esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Soil Pesticide Residue Detection Using an Electronic Nose Based on Hybrid Models
## 2 Experimental Materials and Methods
### Gas Chromatography-Mass Spectrometry Detection  
The analysis of volatile components within the samples was conducted utilizing headspace gas chromatography-mass spectrometry (HS-GC/MS). The extraction of volatile organic compounds was achieved through the application of a PDMS/DVB fiber (65 \(\upmu\)m; Supelco, Bellefonte, PA, USA). An Agilent Technologies 5975 gas chromatography-mass spectrometer (Agilent Technologies, Palo Alto, CA, USA) was utilized for analysis, paired with an HP-5 MS-fused silica capillary column (30 m \(\times\) 0.25 mm, 0.25 \(\upmu\)m). The procedure for detection entailed the equilibration of pesticide samples at 60 \({}^{\circ}\)C for 30 min, followed by the extraction of volatile organic compounds using PDMS/DVB fibers (65 \(\upmu\)m; US Supelco) for 20 min at the same temperature. Subsequent to extraction, the fibers were introduced into the GC-MS inlet and desorbed at 250 \({}^{\circ}\)C for 1 min, during which data collection ensued. The separation of volatile compounds was facilitated by an HP-5 MS-fused quartz capillary column (30 m \(\times\) 0.25 mm, 0.25 \(\upmu\)m), employing a specialized temperature schedule: initiating at 40 \({}^{\circ}\)C for 3 min, followed by incrementing up to 280 \({}^{\circ}\)C at a rate of 10 \({}^{\circ}\)C/min, and then being sustained for 15 min. Helium was used as the carrier gas, with a flow rate maintained at 1 mL/min. The mass-selective detector operated with an electron impact potential of 70 eV, a centrifugal source temperature of 230 \({}^{\circ}\)C, a quadrupole temperature of 150 \({}^{\circ}\)C, and a scan range from 20 to 550 Arnu. The identification of compounds was achieved through a comparison with the NIST 2008 library database.
The aforementioned work revealed that although bonding condition is necessary to guarantee seam continuity [22], it is not sufficient to understand the impact of transverse micro cracks on joint mechanical strength and fatigue durability, particularly in the fillet lap joint configuration. This is especially important for battery tray application which needs to satisfy water and gas tightness requirements [30]. The aim of this paper is to experimentally examine the impact of transverse micro cracks on the mechanical strength and fatigue durability of the remote laser welded AA6063 filler lap joints for battery tray assembly. Both spatial beam oscillation and temporal laser power modulation were integrated with the laser welding system, and three key processing parameters: welding speed, beam oscillation frequency and laser power were considered. Distribution and morphology of transverse micro crack were acquired by scanning electron microscope (SEM) on cross-sections. Grain morphology in the weld zone was determined by electron backscatter diffraction (EBSD). Furthermore, mechanical tests including static tensile and dynamic fatigue tests were carried out to evaluate weld mechanical performance and fatigue durability.
# Point-Graph Neural Network Based Novel Visual Positioning System for Indoor Navigation
## 5 Discussion  
Figure 11 depicts the object when the point-GNN object detection learning is not performed based on the size of the object. Although the object was fixed in indoor space, with the size of the object being relatively small and the learning being performed using the improved point-GNN with a mAP of approximately 0.3, the learning was not performed with precision and recall values of 0. This is because when the graph of the improved point-GNN is generated, only a small number of key-points are included in the object. Therefore, the graph learned from the network is not generated with sufficient accuracy. This can be achieved by increasing the number of computations in the applied point cloud. However, as the down-sample and learning parameters of the point cloud were identical to the other objects used, learning of objects with dimensions 30 \(\times\) 30 \(\times\) 30 cm or less was not performed. In addition, our method synchronized the classification learning rate and localization learning rate by setting the class loss weight and localization loss weight parameters for learning of multi-object classes for dynamic positioning system.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**7-Scenes** & **Chess** & **Fire** & **Heads** & **Office** & **Pumpkin** & **RedKitchen** & **Stairs** \\ \hline PoseNet [35] & 0.32 m & 0.47 m & 0.29 m & 0.48 m & 0.47 m & 0.59 m & 0.47 m \\ \hline PoseNet2 [36] & 0.13 m & 0.27 m & 0.17 m & 0.19 m & 0.26 m & 0.23 m & 0.35 m \\ \hline Ours & 0.25 m & 0.24 m & 0.19 m & 0.26 m & 0.28 m & 0.20 m & 0.32 m \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of localization error on the 7-scenes dataset.  
Figure 11: Fixed object with a small indoor space.  
Figure 10: Localization on the 7-Scenes dataset; Chess, Fire, Heads, Office, Pumpkin, Red Kitchen and Stairs.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**VPS Error Distance** & **Air Con-** & **chair** & **Desk** & **Sofa\_01** & **Sofa\_02** & **Flower Table** & **Flower Pot** & **Refrigerator** & **Round Table** & **Podium Table** \\ \hline Ours & 0.18 m & 0.18 m & 0.17 m & 0.20 m & 0.30 m & 0.15 m & 0.35 m & 0.14 m & 0.19 m & 0.20 m \\ \hline \hline \end{tabular}
\end{table}
Table 5: Visual positioning system (VPS) error distance.
# Vehicle Acceleration Prediction Based on Machine Learning Models and Driving Behavior Analysis
## 2 Methodology  
In this paper, a hybrid model for vehicle acceleration prediction is developed, and the process is shown in Figure 1. Firstly, we use MHMM to divide the driving behavior semantics of car-following data. Secondly, a KS test is used to evaluate the similarity of the driving behavior semantics of different drivers. Thirdly, the drivers are grouped according to their degree of similarity. Finally, LSTM and GRU are trained to predict the acceleration of different groups of drivers.
## 4 Discussion
### Mechanism of Tribochemical Oxidation Reaction on the Surface of SiC  
(1) Solid-phase oxidant NaOH  
See Equation (6), the solid-phase oxidant NaOH readily deliquesces in air and reacts with CO\({}_{2}\) to form Na\({}_{2}\)CO\({}_{3}\) and H\({}_{2}\)O [35]. In addition, under the action of friction, the suspended silicon bond in SiC will also undergo the following tribochemical reaction. The main chemical equation is as follows [42; 43]:  
\[\text{Si}+2\text{NaOH}+\text{H}_{2}\text{O}{\rightarrow}\text{Na}_{2}\text{ SiO}_{3}+2\text{H}_{2}\uparrow \tag{16}\]  
The Na\({}_{2}\)SiO\({}_{3}\) produced by the reaction is soluble and can be easily removed from the SiC surface by mechanical action.  
(2) Other solid-phase oxidants  
From Section 3.1, Na\({}_{2}\)CO\({}_{3}\)--1.5 H\({}_{2}\)O\({}_{2}\), KIO\({}_{3}\), KClO\({}_{3}\), and KMnO\({}_{4}\) can produce O\({}_{2}\) by decomposition under the action of friction heat, then the surface of the SiC undergoes a tribochemical oxidation reaction under the action of frictional heat and other media [20; 21].  
\[\text{SiC}+2\text{O}_{2}{\rightarrow}\text{SiO}_{2}+\text{CO}_{2}\uparrow \tag{17}\]  
(3) Tribochemical hydration reaction on SiC surface  
During the polishing process, due to the high flash point temperature at the polishing interface, SiC reacts with H\({}_{2}\)O to produce SiO\({}_{2}\) on the SiC surface. The main chemical reactions are as follows [44; 45; 46; 47]:  
\[\text{SiC}+2\text{H}_{2}\text{O}{\rightarrow}\text{SiO}_{2}+\text{C}+2\text{ H}_{2}\uparrow \tag{18}\]  
\[\text{SiC}+4\text{H}_{2}\text{O}{\rightarrow}\text{SiO}_{2}+\text{CO}_{2}+4 \text{H}_{2}\uparrow \tag{19}\]  
\[\text{SiC}+\text{O}_{2}+\text{H}_{2}\text{O}{\rightarrow}\text{SiO}_{2}+ \text{CO}\uparrow+\text{H}_{2}\uparrow \tag{20}\]  
The flash point temperature during polishing excites the oxidation reaction in Equations (18)-(20). Therefore, the temperature of the test environment is so low that it does not affect the occurrence of the tribochemical reaction and has little effect on friction behavior [48; 49].  
Thus, as described above, the surface of SiC transforms into SiO\({}_{2}\), Na\({}_{2}\)SiO\({}_{3}\), or a surface film composed of SiO\({}_{2}\) and Na\({}_{2}\)SiO\({}_{3}\)[26; 42; 43]. The resulting product, regardless of the state in which the generated product exists, is less hard than SiC. This oxide layer is easily removed using abrasive.
# Optimal Operation of CCHP Smart Distribution Grid with Integration of Renewable Energy
## 7 Results and Discussion
### Scenario 2  
In this scenario, managing both the generation and the demand side is conducted by considering the DR program, which is integrated with a problem as decision variables. In this scenario, the impact of the DR program on the optimal planning of electric, heating, and cooling systems is investigated. The DR of electric loads affects the optimal planning of the heating and cooling system because the electric system supplies the heating and cooling systems with electric energy to fuel some of the heating and cooling generators.  
Figure 12 shows the impact of the DR on the electric load. This figure shows that the peak hour load is shaved and shifted to off-peak hours.  
Figure 11: Hourly cost of electricity, heating, cooling, emissions, and total cost.  
Figure 10: Optimal schedule of the cooling system.  
It can be observed from Figure 15 compared to Figure 6 that the system exports higher electric energy to the main system at hour 13 and sells energy at hour 14 in this scenario, while in scenario 1 it purchases energy from the main grid at this hour. This is because the electric load reaches peak values and the EEOMP has high values; therefore, the load is cut and shifted from these hours to low peak hours, and the CCHP system sells higher power to the main system to reduce the expense and increase the revenue. The SB has the same optimal operation as in scenario 1 with a small change in the charging and discharging energy. The optimal schedule of the cooling system is exactly the same as scenario 1 because this system depends on the electric and heat systems.  
By comparing Figures 8 and 17, it can be observed that the HS is discharged at hours 3 to 5 in this scenario, while it is uncommitted in scenario 1. This is because in this scenario  
Figure 16: Optimal schedule of the heating system with DR.  
Figure 17: Optimal operation of the HS with DR.  
Figure 15: Optimal exchange of electric energy with the utility grid with DR.  
the electric load is recovered; therefore, less electric energy is supplied to the heating system. Figure 18 demonstrates that the CCHP system exports less heat energy to the main heat grid because the electric load is recovered at a high heat load.  
The CCHP systems spend EUR 376.2 when considering the DR technique, whereas they spend EUR 430.1 without considering the DR program during the scheduling day. It is clear that applying the DR program reduces the total operating and environmental damage cost by 12.5% of the total cost. The DR reduces the peak loads, which improves the load factor and the security of the supply and spinning reserve.
# Camera Path Generation for Triangular Mesh Using Toroidal Patches
###### Abstract  
Triangular mesh data structures are principal in computer graphics, serving as the foundation for many 3D models. To effectively utilize these 3D models across diverse industries, it is important to understand the model's overall shape and geometric features thoroughly. In this work, we introduce a novel method for generating camera paths that emphasize the model's local geometric characteristics. This method uses a toroidal patch-based spatial data structure, approximating the mesh's faces within a predetermined tolerance \(\varepsilon\), encapsulating their geometric intricacies. This facilitates the determination of the camera position and gaze path, ensuring the mesh's key characteristics are captured. During the path construction, we create a bounding cylinder for the mesh, project the mesh's faces and associated toroidal patches onto the cylinder's lateral surface, and sequentially select grids of the cylinder containing the highest number of toroidal patches as we traverse the lateral surface. The centers of the selected grids are used as control points for a periodic B-spline curve, which serves as our foundational path. After initial curve generation, we generated camera position and gaze path from the curve by multiplying factors to ensure a uniform camera amplitude. We applied our method to ten triangular mesh models, demonstrating its effectiveness and adaptability across various mesh configurations.  
camera path; bounding volume; toroidal patch; periodic B-spline curve +
Footnote †: journal: Applied  
Article
# Hierarchical Modelling for CO\({}_{2}\) Variation Prediction for HVAC System Operation
###### Abstract  
Residential and industrial buildings are significant consumers of energy, which can be reduced by controlling their respective Heating, Ventilation, and Air Conditioning (HVAC) systems. Demand-based Ventilation (DCV) determines the operational times of ventilation systems that depend on indoor air quality (IAQ) conditions, including CO\({}_{2}\) concentration changes, and the occupants' comfort requirements. The prediction of CO\({}_{2}\) concentration changes can act as a proxy estimator of occupancy changes and provide feedback about the utility of current ventilation controls. This paper proposes a Hierarchical Model for CO\({}_{2}\) Variation Predictions (HMCVP) to accurately predict these variations. The proposed framework addresses two concerns in state-of-the-art implementations. First, the hierarchical structure enables fine-tuning of the produced models, facilitating their transferability to different spatial settings. Second, the formulation incorporates time dependencies, defining the relationship between different IAQ factors. Toward that goal, the HMCVP decouples the variation prediction into two complementary steps. The first step transforms lagged versions of environmental features into image representations to predict the variations' direction. The second step combines the first step's result with environment-specific historical data to predict CO\({}_{2}\) variations. Through the HMCVP, these predictions, which outperformed state-of-the-art approaches, help the ventilation systems in their decision-making processes, reducing energy consumption and carbon-based emissions.  
energy consumption reduction; HVAC systems; CO\({}_{2}\) variations prediction; convolutional neural networks; transfer learning; ensemble learning +
Footnote †: journal:  
020333
# An Admission-Control-Based Dynamic Query Tree Protocol for Fast Moving RFID Tag Identification
## 3 System Model
### Transmission Model  
In this work, we use a frame-slotted model similar to the de facto standard EPC Global C1G2 [16]. In the model, time is divided into multiple frames, and each frame consists of several synchronized slots. The interaction between the reader and tags in each frame is shown in Figure 2, where three types of frames are illustrated, i.e., frames with two, four and eight slots. Here, the number of time slots in a frame is determined by the frame length parameter \(fl\) contained in the \(Query\) command.  
Figure 1: Conveyor-belt-oriented fast-moving RFID system model.  
As shown in Figure 2, each frame begins with the reader's _Query_ command and is followed with \(k\) consecutive slots, and each slot starts with the short _Qrep_ command except for the first slot of a frame. This is because the first slot starts automatically right after the _Query_ command. Each slot has three states, i.e., collision, idle and successful. A collision slot means that two or more tags reply simultaneously in the same slot. In such slots, the reader cannot effectively decode the received signal. In a successful slot, there is only one tag response so that the reader can successfully recognize this tag. Note that we do not consider capture effect as most works in the literature [3; 4; 5; 6; 7] since it has a similar effect on comparable protocols. Finally, an idle slot means that there is no tag response. In each frame, it takes the reader \(t_{Q}\) and \(t_{R}\) time to transmit _Query_ and _Qrep_ commands, respectively. The time for tag response message transmission is \(t_{T}\). According to [16], \(t_{1}\) is the time taken from the reader transmission to the tag response, \(t_{2}\) is the interrogator response time required if a tag is demodulating the interrogator signal, and \(t_{3}\) is the time a reader waits after \(t_{1}\) if there is no tag response.  
Moreover, the structures of commands and tag response messages are given in Table 2. The _Query_ command consists of five components, i.e., head information, length of matching prefix _Lpre_, matching prefix _pre_, number of consecutive colliding bits _cbit_ and CRC-16. The head segment contains the command, address, mask and other information as defined in [16]. The _pre_ and _cbit_ inform tags with the common prefix of the attending tags and the number of slots in the current frame, respectively; \(L_{pre}\) informs tags with the length of _pre_, which guarantees that tags can correctly obtain _pre_ in the _Query_ command. _Qrep_ only contains command information. In the tag response, a 9-bit preamble accompanied by the remaining tag ID is transmitted. Note that we do not use CRC in the tag's response message because the Manchester coding used in tag-to-reader message transmission has the ability to check errors in a bit-wise manner.
# ICN-Oriented Mobility Support Method for Dynamic Allocation of Mobile Data Flows
## 2 Related Work
### Mobility Support in ICN  
We roughly divide the mobility support proposals in ICN into anchor-based proposals and anchorless proposals and discuss them separately.
# A Software-Defined Networking Framework to Provide Dynamic QoS Management in IEEE 802.11 Networks
## Appendix A Minimum Random Variable  
Throughout the analysis it will be often used a random variable defined as the minimum of a set of independent random variables. Therefore, it is convenient to sum up the main formulas related to this random variable.  
Let be the random variable \(D=\min\{U_{1},U_{2},..,U_{n}\}\), where \(U_{i}\) are independent random variables. The density function of \(D\) is:  
\[F(D)=P(D\leq d)=1-P(D>d)\]  
Due to \(D\) is the minimum of \(U_{i}\), all \(U_{i}\) must be necessarily greater than \(d\),  
\[F(D)=1-P((U_{1}>d)\wedge(U_{2}>d)\wedge...\wedge(U_{n}>d))\]Since the random variables \(U_{i}\) are independent each other,  
\[F(D)=1-\prod_{i=1}^{n}P(U_{i}>d)=1-\prod_{i=1}^{n}\left[1-F(U_{i})\right]\]  
If \(U_{i}\) are i.i.d uniform random variables,  
\[P(U_{i})=1/CW_{min}\Rightarrow F(U_{i})=\tfrac{d}{CW_{min}}\quad 1\leq d\leq CW _{min}\]  
Then the density and probability functions of D are,  
\[F(D)=1-\left[1-\frac{d}{CW_{min}}\right]^{n} \tag{1}\] \[P(D=d)=\left[1-\frac{d-1}{CW_{min}}\right]^{n}-\left[1-\frac{d}{ CW_{min}}\right]^{n} \tag{2}\]  
Here, we want to point out two important aspects of the random variable \(D\). First, as it can be foreseen taking into account the definition of the _minimum_, the probabilities are concentrated in the first values of \(d\). A simple way the proof this is by finding the value of \(d_{th}\) that satisfies:  
\[F(D>d_{th})>x \tag{3}\]  
where \(x\) is usually located in \([0.9,1)\). From Equations (1) and (3):  
\[d_{th}>CW_{min}\left(1-\sqrt[n]{(1-x)}\right) \tag{4}\]  
For example, when there are \(n=15\) stations competing for the channel, the 95% of the probability of \(D\) is concentrated approximately in the first 0.39 - \(CW_{min}\) values of \(d\). This means that \(P(D)\) follows a exponential distribution, as can be seen in Equation (2) and Figure 11, that shows \(P(D)\) when \(CW_{min}=32\).  
The second interesting aspect of the _minimum_ function is the complexity. Both, the density and the probability functions depend on the number of competing station \(n\)the backoff \(CW_{min}\). It is not easy to obtain a real-time trace of \(n\) since although the number of playing stations can be assumed fixed, \(n\) represents the number of stations competing for the channel at the same time. On the other hand, as it will be shown later, in a QoS scenario each station can have its own value of \(W\), increasing even more the complexity of the equations.
# Experimental Study on Multi-Cell Counting Using an Inertial Microfluidic Device
## 2 Materials and Methods
### Experimental Variables and Setup  
To comprehensively analyze the sorting mechanism, the following parameters were varied, as shown in Table 2:  
Figure 3: Experimental setup consisting of the syringe pump, microfluidics chip, microscope, and recording mechanism.  
* Microchannel dimensions: Square channels were designed with widths and depths of 150 \(\upmu\)m, 200 \(\upmu\)m, and 250 \(\upmu\)m.
* Particle ratios: Two particle ratios were used: 600:1 for normal conditions and 400:1 for diseased conditions, corresponding to the prevalence of RBC-sized and WBC-sized microparticles measuring 8 \(\upmu\)m and 15 \(\upmu\)m, respectively.
* Flow rates: Flow rates of 1, 5, 10, and 15 \(\upmu\)L/min were employed to assess the impact of flow velocity on particle focusing and counting.
* Optical imaging: The sorting process and particle behavior were monitored using two microscopes: inverted and fluorescent.
# Fourier Singular Values-Based False Data Injection Attack Detection in AC Smart-Grids
### Paper Structure  
The rest of the study is formed as follows: In part 2, the concepts of ISMG and FDIA are introduced briefly. Part 3 explains the suggested FDIA detection scheme and Fourier singular values (FSVs), with detailed statements on the implementation and architecture topics. Section 4 illustrates the cyber-physical model and the results on the tested ISMG with several case studies. Finally, the main conclusion of this research will be presented in Section 5.
# Curve-Localizability-SVM Active Localization Research for Mobile Robots in Outdoor Environments
## 4 Experiments and Results
### Experiments and Analysis
In the four sets of experiments, the state of particles of AMCL 3D algorithm diverged with no signs of convergence within 2 min; the particles of the straight-line-based, entropy-increment-based and curve-localizability-SVM algorithm converged to the wrong poses, and then converged to the correct pose after multiple relocation, but the relocation number of our algorithm is less than other strategy. Considering the impact of the starting point on localization algorithms, we randomly selected other 7 starting points in this scene to test these algorithms. The final results are shown in Table 2.  
From Table 2, we can see that, first of all, after several times of relocation, our algorithm can successfully converge to the correct pose, so the stability of our algorithm is higher. Secondly, compared with AMCL 3D and entropy increment method, our algorithm takes less time and and is more efficient. Because the straight-line method is just to choose the straight-line path between two points, compared with it, our algorithm has no obvious advantage in time cost, but our success rate is higher, this is very important in autonomous navigation.