esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Engineering Application
### Feasibility Verification for the Optimization Scheme  
As shown in Figure 16a, the optimal Pareto set clearly describes the negative correlation between the total weight of the bridge deck pavement and driving comfort. A randomly selected optimization scheme A was evaluated using C1-C3 to verify its feasibility.  
Figure 15: Pareto solutions in objective space.  
As shown in Figure 16, the total weight of the pavement load in scheme A is 1.48307 \(\times\) 10\({}^{3}\) kN, and the maximum difference between the optimized design variables is 11.225 kN. According to Formula (14), the maximum difference of the deck pavement thickness is 0.022 m, which meets the maximum limit (0.025 m) of the specification. The smoothing treatment of the pavement is completed within 4 m to improve the driving comfort.  
The mechanical performance of the concrete girder is shown in Figure 17. As shown in Figure 17, the maximum vertical deviations of the midspan before and after optimization are 12.30 cm and 9.81 cm, respectively. As shown in Figure 17, the maximum positive bending moments of the concrete girder before and after optimization are 2.183 \(\times\) 10\({}^{4}\) kN\(\cdot\)m and 1.702 \(\times\) 10\({}^{4}\) kN\(\cdot\)m, respectively. The maximum negative bending moments of the concrete girder before and after optimization are \(-\)4.375 \(\times\) 10\({}^{4}\) kN\(\cdot\)m and \(-\)4.251 \(\times\) 10\({}^{4}\) kN\(\cdot\)m, respectively. Hence, scheme A can slightly improve the mechanical performance of the concrete girder.  
As shown in Figure 18, the bending moment and longitudinal displacement of the pylon are almost unchanged after optimization, which means that scheme A hardly changes  
Figure 16: The optimal Pareto set and the optimization scheme.  
Figure 17: Mechanical performance of the concrete girder. (**a**) Vertical deviation. (**b**) Bending moment.  
the mechanical performance of the pylon. As shown in Figure 19, the maximum deviation of the cable tension before and after optimization does not exceed 1%, which further indicates that the mechanical performance of the structure is not changed. Hence, scheme A is feasible to control the NRRAP under the live load.  
Figure 19: Cable tension of the stay cables.  
Figure 18: Mechanical performance of the high pylon. (**a**) Bending moment. (**b**) Longitudinal displacement.
# A Cooja-Based Tool for Coverage and Lifetime Evaluation in an In-Building Sensor Network+
## 6 Performance Evaluation
### Multiple Rooms  
In the multiple-room evaluation, we use the datasets from the other four deployments, _i.e._, the ground floor and first floor of the Nimbus building at the Cork Institute of Technology and the second floor and third floor of the Strata Conference building in New York that was held in 2012 [21]. In this paper, we only show the detailed simulation results for CIT-1, as the results from the other deployments support the conclusions we draw. Figures 12-14 show CIT-1's global QoC, the percentage of active nodes over time and the percentage of dead nodes over time, respectively. These results have similar trends with the TMON deployment in Section 6.1, where the greedy algorithm performs better than the localised algorithm and reading correlation improves the results. The summary of CIT-1's simulation results is presented in Table 8. When the simulation finished, nodes that are not active  
The average execution time for each node with greedy-maintain, local-maintain, greedy-maintain with correlation and local-maintain with correlation are 0.0377, 0.0064, 0.0452 and 0.0080 time units, respectively.  
For the other deployments, we only present the lifetime improvement in Figure 15, where the text next to each bar shows how much the lifetime was extended in time units. This figure shows that the lifetime of the network increases in all cases. When the reading correlation method is used, the lifetime improves, except in NY-2 with greedy-maintain. This happens because some nodes near the sink are turned off due to reading correlation, and so, active nodes near the sink need to forward more packets from their children to the sink. This depletes their battery quickly and disconnects the children from the sink. If more nodes near the sink are active, the workload to forward packets to the sink can be shared so as to balance the traffic and reduce the energy consumption of some nodes. The lesson learned from this experiment is that in selecting nodes to be inactive, one needs to be cognisant of the impact on message routing. We do not discuss this issue in this paper. However, one can refer to [25] for a possible solution for identifying critical nodes for network connectivity.
## 2 Related Works
Compared to related works, the HCR-1 protocol proposed in this paper distinguishes itself from them in two aspects: (1) the balance of energy consumption is considered with the limitation of transmission range. Most related works, such as [14,18], are proposed based on the assumption that all sensor nodes have sufficient transmission power to keep the network connected. However, this assumption can hardly be satisfied in large-scale networks. Therefore, we adopt the hybrid clustering and routing strategy [19] in this paper to generate a connected and efficient topology with limited transmission range; (2) HCR-1 focuses on balancing the energy consumption among the nodes that can transmit the data to the sink directly, _i.e._, the nodes in gradient \(k=1\). The simulation results prove that the network lifetime can be significantly prolonged by HCR-1 with minor modification of the network topology.
# Neuroprotective Investigation of Chitosan Nanoparticles for Dopamine Delivery
## 2 Materials and Methods
### Synthesis of the Nanoparticles  
The synthesis of DA-loaded chitosan nanoparticles (CS(DA) NPs) was carried out based on slight modifications of previously published procedures [15; 17]. Briefly, a 0.2% \(w/v\) solution of chitosan (3 mg) in 0.1% \(v/v\) AcOH/H\({}_{2}\)O (1.5 mL) was prepared. After complete dissolution of the polymer, DA (7.5 mg) was added to the solution to reach a final 0.5% \(w/v\) concentration. A 0.07% \(w/v\) aqueous solution (1.6 mL) of TPP (1.1 mg) was added and the mixture was gently stirred. After 15 min, the formed suspension was precipitated by centrifugation and washed 3 times with deionized water at 400\(\times\)\(g\) for 15 min. The resulting CS NPs were resuspended in 1 mL of ultrapure water by manual shaking, before being characterized or directly utilized for the biological experiments. The same procedure was utilized for the preparation of the CS NPs, except the DA was not added into the polymer solution.
## 4 Batched MK-BGV Scheme
### Improved Batched Multi-Key \(Bgv\)
To produce the output ciphertext, compute \(\mathbf{c}_{\text{out}}=\mathbf{MK-BGV.Eval}\big{(}\mathcal{C},\big{\{}\mathbf{b }_{i}\big{\}}_{i=1}^{n},(\mathbf{c}_{1},\ldots,\mathbf{c}_{x})\big{)}\), where every invocation of the homomorphic multiplication \(\mathbf{Mult}\) is replaced by our new multiplication algorithm \(\mathbf{NewMult}\). By employing batch processing, all participating parties can concurrently perform joint homomorphic evaluations on \(\xi\) sets of encrypted data under the \(\mathbf{BMK-BGV}\) scheme. The use of ciphertext packing and batching does not compromise the IND-CPA security inherited from the underlying \(\mathbf{MK-BGV}\) framework. Furthermore, the correctness of \(\mathbf{BMK-BGV}\) naturally follows from the correctness of \(\mathbf{MK-BGV}\), Definition 1, and the ciphertext packing technique.
## 3 Verification by Numerical Simulation
### Sensor Optimization  
The purpose of sensor optimization is to help owners reach a balance between cost and precision requirements in real application. Take a simply supported beam under dynamic moving load for example, four samples are set for comparative analysis: (1) \(n\) = 80, \(L_{m}\) = 0.1 m; (2) \(n\) = 40, \(L_{m}\) = 0.2 m; (3) \(n\) = 20, \(L_{m}\) = 0.4 m; (4) \(n\) = 10, \(L_{m}\) = 0.8 m. The sensor number and sensor length are denoted as \(n\) and \(L_{m}\), respectively. All sensors are distributed by being set on the bottom of the beam. The comparison results of these samples are illustrated in Figure 9.  
Higher calculation accuracy will be obtained when more sensors (or smaller sensor length) are set due to the fact that average strain from LGSSs is closer to actual strain if sensor length is relatively smaller. Based on the analysis, the relative errors of deflection at mid-span at time \(t\) = 0.814 s are 0.93%, 0.98%, 1.16% and 1.89% for samples (1) to (4), respectively. Obviously, this shows that the shorter the gauge length is, the higher the estimation accuracy is. Sensors can be optimized according to the balance between economic cost and accuracy requirements.
###### Abstract  
Purpose: To examine the reliability of scores calculated from virtual reality (VR) games and their association with inhibitory control and cognitive flexibility in young elite rugby players. Methods: Following a familiarization session, seventeen rugby union players completed a session of a modified Stroop test and two sessions of three VR games consisting of (1) memorizing moving targets (_Tracker Master_); (2) selecting moving targets while avoiding pitfalls (_Beat Master_--_Never Stop_); and (3) selecting moving targets with an increasing frequency of appearance (_Beat Master_--_Turbo_). Results: The reliability of _Beat Master_--_Never Stop_ was poor to moderate (0.41 \(<\) intraclass coefficient correlation [ICC] \(<\) 0.62; 3.2% \(<\) standard error of measurement [SEM] \(<\) 26.1%), while it was good to very good for _Beat Master_--_Turbo_ (0.77 \(<\) ICC \(<\) 0.87; 3.2% \(<\) SEM \(<\) 18.2%). Regarding _Tracker Master_, reliability was considered as low to moderate (0.22 \(<\) ICC \(<\) 0.60; 2.2% \(<\) SEM \(<\) 6.0%). We found strong associations between _Tracker Master_ and Stroop flexionly scores (\(-\)0.55 \(<\) r \(<\)\(-\)0.64), as well as strong to very strong associations between _Beat Master_--_Never Stop_ scores and the Stroop inhibition score (0.52 \(<\) |r \(<\) 0.84). Conclusions: Considering their metrological properties and their association level with inhibition and flexibility, the sensibility scores of the _Beat Master_--_Never Stop_ and _Tracker Master_ games should be preferred for monitoring training load, provided at least two familiarization sessions precede them.  
virtual reality; cognitive performance; rugby union
# Removal of Chromium from Synthetic Wastewater Using Modified Maghemite Nanoparticles
## Materials and Methods  
### Sample Preparation  
Stock solution of chromium was prepared using 2.829 g of analytical grade K2 Cr2O7 (Nice Chemicals, Chennai, India). This was dissolved in double-distilled water forming 250 mg/L of chromium stock solution and used within a month of preparation [24]. The sample chromium solutionsfor all experiments were prepared by diluting the stock solution with distilled water to achieve the desired concentration levels. The required pH was adjusted by using either 0.1 N HCl or NaOH.
## 3 Test Results and Discussion
### Displacement of Soil around Pile under Working Load  
In Figures 7 and 8, the two-dimensional soil deformation around variable cross-section inclined piles with 2% and 4% inclinations was analyzed under different section reduction ratios. The test results showed that, under the working load (when the load is below the ultimate vertical load capacity), slight changes in the section reduction ratio and inclination lead to relatively uniform soil deformation, especially at the vari-dimensional cross-section and the pile end. The deformation and its influence are also small at the pile sides and ends.  
In Figure 9, when the pile is inclined 8%, displacement changes little with increasing inclination. The section reduction ratio affects pile displacement more than the inclination degree. As the variable cross-section diameter decreases, the contact area between the pile and surrounding soil increases. This contact area size directly impacts the soil displacement range around the pile, making the variable section's influence on the soil greater than that of the inclination.
## 8 5G IoT Architecture and Infrastructure  
### 5G Architecture  
Fifth-generation (5G) networks are increasingly adopted for their high-speed connectivity and low latency. This fifth-generation technology employs a high-bandwidth spectrum with wider channels to facilitate faster data transfers. It also enhances spectrum efficiency, supporting seamless connections for numerous users. Additionally, 5G technologies lead to more reliable wireless connections and increased data transfer speeds, making them ideal for connecting a multitude of devices to the Internet and advancing IoT applications [122].  
The 5G architecture comprises three primary components: the radio access network, the core network, and the transport network. The radio access network, functioning as a cellular network, connects devices via radio waves. This network includes base stations or towers for wireless coverage, alongside transmitters, receivers, and antennas. The core network connects the radio access network components to the internet, handling tasks like IP address allocation, authentication, and VoIP (voice over internet protocol). The transport network, consisting of wires and fiber-optic cables, is fundamental in facilitating data transfer within the 5G network, ensuring high-speed access and network scalability [123].  
As illustrated in Figure 9, the 5G architecture is structured into five layers. The sensor layer, the first layer, is made up of wireless sensing devices that collect data, includingphysical and environmental parameters, and EEG brain signals in this research context. These sensors, both active and passive, can detect various parameters like temperature, pressure, and light. The gateway layer, the second layer, functions as the access layer, encompassing the radio access network (RAN), which interfaces between the user equipment and the core network of the mobile service provider [124]. The third layer, the network layer, includes the core and transport networks, acting as the backbone connecting multiple access points. The fourth layer, the service layer, is responsible for providing active services to users and applications, managing service access, and bridging the gap between network and application layers to meet user expectations. Lastly, the application layer, the fifth layer, enables user access to services and applications provided by the service layer, offering the user interface and experience for these applications [125].
## 3 Results and Discussion
### Validation of the Multi-Mycotoxin Determination Method in Vegetable Drinks
#### 3.1.1 Selectivity  
This parameter is related to the degree to which other substances interfere with the identification and, where appropriate, the quantification of the analytes in question. It measures the ability of the method to identify/quantify analytes in the presence of other substances, endogenous or exogenous, in a matrix sample under the conditions required by the method. For this, different mycotoxin solutions were prepared at the concentrations of a calibration line from 500 to 2.5 \(\mathrm{\SIUnitSymbolMicro g}\)/mL: in a solution of methanol: water (70:30, _v/v_); in solution with oat extract; in solution with rice extract; in solution with soy extract; and in a solution with almond extract. In all of them, the following was studied: (a) The retention time of each analyte present in the sample coincided with the retention time of the corresponding standard in fortified matrix, with a tolerance of \(\pm\)2.5%; this retention time had to be fulfilled for the two transitions (qualifier and quantifier). (b) The two transitions (product ions) chosen for each analyte were present in the samples. (c) The area ratio of product ion 1 and product ion 2 was within a tolerance of \(\pm\)20% to \(\pm\)50% depending on the relative intensities of the ions. Thus, the two product ions were present in the matrix solutions and their retention time oscillation (Rt) between the matrix solutions, and the solution prepared with solvents was less than (Rt \(\pm\) 2.5%). This is shown in Figure 2, where the chromatograms of five (ENs and ZEA) of the sixteen mycotoxins analyzed in the four matrices studied are shown.
## 3 Results  
### Crestal Bone Loss  
At the 1-year follow-up, 21 patients were evaluated and the DCC implants yielded a mean CBL of 0.13 mm (SD: 0.23; range: 0-0.75) and a mean CBL of 0.12 mm (SD: 0.24; range: 0-0.79) was measured at the MSC implants. After 6 years, a mean CBL of 0.26 mm (SD: 0.42; range: 0-1.79) for the MSC group and 0.17 mm (SD: 0.29; range: 0-1.00) for the DCC group was observed (Table 1 and Figure 3). At 6 years (21 patients), a mean difference in CBL of 0.09 mm between MSC and DCC was estimated, which is not statistically significant (\(p\) = 0.30; 95% CI [\(-\)0.08, 0.25]) (Table 2).  
After 6 years, six implants (four MSC and two DCC implants) had bone loss >0.5 mm, but a statistically significant difference in risk of bone loss >0.5 mm could not be demonstrated between the MSC and DCC groups (\(p\) = 0.14; 95% CI [\(-\)0.03, 0.22]). Peri-implantits was diagnosed in one MSC implant [16].  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Time** & **Type** & **Mean (mm)** & **Lower Bound 95\% CI** & **Upper Bound 95\% CI** \\ \hline
1 Year & DCC & 0.13 & 0.00 & 0.27 \\ \hline
1 Year & MSC & 0.12 & \(-\)0.01 & 0.25 \\ \hline
6 Year & DCC & 0.17 & 0.04 & 0.31 \\ \hline
6 Year & MSC & 0.26 & 0.13 & 0.40 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Estimated mean bone loss (mm) by time and type of implant.
# Health Monitoring of Civil Infrastructures by Subspace System Identification Method:
## 6 Application of Subspace Identification in Civil Engineering Structures
[163]} & \multirow{3}{*}{SSI and DLV} & Five-story shear frame & & \\  & structure with diagonal & & & \\  & bracings. & & & \\  & & & & \\  & & & & \\ \hline \multirow{3}{*}{[PERSON] et al. [164]} & Deterministic-stochastic & Laboratory-scale, three-story, & & \\  & subspace identification & two-bay, infilled RC &  
In most structures, damage occurs in the form of a reduction in the cross-section of structural members. Partial reductions in 2D structures are usually detectable with a high level of precision. The robust applicability of the proposed methods to solve the problem of detecting local damage in real and complicated civil engineering structures has not been validated, even though these methods work relatively well in simple structures. Three-dimensional frames and building structures are generally complex and they pose challenges for both practitioners and researchers. Damage in building structures is usually in the form of a partial reduction in the cross-sectional area in column elements. In some cases, damage is made by opening bolts in a beam-column connection. However, beam damages are less important and they require higher detectability resolution. Bridge structures are a very important element of transportation. An in-service bridge is subject to loads, such as traffic, temperature variation, wind loading, and deterioration, under aggressive environments. Applying SHM to bridge structures poses significant challenges due to the specific types of loading and complexity of the structure. As a general conclusion, it could be derived that damage detection strategies that use modal frequencies, mode shapes or mode shape curvatures as their damage sensitive features [180] are only efficient for  
\begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline  
the detection of global damage and are not generally sensitive enough to detect changes in the local elements of structures [181].
# Two-Stage Short-Term Power Load Forecasting Based on SSA-VMD and Feature Selection
## 3 Sparrow Search Algorithm Optimizes Variational Mode Decomposition
### Variational Mode Decomposition
The formulae for its cyclic update and the termination conditions are shown in Equations (5)-(7):  
\[\sum\limits_{k}\frac{\left\|\hat{u}_{k}^{n+1}-\hat{u}_{k}^{n}\right\|_{2}^{2}} {\left\|\hat{u}_{k}^{n}\right\|_{2}^{2}}<\varepsilon,n<N \tag{5}\]  
The updated formula for the IMF:  
\[\hat{u}_{k}^{n+1}(\omega)=\frac{\hat{f}(\omega)-\sum\limits_{i\
eq k}\hat{u}_ {i}(\omega)+\frac{\lambda(\omega)}{2}}{1+2\alpha(\omega-\omega_{k})^{2}} \tag{6}\]  
Update formula for central frequency:  
\[\omega_{k}^{n+1}=\frac{\int_{0}^{\infty}\omega|\hat{u}_{k}(\omega)|^{2}d \omega}{\int_{0}^{\infty}\left|\hat{u}_{k}(\omega)\right|^{2}d\omega} \tag{7}\]  
where \(\hat{u}_{k}^{n+1}(\omega)\) is the Navier filtering corresponding to each mode function; \(n\) is the number of iterations and \(N\) is the maximum number of iterations; \(\omega\) is the frequency value; \(\hat{f}(\omega)\) and \(\hat{\lambda}(\omega)\) represent the Fourier transforms of \(f(t)\) and \(\lambda(t)\), respectively; \(\omega_{k}^{n+1}\) is the central frequency of each mode function. VMD has the advantage that the number of IMFs can be determined, while the setting of important parameters such as the number of mode decompositions \(K\) directly determines the decomposition effectiveness of the VMD. When dealing with time series data, the parameters are selected mainly through the experimenters' experience and spectral analysis. Setting parameters through experience is more random, and lacks sufficient theoretical support. In addition, because of the complex types of power consumption of various customers in the grid and the factors influencing the load quantity at the same time, the power load value itself has strong volatility, and the results of setting parameters through spectrum analysis are often poor. Therefore, a solution is proposed in this paper using the sparrow search algorithm (SSA) to determine the optimal combination of the number of mode decompositions \(K\) and the penalty factor \(\alpha\).
## 3 Traffic Data Sensing and Gathering
### Mobile Sensing  
In context of the overall discussion in this section, mobile sensing by utilizing the mobile terminals is expected to enable the fast detection of relevant events on the roads as well as increase the accuracy of the traffic condition monitoring. Following the recent findings in [50; 51; 52], mobile crowd-sensing systems using modern smartphones have been successfully applied to provide with a more accurate real-time road traffic information over large areas (e.g., enabling precise localization of vehicles, improving end user/commuter travel experience, etc.).  
A key requirement for the proliferation of mobile sensing technology is the voluntary participation by the end users. On the other hand, to protect this user-provided data, commuters might demand high levels of privacy and stringent security guarantees before they agree to participate in such data collection. These considerations become the main hurdles and have to be carefully addressed to motivate a large number of people to join mobile sensing applications. The indicated issues may be solved by the following: (i) trust management of mobile sensing data sources; (ii) privacy preservation of user device owners; and (iii) utilization of robust authentication techniques [53].