esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion  
Table 3 summarizes the considered features for both investigated polymers DF-PA and Orgasol-IS in some key words and depicts several prominent findings in this connection. In general, the manufacturers of PA12 improve their polymers for their use in LS regarding several properties. However, the companies Evonik and Arkema have different strategies to adjust their materials marketed as Duraform(r) PA and Orgasol(r) Invent Smooth for the laser sintering process. Principally due to the different production technologies, they supply materials with different particle size distributions and particle shapes, which have severe influences on the processing, packing density, and surface roughness of the parts. The thermal situation is controlled through the different production processes. The melting and crystallization of both materials is adjusted for a good and stable behavior during LS-processing. However, the precipitation process offers better possibilities to fine-tune the crystalline structure of the polymer and DF-PA presents a much larger sintering window, resulting in a better and more forgiving processing behavior.  
The biggest advantage of DF-PA can be identified in the improved homogenous mechanical properties of the LS-parts in all directions. The anisotropy for Orgasol-IS is pronounced and LS-part producers (service bureaus) must be aware of this fact. However, the regulated viscosity of Orgasol-IS leads to reduced powder \"aging\" (post-condensation), which is beneficial from an economical point of view because the refresh rate is about 10% (statement of producer), and is thus much lower than for DF-PA (refresh rate from 30% to 50%). Moreover, Orgasol-IS typically supplies parts with smoother surfaces, with a higher detailing grade due to the more homogenous particles.  
Figure 10: Comparison of surfaces from different part orientations of DF-PA and Orgasol-IS.
## 4 Experimental Validation  
This section describes the document-, sentence-, and aspect-level datasets (Section 4.1), the implementation design decisions and the parameters' actual selection of our proposed GAN architecture model (Section 4.3) used in the experimental evaluation, and, finally, the experimental results (Section 4.4).  
Figure 2: Architecture of the proposed GAN for learning representation at the document, sentence, and aspect level. The network has the following parts: generator \(G\), denoising autoencoder (DAE) encoder \(Enc\), and decoder \(Dec\), a corruption process \(C\), and a discriminator \(D\).
## Appendix B Factor Amplification Calculation  
To define the factor \(\alpha\) used in the magnification, it is considered that the Eulerian motion magnification method is valid for small motions and for slow changes in the image function, i.e., where the first-order Taylor series approximation in Equation (4) is fulfilled. Thus, as reported in [22] there is a direct relationship between the amplification factor and the spatial wavelength (\(\lambda\)) in the current level of the image decomposition:  
\[(1+\alpha)W(t)<\frac{\lambda}{8}, \tag{14}\]To overcome this limitation, a maximum \(\alpha_{max}\) factor must be proposed. Then, in each pyramid level \(j\) a new amplification factor \(\alpha_{j}\) is calculated as follows:  
\[\alpha_{j}=\frac{\lambda_{j}}{8W(t)_{max}}-1, \tag{10}\]  
where \(\lambda_{j}\) is the representative spatial wavelength for the lowest spatial frequency band \(j\) and \(W(t)_{max}\) is the maximum displacement for the spatial wavelength of interest \(\lambda_{s}\) in the image sequence:  
\[W(t)_{max}=\frac{\lambda_{s}}{8(1+\alpha_{max})} \tag{11}\]  
Thus, the amplification factor used in each level of the spatial decomposition is defined by:  
\[\alpha=\begin{cases}\alpha_{max}&\text{if }\alpha_{j}>\alpha_{max}\\ \alpha_{j}&\text{another case}\end{cases} \tag{12}\]
# Rapid Classification of Wheat Grain Varieties Using Hyperspectral Imaging and Chemometrics
## 3 Results and Discussion  
### Analysis of Spectral Profiles  
After extracting the spectral information of batches of wheat samples, the spectral curves of five varieties are displayed in Figure 1. It could be observed that the beginnings and ends of all the spectral curves presented strong noise caused by the instability of instrument or other factors. Thus, the spectral information of the middle wavebands, 975-1660 nm, were selected from the origin bands of 874-1734 nm for further analysis.  
Figure 2 displays the average spectra and corresponding standard deviation (SD) of each variety. It can be seen that the trends of five spectral curves were similar on the whole. However, the reflectance of these five varieties of wheat seeds also exhibited certain discrepancy, which was attributed to the differences of internal physiological and biochemical components among them [20; 21]. For example, the crest around 1100 nm was associated with the second overtone of C-H stretching, as are the crests and troughs around 1200 nm and 1300 nm [30; 31; 32]. Additionally, the trough around 1450 nm was associated with the first overtone of O-H and N-H inherent stretching and vibration [33; 34; 35]. These were related to the composition and content of chemicals, such as starch and protein [36], which resulted in diverse reflectivity. The spectral reflectance of five varieties of wheat seeds had strong fluctuation, especially variety 5, among which the overlapping degree among variety 1, 2, and 3 was comparatively high. All of these lay the foundation for the theoretical basis for the accurate classification of different varieties of wheat seeds. Thus, further chemometric analysis need be introduced for subsequent investigation.  
Figure 1: The spectra of wheat seeds samples.  
Figure 2: Average spectral curves with their standard deviation (SD) of five varieties of wheat seeds.; Note: 1, 2, 3, 4, 5 refer to Anong1124, Longpingmai6, Shannon102, Weilong169, Zhenmia9, respectively.
# QRS Detection Based on Medical Knowledge and Cascades of Moving Average Filters
## 2 Materials and Methods
### Methods
#### 2.2.1 Noise Reduction  
The target of this step is to reduce the effects of two non-QRS components on the signal. The first one is undesired noise, which is a ubiquitous aspect of biosignals. In ECG, noise can be observed as baseline wander, power line interference, spike noise, and myographic noise. The other undesired aspect in heartbeat detection are prominent T waves, as they could be mistaken for a QRS complex. Thus, the first step of the algorithm reduces the amplitude of these two sources of error.  
Figure 1: Impulsive response of a moving average cascade. The straight line with the \(\circ\) marker is a single moving average. The triangular shape with the \(\times\) markers results from two moving averages of the same length in cascade. The curve with the + markers results from a cascade of four moving averages.  
Figure 2: Detection algorithm diagram.  
In the algorithm we present reduction of noise and undesired waves is performed by applying to the signal two different moving average cascades (MACs) of different length. The synchronized outputs of the two MACs are then subtracted. The first MAC consists of the cascade of a 20 ms moving average with a 8 ms one. Thus, we refer to it as MAC(0.02, 0.008). At a sampling frequency of 250 Hz, the impulsive response of MAC(0.02, 0.008) is 24 ms long. This length reduces narrow peaks of noise, while only slightly impacting QRS peaks. The second MAC is MAC(0.12, 0.024). For a sampling frequency of 250 Hz, this filter has a 140 ms long impulsive response interval. The output of MAC(0.12, 0.024) tracks baseline wandering and, partially, T waves. Thus, their amplitude at the output of the synchronized difference of the two MACs is reduced. Figure 3 shows the resulting impulsive response of the noise reduction filter.  
The design of this QRS detector is based on examination of the shape, time of occurrence and duration of the waves that represent ventricular depolarization. These waves are distinguished from artifacts and the P, T characteristic waves that concern other events. The QRS complex is typically 70-110 ms long, with two to four rising and falling fronts. Considering the largest amplitude wave (either Q R or S), its duration can vary from 30-70 ms. Therefore, a MAC(0.02, 0.008) system that performs a moving average over approximately 24 ms reduces the fast variations caused by noise without consistently reducing the QRS peaks. The MAC(0.12, 0.024) system is designed to follow the slow baseline variations but not the fast QRS variations. In fact, this system averages over 120 ms of signal and does not track the variations of the QRS waves.
# Quadratic Clustering-Based Simplex Volume Maximization for Hyperspectral Endmember Extraction
## 2 Proposed Methods
### Spectral Clustering  
Since the wide area covered by one class, such as ocean and forest, will be segmented into several partitions due to the constraint of the search scope, representative endmembers from these partitions exhibit the same feature. The spectral clustering plays a role in merging the representative endmembers separated. On the one hand, it is of great significance to eliminate the effect of large-scope spectral variability. On the other hand, it can reduce the number of candidate endmembers, improving the computational efficiency of the subsequent processing.  
The spectral clustering method is based on the k-means clustering algorithm [32]. The clustering criterion adopts a weighted spectral measurement strategy, which takes both spectra shape and distance into consideration. It is defined as follows:  
\[\mathrm{D}=\lambda_{2}\times\mathrm{ED}_{\mathrm{spe}}+(1\;-\;\lambda_{2})\; \times\mathrm{SAD}, \tag{8}\]  
where \(\lambda_{2}\) is a balance constant.  
In spectral clustering, initialize k cluster centers first. Then calculate D between each representative endmember and the cluster centers. Each endmember is assigned to the center with the smallest D. Next, update the cluster centers according to the classification results. Perform the above operations iteratively. Representative endmembers belonging to the same cluster center form a class. The spatial clustering ends when all classes no longer change. Finally, the cluster centers are regarded as candidate endmembers, which further suppresses the effect of large-scope spectral variability.  
As the final endmembers will be chosen from candidate endmembers, that is, from the cluster centers, the performance of spectral clustering is particularly key, as is the determination of the two parameters involved.
## 2 Preliminaries
### a Stable Distribution  
In the real system, non-stationary, non-Gaussian, spiky signals are usually regarded as outliers and thus discarded by engineers during the signal processing. In fact, however, each point of data from the real system could tell a \"story\" such as malfunctioning, inappropriate control philosophy, abnormal human intervention, etc. Non-Gaussian signals and noises tend to produce large-amplitude fluctuations from the average value more frequently than Gaussian ones do. Where did non-Gaussian signals come from? Many researchers have found that they are from fractional calculus or even more specifically, \(\alpha\)-stable processes [22].  
The \(\alpha\) stable distribution, which is the generalization of the Gaussian distribution, is used to model the non-Gaussian distribution. The \(\alpha\)-stable distribution based techniques have been applied to describe many natural or man-made phenomena in various fields, such as physics, hydrology, biology, financial and network traffic. Stable distributions provide a useful theoretical tool for this type of signals and noises [23]. The \(\alpha\)-stable characteristic function (or distribution) is determined by four parameters: \(\alpha\), \(\beta\), \(\gamma\) and \(\delta\). A univariate distribution function \(F(x)\) is stable if and only if its characteristic function has the form:  
\[\phi(t)=exp\{j\delta t-\gamma\left|t\right|^{\alpha}\left[1+j\beta\text{sign }(t)\omega(t,\alpha)\right]\}, \tag{6}\]  
where  
\[\omega(t,\alpha)=\left\{\begin{array}{ll}\tan\frac{\alpha\pi}{2},&\text{if } \alpha\
eq 1\\ \frac{2}{\pi}\log\left|t\right|,&\text{if }\alpha=1\end{array},\right. \tag{7}\]  
\[\text{sign}(t)=\left\{\begin{array}{ll}1,&\text{if }t>0\\ 0,&\text{if }t=0\\ -1,&\text{if }t<0\end{array},\right. \tag{8}\]  
and  
\[-\infty<\delta<\infty,\;\gamma>0,\;0<\alpha\leqslant 2,\;\;-1\leqslant\beta \leqslant 1. \tag{9}\]  
\(\alpha\) is called the characteristic exponent. A small value of \(\alpha\) will imply considerable probability mass in the tails of the distribution. That is, the smaller \(\alpha\) is, the heavier are the tails. \(\alpha=2\) corresponds to the Gaussian distribution (for any \(\beta\)). \(\gamma\) is a scaling parameter called the dispersion. It is similar to the variance of the Gaussian distribution. \(\beta\) is a symmetry parameter. \(\beta=0\) indicates a distribution symmetric about \(\delta\).  
There are two algorithms provide consistent estimators for all four parameters--[PERSON]'s method, which is based on empirical characteristic function methods of Koutrouvelis [24] and [PERSON]'s method, which is a simpler, faster but less accurate method [25]. In this paper, we apply [PERSON] method for estimation of these parameters in Section 4.
## 3 Materials and Method
### Calculation of \(f_{korr}\) through the Findley's Fatigue Criterion  
The \(\overline{\sigma}(t)\) has been extracted for each grid node (31 nodes) belonging the tooth root radius in each simulated condition (STBF and MG). These represent an entire load cycle discretized in 40 time steps. The approach presented in Section 2.2 has been implemented to calculate \(f_{korr}\). More specifically:  
Figure 6: Finite Element Modelling of MG tests. The torque is applied to bottom gear while the motion has been set consistently to the top gear. The latter has the studied tooth root (the stress history of the red-highlighted nodes has been elaborated). The nodes highlighted in green are those in which the contact has been set. More specifically, the nodes belonging to the bottom gear are master and those of the top gear are slave.  
* For each node the critical plane has been identified using the cylindrical coordinates because the case is 2-dimensional. This means that the angle defining the representation plane of the gears has been kept constant and equal to 90\({}^{\circ}\);
* On each critical plane (of each node) the DP and, therefore, the \(S_{F}\) was calculated;
* On each tooth root radius, the maximum DP and, therefore, the minimum \(S_{F}\) was individuated.  
Based on the above-mentioned values evaluated for the MG and STBF conditions of the same gear geometry, it has been possible to calculate \(f_{korr}\).  
Results of the \(S_{F}\) (related to the MG and STBF) and the \(f_{korr}\) of each gear geometry are presented in Table 2. It is interesting to notice how, in the majority of cases, safety factors assume values approximately the unit. This is a symptom of the correct setting of the forces/torques (using the ISO 6336 approach) combined with the good ability of the Findley criterion to characterize the DP.
# Saturation-Based Airlight Color Restoration of Hazy Images
## 3 Proposed Dehazing Method
### Extraction of Color Components in Airlight
#### 3.1.2 Division of the Image Using K-Means Clustering  
The well-known K-means clustering is a non-training-based method that forms a cluster by placing k arbitrary central points on the closest objects. It is expressed as follows:  
\[argmin_{S}\sum_{i=1}^{k}\sum_{x\in S_{i}}\|x-\mu_{i}\|^{2} \tag{11}\]  
where \(x\) is the data, \(S\) is a cluster, \(\mu\) is the average of the cluster components, and \(k\) is the number of clusters. To execute K-means according to Equation (11), the number of clusters \(k\) and the data with which sorting is performed \(x\) must be established.  
Generally, K-means algorithms for dominant color output in the CIELAB color space [28,29] use the saturation channels a and b of the image as input data. However, if an object with an achromatic color is present, it will form the same cluster as the airglow because its a and  
Figure 3: Airlight extraction using area division: (**a**) clustering of the hazy image with the sky, (**b**) extraction of the brightest pixels in the L channel, and (**c**) airlight area extraction using the proposed method.  
b values are almost identical. Also, if there is a color cast across the scene, the difference in the saturation of the entire image may be small and clustering may be less effective.  
Accordingly, this study sets the input value as follows:  
* The whole LAB color image from Equation (7) is used as the input image.
* The number of clusters \"k\" is set to 6 for better separation of bright objects.
* The number of repetitions is 3, which is the default value of the algorithm.
* K-means is initialized using cluster centroid initialization and the squared Euclidean distance measurement method.  
In this paper, we segmented the images based on the K-means++ algorithm, and all k values were set to 6, which is the number that satisfies [PERSON]'s first hypothesis.  
K, the number of clusters, affects the detection results for the sky area. Unlike traditional saturation-based methods, the proposed method uses the LAB color channel, which bases clusters on shading and saturation differences. We measured the proposed method for k = 3-9 and found that when k is small, the objects in the fog image are detected in the same cluster as the sky area. Conversely, for a large k, sky areas are detected in multiple clusters. The experimental results show that the optimal value of k is 6, so we used k = 6 for clustering in this paper.  
Based on the designed value, K-means clustering is performed to divide the LAB color image. The clustering results are shown in Figure 4, which also compares the saturation channel-based K-means and the proposed method.  
Figure 4a,b show the sky area in the orange cluster area. However, Figure 4b also contains objects with low saturation; therefore, the proposed method is more favorable for separating the sky area.
## 2 Architecture of Dima System
### Hardware Layer
#### 2.2.2 Core Processing Module  
CPM provides computation resources and storage for the tasks on the DIMA system. Core Processor Input/Output Module (CPIOM) is a variant that provides a combination of compute resources, input and output ports for distributed data pre-processing or tightly coupled control loops. By using CPIOMs, one may ease the need for high-speed field buses or dedicated I/O communication devices. However, the disadvantage of such I/O integration may come at the increasing number of different CPIOMs required for specific needs [27].
# Hardware-Accelerated Data Readout Platform Using Heterogeneous Computing for DNA Data Storage
###### Abstract  
DNA data storage has emerged as a promising alternative to traditional storage media due to its high density and durability. However, large-scale DNA storage systems generate massive sequencing reads, posing substantial computational complexity and latency challenges for data readout. Here, we propose a novel heterogeneous computing architecture based on a field-programmable gate array (FPGA) to accelerate DNA data readout. The software component, running on a general computing platform, manages data distribution and schedules acceleration kernels. Meanwhile, the hardware acceleration kernel is deployed on an Alveo U200 data center accelerator card, executing multiple logical computing units within modules and utilizing task-level pipeline structures between modules to handle sequencing reads step by step. This heterogeneous computing acceleration system enables the efficient execution of the entire readout process for DNA data storage. We benchmark the proposed system against a CPU-based software implementation under various error rates and coverages. The results indicate that under high-error, low-coverage conditions (error rate of 1.5% and coverage of 15\(\times\)), the accelerator achieves a peak speedup of up to 373.1 times, enabling the readout of 59.4 MB of stored data in just 12.40 s. Overall, the accelerator delivers a speedup of two orders of magnitude. Our proposed heterogeneous computing acceleration strategy provides an efficient solution for large-scale DNA data readout.  
heterogeneous computing; DNA data storage; hardware acceleration; FPGA +
Footnote †: journal: Applied Sciences
# Pneumatic Rotary Actuator Position Servo System Based on ADE-PD Control
## 5 ADE-PD Controller Design  
The PD controller can make the system respond quickly and have little lag. So, it is used to optimize the control of the pneumatic rotary actuator servo system, and the parameters \(k_{p}\) and \(k_{d}\) of PD controller are tuned by Adaptive Differential Evolution (ADE) algorithm [39]. The first order low pass filter is also used in the system in order to reduce other interferences of the measurement channel and ensure the stability of the deviation \(e(n)\). The schematic diagram of the control system with the ADE-PD controller is shown in Figure 6. The controller can be expressed in a time-discrete way [40].  
The relationship between the input and output signals of the first order low pass filter is:  
\[et(n)=(1-\alpha)et(n-1)+\alpha e(n) \tag{41}\]  
where \(e(n)\) and \(et(n)\) are input and output of the filter at the \(n\)-th sampling, \(n=1\), \(2\), \(3\)... ; filter coefficient \(\alpha=1-e^{-T_{P}/\tau}\), where \(T_{P}\) is sampling period, and \(\tau\) is time constant.  
The discrete PD algorithm can be expressed as:  
\[z(n)=k_{p}\bigg{\{}et(n)+\frac{T_{d}}{T_{P}}[e(n)-e(n-1)]\bigg{\}}=k_{p}e(n)+k_ {d}[e(n)-e(n-1)] \tag{42}\]  
where \(z(n)\) is output of the controller, \(T_{d}\) is differential time constant, \(k_{p}\) is proportional coefficient, and \(k_{d}\) is differential coefficient, \(k_{d}=k_{p}T_{d}/T_{P}\).  
In order to obtain the satisfactory dynamic characteristics of the transient process, the discrete integral of time-weighted absolute value of the error (ITAE) is used as the minimum target function of the parameter selection. The square of the input is added to the target function, so as to prevent excessive control. The optimal index in the process of optimizing parameters \(k_{p}\) and \(k_{d}\) is given as:  
\[I=\sum_{n=1}^{Z}\Big{[}w_{1}|e^{\prime}(n)|+w_{2}\theta_{r}^{2}(n)\Big{]}T_{P} \tag{43}\]  
where \(Z\) is total points of sampling; \(w_{1}\) and \(w_{2}\) are weights.  
In order to avoid overshoot, the penalty function is used, that is, once the overshoot is produced, the overshoot is added as one part of the optimal index. At this time, the optimal index is:  
\[\text{when }e^{\prime}(n)<0,I=\sum_{n=1}^{Z}\Big{[}w_{1}|e^{\prime}(n)|+w_{2} \theta_{r}^{2}(n)+w_{3}|e^{\prime}(n)|\Big{]}\;T_{P} \tag{44}\]  
where \(w_{3}\) is weight, \(w_{3}\gg w_{1}\).  
Figure 6: Schematic diagram of control system with ADE-PD controller.  
The flowchart of optimizing parameters with Adaptive Differential Evolution (ADE) algorithm is described in Figure 7.