esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Development of the 7-DOF Human Model  
### Model Description  
The proposed 7-DOF biodynamic model consists of lumped parameters, including mass, spring, and damper elements. The model considers distinct body segments, comprising the upper body parts (including the head, neck, trunk, and upper extremities) and the lower body parts (including the pelvis, thigh, knee, leg, ankle, and foot). Each segment is treated as nonoverlapping, with the center of mass identified close to the proximal joints. The transmission of vibration from the seat to other segments is influenced by the viscoelastic properties of the human-seat coupling. Viscoelastic seats exhibit higher resonance frequencies compared to rigid seats [12; 20]. To account for this, we represent the human-seat coupling using effective translational spring-damper combinations attached to the trunk and thigh, mimicking the viscoelastic properties of human skin and seat foam. The coupling between the shoe, foot skin, and foot segment is also modeled as an effective translational spring damper. These spring-damper combinations are attached perpendicularly to their respective body segments. Additionally, the joints at the hip, neck, knee, and ankle are modeled as torsional spring-damper elements, allowing rotation in the x-z plane.  
Figure 1 illustrates the 7-DOF seated human model, consisting of five rigid bodies connected by dynamic joints and supported by a backrest. The model is subjected to vertical excitation, with \(m_{1}\), \(m_{2}\), \(m_{3}\), \(m_{4}\), \(m_{5}\) representing the mass and \(J_{1}\), \(J_{2}\), \(J_{3}\), \(J_{4}\), \(J_{5}\) representing the moment of inertia of the head, trunk, thigh, leg, and foot, respectively. The lengths of the segments are denoted as \(L_{1}\), \(L_{2}\), \(L_{3}\), \(L_{4}\), \(L_{5}\), and the trunk, thigh, and foot have thicknesses \(T_{2}\), \(T_{3}\), \(T_{5}\). The translational spring-damper elements attached to the trunk, thigh, and foot are characterized by stiffness coefficients \(k_{21}\), \(k_{22}\), \(k_{31}\), \(k_{32}\), \(k_{51}\) and damping coefficients \(c_{21}\), \(c_{22}\), \(c_{31}\), \(c_{32}\), \(c_{51}\). The distances between the joints and the spring-damper combinations on trunk, thigh, and foot are represented by \(d_{21}\), \(d_{22}\), \(d_{31}\), \(d_{32}\), and \(d_{51}\). The torsional spring-damper elements at the joints have stiffness coefficients \(k_{1}^{T}\), \(k_{2}^{T}\), \(k_{3}^{T}\), \(k_{4}^{T}\) and damping coefficients \(c_{1}^{T}\), \(c_{2}^{T}\), \(c_{3}^{T}\), \(c_{4}^{T}\) and \(z_{b}\) is the seat base excitation.  
Figure 1: The proposed 7-DOF seated human model.
# Reinforcement Learning with Self-Attention Networks for Cryptocurrency Trading
## 4 Exchange Fee Minimization
### Approximate Solution for LAT  
Given a certain value of \(\rho^{k}\), the transfer direction of any asset (buy or sell) is determined by the difference between \(\hat{w}_{i}^{k}\) and \(\rho^{k}w_{i}^{k+1}\) (i.e. \(\hat{w}_{i}^{k}-\rho^{k}w_{i}^{k+1}\)), denoted by \(r_{i}\). If this residual is positive, then the asset has an excess in value that has to be sold to balance Equation (2); on the contrary, if \(r_{i}\) is negative, shares of that asset need to be bought to balance the equation. To reduce expenses, the transfer fees (\(f_{ij}\)) are sorted; by doing this, the transfer values are determined one by one from the pairs of assets (\(ij\)) corresponding to the lowest fees. First, the smallest fee is selected and the corresponding asset indices are retrieved. With them, two conditions are checked: \(r_{i}>0\) and \(r_{j}<0\). If both of the constraints are satisfied, \(\tau_{ij}^{k}\) is non-negative, and its value is determined by Equation (6). Then, \(r_{i}\) and \(r_{j}\) are updated using Equation (7). In this way, the value of \(\tau_{ij}^{k}\) becomes uniquely determined by \(\rho^{k}\). This procedure is repeated for all pairs of assets corresponding to the sorted transaction fees from lowest to highest. When the routine is finished, a new value for \(\rho^{k}\) is computed using Equation (4), which is used to start a new iteration of the process. The procedure is repeated until \(\rho^{k}\) converges. The values of the variables corresponding to the final value of \(\rho^{k}\) are the solution to the problem. This procedure is summarized in Algorithm 1. \[\tau_{ij}^{k}=\min(r_{i}/(1+f_{ij}),-r_{j}) \tag{6}\]  
\[r_{i}\gets r_{i}-(1+f_{ij})\tau_{ij}^{k} \tag{7}\]  
\[r_{j}\gets r_{j}+\tau_{ij}^{k}\]  
The solutions of Algorithm 1 are generally better than those of Algorithm 2 because Algorithm 1 uses IAT. Plus, it is much faster than Algorithm 3 because it does not use convex optimization to compute the solution. Therefore, Algorithm 1 is adopted and integrated into the cryptocurrency trading environment because it best balances the tradeoff  
Figure 1: Block diagram of the trading process. between speed and optimality. A comparison of performances of these methods is shown in Section 6.
# Power Control Techniques for Interference Management-
## 4 Power Control Technique Evaluation
### Keywords Analysis
The keywords displayed in Figure 7 are classified in terms of the underlying technology standard, concepts underlying management of resources, technology standard in use, models for implementing resource management, and network performance indicators. The keywords related to the technology standard are \"5G\" and \"SC\". The keywords \"RM\" and \"RA\" are aligned with the management and allocation of network resources. Keywords specific to modelling are \"PC\", \"GT\", \"RL\", \"DL\", and \"OPT\". The performance-related keywords are \"EE\", \"SE\", and \"QoS\". The box plot in Figure 7 gives us a basic idea of the distribution of co-occurrence data. The data are spread out if the box plot is relatively tall. The box plot of the keyword \"RM\" is relatively tall. In this study's context, \"RM\" has a high occurrence concerning other keywords. By observation, the means and standard deviations of both \"RM\" and \"PC\" are high compared to other keywords, as confirmed by the summary statistics in Table 17. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Keywords** & **Count** & **Mean** & **Standard Deviation** \\ \hline NM & 3 & 29.3 & 24.4 \\ PC & 3 & 74.7 & 24.4 \\ QoS & 3 & 21.7 & 23.1 \\ 5G & 3 & 27.3 & 29.1 \\ RM & 3 & 108.0 & 107.0 \\ RA & 3 & 31.0 & 19.3 \\ RL & 3 & 14.7 & 8.5 \\ DL & 3 & 12.0 & 6.6 \\ OPT & 3 & 39.7 & 48.9 \\ SE & 3 & 8.3 & 11.9 \\ EE & 3 & 30.3 & 18.0 \\ GT & 3 & 8.0 & 5.6 \\ IOT & 3 & 9.0 & 11.5 \\ SC & 3 & 4.0 & 5.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 17: Summary statistics by groups—count, mean, standard deviation. Figure 7: Keyword co-occurrence box and whiskers plots. 2. Trending keywords and topics The review shows that researchers are interested in radio resource management and power control in wireless networks. This is confirmed by the pie chart in Figure 8. The frequencies of keywords \"SC\" and \"GT\" are relatively low, showing less research interest in this study area. For bibliometric analysis, high-frequency keywords are typically recognized as significant research themes. In addition to the keywords previously discussed above, the occurrence of keywords can be analyzed to reflect the central ideas of a research area and the trending topic in the research field. As discussed previously, \"resource management\" is seen as the most occurring keyword in this review. It is interesting to note that \"power control\" and \"optimization\" are still trending. There has been significant growth for the \"resource management\", \"power control\", and \"optimization\" keywords after 2018, indicating their continued relevance in 5G mobile communication. Several factors and trends inform the continued relevance of these keywords in wireless technology. These include the power control and optimization techniques' role in reducing intra-cell and inter-cell interference and optimizing user equipment (UE) power consumption in mobile networks.
# Investigation of Nanogrids for Improved Navy Installation Energy Resilience
## 3 Methodology
### Step 4: Calculate Resilience  
After sufficient information and models are constructed in the above steps, it is now possible to calculate the energy resilience of various nanogrid configurations to various threats. This will allow a comparison between nanogrid architectures, and against the baseline microgrid architecture in a future step.
## 4 Performance Evaluations
### Complexity Comparison  
The complexity mainly includes two aspects, i.e., communication complexity and computation complexity. The LSRR-LA method is similar to the DV-Hop, Amorphous, MDS-MAP, PDM, and LSVR methods. Each node needs to calculate the hop counts by flooding to other nodes, so their communication cost is equal, which is about \(O(n^{2}m)\). Here, \(n\) denotes the number of sensor nodes while \(m\) indicates the number of anchor nodes. DV-hop and Amorphous use the Least Squares (LS) method to estimate the locations of sensor nodes, so they require computation complexity of about \(O(m^{3})\)[24]. MDS-MAP is a centralized localization method, and its location process can be divided into three steps: constructing the global shortest paths, executing the MDS algorithm, and converting the relative coordinates to absolute coordinates. Accordingly, the computation complexity is \(O(n^{3})\), \(O(n^{3})\), and \(O(m^{3}+n)\), respectively. The PDM method uses TSVD to process data in advance, which has a computation cost of \(O(m^{3})\)[25]; then, it uses the LS method to continue calculating data, so the PDM method costs more than DV-hop and Amorphous do. LSVR and MSVR use regression methods based on SVM, which needs to solve a quadratic programming problem [26]; thus, its computational complexity is \(O(m^{2})\)-\(O(m^{3})\). LSRR-LA uses the Least Square Regularized Regression method, so the computation cost is \(O(m^{3})\). Furthermore, its computation cost can be reduced to \(O(m^{2}\text{log}m)\) by using the method introduced in [27].
# A Review on Rainfall Measurement Based on Commercial Microwave Links in Wireless Cellular Networks
## 4 Signal Processing from RSL to Rainfall Map
### Wet/dry Classification
#### 4.1.1 Time or Spectrum Series Analysis  
Based on the assumption that the correlation of RSL time series between two frequencies is higher in the rainy period than in the dry period, [PERSON] et al. (2015) proposed the nearby link approach (NLA). If the RSL value of a microwave link decreases in a time interval and the RSL values of at least half of all the neighbouring links within a radius of 15 km also decrease, then the selected time interval is considered to be wet. [PERSON] et al. (2015) applied the local variability of the link signal to distinguish the wet and dry weather. Basically, the standard deviation of the RSL data, which are collected within a predetermined 15-30 min window, is calculated, and so the weather during this time period would be regarded as wet if the standard deviation is greater than a predefined threshold. Based on the assumption that rainfall would cause more generation of high-frequency RSL samples, [PERSON] et al. (2015) analyzed the power spectrum of RSL time series based on STFT to determine the wet/dry weather. [PERSON] et al. (2015) used the Markov model to distinguish wet/dry period.  
Figure 3: Typical steps in CML-based rainfall measurement.
## 4 Classification of Routing Protocols
### Cluster-Based Routing Protocols
#### 4.3.2 AnyBody  
AnyBody [61] is a cluster-based self organizing data gathering protocol, designed to reduce the direct transmission of the sensor nodes with remote based stations. AnyBody uses LEACH given in [62]--which selects the cluster head at regular time intervals to balance the energy consumption and cluster head aggregates the data and sends to the remote station. In LEACH it is assumed that all nodes are within the range of remote base station. AnyBody addresses this problem by using a density-based cluster head selection method and using these cluster heads to build a backbone network. The five steps of AnyBody are: neighbor discovery, density calculation, constructing cluster head, setting up the backbone, and setting up the routing path.  
In the first step, the one-hop neighbors are discovered by sending a Hello1 message during the first time frame and two-hop neighbors by sending a Hello2 message during the second time frame. In the second step, each node calculates its density based on its two-hop neighborhood information and shares the density information with its neighbor nodes by broadcasting a Hello3 message. In the third step, each node sends the list of its one-hop neighbor nodes to the neighbor node with highest density using a Join message and if it has the highest density then it will not send any messages. The cluster head is selecting based on the highest local density and nodes are grouped in clusters. In the fourth step, each cluster head figures out the gateway nodes. The gateway nodes are the nodes having neighbors outside its cluster. They are used to forward the messages from the gateway nodes of other clusters to its cluster head and forward the messages of its cluster head for other clusters to their corresponding gateway nodes. In the last step, the sink sends a gradient_setup(1) message to its all directed connected cluster heads, then these cluster heads send gradient_setup(2) messages to other cluster heads directly connected with them and this process continues until all cluster heads construct their routing paths. Each cluster head sends the gradient setup messages to all directed connected cluster heads except of the one from it has received. Figure 12 show the hierarchical structure of cluster-based routing.
# Supply Chain Data Analytics for Digital Twins:
## 4 Case Studies  
### Pilot Descriptions/Demonstration  
#### 4.1.1 Greek Pilot  
The analytics service as described in Section 3, is applied to a Greek manufacturing factory that produces orange juice and other related products. This factory operates a production pipeline that integrates various components, each responsible for processes that are essential to its diverse product line. Their basis of their production pipeline is oranges, lime and water. A complex process is followed to efficiently transform raw materials into multiple valuable products, including pellets, orange juice, oil, molasses, and sludge, utilizing multiple processing components. The main goal of the factory is to maximize the utilization of oranges as raw materials, promote data-driven decision making and establishing optimal and environmental-friendly processes.  
Upon detailed analysis of the production components, the decanter has been identified as a critical area requiring further investigation and optimization. The decanter plays a key role in the orange juice production process that separates solid waste (sludge) from the liquid, which is further processed to create molasses. This component stands out due to its high energy consumption, presenting a significant opportunity for efficiency improvements.  
The measurements regarding molasses in the input of the decanter, can support insight regarding the subsequent steps--for example:  
_Molasses pH_: Measures the acidity or alkalinity of the molasses and having a stable and correct pH is vital for molasses stability and spoilage prevention.  
_Molasses Soluble Solids Brix_: Measured in degrees Brix (\"Bx), this indicates the concentration of dissolved solids, primarily sugars, in the molasses and can affect the performance of the decanter.  
_Molasses Sediment Pulp_ %: Represents the percentage of insoluble solid particles, such as fruit pulp and fiber. By keeping sediment pulp in a low percentage, is crucial for product clarity and for preventing equipment failures.  
By integrating in the digital twin of the factory, real-time data from decanter and molasses products become accessible in the data analytics platform, where algorithms continuously monitor and analyze the production process for anomalies.  
For example, the system can detect an unusually high value in the molasses sediment pulp, indicating a potential production anomaly. This can trigger a notification to the factory's production engineers, alerting them to the issue and prompting immediate action. With this information, the production engineer can use the platform's process simulation and modeling tool to experiment with different production parameters.  
Moreover, the analytics dashboard can provide a comprehensive view of historical production and environmental data, including the previous and current measurements, and carbon footprints enabling the comparison of different, real and simulated, production parameters.
## 3 The CsrA-Containing Ribonucleoprotein Complexes
### Deeper Understanding of the Ribonucleoprotein Complexes Formed by CsrA  
Although considerable sequence variation exists among the known CsrA binding sites, a GGA motif was identified as a highly conserved and essential element [79]. This was confirmed by studies using the SELEX method (systematic evolution of ligands by exponential enrichment). The SELEX-derived consensus was determined as RUACARGGAUGU (R being a pyrimidine and the underlined GGA motif being essential) [79]. As described above, the GGA motif is often located in single-strand loops of predicted short RNA hairpins [40]. A single-strand structure appears to be mandatory for regulation since several sRNAs (GcvB, RprA and OmrA) that carry potential CsrA-binding sites with GGA motifs located in double-strand structures (secondary structures of these sRNAs were predicted using Mfold [53], data not shown) do not regulate nor bind CsrA [67]. Spacing distance between CsrA binding sites appears to be important for CsrA-target complex stability [83]. The optimal distance is 18 nt although CsrA dimers are able to bind to two target sites separated by 10 to 63 nt. In CsrB, the 22 potential CsrA target sites are separated by an average of 12.25 nt. This suggests that a CsrA dimer would preferentially bridge non-adjacent sites (_i.e._, separated by at least 18 nt), giving rise to an energetically stable globular complex. [PERSON] _et al_. [79] proposed a model for CsrA binding. Translation repression mediated by CsrA would involve initial binding to a high affinity site located within a hairpin loop upstream of the SD sequence. This first binding would then allow the free RNA-binding surface of CsrA to interact with downstream low-affinity sites overlapping the SD sequence. These two successive binding events would result in formation of a repression loop blocking ribosomal binding for translation initiation [83].
## 1 Introduction
In recent years, with the exponential increase in the quantity of geoscientific data, machine learning has gradually evolved towards deep learning, including techniques like convolutional neural networks [26], deep autoencoders [27], generative adversarial networks [28], and restricted Boltzmann machines [29] that leverage their mechanisms to capture high-dimensional data features from massive geoscientific datasets [30]. [PERSON] has proposed a comprehensive geochemical data analysis framework using a causal discovery algorithm and a hybrid deep learning model of VAE-CAPSNET-GAN, effectively identifying geochemical anomaly areas related to mineralization [31]. [PERSON] used Laplace-Beltrami eigenfunctions, surface normals, and surface distances to describe the geological boundaries of predictive variables and organized them into multi-channel images through perspective projection, combined with a CNN architecture to perform three-dimensional mineralization prediction assessments [32]. Under the precondition of accuracy, interpretability, and physical reality of machine learning algorithms applied to geoscientific applications, the features of geoscientific systems and data [33], including their multi-source and multi-scale nature, auto-correlation and cross-correlation, and spatiotemporal heterogeneity, have been fully demonstrated [34; 35]. [PERSON] explored the contributions of geochemical elements in identifying multivariate geochemical anomalies using the SHAP explainability framework and variational autoencoder models [36]. [PERSON] proposed a BPUL structure integrated framework to address system uncertainties caused by imbalanced data and cost-sensitive issues in data-driven machine learning algorithms [37]. This study aims to address issues such as ore-rock mixing and depletion of deep reserves by integrating and constructing a multi-source geoscientific dataset, including geological data (such as drilling data, geological profiles, and geological topographic maps), geophysical data (aerial magnetic survey data based on drones), geochemical data (XRF trace element detection data from mining area samples), and remote sensing data (drone digital elevation data, drone LiDAR images, and drone multispectral imaging). Using big data and artificial intelligence methods, this study conducts mining big data integration analysis and knowledge discovery from multiple dimensions and modalities, performing an in-depth analysis of the geological genesis of the deposit. The goal is to accurately locate the surface and deep potential ore bodies in the study area, achieve real-time detection of surface ore bodies in the Yanshan Iron Ore Mine open-pit mining area, identify deep exploration indicators and exploration directions, and promote the development and construction of the Yanshan Iron Ore Mine as an intelligent mine. This study aims to increase production efficiency and improve iron ore recovery rates.
# Bluetooth-Based Healthcare Information and Medical Resource Management System
## 2 Related Work and Literature Review
### Message Queuing Telemetry Transport Protocol  
Message Queuing Telemetry Transport (MQTT) Protocol is an important protocol that is more commonly used in IoT. It is based on TCP/IP connection and can transmit and receive messages with light network bandwidth and computational load in restricted environments such as low bandwidth and unstable network connections.  
MQTT's message delivery uses the Publish/Subscribe mode, which contains four important elements: Publisher, Subscriber, Topic, and Broker, and provides three QoS levels for message delivery services for users to choose from. The data flow of transmission is shown in Figure 2. The Broker is a message relay station. It transfers messages within a Topic from a Publisher to a Subscriber. A Topic is a keyword used by the MQTT Broker to filter messages against the MQTT client. Users in Subscriber mode inform the message relay (Broker) of the Topic they want to register and become the Subscriber of this Topic. When messages from Publishers were sent to this Topic, the relative Subscriber would obtain these messages. Each Subscriber can register for more than one Topic.  
The Publisher represents the user who sent the message. When the Publisher sends a message, it is not sent directly to the Subscriber, but the message is sent to the message relay (Broker), and the message relay (Broker) is told to send the message to the specified Topic, and the message relay (Broker) will re-send the message to all the Subscribers who have registered for the specified Topic. The MQTT protocol provides three QoS (Quality of Service) Levels for users to choose from during message transmission.