esa-sceva/satcom-chunk-collection huggingface dataset:
# An Earthquake Early Warning Method Based on Bayesian Inference
## 3 Traditional Calculation Method for Earthquake Magnitude and PGV
### Magnitude Calculation using Average Period \(\tau_{c}\)  
The average period \(\tau_{c}\) is often used in earthquake early warning systems to calculate the magnitude. In particular, the magnitude is estimated by establishing a linear statistical relationship between \(\tau_{c}\) and the magnitude within a few seconds prior to the P-wave [11; 12]. \(\tau_{c}\) can be calculated as follows:  
\[\tau_{c}=\frac{2\pi}{\sqrt{r}}, \tag{1}\]  
for  
\[r=\frac{\int_{0}^{\tau_{0}}\dot{u}^{2}(t)dt}{\int_{0}^{\tau_{0}}u^{2}(t)dt}, \tag{2}\]  
where \(u(t)\) is the displacement of the ground motion at time \(t\); \(u(t)\) is the velocity of the ground motion at time \(t\), which is counted from the trigger of the station and recorded as \(t=0\); and \(\tau_{0}\) is the upper integration time window length.  
In order to process the earthquake data, \(\tau_{c}\) of the first-three-second P-wave was calculated and the fitting relationship with magnitude \(M_{\rm j}\) was established. Note that the magnitude difference between Japan and China is negligible between 4 and 8 [13], and this paper ignores the difference.  
\[lg\tau_{c}=aM_{j}+b\pm\delta, \tag{3}\]  
In the above equation, \(M_{\rm j}\) is the magnitude, a and b are the fitting parameters, and \(\delta\) is the standard deviation of Equation (3). Using the least-squares method, these parameters are determined as \(a=0.1353\), \(b=-0.0495\), and \(\delta=0.1149\).
# Fast Conflict Detection for Multi-Dimensional Packet Filters
## 2 Related Work
### Conflict Detection for 2D Filters  
[PERSON] and [PERSON] [12] determined that when a 2D filter is represented by a geometric area, two conflicting filters will generate an overlapping area in the plane, and at least one perfect crossing among the segments of two areas must exist. [PERSON] and [PERSON] defined perfect crossing as two line segments of the share point perfectly crossing if and only if they cross and the crossing point is not an endpoint of either line segment. In addition, they proposed a magnifying mechanism to ensure that each filter conflict has the characteristics of perfect crossing. They then used [PERSON]'s and [PERSON]'s algorithm [28] to detect all existing perfect crossings and finally to determine all conflicts. The time complexity of this algorithm is \(O(n\text{log}n+S)\) and the space complexity is \(O(n)\). [PERSON] et al. [29] proposed a 2D tuple space search algorithm (TCDA) based on a tuple space data structure and hash search. The execution of conflict detection in each filter can be accelerated by adding a marker pointer and filter pointer. The TCDA can determine all conflicts in \(O(nW+S)\) time. [PERSON] and [PERSON] [30] studied the 2D packet conflict problem and discovered that the problem can be reduced to the persistent predecessor problem. They used a balanced binary search tree to perform 2D conflict detection, with the time complexity further improved to \(O(n\text{ min}\left\{\frac{\log\
u\log\log n}{\log\log\
u},\sqrt{\frac{\log n}{ \log\log n}}\right\}+S)\). [PERSON] et al. [31] converted all arbitrary 1D range filters into consecutive slabs and defined all conflict relationships between all consecutive slabs. They used a slab-detect algorithm and proposed an output-sensitive algorithm that was able to detect all filter conflicts in \(O(n\text{log}n)\) time with the space complexity being \(O(n)\). [PERSON] et al. [9] used a formal method to analyze the meaning of IPv6 firewall filters and took the formal validation tool (satisfiability modulo theories solver Z3) [32] to find all the conflicts between every two firewall filters.  
These 2D conflict detection algorithms cannot be directly extended for 5D conflict detection due to the limitation of the constructed data structures. More specifically, it is difficult or even impossible to represent 5D filters as geometric areas [12], tuple spaces [29], or binary trees [30]. A simple way to make a 2D conflict detection algorithm capable of handling 5D filters is to compare prefix fields and non-prefix fields separately. However, without a good design of data structure and algorithm for non-prefix fields, it is difficult to achieve a satisfactory detection speed.
## 1 Introduction
Therefore, the explicit dependencies of transfection efficiency and cell viability on plasmid concentration remain unclear. To address this, we present an investigation where both transfection efficiency and cell viability dynamics in dependence on plasmid concentration are evaluated in a single study at the same experimental conditions using different number of high voltage (HV) pulses. Additionally, we have evaluated the distance between cell plasma membrane and the nearest plasmid DNA, which seems to be important for the DNA electrotransfer to the cell as the diffusion of plasmid DNA is limited due to its large size [30]. For the first time, we show a quantitative, correlation-based dependency on the distance between the cell and plasmid DNA molecule with cell transfection efficiency and with cell viability. We propose the distance between the plasmid and the cell as the new value for the optimization of molecular delivery.
# An Innovative Information Hiding Scheme Based on Block-Wise Pixel Reordering
## 3 Proposed Scheme
### Image Encryption  
Content owners can use any of the existing encryption methods to protect the privacy of the original images and send the encrypted images to third-party data hiders. The Stream Cipher technique, an exclusive OR operation to encrypt a cipher stream generated by using an encryption key \(k_{e}\), is a well-known, simple, and efficient method to encrypt images. When there is an original image \(I\) of size \(M\times N\), a pseudorandom matrix used as an encryption key \(k_{e}\) with the matching size of the image \(I\) and whose values range between 0 and 255 is generated by using a random seed value. The stream cipher is an encryption algorithm which uses the encryption key \(k_{e}\) in both encryption and decryption. A pixel value and its corresponded encryption key value are converted to 8-bit binary system using Equations (4) and (5):  
\[I_{ij}^{x}=\left\lceil\frac{I_{ij}}{2^{x-1}}\right\rceil\ mod\ 2, \tag{4}\]  
where \(ij\) represents the \((i,j)\) coordinates, \(1\leq i\leq M\) and \(1\leq j\leq N\).  
\[k_{eij}^{x}=\left\lceil\frac{k_{eij}}{2^{x-1}}\right\rceil\ mod\ 2, \tag{5}\]  
where \(ij\) represents the \((i,j)\) coordinates, \(1\leq i\leq M\), \(1\leq j\leq N\), and \(x\) is the bit order from right to left and can be represented as \(x=1\), \(2,\cdots,8\). After binary conversion, a bit-level XOR as Equation (6) is applied:  
\[I_{eij}^{x}=I_{ij}^{x}\ \oplus\ k_{eij}^{x}. \tag{6}\]  
The encrypted image \(I_{eij}^{x}\) is converted back to a decimal encrypted image  
\[I_{ij}^{\prime}=\sum_{x=1}^{8}I_{ij}^{x}\times 2^{x-1}. \tag{7}\]  
Figure 5: Framework of block-group index reordering scheme.
# Improving Spatial Reuse of Wireless LAN Uplink Using BSS Color and Proximity Information
## 3 Proposed Method
### Protocol Details
#### 3.2.2 Proximity Information  
When a node transmits a UL packet to its AP, it includes the proximity information in the preamble, along with the BSS color. The proximity information is basically the RSSI of the associated AP, measured from the node. Suppose node A receives beacons from AP\({}_{1}\) at an average RSSI of \(-40\) dBm. Then, \(-40\) dBm is the proximity information node A includes in the preamble of its packets.  
The proximity information should be included in the signal field of the preamble, such as HE-SIG-A (High-Efficiency SIG-A) of 802.11 ax. In HE-SIG-A, 6 bits are allocated for BSS color, which allows up to 64 unique colors for representing a BSS. The proximity information can be encoded into 4 bits, similar to the Spatial Reuse field in the HE TB PPDU (HE Trigger-Based PLCP Protocol Data Unit) which is used for sending PSR values. An example field encoding for proximity information is shown in Table 2. Using more bits allows sending a fine-grained proximity information which can increase spatial reuse, but at the same time increases the overhead of sending more bits in the preamble. For example, in Table 2, the proximity information is encoded in steps of 4 dB. To encode the proximity information, the RSSI of the AP is quantized to a lower value. For example, if RSSI of the AP is \(-74\) dBm, then the proximity information field is set so '2', which means \(-76\) dBm.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{2}{c}{(a)} & \multicolumn{3}{c}{(b)} \\ \hline \multicolumn{2}{c}{**Neighbor\_Table of B**} & \multicolumn{3}{c}{**Neighbor\_Table of AP\({}_{2}\)**} \\ \hline
**BSSID** & **Color** & **RSSI** & **BSSID** & **Color** & **RSSI** \\ \hline
00:00:00:00:00:01 & 1 & \(-76\) & 00:00:00:00:00:01 & 1 & \(-72\) \\ \hline
00:00:00:00:00:02 & 2 & \(-44\) & 00:00:00:00:00:03 & 3 & \(-63\) \\ \hline
00:00:00:00:00:03 & 3 & \(-68\) & 00:00:00:00:00:04 & 4 & \(-88\) \\ \hline
00:00:00:00:05 & 5 & \(-87\) & 00:00:00:00:00:05 & 5 & \(-83\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Neighbor table of node B and AP\({}_{2}\). APs periodically send its neighbor table to the stations, so node A will have both the tables. (a) Neighbor table of B. (b) Neighbor table of AP\({}_{2}\).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Value** & **Meaning** & **Value** & **Meaning** \\ \hline
0 & PI\_UNKNOWN & 8 & PI = \(-52\) dBm \\ \hline
1 & PI \(\leq-80\) dBm & 9 & PI = \(-48\) dBm \\ \hline
2 & PI = \(-76\) dBm & 10 & PI = \(-44\) dBm \\ \hline
3 & PI = \(-72\) dBm & 11 & PI = \(-40\) dBm \\ \hline
4 & PI = \(-68\) dBm & 12 & PI = \(-36\) dBm \\ \hline
5 & PI = \(-64\) dBm & 13 & PI = \(-32\) dBm \\ \hline
6 & PI = \(-60\) dBm & 14 & PI = \(-28\) dBm \\ \hline
7 & PI = \(-56\) dBm & 15 & PI \(\geq-24\) dBm \\ \hline \hline \end{tabular}
\end{table}
Table 2: Proximity information field encoding. The RSSI of the AP is quantized to a lower value.
# Receiver-Initiated Handshaking MAC Based on Traffic Estimation for Underwater Sensor Networks +
## 3 Related Work
### Related Work on Receiver-Initiated MAC  
The idea of receiver-initiated handshaking MAC is not new. Aiming to reduce the overhead of the handshaking procedure, [PERSON] et al. proposed MACA by invitation (MACA-BI) [13], a simplified version of multiple access collision avoidance (MACA) protocol by removing the transmission of \(RTS\) in the sender. In this method, a traditional _three-way_ handshaking process is reduced to a _two-way_ handshake. MACA-BI saves one round \(RTS\) message transmission; however, it suffers the hidden terminal problem in multi-hop networks.  
[PERSON] et al. designed the receiver-initiated multiple access (RIMA) protocol by reversing the handshaking process in wireless networks [14]. A _three-way_ receiver-initiated handshaking process is employed in RIMA. The authors also extended the protocol to a version with dual-use polling (RIMA-DP), which allows both the polling and polled nodes to send data in one round of collision avoidance. Since no parallel reservation or packet train is implemented in the RIMA protocol, the overhead of the handshaking process is significant when it is applied to the underwater networks.  
More recently, the receiver-initiated packet train (RIPT) protocol [15] was proposed for UWSNs. It employs both parallel reservation and a packet train to mitigate the long propagation delay problem of underwater acoustic channels and to enhance the handshaking efficiency. The RIPT utilizes a _four-way_\(RTR/SIZE/ORDER/DATA\) handshaking process. The \(SIZE\) message is a response to the \(RTR\), based on which the receiver allocates the channel recourse among multiple senders. The new \(ORDER\) control message is introduced to schedule the packet train transmission of all involved sending nodes. Similar to RIMA-DP, the RIPT protocol also allows the receiver to transmit data in one round of collision avoidance. Without the prediction of when data will be available in the senders, the receiver simplystarts one round of handshaking when it has data to transmit. When the network traffic features high dynamics, RIPT will suffer from poor handshaking efficiency.  
The design of efficient data polling is a common challenge to all receiver-initiated MAC protocols in the literature. Without knowing the number of packets that have cumulated on the surrounding nodes, it is difficult for the receiver to decide a good time to initiate the next receiving request, especially with the dynamic traffic pattern. Therefore, in this work, we propose TERI-MAC, a receiver-initiated MAC based on traffic estimation. An adaptive data polling will enable stable energy efficiency in the networks with arbitrary traffic patterns. With the assistance of traffic estimation, the receivers in TERI-MAC are able to arrange the transmission scheduling on the \(RTR\) message without waiting for the \(ATS\) response from senders. The \(ORDER\) message can thus be removed in TERI-MAC to further reduce the overhead of the MAC protocol.
# Adaptive Multi-Path Routing Protocol in Autonomous Vehicular Networks
## 3 Motivation
### Payload Size Determines Optimal Path
However, the main effect of using min-hop instead of MTM is the reduction in medium time spent per packet delivery as shown in Figure 4c. By reducing the medium time spent for small-sized packets, some other flow that transmits large-sized data packets can utilize the saved medium time and increase the overall network throughput. Furthermore, by using the min-hop metric for small-sized packets, the end-to-end delay would decrease compared to MTM, which can be easily figured out in Figure 2. We will further discuss these issues with the simulation results in Section 6. Figure 3: A simple 3-node topology. Figure 2: MAC medium time when sending (a) a 1500-byte and (b) a 40-byte data payload on 1, 2, 5.5, and 11 Mbps links.
# An Improved Radio Resource Management with Carrier Aggregation in LTE Advanced
## 3 System Model
### Problem Formulation
No packets can be transmitted successfully in this state due to excessive error and this can make the system unusable. Therefore, by considering the success state, Equation (9) can be written as follows  
\[\log(\delta_{i})=\tau_{i}\log(1-P_{i})+\log(\varepsilon_{i}) \tag{11}\]  
Considering the error probability and the probability from failure state to success state in (9), Equation (7) can be replaced by the following Equation (12). \[\alpha_{i}=\frac{-\{\tau_{i}\log(1-P_{i})+\log(\varepsilon_{i})\}}{\tau_{i}} \tag{12}\]  
A greedy-based method is proposed in this paper to give a solution with low computational complexity to the scheduling problem in (1) while satisfying the conditions in (2)-(6).
# Research on the Cleaning Method of Unmanned Sweeper Based on Target Distribution Situation Analysis
## 3 Proposed Methodology
### Optimal Sweeping Path Planning Based on Simulated Annealing Algorithm
#### 3.2.1 Generation of Convex Hull in the Region to Be Cleaned  
Convex Hull refers to the convex polygonal region where all the garbage exists in the two-dimensional plane space, forming the Convex Hull set for the garbage region. In this paper, we will use the Graham scanning method; the idea is to find a point on the Convex Hull first, and then start from that point to look for the points on the Convex Hull one by one in a counter-clockwise direction. The main steps are as follows:  
1. [label=(0)]
2. Put all the points in a two-dimensional coordinate system as shown in Figure 10; then, the point with the smallest vertical coordinate must be the point on the Convex Hull with \(S_{0}\) as the starting point.
3. Calculate the magnitude angle \(\alpha\) of each point with respect to \(S_{0}\) and sort each point in order from smallest to largest. When \(\alpha\) is the same, the point closer to \(S_{0}\) is ranked first. For example, the following figure shows that the results are \(S_{1}\), \(S_{2}\), \(S_{3}\), \(S_{4}\), \(S_{5}\), and \(S_{6}\).
4. Put \(S_{0}\), and \(S_{1}\) into the stack, and then repeat step 2 above with \(S_{1}\) as the current point to obtain the stack that characterizes the vertex of the convex packet.
5. Sequentially connect the neighboring points in the stack, and finally connect \(S_{0}\) and the point \(S_{6}\) at the top of the stack, as in Figure 10, to map the final convex envelope region.  
Figure 9: Calculated process of road image complexity. Where (**a**) is the junction of garbage and road, (**b**) is the part of the clean road, (**c**) is the complex garbage pile.  
Specifically, first, we randomly take some farther points inside the GMM distribution and then form a Convex Hull set, as shown in Figure 9 for the set of Convex Hulls formed using the above Graham scanning method.
# Multi-Hypergraph Neural Networks for Emotion Recognition in Multi-Party Conversations
## 3 Methodology
### Model
#### 3.3.2 Hypergraph Convolution (HGC) Layer  
We utilized the two hypergraphs to perform separate hypergraph convolutions, and then obtained different utterance representations. The process of performing hypergraph convolution for each graph can be divided into the following three steps.  
**Node-to-Edge Aggregation.** The first step was the aggregation from the nodes to the hyperedges. Here, we used the position weight \(m_{j}^{i}\) to calculate the weight \(\alpha_{ji}^{pos}\) of the weighted average aggregation. Since some nodes on a hyperedge are informative, but others may not be, we should pay varying attention to the information from these nodes while aggregating them together. We utilized an attention mechanism to model the significance of different nodes. Here, we used a function \(S(\cdot,\cdot)\) to calculate the attention weights \(\alpha_{ji}^{ATT}\). Function \(S(\cdot,\cdot)\) was derived from the scaled dot-product attention formula [23]. Then, the  
Figure 2: Overview of our proposed model. In the hypergraph convolutional layer module, the red dotted line represents the information transfer between the hyperedges.  
where \(H_{i}^{HG}\) represents the result of the hypergraph convolution performed on the hypergraph, \(HG\) can be SSHG and NSHG, and \(L_{HG}\) is the number of layers for the hypergraph convolution of the corresponding hypergraph.  
For the training of ERMC-MHGNN, we employed the standard cross-entropy loss as an objective function. The specific function is shown in Equation (12).  
\[\mathcal{L}(\theta)=-\sum_{i=1}^{C}\sum_{t=1}^{N_{i}}\text{Log}P_{i,t}[y_{i,t}] \tag{12}\]  
where \(C\) is the number of training conversations, \(N_{i}\) is the number of utterances in the \(i\)-th conversation, \(y_{i,t}\) is the ground-truth label, and \(\theta\) is the collection of trainable parameters of ERMC-MHGNN.
## 2 Materials and Methods
### Electroencephalography (EEG)  
To record brain potential changes in response to stimulus presentations, a 64-channel actiCHamp Plus System from Brain Products (Recorder: Version: 1.23.0003; Germany; Gilching) with active electrodes embedded in an actiCAP connected to an amplifier was used. An electrode layout corresponding to the 10-20-electrode placement standard was used. To ensure correct electrode placement, both nasion and inion locations were identified, and the cap was placed accordingly. The amplifier was operated by a powerful lithium-ion battery pack. Brain potentials were sampled with a rate of 1 kHz (filtered: DC to 100 Hz). Impedance was kept equal to or below 10 k\(\Omega\). Cz was used as reference (no relevant activity was expected at this location), and a mid-frontal position on the forehead was used as ground electrode. Offline, all EEG data were down-sampled to 250 Hz, and a bandpass filter from 0.1 (high pass; Zero Phase) to 30 (low pass; Zero Phase) Hz was applied in preparation for following EEG data processing.  
Eye blink artefacts were corrected using a set of linear regression weights at each EEG electrode derived from an averaged eye blink [37]. Segments of the EEG record containing gross artefacts were detected by an automated procedure that applied amplitude thresholds within a number of frequency bands. Noisy channels (i.e., with potential deviations above 100 \(\upmu\)V; less than 5% for each participant) were interpolated prior to data reduction. For this purpose, selected curves were replaced with the 1/R squared weighted average of the 4 nearest neighbouring curves. For each stimulus, EEG epochs were extracted from 100 ms pre-stimulus to 1000 ms post-stimulus and baseline corrected across the pre-stimulus interval. Entire trials containing artefacts exceeding \(\pm\) 100 \(\upmu\)V were excluded. Finally, all remaining epochs (mean number of trials across all participants for words is 24.7 (SD = 3.9), for number words it is 25.5 (SD = 3.1), for digits it is 24.2 (SD = 4.2), and for non-words it is 25.1 (SD = 3.4)) within each of the four stimulus categories were averaged to generate event-related potentials (ERPs). Those were then used to display stimulus category-specific neurophysiological activity changes over time. All EEG data processing was performed with the EEGDisplay (version 6.4.9) software (by [PERSON]).
# Observation and Numerical Simulation of a Windshear Case at an Airport in the Qinghai-Tibet Plateau
## 4 Conclusions  
This short letter documents a windshear case at an airport at the Plateau and for the first time attempts to simulate the main features of the wind at such an airport using a numerical weather prediction model with high spatial resolution. The actual observations of the windshear are provided by a DWL. The main features of the wind are reasonably well simulated by the numerical model, though there are still a number of limitations such as the maximum wind speed of the jet core, the actual values of EDR, and the timing and height of the intrusion of westerly wind on the following day. Nonetheless, it is shown for the first time that a numerical model with high spatial resolution appears to have the potential for forecasting and fore-warning the occurrence of windshear at an airport at the Plateau. In the previous works (e.g., [6; 7; 8]), only isolated terrain near the airport is considered. In the present work, the extensive complex terrain of Qinghai-Tibet Plateau is considered for the first time. The forecasting of windshear using a high-resolution numerical weather prediction model, though with various limitations, could be useful as a tool for an earlier alert of the occurrence of terrain-induced windshear.  
More cases of windshear for Plateau airport are being collected. Additional information would be available for more recent cases, such as Quick Access Recorded (QAR) data from the aircraft experiencing windshear, which would provide further insights into the windshear event and the study of the application of numerical modeling in forecasting such wind features. The results will be reported in future papers.  
Methodology, P.W.C.; Software, K.K.L.; Formal analysis, J.Z. and Y.Z.; Investigation, X.S.; Data curation, H.C. All authors have read and agreed to the published version of the manuscript.  
H.C. and X.S. thank the funding support from the World Meteorological Organization's Aviation Research and Development Project Phase 2 (AvRDP2).  
Not applicable.  
The participants of this study did not give written consent for their data to be shared publicly, so due to the sensitive nature of the research, supporting data are not available.  
The authors declare no conflict of interest.