esa-sceva/satcom-chunk-collection huggingface dataset:
# Gap Analysis of Ambient Electromagnetic Noise Measurements Stored in the ITU Data Banks
## 3 Gap Analysis
### Spectral Coverage of the Measurements  
The man-made noise level is frequency dependent. A close look at the data set reveals that the number of frequencies observed is limited and that not every country has measured over the entire spectral range. The USA, UK and Spain measured on 3 frequencies or less. This is shown in Figure 8. Table 5 shows the number of frequencies per environment in each of the countries.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Country** & **REMR** & **RURL** & **RESD** & **CITY** & **URBN** & **INDA** & **ROAD** & **OFFC** & **Other** \\ \hline Germany & & 1336 & 10,804 & 3207 & & & & & \\
**Japan** & & & 15,663 & 19,008 & 12,960 & 11,205 & 12,960 & & & 24 \\
**The Netherlands** & 80 & 120 & 430 & 96 & & & & & 24 \\
**Spain** & 22 & & & & & & & & \\
**UK** & & 465 & 286 & 306 & 218 & 242 & 178 & 367 & \\
**USA** & & & & 137 & 91 & & & & \\ \hline
**Total** & 102 & 1921 & 27,183 & 22,754 & 13,269 & 11,447 & 13,138 & 367 & 24 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Number of measurements per environment type.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Country** & **REMR** & **RURL** & **RESD** & **CITY** & **URBN** & **INDA** & **ROAD** & **OFFC** & **Other** \\ \hline Germany & & & 70\% & 40\% & 14\% & & & & \\ Japan & & & 58\% & 84\% & 98\% & 98\% & 99\% & \\ The Netherlands & 78\% & 6\% & 2\% & 0.4\% & & & & & \\ Spain & 22\% & & & & & & & \\ UK & & 24\% & 1\% & 1\% & 2\% & 2\% & 1\% & 100\% \\ USA & & & & 0.6\% & 0.7\% & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Relative number of measurements per environment type. The dominant contributions are highlighted in yellow.  
\begin{table}
\begin{tabular}{l l} \hline \hline REMR & Remote Rural (P.372: Quiet Rural) \\ \hline RURL & Rural \\ \hline RESD & Residential \\ \hline CITY & City \\ \hline URBN & Urban \\ \hline INDA & Industrial Area \\ \hline ROAD & Highway \\ \hline OFFC & Office area \\ \hline \hline \end{tabular}
\end{table}
Table 5: Number of frequencies per country per environment type.
# Machine Learning Applications and Uncertainty Quantification Analysis for Refload Tests
## 4 Conclusions  
The UQ for the FLECHT SEASET tests using the SPACE was examined using the STARU. We found that all the test case predictions were improved and effectively approximated the experimental data, resulting in an efficient enhancement of SPACE predictions and the effectiveness of the STARU. Moreover, the most influential physical models obtained were consistent with the results of previous studies. Furthermore, we deployed a machine learning model to predict the dependency of the system behaviors corresponding to their physical models and suggest the most influential physical model. As a result, DNN showed a good performance in system state predictions and consistent results in the most influential parameter identification for this problem, delivering a promising application of DNNs in UQ analysis. However, the results are strongly dependent on the dataset structure and complexity of the input parameters and system state relationship. Therefore, future studies should focus on enhancing the performance of the machine learning model and the sampling algorithm in the DA framework to adapt to various datasets and correlations of simulation problems, contributing to obtaining more reliable results and enhancing the efficiency of UQ analysis.  
Conceptualization, N.H.T., K.-D.K. and H.-Y.J.; methodology, N.H.T., K.-D.K. and H.-Y.J.; software, N.H.T., K.-D.K. and H.-Y.J.; validation, N.H.T.; formal analysis, N.H.T.; investigation, N.H.T.; resources, K.-D.K., H.-Y.J. and M.T.V.; data curation, N.H.T.; writing--original draft preparation, N.H.T.; writing--review and editing, N.H.T., K.-D.K., H.-Y.J., N.X.-M., N.N.A., V.-K.H. and M.T.V. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to the computer code licensing requirements.  
The authors declare no conflicts of interest.  
Figure 14: The absolute relative permutation importance scores of DNN. The red-dotted line indicates the maximum value of the absolute relative permutation scores.
## 3 Materials and Methods
### Collaborative Algorithm  
The cooperative scheme is described in Algorithm 1. The collaborative algorithm consists of sensors monitoring their metric regularly in seconds by default. All sensors start with a default network hierarchy value of zero. When the sensor system is turned on, each sensor stores a difference between the current measured value and the next. If that difference increases by 50%, the hierarchy of that node increases by one. If a node's hierarchy exceeds the value of 3, this sensor reprograms the nature of the routing protocol to reactive mode. Otherwise, the routing protocol remains in proactive mode. When a sensor receives a HELLO packet, it returns an ACK packet with ID, LQI, RSSI and hierarchy. Moreover, the coordinator node every day requests each node to ask for the neighbor tables, with which it builds a hierarchy table. In that order, the coordinator node sets the route priority based on the node's hierarchy, RSSI and LQI. In this scheme, the packets requestedby the coordinator node are prioritized so that it establishes the rules that best suit the network and its performance.  
```
Start Nodes ON; set request_time; Require: coordinator_nodes starts; coordinator_nodesends broadcast HELLO; Per each nodedo: hierarchy_i = 0; set default_measurement_i; Per each request_timesecdo: set measurement_i; end delta_measurement_i = |default_measurement_i - measurement_i|; if(delta_measurement_i > (0.05 * default_measurement_i)) { hierarchy_i++; } } if(hierarch_i > 3) { node_i under proactive mode; } else { node_i under reactive mode; } if(receive HELLO) { node_i sends ID_i; node_i sends LQI_i; node_i sends RSSI_i; node_i sends hierarchy_i; } coordinator_nodes a REQUST for each node; coordinator_nodes a hierarchy_table; coordinator_nodes a LQI_table; coordinator_nodes a RSSI_table; coordinator_nodes chooses the best route from hierarchy_table, RSSI_table, LQI_table; end
```  
**Algorithm 1** Collaborative algorithm pseudocode
# Password Guessability as a Service (PGaaS)
## 4 Architecture Implementation
### Password Guessability Estimator Component
#### 4.2.2 Guessability Problem
Assuming the cloud components are untrustworthy means the servers may either solve the computational tasks incorrectly or learn what should not be known. Depending on the server's behavior, there are two adversarial models: the first is called the 'honest-but-curious' or'semi-honest' model and the second is the'malicious' model. In the honest-but-curious model, servers perform the operations complying with the required computational processes, yet are still curious about the sensitive data submitted by users. Apart from the curiosity of users' data, malicious servers may go against the requested computations and return incorrect results to save their computing power or achieve other intentions for benefits. Hence, if we want the guessability component to process the input passwords in the cloud and provide privacy guarantees to users, this component will require implementing cryptographic techniques. Such cryptographic approaches include homomorphic encryption or secure multiparty computation techniques [65; 66]. Instead, for concerned users, a simple approach to guarantee their data privacy is to delegate this computation to their requesting client software application by providing the respective option within it. If such an option is selected, the client application will frequently download the lists returned by EsRank after each initialization (see Step 4) and update previously stored lists (if any). When computing the strength of a given password, the client application will run the corresponding algorithm with the recently downloaded lists and the target password as input, so the passwords never leave the client application.
## 4 Data-Driven Framework for Building Energy Consumption Prediction
### Model for Prediction
#### 4.4.9 Ensemble Models  
Ensemble models combine multiple different models to make full use of the advantages of each model and improve the overall prediction performance. In building energy consumption prediction, common ensemble strategies include Bagging and Boosting.  
Bootstrap aggregating (Bagging) is a parallel ensemble method, and the random forest is a typical representative based on the Bagging strategy. It generates multiple sub-datasets by sampling with replacement from the original dataset and then trains different models (such as decision trees) on these sub-datasets, respectively. Finally, the prediction results of these models are synthesized (voting is usually used for classification tasks, and averaging is usually used for regression tasks). Bagging can reduce the variance of the model, improve the stability and generalization ability of the model, and have a good effect on processing complex building energy consumption data.  
Boosting is a sequential ensemble method. It trains multiple models in turn, and each model is improved based on the errors of the previous model. In addition to the Gradient Boosting Tree mentioned above, Adaptive Boosting (Adaboost) is also a commonly used Boosting algorithm. In building energy consumption prediction, Adaboost first assigns an initial weight to each training sample and then trains the first model. According to the prediction results of the first model, the weights of the samples are adjusted so that the weights of the misclassified samples are increased and the weights of the correctly classified samples are decreased. Then, the second model is trained based on the adjusted weights, and so on. Finally, the prediction results of all models are linearly combined according to certain weights to obtain the final prediction result. The Boosting method can improve the accuracy of the model, but it may be more sensitive to noisy data, so appropriate data preprocessing is required when using it.
## 3 Reliability Modeling of PV Systems
### Modeling Solar Cell  
The analytical model used in evaluating the power output of solar cells depends on two main factors: solar cell efficiency and solar cell irradiation. The efficiency of a solar cell varies with the amount of solar irradiation, and it can be evaluated using Equations (8) and (9) [15]. The power output from a solar cell can be calculated using Equations (10)-(12), as shown in Figure 5 [15].  
\[Eff=\frac{\eta_{c}}{R_{c}}\times G_{bi}\ 0\leq G_{bi}<R_{c} \tag{8}\]  
\[Eff=\eta_{c}\ R_{c}\leq G_{bi} \tag{9}\]  
\[P=P_{sn}\times\frac{G_{bi}{}^{2}}{G_{std}\times R_{c}}\ 0\leq G_{bi}<R_{c} \tag{10}\]  
\[P=P_{sn}\times\frac{G_{bi}}{G_{std}}\ R_{c}\leq G_{bi}<G_{std} \tag{11}\]  
\[P=P_{sn}\ G_{bi}>G_{std} \tag{12}\]  
where \(P\) is the power output of the solar cell (W); \(G_{bi}\) is global solar irradiation (W/m\({}^{2}\)); \(G_{std}\) is solar irradiation in a standard environment set as 1000 (W/m\({}^{2}\)); \(R_{c}\) is a certain irradiation point set as 150 (W/m\({}^{2}\)); and \(P_{sn}\) is the equivalent rated capacity of PV (W). The approach described in Figure 5 is used to create the multi-state model of the power output of the solar cell. The multi-state model is obtained by dividing the solar irradiation into segments. A step size of 50 W/m\({}^{2}\) is used to create these segments. The number of obtained states from these segments is 22 states. Zero solar irradiation is used as an individual state. To calculate the probability for given states, Equation (13) is created, where \(N_{i}\) is the number of occurrences of each state of \(i\):  
\[\text{Probability}_{i}=\frac{N_{i}}{\text{Total Samples}} \tag{13}\]  
The model is applied to an example system considering historical solar irradiation data of Al-Madinah located in Saudi Arabia [16]. The data include solar irradiation at five-minute intervals from 2000 to 2005 for different sites [16]. The total collected data of solar irradiation at five-minute intervals for five years is 525,600 samples. The power output of the solar cell device depicted in Table 1 is in per unit (pu) and was created using Equations (10)-(13). The probability of zero output is  
Figure 4: Series reliability block network with \(n\) subsystems.  
0.4774. In this work, different PV capacities are considered to assess the reliability contribution of the PV system.
###### Abstract  
Patients at risk of chronic kidney disease (CKD) must be identified early and precisely in order to prevent complications, save lives, and limit expenditures for patients and health systems. This study aimed to develop a simple, high-precision machine learning model to identify individuals at risk of developing CKD in the near future, using a novel metabolic index with or without creatinine. This retrospective cohort study used data from the MJ medical record database collected between 2001 and 2015 in Taiwan. We used Cox hazard regression to identify potential predictors, including the novel metabolic index, for use as variables in the models. To develop a machine learning-based CKD risk model with fewer variables, we performed several experimental analyses to combine interacting variables into subsets. Those subsets were used to train three models, random forest, logistic regression, and XGBoost, with or without adding creatinine. The study included 12,189 participants, 20% with and 80% without CKD. The most important conventional predictors of CKD are age and gender. The novel metabolic index, TyG-Index, TG/HDL-ratio and VAI, had stronger predictive power than the conventional risk factors. Without including creatinine data, the XGBoost provided the best predictive performance. After adding creatinine, the performance of all the models was excellent, outperforming both conventional indicators and existing clinical algorithms for CKD. Using novel metabolic index in machine learning-based CKD risk prediction can accurately identify individuals at risk of diagnosis with CKD in the next year, with or without including creatinine.  
machine learning; chronic kidney disease (CKD); risk factors; novel metabolic indices Article
## Materials and Methods
### Far-Field Modal Analysis  
After etching, the diameter of the sapphire fiber was measured using an Olympus BX51 (Waltham, MA, USA) optical microscope and StreamMotion software. After polishing the tip of the fiber using 100 nm diamond lapping film (Ultra Tec, Santa Anna, CA, USA) to remove facets caused by preferential etching of the \(r\)-plane, the far-field patterns of the reduced-diameter sapphire fiber were captured between etching sessions to collect data at many different diameters. Three 5 mW laser wavelengths were used: 532 nm, 783 nm, and 983 nm (Thorlabs, Newton, NJ, USA) in order to expand the breadth of data collected per measured diameter. The lasers were linearly polarized with a polarization extinction ratio of 4 dB and were mounted at the same angle of rotation for all experiments. These lasers were focused into the fiber using a 40\(\times\) objective lens, whose numerical aperture (NA) is larger than that of sapphire, ensuring the modal state would be \"overfilled\" [13]. A BC-106 VIS charge coupled device (CCD) beam profiler (Thorlabs) was used to capture the far-field profiles, which were then catalogued using Thorlabs' Beam 6.0 software. Most components were mounted on 2-or 3-axis stages to aid alignment. A sample under evaluation using the described setup is visible in Figure 2.  
Figure 1: Simplified schematic of the etching system. For sake of clarity, the enclosing alumina tube and insulation are omitted from the diagram, as is the fused silica thermocouple-shielding tube to which the fiber was attached during etching.  
Figure 2: Far-field measurements were taken using a CCD beam profiler and minimal focusing optics. The sapphire fiber with its glass-shielded region, FC/PC connector, and exposed sapphire fiber before etching is seen during analysis using a 5 mW 532 nm laser.
# Integrating Machine Learning Techniques for Enhanced Safety and Crime Analysis in Maryland
## 3 Methodology  
The methodology for this crime analysis research, shown in Figure 1 focuses on performing extensive data analysis and developing a robust predictive model to estimate the crime index in Maryland counties. This model integrates various stages of data preprocessing, feature engineering, and advanced machine learning techniques to ensure accurate and reliable predictions. By incorporating statistical methods, machine learning algorithms, and ensemble techniques, we aim to identify patterns of crime and predict crime trends effectively. This study specifically employs non-spatial predictive models, acknowledging that spatial correlations, which can significantly influence crime patterns, are not incorporated in the current analysis. This methodological focus was chosen to initially explore and understand the broader, non-spatial factors affecting crime rates across various counties, due to both the initial scope of our research and the limitations of the dataset available. Future research will aim to integrate spatial statistical models to address these limitations and provide a more comprehensive analysis of regional crime dynamics.
## 5 Performance and CPU Usage Analysis
### Experimental Setup  
For the experiments, we utilize a host server equipped with an Intel Xeon processor (E5-2650 [EMAIL_ADDRESS] GHz, 10 cores), 256 GB RAM, and Intel 400 GB PCIe 3.0 x4 NVMe SSD as a local disk, running Ubuntu 18.04 LTS (Linux kernel v5.4.0). Table 2 lists the specifications of the host server and the versions of the runtimes. We construct a single container (and also a VM) running Ubuntu 18.04 LTS on each runtime and configure it to have a single virtual CPU (vCPU). Note that we do not limit the memory size of the container (and the VM) to eliminate performance bottlenecks in the memory. The host server is based on an ext4 filesystem, while the container (runc and gVisor) or the VM (Kata containers and Firecracker) in each runtime utilizes the following filesystems: overlayfs (runc), ext4 (gVisor and Firecracker), and virtio-fs (Kata containers).  
We measure the I/O bandwidth and CPU usage with different block sizes (i.e., 4 KB, 16 KB, 64 KB, 256 KB, and 1 MB). First, for the I/O bandwidth, we utilize _flo_[44], a standard Linux I/O benchmark that performs read/write operations of various sizes. We conduct four different operations (i.e., sequential read/write and random read/write) on a 1 GB file. We run each operation for 10 s and perform three iterations of each experiment. To eliminate the effect of the page cache on the I/O bandwidth, we restart the container and VM before each iteration, and the file is flushed to the disk so that the page cache is cleared between each iteration. Also, for CPU usage, we utilize _mpstat_[45], a standard Linux CPU profiling tool, which monitors the number of CPU cycles spent for the following categories: User, System, Iowait, Softirq, and Guest. User, System, and Guest indicate the percentage of CPU cycles spent in the user level, the system level, and the VM level, respectively. Iowait means the percentage of CPU cycles that are idle while the system has outstanding disk I/O requests, while Softirq indicates the CPU consumption for processing software interrupts.  
Figure 4: File operations of Firecracker. (**a**) Overview, and (**b**) symbol-level analysis of the file I/O stack.
# Cooperative Admission Control with Network Coding in 5G Underlying D2D-Satellite Communication
## 4 Simulation Results
### Joint Admission Control (ATSPSN)  
Figure 6 shows the capacity analysis of the different techniques. ATSPSN GAMS branch cut outperforms ATSPSN NOMAD, and GAMS branch bound. Moreover, when the satellite is used as a supported network, the overall capacity increases. Figure 7 shows mode analysis in terms of the capacity. The hybrid mode, which includes joint optimization of the cellular supported by the D2D satellite network, performs better than the standalone mode. Moreover, at higher admitted users, the satellite capacity increases compared to D2D because of more interference due to an increase in operating devices. The satellite is selected in our algorithm when deemed essential, leading to less link budget costs.  
Figure 8 shows that the ATSPSN GAMS branch cut has better energy efficiency as compared to NOMAD and Branch bound due to faster convergence to the optimal solution. So our algorithm has optimum power allocation as compared to other techniques.  
The detailed analysis of the ATSPSN algorithm is made in the conference version of the paper [25], which includes admitted user's analyses, mode analyses, and complexity. It is concluded via [25] that the overall ATSPSN GAMS branch-cut has better performance in terms of maximization of user selection, energy efficiency, capacity, and complexity as compared to other techniques. ATSPSN GAMS branch-cut is used further to cooperative network coding (JAMANC algorithm).  
Figure 6: Capacity: NOMAD, Branch cut, Branch Bound.
# DMFO-CD: A Discrete Meth-Flame Optimization Algorithm for Community Detection
## 5 Experimental Evaluation  
The performance of the proposed DMFO-CD algorithm was evaluated on eleven real-world datasets, and the results were compared with five state-of-the-art algorithms in community detection, consisting of DPSO-PDM [13], GA-Net [58], GACD [14], EGACD [60], and DECS [76]. The proposed and comparative algorithms were implemented in MATLAB 2020b except for GA-Net and DECS; its executable MATLAB code [89; 90] was used. All experiments were run on a CPU, Intel Core (TM) i7-3770 3.4 GHz with 12.0 Gb real memory. The performance comparison was based on various metrics consisting of modularity (Q), normalized mutual information (NMI) [91], and the number of detected communities. The overall performance of the algorithms was statistically analyzed by two non-parametric statistical tests Friedman [77] and Wilcoxon signed-rank [78].
## 3 Simulation Results  
The proposed ET HSM was designed using a 130 nm BCD process, occupying a chip area of 6.36 mm\({}^{2}\). Figure 13 shows the chip layout of the proposed LA. Figure 14 presents the transient simulation results for a 50 MHz envelope input and the corresponding output voltage. Figure 14 illustrates the current waveforms of LA, SA, and the overall output. The HSM achieves a simulated power efficiency of 83%. Table 3 summarizes the performance metrics of the proposed HSM and provides a comparison with previously reported works.  
Figure 14: Transient simulation results of ET HSM for 50 MHz envelope signal V\({}_{\text{env}}\): (**a**) input and output voltage waveforms; (**b**) current of LA I\({}_{\text{linear}}\), SA I\({}_{\text{buck}}\), output L\({}_{\text{output}}\).  
Figure 13: Chip layout of the proposed LA.  
This work is the first to implement HSM for base station PA applications, which require a wide output voltage range, 6 V to 28 V, and high output power. To meet these requirements, we employed LDMOS devices in a 130 nm BCDMOS process. Unlike conventional CMOS used in previous UE PA applications, LDMOS transistors include a drift region to support high-voltage operation, which increases the device parasitic capacitance and degrades bandwidth performance. Despite the bandwidth limitations of LDMOS, the proposed HSM successfully tracks a 50 MHz signal bandwidth.  
While the efficiency is slightly lower than in previous low-voltage domains [4, 21, 23], this is mainly due to the higher supply voltage, 30 V, used in this work. Since power loss increases with higher supply voltage (P = V \(\times\) I), achieving high efficiency becomes more challenging in high-voltage domains.