esa-sceva/satcom-chunk-collection huggingface dataset:
# Pedestrian Counting Based on Piezoelectric Vibration Sensor
## 3 Problem Formulation
### Problem Definition  
This paper focuses on counting the number of people based on floor vibration signals from piezoelectric sensors. Our system is designed to detect up to four pedestrians in an area of 3 m by 3 m, which can cover most indoor scenarios where multiple pedestrians walk in parallel with different stepping patterns, frequency, and directions. The piezoelectric sensors are deployed in the area of 3 m by 3 m as shown in Figure 1. The layout of the sensor deployment should guarantee that the signal from any vibration source in this area could be detected by any of the sensors. Previous studies showed that this particular layout works with good performance [27; 28]. Overall, our system can detect the number of walking pedestrians who step in this area. The system supports the real-life scenarios where multiple persons are walking together at the same time and different people may walk in different directions. Our system is designed to handle the following stepping patterns [15]:  
1. Footsteps from different pedestrians are fully synchronized in terms of striking timing.
2. Footsteps from different pedestrians are off-sync, but induced vibration signals presents temporal overlapping.
3. Footsteps are temporally staggered.  
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Approaches** & \begin{tabular}{l} **Support** \\ **Extreme** \\ **Environment** \\ \end{tabular} & \begin{tabular}{l} **Support More** \\ **than One Person** \\ **in the Detected** \\ **Area** \\ \end{tabular} & \begin{tabular}{l} **Support More** \\ **than One** \\ **Group of** \\ **People** \\ \end{tabular} & \begin{tabular}{l} **Device-Free** \\ **Group of** \\ **People** \\ \end{tabular} & \
\begin{tabular}{l} **Privacy** \\ **Protection** \\ \end{tabular} &
\begin{tabular}{l} **Resilient to** \\ **Destruction** \\ \end{tabular} \\ \hline Camera-based [21; 36] & - & ✓ & ✓ & ✓ & - & - \\ Device-based [2; 4; 2] & - & ✓ & ✓ & - & - & - \\ [PERSON] et al. [23] & ✓ & - & - & ✓ & ✓ & ✓ \\ [PERSON] et al. [14; 25; 26] & ✓ & - & - & ✓ & ✓ & ✓ \\ [PERSON] et al. [15; 16] & ✓ & ✓ & - & ✓ & ✓ & ✓ \\ Our approach & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ \\ \hline \hline \end{tabular}
\end{table}
Table 2: Capabilities of different approaches.
## 4 Numerical Evaluations
### Numerical Evaluation Parameters  
The list of parameters used in the numerical evaluation is shown in Table 5, which are compatible with the IEEE802.11 ad standard also used in our demonstration experiments. The transmit spectrum conforms to the spectrum mask shown in Figure 15[68]. The bandwidth is 2.16 GHz, but whole band cannot be used for communication because of the guard bands. In this paper, 1.88 GHz is considered the effective bandwidth [68]. Based on the antenna model in [63], as the peak of the antenna gain is 25.4 dBi, we found that the antenna 3 dB beam width corresponds to 9.98 deg. Numerical evaluations were performed by assuming that perfect beam alignments were operated on both sides of the transmitter and the receiver. For comparison, we also performed theoretical numerical evaluations with antenna gains of 15, 20, and 30 dBi. The corresponding 3 dB beam widths are 33.47 deg, 18.65 deg, and 5.87 deg, respectively.  
Figure 14: Two-ray ground reflection model.  
Figure 15: Transmit spectrum mask [68].  
\begin{table}
\begin{tabular}{c c} \hline Frequency Band \(f\) & 60 GHz \\ \hline Wavelength \(\lambda\) (speed of light: \(\mathbf{3.0\times 10^{8}}\) m/s) & \(5.0\times 10^{-3}\) m \\ \hline Transmission Power \(\mathbf{P_{t}}\) & 15 dBm \\ \hline Atmospheric Attenuation \(\mathbf{L_{ox}}\) & 16 dB/km \\ \hline Permititivity of Vacuum \(\epsilon_{0}\) & \(8.854\times 10^{-12}\) F/m \\ \hline Boltzmann’s Constant \(\mathbf{k_{q}}\) & \(1.38\times 10^{-23}\) J/K \\ \hline Temperature \(K\) & 290 K \\ \hline Noise Figure & 10 dB \\ \hline Bandwidth \(\mathbf{B}\) & 1.88 GHz \\ \hline Max Antenna Gain & 15, 20, 25.4, 30 dBi \\ \hline Scan Angle (deg.) & \(\pm 13.5\) \\ \hline \end{tabular}
\end{table}
Table 5: Numerical evaluation parameters.
# Multiuser Equalizer for Hybrid Massive MIMO mmWave CE-OFDM Systems
## 2 System Characterization
### Receiver Characterization  
The considered receiver structure is presented in Figure 2. The received signal, \(\mathbf{y}_{k}\), at the \(k\)th subcarrier, is given by:  
\[\mathbf{y}_{k}=\sum_{u=1}^{U}\mathbf{H}_{u,k}\mathbf{x}_{u,k}+\mathbf{n}_{k}, \tag{6}\]  
where the vector \(\mathbf{n}_{k}\in\mathbb{C}^{N_{rx}}\) is the zero mean Gaussian noise with variance \(\sigma_{n}^{2}\). First, the analog part of the equalizer, \(\mathbf{W}_{a}\in\mathbb{C}^{N_{rx}\times N_{rx}^{RF}}\), with \(\big{|}\mathbf{W}_{a}(n,l)\big{|}_{2}=N_{rx}^{-1}\), is applied to the received signal. Then, it is performed the baseband processing, composed by \(N_{rx}^{RF}\) RF chains and the iterative digital multi-user equalizer. In its turn, a forward path, and a feedback path compose the iterative digital equalizer. In the feedforward path, it is applied the digital linear filter \(\mathbf{W}_{d,k}^{(i)}\in\mathbb{C}^{U\times N_{rx}^{RF}}\) on each subcarrier \(k\), such that, \(\mathbf{W}_{d,k}^{(i)}\big{(}\mathbf{W}_{a}\big{)}^{H}\mathbf{y}_{k}\), where \(i=0,1,\ldots,i_{\max}\), and \(i_{\max}\) is the maximum iteration number. Then, the samples \(\big{\{}\widehat{\tau}_{u,k}^{(i)}\big{\}}_{k=1}^{N_{c}}\) are moved to the time domain, where it is applied the phase demodulator obtaining the samples \(\big{\{}\widehat{\tau}_{u,k}^{(i)}\big{\}}_{t=1}^{N_{c}}\). Finally, a \(N_{c}\) - point fast Fourier transform (FFT) and a selection operation are performed to obtain the soft estimates \(\big{\{}\widehat{\tau}_{u,k}^{(i)}\big{\}}_{k=1}^{L}\in\mathbb{C}^{L}\), \(u=1,\ldots,U\), and the corresponding hard estimates, \(\big{\{}\widehat{\tau}_{u,k}^{(i)}\big{\}}_{k=1}^{L}\in\mathbb{C}^{L}\).  
In the feedback path, the inverse processing is done, i.e., the data recovered from the forward path is again modulated in the symbols \(\left\{\hat{s}_{u,k}^{(i-1)}\right\}_{k=1}^{L}\in\mathbb{C}^{L}\), \(u=1,\ldots,U\), the Hermitian symmetric zero-padded data vector is made using the structure of (1), and it is applied a \(N_{c}\) - point IFFT and the phase modulator. Finally, the resulting signal, \(\mathfrak{c}_{k}^{(i)}=\left[\mathfrak{c}_{1,k}^{(i-1)},\ldots,\mathfrak{c}_{ U,k}^{(i-1)}\right]^{T}\in\mathbb{C}^{U}\), is filtered by the feedback equalizer matrix, \(\mathbf{B}_{d,k}^{(i)}\in\mathbb{C}^{U\times U}\), at \(k\)th subcarrier.  
After all this processing, we obtain the processed received signal for iteration \(i\), \(\overline{\mathfrak{c}}_{k}^{(i)}=\left[\overline{c}_{1,k^{\prime}}^{(i)}, \ldots,\overline{c}_{U,k}^{(i)}\right]^{T}\in\mathbb{C}^{U}\), expressed by:  
\[\overline{\mathfrak{c}}_{k}^{(i)}=\mathbf{W}_{d,k}^{(i)}(\mathbf{W}_{a})^{H} \mathbf{y}_{k}-\mathbf{B}_{d,k}^{(i)}\mathfrak{c}_{k}^{(i-1)}. \tag{7}\]
# A Replica-Selection Algorithm Based on Transmission Completion Time Estimation in ICN
## 3 NDC Transmission and NRS Overview
### Name Resolution System  
Name resolution services are provided by the name resolution system (NRS). NRS types include the enhanced name resolution system (ENRS) [13] and global name resolution (GNR). ENRS is a name resolution service of the SNR approach. It partitions space into hierarchical domains based on distance constraints. No two domains at the same level overlap. ENRS can implement low-latency name resolution services within a limited domain, while inter-domain name resolution services are provided by global name resolution (GNR). The number of layers of ENRS has no effect on our study of replica-selection algorithms, so in this paper, we use a single layer of ENRS to implement our algorithm.  
Figure 2 is a schematic diagram of a single-layer ENRS. The ICN router will register its network address (NA) and cached NDC ID in the ENRS and GNR of its domain. When obtaining content, the content-requesting entity will first query ENRS for a node NA list with the content according to the ID of the content. If the query result is empty, it will continue to query GNR. Finally, the replica-selection algorithm is used to select a replica node for data transmission.  
Figure 1: Diagram of NDC transmission process.
## 4 Simulation and Experiment Results
### Experimental Results in a Realistic Environment  
#### 4.2.1 Experiment Setup  
Three speakers (s1, s2, and s3) were arranged in an equilateral triangle to form a sound source array mounted on the robot, and the center point of the equilateral triangle coincided with the robot's center point. There are two advantages to this arrangement of equilateral triangles. One is to ensure uniform dispersion of the sound signals. The other is that if the robot center point is taken as the coordinate origin, it will help with distance calculations and coordinate transformations to reduce the amount of system computation, because of the equal distance from the center point of the robot to the three speakers. All three speakers were connected to the computer via a Bluetooth module to control their sound. At the same time, a microphone was placed at the center of the robot as a reference microphone (m0), also, the distance from it to the three speakers was equal, known, and fixed. The sound source array and the mobile robot for the experiment are shown in Figure 12. The mobile robot Pioneer3-DX was selected as the main operating platform. The robot controller was connected to the Pioneer3-DX through the RS232 serial port. The controlling program sent a control command to the mobile robot according to the robot's work requirements.  
The Seeed ReSpeaker Core v2.0 was selected as the microphone in this paper, as shown in Figure 13. The chip is designed for voice interface applications. It is based on the Quadcore ARM Cortex A7 Rockchip RK3229 and runs at up to 1.5 GHz with 1 GB of Random Access Memory (RAM). It integrates six microphone arrays, and only one of the microphones was used in our case. The sampling rate of the microphone is 16 kHz. ReSpeaker Core v2.0 runs the GNU/Linux operating system. Xshell can control multiple servers at the same time, and only needs to know their IP address. Therefore, this paper mainly used the Xshell software to remotely control the chip under the same Local Area Network (LAN). Compared to traditional microphone systems that collect sound signals, this method does not require the use of many wires to connect devices. The use of the chip greatly reduced the cumbersomeness of the hardware device and improved the efficiency of the experiments.  
Figure 12: Positional relationship of the sound source array, reference microphone, and mobile robot.
# Metaheuristics for a Flow Shop Scheduling Problem with Urgent Jobs and Limited Waiting Times
## 1 Introduction
This study considers a scheduling problem for a flow shop with urgent jobs and a waiting time constraint. As mentioned, the urgent jobs want to be processed without causing tardiness. Due to the urgent jobs, normal jobs may be relatively delayed, but their waiting times must also be kept. Therefore, it is essential to develop an effective scheduling algorithm to efficiently operate such a manufacturing system. This scheduling problem can be proved to be NP-hard, because a two-machine flow shop total tardiness problem is NP-hard in the strong sense [4], and also a two-machine flow shop makespan problem with limited waiting time is NP-hard [5]. Since the considered problem is NP-hard, it is very tough to find an optimal solution. Hence, this study adopts metaheuristic algorithms to find a good (or near-optimal) solution. To the best of our knowledge, this study is the first attempt to consider the urgent jobs and waiting times simultaneously. In addition, the main contribution is that this work is expected to contribute to the improvement of productivity of semiconductor manufacturing systems. The remainder of this paper is structured as follows. Section 2 introduces previous studies related to this study. Section 3 details the considered scheduling problem with assumptions and presents a mixed integer programming formulation. Section 4 describes the heuristic algorithms for the problem, and Section 5 reports the performance of the proposed algorithms through computational experiments. Finally, Section 6 discusses and concludes this paper with future research directions.
# To What Extent Are the Effects of UV Radiation on Grapes Conserved in the Resulting Wines?
## 3 Discussion
### Wine Phenolic Composition
The effect of UV radiation on wine total stilbenes was inconsistent and not supported by any individual stilbene. Stilbenes respond to both biotic and abiotic stressors (including pathogen attacks and UV-C radiation) in grapes [17; 18; 19], and they also vary along berry development and during unification [7; 21]. However, their responses to other UV wavelengths than UV-C in grapes and the resulting wines ranged from significant [9; 11; 23] to negligible [8; 14; 16]. As occurs with other compounds, this topic requires more research, because stilbenes, and specifically resveratrol, are considered health-promoting compounds [21]. The responses of total and individual flavanols in wine to ambient UV levels were incongruent or non-significant, which agrees with previous results obtained in both grapes and wine [23]. However, high UV-B levels increased several individual flavonoids in grapes and/or wines [16]. Thus, as occurred with hydroxycinnamic acids, flavanol induction in grapes and further persistence in wines may require higher-than-ambient UV levels. This is important becauseflavanols contribute to wine's astringency and bitterness [31] and have antioxidant and anticarcinogenic properties. Hydroxybenzoic acids showed no significant response to radiation regimes, which agrees with previous results [23]. Total polyphenol index, total phenols, and the bulk levels of UVACs showed no definitive response to UV radiation. This may reflect, to a certain extent, the lack of response of most phenolic compounds, including the most abundant ones (anthocyanins), and also the fact that these comprehensive variables integrate the individual responses of many different compounds, which can be diverse and even contradictory. Nevertheless, in some studies, total polyphenol index and total phenols increased (significantly or not) in wines made with grapes previously exposed to ambient UV levels [22; 23], but this increase did not occur in other studies [11; 16]. Factors underlying this variability could be the grapevine variety, UV levels applied, and winemaking process.
## 6 Results
### Influence of Multiple Flight Heights  
In the second dataset, nadir images of different flight heights (s2-n3070) are added to the first dataset (s2-n50, s2-o50). Without any GCP, the addition of images of different flight heights eases the correlation of the focal length with other two parameters (\(\delta_{LA_{z},f}\) = 0.07 and \(\delta_{C_{z},f}\) = 0.07) while the lever-arm on \(z\)-axis is still strongly correlated to camera height (\(\delta_{C_{z},LA_{z}}\) = 0.99). The RMS of 3D point residuals is improved by 2.4 cm and the lever-arm estimation of nadir image is diminished by 2 cm. With one GCP being included, the addition of images of different flight heights largely reduces the correlations among these three parameters (\(\delta_{LA_{z},f}\) decreases from 0.84 to 0.55, \(\delta_{C_{z},LA_{z}}\) decreases from 0.80 to 0.62, and \(\delta_{C_{z},f}\) decreases from 0.68 to 0.33). The internal and the external photogrammetric accuracies are further improved and the RMS of 3D point residuals is decreased to 1.0 cm. For camera model and lever-arm calibration, all GCPs of Segment 2 are included for computing an optimal BBA solution. Parameter estimates issued from this BBA procedure are then used as a priori information for the Flight s1-n50.
# Optimal Pattern Synthesis of Linear Array Antennas Using the Nonlinear Chaotic Grey Wolf Algorithm
## 5 Array Antenna
### Pattern Synthesis of Array Antennas  
The objective is to minimize the decrease in SLL below the predicted value while maintaining a constant main lobe width of the array and achieving deep nulls in specific directions. To achieve this objective, the following fitness function was used [23]:  
\[Fit=\eta_{1}|PSLL-TPSL|+\eta_{2}\Big{|}\text{max}\{F(\theta_{K})\}_{k=1,...N_{ 0}}-DDNL\Big{|} \tag{21}\]  
where \(PSLL\) is the peak sidelobe level; \(TPSL\) is the target peak sidelobe level; \(F(\theta_{k})\) is the direction of the \(k\)th deep null; \(DDNL\) is the desired deep null level; \(\eta_{1}\) and \(\eta_{2}\) are the weighting factors and subject to the condition \(\eta_{1}+\eta_{2}=1\).
# Melamine Foams Decorated with In-Situ Synthesized Gold and Palladium Nanoparticles
## Materials and Methods  
### Materials  
Commercial grade Basotec G+ (BASF, Schwarzheide, Germany) Melamine (ME) foams with very high porosity (0.994), open cellular structure, and a broad cell size distribution (average cell bellow 500 \(\upmu\)m) were kindly provided by LAPE HD SrI (Empoli, Italy). PDMS Sylgard 184 kit, composed by a PDMS base and a curing agent, was purchased from Dow Corning (Midland, MI, US). Chloroauric acid (HAuCl\({}_{4}\)) and sodium tetrachloropalladate (Na\({}_{2}\)PdCl\({}_{4}\)) were purchased from Sigma-Aldrich (St. Louis, MO, US) and used as Au and Pd NPs precursors, respectively. Ethyl acetate and ethanol were also purchased from Sigma-Aldrich (St. Louis, MO, US) and used as solvents. Distilled water was employed for the rinsing of the foams.
# Adaptive Pitch Control of Variable-Pitch PMSG Based Wind Turbine
## 3 Perturbation Observer-Based Nonlinear Adaptive Controller Design  
In this section, the design of NAC for the PMSG-WT based on the feedback linearization will be presented. The NAC based on perturbation estimation proposed in [29] will be used. First, a nonlinear system is transformed as interacted subsystems through input/output linearization. Secondly, in each subsystem, uncertainties, nonlinearities and interaction among subsystems are contained in a defined perturbation term. The perturbation term is estimated via a designed observer. The estimated perturbation is used for compensating the real perturbation and obtaining adaptive linearizing control of the original nonlinear system.
# Exploring Multi-Channel GPS Receivers for Detecting Spoofing Attacks on UAVs Using Machine Learning
## 4 Machine Learning Classifiers
### Random Forest  
Random forest [40] is an ensemble learning algorithm that constructs multiple decision trees, where each tree is trained on a random sample of the data considering random feature selection at each split. The final predictions are made using majority voting and averaging for the classification and regression tasks, respectively. This approach helps to avoid overfitting issues faced in single decision trees. Moreover, random forest provides feature importance scores; this capability is particularly valuable for understanding which GPS signal features contribute most to detect spoofing attacks. Nevertheless, employing a large number of individual decision trees within the ensemble can result in significant computational overhead.  
Our random forest model comprises 100 decision trees, with bootstrap sampling to ensure diverse subsets for training each tree. As the previous explained decision tree, Gini impurity is employed, and the maximum depth of trees is not set. However, the square root of the total number of features is applied to reduce correlation between trees.
# Application of Deep Learning and Symmetrized Dot Pattern to Detect Surge Arrester Status
## 2 Surge Arrester Fault Construction
### Normal State (Type 1)  
The Type 1 surge arrester model built for this paper is shown in Figure 1, which shows the supply side connecting terminal (line terminal), porcelain bushing (porcelain insulator), varistors made of ZnO (ZnO element), spring, gap tube, and ground side connecting terminal (earthing terminal) of the surge arrester. The construction specifications were complied with before the operation of the surge arrester to avoid endangering the power system and to ensure work safety.
## 3 Results and Discussion
### Electronic Nose Analysis  
(1) Electronic Nose Radar Charts  
Radar charts for Petit Manseng sweet white wine made from three groups of grapes were analyzed. The W2S, W1S, W2W, W5S, and W1W response values were high (Figure 5).  
The W2S sensor, sensitive to organosulfur compounds (e.g., thiols), correlates with tropical fruit aromas in wine. W1S responds to methylated compounds, linked to fruity and floral notes, while W2W specifically detects alcohols, with signal intensity reflect  
Figure 4: Principal Component Analysis Load Plot.  
Figure 5: Radar chart of electronic nose data.  
ing overall alcohol content trends. W5S targets nitrogen oxides (potentially from fruit stress metabolism), and W1W measures trace inorganic sulfides, which may influence aroma balance.  
Sensors with low responses (W1C for benzenes, W6S for hydrides, W3S for long-chain alkanes, W5C for short-chain alkanes, W3C for aromatic amines) showed stable weak signals, consistent with GC-MS data indicating low levels of these compounds.  
W2W responses aligned with GC-MS alcohol trends: higher signals in Group A mirrored elevated higher alcohol levels (e.g., phenylethanol, isoamyl alcohol), validating reduced alcohol content in Botrytis-infected groups (B, C). W2S exhibited significantly stronger responses in Group C, corroborating GC-MS findings of 3-methylhiopropanol (a characteristic Muscadelle thiol) exclusively in Group C and reinforcing its role in tropical fruit aromas. Additionally, W1S responses were higher in Group A, consistent with GC-MS data showing elevated ester levels, supporting the conclusion that Botrytis infection diminishes ester content.  
(2) PCA of the electronic nose data  
PCA was used to reduce the dimensions of the electronic nose data and extract the main features for linear analysis [37]. The contributions of PC1 and PC2 were 79.2% and 19.7%, respectively; the cumulative contribution was 98.9%, indicating that the data are representative of the samples (Figure 6). Notably, 3-fold cross-validation of the PCA results showed that the cumulative variance explained post-validation (78.6%) was comparable to the original (80.2%), with stable intra-group clustering. These confirm the statistical robustness of the PCA outcomes. PCA showed that wines made from normal grapes were distinct from those made from grapes infested with gray mold, and that group A-C wines were clearly separate.