esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiments
### Experimental Settings
#### 4.1.2 Baseline OOD Model  
We selected two state-of-the-art defect detection algorithms, Fastflow [14] and Cflow [39], as our baseline models.  
Fastflow is a 2D-normalizing flow designed for anomaly detection and localization. It leverages fully convolutional networks and a two-dimensional loss function to effectively model both global and local feature distributions. It is constructed with a lightweight network structure characterized by the alternating stacking of large and small convolution kernels. This design enhances its efficiency and allows for end-to-end inference. FastFlow is adaptable and can function as a plugin model with various feature extractors. In the following experiment, we retain the original design that uses the features of the last layer in the first three blocks and put these features into the 2D flow model to obtain their respective anomaly detection and localization results. The training process involves an Adam optimizer with a learning rate of 1 \(\times\) 10\({}^{-3}\), weight decay of 1\(\times\) 10\({}^{-5}\), 500 epochs, and a batch size of 32.  
Cflow is based on conditional normalizing flows and shares similarities with CNNs in terms of feature map spatial dimensions. This similarity results in higher accuracy metrics and lower computational and memory requirements. The main idea behind CFLOW-AD is to learn the distribution of anomaly-free image patches and transform it into a Gaussian distribution. This transformation allows the model to separate in-distribution patches from out-of-distribution patches using a threshold. The training hyperparameters for CFLOW-AD include the use of the Adam optimizer with a learning rate of 1 \(\times\) 10\({}^{-4}\), 100 training epochs, and a mini-batch size of 32 for the encoder. Cosine learning rate annealing is applied with two warm-up epochs. The CFLOW-AD decoders are unable to feature map dimensions and have low memory requirements. Both training and testing phases involve the sampling of feature vectors.  
Their source codes are publicly available. Experimental settings in the following sections are given in Table 1. We compare their performance with revised models that incorporate our proposed post-processing model.
## 3 Results and Discussion
### Estimated Energy Production  
The assessment of energetic effectiveness is extremely important to the evaluation of process viability, especially on the large scale [61]. Analyzing the amount of methane production and its energy value of 9.17 Wh/dm\({}^{3}\), the highest gross energy gain was shown in S2V2 with an OLR of 3.0 gVS/dm\({}^{3}\).day and amounted to 187.07 \(\pm\) 1.5 Wh/day (Table 4). In S1V2, only 148.55 \(\pm\) 1.3 Wh/day was obtained. The use of pre-treatment with SCO\({}_{2}\) to intensify the methane fermentation process is justified when a closed CO\({}_{2}\) cycle is used in these solutions in the cycle: biogas production--biogas enrichment--SCO\({}_{2}\) production--sludge disintegration--fermentation--biogas production. This is an important argument that improves the economic, technological, and environmental efficiency of MSS anaerobic digestion [20].  
Figure 8: Anaerobic bacterial community composition in particular experimental variants.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**S1**} & \multicolumn{4}{c}{**S2**} \\ \cline{2-13}  & **OLR** & \(\mathbf{M_{\mathrm{RMS}}}\) & \(\mathbf{Y_{\mathrm{menhance}}}\) & \(\mathbf{Y_{\mathrm{menhance}}}\) & \(\mathbf{CV_{\mathrm{menhance}}}\) & \(\mathbf{E_{\mathrm{out}}}\) & **V** & **OLR** & \(\mathbf{M_{\mathrm{RMS}}}\) & \(\mathbf{Y_{\mathrm{menhance}}}\) & \(\mathbf{Y_{\mathrm{menhance}}}\) & \(\mathbf{CV_{\mathrm{menhance}}}\) & \(\mathbf{E_{\mathrm{out}}}\) & \(\mathbf{E_{\mathrm{out}}}\) \\ \cline{2-13}  & **gVS/dm3** & **by** & **gVS/Dary** & **dm3/kgVS** & **dm2/Dary** & **wh/dm3** & **wh/dm3** & **gVS/Dary** & **dm2/kgVS** & **dm2/Dary** & **wh/dm3** & **wh/dm3** & **wh/dm3** & **wh/dm3** \\ \hline
1 & 2 & 40 & \(310\pm 14\) & \(124\pm 1.2\) & & \(113.71\pm 1.2\) & 1 & 2 & 40 & \(340\pm 9\) & \(13.6\pm 1.4\) & & \(124.71\pm 1.4\) & \(11\pm 0.2\) \\
2 & 3 & 60 & \(270\pm 14\) & \(16.2\pm 1.3\) & & \(148.55\pm
# Audio Anti-Spoofing Based on Audio Feature Fusion
## 1 Introduction
The evaluation results on the ASVspoof 2021 LA and DF databases [8] show that our proposed model outperforms most of the previous works in terms of the minimum-detection cost function (min t-DCF) [14] and equal error rate (EER) evaluation criteria. In particular, it surpasses the best results mentioned in [15] on the DF dataset.
# Refractive Index Sensing through Surface Plasmon Resonance in Light-Diffusing Fibers
## 1 Introduction  
Refractive index (RI) sensing is important for biological and chemical applications since a number of substances can be detected through measurements of the refractive index. Among RI sensors, those based on surface plasmon resonance (SPR) are especially useful as they permit label-free and real-time detection of biological/biochemical binding reactions [1; 2; 3]. In particular, fiber-optic SPR probes have the advantages of remote sensing, continuous analysis, and in situ monitoring. Fiber-optic SPR sensors meet the demands of time- and space-saving, low sample volume, low cost, high sensitivity, portability, and miniaturization. Therefore, these biosensors have recently been extensively investigated [4; 5; 6]. Numerous versions have been introduced, exploiting several kinds of optical fibers and/or different manufacture procedures, in an attempt to optimize the sensitivity of the plasmonic sensor platforms [7; 8; 9; 10; 11; 12; 13; 14].  
In general, the excitation of a surface plasma wave at the boundary between a thin metal film and the surrounding medium requires access to the evanescent part of propagating light [13]. In single-mode fibers, this requires a chemical etching of the cladding, which increases the cost of manufacturing and impacts on the robustness of the sensor. In multimode fibers with polymeric cladding (the so-called hard-clad fibers), the cladding may be more simply removed by mechanical tools. However, the multimode SPR sensor exhibits a strong dependence of its response on the angle of incidence of the input light [7; 12].  
Herein we demonstrate a novel fiber-optic SPR sensor using a light diffusing fiber (LDF) as the host element. LDFs are designed with light-scattering centers in the core, providing very efficient scattering of light through the sides of the optical fiber and along their entire length. Therefore, the SPR response of an LDF is virtually independent of the angle of incidence of excitation light. Note thatan SPR platform based on a plastic LDF has been recently demonstrated [14]. However, as seen in Reference [14], a polishing procedure was still adopted in order to remove the cladding and produce a side-polished surface.  
In the following, we first characterize, numerically and experimentally, the LDF in terms of transmission loss as a function of the surrounding refractive index (SRI). Through this analysis, we estimate that the optical energy within the fiber core is completely scrambled over all the possible angles. Successively, we present the results of an SPR sensor, realized by depositing a thin gold film around the unclad region. The experimental results are finally compared to those found in the literature for a hard-clad silica (HCS) fiber.
# Development and Evaluation of Doppler Ultrasound Training Phantom for Human Vessel Simulation
## 1 Introduction
To mimic human tissues, researchers have utilized several materials, including polyvinyl chloride (PVC), gelatin, and agarose. While the PVC approach resulted in a robust phantom, with excellent imaging attributes, it necessitated the use of additional materials, such as diethyl hexyl adipate, plasticizer softener, chalk powder, and mineral oil. The fabrication process was also comparatively lengthy [11]. Additionally, the acoustic impedance of the phantom range is from 1360 to 1400 m/s, which is different from the acoustic impedance of human soft tissue (i.e., 1540 m/s) [22]. Gelatin is relatively inexpensive, and provides a similar acoustic characteristic to human tissue, from 1518 to 1535 m/s [23]. However, its quality and elasticity characteristics degrade over time. To minimize biological degradation, a hydrocolloid skin dressing is applied [24]. Nonetheless, its production takes more than 12 h, making it less suitable. In contrast, agarose offers the advantages of a shorter production time and cost-effectiveness, and is simple to make, and may be shaped in a variety of ways. The acoustic impedance of agarose gel ranges from 1489 to 1600 m/s, which is closer to human soft tissue (1540 m/s) than that of PVC and gelatin [25]. In previous studies, the blood flow system used a water pump and an automatic injection [26; 27; 28]. This system offered significant advantages, such as precise velocity assessment and real-time blood flow regulation. However, due to the complexity of this system, and the requirement for technical understanding, beginners encountered difficulties in using it. Additionally, when scanning the phantoms at other sites, beginners face limitations in mobility, due to the weight of the blood flow systems. This study proposes a method for manufacturing a Doppler ultrasound training phantom that is suitable for beginners to learn color Doppler. For beginners, we constructed a Doppler ultrasound training phantom, including normal blood vessels instead of abnormal vessels. To mimic human soft tissue, we used agarose, which has a similar acoustic impedance to human soft tissue. The blood flow systems were developed using a cascaded design, with the bottom surface made of ABS in the form of LEGO blocks and LEGO bricks. Our intention is to facilitate the comprehension and application of these essential parameters by beginners during the process of learning Doppler ultrasound. The previously mentioned color Doppler, color box, scale, and color gain are important parameters for understanding color Doppler imaging. Therefore, when designing the Doppler ultrasound training phantom, we focused on the color Doppler, color box, scale, and color gain. Furthermore, we focus on the development of a Doppler ultrasound training phantom that demonstrates significant advantages in terms of cost-effectiveness, user-friendliness, and intuitive design.
###### Abstract  
Dehydration or drying of grapes is one of the most important steps in the production of Croatian traditional dessert wine Prosek. The natural sun drying of grapes is the traditionally used method in Prosek production. Alternative methods, such as dehydration under controlled conditions, have been studied as safer and faster methods than the traditional sun drying but without precise knowledge of the effect on volatile compounds. The objective of this work was to study how dehydration of grapes carried out in a greenhouse and an environmentally controlled chamber impacts on the free and glycosidically bound volatile compounds of native grape cv. \"Marastina'. The 36 volatile compounds were identified and quantified using headspace solid-phase micro extraction coupled with gas chromatography-mass spectrophotometry (HS-SPME-GC/MS). The results showed that the aroma profile of dehydrated grapes was significantly different from that of fresh grapes. Regarding free forms, significant increases in the concentration of 2-methyl-1-propanol, 1-butanol, 2-hexen-1-ol, 1-hexanol, ethyl hexanoate, hexyl acetate, o-cymene, linalool oxide, and terpinen-4-ol and geraniol were found in greenhouse-dried grapes, whereas increases in cis-limonene-epoxide, trans-limonene epoxide, and \(\gamma\)-hexalactone were higher in chamber-dried grapes compared to greenhouse-dried grapes. Glycosidically bound forms of o-cymene, linalool oxide, linalool, and terpinen-4-ol were increased in both types of drying, whereas \(\beta\)-damascenone was increased only in greenhouse-dried grapes.  
free and bound volatiles; varietal aroma; terpen; \(\beta\)-damascenone; drying +
Footnote †: journal: Applied Sciences
## 3 Results and Discussion
### Thermal Diffusivity by the Flash Method  
Figure 11 exhibits a representative example (sample S44) of the procedure used for the calculation of the thermal diffusivity using the flash method (PT in transmission mode) and partial times. The raw data (blue dots) are first fitted using the Savitzky-Golay filter (red dotted curve) seen in Figure 11a, from which the maximum temperature and the corresponding time \(t_{\text{max}}\) are obtained, followed by the calculation of the partial times: \(t_{1/3}\), \(t_{1/2}\), \(t_{2/3}\), and \(t_{5/6}\). The thermal diffusivity is then obtained from Equations (4) through (6), and finally averaged (\(\alpha_{\text{mean}}\)).  
The procedure is repeated for every pixel so a diffusivity map can be reconstructed as shown in Figure 11b, in which the three defects can be clearly identified as the thermal diffusivity is affected by the presence of the artificial porosity areas. From this figure, it can be observed that healthy areas have diffusivities above 4.7 \(\times\) 10\({}^{-7}\) m\({}^{2}\)/s (appearing mostly gray in Figure 11b), while defective areas are below this threshold (appearing mostly orange-yellow in Figure 11b).  
The thermal diffusivity of all specimens was estimated following the same procedure. The average values for sound areas in all samples are summarized in the last column of Table 1.  
Figure 10: Line scan thermography at 25 mm/s. Impact of advanced signal processing and Gaussian filtering on **single layer porosity** at the **deepest depth**: sample S45, defect 3 (insert at \(z\) = 3.115 mm, thickness = 0.19 mm).
# Ultra-Reliable Link Adaptation for Downlink MISO Transmission in 5G Cellular Networks
## 2 System Model  
A downlink multi-cell system is considered, where a number of base stations are deployed to serve mobile terminals (users). The association of users to base stations is fixed and known _a priori_,so that each user is served by a unique base station. The total bandwidth is shared by all cells in a universal frequency reuse manner, and the intra-cell resource allocation is orthogonal. Each base station has \(N_{T}\) transmit antennas, and users have a single receive antenna. The multiple transmit antennas at base stations are used for single-stream beamforming towards their respective users.
## 2 Preliminaries and Related Work
### Preliminaries
#### 2.1.2 Federated Learning Application  
Federated learning has emerged as a promising approach for collaborative model training across distributed devices while preserving data privacy; it can be utilized in different applications such as healthcare, smart city, smart transport, and finance industry applications.  
For example, in the smart healthcare industry, sharing patient records with a central server or cloud is necessary to develop intelligent approaches, e.g., intelligent imaging for disease detection. However, patient records are sensitive, and simply removing personal information is not enough to protect their privacy. This is especially true in complexhealthcare settings where multiple parties, such as hospitals and insurance companies, have access to healthcare databases for data analysis and processing. To address this issue, using FL can provide intelligence and knowledge from different patient records while protecting patient privacy, to enhance the healthcare system to provide a robust predictive model for disease diagnosis, healthcare management, etc. [1; 28; 29].  
On the other hand, smart devices deployed in smart cities are assisting city officials in improving the efficiency of city operations while also enhancing the quality of life for residents by ensuring the seamless delivery of food, water, and energy to end-users. To facilitate smart cities, machine learning techniques have been widely adopted to process real-time big data from sensors, devices, and human activities. However, due to privacy concerns and the massive traffic generated, a centralized learning approach is not scalable. Therefore, FL can be leveraged in this field to enable decentralized smart city applications with high privacy and minimal communication delays. For example, it can be used in a smart grid system to learn power consumption patterns without revealing individual power traces; this can help create an interconnected and intelligent energy exchange network in the city [1; 29].  
Intelligent transport systems aim to ensure safe and efficient traffic flow by using various technologies to monitor and assess their performance. However, traditional intelligent transportation systems share data in untrusted environments, which may raise privacy concerns. Therefore, FL can be utilized in transport applications to enable intelligence without the need to share data; FL can be used in intelligent transport systems to enable traffic prediction and manage resource strategies for vehicle-to-vehicle networks [1; 30].  
Collaboration among various financial institutions has become a growing trend in the financial and insurance industry. Federated learning is a technology that financial institutions can utilize for risk management, fraud detection, marketing, and other purposes. The use of FL in this sector facilitates collaboration between different financial institutions without the need to share their clients' information, thus helping them to develop various financial task models, such as risk assessment machine learning models [31; 32].
## 3 Results
### Machine Learning Models  
#### 3.2.1 Multiple Linear Regression  
After conducting multiple linear regression (MLR) analyses, the most suitable regression model was identified. The comparison between the predicted values generated by the MLR model for training, testing, and 10-fold CV data and the actual experimental values of shear strength in the direct shear tests is presented in Figure 12. Based on the findings, it can be concluded that the MLR model has a high level of accuracy.  
The training database included 36 observations, with an MAE of 8.31, RMSE of 11.87, RMSLE of 0.29, and R\({}^{2}\) value of 0.95, indicating a high level of prediction accuracy. The model was then tested on a separate dataset consisting of 10 observations, where it demonstrated a slightly improved MAE of 7.67 and a reduced RMSE of 10.08, and an impressive decrease in RMSLE to 0.17, maintaining a high R\({}^{2}\) value of 0.94. Furthermore, a 10-fold cross-validation (CV) was performed on all 46 observations, yielding an MAE of 9.28, RMSE of 13.57, and RMSLE of 0.35, along with an R\({}^{2}\) of 0.93. The model performance across  
\begin{table}
\begin{tabular}{c|c c c} \hline \hline  & **Training Database** & **Testing Database** & **10-Fold CV** \\ \hline
**Observations** & 36 & 10 & 46 \\ _MAE_ & 8.31 & 7.67 & 9.28 \\ _RMSE_ & 11.87 & 10.08 & 13.57 \\ _RMSLE_ & 0.29 & 0.17 & 0.35 \\ \(R^{2}\) & 0.95 & 0.94 & 0.93 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The performance of MLR model to predict shear strength.  
Figure 12: Multiple linear regression was performed to compare actual shear strength with predicted shear strength using (**a**) the training database, (**b**) the testing database, and (**c**) 10-fold cross-validation.  
the training, testing, and cross-validation demonstrates its robust predictive capability for shear strength. This outcome promotes confidence in the predictive capability of the model and its applicability to new data. Thus, an empirical equation was generated to predict the shear strength of sand with high level of accuracy. The empirical equation is as follows:  
\[\begin{split}\tau&=15.57+(7.28\times\text{D}_{50})+(6.75 \times\text{C}_{u})-(24.53\times\text{C}_{c})+\\ &\quad\quad\left(53.90\times\rho_{dry}\right)+(121.64\times\sigma _{n})-(36.45\times\rho_{r})\end{split}\]  
where \(\text{D}_{50}\) is the mean particle size, \(\text{C}_{u}\) is the coefficient of uniformity, \(\text{C}_{c}\) is the coefficient of curvature, \(\rho_{dry}\) represents the dry density, \(\sigma_{n}\) is the normal stress, and \(\rho_{r}\) refers to the sand particle shape regularity.
# Underwater-Acoustic-OFDM Channel Estimation Based on Deep Learning and Data Augmentation
## 3 The UWA-OFDM-Channel-Estimation Framework Based on Deep Learning
### GAN-Based UWA-OFDM Channel Sample Enhancement
Therefore, the loss function can be expressed as  
\[ \begin{split}\min_{\theta_{g}}\max_{\theta_{d}}V(D,G)=& \mathbb{E}_{h\sim p_{data}(h)}[D(h;\theta_{d})]+\\ &\mathbb{E}_{z\sim p_{z}(z)}[-D(G(z;\theta_{g});\theta_{d})]- \lambda GP(D),\end{split}  \tag{6}\]  
where \(\lambda\) is the penalty coefficient, and the last term \(GP(D)\) can be written as  
\[GP(D)=\mathbb{E}_{h\sim p_{sample}(\hat{h})}[(||\
abla_{\hat{h}}D(\hat{h}|; \theta_{d})||_{2}-1)^{2}], \tag{7}\]  
where the distribution \(p_{sample}(\hat{x})\) fits the true data distribution \(p_{data}\) and the generated data distribution \(p_{g}\) point pairs are evenly sampled along the line. However, for the unconditional generation model, the generation of the data is uncontrollable. We hoped to use the posterior information in the training data, by attaching some channel information of the real data to the model, which could guide the model to generate our desired channel data. The specific generator and discriminator network structures are shown in Figure 5. Eventually, the loss function was rewritten as  
\[ \begin{split}\min_{\theta_{g}}\max_{\theta_{d}}V(D,G)=& \mathbb{E}_{h\sim p_{data}(h)}[D(h|c;\theta_{d})]+\\ &\mathbb{E}_{z\sim p_{z}(z)}[-D(G(z|c;\theta_{g});\theta_{d})]- \lambda GP(D).\end{split}  \tag{8}\]  
Figure 3: The GAN structure.