esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Works
This study can be extended by employing DL techniques in order to improve its accuracy even further. Because the computing complexity increases with the order of modulation and number of transmit antennas, the TAS algorithm is implemented with DNN for the MIMO system [22]. Algorithms for joint transmit and receive antenna selection (JTRAS) are suggested for SM systems, with the goal of optimizing both capacity and ED [23]. The authors of [23] used DL architectures for the basic SM, which are lacking in SE and may not be useful for future-generation networks. Later, DL methods are implemented to determine the optimal receive and transmit antenna configurations. In [24], DNN is used to implement the TAS algorithm for SM systems. To further improve the performance of the suggested technique, the processing load of the DNN is decreased by extracting the unique portion of the EDAS measure. In [25], a DL-based receiver model is implemented in which a DNN is used to do all of the information recovery tasks typically performed by a receiver, from decoding the incoming IQ signal to generating the final bit stream of data. Simulated findings reveal that the proposed DL model's performance is comparable to that of an ideal soft ML decision and significantly better than that of the hard decision technique. In spite of this, ML and DL-based TAS algorithms have only been tested for fundamental SM, which may not meet SE demands. These AI-based strategies have been developed and incorporated only for traditional MIMO and SM systems. To the best of our knowledge, no research study has given considerable attention to the problem of TAS employing DL for SM or advanced variants of SM. The primary contributions of the proposed work are as follows:  
* There are no repositories that have the TAS dataset for FGSM. In total, two datasets of size 25,000 are created by applying the EDAS algorithm repeatedly. These datasets include information about channel setups and antenna counts for various MIMO configurations, as well as basic performance metrics such as ED. * Three distinct supervised DL methods are incorporated to demonstrate the improvements of DL over ML algorithms, including feed-forward neural networks (FNN), recurrent neural networks (RNN), and 1D convolutional neural networks (CNN). * ML- and DL-based TAS methods are compared to WTAS and EDAS in terms of ABER and computational complexity.
## 12 Simulation and Results
### Analysis of Normalized Number of RREQ and RO  
In Figure 6a, for route discovery, the performance metrics directly affect the performance of the optimization method. With increasing forwarding, the RREQ is directly meant to increase the node density. For example, RREQ propagation is not restricted by the routing scheme; it may be propagated to those areas that are far from the destination nodes. Therefore, to re-ensure the node's availability and its accurate position, the flow of RREQ increased.. On average, RREQ will decrease by 10% for the proposed scheme compared to a 2% increase for EEFDBR, along with an increase of 6% UDB, 4.5% 3 DUL, 4.1% UL, and 3.9% RSMB schemes, respectively. Under some constraints, it will further decrease by more than 10% compared with EEFDBR. Consequently, from the result, we determined that both SRAL and RDBF can significantly reduce the normalized numbers of RREQ against the different density of nodes. Moreover, the traditional gossip protocol shows that for the normalized number of RREQ packets, the information with varying numbers of nodes from all observing techniques like RDBF, EEFDBR, UDB, UL, 3 DUL, and RSMB.  
To evaluate the performance of the ROs in Figure 6b, the combination of RREQ, RREP, and RERR was included. Both have two extra overhead trends when compared with the proposed scheme. On average, the RO is reduced when the control number of RREQ is maintained for all schemes like 6.2% RDBF, 4.1% EEFDBR, 4.3% UDB, 4.0% 3 DUL, 2.5% UL, and 2.9% RSMB, respectively.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Simulation Parameters** & **Values** \\ \hline Number of nodes & 700 \\ \hline Network range & 1600 \(\times\) 1000 \(\times\) 1000 \\ \hline Initial energy & 100 j \\ \hline Bandwidth & 2 Kh \\ \hline Acoustic network speed/link & 1500 m/s \\ \hline Transmission range & 200 m \\ \hline Receiving & 0.8 W \\ \hline Data packet size & 50 B \\ \hline Beacon message size & 52 Bits \\ \hline Data rates & 10 kbps \\ \hline Network simulator with AquaSim framework & NS-3 (v3.35) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Simulation parametric values.
# An Unsupervised Weight Map Generative Network for Pixel-Level Combination of Image Denoisers
## 4 Experiments
### Experimental Results with Other Denoising Methods
In particular, our method yields a notably superior denoising effect to any other stand-alone denoising method since it makes full use of both internal and external information. As a pixel-level combination denoiser, it employs two complementary denoisers to boost the denoising effect, which is better than that of CsNet, U2 Fusion, MFNetand and SPD under various noise levels in different datasets. In the following section, a visual comparison of images denoised by typical methods is presented to further support our conclusions. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Noise Level** & **BM3D** & **FFDNet** & **NCSR** & **DnCNN** & **WNNM** & **VDNet** & **CsNet** & **SPD** & **U2 Fusion** & **MEFNet** & **Proposed** \\ \hline \(\sigma=10\) & 33.25 & 33.56 & 33.42 & 33.69 & 33.53 & 33.43 & 33.59 & 33.57 & 28.47 & 34.19 & **34.22** \\ \(\sigma=20\) & 29.14 & 29.65 & 29.58 & 29.70 & 29.73 & 29.93 & 29.67 & 29.59 & 27.14 & 30.03 & **30.82** \\ \(\sigma=30\) & 27.34 & 27.88 & 27.60 & 27.87 & 27.81 & 28.08 & 27.90 & 27.80 & 26.14 & 28.06 & **28.16** \\ \(\sigma=40\) & 26.11 & 26.73 & 26.27 & 26.87 & 26.54 & 26.87 & 26.75 & 26.60 & 25.36 & 26.80 & **26.91** \\ \(\sigma=50\) & 25.28 & 25.89 & 25.35 & 25.85 & 25.63 & 25.99 & 25.91 & 25.80 & 24.75 & 25.92 & **26.04** \\ \(\sigma=60\) & 24.30 & 25.23 & 24.59 & 25.13 & 24.92 & 25.31 & 25.20 & 25.13 & 24.25 & 25.25 & **25.35** \\ Average & 27.63 & 28.16 & 27.80 & 28.19 & 28.03 & 28.27 & 28.17 & 28.08 & 26.02 & 28.38 & **28.46** \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Average performance of various methods on BSD with different noise levels. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Noise Level** & **BM3D** & **FFDNet** & **NCSR** & **DnCNN** & **WNNM** & **VDNet** & **CsNet** & **SPD** & **U2 Fusion** & **MEFNet** & **Proposed** \\ \hline \(\sigma=10\) & 34.81 & 34.83 & 34.81 & 34.94 & 34.94 & 34.63 & 34.62 & 34.76 & 29.66 & 35.04 & **36.46** \\ \(\sigma=20\) & 31.38 & 31.68 & 31.38 & 31.69 & 31.61 & 31.53 & 31.40 & 31.37 & 28.52 & 31.56 & **31.81** \\ \(\sigma=30\) & 29.53 & 29.92 & 29.43 & 29.83 & 29.80 & 29.78 & 29.75 & 29.67 & 27.63 & 29.81 & **30.82** \\ \(\sigma=40\) & 28.10 & 28.68 & 28.06 & 28.52 & 28.48 & 28.57 & 28.55 & 28.41 & 26.83 & 28.54 & **28.78** \\ \(\sigma=50\) & 27.47 & 27.71 & 27.02 & 27.56 & 27.51 & 27.64 & 27.61 & 27.54 & 26.24 & 27.58 & **28.49** \\ \(\sigma=60\) & 26.33 & 26.90 & 26.08 & 26.65 & 26.68 & 26.88 & 26.79 & 26.77 & 25.67 & 26.79 & **27.03** \\ Average & 29.60 & 29.96 & 29.86 & 29.84 & 29.84 & 29.84 & 29.79 & 29.75 & 27.43 & 29.89 & **30.14** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Average PNSR results of different denoising methods on 10 commonly used images with various noise levels.
## 6 Experimental Results
### Compressed Sparse Row Format  
In Table 3, the exact execution time on the GPU of the evaluation stage for one generation and population size 256, for the four datasets used and for the implementations, with the full conflict matrix and with the CSR format are presented, along with the achieved speedup and the conflict density. The speedups are the same for all of the population sizes, something expected because the use of the CSR format is only related to the conflict density of the specific dataset. It can be observed that the dataset with the highest conflict density (_hec-s-92_) has the smallest speedups, while the dataset with the smallest conflict density (_pur-s-93_) has the biggest speedups. The results shown here were obtained from the previous set of measurements.
# Forecasting the Number of Road Accidents in Polish Provinces Using Trend Models
## 5 Conclusions  
Forecasts of the number of accidents in Poland for individual provinces were determined by selected trend models using Excel. The results show that we can still expect a decrease in the number of traffic accidents in the coming years. It should be noted that the COVID-19 pandemic has distorted the results obtained, and if it continues and traffic restrictions are introduced, the proposed model may not be adequate. The value of the average error of 0.52% for annual data can testify to the choice of an effective forecasting \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Province/Model** & **Model** & **ME** & **MPE** & **The Sum of the Squares** & **MSE** & **MAPE (\%)** & **MAE (\%)** \\ \hline Lower Silesia & Exponential & 14.26 & 184.47 & 1,305,179.18 & 62,151.39 & 0.26 & 7.22 \\ \hline Kuyavia–Pomerania & Exponential & 10.95 & 70.32 & 149,986.76 & 7142.23 & 0.35 & 5.00 \\ \hline Lublin Province & Polynomial of 2 nd degree & 0.07 & 93.93 & 301,100.51 & 14,338.12 & 0.32 & 5.09 \\ \hline Lubusz Province & Exponential & 2.76 & 55.03 & 119,113.40 & 5672.07 & 0.47 & 6.93 \\ \hline Lodz Province & Exponential & 6.86 & 317.71 & 3,302,784.34 & 157,275.44 & 1.01 & 8.72 \\ \hline Lesser Poland & Exponential & 26.74 & 290.75 & 2,392,974.36 & 11,3951.16 & 0.14 & 8.17 \\ \hline Masovia & Logarithmic & 176.78 & 497.81 & 10,533,523.06 & 501,596.34 & 1.77 & 8.75 \\ \hline Opole Province & Exponential & 4.25 & 54.32 & 107,325.17 & 5110.72 & 0.02 & 5.98 \\ \hline Subcarpathia & Exponential & 11.57 & 120.66 & 495,205.41 & 23,581.21 & 0.11 & 6.41 \\ \hline Podlasie Province & Exponential & 2.39 & 52.82 & 81,724.62 & 3891.65 & 0.20 & 6.66 \\ \hline Pomerania & Exponential & 12.34 & 168.89 & 853,023.82 & 40,620.18 & 0.14 & 6.61 \\ \hline Silesia & Polynomial of 3 rd degree & 0.02 & 144.78 & 755,411.76 & 35,971.99 & 0.17 & 3.24 \\ \hline Holy Cross & Polynomial of 2 nd degree & 0.01 & 72.91 & 134,675.61 & 6413.12 & 0.41 & 5.26 \\ \hline Warmia–Masuria & Exponential & 4.82 & 150.77 & 765,403.02 & 36,447.76 & 1.00 & 9.60 \\ \hline Greater Poland & Exponential & 48.45 & 563.32 & 9,570,955.89 & 455,759.80 & 1.81 & 16.12 \\ \hline West Pomerania & Exponential & 1.99 & 75.89 & 166,539.04 & 7930.43 & 0.16 & 5.12 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Summary of errors for annual data.
###### Abstract  
Peatlands play a fundamental role in climate regulation through their long-term accumulation of atmospheric carbon. Despite their resilience, peatlands are vulnerable to climate change. Remote sensing offers the opportunity to better understand these ecosystems at large spatial scales through time. In this study, we estimated water table depth from a 6-year time sequence of airborne shortwave infrared (SWIR) hyperspectral imagery. We found that the narrowband index NDWI\({}_{1240}\) is a strong predictor of water table position. However, we illustrate the importance of considering peatland anisotropy on SWIR imagery from the summer months when the vascular plants are in full foliage, as not all illumination conditions are suitable for retrieving water table position. We also model net ecosystem exchange (NEE) from 10 years of Landsat TM5 imagery and from 4 years of Landsat OLI 8 imagery. Our results show the transferability of the model between imagery from sensors with similar spectral and radiometric properties such as Landsat 8 and Sentinel-2. NEE modeled from airborne hyperspectral imagery more closely correlated to eddy covariance tower measurements than did models based on satellite images. With fine spectral, spatial and radiometric resolutions, new generation satellite imagery and airborne hyperspectral imagery allow for monitoring the response of peatlands to both allogenic and autogenic factors.  
hyperspectral; Sentinel-2; Landsat TM5; Landsat 8 OLI; bog; CASI; SASI; northern peatland; hydrology; Mer Bleue +
Footnote †: journal: Remote Sens. **2018**, _10_, 687; doi:10.3390/rs10050687
# New and Efficient Algorithms for Producing Frequent Itemsets with the Map-Reduce Framework
## 2 Background and Related Work
### Apriori Map-Reduce Algorithms  
There are several known Map-Reduce Apriori algorithms. The PApriori algorithm [11] is a porting of the classical algorithm to Map-Reduce. Everything is done inside the main program in a sequential way, except for the frequency count which is done in parallel with the Map-Reduce algorithm. This algorithm is depicted in Figure 2. Apriori-Map/Reduce [12] is similar to PApriori but also performs the candidate generation in parallel by using another Map-Reduce job. Both of these algorithms require K steps to find the frequent itemsets of length K, with one or two MR jobs per candidate length/step.  
MRApriori [13] is different and presents a two-step algorithm. The first step is to divide \(\mathcal{D}\) into Splits (done by the MR framework) and run the classical Apriori on each Split inside the Mapper (it reads the whole input into the memory of the Mapper as one chunk) to find \"locally\" frequent itemsets. After that, the Reducer joins the results of all Mappers and they become candidates for final frequent itemsets (if the itemset is frequent in \(\mathcal{D}\), then it must be frequent in at least one of its Splits). The second job/step is just counting of all candidates' appearance in each Split and filtering only the frequent itemsets that pass the minimum support level. Figure 3 demonstrates the algorithm's block diagram for clarification.  
IMRApriori [10] works similarly to MRApriori with one add-on/observation that an itemset may become a candidate itemset only if it appeared locally frequent in \"enough\" Splits. More precisely,  
Figure 2: Map-Reduce Apriori Algorithm [11].  
let \(\text{S}_{1},\dots,\text{S}_{\text{m}}\) be Splits of \(\mathcal{D}\) in step 1. Denote their sizes to be \(\left|\text{S}_{i}\right|\) if itemset \(x\) is locally frequent in k (k \(\leq\text{ m}\)) Splits without loss of generality, called \(\text{S}_{1},\dots,\text{S}_{\text{k}}\). Let \(\text{C}_{i}\) be the count of occurrence of X in Split Si. Let \(minSup\) be the minimum support. Then:  
\[sup(x)\leq\sum_{1}^{k}\text{C}_{i}+\sum_{k+1}^{m}(\left|S_{i}\right|*minSup-1)\]  
If this number is less than \(minSup*\left|\mathcal{D}\right|\), then there is no need to calculate the occurrence of \(x\) in \(\mathcal{D}\) in step 2 as it does not have a chance to be frequent any more (early pruning). This observation is applied in the first Reducer (\"Union of all local FI\") of IMRApriori and can be seen in the block diagram in Figure 3. This algorithm is shown to outperform all previous ones and a variation of it will be used by us.
# Rotor Location During Atrial Fibrillation: A Framework Based on Data Fusion and Information Quality
## 3 Results
### Explainability and Performance in Rotor Detection for AF  
Our JDL-based system effectively constructs a decision-support framework for rotor detection in AF while maintaining robustness against noise and artifacts. Unlike black-box machine learning models, our approach improves interpretability by assessing IQ at multiple stages, offering explainability, a key feature missing in electroanatomical mapping systems and traditional machine learning-based methods [17; 49; 50]. Recent advancements in artificial intelligence emphasize the growing importance of explainable machine learning techniques [51], which hold significant potential in cardiac electrophysiology. Research in this field has explored their integration, such as using quantitative EGM features from electroanatomical mapping systems to identify AF-driving mechanisms [52]. This approach enabled the formation of clusters based on arrhythmia properties. Similarly, explainable models have been used to predict rotor formation post-AF ablation [53] and to develop patient-specific cardiac propagation models using imaging data. Other studies have leveraged demographic and medical records to predict post-catheter ablation outcomes in paroxysmal AF patients [54]. However, despite their promise, these approaches remain susceptible to artifacts and noise, which can impact both accuracy and interpretability.  
Addressing this challenge, our work aligns with the principles of explainable machine learning, focusing on answering questions such as \"What additional insights can the model provide?\" Future research should explore the integration of explainability-driven methodologies to enhance decision support systems for AF electrophysiological procedures. The experimental results show that our system achieves a rotor localization accuracy above 90%, with up to a 10% improvement when tuning parameters based on IQ metrics. However, performance remains sensitive to noise levels. The framework is adaptable, allowing the integration of alternative techniques at various levels, including predictive models, EGM classification methods, and rotor localization strategies. Additionally, our results highlight SampEn as a key feature for constructing electroanatomical maps while identifying the mean as a viable alternative. The flexibility of the system enables researchers to consolidate diverse methodologies into more robust, interpretable, and reliable decision-support models.
# Mechanical Properties of Disconnectable Coupling Joints for Steel Bracing under Eccentric Load
## 5 Flexural Performance under Eccentric Load
### Parameters Analysis on Flexural Performance
#### 5.2.2 Influence of Steel Wedge Length  
Figure 10a shows the effect of steel wedge length on the moment-rotation curves. Length significantly improves the flexural performance of DC joints when the length is less than 350 mm. Figure 10b shows the relationship of the steel wedge length with the initial rotational stiffness and moment resistance. When the length increases from 300 mm to 350 mm, the initial rotational stiffness and moment resistance increase by 9.5 MN\(\cdot\)m\(\cdot\)rad\({}^{-1}\) and 27.9 kN\(\cdot\)m, respectively, and their increase rate is about 30.2% and 12.9%, respectively. When steel wedge length is greater than 350 mm, the effective compression area is unaffected because the length exceeds the lower edge length of the middle-rib plate. Therefore, when steel wedge length increases from 350 mm to 400 mm, the increase rate of the initial rotational stiffness and moment resistance is only 8.7% and 5.5%, respectively. The increase rate decreases to 3.2% and 0.7%, respectively, as the steel wedge length increases from 400 mm to 450 mm. Therefore, in practical engineering, the steel wedge length should be greater than that of the lower edge of middle-rib plate, and excessively long steel wedges are unnecessary.
# Influence of Earthquakes on Landslide Susceptibility in a Seismic Prone Catchment in Central Asia
## 2 Materials and Methods
### Data Collection
DEM refers to digital elevation model, ASCII refers to American Standard Code for Information Interchange, LSI refers to landslide susceptibility index, and VOL refers to volume of landslide. based on the _in situ_ measured values (ranged between 6.0 and 13.8 kPa with an average of 10.0 kPa) [62]. In this sense, the value of \(R_{u}\) coefficient could be calculated for each layer with an average value of 0.15 (Table 1). To reflect the effect of the second triggering factor, the seismic loading by means of a specified horizontal pseudo-acceleration coefficient (_keq_, dimensionless and scaled as a fraction of gravity, \(g\)) was employed [33]. This specified coefficient is applied as a uniform horizontal force to represent the effects of peak ground acceleration from an earthquake [63; 64]. The USGS (United States Geological Survey) developed a scale of the earthquake magnitude and the corresponding peak ground acceleration (Table 2). In this study, we applied this information and selected five typical earthquake scenarios, namely M 5.0 (_keq_ = 0.092, moderate), M 6.0 (_keq_ = 0.18, strong), M 7.0 (_keq_ = 0.34, severe), M 8.0 (_keq_ = 0.65, violent), and M 9.0 (_keq_ = 1.24, extreme) (Table 2). Landslide hazards are caused by a series of variables and their interactions, including both the internal geological conditions and external environmental conditions [65; 66]. In this study, nine environmental variables were selected to perform the relationship analysis, including elevation, slope, aspect, land cover, soil type, annual precipitation, distance to faults, roads, and streams. First, three topographic variables, including elevation, slope, and aspect, were extracted using the above-mentioned DEM with a spatial resolution of 10 m in ArcGIS. The values of elevation were the original values of DEM, whereas the values of slope and aspect were calculated using the slope-aspect function of ArcGIS. Elevation and slope were classified into four classes in order to compare the variations of LSI and VOL across the spatial scales: low (576-1000 m), moderate (1000-2000 m), high (2000-3000 m), and very high (>3000 m) for elevation; gentle (0-10\({}^{\circ}\)), moderate (10-25\({}^{\circ}\)), steep (25-40\({}^{\circ}\)), and very steep (>40\({}^{\circ}\)) for slope. Aspect was classified into eight classes: north (0-22.5 and 337.5-360), northeast (22.5-67.5), east (67.5-112.5), southeast (112.5-157.5), south (157-202.5), southwest (202.5-247.5), west (247.5-292.5), and northwest (292.5-337.5). Second, the used land cover data were Globcover 2009 V2.3 from European Space Agency ([[http://due.esrin.esa.int/page_globcover.php](http://due.esrin.esa.int/page_globcover.php)]([http://due.esrin.esa.int/page_globcover.php](http://due.esrin.esa.int/page_globcover.php)); accessed on 20 February 2021) with a resolution of 300 m [67]. In these data, 23 types of land cover are developed. Among them, 14 types are occurring in the Mailuu-Suu catchment. To simplify the analysis, eight
## 2 Principles and Methodologies
### OSP Methods
#### 2.3.3 Selection of the Number of Sensors  
In this paper, the selection of the number of sensors is carried out by MAC, fitness values, and S criteria [12]. The specific steps are as follows:  
1. Constructing modal shape matrix \(\Phi\) based on target modes and optimized DOFs.
2. Conduct QR decomposition on the transpose of the matrix \(\Phi\) to determine the initial sensor locations and calculate the corresponding MAC matrix.
3. From the remaining DOFs outside the initial sensor locations, select point m to be added to the optimized configuration. Recalculate its MAC matrix, denoted as \((MAC_{ij})_{m}\), with the maximum off-diagonal element value, denoted as \((f_{max})_{m}\).
4. Loop the operation in step (3) to plot the curve of the maximum off-diagonal element value of the MAC matrix with the number of sensors.
5. The appropriate interval segments are selected for the graph in (4) and the ONS is selected using the preferred intelligent algorithm in combination with the maximum value of the off-diagonal elements of the MAC matrix, the value of the function fitness, and the maximum singular value ratio criterion.  
Traditionally, most scholars determine the number of sensors based on MAC [12; 29] and also use the fitness value [30] to determine the number of sensors. This paper simultaneously considers MAC, fitness value, and S to mutually verify the ONS. These three evaluation indicators all show that the lower the value, the better the sensor layout effect. Therefore, when these three values are calculated multiple times within the selected sensor number interval, the lowest value corresponding to the number of sensors should be the optimal number. The results indicate that the number of sensors determined based on these three criteria is the same for each operating condition, which well verifies the rationality of the optimal sensor number selection method in this paper.