esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods  
### Study Area  
The 8.0 Richter magnitude (China Earthquake Administration) Wenchuan earthquake occurred on 12 May 2008 and caused the most serious damage and had the widest impact of any earthquake in China since the establishment of the People's Republic of China [24]. The earthquake caused an enormous number of casualties and many geological hazards, such as landslides, rock falls, debris flows, _etc._, which substantially damaged buildings, infrastructure and the ecological environment [24]. The study area for this paper involves 12 counties or cities seriously damaged by the earthquake, including Wenchuan, Dujiangyan, Pengzhou, Shifang, Mianzhu, Anxian, Jiangyou, Qingchuan, Pingwu, Beichuan, Maoxian and Lixian.  
The intensity of the earthquake varied within an elliptical-shaped area centred on Wenchuan and Beichuan. The Wenchuan intensity-XI zone was distributed along Wenchuan-Dujiangyan-Penghzhou, and the Beichuan intensity-XI zone was distributed along Anxian-Beichuan-Pingwu. The XI, X and IX intensity zones converged in a long and narrow area, 300 km long and 50 km wide, in the northeast. Because the intensity-VI zone was small, it will not be discussed below.  
The study area (Figure 1) is located in northern Sichuan Province in the transitional zone between the Qinghai-Tibet Plateau and the Sichuan Basin and covers an area of 33,147 km\({}^{2}\). The terrain tilts from northwest to southeast. The lowest elevation is 474 m, while the highest elevation is 6072 m, a difference of 5598 m. This area has a complex terrain with deep valleys and steep mountains; therefore, landslides, debris flows and other geological hazards are likely. There are 20 rivers in this area, which provide abundant water resources. In addition, this area is the source of the Tuojiang River and the Fujiang River.  
The Longmenshan fault zone extends across the study area from northeast to southwest, and three near-parallel fault zones extend in a northeast direction: the Wenchuan-Maoxian fracture (mountain fracture), the Yingxiu-Beichuan fracture (central fracture) and the Guan-An fracture (piedmont fracture) [25] (Figure 1). The Yingxiu-Beichuan and Guan-An fractures were triggered by the Wenchuan  
Figure 1: The severely damaged counties caused by Wenchuan earthquake, which is located in northern Sichuan province of China.  
earthquake at the same time. Beichuan County and Yingxiu Town, which suffered massive devastation during the earthquake, are located in along the Yingxiu-Beichuan fracture.
# Call Redistribution for a Call Center Based on Speech Emotion Recognition
## 2 Materials and Methods
### Comparison of the SER Results with Other Studies
For the speaker-dependent test, 10-fold cross-validation for the whole corpus is applied, as in our case. The reported [37] average recognition accuracy on the GEES corpus is 94.1%. It is achieved with the proposed 3 DEC combination of SVM classifiers in the speaker-dependent test. Recognition accuracy in their research is obtained as an unweighted average accuracy (UA), i.e., accuracy per class is averaged by the total number of classes. It should be noted that in the case of the GEES corpus, UA accuracy is identical to weighted average accuracy (WA) due to equally balanced emotion classes. Comparing our result with the result of [PERSON] et al. [37], it can be seen that our average recognition accuracy is lower by 3%. It should be noted that our result is obtained with a significantly smaller feature vector (384 features against 6552 features in [37]). Additionally, the classification methods applied are different. In our experiments, the linear Bayes classifier is used as a simple and fast method for training and test stages, and their proposed 3 DEC combination of SVMs requires training of four SVMs. We consider that our proposed SER achieves a slightly lower result compared to the best recognition accuracy reported for the GEES (94.1% in [37]), but having significantly smaller feature vectors and computationally less demanding classification method. One more study on the GEES corpus, by [PERSON] et al. [45], applied the multistage (hierarchical) emotion categorization with SVM. In their research, the extracted utterance-level vectors of 318 features, among which are pitch, energy, MFCC, formants and their statistical functionals (e.g., mean, variance, maximum, minimum, etc.). In the experiment on the GEES corpus, they reported the average emotion recognition rate of 90.63%. Comparing our result with the result of [PERSON] et al. [45], it can be seen that our average recognition accuracy is higher by 1%. They applied a hierarchical classification techniques with 4 SVMs, thus training of all 4 SVMs is necessary. It should be noted that feature vector set used in [45] is smaller, but an important difference is that their experiments were performed on individual speaker sub-corpora and overall recognition accuracy was calculated as an average value of recognition accuracies obtained for each individual speaker. Our recognition accuracy is evaluated after 10-fold cross-validation on the whole corpus, like in the study of [PERSON] et al. [37], which we consider as a more objective measure of recognition performance.
## 2 Methods
### Procedures  
The conventional impression technique involves capturing the implant position using light-body and heavy-body vinyl polysiloxane (VPS) impression materials with a one-step open-tray approach for the full dental arch. The impression may was first tried in the patient's mouth. The healing abutment was removed, and an impression coping was securely attached to the implant with a tightening torque of 10 Ncm. A periapical radiograph was taken using the parallel technique to confirm the precise seating of the impression coping and implant. The VPS impression material was then loaded into the tray and placed onto the dental arch. Once the material had fully set, the tray was carefully removed. The impression was meticulously inspected for accuracy, ensuring it was free of bubbles, distortions, or incomplete details. The impression coping remained firmly embedded in the impression material. A bite registration was recorded in maximum intercuspation using a bite registration material (Futar, Kettenbach Dental, Huntington Beach, CA, USA). An alginate impression (Tropicalgin; Zhermack Dental, Badia Polesine, Italy) was taken of the opposing arch to capture its anatomical details.  
Digital impressions were taken using an intraoral scanner (3 Shape Trios 3 Move, 3 Shape A/S, Copenhagen, Denmark), and the scanning process followed the manufacturer's guidelines. It began by scanning the opposing arch relative to the implant site. The operator positioned themselves on the patient's right side and performed a continuous scan, starting from the occlusal surface of the distalmost tooth, moving through the occlusal/incisal edges of the teeth, and ending at the distalmost tooth on the opposite side. The lingual and buccal surfaces were then scanned. For the maxillary arch, the scan included the relevant palatal area, distal surfaces of the terminal teeth, edentulous regions (if present), and both tuberosities. For the mandibular arch, the scan extended to the distal surfaces of the terminal teeth and edentulous regions (if present). The healing abutment was removed, and the entire dental arch, including the soft tissue emergence profile around the implant, was scanned. The digital impression coping was then connected to the implant with a tightening torque of 10 Ncm. A periapical radiograph was taken to verify the precise seating of the digital impression coping and implant. The scan was repeated until the TRIOS Patient Monitoring software version 21.2 confirmed a successful scan. The digital impression coping was removed, and the bite registration was scanned in maximum intercuspation, starting from the right to the left side.
## 2 Numerical Analysis
### Boundary Conditions  
A numerical analysis was conducted using ANSYS CFX ver. 2020 R2, and Table 2 describes the boundary conditions applied in this study. Here, the inlet velocity, \(V_{\infty}\) according to the inlet condition is 0.78 m/s and the Reynolds number, \(Re_{D}=\rho V_{\infty}D_{p}/\mu\) is 7.43 \(\times\) 10\({}^{4}\), which indicates high turbulence. Due to the absence of a guide vane in this model, the water flows straight until just before the runner entrance. For this reason, the steady state with frozen rotor interface was set to an analysis state, because the flow is uniformly distributed to the blades, and there is no wake at the runner inlet. With respect to the turbulence model, shear stress transport (SST), which is known to be able to simulate the secondary flow at the blade [37; 38], was selected to consider the effects of adverse pressure gradient on the blade.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Classification** & **Value** \\ \hline Analysis state & Steady state \\ Interface model & Frozen rotor \\ Turbulence model & SST \\ Working fluid & Water at 25 \({}^{\circ}\)C \\ Inlet condition & Flow rate (15.95 m\({}^{3}\)/h) \\ Outlet condition & Static pressure (1 atm) \\ Rotational speed & 750 rpm \\ \hline \hline \end{tabular}
\end{table}
Table 2: Boundary conditions applied in this study.  
Figure 3: Grid dependency test (**a**–**c**).
# Short Text Aspect-Based Sentiment Analysis Based on CNN + BiGRU
## 2 Literature Review
At the same time, existing heuristic methods cannot extract the semantic features of polysemous words efficiently, resulting in a poor classification effect and poor generalization of existing deep learning classification models. Therefore, how to effectively solve the above problems and improve the accuracy and generalization of aspect sentiment analysis is attracting extensive attention. In order to solve the above technical problems, [PERSON] [22] proposed a short-text sentiment analysis algorithm based on Bi-LSTM, which aimed at solving the problem that statistics-based feature selection methods ignore semantic information and deep learning methods do not contain the statistical and sentiment information of the feature. [PERSON] et al. [23] proposed a model which uses BiGRU and did an experiment by training [PERSON] on the SemEval 2014 to prove the effectiveness of this model. [PERSON] [24] proposed an sentiment classification model based on BiGRU and knowledge transfer which uses BiGRU to classify sentiments more accurately according to the semantics of aspect words, and obtains domain knowledge by combination with the knowledge transfer method. [PERSON] et al. [25] used a network model based on a bidirectional gating cyclic neural network which used BiGRU to ensure that the model would have fewer network parameters and work faster than CNN. In recent years, aspect-level sentiment analysis has also been applied in other fields. [PERSON] et al. [26] applied aspect-level sentiment analysis to research on the public's acceptance of vaccines during the COVID-19 pandemic. [PERSON] et al. [27] applied aspect-based sentiment analysis based on parallel dilated convolutional neural networks to smart city applications. In summary, aspect-based sentiment analysis has become a hot topic in the field of sentiment analysis in the past two years, and has attracted the attention of many sentiment analysis scholars. In actual application scenarios, aspect-based sentiment analysis has also been improved. The advantage of accuracy is resulting in the gradual replacement of sentence-level and text-level sentiment analysis. Chinese short text reviews usually contain both explicit aspect-level information and implicit aspect-level information. Therefore, aspect-level sentiment analysis technology requires not only explicit structural analysis, but also that attention be paid to implicit expression. Therefore, this paper combines CNN and BiGRU for aspect-level semantic sentiment analysis of short texts.
## 3 Case Study
### Sensitivity Analysis  
In this section, we perform a sensitivity analysis on several parameters to analyze the results of the impact of different parameters on the planning avoidance risk of the model. We show how different propulsion efficiencies \(\eta_{D}\) and the roughness allowance factors accounting for the roughness of the hull surface \(\Delta C_{f}\) differ in the average risk of a planned route to be analyzed.  
To analyze the effect of the ship's propulsion efficiency on the risk caused by bad weather and the sea state during the ship's voyage planning route, we applied the proposed model under different propulsion efficiency scenarios and calculated the average risk for the whole planned route. Figure 14 shows that the average risk value of the navigation will decrease with the increase in propulsion efficiency under the planned route. This means that the new model can avoid high risk in the route and further help the ship to better cope with the dangers of bad weather and the sea state in the ocean.  
Second, we analyzed the average risk of the planned route under different roughness allowance factor scenarios. Figure 15 shows that the value of the navigational risk will increase with the increase in the roughness allowance factor under the planned route. This implies that the roughness subsidy coefficient of different hulls has an impact on the navigational risk.  
Figure 14: Average risk value of planned routes under different propulsion efficiency scenarios.  
Figure 13: Comparison of the actual route and planned route of the validation case.
## 1 Introduction
\"Living\"/controlled radical polymerization (LCRP) approaches such as inferter-mediated polymerization [33], nitroxide-mediated free-radical polymerization (NMRP) [34], atom transfer radical polymerization (ATRP) [35, 36], and reversible addition-fragmentation chain transfer (RAFT) polymerization [37, 38], have been efficiently used to synthesize a variety of star polymers with controlled composition and molecular weight. Among these methods, RAFT polymerization is a facile and versatile method to prepare star polymers because of its many advantages such as relatively mild reaction conditions, wide range of monomers, tolerance of various functional groups, and lack of metal catalyst. Regardless of the polymerization methods (ionic or radical), preparations of star polymers described in the literature can be categorized into two broad approaches, namely, (i) the arm first [39, 40, 41] and (ii) the core first approaches [42, 43]. The arm first approach involves the synthesis of prefabricated arms, usually through \"living\"/controlled polymerization, followed by coupling reaction of living polymer chains with a cross-linking reagent (e.g., divinylbenzene or tetrachlorosilane). The core first approach involves the use of multifunctional initiator (the core) followed by the extension of arms through polymerization. In comparison with the core first approach, arm first approach can produce the stars with a larger number of arms. Thus the arm first method has been extensively utilized in the synthesis of various star polymers via \"living\"/controlled radical polymerization. Although star polymers have unique properties compared to their linear analogues, they have small intermolecular forces and lack a significant entanglement in the solid state, indicating that it is very difficult to prepare tough membranes with a good mechanical strength [24]. Therefore, in order to improve the mechanical properties, we developed a novel facilitated transport membrane by blending chitin (Cs), a good material for preparing membranes, with star PDMAEMA via RAFT polymerization, and investigated its gas permeation properties by using humidified feed gas and sweep gas because star PDMAEMA only contains a tertiary amine group as the CO\({}_{2}\) facilitated carrier.
## 4 Experimental Case Analysis and Discussion
### Imbalance Dataset
#### 4.4.3 Different Load Condition  
In the case analysis in Table 12, it is mentioned that the accuracy of different signal processing under the actual data is sufficient and balanced. This section discusses the impact of generated data on the cross-load diagnosis. Take time-frequency analysis and low-load measurement as the training set. For example, the unbalanced training data are expanded through the over-sampling-expanding (Pro-Expand) method. The data in the training set can make the accuracy rate comparison under the same number. The result of the comparison is shown in Table 8. We can obtain the time-frequency analysis and 95% accuracy in the cross-load test when the training data are adequately balanced. However, under the imbalance of the training data, the accuracy of the cross-load test with wavelet transformation can still reach close to 85%. We also found that if the imbalance rate is lower, the accuracy rate is lower. When the data are unbalanced, the DCGAN generated data and the amplified training dataset cannot achieve the diagnosis effect of the actual and balanced data. However, it still has an accuracy rate of 80%, and through VGG16 classification, an accuracy rate of up to 94.20% can be obtained. This shows that the generated data have learned distinguishable fault characteristics, and it also shows the ability of DCGAN to learn the characteristics of the input signal.
# A Platform of Unmanned Surface Vehicle Swarms for Real Time Monitoring in Aquaculture Environments
## 6 Data Acquisition and Analysis
### Direct Data Acquisition  
The option to gather data directly from each USV is also available, using a mobile app. The user may go directly to the USV and use the mobile app to transfer the data from the USV to the mobile device, through Bluetooth. All data can then be watched on the mobile app itself, as can be observed in Figure 14.  
The mobile app is created with the purpose of collecting the values in real-time obtained by node's sensors, once a Bluetooth session is established. The goal is to exhibit the values obtained by each node's sensor relatively to the acquired time. This is done through iterative graphs per node's sensor. The dashboard is developed in order to display the node's sensory data sent through LoRa.  
Both modules suffered modifications throughout this study in order to receive and interpret correctly the system's message formats sent by the USV and buoys of the system in study.  
Figure 13: Mission-related information on the dashboard.
# Differential Characteristics Based Iterative Multiuser Detection for Wireless Sensor Networks
## 5 Conclusions  
In this paper, an iterative multiuser detection algorithm based on differential characteristics for DS-UWB systems is explored. In this three stage receiver, the differential characteristics function  
Figure 8: The BER performance with different number of users. (**a**) AWGN; (**b**) IEEE 802.15.4a CM1.  
is calculated; then threshold detection is utilized to get a preliminary classification; the third stage is to further correct the error bits. The above three stage process procedure is iterated to further lower BER. The computational complexity of the proposed receiver is linear to \(nK\), where \(n\) is the number of iterations and \(K\) is the number of users. Simulation results illustrates that the proposed iterative multiuser detection algorithm convergences after only a few iterations. Furthermore, the BER performance, near-far resistance performance and user capacity performance are superior compared with MF, DEC, and MMSE.
## 5 Machine-Learning-Based Transfer Point Optimisation of Journey Profiles (Online)
### Many-to-Many Open-Network Shortest-Path Algorithm
#### 5.1.1 Speed-Up Techniques  
Our speed-up techniques build upon the same logic as _arc flags_ and _geometric containers_[51], in which the nodes of a transport network graph are partitioned into a set of distinct regions. Within each region, boundary nodes are identified as those which have edges leading to other regions. For each boundary node, a Dijkstra's search to all other nodes is performed, the results of which are used to define a bit vector for each edge leaving a boundary node, which specifies each region that can be reached via a shortest path including that edge. Then, future [PERSON]'s searches can prune edges leaving boundary nodes which are known not to be included on a shortest path to anywhere in the final target region. In our case, we define our set of regions using Algorithm 1.  
As an extra pruning step, we also introduce a _cross-region shortest-path pruning_ technique. Since we know that if the current \"resolved/scanned node\" in a [PERSON]'s search is not in the origin or destination region, the completed shortest path must include a shortest path between a pair of boundary nodes of the current region, because all sub-paths of shortest paths are also shortest paths. Based on this, we monitor which edges are included in shortest paths across each region between each pair of its boundary nodes, which is information that can be yielded during the pre-processing step of the arc flags speed up referred to above. If, in future [PERSON]'s searches, the current \"resolved/scanned node\" is not in the origin or destination region, we can prune edges which are never in shortest paths across that region. We refer to this speed-up technique as _cross-region edge labels_.  
Figure 4 displays the boundary nodes (green dots) of partitioned regions and cross-region shortest paths (blue); all of the grey edges can be pruned in a [PERSON]'s search when the current \"resolved/scanned node\" is not in the origin or destination region.  
Figure 4: Cluster boundary nodes (green squares) and cross-region shortest-path edges (blue lines) of the _cross-region edge labels_ speed-up technique applied to a pedestrian network.  
Appendix A contains experiment results for these speed-up techniques. In summary, the pre-processing requirements of these techniques increase with transfer-zone sample size, never exceeding an hour. The _arc flags_ technique alone halves query times on average, while the _cross-region edge labels_ technique alone reduces query times by two-thirds on average. When both techniques are applied simultaneously, query times can be reduced by 93%.
# A Polarization Independent Quasi-TEM Metamaterial Absorber for X and Ku Band Sensing Applications
###### Abstract  
In this paper, a dual-band metamaterial absorber (MMA) ring with a mirror reflexed C-shape is introduced for X and Ku band sensing applications. The proposed metamaterial consists of two square ring resonators and a mirror reflexed C-shape, which reveals two distinctive absorption bands in the electromagnetic wave spectrum. The mechanism of the two-band absorber particularly demonstrates two resonance frequencies and absorption was analyzed using a quasi-TEM field distribution. The absorption can be tunable by changing the size of the metallic ring in the frequency spectrum. Design and analysis of the proposed meta-absorber was performed using the finite-integration technique (FIT)-based CST microwave studio simulation software. Two specific absorption peaks value of 99.6% and 99.14% are achieved at 13.78 GHz and 15.3 GHz, respectively. The absorption results have been measured and compared with computational results. The proposed dual-band absorber has potential applications in sensing techniques for satellite communication and radar systems.  
Keywords:absorber; dual-band; metamaterial; quasi-TEM; sensing; X band +
Footnote †: journal: Sensors