esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology  
In conjunction with the three-dimensional Monte-Carlo (MC) and drift-diffusion (DD) simulation techniques [12; 16], this paper draws on Poisson-Schrodinger (PS) quantum corrections [17; 18]. See Figure 2 for the author's approach to simulation. As will be clear to the reader, the quantum-corrected MC simulations, which valuably reflect the non-equilibrium transport effects [17; 19; 20; 21; 22], generate predictive simulation results. After conducting the DD simulations and, in turn, calibrating these in view of the MC results, the former can be used to reflect the contact resistance impacts [4]. Furthermore, it is possible to use the calibrated DD simulations to examine relative changes in I\({}_{\text{on}}\), thus facilitating design optimization and, in addition to this, the creation of efficient statistical variability (SV) and statistical reliability (SR) simulations [12; 23; 24].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Z (nm) \(\times\) Y (nm)** & **Y/Z** & **Area (nm\({}^{2}\))** \\ \hline
4.440 \(\times\) 9.0 & 2.020 & 10\(\pi\) \\
5 \(\times\) 8.10 (Phi) & 1.620 & 10\(\pi\) \\
5.7 \(\times\) 7.0 & 0.810 & 10\(\pi\) \\
6.0 \(\times\) 6.66 & 0.900 & 10\(\pi\) \\
6.32 \(\times\) 6.32 & 1.000 & 10\(\pi\) \\
6.66 \(\times\) 6.0 & 1.110 & 10\(\pi\) \\
7.0 \(\times\) 5.7 & 1.220 & 10\(\pi\) \\
8.10 \(\times\) 5 (1/Phi) & 0.620 & 10\(\pi\) \\
9.0 \(\times\) 4.44 & 0.490 & 10\(\pi\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulated devices’ physical cross-section dimensions.  
Figure 1: Three-dimensional representation of Si-NWTs (with cross-sectional dimensions of 5 nm \(\times\) 8.1 nm), as well as chemical constituents. The straight, white lines indicate that golden ratio (Phi) can be observed on the cross-sectional slice with respect to the middle of the nanowire transistors (NWT) channel.
# Roller-Integrated Acoustic Wave Detection Technique for Rockfill Materials
## 3 Case Study
### Testing Program  
To analyze the effectiveness of SCV for assessing the compaction quality of rockfill materials, a two-part field experiment was performed. The first part of the experiment studied the correlation between the compaction parameters and SCV. Four testing strips were filled with NGM with consistent moisture content and gradation. Each strip had a length of 30 m and a width of 3 m. Figure 12 shows photos of the field rolling test in the rock-fill zone located in Elevation 1 and Elevation 2 (upstream cofferdam), and the test scheme is shown in Table 2. NGM represents natural gravel materials, and HFHA represents high-frequency/high-amplitude. The second part of the experiment was traditional point measurements (water-filling method) aimed at analyzing the correlation between SCV and the number of compaction times as well as the compactness of the NGM.  
Figure 11: Grading curve of gravel material taken from in situ VI zone material field.  
To evaluate this technique, the field study was conducted in 30-m test strips (Figure 12) using NGM. A set of field data was obtained, including compaction parameters, SCVs and results of traditional point measurements that were used for correlation analysis and verifying the effectiveness of this technique. The test strips were compacted using a prototype SSR260C-6 vibratory smooth drum roller. The depth of the filling layer was approximately (and not exceeding) 0.8 m. Test sites of two different elevations were used for this field test. Each strip was divided into a start region, an effective rolling region, and a stop region (Figure 12, Strip 4). The length of the start region and the stop region was 3 m, and the length of the effective rolling region was 24 m. Considering the diameter of the test pit in the water-filling method and the accuracy of this technique, the effective rolling region was divided
# How Time Window Influences Biometrics Performance: An EEG-Based Fingerprint Connectivity Study
## 4 Discussion
Furthermore, we speculate that the poor performance obtained with short time windows mainly depends on the difficulty of correctly estimating the connectivity metrics from very small EEG epochs (shorter than 8 s). When the EEG epoch is too short, there may not be enough data to provide a stable and accurate estimation, leading to poorer performance in biometric applications. This insight highlights the importance of selecting an appropriate epoch length in designing and implementing EEG-based biometric systems, ensuring that the time windows used are sufficiently long to capture the necessary information for robust feature extraction and system performance.
# A Survey of Wi-Fi 6: Technologies, Advances, and Challenges
## 3 Wi-Fi 6 Overview
### Novel Features
#### 3.1.2 Spatial Reuse (SR)  
Wi-Fi technology has only a few different frequency channels that may be utilized by neighboring Basic Service Sets (BSSs). When multiple access points are close to each other, the BSSs are overlapping. As a consequence, a transmitting node blocks other transmissions in its BSS and in other BSSs with the same channel, which makes frequency reuse difficult [20].  
Wi-Fi 6 utilizes the concept of BSS coloring from IEEE 802.11 ah to indicate the BSS in the packet header. A node takes advantage of this information to overhear transmissions of packets from other BSSs. As a result, a node may start a transmission if it estimates that it will not create a collision with ongoing transmissions that are part of another BSS. This improves SR in Wi-Fi 6 [21]. Section 5 explains the details of this feature.
## 5 Discussion
### Empirical Implications  
The successful promotion of sustainable development through digitalization depends on a robust legislative and regulatory framework tailored to individual EU countries' specific needs and capacities. Recognizing the diversity in economic and digital development levels, EU governments must adopt differentiated policies that support innovation, ensure data protection, enhance cybersecurity, and foster sustainable economic practices. For countries with advanced digital infrastructure, these policies could focus on optimizing existing systems and fostering frontier innovations, while less developed regions may prioritize foundational investments in connectivity and digital literacy.  
Collaboration between the public and private sectors, non-governmental organizations, and local communities is essential to address these disparities. Establishing partnerships can facilitate the transfer of knowledge and resources, enabling less digitally advanced regions to benefit from the experiences of their peers. Highly developed countries can share best practices in integrating digital technologies into public services, while economically weaker regions can leverage these insights to accelerate their digital transformation.  
Companies close these gaps by integrating sustainability into their digital strategies and collaborating with governments to develop innovative solutions to address local, social, and environmental challenges. Tailored training and education programs are critical to ensuring that workforces in all regions are equipped for digital transformation. To maximize inclusivity and effectiveness, these programs must consider regional needs such as language barriers, technological access, and economic constraints. Investments in human capital will help to reduce inequalities between countries, ensuring that all EU nations can participate in, and benefit from, the digital revolution.  
Green digital technologies, such as energy-efficient solutions and electronic waste management systems, should be prioritized to minimize environmental impacts. Less developed countries can adopt low-cost, sustainable technologies, while wealthier nations can lead in developing cutting-edge solutions. A circular economy model, supported by research and development investments in sustainable technologies, can serve as a unifying framework for all EU countries, promoting resource efficiency and environmental conservation.  
By addressing these practical implications and emphasizing regional adaptation, European Union countries can ensure that digitalization accelerates progress toward the Sustainable Development Goals and promotes equitable and inclusive development across all member states. This differentiated approach enables countries at varying stages of development to harness the transformative power of digital technologies in alignment with their specific economic and digital contexts.
# Adaptive QoS-Aware Multi-Metrics Gateway Selection Scheme for Heterogenous Vehicular Network +
## 4 Simulation Experiment Settings
### Average Delay  
The average delay refers to the amount of mean time it takes for a packet to go from a point source to a destination. In general, this delay is caused by several factors, such as propagation delay of the packet, retransmission delay on MAC layer in the route discovery process, clustering, re-clustering, and cluster head election process. All of these factors play a major role in elevating the delays. However, a lower value of this metric indicates a higher performance of the scheme in terms of the gateway discovery and selection processes. Equation (15) shows the delay metric computation [47].  
\[Delay=t_{r}-t_{s} \tag{15}\]  
where \(t_{r}\) is the time at which the response is received by the IV and \(t_{s}\) is the time at which the request is transmitted by the IV.
# Urban PM2.5 Concentration Prediction via Attention-Based CNN-LSTM
## 3 Data and Method
### Method  
#### 3.5.1 Convolutional Neural Network  
The CNN has excellent performance in image processing [22], and it can be effectively applied to time series analysis [2]. CNN's local perception and weight sharing features can reduce the number of parameters for processing multivariate time series, thereby improving the learning efficiency [2]. Spatiotemporal features can be easily extracted by the one-dimensional (1D) CNN (1D-CNN) from the model input. Let the given model input be \(X=[x_{1},x_{2},\cdots,x_{t}]\), consisting of meteorological data, pollutant concentrations, and PM\({}_{2.5}\) concentrations at adjacent stations in the past. Firstly, the model input \(X\) is input to the 1D-CNN layer; hence, we have  
\[l_{t}=\tanh(x_{t}*k_{t}+b_{t}), \tag{3}\]  
where \(x_{t}\) represents the input vector, \(k_{t}\) is the convolution kernel, \(b_{t}\) represents bias vector, and \(l_{t}\) is the output vector of the 1D-CNN layer. The output of the 1D-CNN layer is a spatiotemporal feature matrix \(L=[l_{1},l_{2},\cdots,l_{t}]\).
## 3 Results
### Training, Test and Results of Simple Perceptron Neural Network  
As mentioned, we used two different models to process the data: a simple Perceptron implemented with the Sequential Neural Network Model, which is the most basic approach, and a more sophisticated and advanced design that uses several layers, known as Deep Neural Network (DNN). In this section, we present the results of the Perceptron. In Figure 1, the Perceptron architecture can be seen.  
This ANN has a simple structure: one normalization layer and one processing layer, with a total of five parameters. It uses Mean Absolute Error as the loss function and Adam as the optimizer function. The training process used 150 epochs with batch sizes of 50 cases. It took 5 min, 55 s and 930 milliseconds. In Figure 2, it is possible to see the evolutions of the values of the loss function during the training process. In the first 60 epochs, the process improved the adaptation of the network results. From that moment, the improvement was negligible.  
The dataset has data of patients that belong to 100 different DRGs. We tested the Neural Network on a set of DRGs of patients chosen randomly, predicting their Average Total Payments. We selected seven of them, and we present the results obtained in Table 1. The columns of this table are the DRG code, the number of patients that belonged to the diagnostic group, the time spent by the network to make the predictions and the accuracy achieved by the model for each group.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**DRG** & **Number of Patients** & **Time** & **Test** \\  & & & **Accuracy** \\ \hline
39 & 1079 & 3.957 s & 85.04\% \\ \hline
64 & 1669 & 4.344 s & 79.48\% \\ \hline
178 & 1953 & 4.250 s & 83.68\% \\ \hline
473 & 846 & 4.188 s & 83.55\% \\ \hline
536 & 1142 & 4.480 s & 83.75\% \\ \hline
690 & 2989 & 4.105 s & 83.04\% \\ \hline
811 & 1135 & 4.169 s & 80.14\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Sequential Neural Network results on DRG billing prediction.  
Figure 1: First Neural Network design. Prepared by the authors.  
Figure 2: Sequential Neural Network training evolution. Prepared by the authors.  
The accuracy achieved was around 80%, and the time spent running the Neural Network was around 4 s. It can be concluded that the Neural Network has a good prediction capacity and that the time for running is acceptable. We were not able to find a relation between the number of cases in each group and the accuracy value or the execution time.  
In Figure 3a,b, the prediction results and error count for a particular DRG, group 178, are plotted.  
As a final comment, the simple Sequential Model had good performance, and it was mainly because the relationship among the variables was linear. This model is a simple and effective approach to solve the problem under the conditions defined, but we do not believe that it could be used in real applications.
# Radio Frequency Fingerprint Identification for 5G Mobile Devices Using DCTF and Deep Learning
## 1 Introduction
Compared with the PRACH preamble of LTE, 5G NR supports more preamble formats. The formats A, B and C have been introduced to adapt to the flexible frame formats of NR. These preamble formats are much shorter than that of LTE. Moreover, multiple root values are required to generate the 64 preamble sequences of one cell, while for the long preamble of LTE, one root value is sufficient. With the study in this paper, we note that it is difficult to use a single CNN model to uniformly identify all the 64 preamble signals of formats A, B and C. Hence, an index-based DCTF identification scheme is proposed where the CNN model is trained for each PRACH preamble sequence. Moreover, for the case where the gNodeB (gNB) does not have the database of all the preamble sequences, the unknown sequence can also be identified through a _group_-based DCTF identification scheme. The transient features of a 5G mobile phone were studied in [31], but the identification accuracy was only 75.8% with four devices. The main contributions of this paper can be summarized as follows:  
* An index-based DCTF identification scheme is proposed for the RFF identification of 5G mobile phones. The identification accuracy reaches 92.78% with six devices. * When the database of all the preamble sequences is not available at the gNB, a _group_-based DCTF identification scheme is proposed to identify the unknown sequence based on known sequences within the same _group_, where preamble sequences generated from the same root value are grouped together. The classification accuracy of this case is 89.59%. * An experimental system has been set up using 5G gNB and six 5G mobile phones of three models. The 5G gNB and core network are built with the OpenAirInterface (OAI) platform and universal software radio peripheral (USRP) B210. The PRACH signals are collected using a USRP N210. Due to the frequency calibration between the mobile phone and the base station, CFO cannot be used as a standalone RFF feature for 5G phones. For comparison purposes, the frequency domain equalization (FDE) method [15], which consists of using the frequency domain-equalized I/Q samples with a rebuilt CFO, and an I/Q imbalance feature [27] are also simulated. The experimental results show that the index-based DCTF identification scheme outperformed the other methods for all the SNR values. The _group_-based DCTF identification scheme performed similarly to the FDE method at a low SNR, while at a high SNR, this scheme outperformed the FDE method. The rest of this paper is organized as follows. In Section 2, the DCTF extraction method for a 5G PRACH preamble is introduced. Section 3 presents two CNN models for DCTF classification and proposes two identification schemes for two scenarios with different levels of knowledge of the preamble sequences. The experimental system and results are presented in Section 4. Section 5 concludes the paper.
# Novel Feature Extraction Method for Detecting Malicious MQTT Traffic Using Seq2 Seq
## 4 MQTT Attack Analysis
### Flood Attack  
The flood and DoS attacks are similar. As shown in Figure 8, although the client has many connections with the broker during a DoS attack, it has only one connection with the broker during a flood attack and sends many packets through the connection. The client sends many large packets to the broker, which negatively affects the broker's performance. Flood attack traffic was generated using the IoT-Flock tool [23].
# Review of Physical Layer Security in Integrated Satellite-Terrestrial Networks
## 5 Future Directions
### Emerging Techniques
Cross-layer security approaches are being pursued to provide comprehensive end-to-end protection for satellite communication systems. This involves integrating physical layer security with higher-layer security protocols, creating a holistic security framework that addresses vulnerabilities across all layers of the communication stack. By considering security at every layer, researchers aim to develop systems that are resilient to a wide range of threats. Standardization and policy development are also critical for ensuring the security of satellite communications on a global scale. International collaboration between academia, industry, and government agencies is necessary to create standardized security protocols, certification processes, and regulatory frameworks that protect global satellite systems. These efforts are particularly important as the number of satellites in orbit continues to grow, increasing the complexity and potential attack surfaces of satellite communication networks. There is growing interest in understanding the impact of space environment effects, such as space weather and cosmic radiation, on the physical layer security of satellite communications. Space weather events, such as solar flares and geomagnetic storms, can disrupt satellite signals and potentially compromise security. Research is therefore focused on developing techniques to mitigate the effects of these environmental factors, ensuring that satellite communication systems remain secure and reliable even in challenging conditions. The inclusion of signatures in NTN watermarking offers a promising avenue for enhancing data authenticity and traceability. While this technique is well established in GNSS, its application to NTNs warrants further investigation [129]. By embedding signatures within the message or spreading code, it becomes possible to verify the origin and integrity of transmitted data, mitigating the risks associated with unauthorized modifications or fraudulent activities. The dissemination of signatures in NTN environments presents unique challenges due to the distributed nature of these networks. The TESLA protocol, designed for the secure dissemination of cryptographic keys, can be adapted to address this issue [130]. By carefully considering the signature type, placement, and dissemination mechanisms, researchers can develop effective and secure signature-based watermarking techniques that cater to the specific requirements of NTN applications. Collectively, these research efforts aim to enhance the resilience and security of satellite communication systems in an increasingly connected and threatened world, addressing the unique challenges posed by the space environment and the evolving nature of cyber threats.
# Effect of Buildings on the Radiation Characteristics of MF Broadcast Antennas
## 4 Discussion
Moreover, it is interesting to note that, even at such high frequencies, the presence of buildings may distort the radiation patterns and produce side lobes [4; 6; 13]. For the modeling of buildings, it is common practice to assume that they are conductive bodies (as in the present study) not only at the MF/HF bands [2, 11, 12], but even at higher frequencies [4, 13, 29]. The preferred shapes are either cylindrical [4, 12, 13] or rectangular [2, 13, 29]; the latter is also assumed herein. The wire-grid method, adopted in our study to model the buildings, has also been used by other researchers [29], albeit in conjunction with the MoM. Recently, [PERSON] et al. [2] adopted a mesh of metallic wire segments for the simulation of buildings combined with a completely numerical method based on a commercially available software package (FEKO).
# Effect of Magnetic and Electrical Fields on Yield, Shelf Life and Quality of Fruits
## 2 Effect of Magnetic Fields and Electrical Fields on the Growth, Development, and Yield of Fruit
### Effect on Plants  
[PERSON] (2003) treated whole strawberry plants with an MF. He showed that increasing MF strength to 0.096 T increased fruit yield per plant by about 18%; however, higher field values reduced fruit yield and had a negative effect on plant growth [52]. All the MF strengths expanded average fruit mass compared with the control, even though the largest fruits of about 8.92.g were set on at 0.096 T. On the other hand, MF had beneficial effect in terms of number of leaves, and fresh and dried root heaviness. Tomato plants cultivated in magnetic treatment by [PERSON] et al. (2017) also showed significant increases in the number of leaves, shoot length, and root length, with an increment of 152.9% in number of leaves, 14.4% in shoot length, and 23% in root length [53]. These results are in line with those of [PERSON] et al. (2006) who observed that pre-treatment of seeds with magnetic field or irrigation with magnetic treatment increased leaf, stem, and root fresh and dry weight of tomato [42]. The same results were confirmed by [PERSON] and [PERSON] (2004) [68]. Additionally, they stated that an increase in strength of the MF up to 0.384 T increased the concentration of macroelements and Zn of flora leaves. However, it decreased contents of P as well as S compared with the control. It was similarly reported by [PERSON] et al. (1993) that MF had a beneficial effect on crop in strawberry [75]. In shoots of strawberry plants cultivated in a culture medium subject to magnetic treatment, [PERSON] et al. (2017) observed an increase in magnesium content by 26.8%; calcium content by 30.2%; iron content by 62.4%; potassium content by 25.3%; phosphorous content by 12.8%; and sodium content by 26.9% [57]. In roots, mineral uptake was increased by 36.6% for magnesium; 23.1% for calcium; 184.8% for iron; 19.3% for potassium; 32.2% for phosphorous; and 58.9% for sodium. [PERSON] et al. (2015) stated that there was a positive effect from treatment of young plants of the tomato variety \"Pavlina\" with magnetic fields of 40 mT. The mean weights of the ripe fruit and the aboveground parts were higher on average by 55.2% and 11.2% than the control variant, respectively [58].  
Many studies report the positive effects of treating young plants with a magnetic field. The use of appropriate magnetic field values increases plant growth and crop yield. However, the use of high field values reduced fruit yield and had a negative effect on plant growth.