esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Design of the Multi-Agent System Architecture (MAS)
#### 4.2.2 Selection of Algorithms and Technologies for Optimization  
The selection of the algorithms used in the model is based on their ability to address the specific challenges of port management in Ro-Ro terminals. Table 2 presents the advantages and disadvantages of each of these algorithms in the port context, highlighting their applications within the proposed multi-agent system. The integration of these algorithmic \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Entity** & **Main Function** & **Required Data** & **Human Interaction** \\ \hline EOO—Operations & Coordinates communication and synchronization between entities. Make strategic decisions and resolve conflicts in real time. & Terminal status (occupied spaces, traffic, ramp availability). Operational reports of each entity. & Supervisors can manually modify priorities and approve critical decisions. Operators can report incidents. \\ \hline EDV—Vehicle & Determines the best location for each vehicle in the field, reducing travel times. & Availability of spaces in the field. Type and dimensions of the vehicle. Estimated boarding time. & Operators can manually reassign locations in special cases. \\ \hline ENR—Navigation and Routes Entity & Calculate optimal routes to minimize transit times and energy consumption. & Current location and destination of each vehicle. Internal traffic status. & Operators can modify routes in the event of blockages or adverse conditions. \\ \hline ECF—Flow Control & It regulates traffic within the terminal to avoid congestion and high waiting times. & Traffic status at the terminal. Movement restrictions. & Controllers can manually activate restrictions or release locked zones. \\ \hline ECE—Shipping & Manage the arrival of vehicles at the boarding ramp, synchronizing the flow with the departure of the ship. & Departure time of the vessel. Capacity and availability of the boarding ramp. Condition of vehicles in transit. & Supervisors can modify boarding shifts. Managers can adjust load strategies. \\ \hline ESO—Supervisory and Optimization Entity & Evaluate system performance and adjust operating parameters to improve efficiency. & Operation KPIs of each entity. Performance history and data analysis. & Managers can review efficiency reports and make strategic adjustments. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Intelligent entities of the multi-agent system: function, required data, and human interaction. approaches supports adaptive route planning, dynamic space allocation, and decentralized traffic coordination aligned with terminal conditions. Table 2 provides a qualitative comparison of optimization approaches relevant to intelligent port management. It does not constitute direct algorithmic benchmarking. The algorithms used for each intelligent entity allow port processes to be optimized (Table 3):
# Unlicensed Spectrum Allocation for LTE and Wi-Fi Coexistence with HAP
## 2 Related Technology and Works
### LTE-Unlicensed (LTE-U)  
LTE-U [2] and Wi-Fi share the same unlicensed spectrum in a time-division manner. A certain portion of individual repeated cycles is allocated to LTE, and the rest is allocatedto Wi-Fi. LTE-U is favorable in that no PHY/MAC layer change is required. Carrier sense adaptive transmission (CSAT) [5] is a medium access procedure proposed by Qualcomm. Based on modifications of the carrier aggregation [6], CSAT enables the coexistence of LTE and Wi-Fi. The CSAT technique calculates the most appropriate coexistence period, ensuring that Wi-Fi is minimally affected. First, the LTE-U eNB performs CSAT to detect and analyze channel conditions of the unlicensed band, including the number of adjacent LTE-U eNBs, Wi-Fi APs, the types and lengths of packets, and so on. Next, according to the measurements, the adaptive duty cycle (ADC) will be divided into the on/off cycle, which defines the access periods of LTE-U eNB and Wi-Fi. The percentage of allocation (the duty cycle of the ADC) is a critical parameter for system performance. An informed choice of the allocation percentage is traffic dependent. It strongly affects the total system throughput and the fairness between the LTE and Wi-Fi networks. This issue is precisely the primary concern of this study.
## 2 Modeling and Simulation Method
### Modeling of the Modified MHD ARS Combing with Coriolis Effect (Abbreviated as C-MHD ARS)
#### 2.1.2 The Design of Fluid Channel  
The radial rate generation method by a MHD pump has been verified in paper [24] according to the numerical calculation and experimental results of a designed device. Figure 2 presents the streamline for inducing the radial velocity in the annulus and the whole fluid channel of the C-MHD ARS. The fluid in the middle channel is driven along the \(z\)-axis by the orthogonal magnetic field \(B_{x}\) and electrical current \(I_{y}\). Since the smaller flux of the middle channel is compared with the outside channel, the gravity is expected to be utilized in the reflux process. The direction of the velocity \(u_{0}\) generated by the MHD pump is advised upward instead of downward. Then the fluid is forced radially outward in the upper channel and travels through the outside channel to the bottom channel. In order to leave room for the energized electric current leads, the upper fluid channel may be incomplete. Therefore, the bottom channel is chosen as the sensing part and the magnetic field \(B_{z}\) is applied to generate the electromotive voltage \(\varphi\) as the case rotating with angular velocity \(\Omega\). Thus, the sensing part works as shown in Figure 1b. As it rotates, the Coriolis effect is induced by the radial flow to accelerate the relative circumferential velocity \(u_{\theta}\) especially at low frequency.
# Issues with Modeling a Tunnel Communication Channel through a Plasma Sheath
###### Abstract  
We consider two of the most relevant problems that arise when modeling the properties of a tunnel radio communication channel through a plasma layer. First, we studied the case of the oblique incidence of electromagnetic waves on a layer of ionized gas for two wave polarizations. The resonator parameters that provide signal reception at a wide solid angle were found. We also took into account the unavoidable presence of a protective layer between the plasma and the resonator, as well as the conducting elements of the antenna system in the dielectric itself. This provides the first complete simulation for a tunnel communication channel. Noise immunity and communication range studies were conducted for a prospective spacecraft radio line.  
blackout problem; electromagnetic wave tunneling; numerical modeling; waves in plasma; re-entry spacecraft radio line +
Footnote †: journal: Sensors
## 4 Discussion
### Interference Effects of Wind Loads
The contour plots of the horizontal wind velocity for both states of group-arranged cranes are shown in Figures 15 and 16 respectively. The boom height (57 m in prototype) is selected for the slice plane. And the wave flow around the jib and machine house can be demonstrated. It is found that when \(\alpha\) is or near \(90^{\circ}\), more members are hidden behind from the windward structures, 1# or 2# crane. As shown in Figure 15a, compared with that of the boom-up state, when the jib is horizontally settled in the boom-down state, more windward members are shielded. Though the wake flow may enhance turbulence, the shading effect is always dominant and tends to reduce wind loads. Thus, the IE is thought as the combined effects of both the shielding effect and the amplification of turbulence. As for the boom-down crane at \(\alpha=165^{\circ}\), the shielding effect is so wake that the turbulence generated by the wave flow amplified the extreme wind loads. However, because of the lifting boom, the windward boom-up crane mainly affects the seaside girder and part of the machine house. Thus, IE is less obvious than that of the boom-down crane and the factor is near 1. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{3}{*}{**Nondimensional**} & \multicolumn{8}{c}{**Interference Factor**} \\ \cline{2-13}  & \multicolumn{4}{c}{**DIF\({}_{\text{Ms}}\)**} & \multicolumn{4}{c}{**DIF\({}_{\text{My}}\)**} \\ \cline{2-13}  & \multicolumn{2}{c}{**Boom-Down**} & \multicolumn{4}{c}{**Boom-Up**} & \multicolumn{4}{c}{**Boom-Down**} & \multicolumn{4}{c}{**Boom-Up**} \\ \cline{2-13}  & **Value** & **Position** & \(\alpha\) & **Value** & **Position** & \(\alpha\) & **Value** & **Position** & \(\alpha\) & **Value** & **Position** & \(\alpha\) \\ \hline  & & **No. ** & (°) & & **No. ** & (°) & & **No. ** & (°) & & **No. ** & (°) \\ \hline
0.484 & 1.165 & 3\# & 165 & 0.983 & 1\# & 60 & 1.243 & 3\# & 165 & 0.987 & 1\# & 60 \\
0.848 & 1.126 & 3\# & 165 & 1.009 & 3\# & 45 & 1.217 & 3\# & 165 & 1.014 & 3\# & 45 \\
## 3 Analysis of Bearing Capacity of Shield Tunnels with Different Segment Thickness  
### Structural Convergence Deformation under Normal Design Loading  
Figure 15 shows the convergence deformation of the shield tunnel with different segment thicknesses. It can be observed that as the burial depth increases, the deformation of the tunnel with different segment thickness increases to varying degrees. The convergence deformation of the tunnel with 350 mm segment thickness increases nonlinearly with the burial depth, while the tunnel with other thickness segment thickness increases linearly. When the burial depth increased from 10 to 20 m, the tunnel horizontal deformations with 350 mm and 375 mm segment thickness increase by 3.8 and 2.4 times, respectively, while the tunnel horizontal deformations with 400 mm and 425 mm segment thickness both increase by 2.2 times. Figure 16 shows the normalized graph of tunnel convergent deformation under different buried depths. Under the shallow burial condition, when the segment thickness increases by 25 mm, 50 mm and 75 mm, the horizontal deformation decreases by 18.0%, 20.0%, and 4.4%, respectively. Under the deep burial conditions, the horizontal deformation decreases by 42.8%, 16.7%, and 2.5%. Therefore, increasing the thickness of the segment from 350 to 400 mm can significantly reduce the convergence deformation of the tunnel structure. However, if the thickness of the segment continues to increase, the reduction in tunnel structural deformation is not significant.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Load Ratios** & **P\({}_{\textbf{1}}\)/kN** & **P\({}_{\textbf{2}}\)/kN** & **P\({}_{\textbf{3}}\)/kN** & **P\({}_{\textbf{4}}\)/kN** \\ \hline
0.75 & 267 & 298 & 200 & 285 \\
0.72 & 267 & 298 & 192 & 274 \\
0.70 & 267 & 298 & 187 & 261 \\
0.68 & 267 & 298 & 182 & 258
# A Review on the Lifecycle Strategies Enhancing Remanufacturing
## 4 Recent Trends in Remanufacturing
### Industry 4.0 (I4.0)  
Industry 4.0 is a term used to refer to the fourth industrial revolution, which has brought advancements in digital technologies that will completely change traditional man ufacturing architecture [112; 113]. It can be efficiently used to aid digital transformations of an organization to achieve sustainable development goals, and it can also help manufacturers enhance advanced manufacturing capabilities and further meet their sustainable development goals [114]. However, I4.0 technologies pose a challenge because they are relatively new and manufacturing companies face difficulties such as skill gaps, financial constraints and operational complexities in I4.0 projects [62; 99]. The key impact of I4.0 is its ability to produce and access real-time information to allow increased visibility and to mitigate risks in the supply chain network [112].  
Globally, firms are focusing on developing sustainable production and consumption strategies to reduce their negative environmental and social impacts. This thereby helps them to carry out manufacturing and remanufacturing activities in an environmentally friendly manner. [PERSON] et al. [115] discussed the opportunities for I4.0 to support remanufacturing and the three main factors they describe are smart lifecycle data, smart factories and smart services. There are a lot of factors that must come into action to ensure that I4.0 can be appropriately used in remanufacturing, and since it is a new domain, research institutes and universities are playing an important role and are constantly working to find the methods that can be used to make the running of I4.0 successful. The use of big data to collect information so as to improve I4.0 has been closely studied by [53; 98; 116]. Moreover, the IoT has also been listed as a key factor for I4.0 with regard to scheduling of various I4.0 processes [52; 99; 100]. For remanufacturers to fully integrate into I4.0, the remanufacturing companies will have to upgrade their technology to modern day technology, such as by establishing a digital technology link between manufacturers and users [117].
# Smart Waste Collection System with Low Consumption LoRaWAN Nodes and Route Optimization
###### Abstract  
New solutions for managing waste have emerged due to the rise of Smart Cities and the Internet of Things. These solutions can also be applied in rural environments, but they require the deployment of a low cost and low consumption sensor network which can be used by different applications. Wireless technologies such as LoRa and low consumption microcontrollers, such as the SAM L21 family make the implementation and deployment of this kind of sensor network possible. This paper introduces a waste monitoring and management platform used in rural environments. A prototype of a low consumption wireless node is developed to obtain measurements of the weight, filling volume and temperature of a waste container. This monitoring allows the progressive filling data of every town container to be gathered and analysed as well as creating alerts in case of incidence. The platform features a module for optimising waste collection routes. This module dynamically generates routes from data obtained through the deployed nodes to save energy, time and consequently, costs. It also features a mobile application for the collection fleet which guides every driver through the best route--previously calculated for each journey. This paper presents a case study performed in the region of Salamanca to evaluate the efficiency and the viability of the system's implementation. Data used for this case study come from open data sources, the report of the Castilla y Leon waste management plan and data from public tender procedures in the region of Salamanca. The results of the case study show a developed node with a great lifetime of operation, a large coverage with small deployment of antennas in the region, and a route optimization system which uses weight and volume measured by the node, and provides savings in cost, time and workforce compared to a static collection route approach.  
s smart waste collection; low powered sensors; wireless sensor networks; data analytics; decision-making; LoRaWAN; open data; VRP; CVRP
# State-of-the-Art DC-DC Converters for Satellite Applications: A Comprehensive Review
## 5 Future Directions
### Solar Power Satellites  
Space-based solar power (SBSP) is a method in which solar energy in space is harvested for utilization on Earth. SBSP necessitates wireless power transmission via microwaves, laser, or by reflecting sunlight directly. It is expected to change the future of electricity generation and distribution by producing electricity in an efficient and pollution-free manner. A wireless energy grid may involve beaming power from Earth to space and back. The spacecraft may also be located in different orbits, such as LEO or GEO. Although the technology has enormous potential, it is still in the conceptual stage. Several obstacles must be overcome, resulting in a new area of research for future scientists and engineers [144; 145; 146].
# Structural Damage Detection Based on One-Dimensional Convolutional Neural Network
## 2 Formulation of Analytical Model
### Convolutional Neural Networks
However, the ReLU activation function has a relatively broad excitatory boundary. It solves the vanishing gradient problem. Additionally, the ReLU activation function is the most used activation function in deep learning. (3) Pooling layer  
The role of the pooling layer is to reduce the size of the feature map. The pooling layer is similar to the convolutional layer, it also has a kernel. However, the pooling kernel does not contain any parameters. The pooling layer is divided into maximum pooling layer and mean pooling layer. The pooling operation starts at the upper left corner of the input data. For the maximum pooling, the maximum value of all the parameters in the coverage area is outputted. For the mean pooling, the mean value of all the parameters in the coverage area is outputted. The movement of the pooling kernel is the same as the movement of the  
Figure 4: Convolution operation diagram. Figure 5: Activation function. convolutional kernel. The pooling layer is also called the down sampling layer, which can reduce the parameters in the feature map and improve the training efficiency of the model. (4) Fully connected layer  
The fully connected layer can be seen as a \"classifier\" in the convolutional neural network. The convolutional layer maps the raw data to feature space of the hidden layer, and the fully connected layer maps the learned distributed feature to the sample tag space. Each neuron in the fully connected layer is connected to all neurons in the anterior layer. The output signal of the fully connected layer is passed to the objective function (loss function) for classification. A dense layer is a classic fully connected neural network layer. The output shape of last fully connected layer is 4 in this CNN. This value is consistent with the number of categories. (5) Loss function  
The inconsistency between the predicted value and the actual label of each sample is called loss. The smaller the loss, the better the detection performance of the training model. The function that is used to calculate the loss is called the loss function. The most used loss function in deep learning is softmax, which is used for multi-classification problems. It can map the outputs of multiple neurons into intervals of 0 to 1. These values can be understood as prediction probabilities. The label corresponding to the highest probability is outputted as our prediction result.
# Determination of Geometrical REVs Based on Volumetric Fracture Intensity and Statistical Tests
## 2 The Establishment of a 3D Fracture Network Model  
### Data Collection from the Study Area  
In this study, the Maji hydropower station dam site was used as a study area, and the REVs of fractured rock masses were discussed based on the volumetric fracture intensity P\({}_{32}\) and two statistical tests. The Maji hydropower station is located in the middle and lower reaches of the Nu River in China (Figure 1). A 290-m-high concrete arch dam, which has a hydroelectric generating capacity of 4200 MW, is planned for this site. An approximate mountain-canyon topography is present at the dam site area; the difference in the bank slope elevation is generally above 2000 m, and the riverbed elevation ranges from 1320 to 1403 m.  
Figure 1: The location map of the Maji dam site area.  
The 12,339-m-long tunnel that is located on both sides of the dam site was surveyed. The results indicated that the mixed granite and mixed gneiss, which are more than 400 m thick, have a lenticular grain structure and are exposed in the right bank slope; the general orientation of the gneissosity is 330\({}^{\circ}\)N-340\({}^{\circ}\) W/SW\(\angle\)75-85\({}^{\circ}\). Neither a large-scale fault nor a fault zone is located on either bank. Fractures with steep dip angles are better developed, while others with low dip angles are less developed. Except for the fractures that were located in the strong unloading zones and weak unloading zones and were located near large faults, the fractures are commonly closed. A few joints were filled with papery rock debris, the others had sparse black mica precipitation.  
Tunnel PD222, which is striking northwest and located in the right bank slope, is almost orthogonal to the Nu River. The 979 fracture data points from five tunnels near tunnel PD222 were surveyed by the rectangular window sampling method and included data such as orientation, coordinates, trace length, fill and ground water [37]. In this study, due to engineering requirements, tunnel PD222 was used as the main research object, and the 2D fracture traces are described in Figure 2. Of all the data, 213 fracture data points, including fracture orientation, trace length, and fracture spacing, were selected to generate a 3D fracture network model for engineering purposes.
## 4 Methodology  
### Model Overview  
The goal of our model is to better fuse different modal representations of heterogeneous data and alleviate the performance degradation caused by missing modalities. The core idea is to use contrastive learning to generate pseudo-labels to \"complete\" the missing data of each modality, and then apply a two-level fusion method of node and meta-path to fuse the \"completed\" data. The overall structure is shown in Figure 1.  
**Feature extraction.** The input of our model consists of four types of data: text, emoji, meme, and inner text. In the first part of the process, as shown in the upper left of Figure 1, feature extraction is performed on these four types of data. Through this step, the features of each modality are extracted and represented as embeddings.  
**Partition.** The data are divided into two categories: complete modalities and missing modalities, as shown in the upper middle of Figure 1. This partition is based on whether data from a particular modality are missing or available. The incomplete modalities are separated and will undergo a different processing flow.  
**Complete.** The complete modal data are used to train a model for modality completion, as shown in the upper-right section of Figure 1. The model learns to generate pseudo-labels and complete the missing parts of the data. This enables the system to predict the missing modality data, thus \"complete\" the missing data of each modality. When this step is completed, all the data have all the modes (original or \"completed\").  
**Prediction.** A heterogeneous graph neural network (H-GNN) is used to model multimodal data and their relationships, as shown in the bottom part of Figure 1. This model aggregates features using meta-paths and adds a virtual node that associates all modalities, facilitating the fusion of information from different meta-paths. The fused node representations are then used to perform sentiment classification tasks.  
The following details the core modules in the \"complete\" and \"prediction\" sections: \"contrastive learning based on multimodal data completion\" and \"heterogeneous graph neural networks\".