esa-sceva/satcom-chunk-collection huggingface dataset:
# Effects of Velocity and Permeability on Tracer Dispersion in Porous Media
## 3 Experimental Results and Discussion  
### Experimental Results  
After the completion of the displacement experiment, the concentration of the produced tracer solution was measured. Figure 2 illustrates the tracer breakthrough concentrations from the natural cores (Nos. 1-3) for the high- and low-velocity displacement experiments. Figure 3 shows the tracer breakthrough concentration from the sand-filled cores (No. 4-7) at different displacement velocities.  
Figure 4: Data fitting for tracer concentration ratio versus pore volume injected (PVI) at low flow rate (0.02 cm\({}^{3}\)/s) in natural cores: (**a**) core No. 1 (1570 mD), (**b**) core No. 2 (610 mD), and (**c**) core No. 3 (230 mD).  
Figure 5: Data fitting for tracer concentration ratio versus pore volume injected (PVI) at high flow rate (0.04 cm\({}^{3}\)/s) in natural cores: (**a**) core No. 1 (1570 mD), (**b**) core No. 2 (610 mD), and (**c**) core No. 3 (230 mD).
# Data-Driven Network Anomaly Detection with Cyber Attack and Defense Visualization
## 4 Methodology and Implementation
### Network Model
#### 4.4.2 Contention Delays (\(\xi(D)\))  
We assume that network synchronization occurs at fixed time intervals, as this augments simulation speed with minimal accuracy loss, as described in [52]. Consequently, packets are sent to the network asynchronously until the next synchronization interval \(\Delta t\). Our model calculates \(\xi(D)\) by evaluating simulated destination hosts using a uniform distribution of arrival times, which represents a queue over the time interval \(\Delta t\). This can introduce additional delays if consecutive packets overlap upon arrival. For each packet at a time interval, we identify the simulated destination host and compute distributed arrivals including new packets, and then, as per the destination queue and network synchronization, we compute \(\xi_{\text{latency}}\). To avoid recursive dependencies, we define an initial latency, \(\xi_{0}\), which represents the system latency at the start of the simulation or the latency of the first packet when the queue is empty. The \(\xi(D)\) of packet \(y\) per destination \(n\) as follows:  
\[\xi(D)_{y}=\begin{cases}\xi[y-1]+f(P)_{(y-1)}+\frac{T_{i}}{T_{B_{s}}},&\text {if }\xi[y-1]>0\\ f(P)_{(y-1)}+\frac{T_{i}}{T_{pk_{s}}},&\text{if }\xi[y-1]\leq 0\end{cases} \tag{4}\]  
where \(\xi_{0}\) is the latency before any packets are processed, \(f(P)_{(y-1)}\) denotes the physical delay of the previous packet in the queue, i.e., \(y-1\), \(\xi[y-1]\) denotes the contention delay of the previous packet, \(T_{i}\) denotes the synchronization time interval, and \(T_{pk_{s}}\) denotes total packets processed in \(T_{i}\), all with simulated destination \(n\). The condition \(\xi[y-1]\leq 0\) could occur if a packet is processed without any queuing delay, in which case its latency is only due to processing and transmission times. The initial condition \(\zeta_{0}\) is a predetermined latency that is assigned before the arrival of the first packet. This latency could be due to various factors such as initial processing delay, propagation delay, or any inherent delays present in the network. The condition \(\xi[y-1]\leq 0\) would imply that the preceding packet encountered no queuing delay and was processed within its synchronization interval, which could occur under light network load conditions or when a packet arrives exactly at the synchronization boundary. These conditions ensure that \(\xi(D)_{y}\) represents the total latency experienced by packet \(y\), considering both queuing and processing times. These approaches allow us to test network behavior under different realistic inputs, providing insightful results for our proposed solution. We are also able to model the accumulation of delays due to contention in the network and test behavior under different realistic inputs, providing great results for our proposed solution.
## 1 Introduction
(2023) [25] investigated the elastic tensile stiffness of perfobond rib connectors in steel-concrete composite pylons of cable-stayed bridges, proposing predictive equations based on tests, FE analysis, and a spring model. [PERSON] et al. (2020) [26] analyzed the shear behavior of grout-filled perforated plate connectors at the steel-concrete joints of composite bridges. [PERSON] et al. (2020) [27] developed a novel PBL anchorage system for the 4 th Nanjing Yangtze River Bridge, while [PERSON] et al. (2021) [28] focused on the pullout behavior of composite structures with perfobond connectors wrapped in carbon fiber-reinforced polymer (CFRP). [PERSON] et al. (2024) [29] proposed a novel energy dissipation beam-column joint based on CL composite dowels and low-yield-point steel, and [PERSON] et al. (2025) [30] identified a new restricted cone failure mode under tensile-shear coupling load for thin-covered composite dowels. These studies indicate that, despite the progress made in applying perfobond connectors in composite beams, their application in cable-pylon anchorage zones remains limited. With the increase in the span of cable-stayed bridges, the forces on the pylon anchorage zone also increase. This paper draws inspiration from these findings and incorporates transverse steel structure connections into the perforated plate connectors. This has led to the proposal of the integral steel wall plate (IWP) structure, shown in Figure 1a, to further improve the load-bearing performance of cable-pylon anchorage zones. The applications of IWPs along with steel anchor beams and boxes are illustrated in Figure 1b,c, respectively. The proposed IWP structure contains an end plate inside, a surface plate outside, and several perforated side plates embedded in the pylon wall. The end plate and surface plate are connected via several side plates with perfobond connectors, so that cable forces acting on the steel plates can be transferred into the concrete pylon. Several steel cabins were formed by the steel plates, and the material properties of concrete can be increased from the confining action. To this end, this paper investigates the performance of the proposed IWP detail with a steel anchor beam and box in the pylon-cable anchorage zone. A half-scaled experiment and a finite element (FE) analysis were first conducted on an IWP with a steel anchor beam, and the stress distribution and the deformation of the steel-concrete interface were obtained and compared with conventional data. Then, finite element models of three types of cable-pylon anchorage zones with a steel anchor box, namely the proposed IWP type, the exposed type, and the internal type, were established and compared. Finally, a parametric study was conducted to investigate the effects of design parameters such as the thickness of steel plates and the connector arrangement, to provide reference for the future design of IWPs.
# SVIoT: A Secure Visual-IoT Framework for Smart Healthcare
## 3 Proposed Framework: SVIoT
### Data Embedding
#### 3.3.2 Technique-2: Embedding with LDS  
In this technique, the data are first subjected to LDS, so that all decimal equivalents are shifted left by four steps; the data ranging from zero through seven, before LDS, range between negative four and three after LDS. As discussed earlier, in Section 3.2, LDS reduces the probability of changing the value of a pixel by a wider margin than embedding directly with LSB substitution, and thus leads to a better stego-image. With LDS, some boundary conditions do arise during the process, which are taken care of in such a way that, after any arithmetic operation, the pixel becomes confined within its designated value as explained by Equations (4)-(6).  
\[C(i,j)=C(i,j)\ \ for\ 3<C(i,j)<253 \tag{4}\]  
\[C(i,j)=C(i,j)+4\ for\ {C(i,j)}\leq 3 \tag{5}\]  
\[C(i,j)=C(i,j)-3\ for\ {C(i,j)}\geq 253 \tag{6}\]  
The payload in this technique remains the same as in Scheme-1 (1.5 bpp), but a considerable improvement is observed in the imperceptibly, with PSNR raised from 40.95 dB to 43.75 dB, thus contributing to the perceptual quality enhancement of the stego image. In comparison to Scheme-1, this technique takes less time to embed the same amount of secret data; the average data embedding time is slightly reduced to 1.408 s. Algorithm 1 describes in detail the various steps involved in data/watermark embedding.  
```
INPUT: Original Image \"I\", Watermark, Secret Data
``` Step: 1 Take an input image \"I\" of size \(M\times N\). Step: 2 Generate cover \"C\" of size \(M\times 2N\), by interleaving columns at alternate levels using ODNMI. Step: 3 Take a fragile watermark sized (\(n\times n\)). Step: 4 Convert the watermark to a single row vector. Step: 5 Take the secret data as a bitstream of a single-row vector and interleave the watermark bits in the data at regular intervals. In the proposed case one watermark bit is being interleaved after every 47 secret data bits. Apply AES-128 encryption to the secret data. Step: 6 Cover all three-bit chunks into corresponding decimal equivalents by using binary to decimal conversion. Step: 7 For Technique 1: Set three LSBs of all data pixels to zero, excluding seed pixels. For Technique 2: Apply LDS to the resultant decimal equivalents. Step: 8 Add the decimal equilatents to the corresponding data pixels. Step: 9 Repeat Step 8 till all decimal equilatents of the secret data are embedded to obtain stego-image. ```
**OUTPUT: Stego Image \"S\"**
## 2 Architecture of CCSDS-like QC-LDPC Code for Overlap
### Overlapped Decoding Scheme  
In traditional partially parallel decoder architectures, CNU and VNU are performed sequentially in each iterative cycle, as shown in Figure 2a, resulting in low HUE and low throughput. To accelerate the decoding process, and keep low memory consumption at the same time, an overlapped decoding scheme is proposed [25]. An overlapped decoding scheme allows CNU and VNU work independently at the same time, as shown in Figure 2b,  
Figure 1: PCM of CCSDS (8176, 7154) LDPC.  
and in ideal situations, CNU and VNU can seamlessly overlap to achieve maximum throughput like Figure 2c. In this paper, we adopt an overlap scheduling structure based on the flooding decoding algorithm, which lets VNU's start iteration after a number of CN's have been updated rather than all of them. The proposed PCM ensures that the elements required for subsequent column computations have been updated, as elaborated upon in Section 2.3. Through this approach, our decoder not only significantly enhances decoding efficiency and throughput, but also simplifies the scheduling logic and reduces logical resource consumption of the overlap controller.
## 2 Methodology
### Complexity Analysis  
According to the algorithm's design, the proposed method primarily consists of the following key components: Voronoi vertex acquisition, heuristic-based vertex selection, A* connection, and interpolation-based path optimization. The theoretical computational complexity analysis of these components is as follows.  
The computational complexity of Voronoi vertex acquisition is O\(((\mathrm{M}\times\mathrm{N})\cdot\log(\mathrm{M}\times\mathrm{N}))\), where M and N denote the width and height of the flight plane map, respectively, corresponding to a space complexity of O\((\mathrm{M}\times\mathrm{N})\).  
Assuming that the number of vertices in the Voronoi diagram on the map is Q, the heuristic-based vertex selection method exhibits a computational complexity of O\((\mathrm{Q}\cdot\log(\mathrm{Q}))\) and a space complexity of O\((\mathrm{Q})\).  
The A* connection algorithm exhibits a computational and space complexity of O\((\mathrm{b}\delta)\), where b denotes the branching factor (number of expanded nodes per level) and d represents the depth of the optimal path segment. The proposed Voronoi-A* framework strategically applies A* only to short path segments, where the limited depth d guarantees low computational complexity.  
The interpolation-based path optimization has a computational complexity of O\((\mathrm{P})\) and a space complexity of O\((\mathrm{P})\), where P denotes the number of interpolation points along the path.  
The algorithm demonstrates well-bounded computational and space complexity. The Voronoi vertex acquisition stage dominates the overall cost with a time complexity of O\(((\mathrm{M}\times\mathrm{N})\cdot\log(\mathrm{M}\times\mathrm{N}))\) and a space complexity of O\((\mathrm{M}\times\mathrm{N})\). Subsequent heuristic-based vertex selection operates at O\((\mathrm{Q}\cdot\log(\mathrm{Q}))\) time complexity and O\((\mathrm{Q})\) space complexity, while localized A* connections maintain manageable O\((\mathrm{b}\delta)\) complexity through constrained path segments. The final path optimization only requires linear O\((\mathrm{P})\) time/space complexity. This phased complexity design ensures efficient scalability, while initial map processing requires more computation, with subsequent steps slowly increasing in computational demands, maintaining high efficiency even when handling large-scale problems.
## 1 Introduction
### Bibliometric Analysis and Research Clusters
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Cluster** & **Color** & **Main Keywords** & **Topic** \\ \hline \multirow{2}{*}{1} & \multirow{2}{*}{Red} & Fault diagnosis, Vibration analysis, & \multirow{2}{*}{Advancements in Electrical System Fault Diagnosis} \\  & & Frequency domain analysis, Uncertainty & \\  & & analysis, and electric fault currents. & \\ \hline \multirow{2}{*}{2} & \multirow{2}{*}{Green} & Cracks, Sensors, Actuators, Catastrophic & Innovations in Detection and Fault Tolerance \\  & & failures, and Fault tolerance. & \\ \hline \multirow{2}{*}{3} & \multirow{2}{*}{Blue} & Inspection, Image analysis, Aircraft detection, & \multirow{2}{*}{Innovations in Wind Turbine Blade Inspection} \\  & & Fracture mechanics, Wind turbine blade. & \\ \hline \multirow{2}{*}{4} & \multirow{2}{*}{Yellow} & Ultrasonic applications, Non-destructive & \multirow{2}{*}{Structural health monitoring} \\  & & testing, Ice detection, Guided waves, and & \\  & & Ultrasonic waves. & \\ \hline \multirow{2}{*}{5} & \multirow{2}{*}{Purple} & Composite materials, Acoustic emission & \multirow{2}{*}{Advancements in Composite Materials design} \\  & & testing, Adhesives, Intelligent materials, and & \\  & & Reinforced plastics. & \\ \hline \multirow{2}{*}{6} & \multirow{2}{*}{Cian} & Failure mechanical, Fatigue testing, & \multirow{2}{*}{Exploring Mechanical Failures in Composite Structures} \\  & & composite structures, Fracture mechanics, & \\  & & and Finite element method. & \\ \hline \multirow{2}{*}{7} & \multirow{2}{*}{Orange} & Adhesive, Adhesive thickness, & \multirow{2}{*}{Adhesive Joints} \\  & & Glass-ceramics, Turbine components, and & \\  & & Composite. & \\ \hline \multirow{2}{*}{8} & \multirow{2}{*}{Brown} & Lightning, Lightning protection, Lightning & \multirow{2}{*}{Advances in Lightning Protection Systems} \\  & & strikes, Lightning attachment, and Lightning & \\ \multicolumn{4}{c}{protection systems.} \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The main keywords used by the communities detected in the topic Wind Turbine and Failure.
## 3 Asst
### Analysis
#### 3.4.3 Time and message  
For the data transmissions from a child sensor node to a parent node, CSMA is used to reserve the channel. We assume that each node knows the number of contending neighbor nodes (m) and contends the channel with the optimal probability p = 1/m. The probability that one contending node wins the channel is p\({}_{\text{succ}}=(1-1/\text{m})^{\text{m}-1}\). Since the number of slots needed until the successful reservation is a geometric random variable, the average number of contending slots (ACS) is given by:  
\[ACS=\frac{1}{(1-\frac{1}{M})^{M-1}}\]  
where M is the Average number of Neighbor nodes.  
One node on the grid included to the Trajectory Zone has to forward the packet to next virtual block and therefore, the required time slots for Trajectory Zone (STZ) is given by:  
\[STZ=ACS\times NUM_{VB}=\frac{1}{(1-\frac{1}{M})^{M-1}}\times\frac{8H}{W}\]  
The main part of procedure in Table 2 is finding the next node which is closest to the trajectory Edge and the time for this routine is in proportion to the number of nodes in a virtual zone, which is in proportion to the width of Trajectory Zone (W). Sorting with distance and finding a node minimum distance requires O(n log n) time. The required time for inter communication between backbone nodes is in proportion to the width of Trajectory Zone (W) and the distance H as shown above and thus can be also O(n log n). As a consequence, the total time complexity is O(n log n).  
The number of message for Trajectory Zone formation is in proportion to the number of virtual block in Trajectory Zone since every backbone node in virtual blocks has to forward the formation message to next virtual block and thus the number of message for Trajectory Zone formation is given by O(N).
# Sulfate Resistance in Cements Bearing Bottom Ash from Biomass-Fired Electric Power Plants
## 3 Results and Discussion
### Composition and Microstrutural Analysis  
The 180 d XRD patterns for OPC, OPC + 10 BA, and OPC + 20 BA soaked in water or Na\({}_{2}\)SO\({}_{4}\) (reproduced in Figure 5) confirmed the presence of ettrinigite and portlandite, the hydrated phases normally identified in cements. Unreacted BA (i.e., quartz) and calcite resulting from the carbonate formed when carbon dioxide dissolved in the solution were also detected in the blended pastes [45]. The reflections for ettrinigite and gypsum, the two products primarily associated with sulfate attack, were more intense in the samples exposed to sulfates for 180 days. These findings were consistent with earlier reports for Portland cement pastes [46], pastes bearing blast furnace slag [47], and cements prepared with granite sludge [29].  
Figure 4: Expansion in cements exposed to Na\({}_{2}\)SO\({}_{4}\).  
The secondary electron (SE) SEM micrographs for the OPC and OPC + 20 BA pastes soaked in Na\({}_{2}\)SO\({}_{4}\) for 180 days reproduced in Figure 6a,b attest to the microcracks resulting from the generation of internal stress (\(\varepsilon_{\rm local}\)) higher than the tensile strength (\(\varepsilon_{\rm macro}\)) of the matrix. Such fissures appeared in stages 3 and 4 of the mechanism proposed by [PERSON] et al. [42] to describe sodium sulfate attack in mortars. As noted earlier, these circumstances are the result of the expansive nature of gypsum and ettrinigite, the most prominent products of sulfate attack, characterized by expansion factors ranging from 1.25 to 2.76 [4].  
Figure 5: XRD patterns for pastes of ordinary Portland cement (OPC), OPC + 10 bottom ash (BA), and OPC + 20 BA soaked for 180 days in (**a**) water or (**b**) a 0.3 M Na\({}_{2}\)SO\({}_{4}\) solution (from the bottom to the top: OPC, OPC + 10 BA, and OPC + 20 BA).  
Figure 6: SEM micrographs of the compounds formed in cement pastes after 180 days exposure to 0.3 M Na\({}_{2}\)SO\({}_{4}\): (**a**) OPC; (**b**) OPC + 20 BA; gypsum plates in (**c**) OPC and (**d**) OPC + 20 BA; and ettrinigite needles in (**e**) OPC; (**f**) OPC + 20 BA.  
The presence of gypsum deposits resulting from the portlandite-sodium sulfate reaction are visible in Figure 6c,d, where they can be seen to have clustered primarily inside pores (Figure 6d), the sites most favorable to nucleation [42; 48]. Microracking originated in this region and subsequently extended across the matrix. Figure 6e,f, in turn, attest to the presence of ettringit, primarily in the form of elongated needles [49; 50]. The micrographs provide support for the premise that ettringit forms primarily inside pores [51]. In addition to cracks, these are the sites at which it normally crystallizes, given the favorable pressure conditions and the presence of the ions required for the needles to form there [52; 53].
## 5 Conclusions  
To classify the coffee grinder burr wear level, instead of burr surface images obtained from costly scanning electron microscopes, coffee granule images taken by mobile phones are used for analysis. The analysis process running on a server computer is divided into two main steps. First, it applies image processing to obtain the coffee granule size distribution. Second, the input data with a unified length based on the coffee granule size distributions are used to train deep learning model with a compact structure and high accuracy. A chatbot interface using a LINE bot was built for remote users to conveniently send the coffee granule image, make requests to the server computer for analysis, and receive the burr wear level classification result.  
In the performance-related analysis, low mean squared error (MSE, 0.00399-15.22) and high peak signal-to-noise ratio (PSNR, 20.31-72.12 dB) values indicate that the errors caused by morphological operations in image processing are small or acceptable. High confidence scores (0.75-0.99) and low standard deviations (0.05-0.18) from the Monte Carlo dropout analysis ensure the robustness of the prediction consistency of the deep learning model. With a prediction accuracy that is higher than 96%, the proposed design approach in both the image processing and deep learning model have been proven to be very effective.  
The system has the advantages of low implementation cost, low operating cost, fast response speed, high accuracy, a user-friendly interface, and ease of use. Therefore, it is very suitable for both coffee chains and coffee lovers.  
The proposed method of combining image processing, a deep learning model, and chatbot has potential to be further modified and applied to predict tool wear for machines that generate particles or debris and has wider applications in the future.