esa-sceva/satcom-chunk-collection huggingface dataset:
# Privacy-Preserving Authentication Based on PUF for VANETs
## 3 Preliminaries  
In this section, we introduce some basic knowledge, including the system model, the design goals of the authentication protocol for VANETs and the Physical Unclonable Function.
## 3 Results and Discussion
### Bacterial Cellulose Biosynthesis in AW Media  
Figure 9 shows the mass of BC produced by the _K. xylinus_ K2G30 strain in the media of deproteinized AW and NDAW. As a result of the cultivation of the _K. xylinus_ K2G30 strain in AW media, the highest mass of BC was obtained in NDAW, i.e., 1.57 g\(\cdot\)dm\({}^{-3}\). The effectiveness of BC biosynthesis in deproteinized and NDAW media was compared with the BC mass obtained in the classical YPM medium (Figure 1). As expected, the most BC (5.23 g\(\cdot\)dm\({}^{-3}\)) was obtained in the YPM medium. However, it should be noted that the YPM medium is one of the media that are characterized by the best conditions for the growth of _K. xylinus_ K2G30 bacteria in the literature [28]. This difference indicates that further research is required to increase the efficiency of BC production in AW. However, the results obtained in this study show that the de-proteinization of whey is not an appropriate direction for increasing the intensity of BC biosynthesis and deproteinization negatively affected the production of BC. The least BC was obtained in deproteinized medium D7 and D7.5 (0.63 g\(\cdot\)dm\({}^{-3}\) and 0.61 g\(\cdot\)dm\({}^{-3}\), respectively). This could be because some nutrients such as proteins, organic acids, or minerals could be precipitated and separated from the medium after centrifugation under the influence of a high temperature and pH.  
The wet and dry BC obtained in deproteinized AW media and NDAW media are shown in Figure 10.  
Figure 9: Dry mass of bacterial cellulose produced by _K. xylinus_ K2G30 on NDAW and deproteinized AW media columns marked with the same letter belong to one homogeneous group and there are no significant differences between them. Columns marked with different letters belong to other homogeneous groups and there are statistically significant differences between them.
## 1 Introduction
However, the unsupervised clustering-based malware detection methods that have been developed for detecting zero-day type malware cannot adopt the feature reduction technique to reduce the complexity and dimensionality of the framework [15]. The supervised model developed based on a handcrafted method with a feature reduction mechanism has resulted in a less complex malware identification system. The supervised method consisting of both handcrafted and deep learning-based approaches has been developed by researchers to decrease the complexity while preserving the performance of the model. Various deep learning methods require the conversion of malware into an image in the preprocessing stage followed by the feature extraction and classification stages. The deep learning methods have not only improved the classification performance but also reduced effort for the identification of robust features in the overall system. However, the deep learning model requires a huge set of examples for efficient training. In practice, it is not possible due to limited set of malware samples available. Therefore, the deep learning frameworks are bounded to achieve a constrained accuracy. To withstand the hurdle of limited data samples, various researchers have employed the oversampling phenomena by creating the syntactic samples in SMOTE [16]. The limitation of such methods is that mostly, the re-using of existing samples is adopted instead of creating new samples. Generative Adversarial Networks (GAN) [17] developed consisting of generator and discriminator neural networks produce the adversarial samples to extend the dataset, leading to false classification results. The generator part produces a fake set of samples, while the discriminator part differentes between the fake and real samples. The direct supply of real samples to the GAN leads to false classification results; therefore, a preprocessed sample is required to increase the invariance and robustness in the overall framework. To increase the robustness and invariance of the classification model, a Multifaceted Deep Generative Adversarial Networks Model (MDGAN) has been developed in this work. Various GAN variants exist in the literature which includes the deep convolutional generative adversarial networks (DCGAN) [18], which was developed for unsupervised learning consisting of multiple architectural constraints. The SinGAN [19; 20] is developed to learn the image patch distribution at various scales for recognition based on a single train image. The models require RGB input images and mostly focus on the input data type; however;the proposed MDGAN employs the multi-face input consisting of the 2D grayscale image features concatenated to the LSTM binary sequence features set for the detection of malware types. The remaining paper is organized as follows. Section 2 includes the literature review portion that introduces the related works. The proposed methodology portion includes the details of the proposed MDGAN framework. The results section presents the performance of the proposed model in comparison to recently reported works.
# Coordinated Optimization of Feeder Flex-Route Transit Scheduling for Urban Rail Systems
## 3 Mathematical Model
### Flex-Route Transit Operation Organization Model
#### 3.3.1 Objective Function  
The Objective Function (3) of the feeder flex-route transit timetable formulation model is to minimize the system cost \(U\). The system cost \(U\) includes the waiting time cost \(U_{W}\) for passengers/flex-route transit, the late arrival time cost \(U_{L}\) for passengers taking flex-route transit, the transfer failure cost \(U_{F}\), and the enterprise operating cost \(U_{O}\).  
\[\min\,U=U_{W}+U_{L}+U_{F}+U_{O} \tag{3}\]  
Due to the involvement of multiple vehicle systems and vehicle departure intervals, the waiting time is defined as Formula (4); The cost of late arrival time is defined as Formula (5) according to the above rules; The cost of transfer failure during the transfer process is related to the number of failed passengers, as shown in Formula (6); The operating cost of a company depends on the cost of multiple vehicle travel time, which is the total distance traveled by multiple vehicles divided by the operating speed of the vehicles, as shown in Formula (7).  
\[U_{W}=\theta_{W}\sum_{q\in Q_{1}}\sum_{k\in K}y_{q,k}\Big{|}P_{p,q,k}-t_{p,q} \Big{|} \tag{4}\]  
\[U_{L}=\begin{cases}0,P_{d,q,k}\leq t_{d,q}\\ \theta_{L}\sum_{q\in Q_{1}}\sum_{k\in K}y_{q,k}\Big{(}p_{d,q,k}-t_{d,q}\Big{)},P_{d,q,k}>t_{d,q}\end{cases} \tag{5}\]  
\[U_{F}=\theta_{F}\Big{(}q_{m}-\sum_{k\in K}q_{k}\Big{)} \tag{6}\]  
\[U_{O}=\theta_{O}\sum_{i\in S}\sum_{j\in S}\sum_{k\in K}x_{ij,k}d_{ij,k}/\
u \tag{7}\]
## 2 Materials and Methods
### Workflow  
The schematic outline of the conducted research is shown in Figure 3. In the first stage, data were extracted from the LUCAS 2018 database, and samples with specific codes were selected and validated using up-to-date orthophotos from 2023. The data were then divided into training (80%) and testing (20%) sets. In the next step, Sentinel-2 satellite data from 2023 were prepared, and classification was performed using three methods: Random Forest (RF), Support Vector Machine (SVM), and CART. Finally, an accuracy analysis of the results was conducted. The classification was performed in GEE, which meant that any registration period for the collection could be specified. In our study, we conducted classifications for  
Figure 1: LUCAS 2018 (23,087 points) image of the whole country (Poland) with the ROI (Region Of Interest) near Kraków (438 points).  
Figure 2: LUCAS 2018 (438 points) overlaid on Kraków ROI (100 \(\times\) 50 km).  
the data from both 2023 and 2018. The code is publicly available, including the LUCAS 2018 data, so anyone can perform classification for any time period.
## 5 Conclusions and Future Work  
### Conclusions  
This study presents an innovative integrated dietary recommendation system that utilizes advanced multimodal deep learning techniques to automate and enhance dietary assessment. The system effectively integrates zero-shot learning models for food classification, learning-based MVS methods for food volume measurement, and large language models to generate personalized nutritional recommendations. The main contributions of this study include the following:  
* Zero-Shot Food Classification. By fine tuning the CLIP model, the system can accurately classify various foods, including categories not present in the training data, overcoming the limitations of traditional food classification models.
* Convenient Volume Estimation. Using learning-based MVS technology, the system can measure food volume from multi-angle images, ensuring nutritional analysis accuracy without requiring specialized hardware.
* Personalized Dietary Recommendations. The system combines LLMs to analyze nutritional content and provide personalized dietary recommendations based on individual health conditions and goals.
* Enhanced User Experience. The automated food classification and nutritional analysis process significantly reduces user manual recording work, making dietary management more convenient and accurate.
* Comprehensive Evaluation. The research results show that the system can effectively identify various common foods and accurately estimate their nutritional value, enhancing the convenience and precision of dietary control.
## 7 Critical Discussion  
Although the ARIMAX model has demonstrated inferior predictive capability compared to the neural network, it should not be automatically assumed that the latter is devoid of limitations. Deep learning techniques employed in predictive maintenance offer broad applications and future potential, but they face significant challenges in real-world environments--particularly in non-stationary processes. The solution proposed herein encounters the same limitations as those reported by [75,76].  
The neural network-based approach presents the following critical limitations:  
* Difficulty in failure prediction and/or future asset behavior forecasting, especially under dynamic operational conditions.
* High sensitivity to noisy data.
* Poor transferability/replicability across similar scenarios due to context-dependent learning.
* Dependence on large volumes of labeled data are often impractical in industrial applications.
* High computational demands (e.g., GPU requirements), raising deployment costs. Beyond technical barriers, organizational and human factors further hinder implementation:
* Resistance to change within traditionally structured organizations.
* Limited awareness among middle and senior management regarding the tools' capabilities, leading to unrealistic expectations or premature abandonment.
* Black-box nature of the methodology, which undermines trust and decision-making transparency for equipment managers and project leaders.
* Shortage of trained personnel, creating dependency on specialized expertise.  
From all the above, the authors consider that the use of this forecasting tool is suitable for supporting the implementation of predictive maintenance, even when dealing with models affected by data noise. It can serve as a starting point to identify the shortcomings of the current system (both technical and organizational) in order to reduce or eliminate them, thereby achieving more accurate results in the future.
# Status of the SXFEL Facility
## 2 The SXFEL Test Facility
### Undulator Line  
The main purpose of the SXFEL-TF is to test two-stage harmonic generation, such as the HGHG-HGHG or EEHG-HGHG cascading schemes. As shown in Figure 5, the undulator system consists of a seed laser system, three modulators, two radiator sections, and a couple of chicanes serving for laser injection, dispersive section, and fresh bunch delay purposes.  
Figure 4: Twiss functions along the main linac.  
Figure 3: Layout of the main linac of the SXFEL-TF. (Reprint from reference [26]).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Parameters** & **L1** & **LX** & **L2** & **L3** \\ \hline Effective accelerating gradient (MV/m) & 15 & 25 & 32 & 32 \\ Accelerating phase (deg) & \(-\)52.6 & \(-\)180 & 14 & 14 \\ Energy at the section exit (MeV) & 184 & 165 & 389 & 840 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Designed working parameters of the main linac. (Reprint from reference [26]).  
The electron bunch at the exit of the linac is about 1 ps (FWHM) long. A small part is modulated in the modulators by a seed laser pulse with a pulse length of about 100 fs (FWHM), and the modulated part of the electron bunch generates coherent radiation in the first stage radiator. This radiation is shifted ahead to a fresh part of the electron bunch by the fresh bunch chicane and serves as the seed for the following stage. A short undulator of the same type as the first stage radiator is employed as the modulator and a small chicane is used as the dispersion section in the second stage. The harmonic up-conversion numbers of the two stages are six and five, respectively, which makes the final output wavelength ~8.8 nm.
## 1 Introduction  
Reinforced concrete structures exhibit degraded durability over time when exposed to various external environments such as chlorides, CO\({}_{2}\), and water. When the durability of concrete is reduced, the corrosion of steel reinforcement may occur due to the penetration of deterioration factors caused by cracks and fractures on the surface. In particular, cracks in concrete and pores on the concrete surface serve as transport pathways for water and chlorides [1]. To address these problems, technologies to improve the durability of the concrete surface layer by applying liquid-type water repellents to the concrete surface without forming a separate sheet protection layer were developed as methods to reduce water penetration, and they have been used in References [2; 3; 4; 5]. As for the specific mechanism of the surface treatment technique, a material to prevent water penetration is applied to the concrete surface, and the water-repellent active ingredient blocks water as it hardens in the pores of the concrete's surface [6]. The surface treatment technique, however, cannot prevent water penetration when cracks or fracture occurs on the concrete surface due to the various external factors, because the water penetration prevention layer on the surface is also fractured. Therefore, cost and time are required to repair or reconstruct the concrete's surface. To complement this shortcoming, if the water-repellent's performance can be implemented inside the concrete, it is possible to prevent a reduction in the durability of concrete due to the water penetration despite the occurrence of cracks and deformation. If the water-repellent's active ingredient is directly added to implement the water-repellent's performance inside the concrete, the cementitious material and silane/siloxane, which is the water-repellent's active ingredient, can be separated and the water-repellent active's ingredient cannot be evenly distributed inside concrete. Unlike existing surface treatment methods applied only to concrete's surface, previous studies on water repellency inside concrete are not sufficient. Therefore, in this study, water-repellent carriers were fabricated and mixed with cement mortar to prevent a reduction in the durability of concrete due to the fact of water penetration and the separation of water-repellent active's ingredient, and then tests were conducted [1]. Water repellency was provided to (1) sand, (2) silica fume (SF), (3) fly ash (FA), and (4) natural zeolite, and they were mixed with (5) cement mortar. The currently used water-based water repellent was directly mixed with cement mortar to provide water repellency. The physical properties (i.e., air content, flow, compressive strength, and surface contact angle) and durability performance (i.e., water penetration test, resistance chloride penetration resistance test, and mercury intrusion porosimetry (MIP)) of cement mortar were evaluated through tests for the selection of the optimum water-repellent impregnation powder.
# Sensors, Techniques, and Future Trends of Human-Engagement-Enabled Applications: A Review
## 4 Approaches
### Data-Driven Approaches
[138] proposed DRNet, a CNN\(+\)MLP architecture, to estimate gaze direction in images, achieving state-of-the-art performance. The authors of the DAiSEE dataset [57] conducted a comparative study among baseline Deep Learning models. A CNN, specifically InceptionV3 [139], was used to classify engagement at frame level just accomplishing a 47% accuracy of classifying learner engagement state. Engagement could be viewed as a video classification problem by integrating temporal information. The authors tested pre-trained 3D CNNs, specifically C3D [140] and Long-Term Recurrent Convolutional Network (LRCN), raising the accuracy to 56% and 58%, respectively. It is proven that temporal information provides effective engagement estimation and time series modelling is promising. [PERSON] et al. [13] tested signature models for time series prediction, i.e., Gated Recurrent Unit (GRU) and LSTM. They achieved up to 78% accuracy in challenging multimodal HRI tasks. [PERSON] et al. [14] proposed a CNN\(+\)LSTM architecture to regress engagement from a live video feed delivering MSE of 0.126. [PERSON] et al. [75] leveraged Reinforcement Learning (RL) combined with conventional DNNs to push the boundaries of video-based engagement estimation. In Figure 2, several popular DNN architectures for engagement estimation are shown. These models vary in complexity, training paradigm, and efficacy in different application domains. The choice should rely on overall consideration of all factors. In summary, it can be seen from the literature that there is a need for greater accuracy and reliability in engagement estimation approaches. In the review paper [3], the authors remarked on the rise of Deep Learning algorithms, which have been surpassing SVMs and Decision Trees since 2019. This has been verified in this article. Rule-based methods [17; 130]become dramatically ineffective on out-of-domain, unseen, and large-scale tests. As data-driven approaches, especially Deep Learning, triumph in tackling complex, real-world, noisy, multimodal engagement estimation tasks, they are setting new heights in datasets (Table 2), albeit scattered in different application domains.
# Application of Classified Elastic Waves for AE Source Localization Based on Self-Organizing Map
## 6 Discussion
According to the ray-tracing algorithm [21], all possible ray paths are computed on the basis of the candidate points and an approximated velocity distribution to approximate a ray path; a first travel time is then determined as an approximated ray path. In the application of classified waves, approximated ray paths are not required in the source localization because straight propagation waves are classified. Thus, the computation time is expected to be optimized.
## 5 Experimental Results  
In this section, experiments were conducted to evaluate the effectiveness and accuracy of the proposed HTPR. Experiments for item clustering and user clustering are conducted to evaluate the effectiveness of HTPR, and to determine the values of relevant parameters used in the clustering process. In addition, to evaluate accuracy, HTPR was compared with the other three recommendation methods: basic CF, user clustering based CF (UCCF) and item clustering based CF (ICCF). Four metrics were used to evaluate the accuracy: mean absolute error (MAE), precision, recall, and F1 score [42; 43]. All the experiments are carried out with the spark framework and run on a workstation equipped with an Intel 12-core 3.5 GHz CPU, a GTX1080 TI GPU, and 16 GB memory.
# Measurement and Model-Based Control of Solidification in Continuous Casting of Steel Billets
## 1 Introduction
On the other hand, problems of coupling the signal into the strand have to be solved. Additionally, a lot of energy was needed in such an application. Another invention of BFI [12] relates to a method for determining a liquid phase inside a billet that has already solidified from the surface towards the centre during a continuous casting process. By this method, the oscillation of the billet surface is determined at a measurement point along the billet, and the presence of a liquid phase in the billet at the measurement point is established by comparing the surface oscillation measured at the measurement point with at least one reference value. The latter vibrometer technology, in contrast to the two methods described before, provides the advantage of being contactless and the possibility to install it at different positions along the strand. This allows gathering data of different solidification grades during the plant trials. Within the RFCS project ConSolCast [13], where the reported work has been carried out, innovative measurement techniques and model-based process simulation, monitoring, optimisation and control tools have been developed, adapted and tested to minimise casting anomalies (such as strand breakouts and surface defects) and to maximise the productivity of the casting machine (e.g., in terms of increased casting speeds). A dynamic temperature and solidification model (DynSolidCC) developed by BFI has been installed at the five-strand billet caster of ESF and used within an online operator information and advisory system for monitoring of the strand evolution as well as control of the crater end position by appropriate adaptions of casting speed and secondary spray water flows. Measurements of strand surface temperatures with an infrared camera allowed adjusting the respective boundary condition model parameters for the spray water and radiation cooling zones of the caster. Moreover, measurement campaigns with a laser vibrometer revealed that the evolution of the crater end position simulatedby the DynSolidCC model fits well with the measured footprint of a completely solidified strand section, which could be identified in the recorded vibrational spectra by significant differences to those from strand sections with some liquid core. Another topic studied in the ConSolCast project, but not reported in detail within this paper, dealt with the recording of fibre optical temperature sensor (FOTS) signals within the mould walls, which could successfully provide measurement data used for validation and adaptation of the mould boundary condition submodel.