esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Preliminaries
### Recurrent Neural Network  
The recurrent neural network (RNN) does not rigidly memorize all fixed-length sequences. On the input sequence \(x_{t}\), it determines the output sequence \(y_{t}\) by storing the hidden state \(h_{t}\) of the time step information. The network structure is shown in Figure 2, where the calculation of \(h_{t}\) is determined by the input of the current time step and the hidden variables of the previous time step:  
\[h_{t}=\phi(x_{t}\omega_{xh}+h_{t-1}\omega_{hh}+b_{h}) \tag{9}\]  
where \(\phi\) is the activation function, \(\omega_{xh}\), \(\omega_{hh}\) are the weight, \(b_{h}\) is the hidden layer deviation.  
Long-Short term Memories (LSTM) is a kind of gated recurrent neural network, which is carefully designed to avoid long-term dependence. It introduces three gates on the basis of RNN, namely, input gate, forget gate and output gate, as well as memory cells with the same shape as the hidden state, so as to record additional information. As shown in the Figure 3, the input of the gate of LSTM is the current time step input, such as \(x_{t}\) and the previous time step hidden state \(h_{t-1}\), and the output is calculated by the full connection layer. In this way, the values of the three gates are all in the range of \([0,1]\). Suppose the number of hidden units is \(l\), the input \(x_{t}\) of time step \(t\) and the hidden state \(h_{t-1}\) of the previous time step, memory cell \(\mathbb{C}_{t}\), candidate memory cell \(\mathbb{C}_{t}\), input gate \(I_{t}\), forget gate \(F_{t}\) and output gate \(O_{t}\). The calculations are as follows:\[I_{t}=\sigma(x_{t}\omega_{xi}+h_{t-1}\omega_{hi}+b_{i}) \tag{10}\] \[F_{t}=\sigma(x_{t}\omega_{xf}+h_{t-1}\omega_{hf}+b_{f})\] (11) \[O_{t}=\sigma(x_{t}\omega_{x0}+h_{t-1}\omega_{h0}+b_{o})\] (12) \[\tilde{C}_{t}=tanh(x_{t}\omega_{xc}+h_{t-1}\omega_{hc}+b_{c})\] (13) \[C_{t}=F_{t}\odot C_{t-1}+I_{t}\odot\tilde{C}_{t} \tag{14}\]  
where \(\sigma\) is the activation function, \(\omega_{xi}\), \(\omega_{hi}\), \(\omega_{xf}\), \(\omega_{hf}\), \(\omega_{x0}\), \(\omega_{ho}\), \(\omega_{xc}\), \(\omega_{hc}\) are the weight, \(b_{i}\), \(b_{f}\), \(b_{o}\), \(b_{c}\) are the hidden layer deviation. Once the memory cell is obtained, the flow of information from the memory cell to the hidden state \(h_{t}\) can be controlled by output gate:  
\[h_{t}=O_{t}\odot tanh(C_{t}) \tag{15}\]  
where the tanh function ensures that the hidden state element value is between \(-1\) and \(1\).
## 3 Principals of Routing Protocols and Their Breakdowns
### Acar [57]
#### 3.3.3. Rmtg [60]  
Ref. [60] proposes a new geocast approach for UWSNs that includes a hole-detecting algorithm. Routing and multicast tree-based geocasting (RMTG) is the suggested model's name, and it consists of six parts:  
* Neighbor table creation, which is utilized to supply all sensor nodes with information about their neighbors' whereabouts.
* Route discovery, in which the nearest neighbor node to the destination is selected as the next hop node.
* Route maintenance is utilized to tackle the link break problem and resolve the issue of no neighbor node being discovered as the next hop.
* Routing around the geocast region's boundaries.
* Comparison  
Both protocols have different routing techniques, and RMTG can avoid voids whereas, RDBF can not. Both have high energy consumption, but RDBF has a lower delay and lower overhead than RMTG. Table 14 illustrates the differences between the protocols.  
* Cluster-based routing protocol  
CBRP stands for cluster-based routing protocol, and it is a routing protocol for mobile ad hoc networks. In a distributed way, the protocol splits the nodes of the ad hoc network into several overlapping, or disjoint clusters [61]. Each cluster has a cluster head responsible for keeping track of cluster membership. The cluster membership information stored at each cluster head is used to discover inter-cluster paths dynamically. The technique efficiently reduces flooding traffic during route discovery while also simplifying the process by grouping nodes into groups. Furthermore, the protocol recognizes the presence of unidirectional links and uses them for both intra-cluster and inter-cluster routing.
# Multi-Granularity User Anomalous Behavior Detection
## 5 Experiments
### Ablation Study
#### 5.4.2 Ablation of Oversampling  
The MG-UABD system employs a combination of oversampling and undersampling techniques to address the imbalance between positive and negative samples. Specifically, for anomalous users with a relatively large number of anomalous behavior samples, the SMOTE algorithm is used for oversampling. However, since the SMOTE method requires a sufficient number of baseline anomalous samples, for anomalous users with very few anomalous behavior samples that do not meet the requirements for SMOTE, random selection and duplication of anomalous behaviors are employed to oversample the negative samples. To preserve the original data distribution, the oversampling ratio should not be too high, and the number of oversampled instances should not increase excessively. Correspondingly, undersampling is applied to the normal samples, which involves randomly  
Figure 9: The performance of the coarse and fine granularity modules and the overall model is affected by the proportion of the training set (FPR).  
removing positive samples to achieve balance between positive and negative samples in the training set.  
To specifically validate the effectiveness of oversampling for anomaly detection tasks, we implemented comparative experiments with Random Forest, XGBoost, and MG-UABD models, applying oversampling, and visualized the changes in the False Positive Rate (FPR) in Figure 10. From the figure, it can be observed that after applying oversampling, the FPR values for all three models decreased, indicating a reduction in the misclassification of anomalous behavior samples in the test set. This demonstrates that data oversampling can address the issue of insufficient anomalous behavior samples during anomaly detection, thereby enhancing the detection performance of the models.
# CT Scanning of Structural Characteristics of Glacial Till in Moxi River Basin, Sichuan Province
## 4 Results  
### Meso-Structure Characteristics of Intact Glacial Till  
#### 4.1.1 Analysis of Test Results  
According to the CT scanning images obtained in the experiment (Figure 10), the features of the meso-structure of the intact glacial till are as follows: (1) Particles are poorly sorted with filling in between. There are pores of different sizes inside and the shape is irregular; (2) there is no obvious orientational arrangement of coarse particles in the soil sample, and most of the gravel particles are sharp and angular; (3) the gravel particles mostly contact the fine particles in the form of point contact.
# Comparison of Various Reinforcement Learning Environments in the Context of Continuum Robot Control
## 2 Materials and Methods
### The Continuum Robot Environment
The third reward is calculated as the weighted portion of the distance:  
\[r_{3}=-0.7\cdot d_{u} \tag{8}\]  
The third reward function is calculated by multiplying the Euclidean distance by a weight factor of \(0.7\). Specifically, the Euclidean distance is computed between the current position of the robot and the goal position, as described in Equation (5). The aim of this reward function is to assign a numerical value to the distance between the robot and its goal. The weight factor enables the influence of this distance on the learning process to be adjusted. As the robot moves further from its goal, the reward will decrease, promoting the robot's movement towards the goal state. Conversely, as the robot approaches the goal, the reward will increase. Regarding the weight of \(0.7\) in Equation (8), this was chosen based on empirical observations during our initial experiments. We found that this value provided a good balance between encouraging the robot to reach the goal and avoiding excessive movements. However, we acknowledge that this value may not be optimal for all situations and could depend on the specific characteristics of the robot and the task. The final reward is defined as follows:\[r_{4}=\left\{ \begin{array}{ccc}200&d_{u}\leq 0.025\\ 150&d_{u}\leq 0.05\\ 100&d_{u}\leq 0.1\\ 100\times(d_{u-1}-d_{u})&d_{u}>0.1\\ -100&d_{u}=d_{u-1}\end{array} \right. \tag{9}\]  
The final reward function, which is described in Equation (9), aims to incentivize the robot to reach the goal state in the shortest possible time. This reward function takes into account the Euclidean distance (\(d_{u}\)) between the robot's current position and the goal position. The reward is divided into multiple intervals based on the value of the Euclidean distance. The highest reward of 200 is given if the distance is less than or equal to 0.025. A reward of 150 is given if the distance is between 0.025 and 0.05, and a reward of 100 is given if the distance is between 0.05 and 0.1. If the distance is greater than 0.1, the reward is calculated using the formula \(100\times d_{u-1}-d_{u}\). If the Euclidean distance is the same as the previous distance, the reward is \(-100\) to discourage the robot from staying in the same place. To recap, we have established four distinct rewards that consider both the target position and the current position. Table 3 presents information about the attributes of each reward function. The reward Equations (6)-(9) are relatively straightforward and based on a standard measure, such as Equation (5), which is a common choice for this type of problem. Our goal was to show that effective control can be achieved using RL, even with such simple reward functions.
# Getting over High-Dimensionality: How Multidimensional Projection Methods Can Assist Data Science
## 4 Multidimensional Projection Approaches and Domains
### Graph Regularization Multidimensional Projection  
The recently introduced Graph Regularization Multidimensional Projection (GRMP) [77] generates two-dimensional visualizations (2D) that reproduce the groups present in the high-dimensional space while preserving distance relationships between dataset instances. In addition, based on a similarity graph, GRMP uses the Graph Signal Processing theory (GSP) [88] and graph regularization as fundamental tools to incorporate these groups' information into the projected space.  
A graph is a structure that represents data instances (vertices) and their relationships (edges). Signal theory adds extra information to the graph allowing the processing of a signal through the graph structure. In this context, graph regularization is based on signal smoothing by solving a minimization problem, which is equivalent to applying a low-pass spectral filter [77].  
The GRMP method can be divided into three main steps. The initial step is related to the construction of the similarity graph. Similar to the previous topological-based methods, GRMP builds a similarity graph from the \(m\)-dimensional dataset matching data instances as the graph vertices set, establishing a one-to-one correspondence between instances and vertices. Every two vertices are connected according to the similarity relationship between their corresponding instances. In the second step, GRMP defines the coordinate graph signals in the 2D space through the spreading control given by a phyllotactic distribution. Note that the method moves away from all points using this distribution and only approximates the points that should be close in the final step, using regularization. In the third and last step, the coordinate graph is seen as signals to be processed and regularized using GSP tools.  
GPS application is motivated because the neighbor points in the 2D space tend to have similar coordinates in the high-dimensional space. The smoothing effect of the graph regularization resets the coordinates of the points given by a phyllotactic distribution. The final GRMP projection is allocated in the visual space using control points selected from the medoids of each component. The control points are projected using MDS or other multidimensional projection techniques that preserve distances. Then, the smoothed components are translated and positioned, matching the control points' projection counterparts.  
GRMP uses the k-NN graph [89] to build the similarity graph, which means GRMP inherits its main properties and difficulties in dealing with outliers. Parameterization is simplified in GRMP, comprising essentially only two parameters: the number of neighbors and the graph regularization parameter. However, the authors based their parameter choices on empirical tests referring to the need for fine-tuning to achieve better groupings.
# The Less Known Cyclins--Uncovered
## 2 Cyclin C
[PERSON] et al., 2010 [13] by studying hematopoietic cell line BAF-B03, showed that cyclin C can cooperate with c-Myc and affects the proliferation of the cells in a cytokine-independent manner, but also participates in the formation of cell clusters. The interaction of cyclin C with c-Myc is responsible for cell proliferation and passage through the G1/S restriction site. In addition, cyclin C together with c-Myc affects the induction of the cdc2 gene, which is a key regulator in the G2/M phase [14]. In turn, [PERSON] et al., 2014 [15] showed that the CCNC gene is missing in the subset of acute lymphoblastic leukemia, suggesting that cyclin C deficiency is sufficient to promote tumorgrowth. It has been presented that cyclin C controls the level of well-known oncogene Notch1. Cyclin C activates phosphorylation of the Notch1 intracellular domain (ICN1) via CDK 3/8/19 kinases and promotes its degradation. Ablation of cyclin C significantly increased ICN1 levels and enhanced T-ALL progression in vivo [15]. Moreover, [PERSON] et al., 2006 [16] described cases of osteosarcoma with comparative genomic hybridization (CGH). The results of their studies indicated that transfection with CCNC cDNA caused significant suppression of the osteosarcoma cell lines proliferation. After the mapping of the region commonly deleted in osteosarcoma and transfection assay, they showed that CCNC may be a potential tumor suppressor gene [16]. By contrast, the overexpression of cyclin C was demonstrated in colon adenocarcinoma. [PERSON] et al., 2005 [17] confirmed the enhanced expression of cyclin C caused by the amplification of its gene in 219 colon cancer cases, which was associated with an unfavorable prognosis [17]. The in vitro experiments showed that high levels of cyclin C/CDK8 complexes are associated with increased proliferation and hyperactivation of the WNT/beta-catenin pathway [18]. Moreover, mutations of the cyclin C and Cdk8 complex were also observed in other tumors, i.e., melanoma and colorectal cancer [19]. Cdk8 and Cdk19 inhibitors are promising anticancer agents. Currently, two compounds received clinical trial approval: SEL120 in patients with Acute Myeloid Leukemia or high-risk Myelodysplastic Syndrome (NCT04021368) and BCD-115 in Women with ER(+) HER2(-) Local Advanced and Metastatic Breast Cancer (NCT03065010).
## 6 Discussions  
The results obtained in this study provide valuable information about the performance of different segmentation architectures in the context of fish image analysis.  
Experiment 1, which used the UNET architecture with images resized to 256 \(\times\) 256 pixels, demonstrated remarkable precision, achieving an IoU of 0.942854 after 20 training epochs. This superior performance, achieved with MSELoss and the Adam optimizer, suggests that for this resolution and dataset size, UNET offers a highly efficient and accurate solution. Rapid convergence and high IoU indicate that this configuration is particularly well-suited for applications where accuracy and computational efficiency are critical.  
In contrast, Experiment 2, which employed higher-resolution images (512 \(\times\) 512 pixels), showed a decrease in IoU to 0.803244, although it required 50 training epochs using L1 Loss and Adam. This reduction in performance, despite the increase in resolution, can be attributed to the inherent complexity of processing higher-resolution images. Although the result remains satisfactory, it underscores the importance of considering the trade-off between image resolution and model processing capacity.  
Experiment 3, which explored the UNETR architecture with 256 \(\times\) 256 pixel input images, revealed slightly lower performance compared to the UNET models. This observation aligns with the literature, which indicates that Transformer-based models, such as UNETR, require large datasets to reach their full potential. The limited dataset size in this study likely prevented UNETR from learning robust and generalizable representations. This result highlights the necessity of considering data availability when selecting a segmentation architecture, especially for models that rely on attention and transformation.  
A crucial aspect of this study was the use of a coin as a reference scale, allowing precise measurements of the dimensions of the segmented fish. The implementation of OpenCV for contour detection and conversion of pixel coordinates to length units proved to be effective. This methodology is fundamental for morphological analysis and aquaculture management strategies, as it facilitates detailed monitoring of fish growth and development.  
In summary, the choice of architecture and image resolution must be aligned with the specific application requirements and dataset limitations. For practical applications  
Figure 12: Process for calculating trout’s measurement. (**a**) Segmented image of trout; (**b**) binary image of trout segmentation; (**c**) edge detection for boundary identification.  
with datasets similar in size to the one used in this study, the UNET architecture with 256 \(\times\) 256 pixel images (Experiment 1) offers the best balance of accuracy and computational efficiency. Although UNETR represents the state-of-the-art in many contexts, its performance in this study suggests that the size of the dataset is a key limiting factor. Future work could explore the performance of UNETR with larger datasets or data augmentation techniques to overcome this limitation.
## 2 Background on MRAM and Its Variation
### Basics of MRAM  
MRAM bitcell (Figure 2) contains one MTJ and one NMOS access transistor. The MTJ lies between a pair of metal-lines named digit-line and bit-line to facilitate write operation. The metal-lines are parallel to the cell plane and placed orthogonal to each other. An induced magnetic field is created with appropriate polarity by passing current through the lines during write operation. The field exerts a torque on the free layer magnetic orientation, causing it to flip. During read operation, the access transistor is turned ON, and a fixed voltage is applied across the cell to sense the equivalent resistance.  
A commercial toggle MRAM chip (MR4A08B) with 2 million rows and 8 columns is used in this work to validate the proposed morphable security primitive. Table 1 captures the key features of the MRAM chip. It should be noted that we have used the first 128 KB (1 Mbit) out of 16 Mbit of the chip. Figures 3 and 4 presents the timing diagram of the write and read operations of the MRAM chip. From the data sheet, it is evident that the chip requires maximum of 15 ns of write enable (low) signal for successful write and data is valid after 25 ns of read enable signal. In this work, we modified the specified read and write times to realize the proposed PUF and TRNG.  
Figure 2: Toggle MRAM bitcell containing one Magnetic Tunnel Junction (MTJ) and one access transistor.
## 3 Validation
### Experimental Validation  
The present methodology was verified using a wind tunnel test conducted at the Central South University. A wind barrier was installed on the bridge, which gave a similar condition as the present simulations. The low-speed test section of the wind tunnel had a length of 18 m, a width of 12 m, and a height of 3.5 m. The wind speed varied between 2 and 20 m/s, and the turbulence rate was less than 2%. The wind pressure is measured using a DTC electronic pressure scanning system. The sampling time for each measurement was 30 s, and the sampling frequency was 330 Hz. As shown in [17], the two-car train model was a 1:25 CRH2 train widely used in China's high-speed railways. The bridge model used a five-span bridge with 32 m of simply reinforced box girders. Each span was 1280 mm in length. The bridge pier was 400 mm tall, and the gap between the two tracks was 200 mm in the middle. The wind barrier was 0.1 m high, the wind speed was 10 m/s, and the train was stationary, resulting in a 90\({}^{\circ}\) yaw angle. The same measuring points were arranged according to [17; 20], and the mean pressure coefficient of measuring points 1-9 (can be seen in [17]) on the windward side and the top of the middle carriage are shown in Figure 6.  
Figure 5: Maximum rolling moment of three cars predicted by different meshes (in \(V_{t}\) = 300 km/h; \(V_{w}\) = 20 m/s).  
It can be seen that the numerical simulation results obtained by using the present methodology in this study were generally in good agreement with the previous studies. However, the transition between the windward and the top surfaces of the train (i.e., the measurement points 5 and 6) were found to be slightly smaller than that reported by [17]. The discrepancy of the current results is due to the insufficient prediction accuracy of URANS to solve the flow separation at this region. Nevertheless, the error between the current numerical simulation results and the experimental results is within 10%, sufficient to guide engineering problems.
# Development of the First Portuguese Radar Tracking Sensor for Space Debris
## 2 Implemented System
### Data Acquisition  
The data acquisition, depicted in Figure 1 in yellow, contains the acquisition board (AQS) and a single board computer (SBC). The acquisition board is triggered by the controller board and is responsible for converting the I/Q signal components to the digital domain, by employing two 16 bit ADCs at a maximum sample rate of 100 MS/s. The computer (MIO 2360 N-32A1E by Advantech [32]) receives the I/Q signal in the digital domain and performs the necessary digital signal processing. Communication between the AQS and the SBC is done via full speed USB 3.0 (100 MS/s) and a Fast/Slow mode buffer is selectable, where a compromise between sample resolution and sample rate can be chosen.
## 6 Experimental Results
### Results for Individual Vowels
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & & \multicolumn{3}{c}{**SVC Accuracy (\%)**} & \multicolumn{3}{c}{**KNN Accuracy (\%)**} & \multicolumn{3}{c}{**RFC Accuracy (\%)**} \\ \hline \multirow{4}{*}{**Vowel**} & \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Cross**} & \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Cross**} & \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Cross**} \\  & & & & & & & & & & & \\ \cline{1-1}  & \multirow{2}{*}{**Training**} & \multirow{2}{*}{**Testing**} & \multirow{2}{*}{**Validation**} & \multirow{2}{*}{**Training**} & \multirow{2}{*}{**Testing**} & \multirow{2}{*}{**Validation**} & \multirow{2}{*}{**Training**} & \multirow{2}{*}{**Testing**} & \multirow{2}{*}{**Validation**} \\ \hline \multirow{4}{*}{**Vowels**} & \multirow{2}{*}{_/u-u_} & 72.4 & 67.35 & 70.9 (\(\pm\)4) & **73.0** & 65.06 & 69.6 (\(\pm\)5) & 70.3 & 61.22 & 70.7 (\(\pm\)6) \\  & /u/ & **74.3** & **83.67** & **78.3 (\(\pm\)5)** & 68.4 & 71.43 & **71.4 (\(\pm\)7)** & **77.4** & **85.71** & **77.1 (\(\pm\)9)** \\ \cline{1-1}  & /l/ & 72.8 & 77.55 & 71.6 (\(\pm\)5) & 64.3 & **73.47** & 66.8 (\(\pm\)7) & 64.9 & 73.47 & 68.6 (\(\pm\)7) \\ \cline{1-1}  & /u/ & 65.1 & 71.43 & 65.8 (\(\pm\)7) & 65.4 & 63.27 & 66.7 (\(\pm\)6) & 71.6 & 65.31 & 70.4 (\(\pm\)5) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparing the classification results by gender and individual vowels. /a-n/ means /a/ vowel pronounced in normal intonation, other vowels are pronounced in all intonations (normal, low, high, and low-high-low.