)), respectively. These figures underscore the superior performance of the Galileo/GPS fusion approach in maintaining stability over extended periods, which is a crucial attribute for high-precision time transfer applications using Galileo HAS service.
# A New Prediction Model of Annular Pressure Buildup for Offshore Wells
## 7 Results and Discussion  
### Wellbore Temperature  
In this section, the example well in Section 6.1 was used for analysis. The well parameters are described in Section 6.1. The average temperature of the annulus at different production times was calculated and is shown in Figure 13. The increase in temperature is the main factor causing annulus pressure buildup, and the annulus pressure buildup increases as the temperature rises. The temperature profile of the annular fluid is related to the production rate and production time of oil and gas wells. As shown in Figure 11, the temperature of the annular fluid increased rapidly in the early stage of production, then slowly increased and finally stabilized. The temperature of the annular fluid changed significantly in the early stage of production. The temperature profile of the annular fluid slowly increased with the production of oil and gas wells, as shown in Figure 14.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Temperature** & **Measured Pressure** & **Wellcat Calculated Value** & **Error** & **The Calculated Values** & **Error** \\ (\({}^{\circ}\)**C)** & **(MPa)** & **(MPa)** & **(\%)** & **of This Model (MPa)** & **(\%)** \\ \hline
30 & 10.52 & 13.51 & 28.42 & 11.00 & 4.56 \\
40 & 17.98 & 23.7 & 31.81 & 19.07 & 6.06 \\
50 & 21.54 & 28.94 & 34.35 & 23.06 & 7.05 \\ \hline \end{tabular}
\end{table}
Table 2: Comparison between calculated and measured values.  
Figure 12: Comparison of measured pressure and temperature with those calculated by model in this paper.  
In the early stage of production, due to the unstable temperature distribution in the wellbore, there were significant changes in annulus pressure buildup, as shown in Figure 15. Within the first 20 days of production, the pressure in the B annulus was the highest. As the temperature gradually stabilized due to the location of the C annulus fluid at the wellhead, the average temperature of the annulus fluid changed greatly, and the C annulus pressure was the highest. Although the fluid temperature in the A annulus was the highest, the temperature change in the A annulus was minimal, resulting in the lowest pressure formed in the A annulus.  
Figure 14: Temperature profile of annulus at different production rates.  
Figure 13: Average temperature of the annulus at different production times.  
With the increase in wellbore production, the annular pressure gradually increased. Due to the maximum temperature change in the C annulus, the annular pressure was the highest, while the temperature change in the A annulus was the smallest, and the annular pressure was the smallest, as shown in Figure 16.
## 2 Background and Related Works
### Background
#### 2.2.5 Group Relative Policy Optimization (GRPO)  
Group Relative Policy Optimization (GRPO) [8] is a policy optimization that introduces the concept of group-based learning in reinforcement learning (RL). It addresses some of the limitations found in traditional policy optimization methods like Proximal Policy Optimization (PPO) [23], which require a critic model to estimate how good a model's action or output is. GRPO optimizes model behavior based on relative rankings among responses generated for the same input. This ranking-based feedback is inherently more robust to noise and does not require training a separate reward model, which can introduce instability and additional complexity. Moreover, by comparing outputs within localized groups, GRPO effectively regularizes updates, reducing variance and preventing over-optimization on individual examples.  
GRPO takes a simpler and more intuitive approach. Instead of trying to estimate absolute values of model outputs, it works by comparing multiple responses generated by the model for the same input (e.g., a user prompt). For each prompt, the model generates a set of possible outputs (responses), and each one is assigned a reward using a predefined reward function (e.g., right format, or similarity to a reference answer).  
Then, GRPO calculates the advantage of each response by subtracting the average reward of all responses in that group from the reward of the individual response. This advantage reflects how much better or worse the response is compared to others in the same group. By using this relative comparison within a group, GRPO avoids needing an external value function.  
The model is then updated using a clipped surrogate loss, which helps prevent overly large changes in the model's behavior. Additionally, a KL-divergence penalty is added to the loss to ensure that the new model does not deviate too far from the original model. This combination allows GRPO to make safe and meaningful updates to the policy.  
Overall, GRPO offers a simpler, more robust alternative to traditional reinforcement learning methods for language model fine-tuning. It improves sample efficiency, reduces training instability, and works well in applications like instruction tuning and reward-based optimization of LLMs.
# Energy-Aware Wireless Sensor Networks for Smart Buildings: A Review
## 5 Application Layer
### A Simple, Single Cluster Model Using Data Correlation  
This model is based on data correlation exploitation [49]. In this model, the WSN nodes that belong to the same cluster have a strong internal correlation. Therefore, the model takes advantage of this correlation to analyze the mutual effect of cluster size and the distance from the base station on reducing total WSN energy consumption. It is difficult to find clusters with optimal size; however, this model tries to obtain a near-optimal solution to construct energy-efficient clusters in the WSN network. In this model, heterogeneous-sized clusters demonstrated better energy efficiency in WSN.
# Two-Dimensional Human Pose Estimation with Deep Learning: A Review
## 2 Two-Dimensional Human Pose Estimation
### Two-Dimensional Multi-Person Pose Estimation  
Multi-person pose estimation extends the single-person setting but introduces substantially greater challenges. In addition to localizing the keypoints of each individual, the task must address keypoint grouping, inter-person occlusion, and body overlap--factors that significantly increase computational complexity and recognition difficulty.  
Based on their processing pipelines, existing multi-person pose estimation methods are broadly classified into two paradigms: top-down and bottom-up approaches. Top-down approaches (e.g., Figure 4a) first detect all individuals in the image, followed by single-person pose estimation within each bounding box. These methods typically provide high accuracy; however, their computational cost increases linearly with the number of people, making them inefficient in crowded scenes. In contrast, bottom-up approaches (e.g., Figure 4b) bypass the person detection stage and detect all keypoints across the entire image in a single pass. The detected keypoints are subsequently grouped into individual poses using graph-based models or associative embedding techniques. Bottom-up methods offer greater parallelism and scalability, making them well-suited for large-scale scenarios. However, under heavy occlusion or in the presence of similar body postures, keypoint association becomes ambiguous and error-prone.
## 3 Findings
### Prognostics and Health Management (PHM) for Unit Systems
#### 3.1.6 Resource Management  
Resource management is the way towards planning, scheduling, and allocating resources in the best possible way. More observation is on future manufacturing, where resource management is a greater concern and it must handle more proficiently. Particularly different manufacturing, as well as automotive industries, are advancing towards utilization of resources to improve proficiency and profitability without trading off the current manufacturing capacity. [PERSON] et al. [41] proposed a methodology that makes resource management in automated guided vehicle systems more effective. The resource management aims at providing robust strategies in manufacturing systems to accomplish the resource allocation and to solve related issues, for example, resource levelling, and production layout adjustment in production planning.
# A Near-Ultraviolet Photodetector Based on the TaC: Cu/4 H Silicon Carbide Heterostructure
## 3 Results and Discussions  
### Morphological Properties  
The images of surface morphology, elemental composition and cross-sections of the TaC: Cu alloy deposited on a glass and 4H SiC substrates are illustrated in Figures 2 and 3. Figure 2a presents an SEM image that reveals the surface characteristics of the alloy. The image predominantly displays a relatively grainy surface. Figure 2b provides a cross-sectional view of the TaC: Cu alloy deposited on the glass substrate, where the measured thickness is 126.5 nm.  
Figure 1: The fabrication process of the proposed photodetector TaC: Cu/4 H SiC.  
The elemental composition of the deposited TaC: Cu alloy was analyzed using energy-dispersive X-ray spectroscopy (EDX) to quantify the relative contributions of each element. Figure 3a shows the EDX spectra of the TaC: Cu alloy, clearly identifying the elemental composition. A prominent peak at approximately 0.89 keV corresponds to tantalum (Ta), confirming its role as the primary component in the TaC film, followed by copper (Cu) and oxygen (O). Carbon (C) represents the element with the lowest content in the alloy. The presence of Cu, carbon, and tantalum indicates the formation of the tantalum carbide (TaC: Cu) alloy, with potential surface oxidation, which is common in thin-film deposition processes.  
After characterizing the surface morphology and elemental composition of the TaC: Cu alloy, the final device was deposited onto a 4H-SiC substrate via sputtering for 37 min. Figure 3b presents a schematic of the fabricated device, where the upper and lower electrodes are depicted in dark contrast. To investigate the cross-section of the photodetector, a focused ion beam (FIB) system integrated with scanning electron microscopy (SEM) was employed. The FIB system facilitated precise localized etching, enabling the acquisition of high-resolution cross-sectional images. During the scanning process, the sample was  
Figure 2: **(a)** The top-view SEM image for the TaC: Cu alloy **(b)** The alloy cross-section on a glass substrate.  
meticulously positioned 2.5 nm from the FIB lens, and a voltage of 30 kV was applied to ensure optimal imaging. Figure 3c showcases the cross-sectional image of the fabricated device on the 4H n-type SiC substrate, clearly delineating three distinct layers: the protective platinum (Pt) top layer, designed to shield the sample from FIB-induced damage, the TaC: Cu layer with a measured thickness of 168 nm, and the 4H n-type SiC substrate with black color.
## 4 Results and Discussion
### Practical Applications of NAS in GANs
#### 4.5.1 Medical Imaging and Synthetic Data Generation  
Medical imaging faces challenges such as limited annotated data and privacy concerns. NAS-GAN methods have been employed to automatically design generators that produce high-fidelity synthetic images (e.g., MRI, CT, X-ray) preserving important diagnostic features. Such synthetic images facilitate training robust models and augment scarce datasets. For example, [PERSON] et al. reviewed the role of GANs in radiology and demonstrated their potential in generating realistic synthetic images for clinical applications [57]. In addition, work by [PERSON] et al. on AdversarialNAS [39] further highlights how NAS can optimize GAN architectures for medical image synthesis.
# Efficient Inorganic Stabilization Materials for Chromium and Arsenic Pollution in Water and Soil
## 3 Mechanism of Chromium and Arsenic Stabilization
### Oxidation-Reduction Reaction Mechanism  
Relying solely on adsorption is difficult to eliminate the toxicity of chromium and arsenic, so researchers have found that Cr and As were also converted into non-toxic forms through redox reactions by stabilization materials. [PERSON] et al. [83] modified the maceration of green tea with attapulgite (ATP, Mg\({}_{5}\)Si\({}_{8}\)O\({}_{20}\)(OH)\({}_{2}\)(OH)\({}_{2}\))\(\cdot\)4H\({}_{2}\)O) to obtain GATP. FT-IR spectroscopy showed the presence of polyphenols and other tea components on GATP (Figure 10a). Small molecular weight polyphenols (SMWP) undergo redox reactions with Cr(VI); C-O is oxidized to C=O, and 62.98% of Cr(VI) is reduced to Cr(III) (Figure 10b,c). Applying materials to actual contaminated sites, [PERSON] et al. [84] used a mixture of clay minerals (CMs) and pyrogenic carbons (PCs) to remediate Cr-contaminated soil. There are a large number of Fe(II) species in CM, which directly transfer electrons to Cr(VI) to form dissolved and solid Cr(III). PC can be used as an electron medium to facilitate this process, and PC surface functional groups can also participate in the reduction of Cr(VI). The resulting Cr(III) after reduction is fixed on the CM surface (Figure 10d). The Fe(III) in Fe\({}_{3}\)O\({}_{4}\) NPs can be used to oxidize the more toxic form of arsenic, As(III), into the less toxic form, As(V) [85].
# Complete Systematic Error Model of SSR for Sensor Registration in ATC Surveillance Networks
## 2 Measurement Model
A typical solution is to use stereographic projection to project all measures expressed in local Cartesian reference systems to the same plane [4]. Afterwards, the multisensor tracking process will be performed in the stereographic reference system for horizontal track. Aircraft barometric altitude is usually tracked separately in ATC applications.