esa-sceva/satcom-chunk-collection huggingface dataset:
# Wireless Communication Technologies for Safe Cooperative Cyber Physical Systems
## 4 Safety and Security for SafeCOP Co-CPSs
### Autonomous Hospital Beds  
An initial safety analysis of the concept has concluded that the network of external cameras is an enhancement to the basic functionality of the MiR100s, and should not be considered as part of the safety system. The MiR100-to-MiR100 communication, on the other hand, is an integral part of the safety system, and must be certified as safe according to the relevant safety communication standard. Unfortunately, there is currently no relevant safety standard for small, autonomous robots with wireless communication operating in a hospital environment. However, in the railway domain, there are similar challenges in signaling systems and train communication, where wires cannot be used due to the mobility of the application. The safe communication architecture will thus be based on the requirements of EN 50159 [62]. To avoid full safety certification of the communication protocol, it is proposed to use an end-to-end architecture with a safety layer inserted between the \"black box\" communication system and the safety application. In addition, since wireless communication by definition is an open communication system, which is vulnerable to attacks from actors with malicious intent, information security mechanisms (authentication, cryptography) are a prerequisite for achieving safe communication. The combination of threats random failures and external attacks will lead to one or more of the following basic message errors: repetition, deletion, insertion, resequence, corruption, delay and masquerade. In line with EN 50159, the XBee communication shall be enhanced with a safety layer implementing defenses against these threats. They consist of a protocol with the following parameters and functionalities: sequence number, time stamp, time-out, source and destination identifiers, feedback message, safety code, identification procedure, and cryptographic techniques.
# Comparison of Palliative Care Models in Idiopathic Pulmonary Fibrosis
###### Abstract  
Introduction: Palliative care (PC) is recommended in idiopathic pulmonary fibrosis (IPF) patients but poorly implemented. Integration of PC into routine management by pulmonologists may improve overall and end-of-life (EOL) care, but the optimal model of PC delivery is unknown. Objective: To describe three PC care delivery models and their impact on EOL; the Multidisciplinary Collaborative ILD clinic, Edmonton, Canada (EC) and the Bristol ILD Service, UK (BC) that provide primary level PC; and the Queen's University ILD Clinic, Kingston, Canada (QC), which refers IPF patients to a specialist PC Clinic using specific referral criteria. Methods: A multicenter retrospective observational study of IPF patients receiving care in the identified clinics (2012-2018) was designed. Demographics; PC delivery, including symptom management; advance care planning (ACP); and location of death data were examined. Results: 298 IPF patients were included (EC 95, BC 84, and QC 119). Median age was 71 years with 74% males. Overall, 63% (188) patients received PC. Primary PC approach in EC and BC led to more patients receiving PC (98% EC, 94% BC and 13% QC (\(p<0.001/<0.001\))) with earlier initiation compared to QC. Associated higher rates of non-pharmacologic dyspnea management [98% EC, 94% BC, and 2% QC (\(p<0.001/<0.001\)); opioids (45% EC and BC, and 23% QC (\(p<0.001/<0.001\))); and ACP (100% EC and BC, and 13% QC patients (\(p<0.001/<0.001\))) were observed. Median follow up (IQR) was 16 months (5-28) with 122 deaths (41%). Primary PC model in EC and BC decedents was associated with more PC delivery (91% EC, 92% BC and 19% QC (\(p<0.001\))) with more symptoms management, oxygen, and opiate use than QC (\(p<0.001\); \(p=0.04\); \(p=0.01\)). EOL discussions occurred in 73% EC, 63% BC, and 4% QC decedents (\(p=0.001\)). Fifty-nine% (57) died at home or hospice and 38% (36) in hospitals. Concordance rate between preferred and actual location of death was 58% in EC (0.29 (\(-\)0.02-0.51)) and 37% in BC models (\(-\)0.11 (\(-\)0.20-0.15)). Conclusions: Primary PC approach for IPF is feasible in ILD clinics with concurrent disease management and can improve access to symptom management, ACP, PC and EOL care. Reliance on PC specialist referral for PC initiation outside of the ILD clinic can result in delayed care.  
palliative care; integrated; ambulatory; care models; IPF; advance care planning; end-of-life care
## 2 Data and Methodology
### Data Sets  
#### 2.2.1 Sentinel-1  
Sentinel-1 (S1) is a vital component of the Global Monitoring for Environment and Security (GMES) proposed by the European Space Agency (ESA). It includes two satellites, namely Sentinel-1A and Sentinel-1B, which were successfully launched on 3 April, 2014, and 25 April, 2016, respectively. The time resolution of a single satellite is 12 days, which decreases to 6 days after two satellites are networked. S1 is equipped with a C-band SAR sensor and has two kinds of VV and VH polarization, with a working frequency of 5.4 GHz. There are four data acquisition modes (Table 1). The S1 images used in our research are Level 1 products with a single-look complex (SLC) format from an IW mode. Two S1 images were obtained on 28 April 2019 and 29 April 2021 from the ESA ([[https://scihub.copernicus.eu/](https://scihub.copernicus.eu/)]([https://scihub.copernicus.eu/](https://scihub.copernicus.eu/))) to test the developed SSM estimation method (Table 2). The obtained S1 SAR images needed to be preprocessed before being analyzed. The SARscape 5.2.3 software was applied to conduct the radiometric calibration and geocoding to obtain the backscattering coefficient. The backscatter radiation was calculated from previously calibrated S1 images and converted to decibels (dB) by the transformation relation [13]:  
\[\sigma^{0}(dB)=10\log_{10}\left(\sigma^{0}\right) \tag{1}\]
# ERCP: Energy-Efficient and Reliable-Aware Clustering Protocol for Wireless Sensor Networks
## 5 Performance Evaluations
### Simulation Results
#### 5.3.5 Complexity Evaluation  
Throughout this experiment, the overall complexity of the proposed algorithm is evaluated in terms of the processing time required in comparison to HMBCR [10], ECRP [11], and EPSO-CEO [24]. Figure 9 shows the overall complexity in terms of the processing time required. The figure shows that the proposed algorithm requires a longer processing time than the others. Therefore, the complexity of the proposed algorithm is higher than that of the other algorithms. That is to say, the proposed algorithm needs more computational energy. However, in WSNs, energy consumption in communication has been recognized as the main source of energy consumption, and it costs much more than the energy consumption of computation. However, this disadvantage in complexity is compensated for by the good performance in network PDR, network lifetime, delivery delay, and energy balance.  
Figure 8: Energy Imbalance Factor (EIF) vs. the running time for heterogeneous networks.  
Figure 7: Energy Imbalance Factor (EIF) vs. the running time for homogenous networks.
# Comparison of Embankment Properties with Clay Core and Asphaltic Concrete Core
## 3 Results
### Result of the Slope Stability Analysis of the Embankment  
The slope stability analysis results of the embankments by calculating FOS (refer to Table 1) can be explained as follows.  
Clay and asphaltic concrete core embankments in a rapid drawdown state with and without a surcharge load have derived FOS values for upstream slopes of 1.2 to 0.7. The case of the slope ratio 1:1 provides the minimum FOS for all cases, as indicated in Tables 12 and 13.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{
\begin{tabular}{c} **Slope** \\ **Ratio** \\ \end{tabular} } & \multicolumn{6}{c}{**Asphaltic Concrete Core**} & \multicolumn{6}{c}{**Clay Core**} \\ \cline{2-10}  & \multicolumn{2}{c}{**Normal Water Level**} & \multicolumn{2}{c}{**Rapid Drawdown**} & \multicolumn{2}{c}{**Normal Water Level**} & \multicolumn{2}{c}{**Rapid Drawdown**} \\ \cline{2-10}  & **U/S** & **D/S** & **U/S** & **D/S** & **U/S** & **D/S** & **U/S** & **D/S** \\ \hline
1:2 & 2.1 & 2.1 & 1.2 & 2.1 & 2.0 & 2.0 & 1.1 & 2.0 \\
1:1:5 & 1.8 & 1.7 & 1.0 & 1.7 & 1.5 & 1.6 & 0.9 & 1.6 \\
1:1 & 1.4 & 1.3 & 0.9 & 1.3 & 1.4 & 1.2 & 0.7 & 1.2 \\ \hline \hline \end{tabular}
\end{table}
Table 12: FOS of upstream slope without a surcharge load.  
Figure 13: Vertical displacement of the core zone in a normal water level state with a surcharge load for slope ratio 1:1.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{
\begin{tabular}{c} **Slope** \\ **Ratio** \\ \end{tabular} } & \multicolumn{6}{c}{**Maximum Displacement (cm)**} \\ \cline{2-10}  & \multicolumn{6}{c}{**No Surcharge Load**} & \multicolumn{6}{c}{**Surcharge Load = 50 kPa**} \\ \cline{2-10}  & \multicolumn{2}{c}{**Upstream Slope**} & \multicolumn{2}{c}{**Core**} & \multicolumn{2}{c}{**Upstream Slope**} & \multicolumn{2}{c}{**Core**} \\ \cline{2-10}  & **Vertical** & **Horizontal** & **Vertical** & **Horizontal** & **Vertical** & **Horizontal** & **Vertical** & **Horizontal** \\ \hline
1:2 & 4.6 & 3.9 & 0.0 & 1.2 & 4.6 & 3.9 & 1.5 & 1.2 \\
1:1.5 & 4.0 & 4.6 & 0.0 & 1.7 & 4.1 & 4.5 & 1.6 & 1.7 \\
1:1 & 3.0 & 5.5 & 0.0 & 2.7 & 3.2 & 5.4 & 1.6 & 2.7 \\ \hline \hline \end{tabular}
\end{table}
Table 11: Summary of the displacement analysis of the embankment.  
The study conclusion is to explain how seepage through the embankment has been influenced by two factors, the core thickness and core material, whereas the displacement of the soil embankment relates to three factors, the slope, water level state, and surcharge load. The slope stability of the embankment is the most sensitive factor due to influences from four factors. We summarize the factors that influence to the behavior of the soil embankment for designing in Table 14.
# Deep Learning-Based Log Parsing for Monitoring Industrial ICT Systems
## 5 The Deep Learning-Based Log Parsing Method
### Offline Parser  
As shown in Figure 3, as a specifically designed _recurrent neural network_ (RNN), our offline parser has three major components:  
* _A word embedding layer_ that transforms \(x\) to \(\mathbf{e}\);
* _An LSTM layer_ that extracts features from log records;
* _A decision layer_ that identifies the log event type.  
Additionally, to enhance the generalization capability, we integrate two _dropout layers_ into the offline parser, i.e., one between the word embedding layer and the LSTM layer and another between the LSTM layer and the decision layer.  
Fundamentally, the core layer of our offline parser can be formalized as follows:  
\[h_{t} = H(W_{e^{\prime}h}e^{\prime}_{t}+W_{hh}h_{t-1}+b_{h}), \tag{1}\] \[o_{t} = W_{ho}h_{t}+b_{o} \tag{2}\]  
where \(x\) is the input log record, i.e., the word sequence; \(h\) is the hidden vector sequence; \(o\) is the output sequence of the LSTM layer; the \(W\) terms denote the weight matrices; \(H\) is the hidden layer function; and the \(b\) terms denote the bias vectors.  
Figure 3: Deep learning-based log parsing framework: offline architecture.  
Specifically, our hidden layer function \(H\) is designed as a standard LSTM, which is built upon LSTM cells with four major components, i.e., input gate \(i\), forget gate \(f\), output gate \(o\), and memory cell \(c\). Explicit cell implementation is as follows:  
\[i_{t} = \sigma(W_{e^{\prime}i}e^{\prime}_{t}+W_{hi}h_{t-1}+W_{ci}c_{t-1}+b_ {i}), \tag{3}\] \[f_{t} = \sigma(W_{e^{\prime}f}e^{\prime}_{t}+W_{hf}h_{t-1}+W_{cf}c_{t-1}+ b_{f}),\] (4) \[c_{t} = f_{t}c_{t-1}+i_{t}\tanh(W_{e^{\prime}c}e^{\prime}_{t}+W_{hc}h_{t -1}+b_{c}),\] (5) \[o_{t} = \sigma(W_{e^{\prime}o}e^{\prime}_{t}+W_{ho}h_{t-1}+W_{co}c_{t}+b_ {o}),\] (6) \[h_{t} = o_{t}\tanh(c_{t}), \tag{7}\]  
where \(\sigma\) is the sigmoid function.  
Then, the LSTM layer's output \(o\) is transformed into \(o^{\prime}\) by the dropout layer. Finally, the decision layer uses the Softmax function to calculate the probability distribution over all log event types \(y\) as well as the predicted label \(\hat{y}\):  
\[y = softmax(W_{o^{\prime}y}o^{\prime}+b_{y}), \tag{8}\] \[\hat{y} = \operatorname*{argmax}_{i}(y_{i}). \tag{9}\]  
One thing that should be noted is that, different from other kinds of data for mining, a single log record contains limited words. Therefore, we believe that _our offline parser can be effectively trained upon a small training set_, which will be verified further and discussed in Section 6.
## 4 Discussion and Conclusions  
In this paper, the concept of trajectory generation where a quadrotor modifies its global trajectory and generates a local trajectory to avoid an obstacle in an unknown environment is covered and optimized. A new replanning method based on geometrical map information and closed-form formulation is proposed. This analytical solution can be computed much faster than other optimization methods because it does not conduct an iteration process. This closed-form method is free from local minima problems, and is thus safer and more robust than numerical methods. This method took 0.0000035 s for replanning, while other methods take about 0.03 s [11] or 0.00018 s [3] for replanning. This trajectory also considers dynamical and kinematic feasibility by using differential flatness and polynomial continuity. This trajectory generation method has strengths in computation speed, flying velocity, feasibility, non-collision, and resolution of local minima. It was tested both in simulation and in a real-world experiment, and it was proven that this algorithm is more robust and powerful than othersUsing a geometrical and analytical method, a safer and faster algorithm can be calculated allowing quadrotors to fly in messy and cluttered environments.  
[PERSON] and [PERSON] developed the idea and derived formulations. [PERSON] performed the experiment and analyzed the data with [PERSON] and [PERSON]. All wrote and revised the paper. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the MSIT (Ministry of Science and ICT), Korea, under the ITRC (Information Technology Research Center) support program (IITP-2021-2020-0-01460) supervised by the IITP (Institute of Information & Communications Technology Planning & Evaluation.  
Not applicable.  
The simulated data presented in this study are available on request from the second author.  
The authors declare no conflict of interest.
# Detection of Personality Traits Using Handwriting and Deep Learning
## 5 Experimental Results
### Results  
Using the standard indicators for evaluating machine learning models and their performance--true positive (TP), true negative (TN), false positive (FP), and false negative (FN)--we evaluated the classification performance using the following measures: accuracy, precision, recall, and F1 score. The performance measures were calculated using the following equations:  
\[\text{Accuracy}=(\text{TP}+\text{TN})/(\text{TP}+\text{TN}+\text{FP}+\text{ FN})\]  
\[\text{Precision}=\text{TP}/(\text{TP}+\text{FP})\]  
\[\text{Recall}=\text{TP}/(\text{TP}+\text{FN})\]  
\[\text{F1 score}=2\times(\text{Precision}\times\text{Recall})/(\text{Precision}+\text{Recall})\]  
The results obtained after training the networks are presented in Table 6.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Handwriting Feature** & **Type** & **Accuracy** & **Precision** & **Recall** & **F1 Score** \\ \hline \multirow{4}{*}{Baseline} & Rising & 0.96 & 0.89 & 1.00 & 0.94 \\  & Normal & 0.90 & 0.81 & 0.92 & 0.86 \\  & Falling & 0.91 & 0.84 & 0.91 & 0.88 \\ \hline \multirow{4}{*}{Slant} & Extreme left inclined & 0.89 & 0.72 & 0.81 & 0.76 \\  & Left inclined & 0.90 & 0.76 & 0.81 & 0.79 \\  & Vertical & 0.94 & 0.81 & 0.93 & 0.87 \\  & Right inclined & 0.90 & 0.73 & 0.79 & 0.76 \\  & Extreme right inclined & 0.89 & 0.70 & 0.88 & 0.78 \\ \hline \multirow{4}{*}{Spacing} & Narrow & 0.93 & 0.81 & 1.00 & 0.89 \\  & Normal & 0.90 & 0.85 & 0.88 & 0.87 \\  & Wide & 0.93 & 0.88 & 0.92 & 0.90 \\ \hline \multirow{4}{*}{Pressure} & Light & 0.96 & 0.90 & 1.00 & 0.95 \\  & Medium & 0.93 & 0.83 & 1.00 & 0.91 \\ \cline{1-1}  & Heavy & 0.91 & 0.85 & 0.92 & 0.88 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Handwriting feature prediction accuracies.  
Figure 6: Training progress for baseline analysis.  
To test the system, we asked 70 subjects aged between 18 and 30 to complete the MBTI questionnaire online at the web address [31] and to provide 1-2 pages of written text. Figure 7 shows part of the text written by one of the subjects. The recognition accuracy of the four Myers-Briggs indicators is presented in Table 7.  
From the results presented in Tables 6 and 7, it can be seen that there is a correlation between handwriting characteristics and Myers-Briggs indicators. Although the data used to train the neural network (extracted from the IAM database) are different from those used to test the application, the results obtained are very good.
## 1 Introduction
To secure more than 24 h of operating time for the iTDS-2, _i.e._, one full day of usage without any anxiety of running low on battery, we reduced the iTDS-2 power consumption by removing the microcontroller (MCU), which turned out to be the most power consuming block in the iTDS-1. Instead, we employed an ultra-low power field-programmable gate array (FPGA) and a built-in successive approximation register analog-to-digital converter (SAR-ADC), which replaced the MCU functions while consuming less power. Section 2 explains the iTDS-2 SoC architecture with circuit blocks for the wireless link and implementation of an arch-shaped dental retainer. Section 3 describes measurement results of each block and the overall system along with experiments done by two able-bodied participants for system evaluation, followed by concluding remarks in Section 4.
# Experimental Study on Dynamic Response Characteristics of RPC and RC Micro Piles in SAJBs
## 3 Experimental Results
### Bending Moment of Piles
#### 3.3.1 The Influence of Frequencies  
In order to discuss the influence of different frequency loads on the bending moment of piles, the bending moments of RPC and RC micro piles under sinusoidal wave loads with different frequencies (from case 1 to case 5) are shown in Figure 8. As seen in Figure 8, the bending moment distribution laws of piles along the buried depth are basically consistent. It is noteworthy that the bending moments of piles increase linearly from the pile head to the soil surface under different frequency sinusoidal waves.  
Figure 8: Bending moments of micro piles under different frequency sinusoidal wave loads shows **(a)** RPC micro pile, and **(b)** RC micro pile.  
Figure 7: The strain time history response of micro piles. **(a)** RPC micro pile. **(b)** RC micro pile.  
The bending moments of RPC and RC piles increase continuously with the rise in buried depth and reaches the maximum positive value at the buried depth of 0.637 m (6.4D) and 0.425 m (4.3D), respectively. Then, the bending moments of piles gradually diminish to 0 kN\(\cdot\)m or even a slight negative value as the further one increases the buried depth. It is shown that the influences of elastic modulus and ductility of pile body materials on the bending moment of piles are clear, and the peak bending moment and the corresponding buried depth increases to a certain extent, as the increase of the elastic modulus.  
In addition, it is also found that the bending moments of piles along the buried depth first increase and then decrease as the frequencies increase the sinusoidal waves. In this case, the bending moments of the RPC pile are 0.31 kN\(\cdot\)m, 0.38 kN\(\cdot\)m, and 0.92 kN\(\cdot\)m, respectively, under the frequencies of 1 Hz, 2 Hz, and 4 Hz, and the bending moments are 0.73 kN\(\cdot\)m and 0.30 kN\(\cdot\)m under harmonic loads with 8 Hz and 16 Hz, as drawn in the ellipse dashed in Figure 8a. However, for the RC pile, the bending moment also reaches the maximum under the frequency of 4 Hz, and the follow is that of the 2 Hz frequency load, as plotted in Figure 8b. It can be seen that the bending moments ordering of RPC and RC micro piles are different under the same frequency loads. The reason is that the first natural frequency of the RC pile is 3.62 Hz, and the bending moment response of the pile is significant when the dynamic load is close to the frequency of 2 Hz or 4 Hz. However, the first natural frequency of RPC is 4.74 Hz, so the bending moment responses are more significant under the frequency loads of 4 Hz and 8 Hz. In addition, compared to Figure 8a,b, the maximum bending moment of RPC and RC piles are 0.92 kN\(\cdot\)m and 0.76 kN\(\cdot\)m, respectively. It is noted that the bending moment of the RPC pile is greater than that of the RC pile, and increased by 21%.
## 5 Experimental Evaluation
### Functional Validation  
We have considered three different test cases to validate the functionalities and V2X communication interfaces of the OBU. For each test case, we have used a different combination of V2X communication protocols between the OBUs connected with one edge cloud, which consists of an Edge Geoserver and an Edge MQTT broker, as depicted in Figure 1. The schematic representation of the test cases is shown in Figure 7 and the operation of the OBUs in each test case is described below.
# A Novel Feeder Link Handover Strategy for Backhaul in LEO Satellite Networks
## 3 System Model
### LEO Network Scenario  
As illustrated in Figure 1, we present an LEO satellite network scenario where the satellites offload traffic from UEs worldwide and backhaul them to GSs through feeder links. Satellites are denoted as \(S_{n}|n=1,2\ldots,N\), where \(N\) represents the number of satellites in the network. Each satellite constantly moves along a circular orbit around the Earth and communicates with four adjacent satellites (two in-orbit satellites and two inter-orbit satellites) via ISLs. GSs are denoted as \(G_{m}|m=1,2\ldots,M\), where \(M\) represents the number of GSs. Considering the geographical and political constraints, all GSs are deployed in a limited area, as shown by the blue circle in the figure. All GSs connect to the same terrestrial network via ground links. The GS is simultaneously covered by multiple visible satellites, as shown by the dotted circle in the figure. Each GS can establish \(L_{g}\) feeder links with visible satellites to offload backhaul traffic to terrestrial networks, while each satellite can connect with \(L_{s}\) GSs at most. \(L_{g}\) and \(L_{s}\) are determined by the number of antennas equipped with satellites and GSs.  
The satellite network is time-varying since the LEOs move at high speeds. The GSs must continuously switch their feeder links to suitable visible satellites to avoid interruptions of ongoing backhaul services. Virtual topology (VT) is adopted to describe the network dynamics. The VT method envisions the time-varying topology as a discrete-time network model and assumes a fixed topology in each time slot [28]. The handover decisions are only required at the beginning of each time slot \(t=1,2\ldots,T\).  
As the red lines show in Figure 1, the feeder links and ISLs near the GSs carry a large amount of traffic since the global backhaul traffic converges to the GSs located in the limited area. Network congestion and packet loss frequently occur in the network around the GSs. Expanding the link capacity and load balancing has been widely adopted to mitigate the congestion in terrestrial networks. In satellite networks, a handover strategy based on maximizing feeder link quality can improve the channel capacity and mitigate congestion. In addition, the handover strategy affects the traffic distribution and causes the ISLs become the backhaul bottlenecks, so it is also essential to consider the ISL capacity in handover decisions.