esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Design Parameters of the Metamaterial and Simulation Setup
### Equivalent LC Circuit of the Unit Cell  
An estimated electrical equivalent circuit is drawn and executed by ADS to validate the CST results of the proposed metamaterial. The unit cell is designed with a combination of both metal strips and some split gaps. Every metal strip represents a conductor, whereas every split gap represents a capacitor [39]. In the microwave band, metallic conductor copper can be treated as a perfect conductor that can ignore the ohmic losses [40]. Therefore, the whole unit cell is represented by an LC resonance circuit. The inductance and the capacitance are the main parameters of an LC circuit, denoted by \((L)\) and \((C)\), respectively. Using these two parameters, resonance frequency \((f)\) can be calculated by applying Equation (13).  
\[f=\frac{1}{2\pi}\frac{1}{\sqrt{(LC)}} \tag{13}\]  
The quasi-state theory can be applied to measure the capacitance within a distance or in a split gap in a circuit [41].  
\[C=\varepsilon_{0}\varepsilon_{r}\frac{A\ (F)}{d} \tag{14}\]  
where \(\varepsilon_{o}\) is the permittivity in free space, \(\varepsilon_{r}\) is the relative permittivity, \(A\) is the cross-sectional area of the conducting strip and \(d\) is the split gap.  
The inductance of a rectangular metal bar can be calculated according to Equation (15) [42].  
\[L(nH)\ \frac{1}{l}=2\times 10^{-4}\bigg{[}\ln\bigg{(}\frac{l}{w+t}\bigg{)}+1. 193+0.02235\bigg{(}\frac{w+t}{l}\bigg{)}\bigg{]}K_{G} \tag{15}\]  
where \(K_{G}\) is the correction factor, \(w\) is the width, \(l\) is the length and \(t\) is the thickness of the strip.  
An equivalent LC circuit of the proposed MM is illustrated in Figure 11. The whole equivalent circuit comprising eleven (L1 to L11) inductors and twelve (C1 to C12) capacitors is simulated by ADS software. The first resonator on the upper layer is a split-ring resonator (SRR), as indicated by (L1, C1) and (L2, C2), contributing the first resonance frequency of 2.39 GHz, whereas (L3, C3) and (L4, C4) are used for the first octagon that belongs to 7.23 GHz. On the contrary, the second octagon is replaced by (L5, C5) and (L6, C6),  
Figure 10: Electric field distribution at **(a)** 2.35 GHz **(b)** 7.72 GHz **(c)** 9.23 GHz and **(d)** 10.68 GHz.  
Figure 9: Magnetic field distribution at **(a)** 2.35 GHz **(b)** 7.72 GHz **(c)** 9.23 GHz and **(d)** 10.68 GHz.  
which partially contribute to the frequency of 9.21 GHz. C7 and C8 are the coupling capacitors. The joining metal bars and associate gaps are represented by (L9, C10) and (L10, C11), whereas L11 is used for the small central octagon. These components are jointly responsible for the resonance frequency of 10.72 GHz. A comparison between two transmission coefficients that are determined by CST and ADS is shown in Figure 12.
# XSQ-Learning: Adaptive Similarity Thresholds for Accelerated and Stable Q-Learning
## 3 Xperience Similarity-Based Q-Learning (XSQ-Learning)
### Key Components and Pseudocode  
\(1\). **Adaptive Similarity Thresholding**:  
\[\mathcal{N}(s_{t},a_{t})=\{(s,a)\in S\times A\mid f(s_{t},a_{t},s,a)\geq\delta\} \tag{5}\]  
where \(\delta\) dynamically adjusts based on state space density.  
The threshold \(\delta\) acts as a quality filter, ensuring only sufficiently similar pairs participate in updates. This prevents irrelevant information propagation while maintaining useful generalization.  
\(2\). **Adaptive rate**:  
\[\beta=\alpha\cdot f(s_{t},a_{t},s,a)\cdot\frac{|\text{TD-error}|}{|\text{TD- target}-Q(s,a)|+\iota} \tag{6}\]This triple product ensures that updates are (1) scaled by the learning rate \(\alpha\), (2) weighted by similarity, and (3) adjusted according to the relative magnitude of the temporal-difference error. The \(\iota\) term prevents division by zero in novel states.  
3. **Bounded Propagation**:  
\[\beta_{\text{clip}}=\min(\beta,\text{sim\_clip\_value}) \tag{7}\]  
The clip value acts as a safeguard against overly aggressive updates, which are particularly important early in training when Q-value estimates are unreliable.  
Algorithm 1 presents the complete _XSQ-Learning_ procedure, which executes three distinct computational phases during each timestep: (i) a conventional Q-learning update for the current state-action pair (lines 7-9); (ii) identification of sufficiently similar state-action pairs through the similarity metric \(f\) (line 10); and (iii) propagation of temporal-difference updates weighted by both similarity confidence and error magnitude (lines 11-13). The nested loop structure guarantees exhaustive consideration of all potentially relevant state-action pairs while the similarity threshold \(\delta\) preserves computational tractability by limiting updates to only sufficiently similar pairs.  
```
Input: State space \(S\), action space \(A\), episodes \(N\), steps \(T\), learning rate \(\alpha\), discount factor \(\gamma\), similarity function \(f\), threshold \(\delta\), residual \(\iota\), clip value \(c\) Output: Learned Q-function \(Q(s,a)\)
1 Initialize \(Q(s,a)\gets 0\), \(\forall(s,a)\in S\times A\)
2 forpisode\(i\gets 1\) to \(N\)do
3 Observe initial state \(s_{1}\) forstep\(t\gets 1\) to \(T\)do
4 Select \(a_{t}\) via \(c\)-greedy policy
5 Execute \(a_{t}\), observe \(r_{t}\), \(s_{t+1}\)
6 Compute TD-target: \(r_{t}+\gamma\max_{a}Q(s_{t+1},a)\)
7 Compute TD-error: TD-target \(-Q(s_{t},a_{t})\)
8 Update \(Q(s_{t},a_{t})\) using standard Q-learning update
9 foreach\((s,a)\in S\times A\)do
10 if\(f(s_{t},a_{t},s,a)\geq\delta\) and \((s_{t},a_{t})\
eq(s,a)\)then
11 Compute adaptive rate \(\beta\)
12 Clip rate: \(\beta_{\text{clip}}\leftarrow\min(\beta,c)\)
13 Update \(Q(s,a)\leftarrow(1-\beta_{\text{clip}})Q(s,a)+\beta_{\text{clip}}\cdot\text{ TD-target}\)
14
```  
**Algorithm 1**XSQ -Learning Algorithm
## 5 Simulation Results and Discussions
### Accelerations  
Figure 10 shows the time-history curves of the mid-span acceleration of the bridge with a single line train loading at the speed of 60 km/h. The maximum lateral and vertical accelerations occur when the train travels to the mid-span. The peak value of the transverse acceleration of the bridge is slightly larger than the peak value of the vertical acceleration.  
Figure 11 shows the simulation results of the amplitudes of the vertical and transverse accelerations of the track beam of the bridge under running trains. The results indicate that the dynamic accelerations of the bridge approximately linearly increase with the train's speed. In the single line loading case, the discrepancies of the amplitudes of the vertical and transverse accelerations of different parts of the bridge are no more than 18%. In the double line loading case, the amplitudes of the vertical and transverse accelerations of the quarter of the main span and the middle of the Y-shape frame are still very close to each other, but the amplitudes of the vertical and transverse accelerations of the middle of the main span of the bridge are significantly greater than those of the quarter of the main span and the middle of the Y-shape frame. Their difference increases with the train's speed and is up to 44% as the train's speed is increased from 5 to 75 km/h.  
Figure 11: The amplitude of the accelerations of the bridge: (**a**) and (**b**) single line; (**c**) and (**d**) double line.  
Figure 10: The time–history curves of the mid-span acceleration of the bridge under a monorail train running on a single line at a speed of 60 km/h: (**a**) the vertical direction; (**b**) the transverse direction.  
Figure 12 shows the calculated results of the acceleration of the arch rib at the mid-span. It can be seen that the vertical acceleration is greater than the transverse acceleration and the acceleration of the arch rib with two trains running on the double line is greater than that with one train running on a single line. The maximum acceleration is 9.8 cm/s\({}^{2}\), which is much less than 140 cm/s\({}^{2}\), which is the limit of the transverse acceleration specified in the code for rating existing railway bridges [43]. The comparison of Figures 11 and 12 shows that the acceleration of the track beam is much greater than that of the arch rib. The reason could be that the track beam is subjected to direct loading and the vehicle is coupled with the track beam, while the arch rib is subjected to indirect loading.
## 3 Results and Discussions
### Results of Predicting Age Information for Unlabeled Datasets and Exploration
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Age Group Pairs** & \multicolumn{6}{c}{**Section \(S\)**} \\ \cline{2-9}
**(\(g_{i}\), \(g_{j}\))** & **Politics** & **Economy** & **Society** & **Lifestyle/Culture** & **World** & **IT/Science** \\ \hline (10s, 20s) & 0.7400 & 0.6761 & 0.7243 & 0.7498 & 0.7631 & 0.7600 & \\ \hline (10s, 30s) & 0.5779 & 0.4480 & 0.4790 & 0.5371 & 0.5942 & 0.5587 & \\ (10s, 40s) & 0.4802 & 0.3333 & 0.3909 & 0.4747 & 0.5296 & 0.4574 & \\ \hline (10s, \(\geq\)50s) & 0.4008 & \(\vee\) & **0.2796** & \(\vee\) & 0.2841 & \(\vee\) & 0.3761 & \(\vee\) & 0.3893 & \(\vee\) & 0.3378 & \(\vee\) \\ \hline (20s, 30s) & 0.8182 & \(\wedge\) & 0.7863 & 0.7683 & 0.8076 & \(\wedge\) & **0.8347** & \(\wedge\) & 0.8221 & \(\wedge\) \\ \hline (20s, 40s) & 0.5766 & 0.5243 & 0.5463 & 0.5893 & 0.6321 & 0.5885 & \\ \hline (20s, \(\geq\)50s) & 0.4843 & 0.4367 & 0.4080 & 0.4769 & 0.4915 & 0.4614 & \\ \hline (30s, 40s) & 0.8019 & 0.8004 & \(\wedge\) & 0.8023 & \(\wedge\) & 0.8059 & 0.8010 & 0.8062 & \\ \hline (30s, \(\geq\)50s) & 0.5628 & 0.5694 & 0.5209 & 0.5443 & 0.5371 & 0.5400 & \\ \hline (40s, \(\geq\)50s) & 0.7834 & 0.7866 & 0.7507 & 0.7427 & 0.7508 & 0.7164 & \\ \hline \hline \end{tabular} Note: “\(\wedge\)” indicates that the similarity is the largest within a section, while “\(\vee\)” represents the smallest similarity within the section.  
\end{table} 
Table 9: The measured similarities between two age groups in different sections, i.e., \(similarity_{section}(S,g_{i},g_{j})\).
## 2 Materials and Methods
### Analysis of Phenolic Composition
#### 2.6.2 HPLC-DAD-ESI-MS/MS  
In order to characterize the phenolic composition, the extracts were analyzed in a Waters Alliance 2695 HPLC system (Waters, Milford, MA, USA) equipped with a quaternary pump, solvent degasser, auto sampler, and column oven, coupled to a diode array detector (DAD), namely the Detector Waters 2996 (Waters, Milford, MA, USA). For the separation of compounds, a pre-column (100 RP-18, 5 mm) and reversed phase C18 column (LiCrospher 100 RP-18, 250 \(\times\) 4 mm; 5 mm) in a thermostatic oven at 35 \({}^{\circ}\)C were used. The mobile phase consisted of water:formic acid (99.5%:0.5%) as eluent A and acetonitrile:formic acid (99.5%:0.5%) as eluent B at a flow rate of 0.30 mL/min. All solvents were filtered through a 0.22 mm PVDF membrane (Millipore, Billerica, MA, USA) prior to analysis. The system was run with the following gradient elution program: 0-10 min from 99 to 95% A; 10-30 min from 95 to 82% A; 30-44 min from 82 to 64% A; 44-64 min at 64% A; 64-90 min from 64 to 10% A; 90-100 min at 10% A; 100-101 min from 10 to 95% A; 101-120 min at 95% A; and finally returning to the initial conditions. The injection volume was 20 uL-DAD was used to scan the wavelength absorption from 200 to 650 nm. Tandem mass spectrometry (MS/MS) detection was performed using an electrospray ionization source (ESI) at 120 \({}^{\circ}\)C and applying a capillary voltage of 2.5 kV and cone voltage of 30 V. The compounds were ionized in the negative mode and spectra were recorded in the range of \(m/z\) 60-1500. Analytical conditions were optimized to maximize the precursor ion signal ([M\(-\)H]\({}^{-}\)). Ultra-high purity argon (Ar) was used as a collision gas. High purity nitrogen (N\({}_{2}\)) was used both as a drying gas and nebulizing gas. For data acquisition and processing, MassLynx software (version 4.1,Waters Corporation, Milford, MA, USA) was used.
## 2 Background of DSRC
### Dsrc Spectrum and Channelization  
The spectrum allocation for DSRC is not uniform across different countries. Each country has its own specific regulations and frequency bands for DSRC operation. This ensures efficient and reliable communication for vehicular applications while addressing regional requirements and considerations. DSRC spectrum allocations vary across different regions, and It is important to note that these allocations are subject to regulatory updates and changes.  
In Europe, the primary frequency band for DSRC is typically within 5.855-5.925 GHz. However, there can be variations among European Union (EU) member states. Some countries allocate the entire band for Intelligent Transport Systems (ITS) use, while others assign a portion of the band specifically for DSRC. In Japan, DSRC operates in the 5.8 GHz frequency band, specifically from 5.780 to 5.825 GHz. This band is further divided into channels dedicated to various applications, including V2V and V2I communication. In China, DSRC is allocated to the 5.725-5.850 GHz frequency band. Within this band, different channels are designated for specific applications, such as V2V and V2I communication. Various other countries have also designated specific frequency bands for DSRC. For example, Canada, South Korea, and Australia have their own allocations. The exact frequency bands and channelization may vary among these countries.
## 2 System Model
### MAC Protocol Operation and Performance Metrics
\(p_{d}\) is expressed as:  
\[p_{d}(k)=\Pr\left[u\text{ transmits successfully in frame }k|u\text{ has new data in frame }k\right]. \tag{2}\]  
Note that the successfully transmitted packets by EH-ST \(u\) in Equation (2) include both the noncollided packets from the beginning and the packets that have been resolved during its frame time. Moreover, the statistical probability (Equation (2)) is made by each EH-ST independently. Since EH technologies can provide EH-STs with potentially perpetual operation, the average long-term data delivery probability can be expressed as \(p_{d}^{LT}=\lim_{k\to\infty}p_{d}(k)\). Figure 2: Contention resolution diversity slotted Aloha (CRDSA) frame structure and interference cancellation procedure (Packet replica \(l=2\)). Packet delay is another metric to describe the average delay of a packet that is successfully received at the gateway station. Each EH-ST transmits \(l\) packet replicas with the probability \(\eta\) independently. If there is not enough energy in the battery, it will wait until it has enough energy to transmit the packets. The evolution of the available energy in the battery can be modeled as a Markov chain, which is introduced in detail in Section 3. Moreover, if a packet fails to be received at the gateway station, it will be retransmitted with the same policy \(\omega\), according to the residual energy. Therefore, the packet delay \(D_{pk}\) is measured as the number of frames that elapse from the start of one frame where the packet was transmitted for the first time, until the end of the frame where the packet was received successfully. According to the description above, the packet delay distribution can be expressed as [35]:  
\[\Pr\left(D_{pk}=f\right)=\left\{ \begin{aligned} &\sum_{\epsilon=0}^{e_{max}}\pi_{ \eta}(e)\eta(e)(1-PLR),&&\text{for }f=1\\ &\sum_{\epsilon=0}^{e_{max}}\Big{\{}\left[\pi_{\eta}(e)\eta( \epsilon)PLR\right]\cdot\left[(\pi_{\eta}(e)\eta(e)\!\cdot\!(1-PLR))\right]\\ &\times\left[(1\!-\!\pi_{\eta}(e)\eta(e))\!+\!PLR\!\cdot\!\pi_{ \eta}(e)\eta(e)\right]^{f-2}\Big{\}},&&\text{for }f>1\end{aligned} \right., \tag{3}\]  
where \(\pi_{\eta}(e)\) is the steady-state probability of the available energy \(e\), and the derivation of \(\pi_{\eta}(e)\) is presented in the next section. \(\eta(e)\) is the packet transmission probability and \(e_{max}\) is the capacity of the battery. Specifically, the term \(\pi_{\eta}(e)\eta(e)(1-PLR)\) represents that the packet is received successfully. For \(f>1\), the term \(\pi_{\eta}(e)\eta(e)PLR\) means the packet is transmitted in the first frame, but it is not received by the gateway station. Furthermore, the term \(\left[(1\!-\!\pi_{\eta}(e)\eta(e))\!+\!PLR\!\cdot\!\pi_{\eta}(e)\eta(e)\right] ^{f-2}\) can be explained as that the packet is still not received in the next \(f-2\) frame periods due to the energy shortage or packet collisions. Thus, the long-term average packet delay \(D_{pk}^{LT}\) is given by:  
\[D_{pk}^{LT}=\sum_{f=1}^{\infty}f\cdot\Pr\left(D_{pk}=f\right). \tag{4}\]
## 3 Experimental Results
### Data Collection  
The six common receipt formats in Taiwan are electronic receipt certification sheets, cash register unified recepits, triple unified recepits, double unified recepits, special unified recepits, and electronic computer unified recepits (Figure 11).  
In this experiment, the receipt characters were classified as handwritten or printed characters during the recognition process. Accordingly, approximately 300 printed and 100 handwritten receipt characters were obtained. Therefore, the dataset was divided into the following categories: handwritten and printed datasets.  
The numerical characters in the printed dataset were divided into 10 categories: 0 to 9. Each category comprised 500 numerical characters, of which 400 were used as training data and the remaining 100 were used as test data. Figure 11(a) presents the numerical characters. Moreover, the alphabetical characters were divided into 26 categories: A to Z. Each category comprised 200 characters, of which 160 were used as training data and the remaining 40 were used as test data. Figure 11(b) depicts the alphabetical characters. Figure 11(a),b display the accuracy and loss function for the numerical and alphabetical characters in the printed dataset, respectively.  
In the handwritten dataset, the numerical characters were also divided into 10 categories: 0 to 9. Each category comprised 900 characters, of which 720 were used as training data and the remaining 180 were used as test data. Figure 13 depicts these numerical characters. Figure 13(a),b illustrate the accuracy and loss function of the numerical characters in the handwritten dataset, respectively.
# Heterogeneity of Ex Vivo and In Vivo Properties along the Length of the Abdominal Aortic Aneurysm
## 3 Results
### Ex Vivo Analysis
#### 3.1.2 Aortic Media Composition  
Histological samples were collected from a subgroup of 9 patients and aortic composition was evaluated by means of Musto/Movat pentachrome staining to identify elastin, proteoglycans, and smooth muscle cells content for a total of 109 samples. Fifty-six samples (51%) had no discernible media layer and had to be excluded. Figure 4 shows a representative histology for two AAA specimens. The average elastin content for the histological specimens was 23.6% \(\pm\) 10.3%, average smooth muscle cells content was 55.0% \(\pm\) 16.5% and average proteoglycans content was 21.5% \(\pm\) 6.5%.  
The composition of the media layer showed no statistically significant effect of specimen position along the length of the aorta (positions 1 to 6). A significant effect of circumferential position (right-posterior, left-posterior, right-anterior, left-anterior) on elastin, smooth muscle cells and proteoglycans content was observed (Figure 5), with specimens in anterior positions showing lower elastin content, higher smooth muscle cells content and lower proteoglycans content compared to posterior regions. Specifically left-anterior versus left-posterior, left-anterior versus right-posterior, right-anterior versus right-posterior, and  
Figure 4: Representative histology showing aortic composition visualized with [PERSON]/Movat pentachrome stain in the neck—region RA1 (column **a**) and aneurysmal dilatation—region LA3 (column **b**) for one AAA patient in the population. The layers in each histology from top to bottom are: adventitia, media, and intima. Images at 2\(\times\) (top) and 10\(\times\) (bottom) magnification are shown.  
right-anterior versus left-posterior (\(p<0.01\) for elastin and smooth muscle cells contents and \(p<0.04\) for proteoglycans content) (Figure 5).
# Compressive Sensing-Based Coding Iterative Channel Estimation Method for TDS-OFDM System
## 5 Conclusions  
In this paper, we study the TDS-OFDM system with high spectrum efficiency based on the requirement of high spectrum efficiency in satellite Internet communication. In this study, RS-LDPC cascaded channel coding is combined with compressive sensing iterative channel estimation to improve the data accuracy of a sensing matrix in the compressive sensing channel estimation algorithm and solve the problem that all PN sequences cannot be used for channel estimation in traditional methods. Based on the accurate recovery of the aliased data, the proposed method utilizes the iterative optimization of the compressive sensing algorithm and equalization feedback loop to realize the channel estimation and improve the anti-interference and error correction capabilities of the data through RS-LDPC cascaded channel coding. Furthermore, in order to improve the accuracy of the sensing matrix, the error-corrected data are re-encoded, and then the compressive sensing iterative channel estimation is conducted again to enhance the compressive sensing channel estimation performance and the communication performance of the TDS-OFDM system. The simulation results demonstrate that the cyclic compressive sensing channel estimation module based on all PN sequences provides better MSE performance and BER performance in a complex multipath channel, and the performance of the cyclic compressive sensing channel estimation method combined with RS-LDPC coding is superior to the simple cyclic compressive sensing channel estimation method. It realizes high-accuracy channel estimation in a multipath fading environment, and verifies the applicability and effectiveness of the proposed method. In this paper, we do not consider the modules of filtering and frequency conversion at the transmitter. However, we will consider the data rate limitation and the large frequency offset caused by high-frequency carriers in the future.  
Conceptualization, Y.Y. and H.W.; validation, Y.Y. and X.Z.; writing--original draft preparation, Y.Y. and X.Z. All authors have read and agreed to the published version of the manuscript. This research received no external funding.  
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.