esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Validation of General ESS-Based VSM Control
### Simulation Test of a Single ESS-Based VSM under Step Load Change
#### 4.1.4 Summary and Discussion  
The control performance in the above tests is summarized in Table 3. The key findings, as also analyzed above, are that the steady-state energy variation of the single-ESS-based VSM is proportional to the sum of virtual governor gain and damping coefficient, while reciprocal to the frequency integral gain of frequency control. The selected PI parameters for SoC recovery produces similar immediate inertial response, but larger PI parameters result in instability because the bandwidth separation rule is defied.  
Similar to [PERSON] et al.'s research [21] and the authors' previous work [1], the test results here also demonstrated the energy variation of ESS-based VSM due to inertial and damping support, as well as the need and effectiveness of energy recovery control. However, compared to the research [21], the oscillation due to improper energy recovery control design is emphasized, and the bandwidth separation rule is highlighted. The work [1] mainly studied VSM based on hybrid ESS, and pointed out the proportional relation between energy deviation and the virtual damping coefficient, while the research work  
Figure 15: Simulation results after adding single ESS-based VSM without energy recovery control. (**a**) frequency integral gain (\(k_{i,SG}=10\) p.u.). The waveforms show the dynamics of synchronous generator power (\(P_{\text{SG}}\)), the power component of VSM in inertial and damping support (\(P_{\text{HD}}\)), virtual governor control (\(P_{\text{Gov}}\)) and the total power (\(P_{\text{VSM}}\)). There is steady-state energy deviation. (**b**) Frequency integral gain (\(k_{i,SG}=5\) p.u.). The steady-state energy deviation is approximately twice the amount in (**a**).  
here generalized the energy storage installation for VSM implementation, and validated the reciprocal relation between energy variation and the integral gain of frequency control. Therefore, this work is an efficient supplementary of the previous research, and provides a complete guideline for design VSM control based on various forms of energy storage.
## 2 Materials and Methods of Experimental Research
The reactor with a fuel sample was heated by a stream of air heated to a temperature of 873 K during the experiment. The heating process was carried out continuously until the concentrations of the detected oxides were reduced to a minimum. After that, the reactor was cooled to room temperature (293 K). Two holes were drilled in the reactor during the preparation of the stand. The first was intended for the removal of thermal decomposition products of the studied fuel samples into the gas analyzer (4) through the channel and the filter system (5). Filtration of the gaseous products of thermal decomposition of the studied fuels and their mixtures was carried out in order to dehumidify the gases and prevent adsorption of the vapor-gas mixture on the channel walls, as well as to capture the fly ash of solid pyrolysis products at the entrance to the gas analyzer. The second technological opening was designed to control the temperature of the heated medium in the reactor using a thermocouple (6). The parameters recorded by the gas analyzer and the thermocouple \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Fuel** **(Wood/Coal), \%**} & **Moisture Content,** & **Ash Content,** & **Volatile Matter** & **Net Heating Value,** & & \multicolumn{3}{c}{**Elemental Composition 1**} \\  & **Content,** & **A** & **A** & **Content,** & **V** & **daf** & **C** & **H** & **N** & **S** & **O** \\ \cline{4-10}  & & & **wt.\%** & & **M/J/kg** & & & & **wt.\%** & \\ \hline
100 D & 5.83 & 15.76 & 12.36 & 26.20 & 74.20 & 3.60 & 1.60 & 0.1000 & 20.50 \\
10 W/90 D & 5.42 & 14.00 & 14.55 & 24.92 & 63.20 & 4.50 & 1.80 & 0.1034 & 30.40 \\
25 W/75 D & 5.23 & 11.91 & 38.89 & 24.31 & 60.60 & 4.70 & 1.50 & 0.1079 & 33.09 \\
50 W/50 D & 5.35 & 10.44 & 40.96 & 23.84 & 56.20 & 5.20 & 1 & 0.1179 & 37.48 \\
100 T & 5.52 & 18.37 & 24.93 & 25.72 & 84.30 & 6.40 & 2.90 & 0.40 & 6.00 \\
10 W/90 T & 5.42 & 14.24 & 26.46 & 25.60 & 53.50 & 4.10 & 1.40 & 0.15 & 40.85 \\
25 W/75 T & 5.34 & 13.65 & 28.33 & 25.22 & 52.20 & 4.40 & 1.20 & 0.13 & 42.07 \\
50 W/50 T & 5.41 & 11.08 & 39.95 & 24.79 & 51.00 & 4.90 & 0.80 & 0.09 & 43.21 \\
100 W & 5.40 & 0.30 & 80.30 & 21.70 & 58.90 & 6.90 & - & - & 34.20 \\ \hline \hline \end{tabular} Indexes a, d, and daf are the analytical, dry, and dry-ash-free mass of the sample. 1 In relation to the mass of a dry sample.  
\end{table} 
Table 1: Characteristics of fuel components and their elemental composition. were recorded and then graphically visualized using a thermocouple signal converter (7) and a laptop (10). The sensors of the above equipment were polled every 3 s. At least five experiments were conducted for each type of fuel under identical conditions (with fixed main factors) in order to minimize random errors. The experiments continued until the pyrolysis of the studied fuels and their mixtures was fully completed (the main indicator of the process is termination of the SO\({}_{2}\) formation process).
# Lightweight Sensor Authentication Scheme for Energy Efficiency in Ubiquitous Computing Environments
## 4 Performance Evaluation
### Analysis of Energy Efficiency  
The following simulation was performed using the MATLAB program in order to analyze the efficiency through the measurement of time efficiency for each IoT device for proposed protocol and the previous encryption technology.  
Table 2 is the initial setting value for performing the simulation and the time for simulation was set considering the amount of node energy. For simulation, the time efficiency was measured according to the increase in IoT devices by setting a total of 10-200 nodes, and the test was performed by configuring an independent environment for each encryption technology and the proposed scheme.  
The sensor nodes were distributed randomly in a 20 m \(\times\) 20 m area regardless of the number of nodes. The gate was assigned in a specific position considering the placement area.  
According to the placement, the distance between all sensor nodes and gateway remained between 25 m and 10 m. Each round was set to last for 20 s.  
Figures 7 and 8 show the simulation result after the environment was configured as shown in Table 2. Simulation results have shown that in the case of the proposed scheme, the operation being performed in the device was relying on simple hash operation and arithmetic operation with less operation consumption time compared to other authentication techniques.  
In addition, in the case of group key distribution and renewal process, all polynomial operation was performed by the server, thereby reducing the burden of the device and providing a lightweight characteristic compared to other authentication techniques.  
Figure 8: Analysis of authentication time—server.  
Figure 7: Analysis of authentication time—client.
## 4 Results and Discussion
### Research Results
#### 4.1.1 Establishment of Integrated Evaluation System
\begin{table}
\begin{tabular}{c c c c} \hline
**Primary Index** & **Secondary Index** & **Three-Level Index** & **L.D.** \\ \hline \multirow{8}{*}{Systems performance} & & 3-2-6 Effective service: Continuously & \\  & & improve the algorithm and model, & \\  & Service stability & optimize the service process and user & \\  & & experience, and improve the accuracy & \\  & & and quality of the generated effect to & \\  & & ensure the effectiveness of the service. & \\ \cline{2-4}  & & 3-3-2 Stable response time: Ensure that & \\  & & the response time of the image generation & \\  & & model remains stable under different & \\  & & loads and situations to avoid a decline in & \\  & & user experience due to load fluctuations. & \\ \cline{2-4}  & & 3-3-3 Timely response rate: Measures the & \\  & & system’s ability to respond immediately & \\  & & to user requests, ensuring that users can & \\  & & quickly receive processing and feedback & \\  & & after submitting requests. & \\ \hline \multirow{8}{*}{Access security} & 4-1-1 Authentication: Verify the user’s & \\  & identity through a valid authentication & \\  & method. & & \\ \cline{2-4}  & 4-1-2 Access Control: Manage and control & \\  & user access to image generation models, & \\  & ensuring that only authorized users can & \\  & perform certain actions. & \\ \cline{2-4}  & 4-1-3 Information Encryption: Encrypts & \\  & sensitive information during & \\  & transmission and storage to protect user & \\  & data security. & \\ \cline{2-4}  & Access security & 4-1-4 Anti-repudiation: Prevent users & \\  & from denying their operations in the & \\  & system, and ensure that users’ operations & \\  & are traceable and immutable. & \\ \cline{2-4}  & 4-1-6 Data Privacy Settings: Provides & \\  & users with Settings to control the level of & \\  & privacy of their data. & \\ \cline{2-4}  & 4-1-7 Anti-Cheating measures: Prevent & \\  & users or systems from being affected by & \\  & fraud or misconduct. & \\ \cline{2-4}  & 4-1-8 Identity ambiguity: Provides & \\  & ambiguity to protect the identity of a user & \\  & or object. & \\ \hline \multirow{8}{*}{Privacy security} & 4-2-3 Application Software Security: & \\  & Protect the application software used by & \\ \cline{1-1}  & the image generation model from & \\ \cline{1-1}  & malicious attacks, exploits, and & \\ \cline{1-1}  & unauthorized access. & \\ \hline \end{tabular}
\end{table} 
Table 4: _Cont._
# Multi-Scale Cross-Attention Fusion Network Based on Image Super-Resolution
## 2 Related Works
### Attention Mechanism Based on Image Super-Resolution  
Attention usually means that the human visual system adaptively focuses on salient areas in visual information. Therefore, the attention mechanism can help the network focus on essential details. A non-local neural network for image classification tasks [23] was first proposed by [PERSON] et al. After that, [PERSON] et al. designed a Squeeze and Excitation Network (SENet) [24] to improve image classification performance by introducing a channel attention mechanism. Attention-based networks have also been increasingly applied in image super-resolution (ISR) tasks. Inspired by the SENet network [25], [PERSON] et al. referred to the channel attention mechanism in SR [26] to improve image quality. The SAN [27] algorithm recently used a second-order channel attention mechanism to refine features adaptively. In the AIDN [28] algorithm, information recognition ability is enhanced using a refined attention mechanism to improve network performance. In the MSAR [14] algorithm, the multi-scale attention residual module of feature refinement is used to refine the edge of parts at each scale to improve performance. Therefore, using a multi-scale attention mechanism for feature correlation learning can achieve a more comprehensive and in-depth improvement in performance. We propose a multi-scale cross-attention fusion network (MCFN) to extract and effectively fuse image feature information fully.
## 5 Experimental Findings
### Processing Times  
In order to evaluate the processing times, all segmentations were performed on the entire dataset, by calculating the average elapsed time and the corresponding standard deviation for each of the four investigated algorithms over all the analyzed images, obtaining the following values: SMRG: \(2.78\pm 2.79\) s; k-means: \(1.35\pm 0.78\) s; FCM: \(1.65\pm 0.88\) s; sFCM: \(1.76\pm 0.83\) s. These processing times were measured using the Matlab R2019b IDE (by means of the tic and loc stopwatch timer functions) running on a Windows 10 Pro general-purpose PC equipped with an Intel I7-3630 [EMAIL_ADDRESS] GHz CPU and 8 GB RAM.  
As expected, SMRG had the longest processing time due to the two-stage approach and also considering the iterative processes employed during the Split-and-Merge and Region Growing executions. Interestingly, the introduction of the fuzzy logic is negligible compared to the crisp k-means, as well as the integration of the spatial constraints into the sFCM algorithm does not require a remarkable computational overhead in addition to the standard FCM clustering.  
Overall, these results demonstrate the clinical feasibility of the investigated classic unsupervised methods also in terms of both computational resources and processing times, by considering that supervised CNN-based approaches for segmentation require a training phase and then an inference phase typically performed on Graphics Processing Units [18].
## 2 Materials and Methods
### Experimental Apparatus and Sensing Measurement Protocol  
The UV-Vis (range 200-700 nm) absorption spectra of the matrix compounds were collected using a Perkin-Elmer Lambda 19 spectrophotometer. The photoluminescence characterization was accomplished using a LED source (\(\lambda\) = 340 nm, M340L4, Thorlabs, Newton, MA, USA) as excitation, a 25 cm monochromator (Model 1231, Instruments SA Inc., Edison, NJ, USA), and a CCD (S7011-1007, HAMAMATSU, Hamamatsu city, Japan) as detector. The experiments were carried out in triplicate and for each sample, four emission spectra were recorded, one for each face of the cuvette. The fluorescence measurements of the different sides of the cuvette are reported in the Figure S2 of the Supplementary Materials, showing a very good reproducibility and a good homogeneity of the dispersion of the NCs in the solid matrix. For the sensing measurements, a long pass filter of 550 nm (FGL550, Thorlabs) was employed to cut the fluorescence band related to the solid matrix while maintaining the AgNPs-PMAA emission. The power of the LED was measured before and after each acquisition to guarantee the same light intensity for each sample. For the sensing protocol, one solid sample was taken as reference, while another was kept in contact with Pb(II) contaminated water (with a specific lead concentration) and subsequently the fluorescence was recorded. A volume of 1 mL of polluted water was inserted in the cuvette over the hydrogel matrix; in the case of the reference, simple deionized water was employed. The sensing protocol was repeated for each AgNCs-PMAA concentration. The kinetics of interaction was followed recording the emission spectra of the hybrid hydrogel sensor polluted with 50 uM of Pb(II) for 25 h. The equilibrium value was reached after 18 h as reported in the Figure S3. After each measurements the sample was stored at T = 4 \({}^{\circ}\)C. Sensing tests were performed after 18 h of interaction. In addition, to evaluate the time stability of the matrix with AgNCs-PMAA and the reproducibility of the data, the fluorescence was studied over 5 days, showing that the PL intensity during this period is highly stable as reported in the Figure S4. The interaction kinetics represent a point of weakness due to the long time to reach the equilibrium, but this drawback could be easily overcome by photolithography techniques since the photopolymerizable material can be easily microstructured, increasing the surface interaction between AgNPs-PMAA and Pb(II) ions and decreasing the time kinetics.  
The size and shape of the AgNPs-PMAA have been investigated by HRTEM. An image corrected FEI Titan Themis 60-300 X-FEG has been used for these studies. A 4 k \(\times\) 4 k pixels on a metal-oxide-semi-conductor (CMOS) camera has been applied to acquire the HRTEM images.
# An Experimental Tethered UAV-Based Communication System with Continuous Power Supply
## 3 Methodology
### Elements for the Prototype  
Once the project elements have been identified, the equipment and software needed to meet the requirements must be determined.
## 4 Optical Measurement Process  
### Optical Waveguide Measurement Set-Up  
Insertion loss measurements on the test waveguides were carried out at Seagate by both Seagate and Keio University staff using the measurement set-up shown in Figure 10. This is one of the principal multimode fiber launch configurations recommended in the optical circuit board measurement standard: \"IEC 62496-2--General guidance for definition of measurement conditions for optical characteristics of optical circuit boards\" [11]. The continuous wave optical output from a commercial 850 nm source was conveyed along 50/125 \(\upmu\)m OM3 graded-index multimode fiber (GI-MMF) through an Arden Photonics Modcon mode conditioner to produce an encircled flux (EF) profile at the fiber launch facet, which complies with the EF profile defined in the international standard IEC 61280-4-1 [12]. The output of the waveguide under test was collected by an integrated sphere photodetector through an OM2 GI-MMF fiber. The reference power for the insertion loss measurements was obtained by butt-coupling the input and output fibers together and measured to be \(-8.87\) dBm. Both input and  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Wavelength** & \multicolumn{2}{c}{**Set 1**} & \multicolumn{2}{c}{**Set 7**} \\ \hline GI 50 launch fiber & PL (dB/cm) & IL (dB) & PL (dB/cm) & IL (dB) \\
850 nm & 0.11 \(\pm\) 0.01 & 1.71 \(\pm\) 0.19 & 0.05 \(\pm\) 0.01 & 2.29 \(\pm\) 0.14 \\
1310 nm & 0.07 \(\pm\) 0.008 & 0.41 \(\pm\) 0.12 & 0.04 \(\pm\) 0.008 & 0.25 \(\pm\) 0.1 \\ Large Core Fiber & \multicolumn{2}{c}{Set 1} & \multicolumn{2}{c}{Set 7} \\
850 nm & 0.04 \(\pm\) 0.014 & 2.17 \(\pm\) 0.24 & 0.03 \(\pm\) 0.004 & 1.87 \(\pm\) 0.06 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Derivation of Insertion Loss (IL) and Propagation Loss (PL) from cut-back measurements.  
Figure 9: Cut-back measurements on both glass waveguide samples under test to compare end facet coupling losses at both 1310 nm and 850 nm with a standard fiber launch and 1310 nm only with a large core fiber (200 \(\upmu\)m) launch in accordance with IEC measurement standard [11].  
output fibers were end-fire coupled to the input and output waveguide facets respectively with no refractive index matching oil applied. The input and output fibers were held in a brace on an x-y-z translation stage to provide accurate mechanical alignment.
## 4 Experimental Results and Analysis
### Evaluation Criteria  
In this paper, the gravity kernel density clustering algorithm was compared with traditional and deep learning methods. The algorithm was compared with the K-means algorithm and the mean shift algorithm in terms of the execution time, number of iterations, and segmentation accuracy. The lower the execution time and the number of iterations, the faster the convergence rate of the algorithm. The higher the segmentation accuracy, the more accurate the segmentation of the diseased leaf region.  
By analyzing and comparing the segmentation accuracy, over-segmentation rate, and under-segmentation rate of the FCN and U-Net segmentation algorithms based on deep learning, the performances of different algorithms in time and space were obtained. Segmentation accuracy (SA) refers to the ratio between the pixel value of the lesion area correctly segmented with the algorithm and the pixel value of the lesion area manually marked. The over-segmentation ratio (OSR) refers to the ratio of the pixel value of the lesion incorrectly segmented with the algorithm to the pixel value of the manually labeled lesion area. The under-segmentation rate (USR) is the ratio of the pixel value of an unsegmented lesion to the pixel value of a manually labeled lesion area. The relevant evaluation indicators were calculated with Equation (13).  
\[SA=\frac{N_{tp}}{N_{gt}},\ SR=\frac{N_{fp}}{N_{gt}},\ USR=\frac{N_{fn}}{N_{gt}}, \tag{13}\]where \(N_{gt}\) represents the number of pixel values in the manually labeled lesion area, \(N_{tp}\) represents the number of pixel values in the correctly segmented lesion area, \(N_{fp}\) represents the number of pixel values of the incorrectly segmented lesions, and \(N_{fn}\) represents the number of pixel values in the unsegmented lesion area.
## 2 Port Cranes Overview
### Evolution Timeline of Port Cranes
#### 2.2.4 Containerization Revolution (1950s-1970s)  
* PACECO Container Crane Invention (1959)--In 1959, a game-changing event occurred in the port crane world. The Pacific Coast Engineering Company (PACECO) introduced the first high-speed container crane for Matson Navigation. This crane was a revolutionary design featuring all-welded box girders that provided enhanced structural integrity. With a lifting capacity of 27.5 tons, it can handle containers at a much faster rate than previously used cranes. The introduction of this crane reduced ship turnaround times from weeks to hours, setting a new standard for container handling globally and paving the way for the containerization revolution [24].
* Widespread Containerization Adoption (1960s-1970s)--By 1966, 16 PACECO cranes were operational in various ports, and containerization had begun to gain widespread acceptance. The standardization of containers has led to the development of specialized port cranes designed specifically for container handling. These cranes, including STS cranes, are essential for efficient port operations. The growth of container shipping has also spurred the development of larger container ships, which in turn require cranes with greater reach and lifting capacities [24].
# An Unequal Clustering and Multi-Hop Routing Protocol Based on Fuzzy Logic and Q-Learning in WSNs
## 6 Conclusions  
This paper presents FQ-UCR, an unequal clustering routing algorithm that integrates fuzzy logic and Q-learning to enhance network longevity and effectively address the hotspot issue in WSNs.The proposed FQ-UCR leverages multiple factors as inputs for its FIS for CH selection. Moreover, Q-learning enhances inter-cluster communication to minimize total energy usage.  
The simulation results were analyzed across two distinct scenarios: Scenario 1, in which the BS was situated at the network's center, and Scenario 2, where the BS was positioned outside the network.  
In Scenario 1, FQ-UCR demonstrated superior performance, extending the network lifetime by 111.7% compared to CHEF and 228.5% compared to EEUC. Additionally, the stability period saw a substantial enhancement, with increases of 69.3% over CHEF and 120.9% over EEUC.  
In Scenario 2, where the BS was located farther from the network, FQ-UCR further excelled by increasing the network lifetime by 202.1% and 169.5% compared to CHEF and  
Figure 28: Comparative analysis based on network stability period with BS located at (100, 250).  
Figure 29: Comparative analysis based on throughput in Scenario 2.  
EEUC, respectively. Similarly, the stability period was significantly boosted, showing a 118.6% improvement over that of CHEF and a 62.2% improvement over that of EEUC.  
As the BS moved farther away, the performance of the CHEF algorithm became increasingly unstable, while FQ-UCR continued to maintain strong performance across transmission efficiency, network stability, and throughput. Future work will focus on refining the fuzzy logic design to further optimize cluster formation, with the ultimate goal of achieving greater energy efficiency and scalability in large-scale WSNs.  
Conceptualization, Z.W. and J.D.; methodology, Z.W.; software, Z.W.; validation, Z.W. and J.D.; formal analysis, Z.W.; investigation, Z.W.; resources, Z.W.; data curation, Z.W.; writing--original draft preparation, Z.W.; writing--review and editing, Z.W.; visualization, Z.W.; supervision, Z.W.; project administration, Z.W.; funding acquisition, J.D. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Project of Industrial Technology Research and Development in Jilin Province (2023C031-3), Science and Technology Development Program of Jilin Province (20220508152 RC).  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.