esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Definition and Discussion of the Coupling Coefficient
#### 3.2.1 The Response Relationship Between the SSTA and SCA  
As an example, the maps of the _SSTA_ (colors) and _SCA_ (contours) fields over the four typical sea areas are shown in the left images of Figure 5. And the corresponding scatter plots are presented in the right images of Figure 5. The x-axis of the scatter plots represents the _SSTA_, while the y-axis represents the _SCA_. In the Southern Hemisphere (i.e., ARC and BMC), the data in January 2005 were used. In the Northern Hemisphere (i.e., KE and GS), the data in June of the same year were employed. The reasons for choosing different data dates for the Southern and Northern Hemispheres are explained in the subsequent section. Just as expected, the _SCA_ is positively correlated with the _SSTA_, i.e., the larger (smaller) scattering coefficient is over the warmer (colder) water. The scatter plot shows a moderate correlation between the _SSTA_ and _SCA_. The correlation coefficients are 0.60, 0.51, 0.33, and 0.60 in the four sea areas, respectively. Based on the definition, the slope of the fit line in Figure 5 represents the coupling coefficient between the _SSTA_ and _SCA_. And the values of the coupling coefficient \(k_{ssta}\) in the ARC, BMC, KE, and GS regions are 0.34, 0.35, 0.32, and 0.36, respectively. The results in reference [25] showed that the wind speed anomaly is more sensitive to the _SATDA_ than the _SSTA_. This means that the _SATDA_ maybe has a more significant effect on the _SCA_ than the _SSTA_.  
Figure 5: The map of the _SCA_ overlaid as contours on the _SSTA_ at the (**a**) ARC, (**c**) BMC, (**e**) KE, and (**g**) GS, and the scatter plot distribution of data for the (**b**) ARC, (**d**) BMC, (**f**) KE, and (**h**) GS sea areas.
# A Multi-Objective Approach for Optimizing Virtual Machine Placement Using ILP and Tabu Search
## 2 Problem Description and Related Work  
### Problem Overview  
The VMPP, also called the VM mapping problem, involves assigning a set of VMs to a set of PMs while satisfying certain constraints and optimizing an objective function as previously discussed. Specifically, given a set of PMs with defined resource capacities (CPU, memory, and storage) and a set of VMs with resource requirements, the goal is to allocate physical resources such that VM requirements are met, constraints are satisfied, and the objective function is optimized. For example, we consider three PMs with specified capacities for CPU cores, memory, and storage (Table 1), and five VMs with specific resource requirements (Table 2). The task is to allocate the VMs across the PMs to maximize the number of hosted VMs, ensure a one-to-one VM-PM assignment, and avoid resource overcommitment on any PM.  
As shown in Figure 1, multiple solutions exist for the VMPP, each with its own set of advantages and trade-offs. In the first solution, depicted in Figure 1a, VM1 and VM2 are hosted on PM1, utilizing six CPU cores, 24 GB of RAM, and 150 GB of storage. VM3 and VM5 are placed on PM2, consuming eight CPU cores, 32 GB of RAM, and 250 GB of storage, while VM4 is placed on PM3, fully utilizing its resources. Although this configuration hosts all VMs, it results in higher energy consumption since all three PMs are active.  
Alternatively, the second solution, shown in Figure 1b, optimizes resource utilization and minimizes energy consumption. In this solution, VM1, VM2, and VM5 are allocated to PM1, fully utilizing its resources (eight CPU cores, 32 GB of RAM, and 200 GB of storage). VM3 and VM4 are placed on PM2, consuming 10 CPU cores, 40 GB of RAM, and 300 GB of storage. Notably, PM3 remains unused, reducing energy consumption by activating only PM1 and PM2. This configuration maximizes the number of accepted VMs while minimizing resource waste and energy expenditure, demonstrating the effectiveness of strategic VM placement in optimizing resource utilization and cost-efficiency in cloud environments.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**PM** & **CPU (Cores)** & **RAM (GB)** & **Storage (GB)** \\ \hline PM1 & 8 & 32 & 500 \\ \hline PM2 & 16 & 64 & 1000 \\ \hline PM3 & 4 & 16 & 250 \\ \hline \hline \end{tabular}
\end{table}
Table 1: PM resource characteristics.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**VM** & **CPU (Cores)** & **RAM (GB)** & **Storage (GB)** \\ \hline VM1 & 4 & 16 & 100 \\ \hline VM2 & 2 & 8 & 50 \\ \hline VM3 & 6 & 24 & 200 \\ \hline VM4 & 4 & 16 & 100 \\ \hline VM5 & 2 & 8 & 50 \\ \hline \hline \end{tabular}
\end{table}
Table 2: VM resource requirements.  
This example highlights the variability in VMPP solutions and emphasizes the need to optimize multiple criteria simultaneously. A comprehensive approach that sequentially optimizes several objective functions is essential for achieving more efficient and effective VMP solutions.
# Low-Light Mine Image Enhancement Algorithm Based on Improved Retinex
## 1 Introduction
The above process mainly includes three parts: (1) an estimation of the illumination and reflection components from the brightness component; (2) brightness enhancement and denoising on illumination and reflection components; and (3) color correction is applied to the saturation component. The main contributions of this paper are as follows:  
1. An improved guided filtering algorithm is proposed to replace the Gaussian filter in the Retinex algorithm to more accurately estimate the illumination component and reflection component from the brightness component. 2. Fusion of the contrast-limited adaptive histogram equalization algorithm and the improved guided filtering algorithm to process the reflection component to achieve brightness enhancement and denoising at the same time. 3. Propose an improved adaptive stretching method to process the saturation component to avoid color distortion. The rest of this article is organized as follows. Section 2 introduces the relevant theoretical knowledge of Retinex. Section 3 is the algorithm proposed in this article. Section 4 conducts experimental verification and comparative analysis. Section 5 summarizes this article.
# An Efficient Probe-Based Routing for Content-Centric Networking
## 3 Design and Description of Our Proposal
### Probe-Based Routing Algorithms
#### 3.2.1 During Interest Sending Phase  
In basic CCN, when a router receives an interest, it performs three steps:  
**Step 1**: Check its CS. If the _Name_ matches in CS, the content data is returned as a data packet via the interest's incoming interface. Otherwise, go to step 2.  
**Step 2**: Check its PIT. If the _Name_ matches in PIT, the incoming interface is aggregated into the same entry and the interest is dropped. Otherwise, PIT creates a new entry with the _Name_ of the interest and then goes to step 3.  
**Step 3**: Check its FIB. If the _Name_ matches in FIB, the interest is forwarded through the minimum cost of the matched outgoing interface. Otherwise, the interest is broadcast by the routers.  
The adjustments of basic CCN in this paper are described as follows. Figure 3 illustrates an example of our _probe_-based approach. When a router receives an interest, it can play one of three roles: Initial (I), Miss (M), or Hit (H). If the interest is sent from the consumer, the router is called _I router_. If the router fails to retrieve data of _Name_ from its CS, it is called _M router_. If the router owns the data of _Name_, it is called _H router_.  
_I router_ adds a _probe_ to interest before forwarding it. The _probe_ is selected from one of two options: PIT or FIB (see lines 1 and 2 in Figure 3). _I router_ then seeks the best candidate content provider with the minimum cost in SPT, stored in the matched FIB entry in Figure 3 (see line 3 in Figure 3). To forward the interest, the outgoing interface to the best candidate content provider is selected (see line 4 in Figure 3).  
_M router_, in addition to the basic CCN routing steps, checks if it is the _probe's_ content provider. If affirmative, the router ID is added to _probe-response_ (see line 5 in Figure 3) and then the interest is forwarded as _I router_ without adding a new _probe_ (see line 5 and 4 in Figure 3).  
Figure 3: Packet format.  
_H router_ adds its ID to the data packet. In addition to the _M router_ steps, the _probe_ and _probe-response_ in the interest are replicated to the data packet (see line (c) in Figure 4).
## 9 Conclusions  
New and advanced GO algorithms have been continuously introduced to solve complex design optimization problems. These problems are characterized by using computation intensive numerical analyses and simulations as objective and constraint functions with a large number of design  
Figure 16: Cost for FOWT problem results.  
Figure 17: Required CPU time for FOWT problem.  
variables, forming the so-called computation intensive, black-box GO problems. The relatively new, nature-inspired global optimization methods, Artificial Bee Colony (ABC), Firefly Algorithm (FFA), Cuckoo Search (CS), Bat Algorithm (BA), Flower Pollination Algorithm (FPA) and Grey Wolf Optimizer (GWO), have been introduced to improve search efficiency by reducing the needed number of objective function evaluations and increasing the robustness and accuracy of the results. This study is aimed at gaining a better understanding of the relative overall performance of these six well-known new GO search methods. The systematically conducted comparative study under the same evaluation criteria has quantitatively illustrated each algorithm's ability to handle a variety of GO problems to enable a user to better select an appropriate GO tool for their specific design applications.  
Fifteen high-dimensional, unconstrained benchmark functions with unimodal and multimodal shapes; four constrained engineering optimization problems; and a real-life floating offshore wind turbine design optimization problem have been used to conduct the performance and robustness tests on these newer GO methods. These selected complex benchmark functions and real-life computationally expensive design optimization examples present either convergence or computation challenges.  
The GWO and the ABC algorithms have been found most capable in providing the optimal solutions to the high-dimensional GO problems. The main advantages of the GWO method include quick convergence, more accurate results, highly robust, and strong exploration ability over the search space with an overall efficiency of 86%. The ABC method ranks as the second best with an overall efficiency of 80%. The FFA, CS, PFA and BA methods were found to be able to provide competitive results with other well-known algorithms, including SA, GA and PSO. Results from this comparative study show that the ability of these newer global optimization methods to obtain a good solution also declines as the dimension of the problem increases, and each specific application requires a particular type of GO search algorithm. The research contributes to future improvements of global optimization methods.  
**Acknowledgments:** Financial supports from the Fellowship funds of the Libyan Ministry of Education, the Natural Science and Engineering Research Council of Canada, and University of Victoria are gratefully acknowledged.  
**Author Contributions:** [PERSON], [PERSON] and [PERSON] conceived and designed the experiments; [PERSON] performed the experiments. [PERSON], [PERSON] and [PERSON] analyzed the data and wrote the paper.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# A Survey on Energy-Efficient Design for Federated Learning over Wireless Networks
## 3 Wireless Network Architectures for Energy-Efficient CFL
### Non-Terrestrial-Based Architectures
#### 3.2.1 CFL in UAV Communication Networks
The study in [72] proposed a fairness-enhanced scheduling mechanism based on a multiarmed bandit algorithm, where device selection was guided by a weighted reward function that balances model freshness and energy consumption. This multicriteria scheduling promotes fair participation among ground devices, optimizing long-term system performance and preventing drained energy. In the context of UAV-MEC networks, navigation policies must consider UAVs' diverse capabilities to ensure energy efficiency and service quality. The study in [85] proposed a decentralized navigation solution based on the soft hierarchical deep reinforcement learning network and dual-end federated reinforcement learning. These methods not only enhance task-offloading energy efficiency but also reduce waiting times. These studies collectively emphasize the importance of advanced optimization techniques in UAV-enabled FL systems to enhance energy efficiency and system performance. Figure 4: CFL over non-terrestrial-based wireless architectures.
# Wavelength Selection for Satellite Quantum Key Distribution
## 4 QKD Performance  
### QKD Protocol  
The BB84 QKD protocol can be used to derive the secret key rate \(R\) performance metric to identify wavelengths suitable for satellite downlink quantum communication. Ideally, this protocol is implemented using a single-photon source, but attenuated laser pulses (weak coherent states) are commonly used in real-world QKD systems. The decoy-state protocol was proposed by [PERSON] [41] and introduces extra states of different intensity to overcome eavesdropping attacks, such as photon number splitting (PNS). Here, we adopt the decoy-state BB84 protocol and begin by defining some important quantities. The secret bit yield is the key generation rate per signal-state and can be expressed as [59]  
\[R=q\Big{(}-Q_{\mu}f_{ec}H_{2}\big{[}E_{\mu,\mu}\big{]}+Q_{1}(1-H_{2}[e_{1}]) \Big{)} \tag{9}\]  
where \(q\) is \(1/2\) for the BB84 protocol, \(f_{ec}\) is the error correction efficiency set to 1.22, a commonly used value associated with cascade error correction [60], and \(H_{2}(x)\) is the Shannon binary entropy formula defined as  
\[H_{2}(x)=-x\log_{2}(x)-(1-x)\log_{2}(1-x) \tag{10}\]  
The signal or decoy-state gain, \(Q_{n}\), is given by  
\[Q_{n}=Y_{0}+1-e^{-\eta n} \tag{11}\]  
where \(n\) is mean photon number (MPN) of the signal-state \(\mu\), or decoy-state \(\
u\). We choose the signal-state and decoy-state MPNs to be 0.7 and 0.1 for \(\mu\) and \(\
u\), respectively. The signal/decoy quantum bit error rate \(E_{\
u,n}\) (QBER) is given by  
\[E_{\
u,n}=\frac{e_{0}Y_{0}+e_{\rm d}(1-e^{-\eta n})}{Y_{0}+1-e^{-\eta n}} \tag{12}\]  
where the polarization cross-talk error, \(e_{d}\), is 0.01 and the background error rate, \(e_{0}\), is 0.5 for randomly occurring dark and background counts. The single-photon gain, \(Q_{1}\), is calculated using  
\[Q_{1}=\frac{\mu^{2}e^{-\mu}}{\mu\
u-\
u^{2}}\Big{(}Q_{\
u}e^{\
u}Q_{\mu}e^{\mu }\frac{\
u^{2}}{\mu^{2}}-\frac{\mu^{2}-\
u^{2}}{\mu^{2}}Y_{0}\Big{)} \tag{13}\]  
The single-photon yield, \(Y_{1}\), is  
\[Y_{1}=\frac{\mu}{\mu\
u-\
u^{2}}\bigg{(}Q_{\
u}e^{\
u}-Q_{\mu}e^{\mu}\frac{\
u ^{2}}{\mu^{2}}-\frac{\mu^{2}-\
u^{2}}{\mu^{2}}Y_{0}\bigg{)} \tag{14}\]  
Finally, the single-photon state error rate, \(e_{1}\), is given by  
\[e_{1}=\frac{E_{\
u,\
u}Q_{\
u}e^{\
u}}{Y_{1}\
u}-\frac{e_{0}Y_{0}}{Y_{1}\
u} \tag{15}\]
# A Cluster-Based Channel Assignment Technique in IEEE 802.11 Networks
###### Abstract  
Channel assignment has become a critical configuration task in Wi-Fi networks due to the increasing number and density of devices which use the same frequency band in the radioelectric spectrum. There have been a number of research efforts that propose how to assign channels to the access points of Wi-Fi networks. However, most of them ignore the effect of clients (also called stations or STAs) in channel assignment, instead focusing only on access points (APs). In this paper, we claim that considering STAs in the channel assignment procedure yields better solutions in comparison with those obtained when STAs are ignored. To evaluate this hypothesis we have proposed a heuristic technique that includes the effect of interferences produced by STAs. Results show that taking STAs into account clearly improves the performance of the solutions both in terms of the achieved utility and in terms of the variability of results. We believe that these results will be useful to the design of future channel assignment techniques which consider the effect of STAs.  
channel assignment; Wi-Fi networks; multilayer graphs Article
# Experimental Study on Slender CFRP-Confined Circular RC Columns under Axial Compression
## 2 Experimental Program
### Testing Procedure and Instrumentation  
The axial compression tests on 12 RC columns were carried out by using a pressure testing machine with a maximum capacity of 5000 kN (manufacturer is Sinotest Equipment Co., Ltd., Changchun, China). The top and bottom of the specimens were hinged. Four horizontal strain gauges and four vertical strain gauges were positioned at the half height of the specimen to measure the hoop and axial strains, respectively. Lateral deflections were recorded using three linear variable differential transformers (LVDTs) posed at both ends and the half height of the columns. As shown in Figure 1, LVDTs were placed horizontally of the columns 90 degrees around the column. The test setup and several instrumentations used for the testing of the columns are illustrated in Figure 1b.  
Before testing, the columns were preloaded in the elastic range to ensure that the loading point is located at the mechanical center. The specimens were tested using gra  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Specimens** & **Slenderness Ratio** & \(f_{\text{cu}}\) **(MPa)** & **L (mm)** & **D (mm)** & **CFRP layers** & **Reinforcement** \\ \hline C-0-12-1 & 12 & 32 & 600 & 200 & 0 & 6\(\phi\)8 \\ C-1-12-2 & 12 & 32 & 600 & 200 & 1 & 6\(\phi\)8 \\ C-0-20-3 & 20 & 32 & 1000 & 200 & 0 & 6\(\phi\)8 \\ C-1-20-4 & 20 & 32 & 1000 & 200 & 1 & 6\(\phi\)8 \\ C-0-32-5 & 32 & 32 & 1600 & 200 & 0 & 6\(\phi\)8 \\ C-1-32-6 & 32 & 32 & 1600 & 200 & 1 & 6\(\phi\)8 \\ C-0-40-7 & 40 & 32 & 2000 & 200 & 0 & 6\(\phi\)8 \\ C-1-40-8 & 40 & 32 & 2000 & 200 & 1 & 6\(\phi\)8 \\ C-0-48-9 & 48 & 32 & 2400 & 200 & 0 & 6\(\phi\)8 \\ C-1-48-10 & 48 & 32 & 2400 & 200 & 1 & 6\(\phi\)8 \\ C-0-56-11 & 56 & 32 & 2800 & 200 & 0 & 6\(\phi\)8 \\ C-1-56-12 & 56 & 32 & 2800 & 200 & 1 & 6\(\phi\)8 \\ \hline \end{tabular}
\end{table}
Table 1: Characteristics of test specimens.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Material** & **Thickness,** & **Density** & **Tensile Strength,** & **Elastic Modulus,** & **Fracture Strain,** \\  & \(t_{\text{f}}\) **(mm)** & **(g/m2)** & \(f_{\text{fu}}\) **(MPa)** & **E\({}_{\text{f}}\) (MPa)** & **E\({}_{\text{fu}}\) (\%)** \\ \hline CFRP & 0.167 & 300 & 4330 & 237,000 & 1.7 \\ epoxy adhesive & & & 41.1 & 3068 & 1.57 \\ \hline \end{tabular}
\end{table}
Table 2: Mechanical properties CFRP and epoxy adhesive.  
dation loading. The compressive loading was applied by an increment of 20% of the estimated specimen capacity (N\({}_{\text{max}}\)) before the load arrived at 60% of N\({}_{\text{max}}\). Then, the loading increment was changed to 10% of N\({}_{\text{max}}\). Each load level lasted 10 min. The load level was reduced gradually after reaching N\({}_{\text{max}}\).
# A New Compact Double-Negative Miniaturized Metamaterial for Wideband Operation
## 2 Proposed Unit-Cell Construction  
The diagram of the proposed Modified-Z-shape unit cell configuration is specified in Figure 1. The developed structure consists of a Z-shape split ring resonator of copper with a thickness (\(h\)) of 0.035 mm. FR-4 is used as the substrate material, which has a dielectric constant of 4.5 and loss tangent of 0.002. The length (\(l\)) and width (\(w\)) of the unit cell metal strip are both 8 mm. However, the width (\(d\)) of every metal strip is 0.5 mm, and the split width (\(g\)) is 0.5 mm. In this paper, CST Microwave Studio, which is based on the finite integration technique (FIT), is adopted to investigate this design in which an incident electromagnetic wave travels along the positive \(z\)-axis to the negative \(z\)-axis. The schematic and a fabricated diagram of the proposed design are illustrated in Figure 1a,b. Table 1 demonstrates,the design parameters of the proposed metamaterial unit cell, where \(a\), \(b\), \(t\) are respectively the substrate material width, length and height.
## 5 Results and Discussion
### DCNA-Based Identification Performance Analysis
Those methodologies were selected to evaluate the performance of the proposed method in terms of the improvement of LADT for denoising in comparison with the initial method (ANR-GRS), the effective performance between the automatic feature engineering methods (feature extraction, feature selection, and classification) based on DNN from the enriched feature pool (CWT + DCNA), handcraft-method-aided shallow neural networks (SFE + GA + KNN, SFE + SVM), and the effect of enriching feature pool configuration methods (CWT and STHT). To evaluate the proposed method against the reference methods, the overall classification accuracy (R\({}_{f}\)) for each framework was calculated using Equation (10). \[R_{f}=\frac{\sum TP}{\sum TS}\cdot 100\% \tag{10}\]  
Figure 14: The confusion matrices of the experimental scenario 2: (a) Experiment 1, (b) Experiment 2, (c) Experiment 3, (d) Experiment 4.  
where, \(\sum TP\) denotes the summation of the true positives and \(\sum TS\) refers to the total number of samples used in the classifying process. Each framework was executed several times to achieve the average results of overall classifying accuracies for seven defect types. The classification results of all frameworks through two scenarios are shown in Table 6.  
As can be seen from Table 6, the LADT approach performed denoising better than the ANR-GRS method in the three frameworks: Fw1, Fw2, Fw3; however, the identification accuracy results were lesser than the proposed method from 54.69% to 25.81% due to the limitations of those frameworks in engaging with handcraft feature extraction and shallow learning networks. The different results (from 12.7% to 18.49%) between Fw4 and the proposed framework in this paper confirm the high improvement in denoising delicacy of LADT. The Fw5 results (from 13.15% to 8.32% as lower) demonstrate that the wavelet-based vibration imaging to configure the wealth feature pool achieved a better performance than using STFT. By comparative analysis, the applied framework in this paper outperformed the defect type identification for an MDTF gearbox under variable speeds condition in comparison with those state-of-the-art frameworks, yielding an average classification performance of 100% during two scenarios. To establish an accurate fault identification framework, an effective denoising technique for the complex gearbox vibration signals is critically needed. The disturbance noises in the vibration signals make the subsequent processes of feature engineering and classification less effective. Therefore, this paper combined LADT for highly effective denoising, VWI for wealthy visual feature pool configuration, and DCNA for high dimensional and automated feature extraction, feature-optimizing selection and classification, and to draw the accurate and stable fault identification framework for an MDTF gearbox under inconsistent speed conditions. Through analysis and experimentation, our proposed methodology achieved the highest classification result, verifying the effectiveness of the proposed model.