esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
While IoT devices are popular in industrialized countries, their deployment in some developing countries faces additional hurdles such as the prohibitive cost of Internet access or its unavailability. The most appealing applications focus on environmental data: water and air quality monitoring, landslide detection, water levels in rivers, meteorological data and so on. In many countries, there is little information about the environment, creating a knowledge divide that adds to the digital divide. Those data can nevertheless be made available with a local infrastructure that can generate and publish the relevant information. Also, it is common to see people owning smart phones and tablets with no access to data, or not able to afford the extra cost charged by operators for data services. We then propose a sort of _\"Intranet of Things\"_ in which data are locally generated and locally consumed even in the absence of Internet connectivity, or when Internet connectivity is either intermittent or painfully slow. Environmental data collected in the region by low cost and low power end-devices will be transmitted to one or more \"Base Stations\" using the LoRaWAN protocol [1] and made accessible through a WiFi access point or \"Hot Spot\" included in the Base Station (BS). Users can then have access to real time environmental data and other relevant information using their standard devices. They can also have access to stored static materials on health, education, legislation, environment protection and any other sort of content deemed of interest, including folklore and traditional knowledge shared by the community members.  
Optionally, if there is Internet connectivity at the Base Stations, selected time-sensitive content can also be accessed by the whole community at a lower cost, given that a single Base Station can provide services to many users, performing then a \"proxy\" service. So, the Base Station will act as a LoRaWAN Gateway, a LoRaWAN Network Server, a LoRaWAN Applications Server, a local data and content repository, a Bulletin Board and a WiFi Access Point that can be accessed by any WiFi client device. Since the Base Station cannot be sleeping at any time, its power consumption is considerably greater than that of the end-nodes, but it is nevertheless moderate and easily accommodated by means of a lightweight photovoltaic electric power system (EPS). Despite its many functions, it is also cheap since it is based on a Raspberry Pi with a LoRaWAN hat and Open Source software. Optionally, the BS can be connected to Internet through an appropriate router, in which case, the connection costs could be shared among its several users.
# Comparative Study of Minimally Invasive Microwave Ablation Applicators
## 3 Results
### Performance of the Two-Compartment Model for All Designs
#### 3.3.2 Temperature Distribution of the Two-Compartment Model for All Designs  
This part of the results addresses the temperature distribution across all designs within a two-compartment model. The corresponding results are displayed in Figure 8. It is again clear that only the two applicators Figure 8 gj show the temperature contours above 42 \({}^{\circ}\)C restricted inside or slightly outside the tumor boundary. Since it is desired to cauterize a ring of about 1 cm beyond the tumor boundary, Figure 8g is obtained for the appropriate deposited power of 40 W. However, the applicator of Figure 8j allows for an additional small increase of the source power.  
We analyze the temperature distribution of all the, aforementioned, applicators using both a simple model and a two-compartment model. The temperature contours at 38 \({}^{\circ}\)C, 42 \({}^{\circ}\)C, 46 \({}^{\circ}\)C, and 50 \({}^{\circ}\)C are presented. As mentioned before, these specific contours are of particular interest because irreversible changes in tissue begin at 38 \({}^{\circ}\)C, cell necrosis becomes possible at 42 \({}^{\circ}\)C, and cancer cells are destroyed within a few hours at 46 \({}^{\circ}\)C and within a few minutes at 50 \({}^{\circ}\)C.  
The tumor diameter was set to 3 cm for the multi-slot, triaxial, choke, FSD, balun-free base-fed monopole, and balun-free helical dipole antennas, and 2 cm for the monopole, capacitive monopole, dual-slot antennas, and helical open waveguide. This variation is based on the ablation zone dimensions reported in the literature and the attempt to confine the treatment temperature (50 \({}^{\circ}\)C) within the tumor. Both tumor models were assumed to be perfectly spherical. As observed, there are notable differences between the homogeneous model and the two-compartment model.  
A comparative description of Figures 2-7 is given next in the discussion section.  
Figure 7: Specific Absorption Rate (SAR) with a deposited power of 40 W of monopole antenna (**a**), capacitive capped monopole antenna (**b**), multi-slot coaxial antenna (**c**), dual-slot antenna (**d**), triaxial antenna (**e**), choke dipole antenna (**f**), balun-free base-fed monopole (**g**), floating sleeve dipole antenna (FSD) (**h**), balun-free helical dipole antenna (**i**), and helical open waveguide structure (**j**) in the two-compartment model (healthy tissue and tumor). The blue, purple, red, and yellow colors indicate SAR contours of 2.5, 5, 7.5, and 10 W/kg, respectively.
## 3 Results and Discussion
### Influence of Self-Compaction on the Airflow Resistance
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{1}{c}{**Storage Time**} & \multicolumn{1}{c}{**Bulk Depth**} & \multicolumn{1}{c}{**Pore Volume**} & \multicolumn{1}{c}{**Bulk Density**} & \multicolumn{3}{c}{**Statistical Indicators**} \\ \cline{2-7} \(t\)**, h** & \(H_{b}\)**, m** & \(\varepsilon_{\text{ }}\)**-** & \(\rho_{b}\)**, kg**\(\cdot\)**m\({}^{-3}\)** & **R\({}^{2}\)**, -** & **RMSE**, Pa** & _MAPE, \%_ \\ \hline \multirow{4}{*}{1.0} & 1.0 & 0.413 & 793.00 & 0.995 & 15.18 & 8.21 \\  & 2.0 & 0.404 & 805.70 & 0.995 & 33.28 & 8.32 \\  & 3.0 & 0.397 & 814.62 & 0.995 & 52.39 & 7.19 \\  & 3.4 & 0.391 & 822.60 & 0.996 & 61.93 & 7.62 \\ \hline \multirow{4}{*}{65.0} & 1.0 & 0.406 & 802.33 & 0.983 & 29.19 & 14.75 \\  & 2.0 & 0.400 & 811.38 & 0.983 & 62.74 & 15.12 \\  & 3.0 & 0.391 & 822.46 & 0.983 & 100.65 & 14.40 \\  & 3.4 & 0.385 & 830.98 & 0.982 & 123.77 & 13.97 \\ \hline \multirow{4}{*}{164.0} & 1.0 & 0.400 & 810.98 & 0.987 & 26.58 & 16.16 \\  & 2.0 & 0.395 & 817.73 & 0.987 & 54.16 & 14.44 \\  & 3.0 & 0.385 & 831.11 & 0.987 & 89.30 & 13.28 \\  & 3.4 & 0.379 & 838.95 & 0.987 & 106.91 & 13.66 \\ \hline \multirow{4}{*}{236.0} & 1.0 & 0.390 & 825.03 & 0.993 & 21.04 & 14.61 \\  & 2.0 & 0.383 & 833.27 & 0.993 & 45.24 & 15.11 \\ \cline{1-1}  & 3.0 & 0.375 & 844.08 & 0.993 & 68.35 & 10.66 \\ \cline{1-1}  & 3.4 & 0.370 & 851.65 & 0.993 & 84.17 & 12.75 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Pore volume \(\varepsilon\) and bulk density \(\rho_{b}\) for different storage time \(t\) and bulk depth \(H_{b}\) as well as statistical indicators (\(R^{2}\), _RMSE_, _MAPE_) observed from fitting the experimental data with Matthies and Peterson model.
# A Comparison of Deep Transfer Learning Methods for Land Use and Land Cover Classification
## 4 Discussion
The variation in overall accuracy might be attributed to how we distributed the dataset into three subsets instead of two sets with one of them, the test set, not being involved during the training and validation processes. Our total accuracy was calculated by averaging the accuracy scores from the train, validation, and test sets. Our test set was at least 50% smaller than any of the aforementioned research, which could contribute to this slightly lower overall accuracy level. On the other hand, our test set which was independent of the validation set played a substantial role in our analysis. Inseveral of the previous studies, it was unclear which set was utilized for validation, as their dataset was only divided into train and test sets. The test set should ideally be a dataset obtained separately. The presence of a large number of independent test sets validates the generalization argument [39]. Nevertheless, the key objective of this study was to evaluate deep transfer learning models in terms of accuracy metrics and training time, and the present distribution of train, validation, and test sets was paramount for the analyses. While we were modeling, we faced a hurdle due to the insufficient quantity of labeled data available in the EuroSAT datasets. Specifically, there were only 27,000 labeled images available for 10 classes. To address this challenge, we utilized data augmentation techniques to create additional data samples by introducing variations to the existing data samples. These variations were generated with the aid of algorithms that could automatically create new data samples. Deep transfer learning presents a broad range of models suitable for training in various applications. Although some pre-trained models achieved exceptional accuracy metrics in other domains through deep transfer learning, they did not rank among our top models for LULC classifications. The choice of the most appropriate model for a specific application hinges on various factors. For example, the size of the dataset and the complexity of the problem are key considerations when selecting a model. In addition, the availability of computational resources, such as GPU capacity, can also affect the model selection process. Ultimately, the ideal model for a given application should strike a balance between performance and practical constraints. After conducting our research, we propose that EfficientNetV2 is the optimal choice for transfer learning-based classification of LULC image classifications. Nonetheless, if there are constraints on computational resources, MobileNet could be a viable alternative.
## 3 Computational Setup  
Computational Fluid Dynamics (CFD) simulations using ANSYS CFX (ANSYS R17.0, Academic, ANSYS, Inc., Canonsburg, PA, USA) have been carried out to provide detailed flow field data to gain an even deeper understanding of the flow phenomena. ANSYS Turbogrid was used to mesh a full annulus model with 14 passages. The Low-Reynolds-Mesh features about 2.7 million hexahedral elements  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{3}{c}{**(a) Design specifications of rotor**} \\ \hline \(r/r_{mid}\) & \(\beta_{1}\)[\({}^{\circ}\)] & \(\beta_{2}\)[\({}^{\circ}\)] & solidity & stagger [\({}^{\circ}\)] & \(C_{A}\) \\ \hline
0.39 & 25 & 10.8 & 0.55 & 17.9 & 3.17 \\
1 & 53.9 & 45.7 & 0.89 & 49.8 & 1.84 \\
1.61 & 65.2 & 59.9 & 1.19 & 62.4 & 1.22 \\ \hline \hline \multicolumn{5}{c}{**(b) Datum parameters**} \\ \hline Design speed \(n\) & & & & 22,000 rpm \\ Flow coefficient, \(\varphi=\frac{C}{U_{mid}}\) & & & 0.7 \\ Pressure rise, \(\Psi=\frac{P_{min}-P_{lim}}{\frac{1}{2}U_{mid}}\) & & & 0.53 \\ tip clearance & & & 2.4\% blade height \\ \hline \hline \end{tabular}
\end{table}
Table 1: Compressor design.  
Figure 1: Measurement setup.  
and is divided into the stationary inlet and outlet section and a rotating mid-section. According to the experiments, tip clearance was set to 2.4% of the blade height and is meshed using six layers to capture the tip flow. An exemplary section mesh is displayed in Figure 2. Early attempts modelling only one half of the rotor stage showed disturbances within the frequency spectrum due to the periodic interfaces used therein. Thus, the approach has been changed and the rotor stage was modelled as a whole annulus. Due to computation, all resource limitations Y+ values of 2.5 at the blade's surfaces and 4.9/6.9 at the hub/shroud respectively have been set which sufficiently resolve the sub-layer. Flow is propagated in time-space, resolving each passage period with 13 time steps, resulting in a mean Courant-Friedrichs-Lewy (CFL) condition of 17 results in data, capturing all identification features of RI which could be proved in the experiments. A total pressure and normal flow direction inlet boundary condition and a mass flow rate outlet condition were determined in the experiments and are prescribed at the domain's inlet and outlet respectively. Turbulence is modelled using the SST-SAS model, regularly available in Ansys CFX. The model utilises the Unsteady Reynolds-averaged Navier-Stokes (URANS) approach in stable regions whereas LES-like behaviour is enforced in detached regions. That encourages the resolution of a turbulent spectrum by adjusting the URANS large-scale unsteadiness. The authors thereby suspected vortex decay to be an important flow mechanism regarding RI.
# Continuous Latent Spaces Sampling for Graph Autoencoder
## 4 Present Work: colaGAE
### Relative Distance Loss  
The way nodes in a graph are arranged is often reflected by the tendency of nodes to cluster together. This clustering tendency is commonly measured using the Euclidean distance, where nodes that are close to each other are considered neighbors. However, using the mean square error (MSE) as a measure may not be appropriate for graph data, which often have low density and a degree distribution that follows a power law. This can lead to high-degree and low-degree nodes coexisting, making the assumption of Euclidean spaces invalid and the optimization process ineffective.  
To address this issue, we propose a new loss function that takes distance as a relative concept. Unlike deep clustering methods, which require maximizing mutual information or manually selecting cluster centers, our approach aims to bring neighboring nodes closer while pushing non-neighboring nodes farther apart. Specifically, we introduce the Relative Distance (RD) loss function, which is defined as follows:  
\[\mathcal{L}_{\text{RD}}=-\log\frac{\sum_{(i,j)\in E}Dec(\mathbf{Z}_{i},\mathbf{ Z}_{j})}{\sum_{(i,j)\
otin E}Dec(\mathbf{Z}_{i},\mathbf{Z}_{j})} \tag{7}\]  
In (7), the set \(E\) represents the edges in the graph, while \(Dec(\mathbf{Z}_{i},\mathbf{Z}_{j})\) denotes the distance between two nodes \(i\) and \(j\), computed by the decoding function \(Dec\). The numerator and denominator in the RD loss function correspond to the respective sums of the distances between neighboring and non-neighboring nodes. Minimizing the numerator and maximizing the denominator results in neighboring nodes being pulled closer together while non-neighboring nodes are pushed further apart. This is achieved without the need for mutual information maximization or cluster center selection, making the RD loss function an effective way to model the clustering tendency of graph data.
## 3 Results and Discussion  
### Sensory Analysis  
Figure 3 displays the sensory profiles of representative wines belonging to each of the three varieties, selected to cover as far as possible the different cultivation site locations within the regional territory. Specific tasting notes on the three wine varieties are reported in Appendix A.  
It can be observed that _Pecorino_ wines produced in different locations within the Abruzzo region exhibit small differences in some sensorial attributes, mainly Color Intensity and Mineral. _Trebbiano_ wines, on the other hand, present very stable scores for of all the sensory indicators except Color Shade. The three representative _Passerina_ wines, although coming from relatively close localities in northern Abruzzo, exhibit appreciable variations in the Color Shade and Mineral attributes and, to a lesser extent, in Color Intensity. The mean sensory profiles of the three wine varieties are compared in Figure 4.  
Figure 3: Sensory profiles of representative, from top to bottom, Pecorino (PE), Trebbiano d’Abruzzo (TR), and Passerina (PA) wines. The number refers to the production site, as reported in Figure 1).  
Figure 2: Scheme of the algorithm for SO-PLS (**left**) and SO-CovSel (**right**).  
This figure reveals the high degree of similarity among the three wine varieties, especially _Trebbiano_ and _Pecorino_, as _Passerina_ presents a slightly lower amplitude of most of the sensory indicators. The lower complexity in the olfactory notes of _Passerina_ was attributed by panelists to a scarce acidity (Appendix A). Nevertheless, the dissimilarities among the mean sensory indicators are comparable or lower than the differences internal to _Passerina_ and _Pecorino_ wines. Under this condition, unequivocal attribution of the varietal origin of the wines seems hard to achieve based only on sensory analysis.
## 4 Discussion
### Compounds Obtained by Hydrodistillation and SPDE
Therefore, the overall average relative area for each compound was considered representative for further evaluation of the relatively most abundant (>2% of the total area) compounds: limonene (17.9%); linalool (10.5%); oxime-methoxy-phenyl (10.4%); cynmee <p-> (8.7%); eucalypt (8.4%); hexanal (7.0%); pione <\(\beta\)-> (5.0%); isoborneol (3.4%); unknown (2.7%); geranyl acetone (2.5%); pione <\(\alpha\)>(2.4%); octanal (2.2%); 2,4-heptadien <(2E,4E)> (2.1%); and decanal <n> (2.1%). Some researchers reported several volatiles of relatively high molecular mass by using different extraction techniques [27; 28]. Corroborating with the results of the latest, in this research, essential oils also presented almost 40% of the total relative area situated above an AI of 1565. This upper range comprises the following major identified compounds (above 1% on average): squalene (6.6%); palmitic acid (4.5%); phytol (4.5%), 3-heptadecene <(Z)-> (2.6%); meridol <(E)-> (2.1%); methyl hexadecanoate (2.0%); hexahydrofranesylacetone (1.6%); methyl linolenate (1.5%); tetradecanoic (1.5%); dendrolasin (1.4%); and 6,9-heptadecadiene (1.1%). In combination, the results from hydrodistillation and HS-SPDE are comparable with the findings of [PERSON] et al. [2]. This study presented 32 identified and semi-quantified volatiles, which creates a certain intersection between the results from the two different extraction techniques. It is important to emphasize that in this other study, the addition of a non-polar solvent to the distillation procedure, namely dichloromethane, might have assisted in preventing the loss of the most volatile components. These could be found at appreciable levels in the present study just in the HS-SPDE samples but not in the essential oils. Examples are: limonene, cynmee, and eucalypt, which comprised more than 30% of the area in the chromatograms from HS-SPDE, while in case of the essential oils this sum was lower than 0.5%. Only a few compounds (15) could be obtained both by hydrodistillation and HS-SPDE, namely: benzaldehyde; 5-hepten-2-one <6-methyl-5->; heptadien <(2E,4E)->; cynmee <p->; limonene; eucalypt; colimene <(E)-\(\beta\)->; linalool; decanal <n->; cycloctal <\(\beta\)->; anethole <(E)->; damascenone <(Z)-\(\beta\)->; geranylacetone <(E)->; ionone <(E)-\(\beta\)->; and 1H-2-indenone,2,4,5,6,7,7a-hexahydro-3-(1-methylethyl)-7a-methyl. This reduced number of compounds suggests that the combination of different methods is necessary for more complete screenings of the volatiles in the mate tea samples and that by using other methods other compounds should be found. Other researchers already used combinations of different extraction and analytical methods--e.g., dynamic headspace analysis (DHA), solvent-assisted flavor evaporation-solvent extraction (SAFE-SE), column adsorption extraction coupled to gas chromatography-olfactometry (GC-O), and gas chromatography-mass spectrometry (GC-MS). Using these different methods, unlike results were obtained for the same samples [3].
# Communication Rate Increase in Drill Strings of Oil and Gas Wells Using Multiple Actuators
## 3 Experimental Results on Channel Measurements  
Orthogonal frequency division multiplexing (OFDM) was used for signal transmission. Each actuator transmitted from 2 to 6 kHz, using 1024 sub-carriers, including 128 pilot tones for channel estimation and 96 null tones for noise power estimation. Each OFDM symbol duration was 256 ms, with a 25 ms guard time interval in between. A least squares method was used for channel estimation [7], whereas for data detection a minimum mean squared error (MMSE) algorithm was used (see the Appendix A). The used modulation and coding were quadrature phase shift keying (QPSK) and convolutional coding.  
The magnitudes of channel frequency responses measured by the receiving sensors in Figure 1b are presented in Figure 2. It was observed that the strain channel had a nearly flat frequency response, whereas the acceleration channels' frequency responses exhibited more frequency selectivity. The unequal strain and acceleration magnitudes in Figure 2 can be attributed to different sensor sensitivities: 50 mV/\(\upomega\) for the strain sensor and 10 mV/\(g\) for each channel of the triaxial accelerometer [4]. Here \(\upomega\) represents the strain magnitude unit in micro fractional particle displacement, whereas \(g=9.8\) m/s\({}^{2}\) is the acceleration due to gravity, used as the unit for particle acceleration magnitude. A method is discussed in [4] to scale readouts of the strain and acceleration sensors according to their sensor sensitivities, such that strain and acceleration signal magnitudes can be compared using the same unit. In this paper, for simplicity, sensors readouts are used as they are for demodulating and detecting the transmitted data.  
To better understand the less-frequency-selective behavior of the strain channel compared to the acceleration channels, these channels were also examined in time domain by looking at their inverse Fourier transforms. The magnitudes of the channel impulse responses measured by the receiving sensors in Figure 1b are presented in Figure 3. Note that the strain channel impulse response had a shorter duration compared to the acceleration channels, behaviors that were also observed in References [4;5]. Given the properties of the Fourier transform, the short duration of the strain impulse response corroborates the relatively flat strain frequency response.  
Figure 2: Magnitudes of frequency responses of channels measured by the receiving sensors in Figure 1b.
# FPGA Implementation of a BPSK 1D-CNN Demodulator
## 3 FPGA Implementation  
This paper implements the 1D-CNN demodulator in an FPGA. The implementation block diagram is shown in Figure 3.  
Figure 1: The process of one-dimensional convolution neural network (1D-CNN) demodulation. BPSK: binary phase shift keying.  
Figure 2: The structure of a 1D-CNN.  
Two 1D-CNNs are implemented in an FPGA. The input of the 1D-CNN is a discrete time series from an analog-to-digital converter (ADC). The input layer segments the input vector by a sliding window, and conveys the input vector to the convolution layer. If we suppose that the input series is \(x_{1}\ x_{2}\dots\ x_{n}\), where \(\sigma\) is the length of the input vector, then the input vector \(X_{m}\) can be indicated as  
\[X_{m}=\left[\begin{array}{c}x(m)\\ x(m+1)\\ x(m+2)\\ \vdots\\ x(m+\sigma)\end{array}\right],\ m=1,2,3\dots, \tag{2}\]  
Registers are cascaded to realize a sliding window. The convolution layer includes two convolution kernels, which store the convolution parameters in block read-only memory (ROM) and complete convolution calculations. A rectified linear unit (ReLU) function is selected as the activation function of the convolution layer. The number of neurons in the hidden layer is set to 20, and the activation function of the hidden layer is a sigmoid function. A single neuron is set in the output layer. The existence of a certain type of phase shift can be concluded directly by referring to the state of this neuron. Comparators are used in the decision module to compare the predefined threshold with the results of the two 1D-CNNs. The valid bus gives the enable signal according to the states of the ADC and each layer. All components are driven by the same clock, provided by the external crystal.  
To improve the efficiency of resource utilization and operation speed of the FPGA, three methods were adopted as follows: (1) the use of a time-delay network for convolutional calculation; (2) the use of a look-up table (LUT) together with a piecewise function to achieve the activation function; (3) the use of a parallel structure within layers and a pipeline structure between layers.