esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Terahertz (THz) communication is a crucial technique in sixth generation (6G) mobile networks, which allow for ultra-wide bandwidths to enable ultra-high data rate wireless communication. However, the current subcarrier spacing and the size of fast Fourier transform (FFT) of the orthogonal frequency division multiplexing (OFDM) in 5G NR are insufficient regarding the bandwidth requirements of terahertz scenarios. In this paper, a novel waveform is proposed to address the ultra-wideband issue, namely the generalized filter bank orthogonal frequency division multiplexing (GFB-OFDM) waveform. The main advantages are summarized as follows: (1) The \(K\)-point IFFT is implemented by two levels of IFFTs in smaller sizes, i.e, performing \(M\)-point IFFT in \(N\) times and performing \(N\)-point IFFT in \(M\) times, where \(K=N\times M\). (2) The proposed waveform can accommodate flexible subcarrier spacings and different numbers of the subbands to provide various services in a single GFB-OFDM symbol. (3) Different bandwidths can be supported using a fixed filter since the filtering is performed on each subband. In contrast, the cyclic prefix orthogonal frequency division multiplexing (CP-OFDM) in 4G/5G requires various filters. (4) The existing detection for CP-OFDM can be directly employed as the detector of the proposed waveform. Lastly, the comprehensive simulation results demonstrate that GFB-OFDM outperforms CP-OFDM in terms of the out-of-band leakage, complexity and error performance.  
6G; terahertz; waveform; ultra-wide bandwidth Article
# Opportunistic Multi-Technology Cooperative Scheme and UAV Relaying for Network Disaster Recovery
## 3 Multi-Technology Cooperative Communication and Drone Data Relaying
### Clock Drift  
COPE assumes that mobile nodes are already synchronized since smartphones get the local time from the network providers with millisecond accuracy before disasters occur. In the following, we study the clock drift of the smartphones to check weather an additional synchronization is required during the post-disaster period. We have carried out a first simple experiment to check weather smartphones belonging to different network operators are synchronized. Results have shown that smartphones are a few milliseconds apart. Next, we have conducted an experiment to test the clock drift featuring the six smartphones. We initially synchronized all the mobile phones through Internet via a NTP (Network Time Protocol) time server. Afterwards, we prevent the automatic synchronization and we measure the clock drift referring to the NTP time server.  
Figure 4 shows the clock drift of the different smartphones during a period of 24 h. Results show that mobile phones desynchronize by up to 0.3 s during 1 day which is not significant drift and does not impact the COPE scheme. Therefore, since the time-slot \(\tau\) is at second level, COPE does not require an additional synchronization. We would like to emphasize that we have repeated the experiment considering various scenarios: smartphone display ON/OFF, smartphone in charge/not in charge and by running applications in parallel. We have obtained similar clock drift results. Smartphones synchronize their clock time with the cellular infrastructure. When disconnected from the cellular network, clock drift is not significant while difficult to predict. Indeed, results show that smartphones present different desynchronization behaviors even though we use the same mobile device model (i.e., Wiko Tommry 2).  
\begin{table}
\begin{tabular}{l l l} \hline  & **Bluetooth** & **Wi-Fi Direct** \\ \hline Indoor & 35 m & \(\geq\)100 m \\ Outdoor & 50 m & \(\geq\)100 m \\ \hline \end{tabular}
\end{table}
Table 2: Bluetooth and Wi-Fi Direct transmission range.  
Figure 3: Transmission range testing area.
## 4 Core Insights and Critical Reflections
### Evolution and Breakthroughs of Key Technologies
#### 4.1.1 Evolution of Flight Platforms  
The UAV flight platform is the foundation for the development of UAV-RS. Early UAV platforms had relatively single functions, with limited stability, payload capacity, and endurance. However, with the progress in material science, aerodynamics, and navigation and control technology, UAV platforms have shown a development trend towards diversification and high performance. The UAV-RS hardware platform mainly consists of two parts: the flight platform and the sensors it carries [23]. Among them, Multi-rotor UAVs have become the most popular platform type for near-ground remote sensing applications due to their high safety, easy operation, vertical take-off and landing capability, and adaptability in complex environments. Common configurations such as quadrotor, hexarotor, and octarotor do not require large airports or runways, can flexibly adjust flight altitude and speed, and some high-end models even have obstacle detection and avoidance functions. However, multi-rotor UAVs still have certain limitations in terms of endurance and payload capacity. For example, after careful optimization, consumer-grade UAVs usually have an endurance of about 30 min. In contrast, Fixed-wing UAVs and Unmanned Helicopters as well as VTOL UAVs (Vertical Take-Off and Landing UAVs) have advantages in endurance, flight speed, and coverage, but their take-off and landing conditions and requirements for operational skills are relatively high.  
With the continuous optimization of flight platforms, it has become possible to carry more advanced sensors and perform more complex remote sensing tasks. These new types of platforms have many unique advantages, such as a flight altitude lower than the traditional aviation navigation limit, thus obtaining higher spatial resolution; low flight speed or even zero-speed hovering capability (for multi-rotor); the ability to fly in complex environments, such as between buildings or indoor mapping; and the use of lightweight and high-strength materials like carbon fiber. These advances are thanks to the development of electronic technology, including the Inertial Measurement Unit (IMU) for navigation and stabilization, the Global Navigation Satellite System (GNSS) for positioning and navigation, and efficient ground stations and control systems. However, civilian small UAVs still have problems such as sensitivity to wind and limited endurance, which restrict their coverage, payload, and mission time. Therefore, the improvement of energy storage systems, such as the application of new materials like photovoltaic cells, fuel cells (hydrogen energy), and graphene, is a current research hotspot [156].
###### Abstract
The European Commission, in view of a tentative labeling scheme for local farming, suggested that \"a new label could add value to products generated from local agriculture if it went beyond direct sales and if Member States were to ensure that it is integrated with or linked to other measures\" [9]. The adoption of the TAP label for traditional food products, such as cheese, could represent a concrete acceptance of this suggestion. However, first, it seems necessary to increase knowledge about TAP [4]. A very limited number of studies have focused on TAP cheeses [10; 11]. Although those studies depicted an overall picture about the quality of some TAP cheeses, which were either provided by temporary aggregations of dairy farms or purchased from the retail market of dairies, they did not consider the detailed production protocols or the many biochemical and microbiological traits of each cheese. This study aimed to highlight the distinctive traits of four different TAP cheeses (Caprino, Pecorino, Vaccino, Cacioricotta) manufactured at the same semi-industrial plant. For this purpose, the cheeses were analyzed at the end of ripening to assess the microbiota, enzymatic activities, degree of proteolysis, and concentrations of the main compounds involved in their sensory traits. We chose a cross-sectional experimental plan, rather than a longitudinal one (i.e., sampling cheeses throughout ripening time), because for ripened cheeses the highest degree of distinctiveness is especially found at the end of ripening. In addition, we chose sampling the four cheeses from the same dairy plant, rather than from different plants, aiming to minimize the influence of house microbiota on cheeses' distinctive traits.
## 3 Results and Discussion
### Failure Case 2 Results  
For failure case 2, the ANN's predicted CI-values, the true CI-values from the enveloped FFT data and the normalized difference between them, by Equations (3) and (4), can be seen in Figure 7.  
Here, the ANN's predictions in red closely followed the true CI-values in blue until measurement 1024. This was made apparent in the normalized difference, in the bottom graph, which stayed consistent. Thereafter, a cluster of peaks appeared which even though the rotational speed was high the prediction could not replicate. While this behaviour was not a clear indication of the defect being present, it could indicate a defect appearing and subsequent over-rolling by the rolling elements flattening the defect and lowering the strength of the repeated impact resonances being monitored. At measurement 1226 however, a steady increase of the CI-values could not be replicated by the ANN's prediction. This appeared in the normalized difference as a relative increase to 10 and clearly indicated the defect. The comparative normalized CI-values in the top graph of Figure 2b only reached a level of 3 for the same measurements, with the added uncertainty if this slight increase depended on the rotational speed being consistently high during this period, as hinted as by the high predicted values.  
In Figure 8, the results using the enveloped WPT CI-values can be seen. Compared to the results from Figure 7, the ANN did not predict the CI-values as closely before the signs of the defect appeared. This was deemed to be a result of the enveloped WPT CI-values being less dependent on the rotational speed than the enveloped FFT CI-values, as evident in Figure 2b. The increase from measurement 1226 did appear, however, in this case also to a larger extent than the results in the bottom graph of Figure 2b leading to an improvement of the monitoring by the ANN's implementation.  
Figure 7: Planet bearing failure case 2, enveloped FFT CI-values. Comparison of ANN’s predicted CI-values in red and true CI-values in blue at the top graph and normalized difference between them at the bottom graph.
# A Lyapunov-Optimized Dynamic Task Offloading Strategy for Satellite Edge Computing
## 3 System Model and Problem Description
### Problem Formulation  
The optimization objective of this paper is to reduce the average weighted sum of latency and energy consumption over a long period of task offloading, while ensuring that the system queue is stable.  
We define the cost function of offloading tasks to the \(k\)th satellite in time slot \(t\) as \(Cost=T_{k}(t)+\beta*E_{k}(t)\), where \(\beta\) represents the weight assigned to energy consumption, reflecting its relative importance in the optimization problem. The optimization objective function can be defined as follows  
\[\begin{split} P1:&\min\limits_{T\rightarrow\infty} \frac{1}{T}\sum_{t=0}^{T-1}\sum_{k=1}^{N}Cost.\\ & s.t.(1)(3)(4)(6)\end{split} \tag{14}\]
## 2 Materials and Methods
### Study Methods
#### 2.3.1 _kndvi_ Calculation Method  
_kNDVI_ is an advanced index for remote sensing estimation of vegetation coverage, which aims to overcome the limitations of traditional _NDVI_ under complex surface conditions. The calculation formula of _NDVI_ is  
\[NDVI=\frac{NIR-red}{NIR+red} \tag{1}\]  
In the formula, _NIR_ and _red_ represent the reflectance of near-infrared and red bands, respectively [21]. Although _NDVI_ is widely used because of its simplicity and ease of use, it is easily affected by soil spectral characteristics when the soil background is significant, resulting in inaccurate estimation of vegetation coverage. In order to solve this problem, _kNDVI_ introduces the influence of soil reflectance and uses the following formula:  
\[kNDVI=\tan h\left(\left(\frac{NIR-red}{2\sigma}\right)^{2}\right) \tag{2}\]  
In the formula, _NIR_ represents near-infrared; _red_ represents the red band; \(\tan h\) represents hyperbolic tangent function; and \(\sigma\) represents the length scale parameter specified in each specific application, which is the sensitivity of the index to sparse or dense vegetation [22], and is linearly proportional to the mean of near-infrared and red reflectance using remote sensing data images; when \(\tau=0.5\), the value is a good balance between accuracy and simplicity [23]. Then a reasonable choice is to take the average value \(\sigma=0.5(NIR+red)\). In this case, the formula of _kNDVI_ is as follows:  
\[kNDVI=\tan h\left(NDVI^{2}\right) \tag{3}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sensor** & **Parameters** & & \\ \hline \multirow{6}{*}{GF-6} & & Panchromatic & 0.45–0.90 \\  & & Blue (B1) & 0.45–0.52 \\  & Spectral coverage (\(\mu\)m) & Green (B2) & 0.52–0.60 \\  & & Red (B3) & 0.63–0.69 \\  & & Near-Infrared (B4) & 0.76–0.90 \\  & Spatial Resolution (m) & Panchromatic & 2 \\  & & Multi-spectral & 8 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Spectral characteristics of bands.  
The improvement of _kNDVI_ enables it to provide more accurate vegetation coverage estimation in a soil-vegetation mixed environment, especially in arid or sparse vegetation areas.
## 5 Conclusions  
We designed an optimal training signal for MIMO systems in the presence of colored interference considering the imperfection of both the channel and interference covariance. From the solution on the structure, it was observed that the optimal training strategy was to allocate the training power according to the worst-case eigenvalues of the imperfect covariance matrices. It was also shown that the proposed training structure can cover the cases considered in the previous works [18, 19, 20]. The optimal training power allocation scheme was obtained numerically. An efficient suboptimal power allocation scheme was also proposed in a closed form. Simulation results show that the proposed schemes considerably outperform the semi-robust and nonrobust schemes in terms of the worst-case MSE and BER performances. In particular, the proposed closed-form training scheme shows near-optimal performance, and hence it is more practically useful than the optimal training scheme.  
An important conclusion from our work is that the performance of training signal design for MIMO systems is sensitive to the channel and interference covariance uncertainties, and thus one should carefully select or determine the covariance uncertainty-based training signals in practice for reliable performance according to the system requirements and operating conditions.  
Methodology, J.-M.K.; writing--review and editing, S.Y.; supervision, S.Y. All authors have read and agreed to the published version of the manuscript.  
This work was supported by Kyungpook National University Research Fund, 2024, and the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (RS-2025-00559998).  
Figure 8: BER performance comparison of various training schemes for the 4 \(\times\) 4 MIMO system when the training SIR is set to 15 dB and \(\alpha_{T}=\alpha_{R}=\alpha_{Q}=0.3\). The orthogonal space–time code and the MMSE receiver are used to encode and decode the QPSK-modulated symbols, respectively.
# Unsupervised Detection of Multiple Sleep Stages Using a Single FMCW Radar
###### Abstract
In contrast, some previous studies performed non-learning detection using only biometric information obtained with IR-UWB radar to overcome the signal processing limitations of learning-based, radar-detected sleep-stage detection. However, only two sleep stages can be distinguished [27]. No studies have employed frequency modulated continuous wave (FMCW) radar to detect sleep stages using nonlearning techniques. FMCW radar is easy to popularize because it is less costly than pulse radar and is suitable for breathing and heart-rate detection according to the microdisplacement of the target, as it has a wide bandwidth for radio transmission [28]. Compared with the CW radar, which transmits a single frequency signal and cannot distinguish the target from interfering objects, the FMCW radar obtains Doppler and range information about the detection target through frequency modulation of the transmitted and received signals, making it advantageous for detecting biosignals [29]. Therefore, this study introduces a method for the non-learning detection of the three sleep stages using a single FMCW radar with an intermediate frequency of 61 GHz to overcome the limitations of previous studies on PSG and sleep-stage detection. The _REM_ sleep stage was primarily detected using FMCW radar-detected respiration information. In addition, the subject's in-sleep movement was estimated and reflected in the results of the _REM_ sleep-stage detection to mitigate false _REM_ identification caused by _REM_-sleep behavior disorders. In this modification, the interval during which the subject's body movement was too large was considered as the wake state. Owing to the nonideal nature of clinical data, this step poses various challenges in determining the threshold value based on biometric information. Therefore, the threshold was set experimentally based on the acquired data instead of an arbitrary value to address these issues. Nonlinearning-based three-stage sleep detection was performed using the estimated thresholds and biometric information. The accuracy of the proposed method was verified by comparing it with sleep-stage results from PSG readings.
# Spatio-Temporal Distribution of Ground Deformation Due to 2018 Lombok Earthquake Series
###### Abstract  
Lombok Island in Indonesia was hit by four major earthquakes (6.4 Mw to 7 Mw) and by at least 818 earthquakes between 29 July and 31 August 2018. The aims of this study are to measure ground deformation due to the 2018 Lombok earthquake series and to map its spatio-temporal distribution. The application of DinSAR was performed to produce an interferogram and deformation map. Time series Sentinel-I satellite imageries were used as master and slave for each of these four major earthquakes. The spatio-temporal distribution of the ground deformation was analyzed using a zonal statistics algorithm in GIS. It focused on the overlapping area between the raster layer of the deformation map and the polygon layer of six observation sites (Mataram City, Pameang, Tampes, Sukadana, Sembalun, and Belatnig). The results showed that the deformation includes uplift and subsidence. The first 6.4 Mw foreshock hitting on 29 July 2018 produces a minimum uplift effect on the island. The 7.0 Mw mainshock on 5 August 2018 causes extreme uplift at the northern shore. The 6.2 Mw Aftershock on 9 August 2018 generates subsidence throughout the study area. The final earthquake of 6.9 Mw on 19 August 2018 initiates massive uplift in the study area and extreme uplift at the northeastern shore. The highest uplift reaches 0.713 m at the northern shore, while the deepest subsidence is measured \(-\)0.338 m at the northwestern shore. Dominant deformation on the northern area of Lombok Island indicates movement of Back Arc Trust in the north of the island. The output of this study would be valuable to local authorities to evaluate existing earthquake's impacts and to design mitigation strategies to face earthquake-induced ground displacement.  
Lombok; earthquake; deformation; DInSAR; spatial distribution +
Footnote †: journal: Remote Sensing  
1
Footnote 1: [[https://www.mdpi.com/journal/renotesensing](https://www.mdpi.com/journal/renotesensing)]([https://www.mdpi.com/journal/renotesensing](https://www.mdpi.com/journal/renotesensing))
# The Use of Mixed Reality in Training Trainers--A Single-Centre Study
## 1 Introduction  
Recent times have seen an exponential increase in the utility of mixed-reality (MR) software as a tool for education and training. Due to its immersive and interactive capabilities, MR technology offers an immense potential for training surgical trainees, medical students and healthcare professionals [1, 2, 3]. MR technology allows users to simultaneously visualise a combined \"real\" and \"virtual\" environment in real time. Currently, MR software is supported on a variety of devices, including smartphones and head-mounted devices such as Google Glass, Microsoft HoloLens, Oculus Rift VR, and the Samsung Gear VR [4]. Several surgical specialties have adopted MR technology in medical training as it offers a three-dimensional (3D) visualisation of patient-specific anatomy acquired through CT and MRI images [5]. This provides trainees with an intuitive means of evaluating imaging data in a 3D manner in conjunction with surgical pathologies [6].  
There is increasing evidence to demonstrate that MR technology allows residents, trainees and students to overcome the limitations of didactic video sessions by offering 3 Dvisualisation of structures [7, 8]. This in turn facilitates better comprehension of anatomy and enhances skill progression [9]. Furthermore, MR technology is a cost-effective modality that can be adopted with relatively low preparation requirements when compared to other more expensive hands-on simulation models [10].  
However, these studies provide little information on whether future trainers can be trained to incorporate this MR platform in their education curriculums. To make MR-based training sustainable, it is important to ensure that there are systems set in place where future trainers are also provided regular training on new technologies and software using MR.  
In this study, we evaluated two cohorts of participants who attained a postgraduate certificate in clinical research and education. The authors demonstrate the ease with which MR can be introduced in teaching the trainers. The study also showcases that wider exposure allows the generation of ideas for implementation in different resource settings.
## 2 An Overview of Line Simplification Algorithms  
As the objective is to optimally select points on a continuous material curve, in this section, various line simplification and curve discretization algorithms are explored. Given a continuous curve, a piece-wise linear approximation is obtained by connecting selected points on a curve with linear segments. Approximation is closer to the original curve by using more optimally distributed points [25]. For a function \(f(x)\), piece-wise linear approximation can be obtained for any given value of \(x^{\prime}\), such that \(x_{1}\leq x^{\prime}\leq x_{1+1}\). The function value \(f(x^{\prime})\) is then approximated to a combination of \(f(x_{i})\) and \(f(x_{i+1})\)[26]. Most algorithms work by first discretizing a continuous curve, which transforms it into a finite set of equally distributed discrete values, thus leading to information loss [27]. A large number of data points that are then acquired is reduced using an algorithm, which decreases the number of piece-wise segments and distributes them in the most favorable position according to prescribed error tolerance, keeping only the most significant points [28]. Different line simplification algorithms can determine the significance of a single data point in different ways, such as by removing the points that are grouped together, measuring the absolute curvature at that point [28], measuring the distance from a selected line segment, and measuring the impact of a point on the overall shape of a line.
## 6 Discussion and Future Directions
### Future Directions
#### 6.5.2 Further Integration with Novel and Complementary Technologies  
The convergence of ML, BC, and IoV promises great potential as a remarkable technological breakthrough. However, further improvement can be attained by combining this achievement with other cutting-edge technologies. Some of these high-end technologies include digital twins, MEC, Fog computing, etc. [171; 172; 173]. Consequently, future research efforts should be directed towards discussing the potential of integration with other cutting-edge technologies for improvement in intelligent transportation systems.