esa-sceva/satcom-chunk-collection huggingface dataset:
# Analysis of AC DC Four-Switch Boost-Buck Battery Charger Converter for EV Applications
## 9 Simulation Results
Figure 18: Intermediate voltage \(v_{c}\) in buck mode without oscillation and its normalized FFT: (**a**) Intermediate voltage \(v_{c}\). (**b**) Normalized FFT of intermediate voltage \(v_{c}\). It should be noted that simulation findings indicate that the resistor \(R_{d}\) dissipates approximately 3.2 W, which constitutes about 0.2% of the total power in the buck converter mode. This power loss is calculated from the buck mode, which exhibits the highest voltage ripple due to the low output voltage, as shown in Figure 18. The power loss is determined by multiplying the voltage across the resistor \(R_{d}\) by the current flowing through it. For boost mode operation, the design is aimed for a maximum current ripple of 12% and a voltage output ripple of 8%. These criteria were chosen based on considerations of circuit stability and performance. However, this design choice results in the buck mode exhibiting a voltage ripple of 12% due to the lower output voltage, while the current ripple is at 3%. This trade-off ensures optimal performance across different operating modes of the circuit. The output voltage ripple can be controlled by adjusting the value of the output capacitance to meet the application requirements. In this simulation, the output capacitor is set to 800 uF; for a lower voltage ripple, a higher output capacitor could be used. These simulation results provide valuable insights into the performance of the converter with the implemented RC snubber circuit. The waveforms demonstrate the ability of the converter to regulate the output voltage, maintain high PF operation, avoid internal dynamic problems, and control the output voltage ripple within acceptable limits. The simulation outcomes support the effectiveness of the proposed solution in achieving the stable and efficient operation of the converter under various load conditions.
# The Need for Speed: A Fast Guessing Entropy Calculation for Deep Learning-Based SCA
## 2 Background
### Metrics  
The training process has the minimization of the selected loss function as the main goal. In this paper, we consider the _categorical cross-entropy_ (CCE) as the loss function. As demonstrated in [12], due to the imbalanced dataset problem, the validation loss function values (including CCE) can be inconsistent with SCA metrics, which is also the case of SCA-based loss functions, as already proposed in [15; 16]. Therefore, we must select a more efficient validation metric to assess the model's performance for SCA.  
Metrics such as guessing entropy are commonly used by an adversary to estimate the required effort to obtain the key [17]. A side-channel attack outputs a key guessing vector \(\mathbf{g}=[g_{1},g_{2},\ldots,g_{|K|}]\) in decreasing order of probability, i.e., \(g_{1}\) represents the most likely key candidate and \(g_{|K|}\) the least likely key candidate. Guessing entropy is the average position of \(k^{*}\) in \(\mathbf{g}\). Commonly, the averaged value is calculated over multiple independent experiments to obtain statistically significant results. In this paper, this GE method is called empirical GE, and it is evaluated on a set of \(V\) validation traces, where the results of multiple key rank executions are averaged and performed on a partition \(Q\) from \(V\).
# Accurate Indoor Localization with IoT Devices and Advanced Fingerprinting Methods
## 2 Materials and Methods
### Theoretical Foundations
#### 2.1.3 Fingerprint Location  
One of the most efficient location methods is the fingerprint method. Fingerprint-based wireless indoor positioning is widely used in spatial services, because wireless signals such as Bluetooth and Wi-Fi are used indoors. They are easily accessible [1]. One of the advantages of this method is its simplicity compared to other methods in implementation. Fingerprint-based internal positioning via Wi-Fi or Bluetooth signals has also become a standard method for commercial applications [17]. The basis of this method is to collect a lot of data from different environments such as a room or an entire building so that finally, with the large amount of data collected in the core after implementing the local map, the signals received from each object can be checked with previous data and the location of the object can be displayed on the map.  
Fingerprint technology is associated with a high level of accuracy and reliability. Figure 1 provides an overview of the algorithms used in the fingerprint method [1].
## 2 Literature Review  
This section presents related state-of-the-art literature and the principal advantages of the proposed model.  
Several authors carried out research on building a system for real-time intrusion detection by training it with a dataset.  
However, current systems are only able to detect some but not all the indications of an intrusion. This is because they are not able to monitor all the behaviours in the network On the contrary, projectionist techniques are able to provide a visual overview of the network traffic. Earlier dimensionality reduction techniques were applied to visualize network data using scatter plots [18; 19; 20; 21; 22; 23].  
In the case of [24], several projectionist algorithms, such as PCA, CMLHL, CCA, and SOM network, have been applied to monitor the traffic of the Euskalert network (Honeynet data) [25], to discover behaviour and strategies indicative of an attack. In [26], the same techniques have been applied to GICAP-IDS and DARPA datasets [27], and their performance has been measured according to different variables, such as data volume, system dynamics and network traffic diversity, including first-time attacks (0-day). Then, the authors presented a novel Multi-Agent System which combined Artificial Neural Networks (ANN) with Case-Based Reasoning (CBR) techniques for the detection of attacks in computer networks [28]. This new IDS, known as RT-MOVICABIDS, has been validated using three different datasets. Those datasets have also been used in our study, as described further on in the article. In [29], clustering and visualization techniques have been combined to generate an automatic response to the previously developed MOVICAB-IDS system. The modified MOVICAB-IDS has been applied to the three datasets, to assess the improvement of the proposed approach. Furthermore, in [30], it has been validated using a community search dataset. This type of attack involves guessing the password, it has been detected by MOVICAB-IDS, which demonstrated to perform better than other well-know algorithms for detecting attacks on continuous network flow.  
Finally, in [31], MOVICAB-IDS has been applied to a dataset that contained flow-based information (14.2 M flows). The University of Twente [32] collected this information in September 2008, using a honeypot.  
More recently, a novel EPP algorithm, BHL, has been applied to Android malware families [33; 34], obtaining much better results than other well-known algorithms.  
BHL has also been previously employed in the analysis of the internal structure of a series of datasets [35; 36], providing a clear projection of the original dataset. More specifically, it has been successfully applied to Android malware datasets [33; 34], where its task was to characterize Android malware families. Therefore, this research aims to apply BHL to the datasets that have previously been used by MOVICAB-IDS, with the aim of improving the obtained projections and achieving a better visual representation of the network traffic. This facilitates the early identification of anomalous situations which may be indicative of a cyberattack in the computer network.
###### Abstract  
Synthetic aperture radar (SAR) imagery is a promising data source for ocean activity detection. Ship target detection based on SAR images is widely used in maritime trade and the military. SAR image data are rare, and the amount of public data is small. For applications of SAR image ship target detection, a model with low data dependence, fast iteration and low training cost is needed. In this paper, the balanced positive and negative data enhancement method was used. Through statistical analysis of the training dataset, similar sea areas in the training set are filled with detection targets with comfortable size features. Increasing the proportion of positive samples in the data helps to improve the model detection effect. The regional attention preadaptation mechanism based on statistical analysis was implemented to extract information, and the scale-adaptive loss was combined to improve the detection accuracy of the model. Using the same data, our model exhibited better performance. When using 30% of the data, our model was stable in terms of accuracy and average precision (AP) and maintained detection results similar to the training results achieved using 100% of the dataset.  
SAR imagery; ship detection; regional attention; data enhancement; statistical adaptation loss
## 2 Materials and Methods
### Feature Extraction Using Koopman Spectral Operators  
Time-delay embedding: The state space is reconstructed from time-series data by creating delay vectors. Each vector incorporates past observations to capture temporal dependencies [7]. Delay vectors \(x(t)\), \(x(t+\tau)\), \(\ldots\), \(x(t+(m-1)\tau)\) capture temporal dependencies, with \(\tau\) as the embedding delay and \(m\) as the embedding dimension.  
_Graph kernels_: Graph kernels such as the Weisfeiler-Lehman (WL) kernel, Random Walk kernel, and Graphlet kernel are applied to the adjacency matrices to compute Gram matrices. These kernels encode graph structure and dynamics, facilitating spectral analysis [5; 7]. A detailed description of the expressiveness of these kernels can be found in [23].  
_Koopman operator approximation_: The Koopman operator is approximated using Dynamic Mode Decomposition (DMD), yielding eigenfunctions and eigenvalues representing metastable states. This spectral decomposition enables interpretable latent feature extraction.
# Studying Spatial Unevenness of Transport Demand in Cities Using Machine Learning Methods
###### Abstract
The authors of the work use a neural network to predict the travel purposes (destination) of passengers in taxi cars using GPS data, taking into account the travel request by time during the day [22]. This allows one to more accurately plan the work of taxi cars and select the optimal orders for each taxi car according to the criterion \"length of the route to the passenger's pick-up point\", which will increase the efficiency of transportation and reduce the possible waiting time for passengers. ANNs are actively used to determine the optimal operating modes of traffic lights in traffic control. This is especially true during the transition from local adaptive control at one intersection to network adaptive control in a large area of the city and in the future when creating a top-level ITS. In this case, each traffic light unit at the intersection is a separate agent and is controlled separately in the system, taking into account the operating parameters of other agents [23]. An urgent task for municipalities is to predict the number of bicycle trips in order to determine the streets on which it is necessary to create infrastructure for cyclists, namely bicycle paths, parking areas, and special settings for traffic lights, and for businesses, the locations of bicycle sharing stations. The authors of the work use an ANN to forecast the demand for bicycle rentals and forecast the number of trips in space and time [24]. The purpose of this study is to establish a pattern of changes in the share of trips by private and PT, taking into account the spatial location of the transport area in a large city with a population of 800-900 thousand people and at a distance from the city center. To implement the model of the influence of factors, the method of artificial neural networks was chosen [25; 26; 27]. An analysis of previous papers showed that data related to the \"digital shadow\" of the transport system can be used to develop measures to ensure sustainable urban mobility and can be analyzed using machine learning methods and artificial neural networks.
## 5 Experimental Evaluation
### Comparison of Two Cross-Chain Methods for Single-Span Chain Transactions  
In the case of simple single-span chain transactions, we designed a more efficient crosschain-op approach. Next, we compare the performance differences between the two cross-chain approaches for monad transactions.  
We performed several experiments to compare MultiCross and crosschain-op with a cross-chain transaction. Both methods set the RWRate to 0, 0.3, 0.5, 0.8, 1, respectively, and experiments were conducted with a CrossRate of 0, 0.3, 0.5, 0.8, and 1. Since there was no cross-chain conflict in crosschain-op, only SuccRate and average TPS are compared. The experimental results are shown in Figure 7.  
We can see that the changes of the two cross-chain methods are very different under different parameter changes. The SuccRate of the MultiCross method was not affected by RWRate, but decreased as the CrossRate increased. When the CrossRate was 0, the system SuccRate was between 95% and 100%. As the CrossRate increased, the system SuccRate dropped to about 80%. The crosschain-op SuccRate was not obviously affected by CrossRate but by RWRate. In the optimized crosschain method, SuccRate reached a minimum of 94% when the read/write ratio was balanced. The more unbalanced the read/write ratio was, the closer SuccRate was to 100%. This rule is the same as with traditional fabrics.  
The average TPS of the MultiCross method was significantly affected by the CrossRate compared with that of the optimized cross-chain method. The highest mean TPS for the MultiCross method was 510, and the lowest mean TPS was 130. The highest mean TPS of the crosschain-op was 540, and the lowest mean TPS was 510. The average TPS of the MultiCross method decreased with an increasing CrossRate, and the average TPS of the crosschain-op peaked at read-write equalization.  
It can be seen that the optimized cross-chain method did make the system better than the MultiCross method in terms of average TPS and SuccRate. However, the crosschain-op supports only one cross-chain transaction, which limits its scope of use. We combined crosschain-op with MultiCross to enable our cross-chainable blockchain system to support the collaborative cross-chain of any number of sub-transactions while providing good performance over cross-chain requests from monadic transactions.
# An Improved Approach to DNS Covert Channel Detection Based on DBM-ENSec
## 3 Methodology
### DNS Covert Channel Detection Module
The specific DCC detection algorithm is outlined in Algorithm 1:  
```
Input : Training data \(X_{train}\), test data \(X_{test}\) Output : BinarydecisionresultsindicatingDCCpresence\((1)\)\(ornormaldata(0)\) Step1 : Initialize deep feature extraction model AddDBM\((Dual-BranchModule)\) to the model AddConv1 Dlayer\((filters=64,kernelsize=3,activation=ReLU)\) AddConv1 Dlayer\((filters=128,kernelsize=3,activation=ReLU)\) AddMaxPooling1D layer AddAttention layer AddAttention layer AddFlatten layer Compile model with optimizer \(=Adam\), loss \(=binary\) crossentropy Step2 : Set training callbacks InitializeEarlyStopping\((monitor=validationloss,patience=5)\) InitializeReduceLROnPlateau\((monitor=validationloss,factor=0.2,patience=3)\) Step3 : Train the model on \(X_{train}\) with callbacks Step4 : Extract features from trained model \(features_{train}\leftarrow\) Extract\((model,X_{train})\) features\({}_{test}\leftarrow\) Extract\((model,X_{test})\) Step5 : Initialize base classifiers : LGBM, XGBoost, RF, CatBoost forEach model \(\in\{LGBM, XGBoost,RF,CatBoost\}\) do  Train model on features\({}_{train}\) Predict labels on features\({}_{test}\) Compute weighted F1 score StoreF1 scores in dictionary Step6 : Construct decision matrix \(D\) from model predictions Step7 : For each sample \(i\) in \(D\), compute decision score : \(Score_{i}=\frac{\sum_{i}F_{i}d_{i}}{\sum_{i}^{4}{F_{i}}}\) Step8 : Set decision threshold \(\theta=0.01\) Step9 : forEach \(i\) in decision matrix do  If Score\({}_{i}\geq\ \theta\) then  Setresulty\({}_{i}=1(DCC)\) else  Setresulty\({}_{i}=0(Normal)\) return\(\{y_{i}\}\) for all \(i\)
```  
**Algorithm 1**DCC Detection Algorithm
# Error Investigation on Wi-Fi RTT in Commercial Consumer Devices
## 4 Experimental Results and Analysis
### Summary  
To sum up, the errors caused by the hardware, motion effects, and NLOS effects have been investigated. In different conditions, the errors show distinct effects on the Wi-Fi RTT distance from several aspects, including fixed biases, fluctuations, outliers, and their mixture. As summarized in Table 6, the bias always exists in all conditions and varies according to different hardware/manufacturers. Additional bias occurs when the smartphone is in NLOS condition. Particularly, the bias would be larger when the blocker has better conductivity. Moreover, the Wi-Fi RTT distance shows higher fluctuations when the smartphone is moving rather than in static mode. The motion effects usually lead to moderate fluctuations regardless of the ground truth distance in LOS conditions. However, the motion effects exacerbate the NLOS impact on the Wi-Fi RTT distance, resulting in severe outliers. All the errors except the hardware-dependent bias increase when the blocker has better conductivity in all circumstances. In addition, we infer that the phone with a better CPU could provide better RTT performance against the fluctuations and outliers when the phone is moving in an NLOS environment.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **LOS** & **NLOS (Metal)** & **NLOS (Glass)** & **NLOS (Wood)** \\ \hline GP2 & 0.822 m & **2.487** m & 0.899 m & 0.668 m \\ XM & 0.717 m & **2.936** m & 0.593 m & 0.543 m \\ LG & 0.318 m & **2.264** m & 0.499 m & 1.247 m \\ \hline \hline \end{tabular} The bold number denotes better performance.  
\end{table}
Table 4: Comparison of the RMSE in RVPV environment.  
Figure 12: Wi-Fi RTT distance in RVPV: (**a**) Wi-Fi RTT distance from Google Pixel 2; (**b**) Wi-Fi RTT distance from Xiaomi Redmi K20; (**c**) Wi-Fi RTT distance from LG V50S.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **LOS** & **NLOS (Metal)** & **NLOS (Glass)** & **NLOS (Wood)** \\ \hline GP2 & 2.724 m & **3.303** m & 2.778 m & 2.549 m \\ XM & 2.852 m & **3.327** m & 2.821 m & 2.703 m \\ LG & 2.541 m & **3.062** m & 2.831 m & 2.937 m \\ \hline \hline \end{tabular} The bold number denotes better performance.  
\end{table}
Table 5: Comparison of the standard deviation of the Wi-Fi RTT distance in RVPV environment.
## 6 Pitfalls and Potentials
### Reducing the Energy Consumption  
SDN, NFV, and C-RAN can collectively reduce energy consumption and carbon footprint in the cellular communication network. Here are some features of these technologies that will enhance energy efficiency: SDN provides centralized control of network resources using a software-based controller. It can manage network resources based on real-time network traffic demands and optimize network routing, leading to a more efficient way of using network equipment and reducing the energy needed for data transfer. Using the SDN, energy-aware routing and scheduling algorithms can be formulated that can reduce the power consumption of network devices by prudently selecting the most energy-efficient paths to route the traffic. The SDN-based centralized controller also helps to power down unnecessary networks during periods of low demand, such as un-utilized base stations being switched off.  
NFV virtualizes multiple network functions and deploys them as software instances on standard server hardware or the cloud, instead of dedicated physical machines. As NFV consolidates multiple network functions onto a shared hardware infrastructure, it reduces energy consumption and carbon footprint by reducing the number of physical devices required. The 5G network functions, such as Unified Data Management (UDM), Application Function (AF), Network Repository Function (NRF), and many others, can be virtualized to enhance the energy efficiency of the communication network. NFV also helps for the dynamic scaling of network functions to support varying demands by deploying or decommissioning virtual network functions based on the load on the network. This results in reductions in capital and operating expenditures, including energy consumption [95].  
C-RAN centralizes the baseband processing functions of multiple RRUs in one data center/cloud/centralized location, and it connects to radio units via high-speed fiber optic cables. This mechanism allows for harmonized resource allocation and interference management, reduces the need for physical network infrastructure, and improves energy efficiency in data transmission and processing. For instance, an investigation [128] has shown that using C-RAN architecture leads to approximately 83% energy reduction in the BBUs.  
Collectively, SDN, NFV, and C-RAN provide an efficient way to reduce energy consumption in telecommunication networks by offering centralized control, optimizing resource allocation, reducing the need for physical network infrastructure, and enhancing energy efficiency in data transmission and processing. Nevertheless, the real impact may be different depending on practical implementation and usage scenario.
# Cyber4 Drone: A Systematic Review of Cyber Security and Forensics in Next-Generation Drones
## 6 Drone Forensics
### Drone Forensic Framework
#### 6.1.2 Examination  
Once the devices have been identified and raw data extracted from them during the collection phase, the subsequent step is the examination phase. In this phase, the primary objective is to identify and extract data from the imaged devices. The raw data imaged or collected in the previous phase is forensically examined to identify logical files and logs. In terms of drone forensics, relevant logs and files are extracted from drones, mobile devices, GSC, and memory cards. Three major categories of data looked for during the examination phase are event logs, flight logs, and media files.  
An event log is a chronologically ordered list of recorded events. Event logs are also a core component of OS. The recorded event in \"Event Logs\" originated from OS, network, hardware, or database query and is any significant action recognized by the OS. A general event log contains crucial information such as the date and time of the occurrence, the action of the event, severity, the process involved, and other relevant information such as hardware or logical addresses. They can be located in collected drone chips and the Ground Station Controller (GSC). Entries in event logs are generally due to warnings and errors by the camera, radio, battery, GCS failsafe, GPS, ADSB failsafe, and sensors [138].  
During drone investigations, flight logs play a vital role as they contain extensive data in various formats. These logs primarily encompass crucial information such as the drone's location, speed, flight duration, gimbal angle, and camera shooting timing. Flight logs are typically found in the Ground Station Controller (GSC). Additionally, the examination of recorded media, such as photos and videos stored on the drone's memory card, holds significant importance in the investigative process. Popular drones store media-related files (JPG, MP4, and.DNG) in DCIM or MISC directory. Different media files can be located in GSC, drone chips, mobile devices, and memory cards.  
For all relevant data items, the examiner needs to answer: What data were created?; How was the data created?; Who created the data?; Who edited the data?; and When was the data created?