esa-sceva/satcom-chunk-collection huggingface dataset:
# Dynamic Information Flow Tracking: Taxonomy, Challenges, and Opportunities
## 1 Introduction
Specifically, for hardware-based designs, we mainly discuss the gate-level-based DIFT design and micro-architecture-level-based DIFT design. For software-based solutions, we focus on information flow tracking techniques targeting the software and the operating system. For software and hardware co-designs, we cover a variety of DIFT solutions which use both software and hardware to enforce information flow tracking. Table 1 shows a high-level comparison among all DIFT solutions mentioned herein. Based on the analysis, we further discuss the possible solutions to help overcome these limitations. We also discuss potential research directions for better DIFT techniques. The main contributions of the paper are summarized as follows. * We summarize a variety of existing DIFT solutions and classify them according to their designs and implementations as hardware-based, software-based or software and hardware co-designed. * We analyze the advantages and limitations of different DIFT solutions. Based on the analysis, we present a comparison between different DIFT implementations. * We discuss possible solutions to the limitations of the current work. New research directions for DIFT techniques are also presented. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \# & **Tag** & **Type** & **CFI** & **DFI** & **NI** & **Performance** & **Hardware** \\ \hline
[7] & 1 bit/byte & SW-Program level & \(\times\) & \(\checkmark\) & \(\times\) & 650\% & - \\
[12] & 1 byte, 1 bit/byte & SW-Program level & \(\times\) & \(\times\) & \(\times\) & 224–700\% & - \\
[10] & variable tag & SW-Program level & \(\times\) & \(\times\) & \(\checkmark\) & 3000–5000\% & - \\
[11] & 32 bit/variable & SW-Program level & \(\times\) & \(\checkmark\) & \(\times\) & 14\% & - \\
[13] & Process level & SW-Program Level & \(\times\) & \(\times\) & \(\checkmark\) & 5.35\% & - \\
[1] & kernel object label & SW-OS Level & \(\times\) & \(\times\) & \(\checkmark\) & Unspecified & - \\
[6] & object label & SW-System Level & \(\times\) & \(\checkmark\) & 5600\% & - \\
[2] & 2 bit/byte & SW-Offloading & \(\times\) & \(\times\) & \(\checkmark\) & 48\% & - \\
[15] & gate logic & HW-Gate Level & \(\times\) & \(\checkmark\) & \(\times\) & Unspecified & 70\% ALUT \\
[21] & 4 bit/word & HW-In Core & \(\checkmark\) & \(\checkmark\) & \(\times\) & 234\% & 22\% BRAM, 42\% LUT \\
[22] & 1 bit/byte & HW-In core & \(\times\) & \(\checkmark\) & \(\times\) & Negligible & 12.5\% BRAM, 0.82\% LUTs, \(\leq\)1\% Area \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Details of dynamic information flow tracking techniques. CFI, DFI and NI are abbreviations of control-flow integrity, data-flow integrity and new infrastructure, respectively.
## 1 Introduction
### Definition of Problems  
For IoT, low-power wireless communication environment is often used, which can operate the devices long time, considering the energy efficiency [3]. Bluetooth low energy (BLE) is a typical example [16]. Low-power Bluetooth communication is used between devices in various areas in order to send, receive, and control sensor data. However, the following problems exist when the dynamic encryption key technology providing excellent security is applied to low-power wireless communication environment.
# A Novel Shape Finding Method for the Main Cable of
###### Abstract
[20] estimated the spatial cable shape of the Yongjong Grand Bridge using an improved TCUD method coupled with the initial force method by introducing a 3D elastic catenary cable element. The procedures in these studies are lengthier and more time-consuming for spatially curved cables as compared with the planar cable system. To simplify the calculation procedure and reduce the computation burden, the finite element method (FEM) was employed to estimate the 3D main cable shape. A five-step algorithm was proposed in [PERSON] et al. [30] and [PERSON] et al. [31] using the common FEM software ANSYS to solve both 2D planar and 3D spatial cable shapes, which is convenient to be executed at the preliminary design stage. However, the use of the common FEM program results in the insufficiently theoretical analysis for the core algorithm. As can be seen, most above-mentioned methods are developed in the Lagrangian coordinate system, which expresses the quantities of interest as the function of the undeformed position or unstrained length of the main cable segment. However, the unstrained length of each cable segment is usually unknown, resulting in more loop layers. In this paper, a shape finding procedure for the main cable using the NFEM and the Eulerian description is developed. A modified governing equation for the whole main cable, rather than cable segments, based on the force equilibrium is proposed by introducing a condition function to express the concentrated forces from hangers [14]. All quantities of the main cable are expressed in a fixed coordinate system. That is, if the longitudinal direction along the bridge axis, the vertical and lateral directions of a suspension bridge are defined as the \(x\)-, \(y\)- and \(z\)-axes, the variables of the main cable related to the \(y\) and \(z\) coordinates of the cable are expressed by a fixed \(x\) coordinate instead of the unstrained length of the cable. An FEM discretization process [32] starting from a linear basic function is then conducted to solve the governing partial differential equations to get a group of nonlinear algebraic equations. The iteration schemes for solving the cable shape with known and unknown horizontal axial force are proposed. The commonly used segmental catenary method is compared with the present approach. The shapes of 2D parallel and 3D spatial main cables, which are applied to a 1666-m main span earth-anchored suspension bridge are solved using the present approach and the segmental catenary method, respectively. The self-anchored Yongjong Grand Bridge analyzed by [PERSON] et al. [16] is also examined using the proposed method to validate its accuracy. The proposed algorithm provides an alternative to estimate the main cable shape at the preliminary design stage using only two-layer iteration and less programming. The use of the FEM allows the present method to be readily embedded in some commonly used FE analysis software and easily used by general bridge engineers.
# Methodology for Evaluating the Generalization of ResNet
## Appendix B  
Below, we briefly describe the generalization evaluation methods compared in our work. We denote the model as \(f\).  
**Spectral Norm**[8]. This method evaluates the generalizability of the model using the product of the spectral norm of the layer weights multiplied by the sum over layers of the ratio of Frobenius norm to the spectral norm of the layer weights [3].  
\[\text{SpectralNorm}(f)=\frac{\prod_{i=1}^{d}\|\mathbf{W}_{i}\|_{2}^{2} \sum_{j=1}^{d}\frac{\|\mathbf{W}_{j}\|_{F}^{2}}{\|\mathbf{W}_{i}\|_{2}^{2}}}{\gamma^{2}} \tag{1}\]  
where d is the number of layers in the model \(f\). \(\mathbf{W}\) is the weight matrix of the model. \(\|\mathbf{W}\|_{2}^{2}\) denotes the square of the spectral norm of the tensor \(\mathbf{W}\), and \(\|\mathbf{W}\|_{F}^{2}\) denotes the square of Frobenius norm of the tensor \(\mathbf{W}\). \(\gamma\) is the 10 th percentile of the margin values on the training set.  
**Nuclear Norm**[21]. This method uses the normalized value of the nuclear norm of the prediction matrix to evaluate the generalization ability of a model.  
\[\text{NuclearNorm}(\mathbf{f})=\frac{1}{N}\Bigg{[}\frac{\|\mathbf{P}\|_{*}}{\sqrt{\min( N,K)\cdot N}}\Bigg{]} \tag{2}\]  
where N is the number of samples in the training set, and K is the number of classes. \(\|\mathbf{P}\|_{*}\) is the nuclear norm of \(\mathbf{P}\). \(\mathbf{P}\in\mathbb{R}^{N\times K}\) denotes the prediction matrix of \(\mathbf{f}\) on the training set, and \(\mathbf{P}_{i,i}\) is the softmax vector of \(i\)-th sample (_i.e._, \(\mathbf{P}_{i,i}=softmax(\mathbf{f}(\mathbf{x}_{i}))\)).  
**Effective Invariance (EI)**[22]. This method evaluates the generalization ability of a model by quantifying its invariance to transformations of the input data.  
\[\text{EI}(\mathbf{x},\text{T}(\mathbf{x}),\mathbf{f})=\begin{cases}\sqrt{\hat{p}_{ t}\cdot\hat{p}}\;if\;\hat{y}_{t}=\hat{y};\\ 0\;\;\;\;\;\;otherwise.\end{cases} \tag{3}\]  
where \(\mathbf{x}\) is the input sample in the training set. \(\text{T}(\mathbf{x})\) represents the transformation of \(\mathbf{x}\), including 3 rotationally transformed angles (\(90^{\circ},180^{\circ}\), and \(270^{\circ}\)). The EI value is the average of the values calculated for the three rotation angles. \(\hat{p}_{t}\) is the predicted softmax vector of the model for the transformed input sample, and \(\hat{p}\) is the predicted output of the model for the original input sample. \(\hat{y}_{t}\) and \(\hat{y}\) are the model's classification results for the transformed and original input samples, respectively.
## 3 Proposed Methodology
### Model Training
#### 3.3.3 Bidirectional LSTM-Sentiment  
To further explore temporal dependencies, a bidirectional LSTM model, referred to as Bidirectional LSTM-Sentiment, was developed using the same input features: Bitcoin price and sentiment scores. The bidirectional LSTM architecture incorporated three bidirectional LSTM layers, each containing 50 neurons.  
Figure 3: Training and validation error trends for the tuned Multi-LSTM-Sentiment model across metrics: MSE, MAE, and MAPE. Tuning resulted in a consistent reduction in errors and improved generalisation.  
The bidirectional setup leverages both past and future context, which is critical for datasets where patterns may depend on both preceding and succeeding data points. This architecture enhances the model's ability to learn temporal dependencies, making it particularly suitable for financial time-series data. Employing three layers provided adequate depth for capturing the bidirectional temporal relationships without introducing excessive computational complexity.  
This design allowed the model to capture both forward and backward temporal relationships in the data, providing a more comprehensive understanding of price trends influenced by historical patterns, volume spikes, and sentiment fluctuations.  
The training process mirrored that of the sequential LSTM (described in Section 3.3.1), with the Adam optimiser, MSE loss function, 50 epochs, and a batch size of 25. Similar to the earlier models, a 20% validation split was employed. Figure 4 visualises the training and validation error trends for MSE, MAE, and MAPE, demonstrating the model's ability to generalise effectively.
## 4 Results
### Cell Capacity Fade Measurement and Estimation  
Figure 4 shows the evolution of the capacity degradation \(\Delta C\) of cell B0005 vs. the number of cycles as well as the evolution of the predictor \(\Delta V_{RMS}\). Note that \(\Delta C\) is calculated directly from the experimental data by applying (2), whereas \(\Delta V_{RMS}\) is calculated by applying (6) from the RMS value of the difference between the cell terminal voltage of the initial or reference cycle until the last cycle.  
The results presented in Figure 4 show a similar trend of \(\Delta C\) and \(\Delta V_{RMS}\). To quantify the degree of correlation, Table 2 shows the values of the Pearson correlation coefficient \(R\) between the capacity degradation \(\Delta C\) and the proposed estimator \(\Delta V_{RMS}\) for the four analyzed Li-ion cells calculated over the whole lifetime of the cells.  
The results presented in Table 2 show a high value of the correlation coefficient \(R\) for all analyzed cells throughout their entire lifetime, thus validating the usefulness and accuracy of the \(\Delta V_{RMS}\) capacity degradation estimator. Since it is demonstrated that \(\Delta V_{RMS}\) is a good estimator of the capacity fade, it is used to evaluate the SOH of the cell.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Cell Name** & **Number of Cycles** & **Discharged Voltage** & **Discharge Current** & **Charged Voltage** & **Charge Current** & **Ambient Temperature** & **End of Life** & **Rated Capacity** \\ \hline B0005 & 168 & 2.7 V & & & & & \\ B0006 & 168 & 2.5 V & 2.0 A & 4.2 V & 1.5 A & 24 \({}^{\circ}\)C & 30\% fade in rated capacity(2–1.4 Ah) & 2 Ah \\ B0007 & 168 & 2.2 V & & & & & \\ B0018 & 132 & 2.5 V & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Cell degradation test parameters.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & **Cell Name** & **Cycles Considered** &
\begin{tabular}{c} **Correlation** \\ **Coefficient** \(R\) \\ \(\Delta V_{RMS}\) **vs.**\(\Delta C\) \\ \end{tabular} \\ \hline \multirow{3}{*}{6} & B0005 & 168 & 0.991 \\  & B0006 & 168 & 0.990 \\  & B0007 & 168 & 0.993 \\  & B0018 & 132 & 0.982 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters of the analyzed cells when discharged at a C-rate of 1.
## 3 Shield Tunneling Anomaly Detection and Scenario Identification
### Anomaly Detection Model Based on Dynamic Threshold
#### 3.2.1 Offline Training  
During the offline training phase, the model is trained using data from the normal tunneling scenario to learn the distribution characteristics of data in normal scenario. We first use a sliding window with length \(D\) and step size 1 to cut the original training time-series data \(X=[x_{1},x_{2},\dots x_{N}]\) into subsequences so that the input of the encoder is a vector of the window size. Assuming a total of \(p\) subsequences are obtained after segmentation, given a subsequence input \([x_{1},x_{2},\dots,x_{D}]\), the encoder transforms the input data into a latent representation, and the mean vector and variance vector of the latent space distribution can be obtained. The mean vector and variance vector generate the corresponding latent variable \(z\) that satisfies the unit Gaussian distribution. We use \(E=[e_{1},e_{2},\dots,e_{p}]\) to represent all the embeddings output by the encoder, and \(e_{i}\) represents the embedding of the \(i\)-th subsequence of the input data. We use a sliding window of length \(L\) to segment \(E\) and obtain \(k\) non-overlapping subsequences (where \(k\) is an integer).  
Figure 2: Anomaly detection of shield tunneling process based on DT_VAE-LSTM.  
Afterwards, the LSTM model uses these subsequences of embeddings as inputs. We have the LSTM model take the first \(L-1\) embeddings and predict the next \(L-1\) embeddings. This process can be expressed using Formula (1):  
\[[\hat{e}_{j}^{2},\hat{e}_{j}^{3},\ldots,\hat{e}_{j}^{L}]=\text{LSTM}([e_{j}^{1},e _{j}^{2},\ldots,e_{j}^{L-1}])\ (j=1,2,\ldots k) \tag{1}\]  
where \(e_{j}^{i}\) represents the \(i\)-th embedding in the \(j\)-th subsequence of embeddings.  
When using \(\hat{E}\) as the predicted results output by LSTM model, the decoder in the DT_VAE-LSTM model uses \(\hat{E}\) to reconstruct the data. The reconstructed data are the output of the decoder, represented as \(\hat{X}\). The DT_VAE-LSTM model continuously optimizes its parameters by minimizing the objective function (Formula (2)) to accurately reconstruct the normal time-series data characteristics.  
\[\text{minimize KL}(q(Z|X)\|p(Z))+\sum\limits_{i=1}^{N}{(x_{i}-\hat{x}_{i})^{2}} \tag{2}\]  
The objective function consists of two parts, representing the KL divergence loss and the reconstruction error loss, respectively. \(\text{KL}(q(Z|X)\|p(Z))\) measures the difference between the latent variable distribution and the predefined prior distribution. The reconstruction error loss \(\sum\limits_{i=1}^{N}{(x_{i}-\hat{x}_{i})^{2}}\) measures the error between the reconstructed data \(\hat{X}\) and the original data \(X\).
# Fault Prediction for Rotating Mechanism of Satellite Based on SSA and Improved Informer
## 4 Experiments
### Dataset Processing and SSA Procedure
As can be seen from Figure 5, the main components are determined by calculating the contribution rate of each sub-sequence of motor current data. The main components were determined by calculating the contribution rate of each sub-sequence; the contribution rates from the first sequence to the fifth sequence were 66%, 13.7%, 11.5%, 5.4% and 3.4%, respectively. By observing the waveform of each sub-sequence and using autocorrelation coefficient analysis, it can be determined that first sub-sequence is a trend component, the second sub-sequences and third sub-sequences are oscillation components, and the rest are noise components. Therefore, the reconstruction order is determined to be 3. The sequences were reconstructed by regrouping them according to the first three decomposed sub-sequences. The reconstructed sequence was then compared with the original sequence, as shown in Figure 6.  
The same method was used to decompose the turntable temperature data, as shown in Figure 7. The contribution rates of each sub-sequence were calculated to identify the primary components; by observing the waveform of each sub-sequence and using autocorrelation coefficient analysis, it was determined that a reconstruction order of 2 was required. The reconstructed sequence was then compared with the original sequence, as illustrated in Figure 8.  
Figure 5: SSA decomposition sub-sequence of motor current. Figure 6: SSA reconstruction sequence of current and raw sequence. \[MAE=\frac{1}{n}\sum_{i=1}^{n}|y_{i}-g_{i}| \tag{16}\]0 _Cont_.
# Evaluation of Antioxidant and Anticorrosive Activities of _Ceriops_ _tagal_ Plant Extract
## 3 Results and Discussion
### Antioxidant Studies
#### 3.2.1 DPPH Free Radical Scavenging Assay  
DPPH free radical is a stable compound which, when it receives hydrogen atoms from plant extract, changes its color from purple to yellow. This change is measured by a decrease in absorbance indicating an increase in percentage of free radical scavenged in a concentration dependent manner [40]. The results in Figure 3a, show the antioxidant potential of _C. tagal_ extract in a concentration dependent manner ranging from 30.87 to 83.88% for 10-100 \(\upmu\)g/mL, respectively. Various studies have also found that antioxidant activity is dependent on the structure of substrate molecules apart from concentration [41].
# V2X Wireless Technology Identification Using Time-Frequency Analysis and Random Forest Classifier
## 5 Performance Evaluation
### Simulation Results
#### 5.2.2 Performance Analysis
Therefore, the difference between the cyclostationarity properties of C-V2X signals that have similar configurations becomes more difficult to discern, leading to a higher number of classification errors. Similarly, the statistical approach, based on a comparison between the CAF estimates of the considered signals at their cyclic frequencies and a threshold value determined by setting the probability of false alarm to 0.1, shows the poorest performances and its accuracy strongly depends on the SNR. It achieves a maximum value of 82% at 6 dB then fluctuates around 80% for the higher SNR regimes. On the other hand, the accuracy of the proposed model is almost stable at 100% for all SNR values due to the insensitivity of the time-frequency features to noise level. This discussion demonstrates why the proposed ML approach represents a better choice for fulfilling the high requirements of vehicular applications in terms of accuracy, and why cyclostationarity-based features cannot maintain the same level of performance regardless of SNR value.
## 1 Introduction
### Current Research Status
While some models adopt multi-scale feature fusion strategies, existing fusion methods still struggle to effectively adapt to the scale variation characteristics exhibited by mangroves under different tidal conditions. In transition areas between mangroves, water bodies, and other vegetation, existing models often show boundary blur and inaccurate segmentation, especially in scattered distribution areas with poor boundary localization accuracy. Additionally, these models show unstable performance across different temporal phases and regions of mangrove images, with limited generalization ability causing it to struggle to adapt to complex and varying monitoring requirements. To address these model deficiencies in wetland remote sensing recognition, researchers have conducted a series of improvements. [PERSON] et al. [31] comparatively analyzed the performance of different resolution remote sensing datasets in wetland vegetation classification, improving wetland vegetation classification accuracy through enhanced DeepLabv3+ network structure. Addressing the large-scale variations and complex background characteristics of mangrove wetlands, [PERSON] et al. [32] proposed the Swin-UperNet model based on UperNet, successfully achieving high-precision simultaneous segmentation of mangroves and Spartina alterniflora. [PERSON] et al. [33] innovatively incorporated prior background knowledge of remote sensing images into network design, proposing the LSKNet Large Selective Kernel network, effectively enhancing the modeling capability of contextual relationships between targets in remote sensing scenes. [PERSON] et al. [34] optimized channel and spatial position relationship modeling by designing adaptive local cross-channel vector aggregation attention modules, achieving significant segmentation accuracy improvements on their constructed MO-CSSSD mangrove dataset. These targeted improvements not only validate the optimization potential of the existing models but also provide important theoretical and practical references for designing new network architectures.
# Mining Top-\(k\) High Average-Utility Sequential Patterns for Resource Transformation
## 4 Proposed TKAUS Algorithm
### Sequential Average-Utility-Raising Strategies  
The performance of the algorithm is limited due to more candidates being generated in the process of threshold increasing from a lower value. The sequence average-utility-raising (abbreviated as SAUR) strategy, which increases the rise rate of the minimum utility threshold, was proposed in TKAUS.  
**Definition 10** (**Saur Strategy**).: _Assume that \(TKList=\{s_{1},s_{2},\cdots,s_{n}\}\) is the set of sequences contained in the \(q\)-sequence database \(D\). Let \(au_{i}\) denote the \(i^{th}\) highest average-utility value in \(TKList\). Before the mining task, we can safely increase the minimum average-utility threshold, which is a variable denoted as minau, to \(au_{k}\), where \(k\) is the desired input number of patterns._  
Proof.: Let \(minau\) is the minimum utility threshold of the mining process. When the variable \(minau\) increases to \(au_{k}\) from 0, the set of HAUSPs, denoted as \(H_{minau}\), is reduced from \(H_{0}\) to \(H_{au_{k}}\) (\(H_{0}\supseteq H_{au_{k}}\)), and all the sequences whose average utility is not lower than \(au_{k}\) must be in the set of \(H_{au_{k}}\). According to the definition of top-\(k\) HAUSP, there are more than \(k\) sequences in \(H_{0}\) whose average-utility values are not lower than \(au_{k}\). Thus, the SAUR strategy effectively increases the \(minau\) to a rational level. In addition, we prove that the method with the SAUR strategy will not miss any top-\(k\) HAUSPs.