esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion  
### Formation of Ni-11P Alloy Plated Layer  
Figure 3 shows the effects of the H\({}_{3}\)PO\({}_{3}\) concentration on the P content and thickness of the Ni-P plated layers. With increasing H\({}_{3}\)PO\({}_{3}\) concentration in the bath, the P content in the plated layer increases, whereas the thickness of the plated layer decreases. The mechanism of the Ni-P electrodeposition process involves the following reactions [14; 15]:  
\[\mathrm{Ni^{2+}+2e^{-}\rightarrow Ni}, \tag{1}\]  
\[2\mathrm{H^{+}+2e^{-}\rightarrow H_{2}}, \tag{2}\]  
\[\mathrm{H_{3}PO_{3}+6H^{+}+6e^{-}\rightarrow PH_{3}+3H_{2}O}, \tag{3}\]  
\[2\mathrm{PH_{3}+3 Ni^{2+}\to 3 Ni+2P+6H^{+}}, \tag{4}\]  
where reactions (1)-(3) are electrochemical reactions and reaction (4) is a chemical reaction. Increasing the H\({}_{3}\)PO\({}_{3}\) concentration in the bath clearly accelerates the reactions (3) and (4), which increases the P content in the plated layers. In addition, increasing the H\({}_{3}\)PO\({}_{3}\) concentration in the bath solution reduces the pH, thus accelerating the reaction (2). Consequently, the reaction (1) was significantly inhibited, decreasing the thickness of the plated layer.  
The chemical composition data obtained by XRF analysis are plotted in Figure 3. The results show that a Ni-P plated layer of 20 \(\upmu\)m thickness containing 11 wt.% P can be obtained when the H\({}_{3}\)PO\({}_{3}\) concentration in the bath is approximately 0.4 M under the plating conditions of a current density of 2.0 A/dm\({}^{2}\) and a plating time of 90 min.  
The XRD analysis result for the Ni-11P plated layer is presented in Figure 4. The broad peak in the XRD spectra of the plated layer at 2\(\theta\) of 44.44\({}^{\circ}\) suggests that the electroplated Ni-11P alloy layer has an amorphous structure. The incorporation of phosphorus in the nickel generally increases the number of defects in the crystalline lattice of the Ni-P plating layer, thereby transforming the plating layer from crystalline to amorphous [16]. No peaks from Ni\({}_{3}\)P or other Ni-P compounds were detected in the electroplated Ni-11P layer.  
Figure 3: Effect of H\({}_{3}\)PO\({}_{3}\) concentration on P content and thickness of Ni-P plated layer (C.D.: 2.0 A/dm\({}^{2}\), plating time: 90 min).
## 4 Proposed Greedy Heuristic
#### Time Complexity of GH-MWDDS  
In this section, we analyze the complexity of our greedy algorithm, without the optional reduction mechanism from line 22. The main while loop (line 5) of the algorithm is iterated \(k\) times (rounds), where \(k\) refers to the number of disjoint dominating sets returned by the algorithm. Since the maximum number of dominating sets in a domatic partition of a graph \(G=(V,E)\) is less or equal to \(\delta+1\)[19], where \(\delta\) is the minimum degree of any node of \(G\), it holds that \(k\leq\delta+1\). As a result, checking the condition in line 5 and executing lines 7-9 is both done in \(O(\delta\cdot n)\) time. Further, \(n\) is clearly an upper bound for the number of times that the inner while loop (lines 11-21) is iterated. To determine the time complexity of line 12, we proceed as follows. Remember that for any node \(v\) the calculation of its \(\text{\emph{white\_degree}}(v)\) value requires \(deg(v)\) operations. Moreover, for choosing the first node \(v_{1}^{*}\) of a dominating set, line 12 requires a time of \(deg(v_{1})+deg(v_{2})+\cdots+deg(v_{n})\). The well-known Handshaking Lemma states that \(\sum\limits_{v\in V}deg(v)=2m\), where \(m=|E|\). For choosing the second node \(v_{2}^{*}\), line 12 requires \(m-deg(v_{1}^{*})\) time, etc. Therefore, the total time complexity of line 12 is \(O(n\;m)\). In summary, we can conclude that our greedy algorithm has a worst-case time complexity of \(O(\delta\;n)+O(n\;m)=O(max(\delta\;n,n\;m))=O(n\;m)\).
# Unmanned Aerial Vehicle Based Wireless Sensor Network for Marine-Coastal Environment Monitoring
## 4 Experimentation and Results
### Post-Field Test Analysis  
After retrieving the buoys from the beach, each of these was opened to retrieve the sensor nodes placed inside. This revealed that in one of the three buoys there was a small hole in the PVC structure which allowed water to leak. Additionally, the IP67 case that housed the sensor node was missing a vital toric joint, thus water ended up filling the space where the sensor node was located (Figure 17).  
This unexpected outcome suggests that the upper part of the buoy (Figure 10), although functional for the field test, requires further improvements to avoid this kind of results. Furthermore, in a small percentage of the samples extracted from the sensor nodes, a loss of GPS signal was detected. Analyzing Figure 14, it can be seen that part of the sensor node was below the sea surface, which may have been the reason for the GPS interference. Also, this might be the reason for the LoRa transmission loss at 4 km, instead of the expected 5 km. Although additional tests must be done, these results suggest that modifications should be made to the buoyancy, height of the upper part of the buoy or location of the GPS and LoRa antennas.
# DVCW-YOLO for Printed Circuit Board Surface Defect Detection
## 4 Experiments and Analysis of Results
### Experimental Comparative Analysis
#### 4.3.3 Performance Comparison of Different Detection Algorithms  
To further assess the object detection performance of the DVCW-YOLO model proposed in this study, it was compared with several state-of-the-art models, including Faster R-CNN, SSD, YOLOv3-tiny, YOLOv4-tiny, YOLOv5s, YOLOv7, YOLO-G [32], and YOLOv8-PCB [33]. The detailed comparison results are presented in Table 3.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Model** & **DWConv** & **VOVGSCSP** & **WIOU** & **C2 fCBAM** & **P/\%** & **R/\%** & **mAP0.5/\%** & **FPS** & **FLOPs** & **Params** \\  & & & & & & & & **(f/s)** & **/G** & **/M** \\ \hline YOLOv8n & \(\times\) & \(\times\) & \(\times\) & \(\times\) & 96.1 & 95.0 & 95.3 & 41.6 & 8.9 & 3.15 \\ YOLOv8n-D & \(\surd\) & \(\times\) & \(\times\) & \(\times\) & 98.2 & 99.0 & 99.1 & 42.1 & 7.7 & 2.58 \\ YOLOv8n-DV & \(\surd\) & \(\surd\) & \(\times\) & \(\times\) & 98.5 & 98.5 & 98.7 & 43.6 & 7.0 & 2.46 \\ YOLOv8n-DVDW & \(\surd\) & \(\surd\) & \(\surd\) & \(\times\) & 98.6 & 98.7 & 99.1 & 43.6 & 7.0 & 2.46 \\
**DVCW-YOLO** & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & 99.1 & 99.4 & 99.3 & 43.3 & 7.0 & 2.47 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance comparison of ablation experiment.  
Figure 9: Comparison curve of different models.  
The DVCW-YOLO model achieved the highest [EMAIL_ADDRESS] of 99.3% among all the models evaluated. This represents improvements of 11.4%, 29.2%, 24.7%, 15.5%, 6%, 4.9%, 0.3%, 0.9%, and 4.1% over faster R-CNN, SSD, YOLOv3-tiny, YOLOv4-tiny, YOLOv5s, YOLOv7, YOLO-G, and YOLOv8n, respectively. In addition to its superior accuracy, the DVCW-YOLO model also demonstrates a remarkable efficiency, with a FLOPs value of only 7.0 G. In comparison, it shows a significant advantage in computational efficiency, reducing FLOPs by 122 G, 24.5 G, 19.4 G, 14.2 G, 9.4 G, 2.8 G, and 6.2 G over faster R-CNN, SSD, YOLOv3-tiny, YOLOv4-tiny, YOLOv5s, YOLOv7, YOLO-G, and YOLOv8n, respectively. DVCW-YOLO also had significant advantage in detection speed, and its FPS value was greater by 42.1, 20, 16.6, 13.2, 11.2, 10.8, 34.5, 1.7 than those of faster R-CNN, SSD, YOLOv3-tiny, YOLOv4-tiny, YOLOv5s, YOLOv7, YOLOv8-PCB and YOLOv8 ns. Overall, the results indicate that the DVCW-YOLO model offers clear advantages in both detection accuracy and speed compared to other models, demonstrating its superior performance in object detection tasks.
## 3 Heat-to-Heat Applications
### Transcritical CO\({}_{2}\) Heat Pump Cycles
#### 3.1.2 Experimental Results  
There are numerous test results of domestic CO\({}_{2}\) heat pumps [95]. For heat recovery applications, the experimental facilities of CO\({}_{2}\) heat pumps are rather limited, as explained previously, having mostly to do with the limitations of its heat supply temperature to below 30 \({}^{\circ}\)C. However, there are few drying applications for which this kind of heat pumps has been tested and even commercially applied. The available test results were based on a limited number of experimental studies [16; 99] and data provided by system manufacturers [111; 112]. The main relevant results are shown in Figure 5, depicting the COP as a function of the temperature lift.  
The test results with a COP of 6.5, shown in Figure 5 to deviate from all other results, concern an air-source heat pump drier with small differences between the gas cooler and heated air temperatures, favoring its performance. In the same figure, a fitted (exponential-type) correlation is also presented, with its R\({}^{2}\) greatly reduced due to the aforementioned test result. In general, the heat pump performance can reach a COP of four for a temperature lift of about 40 K, while even for a lift of 90 K, the COP was still high and equal to three, achieved with a single-stage cycle. Similar performance at such high lifts is not possible to be achieved with a subcritical vapor compression heat pump that would require a two-stage cycle [81].  
Figure 5: Coefficient of performance (COP) of CO\({}_{2}\) heat pumps for heat recovery as a function of the temperature lift.
# OTFS-Based Handover Triggering in UAV Networks
###### Abstract  
In this paper, delay Doppler (DD) domain is utilized for enabling an efficient handover-triggering mechanism in highly dynamic unmanned aerial vehicles (UAVs) or drones to ground networks. In the proposed scheme, the estimated DD channel gains using DD multi-carrier modulation (DDMC), e.g., orthogonal time frequency space (OTFS) modulation, are utilized for triggering the handover decisions. This is motivated by the fact that the estimated DD channel gain is time-invariant throughout the whole OTFS symbol despite the entity speed. This results in more stable handover decisions over that based on the time-varying received-signal strength (RSS) or frequency time (FT) channel gains using orthogonal frequency division multiplexing (OFDM) modulation employed in fifth-generation-new radio (5G-NR) and its predecessors. To mathematically bind the performance of the proposed scheme, we studied its performance under channel estimation errors of the most dominant DD channel estimators, i.e., least square (LS) and minimum mean square error (MMSE), and we prove that they have marginal effects on its performance. Numerical analyses demonstrated the superiority of the proposed DD-based handover-triggering scheme over candidate benchmarks in terms of the handover overhead, the achievable throughput, and ping-pong ratio under different simulation conditions.  
U 2025 9 185
# Feasibility Study of Real-Time Monitoring of Pin Connection Wear Using Acoustic Emission
###### Abstract
The results showed that the AE signal strength is a good indicator to judge the wear degree of low rotational speed pin connections, and that it can be used to monitor the wear process of pin connections. Experiments also reveal that the main wear forms of low rotational speed pin connections in this research include abrasive wear and the adhesive wear. Furthermore, AE results demonstrated that the adhesive wear is the main wear form of pin connections, and the signal strength of the adhesive wear is around 190 times larger than that of abrasive wear.
# A Study on the Effect of Drift Factor on Feature Optimization in Electronic Nose Detection
## 3 Results and Discussion  
### Drift of the Electronic Nose  
In the detection compensation of electronic noses, it is common to study the effect of drift of the electronic nose by only controlling the humidity or temperature of the incoming gas, which often does not correspond to the reality of the detection situation. Separating the sample temperature and humidity from the detection temperature and humidity faces the following problems: the detection temperature of the sample is affected by the difference between the operating temperature of the sensor and the temperature of the sample, while the humidity faces the problem of the difference between the detection humidity and the sample humidity [17]. All these lead to the problem that the compensation module needs to consider the intersection and fitting of the two factors. Therefore, we take the approach of controlling the sample detection environment, which is also more in line with the electronic nose detection situation when working in practice.  
By looking at Figure 4, we can visualize the effect of drift on the electronic nose under different detection conditions. The more centralized data for the same tea indicate a smaller drift effect, while the more dispersed data points represent the effect of drift on the baseline of the electronic nose, which leads to the dispersion of the sensor's data detection and ultimately reduces the classification effect of the electronic nose. However, on the other hand, a higher degree of data overlap at the same humidity temperature will also mean a greater difficulty in discrimination. Specifically, the data in the figure show that the higher the humidity, the more dispersed the data points are, which is the same as that observed by [PERSON] et al. [24], but the higher the overlap between the various types of tea under low humidity conditions, which may mean that it is more difficult to categorize. The temperature conditions also showed that the higher the temperature, the more dispersed the data from the tea samples. Therefore, a classification comparison of the gas data under different conditions is also needed to further determine the classification performance of the electronic nose and the specific compensation range.  
Figure 3: Flowchart for differentiation and compensation of datasets.  
Figure 4: Temperature and humidity sensor (W3C) detection response chart. Note: Figure (**a**) shows the response distribution of tea data for different humidity conditions; Figure (**b**) shows the response distribution of tea data for the same temperature conditions. The response value of the sensor is the ratio response value of G/G0, which responds to the concentration of the gas without corresponding unit. And the response of the sensor is similar to the results of the W3C sensor, so only the detection response graph of the sensor is released.
## 4 Comparison Experiments
### Feature Combination Comparison  
A wealth of feature extraction space is essential for model performance improvement. To expand the feature space, numerous features were obtained from the raw data, and an attempt was made to combine them. To compare the pros and cons of different feature combinations in identifying electricity consumption in industrial power scenarios, the authors developed identification models using different classifiers and used the recognition accuracy of the corresponding classifiers as the judging criteria. The feature combinations were used as model inputs. Their accuracy comparisons on different classifiers are presented in bar charts, as in Figure 9. The combinations of two features included time domain features with frequency domain features (abbreviated as TF), entropy value features with time domain features (ET), and entropy value features with frequency domain features (EF). There was also a joint feature set (ETF) that aggregated all features.  
Observing the effectiveness of feature combinations for identifying electrical loads, the authors found variability in the accuracy of different classifiers. Among them, the highest classification accuracy was achieved using the joint feature set, which is very intuitive, with 88.44%, 89.35%, 88.41%, 88.25%, and 89.89% on KNN, SVM, DT, RF, and DA, respectively. SVM was employed as an exemplification to examine the accuracy of various models with varying feature combinations. First, in comparing the two feature combinations, EF had the best accuracy rate of 88.17%, ET gave the next best result, of 88.03%, and TF had the worst result, of 87.64%. Subsequently, ETF had the best result, with an accuracy of 89.35%. Interestingly, the accuracy of feature combinations that included entropy features was usually higher. The results show that combining features is beneficial to exploit the advantages of multiple features jointly. Entropy value features contribute  
Figure 8: Comparison of model accuracy and computation time before and after feature extraction.  
the most to the joint feature set, and introducing entropy value features can improve the overall feature quality.
## 2 Theory Background
### The Theory of SAX-LZ  
The Lempel-Ziv complexity, which was proposed by [PERSON] and [PERSON] [25], has been widely used to recognize the fault severity of bearing, because signals of bearing with different fault severities have different complexities. In general, the LZ indicator is calculated based on a 0-1 sequence [26]. However, the application of SAX to code the time series in the calculation of LZ cannot only describe the details of time series, but also improve the computational efficiency. For a given time series \(X=\{x_{1},x_{2},\cdots,x_{n}\}\), the process of the SAX-LZ is given as follows [27].  
Step 1: The time series is normalized according to the equation, as follows:  
\[NX=\frac{X-\mu}{\sigma}, \tag{2}\]  
where \(NX\) is the normalized series of \(X\), \(\mu\) is the mean of the time series, and \(\sigma\) is its standard deviation;  
Step 2: According to the below equation, the normalized time series is represented by the average of each segment, which is divided according to Piecewise Aggregate Approximation (PAA):  
\[\overline{x_{i}}=\frac{N}{n}\sum_{j=\frac{N}{N}(i-1)+1}^{\frac{N}{N}i}x_{j}, \tag{3}\]  
where \(\overline{x_{i}}\) is the average of the \(i^{th}\) segment, \(x_{j}\) is one point of time series \(X\), \(j\) is the sequence number for each segment, \(N\) is the number of equal sized segments;  
Step 3: According to Table 1, determine the breakpoints \(\beta_{i}\) according to the equiprobable regions \(\alpha\) of the distribution space to be divided;  
Figure 1: The Huffman tree and Huffman coding.  
Step 4: The symbolic sequence \(\{s_{i}\}_{i=1}^{N}\) is obtained based on the symbol which is assigned according to the region divided by the breakpoints;  
Step 5: Initialization, set \(S_{v,0}=\{\}\), \(Q_{0}=\{\}\), \(C_{N}=0\), \(r=1\);  
Step 6: Take \(Q_{r}=(Q_{r-1}s_{r})\) and judge whether \(Q_{r}\) belongs to \(S_{v,r-1}=\{S_{v,r-2}s_{r-1}\}\). If not, \(Q_{r}=\{\}\), then \(C_{N}(r)=C_{N}(r-1)+1\), \(r=r+1\). If so, \(Q_{r}=(Q_{r-1}s_{r})\), \(C_{N}(r)=C_{N}(r-1)\), \(r=r+1\), and repeat this step until symbolic sequence is completely covered;  
Step 7: The Lempel-Ziv complexity is normalized by:  
\[0\leq C_{nN}=\frac{C_{N}(N)}{C_{UL}}\leq 1, \tag{4}\]  
where,  
\[C_{UL}=\lim_{N\rightarrow\infty}C_{N}(N)=\lim_{N\rightarrow\infty}\frac{N}{(1 -\beta)\log_{k}N}\approx\frac{N}{\log_{k}N}, \tag{5}\]  
where \(k\) is the number of the alphabets (for binary string, \(k=2\)).
## 4 Results
### Determination of Heating Value  
The results calculated for HHV and LHV are shown in Table 4.  
HHV and LHV are both impacted by feedstock and thermal treatment (at a 95% level of confidence). A generalized upward trend was observed in the values of HHV and LHV  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Materials** & **Test** & **HHV (MJ/kg)** & **LHV (MJ/kg)** \\ \hline \multirow{3}{*}{Sugarcane bagasse} & As received & 19.45 & 16.85 \\  & 300 \({}^{\circ}\)C & 33.45 & 30.89 \\  & 400 \({}^{\circ}\)C & 34.37 & 31.36 \\ \hline \multirow{3}{*}{Cashew nutshells} & As received & 22.05 & 19.52 \\  & 300 \({}^{\circ}\)C & 27.69 & 25.64 \\  & 400 \({}^{\circ}\)C & 28.78 & 27.39 \\ \hline \multirow{3}{*}{PKS} & As received & 19.97 & 17.82 \\  & 300 \({}^{\circ}\)C & 20.42 & 19.01 \\  & 400 \({}^{\circ}\)C & 21.38 & 20.23 \\ \hline \multirow{3}{*}{Coconut shells} & As received & 17.59 & 15.47 \\  & 300 \({}^{\circ}\)C & 25.86 & 24.43 \\ \cline{1-1}  & 400 \({}^{\circ}\)C & 27.21 & 25.92 \\ \hline \multirow{3}{*}{\(p\) value: feedstock} & & 0.0442 & 0.0823 \\ \cline{1-1}  & & 0.0181 & 0.0190 \\ \hline \end{tabular}
\end{table}
Table 4: Values calculated for high heating value (HHV) and low heating value (LHV).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Materials** & **Test** & **Moisture (wt\%)** & **Volatiles (wt\%)** & **Ash (wt\%)** & **Fixed Carbon** \\ \hline \multirow{3}{*}{Sugarcane bagasse} & As received & 3.31 & 82.25 & 1.36 & 16.38 \\  & 300 \({}^{\circ}\)C & 3.79 & 27.10 & 4.39 & 68.52 \\  & 400 \({}^{\circ}\)C & 3.67 & 25.97 & 5.38 & 68.65 \\ \hline \multirow{3}{*}{Cashew nutshells} & As received & 5.85 & 81.32 & 2.04 & 16.65 \\  & 300 \({}^{\circ}\)C & 3.55 & 48.85 & 7.24 & 43.92 \\  & 400 \({}^{\circ}\)C & 5.24 & 22.77 & 7.42 & 69.81 \\ \hline \multirow{3}{*}{PKS} & As received & 2.65 & 67.86 & 9.80 & 22.34 \\  & 300 \({}^{\circ}\)C & 4.54 & 27.90 & 15.32 & 56.78 \\  & 400 \({}^{\circ}\)C & 4.59 & 20.80 & 18.76 & 60.44 \\ \hline \multirow{3}{*}{Coconut shells} & As received & 6.50 & 70.72 & 6.38 & 22.91 \\  & 300 \({}^{\circ}\)C & 3.02 & 27.60 & 11.21 & 61.20 \\ \cline{1-1}  & 400 \({}^{\circ}\)C & 3.75 & 20.84 & 12.81 & 66.35 \\ \hline \multirow{3}{*}{\(p\) value: feedstock} & & 0.2426 & 0.0023 & \textless{}0.001 & 0.1223 \\ \cline{1-1}  & 400 \({}^{\circ}\)C & 3.184 & \textless{}0.001 & \textless{}0.001 & \textless{}0.001 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Results obtained in the proximate analysis (average values shown).  
with increasing test temperature in all materials. The maximum values for HHV and LHV were related with sugarcane bagasse samples at 400 \({}^{\circ}\)C with 34.37 MJ/kg and 31.36 MJ/kg, respectively. PKS samples showed the lower values of HHV and LHV for 300 \({}^{\circ}\)C and 400 \({}^{\circ}\)C with 20.42 MJ/kg, 21.38 MJ/kg, 19.01 MJ/kg, and 20.23 MJ/kg, respectively.
# Proof-of-Concept of a Millimeter-Wave Integrated Heterogeneous Network for 5G Cellular
## 3 mmWave Beamforming Antenna and Backhaul
### Highly Directional Steerable mmWave Antennas
The outdoor field trial setup for LAA is shown in Figure 9. This study demonstrated that the LAA is a practical high-gain steerable millimeter wave antenna for use in future millimeter wave overlay networks (D2D, access and backhaul) within the framework of 5G communication systems. In LAA, we have used one antenna array module with 2 \(\times\) 8 elements including the RFIC chip, and one BB module for digital signal processing. The total power consumption was about 4-6 Watts (from direct measurements) depending on regime of work (RX or TX). For the MAA prototype, we have  
Figure 7: Highly-directional steerable mmWave antennas: (**a**) modular antenna array architecture and (**b**) lens array antenna scheme. used eight antenna array modules (each with 2 \(\times\) 8 elements and one RFIC chip) and one, common, BB module for digital signal processing. Thus, the total power consumption of the eight-module MAA was estimated as 20-25 Watts. Therefore, both the LAA and MAA equipment could be power supplied with PoE.
# Large Scale Community Detection Using a Small World Model
## 6 Implementation
### Datasets Used
#### 6.2.2 Real World Datasets  
Real-world datasets are more complex and heterogeneous as compared to synthetic data. Revealing communities in real-world networks is a NP (non-deterministic polynomial)-hard problem. For measuring the performance of the proposed algorithm, the following real-world datasets have been taken into consideration:  
* DBLP;
* Amazon;
* Youtube.  
All of these datasets have been collected from the Stanford Large Network Dataset Collection (SNAP), which is publicly available for social network analysis [35].