esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Surface Scattering Model
This allows, for example, to compensate for uncertainties of the sensor height, and, even more importantly, to scan the depth. The latter proves to be very useful if the scene consists of targets buried at different depths (as it is shown in the following). Finally, it is remarked that the field in (1), \(\mathbf{E}_{S}\), is not directly the scattered field but it is related to it through a linear transformation, that is  
\[\mathbf{E}_{S}=-j\exp(-jkr)/(2\pi kr)\mathbf{\underline{T}}^{-1}\mathbf{E}_{S} \tag{2}\]  
where \(\mathbf{E}_{S}=(E_{xS},E_{ys})\) is the scattered field projected in the \(x-y\) plane and \(\mathbf{\underline{T}}\) is defined as  
\[\mathbf{\underline{T}}=\left[ \begin{array}{cc}\cos^{2}\theta\cos^{2}\phi+ \sin^{2}\phi&-\sin^{2}\theta\cos\phi\sin\phi\\ -\sin^{2}\theta\cos\phi\sin\phi&\cos^{2}\theta\sin^{2}\phi+\cos^{2}\phi\end{array} \right] \tag{3}\]  
Note that \(\mathbf{\underline{T}}\) is not singular (and, hence, invertible) since in practical cases one can collect the scattered field over an aspect limited configuration so that the singularity point \(\theta=\pi/2\) is excluded. The proposed model leads to a number of advantages. Imaging is cast as a 2D inverse problem and this reduces the computational burden as compared to a 3D formulation. Also, note that the vector problem is actually split into two identical scalar problems. Indeed, \(J_{eq}\) and \(J_{vq}\) can be separately determined from \(E_{xS}\) and \(E_{yS}\) by inverting the same integral operator. Moreover, as mentioned above, there is no need to know or estimate the soil properties. Of course, this is made possible as we renounced to estimate the target's depth. Finally, we want to highlight why source information (or, equivalently, the incident field knowledge) is not necessary. This is because the incident field is embodied within the equivalent surface currents and, hence, does not affect the operator to be inverted for the imaging. However, those sources are frequency dependent. Accordingly, frequency data cannot be simultaneously exploited to get the reconstructions. Rather, each frequency datum needs to be processed separately and eventually fused.
## 3 The Proposed Circuits
### Design of Reversible 1-Bit Comparator Circuit  
A reversible 1-bit comparator can be built by combining the TR gate and FG, as shown in Figure 11. For its creation and design, it requires two TR gates and one FG gate. As can be seen, the proposed design produces only two waste outlets. The block diagram, logic diagram, and QCA implementation are shown in Figure 11a-c, respectively.  
Figure 11: The 1-bit comparator (**a**) logic diagram, (**b**) block diagram, and (**c**) QCA layout.  
Figure 13 represents the simulation outputs of the TR gate; the results are compiled and approved in Table 3. The results show, against the inputs A = 1, B = 1, and C = 0, the outputs were P = 1, Q = 0, and R = 0; against inputs A = 1, B = 1, and C = 1, the outputs values were P = 1, Q = 0 and R = 1; for the remainder, the outputs were similar to those in Table 3.  
Figure 14 represents the simulation outputs for the proposed one-bit comparator; as be seen, the results are compiled and approved in Table 1. The circuit against the inputs A = 1 and B = 0 had outputs of A > B, A < B, and A = B, respectively, 0, 0, and 1. Against inputs A = 1 and B = 0, outputs were 1, 0 and 0, respectively. For the remainder of the inputs, outputs were similar to those seen in Table 1.
## 2 Challenges Faced by 360-Degree Video Streaming  
One of the biggest obstructions in realizing the real-time interaction and communication inside the VR is the high bandwidth requirement. The immersive experience will be broken if a delay occurs or the resolution drops. With the same resolution, 360-degree video requires 4 to 6 times the bandwidth of a regular video [11]. In order to ensure a good immersion experience, the displayed viewport usually has a high pixel resolution, typically 4K or in size of (3840 \(\times\) 2160) and the resolution of the 360-degree movie has to be not less than 12K or in size of (11,520 \(\times\) 6480). [12]. Moreover, an immersive 360-degree video can require a video frame rate equivalent to the Head-Mounted Device (HMD) refresh rate, normally in a range of a hundred (100) frames per second (fps), with the very top frame rate (up to 100 fps) and very huge in resolution (which can reach to maximum 12K). With respect to immersion, 360-degree videos can require a video frame rate equivalent to the HMD refresh rate. For example, a High-Efficiency Video Coding (HEVC) encoded 8K video at 60 fps has a bitrate of roughly 100 Mbps [12]. Table 1 summarizes the network requirement of VR, the bandwidth required for each VR type and the estimated network latency.  
Next, intensive computing power is needed by VR devices, especially in VR information processing. Multiple activities, including scene depth estimation, picture semantic interpretation, three-dimensional reconstruction, and higher realism rendering, for example, need a large amount of computational power and have to be completed in real-time to give a natural and seamless user engagement [13]. The processing latency of VR is affected by the computational power of the computing equipment and the computational capacity of the jobs [14].  
In addition, in order to achieve a high level of immersion and interactivity, high accuracy and high definition (HD) are also owing to the restricted space in the 360-degree film delay tolerance of human eyes. Experiment studies [15; 16] clearly show that users can perceive delays greater than 10 ms as annoying, although users can tolerate the higher latencies [17]. Moreover, the human eye sees precise and smooth motion with a motion-to-photon (MTP) delay of fewer than 20 milliseconds (ms) [18; 19; 20]. The vestibular-ocular reflex (VOR) gets contradictory signals when MTP levels are high, which can cause dizziness and motion sickness. Thus, real-time communication with limited delay tolerance demanded accurate, low-latency communication services.  
The problems can be concluded as: (a) High bandwidth requirement; (b) Intensive computing power requirement; (c) Stringent latency tolerance.
# Enhancing Embedded Space with Low-Level Features for Speech Emotion Recognition
## 2 Materials and Methods
### Deep Neural Network Architectures  
Due to the use of time-frequency representation in this work, we decided to use the ResNet [27] convolutional architecture as the base model of the neural network. This architecture is prevalent in image processing, the classification of emotions in speech signal [28; 29], and it is also used in connection with time-frequency representations [30]. This architecture was designed to solve the problem of vanishing gradient that appears in models composed of many layers. By using residual connections in residual blocks, from which it is built, it is possible to make deep models and train their layers without significant risk of getting stuck in a local minimum. Moreover, this type of mechanism makes it highly efficient, even when using many layers. The ResNet152 variant was used in this work.  
Figure 2: Low–level features (\(\gamma_{2}\)) used in injection process.
## 3 Results
### Morphology  
The microscopic morphology of LFS after carbonation with a water-solid ratio of 0.3 and a water content of 6-8% is shown in Figure 10, magnified by 20,000 times. It can be observed that as the water content increases, the size of carbonation products initially increases and then decreases. Additionally, specimens with higher degrees of carbonation exhibit larger and denser calcium carbonate crystals. The formation of calcite crystals on the particle surface can be observed in specimens with a water content of 6%. These fine particles, approximately 0.3 \(\upmu\)m in size, are cemented to the larger matrix as depicted in Figure 10a. At a water content of 7%, calcite exhibits enhanced crystallinity and grows in rock-like layers, while some small clusters of calcium carbonate crystals fill pores and increase compactness (Figure 10b). This may explain why specimens with a water content of 7% possess higher strength compared to others. Figure 10c reveals that small calcium carbonate crystals measuring around 0.2 \(\upmu\)m are cemented together to form larger particles measuring approximately 1.2 \(\upmu\)m; however, these cemented particles fail to continue growing due to certain limitations, resulting in low crystallinity levels. This phenomenon could be attributed to an abundance of surface moisture hindering CO\({}_{2}\) diffusion on high-water-content specimen particles. Pores associated with high-water-content samples are smaller than those found in low-water-content samples but occur at a greater frequency.  
Figure 9: LFS with different water-solid ratios and water contents: (a) X-ray diffraction pattern; (b) semi-quantitative mineral-phase analysis.  
Figure 11 shows the 5000\(\times\)-magnification electron image, and the EDS surface scan results are shown in Figure 10b. The dense structure of the 7%-water-content specimen can be observed in the electron image. A large number of overlapping regions of Ca, Mg, C, and O elements can be found, which further proves that the carbonation products of the LFS are calcium/magnesium carbonates and predominantly CaCO\({}_{3}\), which is consistent with the results in Figure 9. The areas exhibited smaller crystalline structures and high brightness corresponding to CaCO\({}_{3}\) products, whereas the larger particles with darker regions represent uncarbonated calcium silicate. This distinction arose from the fact that calcium carbonate could solely precipitate on the surface of calcium silicate particles, leading to a higher number of small calcium carbonate crystals formed on larger calcium silicate particles. Carbonation was more challenging for larger calcium silicate particles compared to their smaller counterparts.
## 1 Introduction  
If one had an interest in developing a comprehensive, fully integrated system for collecting science quality (high provenance) spectral data as a means of analyzing crop status at several different scales, how would one proceed? Even an examination of the professional journals dealing with remote sensing documents the fact that many individuals and groups collect and analyze spectral data, and they do so at various scales of investigation ranging from leaf level to regional studies based upon satellite data. Our observation is that the spectral data collected in the field at canopy levels may not always be acquired in the systematic manner required to produce science quality data. Therefore, this paper reviews important considerations in acquiring spectral data at canopy levels using field radiometers, and presents the elements of an integrated system for conducting such work in a systematic, repeatable manner.  
There exists a need to estimate the biophysical characteristics of crops at a variety of spatial scales. Researchers have undertaken work using spectral data acquired at leaf, canopy, field, and even wider areas using a variety of sensors. In addition, such basic research should have an ultimate practical benefit [1]. For example, farmers and managers should benefit from technological and methodological innovations by having a mechanism for assessing the physiological status of their crops at very high spatial resolutions and in real- or near-real time in order to facilitate decision-making. Remote sensing may provide the necessary mechanism; however, the approach must include both measurements made in a systematic, reliable manner and appropriate models to transform the spectral data into accurate, dependable biophysical information for easy agronomic use. The purpose of this paper is to describe one such system.  
Our research group has developed three important components that comprise a unique, efficient system for collecting, processing, and analyzing spectral data acquired by sensors operating in a field setting. We have also developed software and models which transform the acquired data into a variety of useful crop biophysical characteristics. The specific objective of this paper is to provide an overview of: (1) the important considerations and equipment configurations for collecting science quality spectral data; (2) the necessary software algorithms for proper collection and quality control of field data; and (3) a developed suite of spectral transformations for inferring crop biophysical characteristics along with examples of standard products.
# Research on Bidirectional Reservation Method for Anti-Permeation Geomembrane Slack
## 4 Discussion  
Geomembranes require a certain amount of slack when laid, but the existing geomembrane slack reservation method is a unidirectional technique that only addresses the impacts of stresses from a certain direction on the geomembrane. In practice, the tensile stresses generated by ambient temperature changes and uneven foundation settlement are directionally uncertain. Thus, the existing geomembrane slack reservation method is a poor solution to the problem of tensile stresses from different directions on the geomembrane. Therefore, in this paper, based on the existing geomembrane slack reservation methods, we have explored four techniques that can prevent tensile damage to geomembranes by tensile stresses from any direction. In terms of long-term effects, the proposed method in this paper (bidirectional reservation of geomembrane relaxation) can offset the tensile stresses on the geomembrane due to changes in environmental factors, thus achieving the protection of the geomembrane and enhancing the durability of the geomembrane.  
The test described in this paper explores several feasible methods of bidirectional geomembrane slack retention, such as the magnitudes of tensile stresses that different techniques can manage under the same load, the magnitude of external tension required for the same reserved geomembrane slack to be eliminated under different loads, and the amount of external tension required to address different external tensions to protect the geomembrane. The problem of the size of the required slack allowance for geomembranes has not been effectively solved. Therefore, further research on this topic is needed to compile a set of relationships concerning different loads, geomembrane slack reservation methods, reserved geomembrane slack sizes, and external tensions that the geomembrane can manage when the reserved slack is completely consumed to provide a reference for the laying of geomembranes in actual engineering construction projects.  
Figure 10: Schematic of the disadvantages of the three bidirectional reservation methods. (**a**) The process of grinding the sharp corners of the grooves, (**b**) the process of notch excavation, (**c**) neoprene rods cannot be folded \(180^{\circ}\), (**d**) the sharp corner position of bidirectional Z-folded layer, (**e**) failure process of bidirectional cross-Z-shaped slack.
# IMU-Aided High-Frequency Lidar Odometry for Autonomous Driving
## 1 Introduction
The details of the proposed approach are presented from Sections 3.2-3.6. The experimental results and comparison with state-of-the-art approaches are presented in Section 4. Finally, the conclusions are summarized in Section 5. Figure 1: A typical lidar odometry algorithm can be roughly divided into three steps: a pre-processing step, an intermediate step, and an output step. Most existing lidar/Inertial Measurement Unit (IMU) fusion approaches try to fuse lidar odometry and the IMU in the output level [2; 3; 4; 5; 6; 7]. In this paper, we try to utilize the IMU information in all the three steps.
## 4 Results
### Algorithm Performance Analysis
\begin{table}
\begin{tabular}{c l l} \hline \hline
**Algorithm** & **CHT** & **Parameters** \\ \hline \multirow{4}{*}{DE/RAND/1/BIN} & FR & \(CR=0.87\), \(F_{min}=0.51\), \(F_{max}=0.53\) \\  & SR & \(CR=0.91\), \(F_{min}=0.46\), \(F_{max}=0.70\), \(Pf=0.52\) \\  & EC & \(CR=0.92\), \(F_{min}=0.53\), \(F_{max}=0.56\), \(Pg=0.04\), \(Tc=430\), \(Rg=5\), \(cp=9\) \\  & PF & \(CR=0.93\), \(F_{min}=0.52\), \(F_{max}=0.63\) \\ \hline \multirow{4}{*}{GA} & FR & \(CR=0.98\), \(MR=0.11\) \\  & SR & \(CR=0.57\), \(Pf=0.12\), \(MR=0.17\) \\  & EC & \(CR=1\), \(Pg=0.08\), \(Tc=640\), \(Rg=4\), \(cp=6\), \(MR=0.14\) \\  & PF & \(CR=0.02\), \(Pgg=0.21\) \\ \hline \multirow{4}{*}{PSO} & FR & \(r_{min}=0.05\), \(v_{max}=0.24\), \(C1=0.29\), \(C2=3.12\) \\  & SR & \(Pf=0.48\), \(v_{min}=0.04\), \(v_{max}=0.23\), \(C1=1.60\), \(C2=1.07\) \\  & EC & \(Pg=0.06\), \(Tc=600\), \(Rg=2\), \(cp=2\), \(v_{min}=0.04\), \(v_{max}=0.26\), \(C1=1.90\), \(C2=0.86\) \\  & PF & \(v_{min}=0.12\), \(v_{max}=0.34\), \(C1=2.47\), \(C2=0.34\) \\ \hline \multirow{4}{*}{MUMSA} & FR & \(CR=0.99\), \(F_{min}=0.78\), \(F_{max}=0.86\), \(R=0.31\), \(MR=0.03\) \\  & SR & \(CR=0.88\), \(F_{min}=0.71\), \(F_{max}=0.74\), \(R=0.89\), \(MR=0.03\), \(Pf=0.41\) \\  & EC & \(CR=0.91\), \(F_{min}=0.82\), \(F_{max}=0.85\), \(R=0.83\), \(MR=0.03\), \(Pg=0.03\), \(Tc=340\), \(Rg=2\), \(cp=5\) \\  & PF & \(CR=0.98\), \(F_{min}=0.66\), \(F_{max}=0.73\), \(R=0.89\), \(MR=0.12\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Algorithm parameter setting obtained by the irace package in study case 2. \(e\)C, PF, NCH). The acronyms given by the sixteen algorithms are related to the algorithms' acronym, followed by the constraint-handling technique through a dash. For instance, in the case of algorithms related to the DE/RAND/1/BIN, the acronyms are expressed as R1B-FR, R1B-SR, R1B-\(e\)C, and R1B-PF. The algorithm parameters presented in Table 2, and Tables 3 and 4 are considered in the executions. Sixty individuals, particles, or chromosomes (\(NP=60\)) and 600,000 objective function evaluations are established as a stop condition for each algorithm. The results obtained by each algorithm consider the thirty best objective function values obtained through the thirty executions (one value per each execution). This set of values conforms to one sample for the statistical analysis described in this section for a particular algorithm. In this section, the samples of all algorithms are analyzed by using descriptive statistics, confidence intervals, and inferential statistics.
## 4 Methodology
### Data Filtering
Sorting the continuous feature values, identifying and assessing cut issues for splitting or merging intervals, dividing or merging constant intervals depending on particular criteria, and stopping at a specific point are the four general processes of the discretization process [38]. Discretization algorithms can be categorized based on different characteristics [38; 39; 41], as described below:  
Dynamic vs. Static: Static discretization is performed before and does not depend on the learning algorithm and the learning task; while creating a model, dynamic discretization occurs. The most commonly used discretizations are static, whereas the discretized ID3 decision tree is an example of a dynamic discretization. Comparing univariate and multivariate discretization, the former examines a single continuous feature, while the latter considers several features simultaneously. Supervised versus unsupervised discretization methods differ in their approach to determining appropriate intervals during the discretization process. Supervised methods utilize class information to guide the selection of intervals, considering the class membership of the data. On the other hand, unsupervised methods do not consider class membership when discretizing the data. It is important to remember that the majority of discretization methods require supervision. Splitting vs. merging: while merging methods begin with a pre-defined partition and remove a potential cut-point to combine nearby intervals, splitting methods select a cut-point from among all available boundary points to divide the domain into two intervals. Local vs. global: Local discretization bases its partition decisions solely on local data, whereas global discretization handles the discretization of every numeric property during preprocessing. Global discretization techniques typically perform better than local ones. Incremental vs. Direct: Since direct approaches divide the range into \(k\) intervals concurrently, they require an extra criterion to find \(k\). Conversely, incremental approaches begin with a straightforward discretization and proceed through an improvement process; however, they need an extra criterion to decide when to end the process. We used an unsupervised strategy to filter the data after the data-cleaning stage before selecting features. In particular, we applied a discretized filter approach called \"Discrete\"--an instance filter.
## 3 Results and Discussion
### Food Product Quality of Boiled Yam Genotypes
#### 3.3.2 Instrumental Texture Profile of Boiled Yam  
Using the Stable Micro System's TAXT _plus_ Texture Analyzer (Serial Number: 2-P6-Z10447-01-V0038D577), a texture extrusion test was conducted on the boiled yam samples. The key textural parameters were hardness and work completed in extrusion, which is correlated to the ease of chewing or mealiness of the boiled yam [21]. Table 4 shows that hardness ranged from 7440.72 to 25,056.28 g with an average of 1254.28 g and a range of 8592.39 to 237,000.57 g.sec with an average of 117,569.66 g.sec for work completed in extrusion. The cooking qualities and texture of the boiled yam samples were subjected to principal component analysis, as shown in Figure 4. PC1 and PC2 accounted for 73.2% of the total variations in the cooking parameters for the boiled yam samples. Hardness, which occupies the positive score plot of the first principal component, shows a negative relationship with work completed in extrusion and water absorption; this is consistent with previous findings [21; 33]. Most water yam genotypes, such as TDa 0000194, TDa 1401409, TDa1100228, and TDa 10000994, were grouped based on similarity and were influenced by textural hardness. However, CT and WAb are strongly related to work completed in extrusion, and these qualities influence the clustering of other water yam genotypes with the landraces used as control and some white yams. For instance, TDa 1401249, TDa 1401276, TDa 1401270, TDa 1400359, TDa 1100242, and TDa 1000918 show a strong association with Meccakusa and Ojujuyawo and other white yam used as benchmarks. Consumers prefer mealy or easy-to-chew boiled yams, and farmers will adopt the genotypes that cook quickly to minimize the energy cost involved in cooking. [PERSON] et al. [34] also considered ease of chewing in the mouth an important quality characteristic for boiled yam eaters. The current finding emphasizes the importance of work in extrusion and water absorption as key quality characteristics of boiled yam food products. The strong association of the water yam with the landraces means that the selected water yam could produce a boiled yam of food quality comparable to the white yam and the landraces. Therefore, yam breeders and farmers should consider their selection and multiplication. The hierarchical cluster analysis in Figure 5 also corroborates the findings from the PCA, showing the grouping of some highlighted _water yam_ with the white yam genotypes and closely spaced with the landraces.  
\begin{table}
\begin{tabular}{c c c c c} \hline  & **WAb (\%)** & **C T (mins)** & **Hardness (g)** & **Work in Extrusion (g.sec)** \\ \hline Minimum & 1.03 & 9.00 & 7440.72 & 8592.38 \\ Maximum & 5.43 & 15.50 & 25,056.47 & 237,000.57 \\ Mean & 2.37 & 11.17 & 12554.28 & 117,569.66 \\ Standard Deviation & 1.11 & 1.85 & 4447.58 & 45,779.42 \\ \hline \end{tabular}
\end{table}
Table 4: Descriptive statistics of cooking quality parameters.