esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Given these problems, we have conducted some investigations and hope to find a sensor system that can be applied to weight support rehabilitation training and conduct basic research on criteria for activity recognition in patients with hemiplegia. To accurately recognize various patient and robot states in real time, we propose a multisensor fusion system that recognizes three dimensions: kinematics, kinetics, and position distribution. Our system distinguishes between periods of training and rest, detects events such as normal gait and abnormal gait, and achieves a reasonable degree of accuracy. In a previous study, we proposed a two-stage activity recognition classifier, in which a probabilistic neural network (PNN) is first trained to identify a fall in real time, and then data are transferred to a second level for detailed classification. Based on this recognition system, our previous paper also proposed an adaptive neural sliding-mode controller (ANSMC) that provides a continuum of either assistive or challenging rehabilitation training. This controller was applied to a mobile chaperone lower limb rehabilitation-training robot (MCLLRTR). The main contributions of this paper are summarized as follows:  
* We propose a multisensor fusion system that provides accurate activity and motor capability recognition fall prediction and physical fitness assessment during the rehabilitation training process. It uses the multidimensional features provided by multiple sensors with differing properties to achieve a reasonable degree of identification accuracy. To overcome the shortcomings of the kinematic sensor, we propose multisensor activity identification based on kinematics, kinetics, and spatial distribution. * We introduce a two-stage activity recognition classifier in which a probabilistic neural network (PNN) is first trained on regular activities to filter out falls. Then, the fall data are input to an SVM-RBF-KNN algorithm, which classifies specific falls or an abnormal gait in a supervised manner. The first stage predicts falls as early as possible, and the second stage determines fall details and abnormal gait types. These detailed classifications are provided to the therapist for functional assessment and the subsequent formulation of the therapeutic schedule. * We have tried to address the contradictions of sensitivity and specificity. By selecting appropriate sensors whose features can compensate for each other's shortcomings, we can achieve both high sensitivity and reasonable specificity. Additionally, to provide proper safety protection for rehabilitation patients, the motor locking commands are triggered when the algorithm predicts falls, and the bodyweight support slip provides protection. The rest of this article is organized as follows. Section 2 introduces the methods and system setup. Section 3 presents the experimental results. Finally, conclusions are summarized in Section 4.
## 5 Simulation and Experiments
### Dataset Experiment
To assess the robustness of the ICEKF with regard to leap noise from visual measurements, this study incorporates random simulations with an amplitude of 0.5 m into the visual measurement result to imitate harsh visual measurement failure. Figure 17 shows a comparison of the partial trajectory from the ICEKF against the corrupted visual measurement. This indicates that the ICEKF can significantly mitigate the impact of sporadic instability from visual measurements.
# A Real-Time Remote Respiration Measurement Method with Improved Robustness Based on a CNN Model
## 3 Materials and Methods
### Refining Motion Estimation Model's Respiratory Signals  
A time-series motion signal may be obtained by estimating the average motion information over time, as shown in Figure 7. Because the motion estimation model is trained to suppress noise components and detect only motion caused by respiration, a respiration signal that does not contain noise caused by movement other than breathing can be extracted. However, because the proposed motion estimation model compares the amount of motion between adjacent frames without reference, the accumulation of errors in each frame can cause bias in the detected motion signal, which can be considered as noise. Therefore, a post-processing operation must be performed to estimate respiration information by removing the corresponding noise component. In general, frequency-based post-processing methods are commonly considered for signal purification. These methods leave only specific components by using a band-pass filter to allow only the target frequency to pass through the acquired raw signal and to refine various signals, such as heart rate data. Because the deflection that occurs in the signal is a low-frequency component that exists in the entire signal, the trend of the respiratory signal can be removed by suppressing the low-frequency component. The low-frequency components of the signal can be obtained or removed through various methods, such as discrete Fourier transform (DFT) and moving average filtering-based methods. However, because the band-pass filter removes the defined specific frequency components, the pause between breaths is removed with low-frequency components, as shown in Figure 7a. The proposed method uses moving average filtering to obtain the signal. Considering that rates of 10-40 bpm are considered normal breathing, the window size of the filter used to obtain the low-frequency component was 6 s long, which may include the maximum period of breathing. Figure 7 shows an example of the raw signal where the trend occurred, and the breathing signal that removed the trend.  
Figure 6: Constructing a training dataset construction from video and respiration signals with a contact sensor.
## Materials and Methods
### Veterinary Antibiotics Accumulation and Bio-Concentration  
At the end of the growth period, plants were harvested carefully, and different plant parts analyzed for VAs.  
* Lettuce: Root
* The bottom part of the leaf
* The apical part of the leaf
* Carrot: Leaves  
* Foot (base): peel, flesh
* Brain (upper): peel, flesh
* Pepper: Root
* Stem
* Leaves
* Fruit  
The designated plant parts chopped separately, homogenized with liquid N\({}_{2}\) and stored at \(-20\)\({}^{\circ}\)C until analysis. VAs in plant root and soil, after 49 days, were extracted using the QuECHers AOAC LC-MS sample preparation kit. VAs in run-off water, during watering, was not calculated as the purpose of this study does not call for it. Briefly, 1 g sample (in 8 mL DW) extracted with a 12 mL QuECHers buffer solution. Following ultrasonication (15 min), QuECHers buffering salt was added and centrifuged for 15 m, 1500 rpm. The pH of the supernatant was made to 7.5 and centrifuged for 3 m, 3000 rpm. Then, 2 mL supernatant was transferred into a d-SPE cleanup tube, centrifuged (15 m, 15,000 rpm) and filtered through 0.2 uM filter. HPLC conducted in positive electron ionization mode with mobile phase A: 0.1% formic acid in DW and mobile phase B: 0.1% formic acid in acetonitrile kinetex 2.6u C18 50 x 2.1 mm at the flow rate of 0.5 mL min\({}^{-1}\). The gradient program was set as \(-0\) min, A:B = 95:5; 1 min, A:B = 95:5; 10 min, A:B = 0:100; 15 min, A:B = 95:5; and 20 min, A:B = 95:5. VAs concentration was analyzed with Agilent 1200 series HPLC interfaced with Agilent G1314C variable wavelength detector, G1312B binary pump, and G1316B column SL.  
Mass spectrometry analysis was conducted in positive electron ionization mode with the mobile phase flow into the chamber set at 11 mL min\({}^{-1}\), and the source temperature held at 300 \({}^{\circ}\)C. Detector signal for amino acids was recorded from 0-21 min and ions scanned across 190-400 \(m/z\).  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Soil Type** & **Sand (\%)** & **Silt (\%)** & **Clay (\%)** & **OM (\%)** & **pH (DW)** & **EC (\#S/cm)** & **N (mg kg\({}^{-1}\))** & **P (mg kg\({}^{-1}\))** & **K (mg kg\({}^{-1}\))** \\ \hline Sandy clay beam & 59.47 & 9.8 & 30.73 & 1.23 & 5.23 & 72.85 & 518 & 39.1 & 3400 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Properties of the soil used in the study.  
Bioconcentration factor (BCF) is the estimate used to determine the absorption potential of VAs by plants. It is the factor used for describing the rate of uptake of antibiotics from the soil by plants. BCF was calculated using the formula described by [PERSON] et al. [15].  
\[\text{BCF}\left(\%\right)=\frac{\text{Concentration}\,\,\text{root}+\text{Concentration}\,\,\text{ aerial}\,\,\text{part}}{\text{Initial}\,\,\text{concentration}\,\,\text{in external}\,\,\text{medium}}\]
# Path Planning of Inspection Robot Based on Improved Ant Colony Algorithm
## 3 Improved Ant Colony Algorithm
### Improved Pheromone Update Rule and Mathematical Analysis of Convergence
#### 3.4.2 The Improved Pheromone Update Formula  
To accelerate the convergence speed of the algorithm, this paper proposes an improved pheromone update rule designed to enhance global search capabilities. This enhancement is achieved by incorporating the differences between the best and worst paths into the pheromone update process. The improved pheromone update formula is expressed as follows:  
\[\omega_{ij}(t+1)^{*}=(1-\sigma)\cdot\omega_{ij}(t)+\Delta\omega_{ij}(t)+\Delta \omega_{ij}^{*}(t). \tag{17}\]  
where \(\Delta\omega_{ij}^{*}(t)\): the additional pheromone update amount based on the differences between the best and worst paths, used to enhance the probability of selecting the optimal path.  
\[\Delta\omega_{ij}^{*}(t)=\theta\times\frac{Q}{L_{min}}-\
u\times\frac{Q}{L_{ max}} \tag{18}\]\(L_{min}\): the shortest path length found by all ants in the current iteration. \(L_{max}\): the longest path length found by all ants in the current iteration. \(\vartheta\) and \(\
u\): represent the weight factors for the optimal and worst paths, respectively, used to adjust the increase and decrease of the additional pheromone.
## 3 Results
### Analysis and Synthesis
#### 3.1.3 MPC EMSs
A similar idea was proposed in [35], an EMS including two parts: offline modeling and online optimization. The offline modeling contained a Markov chain model. In the online optimization part, a Markov chain model was used to predict demand torque over a finite receding horizon and the sequential quadratic programming (SQP) algorithm was adopted to optimize the comprehensive cost function that included a novel adaptive factor. The proposed SQP-SMPC EMS was also compared with the traditional MPC, DP optimization and an RB strategy. Results indicated that the proposed strategy was effective for the given driving condition, with excellent fuel economy and vehicle-following performance. Finally, in [36], it was highlighted that several works ignored the effects of engine torque on the transmission efficiency. The transmission efficiency was derived based on the efficiencies of different components. An MPC was proposed with the capacity to deal with constrained optimal problems in a finite horizon, as in the EMS. The proposed EMS was benchmarked against an RB strategy, inspired by the Toyota Prius control, representing a strategy with easy implementation and real-time capability. Compared with this, the proposed strategy ensured high powertrain transmission efficiency and, as a result, an improvement in equivalent fuel economy.
# Suppressed Transmission of Long-Range Surface Plasmon Polariton by TE-Induced Edge Plasmon
###### Abstract  
Work on controlling the propagation of surface plasmon polaritons (SPPs) through the use of external stimuli has attracted much attention due to the potential use of SPPs in nanoplasmonic integrated circuits. We report that the excitation of edge plasmon by TE-polarized light passing across gapped-SPP waveguides (G-SPPWs) leads to the suppressed transmission of long-range SPPs (LRSPPs) propagating along G-SPPWs. The induced current density by highly confined edge plasmon is numerically investigated to characterize the extended radiation length of decoupled LRSPPs by the TE-induced edge plasmon. The suppressed transmission of LRSPPs is confirmed using the measured extinction ratio of the plasmonic signals which are generated from the modulated optical signals, when compared to the extended radiation length calculated for a wide range of the input power. It is also shown that LRSPP transmission is sensitive to the excited power of edge plasmon in the gap through the permittivity change near the gap. Such a control of SPPs through the use of light could be boosted by the hybridized edge plasmon mode and a huge field enhancement using nanogap, gratings or metasurfaces, and could provide opportunities for ultrafast nano-plasmonic signal generation that is compatible with pervasive optical communication systems.  
plasmonics; surface plasmon polariton; edge plasmon; waveguide devices; plasmonic signal copier; nanoplasmonic integrated circuit +
Footnote †: journal: 2021  
12
# An Applicable Predictive Maintenance Framework for the Absence of Run-to-Failure Data
## 4 Case Study 1
### Experimental Result  
As shown in Figure 6, the HI model created after setting up the normal interval detected the failure well. In particular, the HI value increased before the failure occurred, enabling the engineer to predict the failure in advance.  
In addition, as indicated by the orange arrow in Figure 6, there is a section where the HI rises sharply. This is difficult to detect with the Statistical Process Control (SPC). In other words, the autoencoder learns the nonlinear structure of the data so that the correlation of the data is reflected. Thus, the change in correlation between the variables affected the rise of the HI value, and when there was a trend, the RUL could be predicted.  
It is also confirmed that each fault class has a different threshold. This can be affected by hyper-parameters and configurations [29]. However, having a different threshold for each failure type means that failure types can be classified if a lot of failure data are accumulated later. In other words, the proposed framework can construct HI, predict RUL, and provide expected failure types.
# Communication Architecture for Grid Integration of Cyber Physical Wind Energy Systems
## 4 Simulation Results
### Met-Mast Results  
We assumed that the dimension of the Met-mast platform were 10 \(\times\) 8 m. Four data collection units (DCU) were installed at different levels (90 m, 70 m, 50 m, and 20 m) for collecting the measurement data. All sensing data were collected at the measurement PC located at the met-mast platform. Each DCU had a dedicated communication link to the measurement PC to maintain reception of data in case of any faults in other DCUs. The electric power needed for the measurement equipment and other devices at the met-mast could be supported from the WF or from photovoltaic panels. Figure 13 shows the received traffic at the measurement PC for pressure, ADCP, humidity, wind speed, and wind direction, which were 150 bytes/s, 400 bytes, 4 bytes/s, 48 bytes, and 24 bytes/s, respectively. Figure 14a shows the total received data at the measurement PC of about 638 bytes/s, while the ETE delay is about 0.54 ms and 0.058 ms for link capacities of 10 Mbps and 100 Mbps, respectively. The amount of data stored at the measurement PC was about 55 Mb per day, and data was transmitted to the met-mast server at the local control center for storage.  
Figure 11: Total received traffic at the SCADA server in the control center (20 wind turbines).  
Figure 12: Average ETE delay for the wind farm data (20 wind turbines).  
In the real system, the data acquisition system at the met-mast uses wireless data transmission using WCDMA communication to transmit the collected data from the measurement PC to the remote control center [32]. We assumed that the met-mast was connected to the nearest wind turbine in the WF (WT16). The real-time measured data were stored at the measurement PC and transmitted to the local control center (met-mast server) through the WF communication network. The IP ETE delay of the met-mast data through the WF network is shown in Figure 15.  
Figure 14: (**a**) Total received traffic at measurement PC for different sensors (**b**) ETE delay for the met-mast sensors.  
Figure 13: Traffic received at measurement PC for different sensors. (**a**) Voltage and current; (**b**) Wind speed, pitch angle, and tilt.  
Figure 15: ETE delay for met-mast data (link capacity 100 Mbps and 1 Gbps).
# Emotion Analysis AI Model for Sensing Architecture Using EEG
## 1 Introduction
### Methods and Scope of Study
#### 1.2.1 Dataset Overview and Preprocessing  
The SEED (Shanghai Jiao Tong University Emotion EEG Dataset) [16] dataset, developed by the BCMI Research Institute at Shanghai Jiao Tong University, was selected as the primary dataset for this study [17]. It comprises EEG recordings from 15 participants, each exposed to emotionally evocative film clips categorised into positive, neutral, and negative states [4]. The EEG signals were collected using a 62-channel NeuroScan system (Compumedics Neuroscan, Charlotte, NC, USA) at a sampling rate of 1000 Hz, which was subsequently downsampled to 200 Hz to optimise computational efficiency [5].  
To prepare the data for model training, EEG signals were converted into a JSONL format, where explicit channel labels were removed while temporal and spectral features were preserved. This transformation enables the model to learn generalised EEG representations without relying on predefined spatial mappings [8].  
Preprocessing was performed to enhance data quality and improve model robustness. Bandpass filtering (0.5-50 Hz) was applied to eliminate noise, ensuring that only relevant frequency components were retained. Independent Component Analysis (ICA) was used to remove ocular and muscular artefacts, minimising contamination in the EEG signals. Channel-wise normalisation was implemented to standardise EEG signals across participants, reducing inter-subject variability and improving model generalisation [9]. While the dataset includes eye-tracking data, this study focuses solely on EEG signals, as they provide a direct and objective measure of neural activity associated with affective states [10].
## 2 Basics of AEPS Reliability Analysis  
### The Architecture of AEPS in the Design Stage  
An AEPS was designed to provide electrical energy for the entire aircraft's electrical load including avionics equipment, de-icing systems, navigation systems, flight control systems, etc. How to select suitable components to build a power distribution network/architecture that meets the power requirements of the entire aircraft is the primary task in the design stage of AEPS [38]. A typical AEPS is shown in Figure 1 and see [39; 40; 41] for more similar AEPS.  
In Figure 1, four generators are included: one left and one right alternating current variable frequency generators indicated as LG and RG separately, an auxiliary power unit generator (APUG), and an emergency power unit-ram air turbine generator (RATG). The generators RG and LG work independently and are the backup for each other; when both of them fail, APUG and RATG serve as backups to provide electrical power for the safety-critical equipment. In addition, there are some batteries (e.g., left and right batteries (LBAT, RBAT)) and flight control batteries (FCBAT). In particular, components such as generators and batteries that serve as an electrical power source in the AEPS architecture are defined as source nodes, and the busbars/buses that directly support the operation of selected electrical loads are defined as sink nodes, from which the required power of the loads (e.g., navigation system, flight control system, etc.), is obtained.  
It can be seen that an AEPS in the design stage is essentially a directed topology network with a \"source-network-sink\" structure including four basic functions: power generation, power storage, power distribution, and load support [16]. Moreover, \"source\" is the starting point of the network used to perform power generation and storage function; \"sink\" is the end point of the network used to perform the load support function; \"network\" includes all the components between sources and sinks (e.g., contactors, inverters, etc.) used to perform the function of distribution.
# Urban Free-Space Optical Network Optimization
## 3 A Genetic Algorithm for FSO Communication Networks
### Generate Initial Population
For the sake of randomness, a relay vertex \(v^{\prime}\in V(G\setminus G^{\prime})\) is chosen, through which the path to \(v\) shall pass. However, this may lead to long and inefficient paths and eventually to poor solutions when a distant vertex (from the shortest path between \(v\) and the connected component \(G^{\prime}\)) is chosen instead of other more appropriate relay vertices. Thus, only vertices that lead to a path at most \(d\) hops long (\(|SP\left(v,v^{\prime}\right)|+|SP\left(v^{\prime},G^{\prime}\right))|\leq d\)) are considered (Algorithm 3, lines 1 and 3). Ultimately, a relay vertex \(v^{\prime}\) is randomly chosen from the set of valid options (line 6), and the resulting (shortest) path \(P\) that goes through \(v^{\prime}\) is returned (lines 7-8). Finally, the path \(P\) is added to the graph \(G^{\prime}\) (Algorithm 2, line 5). ```
Input:\(G=(R,B,E)\) red-blue visibility graph, a subgraph \(G^{\prime}\subseteq G\), and a vertex \(v\). Result: A path \(P\subseteq G\). let \(d\) be a predefined maximal path length, such that \(d\geq|SP\left(v,G^{\prime}\right)|\) \(V^{\prime}=\emptyset\) for each vertex \(v^{\prime}\in V(G\setminus G^{\prime})\), where \(|SP\left(v,v^{\prime}\right))|+|SP\left(v^{\prime},G^{\prime}\right))|\leq d\)do  add \(v^{\prime}\) to \(V^{\prime}\) end  randomly choose \(v^{\prime}\in V^{\prime}\) \(P=SP\left(v,v^{\prime}\right)\cup SP\left(v^{\prime},G^{\prime}\right)\) return\(P\)
```  
**Algorithm 3**Pseudocode of \(findPath\), which finds a random path connecting a new vertex to a graph. Figure 9 illustrates the randomness of \(findPath\) by showing two possible ways to connect a new vertex \(v\). The above \(randomConnect\) constitutes a _constructive_ process of gradually adding vertices (and the paths that connect them) to the connected component of the sink vertex \(s\) until a valid solution is obtained. Another possible approach is to apply a _destructive_ process, in which red vertices are gradually removed from the initial graph \(G\), as long as the graph remains valid (i.e., \"blue-connected\"). In real-world FSO applications, there are commonly many more red vertices than blue ones; thus, the constructive approach has been chosen herein. The \(randomConnect\) process is used not only in \(firstGeneration\), but also in the construction of subsequent generations to ensure the validity of obtained solutions.
## 1 Introduction
Due to its immobility, the cluster head has a lower possibility of losing data when utilized with a static sink. Using the suggested model results in a decrease in energy use and an increase in lifetime. The proposed protocol boosts the cluster's, group's, and network's efficiency in terms of overheads, energy use, and throughput dependability. The lifespan of the network, group members, and cluster head/core are all improved by this efficiency and reliability.