esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Approach and Analysis: Architecture and Methodology
### Learning Algorithm for Sound Classification
#### 3.4.2 Testing  
The testing process comprises input test sound, feature extraction, learning model, and the end result of sound classification, as described in Figure 6. The dog sound was given as the input, and the input sound underwent feature extraction. Based on the features of the sounds, predictions were determined with the model formed using the learning algorithm, which determined the type of sound received as the input. Hence, dog sounds were classified based on the type of input sound given.  
Figure 5: Training process of dog sounds.  
Figure 6: Testing process of dog sounds.  
In the testing process, the proposed model was expected to classify the given sound file. A confusion matrix was used to evaluate the classification model's performance. The confusion matrix describes the classification errors based on the sound predictions. In the matrix representation, diagonal entries correspond to correct classifications, while other entries are incorrect classifications.  
Out of a total of 43 dogs taken for testing, the proposed model was able to correctly identify 39 dog sounds. Based on the increased amount of training and its sound features, the sound classification accuracy was high. The process was repeated with the same datasets for training and testing to compare different hyperparameters. The hyperparameters included actor learning rate, exploration, and environment.  
Sound samples trained for more than 100 epochs did not process valid results of prediction. Table 3 consists of training dog sounds during different scenarios, and training values were updated based on the new states encountered. Updating the values was performed through training in order to achieve the prediction of dog sounds.  
The values in Table 3 were updated based on the training samples presented. Based on the dog sound patterns, the values in the tables were updated for the predictions. This is a learning process, and for each state its corresponding values are updated for classification.
# Q-Learning Empowered Highly Dynamic and Latency-Aware Routing Algorithm for Flying Ad-Hoc Networks
## 5 Simulation Results
### Simulation Environment  
The simulation scenario parameters for this protocol are presented in Table 6, where NS version 3.33 is utilized. The randomization operation is performed multiple times for each configuration using the random number seed (Random Seed) built into NS-3. Simulations are conducted using a random waypoint mobility model [33; 34; 35; 36], where each node is moved from its current position to a new position by selecting the direction and velocity. Specifically, the new position is chosen randomly within the simulation area, while the new velocity is chosen uniformly from the velocity interval. Table 6 is designed to test the protocol in highly dynamic communication conditions.  
NS-3 is a discrete-event network simulator, where each event has a scheduled simulation time that specifies its execution. Conceptually, the simulator keeps track of multiple events that are scheduled to be executed at a predetermined simulation time. The simulator works by executing events in the order of the scheduled simulation time, scheduling them sequentially. Once an event occurs and executes, the simulator moves on to the next event for execution.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Setting** \\ \hline Simulator & NS-3.33 \\ Initialization time & 5 s \\ MAC & 802.11 g/2.4 G \\ Delay model & Constant speed propagation delay model \\ Transmitting power & 20 dbm \\ Receiver sensitivity & \(-85\) dbm \\ \hline \hline \end{tabular}
\end{table}
Table 6: Simulation parameters.
# Reduction of Hexavalent Chromium Using _Sorbaria sorbifolia_ Aqueous Leaf Extract
## Materials and Methods
### Reduction and Analysis of Cr(VI)  
The potential of PLE as a green reductant was assessed for Cr(VI). A 1000 mg/L stock solution of Cr(VI) was prepared from K\({}_{2}\)Cr\({}_{2}\)O\({}_{7}\) in a 1000 mL volumetric flask and stored in a glass bottle at room temperature. PLE was added to 25 mL of freshly prepared Cr(VI) solution (10 mg/L) prepared by diluting the stock solution. Cr(VI) concentration was measured by colorimetric DPC method using a UV-Vis spectrophotometer (Perkin-Elmer Lamda-45, Waltham, MA, USA) at 540 nm [26]. DPC solution was prepared by dissolving 250 mg of DPC in 50 mL acetone and stored in a brown bottle at 4 \({}^{\circ}\)C. The method combines the acidification of the Cr(VI) solution by H\({}_{2}\)SO\({}_{4}\) followed by the addition of DPC. For analysis, 0.25 mL of samples were diluted to 5 mL with ultrapure water and Cr(VI) was analyzed by DPC method after complete color development.  
The effect of different parameters was examined to observe the potential of PLE for Cr(VI) reduction. Solution pH (2-10), contact time (0-90 min), PLE quantity (0.5-2.5 mL), ionic strength (0-500 mM), hardness (0.5-5 mM), temperature (278-333 K) and initial Cr(VI) concentration (5-80 mg/L), were evaluated for Cr(VI) reduction using PLE. Solution pH was adjusted with 0.1 M H\({}_{2}\)SO\({}_{4}\) and NaOH. NaCl and CaCO\({}_{3}\) were utilized to adjust the ionic strength and hardness. Experiments were performed in triplicate and the average value and standard deviations are presented. Functionality of PLE was measured by Fourier Transform Infrared Spectroscopy (FTIR, Nicolet 6700, Thermo Fisher, Waltham, MA, USA) using a spectral range of 4000-400 cm\({}^{-1}\) with resolution of 4 cm\({}^{-1}\).
# The Paradigm Shift in Hyperspectral Image Compression: A Neural Video Representation Methodology
## 4 Experiments and Analysis  
### Dataset  
Chikusei: captured by the Headwall Hyperspec-VNIR-C imaging sensor, these data cover the agricultural and urban areas of Chikusei with a wavelength range of 363-1018 nm and a spatial resolution of 2.5 m. The image size is 2517 \(\times\) 2335, with 128 spectral bands.  
Figure 4: The structure of NeRV block.  
Figure 5: Detailed architecture of the transformer layer.  
Botswana: these data on the Okavango Delta in Botswana were acquired with the NASA's EO-1 satellite with a wavelength coverage of 400-2500 nm, a spatial resolution of 30 m, and a spectral resolution of 10 nm. The spatial size is 1476 \(\times\) 256, with 145 spectral bands.  
Pavia: the image sets of the Pavia Centre and the University of Pavia were both acquired with the Reflective Optics System Imaging Spectrometer (ROSIS), the former with a spatial size of 1096 \(\times\) 715 and 102 spectral bands and the latter with a spatial size of 610 \(\times\) 340 and 103 spectral bands.  
Washington DC: hyperspectral data covering the wavelength range of 400-2400 nm were acquired for the Washington DC Mall area. The spatial size is 1280 \(\times\) 307, with 191 spectral bands.  
Salinas: the Salinas data were acquired by the AVIRIS sensor in the Salinas Valley area in California with a spatial resolution of 3.7 m and an image size of 512 \(\times\) 217. The raw data contain 224 spectral bands, and 204 bands were retained after removing bands heavily affected by water vapor absorption. The data cover 16 crop classes.
## 5 Ca2+ and cAMP Signaling Are Intertwined
Conversely, when PKA is activated through either a gain of function allele in GSA-1 or by depletion of KIN-2/PKA-R, Ca2+ pulses continuously propagate across the spermatheca, even in the absence of oocyte entry. In the spermathecal-uterine valve, which connects the spermatheca to the uterus, loss of GSA-1/G\(\alpha_{\mathrm{s}}\) or KIN-1/PKA-C results in the opposite phenotype: sustained, high levels of Ca2+ and a loss of coordination between the spermathecal bag and sp-ut valve. The phospholipase PLC-1 is required for these Ca2+ pulses. These results suggest activation of PKA has tissue-specific effects onthe timing and intensity of Ca\({}^{2+}\) release, and that KIN-1/PKA-C stimulates Ca\({}^{2+}\) release downstream of GSA-1 in a PLC-1-dependent manner in the _C. elegans_ spermatheca. cAMP and Ca\({}^{2+}\) signaling are also involved in neuronal regeneration in _C. elegans_. _C. elegans_ neurons regenerate after laser axotomy [71; 72]. PLM sensory neuron axotomy triggers a Ca\({}^{2+}\) transient, which correlates with regenerative growth in late larval (L4) stage _C. elegans_. Genetically increasing Ca\({}^{2+}\) or cAMP accelerates this growth, facilitates fusion of axonal fragments, and promotes branching; while inhibiting Ca\({}^{2+}\) release reduces regrowth [73]. Inhibition of the regulatory subunit, through a loss of function allele _kin-2(cc179)_ allele, promoted regeneration regrowth and elevated rates of fusion, while use of the PKA inhibitor H89 resulted in reduced regrowth in a dose-dependent manner [73]. In the ASJ neuron, cAMP signaling elevation or Ca\({}^{2+}\) channel disruption improve DLK-independent regeneration [74]. Therefore, elevation of cAMP promotes neuronal regeneration in a PKA-dependent manner.
## 4 Results
### Variation in QoE Evaluation Due to Perception  
Different users perceive quality differently even if the underlying QoS parameters remain unchanged. These variations in the perception can result in fluctuations in QoE and can be addressed by finding the standard deviation of the Opinion Score (SOS) at a certain QoS configuration as recommended in [36]. Moreover, presenting the individual score of each user can show the distribution of the QoE at a certain PLR. In Figure 6, we addressed this by plotting the MOS, SOS and QoE distributions vs. PLP at 10 ms jitter. Using these results, we tried to evaluate the effect of variations in perception at different PLR values and then compare them with the propagated sampling errors and propagated NetEm error in QoE.  
In Figure 6 it can be seen that SOS is maximum between PLR = 0.001 and PLR = 0.01 and decreases sharply on both sides. This shows that user-rating exhibits more consistency at lower and higher magnitudes of the PLR. The opinion score distribution also highlights this finding, for instance at PLR < 0.0001, 25 subjects out of 26 ranks the video Good (MOS = 4) or Excellent (MOS = 5). The same is true for higher values of PLR as for PLR > 0.02, 25 subjects rank it Bad (MOS = 1). The region between PLR = 0.001 and PLR = 0.01 is of great interest for most service providers as it is the average magnitude of PLP agreed in the SLA, and the results show that user perceptions tends to vary significantly in this region. For instance, at PLR = 0.005, the MOS is equal to 3 but there are equal number of subjects that rank the video as MOS 4 or above and MOS 2 below.  
Moreover, the SOS peak occurs in the same region that has the steepest slope of the MOS function. This shows the significance of using SOS and QoE score distribution rather than using MOS alone, as MOS loses a lot of important information by averaging the user score. In addition, SOS can be used to evaluate user-diversity as reported by author in [36]. The results in Figure 6 show that a small fluctuation in user-ratings at a particular network scenario can result in non-trivial SOS values. For instance, in the case of PLR = 0.0001, only two out of 26 subjects ranked video quality other than Excellent (MOS = 5), and this results in a SOS of 0.23. This poses a challenge to quantitively relate SOS to user-diversity for studies using a smaller number of subjects. In addition, most of the subjective studies do not employ a large number of subjects, as subjective tests can be expensive, laborious and time-consuming. Thus, it is useful to consider QoE distributions to evaluate user perception more accurately.  
Figure 6: Heatmap of variation in perception of QoE of subjects at different PLR scenarios.
## 3 \(V_{oc}\) Losses Analysis  
### \(V_{oc}\) in Ideal Case  
In an ideal case, the UTSi solar cell has a high \(V_{oc}\) and efficiency potential. PC1D [7, 8] is used to calculate the upper limit efficiency and \(V_{oc}\) of a 20 \(\upmu\)m silicon solar cell. Lifetimes of the n\({}^{+}\) front surface field (FSF) layer, p\({}^{+}\) emitter and n base are assumed to be 10 \(\upmu\)s, 10 \(\upmu\)s and 1000 \(\upmu\)s, respectively. The modeled upper values of \(V_{oc}\) and efficiency are 767 mV and 25.4%, respectively [5]. To separate the surface recombination from bulk recombination, we assume the surface recombination velocity (SRV) of both surfaces to be 0 cm/s, so that there is only bulk recombination. Table 1 shows the reverse saturation current densities and the corresponding \(V_{oc}\) values.  
In addition to the PC1D model, first principle calculations are used to calculate \(V_{oc}\). We first separate the surface recombination from bulk recombination by assuming SRV(\(S_{n}\), \(S_{p}\)) to be 0 cm/s so that there is only bulk recombination in the base (\(J_{0b}\)) and emitter (\(J_{0e}\)). The \(J_{0}\) equation becomes \(J_{0}=J_{0b}+J_{oc}=\frac{qD_{n}n_{1}^{2}}{L_{n}N_{A}}\times\tanh(\frac{W_{p} }{L_{n}})+\frac{qD_{p}n_{1}^{2}}{L_{p}N_{D}}\times\tanh(\frac{W_{n}}{L_{p}})\), where \(N_{A}\) and \(N_{D}\) are doping densities; \(L_{n}\) and \(L_{p}\) are minority carriers diffusion lengths; \(W_{p}\) and \(W_{n}\) are the thickness; \(S_{n}\) and \(S_{p}\) are minority carriers surface recombination velocities; \(D_{n}\) and \(D_{p}\) are minority carriers diffusivity at \(p\) and \(n\) silicon, respectively; and \(n_{i}\) is the intrinsic concentration. \(J_{0}\) is determined by \(\frac{W_{p}}{L_{n}}\) and \(\frac{W_{n}}{L_{p}}\), the ratio of thickness over diffusion length. The maximum \(V_{oc}\) is 758 mV for a 20-\(\upmu\)m cell, with an \(n\) base with a thickness of 18-\(\upmu\)m, a doping density \(N_{D}\) of \(5\times 10^{15}\) cm\({}^{-3}\) and a lifetime of 1000 \(\upmu\)s, a p\({}^{+}\) emitter 1 \(\upmu\)m, \(5\times 10^{17}\) cm\({}^{-3}\) and lifetime of 10 \(\upmu\)s. No n\({}^{+}\) FSF layer is considered in this calculation.
# Wintertime Local Wind Dynamics from Scanning Doppler Lidar and Air Quality in the Arve River Valley
## 5 Discussion
### Consequences of the Observed Wind Structures on Air Quality  
As explained in Section 2.1, the Arve valley is urbanized and crossed by one of the major roads for goods transport toward Italy. Figure 2 shows that PM10 concentrations monitored within the Arve river valley exhibit large heterogeneities with on average, \([PM10_{Passy}]>[PM10_{Sallanches}]>>[PM10_{Chumonix}]>[PM10_{Martinez}]\) over a wintertime pollution episode. The \([PM10]\) measured at Marnaz, located at the western exit of the Passy basin are the lowest, with a mean value over the winter period of around 30 \(\mathrm{\SIUnitSymbolMicro g}\)\(\cdot\)\(\mathrm{m}^{-3}\). Consequently, it can be assumed that this broader section of the Arve river valley is more ventilated and less affected by the valley wind systems. The Passy basin and the Chamonix valley have a total population of about 40,000 peoples, which means that winter residential heating (wood burning) is an important source of pollutants. The towns of Passy, Sallanches and Chamonix can be considered to produce similar amounts of such pollution. However, the industrial activity developed at Passy may constitute additional source of emissions. Therefore, one may ask whether the local wind dynamics tend to increase this PM10 variability or homogenise the concentrations within the valley. To answer this question, the following section discusses how the wind patterns may contribute to (i) the higher \([PM10]\) measured within the Passy basin than around and (ii) the local differences within the basin, with larger \([PM10]\) observed at Passy.
# A Parametric Identification Method of Human Gait Differences and its Application in Rehabilitation
## 4 Discussion
### Effects of Shape Parameters in Walking Postures  
In natural walking, the flexion and extension angles of the hip, knee, and ankle joints of healthy individuals changes harmoniously [43], which is different from that of patients. Since each shape parameter corresponds to a wave of the angle period curve, which is exactly the transformation process of walking posture. The abnormality of shape parameters can then assist in judging the abnormality of walking posture in the gait cycle. For the hip joint, the second and third parameters being smaller than the normal range indicated that maximum extension was not achieved in the stance phase, and the forward flexion was not increased enough in the swing phase. In other words, the hip joint was in a slight forward flexion from terminal stance to terminal swing. For the knee joint, the third smaller parameter corresponds to the failure of the patient's flexion to increase effectively during the swing phase, presenting that the patient's activity is limited. For the affected ankle joint, the first and fifth parameters were lower, which corresponded to the limitation of two plantar flexion movements in the whole gait cycle, showing that the patients' feet were still unable to move freely.  
Generally, ankle joint injury had an impact on the normal movement of the lower extremity joints. In order to avoid pain in the affected area or due to psychological factors, the patients used the way of dragging the injured legs to walk, resulting in abnormal movements of the lower limb joints. The explanations above also illustrated= that the shape parameter analysis allowed documentation of the patients' progress after rehabilitation and identified possible directions to adapt the exercise program.
## 2 Drone Clustering Based on the K-Means++ Algorithm
### UAV Clustering Modeling Based on K-Means++  
Currently, there is a lack of unified and clear standard documents for classifying drone performance, which has led to discussions on safety separation mainly focusing on specific drone models. The performance differences between drones mean that there are significant variations in safety separation across different models. Based on this, specific algorithms should be used to deeply explore the intrinsic patterns of indicator feature data, thereby achieving the classification of drones based on their performance. In the process of classifying drones based on performance, clustering algorithms can be used as the primary tool to explore the data features in depth. By pre-setting the number of clusters and performing clustering operations according to a specific objective function, drones with different performances can be effectively grouped into multiple categories. A detailed analysis of the cluster centers can reveal the key parameters of drone performance within each category, thus providing strong support for the study of universal drone safety separation.  
(1) Initialize the data matrix \(X\)  
Let \(m\) be the number of drones and \(n\) be the number of drone performance indicators. The initial performance indicator evaluation data are established as  
\[X=\left(x_{ij}\right)_{m\times n}=\begin{pmatrix}x_{11}&x_{12}&\ldots&x_{1n}\\ x_{21}&x_{22}&\ldots&x_{2n}\\ \ldots&\ldots&\ldots&\ldots\\ x_{m1}&x_{m2}&\ldots&x_{mm}\end{pmatrix} \tag{1}\]  
(2) Standardize the data matrix \(Y\)The elements in the initial data matrix are processed using the range normalization method. Since the selected indicators are all positive indicators, the processing method is as follows:  
\[Y=\left(y_{ij}\right)_{m\times n}=\left(\frac{x_{ij}-\underset{i}{\min}\{x_{ij} \}}{\underset{i}{\max}\{x_{ij}\}-\underset{i}{\min}\{x_{ij}\}}\right)_{m\times n} \tag{2}\]  
(3) K-Means++ Clustering Algorithm Process  
Based on the clustering feature indicators designed in Section 2.1, the K-Means++ algorithm-related parameters are calculated by extracting the performance indicators of the drones. This results in the final outcome, which includes the drone categories and the performance parameters of each category.
# Bounce: A High Performance Satellite-Based Blockchain System
## 5 Implementation  
While this paper has so far mostly summarized the protocols of our book [3], we now describe a concrete implementation and our experiments on that implementation. The prototype is implemented in the Rust programming language. For communication with remote components, we utilize gRPC, except in the case of the Sign Merkle Tree Request, where we open a direct TCP stream. To optimize transaction throughput, each Sending Station entity operates several parallel components. The Sending Station comprises three primary long-running threads: the tx-receiver thread, which handles incoming transactions from clients; the mktree-handler, which performs tasks as described in Section 5.1, Algorithms 1 and 2; and the main sending-station thread, which manages communication with the satellite, Algorithm 3 and 4, and processes messages from Mission Control.  
**Note:** In our implementation, all the algorithms listed here run concurrently. The Sending Station acts like a state machine. After receiving a message, it executes one of these algorithms. A typical message is a multisigned Commit Record or a timer tick. Rust's Tokio library is used for parallelism and asynchronous execution, and the Rayon library is used for parallelizing CPU-intensive operations.
# Heat Transfer Performance and Flow Characteristics of Helical Baffle-Corrugated Tube Heat Exchanger
###### Abstract  
Heat exchangers are widely used in petrochemical and other industries. Improving the efficiency of heat exchangers to increase energy utilization is crucial. Passive enhanced heat transfer technology is widely studied in heat exchanger research due to its low energy consumption and simple operation. Helical baffle-corrugated tube heat exchangers have not been extensively studied as a promising new class of these devices. This paper investigates the key structural parameters of a helical baffle-corrugated heat exchanger through numerical simulation. This study focuses on the factors affecting heat transfer and flow resistance performance. The results show that reducing the helical angle from 28.42\({}^{\circ}\) to 10.81\({}^{\circ}\) increases the total heat transfer coefficient by approximately 20%. The overall performance of the heat exchanger is evaluated using the efficiency evaluation coefficient (EEC). Optimal levels of each structural factor are determined for different working conditions based on this evaluation.  
heat exchanger; heat transfer enhancement; helical baffle heat exchanger; helical corrugated pipe; orthogonal experiments +
Footnote †: journal: Applied Sciences  
0000-0002-4300-3300][PERSON] 0000-0002-1489-0190][PERSON] 0000-0002-1489-0190][PERSON] 0000-0002-1489-0190][PERSON] 0000-0002-1489-0190][PERSON] 0000-0002-1489-0190]  
is relatively straightforward to operate, which has contributed to its widespread adoption in industrial contexts.  
Continuous helical baffles and helical corrugated tubes are innovative variations of baffles and heat exchanger tubes that facilitate efficient heat exchange. However, most of the present experimental studies utilize substitutes of the ideal continuous helical baffles, such as trisection helical baffles [15; 16; 17], helical trapezoidal baffles [18; 19], and helical baffles with central tube [20; 21]. Most studies on improving heat transfer in special-shaped tubes have been directed towards heat exchangers with conventional tube bundle support. However, there has been limited research on heat transfer enhancement in special-shaped tubes with continuous helical baffles.  
In view of the lack of research on helical baffle-corrugated tube heat exchangers in the previous studies, this study is carried out to design orthogonal experiments by selecting different factors for helical baffle-corrugated tube heat exchangers. The effects of the helix angle of the helical baffle on the integrated heat transfer performance and fluid flow characteristics of the helical baffle-corrugated tube heat exchanger under different working conditions are discussed. Meanwhile, the optimum level combinations of various structural factors under different working conditions are discussed. The results are expected to provide data reference for the future use of helical baffle-corrugated tube heat exchanger.