esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
This article presents a compact quad flat no-lead (QFN)-packaged second-order bandpass filter (BPF) with intertwined inductors, a dendritic capacitor, and four air-bridge structures, which was fabricated on a gallium arsenide (GaAs) substrate by integrated passive device (IPD) technology. Air-bridge structures were introduced into an approximate octagonal outer metal track to provide a miniaturized chip size of 0.021 \(\times\) 0.021 \(\lambda_{0}\) (0.8 \(\times\) 0.8 mm\({}^{2}\)) for the BPF. The QFN-packaged GaAs-based bandpass filter was used to protect the device from moisture and achieve good thermal and electrical performances. An equivalent circuit was modeled to analyze the BPF. A description of the manufacturing process is presented to elucidate the physical structure of the IPD-based BPF. Measurements were performed on the proposed single band BPF using a center frequency of 2.21 GHz (return loss of 26.45 dB) and a 3-dB fractional bandwidth (FBW) of 71.94% (insertion loss of 0.38 dB). The transmission zero is located at the 6.38 GHz with a restraint of 30.55 dB. The manufactured IPD-based BPF can play an excellent role in various S-band applications, such as a repeater, satellite communication, and radar, owing to its miniaturized chip size and high performance.  
air-bridge structure; bandpass filter; capacitor; gallium arsenide; inductor; integrated passive device; microwave RF; QFN-package +
Footnote †: journal: materials  
Article
## 3 RAR: Reliability-Aware Routing
### Determination of Neighbors and Their Information
Every node checks its history buffer so that only information about the nodes not in its history buffer are extracted and saved. This strategy reduces saving of redundant information as the history buffer has a limited capacity, although it can save important data of the total nodes in the network. This helps in identification of the reliable routing paths during data forwarding phase. Because sensor nodes move with ocean currents and die too due to battery depletion, they exchange hello packets after regular intervals of time to save energy and remain updated about the vital information of one another. It is because not updating the vital information of the sensor nodes leads to wrong routes selection during data forwarding.
# Multi-Color Channels Based Group Sparse Model for Image Restoration
## 2 GSP Model
### Sparse Representation Model  
After constructing the image group, a sparse representation model is established in the image group. In order to obtain the sparse representation model of the image group, a dictionary, denoted by \(D_{i}\) is given, which is usually learned from the image group, such as the graph-based dictionary [30] and principal component analysis (PCA) dictionary [31]. The aim of the sparse representation is to present the image group using as few atoms of the dictionary as possible. That is, the task is to code the dictionary sparsely using a sufficiently sparse vector. Each sparse representation, denoted by \(\hat{\beta}_{i}\), corresponding to the image group \(X_{i}\), is obtained via minimizing the \(L_{0}\) norm using the following Formula (3).  
\[\hat{\beta}_{i}=\operatorname*{argmin}_{\beta_{i}}\biggl{(}\frac{1}{2}\|X_{i}- D_{i}\beta_{i}\|_{2}^{2}+\lambda\|\beta_{i}\|_{0}\biggr{)},\ \ \ \forall i \tag{3}\]  
Here, \(\beta_{i}\) denotes the group sparse coefficient corresponding to the \(i\)th image group \(X_{i}\). \(\frac{1}{2}\|X_{i}-D_{i}\beta_{i}\|_{2}^{2}\) is the fitting term used to measure the similarity between the image to be repaired via the sparse representation model and the original image. \(\lambda\) is a regularization parameter, and \(\|\|_{0}\) denotes the \(L_{0}\) norm used to calculate the no-zero terms of each column in \(\beta_{i}\). Since \(L_{0}\) norm minimization is NP-hard, \(L_{1}\) norm minimization is used instead of \(L_{0}\) norm minimization via the following Formula (4).  
\[\hat{\beta}_{i}=\operatorname*{argmin}_{\beta_{i}}\biggl{(}\frac{1}{2}\|X_{i} -D_{i}\beta_{i}\|_{2}^{2}+\lambda\|\beta_{i}\|_{1}\biggr{)},\ \ \ \forall i \tag{4}\]  
The original image \(x\) is not available in the image restoration task, and the image groups are from the degraded image \(y\in R^{N}\). The image \(y\) is segmented into \(n\) overlapped patches with the same size of \(\sqrt{b}\times\sqrt{b}\), i.e., a vector with the length of \(b\). The \(m\) similar matched patches are found to generate the image groups corresponding to the \(n\) overlapped patches. Each image group denoted by \(Y_{i}\in R^{b\times m}\) is a matrix with the size of \(b\times m\), which  
Figure 1: The construction diagram of the image group.  
also can be written as \(Y_{i}=\{y_{i,1},\,y_{i,2},\,\ldots,\,y_{i,m}\}\). Now, the image restoration task is to restore \(X_{i}\) from \(Y_{i}\) using the GSR model via the following Formula (5).  
\[\hat{\beta}_{i}=\operatorname*{argmin}_{\hat{\beta}_{i}}\bigg{(}\frac{1}{2} \|Y_{i}-D_{i}\beta_{i}\|_{2}^{2}+\lambda\|\hat{\beta}_{i}\|_{1}\bigg{)},\;\;\; \forall i \tag{5}\]  
After obtaining \(\hat{\beta}_{i}\), \(i=1,\,2,\,\ldots,\,n\), the restored whole image \(\hat{x}\) can be represented by a set of sparse codes \(\{\hat{\beta}_{i}\}_{i=1}^{n}\).
## 4 Practical Application and Result Analysis  
### Data Preparation  
In this research, two exploration wells located in a block of the Ordos Basin in China are studied. The Ordos Basin has the character of stable depression tectonic basin, with the metamorphic crystalline rock series of Archaea and Middle Proterozoic as the rigid sedimentary basement, overlying the platform type sedimentary caps of Middle and Upper Proterozoic, Early Paleozoic, Late Paleozoic and Mesozoic. The Mesozoic is a single fluvial and lacustrine facies terrine detrital coal-bearing sedimentary formation, characterized by the large thickness and wide distribution of lacustrine deltas in the Triassic Yanchang Formation, which is one of the most important oil-gas enrichment horizons in Ordos Basin.  
We designate the two wells as Well A1 and Well A2, respectively, to maintain the privacy of well information. Additionally, since the petrophysical logs are primarily found in the intervals of the oil/gas reservoirs, the interest interval of the two selected wells is  
Figure 2: Flow chart of optimizing parameters of HKEM based on Bayesian algorithm.  
focused on the Upper Triassic Yanchang Formation. The log graphs of Well A1 and Well A2 are shown in Figures 3 and 4, respectively. The petrophysical logs in the studied interval of the two wells are DEPTH, caliper (CAL), neutron porosity (NPHI), density (RHOB), gamma ray (GR), acoustic time difference (DT), and true resistivity (RT). In addition, DEPTH, CAL, NPHI, RHOB, GR, DT, and RT can be used to measure depth, diameter, porosity, density, shale content, acoustic time difference, and true resistivity of the borehole surrounding rock, respectively. The response of the logging sequence is a comprehensive reflection of the lithology, physical property, electrical property and oil-gas property of the corresponding underground formation. Caliper logging is used to indicate hole expansion and aid in lithology identification and borehole correction. Generally, the oil and gas have the physical characteristics of lower density, higher acoustic time difference and higher resistivity compared with water. In a conventional reservoir, oil and gas are usually filled in the pores and fractures of the formation. Meanwhile, the lower the mud content in the formation, the higher the porosity. Therefore, in the conventional reservoir, the logging response characteristics of high porosity, high acoustic time difference, high resistivity, low mud content and low density may indicate hydrocarbon enrichment. In this research, we suppose the RHOB log is missing and forecast the \"missing\" RHOB curve using other logging data by BO-HKELM.  
The statistical properties of the two selected wells are presented in Table 1. There are 1601 and 1202 data points in Well A1 and Well A2, respectively.  
Figure 3: Well logging graph of Well A1 in the studied interval.
## 3 The Virtual-Force-Based Guidance Law
### Spring Mass System: The Source of the Idea  
Consider a spring mass system as shown in Figure 1: before the unit mass (\(m=1\)) reaches its equilibrium position, the deviation (\(d\)) is controlled by the resultant of the spring force (\(F_{t}\)) and the frictional resistance (\(F_{r}\)), where  
\[m\ddot{d}=F_{t}+F_{d},\ F_{t}=-k_{s}d,\ F_{d}=c_{d}\dot{d}, \tag{2}\]  
and \(k_{s}\) and \(c_{d}\) denote the spring constant and coefficient of resistance.  
From (2), we obtain the dynamic of \(d\)  
\[\ddot{d}+c_{d}\dot{d}+k_{s}d=0 \tag{3}\]  
Define \(\mathbf{X}=[d,\dot{d}]^{\prime}\), we obtain the state space description of (3)  
\[\dot{\mathbf{X}}=\mathbf{AX}\] \[\mathbf{A}=\left[\begin{array}{cc}0&1\\ -k_{s}&-c_{d}\end{array}\right] \tag{4}\]  
It is easy to find the only equilibrium state \(\mathbf{X}=\mathbf{0}\). Let \(|\lambda\mathbf{I}-\mathbf{A}|=0\), we obtain the eigenvalues of (4)  
\[\lambda=\frac{-c_{d}\pm\sqrt{c_{d}^{2}-4k_{s}}}{2} \tag{5}\]  
When \(k_{s}>0\), \(c_{d}>0\), \(real(\lambda)<0\),the spring mass system (3) is asymptotically stable, which means the deviation globally converges to 0.  
Figure 1: A spring mass system with a unit mass.  
If \(c_{d}\geq 2\sqrt{k_{s}}\), \(d\) converges to 0 without overshoot or oscillation. From (5), we know that:  
* When \(0<c<2\sqrt{k}\), the spring mass system is a second-order weakly damped control system. Then, there will be oscillation before convergence.
* When \(c=2\sqrt{k}\), the system is a second-order critical damped control system. In this case, \(d\) converges to 0 without overshoot.
* When \(c>2\sqrt{k}\), the system is a second-order over damped system. \(d\) converges to 0 without overshoot, and the convergence rate is slower with a larger \(c\).
# CrossDeFi: A Novel Cross-Chain Communication Protocol
## 5 Proposed Work Overview
### Asset-Specific Bridge Selection (ABS)
#### 5.2.3 Operational Details of Bridge Selection  
The proposed CrossDeFi introduces the asset-specific bridge selection (ABS) process by modeling cross-chain communication between two distinct DeFi applications utilizing various blockchain technologies. This process is visually represented in Figure 4, which illustrates the operational flow of bridge selection. In this scenario, the source blockchain from DeFi Application 1 needs to transfer an asset to the destination blockchain in DeFi Application 2. The ABS employs oracles to gather community information from the cloud server, enhancing the security of the bridge selection process. The optimal bridge between the source and destination is chosen based on a multi-criteria decision process involving community size, asset type, liquidity depth, security level, and adaptability level, all of which aim to minimize the time required for asset transfer while ensuring maximum security and efficiency.  
The bridge hub, depicted in the figure, plays a central role in the bridge selection process. It houses various types of bridges, each evaluated based on multiple criteria. These criteria include: community size, reflecting the bridge's popularity and reliability; asset type compatibility, ensuring that the bridge can support specific DeFi transactions; liquidity depth, crucial for executing large transactions without slippage; security level, guaranteeing that asset transfers are safe from attacks; and adaptability level, assessing the bridge's ability to function efficiently across different blockchain technologies.
# IPCB: Intelligent Pseudolite Constellation Based on High-Altitude Balloons
## 7 Simulations and Discussions  
To verify the effect of the proposed algorithm, simulations are carried out in Matlab 2018b, with the context of IPCB providing independent regional positioning services.
## 2 System Model
### WPT Modelling  
To completely characterise the WPT process, a model describing the current which is effectively available to RN has been developed and implemented by taking into account the results obtained from electromagnetic and ADS simulations, as well as resorting to a set of measurement illustrated in [18].  
The overall final model consists in the 2D spatial current distribution illustrated in the Figure 5. The procedure followed to obtain it starts with the simulated 5.8 GHz radiation pattern of the SBA Figure 2. As described in Section 2.2, it has been evaluated through an EM simulation that consider the total SBA antenna as a whole. This radiation pattern has been translated in a 2D RF power distribution that depends from the planar (\(x\),\(y\)) coordinates. The current distribution has been calculated by considering the RF to DC conversion efficiency of the RN and taking into account the ES antenna gain. In particular, the RF to DC conversion performances have been calculated basing on the measurements illustrated in [18], that correlate the RF power available at the input of the voltage triplier to the DC current. The inclusion of the ES antenna gain into the SBA 2D RF power planar distribution maps it in the 2D RF planar distribution at the input of the RN voltage triplier. The results is the 2D current planar distribution are illustrated in Figure 5.  
Figure 4: Rechargeable Node power and Tx/Rx chains.  
Finally, the derived current distribution has been included in a Matlab framework in order to describe the energy made available from the ES to each RN as a function of its 2D position within the described area [17; 20].
## 2 Materials and Methods
### Synthesis of the Sorbent Materials  
Fe\({}_{3}\)O\({}_{4}\) nanoparticles were synthesized according to the chemical co-precipitation method. 6 g of FeCl\({}_{2}\)-4H\({}_{2}\)O and 10 g of FeCl\({}_{3}\) were dispersed in 50 mL of water with 2 mL of HCl, and then the mixture was added dropwise into 250 mL of 1.5 M NaOH aqueous solution under vigorous stirring. A black precipitate was obtained by centrifugation at 3000\(\times\)\(g\) (20 \({}^{\circ}\)C, 2 min), and the precipitate was washed with water for three times. Then 250 mL of a 0.01 M HCl solution was added to neutralize the anionic charges on the nanoparticles. Synthetic product was collected and dried at 50 \({}^{\circ}\)C under vacuum.  
PNT was fabricated according to a static assembly and freeze-drying method [29]. Briefly, 26.4 mg of ammonium peroxysulfate was dissolved in 200 mL of sodium 4-[4'-(dimethylamino)-phenyldiazo] phenylsulfonate aqueous solution (5 mmol). Then, 1 mL of pyrrole monomer and 0.2 mL of phytic acid was added dropwise into the mixture under vigorous stirring for 2 min at room temperature, the mixture was then allowed to stand without stirring for 12 h at 4 \({}^{\circ}\)C to achieve a black hydrogel. The PNT was washed with water/ethyl alcohol (_v/v_ = 1:1) and then freeze-dried overnight.  
The 3D-rGOPFH was synthesized through a hydrothermal reduction method [22; 30]. First, 18.5 mg of GO powder was dispersed in 20 mL of ultrapure water by ultrasonic treatment (40 kHz) for 5 min. Then, 221.5 mg of PNT and 240 mg of Fe\({}_{3}\)O\({}_{4}\) nanoparticles were added, and the dispersion was continuously stirred for 8 h at room temperature. Then, 380 \(\upmu\)L of hydrazine hydrate (80%) was added dropwise into it. The as-prepared solution was sealed and heated at 90 \({}^{\circ}\)C for 12 h. A self-assembly process then occurred. The scheme shown in Figure 1 is similar to fishing with a fishing net. The fish were the PNT and Fe\({}_{3}\)O\({}_{4}\) nanoparticle, and the fishing net was GO nanosheets. All of them self-assembled together to form a 3D network architecture during the reduction process. After cooling, the resulting intumescent hydrogel was rinsed with water/ethyl alcohol (_v/v_ = 1:1) three times to remove impurities. Finally, the target product was collected with an external magnet and freeze-dried.  
The synthesis of PNT/Fe\({}_{3}\)O\({}_{4}\) refers to the above-mentioned method, the conditions of synthesis were the same except for the addition of GO powder. 3D graphene nanocomposite (3D-G-Fe\({}_{3}\)O\({}_{4}\)) was synthesized by a vacuum free-dried method [31]. The 2D graphene nanocomposite (2D-G-Fe\({}_{3}\)O\({}_{4}\)) was synthesized following the method of [PERSON] et al. [32].
## 10 Simulation Results and Discussion
### Performance Control Parameters  
The performance control parameters directly affect the performance of the network system like transmission range, data rate, energy deviation, and payload of data packets. The nodes were randomly deployed in the form of the layer with the area of 2000 m \(\times\) 2000 m. The 600 nodes were used, including 40 sink nodes, and the transmission range varied from 150 m to 400 m. The data rate is 16 to 32 kbps for performance evaluation against baseline approach L2-ABF, and the deviation of energy for empty regions was 32, 64, 128 kbps. The data packet payload was set to 100-200 bytes. The value of energy consumption was \(P_{t}\) = 2 W, \(P_{r}\) = 0.2 W and \(P_{i}\) = 0.02 W for transmission, reception and idle energy of BIN nodes, respectively. Table 2 presents the constraints used in the simulation environment.
# Loop-Back Quantum Key Distribution (QKD) for Secure and Scalable Multi-Node Quantum Networks
## 3 Loop-Back QKD in a Ring Topology
Such an analysis would provide insights into the feasibility of group key agreement and the practical security of the multiuser ring-based Loop-Back QKD protocol. This structure not only ensures scalability but also reinforces security, as the correlations formed naturally limit an adversary's ability to manipulate the key distribution process without introducing detectable anomalies.
## Appendix C Phase Aberration Correction (PAC) Methods  
PAC I: Time-Shift Correction Based on Periosteal Bone Surface Geometry, Surface Time Correction (ST)  
Figure 11(a) shows an ultrasound wave transmitted from the transducer element 1 to the backside bone surface position F. For the curved model, the waves travel along a longer path in water (red arrows in Figure 11(a)) compared with the flat bone model, resulting in a shift \(\Delta TOF_{geo}\) caused by the different surface geometries. These were determined using the concept of refraction compensation proposed by [PERSON] et al. [24].  
For each transmit channel \(Tx_{i}\) and focus depth \(F\) below the frontside surface, the crossing point of the straight ultrasound wave path and the frontside surface was determined to calculate the height length of the flat plate \(h_{1,plate}\) and curved plate \(h_{1,curved}\), and the width length of the flat plate \(w_{1,plate}\) and curved plate \(w_{1,curved}\) between the crossing point and channel position (Figure 11(a)). In addition, the time-of-flight from the transmitted channel to the focus point \(F\) was calculated for the flat bone plate by  
\[TOF_{Tx_{i},plate}\ =\ \frac{\sqrt{w_{1,plate}{}^{2}\ +\ h_{1,plate}{}^{2}}}{v_{ H_{2}O}}\ +\ \frac{\sqrt{w_{1,plate}{}^{2}\ +\ h_{1,plate}{}^{2}}}{Ct.v_{11}} \tag{5}\]and for the curved bone plate using \(w_{1,curved}\) and \(w_{2,curved}\) instead of \(w_{1,plate}\) and \(w_{2,plate}\) (Figure A3a), respectively. The assumption of \(Ct.v_{11}\) to calculate \(TOF_{\mathit{Fix,plate}}\), was performed by implementing a loop for retrieving \(Ct.Th^{\mathit{MF}}\) and \(Ct.v_{11}{}^{\mathit{MF}}\). The starting value of \(Ct.v_{11,assump}\) was defined at 2500 m/s based on the previous study [19], where \(Ct.v_{11}\) values smaller than 2600 m/s were reported for cortical porosity values larger than 20%. If the difference between the calculated \(Ct.v_{11}{}^{\mathit{MF}}\) and the assumed input \(Ct.v_{11,assump}\) was larger than 10 m/s, the loop continued by replacing the new assumed \(Ct.v_{11}{}_{\mathit{assump}}\) with the previously calculated \(Ct.v_{11}{}^{\mathit{MF}}\). The loop stopped if the difference between calculated \(Ct.v_{11}{}^{\mathit{MF}}\) and assumed \(Ct.v_{11,assump}\) was smaller than 10 m/s. The total time-of-flight from one transmit channel to the receiving channel \(Rx_{i}\) for the plate and curved models was calculated by  
\[TOF_{Rx_{i},plate}\ =\ TOF_{Tx_{i},plate}\ +\ TOF_{64\ -\ Tx_{i}\ +\ 1,plate}, \tag{6}\]  
under the assumption of a straight ultrasound transmitted and reflected travel paths, from transmit channel \(Tx_{i}\) to the focus position \(F\), and back to the receiving channel \(Rx_{i}=64-Tx_{i+1}\).  
After calculating all \(TOF_{Rx}\) for all channels 1 to 64, the corrected delay is determined for each element by  
\[TOF_{goo,Rx_{i}}\ =\ TOF_{Rx_{i},curve}\ +\ TOF_{Rx_{i},plate}. \tag{7}\]  
In summary, \(PAC\)\(I\) corrects for the different propagation travel times caused by the bone curvature compared to a flat plate geometry at each receiving channel.
## 3 Database-Building and Exploratory Analysis
### Data Exploratory Analysis  
Feature engineering plays a crucial role in determining the quality of a prediction model. Therefore, selecting an optimal subset of features can significantly enhance the accuracy of a model's predictions. In the initial database, it is common to encounter redundant features. These may include features that exhibit little variation or hold no relevance to the uplift of a tunnel vault, and their included information can be inferred from other features. Removing such redundant features is important for refining the dataset and improving the overall predictive accuracy of a model.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \multirow{2}{*}{**Symbol**} & \multirow{2}{*}{**Category**} & \multirow{2}{*}{**Unit**} & \multicolumn{2}{c}{**Parameter Description**} & \multirow{2}{*}{**Note**} \\ \cline{3-4} \cline{6-6}  & & & **Min-Max** & & \\ \hline R & Input & m & 6–11 & 6.36 & Circular tunnel diameter \\ L & Input & m & 8.2–867 & 90.04 & Lateral excavation length of an excavation \\ B & Input & m & 9.7–200 & 48.14 & Longitudinal excavation length of an excavation \\ H\({}_{\rm e}\) & Input & m & 4–24.3 & 8.85 & Excavation depth \\ H\({}_{\rm t}\) & Input & m & 0.35–12.4 & 5.20 & The vertical distance between a tunnel vault and the bottom of an excavation \\ L\({}_{\rm c}\) & Input & m & 10–203 & 53.24 & The actual undercrossing length of a tunnel \\ Mud & Input & - & 0–1 & 0.25 & Mucky clay \\ Sof & Input & - & 0–1 & 0.24 & Soft clay \\ Sil & Input & - & 0–1 & 0.25 & Silty clay \\ Gra & Input & - & 0–1 & 0.26 & Gravellly soil \\ SWM & Input & - & 0–1 & 0.28 & Soil mixing wall \\ DW & Input & - & 0–1 & 0.15 & Diaphrag wall \\ BP & Input & - & 0–1 & 0.48 & Bored cast-in-place pile \\ OC & Input & - & 0–1 & 0.21 & Sloping excavation \\ NC & Input & - & 0–1 & 0.70 & Internal support of an excavation \\ SM\(\_\)1 & Input & - & 0–1 & 0.95 & Excavation bottom \\ SM\(\_\)2 & Input & - & 0–1 & 0.98 & reinforcement \\ SM\(\_\)3 & Input & - & 0–1 & 0.68 & Layered excavation of an excavation \\ SM\(\_\)4 & Input & - & 0–1 & 0.21 & Uzlit pile \\ \(S_{\rm max}\) & Output & mm & 20–205 & 87.89 & Maximum displacement of a tunnel’s vault uplift \\ \hline \end{tabular}
\end{table}
Table 1: The definitions, ranges, and detailed descriptions of each variable.
# Landslide Susceptibility Prediction Based on a CNN-LSTM-SAM-Attention Hybrid Model
## 5 Discussion
### Comparative Analysis of the CNN-LSTM-SAM-Attention Model and Traditional Models
Notably, our model maintains a true positive rate (TPR) > 0.9 at a low false positive rate (FPR = 0.2) (Figure 8), demonstrating precise differentiation between landslide and non-landslide events while effectively preventing unnecessary emergency response activations. Comparison results show that the CNN-LSTM-SAM-Attention model comprehensively surpasses traditional models in five indices: accuracy, recall, F1 value, PR-AUC, and ROC-AUC, and provides a high-precision and high-reliability solution for landslide prediction tasks, which can be applied to disaster warning under complex terrain and dynamic climate conditions.