esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### UH Operation Methods and Operational Responsibilities  
This flight was conducted with the permission of the Osaka Regional Civil Aviation Bureau (ORCAB), the Ministry of Land, Infrastructure, Transport, and Tourism (MLITT). When Japan Airlines (JL) applied for permission from the ORCAB based on the \"Approval Procedures for Unmanned Aircraft Flight Permits\" stipulated by the MLIT [16], we notified in advance the date, time, and flight path (same as the result route in brief) to the manned aircraft operating organizations (All Nippon Airways Federation and six other organizations) as the flight was to be conducted \"out-of-sight without an assistant\" and JL applied for the permission for \"transport of dangerous goods\" due to the transport of sputum samples for antigen test specimens of a virus in another UH operation.  
The ORCAB imposed weather restrictions of \(<\)10 m/s wind speed and 5 mm rainfall.  
The flight was performed by the pilots at JL. They were in constant communication with the local pilots at Sasebo and Arikawa ports to assess the weather and wind speed. The UH was controlled by a local pilot under visual observation during take-off and landing  
Figure 1: Blood transport equipment without active transport refrigerator. (**A**) Box A (NeoAce) and four refill chip bags for space; (**B**) Constar II; (**C**) RBC solution sandwiched by Constar II used for box A; (**D**) Box B. Transport heat shield box (Ustunomiya); (**E**) Type III; (**F**) RBC solution sandwiched by the type III used for box B.  
commonly used in Japan. As the ORCAB does not have permission to fly over third parties, flying over navigating ships by hovering to wait risks must be avoided.  
The cruise flight at an altitude of 100 m was performed by pilots at JL. The UH was controlled via satellite telephone communication (Inmarsat). A telephone line using a communications satellite can transmit control communication to a UH in flight, even at sea; thus, the UH could be operated by a pilot at the JL office in Tokyo. The control of the UH flight was based on Inmarsat technologies and Yamaha's technologies [17].  
Although the helicopter was not equipped with a specific collision avoidance system, the flight environment on that day was at an altitude of \(\leq\)150 m, which is restricted by drone regulations to avoid the risk of collision with aircraft and there was no overlapped airspace. There have been no other government-permitted drone operations flying <150 m.
## 3 Numerical Results
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{\(r\)} & \multirow{2}{*}{\(b\)/\(a\)} & \multirow{2}{*}{**Results**} & \multicolumn{6}{c}{**Mode**} \\ \cline{3-10}  & & & **1** & **2** & **3** & **4** & **5** & **6** \\ \hline \multirow{6}{*}{0} & & [26] & 9.88 & 27.6 & 39.48 & 64.88 & 88.84 & 105.56 \\  & & [27] & 9.87 & 27.52 & 39.48 & 64.54 & 88.83 & 105.48 \\  & & present & 9.5114 & 27.4730 & 38.5184 & 64.4515 & 87.2662 & 105.4009 \\ \cline{2-10}  & & [26] & 9.87 & 16.22 & 36.82 & 39.48 & 47.14 & 71.33 \\  & & [27] & 9.87 & 16.13 & 36.71 & 39.48 & 46.73 & 70.73 \\  & & [28] & 9.6314 & 16.1348 & 36.7256 & 38.9450 & 46.7381 & 70.7401 \\  & & present & 9.6296 & 16.1129 & 36.6655 & 38.9360 & 46.6979 & 70.6287 \\ \cline{2-10}  & & [26] & 9.87 & 11.79 & 17.83 & 27.91 & 39.48 & 41.53 \\  & & [27] & 9.87 & 11.66 & 17.66 & 27.73 & 39.48 & 41.18 \\  & & present & 9.7340 & 11.6745 & 17.6572 & 27.048 & 39.1773 & 41.1702 \\ \hline \multirow{6}{*}{0.5} & & 0.5 & present & 13.5055 & 29.1960 & 43.3384 & 67.5191 & 92.4649 & 105.8957 \\  & & 1 present & 13.5969 & 18.8036 & 37.9805 & 43.7159 & 50.7794 & 73.4469 \\  & & 2 present & 13.6752 & 15.1321 & 20.1497 & 29.3911 & 43.4613 & 43.9340 \\ \hline \multirow{6}{*}{0.9999} & & [26] & 22.39 & 36.46 & 61.69 & 83.03 & 110.9 & 120.9 \\  & & present & 22.0267 & 36.0988 & 61.2502 & 82.7627 & 109.5111 & 120.3845 \\ \cline{1-1} \cline{2-10}  & & [26] & 22.39 & 26.61 & 44.13 & 61.69 & 67.66 & 80.76 \\ \cline{1-1}  & & [28] & 22.272 & 26.529 & 43.664 & 61.466 & 67.549 & 79.904 \\ \cline{1-1}  & & present & 22.3421 & 26.5631 & 43.7064 & 61.6519 & 67.6178 & 79.8786 \\ \cline{1-1} \cline{2-10}  & & [26] & 22.39 & 23.51 & 27.72 & 35.96 & 49.02 & 61.69 \\ \cline{1-1} \cline{2-10}  & & present & 22.4337 & 23.5357 & 27.6433 & 35.6897 & 48.6187 & 61.8753 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: First six frequency parameters \(\Omega=\omega a^{2}\sqrt{\rho h/D}\) for isotropic plates with three different rotational fixity factors (\(\
u=0.3\)). Figure 2: Convergence of the fundamental frequency parameter \(\Omega=\omega a^{2}\sqrt{\rho h/D}\) of a square isotropic plate with \(r=0.5\). Furthermore, considerable numerical results have been obtained for R-F-R-F orthotropic plates and compared with existing results reported by [PERSON] and [PERSON] [16]. In the work of [16], five different flexural properties and two different aspect ratios were investigated. In particular, three different degrees of rotational restraints were studied, which was described by the parameter of \(\Phi=D_{x}/Ra\). Consequently, the corresponding rotational fixity factor in Equation (5) can be expressed by  
\[r=\frac{1}{1+3\Phi} \tag{29}\]  
Extensive comparisons are illustrated in Tables 3 and 4 and excellent agreement can be observed. However, it can be found that results of [16] for most results of \(\Phi=0.1\) are lower than the present results, which were denoted in bold. Such findings were also reported by [PERSON] and [PERSON] in [16] when comparing their results of C-C-C-C and C-C-C-F plates with others.
# Evaluation of BLE Star Network for Wireless Wearable Prosthesis/Orthosis Controller +
## 6 Conclusions  
With the rise of wearable, wireless biosensors also come advancements in some commonly used wireless communication protocols. In the case of BLE, version 5.x introduced new features that can help develop lower power and larger biosensor networks for a variety of biosensor applications. In this work, the nRF52840 SoC was used to evaluate the power consumption, maximum network size, and latency at different configurations of the connection interval, event length, and MTU size for a wireless, wearable EMG-driven prosthesis controller. It was observed that increasing the connection interval from 10 ms to 100 ms reduced the power consumption by an average of 16.13%. With respect to the network size, minimizing the event length maximized the number of connections sustained for a given connection interval. Additionally, for a given event length, longer connection intervals allow for more connections to be sustained. Finally, latency measurements showed that, on average, 1.14 connection intervals lapse between the application of a step change at the peripheral node's ADC and when it is detected on the central node in the received BLE data. From these results, it is advised to use connection intervals well under 100 ms for prosthesis control (to minimize latency) and shorter event lengths (to maximize the number of connections sustained). Future works could look to further improve the system's power consumption using power optimization techniques or look to increase the network size or reduce the latency using data compression techniques or through firmware enhancements. Finally, using the latest release of the BLE protocol can improve the performance, as these new releases tend to introduce new features, as well as a more efficient implementation [37].  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app142210455/s1](https://www.mdpi.com/article/10.3390/app142210455/s1)]([https://www.mdpi.com/article/10.3390/app142210455/s1](https://www.mdpi.com/article/10.3390/app142210455/s1)), The raw data for the results presented above.  
Conceptualization, K.J.R. and E.A.C.; Data curation, K.J.R.; Formal analysis, K.J.R.; Funding acquisition, B.E.M., T.R.F. and E.A.C.; Investigation, K.J.R.; Methodology, K.J.R. and A.W.; Project administration, K.J.R.; Resources, K.J.R.; Software, K.J.R. and A.W.; Supervision, E.A.C.; Validation, K.J.R. and A.W.; Visualization, K.J.R.; Writing--original draft, K.J.R. and E.A.C.; Writing--review and editing, A.W., B.E.M., T.R.F., J.L. and X.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by DoD STTR Contract No. W81 XWH-22-C-0049. Any opinions, findings, and conclusions or recommendations are those of the authors and do not necessarily reflect the views of the U.S. Army Medical Research and Development Command (USAMRDC).
## 4 Discussion
### Limitations and Scalability  
Although the UGSAM offers low cost and high accuracy, its application may be constrained in certain urban environments. In regions susceptible to natural disasters or those affected by conflict, the spatial layout alterations may diverge from established social and economic development models, complicating the model's ability to provide consistent estimates. In these specific urban contexts, employing the UGSAM necessitates relabeling the data and training the SCP-Net.  
Fortunately, SCP-Net has a relatively small parameter count, resulting in significantly lower time and cost requirements for training compared to larger models. If deployment is essential in these particular scenarios or if enhanced accuracy is desired, we can opt to freeze both the encoder and decoder of the SAM while flexibly increasing the number of network layers during SCP-Net training to improve precision. This approach will incur a slight increase in cost. However, it remains substantially less than that associated with fine-tuning the SAM.  
Additionally, the WRPM serves as a universal defogging module characterized by strong transferability and is not limited by scene transitions.
# Low Complexity DSP for High Speed Optical Access Networking +
## 6 Conclusions  
We have demonstrated a unique low-complexity and energy-efficient 40 Gb/s PAM-4 PON system using a novel high-performance DSP unit and a 10-G transceiver without requiring a power-hungry RF-driver. The DSP consisted of a low complexity reconfigurable blind adaptive Volterra-based nonlinear FFE/DFE, integrated with an NS filter as a receiver equalizer to alleviate various linear and nonlinear effects from the components and the fiber. The results revealed that a link power budget of 39 dB and 37 dB for 20 km (30 km) SMF transmission was feasible at a BER threshold of \(10^{-3}\), respectively. Up to a 75% reduction in the DSP complexity is feasible without performance degradation. The 17-tap NLE has a complexity that is comparable with a commercial equalized CDR. Simulation results revealed that an APD can potentially be used to avoid pre-amplification, thus, enabling greater cost and energy efficiency in modern PONs, while simultaneously covering the PR30 power budget at 20 km.  
Conceptualization, experiment, writing--original draft preparation, J.W.; formal analysis, investigation, simulation, writing--review and editing, J.W., C.F.L., J.Z., I.A., E.G., A.R., Q.C., R.P. and I.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the European Union under the CEEOALAN project with grant No. 623,515, and by the German Ministry of Education and Research under the KIGLIS project with grant No. 16 KIS1230.  
Not applicable.  
Data Availability StatementData sharing is not applicable to this article.  
The authors thank [PERSON], [PERSON], [PERSON], and [PERSON] from ADVA Optical Networking SE, Germany for useful discussions.  
The authors declare no conflict of interest.  
Figure 6: (**a**) Receiver sensitivity and (**b**) the corresponding power budget at different fiber lengths of the 40 Gb/s PAM-4 system.
# Overview of Distributed Machine Learning Techniques for 6G Networks
## 5 Distributed Machine Learning for 6G
### Collaborative Machine Learning for Energy-Efficient Edge Networks in 6G
#### 5.3.3 Performance Evaluation  
The previously discussed approaches have been evaluated in [51], considering a network of small cells with 6 SBSs, 10 UEs in each cell and 16 orthogonal channels. The sizes of computational operations were randomly distributed between 200 and 300 kB. The energy consumption coefficient for each UE was 1 J/GCycle, and for each SBS it was 100 mJ/GCycle. For the DDPG model adopted, the learning rate and the discount factor were set, respectively, at 0.001 and 0.9. To evaluate the performance of the algorithms, the independent learning algorithm (IL-DRL), the full offloading method, and the local computing one were taken as benchmarks.  
In the IL-DRL algorithm, each SBS agent learns a policy based on its observations and interactions with the environment, there is no exchange or sharing of models between agents. The performances of the algorithms in terms of convergence were compared. As depicted in [64], the MA-DRL algorithm performed best, thanks to using the information regarding observations and actions of the other SBS agents, which allows a clear improvement in terms of stability.  
A comparison of the energy consumption of the various algorithms was also performed in [64]. The MA-DRL and F-DRL algorithms consume less energy than the IL-DRL and full offloading methods, but they still consume more energy than local computing.
## 3 Results and Discussion
### Effect of Process Parameters
#### 3.1.4 Effect of Ultrasonic Power  
In this work, the effect of ultrasonic power on the extraction yield of quercetin and rutin was examined under different ultrasonic conditions. The result is shown in Figure 6, which exhibited a positive influence on the extraction of quercetin and rutin with the increase in ultrasonic power from 25 to 200 W. When higher ultrasonic power was applied, however, an obvious yield reduction was observed for ultrasonic power between 200 and 400 W. An explanation for this phenomenon is that suitable ultrasound wave can facilitate the cell walls of target sample to disrupt, also can accelerate the diffusing and dissolving of target components in the liquid medium. However, higher ultrasonic  
Figure 4: Effect of solvent volume/sample ratio using 60% concentration of ethanol solution with 80 kHz at 30 \({}^{\circ}\)C and 200 W for 30 min on the extraction of quercetin and rutin.  
power can weaken the cavitation effect because the cavitation bubbles in this case more likely grow too big to collapse [39; 40]. Moreover, excessive cavitation bubbles production can hinder the mass transfer and lead the ultrasound waves to scatter, which weaken the effect of ultrasonic power. As already discussed, to extract quercetin and rutin from _Euonymous alatus_, the optimum ultrasound power is selected as 200 W.
## 3 Data and Methods
### Differential SAR Interferometry Displacement Time Series Generation
Occasional snowfall during the snow-free period (usually June until October) also caused low coherence values for few acquisitions. These occasions were also excluded and instead bridged with interferograms of longer temporal baselines (12 or 18 days). The filtered acquisitions were then grouped into continuous time series stacks. Each stack consisting of more than one scene was then processed by pyrate. The processing in pyrate consists of: (i) masking out all pixels below a coherence threshold of 0.3 (=default threshold of pyrate); (ii) automatic search for a stable area to identify and then subtract the reference phase; (iii) applying a residual orbit error correction (linear filter); (iv) atmospheric phase screen (APS) correction using a spatial low-pass Gaussian filter (size: 1 km); (v) APS correction using a temporal low-pass Gaussian filter (size: 6 days); (vi) conversion of the corrected phase to 6-day Line-of-Sight displacement for each individual time step. The APS correction steps (iv) and (v) were excluded for stacks with less than 5 scenes as using the respective filters in pyrate for such few scenes is not recommended due to increased inaccuracies. The 6-day LOS displacement rate time series was then used as input for the displacement decomposition as well as for the calculation of the seasonal movement. Areas affected by radar shadow or layover were masked out.
# Systematic Review of Anomaly Detection in Hyperspectral Remote Sensing Applications
## 5 Unstructured Background Models
### Kernel-Based Models
#### 5.3.3 Support Vector Data Description (SVDD)
It should be noted that the optimization of Lagrangian function \(L\) with respect to \(\alpha\) will typically end with a large fraction of \(\alpha_{i}\) to become zero. The training examples (background pixels) with non-zero \(\alpha_{i}\) are called support objects or support vectors. Expression (29) can be kernelized as [31]:  
\[\begin{array}{l} SVDD_{\Phi}(\underline{x}|\mathcal{B})=\ K(\underline{x}, \underline{x})-2\cdot\sum\limits_{i=1}^{N}\alpha_{i}\cdot K(\underline{x}, \underline{y}_{i})+\sum\limits_{i=1}^{N}\sum\limits_{j=1}^{N}\alpha_{i}\alpha_{ j}\cdot K\left(\underline{y}_{i}\cdot\underline{y}_{j}\right) \begin{array}{l}R^{2} \\ <\\ \end{array}  \tag{30}\]  
\[ \begin{array}{l} H_{1}\\ >\\ <\\ H_{0}\end{array} \]In [31], Gaussian RBF (23) with free parameter \(\sigma\) was applied as the kernel function. For its estimation, the authors proposed a minimax approach that minimizes an approximate upper bound on the average false alarm rate (FAR) over the entire image [31]:  
\[ \begin{array}{l}\hat{\sigma}=\min\limits_{\sigma}\frac{1}{M}\sum\limits_{i=1}^{ M}P_{FA_{i}}\\ \approx\min\limits_{\sigma}\left\{\frac{1}{M}\sum\limits_{i=1}^{M}\frac{\pi 5V_{i}}{N}\right\}\end{array}  \tag{31}\]  
where \(P_{FA}\) denotes the probability of false alarm (FAR), \(M\) is the number of training sets, \(N\) is the number of examples in each training set, and \(\#SV_{i}\) is the number of support vectors in the \(i\)-th training set. Expressions (29) and (30) will lead to the computation of the optimal hypersphere radius for every PUT. That may prevent the distances between PUT and the hypersphere centroid from being used to compare multiple pixels to their local backgrounds mutually. Therefore, a normalized detection statistic is also proposed in [31]:  
\[ \begin{array}{l}{SVDD_{\Phi\_normalized}(\underline{x}|\mathcal{B})=\frac{ SVDD_{\Phi}(\underline{x})}{R^{2}}\begin{array}{l}H_{1}\\ >\\ <\\ H_{0}\end{array}}\lambda.\end{array}  \tag{32}\]  
In the SVDD algorithm, unknown parameters can be estimated globally or locally using the double-sliding window. The global implementation offers better computational efficiency, but the local implementation could lead to better detection performance. The kernel RX algorithm and the SVDD algorithm are related techniques but with two key differences. The kernel RX is a generative model that assumes a Gaussian distribution in the feature space, while SVDD is based on a discriminative model that avoids making such assumptions. The SVDD does not require inversion of the large covariance matrices, which is characteristic of every detector using the Mahalanobis distance (such as the kernel RX). Nevertheless, it should not be overseen that in the optimization step, SVDD requires the inversion of Gramm matrices of the training data, the size of which depends on the number of support vectors.
## 3 Methodology and Details of Data Analysis
### Case Study-I: Analyzing Movements of the Tall Building
From the GPS data analysis, although the significant displacements of the building seemed to be proven, these results do not give an idea to distinguish the components of the movement as static, quasi-static or resonant.
## 5 Conclusions  
The present work deals with the water jet erosion efficiency of HBS under various work parameters, such as jet velocity, standoff distance, and nozzle diameter, and the work parameters were optimized through orthogonal experiments. Numerical models were established based on the ALE method and numerical computation was carried out using LS-DYNA software. The increase in jet velocity enhances the efficiency of the jet erosion of HBS, and there exists a critical jet velocity, namely, the threshold velocity, which describes the minimum jet velocity required to erode HBS. With respect to the standoff distance, when the standoff distance is less than 3 mm, the water jet erosion is stronger since the HBS of the South China Sea is characterized by low cementation strength and easy fragmentation. Moreover, the erosion efficiency of HBS decreases with the increase in standoff distance. For the nozzle diameter, with the increase in nozzle diameter, the erosion efficiency of HBS increases, however, when the nozzle diameter is larger than 1.8 mm, the erosion depth decreases due to the influence of water accumulation. Comprehensive analysis of the results of the orthogonal experiments demonstrates that, when the jet velocity is 150 m\(\cdot\)s\({}^{-1}\), the standoff distance is 0.5 cm, and the nozzle diameter is 2.0 mm, the maximum erosion depth of 4.998 cm is obtained. Moreover, when the jet velocity is 150 m\(\cdot\)s\({}^{-1}\), the standoff distance is 0.5 cm, and the nozzle diameter is 2.5 mm, the maximum erosion volume can be obtained, which is 6.0329 cm\({}^{3}\).  
Figure 13: Stress distribution of HBS at different nozzle diameters. (**a**) \(D=1.2\) mm; (**b**) \(D=1.8\) mm. ; (**c**) \(D=2.4\) mm.  
Nevertheless, it should be noted that only the jet velocity, the standoff distance, and the nozzle diameter were analyzed in this paper. Subsequent investigations need to consider the effects of jet angle and nozzle traverse speed.  
[PERSON] analyzed the data and wrote the paper; [PERSON] and [PERSON] conceived the paper; [PERSON], [PERSON], and [PERSON] commented on the manuscript; [PERSON], [PERSON], and [PERSON] discussed the results; [PERSON] contributed analysis tools; [PERSON] and [PERSON] directed the overall project.  
This study has been supported by the National Natural Science Foundation of China (No. 41672361 and No. 41876218), Scientific and Technological Development Program of Jilin Province (No. 20170414044 GH), the Provincial and School Co-construction project (No. SXGJSF2017-5) and Program for JLU Science and Technology Innovative Research Team (No. 2017 TD-24).  
The authors also thank anonymous reviewers for fruitful discussion and useful comments.  
The authors declare no conflict of interest.
## 2 Methods
The spatial vector data were sourced from the Polish geoportal, encompassing information on land cover (land use), buildings, river and stream networks, boundaries of cadastral districts, city boundaries, and statistical district boundaries. The flood hazard and flood risk data were acquired from the Informatic System of National Protection. Information on local floods was gathered from municipalities based on firefighting service interventions. The digital elevation model raster data were sourced from the Polish National Geoportal. Additionally, raster data sources included satellite images from Landsat 8 (USGS) and Sentinel-2 (ESA), as well as Imperviousness Density 2018 accessible through the Copernicus Europe Eyes On Earth platform. Apart from raster and vector spatial data, basic statistical data such as population figures were obtained from the local database of Polish statistics. Figure 1 presents a simplified diagram illustrating the approach to diagnosing urban areas for climate change adaptation, with a particular focus on the city's hydrological regime and conditions. The upper part includes analyses of archival and modelled flood data (caused by rainfall and riverbank overflow), GIS studies, and multi-criteria analysis. The lower part outlines the activities undertaken within the framework of these analyses, as detailed in the methodology section.