esa-sceva/satcom-chunk-collection huggingface dataset:
# Asynchronous Chirp Slope Keying for Underwater Acoustic Communication
## 2 Materials and Methods
### Channel Model  
We adapted the simulation approach from [37] to estimate the performance of the modulation and demodulation modules in a controlled fashion. The channel model includes simplified Rayleigh fading that multiplies the signal amplitude by the magnitude of two independent, but identically distributed random processes  
\[\begin{split} A_{\text{r}}&=|(\text{randn}\{N_{\text{rx}}\}+\text{i}\cdot\text{randn}\{N_{\text{rx}}\})\,\sigma_{r}|,\quad\text{where}\\ \text{i}&=\sqrt{-1},\end{split} \tag{15}\]  
with a distribution parameter \(\sigma_{\text{r}}=1\). The random sequences are generated through the randn function of Matlab that generates a normally distributed random value. An AWGN in the form of  
\[\epsilon_{\text{n}}=\sigma_{\text{g}}\cdot\text{randn}\{N_{\text{rx}}\} \tag{16}\]  
is used to model the thermal noise of the receiver.  
For simulations, we assume the receiver samples a combination (15) and (16) with the transmitted signal, at a random packet reception time offset \(n_{\tau}\), without additional reverberations from multiple paths  
\[y_{\mathrm{rx}}[n]=A_{\tau}y_{\mathrm{tx}}[n+n_{\tau}]+\epsilon_{\mathrm{n}}. \tag{17}\]
# Feature Fusion Graph Consecutive-Attention Network for Skeleton-Based Tennis Action Recognition
## 5 Materials and Methods
### Datasets
#### 5.1.2 Tennis-Mocap  
The Tennis-Mocap dataset was created in 2020 [16]. It consists of motion capture data of 17 athletes from the Caldas-Colombia tennis league. Five of the participants were stated to be high performers and the others regular performers. Their movements were recorded using an Optitrack Flex V100 including six cameras, with the frequency set to 100 Hz.  
Figure 1: The THETIS model of a tennis player.  
Thirty-four markers, defined by Arena software, were attached for collecting information about their body joints. The study was performed according to the Biovision Hierarchy (BVH) motion capture protocol. All participants were instructed to hit the ball as if they were playing a match for thirty seconds. Each tennis movement was registered separately. They performed a series of the following strokes: groundstrokes (forehand and backhand), serve, volleys, and smash.
## 1 Introduction
### Aim of the Contribution  
The state of research shows the need to find a way of collecting wear-correlated data in polymer gears, that provides better data quality. Current methods, such as bearing block sensors, may not be sufficient for detecting wear in polymer gears due to potential high signal damping for ductile polymers. Other methods, such as thermal analysis based on external sensors, are unable to detect faults of individual gears within a gearbox. The approach followed in this contribution is to integrate the sensors onto the gear and measure the variables in situ.  
Based on the state of research, the research questions that will be investigated in this contribution are as follows:  
Can the integration of sensors into a POM gear lead to better condition monitoring of the gear?  
1. Can integrated acceleration sensors provide more sensitive condition monitoring of POM gears than external acceleration sensors?
2. Can integrated temperature sensors into the POM gear provide reliable data that correlate with the wear of the gear? The aim of this paper is to investigate integrated sensors for the wear condition monitoring of a POM gear.
# Modeling and Spatial Diversity-Based Receiving Improvement of In-Flight UAV FSO Communication Links
## 3 Performance Analysis of the In-Flight UAV FSO Communication Link  
The light carrier satisfies the Gaussian distribution. According to the previous contributions and the characteristics of the real UAV FSO communications, initial parameters are set as follows: the wavelength is 1550 nm, the beam waist is 5 mm, the radius of the receiving aperture is 0.1 m, the transmission distance is 2 km, and the turbulent outer scale \(L_{0}\) and inner scales \(l_{0}\) are 100 m and 0 m, respectively. Modified [PERSON] model is used to simulate the power spectral density function, in which the atmospheric refractive index structure parameter \(C_{n}^{2}\) is \(5\times 10^{-15}\) m\({}^{-2/3}\) and the transmission rate of the QSPK signal is 20 Gb/s. The simulation of system devices has been carried out using the AWGN model, and the simulation of the error caused by the modulation process has been carried out using the load random phase noise on the signal. The influence of atmospheric turbulence on the signal is loaded on the optical carrier by the phase screen method.
# Metal-Organic Frameworks Characterization via Inverse Pulse Gas Chromatography
## 2 Fundamentals of Inverse Gas Chromatography (IGC)  
### Instrumentation and Methods  
In analytical GC, the tested sample mixture, whether gas or liquid, is injected into a well-defined analytical column to separate the composition of that mixture for subsequent qualitative and quantitative analysis. The mixture is separated through different interactions of varying strengths between the analytes and the stationary phase. In IGC, a single known probe is usually injected into a packed column to study its retention behavior and correlation with the physicochemical properties of the packing material. Various probes with different well-known properties are used.  
The composition of the IGC instrument is similar to that of the analytical GC technique (Figure 1). An IGC packed column may be glass, fused silica, copper, stainless steel, or Teflon. Wall-coated open-tubular long columns have also been used in several types of research. An inert carrier with a steady flow conducts the probe through the column at a constant rate. In IGC, the main collected parameter is the retention time, which has been effectively used to evaluate the solid surface's dispersive properties and potential specific interactions [15; 16; 22].  
IGC experiments can be carried out under two different conditions: infinite dilution and finite concentration. Infinite dilution has been identified as appropriate for the heat of sorption and surface energy examination of particles [39]. Infinite dilution at low surface coverage indicates a minimal amount of the tested probe molecule is injected. A low concentration of the adsorbate molecules indicates that the interactions occur with the higher energy sites available at the surface of tested materials. Furthermore, probe-to-probe interactions are considered negligible due to the limited amount of the probe. Therefore, Henry's Law is applied [40], resulting in a linear isotherm of adsorption and a typical chromatographic peak [15; 22]. Infinite dilution is the dominant condition in the IGC technique.  
At finite concentration, a large amount of test probe is injected into the system; hence, interaction with all surface sites is expected [41]. This technique can be used to determine solids' heterogeneity of surface energy [42], or to study the role of both mesopores and micropores in adsorption and thermal desorption methods [43]. However, finite concentration IGC is out of the scope of this review.
# Application of Artificial Neural Networks to Ship Detection from X-Band Kompsat-5 Imagery
###### Abstract  
For ship detection, X-band synthetic aperture radar (SAR) imagery provides very useful data, in that ship targets look much brighter than surrounding sea clutter due to the corner-reflection effect. However, there are many phenomena which bring out false detection in the SAR image, such as noise of background, ghost phenomena, side-lobe effects and so on. Therefore, when ship-detection algorithms are carried out, we should consider these effects and mitigate them to acquire a better result. In this paper, we propose an efficient method to detect ship targets from X-band Kompsat-5 SAR imagery using the artificial neural network (ANN). The method produces the ship-probability map using ANN, and then detects ships from the ship-probability map by using a threshold value. For the purpose of getting an improved ship detection, we strived to produce optimal input layers used for ANN. In order to reduce phenomena related to the false detections, the non-local (NL)-means filter and median filter were utilized. The NL-means filter effectively reduced noise on SAR imagery without smoothing edges of the objects, and the median filter was used to remove ship targets in SAR imagery. Through the filtering approaches, we generated two input layers from a Kompsat-5 SAR image, and created a ship-probability map via ANN from the two input layers. When the threshold value of 0.67 was imposed on the ship-probability map, the result of ship detection from the ship-probability map was a 93.9% recall, 98.7% precision and 6.1% false alarm rate. Therefore, the proposed method was successfully applied to the ship detection from the Kompsat-5 SAR image.  
synthetic aperture radar (SAR); ship detection; artificial neural network (ANN); Kompsat-5
## 2 Review of Related Literature and Applications
### Review of Existing Market Solutions for Controlling Smart Device Use
#### 2.3.2. AI-Driven Monitoring and Alert Systems  
To alleviate the burden of direct parental supervision, some commercial tools now incorporate AI to automate the monitoring of children's digital activity. For example, platforms such as Bark (Bark Technologies Inc., Atlanta, GA, USA, reviewed in November 2024) [49] and Qustodio (Qustodio LLC, Barcelona, Spain, reviewed in November 2024) [50] use natural language processing and keyword recognition to detect potentially harmful content--such as violence or cyberbullying--and deliver real-time alerts to caregivers [36]. By doing so, these systems minimise the need for constant adult oversight.  
In addition to passive monitoring, certain platforms seek to involve children more actively in the management of their digital experiences. Notable examples include Samsung  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Category** & **Applications** & **Key Features** \\ \hline Basic device management & ZEM, U+ Child Protection, Google Family Link, Apple Screen Time, Qustodio, MobileFence & Screen time limits, app and website blocking, location tracking, parental monitoring \\ \hline Enhanced online risk & Bark, Qustodio & AI-powered content filtering; monitoring of texts, emails, and social media; real-time risk alerts \\ \hline UX tailored for young & Samsung Kids Mode, Amazon Kids+ & Age-appropriate content, learning-time settings, restricted access to parent-approved apps \\ \hline Remote device control and flexible mode-switching & Kidslov, MobileFence & Remote device locking, mode switching (e.g., allowed/restricted/locked), flexible time-based restrictions \\ \hline \hline \end{tabular}
\end{table}
Table 1: Classification of commercial smart device management tools by intervention type and key features.  
Kids Mode (Samsung Electronics Co., Ltd., Suwon, South Korea, reviewed in November 2024) [51] and Amazon Kids+ (Amazon.com Inc., Seattle, WA, USA, reviewed in November 2024) [52], which offer curated content, restricted entertainment access, and child-friendly user interfaces. While these features create safe and age-appropriate environments, the underlying design remains largely control-oriented.  
Children engaging with such platforms continue to operate within adult-defined parameters, with limited opportunities for autonomy or reflective decision-making. UX in this case tends to emphasise compliance over internalisation, and although playful interfaces may reduce the rigidity of control, few tools are intentionally designed to encourage disengagement or cultivate intrinsic motivation. Consequently, content safety is prioritised over behavioural development, ultimately constraining the potential for fostering autonomous digital habits.
## 4 5G and V2X Deployment Projects
### Quality-on-Demand API for Automated Valet Parking  
Deutsche Telekom, in partnership with the Fraunhofer Institute for Material Flow and Logistics (IML) and BMW, has successfully demonstrated automated valet parking using 5G technology. The trial was conducted at the Hamburg Airport in Germany, where a vehicle equipped with automated driving technology and a 5G connection was able to park itself in a designated parking space.  
Automated valet parking has the potential to revolutionize the parking industry, reducing the need for parking infrastructure and improving the overall driving experience. Drivers would no longer need to spend time searching for parking spaces or maneuvering in and out of tight spaces, as the vehicle would be able to park itself. This would also reduce the risk of accidents and increase the efficiency of parking operations.  
The successful demonstration of automated valet parking using 5G technology also highlights the potential for other applications of connected and automated driving technology. For example, vehicles could communicate with each other and with their surroundings, improving road safety and reducing congestion. Additionally, 5G connectivity could enable new services and business models, such as in-car entertainment and e-commerce [96].
# mGEODAR--A Mobile Radar System for Detection and Monitoring of Gravitational Mass-Movements
## 3 Measurements and Results
### Signal Strength Optimization
Thus, the larger the error bars, the greater is the background signal variation and the larger are the unwanted spectral components in comparison to the average spectrum magnitude. The curves for the intensities \(P\) and \(B\) of both measuring modes in Figure 5A have a similar shape with a signal drop around \(f_{lo}=8\) GHz as well as below 7.8 GHz and above 8.2 GHz. The signal of the loop cable target \(P\) maximizes at \(f_{lo}=8.12\) GHz. The largest error bars for the background of the spectrum are found for the smaller values of \(f_{lo}\), which correlates with the occurrence of the spurs in the deramp signal at instantaneous chirp frequencies \(f_{ch}=f_{lo}/3\). Beside the absolute intensities, the signal-to-noise ratio (SNR) is of similar importance. We define the SNR as the ratio between the cable intensity \(P\) and the background intensity \(B\), and is shownin Figure 5B. The SNR follows roughly the same trend as for the individual intensities \(P\) and \(B\). Even though the SNR is highest for around \(f_{lo}=7.9\) GHz, the spectra are more disturbed for this settings as shown with the larger error bars. Again the optimal setting found for \(f_{lo}\) is 8.12 GHz. The deramp signal and the corresponding spectrum for the optimal frequency setting \(f_{lo}=8.12\) GHz is the content of Figure 4. The top row shows the data for the research mode and the bottom row shows the detection mode. The zoom in panel (A) focus on the area of the deramp signal that has the same extend as the deramp signal for the detection mode in panel (B). It is easy to see, that the deramp signal \(f\) is very similar in both plots as 11 periods are clearly visible and the signal strength is comparable with around \(\pm 0.25\) V.  
The range resolution for both measuring modes manifests in the spacing of the points in the frequency spectrum. For the detection mode, the peak of the cable measurement spreads across neighbouring value to about 10 m. For the research mode, the sampling of the spectrum is much narrower and the cable peak significantly spreads around 1.5 m. Additionally, the spike-like unwanted spectral components in the frequency spectrum are more pronounced in the research mode measurements and only the strongest ones are found also in the detection mode due to the lower frequency sampling. We believe these unwanted spectral components are caused from internal electronic noise of the radar components as their position in the spectrum are independent on any frequency setting \(f_{lo}\) or \(f_{ch}\). Being aware of the disturbed ranges enables to discard them for any further data processing.
# Research on Performance Metrics and Augmentation Methods in Lung Nodule Classification
## 3 Methodology
### Augmentation Methods in Lung Nodule Detection
#### 3.4.2 Shifting the Image Around  
Image shifting involves moving the image a certain number of pixels in one or more directions. This technique is beneficial because medical images, such as CT scans or MRI, often feature a region of interest, like a tumor or lesion, that may not be perfectly centered. By training the model on images where the nodules have been shifted slightly, it becomes robust to variations in nodule placement, which commonly occur due to patient movement or differences in scanning procedures. Shifting can be implemented in small increments to ensure that the integrity of the nodule's context within the surrounding anatomical structure is maintained. The model trained with shifted images can better handle cases where nodules appear near the edges of the image, which are typical scenarios in clinical settings, thereby enhancing the model's diagnostic accuracy.
## 1 Introduction
Moreover, the influence of the fundamental frequency \(F0\) on HFE content has also been examined. Several plausible combinations of \(R_{d}\) and \(F0\) were considered, thus covering to a large extent the phonation range for male speech. Finally, aspiration noise was also evaluated to study its impact on HFE levels. A preliminary version of this work was presented in [17]. The paper is structured as follows. Section 2 details the methodology we propose to study the production of vowels [a], [i], and [u] for different phonation types and for both, the realistic and simplified vocal tract geometries. Computations are carried out and the results are analysed and discussed in Section 3. Finally, conclusions and future work close the paper in Section 4.
## 3 Results and Discussion
### Mechanical, Thermal and Radiation Resistance of Materials
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Element** & **Phase \#1** & **Phase \#2** & **Phase \#3** \\ \hline Na & 5.2 & 1.6 & 30.1 \\ P & 24.3 & 19.7 & 5.5 \\ Fe & 13.5 & 27.8 & 6.2 \\ Al & 4.7 & 3.7 & 0.9 \\ K & 2.1 & 2.2 & - \\ Cs & 0.4 & 0.3 & 0.2 \\ La & 2.5 & 3.1 & 0.8 \\ Sr & 0.3 & 0.3 & 0.1 \\ Ba & 0.6 & 0.6 & 0.4 \\ Cl & 2.1 & 0.4 & 35.0 \\ O & 43.4 & 39.4 & 20.6 \\ Si & 1.0 & 1.0 & 0.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The elemental composition (wt%) of NAFP glass after heat treatment at 450 \({}^{\circ}\)C.  
Table 4. It is shown that all samples have a high compressive strength of about 40-50 MPa and comply with the regulatory requirements for immobilized HLW (at least 9 MPa) [26]. Radiation stability is one of the important characteristics of solidified radioactive waste, which is characterized, first of all, by the stability of the structure of the samples. Electron irradiation of samples is used to simulate beta decay of fission products [33]. It was found that the phase composition of samples of the MPP-Z compound, NAFF glass and ceramics based on bentonite clay after their electron irradiation remained stable, the diffractograms of original samples of the studied compounds and after their irradiation are identical (Figures 5 and 6). Thus, the MPP-Z compound after its dehydration (Figure 5) consists of the same phases as in the original sample: the KCl phase and the main mineral phases of zeolite (Phase #1, clinoptiolite-Na and Phase #2, heulandite) and quartz (Phase #3) contained in zeolite (74% and 12%, respectively [19]). At the same time, for ceramics based on bentonite clay (Figure 6), only a relatively small broadening of the peaks and an insignificant change in the relative content of minor mineral phases were observed, but, most likely, this is due to the heterogeneity of the samples. Thus, the observed stability of the studied materials at absorbed doses of 10\({}^{8}\)-10\({}^{9}\) Gy allows one to predict the stability of solidified forms of RW for at least 10,000 years of storage [26].
# Underlay Loosely Coupled Model for Public Safety Networks Based on Device-to-Device Communication
## 5 Material and Methods
### Network Update Based on the Optimization Interval  
In the \"optimized tree\" approach, link optimization, based on a CQI comparison, takes place every second. However, it is possible to customize this optimization interval by choosing different interval values. As explained in Algorithm 3, with the defined optimization interval \(i\), this approach will be executed intermittently using either CQI threshold = 15 (corresponding to the \"less cost\" approach) or a predefined CQI threshold.  
```
Set optimization_interval to i ifsim_Step % optimization_interval = 0 then execute network update function with defined CQI_threshold else execute network update function with CQI_threshold = 15 end
```  
**Algorithm 3** Network update function according to the optimization interval.
## 1 Introduction  
During the most recent couple of decades, there have been considerable improvements in wireless system technology [1, 2]. As is clear and evident from Figure 1, the present patterns and requirements in the area of wireless technology involve providing ongoing mixed-media applications over different dissimilar kinds of wireless systems with ensured quality of service (QoS) [3]. Client requests to use and apply these technologies in various scenarios, at any place or time, are continuously rising. The advancement of new innovative frameworks (for example, the fourth generation (4G) remote frameworks and their mix) provides these productive applications with high information movement rates and offers a global range and consistent versatility through a different scope of heterogeneous wireless systems [4]. Mobile stations (MSs) in a conventional 4G network will be outfitted with numerous interfaces [5]. They will have the required information to make improved choices when associating with an assortment of access networks (ANs) to provide productive, interactive media applications [6]. Figure 1 shows the evolution of wireless communication [7, 8].  
Cuckoo search (CS) [9] is one of the numerous nature-inspired methods utilized broadly to solve issues in various fields of engineering. It is a compelling option for global streamlining because it can maintain harmony among neighborhood and worldwide arbitrary strolls utilizing the exchanging parameter. The exchanging parameter for the first CS calculation is fixed at 25%, and insufficient examinations have been performed to evaluate the effect of the dynamic turning parameter on the exhibition of the CS calculation.  
Genetic algorithm (GA) [10]--based methodology is utilized to assess the likelihood of effective handoff in heterogeneous remote systems (HWNs) to expand, limit, and or execute a system. GA has been comprehensively explored for various IoT-based applications [11; 12; 13; 14; 15; 16; 17], so it is interesting to see its performance for wireless communication.