esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 System Structure and Working Principle
### Modeling of Energy Storage System  
The ESS can effectively alleviate the intermittency and instability of PV power generation. However, the life of lithium-ion batteries is limited by many factors. Different charge-discharge rates, different states of charge (SOC) during charging, different operating temperatures, and the internal resistance of the battery have a certain effect on the battery life. In the PV-ESS system, because of the characteristics of PV power generation, the charging and discharging behaviors of the ESS are quite different during the day and night, and during its operation, the instability in the charging and discharging currents will lead to excessive degradation of the battery. Compared with lithium-ion batteries, SCs have a near-infinite life and an extremely high power density, which can be utilized to smoothen the charge-discharge curve of the lithium-ion battery during system operation, prolong the battery life, and improve system economy.
## 4 Experimental Results and Discussion
### Hyperparameters Search
#### 4.1.1 SI Parameters Search  
Before generating the CSI, it is important to investigate thoroughly the optimal SI parameter combination. The SI generator requires four parameters: \(N_{\rho}\), \(N_{\gamma}\), \(\Delta\rho\), and \(\Delta\gamma\). In order to limit the feature dimension, \(N_{\rho}\) and \(N_{\gamma}\) were fixed to 10, resulting in 100 bins of SI. We performed a grid search for \(\Delta\rho\) and \(\Delta\gamma\) ranging from 0.05 to 1. For fair comparison among different SI combinations, the number of trees and maximum depth for the RF models were initially fixed to 500 and 32, respectively. The F1 scores of the RF models of different SI combinations were compared, as shown by the plot in Figure 6. The plot produces a smooth and concave shape with a peak value of 0.8730 at (\(\Delta\rho\), \(\Delta\gamma\)) = (0.6, 0.5). Since the variation of F1 score is very small when both \(\Delta\rho\) and \(\Delta\gamma\) are greater than 0.2, the performance of the RF model is insensitive to the SI parameters near the optimal combination. It is noteworthy that the average relief width of the stele is about 2 mm. Since SI of \(\Delta\rho=0.2\) and \(N_{\rho}=10\) covers a circular area with a radius of 2 mm, it is expected that the SI with a higher \(\Delta\rho\) contains sufficient information to represent reliefs.  
Consequently, other parameter combinations near \(\Delta\rho=0.6\) and \(\Delta\gamma=0.5\) does not reduce the performance much. This SI parameter combination that yielded the highest F1 score of 0.8730 is the optimal.  
Figure 5: Musul-ojakbi stele: (**a**) 2D photograph; and (**b**) 3D scanned data.
# Flow Sensing-Based Congestion Detection for D2D Streaming on a 5G gNB
## 3 System Model
### D2D Flow Congestion Control
#### 3.3.1 D2D Flow Control  
D2D flow control which considers the transmission rate in the buffer cache of \(DUE_{buffer}(x)\) detects the imbalance between the encoding rate (\(E_{r}(k)\) and the decoding rate \(D_{r}(k)\)). If the operation is violated, the D2D flows suffer from congestion due to transmission rate imbalance. The upper and lower limits of the buffer cache of \(DUE_{buffer}(x)\) are set for the optimization of D2D streaming. Equation (8) defines D2D flow control (\(DF_{control}(x)\)) with consideration of the transmission rate.  
\[DF_{control}(x)=Burst(x)-\frac{T-\{(E_{r}(k)-D_{r}(k)\}}{2}\times\mu \tag{8}\]  
where \(T\) is the transmission rate. If the value of \(\mu\) in Equation (8) is small, the variation in the encoding rate becomes large. Hence, the variation in the encoding rate must be considered for D2D flow rate and size.  
The encoding rate between D2D pairs for D2D streaming operates a mapping process that converts input data \(D_{in}\) to output data \(D_{out}\), and is defined as follows:  
\[D_{out}=f(\mu D_{in}+TV) \tag{9}\]  
where \(TV\) is a control variable for controlling and managing flow congestion, and \(f\) is a non-linear function. The weights of all flows are joined and optimized for a encoding rate. \(D\) is input/output data for data transmission between \(DUE_{s}\), and the input/output data set includes all stream flows.  
During D2D encoding, gNB performs the operation for the congestion and flow error detection, and the operation is defined as follows:  
\[L(T_{values},\overline{T}_{values})=-\log p(T_{values}|\overline{T}_{values}) \tag{10}\]  
Here, the congestion error is defined by a cross entropy loss (CEL) value, and CEL is defined as follows:  
\[L(T_{values},\overline{T}_{values})=-\sum_{k}T_{values}(k)\log(\overline{T}_{ values}(k))+(1-T_{values}(k))\log(1-\overline{T}_{values}) \tag{11}\]  
where \(L(T_{values},\overline{T}_{values})\) is \(P(T_{values}(k))=\frac{1}{T_{values}(k)}\).  
\(T_{values}(k)\) represents the target values that affect the \(DUE\) in the operation of D2D streaming, and \(\overline{T}_{values}(k)\) is the target values that do not.  
When the encoding rates on the D2D channel effectively are controlled, the optimization of D2D streaming is defined as follows:  
\[P_{optimization}=\prod_{TV}\text{argmin}\left[Channel_{T_{values},N,\mu}(L(T_{ values},\overline{T}_{values}))\right] \tag{12}\]  
where \(P_{optimization}\) is an optimization function, and \(N\) is the additive white Gaussian noise (AWGN) in D2D channel. In this paper, the D2D flow control with the encoding rate is optimized when \(\mu\geq 0.7\).
# Systematic Review: Malware Detection and Classification in Cybersecurity
## 4 Discussion
### Implications for Future Research
#### 4.6.5 Improving Defense Against Emerging Threats  
As cyber threats continue to evolve, future research must also focus on developing proactive defense strategies capable of anticipating attacks before they occur. Current detection techniques, while effective, are primarily reactive, meaning they detect malware once it has already infiltrated the system. However, advances inpredictive detection could help identify suspicious behavior even before malware manifests as an active threat.  
In this context, the use of predictive artificial intelligence, combined with models based on historical data and known attack patterns, could allow security systems to predict potential attacks and strengthen vulnerable points before an attacker can exploit them. Research in this area could include the use of predictive algorithms and real-time data analysis to detect early signs of malicious activity.
# System-Level Statistical Eye Diagram for Signal Integrity
## 5 Conclusions  
This article reviews the previous works on statistical approaches at the system level. Even though the eye diagram is a critical metric in an SI analysis, its time-consuming nature makes an SI analysis inefficient. Furthermore, the recent high-speed systems include several techniques to enhance SI performance. This makes an SI analysis more complicated than it was before. For the above, statistical approaches for high-speed systems have been introduced. This paper reviews not only the basics of a statistical eye diagram, but also the applications: encoding, equalizers, multi-level signaling, scrambling, and the ECC. The stochastic models for encoding were introduced for the 8B/10B and TMDS. The equalizers which are the CTLE, DFE, and pre-/de-emphasis are addressed. The obtained equalized SBRs were converted to the statistical eye diagram in the proposed method. Multi-level signaling has been used to increase bandwidth. However, it has a critical drawback of conversion loss. Hence, the insertion loss and the conversion loss have to be compared before applying multi-level signaling. The scaled channel responses were converted to the statistical eye diagram to compare the above losses. Scrambling has been used to suppress the radiated EMI noise. As a result of the scrambling, the bit probabilities were determined, which also affect the eye diagram. Thus, the statistical eye diagram for scrambling was introduced. The ECC has been used by an upper hierarchy for data integrity. It also affects the eye diagram by encoding the data bits, especially the RS code which lowers the ONE's probability, which corresponds to the lower probability of the lower area of the statistical eye diagram. In conclusion, this paper thoroughly reviews the previous works on statistical eye diagrams at the system level.  
Conceptualization, J.P.; investigation, J.P.; writing--original draft preparation, J.P.; writing--review and editing, J.P. and H.P.; All authors have read and agreed to the published version of the manuscript.  
The present research was supported by the research fund of Dankook University in 2024.  
The author declares no conflicts of interest.
## 4 Design and Implementation
### BFSOOK Modulation
#### 4.2.1 Encoding in BFSOOK Modulation  
The steps involved in encoding data under a BFSOOK Modulation technique are as shown in Algorithm 4. Here, the \(Setup\) function initializes parameters such as pins connected to the microcontroller, delay values, number of stripes, and test message. **Delay1** and **Delay2** is used for having different frequencies for On-Off Cycles, whereas, **NumberOfStripes1** and **NumberOfStripes2** are used for presetting observable stripes at the receiver for fixed distance of 20 cm. The function \(Frequency\) accepts two parameters, namely as **NumberOfStripes** and **Delay**, based on which the frequency and number of iterations of On-Off Cycles are decided. \(Send\) function accepts binary values as input, depending upon which the corresponding frequency is selected. \(Loop\) function is used to repetitively transmit start bit frequency followed by individual bits from the defined message.  
```
1:functionSetup
2: Set pin mode to output for all the LED connected Pins
3: Set Delay1 and Delay2
4: Set NumberOfStripes1, NumberOfStripes2
5: Set message
6:endfunction
7:functionFrequency(NumberOfStripes,Delay)
8:fori = 0 to NumberOfStripes do
9: LED_On
10: delay(Delay)
11: LED_Off
12: delay(Delay)
13:endfor
14:endfunction
15:functionSend(\(a\))
16:if\(a\) is 0 then
17:Frequency(NumberOfStripes1,Delay1)
18:elseif\(a\) is 1 then
19:Frequency(NumberOfStripes2,Delay2)
20:endif
21:endfunction
22:functionLoop
23:Send(Start Bit Pattern)
24:for each byte in message do
25:for each bit in the byte do
26:Send(bit)
27:endfor
28:endfor
29:endfunction
```  
**Algorithm 4** BFSOOK Modulation for VLC Transmitter.
# Research on the Compactness of Lunar Soil Simulant Based on Static Cone Penetration Test
## 2 Experimental Design  
### Test Material  
In this experiment, CUG-1A lunar soil simulant was used, which is mainly made of olivine basalt. Its primary mineral components are olivine, pyroxene, and plagioclase, with minor amounts of minerals such as magnetite and apatite also observable. It bears a relatively close resemblance to the real lunar soil in composition. After a series of crushing, screening, and drying processes, the lunar soil simulant met the required physical and mechanical properties. The mineral compositions of some real lunar soils and the CUG-1A lunar soil simulant are presented in Table 1. The particles of this lunar soil simulant are predominantly angular and sub-angular, with a small number of sub-rounded particles. The mineral composition of the CUG-1A lunar soil simulant is highly similar to that of real lunar soil. The microscopic diagram of CUG-1A lunar soil simulant is presented in Figure 1.  
Figure 1: Microscopic diagram of CUG-1A lunar soil simulant.  
The key physical-mechanical parameters of the CUG-1A lunar soil simulant are as follows: the minimum dry density is 1.1 g/cm\({}^{3}\), the maximum dry density is 2.0 g/cm\({}^{3}\), the minimum porosity is 24.9%, the maximum porosity is 58.2%, the specific gravity of particles is 2.662, and the median particle diameter is 70 \(\upmu\)m.
# Research on Application of Improved Quantum Optimization Algorithm in Path Planning
## 2 Bloch Sphere Quantum Genetic Algorithm
### Updating and Vication of Quantum Chromosome  
Thirdly, perform update and variation operations on the quantum chromosomes. In BQGA, the update of quantum chromosomes is generally realized by changing the quantum phase through a revolving gate [36]. The purpose of qubit phase rotation is to use each chromosome in the current population to approximate the contemporary optimal chromosome. In the process of approximation, it is possible to produce better contemporary optimal chromosomes, such that the population continues to evolve. The form of updating will be:  
\[U=\begin{bmatrix}\cos\Delta\varphi\cos\Delta\theta&-\sin\Delta\varphi\cos \Delta\theta&\sin\Delta\theta\cos(\varphi+\Delta\varphi)\\ \sin\Delta\varphi\cos\Delta\theta&\cos\Delta\varphi\cos\Delta\theta&\sin\Delta \theta\sin(\varphi+\Delta\varphi)\\ -\sin\Delta\theta&-\tan(\frac{\varphi}{2})\sin\Delta\theta&\cos\Delta\theta \end{bmatrix}, \tag{15}\]  
The corresponding rules are proposed for the direction of the turning angle, which can be judged by the algebraic operation of the three-qubit coordinates of the current chromosome and the optimal chromosome. Based on the optimization theory and the changing trend of the objective function, this paper proposes a progressive Bloch spherical search method, which makes the search process more convenient, and avoids the blindness of the search.  
As the number of iterations increases, \(\Delta\theta\) and \(\Delta\varphi\) should gradually become stable. Therefore, \(\Delta\theta\) and \(\Delta\varphi\) can be calculated asymptotically on the Bloch sphere with the least-squares theory. The sizes of the two corners of the quantum revolving doors can be derived by fitting the calculation to prevent the algorithm from losing the global optimal solution or prematurely converging.  
Assuming that the algorithm evolves to the \(t\) generation, the updated formulas of \(\Delta\theta_{i+1}\) and \(\Delta\varphi_{i+1}\) would be:  
\[\Delta\theta_{i+1}=(i+1)\frac{\sum_{i=1}^{t}i\Delta\theta_{i}-\sum_{i=1}^{t} \Delta\theta_{i}}{\sum_{i=1}^{t}i^{2}-\sum_{i=1}^{t}i}, \tag{16}\]  
\[\Delta\varphi_{i+1}=(i+1)\frac{\sum_{i=1}^{t}i\Delta\varphi_{i}-\sum_{i=1}^{t} \Delta\varphi_{i}}{\sum_{i=1}^{t}i^{2}-\sum_{i=1}^{t}i}, \tag{17}\]  
where \(i=0,1,2,\cdots,t\).  
Since the purpose of mutation operation is to achieve population diversity and break through premature convergence, in BQGA, a quantum revolving gate is used to operate \(\theta\) and \(\varphi\).  
\[\theta=\frac{1}{t}\sum_{i=1}^{t}\theta_{i}, \tag{18}\]  
\[\varphi=\frac{1}{t}\sum_{i=1}^{t}\varphi_{i}, \tag{19}\]  
The quantum mutation operation seeks to change the state of the superposition of the qubit states, so that the original tendency to collapse to the state \"1/0\" becomes the tendency to collapse to the state \"0/1\". At the same time, the variation of quantum chromosomes helps to increase the diversity of the population and helps to avoid premature convergence.
# Urban Traffic State Sensing and Analysis Based on ETC Data: A Survey
## 6 Current Challenges and Future Research Directions
### Technical Challenges
#### 6.1.3 System Integration and Standardization  
The current situation of inconsistent ETC system construction standards and varied data formats across different regions seriously impedes data-sharing applications and collaborative system development. This lack of standardization not only affects technology promotion and application but also increases the complexity and costs of system maintenance and upgrades.  
Interface standard unification serves as the fundamental prerequisite for achieving system interconnection and interoperability. Although basic standard unification has been achieved for highway ETC systems domestically, significant differences still exist in system standards among different manufacturers, regions, and applications in urban application scenarios. This lack of standardization has led to data silo phenomena, limiting the full realization of the ETC data value.  
The absence of data format standardization further exacerbates system integration difficulties. Different systems employ different data structures, encoding methods, and transmission protocols, making data exchange and fusion extremely complex. Although the relevant standardization organizations have conducted substantial work in promoting data format standardization in recent years, standard implementation varies considerably in practical applications, with compatibility issues remaining prevalent. This not only increases system development and maintenance costs but also affects the rapid promotion and application of new technologies.
# Channel Access in Wireless Smart Grid Networks Operating under ETSI Frame-Based Equipment Rules
## 3 Channel Access  
In this section, we describe the channel access procedures for FBE, first as standardized by ETSI and then several modifications proposed in the literature.
# Handover Decisions for Ultra-Dense Networks in Smart Cities: A Survey
## 3 Related Works
### Hybrid Approaches
The authors developed a hybrid handover decision model combining an ANN-based prediction algorithm and a fuzzy logic system, which functions by first predicting the RSS using a k-step-ahead ANN model and then making handover decisions using fuzzy logic based on predicted RSS, distance, channel availability, and other network parameters. Moreover, they investigated the effectiveness of their approach by comparing it with traditional handover algorithms and analyzing its ability to minimize unnecessary handovers and improve network performance. To validate their approach, the study was conducted using simulations with real and synthetic network data, implementing ANN-based RSS prediction and fuzzy logic-based handover decision-making. The evaluation was based on prediction error, relative error, handover success rate, and the ability to reduce ping-pong effects. Nonetheless, the study had certain limitations, such as the lack of integration with real-time mobility models and limited evaluation in large-scale heterogeneous networks, which should be addressed in future research. In 2023, [PERSON] et al. proposed a unified and flexible framework for optimizing handover procedures across 5G, 5G-Advanced, and 6G networks ([PERSON] et al., 2017). The authors introduce an eight-step HO optimization cycle that supports both measurement and decision phases, emphasizing auto-tuning of handover control parameters and optimal base station selection. The framework integrates AI-based techniques, fuzzy logic, and multi-attribute decision-making, aiming to ensure seamless mobility in both terrestrial and non-terrestrial scenarios. The study classifies HO types based on their initiation point and promotes contextualized, infrastructure-independent triggering mechanisms. However, the work remains largely conceptual and lacks empirical evaluation or implementation details. There is no simulation or case study to validate the proposed methods, particularly the fuzzy or AI components. Moreover, challenges related to computational complexity, real-time performance, or energy efficiency are not quantitatively addressed. While the framework is comprehensive and forward-looking, its practical applicability in ultra-dense and heterogeneous smart city environments is still untested, leaving room for further research focused on implementable and lightweight handover decision systems.
# In-Orbit Results and Attitude Analysis of the SNUGLITE CubeSatellite
## 3 In-Orbit Results  
### Satellite Status  
The following section describes the satellite status data required for the analysis of the attitude system. Calculated GPS results, battery voltage level, and power generation were evaluated to determine whether the attitude system was performing efficiently.
## 4 Conclusions
### Limitations and Future Development  
The maintenance information management architecture defined presents some limitations; the validation of the model will be carried out as soon as the dataset of responses becomes consistent. First of all, the model is general and not customised to specific industrial sectors; this would require additional evaluation criteria and, above all, different weights for the identified criteria. Furthermore, the current weights are equally distributed; an Analytic Hierarchy Process (AHP) approach, based on the expert opinions, will be used to set the weights expressed in the mathematical formulation of the method proposed for future development. The proposed model supports small companies in improving the effectiveness and efficiency of their MMIS through a well-defined and organised database. The improvement programme will consist of a growing path of sequential steps for improving the management of each DB by the definition of the best practices for each one; therefore, a well-defined approach will be structured in the future development. The automatization and cost-benefit analysis of the improvement path drawn could be a future step. The future development will include a pilot test of the assessment survey to validate the approach proposed. Conceptualization, A.M.R.T, V.D.P. and R.I.; Investigation, A.M.R.T. ; Methodology, V.D.P. ; Supervision, V.D.P. and R.I.; Validation, R.I.; Writing--original draft, A.M.R.T. ; Writing--review & editing, V.D.P. and R.I. All authors have read and agreed to the published version of the manuscript. This research received no external funding. The data presented in this study are available on request from the corresponding author. The authors declare no conflict of interest.