esa-sceva/satcom-chunk-collection huggingface dataset:
# LP-Order Time-Frequency Spectra and Its Application in Edge Detection
## 3 L\({}^{p}\)-Order Time-Frequency Spectra (LTFS)
### Theory of LTFS  
Considering the amplitude spectra dynamic range [16; 27; 28], the actual signal belongs to the frequency band limited signals; and it means that amplitude spectra of actual signal ranges only in a certain frequency range, not from zero to the Nyquist frequency. Thus, in order to improve the computation efficiency and output resolution of SGHT, and finally obtain high-efficiency and high-resolution results; we extend SGHT by introducing the two frequency integration parameters, and then propose the LTFS given by  
\[h_{f_{1},f_{2}}(t)=h_{3}(t)+i\cdot h_{4}(t) \tag{13}\]  
and  
\[\left\{\begin{array}{l}h_{1}(t)=\int_{f_{1}}^{f_{2}}\text{Sign}[S_{1}(t,f)] \cdot|S_{1}(t,f)|^{p}df\\ h_{2}(t)=\int_{f_{1}}^{f_{2}}\text{Sign}[S_{2}(t,f)]\cdot|S_{2}(t,f)|^{p}df\\ h_{3}(t)=\text{Sign}[s_{1}(t)]\cdot|s_{1}(t)|^{p^{-1}}\\ h_{4}(t)=\text{Sign}[s_{1}(t)]\cdot|s_{1}(t)|^{p^{-1}}\end{array}\right. \tag{14}\]  
where \(f_{1}\) and \(f_{2}\) both are the frequency integration parameters, and \(0\leq f_{1}\leq f_{2}\leq f_{N}\); for actual seismic data, usually \(f_{1}=0\) Hz and \(f_{2}\leq 120\) Hz.  
Correspondingly, instantaneous amplitudes, phases, and frequencies of \(h_{f_{1},f_{2}}(t)\) can be obtained by  
\[\left\{\begin{array}{l}\tan[\theta_{3}(t)]=h_{3}{}^{-1}(t)\cdot h_{4}(t)\\ f_{3}(t)=\frac{1}{2\pi}\frac{\partial\theta_{1}(t)}{\partial t}\\ A_{3}(t)=\sqrt{h_{3}{}^{2}(t)+h_{4}{}^{2}(t)}\end{array}\right. \tag{15}\]  
and \(A_{3}(t)\), \(\theta_{3}(t)\), and \(f_{3}(t)\) are referred to as the instantaneous amplitudes, phases, and frequencies of \(x(t)\) obtained by LTFS, respectively.
## 2 Background
### Metaheuristics and Evolutionary Algorithms  
Searching for the full Pareto set of a given MOP can be unpractical for two reasons. On the one hand, providing a huge number of the solutions can complicate the decision making process; in general, the algorithms used for solving MOPs are aimed at obtaining an approximation of the Pareto set having a relatively low number of solutions. On the other hand, MOPs can be characterized by a number of features, making them very difficult to solve, such as the NP-hard complexity, non-linear functions, epistasis, or several objectives or a large number of decision variables, to mention a few [21].  
As a consequence, the use of exact optimization techniques is frequently unfeasible in practice. The alternative is to shift to other kinds of algorithms. In this context, metaheuristics appear as a family of non-exact techniques that have gained much popularity since the 1990s. They can be defined as iterative strategies that guide and manage the operations of subordinate heuristics to efficiently find high-quality solutions [13]. Metaheuristics are usually non-deterministic and not problem specific, although they can incorporate domain knowledge by virtue of the flexibility of their algorithmic design.  
Sub-classes of metaheuristics include evolutionary algorithms (by far, the most well-known metaheuristic), ant colony optimization, simulated annealing, and particle swarm optimization. In fact, many metaheuristics have been designed, so a number of approaches to classify them have been proposed. The most accepted taxonomy distinguishes between _nature-inspired_ and non-nature-inspired metaheuristics. Table 1 contains a list of representative metaheuristic algorithms according to this classification. A more thorough compendium of nature-inspired metaheuristics can be found in any of the taxonomic studies recently contributed by the community (see, e.g., [22] and the references therein).  
Among nature-inspired metaheuristics, evolutionary algorithms are techniques inspired by biological evolution, where solutions are termed as individuals and grouped into populations that are refined over the search process. The individuals evolve by the iterative application of selection, reproduction, and replacement operators over the population of individuals, which mimic the evolution of beings in an ecosystem. Within evolutionary computation, we highlight genetic algorithms, which apply recombination and mutation in the reproduction step. As in traditional single-objective optimization, metaheuristics have been widely used to solve MOPs [18; 19]. In particular, well-known multi-objective metaheuristics comprise evolutionary computation search operators and concepts at their core, such as NSGA-II [23], MOEA/D [24], and SMS-EMOA [25]. These three exemplified MOP metaheuristics are, respectively, representatives of the three commonly accepted categories of multi-objective metaheuristics: Pareto dominance, decomposition, and indicator-based MOP metaheuristics.
# A Novel Reconfigurable Vector-Processed Interleaving Algorithm for a DVB-RCS2 Turbo Encoder
## 5 Portability and Architectural Considerations
### Required Modifications for Portability  
To adapt the algorithm to other platforms, the following components must be reconsidered:  
* Vector Register Width and Alignment: Modify the iteration tiling or blocking strategy based on the available register width and alignment requirements [35, 42].
* Instruction Mapping: Replace CEVA-specific VLIW operations with equivalent SIMD instructions or pipelined hardware operators.  
* Memory Layout Adjustments: Adapt the memory access code to fit the memory hierarchy and bus architecture of the new platform (e.g., cache-coherent memory on ARM vs. block RAM on FPGA).
* Threading/Concurrency: For multicore CPUs or GPUs, introduce parallelism at a coarser granularity using OpenMP, CUDA, or thread-level task splitting [43].
# A Blockchain Solution for Remote Sensing Data Management Model
## 5 Discussion  
The paper discusses the potential benefits of using blockchain technology for remote sensing data sharing, and it is important to consider the potential drawbacks and limitations of using blockchain technology for remote sensing data sharing. Here are some key points to consider:  
Scalability: Blockchain technology faces scalability challenges, particularly when dealing with large-scale remote sensing datasets. The decentralized nature of blockchains requires every node in the network to process and store a copy of the complete blockchain. This can result in increased storage requirements and slower transaction processing times as the numbers of data grow, potentially hindering real-time data sharing and analysis.  
Computational Complexity: Blockchain transactions involve complex consensus algorithms, cryptographic operations, and validation processes. These operations require substantial computational resources, including processing power and energy consumption. Handling large volumes of remote sensing data on a blockchain could lead to significant computational complexity and may require powerful computing infrastructure.  
Regulatory and Legal Concerns: Blockchain technology operates across borders, which raises regulatory and legal considerations. Compliance with data protection regulations, intellectual property rights, and any restrictions on data sharing in specific regions needs to be carefully addressed. Ensuring compliance with local laws and regulations while utilizing a blockchain for remote sensing data sharing may present challenges.  
Addressing these challenges and limitations will be critical for the successful implementation of blockchain technology in the context of remote sensing data sharing. A careful evaluation of trade-offs, and the consideration of alternative approaches are required to mitigate these limitations, while still harnessing the potential benefits offered by blockchain technology.  
Currently, we have conducted some prototype experiments in the Asia Oceania GEO (AOGEO) data hub project. These experiments focused on data transmission, traceability, and security. These experiments serve as an initial exploration within these areas. Moving forward, it is likely that further experimentation will be conducted in various other projects to broaden our understanding and application.
## 3 Research Methodology
### LiDAR Traffic Verification  
To ensure the 5G uplink system was tested under realistic and representative traffic conditions, we began by analyzing the actual data output characteristics of the LiDAR sensor. Understanding the volume and variability of traffic generated by different LiDAR configurations was essential for selecting appropriate parameters and validating whether specific base station configurations could meet the performance requirements of industrial use cases.  
To this end, we calculated the average throughput of the data stream under various sensor configurations. Each 10-min measurement was divided into 1-min intervals, from which ten average values were obtained and subsequently averaged again. The standard deviation was computed to assess the temporal variability of the generated traffic.  
In this way, we tested configurations created by combining multiple parameter values in all possible ways. These were as follows:  
* Profile: Low Data Rate, Single Return, and Dual Return;
* View angle (\(\lx@math@degree\)): 90, 180, 270, and 360;
* Resolution and frames per second: 512 \(\times\) 10, 1024 \(\times\) 10, 2048 \(\times\) 10, 512 \(\times\) 20, and 1024 \(\times\) 20.  
The Ouster OS0 Ultra-Wide View High-Resolution Imaging LiDAR sensor used in our setup supports several configurable data output profiles that allow users to balance between data richness and bandwidth or processing constraints [44]. These profiles include the Single Return, Low Data Rate, and Dual Return modes. The default Single Return profile (RNG19_RFL8_SIG16_NIR16) provides high-resolution measurements, including 19-bit range, 8-bit reflectivity, 16-bit signal intensity, and 16-bit near-infrared (NIR)  
Figure 2: Overview of the research methodology used in this study, including LiDAR traffic analysis, configuration preparation, two-stage testing, and final evaluation.  
data. This mode is ideal for applications that require complete measurement fidelity. For scenarios with limited bandwidth or processing capacity, the Low Data Rate profile (RNG15_RFL8_NIR8) reduces each pixel's data to a single word, containing scaled 15-bit range, 8-bit reflectivity, and 8-bit NIR, which makes it suitable for lightweight systems. The Dual Return profile (RNG19_RFL8_SIG16_NIR16_DUAL) enables the capture of up to two returns per laser pulse, which is useful for detecting partially transparent or multi-layered surfaces. In this mode, the sensor outputs both the strongest and second strongest returns, each with full data fields. These profiles provide flexibility for adapting the LiDAR output to different industrial tasks, such as perception, mapping, and localization [45].  
The view angle refers to the area covered by the sensor's scan. The direction the LiDAR was pointing was not important for measuring throughput. As for resolution and frames per second, all available values were included. Figure 3 demonstrates the typical output of the LiDAR sensor used in the test setup, showing the point cloud produced during operation.
## 3 Results and Discussion  
In order to illustrate our theoretical results, we present, in this section, some simulations obtained by numerically solving the optimality system given by Theorem 2. This system consists of the state system, adjoint system, initial and final time conditions, and the control characterization.  
The optimality system is solved based on an iterative discrete scheme that converges following an appropriate test, similar the one related to the Forward-Backward Sweep Method (FBSM) [15]. The state system with an initial guess is solved forward in time, and then the adjoint system is solved backward in time because of the transversality conditions. Afterwards, we updated the optimal control values using the values of state and adjoint variables obtained at the previous steps. Finally, we executed the previous steps until a tolerance criterion was reached.  
In this simulation, we propose three types of strategies. The first is to find the optimal controls \(u\) and \(v\) only. Their roles allow the distance between people and therefore the reduction in contact between susceptible people or people being exposed at the same time. The second strategy aims only to find an optimal treatment. The third strategy is to combine the two previous strategies, i.e., reducing contact and improving treatment.  
We consider system (1) with the following parameter values (see Table 2), \(A_{1}=10^{-2}\), \(A_{2}=10^{-6}\), \(A_{3}=10^{-2}\), \(\tau_{1}=\tau_{2}=0.5*10^{3}\), \(\tau_{3}=2*10^{2}\), \(u_{min}=v_{min}=0\), \(u_{max}=v_{max}=1\), \(T_{min}=0.0438013932330731\) and \(T_{max}=1\).
# Mechanisms of Noise Transmission in Velocity Broad-Band Seismometers: Modeling and Analysis
## Appendix B Approximation of the Impedance Ratio  
Typically, the values of \(R_{1}\) and \(R_{2}\) are in the hundreds of kilo-ohms, \(C_{1}\) is in the microfarad range, and \(R_{\text{coil}}\) is typically several tens of ohms. Within the passband of the seismometer, we can make the following approximation:\[Z_{1}(s)=\frac{1}{\frac{1}{R_{1}}+C_{1}s+\frac{1}{R_{\rm coil}}}, \tag{10}\] \[\therefore\left\{\begin{array}{l}\frac{1}{R_{1}}\ll\frac{1}{R_{\rm coil }},\\ C_{1}s\ll\frac{1}{R_{\rm coil}}\end{array}\right.\] \[\therefore\,Z_{1}(s)\approx R_{coll}.\] \[\therefore\,\frac{Z_{1}(s)}{(Z_{1}(s)+R_{2})\cdot R_{coll}}=\frac{1} {R_{coll}+R_{2}}.\] \[\therefore\,R_{2}\gg R_{coll}\] \[\therefore\,\frac{Z_{1}(s)}{(Z_{1}(s)+R_{2})\cdot R_{coll}}\approx \frac{1}{R_{2}}.\] \[Z_{3}(s)=\frac{1}{\frac{1}{R_{1}}+C_{1}s+\frac{1}{R_{2}}},\] when: \[f\to 0,\] \[|C_{1}s|\ll\frac{1}{R_{1}},\frac{1}{R_{2}}\] \[Z_{3}(s)\approx\frac{1}{\frac{1}{R_{1}}+\frac{1}{R_{2}}}=\frac{ R_{1}R_{2}}{R_{1}+R_{2}}\] \[\left|\frac{1}{Z_{3}(s)+R_{coll}}\right|\sim 10^{-5}\] when: \[f=100 Hz,\] \[Z_{3}(s)=\frac{1}{\frac{1}{R_{1}}+C_{1}s+\frac{1}{R_{2}}}>Z_{3}^ {\prime}(s)\] \[\left|\frac{1}{Z_{3}(s)+R_{coll}}\right|\gg 10^{-5}\]
## 2 Materials and Methods  
### Sample Preparation and Characterization  
Copper anode slime was kindly provided by a copper smelter in Yunnan, China. First, the CAS was crushed by a crusher and then passed through 300 mesh sieves. A representative sample was obtained, which was analyzed for Ba, S, Fe, Cu, Pb, Bi, Zn, Au, Ag, and other elements. The chemical composition of the dry anode slime was measured using inductively coupled plasma atomic emission spectroscopy (ICP-AES, OPTIMA 8000, PerkinElmer, America), chemical analysis, inductively coupled plasma atomic absorption spectroscopy (AAS, WFX-320, Beijing Beifen-Ruili, China), and X-ray fluorescence spectrometer (XRF, Bruker S6 Jaguar, Germany), and the data are listed in Table 2.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & **Element** & **Ag** & **Au (g/t)** & **Bi** & **Pb** & **As** & **Ba** & **S** & **O \({}^{*}\)** \\ \hline \multirow{3}{*}{Raw materials} & Content (wt. \%) & 5.24 & 927.9 & 12.92 & 12.1 & 9.35 & 7.64 & 6.94 & 15.52 \\  & Element & Sb & Te & Se & Cu & Ni & Fe & Zn & \\ \cline{1-1}  & Content (wt. \%) & 4.94 & 1.64 & 0.71 & 0.7 & 0.41 & 0.27 & 0.007 & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Chemical composition of the raw materials (\({}^{*}\) O content obtained by XRF).  
The phase of the raw material was analyzed using X-ray diffraction (XRD, D-MAX/2600, Rigaku, Japan) (Figure 1), and the components in the anode slime were mainly Ag, PbSO\({}_{4}\), Pb\({}_{3}\)(AsO\({}_{4}\))\({}_{2}\), Pb\({}_{2}\)As\({}_{2}\)O\({}_{7}\), Bi\({}_{2}\)(SO\({}_{4}\))\({}_{3}\), BiAsO\({}_{4}\), As\({}_{2}\)O\({}_{3}\), BaSO\({}_{4}\), Sb\({}_{2}\)O\({}_{3}\), Sb\({}_{2}\)O\({}_{3}\), and SiO\({}_{2}\). The morphology and elemental distribution of the CAS were analyzed with scanning electron microscopy (SEM, ZEISS Gemini SEM 300, Germany). The SEM results showed a clear overlap of Pb- and Bi-enriched areas. This implies that the physical phase of Bi-containing compounds is similar to that of lead-containing ones. The distribution of Au was relatively uniform and did not overlap with those of the other elements. Combined with the XRD results, the presence of the corresponding arsenate phase was also demonstrated. Pb-enriched and S-enriched regions overlapped, confirming the presence of the lead sulfate phase.
# Evaluating the Energy-Saving Potential of Solar Dedicated Ventilation Systems Based on ADRC
## 2 Photovoltaic and Solar Thermal System Combined with Dedicated Ventilation System  
The proposed solar dedicated ventilation system involves a photovoltaic system, a solar thermal system, and dedicated a ventilation system. These three systems are combined to realize energy conversion, management, and energy saving compared with the former systems. The photovoltaic system supplies all the electrical power to the proposed system. The solar thermal system supplies all the thermal energy to the heater, which can be applied to heat fresh air from state 2 to state 3 and realize accurate control. The dedicated ventilation system processes the fresh air and return air and supplies combined fresh and return air to the indoor space to meet the sensible load and latent load requirements.
# The State of Ethereum Smart Contracts Security: Vulnerabilities, Countermeasures, and Tool Support
## 4 Smart-Contracts Vulnerabilities and Countermeasures
### Vulnerabilities and Countermeasures
#### 4.2.13. Short Address/Parameter Issues  
The parameters of smart-contract functions are encoded before passing to it. The encoded parameter is 32 bytes, and the EVM will concatenate all the encoded parameters together and pad 0 at the end if needed [83]. If the first parameter does not have enough length, e.g., 30 bytes, this will cause a 2-byte left shift after the EVM padding. The shift and padding will increase the value of the second parameter, which could cause severe issues if the second parameter denotes the number of tokens or Ether.  
_Countermeasures_. To address this vulnerability, parameters sent to the smart contracts on Ethereum should be validated first. This is a best practice for third-party applications that interact with smart contracts. For smart-contract developers, parameter order matters since the padding only happens at the end of concatenated parameters.
## 4 Implementation and Discussion
### Limitation and Future Work
#### 4.3.4 Real-Life System Implementation  
Blockchain technology faces scalability issues during real-life deployment [52]. Proposed PoF based blockchain technology is designed with the best possible scalability. As medical collaboration is not meant for a large group, proposed consortium blockchain can accommodate adequate participating entities. Another issue of blockchain is a large block size (storage issue) and transaction delay. The off-the-chain data storing used in this study reduced block size and transaction delay. This rapid transaction among peers increases the chance of commercial deployment of PoF based on collaborative medical decision-making. Last but not least, no reward is introduced in the PoF consensus algorithm. As IFI totally depends on the individual's achievement, no unwanted money and energy are spent on mining and computing. Therefore, PoF based collaborative medical decision-making system is scalable enough for commercial deployment. Alternatively, management of a local database for each participating entity (P, D, I\({}_{c}\), and C\({}_{p}\)) can be difficult. In a future version, we plan to deliver in detail node management strategies designed for PoF based medical collaborative decision-making system.
# A Bug Triage Technique Using Developer-Based Feature Selection and CNN-LSTM Algorithm
## 5 Experimental Result
### Research Questions  
To compare the performance of the proposed model and baseline, the following research questions were considered.  
**ROI: Is the developer recommendation performance of the proposed model appropriate?**  
This question verifies the performance before making comparisons with the baseline. It was confirmed that the developer recommendations are appropriate.  
**RQ2: Does the proposed model perform better than the baseline?**  
This question confirms whether the proposed model performs better than the baseline and compares whether there exist statistically significant differences between them. For statistical verification, the Wilcoxon test [9] and \(t\)-test [10] were performed, and it was observed that there was a significant difference between the proposed method and baseline.
## 3 Damage Location Prediction Model Using AE Signal Data
### Data Collection of AE Signals  
The damage was determined as the impact damage in this study. To generate the impact damages, the test pile was hit with a small iron hammer as illustrated in Figure 3d. Each hit point (damage location) was hit five times with a constant force. The waveforms of the input signals are shown in Figure 3f; the maximum amplitude was 10 V and the average frequency was 3-6 kHz. The vibration time is 0.02 s.  
Five hit points were determined to generate AE signals of damage locations from ground level to the height of 2 m every 0.5 m on one side of the test pile. There were four perpendicular sides on this test pile (Figure 3c), and each side had five hit points. In total, there were 20 hit points on this test pile. Each point was hit five times with a constant strength, and five AE signals were generated corresponding to five hits. One hundred AE signals were generated in total, and all signals were received by six AE sensors; in other words, one AE sensor collected 100 AE signal's data, and thus 600 AE signal's data were collected by six AE sensors. According to the grouping of the AE sensors, there were 300 AE signals collected in group 1, 300 AE signals in group 2, and 600 AE signals in group 3, respectively.
###### Abstract  
Visual comfort and energy consumption for lighting in large office buildings is an area of ongoing research, specifically focusing on the development of a daylight control technique (light shelf) combined with solar energy. This study aims to investigate the optimum performance of light shelf photovoltaics (LSPV) to improve daylight distribution and maximize energy savings for the hot desert-like climate of Saudi Arabia. A radiance simulation analysis was conducted in four phases to evaluate: appropriate height, reflector, internal curved light shelf (LS) angle, and the integrated photovoltaic (PV) with various coverages (25%, 50%, 75%, and entirely external LS). The results revealed that the optimum is achieved at a height of 1.3 m, the addition of a 30 cm reflector on the top of a window with an internal LS curved angle of 10\({}^{\circ}\) with 100% coverage (LSPV1, LSPV2). Such an arrangement reduces the energy consumption by more than 85%, eliminates uncomfortable glare, and provides uniform daylight except for during the winter season. Hence, the optimization of the LSPV system is considered to be an effective solution for sustainable buildings.  
LSPV; visual comfort; energy consumption; angle +
Footnote †: journal: Applied Sciences