esa-sceva/satcom-chunk-collection huggingface dataset:
# Model-Based Control and Model-Free Control Techniques for Autonomous Vehicles: A Technical Survey
## 2 Control of Autonomous Vehicle
### Integrated Lateral and Longitudinal Control of Autonomous Vehicles
#### 2.3.1 Integrated Lateral and Longitudinal Model-Based Control Techniques
These controllers guarantee robust tracking of the reference trajectory and desired speed while considering the strong coupling between the vehicle's lateral and longitudinal dynamics. Validation was conducted via simulation in Matlab/Simulink and experimental tests using a robotized vehicle (Renault-ZOE). The proposed approach by [81] for autonomous driving involves coordinated longitudinal and lateral control, prioritizing stability and comfort. It introduced an enhanced particle swarm optimized proportional-integral-derivative (PSO-PID) method for speed tracking and an improved linear parameter varying model predictive controller (LPV-MPC) for lateral dynamics control. The LPV-MPC incorporates an adaptive LPV model and an enhanced cost function to enhance performance and stability. Matlab/Carsim co-simulations validated the controllers for general trajectory tracking and double-lane-change scenarios, demonstrating reasonable performance and robustness against wind disturbances. A control framework that improves lateral stability and trajectory tracking accuracy by jointly estimating multiple parameters using an adaptive unscented Kalman filter was developed in [82]. This estimation was used to design a lateral control system focusing on large lateral acceleration and a longitudinal control system for accurate speed tracking, including compensation for drive and brake forces using road slope estimation. The proposed framework was validated through co-simulation and experimental tests on a hybrid LincolnMKZ autonomous vehicle platform, showing excellent performance in enhancing lateral stability and tracking accuracy. Wireless communication-based platoon control for vehicles was investigated in this paper. An integrated approach for longitudinal and lateral control within a designated lane was proposed in [83]. The follower vehicle speed is regulated, and the inter-distance is maintained proportionally to the vehicle speed through longitudinal control. Stability conditions were formulated using a Lyapunov candidate function and BMIs. String stability and robust platoon control were ensured through additional conditions. Constraints such as actuator saturation and limited controller information were taken into account. Vehicle lateral control was achieved using a multi-model fuzzy controller, which maintains road position. The design conditions were expressed as LMIs solvable with numerical solvers. The effectiveness of the proposed control method was validated using the CarSim software package. Table 4 summarizes the integrated lateral and longitudinal model-based control techniques.existing in the literature.
## 4 Discussion
### Se  
Regarding the efficacy of SE on the WOMAC pain subscale in KO patients, the meta-analysis showed a small effect in favor of the SE compared to controls (effect size = 0.364; \(p\) = 0.046; IC 95%= 0.006 to 0.722; \(p\) = 0.046). It should be noted that the study [48] showing an effect in favor of the SE included non-weight-bearing exercises for the quadriceps in contrast to studies [39; 46] that included activities for various muscles of the lower limb and not just the quadriceps. It is important to note that the exercise programs in these studies lasted between 8 and 10 weeks. Therefore, it seems necessary to include long exercise programs to obtain benefits on pain.  
Other variables were assessed immediately after the intervention and showed contradictory results. For example, the VAS did not improve in the SE group compared with the controls [39; 41], nor did the ICOAP [38] and JKOM [44]. However, the NRPS and WOMAC showed better results in the SE group [45], as did the pain score [42] and the knee PPT [43]. Although the level of evidence is very low, it is noteworthy that the studies that showed improvements only included exercises that focused on the quadriceps [42; 43; 45].  
Only two studies [39; 42] followed up, with conflicting results. [PERSON] et al. [39] found no differences between SE and controls at either week 32 or week 52. On the other hand, [PERSON] et al. [42] found differences in favor of the SE group at month 6 but not at month 12. It should be noted that in patients with osteoarthritis of the hip and knee, exercise, regardless of type, showed a moderate benefit on pain at 8 weeks [62].  
According to the results of the meta-analysis, ES is effective in improving pain, as measured by the WOMAC pain subscale, immediately after the intervention (high level of evidence). These results are consistent with other reviews concluding that exercise is a safe and effective way to conservatively manage osteoarthritis pain, and is currently the most studied and effective therapy for osteoarthritis pain [62; 63; 64; 65; 66]. Furthermore, within exercise, the effects of resistance and strengthening programs have been highlighted [67], showing improvements in both pain and function [64]. Our review agrees with the conclusion of [PERSON] et al. [66], that exercise is a statistically significant and effective treatment for pain reduction compared with no treatment or minimal intervention [66]. However, our study was focused only on SE. Exercise triggers the release of endogenous opioids, resulting in a decrease in pain perception. This reduction in pain occurs during and after exercise [68; 69]. Exercise improves the descending pain chain by releasing serotonin [68] and other neurotransmitters such as norepinephrine and dopamine [70]. On the other hand,exercise has been shown to have an anti-inflammatory effect, reducing pro-inflammatory cytokines and increasing anti-inflammatory cytokines [68; 71]. In addition, exercise affects the nerves by inducing axonal regeneration, which may improve the proper conduction of pain signals [71].
## 3 Results
### Question 4: How to Collect Data
#### 3.4.1 On-Site Measurement  
Technological advances have made measurement more accurate and convenient in the past few decades since several multifunctional devices that simultaneously measure numerous indicators have now been invented. However, the selection of measurement tools in the literature heavily depended on the study purposes or the amount of available funding. Moreover, measurement procedures, including time duration, time intervals, and the number and location of sensors were also decided, mainly based on the researchers' personal experience. It seems that no consistent guidance for IEQ-related indicators measurement exists as yet. According to a comparison study between two measurement durations--15 min and 2 h--the exposure time significantly impacted the final IEQ model, specifically the weighting scheme [16].  
Furthermore, a recently conducted full-scale laboratory study observed that CO\({}_{2}\) concentrations varied significantly between different locations in the same room, especially under natural ventilation conditions [78]. The same results were also expected for other IEQ indicators. Therefore, future studies must develop a clear IEQ measurement guideline to instruct and unify the measurement procedure.
# Remote Radio Frequency Sensing Based on 5G New Radio Positioning Reference Signals
## 4 OFDM Radar Sensing Method
### Range Estimation  
The maximum achievable sensing range \(r_{max}\) is equal to the following:  
\[r_{max}=\frac{(N_{limit}^{range}-1)\cdot c\cdot dt}{2}, \tag{13}\]  
where \(N_{limit}^{range}\) is defined as follows:  
\[N_{limit}^{range}=min(N_{cp}^{long},N_{pilots}^{symbol}), \tag{14}\]  
and \(N_{cp}^{long}\) denotes the length of the 5G NR longer cyclic prefix, \(N_{pilots}^{symbol}\) denotes the number of PRS pilots in one OFDM symbol, and \(dt\) is the sampling period (inverse of the sampling frequency: \(\frac{1}{f_{s}}\)). The resolution of range estimation \(r_{res}\) is given by the following formula:  
\[r_{res}=\frac{r_{max}}{(N_{limit}^{range}-1)}=\frac{c\cdot dt}{2}. \tag{15}\]  
Let us define \(ind_{r}\) as the index of the CIR row with a maximum value. Then, the estimated range can be expressed as follows:  
\[r_{est}=\frac{ind_{r}\cdot c\cdot dt}{2}. \tag{16}\]
## 3 Results and Discussion
### Primary Metabolite Profiling of Roasted/Unroasted Mahlab Seeds via GC-MS Analysis (Post-Silylation)
#### 3.1.2 Organic Acids/Alcohols  
Organic acids can enhance the digestive process and, alongside their salts, boost the utilization of protein in animals, besides their preservative action in food [18]. The higher levels of organic acids in unroasted RE and roasted red mahlab, \"RRE\", from Egypt (5.9-9.7%), with an abundance of malic acid (4.7-7.4%), account for their sour taste compared to other samples. Succinic (peak 4), \(\gamma\)-hydroxybutyric (peak 3), oxalic (peak 2) and glycolic (peak 1) acids were also present in all mahlab samples. Malic acid (peak 5), a common acid in unripe fruit, is used to enhance beverages as a food acidulant [19]. \(\gamma\)-Hydroxybutyric acid, a natural neurotransmitter in the mammalian brain, is used therapeutically to treat alcoholism and narcolepsy [20].  
It is noteworthy to mention that phytic acid, an anti-nutrient that interferes with the bioavailability of proteins and minerals owing to its strong chelating property, was absent from all seeds, indicating their good food quality [10].  
Glycerol (0.3-7.4%) and glycerol-3-phosphate (peak 10) were identified as alcohols in all mahlab seeds except WG and RE. Although glycerol (peak 9) has a role in Maillard reactions as an active flavor precursor during roasting, its level interestingly showed an increase post-roasting, likely due to the increased lipid degradation [21].
## 2 Design of Proposed Frequency Selective Surface with Harmonic Suppression
### Design of Proposed Loop Structure with Band-Stop Filter Characteristics
The optimized design parameters of the proposed Frequency Selective Surface (FSS) with the characteristics of a Band Pass Filter and a Band Stop Filter are shown in Table 1.  
Figure 2: Configuration of the proposed loop structure: (**a**) equivalent circuit model (**b**) frequency selective surfaces loop design.
## 3 Theoretical Framework
### SHT Approach  
According to [6], the temperature rise of thermally thick sample is given by  
\[\Delta T=2\dot{q}\cdot\sqrt{\frac{t}{\pi\cdot\lambda c_{P}\rho}} \tag{1}\]  
where \(\dot{q}\) is the step heating irradiation in \(\left[\mathrm{W}/\mathrm{m}^{2}\right]\), \(t\) the irradiation time, \(\lambda\) the heat conductivity in \(\left[\mathrm{W}/\mathrm{m}\mathrm{K}\right]\), \(c_{P}\) the specific heat in \(\left[\mathrm{J}/\mathrm{kg}\right]\) and \(\rho\) the density of the material in \(\left[\mathrm{kg}/\mathrm{m}^{3}\right]\).  
Considering the thermal effusivity, which is given by:  
\[b=\sqrt{\lambda c_{P}\rho} \tag{2}\]  
The formula can be rewritten as:  
\[b=\frac{2\dot{q}}{\Delta T}\cdot\sqrt{\frac{t}{\pi}} \tag{3}\]  
This formula calculates the thermal effusivity in a step-heating context on thermally thick materials (materials where the thermal wave can properly develop and travel without reflecting). Generally, thermally thick materials have a thickness \(d\gg\mu\), where \(\mu\) is the thermal diffusion length in \(\left[\mathrm{m}\right]\). The thermal diffusion length is given by:  
\[\mu=\sqrt{\frac{2\lambda}{\rho c_{P}\cdot\omega}} \tag{4}\]  
Figure 1: Correction of GHI to GVI on a 200\({}^{\circ}\) facing angle. Note that the theoretical values for DHI and DVI were calculated according to [8] and were used to correct the measured GHI data to infer the irradiance on the vertical standing wall structures (GVI).  
where \(\omega\) is the angular frequency of the incident thermal wave in \(\left[\mathrm{s}^{-1}\right]\).  
In this work, the angular frequency of the step-heating pulse was determined using \(\omega=\pi/t\). In practice, objects with a thickness of \(d>1.5\mu\) show negligible error when considered thermally thick [6]. This cutoff value of \(1.5\mu\) is not universally applicable and was only used in this context because it generated reasonable results. In other setups with different excitation conditions and materials, other values distinguishing thermally thick from thermally thin materials may be necessary.
## 3 Results and Discussion
### Adsorption Isotherm Studies
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Adsorbent** & **C\({}_{\textbf{0}}\) (mg L\({}^{-1}\))** & **q\({}_{\textbf{max}}\) (mg g\({}^{-1}\))** & **Model** & **Reference** \\ \hline Mesoporous & 20–100 & 230.0 & [PERSON] & [29] \\ activated carbon-alignate beads & & & & \\ Iron oxide/carbon nanocomposites & & & & \\ \hline Poly(vinyl alcohol)-sodium alginate- chitosan-montmorillonite hydrogel beads & & 137.2 & [PERSON] & [56] \\ \hline Chitosan-g-poly(acrylic acid) hydrogel modified with cellulose nanowchiskers & & 1968 & & [57] \\ \hline Alginate-halloysite nanotube beads & & 250.0 & & [58] \\ \hline MWCNTs untreated & & 132.6 & & [59] \\ MWCNTs alkali-activated & & 399.0 & & [60] \\ \hline Alginate-bentonite-activated carbon (ABA) & 25–500 & 756.9 & [PERSON] & [5] \\ \hline Alginate/acid-activated organbentonite beads (A-OAB) & 20–500 & 799.4 & [PERSON] & [33] \\ \hline Alginate (A) & 20–500 & 483.6 & [PERSON] & [33] \\ \hline Functionalized MWCNTs & 20–1000 & 260.8 & [PERSON] & [61] \\ \hline \(\gamma\)-Fe\({}_{2}\)O\({}_{3}\) & 20–1000 & 155.3 & [PERSON] & [61] \\ \hline Alginate/\(\gamma\)-Fe\({}_{2}\)O\({}_{3}\) & 20–1000 & 438.6 & [PERSON] & [61] \\ \hline Alginate/maghemite/functionalized multiwalled carbon nanotubes beads (A/\(\gamma\)-Fe\({}_{2}\)O\({}_{3}\)/\(t\)-CNTs) & 5–270 & 320.5 & [PERSON] & This work \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of several MB adsorption capacities reported in the literature.
# A Personalized Recommendation Algorithm Based on the User's Implicit Feedback in E-Commerce
###### Abstract  
A recommendation system can recommend items of interest to users. However, due to the scarcity of user rating data and the similarity of single ratings, the accuracy of traditional collaborative filtering algorithms (CF) is limited. Compared with user rating data, the user's behavior log is easier to obtain and contains a large amount of implicit feedback information, such as the purchase behavior, comparison behavior, and sequences of items (item-sequences). In this paper, we proposed a personalized recommendation algorithm based on a user's implicit feedback (BUIF). BUIF considers not only the user's purchase behavior but also the user's comparison behavior and item-sequences. We extracted the purchase behavior, comparison behavior, and item-sequences from the user's behavior log; calculated the user's similarity by purchase behavior and comparison behavior; and extended word-embedding to item-embedding to obtain the item's similarity. Based on the above method, we built a secondary reordering model to generate the recommendation results for users. The results of the experiment on the JData dataset show that our algorithm shows better improvement in regard to recommendation accuracy over other CF algorithms.  
collaborative filtering; comparison behavior; item-pairs; item-embedding; secondary-reordering +
Footnote †: journal:  
10.117
# Recent Advances in Applications of Ionic Liquids in Miniaturized Microextraction Techniques
## 2 Ionic Liquids in Miniaturized Microextraction Techniques
### Dispersive Liquid-Liquid Microextraction (DLLME)  
Dispersive liquid-liquid microextraction (DLLME) was first introduced by [PERSON] et al. in 2006 as a novel alternative technique for the extraction and preconcentration of organic compounds in water samples [25]. The fundamentals of DLLME technique are based on the mixing of an aqueous sample containing the analytes with a non-miscible with water organic solvent, used as an extractant, together with a small amount of a dispersive solvent, which is miscible in both water and the extractant solvent. The mixing of the two solvents should be preceded their injection into the sample by a syringe or a micropipette. Gentle manual shaking of the mixture disperses the organic extractant as fine droplets to form a homogenous cloudy solution in which partition of the analytes takes place. Then, the sample is centrifuged, and the sediment phase is collected and determined with the appropriate analytical method [26].  
Since its introduction, DLLME has been performed as an extraction technique in various fields of chemistry, such as analytical, environmental, biochemistry, medicinal, pharmaceutical, toxicology and many others. Its increasing popularity is owed to some significant properties, such as the simplicity,  
Figure 3: Headspace-solid phase microextraction procedure for the extraction of PAHs in water sample using a polymeric ionic liquid-based fiber.  
the low cost, and the environmentally friendly profile. Furthermore, the dispersive mode improves the extraction kinetics by increasing the contact surface between the extractant and the sample. The most critical part of this technique is the choice of the extraction solvent, which should have a density greater than water's density and to form a cloudy solution with the dispersive solvent [27; 28].  
Taking into consideration the properties of ionic liquids as extraction solvents described before, the combination of ILs in DLLME process could be characterized promiscuous. [PERSON] et al. and [PERSON] and [PERSON] were the first researchers who performed the first successful applications of ILs in DLLME process, namely IL-DLLME, for the determination of organophosphorus pesticides in environmental samples and for the extraction of mercury from water samples, respectively [29]. While, [PERSON] and coworkers introduced the conventional IL-DLLME combined with HPLC-DAD for the determination of heterocyclic insecticides in water using the IL 1-hexyl-3-methylimidazolium hexafluorophosphate, ([C\({}_{6}\)MIM][PF\({}_{6}\)]), as the extractant and methanol as the dispersive solvent [30].
## 2 Simulation Based Resource Optimization  
Using detailed simulation models, we search to obtain the minimal cost number of resources that would guarantee a desired mean service level. As simulation is very time consuming for searching over the space of resources, we exploited three structural properties of the problem to decrease the number of simulation cases that would be required and thereby accelerate the optimization procedure.
# OADC: An Obstacle-Avoidance Data Collection Scheme Using Multiple Unmanned Aerial Vehicles
## 1 Introduction
The contributions of the paper are summarized below:  
* The proposed OADC scheme addresses an obstacle-aware path-planning problem named obstacle-constrained distance minimization (OCDM) for multiple UAVs in a WSN data gathering scheme. The OCDM problem is formulated as an integer linear programming (ILP) problem that considers the UAV flight time constraint. The objective of the problem is to minimize the total traveling distance of all UAVs. * First, an algorithm has been developed for selecting detour points around an obstacle to take a detour named the vector rotation angle-based obstacle avoidance (VRAOA) algorithm. VRAOA converts the shape of each obstacle in the environment into a convex polygon by selecting detour points from the borderline points of an obstacle such that the total distance traveling around the obstacle is minimized. * Second, a genetic algorithm with VRAOA (GA w/VRAOA) is designed to determine the UAVs near-optimal paths. In GA w/VRAOA, the detour path between any two sensors when there is an obstacle is obtained by combining the VRAOA and a source-to-destination-based Dijkstra algorithm. * Finally, simulations are performed and analyzed under different scenarios, where GA w/VRAOA shows its efficiency in terms of total traveling distance, total energy consumption, and average data delivery latency. The remaining paper is organized as follows. The related works are discussed in Section 2. The proposed system model and problem formulation are presented in Section 3. Section 4 outlines the proposed algorithms. The experimental settings and performance analysis are demonstrated in-depth in Section 5. Finally, in Section 6, this study is concluded.
# Smart Healthcare Network Management: A Comprehensive Review
## 5 Reference Architecture for Smart Healthcare System Network Management
### Resource Management Layer
#### 5.4.2 Configuration Management  
It encompasses the functions needed to control network elements, identify and gather their data, and provide them with required information. It also facilitates network planning, engineering, installation, service planning and negotiation, provisioning, status checks, and control.
# A Bandwidth-Efficient Dissemination Scheme of Non-Safety Information in Urban VANETs +
## 4 Parameter Tuning and Evaluation through Simulations
### Simulations Configuration  
As in our preliminary studies [12; 13], we have used Veins [14] as our simulation framework because it offers the possibility of importing real maps through OpenStreetMap [15], realistic movements thanks to SUMO [16], an implementation of 802.11p for OMNeT++ [17] and the Simple Obstacle Shadowing [18] propagation model for building shadowing. We have also used VACaMobil [19] in order to maintain a constant traffic density throughout every run of the simulations. The traffic densities go in the range from 25 to 100 vehicles per square kilometer.  
As discussed in [3; 10], the results can be quite different depending on the urban scenario in which the dissemination happens. We have used two different areas of 2 km \(\times\) 2 km, shown in Figure 2. The Manhattan, New York, map is almost a grid, while the area around Castellana Street in Madrid is more irregular.  
After running the simulations, we could appreciate a clear difference in absolute values, which confirms what the mentioned works had already found out: different scenarios yield different results. However, we could also see that the tendency of the curves and the inflection points were always the same for both scenarios. We have already discussed this finding previously in [12]. Therefore, we can extrapolate conclusions from one scenario that will be valid for the rest. Therefore, for the sake of brevity, we only show the results of the New York scenario in this article.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline MAC & 802.11p \\ Frequency band (10 MHz channel) & 5.900 GHz \\ Propagation model & Simple Obstacle Shadowing \\ Transmission power & 1 W \\ Sensitivity & \(-\)77 dBm \\ SNR & 13 dBm \\ Thermal noise & \(-\)104 dBm \\ LOS communication range (\(r\)) & 232 m \\ Modulation & QPSK \\ Bitrate & 6 Mbps \\ \hline \hline \end{tabular}
\end{table}
Table 2: Network parameters in the simulations.  
Figure 2: Simulation scenarios for the urban environment.  
The origin of the message is a fixed unit in the center of the scenario. The preset radius of interest (the ROI) is 1 km. Finally, we have configured the network parameters according to typical values described in [20]. The specific overall configuration is listed in Table 2.
# Application of Machine Learning Methods for Pallet Loading Problem
## 3 Methodology
### Machine Learning Methods for Classification
Similar to a forest consisting of numerous trees, the random forests generate decision trees on a randomly-selected data samples, each making a guess from the tree and choosing the optimal solution by voting [55]. Furthermore, this method provides a reasonably good indication of the feature's importance.