esa-sceva/satcom-chunk-collection huggingface dataset:
# Carousel Greedy Algorithms for Feature Selection in Linear Regression
## 3 Algorithm Description and Preliminary Experiments
### Gurobi Implementation of Best Interchange to Find an FSI(1) Minimum
As shown in Table 4, the final RSS by Gurobi is very close to CG (the best results in Table 4 for each \(k\) are indicated in bold), but the running time is much longer for small \(k\) and not much faster for larger \(k\). This is the case even though Gurobi uses all of the 16 threads by default while CG uses only one thread by default in Python. Therefore, our algorithm is simpler and does not require a commercial solver like Gurobi. It is also more efficient than the BI-MIP by [PERSON] in terms of finding a local optima when \(k\) is small. The circumstance can be different for an \"ill-conditioned\" instance. We tried to apply Algorithm 4 to the Building dataset, but it is a very simple model where \(k=1\) cannot be solved. Meanwhile, CG can solve it without any difficulty. The source of the issue is the quadratic coefficient matrix. The objective of BI-MIP is quadratic. When Gurobi solves quadratic programming, a very large difference between the largest and smallest eigenvalues of the coefficient matrix can bring about a substantial numerical issue. For the Building dataset, the largest eigenvalue is of order \(10^{15}\), the smallest eigenvalue is of order \(10^{-10}\). That's intractable for Gurobi. Therefore, CG is numerically more stable than Algorithm 4 using Gurobi. \begin{table}
\begin{tabular}{l l l l l} \hline  & \(k=5\) & & & \(k=7\) & \\  & RSS & Time (s) & RSS & Time (s) \\ \hline Stepwise regression & 2,129,794 & 3.41 & 1,653,208 & 5.55 \\ CG with stepwise initialization & **2,104,917** & 10.03 & **1,471,932** & 28.45 \\ Iterated BI-MIPs by Gurobi with stepwise initialization & 2,106,992 & 59.89 & **1,471,932** & 99.64 \\ \hline  & \(k=10\) & & \(k=15\) & \\  & RSS & Time (s) & RSS & Time (s) \\ \hline Stepwise regression & 1,307,711 & 8.22 & 1,029,899 & 16.08 \\ CG with stepwise initialization & **1,198,650** & 47.63 & 973,695 & 97.82 \\ Iterated BI-MIPs by Gurobi with stepwise initialization & **1,198,650** & 161.9 & **970,712** & 286.43 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Iterated BI-MIPs by [PERSON] (Algorithm 4) for the CT slice dataset (using up to 16 threads).
# Traffic Aware Scheduler for Time-Slotted Channel-Hopping-Based IPv6 Wireless Sensor Networks
## 3 Related Work
### Adaptive Approaches  
The adaptive approaches are designed for applications where the traffic load tends to create congestion. In all discussed approaches, additional resources are allocated to cope with the traffic increase. This obviously leads to an increase in the duty cycle.
# WaQuPs: A ROS-Integrated Ensemble Learning Model for Precise Water Quality Prediction
## 4 Materials and Methods
### Methods to Develop the WaQuPs Application
#### 4.5.6 Mqtt  
MQTT (message queuing telemetry transport) is a lightweight and open communication protocol specifically designed for devices within the Internet of Things (IoT) network [36].  
This protocol is constructed with a focus on efficiency, reliability, and minimal resource usage, making it well-suited for constrained or low-power environments, such as micro-controller devices. MQTT follows a publish-subscribe model, where one or more devices can publish messages to specific topics, and other interested devices can subscribe to those topics to receive the messages.  
Within WaQuPs, the water quality server operates as the publisher, while the mobile application users function as the subscribers. To illustrate this interaction, consider user AB who subscribes to water quality information 1. When an administrator, using the website application, modifies and saves certain data related to water quality information, the changes are initially transmitted to the server via FastAPI. Subsequently, the server processes the new classification, updates the database, and then promptly publishes the altered information for water quality 1. As a result, all subscribed users receive real-time updates on water quality 1, ensuring they stay informed with the latest information. This real-time functionality enhances the overall user experience and facilitates the rapid dissemination of critical data.
## 2 Literature Review
### Environmental Impact
Effective emission reduction strategies must consider different greenhouse gases (GHGs), such as CO\({}_{2}\), N\({}_{2}\)O, and CH\({}_{4}\)[26]. These gases have different abilities to heat the atmosphere per unit mass and vary in their persistence in the atmosphere. Therefore, a key element is the framework for multiple greenhouse gases that determines the exchange ratios between them. The Kyoto Protocol introduced the 100-year global warming potential (GWP) as a metric to convert CH\({}_{4}\), N\({}_{2}\)O, and fluorinated gases (F-gases) to CO\({}_{2}\) equivalents, which is proposed in the research of [PERSON] and [PERSON] [27] and updated in the IPCC assessments (for the most recent values, see [28]). This method reduces the mitigation costs through flexibility in reducing the emissions of different GHGs. The EMF21 study compared multi-GHG strategies with CO\({}_{2}\)-only strategies and found significant cost reductions in achieving the same target if a multi-GHG strategy was applied [29]. [PERSON] and [PERSON] [30] discussed the assessment of GHG emissions and the uncertainty associated with the cost-benefit analysis (CBA) of transport projects. They outline the importance of accurately and objectively including the impact of GHG emissions in the CBA, given the growing role of the transport sector in GHG emissions. The research discusses the evolution of CO\({}_{2}\) values in France and proposes a more representative indicator to compare national CBA methods. [PERSON] et al. 's [31] research quantifies fuel consumption and CO\({}_{2}\) emissions based on real traffic data at an intersection. The research was conducted in the Slovenian city of Celje and aimed to identify approaches to reduce the environmental and economic impacts of traffic operations through proper traffic flow management. [PERSON] et al. [32] looked at the potential for reducing emissions in the road transport sector in developing countries through biofuels. The research conducted by [PERSON] et al. [33] focuses on analyzing the impact of the introduction of fuel cell vehicles (FCVs) into the on-road vehicle fleet in China on greenhouse gas (GHG) emissions. The research conducted by [PERSON] et al. [34] analyzed the options for reducing CO\({}_{2}\) emissions in European road transport by 2050, with a focus on measures to improve vehicle efficiency and to consider the emissions of electric vehicles (EVs).
# Early Risk Prediction of Diabetes Based on GA-Stacking
## 5 Discussion  
In this work, we proposed a GA-stacking ensemble learning model for diabetes risk prediction. Experiments have shown that the average precision, sensitivity, specificity, and F1-score of the proposed model are better than other machine learning models such as KNN, SVM, LR, NB, CNN, XBNet, etc. The average accuracy is 85.88%. So, GA-stacking has obvious advantages for diabetes risk prediction.  
1. Using GA based on DT for feature selection effectively improves the speed of prediction and accuracy of the model.
2. Based on stacking, using a fully connected layer combined with two primary learners, CNN and SVM, can process the input more accurately and make the model have great generalization capabilities.  
Although GA-stacking effectively improves the accuracy and speed of diabetes risk prediction and has a good generalization ability, it still has some limitations. For example, the model faces challenges in datasets with small attribute sets and unbalanced data. In the future, we will conduct research on how to scientifically and effectively expand the feature, solve the problem of class imbalance, and improve the accuracy and generalization ability of the model.  
Conceptualization, Y.T.; methodology, Y.T. and H.C.; software, H.C.; resources, J.Z.; data curation, J.Z.; writing--original draft preparation, Y.T.; writing--review and editing, Y.T., and R.T.; supervision, R.T. and P.L.; funding acquisition, J.Z. All authors have read and agreed to the published version of the manuscript.  
This work is supported by Qingdao Science and Technology Development Plan (21-1-4-rjkjk-14-nsh).  
This work is supported by Qingdao Science and Technology Development Plan (21-1-4-rkjkk-14-nsh). We are grateful to the anonymous reviewers for comments on the original manuscript.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Study** & **NB** & **BN** & **J48** & **RF** & **RT** & **KNN** & **SVM** & **LR** & **AdaBoost** & **XBnet** & **GA-stacking** \\ \hline
[3] & 87.4 & - & 95.6 & 97.4 & - & - & - & 92.4 & - & - & - \\
[4] & 87.11 & 86.92 & 95.96 & 97.50 & 96.15 & 98.07 & 92.11 & - & - & - & - \\
[5] & 86.92 & 98.08 & 97.31 & 96.92 & - & 97.31 & 97.1 & - & - & - & - \\
[17] & - & - & - & - & - & - & - & - & 80.00 & - \\
**This study** & 89.10 & - & - & - & 96.79 & - & 96.79 & 93.54 & - & - & 98.71 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Studies results comparison.
## 1 Introduction
Based on the chance-constrained programming theory, the actual processing times are predicted utilizing the quantiles of the given normal distribution and the permissible violation degree of the constraints. This approach enhances the robustness of the obtained initial scheduling solution, reducing the impact of dynamic events on the original schedule. 3. We propose a heuristic scheduling method to solve the associated model. Using this method, a left-shift adjustment strategy, an insertion-based local adjustment strategy, and a global adjustment strategy based on a discrete multi-objective grey wolf algorithm (DMOGWO) are developed. 4. An initialization strategy that combines multiple heuristic rules is designed to generate the initial populations with certain levels of quality and diversity. Additionally, discrete crossover and mutation operators are used for position updates in the wolf pack, enabling the GWO to adaptively resolve the scheduling problem. To evaluate the effectiveness of the proposed methods, a series of experiments are conducted with four main purposes: (i) conducting a sensitivity analysis of the population size in DMOGWO to investigate its impact on the algorithm's performance; (ii) comparing the performance of DMOGWO with other representative algorithms on solving a static DFJSP and DDFJSP-VPT to assess its competitiveness in terms of both static and dynamic scenarios; (iii) evaluating the performance of the proposed scheduling method against other dynamic scheduling methods with regard to dynamic environments; and (iv) analyzing the impact of the prediction scheme on scheduling efficiency and robustness. The structure of the study is as follows: Section 2 presents a review of the literature on dynamic DFJSPs and FJSPs with variable processing times. The problem description and mathematical model are presented in Section 3. Sections 4 and 5 detail the processing time prediction methods and the proposed scheduling method, respectively. Experimental results are discussed in Section 6. Finally, Section 7 concludes the paper and outlines potential directions for future research.
# Removal of Chromium(VI) by Chitosan Beads Modified with Sodium Dodecyl Sulfate (SDS)
## 1 Introduction
Moreover, silicon dioxide can be employed to offset the defects of chitosan because it has many properties, such as a rigid configuration, porosity, and a high surface area. In the case of silicon dioxide, modified silicon dioxide produced through the graft between silanol groups and ligands has been developed [27; 28; 29]. We have synthesized a hybrid membrane of carboxymethyl chitosan and silicon dioxide as adsorbents for the removal of Cr(VI) in our previous work [30]. Furthermore, we have carried out an adsorption experiment of chromate ions onto cross-linked chitosan using epichlorohydrin (EP) and glutaraldehyde (GA) as cross-linked agents [31]. In this work, we evaluated the adsorption of chitosan modified with sodium dodecyl sulfate (SDS) as part of the adsorption study of Cr using modified chitosan. It has been reported that SDS-modified chitosan beads can be used to remove cationic dyes. [32]. The metal ion strength and the presence of key functional groups on the polymer chain allow its adsorption on surfaces [33; 34; 35]. The aggregation of particles through a bridging structure can be described as a two-step pathway: (1) initial chain adsorption and bridging, followed by (2) floc maturation/reconfiguration. Before the interparticle connection occurs, the chain of SDS must be adsorbed on a chitosan surface [36]. Furthermore, chitosan modified with SDS has recently been used for the removal of heavy metals, such as cadmium [37; 38]. However, the use of SDS-modified chitosan as an adsorbent of Cr, with varying initial concentrations of SDS for optimizing the adsorbent, has rarely been evaluated. The objective of the present research was to investigate the efficiency of SDS-modified chitosan beads as a sorbent for Cr(VI) for more practical uses in the future, and to reveal the adsorption mechanism. After the characterization of SDS-chitosan by scanning electron microscopy-energy dispersive X-ray spectroscopy (SEM-EDS), X-ray photoelectron spectroscopy (XPS) and Fourier transform-infrared spectroscopy (FT-IR), batch experiments using SDS-modified chitosan beads were conducted to optimize the parameters, in order to obtain the maximum removal of Cr(VI). The effects of various parameters, such as the solution pH, contact time, adsorbent dose, and initial concentration, were examined.
## 3 Results and Discussion  
### Deaeration of juice  
Oxygen, present in the air, fills the spaces between fruit cells and saturates the juice during the disintegration of plant tissues and extraction of the juice, initiating oxidation reactions that can result in enzymatic browning, changes in aroma and a loss in nutritional value [16; 18]. To mitigate the oxidative degradation of juice components, juices undergo a deaeration process prior to preservation [40]. This process leads to a reduction in dissolved oxygen concentration in the juice [41]. Figure 1 illustrates the relationship between the oxygen content in apple juices and the speed of the rotating disc at 25 \({}^{\circ}\)C and 50 \({}^{\circ}\)C, respectively.  
As anticipated, low-temperature deaeration demonstrated superior efficiency in oxygen removal compared to the traditional deaeration method (4.6 mg oxygen/L). The most promising result was achieved when deaeration was carried out at 25 \({}^{\circ}\)C with a rotation speed of 150 rpm. This could be a result of increased oxygen solubility in the juice at lower temperatures and a potentially higher efficiency in the gas removal process. Moreover, this process resulted in an 80.4% reduction in oxygen levels compared to the traditional industry deaeration method and an impressive 86.4% reduction compared to high-temperature deaeration (150 rpm/50 \({}^{\circ}\)C). Certainly, the sample deaerated at a high temperature with a rotation speed of 150 rpm, reaching 6.6 mg oxygen/L of juice. Unexpectedly, high-temperature deaeration did not yield the desired results and the oxygen concentration was even higher compared to conventionally deaerated juice. It is worth noting that the effectiveness of high-temperature deaeration can be influenced by the juice's inherent composition. Different juices may possess varying levels of dissolved oxygen, and certain juices might contain compounds that react with oxygen, thereby increasing their concentration during the deaeration process. During the deaeration process at higher temperatures, molecules in the juice acquire higher kinetic energy due to increased thermal activity. This increased kinetic energy may make the oxygen removal process more challenging, as oxygen molecules bound to juice molecules may be less likely to be released.  
Figure 1: Concentration of oxygen in the apple juices after the deaeration process at 25 \({}^{\circ}\)C and 50 \({}^{\circ}\)C. Mean values denoted with the same letters do not differ significantly statistically, \(p\leq 0.05\).  
Furthermore, factors such as pH, temperature and processing conditions can significantly impact the solubility of oxygen in the juice [40]. Understanding these variables is essential for optimizing the deaeration process and achieving consistent and desirable results.
## 3 Results
### Penetrometer Tests
#### 3.2.2 Phase 2: Effect of Biopolymer Application Rate on Crust Penetration Resistance  
_Medium-grained sand_. The penetrometer test results of biopolymer-treated medium-grained sand samples prepared at different application rates and the biopolymers' respective plateau concentrations are shown in Figure 10. The control group showed no measurable penetration resistance, while all the biopolymer treatments formed crusts. Although the absolute crust strengths of the samples were relatively weak, the penetration resistances generally increased at higher application rates. Despite differing plateau concentrations, most of the treatments achieved relatively similar penetration resistances at the individual application rates. Notably, treatments with FBPC, XG, and CMC formed the strongest crusts and displayed a clear trend of increasing penetration resistances at higher application rates. Contrarily, the WP and CS treatments did not exhibit a clear trend, with the crust strength of CS-treated samples peaking at 0.2 L/m\({}^{2}\) and that of WP-treated samples peaked at 0.5 L/m\({}^{2}\), respectively.  
_Fine-grained silica sand_. Figure 11 displays the results of the penetrometer tests performed on fine-grained silica sand treated with different application rates at the biopolymers' respective plateau concentrations. All the biopolymer treatments exhibited relatively low penetration resistances, mostly ranging from 0.5 to 1.5 N, with the penetration resistance increasing slightly at higher application rates. Despite the differing plateau concentrations used for the different biopolymer types, most of the treatments resulted in similar penetration resistances at the respective application rates. The proteins (FBPC and WP) and CMC produced the strongest crusts, but they were also applied at higher concentrations than CS and XG were.  
Figure 9: Penetration resistance of crusts after last wind tunnel test on day 28 of fine-grained silica sand. Penetration resistance tests were performed with six replicates (n = 6) with two penetrations per sample. Numerical data (mean (M) and standard deviation (SD)) are appended in Table A4.
# A Two-Stage Kalman Filter-Based Carrier Tracking Loop for Weak GNSS Signals
## 2 Signal Model  
The GNSS radio frequency (RF) signal is received by a user antenna. After down-conversion and analog-to-digital conversion (ADC), the RF signal is converted into the discrete intermediate frequency (IF) signal. The digital IF signal can be modeled as [10]:  
\[r_{IF}(nt_{s})=A\cdot D(nt_{s}-\tau)\cdot c(nt_{s}-\tau)\cdot\cos(2\pi(f_{IF}+f _{d})nt_{s}+\varphi_{0})+w(nt_{s}) \tag{1}\]where \(t_{s}\) is the sampling duration, \(A\) is the IF signal amplitude, \(D(nt_{s})\) denotes the navigation data bit, \(c(nt_{s})\) is the pseudo-random number (PRN) spreading code, \(\tau\) is the code delay of the received signal, \(f_{IF}\) denotes the intermediate frequency in hertz, \(f_{d}\) represents the carrier Doppler shift in hertz, \(\varphi_{0}\) is the initial carrier phase in radians, and \(w(nt_{s})\) represents the additive zero-mean white Gaussian noise (AWGN).  
In each tracking channel of the receiver, the IF signal is multiplied with two local carriers that are in-phase and \(90^{\circ}\) shifted, to wipe off the carrier frequency. Then, the result is multiplied by the local code. Finally, its output is passed to the integration and dump blocks to generate \(I\) and \(Q\) correlated signals. The prompt branch signals, \(I\) and \(Q\), can be described as [4]:  
\[I(k)=A\cdot N(k)\cdot R(\tau-\hat{\tau})_{k}\cdot\frac{\sin(\pi\cdot\delta f(k) \cdot T_{s})}{\pi\cdot\delta f(k)\cdot T_{s}}\cdot\cos(\overline{\delta\varphi }_{k})+w_{I}(k) \tag{2}\]  
\[Q(k)=A\cdot N(k)\cdot R(\tau-\hat{\tau})_{k}\cdot\frac{\sin(\pi\cdot\delta f(k )\cdot T_{s})}{\pi\cdot\delta f(k)\cdot T_{s}}\cdot\sin(\overline{\delta \varphi}_{k})+w_{Q}(k) \tag{3}\]  
where \(k\) is the updated index of the correlated values, \(A\) denotes the signal amplitude, \(N(k)\) is the number of samples accumulated from the correlator, \(R(\tau-\hat{\tau})_{k}\) represents the autocorrelation function of the PRN code, \(\hat{\tau}\) denotes the code delay of the local prompt PRN code, \(\delta f(k)\) is the carrier frequency error in hertz, \(T_{s}\) is the coherent integration time (CIT) in seconds, \(\overline{\delta\varphi}_{k}\) is the average carrier phase error over \(T_{s}\), and \(w_{I}(k)\) and \(w_{Q}(k)\) are both white Gaussian noise that are uncorrelated and have the same power.
# Low-Cost Data-Driven Robot Collision Localization Using a Sparse Modular Point Matrix
## 2 Materials and Methods
### Collision Localization Results across the Entire Template
Regarding the deviation in collision localization, the RMSEs of various models for collision localization are presented in Table 4. Due to the utilization of the center position of the SMPM as the estimated location for individual points within the SMPM, additional localization biases are introduced in the RMSEs, even when the collision area is correctly predicted. Therefore, the RMSEs of various models are consistently greater than 1 mm. Without any reduction in data scale, the CNN-SVM model achieves the lowest RMSE in collision localization. However, with the introduction of varying degrees of sparsity, the CE-SVM consistently exhibits a notable advantage. When the data scale is reduced to 51.85%, the RMSE of the CE-SVM method increases by only 0.21 mm, compared to the increase in RMSE for CNN-SVM, which is only 55% of its value. In conclusion, it is evident that the SMPM is capable of maintaining a high level of collision localization performance even in scenarios involving significant data scale reduction. From both the collision localization accuracy and RMSEs perspectives, the SMPM maintains a high level of performance even in scenarios of substantial data scale  
Figure 9: Illustration of SMPMs in the Cell3-2 form with varying degrees of sparsification. reduction. Particularly noteworthy is its outstanding collision localization performance when employed in conjunction with the CE-SVM method.