esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Design Method for the Vibration Isolation System
### Air Spring  
This newly modified QZS system becomes very essential because the weight of the driver is not fixed when the driver sits on the seat, which in turn affects the inclination angle of of the oblique springs in the QZS system.  
Figure 8: Force and stiffness versus displacement using different oblique angles for exact and third-order expressions.  
Figure 7: Force and stiffness versus displacement for exact and third-order expressions.  
Therefore, the proposed solution for this problem is to introduce an air spring, which can deal with the mass of the driver by inflating or deflating the air spring. In addition, this will help to control the initial position to be at the equilibrium, control the inclination angle of the oblique springs, and finally introduce an adequate amount of damping, which also helps in reducing the force transmitted to the driver.  
**Remark 1**.: _Although air spring introduces another stiffness element in the vertical direction, which leads to higher natural frequency and hence less isolation, this amount of stiffness is already present in the vertical spring. Thus, the two systems (the air spring and the vertical spring of the QZS system) will share the same amount of stiffness instead of having a single system, i.e., nothing has changed in terms of the overall vertical stiffness._  
**Remark 2**.: _It is worth noting that introducing a damper instead of the air spring is not a good solution because obviously having a high amount of damping means losing the opportunity to isolate the system at large frequency ratios. In contrast, the air spring introduces a very small amount of damping and it marginally affects the overall isolation, but majorly isolates the force from being transmitted to the driver._  
In summary, the presence of the air spring is indeed one of the main pillars that this system stands on. It gives the advantage of supporting a variety of loads while keeping the system at the equilibrium position for different types and values of the load. Thus, it achieves the optimum isolation performance without introducing high damping into the system.
## 5 Synergistic Effects of Integrating IoT, LLMs, and Federated Learning
### Challenges Arising from the Synergy  
While powerful, the tight integration of IoT, LLMs, and FL introduces unique challenges beyond those of the individual components:  
Cross-Domain Data Alignment and Fusion: Effectively aligning and fusing heterogeneous, multimodal IoT data streams within an FL framework before feeding them to an LLM requires sophisticated alignment and representation techniques [105].  
Resource Allocation Complexity: How to jointly optimize computation (LLM inference/training, FL aggregation), communication (IoT data upload, FL updates), and privacy (PET overhead) across heterogeneous IoT devices, edge servers, and potentially the cloud specifically for this integrated task [13].  
Model Synchronization vs. Real-time Needs: Balancing the need for FL model synchronization (potentially slow for large LLM updates [10]) with the real-time data processing and decision-making requirements of many IoT applications.  
Emergent Security Vulnerabilities: New attack surfaces emerge at the interfaces, e.g., malicious IoT data poisoning FL training specifically to mislead the LLM's interpretation [109], or FL privacy attacks aiming to reconstruct sensitive IoT context interpreted by the LLM [110]. Verifying the integrity of both IoT data and FL updates becomes critical [15].
## 2 Related Work
It offers an approach to improve both data transmission and energy efficiency in WSNs. The ETAAR protocol selects the CHs based on multiple factors such as their distance to the BS, residual energy, throughput, and delay. A TDMA-based sleep-scheduling mechanism is applied to reduce energy consumption. Additionally, the protocol forwards data to the BS using a multi-hop technique. The Dynamic Energy-Aware MAC Extension (DE-MAC Extension) protocol was developed in [18]. It aims to reduce unnecessary transmissions and prolong the WSN's lifetime. During each round, sensor nodes send their residual energy to their respective CHs, which then allocate a specific number of TDMA time slots to each node. Nodes with higher energy are granted more transmission slots, whereas nodes with lower energy receive fewer slots and remain in sleep mode for longer periods. The DE-MAC Extension protocol utilizes single-hop communication to transmit the aggregated data from the CHs to the BS. The Reinforcement of MIS-Based Sleep Scheduling (RMIS) protocol introduces a hybrid approach in [19]. This approach incorporates the Maximum Independent Set (MIS) theory with reinforcement learning to determine effective sleep scheduling. The protocol divides the network into clusters; within each cluster, a subset of nodes is selected to remain active. The selection of the nodes is based on their energy levels, historical activity, and neighborhood density. The RMIS protocol significantly minimizes energy dissipation by reducing the number of active nodes while preserving the network's coverage. However, it employs a uniform sleep strategy and does not consider the specific requirements of WSN applications. The Reinforcement Learning-based Cooperative Event-driven (RL-Coop Event) protocol was proposed in [20]. It provides an event-driven sleep scheduling strategy designed for large-scale clustered WSNs. In this protocol, clusters are dynamically formed using the K-means algorithm, typically after the detection of an event. To determine the active nodes within each cluster, the Deep Deterministic Policy Gradient (DDPG) model is employed. The selected active nodes are responsible for sensing and processing the event, while the remaining nodes are deactivated. By only activating the necessary nodes, the RL-Coop Event protocol reduces energy dissipation and extends the network's lifetime. Nonetheless, the RL-Coop Event protocol lacks multi-hop communication support. In addition, it does not provide adaptability based on the application requirements. Table 1 presents a comparative summary of the protocols discussed in this section, including the proposed EDMR protocol. The comparison is based on their sleep-scheduling mechanisms; the control level of sleep decisions, routing methods, application-adapted sleep strategies; and the use of INs.
# Design and Numerical Study of Induction-Heating Graphitization Furnace Based on Graphene Coils
## 2 Numerical Model
### Mathematical Model
#### 2.2.2 Equation of Electromagnetic Field  
To accurately calculate the electromagnetic field, a computational domain ten times larger than that of the induction furnace is chosen. Eddy currents are generated during induction heating when the workpiece is subjected to alternating magnetic fields, and the behavior and generation of these fields are controlled by [PERSON]'s equations [39]. Solving [PERSON]'s equations is the starting point for solving induction-heating problems. In general, [PERSON]'s equations can be written as follows:  
\[\
abla\times\mathrm{H}=\mathrm{J}+\frac{\partial\mathrm{D}}{\partial\mathrm{t}} \tag{1}\]  
\[\
abla\times\mathrm{E}=-\mathrm{j}\omega\mathrm{B} \tag{2}\]  
\[\
abla\cdot\mathrm{D}=\rho\mathrm{e} \tag{3}\]  
\[\
abla\cdot\mathrm{B}=0 \tag{4}\]  
In these equations, H represents the magnetic-field strength; J represents the current density; E represents the electric-field strength; D represents the electric-flux density; B represents the magnetic-flux density; j represents the complex number j\({}^{2}=-1\); \(\omega\) represents the angular frequency; \(\rho\mathrm{e}\) represents the charge density; and t represents the time. The constitutive relations are as follows:  
\[\mathrm{B}=\mu\mathrm{H} \tag{5}\]  
\[\mathrm{D}=\epsilon\mathrm{E} \tag{6}\]  
\[\mathrm{J}=\sigma\mathrm{E}+\mathrm{J}\mathrm{e} \tag{7}\]  
The magnetic-flux density and electric-field strength can be expressed as follows:  
\[\mathrm{B}=\
abla\times\mathrm{A} \tag{8}\]  
\[\mathrm{E}=-\
abla\mathrm{V}\times\partial\mathrm{A}/\partial\mathrm{t} \tag{9}\]  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Component** & **\(\sigma\), S/m** & **Cp, J/(kg\(\cdot\)K)** & **\(\rho\), kg/m\({}^{3}\)** & **k, W/(m\(\cdot\)K)** \\ \hline Copper coil & \(6\times 10^{7}\) & 385 & 8960 & 400 \\ Graphene coil & \(1.1\times 10^{6}\) & 710 & 1950 & 150 \\ \hline \end{tabular}
\end{table}
Table 1: Parameters of coils.
###### Abstract  
Heavy metals such as Cu(II), if ubiquitous in the runoff, can have adverse effects on the environment and human health. Lime sand bricks, as low-cost adsorbents to be potentially applied in stormwater infiltration facilities, were systematically investigated for Cu(II) removal from water using batch and column experiments. In the batch experiment, the adsorption of Cu(II) to bricks reach an equilibrium within 7 h and the kinetic data fits well with the pseudo-second-order model. The sorption isotherm can be described by both the Freundlich and Langmuir model and the maximum adsorption capacity of the bricks is 7 \(\pm\) 1 mg/g. In the column experiment, the best removal efficiency for Cu(II) was observed at a filler thickness of 20 cm, service time of 12 min with a Cu(II) concentration of 0.5 mg/L. The Cu(II) removal rate increases with the increasing bed depth and residence time. The inlet concentration and residence time had significant effects on the Cu(II) removal analyzed by the Box-Behnken design (BBD). The Adams-Bohart model was in good agreement with the experimental data in representing the breakthrough curve. Copper fractions in the bricks descend in the order of organic matter fraction > Fe-Mn oxides fraction > carbonates fraction > residual fraction > exchangeable fraction, indicating that the lime sand bricks after copper adsorption reduce the long-term ecotoxicity and bioavailability to the environment.  
Keywords:lime sand bricks; copper; batch adsorption; column; sequential extraction +
Footnote †: journal: Applied Sciences
# Research on Decision-Making Methods for Autonomous Navigation in Inland Tribitary Waterways
## 3 Methodology  
### Digitalization of Transportation Environment  
The perception and extraction of aquatic environmental data, coupled with the construction of a digital navigation environment, form the foundation of the autonomous decision-making method. Environmental information is classified and reconstructed based on the characteristics of the generalized navigation environment components, enabling the mathematical expression of static traffic environment information. Key information in navigational channels, including channel boundaries, recommended routes, channel separation lines/zones, and anchorages, is abstracted into computer-recognizable formulas composed of points, lines, and surfaces. According to traffic element information, a long waterway can be decomposed into multiple short straight waterways, each of which is typically a rectangular one-way navigation area defined by four points. As shown in Figure 6, a curved waterway is deconstructed into three straight segments, each of which includes four vertex coordinates.  
Thus, the \(i\)th segment in inland waterways can be mathematically represented as follows, where \(p\) denotes the coordinate information of the four vertices. The recommended route is defined as the polyline connecting midpoints of channel entry/exit boundaries.  
\[SE_{i}=\{P_{a},P_{b},P_{c},P_{d}\} \tag{11}\]  
A digitized instream tributary scenario is shown in Figure 7. The area \"A\" surrounded by green lines represents the upstream waterway, the area \"B\" surrounded by red lines represents the downstream waterway, and the interval area \"D\" between the upstream and downstream channels represents the channel divider. The area \"C\" surrounded by black lines represents the tributary waterway.
# Investigation on the Tip Positioning Accuracy of Cable-Driven Serpentine Manipulators
## 3 Simulation Analysis and Experimental Validation
### Parameters Setting  
Simulations were performed by taking a 3-DOF planar CDSM as an example. This manipulator had 3 joints and 6 cables (i.e., \(n\) = 3, \(m\) = 6), which could realize three the degrees of freedom of movement in the plane. Its structure sketch is shown in Figure 4. There were a total of six cables and three disks in the experimental device. Each disk was controlled by two cables. The first, third, and fifth cables, corresponding to the upper cables of disk_1, disk_2, and disk_3, are cable_disk(1)_1, cable_disk(2)_1, and cable_disk(3)_1 in Figure 4, respectively.  
The structure parameters of the manipulator are shown in Table 1. The height of the hole represented the distance between the center of hole and the geometric center of disk. As shown in Figure 2, the height of the hole in disk_i was the distance between \(O_{i,\mathrm{a}}\) and \(A_{i\_1}\), which was the same as the distance from \(O_{i,\mathrm{a}}\) and \(A_{i\_2}\). In Table 1, 0.030 m is the height of hole in disk_1; 0.028 m is the height of hole in disk_2; and 0.026 m is the height of hole in disk_3.  
\begin{table}
\begin{tabular}{c c c c c} \hline  & **Dimension Parameter (m)** & **Mass (kg)** & **Height of Hole (m)** \\ \hline Fixed End & link\({}_{0,\mathrm{f}\_1}\) & 0.033 & & \\  & link\({}_{\mathrm{f}\_1,\mathrm{1}}\) & 0.033 & & \\ Linkage\_1 & \(B\) & 0.010 & 0.0322 & 0.030 \\  & link\({}_{1,\mathrm{f}\_2}\) & 0.033 & & \\  & link\({}_{\mathrm{f}\_2,\mathrm{2}}\) & 0.033 & & \\ Linkage\_2 & \(B\) & 0.010 & 0.0334 & 0.028 \\  & link\({}_{\mathrm{f}\_3}\) & 0.033 & & \\ Linkage\_3 & \(B\) & 0.010 & 0.0210 & 0.026 \\ \hline \end{tabular}
\end{table}
Table 1: Structure parameters of the 3-DOF planar CDSM.  
Figure 4: 3-DOF planar CDSM (driven by 6 cables).
# Kinematics and Performance Analysis of the 2T1R/1T2R Reconfigurable Decoupled Parallel Mechanism
## 4 Performance Analysis
### Performance Index in 2T1R Mode  
The mode of serial chain 1 is P\({}_{11}\)R\({}_{12}\)R\({}_{13}\)R\({}_{14}\), and R\({}_{12}\) is selected as the driving joint of serial chain 1, that is, the input motion screw \(\mathfrak{F}_{\rm I1}\) of serial chain 1 is  
\[\mathfrak{F}_{\rm I1}=\mathfrak{F}_{\rm 13}=\left(\begin{array}{cccccc}0&1&0&;&-Z _{A1}&0&X_{A1}\end{array}\right) \tag{43}\]  
Similarly, the input motion screws \(\mathfrak{F}_{\rm I2}\) and \(\mathfrak{F}_{\rm I3}\) for the remaining serial chains can be obtained as follows:  
\[\mathfrak{F}_{\rm I2}=\mathfrak{F}_{\rm 21}=\left(\begin{array}{cccccc}1&0&0&;&0&0 &0\\ \mathfrak{F}_{\rm 31}=\mathfrak{F}_{\rm 31}=\left(\begin{array}{cccccc}0&0&0&;&1&0&0 \end{array}\right)\end{array}\right) \tag{44}\]  
Assuming that R\({}_{13}\) is locked, \(\mathfrak{F}_{\rm 11}\) is removed from the kinematic screw system of serial chain 1, and solving the reciprocal product of the remaining kinematic pairs can obtain the transmission force screw of serial chain 1:  
\[\mathfrak{F}_{\rm T1}=\left(\begin{array}{cccccc}0&0&1&;&0&-X_{B1}&0\end{array}\right) \tag{45}\]  
Similarly, the transmission force screws \(\mathfrak{F}_{\rm 2}\) and \(\mathfrak{F}_{\rm 3}\) for the remaining serial chains can be obtained as follows:  
\[\mathfrak{F}_{\rm T2}=\left(\begin{array}{cccccc}0&0&0&;&1&0&0\\ \mathfrak{F}_{\rm 33}=\left(\begin{array}{cccccc}1&0&0&;&0&0&0\end{array}\right) \end{array}\right) \tag{46}\]  
If the driving unit of serial chain 1 is retained, then the five-dimensional constraint screw system, composed of the constraint screw system provided by the serial chain, and the transmission force screw of the remaining serial chain act on the moving platform. At this point, the mechanism is a single-degree-of-freedom mechanism. Solving for its reciprocal product, the output motion screw \(\mathfrak{F}_{\rm O1}\) of serial chain 1 can be obtained as follows:  
\[\mathfrak{F}_{\rm O1}=\left(\begin{array}{cccccc}0&0&0&;&0&0&1\end{array}\right) \tag{47}\]  
Similarly, the output motion screws \(\mathfrak{F}_{\rm O2}\) and \(\mathfrak{F}_{\rm O3}\) for the remaining serial chains can be obtained as follows:  
\[\mathfrak{F}_{\rm O2}=\left(\begin{array}{cccccc}1&0&0&;&0&Z_{A1}&0\\ \mathfrak{F}_{\rm O3}=\left(\begin{array}{cccccc}0&0&0&;&1&0&0\end{array} \right)\end{array}\right) \tag{48}\]  
By substituting \(\mathfrak{F}_{\rm Ii}\), \(\mathfrak{F}_{\rm Ti}\), \(\mathfrak{F}_{\rm Oi}\) (\(i\) = 1, 2, 3) into Equations (40)-(42), the motion/force transmission performance indexes of the mechanism can be obtained, as shown in Figure 7.  
From Figure 7, it can be seen that the motion force transmission performance of the reconfigurable mechanism increases with the increase of the \(Z\) value in 2T1R mode, and is evenly distributed in the horizontal and vertical axes.
# A Distributed RF Threat Sensing Architecture
## 2 Related Work
### Jamming Sensing
Such metrics have been used in several scenarios and communication technologies, e.g., WiFi IEEE 802.11, IoT LWAN, 5G+, MaMIMO, GNSS, MANET, and other spread spectrum-based communication technologies. The proposed methods tend to be assisted by ML and DL models to improve jamming detection. Such tools include neural networks, Decision Trees, random forest, support vector machine,and autoencoders. Different from what is proposed in other research work, we approach the jamming detection challenge at a more holistic, strategic level, identifying the necessity of centralized--at a SOC level--simultaneous monitoring of multiple heterogeneous wireless network technologies, offering a bird's view sensing and localization capability of malicious jammers' activity to various bands of interest. The proposed approach presents the applicability of the deployment of a large number of low-cost sensors, with wideband RF detection capabilities that are technology neutral. The wideband RF capabilities can be RF band restricted through selective RF filtering for the purpose of increasing selectivity and band identification. The RF jamming detection sensors can be geographically distributed, allowing localized jamming detection and jamming localization based on the use of directional antennas. RF jamming detection capabilities can be also further enhanced through the overall architecture that incorporates an SOC with centralized machine learning processing capabilities.
## 2 Background
### Hybrid Approaches  
There are a number of existing approaches that combine data-level sampling techniques with ensemble learning. SMOTEBoost combines SMOTE with a boosting ensemble algorithm to improve the prediction performance of minority class samples. Boosting proved to be an effective ensemble algorithm as it decreases the bias and variance in the final ensemble; however, it might be less effective on imbalanced datasets as it deals with the majority and minority classes similarly. Thus, SMOTE was used to introduce new samples at each boosting iteration and handle the imbalance issue. Moreover, SMOTE increases the diversity of samples as it creates different samples at each boosting iteration. The introduced data are removed after the learning process of each weak learner. So, the error estimate after each iteration is calculated based on the original dataset [14]. Another hybrid model named RUSBoost combines RUS with boosting ensembles. It uses RUS at each boosting iteration to alleviate the imbalance problem. Computationally, RUSBoost is less expensive than SMOTEBoost and requires less training time [15].
# Iteratively Refined Multi-Channel Speech Separation
## 1 Introduction
In the field of single-channel speech separation, demonstrated results [16; 17; 18] have shown that the accuracy of speech separation can be significantly improved through iterative optimization. In the field of multi-channel speech separation, the introduction of iterative structures has also demonstrated great potential. For example, an MVDR beamformer and TasNet were used as the iterative structure in [19], a time-domain real-valued generalized Wiener filter (TD-GWF) and TasNet were used as the iterative structure in [14], and a time-domain dilated convolutional neural network (TDCN) and multi-channel Wiener filter (MCWF) were used as the iterative structure in [20]. The performance of these methods has improved a lot compared with the no-iteration version. Therefore, in this paper, we also consider using an iterative structure to improve the performance of the neural beamforming. In this paper, we greatly extend the iterative version of our previous work [15], and a new neural beamforming structure is proposed, that is, improved Beam-TFDPRNN (iBeam-TFDPRNN). The main contributions of this paper are summarized as follows:  
* The structure of the original neural beamforming is revised. Specifically, two main changes are made. First, the number of the time-frequency domain path scanning blocks in the neural network is reduced to three from the original six. This simplification improves the training efficiency and inference speed of the model, while reducing the complexity and resource consumption of the model. Second, an iteratively refined separation method is proposed, which combines the initially separated speech with the original mixed signal as an auxiliary input for the iterative network. By repeating this process in \(N\) iteration stages, the MVDR beamformer and post-separation network are mutually promoted. As a result, the separation results are effectively improved;
* The proposed method not only evaluates each stage of the multi-stage iterative processes, but also uses more evaluation metrics to obtain a more comprehensive evaluation. The experimental results show that the proposed method worked well on the spatialized version of the WSJ0-2 mix data corpus and greatly outperformed the current popular methods. In addition, it is noted that our proposed method also performed well in the dereverberation task. The rest of this paper is organized as follows: Section 2 presents the details of our proposed method. Section 3 describes the experimental setup and analysis. Finally, Section 4 concludes the paper.
## 4 Discussion  
### Microplastic Pollution of the Sediments of the Tisza River in 2019 and 2020  
The entire length of the Tisza (946 km) was sampled twice, in 2019 and 2020. Between the measurements, the MP pollution of the sediments considerably decreased, as in 2019, the average MP content of the sediments was 3149 \(\pm\) 2010 item/dry kg, but by 2020, it had decreased by 30% to 2220 \(\pm\) 1697 item/dry kg. The MP content in the tributaries decreased even more, by 48% (2019: 3827 \(\pm\) 1584 item/dry kg; 2020: 2007 \(\pm\) 1821 item/dry kg). These data reflect that a considerable amount of the MP has been transported away from the fluvial system of the Tisza River during the surveyed year.  
The results suggest that the Tisza is a highly polluted river. Similarly, the high MP contamination was measured in the Rhine (228-3763 item/dry kg) and Main rivers (786-1368 item/dry kg) in Germany [49], in the sediments of the St. Lawrence River (65-7562 item/dry kg [50]), in the Solimoes, Negro and Amazonas rivers (418-8178 item/dry kg [30]) and the Haihe River (4980 \(\pm\) 2462 item/dry kg) in China [15]. Values similar to the less polluted sections of the Tisza were measured in the sediments of Chinese rivers (800-1100 item/dry kg [19; 51; 52]) or the Vistula River, Poland (190-580 item/dry kg [17]). However, there are also rivers, even with urbanized, densely populated catchments, where the average MP content of their sediments was even less than the least polluted sampling points of the Tisza, i.e., below 200 item/dry kg. Such slightly polluted rivers were reported from South Africa [25; 50; 53] or Scotland [54].  
In the samples, 98% of the MP particles were fibers, two-thirds of which were colored fibers. This suggests that the MP in the river was derived from synthetic clothing materials [6; 55], which entered the water system of the Tisza through the discharge of treated or untreated wastewater [29]. The dominance of fibers in sediments has been reported in several other studies [12; 15; 30; 54; 56; 57]), especially near settlements [16; 17], although the presence of spheres and fragments is more common in other rivers [13; 20; 51].