esa-sceva/satcom-chunk-collection huggingface dataset:
# Sequence Deep Learning for Seismic Ground Response Modeling: 1D-CNN, LSTM, and Transformer Approach
## 3 Prediction Models
### Long Short-Term Memory (LSTM) Networks-Based Model  
#### 3.3.1 Overview  
Long short-term memory (LSTM) networks [27] are a type of recurrent neural network (RNN) specifically designed to effectively handle sequential data and capture long-term dependencies. Unlike traditional RNNs, which struggle with vanishing or exploding gradients when dealing with long sequences [44], LSTMs introduce a memory cell and gating mechanisms to selectively remember and forget information over the sequence. This allows LSTMs to maintain long-term memory and learn complex patterns in sequential data. LSTMs have proven to be successful in various sequence learning tasks in geotechnical and structural areas [30; 31; 34; 45] where capturing long-range dependencies is needed.  
LSTMs learn sequential dependencies by utilizing three gating mechanisms in the memory cell: input gate \(\hat{t}_{t}^{\Theta}\), forget gate \(f_{t}^{\Theta}\), and output gate \(o_{t}^{\Theta}\) (Figure 6), which include learnable parameters. These gates selectively regulate the previous cell information (\(C_{t-1}\) and \(h_{t-1}\)), new information (\(x_{t}\)), and output of the current cell \(C_{t}\) and \(h_{t}\). Here, \(C\) denotes the cell state, and \(h\) denotes the hidden state. The input gate \(\hat{t}_{t}^{\Theta}\) controls the flow of input information (\(x_{t}\) and \(h_{t-1}\)) into the memory cell, deciding which information should be stored. The forget gate \(f_{t}^{\Theta}\) determines what information should be discarded from the previous cell state \(C_{t-1}\) and the new information \(x_{t}\), allowing the network to selectively forget irrelevant or outdated information. The output gate \(o_{t}^{\Theta}\) regulates the flow of information in the memory cell (\(x_{t}\) and \(h_{t-1}\)) to its output, referring to the cell state \(C_{t}\). For more details, refer to [PERSON] et al. [29].  
Figure 5: One-dimensional CNN-based model for seismic ground response modeling.  
Figure 6: Long short-term memory (LSTM) cell structure.  
Compared to 1D-CNNs, LSTMs excel at capturing long-term dependencies within the data. While 1D-CNNs are effective at learning local patterns and short-term dependencies through convolutional kernels, they struggle with long-range correlations due to their localized receptive fields. In contrast, LSTMs' memory cells and gating mechanisms allow them to retain and utilize information over extended sequences, making them more suitable for modeling temporal information change like seismic response data.
## 6 Numerical Experiments
### Results of Crack Identification
For sample 8, the reason for the error was the same as sample 1 without noise shown in Table 3, which was the relaxed threshold of the stop criterions. While for sample 2, the reason for the identification error was the prediction error at the \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Under-Test**} & \multicolumn{2}{c}{**Actual Parameters**} & \multicolumn{2}{c}{**Identified Results**} & \multicolumn{2}{c}{**Criterion Values**} \\ \cline{2-7}  & **Crack Location** & **Crack Depth (\(D\))** & **Crack Location** & **Crack Depth (\(D\))** & **Updating Times** & **for the Final Updated Model** \\ \hline
1 & 34 & 0.1 & 34 & 0.1 & 2 & \((1,1)\) \\
2 & 46 & 0.05 & 50 & 0.1 & 8 & \((1,0.95)\) \\
3 & 50 & 0.35 & 50 & 0.35 & 9 & \((1,1)\) \\
4 & 28 & 0.25 & 28 & 0.25 & 1 & \((0.99,0.99)\) \\
5 & 20 & 0.4 & 20 & 0.4 & 2 & \((1,1)\) \\
6 & 12 & 0.3 & 12 & 0.3 & 10 & \((1,1)\) \\
7 & 42 & 0.2 & 42 & 0.2 & 4 & \((1,1)\) \\
8 & 38 & 0.45 & 36 & 0.45 & 1 & \((1,0.96)\) \\
9 & 16 & 0.15 & 16 & 0.15 & 2 & \((1,1)\) \\
10 & 24 & 0 & 24 & 0 & 1 & \((1,1)\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Crack identification results of the under-test samples with 5% noise. Figure 10: Update process of the under-test samples with 5% noise. (**a**) Identification process for all samples; (**b**) identification process and corresponding value of identified feature for sample 6.  
boundary. Therefore, noise mainly affects the identification efficiency and accuracy for samples at the boundary, which can be improved by adding more samples at the boundary to reduce the prediction error. For the non-boundary cases, the proposed method is robust. To conclude, after proper sampling plan and adding samples, the proposed method is efficient, accurate and robust.
## 3 Methods
### Experimental Overview  
The experiment was conducted for a month, from July 6 to August 6, 2019, during which traffic congestion is known to occur owing to consecutive holidays in Japan. The experimental area was the Yatsugatake area. The experiment focused on tourists who returned to the Tokyo metropolitan area using the Chuo Expressway from the Nagano or Yamanashi Prefectures in Japan. Participants were recruited using leaflets placed at 10 locations in the area under consideration, such as the Kobuchizawa Tourist Information Center road station, Haramura Tourist Information Center, and Fujimi Kogen Resort. Flyers were also distributed at popular tourist spots such as the Yamanashi Prefectural Makiba Park. Participants were also recruited via the Internet prior to the experimental period. The leaflet used in the experiment is provided online [18].  
First, the process and purpose of the experiment were explained to the participants, and their approval concerning their participation was obtained. The participants then installed the developed application, which is described in Sections 3.3 and 3.4, on their mobile phones. On launching the application for the first time, the participants answered questions on personal attributes, their current practice for changing their departure time considering traffic congestion, and their intention to change their departure time using the questionnaire function of the application. They used the application to search for routes to return home and checked the estimated travel times as required. Upon departing, a car navigation application linked with \"Sui-sui tabi\", which records the travel trajectory, is launched. After returning home, they completed the post-experiment questionnaire on their actual travel behavior and consumption via the application.
## 3 System Model
### Delay Model  
Since the tasks offloaded to multiple edge servers and the tasks calculated on the local terminal device are run simultaneously during the computing process, the delay in running tasks in the current epoch is determined by the higher of computing delay \(T^{c}_{i,t}\) and the transmission delay \(T^{trans}_{i,j,t}\). The delay can be expressed as  
\[T_{i,t}=\max\{T^{c}_{i,t},T^{trans}_{i,1,t},T^{trans}_{i,2,t},\ldots,T^{trans}_{ i,N,t}\}, \tag{12}\]  
where the computing delay \(T^{c}_{i,t}\) of the local terminal device task is determined by the highest computing time among the \(K\) CPU cores of the device. The computing delay is given by  
\[T^{c}_{i,t}=\max\{T^{u}_{i,1,t},T^{u}_{i,2,t},\ldots,T^{u}_{i,K,t}\}. \tag{13}\]
# Mesenchymal Stem Cell Differentiation Driven by Osteoinductive Bioactive Nanoscale Topographies
## 2 Materials and Methods
### RT q-PCR  
Total RNA from hMSC cultured for 2 weeks was isolated using RNeasy Mini Kit, genomic DNA was removed using TURBO DNA-free kit. Total RNA was quantified using spectrophotometer NanoDrop 1000 (ThermoFisher Scientific) and RNA integrity was evaluated using an Agilent bioanalyzer 2100 with an RNA 6000 Nano kit (Agilent, Santa Clara, CA, USA) (RIN values from 7 to 10). cDNA was synthesized from 500 ng of total RNA with the help of random primers and Maxima Reverse Transcriptase. RNA retrotranscription reaction was performed in two steps: incubation at 50 \({}^{\circ}\)C for 1 h, and subsequent incubation at 72 \({}^{\circ}\)C for 15 min. Aliquots of cDNA underwent dye-based RT-qPCR for the study of three genes expressed during osteoblastic differentiation (primers listed in Table 1). RT-qPCR was performed using 4 ng of cDNA, and primers at a concentration of 500 nM, for a final volume of 10 \(\mu\)L. RT-qPCR was performed using a CFX Connect(tm) Real-Time PCR System (Bio-Rad), using 2 genes of reference: RPC53 and PPIA. Forty PCR amplification cycles were performed for each experiment, consisting of incubating the solution at 95 \({}^{\circ}\)C for 5 s, followed by an incubation at 60 \({}^{\circ}\)C for 10 s. Cq for the genes of interest were normalized against the reference genes selected using the software BestKeeper: PPIA and RPC [24; 34; 47]. Relative expression levels were calculated using the comparative method (\(\Delta\)\(\Delta\)Cq).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Gene (Protein)** & **Primer Sequence** \\ \hline POLR3D (RPC53) & 5\({}^{\prime}\)-ACCCTGGCTGACCTGACAGA-3\({}^{\prime}\) (Forward) \\ \cline{2-3}  & 5\({}^{\prime}\)-AGGAGATTGCACCTTCCAGA-3\({}^{\prime}\) (Reverse) \\ \hline PPIA (PPIase A) & 5\({}^{\prime}\)-CGGGCTCTCGCATCTGTGT-3\({}^{\prime}\) (Forward) \\ \cline{2-3}  & 5\({}^{\prime}\)-CAGTCTTGCAGGTCAGATGA-3\({}^{\prime}\) (Reverse) \\ \hline RUNX2 (Runx2) & 5\({}^{\prime}\)-AAGTGCGGGTCAAACTTTCT-3\({}^{\prime}\) (Forward) \\ \cline{2-3}  & 5\({}^{\prime}\)-TCTCGGCTGCCTGTAGGA-3\({}^{\prime}\) (Reverse) \\ \hline COL1A1 (COLIA1) & 5\({}^{\prime}\)-ACATGTTCAGCTTGTGGAC-3\({}^{\prime}\) (Forward) \\ \hline BGLAP (OCN) & 5\({}^{\prime}\)-GACTGTGACGAGTGGCTGA-3\({}^{\prime}\) (Forward) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Primers used for RT-qPCR.  
Gene expression was normalized using flat silicon sample as control. For each condition, two replicates were considered.
## 4 EGCIR Protocol
### Graph-Based Clustering with Supervised System
The structure of the generalized table for node \(i\), which is created at the BS, is given in Table 1.  
When the clustering phase is over, the proposed protocol initiates the steps for intercluster routing. During intercluster routing, all cluster heads are arranged in the form of an undirected graph. In the EGCIR protocol, we assume that the cluster heads are modeled as undirected graph \(G\). Let us consider that the cluster head is set as undirected graph \(G\) and all the edges are bidirectional, i.e., \(G=(N,\epsilon)\), where \(N\) is a set of selected cluster heads and \(\epsilon\) is a set of undirected edges. Whenever the source cluster head needs to send its cluster data towards the BS, it extracts the information for the next-hop cluster head from graph \(G\) and forwards the data towards it. The EGCIR protocol makes use of the following rules to identify the optimal routes:  
1. If the source cluster head in graph \(G\) is only one hop away from the BS, then the route is marked as optimal and data packets are forwarded directly to the BS;
2. If the source cluster head identifies multiple upstream cluster heads in the graph, the cluster head which meets energy, time edge and least transmission power requirements has given a higher priority;
3. If the upstream cluster head is already busy in data transmission, then it simply drops the route request (RREQ) packet and the source cluster head extracts the information for the next upstream cluster head in the graph. This leads to distribute the data traffic evenly and avoid the congestion on nodes. The BS continues to update the information of selected upstream cluster heads in its generalized table. It may be a case that during data forwarding, the cluster head in Graph \(G\) drops its energy power and time edge to a preset value, then the BS reselects the new cluster head, which meets the energy, time edge and distance requirements. \begin{table}
\begin{tabular}{c c c c c} \hline
**Id** & **Position (\(x_{j}y_{j}\))** & **Energy \(e_{i}\)** & **Time Edge \(E_{ti}\)** & **Keys Information \(PU_{ki}\) and \(s_{k}\)** & **Status** \\ \hline — & — & — & — & — \\ \hline \end{tabular}
\end{table} 
Table 1: Structure of the generalized table.
# Windowed Hamming Distance-Based Intrusion Detection for the CAN Bus
## 6 Discussion
### Limitations and Future Work  
Our model is very effective against large-scale injection attacks or tampering attacks. The influence of the small-batch attack model remains to be verified; however, we believe that small-batch, high-period injection attacks will not cause a significant influence on or harm to the car because the second normal ID of the vehicle would quickly overwrite the previous information.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Parameters (million)** & **Time (ms)** \\ \hline DCNN [9] & 9.8 & 6.70 \\ Ours & 0.008 & 0.32 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Model complexity comparison.  
The selection of appropriate benchmarks plays a critical role in our method. Consequently, it is necessary to conduct prior analysis on the car's data to carefully choose a benchmark that accurately represents the normal state of the vehicle. This selection process may require significant time and testing to ensure its effectiveness. As the number of complex attack types continues to grow, our method may require the inclusion of additional signature thresholds to detect these new attack types. This expansion of signature thresholds may introduce an increased computational burden.  
In our future work, we aim to enhance the benchmark selection process, identify more efficient methods for benchmark acquisition, or create a standardized benchmark that caters to all types of vehicles. In addition, our objective is to develop an adaptive sliding window method capable of dynamically adjusting the window size based on variations in the scale and nature of attacks. Lastly, we will focus on refining our methodology to enhance its ability to detect a broader range of attack types, thereby bolstering the comprehensiveness of our intrusion detection system.
## 3 Results and Discussion
### TOF-SIMS Analysis  
For the spruce wood (Figure 4) and the cup-plant PB (Figure 5), cross-sections were prepared for the detection of carbon at mass-to-charge _m/Q_ of 12, hydrogen at a _m/Q_ of 1, oxygen at a _m/Q_ of 16, and hydroxyl ion cluster OH at a _m/Q_ of 17. Secondary ion molecular clusters typical for pMDI adhesives were also detected. The cyanide ion anion (CN) was identified at the _m/Q_ peak of 26, hydrogen isocyanide (CNH) at the _m/Q_ peak 27, while cyanate (CNO) was detected at a _m/Q_ of 42. Due to the detection of molecules associated with pMDI (CN, CNH, and CNO), the resin distribution within the composite could be displayed. Results showed that in the pMDI-bonded PB, the pMDI-wood bondlines were not spot-like, but appeared rather even and non-regular, with a penetration deep into the wood structure. This finding corresponded to data presented by [PERSON] et al. [37]. We are showing that C-TOF attached to FIB-SEM with Ga\({}^{+}\) ion source could detect ion molecular clusters specific to pMDI adhesive and wood. Additionally, with the Ga\({}^{+}\) ion source, it should be possible to detect G-lignin at peak _Q/m_ 137 [30], however, the applied C-TOF setup delivered only a low secondary ion signal (although visible), which did not allow an elemental mapping. This could be further elaborated in a future study. The relevance of CN, CNH, CNO being related to the pMDI distribution in the wooden structure was also confirmed by the FIB-SEM image (Figures 4 and 5).  
Figure 3: Cross-sectional SEM images of the spruce (**A–C**) and cup-plant core layer—spruce surface layer (**D–F**) PBs.  
showed that the pMDI adhesive was more dispersed in the cup-plant PB than in the spruce PB (Figures 6 and 7). While the spruce PB showed very narrow particle-particle bondlines, with penetration in non-compressed cell lumina regions (Figure 6), the adhesive in cup-plant PBs appeared to be more spread-out (Figure 7). This could be related to the greater and more dispersed porosity present in the cup-plant. An essential and novel outcome of this research was also that individual particle-particle bondlines could be visualized through molecular C-TOF SIMS identification. Here, no additional sample preparation such as staining was required, with the samples not getting modified in any way, as it is the case with other bondline identification methods [27]. With C-TOF, it is possible to visualize bondlines in 3D, different to regular electron or light microscopy imaging. The capability of 3D imaging also approved Hypothesis 2. The obtained approach delivered data for in-depth analysis of bondline mechanics and substrate interaction, since the dataset could be transformed into the finite element model, with the stress and strain distributions of the structural components to be further assessed [38].
# Evaluating Commercial Electrical Neuromodulation Devices with Low-Cost Neural Phantoms
## 1 Introduction  
### Overview  
Non-invasive transcranial electrical neuromodulation is a category of techniques for treating various neurological disorders [1; 2; 3; 4; 5; 6; 7; 8]. A variety of electrical neuromodulation devices are sold as non-medical devices, which differ in modality and quality [7; 9]. However, these often involve significant financial costs and ethical considerations. Obtaining and maintaining living tissues, along with the potential need for regulatory approval due to working with living subjects, can substantially increase the complexity and cost of experimental setups. A brain phantom, or material simulating the brain's electrical properties, can be used to acquire experimental results without animal or human testing [10; 11; 12]. Material selection, electrode placement, and costs in money and time are all factors in experimental design [13; 14; 15; 16]. A low-cost neural phantom could be used to reliably measure and verify the power delivered by a device and its mechanism of action. Prior phantoms, however, canoften take days of preparation or require specialized, expensive materials [13; 14; 15; 16]. Separate performance metrics have also been used to compare phantoms, such as the electric field. Such limitations preclude rapid, iterative adjustments of stimulation parameters. Calculating the electric field often assumes a uniform distribution across the phantom material, which may not be the case with heterogenous materials and unconventional stimulation modalities. By determining a cost-effective, easily prepared brain phantom material and proposing a common performance metric, electric neuromodulation parameters could be rapidly tested and compared. By proposing a common performance metric, three-dimensional electric field measurement technique, and material preparation comparison, several low-cost phantoms and measurement parameters were explored to validate two consumer electrical neuromodulation devices.
## 6 Conclusions  
In this paper, a method is established to determine the stress rainf range PDF using neural networks for the given non-Gaussian stress PSD and kurtosis. A bi-Gamma model is proposed to describe the PDF, and the parameters of the model are determined through neural networks. A broadband stress PSD expression with bimodal peaks is provided, and a non-Gaussian stress time-domain dataset based on this PSD and specified kurtosis is generated. The neural network model is trained and validated with the dataset. The conclusions of the article are as follows:  
The PDFs of the non-Gaussian stress rainflow ranges can be described accurately with the bi-Gamma model proposed in this article. The model demonstrates strong stability for stress PSDs with different parameters. The correlation coefficients between the predicted PDFs from neural network model and the PDFs obtained from the rainflow counting method all remain above 0.9.  
When the PDFs of the non-Gaussian stress rainflow ranges obtained by the method of this paper are used for life calculation, the results are within the second scatter band compared to the rainflow counting results. Compared to the correction method, the results by this article method are more accurate.  
In addition, through the sensitivity coefficient test of the training set size and robustness test, the impact of the training set size on the neural network's training results was investigated, and the robustness limits during prediction were given.  
The PDF model proposed in this paper can not only be used for predicting the stress rainflow range PDF under non-Gaussian stress but also has the potential to be applied to other more complex scenarios.  
In practical engineering problems, the loads to which structures are subjected are more complex, and the influences on the structural response are not limited to non-Gaussian effects, but also include transient and other nonlinear effects. Therefore, when performing a fatigue analysis for structures, it is necessary to provide a model that takes into account complex parameters and can be used for training and predicting PDF models under various complex loading conditions.  
J.W.: Conceptualization; Investigation; Methodology; Validation; Data Variation; Writing--Original Draft; H.C.: Supervision; Methodology; Formal analysis; Writing--Review and Editing. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.  
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.  
Figure 11: The robustness test of the neural network.
## 2 Problem Description and Model Formulation
### AGV-Referenced Action Space  
In traditional MAPF problems, the action space for AGVs is typically defined in terms of global movement directions, including move up, move down, move left, move right, and wait. However, this formulation ignores orientation and turning actions, assuming that AGVs can move omni-directionally.  
In real-world manufacturing environments, most AGVs cannot move omnidirectionally; they must rotate before changing direction. Like the Figure 1 shows, a left turn requires an AGV to stop, rotate 90\({}^{\circ}\) to the left, and then move forward. Although turning does not change the AGV's position, it incurs a time penalty similar to moving forward.  
To better simulate AGV movement, this paper redefines the action space based on the AGV's own reference frame, allowing the AGV to move forward, rotate 90\({}^{\circ}\) left, rotate 90\({}^{\circ}\) right, rotate 180\({}^{\circ}\) back, and then wait. Because most of the rotation time of an AGV's is spent on stopping and re-accelerating; the actual cost of the rotation itself is minimal. Although a single 180\({}^{\circ}\) turn takes longer than a 90\({}^{\circ}\) turn, it still takes much less time than performing two consecutive 90\({}^{\circ}\) turns. Therefore, in this work, a 180\({}^{\circ}\) rotation is treated as a basic action and occupies exactly one time step.  
By incorporating rotation actions, this action space accurately models the time penalty for turning, improving the realism of AGV motion planning. Unifying all basic actions  
Figure 1: Comparison Between Four-Direction Action Space and AGV-Referenced Action Space.  
under the same time step duration simplifies subsequent model training; during training, each agent can select one basic action per time step, and each action receives environmental feedback within that same step.