esa-sceva/satcom-chunk-collection huggingface dataset:
# Numerical Study of Internal Flow Field in a Disc Stack Centrifuge Based on Mixture-PBM Model
###### Abstract  
Disc stack centrifuge belongs to one kind of sedimentation centrifuge, widely used in the environmental protection, pharmacy, and chemical industries, etc. The flow process inside the disc stack centrifuge seriously affects the separation efficiency. However, the flow process inside the disc stack centrifuge and its influence on the separation efficiency have not yet been detailed. We plan to study the flow process of oil and water phases inside the disc stack centrifuge and to explore the process of fragmentation and accumulation of water droplets. In this study, the Mixture-PBM (Population Balance Model) model is used to numerically simulate the two-phase flow of oil and water in the disc stack centrifuge and compare it with the tests. The research found that with the increase in rotational speed, the separation efficiency rises in both the test and numerical simulation results, and the difference between the test and simulation results is below 1%. The effect of ribs on the flow is considered, and the results show that the hysteresis of the liquid flow in the disc stack centrifuge is significantly reduced after considering the ribs, and the numerical simulation results can reach 98% of the theoretical results. Fluid entering the separation channel from the neutral pore creates a vortex, and as the dimensionless number \(\lambda\) increases, the degree of deviation of the fluid's trajectory from the generatrix increases. The circumferential and radial velocities in the separation channel appear large in the center and small near the wall. The water content in the rising channel gradually decreases, and 90% of the water finishes settling in the distributor. The processing volume of the separation channel in each layer shows a small bottom layer, a large top layer, and a uniform law in the middle. The coalescence of water droplets occurs mainly in the separation channel, as found by analyzing the laws of the internal flow of the disc stack centrifuge, which provides the basis for improving the structure of the disc stack centrifuge, increasing the separation efficiency and reducing the floor space.  
disc stack centrifuge; two-phase flow; Mixture-PBM coupling model; internal flows +
Footnote †: journal: Applied  
0000-0001-6000-0000][PERSON]  
1 Systems Engineering Research Institute, China State Shipbuilding Corporation Limited, Beijing 100194, China; [EMAIL_ADDRESS] (H.D.)1  
Correspondence: [EMAIL_ADDRESS] (S.L.); [PERSON]ESS] (S.L.)
# High-Efficiency Clustering Routing Protocol in AUV-Assisted Underwater Sensor Networks
## 4 Simulation and Performance Evaluation
### Simulation Results  
#### 4.2.1 Network Lifetime  
The network lifetime is generally defined as the total time that all nodes survive [19]. When the residual energy of a node is 0, the node dies; this paper measures the network lifetime in terms of the number of deaths of the nodes. To evaluate the network lifetime of HECRA in different network environments, experiments are carried out with different numbers of nodes and different transmission range scenarios.  
Figure 5a shows the number of deaths of nodes for 1000 rounds of network operation when the transmission range is 200 m, and the number of sensor nodes is 450. The graphical results show that LEACH has an excessive number of dead nodes owing to unbalanced node energy consumption caused by the randomness of CH node selection, whereas the EERBLC, EECMR, and HECRA node deaths are slower overall because all three protocols are optimized for cluster head selection. HECRA further reduces the number of node deaths by implementing a node sleep-wake strategy. After 1000 rounds of network operation, HECRA reduces the number of dead nodes by 1.88%, 3.86%, and 36.57% compared to that of EECMR, EERBLC, and LEACH, respectively.  
Figure 5b shows the number of dead nodes for a transmission range of 150 m and a varying number of sensor nodes between 50 and 450, where the number of sensor nodes is varied every 1000 rounds of operation. The graphical results show that the average number of dead nodes in HECRA is reduced by 0.8%, 12.87%, and 20.18% compared to that in EECMR, EERBLC, and LEACH, respectively, for varying numbers of nodes, where  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Simulation Parameters** & **Value** \\ \hline Initial node energy & 5 J \\ Data packet size & 200 bits \\ Transmit power & 2 W \\ Receive power & 0.1 W \\ Number of sink nodes & 1 \\ Acoustic frequency & 30 kHz \\ Rounds & 1000 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation parameters.  
Figure 4: Network after HECRA.  
LEACH performs the worst in terms of the number of node deaths because all the nodes are randomized to become CHs sequentially in this protocol, which leads to unbalanced energy consumption. The EERBLC achieves more balanced energy consumption by optimizing the cluster head selection considering the node degree and residual energy; hence, the number of node deaths is lower than that of the LEACH algorithm. EECMR stratifies the network based on depth on the basis of CH selection and introduces relay nodes to reduce energy consumption further; hence, the number of node deaths is lower. HECRA is optimized for the CH data at the data transmission stage for multihop transmission, and dynamic relay nodes are introduced to save energy more effectively; hence, the number of node deaths is slightly lower than that of EECMR.
# Seamless 5G Multi-Hop Connectivity Architecture and Trials for Maritime Applications
## 4 Trials in the Baltic Sea Corridor (Tallinn-Helsinki)
### Performance Measurements  
The main purpose of the performance evaluation is to assess how well the network and the use cases developed in the 5G-ROUTES project fulfill the requirements. Starting from the requirements of the use cases, as described in Section 4.1, a set of target KPIs were defined related to latency, throughput, coverage, reliability and position accuracy. The KPIs are aligned with the KPIs identified by the 5G PPP [36]. The network KPIs address the performance and capability of the network, such as the peak data rate and user plane latency and are measured using standardized procedures using synthetic traffic [37]. A tool called tenant web portal has been developed, acting as a data repository for the measurements and as a visualization tool for the KPIs.  
The use-case-specific KPIs will be measured within trials from the first half of 2023 until spring 2024. Test scenarios were defined to measure the data needed for the KPIs during ferry operation, including cross-border/-MNO service continuity. After the tests, the data will be analyzed, and KPIs will be calculated and visualized. Starting from the results of the different test scenarios, conclusions will be drawn on the performance of the network and the use cases. At least two iterations will be applied within the trials for the possibility of performing actions to improve the performance of the network and use cases.
# Research on Fault Diagnosis Method for Rotating Machinery Based on Edge Computing
## 4 Fault Diagnosis Method Based on Edge Computing
### Construction of the Dual-Channel CNN Decision-Level Fusion Model  
This paper proposes three optimizations for the traditional CNN. Firstly, a dual-input channel design is introduced, combining temperature and vibration sensors for fault analysis and diagnosis. Independent CNN convolutional and pooling layers are constructed for each modality of data, and the decision probabilities output by each CNN are fused. Secondly, in the vibration sensor channel, the envelope spectrum is used as a substitute for the raw measurement input, as described in Section 4.3.1. Finally, the Dempster-Shafer evidence theory method is incorporated into the fully connected layer to perform decision-level fusion of the dual-input CNNs, as detailed in Section 4.3.2. These optimizations significantly enhance the model's robustness, generalization ability, and performance. The optimized model in this study is denoted as CNN-DS. This optimization effectively improves the accuracy of fault diagnosis and, by integrating data from different sensors, provides a more comprehensive approach to analyzing and identifying fault patterns in rotating machinery. The results of the model are shown in Figure 6.
## 3 Results and Discussion
### Physical-Chemical Analysis of Fruits
#### 3.2.7 Color Index  
The color of the epidermis fruit is a benchmark of the maturity index [6]. This is due to the chlorophyll degradation starting with the phytol dissociation residue and the porphyrin ring of the chlorophyll molecule. This reaction is catalyzed by an enzyme called chlorophyllase [60]. This reaction allows the color of the fruit to increase its intensity, as shown in Figure 3. We determined the color changes using a colorimeter in the CIE L * a * b * scale, where L * indicates lightness and a * and b * are the chromaticity coordinates (rectangular coordinates). On the other hand, +a * is the red direction, -a * is the green direction, +b * is the yellow direction, and -b * is the blue direction [58].  
Figure 3 shows an ascent of this parameter from day 0 to day 12 in all treatments and control, indicating that during the days evaluated the effect of chlorophyll degradation affects the red/green coordinates (a *) and the yellow/blue coordinates (b *) intensifying their value. These results depend on the postharvest characteristics, and the state of maturity (3-4) due to this it is expected that the color index and the evolution of the carotenoid content are high in this phase [6].  
Statistical data show that there was a significant difference (\(p<0.05\)) concerning days and treatments. The multiple range test showed that the most significant difference concerning the procedures was for the fruits coated with chitosan and 1.0%RGEO. An additional analysis of the coatings of CS + RGEO (0.5%, 1.0%, and 1.5%) showed that in the evaluated color scale, a lower presence of yellow and red tones was observed in comparison with the uncoated samples. Similar results were previously obtained in other researches with chitosan-essential oil coatings [23].  
Figure 3: The color index in the fruits of cape gooseberries during the 12 days of storage covered with each treatment. The superscript letter (a–d) refers to the significant differences (\(p<0.05\)) between each treatment.
# Wearable Vibration Based Computer Interaction and Communication System for Deaf
## 3 Materials and Methods
### Localization Algorithm Based on the Microphone Array  
Position estimation methods in the literature are generally time of arrival (TOA), arrival time difference (TDOA) and received signal strength (RSS) based methods [36]. TDOA-based methods are highly advantageous because they can make highly accurate predictions. TDOA-based methods that use the maximum likelihood approach require a starting value and attempt to achieve the optimal result in an iterative manner [37]. If the initial values are not properly selected, there is a risk of not reaching the optimum result. In order to remove this disadvantage, closed form solutions have been developed.  
Closed-loop algorithms utilize the least squares technique widely used for TDOA-based position estimation [38]. In TDOA-based position estimation methods, time delay estimates of the signal between sensor pairs are used. Major difficulties in TDOA estimation are the need for high data sharing and synchronization between sensors. This affects the speed and cost of the system negatively. The traditional TDOA estimation method in the literature uses the cross-correlation technique [39].
# MT-CMVAD: A Multi-Modal Transformer Framework for Cross-Modal Video Anomaly Detection
## 4 Experiment
### Performance Metrics Formulation  
To rigorously evaluate model performance, we provide formal definitions of key metrics with mathematical formulations.  
Area Under ROC Curve (AUC)  
Given anomaly scores \(s_{i}\in\mathbb{R}\) for \(N\) test frames and binary labels \(y_{i}\in\{0,1\}\), the ROC curve plots the True Positive Rate (TPR) against the False Positive Rate (FPR) across decision thresholds \(\tau\). Let \(\mathcal{S}^{+}=\{s_{i}|y_{i}=1\}\) and \(\mathcal{S}^{-}=\{s_{i}|y_{i}=0\}\). The AUC computes the probability that a randomly chosen anomaly frame has a higher score than a normal frame:  
\[\text{AUC}=\frac{1}{|\mathcal{S}^{+}||\mathcal{S}^{-}|}\sum_{s^{+}\in \mathcal{S}^{+}}\sum_{s^{-}\in\mathcal{S}^{-}}\mathbb{I}(s^{+}>s^{-}) \tag{28}\]  
where \(\mathbb{I}(\cdot)\) is the indicator function. Higher AUC values (range: [0, 1]) indicate better separability between anomaly/normal frames,  
\[\text{with TL-P}=\frac{|\mathcal{P}_{\text{matched}}|}{|\mathcal{P}|},\quad \text{TL-R}=\frac{|\mathcal{G}_{\text{matched}}|}{|\mathcal{G}|}\]  
All metrics are computed under non-overlapping cross-validation protocols to ensure statistical reliability.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & **Train-** & **Train-** & **Evaluation-** & **Evaluation-** \\  & **Normal** & **Abnormal** & **Normal** & **Abnormal** \\  & **(Videos)** & **(Videos)** & **(Videos)** & **(Videos)** \\ \hline UCF-Crime & 800 & 800 & 150 & 150 \\ UCF-Fights & 627 & 157 & 172 & 44 \\ Uhnormal & 239 & 240 & 26 & 38 \\
**Total** & 1666 & 1197 & 348 & 232 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Dataset Partition for Training and Evaluation.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & **Normal Event** & **Abnormal Event** & **All** \\  & **(Videos)** & **(Videos)** & **All** \\ \hline UCF-Crime & 950 & 950 & 1900 \\ UCF-Fights & 784 & 216 & 1000 \\ Uhnormal & 265 & 278 & 543 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Distribution of Normal and Anomalous Events in Benchmark Datasets.
# Smart Environmental Data Infrastructures: Bridging the Gap between Earth Sciences and Citizens
## 2 System Architecture
Recently, the broad proliferation of the use of mobile devices connected to the Internet has opened new opportunities to complement the above Earth observation means by people supported sensing. Generally, this kind of new sensing may have the form of either crowdsensing applications that make use of sensors already installed or connected to the mobile devices or virtual sensors that extract useful information from social media. The main challenge to be overcome by this component is the effective integration and fusion of the above highly heterogeneous types of data sources, taking into account both syntactic and semantic conflicts. **Data and metadata storage (Section 3). ** Various data modeling and representation approaches have been proposed for geospatial and environmental data in the areas of GIS and spatial and environmental data management. As a consequence, well-established solutions exist for both vector features and coverages, and relevant standards are implemented by current tools. General models for the representation of geospatial metadata at different levels of the dataset structures have also been proposed, including specific models for sensor observation semantics. The adoption of semantic web technologies for geospatial and environmental datasets have also been investigated, leading to some general-purpose solutions for representation and storage. More specific ontologies and vocabularies have also been designed that foster interoperability between disconnected data silos. The main research challenges in the scope of this component are related to the effective and efficient incorporation of raster coverage representation and storage within semantic web ecosystems. **Data searching and browsing (Section 5). ** Efficient geospatial data querying has been a topic of research in the area of spatial data management since more than 30 years ago and it is supported in current spatial databases. Additionally, relevant mature OGC web service interfaces exist for filtering over datasets of either vector features or raster coverages. Catalog services enable geospatial data discovery based on standard metadata models. Web map services enable the server-side rendering of maps for geospatial dataset browsing. Regarding semantic web technologies, federated querying is supported for vector entities through GeoSPARQL endpoints. Figure 2: Main components of an environmental data infrastructure. Main open issues and challenges for research are related to the integration of very large datasets of vector and raster data during efficient federated querying, effective and efficient keyword-based searching and efficient query processing for interactive exploratory analysis workloads.
# Compression Law and Settlement Calculation Method of Over-Wet Soil Based on Large Samples
## 3 Results and Discussion
### Analysis of Water Changes in Different Compression Stages of Over-Wet Soil
However, the decrease in the secondary and tertiary consolidation stages is insignificant. Weakly bound water minimally decreases during the primary consolidation stage (Figure 15b). However, it significantly decreases in the tertiary consolidation stage at approximately 77.9% (200 kPa) and 57.6% (400 kPa) for the two sets of data. Figure 15c shows that the content of strongly bound water is lower than that of free and weakly bound water. However, except for a decrease in the third consolidation stage at 400 kPa, there is almost no change (Figure 15d). Studies have indicated that bound water affects the creep deformation of soil [26; 27]. Based on the data in Figures 10 and 13, the discharge of weakly bound water during creep is approximately  
Figure 14: Thermogravimetric curve of the sample before loading at 200 kPa. 15 times that of strongly bound water, indicating that the discharge of weakly bound water is the main cause of creep. Figure (a)a shows the relationship curve between the water content and load for water with different binding forces. According to Equation (5), the proportion of water with different binding forces to the total water content was calculated, and a curve was drawn, as shown in Figure (b)b. Equation (5) is as follows:  
\[Z=\frac{w_{ij}}{w_{t}}\times 100\%, \tag{5}\]  
where \(Z\) = proportion of water with different binding forces (%). \(w_{i,\ j}\) = water content of the \(j\)th type of binding force at the \(i\)th load (%). \(w_{i,\ t}\) = total water content at \(i\)th load (%). Figure (a)a shows that the free and weakly bound water contents continuously decrease during loading. The decrease at 200 kPa is below that at 400 kPa, and the ratios of the two load levels are 1:2.08 (free water) and 1:2.18 (weakly bound water). The content of strongly bound water minimally changes as the load increases. Figure (b)b shows that despite the content of free and weakly bound water decreasing significantly, their ratio to the total moisture content slightly changed by 9.97% and 4.04%, respectively. However, owing to the insignificant change in the content of strongly bound water, its proportion increased from 4.97% to 20.98%. Figure 15: Changes in water with different binding forces. Thermogravimetric data indicate that the creep of over-wet soil is related to the discharge of bound water, but researchers have not yet obtained similar research results. In the process of foundation treatment, the effect of reducing post-construction settlement of excessively wet soil can be achieved by accelerating the discharge of weakly bound water.
## 4 Curve Calibration Using an Axisymmetric Constitutive Model  
To represent the geometry of the cylindrical experimental specimens, an axisymmetric model was implemented in Plaxis 2D, using dimensions of 1 m \(\times\) 1 m to represent the soil domain under analysis. Once the geometric model was defined, the domain was discretized, and initial stress conditions, boundary conditions, and flow conditions were established [42]. Figure 15 shows the configuration of the specimen, including boundary and flow conditions.  
The calibrated parameters for both constitutive models were directly applied to this geometry. The staged construction process integrated into the software was used to simulate the phases of the CIU triaxial test, which correspond to the red, blue, and green curves shown in Figures 16 and 17. The process was divided into the following stages: (i) initial phase, (ii) consolidation phase, and (iii) shearing phase.  
The initial phase initializes the specimen's stress state using the \"K\({}_{0}\) procedure\" calculation type. The consolidation phase simulates the isotropic consolidation stage of the experimental test, gradually applying vertical and lateral stress conditions [43].  
It is important to note that the NS constitutive model, which is designed to capture the plastic behavior of soil, does not accurately represent the initial effective stress equilibrium or the volumetric strains associated with soil compressibility, due to its limited treatment of initial elastic behavior. Therefore, initial stress conditions must be established using the HSS model, which ensures a stable stress state before proceeding to the shearing phase with NS [44].  
Figure 14: Calibration results of the pore pressure-strain curve. (**a**) Comparison of experimental curves and simulated curves from the HSS model. (**b**) Comparison of experimental curves and simulated curves from the NS model.  
Figure 15: (**a**) Three-dimensional model of the specimen. (**b**) Consolidation phase. Flow boundary conditions. (**c**) Shearing phase. Deformation boundary conditions.  
To rapidly apply the confining pressure, the \"Plastic\" calculation type was used with an initial time step (t = 0). Simultaneously, undrained conditions were simulated by keeping the flow boundaries closed, allowing the buildup of excess pore pressure in the modeled sample. Subsequently, drainage was allowed to simulate the consolidation process, enabling the gradual dissipation of pore pressures and reaching stress equilibrium. Finally, during the shearing phase, an axial deformation load was applied to the soil. Deviatoric stress and pore pressure behavior were monitored. As in the previous phase, a \"Plastic\" calculation type was used, and water flow was restricted to maintain undrained conditions.  
This procedure was conducted for three confining pressures--100, 200, and 400 kPa--allowing the simulation of curves corresponding to the segmented lines (Axis), which were then compared with the experimental curves (semi-transparent color curves, HSS) previously defined.