esa-sceva/satcom-chunk-collection huggingface dataset:
# CommC: A Multi-Purpose COMModity Hardware Cluster
## 4 Technologies and Services  
### Proxmax Virtual Environment (Proxmax VE)  
Proxmax Virtual Environment (Proxmax VE) [26] is an open-source platform for efficiently managing virtual machines (VMs) and containers. Built on Debian Linux, it integrates Kernel-based Virtual Machine (KVM) for full virtualization and Linux Containers (LXC) for lightweight containerization, providing flexibility to handle diverse workloads. Proxmax VE supports both single-node setups and scalable cluster deployments, making it suitable for various infrastructure requirements.  
The platform features a centralized management interface accessible through a web-based graphical user interface (GUI), which simplifies tasks such as live migration, back-ups, and high-availability configurations. Advanced users can utilize a RESTful API, a command-line interface, and role-based access control for more granular management. The GUI is designed for ease of use, featuring a resource tree, real-time log monitoring, and secure SSL-encrypted access. It also supports multi-factor authentication and integrates with authentication sources like LDAP and Microsoft Active Directory.  
Proxmax VE offers a flexible storage model, supporting both local and shared storage solutions such as NFS, SAN, Ceph RBD, and ZFS. This flexibility enables live migration and efficient management of storage resources across cluster nodes. An integrated backup tool, vzdump, is included to create consistent snapshots of running VMs and containers, ensuring data integrity and simplifying restoration processes.  
In comparison with other similar systems, like OpenStack, Proxmax is a more lightweight and straightforward platform, making it well-suited for labs or small-to-medium enterprises (SMEs), where simplicity and ease of setup are key. Its intuitive web interface, which bundles virtualization (KVM) and containers (LXC) into one cohesive system, minimizes the need for multiple specialized services and extensive configuration. This makes Proxmox faster to deploy and easier to maintain, especially for teams with limited time or resources. In contrast, OpenStack is a full-fledged private cloud solution that offers a rich ecosystem of components for large-scale, multi-tenant environments. However, it is typically more complex to install, operate, and scale, requiring specialized expertise. For lab environments or SMEs that value a more direct, out-of-the-box virtualization solution without extensive overhead, Proxmox's simplicity, resource efficiency, and robust feature set present a compelling choice.
## 2 Materials and Methods
### EMG Recording and Data Analysis  
EMG data were collected using a Noraxon Ultium EMG sensor system (Noraxon USA, Inc., Scottsdale, AZ, USA) sampling frequency of 4000 Hz. per channel; gain: 1000 (signal to noise ratio; 1 uV RMS); common mode rejection rate (CMRR): \(-\)100 dB; input impedance \(>\)100 m\(\Omega\)). Any hair on the skin was first removed, and then the area was cleansed with an alcohol swab before electrodes were connected to detect EMG signals. The electrodes were positioned bilaterally on the ilicocstalis lumborum pars lumborum (convex side ICL and concave ICL) at the L3 level, midway between the lateral-mostpalpable border of the erector spinae and a vertical line through the posteosuperior iliac spine; on the longissimus thoracis (convex LT and concave LT) at the T9 level, midway between a line through the spinous process and a vertical line through the posteosuperior iliac spine, located approximately 5 cm laterally; and on the ilicocstalis lumborum pars thoracics (convex ICT and concave ICT) at the T10 level, midway between the lateral-most palpable border of the erector spinae and a vertical line through the posteosuperior iliac spine [18]. The sample rate was set to 2000 Hz. Two filters were applied, including a bandpass filtered from 10 to 500 Hz using a first-order high-pass and fourth-order low-pass Butterworth filter to remove unacceptable artifacts and a notch filter (60 Hz) to eliminate noise. A moving 100-ms window was used to calculate the root mean square (RMS) values. The Noraxon MyoResearch XP program was used to process the data (version 3.16; Noraxon Inc., Scottsdale, AZ, USA). The data for each trial were expressed as a percentage of the calculated mean RMS of the MVIC (%MVIC), and the mean %MVIC of 3 trials was used for analysis [19].
## 2 Methods
### Radiomics Feature Extraction and Feature Selection  
After resampling each phase of the CT scan with a resolution of 1 mm \(\times\) 1 mm \(\times\) 1 mm, 1288 radiomic features from 1548 CT scans were extracted from the segmented regions of interest (ROIs) in the original CT scans, wavelet-filtered CT scans, and Laplacian of Gaussian-filtered scans by using the Python package pyradiomics (version 3.1.0) [13] with Python 3.7. The extracted radiomic features included first-order features, three-dimensional shape features, the Gray Level Co-occurrence Matrix (GLCM), the Gray Level Run Length Matrix (GLRM), the Gray Level Size Zone Matrix (GLSM), the Neighborhood Gray Tone Difference Matrix (NGTDM), and the Gray Level Dependence Matrix (GLDM).  
By using various feature selection methods and a dimensionality reduction method exclusively for the training set, we selected 160 radiomic features from 1288 radiomic features to classify malignant renal tumors. First, we conducted the ANOVA F-test for 1288 z-score normalized radiomic features while applying the FDR correction with the Benjamini-Hochberg procedure (\(\alpha_{\text{fdr}}\) = 0.05). This method selected 801 features for the classification of malignant renal tumors. Second, we applied model evaluation-based feature selection methods with stratified 10-fold cross-validation. We performed the Principal Component Analysis (PCA) of the selected radiomic features to reduce the overall feature space dimensionality. By evaluating the accuracy of the predictions made by the models in the validation set, we decided to set the number of principal components at 20% of the number of radiomic features selected earlier (160 features) for malignant renal tumor classification. We tested various ratios (0.1, 0.2, and 0.3) of the number of principal components to the total number of radiomic features selected in the earlier feature selection step. Then, we standardized these principal components (i.e., PCA whitening) for further analyses. Wealso utilized Sequential Feature Selection (SFS) for standardized principal components to select principal components that could maximize the model evaluation performance for the validation set. Through a sequential removal process, the SFS systematically selected the features based on their impact on the model performance for the validation set.
## 4 Lower Bound  
Compression researchers (see, e.g., [16] for a survey) have shown how to store \(S\) in \(nH_{k}(S)+o(n\log\sigma)\) for all \(k\leq(1-\epsilon)\log_{\sigma}n\) simultaneously, where \(\epsilon\) is a positive constant. In a previous paper [11] we showed that it is impossible to prove a worst-case bound of this form when \(k\geq\log_{\sigma}n\):  
* in \(\sigma\)-ary De Bruijn cycles [17] of order \(k\), each \(k\)-tuple appears exactly once, so such cycles have length \(\sigma^{k}\) and \(k\)th-order empirical entropy 0;
* there are \((\sigma!)^{\sigma^{k-1}}/\sigma^{k}\) such cycles [18] and \(\log_{2}\left((\sigma!)^{\sigma^{k-1}}/\sigma^{k}\right)=\Theta(\sigma^{k} \log\sigma)\);
* by the pigeonhole principle, there is no injective mapping from \(\sigma\)-ary strings of length \(n\) with \(k\)th-order empirical entropy 0, to binary strings of length \(o(n\log\sigma)\);
* therefore, if \(k\geq\log_{\sigma}n\), then in the worst case we cannot store \(S\) in \(\lambda nH_{k}(S)+o(n\log\sigma)\) bits for any coefficient \(\lambda\).  
In a recent paper [12] we used similar but more sophisticated arguments to show that, if \(k\geq(1+\epsilon)\log_{\sigma}n\) for some positive constant \(\epsilon\), then in the expected case we cannot store \(S\) in \(\lambda nH_{k}(S)+o(n\log\sigma)\) bits for any coefficient \(\lambda=o(n^{\epsilon})\); if \(k\geq(2+\epsilon)\log_{\sigma}n\), then with high probability we cannot store \(S\) in that many bits for any coefficient \(\lambda\).  
We now turn our attention to proving lower bounds for prediction and show that, if \(k\geq\log_{\sigma}n\) and the elements of \(S\) are chosen uniformly at random, then no predictor without advance knowledge of \(S\) can achieve an expected success rate close to \(P_{k}(S)\). Notice that \(nP_{k}(S)\) is, by definition, at least the number of distinct \(k\)-tuples in \(S\) minus 1: for any distinct \(k\)-tuple \(\alpha\) that occurs in \(S\) and is not a suffix of \(S\), the optimal static predictor described in Section 2 correctly guesses the element after at least one occurrence of \(\alpha\) in \(S\). Suppose \(k\geq\log_{\sigma}n\)--implying that \(\sigma\geq 2\)--and let \(c=\sigma^{k}/n\geq 1\). [PERSON], [PERSON] and [PERSON] [19] showed that the expected number of distinct \(k\)-tuples in \(S\) is  
\[\sigma^{k}\left(1-\frac{1}{e^{n/\sigma^{k}}}\right)+\mathcal{O}(k)+\mathcal{ O}\!\left(\frac{nk}{\sigma^{k}}\right)=cn\left(1-\frac{1}{e^{1/c}}\right)+ \mathcal{O}(k)+\mathcal{O}(k/c)\]  
so  
\[\mathrm{E}[P_{k}(S)]\geq c\left(1-\frac{1}{e^{1/c}}\right)\geq 1-\frac{1}{e}>0.632\]  
On the other hand, no predictor without advance knowledge of \(S\) can achieve an expected success rate greater than \(1/\sigma\leq 1/2\).  
**Theorem 2**: _If the \(n\) elements of a sequence \(S\) are chosen uniformly at random from an alphabet of size \(\sigma\) and \(k\geq\log_{\sigma}n\), then \(S\)'s expected \(k\)th-order empirical predictability \(\mathrm{E}[P_{k}(S)]\geq 1-1/e>0.632\) but no predictor without advance knowledge of \(S\) can achieve an expected success rate greater than \(1/\sigma\leq 1/2\)._
## 4 Process of Remediation for Oil-Contaminated Soil
### Techniques for Physical Remediation  
[PERSON] et al. discovered that a soil-washing agent might remove benzene, toluene, ethylbenzene, and xylene (BTEX) from oil-polluted soil with elimination percentages of 97%, 95%, and 95% for acidic, neutral, and basic pH bands, respectively [35]. Although there was a high elimination percentage, the process did not remove polycyclic aromatic hydrocarbons from the crude oil. The elimination percentages were less than 27%, 41%, and 3% for polycyclic aromatic hydrocarbons in acidic, essential, and neutral bands. Persulphate should not be employed alone to remove hydrocarbons from crude-oil-contaminated soil, according to the study. [PERSON], [PERSON] et al. found that the efficiency of thermal adsorption and desorption in gas oil-polluted soil at low temperatures depending on the soil structure, texture, treatment duration, and temperature of 175 \({}^{\circ}\)C was adequate to desorb diesel-polluted sandy and silt soils, whereas a temperature of 250 \({}^{\circ}\)C was required for clay soils [36].  
The ultrasonic desorption of petroleum hydrocarbons in fine soil was 61 and 49% from 27.6% silt and 55.3% clay, respectively. Increasing the power boosted the removal rates and found a 61% elimination of total petroleum hydrocarbons in fifteen minutes utilising one hundred and sixty watts of electricity but no further drop, presumably owing to steady-state re-adsorption on the soil [37]. Although studies on ultrasonic remediation are limited, it is expected that it aids bioremediation to decompose clayey soil, whether used simultaneously or as a pre-treatment.
# A Practical Deep Learning Architecture for Large-Area Solid Wastes Monitoring Based on UAV Imagery
## 4 Experiments and Discussion
### Effectiveness of Comprehensive Monitoring and Management  
The trained SWM was used to monitor the entire study area using UAV imagery, and a total of 3462 solid waste piles were identified (Figure 10). It can be observed that the garbage is relatively dispersed throughout the entire study area. This study randomly selected 100 solid waste piles, among which the detection results of 89 were accurate. Due to the large-area of the study site, in order to demonstrate the reliability of the SWM's segmentation over the entire study area in detail, this paper selected the northwest area, which has a high concentration of solid waste, as a representative area to analyze the model's segmentation results. These waste piles are widely distributed in our living environment (D1-D4). Long-term outdoor waste dumping and improper disposal will lead to the proliferation of rats and mosquitoes, causing the spread of certain diseases and posing a threat to human health. The spatial location map produced in this study can intuitively demonstrate the distribution of solid waste in different areas.  
The environmental protection department has implemented governance measures on the solid waste piles, and a portion of the before-and-after comparison results are shown in Figure 11. From the results, it can be seen that the waste identified in this study has disappeared after the governance, indicating targeted management of the waste piles. This also demonstrates the high reliability of the monitoring results presented in this paper. Our work provides data support for ecological environment improvement efforts, effectively reducing the regulatory costs of manual inspection of solid waste in urban management  
Figure 10: Overall monitoring results of the study area.  
Figure 9: Segmentation results in diverse scenes.  
through intelligent monitoring methods. It also promotes waste recycling and reduces environmental pollution.
# Quantifying the Occurrence of Multi-Hazards Due to Climate Change
## 3 Results and Discussion
### Multi-Hazard Probability Maps
This outcome is associated with the Arctic amplification and possible connection to the weakening of mid-latitude storm tracks [61]. Figure 8 presents the multi-hazard occurrence in Greece for the examined periods. The value indicates the cumulative annual probability that at least one of the studied extreme hazards will occur. It demonstrates that patterns of highly exposed areas in Greece over the historic period will be spatially shifted in the far future as the increase in hotter climate regimes will be dominating the risk landscape. Figure 7: Dominant-hazard maps using EC–Earth–WRF downscaled data for: (**a**) the historical period 1980–2004, (**b**) RCP4.5 in near future (2025–2049), (**c**) RCP8.5 in near future (2025–2049), (**d**) RCP4.5 in far future (2075–2099) and (**e**) RCP8.5 in far future (2075–2099). In the historic period (Figure 8a), mountainous areas in the northern parts appear to be the most exposed regions in Greece, with TN and RR being the most significant risks (Figure 7a). The plains areas in the mainland and Crete exhibit lower risk levels through a combination of TX and extreme winds. The coastal zones and the Aegean islands appear to lie at the lower risk level. In the near future, the RCP4.5 scenario (Figure 8b) appears to demonstrate the smallest variability in the risk levels when compared to RCP8.5 (Figure 8c). The mountainous areas will be exposed to lower risk levels by a factor between 5 and 14% compared to present times, contrasting a similar increase due to TX in the lowlands and western Greece. These relative changes were determined to be statistically significant at the 95% confidence level using the student t-test. The southern parts of the Eastern Aegean islands and Crete will also experience increased level of hazards. For the RCP8.5 scenario, in the near future, a greater number of regions are projected to be exposed to higher levels of risk due to both higher TX values and stronger winds (Figure 8c). These appear to be located in the southern and the western parts of the mainland and the Aegean Sea. For the far future (Figure 8d,e), both scenarios will exhibit similar patterns of risk changes compared to the present period, although RCP8.5 will be associated with higher hazard risk, often exceeding a 5% increase. Plains, agricultural lands and islands will be especially exposed to increased risk. Figure 8: Annual percentage of multi-hazard using EC–Earth–WRF downscaled data for: (**a**) the historical period 1980–2004, (**b**) RCP4.5 in near future (2025–2049), (**c**) RCP8.5 in near future (2025–2049), (**d**) RCP4.5 in far future (2075–2099) and (**e**) RCP8.5 in far future (2075–2099).
# A More Efficient Conditional Private Preservation Scheme in Vehicular Ad Hoc Networks
## 4 Long-term Unlinkability  
In order to protect the privacy of the driver, we require that the information sent by the same vehicle be unlinkable in the long-term. We calculate the probability to quantify the risk that the victim OBU is tracked by some compromised RSUs. Here, we give some assumptions:  
* The RSUs may be compromised because of the insecure environment, but will be quickly rescued in the next period. We assume that the number of RSUs is \(N_{\textit{rsu}}\), and that \(p_{\textit{c}}\) RSUs can be compromised. Then, the number of compromised RSUs is \(N_{\textit{c}}=N_{\textit{rsu}}*p_{\textit{c}}\).
* We assume that the number of anonymous keys that an OBU requests at some period is \(N_{\textit{k}}\).  
Let \(\textit{Pr}\{i\}\) represent the probability that exactly \(i\) (\(i\geq 2\)) among \(N_{\textit{k}}\) anonymous keys are requested from different compromised RSUs, we have \(\textit{Pr}\{i\}=\frac{\binom{N_{\textit{rsu}}-N_{\textit{c}}}{N_{\textit{k} }-i}(N_{\textit{k}})}{\binom{N_{\textit{k}}}{N_{\textit{k}}}}\). Then, the probability is  
\[\textit{Pr}\{i\geq 2\} =1-\textit{Pr}\{i=0\}-\textit{Pr}\{i=1\}\] \[=1-\frac{\binom{N_{\textit{rsu}}-N_{\textit{c}}}{N_{\textit{k}}} \binom{N_{\textit{c}}}{0}+\binom{N_{\textit{rsu}}-N_{\textit{k}}}{N_{\textit{ k}}-1}\binom{N_{\textit{c}}}{1}}{\binom{N_{\textit{rsu}}}{N_{\textit{k}}}} \tag{12}\]  
From Figure 3 below, it can be seen that the tracking probability increases very slowly with the increase of the number of anonymous keys and the number of compromised RSUs. So it is long-term unlinkability.
# Field Trials of SC-FDMA, FBMC and LP-FBMC in Indoor Sub-3.5 GHz Bands
###### Abstract  
LP-FBMC (low peak-to-average power ratio filter bank multicarrier) was recently proposed to ameliorate the high peak-to-average power ratio (PAPR) issue of filter bank multicarrier (FBMC). The previous simulation study showed that LP-FBMC achieves a similar PAPR as that of single carrier frequency division multiple access (SC-FDMA) while being very robust to inter-user timing/frequency offsets. However, the simulation results that were obtained assuming the stereotyped channel model and the simple nonlinearity model of analog circuits substantially differ from the performance results in a real channel with a real transceiver. To address this, the main purpose of this work is to compare the performances of three waveforms, i.e., SC-FDMA, FBMC, and LP-FBMC, in a real uplink indoor channel. We investigate how the bit error rate (BER) performance gaps of three waveforms in the indoor channels change by the system parameters, such as the carrier frequency within sub-3.5 GHz band and the number of sub-carriers or the sub-carrier spacing, which was not found in the previous simulation study. Our investigation confirms that LP-FBMC is a suitable waveform for real indoor applications.  
5G waveform; SC-FDMA; FBMC; Low PAPR FBMC (LP-FBMC); access timing offset; carrier frequency offset; high-power amplifier (HPA) nonlinearity; software defined radio (SDR) device; uplink indoor channel; out-of-band (OOB) emission
# Quantum Approximation for Wireless Scheduling
## 6 Concluding Remarks and Future Work  
Wireless scheduling was modeled with the MWIS problem, which is one of the well-known NP-hard problems. In order to solve the MWIS problem, a QAOA-based scheduling algorithm, so-called quantum approximate optimization for scheduling (QAOS), was proposed. The proposed QAOS was implemented using Cirq and TensorFlow-Quantum. QAOS outperformed greedy search and random search in the performance evaluation on the random conflict graphs. Therefore,our novel quantum approach to the wireless scheduling problem via QAOS was meaningful.  
Future research will focus on improving the performance of QAOS. In one method, introducing an error correction code to QAOS is considered. This method is expected to improve the sampling quality. Another method is to develop a new optimizer that can more accurately find the optimal parameters of QAOS. A novel optimizer is needed that is more suitable for quantum models than the mainly used optimizers such as Adam, Nelder-Mead (NM), and Broyden-Fletcher-Goldfarb-Shanno (BFGS). From the perspective of quantum machine learning, developing a novel optimizer for the parameterized quantum circuit like the QAOS circuit will be a meaningful challenge.  
[PERSON] was the main researcher who initiated and organized the research reported in the paper, and all authors including [PERSON] and [PERSON] were responsible for analyzing the simulation results and writing the paper. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the National Research Foundation of Korea (NRF-2019M3E4A1080391 and NRF-2019M3E3A1084054).  
[PERSON] is the corresponding author of this paper.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**QAOS, \(p=10\)** & **QAOS, \(p=9\)** & **QAOS, \(p=8\)** & **Greedy** & **Random** \\ \hline
69.50\% & 49.67\% & 42.83\% & 33.83\% & 15.17\% \\ \hline \end{tabular}
\end{table}
Table 1: Percentage of optimal solution computation.  
Figure 3: Performance evaluation results. \(G(\eta)\) is the cumulative distribution function (CDF) of \(\eta\).
## 3 Results and Discussions
### Analysis of Room Energy Consumption
#### 3.2.1 ANOVA of Room Energy Consumption  
As in Section 3.1.1, ANOVA is used to study the relationships between the four factors and room energy consumption. As shown in Table 7, the \(R^{2}\) is 0.996, which means that these four factors can explain 99.6% of the variation of the room energy consumption. The variance ratios of the four factors are all higher than \(F_{0.01}\) (7.01), indicating that the influence of all four factors on room energy consumption is highly significant.