esa-sceva/satcom-chunk-collection huggingface dataset:
# Continual Learning for Table Detection in Document Images
## 2 Related Work
### Applications of Continual Learning  
Catastrophic forgetting has been tackled by researchers with various techniques in different domains [15; 46]. In some works, a regularization technique is applied to different parts of the model such as the loss function, learning rate, and the optimizer; and in others, a form of dynamic architecture or parameters isolation is practiced for learning different tasks continually. Some means of rehearsal process is also exercised [47]. For image classification, [PERSON] et al. [48] introduced the elastic weight consolidation (EWC) to alleviate forgetting. In their approach, any modification to the important weights of the network is penalized. In [49], authors present an EWC-based method for incremental object detection. According to their findings, when the annotations of the old classes are missing, EWC misclassifies previous categories as background. Therefore, they proposed the pseudobounding process for annotating old classes on the new set of images.  
[PERSON] et al. [50] proposed iCaRL for image classification. In iCaRL, a set of examplars for each class is selected dynamically and used for replay with a knowledge distillation technique [51]. In [52], authors proposed deep model consolidation (DMC) for incremental learning that can be applied in both image classification and object detection. In their approach, a double distillation loss is used to combine the two models that one is trained on the old classes and the other is trained on the new ones.  
In [53], authors developed a variant of Fast R-CNN [54] with a class agnostic region proposal [55] for object detection in a class incremental setting. A distillation loss was also used to reduce forgetting when training on the new objects with a frozen copy of the learned model on the previous set of classes. RODEO [56] applied the experience replay procedure using a buffer memory comprising of compressed representations of the past samples. Another recent work that applied experience replay is presented by [PERSON] et al. [57]; in their method, images from the former task are concatenated with the new samples for class incremental object detection.  
Considering the performance and adaptability of the experience replay approach, we employed it for detecting tables in a continual manner. It is expected that by satisfying the shortage of lifelong learning for table-detector networks, the employment of emerging datasets becomes easier for this step of OCR pipeline. The following sections will explain and examine the proposed solution.
# The Bearing Surface Defect Detection Method Combining Magnetic Particle Testing and Deep Learning
## 5 Data Augmentation
As one of the state-of-the-art high-resolution image generation methods, StyleGAN has its shortcomings, including slow network-training speed and the presence of noticeable speckled artifacts in generated feature images. To address these issues, NVIDIA released StyleGAN2 [26]. Experimental findings revealed that the adaptive instance normalization  
Figure 14: StyleGAN network architecture. operation in the StyleGAN network structure causes feature images to contain speckled artifacts. StyleGAN2 redesign the network architecture based on StyleGAN. Firstly, it decomposes the adaptive instance normalization operation into normalization and modulation steps. Secondly, it moves redundant biases and noise broadcast operations between different resolution layers. Finally, it uses demodulation instead of normalization. These adjustments in StyleGAN2 have been proven to effectively reduce the occurrence of speckled artifacts and accelerate network-training speed. The StyleGAN2 network structure is depicted in Figure 15.  
A comparison between Figures 14 and 15 shows that the StyleGAN2 generator incorporates improvements by using Mod and Demod modules to replace the AdaIN module in StyleGAN. Additionally, the overlay position of noise B has been adjusted from between the Conv module and AdaIN module to different resolution layers. These changes signify beneficial adjustments to the generator structure, making it more optimized and adaptable to the needs of image generation. This study utilizes the generative adversarial network StyleGANv2 to generate entirely new defect images through the competition between the generator and discriminator. The generated defect images are then weighted and superimposed on defect-free bearing images, thereby constructing novel bearing defect images. This approach is employed to enhance the dataset with augmented samples of minor defects, enriching the training data and significantly improving the robustness of the detection model.
# Communication Interface Manager for Improving Performance of Heterogeneous UAV Networks
## 7 Conclusions and Future Work  
Collaborative missions or missions with shared tasks that employ wireless multi-UAV networks require higher link stability and special attention to the reliable delivery of messages. An important part of this challenge relates to the wireless interface technology that is used. In this context, in this paper, we proposed a heterogeneous interface manager (IM) that is capable of dynamically defining the best communication interface to be used when more than one option is available. It works by dynamically evaluating parameters collected from the medium. The benefits of the proposed method are that U2U connectivity is improved and message delivery reliability is enhanced.  
The current version of the proposed IM works with a heuristic formulated as a decision three. For validating the proposed method, a close-to-reality simulation scenario was constructed with the use of different simulation tools. From obtained results, the proposed IM presented a lower performance fluctuation in the network flow, even with the increase in the number of nodes, presenting good reception of signal by nodes during all missions, with fewer communication interruptions. The IM was able to maintain the link between \(-60\) and \(-75\) dbm, with a distance of up to 100 m between the nodes, with the lowest signal loss indexes (80 dB to 85 dB), and with SNR up to 0.52. Thus, the use of the IM promotes higher link stability, enabling the dynamic selection of interfaces in order to adjust the network to the conditions of the medium and to promote U2U communication with increased reliability. The IM enabled almost twice the sum of the average throughput from both interfaces applied homogeneously.  
In future work directions, the design of a new heuristic that considers different classes of communication messages should be addressed. This could, for instance, favor some of the metrics over the others. In Non-delay tolerant networks (NDTN), for example, it could be interesting to assign a higher weight for delay and throughput metrics. In this respect, our previous results in [15] will be considered regarding homogeneous network behavior (802.11n and 802.11p) using different message classes.  
Investigation methodology, I.M. and L.B.B.; Interface manager design and experimentation, L.M.B.R. All authors have read and agreed to the published version of the manuscript.  
This research was funded by by the Federal Government of Brazil by means of the Coordination for the Improvement of Higher Education Personnel (CAPES) and by the State Government of Amazon through its Research Foundation (FAPEAM).  
Not applicable.
_J. Sens. Actuator Netw._**2013**, \(2\), 631-652; doi:10.3390/jsan2030631  
_Journal of Sensor and Actuator Networks_  
**ISSN 2224-2708**  
www.mdpi.com/journal/jsan  
_Article_  
**Marmote SDR: Experimental Platform for Low-Power Wireless Protocol Stack Research**  
**[PERSON] *, [PERSON], [PERSON] and [PERSON] ***  
Institute for Software Integrated Systems, Vanderbilt University, 1025 16 th Ave S, Nashville,  
TN 37212, USA; E-Mails: [EMAIL_ADDRESS] (B.B.);  
[EMAIL_ADDRESS] (P.V.)  
* Authors to whom correspondence should be addressed;  
E-Mails: [EMAIL_ADDRESS] (S.S.); [EMAIL_ADDRESS] (A.L.);  
Tel.: +1-615-343-8197 (S.S.); Tel.: +1-615- 343-8307 (A.L.).  
_Received: 10 July 2013; in revised form: 20 August 2013 / Accepted: 27 August 2013 / Published: 9 September 2013_  
**Abstract:** Over the past decade, wireless sensor network research primarily relied on highly-integrated commercial off-the-shelf radio chips. The rigid silicon implementation of the radio stack restricted access to the lower layers; thus, research focused mainly on the medium access control (MAC) layer and above. SRAM field-programmable gate array (FPGA)-based software-defined radios (SDR), on the other hand, provide a flexible architecture to experiment with any and all layers of the radio stack, but usually require desktop computers and draw high currents that prohibit mobile or longer-term outdoor deployments. To address these issues, we have developed a modular flash FPGA-based wireless research platform, called Marmote SDR, that has computational resources comparable to those of SRAM FPGA-based radio platforms, but at a reduced power consumption, with duty cycling support. We discuss the design decisions underlying Marmote SDR and evaluate its power consumption. Furthermore, we present and evaluate an asynchronous and multiple access communication protocol specifically designed for data-gathering wireless sensor networks.  
**Keywords:** flash FPGA; WSN; SDR; PHY layer; DS-CDMA
# Fault Reconstruction for a Giant Satellite Swarm Based on Hybrid Multi-Objective Optimization
## 2 Construction of On-Orbit Reconstruction Indicators for a Giant Satellite Swarm
### Model Assumptions of Satellite Swarm Failure  
Satellite swarm fault reconstruction involves multi-satellite deployment. Without loss of generality, it is assumed that the satellites participating in the reconstruction are near the faulty satellite swarm, and the faulty satellites retain deorbiting capabilities. The entire satellite swarm failure reconstruction process is shown in Figure 1.  
Self-assembled space telescopes are representative of mission-oriented multi-satellite scenarios. Its health and stability will impact the space telescope's imaging mission. Therefore, this paper studies the self-assembled mirror satellite swarm of the space telescope. To simplify the decision-making problem of reconfiguration and allocation of satellite clusters, a two-dimensional schematic diagram shows the status of satellite swarm reconfiguration. The three main states of the satellite swarm are shown in Figure 2: Figure 2a shows the normal satellite arrangement, and Figure 2b,c show the process of the satellite swarm returning to health from the fault state during the reconfiguration process.  
Figure 1: Schematic diagram of satellite swarm fault reconstruction.  
Figure 2: Schematic diagram of the two-dimensional structure of two reconstruction states: (**a**) Before failure; (**b**) Before reconstruction; (**c**) After reconstruction.  
Considering the characteristics of the satellite swarm, the following assumptions are made for the model:  
1. The mission-oriented satellite swarm is composed of individual star satellites that are connected via inter-satellite communication links.
2. The number of satellites in the satellite swarm is \(N\), each satellite is of the same type, and each satellite is a regular hexagon [28] with a side length of \(L\).
3. The satellites in the satellite swarm are evenly distributed, and the distance between adjacent satellites is \(a\), which is much smaller than the orbital radius \(r\), i.e., \(a\ll r\).
4. The satellites are numbered clockwise from the virtual center of the satellite swarm, and the inter-satellite distance correlation matrix is \(\mathbf{D}_{n\times n}\).
# Metallurgical Alchemy: Synthesizing Steel Microstructure Images Using DCGANs
## 2 Materials and Methods
### Image Generative Models
#### 2.2.2 Discriminator Architecture  
The discriminator distinguishes between real images from the dataset and synthetic images generated by the generator. The discriminator network takes an input image of size 512 \(\times\) 512 with 3 channels (RGB) and processes it through a series of convolutional layers. The layer structure and function of the discriminator network is described below:  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Layer (Type: Depth-Index)** & **Output Shape** &
\begin{tabular}{c} **Number of** \\ **Parameters** \\ \end{tabular} \\ \hline Sequential: 1-1 & [16; 3; 512; 512] & – \\ ConvTranspose2d: 2-1 & [16; 1024, 4; 4] & 1,638,400 \\ BatchNorm2d: 2-2 & [16; 1024, 4; 4] & 2048 \\ ReLU: 2-3 & [16; 1024, 4; 4] & – \\ ConvTranspose2d: 2-4 & [16; 512, 8, 8] & 8,388,608 \\ BatchNorm2d: 2-5 & [16; 512, 8, 8] & 1024 \\ ReLU: 2-6 & [16; 512, 8, 8] & – \\ ConvTranspose2d: 2-7 & [16; 256, 16; 16] & 2,097,152 \\ BatchNorm2d: 2-8 & [16; 256, 16; 16] & 512 \\ ReLU: 2-9 & [16; 256, 16; 16] & – \\ ConvTranspose2d: 2-10 & [16; 128, 32; 32] & 524,288 \\ BatchNorm2d: 2-11 & [16; 128, 32, 32] & 256 \\ ReLU: 2-12 & [16; 128, 32, 32] & – \\ ConvTranspose2d: 2-13 & [16; 64, 64, 64] & 131,072 \\ BatchNorm2d: 2-14 & [16; 64, 64, 64] & 128 \\ ReLU: 2-15 & [16; 64, 64, 64] & – \\ ConvTranspose2d: 2-16 & [16; 32, 128, 128] & 32,768 \\ BatchNorm2d: 2-17 & [16; 32, 128, 128] & 64 \\ ReLU: 2-18 & [16; 32, 128, 128] & – \\ ConvTranspose2d: 2-19 & [16; 16, 256, 256] & 8192 \\ BatchNorm2d: 2-20 & [16; 16, 256, 256] & 32 \\ ReLU: 2-21 & [16; 16, 256, 256] & – \\ ConvTranspose2d: 2-22 & [16; 3, 512, 512] & 768 \\ Tanh: 2-23 & [16; 3, 512, 512] & – \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary of the generator network architecture.  
Input Layer: The initial convolutional layer reduces the image dimensions to \(256\times 256\) while increasing the number of feature maps to \(16\).  
Intermediate Layers: Subsequent convolutional layers continue this process, halving the spatial dimensions and doubling the number of feature maps through layers \(16\), \(32\), \(64\), \(128\), \(256\), \(512\), and \(1024\). Batch normalization is applied to stabilize learning, and LeakyReLU activations introduce non-linearity.  
Output Layer: The final layer outputs a single value using a Sigmoid activation function, representing the probability that the input image is real.  
This architecture allows the discriminator to effectively learn and discern the nuanced differences between real and generated images, thereby providing feedback to the generator to improve its output.  
A comprehensive overview of the network's structure, including the layer types, output shapes, and parameters, is presented in Table 3. The network comprises 11,203,264 parameters, all of which are trainable, resulting in a total of 3.42 giga MACs.
# Implementation and Evaluation of Activity-Based Congestion Management Using P4 (P4-ABC)
## 3 Activity-Based Congestion Management (ABC)
### Activity Meter  
Ingress nodes run an activity meter per monitored traffic aggregate. The activity meter measures a time-dependent traffic rate \(R_{m}\) over a short time scale \(M_{AM}\) which is called memory. We chose the  
Figure 1: Activity metering and marking is performed only by ingress nodes. Both ingress and core nodes apply activity active queue management (AQM) during packet forwarding.  
TDRM-UTEMA method [24] for this purpose. The meter is configured with a reference rate \(R_{r}\) and computes the activity of a packet by  
\[A=\log_{2}\left(\frac{R_{m}}{R_{r}}\right). \tag{1}\]  
The activity is written into the header of the packet before passing it to the ABC domain.
## 3 Results  
### Tropical Cyclones  
The total number of TC landings on the YP, from 1851-2019, was 151 (Figure 4); 90 storms crossed Quintana Roo while 55 landed on Belize northern region. Landfall strength was variable, with most events arriving as tropical storms (58) followed by tropical depressions (42), and category-one (H1,18) and category-two (H2, 10) hurricanes (Figure 4B). A lower number of events were identified as category-three (H3, 12), category-four (H4, 8), and category-five (H5, 3) hurricanes; these major hurricanes are characterized by destructive impact on vegetation structure and causing infrastructure damage in urban centers (Figure 4B). The highest landfall frequency (62%) occurred from mid-August through early October, with a general peak period from 21-30 September. The month with the most landings was September (54) followed by August (32) and October (31) (Figure 4C). The only category-5 hurricanes registered in the period were Janet (278 km/h at landfall; September 1955), Gilbert (259 km/h; September 1988), and Dean (278 km/h; August 2007). Additionally, two H5 hurricanes passed within 175 km of Cancun in August 1980 (Allen; 306 km/h) and September 2004 (Ivan; 259 km/h). Using wind speed as a TC-strength metric, Allen is considered the strongest in the whole Atlantic basin while crossing the Yucatan Channel [79] (Figure 1A). Three of the four storms selected for further regional environmental impact analysis occurred during the relatively active season of 2005 (Figure 3D). Dean (2007) is one of the 33 H5 hurricanes that developed in the North Atlantic since 1924 and the most powerful storm crossing the peninsula since 1988. Although Stan (2005) was a tropical storm providing heavy rainfall elsewhere in Mexico, accumulations were moderate across the YP [48].  
Given their strength differences, Emily, Stan, Wilma, and Dean show distinct wind speeds at landfall (Figure 3D, Figure 5; Table S1). Wilma (213 km/h) and Emily (213 km/h) were H5 hurricanes over the Caribbean while Dean was the strongest (278 km/h) TC to cross Quintana Roo. Further, Wilma became an H5 hurricane 57.5 h before landfall but was weaker while landing on the YP coast (213 km/h). Due to the interaction with land, all TCs weakened during the next 6-12 h; although, after crossing the  
Figure 4: Tropical cyclone frequency (N = 151) in the Yucatan Peninsula from 1851–2019. (**A**) Number of tropical cyclones crossing per state in the Yucatan Peninsula; (**B**) Storm intensity frequency per category at landfall; (**C**) Monthly landfall frequency from June to November.  
YP, Stan became stronger over the southern GOM and eventually crossed the state of Veracruz as an H1 hurricane (Figure 3D).
# Fast Frequency Response of a Doubly-Fed Induction Generator for System Inertia Support
## 4 Short-Term Frequency Support of a DFIG
### Proposed Short-Term Frequency Support Method of a DFIG  
The objectives of this study are to (1) provide the approximate performance of improving the frequency nadir with less released energy from a DFIG compared to the conventional method and  
Figure 3: Power-rotor speed trajectory of the conventional short-term frequency support (STFS) method.  
(2) speed up the restoration of the rotor speed. As in the conventional method, the proposed \(P_{STFS}\) consists of two periods: deceleration and acceleration periods. Unlike in the conventional method, the used incremental power for STFS is a function of the rotor speed in the deceleration period, and \(P_{STFS}\) is a function of the rotor speed and time in the acceleration period.
###### Abstract  
This paper presents an innovative piezo-actuated fast-steering mirror (FSM) that integrates control design and system operation to improve the tracking performance of laser beam steering (LBS) systems. The proposed piezoelectric FSM is centered on two pairs of stacked actuators functioning in the tip-tilt direction via novel flexible hinges with strain-gauge sensors for position measurement. The suggested flexible hinge scheme allows the first fundamental resonance mode with the optical mirror to exceed 400 Hz while achieving an actuation range of \(\pm 5\) mrad. Thus, the design offers a wider mechanical actuation range than conventional piezoelectric FSMs. Moreover, LBS systems that use fast-steering motion controllers should be robust against dynamic disturbances, such as periodic external vibrations. Such disturbances, inherently associated with the operating conditions for LBS systems, typically reduce the stability of the tip-tilt motion. To attenuate the effects of such disturbances, a high-precision control system is necessary for the tip-tilt motion. Therefore, a control method integrating a proportional-integral controller with an adaptive feedforward control (AFC) algorithm is outlined to enhance tip-tilt tracking performance during high-speed scanning, compared with conventional LBS systems. Based on experimental findings, the AFC algorithm boosted control performance under dynamic disturbances, such as sinusoidal vibrations with multiple frequencies.  
piezoelectric actuator; fast-steering mirror; laser beam steering: tip-tilt control system; adaptive feedforward control; disturbance-attenuation performance +
Footnote †: journal: Sensors
# Multi-Agent Modeling and Jamming-Aware Routing Protocols for Movable-Jammer-Affected WSNs
## 3 Methodology
### Simulation Parameters  
We conducted simulation experiments to verify our approach using self-developed simulation code in NetLogo. The simulation environment consisted of a 60 \(\times\) 60 area, which wraps around horizontally and vertically. The 400 sensor nodes were randomly positioned within the area. The initialized network topology was based on the JRP, and an example of the network topology is shown in Figure 5. As the sensor nodes always select another sensor node with the largest target value within their communication transmission range as the information successor nodes, the network is always a preferential attachment structure. This structure is similar to the network generated by the Barabasi-Albert algorithm. Among them, the brown dot represents the sensor node, the green pentagon represents the base station, the blue directed link represents the successor node of the sensor node, and the red bug represents the jammer.  
The network was subject to jammers, and the interference in the experiment decreases in intensity as the distance from the jammers increases. The central interference intensity of the jammers in this experiment destroys the communication function of the sensor nodes. However, as the distance increases, the interference intensity decreases, and the soft-failure probability of the sensor nodes decreases accordingly. The focus of this paper is on the influence of movable interference sources on the reliability of WSNs. To simplify the experimental design and speed up the simulation experiment, we set two influence ranges for the jammers. Within the smaller influence range, the interference intensity is high, and the affected sensor nodes cannot maintain the communication transmission function. Within a relatively large influence range, the interference intensity decreases, and the affected sensor nodes can maintain normal operation, but they can perceive that they are being interfered with.  
The communication transmission range of all sensor nodes was set to 10, and the base station node was located in the center of the simulation area. All simulation parameters are summarized in Table 1, and each simulation experiment design has 10 independent runs.