esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Coal  
Coal is formed from biomass of terrestrial origin through the combination of a number of biological, physical, and chemical processes, controlled by factors such as temperature and pressure, over time. Transformations caused by the carbonization processes during the period of time in which this occurs determine their classification in the carboniferous series, which begins with peat, goes on to lignite, sub-bituminous coal, bituminous coal and, finally, anthracite. The relative amounts of moisture, volatile matter, and fixed carbon content vary throughout the carboniferous series. Moisture and volatile matter decrease with increasing degree of carbonization in the inverse ratio of the increase in carbon content [9]:  
* Peat is not yet considered a coal and is used as a fuel in some regions, notably in Northern Europe, such as Scotland or Germany. When dehydrated, peat can be used as an absorbent to contain fuel and oil spills. It is also used in agriculture, where it is incorporated into soils to fix and release water slowly.
* Lignite is considered the lowest grade coal and has a heating value that can vary, depending on the humidity, between 9.5 and 19.3 MJ/kg.
* Sub-bituminous coal usually has a heating value within the range of 19.3 to 30 MJ/kg and usually has more moisture, with a fixed carbon content of 35% to 45%.
* Bituminous coal is formed when lignite is subjected to an increase in temperature and pressure. It has a heating value that varies between 25 and 60 MJ/kg. Bituminous coal is the coal most used in the production of electricity and in the iron and steel industry. It has a fixed carbon content of 45% to 86%.
* Steam coal is a coal that is between bituminous and anthracite types and was once widely used as fuel for steam locomotives for trains.
* Anthracite is formed when the pressure and temperature in the carbonization basin are very high. It presents a dark black tint with a metallic luster. It has a heating value close to 60 MJ/kg and has a fixed carbon content between 86% and 97%.
# Soil Moisture Influence on the FTIR Spectrum of Salt-Affected Soils
## 2 Materials and Methods
### Laboratory Experimental Design  
#### 2.2.1 Analysis of Soil Characteristics in the Laboratory  
The moisture content of the soil collected in the field and the moisture content of the soil samples in the laboratory were determined according to laboratory standard TCVN 4080:2011 [49]. A total of 5 g of air-dried soil sample was weighed on an analytical balance, and put into a weight-fixed aluminum box. The box was oven-dried at a temperature of 105 \({}^{\circ}\)C to constant mass weight. The sample was cooled in a desiccator for about 20 to 30 min, and the mass was determined with an analytical balance. The moisture content of the sample was subsequently determined.  
The particle size distribution (PSD) of soil samples was determined according to laboratory standard TCVN 8567:2010 [50]. The air-dried soil sample was processed through a 2 mm sieve to determine the PSD. Soil samples were soaked with a mixture of sodium pyro phosphate (Na\({}_{4}\)P\({}_{2}\)O\({}_{7}\)) and sodium carbonate (Na\({}_{2}\)CO\({}_{3}\)) at the ratio of 1:2 (\(w/v\)), left to disperse overnight, and passed through a 0.02 mm sieve to determine the sand. The  
Figure 1: The study area: Hai Phong-Thai Binh coastal area, Vietnam. (1) Van Uc estuary; (2) Thai Binh estuary; (3) Tra Ly estuary; (4) Red River estuary.  
suspension was subsequently brought to a 1 L cylinder for the determination of silt and clay particles by the pipette method.  
The results of the textural composition of the soil samples in the coastal estuaries of Hai Phong-Thai Binh (Table 1) shows a USDA textural class variability from Sand in Kien Thuy 2 to Clay Loam in Tien Lang 2. The following soil samples were selected for further salinity experimentation: Tien Lang 2 sample (symbol: TL2) represents an alluvial soil and Kien Thuy 2 sample (Symbol: KT2) represents a sandy soil from a river bank.  
The electrical conductivity of the soil samples collected in the field and soil samples generated in the laboratory was determined according to the laboratory standard TCVN 6650:2000 (ISO 11265:1994) [51]. A sample of the air-dried soil was pre-processed in accordance with ISO 11464:2006 [48] to prepare a 1:5 (\(w/v\)) ratio of soil to water extract. KCl solutions at concentrations of 0.1 mol/L, 0.02 mol/L, 0.01 mol/L were used for calibration.
## 1 Introduction
For the image-level part, the Euclidean distance between the scene and the item embeddings is directly computed. In addition, for the region-level part, the scene is divided into several regions, where the distance is defined as the weighted sum of visual distances between each of the region embedding and the item embedding. The goal of using attentive local distance is to focus on the regions which contain the human subject and shield the complex background, since the background is already considered by the image-level distance. Figure 1: Two types of incomplete outfit images with a query box, with the ground-truth item listed beside the outfit. As shown in (**a**), traditional outfit completion methods focus on high-quality product-based images. Our model focuses on outfit completion in (**b**), where scene information and the full-body human subject are provided in the image. To measure the compatibility of items in an outfit, we further define a novel object-level distance. Object-detection technology is adopted to extract clothing item objects from the masked scene. We then calculate the sum of visual distances between the query item and each of the detected item object to obtain the object-level distance. Meanwhile, we take a step towards predicting the missing item category by analyzing the detected items in the scene. With the help of image-fitting technology, the human mesh can be directly reconstructed from a single image to obtain the human body-shape vector, which describes the body information of the subject, such as height, weight, waist, etc. The detected objects and items can also be projected into a body space by embedding functions. The matching score between the human body shape and an item/object is obtained by computing the inner product of their embedding vectors. We assume that the body shape-matching difference between the missing item and the detected objects should be as small as possible. We conduct comprehensive experiments on Street2 Shop [1] and STL-Fashion [2] datasets. The experimental results demonstrate the effectiveness of our proposed model for item compatibility learning and personalized outfit completion compared to state-of-the-art methods.
# DeepGOMIMO: Deep Learning-Aided Generalized Optical MIMO with CSI-Free Detection
## 3 Detection Schemes for GOMIMO Systems
### Proposed CSI-Free DNN Detection
Because the DNN detector takes the input binary bits corresponding to a transmitted signal vector as the output, both the output layer and the decision layer have the same number of neurons, which is equal to the spectral efficiency of the GOIMO system, i.e., \(S=\eta_{\text{GOMIMO}}\). Therefore, letting \(\mathbf{z}_{k}\) denote the output of the \(k\)-th (\(1\leq k\leq 6\)) layer of the feedforward DNN module, the corresponding input-output relationship can be described by  
\[\mathbf{z}_{k}=\left\{ \begin{array}{ll}\alpha\mathbf{F}\mathbf{y},&k=1\\ f_{\text{ReLU}}(\mathbf{W}_{k-1}\mathbf{z}_{k-1}+\mathbf{b}_{k-1}),&2\leq k \leq 5\\ f_{\text{Sigmoid}}(\mathbf{W}_{k-1}\mathbf{z}_{k-1}+\mathbf{b}_{k-1}),&k=6 \end{array} \right., \tag{11}\]  
where \(\mathbf{W}_{p}\) and \(\mathbf{b}_{p}\) with \(1\leq p\leq 5\) represent the corresponding weight matrix and the bias vector, respectively. According to (11), the mean-square error (MSE) loss can be calculated as follows:  
\[e_{\text{MSE}}=\frac{1}{S}\|\mathbf{z}_{6}-\mathbf{b}\|^{2}, \tag{12}\]  
where \(\mathbf{b}\) denotes the corresponding transmitted bit vector ans \(S\) is the length of \(\mathbf{b}\). Finally, the decision layer is utilized to determine the noninteger output of each neuron in the output layer to be 0 or 1. Letting \(\mathbf{\hat{b}}=[\hat{b}_{1},\hat{b}_{2},\cdots,\hat{b}_{S}]^{T}\) denote the final output binary bit vector, the \(q\)-th (\(q=1,2,\cdots,S\)) binary bit in \(\mathbf{\hat{b}}\) can be estimated by  
\[\hat{b}_{q}=\left\{ \begin{array}{ll}0,&z_{6,q}<0.5\\ 1,&z_{6,q}\geq 0.5\end{array} \right.. \tag{13}\]
## 2 Methodology
### Computational Details
Thus a positive formation energy means that energy must be supplied to form the material, while a negative formation energy implies that the formation is energetically favorable and spontaneous. For the purpose of comparison of DFT functionals, the formation energy is not a very informative indicator because Equation (1) mixes together the energies of substances with very different types of primary chemical bonding (metallic, ionic, and covalent) for which the DFT functionals perform very differently. For instance, semi-local functionals like PBE perform very well for metals, while hybrid functionals describe the bonding in insulators and molecular substances more accurately than in metals [39]. Another approach [40; 41] is to compute the cohesive energy, that is the energy needed to form a solid crystal from the free atoms, which may be defined by replacing the chemical potentials \(\mu\) in Equation (1) with the energies of the corresponding atomic species. Still, such a definition operates the energies of very different systems (namely, a strongly bonded solid versus free atoms), so it also suffers from the transferability errors of DFT functionals that are quite large for atomization energies [42]. Yet another possibility is to consider the energy of a chemical reaction in which Cu\({}_{2}\)P\({}_{2}\)O\({}_{7}\) compound is formed from a basic oxide, CuO, and an acidic oxide, P\({}_{2}\)O\({}_{5}\)[22],  
\[2\mathrm{CuO}+\mathrm{P}_{2}\mathrm{O}_{5}\rightarrow\mathrm{Cu}_{2}\mathrm{ P}_{2}\mathrm{O}_{7},\]  
calculated as follows:  
\[\Delta E_{f}^{ox}=\frac{1}{2}\{E(\mathrm{Cu}_{4}\mathrm{P}_{4}\mathrm{O}_{14}) -E(\mathrm{Cu}_{4}\mathrm{O}_{4})-\frac{1}{2}E(\mathrm{P}_{8}\mathrm{O}_{20})\}. \tag{2}\]  
Here Cu\({}_{4}\)O\({}_{4}\) represents the monoclinic unit cell of CuO structure, space group C2/c, containing four formula units (\(Z=4\)) with an antiferromagnetic alignment of magnetic moments on Cu ions. The calculated lattice parameters by PBE functional is \(a=4.667\) A, \(b=3.419\) A, \(c=5.157\) A, \(\beta=99.4767^{\circ}\), which is very well consistent with the experimental findings [43]. P\({}_{2}\)O\({}_{5}\) crystallizes in the orthorhombic structure, space group Pnma, whose unit cell contains four formula units. The calculated lattice parameters by PBE functional are \(a=9.613\) A, \(b=4.941\) A, \(c=7.386\) A, and \(\alpha=\beta=\gamma=90^{\circ}\). The optimized crystal structures of CuO and P\({}_{2}\)O\({}_{5}\) are shown in Figure S1.
###### Abstract  
The measurement of the shear wave velocities (V\({}_{\text{s}}\)) of soils is an important aspect of geotechnical and earthquake engineering, due to its direct relation to the shear modulus (G), which in turn influences the stress-strain behavior of geomaterials. V\({}_{\text{s}}\) can be directly measured or estimated using a variety of onsite tests or in a laboratory. Methods such as downhole PS logging require boreholes and may not be logistically and economically feasible in all situations. Many researchers have estimated V\({}_{\text{s}}\) from other geotechnical parameters, such as standard penetration test resistance (SPT-N), by means of empirical correlations. This paper aimed to contribute to this subject by developing an empirical relationship between V\({}_{\text{s}}\) and SPT-N. Data from twenty sites in Metro Manila were obtained from geotechnical investigation reports. V\({}_{\text{s}}\) profiles of the same sites were also acquired using the refraction microtremor method. New empirical relationships were developed for all, sandy, and clayey soil types, using a non-linear regression method that is applicable for Metro Manila soils. Statistical evaluation and comparison of the proposed correlations with other previous works suggested the viability of the empirical model.  
Keywords:shear wave velocity; standard penetration test; refraction microtremor; empirical correlation; Metro Manila +
Footnote †: journal: Applied Sciences
# Learning an Efficient Convolution Neural Network for Pansharpening
## 5 Conclusions  
In this paper, we proposed an efficient model motivated by three goals of pansharpening: spectral preservation, spatial enhancement, and model robustness. For the spectral and spatial domains, we employed an end-to-end CNN model that breaks the limitation of linear pansharpening algorithms. Experimental results demonstrate that our model tended to return well-balanced performance in spatial and spectral preservation. For the improvement of robustness, our CNN model is shallow while efficient, which can be less prone to overfitting. By adopting dilated convolution, our model achieved a larger receptive field, and greatly made up the shortcoming of network depth. Among the model, we also employed a multilevel structure to make full use of the features extracted under different receptive fields. Compared with state-of-the-art algorithms, the proposed model makes a better trade-off between spectral and spatial quality as well as generalization across different satellites.  
Our model was motivated by the aim of being robust when generalized to new satellites, which was designed under the guiding concepts of simplicity and efficiency. As the generalization was improved, the shallow network architecture also restricted the fusion accuracy. In our future work, we will stick to the fusion of one specific satellite dataset; in that case, we can remove the effect of generalization and focus on optimizing the architecture of a deeper network to further boost the fusion performance.  
**Supplementary Materials:** The following are available online at [[http://www.mdpi.com/1999-4893/12/1/16/s1](http://www.mdpi.com/1999-4893/12/1/16/s1)]([http://www.mdpi.com/1999-4893/12/1/16/s1](http://www.mdpi.com/1999-4893/12/1/16/s1)).  
**Author Contributions:** Conceptualization, F.Y. and H.G.; methodology, F.Y.; software, H.G.; validation, F.Y., and H.G.; formal analysis, H.G.; investigation, F.Y.; resources, F.Y.; writing--original draft preparation, F.Y.; writing--review and editing, F.Y.; visualization, H.G.; supervision, Y.G.; project administration, Y.G.; funding acquisition, Y.G.  
Figure 11: Results of the original scale experiment on an area extracted from a WorldView-3 image. **(a)** LRMS; **(b)** PAN; **(c)** BDSD; **(d)** NIHS; **(e)** Indusion; **(f)** NMRA; **(g)**\(\ell 1/2\); **(h)** PNN; **(i)** MSDCNN; **(j)** Proposed.  
**Funding:** This research was funded by the National Natural Science Foundation of China under Grant 61673222.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# The Contribution of Near-Surface Geophysics for the Site Characterization of Seismological Stations
## 5 Results and Discussion
Taking into consideration the local geology, the higher frequency peak at 5.1 Hz is probably attributed to the contrast impedance between the talus cones-scree and the shales-chert formations, while the lower frequency peak at 2.7 Hz is probably attributed to the shales-chert and the limestone formations. Additionally, the transition from the talus cones-scree to the shales-chert formation at the depth of 16.0 m is visible to the 1D Vsmodel, from the MASW technique (Figure A4), as below that depth the Vs value is around 900 m/s. The geophysical subsurface investigation at station _THAL_ revealed low resistivity values (close to 50 Ohm) and relatively low P-wave velocity values (550-1300 m/s) (Figure 14), which seem to correspond to the great thickness of the post-alpine marine-brackish marks (_PI-Pt.m_); no hard rock basement is depicted. This is also consistent with the 1D _Vs_ model derived from the MASW technique (Figure A5), where relatively low shear-wave velocities, with an average value of 300 m/s, are present until the depth of 28.0 m. Regarding the H/V curves (Figure 13), a fundamental frequency peak could be interpreted at 1.4 Hz. With an average _Vs_ velocity of 300 m/s, the 1.4 Hz peak corresponds to a depth of 54.0 m (Table 5). This peak may be caused by the contrast impedance between the marine-brackish marks and underlain compact conglomerate formation that exist in the area. The multiple peaks that are present in the entire spectrum above 2.0 Hz indicate several lithological variations inside the marine-brackish marks formation, known also from the geological description of the formation. The geophysical results at the seismological station _EPID_ (Figure 20) reveal comparably low resistivity values and seismic velocities. These could be explained by the existence of Quaternary sediments (\(Q\)). Regarding the 1D _Vs_ model from the MASW technique (Figure A7), the Quaternary deposits could extend down to a depth of 13 m with an average shear-wave velocity of 350 m/s. From the depth of 13.0 m until the depth of 30.0 m, the shear-wave velocity rises to an average value of 430 m/s, probably due to the existence of the trachytes and trachytic tufts (\(\tau\), \(tf\)) formation underlying the Quaternary sediments. Two clear peaks were identified in the H/V curves (Figure 19), at 1.5 and 7.1 Hz. With an average _Vs_ value of 350 m/s, these peaks correspond to 58.0 and 12.0 m depths, respectively. The 7.1 Hz peak (12 m depth) could be related to the existence of a high impedance contrast between the quaternary sediments (\(Q\)) and the trachytes/trachytic tufts (\(\tau\), \(tf\)) formation and can be interpreted as the fundamental one (Table 5). Furthermore, this broad frequency peak indicates that subsurface structural variations (thickness and transition zones) also exist.
## 5 Experiments and Results
### Experiment 1: State Farm Distracted Driver Detection Dataset  
ReSVM outperformed the existing state-of-the-art approaches on SFDDD as can be seen from Table 2 and Figure 7. The reasons include the combination of deep features of ResNet-50 along with the SVM classifier in the ReSVM. SVM scales relatively well to high-dimensional data and also reduces the risk of overfitting [60]. This modification increased the percentage accuracy of the proposed approach from 89%, using simple ResNet-50, to 95.5%. It can be empirically observed that this combination was helpful for datasets containing high intraclass variations such as SFDDD. This dataset has a variety of distractions including texting, talking, operating the radio, nodding, panning, drinking, combing, applying makeup, and cognitive distractions.  
Table 3 shows the optimal parameters (number of epochs and learning rate) of the proposed approach (ReSVM) and existing deep networks.  
Figure 6: Various frames of the DrivFace dataset depicting various distractions, namely: (**a**) looking right, (**b**) looking forward, (**c**) talking and laughing, (**d**) looking forward and waving a hand, (**e**) head panning left, and (**f**) head panning right and signaling.  
Figure 8 shows the ROC-AUC plot obtained by applying ReSVM on SFDDD, BU, DrivFace, and FT-UMT datasets. The value of AUC-ROC for the SFDDD dataset is highest, showing that ReSVM has the highest measure of separability. It shows that the proposed model is better in distinguishing between a distracted driver and one who is not distracted. The value ROC-AUC for BU and DrivFace datasets is relatively lower than that of SFDDD. The reasons include the dim light conditions, lack of clarity, and high intraclass variations of these datasets.
## 2 Materials and Methods
### Proposed Method
#### 2.3.1 Preprocessing  
For the task of crackle detection, the signals are low-pass filtered and resampled to 4 kHz. The window length for the short-time analysis was chosen to be 256 samples (64 ms) with a hop size of 16 ms. This interval was a good compromise between parameter accuracy and stationary requirements. Finally, the sliding segments were weighted by a Hamming window to reduce the spectral leakage.  
Figure 3: Magnitude spectrogram of the first eighteen spectral patterns combining the parameters \(t_{IDW}\) and \(t_{2 CD}\) as previously mentioned. Higher energy is indicated by darker colour.
## 6 Experiment 1: Comparison of SER Methods for Monolingual SER
### Experiment 1.1: Independence of Speakers  
The results of this experiment, expressed as accuracy, are shown in Table 5. Recall that each of the four datasets is monolingual and that we are training and testing on the same language here. We can see that VGGE was the best on ASED (Amharic) and EMO-DB (German), ResNet50 was the best on RAVDESS (English), and AlexNet was the best on URDU (Urdu).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Datasets** & \multicolumn{2}{c}{**Train**} & \multicolumn{2}{c}{**Test**} & \multicolumn{2}{c}{**Validation**} \\
**Labels** & **Positive** & **Negative** & **Positive** & **Negative** & **Positive** & **Negative** \\ \hline ASED & 693 & 804 & 199 & 230 & 99 & 115 \\ \hline EMODB & 95 & 118 & 27 & 34 & 14 & 17 \\ \hline RAVDESS & 456 & 524 & 140 & 160 & 56 & 64 \\ \hline URDU & 140 & 138 & 40 & 40 & 20 & 22 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Class distribution between the train, validation, and test partitions.  
Figure 3: Class distribution within Datasets.  
It is interesting to look at the average figures on the bottom row of the table. ASED (82.53%) and RAVDESS (82.71%) are very close, EMO-DB (77.78%) is 4.75% lower than ASED, and URDU (84.58%) is 2.05% higher than ASED. Generally, the differences are not that large when we consider that the languages have very different characteristics and that the datasets were created independently by different researchers. Moreover, recall that the original data are being mapped onto two sentiment classes from the original four to eight classes (see Section 6.1 and Table 2).  
Subject to these points, we might conclude that Amharic and English monolingual mono-corpus SER are of similar difficulty, German is more difficult, and Urdu is easier. As languages, English and German are perhaps the most similar, since they are both within the Germanic branch of the Indo-European language group. Urdu is also Indo-European but from the Indo-Iranian branch. Finally, Amharic is from the Semitic branch of the Afro-Asiatic group.
# Analysis of Lightweight Feature Vectors for Attack Detection in Network Traffic
## 6 Extracting Features from Encrypted Traffic  
Due to privacy and security concerns, network traffic is now becoming more and more encrypted. Using VPNs to securely connect through the Internet or HTTPS as a secure protocol for web browsing has become common on the Internet. Beyond web browsing, also in cyber-physical systems--for instance, smart grid machine-to-machine communication and smart factory environment--encryption is progressively being adopted. On the other hand, malware communications--such as worm propagation or botnet command and control structures--usually uses encrypted communication to hide the content of their data exchange [25]. With encryption, the access to the data-payload is restricted to the owners of the encryption key and deep packet inspection in the network is no longer possible. Therefore, a big challenge for today's attack detection methods is to detect anomalous behavior without the ability to access packet contents.  
The most commonly used standard protocols for the encryption of TCP/IP communication are IPsec [26] and TLS (Transport Layer Security) [27]. TLS works on top of TCP and just encrypts the payload data from the application layer. IPsec, on the other hand, works at the network layer and also encrypts the TCP header, meaning that fields like source and destination ports or TCP flags are unavailable for analysis. Some of the feature vectors studied here include TCP header fields. Nevertheless, in the conducted analysis, TLS or IPsec encrypted fields show low relevance for detection as a general trend.