esa-sceva/satcom-chunk-collection huggingface dataset:
# 2D Ultrasonic Antenna System for Imaging in Liquid Sodium
## 7 Conclusions  
We presented a technique for long-distance vision in liquid sodium that enables imaging of large areas using a system of two orthogonal linear phased arrays (antennas) and the total focusing method. The use of CIVA to compute the antenna-radiated beams allows to easily understand and illustrate the effect of all the antenna parameters (frequency, aperture, pitch, etc.). CIVA also enables to study many combinations of parameters that was not possible in the first studies of this system and to propose an optimized set of values regarding the targeted application of long-distance viewing. We presented some examples of this simulated parametric study. The comparison of CIVA simulations with experimental results shows a good agreement aside from the noise that is not taken into consideration because CIVA does not compute it. The FMC/TFM imaging method was used to image the antennas' field of vision when, in the first study, the image was performed thanks to successive focalization at the different points of the field. This imaging method is very efficient as illustrated by the experimental results obtained in water, but as we deal with long-distance imaging and large imaging areas, small SNR and computation time appeared as limitations for this method. We proposed several leads to improve both of them that will be soon experimentally evaluated in water.  
Supervision, F.B.; Project administration, L.T.; Investigation, G.T. and R.R.; Writing-review and editing, R.R. and L.L.J.  
This work was supported through the \"Generation IV/Sodium Fast Reactors/ASTRID\" program with CEA, EDF, and FRAMATOME funded by the French government. In the same framework, other ultrasonic transducers are being developed for NDE: TUOSS of FRAMATOME [3] and EMAT of CEA [2].  
The authors would like to thank Toshiba Corporation for manufacturing the 2-antenna first prototype able to operate within liquid sodium at 200 \({}^{\circ}\)C in the frame of the CEA-Toshiba running agreement.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
# Transfer Learning Approach for Human Activity Recognition Based on Continuous Wavelet Transform
## 4 Results
### Model Testing Results  
The whole UCI-HAPT dataset and its subset were used for assessing the performance of the selected pre-trained model. The subset consists of 30% of randomly selected samples from the preprocessed UCI-HAPT dataset. Configurations with various numbers of frozen layers were considered, namely, only the top layer trainable, the first 308 layers frozen, the first 136 layers frozen, and all layers trainable. Table 3 compares the best classification results achieved in five attempts obtained by the pre-trained and non-pre-trained models.  
As seen in Table 3, model pre-training led to superior classification accuracy in both the preprocessed UCI-HAPT dataset and its subset. Concerning the whole target dataset, the usage of the pre-trained model led to a maximal accuracy and F1 increase of 0.21% and 0.33% using 308 layers of freezing, respectively. For other configurations, however, pre-training resulted in a decreased performance. This indicates that the preprocessed UCI-HAPT dataset is sufficiently large to potentially degrade the performance of the pre-trained model and result in a negative transfer. Concerning the UCI-HAPT subset, all pre-trained  
Figure 5: The confusion matrix of the KU-HAR dataset classification using the selected model.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**UCI-HAPT**} & \multicolumn{2}{c}{**UCI-HAPT Subset**} \\ \cline{2-5}  & **Accuracy (\%)** & **F1-Score (\%)** & **Accuracy (\%)** & **F1-Score (\%)** \\ \hline \hline Not pre-trained DenseNet121 & 92.23 & 92.19 & 86.29 & 86.38 \\ Pre-trained DenseNet121, only top layer trainable & 80.00 & 77.99 & 75.60 & 64.08 \\ Pre-trained DenseNet121, first 308 layers frozen & 92.44 & 92.52 & 86.90 & 87.11 \\ Pre-trained DenseNet121, first 136 layers frozen & 92.23 & 92.24 & 89.11 & 89.27 \\ Pre-trained DenseNet121, all layers trainable & 91.89 & 91.92 & 88.31 & 88.26 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance comparison of pre-trained and non-pre-trained models during fine-tuning.  
models, with the exception of the one with the only top trainable, produced superior results compared to the control model. Usage of the pre-trained model with 136 frozen layers led to the highest performance increases of 2.82% and 2.89% for classification accuracy and F1, respectively. Figure 6 illustrates the typical progress of the accuracy and loss metrics during the training of the raw DenceNet121 and the pre-trained models.  
It can be seen in Figure 6 that the process of gradient descent of the pre-trained model is more stable compared to the non-pre-trained model, which can be observed in the advancement of the validation loss value. In Figure 6, for instance, the non-pre-trained model reaches the \"stable point\" after approximately 50 epochs. At the same time, about 30 epochs are enough for the pre-trained model. As a result, the usage of the proposed model results in faster learning compared to the control model.
# Negotiation-Free Encryption for Securing Vehicular Unicasting Communication
## Modeling  
In the unicast transmission process, IKE is used to exchange and manage the encryption key, which solves the problem of safely establishing or updating the shared key in an insecure network environment such as the Internet. With the development of VANET, we found that the key negotiation takes up a lot of time in the transmission process. Compared to the traditional unicast transmission method, the NFEM improves this problem. This section mainly builds the overall system model of the NFEM and enumerates the possible attack model.
# Minimum-Cost Offloading for Collaborative Task Execution of MEC-Assisted Platooning
## 2 Related Work
The main contributions of this article are summarized as follows:  
* The optimization problem of the combined mobile application of each vehicle terminal in a platoon and an MEC server is modeled as a constrained minimizing cost problem. * By analyzing the characteristics of task model in a linear topology, combining the dynamic programming algorithm and the Lagrangian aggregation cost algorithm, the optimal task decision strategy is obtained under the condition that the deadline of the tasks is satisfied. * The effectiveness of the proposed algorithm and strategy is verified by simulation. The simulation results show that, in comparison with task platoon execution and the MEC server execution, collaborative task execution can greatly reduce the cost of tasks offloading.
# Dual Attention Fusion Enhancement Network for Lightweight Remote-Sensing Image Super-Resolution
## 4 Results
### Results on SR Benchmark Datasets
Furthermore, for continuous and densely structured block-like scenes, such as 'Urban100: img019', the reconstruction results from other methods exhibit varying degrees of blurriness in the upper-left regions of the image patches, while our method achieves more accurate  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{**Scale**} & \multirow{2}{*}{**Params**} & \multirow{2}{*}{**Multi-Adds**} & \multirow{2}{*}{**SetS PSNR/SSIM**} & \multicolumn{2}{c}{**Set14 PSNR/SSIM**} & \multicolumn{2}{c}{**BSD100**} & \multicolumn{2}{c}{**Urban100**} \\  & & & & & & & **PSNR/SSIM** & **PSNR/SSIM** \\ \hline Bicubic & \(\times\)2 & - & - & 33.66/0.9299 & 30.24/0.8688 & 29.56/0.8431 & 26.88/0.8403 \\ LGCNet [21] & \(\times\)2 & 193 K & 178.1 G & 37.31/0.9580 & 32.94/0.9120 & 31.74/0.8939 & 30.53/0.9112 \\ IDN [51] & \(\times\)2 & 553 K & 124.6 G & 37.83/0.9600 & 33.30/0.9148 & 32.08/0.8985 & 31.27/0.9196 \\ MADNet [74] & \(\times\)2 & 878 K & 187.1 G & 37.85/0.9600 & 33.39/0.9161 & 32.05/0.8981 & 31.59/0.9234 \\ FeNet [46] & \(\times\)2 & 158 K & 35.2 G & 37.77/0.9597 & 33.28/0.9151 & 31.98/0.8973 & 31.46/0.9215 \\ FeNet [46] & \(\times\)2 & 351 K & 77.9 G & 37.90/0.9602 & 33.45/0.9162 & 32.09/0.8985 & 31.75/0.9245 \\ FDFNet [57] & \(\times\)2 & 480 K & 138.7 G & 37.89/0.9594 & 33.50/0.9170 & 32.15/0.8988 & 32.02/0.9270 \\ DARN-S [56] & \(\times\)2 & 350 K & 78.9 G & 37.97/0.9609 & 33.54/0.9172 & 32.19/0.9005 & 32.14/0.9284 \\ IFN-S [55] & \(\times\)2 & 451 K & 110.6 G & 38.00/0.9606 & 33.66/0.9181 & 32.18/0.8996 & 32.14/0.9284 \\ BMFNet [58] & \(\times\)2 & 465 K & 115.0 G & 38.04/0.9605 & 33.62/0.9180 & 32.22/0.9004 & 32.29/0.9300 \\ TARN [49] & \(\times\)2 & 687 K & - & 38.09/0.9608 & 33.65/0.9183 & 32.22/0.9003 & 32.20/0.9289 \\ DAFEN-S (ours) & \(\times\)2 & 188 K & 37.5 G & 37.94/0.9605 & 33.41/0.9159 & 32.12/0.8991 & 31.76/0.9248 \\ DAFEN (ours) & \(\times\)2 & 416 K & 83.6 G & 38.04/0.9617 & 33.55/0.9175 & 32.22/0.9010 & 32.20/0.9291 \\ \hline Bicubic & \(\times\)3 & - & - & 30.39/0.8682 & 27.55/0.7742 & 27.21/0.7385 & 24.46/0.7349 \\ LGCNet [21] & \(\times\)3 & 193 K & 79.0 G & 33.32/0.9172 & 29.67/0.8289 & 28.63/0.7923 & 26.77/0.8180 \\ IDN [51] & \(\times\)3 & 553 K & 56.3 G & 34.11/0.9253 & 29.99/0.8354 & 28.95/0.8  
reconstruction results. The qualitative and quantitative analyses above suggest that our proposed DAFEN and DAFEN-S are competitive in both natural and remote-sensing image SR tasks.
## 6 Conclusions  
In two study areas located in Xinjiang Province, China, the ESTARFM and STDFA methods were applied to combine HJ CCD, GF-1 WFV and MODIS reflectance data. We found the following conclusions:  
1. Both ESTARFM and STDFA methods can be applied to combine HJ CCD and MODIS reflectance data, and GF-1 WFV and MODIS reflectance data. Synthetic HJ CCD data are very similar to actual HJ CCD data, with \(r\) values greater than 0.8989 generated using these two  
Figure 4: An example of the difference in shadow direction and length.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Image Information** & **3 October 2013** & **7 October 2013** & **15 October 2013** \\ \hline Receive Time (local time) & 13:34:11 & 13:33:24 & 13:31:47 \\ Solar Azimuth & 166.147 & 166.601 & 167.415 \\ Solar Zenith & 42.4117 & 40.9225 & 37.9947 \\ Satellite Azimuth & 235.912 & 121.314 & 106.892 \\ Satellite Zenith & 89.1838 & 88.3384 & 84.7204 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Sensors and solar azimuth information from GF-1 WFV data.  
methods. Synthetic GF-1 WFV data are very similar to actual GF-1 WFV data, generating \(r\) values greater than 0.8643.
2. The accuracy differences between the fusion of HJ CCD and MODIS data and the fusion of GF-1 WFV and MODIS data were lower for STDFA than for ESTARFM. STDFA had better noise immunity than ESTARFM.
3. The spatial resolution differences between MOD09 GQ and MOD09 GA had a more significant impact on the fusion accuracy for ESTARFM than for STDFA.
4. Synthetic red- and NIR-band data generated by ESTARFM are more suitable for the calculation of NDVI than the data generated by STDFA.  
This work was supported by the National Natural Science Foundation of China (41301390), the National Science and Technology Major Project (2014 AA06A511), the Major State Basic Research Development Program of China (2013 CB733405, 2010 CB950603) and the National Science and Technology Major Project of China. The funders had no role in choosing the study design, in the collection, analysis and interpretation of the data, in the writing of the report, or in the decision to submit the article for publication.  
[PERSON], [PERSON] and [PERSON] conceived and designed the experiments. [PERSON] performed the experiments and the manuscript draft. [PERSON], [PERSON] and [PERSON] revised the manuscript draft. All authors read and approved the final version.  
The authors declare no conflict of interest.
# Bingo: A Semi-Centralized Password Storage System
## 4 Key Challenges  
We return to the enabling factor in most attacks aimed at retrieving users' passwords: a weak centralized storage entity. Countless inexperienced developers are tasked daily with implementing scalable web services for the most profitable online services around the globe. Without an understanding of security best practices, these developers are likely to leave threat loopholes such as logging plain text passwords to development logs and forgetting to use salt rounds when hashing a password. There is simply too much variability in designing a secure user profile system, which is the reason why most companies that value security turn to a 2 FA service to add an indirection layer to their authentication scheme.  
* As such, Bingo aims to leverage the best qualities of all of the aforementioned solutions for insecure password storage. We define a series of key design challenges for Bingo to ensure that it provides seamless usability while also employing best security practices:
* Bingo should never store a user's password in a centralized manner. This means that the original plain text password should never be held in persistent storage, nor should Bingo independently store enough shares of a user's password such that an attacker can recover all shares without attacking peers in the network.
* Bingo should never expose the user's password to the online service for which it is middle manning. The online service is tasked with maintaining a profile for the user, as is the norm today. With that said, Bingo takes full ownership of a user's password and acts as the single source of truth for confirming a user's identity, thus offloading this responsibility from the online service.
* A retrieved password from a collection of peers in the network should be verifiable by Bingo, meaning that an attacker cannot inject fake information into the system.
* The user profiles stored in Bingo's database should not provide a meaningful advantage to an attacker seeking to crack a user's password.
* Bingo should provide strong security guarantees but not be an overly opinionated system. Online services which register with Bingo should be granted the ability to prioritize availability or security.
* Bingo should maintain the highest availability possible given a set of configurations, meaning that there should be as few instances as possible where a user cannot register or log in to an online service.  
We summarize these overarching challenges as a series of basic principles: decentralization, anonymity, verifiability, configurability, and availability. The following section describes the system architecture for Bingo which allows it to uphold these principles.
## 6 Results and Discussion
### Optimization Results of Energy Savings for Different Types of Batches
The data are compared with the mixed batch data in the previous section, as shown in Table 10. It can be seen from the table that since the mixed batch is a collection of batches of cold and hot batch types, which generally includes the four batch type conversions described in Section 3, the gas waste is the largest, and the proportion of energy saved reaches 2.46%. The hot-charge batches include DHCR and HCR, which not only have the same type of conversion but also have conversion between the two batches. The amount of gas waste is also relatively high, and the energy savings reach 1.67%. And the cold-charge batch only contains cold-charging slabs from the slab warehouse, so its internal conversion consumes less energy and saves 0.77% of energy. In summary, compared with the minimum time model, the amount of gas resources saved by the cold and hot conversion model proposed in this paper is substantial. Among them, the mixed charging batch has the largest amount of energy wastage, and accordingly, its energy-saving space is the maximum. If the model is applied to an actual steel mill, it will save more than 5000 GJ per month, and the profit can be up to 1 million RMB if all the energy is used for electricity generation. Therefore, the model proposed in this paper can effectively save energy and increase the profit of the enterprise.
## 3 Results and Discussion
### Volatile Compounds
#### 3.1.1 Aldehydes
On day 21 of storage, the pH values for the goat dessert yogurt without _Lactobacillus acidophilus_ LA-5 were: 4.29 for the mild culture, 4.26 for the classic culture, 4.24 for the acidic culture and with _Lactobacillus acidophilus_ LA5: 4.24 for the mild culture, 4.21 for the classic culture and 4.12 for the acidic culture. The lower pH values observed with the addition of _Lactobacillus acidophilus_ LA-5, compared to the addition of _Bifidobacterium_ BB-12, confirms the stronger acidifying effect of the former bacterial strain. According to [PERSON] et al. [24], good yogurt flavor is produced when 8.0 ppm or more of acetaldehyde is produced. In the present study, the acetaldehyde concentrations recorded were, in most cases, quite high, ranging from 6.60 \(\pm\) 0.12 to 66.09 \(\pm\) 18.12 mg kg\({}^{-1}\). The lack of alcohol dehydrogenase enzyme in the bacteria, which is responsible for the conversionof acetaldehyde into ethanol, is suggested to be the reason behind the high acetaldehyde content [12]. As a result, ethanol, a documented volatile compound of yogurt [6; 7] was not determined in any of the dessert yogurts in the present study. According to the literature [25] yogurt products with a very low acetaldehyde content still have a typical yogurt aroma, suggesting that acetaldehyde is only one component of yogurt aroma and does not account for the overall yogurt aroma. Pentanal, hexanal and octanal and nonanal have also been reported as components of the volatile fraction of yogurt [6] formed during the oxidation of milk fat. Of these, pentanal, hexanal and nonanal were determined in the present study in cow yogurt with the addition of _Bifidobacterium_ BB-12. The presence of such aldehydes in the probiotic yogurt prepared with _Bifidobacterium_ BB-12 may be associated with its relatively low acceptability by consumers (see organoleptic evaluation).
# FedOpt: Towards Communication Efficiency and Privacy Preservation in Federated Learning
## 2 System Model and Problem Statement
### Problem Statement  
As mentioned in Section 1, massive communication overhead and malicious users can make the FL infeasible. In this context, we consider the typical environment of FL, where local users  
Figure 2: System Model.  
collaboratively learn a global parametric neural network. Thus, we propose an approach that use data compression technique for efficient communication and integrates additively homomorphic encryption with differential privacy to prevent data from being compromised. The major objective in this approach is to obtain a parameter vector \(\
u\) in Deep Neural Network (DNN) that is required in order to minimise the expected loss \(\varrho\):  
\[\ell(\
u)=\sum_{D_{i}\in D}\sum_{(\mathbf{x},\,\mathbf{y})\in D_{i}}\varrho(f_{ \mathrm{II}}(\mathbf{x},\
u),\mathbf{y}) \tag{1}\]  
As described in the system model, the users learn their local models on their personal datasets and then upload their gradients which are calculated using this loss function to the cloud server. Meanwhile, \(\varrho(x,y)\) denotes the loss function and each user \(\mathrm{II}\) computes the local gradient using gradient function \(f_{\mathrm{II}}\) on its private dataset \(D_{i}\). In order to further ensure the privacy, we apply differential privacy with additively homomorphic encryption on the uploaded gradients during the training process to achieve the highest accuracy.
## 2 Results  
This study investigated the effect of carvacrol on the expression levels of genes coding three main enzymes, farnesyl diphosphate synthase, squalene synthase, and dammarendiol synthase, involved in ginsenoside biosynthesis in hairy root cultures of _P. quinquefolium_ cultivated in shaken flasks. Additionally, in these cultures, the content of eight ginsenosides (Rb1, Rb2, Rb3, Rc, Rd, Rg1, Rg2, and Re) was examined after carvacrol treatment at different concentrations and during different elicitation times.  
Effect of Carvacrol Treatment on Expression Level of Studied Genes in Hairy Roots of P. quinquefolium  
The expression level of three genes, _FPS_, _SS_, and _DS_, involved in ginsenoside biosynthesis was estimated by RT-PCR. An analysis was carried out after 6, 12, and 24 h of treatment with carvacrol at 5, 10, 25, 50, 100, 250, and 500 uM concentrations. It was observed that the elicitation process leads to the overexpression of _FPS_, _SS_, and _DS_ genes. However, the profile of up-regulation depends on the carvacrol concentration and the time of its treatment (Figure 3). Moreover, it was found that carvacrol, at all the used concentrations, brought about an increase in the transcript abundance of farnesyl diphosphate synthase, squalene synthase, and dammarenediol synthase. After 6 h of elicitation, the dynamics of expression changes were similar for all tested genes. In this condition, the maximum level of gene expression, different for each one, was noted for 10 uM carvacrol. It was also observed that a longer-term (12 h) application of carvacrol at a 10 uM concentration showed the strongest overexpression of two studied genes: _FPS_ and _SS_. Then, the transcripts level of _FPS_ was 70-fold higher in comparison to that of untreated samples. Meanwhile, the up-regulation of the _SS_ gene was lower, i.e., it increased 12-fold. The quantitative RT-PCR analysis confirmed the up-regulation of the _DS_ gene, which was the most intense after 24 h of treatment with 25 uM of carvacrol. Furthermore, the performed analyses showed the lowest (10-fold) increases in the maximum relative expression of the _DS_ gene in comparison to this parameter estimated for _FPS_ and _SS_.
# WISCANet: A Rapid Development Platform for Beyond 5G and 6G Radio System Prototyping
## 3 System Design
### GPU Acceleration  
We explore different acceleration approaches by updating the most computationally-demanding computations to run on Graphics Processing Units (GPUs). Some WISCANet applications have used general-purpose GPUs (GPGPUs) to improve processing speed using CUDA, an API developed by Nvidia to expose the GPU architecture and parallel computing elements [74]. AMD ROCm, a similar open-source software platform, can also be used in this context [75]. These tools enable characterization of novel GPU acceleration algorithms using real data.