esa-sceva/satcom-chunk-collection huggingface dataset:
# A Hybrid Genetic Algorithm for Ground Station Scheduling Problems
## 6 Conclusions  
The hardware resource scheduling problem for satellite data transmission ground stations is a multi-constraint optimization problem characterized by limited resources, complex constraints, and significant difficulties standing in the way of solutions. In this paper, based on the actual hardware resource situation of satellite ground stations, a constraint satisfaction model for satellite data transmission tasks is established by analyzing the optimization objectives and constraints of the ground station hardware resource scheduling problem. During the solution process, the set of pending data transmission tasks is first divided into several subsets according to certain principles, and each subset is solved separately. The results are then aggregated to form an initial solution, which is further screened to form an initial population for subsequent algorithm iterations. By comparison with benchmark algorithms, the effectiveness and feasibility of the hybrid GA in solving the hardware resource scheduling problem for satellite data transmission ground stations are demonstrated. The experimental results show that the improved algorithm proposed for the first time in this paper not only enhances the convergence speed and stability of the TGA but also improves the efficiency of hardware resource utilization in ground stations, effectively addressing the hardware resource scheduling problem for satellite data transmission ground stations.  
Due to the limitations of constraints, this paper only considers conventional scheduling scenarios. Future research could explore the handling of emergency situations, such as ground resource failures and temporary urgent tasks. In the event of such emergencies, the system could automatically replan and redistribute hardware resources at ground stations to further validate and strengthen our research.  
Conceptualization, C.Y. and M.G.; methodology, L.X.; software, L.X. and B.W.; validation, L.X., M.G. and C.Y.; data curation, L.X.; writing--original draft preparation, L.X.; writing--review and editing, L.X. and C.Y.; supervision, L.X. and C.Y.; funding acquisition, C.Y. All authors have read and agreed to the published version of the manuscript.  
This work is supported by the National Natural Science of Foundation of China (Grant No. 61871468), the Natural Science of Foundation of Zhejiang Province (Grant No. LY22F010013), and the Natural Science of Foundation of NingBo city (Grant No. 2022J067).  
Data Availability Statement: Data is contained within the article.  
The authors declare no conflicts of interest.  
Figure 12: Comparison of algorithm stability.
# Collective Communication Performance Evaluation for Distributed Deep Learning Training
## 3 Distributed DL Communication: Libraries and Architectures Compared
### Linux Shell Environment  
Within the Linux shell environment, measurements were taken using MPICH [23], OpenMPI [24], CUDA-aware OpenMPI, and the NCCL libraries. Figure 4 provides a flowchart illustrating the communication library architecture in the Linux shell environment. Figure 4a shows broadcast, Figure 4b shows gather (allgather), and Figure 4c shows a flow chart of ring all-reduce. Note that since the architectures of MPI, CUDA-aware MPI, and NCCL operate differently, the function call methods are uniquely configured for each subroutine. Figure 4a,b represents the communication primitives employed in the parameter server method. In the case of using the MPI library, it is essential to invoke CUDA-aware MPI. For non-MPI scenarios, the NCCL library is utilized. There are also variations among libraries in how memory is managed. Given that the MPI library solely supports communication between CPU memories, the worker node must execute the cud-aMemcpy function to facilitate data transfer between CPU and GPU memories. Conversely, CUDA-aware MPI allows for direct communication between GPUs, obviating the need for the cudaMemcpy process. NCCL exclusively supports GPU-to-GPU communication, thus employing a representative worker node known as the chief worker to facilitate communication between the parameter server and workers by enabling communication between CPU memory and GPU memory.  
As described above, the all-reduce method presented in Figure 4c operates without a central server and involves direct communication among workers. Like the parameter server method, MPI mandates the inclusion of the cudaMemcpy process. After parameter updates are executed through the rotation of worker nodes forming a ring, parameters stored in CPU memory are copied to GPU memory. Following the completion of gradient operations in the GPU, there is a subsequent step of transferring the gradients to CPU memory. Conversely, in the case of CUDA-aware MPI and NCCL, which permit direct GPU communication, parameter communication is exclusively accomplished through the all-reduce function. The architectural intricacies of each function are expounded upon in detail in Figures 5-7.  
Figure 4: Linux shell flowchart.
## 5 Discussion
Guayaquil is a city that has a considerable solar resource [76], where the implementation of SPVSs could considerably reduce the electricity bills and the carbon footprint of buildings. However, the expansion of these systems is limited by the subsidies that have certain countries on electricity tariffs [77], as in the case of Ecuador. The obtained results from EnergyPlus simulations could present uncertainties of around \(\pm 10\%\)[78; 79], which is in accordance with the ASHRAE Guideline 14 [80]. These uncertainties depend on external errors related to weather data, schedules, and others, but also, internal errors such as the difference between actual HVAC and its simplified model in the simulation, and the inaccuracies of the mathematical models [81]. Overall, this study showed the potential for energy savings of various passive and actives measures applied in a university building from Guayaquil, Ecuador. From these results, some research questions emerged that could be addressed in future works:  
* What could be the best shading configurations to be applied in Guayaquil taking into account the incident solar radiation in each facade?What could be the best cooling set point to save energy considering the thermal comfort of the occupants in these type of buildings? \(\odot\)  
Should this cooling set point change depending on the season (wet/dry)? What could be the best cooling strategy to provide energy savings and reduce the building's carbon footprint without compromising indoor thermal comfort in this climate? Apart from the implementation of daylighting controls, what could be another low-payback alternative to reduce energy in this climate?
# Macroporous Activated Carbon Derived from Rapeseed Shell for Lithium-Sulfur Batteries
## 3 Results and Discussion
Therefore, the AC/S composite exhibited good rate performance. Figure 4: (**a,b**) SEM images of AC/S composite at various magnifications; (**c**) SEM image of AC/S composite and corresponding energy dispersive spectroscopy (EDS) mapping of elemental (**d**) carbon and (**e**) sulfur. Figure 5: (**a**) Cyclic voltammetry (CV) curves of the AC/S composite cathode in the initial two cycles at 0.05 mV s\({}^{-1}\); (**b**) galvanostatic charge/discharge curves of AC/S composite cathode at 200 mA g\({}^{-1}\); (**c**) galvanostatic charge/discharge curves of the AC/S composite cathode at various current densities; (**d**) rate performance of AC/S composite. To demonstrate long cycle performance, the cell was tested at 800 mA g\({}^{-1}\) for 500 cycles under the galvanostatic mode (Figure 6a). The initial specific discharge capacity reached 942 mA h g\({}^{-1}\), and the maximum reversible discharge capacity of 819 mAh g\({}^{-1}\) was obtained in the second cycle. The cells still possessed a reversible discharge capacity of 486 mAh g\({}^{-1}\) after 500 cycles, representing a high capacity retention of 59%. To further suppress the polysulfide shuttle, 1 wt % LiNO\({}_{3}\) was added to the electrolyte (Figure 6b). After 500 cycles, the average Coulombic efficiency of the sample reached 97%, with LiNO\({}_{3}\) as an electrolyte additive. At 1600 mA g\({}^{-1}\), an initial discharge capacity of 906 mAh g\({}^{-1}\) can be obtained. A maximum reversible discharge capacity of 792 mAh g\({}^{-1}\) was achieved in the second cycle (Figure 6c). The specific capacity remained at 385 mA h g\({}^{-1}\) after 1000 cycles (49% capacity retention). The composite cathode delivered an extremely low decay rate of 0.051% per cycle, indicating notable long-term cycle performance. This superior cycle stability was ascribed to the restraining effect for the diffusion of polysulfides to bulk electrolyte and the loss of active material sulfur by the AC matrix with a small-size nanopore structure. During the 1000 cycles, charge/discharge curves maintained the two-plateau characteristic of Li-S batteries (Figures S1-S3). In addition, the AC/S composite displayed good cycling performance in comparison with other porous carbon/sulfur composite cathodes (Table 2). Figure 6: Long-term cycle performance and coulombic efficiencies of AC/S composite. (**a**) After 500 cycles under 800 mA g\({}^{-1}\); (**b**) after 500 cycles under 800 mA g\({}^{-1}\) with the addition of 1 wt % LiNO\({}_{3}\) in electrolyte; (**c**) after 1000 cycles under 1600 mA g\({}^{-1}\) (the sudden increase of capacity during cycling is due to the increase in room temperature).
# Microbial Bioremediation and Biodegradation of Petroleum Products--A Mini Review
## 2 Petroleum Products
### Types of Hydrocarbons Degradation  
For several decades, enzymatic reactions involved in the aerobic and anaerobic degradation of hydrocarbons through microbes have been considerably reviewed [14]. This section highlights the aliphatic, aromatic, resin, and asphaltene degradation.
# Detecting SPIT Attacks in VoIP Networks Using Convolutional Autoencoders: A Deep Learning Approach
## 5 Experiments
### Results and Discussion  
The experiments commenced by fine-tuning the hyperparameters of the D1-DCAE model that were mentioned in Section 5.2, in addition to adjusting the reconstruction error threshold that was explained in Section 3.2 to achieve a high F1 score. As shown in Table 1, the D1-DCAE model achieved 99.32% and 99.56% F1 scores over the INRIA and RIT datasets, respectively. In addition, the number of misclassified messages was very low, as shown in Figure 6  
Figure 4: F1 score of the D1-DCAE model over INRIA and RIT datasets for several reconstruction error thresholds.  
Figure 5: AUC of the D1-DCAE model over INRIA and RIT datasets for several reconstruction error thresholds.  
To compare the D1-DCAE model with classical anomaly detection approaches, the most common models [8], namely, isolation forest [39], OC-SVM [6], KDE [7], and Gaussian Mixture Model (GMM) [40] were chosen. Furthermore, the parameters of these models were fine-tuned to achieve their best performance for the validation dataset.  
The results of the aforementioned models over the INRIA dataset are shown in Table 2. The D1-DCAE model achieved the best accuracy, F1 score, and AUC. Furthermore, FPR was 1.23%.  
Testing the same models over the RIT dataset achieved better performance than the INRIA dataset as shown in Table 3. The D1-DCAE model achieved the best accuracy and F1 score with only 0.1% FPR.  
\begin{table}
\begin{tabular}{c c c} \hline
**Dataset** & **F1 Score** & **AUC** \\ \hline INRIA & 99.32\% & 99.25\% \\ \hline RIT & 99.56\% & 99.18\% \\ \hline \end{tabular}
\end{table}
Table 1: Performance of the D1-DCAE model over the INRIA and RIT datasets.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Approach** & **Accuracy** & **F1 Score** & **Precision** & **Recall** & **AUC** & **FPR** \\ \hline D1-DCAE & **99.07\%** & **99.32\%** & **99.87\%** & 98.77\% & **99.25\%** & 1.226 \\ \hline GMM [40] & 81.29\% & 87.43\% & 81.53\% & 94.24\% & 73.32\% & 5.756 \\ \hline OC-SVM [6] & 85.66\% & 90.52\% & 83.26\% & **99.17\%** & 80.12\% & **0.862** \\ \hline KDE [7] & 86.78\% & 91.05\% & 85.51\% & 97.35\% & 80.29\% & 2.653 \\ \hline Isolation Forest [39] & 82.05\% & 87.39\% & 84.86\% & 90.07\% & 86.58\% & 9.935 \\ \hline \end{tabular} _Note:_ The highest value in each metric is in bold.  
\end{table}
Table 2: Comparing performances of the D1-DCAE model and the classical anomaly detection models over the INRIA dataset.  
Figure 6: Confusion matrix of the D1-DCAE model over the INRIA and RIT datasets.
## 1 Introduction  
Elemental analysis of materials, qualitative and quantitative, is used in a broad range of scientific fields. Depending on the type of application, several elemental analysis techniques have been developed over the years, broadly categorized into two types: destructive and non-destructive. The different destructive methods include Auger Electron Spectroscopy [1], Scanning Electron Microscopy/Energy Dispersive X-ray Spectrometry [2], Secondary Ion Mass Spectrometry [3], Inductive Coupled Plasma Atomic Emission Spectroscopy [4] and Inductive Coupled Plasma Mass Spectroscopy [5]. With these destructive techniques, one can study trace elements (down to parts per quadrillion (ppq) levels) near the surface of the material, but this comes with the cost that the investigated sample cannot be retained in its original form. Thus, such techniques are not appropriate with rare and precious samples for which even a partial destruction is excluded. The different non-destructive techniques include X-ray Photoelectron Spectroscopy [6], X-ray Fluorescence (XRF) [7], Proton-induced X-ray Emission (PIXE) [8], Rutherford Backscattering Spectrometry (RBS) [9], Nuclear Reaction Analysis (NRA) [10], Prompt Gamma-ray neutron Activation Analysis (PGAAA) [11], Neutron Activation Analysis (NAA) [12], and Neutron Depth Profiling [13]. All these non-destructive techniques, with the exception of PGAA and NAA, are able to provide information from near (i.e., up to \(\sim\)10 micrometer) the surface of the material only. On the other hand, the PGAA and NAA techniques are bulk measurements and not depth-sensitive, and the sensitivity is strongly isotope dependent. The technique of Muon Induced X-ray Emission (MIXE) (Reference [14] called this technique as Muonic X-ray Analysis (MXA). The Refs. [15; 16; 17] used Muon Induced X-ray Emission (MIXE), while the Ref. [18] used Muonic Atom X-ray Spectroscopy (MAXRS); all of which describe the same technique, a non-destructive technique, which was developed more than 40 years ago [14; 19; 20; 21], has recently been used extensively with pulsed muon beams for elemental analysis [16; 18; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34]. The advantage of this technique is that it is able to probe deep into the material, up to a few millimeters, and does not lead to a severe radiation damage of the sample. The aim of the present manuscript is to demonstrate the performance of the MIXE technique using a continuous muon beam.
# Sensitivity Analysis of Numerical Coherency Model for Rock Sites
## 2 Numerical Coherency Evaluation Methodology
### Estimation of Plane-Wave Coherency
Slowness is defined as the reciprocal of the apparent velocity. It is the wave-passage effect that considers the source effect, and plane-wave coherency is equivalent to removing this effect. To consider the source effect in the 2D numerical model, we calculated the plane-wave coherency using the slowness values measured at the Pinyon Flat array. The estimated plane-wave coherency was modeled using the following functional form proposed by [PERSON] [1]:  
\[\gamma^{PW}(\xi_{ij},f)=\Bigg{[}1+\Bigg{(}\frac{f\text{Tanh}\big{(}a_{3}\xi_{ ij}\big{)}}{a_{1}f_{c}\big{(}\xi_{ij}\big{)}}\Bigg{)}^{n1(S_{ij})}\Bigg{]}^{-1/2} \Bigg{[}1+\Bigg{(}\frac{f\text{Tanh}\big{(}a_{3}\xi_{ij}\big{)}}{a_{2}}\Bigg{)} ^{n2}\Bigg{]}^{-1/2} \tag{11}\]  
where \(a_{1}\), \(a_{2}\), \(a_{3}\), \(n_{1}\), \(n_{2}\), and \(f_{c}\) are site-specific parameters derived through regression analysis of the calculated plane-wave coherency data. \(f\) is frequency. To develop the numerical coherency functions, regression analysis was conducted following the approach of [PERSON] et al. [5]. A nonlinear regression was performed within the frequency range of 1 to 30 Hz, focusing on the site-specific parameters \(f_{c}\) and \(n_{1}\) in Equation (11), while other parameters were set to the values used in the Pinyon Flat database. This approach was necessary due to convergence issues during the regression process. Consequently, it is important to note that the coherency functions developed in this study are subject to these limitations.
# Collecting, Processing and Secondary Using Personal and (Pseudo)Anonymized Data in Smart Cities
## 5 Application of Anonymization Tools and Privacy-Preserving Techniques
### Privacy-Preserving Techniques
#### 5.2.1 Homomorphic Encryption  
In a context of smart city vehicular data sharing, there are electricity providers that have to receive location data and weather data from cars that do the measurement. With HE, it is possible for the electricity supplier to compute on this data without having access to the actual location of the vehicles and still having the supposed results [152].  
Another application of HE is given by [153], where fully homomorphic encryption (FHE) is applied to encrypt patient average data for a health-based smart city initiative.  
The authors also shows that is possible to compute the heart rates and detect long QT syndrome privately.
## 6 Training and Validation
### Training and Validation Metrics
#### 6.2.4 Performance Assessment in the Production Phase  
The performance of the framework was further measured against the actual quality control reports generated in the first stage of visual quality control in the production line. Only the occurrence of missed nodes was reported daily, hence, the comparison was only made on the missed-weld class prediction versus factory reports for three weeks of production.  
The total number of detected classes was 103,138. The Node-OK, Missed-Weld, Node-Blocked, Spacer, Seat, and Endnode classes were detected 63,659, 116, 2135, 20,381, 5611, 11,236 times, respectively. Table 10 presents framework defect predictions versus the actual classes.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Class** & **Node-OK** & **Missed-Weld** & **Node-Blocked** & **Spacer** & **Seat** & **Endnode** & **Back-Ground** \\ \hline Prediction & Missed-Weld & 9 & 103 & 21 & 1 & 2 & 0 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Match Rate of Automated Detection to Reported Missed Welds.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Class** & **AP50** & **AP75** & **mAP** \\ \hline Node-OK & 0.994 & 0.984 & 0.823 \\ Missed-Weld & 0.997 & 0.971 & 0.770 \\ Node-Blocked & 0.977 & 0.963 & 0.733 \\ Spacer & 0.995 & 0.976 & 0.793 \\ Seat & 0.994 & 0.962 & 0.802 \\ Endnode & 0.990 & 0.868 & 0.684 \\
**Average** & **0.991** & **0.954** & **0.767** \\ \hline \hline \end{tabular}
\end{table}
Table 8: Train-validation-test metrics.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Class** & **Precision** & **Recall** \\ \hline Node-OK & 0.994 & 0.999 \\ Missed-Weld & 1 & 0.992 \\ Node-Blocked & 0.981 & 0.929 \\ Spacer & 0.996 & 0.989 \\ Seat & 0.981 & 1 \\ Endnode & 0.972 & 0.981 \\
**Average** & **0.9873** & **0.982** \\ \hline \hline \end{tabular}
\end{table}
Table 9: Precision and recall.  
Regarding the data presented in Table 10, it is important to note that this table is specifically added for assessing the detection accuracy of missed welds. The data acquired from the job site during the first stage of quality assessment in the production phase primarily includes precise figures concerning missed welds, with a scarcity of data for other categories. Therefore, the table is intentionally structured to highlight the accuracy and performance of our model in identifying missed welds. This focus aligns with our research's primary aim and addresses the critical aspect of quality control in welding processes.  
The framework counts the defects based on nodes, while missed welds are reported based on the welding path in the shop logs. So, in order to compare the framework output with the shop logs, a conversion was done based on the number of missing welding paths. The framework overall performance is reported in Table 11.
# Binary PSO with Classification Trees Algorithm for Enhancing Power Efficiency in 5G Networks
## Nomenclature  
\begin{tabular}{l l}
**Symbol** & **Description** \\ \(a_{1},a_{2},\ a_{3}\) and \(a_{4}\) & Random numbers uniformly distributed between 0 and 1 \\ \(B_{k}\) & Bandwidth allocated to UE \(k\) \\ \(B_{RB}\) & Resource block (RB) bandwidth \\ \(c_{1}\) and \(c_{2}\) & Acceleration parameters \\ \(C_{sys}\) & Total throughput of the system \\ \(D_{f}\) & Distance between the SC and the closest SC using sub-band \(f\) in its edge region \\ \(d_{m,k}\) & Distance between SC \(m\) and UE \(k\) \\ \(G_{m,k}\) & Channel gain between UE \(k\) and SC \(m\) \\ \(G_{best}\) & Global best position of all the particles \\ \(L_{k,m}\) & Number of requisite RBs for UE \(k\) in SC \(m\) to achieve the minimum data rate \\ \(M\) & Number of SCs \\ \(N\) & Noise power \\ \(P_{best_{j}}\) & Best position of the particle \(j\) \\ \(P_{m}\) & Transmission power of SC \(m\) \\ \(P_{m_{tot}}\) & Total power consumption of SC \(m\) \\ \(P_{m_{tot}}\) & Transmission power consumption of SC \(m\) \\ \(P_{sys}\) & Total power consumption of the system \\ \(PE_{sys}\) & PE (power efficiency) of the system \\ \(R_{k,m}\) & Data rate of UE \(k\) in SC \(m\) \\ \(SINR_{k,m}\) & Signal to interference noise ratio of UE \(k\) in SC \(m\) \\ \(U_{m}\) & Number of UEs in SC \(m\) \\ \(v_{j}\)\({}^{z}\) & Velocity of the particle \(j\) in the \(z^{th}\) iteration \\ \(\omega_{z}\) & Inertia Weight (IW) in the \(z^{th}\) iteration \\ \(x_{j}\)\({}^{z}\) & Position of the particle \(j\) in the \(z^{th}\) iteration \\ \(\alpha\) & Path loss exponent \\ \(\gamma_{m}\) & SC \(m\) on/off indicator \\ \(\varphi_{m,k}\) & UE \(k\) association indicator with SC \(m\) \\ \end{tabular}
# Deterministic Propagation Modeling for Intelligent Vehicle Communication in Smart Cities
## 1 Introduction
Homogeneous clusters of information (in terms of data dispersions) were used to identify subtle variations in the large-scale and small-scale statistics considering factors as relative distance and position from the antenna transmitters (TX), geometry, density and electric properties of the obstacles and permit estimation of the variations in time and frequency of small-scale fading given the non-stationary nature of vehicular communications channel [23]. The statistical analysis concludes that Lognormal, Gamma and Weibull distribution can explain the fading behavior of this multipath environment where the amplitude of fading is highly affected by the data dispersion. The remaining parts of the paper are outlined as follows: Section 2 presents the materials and methods used in this work, with a brief explanation of the 3D-RL technique, the description of the urban scenario considered for radio wave propagation characterization and the experimental measurements performed in the same scenario for the analysis of network parameters of the V2V and V2I communication links. Section 3 reports the large-scale and small-scale characterization of the urban vehicular scenario. Statistical analysis of radio wave propagation characterization is presented in Section 4. Section 5 shows a network parameter communication link analysis based on the experimental campaign of measurements performed in the same vehicular scenario, obtaining in this way a complete characterization (physical and network layer) of vehicular communication links. An application example is presented in Section 6, and conclusions and future work are summarized in Section 7.