esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results  
The results are shown with a 95% confidence interval (as per the ITU recommendation [12]) in Figure 2 and in Table 1. It is seen clearly in Figure 2 that the rule that stands out is matching first partial (m1). The range of the rule m1 is higher than the rest, and does not overlap with the other ranges. It can be concluded that the listeners thought that the samples tuned using rule m1 were best tuned on average.  
Figure 1: Screenshot of the listening test environment.  
The results for each octave were also similarly plotted, and some of these are shown in Figure 3. It can be seen that the outcomes are not exactly the same for every octave. Although they follow a similar trend to the overall plot, two plots are significantly different. The plot for octave D6-D7 in Figure 3b does not have rule m1 with the best rating, as is the trend. This is possibly because it is harder to hear beats at higher octaves due to really fast decay [9].  
The second octave with deviated results was A5-A6, in which it appears that every method has roughly equal scores on average, with overlapping confidence intervals, as shown in Figure 3a. This may be because of the increasing inharmonicity with frequency, which makes it possible to match multiple partials at once [13]. However, if we look at the results overall, we can see that rule m1 seems to be doing clearly best in most cases. In no octave has any other rule clearly gotten a better score. The professional tuning has gotten a higher average score than rule m1 in a few octaves, but is not clearly the best tuning for any single octave. Rule m3, the anchor, has clearly gotten the worst scores in all octaves, as expected. Figure 3c shows an example of a typical result for a single octave in which rule m1 gets the best rating and anchor m3 the worst one.  
It is somewhat surprising that listeners did not in fact find the professional tuning to be the best tuning in every octave. This can be explained by the fact that tuners sometimes use additional tests, for example double octaves, for fine tuning the tone [8]. These tests are subjective, and used differently on different pianos and on different octaves. We can thus say that despite some variations in individual octaves, it appears that, on average, the octaves tuned using rule m1 have been perceived to be the best in tune by listeners.  
The results of the listening test have also been used to substantiate the claims of beating rates being a better identifier of the quality of tuning than Railsback curves [1]. This is explained in further detail in the next section.  
Figure 2: Average results of the listening test and the 95% confidence intervals. The rules tested were (1) professional tuning, (2) geometric mean rule (GM), (3) matching first partial (m1), (4) matching second partial (m2), and (5) matching third partial (m3), which was the low-quality anchor.  
Figure 3: Results of the listening test with their 95% confidence interval, for example octaves (**a**) A5–A6, (**b**) D6–D7, and (**c**) Bb5–Bb6 (typical plot). The rules are explained in the caption of Figure 2.
# A Multilevel Adaptive Path-Planning Model in Off-Road Environments
## 3 Construction of the Environment Model
### Contributing Factors to Trafficability
#### 3.1.2 Impacts of Terrain on Trafficability  
The speed influence coefficient of surface features introduced in the previous section can be better applied to path-planning problems with a smaller scale. However, when it comes to the path-planning problem with a larger scale, for example, when the total length of the path reaches tens of kilometers, the environment modeling will be too complex if only the surface feature description is used. In geography, terrain is a more macroscopic description of ground attributes than surface features. Therefore, this section considers the impact of terrain on trafficability. Terrain is a general term for surface features and geomorphic features. Geomorphic features combined with different surface features comprise different terrains. For instance, deserts comprise the geomorphic feature of plains combined with sandy soil. The speed influence coefficient of terrain is introduced in environment modelling, and the definition is the same as in Section 3.1.1. Table 2 shows the set values of the speed influence coefficient of terrain in this study.
## 2 Materials and Methods
### Material Characterization  
The morphologies of the sample surfaces were observed using transmission electron microscopy (TEM, JEM-2100F), high-resolution TEM (HR-TEM, JEM-2100F) and scanning electron microscopy (SEM, MIRA3, Tescan). Elemental mapping was performed using energy-dispersive X-ray spectroscopy (EDS) attached to the TEM. The structural characteristics were determined by X-ray diffractometer (XRD) using filtered Cu \(\alpha\) radiation (SmartLab, RIGAKU). N\({}_{2}\) adsorption-desorption isotherms were obtained at 77 K using a N\({}_{2}\) adsorption analyzer (MicrotracBEL Corp., BELSORP-max). Specific surface areas were evaluated using the Brunauer-Emmett-Teller (BET) method. Pore sizes were determined using the Barrett-Joyner-Halenda (BJH) method and micropore analysis (MP).  
Figure 1: Schematic illustration of the experimental set up for plasma-in-liquid process.
# Heterogeneous Parallel Implementation of Large-Scale Numerical Simulation of Saint-Venant Equations
## 3 Heterogeneous Implementation
Because of the large scale of computation, a node may need to exchange data bidirectionally with four adjacent nodes (maybe 2 or 3 times for nodes at the edge). We use four MPI_Sendrecv() functions to implement this communication operation. MPI_Sendrecv() combines sending a message to a destination and receiving a message from another process into one call. Figure 4 shows how MPI_Sendrecv() in the four directions exchanges data. For the intermediate nodes, each node must send data to the surrounding adjacent nodes, and also receive data from the surrounding adjacent nodes, which can be easily implemented by calling MPI_Sendrecv(). \begin{table}
\begin{tabular}{c c c} \hline \hline
**Hard/Software** & **Name** & **Details/Version** \\
**Environment** & MPI & Openmpi 4.0.4 \\  & gcc/g++ & 4.8.5 \\  & OpenMP & 3.1 \\  & Phread & NPTL 2.17 \\  & & Platform: AMD Accelerated Parallel \\  & & Processing \\  & & Driver version: 2982.0 \\  & & OpenCL Standard: OpenCL 2.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._  
Figure 3: Schematic of the heterogeneous parallel framework. When dealing with nodes at edges, we introduce MPI_PROC_NULL, a constant that represents a dummy MPI process rank. This allows all MPI processes to issue the same calls regardless of their position. After the communication ends, a complete iteration is over, and after that, the iterative loop continues until the set time is reached. Then we output the result and perform post-processing to generate a picture, such as the visualization of the output of the z value shown in Figure 5 (Only the parts with numerical changes are displayed). In this way, you can intuitively see the change of the water level in the river channel with time.
# A Scalable Bidimensional Randomization Scheme for TLC 3D NAND Flash Memories
## 1 Introduction
In this work, we tackle the data randomization challenge in Triple Level Cell (TLC) 3D NAND Flash memories by presenting a simple yet scalable bi-dimensional randomizer that guarantees both the horizontal and the vertical randomization while defining an upper bound on the maximum sequence length of consecutive ones and zeros along the dimensions. The contributions of this paper can be summarized as follows:  
1. We show that a chained structure of two \(k\)-bits LFSRs can provide, from a statistical standpoint, both the horizontal and vertical data randomization while guaranteeing a \(k\)-bit upper bound on the maximum sequence length of consecutive ones and zeros;
2. We show that our proposed randomization scheme introduces a low-complexity hardware overhead, most of which scales automatically with the memory size and is independent of cumbersome heuristics, to achieve seed randomization or look-up tables (LUTs) for seed storage, to potentially be adopted by different memory technologies and vendors;
3. We benchmark the effectiveness of our scheme by measuring the RBER characteristics of a Triple Level Cell (TLC) 3D NAND Flash memory during both endurance and data retention stress.
## 4 The Proposed Problem Formulations
### Problem Variant #1: Offset Optimization
To date, there is a potential issue when non-synchronized trips lead to negative values on the right term of Equation (3), which produces unfeasible constraints. The formulation only needs one value, \((A^{i}_{rb}+d^{ij}_{b}+W^{ij}_{b})-A^{j}_{sb}\) or \(A^{j}_{sb}-(A^{i}_{rb}+d^{ij}_{b})\), to be lower than zero, so the synchronization variable \(Z^{ij}_{rsb}\) is deactivated. Thus, it is enough to introduce a constant value \(M\), large enough to guarantee that both Equations (2) and (3) are always feasible. In a real solver implementation, considering large values of \(M\) might cause numerical stability problems. Thus, appropriate and relatively low values for \(M\) are computed as the maximum value within the union of sets \(\{(H_{i}(j)+(f_{i}(j)-1)\times\Delta X^{i}+T^{j}_{b})-(T^{i}_{b}+d^{ij}_{b}+W^ {ij}_{b})\}\) and \(\{(H_{i}(i)+(f_{i}(i)-1)\times\Delta X^{i}+T^{i}_{b}+d^{ij}_{b})-T^{i}_{b}\}\) for all transfer zones \(b\)\(inB\). These values of \(M\) can be easily calculated during the process of crafting the MIP formulation before using any solver implementation, since finding \(M\) is a polynomial complexity problem. Equation (4) indicates that the maximum value for the offset of each line is the minimum between \(T\) (mod \(F^{i}\)) and the maximum headway \(H_{i}\). No constraints are defined over headways, since a fixed frequency \(F^{i}\), which satisfies the bounds for headways, is assumed for all subsequent trips. Finally, Equation (5) defines that decision variables \(Z^{ij}_{rsb}\) are within the domain of binary variables. Without losing generality, the proposed problem formulation assumes that \(F^{j}>W^{ij}_{b}\), \(\forall j\in I\), i.e., headways of bus lines are larger than the waiting time thresholds for users. The case where \(F^{j}\leq W^{ij}_{b}\) corresponds to a scenario in which the headway of line \(j\) is lower than the time users are willing to wait; thus, all transfers with line \(j\) would be synchronized, and they would not be part of the problem to solve (i.e., those lines can be removed for the specific problem instance to solve).
# Joint Content Recommendation and Delivery in Mobile Wireless Networks with Outage Management
## 4 Heuristic Algorithms for Joint Content Recommendation and Delivery
### Content Recommendation
#### 4.2.1 Maximum File Size Constraint  
\[\begin{split}\text{maximize}&\sum_{u=1}^{U}\sum_{ f=1}^{F}r_{uf}x_{uf}\\ \text{subject to}& l_{max}^{u}=\max_{f=1\cdots F}\{x_{uf} l_{f}\}\leq\delta*R_{u}*T_{s}\qquad\forall u\\ &\sum_{f=1}^{F}x_{uf}=N.\end{split} \tag{25}\]  
In this problem, maximizing the sum interest is equivalent to maximizing each user's interest because the capacities of users are already fixed and decoupled. The optimal algorithm that solves this problem runs as follows: For each user, we exclude contents whose size exceeds the data rate \(R_{u}\) and then recommend the top \(N\) contents with the highest interests in the remaining content. The minimum heap algorithm (time complexity \(O(\text{Flog}N)\)) or partial sorting algorithm (time complexity \(O(FN)\)) can be used here.
## 3 Results and Discussion  
### Dissolution of SO4\({}^{2-}\) from Lithium Mica Slag  
In order to study the effect of SO4\({}^{2-}\) in lithium mica slag on the properties of cementitious materials, the concentrations of SO4\({}^{2-}\) at different pH value solutions and stirring immersion times were tested using an ion chromatography instrument. The results for the test from Figure 2 show that (1) the concentration of SO4\({}^{2-}\) in the solution remains relatively stable after stirring for 1, 2, and 3 h at the same pH value. When the pH value is 9, the concentrations of SO4\({}^{2-}\) in the solution after stirring for 1, 2, and 3 h are 0.00819 mol/L, 0.00808 mol/L, and 0.00803 mol/L, respectively. Within 1 h of stirring, the majority of SO4\({}^{2-}\) in lithium mica slag dissolved. (2) As the pH value increased, the concentration of SO4\({}^{2-}\) in the solution decreased, indicating that a lower pH value facilitates the solubility of SO4\({}^{2-}\) in lithium mica slag. As the dissolution medium used in this test was a solution comprising calcium hydroxide, at higher pH levels, the increased concentration of Ca\({}^{2+}\) caused the formation of microsoluble calcium sulfate (CaSO4) when combined with dissolved SO4\({}^{2-}\), resulting in the lower measured concentration of SO4\({}^{2-}\). Additionally, the slurry contained a certain amount of Ca\({}^{2+}\), rendering calcium hydroxide an appropriate choice for simulating the mixing pore solution.
## 4 Discussion and Outlook
Except for the GIS and the other systems mentioned above, there are a lot of advanced monitoring and control systems emerging, such as WAMC (Wide Area Monitoring and Control) system. However, whatever control and monitoring systems emerge in the future, as long as these systems can be applied to specific practical problems and engineering projects to achieve good results, the efforts of researchers around the world who are researching the innovative system will not be in vain.
## 3 Hybrid FE-SEA Modeling Parameter Identification Algorithm
### The Algorithm of a Case Analysis  
#### 3.6.1 Numerical Example  
According to the multi-system coupling model, a hybrid FE-SEA model of six subsystems was established, as shown in Figure 7. The hybrid model consists of six square steel plates welded together. The material parameters are shown in Table 1. The density of the steel plate was 7800 kg/m\({}^{3}\); the Poisson's ratio was 0.3; the elastic modulus E = 200 GPa; the length and width of the steel plate were 1000 mm and 1000 mm, respectively; the thickness was 1 mm; and the damping loss factor of the steel plate was 0.01. The analysis frequency range of the hybrid model was 0-2000 Hz, and unit-frequency excitation was performed on each plate to obtain the average vibration energy, the modal density, and the input power for each plate. The Monte Carlo calculation method was used as the reference solution to compare the effects of the mixed FE-SEA method and the improved FE-SEA method [20]. As shown in Figure 8, the test device was built according to a 1:1 ratio between the model and the real object. The effective coupling loss factor was improved and verified. The test system was composed of a physical sample, an acceleration sensor, a shaker, a rope, and a test data line. Each measuring point on the surface of the physical sample was excited separately, and the acceleration and velocity signals of each measuring point were tested. The shaker selected a sinusoidal peak force of 100 N and a frequency range of 5-10 KHz. Nine measuring points were selected for each board of the physical sample, and each measuring point was tested three times to obtain an average value. For the acceleration sensor PCB, \(\pm\)50 g was selected, and the sensitivity was 100 mv/g. The acceleration and velocity of each measuring point and the force of each measuring point were measured by the power input method. The input power and energy of each measuring point were calculated according to the statistical-energy power input method.  
Figure 6: Iterative flow of ill-conditioned energy matrix.  
Figure 7: FE-SEA statistical energy model.
## 2 Study Area
### Geological Setting  
Different geological units were identified from [26]. The basement units of the study area are mainly represented by medium-to high-grade metamorphic rocks, ophiolite serpentine including talc carbonate rocks, undifferentiated metagabbro to metadiorite, ophiolitic metagabbro, undifferentiated metavolcanics, ophiolitic metavolcanics, intermediate to acid metavolcanics and metapyroclastics, metamorphosed shelf sediments and volcanogenic rocks, molasse-type hammamat clastics, fresh olivine gabbro, deeply weathered grey granite (older granite), weakly deformed pink granitic (younger granite) rocks, unreformed granitic to alkali-feldsparanitic rocks, syenitic to alkali-feldsparanitic rocks as a ring complex, alkaline andesitic to hylolitic dokhan volcanics, post-hammamat felsite, and trachyte plugs and sheets, respectively [29] (Figure 2). The Upper Cretaceous period was recorded in the study area by Turonian Taref Formation, which is composed of fine- to medium-grained fluvialtile and eolian sandstone, Coniacian-Santonian Timsah Formation,which includes shale, silt, and sandstone with two major oolitic iron-ore beds, Campanian Taref Formation, Campanian Qussier Formation, and Maastrichtian Duwi Formation [29] (Figure 2). The study area presents the Tertiary time through Tarwan Formation, Thebes Group, Nakheil Formation, Umm Mahara Formation, Umm Gheig Formation, and Shagra Formation, respectively [29] (Figure 2). The Quaternary rocks cover the study area by sabkha and wadi deposits [29] (Figure 2).  
Figure 1: (**a**) Google earth image defines the location of the study area by a yellow polygon; (**b**) Map illustrating the catchments and sub-catchments of the study area is overlying a digital elevation map. Yellow squares in (**a**) and stars in (**b**) indicate the main cities. C, catchment; SC, sub-catchment.  
Regarding the general geomorphology of the Egyptian Eastern Desert, the studied zone is categorized into different linear zones [30]. The topographic maps and digital elevation model were preliminarily processed to examine the geomorphology of the study zone (Figure 4). The raster surface analysis tools in ArcGIS software, such as elevation, contour, slope, and aspect, aid in recognizing the different linear zones; the eastern coastal and inland strip zone, the high plateaus (mostly Miocene and Eocene), and the Pre-Cambrian Red Sea mountain hills (Figure 5a-d). Eastern desert of Egypt always reflects arid climatic conditions that vary from season to season and influence the hydrological characteristics of the drainage catchments in the study area. The temperatures range from 9.7 \({}^{\circ}\)C to 36.7 \({}^{\circ}\)C  
Figure 2: Detailed Geological map of the study catchments and sub-catchments, Modified after [29].  
and reach 47 \({}^{\circ}\)C in the southern parts [21; 31]. The humidity has been recorded as 43% in the summer seasons and 48% during winters [21; 27].
# A Survey of the Real-Time Metaverse: Challenges and Opportunities +
## 4 State-of-the-Art Real-Time Metaverse
### Communication and Computational Infrastructure  
For the real-time metaverse to function efficiently, robust infrastructure is necessary. Figure 4 highlights key infrastructure components--communication, computation, and storage--that support the real-time data flow between the physical and virtual worlds. Scalability is essential, as the system must handle ultra-dense connectivity with high data rate reliability and low-latency communication. Technologies like 5G and edge computing are pivotal in ensuring that real-time data can be collected and transmitted without delays [109]. These communication technologies facilitate real-time data transmission and control signals between sensors, robots, and user interfaces, ensuring low latency and high reliability [110]. Real-time connectivity is essential for maintaining the interactive nature of the real-time metaverse, allowing users to experience immediate responses to their actions.  
In addition to scalability, the infrastructure must be ubiquitous [37] and shardless [111]. Trustworthiness [80] is another critical factor in real-time metaverse systems, as users must trust the data and services they interact with. Localized computing, with cloud-edge-assisted rendering, cloud-edge AI model training, and blockchain mining are essential for delivering real-time experiences at scale, whether users are in urban areas with high connectivity or remote locations with limited infrastructure [90]. Technologies like blockchain provide decentralized storage, ensuring secure and verifiable transactions within the metaverse, from virtual goods to real-world e-commerce logistics.
# Prosody-Based Measures for Automatic Severity Assessment of Dysarthrie Speech
## 5 Results
### QoLT  
Results from the QoLT dataset indicate higher accuracy when training on all acoustic features as shown in Table 11. However, compared to solely using MFCCs, there is still an accuracy improvement from 60.00% to 66.73% when including prosody-based features with an RF classifier. As shown in Table 12, most improvements are with mild and healthy utterances. There is a recall increase from 36.73% to 80.00% and a precision increase from 52.52% to 66.67% for mild utterances. Furthermore, healthy utterances are more accurately classified when including prosody-based features and reach 100% for all evaluation metrics. This implies no healthy utterance is misclassified as dysarthric and no dysarthric utterance is misclassified as healthy.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Features** & **Evaluation Metrics** & **Healthy** & **Mild** & **Moderate** & **Severe** \\ \hline \multirow{3}{*}{Only MFCC} & F1-score \% & 84.30 & 52.34 & 50.32 & 73.68 \\  & Precision \% & 82.26 & 68.29 & 70.91 & 59.32 \\  & Recall \% & 86.44 & 42.42 & 39.00 & 97.22 \\ \hline \multirow{3}{*}{MFCC + Prosody} & F1-score \% & 86.23 & 82.11 & 57.14 & 68.02 \\  & Precision \% & 82.81 & 80.90 & 52.33 & 82.92 \\ \cline{1-1}  & Recall \% & 89.84 & 83.34 & 63.00 & 57.61 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Evaluation metrics for all severity groups using an NN classifier.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Prosodic Group** & **Optimal Features for TORGO** & **Optimal Features for QoLT** \\ \hline \multirow{3}{*}{Pitch} & F0 mean, F0 median, F0 quantile & F0 median, F0 quantile 75, F0 quantile 25, 25, F0 quantile 75 & F0 max, F0 min, F0 mean, F0 std \\  & number of pauses, speaking & total duration, number of pauses, \\  & duration, total duration, balance & speaking duration, balance, speaking rate \\  & Delta-V, Delta-C, Varco-C, VrPVI, & \%V, VrPVI, VnPVI, Varco-C, CnPVI, \\  & CrPVI, VnPVI & Varco-V, Delta-C, Delta-V, CrPVI \\ \hline \hline \end{tabular}
\end{table}
Table 7: Prosody-based features selected for TORGO and QoLT datasets.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Features** & **TORGO Accuracy \%** & **QoLT Accuracy \%** \\ \hline Prosody & 66.39 & 66.67 \\ MFCC & 64.02 & 60.00 \\ Voice Quality & 53.82 & 58.76 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Severity assessment accuracy scores with acoustic measures.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Features** & **Accuracy \%** & **Accuracy \%** \\  & **MFCC + Prosody** & **MFCC + Voice Quality** & **Prosody + Voice Quality** \\ \hline RF & 60.50 & 57.98 & 58.40 & 62.18 \\ SVM & 68.91 & 67.65 & 68.07 & 71.00 \\ NN & 75.63 & 69.33 & 65.97 & 73.89 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Accuracy results for various acoustic feature combinations.