esa-sceva/satcom-chunk-collection huggingface dataset:
# Privacy-Preserving Data Analytics in Internet of Medical Things
## 2 Background
### K-Anonymity  
Researchers have proposed multiple other methods for privacy preservation to overcome the shortcomings of simple data anonymization. K-anonymity is considered a widely used method for protecting privacy. It ensures that individuals cannot be reidentified from anonymized datasets by making sure that every person in the record can be distinguished from at least \(k-1\) other person [7; 8]. Elements of data like age, sex, and occupation that could potentially identify individuals are grouped into categories. Individuals who have similar characteristics are grouped together. Instead of recording exact ages, age can be grouped into ranges like (30-35 years). Each group should contain at least \(k\) individuals. By organizing the data this way, it is much harder for someone to figure out who a specific person is. However, this technique is still vulnerable to homogeneity and background knowledge attacks.
# ReBiDet: An Enhanced Ship Detection Model Utilizing ReDet and Bi-Directional Feature Fusion
## 2 The Proposed Method  
This section presents the architecture of ReBiDet. Figure 1 illustrates the five modules of ReBiDet: the feature extraction module ReResNet [21], the feature pyramid construction module ReBiFPN, the bounding box generation module RPN, the RoIAlign module for extracting feature maps of horizontal bounding boxes and performing preliminary classification and rotation bounding box generation, and the RiRoIAlign [21] module for extracting feature maps of rotation bounding boxes and performing classification refinement and rotation bounding box parameter refinement.  
Figure 1: Architecture of rotation-equivariant bidirectional feature fusion detector (ReBiDet).  
ReBiDet is an improved version of ReDet that specifically addresses the characteristics of optical remote sensing images. The objective is to tackle the challenge of detecting ships in complex scenes with scale differences and enhance the detection accuracy of ships in remote sensing images using specialized modules. Three changes were made to the original ReDet architecture. Firstly, we introduced ReBiFPN, constructed based on \(e2 cm\)[22], to enable the network to capture multi-scale feature information from both bottom-up and top-down paths, facilitating more comprehensive fusion of low-level positional information and high-level semantic information. Secondly, we utilized the K-means [26] algorithm to cluster the aspect ratios of ground truth boxes in the dataset and adjusted the size of the anchor boxes generated by the anchor generator accordingly. Finally, in the RoAlign stage, we employed the difficult positive reinforcement learning (DPRL) sampler to address class imbalance in the dataset and make the model more sensitive to challenging positive samples. Experimental results demonstrate the effectiveness of these improvements in enhancing the detection performance of small objects in optical remote sensing images.
## 1 Introduction
In UM, the material removal rate of Inconel 718 was seriously reduced. To improve the material removal rate through the UM method, this study conducted hydrogen embrittlement (HE) for Inconel 718. Hydrogen embrittlement, the most common form of metal failure, limits many high-strength alloy applications. Hydrogen usually comes from the environment in which it is used and when it is absorbed by the alloy, it is randomly dispersed and eventually trapped by defects, dislocations, etc. The trapped hydrogen becomes enriched over time, weakening interfacial bonding, known as hydrogen-enhanced debonding (HEDE), and promoting dislocation motion, known as hydrogen-enhanced local plasticity (HELP) [20]. The chemical composition of the Inconel 718 alloy is shown in Table 1. The Inconel 718 alloy, after being subjected to HE, exhibits reduced ductility, making it more suitable for UM material removal. [PERSON] et al. [21] reported that the mechanical properties of Inconel 718 alloy were deteriorated in the presence of hydrogen. [PERSON] et al. [22] analyzed the HE behavior of Inconel 718 at room temperature through slow-strain-rate tensile tests, TEM, and SEM, and the results showed that Inconel 718 exhibited the Portevin-Le Chatelier (PLC) effect at current densities of 220 mA/cm\({}^{2}\) and 590 mA/cm\({}^{2}\). [PERSON] et al. [23] observed mixed cracks on the fracture surface of Inconel 718 after HE modification, making it easier to machine by the UM method. To enhance the material removal rate of Inconel 718, this study introduces a hybrid material removal technique known as \"hydrogen embrittlement enhanced ultrasonic machining (HEUM)\". The influence of machining parameters on the specific material removal rate and surface roughness is examined through experiments. In order to explore the side-wall material removal mechanism, nanocatch testing was performed on HEUM samples with reference to the study by [PERSON] et al. [24] to simulate a single abrasive machining \begin{table}
\begin{tabular}{c c c c c c c c c} \hline
**Ni** & **Cr** & **Mo** & **Nb** & **Ti** & **Al** & **Si** & **Mn** & **Co** & **Fe** \\ \hline
50.0–55.0 & 17.0–21.0 & 2.8–3.3 & 4.75–5.50 & 0.65–1.15 & 0.20–0.80 & 0.35 & 0.35 & 1.0 & The rest \\ \hline \end{tabular}
\end{table} 
Table 1: Inconel 718 alloy chemical composition (mass fraction/%). process. The material removal mechanism of HEUM is analyzed by examining results from nanoindenter tests and the morphology of the bottom surface of blind holes. This research holds significant implications for the HEUM strategy and the precise small hole machining of Inconel 718.
## 4 Curve Passage Analysis  
The safety of engineering vehicles traversing R20m minimum radius curves is structurally limited by the maximum allowable steering angle of the bogie. When a vehicle passes through a curve, the bogie is deflected and forms an angle with the body. The smaller the curve radius, the greater the steering angle of the bogie relative to the body. When the outer axle of the bogie approaches the outer track and the inner axle nears the inner track, and each axle end of the bogie reaches its lateral moment limit, the minimum radius \(R_{min}\) of curvature through which the rail vehicle can pass is determined, as shown in Figure 10.  
Figure 9: Curves of wheelset transverse displacement for the rigid–flexible coupled model at 66 m/s (**a**) and 67 m/s (**b**).  
With reference to the design parameters of a certain type of inner axle-box bogie in China, the parameters of the track and wheel pairs required to analyze the ability of the battery track engineering vehicle to pass through curves are shown in Table 3.  
\(L_{K}\) According to the report \"Locomotive General and Bogie\" published by China Railway Publishing House in 2010, to simplify the calculation, assuming no transverse movements occur in any axes, the minimum curve radius that the engineering vehicle can safely navigate can be determined as follows:  
\(X\) is the rotating heart distance of the wheelset and is calculated as  
\[X=\frac{l}{2}+\frac{R\big{(}\overline{Y_{3}}-\overline{Y_{1}}\big{)}}{l}, \tag{1}\]  
where \(\overline{Y_{3}}=1199.55\) mm and \(\overline{Y_{1}}=-400.5\) mm  
\(\theta\) is the maximum angle of rotation permitted for the bogie design.  
\[\theta=\frac{\frac{L_{K}}{2}+\frac{l}{2}-X}{R}. \tag{2}\]  
\(\theta^{\prime}\) is the angle of the bogie due to the inner and outer rails.  
\[\theta^{\prime}=sin^{-1}\frac{\sigma+\Delta}{l}. \tag{3}\]  
\(R_{min}\) is the minimum curve radius.  
\[R_{min}=\frac{L_{K}}{2 sin(\theta+\theta^{\prime})}. \tag{4}\]  
Figure 10: Scenario in which the vehicle can navigate the minimum radius.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Values** \\ \hline Track gauge & A = 1435 mm \\ Wheel pair inside distance & B = 1380 mm \\ Wheel rim thickness & T = 21.22 mm \\ Full clearance between inside of the rail and & \(\Sigma=12.6\) mm \\ outside of the rim & A = 3 mm \\ Gauge widening on curves & A = 15 mm \\ Free transverse motion of the wheelset & \(\Delta=15\) mm \\ Bogie fixed wheelbase & L = 1600 mm \\ Vehicle fixing distance & \(L_{K}=6000\) mm \\ \hline \hline \end{tabular}
\end{table}
Table 3: Track and wheelset parameters.  
Calculations based on the data in Table 3 revealed that the battery track engineering vehicle passed through a minimum curve radius of \(R_{min}=19.49\) m, demonstrating that it can navigate curves with a minimum radius of R20m.
# Farm Monitoring System with Drones and Optical Camera Communication
## 4 Computer Simulation
### Simulation Results  
#### 4.2.1 Total Trajectory Length  
Figure 6 shows the total trajectory length of the drone to collect the data from all of the deployed sensor nodes. The points represent the average. The error bars show the maximum and minimum values. The trajectory length increased according to the field size. As the field size increased beyond 120 m, a clear separation in performance became more apparent. The shortest path consistently resulted in the longest trajectory across all field sizes, whereas the proposed algorithm demonstrated slightly more efficient trajectory lengths. The proposed algorithm using k-means tended to yield the shortest trajectory, closely followed by the group average method and [PERSON]'s method. This suggests that the proposed method is more efficient in optimizing the total trajectory length in larger fields.
## 3 Materials and Methods
### Hs-SPME/GC-Ms  
HS-SPME was performed using a manual SPME holder and PDMS/DVB and DVB /CAR/PDMS fibers purchased from Supelco Co. (Bellefonte, PA, USA). The fibers were conditioned according to Supelco Co. (Bellefonte, PA, USA) instructions. The 2 mL of the samples were divided into separate 5 mL glass vials and hermetically sealed with PTFE/silicone septa. The liquid phase of the sample was kept during equilibration (15 min) and HS-SPME (45 min) in the vials in a water bath at 60 \({}^{\circ}\)C below the water surface. A magnetic stirrer was used to perform the extraction with constant stirring of the sample (at 1000 rpm). The SPME fiber was drawn into the needle, removed from the vial, and placed in the injector (250 \({}^{\circ}\)C) of a gas chromatograph with a mass spectrometer (GC-MS). After 6 min, the extracted volatiles were thermally desorbed and added directly to the GC column. GC-MS analyses of volatiles were performed using a model 7890A gas chromatograph (Agilent Technologies, Palo Alto, CA, USA) equipped with a HP-5 MS capillary column (5% phenylmethylpolysiloxane, Agilent J and W; 30 m \(\times\) 0.25 mm i.d., coating thickness 0.25 \(\upmu\)m) and a model 5977E mass selective detector (MSD) (Agilent Technologies, Palo Alto, CA, USA). The carrier gas was helium (He 1.0 mL min\({}^{-1}\)). The oven temperature was set at 70 \({}^{\circ}\)C for 2 min, and then increased to 200 \({}^{\circ}\)C (3 \({}^{\circ}\)C/min) and held for 15 min. MSD (EI mode) was used at 70 eV with a mass range of 30-300 amu. Retention indices (RIs) were calculated in terms of the retention times of the \(n\)-alkanes (C9-C25) and their comparison with data from the literature (National Institute of Standards and Technology, Gaithersburg, Maryland, USA) and the mass spectra of the compounds, which matched those of the mass spectral libraries Wiley 9 (Wiley, New York, NY, USA) and NIST 17 (D-Gaithersburg). All extractions (HS-SPME) were performed in duplicate (\(n\) = 2 replicates), and the results are expressed as mean values of percent composition.
# Fault Detection of Resilient Navigation System Based on GNSS Pseudo-Range Measurement
## 2 Methods
### Factor Graph Model  
Because of the flexible optimization structure, the factor graph framework has good research prospects. It can quickly isolate the fault navigation subsystem under vibration disturbance and electromagnetic interference.  
The factor graph originates from the bipartite graph. The fixed points of a factor graph can be divided into two different types of information, and the relationship between the two nodes is connected by edges. The complex navigation system can be simplified by factorization in the factor graph.
## 2 Materials and Methods
### Case-Study: Tapiola Bridge
The numerical analysis of the Tapiola Bridge deck starts at the end of the construction time (April 2019) until October 2020, see the weather data in Figure 9, while the sensor-based monitoring started later (October 2019) and is on-going. The earlier starting of the numerical analysis demonstrates that the numerical models can assist the monitoring by predicting the hygro-thermal response of the SLTD also in the absence of measurements. The initial relative humidity and temperatures are equal to those of the air (\(RH_{0}=65\%\), \(T_{0}=0.85\)\({}^{\circ}\)C) and the initial moisture content in equilibrium with \(RH_{0}\) is \(MC_{0}=15.3\%\). Figure 8: Tapiola Bridge. (**a**) The scheme of the transverse prestressed gullam wooden slabs of the bridge where T2, T3 and T4 indicate the support, reproduced from [30] with permission from VTT publications. Detail A is shown in Appendix B. (**b**) A picture with the view of the bridge. \begin{table}
\begin{tabular}{l l} Industrial PC: & Advantech [31] \\ Measurement software: & Labview [32] \\ Remote desktop software: & DWAgent [33] \\ Data acquisition chassis: & NI cDAQ-9174 [32] \\ Data acquisition devices: & NI 9211 (thermoceptive) [32] \\  & NI 9205 (temperature, moisture) [32] \\  & NI 9237 (Force) [33] \\ Power supply: & Quint Power [34] \\ Enclosure internal thermostat & \\ Enclosure heater & \\ Thermocouples & \\ \end{tabular}
\end{table} 
Table 3: The devices and software of the monitoring unit.
## 2 Materials and Methods  
### Materials Preparation and Characterization  
The current study utilizes the same slag samples used by the authors in previous experiments [15; 16]. The samples are taken by two different Swedish steel plants, namely Outkumpu Stainless AB (Avesta, Sweden) and Sandvik Materials Technology (Sandviken, Sweden). Two slag samples from each company were taken from their respective landfill sites (O1 and S1). Moreover, one slag sample was taken directly from the Electric Arc Furnace (EAF) process (O2) and one from the Argon Oxygen Decarburization (AOD) converter process (S2). The samples were ball milled and sifted by using a 25-50 \(\upmu\)m mesh, to obtain a well-defined size interval of slag particles to be used for the experimental trials.  
To assess the mineral composition of the slag samples, identification and semi-quantitative analysis of the mineral phases within each slag sample was performed using scanning electron microscopy (SEM) and powder X-ray powder diffraction (XRD). Energy spectra for distinct mineral phases were obtained using an FEI Quanta 650 field-emission scanning electron microscope (FEG-SEM) equipped with an 80 mm\({}^{2}\) X-MaxN Oxford Instruments energy-dispersive spectroscopy (EDS) detector operating with an accelerating voltage of 20 kV. The spectra were reduced to chemical data within the AZtec software and subsequently calculated to approximate mineral formulas. Thereafter, semi-quantification of the slag minerals in each sample was achieved using a PANalytical X'Pert [3] Powder diffractometer equipped with an X'celerator silicon-strip detector and operated at 40 mA and 45 kV (CuK\(\alpha\)-radiation, \(\lambda\) = 1.5406 A). Peak positions were determined using the X'PertHighScore Plus 4.6 program (peak positions were corrected against an external Si Metal Standard, NBS640b) and matched with mineral phases identified during SEM analyses.  
To also have a precise quantitative assessment of the elements present in the slag samples (such as Ca, Si, Al, Mg among others), the chemical compositions of all four slag samples have been identified using Sector Field Inductively Coupled Mass Spectrometry (ICP-SFMS). The samples have been digested in HNO\({}_{3}\), HCl and HF prior to ICP-SFMS to extract all the metal ions present. Thereby, the concentrations of 32 metallic elements were determined.
###### Abstract  
In this paper, a new method is presented for sensitive quench detection in high-temperature superconductor (HTS) rotating machinery. The normal zone propagation velocity of an HTS is about 1000 times slower than that of a low-temperature superconductor. Therefore, the propagation of normal zone resistance, which occurs when the HTS transits from the superconducting state to the normal state, is also slower. Thus, it is difficult to detect the abnormal signals by voltage measurement using voltage taps. Moreover, the monitoring signal includes noise generated by interaction between the HTS rotating machinery and the industrial environment. Therefore, when quenching occurs in the HTS rotating machinery, a thermal runaway occurs in the hot spot. Furthermore, the magnetic energy stored in the HTS coil can damage the machinery. For these reasons, a new method is proposed for sensitive quench detection that reduces the noise generated from the power supply and from the HTS rotating machinery, using both an RLC resonance circuit and fast Fourier transform analysis.  
high-temperature superconductor; noise; resonance circuit; rotating machinery; quench detection +
Footnote †: journal: Applied Sciences
# Reliability Analysis of an Air Traffic Network: From Network Structure to Transport Function
## 4 Empirical Analysis
### Calculation of the CORN, TTRN and CARN
In stormy weather, flights must fly around the thunderstorm area, which increase the flight distance and travel time. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Index\(\backslash\)Month** & **September** & **October** & **November** \\ \hline \(\mu_{tra}\) & 0.9615 & 0.9610 & 0.9647 \\ \(\sigma_{tra}\) & 0.0468 & 0.0357 & 0.0367 \\ \(R_{tra}\) & 0.9147 & 0.9253 & 0.9280 \\ \hline
**Index\(\backslash\)Month** & **December** & **January** & **February** \\ \hline \(\mu_{tra}\) & 0.9547 & 0.9601 & 0.9601 \\ \(\sigma_{tra}\) & 0.0393 & 0.0422 & 0.0420 \\ \(R_{tra}\) & 0.9154 & 0.9179 & 0.9181 \\ \(\overline{R}_{tra}\) & 0.9199 & & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The travel-time reliability of the air traffic network (TTRN) of the US air traffic network. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Ranking** & **Airports** & **TTR** \\ \hline
1 & GUM & 0.998 \\
2 & EYW & 0.991 \\
3 & RSW & 0.969 \\
4 & MHT & 0.963 \\
5 & HNL & 0.962 \\
6 & DAL & 0.959 \\
7 & MSY & 0.958 \\
8 & ELP & 0.957 \\
9 & CHS & 0.957 \\
10 & DFW & 0.956 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The top 10 Airports for travel-time reliability. (3) Calculation of the CARN  
First, the capacity reliability of Atlanta Airport (ATL) is calculated based on the method in Section 3.3. The flight operation at ATL on 15 January is shown in Figure 5. The capacity reliability of ATL is 0.8068, which is relatively high. Then, we calculate the capacity reliability of other airports. We find that the capacity reliability of the top 28 large-flow airports is above 0.8, and the airport connectivity, with relatively high capacity reliability, is generally high. Then, we calculate the monthly CARN of the US air traffic network, as shown in Table 6. From Table 7, we find that the CARN of the US air traffic network is much higher and the monthly variation of the CARN is much smaller. The capacity reliability of December is the lowest, which may be due to snowy weather; snowy weather will cause airport lanes to close, route capacity to drop, etc.
## 3 Experimental Setup and Results
### Measurement System  
Figures 7 and 8 show the RF measurement sensor and supervisory part, respectively.  
Figure 7 is the radiometer sensor and Figure 8 is the supervisory system. Sensor nodes used for measurement consisted of four major sub-systems including the RF front end, signal conditioning, microcontroller and the wirelessHART unit. Such sensors are simple and cost-effective and can be deployed for continuous monitoring of PD.  
The explanation about each part of the sensor system is given next.
## 4 Conclusions  
The _SQI_ developed in this study demonstrated its ability to differentiate the quality of soils used in corn cultivation. The PCA greatly simplified the number of indicators assessed, reducing them from 27 to only two. This reduction not only increases the applicability of the _SQI_ in other agricultural areas but also reduces the cost of implementation and facilitates the interpretation of results. The SMR and qCO\({}_{2}\) indicators that comprise the _SQI_ focus on key biological aspects that are fundamental to understanding nutrient cycling, microbial activity, and stress, as well as overall soil health, all of which are related to soil quality. The inclusion of these biological indicators provides the developed _SQI_ with greater sensitivity to detect minor disturbances in agricultural soils due to human activity, compared with _SQIs_ consisting only physicochemical indicators. The usefulness of the developed _SQI_ lies in its ability to provide farmers and decision-makers with a practical tool to take concrete measures to maintain and improve the quality of their soils. The developed _SQI_ can be used to ensure high-quality food production in soils under corn cultivation and similar conditions, both nationally and internationally.  
Conceptualization, E.C.-B.; Data curation, A.S.-G. and H.I.B.-R.; Formal analysis, H.I.B.-R., M.A.L.-H. and F.P.G.-V.; Funding acquisition, D.A.-B.; Investigation, H.I.B.-R. and F.P.G.-V.; Methodology, A.S.-G., H.I.B.-R., M.A.L.-H. and F.P.G.-V.; Project administration, D.A.-B.; Resources, E.C.-B.; Software, A.S.-G. and H.I.B.-R.; Supervision, E.C.-B., M.d.I.L.X.N.-R. and D.A.-B.; Validation, H.I.B.-R.; Writing--original draft, A.S.-G., M.A.L.-H. and D.A.-B.; Writing--review and editing, H.I.B.-R., E.C.-B., M.d.I.L.X.N.-R. and D.A.-B. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Secretaria de Investigacion y Posgrado del IPN (20220196/20231673).  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflicts of interest.
## Materials and Methods
### Study Setting
#### 2.1.3 Socioeconomic Context  
Malawi has significant socioeconomic challenges. The nation ranked 170 th out of 188 in the 2015 Human Development Index [37]. Its population of 18.2 million is rapidly growing, at 2.9% per annum. Furthermore, the economic prosperity of Lake Chilwa gravitates populations and has become one of Africa's most densely populated wetland areas at 321 persons per km\({}^{2}\)[38]. The Phalombe District population of 430,000, of 325 persons per km\({}^{2}\) density (2018 census data), is approximately 98% rural and hosts some of Africa's poorest, with 70-80% below the poverty line and 30-50% ultra-poor [22]. Some 85% of households are subsistent, reliant on smallholdings at perennial risk of dry-season food shortage. Nevertheless, Lake Chilwa has been one of Africa' most productive, but variable fisheries with incomes of fishers exceeding farmers unless the lake dries out [22].  
Land-use change calculations for the Chilwa Basin based on data from [PERSON] et al. [10] reveal decreases of 21% in bare land, 67% in wetland and 51% in forest, but increases of 62% in cultivation and 69% in grassland over the 40-year period of 1973-2013. These accord with observations in the Phalombe District of livestock holdings and forest cover decrease, and cultivation increase, including rice production [22]. Intensified use of stream banks and wetlands for agriculture poses basin-wide erosion risks [24] that continue. Such land-use changes will have significantly perturbed the basin water cycle from around the 1960s onwards.