esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel TLS-Based Fingerprinting Approach That Combines Feature Expansion and Similarity Mapping
## 4 Evaluation
### Visualisation of the Datasets
#### 4.3.3 Dataset A--Mixed Host  
For the Mixed Host dataset, malicious nodes formed natural groups--indicating that there is a high similarity between malware domains across TLS and HTTP features. Figure 10 shows a proportion of the final graph, where the diversity between known good domains is evident by the high level of branching. This dataset had dimensions n = 17,711 (fingerprints) and d = 2133. The statistical analysis of the domain evaluations can be found in Table 5.  
Figure 10: The LSH forest of dataset A visualised using Fearun. Known bad domains are colored red, known good are colored blue and unknown domains are colored orange.  
Figure 9: An example of a domain on the threshold for further investigation. The domain has three vendors confirmed as malicious—BitDefender, CRDF and G-Data—but a further suspicious flag from vendor Trustwave. The left-hand shows the heuristic scan performed by URLQuery, indicating that ClearFake malicious JavaScript library was detected.  
\begin{table}
\begin{tabular}{c c} \hline \hline Total Nodes: & 211 \\ \hline Known Good: & 32 \\ \hline Known Bad: & 166 \\ \hline Unknown: & 13 \\ \hline Newly identified malicious domains: & 27 \\ \hline \hline Area with 91.47\% confirmed malicious nodes and 12 distinct fingerprints. \\ Of the total good or unknown domains, 60\% were reclassified to bad. \\ \hline \hline \end{tabular}
\end{table}
Table 5: Breakdown of the statistics across three blended areas of the similarity mapping for dataset A.
# Optical Transmitters without Driver Amplifiers--Optimal Operation Conditions
## 1 Introduction
More precisely, we show that increasing the ROF of a square-root-raised-cosine (SRRC) signal results in a larger electrical eye-opening; however, increasing the ROF beyond 0.4 does not bring benefit with respect to electrical eye-opening. Furthermore, we investigate the impact of clipping to SRRC signals. We also report that DPreD reduces the root-mean square (RMS) amplitude. Instead, DPostD, rather than DPreD, can be used to correct for the transmitter frequency response without influencing the transmitter electrical signal power, and subsequently, the OSNR after the optical modulator. Our simulative analyses were obtained with MATLAB and Mathematica. We substantiated our findings with measurement results showing the gain by DPostD in dependence of DAC output swing, roll-off factor, and symbol rate.
## 5 Experiment
### Implementation
#### 5.3.3 Implementation of Modified GCN Model  
The above graph was fed into the modified GCN model to create a node classification model. The modified model was described in Figure 6. In each SAGEConv layer, the number of hidden units is an important factor that affects the model's representational capacity and is also the dimension of the node embedding. This parameter determines the model's ability to leverage richer features. Another critical parameter is the learning rate, which dictates the size of the gradient descent step and influences whether the model can converge to the global optimum. To select appropriate values for these parameters, numerous experiments were implemented while keeping other parameters constant and adjusting a specific parameter. The results indicated that the model achieves optimal performance when the number of hidden units is set to 500 and the learning rate is set to 0.001. The model training process consists of 500 epochs, using the cross-entropy loss function and the Adam optimization function. Details regarding the selected parameters are provided in Table 4.
## 4 Numerical Modeling
### Single External Source  
Below are the results of the numerical simulation of the operation of Algorithms 1 and 2 if there is the single point source with an amplitude of \(a_{1}=1\), an angle of arrival of \(\theta_{1}=-22.5^{\circ}\), and phase \(\psi_{1}=0.2\pi\). On the left, vertically, are the results for Algorithm 2 and on the right, vertically, are the results for Algorithm 2. For example, Figure 2b shows the plot for a FFT (black) and Algorithm 1 and resulting basis components coefficients \(\mid x\mid\) (blue).  
It is evident from the obtained results that for the phase value \(\psi_{1}=0.2\pi\), (i.e., provided that the phases of the basis functions (7) and the source phase do not coincide), the use of both algorithms leads to a solution with only one main non-zero basis coefficient, which corresponds to the amplitude, direction, and phase of the source, as shown in Figure 2a,b,g,h. In this case, the loss function characterizing the error in representing the input signals P becomes less than \(10^{-13}\)-\(10^{-15}\), and the rate of change of the coefficients \(\Delta_{x}(t)\) for Algorithm 2 drops to \(10^{-12}\), i.e., practically to zero. At the same time, the rate of change of the coefficients for Algorithm 1 decreases only to \(10^{-7}\), which means that the process of changing the coefficients continues even after reaching the exact value.  
For Algorithm 1, the result for \(\mu=2\), \(\alpha=1\) converges after 40 cycles of 25,000 steps, while Algorithm 2 converges after 10 cycles of 6000 steps. Increasing \(\mu\) to a value greater than 4 leads to a divergence of the iteration process for Algorithm 1. Intermediate values of \(\mu\) from 2 to 4 allow for acceleration of the convergence, but require selecting \(\alpha\), and lead to a significant increase in random fluctuations in the coefficient values. They can be seen in Figure 3b, which shows the change in the iteration steps of \(\Delta_{x}(t)\) (blue) and \(P(t)\) (red), and in Figure 3c, which shows the change in \(\mathrm{Re}(x(t))\) (red) and \(\mathrm{Im}(x(t))\) (blue), for \(\mu=3.9\), \(\alpha=0.99\).  
Table 2 shows the number of excess basis coefficients, the value of which is normalized to the maximum value \(\max_{i}\mid x_{i}\mid\) exceeding 0.1, 0.01, and 0.001.  
From the obtained results, it follows that for single sources, Algorithm 1 not only has no advantage over Algorithm 2 in terms of speed, but also, when using universal values \(\mu=2\), \(\alpha=1\), which ensure stability and small fluctuations in the coefficients, it turns out to be 17 times slower.  
One more peculiarity of the coefficient behaviors for Algorithm 1 can be noted: shown in Figure 2d,f--the bulk of the coefficient values that differ from the exact solution very quickly become negligibly small, and during the same time, the loss function \(P\) also falls almost to zero, and the main coefficients stop changing much later. At the same time, for Algorithm 2, the changes in the coefficients and the loss function, as seen in Figure 2c,e, change in the same way and reach a stationary value simultaneously.
## 5 Experimental Results
### Performance of FFSP-AGV Parameters  
#### 5.4.1 The Number of Jobs  
In this sub-section, we investigate the effect of the number of jobs (i.e., n) on model complexity while ensuring that all the other parameters (such as s, u, r and other time-related parameters) are the same. To this end, we generate eight sets of test instances by varying the number of jobs (from two to nine) based on the existing instance J3-S2-M3-A2 reported in Table 1. The solution time of CPLEX is set to 600 s, and the experimental results are shown in Table 2 below.  
In Table 2, it can be seen that when the number of scheduled jobs n increases, the following conclusions can be drawn: (1) the NBV, NC and NCV indices of the corresponding MILP model also increase; (2) the NBV and NC indices of the MILP model show the same and sharp change trend, while NCV changes more slowly by comparison.
# Cross-Modal Adaptive Interaction Network for RGB-D Saliency Detection
## 3 Methodology
### Modal Integration Network
#### 3.3.2 Adaptive Feature Fusion Module  
As illustrated in Figure 6, we introduce an adaptive feature fusion module to dynamically compute weights for different modalities and facilitate effective fusion. Specifically, leveraging the skip connection structure of the U-Net network, we derive modality-specific features \(g_{r}^{i}\) and \(g_{d}^{i}\) (i = 1, 2, 3, 4, 5) after integrating skip connections. Notably, when i = 1, \(g_{r}^{i}\) and \(g_{d}^{1}\) correspond to the high-level features \(f_{r}^{S}\) and \(f_{d}^{S}\) generated by the modality-specific encoder. Similarly, \(g_{s}^{1}\) represents the high-level feature \(f_{S}^{S}\) generated by the modality integration encoder. Initially, these three features undergo a 3 \(\times\) 3 convolutional layer to reduce channel dimensions and smooth the features. Subsequently, we devise an attention weight layer, GSA, which integrates Global Average Pooling (GAP), a 1 \(\times\) 1 convolutional layer, and sigmoid function to compute weights for the RGB and depth branches, thereby enhancing their respective features. Finally, leveraging features generated by the preceding modality integration network, we further amplify and fuse the features of both modalities. The detailed procedure is outlined as follows:  
\[\begin{cases}g_{r_{1}}^{i}=conv3\big{(}g_{s}^{i-1}\big{)}\bigotimes{(}conv3 \big{(}g_{r}^{i}\big{)}\bigotimes{GSA\big{(}conv3\big{(}g_{r}^{i}\big{)}\big{)} \big{)}}\\ g_{d_{1}}^{i}=conv3\big{(}g_{s}^{i-1}\big{)}\bigotimes{(}conv3\big{(}g_{d}^{i} \big{)}\bigotimes{GSA\big{(}conv3\big{(}g_{d}^{i}\big{)}\big{)}\big{)}}\end{cases} \tag{7}\]  
\[g_{s}^{i}=conv3\Big{(}g_{s}^{i-1}\Big{)}\bigoplus{conv3\Big{(}concat\Big{(}g_{ r_{1}}^{i},g_{d_{1}}^{i}\Big{)}\Big{)}} \tag{8}\]  
Among them, \(\bigotimes\) represents element-wise multiplication, and concat represents concatenating two features. After obtaining the fused result \(g_{s_{1}}^{i}\), a 3 \(\times\) 3 convolutional layer smooths the feature representation. Subsequently, it is connected with \(g_{s}^{i-1}\) using addition to generate an enhanced feature with rich cross-modal information. This enhances the model's ability to perceive multi-scale features. It is particularly important to note that the input to all AFFM modules is preprocessed through the RFB module [40] to enhance the model's perception and detection capabilities of features across different scales and complexities.  
Figure 6: This figure depicts the structural diagram of the AFFM module, where “GAP” signifies the Global Average Pooling operation, “\(\times\)” and “+” denote element-wise multiplication and addition, respectively, and “C” represents feature concatenation.
## 2 The Orientation Compressed Map
### Extract Possible Object Locations Based on the Orientation Compressing Map  
The similarity measure is used to yield a set of possible object locations. In the D-Textureless dataset [23], models are rotated and scaled in the test images and the test images are 640 \(\times\) 480 pixels as shown in Figure 4. For the red plier model, it appears in Figure 4e,f respectively, which are the positive test samples. Figure 4g,h are the negative test samples. Before matching with these test images, the model is performed scale and rotation transformation, which can generate 252 templates by rotating the model in the range of \([0^{\circ},350^{\circ}]\) with \(10^{\circ}\) increment and scaling the model in the range of [60%, 90%] with 5% increment. The model can be easily learned because the learning process only requires generating and storing the orientation compressing maps of these templates (CMT) with different scales and rotations. For the precision and computational load, \(r^{\mathrm{c}}=6\) is a good trade-off with respect to our experiments, which will be explained in detail in Section 4.1.  
The compressing map can be used for generating possible object locations efficiently. The whole time of generating orientation compressing map of the test image (CMI) and matching with the 252 orientation compressing maps of templates takes 0.19 s on a laptop with Intel Core i5 processor and 4 GB memory. The similarity score of the CMI of the four test images are shown in Figure 4a-d. Note that each pixel of similarity score in Figure 4a-d is the max similarity score of the all the CMTs matching with CMI in its location. Using the non-maximal suppression, a set of possible object locations with different scale and rotation are selected as shown in the yellow boxes in Figure 4e-h. In Figure 4e,f, it can be seen that the compressing map generates a small set of possible object locations which contain the object with correct position, scale and rotation. Especially, for some negative test samples as shown in Figure 4h, the orientation compressing map excludes all possible object locations.
# Experimental and Parametric Analysis of Pull-Out Resistance of Notched T-Perfobond Connectors
## 3 Test Results and Analysis
### Pull-Out Mechanism  
The tensile strength of the perfobond connectors is a collective result of the perfobond rib, the flange, the concrete, and the rebar that passes through the perforations. An external force applied to the perfobond rib was transmitted from the rib, through the concrete, and into the rebar and flange. The force exerted on the perfobond rib and its immediate vicinity was the most significant and diminished axially towards the rebar ends. The concrete dowel effect and the rebar's role are analogous to that of an elastic foundation beam. The surrounding concrete serves as a foundation, providing the reaction force for this composite elastic beam. When the perfobond rib's strength is less than that of the concrete dowel within the hole, the constraining effect of the rebar on the flange becomes critical. The fracture of the perfobond rib is the decisive factor that leads to the specimen's loss of load-bearing capacity. On the other hand, if the concrete dowel's strength predominates, an inverted pyramid-shaped wedge of concrete is formed diagonally upwards from the flange's edge. In this scenario, the concrete's punching shear failure is the determining factor that causes the ultimate loss of the specimen's load-bearing capacity, as depicted in Figure 7.  
Figure 6: Load–separation curves. (**a**) PFR; (**b**) PMFR.
## 2 Materials and Methods
### Data Analysis
The n_estimators hyperparameter represents the number of trees that the algorithm constructs before determining the final forecast by maximum voting or averaging. Typically, a greater quantity of trees enhances performance and stabilizes forecasts while also hindering computational speed. Another crucial hyperparameter is max_features, which represents the upper limit on the number of features that the Random Forest algorithm takes into account when deciding how to split a node. The final crucial hyperparameter is min_sample_leaf. This refers to the minimal number of leaf nodes needed to divide an internal node. The n_jobs hyperparameter specifies the maximum number of processors that the engine can utilize. If the value is one, it can only utilize a single processor. A value of \"\(-1\)\" indicates an absence of any restriction or limit. The random_state hyperparameter ensures that the model's output can be reproduced. The model will consistently yield the same outcomes when it possesses a certain value for random_state, as well as when it is provided with identical hyperparameters and training data. Finally, there is the oob_score (also known as oob sampling), which is a cross-validation method used in random forests. Approximately one-third of the data in this sampling are allocated for evaluation purposes rather than being utilized for training the model. This portion of the data is reserved to assess the performance of the model. The term \"out-of-bag samples\" refers to these specific samples. It bears a strong resemblance to the leave-one-out cross-validation method, although it incurs minimal additional computational effort. For the hyperparameter tuning of our Random Forest classifier, we used the following settings: 'n_estimators' = [100, 200, 500, 1000], criterion = 'gini','max_depth' = [3, 5, 7, 9, 11, None],'min_samples_split' = [2, 5, 10],'min_samples_leaf' = [1, 2, 4],'max_features' = ['sqrt', 'log2', None]. A randomized search was used.
# Hybrid Particle Swarm Optimization-Jaya Algorithm for Team Formation
## 6 Experimental Results
### Experimental Results for Communication Cost Analysis
Efficiency of the I-PSO-Jaya algorithm for the ACM dataset: By examining Table 6, the results from the ACM dataset further highlight the superior efficiency of the I-PSO-Jaya algorithm, particularly in the more complex experiments 7, 8, and 9. In experiment 7, I-PSO-Jaya achieves a communication cost of 0.04, significantly lower than the costs observed for the other methods, ranging from 0.05 to 0.86. This trend continues in experiment 8, where I-PSO-Jaya maintains a communication cost of just 0.03, outperforming all other methods, which range from 0.04 to 1.22. Similarly, in experiment 9, the I-PSO-Jaya algorithm records a communication cost of 0.04, again demonstrating its efficiency compared to the other methods, which have costs ranging from 0.05 to 1.62. These results underscore the algorithm's ability to maintain low communication costs even as the number of skills and iterations increases, thereby highlighting its effectiveness in handling more complex scenarios with greater efficiency.
## 3 Case Analysis and Discussion  
### Data Sources  
In this study, the vehicle trajectory dataset from the I-80 road section in the NGSIM dataset was selected. The detected road section is located on Interstate 80 in Emeryville, as shown in Figure 1.  
The detection area comprises eight lanes, of which lane 1 is the high-occupancy vehicle lane, lane 6 is the distribution lane, lane 7 is the entry ramp, lane 8 is the exit ramp, and lanes 1-6 are unidirectional lanes with a length of about 503 m. The driving direction is from south to north, the average flow rate is 7124 veh/h, and the average speed is 8.32 m/s. This study selected the data from the congestion period, which contains a large number of trajectory data of car-following and lane-changing behavior and well meets the data requirements.
## 3 Results
### Real-World Applications and the Impact of JAVIS in Hospital Workflows
#### 3.8.2 Scalable Clinical Data Labeling for AI Research  
Developing AI-driven clinical models requires large-scale annotated datasets; however, the deployment of fine-tuned models for disease classification, symptom extraction, and test result identification has historically been constrained by hospital data privacy regulations and the absence of on-premises infrastructure for LLM hosting. JAVIS addressed this limitation by enabling the deployment of fine-tuned LLMs within the hospital network, facilitating the annotation of over 10,000 medical records for structured information extraction.  
This capability has been instrumental in supporting AI research for clinical information retrieval and natural language processing (NLP) applications, as prior attempts to host large-scale models within hospital networks had been infeasible due to infrastructure constraints. JAVIS ensures that researchers can train, fine-tune, and deploy AI models in a privacy-compliant manner, allowing hospitals to develop AI-driven tools without reliance on external cloud services.  
This use case highlights JAVIS's role in bridging the gap between AI development and hospital-based implementation, providing a secure, scalable solution for clinical data processing and annotation.
# Virtual Grid-Based Routing for Query-Driven Wireless Sensor Networks
###### Abstract  
In the context of query-driven wireless sensor networks (WSNs), a unique scenario arises where sensor nodes are solicited by a base station, also known as a sink, based on specific areas of interest (AoIs). Upon receiving a query, designated sensor nodes are tasked with transmitting their data to the sink. However, the routing of these queries from the sink to the sensor nodes becomes intricate when the sink is mobile. The sink's movement after issuing a query can potentially disrupt the performance of data delivery. To address these challenges, we have proposed an innovative approach called Query-driven Virtual Grid-based Routing Protocol (VGRQ), aiming to enhance energy efficiency and reduce data delivery delays. In VGRQ, we construct a grid consisting of square-shaped virtual cells, with the number of cells matching the count of sensor nodes. Each cell designates a specific node as the cell header (CH), and these CHs establish connections with each other to form a chain-like structure. This chain serves two primary purposes: sharing the mobile sink's location information and facilitating the transmission of queries to the AoI as well as data to the sink. By employing the VGRQ approach, we seek to optimize the performance of query-driven WSNs. It enhances energy utilization and reduces data delivery delays. Additionally, VGRQ results in \(\approx\)10% and \(\approx\)27% improvement in energy consumption when compared with QRRP and QDVGDD, respectively.  
wireless sensor networks; mobile sink; grid-based routing; query transmission +
Footnote †: journal:  
0000-0001-5000-0001-5000][PERSON]  
0000-0001-5000-0001-5000]R.K.Bhatia  
0000-0001-5000-0001-5000]N.K.Bhatia  
0000-0001-5000-0001-5000]N.K.Bhatia  
0000-0001-5000-0001-5000]N.K.Bhatia
# Positron Emission Tomography in Cerebral Amyloid Angiopathy: A Scoping Review
## 2 Materials and Methods
### Definitions
#### 2.1.2. Positron Emission Tomography with Amyloid Tracers  
PET imaging with [11C]Pittsburgh compound B (PiB) [19], [18F]Iforbetapir [20], or [18F]Ibuttemetamol [21] tracers has been shown to measure the burden and location of fibrillar cerebrovascular (in addition to parenchymal) b-amyloid deposits [20,21]. While it is clear that CAA is detectable by amyloid-PET [19, 20, 21], the available tracers have no selectivity for vascular or parenchymal amyloids.