esa-sceva/satcom-chunk-collection huggingface dataset:
# An Ante Hoc Enhancement Method for Image-Based Complex Financial Table Extraction
## 3 Methodology
### The Split Module  
The split module is based on an OCR model and a heuristic algorithm, as shown in Figure 3. The image of a table is regarded as an M \(\times\) N 2-dimensional matrix, where M is the width of the image in pixels and N is the height of the image. A Cartesian coordinate system is established with the origin at the top-left vertex of the image, the \(x\)-axis along the top edge of the image, and the \(y\)-axis along the left edge of the image. First, PaddleOCR [33] is used to detect text boxes in the image, as shown in Equation (1). In this Equation, T is the set of all text boxes detected by the OCR model, t\({}_{\text{i}}\) is a text box, c\({}_{\text{i}}\) is the text line within the textbox, x\({}_{\text{i1}}\) is the x-coordinate of the top-left vertex of the text box, y\({}_{\text{i1}}\) is the y-coordinate of the top-left vertex of the text box, x\({}_{\text{i2}}\) is the x-coordinate of the bottom-right vertex of the text box, and y\({}_{\text{i2}}\) is the y-coordinate of the bottom-right vertex of the text box.  
\[\text{t}_{\text{i}}=[\text{c}_{\text{i}},\,\text{x}_{\text{i1}},\,\text{y}_{ \text{i1}},\,\text{x}_{\text{i2}},\,\text{y}_{\text{i2}}],\,\text{t}_{\text{i }}\in\text{T} \tag{1}\]  
Second, the text boxes are projected onto the \(x\)-axis and \(y\)-axis, resulting in two arrays, p\({}_{\text{x}}\) and p\({}_{\text{y}}\), indicating the number of text boxes on each coordinate for the \(x\)-axis and \(y\)-axis, respectively, as shown in Equations (2) and (3). The initial value of each p\({}_{\text{i}}\) of the arrays is set as 0, and if a text box occupies a pixel corresponding to coordinate i, then the value of p\({}_{\text{i}}\) is increased by 1.  
\[\text{p}_{\text{x}}=[\text{p}_{\text{1}},\,\text{p}_{\text{2}},\,\ldots,\, \text{p}_{\text{M}}] \tag{2}\]  
\[\text{p}_{\text{y}}=[\text{p}_{\text{1}},\,\text{p}_{\text{2}},\,\ldots,\, \text{p}_{\text{N}}] \tag{3}\]  
Finally, candidate demarcation lines are obtained from the zero-value regions of p\({}_{\text{x}}\) and p\({}_{\text{y}}\); thus, the image is split into grids. In detail, we extracted the continuous zero-value regions in p\({}_{\text{x}}\) and p\({}_{\text{y}}\). For instance, if the p\({}_{\text{x}}\) array of a certain image is [0, 0, 2, 2, 2, 0, 0], then its \(x\)-axis zero-value regions are [0, 1] and [5, 6]; this indicates that the pixels from x = 0 to x = 1 and from x = 5 to x = 6 are all blank, from which we can obtain the candidate column demarcation lines. If a zero-value region is on an edge of the image, the candidate demarcation line for the region is set as the edge line, such as x = 0 or x = M. If the zero-value region is not on the edge, then the candidate demarcation line is set as the middle line of the blank region corresponding to the zero-value region. For example, if the p\({}_{\text{y}}\) array of a certain image is [0, 0, 1, 1, 0, 0, 0, 1, 1, 0, 0], then its zero-value regions are [[0, 1], [4, 6], [9, 10]] and the candidate row demarcation lines are \(\{\text{y}=0,\,\text{y}=5,\,\text{y}=10\}\).
## 3 Results
### Relationship with the Assimilation Rate  
There was a good correlation between the assimilation rate and the indices derived from the thermal and hyperspectral images (Figure 7). The relationship between the CWSI and the assimilation rate on DOY 76 was different for the two species (Figure 7a), although this relationship was unique on DOY 126 (R\({}^{2}\) = 0.54, \(p\) \(<\) 0.05; Figure 7b). The assimilation rate was slightly higher for durum wheat than for the bread wheat. The well-watered conditions during the flight on DOY 76 can be observed in Figure 5 and were confirmed by the lower CWSI values, which were below 0.15. The CWSI values during the second flight ranged between 0.7 and 0.9 (Figure 7b).  
The relationship between the assimilation rate and the FLD was unique for the two species on both dates (Figures 7c,d). The coefficient of correlation was similar for the two dates with different water status, and the slope was positive on both dates. On the other hand, the relationship with NDVI was significant (R\({}^{2}\) = 0.45 and 0.28 for the first and second flight, respectively) but weak, as the slope of the relationship with the assimilation rate was reversed on the two dates. On DOY 76, before the start of water stress, plots displaying a higher NDVI value (indicating a better developed canopy) exhibited a higher assimilation rate (Figure 7e). No difference between the two species was observed. Nevertheless, once the water stress was imposed on DOY 126, the tendency was the opposite, and the larger canopy volume was related to a further fall in photosynthetic capacity (Figure 7f).
## 5 Publicly Available Datasets and Benchmarks
### MIT Indoor-67 scenes  
This dataset contains 15,620 color images distributed across 67 categories [80]. This is particularly complex as the indoor scenes suffer from huge intraclass variation, with confusing indoor scenes having similar backgrounds and sharing repeated objects.
# Research on Short-Term Prediction Methods for Small-Scale Three-Dimensional Wind Fields
## 3 ConvLSTM-DSConvGRU Hybrid Prediction Model
### Small-Scale Wind Field Prediction Model  
The small-scale wind field prediction method proposed in this study consists of three parts: the WRF module, the ConvLSTM-DSConvGRU hybrid prediction module, and the CFD module. The overall framework is shown in Figure 9. In the WRF module, the NCEP meteorological reanalysis data, land data, and terrain data are used to drive WRF for mesoscale meteorological field simulation. The ARWpost tool is then used to extract the wind speed components along the longitude, latitude, and vertical directions, i.e., \(u,v,w\), and temperature \(tk\), as historical boundary conditions from the WRF outputs. Further, the ConvLSTM-DSConvGRU hybrid prediction module with an encoder-decoder structure is used to predict the boundary conditions of wind speed and temperature. Using the proposed model, the boundary conditions of wind speed and temperature are predicted with a limited number of parameters and high speed. In the CFD module, DEM elevation data, OSM map data, and real building information are first combined to accurately model the terrain and buildings. Then, the model is subjected to unstructured body grid partitioning, and the boundary conditions predicted by the neural network are assigned to the inflow boundary grid points using the inverse distance weighting method. Finally, the CFD module is driven to solve and complete the wind field prediction.  
Figure 8: Structure of the ConvLSTM–DSConvGRU hybrid prediction model.  
Figure 9: Small-scale wind field prediction model.
## 3 Results and Discussion
### Cybersecurity and Privacy in Energy Management Systems  
The digitalization of smart microgrids has improved operational flexibility but has also introduced new cybersecurity and data privacy risks. As microgrids become more dependent on interconnected platforms, real-time data exchange, and distributed control systems, protecting these infrastructures from cyber threats is now a research priority.
# Analytical Solutions of Water Inflow for Foundation Pit in Confined Water Stratum
## 2 Water Inflow Solutions of Circular and Strip Foundation Pits
### Water Inflow Calculation in the Foundation Pit  
The water in Region 1 flows around into Region 2, then the flow direction is turned in Region 2 and there is only water flowing vertically in Region 3. Therefore, the shortest seepage path \(L_{\text{min}}\) of water flow in Region 2 and Region 3 is denoted by:  
\[L_{\text{min}}=H_{d}-M+L \tag{8}\]  
The longest seepage path \(L_{\text{max}}\) is:  
\[L_{\text{max}}=r_{0}+H_{d} \tag{9}\]  
It should be pointed out that if the hydraulic head is in the impermeable layer, that is, \(H_{d}>M\), then \(H_{d}\) in Equations (8) and (9) is replaced by \(M\). When [PERSON]'s law is used to calculate the water inflow in the foundation pit, most of the existing analytical solutions are to replace the equivalent seepage length by the average of the longest seepage path and the shortest seepage path [13; 18], or \(2L_{\text{min}}\). The water inflow calculated by those two methods is only reasonable in the specific intervals of \(r_{0}/M\) and \(L/M\). In fact, the length of the equivalent seepage path that is used to calculate the water inflow in the foundation pit should vary with \(r_{0}/M\) and \(L/M\), and it is unreasonable to substitute by \((L_{\text{max}}+L_{\text{min}})/2\) or \(2L_{\text{min}}\). In order to accurately calculate the water inflow in the foundation pit in the confined water stratum, the seepage path influence coefficient \(\alpha\), which can reflect the change in the equivalent seepage path length with \(r_{0}/M\) and \(L/M\), is introduced, and the fitting formula is explored through numerical simulation in the next section. The length of the equivalent seepage path can be expressed as:  
\[\overline{L}=\alpha\cdot\frac{(L_{\text{max}}+L_{\text{min}})}{2}=\frac{\alpha (2H_{d}+r_{0}-M+L)}{2} \tag{10}\]  
where \(\alpha\) is the function whose variables are \(r_{0}/M\) and \(L/M\):  
\[\alpha=f\bigg{(}\frac{r_{0}}{M},\frac{L}{M}\bigg{)} \tag{11}\]  
The water inflow for the circular and strip foundation pit in Region 2 and Region 3 is calculated by:  
\[\begin{array}{l}Q_{2}=Q_{3}=k_{v}\frac{2\pi r_{0}^{2}(h_{0}-H_{d})}{\alpha( 2H_{d}+r_{0}-M+L)}\\ Q_{2}=Q_{3}=k_{v}\frac{l_{v}(h_{0}-H_{d})}{\alpha(2H_{d}+r_{0}-M+L)}\end{array} \tag{12}\]  
where \(k_{v}\) is the vertical permeability coefficient.
### Network Architecture and Requirement of Uwsn
#### 1.3.4 4D-Uwsn Architecture  
4D-UWSN consists of remotely operative underwater vehicles (ROVs) and clusters deployed at different depths of the sea such as 3D-UWSN [12]. Here, the ROV collects the data from the cluster heads, then, depending on the position, either relays the signals to the buoy or directly transmits them.
# KACFormer: A Novel Domain Generalization Model for Cross-Individual Bearing Fault Diagnosis
## 4 Conclusions  
In this work, a cross-individual bearing fault diagnosis scenario is explored. By integrating KART into traditional convolution and attention mechanisms, a novel model, KACFormer, is proposed to explore the potential of leveraging the nonlinear modeling capabilities and individual generalization of KANs. KACFormer enhances feature representation while preserving the translation invariance of convolution and the context-aware semantic modeling ability of attention mechanisms. Extensive experiments on two public datasets demonstrate the superior generalization performance of KACFormer, achieving state-of-the-art accuracy rates of 95.73% and 91.58% in cross-individual scenarios. These results highlight the potential effectiveness of KACFormer in improving fault diagnosis under varying operational conditions and individual differences. Beyond bearing fault diagnosis, the proposed KACFormer framework exhibits potential for generalization to other similar industrial scenarios involving vibration signal processing and domain adaptation, such as health monitoring for gearboxes and wind turbine condition monitoring [37].  
Despite the advancement achieved, limitations remain. (1) Compared to MLP-based models, KANs-embedded models take a relatively longer time to train. Further study will be carried out to reduce the complexity of the model while maintaining the cross-individual accuracy. (2) Furthermore, the model should prioritize detecting smaller, incipient faults to better align with industrial requirements, as early-stage fault diagnosis is crucial for predictive maintenance and to minimize machine downtime. However, due to limitations in the current datasets, experiments regarding the model's minimum detectable fault size were not conducted. This constitutes an important direction for future research.  
Conceptualization, S.S.; methodology, S.S.; software, S.S.; validation, M.X. and P.L.; investigation, P.Y.; resources, J.Y.; writing--original draft preparation, S.S.; writing--review and editing, S.S.; visualization, P.Y. and T.W. supervision, S.S.; project administration, J.Y.; funding acquisition, J.Y. All authors have read and agreed to the published version of the manuscript.  
The work is supported by the National Natural Science Foundation of China, General Project, 52374167.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflicts of interest.
# Fault Injection Emulation for Systems in FPGAs: Tools, Techniques and Methodology, a Tutorial
## 6 An Emulation Framework for Fault Injection
### Soft Error Mitigation (SEM) IP Controller  
SEM IP core is the fault injection engine for the emulation process. This IP core, supported by Xilinx, is a solution to detect and correct soft errors in the Configuration Memory of Xilinx FPGAs.  
This module can read the configuration memory to look for errors. In case of discovering any altered bit, it is flipped by the SEM IP to correct it. This ability to read and write into the configuration memory is used to manage the fault injection in a non-invasive way. The manager based on SEM IP could inject bit flips into the configuration memory, testing the reliability of the solution implemented for an FPGA after the injection. SEM IP controller is employed because it is the only tool in Xilinx FPGAs that allows checking the status of the Configuration Memory of the devices to look for faults. Customized controllers were not considered as SEM IP was already designed, validated and tested by the manufacturer of the boards.  
In order to add the SEM IP core to a Vivado project, as it can be seen in Figure 6, it requires to be instantiated from the IP catalog.  
To do so:  
* The clock input signal (clk) should be mapped to the global clock of the design.
* The Monitor Interface is an UART that serializes status information generated by the SEM IP controller for serial transmission between the host computer and the FPGA over the TX/RX lines:
* Monitor RX signal receives as inputs the commands that SEM IP core interprets in order to perform functions like, for example, injections.
* Monitor TX signal reports from the SEM IP core, the new states achieved among these: idle, initialization, observation, injection, correction, fatal error and classification.  
Other signals will be described in later sections. Finally, it is important to remark that the FPGA families supported by the SEM IP core are the Zynq-7000 all Programmable SoC and the 7 Series.
###### Abstract
[26] studied important parameters to foreground the small-size issues. [PERSON] et al. [27] reviewed both UAV remote sensing images and DL in a review paper. [PERSON] et al. [28] studied a review based on an extant UAV-assisted system. [PERSON] et al. [29] monitored the traffic based on the UAV system, while [PERSON] et al. [30] reviewed the vision processing techniques. [PERSON] et al. [31] reviewed the information and communication technology approaches based on DL application to analyze the target in traffic in real-time using UAVs. [PERSON] et al. [32] presented correlation filtering and other tracking algorithms to solve the target occlusion problem in a review paper. From the above body of work, it is worth noting that all the above-mentioned works consider either only UAV-based target detection or one specific kind of DL for target detection. None of the above-mentioned reviews considered applications of DL and UAVs in target detection at the same time. The purpose of this review paper is to discuss different issues rather than one specific issue using different methods in contrast to other review papers and provide the future direction related to more preferable methods to handle those issues in the upcoming years. It also presents a brief overview of DL-based approaches for UAV-based target detection. The main contribution of this review paper is to further address the target detection problem under various circumstances with the help of DL approaches instead of exploring one specific DL technique for target detection entirely and inspect the best execution performance model built on current research. The statistics of this paper are taken from different sources, for instance, conference proceedings, journals, and workshops to offer readers a glimpse of the common relationship between DL techniques and target detection through UAVs in traffic congestion at an advance level. The rest of the paper is organized as follows: Section 2 narrates the concept and research tasks affiliated with UAVs classifying the area of target detection in traffic congestion along with major metrics and crucial issues. Next, Section 3 introduces DL approaches used for handling crucial issues in target detection. Section 4 inspects the existing implementation of DL techniques in target detection. Section 5 puts forward a comprehensive discussion on utilization of DL techniques in target detection and outlines the limitations to denominate future research trends. Finally, Section 6 concludes this review paper. For more clarity, the organization of the paper is presented in Figure 3.  
Figure 3: Paper Organization.
# EAOA: Energy-Aware Grid-Based 3D-Obstacle Avoidance in Coverage Path Planning for UAVs
## 4 Evaluation Metrics and Results
### Evaluation Metrics
#### 4.1.2 Completion Time  
Optimizing the trajectory length and the mission completion time is essential in energy-efficient path planning techniucqs for UAVs [7; 10; 33; 34]. The mission completion time includes flying time and hovering time. We lean on the following formula from [4] to find the completion time denoted \(\tau\):  
\[\tau=\frac{\top}{v}+\sum_{\theta=1}^{U}\frac{\Phi_{\theta}}{\varpi} \tag{11}\]  
where \(\top\) is the path length, \(v\) is UAV speed, while \(U\) is the number of turns, \(\Phi_{\theta}\) is the angle of \(\theta\)th turn, and \(\varpi\) is the UAV rotation rate.
## 5 Use Case: IWSN Resilience Using Maintenance 5.0
### Applying Novel Maintenance 5.0 to Previos Study
#### 5.2.2 Control Level  
At a control level (see Figure 17), after setting up the network there are no need to detect of mitigate jamming attacks; in this time interval \((t_{0}-t_{R})\) the wireless sensors communicate with the gateway through Zigbee and LoRaWAN protocols, and the sniffer provides real-time Energy (J) performance metric at their optimal level \(P_{1}(t)\), saving data history in a \".csv\" file. After this, the IWSN works correctly until time \(t_{0}\), which is when a jammer (in this case is a constant jammer) appears and distorts the energy (J) performance; after some time \((t_{1}-t_{0})\) the skewed metrics \(P_{2}(t)_{n}\) will be in a steady state while the detection and mitigation algorithm runs, and repair changes are applied. The detection algorithm provides the status of the algorithm counters \(\big{(}C_{CO},\ C_{DE},C_{RA},C_{RE},C_{NJ}\big{)}\) (Figure 16) as well as the detection algorithm processing time \((t_{2}-t_{1})\).  
Figure 17: The worker-in-the-loop jamming attack control mechanism.  
During the time span \((t_{3}-t_{2})\) a Q-learning mitigation algorithm is executed in python verifying the optimal route in which the data packets could arrive from a node to the coordinator. Resilience can be reached a) by switching collaborative and cooperative network schemes and b) by looking for the best route for data packets; In both cases, worker 5.0 acts as an adviser. As seen in Figure 18, to communicate worker 5.0 with the cyber world and with the real world, a Telegram Bot has been implemented, which communicates through an MQTT Broker and Node-RED [132; 133].  
Two approaches taken to measure asset resilience without human intervention versus human advice are shown in the next subsection.
# Autonomous Navigation Framework for Intelligent Robots Based on a Semantic Environment Modeling
## 2 Related Work
### Recognition
#### 2.3.3 Localization  
The robotic research community is making steady progress in determining the autonomous robot localization concerning the environment using recognition techniques. [PERSON] et al. [55] proposed a system for mobile robot localization with an appearance-based place recognition approach in conjunction with the dead reckoning and gyrodometry model. Ref. [56], presented a vision-based localization approach in the outdoor environment against a known map by combining a place-dependent feature detector with prior experience of place. Instead of representing edges and corners, these features represented mid-level patches of different elements like windows. Ref. [57], presented the Visual Landmark Sequence-based Indoor Localization (VLSIL) approach and addressed environmental variations using semantic information of steady indoor objects (landmarks) while performed the localization using objects' occurrence order in the video. [PERSON] et al. [58] proposed a mobile robot's self-localization method using recognition of artificial visual landmarks which were identified using a bilayer recognition algorithm. The geometric relation was established between image and environmental frames to get the orientation and position of the camera for robot localization.
# Virtual Design and Construction (VDC) Framework: A Current Review, Update and Discussion
## 2 Background
### Interrelationship between POP Components  
From its beginnings, VDC has conceptually interrelated product, organization and process in various ways. One of the most widely used and standard representations is in the form of a matrix, in which the components of the POP models are related to the concepts of function, form and behavior [12].  
The function, form (also called scope or structure) and behavior terms correspond to the key components of developing a design prototype, a concept developed by [PERSON] in 1990 [33]. Considering the design of any artifact as an example, the desired requirements for its use are transformed into functions that the artifact itself must fulfill. These functions will be performed by the elements that make up the artifact's form. Finally, identifying the artifact's expected and actual behavior is necessary to evaluate its design correctly [33].  
[PERSON] et al. [34] adapted [PERSON]'s concepts to building design through a modeling system incorporating design processes with digital methods and tools. [PERSON]'s work defined the terms of function, form and behavior adopted from Kunz & Fischer [12] in the following way:  
* The function represents the project's requirement, either due to the design or a specific request from the client.
* The form comprises the design chosen based on the requirement of the function, including all physical and abstract elements of the building, such as equipment, systems and project activities.
* The behavior is composed of the predicted or observed measurements of an aspect or element of the design, such as cost or schedule.  
These components are interrelated in a matrix called the POP matrix (Table 1) that represents the interdependence of the POP components, such as the effects of activity in the organization or the physical product, as well as the impact of a design change in the construction process, among other correlations [16].  
The POP matrix application focuses on segregating a decision's impact on the entire project [20]. For example, decisions made in construction are often based on the impact of the product, the people involved and the process required.  
[PERSON] & Fischer [18] developed a reference framework to interrelate the POP components, which indicates how the information flow from the client's requirement and moves toward the product, organization and process models, updated by the feedback cycles they receive over time. Furthermore, this feedback is given interdependently between components and project objectives, influencing client objectives.  
Next, the VDC project management framework, its elements and its relationship to the POP model components are presented below.