esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiments and Results
### Experimental Results
#### 4.3.6 Significance Test
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Algorithm** & **Unadjusted \(p\)** & \(P_{Name}\) & \(P_{Holm}\) & \(P_{Reg}\) & \(P_{Shaf}\) \\ \hline HDELC vs. Stacking & 0.0037 & **0.1052** & 0.0489 & 0.0396 & 0.0472 \\ HDELC vs. Bagging & 0.0012 & 0.0069 & 0.0038 & 0.0038 & 0.0038 \\ HDELC vs. Boosting & 0.0056 & **0.1398** & **0.0540** & 0.0481 & **0.0527** \\ HDELC vs. BOA [34] & 2.3006 \(\times\)\(10^{-5}\) & 6.4290 \(\times\)\(10^{-4}\) & 4.6833 \(\times\)\(10^{-4}\) & 3.5772 \(\times\)\(10^{-4}\) & 4.2085 \(\times\)\(10^{-4}\) \\ HDELC vs. & 1.4489 \(\times\)\(10^{-6}\) & 5.7897 \(\times\)\(10^{-5}\) & 4.9836 \(\times\)\(10^{-5}\) & 2.5539 \(\times\)\(10^{-5}\) & 4.6791 \(\times\)\(10^{-5}\) \\ PCA-Decision Tree [35] & \(5.3924\times\)\(10^{-10}\) & 1.2764 \(\times\)\(10^{-8}\) & 1.2273 \(\times\)\(10^{-8}\) & 1.0784 \(\times\)\(10^{-8}\) & 1.0784 \(\times\)\(10^{-8}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Adjusted \(p\)-values in multiple tests (Nemenyi, Holm, Bergmann and Shaffer tests, where the bold values denote that the results are not significant, where the bold values denote that the results are not significant). of the adjusted \(p\)-values are less than the significance threshold, and the corresponding comparisons can be considered as statistically significant, which further confirms the significant performance improvement and enhancement of the HDELC algorithm over other current state-of-the-art classifiers ensemble algorithms. Nonparametric tests can combine accurate, reliable statistics test references with complexity in multiple comparisons.
## 4 Discussion
### Factors Influencing Variations in the Phytoplankton Size Structure  
The environmental factors influencing phytoplankton growth in coastal seawater regions include water temperature, nutrient levels, and turbulence; however, the most fundamental factor is light [32, 33]. Accordingly, in the present study, an increase in transparency, which directly influenced the light available for phytoplankton, was found to contribute to an increase in phytoplankton biomass, especially for net-sized phytoplankton (Figures 5f and 6b). However, although phytoplankton may exhibit the maximum photosynthetic rate under an optimum light intensity, photoinhibition may occur with a reduced photosynthetic rate under a light intensity above the optimum level [34]. Furthermore, an increase in PAR was shown to adversely affect phytoplankton biomass (total and net-sized), with a particularly significant effect across high PAR levels (Figure 5g). On the contrary, an increase in solar radiation, a seasonal factor, significantly contributed to increased phytoplankton biomass (especially total and net-sized), whereas an increase in DS had a negative effect. This indicates the potential influence of seasonal factors on net-sized phytoplankton variations.  
stratified water layers due to the lower salinity and higher water temperature. In the present study, stratification due to high water temperatures and low salinities increased the biomass of nano-sized phytoplankton (Figure 5d,e). However, the semidiurnal and fortnightly tidal forcing cycles in the estuary may induce variations in the direction and intensity of the tidal current, consequently affecting salinity and water temperature. Thus, in the future, short-term variation factors such as tidal forcing should also be considered as environmental factors.  
In summary, the environmental factors that significantly contributed to phytoplankton variations in the YR estuary were mostly seasonal factors, including water temperature, solar radiation, and duration of sunshine, as well as factors related to the artificial inflow of freshwater, including discharge, salinity, nutrients, transparency, and the difference between surface and bottom water temperature. The contribution of the environmental factors varied according to phytoplankton size, and seasonal factors generally had a greater influence on the variation of net-sized phytoplankton. In contrast, the most influential factors for the variation of nano-sized phytoplankton, including the red tide species such as _Heterocaps_ sp. [15], were factors related to freshwater inflow. Taken together, the results of our study demonstrate that the management of anthropogenic freshwater discharge is essential for preventing and controlling HABs in an altered estuarine system, such as the YR estuary, and for managing overall aquatic ecosystem health.
# Swin-YOLO for Concealed Object Detection in Millimeter Wave Images
## 4 Experiments
### Ablation Experiments  
In this section, we conducted ablation experiments on the improvements proposed in this paper to better understand the performance of the neural network. The experimental results are shown in Table 5. The detection \(mAP\) of YOLOv5 is 87.3%. After the improvement of using the added small object detection head (termed as P2), the model's \(mAP\) was increased by 0.7% to reach 88%, which demonstrates the effectiveness of the small object detection branch. Subsequently, we evaluated the Local Perception Swin Transformer layer and Coordinate Attention. The addition of the LPST Layer has brought a further 3.5% improvement, indicating that the LPST improves the detection performance of small objects. It provides the network with an enhancement in self-attention and global contextual information, which is critical. Due to the large number of small objects in MMW images, such  
Figure 14: Some detection results of Swin-YOLO on MMW dataset.  
improvements are necessary. Building upon the improvement of the small object detection head, the addition of Coordinate Attention resulted in a 2.4% increase in performance, demonstrating that coordinate-based attention can optimize the feature information of small objects and improve the performance of the small object detection head. The addition of BiFPN improved the network's _mAP_ by 0.2% compared with the modification of P2, CA, and LPST, proving the effectiveness of cross-scale fusion and normalized feature map weights. In comparison with the Swin Transformer, the _mAP_ of LPST was 0.6% higher than the Swin Transformer, which proved the effectiveness of LPM in providing the Swin Transformer with local perception.  
When all these improvements were combined, the object detection network achieved a 4.7% improvement over the YOLOv5 baseline. Therefore, Swin-YOLO achieves competitive detection performance, demonstrating its potential for MMW object detection.
## 1 Introduction
The aim of this communication is to evaluate the feasibility of using virtual reality gloves with pressure sensors, such as the VMG30, for hand kinematics characterization during product manipulation, testing its accuracy for motion recording when compared with CyberGlove as well as its ability to differentiate between free motion and manipulation using its pressure sensors in comparison to visual analysis. The main advantages of validating the VMG30 glove would be: (1) automatic distinction between free motion and manipulation without the human error associated with visual analysis, (2) the possibility of using a more affordable commercially available data glove, and (3) the option of recording pressure applied to specific hand spots during grasps. Figure 1: Pressure sensor’s location: s1 to s5 on the fingertips; s6, s7, s9 and s10 on the palm; s8 on the middle proximal phalanx. Additional sensors s6 to s10 marked in red.
## 3 Results and Discussion
### Cell Culture  
#### 3.2.1 Cell Morphology  
The growth of HepaRG cells on PET and PET-COL scaffolds was observed using a cold-field emission scanning electron microscope (refer to Figure 7). The findings indicated that, on the first day of incubation, cells could be seen adhering to and accumulating on the surface of the PET-COL fibers. As incubation progressed, the cell population on the PET-COL fibers increased significantly, eventually leading to the formation of cellular clusters by the fifth day, displaying a three-dimensional organizational structure. This implies that  
Figure 5: SHG images of PET and PET-COL scaffolds. Scale bar: 50 \(\upmu\)m.  
Figure 6: Hydroxyproline content on PET fibers after type I collagen coating. **(A)**: Standard curve of Hydroxyproline; **(B)**: Hydroxyproline peak graph of PET-COL surface.  
PET-COL can create a favorable environment for cell growth, promoting cell adhesion and proliferation. In contrast, cell growth on PET fibers remained minimal, highlighting its restrictions in supporting cell development.
# Differential Signalling in Free-Space Optical Communication Systems
## 6 Combined Atmospheric Turbulence and Pointing Errors
### Moderate to Strong Turbulence and PE  
Similar to the weak turbulence regime, the mean and variance for moderate-strong turbulence under the GG channel with the PE are given by  
\[Mean(h_{i})=E(h_{a})E\big{(}h_{p}\big{)}=h_{o}\frac{A_{0}\gamma^{2}}{1+\gamma ^{2}}exp\left(-\frac{r_{b}^{2}}{2(1+\gamma^{2})\sigma_{j}^{2}}\right),\] (34a) \[Var(h_{i})=E\big{[}h_{a}^{2}\big{]}E\Big{[}h_{p}^{2}\big{]}- \big{(}E[h_{a}]E\big{[  
Using Label (6), and Label (34b), \(\text{Var}(V_{thresh})\) is determined as given in Table 1. Deriving the expression for the BER in the moderate to strong turbulence regime with the presence of PE again do not result in a simple closed-form expression, however, the instruction to simplify the numerical approach of BER is given in Appendix A.
# End Face Attitude Detection of Special Steel Bars Based on Improved DBSCAN
## 3 Proposed End Face Attitude Detection Algorithm for Special Steel Bars  
As previously mentioned, the bar end face attitude is determined primarily by two feature points. Since the two-dimensional code has a more mature identification and positioning method, the accuracy of the bar end face attitude angle is primarily determined by the accuracy of the center of a steel bar's end face. For illustrating our detection algorithm, we use the algorithm flow chart shown in Figure 4 to illustrate its implementation. The flow chart not only illustrates the detection process of the steel's end face attitude, but also the key steps involved in its implementation. The detection process mainly includes the following steps: steel bar end face image acquisition, connected region marking, circle parameter clustering based on improved DBSCAN, actual circle parameter detection, and calculating steel bar end face attitude angles. The specific implementation is as follows.
## 6 Conclusions  
Remote sensing technology can improve the efficiency of disaster monitoring. This study used airborne optical images to monitor the development of secondary hazards and vegetation recovery in post-earthquake areas, and the results were validated through field investigation.  
During the first year after the earthquake, debris flow activity occurred frequently with high intensity. After the source material became less available and the slope structure stabilized, the intensity of the debris flows trended down. The average annual rate of newly developed debris flows between 2008 and 2011 was approximately 3%. After the first year, the debris flow and vegetation recovery activity gradually diminished, and the hazard area gradually decreased to 12%. The lithology and topography had an influence on the debris flow development process.  
Figure 11: Relationship between vegetation recovery and geological hazards development from 2008 to 2011.  
Figure 12: Relationship between vegetation recovery and topographic factors.  
Observations in the post-earthquake hazard areas from 2008 to 2011 showed vegetation recovery in these areas was possible, and the greatest vegetation recovery occurred two or three years after the earthquake. However, the vegetation recovery rate was related to debris flow development, and was in agreement with post-earthquake geologic hazard development. The fastest vegetation recovery period was the first one to two years after the earthquake. Afterwards, the vegetation recovery gradually slowed. The average recovery rate between 2008 and 2011 was 15% to 20%. Thus, the results of this paper predict that at least five to ten years are needed to achieve total vegetation recovery. In addition, the elevation below 1500 m and slope between 31 \({}^{\circ}\)and 50 \({}^{\circ}\)has the fastest rate of vegetation growth.  
Given the various performances of the different hazard sites, future studies should consider the whole disaster area as the research area. Other factors such as the soil structure [56,57], terrain [58,59], and regional climate [60,61] provide more accurate data for decision making. To acquire a better understanding of the hazard development and vegetation restoration processes, longer-term observations, such as ten to twenty years, must be conducted [62].
## 3 Results and Discussion
### Oil-Water Separation Performance of MFA@PDMS Sponge
#### 3.3.2 Separation Performance for Oil-Water Emulsion  
In general, the particle size of the dispersed phase of oil-water emulsions is much smaller than 20 \(\upmu\)m, and a stable protective layer is formed on the surface of droplet molecules. As such, it is difficult to break emulsions, making oil-water separations extremely difficult. In this study, the separation efficiency of MFA@PDMS for different types of emulsified oils (n-hexane, phenol and canola oil) was investigated via the experimental method shown in Figure 9. The results are shown in Figure 10.  
Figure 8: Comparison of adsorption times.  
Figure 10: Schematic diagram of emulsion oil–water separation efficiency changes over time.  
Figure 9: Schematic diagram of oil–water separation process of emulsion mixture.  
This oil-water separation device was based on the hydrophobicity and lipophilicity of the MFA@PDMS based material. The MFA@PDMS was clamped in the middle of the left tube and the oil-water mixture was poured into the wide-mouth collection container on the right side. The red oil quickly passed through the MFA@PDMS and flowed into the collection container on the lower left side, while the blue deionized water was blocked on the top side of the MFA@PDMS. Finally, the oil-water mixture was successfully separated. When the oil-water mixture came into contact with the MFA@PDMS surface, its superhydrophobic and super lipophilic properties caused the oil contaminants to penetrate and pass through the material instantly, while the aqueous solution was blocked by its hydrophobic nature. In this study, after each separation, the sample was washed and dried before being reused for the next oil-water separation.  
As shown in Figure 10, the separation efficiency of magnetic MFA@PDMS sponge for n-hexane emulsified oil was the highest, reaching 97.12%, followed by phenol, which reached 95.27%. This was due to the unique structure of fly ash, i.e., its large specific surface area, porosity and abundant aluminum-silicon components, as well as the superhydrophobic performance of PDMS, which can attract organic molecules and chain-like structures to the inner surface of sponge materials through \(\pi\)-\(\pi\) dispersion interactions and the donor receptor effect [6].
## 1 Introduction
Whether the formulated method can achieve the required accuracy in horizontal position within 10 mm under railway operating conditions with different horizon shading and whether it can be further simplified. 2. The extent to which operating conditions degrade the accuracy of the measurement result. 3. Whether it is appropriate to use GNSS receivers with an integrated tilt sensor and what the risks are to accurate positioning. 4. Whether the applied method of precise GNSS positioning of points of the primary system in real time is sustainable in the long term with respect to the gradual refinement of the geometric properties of the uniform transformation key in relation to the Czech network of permanent GNSS stations. The article presents the verification (testing) of a method of GNSS RTK measurement in railway operating conditions, which enables achieving a high accuracy of \(\sigma_{x,y}=5\) mm by means of independently repeated and scheduled GNSS observations. The repeated measurements achieve the randomness of the multipath phenomenon, which does not affect the resulting mean value in a series of independent measurements. Efforts are being made to virtualize the primary railway system with long-term sustainable GNSS RTK technology, taking into consideration the instability of the area close to the railway line due to traffic.
# Performance Evaluation of Routing Protocols for Underwater Wireless Sensor Networks
## 4 Conclusions  
This research evaluated seven cooperative energy consumption routing protocols through a simulation study. The performance metrics used in this study are energy consumption, end-to-end delay, PDR, and the number of alive nodes. Based on the simulation results, RACE-SM outperforms all performance metrics from all other evaluated routing protocols. MulSi performs better than other routing protocols in terms of end-to-end delay because it uses a single forwarder node for data transfer, which reduces latency and decreases end-to-end delay. EH-UWSN performs better after RACE-SM in terms of energy consumption, PDR, and the number of alive nodes. EERBCR, GCORP, and EEDG are approximately the same in all performance metrics. In the future, we will test these and other recently designed routing protocols with other performance metrics within different  
Figure 5: The Number of Alive Nodes.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Rounds** & **RACE-SM** & **EH-UWSN** & **MulSi-Co** & **EERBCR** & **MulSi** & **GCORP** & **EEDG** \\ \hline
0 & 200 & 200 & 200 & 200 & 200 & 200 & 200 \\
1000 & 195 & 180 & 175 & 160 & 150 & 155 & 160 \\
2000 & 185 & 140 & 135 & 130 & 120 & 130 & 125 \\
3000 & 150 & 120 & 110 & 107 & 95 & 100 & 95 \\
4000 & 120 & 90 & 80 & 77 & 50 & 70 & 65 \\
5000 & 100 & 65 & 50 & 47 & 25 & 30 & 33 \\
6000 & 70 & 20 & 15 & 10 & 0 & 5 & 0 \\
7000 & 30 & 5 & 0 & 0 & 0 & 0 & 0 \\
8000 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Addressing the Number of Alive Nodes.  
types of network structures, such as dense networks, to understand which protocol is better for different types of network structures.  
Conceptualization, T.R. and I.A.; methodology, I.K.; software, A.Z.; validation, I.K., T.R. and A.Z.; formal analysis, I.A.; investigation, G.A.; resources, M.E.; data curation, T.R.; writing--original draft preparation, I.A.; writing--review and editing, I.K.; visualization, T.R.; supervision, T.R.; project administration, G.A.; funding acquisition, M.E. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the EIAS Data Science and Blockchain Lab, College of Computer and Information Sciences, Prince Sultan University, Riyadh, Saudi Arabia.  
The study did not require ethical approval.  
The materials supporting this study are available upon request from the first author.  
The authors would like to acknowledge the support of Prince Sultan University for Article Processing Charges (APC) of this publication.  
The authors declare no conflict of interest.
# CT-RPL: Cluster Tree Based Routing Protocol to Maximize the Lifetime of Internet of Things
## 3 Cluster Tree-Based Routing Protocol
### System Model
#### 3.1.2 Energy Model  
The energy consumption model adopts the free space channel with _dis_\({}^{2}\) energy consumption and the multipath channel with _dis_\({}^{4}\) energy consumption, depending on the distance between the transmitter and the receiver. Figure 2 depicts the CT-RPL energy model.  
The amount of energy required for transmitting \"s\" bits of data from node \"x\" to node \"y\" with respect to the distance _dis_(_x,y_) and its calculation is given in Equation (1) [55].The total transmission energy \(E_{TX}\) calculation is given in Equation (1).  
\[E_{TX}(s,dis)=s\times E_{elec}+s\epsilon\times dis(a,b)^{\alpha}\ =\ \left\{\begin{array}{ll}s\times E_{elec}+s\times e_{fs}\times dis(a,b)^{2}& \mbox{where }dis(a,b)<dis_{0}\\ s\times E_{elec}+s\times e_{mp}\times dis(a,b)^{4}&\mbox{where }dis(a,b)\geq dis_{0} \end{array}\right. \tag{1}\]  
where \(\epsilon_{fs}\) energy dissipated in the amplifier while _dis_\(\leq dis_{0}\), and \(e_{mp}\) is the energy dissipated in the amplifier while _dis_\(\geq dis_{0}\), \(E_{elec}\) is the consumed energy in the transmission unit, \(\epsilon\) is a constant, and \(s\) is number of data bits.  
The distance between the transmitter and receiver threshold _dis_\({}_{0}\) is given thus:  
\[dis_{0}=\sqrt{\frac{e_{fs}}{e_{mp}}} \tag{2}\]  
The amount of energy needed for receiving \(s\) bits \(E_{RX}\) of data packets from the participant RPL router is given thus:  
\[E_{RX}(dis)=dis\times E_{elec} \tag{3}\]  
Figure 2: CT-RPL energy model.
## 5 Arcing Characteristics
### Relation to Temperature  
The resistance of the contact area was measured between two contact points at various temperatures using an infrared thermal imaging camera (G100 EX, Nippon Avionics Co., Ltd., Yokohama, Japan) and is shown in Figure 5. The current flow between the two contact points was set to 8.33 A. The reason for using a current of 8.33 A is that the generated arc fault is usually energetic enough to increase the temperature at the contact point beyond 600 \({}^{\circ}\)C. At 600 \({}^{\circ}\)C, the heat is sufficient to generate copper oxide. At a lower current setting, such as 4 A, the arc fault is not sporadic enough to raise the temperature of the contact area above 600 \({}^{\circ}\)C. The temperature data was taken from room temperature at 21 \({}^{\circ}\)C up to the highest temperature possible. Upon initiation of the arc-fault state, the temperature rapidly increased to around 130 \({}^{\circ}\)C then gradually rose to 700 \({}^{\circ}\)C. Two multimeter probes (FLUKE 8846A Precision Digital Multimeter, Fluke Co., Ltd., Washington, DC, USA) were inserted to measure the resistance between the two points of contact. The results show that when the temperature between the two points of contact was higher, the resistance was lower than it was at room temperature. This is shown in Table 1. The oxide bridge at the contact points acts as an electrical insulator. When the contact was perturbed while making and breaking the contact arc, the arc fault became self-sustaining. When the contact  
Figure 4: The voltage and current waveforms in the arcing state. The red circles show the voltage drops at every zero-crossing point of the current. The green circles show where the current shoulder occurs. (**a**) A significant voltage drop and a longer current shoulder for a 100 W load. The greater power draw by the load results in a smaller current difference and drop between the hot and neutral lines. (**b**) The current shoulder and voltage drop for a 400 W load. (**c**) The current shoulder and voltage drop for an 833 W load.  
point reached a temperature at and above 520 \({}^{\circ}\)C, the resistance fluctuated between 1 k\(\Omega\) and less than 100 \(\Omega\). Therefore, in this experiment, the resistance value was measured for temperatures ranging from 21 \({}^{\circ}\)C to 520 \({}^{\circ}\)C. It was observed that at around 300 \({}^{\circ}\)C, the resistance value fell to the kiloohm range, indicating that there were semiconductor properties at the contact point. The semiconductor properties of copper oxide, which has a negative temperature coefficient, are well understood. The results we gathered indicate that current flow may be facilitated by the semiconductor properties of the contact surface.
## 4 Numerical Results
### Simulation Settings
#### 4.1.1 Environment Parameters  
The environment parameters define the physical characteristics, resource configuration, communication model, and task load of the simulated scenario. The specific parameter settings are shown in Table 3.