esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Fall Detection Application
### Experimental Results
#### 5.2.3 Walking Experiment  
For the scenario of a walking person, we measured the subject's spectral signature using the radar, as shown in Figure 6c. We also tracked the movement of his head, shoulder and right hand through video processing. Figure 11 shows three frames from the video of the walking person; the tracking of the head movement is indicated by red markers, shoulder tracking by green markers and tracking of the right hand by blue markers.  
Figure 12 shows the results obtained in the experiments described above. In this figure, correspondence between the spectrogram and the video analysis results can be seen. In the  
Figure 11: Process for the video analysis of a walking man, tracking motion of the head (red markers), shoulder (green markers) and right hand (blue markers) (**a**) at the beginning of walking; (**b**) during walking, hand forward; (**c**) during walking, hand backward.  
Figure 10: Presentation of the measurement results for a sitting man. The spectral signature was measured using the MMW radar, and the red line indicates the data obtained through head tracking in a recorded video.  
movement of the right hand, indicated as a blue line, two \"hills\" can be seen. The second hill is in the time interval \(4.07\) s \(<t<5.14\) s. In the time interval \(4.07\) s \(<t<4.5\) s, the frequency increases from 0 to its highest value of \(f_{d}=1.4\) kHz; thus, there is a positive slope of \(m_{p}=3.25\) kHz/s. In the time interval \(4.5\) s \(<t<5.14\) s, the frequency decreased to 0 with a slope of \(m_{n}=2.18\) kHz/s. The two other \"hills\" observed in the spectrogram, which look similar to those attributed to the right-hand tracker signal, are due to movement of the left hand.  
The power due to shoulder and head movement is higher than the power due to right-hand movement, but their characteristics are different from those of the falling spectrogram. Therefore, head and shoulder movement during walking is not likely to cause an erroneous result for radar detection.
# Emotion Analysis AI Model for Sensing Architecture Using EEG
## 2 Literature Review
### Research Gaps and Contributions of This Study  
Despite progress in EEG-based emotion recognition and AI-driven affective computing, their integration into adaptive architectural environments remains largely unexplored [41]. Most research has been conducted in controlled laboratory conditions, limiting the application of EEG-based emotion analysis in real-world built environments [42]. Existing AI models for emotion recognition have primarily been applied to HCI and medical fields, with limited research exploring their potential role in adaptive architecture [43].  
One critical research gap is the lack of real-world validation for EEG-based emotion recognition models in architectural contexts. Most studies are conducted under highly controlled experimental settings, where participants are exposed to predefined emotional stimuli while their neural responses are recorded [44]. While these studies provide insights into EEG-emotion correlations, they fail to account for the complexity of real-world architectural environments, where multiple environmental stimuli interact simultaneously [45].  
Another major gap is the absence of AI models specifically tailored for EEG-based emotion detection in architecture. Existing emotion recognition models have been developed primarily for text-based sentiment analysis, speech emotion recognition, and facial expression analysis, while EEG-based models remain largely focused on clinical and neuroscientific applications [46]. Although deep learning architectures such as CNNs and RNNs have demonstrated high accuracy in EEG emotion classification, their adaptation to real-time adaptive built environments has not been extensively studied [37].  
Furthermore, current adaptive architecture systems rely on predefined environmental settings, such as automated lighting adjustments or temperature control, based on static user preferences. However, AI-driven real-time adaptation mechanisms that dynamically modify spatial configurations based on EEG-derived emotional feedback are currently lacking.  
To address these gaps, this study introduces a fine-tuned AI-driven EEG emotion recognition framework designed for real-time application in built environments. Unlike previous studies that rely on static, pre-labelled datasets, this research integrates real-time EEG data collection and processing within an interactive architectural setting. The model is validated in a 360\({}^{\circ}\) virtual reality (VR) environment, shifting beyond controlled experimental conditions to evaluate the practical feasibility of EEG-based emotion recognition in dynamic architectural spaces.  
By integrating neuroscience, affective computing, and architectural intelligence, this study proposes a novel approach to incorporating real-time emotional feedback into adaptive spatial design. The findings are expected to contribute to the development of intelligent, user-responsive environments, enhancing occupant experience, psychological well-being, and architectural functionality.
# SALMA: A Novel Middlebox Infrastructure System Based on Integrated Subnets
## 7 Evaluation  
In this section, we evaluate our proposed solution through various sets of experiments. Before starting our extensive evaluation, we built a proof of concept for the SALMA; the topology is depicted in Figure 6 and the policies in Figure 5. After that, we evaluated SALMA using larger, more realistic topologies, such as Internet2, Geant, and the abstracted enterprises network, as shown in Figure 8, to be able to evaluate the performance on various topologies. Moreover, we evaluated the overhead of SALMA on the abstracted enterprise network using various scenarios.
# Efficient Wireless Monitoring and Control of a Grid-Connected Photovoltaic System
## 3 Description and Design of the Photovoltaic System
### Wireless Conversion Unit (WCU)
#### 3.2.3 MPPT  
The WGPVS must search for the MPP to transfer the maximum power to the electrical network. For that, an MPPT algorithm is required. In this case, it includes a regression plane [26] in which all the MPPs are obtained for different values of temperature and irradiance (providing the theoretical reference voltage \(V_{\textit{ref}}\)) and a modified P&O [26], which provides an incremental value of the reference voltage, \(\Delta V_{\textit{ref},p}\), where the MPP is located. Both the regression plane and the modified P&O are used to calculate the reference voltage to be reached by the adaptive Lyapunov controller, \(V_{PV,ref}\), to obtain the MPP. Figure 7 depicts the used MPPT algorithm.  
Figure 6: DC/AC converter’s PI control.  
Figure 7: Block diagram of the maximum power point tracking (MPPT) algorithm.
# A Review: Factors Affecting Internal Two-Phase Flow-Induced Vibrations
## 2 Factors Affecting FIV in Internal Two-Phase Flow
### Effect of Void Fraction
A relationship between void fraction, liquid and gas flow rates and flow regimes based on data available in the literature [95] has been developed as illustrated in Figure 5. Notably, these data are for upward vertical flow, a flow type widely used in the study of FIV. This figure concludes the effect of void fraction on excitation forces and its relation to flow regime. For a fixed mixture velocity, the excitation forces increase as gas void fraction increases, where the maximum forces were reported to be between 50% and 75% void fraction value, and upon further increase, the excitation forces start to decrease, as shown in Figure 5. In addition, Figure 5 shows that this range covers the slug/churn flow regime, which shows maximum momentum fluctuation and, in turn, produces the highest forces. The annular and bubbly flows occur outside this range of void fraction, which shows lesser forces due to the less turbulent nature. This change is attributed to the change in flow regime as we increase the void fraction, rather than the effect of void fraction.
## 4 Experiments  
### Experimental Settings  
The experiments follow the sequence in Figure 4 and are presented in this paper in the same order. Firstly, the image to be encrypted is signed using the ElGamal Digital Signature algorithm. Then, the 3D Arnold transform is performed. Next, the key parameters of Arnold are encrypted using the ElGamal Encryption Algorithm. After that, the encrypted image is sent to the recipient with the public key, who first decrypts the cipher test using the key to obtain the Arnold parameters. Afterwards, it decrypts the image using the parameters and finally uses the ElGamal Digital Signature algorithm to verify that the image has not been tampered with and that it has been restored to the original image after decryption. As shown below, M is plaintext, E is the signed file, D is ciphertext, SK\({}_{A}\) is the private key of Sender A, PK\({}_{A}\) is the public key of Sender A, SK\({}_{B}\) is the private key of Receiver B, and PK\({}_{B}\) is the public key of Receiver B.  
The proposed algorithm is implemented using Python 3.10.9 by PyCharm 2022.2.3 on the Windows 11 platform using a personal computer with an 11 th Gen Intell CITM) i7-1195G7 @ 2.90 GHz and 2.92 GHz Processor. To verify the performance of the proposed framework, we tested it on three distinct modalities coming from three biometric datasets. The face dataset is the FEI (Fundacao Educacional Inaciana) dataset [39]. There are 2800 images in this database with 200 subjects, and the images are colored on a homogeneous white background. The face images are in JPG (*jpg) format, where the size of each face image is 640 * 480 pixels. The palmprint dataset is the IIT Delhi Palmprint Image Database version 1.0 [40]. The database consists of hand images collected from students and staff at IIT Delhi, New Delhi, India. The database was collected from 230 users, with all images in bitmap (*.bmp) format. The resolution of these images is 800 * 600 pixels, and all  
Figure 4: Public key encryption with confidentiality, authentication, and signature.  
of these images are available in bitmap format. The dataset contains 5202 images. The iris dataset is from IIT Delhi Iris Database version1.0 [41]. This dataset mainly consists of the iris images collected from students and staff at IIT Delhi, New Delhi, India. The available database is from 224 users, and all the images are in bitmap (*.bmp) format. The resolution of these Images is 320 * 240 pixels and contains a total of 1120 images. The experiments selected three images randomly from each database. Figure 5 illustrates the original images regarding the biometric data, where samples 1 to 3 are face images, samples 4 to 6 are palmprint images, and samples 7 to 9 are iris images.
# Urban Quarry Ground Vibration Forecasting: A Matrix Factorization Approach
## 5 Methodology
### Prediction of PPV Using WNMF
#### 5.2.2 Step 2: Data Processing  
In Step 1, the range of values varies greatly for each parameter. Therefore, if these measurement data are used directly in WNMF, the learning may not proceed properly, potentially reducing the accuracy of PPV prediction. Thus, in Step 2, to apply WNMF for PPV prediction, normalization using ANN is performed on the measurement data from Step 1. Figure 8 presents a conceptual diagram of the neural network. During the learning process of the neural network, parameters are first input at the input layer. Then, in the hidden layer, \"weights\" are applied to each parameter, forming hidden neurons.  
Figure 7: Memory settings.  
The number of these hidden neurons can be pre-specified. Based on these hidden neurons, a single predicted value (PPV) is output in the output layer. The output predicted value is compared with the actual value to calculate the error. The neural network is then feedback-adjusted to optimize the \"weight magnitude\" for each parameter. This is the learning cycle of ANN. In our proposed method, the \"weight magnitude\" calculated by this ANN is quantified as the degree of influence on PPV, and the normalization value range is changed according to this weight magnitude. Parameters with a large influence on PPV are prioritized, and their normalization value range is made larger to prioritize their learning in WNNF. The method of normalization is min-max normalization. When aligning the value \(a_{ij}\) of the parameter at measurement point \(o_{i}\) to an arbitrary value range \([b_{\max},b_{\min}]\), the normalized value \(\text{Normalized}(a_{ij})\) is determined using Equation (8).  
\[\text{Normalized}(a_{ij})=\frac{a_{ij}-\min(p_{j})}{\max(p_{j})-\min(p_{j})}(b_ {\max}-b_{\min})+b_{\min} \tag{8}\]  
Here, \(p_{j}\) indicates the set of values for the \(j\)th parameter. \(\max(p_{j})\) represents the maximum value of \(p_{j}\), and \(\min(p_{j})\) denotes its minimum value. In our proposed method, the impact on PPV is evaluated in three stages. Parameters with the highest influence have their normalization value range set to [1; 11], the second highest to [1; 6], and all other parameters to [1; 2]. The normalization value range for PPV is also set to [1; 2].
## 4 Techniques Used in Academia to Improve Performance Metrics
### Frequency Diversity  
Diversity can be used over frequency, in case of a frequency-selective channel. Transmitting the same information on two frequency channels that are separated by more than the coherence bandwidth value (_i.e._, the frequency-shift by which a link has to undergo transition from deep fade) leads to independent fading.  
In the physical layer, the frequency hopping spread spectrum (FHSS) mechanism is an example of frequency/spectral diversity, in which multiple channels are used to communicate. Modulation techniques, such as the direct-sequence spread spectrum (DSSS), minimize the effect of noise on a given channel.  
In the MAC layer, frequency hopping is a diversity technique, in which each node-to-node transmission occurs on a different frequency than the previous one. The frequency-hopping scheme is studied in [26, 27, 28, 29, 30, 31]. In [26], authors show that the multipath effects are handled by channel hopping, while Reference [30] argues that the adaptive routing provides sufficient results to mitigate multipath fading. Furthermore, Reference [31] evaluates the impact of channel hopping and adaptive routing on the delay and reliability.
## 6 Security Analysis
### Informal Security Analysis  
The proposed scheme prevents assorted security attacks, including sensor capture, replay, ESL, and DoS attacks. Furthermore, the proposed scheme guarantees mutual authentication, user anonymity, and untraceability.
###### Abstract  
For the objective of essentially decreasing computational complexity and time consumption of signal acquisition, this paper explores a resampling strategy and variable circular correlation time strategy specific to broadband multi-frequency GNSS receivers. In broadband GNSS receivers, the resampling strategy is established to work on conventional acquisition algorithms by resampling the main lobe of received broadband signals with a much lower frequency. Variable circular correlation time is designed to adapt to different signal strength conditions and thereby increase the operation flexibility of GNSS signal acquisition. The acquisition threshold is defined as the ratio of the highest and second highest correlation results in the search space of carrier frequency and code phase. Moreover, computational complexity of signal acquisition is formulated by amounts of multiplication and summation operations in the acquisition process. Comparative experiments and performance analysis are conducted on four sets of real GPS L2C signals with different sampling frequencies. The results indicate that the resampling strategy can effectively decrease computation and time cost by nearly 90-94% with just slight loss of acquisition sensitivity. With circular correlation time varying from 10 ms to 20 ms, the time cost of signal acquisition has increased by about 2.7-5.6% per millisecond, with most satellites acquired successfully.  
signal acquisition; bandpass sampling; circular correlation time; computational complexity; time consumption; GNSS receivers +
Footnote †: journal: Sensors
## 5 Experimental Results
### Experimental vs. Simulation Results  
Table 3 presents some comparisons between simulation and experimental results when load increases: (1) for Hybrid SMC-PI, the load is increased 25%; and (2) for the proposed SMC, the load is increased 50%. The stabilization time and undervoltage for \(v_{o}\), the cycles to reach steady state for \(i_{s}\) and its corresponding \(THD_{i}\) were compared. It can be observed that there are small differences for both cases.  
Figure 21: Experimental results with SMC for load changes: (**a**) load increases to 50%; and (**b**) load decreases to 50%.  
Figure 22: Zero crossing of AC current: (**a**) PI-SMC control; and (**b**) SMC control.
###### Abstract  
Computational models of cardiac electrophysiology are promising tools for reducing the rates of non-response patients suitable for cardiac resynchronization therapy (CRT) by optimizing electrode placement. The majority of computational models in the literature are mesh-based, primarily using the finite element method (FEM). The generation of patient-specific cardiac meshes has traditionally been a tedious task requiring manual intervention and hindering the modeling of a large number of cases. Meshless models can be a valid alternative due to their mesh quality independence. The organization of challenges such as the CRT-EPiggy19, providing unique experimental data as open access, enables benchmarking analysis of different cardiac computational modeling solutions with quantitative metrics. We present a benchmark analysis of a meshless-based method with finite-element methods for the prediction of cardiac electrical patterns in CRT, based on a subset of the CRT-EPiggy19 dataset. A data assimilation strategy was designed to personalize the most relevant parameters of the electrophysiological simulations and identify the optimal CRT lead configuration. The simulation results obtained with the meshless model were equivalent to FEM, with the most relevant aspect for accurate CRT predictions being the parameter personalization strategy (e.g., regional conduction velocity distribution, including the Purkinje system and CRT lead distribution).  
electrophysiology; parameter optimisation; smoothed particle hydrodynamics; meshless model; cardiac resynchronization therapy; CRT-EPiggy19 challenge
# Vibration Control of Multi-Modular VLFS in Random Sea Based on Stiffness-Adjustable Connectors
###### Abstract
Thus, the cylindrical spring is the passive component, and the actuator is the active component. During a control process, the cylindrical spring shares a part of the connection forces with the actuator. The combination of both of the components actually makes the connector stiffness adjustable. A feedback control scheme that is based on the responses of floating bodies is proposed, and the feedback gains are determined by Sequential Quadratic Programming (SQP) [30] with the minimization of spectral VLFS responses in random sea conditions. By using the novel connectors, the resonances of VLFS systems can be eliminated in all wave conditions, and the vibration response can be effectively reduced in random sea conditions with reasonable small control forces. This paper is organized as follows: In the next section, the model of the stiffness-adjustable novel connector is proposed for a VLFS. Then, a vibration control method is developed based on the mathematical VLFS model and is connected by the novel connectors and Sequential Quadratic Programming to determine feedback gains of the different states and the hydraulic parameters of the actuators. Numerical examples are provided to verify the effectiveness of the vibration control method in different wave conditions. To highlight the performance of the control scheme, a comparison study for a VLFS that is connected using the flexible and hinged connectors [31; 32] is conducted. Finally, the results are discussed.
###### Abstract  
Envelope analysis is a widely used tool for fault detection in rotating machines. In envelope analysis, impulsive noise contaminates the measured signal, making it difficult to extract the features of defects. This paper proposes a time-frequency envelope analysis that overcomes the effects of impulsive noises. Envelope analysis is performed by dividing the signal into several sections through a time window. The effect of impulsive noises is eliminated by using the frequency characteristics of the short time rectangular wave. The proposed method was verified through simulation and experimental data. The simulation was conducted by mathematically modeling a cyclo-stationary process that characterizes rotating machinery signals. In addition, the effectiveness of the method was verified by the measured data of normal and defective air-conditioners produced on the actual assembly line. This simple proposed method is effective enough to detect the faults. In the future, the approaches of big data and deep learning will be required for the development of the prognostic health-management framework.  
fault detection; impulsive noise environment; rotating machinery; envelope analysis +
Footnote †: journal: Applied Sciences
# On Non-Completeness and G-Equivariance
## 1 Introduction
More precisely, while the original concept of G-equivariance is defined for 2-share masking setting, we use the extended notion of G-equivariance to an arbitrary number of shares and prove that the non-complete masked circuit is actually G-equivariant, which, in turn, implies that any non-complete TI can successfully prevent the glitch attack. Finally, we show that there are some G-equivariant gates that are not non-complete; thus, the notion of G-equivariance is broader than that of non-completeness. Consequently, the contribution of this paper is three-fold. First, we review the proof in [14] that any non-complete TI with uniform input sharing is secure against the 1 st order DPA even in the presence of glitches and claim that the proof is not satisfactory by introducing a contradictory example. Next, extending the definition of G-equivariance, this paper proves that non-completeness implies G-equivariance, which fills the gap in the security proof in [14]. Finally, we give some exemplary gates which are not non-complete but are G-equivariant. The paper is organized as follows. In Section 2, we review some prerequisite knowledge for our subsequent discussions. In Section 3, we show why the proof that non-completeness guarantees the 1 st-order DPA-resistance is not satisfactory. Then, in the next section, we will prove that non-completeness implies G-equivariance and give some examples that are G-equivariant without being non-complete. The conclusion is given in Section 5.