esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 NURBS-Based Isogeometric Analysis
### A Brief on NURBS  
A knot vector \(\mathbf{\Sigma}=\left\{\xi_{1},\ldots,\zeta_{n+p+1}\right\}\) is assigned on the one-dimensional parameter axis \(\zeta\), where \(\zeta_{i}\) is the \(i\)th knot, and \(\zeta_{i+1}\geq\zeta_{i}\). Knots in \(\mathbf{\Sigma}\) may be repeated. An open knot vector is usually used, where the start and end knots repeat \(p+1\) times to create additional control points at the ends. The knot vector \(\mathbf{\Sigma}\) recursively defines a group of one-dimensional polynomial B-splines as follows [44; 45]:  
\[\begin{split}&\mathcal{N}_{i,0}(\zeta)=\left\{\begin{array}{ l}1,\ \ \zeta\in\left[\zeta_{i},\zeta_{i+1}\right)\\ 0,\ \ \text{otherwise}\end{array}\right.\\ &\mathcal{N}_{i,p}(\zeta)=\frac{\zeta-\zeta_{i}}{\zeta_{i+p}-\zeta_{i}} \mathcal{N}_{i,p-1}(\zeta)+\frac{\zeta_{i+p+1}-\zeta_{i+1}}{\zeta_{i+p+1}- \zeta_{i+1}}\mathcal{N}_{i+1,p-1}(\zeta),\ \ \ p\geq 1\end{split} \tag{14}\]  
where \(p\) and \(n\) are the order and the total number of the B-splines, respectively. \(i=1,\ldots,n\) denotes the sequence number. Polynomial B-splines cannot accurately represent conic curves; hence, rational B-splines are constructed as [44; 45]  
\[\mathcal{R}_{i,p}(\zeta)=\frac{\alpha_{i}\mathcal{N}_{i,p}(\zeta)}{\sum_{j=1}^ {n}\alpha_{j}\mathcal{N}_{j,p}(\zeta)} \tag{15}\]  
where the constants \(\alpha_{i}>0\) denote the weights. The weights \(\left\{\alpha_{i}\right\}_{i=1}^{n}\), the knot vector \(\mathbf{\Sigma}\), and the order \(p\). define a group of rational B-splines. The rational B-splines based on non-uniform knot vectors are called NURBS.  
Multi-dimensional NURBS basis functions used to discretize multivariable functions can be readily obtained by tensor product operations. Specifically, the NURBS representation of a bivariable function can be simplified as [44; 45]  
\[\begin{array}{l}\mathbf{s}(\zeta,\eta)=\sum_{[\hat{1}]=1}^{Q}\mathbf{s}_{[\hat{ 1}]}\mathcal{R}_{[\hat{1}]}(\zeta,\eta)\\ \mathcal{R}_{[\hat{1}]}(\zeta,\eta)=\mathcal{R}_{i_{1},p_{1}}(\zeta)\mathcal{ R}_{i_{2},p_{2}}(\eta)\end{array} \tag{16}\]  
where \([\hat{1}]\) is the lexicographical order associated with the two-tuple \(\ \mathbf{i}=(i_{1},i_{2})\), which contains the sequence numbers of \(\zeta\)-dimensional and \(\eta\)-dimensional NURBS basis functions. The \(\mathbf{s}_{[\hat{1}]}\) represents control points or control vectors, while \(\mathcal{R}_{[\hat{1}]}\) represents the two-dimensional (2D) NURBS basis functions. \(Q=n_{1}\times n_{2}\) denotes the total number of 2D NURBS basis functions. Additionally, \(p_{1}\) and \(p_{2}\), along with \(n_{1}\) and \(n_{2}\), indicate the orders and the total numbers of the one-dimensional (1D) NURBS basis functions in the \(\zeta\) and \(\eta\) directions, respectively.  
NURBS basis functions are widely used for creating parametric representations of geometric models [44; 45]. Figure 3a,b illustrate an instance of a 2D NURBS plane in Euclidean and parameter space, respectively. For a deeper understanding of NURBS and NURBS-based IGA, refer to the works of Piegl and Tiller [45], Farin [46], and Hughes and Cottrell [44].
# A Comprehensive Survey on Knowledge-Defined Networking
## 7 Application Plane
### Application Scenarios
#### 7.3.8 Business Applications  
Business applications are typically revenue-generating applications that define policies that help organizations achieve their business goals. In [544], an intent-based northbound interface with micro-services, and service-oriented design principles having a three-tier application architecture and domain-driven design is proposed to define business-like applications. A framework known as FlowVista, which identifies flows using the Northbound interface, where flows are matched directly by interacting with a business application, translates high-level business policies into low-level network flows [545]. Voice Over IP (VoIP) is a very good example for the realization of FlowVista, as VoIP is driven by high-level business policies. There has been a need for KDN-based high-performance computing solutions for enhanced throughput and better utilization of bandwidth to meet business needs [546]. Enterprise Integration Pattern (EIP) is a combination of design patterns that combines existing and new business applications in an enterprise environment. A communication framework that enables communication between applications in an enterprise environment and creates virtual local area networks using EIP by integrating with KDN to improve programmability and global view has been studied in [547]. Similarly, in an enterprise network, a context-aware communication framework using EIP to offer host anonymity by replacing IP addresses with spoofed IP addresses and achieve context awareness through KDN global visibility, where business application services route traffic based on business service requirements rather than network layer information [548].
# Positioning Systems for Unmanned Underwater Vehicles: A Comprehensive Review
## 4 Methodology in Reviewing Positioning Systems Applications Based on Positioning Systems  
In our research methodology, we primarily relied on Scopus and Google Scholar as our principal sources of information. Initially, we conducted a comprehensive search using the query \"UUVs and positioning systems\", which yielded a range of insightful results and review papers. These sources significantly contributed to refining our perspective and structuring our approach to the topic.  
To narrow down the vast amount of information, we employed a strategic approach utilizing specific keywords. These keywords included \"positioning underwater\", \"AUV\" (Autonomous Underwater Vehicle), \"ROV\" (Remotely Operated Vehicle), \"INS\" (Inertial Navigation System), \"DVL\" (Doppler Velocity Log), and \"unmanned underwater vehicle positioning\". By incorporating these targeted keywords, we identified over 152 relevant papers on Google Scholar and approximately 120 on Scopus.  
The initial search results from both databases included a substantial number of articles, reviews, and conference papers. To manage this extensive collection of resources and maintain focus on our specific research topic, we employed a rigorous filtering process. This process was essential to ensure that our review remained concentrated on pertinent information and did not deviate from the core subject matter. Filtering was a critical step in our methodology, enabling us to systematically sift through the vast amount of data and extract only the most relevant and valuable insights for our study. In Section 4.1, we explain how we filtered out some results by using keywords and personal criteria.
## 3 Results and Analysis  
### Compaction Characteristics and Pore Size Distribution  
#### 3.1.1 Compaction Characteristics  
Figure 4 shows the relationship between the dry density and moist content under different compaction energies. Note that \(V_{a}\) is the contour when the volume of gas in the soil is equal to zero. At the same moisture content, with increasing compaction energy, the maximum dry density increased, and the optimal moisture content decreased. Regarding the compaction curve, with an increase in compaction energy, the range of moisture content with a compaction degree greater than 90% increased. The relationship of the compaction degree K with the compaction energy N varied with the change in moisture content. As Figure 5a illustrates, the impact of the compaction energy on the development of dry density differed according to the moisture contents. Figure 5b indicates that when the moisture contents are less than the optimal moisture content, the compaction degree increases from 82% to 100% with an increase in compaction energy, with a large range of change; when the moisture contents are slightly above optimal (<3%), the compaction degree increases from 91.4% to 96.4% with an increase in compaction energy; this is a small change but satisfies the requirements of embankment compactness. When the moisture contents exceed the optimal content (>3%), the change in compaction degree with the increase in compaction energy is the smallest, and it remains below 90%.  
Figure 4: Compaction curves of samples with different compaction energies and moisture contents.  
Figure 5: (**a**) Curves of dry density and stroke numbers with different moisture contents; (**b**) curves of compaction degrees and stroke numbers under different moisture contents.
# Single Image Super-Resolution Method Based on an Improved Adversarial Generation Network
## 4 Experiments
### Implementation Details
#### 4.2.3 Perceptual Loss Function Performance  
As stated in Section 3.3, the implementation of the perceptual loss function can be trained to alleviate losses through the adversarial network. In our experiment, we evaluate  
\begin{table}
\begin{tabular}{c c c} \hline
**PSNR(dB)** & **Dataset** & **BSD100** & **Urban100** \\ \hline
**Inputs** & 32.30 & 30.79 \\ \hline Original size + 1/2 size & 32.38 & 30.88 \\ \hline Original size + 1/2 size +1/4 size & 32.41 & 30.93 \\ \hline \end{tabular}
\end{table}
Table 1: Comparison with different sized inputs on BSD100 and Urban100.  
Figure 5: Visual comparison with different inputs on the convolutional skip connections. (**a**) The generative network has only red convolutional skip-connections. (**b**) There are 2 convolutional skip-connections, including red and blue, in the generative network. (**c**) Our generative network is a parallel architecture, which contains red, blue, and green convolutional skip-connections.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Inputs** & **CSC** & **1 st CSC (Red)** & **2 nd CSC (Blue)** & **3 rd CSC (Green)** \\ \hline (a) & \(\surd\) & & \\ \hline (b) & \(\surd\) & \(\surd\) & \\ \hline (c) & \(\surd\) & \(\surd\) & \(\surd\) \\ \hline \end{tabular}
\end{table}
Table 2: Comparison with different convolutional skip connections in each input (for the color of CSC module, refer to Figure 3).  
the performance of three loss functions: SRGAN, EGAN without energy-based regularization, and EGAN with energy-based regularization. The results are shown in Figure 6. On one hand, EGAN has a smaller loss compared with SRGAN, which accelerates the convergence speed. On the other hand, the energy-based regularization overcomes over-fitting effectively, and gives the generated images more robust details. When the epochs reach 5000, the loss caused by the generator and the discriminator becomes stable. The converging speed of EGAN with energy-based regularization is the fastest, indicating that the network has a better convergence speed. In other words, G obtained the capacity of generating fake images, and D can also provide real-time feedback for G, which is beneficial for tuning hyperparameters.  
Although most GAN algorithms have momentum during the training progress, we use Adaptive Moment Estimation (Adam) [40] for all hyperparameters. This method not only stores the average exponential decay in AdaDelta, but also keeps the average exponential decay from the gradient \(M(t)\), which is similar to the momentum method.  
\[M^{*}(t)=\frac{M(t)}{1-\beta_{1}^{t}} \tag{18}\]  
\[V^{*}(t)=\frac{V(t)}{1-\beta_{2}^{t}} \tag{19}\]  
Here, \(M(t)\) and \(V(t)\) are the gradients of first-order and second-order moment estimations, respectively. We set the momentum to \(\beta_{1}=0.9\) and \(\beta_{2}=0.999\). The variables \(M^{*}(t)\) and \(V^{*}(t)\) can be used as unbiased estimation to correct \(M(t)\), \(V(t)\).
## 4 Discussion
### Implication for Land Vover Classification  
Vegetation phenology also represents a potentially significant source of land cover information [47]. To some degree, asymmetric indices are able to distinguish between natural and cultivated vegetation, especially as shown in the geographic distribution of AsyR and AsyV (Figure 4b,c and Figure 5b,c). That may be attributed to the inherently phenological features of specific species, as well as human activities (e.g., irrigation, fertilization, harvesting etc.), but it is merely an approximate matching of AsyR and AsyV to the different vegetation types. Asymmetric indices may be capable of classifying vegetation types through developing more growth curve shape parameters and using datasets with high spatiotemporal resolution (e.g., daily carbon flux, web camera images). Previous studies also confirmed this point using eddy flux records to explore seasonal patterns of net carbon exchange in various forest types [27]. Similarly, other analyses based on multitemporal remote sensing data with high spatial resolution (e.g., Landsat, MODIS) have obtained credible results in vegetation classification that primarily use various plant phenological indicators [48; 49; 50; 51]. These studies confirm the potential use of asymmetric indices in land cover classification, but advances are needed, requiring new data sources and more refined methods. Overall, asymmetric indices are able to provide new insights into the interannual variation in vegetation changing signals and play a key role in accurately modeling vegetation phenology or the seasonality of ecosystem processes.
## 2 Materials and Methods
### Proteomic Analysis
#### 2.7.5 MALDI TOF/TOF-MS Analysis for Protein Identification  
After tryptic digestion and evaporation, the peptides were resolubilized in 10 \(\mu\)L of 2% ACN/0.05% trifluoroacetic acid (TFA) for analysis by Nano-LC MS/MS. This system was composed of an Ultimate 3000 liquid Nano-chromatography (Thermo Scientific, Bleiswijk, The Netherlands) and the Probot (Thermo Scientific, The Netherlands) for spotting, coupled offline to a 4800 MALDI TOF/TOF mass spectrometer (Sciex, Framingham, MA, USA). For each sample, 5 \(\mu\)L was injected into a pre-column (C18 PepmapTM 300 \(\mu\)m ID \(\times\) 5 mm, Thermo Scientific, The Netherlands) with a flow rate of 20 \(\mu\)L/min of 2% CAN/0.1% TFA. After a desalting step of 5 min, the pre-column was coupled to the analytical column (C18 PepmapTM 75 \(\mu\)m ID \(\times\) 150 mm, Thermo Scientific, The Netherlands) previously equilibrated with 100% of solvent A (ACN 2%/TFA 0.1%). The peptides were eluted from the pre-column to the analytical column using a linear gradient from 0% to 50% of solvent B (ACN 90%/TFA 0.1%) for 45 min at a flow rate of 250 nL/min. At the column outlet, the eluate was mixed with the MALDI matrix (\(\alpha\)-Cyano-4-hydroxycinnamic acid, CHCA at 3.3 mg/mL) containing 0.3 fmol/\(\upmu\)L of Glu-1-Fibrinopeptide B (used for internal calibration), and spotted on the MALDI plate at the rate of one spot every 15 s. The plate was calibrated in MS and MS/MS with the PepMix4 mixture (Laser Biolabs, Sophia Antipolis, France). MS spectra were acquired by the MALDI TOF/TOF over a \(m/z\) mass range of 800 to 4000 with a laser intensity set at 3100 (arbitrary units) with 600 laser shots per spot. A list of precursors was established on the basis of the following criteria: the 100 most intense peaks per spot with a signal-to-noise ratio of at least 5. The precursors were then fragmented in MS/MS starting with the weakest (intensity of 4000 laser).
## 3 Results
### Extent of Irrigated Area
#### 3.1.3 China  
In contrast to the findings in Sudan and USA, the identified irrigated area in China almost remains constant across all spatial resolutions. The differences between the spatial resolutions are small (\(\sim\)1%). The tile shows two large agglomerations and two smaller agglomerations of irrigated area. The fields are more densely organized than in the USA and Sudan tiles and the irrigated area is affected differently by the decrease in resolution. Instead of decreasing the irrigated area, the small space between the fields is averaged out and also classified as irrigated and the original pattern of the agglomeration of the fields remains. This results in a smaller decrease in the irrigated area at resolutions up to 1000 m compared to the results in the USA and Sudan.
# Mental Workload Assessment Using Machine Learning Techniques Based on EEG and Eye Tracking Data
## 3 Materials and Methods
### Brief Overview of Machine Learning Algorithms
#### 3.5.7 Light Gradient Boosting Machine (LightGBM)  
LightGBM, a gradient-boosting decision tree method, is an open-source implementation that employs a leaf-wise approach to identify optimal splits for maximizing profits [68]. To enhance prediction accuracy, various model parameters, such as the number of leaves, learning rate, maximum depth, and boosting type, need to be fine-tuned [68].
## 1 Introduction
First, GB-RAR was utilized to derive the dynamic deformation information of the WGC from the north-south and east-west directions. Wavelet analysis was used to eliminate the noise in dynamic deformation signals and the accurate dynamic characteristics of the building, such as horizontal displacement, oscillation amplitude, and displacement trajectory, were extracted. Finally, the correlation between the building displacement and temperature was discussed and analyzed. A set of technical methods for monitoring and analyzing the dynamic characteristics of super high-rise buildings using GB-RAR was studied and established.
# Parameter-Free Ordered Partial Match Alignment with Hidden State Time Warping
## 3 Results
### Experimental Setup--Speech Alignment & Verification  
The second task was to verify the legitimacy of a short speech recording (e.g., a viral video clip of a presidential speech on social media) by comparing it to a trusted reference recording (e.g., an official recording of the entire speech from a major news outlet). The goal was to be able to perform verification in the presence of common tampering operations such as insertions, deletions, and replacements. Note that, while there is a rich literature in audio tampering detection by detecting internal inconsistencies and tampering artifacts (see [28] for a survey), our task instead focuses exclusively on _positively_ verifying the legitimacy of an audio recording by aligning and comparing it to a trusted external reference recording. This problem has become particularly relevant in recent years, as digital audio editing software and deep fake technology has become readily accessible [29].  
To simulate a realistic scenario involving a high-profile political figure, we used recordings of former U.S. President [PERSON]. We collected 50 speech recordings of [PERSON] from 2017 to 2018, all taken from the official White House Youtube playlist when he was in office. Each of these recordings is single-speaker and is 1-3 minutes long.  
We generated audio queries from the raw data in the following manner. We randomly selected ten 10-second segments from each recording and then generated four different versions of each segment by applying various tampering operations. The first version has no tampering: it is simply an unedited version at a lower bitrate quality (160 kbps) than the reference (320 kbps). The second version has an insertion: we randomly selected an \(L\) second fragment from a different audio recording (within the dataset) and inserted the fragment into the 10-second segment at a random location. The third version has a deletion: we randomly selected an \(L\) second interval from the segment and deleted it. The fourth version has a replacement: we randomly selected an \(L\) second fragment from a different audio recording and used it to replace a randomly selected \(L\) second fragment in the 10-second segment. For a given value of \(L\), this process will generate a total of \(4\times 50\times 10=2000\) queries. We divided the queries by speech recording and used 50% for training and 50% for testing. Each query was aligned against the corresponding high-resolution reference audio recording from which it was taken.  
We evaluated our speech verification system based on how well it classified each audio frame in the query as matching or non-matching. Again, we note that solving the classification task requires solving the alignment task accurately, and is therefore a more reliable indicator of system-level performance. We evaluated this binary classification task using a standard ROC curve and corresponding equal error rate (EER). We excluded frames that lie within a \(\pm\Delta=100\) ms scoring collar around tampering locations.
## 4 Experiments and Analysis  
### Datasets  
In part of datasets, we select datasets that are widely used in aspect level sentiment analysis tasks, which are derived from SemEval ABSA challenges ([PERSON] et al., 2014, 2015, 2016) [25; 26; 27]. In addition, the first dataset was modified by [PERSON] et al. [17] according to the ASTE task, and the second dataset was annotated by [PERSON] et al. [16], whilst [PERSON] et al. [7] further proposed a corrected version of the dataset. The above two datasets are labeled \(D_{1}\) and \(D_{2}\), respectively. The options include Laptop 14, Restaurant 14, Restaurant 15, and Restaurant 16. Statistics for these two groups of datasets are shown in Table 3.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multicolumn{3}{c}{**14 res**} & \multicolumn{3}{c}{**14 lap**} & \multicolumn{3}{c}{**15 res**} & \multicolumn{3}{c}{**16 res**} \\  & **\#S** & **\#T** & **\#S** & **\#T** & **\#S** & **\#T** & **\#S** & **\#T** \\ \hline \multirow{3}{*}{\(D_{1}\)} & train & 1259 & 2356 & 899 & 1452 & 603 & 1038 & 863 & 1421 \\  & dev & 315 & 580 & 225 & 383 & 151 & 239 & 216 & 348 \\  & test & 493 & 1008 & 332 & 547 & 325 & 493 & 328 & 525 \\ \hline \multirow{3}{*}{\(D_{2}\)} & train & 1266 & 2338 & 906 & 1460 & 605 & 1013 & 857 & 1394 \\  & dev & 310 & 577 & 219 & 346 & 148 & 249 & 210 & 339 \\ \cline{1-1}  & test & 492 & 994 & 328 & 543 & 322 & 485 & 326 & 514 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Data information of datasets.
# BDS Satellite Clock Prediction Considering Periodic Variations
## 3 Results  
In this section, based on the clock products of the WHU and the GFZ, the performance of the TFAM is analyzed. First, the fitting performance of the TFAM is evaluated and compared with that of the SAM. Then, the prediction results of the TFAM and the SAM are discussed. Finally, the prediction results of the satellites with marked periodic variations are investigated to show the performance of TFAM fully.