esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimization of Carsharing Fleet Placement in Round-Trip Carsharing Service
## 3 Optimization Model  
The focus of this article is on the round-trip carsharing service, which received relatively less attention in the research community [52]. Hence, a novel approach for round-trip carsharing fleet placement is proposed. There are three benefits that the approach offers. The first benefit is that automation of the fleet placement process removes the need for traditional manual allocation. The second benefit is the higher placement precision with the inclusion of a Geographic Information System (GIS). Finally, the third benefit is in proposing an approachable fleet management problem to the research community that may also be beneficial to similar applications.  
The proposed approach emphasizes the user coverage and ease of access with a constraint of being applicable to the real-world scenario by utilizing the two components mentioned below. These two goals are of real concern in practice and are often expressed by experts in this area.  
1. Utilizing graph theory to implement graph model representing a street network
2. Multiobjective Optimization model with two objectives, maximizing user coverage and minimizing global walking distance between cars and users  
In this section, the graph instance used in this article is first defined for the FPP since the graph instance is closely related with the problem definition. Second, the Fleet Placement Problem (FPP) model is formulated. Finally, FPP is proven that it is an NP-hard in a strong sense.
# Millimetre-Wave Backhaul for 5G Networks: Challenges and Solutions
## 4 Emerging Mm-Wave Physical Layer Techniques  
Physical layer techniques are the most fundamental ones to satisfy data demands and basic service requirements. Mm-Wave standards, such as IEEE 802.11 ad, offer various modes for different application scenarios. Due to large path loss in the mm-wave system, beamforming has been undoubtedly conceived of as a vital component for mm-wave systems. However, constraints of one radio frequency (RF) chain in each link and point-to-point transmission have made it hard for the current analogue beamforming techniques to handle the rapid increasing data demands. Beamforming techniques that support multiple RF streams become more appealing, and the emergence of a related simple prototype [8] shows the feasibility of implementation. Meanwhile, the progress in self-interference cancellation technology is likely to enable full duplexing, which helps to further improve the link capacity. Figure 2 shows an overview of these two physical layer techniques.
## 5 Numerical Characterization
### Comparison and Validation  
Figure 24a,c,e compare the normalized \(Q/Q_{y}\) versus \(\delta/\delta_{y}\) curves obtained experimentally for specimens SS-TTD4, SS-TTD5 and SS-TTD6, with the prediction provided by the polynomial and the Bouc-Wen models under the same history of loading applied during the tests. The shape of the hysteretic loops provided by both numerical models is found to be close to the experimental results. The normalized \(Q/Q_{y}\) versus \(\delta/\delta_{y}\) curves obtained with the numerical models was decomposed into the skeleton part and the Bauschinger part (as explained in Section 4.2) and the energy dissipation paths in the \({}_{eq}\eta\)-\(\eta\) plane were obtained. These paths are compared with the ones obtained experimentally in Figure 24b,d,f. In general, both models approach the energy dissipation paths obtained from the tests reasonably well. The approach is better?particularly in specimen SS-TTD6? with the polygonal model than with the Bouc-Wen model, as was to be expected since the polygonal model is explicitly calibrated from energy dissipation considerations. Both numerical models predict the point of failure very well, with errors below 10%. It is also worth noting that the polynomial model is not able to capture strength degradation effects under cycles of constant amplitude, whereas in the refined Bouc-Wen model implemented in OpenSees there are two parameters that allow this degradation to be reproduced. Nonetheless, the strength degradation is negligible as noted in previous sections, and the error accumulated in the predicted energy dissipation capacity due to ignoring this strength degradation is below 10%, which is not significant in the overall response.  
Figure 24: _Cont._
## 2 Materials and Methods
### Methodological Quality Assessment  
The risk of bias in the studies was determined using the Tool for the Assessment of Study Quality and Reporting in Exercise (TESTEX) scale [32] applied in similar CT studies [15; 17]. This tool comprises 12 assessment criteria, with a maximum score of 15 points. Higher scores reflect lower study bias. Reference values categorize a study's risk of bias as high (\(\leq\)4 points and below), medium (4-11 points), and low (\(\geq\)11 points) [15]. Two independent researchers (EFG and AV) conducted the assessments, and in the event of conflicts, a third author (ARF) facilitated discussions until a consensus was reached.  
\begin{table}
\begin{tabular}{c c c} \hline
**PICOS Category** & **Inclusion Criteria** & **Exclusion Criteria** \\ \hline P (Population) & Basketball players from any sex (female/male) or competitive level without age restrictions (amateur to professional level). & Sports other than basketball (e.g., soccer, football, volleyball) or with injuries. \\ \hline I (Intervention) & Studies analyzing acute (effect of CT implemented how warm-up) or chronic (effect of CT implemented how training strategy) effects of CT with a specified applied load & Interventions testing the effects of other types of strength training (i.e., Bodyweight Training) or including alternative methods in addition to strength training \\ \hline C (Comparators) & Studies where groups are compared (i.e., CT vs. control group or vs. other type or training or different load) & Single-group studies \\ \hline O (Outcomes) & The studies must analyze the effects in physical fitness tests (jump, speed, strength...) & Interventions that analyze other variables (i.e., cognition-related, nutrition) \\ \hline S (Study designs) & Randomized and non-randomized controlled studies & Single group studies; Case studies (e.g., \textless{}5 participants per group). \\ \hline \end{tabular}
\end{table}
Table 1: Selection criteria used according to PICOS model.
## 3 Results
### Changes in Cytokine and miRNA Expression  
Comparisons of all hemodynamic and biochemical parameters between the sepsis group resuscitated with 10 mL/kg/h and the sepsis group resuscitated with 17 mL/kg/h were not significantly different (\(p\) > 0.1 for all the hemodynamic and biochemical comparisons). Therefore, these two sepsis groups (resuscitated with 10 mL/kg/h and resuscitated with 17 mL/kg/h) were combined (high resuscitation (HiR)) for comparison with the sepsis group resuscitated with 4 mL/kg/h (low resuscitation (LoR)) and the control (non-septic) groups. The samples available for analysis for the control (non-septic), LoR septic, and HiR septic groups were between 5 and 6, between 3 and 4, and between 3 and 10, respectively (Table S3).  
The serum concentrations of IL-1\(\beta\), TNF\(\alpha\), IL-6, and IL-1\(\beta\) in the renal medulla were higher in the septic group. The IL-1\(\beta\) concentrations in the renal cortex trended higher in the septic group (not significant) (Table S4). The concentration of all cytokines studied tended to decrease in the sepsis HiR group compared with the sepsis LoR group, the difference reaching statistical significance for the TNF-\(\alpha\) serum concentration (Table S4).  
The serum expression of miRNA34a-5p and miR-146a-5p increased significantly in the septic groups (\(p\) = 0.010 and \(p\) = 0.016, respectively, for the overall between group differences) (Table S5). The _post hoc_ comparison between the LoR and HiR groups (effect of resuscitation) reached statistical significance only for miRNA146a-5p (Table S5). miR-155-5p tended to increase after the septic challenge and decrease with HiR as compared with LoR, with changes that did not reach statistical significance (Figure 1, Tables S5 and S6).
# A Critical Examination of the Beam-Squinting Effect in Broadband Mobile Communication: Review Paper
## 2 Broadband Communication
### Effect of Beam Squint on the Communication System Performance  
\(\bullet\) Bandwidth and Capacity:  
The system bandwidth in wideband communication is constrained by the beam-squint phenomenon [41]. The beam squint effect is closely correlated to bandwidth; as bandwidth rises, the beam squint effect becomes more pronounced, and a larger fraction of the subcarriers experience tiny array gains. Even outside the main lobe, certain subcarriers may exist. As a result, the whole system's capacity starts to decline [42]. Figure 7 shows the channel capacity versus the bandwidth with and without beam squint. It is clear from this figure that the capacity decreases as the array's element count, fractional bandwidth, and beam focus angle rise.  
\(\bullet\) Channel estimation:  
Channel estimation algorithms for Orthogonal Frequency Division Multiplexing (OFDM), Maximum Mean Square Error (MMSE) estimator, and Maximum Likelihood Estimation (MLE) estimator are widely used [41]; however, these estimators do not consider beam squint phenomena. In [42], the MLE channel estimator was used to investigate the effect of beam squint on the estimation process, Fading was considered fixed, and the results showed that beam squint increases the error of channel estimation, even without any noise. The error also increases with increasing the number of antenna elements in the array, fractional bandwidth, and the magnitude of beam focus angle. Figure 8 shows an example of the MLE channel estimation with beam squint, where \(H\)(n) represents the true channel, and \(H\)(n) represents the estimated channel. It is clear from this figure that there is a difference between the actual channel and the estimated channel, even without considering the noise.  
Figure 7: Capacity as a function of bandwidth with beam squint (CBS) and without beam squint (CNBS) [41].
# Design and Validation of a U-Net-Based Algorithm for Star Sensor Image Segmentation
## 4 Discussion  
Previous results have proved the high quality of night sky image segmentation provided by the algorithm. The unparametric nature of it makes the calibration operation unnecessary and maintains the high robustness to strong stray light variation scenarios. These features are welcome both for attitude determination and RSO detection through electro-optical sensors such as star sensors. Moreover, the absence of a calibration activity would make an optical camera based on this technology ready to work when the platform is released into its orbit. This algorithm could be used as an image processing main module for lost in space [36] or star tracking routines in common star sensors or RSO detection modules for onboard space surveillance camera or star sensors [40,46]. Both in the former and latter applications, good noise filtering capability is crucial for the success of the star and objects information extraction. This suggests a star sensor based on the U-Net image processing algorithm can be used to validate this technology in a real space mission and increase its technology readiness level (TRL) up to eight. This module will serve both the AD routines and the RSO detection modules. As the image is acquired by the optical head (OH), it is fed into the U-Net-based image segmentation module and then the mask will be used both by lost in space (LIS)/star tracking and RSO detection routines (Figure 15).  
The selected components of the payload are the following:  
* ARCSEC SPACE Sagitta Star Tracker as OH;
* PHOEBE board as Payload Processing Unit.
# Passivity-Based Control Design for Magnetic Levitation System
## 3 Performance: Sensitivity Function  
The feedback control system block diagram is shown in Figure 1, where \(r\) is the reference input, \(e\) is the error signal, \(K\) is a controller, \(u\) is control law, \(G\) is the plant requiring control, \(y\) is the system output.  
The sensitivity function \(S\) of the system is defined as:  
\[S=\frac{\mathrm{d}\ln T}{\mathrm{d}\ln G}=\frac{\mathrm{d}T/T}{\mathrm{d}G/G} =\frac{G}{T}\frac{\mathrm{d}T}{\mathrm{d}G}=\frac{1}{1+KG}\]  
where \(T\) is the closed-loop transfer function of the system. We can know that the system sensitivity quantitatively represents the degree of sensitivity of the closed-loop transfer function \(T\) relative to the plant parameter changes. If the system sensitivity is low, the design is robust to plant modeling errors. If \(S\) is seen as the transfer function, it is characterized by the capability of the system to track the input signal (the transfer function from the reference input \(r\) to the error signal \(e\) is equal to the sensitivity) and the disturbance rejection ability (if there is a disturbance at the output, the transfer function from the disturbance to the system output \(y\) is also equal to the sensitivity). It is thought that the sensitivity should be as small as possible. The design should be such that the system achieves the goal of better feedback features, specifically, low sensitivity and good disturbance rejection ability. In addition to the description above, the sensitivity function also reflects the other important features of the system, and it is a very important design indicator. When an actual system is designed, it is necessary to reasonably design the peak value of the sensitivity function and the frequency of the peak value. However, the sensitivity function cannot be arbitrarily specified in the design. In the comprehensive design of a system, one important problem often encountered is the degree to which the system performance can be achieved [17]. The gap of the magnetic levitation system is generally small. If the anti-interference ability or the robustness is poor, collisions are likely to occur and the system cannot run stably. If it is in a magnetic levitation train, then a serious accident will occur. Reduced sensitivity is necessary. The optimal sensitivity function characteristics are given in the paper. In Section 4, in order to obtain a better sensitivity function, \(c_{1},\alpha,R_{\alpha}\) is reasonably selected. \(\alpha\) (in Equation (30)) provides the coupling of displacement and velocity to the flux linkage, making system (29) a passive system, \(R_{\alpha}\) is added to the dissipative matrix (31) to increase the dissipation of energy and improve the response speed, negative feedback \(-c_{1}\overline{x}_{1}\) (39,40) is applied to the flux linkage that forms the electromagnetic force to stabilize.
## 4 CFD Simulation of Mixing Processes
### Types of Mixers and Their Working Principles
Shear-induced mixing results from velocity gradients and is responsible for droplet breakup or structural modification, especially in rotor-stator and extrusion systems. Turbulent mixing, characterized by eddy formation and energy cascades, enhances mass and heat transfer in high-Reynolds-number flows. Flow patterns within mixers--ranging from laminar to turbulent--are determined by the Reynolds number, geometry of the mixing vessel, and material properties such as viscosity and density. Understanding these patterns is critical for accurate prediction of mixing efficiency, energy input, and thermal behaviour [50]. Mixer selection in food applications must consider material rheology, shear sensitivity, sanitation requirements, and operational mode. High-viscosity or yield-stress materials require high-torque mixing systems, whereas low-viscosity liquids benefit from high-speed agitation. Shear-sensitive materials, such as probiotics or enzymatic systems, demand gentle handling to avoid degradation. In hygiene-critical processes, such as infant formula or dairy production, mixers must support Clean-in-Place (CIP) procedures or be easily disassembled for thorough sanitation. Furthermore, batch mixers offer flexibility and control for small-scale or variable production, while continuous mixers provide greater throughput and consistency for large-scale operations [51]. A comprehensive understanding of mixing technologies and their underlying principles is essential for appropriate equipment selection and process design. When combined with CFD-based modeling of flow fields, shear zones, and residence time distributions (RTD), this knowledge enables the optimization of mixing performance and supports the development of high-quality, consistent food products.
# Analysis and Prediction of the IPv6 Traffic over Campus Networks in Shanghai
## 4 IPv6 Traffic Prediction Model
### The LS6 Model
#### 4.2.1 Model Overview  
As shown in Figure 5, there are two key components of our approach. First, we use a traffic encoding component to take historical traffic data as input and automatically extract two types of features with deep learning and statistical methods. Second, we introduce an integrated predictor component, focusing on combining the outputs of deep learning and statistical methods to get the final prediction. In the traffic encoding component, we leverage two independent seasonal ARIMA (SARIMA) modules and two independent LSTM networks to predict IPv6 and IPv4 network traffic volume in the next time slot with previous traffic volume. To capture the relationship between IPv6 and IPv4 traffic and combine the advantages of LSTM and SARIMA, we feed \(y^{L}_{1},y^{S}_{1},y^{L}_{2}\), and \(y^{S}_{2}\), the extracted features of IPv6 and IPv4 traffic from LSTM and SARIMA to a multilayer perceptron (MLP) model in the integrated predictor module and get the final prediction \(\hat{y}\). In the following subsections, we introduce the two modules in detail.
# Preliminary Analysis of the Water Quality Status in an Urban Mediterranean River
## 3 Results
### Physicochemical Fluctuations Analysis per Site  
Figure 6 represents electrical conductivity (EC) (left axis) and temperature (T) (right axis) observations per sampling site. It was observed that the EC concentrations ranged from 286.3 \(\upmu\)S/cm to 8146 \(\upmu\)S/cm in water obtained from K Ekv. The wide variation at this sampling site, which is considerably different from the rest, is mainly due to seawater influence. The highest EC values in most all other cases were close to 1000 \(\upmu\)S/cm. Such a value is within the expected range for fresh water in an urban river. K 17 and K MD had proximity in the EC minimum values (K 17: 513 \(\upmu\)S/cm and K MD: 519 \(\upmu\)S/cm), median (K 17: 1012 \(\upmu\)S/cm and K MD: 1043.5 \(\upmu\)S/cm) and maximum values (K 17: 1117 \(\upmu\)S/cm and K MD: 1160 \(\upmu\)S/cm). Water temperatures ranged from 9.4 to 25 (\({}^{\circ}\)C), which would pose no adverse effects on living organisms. K 12 and K 17 have proximity in their values, while K MD maximum, median and minimum values are lower compared to the other four sites.
# Hydrodynamic Investigation on a Land-Fixed OWC Wave Energy Device under Irregular Waves
## 1 Introduction
Their research focused on the first-stage and second-stage efficiencies (calculated from the turbine system) under various test conditions. [PERSON] et al. [20] performed a scaled model test to investigate the sloshing phenomenon at the chamber interior in irregular wave conditions. Real ocean wave conditions are manifest as various irregular wave forms. These irregular sea conditions can be investigated by employing various forms of random wave spectra calculated from measured and statistical wave data. Due to the presence of the air phase within the chamber, the interaction between the wave and the OWC device is complex and nonlinear [21]. Based on the fully nonlinear potential flow theory, the hydrodynamic characteristics of a land-fixed OWC device in the irregular wave conditions are numerically investigated using higher-order boundary element method (HOBEM) in the present study. Due to the fact that the numerical tests conducted using irregular waves require longer simulation times to ensure sufficient statistical data, this model adopts an inner-source wave generation technique to generate waves, which can eliminate re-reflection and maintain model stability in the long duration simulations [6]. The characteristics of the wave height and pneumatic pressure inside the chamber, a comparison of the hydrodynamic performance between the irregular and regular waves and, specifically, the water motion inside the chamber is analyzed. The overall structure of this paper is as follows. In Section 2, the numerical model is introduced. The wave generation and model capability validations are presented in Section 3. The influence of irregular wave conditions on the hydrodynamic characteristics of the fixed OWC device are analyzed in Section 4. The main conclusions are summarized in Section 5.
# Gas-Liquid Mixability Study in a Jet-Stirred Tank for Mineral Flotation
## 1 Introduction
[PERSON] and [PERSON] [20] investigated various impeller types and concluded that impellers with 45\({}^{\circ}\) and 60\({}^{\circ}\) slopes achieve more uniform gas-liquid mixing. Despite these advancements, research specifically addressing the uniformity of gas-liquid mixing in jet stirring remains limited. In the jet-stirred tank examined here, factors influencing gas-liquid mixing include jet velocity, nozzle orientation, nozzle height, nozzle inner diameter, and nozzle radius ratio. These parameters affect the dispersion and stability of bubbles in the liquid phase, which in turn impacts flotation effectiveness and resource recovery. Therefore, simulations were conducted with different feed and aeration volumes to study their effects on the flow field distribution within the jet-stirred tank. Structural parameters such as nozzle direction, height, inner diameter, and radius ratio were analyzed to determine their impact on gas-liquid mixing. The originality of our work lies in the introduction of dimensionless variance as a statistical measure to evaluate gas-liquid mixing uniformity. This method allows for a more precise quantification of the effects of different parameter combinations on mixing performance by normalizing variance with the dimensionless Reynolds number. This dimensionless approach facilitates effective comparison and validation of results across various scales and conditions, ensuring that our research conclusions have broad applicability. Unlike previous studies that mainly focused on operational parameters, our method provides a more accurate and generalizable tool for quantifying mixing effects. This innovation not only addresses gaps in current research on mixing uniformity but also offers a more scientific basis and practical guidance for the design and application of jet-stirring technology. By enhancing mixing uniformity, this study significantly improves flotation efficiency, which is its primary practical significance. Greater mixing uniformity leads to higher flotation efficiency, thus improving the recovery rate of mineral resources and the efficiency of resource utilization. These findings not only have theoretical value but also provide practical design guidance for industrial applications, advancing the practical use and optimization of jet-stirring technology in mineral flotation.
## 5 Energy Consumption Optimization Strategy  
One of the main requirements of the project is to ensure the system works for as long as possible. Therefore, the node is designed to be sleeping (consuming only several \(\upmu\)A) almost all the time. That is, the node will wake up only when the train is approaching to be able to perform two key tasks:  
1. Enable accelerometers to collect data from vibrations produced by the approaching train.
2. Transmit data collected during the passing of the next train.  
To go from the state asleep to awake, the node needs an external interrupt that alerts the node to the approaching train. In the following sections two solutions are designed to awake the node in time to perform the two aforementioned tasks.