esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion
### Congestion in the Network  
Congestion presents a significant challenge in IoT networks, particularly those incorporating diverse network types, such as wireless sensor networks and vehicular ad hoc networks. The increasing complexity and size of these networks often lead to congestion, resulting in delays in communication and degraded system performance [43].  
Congestion negatively impacts QoS in IoT systems by increasing power consumption and reducing the overall efficiency of network operations. Effective routing protocols play a crucial role in mitigating congestion by optimising the paths data take through the network, selecting the shortest and least congested routes for data transmission [44]. Addressing congestion effectively requires robust control over routing paths and balanced workloads among network nodes to maintain the longevity and reliability of IoT networks [45].
## 6 Model Results  
### Hyperparameter Optimization: BayesSearchCV  
This paper's forecasting model employs the Scikit-Learn library, the preeminent machine learning library for Python, utilized specifically on the Python 3.7 platform. In this research, the XGBoost algorithm was utilized to create a predictive model for cubic CS resulting from machine measurements. By utilizing the BayesSearchCV method from the Skopt package for the parameter search, it was feasible to identify the parameters that best fit the predictive model attributes. Locating parameters that yield the highest model prediction accuracy becomes more streamlined with the Python BayesSearchCV function [58]. The perfect blend of XGBoost hyperparameters was derived by setting the values and ranges of the XGBoost prediction model hyperparameter and employing the BayesSearchCV function. The effectiveness of different hyperparameters in XGBoost can vary depending on the specific dataset and problem at hand. However, some hyperparameters are generally considered more crucial to optimize. Here are a few:  
* \(n\_\)estimators and learning_rate: These parameters control the number of sequential trees to be modeled (\(n\_\)estimators) and the contribution of each tree to the final outcome (learning_rate). They can have a significant effect on the model's performance and are often tuned together.
* max_depth: This parameter sets the maximum depth of a tree and can help control overfitting, as it makes the model more specific to the training data. Generally, a smaller max_depth is preferred to prevent the model from learning relations which might be highly specific to the particular sample selected for a tree.
* subsample: This is the fraction of observations to be randomly sampled for each tree. Lower values make the algorithm more conservative and prevent overfitting but too small values might lead to under-fitting.
* colsample_bytree: This is the subsample ratio of columns when constructing each tree. Subsampling occurs once for every tree constructed.  
Table 2 provides a view of the search parameters' range and the most suitable hyperparameters identified for the XGBoost model.  
Figure 3: A heatmap illustrating the PCC between the dataset’s CS and input variables.  
The evaluation results of the model are summarized in Table 3, presenting the three chosen metrics for both the training and testing sets. The model exhibits outstanding performance in forecasting CS values. In the case of the training data set, an R\({}^{2}\) value of 0.9993 signifies an almost perfect alignment with the actual values. Furthermore, the model achieves a MAE of just 0.3544 MPa and a RMSE of 0.4991, reinforcing the model's exceptional learning and predictive capabilities. When evaluated on the testing data, the model maintains a robust performance with an R\({}^{2}\) value of 0.9756, which is remarkably close to 1. The MAE is slightly higher at 1.7737, yet this increase remains within acceptable limits from an engineering practice perspective. Overall, the BO-XGBoost model accurately predicts the CS, demonstrating its high efficacy.
## 2 Background
### Theoretical Context
#### 2.1.4 Mobile Applications  
Nowadays, cell phones have become essential, and thanks to advances in their development in conjunction with the Internet, a wide range of tasks can be performed through them. By using a mobile device, users can access statistics on energy consumption and control household electrical power remotely [54]. There are several studies on the developing mobile smart grid applications that focus on monitoring and controlling energy consumption to interact with an SG network [55; 56; 57]. These studies only focus on the technical method of the smart grid system process so that energy consumption can be monitored, organized, and scheduled using a mobile application without going into the process of developing smart-grid-oriented mobile applications.
# Ground Control System Based Routing for Reliable and Efficient Multi-Drone Control System
## 4 Performance Evaluation
### Simulations
#### 4.3.3 Performance Evaluation for the Replacement Scenario  
As discussed above, drone replacement is an essential function to prolong the drone system service time. Therefore, we evaluated GCS-routing for the replacement scenario by simulation, as shown in  
\begin{table}
\begin{tabular}{l c c} \hline  & **Throughput (Kbps)** & **Standard Deviation** \\ \hline GCS-routing & 1000.1 & 3.1 \\ DSDV-2s & 814.0 & 370.9 \\ OLSR & 743.9 & 416.9 \\ \hline \end{tabular}
\end{table}
Table 4: Simulated throughput for the mobility scenario without background traffic.  
Figure 16: Simulated throughput for the mobility scenario.  
\begin{table}
\begin{tabular}{l c c} \hline  & **Throughput (Kbps)** & **Standard Deviation** \\ \hline GCS-routing & 989.2 & 90.7 \\ DSDV-2s & 581.6 & 446.0 \\ OLSR & 669.7 & 452.6 \\ \hline \end{tabular}
\end{table}
Table 5: Simulated throughput for the mobility scenario with background traffic.  
Figure 15b. The user located in the service area of Drone 4 sent 1 Mbps UDP data traffic to Drone 1. At \(T_{1}\), Drone 3 left the fleet and was replaced by Drone 5. At \(T_{2}\), Drone 2 was replaced by Drone 6.  
Figure 17 shows the resultant GCS-routing, DSDV, and OLSR throughputs, and Tables 6 and 7 show network disconnected time and throughput standard deviation, respectively. Similar to the real-drone experiment case in Section 4.2.3, GCS-routing exhibited superior performance compared with the other protocols, and immediately reacted to new network topologies.
## 2 MPS Method
### Mathematical Formulation
#### 2.1.4 Boundary Condition  
The MPS method needs to decide on boundary conditions for solving the pressure Poisson equation. The free surface boundary and solid boundary conditions used in this paper are described as follows:  
1. Free surface boundary. Since no particle can exist outside the fluid domain, the particle number density will inevitably decrease on the free surfaces. Therefore, the particles nearing the free surface can be detected by the following condition: \[\langle n\rangle_{i}^{*}<\beta n^{0}\] (11) where \(\beta\) is a parameter, and is less than 1.0. Furthermore, the pressure of free surface particles is set to zero.
2. Solid boundary. For the moving boundary condition, two kinds of solid particles are adopted to prevent the fluid particles from penetrating the boundary. One is the wall particles, which are set along the solid boundary. The other one is the dummy particles, which are placed outside the solid wall. It should be noted that the position of all solid particles will not update velocity and position after they gained pressure, the pressure of dummy particles is obtained by extrapolation, and the pressure of wall particles are involved in solving the PPE.
###### Abstract  
The classification of the wireless propagation channel between Line-of-Sight (LOS) or Non-Line-of-Sight (NLOS) is useful in the operation of wireless communication systems. The research community has increasingly investigated the application of machine learning (ML) to LOS/NLOS classification and this paper is part of this trend, but not all the different aspects of ML have been analyzed. In the general ML domain, poisoning and adversarial attacks and the related mitigation techniques are an active area of research. Such attacks aim to hamper the ML classification process by poisoning the data set. Mitigation techniques are designed to counter this threat using different approaches. Poisoning attacks in LOS/NLOS classification have not received significant attention by the wireless communication community and this paper aims to address this gap by proposing the application of a specific mitigation technique based on outlier detection algorithms. The rationale is that poisoned samples can be identified as outliers from legitimate samples. In particular, the study described in this paper proposes a recent outlier detection algorithm, which has low computing complexity: the sparse data observers (SDOs) algorithm. The study proposes a comprehensive analysis of both conventional and novel types of attacks and related mitigation techniques based on outlier detection algorithms for UltraWideBand (UWB) channel classification. The proposed techniques are applied to two data sets: the public eWINE data set with seven different UWB LOS/NLOS different environments and a radar data set with the LOS/NLOS condition. The results show that the SDO algorithm outperforms other outlier detection algorithms for attack detection like the isolation forest (iForest) algorithm and the one-class support vector machine (OCSVM) in most of the scenarios and attacks, and it is quite competitive in the task of increasing the UWB LOS/NLOS classification accuracy through sanitation in comparison to the poisoned model.  
s _future internet_2025, 17, 60
# User-Centric Cell-Free Massive MIMO with Low-Resolution ADCs for Massive Access
###### Abstract  
This paper proposes a heuristic association algorithm between access points (APs) and user equipment (UE) in user-centric cell-free massive multiple-input-multiple-output (MIMO) systems, specifically targeting scenarios where UEs share the same frequency and time resources. The proposed algorithm prevents overserving APs and ensures the connectivity of all UEs, even when the number of UEs is significantly greater than the number of APs. Additionally, we assume the use of low-resolution analog-to-digital converters (ADCs) to reduce fronthaul capacity. While realistic massive access scenarios, such as those in Internet-of-Things (IoT) environments, often involve hundreds or thousands of UEs per AP using multiple access techniques to allocate different frequency and time resources, our study focuses on scenarios where UEs within each AP cluster share the same frequency and time resources to highlight the impact of pilot contamination in dense network environments. The proposed algorithm is validated through simulations, confirming that it guarantees the connection of all UEs and prevents overserving APs. Furthermore, we analyze the required fronthaul capacity based on quantization bits and confirm that the proposed algorithm outperforms existing algorithms in terms of SE and average SE performance for UEs.  
massive MIMO; cell-free massive MIMO; fronthaul capacity; low-resolution ADCs; massive access; AP-UE association; user-centric network +
Footnote †: journal: _Sensors_  
_Sensors_**2024**, 24, 5088. [[https://doi.org/10.3390/s24165088](https://doi.org/10.3390/s24165088)]([https://doi.org/10.3390/s24165088](https://doi.org/10.3390/s24165088))
## 5 Conclusions  
This study has successfully obtained analytical thermal solutions for radially FGM-coated or FGM-undercoated circular plates subjected to transversely and radially thermal loads, utilizing classical plate theory. The findings lead to the following conclusions:  
1. Incorporating FGM as a coated or undercoated layer can effectively reduce the maximum thermal stress experienced by the circular plate. Notably, the maximum thermal stress in the FGM-coated or FGM-undercoated plate is located within the radius of the circular plate (\(r\
eq 0\)), which differs from the behavior observed in homogeneous circular plates.
2. For the case where the index \(P=0\), the thermal stresses and moments of the FGM-coated circular plate under the thermal load \(T(r,z)=T_{0}(1+\bar{r}^{m})\bar{z}^{2P+1}\) demonstrate the proportional relations of \(M_{j}=h^{3}\sigma_{j}/(12z),\ j=r,\ \theta\).
3. Under transversely thermal loads (\(m=0\)), the stresses at the homogeneous portion of the FGM-coated circular plate remain unaffected by the radius \(r\). However, the stresses at the FGM-ring portion vary depending on the radius, owing to the radially varying material properties of the FGM layer.
4. When subjected to radially thermal loads only (\(P=-0.5\)), the stresses and moments of the homogeneous portion depend on the product of \(r^{m}\) influenced by the radially thermal load. Conversely, the stresses and moments of the FGM layer are determined by the function \(r^{m+n}\), which takes into account both the thermal load and material gradation.
5. The stresses \(\sigma_{r}\) and \(\sigma_{\theta}\) of the radially FGM-coated circular plate, whether subjected to transversely thermal load only (\(m=0\)) or radially thermal load only (\(P=-0.5\)), exhibit continuity with noticeable inflection points in slope at the interfaces.
6. The numerical findings obtained from this study can serve as a benchmark for researchers in validating their numerical methods and results when analyzing similar problems involving FGM-coated and FGM-undercoated circular plates.
7. The developed analytical solutions can be applied to analyze the mechanical behavior of various FGM-coated and FGM-undercoated circular plates subjected to different thermal loading conditions. Further investigations can explore their application in specific engineering problems.  
Figure 13: The distributions of stresses \(\sigma_{r}\) and \(\sigma_{\theta}\) of the FGM-undercoated circular plate (\(n=2\)) subjected to thermal load (\(P=0\),\(m=2\)) when the undercoated thickness changes with \(R_{1}/R=0.1,\ 0.2,\ 0.3,\ 0.5\) and \(R_{2}=0.8R\).  
**Author Contributions:** Conceptualization, Y.-L.C.; Methodology, Y.-L.C.; Validation, Y.-L.C.; Formal analysis, Z.-H.L.; Writing--original draft, Y.-L.C.; Project administration, Y.-L.C. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Millimeter-Wave Smart Antenna Solutions for URLLC in Industry 4.0 and Beyond
## 5 Research Opportunities and Future Directions
### Machine Learning for Antennas in Industry 4.0 and Beyond  
The potential of exploiting machine learning (ML) is imminent in 5G and beyond-enabled Industry 4.0 and beyond. At each abstraction layer, ML is anticipated to be used to predict service demands of Industry 4.0 and beyond as well as the evolution of the wireless channel in order to design self-optimizing and self-updating networks [197; 198; 199; 200]. It is unveiled in [191] that the traditional multi-antenna signal processing approaches are no longer sufficient for broadband mmWave massive MIMO systems due to a large number of radiating elements involved, high data rates, and a high number of mobile users. Unlike traditional communication systems, which demand many computing resources and result in unacceptable latency, deep-learning-based approaches take advantage of natural channel sparsity to efficiently precode and modulate data into several streams and send it to the distributed system [191; 201; 202; 203]. Moreover, a physics-inspired neural network is used in [204] to design a reconfigurable coded metasurface for dynamic beam steering. Such type of ML-based reconfigurable antennas possess huge potential for Industry 4.0 and beyond applications, where the smart antennas system is required to reconfigure its radiation pattern in real-time based on blockage, jamming, and NLOS scenarios.
## 3 Antenna Testing and Result Analysis
### Mode 3 (\(D_{1}\) Is On While \(D_{2}\) Is Off)  
In mode 3, when D\({}_{1}\) is switched on while keeping D\({}_{2}\) switched off, the antenna offers the tri-band behavior with wideband behavior similar to mode 1, and two distinguished resonances across 2.15 GHz and 2.6 GHz, as depicted in Figure 9a. Simulated results offer the impedance bandwidths of 2.1-2.19 GHz, 2.54-2.65 GHz, and 4.2-7.2 GHz, which correspond to 4.18%, 4.23%, and 64.5%, respectively. On the other hand, the measured result offers an S11 \(<-\)10 dB bandwidth of 2.09-2.21 GHz, 2.55-2.69 GHz, and 4.25-6.87 GHz, as depicted in Figure 9a. The surface current distribution is also studied, where at 2.6 GHz, the maximum current is concentrated in the outer stub, while a small amount of current is also induced in the inner stub. Contrary to that, at 2.15 GHz, the maximum current is observed in the inner stub, which is due to coupling among the stub and the radiator, as shown in Figure 9b. The maximum current is observed in the outer stub, which is due to coupling among the stub and the radiator, as shown in Figure 9b. The maximum current is observed in the outer stub, while a small amount of current is observed in the inner stub.  
Figure 8: Mode 2 performance parameter comparison of (**a**) return loss, (**b**) surface current, and (**c**) peak gain.  
depicted in Figure 9b. Since the inner stub offers the bigger electric length, the coupling generates a lower frequency of 2.15 GHz, unlike the generation of 2.6 GHz due to the outer stub, which offers the smaller electrical length.  
A comparison among the radiation pattern of the antenna at the selected frequencies of 2.15 GHz, 2.6 GHz, and 4.65 GHz is also illustrated in Figure 9c. For all frequencies, an omni-directional radiation pattern is observed for Phi = 90\({}^{\circ}\), while a monopole-like radiation pattern is observed for Phi = 0\({}^{\circ}\). For all frequencies, a strong comparison among simulated and measured results is offered, which validates the working of mode 3, as depicted in Figure 9c. Moreover, for the lower band, the antenna offers a gain of more than 1.1 dBi, while for the upper band, the gain is observed to be greater than 2.5 dBi. Furthermore, in mode 3, the proposed antenna covers the globally allocated band spectrum for 3G, LTE-band-7, LTE-4G-band-1/band-4/band-66, 5G-sub-6-GHz, Wi-Fi-6E, and C-band applications.
# Authenticated Multicast in Tiny Networks via an Extremely Low-Bandwidth Medium
## 4 Sender Authentication
### Optimal Values for Small Number of Nodes
#### 4.5.1 Preliminary Facts  
Let us start with some obvious facts for which the proofs are constructive.  
**Lemma 2**.: \(\Psi(r+1)\leq\Psi(r)+1\)_._  
\begin{table}
\begin{tabular}{l c c c c c c c c c c c c c c c c c c} \hline \hline \(r\): & **4** & **5** & **6** & **7** & **8** & **9** & **10** & **11** & **12** & **13** & **14** & **15** & **16** & **17** & **18** & **19** & **20** & **21** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of the number of elements in a separating family obtained by the two algorithms.  
Figure 5: A separating family \(F_{1},\ldots,F_{5}\) obtained by connecting the nodes \(F_{1},\ldots,F_{5}\) by edges labeled \(1\) through \(10\). The obtained solution is \(F_{1}=\{1,2,4,6\}\), \(F_{2}=\{2,3,7,10\}\), \(F_{3}=\{5,6,7,8\}\), \(F_{4}=\{4,8,9,10\}\), \(F_{5}=\{1,3,5,9\}\).  
Proof.: If \(\mathcal{SF}\) is a separating family for \(\{1,\ldots,r\}\) with \(\Psi(r)\) elements, then \(\mathcal{SF}^{\prime}=\mathcal{SF}\cup\{r+1\}\) is a separating family for \(\{1,\ldots,r+1\}\).  
**Lemma 3**.: \(\Psi(2r)\leq\Psi(r)+2\)_._  
Proof.: Let \(\mathcal{SF}\) be a separating family for \(\{1,\ldots,r\}\) with \(\Psi(r)\) elements. For each \(F\in\mathcal{SF}\), we define a set \(F^{\prime}=\{j\leq 2r\mid j\in F\lor j-r\in F\}\). Note that each \(F^{\prime}\) is twice the size of its corresponding \(F\). Indeed, this construction'stacks' two separating families on top of each other: one for \(\{1,\ldots,r\}\), and an isomorphic one for \(\{r+1,\ldots,2r\}\). Let \(\mathcal{SF}^{\prime}\) be a family comprising the sets \(F^{\prime}\) (one for each \(F\in\mathcal{SF}\)) as well as two additional sets: \(F_{L}=\{1,\ldots,r\}\) (the lower half of the nodes) and \(F_{U}=\{r+1,\ldots,2r\}\) (the upper half).  
We must show that \(\mathcal{SF}^{\prime}\) is a separating family for \(\{1,\ldots,2r\}\). If \(i,j\leq r\), then \(i\) and \(j\) are separated, as \(\mathcal{SF}\) is a separating family for \(\{1,\ldots,r\}\) and \(F^{\prime}\cap\{1,\ldots,r\}=F\). Similarly, if \(i,j>r\), then they are separated, as \(\mathcal{SF}\) can be applied as a separating family to \(\{r+1,\ldots,2r\}\) under a suitable map \(\varphi:i\mapsto i-r\) and \(\varphi(F^{\prime}\cap\{r+1,\ldots,2r\})=F\). If \(i\leq r<j\), then the set \(F_{L}\) separates \(i\) from \(j\); similarly, if \(i>r\geq j\), then the set \(F_{U}\) separates \(i\) from \(j\).
## 5 Discussion
### Single-Hop vs. Multi-Hop Communications  
Another key requirement in sensing application is network coverage. TSCH is designed to support mesh networking, which enables to cover large areas by means of multi-hops. Multi-hop mesh networks enable flexible topologies and provide redundancy against environment changes, which is crucial in certain communication environments. Nevertheless, the realization of multi-hop transmissions requires a dense network deployment to ensure path availability. Unfortunately, this is an unrealistic assumption in many real scenarios. In addition, the end-to-end bandwidth is reduced, because the channel is busy and the delay increased, due to processing overhead. However, the per-hop average energy consumption of a given node can be lower, because the transmit power at each hop can be reduced. However, this comes at the price of increasing the overall network energy consumption, as many nodes are involved in the process of forwarding a packet to its final destination. Furthermore, multi-hop communications require routing, which increases energy consumption, due to network management overhead, and becomes less scalable. In addition, selecting the appropriate routing metric is application dependent and introduces significant complexity to the network.  
Contrarily, D7 AM is mainly designed to support star topologies by means of using single-hop communications, though it also supports multi-hops through an optional feature at the network layer, where certain nodes can forward packets to extend the network coverage. In single-hop communications, the transceiver characteristics, e.g., transmit power and sensitivity, are crucial, because they determine the communication range. D7 AM operates in the 433 MHz band with lower data rates and optional channel encoding, which enables to have longer range communications, due to its better propagation and sensitivity characteristics. However, operating in the 433 MHz band has some downsides, as well. First, narrow-band channels are more subject to the effects of interference, both internal and external, and multi-path propagation. Second, due to the limitations in the available bandwidth, the channels in the 433 MHz band are not orthogonal, meaning that certain channels interfere with other channels in the same band. Third, regulations in the 433 MHz band are not harmonized world-wide, and, thus each application needs to be tailored depending on the specific country regulations.