esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
In this context, a dry deposition measurement network across the Mediterranean (XMed-Dry) has been recently established [27]. In this network, a set of seven newly developed dry deposition-only collectors has been installed at different locations across the Mediterranean (Huelva, Barcelona, Spain; Ile-Rousse, France; Gozo Island, Malta; Lecce, Italy; Athens, Greece; Nicosia, Cyprus) to capture spatial and temporal variability of atmospheric aerosols including their size and composition [28]. In the context of this study, deposition data from three XMed-Dry stations--namely: Lecce, Athens, and Malta have been utilized to validate the WRF-Chem model. It is utilized to analyze an intense Saharan dust outbreak event affecting the Italian peninsula in 15 and 16 April 2018. This intrusion was characterized by an intense aerosol optical depth (AOD) peak value in the southern Mediterranean, according to the MODIS retrievals. The WRF-Chem model, that actually implements two different dust emission schemes, namely the GOCART-Air Force Weather Agency (AFWA; [29]) and the University of Cologne (UoC; [30]), will be tested and compared with multiplatform observations, for a simulation period covering the dust outbreak in April, 2018. Generally, analysis shows that the AFWA emission scheme significantly over-predicts the emission of mineral dust, while the UoC parameterization gives overall better results in terms of optical properties and total mass. It is important to remark that rather than discovering new dust-emission parameterizations we are mainly interested in finding the optimal WRF-Chem configuration in term of physics and chemistry setup. We partially did this in a previous work [25] were we tested the UoC dust emission scheme using two different WRF land surface schemes and found very different results only by changing the surface land model. The paper outline is the following: Section 2 describes materials and methods utilized for this study; Section 3 depicts results and the relative analysis; and finally, in Section 4, the conclusions of the study are drawn.
## 5 Discussion
As such, the introduction of erroneous labels to these types of images confused the DNNs and caused a significant efficiency drop. Notably, this behavior was observed for each of the tested DNN architectures, as demonstrated by the faded region around the IDN curve for the MNIST dataset (see Figure 6). The GTSRB database produces distinctive behavior, with the performance results for all algorithms close to 100% (see Figure 8). The application of noise of any type or level had no significant statistical effect on DNN efficiency. We hypothesize that this effect has two sources. The first is the ease with which each of the architectures can classify well-defined images, such as traffic signs. The impact of sign scaling and background variation does not pose a substantial problem for current DNN architectures. The second presumed source is highlighted by the ineffectiveness of noise on classification performance. This suggests that the database itself is highly robust and redundant. Thus, the presented analysis can be considered as a means of evaluating databases for their robustness against intrinsic label noise. The graphs presented in Figure 6 also show that UNI and CDN noise result in very similar misclassification rates. The majority of the literature concerning label noise uses CDN noise and suggests that UNI noise is both unrealistic and easier for DNNs to handle. Our experiments prove that both noise types have similar effects on final classification. Only IDN noise introduces a qualitatively different degree of difficulty. This effect is less apparent for the CIFAR10 and CIFAR100 databases and more apparent for the MNIST database. The graphs shown in Figures 7 and 8, in addition to the AUC results presented in Table 3, confirm that the proposed MultiNET algorithm achieves significantly better mislabel detection performance than the original CL method. For the CIFAR10 and CIFAR100 databases, a clear improvement can be observed in the precision-recall relationship for all noise types when a combination of DNNs was utilized. The use of the MNIST database produces the smallest increase in detection efficiency. This is due to the inherent high classification performance of DNNs when applied to simple written digits. Nevertheless, the improvement is still noticeable. The improvement in detection performance when using the GTSRB database could not be measured due to the 100% detection precision in all noise scenarios. In addition to improved performance when using multiple DNN architectures, the introduction of the \(q\)-quantile value allows for smooth movement along the precision-recall curve. This presents a variety of mislabeling detection strategies. It is possible to optimize either precision, recall, or a combination of both, according to the needs of the user.
## 3 Models
### The LCPSO Algorithm
#### 3.4.2 LCPSO, a CPSO Variant to Deal with Some Real-World Constraints  
First, to reflect limitations in communication links, a local communication constraint is added to the model. Indeed, the best tracker position of the swarm \(\mathbf{p}^{s}(t)\) in Equation (9) is global, shared with each agent of the swarm. We use the local-best vector position \(\mathbf{p}_{i}^{l}(t)\) (1 for local), which is the position of tracker \(j\) in the neighborhood set of \(i\) where measure\(f(\mathbf{p}_{j},t)\) is the greatest. The neighborhood set \(\mathcal{N}_{i}(t)\) is based on parameter \(r_{com}\) which denotes the maximum communication range between tracker \(i\) and its neighbors [12]. Beyond maximum communication range \(r_{com}\) data transmission is impossible, below \(r_{com}\) transmission is perfect :  
\[\mathcal{N}_{i}(t)=\{j|\big{\|}\mathbf{p}_{i}(t)-\mathbf{p}_{j}(t)\big{\|}<r_{ com},i\
eq j\}\subset\{1,\cdots,N\}. \tag{10}\]  
This decentralization was already proposed by [14; 15] but to the best of our knowledge it was never used in the aim of mobile target tracking. Each vehicle will have its own local best position and will move towards its best neighbor.  
Second, the best historical position \(\mathbf{p}_{i}^{b}(t)\) is removed in the proposed approach. This is because the target is not static: it changes position with time.  
Finally, to obtain a stable swarm formation, we set our random component \(\alpha_{i2}(t)\) to \(1\). The analysis of the algorithm with random components can be the object of future work. Those considerations lead to the following model, originally introduced in [40], and which we named LCPSO:  
\[\mathbf{v}_{i}(t+\Delta t)= c_{0}\mathbf{v}_{i}(t)+c_{2}\big{(}\mathbf{p}_{i}^{l}(t)- \mathbf{p}_{i}(t)\big{)}-c_{3}\sum_{j\in\mathcal{N}_{i}(t)}\
abla_{\mathbf{p} _{i}}P^{r}\big{(}d_{i,j}(t)\big{)}. \tag{11}\]  
From Equation (11), we can deduce the potential function of LCPSO as follows:  
\[P_{i}(t)=P^{a}\big{(}\big{\|}\mathbf{p}_{i}^{l}(t)-\mathbf{p}_{i}(t)\big{\|} \big{)}-\sum_{j\in\mathcal{N}_{i}(t)}P^{r}\big{(}d_{i,j}(t)\big{)} \tag{12}\]  
This potential meets Definition 1, and is illustrated in Figure 1, with the corresponding attractive (7) and repulsive potentials (8) used for the LCPSO. The Euler integration scheme is the same as in Equation (3).
# A Study of the Thermodynamic Properties of Nd-C and Ce-C TRISO Fission Product Binary Systems
## 2 Materials and Methods
### Differential Scanning Calorimetry  
The Netzsch 449 F1 Jupiter Simultaneous Thermal Analyzer (STA) was employed (sourced from Netzsch, Bavaria, Germany), as it can perform multiple types of analysis, including ThermoGravimetric Analysis (TGA) and DSC. For this work, the STA was used in DSC-TGA mode to perform the experimental measurements, similar to other experimental works completed at Ontario Tech University [44; 45; 46]. The SiC furnace of the STA was employed, permitting a peak measurement temperature of 1550 \({}^{\circ}\)C but maximum setpoint temperature of 1600 \({}^{\circ}\)C with a continuous flow of 99.999% Ar gas at 20 mL/min passed over an Oxygen Trap System (OTS) composed of a zirconium oxygen-getter ring and alumina support tube (sourced from Netzsch product no. HTP40000A97.010-00, Bavaria, Germany). Measurements were performed using an alumina sample carrier, Type S TGA-DSC thermocouple, with a Pt/Rh head (sourced from Netzsch product no. HTP40000A54.000-00, Bavaria, Germany), having a mass balance resolution of 25 ng and a maximum sample weight of 5 g including the two DSC crucibles [47]. A photograph of the DSC setup is shown in Figure 7, and a schematic showing the layout of the thermocouple is shown in Figure 8. A dissimilar metal junction is under each crucible and wired together, composing the thermocouple assembly, as shown in blue.  
Prior experimentation has shown that below 800 \({}^{\circ}\)C, the OTS was ineffective at reducing oxygen concentration [44; 45], as measured with a Zirox ZR 5 oxygen probe (sourced from Zirox, Greifswald, Germany). Despite this observation, residual oxygen remained at approximately 180 ppm within this temperature range which is favorable to prevent oxygen ingress during the low-temperature calibrant testing. Additionally, mass changes during each experiment were found to be negligible, with mass changes increasing up to 5% at the peak temperature of a heating cycle likely due to buoyancy effects, then returning to nominal at the end of the cycle and nominal at the end of the overall DSC experiment. This indicated that oxidation during testing was unlikely to have occurred.  
Figure 7: STA 449 F1 Jupiter STA setup with OTS ring, alumina support tube, alumina sample carrier, Type S TGA-DSC thermocouple with a Pt/Rh head, and boron nitride measurement and reference crucibles.
## 6 ADRC's ESO, NLSEF, and Disturbance Compensation  
### Eso  
According to the ADRC law, Equation (18) is rewritten as follows:  
\[\left\{\begin{array}{l}\frac{\partial^{2}y_{y}}{\partial t^{2}}=b_{y}i_{cl}+ w_{y}(t)\\ \frac{\partial^{2}e_{y}}{\partial t^{2}}=b_{z}i_{cr}+w_{z1}(t)\end{array}\right. \tag{61}\]  
where \(w_{y}(t)\) and \(w_{z1}(t)\) are the equivalent disturbances of the system, including coupling and other disturbances. \(i_{cl}\) and \(i_{cr}\) are the control variables. In Equation (61), \(b_{y}\) and \(b_{z}\) are the control parameters of the control variables \(i_{cl}\) and \(i_{cr}\), respectively.  
From this, the current variations of the four horizontal control coils can be obtained. The ADRC can regard \(w_{y}(t)\) and \(w_{z1}(t)\) as the system's comprehensive disturbance and observe and compensate for them through its ESO. The control variables are selected as follows:  
\[\left\{\begin{array}{l}i_{cl}=\frac{1}{b_{y}}[i_{cl}^{*}-w_{y}(t)]\\ i_{cr}=\frac{1}{b_{z}}[i_{cr}^{*}-w_{z1}(t)]\end{array}\right. \tag{62}\]  
After compensation, Equation (20) can be rewritten as follows:  
\[\left\{\begin{array}{l}\frac{\partial^{2}y_{y}}{\partial t^{2}}=i_{cl}^{*} \\ \frac{\partial^{2}e_{y}}{\partial t^{2}}=i_{cr}^{*}\end{array}\right. \tag{63}\]  
After compensation, \(y_{b}\) and \(\psi_{z}\) are decoupled and controllable. Taking \(y_{b}\) as an example, it can be seen from Equation (63) that the system controls \(y_{b}\) through \(i_{cl}\). InEquation (63), after parameter adjustment, \(i_{cl}\) becomes part of the equivalent disturbance of \(w_{z1}(t)\) and is then observed and compensated by the ESO.  
Similarly, when controlling the other degree of freedom \(\psi_{z}\), \(i_{cr}\) will also cause changes in \(y_{b}\). In Equation (63), after parameter adjustment, \(i_{cr}\) becomes part of the equivalent disturbance of \(w_{y}(t)\) and is then observed and compensated by the ESO. The ESO of the HCS of the MAR system is as follows:  
\[\left\{\begin{array}{l}e(m)=z_{1}(m)-y(m)\\ z_{1}(m+1)=z_{1}(m)+h[z_{2}(m)-\beta_{c1}e(m)]\\ z_{2}(m+1)=z_{2}(m)+h\{z_{3}(m)-\beta_{c2}fal[e(m),0.5,\delta]+\,b_{0}u(m)\}\\ z_{3}(m+1)=z_{3}(m)-h\beta_{c3}fal[e(m),0.25,\delta]\end{array}\right. \tag{64}\]  
where:  
\[fal(e,a_{1},\delta)=\left\{\begin{array}{l}|e|^{a_{1}}sign(e)\quad|e|>\delta \\ \frac{e}{\delta^{3-a_{1}}}\quad|e|\leq\delta\end{array}\right.\]  
where \(z_{1}(m)\), \(z_{2}(m)\) and \(z_{3}(m)\) are the state variables of the ESO. \(z_{1}(m)\) and \(z_{2}(m)\) are used to track the state variables of the system, and \(z_{3}(m)\) is used to track the state variable of the total disturbance. \(\beta_{c1}\), \(\beta_{c2}\), and \(\beta_{c3}\) are the gains of the ESO. \(a_{1}\) and \(\delta\) are undetermined parameters. If \(a_{1}\) is less than 1, the function has nonlinear characteristics such as small gain for large errors and large gain for small errors. \(\delta\) represents the linear range, aiming to avoid oscillation caused by large gain for extremely small errors.
## 4 Results & Discussion
The experimental scenario specifically evaluated an 180,000 ton bulk carrier under Sea State 5 conditions (wave height 2.5-4.0 m, wind force 5). Comparative analysis revealed that under milder conditions--Sea State 1 (wave height 0-0.1 m, wind force 1) with equivalent angular hull motion below 0.4\({}^{\circ}\)/s and oscillation periods exceeding 10 s and Sea State 2 (wave height 0.1-0.5 m, wind force 2) with angular motion under 2\({}^{\circ}\)/s--the scanning errors would diminish substantially due to reduced disturbance amplitudes and frequencies relative to Sea State 5. Conversely, harsher conditions beyond Sea State 5 would exacerbate scanning errors, though system performance would remain adequate for typical operational maritime environments. Practically, the Fuzzy PID's efficacy fundamentally depends on the construction of its fuzzy inference rule base. Notably, the computational load increases exponentially with rule base expansion, potentially exceeding the real-time processing capabilities of marine-grade embedded processors like the STM32F4 series MCU. Frequent parameter updates further intensify computational demands, necessitating careful trade-offs between control precision and processing latency. Implementation strategies should incorporate hierarchical inference architectures to distribute computational burdens while considering upgraded processor specifications (e.g., Cortex-R series or FPGA hybrids) to maintain deterministic performance under high-frequency disturbance spectra. This balance becomes crucial when addressing the sub-millisecond control cycles required for precision photoelectric stabilization in dynamic marine environments.
# Detection of Volatile Organic Compounds (VOCs) in Livestock Houses Based on Electronic Nose
## 2 Materials and Methods
### Data Processing  
The change trend of sensor response is related to the composition and concentration of odor. The response curve of electronic nose sensors are shown in Figure 2 (pig farm gas as an example). The collection time of the sensor was 80 s. Except for sensor TGS2602, other sensors tend to be stable after exposing to the sample gas for a period of time. The maximum and average values are important information of sensor response, which are the most used as feature in electronic nose analysis [24; 25]. So, the maximum and average values were used as the feature of the sensor response in this paper. First, the original data and mark the serial number were classified. MATLAB software was used to acquire two feature value sets, which are the maximum feature value set and the average feature value set. The features in each feature set are features composed of 7 sensors.  
Pattern recognition was based on the characteristics of the research object, using statistical methods to process and analyze the information that characterizes things or phenomena, to describe, classify and explain the things or phenomena under study [26]. The pattern recognition method has a great impact on the performance of the electronic nose and is a very important part of the electronic nose. In this paper, LDA, KNN and SVM are used to classify the gas in the livestock farms, and the most suitable pattern recognition method for the VOC gas in the livestock farms was obtained by comparing the recognition rates.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Sensor Model** & **Main Sensitive Substances** & **Sensitive Range** \\ \hline TGS826 & Ammonia & 30–300 ppm \\ TGS2611 & Methane, Natural gas & 500–10,000 ppm \\ TGS2603 & Trimethylamine, Methyl mercaptan & 1–10 ppm \\ TGS2602 & VOC, Ammonia, Hydrogen sulfide & 1–30 ppm \\ TGS2600 & Ethanol, Hydrogen & 1–30 ppm \\ TGS2610 & Butane, Propane & 500–10,000 ppm \\ TGS2620 & Organic solvent, Ethanol & 50–5000 ppm \\ \hline \hline \end{tabular}
\end{table}
Table 1: Sensor models and characteristics of electronic nose.  
Figure 1: The developed electronic nose system.
## 4 Results and Discussion
Notably, the analysis of Figure 12 highlights insights provided by the CGO-ANN and GA-ANN models regarding specific deviations, aiding in pinpointing areas for model improvement. Specifically, these models exhibit deviations when the residuals of PPV predictions exceed 2 cm/s or fall below \(-2\) cm/s. Conversely, the single ANN model and the USBM model demonstrate pronounced skewness in the residuals, indicating heavy tails or other deviations from normality. These findings underscore the need for further enhancements to these models to ensure more accurate and reliable PPV estimation. Figure 12: Q–Q plot of residuals for each PPV predictive model developed. From the findings and analyses of this study, it is evident that the proposed CGO-ANN model emerges as a novel and robust approach for predicting PPV, demonstrating superior performance and reliability. Compared to alternative optimized models such as GA-ANN and PSO-ANN, the CGO-ANN model exhibits notable proficiency in PPV prediction, indicating the compatibility of chaos theory with the inherent uncertainties of PPV propagation in the ground. Furthermore, this study underscores the unsuitability of the USBM model for PPV prediction due to its low reliability.
## 3 Methods
### Verification and Evaluation
Figure 9: Point cloud mesh result of 3D laser scanning used to compare the desired and obtained thermoformed modules  
Careful planning and execution were crucial to avoid introducing new issues or endangering other design elements. The retopology of the surface was performed using mesh smoothing techniques in Rhino. This involved reorganizing and optimizing the network of polygons that defines the surface geometry to enhance its structural and visual characteristics. Refining the network eliminated irregularities, such as uneven polygon sizes, twisted faces, or unnecessary vertices, that could compromise the smoothness of the surface. Mesh smoothing helped to distribute curvature more uniformly, reducing areas of high stress concentration that could lead to deformations or failures during manufacturing. This process not only enhanced the visual fluidity of the geometry but also ensured a more predictable and stable behavior of the material under heat and pressure during thermoforming. Maintaining the curvature within allowable thresholds was required for preserving material integrity while achieving the desired ergonomic and aesthetic outcomes. Equally, the filler materials used had to be compatible with the mold's base material to maintain structural stability under the pressures and temperatures of thermoforming. Furthermore, the modified mold surfaces were meticulously sanded and finished to have the smoothness required for high-quality surface reproduction in the final product. The iterative modifications effectively eliminated defects such as folds and wrinkles, enhancing structural integrity, visual appeal, and long-term durability. The developed modular, stone-like surface (Figure 10) was successfully integrated into the play space and activity container. The modules were designed for efficient on-site assembly, featuring precise alignment elements to guarantee seamless connections between pieces. On-site post-processing included sanding, filling, and polishing the assembly seams to create a continuous, aesthetically pleasing surface that met both safety standards and design objectives. The design offered additional benefits, including ease of transportation, flexibility in installation, and adaptability for various play environments. By rearranging or rotating the modules, different shapes and spatial experiences could be generated, enhancing the playground's versatility and appeal. As shown in Figure 10, rotating the same piece by 180 degrees provided varied configurations, sustaining children's interest and engagement by fostering dynamic and stimulating play environments. Figure 10: Assembly of 4 pieces of module B, composing the integrated modular part of the installed crawling area. Module A, not present, would attach to the left side.
## 3 Results and Discussion
### Radiation Pattern Optimization
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \multirow{2}{*}{\begin{tabular}{c} **Analysis** \\ **Number** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Notch** \\ **Parameter y** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Notch** \\ **Parameter u** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **S\({}_{11}\) (dB)** \\ **28** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Resonant** \\ **(GHz)** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Gain (dBi)** \\ **(dB)** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **SLL** \\ **(dB)** \\ \end{tabular} } \\  & & & **28** & **38** & & **(GHz)** & & **28** & **38** & **28** & **38** \\  & & & **GHz** & & & **GHz** & & **GHz** & **GHz** & **GHz** \\ \hline
1 & 1.43 & 0.66 & \(-39.87\) & \(-42.82\) & 28.08 & 37.89 & 7.83 & 9.02 & \(-2.3\) & \(-6.5\) \\ \hline
2 & 1.43 & 0.56 & \(-38.53\) & \(-42.02\) & 28.08 & 37.90 & 7.82 & 9.03 & \(-2.4\) & \(-6.4\) \\ \hline
3 & 1.43 & 0.46 & \(-36.86\) & \(-39.70\) & 28.08 & 37.90 & 7.81 & 9.03 & \(-2.4\) & \(-6.6\) \\ \hline
4 & 1.43 & 0.36 & \(-35.80\) & \(-39.63\) & 28.08 & 37.88 & 7.82 & 9.03 & \(-2.4\) & \(-6.6\) \\ \hline
5 & 1.23 & 0.66 & \(-37.17\) & \(-39.63\) & 28.08 & 37.88 & 7.85 & 9.03 & \(-2.4\) & \(-6.6\) \\ \hline
6 & 1.23 & 0.56 & \(-35.27\) & \(-40.00\) & 29.09 & 37.90 & 7.84 & 9.03 & \(-2.4\) & \(-6.6\) \\ \hline
7 & 1.23 & 0.46 & \(-35.36\) & \(-38.04\) & 28.07 & 37.88 & 7.84 & 9.03 & \(-2.4\) & \(-6.6\) \\ \hline
8 & 1.23 & 0.36 & \(-32.76\) & \(-39.34\) & 28.06 & 37.88 & 7.85 & 9.03 & \(-2.4\) & \(-6.6\) \\ \hline
9 & 1.03 & 0.66 & \(-35.05\) & \(-37.77\) & 28.09 & 37.90 & 7.86 & 9.03 & \(-2.4\) & \(-6.6\) \\ \hline
10 & 1.03 & 0.56 & \(-34.06\) & \(-39.36\) & 28.07 & 37.88 & 7.86 & 9.04 & \(-2.4\) & \(-6.6\) \\ \hline
11 & 1.03 & 0.46 & \(-34.01\) & \(-38.75\) & 28.09 & 37.88 & 7.86 & 9.04 & \(-2.4\) & \(-6.6\) \\ \hline
12 & 1.03 & 0.36 & \(-34.01\) & \(-39.04\) & 28.09 & 37.88 & 7.86 & 9.03 & \(-2.4\) & \(-6.6\) \\ \hline Proposed Antenna & 1.63 & 0.76 & \(-53.00\) & \(-83.64\) & 28.07 & 37.90 & 7.82 & 8.98 & \(-2.2\) & \(-6.4\) \\ \hline \end{tabular}
\end{table} 
Table 5: Parametric analysis of radiation pattern optimization using smaller values of y and u for EBG-like notches.