esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Simulation Analysis
### Computational Efficiency Evaluation  
To further synthesize the superiority of the proposed RMSDE in terms of computational efficiency, this section extends all types of entropy calculation methods to multiple scales and achieves the evaluation of computational efficiency by measuring the actual computational elapsed time of each entropy method for the same time series under different scale factors \(\tau\).  
The time series for this simulation test uses the composite signal constructed in Section 4.2, and the first 5000 points in this composite signal are taken as the calculation object. The actual running time of the multidimensional entropy value of the time series is calculated by counting each entropy method when taking different scale factors \(\tau\) so as to reflect the actual operational efficiency of the algorithm. In this test, the parameter settings  
Figure 6: Time domain waveform of the composite signal.  
Figure 7: Trends in increasing rates of different entropy methods.  
of the four entropy methods are still the same as in the previous two sections. The scale factor \(\tau\) is varied from 1 to 15. The computational time consumption curves of the four entropy methods for different values of \(\tau\) are shown in Figure 8. Subsequently, the average values of the four curve sequences are calculated, respectively, as shown in Table 1. The operating system used in this simulation test is Windows 10, and all the codes are compiled on MATLAB R2017b. The configuration of the computer is Core i5-9500 HQ [EMAIL_ADDRESS] GHz, and the memory size is 16 GB.  
Combining Figure 8 with Table 1, it can be seen that MPE has the highest computational efficiency, with an average computation time of only 0.091 s for different scale factors, which is mainly due to its computational principle of using the magnitude ranking of track amplitudes to measure complexity. However, this computational principle also makes MPE, in the more significant complexity estimation performance and anti-noise performance, has obvious disadvantages. Although the RMSDE proposed in this chapter is slightly lower than the MPE in terms of computational efficiency, the difference is only about 0.18 s. This difference is mainly due to the number of operations caused by the refined symbol encoding process of RMSDE. Unlike MPE, this process has resulted in a key improvement in other performance aspects of RMSDE at the cost of only adding a bit of computational time. RMSDE is still significantly superior to MSE and MFE in computational efficiency. In terms of average calculation time, MSE and MFE take approximately seven times longer than RMSDE. Combining the analysis in Sections 4.1 and 4.2, it is demonstrated that RMSDE has the best comprehensive performance among these four entropy methods.
## 6 Seismic Sedimentology Technology
### Spectral Decomposition Technique  
Spectral analysis of seismic data from the Dainan Formation in the Gaoyou Sag revealed a dominant frequency range of 20-30 Hz (Figure 5). The study computed spectral energy distributions across 8-64 Hz, segmenting 3D seismic volumes into six frequency bands (8-12 Hz, 12-16 Hz, 16-24 Hz, 24-32 Hz, 32-48 Hz, and 48-64 Hz). Each band generates a corresponding root mean square (RMS) amplitude slice. Comparative analyses have demonstrated significant frequency-dependent geological responses, with the 16-24 Hz band exhibiting exceptional resolution for thin-bed identification.  
Frequency-partitioned slices surpass conventional amplitude/phase slices in detecting subtle lateral depositional variations and resolving heterogeneous sedimentological features. Notably, spectral energy maps maintain diagnostic clarity even when attribute boundaries appear ambiguous. The technique's primary advantage lies in its capacity to reveal subtle attribute variations through animated frequency slice visualization. When integrated with core-derived sedimentary sequences and depositional models, this approach enables effective analysis of lateral reservoir heterogeneity within target intervals.
## 6 Numerical Results
### Analysis of Network Performance
#### 6.4.2 PP Capacity  
Figure 10 presents the value of \(z\) as a function of the PP capacity, expressed by capacity multiplier \(M\). The results were obtained for different numerologies and averaged over the network scenarios with \(10\leq R\leq 70\) RUs.  
It can be seen that increasing the PP capacity can reduce the number of required PPs in the network up to some level above which no significant gains are achieved. The reduction in \(z\) is higher for higher numerologies and reaches about 20% for \(\mu=3\), comparing the values of \(z\) for \(M=1.5\) and \(M=4\), whereas it is about 12% for \(\mu=2\) and 8% in MESH-20 and 0% in MESH-38 for \(\mu=1\).  
Figure 9: Number of active PPs as a function of the URLLC traffic ratio assuming different numerologies and \(R=30\) RUs.
## 3 UAN Generic Architecture
### Gateway  
A UAN gateway is a device with multiple communication interfaces which interconnects UAN backhauls with a core network (e.g., an Intranet or the Internet), generally by means of Metropolitan Area Network (MAN) or Wide Area Network (WAN) technologies, such as fiber-optics, Digital Subscriber Line (DSL), 2.5G/3G/4G, Power Line Communication (PLC), Ethernet variants, _etc_. For the sake of service availability, the gateway is commonly required to be mains-powered. In order to provide high UAN reliability, it is recommendable that a given sensor node can reach more than one gateway.
## 4 Experimental Results and Discussion
### Output Power Fluctuation and Longitudinal Mode Shift of the SLM Laser
#### 4.2.4 LT Etalon's Compensation for Optical Path-Length Change  
To maintain the laser's SLM operation, we replaced the K9 etalon with an LT etalon. A 160V voltage was applied to the LT etalon to generate SLM oscillation at the beginning. The light green and orange waveforms in Figure 6a are the oscillating modes before and after longitudinal mode drift, respectively. This drift of about 0.19 GHz happened and a second longitudinal mode oscillated during the first 25 s in Figure 6b. In this interval, the laser spot's centroid positions along the x- and y-axes move down slightly, as Figure 6b records. In the next 15 s, we changed the voltage on LT etalon to \(-240\) V. The bottle green line in Figure 6b demonstrates that the SLM oscillation returned while another 0.06 GHz drift developed relative to MLM operation.  
According to Formula (10), a voltage difference of 400 V could bring about a 0.05 \(\upmu\)m change in the laser's optical path and a 0.23 GHz frequency shift for one longitudinal mode. From Formula (11), the same voltage also caused about 2.6 GHz movement of the LT etalon's transmission peaks. The movement also contributed to the SLM operation in the last 15 s in Figure 6b. On the other hand, the duration of the SLM oscillation was shorter than that of the K9 etalon with the same reflectivity, due to the absorption of the LT crystal to the laser.
# Adaptive Hybrid Storage Format for Sparse Matrix-Vector Multiplication on Multi-Core SIMD CPUs
## 2 Background  
### Sparse Matrix-Vector Multiplication  
Sparse matrix-vector multiplication (SpMV) is of the form \(\mathbf{y}\leftarrow\mathbf{A}\cdot\mathbf{x}\), where \(\mathbf{A}\) (\(M\times N\)) is a sparse matrix, and \(\mathbf{x}\) (\(N\times 1\)) and \(\mathbf{y}\) (\(M\times 1\)) are dense vectors. Figure 1 gives a simple example of SpMV, where the number of non-zero elements (\(nnz\)) of \(\mathbf{A}\) is 8 and \(M=N=4\).  
SpMV is a numerical algorithm based on sparse matrix. The use of compressed storage formats reduces memory footprints, but it also increases the number of discontinuous memory accesses and increases the memory access overhead. Since the compressed storage destroys the temporal and spatial locality in the calculation, it is difficult to optimize the SpMV on the cache-based CPU. To improve the computational efficiency of SpMV, the existing implementation needs to be improved by combining the characteristics of the target architecture and matrix storage format with the characteristics of the SpMV algorithm itself.
## 3 System Architecture
### Virtual Vehicular Server
#### 3.3.2 Server Selection  
The VVS zones are typically chosen as intersections, to ensure the continuous presence of vehicles that will serve as DIFTOS-Server. When a vehicle enters a VVS region and stops at traffic lights, it announces itself as a server, by broadcasting a \"Server_Candidate\" message to a nearby vehicle and  
Figure 2: VVS hierarchical partition.  
initialize a wait response timer, in case a \"Server_Exist\" message is received before timer termination, the candidate will stop assuming itself as a server. Otherwise, it will broadcast \"Server_Elected\" message to nearby vehicles, and start doing server responsibility, which is to resolve path requests and update the RRM. When the server leaves the VVS region, it forwards the RRM to one of the nodes that are waiting for the traffic light and announce it as a server by broadcasting \"Server_Replaced\" message.  
It is worthwhile to mention that in high density scenarios, the server selection scheme might suffer from broadcast storm problem [21]. To alleviate the effects of broadcast storm problem, the server selection in DIFTOS includes a broadcast suppression scheme. The latter is based on two techniques. (1) Number of hops: In which the number of hops is limited to prevent the vehicles from retransmitting the message to vehicles that already received it; (2) Transmitting location: The vehicles check whether they are still inside the region of interest (VVS region) before retransmitting the message.
## 2 Results and Discussion
### Nephroprotective Effect of APTM
#### 2.4.8 Effect of APTM on Serum Electrolytes Level  
Table 4 summarizes the effects of aqueous APTM extract on serum electrolyte concentrations in GM-poisoned rats. Indeed, as compared to healthy rats (NCG), GM treatment substantially raised sodium (\(p<0.01\)), chloride (\(p<0.05\)), calcium (\(p<0.01\)), and lowered potassium in a non-significant way (NCG). These outcomes align with some investigations where GM led to rat electrolyte imbalance [10; 44]. This could be explained by how GM affects the basolateral membranes and the membranes that transport the epithelial cells' brush borders, causing an electrolyte imbalance. Additionally, GM inhibits several membrane transporters from brush borders and basolateral membranes, regardless of the cellular lesion, which results in aberrant electrolysis [10]. Transport inhibition impacts tubular reabsorption and cell viability, leading to necrosis or apoptosis in some cases [45; 46]. However, treatment of rats (intoxicated by GM) with aqueous APTM extract reduced sodium, chloride, and calcium levels in a non-significant way, while increasing potassium levels in a non-significant way. These findings agree with other researchers, who found that plant extract restored electrolyte concentration in rats poisoned by GM [30; 44].  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline \multicolumn{1}{c}{**Groups**} & \multicolumn{1}{c}{**Sodium (mmol/L)**} & \multicolumn{1}{c}{**Potassium (mmol/L)**} & \multicolumn{1}{c}{**Chloride (mmol/L)**} & \multicolumn{1}{c}{**Calcium (mg/L)**} \\ \hline
**NCG** & 117.00 \(\pm\) 20.70 & 3.45 \(\pm\) 1.20 & 77.50 \(\pm\) 18.94 & 70.55 \(\pm\) 19.67 \\ \hline
**GGM (80 mg/kg)** & 154.20 \(\pm\) 12.00 **\#** & 2.60 \(\pm\) 0.78 \({}^{\rm{Ns}}\) & 102.25 \(\pm\) 3.59 \({}^{\#}\) & 104.27 \(\pm\) 4.18 **\#** \\ \hline
**GGM+TM (200 mg/kg)** & 145.50 \(\pm\) 4.65 \({}^{\rm{Ns}}\) & 2.84 \(\pm\) 0.53 \({}^{\rm{Ns}}\) & 99.25 \(\pm\) 5.85 \({}^{\rm{Ns}}\) & 97.06 \(\pm\) 6.94 \({}^{\rm{Ns}}\) \\ \hline
**GGM+TM (400 mg/kg)** & 143.50 \(\pm\) 11.93 \({}^{\rm{Ns}}\) & 3.11 \(\pm\) 0.29 \({}^{\rm{Ns}}\) & 94.60 \(\pm\) 7.60 \({}^{\rm{Ns}}\) & 94.17 \(\pm\) 10.96 \({}^{\rm{Ns}}\) \\ \hline \multicolumn{1}{c}{The findings are displayed as mean \(\pm\) SEM, (\(n=6\)). \({}^{\#}\)\(p<0.05\), \({}^{\#}\)\(p<0.01\) comparing to NCG. N’s: not significant related to GGM (80 mg/kg).} \\ \hline \hline \end{tabular}
\end{table}
Table 4: Effect of APTM extract on serum electrolyte amounts in GM-exposed rats.  
Figure 7: Effect of aqueous APTM extract on kidney MDA concentrations in GM-poisoned rats. The results are posted as mean \(\pm\) SEM, (\(n=6\)). \({}^{\##}\)\(p<0.001\) related to NCG. \({}^{*}\)\(p<0.05\), \({}^{**}\)\(p<0.01\) compared to GGM (80 mg/kg).
## 4 Performance Evaluation of the Proposed Framework
### Resources Utilisation and Data Locality  
During the peak workload, fixed and auto-scaling clusters do not have idle slaves. When the workload falls back, the fixed clusters have more idle slave nodes than auto-scaling cluster. To process and efficiently analyse big data, the MapReduce jobs can be carried out on both the Hadoop fixed and auto-scaling clusters. Figure 5 illustrates the aggregated time consumed to accomplish all the workloads. In the auto-scaling cluster, there are always a minimum of 2 and a maximum of 14 machines available depending upon the workload. The results show that for the same MapReduce job when running on the auto-scaling cluster compared to the fixed fourteen-slaves cluster, takes 9.6 h and 9.72 h to finish respectively. This accordingly demonstrates that the auto-scaling cluster processes and finishes the jobs in an efficient manner and thus saving not only 12 min, but also number of instances.  
Figure 4: Time consumption for each workload last three hours.  
Figure 3: Time consumption for each workload last hour.
## 3 Results
### Comparison of Time for Training and Testing between Segmented and Non-Segmented Images  
In this paper, we also investigate the leaf disease classification performance and time between non-segmented and segmented images. In particular, Table 6 presents the results of the leaf disease classification performance as well as time for training and testing between the two sets of non-segmented and segmented leaf disease images using the HSV color space method. The performance of leaf disease classification when using the segmented method is higher than that of using the non-segmented one. In particular, the classification accuracy of leaf diseases in the two cases are 99.72% and 99.63%, respectively. It is obvious that time for training and testing of the leaf disease classifier in the case of the segmented images is faster than that in the non-segmented ones; specifically, it takes the network using the segmented images 2.75 \(\times\) 10\({}^{5}\) s (training) and 29.30 s (testing), while times of the network using the non-segmented images are 2.98 \(\times\) 10\({}^{5}\) (training) and 35.27 (testing). The reason for this achievement of better performance and time is that the network using the segmented images only focuses on the feature region of the leaf image and the same background of the leaf images is black. Meanwhile, for the original image, the network is trained for both the leaf image region and background.
## 2 Materials and Methods
### Cell Viability Assays  
To ascertain the extent of the cytotoxicity exerted by DMH on C2C12 cells, cell viability was assessed. Cell viability was determined using the 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl-2H-tetrazolium bromide (MTT) assay. The C2C12 cells were seeded in 24-well plates at a density of 1 \(\times\) 10\({}^{4}\) cells/well and incubated for 24 h. Subsequently, the cells were treated with DMH at varying concentrations (0, 1, 5, 10, 25, and 100 \(\upmu\)g/mL) for 24 h. Furthermore, 1000 \(\upmu\)L of 2.5 \(\upmu\)g/mL MTT reagent was added to each well and incubated for 3 h at 37 \({}^{\circ}\)C with 5% CO\({}_{2}\) in a humidified incubator. Moreover, the supernatant was removed, and 1000 \(\upmu\)L of dimethyl sulfoxide was added to each well to dissolve the resulting formazan crystals. The absorbance was then measured at 570 nm using an MMR SPARK(r) spectrophotometer (Tecan, Mannedorf, Switzerland). Cell viability is expressed as a percentage of the absorbance of the control. We investigated the effect of DMH on the protection of C2C12 cells against damage induced by DEX. C2C12 cells were seeded in 24-well plates at a concentration of 1 \(\times\) 10\({}^{4}\) cells/well and incubated for 24 h. Subsequently, the cells were treated with 100 \(\upmu\)M DEX and DMH at varying concentrations (0, 1, 5, 10, 25, and 100 \(\upmu\)g/mL) for 24 h. Cytoprotective effects of DMH were investigated by measuring cell viability using the aforementioned procedure.
# A Review of Research on Spectrum Sensing Based on Deep Learning
## 2 Spectrum-Sensing Methods
### Cooperative Spectrum Sensing
#### 2.3.2 Distributed Cooperative Spectrum Detection  
Unlike centralized cooperative spectrum sensing, distributed cooperative spectrum-sensing networks do not require wired infrastructure support or data fusion centers. The basic process of distributed cooperative spectrum detection is as follows: in the sensing environment, each local node first senses independently, then each node shares the sensed data with each other, and finally, each local node combines all the received sensing information to make a final judgment. Figure 7 below shows the basic model for distributed cooperative spectrum detection.  
Although compared to centralized cooperative sensing, distributed cooperative sensing does not rely on data fusion centers to make cooperative decisions; local cognitive nodes need to interact more regularly with local sensing information, which puts higher demands on control channel resources. In order to improve the reliability of robust cooperative spectrum sensing in dense network environments, a current data fusion scheme based on clustering algorithms and distributed sensing by [PERSON] et al. [42] is suitable for environments with a high degree of relay channel fading and is designed to distribute the clusters and select the fusion nodes (FNs) in two phases. The results show that there is an improvement of 0.42 in the error through the two processes; moreover, the detection sensitivity is also improved to 0.95. Multi-frequency CSS is also a research focus in CR networks, where SU scheduling with a subset of channels in a distributed CR network poses a strong challenge. To address this difficulty, [PERSON], in [43], proposed a two-stage multi-frequency CSS scheme where, in the first stage, a master channel is selected for each channel by optimization, and then collaborating SUs are selected for each channel. The second stage allows the original SUs to participate in the sensing process. It is shown that this scheme minimizes sensing consumption compared to existing schemes.
# HYDRUS-1D Simulation of Soil Water Dynamics for Sweet Corn under Tropical Rainfed Condition
## 2 Material and Methods
### Model Evaluation  
To evaluate the model performance, the agreement between observed and modeled data was assessed through the statistical procedure. The coefficient of determination R\({}^{2}\), modeling efficiency (EF) and root mean square error (RMSE) were used for this purpose, which are given as [28].  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Depth (cm)** & **Soil Type** & \(\theta_{s}\) **(cm\({}^{3}\)cm\({}^{-3}\))** & \(\theta_{s}\) **(cm\({}^{-3}\))** & \(a\) **(cm\({}^{-1}\))** & \(n\)** & **Ks (cm Day \({}^{-1}\))** & \(L\)** \\ \hline
0–20 & Clay & 0.0973 & 0.4750 & 0.0250 & 1.1625 & 12.86 & 0.5 \\
20–40 & Clay & 0.0930 & 0.4561 & 0.0252 & 1.2019 & 12.43 & 0.5 \\
40–60 & Clay & 0.0952 & 0.4661 & 0.0254 & 1.1828 & 12.78 & 0.5 \\
60–80 & Clay & 0.1020 & 0.5084 & 0.0201 & 1.1903 & 18.11 & 0.5 \\
80–140 & Clay & 0.1017 & 0.5066 & 0.0203 & 1.1825 & 17.56 & 0.5 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Optimized value of soil hydraulic properties.  
Coefficient of determination  
\[R^{2}\ =\ \left\{\frac{\sum_{i=1}^{n}\bigl{(}Oi\ -\ \overline{O}\bigr{)}\bigl{(}Pi\ -\ \overline{P}\bigr{)}}{\biggl{[}\sum_{i=1}^{n}\bigl{(}Oi\ -\ \overline{O}\bigr{)}\biggr{]}^{2}\biggl{[}\sum_{i=1}^{n}\bigl{(}Pi\ -\ \overline{P}\bigr{)}\biggr{]}^{2}}\right\}^{2}. \tag{9}\]  
Modeling efficiency  
\[EF\ =\ 1\ -\ \frac{\sum_{i=1}^{n}\bigl{(}Oi\ -\ \Pi i\bigr{)}^{2}}{\sum_{i=1}^{n} \bigl{(}Oi\ -\ \overline{O}\bigr{)}^{2}}. \tag{10}\]  
Root Mean Square Error  
\[RMSE\ =\ \sqrt{\frac{\sum_{i=1}^{n}\bigl{(}Oi\ -\ \Pi i\bigr{)}^{2}}{n}}, \tag{11}\]  
where \(Pi\) are predicted values; \(\overline{P}\) is mean of predicted values; \(O\) are observed values; and \(\overline{O}\) is mean of observed values. The optimum value of \(R^{2}\) and \(EF\) is 1 while for \(RMSE\) it is 0.
# Segmentation of Preretinal Space in Optical Coherence Tomography Images Using Deep Neural Networks
## 6 Results
### Effect of Removing Anomalous Data Samples  
In Table 5, we demonstrate how removing anomalous data samples from the dataset can improve the training of the network. The results show that the accuracy improved for all the tested methods. Here the LFUNet and original UNet present the best accuracy, while ReLayNet stays the worst in all areas, although its results have improved the most.  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{5}{c}{**Average Dice Score (SD)**} & \multicolumn{5}{c}{**MAE (SD) [px]**} \\ \cline{2-9}  & \multirow{2}{*}{**Vitreous**} & \multicolumn{2}{c}{**Preretinal Space**} & \multirow{2}{*}{**Retina**} & \multicolumn{2}{c}{**Region**} & \multirow{2}{*}{**PCV**} & \multirow{2}{*}{**ILM**} & \multirow{2}{*}{**RPE**} \\ \cline{2-9} \cline{5-9} Graph search & 0.9842 (0.033) & 0.8216 (0.207) & 0.9385 (0.163) & 0.9888 (0.028) & 7.29 (13.7) & 5.10 (12.1) & 5.20 (15.5) \\ UNet & **0.9942 (0.015)** & **0.9458 (0.088)** & **0.9929 (0.002)** & **0.9976 (0.003)** & **2.83 (6.05)** & **0.56 (0.14)** & **0.75 (0.35)** \\ LFUNet & 0.9928 (0.017) & 0.9337 (0.099) & 0.9922 (0.003) & 0.9972 (0.003) & 3.11 (5.72) & 0.60 (0.18) & 0.84 (0.46) \\ Attention UNet & 0.9822 (0.037) & 0.8679 (0.197) & 0.9918 (0.012) & 0.9953 (0.010) & 5.40 (10.04) & 0.63 (0.77) & 0.87 (1.11) \\ ReLayNet & 0.9529 (0.056) & 0.7827 (0.245) & 0.9906 (0.008) & 0.9814 (0.032) & 25.66 (26.34) & 0.84 (1.30) & 1.45 (1.77) \\ \hline \hline \end{tabular}
\end{table}
Table 4: The baseline Dice score and Mean Absolute Error (with Standard Deviation) results of 4 class pixel segmentation with various neural network models. The best results are highlighted in bold.
# Reducing Forecast Errors of a Regional Climate Model Using Adaptive Filters
###### Abstract  
In this work, the use of adaptive filters for reducing forecast errors produced by a Regional Climate Model (RCM) is investigated. Seasonal forecasts are compared against the reanalysis data provided by the National Centers for Environmental Prediction. The reanalysis is used to train adaptive filters based on the Recursive Least Squares algorithm in order to reduce the forecast error. The K-means unsupervised learning algorithm is used to obtain the number of filters to employ from the climate variables. The proposed approach is applied to some climate variables such as the meridional wind, zonal wind, and the geopotential height. The forecast is produced by the Eta RCM at 40-km in a domain covering most of Brazil. Results show that the proposed approach is capable of reducing the forecast errors, according to evaluation metrics such as normalized mean square error, maximum absolute error, and maximum normalized absolute error, thus improving the seasonal climate forecasts.  
adaptive filtering; regional climate model; forecast error; signal processing