esa-sceva/satcom-chunk-collection huggingface dataset:
# Lateral Capacitance-Voltage Method of NanoMOSFET for Detecting the Hot Carrier Injection
## 2 Capacitances of Lateral Junctions  
The proposed method is based on the measurement of capacitance dependence on the voltage of the drain-substrate and source-substrate lateral junctions. The junction capacitances can be split into a side wall portion \(\mathrm{C_{sw}}\) and a bottom wall portion \(\mathrm{C_{bw}}\):  
\[\mathrm{C_{ds}}=\mathrm{C_{sw}}+\mathrm{C_{bw}} \tag{1}\]  
The same expression (see Equation (1)) can also be written for \(\mathrm{C_{ss}}\). The capacitance associated with the side wall portion is obtained by multiplying the length of the side wall perimeter by the effective side wall capacitance per unit length. The capacitance for the bottom wall portion is obtained by multiplying the area of the bottom wall by the bottom wall capacitance per unit area. The side wall portion of the capacitance \(\mathrm{C_{sw}}\), in turn, consists of two parts, namely the capacitance associated with the side wall of the contactless channel \(\mathrm{C_{swn}}\) and the capacitance associated with the side in contact with the channel \(\mathrm{C_{swc}}\):  
\[\mathrm{C_{sw}}=\mathrm{C_{swn}}+\mathrm{C_{swc}} \tag{2}\]  
Note that all parts of the lateral junction capacitance \(\mathrm{C_{ds}}\), as well as \(\mathrm{C_{ss}}\), depend on the geometry of the junctions and the applied voltage. Besides, the part of a side wall capacitance connected with the side in contact with the channel \(\mathrm{C_{swc}}\) depends on the potential profile of the channel, which is controlled by the applied voltage and is influenced by the charge trapped in the oxide layer or at the interface. Therefore, the potential profile depends on the linear size of the trapped charge domains along the channel. Small signal drain-substrate capacitance (\(\mathrm{C_{ds}}\)) and source-substrate capacitance (\(\mathrm{C_{ss}}\)) are functions of the transistor's channel potential profile, which is controlled by the applied bias. The presence of trapped charges in the oxide layer or at the interface leads to a local change of this potential and consequently to capacitances \(\mathrm{C_{ds}}\) and \(\mathrm{C_{ss}}\), which are different from those without a trapped charge. Measurement of changing the capacitance before and after charge trapping is therefore a practical tool for studying the properties of the trapped charge due to hot carrier injection. Therefore, the main objective of this work is to simulate the capacitances of lateral junctions \(\mathrm{C_{ds}}\) and \(\mathrm{C_{ss}}\), analyze the dependences between these capacitances, and analyze the linear size of the charge-trappeddomains in the oxide layer at the drain end. The dependence studied can be used in measurements to estimate the linear size of the trapped charge induced by electrical stress.
# Configurable Hardware Core for IoT Object Detection
## 3 Hardware Core for Object Detection with YOLO
### Architecture of the Computing Core Unit  
Each computing core receives as input the feature map values, biases, and weights from the on-chip memories. The core outputs the computation result to an OFM memory. The core can be configured to compute multiple IFM channels in parallel using multiple MACs (nMAC parameter). This parallelism requires multiple words of IFM data and kernel weights.  
The core supports multiple functions that can be selected during runtime. This runtime configuration allows the acceleration of multiple types of layers.  
The convolution function takes the outputs of the several MAC blocks and adds them together. The result of the convolution can be used as-is or be subject to either leaky or sigmoid non-linear activation. The non-linear activations are implemented for the fixed-point format. The core shifts the final result according to the quantization format being used. For the convolution and maxpool function, each computed convolutional result is compared to the previous convolution results of the same \(2\times 2\) maxpool region. The maxpool function can be used in standalone mode using directly one of the words from the IFM data.  
To exemplify the versatility of the core, the configurability of the core to run the Tiny-YOLOv3 layers presented in Table 1 is presented in Table 3.  
The layers 1 to 8 can be computed in convolutional + maxpool pairs. These layers disable the convolution bypassing and activate the maxpooling. All convolutional layers have leaky activation.  
The layers 10 and 12 are maxpool only. These layers bypass the convolution, ignoring all convolution specific configurations.  
The 16-17 and 23-24 layers are convolutional + YOLO pairs. These layers can be computed together since the convolution has linear activation, and the YOLO layer consists in performing sigmoid activation to the input feature maps. So, these two layer types can be combined into a convolution with sigmoid activation. Note that only part of the YOLO layer output channels is activated.  
The remaining layers perform convolution with leaky activation. All convolutional layers in the neural network use bias.  
The custom computational core is controlled by an AGU. The AGU period controls the number of operations required to obtain one result. For example, a maxpool requires a period of \(2\times 2=4\), while a convolution with a \(3\times 3\times 16\) kernel takes a \(3\times 3\times 16\)/nMAC  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Tiny-YOLOv3 Layers** & **Maxpool** & **Bypass** & **Leaky** & **Sigmoid** & **Bias** \\ \hline (1–2);(3–4);(5–6);(7–8) & 1 & 0 & 1 & x & 1 \\
9;11;13;14;15;19;20;22 & 0 & 0 & 1 & x & 1 \\
10;12 & 1 & 1 & x & x & x \\ (16–17);(23–24) & 0 & 0 & 0 & 1 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Custom core configurations for Tiny-YOLOv3 layers.  
period. Note the division since nMAC input channels are computed in parallel. The number of final outputs for a phase execution is defined by the iterations of the address generator.
# Improved PBFT Algorithm Based on Comprehensive Evaluation Model
## 3 TB-PBFT Algorithm
### Grouping Strategy Based on UAV Cluster  
The UAV cluster formation with a distributed control strategy has strong adaptability, good scalability, and fault tolerance. However, the UAV cluster may face the situation of multiple targets when performing tasks. It is difficult to achieve encirclement and tracking of dispersed targets only by a single formation. At this time, it is necessary to group the clusters and complete the encirclement task through collaboration between groups [29], which can significantly improve the operational efficiency. To maintain the formation, each group can adopt the leader-follower method [30]. The leader is responsible for controlling the formation and managing the team, and other UAVs, as followers, follow the leader's movement. Multiple leaders reduce the dependence on the control center, reduce the amount of information interaction, and shorten the calculation time. Therefore, this study adopts the grouping strategy based on UAV cluster formation, and the blockchain nodes are divided into several groups according to the formation results of the UAV cluster. The grouping process is shown in Figure 3. Each formation selects a leader as the primary node, so the consensus mode of multiple primary nodes can improve the degree of decentralization of the consortium blockchain, and also reduce the impact of a single primary node on the consensus system. Each primary node participates in intergroup communication on behalf of each group, thus greatly reducing the communication complexity. However, the consensus mechanism is similar to the UAV cluster based on the leader-follower formation method, which relies too much on the leader in the system structure. Failure of the leader will easily lead to failure of the whole formation control [31]. Therefore, to reduce the probability that malicious nodes are selected as the primary node, we evaluate the reliability of the node through a comprehensive evaluation model and select the node with the highest comprehensive score as the primary node. Details are described in Section 3.3.  
Figure 2: TB-PBFT Algorithm Framework.
## 2 Related Work
### Robot Motion Planning in Dynamic Environments  
Motion planning in unknown environments with dynamic obstacles presents significant challenges. Frontier-based methods [11; 12; 13] and sampling-based approaches [12; 13; 14] are effective for incremental exploration of unknown spaces, achieving completeness or near-optimality through boundary identification, random sampling, hierarchical planning, and viewpoint integration. However, these techniques primarily focus on exploration and static mapping; their effectiveness diminishes significantly when confronted with unpredictable dynamic obstacles.  
The existing motion planning approaches for dealing with dynamic obstacles include prediction-based motion planning methods, artificial potential field, and model-based methods [15; 16; 17; 18]. The work in [15] uses avoidance actions based on the position and velocity of the robot and the obstacles. The work in [16] further refined the potential field of obstacles in the APE framework to improve the performance of online obstacle avoidance. The work in [17] uses the MPC method to represent uncertainty using the chance constraint. The work in [18] added gradient optimization and further improved the dynamic model to achieve efficient dynamic obstacle avoidance.  
Effective prediction and tracking of dynamic obstacles remain challenging. To address this, we propose the \"K-GRU Kalman\" framework. This hybrid approach strategically integrates Kalman filtering (KF) [19; 20] and Gated Recurrent Unit (GRU) networks [21; 22], motivated by their complementary strengths in handling uncertainty and complex dynamics. Critically, raw sensor data undergoes initial processing via K-means clustering [23; 24] for efficient obstacle segmentation (e.g., velocity-based classification [25]). The Kalman filter (KF) provides uncertainty-aware filtering and short-term state propagation, leveraging its recursive structure for real-time efficiency. The gated recurrent unit (GRU) then processes the KF estimates as refined inputs, enabling explicit recognition of motion patterns and capturing complex, nonlinear velocity variations that challenge purely stochastic models. Thus, the information flow progresses from segmentation and stochastic filtering (KF) to learned pattern modeling (GRU). This synergistic integration mitigates the limitations of each component: KF's linearity assumptions are compensated by GRU's ability to learn complex trajectories, while GRU's reliance on large datasets is alleviated through KF's robust preprocessing. By jointly leveraging uncertainty quantification through the Kalman filter (KF) and adaptive learning via gated recurrent units (GRU), the framework aims to enhance robustness in environments with mixed static and variable-speed dynamic obstacles under uncertainty.
# An Improved Collaborative Filtering Recommendation Algorithm Based on Retroactive Inhibition Theory
###### Abstract
For example, given samples as in Table 1, to predict user \(a\)'s rating of movie 4 on 11/12/2018, traditional itemCF calculates the contribution of each similar movie directly (namely, rating \(\times\) similarity), so user \(a\)'s rating of movie 1 has contributed 2.5 \(\times\) 0.9 = 2.25 rating to the prediction of user \(a\)'s rating of movie 4. However, we need to adjust the user's previous ratings to fit real scenarios because of the evolution of user's preference. Specifically, on 11/12/2018, for the preference that user \(a\) had to movie 1, it suffered retroactive inhibition from 05/04/2018 to 11/12/2018, namely the inhibition from movie 3 rated by user \(a\), which exists in a different preference cluster and has a higher rating. Movie 3 subsequently affects movie 1's contribution to the prediction of movie 4's rating  
Figure 1: Demonstration for collaborative filtering (CF) algorithms. (**a**) user-based CF; (**b**) item-based CF. The contributions of this paper are described as follows,  
1. This paper introduces the theory of retroactive inhibition into recommendation systems to more interpretably measure the decay of a user's preference over time. Specifically, we modified the application of the forgetting mechanism of brain memory to the recommendation system by using retroactive inhibition instead of using forgetting over time directly, in order to calculate the change of user's preference more accurately. 2. The proposed RICF algorithm not only takes into account the evolution of user preferences but also uses more powerful item embeddings by fusing user, item and rating information to alleviate the problem of data sparsity and improve the accuracy of rating prediction. In addition, the embeddings trained by the model (using rating-prediction as an optimization goal) can help to reduce the number of similar neighbors in the collaborative process. 3. Differing from previous related studies, this paper proposes to use a clustering of embeddings to obtain a preference points model. Meanwhile, RICF combines the Canopy and K-Means algorithms to overcome the problem that the clustering efficiency decreases with the increasing size of the dataset, as well as the feature dimension. 4. To show the practicability of the proposed algorithm, this paper applies real datasets with real timestamps, which are the live movie rating dataset collected from Twitter [13] and the digital music dataset collected from Amazon [14]. The experiment results show that RICF performs better and is more interpretable than the traditional itemCF as well as the state-of-the-art sequential algorithms that focus on the research of preference decay. The remainder of this paper is organized as follows. Section 2 summarizes the related work. Section 3 provides some preliminaries and describes the proposed RICF algorithm. Section 4 presents results from experiments conducted on two evaluation datasets. Section 5 concludes the paper.
## Materials and Methods
### Imagery Data and Processing
#### 2.2.2 Sample Data  
It is necessary to determine the number of sample points for crop mapping. We determined the total number of samples with Equation (1) [37; 43], and the number of sample data involves the expected accuracy and the uncertainty of estimation.  
\[n=\frac{z^{2}O(1-O)}{d^{2}} \tag{1}\]  
where \(n\) is the total sample size, \(z\) is the percentage of the standard normal distribution, \(O\) is the expected overall accuracy, and \(d\) is the error margin. If the aim is to obtain 80% overall accuracy, \(z\) = 1.96 at 95% confidence interval, and the error margin is 1.5%. Therefore, the total sample is 2731. We know that in order to achieve 80% overall accuracy, at least 2731 sample points are needed to avoid potential bias [44; 45]. Meanwhile, the sample points of each class should be consistent with the actual land covers. Owing to these sample data being unable to obtain a good classification accuracy, we constantly adjust the number of different classes of sample points to reduce the misclassification as much as possible, and finally we determined the number of each class in Table 2.  
The wheat, vegetation, urban, water, and others classes (Table 2) are labeled in GEE, and all sample points are selected by visual interpretation. Since different bands of images have various spectral information, a combination of different bands can better highlight feature information [46]. The optimal sample points are selected after comparing the Sentinel-2 false color (B8-B4-B3)(B9-B4-B3) and true color (B4-B3-B2) images. Finally, all of the selected sample data are compared to the high-resolution image in Google Earth (GE). If the selected sample label is not matched with the actual feature in GE, the sample is deleted to ensure the accuracy and reliability of the selected sample. With machine learning algorithms, the sample data is usually split into training data sets and validation data sets. This facilitates the usage of the available sample data as efficiently as possible in the absence of independent validation data. In addition, the model is trained by training data, and validated by unseen validation data, which can test the classification performance of algorithms in the face of unseen data. In order to split data into two sets, the _randomColumn_ function of GEE was used to generate a random number in all sample points, with the random values ranging from (0-1). Therefore, all of the sample data generate an extra random value. If sample values were \(\geq\)0.7, they were taken as the validation set, and the sample with values \(<\)0.7 as the training set. Finally, the training set was used for training, and the validation set was utilized to validated the performance of algorithms.
## 3 Present
### CAD in Lung Cancer Diagnosis
#### 3.1.3 Algorithms Proposed for Detection and Nodular Classification  
[PERSON] et al. [30] propose a novel Fuzzy Particle Swarm Optimization (FPSO) algorithm used to choose the most specific nodular feature after extracting data from the image so that the computational complexity of CNN is reduced. [PERSON] et al. [31] use the LIDC-IDRI database to propose two models: one for CT and one for chest radiography. With the CT model, they achieve a sensitivity of 98.09%, a specificity of 95.63% and an accuracy of 97.27%. [PERSON] et al. [32] propose a DL model using ResNet, for the detection and classification of the pulmonary nodule. They consider the process of differentiating a malignant nodule from a benign one difficult work due to the minuscule differences that may exist between the two, and for this, they propose 18 morphological features to take into account when classifying the nodule such as perimeter, orientation, area, eccentricity, etc. [PERSON] et al. [33] use a Deep learning model combined with a Transfer Learning method in order to save time when training their new model, taking another already trained and whose obtained values are known and re-applying it to the new categories concerned in the study. [PERSON] et al. [34] propose a 3D-DCNN model to detect and classify nodules greater than or equal to 3 mm that achieves a higher sensitivity and FP/scanner than the other models with which it is compared. This model classifies findings into nodules (benign or malignant) and non-nodules. In micronodules of less than 3 mm, the performance is lower, which is why they conclude that in future works it will be an objective to achieve their detection while maintaining a good relationship between sensitivity and FP by a scanner. [PERSON] et al. [35] propose a three steps 3D FasterRegion-CNN system in which they first detect lung nodules, second classify them as benign or malignant, and thirdly the results obtained are combined with multiple clinical factors such as family history, age, tobacco, biomarkers, size and location of the nodule. [PERSON] et al. [36] develop a CAD model of DL using the Deep Belief Network (DBN), another type of Deep Neural Network. [PERSON] et al. [37] propose a DL algorithm that performs the detection and classification processes in a unified way in contrast to most studies, which apply a sequential algorithm since this reduces the possibility of accumulating errors. From the databases of various centers, the model was validated and a subset of 50 patients was obtained where the model was tested, comparing it with 25 physicians (including radiologists, thoracic surgeons, and pulmonologists) obtaining better detection performance and classification of pulmonary nodules.
###### Abstract  
Probabilistic solar power forecasting has been critical in Southern Africa because of major shortages of power due to climatic changes and other factors over the past decade. This paper discusses Gaussian process regression (GPR) coupled with core vector regression for short-term hourly global horizontal irradiance (GHI) forecasting. GPR is a powerful Bayesian non-parametric regression method that works well for small data sets and quantifies the uncertainty in the predictions. The choice of a kernel that characterises the covariance function is a crucial issue in Gaussian process regression. In this study, we adopt the minimum enclosing ball (MEB) technique. The MEB improves the forecasting power of GPR because the smaller the ball is, the shorter the training time, hence performance is robust. Forecasting of real-time data was done on two South African radiometric stations, Stellenbosch University (SUN) in a coastal area of the Western Cape Province, and the University of Venda (UNV) station in the Limpopo Province. Variables were selected using the least absolute shrinkage and selection operator via hierarchical interactions. The Bayesian approach using informative priors was used for parameter estimation. Based on the root mean square error, mean absolute error and percentage bias the results showed that the GPR model gives the most accurate predictions compared to those from gradient boosting and support vector regression models, making this study a useful tool for decision-makers and system operators in power utility companies. The main contribution of this paper is in the use of a GPR model coupled with the core vector methodology which is used in forecasting GHI using South African data. This is the first application of GPR coupled with core vector regression in which the minimum enclosing ball is applied on GHI data, to the best of our knowledge.  
core vector regression; gaussian process; lasso; minimum enclosed ball; solar power Article
# Image Watermarking between Conventional and Learning-Based Techniques: A Literature Review
## 3 Performance Requirements in Digital Watermarking
### Security  
The security issue refers to the watermark's ability to resist attacks that intend to thwart the watermark's purpose. The watermarking scheme is considered to be secure if it  
Figure 7: Constraints of digital watermarking.  
is difficult to remove or alter the watermark without causing damage to the cover image or without knowledge of the watermarking embedding and detector [57]. Encryption methods, such as DCT- and chaos-based have been used to ensure watermark security, where the encryption key identifies the degree of security [58; 59].  
**Number of Changing Pixel Rate (NPCR)** and **Unified Averaged Changed Intensity (UACI)**: Those two measures are used to evaluate the efficiency of image watermarking against potential attacks. They are usually used to analyze the resistance of the watermarked images to pixel-level changes. NPCR and UACI scores should always be close to 1 and 0.33, respectively, to achieve good security. Higher values mean resistance will be better. If \(W\) and \(\overline{W}\) denote the original and the extracted watermark, respectively, NPCR and UACI are defined as:  
\[NPCR=\frac{\sum_{i=1}^{M}\sum_{j=1}^{N}D(i,j)}{M\times N}\times 100\ \ \ (\%) \tag{9}\]  
where:  
\[D(i,j)=\left\{\begin{array}{cc}1&if\ W(i,j)=\overline{W(i,j)}\\ 0&Otherwise\end{array}\right. \tag{10}\]  
\[UACI=\frac{\sum_{i=1}^{M}\sum_{j=1}^{N}|W(i,j)-\overline{W(i,j)}|}{M\times N \times 255}\times 100\ \ \ (\%) \tag{11}\]
# Multi-Tag Fusion Localization Method Based on Geometric Constraints
## 4 Fusion of Multiple Observations
### Geometric Constraint Effectiveness  
A critical aspect of this research was to demonstrate the effectiveness of geometric constraints in enhancing the accuracy of the positioning system.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Group** & **State of Motion** & **Tag Size** & **Tag Interval** & **Whether Obstructions Exist** \\ \hline
1 & still & 0.04 m \(\times\) 0.04 m & 0.15 m & no \\
2 & moving & 0.04 m \(\times\) 0.04 m & 0.15 m & no \\
3 & moving & 0.04 m \(\times\) 0.04 m & 0.15 m & yes \\
4 & still & 0.03 m \(\times\) 0.03 m & 0.1 m & no \\
5 & moving & 0.03 m \(\times\) 0.03 m & 0.1 m & no \\
6 & moving & 0.03 m \(\times\) 0.03 m & 0.1 m & yes \\ \hline \hline \end{tabular}
\end{table}
Table 1: Experimental setup.  
Figure 7: **(a)** Simulation Scenario 1; **(b)** Simulation Scenario 2; **(c)** Simulation Scenario 3; **(d)** Simulation Scenario 4.