esa-sceva/satcom-chunk-collection huggingface dataset:
# Effect of Temperatures on Polyphenols during Extraction
## 5 Formation of Maillard Reaction Products (MRP) at High PLE Temperatures  
There is limited understanding of the composition, quantity and function of new compounds that are formed under PLE extractions at high temperatures. These compounds are commonly referred to as Maillard reaction products or MRPs. They have been reported to possess antioxidant activity and be toxic, mutagenic compounds [32; 76; 77]. [PERSON] et al. [78] found that newly formed compounds possessed AOA when extracted using PLE at 200 \({}^{\circ}\)C. [PERSON] et al. [45] showed that MRPs increased when the temperature increased from  
\begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Source** & **Extraction** & **Effect** & **Reference** \\ \hline \multirow{2}{*}{Thyme} & \multirow{2}{*}{PLE} & T \(\uparrow\), hydroxyphenyl propanoic acids (HPPA) \(\uparrow\), hydroxycinamic acids \(\downarrow\), flavones \(\downarrow\), and flavanols \(\downarrow\) & [47] \\ \hline Rosemary & PLE & T \(\uparrow\), rosmanol \(\downarrow\), carnosic acid \(\uparrow\) & [37] \\ \hline \multirow{2}{*}{Mint} & TSE followed by acid hydrolysis & All phenolic increased massively after hydrolysis, except caffeic acid & [73] \\ \hline \multirow{4}{*}{Buckthorn} & TSE followed by acid hydrolysis & Ferulic acid, myricetin, quercetin, naringenin, luteolin and apigenin appeared after hydrolysis. The content of the phenolics also increased with the exception of gallic acid, which slightly decreased. Vanillic acid was present in the normal extract but not present after hydrolysis & [73] \\ \hline \multirow{2}{*}{Birch} & TSE followed by acid hydrolysis & Myricetin, quercetin and kaempfer appeared after hydrolysis. Hydrolysis caused an increase in the content of gallic acid, protocatechuric acid and apigenin but a decrease in the content of caffeic acid and chlorogenic acid & [73] \\ \hline \multirow{2}{*}{Caraway} & TSE followed by acid hydrolysis & Protocatechuric acid was found in the normal extract but not found after hydrolysis. Caffeic acid decreased after hydrolysis & [73] \\ \hline \multirow{2}{*}{Parsley} & TSE followed by acid hydrolysis & Quercetin appeared after hydrolysis. Increase in concentrations of all other phenolics after hydrolysis. Increase in gallic acid again explained by hydrolysis of galactannins & [73] \\ \hline \end{tabular}
\end{table}
Table 2: Impact on individual phenolic compounds.  
150 \({}^{\circ}\)C to 200 \({}^{\circ}\)C, as did the TPC and AOA. With extraction temperatures above 150 \({}^{\circ}\)C, studies have reported the formation of MRPs and an increase in AOA [45; 50; 78; 79]. Due to the possible toxic effects from MRPs, extraction products obtained at these temperatures should be carefully analysed and studied.
## 2 Materials and Methods  
### Materials  
#### 2.1.1 HadISST1 Datasets  
Global monthly average sea ice concentration data from 1988 to 2017 was collected from the Met Office Hadley Centre. The grid resolution of the data set was \(1^{\circ}\times 1^{\circ}\). These data sets had high temporal and spatial resolution than other products. By calculating the average of three months, the autumn (September-November) sea ice concentration in the Arctic was calculated (180\({}^{\circ}\) E-180\({}^{\circ}\) W, 60\({}^{\circ}\) N-90\({}^{\circ}\) N). Sea ice extent was defined as the sum area of the grid points, where the sea ice concentration was greater than 15%.
# Hardware-Based Architecture for DNN Wireless Communication Models
## 3 Background
### Conventional Detector
#### 3.2.2 MMSE Detector  
For the MMSE algorithm, the transmitted signals \(S(n)\) is recovered by multiplying the received signals \(Y(n)\) by the pseudo inverse of channel matrix \(W_{MMSE}=(H^{H}H+\rho^{-1}I)^{-1}H^{H}\) where \(\rho\) denotes SNR value, \(I\) represents identity matrix. Furthermore, the MMSE algorithm also recognizes the existence of practical noises and tries to eliminate them. The formula of the MMSE algorithm is given by the following equations  
\[\begin{split} Y(n)&=HS(n)+W(n)\\ W_{MMSE}Y(n)&=W_{MMSE}HS(n)+W_{MMSE}W(n)\\ W_{MMSE}Y(n)&=S(n)+W_{MMSE}W(n)\\ =>Y_{MMSE}(n)&=W_{MMSE}Y(n)\end{split} \tag{7}\]  
From each element in \(Y_{MMSE}(n)\), the component-wise detection is used for calculation. Then, the MMSE detection is completed. By comparing the ZF algorithm with the MMSE algorithm in terms of low complexity and performance detection algorithm, we can see that the MMSE algorithm can reduce the noise effect, which helps to improve the signal detection performance. However, the performance is still not close to the ideal value since some noise effects still exist. These noise effects can be removed by the MLD algorithm.
# Temporal Convolutional Network-Based Axle Load Estimation from pavement Vibration Data
## 2 Methods  
This section presents the sequence-to-sequence method for estimating axle loads using DOVS and TCN. The method's overview is depicted in Figure 1. Firstly, data acquisition, preprocessing, and augmentation were carried out, wherein a sufficient amount of data, comprising vibration signals from DOVS and load-time curves, were collected from the experiment. The original data were then subjected to filtering, normalization, down-sampling, and random window selection. Next, the preprocessed data were partitioned into training, validation, and testing samples. Subsequently, a TCN-based network was constructed and trained to handle various types of axle load estimation tasks. Finally, the network's performance was verified and applied to estimate the tested moving loads.
## 3 Prediction Results of Mining Surface Subsidence of Northern Pei County for Reusing Land Resources
### The Influence of Geological and Mining Conditions on Prediction Parameters
#### 3.1.2 Mining Thickness  
Mining thickness is an important factor affecting surface subsidence. The bigger the mining thickness, the greater damage to the overburden. Thus, the subsidence coefficient increases with the increase of the mining thickness. The influence of mining thickness on the subsidence coefficient is related to the mining depth. The ratio of depth and thickness can be used to reflect the relationship between the subsidence coefficient, the mining depth and the mining thickness, and their relationship can be expressed as [23]:  
\[q=1-0.292\ 35\bigg{(}\frac{H-h}{M}\bigg{)}^{0.054\ 573} \tag{6}\]  
where:  
\(q\) is the surface subsidence coefficient;  
\(H\) is the mining depth, m;  
\(M\) is the mining thickness, m;  
\(h\) is the unconsolidated layer thickness, m.  
The mining thickness does not have much effect on the tangent of major influence angle, the mining influence propagation angle and the inflection point offset.
# Resting-State Functional Connectivity in Mathematical Expertise
## 4 Discussion
### Classification Accuracy  
Statistical analyses of functional or structural group differences have been widely used to investigate fMRI-based neuroimaging features, thereby revealing psychophysiological traits of specific population groups [1; 2; 81]. In the present study, together with this approach, we employed the machine learning technique to discriminate between professional mathematicians and non-mathematicians in their functional connectivity, using significant features extracted from resting-state fMRI data. We obtained a high classification accuracy of 91.19%, thereby cross-validating our statistical results from the aspect of machine learning. Particularly, we suggest that using resting-state functional connectivity features would play an important role in obtaining a relatively high classification accuracy. Indeed, several previous studies reported that functional connectivity features, compared to BOLD activities, showed better classification performances [82; 83; 84]. Therefore, it is necessary to look into functional connectivity from the resting-state fMRI data, as well as the various patterns of BOLD activations to understand the brain's complex processes [85; 86; 87].  
The machine learning approach can be used to develop practical fMRI-based applications. For example, as mathematical ability is closely linked to learning ability [3], discriminating between different levels of mathematical proficiency with a high classification accuracy may be an appropriate way to evaluate individuals' learning capability and to provide educational feedback depending on their performances over the course of learning. To develop such an educational application, it would be better to use a large sample size in each group in the future study.
# Cluster Analysis of Pedestrian Mobile Channels in Measurements and Simulations
## 2 Cluster Tracking in Mobile Measurements
### Measurement Setup and Data Processing
#### 2.1.1 K-Power Means Part  
Generally, KPM minimizes the total weight of multipath component distance (MCD) to the corresponding cluster centroid by iterations [19]. The MCD between the \(i\)th and \(j\)th MPC is calculated by  
\[MCD_{ij}=\sqrt{\left\|MCD_{\tau,ij}\right\|^{2}+\left\|MCD_{Txj}\right\|^{2} +\left\|MCD_{Rx,ij}\right\|^{2}} \tag{3}\]  
of which \(MCD_{\tau,ij}\) is the delay distance given as follows:  
\[MCD_{\tau,ij}=\eta\cdot\frac{\left|\tau_{i}-\tau_{j}\right|}{\Delta\tau} \cdot\frac{\tau_{sd}}{\Delta\tau} \tag{4}\]  
\(\eta\) is a scaling factor to adjust the weight of delay in the distance function. \(\Delta\tau\) means the range of delay, and \(\Delta\tau=max_{ij}\left|\tau_{i}-\tau_{j}\right|\). \(\tau_{sd}\) is the standard deviation of delay.  
Figure 4: Flowchart of the K-power means-Kalman filter (KPMKF) algorithm.  
Angle distance \(MCD_{Tx/Rx,ij}\) is given as  
\[MCD_{Tx/Rx,ij}=\frac{1}{2}\left|\begin{pmatrix}sin(\theta_{Tx/Rx,i})cos(\phi_{Tx/ Rx,i})\\ sin(\theta_{Tx/Rx,i})sin(\phi_{Tx/Rx,i})\\ cos(\theta_{Tx/Rx,j})\end{pmatrix}-\begin{pmatrix}sin(\theta_{Tx/Rx,j})cos( \phi_{Tx/Rx,j})\\ sin(\theta_{Tx/Rx,j})sin(\phi_{Tx/Rx,j})\\ cos(\theta_{Tx/Rx,j})\end{pmatrix}\right| \tag{5}\]  
First, we randomly choose the \(K\) positions of cluster centroids \([c_{1}^{(0)},c_{2}^{(0)},\cdots,c_{K}^{(0)}]\) as the initial state. Then, we start iterations in the following steps:  
1. Assign MPCs to cluster centroids and store indices: \[\begin{array}{c}\mathbf{F}_{l}^{(i)}=argmin(P_{l}\cdot MCD(X_{l},c_{k}^{(i-1 )}))\\ \mathbf{F}^{(i)}=[\mathbf{F}_{1}^{(i)},\mathbf{F}_{2}^{(i)},\cdots,\mathbf{F }_{L}^{(i)}],\overrightarrow{c_{k}^{i}}=\underset{l}{Indices}(\mathbf{F}_{l}^{ i}=k)\end{array}\] (6) where \(\mathbf{F}_{l}^{(i)}\) is the cluster index of the \(l\)th MPC in the \(i\)th iteration. \(\overrightarrow{c_{k}^{i}}\) is the set of MPCs indices belonging to the \(k\)th cluster in the \(i\)th iteration.
2. Recalculate positions of cluster centroids \(c_{k}^{(i)}\) from the allocated MPCs to coincide with the clusters' centers of gravity: \[c_{k}^{i}=\frac{\sum\limits_{j\in\overrightarrow{c_{k}^{i}}}(P_{j}\cdot X_{ j})}{\sum\limits_{j\in\overrightarrow{c_{k}^{i}}}P_{j}}\] (7)
3. If \(c_{k}^{(i)}=c_{k}^{(i-1)}\) for all \(k=1,\cdots,K\), then we go out of the iterations. Otherwise, we keep performing iterations up to the setting maximum iteration times. Finally, we obtain the optimal cluster set. Here, the maximum iteration times were set as 100.
# A Full-Duplex 60 GHz Transceiver with Digital Self-Interference Cancellation
## 3 Design and Implementation
### Digital SI Cancellation Scheme
A 3 rd-order LaGrange interpolation module is used to regenerate the symbol data based on the estimated sampling time offset. Then, the SI leakage signal is regenerated after the phase rotation, and it is subtracted from the received baseband signal. In the proposed system, the DSIC is executed in MATLAB. The transmission baseband signals \(\mathrm{I_{rx}}\) and \(\mathrm{Q_{rx}}\) must be synchronized to the received sequence again before the SI cancellation runs. The evaluation of the DSIC assumes that the SI signal is 12 dB lower than the wanted signal with a given phase rotation and sampling offset. The concept simulation runs based on the ideal test patterns with a few specified effects, including phase rotation, sampling offset, and additional Gaussian white noise. The phase noise and channel flatness degradation are not included in the DSIC simulation. The simulation achieves more than a 20 dB SI cancellation by using a 6-bit ADC, with perfect alignment of the phase and sampling time. The finer ADC resolution achieves better performance based on a 6 dB improvement per bit. However, the performance is sensitive to the symbol's sampling time offset. It will drop to 5 dB with the worst estimated sampling timing offset. The DSIC module achieves an 8 dB SNR improvement by using the 41-tapped FIR filter with an 8-bit ADC at four times over-sampling with measured data. The SINR of the input signal is around 12 dB and the input signal is fully occupied by the 10-bit ADC's input range. Detailed information is introduced in the next section. Figure 5: A block diagram of the digital SI cancellation.
## 2 Basics and Contribution Overview  
### Basic Concepts  
We denote by \(T\) the **text** string, by \(P\) the **pattern** string, and by \(O\) an occurrence of \(P\) in \(T\); by \(\Sigma\) the strings' **alphabet**, of size \(\sigma\); by \(T[i]\) the symbol (or character) at position \((i\bmod u)\) of \(T\); by \(S.S^{\prime}\) the **concatenation** of strings; by \(S=S[..i-1].S[i..j].S[j+1..]\) respectively a **prefix**, a **substring**, and a **suffix** of a string \(S\); by \(S\sqsubseteq S^{\prime}\) that \(S\) is a substring of \(S^{\prime}\); and by \(\varepsilon\) the empty string.  
A **trie** is a character-labeled tree where no two children of a node have the same label. The concatenation of the labels from the root to a node \(v\) is called the **path-label** of \(v\), and also denoted \(v\). A **compact trie** is obtained by collapsing the unary paths in a trie into single edges, labeled with the concatenation of the collapsed symbols. The **suffix tree**[10; 42] of \(T\) is a compact trie such that each suffix of \(T\$\) is the path-label of a leaf and vice versa, where \(\$\
ot\in\Sigma\) is a terminator symbol. String labels in a suffix tree are substrings of \(T\), thus they can be represented by a starting position in \(T\) plus a length. We will assume that \(u\) is the length of \(T\$\). The **suffix array**[11]\(SA[0,u-1]\) stores the suffix indexes of the leaves in lexicographical order. The suffix tree nodes can be identified with suffix array intervals: each node \(v\) corresponds to the range of leaves that descend from \(v\). For a detailed explanation see, _e.g.,_ [PERSON]'s book [43]. Figure 1 shows an example of a suffix tree and a suffix array.  
**Ziv-Lempel** compression [23; 44; 45; 46] is based on cutting a text \(T\) into **phrases** of varying length, so that, essentially, each phrase already appears somewhere earlier in the text. Each phrase is encoded usually with a fixed number of bits, so more compressible texts generate longer phrases. There are many variants of this family, which basically differ in the rules to form the phrases from the previous text.
## 5 Conclusions  
This study presented the effects of diaphragm deformation and buckling on the fatigue performance and stress characteristics of diaphragm-to-U-rib (DU) welds. A classified maintenance scheme was proposed to relieve the adverse impact caused by diaphragm deformation. The conclusions are as follows:  
1. Field measurements reveal distinct stress responses to diaphragm deformation. While the structural stress and fatigue performance at the normal regions of DU welded joints remain largely unchanged, the distorted regions show significant stress amplification, with stress ranges increasing by 50%. These results highlight the importance of prioritizing inspection of distortion-affected areas in operational bridges.
2. On the distorted side of the diaphragm, stresses in the DU weld and both in-plane and out-of-plane deformations perpendicular to the weld increase significantly. Stress concentrations shift from the arc notch section toward the depressed zone of the diaphragm, accompanied by a rise in both the number of stress concentration points and the simulated stress magnitude, the latter increasing by 72.6%, elevating the risk of DU welded joint cracking.
3. Based on the mechanical behavior of the deformed region, it is suggested to implement regular inspections and graded maintenance strategies corresponding to the progression of the deformation. For areas exhibiting minor and stable deformation, hammer peening proves effective for enhancing fatigue resistance at this stage. However, steel plate reinforcement is more appropriate when addressing advanced diaphragm deformation.  
This study investigates the effects of diaphragm deformation on the stress characteristics of orthotropic steel decks (OSDs) and proposes scientifically grounded treatment strategies. While the primary causative factors of diaphragm deformation in this case remain undetermined, subsequent investigations will examine the following three potential contributors: material defects, manufacturing deviations, and load sensitivity. These analyses are expected to establish fundamental guidelines for the optimal design and construction of comparable structures.  
Conceptualization: C.L.; methodology: C.L.; software: C.L. and Y.Y.; validation: C.L. and Y.Y.; formal analysis: C.L. and Z.L.; investigation: C.L. and Y.Y.; resources: B.J.; data curation: C.L. and Z.L.; writing--original draft preparation: C.L.; writing--review and editing: B.J. and Y.Y.; visualization: Z.L.; supervision: [PERSON]; project administration: [PERSON]; funding acquisition, B.J. and Y.Y. All authors have read and agreed to the published version of the manuscript.  
This research is funded by the National Natural Science Foundation of China [Grant No. 52378153] and the Construction System Technology Project of Jiangsu [Grant No. 2024 ZD035].  
Not applicable.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.
## 5 Possible Signal Model Improvement  
The new civil frequency at L5 will have more signal power than the current civil signal at L1 [1]. The higher civil signal power will enable users to acquire GPS satellites earlier for smoothing before using them for position estimation. Thus, the floor of the residual user receiver noise and multipath error (\(\sigma^{2}_{i,air,\,\text{L1-L5}}\) in Equation (6)) would be lower than the current model [15]. An upper bound of the benefit is found by setting the residual user receiver noise and multipath error to be _zero_. The MAAST simulation result is shown in Figure 8. As can be seen in Figure 8, however, the VPL values still does not meet the CAT I requirement (CAT I VAL = 12 m), despite improvement of some region to VPL \(<15\) m.  
For an L1-L5 dual-frequency WAAS user avionics sensor, the dominant term in the confidence calculation of Equation (6) is the \(\sigma^{2}_{i,air}\), which is the confidence bound on satellite clock and ephemeris corrections. However, any reduction in the confidence calculation of \(\sigma^{2}_{i,air}\) will require a substantial change in the WAAS UDRE (User Differential Range Error) algorithms [7].
## 5 Conclusions  
This study addresses the technical challenge of achieving stable control for Unmanned Aerial Vehicles (UAVs) operating in complex nonlinear systems and under random airflow disturbances by proposing a hybrid control algorithm that integrates Active Disturbance Rejection Control (ADRC) with Super Twisting-Sliding Mode Control (ST-SMC). By leveraging the advantages of ADRC in disturbance estimation and compensation and the strengths of ST-SMC in enhancing system robustness and suppressing high-frequency chattering, the proposed approach achieves complementary improvements in control performance. The hybrid algorithm effectively mitigates external disturbances, reduces system oscillations, and significantly enhances the accuracy and stability of attitude and trajectory control.  
Simulation results demonstrate that the proposed algorithm exhibits strong disturbance rejection and fast convergence during the dynamic response phase. Even under severe airflow disturbances, the UAV quickly returns to the desired trajectory. In the steady-state phase, the output error approaches zero, indicating excellent control precision and tracking performance. Compared with traditional PID control and single control strategies such as ADRC, the hybrid algorithm shows clear advantages in terms of response speed, robustness, and control accuracy.  
This research not only enriches the theoretical framework of UAV attitude control but also provides a reliable solution for stable UAV operation under complex weather and disturbance conditions. In the future, this algorithm can be extended to various types of unmanned systems and more complex three-dimensional flight environments, laying a foundation for the advancement of intelligent flight control technologies and the realization of highly reliable autonomous flight missions.  
Conceptualization, Y.G. and B.W.; software, Y.G.; formal analysis, Y.G.; investigation, Z.L., J.G. and J.Z.; resources, B.W.; data curation, N.H.; writing--original draft preparation, Y.G. and J.Z.; writing--review and editing, Y.G., N.H., and B.W.; visualization, Y.G.; supervision, B.W.; project administration, X.H.; funding acquisition, B.W. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Shenyang Science and Technology Plan Project (No.23-407-3-33).  
Not applicable.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
The authors declare no conflict of interest.
## 4 Result
### Path Loss  
By comparing the analyzed path loss and the 3 GPP path loss models, crossings #1 and #2 match the model for the LoS environments. Since 3 GPP path loss of LoS for urban, suburban, and rural is similar, it is hard to define a similar environment than a status of sight. Crossings #3 and #4 can be considered NLoS environments for suburban and urban, respectively. As expected, the path loss of crossing #5 is in between crossings #2 and #3. At shorter distances to the crossing, from \(-\)100 m to 0 m, the results for crossing #3, however, matched the suggested values for LoS environments. At larger distances, they match 3 GPP's SMa NLoS path loss models. We assume crossing #3 can be considered a suburban environment. Interestingly, the evaluated path loss for crossing #4 is almost identical to the UMa NLoS before and worse than UMa NLoS after the crossing. Before crossing #4, the environment has combinations of small buildings, trees, and open spaces. After crossing #4, the environment has a small window of LoS and mainly has a large building near the track, which has worse conditions than before the crossing. Crossing #4 has the highest path loss and can be considered an urban-like environment.  
We could expect what 3 GPP path loss model to use for T2I communication from the evaluations. If the environment near the crossings is mainly trees and open spaces, the GPP RMa LoS model is preferred. If the environment has numerous buildings and trees around the crossing, the 3 GPP SMa or UMa NLoS model is preferred.