esa-sceva/satcom-chunk-collection huggingface dataset:
# Opportunistic and On-Demand Network Coding-Based Solutions for LPWAN Forwarding
## 6 Implementation and Evaluation  
### Implementation Detail  
We evaluated the proposed method performance using a simulator program that is built based on the LoRaSim [5]. This simulator mimics LoRa communication behavior, including its communication range and collision behavior through LoRa design [5; 35; 37].  
For the evaluation, we used three data-rate scenarios: LoRaWAN slow data-rate (SDR), fast data-rate (FDR), and adaptive data-rate (ADR). Each scenario has different configurations of bandwidth (BW) and spreading factor (SF), as shown in Table 1. While both SDR and FDR apply static configurations, ADR scenario calculates the best SF to transmit the packets efficiently (i.e., utilize faster transmission). These different configurations affect not only the transmission range but also transmission time. According to communication range calculation, the transmission range in the SDR scenario is estimated to be up to approximately 8900 m, with a transmission time \(T_{frame}\) of 1712.13 ms. Conversely, FDR may have a smaller coverage that is approximately 1100 m. However, the transmission time is only 14.14 ms.  
The end-device packet is generated following a Poisson distribution with an average inter-packet time \(lambda=16.7\) m. In our simulations, the transmissions from the end-devices are not active during the whole simulation run-length time. End-devices are active only for the first \(T\) long interval after the start, leaving a short additional time for the relay to forward the remaining packet. During this \(T\), the feedback message is transmitted periodically to the relay to start forwarding the missing packets. We performed 10 simulation for each evaluation and took the average to provide the results.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **SDR** & **FDR** & **ADR** \\ \hline Transmission Power (dBm) & 14 & 14 & 14 \\ Carrier Frequency (MHz) & 868 & 868 & 868 \\ Bandwidth (kHz) & 125 & 500 & 125 \\ Spreading Factor & 12 & 7 & 7–12 \\ Coding Rate & 4/5 & 4/5 & 4/5 \\ Payload Size (byte) & 20 & 20 & 20 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Long-range radio (LoRa)WAN configurations for different data-rate scenarios.  
Figure 5: Network coding could recover linearly independent coded packets from different relays.
## 3 Background
### Propulsion System Technologies Overview
#### 3.3.2 Electrical Propulsion Systems
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Product** & **Manufacturer** & **Thrust** & **Power** & **Specific Impulse** & **Propellant** & **Status** \\ \hline \multirow{2}{*}{PPTCUP} & Mars Space, Southampton, UK & \multirow{2}{*}{40 \(\upmu\)N} & \multirow{2}{*}{2 W} & \multirow{2}{*}{655 s} & PTFE & \multirow{2}{*}{TRL6} \\  & and Clyde Space, Glasgow, UK & & & & & \\ \hline NanoSat PPT & Mars Space and Clyde Space & 90 \(\upmu\)N & 5 W & 640 s & PTFE & \multirow{2}{*}{TRL5} \\ \hline \multirow{2}{*}{\(\upmu\)CAT} & GWU, Washington, DC, USA, & \multirow{2}{*}{1-50 \(\upmu\)N} & \multirow{2}{*}{2-14 W} & \multirow{2}{*}{2500–3000 s} & Titanium & \multirow{2}{*}{TRL7} \\  & and USNA, Annapolis, MD, USA & & & & & \\ \hline BmP-220 & Busek & 20 \(\upmu\)N-s & 1.5 W & 536 s & PTFE & \multirow{2}{*}{TRL5} \\ \hline MPACS & Busek & 80 \(\upmu\)N-s & 10 W & 827 s & PTFE & \multirow{2}{*}{TRL8} \\ \hline P4 RFT & Phase Four, El Segundo, CA, USA & 1–15 mN & 3 W & 500–1000 s & PTFE & \multirow{2}{*}{TRL9} \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Performance Data of off-the-shelf Pulse Plasma Thrusters and Vacuum Arcs [23; 38]. Figure 20: P4 RFT Cross Sectional Diagram [38]. The Busek 200 (Figure 22) however is aimed at much larger CubeSats with increased electrical capacity. Power consumption of this component ranges between 100-300 W [35]. Miniaturized Hall effect thrusters are under development by the University of Toronto's Space flight Laboratory (Figure 22) to consume sub 200 W values, but these thrusters are still far too electrically demanding for the current capabilities of a 3U CubeSat.
## 3 O-MAC Protocol Design Goals and Architecture
### Interior Communication
#### 3.1.2 Preamble Tradeoff Validation Model
\begin{table}
\begin{tabular}{c c} \hline \hline
**Notations** & **System Definitions** \\ \hline C & Collision on the channel \\ C\({}_{\text{drift}}\) & Clock drift \\
**E\({}_{\text{ct}}\)** & Energy consumed during the channel access time \\
**E\({}_{\text{sp}}\)** & Energy consumed by the short preamble \\
**E\({}_{\text{svn}}^{2}\)** & Energy consumed by synchronization at both the transmitter and receiver sides \\ i & Starting number of the short preamble \\ N & Ending number of the short preamble \\
**S\({}_{\text{i}}\)** & Short preamble \\ \(\dot{\text{s}}\) & Symbol sent over the channel to determine the availability of the channel \\ (**U\({}_{\text{bt}}\))\({}^{2}\)** & Unit backup time to avoid deadlock at the channel \\ \(\Delta\varphi\) & Size of the short preamble \\ \(\Delta\text{p}\) & Average energy consumed by carrier sensing \\ \(\Delta\varphi\) & Initial backup time \\ \(\Delta_{\text{t}}\) & Time consumed for sending the short preamble \\ \(\Delta\textbf{v}^{2}\) & Short preamble speed \\ \(\dot{\text{t}}\) & Check time \\ \(\beta\) & Energy consumed by the source node \\ \(\boldsymbol{\delta}\) & Energy consumed by the particular node (principal or backup node) \\ \(\boldsymbol{\mu}\) & Nature of the location \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Notations and system definitions. Furthermore, to confirm the analytical results shown in Table 4, we have programmed and simulated the preamble-enabled protocols using the network simulator-3 (NS3). The simulation results were identical to those obtained through the analytical calculation. Based on the simulation results, we depict the energy consumed for the channel access, short preamble and data forwarding in Figure 5. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Parameters**} & \multicolumn{4}{c}{**Preamble-Enabled MAC Protocols**} \\ \cline{2-6}  & **WiseMAC** & **B-MAC** & **X-MAC** & **O-MAC** & **IEEE802.15.4** \\ \hline Energy consumed to & \(0.886\times 10^{-6}\) & \(0.896\times 10^{-6}\) & \(0.839\times 10^{-6}\) & \(0.619\times 10^{-6}\) & \(0.936\times 10^{-6}\) \\ access the channel & Joules & Joules & Joules & Joules & Joules \\ \hline Energy consumed to & \(1.832\times 10^{-6}\) & \(1.842\times 10^{-6}\) & \(1.659\times 10^{-6}\) & \(1.152\times 10^{-6}\) & \(1.805\times 10^{-6}\) \\ send the short preamble & Joules & Joules & Joules & Joules & Joules \\ \hline Energy consumed to & \(0.964\times 10^{-1}\) & \(0.962\times 10^{-1}\) & \(0.928\times 10^{-1}\) & \(0.821\times 10^{-1}\) & \(0.976\times 10^{-1}\) \\ forward 5 MB of data & Joules & Joules & Joules & Joules & Joules \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Time and energy consumption of MAC protocols. Figure 5: **(a)** Average energy consumed to access the channel; **(b)** Average energy consumed to forward the short preamble and **(c)** Average energy consumed to forward 5 MB of data (Joules).
## Appendix A Simulation Environments
### Checking the Quality of the Simulation Environment Base Model
#### a.4.2 Measuring If a Base Model Captures the Variance in the Data  
We measure how well the fitted base models captured (1) whether or not the user brushed and (2) the variance of the brush time when the users did brush. To measure point (1) for each user model \(i\), we calculate the statistic:  
\[U_{i}:=\frac{1}{T}\sum_{t=1}^{T}\bigg{(}\frac{\mathbb{I}[D_{i,t}>0]-\widehat{ \mathbb{E}}[\mathbb{I}[D_{i,t}>0]|S_{i,t}]}{\widehat{\text{Var}}[\mathbb{I}[D_{ i,t}>0]|S_{i,t}]}\bigg{)}^{2} \tag{1}\]  
where \(\widehat{\mathbb{E}}[\mathbb{I}[D_{i,t}>0]|S_{i,t}]=1-\text{sigmoid}(S_{i,t}^ {T}w_{i,b})\) and \(\widehat{\text{Var}}[\mathbb{I}[D_{i,t}>0]|S_{i,t}]=\widehat{\mathbb{E}}[ \mathbb{I}[D_{i,t}>0]|S_{i,t}]\cdot\text{sigmoid}(S_{i,t}^{T}w_{i,b})\).  
To measure point (2) for each user model \(i\), we calculate the statistic:  
\[U_{i}:=\frac{1}{\sum_{t=1}^{T}\mathbb{I}[D_{i,t}>0]}\sum_{t=1}^{T}\mathbb{I}[D _{i,t}>0]\bigg{(}\frac{D_{i,t}-\widehat{\mathbb{E}}[D_{i,t}|S_{i,t},D_{i,t}>0] }{\widehat{\text{Var}}[D_{i,t}|S_{i,t},D_{i,t}>0]}\bigg{)}^{2} \tag{2}\]  
Definitions of \(\widehat{\mathbb{E}}[D_{i,t}|S_{i,t},D_{i,t}>0]\) and \(\widehat{\text{Var}}[D_{i,t}|S_{i,t},D_{i,t}>0]\) for the nonzero component of each model class are specified in Table 11. For a user model to capture the variance in the data, \(U_{i}\) should be close to 1. We calculate the empirical mean \(\overline{U}=\frac{1}{N}\sum_{i=1}^{N}U_{i}\) and standard deviation \(\overline{\sigma_{U}}=\text{std}(U_{i})\), and the approximate 95% confidence interval is \(\overline{U}\pm 1.96\times\frac{\overline{\sigma_{U}}}{\sqrt{N}}\).  
Results are in Table 12. We can see that after computing the statistic for each user, the confidence interval is close to 1. We understand that the confidence intervals do not contain 1, which implies that we are overestimating the amount of variance in \(\mathbb{I}[D>0]\) and underestimating the amount of variance in \(D|D>0\). In the future, we hope to improve upon this statistic by considering nonlinear components in our base models.
# Blood Pressure Monitoring Based on Flexible Encapsulated Sensors
## 4 Blood Pressure Measurement
### Experimental Method  
A blood pressure measurement device was developed and used along with a commercial blood pressure monitor to measure the blood pressure of 20 volunteers in different states (10 measurements at rest and 10 measurements taken after running for 5 min and resting for 10 min). The specific method involved the use of a commercial blood pressure monitor to measure the systolic pressure and diastolic pressure (_SYS_ and _DIA_, respectively) of the subject's left wrist while using an encapsulated sensor to collect the pulse waveforms from the subject's right wrist to extract the peak and valley values and other pulse wave parameter features. Based on Formulas (2) and (3), the blood pressure systolic and diastolic values were calculated using the arterial tonometry method. Then, all the extracted features were input into a machine learning regression model. The values measured by the commercial blood pressure monitor were considered as the true blood pressure values, while those calculated by the self-made blood pressure device were considered as the predicted values. To test the effectiveness of the blood pressure measurement method combining arterial tonometry and pulse wave parameter, this study calculated the blood pressure prediction error of the arterial tonometry method, the pulse wave parameters method, and the method combining arterial tonometry and pulse wave parameters.  
The blood pressure prediction error refers to the difference between the true blood pressure value and the predicted value. To evaluate the prediction accuracy, commonly used regression prediction evaluation metrics were used, including the mean absolute error (_MAE_) and standard deviation (_SD_).  
\[MAE=\frac{1}{n}{\sum\limits_{i=1}^{n}{{{\left|{{y_{i}}-{{\hat{y_{i}}}}}\right|}}}} \tag{6}\]  
\[{z_{i}}={y_{i}}-{{\hat{y_{i}}}} \tag{7}\]  
\[{z_{mean}}=\frac{1}{n}{\sum\limits_{i=1}^{n}{{z_{i}}}} \tag{8}\]  
\[SD=\sqrt{{\sum\limits_{i=1}^{n}{{{\left({{z_{i}}-{z_{mean}}}\right)}^{2}}}}} \tag{9}\]  
In the equations, n represents the number of samples, \(y_{i}\) represents the true blood pressure value, \({{\hat{y_{i}}}}\) represents the predicted blood pressure value, \({{z_{i}}}\) is the difference between the predicted and true values of blood pressure for the ith sample, and \({z_{mean}}\) is the average of \({{z_{i}}}\).
###### Abstract
The use of mechanisms based on machine learning [18] (ML) and deep learning (DL) are efficient solutions that allow the detection of cyberattacks after the application of classic solutions such as the Intrusion Detection System (IDS), e.g., Snort, Suricata, or the Security Information and Event Management (SIEM) systems. Additional detection solutions based on ML/DL supplement the classic mechanisms that allow the detection of unknown and unauthorized operations in cyberspace. They may enable additional advanced safety data analysis, thus allowing us to find non-obvious connections. It is possible to discover valuable relation patterns between, e.g., adversaries and attacks, which are originally undetectable in the dataset acquired from the IT system [19; 20]. This study addresses the challenges of anomaly detection by focusing on the potential of autoencoders and their enhancements to improve detection performance. While autoencoders have shown promise, there remains a need for a thorough evaluation of how architectural adjustments, training strategies, and loss functions affect their ability to detect anomalies in complex data environments. The motivation behind this work stems from the need to enhance anomaly detection systems by systematically evaluating the performance of neural network models in a practical, network-focused context. While prior research has introduced numerous variationsof different architectures, their direct application and comparison in the context of network anomaly detection require further exploration. We systematically compare multiple autoencoder architectures across various parameter settings, including standard and Variational Autoencoders. This analysis highlights their strengths, limitations, and suitability for network anomaly detection. We design and implement a robust, scalable experimental environment to evaluate autoencoders as anomaly detectors. This framework supports real-time network monitoring and integrates seamlessly with Big Data processing pipelines. By focusing on the practical application and comparison of autoencoder-based models, this study advances the understanding of how deep learning can be effectively leveraged for anomaly detection in complex environments. The insights gained from our work contribute to developing more robust, efficient, and scalable solutions for network security.
## 4 Model Verification
### Constant Radius Cornering Tests
#### 4.1.2 Yaw Rate Gain
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Designation** & **Symbol** & **Unit** & **Value** \\ \hline Front axle weight & \(W_{f}\) & N & 8829 \\ Rear axle weight & \(W_{r}\) & N & 5886 \\ Front wheel cornering stiffness & \(C_{af}\) & N/rad & 78,855.7 \\ Rear wheel cornering stiffness & \(C_{ar}\) & N/rad & 71,687 \\ Pneumatic trail & \(P\) & m & 0.018754 \\ Wheelbase & \(L\) & m & 2.8 \\ Steering angle per unit lateral force & \(q\) & \({}^{\circ}\)/N & \(1.194\times 10^{-4}\)(f)/\(1.0498\times 10^{-4}\)(r) \\ Roll steer coefficient & \(\varepsilon\) & \({}^{\circ}\)/\({}^{\circ}\) & \(-0.117213\)(f)/\(0.02272\)(r) \\ Roll angle & \(\emptyset\) & \({}^{\circ}\) & 0.7047 \\ Lateral acceleration & \(A_{y}\) & G & 0.2521 \\ Camber stiffness & \(C_{y}\) & N/rad & 7885.57(f)/7168.7(r) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Full-vehicle parameters. Figure 7: Relationship between yaw rate gain and cornering speed for target vehicle. When the vehicle performs cornering, the members of the suspension system may undergo lateral deformation. Since the understeer effect is directly correlated with the steering angles of the front and rear axles, respectively, the effect of the compliance steer of the members resulting from the lateral force described above on the understeer can be quantified as:  
\[K_{\textit{ifcs}}=q_{f}W_{f}-q_{r}W_{r} \tag{4}\] \[K_{\textit{ifcs}}=0.0001194\times 9417.6-0.00010498\times 6278.4=0.4 65\,(^{\circ})\]
# Experimental Test of Continuous Wave Frequency Diverse Array Doppler Radar +
## 2 Data Collection I: Far-Field Beampattern
### Expected Behavior at Baseband  
We expect the same far-field magnitude behavior seen at RF to also exist at baseband. To show this, we begin by rearranging Equation (1) as follows  
\[|F(t;R,\theta)|=\bigg{|}e^{j2\pi\beta t}\sum_{n=0}^{N-1}e^{j2\pi\Delta f(t-R_{ n}/c)}\bigg{|}. \tag{3}\]  
The magnitude of Equation (3) can be simplified using \(|z_{1}z_{2}|=|z_{1}||z_{2}|\) (where \(z_{1},z_{2}\) are complex numbers), resulting in the simplified expression  
\[\begin{split}|F_{RF}(t;R,\theta)|&=\bigg{|}e^{j2 \pi\beta t}\bigg{|}\bigg{|}\sum_{n=0}^{N-1}e^{j2\pi\Delta f(t-R_{n}/c)}\bigg{|} \\ &=\bigg{|}\sum_{n=0}^{N-1}e^{j2\pi\Delta f(t-R_{n}/c)}\bigg{|}. \end{split} \tag{4}\]  
\begin{table}
\begin{tabular}{c c c c} \hline
**Collection** & \(f_{0}\) **(GHz)** & \(N\) & \(R_{ff}\) **(m)** \\ \hline I & 2 & 3 & 0.2998 \\ II & 2 & 3 & 0.2998 \\ III & 2 & 3 & 0.2998 \\ IV & 5.9 & 2 & 0.0254 \\ \hline \end{tabular}
\end{table}
Table 3: Far-field distance of each collection calculated using Equation (2).  
The magnitude of the baseband signal when down converted at the carrier frequency \(f_{0}\) is  
\[\begin{split}|F_{bb}(t;R,\theta)|&=\left|e^{2\pi f_{0} t}\right|\left[\sum_{n=0}^{N-1}e^{2\pi\Delta f(t-R_{n}/c)}\right]\cdot e^{-2\pi f_{0} t}\right|\\ &=\left|\sum_{n=0}^{N-1}e^{2\pi\Delta f(t-R_{n}/c)}\right|.\end{split} \tag{5}\]  
Thus, it can be seen through comparison of Equations (4) and (5) that \(|F_{RF}(t;R,\theta)|=|F_{bb}(t;R,\theta)|\).
## 2 Design and Simulation Results  
Figure 1a,c shows the front and rear-view geometry and dimensions of the reference UWB antenna while Figure 2a,c shows the front and rear view prototype of the conventional UWB antenna designed [35] and termed as a reference UWB antenna. This antenna was designed on RogersRO5880 substrate with a thickness of 31 mils, a relative dielectric constant of 2.2, and a loss tangent of 0.0009.  
A modification was carried out for the reference UWB antenna in order to efficiently reject the interfering frequency bands that fall within the UWB frequency band. Figure 1b,c show the front and rear-view geometry and dimensions of the proposed quintuple band-notched UWB antenna. The prototype of the proposed antenna is also fabricated as shown in Figure 2b,c. The dimensions of the reference and proposed antenna is listed in Table 1.  
Figure 1: The geometry of (**a**) the reference antenna (front view); (**b**) the proposed antenna (front view); and (**c**) the reference and proposed antenna (rear view).
# MB-OFDM-UWB Based Wireless Multimedia Sensor Networks for Underground Coalmine: A Survey
## 5 Discussion for Future Research Directions  
While we expect smart coalmines to continue to evolve in the future, it is necessary to consider the most robust and reliable technology to facilitate the underground communication in wireless multimedia sensor network. In the sections above, we highlight the superiority of MB-OFDM-UWB in data rate, normalized energy consumption, robustness against fading, immunity to multipath and localization performance, and stress choosing it in underground WMSNs as it performs better than ZigBee and Wi-Fi.  
It should be stressed, however, that underground mines have many different types of communication such as ground, in-mine, through-the-earth, and disaster communications, and it is difficult to present a single-structure system that can provide solutions to all the applications simultaneously. Except for the research of applying MB-OFDM-UWB into underground coalmine WMSNs, we are still paying our attention to the hybrid schemes which will use multiple short-range wireless transmission techniques.  
The emerging standard IEEE 802.11 ac [74] has brought some new features like providing high-throughput to the 802.11 family, but the industrial applications of it, especially for coalmine WMSN, are almost blank. In addition, the techniques of multiple-input multiple-output (MIMO) facilitate the 802.11 ac with higher speed. Thus, combining 802.11 ac with UWB maybe a good choice to establish a hybrid WMSN that supports multimedia information transmission since both of them may deliver gigabit wireless. In addition, some antenna selection techniques such as using multiple antennas can also be utilized in UWB to solve the problem of data rates, transmission range, and interference [75].  
As Cross-layer design is emphasized in Section 3.4, the latest findings from different layers, e.g., wave propagation in the specific type of mine tunnel, are related to the system performance or complexity. The UWB technology is different to the classical narrowband communication in many aspects and thus there are still underutilized potentialities in underground mine applications despite recent drawbacks caused by the market meltdown and delays in delivering new products. In addition, some new concepts like mashup middleware [76], internet of things (IOT) and cloud computing [77] are introduced into the sensor network for underground coalmines which help to increase the operational efficiency and improve the overall level of mining safety.
# A Low-Complexity Ordered Statistics Decoding Algorithm for Short Polar Codes
###### Abstract  
In this paper, we propose a low-complexity ordered statistics decoding (OSD) algorithm called threshold-based OSD (TH-OSD) that uses a threshold on the discrepancy of the candidate codewords to speed up the decoding of short polar codes. To determine the threshold, we use the probability distribution of the discrepancy value of the maximal likelihood codeword with a predefined parameter controlling the trade-off between the error correction performance and the decoding complexity. We also derive an upper-bound of the word error rate (WER) for the proposed algorithm. The complexity analysis shows that our algorithm is faster than the conventional successive cancellation (SC) decoding algorithm in mid-to-high signal-to-noise ratio (SNR) situations and much faster than the SC list (SCL) decoding algorithm. Our addition of a list approach to our proposed algorithm further narrows the error correction performance gap between our TH-OSD and OSD. Our simulation results show that, with appropriate thresholds, our proposed algorithm achieves performance close to OSD's while testing significantly fewer codewords than OSD, especially with low SNR values. Even a small list is sufficient for TH-OSD to match OSD's error rate in short-code scenarios. The algorithm can be easily extended to longer code lengths.  
channel coding; polar codes; ordered statistics decoding; short codes +
Footnote †: journal: Applied Sciences
# A Review on the Thermal Hazards of the Lithium-Ion Battery and the Corresponding Countermeasures
## 5 Management and Countermeasures
### Safety Devices within Battery
#### 5.1.1 Safety Vent  
During the operation of the LIB, especially under abusive conditions, it is common to see that gases will be released inside battery. Thereby, the safety vent was proposed in response to the continuous increase of pressure inside battery, allowing the escape of excessive gases. Generally, a safety vent is composed of a gasket containing a puncture film and a spike. The spike is located at the top of the battery, while the gasket lies below it. Once the pressure inside battery builds up, the gasket squeezes upward and deforms, enabling the puncture film punctured by the spike, therefore the dangerous rupture of battery casing can be terminated [190; 191; 192; 193]. Moreover, safety vents can be designed to operate at pre-set internal battery temperatures to meet the requirement of practices.