esa-sceva/satcom-chunk-collection huggingface dataset:
# Modeling Intelligent Control Switch IEC 61850 Based Substation Automation Communication
## 7 Conclusions  
Ethernet switches in the electric power system are experiencing significant changes from traditional switches architectures to intelligent switches design that simplify and unify the transport packets in the network. This research investigated the issues of packet loss in the substation automation network. The loss occurs because the switch is unable to cope with the high volume of incoming traffic of the network. On the other hand, in the substation automation network, data delivery is crucial for proper functioning of the system. Consequently, a high-speed Ethernet network based on high-performance switches is required to ensure the system reliability and security. Only a high-quality network of IEC 61850 standard and GOOSE protocol of below 4 ms delay can cope that requirements.  
Investigations were carried out in this paper on various switch configurations and architectures to reduce the number of the drop packets. The investigation was implemented on the standard switch architecture with different buffer capacity, SDS-3008 and four switches architecture. The proposed switch architecture is integrated with an intelligent control mechanism that has the capability to distribute the load between the switches based on their availability. All the proposed architectures  
Figure 16: Comparison ratio of packet loss between one buffer capacity and four switches.  
Figure 15: Comparison number of drop packets of the four mechanisms.  
implemented by using MATLAB/SimEvent blocks and subsequently, all the simulations were carried out on this platform. Finally, the simulation results showed an outstanding performance of the four switches methodology as compared with other architectures.  
K.S.N. and S.N.F. contributed to the design and implementation of the research, to the analysis of the results and to the writing of the manuscript.  
The authors declare no conflict of interest.
## 2 Duqu
### Duqu Detector Toolkit
Both Duqu and Stuxnet put components in encrypted form into folder %WINDIR\(\lx@sectionsign\)inf with a PNF extension. Encrypted and compressed files generally have a distinct characteristic: their entropy calculated over the binary file is larger than those of other standard binary files. This tool calculates entropy of all files in %WINDIR\(\lx@sectionsign\)inf. Files with entropy above 0.6 are marked suspicious (calibrated by real-life Duqu samples with a typical entropy around 0.9). 5. _FindDuquReg_. This tool looks up the registry recursively from a given key node to identify suspicious entries with high entropy. In this regards, it works similarly to _CalcPNFEntropy_, but due to the very small size of binary data in the registry entries, instead of performing the entropy calculation over bytes, we use four consecutive bits as one symbol in the calculation (which is otherwise identical to that of _CalcPNFEntropy_). 6. _FindInjectedSections_. This tool builds upon the fact that Duqu injects itself into running processes such as svchost.exe, lsass.exe and creates a view of sections with read/write/execute rights. This technique is a well-known code injection method, allowing Duqu to start itself from a memory region that it previously wrote. The problem with detecting injection into running processes is that it may happen in case of benign software as well; therefore, this tool may generate false alarms. To limit the number of false positives, we only consider specific processes where Duqu typically injects itself. We tested our toolkit on virtual machines infected by our Duqu sample and an available Stuxnet sample (Stuxnet.A). All the six tools in the toolkit generated alarms for Duqu-infected machines. For the Stuxnet-infected machines, naturally, the Duqu signature scanner and the temporary file detectors did not signal any problems, however, the remaining four tools raised alarms. In all cases, we had a small number of false positive alarms, e.g., we found a few innocent PNF files without a corresponding INF file. Our Duqu detector toolkit has been downloaded from more than 12,000 distinct IP addresses distributed over 150 countries. The highest number of downloads originate from Vietnam, followed by the US, France, Iran, India, Poland, Norway, Hungary, Indonesia, and Great Britain.
# Configuration Optimization of Mobile Photovoltaic-Diesel-Storage Microgrid System Based on CPS-MOEA
## 4 Two-Step Approach
### First Step: CPS-MOEA  
The multi-objective evolutionary algorithm with classification based on preselection is expressed as CPS-MOEA in this work. The selection process in the multi-objective evolutionary algorithm can be regarded as a classification problem.  
The CPS-MOEA follows the idea of marking some solutions as positive sample points, expecting that they are non-dominated, while marking other solutions as negative sample points. With these sample points, a classifier can be trained and used to classify the newly generated candidate offspring solutions. The simulated process flow diagram of its implementation is shown in Figure 3.  
To implement classification-based preselection (CPS), two external populations were introduced, namely, \(P_{+}\) and \(P_{-}\): \(P_{+}\), which consist of \"good\" solutions each labeled with \(+1\), are hoped to be non-dominated and have been discovered so far, while \(P_{-}\) contains \"bad\" solutions, each labeled with \(-1\). The selection process, denoted as \(Q=NDS(P,N)\), is based on a non-dominated sorting scheme, referring to the classic multi-objective evolutionary algorithm- NSGA-II.  
Figure 2: Two-step approach.  
First, the population \(P\) is divided into several clusters based on Pareto dominance, ensuring that solutions within each cluster do not dominate each other. Second, the crowding distance for each solution is calculated within its cluster. Third, all solutions in \(P\) are sorted based on their cluster membership and crowding distance. This process selects the top \(N\) solutions, which are then stored in \(Q\).  
In each generation, CPS-MOEA maintains a population \(P\) consisting of \(N\) solutions, an external population \(P_{+}\) containing the best \(N/2\) \"good\" solutions found so far, and another external population \(P_{-}\) containing the worst \(N/2\) \"bad\" solutions discovered.  
To facilitate the comprehension of CPS-MOEA, we provide a pseudocode as shown in Algorithm 1 (Pseudocode of CPS-MOEA).  
Figure 3: The framework of CPS-MOEA.  
```
Function InitializePopulation(): // Create an initial population of random individuals Function TrainClassifier(): // Train a classifier with the data set Function ClassifyPopulation(): // Classify individuals in the population using the classifier Function NonDominatedSort(): // Perform non-dominated sorting on each individual in the population Function CrowdingDistance(): // Compute the crowding distance of each individual in the population Function GenerateOffspring(): // Generate offspring by applying crossover and mutation operations on selected parents Function MergePopulation(): // Merge parents and offspring to create a combined population Main():  InitializePopulation() Repeat until termination criteria are met:  TrainClassifier() For each individual in the population:  GenerateOffspring()  ClassifyPopulation()  MergePopulation()  NonDominatedSort() CrowdingDistance ()  UpdateDataSet() Output the final results Main()
```  
**Algorithm 1** Framework of CPS-MOEA
# Quantum 3D FFT in Tomography
## 2 Quantum Computational Units  
### Quantum Fourier Transform Theory  
The Fourier transform occurs in many different versions in all areas from signal processing to complexity theory and data compression [35; 36; 37]. The QFT is the classical discrete Fourier transform applied to the vector of amplitudes of a quantum state, where we usually consider vectors of length \(N\).  
The discrete Fourier transform acts on a vector \((x_{0},\ x_{1},\ \ldots,\ x_{N-1})\in C^{N}\) and maps it to the vector \((y_{0},\ y_{1},\ \ldots,\ y_{N-1})\in C^{N}\), according to the formula:  
\[y_{k}=\tfrac{1}{\sqrt{N}}\sum_{j=0}^{N-1}x_{j}\omega_{N}^{jk},\quad k=0,1,2,3, \ \cdots,\ N-1 \tag{1}\]  
where \(\omega_{N}^{jk}=e^{\frac{2\pi i}{N}jk}\) and \(\omega_{N}^{j}\) is an j-th root of unity.  
Similarly, the QFT acts on a quantum state \(|x\rangle=\sum_{j=0}^{N-1}x_{j}|j\rangle\) and maps it to a quantum state \(|y\rangle=\sum_{k=0}^{N-1}y_{k}|k\rangle\), according to the formula:  
\[y_{k}=\tfrac{1}{\sqrt{N}}\sum_{j=0}^{N-1}x_{j}\omega_{N}^{jk},\quad k=0,1,2,3,\ \cdots,\ N-1 \tag{2}\]  
Since \(\omega_{N}^{jk}\) is a rotation, the Inverse Quantum Fourier Transform (IQFT) acts similarly:  
\[\mathrm{x_{j}}=\tfrac{1}{\sqrt{N}}\sum_{k=0}^{N-1}y_{k}\omega_{N}^{-jk},\quad j =0,1,2,3,\ \cdots,\ N-1 \tag{3}\]  
In case that \(|j\rangle\) is a basis state, the QFT can also be expressed as the map:  
\[|j\rangle\rightarrow\frac{1}{\sqrt{N}}\sum_{k=0}^{N-1}\omega_{N}^{jk}|k\rangle \tag{4}\]  
Equivalently, the QFT can be viewed as a unitary matrix (or quantum gate) acting on quantum state vectors, where the unitary matrix \(F_{N}\) is given by  
\[F_{N}=\frac{1}{\sqrt{N}}\begin{bmatrix}1&1&1&1&1\\ 1&\omega&\omega^{2}&\omega^{3}&\omega^{N-1}\\ 1&\omega^{2}&\omega^{4}&\omega^{6}&\omega^{2(N-1)}\\ 1&\omega^{3}&\omega^{6}&\omega^{9}&\cdots&\omega^{3(N-1)}\\ \vdots&\vdots&\vdots&\vdots&\vdots\\ 1&\omega^{N-1}&\omega^{2(N-1)}&\omega^{3(N-1)}&\omega^{(N-1)(N-1)}\\ \end{bmatrix} \tag{5}\]  
where \(\omega=\omega_{N}\).  
Most of the properties of the QFT follow from the fact that it is a unitary transformation. This can be checked by performing matrix multiplication and ensuring that the relation \(FF^{+}=F^{+}F=I\) holds, where \(F^{+}\) is the Hermitian adjoint of \(F\). Alternately, one can check that orthogonal vectors of norm \(1\) are mapped to orthogonal vectors of norm \(1\).  
From the unitary property, it follows that the inverse of the QFT is the Hermitian adjoint of the Fourier matrix, thus \(F^{-1}=F^{+}\). Since there is an efficient quantum circuit implementing the QFT, the circuit can be run in reverse to perform the IQFT. Thus, both transforms can be efficiently performed on a quantum computer.  
The QFT transforms between two bases, the Fourier basis and the computational basis (Z). The H-gate is the single-qubit QFT, and it transforms between the Z-basis states \(|0\) and \(|1\rangle\) to the X-basis states \(|+\rangle\) and \(|-\rangle\). In the same way, all multi-qubit states in the computational basis have corresponding states in the Fourier basis. The QFT is simply the tool that transforms between these bases.
# Thermal Interaction and Cooling of Electronic Device with Chiplet 2.5D Integration
## 5 Conclusions  
The focus of this paper is to investigate the factors influencing the thermal interaction between chiplets and evaluate the cooling performance of 2.5D integration with different TIMs. Through FEM simulation, the effects of different parameters on chiplet thermal interaction are verified, and the simulation results are consistent with the trends predicted by the collective parameter model and distributed parameter model proposed. Furthermore, a near-chip cooling vehicle for chiplet 2.5D integration based on simulation results utilizing different TIMs is employed and experimentally investigated. Compared with DOW5888, the utilization of liquid metal can mitigate thermal interaction by 56% and 76% at the flow rates of 0.2 L/min and 0.8 L/min, and the experimental results agree with the trends shown in the collective parameter model. At a flow rate of 0.8 L/min, when the temperature rise reaches 60 \({}^{\circ}\)C, the cooling scheme utilizing the liquid metal TIM can achieve heat fluxes of 330 W/cm\({}^{2}\) and 520 W/cm\({}^{2}\) for chiplet1 and chiplet2, respectively. For high-heat-flux chiplets in the future, near-chip cooling with a high thermal conductivity TIM is a reasonable solution.  
Conceptualization, J.F. and C.C.; methodology, J.F.; software, J.F.; formal analysis, M.Z.; investigation, J.F.; data curation, J.F. and C.C.; writing--original draft preparation, J.F.; writing--review and editing, J.F., C.C. and Q.W.; supervision, L.C.; funding acquisition, C.C. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the National Natural Science Foundation of China (Grant No. 62104249), the Youth Innovation Promotion Association and the Chinese Academy of Sciences under grant 2023126.  
Figure 13: Temperature rise of chiplets in different heat modes. (**a**) When TIM is DOW5888, heat mode 1. (**b**) When TIM is liquid metal, heat mode 1. (**c**) Heat mode 2.
# Influence of Aggregate Gradation on the Engineering Properties of Lightweight Aggregate Concrete
###### Abstract  
Expanded shale lightweight aggregates, as the coarse aggregates, were used to produce lightweight aggregate concrete (LWAC) in this research. At the fixed water-cement ratio, paste quantity, and aggregate volume, the effects of various aggregate gradations on the engineering properties of LWAC were investigated. Comparisons to normal-weight concrete (NWC) made under the same conditions were carried out. From the experimental results, using normal weight aggregates that follow the specification requirements (standard gradation) obtained similar NWC compressive strength to that using uniform-sized aggregates. However, the compressive strength of LWAC made using small uniform-sized aggregates was superior to that made from standard-grade aggregates. This is especially conspicuous under the low water-cement ratio. Even though the workability was affected, this problem could be overcome with developed chemical additive technology. The durability properties of concrete were approximately equal. Therefore, it is suggested that the aggregate gradation requirement of LWAC should be distinct from that of NWC. In high strength LWAC proportioning, following the standard gradation suggested by American Society for Testing and Materials (ASTM) is optional.  
lightweight aggregates; aggregate gradation; standard gradation; uniform-sized aggregates; compressive strength; durability +
Footnote †: journal: Applied Sciences
# On the Analysis of Coverage Feedback in a Fuzzing Proprietary System
###### Abstract  
Coverage feedback is one of the key mechanisms for improving the effectiveness of fuzzers by measuring and comparing the executed code regions while processing input data. In general, such guidance should always improve the performance of fuzzers to better find unexplored code regions. However, proprietary systems with uncommon I/O interfaces (e.g., UAV system, IoT devices, satellite firmware) require extensive engineering/porting efforts to apply coverage feedback support in developing their fuzzing platform. In this paper, we evaluate the detailed efficacy of _coverage feedback_ in fuzzing based on 44 real-world bugs we found using OSS-Fuzz. Our analysis uncovered when and how code coverage information can be helpful, and our experiment demonstrates that although coverage guidance is always helpful to some extent, its effectiveness depends on various external factors. Therefore, such factors should be carefully considered for optimizing the cost and efficiency in designing the fuzzing architecture of proprietary systems.  
fuzzing; coverage feedback; bug detection +
Footnote †: journal: _Applied_  
_sciences_
# Study of Physicochemical and Gelatin Properties of Fish Gelatin from Different Sources
## 4 Conclusions  
The source of fish gelatin determines its basic physicochemical properties, such as protein content, molecular weight distribution, amino acid composition and consequently gel strength. The gel strength of warm-water fish gelatin M04 and M03 was significantly lower than that of cold-water fish gelatin M06 and M08 at low temperatures under the same gelation conditions (temperature and time). Because M08 is derived from cold-water fish, it has the characteristics of faster gelation, higher protein content and wider protein molecular weight distribution at low temperatures. Accordingly, because of the good performance of M08 at low temperatures, such as higher gel strength and better stability, the M08 gelatin was selected for the generation of low-temperature composite salt-soluble protein gels. Following TG enzyme-induced modifications, the strength of M08-derived enzymatic gels was enhanced by a minimum of 200% at low temperatures.  
At present, to keep the cold-water fish gelatin in a good state, the reaction of TG enzyme crosslinking fish gelatin solution is carried out at 4 C, but not at the optimum reaction temperature of TG enzyme, which will affect the activity and crosslinking time of TG enzyme to a certain extent.  
We improved the gel strength of fish gelatin through TG enzyme reaction so that it can be used in a more special environment. Therefore, this study broadened the application range of fish gelatin and improved its application potential of fish gelatin.  
Conceptualization and methodology: [PERSON] and [PERSON]; validation: [PERSON] and. [PERSON]; data analysis: [PERSON]; investigation: [PERSON]; writing--original draft preparation: [PERSON] and [PERSON]. ([PERSON]); writing--review and editing: [PERSON] and [PERSON]; funding acquisition: [PERSON]. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Major Science and Technology Innovation Project in Shandong Province (2019)ZZY011001).  
**Institutional Review Board Statement:** Not applicable.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Gelatin** & & **Gelatin** & \\  & **Composite Salt** & & **Composite Salt Soluble** & **Enzymatic Gel Salt Soluble** \\
**Gelation** & **Soluble Protein** & **Protein Gel Strength** (g/cm) & & **Protein Gel Water** & **Protein Gel** \\  & **Gel Strength** & & **Holding Capacity (\%)** & **Water Holding Capacity (\%)** \\  & **(g/cm)** & & **Capacity (\%)** & \\ \hline M03 & 192.8 a & 326.3 b & 95.03 a & 91.46 a \\ M04 & 163.8 b & 209.6 c & 95.93 a & 91.45 a \\ M06 & 125.3 c & 164.9 d & 93.28 a & 90.63 a \\ M08 & 198.5 a & 416.7 a & 95.84 a & 91.53 a \\ \hline \hline \end{tabular}
\end{table}
Table 6: Gel strength and water holding capacity of enzymatic and self-gelatinized salt-soluble protein gels.
## 2 Theoretical Analysis
### Axial and Radial Dependence of the Sound Field  
The sound field in the tube can be analyzed in terms of the formal solution in Equation (8) for the displacement potential function in the water. If the radial wavenumber \(k_{r}\) is negligible, then only the axial dependence of the wave needs be considered. When \(k_{z}\) is real, \(e^{ik_{z}z}\) is a periodic function, and the sound wave is able to propagate for long distances along the axial direction. When \(k_{z}\) is imaginary, \(e^{ik_{z}z}\) is an exponential function, and the normal wave is transformed into a nonuniform wave attenuated according to an exponential law along the axial direction, and thus has very little influence on the sound field far from the pipeline axis: The sound wave cannot propagate for long distances in the pipeline.  
If the axial wavenumber \(k_{z}\) is negligible, then only the radial dependence of the wave needs be considered. It can be seen that this is given by the zeroth-order Bessel function \(J_{0}(k_{r}r)\), and so the sound pressure is greatest close to the axis.
# Multi-Device to Multi-Device (MD2 MD) Content-Centric Networking Based on Multi-RAT Device
## 4 Performance Evaluation and Analysis  
As described above, a testbed based on the Raspberry Pi was constructed to evaluate the proposed method. In this section, the structure of the testbed is described, together with an evaluation of its performance.
## 4 Results
### Comparison of Machine Learning to Queueing Theory Predictions  
To gauge how well the proposed ML framework performs against classical methods, its outputs were compared to those of an M/M/c queueing model on both the synthetic test dataset and real Singtel data. Table 5 shows the head-to-head comparison on major metrics:
## 2 Study Sites and Data Sources
### Data Sources
#### 2.2.3 Ground References  
The location and crop type of each sample in the study area were recorded with a handheld Global Positioning System (GPS) instrument; the study area's main object types included corn, rice, soybeans and other land cover types (rivers, water bodies, etc.). Our main target objects are three main crops, so 218 corn samples, 205 soybeans samples, and 156 rice samples were selected to evaluate the classification results of all images for each period. Their spatial distribution is shown in Figure 1.
# A Survey on Applications of Reinforcement Learning in Flying Ad-Hoc Networks
## 3 Fundamentals of FANET
### FANET Architecture  
The architecture of FANET is similar to MANET as it is a subset of MANET. FANET contains multiple manned or unmanned aerial vehicles and ground gateway units (GGUs) communicating with each other in an ad-hoc manner [9; 27]. There are different types of topologies in FANET, such as:  
Figure 5: DQL framework.  
* **Centralized topology:*
* An example of centralized topology is shown in Figure 6, where all UAVs are communicating with a GGU directly to transmit data to the control center. In this topology, UAVs also communicate with each other via the GGU [28]. This topology is more fault-tolerant but requires higher bandwidth, causes high latency, and constrains high-speed UAV mobility. Furthermore, putting up GGUs for multiple UAV groups is not economically feasible.
* **Decentralized topology:*
* In this topology, UAVs can communicate with each other as well as with the GGUs as shown in Figure 7[9]. This topology provides the UAVs more flexibility for mobility and requires less bandwidth but increases the power consumption owing to the large overheads.
* **Multigroup topology:*
* In this topology, UAVs are divided into multiple clusters, and each group contains a cluster head (CH), which is responsible for communicating with the GGU and other groups of UAVs as shown in Figure 8[29]. With this topology, a large number of UAVs can be covered. However, it increases the probability of a single-point failure problem.  
Figure 6: Centralized topology of FANET.  
Figure 7: Decentralized topology of FANET.
## 3 Mathematical Modelling and Analysis
### Voltage Stress Across the Capacitive Interface  
By having the DC-DC buck converter on the secondary side in the proposed system, the equivalent AC load increased with decreasing \(D\) as expressed in Equation (4). Therefore, the input voltage had to be boosted accordingly per Equation (5) and the power could be transferred with smaller current (\(I\)) to the load in the same amount of transferred power. The current flowing through the interface (\(I\)) can be found using Equation (10), which can be expressed as a function of the duty cycle and the interface current of the conventional system at the resonance frequency, as shown in Equation (13).  
Figure 6: The change in the capacitive interface value effect on the \(Q\) value.  
Figure 7: \(Q\) value change with the load.  
The reduction in the current through the circuit results in less voltage stress \(V_{c}\) across the one pair of the coupling plates as described in Equations (11) and (12). Figure 8 that shows how \(V_{c}\) varies with the duty cycle of the DC-DC converter. The voltage stress across one pair of the coupling plates (\(V_{c}\)) decreases proportionally with \(D\) at the resonant frequency, as given in Equation (14). At low frequencies, the terms 2 \(C_{c}\)\(L\)\(\omega^{2}\) and \(C_{c}{}^{2}\omega^{2}\) (\(R^{2}+L^{