esa-sceva/satcom-chunk-collection huggingface dataset:
# Analysis of the Motion Sickness and the Lack of Comfort in Car Passengers
## 2 Comfort Evaluation Methods
The way to evaluate it is also defined in ISO 2631 Standard by a \(w_{f}\) filter (see Figure 1) and Equation (3), where \((a_{i}(t),w_{f})\) is the acceleration in the \(i\)-th axis weighted with \(w_{f}\) filter in m/s\({}^{2}\). ISO 2631 only provides guidelines for the interpretation of the \(MSDV\) in the vertical direction, \(MSDV_{z}\). However, in [35; 39; 40; 41], [PERSON] justified that this variable may not be optimum for the prediction of motion sickness. Moreover, he experimentally demonstrated that motion sickness dose values are similar in longitudinal and lateral axes when calculated using the frequency weighting in current standards and that, at frequencies less than 0.1 Hz, fore-and-aft acceleration is greater than lateral acceleration. Based on those studies, he proposed an alternative to the \(w_{f}\) ISO standard filter. This filter is represented in Figure 1 as \(w_{f\; Griffin}\). Moreover, [PERSON] provided an interpretation of the \(MSDV\) for the lateral direction in [6]. So, in this paper, \(MSDV\) in all directions is considered in order to analyze the effect on the passenger of each of the axes, and thus conclude if they could be combined as in the case of the \(a_{v}\) variable or if, on the contrary, some contributions could be neglected. \[MSDV_{i}=\left(\int_{0}^{T}(a_{i}(t),w_{f})^{2}\;dt\right)^{1/2} \tag{3}\]  
Power Spectral Density (PSD) of the acceleration data could be another way of analyzing the lack of comfort. It has been mainly used when evaluating vehicle dynamics and suspensions as it is done in [45] for motorcycles, in [46] for rail vehicles, and in [47] for tractors. PSD represents the power content across the frequency spectrum and so it can be very useful in order to determine the power distribution at certain frequencies [48]. One of the main methods used to estimate the PSD is Welch's Method [49]. Furthermore, in order to precisely analyze the details of the journey, time content can be added to the PSD, resulting in what it is known as a spectrogram [50]. Briefly, [PERSON]'s method is based on dividing the given data sequence \(x[0],x[1],\ldots,x[N-1]\), where \(N\) is the number of acquired points, into \(K\) segments of \(M\) points with a shift of \(S\) between segments. Segment 1 : \(x[0],x[1],\ldots,x[M-1]\)  
Segment 2 : \(x[S],x[S+1],\ldots,x[M+S-1]\)  
Segment \(K\) : \(x[N-M],x[N-M+1],\ldots,x[N-1]\)  
For each segment, a windowed Discrete Fourier Transform (DFT) is computed at some frequency \(\
u=i/M\) with \(-(M/2-1)\leq i\leq M/2\):  
\[X_{k}(\
u)=\sum_{m}x[m]w[m]exp(-j2\pi\
u m) \tag{4}\]  
where \(m\) varies from \((k-1)S\) to \(M+(k-1)S-1\) and \(w[m]\) is the window function (Hann, Hamming, Blackman or Kaiser-Bessel, for example). Then, for each segment, the modified periodogram value is generated from the DFT:  
\[P_{k}(\
u)=\frac{1}{W}|X_{k}(\
u)|^{2} \tag{5}\]  
where  
\[W=\sum_{m=0}^{M}w^{2}[m] \tag{6}\]Finally, the periodogram is averaged in order to obtain [PERSON]'s estimate of the PSD:  
\[S_{x}(v)=\frac{1}{K}\sum_{k=1}^{K}P_{k}(v) \tag{7}\]
## 3 Results
### Effects of Cultivation and Effluent Use on Soil Properties
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Parameter 1** & **Control** & **Pasture** & **Sorghum** \\  & **(0.30–0.60 m)** & **(0.30–0.60 m)** & **(0.30–0.60 m)** \\ \hline pH & 7.62 \(\pm\) 0.19 b ** & 7.35 \(\pm\) 0.16 ab  & 7.31 \(\pm\) 0.07 a  \\ EC (dS/m) & 7.83 \(\pm\) 1.68 & 4.67 \(\pm\) 0.08 & 6.94 \(\pm\) 2.40 \\ SV (g/100 g soil) & 88.00 \(\pm\) 4.00 a  & 95.00 \(\pm\) 1.15 ab  & 109.00 \(\pm\) 12.22 b  \\ CO3\({}^{2-}\) (meq/L) & 0.00 \(\pm\) 0.00 & 0.00 \(\pm\) 0.00 & 0.00 \(\pm\) 0.00 \\ HCO3\({}^{-}\) (meq/L) & 3.20 \(\pm\) 0.11 & 3.53 \(\pm\) 0.34 & 2.74 \(\pm\) 1.04 \\ Cl\({}^{-}\) (meq/L) & 23.39 \(\pm\) 29.21 & 6.86 \(\pm\) 0.00 & 36.26 \(\pm\) 32.09 \\ SO4\({}^{2-}\) (meq/L) & 74.34 \(\pm\) 20.00 & 57.73 \(\pm\) 5.59 & 50.21 \(\pm\) 19.34 \\ Na (meq/L) & 24.22 \(\pm\) 9.23 & 12.13 \(\pm\) 1.02 & 21.21 \(\pm\) 10.17 \\ Ext-K (mg/kg) & 38.67 \(\pm\) 5.05 b  & 17.28 \(\pm\) 1.39 a  & 29.62 \(\pm\) 11.14 ab  \\ Ca (meq/L) & 24.91 \(\pm\) 2.54 a  & 29.29 \(\pm\) 1.75 ab  & 29.96 \(\pm\) 2.54 b  \\ Mg (meq/L) & 13.13 \(\pm\) 3.03 & 9.43 \(\pm\) 4.98 & 8.42 \(\pm\) 3.09 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Final soil properties at sub-surface (0.30–0.60 m). \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Parameter 1** & **Control** & **Pasture** & **Sorghum** \\  & **(0–0.30 m)** & **(0–0.30 m)** & **(0–0.30 m)** \\ \hline pH & 7.57 \(\pm\) 0.16 & 7.28 \(\pm\) 0.07 & 7.37 \(\pm\) 0.19 \\ EC (dS/m) & 9.11 \(\pm\) 5.16 & 5.67 \(\pm\) 0.90 & 10.21 \(\pm\) 6.00 \\ SV (g/100 g soil) & 92.0 \(\pm\) 2.0 & 116.00 \(\pm\) 14.42 & 115.00 \(\pm\) 17.01 \\ CO3\({}^{2-}\) (meq/L) & 0.26 \(\pm\) 0.45 & 0.00 \(\pm\) 0.00 & 0.00 \(\pm\) 0.00 \\ HCO3\({}^{-}\) (meq/L) & 3.53 \(\pm\) 0.68 & 3.01 \(\pm\) 0.30 & 3.72 \(\pm\) 1.02 \\ Cl\({}^{-}\) (meq/L) & 50.63 \(\pm\) 77.57 & 9.80 \(\pm\) 0.98 & 27.05 \(\pm\) 13.
## 4 Performance Analysis of QoS Metrics  
This section conducts the analysis of different Zigbee QoS metrics--power consumption, packet delivery ratio (PDR), network lifetime, link quality based on RSSI, latency, and throughput and depict an overview, explanation, and evaluation of the performances based on deployment scenario and performance parameters, such as data encryption, transmission power, transmission distance, baud rates, and many hops. At first, this work analyzes the PDR and power consumption to find the optimal transmission power level. Energy consumption analysis was conducted for various transmission power levels, which led to the formulation of the equations to find the node lifetime from the current consumption captures of a data packet transmission. The link quality evaluation analyzes the variation of RSSI with distance by varying the different parameters, such as the number of hops, deployment scenarios--indoors and outdoors. It also rechecks and verifies if the RSSI performance maintains the same trend as PDR with the optimized transmission power level. Finally, by setting the transmission power to the optimal level, throughput and latency are analyzed to find the optimum baud rate, deployment scenario.
# A Deep-Learning-Based Secure Routing Protocol to Avoid Blackhole Attacks in VANETs +
## 3 System Model
### The Proposed DLSR Protocol
#### 3.3.2 Route Reply Process
Here, we introduce the penalty term, as follows:  
\[\mathtt{P}_{i}=\bar{\mathtt{P}}\times\mathtt{blackhole}, \tag{8}\]  
where \(\bar{\mathtt{P}}\) is a large positive constant, and \(\mathtt{blackhole}\) is a binary variable (1 if a blackhole node is detected, 0 otherwise). Thus, the updated fitness function with a penalty term can be expressed as  
\[\mathtt{F}_{\mathtt{P}_{i}}=\mathtt{F}_{i}+\mathtt{P}_{i}, \tag{9}\]  
where \(\mathtt{F}_{i}\) is the original fitness function, and \(\mathtt{P}_{i}\) is the penalty term. The idea is to make the fitness value higher than a normal node, effectively discouraging the selection of routes with blackhole nodes, then go to **Step 7.5**. **Step 7.5:** The best route is selected by choosing the minimum fitness function value for data transmission that can be mathematically formulated as  
\[i^{*}=\arg\min_{i\in\mathtt{R}}\mathtt{F}_{i}, \tag{10}\]  
where \(\mathtt{F}_{i}\) indicates the fitness function value of the \(i^{th}\) route. Then, go to **Step 7.6**. * **Step 7.6:** CHi records the sender's ID, updates the routing table, and sends the packet to \(S\) by using unicast and increasing the number of hop counts, then goes to **Step 7.7**. * **Step 7.7:** If \(S\) receives RREP, then it goes to **Step 8**. Otherwise, wait until \(S\) receives the RREP packet. * **Step 8: Data Transmission Process: Source Received RREP and Preparing for Data Transmission** The data transmission is sent by \(S\) to \(D\) based on the routing table, which is determined in **Step 1** to **Step 7**. The routing table can be summarized in Table 4, where PN is a previous node, NX is the next node, F is the fitness function, NID is the node ID, SID is the source ID, DID is the destination ID, BID is the broadcast ID, Sseq is the source sequence, Dseq is the destination sequence, and H is the hop count, respectively. Therefore, based on the explanation above, the summary of the packet list involved in the proposed DLSR protocol can be seen in \[\min_{\{x_{1},x_{2},x_{3}\}}\mathrm{F}_{i} =x_{1}\mathrm{E}_{i}+x_{2}\mathtt{Dist}_{i}+x_{3}\mathtt{H}_{i},\]0
# Hyperspectral Data: Efficient and Secure Transmission
###### Abstract  
Airborne and spaceborne hyperspectral sensors collect information which is derived from the electromagnetic spectrum of an observed area. Hyperspectral data are used in several studies and they are an important aid in different real-life applications (e.g., mining and geology applications, ecology, surveillance, etc.). A hyperspectral image has a three-dimensional structure (a sort of datacube): it can be considered as a sequence of narrow and contiguous spectral channels (bands). The objective of this paper is to present a framework permits the efficient storage/transmission of an input hyperspectral image, and its protection. The proposed framework relies on a reversible invisible watermarking scheme and an efficient lossless compression algorithm. The reversible watermarking scheme is used in conjunction with digital signature techniques in order to permit the verification of the integrity of a hyperspectral image by the receiver.  
digital watermarking; hyperspectral data; lossless compression; efficient transmission +
Footnote †: journal:
## 2 Modeling of Receiver Pulsed Environment Interference Effect Based on BER  
### Signal Processing in Communication Receiver  
Although the communication system has a variety of division modes, such as frequency band, modulation mode, coding mode, etc., and different types of communication systems use different technologies, they all have the basic structure of the receiver [39] as shown in Figure 1. The characteristics of its components need to be determined according to the performance of the communication system. The flow of signal processing is that the functional signal enters the receiver through the receiving antenna and band-pass filter together with the interference signal. After mixed processing, the processed signal is converted to the baseband through down-conversion circuit, which include low-pass filter and sampling decimator.  
Now let us consider interference signals from the electromagnetic environment. For the interference signal, the out-of-band signals are filtered out after passing through the antenna and band-pass filter due to the frequency selection characteristics. The pulse signal near the working frequency band of the antenna or the band-pass filter is coupled into the receiver and enters the low-noise amplifier. Due to the non-linear characteristics of the low-noise amplifier and other devices, the frequency of the interference signal is converted to the tuning frequency of the receiver. By mixing and demodulating with the useful signal, it will eventually cause the input waveform of the sam  
Figure 1: Typical communication receiver block diagram.  
fall, resulting in sampling and judgment errors, and ultimately making decoding errors. Thus, the communication efficiency is affected, and at the same time, it is manifested as an increase of the BER.  
From the perspective of EMC between systems, we focus on the impact of pulse signals in the same frequency band on the BER of receiver, which can be carried out in two steps:  
1. Analyze the interference mechanism of the pulse signal in the receiver;
2. Establish the quantitative characterization model of BER and the electromagnetic parameters of the pulse signal.
## 3 Results and Discussions  
### Calibration of VSFlab  
To achieve the absolute magnitude of VSF in m\({}^{-1}\) sr\({}^{-1}\), in addition to baseline correction and angular calibration, we implemented amplitude calibration by utilizing solutions with various concentrations of 0.203 \(\upmu\)m standard beads for the VSFlab at 532 nm. In this way, the calibration coefficient \(k\) at each scattering angle was determined. Figure 5a shows the correlation between the raw signal voltage and the theoretical VSF for the new prism at four scattering angles of 6\({}^{\circ}\), 90\({}^{\circ}\), 177\({}^{\circ}\), and 178.5\({}^{\circ}\). The high coefficients of determination R\({}^{2}\) indicated that the calibration procedure was reliable. The estimated calibration coefficient \(k\) for each scattering angle is shown in Figure 5b. The angular distribution of \(k\) for both prisms was relatively smooth between 3\({}^{\circ}\) and 177\({}^{\circ}\), with the average \(k\) being 65.1 \(\pm\) 4.68 m\({}^{-1}\) sr\({}^{-1}\)/V. Such characteristic was determined by the fact that the single detector used herein ensured the constant photoelectric conversion efficiency. Sharp increases were observed at scattering angles close to 0\({}^{\circ}\) and 180\({}^{\circ}\) (typically from \(<\)3\({}^{\circ}\) and \(>\)165\({}^{\circ}\)) because of the prism's shadowing effect. As shown in Figure 5d, the scattering light at small angles close to 0\({}^{\circ}\) and 180\({}^{\circ}\) was shadowed by the prism, which limited the minimum and maximum detectable angles [23]. We applied the estimated calibration coefficient \(k\) to the measurements of 3.0 \(\upmu\)m standard beads. It was found that such loss of light intensity due to the shadow effect could be compensated through amplitude calibration (Figure 5c). At scattering angles from 1\({}^{\circ}\) to 3\({}^{\circ}\) and from 177\({}^{\circ}\) to 178.5\({}^{\circ}\), the significant underestimation of measured VSF was observed before the amplitude calibration based on the comparison with the theoretical one calculated from Mie theory. After the calibration, the VSF was more consistent with the theoretical curve in the two angle ranges. It indicated that our calibration procedure for the VSFlab was reasonable.  
Figure 4: All 21 Stations (blue circles) at the East China Sea (ESC) including 9 stations for microscopic imaging (red plus). (**a**) The observation in May 2021. (**b**) The observation in August 2021.The gray part represents the land and the white part represents the sea.
# The Application of Game-Based Teaching for Water Resources Science Learning
## 3 Research Methodology  
The course program is designed based on the research team's discussion, taking into account the learning level of the participants and selecting the course theme (water cycle) that best matches them. Following the establishment of a half-day course schedule, the content of the activity execution is finished in accordance with this schedule. For the purposes of this study, primary school students were given a game-based teaching course and then a questionnaire survey was conducted after the course. A total of 420 questionnaires were issued, of which 388 questionnaires were effective, with an effective ratio of 92%. The satisfaction assessment questionnaire used in this study was produced by combining and summarizing relevant research (i.e., [27; 28]), which serves as the fundamental framework of the questionnaire, and then producing the questions and completing the reliability and validity analysis. The following list represents the learning satisfaction questionnaire's items: (1) You like this activity; (2) You like the contents taught in class; (3) You like game-based teaching; (4) You like hands-on experience; (5) You concentrate on the lecture in class; (6) You are eager to answer questions in class; (7) You work hard with members of your group in class; (8) You understand the contents taught in class; (9) Game-based teaching makes it easier for you to understand the contents; (10) Hands-on experience makes it easier for you to understand the contents; (11) Understanding what the teacher is teaching gives you a sense of achievement; (13) Knowing how to operate the teaching tools gives you a sense of achievement; (14) You will participate in the next science related activity; (15) You will recommend that friends participate in related science activities. The responses were recorded on a five-point Likert scale [29].  
In order to satisfy the demands of guiding students to learn through scientific toys and science games ([PERSON] and [PERSON], 2010), water resources science teaching materials were divided into two parts; one is a monopoly game, and the other is a jigsaw puzzle. Monopoly uses a water world map (as illustrated in Figure 1) designed by this study for students to learn about the water cycle and water resources science concepts. When playing monopoly, students are separated into groups and move by rolling dice. At each step, there are a number of cards containing water resource related knowledge. Students who answer correctly can move forward. The card's contents comprise knowledge about each facility. Students that play the game will not only have a good time, but they will also obtain knowledge about water resources science. The jigsaw puzzle is cut at the red line and folded at the black line (as illustrated in Figure 2). Students have to figure out how to fold the puzzle into four different pictures, including a \"water world map,\" \"Taiwan's 25 main rivers,\" and a \"Taiwan reservoir distribution map.\"
## 8 Cramer-Rao Bound on Covariance Matrix of Localization Errors
### Cramer-Rao Bound on Position Estimate in Presence of Array Model Errors  
This goal of this subsection is to derive the CRB for the position estimate in the presence of array uncertainties. Because in the present case the full parameter set contains both the deterministic parameters \(\mathbf{p}\), \(\mathbf{\beta}\), \(\overline{\mathbf{s}}\), and \(\sigma_{\varepsilon}^{2}\) and the stochastic parameter \(\widetilde{\mathbf{\varphi}}\), the CRB derivation should follow the Bayesian theory frame [68; 69; 70]. It is noteworthy that the CRB derivation can also be used for stochastic parameters, as processed in [68; 69; 70]. To this end, a novel parameter vector that comprises all the deterministic and stochastic unknowns is introduced  
\[\mathbf{\eta}_{\text{b}}=\left[\sigma_{\varepsilon}^{2}\ \mathbf{p}^{\text{T}}\ ( \text{Re}\{\mathbf{\beta}\})^{\text{T}}\ (\text{Im}\{\mathbf{\beta}\})^{\text{T}}\ (\text{Re}\{\overline{\mathbf{s}}\})^{\text{T}}\ (\text{Im}\{\overline{\mathbf{s}}\})^{\text{T}}\ (\text{Re}\{ \widetilde{\mathbf{\varphi}}\})^{\text{T}}\ (\text{Im}\{\widetilde{\mathbf{\varphi}}\})^{\text{T}} \right]^{\text{T}}=\left[\sigma_{\varepsilon}^{2}\ \mathbf{\mu}_{\text{b}}^{\text{T}}\right]^{\text{T}}, \tag{94}\]  
where  
\[\mathbf{\mu}_{\text{b}}=\left[\mathbf{p}^{\text{T}}\ (\text{Re}\{\mathbf{\beta}\})^{ \text{T}}\ (\text{Im}\{\mathbf{\beta}\})^{\text{T}}\ (\text{Re}\{\overline{\mathbf{s}}\})^{\text{T}}\ (\text{Im}\{\overline{\mathbf{s}}\})^{\text{T}}\ (\text{Re}\{ \widetilde{\mathbf{\varphi}}\})^{\text{T}}\ (\text{Im}\{\widetilde{\mathbf{\varphi}}\})^{\text{T}} \right]^{\text{T}}. \tag{95}\]  
By performing similar algebraic manipulation in [68; 69], the CRB matrix for vector \(\mathbf{\mu}_{\text{b}}\) is formulated as  
\[\mathbf{CRB}(\mathbf{\mu}_{\text{b}})=\left(\frac{2}{\sigma_{\varepsilon}^{2}}\cdot \text{Re}\{\mathbf{\Omega}_{\mathbf{\mu}_{\text{b}}}^{\text{H}}\mathbf{\Omega}_{\mathbf{\mu}_ {\text{b}}}\}+\left[\begin{array}{cc}\mathbf{O}&\mathbf{O}\\ \mathbf{O}&\mathbf{\Phi}^{-1}\end{array}\right]\right)^{-1}, \tag{96}\]  
where  
\[\mathbf{\Omega}_{\mathbf{\mu}_{\text{b}}}=\frac{\partial\overline{x}_{0}}{\partial\bm {\mu}_{\text{b}}^{\text{T}}}=\left[\mathbf{\Omega}_{\mathbf{p}}\ \mathbf{\Omega}_{\text{Re}\{\mathbf{\beta}\}}\ \mathbf{\Omega}_{\text{Im}\{\mathbf{\beta}\}}\ \mathbf{\Omega}_{\text{Re
# Security Analysis of the MQTT-SN Protocol for the Internet of Things
## 6 Threat Detector Proposed
Table 7 shows the results of the proposed threat detector against the attacks that were presented in this paper. It should be noted that it was used to detect attacks that are more difficult to detect using rule-based threat detectors. In this case, a single rule was generated that tries to highlight all traffic that is malicious. We can see how the different types of traffic are detected by the detector (Table 8); in this case, it was analyzed packet by packet. The classifier achieves a True Positive (TP) when a packet, which belongs to an attack, is classified as an attack, a True Negative (TN) when a packet, which belongs to normal traffic, is classified as such, a False Positive (FP) when a packet, which belongs to normal traffic, is classified as an attack, and finally, a False Negative (FN) when a packet that belongs to the attack traffic is classified as normal traffic. To accurately assess the effectiveness of the classifier, the following metrics are used:  
* Precision = \(\frac{\text{TP}}{\text{TP+FP}}\)
* Recall = \(\frac{\text{TP}}{\text{TP+FN}}\)
* F1 Score = \(2\cdot\frac{\text{Precision-Recall}}{\text{Precision+Recall}}\)  
All metrics are between 0 and 1, with 1 being a perfect score. A high precision metric score indicates that the classifier does not generate many false positives. A high recall score indicates that the classifier does not generate many false negatives, and the F1 score metric combines the two previous metrics to obtain the combined performance. As we can see, this configuration causes some normal packets to be detected as attacks, but in contrast, allows all attacks to be detected. Prioritizing the recall metric over the precision metric is very common in the cybersecurity field. In addition, the threat detector we suggest has been tested in other contexts to detect attacks in the IoT environment with very positive results [39], being able to detect attacks against the MQTT protocol with very good computational performance figures. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Packet Type** & **TP** & **TN** & **FP** & **FN** \\ \hline Normal traffic & 0 & 653 & 24 & 0 \\ Disconnect wave & 2357 & 0 & 0 & 0 \\ Trash injection & 76 & 0 & 0 & 0 \\ Congestion attack & 824 & 0 & 0 & 0 \\ Fake Id & 8 & 0 & 0 & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Suggested threat detector results by packet type. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Precision** & **Recall** & **F1 Score** \\ \hline
0.9927 & 1 & 0.9963 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Overall results of the classifier.