esa-sceva/satcom-chunk-collection huggingface dataset:
# Routing Protocols Performance on 6 LoWPAN IoT Networks
###### Abstract  
IPv6 over Low-Power Wireless Personal Area Networks (6 LoWPAN) are specifically designed for applications that require lower data rates and reduced power consumption in wireless internet connectivity. In the context of 6 LoWPAN, Internet of Things (IoT) devices with limited resources can now seamlessly connect to the network using IPv6. This study focuses on examining the performance and power consumption of routing protocols in the context of 6 LoWPAN, drawing insights from prior research and utilizing simulation techniques. The simulation involves the application of routing protocols, namely Routing Protocol for Low-power and Lossy (RPL) Networks, Ad hoc On-demand Distance Vector (AODV), Lightweight On-demand Ad hoc Distance-vector Next Generation (LOADng), implemented through the Cooja simulator. The simulation also runs in different network topologies to gain an insight into the performance of the protocols in the specific topology including random, linear, and eclipse topology. The raw data gathered from the tools including Powertrace and Collect-View were then analyzed with Python code to transfer into useful information and visualize the graph. The results demonstrate that the power consumption, specifically CPU power, Listen Power, and Total Consumption Power, will increase with the incremental of motes. The result also shows that RPL is the most power-efficient protocol among the scenarios compared to LOADng and AODV. The result is helpful because it brings insights into the performance, specifically power consumption in the 6 LoWPAN network. This result is valuable to further implement these protocols in the testbed as well as provide an idea of the algorithmic enhancements.  
AODV; Cooja; LOADng; RPL; 6 LoWPAN
# Robust Low-Sidelobe Transmit Beamforming under Peak-to-Average-Power Ratio Constraint
## 4 Numerical Results and Analyses  
This section describes several representative experiments that were performed to evaluate the effectiveness of the proposed robust TBF method. The following experiments are based on a ULA, whose structure is shown in Figure 1. Taking the first array element as the reference element, suppose that the ULA has \(N=32\) isotropic radiation elements, and there is no mutual coupling effect among the elements. To prevent the appearance of grating lobes, the element spacing is set at a half wavelength. It is assumed that the mainlobe direction of the beampattern is \(\theta_{0}=0^{\circ}\), the scanning range is \([-90^{\circ},90^{\circ}]\), and the sampling interval is \(0.1^{\circ}\). The sidelobe regions are \([-90^{\circ},-5^{\circ}]\) on the ground side and \([5^{\circ},90^{\circ}]\) on the air side. Training snapshots were set as 500 for adaptive TBF. The convex problems were solved by using the CVX toolbox. The basic parameter settings of the numerical experiments are shown in Table 1.  
In the follow-up experiments, we first compared the transmit beampatterns generated by the proposed method and other typical beamforming methods, including SMI beamforming, static beamforming, NCCB beamforming, and WCPO beamforming, so as to evaluate the effectiveness of the proposed TBF method. Then, to quantize the influence of the PAPR constraint and norm constraint on TBF, the performance was studied under different parameter configurations, and simulation experiments were conducted with different parameter settings.  
Two typical metrics are introduced to measure the performance of sidelobe control, namely, the PSL and the ISL. The PSL, which the proposed method aims to minimize,  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Array Model** & **ULA** & **Element Spacing** & _A/2_ \\ \hline Scanning Range & \([-90^{\circ},90^{\circ}]\) & Expected Signal & \(\theta_{0}=0^{\circ}\) \\ Sampling Interval & \(0.1^{\circ}\) & Ground Side & \([-90^{\circ},-5^{\circ}]\) \\ Snapshots & 500 & Air Side & \([5^{\circ},90^{\circ}]\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: General parameter settings.  
measures the ability of the radar system to distinguish neighboring targets. On the other hand, the ISL measures the impact of the whole sidelobe region in the desired target direction. The smaller the PSL and ISL, the lower the energy of the sidelobe returned by the strong clutter and undesired targets, and the higher the probability of detecting the weak desired target, thus improving the detection performance. The following formulas can be used to calculate the two metrics.  
\[\text{PSL}=10\log\left(\frac{\max_{\theta\in\Theta_{\text{G}}}\abs{\mathbf{\omega}^ {H}\mathbf{\mathrm{a}}(\theta)}^{2}}{\abs{\mathbf{\omega}^{H}\mathbf{\mathrm{a}}(\theta_{ 0})}^{2}}\right) \tag{25}\]  
and  
\[\text{ISL}\ =10\log\left(\frac{\int_{\theta\in\Theta_{\text{G}}}\abs{\mathbf{\omega}^ {H}\mathbf{\mathrm{a}}(\theta)}^{2}}{\abs{\mathbf{\omega}^{H}\mathbf{\mathrm{a}}(\theta_{ 0})}^{2}}\right),\theta\in\Theta_{\text{G}} \tag{26}\]
# Dynamic Coal Flow-Based Energy Consumption Optimization of Scraper Conveyor
## 5 Case Study and Validation
### Correlation Parameter Analysis
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Run** & \(\mathbf{V_{q}}\) & \(\mathbf{V_{j}}\) & \(\mathbf{n}\) & \(\mathbf{S_{a}}\) & \(\mathbf{E\) (\(\times\)\(10^{9}\)) \\ \hline
1 & 2 & 30 & 40 & 5700.43 & 4.1747 \\
2 & 7 & 30 & 40 & 15,983.1 & 1.06096 \\
3 & 2 & 100 & 40 & 5700.43 & 13.0266 \\
4 & 7 & 100 & 40 & 15,983.1 & 3.21057 \\
5 & 2 & 65 & 30 & 6282.62 & 8.09433 \\
6 & 7 & 65 & 30 & 27,460.8 & 2.06738 \\
7 & 2 & 65 & 50 & 5440.57 & 9.09712 \\
8 & 7 & 65 & 50 & 11,518.1 & 2.19038 \\
9 & 4.5 & 30 & 30 & 12,641.3 & 1.60108 \\
10 & 4.5 & 100 & 30 & 12,641.3 & 5.00289 \\
11 & 4.5 & 30 & 50 & 7440.23 & 1.76725 \\
12 & 4.5 & 100 & 50 & 7440.23 & 5.3341 \\
13 & 4.5 & 65 & 40 & 8981.45 & 3.42053 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: BBD experimental matrix with objective function values. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Level** & \(\mathbf{V_{q}}\) **(m/min)** & \(\mathbf{V_{j}}\) **(mm)** & \(\mathbf{n}\) **(r/min)** \\ \hline \(-1\) & 2 & 30 & 20 \\
0 & 4.5 & 65 & 30 \\
1 & 7 & 100 & 50 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Factor level coding. transport efficiency and friction losses. An increase in chain speed leads to greater friction between the coal and the conveyor trough, raising operating resistance and consequently increasing motor energy consumption and chain wear. Improper chain speeds may cause coal accumulation, idling, or overload conditions, further increasing energy consumption and reducing efficiency. Moreover, the mining machine's drum speed indirectly affects the conveyor system by influencing the fragmentation and uniformity of the coal flow. In conclusion, the traction speed of the mining machine and the scraper chain speed are critical factors in the analysis and optimization of scraper conveyor energy consumption as they directly influence coal flow rate and transport efficiency. From Table 12, it is clear that the mining machine's traction speed and drum speed significantly affect the lump coal rate. The traction speed determines how fast the drum moves through the coal seam. A higher traction speed increases the impact and shear forces during cutting, leading to more coal fragmentation and a lower lump coal rate. Conversely, lowering the traction speed reduces coal breakage and improves the lump coal rate. The drum speed also plays a key role by controlling the frequency of the picks' cutting and the evenness of coal fragmentation. A higher drum speed causes more frequent forces on the coal, producing smaller coal pieces and lowering the lump coal rate. A slower drum speed allows for more even cutting, helping the coal break more cleanly and increasing the lump coal rate. In contrast, the scraper chain speed mainly affects the coal flow during transport and has less of an impact on coal fragmentation. Regardless of whether the chain speed is fast or slow, it does not significantly change the coal flow pattern during transport, and thus does not notably affect the lump coal rate.
## 2 Materials and Methods
### 2.4.1. Sorption Kinetic Models
#### 2.4.2 Sorption Thermodynamic Models  
The sorption isotherm, which describes the mechanism of adsorbent-adsorbent interaction, is essential to optimize the use of adsorbents. The Langmuir isotherm model is often used under the assumption of a fixed number of homogeneous positions, where reversible monolayer sorption occurs in the absence of interaction between adsorbent species. It can be expressed by the following equation:  
\[Q_{e}=\frac{Q_{max}K_{L}C_{e}}{1+K_{L}C_{e}} \tag{5}\]  
where \(Q_{max}\) (mg\(\cdot\)g\({}^{-1}\)) is the maximum saturated monolayer sorption capacity, \(K_{L}\) (L\(\cdot\)mg\({}^{-1}\))is the Langmuir sorption equilibrium constant related to the affinity between the adsorbent and the adsorbent, and \(Ce\) is the equilibrium concentration of Mn(II) in solution (mg\(\cdot\)L\({}^{-1}\)) [29].  
The Freundlich model primarily describes multilayer sorption of surface inhomogeneous adsorbents which applies to the sorption of solutes in solution on solid surfaces. The model describes reversible, non-ideal, multilayer sorption, and is commonly used for non-homogeneous adsorbents such as biomass. It is expressed as follows:  
\[Q_{e}=K_{F}C_{e}^{\frac{1}{2}} \tag{6}\]  
where \(Q_{e}\) (g\(\cdot\)kg\({}^{-1}\)) is the amount of metal adsorbed by the sediment at equilibrium, \(C_{e}\) (g\(\cdot\)kg\({}^{-1}\)) is the equilibrium concentration, \(K_{F}\) (g\(\cdot\)kg\({}^{-1}\)) is the Freundlich isotherm constant, and n is the dimensionless constant indicating the heterogeneity factor [30].
## 2 Related Work
They also presented an optimization model aimed at minimizing service delay and energy consumption while ensuring load balancing across the network. To the best of our knowledge, no existing literature addresses the DRL-based optimization of TSN routing and scheduling for IoV and I2I communication networks. Furthermore, the solution proposed in this study is the first to employ MARL to tackle TSN's routing and scheduling challenges. A related study, ref. [12], developed an ILP-based routing and scheduling model for TSN-SDN in-vehicle networks considering some vulnerabilities in the Time-Aware Shaper (TAS)--a critical traffic shaper for TSN networks. The proposed approach mitigated TAS vulnerabilities, making it applicable to in-vehicle networks. Ref. [21] proposes an algorithm that solves Edge Disjoint Rooted Distance-Constrained Minimum Spanning trees problem with a limited budget. The proposal develops two local operators, which obtain disjoint routes between clients and facilities with budget limits based on a local search algorithm ensuring fault tolerance. The algorithm requires that all requests between clients and facilities are initially known. In [22], the authors propose a static routing and scheduling algorithm based on mixed integer programming (MIP) in the in-vehicle network. The algorithm controls the TSN Gate Control Lists by simultaneously optimizing the routing and scheduling of dynamic traffic as the vehicle drives off. The work focuses on the static scheduling of load-balanced flows across TSN slots by optimizing dynamic requests. This offline model is evaluated for a small number of nodes with a priori knowledge of the demands. Similarly, ref. [23] evaluated an ILP-based routing and scheduling model for TSN networks, analyzing its performance under increasing network sizes and the traffic loads across different topologies. While the schedules produced were near-optimal, the high computational runtime rendered the model unsuitable for real-time scenarios, particularly in automotive environments. In contrast, ref. [24] proposed a DRL-based model to solve the routing problem in TSN networks. A key innovation of their work was the integration of graph convolutional networks within the DRL agent. The model accounted for the coexistence of time-critical and best-effort traffic by using multiple priority-based queues. However, the DRL agent did not address the assignment of data flows to these priority queues, leaving a gap in the comprehensive handling of TSN scheduling.
## 5 Metrics
### Security Metrics  
As security measures, we define system cost and confidentiality, which are functions of the steady-state probabilities of the CTMC model. The steady-state probabilities \(\pi_{i}\) may be interpreted as the proportion of time that the CTMC spends in state \(i\), where \(i\in\{R,G,T,C\}\). The WSN suffers from cost in two states, the compromised state \(C\) and the rekeying state \(R\). The system loses sensitive information in the compromised state, and cost is also incurred when the clustered sensor network deploys a rekeying process due to the extra communication. The rekeying cost and the data disclosure cost are both interpreted as the proportion of system life time, that is the steady-state probability of the  
\begin{table}
\begin{tabular}{c l l} \hline \hline \(\pi_{i}\) & \(\triangleq\) & The steady-state probability that the continuous-time Markov process is in state \(i\), \(i\in\{R,C\}\) \\ \(\Theta\) & \(\triangleq\) & System cost metric \\ \(\Lambda\) & \(\triangleq\) & Confidentiality metric \\ \(X\) & \(\triangleq\) & System throughput \\ \(T\) & \(\triangleq\) & Tradeoff metric \\ \hline \hline \end{tabular}
\end{table}
Table 1: Table of Notation.  
CTMC. We define a weight \(w\) and its complement \(1-w\) for the two kinds of cost. We use normalized weights for simplicity. Mathematically, the system cost is computed with:  
\[\begin{array}{rcl}\Theta&=&C_{\mathit{rekeying}}&+&C_{\mathit{ disclosure}}\\ &=&w\pi_{R}&+&(1-w)\pi_{\mathrm{C}}\end{array} \tag{1}\]  
where \(\pi_{i},i\in\{R,\mathrm{C}\}\) denotes the steady-state probability that the continuous-time Markov process is in state \(i\). \(0\leq w\leq 1\) is the weighting parameter used to share the relative importance between the loss of sensitive information and the power consumption needed to rekey regularly.  
If a timing attack on the WSN cluster is successful, the attacker obtains the cluster key and can browse unauthorized data thereafter. The entered states denote the loss of confidentiality. Therefore, the steady-state confidentiality measure can be computed as:  
\[\Lambda=1-\pi_{\mathrm{C}}\;. \tag{2}\]  
For quantifying the reliability of a software system, mean time to failure (MTTF) is a widely-used reliability measure. We also use the mean time to security failure (MTTS) metric as a measure for quantifying the security of WSNs in Section 6.3.
## 4 Experiments
### Model Comparison by Target Variable  
The performance of the selected ML models was compared across three target variables, as shown in Figure 9. The analysis involved extracting and evaluating key metrics, including AUC, accuracy, sensitivity, and specificity, across various feature subsets to identify the most effective combinations for each target.  
For the Diagnosis target, LR, RF, and XGBoost models demonstrated superior discriminative power when using Laboratory and Imaging features, with AUCs reaching up to 0.9868 and accuracies around 94%. In contrast, models based on Demographic and Morphological features consistently underperformed, with AUCs as low as 0.5131 and accuracies just above 50%. MLPs further underscored the effectiveness of Laboratory and Imaging, and Literature-derived features in accurate diagnosis.  
In the Management context, Clinical Symptoms and Observations, along with Literature-Based features, achieved the highest predictive accuracy across all models, with AUCs ranging from 0.9434 to 0.9596 and accuracies close to 90%. These features enabled robust discrimination between conservative and surgical management approaches. While Laboratory and Imaging features also performed well, with AUCs around 0.877 and accuracies of 80-83%, they were slightly less effective compared with the top-performing subsets. Demographic and Morphological features exhibited poor discriminative power, with AUCs between 0.4962 and 0.5867.  
For Severity classification, models performed best using Literature-Based and Clinical Symptoms and Observations features, achieving near-perfect AUCs of 0.9986 and demonstrating strong sensitivity and specificity. LR, RF, XGBoost, and MLPs all highlighted these features as critical for accurate severity prediction. Conversely, Demographic and Morphological features were notably less effective, yielding AUCs as low as 0.4642, underscoring their limited utility in distinguishing severe from non-severe cases.  
Overall, Laboratory and Imaging and Literature-Based feature sets consistently outperformed others across all three target variables, demonstrating their robustness and reliability. In contrast, Demographic and Morphological features consistently underperformed across all targets. The Clinical Symptoms and Observations feature set produced moderate to strong results depending on the target variable, reinforcing the need for careful feature selection in optimizing model performance.
# A Top-N Movie Recommendation Framework Based on Deep Neural Network with Heterogeneous Modeling
## 4 Experiments
### Discussions  
In this section, we further analyze the architecture of MFDNN and discuss the experimental results to illustrate the performance of MFDNN.  
(1) Table 4 clearly shows that MFDNN outperformed the other baseline methods, which indicates that MFDNN improved the top-N recommendation performance of HINs to an extent.  
(2) We see that, by combining explicit and implicit feedback information, we can improve the recommendation performance significantly (+3.0% and +2.5%, respectively, for MFDNN+ and MFDNN- in terms of HR, and +1.9% and +1.0%, respectively, for MFDNN+ and MFDNN- in terms of NDCG).  
(3) We also found that configuring MF and DNN to learn embedding factors independently improved the recommendation performance (+0.6% and +0.7%, respectively, for MFDNN+ and MFDNN- in terms of HR and +0.8% and +0.9%, respectively, for MFDNN: (share MF) and MFDNN: (share DNN) in terms of NDCG). Although it is not obvious, learning embedding factors independently can improve the performance to a certain extent.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline  & **MovieLens 1m** & \multicolumn{2}{c}{**Netflix**} \\ \hline
**Method** & **HR@10** & **NDCG@10** & **HR@10** & **NDCG@10** \\ \hline MFDNN & **0.7278** & **0.4319** & **0.6828** & **0.4214** \\ DMF & 0.6735 & 0.3975 & 0.5776 & 0.3459 \\ NCF & 0.7048 & 0.4252 & 0.6245 & 0.4000 \\ HeteCF & 0.7097 & 0.4268 & 0.6601 & 0.4013 \\ HeteMF & 0.7123 & 0.4271 & 0.6609 & 0.4062 \\ CMF & 0.7235 & 0.4308 & 0.6445 & 0.3893 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The experimental results compared with the results from the baseline methods.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline  & **MovieLens 1m** & \multicolumn{2}{c}{**Netflix**} \\ \hline
**Top-N** & **HR** & **NDCG** & **HR** & **NDCG** \\ \hline N=5 & 0.5303 & 0.3672 & 0.4583 & 0.3058 \\ N=10 & 0.7279 & 0.4319 & 0.6828 & 0.4214 \\ N=15 & 0.7869 & 0.4443 & 0.7542 & 0.4386 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The HR and NDCG values for different top-N values.  
Although MFDNN provided significantly improved performance compared with baseline methods, there is room for further improvement in terms of the metrics. We will continue our best efforts to improve the performance of MFDNN.
## 3 Results
\[\text{Accuracy}=\ \frac{TP+TN}{TP+TN+FP+FN} \tag{4}\]  
\[\text{Precision}=\ \frac{TP}{TP+FP} \tag{5}\]  
\[\text{Recall}=\ \frac{TP}{TP+FN} \tag{6}\]  
\[\text{F1}-\text{Score}=2\times\frac{Precision\times Recall}{Precision+Recall} \tag{7}\]  
A true positive (TP) is a value that represents correct classification of an abnormal image as an abnormal image, a false negative (FN) is a value that represents incorrect classification of an abnormal image as a normal image, a false positive (FP) is a value that represents incorrect classification of a normal image as an abnormal image, and a true negative (TN) is a value that represents correct classification of a normal image as a normal image. Precision refers to the ratio of correctly predicted abnormal images to total predictions of images as abnormal images, and recall refers to the ratio of correctly predicted abnormal images to total actual abnormal images. Accuracy refers to the correctly classified proportion in all the cases, and the F1 score is the harmonic average of precision and recall. After increasing the data using both the DCGAN and CIFAR-10, the method has an accuracy of 0.851 and F1 score of 0.841, which improves the performance by approximately 0.06, compared to the model without data augmentation, as shown in Table 2. The values in brackets in the table are the results of the model without the image quality-measurement method. Thus, the results confirmed that the model with the image quality-measurement method delivered a better overall performance than the model without the method. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Accuracy** & **Precision** & **Recall** & **F1 Score** \\ \hline Original & 0.796 & 0.839 & 0.732 & 0.782 \\ DCGAN & 0.811 (0.814) & 0.823 (0.825) & 0.793 (0.774) & 0.807 (0.804) \\ CIFAR-10 & 0.832 (0.805) & 0.819 (0.825) & 0.854 (0.774) & 0.836 (0.799) \\ DCGAN+CIFAR-10 & 0.851 (0.820) & 0.896 (0.800) & 0.793 (0.854) & 0.841 (0.826) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Model performance for gastric cancer classification. Figure 10: Gastric cancer classification model performance based on the ROC curve: (**a**) models with no image quality measurement method and (**b**) models with the image quality measurement method.
# Determination of Cyanotoxins and Prymnesins in Water, Fish Tissue, and Other Matrices: A Review
## 4 Sample Preparation and Analytical Methods for Cyanotoxins and Prymnesins Detection
### Sample Preparation
#### 4.1.6 Prymnesins  
Evaluation of extraction techniques for prymnesins and other toxins associated with _P. parvum_ is difficult owing to the many questions related to the toxic species, their structure, and the lack of available analytical standards. The protocols listed in Table 3 illustrate these challenges, with [46; 47; 63] repeatedly re-extracting the water/algal cell lysates with solvents of varying polarity in an attempt to isolate and characterize potential toxic species. In [46], cold acetone was used as a pre-extraction solvent to remove chlorophyll, followed by methanol extraction, in which prymnesins were observed by LC-HRMS. In [47], _P. parvum_ lysates were extracted with dichloromethane, ethyl acetate, methanol, and water and while prymnesins were not observed under LC-HRMS analysis, cytotoxic activity was seen in the ethyl acetate and methanol extracts and a variety of fatty acid amides and one hydroxamic acid were observed. Finally, in [63], cold acetone was again used as a chlorophyll-removal step before sequential methanol and _n_-propanol extraction of the cellular material, which was then followed by solvent exchange to water. The aqueous material was then extracted with ethyl acetate four times to remove fatty material, before a final SPE step for clean-up prior to analysis. In this study, two prymnesins and a variety of related ions and fragments were characterized by LC-HRMS.