esa-sceva/satcom-chunk-collection huggingface dataset:
# Using Explainable AI (XAI) for the Prediction of Falls in the Older Population
## 2 Materials and Methods
### Workflow Summary  
The workflow process of the present study is summarised as follows:  
1. Extraction of data from the TILDA database.
2. Data processing and cleaning (removing data with missing values, removing duplicate data, and encoding binary variables).
3. Building random forest prediction models (all falls and syncope model; simple falls model; complex falls model; syncope model).  
1. Python 3 programming language was used on the Anaconda platform.
2. _GridSearchCV_ package was implemented for tuning of hyperparameters.
4. Assessing the model's performance by calculating the precision, recall and F1 scores.
5. Feature relevance: SHAP and random forest feature importances were derived from the four models.
## 1 Introduction
The Frequency Hopping Spread Spectrum remains one of the most reliable and ruggedized options for modern Internet of Things communications. Therefore, despite the rapidly changing technology landscape, the FHSS remains a good choice for long-range communication and has the potential to accommodate the growing number of devices in the field thanks to its ability to efficiently and dynamically share network spectral capacity among devices. This work, therefore, proposes a new FHSS-based spread spectrum approach, adapted to the NB-IoT PHY layer characteristics, which, when coupled with a novel clustering approach, aims at enhancing the NB-IoT network scalability while preserving its energy efficiency and network reliability. Due to the limited available spectrum (roughly 200 kHz), as allocated to the NB-IoT within its three deployment modes (in-band, standalone, and guard band); one of the key issues is how to efficiently use these resources to support massive IoT devices. Because the NB-IoT technology is deemed to have a higher level of computational complexity at Base Station as compared to its unlicensed band competitors such as the LoRa and Sigfox; the concepts of time offset as well as transmission repetition have been proposed by previous work such as [12; 13]. Finally, there is also the fact that frequency band allocation within the cellular network's band (in-band deployment) or as standalone does often causes non-synchronization among different cells which results in inter-cell interference from the neighbor cells' LTE users or NB-IoT users. The above three reasons coupled with the fact that most NB-IoT technology designs, as currently proposed, mainly use simple ALOHA or CSMA based MAC protocols which do not scale well with a large number of connected devices; have been considered by the present study as a trigger for a need for a novel spread spectrum and clustering approach for the NB-IoT. Therefore, the present work proposes a mixed-strategy of frequency hopping spread spectrum and clustering approach to mitigate the inter-cell interference and reduce computational complexity at the BS. This is expected to enhance NB-IoT system designs as a way to enable network scalability, data rate enhancement, and also maintain energy efficiency and network reliability. The rest of this paper is organized as follows. Section 2 presents a summary of existing spectrum sharing and clustering techniques for NB-IoT as found in the literature. Section 3 presents a detailed development of the proposed mixed spread spectrum and clustering algorithm. Section 4 presents the performance evaluation of the proposed followed by the analysis and discussion of the obtained results. Section 5 concludes the paper and presents future work guidelines.
## 3 Field Measurements
### Dynamic-Wheel-Load Measurement  
The levels of the dynamic responses of the track were analyzed as the train passed along the ballasted track. As a result of measuring the dynamic wheel load for each passed-train speed, the dynamic wheel load increased significantly at 115 km/h. Figure 13 shows examples of the measured waveforms of the wheel load, vertical rail displacement, and vertical railroad tie displacement obtained from the field measurements [17].  
As shown in Figure 14[17], to calculate the representative value of the dynamic wheel load of the ballasted track, the train speed was set to 80-120 km/h in consideration of the average train-operating-speed range of the target section of this study, and the dynamic-wheel-load data for the corresponding speed range was used and applied to a Gaussian probability-density-function analysis.  
As a result of the Gaussian probability-density-function analysis based on the dynamic-wheel-load measurement outcomes according to the train-speed change that was measured  
Figure 14: Analysis of probability density function of dynamic wheel load for the ballasted track which did not contain irregularities. (**a**) Gaussian probability density function of dynamic-wheel-load measurements; (**b**) Analysis of representative values of dynamic wheel load for the ballasted track which did not contain irregularities.  
Figure 13: Measurements of dynamic responses of the track. (**a**) Example of dynamic-wheel-load waveforms; (**b**) Dynamic-wheel-load measurements with respect to the train speed.  
on the ballasted track on a serviced railway line (which did not contain any track irregularities), the dynamic wheel load which corresponded to the train-speed range of 80-120 km/h was 112.65 \(\pm\) 14.62 kN, and the range of the total wheel load was analyzed to be in the range of 98-127 kN. The correlation coefficient (R\({}^{2}\)) of the probability density function was 0.98, which verified the appropriateness of the probability density function for the measured data.  
In addition, the standard deviation was analyzed to be within \(\sim\)10% of the probability mean value, thus indicating a good data spread. This is because the dynamic-wheel-load values were measured on a ballasted track which did not contain any track irregularities.
## 3 Physical (PHY) Layer and Media Access Control (MAC) in IoT
### Modulation Scheme  
Modulation scheme and index depend upon the severity of the channel noise, as well as the type of application run on/for a node in IoT. Generally, IoT aims for low cost and complexity. For this reason, computationally expensive and advanced modulation schemes such as trellis-coded modulation, etc. are excluded at first. In the case of wider frequency bands for wireless connectivity, DSSS is employed, but since IoT nets are deployed with a massive number of nodes, DSSS is precluded because of regulatory constraint and narrowband interference. This will save channel bandwidth for other communications. DSSS increases channel bandwidth and requires more power for transmission [23]. For task-specific communication, direct burst modes are achieved using single channel communication with no spectrum hopping. It is a high-power low-bandwidth communication scheme.
# LDPC Decoder Design Using Compensation Scheme of Group Comparison for 5G Communication Systems
## 2 Low-Density Parity-Check Codes
### Quasi-Cyclic (QC) LDPC Codes  
Quasi-cyclic (QC) LDPC codes [13] are more efficient in the hardware implementations of LDPC codes. In the \((N,M)\) QC-LDPC \(\mathbf{H}\) matrix, \(N\) represents the number of base columns, and \(M\) represents the number of base rows. The maximum number of VNs connected to a row is defined as \(w_{r}\) (row weight), and the maximum number of CNs connected to a column is defined as \(w_{c}\) (column weight). Each element of the \(\mathbf{H}\) matrix can be treated as an identity matrix, which can be expanded to provide a \(z\times z\) sub-matrix with a right shift. Therefore, the \(\mathbf{H}\) matrix is a simplified version of the \((N,M)\) QC-LDPC. The code rate \((R)\) is the proportion of real information \((N-M)\) in the transmission data \((N)\), and is regarded as \(R~{}=~{}\frac{N-M}{N}\). Each element in the \(\mathbf{H}\) matrix represents the number of right shifts. In the \(\mathbf{H}\) matrix, all elements in a single layer are independent, enabling the operations in a single layer to run synchronously. The maximum parallelism of the hardware design can be up to a maximum value of \(z\). This efficiently speeds up the overall operations and improves the overall throughput. The QC-LDPC is composed of a cyclic-shifted identity matrix; thus, a memory address can be obtained using the cyclic-shifted characteristic. Only the starting point of the memory address is required to make the design of the reading memory address more flexible.
# Intrusion Detection Model Based on Improved Transformer
## 3 Materials and Methods
### Data Preprocessing Strategy
#### 3.2.1 Numericalization and Normalization  
Intrusion detection datasets often contain character-based features, which cannot be processed by computers as they only recognize numerical data. Therefore, character-based features are encoded into numeric values by using one-hot encoding. However, the resulting dataset may contain discrete and continuous states, leading to significant differences in individual feature values. This, in turn, can cause the gradient to disperse during backpropagation, slowing down the learning process and reducing the model's ability to extract deeper features. To address this issue, we propose normalizing the dataset after one-hot encoding. In this paper, we utilize minimize-maximize normalization to rescale the data and map it to a range of [0, 1]. The calculation formula is shown as follows:  
\[x^{*}=\frac{x-x_{\min}}{x_{\max}-x_{\min}} \tag{1}\]  
In the formula, \(x\) represents a specific data value, \(x_{\min}\) is the minimum value of the column feature, \(x_{\max}\) is the maximum value of the column feature, and \(x^{*}\) is the resulting normalized data value.
## 3 Valve Characteristics
### Dynamic Characteristics  
We executed a pressure-control experiment connecting a pressure sensor directly to the control port. During the experiment, the dead volume of 1.4 \(\times\) 10\({}^{3}\) mm\({}^{3}\) remained even when directly connecting the sensor to the valve.  
A PI feedback controller was used for pressure control. A PC was used as the controller; the measured pressure was entered into the PC by an AD converter. The control signal was provided to the DC motor through a DA converter. A block diagram of the pressure control is shown in Figure 10. The control parameters were determined by trial and error, and are listed in Table 3. The parameters were determined to be as large as possible to minimize offset during stable conditions. In this experiment, sinusoidal reference pressures with an amplitude of 20 kPa and an offset of 200 kPa were entered into the controller. The frequency of the reference input ranged between 0.1 and 30 Hz.  
The experimental results at frequencies of 0.1 and 5.0 Hz are shown in Figure 11. The upper figures show the pressure, and the lower figures show the cam angle. It is clear that the valve can control the pressure at a frequency of 0.1 Hz. A small phase delay is observed at a frequency of 5.0 Hz. The results were summarized in a Bode diagram, as shown in Figure 12. We confirmed that bandwidth was higher than 30 Hz when using the proposed valve. The phase lag became \(-\)180 deg at 40 Hz. This is considered to be a result of the motor dynamics. To enable comparison, we performed the same experiments using a commercially available spool-type servo valve (FESTO MPYE-5-M5-010-B), in which the maximum flow rate is approximately the same as that of the developed valve. The results for the spool-type valve are plotted in Figure 12. Up to 30 Hz, it is clear that the developed valve  
Figure 10: Block diagram of pressure control.  
\begin{table}
\begin{tabular}{c c} \hline \(K_{ap}\) & 4.0 V/kPa \\ \hline \(K_{ai}\) & 0.25 V/kPa s \\ \hline \end{tabular}
\end{table}
Table 3: Control gains of pressure control.  
Figure 9: Pressure gain characteristics of developed valve.  
demonstrated almost the same performance as the spool-type servo valve. The dynamic response could be improved by changing the DC motor to one having a higher response.
# Fault Prediction and Reconfiguration Optimization in Smart Grids: AI-Driven Approach
## 3 Data Source Search, Analysis, Purge, and Selection
### SustDataED and PVWatts Tool--Final Dataset (KeyMonData)  
After identifying and addressing the limitations of SustDataED by incorporating the PVWatts tool for photovoltaic power generation data, the entire dataset was processed to create the final dataset. The data processing workflow is outlined in Figure 5. As shown, there were two main branches: the first branch originates from the SustDataED dataset and included all household consumption data, while the second branch corresponded to the photovoltaic generation data. Out of the 50 households, only 23 were selected from the first deployment, as previously mentioned. Subsequently, 23 photovoltaic generation profiles were generated, ultimately forming 23 files of real net loads, representing the sum of consumption and generation data.  
This new dataset required further processing to limit both its size and the temporal series to align with the specified deployment. Additionally, to make the dataset more manageable, the data were synthesized. Specifically, SustDataED provides measurements at a minute-level resolution; however, working with minute-by-minute samples is unnecessary, as the variations in consumption are negligible. Therefore, the resolution was reduced to hourly samples. Thus, if the first deployment was limited to a duration of one year, there were 8760 (24 \(\,\mathrm{h}\times 365\,\) days) time intervals per household, for a total of 23 households.  
Figure 4: Correlation patterns between production parameters in PVWatts.  
Figure 3: Comparison using correlation matrices of photovoltaic production parameters. (**a**) SustDataED—Photovoltaic parameters. (**b**) PVWatts—Photovoltaic parameters.  
Figure 8: Data processing scheme for the generation of the final dataset (7).  
Regarding the file naming convention, each file was labeled as _load_x.csv_, where \(x\) refers to the identifier of the corresponding household for the calculated loads. Consequently, this resulted into a total of 23 load files (_load_x.csv_), each containing 8760 time intervals. The final fields included in the new dataset are outlined in Table 3. After being processed, cleaned, and synthesized, the new dataset was named _KeyMonData_ and has been made publicly available for use by anyone [40].
## 2 Overview of the Relevant Standards
### EN 50530:2010 Standard  
EN 50530:2010 is a European standard that specifies a methodology for testing and evaluating the performance of PV inverters [28]. It defines procedures for evaluating inverters' efficiency under different operating conditions and establishes a uniform methodology for measuring the efficiency of grid-connected PV inverters. The standard applies to single-phase and three-phase inverters, ensuring that they are tested under controlled conditions to determine their overall efficiency and performance under different conditions. The description provided in this section is limited to MPPT efficiency test conditions [28].  
The standard specifies testing over a defined range of DC input voltages and loads connected to the inverter output. The tests consider different environmental conditions, such as temperature and radiation, but usually assume nominal parameters. During testing, the inverter's performance is compared to its nominal efficiency to predict its performance under field operating conditions [28].  
In EN 50530:2010, efficiency serves as the primary performance indicator. This concept includes maximum efficiency, usually evaluated under full load or optimal operating conditions. In addition, weighted efficiency is also defined, which considers different load scenarios to reflect use in real-world conditions. In addition to assessing the inverter's performance during unchanging conditions (static MPPT efficiency), the testing also assesses its ability to cope with fluctuations in input power caused by different environmental conditions (dynamic MPPT efficiency). For this research, test specifications and conditions for static MPPT efficiency are used to evaluate PV inverters regarding the generation of current harmonics in steady-state operations. Measurements were performed for MPP normalized to 100%, 75%, 50%, 30%, 25%, 20%, 10%, and 5% of the nominal DC power,  
\begin{table}
\begin{tabular}{c c c c} \hline
**Odd Harmonic** & **\%** & **Even Harmonic** & **\%** \\ \hline
3 \(<\) h \(<\) 9 & 4.0 & DC & 1.0 \\
11 \(\leq\) h \(<\) 15 & 2.0 & 2 \(<\) h \(<\) 8 & 1.0 \\
17 \(\leq\) h \(<\) 21 & 1.5 & 10 \(<\) h \(<\) 32 & 0.5 \\
23 \(\leq\) h \(<\) 33 & 0.6 & & \\ \hline \end{tabular}
\end{table}
Table 2: Maximum DC injection and harmonic current distortion, given in percent of the rated current capacity, \(I_{\rm r}\).  
with measurements being conducted for minimum MPP voltage, rated input voltage, and maximum input voltage [28].
# Identifying Influential Nodes of Complex Networks Based on Trust-Value
## 3 Experiments and Discussion
### Estimate for Parameter \(k\)  
The trust-value proposed above consists of both the similarity ratio and the degree ratio. To determine their exact proportion in the trust-value to ensure the accuracy of the experimental results, we took parameter \(k\) as ranging from 0.1 to 1. The infection probability and recovery probability in the SIR model were set to 0.3 and 0.6, respectively, 10 steps were propagated, and 100 iterations were run. The Kendall correlation coefficients between the SIR and TPR algorithms were calculated in the PolBooks, Dolphins, Hamster, and Elegans networks. The experimental results are shown in Figure 4.  
Figure 4 shows that in the Dolphins, Hamster, and Elegans networks, the values of the correlation coefficient \(\tau\) increase slightly with the change in the value of the parameter \(k\). In the PolBooks network, the maximum of \(\tau\) is reached when \(k\) is between 0.85 and 0.9. To accurately evaluate the influence of nodes in the network and avoid adopting too many parameters in the experiments, the value of \(k\) was set to 0.85.
## 4 Equivalent Block Diagrams for GNSS and Spoofing Signals
### Equivalent Block Diagram of the Full Transmitter-Channel-Receiver Chain
Moreover, the impact of the receiver sampling rate on RFF accuracy remains to be addressed in the GNSS context. Some studies of the effect of quantization and sampling rates on RFF in the context of non-GNSS signals can be found in [56] (for WiFi signals) and [57] (for BLE signals) and  
Figure 8: The illustration of the EVM principle. The blue arrow denotes the transmitted symbol, the yellow arrow denotes the received symbol, the blue arrow denotes the estimate, and the crimson arrow denotes the estimation error. the current understanding is that, typically, higher sampling rates give better RFF accuracy. Such findings are still to be confirmed in the GNSS context.
## 6 Mathematical Models for SNR and Signal Combining in ADRs  
### SNR in ADRs  
The SNR for each signal received by a photodiode \(k\) is defined as the ratio of the signal power received to the noise power on that photodiode. For the photodiode \(k\) in an ADR, the received power is \(P_{r,\text{PD}_{k}}\), and the noise power \(P_{\text{noise}}\) is modeled as thermal and shot noise. The SNR at photodiode \(k\) is given by  
\[\text{SNR}_{k}=\frac{P_{r,\text{PD}_{k}}}{P_{\text{noise}}}. \tag{35}\]  
The noise power \(P_{\text{noise}}\) can be expressed as  
\[P_{\text{noise}}=N_{0}B. \tag{36}\]  
where \(N_{0}\) is the noise spectral density (W/Hz), and \(B\) is the bandwidth of the communication system (Hz).  
Thus, the SNR for the photodiode \(k\) can be written as  
\[\text{SNR}_{k}=\frac{P_{r,\text{PD}_{k}}}{N_{0}B}. \tag{37}\]
## 4 Discussion of Results
For example, [PERSON] et al. [22] applied a NN combined with a particle swarm optimization (PSO) algorithm to predict the BOF's endpoint carbon content and endpoint temperature. Their results show that when the carbon content prediction error is within \(\pm\)0.02, the prediction accuracy rate of carbon content is 92.85%. On the other hand, when the endpoint temperature prediction error is within \(\pm\) 10 \({}^{\circ}\)C, the prediction accuracy rate of the endpoint temperature is 89.28%. They tested NN with PSO on 30 melts. The absolute error in carbon prediction was in the range of 0.005-0.025 vol.% and 5-10 \({}^{\circ}\)C in temperature prediction. [PERSON] et al. [25] compared an artificial NN and proposed a least-squares support-vector machine (LSSVM) model for temperature prediction based on 13 input observations. The best obtained RMSE was 16.36 in ANN and 13.21 in LSSVM. These results are better than in our research. In our research, the RF model with the basic observations achieved an RMSE with a value of 17.46 when predicting temperature from static data and 0.0125 when predicting carbon. For comparison, [PERSON] et al. [34] investigated a data-driven model based on ridge regression, random forest (RF), and gradient-boosted regression trees (GBRT) on BOS variable prediction. They modeled melt temperature and its chemical composition in the endpoint. In ridge regression and carbon prediction, the obtained RMSE was 0.93, and temperature prediction reached 0.73. In RF regression and carbon prediction, the RMSE was 1.05, and in temperature prediction, the RMSE was 0.92. They obtained the best carbon prediction result with GBRT, where RMSE was 0.88. In temperature prediction with GBRT, the obtained RMSE was 0.79. In future research, selected machine learning-based models and the deterministic model will be improved for practical use in steelmaking plants. As an alternative to the existing machine learning methods, extreme learning machines also have excellent potential for developing efficient and accurate modeling in steelmaking. In future research, we plan to optimize the NN. It would be interesting to change the network structure and increase the training set to assess the impact on the NN's performance. It would also be interesting to investigate the prediction performance of online models.
# CONE: A Connected Dominating Set-Based Flooding Protocol for Wireless Sensor Networks
## 2 Related Work
### Backbone Construction
#### 2.2.3 Connected Dominating Sets  
A DS is a subgraph of a graph G that represents a network (see Figure 5). Each node of G is either in the DS or adjacent to a node of the DS. We refer to a node that is in the DS as a \"dominator\". Accordingly, we refer to a node that is not in the DS as a \"non-dominator\".  
A DS is connected and, therefore, a CDS if there is a path from each dominator to each other dominator and this path includes only dominators. As mentioned in Section 1, a MCDS contains the  
Figure 4: A spanning tree given by the white nodes. Leaf nodes are colored in grey.  
Figure 5: Connected dominating set. The dominators are colored in green.  
minimal number of dominators that are required to cover the whole network. Finding a MCDS is referred to as the \"MCDS problem\" [11].  
CDSs are usually used as a backbone [18]. As previously mentioned, many nodes in network flooding unnecessarily forward a packet which results in a broadcast storm. Using a CDS for network flooding can decrease broadcast storms by decreasing the number of forwarder nodes [11]. Furthermore, a node's radio transceiver must be turned off as often and as long as possible in order to achieve a long lifetime of the node [19]. When a CDS is used, non-dominators can turn off their radio transceiver for a longer time period in order to save energy.