esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Control Methods Used in Agricultural Vehicle Path Tracking Control
### Agricultural Robots and Facility Agricultural Machinery
[87] solved the path-tracking problem of autonomous ground vehicles (AGV) using nonlinear model predictive control (NMPC) and linear model predictive control (LMPC) and proved through experiments that LMPC has better real-time performance than NMPC. [PERSON] et al. [88] proposed a path-tracking method on the basis of linear time-varying model predictive control (LTVMPC). The reference path of agricultural robots usually consists of straight lines and arcs, with significant curvature changes, so linearized prediction models are not suitable for optimizing real-time performance. [PERSON] et al. [89] proposed a path-tracking method based on nonlinear model predictive control. Test resultsshowed that the developed controller has a smaller deviation than the linear model predictive controller when tracking linear and circular paths. Aiming at the poor real-time performance of nonlinear model predictive control, [PERSON] et al. [90] proposed to optimize by reducing the number of control steps or lowering the control frequency. Experimental results show that reducing the number of control steps is more suitable for agricultural robots with high flexibility.
## 3 MZM Parameter Optimization
In summary, for a 1 mm long MZM, the optimal parameters are \(V_{\pi}=11.7\) V and \(\text{IL}_{\text{MZM}}=1.7\) dB which leads to an electrical bandwidth of \(\text{BW}_{\text{MZM}}=23.5\) GHz and a system optical budget of \(\text{IL}_{\text{PATH}}=23.5\) dB for an input laser power \(P_{\text{LAS}}=+10\) dBm. If a MZM with higher modulation efficiency was used (further to the right on the black line), the improvement in extinction ratio would not be sufficient to compensate the inevitable higher insertion loss and thus, the tolerable path loss would decrease. The reverse applies if we considered a MZM with lower insertion loss. It is important  
Figure 7: MZM Parameter space limits: upper limit linked to insertion loss (**blue**); and lower limit due to technology (**black**). \(l_{\text{MZM}}=1\) mm. to underline that the optimal parameters found above are valid whatever the sensitivity of the receiver or the laser power. Indeed, if the RX sensitivity is degraded or the laser power is lowered, the tolerable path loss decreases (translation along the \(x\)-axis of the blue curve) but the optimal MZM parameters do not change. This optimization can be repeated for different values of \(l_{\mathrm{MZM}}\) in order to find the optimal electrode length (Figure 9). We plot in Figure 10 the evolution of the maximum \(\mathrm{IL}_{\mathrm{PATH}}\) as a function of \(l_{\mathrm{MZM}}\). The optimum MZM electrode length is found to be 4 mm, which leads to \(\alpha_{\mathrm{MZM}}=0.52\) dB/mm and a product \(V_{\pi}L_{\pi}\) of 1.87 V\(\cdot\)cm at \(-2.4\) V (\(\Delta\varphi=23^{\circ}\)/mm) or, in other terms, \(V_{\pi}=4.6\) V and \(\mathrm{IL}_{\mathrm{MZM}}=2.1\) dB. For a laser power \(P_{\mathrm{LAS}}=+10\) dBm and a receiver sensitivity \(P_{\mathrm{SEN}}=-20.8\) dBm at 25 Gbps, we find a tolerable path loss \(\mathrm{IL}_{\mathrm{PATH}}=25.9\) dB. Figure 8: Maximum tolerable path loss as a function of \(V_{\pi}l_{\mathrm{MZM}}\) for \(l_{\mathrm{MZM}}=1\) mm. Figure 9: MZM optimal parameter search for different electrode lengths.
# A Proof-of-Multiple-State Consensus Mechanism for Mobile Nodes in Internet of Vehicles
## 4 Consensus Mechanism Based on Integrated State
### PoMS Consensus Algorithm  
The flow of this consensus algorithm is as follows: Given the inefficiencies inherent in the traditional proof-of-work consensus algorithm, especially its wasteful use of system resources, this study introduces a modified proof-based consensus mechanism. This adjustment replaces the computationally intensive hash value verification with a scenario-adapted, comprehensive quality score verification process.  
(1) Initialization: This study establishes a blockchain-based vehicular mobility network, described as an open, distributed peer-to-peer network in the broader vehicular network context. It denotes the total number of vehicle nodes in the network as \(n\), where each node potentially becomes a ledger-maintaining node through competitive rotation. The total number of Roadside Unit (RSU) nodes is represented by \(m\), where each RSU is part of a smaller network and maintains a local blockchain record.  
(2) Mobility Process: Once the vehicle nodes enter the mobile stat, RSU nodes start sending messages continuously. Vehicles that receive broadcasts from RSUs respond by initiating mining activities. Using reliable on-board sensors, the vehicle collects position, speed and additional data, uses a pre-trained network model and inputs travel information to predict its state over an upcoming period, and then logs the predicted state data. The vehicle calculates its value, summarizes these findings in a block, documents essential parameters and forwards this to the mining node to complete the uplink and storage processes.  
(3) Relay Nodes Selection: After receiving the position information and state prediction information of each vehicle, the RSU nodes begin to analyze all values for analysis. The time taken by each node to submit the block and the calculated value show a reverse trend, such as:  
\[t_{w}=\frac{\tau}{SF} \tag{1}\]  
where the \(SF\) represents a special value, State-Factors, which will be explained in the next section, and the parameters \(\tau\) are used to ensure that the waiting time is within reasonable limits. Nodes with better channel states can transmit blocks earlier, reducing the waiting time for information transmission. At the same time, differentiate the period during which different nodes transmit blocks to prevent multiple nodes from initiating communication requests at the same time, causing transmission conflicts, and to avoid the calculations becoming untrustworthy.  
(4) Messages Delivery: After the relay node is selected, the message is delivered through the chosen node, repeating the selection process throughout the delivery phase until the message count reaches the system-defined threshold. The threshold specifies both the required number of messages for delivery and the maximum hop count for message forwarding. It is determined by the road department based on the different communication requirements.
# An Energy Efficient Routing Approach for Cloud-Assisted Green Industrial IoT Networks
## 6 Experimental Results and Evaluation  
In this section, we present the performance evaluation of our proposed routing scheme based on RAROF by using simulation. Furthermore, we describe the simulation environment, simulation parameters, and depict the comparative simulation results. The extensive simulation experiments are performed in the COOJA simulator and compared with other OFs to validate the proposed OF in the IIoT scope.
## 3 Denoising Transformer-Based Audio Music Genre Classification
### Fine-Tuning Stage  
In the fine-tuning stage, the Deformer that has already learned the deep representation of audio music is applied to music genre classification. Figure 6 shows the process of fine-tuning the pre-trained Deformer to a classification network. Different from the pre-training stage, the fine-tuning stage is not unsupervised learning, and the prior decoder is not utilized. Normalized Mel spectrogram patches \(\{p_{1},p_{2},p_{3},...,p_{n},\ldots,p_{N}\}\) without added noise are fed into the model, along with a classifier token (_cls_) [21]. The _cls_ token serves as a condensed representation of all input patches. As opposed to pre-training, which involves decoding layers, fine-tuning employs a genre prediction layer connected to the final Deformer position. This layer is a linear component that uses a SoftMax function to predict the probability distribution for the genre classes based on the _cls_ token's hidden state. The cross-entropy loss is then computed using the predicted and target genres to fine-tune the Deformer and the genre prediction layers, enhancing Deformer's ability to classify music genres effectively.  
Algorithm 2 details the fine-tuning process, where \(p_{N}\) represents the normalized patches, \(g\) represents the target genre, \(g^{\prime}\) represents the predicted genre of Deformer, and \(cross\_entropy(\bullet)\) calculates the loss of \(g^{\prime}\) and \(g\) for performing updates.  
```
Input: \(\{p_{1},p_{2},p_{3},...,p_{n},\ldots,p_{N}\}\) Output: \(g^{\prime}\)
1: Load pre-trained Deformer (\(\theta\))
2: for \(i=1\) to \(I\) do:
3: Forward pass: \(g^{\prime}\) = Deformer (\({p_{n}}^{\prime},\theta\))
4: Compute \(L=cross\_entropy(g^{\prime},\,g)\)
5: Update \(\theta=\theta-\eta^{*}\
abla L(\theta)\)
6: End for
```  
**Algorithm 2** Fine-tuning  
Algorithm 3
## 3 Case Studies and Analysis
### Test Results and Analysis for PFG at SCP1
#### 3.2.2 Test Results and Analysis in Three-Dimensional Spaces  
In this section, the PFG to each CIV at SCP1 will be further analyzed in three-dimensional spaces.  
According to Equations (1)-(3) and the theoretical analysis in Section 2.3, the PFG to CIV1 and CIV3 only have one degree of freedom for regulation and present simple principles, while the PFG to CIV2 has two degree of freedom for regulation and more complex principles. Therefore, only the principles of PFG to CIV2 in spaces are essential to be analyzed, and the spatial surfaces of PFG to CIV2 at SCP1 are listed in Figure 3.  
Figure 2: Curves of power flow gradients (PFG) to each characteristic independent variable (CIV) at SCP1 in planes: (**a**) Curves of the PFG to CIV1; (**b**) Curves of the PFG to CIV2. (**c**) Curves of the PFG to CIV3; (**d**) Curves of active power flow gradients (APFG) against reactive power flow gradients (RPFG) to CIV1; (**e**) Curves of APFG against RPFG to CIV2; (**f**) Curves of APFG against RPFG to CIV3.  
On the one hand, the pictures in (a)-(d) of Figure 3 depict the spatial surfaces in all the operation conditions by Reg.1 mode of OSIV. It is evident that the spatial surfaces in all operation conditions present similar funnel shapes humping downwards, and the surface areas are proportional to CIV1. The difference among them mainly lie in the distribution positions of the projection loops on the P-Q plane regarding certain values of CIV1, revealing that the PFG to CIV2 at SCP1 are relatively uniform and have not been impacted much when the operation condition varies.  
On the other hand, (e)-(h) of Figure 3 also describe the spatial surfaces in all the operation conditions by Reg.2 mode of OSIV. It can be seen that, the spatial surfaces in all the operation conditions still present similar shapes, but the surface area are larger than those of Reg.1 mode, revealing that the PFG to CIV2 at SCP1 by Reg.2 mode have larger regulation regions or redundancies and may be more adaptive to different regulation requirements.
# Enhancing the Recommendation of Learning Resources for Learners via an Advanced Knowledge Graph
## 2 Related Work
### Recommendation Algorithms Based on Knowledge Graph Representation Learning
#### 2.1.3 Propagation-Based Methods  
Propagation-based methods refine entity representations by aggregating embeddings of multi-hop neighbors in the knowledge graph, starting from the user's initial records. These methods are typically implemented using graph neural network technology, effectively leveraging the structured information in the knowledge graph. For example, the KGNN-LS model employs graph convolutional neural networks to obtain the representations of items by aggregating their neighboring nodes, thereby enhancing the use of relationships in the knowledge graph and improving recommendation accuracy [18]. [PERSON] et al. [19] proposed a knowledge-graph-based exercise recommendation method, KG4 Ex, where the knowledge graph includes three key entities (knowledge concepts, students, and exercises) and their interrelationships, which can be used to recommend suitable exercises. These methods, through multi-hop propagation, can capture implicit relationships between users and items, thereby enhancing the personalization of recommendations.  
However, propagation-based methods also present some challenges. Firstly, these methods often involve substantial computational load, especially when dealing with large-scale knowledge graphs, as the training and inference processes of graph neural networks can consume significant computational resources. Secondly, to reduce computational costs, random sampling methods are often used to select neighbor nodes, but this may lead to information loss, thereby affecting the accuracy and completeness of recommendation results. Additionally, the issue of information decay during the propagation process needs to be addressed through appropriate design, such as introducing residual connections to preserve original information.  
In recent years, recommendation algorithms based on knowledge graphs have also been evolving with the continuous development of deep learning and graph neural network technologies. For example, in the latest research, [PERSON] et al. [20] proposed a novel recommendation model called KGLN based on graph neural networks. This model first merges node features in the graph using a single-layer neural network then adjusts the aggregation weights of neighboring entities by incorporating influencing factors and iterates from a single layer to multiple layers to capture multi-order associated entity information. Finally, it integrates entity and user functionalities to generate recommendation scores, thereby improving the accuracy and effectiveness of personalized recommendations. In addition, [PERSON] et al. [21] began exploring the integration of large language models (LLMs) with knowledge graphs, leveraging their powerful language generation capabilities and knowledge comprehension abilities to provide richer contextual information for recommendation systems.
# Broadcasting in Stars of Cliques and Path-Connected Cliques +
## 4 Decision Algorithm for the Broadcast Time of Star of Cliques
### Improved Decision Algorithm for the Broadcast Time of Star of Cliques  
Algorithm 3 can compute \(b(u,G_{k})\) for an arbitrary star of cliques in \(O(n^{3})\) time. However, in Algorithm 5, we present a more efficient approach with a time complexity of \(O(n\cdot\log n)\) for calculating \(b(u,G_{k})\) in an arbitrary star of cliques.  
The algorithm utilizes base \(10\) digits, which are stored in a max heap. In each iteration, we extract the two largest cliques from the top of the heap. At each time unit \(j\), where \(1\leq j\leq t\), the algorithm can inform \(2^{t-j}\) vertices. If any clique, other than the first, requires at least \(2^{t-j}\) vertices in the current time unit, the algorithm returns False. Otherwise, \(2^{t-j}\) vertices from the first clique are informed. The first element in the heap is then updated to \(l_{1}-2^{t-j}\). This process is repeated for \(t\) iterations.  
Complexity Analysis  
The algorithm requires \(O(\log k)\) time to perform one iteration of all heap operations in Steps 2, 8, and 11, where \(k\) denotes the number of elements in the heap (i.e., the number of cliques in the graph). Consequently, performing \(t\) iterations, where each iteration corresponds to a time unit, incurs a total time complexity of \(O(t\cdot\log k)\). Additionally, the preliminary operations of sorting the input and calculating the upper bound \(t^{\prime}\) require \(O(k\cdot\log k)\) time, while initializing the heap incurs \(O(k)\) time.  
It is evident that \(t,k=O(n)\) as \(t\) is bounded by \(k+\lceil\log l_{1}\rceil\), and \(k,l_{1}\leq n\), where \(n=l_{1}+l_{2}+\ldots+l_{k}\) denotes the total number of vertices in the graph. Therefore, the overall time complexity of the algorithm is \(O(n\cdot\log n)\).
## 5 Discussion
### Accuracy and Validity of LPS Systems
A LPS using a Wireless Ad hoc System for Positioning (WASP) using 12 antennae and a sampling rate of 10-Hz was tested for its validity [32]. The results for mean error (%) varied between 1.26 (walking distance in a linear course) and 3.87% (sprinting distance in a nonlinear course). Results in indoor and outdoor conditions were consistent and revealed validity [32]. One of the included studies proposed to analyze the interchangeability of a multicamera, semiautomatic system, LPM (Inmotio) and GPS units [29]. Comparing the distance run at different speeds, the Immotio tended to largely and moderately underestimate the distances run at 7.2 and 14.4 km/h\({}^{-1}\), respectively, while the multicamera system and the GPS tended to overestimate the distance run at all intensities [29]. In the study, the authors [29] proposed calibration equations for interchangeability of the systems, revealing that most of the calibration equations calculated were associated with small-to-moderate typical errors of the estimate. An ultra-wide band (UWB) from RealTrack systems was tested in indoor conditions for its accuracy revealing mean absolute error of all position estimations of 5.2 cm (0.97%) for the x-position and 5.8 cm (0.94%) for the y-position [17]. Additionally, the estimation of errors was between 2.1 and 8.3 cm on the x-axis and 3.5 and 8.2 cm on the y-axis [17]. The results of the study [17], suggested acceptable accuracy levels of the UWB for monitoring the position of players. The same UWB (RealTrack systems) tested in outdoor conditions [24] revealed a mean absolute error of 41.23 and 47.6 cm for x-axis and y-axis, respectively. The findings confirmed the high accuracy and high transmission path of the UWB, mainly considering comparisons with GPS systems [24]. A third included study [23], testing the UWB from RealTrack systems showed a bias (%) of 0.55 to 5.85% for determining distance covered, and, moreover, a bias between \(-\)0.56 and 0.67 for determining mean velocity [23]. An additional comparison with GPS also revealed the better accuracy of UWB [23]. In brief, the studies [17; 23] testing the accuracy of UWB of RealTrack systems showed a good accuracy of the system to determine players' positions, distances covered and mean velocities. An alternative brand of UWB (Ubisens Series 7000 compact tag) was also tested for its accuracy [36], also showing sufficient accuracy to test positions of players independently of the length of the recorded runs. Summarizing the evidence about validity of LPS, all the tested systems (e.g., Catapult ClearSky T6; Kineson One; NBN23; WASP; LPM using glass fiber technology; Imnotio) revealed mean error below 5% measuring distances and average speeds, although not in measuring instantaneous speed and decelerations. It was clear that all studies confirmed good and acceptable accuracy of LPS systems to estimate the position and the distance and velocities achieved by players, although a decrease in accuracy occurs in some conditions (e.g., turns, changes of direction, sport-specific actions) and intensities (e.g., peak accelerations or decelerations).
# Comparison of the Heights of Sporadic E Layers and Vertical Ion Convergence Parameters
## 1 Introduction
The black star represents the S4 max; **(b)** The red curve represents the profile of ion vertical velocity, and the black regular triangle represents the ion vertical velocity null (IVN); **(c)** The green curve represents the vertical ion convergence (VIC). The black inverted triangle represents the VICmax; **(d)** The black solid curve represents the zonal wind, and the black dash-dotted curve represents the meridional wind. value of vertical ion convergence. The VICmax signifies the altitude of the strongest convergence, where metallic ion velocity changes the fastest. This suggests the potential for a high-density ion layer to swiftly form in this region, making its altitude an accepted proxy for the height of the Es layer in previous studies [11; 12]. The results of [PERSON] et al. (2014) showed consistency between the height of Es and VICmax [12]. However, [PERSON] et al. (2021) highlighted that the heights of Es layers and VICmax are not completely consistent in the Es layer formation process [11]. The ion vertical velocity null (IVN) refers to the point at which ion movement transitions from an upward direction at lower altitudes to a downward direction at higher altitudes. This demarcates the altitude where ions come to a standstill and can also be considered the altitude where Es layers are stationed. Numerical simulations suggested that the height of the Es layer agrees well with the height of the IVN above 110 km [22]. A theoretical study by [PERSON] et al. (2020) also suggested that the height of the Es layer is in closer proximity to the height of IVN [23]. [PERSON] et al. (2022) verified the role of IVN in the height of Es layers based on the observations of ionosondes and ICON/MIGHT [24]. Previous studies have utilized various parameters from the wind shear theory to quantitatively assess its contribution to sporadic E layers. These investigations have revealed both agreements and discrepancies between wind shear theory and Es layers across different parameters [11; 12; 19; 21; 24]. However, most of these studies typically focused on individual parameters. Using the WACCM-X (The Whole Atmosphere Community Climate Model with thermosphere and ionosphere extension) model and COSMIC-1 RO observations, this study reexamines the relationship between VICmax, IVN, and Es layer heights at different seasons and latitudes. In Section 2.1, the COSMIC data and WACCM-X model are described. Section 2.2 introduces the data analysis methods used in this paper, including the definitions of IVN and VICmax. The statistical results are displayed and discussed in Section 3. Section 4 contains our conclusions.
# FedCrow: Federated-Learning-Based Data Privacy Preservation in Crowd Sensing
## 3 FedCrow Design  
In order to effectively protect the data security in crowd-sensing applications, based on the scenarios and characteristics of crowd sensing, this study designed a federated-learning-based data security system for crowd sensing. By introducing federated-learning models into the crowd-sensing system, it ensures that user data are only stored locally to mitigate the risk of data leakage. For this purpose, a federated-learning framework suitable for crowd sensing was designed using the TensorFlow framework based on the principles of federated learning.  
Taking into account the unnecessary data leakage that can occur during data interactions in federated-learning systems, we aimed to construct a secure federated-learning framework for safeguarding data in crowd-sensing applications. To achieve this, we utilized appropriate data encryption methods to encrypt the data transmission process by considering the data volume typically encountered in crowd-sensing application scenarios.  
Furthermore, the greatest threat in federated-learning systems stems from internal malicious users participating in the training process. Each user in federated learning possesses a dataset, allowing internal malicious actors to easily manipulate datasets, the training process, and the model, consequently compromising the model performance. To mitigate the risk of internal attacks, it is imperative to rigorously scrutinize users participating in the training to ensure their legitimacy. This paper proposes a legitimate user-screening method based on client-side contributions to the global model. It evaluates the contribution level of user models to the server's global model by computing the gradient similarity between different models, thereby identifying trustworthy users.