esa-sceva/satcom-chunk-collection huggingface dataset:
# Deep Autoencoder Framework for Classifying Damage Mechanisms in Repaired CFRP
## 2 Materials and Methods  
### Materials  
#### 2.1.1 Material Properties  
The CFRP laminates were fabricated using an epoxy-carbon fiber prepreg, ER450 epoxy (SAATI CIT CC206 ER450 43%). The carbon fibers are arranged in a woven configuration, with layers of fibers overlapping one another. The adherends were cured using the autoclave method. The composite laminates for both JLS specimens (Figure 1) were cured using the autoclave method. Figure 1 illustrates the nominal dimensions of the specimens, including side and top views. Table 1 summarizes the geometric properties and material characteristics of the specimens, covering the dimensions (length, width, and thickness), number of plies, and stacking sequence for both flat and curved adherends, as well as the geometry of the overlapping adhesive region. Specifically, the flat and curved adherends have nominal lengths of 101.6 mm, widths of 26.09 mm, and thicknesses of 2.0 mm, while the overlapping adhesive region has a width of 2.0 mm and a thickness of 3.67 mm. The stacking sequences for the adherends are \([+45/-45]_{3}/-45/+45\), and the nominal ply thickness is 0.244 mm.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Property** & **Flat Adhered** & **Curved Adhered** & **Overlapping Region (Adhesive)** \\ \hline Length (mm) & 101.6 \(\pm\) 0.12 & 101.6 \(\pm\) 0.17 & 101.6 \(\pm\) 0.17 \\ Width (mm) & 26.09 \(\pm\) 0.07 & 26.09 \(\pm\) 0.05 & 2.0 \(\pm\) 0.04 \\ Thickness (mm) & 2.0 \(\pm\) 0.04 & 2.0 \(\pm\) 0.04 & 3.67 \(\pm\) 0.05 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Description of geometry and properties for joggled lap shear (JLS) specimens.  
The adhesive used to join the laminates is a high-strength structural adhesive characterized by a shear strength of 25 MPa and a peel strength of 65 MPa.
## 4 Problem Space for DL Applications
### Application Scenarios with DL
#### 4.3.2 Risk Assessment
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Use Case** & **Description** & **Example** & **Impact** \\ \hline \multirow{3}{*}{Real-time health monitoring} & IoMT devices support & \multirow{3}{*}{Smartwatches track heart rate, blood pressure, and oxygen saturation} & Improved chronic disease management and real-time health insights. \\  & & & \\ \hline \multirow{3}{*}{Remote patient monitoring (RPM)} & Devices monitor patients & \multirow{3}{*}{IoMT devices managing chronic diseases like diabetes remotely.} & Reduced hospital burden and enhanced rural healthcare \\  & & & \\  & & & \\ \hline \multirow{3}{*}{Predictive healthcare analytics} & IoMT data combined with AI & \multirow{3}{*}{Platforms predicting sepsis in ICU patients using IoMT data.} & Better preventive care and fewer emergency interventions. \\  & & & \\  & & & \\ \hline \multirow{3}{*}{Smart surgical systems} & IoMT-enabled robotic surgical & \multirow{3}{*}{Da Vinci Surgical System using IoMT for minimally invasive surgeries.} & Increased accuracy and safety in complex surgical procedures. \\  & & & \\  & & & \\ \hline \multirow{3}{*}{Virtual healthcare (telemedicine)} & Connected devices facilitate remote consultations and diagnostics through telemedicine platforms. & Connected stethoscopes and diagnostic kits for virtual consultations. & Broader access to healthcare, especially in remote regions. \\ \hline \multirow{3}{*}{Medication adherence monitoring} & IoMT systems track medication usage with smart devices like pillboxes or connected dispensers. & Smart inhalers tracking usage and sending reminders. & Enhanced patient compliance and reduced treatment failures. \\ \hline \multirow{3}{*}{Assisted living and elderly care} & IoMT devices like fall detectors and trackers improve safety and independence for the elderly. & Wearable fall detectors alerting caregivers in emergencies. & Improved safety and quality of life for aging populations. \\ \hline \multirow{3}{*}{Personalized medicine} & The IoMT collects data to deliver tailored treatment plans based on health metrics, lifestyle, and genetics. & Continuous glucose monitors adjusting insulin dosages automatically. & Precise treatments with reduced side effects. \\ \hline \multirow{3}{*}{IoMT in mental health} & Wearables and apps monitor indicators like stress and sleep patterns to detect mental health issues. & Smartwatches tracking sleep disturbances for early mental health intervention. & Proactive mental health management and early intervention. \\ \hline \multirow{3}{*}{Connected medical imaging} & IoMT-enabled imaging devices provide real-time transmission of images for faster diagnosis. & Portable X-ray devices uploading images for real-time analysis. & Faster and more accurate diagnostic imaging in critical cases. \\ \hline \hline \end{tabular}
\end{table} 
Table 8: IoMT use cases in healthcare.
## 3 Materials and Methods
### Generator  
The generator model was designed to transform a single latent variable into a high-dimensional output through multiple layers. Each layer is progressively larger, facilitating the generation of complex patterns:  
Input layer: A single dimension input.  
Hidden layers: Four fully connected layers with increasing units (256, 512, 1024, and 2048), each followed by ReLU activation.  
Output layer: A single-dimension output to match the real data's dimensionality.  
Figure 2: WGAN-GP architecture.  
Discriminator  
The discriminator model aims to distinguish between real and generated data through several layers, incorporating dropout for regularization and LeakyReLU for stable gradient flow:  
Input layer: A single-dimension input.  
Hidden layers: Four fully connected layers with decreasing units (512, 256, and 128), each followed by LeakyReLU activation (with a negative slope of 0.2) and dropout (0.3).  
Output layer: A single-dimension output representing the authenticity score.  
Training Procedure with WGAN-GP  
Training GANs is challenging due to their instability. WGAN-GP improves stability by enforcing a Lipschitz constraint with a gradient penalty. The training process involves alternating updates to the discriminator and generator:  
1. Initialize models and optimizers: Initialize the generator, \(G_{\theta}\), and discriminator, \(D_{\omega}\), models. Both are optimized using the Adam optimizer learning rate, \(lr=0.001\), and betas (\(0.5,0.999\)).
2. Gradient penalty (GP): GP is calculated to enforce the Lipschitz constraint.
3. Discriminator update: the discriminator is trained on real and fake samples; the loss is calculated, to which the gradient penalty is added and backpropagated.
4. Generator update: the generator is updated less frequently (every 5 batches) to ensure discriminator training.
## 4 Discussion  
### System Analysis  
The mathematical model of the system is given by Equations (3) and (4) in the Laplace domain. The unit step response of the system is shown in Figure 6. Besides the gain and time constant, other essential characteristics of the system are the rise time, transient time, settling time, and steady-state. These system characteristics are calculated from the transfer function or are found by plotting the step response in MATLAB.  
Figure 4: Input: activation voltage step protocol (**above**); output: measured macroscopic ion-current (**below**) for system identification.  
Figure 5: Input-output data for nominal operating point at 70 mV (**a**); output: macroscopic ion current (**b**) used for system identification.  
In Figure 6, the rise time is 0.104 s, the transient time is 0.184 s, and the final value corresponds to a gain of 0.174 nA; thus, the system does not have an overshoot. Experimental output data and the mathematical model indicate that stability is enforced. The same conclusion was drawn by conducting a pole location analysis. The model has one pole with a negative real part, and the system was determined to be stable [22; 24; 25].  
The model in the Laplace domain corresponds to the differential equation under the assumption that all initial conditions are zero [22]. The inverse Laplace transformation gives a solution of a differential equation in the time domain, and the system described with the solution to Equation (4) in the time domain is  
\[g(t)=0.1738\Big{(}1-e^{\frac{t}{0.0026s}}\Big{)}h(t) \tag{5}\]  
where \(g(t)\) corresponds to \(G(s)\), representing the unit step response of the system \(G(s)\) (see Figure 6) when all initial conditions are equal to zero, and \(h(t)\) is the input function, which corresponds to the unit step or Heaviside function \(h(t)\).  
We simulated outputs of the model using Equation (4) with inactivation and deactivation stimuli. The compared results of the measured outputs and simulated outputs are shown in Figure 7. The model satisfies the dynamic behavior of the Kv10.1 voltage-gated ion channel based on the adopted assumptions.  
Since different cellular mechanisms can affect the opening behavior of ion channels, we believe that time-dependent transfer functions can be used to provide a more accurate description of the dynamic characteristics of ion channels. This hypothesis is supported by the fact that all mathematical systems are non-stationary and that the mathematical origin of non-stationarity can be proved [31].
## 2 Experimental Setup and Methods  
In order to investigate film-formation phenomena in an SCR environment, a generic test bench was set up in previous work [37]. It was designed to meet operational conditions typically found in SCR systems, allowing performance of reproducible experiments with well-defined boundary conditions. This was needed to deliver a systematic and comprehensive database for the validation of numerical simulations.  
To achieve a fully developed turbulent velocity profile in the optically accessible measurement region, the test rig consists of a wind tunnel in which the heated gas first flows into a plenum and is then accelerated through a nozzle. In the plenum, three grids ensure a homogeneous temperature profile. A turbulence grid downstream from the nozzle increases the degree of turbulence (Figure 2). To achieve a fully developed turbulent block velocity profile at the measurement section, it is located 25 D downstream from the nozzle. By the well-defined turbulent inlet boundary conditions at inlet to the measurement section, the numerical domain is reduced (Section 4.2).  
The test bench includes two optical accesses. All measurements discussed in this study were conducted in the upstream test section. Both test sections are designed such that the processes of interest (spray injection, film evaporation, film evolution) can be monitored through windows from each direction perpendicular to the direction of flow. To prevent flow disturbances or separation, the measurement chambers are designed without edges. For the experiments presented here, the lower window was replaced by a stainless-steel plate on which the AdBlue film is to be formed. The AdBlue injector is mounted upstream of the optical access. A one-hole, pressure-assisted injection nozzle (Cummins S5.3) with a maximum mass flow of 11.7 kg/h and a spray angle of 50\({}^{\circ}\) was used, operated at 9 bar and an injection frequency of 1 Hz. The injector is continuously cooled during operation to avoid nozzle clogging with solid urea, which would alter the spray characteristics.  
The generic test bench provides a gas flow of a specific velocity and temperature similar to diesel exhaust. The gas flow velocities can be set between 1 and 15 m/s and gas temperatures of up to 700 K with 5 K increments that can be reached in the measurement section. Sensors for recording temperature, pressure and velocity allow for monitoring the process parameters. The temperature of the gas flow and the AdBlue mass flow can be controlled via custom software. All measurements presented in this study were performed  
Figure 2: Schematic layout of the generic exhaust gas test bench. The plenum, the inflow nozzle and the 25 D-long mixed pipe are designed with integrated turbulence grids to prepare the flow. The injection is placed close to the “optical access l”, where the reported measurements took place.  
with dry air (dew point of 255 K). Between the individual measurements, the optical chamber was thoroughly cleaned to ensure reproducible initial conditions.
## 3 Results and Discussions  
### Physicochemical Properties of Investigated PIL  
The physical properties of synthesized PIL are shown in Figure 6.  
Due to the long alkyl chain erucic acid anion, the synthesized protic ionic liquid bis(2-hydroxyethyl)ammonium erucate exhibited very high viscosity at room temperature. The erucic acid itself has a melting point of 28-32 \({}^{\circ}\)C. Therefore, such anion containing protic ionic liquid possesses high viscosity. However, the kinematic viscosity sharply drops at higher temperatures, showing the exponential temperature-viscosity relationship (Figure 6a). The exponential relationship is a characteristic feature of lubricants. Furthermore, it has been determined that IL-DEE has a viscosity index of 88.6. The viscosity index is a unit-less parameter showing how viscosity change relates to temperature change. The lower value shows a higher viscosity change. Therefore, there was a dramatic difference in viscosity between temperatures of interest, namely--1271.7 mm\({}^{2}\)/s at 30 \({}^{\circ}\)C and 74.2 mm\({}^{2}\)/s at 80 \({}^{\circ}\)C. Such a difference can undoubtedly lead to a different tribological response. Generally, having a higher viscosity index is better, because it leads to fewer viscosity changes during operation. The temperature-density relationship has a linear trend, decreasing in density while the temperature increases (Figure 6b).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Lubricant**} & **Test** & **Test** & **Stroke** & **Load, N** & **Reciprocation** \\  & **Temperature**, \({}^{\circ}\)**C** & **Duration**, **min** & **Length, mm** & & **Heavy,** \\  & **\({}^{\circ}\)C** & **min** & & **Length, mm** & & **Hz** \\ \hline IL-DEE & 30 and 80 & & & & & \\ W + IL-DEE & 25 & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of ball-on-plate tribo-test.  
Figure 6: Kinematic viscosity (**a**) and density (**b**) of bis(2-hydroxyethyl)ammonium erucate as a function of temperature.  
Introducing such a high-viscosity additive into water increased the solution's viscosity (Figure 7). However, the increment is temperature-related--higher differences occur at a lower temperature. This relationship is related to the physical properties of IL-DEE, where a wide viscosity range, moving from a low to a high temperature, was observed.
###### Abstract  
Business models built upon multimedia/multisensory setups delivering user experiences within disparate contexts--entertainment, tourism, cultural heritage, etc.--usually comprise the installation and in-situ management of both equipment and digital contents. Considering each setup as unique in its purpose, location, layout, equipment and digital contents, monitoring and control operations may add up to a hefty cost over time. Software and hardware agnosticity may be of value to lessen complexity and provide more sustainable management processes and tools. Distributed computing under the Internet of Things (IoT) paradigm may enable management processes capable of providing both remote control and monitoring of multimedia/multisensory experiences made available in different venues. A prototyping software to perform IoT multimedia/multisensory simulations is presented in this paper. It is fully based on virtual environments that enable the remote design, layout, and configuration of each experience in a transparent way, without regard of software and hardware. Furthermore, pipelines to deliver contents may be defined, managed, and updated in a context-aware environment. This software was tested in the laboratory and was proven as a sustainable approach to manage multimedia/multisensory projects. It is currently being field-tested by an international multimedia company for further validation.  
IoT; multimedia; multisensory; mulsemedia; IoT prototyping; virtual environments; simulation; system planning +
Footnote †: journal: Applied Sciences
# State-Of-The-Art and Prospects for Peer-To-Peer Transaction-Based Energy System
## 5 Frameworks for TE
### Classification Based on Optimization Techniques
#### 5.3.3 Classification Based on Non-Cooperative Game Theory  
In a non-cooperative game, players make decisions independently where each player focuses on predicting the actions and strategies of other players. In energy trading, players use a non-cooperative game to calculate the amount of energy to be sold using the Nash equilibrium. In studying non-cooperative game for storage units among prosumers, [PERSON]. [85] formulated a non-cooperative problem for MGs to trade their stored energy with MGs in other geographical locations. A non-cooperative game is also proposed by [21] using a Nash equilibrium game where each player competes with other players using different strategies. In addition, [PERSON]. [97; 98] used a non-cooperative game to solve the optimal amount of energy exchange among EVs. Furthermore, [PERSON]. [25] proposed a bidding system: Elecbay based on game theory using Nash Equilibrium for energy trading.  
The studies in [68; 83] considered the interaction between all parties involved including utility companies, MGs and customers to propose a distributed algorithm as a two-stage Stackelberg game. The first stage involves the utility companies and MGs as game leaders setting electricity price as a function of the generation cost, power loss and electricity sales income. In the second stage,the consumers are modelled as the game follower adjusting their demand based on the set price. Interestingly, [PERSON]. [99] used this game as an incentive for customers to trade their energy surplus during peak hours, while [100] modelled a leader-follower strategy that considers the competitive situations between ESP and a large central production unit. The leader-follower approach was also applied in [101] for energy exchange in vehicle to grid applications, and the game converged to a socially optimal point. Table 2 presents literature classification based on the framework used and the desired outcome.
# Recognition of Stress Activation by Unobtrusive Multi Sensing Setup
## 6 Results
### AE Features  
In general, the observed accuracy of AE features resulted high (>90%), with the worst performance (about 93%) obtained when a single feature was available (Figure 4). When two (or more) features were employed, the accuracy fluctuated around 93-95%. Therefore using more than two AE features did not significantly improve discrimination capabilities. Indeed, a maximal accuracy of (about 95%) was already met using two features.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline  & \multicolumn{2}{c}{**Median RR (bpm)**} & \multicolumn{2}{c}{**Number of GSR Peaks**} \\ \hline
**ID** & **Rest** & **SCWT** & **Rest** & **SCWT** \\ \hline
01 & 14 (1) & 17.3 (2.7) & 0 & 25 \\
02 & 16 (3) & 17 (1.5) & 0 & 19 \\
03 & 16 (2) & 19.5 (2) & 0 & 18 \\
04 & 13 (2) & 13.5 (1) & 0 & 32 \\
05 & 11 (4) & 18 (3.5) & 0 & 25 \\
06 & 10 (2) & 14.5 (4.5) & 11 & 45 \\
07 & 17 (1) & 19.5 (1) & 7 & 36 \\
08 & 15 (1) & 17 (0) & 0 & 0 \\
09 & 13 (1) & 15.5 (1) & 0 & 0 \\
10 & 13 (1) & 13 (2) & 5 & 44 \\ \hline \hline \end{tabular}
\end{table}
Table 4: MCA database: Median RR with interquartile range (in brackets) together with the Number of GSR peaks both at rest and during SCWT. No IQR is available for MCA GSR as only one block is present in data.  
Figure 3: On the **left**: Accuracy of LDA classifier evaluated on the SRAD dataset varying the number of features according to SFFS ranking. On the **right**: accuracy versus number of features evaluated on MCA.  
When each of the 12 models were tested on the MCA dataset, results were found to be affected by a larger variability (Figure 4). With a single feature, the accuracy is 0.5 (chance level). However, also in this case, using two features produces a substantial accuracy boosting. This reached its maximum with three features (at around 90%). The inclusion of additional features results in accuracy fluctuation about lower values.  
To sum up, both SFFS and AE support the finding that two features may be sufficient to reliably recognize activation statuses.
## 2 NB-IoT System Detailed Design and Implementation
### NPDSCH Receiver Chain Implementation
#### 2.2.4 Resource Element De-Mapper  
The PRB consists of \(N_{symb}^{DL}\) consecutive OFDM symbols in the time domain and \(N_{sc}^{RB}\) consecutive subcarriers in the frequency domain, as it comprises \(N_{symb}^{DL}\times N_{sc}^{RB}\) resource elements (REs), corresponding to one time slot and 180 kHz in the frequency domain. PRBs are numbered from 0 to \(N_{RB}^{DL}-1\) in the frequency domain [28; 29]. Resource block pair consists of two resource blocks: the even numbered frame and the odd numbered frame. As in NB-IoT, the normal CP is used, where \(\Delta f=15\) kHz, \(N_{sc}^{RB}=12\), and \(N_{symb}^{DL}=7\). Therefore, the subframe is \(12\times 14\), and only one antenna port is used. Then the NB-IoT carrier uses one LTE PRB in the frequency domain where there are 12 subcarriers of 15 kHz for a total bandwidth of 180 kHz [28; 29].  
As shown in Figure 10, the resource de-mapper takes the output of the FFT and then the output is stored in its memory to be used by the channel estimation, channel equalization, and fine synchronization using the row and column address. The block includes a \(16\times 14\) storage element memory, which stores the unsorted FFT output, and when it is full, an enable signal is raised to let the resource de-mapper get the symbols column by column. Correspondingly, it takes 14 clock cycles to fill the resource de-mapper. Thus, the time in which the resource de-mapper is kept unchanged is approximately 1 ms as stated in the 3 GPP standard [9].  
In the storage element memory, the bit-reversing of the FFT output takes place, and the symbols stored in this memory are in-order. Then, when transferring the data from storage element memory to resource element de-mapper the symbols are resorted as the  
Figure 10: REM detailed block diagram.  
Figure 9: FFT detailed block diagram.  
last six symbols stored in first six symbols and the first six symbols are stored in last six places in memory.