esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Simulation Results
### Search Delay  
In this section, we study the search delay of TS, and compare it with that of ERS and XYLS. The search delay is defined as the duration from the time that the source broadcast the search packet to the time that the actor receives the search packet.  
At first, the performance of search delay is studied with different threshold value of trail age \(\varepsilon\). The \(\varepsilon\) varies from 0 to 100, and the \(\tau_{A}\) is fixed at 5. As shown in Figure 12, with the help of the trail, the search delay in TS and TS-R are less than that in ERS. Moreover, the search delay in TS and TS-R decreases with the growth of \(\varepsilon\). The reason for this is that, when the threshold \(\varepsilon\) increases, the number of search attempt decreases, which further results in a reduced delay. On the other hand, the search delay in XYLS is lower than that in ERS. It is because the failure of search attempt in ERS greatly increases the search delay. Nevertheless, TS has less search delay than XYLS when \(\varepsilon>50\), since the number of search attempts in TS is smaller with a longer trail.  
Then we study the search delay with different waiting time of actor \(\tau_{A}\). The \(\tau_{A}\) grows from 3 to 8, while the \(\varepsilon\) is set to be \(20\times\tau_{A}\) to ensure the actor trails have the same length. As shown in Figure 13, in TS and TS-R, the search delay decreases as \(\tau_{A}\) increases. We have proved in Equation (15) that the difference between the delays in ERS and TS will increase with the growth of \(\tau_{A}\). The results in Figure 13 verify our analysis. A larger \(\tau_{A}\) means the actor moves slower and the packet can catch up with the actor more quickly. The redundancy reduction scheme has no influence on the search delay, thus the search delay in TS and TS-R are almost the same.  
Figure 11: Redundancy ratio of TS-R.
## 3 Construction of the Risk Assessment Model
### Triangular Fuzzy Analytic Hierarchy Process
#### 3.2.1 Introduction to Triangular Fuzzy Numbers  
For a fuzzy number \"M\" defined on the real domain R, if its membership function is expressed as [26]:  
\[\mu_{M}(x)=\left\{\begin{array}{ll}0&(x<1)\\ \frac{x-1}{m-1}&(1\leq x<m)\\ \frac{u-x}{u-m}&(m\leq x<u)\\ 0&(x\geq u)\\ \end{array}\right. \tag{1}\]  
In the equation, if \(\mu_{M}\) (x) \(\in\) [0,1], x \(\in\) R, the fuzzy number M is referred to as a triangular fuzzy number, denoted as M = (l, m, u) (l \(\leq m\leq u\)). Here, l, u, and m respectively represent the lower bound, upper bound, and the most plausible value within the possibility interval. The triangular fuzzy distribution is illustrated in Figure 6, and when l = u = m, M becomes a crisp (precise) number. While the traditional Analytic Hierarchy Process  
Figure 5: Expert system analysis method. Footnote 1: Experts mark a check (\(\surd\)) at the designated impact value for each respective indicator based on subjective comparative judgments.  
(AHP) is straightforward and easy to use, its representation of subjective decisions using a single numerical value limits the accuracy of evaluations to some extent. Triangular fuzzy numbers express a range of possibilities, and the accuracy of the Triangular Fuzzy Analytic Hierarchy Process (FAHP) does not lie in quantifying fuzzy problems in advance but rather in preserving fuzziness to the maximum extent during mathematical operations [27].
# Detection and Localization of Multiple Damages through Entropy in Information Theory
## 5 Results
### Multiple Damage Detection and Localisation  
Four damage scenarios are here reported: 80%/20%/0% and its specular 0%/20%/80%, plus 80%/0%/40% and its specular 40%/0%/80%. In the first two cases, one localized and one large-spread damaged area were considered. In the latter pair, the two smaller damaged areas were included, with alternating severity, while the ring-like damage was decactivated. These two sets are reported, respectively, in Figures 5 and 6. Both figures report the noise-free results for the optic fibers deployed at \  
As expected, the insertion of the damage caused a localized decrease of the Wiener entropy. This confirms the findings of previous studies [10,11,17].  
As can be seen, the variation in the measured WE depended both on the z- and the \(\theta\)-coordinate. This allowed us to locate the damaged portion(s) in cylindrical coordinates.  
In both Figure 5a,b, the ring-like damage #2 is visible in all four fibers (\(\theta=0^{\circ}\), \(90^{\circ}\), \(180^{\circ}\), and \(270^{\circ}\)). It is possible to infer from this the existence of one large example of damage and therefore to evaluate its large spatial extension (although, potentially, the same results might be due to four distinct damages located at the same z coordinates; this ambiguity cannot be solved with the spatial resolution applied in this case study, i.e., 0.10 m). On the other hand, the two smaller examples of damage are only detected by their closest sensor (\(\theta=90^{\circ}\) and \(\theta=0^{\circ}\), respectively).  
In noise-free conditions, the insertion of perfectly identical damage at different locations returned identical changes in the spatial distribution of the spectral flatness index. This can be easily visualized in the second set of results as well (Figure 6).  
Figure 6: The second set of multi-damaged scenarios: (**a**) 80%/0%/40%; (**b**) 40%/0%/80%. Noise-free measurements.  
Figure 7 reports the results for the damage scenarios with an artificially added 10% noise. The two cases portrayed there represent the two combinations, with 80%/20%/0% and 40%/0%/80%. These can be directly compared with Figures 5a and 6b, respectively. One can see that the presence of noise causes the effects of damage to decay more rapidly, moving away from the damage location. In the exact damage location, the inhomogeneity effects are noticeable and even increased significantly in their absolute value. However, this behavior is reached with a contextual absolute value increase of the health state indicator far from the damaged area.
## 1 Introduction
According to [21], by using SACI combustion, it is possible to obtain engine operation and control of the combustion phase at both high and low loads. However, when very lean mixtures are used, it is challenging to initiate combustion by spark discharge, which leads to unstable combustion. The ignition of very lean mixtures could be enabled by providing more fuel in the vicinity of the spark. which is a major driver of the idea of using pre-chamber induced HCCI combustion. The pre-chamber induced HCCI (PC-HCCI) combustion uses flame propagation through an active pre-chamber to induce HCCI in the main chamber. The spark ignition in the pre-chamber is enabled by providing near stoichiometric conditions through direct injection of fuel into the PC, while the conditions in the main chamber are such that the change in pressure caused by combustion in the pre-chamber initiates HCCI combustion. The initial PC-HCCI experimental work has been studied in [22] and it has been found that the PC-HCCI combustion mode ensures stable operation at low loads, extends the lean limit, and maintains higher efficiency when compared to the PCSI combustion mode. Compared to the HCCI mode, PC-HCCI has been achieved at an intake air temperature of 80 \({}^{\circ}\)C, which is a decrease of 50 \({}^{\circ}\)C compared to pure HCCI combustion in the same engine. The initial results showed a proof of concept. However, to explore the benefits and limitations of the concept, further data and research are required. In this study, experiments on the PC-HCCI concept have been continued with the aim of providing insight into the stability of PC-HCCI combustion, i.e., the ability to use the fuel injection and spark timing (PC-HCCI combustion control parameters) for control of combustion timing at changeable boundary conditions. Additionally, the paper provides performance results of PC-HCCI combustion at variable boundary conditions and compares results of PCSI, PC-HCCI, and HCCI combustion modes measured on the same engine. The study was performed at mid to low load (3-3.5 bar) so that previously presented results of HCCI mode [19] and of PCSI [6] can be used for comparison.
# Soil Erosion Assessment and Prediction in Urban Landscapes: A New G2 Model Approach
## 2 Materials and Methods
### Soil Erosion Using the G2 Model
#### 2.4.1 Rainfall Erosivity (Factor R)  
Rainfall erosivity represents the potential of rain intensity to affect the erosion of a certain type of soil in a certain period [29]. The factor is a measure of the erosion intensity of rain, i.e., an index that defines the interdependence of precipitation and the production of erosion material. The annual value of factor R represents the function of EI\({}_{30}\) which is obtained on the basis of the mathematical dependence between energy (E) and a thirty-minute rain intensity (I\({}_{30}\)) [30, 59]. For the purposes of this study, due to the lack of data on thirty-minute values of precipitation, average annual precipitation amounts were used by applying a mathematical relation [60, 61]:  
\[\mathrm{R}=\mathrm{b}_{0}\cdot\mathrm{P}_{\mathrm{m}} \tag{2}\]  
R--rainfall erosivity (MJ\(\cdot\)mm\(\cdot\)h\({}^{-1}\cdot\)ha\({}^{-1}\cdot\)y\({}^{-1}\)); b\({}_{0}\)--empirical coefficient (MJ\(\cdot\)h\({}^{-1}\)); Pm--average annual precipitation (mm).  
The values of the empirical coefficient range from 1.1 to 1.5 [60], and for the purposes of this paper, the adopted value of the coefficient is 1.1. This value has been successfully applied in the territory of the Republic of Serbia [62]. Factor R is presented in the form of a raster with a spatial resolution of 30 m, using the IDW (inverse distance weighting)interpolation method based on the relevant rain gauge cells that have an impact on the study area. The obtained values of the R factor before the calculation of soil loss were compared with the extrapolated European data for the Balkan Peninsula at spatial resolutions of 500 m, which are available from the European Soil Data Centre (ESDAC) of the Joint Research Centre (JRC) [63]. In addition to this database, also consulted data obtained from studies by [PERSON] et al. [64], who used the Rainfall Erosivity Database at European Scale (REDES) and Uncertainties in Ensembles of Regional Reanalyses (UERRA) rainfall data for the period of 1961-2018 with a spatial resolution of 1000 m. Based on the obtained data, the results of the R factor are consistent values with small deviations.. Compared to the European data for the Balkan Peninsula the R factor in the MPB territory was reduced by 9%. Compared to the REDES database, the value of the R factor in the study area was reduced by 12%.
## 6 Results and Discussion
### Static Scheduling Experiment Results  
To illustrate the effectiveness and performance of the proposed hybrid GASAVNS algorithm on a static scheduling dataset, this study utilizes the benchmark problem dataset adapted from [40]. Table 3 shows the results of the experiment, providing a comparison of the makespan obtained using our algorithm with that obtained using other algorithms. The proposed algorithm is compared with the following DRL-based methods: DDQN [15], SPT, LPT, SA, and GA. The results indicate that the proposed hybrid GASAVNS algorithm shows the best result for 38 instances out of 40 compared to the other algorithms. This means that the proposed method is highly efficient for solving the static FJSP.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Instances** & **SPT** & **LPT** & **DQN** & **SA** & **GA** & **Proposed** \\ \hline
**La01 (10 \(\times\) 5)** & 920 & 889 & **666** & 889 & 717 & **666** \\
**La02 (10 \(\times\) 5)** & 901 & 894 & **655** & 871 & 751  
In Figure 4, a Gantt chart illustrates the scheduling solution for problem instance LA28. It displays 20 jobs allocated across 10 machines. Each color represents a specific job; for example, all green sections indicate job number 17. On machine 1, the green-colored section represents job number 17, indicated within brackets as (17,1,1) (75). Here, 17 denotes the job number, 1 signifies the operation number, 1 indicates the chosen machine option from the set of machines, and 75 denotes the processing time. The maximum completion time (makespan) for scheduling all operations is 1305 units.
## 5 Experiments
### Test of the Proposed Method
#### 5.1.3. Deployment Phase and Results (Deployment of Selected AI Models on Selected Edge HWs)  
In Section 5.1.2, we evaluated the proposed method across four distinct scenarios, each designed to address different use cases and operational constraints in UAV context. The results of these experiments highlight the trade-offs among performance, power consumption, cost, and accuracy, which are critical to consider while forming edge hardware and AI model combinations.  
Scenario I demonstrates the suitability of the STM32H7 series microcontrollers when paired with the SSD Mobilenet v1 0.25 model (256 \(\times\) 256 \(\times\) 3) for basic object detection in controlled environments. The STM32H7's affordability, compact size, and low power consumption make it an ideal option for lightweight, resource-constrained applications. The system achieves a slightly low accuracy (lower than 30% mAP) and an average inference time (266.4 ms). While the accuracy is not as high as powerful systems, it is still adequate for basic detection tasks in environments, such as detecting large or stationary objects or general scene understanding. Scenario II pairs the NVIDIA Jetson Nano with the YOLOFastestV2 model to address real-time obstacle detection for mid-level UAVs, delivering high FPS (38.4) at a lower mAP (24.1%), making it suitable for dynamic environments where speed is critical. For general-purpose outdoor detection, Scenario III combines Raspberry Pi 4 with YOLOFastestV2 model, offering a performance of 18.8 FPS and 24.1% mAP. It provides a more cost-effective solution as compared to Jetson Nano, and generally better performance compared to STM32 series. Finally, Scenario IV utilizes the NVIDIA Jetson Xavier NX with Complex-YOLOv3 (Tiny) model for high-performance tasks such as search and rescue or advanced surveillance, achieving a higher mAP (67%) and moderate FPS (17.93). While this scenario prioritizes accuracy and speed for critical missions, it comes with higher costs and power requirements when compared to other combinations.  
Overall, this multi-scenario evaluation highlights the adaptability of the proposed method to varying operational goals and constraints. This approach can be extended to other Edge AI domains, such as autonomous vehicles or smart city systems, by tailoring criteria and metrics to their specific requirements.
## 2 Physical Layer-Based Full-Duplex Underwater Acoustic Communication Systems
### Implementation of Physical Layer FD-UWAC Systems Using FD Modems  
Many companies and researchers have produced a variety of modems that are cost efficient, consume low power, transmit large amounts of data, have a long transmission range, and are highly reliable in the harsh underwater environment [65; 66]. These modems, however, are HD and have very poor throughputs. Researchers were drawn to build FD modems because of the high throughput specifications, multi-user capabilities, and field configurability. A physical layer FD multi-user and parameter reconfigurable underwater acoustic communication modem, for instance, was proposed by the researchers [33]. To achieve multi-user acoustic communication (MUAC) with FD, an OFDM plus CDMA device with spread spectrum implementation is used [33]. The modem's performance is hindered by its long program runtime and high power consumption. As a result, algorithms built in [33] must be optimized to minimize program runtime while consuming minimal resources. It is also essential to investigate its robustness in a deep sea environment with a long range and high data rate.  
Authors in [45], describes a modern FD high data rate FPGA-based underwater acoustic modem. It was developed to improve the FD-UWA modems' low data rate, limited communication range, and low power efficiency. In this modem, multiple digital modulation techniques are used on a polyvinylidene fluoride PVDF ultrasonic emitter transducer to transmit more precise, safe, and high-quality signals [45]. It operates at a high frequency of 1 MHz with a point-to-point communication connection, with a data transfer rate of 1 Mbps and a BER of 1.4, with the added benefit of consuming less electricity. However, it has some limitations, such as the fact that it performs best over short distances and that the need for digital circuitry and filter optimization is still being investigated [45]. Table 2 compares the performance of FD physical layer-based UWAC modems in terms of BER, efficiency, transmission range, power consumption, and SNR.
# Daily Flood Monitoring Based on Spaceborne GNSS-R Data: A Case Study on Henan, China
## 3 Results
### The Occurrence and the Duration of the Flood  
Since CYGNSS reflectivity is sensitive to water bodies, we generated a temporal map of the CYGNSS reflectivity to show the evolution of the flood. As shown in Figure 6, the changes of daily CYGNSS reflectivity are mapped to monitor flood changes (from 19 July to 27 July 2021).  
It can be seen that the CYGNSS reflectivity (black box) began to increase, which means that the flood occurred on 20 July 2021. And the CYGNSS reflectivity (red box) maintained until 24 July 2021. This shows that the flood duration is about 5 days (from 20 July to 24 July 2021). The flood began to fade away gradually on 25 July 2021.
## 3 Proposed Algorithm
### Basic operation of the algorithm  
The proposed algorithm uses four message types: beacon, Route Request (RREQ), Route Reply (RREP), and Route Error (RERR). Originally, any node can broadcast beacon messages but in the proposed scheme, only the main route nodes send beacon message to one-hop neighbor nodes by setting the TTL value of the beacon message to 1. RREQ and RREP are used in the route discovery process. RERR is used in the route maintenance and restoration process. Figure 2 depicts the basic operations of the proposed algorithm.
# A Self-Testing Platform with a Foreground Digital Calibration Technique for SAR ADCs
## 2 Proposed Self-Test Platform
### RDFT Design  
Figure 11 shows an RDFT processor structure used to efficiently compute the desired power spectrum density of the signals, including fundamental and third harmonic tones [11]. According to Reference [11], we found that the z-transform formula of the N-point RDFT computation can be defined as follows:  
\[H\left(z\right)=\frac{W_{N}^{k}-z^{-1}}{1-2\text{cos}\left(2^{nk}/N\right) \times z^{-1}+z^{-2}}, \tag{10}\]  
The coefficients of \(\text{cos}(\theta_{\text{k}})\) and \(2\text{cos}(\theta_{\text{k}})\) can be shared to reduce the multiplication of \(\text{cos}(\theta_{\text{k}})\) using the same computation; hence, Equation (4) can be written as follows:  
\[H\left(z\right)=\frac{j\text{sin}\left(\theta_{\text{k}}\right)+\left(\text{ cos}\left(\theta_{\text{k}}\right)-z^{-1}\right)}{1-z^{-1}\left(2\text{ cos}\left(\theta_{\text{k}}\right)-z^{-1}\right)}, \tag{11}\]  
In the targeted application, the RDFT processor calibration method would have a better performance than the FFT processor because only two frequency bins (i.e., the main and third harmonic tones) should be calculated. Therefore, the computational complexity can be lower than in the FFT processor. In this work, an RDFT processor is implemented in the TSMC 0.18 \(\upmu\)m standard CMOS process to execute the calibration under the transform length of 4096 and the world length of 24 bits. Moreover, the computational complexity includes a total of (2N + 2) multiplications and (4N + 2) additions.  
Figure 11: The recursive discrete Fourier transform (RDFT) structure.  
Figure 10: Output signal waveform of the clock generator.
# Improvements of the xAAL Home Automation System
## 2 The xAAL Architecture  
The architecture of home automation systems, AAL ones or general ones, is usually a centralized architecture. A central hub, a home box or a cloud service, is in charge of managing home devices. The set of the supported technologies depends on the agreements between the manufacturers and this central player. As an alternative, an open distributed architecture could better manage interoperability.  
A home automation facility is usually made of physical devices (sensors, actuators). The basic way for users to interact with a home automation facility is by pressing buttons or switches, that is to say activating dedicated physical sensor devices. Modern home automation solutions provide user interfaces such as mobile applications for smartphones tablets PC or voice interfaces. It is also common to provide some functionalities for scripting sequences of actions to be triggered automatically at a given time or to react to some expected event. There may also be some logging functionalities for monitoring parameters along the time, such as temperature in rooms, consumption of energy or water, so that users may reduce their consumption.  
The xAAL system aims to address the typical uses and needs towards home automation systems.  
Objectives:  
* The system should be robust: the failure of a component should not prevent others from doing their job.
* The system should be lightweight, even in terms of communication. It should use existing communication networks available at home, without consuming too much bandwidth.
* The system should be flexible: adding or removing a component should not require long or complex configuration processes to make it work nor disturb the other components.
* The system should address security considerations, regarding users' privacy, or third party's threat to get control.
* Last but not least, the system should handle interoperability issues, allowing a device from vendor A (e.g., a switch) to talk to a device from vendor B (e.g., a lamp), allowing consumers to buy devices they need without being enclosed into the market or the technology of the vendor of devices they already have bought.