esa-sceva/satcom-chunk-collection huggingface dataset:
# Cognitive Interference Cancellation with Digital Channelizer for Satellite Communication
## 3 Digital Channelizer  
In satellite communication, a satellite operates as a relay between Earth and terminal stations. In general, satellites have multiple purposes, e.g., satellite broadcasting services and satellite communication services, and can change the carrier frequency, bandwidth and gain of the received signal form a transmitter (e.g., an Earth station) before relaying it to the receiver (e.g., a terminal station). A digital channelizer is used for efficient implementation of switching and multiplexing functionality. In this section, a digital channelizer based on a digital filter bank is briefly reviewed.  
The main function of a digital channelizer is to divide the received signal into multiple subchannels and to reconstruct the wideband signal by combining these multiple subchannels. The filter that divides the wideband signal into multiple subchannels with uniform bandwidth is called an analysis filter bank and that which combines the multiple subchannels is called a synthesis filter bank. Discrete Fourier transform (DFT) is one of the simplest filter banks, i.e., digital channelizer, and it is efficiently realized using an FFT algorithm. However, the stopband attenuation and flatness of the passband of the DFT filter bank are not sufficient for switching and multiplexing functions in a digital channelizer. The FFT operation is regarded as a digital channelizer with a rectangular finite impulse response (FIR) filter. Therefore, the spectrum mask of each subchannel becomes a sinc function as shown in Figure 4a. By designing a sophisticated lowpass FIR (prototype) filter instead of a rectangular FIR (prototype) filter, both the stopband attenuation and passband flatness of the subchannel in the digital channelizer can be improved. Therefore, the objective function of the digital channelizer design is given by the weighted sum of two measures: The stopband attenuation of a lowpass (prototype) filter, and sum of the squared magnitude responses of all subchannels. For example, a quadrature mirror filter (QMF) banks design method was proposed by [PERSON] [22]. [PERSON] [23] and [PERSON] [24] also proposed other filter design methods.  
In this study, a near-perfect reconstruction (NPR) polyphase filter bank is used in the design of a digital channelizer with 1024 subchannels. The frequency response of the designed filter bank with 16 subchannels is shown in Figure 4b. Because the frequency response of a 1024-subchannel filter bank is not visible, we show that of 16-subchannel filter bank which is designed with the same method. As shown in the figure, the designed digital channelizer with an NPR polyphase filter bank has negligible error in the sum of magnitude response and significant stopband attenuation. Therefore, is can be concluded that the designed filter bank can be used as a digital channelizer for satellite communications.  
Figure 4: Power spectral density of filter banks with 16 subchannels: (**a**) Discrete Fourier transform (DFT) filter bank and (**b**) near-perfect reconstruction (NPR) polyphase filter bank.
## 3 System Architecture
### Perception Layer  
This layer is comprised of field devices that are resource-constrained in terms of computation, storage, and power; therefore, these devices are not capable of performing heavily complex computation and massive data storage (for example, the blockchain operations, such as proof of work and keeping a copy of the ledger, which is usually undefined in size, in the IoT platform). In our case, we employed IoT-enabled RFID devices of the active type, which are also resource-constrained devices. Particularly, each device's internal storage was sufficient to store a unique identification (ID), a session key, and a hash of IDs in order to initiate communication with an IoT-enabled RFID reader. Each parking slot was installed with an IoT-enabled RFID reader and was able to obtain the reading from the RFID tag embedded in the AV. The number of parking slots in parking stations is entirely dependent on the area it occupies. In the situation of on-road parking, the RFID readers are mounted safely, with a parking panel closer to the edge of the road on the pavement. For both indoor and outdoor parking, their locations are fixed, and each parking slot is active with an IoT-enabled RFID reader via Ethernet to communicate with fog nodes using MQTT (Message Queue Telemetry Transport). In the case of wireless connectivity to fog nodes in WLAN or WPAN, the parking slots are operational using Wi-Fi or ZigBee.  
Figure 3: System Architecture.
# A Four-Port Dual-Band Dual-Polarized Antenna for Ku-Band Satellite Communications
## 3 Experimental Results and Discussion
The designs in [13; 14] are two-port DBDP antennas, whereas the rest, including the proposed design, are four-port DBDP antennas. Although the design in [17] has a higher gain, the proposed antenna offers much wider IBWs and is much more compact. Figure 8: (**a**) \(S\)-parameters of the LB antenna. (**b**) \(S\)-parameters of the HB antenna. (**c**) Tx/Rx isolation. Figure 10: _Cont._  
Figure 9: Simulated and measured realized gains of (**a**) LB antenna and (**b**) HB antenna.
## 4 Methodology
### Unsupervised Classification Using Gaussian Mixture Model (GMM) Clustering  
A GMM is a probabilistic model that assumes the data is generated from a mixture of Gaussian distributions. It is used to classify the compressed features into distinct engine states.  
The GMM models engine data as a weighted sum of K Gaussian distributions. The probability density function can be calculated in accordance with Equation (3).  
\[p(x)=\sum_{k=1}^{K}\pi_{k}N(x;\mu_{k},\Sigma_{k}) \tag{3}\]  
where \(\pi_{k}\) are mixing coefficients (\(\Sigma\pi_{k}=1\)), and \(N\) denotes a Gaussian with mean \(\mu_{k}\) and covariance \(\Sigma_{k}\).  
Feature Aggregation  
Having trained the autoencoder model, compressed features were extracted for each engine. Then, the engine features were aggregated per engine and standardized.  
GMM clustering was performed to classify engines into three states: Healthy, Middle, and Unhealthy. Cluster indices were mapped to engine states, and clustering results were visualized by plotting aggregated and compressed features vs. their values. Health states emerge from GMM clustering of compressed features, not predefined thresholds. Lower latent values (Healthy) correspond to nominal sensor baselines, while higher values (Unhealthy) reflect deviation magnitudes. For example: 20% \(\Delta\) in exhaust gas temperature \(\rightarrow\) Middle state; 35% \(\Delta\) in vibration amplitude \(\rightarrow\) Unhealthy state. This progression aligns with physical degradation models in which early faults manifest as subtle deviations before accelerating toward failure.  
Visualized results for one of the results are presented in Figure 2.  
Figure 2 illustrates the distribution of compressed engine features classified by the GMM into three distinct health states: Healthy, Middle, and Unhealthy. Every colored line refers to the individual engine serial number. As observed, the range of values for crucial compressed engine features tends to be lower for Healthy engines, progressively increasing for Middle engines, and significantly higher for Unhealthy engines. While feature values provide an indication, the trend of these values over time truly signifies changes in engine health status. For the real-life engine dataset, distinguishing clear trends for the Middle and Unhealthy engines presents a significant challenge, highlighting the inherent complexity and noise in real-world operational data.
# Effects of Pulsed Electric Field on Oil Extraction Rate and
## 3 Results and Discussion  
### Electrical Properties  
The high voltage, generated by a home-made pulse power supply, was applied to high voltage electrode of the PEF treatment unit for processing the particles of penoy seeds. Taking the example of applied voltage of 2 kV, the voltage-current waveforms of Pulsed Electric Field (PEF) treatment processes with different particle sizes of penoy seeds are depicted in Figure 4a,b. It can be observed that under the same voltage level, only the capacitive current is detected in the circuit. Within the scope of this experiment, the influence of penoy seed particle size on the electrical properties is relatively small, and can be determined through equivalent circuit analysis. In fact, during the process of PEF treatment of penoy seeds, the circuitry in the processing unit can be equivalently represented as a series or parallel connection of air capacitance and penoy seed capacitance. Taking series connection as an example (the equivalent capacitance relationship is similar after parallel connection), as shown in Figure 5, assuming that the penoy seeds of various particle sizes in the experiment are compacted into cakes with a capacitance \(C_{\mathrm{P}}\), and the remaining thickness forms an air column with a capacitance \(C_{0}\). According to series capacitors analysis, it can be concluded that \(C_{\mathrm{P20}}<C_{\mathrm{P40}}<C_{\mathrm{P60}}\) for equivalent capacitances, where \(C_{\mathrm{P20}}\) represents the capacitance of penoy seed particle with size of 20-mesh. Due to the relatively large value of \(C_{0}\), there is only a small difference in equivalent capacitances among penoy seeds with different particle sizes. Therefore, under the same voltage level, capacitive (displacement) currents are approximately equal. For penoy seeds with the same particle size, their capacitance \(C_{\mathrm{P}}\) remains approximately constant. Therefore, according to the expression for displacement current \(i=C\frac{\mathrm{d}u}{\mathrm{d}t}\), it can be inferred that as the voltage increases, the current also increases, as shown in Figure 4c,d. It is well known that dielectric constants vary at different frequencies, which affects capacitance and further influences displacement current. This results in different current behaviors at two frequencies mentioned in this study. However, since there is little variation in dielectric constants within the range of 100 Hz to 1000 Hz, the difference in current waveforms between Figure 4c,d is not significant. Therefore, subsequent discussions will only consider voltage amplitude and frequency without considering the impact of current on various indexes.  
Figure 4: Voltage and current waveforms during the process of PEF treatment on penoy seeds.
## 3 Background
### Data Imputation  
In many real-world datasets, missing data is a persistent challenge that can compromise the accuracy and reliability of subsequent analyses. This problem occurs when certain values in the dataset are either unobserved or lost due to various reasons such as sensor failures, incomplete surveys, or system errors. Missing data, particularly in heterogeneous datasets containing both continuous and categorical variables, poses a significant problem, as it can lead to biased results or reduce the overall dataset size if handled improperly.  
One common approach to mitigate this issue is through data imputation, a process by which missing values are estimated and filled in to maintain the integrity of the dataset.  
Given a dataset \(X=\{x_{1},x_{2},\ldots,x_{n}\}\), where each \(x_{i}\) is a data point that may include both continuous and categorical features, the Gower distance is a metric that enables the handling of mixed data types. Let each data point, \(x_{i}\), consist of \(p\) features, i.e.,  
\[x_{i}=\{x_{i1},x_{i2},\ldots,x_{ip}\}.\]  
For two data points, \(x_{i}\) and \(x_{j}\), the Gower distance, \(d_{G}(x_{i},x_{j})\), is computed as the weighted sum of individual feature contributions:  
\[d_{G}(x_{i},x_{j})=\frac{\sum_{k=1}^{p}w_{k}d_{k}(x_{ik},x_{jk})}{\sum_{k=1}^{ p}w_{k}},\]  
where:  
* \(d_{k}(x_{ik},x_{jk})\) is the normalized distance between the \(k\)-th features of \(x_{i}\) and \(x_{j}\).
* \(w_{k}\) is a weight assigned to the \(k\)-th feature, which is typically 1 if both \(x_{ik}\) and \(x_{jk}\) are non-missing, and 0 otherwise.  
For continuous variables, the distance between two features is the normalized absolute difference:  
\[d_{k}(x_{ik},x_{jk})=\frac{|x_{ik}-x_{jk}|}{\max(x_{k})-\min(x_{k})},\]  
where \(\max(x_{k})\) and \(\min(x_{k})\) denote the maximum and minimum values of the \(k\)-th feature across all data points.  
For categorical variables, the distance is binary:  
\[d_{k}(x_{ik},x_{jk})=\begin{cases}0,&\text{if }x_{ik}=x_{jk},\\ 1,&\text{if }x_{ik}\
eq x_{jk}.\end{cases}\]  
Once the Gower distance is computed between data points, the k-NN algorithm selects the \(k\)-nearest neighbors of a data point \(x_{i}\) (those with the smallest Gower distance values) and imputes the missing values by using information from these neighbors. For example, missing values in continuous features may be imputed by the average of the corresponding feature values from the nearest neighbors, while missing categorical values may be imputed using the mode of the neighbors.  
In summary, the k-NN imputation process for a missing value in \(x_{i}\) can be expressed as:\[\hat{x}_{i}=\frac{1}{k}\sum_{j\in\mathcal{N}_{k}(x_{i})}x_{j},\]  
where \(\mathcal{N}_{k}(x_{i})\) represents the set of the \(k\)-nearest neighbors of \(x_{i}\) based on the Gower distance.  
This combination of k-NN with Gower distance addresses imputation challenges by leveraging both the proximity of data points in a mixed-type feature space and the ability to handle missing values persistently.
## 5 Digital Modulation and Demodulation Circuits
### Transmitter  
The transmitter generates a 13.56 MHz signal modulated with binary phase-shift keying (BPSK), i.e., a phase modulation with the carrier phases of 0\({}^{\circ}\) and 180\({}^{\circ}\), at a symbol rate of 6.78 MHz. Besides the payload data, a packet will include an initial sequence as shown in Figure 10a: An initial carrier burst will allow the receiver to synchronize its local carrier, which is required for synchronous demodulation; the subsequent preamble is a data pattern containing several transitions between 0\({}^{\prime}\) and '1' (here: 01 01 10 10 01 01 10 10), which allows the receiver to detect the beginning of the following payload data.  
This data sequence is generated by the TX packet handler and fed to the BPSK modulator shown in Figure 10b. A data FIFO and a D-flipflop assure that every data bit is applied to the modulator with exactly the same length of two carrier cycles, creating the signal _data_tx_. The actual modulation is then performed by an XOR gate, which acts as a digital mixer by passing the carrier for _data_tx_='0' and inverts the carrier for _data_tx_='1'. A tristate buffer either applies the modulated signal _rf_out_ to theinput of the impedance matching network (recall Figure 7) or switches the output to a high-impedance state in order not to load the output node in case the receiver is active.
## 3 Materials and Methods
### Data Processing
#### 3.4.5 Error Analysis and Quality Assessment of the Snow Depth Maps  
The produced DEMs are subject to two types of error: (i) a photogrammetric reconstruction error which depends on the overall quality of the photosets, and (ii) a georeferencing error influenced by the GNSS post-processing quality, the GCPs allocation and identification in the images, and the antenna height measurements.  
The quality of the photogrammetric 3D reconstruction was assessed based on the point cloud density and the output resolution of the DEMs, which are directly linked with the number of points that the SfM algorithm could match and thus indicates how well a surface was reconstructed. Attention was paid to visually inspecting the point cloud noise and to the correct representation of snow surface cover at both small (e.g., reconstruction of sastrugi and snow ripples) and large scales (e.g., holes inthe DEM and snow drift accumulation areas) by comparison with in situ imagery. The quality of each reconstructed 3D model is also assessed individually by using an indicator of the reconstruction uncertainty such as the difference between the reconstructed and measured position at the GCPs. This is quantified as the RMSE of the Euclidean distance between the reference coordinates of the GCPs to the corresponding estimated points in the reconstructed 3D model (provided by Photoscan).  
It is challenging to break down each component influencing the georeferencing error. However, the error of the GNSS post-processing is considered to be on the same order of magnitude as the GSD after the images were downsampled, and as the uncertainty inherent to the GCPs identification on the images. As snow depth is derived by subtracting the SDEM from the TDEM, the total error is the sum of both SDEM and TDEM error. The most straightforward validation approach is based on a comparison with the probed snow depths at the SDEMs GCP positions and at the few additional samples taken post survey (for Sval2 and Green4). These points will be termed validation points (VPs). We can then analyze the mean bias, RMSE and error distribution for these areas to provide an estimate of the uncertainty on the snow depth product over these points. As validation is biased around GCP positions [32], an additional validation is performed by comparing corresponding pixels of snow-free areas away from the GCP positions. Assuming no change in the terrain between the winter and summer campaign, the snow depth should equal zero for these areas. The snow-free areas are automatically selected as cells with a mean RGB value lower than 0.2 in the RGB orthomosaics. This simple threshold was chosen to be very conservative based on visual inspection of the snow-free areas for each orthomosaic. While with this threshold not all snow-free pixels are detected, it guarantees that shadowed (dark) snow pixels are not incorrectly selected as snow-free. This was visually verified.
# A Topology Optimization Method for Reducing Communication Overhead in the Kalman Consensus Filter
## 5 Conclusions  
In this paper, we proposed a network topology optimization method. Firstly, we define the communication overhead of the KCF algorithm, which can be transformed into a network topology optimization problem. Secondly, the network topology optimization method is introduced, where the network topology is optimized by properly deleting and adding communication links, as well as considering the constraint on the communication range of two nodes. Simulation results show that the proposed optimization method can improve the algebraic connectivity of network topology and increase the convergence rate of the KCF algorithm under optimized network topologies. Moreover, the proposed network topology optimization method achieves a good trade-off between the estimate error and the communication overhead.  
In the future, the network topology optimization method can be further combined with more structural parameters. Meanwhile, the network topology optimization method can also be considered to improve the stability of the topology.  
Conceptualization, L.L. and H.C.; methodology, L.L. and H.C.; software, L.L.; validation, L.L., L.X. and K.W.; formal analysis, L.L. and H.C.; investigation, L.L.; resources, L.X. and K.W.; data curation, L.L.; writing--original draft preparation, L.L. and H.C.; writing--review and editing, H.C.; supervision, H.C.; project administration, H.C.; funding acquisition, H.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Fund of the Science and Technology Department of Zhejiang Province under Grant 2018R52046 and LGG18F010005, National Natural Science Foundation of China under Grant 61671410 and 61531017.  
Not applicable.  
The authors declare no conflict of interest.
# mmHSE: A Two-Stage Framework for Human Skeleton Estimation Using mmWave FMCW Radar Signals
## 4 Methodology
### Localization Optimizer
#### 4.4.2 Kinematic Constraints  
To ensure anatomical plausibility of the predicted pose, we incorporate structural supervision through two types of kinematic constraints: bone length consistency and joint angle coherence. Both constraints are computed based on deviations from the ground truth skeleton.  
(a) Bone Length Loss: For each bone connection \((s,t)\in\mathcal{C}\), we compute the Euclidean distance between the predicted keypoints  
\[l_{s,t}=\|\mathbf{y}_{s}^{\prime}-\mathbf{y}_{t}^{\prime}\|_{2}, \tag{12}\]  
and its ground truth counterpart  
\[l_{s,t}^{\text{gt}}=\|\mathbf{y}_{\text{gt},s}-\mathbf{y}_{\text{gt},t}\|_{2}. \tag{13}\]  
The bone length loss penalizes discrepancies between the predicted and ground truth lengths  
\[\mathcal{L}_{\text{bone}}=\sum_{(s,t)\in\mathcal{C}}\Big{(}l_{s,t}-l_{s,t}^{ \text{gt}}\Big{)}^{2}. \tag{14}\]  
(b) Joint Angle Loss: For each joint triplet \((s,t,u)\in\mathcal{T}\), the predicted joint angle is measured via the cosine of the angle between vectors  
\[\cos\theta_{s,t,u}=\frac{(\mathbf{y}_{s}^{\prime}-\mathbf{y}_{t}^{\prime}) \cdot(\mathbf{y}_{u}^{\prime}-\mathbf{y}_{t}^{\prime})}{\|\mathbf{y}_{s}^{ \prime}-\mathbf{y}_{t}^{\prime}\|_{2}\cdot\|\mathbf{y}_{u}^{\prime}-\mathbf{ y}_{t}^{\prime}\|_{2}+\epsilon}, \tag{15}\]  
and its ground truth cosine is  
\[\cos\theta_{s,t,u}^{\text{gt}}=\frac{(\mathbf{y}_{\text{gt},s}-\mathbf{y}_{ \text{gt},t})\cdot(\mathbf{y}_{\text{gt},u}-\mathbf{y}_{\text{gt},t})}{\| \mathbf{y}_{\text{gt},s}-\mathbf{y}_{\text{gt},t}\|_{2}\cdot\|\mathbf{y}_{ \text{gt},u}-\mathbf{y}_{\text{gt},t}\|_{2}+\epsilon}. \tag{16}\]  
The joint angle loss is thus defined as  
\[\mathcal{L}_{\text{angle}}=\sum_{(s,t,u)\in\mathcal{T}}\Big{(}\cos\theta_{s,t,u}-\cos\theta_{s,t,u}^{\text{gt}}\Big{)}^{2}. \tag{17}\]
## 4 Performance Evaluation
### Network Model  
We make the following assumptions in evaluating the proposed approach.  
1. All sensor nodes are randomly distributed over a two-dimensional area.
2. All sensor nodes are homogeneous in terms of processing and communication capabilities. Furthermore, they have the same battery, radio, sensing, and storage capabilities.
3. There are no recharging capabilities.
4. The BS is able to estimate the locations of the sensor nodes by using any localization technique. This may be based on utilizing GPS [57; 58] or by adopting GPS-free localization such as weighted centroid localization [59; 60; 61], which is based on the received signal strength. Generally, a small error in localization will not be significant for the overall clustering result since the values are calculated as relative variances between the lowest and highest values. These relative variances are later fuzzified (expressed as linguistic variables). The percentage of cluster heads is supposed to be 5% of the total number of sensor nodes in the WSN.
5. Cluster heads are re-selected periodically.