esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Literature Review on Relevant Studies
### Radar Point Cloud and Camera-Based Sensor Fusion for Object Detection  
Radar sensors actively perceive the surroundings and analyze the reflected waves to determine the position and the speed of objects by constantly emitting radio waves. The world of radar signal processing for autonomous vehicles offers a rich tapestry of techniques to extract meaningful information from the sensor's output, for example, doppler processing [26], occupancy grid maps [27], multi-input multi-output (MIMO) radar [28], and representations using radar point clouds [29]. The work presented in this paper focuses on radar point-cloud data representation for radar signal processing using the proposed deep neural network for 3D object detection.  
Typically, radars in automotive applications determine objects as 2D reference points in bird's eye view (BEV), synthesizing information on the radial distance to/from the object. In each radar detection, information is provided about the object's instantaneous velocity in the radial direction, along with the azimuth angle. The azimuth angle represents the horizontal angle of the object concerning the observer, which, in this case, is the radar sensor. The azimuth angle is crucial for determining the location of detected objects in the radar's field of view. Figure 2 provides a graphical representation of the difference between the radial velocity and the true velocity.  
Figure 2: Comparison between velocities: the true velocity (\(v^{A}\)) for object A is equal to the radial velocity (\(v_{r}\)), whereas, for object B, the radar-reported radial velocity (\(v_{r}\)) indicated by the red arrow is not equal to the true velocity (\(v^{B}\)).
## 3 Proposed Approach  
The proposed approach for secure UAV delivery in delay-tolerant networks consists of using an enhanced CBC approach to encrypt the data of consecutive transactions, as illustrated in Figure 3. The first encrypted block uses an initialization vector (IV). In the proposed approach, this block corresponds to logging the start of the UAV mission and it indicates the date, time, mission number, etc. when leaving the Control Center. Thus, the IV needs only to be stored at the Control Center, and not at the UAV. Afterwards, the UAV follows its planned trajectory. At each delivery location, it captures the relevant information, such as the item delivered, location, person/entity to which it was delivered to, and indication of correct delivery or if a problem occurred, such as theft or pickup by the wrong person. Then, it encrypts this transaction data using symmetric encryption (the blocks labeled \"Encryption\" in Figure 3) and CBC.  
With CBC, the data of the current transaction are XOR-ed with the encrypted data of the previous transaction, and then the result is encrypted using a symmetric key. In traditional CBC, the key used for symmetric encryption is the same for each CBC transaction. Having this key stored at the UAV poses some security risks, e.g., in case the UAV is captured  
Figure 3: CBC process for encrypting the transaction data.  
by malicious end users, who might then be able to locate the key in the memory and trace/manipulate the transactions performed. Therefore, in the proposed approach, we provide a remedy to this problem by adopting the following steps:  
* Use a different key for encrypting each transaction data, i.e., generate a key Ki for transaction number i;
* Generate this key on the spot, using physical layer security (PLS) techniques;
* After symmetric key encryption using CBC, use asymmetric encryption to encrypt the key only, with the public key of the Control Center Kpublic; and
* Store the symmetric key \(K_{i}\) encrypted with \(K_{public}\) as \(\left[K_{i}\right]_{K_{public}}\), but never the unencrypted key \(K_{i}\) as it is.  
After the end of the UAV's flight mission, in the Control Center, the encrypted data can be recovered along with the encrypted keys. The keys can be decrypted using the Control Center's private key and then used to decrypt the CBC data of the various transactions. The process is illustrated in Figure 4.
## 3 Mathematical Formulation of the Reactive Power Supply
### Cost of Reactive Power Generation of the Compensators
#### 3.4.1 Svc  
This device is connected in parallel with buses that might operate in capacitive or inductive modes. It can be modeled as a capacitor with variable capacitance ranging from positive to negative. In power flow equations, the bus with SVC is considered as a generator bus with zero active power generation [12]. Figure 2 shows the SVC model.  
Figure 1: The \(\pi\)-model of the transmission lines with tap changer.  
Figure 2: Single-line diagram of a Static Var Compensator model.  
The total cost of SVC is a second-order function of its nominal capacity. The SVC cost can be calculated by Equation (8) [13].  
\[C_{sync}=0.0003s^{2}-0.3051S+127.38\bigg{(}\frac{USS}{K}var\bigg{)} \tag{8}\]  
In the above equation, S is the rated capacity of the SVC in MVAr. Considering the 5-year lifetime, the cost is calculated in $/h.
# Zernike Coefficient Prediction Technique for Interference Based on Generation Adversarial Network
## 3 Results
### Testing_2 and Transfer Learning with Simulated Images  
To understand the applicability of the GAN model and whether it can predict the coefficient with the actual interference fringes, the simulated images are used to test the GAN model. According to the Testing_2 process, the Generator makes ten fake images using the simulated image of interference fringe and obtains ten RMSE. After averaging the ten RMSE values, the result is about 0.101 \(\pm\) 0.0263\(\lambda\) in which some of the predicted values of Zernike coefficients have significant errors. The prediction accuracy with the simulated image is worse than the ideal image since the image has more image details, and the training datasets do not include this kind of data.  
Figure 6: The ideal images of interference fringe. (**a**) The phase shift is zero. (**b**) The phase shift is \(\pi/4\). (**c**) The input image of the Generator according to Equation (5).  
For these reasons, the GAN model needs to be trained again using the transfer learning process with 100 simulation images. After the transfer learning process, the new GAN model is acquired, and it is tested again using ten of the same simulated images. After averaging the ten RMSE values, the result is about 0.0586 \(\pm\) 0.0183\(\lambda\). The new GAN model is better than the pre-trained model, and the tested RMSE is reduced to 0.0424\(\lambda\). Therefore, we prove that the transfer learning can improve the prediction accuracy of the GAN model.  
Figure 8: The Zernike coefficients and the predicted values.  
Figure 7: The mosaic image for 32 Zernike coefficients. (**a**) Target image (actual image). (**b**) The Generator’s output image (fake image).  
For example, one simulated image of the interference fringe as the input of the GAN model is shown in Figure 9, and it is closer to the actual image than the ideal image. The comparison between the Zernike coefficients and predicted values is shown in Figure 10, and we do not show the difference of the first coefficient because the effect is negligible.
## 5 Recommendation and Future Research Direction
The model is trained incrementally as new data becomes available and is capable of adapting to the changes in the data distribution. [153] proposed an approach based on evolutionary neural network models for time-evolving text classification. In the field of RS, a considerable effort has been recorded on the application of deep learning methods to improve the performance of RS [154; 155; 156; 156; 15]. An extensive review of the application of deep learning techniques in RS has been done by [61] for reference purposes. However, despite a lot of attention given towards deep learning in RS, very few attempts have been made to explore the temporal deep learning approachfor DRS. Therefore, as a future research direction, there is a need for extensive research to explore the full potentials of deep learning techniques to improve the existing DRS models. Another point worth noticing is the evaluation strategy used to assess the performance of DRS systems, as almost all the reviewed studies were evaluated based on the offline approaches. Even though the offline evaluation is considered to be of lower cost with no bias of response from active user involvements as in the case of online and user studies, the results mostly contradict when applied in real-life applications with the online and user studies evaluations. Therefore, there is a huge need for more research on the evaluation strategies to compare performance based on different performance measures other than offline evaluation, like real-time, novelty, coverage, serendipity and diversity among others [157].
# Short-Term Forecasting of Power Production in a Large-Scale Photovoltaic Plant Based on LSTM
## 5 Conclusions  
This paper discussed the LSTM algorithm theoretically and the method was applied in PV power output time series forecasting. Without changing the input layer, optimizer, output layer, and learning rate, this paper discusses the influence on forecasting performance by changing parameters and the number of hidden layers. The developed model is versatile, and that can be used to forecast the solar PV power output without any complicated calculations for any region provided comprehensive historical power and meteorological data. The final LSTM model for four seasons is established by a  
Figure 11: Short-term PV power forecasting in summer by various algorithms.  
Figure 12: Short-term PV power forecasting in autumn by various algorithms.  
Figure 13: Short-term PV power forecasting in winter by various algorithms.  
training set and evaluated by a validation set. Finally, 1-h-ahead forecasting of the power generation is achieved by a test set.  
Furthermore, this paper describes and compares four short-term forecasting models (LSTM, BP networks, WN networks, and LSSVM) for 1-h-ahead forecasting of the power generation using meteorological data in next hour. The RMSE values of forecasting using LSTM models in four seasons are 5.34%, 9.57%, 13.86%, and 9.26%, respectively. While the lowest values using other algorithms are respectively 18.8%, 13.03%, 20.94%, and 17.74%, respectively. The result shows that the accuracy using the final LSTM model for forecasting is sufficiently high and that it can be used in instantaneous control of micro grids. A PV power producer is economically more penalized when larger derivation (error) exists between real power productions and electric energy sale bids of the PV plant. Therefore, the method proposed in this paper contributes to the reduction of economic penalizations in PV plant owners' retributions and, therefore, to increasing net profits for PV plant owners.  
Conceptualization, M.G. and F.H.; M.G. and F.H.; software, J.L. and F.H.; validation, J.L. formal analysis, J.L. and F.H.; investigation, F.H. and J.L.; resources, F.H.; data curation, J.L.; writing--original draft preparation, J.L. and F.H.; writing--review and editing, J.L., F.H. and D.L.; visualization, J.L.; supervision, J.L. and F.H.; project administration, M.G.; funding acquisition, M.G.  
This project was funded by the Project Supported by the Fundamental Research Funds for the Central Universities (2018 ZD05).  
The authors declare no conflict of interest.
# Reducing Defense Vulnerabilities in Federated Learning:
## 4 Attack Scenarios and Results
### Performance Analysis  
Various simulations have been carried out to evaluate the defense performance of the NC-FLD. The first of these includes a label-flipping attack for the data poisoning attack scenario. The second one is sign-flipping and LIE attacks from model poisoning attacks. The following subsections show the robustness of the NC-FLD algorithm against poisoning attacks and pattern attacks, respectively. For each scenario, we run 100 \(FL_{cycles}\) rounds before calculating the test accuracy on the global model. These simulations are run 10 times and the average performance of all the simulations is given in tables in this section.  
Our evaluation covers multiple attack types across various datasets. We examine two primary attack categories:  
Data Poisoning: Implemented as label-flipping attacks where malicious users deliberately mislabel training samples. For CIFAR-10, we flip dog (label 5) to cat (label 3); for F-MNIST, we change the labels for shirt and t-shirt; and for MNIST, we change the labels for digits 7 and 1.  
Model Poisoning: implemented through (1) sign-flipping attacks where malicious users invert gradient signs (\(\Delta\theta_{\text{k}}{}^{\text{t}}\rightarrow-\Delta\theta_{\text{k}}{}^{ \text{t}}\)) and (2) LIE attacks where updates are manipulated to evade detection while maximizing the negative impact on the global model.  
We evaluate the performance across both IID and non-IID data distributions with malicious participation rates ranging from 0% to 50%. For each scenario, we run 100 FL rounds and measure the overall accuracy on the global model.
## 5 Open Challenges and Future Directions
### Towards Integrated, Real-Time, and Edge-Aware Solutions  
To truly bridge theory and practice, future AI models must be designed for deployment in real-time, field-ready environments. This involves reducing computational complexity, optimizing models for embedded hardware, and ensuring resilience to sensor noise or partial occlusion. Advances in edge AI, including model pruning and quantization, can facilitate on-device inference for mobile GPR platforms, such as UAVs, robotic rovers, or handheld units. Moreover, integrating AI models into modular interpretation pipelines, including pre-processing, detection, classification, and visualization, will streamline adoption and reduce operational friction [91; 92; 93; 94; 95; 96; 97; 98; 99; 100; 101; 102].  
Conceptualization, L.Z. and A.M.A.; methodology, L.Z. and Y.L.; software, L.Z. and Y.L.; validation, L.Z., K.M. and A.M.A.; formal analysis, Y.L.; investigation, L.Z.; resources, K.M.; data curation, L.Z.; writing--original draft preparation, L.Z. and Y.L.; writing--review and editing, K.M. and A.M.A.; visualization, L.Z.; supervision, A.M.A.; project administration, K.M.; funding acquisition, A.M.A. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
No new data were created or analyzed in this study.  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} AI & Artificial Intelligence \\ GPR & Ground Penetrating Radar \\ ML & Machine Learning \\ SVMs & Support Vector Machines \\ DL & Deep Learning \\ CNNs & Convolutional Neural Networks \\ KNN & K-nearest Neighbors \\ YOLO & You Only Look Once \\ RNNs & Recurrent Neural Networks \\ LSTM & Long Short-Term Memory \\ PINNs & Physics-Informed Neural Networks \\ IoU & Intersection-over-Union \\ mAP & Mean Average Precision \\ SSD & Shot MultiBox Detector \\ UAVs & Drones \\ UGVs & Ground Robots \\ XAI & Explainable AI \\ \end{tabular}
## 5 Verification and Small-Scale Demonstration
### Application Use Case Description  
The capabilities of the proposed system are demonstrated in the precision agriculture domain, since the deployment of numerous sensors enables the farmers to closely monitor their fields and substantially increase their yield and quality while lowering the production costs. In our use case, the proposed system is used to gather the required data from three different vineyard pilots as Figure 23 depicts. The physical installation of the IoT nodes with their sensors and the corresponding Iota Powered Nodes followed the plan indicated by domain experts (phytopathologists). Each IoT node with its sensors collects various sets of data covering a radius of 25 m with a minimum distance between two IoT nodes of 50 m. One Edge Node is deployed in each pilot. In pilot 1 the following parameters are collected: soil moisture, air temperature, air humidity, and atmospheric pressure. In pilot 2 the following parameters are collected: leaf wetness, air temperature, air humidity, and solar radiation. In pilot 3 the following parameters are collected: rain amount, air temperature, air humidity, and wind direction/speed. Each IoT node is in a hibernate state and every 10 min wakes up, collects the measurements from its sensors and emits them at a pre-agreed speed and frequency channel. Each IoT node has a unique 16 bit ID and does not expect any acknowledgement from the receiving Edge Node assuming that the measurements have reached the destination. Each packet of measurements, with the correct CRC, constitute a text-based dataset of an email which is send to the database of the backend server through GPRS once a day. The above parameter data have been collected uninterruptedly in the backend server for approximately five (5) months. The collected information is valuable to the farmer and his agronomist as it offers a live view of the farm's micro-climate. The agronomist can monitor different meteorological phenomena, which are used to detect various diseases within vineyards such as Downy mildew, Black rot and Botrytis. Details regarding the use of the collected information by the agronomist and the parameters of the smart-agriculture pilot are not the goal of this work and are omitted.
## 3 Results and Discussion
### Dough Rheological Properties during Mixing Stage of the Mixolab
#### 3.5.3. Dough Stability  
The regression model for dough stability (ST) is represented by Equation (8):  
\[\text{ST }=9.86\ +0.1660\cdot\text{X}_{1}-0.4867\cdot\text{X}_{2}\ -0.0490\cdot\text{X}_{1}\cdot\text{X}_{2}\ -0.1240\cdot\text{X}_{1}^{2}\ -0.3848\cdot\text{X}_{2}^{2} \tag{8}\]  
Dough ST was significantly influenced (\(p<0.05\)) by particle size and by the level of QF added in wheat flour. The quadratic model fitted to the experimental results of dough ST showed a good coefficient of determination (\(R^{2}=0.69\)). The statistical analysis of the model coefficients showed that the linear terms were highly significant only for the level of QF added (\(p<0.05\)), while the linear term of particle size, interaction coefficient and quadratic terms were nonsignificant (\(p>0.05\)).  
The dough ST ranged from 8.40 to 10.35 min for the composite flours' formulations. An increase in QF addition level decreased significantly (\(p<0.05\)) dough ST (Figure 1d). Compared to the control sample, it seemed that the weakening of dough caused by the addition of QF was not excessive, probably due to the percentage of lipids from the QF which are able to form lipoprotein complexes, leading to the stabilization of dough [74]. The decrease in dough ST with the increase of QF level may be related to the lower water availability in the dough system because it can be seen that the water absorption capacity of dough decreased with an increase of QF level. Gluten dilution, when nongluten flour was added, diminished the dough's viscoelastic properties [75] and, therefore, the ST decreased. At a high QF level the gluten networks may be disrupted, which can lead to dough weakening.
# Dual-Band MIMO Antenna with Enhanced Isolation for 5G NR Application
## 5 Conclusions  
In this paper, a dual-band MIMO antenna with high isolation has been designed. The size of the proposed antenna is 41 \(\times\) 30 \(\times\) 1.59 mm\({}^{3}\) and the impedance matching (which is better than \(-\)10 dB) covers 5G NR sub-6 GHz n7/n38/n41/n79. The high isolation of a value that is better than 20 dB is successfully realized by adding the defected  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Ref.** & **Operating Bands (GHz)** & **Isolation (dB)** & **ECC** & **Gain (dBi)** & **Size (mm\({}^{3}\))** \\ \hline
[7] & 2.25–2.9 & \textgreater{}19.3 & \textless{}0.03 & 2.4 and 3.8 & 50 \(\times\) 50 \(\times\) 1.6 \\
[12] & 5.05–6.025 & & & & \\
[12] & 2.23–2.46 & \textgreater{}12  
ground structure and the slotted stub between the two antenna elements. Moreover, it is found that the antenna's lower gain and efficiency for the proposed system are 3 dBi and 62%, respectively. The measured ECC is less than 0.005, which meets the requirement of the MIMO systems. Therefore, the experimental results and the designed MIMO antenna structure exhibit that the presented antenna is preferred for 5G communication applications.  
Conceptualization, S.X.; methodology, J.C.; optimization, L.S.; investigation, Q.L.; measurement, L.S.; writing--original draft preparation, S.X.; writing--review and editing, Q.L. and G.L.; supervision and funding acquisition, G.L. All authors have read and agreed to the published version of the manuscript.  
This work was funded in part by the Science and Technology Department of Zhejiang Province, under Grant No. LGG19F010009, the National Natural Science Foundation of China, under Grant No. 61671330, and Wenzhou Municipal Science and Technology Program, under Grant No. C20170005.  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
The authors declare no conflict of interest.
## 4 Results and Discussion
### Comparison of Suction and Discharge Pressure Pulsation Influence  
Figure 12 shows the comparison of suction and discharge pressure pulsation influence on specific work for ideal and real gas models. As can be seen, results illustrate that pressure pulsations with small amplitude in the suction or discharge system have a slight effect on specific work in both gas models, with a increment about 1-2%. However, the specific work will increase significantly as the suction or discharge system is located in the 1 st order resonance state. Furthermore, discharge resonance increases work to compress one cycle working gas than that needed by suction resonance, with the ratio of 13% and 10.7% for the real gas model. In Figures 8 and 11 the indicated power in the real gas model is slightly higher than that in the ideal gas model, and the specific work for the real gas model is less than that for the ideal gas model.
# A Survey of UV Filters Used in Sunscreen Cosmetics
## 5 Conclusions  
Several popular UV filters are present in sunscreen products on the Polish market, i.e., within the EU. The most common organic UV filters found in child sunscreen products, with a frequency of use above 30%, are bis-ethylhexyloxyphenylenol methoxyphenyl triazine (BEMT; Tinosorb S)--60.0%, ethylhexyl triazone (EHT)--52.0%, ethylhexyl salicate (EHS)--46.0%, diethylamino hydroxybenzoyl likely1 benzoate (DHHB)--38.0%, and butyl methoxydibenzoylmethane (BMBM; Avobenzene)--36.0%. The most common adult photoprotectors were butyl methoxydibenzoylmethane (BMBM; Avobenzene)--56.0%, bis-ethylhexyloxyphenyl methoxyphenyl triazine (BEMT; Tinosorb S)--54.7%, ethylhexyl salicate (EHS)--54.7%, ethylhexyl1 triazone (EHT)--50.0%, diethylamino hydroxybenzoyl likely1 benzoate (DHHB)--38.0%, and octocrylene (OC)--36.7%. The most common organic compounds were triazine, salicylic acid, dibenzoylmethane, and benzophenone derivatives.  
Mineral filters, especially titanium dioxide, are much more popular among child sunscreens (TiO\({}_{2}\)--28.0% and TiO\({}_{2}\) nano--22.0%) than for adults (TiO\({}_{2}\)--14.7% and TiO\({}_{2}\) nano--6.7%). Ten of the UV filters listed in Annex VI to Regulation (EC) No 1223/2009 are not used at all in sunscreen products; these are mostly benzylidenecamphor derivatives, with some benzophenone and triazine derivatives.  
Conceptualization, U.K.-L.; Methodology, A.P.; Formal analysis, A.P.; Investigation, A.P.; Data curation, A.P.; Writing--original draft, A.P.; Writing--review & editing, U.K.-L.; Visualization, A.P.; Supervision, U.K.-L.; Project administration, U.K.-L. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Medical University of Lodz, grant No. 503/3-066-02/503-31-001.  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
We would like to thank [PERSON] for her skillful contribution and support for the article.