esa-sceva/satcom-chunk-collection huggingface dataset:
# Nutritional Values of Onion Bulbs with Some Essential Structural Parameters for Packaging Process
## 3 Results and Discussion  
### Nutritional Values  
Figure 1 represents the results of the nutritional values carried out on some onion bulb varieties. Protein content varied overall from 9.22 g/100 g FW (Green) to 13.21 g/100 g FW (Leek), Figure 1a. It was found that protein content agreed with that of onion bulbs investigatedby [PERSON] et al. [22] that rangedfrom 9.84 to 12.09 g/100 g. The protein daily allowance is 50 to 63 g protein/day as per USDA recommendations (2000) [23]. About 80% of protein intake can be overetable origin, especially in developing countries, as it is more economic than animal protein. Therefore, onion consumption might supply a considerable amountfor human needs.  
However, ash contents varied from 3.02 g/100 g FW (Leek) to 4.92 g/100 g FW (Baby), Figure 1b. These results were similar to the other onion varieties [1].  
As shown in Figure 1c, the fiber content values vary between 1.69 g/100 g FW in the Baby variety and 1.81 g/100 g FW in Green samples. Higher fiber content can be related to several factors, including the type and age [24].  
[PERSON] et al. [10] established similar results of crude fat in onion bulbs toour contents, varying from 2.01 to 3.72 g/100 g FW. All onion bulbs have high crude fat, especially the Red variety, Figure 1d.  
The carbohydrate content of different bulbs was varied; the Green variety had the highest at 83.04 g/100 g FW, followed by Leek at 79.88 g/100 g FW, Figure 1e. On the other hand, the Red variety hadthe lowest carbohydrate content at 77.20 g/100 g FWas compared with other onion bulbs. All bulbs had a slightly higher carbohydrate content than the varieties reported by [PERSON] et al. [1].  
The results for acidity were 6.24 (Baby) to 6.61 (Leek), Figure 1f. The low acidity might be a limitation for onion preservation [25].  
Figure 1: Nutritional values: protein (**a**), ash (**b**), fiber (**c**), crude fat (**d**), carbohydrate (**e**), and \(\rm pH\) (**f**) contents of onion bulbs.
## 4 Simulations and Real Data Processing
### Verification with Real Data  
Two sets of real data, obtained from the wideband LFM radar systems, are applied for the verification of the proposed method. The parameters of the two datasets are shown in Table 5, where PRI means pulse repetition interval. The targets are satellites in space. They fly along the radar line of sight, first toward the radar, then leave. For real radar data, the signals are stretched, and the SNR values cannot be obtained. Nevertheless, the noise power can be estimated by averaging the power of the spectrum outside the target's position, and the signal power can be calculated via subtracting the noise power from the power of the stretched signal. In this way, the average estimated SNRs of the two real data sets are \(-5.99\) dB and \(-7.58\) dB, respectively.  
Figure 9 illustrates the spectrums of the cross-correlation results of the two datasets. Figures 10a and 11a show the estimated velocities in comparison with the real velocities of the targets. The absolute values of differences between the estimated velocities and the real velocities of the targets are shown in semilog scale in Figures 10b and 11b. It is seen that the estimated results are consistent with the real velocities for both datasets. The RMSE of the estimated velocity is 0.0561 m/s in the first dataset and 0.2842 m/s in the second dataset.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Time Cost (s)** & **CCAE\_Stretched** & **CCAE\_UR** & **ACCF\_UR** \\ \hline Estimation without acceleration & 81.6 & 2545.1 & 5282.1 \\ Estimation with acceleration & 156.5 & 4869.7 & 10175.3 \\ \hline \hline \end{tabular} Simulation environment: CPU frequency 3.30 GHz, memory 8 GB.  
\end{table}
Table 4: Comparison of the time cost between CCAE on stretched signals, CCAE on UR signals and ACCF on UR signals.  
Figure 9: The spectrums of the cross-correlation results. (**a**) The spectrum of the cross-correlation result of radar Dataset 1. (**b**) The spectrum of the cross-correlation result of radar Dataset 2.  
in Figure 12b. With motion compensation, the RM effect is eliminated, resulting in a focused ISAR image (Figure 13). These results with real data demonstrate that the proposed method is effective in practical radar systems.
## 2 Experimental Design
### General Methodology
#### 2.1.2 In-Situ and Laboratory Experiments  
The present study conducted both in-situ and laboratory experiments to complement each other. The in-situ test was considered to be an important first step from the viewpoint of ecological validity; being physically in the space would allow the subject to perceive auditory attributes in the most natural and accurate way. Although the listener's auditory perception may be biased by other senses such as vision, data collected from the in-situ investigation would reflect real life experience. However, a limitation of the in-situ RGT experiment with a multi-position comparison task was identified; it was not easy for the subject to compare different test conditions, since the subjects had to physically move between different positions whilst remembering the auditory experience of the previous condition. This may have made it difficult for the subjects to detect subtle differences.  
Considering this limitation, a laboratory RGT experiment was also conducted using binaurally auralised stimuli reproduced over headphones. This allowed instant switching between the stimuli, thus enabling the subjects to compare different conditions more effectively and efficiently. The lab experiment also allowed the present authors to control the experimental environment more. However, it is important to verify the ecological validity of the results obtained in the virtual environment against those from the in-situ investigation, which will be discussed in Section 4.1.  
Furthermore, this study extended the traditional RGT with an additional audibility grading phase in order to identify most salient attributes. As mentioned earlier, RGT provides a well-structured framework for the elicitation of personal constructs. However, it does not generate any information about which attributes are perceptually more important. The second stage of the RGT grades each attribute for each stimulus; however, this grading is only used for assessing the level of similarity between different constructs or stimuli. Therefore, the relative difference between different stimuli in perceptual strength is not known from the RGT result. In the context of the present study, it was also of interest to see which of the elicited attributes have stronger audible differences among different position/orientation conditions. Therefore, after the lab RGT test, an audibility test was carried out to compare all of the stimuli conditions for each of the attributes elicited from each subject. The subjects were asked to rate the perceived magnitude of the difference between all of the conditions, for each of their own elicited bipolar constructs. This allowed for further filtering of the constructs based on their reported audibility rating.
# Cellular and Virtualization Technologies for UAVs:
## 4 Testbed Experimentation
### 5G Standalone Benchmark  
By December 2020, Ericsson has activated 5G SA technology at 5G Telefonica Open Network Innovation Centre (5 TONIC) [51]. 5 TONIC is a leading research laboratory focusing on 5G technologies based in Madrid, where members from both industry and academia work together in specific research and innovation 5G experimentation projects. The deployment of 5G SA in this laboratory is a crucial technological milestone for validating the solutions developed by 5 TONIC verticals partners allowing promising capabilities enabled by 5G technology demonstrations. This deployment includes the integration of the Ericsson Radio Access Network (RAN) [52] and the Core solutions for 5G NR standalone [53] in the laboratory facilities. With this achievement, 5 TONIC becomes one of the first open research and innovation laboratories in the world to offer 5G SA capabilities to validate vertical applications.  
Due to the lack of commercial deployment of this technology, the 5 TONIC 5G SA network has been used to benchmark the network performance offered by a real 5G SA  
Figure 5: Performance analysis upon the UAV-controller.  
deployment. Although these measurements have been conducted in a laboratory environment with ideal conditions (compared to Section 4.1 measurements), they demonstrate the upcoming 5G SA technology potential.  
The NETGEAR CPE (Nighthawk M5 5G WiFi 6 Mobile Router: [[https://www.netgear.com/home/mobile-wifi/hotspots/mr5200/](https://www.netgear.com/home/mobile-wifi/hotspots/mr5200/)]([https://www.netgear.com/home/mobile-wifi/hotspots/mr5200/](https://www.netgear.com/home/mobile-wifi/hotspots/mr5200/)) (accessed on 27 April 2020)) was used to perform these experiments. This device connects through the Ethernet interface with the RPi to provide connectivity. Thanks to its compact size (10.5 cm \(\times\) 10.5 cm \(\times\) 2.15 cm) and low weight (240 g), this device can be on-boarded on almost any UAV, as shown in Figure 2(b). Moreover, it includes a rechargeable battery promising battery life-times for one day at total operation. As in the previous section, the Ookla speed test has been used to diagnose Internet quality. We obtained an average value of 387.98 (\(\pm\)90) Mb/s in the downlink and an average value of 52.3925 (\(\pm\)5) Mb/s in the uplink. RTT results are 11.94 (\(\pm\)3.5) ms. These results vastly improve the ones obtained by a commercial 4G network. Accordingly, this technology has to be contemplated for the near future and can be considered as the enabling technology to deploy the full potential of UAV networks. However, an exhaustive performance evaluation will have to be carried out with commercial deployment.
## 3 Neural Networks
### Long Short-Term Networks  
By retaining information for a longer period, unidirectional LSTMs (simply LSTMs) are a type of RNN that excels at learning long-term dependencies [30]. RNNs, specifically LSTMs, are preferred for recognition and prediction tasks in applications involving language translation, time-series data, and speech recognition [31]. LSTMs would therefore be a good option for training with our time-series data.  
A single LSTM layer followed by four fully connected layers was created as an estimator in this study. A total of 512 hidden units made up the LSTM layer with a time step of 100, equivalent to the input dataset row size. Considering the fact that the output target angle values could be positive or negative, a linear activation was employed on the last fully connected layer of the network. This last layer is the same for all the other networks too. The bidirectional LSTM (BLSTM) is another variation of LSTMs. The distinction between both the unidirectional LSTM and BLSTM is that input data flows in both forward and backward directions of the LSTM nodes connected across the timesteps of the network. In other words, BLSTM can be assumed to add one more LSTM layer to reverse the input data flow from the last timestep to the first timestep direction [32]. The fact that the BLSTM also preserves information from the future is the only distinction between the unidirectional LSTM and BLSTM. The full BLSTM network for the eight-joint-angle set is depicted in Figure 7. The diagram is only for the inertial data of any of the four bone segments. For the datasets which include BID, the BID data were fed to a separate dense layer which is later combined with the LSTM output at the fully connected layer with 64 units. For datasets that include the FID, the FID is concatenated with the PID and given to the networks as a 100 \(\times\) 18 array. In this way, the two different quantities, the inertial and the biometric data will be separately fed to the network so that the networks can learn features from them independently. Similarly, this applies to the other neural-network models as well.
###### Abstract  
The problem of estimating the indoor position of a person or an object, also known as indoor localization, has gained a lot of interest in the last decades. Actually, this feature would be valuable in many application contexts, from logistics to robotic and Assistive Technology. Different solutions have been proposed in the literature, exploiting a wide range of approaches. This paper aims to provide a brief review of the state-of-the-art approaches in the field, as well as to present the RESMA case study. The latter exploits an ultrasound-based indoor localization system and a User-Environment Interaction functionality, which allows for performing the continuous estimation of the distance between the end-user and objects in the environment. The latter is valuable to provide the end-user with efficient assistance during the environment exploitation. The main focus of this work is related to the overall description of the system architecture, the trilateration algorithm adopted for the sake of user localization and the estimation of the delay time produced by user-distance computation under different operating conditions.  
indoor localization; ultrasound sensors; trilateration algorithm; user-environment interaction; assistive technology
## 5 Discussion  
The results obtained in this study show that the presented methods respond differently when exposed to different variations in irradiance. Additionally, during steady-state simulations, all algorithms showed effectiveness values exceeding 99%. However, in HIL simulations, the ANN (G, T) exhibits a comparatively lower effectiveness value. Nevertheless, no MPPT algorithm achieves perfect effectiveness. According to our analysis of the response times and oscillations in the voltage and current of the PV, all the algorithms have good response times. In terms of oscillation, the INC, FL, and ANN (G, T) present lower oscillation than the other methods.  
According to our analysis of Table 3, the algorithms with the greatest efficiency are the new IC-INC and the ANN (G, T). The IC-INC method is also effective in creating datasets to train high-performance ANNs that are not dependent on measured values of irradiance and PV cell temperature. The simulation results show that FL, IC-INC, and ANN algorithms achieved efficiencies above 99% in all the tested scenarios. In terms of implementation, the proposed IC-INC algorithm does not require a high number of resources, with values slightly higher than P&O and INC.  
The fuzzy logic and neural networks algorithms also achieve good results in most scenarios; however, more resources are necessary for FPGA implementation.  
The behaviors shown in the results of the HIL and simulation are very similar in most cases; however, in FL implementation, some changes are visible in the slow variation test.  
By comparison to the results reported in Table 4, it was observed that the IC-INC algorithm outperforms the tested ones in steady state. Furthermore, the IC-INC algorithm delivered satisfactory results in most irradiance scenarios.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Algorithm** & **LUTs** & **F7 Muxes** & **F8 Muxes** & **DSPs** \\  & **(218,600)** & **(109,300)** & **(54,650)** & **(900)** \\ \hline P&O & 1.08\% & 0.01\% & 0.00\% & 0.22\% \\ INC & 2.48\% & 0.01\% & 0.00\% & 0.00\% \\ FL & 5.92\% & 2.02\% & 1.67\% & 0.89\% \\ ANN (G, T) & 28.38\% & 0.08\% & 0.02\% & 11.78\% \\ IC-INC & 2.53\% & 0.00\% & 0.00\% & 0.44\% \\ ANN (IC-INC) & 63.76\% & 0.08\% & 0.02\% & 30.67\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Results of FPGA resources for different implemented algorithms.
# From Bottom of Sea to Space: Quo Vadis IoT? So What About Security?
## 3 Security Challenges and Mechanisms
### IoT in Space  
#### 3.5.1 Internet of Remote Things  
Internet of Remote Things (IoRT) has been of interest to researchers in recent years. Gateways are a way for terrestrial networks and IoRT to communicate and allow for IoRT services. Each gateway can possibly have different channel conditions, locations, and other varying parameters, which can affect the performance of communication. This makes gateway placement vital to how IoRT is able to communicate. The authors of [208] propose a way to effectively place gateways to minimize access distance and the number of gateways needing to be created. Distributed resource allocation (DRA) algorithms are designed using principles of alternating direction method of multipliers (ADMM). This allows the algorithm to find the total revenue and cost of gateway placement and cost of deployment and optimize them. A genetic-based gateway placement algorithm is then used based on the data given by the previous algorithms to offer a solution. The proposed scheme shows through testing its ability to effectively place gateways in a way that improves communication and lowers costs.  
The authors are not aware of any research work related to security challenges or mechanisms of this extension.
# A DDPG-Based USV Path-Planning Algorithm
## 5 Conclusions  
This study provides an in-depth comparison and analysis of the performance of multiple path planning algorithms, including USV-based DDPG, Actor-Critic, and A-star algorithms in a simulated environment. The data show that USV-based DDPG outperforms the other methods in terms of average reward, average number of steps, path length, and number of turns.  
Firstly, USV-based DDPG shows significant improvement in average reward compared to other algorithms, which indicates its superior performance in path-planning tasks. Also, its average step count and path length are much lower than other algorithms, which indicates that it can find paths faster, and the paths that have been found are more concise and direct. In addition, USV-based DDPG makes far fewer turns than Actor-Critic, which further proves the efficiency and accuracy of its path planning.  
Although the Actor-Critic and A-star algorithms also have their advantages in some aspects, the USV-based DDPG undoubtedly demonstrates its superiority in terms of overall performance. This may be attributed to its deep reinforcement learning nature and specific optimization of USV.  
However, any research has its own limitations. In future work, further efforts shall be made to explore different model parameters and settings so that the performance of these algorithms in different contexts can be fully evaluated.  
Overall, this study provides valuable insights into the comparison of path-planning algorithms in simulated environments and provides a solid foundation for future research and practical applications.  
Conceptualization, J.Z. and P.W.; methodology, J.Z., P.W. and C.B.; analysis, P.W.; the investigation, J.Z. and C.B.; writing--review and editing, P.W. and B.L.; supervision, C.B. All authors have read and agreed to the published version of the manuscript.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Simulation Algorithmic** & **Average Reward** & **Average Steps** & **Path Length** & **Number of Turns** \\ \hline DDPG based on USV & 170.65 & 366.42 & 2315.76 & 3 \\ Actor-Critic & 163.70 & 432.78 & 2513.41 & 19 \\ A-star & 105.63 & 537.51 & 2607.93 & 6 \\ \hline \end{tabular}
\end{table}
Table 4: Running results of each algorithm.  
**Funding:** This work is partially supported by the National Science Foundation of China (Grant No. 51679024), the Dalian Innovation Team Support Plan in the Key Research Field (Grant No. 2020 RT08), and Fundamental Research Funds for the Central Universities (Grant No. 3132021139).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 4 Experiments
### Computational Performance  
Due to the influence of factors such as computing environment initialization and GPU startup, the computing performance of each algorithm with respect to different data amounts often differed. To test the computational performance of each algorithm under different computational amounts, we selected 300, 1000, and 10,000 HRRS images, respectively, from _Datasets\({}_{10,000}\)_ to construct three datasets. Table 13 shows the computational performance of each algorithm with respect to these three datasets.  
The computational performance of our algorithm was only slightly better than the original Swin-Unet-based algorithm, and was essentially the same as the MultiResUnet  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Model Each**} & \multirow{2}{*}{**300 Images**} & \multicolumn{2}{c}{**1000 Images**} & \multicolumn{2}{c}{**10,000 Images**} \\ \cline{2-6}  & **Average Time** & \multirow{2}{*}{**Total Time (s)**} & \multirow{2}{*}{**Average Time**} & \multirow{2}{*}{**Total Time (s)**} & \multirow{2}{*}{**Average Time**} \\
**Based on** & **(ms)** & & & **(ms)** & \\ \hline MUM-Net & 35.13 & 10.54 & 24.20 & 24.20 & 23.43 & 234.30 \\ MultiResUnet & 44.07 & 13.22 & 33.89 & 33.89 & 32.37 & 323.68 \\ U-net & 21.20 & 6.36 & 13.34 & 13.34 & 12.78 & 127.79 \\ M-net & 27.57 & 8.27 & 17.79 & 17.79 & 15.75 & 157.50 \\ Attention U-Net & 21.47 & 6.44 & 15.60 & 15.60 & 13.38 & 133.82 \\ Attention ResU-Net & 46.83 & 14.05 & 26.60 & 26.60 & 22.12 & 221.24 \\ Attention R2U-Net & 30.40 & 9.12 & 20.84 & 20.84 & 19.24 & 192.36 \\ AAU-Net & 19.70 & 5.91 & 14.89 & 14.89 & 11.73 & 117.34 \\ Swin-Unet & 59.20 & 17.76 & 38.98 & 38.98 & 36.08 & 360.77 \\ Improved Swin-Unet & 49.73 & 14.92 & 33.11 & 33.11 & 31.24 & 312.37 \\ \hline \hline \end{tabular}
\end{table}
Table 13: Computational performance.  
-based algorithm. In fact, Transformer has had a similar problem in other fields, such as Transformer-based image segmentation and image classification; that is, the computing performance of Transformer is lower than CNN.
## Appendix A
### FiniteElement Errors and Adaptivity [26]  
The finite element errors are the deviation of the original finite element results from the recovered results (or exact results). Solution errors are evaluated in in scalar quantity terms i.e., energy norms or L\({}_{2}\) norms. The reliability of error estimations is measured in terms of effectivity. The ratio between the projected error and the exact error i.e., effectivity (\(\theta\)), provide the reliability of the error estimators.  
\[\theta=\frac{\|e\|}{\|e_{ext}\|} \tag{13}\]  
where \(\|e\|_{E}(=\left[\int_{\Omega}e_{o}^{*T}D^{-1}e_{o}^{*}d\Omega\right]^{\frac{ 1}{2}})\) represent the evaluated error, and \(\|e_{ext}\|_{E}\) is the exact error (in energy norms), \(D\) is the elasticity matrix.  
Solution accuracy (\(\eta\)) in finite element analysis is given as follows.  
\[\eta=\frac{\|e\|}{\|\sigma^{*}\|} \tag{14}\]  
where \(\|\sigma^{*}\|^{2}=\|\sigma^{h}\|^{2}+\|e\|_{E}^{2}\), \(\|\sigma^{h}\|(=\left[\int_{\Omega}\sigma^{T}D^{-1}\sigma^{T}d\Omega\right]^{ \frac{1}{2}})\).  
The finite element solution is accepted when target accuracy (\(\eta_{target}\)) is more than the accuracy (\(\eta\)) of the solution. The mesh may be improved in adaptive way i.e., only on locations where the errors are more than the target error. The global target error is calculated as follows.  
\[\|e\|_{target}=\eta_{target}\|e\|/k \tag{15}\]  
where \(k\) is a factor lying between 1.0 to 1.5 to avoid oscillation [32].  
The target error in the ith element is calculated using the following equation [26].  
\[\frac{\|e\|_{target\ (i)}}{\sqrt{\Omega_{i}}}=\frac{\|e\|_{target}}{\sqrt{ \Omega}} \tag{16}\]  
where \(\Omega\) is the volume of the domain, \(\Omega_{i}\) is the volume of the \(i\)th element.  
Mesh improvement parameter (\(\xi_{i}\)) that directs mesh refining is as follows.  
\[\xi_{i}=\frac{\|e\|_{target}}{\|e\|_{target\ (i)}} \tag{17}\]  
The mesh improvement is required when element improvement parameter is greater than one. The updated mesh size (\(h_{updated}\)) can be found as.  
\[h_{updated}=\frac{h_{old}}{\xi_{i}^{1/p}} \tag{18}\]where \(p\) is the order of the approximating polynomial and \(h_{old}\) is the old size of the \(i\)th element.