esa-sceva/satcom-chunk-collection huggingface dataset:
# The First Result of Relative Positioning and Velocity Estimation Based on CAPS
## 2 CAPS Structure and Mathematical Model of Observations  
CAPS is a new concept of satellite-based positioning system and it is quite different than the concept of traditional GNSSs. This section will give a very brief introduction of CAPS, including the operating principle and the original observation model for positioning.
## 4 Experimental Results  
The proposed architecture has been simulated extensively to evaluate its behavior in the presence of attacks of very different nature. Several algorithms have been implemented and used in the reputation server to calculate the reputation of the sensors to make a comparison of its performance: linear algorithm, beta function algorithm and SOM.  
In [10] a simple linear algorithm was used to analyse how a reputation system works when integrated in a wireless sensor network. Beta is the most frequent algorithm [23] used in reputation systems and it is also used for WSNs, as shown in [24]. So, we compare both algorithms with a reputation system with SOM algorithms in the context of a SCADA sensor network. We have defined the following characteristics which let us measure the performance and the effectiveness of a concrete algorithm:  
* **Detection time**. It is the elapsed time since the attack was started until it is detected, _i.e._, the reputation of ill-behaved nodes starts decreasing.
* **Isolation time**. It is the elapsed time since the attack is detected until the reputation of every attacker node gets below a threshold. The nodes with reputations below this threshold are not considered for decision making.
* **Isolation capacity**. It is the portion of ill-behaved nodes that are detected as attackers.
* **System degradation**. It is the portion of well-behaved nodes detected as attackers.
## 3 Material Characteristic and Numerical Analysis
For the \(H\)-field distribution, a complete dipole-type distribution can be found at the top part of the resonator, while, at the bottom part of the resonator, a dipole-type distribution with the opposite direction can be observed, indicating that the higher order magnetic dipole mode, or the \(\text{HEM}_{11,1+\delta}\) mode, is excited. Additionally, two maximal regions can be found in the figure of power loss density, indicating that the higher order dielectric resonator mode has been excited. Finally, the operating principle at the higher frequency \(f_{4}\) is discussed. From Figure 4j,k, we can see that the field distribution is quite different from that at \(f_{1}\) to \(f_{3}\). In this case, the field is mainly distributed at the gap between the adjacent elements of the absorber. Additionally, the field at the top part of the resonator is strong. This phenomenon indicates that the grating mode of the resonator is excited. By exciting different modes at different frequencies and combining the multi-mode resonances, high absorption over an ultra-broadband can be obtained using the designed absorber.
## 3 Security and Privacy-Preserving Solutions for IoE
### Edge AI and Secure Computing in IoE
#### 3.2.1 The Role of Edge AI in Ensuring Security in IoE  
Edge AI offers various advantages, especially in critical applications such as healthcare, smart cities, and industrial systems [106]:  
* Reduces latency and enables rapid response [100; 101].
* Enhances data privacy, since data is processed locally without being sent to a central server [102; 103].
* Detects anomalies in real-time, allowing immediate responses to attacks or unexpected behaviors [106].  
As illustrated in Figure 6, the Edge AI model analyzes data locally from IoE devices, performs anomaly detection, and can instantly make security decisions against potential threats [107; 108].  
Figure 6: Local decision-making pipeline enabled by Edge AI in IoE systems. The model performs data analysis, anomaly detection, and real-time security decisions based on locally processed information.
## 2 Experimental Study and Results
### Determination of Basic Parameters
#### 2.2.2 Determination of Chemical Composition  
The chemical composition analysis and mineral composition analysis were performed using the chemical elemental calibration method combined with the X-ray diffraction method. The experimental test samples were identical to the homogenized specimens used for physical property testing in Section 2.2.1. To ensure the reliability of the results, the values presented in the tables are average values. The chemical composition data in Table 2 were obtained from three parallel tests conducted on the composite sample. In addition, to meet the strength requirements of mine backfilling, P.O 42.5 cement, which exhibits excellent cementation performance, was selected as the binder for the backfill material. The results of the mineral composition analysis of the raw materials are presented in Tables 2 and 3.  
The results of the analysis demonstrated that the mineral fraction of the cementitious material had a high content of CaO and SiO\({}_{2}\). According to the data in Tables 2 and 3, it can be observed that the chemical elemental composition of the tailing sand has a high content of Ca, Si, P, and S, and the mineral composition of the tailing has a high content of CaO, SiO\({}_{2}\), P\({}_{2}\)O\({}_{5}\), and SO\({}_{3}\).
## 5 Results and Analysis
### Application in Psychosomatic Illness Diagnosis  
The use of emotion classification can greatly help in identifying the following psycho-somatic conditions.  
Depression is identified by persistent feelings of sadness, loss of interest, and lack of energy. The relevant emotions include sad, neutral, angry.  
Anxiety disorders are characterized by intense fear and worry along with feeling nervous. The relevant emotions include fear, neutral, surprise.  
Bipolar Disorder includes experiencing extreme mood swings from one end of mania to the other end of depression. The relevant emotions include happy, sad, angry.  
Post-Traumatic Stress Disorder (PTSD) is developed after being exposed to events that evoke high levels of fear, leading to flashbacks coupled with anger or surprise upon recollection. The relevant emotions include fear, angry, surprise.  
Obessive-Compulsive Disorder (OCD) is characterized by intrusive thoughts and urges to perform rituals. It often involves an excessive focus on physical symptoms, like pain, which can be distressing. The repetitive nature of OCD symptoms can lead individuals to feel trapped in a never-ending cycle, contributing to feelings of helplessness and hopelessness. Dealing with these types of symptoms frequently makes individuals become even more frustrated as they find it difficult to cope with their daily activities.
## 5 Experimental Results
### Evaluation of the Algorithms  
From the report of Tables 3-7, it can be seen that the results of NewKLS_FVS and k-opt-LS are obviously better than those of VNS1, VNS2 and SA. Table 8 presents the results of the best algorithm for each instance testing DIMACS. In Table 8, the column _best_ means the best result from the algorithms, and the column _worst_ is the worst result from the algorithms. The column _the-best-algorithm_ is the set of algorithms capable of obtaining the best result.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Instance** & **Best** & **Worst** & **The-Best-Algorithm** \\ \hline anna.col & 126 & 126 & a,b \\ david.col & 66 & 66 & a,b \\ DSJC125.1.col & 58 & 58 & a \\ DSJC125.5.col & 15 & 15 & a,b \\ DSJC125.9.col & 6 & 6 & a,b,c,d \\ fpsol2.i.1.col & 366 & 364 & a \\ fpsol2.i.2.col & 370 & 370 & a \\ fpsol2.i.3.col & 344 & 344 & a,b \\ games120.col & 47 & 46 & a,b \\ homer.col & 520 & 520 & a,b \\ \hline \hline \end{tabular}
\end{table}
Table 8: Results for algorithm analysis.  
Figure 2: Comparison of the algorithms within 100 iterations. (**a**) huck.col; (**b**) inithx.i.1.col; (**c**) le450_25a.col; (**d**) zeroin.i.1.col.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Instance** & **Best** & **Worst** & **The-Best-Algorithm** \\ \hline huck.col & 52 & 52 & a,b \\ inithx.i.1.col & 602 & 554 & a \\ inithx.i.2.col & 549 & 548 & a \\ inithx.i.3.col & 529 & 529 & a \\ jean.col & 63 & 63 & a,b \\ latin\_square\_10.col & 13 & 13 & a \\ le450\_5a.col & 114 & 108 & a \\ le450\_5b.col & 114 & 109 & a \\ le450\_5c.col & 104 & 94 & a \\ le450\_5d.col & 104 & 97 & a \\ le450\_15b.col & 129 & 126 & a \\ le450\_15c.col & 66 & 63 & a \\ le450\_15d.col & 67 & 65 & a \\ le450\_25a.col & 157 & 154 & a \\ le450\_25b.col & 141 & 138 & a \\ le450\_25c.col & 78 & 76 & a \\ le450\_25d.col & 72 & 70 & a \\ miles250.col & 81 & 80 & a \\ miles500.col & 43 & 43 & a,b \\ miles750.col & 27 & 27 & a,b \\ miles1000.col & 20 & 20 & a,b \\ miles1500.col & 10 & 10 & a,b \\ mulsol.i.1.col & 121 & 121 & a,b \\ mulsol.i.2.col & 133 & 133 & a,b \\ mulsol.i.3.col & 129 & 129 & a,b \\ mulsol.i.4.col & 130 & 130 & a,b \\ mulsol.i.5.col & 131 & 131 & a,b \\ mycie13.col & 8 & 8 & a,b \\ mycie14.col & 16 & 16 & a,b \\ mycie16.col & 61 & 61 & a,b \\ mycie17.col & 122 & 122 & a \\ queen5\_5.col & 7 & 7 & a,b,c,d,e \\ queen6\_6.col & 9 & 9 & a,b,c,d,e \\ queen7\_7.col & 11 & 11 & a,b,c,d \\ queen8\_8.col & 13 & 13 & a,b \\ queen8\_12.col & 16 & 16 & a,b \\ queen9\_9.col & 14 & 14 & a,b \\ queen10\_10.col & 16 & 16 & a,b \\ \hline \hline \end{tabular}
\end{table}
Table 8: _Cont._Table 8 shows that NewKLS_FVS obtains the best result for all the tested instances, and k-opt-LS obtains 34 best results. From the report, it can be seen that they both perform better than other test procedures for the feedback vertex set problem.
###### Abstract  
We compared the immediate effects of a cool-down strategy including an inverted body position (IBP: continuous 30-s alternations of supine and IBP) after a short period of an intense treadmill run with active (walking) and passive (seated) methods. Fifteen healthy subjects (22 years, 172 cm, 67 kg) completed three cool-down conditions (in a counterbalanced order) followed by a 5-min static stretch on three separate days. Heart rate, energy expenditure, blood lactate concentration, fatigue perception, and circumference of thighs and calves were recorded at pre- and post-run at 0, 5, 10, 20, and 30 min. At 5 min post-run, subjects performing the IBP condition showed (1) a 22% slower heart rate (\(p<0.0001\), ES = 2.52) and 14% lower energy expenditure (\(p=0.01\), ES = 0.48) than in the active condition, and (2) a 23% lower blood lactate than in the passive condition (\(p=0.001\), ES = 0.82). Fatigue perception and circumferences of thighs and calves did not differ between the conditions at any time point (F\({}_{10,238}<0.96\), \(p<0.99\) for all tests). IBP appears to produce an effect similar to that of an active cool-down in blood lactate removal with less energy expenditure. This cool-down strategy is recommended for tournament sporting events with short breaks between matches, such as Taekwondo, Judo, and wrestling.  
c 5 September 2020; Accepted: 21 September 2020; Published: 23 September 2020
# Research on Human Exposure to Transport-Induced Vibration in Buildings
## 3 Weighting Function Problem  
The first problem with signal analysis appears at the very beginning, where after applying an appropriate correction filter (values available in the standards [18] or [22]), the weight values corresponding to the directions of vibrations should be entered. According to the standard [19], weight values assigned to the directions of vibration reception by humans should be used. The standard [18] makes it possible to use the so-called combined weighting function. In different standards, there are also differences in weight values relating to the same directions. This is evident when comparing information from the two standards that first introduced the VDV method, i.e., British [19] and ISO [18]. In the provisions of both standards, there are differences in weight values in the vertical direction, and in the horizontal directions, these differences are negligible. The problem appears when comparing the combined function \(W_{\text{m}}\) acc. [19] with functions in the z-direction (\(W_{\text{k}}\)[18] or \(W_{\text{b}}\)[22]), which in most cases decides the results of evaluation of human perception of vibration in buildings. In Figure 5, the values of the weight functions in the \"z\" directions with the values of such a function in the combined direction \(W_{\text{m}}\) are shown. It can be seen that while the values of the weight functions in the direction \"z\" do not differ significantly from each other, the value of the combined weighting function \(W_{\text{m}}\) is significantly different from them. This problem was examined on an example building located in Krakow at Mogilska St. Measurements were made on typical masonry building built before World War II (see Figure 6). \begin{table}
\begin{tabular}{l l c c} \hline \hline \multicolumn{1}{c}{**Purpose of the Room**} & \multicolumn{1}{c}{**Daytime**} & \multicolumn{1}{c}{\
\begin{tabular}{c} **Low Probability of** \\ **Adverse Comments** \\ \end{tabular} } & \multicolumn{1}{c}{
\begin{tabular}{c} **High Probability of** \\ **Adverse Comments** \\ \end{tabular} } \\ \hline Hospitals, operating theatres, & Day and night & 0.1 & 0.2 \\ \hline Residential & Day & 0.2 & 0.4 \\  & Night & 0.13 & 0.26 \\ \hline Office & Day and night & 0.4 & 0.8 \\ \hline Workshop & Day and night & 0.8 & 1.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The values of VDV for assessing the probability of complaints from people in buildings. Figure 5: Comparison between \(W_{\text{b}}\), \(W_{\text{k}}\) and \(W_{\text{m}}\) weighting functions. The building is subjected to the following two sources of transport excitation:  
* Car and heavy truck passages on the road located 1 m from the building;
* Tram passages on the track located 10 m from the building. In this case, the VDV evaluation method was analyzed. VDVs for vibration episodes using three different weighting functions were determined (Table 2).
# Optimal Gliding Trajectories for Descent on Mars
## 4 Conclusions  
The research performed here showed the effects of the variation in the \(l/d\) ratio in the descent trajectories of a spacecraft on Mars. It can be used to guide the preliminary design of missions that aim to use a gliding fly in the atmosphere of Mars to descend a spacecraft close to the surface of this planet. The most important result of this research is that the feasible trajectories landed at latitudes around 30 degrees and longitudes close to 26 degrees for the majority of the trajectories. Even though the latitude is lower than expected, it is still not wholly unpredictable. Regarding the trajectories of the spaceplane, it was shown that most of them behaved similarly to the nominal trajectory, both in terms of velocity and altitude time evolution. Almost all those trajectories bounced back when reaching a point of higher density in the atmosphere of Mars, and after that, the spacecraft fell again. At the same time, the velocity had a slow decreasing phase during this bouncing motion and a fast decrease when out of this zone. The trajectories obtained for different values of \(l/d\) were very similar to the nominal trajectories during the whole motion. The angle of attack has a similar behavior, while the banking angle had a more dispersed evolution, compensating for the losses in lift due to lower density to reach the desired point.  
Conceptualization, I.D. and A.F.B.A.P.; methodology, software, validation, and simulations J.M.-P.; formal analysis, I.D. and A.F.B.A.P.; investigation, I.D. and A.F.B.A.P.; resources,A.F.B.A.P. and R.V.d.M.; writing--original draft preparation, I.D., A.F.B.A.P. and J.M.-P.; writing--review and editing, all authors; visualization, J.M.-P.; supervision, R.V.d.M.; project administration, R.V.d.M.; funding acquisition, J.M.-P. and R.V.d.M. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Sao Paulo Research Foundation (FAPESP) [grant numbers 2019/26605-2, 2015/24561-0, and 2016/24561-0]; the National Council for Scientific and Technological Development (CNPq) [grant numbers 3003102/2019-5, 309089/2021-2, and 301338/2016-7]; and the National Council for the Improvement of Higher Education (CAPES) [grant number 88882.317514/2013-01]. This research was partially funded by a grant (FGR10074) from the Italian Ministry of Foreign Affairs and International Cooperation (MAEC) in the framework of the first Executive Programme for Scientific and Technological Cooperation between the Government of the Italian Republic and the Government of the Federal Republic of Brazil.  
Not applicable.  
All the data used for this research are presented in the figures of this article.  
The authors are grateful to the Institute of Science and Technology (ICT) of the Federal University of Sao Paulo (UNIFESP), the National Institute for Space Research (INPE), and the Italian Aerospace Research Centre (CIRA) for supporting this investigation in various capacities.  
The authors declare no conflicts of interest.
## 5 Case Study
### Application of the Model to the Case Study
Integrated Computing Service:  
An automated service was deployed in a virtualized environment to access the data collected and stored in the SQL database. This service has been configured to perform the following:  
* Connect to the SQL database through ODBC drivers, ensuring compatibility and security in data transmission. * Execute pre-defined workflows that include the preprocessing, normalization, and application of machine learning algorithms. 2. Interfaces Used:* Database interface: the service used structured queries to extract data from the SQL database, handling large volumes of records and applying filters for the most recent data (last 24 h). * Results interface: after generating the forecasts, the predicted values were automatically inserted into a dedicated table within the same SQL database, allowing for tracking and integration with the alert system. * Controls and Automation:
* The service has been scheduled to automatically run tasks at regular (hourly) intervals using scheduling tools within the server operating system. * Machine learning algorithms were implemented in a secure virtualized environment designed to ensure system integrity during data processing and analysis. * Interaction with the Alert System:
* After processing and predictive analysis, the service would forward the results to a second automated system (controlled by RPA). This system checked whether values were outside pre-defined limits and, if they were, triggered automatic notifications to the maintenance team. * Libraries Used:
* Pandas and NumPy: data manipulation, normalization, and mathematical operations. * Scikit-learn: decision tree model training and inference (CART). * SQLAlchemy: integrating and managing SQL database connections. In Figure 3, a schematic diagram is presented, illustrating the integrated sequencing of the operational flow of the model applied in the company. This diagram will visually represent the step-by-step process, from the data collection via IoT sensors to the RPA robots' data extraction, processing, predictive analysis, and the final alert generation in case of anomalies, showcasing the seamless interaction between technologies in the predictive maintenance process.
# Recent Advances in Optical Detection of Aminoglycosodies
## 1 Introduction
### Conventional Detection Methods  
Since most of the AGs lack a chromophore and fluorophore, the detection of AGs is a big challenge. Traditional methods for the detection of AGs are based on microbial assays. Although these microbial  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Name of AGs**} & \multirow{2}{*}{**Animals**} & \multirow{2}{*}{**Detection Object**} & \multicolumn{4}{c}{**MRLs (\(\mu\)g kg\({}^{-1}\))**} \\ \cline{4-7}  & & & U.S. & E.U. & **China** & **Japan** \\ \hline \multirow{2}{*}{Streptomycin} & Cattle & Muscle/fat/liver & 500 & 500 & 600 & 600 \\  & & Milk & - & 200 & 200 & 200 \\ \hline \multirow{2}{*}{Neomycin} & Cattle/pork & Muscle/fat/liver & 1200/- & 500 & 500 & 500 \\  & Chicken & Egg & - & 500 & 500 & 500 \\ \hline \multirow{2}{*}{Kanamycin} & Cattle & Muscle/fat & - & 100 & - & 40 \\  & & liver & - & 600 & - & 40 \\  & & Milk & - & 150 & - & 400 \\  & & Muscle & - & 100 & - & \\  & & fat & - & 100 & - & 100 \\  & & liver & - & 600 & - & 100 \\  & Chicken & Egg & - & - & - & 500 \\ \hline \multirow{2}{*}{Gentamicin} & Cattle/pork & Muscle/fat & 100 & 50 & 100/400 & 100 \\  & & liver & 300 & 200 & 2000 & 2000 \\ \cline{1-1}  & & Milk & - & 100 & 200 & 200 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Maximum residue limits (MRLs) of some AGs in different foods.  
Figure 1: Structures of typical aminoglycosoides (AGs).  
assays are relatively cheap, the determination is time-consuming and the sensitivity and specificity are poor. Alternatively, high-performance liquid chromatography (HPLC), gas chromatography (GC), capillary electrophoresis (CE), etc., have been applied to the detection of AGs [6; 7], among which HPLC is the most commonly used analysis method. Since AGs have no chromophore, HPLC coupled with all-purpose detection systems such as evaporative light-scattering detection (ELSD), pulsed amperometric detector (PAD), and mass spectrometry (MS) should be used [8]. Although these novel detection systems make the detection of AGs possible, their price is high and there are no conventional detectors equipped. By using ordinal UV absorption or fluorescent (FL) detectors, pre-column derivation and post-column derivation have been employed for the detection of AGs. The amino group contained in AGs can react with FL derivatization reagents, including o-phthalaldehyde, 1,2-naphthquinone-4-sulfonate, dansyl chloride, 1-fluoro-2,4-dinitrobenzene, to form stable fluorescent derivatives of AGs. However, the pre-column derivation introduces new compounds such as derivation reagents and byproducts in the detection system, which may interfere with the analysis of target AGs. The incomplete derivation reaction may cause the loss of target AGs. Post-column derivation suffers from complex instrumentation because of the requirement for post-column reactors and additional pumps. Due to the complex instrumentation, time-consuming detection and sample preparation processes, these chromatographic methods are difficult to be applied to the point of care test (POCT) for AGs.