esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Selection and Shortlisting of Downscaled GCMs  
The range of projections from global circulation models (GCMs) is quite broad, with high levels of uncertainty [52]. The GCMs were downscaled to higher resolution (0.25\({}^{\circ}\)\(\times\) 0.25\({}^{\circ}\)) by considering local topographic and physical characteristics, which have gained popularity due to accurate and reliable estimation of future earth climate scenarios in regional hydrological impact studies [17; 18; 20; 36]. Even after downscaling, future climate projections can vary significantly from one another, ranging from very wet to extremely dry or from extremely hot to very cold. As a result, the models can be classified as representing the Warm-Wet, Warm-Dry, Cold-Wet, and Cold-Dry corners of the full spectrum. In the present study, model behavior with respect to future in terms of precipitation and temperature is considered. From the available 13 models under the SSP245, SSP370, and SSP585 scenarios, a selection was made based on the changes in average annual precipitation (\(\Delta\)P) and average temperature (\(\Delta\)T) across the Nagavali and Vamsadhara watersheds between the model's historic data (1975-2015) and the projected future data (2022-2100). Additional details on the selection and shortlisting of the models are available in [PERSON] et al. (2022) [47]. According to [PERSON] and [PERSON] (2018) [53], the 10 th, 50 th, and 90 th percentile values for \(\Delta\)P and \(\Delta\)T were first calculated as the goal was to select a few models that best represent the four corners and the center of the entire spectrum as shown in Table 1.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**S.No** &
\begin{tabular}{} \end{tabular} & **Selection Criteria** \\ \hline
1 & Cold-Dry &
\begin{tabular}{} \end{tabular} \\ \hline
2 & Cold-Wet &
\begin{tabular}{} \end{tabular} \\ \hline
3 & Warm-Wet &
\begin{tabular}{} \end{tabular} \\ \hline
4 & Warm-Dry &
\begin{tabular}{} \end{tabular} \\ \hline
5 & Average &
\begin{tabular}{} \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 1: Selection criteria of GCM models.
# MEMS-Based Vibration Acquisition for Modal Parameter Identification of Substation Frame
## 3 Analysis of Experimental Data Characteristics
### Time-Frequency Characteristics of Responses Collected by the 941b System  
Figure 6a shows the time-history response at measurement point 1, obtained by using the 941b system. Since the MEMS acquisition system and the 941b vibration pickup system are two independent systems, the response signals measured cannot be perfectly synchronized. On the other hand, the time-domain response obtained from the MEMS acquisition system shows slightly higher amplitude than that from the 941b system. This may be because the MEMS system captured some noise during data acquisition, resulting in a slightly higher standard deviation of the data compared with the 941b system. However, this does not alter the modal characteristics of the structure, as these characteristics are inherent to the structure itself. Figure 6b presents the root power spectral density plot obtained using the 941b vibration pickup system. It can be seen that the resonance peaks in  
Figure 4: Vibration collection system and field test.  
this plot are generally consistent with the peak regions observed in the MEMS root power spectral density plot, with similar amplitude in the peak regions. The main difference lies in the amplitude of the frequency-domain response in the non-resonant peak regions, where the frequency-domain information is primarily contributed by noise. The comparison indicates that in the resonance peak regions, the modal characteristics identified by both systems exhibit good consistency.
###### Abstract  
Multiferroic systems are of great interest for technological applications. To improve the fabrication of thin films, stereometric and fractal analysis of surface morphology have been extensively performed to understand the influence of physical parameters on the quality of spatial patterns. In this work, GaMnO\({}_{3}\) was synthesized and thin films were deposited on Pt(111)/TiO\({}_{2}\)/SiO\({}_{2}\)/Si substrates using a spin coating apparatus to study the correlation between their stereometric and fractal parameters. All films were studied by X-ray diffraction (XRD), where the structure and microstructure of the film sintered at 850 \({}^{\circ}\)C was investigated by Rietveld refinement. Topographic maps of the films were obtained using an atomic force microscope (AFM) in tapping mode. The results show that the film sintered at 850 \({}^{\circ}\)C exhibited a clear formation of a GdMnO\({}_{3}\) orthorhombic structure with crystallite size of \(\sim\)14 nm and a microstrain higher than other values reported in the literature. Its surface morphology presented a rougher topography, which was confirmed by the height parameters. Topographic differences due to different asymmetries and shapes of the height distributions between the films were observed. Specific stereometric parameters also showed differences in the morphology and microtexture of the films. Qualitative rendering obtained by commercial image processing software revealed substantial differences between the microtextures of the films. Fractal and advanced fractal parameters showed that the film sintered at 850 \({}^{\circ}\)C had greater spatial complexity, which was due to their higher topographic roughness, lower surface percolation and greater topographic uniformity, being dominated by low dominant special frequencies. Our combination of stereometric and fractal measurements can be useful to improve the fabrication process by optimizing spatial patterns as a function of the sintering temperature of the film.  
GdMnO\({}_{3}\); morphology; topography; stereometric and fractal analysis +
Footnote †: journal: Applied Sci.
## 6 Conclusions and Future Work  
In this paper, an approach for the parallelization of algorithms on distributed devices, residing in a Fog Computing layer, has been presented. In particular, the approach has been tested against a real case scenario, provided by the CoSSMic European Project, regarding the clusterization and classification of data coming from sensors previously installed in households. Such clusters are then used to predict energy consumption and plan the use of the devices to maximize the use of solar energy.  
What we wanted to achieve was to demonstrate that, through the application of the approach, it is possible to obtain a performance improvement in the algorithm execution time.  
The initial results seem promising, as with the opportune configuration of data and tasks it is possible to obtain a sensible enhancement of the algorithm performances, provided that a sufficient number of devices (in the test case we used Raspberry PIs) are available.  
However, the approach needs to be polished and to be completely automated, in order to reduce possible setbacks in the selection of the right configuration and to support the auto-tuning of the data and tasks distribution. Furthermore, in the future there will the possibility to automatically detect parallelizable sections of code to support the user in the annotation phase, or possibly to even completely automatize the whole annotation step.  
Conceptualization and supervision: B.D.M.; methodolody: B.D.M., S.V. and A.E.; software: S.D.; data curation: S.D. and A.E.; writing-original draft preparation, writing-review and editing: B.D.M., S.V., A.E., S.D. All authors have read and agree to the published version of the manuscript.  
This work has received funding from the European Union's Horizon 2020 research and innovation programme under the TOREADOR project, grant agreement Number 688797 and the CoSSMic project (Collaborating Smart Solar powered Micro grids - FP7 SMARTCITIES 2013 - Project ID: 608806).  
The authors declare no conflict of interest.  
Figure 6: Comparison between Execution Times for different cluster dimensions.
# Spectrum Demand Forecasting for IoT Services
## 3 A Market Study
### Technologies  
The main driver of the IoT market is the availability of a given wireless technology. Companies and their products will convert to IoT (i.e., their products/services or their information will become 24/7 available on the Internet) if an affordable and fully integrated technology is known for that business. Accordingly, a review of all wireless communication technologies and their recent innovations is the first essential step. For each technology found, the following is a list of important questions to be answered:  
* Identify how close the technology is: in terms of geographic or commercial linkage. Where is the closest manufacturer or designer of this technology and how massive is this technology's scope? Does the manufacturer target your country's market as a goal for production plans?
* Identify the ease of use of this technology: Is it plug-and-play? Does it require hiring a team of engineers to buy, operate and maintain the system? Are these specialized engineers easily available in the labor market? Will this technology reach any business or only big corporations with investment capacities?
* Identify possible new business models based on operators: As in the case of LPWANs, many companies prefer to pay for the services instead of buying equipment and developing their own IoT use cases. Hence, some telecommunications manufacturers have decided to be IoT operators themselves or sometimes in alliance with mobile operators. A notable case is SigFox. Therefore, one should identify which technologies and services may generate new operators.
* Identify the technology bands: Is this a multi-band technology? Are there any official plans from the vendor to extend production in your country's bands? Is this band available in your country? If not, how much is it used and how important are these services in your country? Has your country's regulator discussed the use of this band?
* Identify the strength of the technology versus incoming cellular competition: Can this technology survive after 3 GPP IoT networks (NB-IoT and LTE-M) are fully standardized and commercial? Consider the time frame this will take to be a reality in your country.
## 5 Proposed Hardware Architecture
### Routing Networks  
The proposed architecture, shown in Figure 1, utilizes three routing networks (RN2, RN3 and RN4) to transfer data between different modules. The input data, including base coordinates x and y, the output of RN1, and the output of RF1 and RF2, are fed into RN2  
Figure 1: Proposed BEC architecture.  
and RN3. Control signals RN_OP1_ADDR and RN_OP2_ADDR are used to select the appropriate data for processing. The sizes of RN2 and RN3 are \(5\times 1\). RN4, with a size of \(3\times 1\), is utilized to select the output of the ALU as its input. This architecture efficiently transfers data between the different modules, ensuring smooth operation and minimizing processing delays.
###### Abstract  
Among several usages of unmanned aerial vehicles (UAV), wireless relay systems for high altitudes using fixed-wing UAVs, or high-altitude platform stations (HAPS), are some of the most promising applications. To realize the systems by making the most of advantages of the long flight duration and endurance of fixed-wing airplanes, this paper proposes an antenna pointing control system using mechanical gimbals onboard a fixed-wing UAV continuously turning midair and describes results of the blocking analysis of the antenna driving angles of the gimbal directed to a ground station, the design of the antenna pointing control system, and the evaluation of its performance. It is confirmed by the evaluation that, though the antenna pointing control accuracy is greatly influenced by the noisy antenna pointing direction command, its accuracy is greatly improved by using the highly accurate RF sensor to detect antenna pointing direction.  
HAPS; fixed-wing UAV; antenna pointing control system; wireless relay system; two-axis gimbal 2023
# Wavelength Selection for Satellite Quantum Key Distribution
## 3 Channel Model
### Free-Space Diffraction  
The losses resulting from diffraction-induced beam spread can be accounted for using the following equation to approximate the free-space coupling efficiency \(\eta_{d}\):  
\[\eta_{d}=1-\exp\biggl{(}-\frac{1}{2}\frac{D_{r}^{2}}{w^{2}(\lambda,z,w_{0})} \biggr{)} \tag{2}\]  
Figure 1: Transmittance and spectral radiance plotted as a function of wavelength in Tucson and Waterford. The transmittance and radiance in Tucson are coloured blue and black, respectively, while green and red are used to represent the equivalent data in Waterford.  
where \(D_{r}\) is the diameter of the receiver aperture and \(w(\lambda,z,w_{0})\) is the beam waist at the receiver [14]. It is common practice to approximate a laser beam as a Gaussian beam where the transverse intensity profile follows an ideal Gaussian distribution. As the beam propagates, diffraction causes its profile to change depending on its wavelength \(\lambda\), initial beam waist \(w_{0}\), and propagation distance \(z\). The beam spot size for a Gaussian beam is given by [51]  
\[w(\lambda,z,w_{0})=\sqrt{w_{0}^{2}\Bigg{[}1+\left(\frac{\lambda z}{\pi w_{0}^{ 2}}\right)^{2}\Bigg{]}} \tag{3}\]  
This increase in the beam spot size relative to the size of the receiver aperture is responsible for significant power loss in the satellite downlink scenario. Increasing either the initial beam waist, \(w_{0}\), or the receiver diameter, \(D_{r}\), will increase the geometric coupling efficiency. Alternatively, increasing \(\lambda\) or the propagation distance, \(z\), will decrease the geometric coupling efficiency. So, for a given z, as \(\lambda\) is varied, \(w_{0}\) and \(D_{r}\) can also be varied to keep the coupling efficiency constant. This suggests that larger components can make up for longer wavelengths in terms of the geometric coupling efficiency. However, the channel efficiency is a combination of geometric and atmospheric transmittance, which also varies with wavelength.
## 4 Simulation Tests and Results
### Case Study 3  
The aim in the third case study was to investigate the maximization of the coverage in a network located within a two-dimensional, squared area of \(20\times 20\) provided that k-coverage of six pre-defined spots was satisfied.  
More specifically, the points (5, 5), (10, 5), (15, 5), (5, 15), (10, 15), and (15, 15) were considered to have three-coverage each one. The number of the available sensor nodes was equal to 45 while the sensing range of each of them was equal to 1.5. The size of the population used in the GA algorithm was selected to be equal to 600 while the swarm size in the PSO algorithm was adjusted to be equal to 600.  
The results of the simulations performed are synoptically presented in Table 5.  
Figure 10: Best fitness value for each iteration of the PSO algorithm in case study 2.  
Figure 9: Optimal node positions with the use of the PSO algorithm in case study 2.  
Similarly, the optimal positions of nodes calculated by using PSO algorithm are illustrated in Figure 13 while the corresponding best fitness values for each iteration are graphically presented in Figure 14.
# Variational Autoencoders for Network Lifetime Enhancement in Wireless Sensors
## 1 Introduction
### Variational Autoencoders  
Variational autoencoders (VAEs) are a generative-model-based neural network approach that is widely used for unsupervised learning processes in data representation applications. The concept of VAEs is to extract the continuous latent information from the given high-dimensional data, like text, image, or any other signal, to provide compact data values that reflect the input data distribution. Unlike the regular autoencoders, the VAEs are included with a probabilistic framework based on a Gaussian distribution model that acts like an encoder, which allows for extracting the latent information effectively, in a distributed manner. Further, the output of the encoder is connected to a decoder network for resampling the distributed information received from the input. VAEs utilize an optimized loss function that regulates the reconstruction process on the latent space, and that improves the training process by learning only the meaningful information. Therefore, the performance of VAEs is comparatively better than that of the traditional autoencoders, as it provides scalable information. The reparameterization feature included in VAEs can provide gradient-based optimization from the useful information extracted from raw data. This interpretable ability makes VAEs suitable for big and complex data analysis.
## 2 Materials and Methods
### Problem Description  
There are 18 kinds of factors that directly affect the drift motion of AUV, which are divided into 4 categories, including current, wind, and wave, as well as the size of the AUV. Among them, currents are divided into three types: density currents (m/s), wind currents (m/s), and tidal currents (m/s). Wind is divided into 4 types: wind direction (\({}^{\circ}\)), wind speed (km/s), crosswind direction (\({}^{\circ}\)), and crosswind speed (km/s). Waves include 4 types: action coefficient of wave drifting force, effective wave height (m), wavelength (\(\mu\)m), and wave number (nm). In addition, there is target size (m\({}^{3}\)), temperature (\({}^{\circ}\)), salinity (\(\%\)), density (g/cm\({}^{3}\)), gravitational acceleration (m/s\({}^{2}\)), diving depth (m), and fluid resistance (N).  
Under the combined influence of the torque and force generated by the many above factors, the AUV produces six degrees of freedom of space motion after it is out of control at the great diving depth. The six degrees of freedom are surge, sway, heave, roll, pitch, and yaw. The heave is floating upward under the action of residual buoyancy. During the floating process, the AUV passes through different ocean layers. According to the difference of influencing factors, it is mainly divided into deep ocean layer and thermocline, as shown in the following Figure 1.  
During the floating process, the marine factors affecting drift motion are similar when the AUV is in the ocean deep layer and middle layer. However, there is a huge change of the composition of influencing factors when it drifts to the thermocline. The main reasons for the change are the appearance of the prevailing wind on the ocean surface and the sudden changes of ocean water temperature, salinity, and density with depth, as shown in Figure 2.  
Figure 2a shows that in the thermocline at a depth of 200 m, the temperature of the ocean water rises rapidly. Similarly, Figure 2b,c also show that there is a sudden change in the ocean salinity and density values in the thermocline, which makes the density distribution of the thermocline uneven. Causing the ocean surface to tilt and seawater flow. Finally, a larger density flow and a change in fluid force are caused [19].  
In addition, waves are formed by the wind on the surface of the ocean, and the friction of the wind on the ocean surface and the pressure exerted by the wind on the windward side of the waves force the ocean water to move forward to form wind-driven currents. However, the action range of waves and wind-driven currents can only extend to the thermocline. Based on the above analysis, a new AUV drift track prediction framework based on modified neural network is proposed to solve the problem of feature drift during AUV drift.
# Real-Time Semantic Image Segmentation with Deep Learning for Autonomous Driving: A Survey
## 3 State-of-the-Art Deep-Learning Models
This method is based on the efficiency-oriented approach mentioned in Section 2. Although this approach reduces accuracy, the trade-off between accuracy and inference speed is still satisfactory. ENet [8] is an optimized deep neural network designed for fast inference and high accuracy. It follows a compact encoder-decoder architecture; however, as shown in Table 2, ENet uses a small size decoder for reducing computation cost and increasing inference speed. Furthermore, ENet introduces early downsampling to achieve low-latency operation. More specifically, early downsampling is the process of downsampling the input image at the first layers of the network. The reason behind this technique is that a downsampled version of the input image can be much more effective without losing vital information and thus without sacrificing accuracy. Last but not least, [8] was one of the first semantic image segmentation models that aimed at real-time performance and a milestone for the following research attempts. \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Layer** & **Type** & **Output Channel** & **Output Resolution** \\ \hline
1 & Downsampling block & 16 & 512 \(\times\) 256 \\ \hline
2 & Downsampling block & 64 & 256 \(\times\) 128 \\
3–5 & 3 \(\times\) Non-bt-1D & 128 & 128 \(\times\) 64 \\
5–7 & 2 \(\times\) Conv-module & 64 & 256 \(\times\) 128 \\ \hline
8 & Downsampling block & 128 & 128 \(\times\) 64 \\
9 & Non-bt-1d (dilated 2) & 128 & 128 \(\times\) 64 \\
10 & Non-bt-1d (dilated 4) & 128 & 128 \(\times\) 64 \\
11 & Non-bt-1d (dilated 8) & 128 & 128 \(\times\) 64 \\
12 & Non-bt-1d (dilated 16) & 128 & 128 \(\times\) 64 \\
13 & Conv-module (dilated 2) & 128 & 128 \(\times\) 64 \\
14 & Conv-module (dilated 4) & 128 & 128 \(\times\) 64 \\
15 & Conv-module (dilated 8) & 128 & 128 \(\times\) 64 \\
16 & Conv-module (dilated 16) & 128 & 128 \(\times\) 64 \\ \hline
17 & Deconvolution (upsampling) & 64 & 256 \(\times\) 128 \\
18–19 & 2 \(\times\) Non-bt-1D & 64 & 256 \(\times\) 128 \\ \hline
20 & Deconvolution (upsampling) & 16 & 512 \(\times\) 256 \\
21–22 & 2 \(\times\) Non-bt-1D & 16 & 5125 \(\times\) 256 \\ \hline
23 & Deconvolution (upsampling) & C & 1024 \(\times\) 512 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Network architecture of ENet [8]. Figure 24: EDANet Network Architecture (adapted from [45]). The presented state-of-the-art models are listed in Table 3, along with their backbone and the efficiency-oriented approaches they share in common. The role of Table 3 is to summarize the ameliorative features that are mostly used in real-time semantic image segmentation, with the intention of clearing the way of designing efficient real-time semantic image segmentation models. Furthermore, Table 4 presents a collection of links to the implementation code of the state-of-the-art models.
# Customer Churn Prediction Approach Based on LLM Embeddings and Logistic Regression
## 3 Preliminaries
### Embeddings
#### 3.1.6 Doc2 vec  
Doc2 Vec is a static embedding technique designed to represent documents, paragraphs, or sentences as numerical representations. It allows the model to understand phrases or entire documents. Unlike traditional word embeddings, Doc2 Vec represents documents as vectors, incorporating a unique identifier for each document. This identifier influences the adjustment of word vectors during training. By training the document identifier and word vectors together, the model gains the ability to predict the next word in a sentence. Consequently, it yields a vector representation of the entire document, facilitating tasks like document comparison and clustering.