esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Simulation Results and Discussions
### Single-Correlator Receiver  
In the following simulations, the spreading sequences have been chosen according to the standard for sensor networks [1]. The spreading factor is \(2\beta=32\). The carrier inside in-phase and quadrature branch is modulated by 16 chips representing a symbol. The pulse shaping was done according to the Standard [1]. Figure 2 presents theoretical BER curve (blue) and the curve obtained by simulation (red). The simulated BER is overlapping the theoretical curve from 0 dB to 8 dB.  
Figure 2: BER curves for a single-correlator receiver in presence of additive white Gaussian noise channel (AWGN): theory (blue) and simulation (red).  
Figure 3 presents theoretical BER curve (blue) and the curve obtained by simulation (red) in the case when fading is present in the channel. The BER is significantly worse than in the case when the AWGN is present in the channel which is represented by the third (black) curve in Figure 3. This is the reason a method of fading mitigation using interleavers and deinterleavers is investigated. The results for this case are shown in Figure 4.  
As can be seen from Figure 4, the improvement in BER is substantial if interleaver and deinterleaver are used. In this simulation a block interleaver is used and BER improvement achieved is, for example, from 2.2 \(\times\) 10\({}^{-2}\) to 1 \(\times\) 10\({}^{-4}\) (more than two order of magnitude) for SNR = 10 dB, as can be seen in Figure 4. By using interleavers and deinterleavers the influence of fading inside each symbol is practically randomized and the BER curve is coming closer to the curve representing BER when noise only is present in the channel (black curve). The interleaver curve obtained by the simulation can be easily confirmed by the theoretical Equation (46).  
Further improvement could be achieved if the spreading factor is increased. Therefore, the chip interleaving is efficient technique to mitigate fading influence and contributes directly to the power saving in wireless sensor networks. Namely, for the required BER a smaller value of SNR is required if the system includes interleaver and deinterleaver blocks.
# Renyi Entropy-Based Adaptive Integration Method for 5G-Based Passive Radar Drone Detection
## 8 Conclusions  
In this paper, the successful experiment of drone detection using 5G-based passive radar was shown. The target was a medium-sized drone with a GPS logger used as a reference position. For the first time in the literature, the successful detection of a 1-min flight was shown. To the authors' best knowledge, there are no previous results of such an experiment published. The issue of content dependency was addressed. Some methods to overcome it were evaluated as not sufficient and a new approach, employing the Renyi entropy, was proposed. Simulations were conducted to prove this concept. It was shown that the evaluation of the Renyi entropy of a 5G signal enables a reliable decision on the time moment and its duration for radar processing, which is especially important in small-target detection such as drones. It must also be highlighted that the proposed method can  
Figure 24: Entropy and effective bandwidth values obtained for consecutive 20 ms fragments across 60 s of signal.  
Figure 23: Detection of 1 minute of the drone flight.  
be widely used in content-dependent wireless transmissions, e.g., LTE, used for passive radar purposes. As shown in the paper, the proposed technique for adaptive integration is superior to the technique from the literature. Thanks to dependable and autonomous operation, the proposed method is suitable for operational PCL systems. However, the proposed approach is functional as long as the content is present in the downlink transmission. Otherwise, the 5G network only generates synchronization pulses with a relatively low repetition rate (from 5 to 160 ms), which entails significant limitations [35].  
Additional works may involve real-time implementation of the proposed processing. Moreover, for continuous resource allocation, one can extract micro-Doppler signatures and estimate target parameters. A promising concept is to apply the proposed technique to detect smaller targets such as birds and tiny drones.  
Conceptualization, R.M. and K.A.; Methodology, K.A., P.S. and M.P.; Software, R.M., K.A. and M.P.; Validation, K.A., P.S. and M.P.; Formal analysis, K.A.; Investigation, R.M. and M.P.; Writing--original draft, R.M., K.A., P.S. and M.P.; Visualization, R.M., K.A. and M.P.; Supervision, P.S.; Project administration, R.M. and P.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors would like to thank [PERSON] and [PERSON] for providing access to the 5G infrastructure at the Lodz University of Technology and for their valuable help during the measurement campaigns, as well as [PERSON] for their support during the trials. The authors would like to also express their deepest gratitude to [PERSON], [PERSON] and [PERSON] for their significant contribution to understanding 5G signals.  
The authors declare no conflict of interest.
## 1 Introduction
For the analysis, an underwater experiment was conducted in a lake by using spread 4-FSK, turbo codes with one-third coding rates, and four bands. Through the lake trial tests, the performance was improved as the number of spreading factors and turbo iterations increased. Furthermore, the multi-band performance improved when the proposed EC-BER weighting algorithm was applied. This paper is arranged as follows: Section 2 presents system model of the multi-band FSK with a DSSS is described in detail. In Section 3, the proposed EC-BER algorithm is described, and showed how an optimal weighting value is assigned by simulation results. Section 4 analyzes the performance of the proposed algorithm through lake experiments. Concluding remarks are given at the end of Section 5.
## 2 Background and Related Work
### Deep-Learning Background
#### 2.1.2 Mlp  
The perceptron was proposed by [16] in 1943, representing an algorithm for the supervised learning of binary classifiers. As a linear classifier, a single-layer perceptron is the simplest feedforward neural network. [PERSON] and [PERSON] [17] showed that a single-layer perceptron is incapable of learning the exclusive or (XOR) problem, whereas an MLP is capable of solving the XOR problem.  
An MLP is a fully connected class of ANN. Attempts to solve linearly inseparable problems, such as the XOR problem, have led to different variations in the number of layers and neurons as well as nonlinear activation functions, such as a logistic sigmoid function or a hyperbolic tangent function [18].
## 3 Background on YANG and OPC UA
### Opc Ua
#### 3.2.2. OPC UA Device Model  
The device model for the OPC UA server is established using one of the following methods: (a) leveraging the OPC UA standard-defined models, (b) leveraging the companion models, and (c) utilizing vendor-specific models. These device models are integrated into the OPC UA server and are accessible and manageable by any OPC UA client. OPC UA publishes a base information model, while many vendors publish companion information models, providing a unified view of devices regardless of underlying device variants.  
Figure 5: OPC UA model.
## 4 Classification of Routing Protocols
### Temperature-Aware Routing Protocols  
In wireless body sensor networks, some bio-medical sensors may be implanted inside the human body. Electric and magnetic fields are generated due to the radio signals used in wireless communication, which will result in temperature rise because of antenna radiation absorption, and power consumption of the node's circuitry [28]. This will affect temperature sensitive organs of human body by reducing the blood flow [28], growth of certain tissues, enzymatic reactions and might damage the surrounding tissues if in place for long periods of time [29]. Specific Absorption Rate (SAR) is the measure of the rate at which radiation energy is absorbed by the tissue per unit weight and is given in [28] as:  
\[SAR=\frac{\sigma\big{|}E\big{|}^{2}}{\rho}\big{(}W\,/\,Kg\big{)} \tag{1}\]  
where '\(\sigma\)' is electrical conductivity of tissue, '\(E\)' is induced electric field by radiation and '\(\rho\)' is density of tissue. During last decade, different researchers and engineers have tried to solve the problem of temperature rise and overheating. The aim of all temperature-aware routing protocols for WBSNs is to reduce the temperature rise of the implanted bio-medical sensor nodes. In the following sections, we have discussed the temperature-aware routing protocols for WBSNs proposed during last decade.
## 1 Introduction
### Review Methodology  
This narrative review employed a comprehensive multi-database literature search to identify relevant studies on non-conventional lane design and control coordination optimization at urban road intersections. The primary databases searched included Web of Science Core Collection, Transportation Research International Documentation (TRID), IEEE Xplore Digital Library, Engineering Index (EI Compendex), and ScienceDirect to ensure comprehensive coverage of international literature. Given the significant contributions from Chinese research institutions in this field, China National Knowledge Infrastructure (CNKI) was also searched to capture relevant Chinese studies. Additional sources included proceedings from major transportation conferences (Transportation Research Board Annual Meeting, IEEE Intelligent Transportation Systems Conference) and technical reports from transportation research institutes.  
The search strategy utilized combinations of key terms, including \"non-conventional lanes\", \"unconventional intersection design\", \"contralow left-turn lane\", \"exit lanes for left-turn\", \"reversible lane,\" \"variable lane assignment\", \"dynamic lane control,\" \"intersection optimization\", \"signal control coordination\", and \"spatiotemporal optimization.\" Chinese equivalent terms were used for CNKI searches. The temporal scope focused on publications from 2000 to 2025 to capture the evolution from early non-conventional lane concepts to current intelligent control systems, while foundational earlier works were included when historically significant for understanding the development trajectory.  
The literature selection involved initial screening based on titles and abstracts, followed by full-text review for studies meeting the inclusion criteria. A total of 81 high-quality studies were ultimately selected and synthesized in this review. Given the narrative nature of this review, the synthesis process emphasized thematic organization around key aspects, including (1) concepts and classification of non-conventional lanes, (2) operational mechanisms and traffic flow characteristics, (3) suitability evaluation and effect analysis, (4) optimization design and control methods, and (5) practical applications and case studies. Particular attention was given to research addressing different geographic contexts and traffic conditions to understand the broad applicability of various non-conventional lane designs and control strategies in diverse urban environments.
## 3 Results  
### Interpretation of Feature Extraction Results  
In order to analyze the meaning of the ability feature extraction results, this section uses the SHAP analysis method of machine learning to explore the connotation of the extraction results. The SHAP method is based on the idea of game theory and achieves the importance ranking of the input features by quantifying the contribution of each input feature of the model to the output results. In order to overcome the un-interpretability of RNN and extend the applicability of the encoder-decoder architecture constructed in this paper, the constructed Encoder is interpreted and analyzed with the help of Python's SHAP framework, so as to reflect the importance of the original ability features to the ability sequence features, as shown in Figure 6.  
The hidden layer-to-hidden layer transfer matrix obtained from training is  
\[W_{hh}=\begin{bmatrix}0.9518&-0.0936&-0.1264\\ 0.0419&0.8325&-0.0397\\ 0.1070&-0.0946&0.7910\end{bmatrix} \tag{33}\]  
As can be seen from Figure 6, the \"number of flood control projects\" \"clean water guarantee number\", and \"proportion of urban buildings with lighting protection devices\" have a greater impact on feature 1, and this kind of feature can directly show the physical disaster prevention facilities and provide a reliable guarantee; \"weather radar station coverage\" and hydrological station network density\" have a greater impact on feature 2. These facilities mean that disaster prevention subjects have better forecasting and warning ability for the coming of disasters. \"Automatic weather station coverage rate\" has a greater impact on feature 3, and more automatic weather stations mean that disaster prevention agents can understand weather conditions faster and more comprehensively and make preparations for possible disasters.  
Observing Equation (33), we can find that, with \(W_{hh}\), the absolute values of the main diagonal elements are larger and the absolute values of the elements at other positions  
Figure 6: Visualization of the importance of original capability characteristics on capability sequence characteristics.  
are smaller, which predicts that, as time progresses, a feature at the current moment is more influenced by the same feature at the previous moment and less influenced by other features. This phenomenon is most pronounced for feature 1 and less pronounced for feature 3. This is because the impact of disaster recognition capability is relatively stable and lasting, while the social control capability is highly variable, difficult to evaluate, and unstable.
## 3 Proposed Method
### Data Pre-Processing  
Missing Value Imputation: The method of imputing missing values involves replacing the absent data points with approximated values. The dataset includes a feature referred to as \"Total Bilirubin\", which is absent for certain cases. The process of imputing missing values in this feature involves utilizing \"mean imputation\", whereby the missing values are substituted with the mean value derived from the available non-missing values.  
Normalization of Data: Normalization of data is a method applied to standardize and measure data in the direction to carry them to a comparable range and magnitude.  
There are several approaches available for achieving this objective, including the utilization of min-max normalization techniques. The dataset includes the variable \"Age\". This particular feature has a distinct magnitude in comparison to the remaining features within the given datasets. The feature has been normalized using min-max regularization.  
Selection of Features: The process of identifying the most significant characteristics for a given assignment involves the utilization of a technique known as \"recursive feature elimination\". The dataset includes the variable \"Gender\". There is no observed correlation between this attribute and the target variable. Therefore, the aforementioned feature was excluded from the dataset in order to improve the performance of the proposed model.  
Data transformation: The method involves converting the data into a format that is better suited for deep learning algorithms. The proposed model employed the technique of discretization for the purpose of transforming the data. The dataset includes the variable \"Total Bilirubin\". The aforementioned characteristic exhibits continuity. Therefore, it is necessary to discretize this particular feature into a predetermined number of bins.
## 1 Introduction
More recently, reconstructed sun-induced fluorescence (RSIF) product was created, which utilizes high resolution MODIS data to predict GOME-2 SIF data, and the results indicated that RSIF had much higher seasonal and interannual correlation than the original SIF, especially in dry and cold regions [38]. Most of the above studies were conducted in the USA and Western Europe, and few studies have compared the GPP estimates from SIF retrievals from GOME-2 and OCO-2 in China. If the difference between the results of these methods and whether SIF exhibits the same performance in GPP estimates in the cold and arid region of China remains unclear. Therefore, it is interesting to evaluate the performance of SIF in the cold and arid region of China. Specifically, the differences in SIF retrievals from GOME-2 and OCO-2 must be evaluated to determine whether they perform consistently in GPP estimations and identify the extent to which different spatial resolutions contribute to the differences. In this study, the objectives were to (1) examine the correlations between tower GPP and GOME-2 SIF data at one retrieval band (740 nm), and OCO-2 SIF data at two retrieval bands (757 nm and 771 nm); (2) assess the performance of a SIF-based GPP model and a traditional LUE model (VPM); and (3) find possible reasons for the differences between OCO-2 SIF and GOME-2 SIF.
## 2 Challenges and Routing in 6G Networks
### Routing in 6G Networks
#### 2.2.1 State of the Art of 6G
It was also emphasized that 6G will contribute to sustainable development goals by supporting energy efficiency, greening the economy, and digital participation. A public-private budget of EUR 2 billion euros was committed to carrying out 6G R&D activities. Table 1 contains the studies given in this section. Table 1 presents a compilation of significant survey and visionary articles that laid the foundation for early 6G research. To provide a deeper understanding, a comparative analysis is needed. The authors of [67] uniquely questioned the necessity of 6G and mapped generational progress, serving as an early reflection piece rather than a technical survey. On the other hand, the authors of [68] explored quantum computing and machine learning, bridging future computing paradigms with telecommunications. In [19], one of the first technical explorations of THz communication is offered, emphasizing the  
Figure 4: Architecture of 6G-based space–air–ground combined network. practical hurdles. In addition, the studies in [20; 69] addressed the architectural and security challenges in 6G but lacked detailed implementation insights. The collective work in [70; 71; 72; 73; 74; 75; 76; 77; 78] provided a holistic vision of 6G requirements and architecture but remained conceptual. In [45], a philosophical and AI-centric perspective is provided, while [79] delivered a comprehensive unification of tellurian and non-tellurian domains, covering VLC and THz technologies with greater depth. The 6G Flagship program [80] stands out as the most extensive and ongoing effort, generating a large body of research and strategic whitepapers. Lastly, ref. [81] and the NGMN/ITU initiatives mark global governance perspectives but offer limited technical depth. In terms of pros, the early works are visionary and cover wide-ranging aspects such as frequency bands, intelligent surfaces, AI integration, and sustainability goals. The cons include redundancy across some studies, a lack of implementation scenarios, and the absence of unified modeling or standardized frameworks. Therefore, while these studies lay important groundwork, a need remains for more technically detailed, comparative, and implementation-oriented surveys to guide both academia and industry.
# Modeling Terrestrial Net Ecosystem Exchange Based on Deep Learning in China
## 2 Methods  
To predict NEE, we require a method that can effectively handle multi-source, heterogeneous ecological data, such as meteorological, vegetation index, and soil data. Deep learning models offer significant advantages in processing such diverse data types. These models can seamlessly integrate information from different sources and properties, allowing them to learn complex patterns associated with NEE. Furthermore, deep learning models are well-suited to address the noise and uncertainty inherent in ecosystem data. Due to their multilayered structure and the use of appropriate regularization techniques (e.g., L1/L2 regularization), they can maintain relatively stable prediction performance even in the presence of noisy data and outliers. Compared to traditional machine learning methods, deep learning models are less sensitive to data quality, making them more adaptable to the varied and often imperfect data conditions encountered in ecological research.  
In this study, we used the eddy covariance site data published by ChinaFLUX and MODIS remote sensing data as data sources to model the net ecosystem exchange at selected sites in China. Due to the limitation of data acquisition, only nine sites' data are currently available from the ChinaFLUX website, so we conducted the experiment on this basis.
## 5 Evaluation of Reliability Cost
### Background on Reliability Theory
#### 5.1.2 System and component reliabilities  
The state of \(i^{\text{-}}\) component can also be represented by a random variable \(X_{i}\) such that \(P\{X_{i}=1\}=p_{i}=1\) - \(P\{X_{i}=0\}\), where \(p_{i}\) is the probability that \(i^{\text{-}}\) component is functional, also referred as its _reliability_. The overall reliability of a system is then  
\(r=P\{\phi(X)=1\},\)\(where\)\(X=\{X_{1},X_{2},\)\(\ldots,\)\(X_{\text{-}}\}\).  
If \(p=\{p_{1},\)\(p_{2},\)\(p_{3},\)\(\ldots,\)\(p_{\text{-}}\}\) is a state vector representing the reliabilities of individual components, the system's reliability function is \(r=r(p)\).
# Hybrid Deep Learning Approach for Automatic DoS/DDoS Attacks Detection in Software-Defined Networks
## 3 The Proposed Method
### Dataset Types  
In SDN, various types of datasets are used to test and evaluate the effectiveness of DDoS attack detection and prevention mechanisms. Here are some examples of datasets commonly used for this purpose.
## 6 Experiment  
We evaluated our method using three multi-view datasets and the Daily and Sports Activities (DSA) dataset [3], multi-view IXMAS dataset [39], and the Northwestern-UCLA Multi-view Action 3D (NUMA) dataset [40]). It is important to note that the datasets we used were also used in many studies, such as [3; 6; 8; 27; 28]. Many-to-one and one-to-one were treated here as two cross-view categorization situations. The first was trained on one view and examined another view, but the second was trained on V-1 views and examined the remaining views. The corresponding \(X^{\mathrm{v}}\gets 0\) in Equation (5) was used for the \(v\)th view meant for testing. In addition, we employed the intersection kernel support vector machine (IKSVM) as our classifier with parameters \(C=1\). \(\gamma=1\), \(\beta=1\), \(\alpha=1\), \(p=0\), and \(K=1\) were default parameters with the default number of layers being \(1\). We also took into consideration NUMA and IXMAS. These are datasets for multiple camera view video. We acquired the first from three Kinect sensors in five scenarios comprising \(10\) human actions, while the second was taken from one top-view camera, and four side-view cameras. In addition, we employed a k-means clustering approach to create video words and quantize the descriptors. This led to the likelihood of using a histogram of the feature to represent a given video. As a further contribution, we were curious to see how our model would react if the recording was not done continuously for the performing action; that is, will it be possible for our model to keep the same accuracy if we remove some frame of the video? As described in Section 4.1, for an interval of time \(t\), we have N actions. If we pause the recording during a small period (\(p<<t\)), we can still see a flow described by feature points of the target view, as shown in Figure 7.  
Figure 6: Extraction of features of sources view followed by the computation of the mapping matrix (\(\mathbf{W}\)) as well as the target view (\(t\)) (image obtained from a public dataset [38]).  
It is also worth noting that \(V\) feature vectors constituted a representation of an action taken from \(V\) camera angles.