esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methods
### Data Acquisition and Analysis  
All kinematic and EMG variables to evaluate scapular stability during the movement were computed using a custom-written algorithm in MATLAB (version 2023b, The Mathworks Inc., Natick, MA, USA).  
Kinematics: Kinematic data (the acromioclavicular (AC) joint displacement) exported by Vicon Nexus 2.12 Software (Vicon, Oxford Metrics, Oxford, UK) were low-pass filtered through a fourth-order Butterworth filter with a cut-off frequency of 6 Hz [28].  
EMG: The peak amplitude and burst duration of EMG in each muscle (biceps, triceps, and lower trapezius) were measured. The raw EMG data collected during the movement testing underwent the following processing steps:  
(1) Pre-processing of sEMG signal: Any DC offset was first eliminated using the \"detrend\" function in MATLAB. Next, a median filter was applied to the signal to remove noise, followed by the application of a 20-450 Hz bandpass filter to extract the frequency range where muscular energy is concentrated [29; 30; 31].  
(2) sEMG rectification and linear envelope: sEMG signal values below zero were converted to positive values of the same amplitude to create a full-wave rectified sEMG signal (see Figure 2). To obtain sEMG envelopes, a 2 nd-order Butterworth low pass filter with a 20 Hz cutoff frequency was applied for digital smoothing [29; 30; 31].  
(3) % EMG peak amplitude  
EMG peak amplitude was normalized to the baseline EMG value, calculated as follows:  
\[\%\ \text{EMG peak amplitude}=\frac{(\text{peak amplitude value}-\text{ baseline EMG value})}{\text{baseline EMG value}}\times 100\]  
(4) EMG Burst Duration  
EMG onset time was determined as the moment when the EMG exceeded three standard deviations (SD) of the baseline EMG value. The EMG offset was identified as the time when the EMG activity decreased to a level below three SD of the baseline EMG value [32]. The interval between EMG on and off was defined as \"EMG burst duration\".  
Figure 3: Dots image used for feedback conditions 2 and 3.
# MAToC: A Novel Match-Action Table Architecture on Corundum for 8 \(\times\) 25G Networking
## 4 Implementation
### Thoughput Analysis  
Throughout is the main metric in our experiments. The physical limitation in optical modules is called the line rate, which is around 8 \(\times\) 25 = 200 Gbps in our case and it is also the target throughput. The throughput of the internal data path depends on datawidth and clock frequency. Parameters such as data width are automatically calculated to ensure sufficient bandwidth of the on-board data path. For example, the data width is bound to 512 bits for 100G link mode and 64 bits for 10G, 128 for 25G with the same clock frequency of 250 MHz. Therefore, the original infrastructure modules would not be a restriction. As we mentioned earlier, when traffic from all eight channels is merged into a single pipeline for processing purposes, the pipeline becomes a bottleneck for the entire application. This is why we emphasize that modules in the fast path should not stall packets when the backpressure signal is not asserted. In addition, to maintain a throughput margin, data width adaptation is necessary.  
In MAToC, data width adapting is performed for the MAT pipeline, from 128 bits in the ingress and egress, to 512 bits internally, in order to ensure the headers are transferred as one frame per packet. The working frequency is 250 MHz, which means the total throughput is limited to 250 Mpps. We will show that this choice is a trade-off between routing complexity and performance.  
With eight channels combined, the total throughput seems to be 200 Gbps. However, the actual data rate varies with packet length (PL) due to the bandwidth consumption of interframe gap (IFG, normally 12 bytes), preamble (abbreviated as P, 7 bytes), and start frame delimiter (SFD, 1 byte). The maximum receive rate can be calculated by formula (1), which is coincident with the actual traffic rate from the IXIA generator, tagged as transmit rate upper bound (TX UB) in Figure 3.  
\[\text{Rx Rate }=\frac{\text{PL}}{\text{PL}+\text{IFG}+\text{P}+\text{SFD}} \times\text{Line Rate}, \tag{1}\]
## 6 Conclusions  
In this paper, the composite main reflector of the large deployable high-stability reflector antenna was developed using space-grade high-stiffness carbon fiber and the aramid honeycomb core. First of all, the laminated pattern composed of the composite facesheet and the honeycomb core in the main reflector was determined using modal analysis of the unit structure. The weight of the main reflector model using a low-density honeycomb core was reduced by about 8.8% compared to that of the high-density core. The antenna assembly using the low-density core is approximately 5.7% lighter than that using the high-density honeycomb core. Considering the launch and on-orbit environment, the antenna assembly with the low-density core of Case 2.2 has a 25% better damping performance than the high-density core of Case 2.1 in terms of RMS displace criteria. Although the fundamental mode frequency of both models is higher than the stiffness requirement (\(\geq\)2.5 Hz), the antenna assembly based on Case 2.2 has a 3.7% higher mode frequency than Case 2.1. The DR-A model will be developed based on the main reflector designed in this study. Finally, the environment test of the antenna assembly manufactured by the domestic technology will be performed to experimentally evaluate structural and thermal stability.  
Conceptualization, Y-J.Y.; methodology, D.-G.K.; software, H.-G.K. and D.-G.K.; validation, H.-G.K. and D.-G.K.; formal analysis, D.-G.K.; investigation, H.-G.K.; resources, Y.-J.Y.; data curation, R.-H.D. and D.-G.K.; writing--original draft preparation, H.-G.K. and D.-G.K.; writing--review and editing, K.-R.K.; visualization, H.-G.K.; supervision, K.-R.K.; project administration K.-R.K.; funding acquisition, K.-R.K. All authors have read and agreed to the published version of the manuscript.  
This study was conducted with support from the Korea Research Institute for defense Technology planning and advancement and the Defense Acquisition Program Administration in 2021 (20-207-B00-011-001).  
Not applicable.  
Data Availability Statement: All data are in the paper; there are no additional data.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{l l c c} \hline \hline  & & **Case 2.1** & **Case 2.2** \\
**Antenna Assembly** & & **Stacking Sequence with the** & **Stacking Sequence with the** \\  & & **High-Density Honeycomb Core** & **Low-Density Honeycomb Core** \\ \hline Mass & & 100.25 & 94.54 \\ Modal analysis & 1 st mode (Hz) & 35.38 & 36.47 \\ (Stowed condition) & 2 nd mode (Hz) & 51.76 & 54.26 \\ Modal analysis & 1 st mode (Hz) & 3.34 & 3.47 \\ (Deployed condition) & 2 nd mode (Hz) & 10.93 & 11.77 \\  & (Facesheet MOS & 0.52 & 0.67 \\ Quasi-static analysis & (Fiber direction) & & \\  & Core MOS & 4.81 & 6.89 \\  & (Shear direction) & & \\ Micro-vibration analysis & RMS (m) & \(1.90\times 10^{-4}\) & \(1.34\times 10^{-4}\) \\ \hline \hline \end{tabular}
\end{table}
Table 7: Summary of the structural analyses of the antenna assembly level.
## 2 Performance at Higher Sampling Rates  
_Hybrid models_: Methods that have combined CNN with RNN or attention mechanisms, such as CNN-LSTM with attention, display well-acceptable results in dealing with ECG signals by leveraging both spatial and temporal features [14]. Such models might attain similar or slightly improved MSE and RMSE values but often at an amplified computational cost. The stable performance of the TCN model at varying rates specifies that it offers a good trade-off between precision and computational efficiency, thus making this model a reasonable alternative to the other more intricate models.  
The current transformer model not only matches but, in some cases, even outperforms these hybrids with a much simpler and more efficient architecture.
###### Abstract  
The automatic opening of an ampoule bottle is key to making the operation of the sterility inspection automatic. During the automatic opening, the fracture characteristics on the neck of ampoule bottle need to be deeply understood to avoid the contamination of the samples by preventing the glass fragments from dropping into the ampoule bottle. This paper presents the calculation of fracture characteristic parameters, such as the coefficient \(K\) of the stress intensity factor (SIF) and the coefficient \(F\) of shape factor (shape coefficient), based on the finite element method (FEM) for a non-penetrating three-dimensional crack. Mechanical and mesh modes were built for the special structure of an ampoule bottle with a V-shaped notch, and the influence of mesh size on the coefficient \(K\) was evaluated. The mathematical expressions of stress intensity factor and shape coefficient of three types of cracks were established. The results demonstrate that the crack ellipticity (\(a/c\)) and crack relative depth (\(a/t\)) have significant effects on the \(K_{\rm{L}}\), \(K_{\rm{II}}\), and \(K_{\rm{III}}\) of the SIFs. The \(K_{\rm{I}}\) plays a dominant role, which follows a symmetrical distribution at the symmetrical position on both sides of the deepest crack point, whereas the \(K_{\rm{II}}\) and \(K_{\rm{III}}\) can be negligible. The corrected shape coefficient \(F_{\rm{I}}\) decreases with increasing ellipticity \(a/c\) and increases with increasing relative depth \(a/t\) under the combined tensile stress and bending loads. The comparison to the literature shows the calculation of the corrected shape coefficient has a high accuracy based on the FEM, which will be applicable and reliable for non-penetrating three-dimensional cracks.  
Keywords:stress intensity factor; shape coefficient; non-penetrating crack; finite element method; ampoule bottle; sterility inspection +
Footnote †: journal: Applied
## 4 Proposed Architecture and Implementation
### Blockchain Components  
* Blockchain platform-our implementation is based on Hyperledger Fabric [24], a permissioned blockchain platform that ensures that only authorized vehicles and RSUs participate in the network.
* Consensus algorithm-the system uses a Byzantine Fault Tolerant (BFT) [25] consensus mechanism, in this case Practical Byzantine Fault Tolerance (PBFT) (Algorithm 3) [26], which is suitable for environments with a relatively small number of participants (RSUs and vehicles) and requires fast consensus.
* Cryptographic techniques-the developed system relies on elliptic curve cryptography (ECC) [27] for secure key generation, as it provides strong security with relatively small key sizes, which is ideal for resource-constrained in-vehicle IoT devices.  
```
1:Input: Client request _req_
2:Output: Agreement on the state of the system
3:Initialization:
4: All nodes \(n_{i}\) are initialized with the same initial state
5: The system can tolerate up to \(f\) faulty nodes out of \(3f+1\) total nodes
6: Primary node \(P\) is selected as the leader
7:functionPBFT(req)
8:Phase 1: Pre-Prepare
9: Primary \(P\) receives the request from the client
10: Primary \(P\) assigns a sequence number to the request and multicasts a _pre-prepare_ message to all replicas
11:Phase 2: Prepare
12:for each replica \(r_{i}\)do
13: Replica \(r_{i}\) verifies the _pre-prepare_ message
14:if message is valid then
15:\(r_{i}\) multicasts a _prepare_ message to all other replicas
16:endif
17:endfor
18: A replica waits until it receives \(2f\)_prepare_ messages from different replicas
19:Phase 3: Commit
20:for each replica \(r_{i}\)do
21:if\(r_{i}\) receives \(2f\)_prepare_ messages then
22:\(r_{i}\) multicasts a _commit_ message to all replicas
23:endif
24:endfor
25: A replica waits until it receives \(2f\)_commit_ messages
26: The request is executed and the result is sent to the client
27:return Agreement reached and state updated
28:endfunction
```  
**Algorithm 3** Practical Byzantine Fault Tolerance (PBFT)
# Interference Suppression in EEG Dipole Source Localization through Reduced-Rank Beamforming
###### Abstract  
In this paper, we propose new neural activity indices for the solution of the inverse problem of localizing sources of cortical activity from electroencephalography (EEG) measurements. Such indices are based on reduced-rank beamformers, specifically the generalized sidelobe canceler (GSC), and with the purpose of suppressing the contribution of interfering sources and noise. Here, the GSC is modified with an adaptive blocking matrix (ABM) to optimally estimate and later suppress unwanted brain sources. With respect to the rank-reduction, this is achieved through the cross-spectral metrics (CSM) as they give a sense of the affinity of the beamformers' eigenstructure to the orthogonal subspace of noise an interference. Based on that, two different neural indices are proposed for the assessment of brain activation. Our realistic simulations show that a more consistent source localization is achieved through the proposed indices in comparison to the use of the traditional full-rank approach, specifically for brain sources embedded in high background activity that originates at the brain cortex and thalamus. We also prove the applicability of our methods on the localization of sources on the visual cortex produced by steady-state visual-evoked potentials.  
reduced-rank beamforming; cortical activity; neural indices Article
# Rural Healthcare IoT Architecture Based on Low-Energy LoRa
## 5 Remote and Environment Triggering  
The low power constraints of the architecture result in information pulling as means for the battery-powered devices to communicate in the network. This is sufficient to complete some goals, such as scheduled communication and sending data when needed. However, push notifications are necessary for specific situations, such as when a command is sent from the care providers. To allow for push notifications, we propose utilizing wake-up signals to trigger the devices to exit the sleep state and prepare to receive network communication.  
In an ideal case, a sensor IoT module will not consume any energy until the act of measurement. When measuring parameters on time intervals, this can be accomplished by ULP timers. However, when monitoring for an event that might happen at any time, we either have to conduct measurement the entire time or construct a device that will wake the node upon such event occurs.  
A care receiver might want to obtain sensor information at a specific time. For example, they might want to check if the person is in bed, if there is electricity in the house, or if the medicine disposal device is empty. A remote trigger mechanism is needed to send a wake-up signal to those sensors that are identified as needing these ad-hoc probes. Such a signal must be routed through the communication gateway for the system. The standby energy usage must be low enough not to affect the devices' expected battery life significantly.  
When utilizing the external switch to cut the power supply to the microcontroller, we need a way to send a wake-up signal to start the microcontroller. Fortunately, the ULP timer can receive interruptions. A wake-up signal can be used to trigger this interrupt. To \"wake up\" the TPPL5111, the DELAY/M_DRV pin should be set to high, which can be accomplished by a short connection to \(V_{DD}\) pin.  
In this paper, we consider the following types of wake up signals:  
* Radio frequency (RF) signal;
* Light signal;
* Sound signal;
* Mechanical signal.
# The Design and FPGA-Based Implementation of a Stream Cipher Based on a Secure Chaotic Generator
## 4 Performance Analysis of the Proposed SCbSC
### Cryptanalytic Analysis  
In order to assess the security of the proposed SCbSC system against the most common attacks, we performed the following the key space analysis and assessed its sensitivity; then we used statistical analysis.
## 1 Introduction
In order to meet the highly reliable transmission requirements for satellite communications, this paper presents a new decoding algorithm based on multi-dimensional reliable information addition iteration (MRAI-LDPC). The main innovation of the MRAI-LDPC algorithm is as follows: Firstly, the Hamming distance coefficient is directly calculated by subtraction, avoiding its computer simulation process. Then, the channel received information is subjected to hard-decision and constellation mapping processing, and we design correlated reliability between the extrinsic information and the mapping processing information to improve the correctness of decoding. Finally, the Hamming distance coefficient, the correlated reliability between the extrinsic information and the mapping processing information, and the extrinsic information frequency are simply summed to reduce the system's complexity. The simulation results show that the presented MRAI-LDPC algorithm can achieve better error-correcting performance and throughput, ensuring the reliable transmission of satellite communications. The remaining sections of this paper are organized as follows: Section 2 introduces the system model of LDPC. Section 3 designs a new LDPC decoding algorithm based on multi-dimensional reliable information addition iteration. In Section 4, we complete the performance simulation and complexity analysis of the MRAI-LDPC decoding algorithm. Finally, Section 5 provides a summary of this paper.
# Heat Transfer Enhancement of Nanofluids with Non-Spherical Nanoparticles: A Review
## 2 Thermal Conductivity of NSN Nanofluids
### Numerical Predictions and Mechanism Discussion
#### 2.2.3 Network Effect  
[PERSON] et al. [71] constructed a model based on the thermal resistance network by considering the random CNT orientation and CNT-CNT interaction. Their model effectively agreed with experimental data by assuming that a CNT contacts only two neighboring CNTs. Subsequently, [PERSON] et al. [72] improved the model by considering the excluded volume of cylindrical particles. The revised model describes the effect of particle diameter and aspect ratio of CNTs and CNFs on TCE. The alignment of CNTs and CNFs due to the long-range repulsion force decreases the excluded volume, nonlinearly increasing the convexity and concavity and linearly increasing the thermal conductivity with particle concentration. [PERSON] et al. [44] recently simulated the TCE of nanofluid using the lattice Boltzmann method and discovered that the network effect promotes the enhancement of heat transfer. Their algorithm prevents nanoparticles from forming aggregations by defining a minimum proximity distance.
## 3 Background
They combine an underlying Markov chain of hidden states with observed data, characterised by transition probabilities (\(P(Z_{t}|Z_{t-1})\)) and emission probabilities (\(P(X_{t}|Z_{t})\)) [17]. The model's joint probability distribution is expressed as per the equation:  
\[P(\mathbf{X},\mathbf{Z})=P(Z_{1})\prod_{t=2}^{T}P(Z_{t}|Z_{t-1})\prod_{t=1}^{T }P(X_{t}|Z_{t}) \tag{2}\]  
HMMs are particularly valuable in applications like speech recognition, handwriting analysis, and time-series labelling, where hidden states provide insights into underlying patterns. LSTM networks are a specialised type of recurrent neural network (RNN) designed to overcome challenges like the vanishing gradient problem. LSTMs use gated units--forget, input, and output gates--to selectively retain or discard information, enabling them to capture both short-term and long-term dependencies [18]. The forget gate's function is defined as per the equation:  
\[f_{t}=\sigma(W_{f}\cdot[h_{t-1},x_{t}]+b_{f}) \tag{3}\]  
LSTM networks are widely applied in tasks such as time-series prediction, machine translation, and natural language processing. The GRU is a simplified alternative to LSTM. It uses fewer parameters by combining the forget and input gates into a single update gate, reducing computational complexity while maintaining comparable performance [19]. The update gate is represented as per the equation:  
\[z_{t}=\sigma(U_{z}^{(h)}\cdot h_{t-1}+W_{z}^{(x)}\cdot x_{t}) \tag{4}\]  
GRUs are preferred in resource-constrained environments due to their efficiency. GANs consist of a generator and a discriminator that compete to produce realistic synthetic data [20]. The generator creates samples, while the discriminator evaluates their authenticity. The min-max optimisation objective is defined as per the equation:  
\[\min_{G}\max_{D}\ \mathbb{E}[\log D(x)]+\mathbb{E}[\log(1-D(G(z)))] \tag{5}\]  
GANs are used in various fields, including image generation, data augmentation, and sequence modelling. The DGAN is a GAN-based model tailored for generating sequential data, supporting time-varying features and categorical variables [21]. Reinforcement learning (RL) is a machine learning paradigm where an agent interacts with an environment to maximize cumulative rewards. It emphasises balancing exploration (discovering new strategies) and exploitation (leveraging known strategies) [22]. The RL framework is often formalised using Markov Decision Processes (MDPs), defined by a tuple \((S,A,P,R,\gamma)\), where \(S\) is the state space, \(A\) is the action space, \(P\) represents transition probabilities, \(R\) is the reward function, and \(\gamma\) is the discount factor. RL is widely used in decision-making and dynamic system modelling, offering a robust framework for optimising strategies through iterative learning.
# Biomechanical Analysis of Running in Shoes with Different Heel-to-Toe Drops
###### Abstract  
The heel-to-toe drop of running shoes is a key parameter influencing lower extremity kinematics during running. Previous studies testing running shoes with lower or larger drops generally used minimalist or maximalist shoes, where the factors outside of the drop may lead to the observed changes in running biomechanics. Therefore, our aim was to compare the strike patterns, impact force, and lower extremity biomechanics when running in shoes that varied only in their drops. Eighteen habitual rearfoot strikes performed trials wearing running shoes with four drop conditions: 15 mm, 10 mm, 5 mm, and without a drop. Three-dimensional (3D) tracks of the reflective markers and impact force were synchronously collected using a video graphic acquisition system and two force plates. The biomechanical parameters were compared among the four drop conditions using one-way ANOVA of repeated measures. A greater foot inclination angle (\(p=0.001\), \(\mathrm{np^{2}}=0.36\)) at initial contact and a lower vertical loading rate (\(p=0.002\), \(\mathrm{np^{2}}=0.32\)) during the standing phase were found when running in shoes with large drops compared with running in shoes without a drop. Running in shoes with large drops, as opposed to without, significantly increased the peak knee extension moment (\(p=0.002\), \(\mathrm{np^{2}}=0.27\)), but decreased the peak ankle version moment (\(p=0.001\), \(\mathrm{np^{2}}=0.35\)). These findings suggest that the heel-toe drop of running shoes significantly influences the running pattern and the loading on lower extremity joints. Running shoes with large drops may be disadvantageous for runners with knee weakness and advantageous for runners with ankle weakness.  
heel-toe drop; strike pattern; knee; ankle; injury +
Footnote †: journal: Applied Sciences  
1  
Biomechanics Laboratory, School of Sport Science, Beijing Sport University, Beijing 100084, China; [EMAIL_ADDRESS] (M.Z.); [EMAIL_ADDRESS] (H.S.) 1  
China Institute of Sport and Health Science, Beijing Sport University, Beijing 100084, China; [EMAIL_ADDRESS] 1  
Footnote 1: email: [EMAIL_ADDRESS]