esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion  
### Pareto Front Points  
As discussed earlier, for optimization, we make use of the equations from the MM as the fitness function in genetic algorithms. The objective here is to minimize the pressure losses as well as the cut-off particle size. Hence, the MM has been used to perform multi-objective optimization using MATLAB. The optimal data sets--also known as Pareto front points--have been presented in Figure 4. It becomes apparent that there is a trade-off between the two objective functions, such as the pressure drop and cut-off particle size. All the details have been presented in Table 3. Based on the relevance, three Pareto front points have been selected for analysis, namely A, B, and C. CFD analysis of these points has been carried out for cross-validation with GA predictions to quantify the performance with respect to the baseline (Std) model and to explore the flow field in detail (discussed next).
## 3 Characteristic Analysis
### Axisymmetric Deformation  
Multiplying the first equation in (16) by \(\cos\psi\), the second by \(\sin\psi\) and summing gives  
\[\left(1-\frac{\cos 2\psi}{\sin\phi}\right)\frac{\partial q}{\partial x_{1}}- \frac{\sin 2\psi}{\sin\phi}\bigg{[}1+\frac{x_{2}}{R(x_{1})}\bigg{]}\frac{ \partial q}{\partial x_{2}}+2q\bigg{[}1+\frac{x_{2}}{R(x_{1})}\bigg{]}\frac{ \partial\psi}{\partial x_{2}}=F_{1}. \tag{23}\]  
Multiplying the first equation in (16) by \(\sin\psi\), the second by \(-\cos\psi\) and summing gives  
\[\frac{\sin 2\psi}{\sin\phi}\frac{\partial q}{\partial x_{1}}-\bigg{(}1+\frac{ \cos 2\psi}{\sin\phi}\bigg{)}\bigg{[}1+\frac{x_{2}}{R(x_{1})}\bigg{]}\frac{ \partial q}{\partial x_{2}}+2q\frac{\partial\psi}{\partial x_{1}}=F_{2}. \tag{24}\]  
The terms \(F_{1}\) and \(F_{2}\) include no derivatives.  
Assume that S - curve in Figure 3 is a characteristic curve. Then, \(\psi=\pm(\pi/4+\phi/2)\) on this curve [31]. In particular, \(\partial\psi/\partial x_{1}=0\). Equation (24) supplies the characteristic relation in the form  
\[\pm\cot\phi\frac{\partial q}{\partial x_{1}}=F_{2}. \tag{25}\]  
In the proceeding, it is assumed that S - curve in Figure 3 is an envelope of characteristics. Therefore, Equation (25) is not satisfied even though \(\psi=\pm(\pi/4+\phi/2)\) on this curve. It is possible only if  
\[\left|\frac{\partial q}{\partial x_{2}}\right|\to\infty \tag{26}\]  
as \(x_{2}\to 0\). In this case, the second term in (24) reduces to the expression \(0\cdot\infty\) at \(x_{2}=0\).
# PowerBridge: Covert Air-Gap Exfiltration/Infiltration via Smart Plug
## 6 Data Encoding and Decoding
### Signal Reception and Data Decoding  
PowerBridge utilizes power lines as a communication medium, resulting in half-duplex communication due to the inability of simultaneous exfiltration and infiltration communication.  
In the exfiltration scenario, the software embedded in the air-gapped computer captures power consumption fluctuations from hardware devices to transmit signals through series-connected smart plugs on the power line. Subsequently, these fluctuating signals are forwarded by smart plugs to the receiver and decoded using an algorithm. This approach bears a resemblance to [PERSON]'s PowerHammer technique, which will not be reiterated within this article.  
In the infiltration scenario, the current signal is transmitted by remotely controlling the switch of the smart socket to the open and closed positions. The laptop detects the power supply state through the ACPI interface, switches to battery power when disconnected from the external power supply, and logs this information. The computer software utilizes the kernel.dll dynamic library's API interface GetPowerStatus to detect and record ACoff and ACon as external power status indicators, enabling the determination of signal timing for data decoding. Ultimately, communication content can be decoded based on the agreed-upon communication protocol. Algorithm 2 describes the process of PIM-based signal decoding.  
When utilizing the powercfg command to access the system power usage log, it is observed that the logging time interval is significant, resulting in a failure to record power status changes occurring within less than 3 s. This limitation can lead to a reduced data reception rate at the receiving end. Through experimentation, we have discovered that the GetPowerStatus API provided by the Windows operating system serves as an interface function exported from the dynamic library 'kernel.dll'. This API enables querying of the computer power supply status and related information. Consequently, employing this API for rapid polling effectively resolves the issue of extensive logging time intervals and enhances communication speed.  
Figure 23: Packet frame structure.
## 4 Performance Evaluation, and Result Analysis and Discussion
### Evaluation Environment  
Figure 7 shows the environment we used to evaluate the performance of EDCrammer-based caching. To configure a cloud node, we installed Unbuntu 14.04 and Docker engine 17.03-2-ce on a physical server. EDCrammer has an MQTT client and PID controller and was developed in Python 3 and then containerized to run on the Docker engine. To deploy MQTT server, we used an eclipse-mosquitto container. To configure an edge node, Raspbian Stretch Lite 4.14 and Docker engine 18.09.0 were installed on a Raspberry Pi 3 Model B+. the SDC manager has an MQTT client and the SDC. The SDC manager was also developed in Python 3 and containerized to run on the Docker engine. Within that environment, we used four experimental scenarios to evaluate the proposed rate-control algorithm for in- and outflows and caching of streaming data on resource-limited edge nodes.  
Table 2 shows the specifications for the cloud and edge nodes. In this performance evaluation, we selected a physical server as the cloud node and a Raspberry Pi 3 Model B+, which has lower computing resources than the cloud node, as the edge node.  
Figure 6: A state diagram of the data life cycle on the framework.
# Fuzzy-Based Multi-Modal Query-Forwarding in Mini-Datacenters
## 5 Proposed Methodology  
The proposed methodology, as demonstrated in Figure 6, begins by checking for the presence of a query or a set of query bursts at the command center (CC) queue. The query arrival rate is set greater than the service rate, so with an arrival of a set of query bursts, there will be a backlog of queries at the queue. The queries are dequeued in a FIFO fashion, and once a query with a specific burst size is dequeued, the burst size is set as a count to repeat the query forwarding loop. The framework itemizes the query burst into independent queries, and for each query, it selects a list of fitted gateways from the deployed ones to redundantly forward the selected query. This selection is driven by the current buffer occupancies, which are monitored using the buffer-watching algorithm. Gateways with fitness values greater rather the defined threshold are selected as 'fitted gateways.'  
With the available list of fitted gateways, the gamma factor (backlog at CC) at the time of query forwarding is calculated to estimate the redundant number of gateways to be selected to forward that query. We have defined an overload threshold to list the overloaded gateways among the fitted gateways. Say, if the fitted gateways have fitness values ranging from 30 to 100%, overloading is checked against buffers with occupancies greater than 70%. So, there is a possibility for a gateway to be listed in the fitted and overloaded category at the same time. The proportion of fitted to available gateways and the proportion of overloaded gateways against the fitted gateways enables fuzzy intelligence to estimate the granularity level, resulting in the selection of an appropriate forwarding technique. The forwarding may be either coarse-grain with overloaded buffer occupancies or fine-grain with low or medium overloading. The procedure gets repeated until all queries are forwarded.  
Figure 5: Output membership function.
# Development of Automatic Crack Growth Simulation Program Based on Finite Element Analysis
## 2 Development of AI-FEM  
### The Overall Procedures of AI-FEM  
The crack growth evaluation procedure through AI-FEM is shown in Figure 1. As a first step, information of initial crack is entered such as initial crack shape and location. Then, the program generates a crack tip mesh based on the initial crack information and the modeling criteria described in Section 2.2. If there is an initial stress field such as the weld residual stress considered in the MRP-215 report [19], stress mapping is performed using the 'Map Solution' function provided by Abaqus [20]. If the initial stress field does not exist, this step can be omitted. Once the crack information, load, and stress conditions are all entered, AI-FEM calculates the SIF (\(K_{\text{I}}\)) values at each node along the crack tip and crack growth direction. By applying SIF to the FCG curve or the SCC growth curve, the corresponding crack growth length can be calculated, and the crack growth direction can be determined as the normal direction at each point along the crack tip. In the next step, the AI-FEM checks whether the final operating cycle for FCG or time for SCC has been reached. If the final operating cycle or time is not reached, the crack size is updated by adding the crack growth length to the current crack size at each point along the crack tip. Using the updated crack information, the crack tip mesh is regenerated, and the above-mentioned process is repeated until the final operation cycle or time is reached.
# Research on Defect Detection Method for Composite Materials Based on Deep Learning Networks
## 3 ECA Ghost CNN Method  
The improved ECA mechanism Ghost module model is abbreviated as ECAGhostCNN. The overall structure of the rapid classification model for composite material defects is depicted in Figure 1. The entire model undergoes four rounds of feature extraction, introducing the Ghost module to replace traditional convolutional (conv) operations for a more efficient implementation of the model. Additionally, an enhanced ECA mechanism module is incorporated during the feature extraction process to improve the performance of the model. Each round of feature extraction begins with a layer based on the Ghost module with the ECA mechanism, followed by a layer using the Ghost module without ECA. Finally, three FCs are applied, with the first two utilizing the ReLU activation function, and the last one using the sigmoid activation function. This results in a final prediction of size \(1\times 1\times 1\). The final output represents the binary classification result of whether a defect is present in the image.  
The entire classification network model is based on a traditional CNN, which consists of feature extraction structures, pooling structures, attention modules, and an FC. Two rounds of feature extraction, followed by pooling, are performed consecutively. The model's prediction is obtained from the final FC. In this study, two Ghost modules are used for each feature extraction, and the number of output channels increases in the first iteration. The Ghost module helps save a significant amount of computational resources. In the second round, the channel count remains unchanged, and an attention mechanism is added on top of the Ghost module. This allows the model to prioritize the feature information in the original image.  
The detection results of the ECAGhostCNN model are shown in Figure 1 below.  
The input dimension is a raw image with dimensions of \(1\times 512\times 512\). After being transformed by the ECAGhostCNN model, the final output values range from 0 to 1. If the value is greater than 0.5, the component is considered to have a defect; otherwise, it is considered normal.
## 5 Conclusions  
Traditional sound field control technology is mainly based on sound pressure or sound pressure improvement technology, without considering another physical property of sound: particle velocity; if the loudspeaker array is placed unevenly, it will make the reconstruction system unstable. In order to solve this problem, we introduce a new sound field control method based on the traditional sound field control technology. This method pays attention to both sound pressure and particle velocity, exerts influence on the sound pressure and particle velocity in the dark zone, and tries its best to reduce the reconstruction error in the bright zone and reduce the loudspeaker array effort. The model of our method contains three weight factors: \(\kappa\), \(\sigma\), and \(\zeta\), and their function and selection method are introduced in detail in this paper. Computer simulation experiments are carried out on a system with five unevenly placed loudspeaker, and our method is compared with the ACC method, PM method in the bright zone and the EDPM method in the bright zone, and the comparison indexes are reconstruction error, acoustic contrast, and loudspeaker array effort. Although the ACC method performs best in acoustic contrast and the PM method in the bright zone performs best in reconstruction error, they both focus on only one indicator and perform poorly in the other two. The EDPM method in the bright zone is an improvement of the PM method in the bright zone. Like the PM method in the bright zone, the EDPM method in the bright zone focuses on only one indicator. Although it outperforms the PM method in the bright zone on acoustic contrast and array effort, it is inferior to the PM method in the bright zone on reconstruction error. Compared with the three traditional methods, our method has the best compromise on reconstruction error, acoustic contrast, and array effort. Our method significantly outperforms other comparison methods in array effort performance. The average array effort produced by our method is about 9.4790 dB smaller than that produced by the ACC method, about 8.0712 dB smaller than that produced by the PM method in the bright zone, and about 4.8176 dB smaller than that produced by the EDPM method in the bright zone. Therefore, our method can guarantee optimal system stability in the case of uneven placement of loudspeaker array.  
Conceptualization, S.W.; methodology, S.W.; software, S.W.; validation, S.W.; formal analysis, C.Z.; investigation, S.W.; resources, S.W. and C.Z.; data curation, S.W.; writing--original draft preparation, S.W.; writing--review and editing, S.W. and C.Z.; visualization, S.W.; supervision, C.Z.; project administration, S.W. and C.Z.; funding acquisition, S.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Science and Technology Research Project of the Education Department of Hubei Province (No. B2022245).  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
The authors declare no conflict of interest.
## 3 Research Methods
### Principal Component Analysis (PCA)  
PCA (Figure 2), or principal component analysis, is a basic and commonly used method for the linear dimensionality reduction of hyperspectral image data [25]. Its fundamental idea is to convert a large number of original variable indicators in the data into a small number of principal component indicators that can maximize the retention of the original information while remaining mutually independent of each other; we then sort and filter them according to the amount of information that they encapsulate, thus achieving the purpose of dimensionality reduction for high-dimensional data [26,27].
## 2 Materials and Methods
### Image Acquisition Parameters  
For CT QC, image acquisition parameters were equal for all scanners, except for the tube current (in mA). The recommended tube current is 250 mA for AAPM CT Performance Phantom evaluation [13,14]. However, the CT radiation dose, measured as the CT dose index volume (CTDivol), is not the same among the various CT scanners when the tube current is fixed at 250 mA. As a result, we adopted a radiation dose-based protocol (CTDivol: 20 mGy) for all scanners (i.e., a tube current corresponding to a CTDivol of 20 mGy was selected). However, CT scanners that cannot be set to exactly 20 mGy were set to the nearest value (Table 1), similar to that of a previous study [28]. The other parameters were as follows: tube peak kilovoltage(kVp) = 120 kV, rotation time = 1 s, reconstruction method: filtered back projection, convolution kernels = Siemens, B30; GE, standard; Philips, B; Canon, FC13, display field-of-view = 25 cm, and scan mode = sequential scan of 10-mm slice thickness. All raw data in DICOM format was acquired by one author (K.B.L. with 10 years of experience in QC of CT).  
The specifications of the scanners are as follows: Siemens Healthineers: SOMATOM Definition AS (32), AS+ (64), and Edge (64); GE Healthcare: Lightspe  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Manufacturer**} & \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Tube Voltage**} & \multirow{2}{*}{**Rotation Time**} & \multirow{2}{*}{**Slice Thickness**} & \multirow{2}{*}{**Convolution Kernel**} & \multirow{2}{*}{**Tube Current**} & \multirow{2}{*}{**CTDivol**} \\ \cline{3-3} \cline{5-8}  & & **kVp** & & & & & **mA** & **mGy** \\ \hline \multirow{3}{*}{Siemens} & Edge & & & & & 274 & 19.95 \\  & AS+ & & & & B30f & 300 & 19.92 \\ \cline{1-1} \cline{3-8}  & CT250 HD & & & & & 25 & 19.85 \\ \cline{1-1} \cline{3-8}  & VCT Xte & 120 & 1 & 10 & Standard & 235 & 19.9 \\ \cline{1-1}  
Discovery CT750 HD (64); Philips Healthcare: Brilliance CT (64) and IQon Spectral CT (64); Canon Medical Systems: Aquilion CX (64) and GENESIS (160). CT, computed tomography; CTDIvol, computed tomography dose index volume; kVp, peak kilovoltage.
## 2 Mathematical Formulation
### Objective Function  
The objective function of the OCR coordination in electric distribution networks, labeled here as \(Z\), consists of minimizing the time response of any fault. This is expressed in Equation (1) where \(t_{rf}\) is the operation time of relay \(r\) when fault \(f\) takes place, while \(R\) and \(F\) are the sets of relays and faults, respectively.  
\[MinZ=\sum_{r\in R}\sum_{f\in F}t_{rf} \tag{1}\]
# Application of Multimodal AI to Aid Scene Perception for the Visually Impaired
## 1 Introduction
### Artificial Intelligence in Assistive Systems for the Visually Impaired  
Generative artificial intelligence is already being used to support visually impaired individuals, although in a different context. For example, in [18], ChatGPT was employed to enable conversational interaction for a blind user. The user communicates via voice, which is transcribed into text and sent to the model, while the model's response is converted back into speech using a text-to-speech system. The Be My Eyes platform [19] has integrated AI to provide visual assistance through a virtual volunteer powered by OpenAI's GPT-4, allowing blind users to receive descriptions and contextual understanding of their surroundings via smartphone cameras.  
It is worth noting that traditional artificial intelligence has been used for many years to support visually impaired individuals. As summarized in the review by [PERSON] et al. [20], applications include OCR-based text recognition and text-to-speech systems for reading assistance, smartphone tools for facial and object recognition, and obstacle avoidance systems using computer vision and sensors. These technologies have already contributed significantly to supporting the daily lives of people with visual impairments.  
Furthermore, modern smartphones enable the deployment of lightweight AI object detection models combined with multimodal feedback, such as haptic and audio cues, to assist visually impaired users in real-time navigation. Such mobile applications demonstrate significant potential in improving accessibility and independence by recognizing objects and providing intuitive feedback in diverse environments [20,21].
## 4 Results and Discussion
### Effect of Hydrostatic Pressure
#### 4.1.2 Numerical Results and Comparative Analysis  
It can be seen from Figure 4 that when the hydrostatic pressure was 0.5 MPa, the calculated depth of chloride ion diffusion in the concrete changed with different w/c ratios. Specifically, when the w/c ratio was 0.35, the chloride ion diffusion depth was 12.1 mm; when the w/c ratio was 0.45, the chloride ion diffusion depth was 15.5 mm; and when the w/c ratio was 0.55, the chloride ion diffusion depth was 18.5 mm. In addition, the calculated depths of chloride ion diffusion of the concrete with different w/c ratios and different hydrostatic pressures are shown in Table 8. Obviously, with the increase of the water-to-cement ratio--that is, the diffusion coefficient of chloride increased--the diffusion depth of the chloride increased with the same chloride concentration and the same erosion time. Furthermore, it can be concluded that the multi-physical field calculation method based on COMSOL software had higher accuracy by comparing the experimental results with the calculated results.  
Figure 4: Simulation results of chloride ion diffusion under different w/c ratios and under a hydrostatic pressure of 0.5 MPa.  
Figure 3: Test results of chloride ion diffusion under different w/c ratios while under hydrostatic pressure of 0.5 MPa.
# Artificial Intelligence Applications for MEMS-Based Sensors and Manufacturing Process Optimization
## 2 MEMS Background
### MEMS Components
The accelerometer measures acceleration in terms of movement, shock, or vibration. The basic working principle can be explained with a mass suspended on a spring attached to a fixed frame, i.e., a comb capacitor plate. In the presence of an external force, the mass moves, and the distance between the fixed plate and the seismic mass changes. This further changes the capacitance between the set and the movable plate [20,21]. The challenge of this design is to provide DC accuracy over temperature and reduce the temperature drift and bias drift as much as possible. The drift compensation by AI algorithm has become an attractive research topic. From an application perspective, MEMS accelerometers are used in smartphones, airbags in cars, cameras for anti-blur, real time applications, such as the military, etc. MEMS vibratory gyroscopes measure the angular rate of rotation or displacement by using Coriolis force. Based on the transduction type, a MEMS gyroscope can be of different types, such as silicon tuning fork, quartz tuning fork, vibratory ring, etc. The purpose of a gyroscope is to measure the acceleration of its oscillating mechanical sensing components. The mechanical structure remains in active resonance, and a small displacement as a response is produced due to Coriolis acceleration [22]. Managing this quadrature signal requires a clever gyroscope design so that the small signal is detectable. This is why many electronic compensation methods exist to de-noise the signal. Thus, noise modeling, random drift construction, temperature drift compensation, fault detection, and diagnosis of the gyroscope have become some of the most lucrative fields of interest for data scientists. The main application areas can be found in stability control in the automotive industry, short-range navigation, such as missile navigation, image stabilization in industrial applications, submarines, UAVs, aeronautics navigation, etc.
# Utility-Based Wireless Routing Algorithm for Massive MIMO Heterogeneous Networks
## 4 Preallocated Sequential Routing  
Here, we propose a heuristic algorithm called PSR that contains three parts. Algorithm 1 generates an initialized topology and respective antenna allocation, which connects all BSs to MBS directly. In this topology, MBS allocates all antennas to BSs with maximal incremental utility. Then, the main function, shown in Algorithm 2, traverses each BS and compares the utility of possible parent nodes and finally connects the node that has the maximal presumed utility as the parent. Algorithm 3 shows the subfunction, which calculates the presumed utility of the subtree in which the candidate node is located and returns it to the main function for comparison. Figure 2 presents a flow chart diagram of PSR.  
Figure 2: Flow chart diagram of preallocated sequential routing (PSR).