esa-sceva/satcom-chunk-collection huggingface dataset:
# On the Use of LoRaWAN for Mobile Internet of Things: The Impact of Mobility
## 5 Conclusions  
LoRaWAN, as a member of the LPWAN family, is the most adopted technology for IoT applications; it provides long-range connectivity with a minimal cost in terms of energy consumption. Recent advances in smart connected devices have paved the way for the study of many topics related to LoRaWAN, one of which is mobility and its influence on the performance of LoRaWAN. The work in this paper investigates the effect of mobility on the performance of mobile IoT devices. In addition to the stationary model, the research considers other two popular mobility models--namely, the random waypoint and Gauss-Markov models.  
Through the OMNeT++ network simulator, we extensively evaluated the performance of a mobile LoRaWAN. The experiments were carried out over different network sizes, different numbers of end devices, different mobility speeds, and varying payloads. Our findings indicate that LoRaWAN performs well using both mobility models for intense networks. Moreover, it is also capable of providing good results even with high velocities and larger payloads. The network based on the random waypoint mobility model tends to give better results in terms of the packet delivery ratio, but at the cost of higher collisions and higher energy consumption.  
All of the experiments carried out within this study used the standard network-based ADR mechanism. However, the standard ADR strategy is meant to be more suitable for static networks, due to the unpredictable channel attenuations caused by device movements. Therefore, for future works, different ADR strategies such as blind ADR should be used to investigate the performance of LoRaWAN for mobile applications.  
This research received no external funding.  
Not applicable.  
The author declares no conflict of interest.
# Simplified Derivative-Based Carrierless PPM Using VCO and Monostable Multivibrator
## 5 Experiments and Results
### PPM Signal Synthesis Through Signal Processing  
Building upon the PWM foundation, the PPM signal synthesis employs sophisticated mathematical operations to convert width-modulated information into position-modulated pulses. The synthesis process involves the strategic manipulation of delayed and non-delayed PWM signals, where the temporal relationship between these components determines the final PPM characteristics. The implementation code for this section can be found in Code S2 of the Supplementary Materials.  
Figure 13: MATLAB simulation results demonstrating PWM signal generation and demodulation performance. (**a**) Comparison between the original information signal (smooth curve) and the generated PWM signal (pulse train), showing the direct relationship between signal amplitude and pulse width; (**b**) PWM signal input and successfully demodulated output signal, demonstrating effective signal recovery with minimal distortion.  
Figure 14 presents the comprehensive results of this synthesis approach, demonstrating the subtraction process between delayed and non-delayed PWM signals. The simulation results reveal a critical insight: as higher harmonic components are incorporated into the synthesis process, the resulting PPM signal increasingly approaches the ideal theoretical response. This observation highlights the importance of harmonic content in achieving optimal PPM signal quality and suggests that bandwidth considerations play a crucial role in practical PPM system design.
# Representing and Validating Cultural Heritage Knowledge Graphs in CIDOC-CRM Ontology
## 4 Analysis and Validation
* For example, if the COURAGE P1 property should be translated into CRM P2, then for all facts using the P1 property, the existence of corresponding facts using the P2 property can be checked. In certain cases, the non-existence of other facts using P2 can also be checked. For example, if a collection is located in Budapest only according to COURAGE data, then in its CIDOC CRM translation the same collection should not have other locations either.
## 2 Engineering Overview  
### Layout of Building Monitoring Points  
Shanghai Rail Transit Line 15 is a north-south radial line in the western part of Shanghai, passing through five administrative districts including Minhang District, Xuhui District, Changing District, Putuo District, and Baoshan District. Guilin Road Stationis located in Xuhui District and is surrounded by complex buildings and pipelines. The station is an underground three-story island-type platform station, station center mileage SK20+511.173, the total length of the station body is 486.37 m, the net width of the station center is 22.84 m, and the width of the effective platform is 14 m. The planned ground elevation is +4.06 m, the soil overlay at the center of the platform is about 4.31 m, and the elevation of the rail surface in the center of the station is \(-\)19.42 m. the main body of the station is a concrete box-type structure cast in situ, of which axes 1-48 are a double-column three-span structure, axes 48-56 are a column-free single-span structure, and axes 56-58 are a single-column double-span structure. The main body of the station is a cast-in-place concrete box structure, of which 1-48 are a double-column three-span structure, axes 48-56 are a column-free single-span structure, and axes 56-58 are a single-column double-span structure. The subway station pit is a long pit with an average depth of about 25.8 m. It is divided into six zones from A to F. Among them, A, C, and E are constructed by the open-cut method, and B, D, and F are constructed by the total reverse method. There are three main buildings around Area A (denoted by I, J, K) and two main buildings around Area F (denoted by P, Q). The arrangement of monitoring points of each building is shown in Figure 1.
# Algebraic Point Projection for Immersed Boundary Analysis on Low Degree NURBS Curves and Surfaces
## 5 Results and Discussion
### Curve Tests  
The first curve example is illustrated in Figure 16, where physical points in the underlying domain are projected onto a given NURBS curve using both Newton-Raphson iterations as well as the algebraic point projection. Contour levels indicate the value of parameter \(u_{f}^{N}\) of the predicted footpoint.  
Figure 15: Flowchart for execution of algebraic point projection.  
As can be observed in Figure 16, both first- and second-order algebraic point projection provide exact on-curve solutions and good approximate solutions to the parameter values from points near the curve. In addition, one may observe that there are two regions, at the bottom-left and upper-right of Figure 15(a), respectively, where due to high curvature of nearby curve segments, a jump in parameter value occurs when Newton-Raphson iterations are used. Such discontinuities disappear when algebraic point projection is used. The relationship between distance from the curve and relative error is obtained for both first- and second-order algebraic point projection methods using the Newton-Raphson method as a reference (Figure 17). The test points are picked along an arbitrary vertical trace line in Figure 16. The parameter range of the NURBS curve is \([0,1]\) in the example. As the test point moves closer to the target curve, the projection error decreases quadratically (first-order point projection) and cubically (second-order point projection).  
The second curve example shown in Figure 18 demonstrates the robustness of the algebraic point projection when the footpoint is either discontinuous or non-existent. One can observe in Figure 17(b) that, although Points A and B are continuous in terms of the parametric value on the tracing curve, the Newton-Raphson method results in a large jump in parametric solution \(A_{f}^{N}\) and \(B_{f}^{N}\), whereas the algebraic method yields a continuous parameter value. In general, the algebraic projected solution is smoother and matches the Newton-Raphson solution well, and second-order point projection is more accurate than first-order point projection.  
Figure 16: Parameter values of footpoints obtained using: (**a**) Newton–Raphson method; (**b**) first-order algebraic point projection; and (**c**) second-order algebraic point projection. Parameter range of NURBS curve is \([0,1]\).
## 3 Conventional Geolocation Techniques
### Hybrid Technique of Geolocation Measurements  
The hybrid geolocation technique is designed to obtain a more precise estimation of the target's location and reduce the number of sensors needed compared to a particular technique. In the state-of-the-art, various hybrid techniques have been used in geolocation applications, for example, (TDOA/AOA), (TDOA/FDOA), and (TDOA/PDOA) [59; 60; 61; 62; 63].
## 2 Materials and Methods
### Materials  
#### 2.2.1 Satellite Data  
Two scenes of orbital images from the PlanetScope Instruments mission, from 2018 and 2023, were used, both acquired in October, a period of low rainfall in the region [49]. The analysis of the Caatinga's vegetation cover is more effective during the dry season, as the vegetation is sensitive to minimal moisture, which can cause false positives during rainy periods [45; 50]. The PlanetScope constellation, managed by Planet Labs, consists of over 130 satellites that offer daily global coverage [51]. The images provided by PlanetScope  
Figure 2: Map of the study area. The investigated region, designated for gold mining activities, is outlined by a red rectangle, and encompasses 459.33 km\({}^{2}\), including a 6 km buffer zone around the Serrita-Cedro Project. The boundary of the Serrita-Cedro Project, part of the National Program for the Study of Mining Districts conducted by the National Department of Mineral Production (DNPM), is indicated by a yellow line. The area is located in the semi-arid region of Pernambuco, Brazil.  
(PS) include four spectral bands: blue, green, red, and near-infrared (NIR), with a spatial resolution of 3 m and a radiometric resolution of 16 bits [52]. The images from PSB.SD and PS2 sensors used in this study, along with their characteristics and acquisition dates ([[https://www.planet.com/](https://www.planet.com/)]([https://www.planet.com/](https://www.planet.com/)), accessed on 4 March 2024), are presented in Table 1.  
[PERSON] et al. [53], using the kNN model with PlanetScope images to map native and invasive species distributions in two forest reserves in Pakistan, highlighted the images' good performance in identifying targets compared to the Sentinel-2 MSI sensor.
## 1 Introduction
Simulation results show that the proposed algorithm can achieve a higher performance compared to the other benchmark algorithms in terms of improving system rate. The rest of this paper is organized as follows. In Section 2, we introduce the related works. The system model is presented in Section 3. In Section 4, we formulate the joint user access and resource allocation problem, and then propose a matching-iteration-based algorithm in Section 5. In Section 6, we analyze the properties of the proposed algorithm including convergence, stability, optimality and complexity. Simulation results are provided in Section 7. Finally, we conclude the paper in Section 8.
# Passive Sampling and Analysis of Naphthalene
## Experiments
### Effects of Storage Time and Temperature on Analyte Mass Loss  
Analyte losses were studied for a practical application scenario for a sampling of gas exhaust in the field, cold storage and transportation, and time lag between sampling and analysis in the lab. The following parameters were used: 100 \(\upmu\)m PDMS (\(n\) = 3), \(Z\) = 10 mm, \(C_{g}\) = 2.0 ppm, storage temperature = 23 \({}^{\circ}\)C and 0 \({}^{\circ}\)C. Storage time = 0 (immediate injection), 15, 30, 45 and 60 min, respectively.  
Effects of \(t\), \(C_{g}\), \(Z\) and Adsorption by Metallic SPME Assembly: Verification of Fick's Law Model for TWA-SPME Gas Sampling of Naphthalene  
Fick's Law model for passive sampling with retracted SPME fiber was tested. Effects of several operating parameters for practical gas sampling were tested. These include \(t\), \(C_{g}\), and \(Z\). For the experiments testing the effect of \(t\), the following parameters were used: \(t\) = 30, 60, 180, 540, 1440 and 2880 min, while others were kept constant, i.e., \(Z\) = 10 mm, \(C_{g}\) = 2.0 ppm. Set of \(n\) = 3100 \(\upmu\)m PDMS fibers and a set of \"broken\" fibers (i.e., for testing of the adsorption on metallic parts) of the 100 \(\upmu\)m PDMS type was used. There was no statistical difference between the performance of the SPME fibers or metal assemblies as determined by a t-test. For the experiments testing the effect of retraction depth \(Z\), the following parameters were used: \(Z\) = 7, 10 and 20 mm while \(t\) = 9 h, \(T\) = 40 \({}^{\circ}\)C, \(C_{g}\) = 5.9 mg\(\cdot\)m\({}^{-3}\) (2.0 ppm) were kept constant. For the experiments testing the effect of concentration, i.e., calibrations of SPME and direct injection method, the following parameters were used: \(C_{g}\) for naphthalene ranged from 0.1 to 12 ppm while other parameters were kept constant: i.e., \(Z\) = 10 mm retracted fiber, \(t\) = 9 h, \(T\) = 40 \({}^{\circ}\)C.  
Effect of temperature was not tested. It was assumed that the effect of \(T\) would be insignificant, similarly to our previous work [30] on TWA-SPME and BTEX. Increasing \(T\) slightly increases the \(D_{g}\) while the effect is offset by lower analyte partitioning to SPME coating at higher \(T\)[43]. Compounds with lower molecular weight (BTEX) were affected by the effects of \(T\) more those with greater molecular weight (naphthalene).
## 5 TSN Scheduling Problem Formulation
### TSN Scheduling Problem Formulation Assumptions
#### 5.3.2 Assumptions on the Queues  
The management of the queues, although not part of the shaping, is very crucial for the problem formulation. Different works try to reduce the complexity of the scheduling problem by assuming a specific queue setup. For example, [11, 36, 38, 68] attributes the highest priority to the HTS queues. As the mapping between the traffic classes and priority queues is configurable, assigning the highest priority to HTS queues is a reasonable choice. Other works made different assumptions on the queues, such as fixing the number of HTS queues to one (as in [46, 47, 49, 57, 79, 122]) or preventing the opening of more than one queue gates at the same time (as in [50]). Assuming the highest priority for the HTS queue could be used to prioritize HTS traffic when multiple queues are eligible for transmission. However, limiting the number of queues with an open gate at the time to one marginalizes the priority of the queues since only one would be always eligible for transmission. Therefore, combining the former two assumptions is not beneficial.
## 3 Methodology
### Parcel-Based Time-Series Features  
Multi-temporal processed Sentinel-2 images (including spectral bands and derived vegetation indices) were overlaid onto parcel maps of farmland to generate parcel-based time series. Spectral values were averaged over the bounds of parcel geometry. In Sentinel-2 bands, pixels within a parcel polygon were first searched. Then, the average spectral value of these pixels was taken as the feature value of this parcel. When parcels were entirely covered by clouds and shadows, their features were assigned the default masking value of 0. When parcels were partially covered, the spectral values of clean pixels were averaged. Finally, for each parcel, a feature vector \(X\in R^{D\times T}\) was generated, where \(D\) is the number of spectral bands, and \(T\) is the number of satellite observations.
# The Car-Following Model and Its Applications in the V2X Environment: A Historical Review
## 3 Research on Car-Following Behavior in the V2X Environment
### Modelling the Car-Following Behavior
#### 3.2.4. Electronic Throttle Open Angle Information
introduced the ETOA information of the preceding vehicle into the BLFVD model and established an extended model considering multiple kinds of information of the preceding vehicle and the Back Looking Effect [67]. In the same period, [PERSON] et al. incorporated the ETOA information of multiple preceding vehicles to extend the FVD model [68]. Recently, [PERSON] et al. further introduced the mean headway of multiple preceding vehicles into the T-FVD model and proposed a new extended model [69]. The results of the above works reveal that the ETOA information can further optimize the car-following behavior along with other kinds of motion state information, which would contribute to the operation of traffic flow. With the increase in correlation coefficients, the positive influence of ETOA information will increase accordingly. Although parts of vehicles are no longer equipped with throttle due to the replacement of the internal combustion engine, the ETOA information can still be applied in the future. This is because the vehicles with or without the internal combustion engine all have the accelerator pedal, and the ETOA information or equivalent can be collected through CAN or other vehicle control units. Considering this, the ETOA information will contribute to controlling and optimizing the vehicle motion and should be considered in modeling the car-following behavior in the V2X environment.
# Improved Anti-Collision Algorithm for the Application on Intelligent Warehouse
## 3 Group Mapping Query Tree Algorithm
### Protocol Description  
Based on the grouping strategy, we divide tags into four groups. The reader queries tags and receives ID and group number information from tags, infers possible groups according to Manchester code of the mapped group number, and then queries the possible group numbers in turn.  
The flow chart of GMQT algorithm is shown in Figure 2. In addition, the procedures of this GMQT algorithm are as follows:  
Step1: Initialization, the reader sets the query prefix stack PS and group number stack GS to be empty, and these stacks follow the first in first out principle.  
Step2: The reader sends the command QUERY(prefix), and the query prefix is \(\varepsilon\) when the command is first sent, that is, all the tags within the reading range respond to the reader.  
Step3: If there is no tag response, it means idle slot, and jump to Step 10; otherwise, jump to Step4.  
Step4: If there is no collision, the reader sends a ACK command to identify the tag and puts it to sleep, then jump to Step10; otherwise, jump to Step5.  
Step5: The reader receives decoded data 'pcode' of tags and mapping data 'gcode' of group, assuming that the structure of pcode is 'p\({}_{1}\)p\({}_{2}\)...p\({}_{c-1}\)p\({}_{c}\)p\({}_{c+1}\)...', p\({}_{i}\)\(\in\)0,1, and q\({}_{c}\) is the first collision bit.  
* If c \(>\) 3, set p\({}_{c}\) to '0' and '1', respectively, and PUSH(PS, prefix \(\mid\)  p1p2... Pc), then jump to Step10.
* If there is only one collision bit in p\({}_{1}\)p\({}_{2}\)p\({}_{3}\), set the collision bit to '0' and '1' respectively, and PUSH(PS, prefix \(\mid\)  p1p2p3); then, jump to Step10.
* Predict the possible group number group\({}_{1}\)...group\({}_{n}\), and PUSH(GS,group\({}_{1}\))...PUSH(GS,group\({}_{n}\)) in turn, and then jump to Step6.  
Step6: group = POP(GS), and the reader sends command QUERY(prefix, group).  
Step7: If only one tag responds, the reader identifies the tag directly, and jump to Step9. Otherwise, jump to Step8.  
Step8: According to the decode data the reader receives and the group, the reader can infer the bit string combinations as bc based on the rule of grouping, and PUSH(PS, prefix \(\mid\) |bc).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Group Number** & **Mapping Code** \\ \hline
00 & 0001 \\
01 & 0010 \\
10 & 0100 \\
11 & 1000 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Mapping rule.  
Step9: When GS is empty, it means that all groups have been queried; then, jump to Step10. Otherwise, jump to Step6.  
Step10: When PS is empty, it means that all tags have been identified. Otherwise, set prefix = POP(PS), and jump to Step2.
###### Abstract  
Cockle shells are a natural reservoir of calcium carbonate (CaCO\({}_{3}\)), which is widely used in bone repair, tissue scaffolds, and the development of advanced drug delivery systems. Although many studies report on the preparation of CaCO\({}_{3}\), the development of a nanosized spherical CaCO\({}_{3}\) precursor for calcium oxide (CaO) that is suitable to be incorporated in dental material was scarce. Therefore, this study aimed to synthesize a nanosized spherical CaCO\({}_{3}\) precursor for CaO derived from cockle shells using a sol-gel method. Cockle shells were crushed to powder form and mixed with hydrochloric acid, forming calcium chloride (CaCl\({}_{2}\)). Potassium carbonate (K\({}_{2}\)CO\({}_{3}\)) was then fed to the diluted CaCl\({}_{2}\) to obtain CaCO\({}_{3}\). The effect of experimental parameters on the morphology of CaCO\({}_{3}\), such as volume of water, type of solvents, feeding rate of K\({}_{2}\)CO\({}_{3}\), and drying method, were investigated using field-emission scanning electron microscopy, energy-dispersive X-ray spectroscopy, X-ray diffractometry (XRD), Brunauer-Emmett-Teller surface area analysis, and thermogravimetric analysis. Optimized CaCO\({}_{3}\) was then calcined to form CaO. XRD analysis of CaCO\({}_{3}\) nanoparticles was indicative of the formation of a calcite phase. The well-structured spherical shape of CaCO\({}_{3}\) was obtained by the optimum condition of the addition of 50 mL of water into CaCl\({}_{2}\) in ethanolic solution with a 1 h feeding rate of K\({}_{2}\)CO\({}_{3}\). Less agglomeration of CaCO\({}_{3}\) was obtained using a freeze-drying technique with the surface area of 26 m\({}^{2}\)/g and average particle size of 39 nm. Spherical shaped nanosized CaO (22-70 nm) was also synthesized. The reproducibility, low cost, and simplicity of the method suggest its potential applications in the large-scale synthesis of the nanoparticles, with spherical morphology in an industrial setting.  
calcium carbonate; calcium oxide; cockle shell; sol-gel method; morphological images; nanoparticles +
Footnote †: journal: Applied Soft. 2020, 10, 7170; doi:10.3390/app10207170
# Principal Subspace of Dynamic Functional Connectivity for Diagnosis of Autism Spectrum Disorder
## 3 Material and Method
### Functional Connectivity (FC) of BOLD Time-Series Signals
#### 3.2.1 Static FC Using Pearson Correlation Coefficients (PCC)  
For static FC (SFC), the use of Pearson correlation coefficients (PCCs) of BOLD signals is a common linear method for determining functional connectivity between different areas of the brain and provides useful information about brain activity [17; 18]. Calculating the PCC between two BOLD signals involves finding their covariance (cov) and dividing by the product of their standard deviations. If \(x(t)\) and \(y(t)\) represent the BOLD signals of two nodes, the PCC of the two nodes is  
\[\beta_{x,y}=\frac{cov(x,y)}{\sigma_{x}\sigma_{y}}, \tag{1}\]  
where for \(x(t)\) and \(y(t)\), the standard deviation is represented by \(\sigma_{x}\) and \(\sigma_{y}\), respectively. By subtracting the mean value of the BOLD signals and determining the expected value, the covariance can be calculated as follows  
\[cov(x,y)=E[(x-\mu_{x})(y-\mu_{y})], \tag{2}\]  
where \(E[.]\) denotes the expected value, and \(\mu_{x}\) and \(\mu_{y}\) denote the mean of \(x(t)\) and \(y(t)\), respectively.