esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experiment and Results
### Comparison of Different Feature Extraction Combinations
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Datasets** & **Method** & **LDA** & **SVM** & **KNN** & **ANN** \\ \hline \multirow{8}{*}{Ninapro DB4} & TD & 58.96 & 50.89 & 63.85 & 75.78 \\  & mDWT & 56.18 & **66.47** & 73.03 & 75.79 \\  & ICC & 55.92 & 54.76 & 69.74 & 76.61 \\  & TD and mDWT & 57.26 & 65.30 & 74.94 & 75.11 \\  & TD and ICC & 62.27 & 55.55 & 75.51 & 76.09 \\  & ICC and mDWT & 60.37 & 62.30 & 74.73 & 76.19 \\  & ICC and TD and mDWT & **62.91** & 63.15 & **75.70** & **77.64** \\ \hline \multirow{8}{*}{Ninapro DB5} & TD & 69.43 & 66.54 & 69.88 & 75.37 \\  & mDWT & 67.08 & 70.97 & 73.78 & 78.83 \\  & ICC & 66.68 & 68.86 & 69.38 & 79.12 \\  & TD and mDWT & 69.55 & 70.27 & 74.52 & 79.73 \\  & TD and ICC & 70.55 & 70.91 & 70.91 & 79.19 \\  & ICC and mDWT & 69.21 & 70.89 & 74.47 & 79.97 \\  & ICC and TD and mDWT & **70.59** & **71.25** & **75.41** & **81.88** \\ \hline \multirow{8}{*}{BioPatRec DB1} & TD & 52.15 & 36.59 & 65.65 & 85.95 \\  & mDWT & 48.55 & **65.19** & 77.05 & 81.52 \\  & ICC & 41.76 & 38.94 & 70.51 & 78.08 \\  & TD and mDWT & 55.60 & 63.90 & 80.41 & 87.25 \\  & TD and ICC & 55.59 & 44.86 & 79.27 & 86.84 \\  & ICC and mDWT & 53.43 & 59.28 & 77.24 & 85.09 \\  & ICC and TD and mDWT & **57.81** & 61.00 & **81.57** & **88.40** \\ \hline \multirow{8}{*}{BioPatRec DB2} & TD & 40.93 & 19.78 & 55.11 & 83.33 \\  & mDWT & 36.17 & **68.20** & 76.59 & 83.74 \\  & ICC & 31.12 & 23.37 & 70.96 & 82.31 \\  & TD and mDWT & 43.24 & 64.32 & 77.29 & 84.60 \\  & TD and ICC & 46.39 & 27.03 & 72.15 & 85.16 \\  & ICC and mDWT & 44.18 & 55.19 & 77.15 & 85.45 \\  & ICC and TD and mDWT & **48.06** & 55.52 & **78.67** & **86.37** \\ \hline \multirow{8}{*}{BioPatRec DB3} & TD & 44.39 & 28.76 & 57.18 & 82.31 \\  & mDWT & 43.90 & 64.97 & 77.20 & 82.77 \\ \cline{1-1}  & ICC & 37.82 & 30.92 & 65.67 & 73.59 \\ \cline{1-1}  & TD and mDWT & 48.36 & **63.87** & 79.83 & 85.84 \\ \cline{1-1}  & TD and ICC & 49.74 & 34.10 & 71.98 & 82.36 \\ \cline{1-1}  & ICC and mDWT & 47.99 & 58.08 & 77.73 & 85.99 \\ \cline{1-1}  & ICC and TD and mDWT & **52.27** & 59.00 & **81.04** & **87.08** \\ \hline \multirow{8}{*}{Mendley Data} & TD & 54.87 & 41.93 & 44.62 & 70.14 \\  & mDWT & 49.83 & 61.72 & 66.90 & 72.64 \\ \cline{1-1}  & ICC & 34.70 & 27.61 & 59.60 & 73.59 \\ \cline{1-1}  & TD and mDWT & 56.19 & 61.67 & 69.92 & 73.50 \\ \cline{1-1}  & TD and ICC & 57.55 & 39.15 & 52.54 & 76.77 \\ \cline{1-1}  & ICC and mDWT & 54.84 & 59.59 & 68.11 & 77.13 \\ \cline{1-1}  & ICC and TD and mDWT & **58.71** & **62.00** & **70.06** & **78.23** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Classification accuracy (%) on Ninapro DB4, DB5, BioPatRec DB1, DB2, DB3, and Mendeley Data using four classifiers. Furthermore, all methods are processed by SPS. Bold indicates the best results under the same method for the same dataset.
# Enhanced Matching Game for Decoupled Uplink Downlink Context-Aware Handover
## 5 Simulation and Results
### Results and Discussion
In order to study the impact and the gain brought by the femto BS (thus contrary to the initial topology), the number of femto BSs will be variable up to 100 femto BSs, which allows us to study the performance of the handover decision in a dense network environment. Figure 4 demonstrates the average utility per UE according to the number of femto BSs in a network with \(N=100\) UEs. For the proposed context-aware approach, the UEs moved toward becoming related to femto BSs, which is mutually not loaded and gives an improved rate. Accordingly, during context-aware decision making, the subsequent UE-BS association considerably expands the UEs utilities. The proposed context-aware handover yields a huge performance gain, growing with the network dimension M, reaching 31% and 43% over the traditional downlink handover and traditional DUDe handover schemes, respectively. \begin{table}
\begin{tabular}{l|l l l l l l} \hline
**Number of \(N^{Req}\) Set** & **50** & **60** & **70** & **80** & **90** & **100** \\ \hline Delay (ms) & 14.440 & 27.340 & 39.490 & 52.615 & 64.340 & 76.167 \\ \hline \end{tabular}
\end{table} 
Table 4: Delay based on the number of \(N^{Req}\) set. Figure 3: The average rate of UEs served by femto BSs (FBSs) in terms of the total number of UEs.
## 4 Discussion
### Transmission Factors and Data Analysis  
The translocation factor (TF) plays a crucial role in assessing heavy metal contamination. It provides a quantitative measure of how metals move within plants from the roots to shoots and helps researchers and environmental managers understand the extent of the contamination and develop effective mitigation strategies. High TF values signify that metals are being effectively transferred from plant roots to the aboveground parts, such as the leaves and stems. This can be concerning because it increases the likelihood of metal uptake by livestock and humans who consume these plants and may lead to adverse health effects. In phytoremediation, high TF plants reduce soil pollution by effectively removing and transporting metals from the soil to harvestable parts, which are then removed. Previous TF studies have investigated plant physiological mechanisms and the results have been used to guide genetically modified crop development. The TF variations across species reflect ecological adaptations and pollution impacts. Monitoring the TFs for an indicator species can help in the assessment of contamination levels and track changes. This information can then be used to develop remediation strategies. The application of TFs extends beyond Cd to other heavy metals and highlights plant-specific uptake and translocation mechanisms.
# An Optimization Route Selection Method of Urban Oversize Cargo Transportation
## 3 Materials and Methods
### TOPSIS Application  
#### 3.4.1 Step 1  
The same trend processing can make any scheme with the better standardized attribute value have a larger attribute value after processing.  
Normalize benefit attributes and cost attributes and establish a corresponding matrix. Convert the values in the table to the interval [0,1].  
The standardized decision matrix is recorded as: \(Z=\left(z_{ij}\right)_{4\times 7}\).  
The transformed decision matrix is recorded as: \(A=\left(a_{ij}\right)_{4\times 7}\).  
Set \(z_{j}^{\text{max}}\) as the maximum value in j column of the decision matrix, and \(z_{j}^{\text{min}}\) as the minimum value in j column of the decision matrix.  
If \(z_{j}\) is a benefit attribute, then:  
\[a_{ij}=\frac{z_{ij}-z_{j}^{\text{min}}}{z_{j}^{\text{max}}-z_{j}^{\text{min}}} \tag{10}\]  
In this paper, road mileage with a width greater than 25 m is a benefit attribute.  
If \(z_{j}\) is a cost attribute, then:  
\[a_{ij}=\frac{z_{j}^{\text{max}}-z_{ij}}{z_{j}^{\text{max}}-z_{j}^{\text{min}}} \tag{11}\]  
In this paper, cost attribute includes: total mileage, number of horizontal curves with a turning radius less than 20 m, number of switch-back curves, road mileage with a longitudinal gradient greater than 5%, road mileage with a width of less than 15 m, and the number of bridges with a headroom less than 5 m.  
Although this paper does not involve interval attributes, for the completeness of theoretical system, supplementary explanations on interval attributes are provided.  
If there are interval attributes, set the optimal attribute interval be \([z_{j}^{0},z_{j}^{d}]\); \(z_{j}^{u}\) is the intolerable lower bound and \(z_{j}^{v}\) is the intolerable upper bound.  
\[a_{ij}=\left\{\begin{array}{ccc}1-\left(\frac{a^{0}-z_{ij}}{z_{j}^{0}-z_{j} }\right)&,&z_{j}^{u}\leq z_{ij}\leq z_{j}^{0}\\ -\frac{1}{z_{j}^{0}-z_{j}^{u}}&,&z_{j}^{0}\leq z_{ij}\leq z_{j}^{n}\\ \frac{1-\left(z_{j}-z_{j}^{d}\right)}{z_{j}^{0}-z_{j}^{u}}&,&z_{j}^{d}\leq z_{ ij}\leq z_{j}^{v}\\ 0&,&\quad\quad\quad\quad\quad\text{others}\end{array}\right. \tag{12}\]  
The worst attribute value after transformation is 0, and the best attribute value is 1. After same trend processing, each datum retains two decimal places, and the matrix is recorded as:  
\[A=\left(\begin{array}{cccccc}0.8&1&0.8&1&0.29&0.12&1\\ 0&0&0&0&1&0&0.6\\ 1&0.5&0.56&0.67&0&1&0\\ 0.4&0.5&1&0.87&0.39&0.73&1\end{array}\right)\]
## 4 Results
### EASY Concept Design Details: System Functionality for Different Users
#### 4.4.1.2 Provision of Executable Models  
Few environmental and process models used in agricultural and environmental planning and management have existing web-accessible interfaces or programming interfaces. Unlike other spatial information services such as the Open GIS web services--web mapping service (WMS) and web feature service (WFS), standards for interacting with environmental and landscape models do not exist or have not been widely adopted.  
For relatively simple models, EASY will facilitate the development of interfaces and services to allow end-users to directly interact and create new outputs. While these models may still be run externally to the EASY system, the EASY application will provide the necessary user interface and services for interacting with the outputs of the models and/or the models themselves. An efficient solution for EASY to provide modelling functionality would be for other existing systems to host the models. EASY could then be designed to link through a published web service to the model outputs and in some cases inputs. For example the National Data Grid (NDG) prototype cited earlier already provides a sophisticated web-enabled data storage, visualisation and modelling environment for raster spatial datasets including modelled inputs and outputs. For raster data then it may be most efficient forEASY to provide a linking interface to the NDG system via open source web mapping services, so that raster modelling outputs can be visualised and queried within the EASY user interface.
###### Abstract  
The cold atmospheric pressure plasma jet interaction with the bio-target is studied in the plasma experiment, 2D fluid model simulations, and with MTT and iCELLigence assays of the viability of cancer cells. It is shown, for the first time, that the use of the grounded substrate under the media with cells considerably amplifies the effect of plasma cancer cell treatment in vitro. Plasma devices with cylindrical and plane geometries generating cold atmospheric plasma jets are developed and tested. The sequence of the streamers which forms the plasma jet is initiated with a voltage of 2.5-6.5 kV applied with the frequency 40 kHz. We suggest using the grounded substrate under the bio-target during the plasma jet treatment of cancer cells. The analysis of the measured plasma spectra and comparison of OH-line intensity for different voltages and gas flow rates allows us to find a range of optimal plasma parameters for the enhanced OH generation. The time-dependent viability is measured for human cell lines, A431 (skin carcinoma), HEK 293 (kidney embryonic cells), and A549 (human lung adenocarcinoma cells) after the plasma jet treatment. The results with cell-based experiments (direct treatment) performed with various plasma jet parameters confirm the maximum efficiency of the treatment with the optimal plasma parameters.  
Keywords:cold atmospheric plasma jet; plasma device; bio-target; plasma-surface interaction +
Footnote †: journal: Applied Physics Letters B
## 4 Results
### Estuarine Mapping and Area Calculation
The first available aerial photo of the zone (1947, top right image in Figure 5) shows the old mouth of the river, presently located in the southern part of San Antonio island (abandoned channel in Figure 4), and the current active mouth derives from the breakage of the old channel on its left side as a consequence of a river flooding in 1937 [59]. In 1956 (Figure 5), the original mouth was already closed, and the active mouth opened very widely, initiating the shift towards the present shape. Huge sand sheets covered San Antonio island in 1956, which could be considered as a complex sandy system, i.e., shoreline sandy environments and dunes, with a virtual absence of muddy features, except for the proper deltaic plain. This area did not suffer remarkable changes in anthropic features due to its qualification as a Natural Park in 1989. However, it has undergone important eco-geomorphological changes during the last 30 years (Figure 4). The most evident change was the development of the beach and dune ridges in El Garxal (a submerged meadow in the left side of the main channel). These sandy features protect and make possible the formation of a wide wetland, which opens to the main channel. During the study period, an important input of sediment arrived to El Garxal, supplied by the main littoral drift transport and enabling the formation of new sandy hooks (see left black dotted arrow in Figure 4). These hooks slowly connected to land and enabled the development of a new coastal lagoon (dark blue feature in Figure 4). Besides, on the western side of the wetland, the parabolic dunes increased in area, even burying part of the vegetated wetland. On the eastern side of the channel (San Antonio island), an erosive trend was maintained in the last years. Despite the main littoral drift flowing to the south, other remarkable currents affect the zone. Prevailing winds and waves come from the east, being responsible for the main sediment transport and shoreline morphology in this area (black dashed arrows in Figure 4). The total areas on the GUA (Figure 3) and EBR (Figure 4) estuaries are dependent on the state of the sublittoral sand bars attached to the coast and the tide level at the time of the photo (only relevant for GUA as EBR is a microtidal environment). Seasonality is also relevant for the degree of the feature development; however, it is difficult to include this variable in the general calculation of areas.
## 4 A Novel Bagging Method Based on Ensemble Margin  
Compared to binary classification data imbalance problems, multi-class imbalance problems increase the data complexity and negatively affect the classification performance regardless of whether the data is imbalanced or not. Hence, multi-class imbalance problems cannot be simply solved by rebalancing the number of examples among classes in the pre-processing step. In this section, we propose a new algorithm to handle the class imbalance problem. Several methods proposed in the literature to address the problem of class imbalance as well as their strengths and weaknesses have been presented in the previous section. Ensemble classifiers have been shown to be more effective than data sampling techniques to enhance the classification performance of imbalanced data. Moreover, the combination of ensemble learning with sampling methods to tackle the class imbalance problem has led to several proposals with positive results in the literature.  
In addition, as mentioned in the previous section, boosting based methods are sensitive to noise. On the contrary, bagging techniques are not only robust to noise but also easy to develop. [PERSON] et al. pointed out that bagging ensembles would be powerful when dealing with class imbalance if they are properly combined [5; 63]. Consequently, we chose to found our new imbalance ensemble learning method on bagging.  
Enhancing the classification of class decision boundary instances is useful to improve the classification accuracy. Hence, for a balanced classification, focusing on the usage of the small margin instances of a global margin ordering should benefit the performance of an ensemble classifier. However, the same scheme is not suited to improve the model built from an imbalanced training set. Although most of the minority class instances have low margin values, selecting useful instances from a global margin sorting still has a risk to lose partial minority class samples, and even causes the classification performance to deteriorate. Hence, the most appropriate method for the improvement of imbalanced classification is to choose useful instances from each class independently.
## 3 Results and Analysis  
### Change in TCF for Agricultural Production  
The TCF induced by 10 main types of agrochemical and energy inputs rose from 194.30 to 309.89 Kt CE (1 Kt = 10\({}^{3}\) t) over 1995-2016. It had a mean value of 222.22 Kt CE and a yearly rate of increase of 2.83% over the study period (Figure 3 and Table 2). Three time periods could be found for the TCF changing trend over 1995-2016, i.e., a five-year period over 1995-1999, an eight-year period over 2000-2007, and a nine-year period over 2008-2016 (Figure 3). The TCF was roughly stable over 1995-1999, with a lower yearly rate of increase of 1.57%; slowly declined over 2000-2007, with a yearly decreasing rate of 1.02%; and rapidly rose over 2008-2016, with a higher yearly increasing rate of 4.85%. The mean value of TCF decreased from the first period to the second period; yet, it increased from the second period to the third period. Distinct change in TCF occurred from 2007 to 2008. Indicated by Figure 3, the relative TCF was lower during the GFGP in the study area, which was obviously different from that in the whole country [3].  
Figure 3: Changing trends of carbon footprints induce by agrochemical and agricultural energy inputs in Qinghai province over 1995–2016. TCF—total carbon footprint.
## 2 Materials and Methods
### Monitoring Equipment: Soil Moisture and Temperature Sensors
Through the horizontal installation of the 30 cm rods in order to determine vertical water fluxes, soil water content is 'averaged' over at least this length at the specific depth reducing small scale soil variation and capturing possible vertical preferential flow. [PERSON] et al. [13] state that a horizontal buried probe at a particular depth will give the mean soil water content at that depth \(\pm\) approximately 1.5 cm. Sensors were inserted at 4 depth intervals: 0-5 cm, 5-20 cm, 20-40 cm and 40-80 cm maximally overlapping the sampling depths of the long-term Level II soil monitoringprogram (Table 2) in order to relate physico-chemical data with time series of soil water dynamics for each layer. At each site, the CR1000 datalogger was situated in the middle of the triangle formed by the profile pits A, B, and C (Figure 1), limiting the cable length between CS616 probe and logger to maximum 10 m. The T107 temperature sensors were installed at Pit A only (at all four depths) and at least laterally 10 cm away from the CS616 probe to avoid interference. The soil temperature readings of pit A were applied for possible temperature correction in the other pits. After installation of all sensors, the wiring holes were carefully covered with soil and the profile refilled layerwise. All sensors have continued operation since 2010, whereas some of the central CR1000 data loggers required periodical revision or replacement. The accuracy specifications mentioned by the manufacturer for the \(\theta_{FDR}\) measurement using the CS616 probes is based on laboratory measurements in a variety of soils and over the water content range air-dry to saturated. The soils were typically sandy loam and coarser. The CS616 accuracy is \(\pm 0.025\) m\({}^{3}\) m\({}^{-3}\) using standard calibration with bulk electrical conductivity \(\leq 500\) uS cm\({}^{-1}\) and soil bulk density \(\leq 1550\) kg m\({}^{-3}\) in a measurement range from 0 to 0.50 m\({}^{3}\) m\({}^{-3}\). Resolution (i.e., the minimum change in the dielectric permittivity that is reliably detectable) is better than 0.001 m\({}^{3}\) m\({}^{-3}\). Precision or repeatability of the \(\theta_{FDR}\) measurements in the same soil material is also better than 0.001 m\({}^{3}\) m\({}^{-3}\). Probe-to-probe variability ranges from \(\pm 0.005\) m\({}^{3}\) m\({}^{-3}\) in dry soil to \(\pm 0.015\) m\({}^{3}\) m\({}^{-3}\) in a typical saturated soil [20].
# TransPGC: Point Cloud Geometry Compression Based on Transformers
## 2 Related Work  
### Non-Deep Learning Methods  
Over the past few years, a series of Point Cloud Compression (PCC) standards have been developed under the MPEG consortium. Prominent examples include the geometry-based PCC (G-PCC) v14 [20] and V-PCC [21]. Meshes, voxels, and point clouds serve as widely used 3D representation forms. Mesh models consist of vertices and faces formed by the connections/topology between vertices. Mesh compression methods [22] save topology memory through intermediate representations, where vertices can also be compressed using topological information, resulting in improved subjective quality with fewer encoding bits. For geometry compression based on octrees, the octree structure is commonly utilized to provide better context for entropy coding. Point cloud encoding is executed through manually defined rules and data structures. The octree geometry codec in PCL [23] and MPEG standard G-PCC [24] are two popular octree-based methods. Google's Draco [25] employs a kd-tree to partition space and encodes points based on the occupancy of partitioned space. Some algorithms do not directly compress the 3D geometric structure of point clouds; they project the point clouds onto 2D images and apply video compression algorithms, as seen in the V-PCC method in the MPEG standard [20]. [PERSON] et al. [26] presented a dynamic point cloud compression based on different projection types and bit depth combined with the surface reconstruction algorithm and video compression for obtained geometry and texture maps. [PERSON] et al. [27] proposed an improved compression means for dynamic point cloud based on curvature estimation and hierarchical strategy. Certain works [28] also explored the compression of dynamic point cloud sequences by introducing motion estimation between consecutive frames. These non-learning-based methods encode coordinates via predefined rules and generally exhibit robustness across different types of point clouds, but their encoding performance may be limited.
## 4 Simulation Results and Discussion  
In this section, we simulated the model as shown in Figure 7. The original signal \(u[k]\in\{0,1\}\) with dimension \(1\times 1,\)440,000 is converted into a 2D signal \(a[j,k]\in\{-1,1\}\) with dimensions of 1200 \(\times\) 1200 by modulation, which is also the size of a data page. Input data \(a[j,k]\) pass through the BPMR channel with AWGN. The output of channel \(y[j,k]\) becomes the input to a 2D equalizer. We used the 2D equalizer with dimensions \(5\times 5\); the equalizer coefficients and the GPR target were estimated by calculating the error signal \(e[j,k]\) and applying the MMSE algorithm [20]. Then, the output of equalizer z[\(j,k]\) is passed through our detection scheme. Finally, the output \(\bar{a}[j,k]\) from the detection scheme is demodulated to obtain \(\bar{a}[k]\). In this paper, we define the channel signal-to-noise ratio (SNR) as 10 log\({}_{10}(1/\sigma^{2})\), where \(\sigma^{2}\) is the AWGN power. In this experiment, we simulated 10 pages of sample data and an AD of 3 Tb/in\({}^{2}\) (0.465 Tb/cm\({}^{2}\)) (\(T_{x}=T_{x}=14.5\) nm) [9]. Our program is built in Matlab R2018a. The coefficients used in the channel simulation are as follows:  
\[\mathbf{H}=\left[\begin{array}{ccc}0.0824&0.3876&0.0824\\ 0.2125&1&0.2125\\ 0.0824&0.3876&0.0824\end{array}\right] \tag{53}\]