esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Methods
#### 2.2.5 Instrumental Measurement of Colour  
Measuring the colour components in the CIELab system, where L* stands for (lightness), a* (chromaticity from green (\(-\)a) to red (\(+\)a)), b* (chromaticity from blue (\(-\)b) to yellow (\(+\)b)), was performed using a retro-reflective Minolta CR-300 spectrophotometer (Konica Minolta, Tokyo, Japan). During the measurements, a standard CIE observer was used: 2\({}^{\circ}\), illuminant D65, measurement area 8 mm, calibration was done using the white tile standard (L* 99.18, a* \(-\)0.07, b* \(-\)0.05) (CIE, 1976). L* a* b* colour components were determined in meat products receiving illumination at 0, 2, 5, 24, and 48 h. Exposure to light was performed in the refrigerated counter at a temperature of 7 \({}^{\circ}\)C. Fluorescent white light with 180 to 190 lx intensity was applied. Preserved blocks cut across were the research samples. During exposure to light the samples were wrapped in cling film in order to prevent the surface of the product from drying. The studies on model products were performed after production (time 0) and 8 weeks of storage at 4 \({}^{\circ}\)C.  
The measurement was performed in a room at 24 \({}^{\circ}\)C \(\pm\) 2 \({}^{\circ}\)C. Four measurements were carried out for each of the four replicates of the treatment (C, M, SL).  
The hue angle (h\({}^{\circ}\)) was calculated according to Equation (1),  
\[\text{h}^{\circ}=\tan^{-1}\frac{\text{b}^{*}}{\text{a}^{*}}, \tag{1}\]  
and chroma (C*-saturation index) was calculated according to Equation (2),  
\[\text{C}^{*}=\sqrt{\text{a}^{*^{2}}+\text{b}^{*^{2}}}, \tag{2}\]  
where a* and b* were data from an instrumental measurement of colour.  
The total change in colour (\(\Delta\)E*) was calculated after storage according to Equation (3),  
\[\Delta\text{E}^{*}=\sqrt{\left(\text{L}1-\text{L}2\right)^{2}+\left(\text{a} 1-\text{a}2\right)^{2}+\left(\text{b}1-\text{b}2\right)^{2}}, \tag{3}\]  
where 1 is the value before storage or illumination; 2 is the value after storage or illumination [22].
# Image Retrieval Method Based on Image Feature Fusion and Discrete Cosine Transform
## 4 Experimental Results and Analyses
### Experimental Results
#### 4.2.4 Oxford building Dataset  
We continue to evaluate the proposed method on Oxford Buildings dataset. The Oxford Buildings dataset contains 5063 images downloaded from Flickr by searching for particular landmarks. For each image and landmark in the dataset, four possible labels (Good, OK, Bad, Junk) were generated. The dataset gives a set of 55 queries for evaluation. The collection is manually annotated to create comprehensive ground truth for 11 different landmarks, each represented by five possible queries. The mean average precision (mAP) [71] is used to measure the retrieval performance over the 55 queries. In the experiment, each image is resized to the shape of 224 \(\times\) 224. Some typical images of the dataset are shown in Figure 15.  
A radar chart of the compared results on each category is shown in Figure 16. The results show that the mAP based on shallow features is significantly lower than the mAP based on the in-depth part. The maximum value is obtained on the 'RadCliffe_camera'  
Figure 14: APs bar plot of each category on the Corel-DB80 dataset using the proposed method with Top NR = 10.  
Figure 15: Typical images of the Oxford buildings dataset (the first row is five different queries of the first category, and the last two rows of images are specific queries of the other ten types).  
class, but only slightly greater than 0.7, the minimum value is received at the 'Magdalen' category, and the value is not more than 0.1. The fused deep feature did not significantly improve the retrieval results as a whole, and when the weight of each part is the same, the results will even decrease. Given that the performance of the shallow feature is deficient in the landmark categories, the performance of the ObjPlaDctHld is not excellent. Akin to the test results on the previous dataset, the DCT-based method still performed better than using the fully connected layer.  
Figure 17 shows the mAP of the total dataset using different image retrieval methods. Here, we use the extended bag-of-features (EBOF) [72] method as the compared method. Its codebook size is K = 1000. The performance of the EBOF method is much better than that using shallow features and is much lower than that using the deep features-based method. For the Oxford 5K dataset, the PlaDct method based on places-centric features has the best performance.  
Figure 18 shows the APs of each query using the PlaDct method. Retrieving results on several categories such as 'All Souls', 'Ashmolean', and 'Magdalen' is not ideal, especially in the 'Magdalen' category.  
Figure 16: Radar chart of mAPs using different image retrieval methods on the Oxford 5K dataset.  
Figure 17: Boxplot of mAPs on the Oxford building dataset.
## 2 Maritime Communications Systems
### Overview of Maritime Communications
Moreover, Figure 4a also shows an  
Figure 3: Overview of the AIS communication. example of the message in the form of the bit patterns with explanations of the information they contain, and the ASCII code characters assigned to each six-bit group. Finally, the transmitted AIS message obtained by the described data encoding is shown in Figure 4b. The described data encoding converts the decimal numeric values to the ASCII code in the following way: decimal numbers in the range 0-39 are converted to hexadecimal numbers in the range 0 \(\times\) 30-0 \(\times\) 57, while those in the range 40-63 correspond to the hexadecimal range 0 \(\times\) 60-0 \(\times\) 77. The six-bit binary field conversion used for AIS data encoding is proposed by the International Electrotechnical Commission (IEC) in the document IEC 61162-1 (Table C.1) [38]. IEC 61162-1 represents the international standard for the digital interfaces used in maritime navigation and radiocommunication equipment and systems, particularly in single talker and multiple listener systems.
## 2 System Model
### Synchronization Sequence  
The synchronization scheme adopts the PSS and SSS. The PSS and SSS are located on SS/PBCH Block (SSB). The structure of SSB is shown in Figure 3. PSS and SSS occupy 127 subcarriers in frequency domain subcarriers in the frequency domain. The PSS sequence is defined by  
\[d_{\text{PSS}}(n)=1-2x\Big{(}\Big{(}n+43N_{\text{ID}}^{(2)}\Big{)}\text{ mod }127\Big{)}, \tag{4}\]  
\[0\leq n<127\]  
\[x(i+7)=(x(i+4)+x(i))\text{ mod }2 \tag{5}\]  
\[[x(6),x(5),x(4),x(3),x(2),x(1),x(0)]=[1110110] \tag{6}\]  
The SSS sequence is defined by  
\[d_{\text{SSS}}(n)=[1-2x_{0}((n+m_{0})\text{ mod }127)]\cdot \tag{7}\] \[[1-2x_{1}((n+m_{1})\text{ mod }127)]\] \[m_{0}=15\left\lceil\frac{N_{\text{ID}}^{(1)}}{112}\right\rceil+5 N_{\text{ID}}^{(2)}m_{1}=N_{\text{ID}}^{(1)}\text{ mod }112\] \[0\leq n<127\]where \(N_{\rm ID}^{(1)}\in\{0,1,\ldots 335\}\), \(N_{\rm ID}^{(2)}\in\{0,1,2\}\) represents the physical-layer cell identities.
# Enhancing Beamforming Efficiency Utilizing Taguchi Optimization and Neural Network Acceleration
## 3 Taguchi Method
### Amplitude Synthesis of Antenna Array Radiation Pattern under Constraints
#### 3.1.2 Sixteen-Element Antenna Array  
In this example, the array factor is described by Equation (26).  
\[AF(\theta)=2\sum_{n=1}^{8}a(n)cos[\beta d(n)cos\theta] \tag{26}\]  
The fitness function is chosen based on the following optimization objective:  
\[fitness=min(max\{20 log|AF(\theta)|\}) \tag{27}\]  
under constraint \(\theta\in[0^{\circ},80^{\circ}],[100^{\circ},180^{\circ}]\).  
To use the Taguchi method, the following steps [3.3]-[3.1] must be followed:  
* Step 1: Determine the number of parameters (\(k=8\)).
* Step 2: Determine the number of levels (\(s=3\)).
* Step 3: Determine the strength (\(t=2\)).
* Step 4: Determine the OA experimental design (\(27,8,3,2\)).
* Step 5: Determine the reduced function (\(rr=0.9\)).
* Step 6: Determine the convergence value \(=0.01\).  
Figure 5a shows that the level of sidelobes (SLL) is minimized down to SLL = \(-31.3151\) dB. The gain is approximately 18.17 dB compared to the level of side lobes of the uniform array (SLL = \(-13.148\) dB). As shown in Figure 5b, the optimization objective is achieved after 66 iterations. The results obtained compared to those of PSO (see Figure 5) show a gain of 0.8 dB in terms of minimizing the side lobes, a convergence speed of 66 iterations for the Taguchi method, and a time requirement of 9 s for our digital optimization tool.  
Figure 4: Radiation pattern of ten-element linear antenna array (Taguchi, PSO, and Uniform).
## 4 Discussion
In this in vitro study, the direct viewing technique using SEM was chosen because it is noninvasive, less time-consuming, and easy to repeat. Most importantly, it allowed for the evaluation of simulated clinical experiments. To achieve clinically relevant information and a consistent estimate of the precision of fit, the literature suggests that at least 50 measurements on a specimen should be collected [45]. In addition, data obtained from measuring the experimental crown margin at fewer than 18 points might be misleading [46]. Thus, the number of measurements in this study was 210 per specimen to ensure accurate results, and the same operator performed all measurements. In the present study, the ultimate fracture strength was also evaluated, as this is another important factor in predicting the clinical service and failure rates of the prosthesis material. The titanium FDPs fractured at a mean load of 3018 \(\pm\) 385.3 N. This value is similar to a previous study reporting a fracture load of 3500 N for three-unit metal-ceramic FPDs [47]. The PEKK frameworks showed a significantly lower mean fracture load of 1359.14 \(\pm\) 205.49 N as a result of lower compressive and tensile strengths than the titanium group. Several previous in vitro studies have reported a mean fracture load in the range of 1070-2350 N for PEKK and PEEK FPDs [11; 15; 16; 48]. Experimental designs and testing parameters, such as the dimensions of the framework connector, cementation, abutment material, fabrication technique, veneering material, applied load, and aging conditions, differ from previous in vitro studies. Thus, it is difficult to compare the results of these studies with those obtained here. However, the value presented in this study is still higher than the maximum bite force reported in the posterior region, ranging from 600 to 920 N [49; 50]. This indicates the potential for clinical use of PEKK in implant-supported FPD frameworks in the molar region. There are some limitations to the present study. All FDPs were fabricated under standardized conditions, which may not reflect the conditions in clinical practice. The fit and fracture strength of the restoration may be influenced by numerous factors, such as the fabrication technique, milling machine, and cementation [11; 15; 16]. Further studies are needed to analyze the factors that affect the marginal gap of PEKK restorations, including the technique of fabrication, different milling machine axes, and the luting agent used. In addition, further investigations with larger samples and clinical trials are needed to validate the obtained results.
# Spatio-Temporal Variability of Water Temperature of Arctic Rivers in Russia over the Past 60 Years
## 2 Materials and Methods
### Study Area and Rivers  
The large spatial coverage of this study allowed for the averaging of values over the Russian Arctic regions. The areas were assigned according to the hydrographic regions of the State Water Register: (1) Kola Peninsula, (2) Karelia, (3) north of the European part of Russia (EPR) (excluding Kola Peninsula and Karelia, excluding rivers of the Baltic Sea basin and lakes Onega and Ladoga), (4) north of Western Siberia (WS) (Ob, Nadym, Pur and Taz basins), (5) Yenisei (Yenisei basin), (6) Lena (Khatanga, Anabar, Olenyok, Lena, Yana, Indigirka and Alazei basins), (7) Northeast (Kolyma and Anadyr basins). The southern boundary of the study area was set at 60\({}^{\circ}\) N latitude. There are rivers of different sizes in these studies: small (watershed area less than 2000 km\({}^{2}\)), medium (watershed are greater than 2000 but less than 50,000 km\({}^{2}\)), and big ones (watershed area greater than 50,000 km\({}^{2}\)).  
The territory covers in whole or in part the catchment areas of all the major rivers flowing into the Arctic Ocean from Russia. It includes a variety of landscapes, both flat and mountainous. The main mountain ranges are the Ural Mountains and several ridges in Eastern Siberia. Most rivers flow over relatively wide plains, although some flow through mountainous areas as well. Their tributaries usually come from mountainous regions. Climate in most parts of the territory is warm in summer (up to 20 \({}^{\circ}\)C) and relatively dry. In the European part of the Arctic zone, it is somewhat warmer and more humid, with a marine climate on the coast. Permafrost is common in some form or another in the territory from the western slopes of the Urals to Chukotka, but the only basin entirely located in the continuous permafrost zone is the Kolyma River basin.  
Most of the rivers in this study have unregulated water flow, with the exceptions being the Vilyui, Yenisei, and Ob rivers, as well as a number of rivers in Karelia and the Kolyma River. As shown in [41], on the largest rivers in Russia, the impact of reservoirs does not significantly affect the water temperature in the Arctic zone during May-October. The change point years in water temperature series during these months on these rivers are not correlated with the construction periods or the years when hydroelectric power stations (HPS) were launched. At the same time, changes in the thermal regime of the Kolyma River can be observed, but the series of observations after the construction of the Ust-Srednekanskaya HPS (in 2014) are too short at the time of writing for statistical analysis. Our field work has shown that, in the lower reaches of the Kolyma, below the village of Srednekolymsk and at the inflow of the Anyuy, at least during the second half of the summer, there is no significant effect of hydroelectric power plants on water temperature [55].
## 2 Dynamic Modeling of Fully Flexible Base-Link-Joint Space Robot  
### Fully Flexible Base-Link-Joint Space Robot Model  
As shown in Figure 1, the floating flexible base-link-joint space robot consists of a carrier \(B_{0}\), a flexible link \(B_{1}\) at the near the base, and a flexible link \(B_{2}\) at the far base. \(O_{\text{C0}}\) is the center of mass of base \(B_{0}\), \(O_{\text{C}}\) is the total center of mass of the system, and \(O_{k}\) is the geometric center of the joint hinge connecting \(B_{k-1}\) and \(B_{k}(k=1,2)\). The inertial coordinate system \(OXY\) and each split contiguous coordinate system \(O_{k^{\prime}}X_{k^{\prime}}Y_{k^{\prime}}\) (\(k^{\prime}=0,1,2\)) are established, where \(O_{0}\) overlaps with \(O_{\text{C0}}\).\(r_{0}\) and \(r_{C}\) denote the position vectors of the base center of mass and the total center of mass of the system in the inertial coordinate system, respectively, and \(r_{k}\) denotes the vector diameter of any point on the flexible link \(B_{k}\) with respect to \(O\) (\(k=1,2\)). \(q_{b}\) is the base elastic deformation at the articulation of base and link \(B_{1}\), \(q_{0}\) is the base attitude angle, \(q_{k}\) is the relative rotation angle of link \(B_{k}\), and \(q_{mk}\) is the rotation angle of the joint motor rotor (\(k=1,2\)). \(x_{k}\) is the arbitrary distance in the direction of the longitudinal axis \(X_{k}\) of the flexible link \(B_{k}\), and \(v_{k}(x_{k},t)\) is the transverse elastic deformation of the link \(B_{k}\) at the \(x_{k}\) cross section. The mass of the base is \(m_{0}\) and the inertia is \(J_{0}\). The initial length of link \(B_{k}\) is \(l_{k}\) and the density is \(\rho_{k}(k=1,2)\). The inertia of the motor rotor at joint \(O_{k}\) is \(l_{mk}\). In this paper, the elastic part of the base is equated to a massless linear telescopic spring, the stiffness factor \(k_{b}\) is taken as a constant value, and the distance between \(O_{\text{C0}}\) and \(O_{1}\) is \(l_{0}\) when the initial amplitude of the base is \(0\) m.
## 2 Method
### Numerical Simulations
#### 2.2.3 Turbulence Model  
The Standard K-Epsilon model was chosen for mesh sensitivity, thereby to determine the appropriate mesh in numerical simulation.  
Previous research indicated that the modified k-\(\varepsilon\) models were suitable for industrial applications in comparison with the LES models and the DNS models. A range of modified k-\(\varepsilon\) models (e.g., models incorporating wall functions or damping functions) were selected for comparison purposes, including the Standard K-Epsilon (SKE) model [44], the Standard Two-Layer K-Epsilon (STLKE) model [45], the Standard Low-Reynolds Number K-Epsilon (SLRNKE) model [46], the Realizable K-Epsilon (RKE) model [47], the Realizable Two-Layer K-Epsilon (RTLKE) model, the Abe-Kondoh-Nagano Low-Reynolds Number K-Epsilon (AKNKE) model [48], the Elliptic Blending K-Epsilon (EBKE) model [49], and the V2F Low-Reynolds Number K-Epsilon (V2 FKE) model [50,51]. The source files of numerical simulation can be find in Supplementary Materials. The simulation results of these models were evaluated against the experimental results based on wind tunnels.
## 3 Background
### Data Parallel Primitives  
In this paper, we use parallel data primitives to parallelize the algorithms for histogram and GMM modeling. To form the basic units of parallelized algorithms, various algorithms can be produced through the permutations and combinations of many DPPs. In this section, we will briefly introduce a few DPP functions used in this paper.  
**Map** receives \(N\) arrays with length \(K\) and an operator as arguments, and it returns \(M\) arrays of length \(K\). \(N\) and \(M\) are determined by the operators in the parameters. **Map** will map the input arrays to output arrays by running the input operator on each element independently. For example, C=MAP(A,B,operator=Add(0)) will compute C[i]=A[i]+B[i] for all elements. **Map** primitive and user-defined operators are generally used to replace the traditional loop procedure.  
**Reduce** receives an array and an operator as arguments and returns an output value. REDUCE builds a merge tree and uses multiple threads to merge all elements of the input array into a final output at the same time through the input operator. For example, C=REDUCE(A, operator=ADD(0)) will add all values together and store the result in C. **ReduceByKey** is similar to REDUCE, but needs an additional key array with the same length as the input array. **ReduceByKey** merges values in the input array which have the same corresponding elements in the key array into a single value using the operator. If the key array has \(N\) unique values, the length of the output array should be \(N\).  
**Gather** receives an array \(A\) of length \(N\) and an index array \(B\) of length \(M\), and returns an array \(C\) of length \(M\). For each element \(B_{i}\) in array \(B\), **Gather** copies the \((B_{i})^{th}\) value in array \(A\) to \(C_{i}\).
## 5 Experimental Validation
### Data Preprocessing  
As mentioned in Section 5.1, the measurement in this experiment contains the gear meshing component, which may have a great impact on the result of the proposed method. Therefore, the band-pass filtering should be carried out for the resonance frequency band. Figure 14b shows that the components of the gears meshing are focused on the lower frequencies and the centers of the resonance frequency band can be chosen as 6000 Hz, 12,000 Hz, 18,000 Hz and 24,000 Hz. In this paper, the filter band is set as [12,000 \(-\) 1000, 12,000 \(+\) 100] Hz. The filtered signal can be seen in Figure 14c. The impulses generated by the outer race fault are relatively clear compared with that unfiltered (seen in Figure 14a). The spectrum of the filtered signal is displayed in Figure 14d, which turns out the feasibility of the filtering. Then, the filtered signal with fewer gear meshing components can be used for fault feature extraction.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Type** & **Rolling Balls Number** & **Rolling Element Diameter** & **Pitch Diameter** & \(\mathbf{\kappa_{\text{BPOO}}}\) \\ \hline
6203 & 8 & 6.747 mm & 28.5 mm & 3.05 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The parameters of the rolling bearing that is tested.  
Figure 13: The layout of the experiment. (**a**) the experiment rig; (**b**) two shafts in the gearbox; (**c**) the point corrosion defect on the outer race.
## 3 Methodology
### FFISS Application  
All the information describing the flood impacts of both flood events was included in a geo-database specially designed for the purpose of this study in Geographic Information System (GIS) environment. Each entry in this database represented one particular impact of the flood and was plotted in the GIS as a point geometry.  
Each of these points, named unique flood impact elements (UFIEs), was also accompanied by a description of the observed effect, as well as the type of damage and its severity based on the FFISS proposed by [PERSON] et al. [18].  
The FFISS is arranged into four categories of impacts on the following:  
1. The built environment: This category encompasses impacts on all man-made structures, comprising individual buildings (property) and various forms of infrastructure, with the exception of mobile objects. Minor impacts range from basic flooding of yards and roadways, while more severe consequences extend to significant buildings and other structures, including instances of dam failure due to flash floods.
2. Man-made mobile objects: This category encompasses very light (<1 kg) to very large (e.g., aircraft, train cars) objects being transported, including but not limited to vehicles, household items, and others.
3. The natural environment: This category includes 4 sub-categories of impacts, namely on geomorphology, agriculture, and vegetation, as well as regarding pollution. Low-severity classes start from vegetation debris carried away by floods and simple inundation of agricultural plots, whereas high-severity classes reach up to major pollution episodes.
4. The population: This category begins with people just noticing flooding or having trouble commuting in the low-severity classes, and reaches up to clusters of fatalities in the higher-severity levels.  
Figure 7: Diagram depicting the fundamental steps of the methodology employed in this study.  
The scale also encompasses ten classes (\"Class I\" to \"Class X\") to account for the wide range of flood effects and to ensure the capacity to differentiate between different levels of severity. This approach aims to prevent the saturation phenomenon observed for other impact-based scales in the literature [27]. The scale has been developed through a comprehensive review of the impacts observed in numerous flash flood events worldwide [18], ensuring it encompasses a diverse range of flood effects.