esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiment Results and Comparison
### The Baseline Algorithms  
To evaluate the performance of the proposed approach, we compare it with two relevant semi-supervised labeling techniques, namely the probabilistic graphical model (PGM) [7] and various widths clustering(kNNVWC) [28]. These algorithms were selected because of their relevance, simplicity, and ease of construction and implementation.  
* **Probabilistic graphical model (PGM)** This algorithm applies a self-training procedure, and probabilistic graphical modules are used to label unlabeled flows. It is a Naive Bayes extension designed to learn from both labeled and unlabeled instances. The first step is used to assign a probability distribution among all known and unknown variables in labeled traffic flow using Naive Bayes. In the second step, this algorithm identifies a rule that describes the relationship between the probability distribution and the decision rule.
* **Various-Widths Clustering ( kNNVWC)** This approach can produce clusters of various distributions, sizes, and radii from high-dimensional data. The clustering process is divided into three sub-phases: cluster width learning, partitioning, and merging. These phases are repeated and executed until the user-defined criteria are met. In the first stage, we estimate the global width of the data set, which is the average of the widths of a set of observations picked randomly from the data set. Then, the determined width will be used in the partitioning phase to recursively partition a data set into several clusters. The third stage aims to reduce cluster overlap as much as possible in order to explore the most representative observations. The labeling process of unlabeled observations is based on the majority class (or label) of the labeled observations contained in the cluster, whereas the class label that receives the largest number of votes is assigned to this observation.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Protocol** & **Size(MB)** & **Pct** \\ \hline \multicolumn{4}{c}{**(a) Total traffic composition**} \\ \hline
**IP** & 76,570.67 & 99.99 \\
**ARP** & 4.39 & 0.01 \\
**IPV6** & 1.74 & 0.00 \\
**IPX** & 0.00 & 0.00 \\
**STP** & 0.00 & 0.00 \\
**Other** & 0.00 & 0.00 \\ \hline \multicolumn{4}{c}{**(b) TCP/UDP traffic composition TCP**} \\ \hline
**TCP** & 75,776.00 & 98.96 \\
**UDP** & 792.00 & 1.03 \\
**ICMP** & 2.64 & 0.00 \\
**ICMPV6** & 0.00 & 0.00 \\
**Other** & 0.03 & 0.00 \\ \hline \multicolumn{4}{c}{**(C) TCP/UDP traffic composition**} \\ \hline
**FTP** & 200.30 & 0.26 \\
**HTTP** & 72,499.20 & 94.69 \\
**DNS** & 288.60 & 0.38 \\
**Netbios** & 36.40 & 0.05 \\
**Mail** & 119.80 & 0.16 \\
**SNMP** & 0.01 & 0.00 \\
**SSH** & 241.40 & 0.32 \\
**Messenger** & 0.54 & 0.00 \\
**Other** & 3179.08 & 4.15 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Traffic composition in the ISCX data set.
# An Energy Efficient MAC Protocol for Wireless Passive Sensor Networks
## 3 IS-MAC Protocol
### The Improved Backoff Algorithm  
1. Node fails to access channel. \(SC\) is reset and \(FC\) plus one. It is divided into two cases as follows. 1. When the number of continuous failed competition of channel is more than its corresponding threshold, the value of \(FC\) is greater than \(FC_{\text{lim}}\). The current contention window is too small and needs to be adjusted much larger via multiplicative increase. The new contention window is expressed as \(CW=\min(CW_{\text{max}},CW\times 2)\), which rapidly makes the contention window depart from area where collision happened just now. 2. When the number of continuous failed competition of the channel is less than its corresponding threshold, the value of \(FC\) is less than \(FC_{\text{lim}}\). If the current contention window is smaller than \(CW_{\text{init}}\), then the new contention window becomes \(CW=CW_{\text{min}}\). If the current contention window is larger than \(CW_{\text{init}}\), then the new contention window is \(CW=CW_{\text{init}}\).
2. Node succeeds in accessing channel. \(FC\) is reset and \(SC\) plus one. It is also divided into two cases as follows. 1. When the number of continuous successful competition of channel is more than its corresponding threshold, the value of \(SC\) is greater than \(SC_{\text{lim}}\). It is considered that the current contention window is too large. There are not so many nodes in the network or there is not so high data traffic. Therefore, the competition of the channel is not intense. In this case, it is not essential to set such a large contention window, so the current contention window value needs to be appropriately adjusted. The new contention window is expressed as \(CW=\min(CW/2,CW_{\text{init}})\). The contention window will be maintained within a certain area in case of its frequent changes. 2. When the number of continuous successful competition of channel is less than its corresponding threshold, the value of \(SC\) is less than \(SC_{\text{lim}}\). The traffic load is relatively large right now, and the competition of channel is intense. Therefore, the current contention window merely requires minor adjustment, so it will be reduced by two to get the new contention window, \(CW=CW-2\).  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Name** & **Value** \\ \hline \(CW_{\text{min}}\) & 3 \\ \(CW_{\text{max}}\) & 63 \\ \(SC_{\text{lim}}\) & 5 \\ \(FC_{\text{lim}}\) & 5 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters and their values in the improved backoff algorithm.  
The flow chart of this improved backoff algorithm is shown in Figure 1.
# Nanomechanical, Structural and Antioxidant Characterization of Nixtamalized Popcorn Pericarp
## 3 Results and Discussion
### Nanostructure and Nanoindentation  
Pericarp mechanics play a pivotal role in grain firmness and protection, but their relationships with structure and stiffness remain unknown to a certain extent. For this reason, topography, stiffness, and the structure of the pericarp were studied using AFM. The surface of the popcorn pericarp is a heterogeneous topography with roughness values (Ra) of around 4.32 \(\pm\) 0.40 nm. The pericarp's surface is associated with a wide distribution of Young's modulus (YM) values shown in the histogram of Figure 7 (261.72 \(\pm\) 23.58 MPa). Furthermore, a Gaussian function was the most appropriate model for fitting the distribution of YM values according to the mathematical modeling optimization employed. Similar topography was observed in corn kernels from Jilin province of China, where they had values of Ra 3.41 \(\pm\) 0.89 nm [45]. Another study of eggshells reported similar values (5.30 to 8.81 nm) to pericarp, but they had different surfaces because the eggshell showed pores [46]. In contrast, [PERSON] et al. [27] reported higher values of Ra around from 20.63 to 38.65 nm in apple tissues, but they have lower values of Young's modulus compared with pericarp (0.90 \(\pm\) 1.30 to 1.76 \(\pm\) 1.01 MPa). [PERSON] et al. [47] reported values of YM in the pericarp of grain's wheat (cv. Baroudeur and, cv. Scipion) where the values were 20.6 \(\pm\) 2.8 and 28.9 \(\pm\) 6.7 N/mm, respectively. Another study determined YM in bread wheat Buck-Amancay cultivar and ACA 315 cultivar from Argentina. They found values of an outer layer of around 2799 \(\pm\) 659 and 1404 \(\pm\) 121 MPa, respectively, with 10% moisture [48].  
Figure 6: SEM micrographs of pericarp in different parts. Scales correspond to 1 mm. (**a1**): The near pedical part, (**b1**): in middle part, (**c1**): end part, and (**a2**,**b2**,**c2**): close up of pericarp parts, images by fast Fourier transform (FFT).  
Mechanical characterization of pericarp allows knowing the physiological state of cells and the effect of individual biomolecules and assemblies on overall properties on the cell surfaces.
## 3 Methods
### Ore-Forming Simulation
#### 3.3.2 Model Setup  
Previous researchers have generally concluded that fault zones and fractures are conduits for ore-forming fluid flow [81; 82; 83]. Refined architectures of the regional fault are essential, as the strain distribution and fluid migration occurs in fault zones [42; 73]. Here we manually refined the Xiadian fault surface model constructed above to avoid the post-ore cutoff or deformation. A conceptual model (Figure 6), including the Xiadian fault zone, hanging wall (Jiaodong Group Precambrian metamorphic rocks), football (Linglong granite), and erosion cover, is necessary for ore-forming numerical modeling [47; 49; 84]. In this study, the dimensions of the overall model spanned 7 km horizontally and 10 km vertically. Before simulation, the meticulous mesh definition of these models was imperative to ensure accurate numerical results [44; 85; 86; 87; 88]. We used the tetrahedral elements for mesh discretization (Figure 6) via the software GoCAD 17, and converted to FLAC\({}^{\text{3D}}\) tetrahedron-discretization model utilizing a new computer program [45]. This choice was motivated by the undulation features (see below models) generally displayed in the fault zone, which can lead to topological errors after refining the fault model using hexahedral meshes. Tetrahedral meshes, on the other hand, can be small or dense enough to adequately capture the geometric details of the fault model. For different units, meshes are distributed as uniformly as possible and their shape is generally similar as possible. As a result, the whole computational domain is simulated by 1,908,076 tetrahedral elements, including erosion (384,251 elements), fault zone (87,291 elements), footwall (704,938 elements) and hanging wall (731,596 elements). All units were treated as homogeneous porous zones, which are simplified respectively as the Jiaodong Group (hanging wall), Linglong granite (footwall), and fault damage zone with high permeability and low mechanical properties [82; 89]. The specific mechanical, hydraulic and thermodynamic parameters of rocks are given in Table 2. These values are based on rock mechanical experiment data or related hand book and numerical simulation experiments in Jiaodong [49; 84; 90].  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Property** & \
\begin{tabular}{l} **Hanging Wall** \\ **(Metamorphic Rocks)** \\ \end{tabular} &
\begin{tabular}{l} **Footwall** \\ **(Granite)** \\ \end{tabular} & **Fault Zone** \\ \hline Density (kg/m\({}^{3}\)) & 2.67 & 2.6 & 2.0 \\ Bulk modulus (Pa) & 3.28 & 5.33 & 0.52 \\ Shear modulus (Pa) & 0.56 & 0.32 & 0.031 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Numerical simulation parameters used in this study.  
Figure 6: Concept model for 3D numerical modeling in the Xiadian deposit.
## 4 Results  
### Metal Concentrations in Stream Sediments  
Basic statistics, background values, and sediment quality guidelines for metals and other parameters analysed in urban stream sediments are shown in Table 1. V concentrations ranged from 118.00 to 330.33 mg/kg, with an average of 184.69 mg/kg and a relatively high standard deviation (63.44), suggesting spatial variability. Similar high Cr values in sediments have previously been attributed to industrial discharges or weathering of mafic rocks [54]. Co and Ni displayed relatively narrow concentration ranges and lower standard deviations, with mean values of 23.13 mg/kg and 43.30 mg/kg, respectively, indicating more homogeneous distribution, consistent with natural geogenic sources [55]. Cu and Zn had notably high maximum values (216.22 mg/kg and 439.27 mg/kg, respectively), with elevated mean concentrations and high variability, which may point to anthropogenic influence, especially in samples such as K4. These elements are often linked to mining, industrial emissions, and urban runoff [22; 56]. Trace elements such as Tl, Pb, Th, and U were found at relatively low concentrations, with Tl averaging 0.06 mg/kg and Pb averaging 5.72 mg/kg. The low mean and standard deviation values for Th (1.40 mg/kg) and U (0.57 mg/kg) suggest natural background levels with limited enrichment [57].  
Basic statistical parameters for the analysed elements are summarised in Table 1. On average, the elemental concentrations followed a decreasing order of Cr > Zn > V > Cu > Ni > Co > Pb > Th > U > Tl. The spatial distribution of these elements across the sampling sites is illustrated in Figure 3. K8 exhibited high concentrations of most elements except for Zn, Ni, Co, V, and Cu, indicating localised enrichment of certain metals. Conversely, K1 showed elevated levels of V and Co, but comparatively low concentrations of other elements, suggesting different sources or environmental influences at this location. K15 was characterized by high concentrations of U, Th, Pb, Tl, and Ni, while other element levels remained relatively low. These variations demonstrated the heterogeneity of metal distribution in the urban stream sediments and reflected both natural background and anthropogenic influences in the study area.
# Analysis of Dynamic Response of a Two Degrees of Freedom (2-DOF) Ball Bearing Nonlinear Model
## 3 Simulation Results  
The derived mathematical model (Equation (10)) is applied in the dimensionless form with Matlab software using ODE 45 (Runge-Kutta method) solver with a relative tolerance of 0.01 and the same value of time step. Dimensionless terms related to the Hertzian contact model are dependent on the global value of the radial internal clearance and the value of the shaft's eccentricity is taken as the variable parameter. The value of waviness on the rolling surfaces has a weaker impact on the dynamic response and in the real conditions, it can be determined before bearing's assembly or disassembly process. In Table 1, the values of input parameters (with prime) into the model are specified. To determine the velocity of the cage, the internal geometry (pitch diameter, ball diameter, and the number of balls) is assumed as for the single-row ball bearing 6309. In Table 2, the potential physical parameters are specified corresponding to the dimensionless terms. The system is studied by the internal resonance frequency corresponding the shaft's angular velocity [57; 58]. The dynamic output of the analyzed bearing is taken during its stable operation, i.e., after the starting procedure. It is worth emphasizing, that the load is subjected only to the \(x\)-axis (vertical direction) introducing a significant nonlinear effect into the response. The results of the time series, Fast Fourier Transform, phase plots are presented and discussed only from the mentioned direction. In the orbit plots, the results obtained from both axes are taken into account.
## 3 Results and Discussion
### Efficiency and Energy Consumption  
Figure 5 shows the total current efficiency (%TCE) (Figure 5a) and EC (Figure 5b) when a polluted HCQ river water sample was electrochemically treated at different \(j\) (15, 30 and 45 mA cm\({}^{-2}\)). It is an important parameter to evaluate the viability of the EO using BDD anodes [45]. Thus, in the first 30 min for all experiments, a slow %TCE decrease was observed, as a function of time. This behavior can be associated to, on the one hand, the efficient use of current to eliminate HCQ from river water until the process is controlled by mass transport and on the other hand, the existence of parallel reactions, such as oxygen evolution and degradation of persistent by-products formed during HCQ removal (i.e., short-chain aliphatic carboxylic acids), that employ a portion of the electrical energy applied. Meanwhile, the EC increased with \(j\) and electrolysis time, thus the most rapid and efficient process was observed at high \(j\). Obviously, as seen Figure 5b, the \(j\) has a strong effect of the EC; however, agitated beaker reactor could not be representative for energetic requirements and oxidation environment for scale-up conditions, in terms of effectiveness, mass transport and oxidants production [46]. Therefore, more studies are in progress in order to decrease EC and consequently, the relative costs. Another strategy is based on the integration of photovoltaic (PV) to electrochemical technologies to supply the electrical energy for treating different water matrices [47; 48; 49]. The falling price and the more efficient materials used in solar photovoltaic panels are making the process promising. Up to now, few research groups are employing solar-driven for water treatment, and as far as we know, there are few reports on BDD electrodes applied for different water matrices.
# Recent Web Platforms for Multi-Omics Integration Unlocking Biological Complexity
## 2 Methods
### Platforms for Multi-Omics Visualization and Pathway Exploration
#### 2.2.7 Summary and Comparative Insights  
In conclusion, the platforms reviewed in this section--multiSLIDE, MVP, VDJView, ProteomicsDB, PaintOmics 3, and Argonaut--highlight the diverse approaches available for multi-omics visualization and pathway exploration. MultiSLIDE provides real-time, pathway-centric visualizations for transcriptomics and proteomics, ideal for researchers requiring interactive clustering tools, while PaintOmics 3 integrates multiple omics layers for dynamic pathway mapping. MVP extends visualization to include genomics and metabolomics, supporting complex disease studies through integrative pathway analysis. VDJView specializes in immune repertoire sequencing, offering clonotype diversity metrics that are valuable for immunotherapy research. ProteomicsDB excels in protein expression and interaction analysis, particularly in large-scale proteomics datasets. Lastly, Argonaut emphasizes accessibility and collaboration, offering real-time statistical and visualization tools suitable for interdisciplinary research. While each platform caters to specific needs, selecting the right tool depends on the research focus, the type of omics data being analyzed, and the intended depth of exploration.
## Appendix A
### ANN Model Performance by Lag Time
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**No**} & \multirow{2}{*}{**Model**} & \multirow{2}{*}{
\begin{tabular}{c} **R\({}^{2}\)** \\ **Training** \\ \end{tabular} } & \multicolumn{2}{c}{**R\({}^{2}\)**} & \multicolumn{2}{c}{**Overfit**} & \multirow{2}{*}{**Leg Time**} \\  & & **Training** & & & **Validation** & & **Index** \\ \hline  
& VCI3M\_lag1+SP11M\_lag1 & 0.78 & 0.74 & 0.04 & Yes & 1 \\
20 & VCI3M\_lag1+RFE3M\_lag1 & 0.74 & 0.72 & 0.02 & No & 1 \\
21 & VCI3M\_lag1 & 0.68 & 0.66 & 0.02 & No & 1 \\
22* & NDVIDekad\_lag1+SP13M\_lag1 & 0.60 & 0.57 & 0.04 & Yes & 1 \\
23* & NDVIDekad\_lag1+RC13M\_lag1 & 0.59 & 0.54 & 0.05 & Yes & 1 \\
24* & VCI1M\_lag2+SP11M\_lag2 & 0.57 & 0.51 & 0.06 & Yes & 2 \\
25* & VCIDekad\_lag2+SPI1M\_lag2 & 0.58 & 0.51 & 0.07 & Yes & 2 \\
26* & VCIDekad\_lag2+SPI3M\_lag2 & 0.54 & 0.51 & 0.04 & Yes & 2 \\
27* & VCI1M\_lag2+SPI3M\_lag2 & 0.56 & 0.49 & 0.07 & Yes & 2 \\
28* & VCIDekad\_lag2+RC11M\_lag2 & 0.53 & 0.47 & 0.06 & Yes & 2 \\
29* & VCIDekad\_lag2+RFE1M\_lag2 & 0.52 & 0.47 & 0.06 & Yes & 2 \\
30* & VCI1M\_lag2+RC11M\_lag2 & 0.53 & 0.46 & 0.07 & Yes & 2 \\
31* & VCID1M\_lag2+RC13M\_lag2 & 0.53 & 0.46 & 0.08 & Yes & 2 \\
32* & VCIDekad\_lag2+RFE1M\_lag2 & 0.53 & 0.46 & 0.07 & Yes & 2 \\
33* & VCIDekad\_lag2+RC13M\_lag2 & 0.52 & 0.45 & 0.07 & Yes & 2 \\
34* & VCI3M\_lag2+SPI3M\_lag2 & 0.52 & 0.44 & 0.08 & Yes & 2 \\
35* & VCI3M\_lag2+SPI1M\_lag2 & 0.50 & 0.44 & 0.06 & Yes & 2 \\
36* & SPI3M\_lag1 & 0.47 & 0.43 & 0.03 & Yes & 1 \\
37* & NDVIDekad\_lag2+SP13M\_lag2 & 0.48 & 0.43 & 0.05 & Yes & 2 \\
38 & SPI3M\_lag2 & 0.42 & 0.42 & 0.00 & No & 2 \\
39* & VCI3M\_lag2+RC13M\_lag2 & 0.49 & 0.40 & 0.09 & Yes & 2 \\
40* & NDVIDekad\_lag2+RC13M\_lag2 & 0.45 & 0.40 & 0.05 & Yes & 2 \\
41* & VCI3M\_lag2+RC11M\_lag2 & 0.51 & 0.39 & 0.12 & Yes & 2 \\
42* & RC3M\_lag1 & 0.43 & 0.39 & 0.04 & Yes & 1 \\
43* & VCI3M\_lag2+RFE1M\_lag2 & 0.47 & 0.38 & 0.09 & Yes & 2 \\
44* & NDVIDekad\_lag1+RFE3M\_lag1 & 0.47 & 0.37 & 0.09 & Yes & 1 \\
45* & VCIDekad\_lag2+RFE3M\_lag2 & 0.46 & 0.37 & 0.09 & Yes & 2 \\
46* & VCI1M\_lag2+RFE3M\_lag2 & 0.46 & 0.37 & 0.09 & Yes & 2 \\
47 & RC13M\_lag2 & 0.38 & 0.37 & 0.01 & No & 2 \\
48* & NDVIDekad\_lag1+SP11M\_lag1 & 0.43 & 0.36 & 0.06 & Yes & 1 \\
49* & VCI1M\_lag2 & 0.39 & 0.36 & 0.03 & Yes & 2 \\
50* & VCIDekad\_lag2 & 0.39 & 0.36 & 0.03 & Yes & 2 \\ \hline \hline \end{tabular}
\end{table} 
Table 14: ANN models in decreasing order of R\({}^{2}\) with the overfit index provided.
## 4 IRS-Aided Wireless Network Performance and Deployment
### IRS as a Wireless Network Signal Reflector  
An IRS can be employed as a signal reflector. In [68], the authors established that an IRS can reflect the incident signal toward the receiver to establish a reliable virtual link between transceivers and thus overcome the NLoS challenges in mmWave communication. The benefits of using an IRS as a signal reflector can be quantified using two types of performance metrics: probabilistic metric and ergodic metric. Next we discuss these metrics in detail.
## 2 Materials and Methods
### Virtual Inertia Optimization Allocation Model
#### 2.2.2 Inertia Level Constraint  
The total virtual inertia of the autonomous region is closely related to frequency stability [24], and the total virtual inertia of the region can be obtained by Equation (13).  
\[H_{total,k}=\frac{\sum\limits_{j=1}^{N_{PV}}H_{PV,j}S_{PV,j}+\sum\limits_{i=1} ^{N_{ESS}}H_{ESS,j}S_{ESS,i}}{\sum\limits_{j=1}^{N_{PV}}S_{PV,j}+\sum\limits_{i= 1}^{N_{ESS}}S_{ESS,i}} \tag{13}\]  
where \(H_{total,k}\) represents the total virtual inertia of area k; \(H_{PV,j}\) is the virtual inertia time constant of the jth PV virtual synchronous machine; \(H_{ESS,i}\) is the virtual inertia time constant of the ith ESS virtual synchronous machine; \(S_{PV,j}\) is the rated capacity of the jth PV virtual synchronous machine; \(S_{ESS,i}\) is the rated capacity of the ith ESS virtual synchronous machine; and \(N_{PV}\) and \(N_{ESS}\) are the numbers of PV virtual synchronous machines and ESS virtual synchronous machines, respectively.  
To enable the autonomous and stable operation of multiple areas, the total virtual inertia of area k should be greater than the inertia required by the area, that is, it should meet the requirements of Equation (14).  
\[H_{total,k}\geq H_{min} \tag{14}\]  
Meanwhile, the virtual inertia time constants of each PV virtual synchronous machine and ESS virtual synchronous machine should be optimized and adjusted within the allowable range, that is, to meet the requirements of Equation (15).  
\[\left\{\begin{array}{l}H_{PV,j}\leq H_{PV,max}\\ H_{ESS,i}\leq H_{ESS,max}\end{array}\right. \tag{15}\]  
where \(H_{PV,\max}\) and \(H_{ESS,\max}\) are, respectively, the maximum adjustable virtual inertia time constants of PV and ESS obtained from the first part.  
Finally, to achieve the coordinated distribution of virtual inertia among different regions, flexible interconnection devices, namely Soft Open Point (SOP), are added between regions. During the process of optimizing the distribution of virtual inertia, the constraints of voltage and power balance should be satisfied.  
\[V_{m}^{min}\leq|V_{m}|\leq V_{m}^{max} \tag{16}\]  
\[\sum P_{gen}=\sum P_{load}+\sum P_{loss} \tag{17}\]  
where \(V_{m}\) represents the voltage magnitude of node m; \(V_{m}^{min}\) and \(V_{m}^{max}\) are the lower and upper limits of the voltage at node m; \(P_{gen}\) is the active power output of distributed PV and ESS; \(P_{load}\) is the load demand; and \(P_{loss}\) is the line loss.