esa-sceva/satcom-chunk-collection huggingface dataset:
# Predicting Choices Driven by Emotional Stimuli Using EEG-Based Analysis and Deep Learning
## 3 Related Works  
In recent years, there has been an increasing interest in utilizing EEG signals for emotion recognition and prediction for human reactions based on their responses to emotional stimuli. Several studies have explored the use of DL models to develop accurate emotion recognition systems using EEG signals.  
One such study by [PERSON] and [PERSON] [8] introduced the use of deep belief networks (DBNs) for constructing EEG-based emotion recognition models from 62 channels for three types of emotions: positive, neutral, and negative. They extracted five features: power spectral density (PSD), differential entropy (DE), differential asymmetry, rational asymmetry, and the differences between the DE features of 23 pairs of frontal-posterior electrodes. A comparison between DBN and the shallow models (SVM, LR, and KNN) was conducted, with the average accuracies being 86.08%, 83.99%, 82.70%, and 72.60%, respectively. They also proposed a DBN-based method for selecting critical channels and frequency bands using the weight distributions of the trained DBNs. Among the different electrode sets, 12 channels with SVM achieved the highest accuracy (86.65%) and lowest standard deviation (8.62%), surpassing the accuracy of the 62 channels with SVM (83.99%) and DBNs (86.08%).  
[PERSON] et al. [27] extracted features using the discrete wavelet transformation method and the AlexNet model from 62 channels in SEED. They used the bag of deep features as a dimensionality reduction and attained 93.8% accuracy with the SVM classifier.  
[PERSON] et al. [28] extracted features using dynamic sample entropy measures from all channels over time. They utilized SVM and achieved an average accuracy of 85.11% for identifying negative and positive emotions. [PERSON] et al. [29] chose eight channels to classify positive and negative emotional states. They used the wavelet transform (WT) approach to decompose and extract the frequency band and calculate the sample entropy. They proposed LSTM and achieved a final accuracy rate of 90.12%.
## 4 Expected Results  
Based on the detailed analyses of the semi-physical simulations described above, an experimental platform for the semi-physical simulation test of the inter-satellite laser interference is designed. The experimental scenario for the semi-physical simulation test of inter-satellite laser interference is shown in Figure 13a. The dual vacuum tank system under construction is depicted in Figure 13b.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Technical Specifications** \\ \hline Ultimate vacuum & \(5\times 10^{-6}\) Pa \\ Leakage rate & \(<\)1 \(\times\) 10\({}^{-10}\) Pa\(\cdot\)m\({}^{3}\)/s \\ Temperature & \(<\)80 K \\ Temperature uniformity & \(\pm\)3 K \\ \hline \hline \end{tabular}
\end{table}
Table 6: Main technical parameters of the vacuum tank with a heat sink.  
Figure 11: Schematic diagram of a vacuum tank with a heat sink (top view).  
Figure 12: Simulation of the space environment for the two spacecrafts using the dual vacuum tank system (top view).  
With the combination of DWS technology and the piezo-driven hexapod, not only can the residual error of 1 \(\upmu\)rad for the acquisition procedure based on the CCD be verified, but the test used to establish the inter-satellite laser link with a pointing jitter of 100 nrad/\(\surd\)Hz can also be performed. After establishing the stable laser link, the inter-satellite ranging interferometry test can be conducted by measuring the system noise. A ranging noise of better than 100 pm/\(\surd\)Hz can also be achieved.  
Except for the semi-physical simulation test for the two key technologies, the semi-physical simulation demonstration of the in-orbit experiment processes can also be implemented using the experiment platform. These processes composed involve attitude control of the spacecraft, startup and shutdown of payloads, calibration of certain payloads, inter-satellite laser acquisition, weak-light phase locking, laser precision pointing, establishment of the laser link, execution of the scientific experiment mode, and other factors.  
In the future, with the addition of the same vacuum tank with heat sink, the experimental platform can be upgraded to form a triangle, with which the semi-physical simulation test for the final triangle constellation of the Taiji program can also be performed.
# Crowd Monitoring and Localization Using Deep Convolutional Neural Network: A Review
## 5 CNN and Deep CNN Frameworks  
Deep CNNs are special types of Artificial Neural Networks (ANNs) that learn hierarchical representation from the spatial information contained in digital images. It was originally design to process multidimensional (2D and 3D) arrays of high resolution input datasets such as images and videos [69; 70; 71]. The first deep CNN architecture was AlexNet [69] having seven hidden layers with millions of parameters. Deep convolutional neural networks have achieved great success on image classification [70], object detection [71], crowd counting [72] and people localization [73]. The brief structure of deep CNN is shown in Figure 3.  
The success of convolutional neural networks (CNN) and deep convolutional neural networks (DCNN) in various computer vision tasks has inspired researchers to leverage their ability to learn  
Figure 3: Structure of deep convolutional neural networks (CNN) [74].  
nonlinear functions from crowd images to their respective density maps or counts [75]. A variety of CNNs and DCNNs have been proposed in the literature which are used for crowd monitoring. Recent studies are included in Table 3 which represents different methods and techniques used for crowd monitoring i.e., crowd counting, density estimation and localization. The selection of research papers is from 2017-2019 respectively in which deep convolutional neural networks, scale driven convolutional networks and simple convolutional neural networks have been used. The experimental results were evaluated using different datasets namely UCSD [32], world expo'10 [19], UCF-CC-50 [2], Shanghai Tech Part A,B [35], UCF-QNRF [11] and subway-carriage [60] datasets. Further details are presented in Table 3.
# An Islanding Signal-Based Smooth Transition Control in AC/DC Hybrid Micro-Grids
## 3 A Smooth Transition Control Based on Islanding Signal  
### The Origin of Transient Impact  
In the control strategy proposed in this paper, the micro-grid operates in islanding mode, while the battery and fuel cell in the DC sub-network use voltage control. The battery in the AC sub-network uses VF control. When the micro-grid operates in grid-connected mode, all of the DGs operate in power control. In order to implement different control strategies for islanding mode and grid-connected mode, each DG has two sets of controllers: a voltage controller and a power controller. The PI regulator is a very important component in both of the mode structures. The control structure of the PI regulator is shown in Figure 5, where \(K_{i}\) is the integral gain and \(K_{p}\) is the proportional gain.  
The PI regulator's transfer function is  
\[G(s)=K_{p}+\frac{K_{i}}{s} \tag{1}\]  
where \(G(s)\) is the gain function. \(K_{i}\) represents the integral coefficient and \(K_{p}\) represents the proportional coefficient.  
When a micro-grid operates in islanding mode, the voltage controller is put into operation. The power controller is also working during this time, but its output control signal does not act on the converter. Similarly, when a micro-grid operates in grid-connected mode, the power controller is put into operation. The voltage controller is working at the same time, but its output control signal does not act on the converter. When the two modes are switched, due to the memory of the integral PI regulator part, there is a step change of the regulator output, which causes a sudden change in the current reference and leads to impulse current, which may cause instability. In some serious situations, it can even produce oscillation.  
In addition to the impact caused by the output state of the regulator, there are voltage amplitude, phase, and frequency differences between a micro-grid and a utility grid during the switching from islanding mode to grid-connected mode, which results in a great rush current at the PCC.
# Edge AI for Real-Time Anomaly Detection in Smart Homes
## 5 Results and Discussion
### Generalization on Real-World Smart Home Data (CASAS Dataset)  
To assess the proposed anomaly detection framework's robustness and generalizability, we evaluated it using a real-world subset of the CASAS smart home dataset. The selected subset includes multivariate sensor streams--such as motion and temperature readings--from the \"Kyoto\" and \"HH102\" environments over a continuous 7-day window. The data were pre-processed to align with the format of our synthetic dataset, ensuring compatibility with the LSTM-AE and Isolation Forest (IF) models without requiring structural modifications.  
Table 14 summarizes the results of applying each model to the CASAS subset. The LSTM-AE model demonstrated strong performance with an F1-score of 84.9%, while the Isolation Forest maintained faster inference with slightly lower precision and recall. A hybrid approach, leveraging both models, yielded the best overall performance in terms of balanced accuracy and inference efficiency.  
These findings confirm that the framework retains high anomaly detection performance in realistic sensor environments and supports the feasibility of extending the system beyond simulation-based setups.  
Future work will explore adaptive retraining methods, such as reinforcement learning-based model updates, in which the model adjusts its learning rate dynamically based on real-time feedback from anomaly detection results.
## 5 Conclusions  
We have studied the tissue optical clearing effect of four osmotically active agents, PG, PGOA, V and LV using OCT. The application of hyperosmotic agents on skin tissue has been repeatedly shown to reduce random scattering within the OCT imaging volume due to RI matching and tissue dehydration. In this study, PGOA was found to be the most effective agent in lowering the light attenuation through skin tissues over a period of 90 min. At the same time, PG causes significant tissue dehydration which is depicted in the elevated epidermal thickness. OCT is a morphological imaging technique superior to ultrasound in terms of resolution and ease of handling. Topical administration of PG combined with OA (1:1 \(v/v\)) within a clinically relevant timeframe, can enhance tissue differentiation in OCT imaging due to skin dehydration and reduced \(\mu_{\text{OCT}}\). Still, further work is required to elucidate the contribution of each OCA to skin optical permeability. This will include a population of varying ages, different anatomic regions and multiple solution concentrations.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app12168277/s1](https://www.mdpi.com/article/10.3390/app12168277/s1)]([https://www.mdpi.com/article/10.3390/app12168277/s1](https://www.mdpi.com/article/10.3390/app12168277/s1)), Table S1: Mean \(\mu_{\text{OCT}}\) values with standard deviation (SD) estimated by the iterative analysis of four OCT image ROIs at time points \(t_{1}\) and \(t_{2}\); Table S2: Mean epidermal thickness values with SD estimated by the iterative analysis of four OCT image ROIs at time points \(t_{1}\) and \(t_{2}\).  
Conceptualization, G.G., I.D.B. and N.K.; Data curation, M.V., M.Z.V., G.G. and N.K.; Formal analysis, M.V., M.Z.V. and N.K.; Project administration, N.K.; Supervision, N.K.; Validation, M.V.; Writing--original draft, N.K.; Writing--review & editing, M.Z.V., G.G., I.D.B. and N.K. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Institutional Review Board Statement: University Hospital of Ioannina, 15/19-6-2019(5).  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
Data Availability Statement: Data are available on specific request to the corresponding author.  
The authors declare no conflict of interest.
# Multiband Frequency Tuneable Antennas for Selection Combining Strategy in White Space Applications
## 4 Measurement Results  
The antenna prototype was manufactured using the FR4 epoxy board shown in Figure 12. The antenna radiating elements were the bending metal plates soldered on the strip lines at the corners. The DTC components were also placed on these strip lines. Each DTC was connected to the control unit via five metal lines: VCC, GND for the power supply and SDA, SCL, ADDR for the serial interface. By adding RF choke inductors, the control signals could be isolated to the RF path. There was a large unused area in the prototype. In general, this place is reserved for the electronic circuit, battery, and display part of the real device. An Mbed NXP LPC1768 board was soldered onto this zone to program the DTC.  
By loading the embedded code, the capacitance value could be varied. Then, the antenna resonance was switched from 0.47 to 0.7 GHz to cover all white-space frequency bands. The prototype was measured using the N55227A PNA network analyzer and is reported in Figure 13. According to the results, the proposed structure needed only 19 states to cover all required bands with a reflection coefficient lower than \(-6\) dB. Moreover, the continuity coverage scheme was obtained while using a discrete variable capacitor type thanks to the wide bandwidth at each DTC state.  
Figure 12: Top view of the proposed antenna prototype.  
Figure 13: Measured reflection coefficient of proposed antenna.
## 3 Experiments and Discussion
### Material Selection Test
#### 3.2.2 Adhesive Material  
The curing agent and Pressure Sensitive Adhesive (PSA) were tested and contrasted in the same experimental conditions. The instantaneous adhesive (aron alona 707) was chosen as a curing agent, and the double-sided adhesive tape (3M9080) was selected as the PSA. The output response curve is shown in Figure 6. It shows that the output response of the sensor with a double-sided adhesive tape is significantly greater than that of the one with the instantaneous adhesive.  
As for the selection of the adhesive, the instant curing agent forms a layer of film with a certain hardness on the backing surface after solidification, which increases the elastic coefficient of the backing.  
Figure 5: The output curves of sensors with different backing materials.  
According to the backing material test, the greater the elastic coefficient of the backing, the worse the performance of the sensor. In comparison with the solidified instant curing agent (\(6.6~{}\times~{}10^{6}\) kg/m\({}^{2}\cdot\)s), the acoustic impedance of double-sided adhesive tape (\(3.1~{}\times~{}10^{6}\) kg/m\({}^{2}\cdot\)s) is much more closer to that of the PVDF film (\(4.2~{}\times~{}10^{6}\) kg/m\({}^{2}\cdot\)s), and the acoustic reflection at the contact surface is moderate, which is more conducive to matching acoustic impedance [22]. Therefore, the effect of the double-sided adhesive tape is obviously better than an instant curing agent under the same conditions.
## 5 Economic Evaluation
### Changes in the Amount of Electricity Generated by Solar Power Investment in Window Investment Costs  
Finally, we performed the economic feasibility analysis based on regions by synthesizing the change in energy consumption and investment cost due to the change of windows and the increase in energy production due to the investment in solar power generation equipment (Table 12). In Seoul, as the thermal transmittance rate of windows decreases from 1.000 W/m\({}^{2}\)K to 0.880 W/m\({}^{2}\)K, the cost increases by about KRW 9,211,815, and the energy consumption decreases by 3063 kWh/y. If the same cost is invested in a photovoltaic device, an additional 3.4 kW of capacity can be secured, which can produce 1373 kWh/y of energy. In this case, it makes economic sense to invest in passive elements because the decrease in energy consumption is 123% greater than the increase in energy production. In Busan, if the thermal transmittance of a window is reduced from 1.150 W/m\({}^{2}\)K to 1.000 W/m\({}^{2}\)K, the cost increases by about KRW 12,103,977, and the energy consumption decreases by 1074 kWh/y. A solar power unit for that cost is about 4.5 kW, which can produce 2060 kWh/y of energy. In this case, based on the same cost, investing in an increase in energy production rather than a decrease in energy consumption is 92% larger; therefore, investment in active elements is more economical.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Manufacturer** & **Capacity [kW]** & **Cost [KRW]** \\ \hline \multirow{8}{*}{S} & 19.8 & 52,400,000 \\ \cline{2-3}  & 25.2 & 66,300,000 \\ \cline{2-3}  & 30 & 79,400,000 \\ \cline{2-3}  & 35.1 & 92,000,000 \\ \cline{2-3}  & 40.5 & 106,100,000 \\ \cline{2-3}  & 45 & 125,000,000 \\ \cline{2-3}  & 50.4 & 136,500,000 \\ \cline{2-3}  & Average & 2,673,577/kW \\ \hline \multirow{8}{*}{B} & 20.76 & 52,400,000 \\ \cline{2-3}  & 25.95 & 66,300,000 \\ \cline{2-3}  & 30.44 & 79,400,000 \\ \cline{2-3}  & 35.98 & 92,000,000 \\ \cline{2-3}  & 40.48 & 106,100,000 \\ \cline{2-3}  & 45.67 & 125,000,000 \\ \cline{2-3}  & 51.90 & 136,400,000 \\ \cline{2-3}  & Average & 2,618,043/kW \\ \hline \hline \end{tabular}
\end{table}
Table 11: PV product cost by capacity.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Simulation Case** & \
\begin{tabular}{c} **Window** \\ **U-Value** \\ **[W/m\({}^{2}\)K]** \\ \end{tabular} &
\begin{tabular}{c} **Energy** \\ **Demand** \\ **Sum [kWh]** \\ \end{tabular} & **Manufacturer** & **Cost [KRW]** \\ \hline \multirow{8}{*}{Seoul} & A & 1.150 & 85,732 & K & 91,770,149 \\ \cline{2-5}  & & & H & 97,116,420 \\ \cline{2-5}  & B & 1.000 & 83,628 & K & 103,874,126 \\ \cline{2-5}  & C & 0.880 & 80,565 & K & 113,085,940 \\ \cline{2-5}  & & H & 109,004,019 \\ \hline \hline \end{tabular}
\end{table}
Table 12: Correlation between window U-value, energy demand, and cost.
# Research on Mechanical Properties of Core-Filled Desulfurized Gypsum Masonry
## 2 Test Overview  
### Specim Design  
The high-precision desulfurized gypsum hollow non-platering blocks used in this test are made from a mixture of industrial desulfurization gypsum, water, fly ash, calcium hydroxide, and water-reducing agents [23], and are industrially manufactured building infill wall blocks, provided by Spring Building Materials Technology Co., Ltd. (Jinan, China). The main component of industrial desulfurization gypsum is dihydrate calcium sulfate (CaSO\({}_{4}\cdot\)2H\({}_{2}\)O), which contains impurities introduced by the desulfurization process, such as unburned carbon, soluble salts, and organic matter. Based on the \"Standard for Test Methods of Basic Mechanical Properties of Masonry\" (GB/T 50129-2011) [24], standard blocks and half blocks were selected. The dimensions of the standard block are 600 mm \(\times\) 200 mm \(\times\) 250 mm, and the dimensions of the half block are 298 mm \(\times\) 200 mm \(\times\) 250 mm. The gypsum mortar was mixed with water at a water-to-binder ratio of 0.57. The mix design of the core-filling concrete was carried out in accordance with the \"Ordinary Concrete Mix Design Code\" [25], using the mass method for mix proportion calculation. The amount of materials used and the strength for each test of grouted concrete are shown in Table 1.  
All test specimens were constructed using a three-course, fully grouted masonry method. The top and bottom courses were made of standard blocks, while the middle course consisted of two half blocks. The core-filled masonry was designed in 5 groups, with each group containing 6 specimens of C20, C25, C30, C35, and C40 strength core-filled masonry, totaling 30 core-filled masonry specimens, as shown in Figure 1. After curing the masonry for 7 days, axial compression tests were conducted. The reinforced masonry was designed in five groups, with each of the four grout holes containing 1 or 2 vertical ribbed reinforcing bars, i.e., 44014 or 8014. In the C25 and C30 groups, the number of reinforcing bars in each of the four grout holes was increased to 3 or 4 vertical ribbed reinforcing bars, i.e., 12014 or 164014. The length of the vertical ribbed reinforcing bars was 700 mm, the stirrup spacing was 200 mm, and the minimum concrete cover thickness for the reinforcing bars was 25 mm. In total, there were 42 reinforced masonry specimens, as shown in Figure 2.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Specimen** & **Age (In** & **Cement** & **Water (kg/m3)** & **Gravel** & **Sand (kg/m3)** & **Average Compressive Strength (MPa)** \\ \hline C20 & 28 & 15.34 & 18.57 & 21.04 & 21.04 & 14.95 \\ C25 & 28 & 12.63 & 12.63 & 12.63 & 12.63 & 18.24 \\ C30 & 28 & 46.94 & 45.30 & 44.03 & 44.03 & 22.52 \\ C35 & 28 & 45.10 & 43.52 & 42.31 & 42.31 & 25.66 \\ C40 & 28 & 15.34 & 252.5 & 832.4 & 799.8 & 28.89 \\ \hline \end{tabular}
\end{table}
Table 1: The amount of materials used and the strength for each test of grouted concrete.  
Figure 1: Grouted masonry specimen.
# A Three-Dimensional Electrochemical Process for the Removal of Carbamazepine
## 2 Materials and Methods
### Data Analysis  
#### 2.7.1 Modeling of Kinetic and Equilibrium Adsorption Studies  
The following models were applied to fit the data by nonlinear curve fitting using Origin software (Origin Lab Corporation, Northampton, MA, USA).  
The adsorption capacity (\(q\)) was determined as:  
\[q=(C_{0}-C_{t})\times\frac{V}{m} \tag{1}\]  
where \(C_{o}\) and \(C_{t}\) (mg/L) are the initial and instant \(t\) CBZ concentrations, respectively; \(V\) is the solution volume (L); and \(m\) (g) is the adsorbent mass.  
The pseudo-first-order kinetic model [39] is given by Equation (2):  
\[q_{t}=q_{e}\Big{(}1-e^{-k_{1}t}\Big{)} \tag{2}\]  
where \(t\) is time, \(q_{t}\) is the adsorption capacity at time \(t\), \(q_{e}\) is the equilibrium adsorption capacity, and \(k_{1}\) is the pseudo-first-order rate constant.  
The pseudo-second-order kinetic model [40] is given by Equation (3):  
\[q_{t}=\ \frac{q_{e}^{2}k_{2}t}{1+q_{e}k_{2}t} \tag{3}\]  
where \(k_{2}\) is the pseudo-second-order rate constant.  
[PERSON] (1918) [41] and [PERSON] (1907) [42] (Equations (4) and (5)) models were applied to the study of adsorption equilibrium.  
\[q_{e}=\frac{q_{m}k_{1}c_{e}}{1+k_{L}c_{e}} \tag{4}\]  
where \(C_{e}\) is the equilibrium concentration of the adsorbate (mg/L), \(q_{e}\) is the adsorption capacity at equilibrium (mg/g), \(K_{L}\) is the Langmuir equilibrium constant related to the energy of adsorption (L/mg), and \(q_{m}\) is the maximum adsorption capacity (mg/g), which in this model should correspond to a monolayer coverage of the adsorbent surface.  
\[q_{e}=k_{F}(c_{e})^{\frac{1}{k_{F}}} \tag{5}\]  
where \(K_{F}\) is the Freundlich isotherm constant ((mg/g) (L/mg)1/\(n_{F}\))), and \(n_{F}\) (dimensionless) is the Freundlich exponent that describes the strength of adsorption. Typically, the \(1/n_{F}\) value ranges between 0 and 1, and if \(1/n_{F}\) is closer to 0, the adsorption intensity is higher.