esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Overview of the Conventional Techniques for Graphene-Added Biomaterials
The electrospinning technique is a versatile method to realize uniform dispersion, as well as the effective alignment of fillers [54]. Subsequent hot pressing of the aligned electrospun fibres led to the fabrication of polymer composites with enhanced mechanical properties. Electrospinning has several limitations in the preparation of graphene-based materials, including the limited variety of polymers that can be used, friability of the graphene fibres after calcination, high production cost when implemented at the industrial level [55]. Alternatively, in situ polymerization is widely used in the synthesis of graphene-reinforced composites due to its benefit in improving their mechanical, electrical and thermal properties [56]. The reasons for this include the low amounts of organic solvents, the higher dispersion of the filler in the polymer matrix and enhanced graphene-polymer interactions. Moreover, polymer composites with high filler content can be prepared. Basically, this technique consists in blending the graphene or graphene derivative with a monomer or prepolymer, then adding the suitable initiator. The reaction occurs upon irradiation or heating conditions. More recently, [PERSON] and [PERSON] [57] reported the fabrication of graphene reinforced ultra-high molecular weight polyethene nanocomposite by combining the process of ball milling with compression moulding. The high values of flexural strength and flexural modulus recommend the composite with 0.5 wt% of graphene loaded as a material suitable for total joint replacement.
# Improving the Gross Primary Production Estimate by Merging and Downscaling Based on Deep Learning
## 2 Materials and Methods
### Evaluation Indicators  
The evaluation indicators, including mean absolute error (MAE), root mean square deviation (RMSD), unbiased root mean square deviation (ubRMSD), and coefficient of determination (r\({}^{2}\)), have been used to assess the performance of the merged data objectively and find out if it captures the distribution characteristics of the reference data. The calculation formulae are as follows:  
\[MAE=\frac{1}{n}\sum_{i=1}^{n}\lvert R_{i}-M_{i}\rvert, \tag{2}\]  
\[RMSD=\sqrt{\frac{1}{n}\sum_{i=1}^{n}(R_{i}-M_{i})^{2}}, \tag{3}\]  
\[ubRMSD=\sqrt{\frac{1}{n}\sum_{i=1}^{n}\left[\left(R_{i}-\overline{R}\right)- \left(M_{i}-\overline{M}\right)\right]^{2}}, \tag{4}\]  
\[r^{2}=1-\frac{\sum_{i=1}^{n}(M_{i}-R_{i})^{2}}{\sum_{i=1}^{n}(\overline{R}-R_{ i})^{2}}, \tag{5}\]  
Figure 1: Deep-learning model structure [52].  
where \(n\) represents the total number of samples. \(M_{i}\) and \(R_{i}\) represent the merged and reference data at \(i\), respectively. \(\overline{M}\) and \(\overline{R}\) represent the average of the merged and reference data.
# DOW-PR DOlphin and Whale Pods Routing Protocol for Underwater Wireless Sensor Networks (UWSNs)
## 4 Proposed Scheme
### Division of Transmission Range into Different Transmission Power Levels  
The proposed protocol divides the transmission range into six different transmission energy levels as shown in Figure 5. For example, the next forwarder is close to the source node i.e., in transmission zone TZ1, then it will require less transmission energy. On the other hand, if the next potential forwarding node is far away in transmission range from the source node i.e., in TZ6, then higher transmission energy will be required. To increase the network lifetime, the proposed scheme uses different transmission power levels, which range from P1 to PN for broadcasting a DATA packet. The sender node floods a neighbor request message using power intensity level of PN. All the neighboring nodes receive the neighbor request message and reply with an acknowledgment packet. According to the acknowledgment packets received from different neighboring nodes from different transmission levels, the source node sets the transmission power. For example, from Figure 5, the source node S broadcast a neighbor request with a power level PN. The node A in transmission zone TZ1, node B in TZ3 and node C in TZ4 level received the neighbor request. The nodes A, B, and C reply with the acknowledgment. The acknowledgment packet contains the depth field pertaining to the depth of the sender. According to the DP field in the acknowledgment packet, the source node found the nodes in different transmission levels. The node A is lying in transmission portion TZ1, node B placed transmission zone TZ3, while node C is in transmission zone TZ4. Thus, the node C has a smaller depth than all the other nodes. The source node sets the transmission power level to P4 for broadcasting the DATA packet and with this power level all the three nodes received the packet successfully. The nodes then calculate the holding time and set the timer according to their holding time.
# GPRS Sensor Node Battery Life Span Prediction Based on Received Signal Quality: Experimental Study
## 3 Material and Methods
### Data Acquisition Part
#### 3.7.3 Scenario3: GPRS Current Acquisition  
During this step, with the help of AT commands, the microcontroller for the sensor node was programmed for sending temperature data to a remote database every 20 s, then the current consumption for the whole sensor node was recorded. The total current consumed in this situation is the same as sensing current, the current required for connecting to the network, and the current for sending data to a remote server.
# On-Line Partial Discharge Monitoring System for Switchegears Based on the Detection of UHF Signals
## 5 Experimental Results  
To simulate the process of charge accumulation in the actual high-voltage switchgear until the breakdown of the insulating material, an experimental platform as shown in Figure 14 was built in combination with the discharge model. The experimental device consists of a transformer, a discharge model, a partial discharge data collector, a high voltage regulator and PC for monitoring. The insulating plastic is placed between the two metal balls of the discharge model as the dielectric, and the UHF sensor is placed about 0.2 m away from the ball-ball discharge model.  
Figure 14: High voltage laboratory simulation of discharge circuit test site.  
Figure 13: Part of the software interface functions. (**a**) Real-time measurement interface, (**b**) history query interface, (**c**) abnormal data logging interface, and (**d**) report generation interface.  
The test steps of the experiment are as follows:  
1. Start each device, start data collector, and supply power to both ends of the discharge model;
2. Use the high voltage regulator to slowly increase the supply voltage;
3. Record the data monitored by the computer web page.  
Table 2 shows the maximum value of the localized discharge pulse monitored by the system as the voltage applied to both sides of the discharge model increases. Figure 15 shows the results of the amplitude of the discharge pulse collected from both sides of the discharge model at a high voltage of 9000 V, and Figure 16 shows the statistical results of the number of discharges. From the experimental results, it is clear that the system can effectively monitor the UHF signals diffracted from the partial discharges by the UHF method and that the maximum voltage  
Based on the data received from the web page, the front-end software can accurately display the data collected by the data collector. The two-dimensional spectrum shows the amplitude information of the partial discharge signal collected by the data collector and the number of discharges, indicating that the designed detector circuit can meet the demand for UHF signal collection and the data transmission is normal, and there is no loss or repeated data transmission. During the continuous monitoring process, the system operates stably and effectively monitors the discharge signal before the insulation material is penetrated, which provides a reliable basis for the monitoring system designed in this paper to be applied in switchgear monitoring.
# CCAIB: Congestion Control Based on Adaptive Integral Backstepping for Wireless Multi-Router Network
## 4 Performance Evaluation
### Results and Analysis  
In this subsection, simulation results are demonstrated and discussed. For graphs encompassing a wide range, logarithmic Y-axis scales are used for readability. In some graphs, a magnified graph is superimposed to show the detail. Except for the simulation for different routers, other groups only show the results of router 1 since all the routers have same trends.
## 5 Results and Discussion
### Multiple Factor Experiment Analysis
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \begin{tabular}{c} **Variance** \\ **Source** \\ \end{tabular} & \begin{tabular}{c} **Sum of Squares of** \\ **Deviations** \\ \end{tabular} & \begin{tabular}{c} **Freedom \(f\)** \\ \end{tabular} & \begin{tabular}{c} **\(F\) Value** \\ \end{tabular} & \
\begin{tabular}{c} **\(p\)-Value** \\ \end{tabular} &
\begin{tabular}{c} **Significance** \\ **Marker** \\ \end{tabular} \\ \hline Model & 26.81 & 9 & 19.99 & 0.0003 & *** \\ A & 16.59 & 1 & 111.32 & \textless{}0.0001 & *** \\ B & 2.32 & 1 & 15.58 & 0.0055 & ** \\ C & 0.90 & 1 & 6.07 & 0.0432 & * \\ AB & 0.031 & 1 & 0.21 & 0.6640 & \\ AC & 0.081 & 1 & 0.55 & 0.4843 & \\ BC & 0.0064 & 1 & 0.043 & 0.8417 & \\ A\({}^{2}\) & 4.10 & 1 & 27.53 & 0.0012 & *** \\ B\({}^{2}\) & 0.81 & 1 & 5.46 & 0.0521 & \\ C\({}^{2}\) & 1.34 & 1 & 9.00 & 0.0199 & * \\ Residual & 1.04 & 7 & & & \\ Lack of Fit & 0.62 & 3 & 1.98 & 0.2598 & \\ Pure Error & 0.42 & 4 & & & \\ Cor Total & 27.85 & 16 & & & \\ \hline \hline \end{tabular} Note: A is cylinder rotational speed/(r-min\({}^{-1}\)); B is clearance between concave and cylinder/mm; C is feed amount/(kg\(\cdot\)s\({}^{-1}\)); * is significant (\(p<0.05\)); ** is very significant (\(p<0.01\)); *** is extremely significant (\(p<0.001\)); \(F\) is [PERSON]’s variance ratio; \(p\) is probability value.  
\end{table} 
Table 5: ANOVA analysis for the rate of broken grain (BGR). According to ANOVA, to test the validity of significance, posthoc tests of the main effects were carried out by SPSS, as shown in Table 7; a method of least significant difference (LSD) was used for the backtesting. (1) As for the BGR, the factor of the A--cylinder rotational speed was extremely significant; B--clearance between the concave and the cylinder, as well as the C--feed amount were very significant. (2) The differences between level \(-1\) (275 r/min) and level 1 (355 r/min), as well as level 0 (315 r/min) and level 1 of factor A had extreme significance. (3) The differences between level \(-1\) (30 mm) and level 0 (35 mm), as well as level \(-1\) (30 mm) and level 1 (40 mm) of factor B were extremely significant. (4) The differences between level 0 (8 kg/s) and level 1 (10 kg/s) of factor C were extremely significant; the differences between level \(-1\) (6 kg/s) and level 1 were very significant. (5) As for the UGR, factor A was extremely significant, and factor B was very significant. (6) Among the differences between level \(-1\) (275 r/min) and level 1 (355 r/min), as well as level 0 (315 r/min) and level 1, factor A had extreme significance. (7) The differences between level 0 (35 mm) and level 1 (40 mm) were extremely significant, and the differences between level \(-1\) (30 mm) and level 0 (35 mm) were significant. The differences between the levels of factor C were not significant.
# The Diagnostic Classification of the Pathological Image Using Computer Vision
## 7 Integration into Real-World Clinical Workflows  
AI tools for the diagnostic classification of pathological images using computer vision have been successfully integrated into real-world clinical workflows through advancements in digital pathology, deep learning, and computational pathology. The application in clinical workflows includes (i) digital pathology and whole slide imaging (WSI), (ii) automated classification and prediction, (iii) enhanced pathologist support, and (iv) innovative architectures. AI-powered systems now utilize WSIs, which are high-resolution digitized versions of traditional glass slides, for tasks such as tumor grading, histopathological subtyping, and immunohistochemical scoring. These tools enhance diagnostic accuracy and efficiency by automating image analysis and providing objective assessments [16; 176; 177]. FDA-approved AI algorithms, such as those for prostate cancer diagnosis, have facilitated their adoption for primary diagnosis in clinical settings [176]. AI models have demonstrated high diagnostic accuracy in classifying various pathological conditions. For example, studies have shown over 97% accuracy in classifying colon lesions into six categories using convolutional neural networks [178]. These tools also predict clinical endpoints, patient prognosis, and therapy responses by analyzing tissue morphology and biomarker expression [177]. AI systems assist pathologists by identifying subtle features difficult to detect manually, such as isolated tumor cells or rare histopathological patterns. They also provide content-based image retrieval (CBIR), enabling pathologists to compare cases with large databases for better decision-making [16; 176]. Novel AI architectures like DeepTree, mimic the diagnostic process of pathologists by incorporating prior knowledge of tissue morphology. This approach improves accuracy and transparency, fostering trust among clinicians [179]. In addition, there are some benefits to clinical integration, such as efficiency, standardization, and accessibility. AI reduces the time required for routine tasks like tumor grading or biomarker quantification. Standardization ensures consistency in scoring criteria for diseases like prostate or breast cancer. Digital pathology enables remote consultations and secondary reviews, expanding access to expert diagnoses [16; 180].  
Despite their success, challenges remain in scaling these technologies: (i) the need for robust validation to ensure diagnostic accuracy across diverse datasets, (ii) addressing technical barriers like computational costs and integration with existing hospital infrastructure, and (iii) increasing clinician trust by improving interpretability and transparency of AI models [177; 181]. Thus, AI tools are transforming pathology by automating workflows, improving diagnostic precision, and supporting personalized medicine. Their continued development is expected to further integrate them into routine clinical practice.
# Energy-Efficient Hybrid Routing Protocol for IoT Communication Systems in 5G and Beyond
## 5 Performance Evaluation and Discussion
### Cluster Head Distribution  
Here, we study the correlation among \(R_{c}^{0}\) and the number of cluster heads when \(\beta\) and \(\alpha\) take distinct values. Figure 7 shows the generated number of cluster heads in the network for the MIMO-HC. Figure 7 shows that if \(\alpha\) and \(\beta\) are given a value of 0.5 each, the number of generated cluster heads would be smaller than \(\alpha\) and \(\beta\) if their values are 1 each. Besides that, having \(\alpha\) value of 0, and \(\beta\) value of 0 generates the least number of cluster heads. This is mainly attributable to the increase in the Competition Radius. In addition, with the decrease in radius, and \(\alpha\) and \(\beta\) each with value of 1, the number of generated cluster heads increases. This implies the number of cluster heads produced is specified by \(R_{c}^{0}\), \(\alpha\), and \(\beta\).  
In our suggested MIMO-HC, we give \(\alpha\) and \(\beta\) value of 0.5 each, and \(R_{c}^{0}\) have value equal to 50. Figure 8 demonstrates the distribution of cluster numbers in both MIMO-HC and UN-LEACH. The numbers of clusters in MIMO-HC are steadier in contrast to UN-LEACH. This is attributed to the random technique that has been employed in UN-LEACH to create cluster heads, resulting in a totally changeable number of clusters.  
Figure 8: (**a**) Clusters distribution in UN-LEACH [11]: (**b**) Clusters distribution in MIMO-HC.  
Figure 7: Number of Clusters Produced in the Network.
## 2 Methodology
### Simulation Properties  
This research is based on simulations made with the General Simulation Tool for Earth-Orbiting Objects (GSTE) software, written by [PERSON] during his work at Centrum Badar Kosmicznych Polskiej Akademii Nauk (CBK PAN) and developed for the ISL by [PERSON]. The key software modules include an orbit propagator with specific support for navigation satellites, a simulator of the GNSS and the ISL observations, and a parameter estimator based on weighted least squares (WLS) method with use of 4 iterations. The GNSS measurements and ISL observations were simulated based on a Galileo-like constellation (Table 1). Reference orbits were propagated using a set of gravitational and non-gravitational force models (Table 1). To obtain reliable results, each simulation scenario was repeated 10 times with randomly generated errors (i.e., measurement precision) imitating the principles of the Monte Carlo method, to provide more reliable error assessment under various simulation conditions. In the plots presenting orbit and clock estimation errors, we display mean RMS error values from the repetitions together with their standard deviations. It should be noted that estimated corrections using real data depend on many models and parameters such as tidal motion [52], antenna phase center offsets, and satellite attitude, which are not discussed in this study (the simulations are simplified and do not consider these errors in the included models). Three sets of GNSS ground stations were used (Figure 2): 16 stations located globally, 44 stations located globally, and 16 placed only in Europe. The 16-station global set partially represents Galileo Sensor Station used in previous research [32; 33]. Regional stations located in China only with ISLs support were also tested for the BeiDou navigation system with promising results [17; 18; 19]. ISLs in BeiDou compensate for the lack of satellite visibility from a ground station and help to measure the relative inter-satellite clock in nearly real time. ISL observations help in accurate orbit determination of a small regional network comparable with the global network. Additionally, ISL is integrated with observations from regional stations for broadcast clock estimation. The clock errors between satellites are obtained through centralized estimation based on ISLs.
# A Research Study on Protocol Stack of Space-Based Optical Backbone Network
## 4 Simulation and Analysis
### Test in Hardware Platform
#### 4.2.5 Test of Protocol Stack  
In previous work, we tested the functions of each part of the protocol stack. Next, we tested the whole protocol stack by automatically building an LSP. Additionally, we used IPv6 services to test the transmission of the services in the built LSP. We designed a scheme using PING(packet internet groper) services (only IPv6).  
Figure 24 shows the scheme for the PING service (only IPv6). According to the scheme, we used two service sources that kept sending PING packages. Then, we used network manager to send a request for building an LSP.  
As shown in Figure 25, the network manager sent a request to build an LSP with the destination address of fe80:0:0:30:: According to the hardware platform architecture shown in Figure 17, source 2 has the address and it connects to node 3 directly. So, the LSP should be from node 1 to node 3. Then, the routing protocol calculated the route path and signal protocol built the LSP according to the route path.  
Figure 23: Sequence diagram of AOS frame captured by port.  
As shown in Figure 26, the calculated route path consists of 3 nodes: node 1, 2 and 3. In addition, the signal protocol has already built the LSP by distributing the FIB to node 1 and the LPIB to node 2. Similarly, we used the network manager to build another LSP from node 3 to node 1. Then, two-way LSPs were built for the service and used to track the results.  
Figure 27 shows the transmission state of services in the hardware. When one port has received the service data, the corresponding light then lights up, by which we could track the transmission state of services in the hardware in real time. In the Figure 27, the two-way LSPs have already been built and we could see that there are two lights on each FPGA lighting up, which represent the accepting state of the corresponding port. By tracking this, we marked out the transmission state of services in the hardware, as shown in Figure 27. Here, we used a marker to represent a port, for example the marker 2-1 means the port 1 of node 2. Following the arrows marked in the figure, we could see the whole transmission path of services in the hardware platform. Finally, we could see that the services had been transmitted successfully, whose transmission state used to be \"Request timed out\" before.  
Figure 26: Label distribution of the signal protocol in hardware platform.  
Figure 24: Scheme for PING Service (Only IPv6).  
Based on the results from the simulations and tests operated above, we can see that the protocol stack was well designed and implemented to realize the functions designed.
## 3 CSCL-WVE-UDI: Method for Identifying Unknown IoT Devices
### WVE-UDI:UDI Algorithm Based on WVE
#### 3.5.3 WVE-UDI Algorithm  
Based on the process above, we propose the WVE-UDI algorithm. The pseudocode for the WVE-UDI algorithm is described in Algorithm 5. The inputs include new traffic data \(X_{u}\) (which contains traffic data from unknown IoT devices), the embedded vector database \(ED\) of known IoT devices, the trained and saved CSCL-Siamese network model \(M\), and a preset similarity threshold \(\theta\). The output is the identification result of the unknown IoT devices \(\hat{Y}_{u}\).  
```
0:\(X_{u}\);\(ED\);\(M;\theta\)
0:\(\hat{Y}_{u}\) // Unknown IoT device identification results
1:for each \(x_{u}\) in \(X_{u}\)do
2:\(NS\leftarrow\) call Data Preprocess(\(x_{u}\)) // Call Algorithm 1 to perform data preprocessing on new traffic data
3:endfor
4:for each \(ns\) in \(NS\)do
5:\(h_{ns}\leftarrow\) call improved-Nilsimsa(\(x_{u}\)) // Call Algorithm 2 to generate a 512-bit hexadecimal hash value
6:\(g_{ns}\gets h_{ns}\) use Pillow // Generating grayscale images with the Pillow library
7:\(G_{ns}\gets g_{ns}\) // Get a sample set of grayscale images
8:endfor
9:\(NED\gets M(G_{ns})\) // Get all embedding vectors for new traffic data
10:for each \(ned\) in \(NED\)do
11:for each \(ed\) in \(ED\)do
12:\(d_{\min}\leftarrow\) Use Equation (8) to calculate and find out minimum // Calculate the minimum similarity distance using Equation (8)
13:if\(d_{\min}<\theta\)then // Comparison of Similarity Thresholds
14:\(UED\gets ned\) // Save filtered embedding vectors for all unknown IoT devices
15:endifUnknowin IoT Device Identification Models and Algorithms based on CSCL-Siamese Networks and Weighted-Voting Clustering Ensemble
16:endfor
17:endfor
18:\(A\gets Agglomerative(UED)\) // Agglomerative identification results
19:\(B\gets Birch(UED)\) // Birch Recognition Results
20:\(G\gets Gaussian Mixture(UED)\) // GaussianMixture Recognition Results
21:\(W_{A}\gets 0.2;W_{B}\gets 0.2;W_{G}\gets 0.6\) // Assignment of weights
22:for each \(a_{ik_{n}}\) in \(\mathbf{a}\)do
23:for each \(b_{ik_{n}}\) in \(\mathbf{B}\)do
24:for each \(\hat{g}_{ik_{n}}\) in \(G\)do
25:\(labels\_weight\leftarrow\max(W_{A}\cdot a_{ik_{n}};W_{B}\cdot b_{ik_{n}};W_{G} \cdot\hat{g}_{ik_{n}})\)
26:if\(len(labels\_weight)\
eq 1\)then // Label conflicts after weighting
27:\(k\gets g_{ik_{n}}\)
28:else\(\leftarrow labels\_weight\)
29:endif
30:\(\hat{Y}_{u}\gets k\) // Final identification results
31:endfor
32:endfor
33:endfor
34:\(\hat{Y}_{u}\)
```  
**Algorithm 5** WVE-UDI algorithm