esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Preliminaries  
### Polar Codes  
Polar codes are proposed based on the phenomenon of channel polarization: \(N\) polarized sub-channels \(\{W_{N}^{(i)}\}\) (\(i=1,2,...,N\)) can be obtained by channel combining and splitting operation on \(N\) independent discrete memoryless channels (DMCs). In this work, the most reliable \(K\) sub-channels will be selected by a Density Evolution (DE) algorithm [29] to transmit information sets and the rest of the sub-channels will be used to transmit frozen sets, where \(K\) is the number of information bits.  
Polar codes can be uniquely determined by three parameters \(C(N,R,\mathcal{A})\), where \(N=2^{m}\), \(m>0\) is the block length, \(R=K/N\) is the code-rate and \(K\)-element subset \(\mathcal{A}\subset\{1,2,...,N\}\), where \(K=\left|\mathcal{A}\right|\) and \((N-K)=\left|\mathcal{A}^{c}\right|\). We refer to this set as the _information_ set, and the complementary set \(\mathcal{A}^{c}\) is referred to as the _frozen_ set. Let \(\mathbf{u}=(u_{1},u_{2},...,u_{N})\) denote an information vector of size \(1\times N\). The \(K\) information bits are placed in those elements of \(\mathbf{u}\) corresponding to the set \(\mathcal{A}\), and \(N-K\)_frozen_ bits (deterministic values, typically zeros) are placed in \(\mathbf{u}\) corresponding to the complementary set \(\mathcal{A}^{c}\). The codeword \(\mathbf{x}\) corresponding to an information vector \(u^{\mathcal{A}}\subset\mathbf{u}\) to be transmitted is then generated by  
\[\mathbf{x}=\mathbf{u}G_{N}=\left(u^{\mathcal{A}}+u^{\mathcal{A}^{c}}\right) \left(B_{N}\mathbf{F}_{2}^{\otimes n}\right), \tag{1}\]  
where \(G_{N}\) is the generator matrix, \(B_{N}\) is an \(N\times N\) permutation matrix that acts as a bit-reversal operator, \(\mathbf{F}_{2}=\begin{bmatrix}1&0\\ 1&1\end{bmatrix}\) is a kernel of the polarizing transformation and \(\mathbf{F}_{2}^{\otimes n}\) is the \(n\)-th Kronecker power of \(\mathbf{F}_{2}\).
## 6 DRRF and Real-time Traffic
### Earliest Deadline First (EDF)  
Given a set of flows, the first algorithm, EDF [12], compares the packets at the head of the flow queues, and schedules the packet the earliest deadline constraint. One additional complication in the case of Mobile WiMAX is that the entire packet may not fit in the current Mobile WiMAX frame, and a fragment may be left-over. This fragment is possibly transmitted first in the next Mobile WiMAX frame. The entire packet is discarded if the fragment does not meet the deadline (or delay constraint).
## 4 Datasets and Experimental Results
### Analyzing the Impact of the Iteration Number  
In this part, we look into how the number of iterations affected the SF-ICNN method's ultimate OA. By dividing the training samples, the accuracy results were acquired on validation samples, which helped identify the ideal number of iterations for the SF-ICNN approach. To accomplish this, we changed the number of iterations from zero, which means \"no iteration applied,\" to six. Figure 7 reports the outcomes of the OA.  
As shown in Figure 7, the OA metric exhibited an initial increase as the number of iterations reached five. However, the next iterations did not result in any further improvement. Figure 7 shows that for both HSI datasets, the majority of the accuracy improvement happens in the first few rounds. It is important to note that the computation time increases by training more CNNs, resulting from more iteration loops. Therefore, it may be recommended to use a smaller number of iteration loops in studies where the computational time is critical.
## 3 Results and Discussion
### Response Surface Process Optimization
Results from another examination on ultrasound-assisted extraction of polyphenol from PP with water/ethanol solvent reported a maximum Y\({}_{\text{TP}}\) of 6.20 mg gallic acid equivalents g\({}^{-1}\) DM [29]. On the other hand, Y\({}_{\text{TP}}\) as high as 20 mg gallic acid equivalents g\({}^{-1}\) DM has been achieved with subcritical water extraction of PP [30], and 32.87 mg gallic acid equivalents g\({}^{-1}\) DM with sequential hydrothermal extraction [31], whereas extraction under alkaline conditions and high-pressure homogenization has afforded no more than 4.16 mg gallic acid equivalents g\({}^{-1}\) DM [32]. Figure 6: Plot portraying the effect of sulfuric acid, used as catalyst, on the treatment performance. Assignments: 1-prop/SuAc, treatment performed with 40% 1-propanol/1.5% sulfuric acid; 1-prop, treatment performed without sulfuric acid addition; 2-prop/SuAc, treatment performed with 40% 2-propanol/1.36% sulfuric acid; 2-prop, treatment performed without sulfuric acid addition. The resident time used for the treatments with 1-propanol (with or without sulfuric acid) was 60 min. The corresponding time for the 2-propanol treatments was 167 min. Bars on the columns represent standard deviation. Y\({}_{\text{TP}}\) is the yield in total polyphenols, expressed as chlorogenic acid equivalents—CGAE.
# Wearable Inertial Devices in Duchenne Muscular Dystrophy: A Scoping Review
###### Abstract  
In clinical practice and research, innovative digital technologies have been proposed for the characterization of neuromuscular and movement disorders through objective measures. Among these, wearable devices prove to be a suitable solution for tele-monitoring, tele-rehabilitation, and daily activities monitoring. Inertial Measurement Units (IMUs) are low-cost, compact, and easy-to-use wearable devices that evaluate kinematics during different movements. Kinematic variables could support the clinical evaluation of the progression of some neuromuscular diseases and could be used as outcome measures. The current review describes the use of IMUs for the biomechanical assessment of meaningful outcome measures in individuals affected by Duchenne muscular dystrophy (DMD). The PRISMA methodology was used and the search was conducted in different databases (Scopus, Web of Science, PubMed). A total of 23 articles were examined and classified according to year of publication, ambulatory/non-ambulatory subjects, and IMU positioning on human body. The analysis points out the recent regulatory identification of Stride Velocity 95 th Centile as a new endpoint in therapeutic DMD trials when measured continuously from a wearable device, while only a few studies proposed the use of IMUs in non-ambulatory patients. Clinical recognition of reliable and accurate outcome measures for the upper body is still a challenge.  
Duchenne muscular dystrophy; inertial measurement units; wearable devices; outcome measures; movement disorders
# The Challenges of Advanced CMOS Process from 2D to 3D
## 3 Process Integration of New Transistor Architecture  
In process flow for FinFETs, dense fins are patterned by using self-aligned double patterning (SADP), followed by oxide filling, planarization, and recessing to reveal the fin active region and form shallow trench isolation (STI). Then the rest of the process flow proceeds in similar steps as for the planar devices such as well doping, dummy gate deposition and patterning, spacer deposition and etching, epitaxial source/drain (S/D) formation, inter layer dielectric zero(ILD0) and chemical mechanical polish (CMP), dummy gate removal, high-\(k\) and metal-gate (HKMG) formation, self-aligned contact (SAC) formation, local interconnects (LI), and finally back-end-of-line (BEOL) interconnect construction [21; 22].  
Although the overall process sequence for FinFETs (as shown in Figure 1) does not differ much from planar FETs, however the addition of fins adds many new challenges in fabrication, as shown in Figure 2[21; 22; 23; 24]. These challenges relate to issues: precise and uniform fin formation, 3D gate and spacer patterning, uniform junction formation in fin, and severer layout dependent effect (LDE) on stress.  
Figure 1: Process flow for the bulk fin field effect transistors (FinFETs) or planar transistors where additional processes for FinFETs are underlined.  
Figure 2: New challenges in fabrication of FinFETs.
# Blood Pressure Monitoring Based on Flexible Encapsulated Sensors
## 4 Blood Pressure Measurement
### Preliminary Blood Pressure Calculation Based on Arterial Tonometry  
Prior to measurement, calibration using a standard sphygmomanometer was necessary to convert the pressure measurement values obtained from the MEMS pressure sensor into the corresponding blood pressure values. The specific method was as follows: First, a commercial blood pressure monitor was used to measure the systolic blood pressure (\(SYS\)) and diastolic blood pressure (\(DIA\)) on the left wrist of the subject for a certain period of time (e.g., 30 s), while the packaged sensor was used to perform arterial tonometry blood pressure testing on the right wrist of the subject. Based on the pulse waveform, the average values of the peak and trough values during this period, denoted by \(P_{p}\) and \(P_{t}\), respectively,  
Figure 10: Peak and trough location.  
Figure 9: The pulse amplitude frequency map before and after filtering.  
were calculated; a fitted relationship between the systolic and diastolic blood pressure and \(P_{p}\) and \(P_{t}\) was obtained, as follows:  
\[SYS=k_{1}P_{p}+b_{1} \tag{2}\]  
\[DIA=k_{2}P_{t}+b_{2} \tag{3}\]  
At any given time, the output of the sensor is \(y(t)\), and the preliminary blood pressure value \(BP(t)\) after calibration is given by:  
\[BP(t)=\frac{k_{1}+k_{2}}{2}y(t)+\frac{b_{1}+b_{2}}{2} \tag{4}\]  
where \(k_{1},k_{2},b_{1}\), and \(b_{2}\) are constants. After blood pressure calibration, continuous long-term monitoring of blood pressure could be achieved.
## 3 Datasets
### Gridded Precipitation Products (GPPs)
#### 3.1.4 Global Satellite Mapping of Precipitation (GSMaP)  
The Global Satellite Mapping of Precipitation (GSMaP) project, which began in 2002, intended to provide precise high-resolution precipitation estimates for the entire globe [15]. The GSMaP project includes different precipitation datasets, and the gauge-adjusted GSMaP product was employed herein. This GPP is initially produced using observations from various microwave radiometers and sounders based on the 2-D radiative transfer model and climatological precipitation type classification. Subsequent to that, cloud motion vector analysis and the Kalman filtering algorithm were applied to combine previous estimates with infrared data and produce a more accurate hourly dataset with a 0.1\({}^{\circ}\)\(\times\) 0.1\({}^{\circ}\) spatial resolution [19,76]. Finally, the daily unified Climate Prediction Center (CPC) datasets are introduced to the GSMaP estimates for bias correction and enhancement. Additional information can be found in [PERSON] et al. [15] and [PERSON] et al. [19]. GSMaP precipitation data from 2000 to the end of 2020 was considered for performance evaluation.
## 5 The Potential of Computer Vision Research in UAV Datasets
### Leveraging UAV Datasets for Computer Vision Applications
#### 5.1.6 Object Tracking and Robust Vision  
With 600 videos and 304,209 manually labeled frames, BioDrone is a benchmark for single object tracking with bionic drones. It captures challenges such as camera shake and drastic appearance changes, supporting robust vision analyses and evaluations of various single object tracking algorithms.
## 2 Materials and Methods
### Test Specim Geometry Determination  
The dimensions of the specimens for threshold stress intensity factor determination were taken according to the ISO 148-1:2009 standard [17]. The standard distinguishes two types of notches for the specimen, U and V notches. The U notch is intended for testing brittle materials, while the V notch is for tough materials. Since titanium alloys are generally tough, the V-type notch was selected. The same notch type was used on test specimens made by both production processes to ensure comparability between production processes. The standard defines the specimen dimensions as length \(L\) = 55 mm, height \(H\) = 10 mm,and width \(W\) = 10 mm. Length and height values are fixed, while the standard allows three additional width sizes for reduced cross-sections: 7.5 mm, 5 mm, and 2.5 mm. As previously mentioned, the specimen dimensions in this research were limited by the diameter of the drawn rod, so the largest feasible width was 5 mm. Therefore, the dimensions of the specimens were length \(L\) = 55 mm, height \(H\) = 10 mm, and width \(W\) = 5 mm. The specimen with the indicated dimensions and tolerances is shown in Figure 1.  
For the fracture toughness determination, a three-point bending test is required, for which the same test specimen was used with knives subsequently glued on to hold the crack mouth opening displacement (CMOD) measuring instrument. The height of the knife glued to the specimen was 2 mm. The dimensions of this specimen with the knife positions are shown in Figure 2.  
In addition to the test specimens described above, round specimens for fracture toughness testing were also used and dimensioned according to ASTM E466-96 [18]. Those  
Figure 1: Test specimen dimensions.  
Figure 2: Test specimen adjustment for three-point bending test.  
specimens were prepared for fracture toughness testing by subsequently machining the ring-shaped notch shown as detail A in Figure 3, which shows a cylindrical specimen for experimental fracture toughness testing.
# Robust Hessian Locally Linear Embedding Techniques for High-Dimensional Data
## 3 Sensitivity of Hessian LLE to Outliers and Noise  
In this section, we use the \"Swiss Roll with hole\" dataset to illustrate how the HLLE result can be affected by outliers and noises in the data. There are 1500 samples in the dataset. We randomly select  
Figure 1: Embeddings of the synthetic 2D manifolds. (**a**) Swiss Roll with a hole; (**b**) S-curve; (**c**) Uniform Swiss Roll; (**d**) Punched Sphere. The first row shows the four manifolds, and the second (**e**–**h**) and third rows; (**i**–**l**) are the corresponding embedding results obtained by the original HLLE and our reformulated HLLE respectively. In all experiments, the number of data samples is 1000 and the number of nearest neighbors is fixed to \(k=9\).  
150 samples (10%) and impose uniformly-distributed random noise with a large amplitude on them as outliers. We add Gaussian noise to the remaining 90% of the samples to simulate noise-corrupted data. Figure 2a plots these sample points, where the black points are the outliers and the colored points are the noise-corrupted data. As we can see from Figure 2g, due to outliers and noise, HLLE cannot recover the manifold structure well. In fact, the outliers and noise-corrupted points usually deviate from the smooth surface of the underlying manifold. Therefore, the dimension of the approximated local tangent space of the data points corrupted by the outliers or noises may be larger than the intrinsic dimension of the real manifold. Furthermore, the orientation of the approximated local tangent space of of the data points corrupted by the outliers or noise may deviate from the true one of the local manifold. As a result, the estimated local tangent space cannot catch the real local geometry of the manifold; hence, the local Hessian matrix, which uses the local tangent coordinates cannot reflect the local manifold structure well, leading to a large bias with respect to the true embedding result. Based on robust statistics, the RLLE algorithm can identify and remove the outliers successfully (see Figure 2b). However, RLLE would fail when the data have a large amount of small noises (see Figure 2h). In the next section, we present an approach to make HLLE more robust against both outliers and noises.  
Figure 2: Results of HLLE, RLLE and RHLE applied to the “Swiss roll with a hole” dataset with the existence of outliers and noises. (**a**) Dataset with outliers and noises; (**b**) Results of outlier removal using RLLE’s method; (**c**) Results of outlier removal using RHLE’s method; (**d**) Cumulative distributions of reliability scores computed by the RLLE’s method; (**e**) Cumulative distributions of reliability scores computed by the RHLE’s method; (**f**) Results of noise reduction with one iteration of local smoothing; (**g**) HLLE result; (**h**) RLLE result; (**i**) RHLE result.
###### Abstract  
Vegetation plays a crucial role in enhancing residents' quality of life, especially in densely populated urban areas. However, previous research has rarely explored the inconsistency between vegetation greenness and productivity or its potential factors, leaving the reasons for their inconsistency unclear. Taking the three largest urban agglomerations in China as study areas, this study examined the inconsistency between vegetation greenness (LAI) and productivity (GPP) after detecting their dynamics based on the Mann-Kendall test. Then, the impact of land use change on the observed inconsistency was explored by contrasting the variations in vegetation greenness and productivity between regions with and without land use changes. The effect of climate change was evaluated by the Spearman correlation method at the pixel level. The results showed that both vegetation greenness and productivity exhibited a rising trend in three agglomerations from 2001 to 2020. Notably, an obvious inconsistency existed between greenness and productivity. Regions with a consistent change in greenness and productivity accounted for 69.87% in Beijing-Tianjin-Hebei (BTH), while only 45.65% and 42.93% in the Pearl River Delta (PRD) and the Yangtze River Delta (YRD), respectively. Land use change and climate change exerted divergent impacts on greenness and productivity across these agglomerations. The conversion of croplands and grasslands to construction lands had a more severe negative effect on vegetation greenness than on productivity in all regions. However, this transition led to a general decline in both greenness and productivity in the YRD and PRD, whereas in BTH, greenness declined while productivity paradoxically increased. As for climatic factors, the responses of greenness and productivity to rainfall and solar radiation exhibited spatial heterogeneity among the three agglomerations. In the YRD and PRD, they had a negative correlation with rainfall and a positive correlation with solar radiation, whereas in BTH, these correlations were reversed. Our spatial comparative analysis provided insights into the inconsistency between vegetation greenness and productivity as well as their potential reasons, offering a fresh perspective for regional vegetation research.  
vegetation greenness; vegetation productivity; inconsistency; land use change; climate change 2024  
1  
[PERSON], [PERSON], Y. Diverse Responses of Vegetation Greenness and Productivity to Land Use and Climate Change: A Comparison of Three Urban Agglomerations in China  
1  
[PERSON], Y. Diverse Responses of Vegetation Greenness and Productivity to Land Use and Climate Change: A Comparison of Three Urban Agglomerations in China  
1 School of Environmental and Geographical Sciences, Shanghai Normal University, Shanghai 200234, China; [EMAIL_ADDRESS]  
2
# Evolution of Hybrid LiFi-WiFi Networks: A Survey
## 7 Implementing HWLNets
### Physical Layer Integration  
LiFi and WiFi components can be integrated on the same platform, making a universal AP. This can be achieved by integrating front end electronics on the same board due to the fact that they share a lot of components such as power amplifiers, converters, etc. AlreadyRF spectrum devices exist that use a reconfigurable baseband filter, which allows to serve as WiFi or cellular AP [100].  
LiFi uses a different antenna technology, so implementation would prove to be difficult and a possible device might be substantial in terms of dimension compared to a standalone AP. For synchronized signal processing of both bands, signals have to be converted into different frequencies, thus impairing gain and adding noise to the channel.  
Pulse modulation and OOK are the best suited modulation types for VLC with data rates up to 100 Mbps. Both are single-carrier and have low peak to average power ratio with modest data rates. In contrast, DCO-OFDM has high peak to average power ratio but offers the highest data rates. The types of LED used commercially have narrow FOV, thus have limited bandwidth. On the other hand, photo diodes in user's equipment are vulnerable to positioning and changes in environment, especially a high volume of external light, e.g., sun light. Constructing a viable hybrid AP must take into account the compensation between spectral and energy efficiency and the circuit integration of the two different technologies.
## 3 Materials and Methods
### Feature Extraction Methods
#### 3.3.3 Time-Frequency Domain  
Time-frequency domain analysis methods have localized analysis capabilities in both the time and frequency domains simultaneously. The functions used in this work are discrete wavelet transform and wavelet entropy. The feature extraction in this domain is powerful, capturing the transient behavior of EEG signals and the evolution of frequency components over time.
# Glass Defect Detection with Improved Data Augmentation under Total Reflection Lighting
## 2 Composition of Defect Detection System for Mobile Phone Flat Glass
### Image Acquisition and Imaging Process of Mobile Phone Flat Glass  
The data samples used in this paper all come from a mobile phone flat glass manufacturer. A designed LED combination light source worked as the light source, and the system imaging field was made up of a low-angle grazing imaging field and a total internal reflection imaging field. The camera model was GS3-U3-8956M-C, the size of the mobile phone flat glass sample was 70 mm \(\times\) 150 mm, and the designated detection field width was 76 mm. To acquire a complete defect image, the two cameras are required to collect images of both the upper and lower surfaces of the glass sample six times under each imaging field, thereby generating 6 images of the upper surface and 6 images of the lower surface under both the main light source and the auxiliary light source. The detection precision of the system stands at 10 \(\upmu\)m. According to the statistical analysis of defective products in the production process, the defects in mobile phone flat glass fall into four categories: point, slice, dust, and dirt defects, whose images in the main and auxiliary imaging fields are all shown in Figure 5. As shown, the imaging characteristics of each defect manifest as follows: point and slice defects exhibit higher recognition of the target area and the background under primary light source illumination, while, under the auxiliary light source, the recognition of the target area and the background is lower. Dust and dirt present lower recognition between the target area and the background under the primary light source, whereas, under the auxiliary light source, the recognition of the target area and the background is higher.