esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
[36; 37] first used modern data mining techniques integrating two-step clustering, association rule mining, and decision trees to analyze data from the Majiagou landslide and the Zhujiadian landslide in the Three Gorges reservoir area. These studies not only identified landslide disaster factors but also realized the prediction of displacement evolution, which was the earliest research to carry out data mining for single landslide monitoring. [PERSON] et al. [38] and [PERSON] et al. [39] adopted the same data mining technology to analyze the trigger factors of the Basihule landslide and the Shuping landslide in the Three Gorges Reservoir area, and determined the warning threshold. All these studies have fully and comprehensively considered the correlation between multi-source monitoring data and provided causal relationships between different monitoring variables, which are very helpful for the analysis of landslide damage mechanisms and instability patterns. Most of these studies focused on reservoir landslides in the Three Gorges region of China, with monitoring data collected over several years and on a monthly scale. Therefore, these studies were more focused on the long-term deformation patterns of landslides. However, the daily-scale or even hourly-scale short-term deformation patterns of landslides are equally important in landslide early warning studies. Such short-term deformation patterns contain more reliable precursors of landslide disasters than deformation features, which are important for early warning decisions. In addition, these studies all adopted a two-step clustering algorithm, which is a kind of hierarchical clustering and divides clusters through the process of splitting or clustering, so there is no need to determine the number of clusters. However, for the clustering of daily or even hourly monitoring data, we prefer to flexibly adjust the number of clusters. This kind of data is very complex, and human subjective judgment is still needed. At this time, partition clustering represented by k-means is more appropriate. The purpose of this paper was to mine the short-term deformation patterns of landslides, identify the precursors of landslides, and obtain more reliable early warnings. In this study, the Lishanyuan Landslide in Hunan Province was taken as the case study. First, the sliding window method was used to extract features from the original monitoring data, then the k-means algorithm optimized by particle swarm optimization (PSO) was used to cluster the features and construct the item set, and the Apriori algorithm was finally used to mine the association rules between different features and determine the short-term deformation pattern of landslides according to the given confidence levels to analyze the precursors of landslide disasters and provide early warnings.
# Research on High-Performance Fourier Transform Algorithms Based on the NPU
## 4 Methods
### Energy Consumption Measurement Results and Discussion
#### 5.3.3 Discussions of Energy Efficiency for the Internal Units of the NPU  
Next, we analyzed the energy efficiency characteristics of the main acceleration computation units within the NPU, including the cube units, vector units, and embedded CPU. MM-2 DFT primarily utilizes the NPU's cube unit. Despite not having any algorithmic advantages over other methods, MM-2 DFT shows the best energy efficiency in both energy consumption tests. Hence, the cube unit can be deemed as the most power-efficient computation unit among all the units in NPUs and is suitable for the majority of matrix computation tasks.  
The comparison between MV-2 FFTm and MV-2 FFTv demonstrates that MV-2 FFTv is more advantageous in both speed and energy consumption. The main reason for this can be attributed to the optimization technique of replacing a sparse diagonal matrix multiplication with a vector multiplication operation, wherein the vector unit plays a crucial role in enhancing the time and energy efficiency of sparse matrix multiplication operations. Consequently, improving multiplication efficiency in sparse computing scenarios has become a key optimization focus.  
Some NPUs usually provide CPU units to support the execution of algorithms that are not compatible with matrix and vector computations. Hence, we consider the CPU to be an internal unit of NPUs to analyze its energy efficiency. Based on the test outcomes of FFT_CPU, it is evident that the CPU possesses prominent advantages in swiftly processing small-scale inputs and keeping low peak power. Therefore, the CPU still emerges as the optimal choice in the above scenarios.
## 2 Proposed Framework and Experimental Methodology
### Proposed Hybrid CPU-GPU Workflow  
#### 2.4.1 Conceptual Model  
The proposed hybrid CPU-GPU workflow optimizes AES encryption by dynamically allocating computational workloads to CPUs and GPUs based on the characteristics of the data being encrypted. Initially, input data are analyzed to determine its size and computational complexity. Small data blocks and metadata are assigned to the CPU, exploiting its strengths in sequential and control-oriented tasks. In contrast, larger data chunks, which are more suited for parallel processing, are assigned to the GPU for efficient parallel encryption. Following parallel encryption, encrypted outputs from the CPU and GPU are combined to generate the final encrypted result (Figure 4).  
Figure 4: Proposed conceptual hybrid CPU–GPU workflow.  
Figure 3: Comparison of AES encryption and decryption workflows using (**a**) CBC mode and (**b**) CTR mode. In CBC mode, encryption is performed sequentially due to the dependency between blocks, while CTR mode allows parallel processing by precomputing key streams using counter blocks. Arrows represent data flow, and ellipses (“...”) indicate continuation to subsequent blocks.
# Design of an Energy Efficient Future Base Station with Large-Scale Antenna System
## 2 System Model
### Power Consumption Model
#### 2.1.2 Circuits Power Consumption  
The circuit power consumption can also be divided as follows:  
\[P_{Cir}=P_{BB}+N_{t}P_{RF} \tag{4}\]  
Each parameter is a function of bandwidth, \(B\), and we use \(B=10\) MHz. \(P_{RF}\) can also be divided as:  
\[P_{RF}=P_{DAC}+P_{m}+P_{fill} \tag{5}\]  
where \(P_{DAC}\) is the digital-to-analog converter (DAC) power consumption, \(P_{m}\) is the mixer power consumption and \(P_{fill}\) is the TX filter power consumption [25].  
In this paper, we assume that we can adjust the RF power consumption. It would then be obvious for fine DAC/filtering to be difficult if we reduce the RF power consumption. Based on this assumption, we propose the RF power consumption model shown in Figure 1. This model indicates that the performance of the RF front-end circuits depends on the power consumption of those. For example, assuming we can adjust the RF power consumption from 0.1 to 1 W, when the RF power consumption = 0.1 W, the noise from coarse processing is too high, and \(SNR_{RF}\), the signal to RF noise ratio due to coarse processing, is 0 dB, i.e., the noise level and signal level are the same. Meanwhile, \(SNR_{RF}\) is 20 dB, when RF power consumption = 0.7 W, and a relative level of fine RF processing is possible. Regarding the BB power consumption, \(P_{BB}\), we already mentioned that we assume a future BS to have LS-MIMO capability. With that in mind, [PERSON] and [PERSON] presented an LS-MIMO BB computation model, \(\chi(Gflops)\), which is denoted as follows [26; 27; 28]:  
\[\chi(Gflop) = N_{t}\times\frac{(T_{u}B)}{T_{s}}\times\log_{2}(T_{u}B)+N_{t} \times\frac{(T_{d}B)}{T_{sl}}\times\tau_{r}\times\log_{2}(\tau_{r}) \tag{6}\] \[+ N_{t}\times K\times(T-\tau_{r})\times\frac{(T_{d}B)}{T_{sl}}+M \times K^{2}\times\frac{(T_{d}B)}{T_{sl}}\]  
where \(\tau_{r}\) is the length in the samples of the uplink pilot sequences, which are used to acquire channel state information (CSI), and a description of each parameter is shown in Table 1. We took the parameters from [29] and from a current LTE system [30]. The first part of Equation (6) is to operate the FFT/IFFT operation; the second part is to implement precoding/decoding; the third part is to correlate the pilot signals with pilot sequences; and the last part is to have the additional pseudo inverse for zero-forcing (ZF).  
The relationship between \(P_{BB}\) and \(\chi(Gflops)\) can be represented as:  
\[P_{BB}=\frac{\chi(Gflops)}{\varrho(Gflops/W)} \tag{7}\]  
where \(\varrho\) is the very-large-scale integration (VLSI) processing efficiency.  
We assume \(T_{g}=T_{d}\), a short guard interval (GI) and fast pilot correlation. Three pilot symbols are dedicated to one slot for the channel estimation [29]. Note that this model does not include a channel coding part, and the last part of Equation (6) becomes zero, when we use matched filtering (MF) precoding.
## 6 Supervised and Unsupervised Machine Learning Model
### Unsupervised Machine Learning
#### 6.2.2 Long Short-Term Memory Model
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Filtering Methods** & \multicolumn{2}{c}{**Advantages**} & \multicolumn{2}{c}{**Disadvantages**} \\ \hline \multirow{4}{*}{MLR} & \(\bullet\) & Simple to implement & \(\bullet\) & Outliers can have huge effect on \\  & \(\bullet\) & Dimensionality reduction & the regression \\  & \(\bullet\) & Regularization and cross-validation could & \(\bullet\) & Assumes linear relationship \\  & & be used to increase accuracy & between attributes \\ \hline \multirow{4}{*}{HMM} & \multirow{4}{*}{\(\bullet\)} & \multirow{4}{*}{Efficient learning algorithm} & \(\bullet\) & Contain large number of \\  & & & unstructured parameters \\  & & & \(\bullet\) & Unable to express dependencies between \\  & \(\bullet\) & Able to handle inputs of variable length & \(\bullet\) & Unable to capture higher order correlation \\  & & & \(\bullet\) & Viterbi algorithm is \\  & & & computationally expensive \\ \hline \multirow{4}{*}{ANN} & \(\bullet\) & Ability to predict with & \(\bullet\) & Performance depending on computer \\  & & incomplete knowledge & \(\bullet\) & processing power \\  & & Less sensitive to incomplete & \(\bullet\) & Black box nature (hard to interpret) \\  & & (corrupted) data & \(\bullet\) & Heavy depending on trial and error \\  & \(\bullet\) & Capable for parallel processing & \(\bullet\) & Computational time is hard to estimate \\ \hline \multirow{4}{*}{GBM} & \multirow{4}{*}{\(\bullet\)} & \multirow{4}{*}{Considers the prior knowledge of the  target system} & \multirow{4}{*}{Inherit drawback from both WBM and BBM} \\  & & & Applies intelligent statistical technique & \(\bullet\) \\ \cline{1-1}  & & \(\bullet\) & Complement the limitation of both WBM & \\ \cline{1-1}  & & \(\bullet\) & Beam from stored information from \\ \cline{1-1}  & & \(\bullet\) & Previous time-step \\ \cline{1-1}  & & \(\bullet\) & Able to deal from problems with \\ \cline{1-1}  & & \(\bullet\) & missing data \\ \cline{1-1}  & & \(\bullet\) & Less sensitive to tuning parameters such as & \(\bullet\) \\ \cline{1-1}  & & \(\bullet\) & Learning rate \\ \cline{1-1}  & & \(\bullet\) & Able to handle the vanishing & \\ \cline{1-1}  & & & gradient problem \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Advantages and disadvantages of different machine learning techniques [70; 71; 72; 73]. outperforms the HMM and ANN in the fuel prediction when there are missing data in the dataset collected from the sensors.
## 3 Results
### Bit-Error-Rate Based on the AUAS Swarm Routing Model  
We use the BER to understand the mutual communication among drones as it gives a measurement of bits that have errors relative to the total number of bits received in a transmission (usually denoted by ten to a negative power). Following [48], we have shown the BER of the proposed AUAS swarm routing algorithm over a collection of signal-to-noise ratios (SNR). As shown in Figure 6, when SNR increases, the BER displays constant behavior, and hence implies that there is a communication loss or delay among drones. The simulated BER is calculated using the beamformed output signals \(\mathbf{y}^{(u)}\) and source signals \(\mathbf{x}^{(u)}\) multiplied by an offset factor 1.022 for 20 drones, each having an antenna array with 32 elements. If the magnitude of the beamformed output vector \(\mathbf{y}^{(u)}\) is greater than the magnitude of the source signal vector \(\mathbf{x}^{(u)}\), then we set the bit value to zero and otherwise the bit value to one (this is undertaken to convert the vector quantities into bits). Next, the AWGN is added to \(\mathbf{y}^{(u)}\) in order to introduce noise to the beamformed output signal. The AWGN is calculated as \(\frac{1}{\sqrt{2}}n\), where \(n\) is a randomly generated complex number. Following this, the positive real part of the beamformed output signal \(\mathbf{y}^{(u)}\) is compared with the bit value conversions. The BER is encountered when these quantities are different. These bit-errors are summed and divided by the number of bits in the transmit signal and show the resulted BER over SNR as in Figure 6.  
Figure 5: Using the code written in Python: (**a**) shows the execution time; and (**b**) shows the execution time on a log-scaled graph. Each line in the subfigures (**a**) and (**b**) shows the number of elements in the antenna arrays which are 64, 32, 16, 8, and 4, and varies from top to bottom, respectively.
# Anti-Atopic Dermatitis Activity of _Cornus walteri_ and Identification of the Bioactive Compounds
## 2 Results and Discussion
### Effects of the Isolated Compounds **1**-**6** on IL-4/IL-13-Induced Eotaxin-3 Expression  
The effects of the isolated compounds **1**-**6** on cell viability were examined. While some compounds did not display any noticeable cytotoxicity at the tested concentrations, compounds **1**, **2** and **3** reduced cell viability at 20 uM to 84.5, 84.8 and 81.9%, respectively (Figure 5A). Therefore, further experiments were performed at concentrations up to 10 uM. The levels of eotaxin-3 were measured to compare the anti-AD potential of compounds **1**-**6**. At 5 uM, compound **2** exhibited a superior inhibitory effect against IL-4/IL-13-induced eotaxin-3 levels compared with the other compounds (Figure 5B). In addition, 10 uM of compounds **1** and **3** showed the strongest suppressive effect on eotaxin-3 expression (Figure 5B).  
Compounds **5** and **6** displayed relatively weaker suppression compared with compounds **1**-**3** (Figure 5B). These results indicate that the isolated compounds **1**-**6** have the potential to inhibit AD, and compounds **1**-**3** may be the major active compounds in CWS responsible for the anti-AD effect. Interestingly, to the best of our knowledge, lupel (**1**), betulinic acid (**2**), 5\(\alpha\)-stigmast-3,6-dione (**3**), 3-O-acetylbetulin (**4**), betulinic acid methyl ester (**5**) and morphytan (**6**) have not been previously reported to be involved in AD, suggesting that they are novel molecules that could suppress AD. Therefore, further studies are needed to investigate the anti-AD effects of these compounds and their underlying molecular mechanisms.  
Figure 4: Chemical structure of compounds **1**–**6**.  
Figure 5: **Effect of the isolated compounds 1–6 on IL-4/IL-13-induced AD-like human keratinocytes (HaCaT cells).** (**A**) Cell viability was measured after treatment of compounds 1–6 (0, 5, 10 and 20 \(\upmu\)M) for 48 h. (**B**) Cells were treated with compounds 1–6 and induced with IL-4/IL-13 (50 ng/mL each). The production of equaxin-3 in culture supernatants was measured by ELISA (\(n=3\)). The data are expressed as the mean \(\pm\) standard deviation.
## 1 Introduction
By utilizing a parallel network cyclic structure, this mechanism adaptively and dynamically assigns varying weights for feature fusion, thereby constructing a health index (HI) that encompasses a more comprehensive representation of degradation features. Finally, the constructed health index is input into the Bi-GRU model to derive the final RUL prediction results.
## 5 Simulation Experiment
### Comparison Between MISSA and Other Optimization Algorithms  
To verify the performance comparison between the MISSA and common multi-objective optimization algorithms such as NSGA2, NSGA3, and SPEA2, ten repeated comparison experiments were conducted on ten cases, and the performance indicators of the solution results were statistically analyzed. The comparison results are shown in Tables 7 and 8 and Figures 9 and 10. Tables 7 and 8 show the GD, IGD, and \(\Delta\) index results obtained by the four algorithms. It can be seen from the table that compared with the other three algorithms, MISSA achieved smaller GD and IGD mean values in eight cases and smaller \(\Delta\) mean values in seven cases in terms of solution quality, proving that the diversity and convergence of the solution set solved by MISSA are better. The quality of the obtained solution is higher. In terms of the stability of the solution results, it can be seen from the standard deviation data of different evaluation indicators in the table that the  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Case**} & \multicolumn{4}{c}{**Index**} & \multicolumn{4}{c}{**Index**} \\ \cline{3-14}  & \multicolumn{3}{c}{**Mean Value \(\times\) 10\({}^{-4}\)**} & \multicolumn{3}{c}{**Mean Value \(\times\) 10\({}^{-4}\)**} & \multicolumn{3}{c}{**Standardized Proximities \(\times\) 10\({}^{-4}\)**} & \multicolumn{3}{c}{**Standardized Proximities \(\times\) 10\({}^{-4}\)**} \\  & **MSSA** & **NSGA2** & **NSGA3** & **SPEA2** & **MSSA** & **NSGA3** &  
To more clearly demonstrate the distribution of mean and standard deviation of indicators obtained through different algorithms for each test case, Figure 9 shows the distribution curves of mean and standard deviation of indicators obtained by four comparative algorithms. It can be clearly seen from the graph that the differences between different algorithms are small, but the performance of the MISSA in solving the set is superior to the comparison algorithm in the vast majority of cases.  
To observe the distribution of the solution sets obtained by different algorithms more intuitively, Figure 10 shows the frontier solutions obtained by different algorithms, with points of different shapes representing the solution sets obtained by different algorithms. It can be clearly seen from the figure that the solution set solved by the MISSA basically dominates the frontier solutions obtained by the comparison algorithms, while the sets solved by the three comparison algorithms dominate each other, indicating that the solution set solved by the MISSA is closer to the Pareto frontier solutions of the case and has better solution set quality.  
In summary, compared to NSGA2, NSGA3, and SPEA2 algorithms, MISSA has better global search and local optimization capabilities, and can more effectively solve the problems proposed in this paper.
## 5 Discussion
### RQ2: Security Measures and Technologies for Detecting and Mitigating Blockchain Attacks  
To counter malicious blockchain attacks, various security measures are employed:  
* Detection Techniques:
* AI and ML-based anomaly detection for fraud and transaction manipulation.
* Static and dynamic smart contract analysis for vulnerability detection.
* Cryptographic verification ensuring transaction integrity.
* Consensus monitoring for anomaly detection in mining behavior.
* Mitigation Measures:
* Secure smart contract coding practices and formal verification.
* Blockchain-based authentication for secure access control.
* Privacy-preserving mechanisms such as zero-knowledge proofs (ZKP) and homomorphic encryption.
* Hybrid blockchain models integrating public and private blockchains for enhanced security.
* Prevention Techniques:
* Decentralized identity management with multi-factor authentication.
* Intrusion prevention systems (IPS) for blocking unauthorized transactions.
* Tokenization and encrypted storage for securing sensitive data.  
These security mechanisms help ensure blockchain resilience against various threats.
## 2 UAV Three-Dimensional Path Planning
### Path Optimality  
Path planning must achieve optimality under specific criteria to ensure the efficient operation of UAVs. For applications such as aerial photography, surveying, and surface inspection, minimizing the path length is a primary objective.  
The cost function for path length \(F_{1}\) can be expressed as follows:  
\[F(X_{i})=\sum_{j=1}^{n-1}\left\|\overline{B_{ij}P_{i,j+1}}\right\| \tag{1}\]\(X_{i}\) represents the list of \(n\) waypoints that the flight path needs to traverse, with the coordinates of each waypoint denoted as \(P_{ij}=\left(X_{ij},Y_{ij},Z_{ij}\right)\).
## 5 Experimental Scenarios and Results
### ADR-Det Stage 2 Evaluation Using Selected Datasets
#### 5.3.3. ADR-Det Stage 2 Detection Evaluation Using a Dataset of 200 Non-ADR Images and 50 ADR Images  
In this ADR-Det evaluation experiment, 250 photographs of large vehicles were selected, of which 200 were without ADR plates, and the rest depicted vehicles with ADR plates. False positives were detected in 18 out of 250 vehicles (7.2%), while 232 out of 250 vehicles (92.8%) have been correctly detected. Vehicles with ADR license plates have also been detected correctly (50 out of 50), i.e., 100%, with an average certainty of 99.96%. In total, including all 250 photos, the detection success percentage is 87.17% \(\pm\) 9.41%, with a minimum percentage of certainty of 51.82% and a maximum of 100% (3 out of 18 gave a false-positive result). The following Figure 8 includes a false-positive non-ADR result. It is obvious that from these experimental results, detections with a confidence level below 80% are false positive detections.  
Figure 6: Examples of ADR-Det algorithm successful ADR sign detection (green image box), using an evaluation dataset of 500 ADR images.  
Figure 7: Non-ADR sign, from the dataset of the 250 ADR images detected as an ADR sign (green image box), false positive detection result.
## 4 Methodology
### Analysis Setup  
Figure 4 depicts the schematic of the analysis setup used in this study. The initial step involves the selection of user spacecraft that have been supported by the FDF for DOWD analysis, as described in Section 4.3. Once the user spacecraft is identified, the list of the TDRS satellites used for acquiring DOWD measurements is determined. In the subsequent step, the TDRS orbit is reconstructed using TT&C measurements and weighted Batch Least Squares (BLS) techniques.  
As the TT&C-only solution is sensitive to the a priori knowledge of TT&C bias and its covariance, a Monte-Carlo analysis is conducted to account for the uncertainties in the bias and its covariance. In this analysis, over 40 TDRS orbit solutions are created, each varying the covariance of the TT&C bias. The purpose of generating this cluster of TDRS orbits is to understand how TDRS orbit errors are influenced by the choice of a priori covariance for the TT&C bias. The next step involves a comparative analysis of the obtained orbit with respect to the reference solution (based on TT&C and BRTS measurements). This setup enables the evaluation of the accuracy of TDRS orbit determination using only TT&C measurements and the identification of factors affecting the orbit accuracy.  
To determine the user spacecraft orbit, the TDRS ephemerides cluster is used in combination with DOWD measurements and weighted BLS techniques. Following the orbit reconstruction, an orbit comparison is carried out with respect to the FDF operational solution to identify discrepancies and gain insights into the sources of error. This analysis assesses the accuracy and reliability of the TDRS orbits and their impact on user spacecraft orbit determination (Section 5).