esa-sceva/satcom-chunk-collection huggingface dataset:
# Affordable Broadband with Software Defined IPv6 Network for Developing Rural Communities
## 3 Affordable Broadband for Rural Communities
### Digital Subscriber Line and Cable Network  
Fiber to the home (FTTH) has become more popular worldwide to provide broadband communication services [30]. However, in the Nepalese context, FTTH service has just entered into the market as a major competitor, but Digital Subscriber Line (DSL), basically an Asymmetric DSL (ADSL) service, and cable-based services are still recognized as popular connectivity services that are implemented by service providers. In most of the offices where public switched telephone service is available, Nepal Telecom (NT), the incumbent operator, has provided ADSL connectivity throughout Nepal. ADSL became the proprietary of the incumbent operator in Nepal, hence internet and cable TV services through coax connection is also a popular service adapted by ISPs and cable service providers, but they are mostly city/urban centric. Wireless internet connectivity services provided by the Nepal Wireless project ([[http://www.nepalwireless.net/](http://www.nepalwireless.net/)]([http://www.nepalwireless.net/](http://www.nepalwireless.net/)) (accessed on 24 August 2019), [[http://www.nepalwireless.com.np/](http://www.nepalwireless.com.np/)]([http://www.nepalwireless.com.np/](http://www.nepalwireless.com.np/)) (accessed on 24 August 2019)) in some of the rural areas of Nepal and NT's ADSL services to every rural municipality, including 3G/GPRS and WiMAX services [31], are the basic connectivity available in rural areas. Based on the viability and possibility of network expansion, local wireless services utilizing TV White Space (TVWS) also enabled low cost and affordable services for rural communities.
## 10 Research Challenges and Future Directions
### Ethical and Legal Considerations  
AI annotation agents often reflect and amplify biases present in their training data. When LLMs internalize stereotypes or problematic associations, these issues appear in their annotation outputs. For instance, an agent trained on biased data sometimes flags content from certain dialects as inappropriate or performs with reduced accuracy when labeling images representing underrepresented demographic groups. This propagation of bias through annotations raises serious concerns, as the resulting labels directly shape downstream models. Researchers have found that, although crowdsourced annotations exhibit recognizable human bias patterns, LLM-based annotations introduce new forms of bias that are often more difficult to detect, particularly when systems are trusted without sufficient scrutiny [162].  
AI-driven data-annotation pipelines that rely on LLM agents raise security and privacy concerns such as safeguarding personal information and ensuring decision accountability. A recent review of privacy-preserving techniques for generative AI lists differential privacy, federated learning, homomorphic encryption and secure multi-party computation as effective defenses that keep raw inputs inside the data-controller's trust boundary [163]. Recent studies show that on-premise deployment combined with selective disclosure can mitigate membership-inference and data-reconstruction attacks during labeling tasks [164]. A research for clinical LLMs argues that structured logging is essential for reconstructing how an agent arrived at a label and for measuring bias and accuracy over time [165]. Technical guidance on data provenance in healthcare echoes this point and recommends immutable audit trails that capture prompts, model versions, and chain-of-thought justifications [166]. Recent work on the clinical adoption of LLM systems defines accountability as a triad of auditability, risk management, and redress, reinforcing the need for post-deployment monitoring [167]. Surveys of explainable AI in healthcare find that transparent rationales improve clinician acceptance of automatically generated labels and support downstream bias analysis [168].  
Future research must develop rigorous bias auditing protocols tailored to AI-generated annotations. Strategies include the use of diverse calibration datasets, consensus mechanisms involving multiple models with distinct characteristics, and algorithmic bias mitigation applied directly to generated labels. Involving human reviewers from varied backgrounds in the evaluation of sensitive annotation tasks improves the detection of biased outputs. From an ethical governance standpoint, annotation agents require the same fairness assessments and transparency obligations as other AI systems. Standardized documentation formats, such as adapted model cards, support accountability in this context and help formalize responsible deployment practices.
## 5 AP Localization Using Compressed Sensing Method
As seen in the figure, all the AP locations are estimated with a high level of accuracy. Although the localization results contain some errors, it brings limited effect to our RSS smoothing method proposed later.
## 4 Probability Distribution and Relation
### Relation Analysis
Compared with the cold season (fall and winter), the study site in the warm season (spring and summer) has a higher temperature, higher wind speed, higher MLH, and lower visibility (Figures 5 and 6), thus resulting in more serious dust pollution. Figure 8 shows the probability density distribution between the MLH and the SMEs in the warm season. In the preprocessing stage, the invalid values of SMEs are first removed. Secondly, since the temporal resolution of SMEs is 1 h, the calculated MLH represents the average value within 1 h. Finally, the total number of valid samples during the daytime is 3410. In the warm season, the temperature distribution in the desert ranges from \(-5\,^{\circ}\)C to \(45\,^{\circ}\)C, which represents a large temperature difference. In particular, the temperature is mainly concentrated in the range of \(15\,^{\circ}\)C to \(30\,^{\circ}\)C (Figure 8a). When the MLH is lower than 2 km, the relative humidity is concentrated within the range of \(-10\%\) to \(50\%\). However, when the MLH exceeds 2 km, the center of the relative humidity distribution is located near \(15\%\) (Figure 8b). The wind speed is mostly distributed below 2 m/s (Figure 8c), which meets the wind speed requirements for floating dust weather. According to the national meteorological standard, weather with visibility less than 10 km in the desert is categorized as dust weather, and the proportion of visibility below 10 km (Figure 8d) in the local area is very high. It is worth noting that when the height of ML is approximately 3 km, the temperature (30 \({}^{\circ}\)C), the relative humidity (15%), the wind speed (2 m/s), and the visibility (5 km) all show a clear trend of a concentrated distribution, which indirectly supports the research of [PERSON] [9] on the existence of a dust layer at the height of 3 km. From the analysis of the MLH, it is evident that during the daytime of the warm season, the MLH is widely distributed between 2 km and 4 km. A higher MLH is conducive to the upward diffusion and dilution of dust particles, thereby increasing the likelihood of dust transport to greater heights and distances, ultimately affecting the air quality, climate, and environment in the surrounding areas.
###### Abstract  
In this work, a complex examination of vanadium oxide thin films prepared by gas impulse magnetron sputtering with various Ar\(\mathrm{O_{2}}\) gas ratios of 2:1 \(\div\) 8:1 was conducted. X-ray diffraction revealed the amorphous nature of the prepared thin films, and scanning electron microscopy images showed that the thin films were crack-free and homogenous. Optical properties investigations revealed that a higher oxygen content in the Ar\(\mathrm{O_{2}}\) atmosphere during sputtering caused an increase in transparency. The sample prepared with the highest amount of oxygen in the gas mixture during deposition had 51.1% of the average transmission in the visible wavelength range. A decrease in oxygen caused deterioration in the thin film transparency with the lowest value equal to 21.8%. Electrical measurements showed that the prepared thin films had a semiconducting character with either electron or hole conduction type, depending on the sputtering gas composition. A small amount of oxygen in the gas mixture resulted in the deposition of p-type thin films, whereas an increase in the amount of oxygen caused a change to n-type electrical conduction. Resistivity decreased with increasing Ar\(\mathrm{O_{2}}\) ratio. The gas sensing response toward diluted hydrogen was investigated for all the V\(\mathrm{{}_{x}}\)O\(\mathrm{{}_{y}}\) thin films, but at low operating temperatures, only the p-type thin films exhibited a visible response.  
vanadium oxide; magnetron sputtering; optical properties; electrical properties; p-type thin film; hydrogen gas sensing; hardness Article
# The Use of Transfer Learning for Activity Recognition in Instances of Heterogeneous Sensing
## 1 Introduction
### Problem Statement and Contribution  
The underlined problems are that while transfer learning offers a mechanism to leverage small labelled datasets, current research efforts are restricted to sensing technology, which relies on a similar feature-space. Simultaneously, current alternatives tend to be impractical due to the inconvenient method of labelling intervention that increases the time and resources costs, as well as the effort investment.  
T/L modelling presents a new perspective on transfer learning that enables the transferability of knowledge in scenarios where the feature-space is different; however, it introduces some challenges. A major challenge is that the accuracy of the teacher limits the accuracy of the Learner, as the system's only source of a ground truth comes from the teacher, and, thus, the learner is entirely reliant upon the teacher. Hence, it remains to be explored whether the Learner can outperform the teacher.  
This problem presents the opportunity to investigate the benefit of MWE sensing technology and, together with its sensing characteristics, to train a classification model underlined by transfer learning techniques to mitigate the insufficiency of labelled data for ADL problems when the feature-space is different.  
The key contributions of this paper are two:  
* TL-FmRADLs: a framework that integrates the T/L, AL and data fusion techniques that offers to optimise the performance of learner AR models regardless of the sensing technology source;
* InSync: an open-access dataset that consists of subjects performing activities of daily living while data are collected using three different sensing technologies to facilitate the community future research in transfer learning.  
The rest of the paper is organised as follows. The next Section 2 presents the background of the T/L, AL, and fusion techniques in a broader sense to build the foundations and scope of our investigation. Then, in Section 3 we introduce TL-FmRADLs and explain its different algorithms in the subsequent sub-sections. The experiment setup is presented in Section 4, followed by the introduction of InSync Dataset in Section 5. Section 6 presents implementation details. Results and Discussion are presented in Sections 7 and 8, respectively. In the penultimate Section 9, we highlight research issues and potential future directions, whereas the final Section 10 presents the concluding remarks of this paper.
# Performance Analysis of a Stand-Alone PV/WT/Biomass/Bat System in Alrashda Village in Egypt
###### Abstract  
This paper presents an analysis and optimization of an isolated hybrid renewable power system to operate in the Alrashda village in the Dakhla Oasis, which is situated in the New Valley Governorate in Egypt. The proposed hybrid system is designed to integrate a biomass system with a photovoltaic (PV), wind turbine (WT) and battery storage system (Bat). Four different cases are proposed and compared for analyzing and optimizing. The first case is a configuration of PV and WT with a biomass system and battery bank. The second case is the integration of PV with a biomass system and battery bank. The third case is WT integrated with biomass and a battery bank, and the fourth case is a conventional PV, WT, and battery bank as the main storage unit. The optimization is designed to reduce component oversizing and ensure the dependable control of power supplies with the objective function of reducing the levelized cost of energy and loss of power supply probability. Four optimization algorithms, namely Heap-based optimizer (HBO), Franklin's and Coulomb's algorithm (CFA), the Sooty Term Optimization Algorithm (STOA), and Grey Wolf Optimizer (GWO) are utilized and compared with each other to ensure that all load demand is met at the lowest energy cost (COE) for the proposed hybrid system. The obtained results revealed that the HBO has achieved the best optimal solution for the suggested hybrid system for case one and two, with the minimum COE 0.121171 and 0.1311804 $/kWh, respectively, and with net present cost (NPC) of $3,559,143 and $3,853,160, respectively. Conversely, STOA has achieved the best optimal solution for case three and four, with a COE of 0.105673 and 0.332497 $/kWh, and an NPC of $3,103,938 and $9,766,441, respectively.  
PV; wind turbine; biomass system; heap-based optimizer; Franklin's and Coulomb's algorithm; sooty term optimization; energy cost
# Sensor Network Environments: A Review of the Attacks and Trust Management Models for Securing Them
## 4 Attack Types
### Payload/Active Attacks  
These types of attacks are launched from compromised nodes after access is gained and trust is established. These types of attacks can be very destructive to a network and the goal of the trust models would be to detect and eject compromised nodes before they can gain enough power to launch such attacks effectively.  
**Malicious software spreading**. This is the spreading of software such as worms or viruses through a network from a compromised node. These worms and viruses can compromise more nodes and propagate throughout the network, affecting the operation of compromised nodes in any way that the attacker intended when creating the malware. These attacks can be devastating to a network.  
**Packet modification**. In this scenario, a compromised node modifies all or some of the packets that it is supposed to forward [43]. This allows the attacker to insert the data it wants into the packets, such as false data readings or malware. Data pollution attacks are also very similar to packet modification and involve the injection of corrupted data into the network to disrupt normal operation.  
**Selective forwarding**. Otherwise known as packet dropping, this is where a compromised node drops all or some of the packets that it is supposed to forward [43]. This causes data to be delayed in transit or lost completely. Nodes can also store packets and forward them later in a replay attack. A replay attack can be mitigated using random numbers or timestamps.
# Artificial Intelligence of Things as New Paradigm in Aviation Health Monitoring Systems
## 3 Results
### AloT-Enabled Aircraft Communication Architecture: Enhancing Health Monitoring through WDAR
Additionally, the current use of 2G/3G technology suggests that there is room for improvement in data transmission speeds, which could be addressed with the adoption of more advanced cellular technologies like 4G LTE or 5G in the future. The aircraft communication architecture with the WDAR option represents a significant step towards fully realizing the potential of AIoT in aviation health monitoring. By combining extensive data collection capabilities with diverse communication channels and advanced analytical systems, this architecture lays the groundwork for a new era of predictive, efficient, and safe aviation operations. As AIoT technologies continue to evolve, we can expect even greater integration of real-time data analyses and automated decision-making processes, further revolutionizing the field of aviation health monitoring.
## 4 Positioning Algorithms
### Positioning Optimization Model and Computational Complexity Analysis  
In addition to heuristic localization algorithms such as the triangle centroid algorithm (TCA), inner triangle centroid algorithm (ITCA), and intersection midpoint algorithm (IMA), this study incorporates a mathematical optimization model to establish a reference for positioning accuracy. The optimization framework is designed to minimize the discrepancy between the measured and calculated distances from the tag to the anchor nodes [23; 24; 25; 26].  
Let \(A_{i}(x_{i},y_{i})\) denote the coordinates of the \(i^{th}\) anchor node, and \(D_{i}\) represent the measured distance from the tag to anchor \(i\). The unknown tag location is denoted as \(\mathrm{T}(x,y)\). The objective is to minimize the sum of squared errors between the measured distances  
Figure 6: Model of the inner triangle centroid algorithm.  
Figure 7: Model of intersection midpoint algorithm.  
and the Euclidean distances from the estimated position to the anchors. This leads to the following nonlinear least squares optimization formulation [27]:  
\[\underset{x,y}{min}\sum_{i=1}^{N}{(\sqrt{{{{(x-x_{i})}^{2}}+{{(y-y_{i})}^{2}}}}-{{ D_{i}}})}^{2}, \tag{12}\]  
This model represents a nonlinear optimization problem, commonly solved using iterative numerical techniques such as the Levenberg-Marquardt algorithm, Gauss-Newton method, or gradient descent. Such methods iteratively update the estimated coordinates (x, y) until convergence is reached, typically requiring several iterations depending on initial conditions and system noise.  
To evaluate the practical efficiency of each positioning approach, the computational complexity of the proposed algorithms is analyzed. The TCA operates with constant-time complexity O(1), as it requires only basic geometric computations to determine intersection points and the centroid of a triangle. This makes it well suited for deployment on lightweight, low-power embedded devices. Similarly, the ITCA also exhibits O(1) complexity but involves a greater number of intersection evaluations, resulting in a slightly longer execution time, as reflected in the performance data presented in Table 1. The IMA maintains constant-time complexity as well, offering the simplest structure and the shortest execution time among the three heuristic approaches. In contrast, the optimization-based method relies on iterative numerical techniques and has a time complexity of O(kN), where N is the number of anchor nodes and k is the number of iterations required for convergence. Although it typically yields higher accuracy, it imposes a significantly greater computational burden.
# Quad-Port Multiservice Diversity Antenna for Automotive Applications
## 6 Conclusions  
A MIMO/diversity antenna for automotive communications is designed and developed in this paper. Stubs are integrated into the UWB monopole antenna element to achieve resonance at 1.5 GHz and 2.45 GHz. The automotive antenna must receive signals from all directions; therefore, the antenna elements are arranged orthogonally to each other. The orthogonal arrangement of the antenna elements also improves inter-element isolation. The antenna is fabricated and tested for diversity performance, and the obtained results show that the ECC is less than 0.4, DG is greater than 9 dB, TARC is greater than \(-\)10 dB, and CCL is less than 0.4 bits/s/Hz. The proposed MIMO antenna could be helpful for GPS, RFID/Bluetooth/Wi-Fi, and V2V communications.  
Conceptualization, L.K., S.K.P., M.K. and S.K.; methodology, L.K. and S.K.P.; software, L.K.; validation, L.K., S.K.P., M.G.N.A. and T.R.R.; formal analysis, L.K., M.G.N.A. and T.R.R.; investigation, L.K. and L.W.; resources, S.K.P., S.K. and L.W.; data curation, L.K. and M.K.; writing and original draft preparation, L.K.; writing review and editing, S.K., S.K.P. and L.W.; supervision, S.K.P. and S.K.; project administration, S.K.P., S.K. and T.R.R. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the International Science and Technology Cooperation Project of the Shenzhen Science and Technology Commission (GJHZ20200731095804014).  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.
# Vibration Damage Analysis of Bottom Hole Assembly Under Axial Impact Based on Dynamic Analysis
## 2 Impact Simulation Model
### Rock Constitutive and Failure Criteria  
Rock materials exhibit characteristics such as nonlinearity, elastoplasticity, viscoelasticity, dilatancy, and anisotropy. In rock mechanics, the commonly used constitutive models for rock materials include the Mohr-Coulomb and Drucker-Prager (D-P) criteria [22; 23]. The D-P criterion is based on the Mohr-Coulomb model but incorporates the influence of hydrostatic pressure, allowing for a better representation of material behavior under complex stress conditions. In the D-P model, the rock is treated as an isotropic damage material, where the degradation of stiffness is characterized by a damage variable D. The value of D ranges from 0 to 1, with D = 1 indicating a complete loss of strength, representing rock failure, spalling, or fragmentation. The stress-strain relationship of the rock is illustrated in Figure 6.  
The standard definition of rock damage is given as follows:  
\[\varepsilon^{p}\leq\bar{\varepsilon}_{f}^{p\!l} \tag{9}\]  
where \(\bar{\varepsilon}_{f}^{p\!l}\) is the equivalent plastic strain of rock failure and \(\varepsilon^{p}\) is the equivalent plastic strain.  
The rock damage variable is defined as:  
\[D=1-\frac{E\!t}{E} \tag{10}\]  
\(E\!t\) and \(E\) are the elastic moduli of the damaged and undamaged material, respectively.