esa-sceva/satcom-chunk-collection huggingface dataset:
# Mapping irrigated Areas Using Sentinel-1 Time Series in Catalonia, Spain
## 4 Results
### Classification Using Random Forest Classifier  
#### 4.2.1 PC-RF  
As discussed in Section 3.6, the RF classification was first performed using the total number of PC values obtained from the application of the PCA on the four different temporal series (328 variables). Then, the significant PC variables that most contributed in the classification of the irrigated/non-irrigated plots were determined using the increase in the mean square error of the predictions (%IncMSE \(>\) 1%). The use of this threshold value allows reducing the number of predictive variables from 328 to 15 while keeping accurate classification. These variables contain the first, second, fourth and sixteenth PC dimension of \(\sigma^{0}_{\text{P,VV}}\), the first third and sixteenth PC dimension of \(\sigma^{0}_{\text{P,VH}}\), the second and the fifth PC dimension of \(\Delta\sigma^{0}_{\text{P,VV}}\), and the first and second dimension of \(\Delta\sigma^{0}_{\text{P,VH}}\). Figure 7 shows the scatter plot  
Figure 6: Temporal evolution of SAR backscattering coefficient \(\sigma^{\circ}\) in VV polarization at plot scale (green curve) and 10 km grid scale (red curve) with precipitation data recorded at a local meteorological station for (**a**) the non-irrigated plot, (**b**) the irrigated plot.  
of 2000 irrigated and 2000 non-irrigated plots (randomly selected) with different combinations of the obtained important PC values. It clearly shows that irrigated and non-irrigated plots can be separated using several PC combinations as they distinctly appear in two different point clouds. The RF classifier is thus capable of obtaining good results when introducing these variables for classifying irrigated and non-irrigated plots.  
Table 3 summarizes the accuracy obtained when applying the trained RF classifier on the 50% validation data using all the variables and the selected important variables. The validation of the RF classifier using the 328 PC values generally shows very high accuracy. In fact, the overall accuracy reaches 91.2% while the kappa coefficient reaches 0.79 and the F-score reaches 0.91. The F-score of the irrigated class is 0.86 while that of the non-irrigated class is 0.94. The reestablishment of the RF classifier using the 15 important PC variables produced similar accuracy compared to the RF classifier using the total number of variables. In general, both classifiers present nearly the same results. However, the overall accuracy slightly decreased from 91.2% to 90.7% but yet keeping an accurate classification. The Kappa coefficient and F-score remained unchanged for both approaches.
# A Chinese Grammatical Error Correction Method Based on Iterative Training and Sequence Tagging
## 3 Methodology
### Sequence Tagging Method Based on CRF  
Both the grammatical error detector and the grammatical error corrector serve to label each token according to the feature vector output of the Transformer encoder. The sequence tagging problem can be simply understood as one of classifying each character, and the number of categories is the size of the tag set. The only difference between the grammatical error detector and the corrector is that the set of tags differs between the two. The grammatical error detector uses only two tags, while the grammatical error corrector uses the 7462 tags mentioned in Section 3.1. Since this is a classification task, it is easy to consider classifying directly, after encoding with a fully-connected layer and then activating it with the softmax function. When the length of the sequence is \(N\), and the size of the label set is \(L\), the formula is as follows:  
\[h_{i}=o_{i}^{T}W+b \tag{3}\]  
\[P_{\text{sm}}(t_{i})=softmax(h_{i}) \tag{4}\]  
where \(W\) is an \(N\times L\) parameter matrix and \(b\) is a parameter matrix of size \(L\). \(P_{\text{sm}}\) is a probability distribution, based on which we can obtain the tagging result \(T\) for each character:  
\[T=\left(t_{1}^{\prime},t_{2}^{\prime},\dots,t_{N}^{\prime}\right) \tag{5}\]  
However there are associations between the output tag sequences. Label-by-label softmax does not take into account such output-level contextual associations but instead puts these associations into the coding level for learning. Particularly when there are multiple errors in a sentence, it is necessary to comprehensively consider the correction results of each point, to achieve better results. If an input sentence has \(N\) tokens and each token has \(L\) possibilities for its label, then there are theoretically \(L^{N}\) different outputs. As Figure 2 shows, the goal is to select the correct one from \(L^{N}\) paths, which means that if it were considered as a classification problem, it would be a problem of selecting one of the \(L^{N}\) classes! It is natural to consider using linear-chain CRF [5] to ensure the model takes this overall association into account.  
In a CRF network with input sequence \(H=(h_{1},\ldots,h_{N})\) and target sequence \(T=(t_{1},\ldots,t_{N})\), the probability distribution of linear-chain CRF is as follows:  
\[P_{\text{crf}}(T\mid H)=\frac{1}{Z(H)}exp\left(s(t_{1};H)+\sum_{i=1}^{N-1}[f(t _{i},t_{i+1})+s(t_{i+1};H)]\right) \tag{6}\]  
where \(Z(H)\) is the normalization factor and \(s(ti;H)\) is the fraction of \(t\) at position \(i\), which can be obtained from Equations (3) and (4). In addition, \(f(ti,ti+1)\) is a parameter matrix of size \(L\times L\) to be trained and \(L\) is the size of the tag set. In order to solve the problem that calculating all paths requires more resources, we refer to [27] and improve the computational efficiency by selecting the k tags with the highest probability at each position for calculation instead of all the tags in the tag set.
## 4 On-Orbit Thermal Analysis
### Description of the Thermal Model and On-Orbit Conditions
Additionally, we assumed that the antenna maintained Earth pointing at a specified incidence angle for 10 min to conduct image acquisition, lasting for 3.5 min, followed by data transmission to the ground station for 3.3 min before and after imaging.
# Optimizing Lattice Basis Reduction Algorithm on ARM V8 Processors
## 2 Technical Background  
### Foundation of Lattice  
Let n be a positive integer and the lattice \(\mathbf{L}\) be a discrete, additive subgroup of \(\mathbf{R}^{n}\), defined as  
\[\mathbf{L}=\{\mathbf{B}\mathbf{z}\} \tag{1}\]  
where \(\mathbf{z}\) is an n-dimensional integer vector and \(\mathbf{B}\) is an m \(\times\) n (m \(\geq\) n) matrix, called a lattice generating matrix.  
Let \(\mathbf{B}=[\mathbf{b_{1}},\mathbf{b_{2}},\cdots\mathbf{b_{n}}]\), where \(\mathbf{b_{1}},\mathbf{b_{2}},\cdots\mathbf{b_{n}}\) are linearly independent columns. They form the basis of L. For example, matrix  
\[\mathbf{B}=\begin{bmatrix}\mathbf{b_{1}}&\mathbf{b_{2}}\end{bmatrix}=\begin{bmatrix}2&3\\ 1&0\end{bmatrix} \tag{2}\]  
A lattice can have many lattice bases. For example, matrix  
\[\mathbf{C}=\begin{bmatrix}\mathbf{c_{1}}&\mathbf{c_{2}}\end{bmatrix}=\begin{bmatrix}1&2\\ -1&1\end{bmatrix} \tag{3}\]  
Since the lattice \(\mathbf{L}\) can have multiple bases, some bases are preferable to others. We can expect the short basis vectors to be nearly orthogonal. \(\mathbf{c_{1}}\) and \(\mathbf{c_{2}}\) are shorter basis vectors than \(\mathbf{b_{1}}\) and \(\mathbf{b_{2}}\), and they are more orthogonal than \(\mathbf{b_{1}}\) and \(\mathbf{b_{2}}\). The short basis is called a reduced basis.
## 4 Experimental Results
### The Performance of the Frequency Shifting Algorithm
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Subject**} & \multirow{2}{*}{
\begin{tabular}{c} **Frequency Discrimination** \\ **Threshold** \\ \end{tabular} } & \multirow{2}{*}{\(f_{f}\)/Hz} & \multirow{2}{*}{\(f_{h}\)/Hz} & \multirow{2}{*}{\(\gamma\)} & \multirow{2}{*}{\(\beta_{1}\)} & \multirow{2}{*}{\(\beta_{2}\)} & \multicolumn{2}{c}{**The Number of**} \\  & & & & & & & **Interpolated and Sampled** \\ \hline S1 & \(4000\times[1\pm 1.6\%\,\mathrm{Hz}\) & 3936 & 4064 & 4 & 0.95 & 0.95 & 24 \\ S2 & \(3000\times[1\pm 3.2\%\,\mathrm{Hz}\) & 2904 & 3096 & 3 & 0.94 & 0.96 & 24 \\ S3 & \(4000\times[1\pm 3.2\%\,\mathrm{Hz}\) & 3872 & 4128 & 3 & 0.94 & 0.94 & 32 \\ S4 & \(6000\times[1\pm 6.3\%\,\mathrm{Hz}\) & 5625 & 6375 & 2 & 0.93 & 0.81 & 48 \\ S5 & \(4000\times[1\pm 6.3\%\,\mathrm{Hz}\) & 3750 & 4250 & 2 & 0.93 & 0.93 & 32 \\ S6 & \(3000\times[1\pm 1.6\%\,\mathrm{Hz}\) & 2952 & 3048 & 4 & 0.97 & 0.98 & 18 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The parameters of the piecewise-linear frequency shifting algorithm. As shown in Figure 5a,b, the spectrogram of the frequency region \(\left[3750\,\mathrm{Hz}\quad 4250\,\mathrm{Hz}\right]\) of the original signal was stretched in the spectrogram of the output signal. The separation of formant tracks was increased and the energy was diluted. While the spectrograms of the adjacent frequency ranges were compressed with the formants slightly squeezed. The audition in a silent room by a normal hearing person showed that the processed word was clear.
## 2 Duqu
### Duqu Analysis
#### 2.1.6. Communication Module  
Duqu has a backdoor covert channel control communication module that is used to send information to and receive commands from a remote Command and Control (C&C) center. In our case, the remote C&C server was located at the address 206.183.111.97, but later evidence shows that other instances used different servers. The communication protocol uses both HTTP port 80 and HTTPS port 443, and it is encrypted. The communication through port 80 starts with a valid HTTP request, followed by the transmission of (possibly encrypted) binary data obfuscated as jpeg images.
## Abstract  
Electrical stimulation (ES) and its effects on biological systems is an area of research in regenerative medicine. The focus here is on the mechanism of action of ES on cell membrane-related components. A short alternating current (AC) stimulation (10 min) was applied on suspended human MG-63 osteoblasts via a commercially available multi-channel system (IonOptix). The pulsed ES with 1 V or 5 V and frequencies of 20 Hz on cells was performed immediately after cell seeding. The in vitro investigations were conducted by microscopy, flow cytometry, and particle analysis via a Litesizer within 24 h. The short-time ES with the parameter 1 V and 20 Hz was beneficial for the process of cell attachment, which could be related to an enhanced deposition of fibronectin on the glass bottom from the protein-containing medium (10% FBS). The MG-63 cells' spherical coat hyaluron remained constant and did not contribute to this AC-triggered adhesion. In this context, the cells' zeta potential also did not play a role. The membrane potential analyzed via DiBAC\({}_{4}\)(3) was unchanged. Only the aquaporin channel AQP 8 in the cell membrane was significantly enhanced. Suspended cells in an AC electric field were activated during their settlement, and the fibronectin adsorption on the bottom contributed to this effect but not the membrane-related components.  
Electrical stimulation; alternating current; IonOptix system; confocal microscopy; Litesizer; zeta potential; particle size; osteoblasts; aquaporins; hyaluron
# A Comprehensive Survey of Recent Routing Protocols for Underwater Acoustic Sensor Networks
## 3 Routing Protocols for UWSNs
### VA-GMPR  
VA-GMPR [32] works by dividing a UWSN into a virtual three-dimensional grid of equal sized cubic cells. Each cell has an elected gateway. Each gateway node hosts a gateway table that contains information about gateways up to two hops away. This information is used by the grid void avoidance scheme to avoid connectivity voids. Moreover, each sensor node hosts a path table that contains four cell disjoint paths from the underwater sensor node to the surface sink node. Traffic load balancing is achieved by the implementation of round robin selection of paths by each source gateway. In order to deal with connectivity holes or voids, a void avoidance method is proposed. The void avoidance technique used is based on the magnitude of the void area. The techniques used are hole bypassing, path diversion, and path back tracking, which solve lowest-to-highest complexity voids in the presented order. VA-GMPR enhances reliability by considering multiple node disjoint routes connecting a source node to the destination. Out of the available paths, longer and infeasible paths are dropped, thus saving network resource, and improving E2E delay. However, the selection of a few optimal paths out of many available paths introduces complexity.
# Characteristics and Origin of Natural Gas in Yongfeng Sub-Sag of Bogda Mountain Front Belt
## 7 Conclusions  
1. The Permian Lucaogou Formation in the Yongfeng sub-sag and its surrounding areas has developed deep to semi-deep lacustrine facies, with the lithology mainly composed of organic rich black to gray-black mudstone. The source rocks are thick, with high organic matter abundance, good type, moderate maturity, and good hydrocarbon generation ability.  
2. The Yongfeng sub-sag natural gas is mainly composed of hydrocarbon gases, while other gases are mainly nitrogen. For hydrocarbon gases, methane is the main component and the content of heavy hydrocarbon components (\(\rm C_{2+}\)) is relatively low. The drying coefficients of natural gas are all greater than 0.96, showing typical dry gas characteristics. This is quite different from other natural gas components in the southern margin of the Junggar Basin.  
3. The ethane carbon isotope \(\rm\delta^{13}C_{2}\) of natural gas is \(-28.45\%\) and the methane carbon isotope \(\rm\xi^{13}C_{1}\) of natural gas ranges from \(-58.62\%\) to \(-59.42\%\) from Well Xyd1 in the Yongfeng sub-sag. The carbon isotope of natural gas is significantly lighter than in other areas. According to the carbon isotope and natural gas light hydrocarbon composition, the natural gas type in the Yongfeng sub-sag is oil type gas, which is different from that in the central and western regions of the southern margin of the Junggar Basin.  
4. Based on comprehensive data and information on natural gas composition, carbon isotopes, the burial history of source rocks, and thermal history, it is believed that the natural gas in Well Xyd1 is mainly influenced by biological processes. The crude oil generated from the Lucaogou Formation source rocks in the early stage was partially biodegradable due to tectonic uplift, resulting in biogenic methane and the formation of crude oil biodegradation gas.  
**Author Contributions:** Conceptualization and methodology, X.S.; validation, formal analysis, investigation, and resources, L.W. and K.Y.; data curation, Y.C., X.Z., Z.B. and Y.D.; writing--original draft preparation, L.W., K.Y. and X.S.; writing--review and editing, visualization, supervision, and project administration, X.S. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the Geological Survey Project of China Geological Survey (DD20190090, DD20230042 and DD20242210).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** We state that the data are unavailable due to privacy or ethical restrictions of the company and university.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 5 Surface Modification Used for Mg Alloys
It has a noteworthy effect of improving the corrosion resistance of Mg alloys. After the SMAT process, the microstructure of Mg alloys is fine and uniform, the surface is comparatively smoother, and the corrosion rate is considerably lower [97; 112]. The main problem of SMAT is reduced degradation resistance due to the crystal defect density caused by attrition balls [98; 101; 113; 114]. So, SMAT is less effective in improving the biomedical Mg alloy performance [24]. Shot Peening:  
Shot peening is a surface modification technique that introduces compressive residual stress on the Mg surface, similar to the SMAT technique. The shot peening process creates a plastically deformed zone with an extended and refined grain structure [115; 101]. [PERSON] et al. [116] discovered that microhardness and degradation resistance could be improved through shot peening by refining grains and increasing coating density. [PERSON] et al. [117] proved that higher surface roughness developed from shot peening favors rapid degradation. Similar to SMAT, shot peening is limited in improving the biological function of Mg alloys. Several other strategies have recently been researched. This technique is a productive method for modifying a material's surface by melting the surface by scanning it with a high-intensity laser beam (refer to Figure 8). The advantages are high treatment efficiency, zero pollution, and low material consumption [103; 118; 119; 104]. Currently, laser surface melting, laser cladding, and laser surface alloying are extensively used in surface engineering [106; 120; 121; 122]. For the Mg alloys, laser surface melting is currently used to improve the alloys' mechanical performance and corrosion resistance [109; 123]. In addition, laser modification increases the cytocompatibility and corrosion resistance of Mg alloys by modifying the substrate's surface microstructure [24]. Friction stir processing (FSP) is a technique similar to friction stir welding, developed by [PERSON] [124], and is used for mechanical property improvement and surface composite fabrication of light alloys such as Mg alloys [125; 126; 110; 127]. FSP uses a non-deformable tool that is forcibly inserted into the workpiece and revolved in a stirring motion as the device is pushed laterally to the alloy. FSP is a technique that can produce refined grains and uniform microstructures and enhance mechanical performances in base materials. FSP is also used to improve the corrosion resistance of Mg alloys by optimizing surface microstructure or creating a composite layer on the metal surface in biomedical applications [112; 113; 114; 115; 128; 129; 130; 121; 115]. FSP creates severe plastic deformation, making grains refined and eliminating surface defects. Moreover, combining FSP with other technologies or reinforcement materials enhances mechanical and biological properties [24]. To summarize, Table 8 highlights the types of coatings used on magnesium alloys and their benefits.
# Deepwater 3D Measurements with a Novel Sensor System
## 2 Materials and Methods
### Underwater 3D Sensor System
#### 2.1.1 Sensor Hardware
Three-dimensional image data are calculated by evaluating image sequencing pairs, one sequence from each camera, where each image sequence typically comprises ten images. The corresponding measurement principle is described in [29]. To support the determination of point correspondence in the two measurement cameras, the scene is illuminated with a structured stripe pattern by the projection unit. The GoBo method [29] is used for this, in which the stripe pattern is generated by projecting a rotating GoBo wheel. The main components of the projection unit are the lighting unit, rotary motor, GoBo wheel, projection lens, and the associated electronics. The lighting unit consists of a high-performance LED in the blue wavelength range. The projection lens is a typical industrial camera lens. Figure 1: Scheme of the sensor system with five tubes containing measurement cameras (C\({}_{\text{m}}\)), color camera (C\({}_{\text{col}}\)), IMU (I), projection unit (P), and electronic control box (E). Additionally, two flashlights (F) are mounted on the carbon fiber rod (R). The color camera has 7 MPix and essentially fulfills two functions. Firstly, it serves to support the ROV pilot's navigation when the ROV headlights are switched off. The ROV headlights cannot be switched on while the measurement is in progress, as they would disrupt the structured light pattern for the 3D measurement. The necessary lighting of the scene for the color camera recording is provided by the two flashlights, which are switched on and off alternately with the structured lighting. The second function of the color camera is the recording of video streams to create a 3D map of the scanned area of the seabed and the objects to be measured in the water. This is performed with the additional use of IMU data by means of visual odometry [14; 30]. The inertial measurement unit (IMU) is used to register the sensor system's own movement and to export these movement data to the control system of the UWS. Using this information, motion compensation for the 3D reconstruction can be performed online. With the help of IMU data and the color camera images, a trajectory of the sensor movement is generated afterward, which is also used for scaling the 3D data generated by the monocular video sequences using visual odometry. The following features and performance parameters characterize the 3D measurement system UWS1. Distinguishing parameters of the second variant (UWS2) are listed in parentheses. * Size (spatial dimensions): 1.25 m \(\times\) 0.7 m \(\times\) 0.5 m (0.9 m \(\times\) 0.7 m \(\times\) 0.5 m);
* Mass: 65 kg (approx. );
* Color camera frame rate: 25 Hz at 7 Mpix resolution;
* Measurement camera frame rate: up to 900 Hz at 1 MPix resolution;
* Three-dimensional frame rate: up to 50 Hz;
* Measurement distance 2.0 m \(\pm\) 0.4 m (1.3 m \(\pm\) 0.3 m);
* Field of view at a standard distance: 0.9 m \(\times\) 0.8 m (0.7 m \(\times\) 0.6 m);
* Maximum diving depth: 1000 m.
## 2 Hybrid Path Planning Based on Improved ABC and BAS Algorithms
### Analysis of Algorithm Time Complexity  
The time complexity of an algorithm can reflect its feasibility and efficiency in solving path planning problems. Therefore, this section conducts a time complexity analysis of the original ABC and BAS algorithms, as well as the improved IABCBAS algorithm.  
In the global waypoint exploration phase, the size of the artificial bee colony is \(C\), the problem dimension is \(b\), and the maximum number of iterations of the algorithm  
Figure 2: The flow path of the IABCBAS algorithm.  
is \(E\). Therefore, the time complexity of the original ABC algorithm is \(O(c\times b\times E)\). The initialized population of the improved ABC algorithm is disturbed by Tent chaos, and the initialization time complexity is \(O(b)\). Affected by the mutation factor, the time complexity of the waypoint exploration phase is \(O(c\times b\times E)\). Thus, the time complexity of the improved ABC algorithm phase is \(O(b+c\times b\times E)\).  
In the local waypoint optimization phase, the problem dimension at this stage is \(b\). The computational complexity of initializing the beetle is \(O(b)\). During the iteration process, the computational complexity for judging the directions of the two antennae is \(O(2b)\), and the computational complexity for updating the beetle's position is \(O(2b)\). Therefore, the time complexity of the original BAS algorithm is \(O(5b)\). Since the initial position of the beetle in the improved BAS algorithm is generated in the global exploration phase, the initialization computational complexity is reduced. Due to the introduction of random reverse solutions, the computational complexity caused by the increased direction selection is \(O(3b)\). The computational complexity of greedy selection confirmation is \(O(b)\). Thus, the time complexity of the improved BAS algorithm is \(O(4b)\).  
In summary, the combined time complexity of the two original algorithms before improvement is \(O(c\times b\times E)+O(5b)=O(c\times b\times E+5b)\), and the complexity of the IABCBAS algorithm is \(O(b+c\times b\times E)+O(4b)=O(c\times b\times E+5b)\). It can be seen that the IABCBAS algorithm has the same complexity of \(O(c\times b\times E+5b)\). However, through the design of optimization strategies, the IABCBAS algorithm improves the search capability, controls the time, and improves the efficiency of path planning.