esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Performance with Phase Noise  
This section is devoted to the QCPSK and FM-CSK systems performance investigation in the increased phase noise conditions. It is applied during the modulation and demodulation processes and is caused by phase deviations in the LO signals. Considering the proportional dependence between the phase noise and the carrier frequency, the current research's relevance to the WSN applications becomes obvious. A typical WSN node uses narrow bandwidth for the transmission; however, the overall number of the nodes tends to increase, which leads to the boost of the carrier frequency and phase noise.  
The remaining part of this section describes the test setup and details phase noise generation during the experiments. This section ends with the QCPSK and FM-CSK BER graphs for AWGN conditions at different phase noise levels. The experiments are also performed for the conventional modulation schemes (QPSK and FSK) to show the phase noise immunity benefits of chaos-based systems.
## 3 Results
### IQR/Median SR for Three Applications of Strain Elastography  
For the three applications reported here, the IQR/median \(SR\) increased from scanning a tissue mimicking phantom to ex-vivo surgical specimens of bowel pathology, and increased further when the strain imaging was performed endoscopically focusing on focal pancreatic lesions. The difference between the IQR/median \(SR\) was significant (\(p\) = 0.002, Kruskal-Wallis; Figure 4).  
Figure 4: Box plots of the interquartile range (IQR)/median \(SR\) for the different applications of strain based elastography (Real-Time Elastography) reported here. There is significant difference between this quality parameter for the three applications (\(p\) = 0.002). In liver elastography using Transient Elastography, the suggested maximum IQR/Median Shear-Wave speed for good quality assessment in 10 repeated measurements in the same liver is 0.30, which is marked with the dotted horizontal line.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Entity**} & \multicolumn{2}{c}{**Neuroendocrine Tumors (NETs)**} & \multicolumn{2}{c}{**NET**} & \multirow{2}{*}{**Adenocarcinoma**} & \multicolumn{2}{c}{**Focal**} & \multicolumn{1}{c}{**Other Benign**} \\  & **Undetermined or Benign** & & & **Malignant** & & **Pancreatitis** & **Lesion** \\ \hline Number & 11 & 7 & 11 & 8 & 11 \\ Median \(SR\) & 2.19 & 5.74 & 8.36 & 0.91 & 3.34 \\ Range & 7.93 & 17.5 & 24.5 & 8.33 & 35.3 \\ IQR 1 (25-75) & 2.80 & 13.1 & 10.9 & 3.55 & 5.53 \\ IQR/Median & 1.28 & 2.28 & 1.30 & 3.68 & 1.66 \\ \hline \hline \end{tabular}  
* IQR: Inter Quartile Range. Mean IQR/Median \(SR\) for pancreatic lesions: 2.04. SR data with median values and IQR previously published as box-plots [15].  
\end{table}
Table 3: Elastography strain ratio of focal pancreatic lesions by Endoscopic Ultrasound (EUS).
# Enhancing Resilience in Digital Twins: ASCON-Based Security Solutions for Industry 4.0
###### Abstract  
Persistent security challenges in Industry 4.0 due to the limited resources of IoT devices necessitate innovative solutions. Addressing this, this study introduces the ASCON algorithm for lightweight authenticated encryption with associated data, enhancing confidentiality, integrity, and authenticity within IoT limitations. By integrating Digital Twins, the framework emphasizes the need for robust security in Industry 4.0, with ASCON ensuring secure data transmission and bolstering system resilience against cyber threats. Practical validation using the MQTT protocol confirms ASCON's efficacy over AES-GCM, highlighting its potential for enhanced security in Industry 4.0. Future research should focus on optimizing ASCON for microprocessors and developing secure remote access tailored to resource-constrained devices, ensuring adaptability in the digital era.  
Digital Twins; DT; lightweight; ASCON; IIoT; ESP32 2024
## 4 Gated Transformer Models
### Encoder Module  
The Encoder module is mainly composed of multiple Encoder layers connected in series, and the number of layers can be set freely. In this paper, a three-layer structure is adopted. Each layer includes a multi-head attention mechanism, residual and regularization network, linear feedforward network, and GRTU module. The multi-head attention mechanism is set to 8 heads. The module can focus the input multi-dimensional feature information with attention weights, and output the attention diagram of \(\mathbb{R}^{\mathbb{B}\times T_{\text{g}}\times T_{\text{g}}}\) to Decoder, helping Decoder to complete the final power prediction.  
In other words, the Encoder module is the core of the entire model. It can analyze and extract the features of input sequences, and utilize gating systems and attention mechanisms to efficiently capture the core intrinsic features of the data, thereby helping the Decoder to complete sequence prediction. As the Encoder outputs an attention weight map, it reflects the distribution of key information in the sequence. To facilitate understanding and observation, the output was visualized and shown in Figure 7. The figure shows the encoder output with an input length sequence of 96. It can be seen that the attention in the first and second half of the sequence is focused on itself, respectively. The poor attention at the front and back ends also reflects a low correlation.
## Methods
### CTD Measurements  
At each station, a conductivity-temperature-depth (CTD) package equipped with Niskin bottles was deployed to determine the depth profiles of seawater physical parameters (temperature \(T\), salinity \(S\), and density anomaly \(\sigma\)), chlorophyll-\(a\) fluorescence, and non-water optical beam attenuation coefficient at 660 nm, \(c_{mw}\)(z, 660) (i.e., the total beam attenuation of seawater after subtraction of pure seawater contribution). Water samples were also collected at discrete depths with the CTD-Rosette for subsequent determinations of \(Chl\) in the laboratory.  
The CTD package was deployed from water surface to \(\sim\)500 m depth. The acquired raw data were processed using a manufacturer's software, SBEDataProcessing v7.23.2 (Sea-Bird Electronics Inc., Bellevue, WA, USA), and converted to values in physical units with calibration files. The processing software automatically aligned the measurements of each sensor to the common depth vector. The calibrated data were subsequently split into down- and up-casts. Preliminary inspection of the data showed that the up-casts of CTD measurements often exhibited less noise and thus were chosen for further processing. Depth profiles of each parameter were despicked using a running median method and smoothed using a Savitzky-Golay filter in Matlab R2015b (MathWorks, Natick, MA, USA). The profiles were then binned to 0.5 m depth resolution to yield the final data of \(T\), \(S\), \(\sigma\), chlorophyll-\(a\) fluorescence, and \(c_{mw}\)(660). The \(c_{mw}\)(660) was considered to be equivalent to the particulate attenuation coefficient at 660 nm, \(c_{p}\)(660), because the absorption of dissolved material at 660 nm was assumed negligible.
## 3 What Is SDN?  
Software-Defined Networking (SDN) is an architectural approach to computer networking that enhances flexibility, scalability, and programmability by decoupling the network's control plane from the data plane, as illustrated in Figure 4b,c. In traditional networking, both the control and the the data planes are tightly integrated within networking devices such as switches and routers (Figure 4), limiting the flexibility and programmability of the network. SDN manages networks through software applications [60, 61]. It employs software controllers, guided by Application Programming Interfaces (APIs), to interact with the hardware infrastructure and direct the flow of network traffic [62]. This approach offers a more flexible and efficient method for managing and controlling network operations.  
OpenFlow-based transformations (Figure 4b) are executed by the control plane (e.g., ONOS or Ryu), which operates under the OpenFlow protocol. These transformations are supported by OpenFlow-compatible vendors such as HP and Cisco. Additionally, P4  
\begin{table}
\begin{tabular}{c  
networking devices (Figure 4c) enable programmability in the data plane while maintaining a similar control plane architecture.  
An SDN network is distinguished by its agility, effectively combining proactive and reactive capabilities to swiftly adapt to changing network conditions. Centrally managed through one or more controllers, SDN provides a unified view of the network, simplifying management tasks. By enabling programmatic configuration, SDN allows administrators to define network behavior through software-defined policies, thereby enhancing flexibility and automation. SDN embraces open standards and vendor neutrality that fosters interoperability and innovation, allowing organizations to seamlessly integrate diverse network components. These characteristics collectively make SDN networks highly adaptable, efficient, and suitable for addressing evolving networking challenges.
# An Algorithm for Obtaining 3D Egg Models from Visual Images
###### Abstract  
Mathematical models for describing the shape of eggs find application in various fields of practice. The article proposes a method and tools for a detailed study of the shape and peripheral contours of digital images of eggs that are suitable for grouping and sorting. A scheme has been adapted to determine the morphological characteristics of eggs, on the basis of which an algorithm has been created for obtaining their 3D models, based on data from color digital images. The deviation from the dimensions of the major and minor axes measured with a caliper and the proposed algorithm is 0.5-1.5 mm. A model of a correction factor has been established by which the three-dimensional shape of eggs can be determined with sufficient accuracy. The results obtained in this work improve the assumption that the use of algorithms to determine the shape of eggs strongly depends on those of the bird species studied. It is approved with data for Mallard eggs which have a more elliptical shape and correspondingly lower values of correction coefficient '\(c\)' (c = 1.55-4.96). In sparrow (c = 9.55-11.19) and quail (c = 11.71-13.11) eggs, the form tends to be ovoid. After testing the obtained model for eggs from three bird species, sparrow, mallard, and quail, the coefficient of the determination of proposed model was R\({}^{2}\) = 0.96. The standard error was SE = 0.08. All of the results show a p-value of the model less than \(\alpha\) = 0.05. The proposed algorithm was applied to create 3D egg shapes that were not used in the previous calculations. The resulting error was up to 9%. This shows that in the test, the algorithm had an accuracy of 91%. An advantage of the algorithm proposed here is that the human operator does not need to select points in the image, as is the case with some of the algorithms developed by other authors. The proposed methods and tools for three-dimensional transformation of egg images would be applicable not only for the needs of poultry farming, but also in omnibological research when working with different shaped varieties of eggs. Experimental results show that the proposed algorithm has sufficient accuracy.  
egeg measurement; digital imaging; image processing; egg geometry; non-destructive measurements +
Footnote †: journal: Applied Sciences
# Thin-Walled CFST Columns for Enhancing Seismic Collapse Performance of High-Rise Steel Frames
## 3 Seismic Performance of the Thin-Walled SMRFs
### Model Validation  
The numerical model is validated by simulating a static cyclic test on a planar CFST frame with three stories under cyclic loading [23; 28]. The frame specimen was constructed by square CFST columns and H-shaped steel beams. The cross section of the CFST columns is uniformly fabricated as \(150\times 150\times 4.5\) using the steel material STKR400 with \(\sigma_{y}=409\) MPa and \(\sigma_{u}=477\) MPa.  
Figure 4: Statistical databases of various existent high-rise buildings.  
Figure 5: Members, element discretization, plan and elevation view of high-rise SMRF (steel moment-resisting frame) models.  
The BH-200 \(\times\) 100 \(\times\) 6 \(\times\) 6 H-shaped steel using the steel material SS400 with \(\sigma_{y}\) = 337 N/mm\({}^{2}\) and \(\sigma_{u}\) = 444 N/mm\({}^{2}\), is adopted as the beam components. The compressive strength of the infilled concrete in CFST columns is 31.3 MPa based on four concrete cylinder test results. Horizontal cyclic loading is applied on the top of the frame specimen, and constant axial loads with the axial load ratio \(N\) = 0.2\({}_{c}\)N\({}_{u}\)\(\times\) 2 are applied on two CFST columns simultaneously. Figure 6 shows the elevation view of the CFST frame model.  
Comparisons of hysteretic load (H) versus roof drift ratio (R) curve between experimental and analytical results are presented in Figure 7. The hysteretic curve before R \(\leq\) 2% shows slight plastic deformation while the strength degradation was not induced by local buckling. Furthermore, local buckling was triggered, associated with post-peak strength deterioration which was incorporated in the numerical modeling. More than 85% of the maximum strength is maintained; even the roof drift ratio R reaches 5%. Through simulating this cyclic test of the CFST frame, the numerical modeling approach is promising to accurately predict the seismic behavior of CFST frames, including the elasto-plasticity, damages and strength deterioration. The seismic performance of CFST frame structures would be overestimated if the deterioration effects of steel tube and confined concrete were neglected. Hence, deterioration effects should be appropriately considered in simulating the seismic performance of high-rise steel and CFST buildings.
# Internal Curing Effect of Waste Glass Beads on High-Strength Cement Composites
## 3 Results and Discussion
### Compressive Strength  
Figure 4 shows the compressive strength test results of the HSCC specimens mixed with WGBs. The average compressive strengths of SP, S5, S10, and S20 specimens were 120.3, 85.6, 71.8, and 67.4 Mpa, respectively. As shown in Figure 4, the average compressive strengths of RP, R5, R10, and R20 specimens were 62.1, 50.3, 39.6, and 35.5 Mpa, respectively. The compressive strengths of cast-off specimens at 1 d of age were similar for the two test groups. The error bars in Figure 4 indicate the 95% confidence interval.  
The compressive strength of S-group specimens was approximately twice that of R-group specimens. As shown in Figure 4, the strengths slightly increased with age even after the completion of steam curing. Since typical HSCCs are subjected to steam curing at early ages, the maximum hydration reaction occurs in the matrix during this period [36,37]. Therefore, high-temperature and high-pressure curing can significantly improve the mechanical properties of HSCCs [38].  
Figure 4 shows the strengths of R-group specimens. The compressive strength of RP specimen at the age of 56 d is comparable to that of S20 specimen at the age of 56 d in Figure 4. HSCC specimens contain large amounts of silica fume and silica powder. Moreover, the reaction between them is activated only at high temperatures; consequently, steam curing at earlier stages leads to faster strength development [38,39]. Hence, the HSCC specimens subjected to room temperature curing showed slower strength development owing to the insufficient activation reaction of internal silica components.  
WGBs reduced the compressive strengths of specimens to a certain level, and specimens with a maximum content of 20% developed approximately half the strength of the  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**ID.**} & \multirow{2}{*}{**Flow Table (mm)**} & \multicolumn{2}{c}{**Density (g/cm\({}^{3}\))**} & \multirow{2}{*}{**ID.**} & \multirow{2}{*}{**Flow Table (mm)**} & \multicolumn{2}{c}{**Density (g/cm\({}^{3}\))**} \\ \cline{3-3} \cline{5-8}  & & **Age of 3 d** & & & & **Age of 3 d** & **Age of 28 d** \\ \hline SP & 220 & 2.62 & 2.51 & RP & 220 & 2.46 & 2.38 \\ S5 & 205 & 2.44 & 2.16 & R5 & 205 & 2.20 & 2.08 \\ S10 & 180 & 2.42 & 2.02 & R10 & 180 & 2.07 & 1.96 \\ S20 & 175 & 2.26 & 1.88 & R20 & 175 & 1.94 & 1.81 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Flow table and density.  
Figure 4: Compressive strengths of specimens subjected to steam curing and room temperature curing as a function of curing time.  
specimens without WGBs. In general, concrete mixed with LWAs contributes to a decrease in density, which tends to reduce mechanical strength [40,41]. The reduction in compressive strength results from the low mechanical strength of WGBs as LWAs, and the destruction of aggregates results from the increasing volume content of LWAs [38].  
If WGBs are to be used in mixing high-strength concrete and cement composites, an additional review on the mixing rate and curing method of WGBs is needed.
## 6 Utilizing MRSO for Solving Engineering Design Issues
### Overview of Constrained Engineering Design Problems
#### 6.1.1 Pressure Vessel Design  
Designing a pressure vessel, as proposed by [PERSON] and [PERSON] (1994) [40], aims to minimize the overall cost, which includes material, shaping, and welding. The vessel, shown in Figure 3, consists of a cylindrical body with hemispherical heads at both ends. The problem involves four design variables:  
\(S_{t}\) = shell thickness  
\(H_{t}\) = head thickness  
\(I_{r}\) = inner radius  
\(L_{c}\) = length of the cylindrical section  
Here, \(I_{r}\) and \(L_{c}\) are continuous variables, while \(S_{t}\)_and_\(H_{t}\) are discrete values that are multiples of 0.0625 inches.  
Consider the following:  
\[\overset{\rightarrow}{P}=[P_{1},\ P_{2},\ P_{3},\ P_{4}]=[S_{t},\ H_{t},\ I_{r},\ L_{c}]\]  
The objective is to minimize the cost function:  
\[f\left(\overset{\rightarrow}{P}\right)=0.6224P_{1}P_{3}P_{4}+1.7781P_{2}P_{3 }^{2}+3.1661P_{1}^{2}P_{4}+19.84P_{1}^{2}P_{3}\]  
subject to the following constraints:  
\[S_{1}\left(\overset{\rightarrow}{P}\right) =-P_{1}+0.0193P_{3}\leq 0\] \[S_{2}\left(\overset{\rightarrow}{P}\right) =-P_{3}+0.00954P_{3}\leq 0\] \[S_{3}\left(\overset{\rightarrow}{P}\right) =-\pi P_{3}^{2}P_{4}-\tfrac{4}{3}\pi P_{3}^{3}+1,296,000\leq 0\] \[S_{4}\left(\overset{\rightarrow}{P}\right) =-P_{4}-240\leq 0\]  
with variable ranges:  
\[0\leq P_{1}\leq 99,\ 0\leq P_{2}\leq 99,\ 10\leq P_{3}\leq 200,\ 10\leq P_{4}\leq 200\]
# A Review on Deep-Learning-Based Cyberbullying Detection
## 5 Deep-Learning-Based Models
### Hybrid Models (LSTM-CNN, CNN-LSTM)  
The LSTM-CNN architecture for cyberbullying detection is a Deep Neural Network model that combines the advantage of both LSTM and CNN to detect cyberbullying. Processing sequential data, such as text, is where the model performs well.  
The architecture consists of three main components: an embedding layer, an LSTM layer, and a CNN layer as shown in Figure 19. The embedding layer converts the input text into a vector representation. After this, dropout can be applied to prevent overfitting. Then, the main structure of this architecture is built with a Bidirectional LSTM layer followed by a CNN layer, which is an extension of traditional LSTMs that can improve model performance on sequence classification problems that allow the model to capture both local and global context information. The LSTM-CNN architecture can be trained to identify messages or posts as cyberbullying or non-cyberbullying. The model takes in a sequence of words and outputs a probability score for each class.  
The choice between LSTM-CNN and CNN-LSTM in the context of cyberbullying detection may rely on the type of input data. For instance, LSTM-CNN may be more appropriate if the input data are textual, such as social media posttings or chat logs, because it can model the temporal dependencies in the data. On the contrary, CNN-LSTM might be a better fit if the input data are visual, such as images or videos, because it can simulate the spatial and temporal relationship in the data.  
In [63], we found both CNN-LSTM and LSTM-CNN experiments, and they showed that LSTM-CNN performs better than CNN-LSTM because the CNN layer would receive the word embeddings as input, which will further be pooled to a smaller dimension, and then, the LSTM layer will use the ordering of said features to learn about the input's text ordering.
## 5 Method
### Problem Modeling  
This paper designs the system architecture shown in Figure 5, which is designed according to the common characteristics of most emergency response scenarios and disaster relief scenarios, and is highly generalized in terms of geographic environments, meteorological conditions, communication resources, and computational resources. In specific emergency rescue tasks, the system can be disassembled and expanded as needed to meet the actual task needs. To facilitate the modeling of the target system, the system architecture shown in Figure 5 is simplified and abstracted, and the simplified model shown in Figure 6 is established.  
The emergency rescue center mainly handles large-scale tasks, such as disaster situation analysis, situation prediction, public opinion judgment, risk assessment, etc., which require data fusion, intelligence correlation, and situation analysis based on pre-trained large language models, and rely on large computing and storage resources. However, the rescue command center has multi-hop fiber-optic cable network transmission from the rescue site, so the up-linking of intelligence data and the downstreaming of processing results require a certain delay overhead. Therefore, when faced with a large intelligence analysis task, or when there are not enough computing and storage resources on the edge side of the task, the intelligence data are transmitted back to the emergency rescue center for processing.  
The mission edge side mainly deals with lightweight tasks, such as data aggregation, multi-modal intelligence alignment, content distribution, remote information relay, etc. The mission edge side can usually rely on the existing information and communication infrastructure, or motorized communication security vehicles to build an edge cloud, which  
Figure 6: Simplified model of emergency rescue system.  
is flexible in siting and easy to set up, and is therefore closer to the mission area and avoids a lot of the transmission latency. When the computing and storage resources on the edge side are sufficient and the task requires low latency, the rescue intelligence data will be shunted to the edge side of the task for processing.  
The rescue region is mainly oriented to simple tasks, for example, the aggregation and processing of life detection data, IoT sensing node data, and geographic information data, as well as the on-site control of drones, unmanned vehicles, or robot dogs. Therefore, when the task volume is small and the equipment in the rescue task area can autonomously complete simple task processing, it is processed locally. In this model, the specific meaning is shown in Table 2.