esa-sceva/satcom-chunk-collection huggingface dataset:
# Investigating Bayesian Parameter Identification Using Non-Standard Laboratory Specimens
## 2 Modeling of Fracture Propagation
### Crack Nucleation  
The nucleation of cracks within each Q6 ED element is governed by the stress state at the element's four integration points, which determine both the crack's location and orientation. The procedure, first introduced by [10], consists of two key processes: the fracture initiation and the definition of crack position and orientation.  
Fracture initiation is regulated using the Rankine criterion, which assumes that a crack forms when the maximum principal stress exceeds the material's tensile strength. At each simulation timestep, all uncracked finite elements are evaluated using this criterion. First, the principal stress values \(\sigma_{p1,p2}^{e,i}\) at the four integration points are computed. The maximum principal stress at each integration point is determined by \(\sigma_{p}^{e,i}=\max[\sigma_{p1}^{e,i},\sigma_{p2}^{e,i}]\). The highest value among these is selected \(\sigma_{p}^{e}=\max\limits_{i=1,\dots,A}\sigma_{p}^{e,i}\). This value is then compared with the tensile strength of the material \(\sigma_{n}\). If \(\sigma_{p}^{e}\) exceeds this value, the element fractures, and a crack within the element is initiated.  
Once a crack is initiated, its position and orientation need to be defined. The integration points where \(\sigma_{p}^{e}\) reaches its maximum value (within a tolerance) are collected in the set: \(B^{e}=id\in 1,2,3,4:\sigma_{p}^{e,id}=\sigma_{p}^{e}\). The geometric center of the crack is obtained by averaging the global coordinates of the points in \(B^{e}\):  
\[\mathbf{x}_{ED}^{e}=\frac{1}{N_{B^{e}}}\sum_{i\in B^{e}}\mathbf{x}^{e,i}, \tag{3}\]  
where \(N_{B^{e}}\) is the number of contributing points, and \(\mathbf{x}^{e,i}\) are their global coordinates. The average stress at points \(B^{e}\) is calculated as follows:  
\[\sigma_{ED}^{e}=\frac{1}{N_{B^{e}}}\sum_{i\in B^{e}}\sigma^{e,i}, \tag{4}\]  
where \(\sigma^{e,i}\) are the stress values of each contributing point. The crack orientation angle \(\alpha_{ED}^{e}\) is then derived from these averaged stress values:  
\[\alpha_{ED}^{e}=\frac{1}{2}\text{arctan}\left(\frac{2\sigma_{xyED}^{e}}{\sigma _{xED}^{e}-\sigma_{yED}^{e}}\right). \tag{5}\]  
With the calculated position \(\mathbf{x}_{ED}^{e}\) and orientation \(\alpha_{ED}^{e}\), embedding the crack is straightforward (see Figure 2). The crack normal, represented by the angle \(\alpha_{ED}^{e}\), aligns with the vector \(\mathbf{n}\), while the tangential vector \(\mathbf{m}\) is perpendicular to it. Once defined, the crack'sgeometry remains fixed throughout the simulation, with only the separation parameters (\(\alpha_{1},\alpha_{2}\)) evolving over time. Each finite element can have only one crack, but multiple cracks can emerge across the mesh during each timestep.
## 3 SDK-Injection Method
### Technique 1: Curve Fitting-Based Smoothing  
Unlike ECG waveforms visualized by professional equipment in hospitals, which are generally used for heart disease diagnosis by medical experts, an ECG stream, directly collected through a sensor attached to a wearable device, contains noise. Such noise may cause distortion of the characteristics of the ECG waveform. This may degrade the classification performance and also reduce the effectiveness of shallow domain knowledge injection. Therefore, the SDK-Injection method introduces curve fitting-based smoothing to deal with this noise.  
SDK-Injection method uses the Savitzky-Golay filter for curve fitting-based smoothing. The Savitzky-Golay filter is a curve fitting method that fits points within a given window interval, based on a polynomial function of a given order. Figure 2 shows the result of applying the Savitzky-Golay filter to the sample pattern extracted from the datasets used in the experiment in Section 4. The black line is the data before curve fitting, and all of the data contain noise; although, there may be a difference depending on the dataset. The red line is smoothed data after curve fitting by the Savitzky-Golay filter, and because outliers caused by noise are corrected it is easy to check the characteristics and trends of ECG patterns.  
The degree of smoothing by the Savitzky-Golay filter, that is, the degree of noise correction, is controlled by two parameters: window size and polynomial order. However, the window size must always be an odd number, and the polynomial order must be smaller than the window size. At this time, if the polynomial order is set similar to the window size, the curve fitting algorithm cannot work properly. On the other hand, if the polynomial order is too small, important feature points may be lost. Therefore, it is necessary to set a polynomial order of an appropriate size compared to the window size. That is, it is not the window size alone that affects the smoothing degree, but the value of the appropriate polynomial order compared to the window size.  
Figure 3 shows the original patterns and smoothed patterns  
Figure 3: _Cont_.  
Figure 2: Original (black) and smoothed (red) pattern samples from five datasets used in the experiments. Smoothed patterns were generated by Savitzky–Golay filter with window size 21 and polynomial order 13.
## 3 Image processing and evaluation
### Image tracking algorithm
#### Image tracking criteria:  
The criteria to judge that image _m'_ in the first frame and image _n'_ in the second frame are images of the same ant is given as  
\[D\left(m^{\prime},n^{\prime}\right)=\min\left\{\begin{array}{l}D\left(m^{ \prime},j\right)\text{ \ for }j=1,2,3\cdots,N_{o}\text{ and }\\ \left(X_{2,j}-X_{1,m^{\prime}}\right)^{2}+\left\{Y_{2,j}-Y_{1,m^{\prime}}\right\} ^{2}\leq R_{s}^{2}\end{array}\right\} \tag{8}\]  
wherein search radius _R\({}_{s}\)_ is used to limit the search for the image partner _n'_ in the neighbored area of the tracked image _m'_, so that some false tracking can be avoided and the computation time can be reduced.
# Channel Modeling and Analysis for the Sensor Network Inside Tower Buildings
## 1 Introduction
The main contributions and innovations of this paper are generalized as follows:  
1. For performance evaluation of the WSN inside tower buildings, a cluster-based propagation channel model inside tower structure buildings is proposed based on the ray-based channel model, which greatly reduces the complexity to analyze the performance of the communication system. 2. An improved k-means algorithm is developed to cluster the channel, which could obtain approximately equivalent channel impulse response (CIR) with a ray-based channel model. The channel parameters such as cluster delay and cluster power are equivalently calculated according to the clustered rays. Moreover, the performance of WSN, i.e., BER and channel capacity are derived. 3. The simulation results for different positions at different heights of the reconstructed tower building at 1 GHz show that the cluster classification method and cluster parameter calculation method can get more approximate equivalent CIR, and the calculated BER on this basis is in good agreement with the theoretical results, which verifies the accuracy of the cluster model and theoretical derivation. The rest of this paper is arranged as follows. In Section 2, a cluster-based channel model on the basis of the ray-based model is proposed. The improved k-means clustering algorithm and the cluster parameters of the channel are obtained and the performance analysis of the communication system is presented in Section 3. Simulation and validation results are conducted and analyzed in Section 4. Finally, some conclusions are given in Section 5.
## 1 Introduction
### Energy Recovery from Wastewater Sludge  
[PERSON] et al., [25] reviewed the differing technologies for energy recovery from municipal wastewater sludge. The study concluded that moisture content levels within sludge substantially affect the energy requirements for many of the energy recovery options, especially those that require dewatering and drying, such as incineration and pyrolysis [25]. [PERSON] & [PERSON] [26] presented a review of the types of resources that could be recovered from waste sludge, together with the methods employed to convert sludge into valuable resources. According to this study, AD is a well-established methodology and is widely used due to its cost-effectiveness and dual purpose (sludge stabilization and energy production) [26]. However, the main disadvantage is the slow-paced hydrolysis of sludge, which can be overcome by physical and chemical modifications to the process [26]. One major limitation of AD is its inability to decompose organic matter completely, with the by-product consisting of digestate [27]. [PERSON] & [PERSON] [27] concluded that the efficiency of sludge-to-energy conversion can be enhanced through AD followed by a pyrolysis step (fed with digestate), which converts organic matter into bio-oil, biochar, and pyrolytic gas. Another limitation of AD is the prolonged sludge retention time within the digester, approximating a timeframe of 10-20 days. [PERSON] et al., [28] addressed the limitation of municipal wastewater sludge as a feedstock to produce CH\({}_{4}\), since AD sludge typically contributes low CH\({}_{4}\) yields in comparison to other types of organic waste.
# Analysis of the Landfill Leachate Treatment System Using Arima Models: A Case Study in a Megacity
## 3 Results and Discussion
### Treated leachate
On the other hand, findings showed that the term MA of ARIMA models developed for the concentrations of indicator HMs were between 0-1 (Fe and Ni). That is, the results showed short fluctuations in concentrations of the indicator HMs during the operation of the leachate treatment plant. These fluctuations were probably up to a month. The other HMs under study also tended to show short fluctuations from the magnitude of their term MA, between 0-2 consecutive months (Table 2). This trend was possibly associated with controlled treatment conditions at the leachate treatment plant, which prevented the occurrence of atypical variations in treated leachate concentrations. However, COD and BOD\({}_{5}\) concentrations in the treated leachate showed greater fluctuations, up to four consecutive months. The findings hinted that these parameters were more difficult to control during the operation of the leachate treatment plant compared to the HMs considered in this study. Table S4 shows the parameter estimates for the ARIMA model terms selected for the variables considered in the treated leachate. Finally, the following limitations are part of this study and require special attention: (i) there is no information regarding scheduled or emergency shutdowns at the leachate treatment plant under study. This can generate increases or decreases in the concentrations of the chemical parameters analysed. (ii) Due to the high quantity of MSW managed by the study landfill, it is desirable to have detailed information regarding the progress of the landfill operations. For example, the type and advance of the intermediate coverage in the discharge cell may condition the flow of untreated leachate and, indeed, its chemical characteristics. (iii) In this study, outliers were not considered during the development of ARIMA models. These outliers may be relevant for measuring the influence of some abnormal event on the time series, or as it is feared that this abnormal event may affect the estimation of the parameters and results of the ARIMA model. Therefore, we suggest developing future research on similar treatment systems associated with the analysis of time series influenced by interventions. Figure 5: Observed and simulated (ARIMA) values for Fe and Ni concentrations in untreated and treated leachate during the study period. NT = Untreated, T = Treated, UL = Upper limit, and LL = Lower limit.
# Exploring Data Augmentation Algorithm to Improve Genomic Prediction of Top-Ranking Cultivars
## 4 Discussion
### Emphasis on Top Lines and Restricted Augmentation  
A distinctive aspect of our approach involves the deliberate emphasis on augmenting the top-performing lines during the training phase. This strategic decision stems from the recognition of the disproportionate impact that these lines often have on overall model performance, particularly in the context of plant breeding programs where the focus is often on elite or high-yielding genotypes [26]. By targeting augmentation efforts towards these top lines, our aim is to mitigate prediction errors specific to this subset, thereby potentially improving the overall predictive performance of the model. While our findings indeed demonstrate a notable reduction in errors within this targeted subset, it is essential to acknowledge the inherent limitations of this approach. Despite its effectiveness in optimizing performance for select lines, its impact on the overall model performance across the entire testing set may be somewhat constrained [22]. Therefore, further refinement and optimization are warranted to comprehensively address this discrepancy and maximize the utility of data augmentation strategies within genomic prediction frameworks.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **Method** & **Metric** & **Average** & **SE** \\ \hline Across\_data & A & MAAPE & 0.2391 & 0.0143 \\ Across\_data & A & MAAPE\_20 & 0.0671 & 0.0037 \\ Across\_data & A & NRMSE & 1.8357 & 0.0065 \\ Across\_data & A & NRMSE\_20 & 1.4789 & 0.0191 \\ Across\_data & C & MAAPE & 0.1038 & 0.0058 \\ Across\_data & C & MAAPE\_20 & 0.1134 & 0.0058 \\ Across\_data & C & NRMSE & 0.8654 & 0.0047 \\ Across\_data & C & NRMSE\_20 & 2.5089 & 0.0548 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Prediction accuracy results for across dataset using conventional (C) and augmented (A) methods, with metrics MAAPE and NRMSE for the total testing and MAAPE_20 and NRMSE_20 for the best 20% testing.
## 4 Analysis of DoF and User Scaling  
In this section, we first analyze the DoF achievability of our new hybrid opportunistic scheduling method along with the corresponding user scaling law. We then analyze the interference decaying rate with respect to the number of MSs.
## 4 Neuoprotective Peptides and Their Therapeutic Application in HIE
### Studies Using CARPs in Animal Models of HIE
#### 4.4.5 Osteopontin (OPN) and TAT-Fused OPN Peptide (TAT-OPN)  
Osteopontin (OPN) is a secreted acidic glycoprotein containing an arginine-glycine-aspartate (RGD) motif and possesses multiple cellular functions including chemotactic and cytokine-like actions. The OPN protein is expressed in various tissues including bone, placenta, pancreas and the CNS [123]. Following cerebral ischemia, OPN deficient mice demonstrate increased neurodegeneration [124] and when administered ICV, the protein is neuroprotective in several adult rodent cerebral ischemia and hemorrhagic stroke models [125; 126; 127; 128].  
Mechanisms thought to be associated with OPN-mediated neuroprotection include binding to the cell surface \(\alpha\)v\(\beta\)3 integrin receptor to stimulate pro-survival signaling (i.e., PI3K/Akt pathway), anti-inflammatory effects [126; 129; 130; 131], inhibition of inducible NOS [132; 133] STAT1 ubiquitination/degradation and by reducing MMP-9 and NF-\(\upkappa\) activation. Despite OPN's demonstrated neuroprotective effects, in one study OPN deficient mice demonstrated no change in susceptibility to ischemic brain injury [126].  
The first study to examine the neuroprotective efficacy of OPN in HIE used full length recombinant OPN protein (rOPN) in a P7 rat model of HI [134]. Animals treated with rOPN (0.03 or 0.1 \(\upmu\)g per animal: ICV) 60 min after hypoxia, displayed a reduction in infarct volume at 48 h and significant improvements in behavioral outcomes at 7 weeks post-HI. In contrast, treatment with rOPN (0.05 \(\upmu\)g/animal: ICV or 1.2 \(\upmu\)g/animal: IN) or thrombin cleaved rOPN (1.2 \(\upmu\)g/animal: IN) was not neuroprotective in a P5 mouse model of HI [135]. To improve cerebral delivery of OPN, a shorter peptide containing the RGD motif (IVPTVDVPNGRCDSLAYGLR\({}_{134-153}\); net charge 0) was developed. However, the shorter peptide was not neuroprotective when administered ICV (0.2 \(\upmu\)g/animal) or IN (30 \(\upmu\)g/animal) before hypoxia in P5 mice [135]. In a subsequent study the OPN\({}_{134-153}\) peptide was fused to TAT (TAT-OPN; YGRKKRRQRRR-IVPTVDVPNGRCDSLAYGLR, net charge +8) and assessed in a P9 mouse model of HI [136] using several peptide treatment regimens consisting of multiple dosing using IN, IP or ICV administration (see Table 1 for details). No treatment regimen reduced infarct volume or improved functional assessments up to 35 days post-HI. Taken together, current evidence indicates that at the dosing schedules examined for the TAT-OPN peptide, it is not neuroprotective in HIE rodent models.
# Special Issue on Precision Dimensional Measurements
## 2 Surface Profile and Form Error Measurements  
The first paper on 3D surface profile measurement is introduced by [PERSON] and his colleagues at Beihang University on the measurement sensitivity of through-focus scanning optical microscopy (TSOM), which involves scanning a target through the focus and capturing of a series of images [1]. It was found that for enhanced sensitivity, illumination polarization should be perpendicular to the target texture. Professor [PERSON]'s group at Tohoku University presents a different method to increase Z-measurement range of 3D profile using mode-locked femtosecond laser chromatic confocal probe [2]. They also provide an uncertainty evaluation method for measurements of pitch deviation and out-of-flatness of planar scale gratings by a Fizeau Interferometer in Littrow configuration [3]. A collaborative work between Karlsruhe Institute of Technology of Germany and Ohio State University of USA presents a generalized approach using measurement data to enhance the simulation model for designing freeform optics [4]. The boundary of a surface is difficult to measure by normal microscopy. Professor [PERSON] of National Taiwan University employed the digital image correlation (DIC) technique to assist accurate edge detection of 3D surface profilometry [5]. He also proposes a new moire projection triangulation method using a dual optical sensing configuration by engaging two optical sensors at two different viewing angles for achieving simultaneous full-field surface profilometry for measuring semiconductor wafer surfaces with extremely variant reflectivities [6]. An international cooperative work by Shanghai University ([PERSON] et al.), Nanyang Technological University (Asundi) and Linkoping University ([PERSON]) presents the algorithm for surfaces profiles and thickness variation measurement of a transparent plate using a Fizeau interferometer with wavelength tuning [7]. Anotheruseful algorithm of iterative stitching was also developed by this team on high numerical aperture cylindrical surface [8]. [PERSON] et al. proposes an innovative methodology of cloud data compression for free-form surface scanning measurement. It can save substantial data memory while remaining in good quality [9]. We are pleased to have the contribution by Professor [PERSON] at HUST on the Mueller matrix technique for nanostructure scatterometry [10] and ellipsometry [11]. [PERSON] et al. deals with the method of measuring a large aspherical mirror and identifying its vertex position [12]. [PERSON] et al. summarizes and compares the current five-point cylindricity error separation method with proximity sensors [13]. This provides a guideline for accurate evaluation of cylindricity errors.
# Antennas for Licensed Shared Access in 5G Communications with LTE Mid- and High-Band Coverage
## 4 Wideband Antenna for LSA Bands and LTE Mid and High Bands
### Antenna Configuration  
Figure 12 shows the configuration of the novel wideband antenna (Ant#3). The overall size of the antenna is 34 mm \(\times\) 15 mm \(\times\) 4 mm or 0.16\(\lambda_{2}\)\(\times\) 0.07\(\lambda_{2}\)\(\times\) 0.02\(\lambda_{2}\), where \(\lambda_{2}\) is the free-space wavelength at 1.41 GHz. This indicates that the area occupied by the wideband antenna is 15% and 13% smaller than that occupied by Ant#1 and Ant#2, respectively. The substrate size, ground clearance, positioning of the antenna, feed arrangement, and back view are similar to Ant#1, as shown in Figure 1.  
Figure 11: Normalized radiation patterns of the dual-band antenna (Ant#2) at 1.8, 2.6, and 3.6 GHz.  
Ant#3 is composed of three elements: a U-shaped large element PQOR loaded with an L-shaped vertical plate (aligned with the edge PQ) on the left to cover the mid band, an L-shaped loop element NSTS on the right to cover the high band, and an L-shaped element MNO. It is worth mentioning here that the element MNO is the same as that used in Ant#2. The optimized design parameters of Ant#3 are listed in Table 2. The parameters of the mid-element MNO are presented in Table 1.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \(w_{9}=1\) & \(w_{14}=2.5\) & \(w_{15}=3.5\) & \(w_{16}=1\) & \(w_{17}=2.75\) \\ \hline \(w_{18}=1\) & \(w_{19}=0.75\) & \(w_{20}=1.5\) & \(w_{21}=0.25\) & \(w_{22}=0.25\) \\ \hline \(w_{23}=1\) & \(l_{9}=14\) & \(l_{10}=5\) & \(l_{11}=5.5\) & \(l_{12}=5\) \\ \hline \(l_{13}=1\) & \(l_{14}=2\) & \(l_{15}=3.5\) & \(l_{16}=2.5\) & \(l_{17}=3\) \\ \hline \(l_{18}=5\) & \(g_{1}=0.5\) & \(g_{2}=1\) & \(g_{3}=1\) & \(g_{4}=2\) \\ \hline \(h_{2}=4\) & \(w_{t}=2\) & \(r=2\) & \(l_{2}=34\) & \(W_{2}=15\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Design parameters of the wideband antenna (Ant#3). All dimensions are in millimeters.  
Figure 12: Configuration of the wideband antenna (Ant#3) for the LSA bands, as well as the LTE/WWAN mid and high bands: (**a**) perspective view, and (**b**) top view (vertical plate is unfolded along the +y axis).