esa-sceva/satcom-chunk-collection huggingface dataset:
# Anomaly Detection in Blockchain Networks Using Unsupervised Learning: A Survey
## 6 Data Structures Used in Blockchain Anomaly Detection
### Sequence-Based Data Structures  
In a blockchain network, each user node broadcasts the whole block, and therefore, the complexity involved prevents the effective implementation of the process. On the other hand, once a malicious transaction is erroneously admitted by a user it cannot be undone. Thus, an in-time detection of the anomalous transaction would prevent the depletion of valuable resources for the user [128]. In this regard, the identification of anomalous transactions would be effectively supported by representing the blockchain data as time series (i.e., data sequences) and using specialized techniques (e.g., rolling window aggregation method [129]) to extract useful features that describe the properties of the time series data and define the space where the unsupervised learning is to be applied [130].  
For example, times series representation of the Bitcoin data has been involved in address clustering to identify accounts belonging to the same user. A feasible way to do this relies on encoding sequences of transaction data in patterns each of which is described by features related to the information that flows in the transactions (tx_in) and the transactions' output information (tx_out) [131]. For example, considering cybersecurity applications related to blockchain, the tracing and audit of transaction events appear to be most evident, where blockchain transactions can be treated as a sequence of events, the end of which might correspond to a malicious incident [132].  
The utilization of time series representation of blockchain data has been very convenient in defining behavior patterns to identify malicious activities. This is translated in studying the data under the framework of behavior patterns, originating from the very nature of the blockchain structure, because each block contains a ledger (i.e., record) of all transactions that took place since the previous block. Therefore, there exist multiple backup ledgers in a blockchain network. Pattern sequences are generated based on featuresselected in terms of various ways. For example, the changes in the transaction amount over time [133] or the selection of the time taken to perform a transaction together with the transaction amount from one user node to another one [134]. Notably, in both cases, the transaction amount is the most predominant feature.  
The time series representation of the Ethereum can be accomplished by representing the smart contract codes as a sequence of opcodes, which are hashed and used as input to the unsupervised learning [135], such as affinity propagation [111] and k-Medoids [136].
## 4 Test and Analysis
### Energy Havesting  
The working principle of the PVDF piezoelectric wing was to convert the mechanical energy generated by the reciprocating bending vibration into electric energy during wing flapping process. The energy harvesting circuit used in this paper was the classic energy harvesting circuit shown in Figure 14. It is divided into three basic parts: full-bridge rectification, voltage regulation, and filtering.  
The AC signal output by the piezoelectric element was converted into a DC signal (AC-DC conversion) after full-bridge rectification, and the capacitor Ce acts as a filter and a voltage regulator. In order to obtain a relatively ideal DC voltage, a filter circuit composed of a capacitor having an energy storage function is required to filter the pulsating component in the output voltage of the rectifier circuit to obtain a DC voltage.  
In order to verify the power generation effect of PVDF piezoelectric wing, this paper used a rectifier bridge to form an AC-DC conversion circuit, and used a 47 uF filter capacitor to stabilize the voltage. The experimental device in Figure 15 successfully illuminated the letter 'JLU' consisting of 14 red LED lights at the flutter frequency of 15 Hz shown in Figure 16. It offered the feasibility to drive micro electrical components without extra energy consumption of the energy source, which could extend the flying endurance.  
Figure 14: Classic energy harvesting circuit.  
Figure 13: Real time voltage phase difference of divided area.
## 4 Current Approaches to Measure Resilience in Smart ICI
### Economic Approach  
In current research, economic resilience is defined as the \"inherent ability and adaptive response that enables firms and regions to avoid maximum potential losses\" [52]. The foundational model in [52] employs a linear time-independent analysis to examine various aspects that may influence overall resilience, such as generation, flow, and consumption of multiple products within ICIs. This model has also been extended to incorporate nonlinearities and time dependencies to investigate different hazard models within ICIs. Moreover, in [53], the authors characterize dependencies between infrastructures in simple terms, such as the exchange of products, data, and services. They also demonstrate how a disaster event can exacerbate initial damage based on these dependencies, resulting in  
Figure 11: Knowledge discovery process for ICI [51].  
cascading failures. Consequently, understanding the dependencies within ICIs is a crucial aspect for decision makers to consider during the modeling and recovery processes. In their example, recovery is based on assessing infrastructure parameters from graph theory. This methodology was illustrated using a potential infrastructure system, including power, water, and telecommunication infrastructures following a hurricane.  
Additionally, in [54], the authors present a risk-based economic input-output model, which serves as a useful tool for estimating the cascading effects of ICI failures. Their model is applied to provide a framework for evaluating economic resilience. They propose a static model for resilience that incorporates primary resilience concepts of robustness, rapidity, redundancy, and resourcefulness. The general notion of static resilience, or \"the ability of infrastructure to maintain functionality during a disruptive event\" [55], is depicted in Figure 12 and expressed in Equation (11):  
\[Static\;Economic\;Resilience=\frac{\%\Delta DY^{max}-\%\Delta DY}{\%\Delta DY^{ max}} \tag{11}\]  
Here, %\(\Delta DY\) represents the actual percentage difference in infrastructure functionality following a disruptive event, and %\(\Delta DY^{max}\) denotes the maximum rate difference corresponding to the worst-case level of production.  
_Limitations:_ The primary weaknesses of this approach are that many metrics related to this type are only for static resilience and focus on the disturbance event, which lacks the determination of resilience measures in pre/post-event phases, such as preparedness.
# Majority Voting Based Multi-Task Clustering of Air Quality Monitoring Network in Turkey
## 3 Materials and Methods
### K-Means Clustering  
Consider a dataset \(D=\{o_{1},\,o_{2},\,\ldots,\,o_{n}\}\) where each \(o_{i}\) represents an object as a \(p\)-dimensional explanatory variable and \(n\) is the number of objects (instances) in the dataset. Assume that the problem domain is to be divided into \(k\) clusters combination of which is represented as a vector \(C_{KM}=\{C_{1},\,C_{2},\,\ldots,\,C_{k}\}\) and the centroids of \(k\) clusters are denoted by \(\mu=\{m_{1},\,m_{2},\,\ldots,\,m_{k}\}\).  
The first step is to assign \(k\) points as cluster centers at random. The distance between each data point \(o_{i}\) and each cluster centroids \(m_{j}\), where \(i=\{1,\,\ldots,\,n\}\) and \(j=\{1,\,\ldots,\,k\}\), are calculated using one of the distance metrics such as _Euclidean_, _Manhattan_, _Chebyshev_, _Minkowski_ distance, etc. as \(\operatorname*{argmin}_{j}\text{dist}(o_{i},\,m_{j})\) to find the nearest cluster for the respective instance to be assigned. New cluster centroids are calculated by \(m_{j}=\left(1/n_{j}\right)\sum\limits_{o_{i}\in m_{j}}o_{i},\,\) where \(n_{j}\) denotes the number of objects in cluster \(j\), \(C_{j}\). This process iteratively continues until no data point changes cluster membership. According to the method used for the initialization of the process, different techniques instead of random initialization can be used such as K-Means++, Farthest First or Canopy.
# Design and Implementation of LoRa Based IoT Scheme for Indonesian Rural Area
## 3 Design and Implementation
### LoRa Based Dragino Implementation  
The pilot experiment utilizes the Dragino Lora IoT Kit [27] as a Lora framework with a frequency of 925 Mhz, a maximum link budget around 168 dB, and a dynamic range RSSI around 127 dB. The setup of the Lora system requires two devices, which are located separately. The first device called as a client, which is located in a remote area, consists of a LoRa shield and Arduino Uno. The client, in which refer to edge computing paradigm, is surrounded and connected to several sensors such as a DHT11 [28] for air temperature and humidity, a soil moisture sensor for estimation of water content in soil, and a pH soil sensor for measuring soil pH. The client works by collecting data obtained from three different sensors, then broadcasting them via LoRa wireless. The detail of client architecture is shown in Figure 2.  
Thus, to store the recorded data, we construct a client-server communication using Dragino LG-01P LoRa gateway. The server receives and collects the data which is broadcasted by several clients. Figure 3 shows the server architecture. The server then maps the data received from clients by adding one dummy variable such as code \"01\" for NODE 1 and \"02\" for NODE 2. The Arduino IDE is installed to the server so that the users can access the data and data logger.
# Modeling and Analysis of RFI Impacts on Imaging between Geosynchronous SAR and Low Earth Orbit SAR
## 4 Discussion on Probability of Specular Scattering Case
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**GEO SAR** & \multicolumn{3}{c}{**LEO SAR Constellation**} \\ \hline Orbit altitude/km & 35,793 & Orbit type & sun-synchronous \\ Inclination/\({}^{\circ}\) & 16 & Orbit altitude/km & 500–1000 \\ Eccentricity & 0 & Constellation type & Walker \\ True anomaly/\({}^{\circ}\) & 0 & Patten & 20/1/1; 10/1/1 \\ Argument of & 0 & Satellite total number & 20; 10 \\ Perigee/\({}^{\circ}\) & & & \\ Lon.Ascn.Node/\({}^{\circ}\) & 88 & True anomaly/\({}^{\circ}\) & 0–360 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Orbital elements of the GEO SAR and LEO SAR constellation. Comparing Figure 11 with Figure 12, \(p_{GEO}\) can approach 1.9%, but \(p_{LEO}\) cannot reach 0.5%. This is because there is only one GEO SAR with a right look function but there are 20 LEO SARs with right look function and left look function in our study. As a result, the total time for LEO SARs observation in China is much longer than that of GEO SAR, but the time for specular scatter is the same, causing the large difference between \(p_{GEO}\) and \(p_{LEO}\) in this case. Moreover, when the number of LEO SARs doubles, \(p_{GEO}\) increases  
Figure 11: Probability of specular scatter while the GEO SAR is illuminating China. The magenta box line shows the case of a LEO SAR constellation which uses a 20/1/1 walker pattern. The magenta points are the average of the probability for every altitude in 20/1/1 walker pattern. The blue box line shows the case of a LEO SAR constellation which uses a 10/1/1 walker pattern. Figure 12: Probability of specular scatter while the LEO SAR is illuminating China. The magenta box line shows the case of a LEO SAR constellation which uses a 20/1/1 walker pattern. The magenta points are the average of the probability for every altitude in 20/1/1 walker pattern. The blue box line shows the case of a LEO SAR constellation which uses a 10/1/1 walker patten. The blue points are the average of the probability for every altitude in 10/1/1 walker pattern. but \(p_{LEO}\) decreases because the specular scatter time does not grow as the total time for LEO SAR observation of China. This indicates that the growing number of LEO SARs (or GEO SARs) could lead to higher \(p_{GEO}\) (or \(p_{LEO}\)). Furthermore, it is apparent that the probability becomes higher as the orbit altitude increases. Since the orbit altitude increases, the coverage of the LEO SAR is larger. Thus, the specular scatter RFI is more likely to occur. Furthermore, the probability will increase as the altitude becomes higher and the number of satellites grows. Therefore, in the future, it will be necessary to suppress the RFI between the two systems, due to the growing number of LEO SARs. Furthermore, it is also worth noting that the orbit design of LEO SARs should be optimized to avoid this case as much as possible.
# Image Dehazing and Enhancement Using Principal Component Analysis and Modified Haze Features
## 5 Conclusions  
This paper presented haze removal and low-light image enhancement methods using the supervised learning manner. The proposed method simplifies the atmospheric light estimation using the feature of haze patches analyzed by the principal component analysis. To estimate an accurate transmission, we generated the synthesized haze patches using the haze-free patches extracted from the non-hazy region at multiscaled spaces. The transmission was estimated in a supervised learning manner using random forest with two haze-relevant features. Through experiments, we demonstrated that the proposed method can estimate accurate atmospheric light and transmission than existing haze removal methods. As a result, the proposed method can obtain the resulting image without color distortion or brightness saturation. In addition, we demonstrated that the proposed method can be applied to low-light image enhancement problems using the degradation model of an inverted haze image. In conclusion, the proposed method can provide high-quality images for various image processing applications in haze and low-light conditions.  
[PERSON] and [PERSON] initiated the research and designed the experiments. [PERSON] acquired the test images and performed the experiments. [PERSON] analyzed the experimental results and [PERSON] wrote the manuscript.  
This work was partly supported by Institute for Information & Communications Technology Promotion(IITP) Grant, funded by the Korean Government (MSIT) (2017-0-00250, Intelligent Defense Boundary Surveillance Technology Using Collaborative Reinforced Learning of Embedded Edge Camera and Image Analysis), and by the Korea Aerospace Research Institute (NRF-2017M1A3A4A07028434).  
The authors declare no conflicts of interest.
## 3 Results and Discussion  
This section presents the results obtained following the comprehensive post-processing methodology described earlier. In addition, the results are compared with a high-cost system to validate the effectiveness of the introduced low-cost system.
## 3 Results and Analysis  
This section comprehensively validates the performance of OMRoadNet through a four-dimensional analysis framework. First, cross-domain comparative evaluations quantify its superiority over existing methods in mining road extraction accuracy and morphological adaptability. Second, ablation experiments deconstruct the framework's architectural components to justify their design necessity. Third, SNR-based (signal-to-noise ratio) robustness verification demonstrates stable performance across diverse operational scenarios, while final operational constraints analysis reveals practical deployment considerations. Together, these investigations systematically establish OMRoadNet's technical advantages and industrial applicability, spanning quantitative benchmark testing (Section 3.1), critical module verification (Section 3.2), dynamic environment adaptation (Section 3.3), and industrial deployment feasibility (Section 3.4). The experimental design rigorously addresses three research objectives: feature dissociation resolution under domain shifts, annotation-free learning efficacy, and robustness under mining-specific perturbations.
# Research on Short-Term Traffic Flow Combination Prediction Based on CEEMDAN and Machine Learning
## 2 Current Research Status
### Predictive Model Based on Mathematical Statistical Analysis  
Forecasting models based on mathematical statistical analysis have an important assumption--that historical data and prediction results have exactly the same characteristics. These kinds of forecasting models mainly include the Kalman filter prediction method, the time series analysis forecasting method, the historical average forecasting method, and so on. Since [PERSON] [4] proposed Kalman filtering in 1960 [3], due to its excellent performance and good effect, Kalman filtering and its improved algorithms have been widely used in modern projects, such as control, finance, communication, inertial guidance, fault detection, and signal processing. [PERSON] et al. [5] were the first to verify that Kalman filtering has a good predictive effect on short-term traffic flow. [PERSON] [6] and other scholars used the dataset of Xuzhou National-Provincial Highway to verify the short-term traffic flow prediction model based on Kalman filtering. Among many time series forecasting models modeled by parametric modeling, ARIMA [7] is the most widely used. [PERSON] et al. [8] proposed an ARIMA model that takes into account seasonality. Historical average prediction is the earliest forecasting method applied to transportation systems, but the accuracy of prediction is difficult to guarantee, especially when there is an emergency on the road. As a result, the historical averaging method cannot be applied to the increasingly complex modern transportation system [9].
## 3 Results and Discussion
### Characterization of CNCs-Coated Plastics  
#### 3.2.1 Z Potential of CNCs Coated and Uncoated PET Film Versus pH  
Z potential and contact angle were employed to assess the hydrophobicity of uncoated and CNCs-coated PET films. From the data reported in Figure 5, PET films coated with the three types of cellulose nanocrystals showed similar behavior, Z potential increases with the increase in pH values. Uncoated PET film has its isoelectric point at pH 4 while PET films coated with CNC\({}_{\text{SOSH}}\), CNC\({}_{\text{COOH}}\) and CNC\({}_{\text{COOR}}\) have their isoelectric point at pH 1.6, 2.08 and 2.7, respectively. According to analytical instrumentation and data interpretation (Surpass TM 3), the isoelectric point found at a pH close to 4, and lower Z potential values found at pH \(\approx\)6, indicate that the material exhibits less charges density. Based on the above, uncoated PET film appeared to be the material, which exhibits the lowest charges density followed by PET-CN\({}_{\text{COOR}}\), PET-CN\({}_{\text{SOSH}}\) and PET-CN\({}_{\text{COOH}}\).  
Figure 4: Thermal degradation pathways for the three samples.  
In contrast, the hydrodynamic water contact showed a different behavior (Figure 6). There was an immediate decay in water contact angle of PET films coated with esterified CNCs. It was observed that the drop of water was not absorbed by the CNC\({}_{\text{COOR}}\) coated PET film, but it just expanded rapidly on the surface. PET films coated with CNC\({}_{\text{SO3H}}\) and CNC\({}_{\text{COOH}}\) have similar trends and decay rate over measurement time.
# Throughput Prediction of 5G Network Based on Trace Similarity for Adaptive Video
## 4 Experiment and Results
### Results  
In Figure 8, we present a fragment of the F2 trace, which helps to visualize the traffic characteristics and prediction accuracy. The prediction generally followed the traffic pattern, but sometimes, it overestimated the scope of oscillations in absolute values, particularly in fragments where traffic fluctuations had a higher frequency. Similar to Figure 4, the trace had an irregular structure with no identifiable patterns, which made the prediction difficult. However, there were some periods, such as between  
Figure 9 shows the distribution of prediction errors for the LSTM, LSTM + KMeans, and MA(1) models using the M2 trace. The histogram for MA(1) is wider, indicating that the errors were higher than in the LSTM-based models. The difference between LSTM and the clustered approach was more subtle but still visible.  
The results presented in Figures 10 and 11 reveal that the clustering approach achieved the lowest NRMSE in both fixed and mobile scenarios. Both the F1 and F2 traces showed similar performance results for the fixed scenario, as illustrated in Figure 10. The error achieved by the LSTM model trained on the whole continuous traces was moderately higher than that of the specialized LSTM models. The MA(1) error was the highest. The results were similar regardless of the applied clustering algorithms. However, a closer examination of the results showed that KMeans obtained a slightly worse score than the remaining two groupings.  
In the case of the mobile scenario, shown in Figure 10, the NRMSE was higher for all the models compared to the fixed scenario. Additionally, the universal LSTM model's error was significantly higher than that of the cluster-based model.  
Figure 10: NRMSE of different prediction models for a fixed UE device generating F1 and F2 traces.  
Figure 9: Comparison of prediction error distribution by (**a**) LSTM, (**b**) LSTM + KMeans, and (**c**) MA(1) for the M2 trace. The narrower the histogram, the better the prediction accuracy.
## 5 Conclusions  
This paper proposes a method for dynamically predicting traffic demands to improve the efficiency of satellite resource management. Based on real data, the method utilizes a deep learning artificial intelligence algorithm that combines Seq2 Seq and LSTM networks. This approach enhances prediction accuracy and has broad applicability, fully considering the demands of mobile terminals such as aviation and maritime ones without the need for extensive statistical parameterization. The method can be extended to various real systems and excels in predicting long time series, enabling stable forecasts of traffic demands over extended periods.  
Future work should focus on retraining and fine-tuning the model using data from diverse regions and varying traffic patterns to enhance its adaptability and robustness. This includes employing K-means clustering to group beams based on historical demand data that will improve model performance by identifying clusters of similar patterns.  
Conceptualization, L.Z. and C.Z.; methodology, P.Q.; software, L.Z.; validation, Y.Z., Y.C. and C.Z.; formal analysis, C.Z.; investigation, P.Q.; resources, P.Q.; data curation, C.Z.; writing--original draft preparation, L.Z.; writing--review and editing, L.Z.; visualization, P.Q.; supervision, Y.Z.; project administration, Y.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the pre-research project on Civil Aerospace Technologies, grant number D010203.  
Not applicable.