esa-sceva/satcom-chunk-collection huggingface dataset:
# An Overview of FIR Filter Design in Future Multicarrier Communication Systems
## 3 FIR Prototype Filter Design
### Optimization Based Methods  
In optimization based methods, some design parameters are usually given, including the filter length, the passband cutoff frequency, the stopband cutoff frequency, the number of channels, the maximum allowable distortion, and so forth. According to corresponding evaluation criteria, different objective functions and algorithms are proposed to optimize the filter parameters. This type of filter design methods is more flexible due to adjustable objective functions and constraints. Both constrained and unconstrained optimization problems can be formulated for the filter design.
# Can Blockchain Strengthen the Energy Internet?
## 3 Improved Trust, Security and Privacy
### Key Challenges  
1. **Device tampering:** How can we prevent tampering and unauthorized accessing of smart meters and smart sensors to ensure integrity of the obtained energy measurements [40]?
2. **Man-in-the-Middle attacks in EI grids:** How can we establish a secure communication link between the prosumer and the consumer during energy trading and prevent Man-in-the-Middle attacks causing data manipulation?
3. **DDoS attacks in EI grids:** How will it be possible to detect Distributed Denial of Service (DDoS) attacks causing deliberate traffic of energy requests and depriving the legitimate users from consuming energy [41]?
4. **Privacy issues:** Can a consumer participate in DSI initiatives while preserving the privacy of energy consumption data which can trace back to the behavioral patterns of the user?
5. **Authentication:** How can the identity of a node in the energy grid be verified in a decentralized architecture without revealing the connection between the energy signature and the owner's name and location?
6. **AI and ML related-attacks:** How can we mitigate data poisoning attacks related to integration of AI and ML techniques in predictive data analysis [42, 43]?
## 5 Comparison of the Performance between ATD with Multirotor Control  
To compare the performance of the ATD control and multirotor control, comparative experiments were performed. Both experiments were done with manual control. In the case of ATD control, if the input thrust in \(y\) axis (see Figure 3) is 50%, the output force can be estimated as 4.9 N (according to Figure 6). To generate the same force in \(y\) axis in the case of multirotor control, the pitch angle should be controlled to about 10 degrees. Thus, the experiment was done by translating the multirotor at the same force with a different control mode (ATD control mode and UAV control mode). To fix the control input to desired values, we reset the end points of the sticks in the RC transmitter. By limiting the maximum input value from the transmitter, the value can be fixed since we are fully bringing down the stick. The multirotor can perform brake-like motion after releasing the stick of RC transmitter in both control modes.  
Figure 5: Experimental set up for force measurement.  
Figure 6: Experimental result on output force of the ATD for different input thrust. The linear approximation is \(y=0.0826x+0.1274\).  
The experimental results are shown in Figure 7. The figure shows velocity in \(y\) axis and pitch angle change of the multirotor in different control modes. In the case of UAV control, the velocity was increased more quickly than in the case of ATD control, but, since we released the stick, the brake-like motion was performed autonomously by controlling its pitch angle in the opposite direction, and it was observed to be much larger than when translating the multirotor. Continuous images of the motion are shown in Figure 8a. In addition, it took 1.2 s to completely stop the body frame in horizontal direction. In the case of the ATD control, although velocity was increased more slowly than in the case of UAV control, it was increased more smoothly. It took 0.5 s to completely stop the body frame in horizontal direction from when the multirotor started to perform brake-like motion. Continuous images of the motion are shown in Figure 8b. During the flight, the attitude of multirotor remained stable. Through the experiments, we verified that the ATD control was successfully able to translate the multirotor by keeping the attitude horizontal including brake-like motion, and it can be helpful for the multirotor to move finely to accurate position and perform smooth aerial manipulation tasks.  
Figure 8: Continuous images of the brake-like motion in different control mode: (**a**) the multirotor control mode; and (**b**) ATD control mode. Both (**a**,**b**) follow from left to right, and they correspond to Seconds 7–9 in Figure 7. The video of the experiment is available from the supplementary materials.  
Figure 7: Experimental results of the ATD control and multirotor control. The result was recorded from the start of multirotor translating until it stopped: (top) the velocity change of the multirotor in \(y\) axis; and (bottom) the pitch angle change.
## 3 Simulations
### Simulations  
In the simulations, the low-resolution images performance using camera-scanning Fourier ptychography imaging is shown in Figure 3. And the focal length of the focusing lens was \(f=180\) mm, and the diameter of the circular pupil was \(D=8.2\) mm, i.e., the f-number was \(F/22\). The corresponding pupil function is an ideal binary function (all ones inside the pupil circle and all zero outside). The working distance from the object to the pupil plane was \(2\) m, the scanning step between adjacent LR images was \(2\) mm, and \(15\times 15\) sequence images were used to reconstruct the HR image, with the overlap ratio between adjacent sub-spectrum being approximately \(84\%\). We used the USAF resolution test chart (\(512\) pixel \(\times\)\(512\) pixel) as the HR amplitude. For the above image simulation process, we iterated a total of \(20\) times, which required approximately \(1\) min to reconstruct the HR image on a laptop with a \(9\)th Generation Intel Core i7 processor and \(16\) GB RAM. The recovered HR intensity image is shown in Figure 4c, and the phase image is shown in Figure 4d. The algorithm can immediately calculate the synthetic aperture spectrum of a complex object in a Fourier space (on a log scale), as shown in Figure 4e. While improving the image resolution, it can also obtain the phase information of the actual pupil function, as shown in Figure 4f. The relationship between the reconstruction quality of the HR image and the number of iterations is shown in Figure 5.  
Figure 3: Capture of low-resolution images performance using camera-scanning Fourier ptychography imaging. (**a**) The raw image array of the object captured directly by the camera sensor and (**b**) the central raw image.  
We applied long-distance FP imaging to the simulated data with varying spectrum overlap ratios to study the performance of the algorithm [13]. Specifically, the overlap of the adjacent spectrum was to 70%, 50%, and 30%, which showed that the insufficient overlap drastically reduced the resolution of the HR image [11]. The visual results are shown in Figure 6. From the results, we can see that long-distance FP worked worse in reconstructing intensity with decreasing overlap ratio. Holding the individual aperture size and the number of samples constant, we varied the overlap ratio between adjacent apertures; the RMSE plot of HR intensity image is shown in Figure 7a. When the overlap ratio is increased, the overlap ratio is increased.  
Figure 4: Super-resolution simulation using transmission far-field Fourier ptychography. (**a**) USAF resolution test chart; (**b**) the low-resolution central image was captured in the imaging plane after long-distance imaging; (**c**) the reconstructed high-resolution image; (**d**) the phase of the resolution test chart; (**e**) the synthetic aperture spectrum of a complex object in a Fourier space (on a log scale); (**f**) the actual pupil function.  
Figure 5: Relationship between the number of iterations and the RMSE of the image.  
was less than 50%, we were unable to faithfully recover the high-resolution image, as shown in the resolution plot in Figure 7b.
## 1 Introduction
### Novelty of Our Work  
Studying micro-Doppler measurements of vehicle vibrations introduces a hitherto unexplored and novel approach to remotely sensing and characterizing the micro-motions of vehicles, such as engine vibrations and structural oscillations. This technique leverages the micro-Doppler effect, where slight movements within a target induce frequency modulations in the reflected radar signal, providing unique signatures that can be analyzed to extract detailed information about the target's dynamics. Traditionally, vehicle vibrations have been measured using contact methods like accelerometers or non-contact optical techniques such as Laser Doppler Vibrometry (LDV). However, these methods often require proximity to the target without obscurants such as rain, fog, or intervening vegetation, and thus may not be practical for operational applications.  
Micro-Doppler radar is a unique technology that enables the detection and analysis of vehicle vibrations from a distance, enhancing safety and operational efficiency in various scenarios. Very limited work has been done by other researchers and sparsely reported as a side note in micro-Doppler papers on other focused applications, such as human gait and helicopter blades. There is no specific body of work reported in the open literature that specifically explores micro-Doppler radar features of vehicle vibrations. We believe that our work, reported in detail herein, is the first wherein micro-Doppler radar characteristics of vehicle vibrations have been systematically explored and discussed. Its ability to remotely and non-invasively analyze vehicle dynamics offers promising applications in areas such as surveillance, traffic monitoring, and automotive diagnostics.
## 8 Bird Strike  
Keeping an eye on potential future certification of a morphing leading edge concept, one aspect to consider is the proof of bird strike resistance [36]. In particular, it must be shown that the bird does not damage the front spar of the wing and the fuel tank, as this might result in catastrophic failure of the fuel system and subsequent loss of the aircraft [37; 38; 39]. For this work, the proof of the bird strike resistance of the morphing leading edge was demonstrated by means of simulation. For this purpose, simulations of a bird (mass 2 kg) impacting the MLE at a velocity of 180 m/s were conducted using the explicit finite element software LS-DYNA. A summary of the simulation approach and results is given below. For further details, the reader is referred to [40].  
Following the industry standard, the bird was represented by a cylinder with two semi-spherical caps and modelled using smooth particle hydrodynamics. The bird model had previously been validated against experimental data [41]. The composite skin was modelled following the established approach from [42], considering strength-based failure criteria and non-linear shear. The internal architecture of the MLE (actuators, etc.) was simplified. The simulation results clearly indicate that the relatively thin composite skin is not capable of stopping the bird from penetrating into the wing. For some impact locations, the internal architecture is capable of stopping the bird before impacting the front spar. However, for the majority of cases, the bird was sliced by the internal architecture, and large portions of the bird subsequently impacted the front spar. Therefore, a bird strike protection concept had to be sought. Following recommendations from the literature [27; 42], a bird splitter/deflector concept was integrated into the FE model consisting of thin aluminium plates. The simulation results indicated that a thickness of 2.5 mm was sufficient to prevent the bird from impacting the front spar. Figure 32 shows a typical simulation result for the  
Figure 31: Bolted joint between MLE and front spar (**left**), cut-out areas at top and bottom of skin element (**right**) [28].  
Figure 30: Joining elements for MLE attached to the front spar (**left**) and supporting stiffening elements on opposite side (**right**).  
case including a bird strike protection shield. For illustrative purposes, the skin has been hidden in order to increase the visibility of the interaction of the bird with the internal structure.
# Artificial Intelligence in Ophthalmology: Advantages and Limits
## 5 AI and Different Applications in Ophthalmology and Other Fields
### Choroidal Melanoma  
Applications of AI have also been successfully demonstrated in the analysis of choroidal melanoma. For instance, AI could predict five-year mortality from choroidal melanoma considering patient demographics and ultrasound tumor images using an ANN which provided higher precision (86%) in comparison with an ocular oncologist (70%) [56].  
Another ANN-based technique modeled the survival prognosis in choroidal melanoma patients using additional significant risk parameters (age, gender, tumor stage, cytogenetic melanoma type, and histological grade of malignancy) [56].
## 2 Data  
### Flux Site Data  
Flux data originating from FLUXNET2015 ([[https://fluxnet.org/data/fluxnet2015-dataset/](https://fluxnet.org/data/fluxnet2015-dataset/)]([https://fluxnet.org/data/fluxnet2015-dataset/](https://fluxnet.org/data/fluxnet2015-dataset/)), accessed on 17 May 2023) and AmeriFlux ([[https://AmeriFlux.lbl.gov/](https://AmeriFlux.lbl.gov/)]([https://AmeriFlux.lbl.gov/](https://AmeriFlux.lbl.gov/)), accessed on 17 May 2023) have the standard format of eddy covariance (EC) flux data, with better processing and quality control [44], covering different global regions. The time series of the FLUXNET2015 data (ranging from 2001 to 2014) is shorter than that of the AmeriFlux data (from 2000 to 2022). The FLUXNET dataset recently published by AmeriFlux has high quality that is assured through outlier filtering, gap filling, and uncertainty estimation. GPP could be obtained from the difference between net ecosystem exchange (NEE) and respiration based on light-response curves [45]. The quality label is due to the proportion of great data, including observation and excellent filling, and the study uses data whose quality label is over 0.8 and contains at least 90% metrological and flux data. Additionally, we removed data with GPP values of less than 0, out-of-order temporal characteristics, and unrealistic patterns of vegetation growth. The vegetation type of the site was determined by combining the site description and Moderate Resolution Imaging Spectroradiometer (MODIS) Land Cover Version 6.1 (MCD12Q1.061) product. The study aggregated half-hourdata of more than 5 days into an eight-day dataset to coincide with the temporal resolution of the remote sensing data, of which the following variables were selected for the study: GPP, solar shortwave radiation (SWRad), vapor pressure deficit (VPD), and minimum air temperature (T\({}_{\rm{amin}}\)). The study used data from a total of 84 EC sites across North America from 2002 to 2020 for different vegetation types (Table 1): DBF (14), ENF (24), MF (3), CRO (15), WSA (2), OSH (11), SAV (3), and GRA (12) (Figure 1).
# Investigation of Critical Gap for Pedestrian Crossing Using Fuzzy Logic System
## 2 Methodology  
### Critical Gap Analysis  
The gap in the traffic is defined as the duration time between two consecutive vehicles arriving at the exact reference point [38; 39]. Pedestrian crossing behavior is determined by a critical gap, which is defined as the shortest gap that the pedestrian would accept. Each pedestrian will have a certain value for their critical gap. Pedestrians monitor the gaps available to them and compare them to their critical gap; crossing is attempted when the available gap is equal to or greater than the critical gap [38; 39]. Assessing the rejected gaps and accepted gaps for each individual pedestrian would allow the identification of the critical gap for that individual, and for the sample population. In this study, data was collected using video recording [39]. Most previous research on pedestrian behavior has adopted interaction field experimentations and simulation approaches. Very few studies have adopted fuzzy logic controls.
## 4 Experiment and Results
### Experimental Result
#### 4.4.1 Visual Experiment on the Effect of Location Feature Extraction  
Heat map visualization is a tool commonly used in image analysis. It aggregates a large amount of data and uses a progressive color band to show the saliency effect. The more the color tends toward red, the better the recognition effect. Under CPU conditions, a heat map was used to visualize the recognition effect of the improved network with five LFE blocks inserted. In addition, the improved network was compared with the original YOLOv4. The results shown in Figure 3(a) show the results of the original YOLOv4, and Figure 3(b) shows the results of the improved YOLOv4. Compared with Figure 3(a), the red area on the defect in Figure 3(b) has a darker color, a larger area, more concentration, a clearer shape, and a lower location error rate, indicating the improved network can more accurately locate the defect target, and the contour of the identified defect is more detailed. On the whole, the error rate of the improved network was lower, which shows that the LFE structure was effective for the extraction of location features.  
Figure 4: Visualization results of the heat map.
## 3 LiDAR Range Measurement
### Phase-Shift Range Measurement  
Since the laser beam is highly affected by weather conditions, the measurement signal is modulated with a carrier wave for high accuracy. Frequency modulation continuous wave (FMCW) and amplitude modulation continuous wave (AMCW) are commonly used in this measurement. The distance is measured by the phase difference modulation in an indirect time-of-flight method.  
The frequency of the carrier wave is periodically increased and decreased to match the wave motion of the measuring beam in the FMCW method. The phase difference is determined by comparing the emitted and received return signals. If the instrument and object measured are fixed, a single or multi-frequencies can be applied to estimate the residual wavelength [15]. In this case, since the instantaneous frequency corresponding to the wavelength is variable, the frequency value in the residual portion will be proportional to the round ToF (\(\Delta t\)). The \(\Delta t\) is expressed by Equation (2).  
\[\Delta t=(N_{c}+\frac{\Delta\varphi}{2\pi})T \tag{2}\]  
\(\Delta t\): Round trip time-of-flight  
\(N_{c}\): Number of full wavelengths  
\(T\): Period  
\(\Delta\varphi\): Faz angle  
\(N_{c}\) is determined using modulation waves at various wavelengths. The distance (\(\Delta r\)) corresponding to the phase angle is calculated by Equation (3).  
\[\Delta r=c\frac{\Delta\varphi}{4\pi}T=c\frac{\Delta\varphi}{4\pi}\cdot\frac{1 }{f_{mod}} \tag{3}\]  
The residual phase angle is estimated by the cross-correlation of the amplitudes (\(A\)) of emitted and received signals at four different regions of the wavelength at a differential time interval (\(\Delta t_{i}\)) in the AMCW method. (Figure 4). Accordingly, the phase angle corresponding to the residual wavelength is calculated by Equation (4) [28; 29].  
\[\Delta\varphi=\arctan(\frac{A_{1}-A_{3}}{A_{2}-A_{4}}) \tag{4}\]  
Figure 4: Amplitude modulation (AMCW).  
The phase-shift method removes the measurement uncertainty to a certain level. Increasing the frequency reduces the distance uncertainty corresponding to the phase angle. Another way to increase distance accuracy is to increase the integration time (IT). IT should be at a proper level to obtain enough accurate measurements. A high IT increases the measurement period to provide sufficient measurement accuracy [30]. On the other hand, it reduces the measurement speed. The AMCW phase shift can be applied when the returning signal has a certain strength and the SNR reaches a certain value. For this reason, the measuring distance is shorter than the FMCW and pulse methods so that the intensity of the signals does not fall below a certain value.
## 2 Method Description
### The Second Phase of the Proposed Method
The experimental results using the proposed method for the classification datasets are depicted in Table 8 and for the regression datasets in Table 9.

In Table 8, the experimental results indicate a general trend of decreasing error rates as the number of generations \(N_{g}\) increases from 25 to 200. This reduction suggests that increasing \(N_{g}\) improves the performance of the genetic algorithm, as it allows the model to better approximate the optimal solution. For most datasets, the lowest error rate is observed when \(N_{g}=200\). For instance, on the \"Alcohol\" dataset, the error rate decreased from 38.19% to 31.28%, while a significant reduction was also observed in the \"Sonar\" dataset, from 21.77% to 18.25%. Similar reductions were noted on several other datasets, such as \"Spiral\" (31.82% to 22.52%) and \"ZO_NF_S\" (4.19% to 3.63%). However, there are datasets like \"Mammographic\" and \"Lymography\" where the changes are minor and not consistently positive, indicating that increasing the number of generations does not always have a dramatic impact on performance. The overall average error rate across all datasets steadily decreases, from 19.99% when \(N_{g}=25\) to 18.67% when \(N_{g}=200\). This confirms the general trend of the genetic algorithm converging towards improved solutions with more generations. The statistical analysis of the results demonstrates that increasing the number of generations is effective in the majority of cases, enhancing the accuracy of the proposed method on classification datasets. Nonetheless, the performance improvement

Figure 11: Statistical comparison of the obtained results by the application of the proposed method to the regression datasets. For this experiment, different values of parameter \(F\) were used. appears to also depend on the specific characteristics of each dataset as well as the inherent properties of the method.
# Panoptic Segmentation Method Based on Feature Fusion and Edge Guidance
## 3 Method
### Dynamic Multi-Scale Feature Fusion Module  
In panoptic segmentation tasks, effective feature fusion plays a pivotal role in enhancing model performance. However, traditional feature fusion methods, such as simple channel concatenation or weighted averaging, often rely on fixed strategies, making them inadequate for handling the diversity and complexity of targets across different scenes [12]. Particularly when processing multi-scale targets, the absence of an adaptive feature selection mechanism frequently results in insufficient feature representation, compromising segmentation accuracy. To overcome the limitations of traditional feature fusion strategies in adapting to scene diversity, this section introduces a cascaded architecture incorporating Dynamic Large Kernel (DLK) and Dynamic Feature Fusion (DFF), with its innovation manifested in two key aspects: dynamic receptive field expansion and cross-scale adaptive fusion.