esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
Experimental exhaust data obtained for most common light-duty diesel engines was used to construct a 3D model. A complete methodology to build 3D CFD exhaust models is presented, including how to estimate main parameters needed and numerical approaches. A 2D axi-symmetrical complementary model was shown to be useful to overcome the necessity of chemical parameters fitted to a specific model of catalytic converter.  
A computational model incorporating these findings will lead to accurate and faster numerical calculations of analysis of recoverable energy in exhaust systems. This will allow more designs to be explored in a given time.  
It has been pointed out that the particular nature of the exhaust flow leaving catalytic converters has to be taken into account for energy-harvesting calculations, since the end of after-treatment systems is the indicated position to place energy recovery devices.  
Furthermore, temperature losses caused by placing the recovery device distant from the DOC were evaluated. This information can be used in the evaluation process of the position of a recovery device in an automotive exhaust system. Since temperature falls promptly, it is advised to place the device as close as possible.  
In works regarding simulations of recovery devices, if not enough experimental information is available, it is encouraged to include upstream elements of the exhaust that could alter the flow significantly in order to better predict their performance when installed in vehicles.  
**Acknowledgments: Authors wish to thank the financial support provided by the Spanish Ministry of Economy and Competitiveness to the project POWER Ref. ENE2014-57043-R and Universidad de Castilla--la Mancha for the pre-doctoral funding [2015/4062]. Authors also wish to thank the technical support provided by NISSAN Iberica Co. and CMT-Motores Termicos Institute at Universitat Politecnica de Valencia.**Author Contributions: [PERSON] developed the model; [PERSON] directed and supervised all the work done; [PERSON] assisted with the experimental work; [PERSON] assisted with the development of the model.**  
**Conflicts of Interest: There is no conflicts of interest.**  
**Nomenclature**  
\(A\): Pre-exponential factor (m\({}^{3}\)/(mol-s))  
\(B\): Pressure drop linear fit constant (m\({}^{2}\))  
\(C\): Reaction rate linear fit constant \(D\): Reaction rate linear fit constant (K)  
\(c_{p}\): Specific heat at constant pressure (J/(kgK))  
\(d\): Diameter (m)  
\(F\): External body forces (N)  
\(g\): Gravitational acceleration (m/s\({}^{2}\))  
\(G\): Inhibition factor (K)  
\(h\): Convection heat transfer coefficient (W/m\({}^{2}\)K)  
\(J\): Diffusion term (kg/m\({}^{2}\)s)
# An ECC-Based Anonymous and Fast Handover Authentication Protocol for Internet of Vehicles
## 4 Proposed Protocol
### Registration Phase  
#### 4.2.1 Vehicle Registration  
The vehicle registration process is shown in Table 3. In this phase, the \(TA\) delivers registration services to the vehicle \(V_{i}\) through a secure communication channel, ultimately enabling \(V_{i}\) to acquire the private key \(d_{V_{i}}\) and public key \(P_{V_{i}}\).  
1. \(V_{i}\) selects a secret parameter \(y_{v}\in\mathbb{Z}_{p}^{*}\) as a component of its private key, computes \(Y_{v}=y_{v}\cdot P\), and transmits a registration request message \(\{ID_{V_{i}},Y_{v}\}\) through the secure communication channel to the \(TA\).
2. Upon receiving the registration request from \(V_{i}\), the \(TA\) randomly selects \(u\in\mathbb{Z}_{p}^{*}\), computes \(X_{v}=u\cdot P\), generates a component \(x_{v}=u+s\cdot h(ID_{V_{i}}||X_{v}||Y_{v})\) of its partial private key, and transmits the response message \(\{x_{v},X_{v}\}\) through the secure communication channel.
3. Upon receiving the response message from the \(TA\), \(V_{i}\) first sets \(C=h(ID_{V_{i}}||X_{v}||Y_{v})\), and verifies whether equation \(x_{v}\cdot P=X_{v}+P_{Pub}\cdot C\) holds true. If the verification succeeds, \(V_{i}\) proceeds to compute and generate the \(d_{V_{i}}=x_{v}+y_{v}\) and \(P_{V_{i}}=\{X_{v},Y_{v}\}\). Finally, \(V_{i}\) stores \(\{d_{V_{i}},P_{V_{i}},C\}\) in its memory and publishes \(P_{V_{i}}=\{X_{v},Y_{v}\}\). If the verification fails, \(V_{i}\) retransmits the registration request \(\{ID_{V_{i}},Y_{v}\}\) through the secure communication channel.
## 3 YOLOv8_Adv Model
### Integration of Small Target Detection Layer in Head Section  
In infrared images of substation equipment, large equipment such as arresters, circuit breakers, and bushings, as well as smaller equipment like insulators, exhibit varying bounding box sizes. Among these, features of small targets are often weak and challenging to recognize clearly in the images. For the YOLOv8n model with a high downsampling rate, learning and extracting feature information of small targets in deeper feature maps is notably difficult. Additionally, equipment in transmission lines often obscure each other, further increasing the detection difficulty.  
In the original YOLOv8n network, the input image is scaled to a size of 640 \(\times\) 640 \(\times\) 3, resulting in a limited receptive field in the shallow layers and a weak capacity to express semantic information. To better extract defect details and features of small targets, the improved YOLOv8_Adv model incorporates an additional upsampling layer into its existing architecture, linking this layer to the prediction component in the detection head. This modification significantly boosts the model's ability to accurately detect small substation equipment. This improvement enables a detection scale of 160 \(\times\) 160 \(\times\) 32, as illustrated in the feature fusion structure in Figure 7.  
Although the integration of a small target detection layer results in a minor increase in the computational demands, this enhancement substantially improves the detection accuracy for small targets. By focusing specifically on these smaller, often more challenging objects, the layer enables the model to achieve a higher level of precision and reliability. This improvement is particularly crucial in applications where accurately identifying small  
Figure 7: Diagram of the feature fusion structure with the addition of a small target detection layer.  
targets can significantly impact overall performance, such as in surveillance or complex background environments. The trade-off of increased computation is thus justified by the marked gains in accuracy and effectiveness.  
Moreover, it significantly enhances the model's overall performance when processing complex images of electrical equipment. By improving the model's ability to identify and analyze intricate details, this layer ensures more reliable and precise detection, ultimately contributing to better operational outcomes in challenging environments.
# Acquisition of Weak BDS Signal in the Lunar Environment
## 2 Materials and Methods  
In this section, we first define the scenario of a receiver in circumlunar orbit receiving Earth-GNSS signals, and give some simulation parameters. Next, several algorithms for acquisition of weak signals are briefly described and the relevant parameters of the algorithms used in this paper are elaborated.
# BDS-3 Integrity Risk Modeling and Probability Evaluation
## 4 Result and Discussion
### Probabilistic Assessment and Result Analysis of Integrity Risk Based on Actual Data
\begin{table}
\begin{tabular}{c c c c} \hline  & **Number of** & **Predictive Value** & **Probability of Integrity** \\
**Bottom Event Name** & **Historical** & **under 95\%** & **Risk under 95\%** \\  & **Occurrences** & **Confidence Limit** & **Confidence Level** \\ \hline Satellite clock anomaly & 3 & 7.04 & 1.34e-05 \\ Satellite attitude anomaly & 0 & 1.93 & 3.67e-06 \\ Signal measurement anomaly & 7 & 12.5 & 2.37e-05 \\ Signal power anomaly & 1 & 3.91 & 7.43e-06 \\ Monitoring station data anomaly & 1 & 3.91 & 7.43e-06 \\ Injection anomaly of injection station & 0 & 1.93 & 3.67e-06 \\ Calculation anomaly of track/clock offset & 2 & 5.54 & 1.05e-05 \\ Anomaly of ephemeris fitting parameters & 3 & 7.04 & 1.34e-05 \\ Track and time synchronization & 1 & 3.91 & 7.43e-06 \\  & equipment anomalies & 0 & 1.93 & 3.67e-06 \\ \hline  & SAIM parameter broadcast error & 0 & 1.93 & 3.67e-06 \\  & Satellite autonomous & 6 & 11.19 & 2.13e-05 \\ Monitoring & integrity not monitored & & 1.93 & 3.67e-06 \\ miss & Ground integrity & 0 & 1.93 & 3.67e-06 \\  & parameter broadcast error & & & \\  & Ground integrity not & 0 & 1.93 & 3.67e-06 \\  & monitored & & & \\ \hline \end{tabular}
\end{table} 
Table 6: Statistics and prediction of abnormal events in integrity risk trees.
## 3 Results and Discussion
### Impact of Alkalinity (OH/Si)  
Zeolite frameworks (e.g., aluminosilicate) typically crystallize under high alkaline conditions, characterized by a pH range of 9-13 [44]. The negative charge in the zeolite framework can be counterbalanced by cations such as Na\({}^{+}\), K\({}^{+}\), and Cs\({}^{+}\)[45]. An elevated OH\({}^{-}\)/Si ratio promotes the solubility of Si and Al sources within the precursor gel, concurrently reducing the polymerization degrees of silicate anions and accelerating the polymerization of aluminate anions. In particular, increasing alkalinity accelerates zeolite crystallization by shortening the induction and nucleation periods (since the reaction between Al and Si ions at higher pH is accelerated by releasing OH\({}^{-}\)). Additionally, it affects the size and morphology of the crystal and expands the ionic interaction between tetrahedra and OSDA [46]. To study this parameter, five membranes were prepared with different OH\({}^{-}\)/Si ratios from 0.1 to 0.5. As outlined in Table S5, the membrane with the highest alkalinity (OH\({}^{-}\)/Si ratio of 0.5) displayed a pronounced presence of non-zeolitic pathways prior to calcination. Gas permeation measurement revealed that a reduction in the alkalinity of the gel precursor led to a slight decrease in CO\({}_{2}\) permeance but a significant drop in SF\({}_{6}\) permeance (from 0.2 to 0.05 [mol/(m\({}^{2}\)sPa)]), resulting in an increased ideal CO\({}_{2}\)/CH\({}_{4}\) permselectivity from 17 to 85 (Figure 2e). In addition, the obtained permselectivity for CO\({}_{2}\)/N\({}_{2}\) and H\({}_{2}\)/SF\({}_{6}\) increased to 14 and 60, respectively.
## 3 Results
Figure 3: Transfer characteristics of representative FETs before **(a)**; and after **(b)** applying Teflon-AF. The \(y\)-axis is \(I_{d}\) in \(nA\) unites for both cases and the dotted lines are visual guides emphasizing the \(V_{th}\) shift after applying the polymer cap. Figure 5b presents the distribution of gate hysteresis. The hysteresis is calculated by taking the difference between the \(V_{th}\) when \(V_{g}\) is swept from negative to positive direction and vice versa. An improvement was observed in both the average and the standard deviation of the gate hysteresis distribution after applying the cap. The average value reduces from 15.1 V to 8 V. The gate hysteresis is mainly caused by the trapped mobile charges present within the gate dielectric and at the interface of the channel that rearrange under gate bias and drain voltage. The polar structure of Teflon-AF proved significantly effective in neutralizing the mobile charge traps. Figure 6a presents the subthreshold swing (\(SS\)) of tested devices calculated using, \(SS=ln10\frac{dV_{g}}{dln(l_{d})}\). The average value of \(SS\) reduces from 15.9 V/Dec and 10.7 V/Dec after applying the cap. This reduction is in agreement with reduced charge transfer between the surface adsorbates and channel carriers. The reduction of the standard deviation of all three performance metrics here shows an improved device to device consistency. Figure 6b presents the improved on-off switching ratio after applying Teflon-AF. The average value changes from 5 \(\times\) 10\({}^{5}\) to 2 \(\times\) 10\({}^{6}\) after applying the cap, showing a significant improvement of off-current state.
## Concept and Kinematic Model Formulation
### Kinematic Linear n-Sensor Model
The results showed that the main critical directions were the \(y\)-axis (max-displacement close to 30.0 \(\upmu\)m) and the \(x\)-axis (max-displacement \(<\) 10.0 \(\upmu\)m), while the structure highlighted a high stiffness in the \(z\)-axis (max-displacement \(<\) 1.0 \(\upmu\)m). The simulation was used to estimate the coefficients of the regression equations. Figure 2: Description of general limited degree of freedom (DOF) parallel manipulator system. In the same way, the FE analysis was replicated considering the thermal stresses. The objective was to simulate and measure the displacements in different parts of the structure where the FBG sensors were located. By using this information, a further adjustment of the regression model was executed in order to predict the TTP position accurately.
# Enhanced Performance of nZVI/MXene@CNTs for Rapid As(III) Removal from Aqueous Solutions
## 2 Materials and Methods
### Characterization  
The Fourier-Transform infrared spectroscopy (FTIR; Thermo Scientific Nicolet iS5, Waltham, MA, USA) was used to analyze the chemical bonds and functional groups of functionalized CNTs, Ti\({}_{3}\)C\({}_{2}\) MXene and nZVI/MXene@CNTs. The scanning electron microscopy (SEM; ZEISS Sigma 300, Roedermark, Germany) equipped with an energy dispersive X-ray spectrometer (EDS) was employed to observe the morphology and structure of several materials. The chemical state, elemental content and molecular structure of the materials were determined applying X-ray photoelectron spectroscopy (XPS; Thermo Scientific K-Alpha) based on peak location and peak shape. The vibrating sample magnetometry (VSM; LakeShore7404) was employed to evaluate the magnetic characteristics of the samples.
# Comparison of Various Models for Optimal Water Distribution in Canal Systems
## 1 Introduction
considering the uncertainty of water supply, established a water distribution model for three scenarios based on actual water supply conditions. The flow rate computed by the model closely matched the actual flow, making it easier to adjust [19]. [PERSON] and [PERSON] addressed the issue of irrigation scheduling with varying flow rates in lower-level canals, and provided effective and high-quality scheduling solutions for large-scale irrigation systems [20]. [PERSON] et al. developed a nonlinear multi-objective optimization model for complex canal systems, considering the multiple demands of decision makers as well as real-world canal operations. The optimized model successfully shortened irrigation operation time, reduced water consumption, and improved water transfer efficiency [21]. [PERSON] et al. applied a genetic algorithm-based model to the canal system in the North China Plain, demonstrating that the model effectively reduced water delivery losses, saved distribution time, and ensured continuous canal operation with minimal leakage [22]. [PERSON] et al. established a coupled model to develop a more efficient water distribution plan, accounting for the influence of both the water resource allocation model and optimized canal water distribution [23]. The aforementioned optimal water distribution models for canals provide valuable guidance for practical water distribution in irrigation districts; however, no studies have comprehensively examined the advantages and disadvantages of different water distribution models. In this study, therefore, we selected five models for analysis: Irrigation Model 1 (upstream-to-downstream irrigation); Irrigation Model 2 (downstream-to-upstream irrigation); Irrigation Model 3 (continuous irrigation between groups, with rotational irrigation within groups); Irrigation Model 4 (rotational irrigation between groups, with continuous irrigation within groups); and Irrigation Model 5 (rotational irrigation-based optimal distribution). These models were applied to the Fengqigou canal in the Bojili Irrigation District. We then evaluated and analyzed the advantages and disadvantages of each model based on five criteria: total water distribution duration; canal engineering capacity; leakage losses in canal water delivery; water abandonment volume; and operability for irrigation managers.
# Time Modulated Arrays: From their Origin to Their Utilization in Wireless Communication Systems
## 2 State of the Art
### TMA Design under a Signal Processing Perspective
#### 2.3.1 Interaction of TMAs with Information Signals: First Approaches  
Most of the above-referenced investigations exclusively focus on the synthesis of the antenna patterns. The fundamental and/or the harmonic components are manipulated in some way, without considering the interaction between the TMA technique and the signals sent or received by such an antenna. This subsection is devoted to those works that apply TMA to wireless communications.  
As a starting point, it is necessary to find the signal frequency restrictions, i.e., the relationship between the time modulation frequency, the signal bandwidth, and the carrier frequency that must be satisfied in order to keep the integrity of the signals transmitted/received over the TMA. Those restrictions were initially stated by [PERSON] and [PERSON] [9], and completed by [PERSON] et al. [58], where additional closed-form expressions for the total mean SR power and for the TMA power efficiency were properly derived.  
The transmission of narrowband amplitude-modulation (AM) and frequency-modulation (FM) signals was theoretically analyzed by [PERSON] et al. [59] and, for the case of AM, experimentally proved in [60]. Other specific cases were also investigated like a dual-channel AM receiver [61], the transmission of linearly frequency modulated (LFM) signals [62], or the feasibility of implementing a dual function of radar and communication [63; 64].  
The TMA technology was also proposed--originally by [PERSON] et al.--to transmit direction-dependent signals in the context of secure communications [65; 66; 67]. The idea is based on generating a null level of SR in some desired directions and a high level of SR on other directions in order to distort the signal. Conditions of frequency overlapping between the signal replicas placed in adjacent harmonic frequencies must be satisfied, paradoxically contrary to those ones established in [9].  
A first study on the signal-to-noise ratio (SNR) in a system with a receiver TMA was presented in [68] by [PERSON] et al. This work describes a specific case exploiting the fundamental pattern and the first positive harmonic to receive two different binary phase shift-keying (BPSK) signals over additive white Gaussian noise (AWGN) channels. The multibeam characteristics of TMA were also proposed for spatial multiplexing by [PERSON] et al. [69], although the performance with broadband signals was not analyzed. More recent works focus on the hardware design of TMA applied to spatial filtering of signals [70], and even for interference suppression in mm-Wave communications [71].  
Note that, although the TMA topic has been mainly considered from a theoretical point of view, several prototypes have already been described in the literature [41; 42; 46; 49; 50; 56; 60; 69; 70].
# Semantic 3D Reconstruction with Learning MVS and 2D Segmentation of Aerial Images
###### Abstract
With the booming of deep learning methods, 2D segmentation tasks are reaching high performance levels, which makes it possible to acquire a large-scale 3D semantic model easily. Nevertheless, errors within depth maps and semantic maps may lead to inconsistency. This can be alleviated by considering 3D geometry and 2D confidence maps together in an optimization module. Moreover, 3D models with coarse segmentation still need further refinement to filter error points. In a nutshell, the main contributions of our work are three folds:  
* We present an end-to-end, learning-based, semantic 3D reconstruction framework, which reaches high Pixel Accuracy on the Urban Drone Dataset (UDD) [7]. * We propose a probability-based semantic MVS method, which combines the 3D geometry consistency and 2D segmentation information to generate better point-wise semantic labels. * We design a joint local and global refinement method, which is proven effective by computing re-projection errors.
## 4 Experiments and Analysis
### Experimental Results and Analysis
#### 4.4.4 Performance Comparison with AutoML-Based Approaches  
More importantly, we evaluated the scalability limitations of the existing AutoML-based model selection methods under constrained resources. As illustrated in Figure 10, methods such as AutoSklearn, AutoML (AutoKeras), and AutoGluon failed to process the concurrency of model executions that exceeded a certain threshold--typically around 20 to 40 models--regardless of the available CPU levels. This result reflects the heavy computing loads of their model search and selection processes, which often involve repeated retraining, architecture search, and hyperparameter tuning.  
In contrast, the proposed meta-learning framework can simultaneously process up to 100 concurrent model executions. As a result, AutoML methods become infeasible for real-time, large-scale deployment in edge environments. These observations underscore the following critical trade-off: although AutoML methods automate model optimization, their scalability limits their applicability in latency-sensitive systems. In contrast, the proposed meta-learning method offers consistent and efficient service performance even under high concurrency and limited resources.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Feature** & **Selection Impact (\%)** \\ \hline Skewness & 35.7 \\ Variance & 21.3 \\ Entropy & 14.3 \\ Mean & 9.9 \\ Max Value & 8.2 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The top five statistical features influencing the model selection.  
Figure 9: Distribution of the forecasting models selected by the meta-learning framework at different CPU levels.
## 4 WirelessHART Scheduling Schemes
### The Dang Algorithm  
The work in [78] presents a scheduling algorithm for the graph route technique, scheduling end to end communication resources, with the intention of meeting the WirelessHART specification. The algorithm deals with multiple publish rates, thus dealing with multiple superframes, and seeks to order the scheduling of the links according to the connection sequence indicated in the graph tables.  
The algorithm schedules each superframe, from the superframe with the shortest length to the superframe with the largest length. It is worth noting that the devices that are attributed to a given superframe have the same publish rate, and this common publish rate defines the length of that specific superframe. Thus, the fastest publish rate devices have priority over the slower ones. A table is used to store the schedule, which is called SlotNumber, and relates the slot time with the channel used, avoiding time slot and channel conflicts among the transmissions.  
In order to schedule the communication over the actual sequence of devices along the path to its destination, a variable named LastSlot is used. There is a LastSlot variable for each device, and it stores the last slot time in which the device was scheduled. When the next transmission is to be scheduled, it will be scheduled on the next available slot after the LastSlot (first fit technique).  
The algorithm uses the retransmission scheme based on the scheme proposed by the WirelessHART specification as described in Section 2. The algorithm uses two Booleans to control whether the link being scheduled is a reservation for a retransmission: _First_, which indicates whether the transmission is an initial one or a retransmission, and _FirstChannel_, which stores which channel was used in order to schedule the retransmission in a different channel. Transmission trough different devices from the main route should be directly in the superframe.  
In order to validate the proposed scheduling, the paper did an analysis of the proposed scheme and compared it with single, double and triple redundant routes schemes. Unfortunately, no testbed or simulation results were presented.