esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Classic Train Evaluation  
All models presented were trained and tested three times with random selections of data from the same dataset containing 61 DGA families in the order of 70% for training and 30% for testing. The precision, recall, and F1 score of each of the 61 families were recorded for each training and test. Table 4 presents the general average of the metrics among all DGA families during the tests carried out for each of the tested models.  
It is possible to observe that Model 1 obtained significantly higher metrics than the other models tested; on average, Model 1 presented a precision 20.73% higher than the average of the other models and 6.97% higher than the second best, which is Model 2; recall 24.72% higher than the average of the other models and 10.67% higher than the recall obtained by Model 2; and an F1 score 25% higher than the average of the other models, and 10.91% higher than the F1 score obtained by Model 2. It can also be seen that models based on convolutional neural networks obtained better metrics than the others. Therefore, in addition to obtaining metrics superior to the other models on the same set of data, Model 1 obtained, on average, metrics 9.52% higher than those obtained by Model 2, a model that did not use character-level embedding in the input data. For better visualization, Figure 3 graphically presents the results obtained by the models compared in each of the metrics side by side.  
A more detailed overview with results by family is presented in Appendix A. Table 11 presents the results obtained by Model 1 in the classification among the 61 DGA families. Table 12 presents the results obtained by Model 2 in classifying the 61 DGA families. Table 13 presents the results obtained by the Bilbo model in classifying the 61 DGA families. Table 14 presents the results obtained by the DistilBERT model in classifying each of the 61 DGA families. Table 15 presents the results per family obtained by the CNN model. Tables 16 and 17 present the results obtained by the CNN+LSTM and RNN models, respectively, in classifying the 61 DGA families.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Avg. Prec.** & **Avg. Rec.** & **Avg. F1** \\ \hline Model 1 (Proposed) & 86.75\% & 83.06\% & 83.78\% \\ Model 2 (Proposed) & 79.78\% & 72.39\% & 72.87\% \\ Bilbo [35] & 64.39\% & 58.22\% & 58.22\% \\ DistilBERT [36] & 53.71\% & 36.65\% & 38.05\% \\ CNN [28] & 77.06\% & 68.38\% & 69.68\% \\ CNN+LSTM [28] & 73.83\% & 70.28\% & 70.19\% \\ RNN [28] & 47.33\% & 44.11\% & 43.69\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Average of the metrics obtained by Model 1, Model 2, and other models in direct comparison to classifying 61 DGA families.
## 3 Materials and Methods  
Our implementation and experiments were performed in R studio. We downloaded the CSE-CIC-IDS2018 dataset from the Kaggle website [20] with 16,000,000 instances collected over the course of ten days. The attacking infrastructure includes 50 machines, and the victim's organization has 5 departments, which include 420 machines and 30 servers. This dataset is the result of a collaborative project between the Communications Security Establishment (CSE) and The Canadian Institute for Cybersecurity (CIC) that use the notion of profiles to generate cybersecurity dataset in a systematic manner.  
The dataset has several attack profiles in different categories: Botnet, DoS/DDoS, Brute force, Web attack, and Infiltration. There are 80 features in this dataset that are extracted from network traffic using the CICFlowMeter tool. The most important columns within this dataset are: Destination Port, Protocol, Flow Duration, Total Forward Packets, Total Backward Packets, and Label. The Label is the dependent variable which indicates if the flow is benign or attack; the category of attack is mentioned as a label in case of attack.  
In our experiments, samples are selected from 6 days of the dataset using a stratified sampling technique. Stratified sampling is a sampling technique to ensure that a representative sample is obtained from a population with diverse subgroups to reduce the potential sampling bias. In our dataset, each subgroup is a collection of tuples with one type of attack vector. As a result, according to Table 3, we have 8 subgroups. In our sampling attempt, we kept the percentage of each subgroup to the total number of tuples. For example, the attack vector of Infiltration has 1701 number of samples out of 49,545 (in the training set), i.e., 0.034% the same as the percentage in the original dataset.  
The training and test sets are divided based on a 75-25% ratio, which resulted in 49,545 tuples as the training set and 16,860 tuples as the testing set. Table 3 illustrates the number of samples in each set based on their Label according to their real distribution in the original set. The data are normalized based on z-score metric. After preprocessing, which involved the detection of infinity and NaN values, the data are fed into feature selection/reduction models.  
\begin{table}
\begin{tabular}{l l l} \hline
**Label** & **Training Set** & **Test Set** \\ \hline Benign & 29,605 & 10,128 \\ \hline DDOS attack-HOIC & 6501 & 2167 \\ \hline DOS attacks-Hulk & 4401 & 1467 \\ \hline Bot & 2434 & 900 \\ \hline FTP-BruteForce & 1801 & 600 \\ \hline SSH-BruteForce & 1801 & 600 \\ \hline Infiltration & 1701 & 564 \\ \hline DOS attacks-SlowHTTPTest & 1301 & 434 \\ \hline Total & 49,545 & 16,860 \\ \hline \end{tabular}
\end{table}
Table 3: Frequency of label values in training and test set using a stratified sampling from CSE-CIC-IDS2018 dataset [11] that we used in our experiments.
# Deep-Learning-Based Recovery of Frequency-Hopping Sequences for Anti-Jamming Applications
## 4 Performance Analysis
### Performance of Transfer Learning  
In this section, the frequency-hopping communication system with 20 frequency points is used as the object of CNN-GRU network transfer learning. We adopted transfer learning and non-transfer learning, respectively, and the learned network models are denoted as network1 and network2, respectively. We conducted the experiments by choosing different numbers of training samples, and the results are shown in Figure 9.  
It can be seen from Figure 9 that the performance of the network model obtained by using the same number of datasets for transfer learning training was better than that of the network model trained without transfer learning. By sharing the parameters of the trained model, the network's ability to extract some basic features of the received signal can be preserved, and training on this basis will greatly improve the performance. With the transfer learning method, when the number of datasets reached 3300 or more, the performance of the trained network model was not significantly improved. Notably, 7700 samples were needed to train the network model without transfer learning to approximate the performance of the network model trained by transfer learning with 3300 samples.  
Figure 8: Estimation accuracy of sequences of different lengths.  
Transfer learning reduced the data demand by 57.1%, which shows that transfer learning has more advantages under the condition of a few-shot scenario.
# Microscopic Simulation of Heterogeneous Traffic Flow on Multi-Lane Ring Roads and Highways
## 4 System Description
### Heterogeneous Traffic Flow Generation  
In SUMO, to simulate and analyze various types of traffic flow and their interactions, it is essential to define the characteristics of different vehicle and pedestrian types, including speed, acceleration, and driving behavior, and to model their dynamic behavior in urban road networks. The JAD strategy acts as the pivotal decision-making module within the system, dynamically modifying driving behavior based on vehicle and environmental information. These characteristics can be specified through detailed configurationfiles, which include vehicle types (e.g., cars, buses, trucks, bicycles) and pedestrian types (e.g., adults, children, elderly). Each type has specific parameters such as maximum speed, acceleration, deceleration, length, width, and car-following models (e.g., IDM, ACC, CACC). Additionally, vehicle communication capabilities can be configured to simulate vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) interactions. Based on these configurations, SUMO can simulate complex traffic scenarios to explore the characteristics of mixed traffic flow, the causes and mitigation of traffic congestion, the impact of autonomous driving technologies, and the potential of ITSs. This supports traffic planning and management effectively.  
To generate a heterogeneous traffic flow for the road networks, several steps are required. First, the vehicle types must be defined in a.xml file using the <vType> tag, which includes attributes such as ID, vehicle model properties, lane-change model properties, parameters of various vehicle models, vehicle color, and penetration rates. Next, the traffic flow of different vehicle types is defined using the <flow> tag in another.xml file, which includes start and end time, the vehicle types, departure lane, departure speed, and the vehicle paths. Finally, these defined vehicle types and traffic flow files, along with the road networks file, need to be incorporated into a configuration file (.sumocfg). Therefore, by utilizing the vehicle parameter configuration file, the path file, and the heterogeneous traffic flow file (including multiple vehicle types, the distribution ratios of the vehicle types, and lane-change models), it is possible to model heterogeneous traffic flow with different penetration rates.
# RIS-Assisted Multi-Antenna AmBC Signal Detection Using Deep Reinforcement Learning
## 4 Proposed Solution
### TD3-Based Framework
Obtain the AmBC BER according to Equation (14) by the final \(r^{t}\)
```  
**Algorithm 1** The TD3-based detector for AmBC  
It is worth noting that in practical systems, due to the limitations of the physical properties of the device, continuous phase shifts are approximated by multi-bit quantization of discrete phase shifts at the present [23; 24; 25]. For this purpose, the method proposed in this paper is still valid and only requires quantization of the optimal phase shift output to the nearest discrete phase shift. Of course, we can also replace the TD3 agent that solves continuous non-convex optimization with a DQN agent for discrete states and actions within the framework proposed in this paper.
# Numerical Investigation of Thermal-Flow Characteristics in Heat Exchanger with Various Tube Shapes
## 1 Introduction
Moreover, other investigations by numerical and experimental treatments have been carried out by [PERSON] et al. [42; 43; 44], [PERSON] et al. [45], [PERSON] et al. [46], [PERSON] et al. [47], [PERSON] et al. [48], [PERSON] et al. [49; 50; 51; 52], [PERSON] et al. [53], [PERSON] et al. [54], [PERSON] et al. [55], [PERSON] et al. [56], [PERSON] et al. [57], etc. Their studies suggested different models of heat exchangers in order to improve the performance under various limit conditions. As seen from the aforementioned research works, several researchers have studied, separately, the effect of the axis ratio of oval tubes and the flatness of flat tubes in cross-flow FTHEs. However, only one numerical investigation was conducted by [PERSON] et al. [39] to compare the air-side thermal-hydraulic performance of plate FTHEs using circular, oval, and flat tube shapes, whereas no study was performed on the impact of these tube shapes for the case of annular fins. Therefore, in the present work, special attention is paid to the annular fins with circular, oval, and flat tube configurations. The numerical results have been obtained having three-row staggered banks for a range of Re (\(2600\leq\mathrm{Re}\leq 10\),200).
# Implementation of Efficient 5G AKA Protocol for Light-Weight Environment
## 1 Introduction
found structural weaknesses in 5G authentication and key agreement (AKA) protocol, one of the 5G key agreement protocols [14]. Vulnerability is the exposure of users' information in plaintext when authentication fails under certain conditions; this study explores passive attacks that exploit this simply to eavesdrop and active attacks that exploit structural protocols with inexpensive equipment to manipulate data. This situation contradicts the protection of users' information emphasized by 3 GPP, and several studies have been conducted in an attempt to solve it. [PERSON] et al. supplemented the effort by introducing hardware equipment, but that the requirement of additional equipment is a disadvantage [15]. [PERSON] et al. proposed the random replacement of random numbers and sequence numbers proposed to complement the protocol, but this burdens the user equipment (UE) and home network (HN), and it necessitates changes in the protocol structure [16]. Therefore, we propose a method to address this problem and increase the speed of the protocol. The improved protocol encrypts plain text to compensate for vulnerability and applies a Lightweight Encryption Algorithm (LEA) [17] for a lightweight environment. It  
Figure 1: GSMA prediction regarding 5G users (Adapted from [1]). replaces the parts encrypted with an Advanced Encryption Standard (AES) algorithm [18] used in the existing protocol and assumes that it can increase the speed of the protocol. By introducing LEAs that are lighter and faster than an AES algorithm, in future, even low-level devices will be able to successfully carry out authentications in IoT environments. In addition, more efficient and sustainable authentication can be performed because LEA reduce power consumption during encryption and decoding. The implemented improved protocol will be introduced into the core network that will be implemented in our lab in future to experiment with the vulnerabilities of the AKA protocol in a real-world environment.
# Guaranteeing QoS for NOMA-Enabled URLLC Based on \(\kappa\)-\(\mu\) Shadowed Fading Model
## 1 Introduction
### Related Works
Furthermore, by utilizing moment generating function (MGF)-based SNC, probabilistic delay bounds for traffic dispersion, network densification and a hybrid scheme were obtained in millimeter-wave communications over Nakagami-\(m\) fading [47]. A study close to our work is the research by [PERSON] et al. [22], where the stochastic QoS performance in terms of both the delay and error rate metrics was obtained for a downlink CF mMIMO based system model across Rician fading in the FBL regime. However, only traditional fading channels have been considered for discussion in the related literature, while no investigation is available with respect to the performance analysis for CF mMIMO systems with the juxtaposition of shadowing and small-scale fading.
## 3 Results
### Quantitative Detection of PZA and POA Applying the LoC-SERS Device  
The presence of PZA resistant MTB in patient samples can be deduced from the absence of POA in the in vitro cell cultures. The amount of PZA added to these assays is not standardized. For example, in a quantitative Wayne assay PZA concentrations were set to 400 \(\mu\)g/mL (3.2 mM) and 800 \(\mu\)g/mL (6.4 mM) [56], whereas in another test 100 \(\mu\)g/mL (0.8 mM) PZA was used as a critical concentration. In order to determine whether the bacterial culture is susceptible or resistant to PZA using SERS as analytical tool, the limit of detection (LOD) of the prodrug and its metabolite needed to be identified in a first step. Thus, both analytes were dissolved in high purity water. For each analyte a stock solution of 0.1 mM was achieved and diluted to lower concentrations ranging from 100 \(\mu\)M to 2 \(\mu\)M. A flow profile was applied to vary the in-chip concentration during the measurements in the microfluidic device (detailed flow rates are given in the instrumentation and measurement section and SI). The background signal of the in situ synthesized colloids (y-AgNPs) shows some weak bands that can be ascribed to  
Figure 2: (**a**) Varying flowrates for mineral oil, AgNO\({}_{3}\) with NH\({}_{3}\) and N\({}_{2}\)H\({}_{4}\) in the microfluidic chip device. The flowrates of PZA (10\({}^{-4}\) M) and NaNO\({}_{3}\) were kept constant at 10 nL/s. With the addition of citrate (10\({}^{-5}\) M) depicted in black, without citrate plotted in gray. In light gray the standard deviation values are indicated. (**b**) Integrated intensity of the peak at 1040 cm\({}^{-1}\) relative to the different flowrates. With addition of citrate (10\({}^{-5}\) M) depicted in black, without citrate plotted in gray, respectively.  
the sodium citrate used as stabilization agent (Figures 3 and 4: background spectra depicted in gray). The LOD was determined employing the three times standard deviation value of the background signal as a threshold (3\(\sigma\)). For both analytes the SERS signal vs. concentration yields a sigmoidal curve, which is well known to be characteristic for the SERS technique due to saturation at high concentrations. The calculated limit of detection for PZA is 27 uM, while for POA 21 uM (see Figure S2 in the SI for more details).
# An IoT-Based System for Monitoring the Health of Guyed Towers in Overhead Power Lines
## 3 System Architecture
### Hardware Design of the System
#### 3.5.2 Wireless Communication  
The network needs to deliver data traffic efficiently at a low power profile. Then, it is acceptable to work with low speeds and a reasonable degree of data loss. IEEE 802.15.4g is a physical layer created by the IEEE 802.15.4g Smart Utility Networks Task Group (WiSUN) to provide a standard for the framework described in the current investigation. Usually, it facilitates the deployment of enormous scale process control applications, such as the utility smart grid network capable of supporting geographically large networks with minimal infrastructure. In this case, the best-chosen candidate is the RAA604S00 wireless communications IC with built-in RF peripheral components and hardware supporting IEEE 802.15.4g/e. The RF frequency range covers from 863 up to 928 MHz; the modulation is 2 PSK/GFSK with a data rate of up to 300 kbps. It is among the leading LPWAN communication technologies and the industry's lowest level of power consumption.
# The Role of an Industrial Alkaline Wastewater in the Alkali Activation of Biomass Fly Ash
## 3 Results and Discussion
### Hardened State Characterisation: Compressive Strength  
The compressive strength values of the various compositions measured at the 1 st and 28 th days is shown in Figure 6. The results show that at the 1 st day and for incorporation contents up to 15 vol.%, the impact of the effluent on the specimens' compressive strength is minor, while higher amounts induce a decrease. This might be related to the lower dissolution of Al and Si observed in the dissolution assays (see Figure 4), which cause a delay in the geopolymerisation reactions. The lower strength seen at the 1 st day can also be correlated with the lower maximum temperature seen in the calorimetric assays (see Figure 5) for the effluent-containing composition compared with the reference (prepared without adding the effluent). The decrease in the maximum temperature of the pastes can be attributed to the lower alkalinity of the effluent compared with the NaOH, hindering the dissolution of the reactive fraction of the precursors [40]. In fact, previous studies have observed a decrease in the heat evolution (measured by calorimetry) as the ratio of SiO\({}_{2}\)-to-Na\({}_{2}\)O of the activating solution rises [41] in line with our results. The higher earlier strength seen for the reference composition is connected with the superior dissolution of Si and Al from the precursors contributing towards the higher strength of these specimens.  
Figure 5: Temperature evolution upon curing measured for the reference and the 25_Ef formulations.  
After 28 days the composition containing the highest amount of effluent (25 vol.%) shows a compressive strength higher than the reference, and this suggests that the system was able to recover from the delay in the early strength. At this stage, the compressive strength of this composition reaches 22.7 \(\pm\) 2.3 MPa, while the reference reaches 20.0 \(\pm\) 1.2 MPa. The distinct strength evolution for these compositions suggests that the use of high alkalinity (reference), while favouring the earlier strength gain, might be detrimental to the subsequent geopolymerisation, resulting in lower strength development [29; 42].  
In any case, these results demonstrate the feasibility of using an alkaline wastewater to partially replace the commercial activator (e.g., NaOH). It should be noted that this compressive strength is much higher than the minimum values required for masonry applications, belonging at least to the class M10 [43]. Nevertheless, it should be noted that the mechanical strength of the specimens is only one of the requirements for masonry applications. Indeed, future work should be carried out to optimise the pastes' setting times. Moreover, the industrial wastewater variability and the possible impact on the properties of the synthesised materials should be studied.
## 3 Results and Discussion  
### Compressive Strength  
The compressive strengths at 3, 7, and 28 d of age are shown in Figure 4. The compressive strengths after 3, 7, and 28 d of age were 17.88, 23.62, and 28.24 MPa for samples with only OPC; 7.47, 11.12, and 23.99 MPa for BFS50 specimens; 12.33, 16.55, and 27.06 MPa for ABFS25 specimens; and 8.66, 12.29, and 25.48 MPa for ABFS50 specimens, respectively.  
The mortar mixed with BFS showed a lower compressive strength than that with only OPC, regardless of the type of mixing water. The glassy film on the surface of the BFS particles was destroyed by the alkaline water, resulting in a hydration reaction between  
Figure 4: Mortar compressive strength test results (MPa). (1) OPC: Ordinary Portland cement; OPC:BFS = 100:0 (normal water); (2) BFS25: Blast furnace slag; OPC:BFS = 75:25 (normal water); (3) ABFS25: Blast furnace slag; OPC:BFS = 75:25 (alkali water); (4) BFS50: Blast furnace slag; OPC:BFS = 50:50 (normal water); (5) ABFS50: Blast furnace slag; OPC:BFS = 50:50 (alkali water).  
Figure 3: Accelerated carbonation test specimen mold.  
water and BFS. Thus, the mortar using alkaline water as mixing water had a higher strength than that using conventional mixing water regardless of age. Calcium silicate hydrates (C-S-H) and calcium aluminate hydrates (C-A-H) were generated owing to the reactive materials in BFS eluting through the glassy film and densely filling the pores inside the mortar.