esa-sceva/satcom-chunk-collection huggingface dataset:
# Cyclostationary Crosstalk Cancelation in High-Speed Transmission Lines
## 2 Linear Periodic Time-Varying Filtering
### Sliding Window Filtering
The \(v\)th Fourier coefficient of the LTI filter impulse response can be expressed using the inner product of the permutated frequency shifted matrix\(\mathbf{H}_{\mu}\) and matrix \(\mathbf{\Phi}^{v}=N^{-1}\mathbf{F}^{v}\):  
\[G_{v\mu}(n)=\left\langle\mathbf{H}_{\mu}\mathbf{P}_{n},\mathbf{\Phi}^{v} \right\rangle=N^{-1}\langle\mathbf{H}_{\mu}\mathbf{P}_{n},\mathbf{F}^{v} \rangle=N^{-1}\text{tr}\Big{(}\mathbf{F}^{vH}\mathbf{H}_{\mu}\mathbf{P}_{n} \Big{)} \tag{24}\]  
where \(\mathbf{H}_{\mu}=\mathbf{H}\mathbf{F}^{\mu}\) is a frequency shifted version of the matrix \(\mathbf{H}\),  
\[\mathbf{P}_{n}=\left\{ \begin{array}{ll}\mathbf{P}^{nT},&0\leq n\leq N-1\\ \mathbf{P}^{|n|},&-N+1\leq n<0\end{array} \right. \tag{25}\]  
The bank of one-dimensional IRs of the FRESH filter is shown in Figure 6. This structure is functionally equivalent to the SW filter with a two-dimensional IR shown in Figure 4. The single one-dimensional IR is complex valued. Real parts are shown in Figure 6a-c. The corresponding imaginary parts are presented in Figure 6d-f.  
Figure 5: Scheme of frequency shift filtering. The transformation of signal in the LPTV system can be realized by SW filtering or by equivalent FRESH filtering. An example of the output pulse amplitude modulated (PAM) signal is shown in Figure 7 for both types of filters. The analysis of the compact matrix form of the SW LPTV filtering using HSR shows that its FRESH representation includes frequency shifts and MIMO LTI filtering of band-limited components of the input signal. The output full-band signal is obtained by combining the orthogonal components.
## 2 Overview of IoV
### Challenges in the IoV System
#### 2.3.3 Integrity- or Trust-Related Issues  
From an integrity perspective, the malicious nodes modify the routing information to misguide the genuine vehicles in the network. By integrity, we mean that data that have been transmitted from the source node are not modified when they are received at the sink node. In such types of attacks, the attacker somehow enters the IoV system and accesses the packets that are being sent in the IoV network, modifies them, and sends them to the sink node. Trust management is another crucial aspect that significantly impacts the utilization of IoV system. With the distributed nature of its architecture coupled with high volume of messages exchanged, the reliability of the system is often compromised [56].
## 4 Secure Path Algorithm
### Secure-Path based Data Collection  
As long as a source node receives enough secure paths from the sink, it is able to send data via these paths. Therefore we can improve the ADC algorithm in Section 3 by using secure paths. The secure-path based data collection (SPDC) algorithm is illustrated as follows:  
1. When a source node \(S\) intends to send a data share to the sink, it first checks its local cache. If there are secure paths, it selects a secure path \(P=\textless S\), \(L_{R}\), \(C\textgreater\) with the largest counter value from its local data repository. \(S\) adds \(L_{R}\textless\{ }\{d_{1},d_{2},\ldots,\)\(d_{n}\}\) to the beginning of the data share. If there are no secure paths in the local cache of the relay node, it just performs random multipath routing as the ADC algorithm in Section 3. If \(S\) has no secure paths at all, it performs path construction as the FSPC algorithm in Section 4.1.
2. Before sending the share, \(S\) first checks whether the node \(S_{1}\) with identity \(d_{1}\) is in its neighbor list. If the node is not in the list, it just performs random multipath routing. Otherwise, it sends the share to the node \(S_{1}\) with \(d_{1}\).
3. When a sensor node \(S_{k}\) receives a share, it first checks whether there is any secure path in the head of the share. If not, it performs random multipath routing and path construction. Otherwise, it checks whether the node \(S_{k\textless\ 1}\) with identity \(d_{k\textless\ 1}\) is in its neighbor list. If the node is not the list, it just performs random multipath routing. Otherwise, it sends the share to the node \(S_{k\textless\ 1}\) with \(d_{k\textless\ 1}\).  
_If the share reaches the sink successfully_:  
1. On the arrival of the share, if there is a secure path in the share, it means every relay node has used the path and the sink just sends back an empty notification to \(S\). Otherwise, the sink extracts the identity list as a new secure path from the share, updates its local database, and sends back a notification with the newly-constructed secure path to \(S\).
2. The relay nodes on the path update their local cache with sub-paths.
3. On the arrival of the notification, \(S\) extracts new secure path (if any) from the packet, and stores it in its local cache.  
_If the share is dropped or does not reaches the sink within the time span allowed_:  
1. \(S\) does not receive a notification from the sink, and then it just decreases the counter of \(P\) by 1.
2. If the counter of a secure path is cleared, \(S\) will remove it from its local cache. \(S\) will resend the share if possible.  
From this algorithm, we can see that a secure path is not considered secure all the time. Secure paths are evaluated by their quality of service (QoS) for data collection. The SPDC algorithm deals with selective forwarding attacks by using a scoring mechanism. In this way, we can exclude as many compromised nodes as possible from data collection. The local cache for secure paths of a source node therefore changes dynamically to support more secure data collection in WSN.
# A Framework for Bluetooth-Based Real-Time Audio Data Acquisition in Mobile Robotics
## 3 Theoretical Framework and Novel Methods
### Motor Control and Power Regulation Theory  
The L298N motor driver utilized in this system employs a dual H-bridge configuration, enabling bidirectional control of two DC motors through pulse-width modulation (PWM). The relationship between the PWM duty cycle \(\delta\) and the applied motor voltage \(V_{m}\) can be expressed as  
\[V_{m}=\delta\times V_{s} \tag{8}\]  
where \(V_{s}\) is the supply voltage provided to the motor driver. This linear relationship enables precise control over motor speed, though inertial effects and friction introduce non-linearities in the actual mechanical response.  
Power regulation represents a critical aspect of this design, as both motor operation and audio sampling must coexist without interference. The electromagnetic noise generated by DC motors follows an inverse square relationship with distance, with intensity \(I\) at distance \(r\) given by  
\[I\propto\frac{1}{r^{2}} \tag{9}\]  
The physical layout design was inspired by this relationship, with the microphone positioned to maximize distance from the motors while maintaining a compact form factor. Additionally, the power distribution system incorporates decoupling capacitors and separate regulation pathways for the motor subsystem and the sensitive audio acquisition components, minimizing electrical noise coupling.  
The power consumption \(P\) of the system at an instant of time can be represented as the sum of the consumptions of individual components:  
\[P=P_{esp32}+P_{motors}+P_{mic}+P_{bt} \tag{10}\]  
where* \(P_{\textit{ESP32}}\) is the power required by the ESP32 microcontroller (Espressif Systems, Shanghai, China) (\(\sim\)240 mW under processing).
* \(P_{\textit{motors}}\) is the power consumed by the DC motors depending on torque and speed (\(\sim\)100-500 mW per motor in standard operation).
* \(P_{\textit{mic}}\) is the power spent by the INMP441 microphone (InvenSense, San Jose, CA, USA) (\(\sim\)1.4 mW).
* \(P_{bt}\) is the excess power to be added for Bluetooth transmission (approximately 100 mW during active streaming).  
This power model guided the battery selection and power management strategy, ensuring sufficient capacity for extended operation while maintaining stable supply characteristics essential for audio quality and motor performance. The approach to power management used in this study aligns with research by Owfi [15] on deep-learning-based wireless indoor localization, which emphasizes the importance of efficient power utilization in mobile sensing systems.
# Development of a Wideband Slotted Antenna Array with Low Profile and Low Sidelobe
###### Abstract  
In this paper, a novel multi-layered waveguide-fed slotted cavity antenna array operating in the K-band (i.e., 18-27 GHz) is presented. The antenna is composed of 64 (8 \(\times\) 8) groups of 2 \(\times\) 2 subarrays with low profile, and fed by a 1-64 ways waveguide corporate-feed-network. In order to obtain a low sidelobe level (SLL), the Chebyshev power distribution is introduced into the feeding network to accurately taper the power distribution among the subarrays. To realize the amplitude-tapering network, a simple T-junction, which can provide equal phase but unequal power, is used. The antenna array is analyzed and validated by using the finite element method (FEM). Simulation results demonstrate that the proposed antenna array can achieve a broad bandwidth of 21.9%, and a good gain as 29.1 dBi. Additionally, the first SLL can be as small as \(-\)28.3 dB and \(-\)20 dB in the \(E\)-plane and the \(H\)-plane, respectively. The overall size of the slotted cavity antenna array is 169.6 \(\times\) 169.6 \(\times\) 7.23 mm\({}^{3}\).  
antenna array; K-band; low sidelobe; slotted cavity array; wideband; wireless communication +
Footnote †: journal: Electronics Letters
# Small-Sample Bearings Fault Diagnosis Based on ResNet18 with Pre-Trained and Fine-Tuned Method
## 2 Relevant Theoretical Approaches
### Pre-Trained and Fine-Tuned Model  
In the field of machine learning, TL can be divided into sample-based TL, feature-based TL and model-based TL [41]. Sample-based TL helps target domain learning by adjusting the use of source domain samples in the target domain, but it needs to ensure that there is a certain similarity between the source domain and the target domain; otherwise, the effect is limited. Feature-based TL transforms the features of the source domain and the target domain to make them more similar or more discriminative in the common feature space, but the transformation of features may lead to the loss of information and affect the performance of the model. Model-based TL migrates the trained model in the source domain to the target domain and uses the knowledge of the pre-training model to improve the learning effect in the target domain.  
In this study, we utilized model-based TL (the pre-trained and fine-tuned method) to enhance the performance of ResNet18 in the context of small-sample FD. Therefore, we utilized a pre-trained ResNet18 model from the ImageNet dataset (source domain). The ImageNet pre-trained weights are trained on extensive-scale image categorization tasks, which makes them perform well in learning common image features. Because of the scale and variety of the ImageNet dataset, pre-trained weights can capture rich image features. In many cases, employing pre-trained weight can enhance both the performance and generalization capability of the model. First, we froze the weights of all models, except the classifier, to keep the feature representation learned by the ResNet18 model in the pre-trained phase unchanged. Next, we eliminated the initial output layer from the pre-trained model and replaced it with a new output layer, which is designed for our small-sample dataset (target domain). Finally, the model is trained on our small-sample dataset. The output layer is trained from scratch, and the parameters of other layers are fine-tuned according to the parameters of the pre-trained ResNet18 model. The principle of pre-training and fine-tuning is shown in Figure 5.  
Figure 4: ResNet18 architecture.
###### Abstract  
Estimating accurately evapotranspirationspiration (ET) in urban ecosystems is difficult due to the complex surface conditions and a lack of fine measurement of vegetation dynamics. To overcome such difficulties using recent developments of remote sensing technology, we estimate leaf area index (LAI) from Sentinel-2-based Normalized Difference Vegetation Index (NDVI) using the NDVI-LAI nonlinear relationship. By applying Sentinel-2-based LAI and land cover classification (LCC) to a carbon-water coupling model (PML-V2.1) with surface meteorological forcing data as input, we, for the first time, estimate monthly ET at 10m \(\times\) 10m resolution for the Beijing Sponge City. Results show that for the whole sponge city during June 2018, the LAI, ET and gross primary productivity (GPP) are 0.83 m\({}^{2}\) m\({}^{-2}\), 1.6 mm d\({}^{-1}\) and 2.8 gC m\({}^{-2}\) d\({}^{-1}\), respectively. For different LCCs, lakes and rivers have the highest ET (\(\geq\)8 mm d\({}^{-1}\)), followed by mixed forests and croplands (ET is 4-6 mm d\({}^{-1}\) and LAI is 2-3 m\({}^{2}\) m\({}^{-2}\)) with dominant contribution (\(>\)80%) from plant transpiration, while grasslands (2-4 mm d\({}^{-1}\)) have 50-70% from transpiration due to smaller LAI (1-2 m\({}^{2}\) m\({}^{-2}\)). The impervious surfaces occupying \(\sim\)60% of the sponge city area, have the smallest ET (\(\leq\)2.0 mm d\({}^{-1}\)) in which interception evaporation by impervious surface contributes 20-30%, and transpiration from greenbelts (0.5-1.0 m\({}^{2}\) m\({}^{-2}\) of LAI) contributes 40-50%. These findings can provide a valuable scientific basis for policymaking and urban water use planning. This study proposes a Sentinel-2-based technology for estimating ET as a feasible framework to evaluate city-level hydrological dynamics in urban ecosystems.  
evaporation; evapotranspiration; LAI; NDVI; urban ecosystem; sponge city; PML-V2; Penman-Monteith equation; Sentinel-2 2021  
14 May 2021  
21 May 2021  
1
## 2 Configuration of Hybrid Tramway
### Energy Storage System  
#### 2.3.1 Battery Model  
In the hybrid system, the BAT, with high energy density, more efficient, and faster dynamic response than the PEMFC, is utilized to not only compensate for the excess power that PEMFC cannot deliver but also accumulate the regenerative energy from traction load. The behavior of the BAT model is constructed by using the SimPowerSystems of MATLAB/Simulink with the Shepherd curve-fitting model employed [42]. Depending on the charge mode or discharge mode, the charge model or discharge model is respectively operated to keep the BAT's capacity in a reasonable operating performance range.  
In the discharge model, the term polarization voltage is added to the discharge voltage formulation to describe the influence of BAT SOC on power efficiency. Besides, the polarization resistance is also considered to ensure the stability of the simulation model. Thus, the BAT voltage is calculated as follows [42]:  
\[\mathrm{V_{BAT}=V_{0,BAT}-K\frac{Q}{Q-\int i_{BAT}dt}\int i_{BAT}dt-K\frac{Q}{Q -\int i_{BAT}dt}i_{BAT}^{*}+A_{b}\exp\left(-B\int i_{BAT}dt\right)-R_{int}i_{BAT} \tag{7}\]  
where \(\mathrm{V_{BAT}}\) is the BAT voltage (V), \(\mathrm{V_{0,BAT}}\) is the BAT constant voltage (V), \(K\) is the polarization constant (V/Ah), \(\mathrm{Q}\) is the maximum BAT capacity (Ah), \(\mathrm{i_{BAT}}\) is the BAT output current (A), \(\mathrm{i_{BAT}^{*}}\) is the low-frequency current dynamics (A), \(\mathrm{A_{b}}\) is the exponential zone amplitude (V), \(B\) is the exponential zone time constant inverse (Ah\({}^{-1}\)), and \(\mathrm{R_{int}}\) is the BAT internal resistance (\(\Omega\)). In addition, the term \(\mathrm{K(Q/(Q-\int i_{BAT}dt))}\int i_{BAT}dt\) indicates polarization voltage and \(\mathrm{K(Q/(Q-\int i_{BAT}dt))}\) is the polarization resistance.  
For the charging model, the polarization resistance term is adjusted to illustrate the behavior at the end of the charging process because the BAT voltage increases rapidly. In this case, the BAT voltage is represented as follows [42]:  
\[\mathrm{V_{BAT}=V_{0,BAT}-K\frac{Q}{Q-\int i_{BAT}dt}\int i_{BAT}dt-K\frac{Q}{ \int i_{BAT}dt-0.1Q}i_{BAT}^{*}+A_{b}\exp\left(-B\int i_{BAT}dt\right)-R_{int }i_{BAT} \tag{8}\]  
On the other hand, the SOC level of the BAT must be kept in a limited range of capacity to achieve high working efficiency. This SOC can be calculated by the relationship of the current charge and the maximum capacity as [42]  
\[\mathrm{SOC_{BAT}(t)=SOC_{BAT}(t_{0})-\frac{1}{Q}\int\limits_{t_{0}}^{t}i_{BAT }(\tau)d\tau} \tag{9}\]  
where SOC\({}_{BAT}\) is the SOC of the BAT (%), \(t\) is the instant time, and \(t_{0}\) is the initial time.  
From input parameters, nominal current discharge curves of the used BAT model are obtained as depicted in Figure 3.  
Figure 2: Polarization curves of the PEMFC stack.
## 2 Materials and Methods
### Postural Balance Parameters
#### 2.5.1 Total Path Length  
Total path length, the cumulative distance traveled by the body segment during the recording period, was calculated. First, the displacement of the body segment was obtained through double integration of the pre-processed acceleration data, as shown in Equation (2).  
\[d(t)=\iint a(t)dtdt \tag{2}\]  
where \(a(t)\) is the acceleration and \(d(t)\) is the displacement.  
The total path length \(L\) is then calculated by summing the absolute displacements across the three orthogonal axes, i.e., the 3D Cartesian coordinate system (X, Y, Z), as shown in Equation (3). It is necessary to use absolute values to ensure no effect of the directions of the changes was included in the sum of the distance.  
\[L=\sum|D_{x}|+\sum|D_{y}|+\sum|D_{z}| \tag{3}\]  
\begin{table}
\begin{tabular}{c c} \hline
**Task** & **Description** \\ \hline
1 & Sit to stand \\
2 & Standing unsupported \\
3 & Sitting unsupported \\
4 & Stand to sit \\
5 & Transfers \\
6 & Standing with eyes closed \\
7 & Standing with feet together \\
8 & Reaching forward with outstretched arm \\
9 & Retrieving object from floor \\
10 & Turning to look behind \\
11 & Turning 360 degrees \\
12 & Placing an alternate foot on stool \\
13 & Standing with one foot in front \\
14 & Standing on one foot \\ \hline \end{tabular}
\end{table}
Table 1: Berg Balance Scale tasks.  
where \(D_{x}\), \(D_{y}\), and \(D_{z}\) are displacements in the X, Y, and Z axes, respectively. This metric was chosen as it provides a simple yet informative measure of the amount of movement, which could be indicative of balance strategy or overall postural stability [27]. Greater total path lengths might indicate less efficient or more compensatory movements, suggesting poorer balance.
# Recent Advances in Laser-Induced Surface Damage of KH\({}_{2}\)Po\({}_{4}\) [PERSON]
## 1 Introduction
At this time, the nonlinear absorption of the laser will cause physical phenomena such as temperature rise, melting, vaporization, thermal compression shock, plasma expansion, and wave shock, which are extremely complex process ([PERSON] et al., 2015). It is of great significance to understand the mechanism of laser induced damage for improving the laser induced damage threshold of optical crystal materials. To determine the degree of damage in KDP crystal materials caused by the laser irradiation, LIDT is usually the criterion that is used to improve the anti-laser damage ability of materials. [PERSON] and [PERSON] et al. (2015, 2016) proposed the ionization rate model. [PERSON] et al. (2016) integrated the whole ionization process and used the evolution process of free electron density to characterize the trend of laser-induced damage under the influence of incident laser intensity indirectly. The light intensity modulation phenomenon of the incident laser inside the material was introduced to characterize the change of damage threshold by [PERSON] (2016). In addition, the finite-difference time-domain (FDTD) algorithm was also applied to KDP crystals to determine the light intensity enhancement factor (LIEF) inside the material to show the trend of damage ([PERSON] et al., 2015; [PERSON] et al., 2015). However, both the exploration of free electrons and LIEF in an electric field can only give the single trend of damage. The damage trend reflected by LIDT cannot be intuitively characterized. A simulation technique that can intuitively characterize the damage threshold needs to be further explored. To understand the principles of laser-induced damage of KDP crystals deeply, the surface damage caused by the irradiation of laser on KDP crystal materials will be reviewed in detail from three stages of energy deposition, damage initiation, and damage forming. The energy deposition regime of KDP crystals in the early stage of laser irradiation was analyzed, which will be helpful to establish the evaluation criteria of the anti-laser damage ability of KDP crystal elements, so as to delay the service life of the project. In addition, combined with different forms of defects, the experiments and theoretical simulation of laser induced damage mechanism were sorted and reviewed. The initial energy deposition and the damage caused in the middle and late stages were discussed to optimize the processing parameters of KDP crystal materials. The damage threshold prediction method has also been sought.
## 4 Results
### Reference Theoretical Value of the Inverse Kinematic Matrix  
The exact or theoretical value of the inverse kinematic matrix (\(M^{-1}\)) of the APR-02 mobile robot can be computed analytically from Equation (1) as [17]:  
\[M^{-1}=\begin{bmatrix}-0.0854478398400646&0.000000000000&0.0854478398400646\\ 0.0493333333333333&-0.09866666666667&0.04933333333333\\ 0.2529914529914529&0.2529914529914529&0.2529914529\\ \end{bmatrix} \tag{7}\]  
Figure 5 shows the evolution of a sample trajectory followed by the APR-02 mobile robot: the red line displays the ground truth trajectory estimated with SLAM [31] and the blue line the odometry computed using the theoretical value of the inverse kinematic matrix. As could be expected, Figure 5 shows differences between the ground truth trajectory and the odometry due to the existence of systematic odometry errors. At this point, note that the inverse kinematic matrix of a three-wheeled omnidirectional robot is very sensitive to any inaccuracy in the implementation of the parameters of the motion system [17]: radii of the wheels \((r_{a},r_{b},r_{c})\), the distance between the center of the robot and each wheel \((R_{a},~{}R_{b},~{}R_{c})\), and the angular orientation of each wheel in the mobile robot frame \((\delta_{a},~{}\delta_{b},~{}\delta_{c})\). In any case, the effects of the systematic errors are best evidenced in a mobile robot performing curved trajectories (Figure 5) rather than straight trajectories [17]. In the case of an omnidirectional mobile robot, the difference between a curved and a straight trajectory depends only on  
the target angular rotational speed, which is the \(\omega\) parameter in the motion command, \((v,\alpha,\omega)\).