esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Sample Characterization  
The elemental composition of the water samples from the sampling points supplied to the ASEC system (input), besides the water samples collected after the treatment (output), were analyzed using Inductively Coupled Plasma-Mass Spectroscopy (ICP-MS, Thermo Electron Corporation(r), X-series II, Thermo Fisher, Karlsruhe, Germany). Analyses were carried out at certified laboratories at the ICMAN/CSIC (Spain).  
On the other hand, the dried solids and crystallized salt samples collected as the output of the ASEC technology were semi-quantitatively characterized using X-ray fluorescence spectrometry (XRF, in a Panalytical Epsilon 1 model spectrometer equipped with an Ag target X-ray tube and a Be detector window that can be operated at a maximum voltage of 50 kV) and X-ray diffraction (XRD, using a Panalytical X'Pert Pro diffractometer with CuK\(\alpha\) radiation over an angular range (20) from 10\({}^{\circ}\) to 90\({}^{\circ}\)). Acid digestion of the same aliquots enabled the determination of the multi-elemental composition using ICP-MS at the ICMAN/CSIC laboratories [44].
# Review of V2X-IoT Standards and Frameworks for ITS Applications
## 3 Commercial V2X Products
### Commercial V2X Chipsets
While most software with defined RF transceivers could be suitable for 3G/4G/5G base stations, it should be noted that Multiple-Input-Multiple-Output (MIMO) receiver and transmitter chains usually share the same Local Oscillation (LO) generators, hence, only single standards can be supported at a single time and more than one transceiver must be used in a multistandard V2X system. In summary, all reviewed RSU, OBU, and RF transceivers are mainly intended to support one or only a few specific standards and are usually single-channel (SISO). This means that multiple, different frequency standards cannot be maintained simultaneously. For the latter reasons, it is necessary to develop new V2X frameworks based on highly versatile hardware both from analog and digital processing point of view. This would enable the development of new hybrid DSRC and C-V2X technologies and systems, and would enable the use of other complementary wireless solutions (for example, IoT-based) in such systems.
## 3 Data Source and Data Preprocessing
### Data Screening  
To decrease the interference of the external source field in establishing lithospheric magnetic field model, according to the satellite data screening criteria proposed by [PERSON] and [PERSON] et al. [24; 46], the data are selected in local time 22:00-03:00 to minimize the contributions from the ionospheric \(Sq\) field. The \(Dst\) and \(Kp\) index conditions in Equation (14) are utilized to select the data corresponding to a quiet period of geomagnetic activity to weaken the effects of the equatorial ring current and magnetospheric convection intensity:  
\[\left\{\begin{array}{ll}|Dst|\leq 20 nT&\quad\mbox{and}\quad d|Dst|\leq 10 nT\\ Kp\leq 2&\quad\mbox{and}\quad d|Kp|\leq 2\end{array}\right. \tag{14}\]  
Figure 2: Solar radiation index F10.7 from 2000– 2015.  
\(d|Dst|\) and \(d|Kp|\) represent changes over the previous three hours. A selection condition is selected using quality flags indicators, such as spacecraft maneuvers and the on/off status of the star camera, to judge the running state of a star camera. Finally, short-arc tracks without data beyond 240 epochs are eliminated to fully guarantee the quality of the observed data. Figure 3 shows the data preprocessing procedure of extracting a lithospheric magnetic field signal based on satellite magnetic survey data.
# Investigation on Reference Frames and Time Systems in Multi-GNSS
## 2 Quality Check of Broadcast Orbits  
To study the accuracy of the broadcast orbits relative to the precise orbits provided by CODE [6] SP3 (Extended Standard Product 3 Orbit Format), the alignment of the reference systems adopted by broadcast orbits with respect to that provided by precise orbits is analyzed. The reference frames adopted by the several GNSSs in the broadcast ephemeris are different from each other, namely WGS84 (GPS), PZ-90.11 (GLONASS), GTRF (Galileo) and CGCS2000 (BeiDou), whereas the reference frame adopted by precise orbits is typically unique. According to [7; 8], current GLONASS broadcast ephemerides and all those since 31 December 2013 should be referred to PZ-90.11 as it is aligned to ITRF at millimeter level. The reference frame adopted by CODE during week 1950 is IGS14.  
Broadcast and precise orbits are referred to different points of space vehicles: the antenna phase center (APC) and the center of mass (CoM), respectively. The relative distance of APC to CoM is the antenna phase offset (APO). [PERSON] et al. [3] decided not to apply any reference frame transformation in their comparative analysis. They inferred the APO from the comparison between broadcast and precise orbits except for BeiDou, for which they assumed that the broadcast ephemeris is referred to the CoM.  
In this section the coordinate differences in the NSW (radial, along-track, cross-track) reference system (Figure 2) are also analyzed and computed on the post-fit residuals of the seven-parameter Helmert transformation [9] between broadcast and precise ephemeris, for each day and satellite of the four GNSSs: GPS, GLONASS, Galileo, BeiDou. To achieve this goal, the Bernese software [10] was used with a maximum threshold of 5 m as a condition for coordinate rejection.  
Figure 1: Map view of monitored sites.
# DeepProfile: Accurate Under-the-Clothes Body Profile Estimation
## 7 Conclusions and Future Work  
In this work, we propose a human body profile estimation method which generates accurate under-the-clothes human body profiles via deep learning. We established and labeled under-the-clothes body contour keypoints, based on which the precise human body profile was generated. To improve the precision of keypoint detection, we propose an SMSD block, which keeps the details of the image and increases the information flow among different layers. Extensive experimental results clearly demonstrate significant performance gain from the proposed method over state-of-the-art methods. Moreover, the body profiles obtained with our method were extended to the applications of 3D human model reconstruction and non-contact body size measurement.  
Since the images of human subjects in the dataset were captured in a standard standing pose, the body profile extraction results were not satisfactory if the tested pose deviated greatly from the standard pose. In the future, we aim to improve the robustness of the proposed method by expanding our dataset with more, different human poses. Moreover, more potential applications based on our profiling, such as personalized outfit recommendation, could be developed [45,46].  
Conceptualization, S.L., X.S. and S.Z.; methodology, S.L., F.L. and X.S.; data curation, S.Z.; writing--original draft preparation, F.L. and X.S.; writing--review and editing, S.L. and F.L.; supervision, S.L. and S.Z. All authors have read and agreed to the published version of the manuscript.  
This work was funded by the Natural Science Foundation of Zhejiang Province(LY19F020027) and Zhejiang Provincial Science and Technology Planning Key Project of China under Grant No. 2022C01120.  
**Institutional Review Board Statement:** Not applicable.  
\begin{table}
\begin{tabular}{l l c c c c c} \hline \hline
**ID** & **Sex** & **Chest (Gt)/cm** & **Chest (Err)/cm** & **Abdominal (Gt)/cm** & **Abdominal (Err)/cm** & **Calf (Gt)/cm** & **Calf (Err)/cm** \\ \hline
1 & M & 83 & 1.85 & 75 & 1.00 & 38 & \(-\)1.13 \\
2 & M & 89 & 1.52 & 83 & 1.27 & 37 & 0.16 \\
3 & M & 87 & \(-\)1.87 & 80 & 1.76 & 34 & \(-\)0.22 \\
4 & M & 101 & \(-\)0.55 & 104 & \(-\)1.71 & 42 & \(-\)2.03 \\
5 & M & 93 & 1.45 & 83 & 1.25 & 37 & \(-\)2.03 \\
6 & F & 82 & \(-\)2.26 & 67 & 0.56 & 32 & 1.91 \\
7 & F & 83 & 1.03 & 62 & 1.82 & 29 & 1.14 \\
8 & F & 80 & 1.28 & 66 & \(-\)0.72 & 32 & \(-\)0.38 \\
9 & F & 80 & \(-\)0.61 & 72 & 0.3 & 33 & \(-\)0.13 \\
10 & F & 86 & 1.67 & 82 & \(-\)1.34 & 35 & 1.97 \\
11 & M & 89 & \(-\)1.65 & 84 & 0.91 & 38 & \(-\)1.56 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results and error analysis of size measurement: chest girth, abdominal girth and calf girth. (Gt) means ground truth; (Err) means the deviation between the estimated size and the ground truth.
## 3 Methodology
### Speech Emotion Recognizer
#### 3.2.1 Feature Extraction  
For the SER model, we used a pre-trained xlsr-Wav2 Vec2.0 [66] model. This model had the original architecture of a Wav2 Vec2.0 transformer [67]. Unlike Wav2 Vec2.0, the xlsr version was trained in 53 distinct languages, reaching state-of-the-art performance in speech-to-text. Further, xlsr-Wav2 Vec2.0 was a transformer trained in a self-supervised way from millions of raw audio data. After its pre-training on unlabelled data, the model was fine-tuned on labeled data to adapt it to downstream speech recognition tasks of different nature.  
As [PERSON] et al. described in [67], this model consists of three distinct parts (which also appear in Figure 2): the feature encoder, the transformer, and the quantization module.  
Firstly, the feature encoder contains several convolutional layers that receive the raw audio \(X\) and outputs a latent speech representation \(Z\) of each timestep of the recording.  
Secondly, the transformer module receives the latent speech representations \(Z\) and creates the context representations \(C\). This context representation is generated after passing through the 24 transformer blocks and 16 attention heads that conform to the transformer module.  
Finally, the quantization module maps the output of the feature encoder \(Z\) into a discrete space using a product quantization that generates the compact vector \(Q\).  
For our experiments, we employed the LARGE version of xlsr-Wav2 Vec2.0, fine-tuned on the English set of the Common Voice dataset [68] and available in the following Hugging Face repository: [[https://huggingface.co/jonatasgrosman/wav2](https://huggingface.co/jonatasgrosman/wav2) vec2-large-xlsr-53-english]([https://huggingface.co/jonatasgrosman/wav2](https://huggingface.co/jonatasgrosman/wav2) vec2-large-xlsr-53-english) (accessed on 26 December 2021).  
For the feature extraction strategy, we re-used the pre-trained network to obtain a latent speech representation of each recording. First, we subsampled the audios to 16 kHz and converted them into mono-channels using the FFmpeg tool [69]. Internally, the framework divided the audio into windows of 25 ms with an overlap of 15 ms and a stride of 20 ms. When the transformer generated the sequences of 512-dimensional embeddings from the convolutional feature encoder, we calculated the average of these embeddings along its temporal dimension. With the averaged 512-dimensional representation of each recording, we trained several static speech emotion recognizers employing the sklearn library [70]. Among the compared models, we used a support vector machine (SVM) with an 'RBF'kernel, a k-Nearest Neighbours (kNN) with a majority voting to select the class, and a multilayer perceptron (MLP) with one or two layers of 80 neurons each and an output eight-neurons layer.  
In this way, we re-used the original features of the speech-to-text task to solve speech emotion recognition, transferring the learned knowledge contained on the embeddings to the new models.
# Analysis of Drone Flight Stability for Building a Korean Urban Air Traffic (K-UAM) Delivery System
## 4 Drone Flight Data Analysis--Route
In Figure 8, the drone delivery route of the Jinha--Myeongjin Bridge--Jinha course in Figure 7d is converted into x, y, and z coordinates using the UTM-K coordinate system conversion, and (a) represents the entire xyz plane, (b) represents the xy plane, (c) represents the xz plane, and (d) represents the yz plane. Figure 8: _Cont._  
Figure 7: Drone flight path course based on the Jinha Public Parking Lot in Table 2 (Red points: Planned drone delivery route. Blue line: Collected drone flight path data). In particular, analysis of drone delivery route data converted to xy coordinates in Figure (b)b is important. In Figure (b)b, a MATLAB (2022b) code was developed to calculate in real time the distance (d) difference between the two adjacent blue points along the planned delivery route that the drone passes through at each moment on the actual path that the drone passes through, using the distance formula between the point and the straight line in Figure 9. The altitude z-coordinate was also calculated as the difference between the planned altitude and the drone's real-time flight altitude. Therefore, Figure (a)a is the result of calculating the distance deviation from the planned blue path in the Jinha--Myeongjin Bridge--Jinha route of Figure (b)b to the red point, which is the actual drone delivery movement coordinate. The x-axis is expressed as the number of data loaded instead of time. As with the visual observation in Figure (b)b, the calculated numerical distance error in Figure (a)a also shows that the drone is flying along a very stable path within 10 m. Next, the rule is applied to analyze the distance deviation of the z-coordinate, which is the altitude in Figure (b)b, in the order of 10 m, 5 m, and 3 m. The error rate is expressed as the percentage of the number of data exceeding the distance error compared to the total number of data in Equation (1). When the excess data analysis was performed with the most conservative rule of 3 m or less, the altitude error of the Jinha--Myeongjin Bridge--Jinha drone delivery route was analyzed to be 0.11762%. According to the regulations set by the Korea Transportation Safety Authority, the error rate must be within 3%. Figure 8: Orthogonal coordinate transformation of the drone delivery route of Jinha—Myeongjin Bridge—Jinha course in Figure (d)d.  
Figure 9: Analysis of drone delivery route deviation data (path error: xy plane). \[Error\left(\%\right)=\frac{\text{Data exceeding the distance error }\left(\text{N}_{\text{exceeding}}\right)}{\text{Total number of data }\left(\text{N}_{\text{total}}\right)}\times 100 \tag{1}\]  
The results of analyzing the path error and altitude error of the drone delivery routes in the Ulju area summarized in Table 2 are shown in Figure 5:0 It can be seen that all drone delivery flight paths are flying very stably within the planned route and regulations.
# Assimilation of Earth Observation Data Over Cropland and Grassland Sites into a Simple GPP Model
## Results
### Site Examples
In general, we do not observe a systematic underestimation of maize GPP (see Sections 4.2 and 5 for a discussion). Figures 4-6 show the observed NDVI and GPP along with the same variables modelled by the Sim model for three EC grassland sites. Figure 4 shows the grassland site HU-Bug (Hungary, 47.84690\({}^{\circ}\)N, 19.72600\({}^{\circ}\)E; [59]), a semi-arid sandy grassland extensively grazed. The Sim model detects two growing cycles per year that are originated from a biomass reduction during the summer due to grazing and/or sever water  
Figure 6: Observed and modelled GPP and NDVI for the grassland site IT-Amp. Description of plots as Figure 2.  
limitations [60]. In years when no double cycles is visible in the measured GPP, NDVI shows only a moderate reduction during the summer and coherently, the two season modelled by Sim are nearly merged together (e.g., years 2005 and 2006). GPP estimation accuracy is high for both the Sim model and MOD17 for this site (see scatter plots of Figure 4)  
Figure 5 shows an example of a grassland site where the GPP estimated by Sim outperforms that of MOD17. It refers to the grassland site of AT-Neu (Austria, 47.11667\({}^{\circ}\)N, 11.31750\({}^{\circ}\)E; [61]). The Sim model correctly diagnoses one growing cycle per year. Growing cycles are neatly separated by snow cover extending from mid-November to mid-March where measured GPP is close to zero and observed NDVI is very low or missing. While NDVI is nicely fitted by the model, GPP tends to be underestimated by both MOD17 and (to a much lesser extent) by the Sim model. The effect of the fit operated on the upper envelope of the observed NDVI is clearly visible in several years (e.g., 2006, 2007). Figure 6 shows the results for a grassland site where estimation errors are among the largest in the dataset (IT-Amp, Italy, 41.90410\({}^{\circ}\)N, 13.60516\({}^{\circ}\)E; [62]). The site is characterized by a distinct summer drought with senescence occurring in June, followed by a cut and subsequent animal grazing and moderate vegetation regrowth. Although the Sim model is able to reproduce this temporal pattern in both NDVI and GPP, it fails to match the GPP magnitude in the main growing period of some years (i.e., 2004, 2006, 2008). It is noted that, in contrast to MOD17 that does not reproduce the GPP reduction between the two growing cycles, the model simulations do detect the break between the cycles and correctly represent the difference in GPP magnitude of the main (spring) and secondary cycle (late summer to autumn).
# Backscatter Communications: Inception of the Battery-Free Era--A Comprehensive Survey
## 7 BackCom: Range Extension and Reliability
### Environmental Monitoring Using BackCom  
To address the problem of global warming and to improve the environment, backscatter tags can be used for the constant monitoring of environmental parameters (e.g., temperature, humidity, etc.) so that the effects of environmental conditions on human life can be reduced. Ambient FM BackCom can measure the moisture level in plants, with the help of wireless leaf temperature sensors [205]. BackCom-based leaf sensing system for water stress management can measure leaf canopy temperature [206]. A WiFi-powered camera and temperature sensor have been deployed in six homes of a metropolitan area to sense and communicate a real-life scenario. These devices have a communication range varying from 17 and 20 feet [130]. Battery-free connected cameras consume only 250 \(\mu\)W and can be used for environment monitoring [207]. [PERSON] et al. demonstrated the analog (BiS) radio enabled sensors for environmental monitoring. These sensors have a power consumption of less than 1 mW and range over 100 m. Moreover, this design allows receiver-less sensing and communication without collisions between sensors [208].
## 4 Security Issues and Solutions for CAVs
### Cyber-Attacks on Inter-Vehicle Systems
#### 4.2.1 Models of Attacks on Platoon
analyzed the stability and safety of a platoon according to a model including Rician fading channels and jamming attacks [102]. When the attacker launches the jamming signal over the platoon, with the assumption of Rician fading, the probability density function of the instantaneous signal-to-interference-plus-noise ratio (SINR) of the received signal of the i-th vehicle at time k, \(\gamma_{i}^{k}\), is derived by  
\[f_{i}^{k}(\gamma_{i}^{k})=\frac{1+K}{\gamma_{i}^{k}}\exp\left(-K-\frac{(1+K) \gamma_{i}^{k}}{\overline{\gamma_{i}^{k}}}\right)*I_{0}\left(2\sqrt{\frac{K(K+ 1)}{\overline{\gamma_{i}^{k}}}}\right) \tag{6}\]  
The state space representation of the platoon under the Rician fading channel and jamming attacks is derived as  
\[x_{n}[k+1]=\overline{\dot{A}}_{n}x_{n}[k]+\overline{\dot{B}}_{c}\overline{u}_ {n-1}[k]+\overline{\dot{B}}_{s}u_{l}[k] \tag{7}\]  
Considering various scenarios with different settings, including various attacker's locations and vehicle signal transmission power, they proved that platoon stability and safety are highly sensitive to jamming attacks. They also derived the best location and best time to launch the jamming attacks to destabilize the platoon. In addition, they computed the minimum transmission power to maintain string stability and advised that the minimum transmission power needs to increase with the increase in jamming signal power. Currently, much work has been undertaken investigating the string stability under imperfect communication performances [98; 103; 104; 105]. There is little work to consider how cyber-attacks utilize the imperfections of communications to impact the performance of the platoon. The most common one is jamming, which causes severe damage to the safety of the platoon and is hard to prevent. Besides string stability, platoon management involving platoon formation, merging, and splitting is another fundamental issue. It is still challenging to form the stable cluster or platoon, especially in heterogeneous and drastically changing scenarios. In this process, traffic dynamics, communication behavior, and security are supposed to be considered. In addition, a platoon typically consists of a master and multiple following vehicles and operates within one lane, while another cooperative pattern, convoy, which has no master vehicle and operates on multi-lanes, considerably relies on vehicles to self-organize a more complex cooperative pattern. It would be challenging to model such a pattern under a communication.
## 2 Methodology
### Numerical Simulation Method  
After modelling the metamaterial structure in CST Microwave Studio 2018, a numerical simulation was performed to determine the reflection and transmission coefficients. Figure 2 depicts the wave propagation of the metamaterial structure. In this simulation, the proposed metamaterial was put in between the two ports on the positive and negative \(z\)-axis, respectively, as illustrated in Figure 2a. The transmitting port was represented by port 1, and the receiving port was represented by port 2. The boundary conditions for the x- and y-axes were set to perfect electric conductor (PEC) and perfect magnetic conductor (PMC), respectively. Figure 2b illustrates the boundary conditions. Because the unit cell design is smaller, the frequency domain was utilized. The characteristic impedance (\(Z_{0}\)) was 50 \(\Omega\). The frequency was set from 0 GHz to 12 GHz. Once the simulation was completed, scattering parameters (S-parameters) were obtained, in which the S-parameters were composed of reflection coefficient (S\({}_{11}\)) and transmission coefficient (S\({}_{21}\)). The data of the S-parameters were analyzed and extracted to calculate the electromagnetic properties such as electric permittivity (\(\epsilon\)), magnetic permeability (\(\mu\)), refractive index (n), and impedance (z). The values of the electromagnetic properties were defined in Equations (1)-(7). The effective parameters of the electromagnetic properties were retrieved using the Robust method [31]. MATLAB software was used for the result retrieval.  
\[\text{S}_{11}=\frac{\text{R}_{01}(1-\text{ e}^{\text{i}2\text{n}_{\text{k}0}\text{d}})}{1-\text{ R}_{01}^{2}\text{e}^{\text{i}2\text{n}_{\text{k}0}\text{d}}} \tag{1}\]  
\[\text{S