esa-sceva/satcom-chunk-collection huggingface dataset:
# Tourism Sentiment Chain Representation Model and Construction from Tourist Reviews
## 4 Results
### Tourism Sentiment Chain Generation and Scenario Validation  
Building upon the TSC generation methodology outlined in Section 3.3.2, this section first employs an RW-based approach to route generation, computing the integrated attractiveness between attractions to construct the TSC through OSA. The adjacency relationships between attractions in the resulting TSC are then benchmarked against attraction recommendations from two major online platforms, \"Qunar\" and \"Mafengwo\". This comparative analysis highlights the superiority of TSC in two key aspects: geographical accessibility and emotional experience coherence.
## 3 Results  
Adult zebrafish caudal fin regeneration has been used as readout model to analyze the effect of biophysical stimulation on bone regeneration by calcein live staining (Figure 2).  
Different HI LF-PEMF treatments have been evaluated at five days after amputation in terms of duration or number of session, resulting in an effective protocol of 64 min twice a day (Figure 3).  
Figure 3: Regenerated fin rays’ length measurements in different treatment with HI LF-PEMFs. Parameters were expressed as minutes of treatment (min.) and number of daily sessions (n.) (0 vs. 64, * \(p<0.05\)).  
Figure 2: Fin amputation and double-live staining as readout model to study the effects of biophysical stimulations of bone regeneration in vivo.  
Regenerated fins images of untreated and HI LF-PEMF-treated fish were captured (Figure 4) and morphometric parameters were analyzed by imaging software.  
Length of regenerated fin's rays under HI LF-PEMFs condition at five days after amputation, measured at the top of the fin, showed a statistically significant dimensional increase compared with the control group (\(p\) < 0.01, Figure 5A). Similarly, regenerated fin rays' area of PEMFs-treated fish resulted wider compared to controls (\(p\) < 0.05, Figure 5B).  
Figure 4: Calcein-alizarin double staining of fin rays: regenerated bone matrix in untreated (CTR) and treated with HI LFEMFs (HLP) adult zebrafish fin (red staining highlights the old bone tissue and green staining highlights the new regenerated bone tissue).  
Figure 5: Measurements in untreated (CTR, seven fish) and treated with HI LF-PEMFs (HLP, seven fish) adult zebrafish of (**A**) regenerated fin rays’ length (HLP vs. CTR, **\(p\) < 0.01) and (**B**) regenerated fin rays’ area (HLP vs. CTR, *\(p\) < 0.05).  
The whole growth curve, calculated until the end of the regeneration process (10 days), indicated a stimulatory effect of HI LF-PEMFs respect of untreated controls (Figure 6). In fact, the peak of regeneration is achieved before and faster in the treated group compared to the control group (8 days vs. 10 days).
# A Beam Hopping Scheme Based on Adaptive Beam Radius for LEO Satellites
## 3 Model
### Problem Formulation  
Based on the aforementioned model, we first present the problem that this paper aims to address, followed by a mathematical formulation of the issue.  
From the aforementioned model, we observe that the number of concurrently operating beams is limited, and the power provided by the amplifiers for each beam is also constrained. Additionally, to avoid interference between beams, it is necessary to maintain a specific isolation angle. Therefore, we select the cell with the maximum value of A from those that meet these requirements.  
Figure 6: Traffic distribution.  
Below, I use mathematical expressions to represent these constraints and my optimization objectives.  
\[\begin{array}{l}P1:\max Throughput\\ P2:\min Delay\\ \text{s.t.}\\ \text{C1}:0\leq P_{\alpha ml}^{i}\leq P_{max}\\ \text{C2}:0\leq|\mathfrak{E}|\leq N_{\text{beam}}\\ \text{C3}:i=\text{argmax}\big{(}\mathit{VirD}_{t}^{cell}\big{)}\\ \text{C4}:<cell_{i},cell_{j}>\geq\theta_{\text{th}}\big{(}cell_{j}\in \mathfrak{E}\big{)}\end{array}\]  
In the above problem, \(C1\) and \(C2\) means power and bandwidth must not exceed the maximum power and total bandwidth of the transponder. C3 indicates the cells are chosen by its max \(\mathit{VirD}_{t}^{cell_{i}}\) value; we choose the biggest \(N_{beam}\)\(\mathit{VirD}_{t}^{cell_{i}}\) value under the constraint of \(C4\). \(C4\) constrains the angle between the \(cell_{i}\) and the cells which is chosen completely. In conclusion, C3-C4 means we can choose up to \(N_{beam}\) cells from \(N_{cell}\) cells that should have enough \(\mathit{VirD}_{t}^{cell_{i}}\) and \(cell_{i}\) should satisfy interference avoid criterion from \(C4\).
## 1 Introduction
Figure 1 shows the progression from analog to fully digital workflows in maxillofacial prosthetic fabrication, showcasing how imaging, computer-aided design (CAD) modeling, and additive manufacturing streamline the process. Recently, there have been few clinical cases describing the direct printing of maxillofacial soft prostheses. To date, only one printable silicone material, ACEO Silicone (Wacker Chemie AG, Munich, Germany), has been reported for clinical use, employing a Drop-on-Demand technique [9; 10] through their printing service, although its use was  
Figure 1: Various workflows for the manufacturing of maxillofacial soft prostheses. discontinuued in 2021 [11]. Other flexible resins have been employed in the fabrication of interim prostheses, and more recently, Polyjet color 3D printing technology has been utilized for the direct production of a definitive facial prosthesis [12]. While researchers have focused on developing printable maxillofacial materials for soft prosthesis, clinical application reports remain scarce [13, 14, 15, 16].There is a lack of comprehensive reviews assessing the feasibility of a fully digital workflow, including direct 3D printing techniques, material properties, biocompatibility, and clinical outcomes. This scoping review aims bridge these gaps by providing an updated overview of additive manufacturing for direct printing of silicones and flexible resins; highlighting the technology's potential to enhance the fit, function, and aesthetics of maxillofacial soft prostheses; and evaluating the feasibility of a fully digital workflow for their fabrication.
## 3 Ground Motion Propagation of Nonlinear Soil
### Selection of Typical Soft Deposit and Ground Motion Records  
Adopting the Davidenkov model, propagation of ground motion includes determining the deep cover soft deposit and ground motion records. To deal with this, a typical soil site with a thickness of 280 m was selected, designed by [PERSON] et al. [53] based on Shanghai stratigraphic survey data as well as the current Shanghai foundation specifications. The details of the site are listed in Table 3, where \(\rho\) is the density of soil, \(\
u\) is Poisson's ratio, and \(V_{s}\) is the shear wave velocity of soil.  
Strong ground motion records measured at the deep cover soft deposit were further selected. A series of measured bedrock ground motion records were preferred, whose corresponding ground surface acceleration time history responses could be obtained after the nonlinear ground motion propagation analysis. Therefore, 40 bedrock ground motion records were selected from the Pacific Earthquake Engineering Research Center (PEER) database [54]. These records comprehensively consider the main factors affecting the energy spectrum, including ground motion duration, intensity, spectral component, magnitude, hypocentral distance, etc. In addition, we tried to select records from different earthquakes. The selected records and their main characteristics are given in Table 4, including peak ground acceleration (PGA), peak ground velocity (PGV), peak ground displacement (PGD), total duration (\(T_{d}\)), and strong motion duration (\(t_{D}\)) [55]. Figure 11 shows histograms for the distribution of records versus earthquake magnitude and hypocentral distance for the contributing earthquakes.  
Figure 10: Comparison of computed and recorded ground surface acceleration response spectra.  
Figure 9: Computed ground surface acceleration time history.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Layer** & **Soil Type** & **Depth (m)** & **Thickness (m)** & \(\rho\) (**kg.m\({}^{-3}\)**) & \(\
u\) & \(V_{s}\) (m\({}^{-1}\)) & **Gmax (MPa)** \\ \hline
1 & Clay & 0 & 3 & 1900 & 0.33 & 100 & 19.39 \\
2 & Muddy silty clay & 3 & 7 & 1750 & 0.35 & 130 & 30.18 \\
3 & Muddy clay & 10 & 10 & 1750 & 0.38 & 160 & 45.71 \\
4 & Clay & 20 & 5 & 1820 & 0.30 & 190 & 67.04 \\
5 & Clay & 25 & 5 & 2000 & 0.30 & 260 & 137.96 \\
6 & Sand & 30 & 15 & 1920 & 0.28 & 290 & 164.77 \\
7 & Silty clay & 45 & 30 & 1900 & 0.33 & 320 & 198.53 \\
8 & Sand & 75 & 20 & 1950 & 0.28 & 340 & 230.02 \\
9 & Clay & 95 & 15 & 2000 & 0.33 & 400 & 36.53 \\
10 & Sand & 110 & 30 & 2020 & 0.28 & 420 & 363.60 \\
11 & Clay & 140 & 30 & 2040 & 0.35 & 440 & 403.00 \\
12 & Sand & 170 & 60 & 2060 & 0.28 & 500 & 525.51 \\
13 & Clay & 230 & 30 & 2080 & 0.35 & 510 & 552.05 \\
14 & Sand & 260 & 20 & 2100 & 0.28 & 580 & 720.86 \\  & Bedrock & 280 & - & 2390 & 0.20 & 2000 & 9755.10 \\ \end{tabular}
\end{table}
Table 3: Parameters of soil site in urban area of Shanghai: \(\rho\): density; \(\
u\): Poisson’s ratio; \(V_{s}\): shear wave velocity.
## 3 Results
### Functional Properties Evaluation
#### 3.4.1 Electrical Conductivity
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Electrical Conductivity (S/m)**} \\ \hline \multirow{3}{*}{**Sample**} & **Water** & **Water** & **Water** & **Water** & **Water** \\  & **Treatment** & **Treatment** & **Treatment** & **Treatment** & **Treatment** & **Treatment** \\  & **Cycle (0)** & **Cycle (1)** & **Cycle (2)** & **Cycle (3)** & **Cycle (4)** & **Cycle (5)** \\ \hline
0.05\%GSNPs-PCL & \(6.00\times 10^{-9}\) & \(6.67\times 10^{-9}\) & \(6.15\times 10^{-9}\) & \(1.00\times 10^{-8}\) & \(1.06\times 10^{-8}\) & \(6.43\times 10^{-9}\) \\
0.1\%GNPs-PCL & \(5.75\times 10^{-2}\) & \(1.17\times 10^{-2}\) & \(1.05\times 10^{-2}\) & \(3.75\times 10^{-4}\) & \(1.85\times 10^{-4}\) & \(2.40\times 10^{-4}\) \\
0.2\%GNPs-PCL & 0.29 & 0.24 & 0.21 & 0.02 & 0.01 & 0.010 \\
0.5\%GNPs-PCL & 1.61 & 0.21 & 0.09 & 0.03 & 0.03 & 0.025 \\
1\%GNPs-PCL & 3.01 & 0.56 & 0.43 & 0.19 & 0.13 & 0.078 \\
2\%GNPs-PCL & 3.17 & 0.68 & 0.74 & 0.25 & 0.11 & 0.08 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Electrical conductivity results as a function of the concentration of GNPs and the amount of water treatment cycles performed from 1 to 5. Figure 9: Electrical properties of the samples: (**a**) Resistance and uploaded GNPs mass of the GNPs-PCL membranes with an immersion of 1 h and after five consecutive washing/drying treatments; (**b**) Resistance and uploaded GNP mass for 1%GNPs-PCL membrane for several impregnation times (5 min, 15 min, 30 min, and 60 min). by the fact that GNPs concentration increases as the distance between the nanoparticles decreases, leading to a fluid movement of the electrons, forming larger electron clouds, and, as a consequence, decreasing the resistance of the material [20].
## 3 Materials and Methods
### FEM and ASCs  
Since the introduction of U-Net, encoder-decoder networks have achieved state-of-the-art (SOTA) performance in most image classification and semantic segmentation tasks. Compared to traditional CNNs, their unique skip connection mechanism enables the transfer of image details between different layers and the combination of features across layers. Inspired by this, the Football Edge Module (FEM) incorporates an encoder-decoder structure, improving the skip connections by changing them into Asymmetric Skip Connections (ASCs). In the Football Net model, the first six layers function as the encoder, while the last two layers form the decoder. The ASCs connect layers 1 and 2 of the encoder with layers 8 and 7 of the decoder and internally link layers 3 and 4 with layers 6 and 5 in the encoder. The detailed structure of the Football Edge Module is shown in Figure 5.  
During the downsampling process in CNNs, image resolution gradually decreases, leading to the potential loss of high-frequency information and details. To address this issue, the model directly transfers high-resolution features from the encoder to deeper layers, allowing the model to retain and utilize high-frequency features even during downsampling.
## 5 Seismic Performance of the SSSI System
### Performance Indexes and Maps  
To assess the effectiveness of the SSSI system in mitigating the structural response, two performance indices are introduced according to Equation (16):  
\[\alpha_{1}=\frac{\max\lvert u_{1}(t)\rvert}{\max\lvert\bar{u}_{1}(t)\rvert}, \ \alpha_{2}=\frac{\max\lvert\Delta u(t)\rvert}{\max\lvert\bar{\Delta u}(t)\rvert} \tag{15}\]  
where \(\bar{u}_{1}\) and \(\bar{\Delta}u=\bar{u}_{1}-\bar{u}_{2}\) denote the displacement and drift of the 2-degree-of-freedom (2-DOF) model representing the standalone structure. The effectiveness of the SSSI system improves as the values of \(\alpha_{1}\) and \(\alpha_{2}\) decrease relative to unity.  
The parametric analysis involves mapping the values of the performance indices, \(\alpha_{1}\) and \(\alpha_{2}\), onto the parameter plane defined by \(\eta\) and \(\gamma\) using a single earthquake record. This process generates performance maps, which are contour plots illustrating the distribution of the indices \(\alpha_{1}\) and \(\alpha_{2}\) across the \(\eta-\gamma\) parameter space. To enhance readability, the contour levels are delineated using a specific greyscale scheme, where lighter shades correspond to smaller values of the performance indices. Optimal performance of the SSSI system is observed within regions of the maps characterized by clearer greyscale tones. These areas indicate superior system performance, as denoted by lower values of the performance indices \(\alpha_{1}\) and \(\alpha_{2}\).
# Handover Management for Drones in Future Mobile Networks--A Survey
## 5 Handover Challenges in Drone Networks
### Handover Self-Optimization Functions  
Another issue result from the Handover Self-Optimization Functions that deal with handover control parameters, such as Mobility Robustness Self-Optimization and Load Balancing Self-Optimization functions. To date, several handover control techniques have been developed in the literature to optimize handover control parameters; however, existing techniques may not be able to work efficiently with drones. The existing techniques were designed to serve terrestrial users, which have very different mobility, operations, and traffic features. The key distinctions between drones flying in the sky and terrestrial users produce significant challenges. Moreover, most of the literature has been developed with the previous mobile networks, whereas the work on 6G networks is still in its infancy. Existing handover Self-Optimization functions still pose issues that require further enhancements.
# Embedded Deep Learning for Ship Detection and Recognition
## 3 Designing a Recognition Neural Network-DCNet  
DCNet is a two-stage network that consists of a DNet and a CNet as shown in Figure 1. DNet is a fully convolutional network [21] for ship parts detection including ship bow, cabin and stern. CNet is a classifier that can takes an image of any size and output a set of classification scores. We locate the ship parts from the DNet, and feed them into the CNet to get three classification scores (bow score, cabin score, stern score) of ship identify. Finally, a voter is used to recognize the ship as shown in Figure 1.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Algorithm** & **Datasets** & **mAP** & **FPS** & **Proposed Year** \\ \hline SSD300 & VOC 2007 + 2012 & 74.3 & 46 & 2016 \\ SSD500 & VOC 2007 + 2012 & 76.8 & 19 & 2016 \\ Faster-RCNN & VOC 2007 + 2012 & 73.2 & 31 & 2015 \\ YOLO & VOC 2007 + 2012 & 63.4 & 45 & 2015 \\ YOLOv2 & VOC 2007 + 2012 & 76.8 & 67 & 2016 \\ Tiny YOLO & VOC 2007 + 2012 & 57.1 & 207 & 2016 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The performance of the algorithms.
# Efficacy Improvement of Anomaly Detection by Using Intelligence Sharing Scheme
## 4 Proposed Architecture
### Global Reputation Model  
Here, the global reputation model (GRM) is proposed, which uses the information gathered from individual networks to assign globally associated anomaly values with individual hostnames. The overall reputation model structure can be visualized in Figure 2, where the input is a list of flows with associated anomaly scores (produced by the anomaly processing pipeline of the CTA-IDS), and the output is a GRM. The GRM can be used to enhance detection strengths in individual networks. There are three parts of this reputation model represented by blue arrows (Figure 2) that are developed and evaluated in this research work.  
1. Aggregation of anomaly scores over hostnames, producing a set of local reputation models (LRMs). This part can be implemented simply as an average of per-flow anomaly values described in Section 4.2.1.
2. Combination of LRM to form a GRM. This part is much more involved than the others and presents difficult challenges such as normalization of individual local reputation tables. The remainder of this work mainly focuses on the design and implementation of this part of the overall intelligence sharing algorithm.
3. Improvement of anomaly detection in individual networks using the GRM. This part can be implemented by replacing individual flow anomaly scores with hostname reputation values described in Section 4.3.
## 6 UOWC Security Concerns and Challenges and Future Directions
### Challenges and Future Directions
Nevertheless, we must consider more than one optical pulse to increase the accuracy of any possible elongation in the tails of the received photon distribution. To explain this issue, we need to refer to Equation (36). Hence, we need to consider at least two neighbouring symbols of the zero-position symbol, i.e., \(S=1\). In conclusion, executing an MCS based on two transmitted optical pulses would be advantageous in exploring the impact of the ISI on the temporal spreading of the received photons. This means that Equations (76) and (78) will be transformed into the following expressions:  
\[I\big{(}t,\ E_{p},\ K\big{)}=-\Big{(}\frac{n}{v.c}\Big{)}\sum_{s=-S}^{S}a_{s} \sum_{k=1}^{K}h_{p}(t_{0,k}-s\tau)\left[\sum_{m=0}^{M_{k}}\cos\theta_{k,m}\ln (\ q_{k,m}\ )\ \right]_{E_{p}|m} \tag{83}\]  
\[I\big{(}t,\ E_{p},\ K\big{)}=-\bigg{(}\frac{1}{v}\bigg{)}\sum_{s=-S}^{S}a_{s} \sum_{k=1}^{K_{l}}h_{p}(t_{0,k}-s\tau)\left[\sum_{m=1}^{M_{l,k}}\bigg{(}\frac{ n_{l,k,m}.cos\theta_{l,k,m}}{c_{l,k,m}}\bigg{)}\ln(\ q_{l,k,m}\ )\ \right]_{E_{p}|m} \tag{84}\]  
It is essential to notice that the total number of emitted photons \(K\) in Equation (84) will be considered random within practical limits. Hence, we used \(K_{l}\) instead of \(K\), as shown in Equation (78). Equations (83) and (84) can also be applied to simulate MIMO systems. The future of UOWC systems lies in solving challenges related to channel characterisation, range, energy efficiency, multi-modal integration, and real-world implementation. Addressing these challenges requires interdisciplinary research, combining advances in optics, communication theory, artificial intelligence, and marine technology. With these advancements, UOWC systems can significantly enhance underwater connectivity, enabling transformative applications across scientific, industrial, and defence domains. However, several challenges need to be addressed to achieve their full potential. Table 11 summarises the key open research challenges and future directions. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline  & & **Challenges** & **Future Directions** & **Ref. ** \\ \hline \multirow{8}{*}{**1. Channel Characterisation and Modelling**} & \multirow{4}{*}{**Dynamic Environment**: Accurate modelling of UOWC channels under varying conditions of turbidity, temperature, salinity, and turbulence is complex. **} & \multirow{4}{*}{Develop real-time adaptive channel models based on environmental sensing data. Use machine learning to predict channel conditions and optimise system performance dynamically.} \\  & & & & \\
## 4 Conclusions  
Reduced graphene oxide and PEO were prepared and applied to VFQPH solar cells as a hole transport buffer layer and an electron transport buffer layer. The rGO/PEDOT:PSS was selected as the optimal HTBL for cell performance. The highest values of shunt resistance led to the decreased leakage of current and the lowest values of series resistance led to increased current density values. The device's active layer surface morphology was well controlled by coats of thin PEO (SS-4000) between the active layer and the back contact E-GaIn. At the optimized structure of the glass/ITO/rGO/PEDOT:PSS/PbS:PCBM/PEO-(SS-4000)/E-GaIn device, the maximum power conversion efficiency of 4.43% (J\({}_{\text{sc}}\): 7.61 (mA/cm\({}^{2}\)); V\({}_{\text{oc}}\): 0.75 (V); FF:72)) was obtained.  
Our results suggest that thin layers of rGO and PEO are among the most promising candidates for the anode and cathode buffer layers in vacuum-free conventional solar cells.  
Writing--original draft, conceptualization, methodology and data interpretation, M.K. and N.T.N.T.; data curation, validation and resources, N.H.L. and N.L.; visualization and formal analysis, A.M.T.; writing--review and editing, M.S.T. and T.N.L.V.; supervision, project administration and funding acquisition, J.H.J. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the 2015 Yeungnam University Research Grant.  
Not applicable.  
The authors declare that they have no known competing financial interests or personal relationships that could appear to influence the work reported in this paper.  
\begin{table}
\begin{tabular}{l c c c c} \hline
**Device** & **J\({}_{\text{sc}}\)** & **V\({}_{\text{oc}}\)** & **FF** & **Eff** \\
**Structure** & **(mA/cm\({}^{2}\))** & **(V)** & **(\%)** & **(\%)** \\ \hline Without PEO & 6.69 \(\pm\) 0.01 & 0.70 \(\pm\) 0.00 & 70 \(\pm\) 2 & 3.27 \(\pm\) 0.01 \\ With PEO & 7.42 \(\pm\) 0.02 & 0.75 \(\pm\) 0.02 & 65 \(\pm\) 2 & 3.78 \(\pm\) 0.02 \\ (SS-3000 rpm) & 7.61 \(\pm\) 0.02 & 0.75 \(\pm\) 0.00 & 72 \(\pm\) 2 & 4.34 \(\pm\) 0.01 \\ \hline \end{tabular}
\end{table}
Table 2: Device parameters as open-circuit voltage (V\({}_{\text{oc}}\)), short-circuit current density (J\({}_{\text{sc}}\)), fill factor (FF) and power conversion efficiency (PCE) from J-V characteristics without and with different PEO buffer layer thickness under A.M 1.5G illumination at 100 mW/cm\({}^{2}\).