esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Interaction Between DE and GD  
To avoid spurious regression, a unit root test was performed for the DE and GD levels by using two methods: Levin-Lin-Chu (LLC) and Im-Pesaran-Shin (IPS) (Table 4). These models passed the 5% significance level, confirming data stationarity.  
The optimal lag order was determined to be 1 via the Akaike information criterion (AIC), Bayesian information criterion (BIC), and Hannan-Quinn information criterion (HQIC) (Table 5).  
Following the identification of the lag order, GMM estimation was conducted. The results indicated a significant dynamic relationship between the DE and GD. Specifically,  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Variable** & \multicolumn{3}{c}{**Test Methods**} \\ \cline{2-4}  & **LLC** & **IPS** & **Result** \\ \hline DE Levels & 0.00 & 0.01 & Smooth \\ GD Levels & 0.00 & 0.01 & Smooth \\ \hline \hline \end{tabular}
\end{table}
Table 4: Unit root test for the DE and GD levels (LLC and IPS).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Year** & **Area/km2** & **Barycentric Coordinates** & **Offset Direction** & **XStdDist/km** & **YStdDist/km** & **Rotation/\({}^{\circ}\)** \\ \hline
2013 & 3,068,315 & \(33^{\circ}25^{\prime}\) N, \(114^{\circ}20^{\prime}\) E & 887,281 & 1,100,811 & \(20^{\circ}02^{\prime}\) \\
2014 & 3,078,623 & \  
the DE with lagged one-period positively impacted both itself and the GD. Furthermore, the GD with lagged one-period exhibited a positive influence on itself, but failed to exert a substantial impact on the DE (Table 6).  
To further examine the dynamic link between the DE and GD, an impulse response analysis was undertaken. Impulse responses for a six-period lag of variables were obtained at a 95% confidence level.  
The impulse response analysis revealed the following three aspects:  
1. The DE responded positively to its own shocks, with a rapid initial response that gradually diminished over time. The GD reacted positively to its own shocks, with the response continuously decreasing over time, transitioning from a positive response to a negative response.
2. Initially, the GD negatively impacted the DE. However, it transformed into a relatively stable positive impact over time.
3. Initially, the DE had no significant impact on the GD, but it later turned into a negative impact.  
Variance decomposition offers insights into the contribution of structural shocks from each variable to the volatility of a specific variable. The results in Table 7 show the variance decomposition results for DE and GD. From period 10 onward, the variance decomposition values tended to stabilize, which implies that mutual contributions of variables achieved favorable stability by that juncture. The results in period 20 showed that the contribution of the DE to itself and GD reached 79% and 21%, respectively. Similarly, the GD's contribution to itself and the DE was 82% and 18%, respectively, indicating a mutually interdependent relationship between these variables.
## 2 Preparation of IL-Based Gel  
### Classification of Preparation Methods  
The classification of IL-based gels or ionogels and the different routes to synthesize IL-based gel electrolytes or ionogels have been reviewed by a number of research groups [13; 14; 15; 16]. The various kinds of IL-based gels can be simply categorized as physical gels and chemical gels according to the type of matrix formation [13; 14; 15]. In physical gels, internal, three-dimensional, cross-linking networks occur due to weak reversible interactions such as hydrogen bonds, hydrophobic interactions, van der Waals interactions, \(\pi\)-\(\pi\) interactions, crystallite junctions, etc., whereas in chemical gels the cross-links are formed by covalent bonding. Typically, chemical gels offer good mechanical strength compared to physical gels. Another type of IL-based gel classification is organic, inorganic, and hybrid organic-inorganic IL-based gels [16]. Until now, various gel techniques to confine and immobilize ILs into a three dimensional organic or inorganic matrix have been reported, such as the sol-gel process, polymerization, photopolymerization, and solvent casting [16; 17; 18]. Among them, the sol-gel process has gained much interest for producing IL-based gel electrolytes with enhanced mechanical properties for numerous electronic applications related to lithium-ion batteries, supercapacitors, and other electrochemical devices [15; 18; 19; 20]. Moreover, the sol-gel derived IL-based gels have been reported to be used in electrochromic, drug-delivery, photo-stimuli, and luminescence systems in recent years [21; 22]. Meanwhile, polymerization leading to the formation of micro-patternable gel electrolytes, has received significant attention due to its promising application in microdevices such as microcapacitors, flexible sensors, and 3D printing technology [18; 23]. In the following section, several ways for obtaining IL-based electrolytes are presented.
# A Reliable Low-Latency Multipath Routing Algorithm for Urban Rail Transit Ad Hoc Networks
## 3 The Improved Multipath Routing Algorithm
### Local Routing Maintenance Scheme Based on Maintenance Nodes
Case 3: the link failure happens between two cells, and the Intra-cell maintenance node cannot directly establish a temporary path for repair due to the limited communication range of the maintenance node. When the node detecting the failure finds that the next hop is not in the current cell, the node sends an RRER message to the maintenance node to let the maintenance node establish a connection with the maintenance node of the next cell to establish an available temporary path, as shown in Figure 4.  
In summary, the routing repair scheme proposed in this paper predicts link failures by estimating the received signal strength of the nodes, and once a failure is found, it can be repaired quickly with the help of maintenance nodes. The average end-to-end latency using local routing repair schemes is shown in Equation (19). \[\overline{T_{d2d}}=\sum_{i}^{\text{M}}\big{[}\frac{m}{M}*(1-P)*\{\sum_{hc_{i}} T_{i}+T_{f}\}+\frac{m}{M}*P*\{T_{f}+\sum_{hc_{i}}T_{i}+T_{r}\}\big{]}/\frac{m}{M} \big{]}/M \tag{19}\]  
where \(m\) is the number of packets transmitted, \(P\) is the probability of the link failure of each hop, \(T_{i}\) represents the end-to-end latency of the \(i\)-th hop, \(T_{r}\) represents the route maintenance time, and \(T_{f}\) represents the route discovery time.
## 3 SD-QGrid Routing Platform and Algorithms
### SD-QGrid Offline Q-Learning Training Process  
The flow of the SD-QGrid offline Q-learning training process is shown in Figure 6. The information of the vehicle environment is handed over to each module of the SDN Control Node (CN) and RSUs, and finally, generates Q-tables of each grid. The steps of the SD-QGRID offline Q-learning training process are described as follows:  
1. After the initialization of the SD-QGrid, all vehicles on a road segment issue HELLO messages to the corresponding RSUs.
2. The RSU collects vehicle trajectory information from connected road segments and analyzes it to generate the historical trajectory tables.
3. The RSU periodically transmits the analyzed tables and vehicle trajectories of the connected road segments to the SDN CN.
4. The SDN CN aggregates the historical vehicle information of each grid from the information sent by its RSUs in the network environment.
5. The SDN CN extracts important Q-learning parameters from the aggregated information of each grid. It finally uses Q-learning to train the Q-model for calculating the Q-table and Q-values of each grid.  
The following provides a functional description of the modules in the SD-QGrid offline Q-learning training process.
## 4 Applications of Zeolites in Catalysis, Bioprocesses, and Environmental Remediation
### Biomass Conversion
This factor in processes of biomass conversion using zeolites, was studied closely because of little understanding about it, and the reaction was the conversion of glucose to aromatics by catalytic fast pyrolysis. Studying the kinetic diameters of reactants and products and the occurrence of reactions inside the pores as in external surface verified that aromatic products and reactants occurred inside the pores in medium-and large-pore zeolites, while in smaller pore structures the locale was the external surface [304]. The use of beta zeolite for biomass conversion processes is the most highlighted. This structure can be modified with the introduction of heteroatoms to increase the Lewis acidity, improving the conversion. One main structure used is the Sn\(\beta\) zeolite, constructed in an ordered 3D network of SiO\({}_{2}\) and presenting some Si atoms replaced by Sn atoms. The high activity as a catalyst is evidenced in sugar isomerization or epimerization, production of lactates from carbohydrates, Baeyer-Villiger oxidation, and Meerwein\(-\)Ponndorf\(-\)Verley reactions. In this structure, there is a combination of Bronsted and Lewis acidity, giving suitable conditions for biomass conversion reactions, such as conversions from carbohydrates to chemicals, for instance, the conversion of glucose in 5-hydroximethylfurral (HMF), where the Lewis sites catalyze the conversion of glucose to fructose and the Bronsted sites catalyze the dehydration for 5-HMF formation [305]. The production of furfural, a compound derived from lignocellulosic biomass with more than 200,000 annual tons produced, has been achieved by applying H-beta zeolite as catalyst, presenting a bifunctional nature, such as those structures with Sn in the framework. In this case, the isomerization reaction was in xylose and arabin before the dehydration. Depending on the composition of biomass feedstock for this purpose, H-beta can be used together with H-mordenite if the presence of xylose is high in hemicellulose fraction [306]. The acidity in zeolite structure is improved by metal clusters and nanoparticles, making the biomass processes conversion better, solving problems as metal coalescence, sintering and leaching [307]. Figure 17 shows pathways to obtain a product from carbohydrates.
# Handover-Driven Interference Management for Co-Channel Deployment of Femto- and Macro-Cells
## 4 The HO-FIM Architecture
### Definition of Cell and System Loads  
The _load_ to measure the degree of crowdedness of a cell or the entire system is defined. It is assumed that the power resource allocated to a user at a RB is zero unless the RB is assigned to him. Then, the load can be modeled as a function of allocated power as follows:  
* Load \(\rho_{i}\) of cell \(i\) measures the resource usage status of cell \(i\). It depends on the power allocations of the users being served by cell \(i\).
* System load \(\rho\) measures the resource usage status of all users in the entire system. It depends on the power allocations of all users.  
The macrocell load is denoted by \(\rho_{m}\) and the activation and deactivation thresholds of the handover controller are denoted by \(\rho_{m}^{(+)}\) and \(\rho_{m}^{(-)}\), respectively.  
Two properties of the load function are assumed: (1) the cell and system loads are monotonically increasing in the transmit power vector; and (2) the cell load function is additive, i.e., \(\rho_{i}(\sum_{j}\mathbf{p}_{j})=\sum_{j}\rho_{i}(\mathbf{p}_{j})\) when \(\mathbf{p}_{j}\) is the transmit power vector assigned to user \(j\). The monotonicity is straightforward.  
Figure 3: Interactions between the controllers of HO-FIM.  
The additivity is acceptable if the cell load is defined as a sum or an average of the per-RB load since RB assignments are orthogonal between users within a cell (\(\rho_{i}(\mathbf{p})=\rho_{i}(\sum_{j}\mathbf{p}_{j})\)).
# Enhancing Street-Crossing Safety for Visually Impaired Pedestrians with Haptic and Visual Feedback
## 3 Study 1: Haptic and Visual Feedback for Visually Impaired Pedestrians Crossing at Traffic Signals  
This study investigates how well haptic and visual feedback support visually impaired pedestrians with residual light perception in crossing streets at traffic signal intersections. The feedback modes convey timing information to help participants make crossing decisions, allowing them to interpret the traffic light status through the feedback's urgency levels. We systematically examine influencing factors of both haptic and visual feedback approaches in terms of interaction design, user evaluation, and experimental data analysis.
## 4 Preclinical Studies
### In Vivo Studies
The concentration of the SnET2 within the dog prostate was found to be heterogeneous. Thus, computerized modelling must consider the uneven sequestration of photosensitizer and the consequential asymmetrical necrosis of the prostate. In fact, animals treated with PDT showed differences in the necrotic front around the diffuser site [156]. The photosensitizer cyclohexane-1,2-diamino-hypocrellin B (SL052) was investigated in canine prostate models. SL052 was formulated in liposomes or dissolved in dimethyl-sulphoxide and administered intravenously or via the prostate arteries. Irradiation was performed with a laser diode at 635 nm. The intra-arterial route resulted in a more complete photoablation. Associated side effects included acute urinary retention, which resolved over time [157]. Sinoporphyrin sodium toxicity was evaluated in dogs. The animals were irradiated in the skin with a 630 nm wavelength laser for 10 min, once every seven days for five weeks. No deaths were reported; however, the animals that underwent PDT showed oedema and skin ulcerations. Changes in blood clotting time, splenomegaly, and organ pigmentation were also observed [158]. Canine mammary carcinoma cells (SNP) were used to obtain a murine model. For this, the cells were inoculated subcutaneously in female mice. PDT using a glucose-conjugated chlorin e6 (G-Ce6, 10 mg/kg) was achieved by irradiation with a 677 nm diode laser. A significant tumor regrowth delay was observed both with a 5 min and 3 h drug light interval. Nevertheless, the shorter drug light interval was the most effective [159]. The studies where PDT was applied to health animals used as preclinical models are systematized in Table 6.  
\begin{table}
\begin{tabular}{p{42.7 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Ref. ** & **Disease, Model** & **Animals** & **PDT Protocol** & **Main Results** \\ \hline \multirow{4}{*}{[129]} & \multirow{4}{*}{Glioma, cell inoculation in the brain} & & & Animals receiving 2.0–4.0 mg/kg: necrosis and hemorrhage of the 0.75–4.0 mg/kg, iv & \\  & & & **PS:** Portimer sodium, 0.75–4.0 mg/kg, iv & & \\  & & & **Light:** Laser light, 1800 J, 630 nm, 1 session & & \\  & & & & 1 \\ \hline \multirow{4}{*}{[130]} & \multirow{4}{*}{Esophageal cancer, normal tissue} & & & Animals receiving 2.0–4.0 mg/kg: necrosis and hemorrhage of the 0.75–4.0 mg/kg, iv & \\  & & & **Light:** Argon laser, 75–600 J/cm, 630 nm, 1 session & & \\  & & & & **Light:** Argon laser, 75–600 J/cm, 630 nm, 1 session & & \\  & & & & 630 nm, 1 session & & \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\  & &
## 2 Introduction to Basic Theory
### Linear Normalization  
Many dimensionality removal methods were listed in the literature [30], including standardization method, extreme value method, linear ratio method, normalization method, vector normalization method and power coefficient method. Each method has its advantages and disadvantages. This paper combines the extremum processing method  
Figure 1: The overall framework and ideas of the content.  
Figure 2: The process of wavelet threshold noise reduction.  
in the literature [31] to reduce dimensionality the fusion signal. Linear transformation is performed on the fused data to map it to [0,1]. The results of normalization are as follows:  
\[X^{\prime}=\frac{X-\min_{X}}{\max_{X}-\min_{X}} \tag{3}\]  
where \(X^{\prime}\) is the normalized result, \(\min_{X}\) and \(\max_{X}\) are the minimum and maximum values in the sample. The data after normalization does not have dimensions, but the data of each sensor is mapped to [0,1], which will not change the trend and regularity between the data before normalization.
# Modeling of Key Specifications for RF Amplifiers Using the Extreme Learning Machine
## 5 Conclusions  
In this paper, an ELM-based modeling method for the temperature characteristics of RF amplifiers is proposed. Additionally, the effectiveness of this modeling method is verified by taking PA and LNA as examples. The results show that the modeling method proposed in this paper can realize the modeling of RF amplifier temperature characteristics and significantly reduce the number of measured temperature points with very low sensitivity to the temperature points while ensuring the model accuracy. Compared with the existing modeling methods, the modeling method can effectively reduce the measurement time and lower the cost. Furthermore, the model proposed in this paper can characterize the static characteristics of RF amplifiers and be extended to model the behavior of dynamic characteristics of RF amplifiers or other devices.  
Conceptualization, S.Z., C.Y. and J.W.; methodology, S.Z., C.Y. and J.W.; software, S.Z. and C.Y.; validation, S.Z., C.Y. and J.W.; formal analysis, J.W.; investigation, S.Z. and C.Y.; resources, J.W.; data curation, S.Z. and C.Y.; writing--original draft preparation, S.Z.; writing--review and editing, C.Y. and J.W.; visualization, S.Z. and C.Y.; supervision, J.W.; project administration, J.W.; funding acquisition, J.W. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.
# Characteristics of Carbon Fluxes and Their Environmental Control in Chenhu Wetland, China
## 3 Results and Discussion  
### Meteorological Conditions  
Figure 2 illustrates the temporal fluctuations in the monthly mean air temperature (T\({}_{\text{a}}\)), soil temperature (T\({}_{\text{s}}\)) at a depth of 20 cm, wind speed (WS), photosynthetically active radiation (PAR), relative humidity (RH), vapor pressure difference (VPD), soil water content (SWC) at a depth of 20 cm, and total precipitation (PPT) recorded at the Chenhu wetland station throughout the year 2022. The parameters of T\({}_{\text{a}}\), T\({}_{\text{s}}\), PAR, VPD, SWC, and PPT exhibited significant seasonal variation, characterized by a distinct single peak trend. However, the months in which these peaks occurred varied among the parameters. Specifically, the highest values for T\({}_{\text{a}}\), T\({}_{\text{s}}\), PAR, and VPD were recorded in August, whereas the peak values for SWC and PPT were observed in June and July, respectively.  
The average T\({}_{\text{a}}\) in August was recorded at 31.57 \(\pm\) 4.10 \({}^{\circ}\)C, representing the highest monthly average, while the average T\({}_{\text{a}}\) in January was the lowest at 6.06 \(\pm\) 2.83 \({}^{\circ}\)C (Figure 2a). These temperature values exceeded the monthly average temperatures documented in Wuhan from 2011 to 2021 as per the data provided in the Wuhan Statistical Yearbook. T\({}_{\text{s}}\) exhibited a similar trend to that of T\({}_{\text{a}}\), with maximum recorded values of 28.31 \(\pm\) 0.45 \({}^{\circ}\)C, and consistently remained lower than T\({}_{\text{a}}\) from March to August. The PAR levels were observed to be elevated during the summer months in comparison to other seasons (see Figure 2b). In 2022, the total annual rainfall was measured at 571.00 mm, which is significantly below the average annual precipitation of 1320.46 mm recorded in Wuhan from 2011 to 2021, as reported by the Hubei Provincial Water Conservancy Department. The majority of the rainfall occurred in March and April (refer to Figure 2d). Wind speed values were notably higher in March, April, June, and July (Figure 2b), which can be primarily attributed to large-scale atmospheric circulation patterns. Wind speeds exceeding 3.0 m\(\cdot\)s\({}^{-1}\) had an occurrence frequency of 25.3% throughout the year, with a predominant concentration observed from March to August. The RH ranged from 58.49% to 80.38% (Figure 2c), which is lower than the monthly mean RH recorded in Wuhan from 1961 to 2015, with the exception of January [29]. The SWC values were particularly low (<30%) during January and from August to December (Figure 2d), but exhibited a significant increase from February to March, peaking at 43.23% in June. The seasonal variation in SWC closely mirrored that of rainfall, albeit with a temporal lag. This lag can be attributed to the water absorption by the root systems of vegetation, which inhibits a rapid increase in soil water content [7].
# Photoacoustic Imaging for Management of Breast Cancer: A Literature Review and Future Perspectives
## 3 Results
### Description of Studies
#### 3.1.1 Phantom Studies
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Phantom Study**} & **Materials Mimicking Breast** & **Materials Mimicking Tumor/Blood** \\  & **Tissue** & **Vessel/Target** \\ \hline \multirow{3}{*}{[PERSON] et al. [54]} & 10\% gelatin (bulk collagen gels) & \multirow{3}{*}{Gel spheres colored with the bovine hemoglobin (\(\upmu_{\text{a}}=0.75\) cm\({}^{-1}\))} \\  & 1064 nm (\(\upmu_{\text{a}}=0.11\) cm\({}^{-1}\)) & \\  & \(\upmu_{\text{s}}\)\({}^{\prime}=2.92\) cm\({}^{-1}\) & \\ \hline \multirow{2}{*}{[PERSON] et al. [61]} & \multirow{2}{*}{10\% gelatin (\(\upmu_{\text{a}}=0.13\) cm\({}^{-1}\))} & Gel spheres colored with the bovine hemoglobin \\ \hline \multirow{2}{*}{[PERSON] et al. [62]} & \multirow{2}{*}{Gelatin} & Gel spheres colored with the bovine hemoglobin (\(\upmu_{\text{a}}=1.0\) cm\({}^{-1}\)) \\ \cline{1-1}  & & Blood vessels filled with rat blood (\(\upmu_{\text{a}}\) varies from 0.8 to 4.0 cm\({}^{-1}\)) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Details of materials used for breast tissue, tumor and blood vessels in phantom studies.
# A New Method for Large-Scale Landslide Classification from Satellite Radar
## 2 Materials and Methods
### Theory: Landslide Detection with SAR
#### 2.1.4 Sibling-Based Coherence Method
Since the same pair of images is used in the coherence calculation with both methods, the histogram matching step carried out in the ARIA method [21; 39] becomes unnecessary and the potential for other sources of temporal decorrelation is decreased.
# Rapid Tracking Satellite Servo Control for Three-Axis Satcom-on-the-Move Antenna
## 2 Kinematic Modeling of Three-Axis SOTM Antenna
According to Equation (12), it can be obtained that:  
\[\theta_{2}^{*}=\theta_{2}-\pi=\text{atan2}(a,b)-\pi \tag{13}\]  
The inverse kinematic solution for the pitch angle \(\theta_{3}^{*}\) is:  
\[\left\{ \begin{array}{l}\sin\theta_{3}=\frac{r_{\text{2s}}\sin\varphi_{\text{ }}\cos\theta_{2}-r_{\text{32}}\cos\varphi}{\sin^{2}\varphi_{\text{}}\cos^{2} \theta_{2}+\cos^{2}\varphi}=a\\ \cos\theta_{3}=-\frac{r_{\text{31}}\sin\text{cos}\theta_{2}+r_{\text{32}}\cos \varphi}{\sin^{2}\varphi_{\text{}}\cos^{2}\theta_{2}-\cos^{2}\varphi}=b\end{array} \right. \tag{14}\]  
According to Equation (14), it can be obtained that:  
\[\theta_{3}^{*}=\theta_{3}=\text{atan2}(a,b) \tag{15}\]