esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
When the incident wave toward a dielectric bar is a plane-wave, the electromagnetic wave travelling along the dielectric bar or an array of such bars is investigated using scattering theory [13; 14; 15]. However, research has mainly focused on a situation in which the incident direction is perpendicular to the bar. However, when the incident direction is parallel to the bar or the array, i.e., from the left side to the right side as shown in Figure 1, the field distribution along the dielectric bar and the excited eigenmodes has not been discussed. To study the modes and the field distribution along the RDLB under the specific incident plane wave, knowledge of the eigenmodes of the electromagnetic wave travelling along such bars is required; this was investigated once [16; 17]. It was found that there is an energy transition between the guided mode and the leaky mode [18]. As a result, a beat wave pattern can appear when the guided mode and the leaky mode are excited  
Figure 1: Schematic diagram of microwave radiation effect experiment in wind tunnel. A matching port is assumed at the left end for simplicity. The right end extends periodically toward the end of the wind tunnel. simultaneously. The theoretical calculation for the guided modes has been proven to agree well with accurate numerical solutions. However, to the best of our knowledge, the analysis of the leaky mode propagating along rectangular dielectric bars has not yet been presented. The beat wave phenomenon has also been investigated previously for different guiding systems, for example, a raised-strip waveguide [19], a metal-dielectric interface SPP waveguide [20] and a fiber waveguide suspended above a slab [21]. When the incident wave travels along the longitudinal direction of the RDLB, the beat wave pattern can be significant. To the best of our knowledge, a study of the beat wave pattern on dielectric bars under such a plane wave excitation has not yet been presented. In this paper, efforts were made to analyze the leaky mode on the RDLB and the longitudinal beat wave distribution along such bars under the plane wave excitation with a zero incident angle. For microwave absorption in a wind tunnel as shown in Figure 1, the absorbing array of such bars is also studied with numerical simulation. In Section 2, a beat wave pattern is introduced and it is found that the beat wave consists mainly of a leaky wave and a guided wave. In Section 3, the modes are analyzed using field matching theory and the calculated phase constants are verified. In Section 4, an absorbing array of a lossy dielectric bar is investigated with CST software for absorbing microwave power in a wind tunnel microwave radiation experiment. Finally, the conclusion is presented in Section 5.
## 3 Dynamic Routing Algorithm for Inter-Satellite Network Based on Q-Learning
### Q-Learning  
In Q-learning, which belongs to the field of reinforcement learning, agents/decision makers try to interact with the environment by learning the behavior of dynamic systems. In this chapter, the agent receives the current state and rewards of the dynamic system, and then performs corresponding actions based on its experience to increase long-term revenue through state transitions. States and rewards represent data the agent receives from the system, while actions are the only input to the system. Unlike supervised learning, in Q-learning, the agent must find the optimal action to maximize the reward while the agent's actions not only affect the current reward, but also affect future rewards.  
A trade-off exists in Q-learning between exploitation and exploration. Unknown actions are investigated in order to avoid missing better candidate actions, but because they are unpredictable, they may worsen network performance. On the other hand, if action isdependent on the present best action choice, it may result in a local optimal solution even when other as yet undiscovered actions might offer more advantages.  
Figure 4 describes the basic Q-learning algorithm flow, and its specific execution steps are described as follows:  
**Step 1**: Initialize the Q value. This stage constructs a Q-table, where the rows represent the state space and the columns represent the action space, and are initialized to 0.  
**Step 2**: Throughout the lifetime, repeat Step 3-Step 5 until the set number of stop training is reached.  
**Step 3**: Based on the current Q value estimation state, select an action from the optional action space, which involves the trade-off between exploration and exploitation in Q-learning. The most commonly used strategy, for example, \(\varepsilon-greedy\), adopts an exploration rate as the random step size. It can be set larger initially, because the Q value in the Q-table is unknown, and a large amount of exploration needs to be performed by randomly selecting actions. Generate a random number and if it is greater than \(\varepsilon\), the action will be selected using known information, otherwise, it will continue to explore. As the agent becomes more confident in the estimated Q value, \(\varepsilon\) can be gradually reduced.  
**Step 4-5**: Take action \(\alpha\) and observe the output state, evaluate the reward, then use the Bellman equation to update the \(Q(s,a)\) function.
# Evaluating GNSS Receiver Resilience: A Study on Simulation Environment Repeatability
## 6 Discussion
The repository further offers a complete description of the data structure and illustrative examples for data manipulation, written in Python 3.13. Moreover, it contains the scripts used to generate the figures presented in this work. This comprehensive collection aims to showcase the dataset's broad applicability and inspire further research utilizing this resource. Although this publication presents a focused analysis of the collected data, the full dataset unlocks a wide array of potential use cases, ranging from more granular response analysis and early jamming detection to the implementation of AI-driven time series algorithms for jamming mitigation. Future investigations will expand to multi-frequency receivers and simulators, capturing more detailed receiver data, including spectrum observations and, importantly, capturing receiver responses under spoofing scenarios, similar to the dataset presented in this research. We are currently developing a receiver network for automated GNSS spectrum monitoring across a wide geographical area, with plans for long-term signal sampling at diverse locations. This will allow for the observation of region-specific variations in receiver behavior due to satellite visibility and elevation differences, contrasting with data from densely populated areas like Central Europe. Ultimately, laboratory-based interference response evaluations, as presented here, and the planned spoofing response analysis are vital for understanding and predicting receiver behavior in real-world scenarios. Conceptualization, A.B. ; Methodology, A.B. ; Validation, A.B. ; Investigation, A.B. ; Data curation, A.B. ; Writing--original draft preparation, A.B. ; Writing--review and editing, A.B. ; Visualization, A.B. ; Supervision, B.B. All authors have read and agreed to the published version of the manuscript. This research is supported by the Slovenian Research and Innovation Agency (ARIS) under grant numbers V2-2342 and L7-60164. To facilitate further research, the complete dataset, its data structure, and example code demonstrating its use are available in the following public repository for download: [[https://github.com/aljazblatnik/GNSS](https://github.com/aljazblatnik/GNSS)]([https://github.com/aljazblatnik/GNSS](https://github.com/aljazblatnik/GNSS)) (accessed on 20 April 2025). The authors declare no conflicts of interest.
# External Validation of Accelerometry-Based Mechanical Loading Prediction Equations
###### Abstract  
Accurately predicting physical activity-associated mechanical loading is crucial for developing and monitoring exercise interventions that improve bone health. While accelerometer-based prediction equations offer a promising solution, their external validity across different populations and activity contexts remains unclear. This study aimed to validate existing mechanical loading prediction equations by applying them to a sample and testing conditions distinct from the original validation studies. A convenience sample of 49 adults performed walking, running, and jumping activities on a force plate while wearing accelerometers at their hip, lower back, and ankle. Peak ground reaction force (pGRF) and peak loading rate (pLR) predictions were assessed for accuracy. Substantial variability in prediction accuracy was found, with pLR showing the highest errors. These findings highlight the need to improve prediction models to account for individual biomechanical differences, sensor placement, and high-impact activities. Such refinements are essential for ensuring the models' reliability in real-world applications, particularly in clinical and biomechanical research contexts, where accurate assessments of mechanical loading are critical for designing rehabilitation programs, injury prevention strategies, and optimizing bone health interventions.  
ground reaction force; loading rate; accelerometers; validation; biomechanics Article
# Fixed Wireless Access in Flexible Environment: Problem Definition and Feasibility Check
## 3 Assumptions and Inputs
### Time Granularity for Traffic Design  
Assuming both dynamic service usage and existing dynamic traffic in the network [30; 31; 32], the required and available connection parameters are defined for a series of timeslots (e.g., for every hour of the day/week, or even for every 15 min) as provided by the Communications Service Provider (CSP) and service definitions. This granularity must be synchronized in advance. If no timeslots are defined, a single value is used. If timeslots are defined for every 15 min of the week, it is a \(4\times 24\times 7=672\)-dimensional vector and requirements must be fulfilled for all 672 timeslots.
# SVDC: Synchronous Update with the Division and Combination of SRv6 Policy
## 3 Policy-Dividing and Combination Mechanism in SUDC
### SRv6 Policy Pushing  
The SRv6 policy-pushing module distributes the SID blocks derived from the SRv6 policy-dividing module to the target routers. This function is carried out on the control plane. SRv6 policy-pushing module consists of four steps: push initiation, BitString mapping, BIERv6 multicast push, and push completion.  
SRv6 policy pushing relies on BIERv6 [25], which enables the network to specify the set of destination nodes at the multicast source endpoint. In BIERv6, BitString is set at the source endpoint to specify the destination node set for multicast. Subsequent nodes replicate and forward the multicast packet based solely on the BitString. Therefore, BIERv6 avoids the resource and time overhead required to build and maintain multicast trees in the traditional multicast approach.
# Real-time Conflict Resolution Algorithm for Multi-UAV Based on Model Predict Control
## 3 Modeling of the Conflict Resolution Problem
### Modeling of Conflict Management  
In this study, a distributed control model is adopted for UAVs to communicate. For all the UAVs in a certain range, they broadcast their own predicted trajectories to each other. If at _k_th time, thereexist two or more UAVs with distance between them of less than the safe range, then these UAVs will be predicted as \"conflicting\". The predicted conflict UAV set is defined as below:  
\[\begin{split} Cf_{e}(k)=\Big{\{}(S_{i},S_{j})\Big{|}i,j\in\{1\cdots N _{v}\},\exists k\in\Big{\{}1\cdots t_{f}\Big{\}},r_{ij}(k)<r_{safe}\Big{\}}\\ \epsilon\in\{1\cdots N_{Cf}\Big{\}}\end{split} \tag{6}\]  
where, \(N_{Cf}\) is the number of conflict groups at \(k\)th time, \(S_{i},S_{j}\) are the predicted trajectories of UAVs with conflict, as shown in Figure 2. Thus, all the resolutions are calculated and maintained by the members in the coordination group independently, based on the information broadcast by the group members.  
The concept of a state machine is used to model the UAV flight process. The state machine is a mathematical model that represents a limited number of states and behaviors, such as transitions and actions between these states. It mainly consists of the following parts: states, events, actions, and transitions, as shown in Figure 3.  
Considering the issue of multi-UAV trajectory coordination [19], suppose that UAVs in flight have only two states, namely: (1) flying towards the target mission point; (2) performing adjustment for the conflict resolution. To describe the switching condition of the state machine between these two states [20], a reference early warning distance variable \(r_{dec}\) is defined. As shown in Figure 4, when the distance between two UAVs is less than \(r_{dec}\), the UAV will change from the cruising state to the adjustment state, as shown in Figure 5.  
Figure 4: Reference distance of UAV.  
Figure 3: Principle of state machine.  
Figure 2: Information exchange of distributed UAV control system.  
Step1. At \(k\)th time, get all UAV state variables:  
\[\mathbf{x}_{i}(k)=[x_{i}(k),z_{i}(k),\psi_{i}(k)],\ \ i=1,2,3,\ldots,N_{V};\]  
Step2. For UAV \(i\), identify all UAV \(j\) so that  
\[\sqrt{\left[x_{i}(k)-x_{j}(k)\right]^{2}+\left[z_{i}(k)-z_{j}(k)\right]^{2}} \leq r_{dec}\]  
Construct the conflict set \(Cf_{\varepsilon}=\left\{S_{i},S_{j}\right\}\), \((\varepsilon=i)\);  
Step3. If \(Cf_{\varepsilon}=\varnothing\), then the UAV \(i\) flies towards the target point according to the original flight status;  
Otherwise, adjustment optimizations are performed on the UAV \(i\) and \(j\);  
Step4. For \(i=i+1\); \((\varepsilon=\varepsilon+1)\), repeat Step 2-3, until the maximum number of UAVs.
# Microwave and Ultrasound Based Methods in Sludge Treatment: A Review
## 5 Ultrasound Treatment
### Ultrasound-Alkaline Combined Treatment  
As mentioned earlier, ultrasonic irradiation, more specifically the extreme local temperatures caused by ultrasonic cavitation can lead to the production of H and OH radicals. These oxidative radicals can play a significant role in sludge disintegration and solubilization, as well as in anaerobic digestion. However, it is worth noting that the formation of these free radicals depends on the pH of the medium and can be controlled or enhanced by using alkalis.  
This and other phenomena caused by the combined effects of ultrasound and alkaline treatment of wastewater and sludge have been shown in several studies. [PERSON] et al. described that COD solubilization caused by ultrasonication was higher when using it with an alkaline combination [70]. The solubilization of COD may be caused by three different processes: solubilization by alkali, ultrasound or by the synergistic effect of these two. [PERSON] et al. in 2010 have proved that the efficiency of the combined ultrasound/alkaline treatment exceeded the effectiveness of the alkali and ultrasound treatment individually, thus proving that the effects of the two are additive [71]. [PERSON] et al. found that the solubilization of proteins that originated from microbial cells were significant (around 67%) when applying ultrasound and NaOH treatment in a combination [72]. Combined ultrasound/alkaline treatment can also be used for enhancing the anaerobic fermentation of sludge. In the study of [PERSON] et al., it was shown that using 0.05 mol/L alkaline treatment in a combination of 21 kJ/g TS ultrasound irradiation, the biodegradability increased by almost 21%, compared to the control sample [73]. [PERSON] et al. also concluded that ultrasound/alkaline pre-treated sludge resulted in 42.1% higher methane production (with a solids retention time of 10 days) than that obtained for the untreated sludge [74]. [PERSON] et al. showed that methane productivity can be enhanced via US-alkaline combined treatment; methane production rate was enhanced to 0.15 m\({}^{3}\) CH\({}_{4}\)/m\({}^{3}\) reactor/d, which means a 3 times improvement compared to the control [75].
# A Programmable SRv6 Processor for SFC
## 3 Protocol-Independent Pipeline Design
### Programmable SR Forwarding for Network Slice  
A programmable SRv6 processor has wide application scenarios, such as SR, VXLAN, SRoU [24], INT [25], and SFC orchestration [26]. The SR technology support includes MPLS SR [27], SRv6, and multiple semantics for SIDs.  
The MPLS SR and SRv6 are two main application forms of SR technology. SRv6 has much more powerful network programmable ability than MPLS SR, thanks to its flexible SRH extension header.  
Figure 3: Example of PDT processing.
## 3 Results and Discussion  
### FTIR Spectroscopy  
Infrared spectra of the NR starting material and the obtained LNRs at various pH were compared, as shown in Figure 3. The obtained LNR exhibited the absorption peaks of C=C stretching and =C-H bending at 1665 and 834 cm\({}^{-1}\), similar to those observed in the NR. The FTIR spectra of LNR samples prepared at different pH showed a few new peaks compared to the NR. For the LNR sample prepared in an acid medium, a broad peak located at 3400 cm\({}^{-1}\) was assigned for the stretching mode of the hydroxyl groups of LNR chains. Meanwhile, the presence of other groups on the LNR chains prepared in neutral and alkaline media can be attributed to the appearance of carbonyl peaks (C=O) at 1720 cm\({}^{-1}\) for ketone and 1739 cm\({}^{-1}\) for aldehyde, respectively.  
The chain breaking of NR at the carbon-carbon single bond is usually due to an attack of radicals and leaving a hydroxyl end group. In contrast, an oxidizing agent can oxidize the broken carbon-carbon double bond in the NR chains to leave a carbonyl end group. Mechanisms of LNR samples prepared via oxidative degradation using NaNO\({}_{2}\) and H\({}_{2}\)O\({}_{2}\) reagents were found to have different end groups, hydroxyl, and carbonyl, depending  
Figure 3: FTIR spectra of LNR samples prepared at different pHs using 0.4 M H\({}_{2}\)O\({}_{2}\) and 0.02 M NaNO\({}_{2}\) at 65 \({}^{\circ}\)C for 12 h.  
on the pH of the system used. H2O2 and NaNO2 reacted to form peroxnitrous acid, which decomposed to form *OH radicals at pH \(<\) 7. The hydroxyl radicals attacked the carbon-carbon single bond of polyisoprene chains. Breakage of polyisoprene chains by hydroxyl radicals then yielded LNR with hydroxyl end groups, as shown in Scheme 1. At pH \(\geq\) 7, hydrogen peroxide preferred to oxidize carbon-carbon double bond to form epoxy rings, which were attacked by nitrite ion (NO2\({}^{-}\)) during degradation, thus yielding chain breaking. The result was that the polyisoprene chains were getting shorter and had carbonyl end groups (Scheme 2). Similar mechanisms of LNR with different end groups prepared in different pH conditions were proposed by [PERSON] et al. [16].  
**Scheme 1.** The degradation of the NR chain using H2O2 and NaNO2 in an acid medium.  
**Scheme 2.** The degradation of the NR chain using H2O2 and NaNO2 in neutral and alkaline media.
###### Abstract  
Disasters are crisis circumstances that put human life in jeopardy. During disasters, public communication infrastructure is particularly damaged, obstructing Search And Rescue (SAR) efforts, and it takes significant time and effort to re-establish functioning communication infrastructure. SAR is a critical component of mitigating human and environmental risks in disasters and harsh environments. As a result, there is an urgent need to construct communication networks swiftly to help SAR efforts exchange emergency data. UAV technology has the potential to provide key solutions to mitigate such disaster situations. UAVs can be used to provide an adaptable and reliable emergency communication backbone and to resolve major issues in disasters for SAR operations. In this paper, we evaluate the network performance of UAV-assisted intelligent edge computing to expedite SAR missions and functionality, as this technology can be deployed within a short time and can help to rescue most people during a disaster. We have considered network parameters such as delay, throughput, and traffic sent and received, as well as path loss for the proposed network. It is also demonstrated that with the proposed parameter optimization, network performance improves significantly, eventually leading to far more efficient SAR missions in disasters and harsh environments.  
U
# Towards the Unmanned Aerial Vehicles (UAVs): A Comprehensive Review
## 3 Unmanned Aerial Vehicles (UAVs)
### Classification of UAVs  
UAVs are available with different specifications, equipment, sizes, range, and shapes. In the market, UAVs are present with a different number of rotors or propellers as shown in Table 2[44]. UAVs have been developed with different engines and wing structures. UAVs can communicate using both short and long range wireless technologies, and their sizes can be classed as nano, micro, or large. They are excellent possibilities for providing cellular connectivity due to continuing and developing advances. Drones are equipped with First Person View (FPV) goggles, a Global Positioning System (GPS), sensors, stabilizers, and cameras. In this study, four different UAV categories are termed as fixed-wing, fixed-wing hybrid, single rotor, and multirotor as shown in Table 3[45]. Fixed-wing UAVs are based on wings, main body, motor, and propeller. These UAVs require extensive skills-based training to operate and can vertically balance in the air for about sixteen hours, but they cannot move backward, hover, or rotate. Thus, they are not useful for some tasks such as aerial photography. Figure 4a shows an example of a fixed-wing UAV [46]. These UAVs are commonly used for power line inspections and aerial mapping. On the other hand, fixed-wing hybrid UAVs are based on automation and manual gliding. These UAVs are not good at forward flight and hovering. Figure 4b presents fixed-wing hybrid UAV [47]. Furthermore, single rotor UAVs are expensive and require skill training for operation. These UAVs are mechanically complex and vulnerable to challenges like vibrations. Figure 4c shows an example of a single rotor UAV [48]. Moreover, the cheapest and most easily fabricated UAVs are multirotor UAVs. These UAVs are commonly used for imaging and video surveillance. Multirotor UAVs can be tricopters, quadcopters, hexacopters, or octocopters as shown in Table 2. Quadcopters are the most commonly used UAVs, as illustrated in Figure 4d. Quadcopters have gained attention due to their vertical landing, fast maneuverability, high agility, and takeoff capabilities, along with their simple structure, cost-effectiveness, and small size.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Types of UAVs** & **Number of Propellers** \\ \hline October & 8 \\ Hexacopter & 6 \\ Quadcopter & 4 \\ Tricopter & 3 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Categories of UAVs based on number of propellers.  
Figure 3: Classification of UAVs [38; 39; 40; 41; 42; 43].