esa-sceva/satcom-chunk-collection huggingface dataset:
# Multigap Resistive Plate Chambers for Time of Flight Applications
## 3 Applications in Large Experiments
### Developments for Future ToF Systems
#### 3.3.2 The New Time Reconstruction Algorithm  
In recent years, artificial neural networks (ANNs) have become a powerful and popular machine learning (ML) method in many fields, including high energy physics analysis [92; 93; 94; 95]. As mentioned before, the ToT of an MRPC signal is usually discriminated against and digitized by TDC. The main drawback is that the time jitter of each TDC channel is typically 20 ps, which is a big issue to achieve a system resolution better than 20 ps. Moreover, the threshold crossing time is related to the pulse amplitude, so the time-slewing effect should be carefully corrected. [PERSON] et al. [96] proposed neural network (NN) algorithms to reconstruct the MRPC time. The Monte Carlo simulation described before provided labeled training datasets to train the network model, while the experiment datasets are used to test. The FC (fully connected) and long short-term memory network (LSTM) are detailed studied [96]. Moreover, they both give more accurate and precise time resolution results compared to the ToT method.  
[PERSON] also proposed a combined LSTM (ComLSTM) neural network (see Figure 14), which combines the advantages of both the LSTM and FC. The detailed description of the ComLSTM structure can be found in [97]. For the 32-gap MRPC, same as [90], timing performances with both the ComLSTM neural network and the traditional threshold-based method are shown in Figure 15. The best time resolution with the ComLSTM can reach \(23.82/\sqrt{2}=16.84\) ps, which is better than that of the ToT method. Although the neural networks have shown better performance, many efforts need to be put into implementing and validating them in high-energy physics and particle experiments.
# Privacy-Preserving Outsourced Artificial Neural Network Training for Secure Image Classification
## 4 The Framework and Construction for Privacy-Preserving Image Classifier
### Security Requirement  
The main goal of _IPPNN_ is to build an image classifier by training a neural network model: _(i)_ without being able to observe any information belonging to the users; and _(ii)_ without leaking the model parameters trained by the server \(\mathcal{S}_{\mathcal{NN}}\).  
* Honest-but-curious \(\mathcal{S}_{\mathcal{NN}}\): We assume that the server \(\mathcal{S}_{\mathcal{NN}}\) correctly follows the _IPPNN_ protocol, but may try to learn private information belonging to users from the processed information provided by \(\mathcal{S}_{\mathcal{A}\mathcal{U}}\).
* Honest-but-curious \(\mathcal{S}_{\mathcal{A}\mathcal{U}}\): We assume that the server \(\mathcal{S}_{\mathcal{A}\mathcal{U}}\) correctly follows the _IPPNN_ protocol, but may try to learn private training samples from the encrypted information provided by data owners, and try to learn the trained model parameters.
* Trusted \(\mathcal{TPA}\): We assume that the \(\mathcal{TPA}\) is an independent entity trusted by the users and the servers \(\mathcal{S}_{\mathcal{NN}}\) and \(\mathcal{S}_{\mathcal{A}\mathcal{U}}\).  
We now present our privacy-preserving scheme for achieving an image classifier. We first introduce the idea of _IPPNN_ model. Then, the data encryption algorithm and a detailed secure training process are presented.
## Materials and Methods
### Volatile Compounds  
Volatile composition of the samples under analysis was obtained by headspace solid phase microextraction (HS-SPME). Five g of each smoothie + 10 mL ultrapure water were placed into 50-mL vials with polypropylene caps and PTFE/silicone septa. A magnetic stirring bar was added, together with NaCl (15%) and the vial was placed in a water bath with controlled temperature and automatic stirring. A 50/30 \(\upmu\)m DVB/CAR/PDMS fiber (Supelco, Bellefonte, PA, USA) (high capacity of trapping fruit volatile compounds) was exposed to the sample headspace for 50 min at 40 \({}^{\circ}\)C to simulate the mouth temperature during the chewing process. Desorption of the volatile compounds from the fiber coating was carried out in the injection port of the GC-MS for 3 min.  
The identification and semiquantification of the volatile compounds was performed on a gas chromatograph (GC-MS), Shimadzu GC-17A (Shimadzu Corporation, Kyoto, Japan), coupled with a Shimadzu mass spectrometer detector GC-MS QP-5050A. The chromatographic set up and conditions were identical to those recently reported, with the only exception that the column used was a Restek Rai-1301 Sil MS (Restek Corporation, Palo Alto, USA) of 30 m \(\times\) 0.25 mm internal diameter, 0.25-\(\upmu\)m film thickness. Analyses were carried out using helium as carrier gas at a flow rate of 6 mL min\({}^{-1}\) in a split ratio of 6 and a program: (a) initial temperature 80 \({}^{\circ}\)C; (b) rate of 3.0 \({}^{\circ}\)C min\({}^{-1}\) to 210 \({}^{\circ}\)C and hold for 1 min; (c) rate of 25 \({}^{\circ}\)C min\({}^{-1}\) from 210 to 300 \({}^{\circ}\)C and hold for 3 min. Injector and detector temperatures were held at 230 and 300 \({}^{\circ}\)C, respectively.  
Most of the compounds were simultaneously identified by using 3 different analytical methods: (1) retention indices, (2) GC-MS retention times (authentic chemicals), and (3) mass spectra (authentic chemicals and Wiley spectral library collection). Identification was considered tentative when it was based only on mass spectral data. The volatile composition analysis was run in triplicate and results were expressed as percentage of the total area represented by each one of the volatile compounds.
## 2 A General Overview on SSNs
### SSNs Architecture  
The infrastructure of the early stage of traditional WSNs was owned by the publisher, who can dedicate it to a certain application or service. Nowadays, due to the increasing demand on technological applications, it became necessary to separate between the WSN's infrastructure and the offered services. That is to get better utilization of the infrastructure and improve the ability to serve multiple applications. Thus, it became a real need to federate WSNs that belong to different companies and treat them as one Large Federate Sensor Network (LFSN), which is considered to be a multiservice system [15]. Therefore, there are two co-operating partners to implement this issue, the Sensor Infrastructure Provider (SInP) and Sensor Virtualization Network Service Provider (SVNSP) [13], one to federate the deployed sensors' infrastructure from different owners to the service providers, and the other to offer the sensing service to the users, respectively. Thus, the LFSN environment can adjoin a number of heterogeneous sensor networks.  
The architecture of the emerging SSNs paradigm is based on separating the sensor infrastructure from the offered service by it, that is to reduce the required establishment cost, manageability, maintenance, programmability, etc., thus, as can be seen from Figure 1, the SSNs architecture consists of four different layers that will be described briefly, as follows:  
Sensor Infrastructure Provider (SInP): it is the responsible partner for federating and managing the WSNs physical infrastructure, it is able to offer the Sensing Infrastructure as a Service (SlaaS) to a multiple of service providers, it is sensing resources that contain sensors from different vendors (companies), among sensors there are Sensor Gateways Routers (SGR) acting as the sink node, all of them have sufficient power supply, memory, processing, etc. Being connected to high speed wireless networks, it could be able to host different Virtual Sensor Gateways Routers (VSGR), which are offered when the customer requests a special service. SInP can define itself by the type of services that it could provide, and offer the SlaaS through a programmable interface to different SVNSP. As can be seen in the Figure 1, large scale federate sensor networks containing different types of sensors are created by multiple co-operated companies to form the infrastructure.  
Sensor Virtualization Network Service Provider (SVNSP): it is the partner who can lease the infrastructure resources from one or more SInP; it uses the virtualization technology to share the leased infrastructure resources and deploys it as a VSN to the end users. As can be seen in Figure 1, the SVNSP offer different VSNs to the end user.  
Virtual Sensor Network (VSN): it is the logical representation of the required sensing environment offered to the end user by the SVNSP.  
Application Level User (ALU): it is the end user application interface to transact the user queries and allow him/her the ability to instrument, observe, and react to events and phenomena that are required for one or more applications.  
Figure 1: SSNs Architecture.
## 3 Results and Discussion
### Description of the Damage  
To better compare the tunnel damage characteristics under the combined effects of fault dislocation and seismic excitation, this study sets up three cases. In Case 1, the tunnel is subjected only to fault displacement; in Case 2, the tunnel experiences both fault dislocation and seismic excitation; in Case 3, the tunnel is subjected solely to seismic excitation.  
Figure 9 depicts the cloud diagrams illustrating the compression damage and tensile damage of the lining under the three cases. It is evident that when the tunnel experiences fault dislocation alone, both compression and tensile damage of the lining are predominantly concentrated in the region of the fault fracture zone. The most severe damage occurs at the fault plane, where compression damage is primarily concentrated in the tunnel vault and the tunnel invert, while tensile damage extends from the sides of the tunnel wall toward the direction of the tunnel vault and the invert. When the tunnel is simultaneously subjected to fault displacement and seismic action, both the extent and severity of compressive and tensile damage to the tunnel lining increase. The maximum compressive damage factor, for instance, rises from 0.72 in Case 1 to 0.78. Since concrete is a brittle material, its compressive strength greatly exceeds its tensile strength, which is why, in all three cases, the tensile damage to the tunnel is greater than the compressive damage. Under seismic forces alone, the compressive damage to the tunnel is primarily concentrated near the fault surface, particularly at the tunnel vault and the tunnel invert, with the maximum compressive damage factor reaching 0.15. As shown in the figure, most of the damage factors are less than 0.1, indicating that the tunnel has not experienced severe compressive damage under seismic action. Similar to Case 2, when the tunnel is only subjected to seismic action, the tunnel also exhibits significant tensile damage, with a larger distribution of damage compared to Case 1. On the other hand, the maximum damage factor decreases from 0.98 to 0.89, indicating that while the distribution of tensile damage under seismic loading alone is more extensive than under fault displacement alone, the severity of the damage is reduced.  
Figure 9: Damage cloud diagrams of the tunnel lining under different cases. (**a**) Compression damage. (**b**) Tensile damage.
# Analysis of Flame Evolution Generated from Methyl Laurate Droplet Using Deep Learning
## 1 Introduction
Traditional methods for analyzing flame evolution, such as schlieren imaging and thermocouple-based diagnostics, face limitations in resolving transient phenomena at high temporal resolutions [22]. This underscores the need for advanced computational tools to capture complex combustion dynamics. The combustion process or flame evolution can be analyzed by extracting images from video recordings. Each image provides insight into the droplet shape transformation process and flame development. In the realm of deep learning, the computer strives to identify features from a collection of images through a training process. This process allows the computer to recognize specific patterns, such as the size of objects or color channels, which are crucial for detecting objects [23; 24]. Currently, YOLO (You Only Look Once) is a promising technology for object detection. It has several parameters that justify its use, such as the size of the resulting model, the speed of detection, and the accuracy of detection [25]. This algorithm is a widely recognized method for object detection that excels in real-time performance. It necessitates a training process using image data that contain the object class of interest [26]. The calculations for these algorithms can be executed using the primary processing capabilities of a computer or with the assistance of a graphics card to create models for detecting objects. Once the algorithm identifies the droplet and flame, their progression can be quantified over time. Machine learning techniques have shown promising results in predicting flame and droplet evolution. Despite advancements in combustion diagnostics, few studies have leveraged deep learning (DL) to automate the analysis of flame and droplet evolution. While machine learning models like LSTM, CNN-LSTM, the random forest algorithm, and the TSPV model have been applied to flame prediction [27; 28; 29; 30], their adaptation to droplet combustion--particularly for methyl laurate--remains unexplored. This study bridges this gap by implementing YOLOv9, a state-of-the-art object detection model, to segment and quantify flame features in real time. Our approach uniquely combines high-resolution imaging with DL-driven analytics, enabling unprecedented precision in tracking ignition delay, flame morphology, and droplet lifetime. This work aims to characterize the flame evolution of methyl laurate droplets under atmospheric pressure using YOLOv9-based segmentation, to correlate flame dynamics with droplet temperature and size profiles, and to establish a framework for integrating deep learning into combustion diagnostics. The findings provide critical insights for optimizing biodiesel combustion systems and advancing sustainable energy technologies.
# Group Pile Effect on Temperature Distributions inside Energy Storage Pile Foundations
## 4 Analytical Results and Discussions
### Temperature Distribution in Concrete
As the cycle increases, the temperature change starts to slow down and converges at a constant temperature. The group pile cases converge to a significantly higher temperature (about 120 \({}^{\circ}\)C) than the isolated pile case due to the additional heat transferred from the adjacent piles. The cases with larger \(s\) have a lower converging speed compared to the cases with smaller pile spacing. Figure 8: Distribution of the temperature along the radial direction in the 1 st, 10 th, 50 th, 200 th, and 1000 th loading cycles at (**a**) 19:00 and (**b**) 7:00 (next day). Figure 9: The largest temperature ever reached in each cycle for various \(s\) at (**a**) the one-third location; (**b**) the middle location; (**c**) the two-thirds location; and (**d**) the outer surface of the pile section. To determine when the group pile starts to affect the temperature in the concrete section, a parameter \(N_{G}\), which is calculated as the cycle where the maximum temperature difference (\(\Delta\)T) between the group pile case and the isolated pile case starts to be higher than 0.1 \({}^{\circ}\)C at a given location of the pile section, was introduced. Figure 10a demonstrates an example with \(s=2\) m for the outer surface of the pile section on how \(N_{G}\) was determined, where at cycle 3, the maximum temperature difference starts to be larger than 0.1 \({}^{\circ}\)C.  
Figure 10b shows \(N_{G}\) for all group pile cases along the concrete section. As seen from this figure, the inner surface of the concrete (\(r=0.1\) m) is not affected by the heat transferred from the adjacent pile, since the temperature at this surface follows the temperature input from compressed air and has the same profile for all cases, including the isolated pile. Moving further towards the outer surface of the concrete section, \(N_{G}\) gradually decreases, reaching a minimum value at the outer surface of the section (\(r=0.5\) m), which indicates that the heat from the adjacent pile affects the outer surface first, slowly transferring into the region closer to the inner surface. Regarding pile spacing, as the distance between two piles becomes gradually small, \(N_{G}\) also decreases.
## 2 Materials and Methods
### Basic and Complete Regression Analyses  
Regression analysis is a statistical technique used in experimental design to establish the relationship between response values and influencing factors. With the help of convenient commercial software programs, the estimated values of variable parameters can be easily calculated using the least squares method. These software programs also provide statistical tests to validate the regression models, such as analysis of variance tables. An analysis of variance table lists the \(t\)-value and \(p\)-value for each variable, facilitating further analysis. This process is called basic regression analysis.  
Regression analysis does not simply accept all variables in the initial calculation equation. The next step is to screen out those variables that have no significant effect on the response. Due to the interaction and multicollinearity between these variables, it is impossible to remove the variables that have no significant effect on the response at the same time [17, 18, 39, 40, 41, 42, 43].  
Several procedures are employed, including the sequential variable selection procedure, the forward procedure, stepwise regression, and backward elimination. In the establishment of the RSM models, the backward elimination procedure is recommended. For the regression analysis, two criteria are used to evaluate the accuracy (fitting agreement) and prediction [17, 18, 39, 40, 41, 42].  
Besides calculating the estimated values of the parameters and screening the effective variables, the detailed technique includes checking for violations of the assumptions and performing influence diagnostics. The influential data points are checked with other criteria [17, 18, 39, 40, 41, 42, 43].  
In this study, the complete regression analysis includes performing a sequential variable selection procedure, evaluating RSM model accuracy and prediction using different criteria, checking for violations of assumptions, and performing influence diagnostics to identify influential data points.
# The Development of Teleoperated Driving to Cooperate with the Autonomous Driving Experience
## 3 Designs and Developments
### Vehicles
For geolocation, the U-blox C099-F9P GNSS module captures real-time satellite signals to generate precise latitude and longitude data, enhancing localization and 3D mapping accuracy.
# Performance Comparison of Machine Learning Disruption Predictors at JET
## 2 Materials and Methods
### Machine Learning Methods
#### 2.3.3 Convolutional Neural Networks (CNN)  
A CNN is a deep neural network particularly suited to processing images and is able to produce a high-accuracy performance without the need for handmade feature extraction engineering [41]. Its architecture contains a cascade of blocks that filter the input data to extract significant features and perform the classification task: (i) convolutional units \((CU_{k})\), each composed by the cascade of a convolutional layer \((C_{k})\), a batch normalization layer \((N_{k})\), and a non-linear activation layer with rectified linear unit (ReLU) functions \((A_{k})\); (ii) max-pooling \(P_{max}\) and/or average-pooling \(P_{avg}\) layers; (iii) drop-out layers \((D)\); (iv) fully connected MLP \((FC)\) layers; (v) soft-max layers \((S)\); and (vi) the classification output layer \((CO)\). The main advantage of this architecture, for DP tasks, is that 1D profile diagnostics can be directly used as network inputs without the necessity to synthesize 0D signals from them, such as peaking factors [8; 36]. During the supervised training, the network parameters are optimized with stochastic gradient descent algorithms. Once the CNN model is trained, as in the case of the MLP-NN models, the output related to a new discharge provides its disruptive likelihood, as in Figure 2.
## 3 Results and Discussion
### Analysis of Aroma-Active Compounds Based on AEDA and MF  
CP EOs with better sensory properties were selected for GC-O analysis. As shown in Table 2, a total of 59 aroma-active compounds were identified by GC-MS-O in two EOs, including 14 terpenes, 12 alcohols, 11 aldehydes, 2 esters, 2 ketones and 15 unidentified compounds. Moreover, 49 and 41 compounds were detected in HY EO and GF EO, respectively. In terms of the number of aroma compounds, HY EO exhibited a richer aromatic profile.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{**No. \({}^{*}\)**} & \multirow{2}{*}{**Compounds**} & \multirow{2}{*}{**Identification**} & \multirow{2}{*}{**Odor Characteristic**} & \multicolumn{2}{c}{**HY EO**} & \multicolumn{2}{c}{**GF EO**} \\ \cline{4-7}  & & & & **MF\%** & **FD** & **MF\%** & **FD** \\ \hline
1 & \(\beta\)-Myrcene & MS, RI, O & balsamic, fruity & 78.06 & 128 & 81.97 & 128 \\
2 & Octanal & MS, RI, O & fatty, citrus & - & - & 84.52 & 256 \\
3 & \(D\)-Limonene & MS, RI, O & citrus, lemon & 79.06 & 256 & 80.18 & 256 \\
4 & \(\gamma\)-Terpinene & MS, RI, O & engine oil & 73.95 & 1 & - & - \\
5 & 1-Octanol & MS, RI, O & pungent & - & - & 49.74 & 1 \\
6 & Terpinolene & MS, RI, O & pine & 35.36 & 8 & 63.74 & 8 \\
7 & Unknown! & O & sweet, fruity & 55.90 & 2 & - & - \\
8 & Linalool & MS, RI, O & floral & 83.45 & 8 & 85.70 & 256 \\
9 & Nonanal & MS, RI, O & aldehyde & - & - & 43.30 & 2 \\
10 & _cis_-Limonene oxide & MS, RI, O & herbaceous & 37.50 & 1 & - & - \\
11 & Unknown 2 & O & tallowy, pungent & 72.89 & 2 & 39.53 & 4 \\
12 & Citronellal & MS, RI, O & floral, lemon & 87.50 & 4 & 83.85 & 8 \\
13 & Unknown 3 & O & herbaceous & - & - & 35.36 & 1 \\
14 & Unknown 4 & O & metallic & 70.71 & 32 & 66.14 & 64 \\
15 & \(\alpha\)-Terpineol & MS, RI, O & fruity, floral & 68.47 & 32 & 77.06 & 64 \\
16 & Decanal & MS, RI, O & aldehyde, citrus & 84.78 & 4 & 84.78 & 64 \\
17 & Citronellol & MS, RI, O & floral, fresh & 59.51 & 1 & 53.03 & 16 \\
18 & Neral & MS, RI, O & citrus, floral, fresh & - & - & 67.31 & 4 \\
19 & (-)-Carvone & MS, RI, O & minty, fresh & - & - & 43.30 & 8 \\ \hline \end{tabular}
\end{table}
Table 2: Analysis of aroma-active compounds in HY EO and GF EO based on MF and AEDA.
# EVM Loss: A Loss Function for Training Neural Networks in Communication Systems
###### Abstract  
Neural networks and their application in communication systems are receiving growing attention from both academia and industry. The authors note that there is a disconnect between the typical objective functions of these neural networks with regards to the context in which the neural network will eventually be deployed and evaluated. To this end, a new loss function is proposed and shown to increase the performance of neural networks when implemented in a communication system compared to previous methods. It is further shown that a'split complex' approach used by many implementations can be improved via formalisation of the 'concatenated complex' approach described herein. Experimental results using the orthogonal frequency division multiplexing (OFDM) and spectrally efficient frequency division multiplexing (SEFDM) modulation formats with varying bandwidth compression factors over a wireless visible light communication (VLC) link validate the efficacy of the proposed method in a real system, achieving the lowest error vector magnitude (EVM), and thus bit error rate (BER), across all experiments, with a 5 dB to 10 dB improvement in the received symbols EVM overall compared to the baseline implementation, with bandwidth compressions down to 40% compared to OFDM, resulting in a spectral efficiency gain of 67%.  
machine learning; loss function; optics; error vector magnitude; neural networks 2021211049103390/s21041094