esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Ravenna  
The coastal areas overlooking the Adriatic Sea in the proximity of Ravenna form a quaternary sedimentary basin bordered by the Northern Apennines and the Southern Alps [22]. The complex seismotectonic framework of the observed area is interested in the compression and extension processes in the foreland and hinterland, respectively, resulting in the actual seismicity distribution with many strong earthquakes [23]. Moreover, the whole coastal area was interested in intensive groundwater pumping and hydrocarbon exploitation activities, which provided decreasing land settlement phenomena with subsidence rates on the order of magnitude of some cm/year [4; 24].  
The SAR dataset used for studying this area consisted of 97 images acquired in single-look complex (SLC) format by the C-band ENVISAT SAR mission along ascending and descending orbits in the time-span of 2003-2010. They provide a ground coverage of 100 \(\times\) 100 km\({}^{2}\) and a spatial resolution of 25 m \(\times\) 5 m along the range and azimuth direction, respectively. The SBAS approach was used to retrieve the mean deformation velocity and time series. To ensure a reliable stack of interferometric pairs, temporal and spatial baselines were set to maximum values of 450 m and 700 days, respectively. A multi-look factor of 4 \(\times\) 20 in the range and azimuth direction was chosen to reduce the speckle noise, adequately match the digital elevation model (DEM) (Shuttle Radar Topography Mission, SRTM 90m resolution) spatial resolution, and obtain a large density of points in each SAR image.  
The results of multi-temporal InSAR processing are shown in Figure 3. The Line-Of-Sight (LOS) mean velocity maps highlight the overall subsidence along both acquisition orbits in the whole time span 2003-2010. In detail, the mean negative deformation velocities ranged between 2.5-5 mm/year, with maximum subsidence rates of about 12 mm/year. The latter can be observed both along the coastlines and the major urban settlements where anthropogenic activities are intensely concentrated.  
Figure 3: Deformation trend of Ravenna coastal area retrieved by ascending (**a**) and descending (**b**) ENVISAT data. On the right panels, the time series extracted by three points on the two maps are shown.
# Leveraging Machine-Learning for D2D Communications in 5G/Beyond 5G Networks
## 4 Applications of ML in D2D Communications  
In this section, we highlight some vital ML algorithms utilized for D2D in real-life scenarios emphasizing on their pros over traditional solutions. ML techniques can address variety of D2D communications' challenges as given in Figure 3 including the traditional ones given in Figure 2. Furthermore, Table 2 summarizes different ML applications in different D2D scenarios.
# A Weighted Histogram-Based Tone Mapping Algorithm for CT Images
## 2 Problem Statement  
Good local contrast has a very important role in computed tomography (CT) images used for diagnosing pathologies [31]. Unlike traditional photographs, CT images contain a measurement of material densities, and these are unaffected by irradiation. Traditional tone mapping assumes that the image is the product of illumination and reflectance [32].  
CT images also have huge dynamic range, and a global histogram equalization would not yield good enough local contrast, while local methods might yield unwanted halos. Our main goal is to develop an algorithm which is able to compress high dynamic range images into a low dynamic range while presenting as much local information as possible, preserving the main structures, not exhibiting strong halos, and more or less keeping pixel intensity ordering.  
Theoretically, any tone mapping operator could be used for medical images. However, the required contrast strongly depends on the tissue, and the absolute contrast is the smallest for medium CT densities, for instance kidney (20-45 HU), muscle (35-55 HU) [33], while the absolute contrast is large for small CT densities, for instance gas volumes (\(-\)1000 HU) and lung tissue (\(-\)700-500 HU) [34] and also large for dense parts, such as calcification (>150 HU) and bones [35]. Traditional tone mapping operators do not necessarily perform well for these regions, global histogram equalization lacks locality, while local histogram equalization methods (LHE/CLAHE) often lead to halos.  
In practical terms, the aim of this paper is to develop a local tone mapping operator which lies somewhere between global and local histogram equalization and combines their advantages and avoids their shortcomings.
## Appendix B Decision Variables Calculation Method
If the time between the current observation mission and the next observation mission is enough to turn to the Earth-pointing attitude and then turn to the next target observation attitude, the satellite will first turn to the Earth-pointing attitude, and maintain the attitude for a certain time, and then turn to the observation attitude of the next target;
2. Select an observation target \(s_{i}\) according to the order of the observation result sequence. Targets are categorized into selected targets and unselected targets according to \(x_{i}=1\) and \(x_{i}=0\), and the observation target sort sequence \(S=\{s_{i}|1\leq i\leq n_{s}\}\) is the sequence of the selected target serial numbers sorted according to the order of observation targets \(g_{ij}\), where \(n_{s}\) is the number of selected targets, and \(s_{i}\) is the serial number of a target;
3. Start with \(k=1\), then select the \(k\)th data transmission time window and calculate the available time segment \(\left[t_{gusk},t_{guck}\right]\) of the data transmission time window that satisfies the data transmission constraints, which can be expressed as \[\left[t_{gusk},t_{guck}\right]=\left[t_{gsk},t_{gek}\right]\cap\left[t_{oei}, t_{e}\right]\cap\bar{\bar{\bar{\
u}}}\left(\left[t_{gcs1},t_{gcc1}\right] \cup\left[t_{gco2},t_{gco2}\right]\cup\ldots\cup\left[t_{gcsng},t_{gcn_{g}} \right]\right),\] (22) where \(t_{gck}\) and \(t_{gcck}\) are the start time and end time of the occupied segment of the data transmission time window \(k\). 4. Determine whether the available time segment \(\left[t_{gusk},t_{guck}\right]\) of the \(k\)th data transmission time window is sufficient for downloading the observation data of target \(s_{i}\). If there is enough time, the data transmission start time \(t_{dsi}\) is the start time \(t_{gusk}\). Otherwise, set \(k=k+1\), and repeat the calculation of the available time segment and download the time judgement. If there is not enough time for data downloading in all of the data transmission time windows, the data transmission constraints are not satisfied. Hence, the data transmission time calculation is ended;
5. Repeat Steps 2 to 4 until the data download time calculation of all the observation targets are completed. The targets' observation data download time can be calculated and the data transmission constraints can be judged at the same time.
## 4 Attosecond Pulse Trains with Four Harmonics  
The undulator configuration for generating a harmonic comb with four harmonics is shown in Figure 4.  
Each harmonic is generated in one undulator, and the last two undulators are not used for harmonic generation (shown in grey). In the configuration presented, phase scans were carried out with either PS\({}_{2}\) or PS\({}_{3}\) (indicated in red). The presence of four harmonics requires the analysis of two pairs of the correlation plots: (P\({}_{7,8}\), P\({}_{8,9}\)) and (P\({}_{8,9}\), P\({}_{9,10}\)). As a consequence, two phase differences \(\Delta\varphi_{7,8,9}\) and \(\Delta\varphi_{8,9,10}\) are available for manipulation, each of them affected in a different way by the delays introduced by the phase shifters PS  
Figure 4: Undulator configuration for the four harmonics’ generation. Each harmonic is generated using one undulator. H\({}_{10}\) is generated by the undulator U\({}_{1}\); H\({}_{9}\) is generated by the undulator U\({}_{2}\); H\({}_{8}\) is generated by the undulator U\({}_{3}\); H\({}_{7}\) is generated by the undulator U\({}_{4}\). The phase shifters used to conduct a phase scan are indicated in red.  
Figure 3: Amplitude fluctuations. (**a**) Average photoelectron energy spectrum for different phases in a phase scan without the dressing NIR field. Photoelectron spectra for each phase are normalized to the maxima of H\({}_{8}\) of the first phase value. (**b–d**) Distribution of the harmonic amplitudes for one phase value, H\({}_{9}\) (**b**), H\({}_{8}\) (**c**), and H\({}_{7}\) (**d**). In Panels (**b–d**), the amplitudes are normalized to the mean value of the amplitude of the harmonic H\({}_{8}\).  
and PS\({}_{3}\). The variation of the phase difference terms \(\Delta\varphi_{7,8,9}\) and \(\Delta\varphi_{8,9,10}\) as a function of the delay introduced is given by:  
\[\begin{split}\Delta\varphi_{8,9,10}&=\varphi_{10}^{ (0)}+\varphi_{8}^{(0)}-2\varphi_{9}^{(0)}-8\omega_{\text{UV}}\tau_{z2}\\ \Delta\varphi_{7,8,9}&=\varphi_{9}^{(0)}+\varphi_{7}^ {(0)}-2\varphi_{8}^{(0)}+9\omega_{\text{UV}}\tau_{z2}-7\omega_{\text{UV}}\tau_ {z3}\end{split} \tag{5}\]  
\(\Delta\varphi_{8,9,10}\) is dependent only on the delay introduced by the phase shifter PS\({}_{2}\) and independent of the phase shifter PS\({}_{3}\), and \(\Delta\varphi_{7,8,9}\) varies as either of the phase shifters PS\({}_{2}\) or PS\({}_{3}\) is changed. Hence, using the phase shifter PS\({}_{2}\) will affect both the phase difference terms \(\Delta\varphi_{7,8,9}\) and \(\Delta\varphi_{8,9,10}\). The variation corresponds to an evolution of the correlation plots of the oscillating components pair \((P_{7,8},P_{8,9})\) and \((P_{8,9},P_{9,10})\). In Sections 4.1 and 4.2, the evolution of the correlation plots along with the reconstructed attosecond pulse trains for the case of a phase scan with PS\({}_{2}\) and PS\({}_{3}\) are presented.
## 2 Materials and Methods
### Calculations and Statistical Analysis  
The R 3.5.1. software [17] was used to run the statistical analyses. Based on the phyloseq package [18] and an OTU table rarefied at 15,752 sequences, the number of OTU and the Shannon index described the microbiota diversity. Due to the lack of fecal samples at 119 and 140 days of age in male pigs, we chose to analyze the age impact on the microbiota composition in females only. The diversity indexes (number of OTU and Shannon index) and the phyla relative abundance were compared between ages using Kruskal-Wallis tests. To visualize the evolution of the main genera abundance through time, we used a local polynomial regression fitting (degree 2) as proposed by the SplinectomeR package [19].  
A non-metric distance scaling (NMDS) plot was used to represent the similarities between samples based on the Bray-Curtis distance matrix. A pairwise MANOVA was applied on this distance matrix to evaluate the impact of age. The bacterial community was considered significantly different between groups if the adjusted false discovery rate (FDR) was under 0.05 after 999 permutations.  
To identify samples with similar bacterial composition, enterotypes at 52, 99, 119, 140, and 154 days were constituted following the methodology from [PERSON] et al. [9]. Briefly, from the relative genus abundance, a Jensen-Shannon divergence matrix was calculated. The partitioning around medoids clustering algorithm was applied and the optimal number of clusters (corresponding to enterotypes) was assessed using the Calinski-Harabasz index. The silhouette coefficient was used to evaluate the significance of this clustering. Kruskal-Wallis tests compared the genus relative abundance between the resulting enterotypes at each age. Chi-squared tests were used to analyze the repartition of the sex types, or the DON-challenged groups among the enterotypes. The effects of the enterotypes at each age on pigs' performance were analyzed using ANOVA models. The sex, the replicate, the DON challenge, the body weight at 99 days of age, and their interactions were included in the models.
# Compression-Aware Aggregation and Energy-Aware Routing in IoT-Fog-Enabled Forest Environment
## 5 Performance Evaluation
### Result Analysis
#### 5.3.7 CPU Power Utilization  
Figure 10 shows the CPU utilization of the entire simulation with a time period of 45 min. As the number of nodes increases, the CPU utilization increases. The average CPU power consumption of the entire network in the proposed CAA-ERPL is 0.07%, 0.10%, 0.31%, 0.26%, and 0.37% when compared to E-RPL. This is because the routing is formed for the aggregators only. The compressed data are sent via the DODAG parent to reach the border router.
# Identification of Topographic Seismic Site Periods in Sloping Terrains
## 1 Introduction
On the other hand, [PERSON] [19], based on 2D and 3D numerical analysis and experimental measurements using the horizontal to vertical spectral ratio (HVSR) method, identified periods in which the highest spectral amplitudes of the response occur repeatedly along the slope of the terrain, and their magnitude is mainly conditioned by the stiffness and height of the surrounding terrain, rather than by the slope angle, \(\beta\), of the terrain. While some of the variables that affect the seismic response of sloping terrains have been identified through previous research (e.g., [10; 19; 20; 21; 22]), a characteristic period associated with a peak amplification that can be attributed mainly to the slope of the terrain has not been identified yet. The identification of such a characteristic period represents a contribution to the seismic characterization of terrains under potential topographic effects, as well as to the identification of the amplification sources in the response spectra for seismic design purposes. The purpose of this study is to identify and characterize a topographic seismic site period \(T_{t}\) in sloping terrains that could aid in evaluating the variation in seismic response due to topographic effects. Therefore, to identify an amplification pattern attributable to the geometry of the terrain, a parametric analysis was carried out using two-dimensional finite element models subjected to the action of a dynamic signal. The periods in which amplification peaks were generated were evaluated and compared with the amplification response recorded in the free field on horizontal terrain with equal stiffness. Based on the results, it was observed that the frequency content of the dynamic response of the sloping terrain is a combination of the response from the surrounding terrain to the sloping zone and vice versa, and an amplification peak attributable to the topography, i.e., to the discontinuity generated by the slope of the terrain was identified. Thus, a simple expression with a high determination coefficient, R\({}^{2}\), is proposed to define a topographic site period, whose equation form takes the same shape as the equation used for determining the soil fundamental period in terms of \(Vs\) and the total thickness of the soil measured from the bedrock to the crest of the sloping terrain, \(H_{\rm T}\). This research aims to provide a basis for improving seismic response analysis criteria in areas with complex topography by evaluating the relationship between the characteristic site period and the resulting amplification patterns.
## 3 Theoretical Analysis of Ore Pass Wall Damage
### Theoretical Analysis of Tangential Shear  
#### 3.3.1 Calculation of the Tangential Force  
The collision between the ore-rock block and the ore pass wall is a process of oblique impact. The tangential force is controlled by the tangential velocity, the tangential deformation, the impact angle, and the friction coefficient, all of which are important parameters affecting the tangential movement of the ore-rock block. When the ore-rock block comes into contact with the shaft wall, if the adhesive contact between the two materials is not considered and only the slippage of the ore-rock block on the contact zone is considered, the formula for calculating the tangential force is as follows:  
\[F_{\tau}=\mu F_{\text{n}}^{\prime} \tag{31}\]  
where \(\mu\) is the friction coefficient between the rock block and the shaft wall and \(F_{\text{n}}^{\prime}\) is the maximum normal contact force, N.
# Integrated Photonics on Glass: A Review of the Ion-Exchange Technology Achivements
## 3 Telecom Devices
### Waveguide Amplifiers and Lasers
succeeded in creating a comb of 15 lasers with one single Bragg grating [76]. The emitters' wavelengths were spaced by 25 GHz and 100 GHz and set to be on the Dense WDM International Telecommunication Union (ITU) grid. The output power of these devices could be as high as 80 mW for a 350 mW coupled pump power [41], while a linewidth of only 3 kHz has been reported by [PERSON] et al. on their DFB lasers [77]. Figure 9 displays a picture of such a DFB laser pigtailed to HI1060 single mode fibers. The stability and purity of the emission of erbium doped waveguide lasers has been recently used to generate a Radio Frequency signal and successfully transmit data at a frequency of 60 GHz [78]. Bragg gratings on phosphate glass can be made by photolithography steps and etching like in [41; 76; 77] or by direct UV inscription like in [79; 80] and on IOG1. The use of a hybrid un-doped/doped IOG1 substrate allowed [PERSON] et al. [80] to realize UV-written Bragg gratings with high reflectance and selectivity, demonstrating hence a single frequency emission with an output power of 9 mW and a slope efficiency of 13.9%.
# LoRa Technology Propagation Models for IoT Network Planning in the Amazon Regions
## 4 Propagation Model
### Recommendation ITU P-833 and the Vegetation Loss Model
Considering the seasonal characteristics and different vegetation of the Amazon region, we applied the following vegetation attenuation model:  
\[Veg_{loss}(dB)=A*f^{B}*log10(d_{vegetation})*(\theta_{d}+E)^{G}-4 \tag{8}\]  
Factor B represents the frequency-dependent attenuation due to seasonal characteristics and is given by:  
\[B=(0.30281-0.003624*kh)(f/1000)^{(0.0013118-0.026236*kh)} \tag{9}\]  
Figure 8: Representation of the location of the gateway (blue icon) and end node (white icon), and the depth of vegetation (red rectangle), used for the calculations of the ITU-R Recommendation P-833 model. The icons in shades of yellow, orange, and green next to the end node icon represent the measured data. Figure 7: Representative radio path in woodland with vegetation path length (\(d\)), average tree height (\(h_{p}\)), height of the Rx antenna over ground (\(h_{a}\)), radio path elevation (\(\theta\)), and distance of the antenna to the roadside woodland (\(d_{w}\)) [49]. where \(f\) is the frequency (MHz), the distance \(d_{vegetation}\) is the total distance of the vegetation depth (m), \(\theta_{d}\) is the distance-dependent elevation angle (degrees), \(kh\) is the season factor, and in the Southern Hemisphere, \(kh=6-|Month-6.5|\). \(A\), \(B\), \(E\), and \(G\) are empirical parameters and are determined for each area. In this study, we used the Kenyan cedar values in Table 3 of the Recommendation due to their similarity with the trees in the scenario.
## 6 Improvement of the Steering Arm
### Improved Steering Arm  
The steering arm has been repeatedly modified to maximize its strength and to avoid the stress concentration and motion interference, and the final improvement is shown in Figure 19. After the steering arm in the rigid-flexible coupling model mentioned in Section 4.2 is replaced by the improved one, the Von Mises stress distribution can be obtained by solving the model, as shown in Figure 19. It can be seen that the maximum Von Mises stress 176.25 MPa is located in the contact area between the steering arm and the ball pin, and the maximum stress on its original high stress area has decreased to around 85 MPa. By comparing the maximum Von Mises stresses on the steering arm improved before and after, it can be seen that the strength of the improved steering arm has nearly doubled. Furthermore, the enhancement in rigidity apparently decreases the stress on the contact area between the steering arm and the ball pin away from fixed end, which satisfies  
Figure 18: Improvement schemes 2 and 3.  
the real condition. The results illustrate that the improved steering arm has a significant enhancement in strength under the extreme conditions (i.e., obstacle's height 300 mm).  
Then, the improved steering arm is manufactured and installed for the kind of mining truck, and the real product is shown in Figure 20. According to the feedback from the users, there is no fracture of the improved steering arm after it was installed, which illustrates that the issue has been addressed. At present, the improved part has been used for more than half a year without any fault, demonstrating that the current scheme is effective, and the rapid improvement and optimization of the steering arm has been realized.
# CT-RPL: Cluster Tree Based Routing Protocol to Maximize the Lifetime of Internet of Things
## 4 Results and Discussion
### End-to-End Delay  
The end-to-end delay indicates the amount of time taken for transmitting the sensor data between the RPL router and DODAG root. Figure 6 illustrates the end-to-end delay for a varying number of  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Number of Nodes**} & \multicolumn{3}{c}{**Packet Loss Ratio (\%)**} \\ \cline{2-4}  & **RPL** & **E2 HRC-RPL** & **CT-RPL** \\ \hline
10 & 0 & 0 & 0 \\
20 & 1 & 0 & 0 \\
30 & 3 & 2 & 1 \\
40 & 4 & 3 & 1 \\
50 & 5 & 3 & 2 \\
60 & 6 & 4 & 2 \\
70 & 8 & 5 & 3 \\
80 & 9 & 7 & 4 \\
90 & 12 & 9 & 6 \\
100 & 15 & 10 & 7 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Packet loss ratio for different nodes. RPL: routing protocols, E2 HRC-RPL: energy-efficient heterogeneous ring clustering routing protocol, and CT-RPL: cluster tree-based routing protocol.  
Figure 5: Packet loss ratio vs. network size for a network size of 100; the packet loss ratio of RPL, E2 HRC-RPL, and CT-RPL are 15%, 10%, and 7%, respectively.  
hops. It is observed that the delays of RPL, E2 HRC-RPL, and CT-RPL are 2700 ms, 2400 ms, and 1500 ms, respectively, for the hop count of six. It is also noted that the node's end-to-end delay increases for the varying number of hops. It is owing to the formation of clusters in the network nodes, and it applies the game theory for CH selection. Additionally, it considers the metrics _QU_, _ETX_, _RER_, and _CHNC_ for the route establishment.  
Table 5 indicates the end-to-end delay in RPL, E2 HRC-RPL, and CT-RPL for a varying number of hops. The result confirms that the cluster-based routing protocols take less time comparatively when the number of rounds in the network is increased.