esa-sceva/satcom-chunk-collection huggingface dataset:
# Energy-Aware Forwarding Strategies for Delay Tolerant Network Routing Protocols
## 4 Evaluation and Analysis
### Comparison with Other Approaches  
Section 2.3 above presents a number of other approaches for energy efficiency in DTN communications. The papers describing these algorithms present only the general principles of their operation, and we were not able to obtain sufficient details of the algorithms to perform a direct quantitative comparison. However, we can make the following qualitative comparison.  
The other approaches all seek to reduce the overall energy consumption by making choices about the cost of forwarding, and only choose energy-efficient message transfers. However, forwarding decisions are not based on the instantaneous amount of available energy in scenarios where energy is depleted and then replaced, so they cannot change their behaviour depending on whether energy is scarce or abundant. They cannot adjust their behaviour in the presence of energy harvesting, which replenishes energy reserves. The work above shows that such decisions can improve performance metrics.  
The technique proposed here does not replace these other methods; it is a general approach to any forwarding method which stops message forwarding (except to a gateway) when energy is low. Indeed, this new method could be usefully combined with many of the other methods in Section 2.3.
## 1 Introduction
In this context, a very recent application has regarded the detection of source events at the Colima and Vesuvius volcanoes, whose classification is the basis for the construction of a suitable ontology in which the events are semantically annotated. This framework can be embedded in early warning systems to ensure an effective monitoring of dangerous volcanoes and improve the time and accuracy of hazard forecasts [33, 34]. In a few cases, ML approaches have been applied to identify episodic volcanic tremors [30] and (even less) to nonstationary seismic noise of natural and/or artificial origin, usually affecting continuous waveforms [35]. This work proposes a machine learning solution, inspired by [34], for analyzing the background seismic signal acquired in different volcanic areas. To the best of our knowledge, this is the first attempt in the seismological field in which the proposed approach successfully identifies the source that generates it. The ML-based classification of background seismic signal, as a hallmark for recognition of the generating volcanic source, is thus the main contribution of the present research. Specifically, we will study four volcanoes: Campi Flegrei, Ischia, and Vesuvius (Italy), and Colima (Mexico), each one characterized by peculiar patterns of seismic activity. The main aim is to show that the MLP and CNN methods are able to classify the background seismic signal, even containing different contributions (microseism, cultural noise, and volcanic/hydrothermal tremor). The classified signal can be considered as a kind of fingerprint, which allows the identification of a volcano, among others. The proposed approach, when applied to continuous seismic acquisitions, can promptly reveal anomalies or variations of the background motion, thus providing information about changes in the source process much earlier than the occurrence of potentially dangerous events such as volcanic eruptions.
# HMT exerts an Anticancer Effect by Targeting PAK-1
## 2 Materials and Methods
### Cell Viability Assay  
We measured cell viability following HMT treatment in several prostate cancer cell lines using the CELLOMAX viability kit (Precaregene, Hanam, Kyungido, Korea) and the tetrazolium salt WST-8 (2-(2-methoxy-4-nitrophenyl)-3-(4-nitrophenyl)-5-(2,4-disulophenyl)-2H-tetrazolium, monosodium salt). Cells (1 \(\times\) 10\({}^{4}\)) were treated with different concentrations of HMT (0, 31.3, 62.5, 125, and 500 ug/mL) in a 96-well plate for 24 h. Then, 10 uL of CELLOMAX reagent was added and incubated 2 h at 37 \({}^{\circ}\)C in the dark. The optical density (O.D.) was measured with a microplate reader (Tecan, Sunrise, Switzerland) at 450 nm. Cell viability was calculated using the following equation: Cell viability (%) = (O.D. (FFE) - O.D. (blank))/(O.D (control) - O.D. (blank)) \(\times\) 100.
# Metal Surface Defect Detection Using Modified YOLO
## 4 Experiments and Results
### Performance for Real-Time Operation  
The training loss function is shown in Figure 8. As can be seen from the figure, with the increase of iteration times, the loss function value gradually decreases and tends to converge.  
Figure 8: The curve of loss function. Training loss is measured during each epoch while validation loss is measured after each epoch.  
Figure 9 is the mAP graph. The mAP is a comprehensive measurement index commonly used in the field of target detection. It measures the overall detection accuracy of the detection box under different IOUs. The higher the value, the higher the accuracy of the model.
# Upper Limb Orthoses: Integrating Topology Optimization and 3D Printing for Custom Fit and Function
## 4 Design Process for Custom Limb Orthoses
### Framework: Determination of Objective and Constraint Functions  
In the optimization of the wrist brace, the primary objective function employed is the deformation energy, which is also known as weighted compliance. Compliance (\(C\)) is a measure of how much a structure deforms under applied forces, and it is inversely related to the structure's stiffness (\(K\)). Thus, by minimizing the strain energy, we effectively maximize the stiffness of the wrist brace, resulting in a more robust and supportive design. To ensure that the optimization process yields a feasible and practical design, several constraints are applied. This study imposes constraints on the reduction in the initial volume fraction of the brace material, specifying reductions to 25% and 50% of the original volume. These constraints guide the optimization process to balance material usage with structural performance. The topology optimization (TO) problem is solved using an iterative approach where the volume fraction is progressively reduced. This iterative process involves updating the design in each iteration to achieve a structure that is as lightweight as possible while still meeting the imposed volume constraints. The aim is to converge to an optimal design that maximizes stiffness and performance while minimizing material usage and weight. This methodology ensures that the final wrist brace design is not only efficient in terms of material but also meets the required functional and structural performance criteria.
# Evaluation of Viscoelastic and Rotational Friction Dampers for Coupled Shear Wall System
## 3 Shake Table Test of Six-Story Coupled Wall System with Damper
### Experimental Test Results of the Six-Story Frame  
#### 3.5.1 Natural Frequency Under White Noise  
Table 5 presents the natural frequencies of a six-story frame structure subjected to varying intensities of white noise seismic waves, comparing three different structural systems: the frame with the coupled shear walls without damper (Shear Wall), with rotational friction damper (Shear Wall + RF), and with a viscoelastic damper (Shear Wall + VE). The results indicate that as the white noise intensity increases from 40 to 200 gal, the natural frequencies of all systems generally decrease, reflecting a reduction in structural stiffness due to higher excitation levels. The RF dampers excel in high-intensity scenarios due to their predictable force-limiting behavior but offer no adaptability to varying frequencies. The VE damper performs well across moderate intensities but may lose efficiency under extreme loads due to material limits. The Shear Wall system exhibits the lowest natural frequencies across all intensities, starting at 1.48 Hz at 40 gal and dropping to 1.07 Hz at higher intensities. In contrast, the Shear Wall + RF and Shear Wall + VE systems show higher initial frequencies (2.43 Hz and 2.32 Hz, respectively, at 40 gal) but experience more pronounced declines as intensity rises, eventually converging to 1.48 Hz at 200 gal. The analysis for three different models reveals a systematic reduction in natural frequency under higher excitation levels. The frequency stabilizes when the LM guides slide, because  
Figure 16: Measurement device for shake table test.  
during small intensity, LM guides do not move due to friction, and then start to slide under higher intensity.
# Efficient and Secure EMR Storage and Sharing Scheme Based on Hyperledger Fabric and IPFS
## 5 Scheme Implementation
### Data Query Phase
#### 5.4.2 Executing Conditional Query  
When executing query \(Q\), construct all query conditions into a composite key \(k\). This \(k\) contains constraint information for all conditions in query \(Q\). Subsequently, use the key \(k\) as a parameter and execute the \(getState(k)\) function. \(getState\) is an API provided by Hyperledger Fabric for querying the latest state. After the \(getState\) function's execution, the resulting data will contain all the latest records that meet all conditions specified in query \(Q\).
# VLSI Implementation of an Efficient Lossless EEG Compression Design for Wireless Body Area Network
## 3 Methodology
### VLSI Architecture
#### 3.2.2 Entropy Encoder  
The entropy encoder is composed of both the two-stage Huffman and Golomb-Rice components as shown in Figure 5. In the two-stage Huffman encoder component, two Huffman tables were used as look-up tables. The two-stage Huffman encoder checks the value of \(PD_{2}\) which stores the output of the adaptive voting predictor. If the value is within the encoding range of the two-stage Huffman encoder, the value of \(PD_{2}\) will be encoded using the two-stage Huffman encoder and will be directly sent as the final output. Otherwise, the value of \(PD_{2}\) will be passed through a pre-processing stage where Equation (1) is utilized. The pre-processing procedure ensures that the data will be passed on to the Golomb-Rice encoder through a multiplexer, which has a range of values that will better fit what is required by the Golomb-Rice look-up table. The resulting data of the Golomb-Rice encoder is of a variable length code that will be the output of the proposed lossless EEG controller that is ready for transmission.
# STPA-RL: Integrating Reinforcement Learning into STPA for Loss Scenario Exploration
## 3 Proposed Approach: Stpa-Rl
### System Environment Modeling
#### 3.1.5 Setting the Environment State Transition  
Various scenarios that can occur in the actual system are considered to set the environment's state transition. This includes both typical and exceptional behaviors, representing the system transitions comprehensively. The state transition process enables the RL model to simulate the system's behavior according to real-world situations.  
The state transition function is denoted by \(P\), which represents the probability of reaching the next state \(s^{\prime}\) with given current state \(s\) and action \(a\). In mathematical notation, \(P(s,a,s^{\prime})=Pr[S(t+1)=s^{\prime}|S(t)=s,A(t)=a]\). \(S(t)\) is the state at time \(t\), \(A(t)\) is the action at the time \(t\), and \(Pr\) represents the probability. It works as shown in Figure 4.  
We can represent the system environment modeling phase in a more formal and structured way, considering the state space, action space, control policy, hazardous states, and state transition probabilities.
# Pipeline Damage Identification Based on Additional Virtual Masses
## 4 Test of the Pipeline Damage Identification  
### Test Model  
Figures 13 and 14 present the test model of the pipeline structure. The pipeline steel used in this test is Q235, with 2 m in length and 3 mm thickness. Both ends are clamped to simulate fixed support. The net length after deducting the support is 1.9 m. The pipe is divided into 10 substructures with 9 nodes along the length, each of which is 200 mm long.  
Figure 11: Case 1: identified damage extents.  
Figure 12: Case 2: identified extents of damage.  
The nine nodes were selected as the hammer excitation points and node 5 was set as the pick-up point of the acceleration response. The piezoelectric acceleration sensor (sensitivity: 992 mV/g) was fixed to the upper surface of the pipeline with a small piece of magnet with a measured sampling frequency of 10 kHz. The experimental data were obtained according to the model and method. The results analyzed using Matlab software (R2014a; MathWorks, Inc, Natick, MA, USA; 2014) are shown below.  
Figure 15 shows the measured excitation and acceleration response. Figure 16 presents the frequency response obtained by the FFT of the acceleration response, which is shown as the average of the multiple frequency responses. In the same figure, we can also see the first two vertical modes of the pipeline. Table 7 lists the specific frequencies consistent with the calculated frequencies. Such a case indicates the good reflection of the actual structure, which can be used as a benchmark for subsequent damage identification.  
Figure 14: Test model of actual pipeline. (**a**) Test pipeline; (**b**) Hammer excitation.  
Figure 13: Test model of the pipe structure.  
Figure 15: Measured test results. (**a**) Measured excitation; (**b**) Acceleration response.
## 4 Results and Discussion
### Performance Comparison between Real-Time Object Detectors  
Table 5 summarizes the performance results, in terms of accuracies obtained using the chosen \"generic object detectors\" in our tests. As discussed above, we included tests using Haar-based classifier as in [17] to see if any improvement could be obtained, as we included part of their dataset for the testing phase of our models.  
In the table, we also included metrics for performance for the best performing object detectors in the state-of-the-art, which made use of RetinaNet [21] and MobileNet [22]. The reported models attained in general better results than previously using the same configurations ([17; 21; 25], respectively), owing to the use of the extended and quality aware dataset. As we will see next, this was accompanied with comparable or better precisions (and lower recalls), as well as reduced latencies.  
In Table 5, we chose to include a comparison of only four of most studied classes (common in works in the state-of-the-art, as they are the most prevalent); we believe that, otherwise, the comparisons will be less fair and more difficult to appreciate. We have to stress that other methods analyzed in Section 3  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Metric/Class** & **D01 (Long Crack)** & **D11 (Lat. Crack)** & **D20 (Alligator)** & **D40 (Pothole)** \\ \hline LPB-cascade & 0.7080 & 0.7182 & 0.7625 & 0.7476 \\ MobileNet & 0.8124 & 0.9032 & 0.9234 & 0.9675 \\ RetinaNet & **0.9148** & **0.9511** & **0.9522** & **0.9823** \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison between different object detectors reported in the literature making use of our extended dataset, significant improvements were obtained for all, but RetinaNet was chosen owing to its performance.  
yielded good results for individual types of asphalt damages, but in general, those models were not amenable for constrained mobile implementations (as shown in Table 1).  
The sole reference in the state-of-the-art to implement a full-fledged acquisition system for all eight classes in Table 2 and to carry out a thorough analysis is the work presented by the researchers at the University of Tokyo [25]. The authors designed and tested two deep learning-based object detectors (using Inception V2 and MobileNet, respectively). They reported that the best results were obtained with the latter, but in recent years, other object detectors have been developed (i.e., RetinaNet), which yielded better results in our experiments, as can observed in Table 6. For the sake of completeness, we show some instances of the test dataset that were correctly identified and detected in Figure 9. From the figures, it can be verified that, apart from detecting the instances with high confidence, the extent of the structural damages is well localized.
## 3 Results and Discussion
### Compression Tests
#### 3.2.3 Comparison between Reinforced and Unreinforced Pieces  
Based on the analysis of Figure 3, it is possible to deduce the different behavior of reinforced and unreinforced pieces. The shape of the strength-strain graphs is dramatically different. In reinforced pieces, the four phases of strength-strain behavior that we see in unreinforced pieces do not occur (Figure 4). Once the maximum test load is reached, a decrease in the strength values occurs until breakage, which takes place with ultimate strain values that are much lower than for unreinforced pieces.  
FRP reinforcement results in the lateral confinement of the wood fibers, preventing their transversal displacement. At the moment when the reinforcement fabric breaks, due to being fragile when placed under stress, the piece may experience lateral deformation, such that the resilience of the lignin and hemicellulose in the wood is exceeded, resulting in the rapid and explosive breakage of the piece of wood due to the separation of the fibers. This behavior explains why, once the maximum test load has been reached, the subsequent phase of the strength-strain graph is shorter, breakage occurring more suddenly and dramatically.  
There is a significant increase in the load capacity of the reinforced pieces compared to the unreinforced pieces. The reinforced pieces attain at least 96% greater resistance than the unreinforced pieces (FC300), reaching as much as 133% more resistance in the case of the pieces reinforced with FB600. The application of the reinforcement also resulted in an increased modulus of elasticity in the pieces and an increase in the strain occurring at the point of maximum strength (Table 2). However, the confinement of the pieces caused a highly significant decrease in the ultimate strain values, which also meant that the ductility of the reinforced pieces was much lower than that of the unreinforced pieces, by approximately a quarter or a sixth.
## Thermal-Solid Damage Experiment
### Ultrasonic Pulse Measurement  
The ultrasonic pulse measurement principle is to generate a vibration source with a fixed frequency by driving the piezoelectric material within the ultrasonic transducer (Figure 3b,c). When the transmitting transducer makes contact with one side of the test specimen, the vibration source is transmitted within the specimen material as a stress wave and received by the receiving transducer on the other side of the specimen. If the detected distance is known, the wave velocity can be calculated.  
The speed of wave propagation will be affected by the type of medium. It is prevalent that the fewer the voids and defects within the material, the higher the wave transmission speed [14; 15; 16]. It should be noted that the generated stress wave type can be a pressure wave (P wave) defined as the particles in the material vibrating along the wave propagation axis, or a shear wave (S wave) defined as wave vibration direction perpendicular to the wave propagation direction.  
Ultrasonic pulse measurement was first used to detect defects in metal material in 1929. At present, it has been used widely in civil engineering to evaluate rock quality, species, strength, and properties including density, porosity, and elastic modulus [17; 18; 19]. This study is the first attempt at using ultrasonic pulse measurement to examine the thermal-induced damage level of concrete. Ultrasonic pulse measurement was practiced in this study using the UK-1401 dry point ultrasonic tester with P/S wave transmitted and received transducers from Acoustic Control Systems (ACS), as shown in Figure 3. The measured wave velocity and distance of UK-1401 ranged from 1000 to 9990 m/s and from 50 to 250 mm, respectively.  
Figure 3: Dry point ultrasonic tester: (**a**) device; (**b**) pressure wave transducer of 100 kHz; (**c**) shear wave transducer of 50 kHz.  
Figure 2: Uniaxial Compressive test with a circumferential extensometer.
# A Generative Adversarial Network-Based Fault Detection Approach for Photovoltaic Panel
###### Abstract
Although snail trials have no direct effect on power generation efficiency, invisible cell cracks usually reduce their power output [8]. Therefore, it is necessary to inspect the status of photovoltaic panels regularly to find and replace defective photovoltaic panels in time. However, most of the photovoltaic panels are installed on roofs, fish ponds and hillses with a wide area, so manual inspection cannot be carried out. Recently, several artificial intelligence algorithms (e.g., deep convolutional neural networks) were introduced to facilitate the process of PV panel fault detection [9; 10; 11; 12; 13]. Unfortunately, such fully supervised learning-based methods have been proven unsuitable for large-scale PV panel fault detection due to the lack of defective samples [14; 15; 16]. At present, semi-supervised learning-based approaches [17; 18; 19; 20] are being explored by many researchers. To solve these issues, by leveraging adversarial auto-encoders and conditional Generative Adversarial Networks (GAN), we propose a semi-supervised anomaly detection approach (named ppFDetector) to facilitate the process of fault detection in PV panels. The main contributions in this paper can be summarized thus:  
1. A novel model-based semi-supervised generative adversarial network is proposed. Compared with the fully supervised learning model, the semi-supervised anomaly detection model does not require a large number of negative samples, which solves the problem of the anomaly detection model's inability to be trained without negative samples. Figure 1: The fishery light complementary large-scale PV power plant. 2. The original optimization function is implemented with gradient centralization (GC) to regularize weight and output space to prevent the model from overfitting. 3. Convolutional block attention module (CBAM) is used to make the model pay more attention to the defective area, and improve the performance of the model. 4. SmoothL1 loss is used to define the loss, which can combine the advantages of L1 Loss and L2 Loss to speed up the training of the model. The remainder of this paper is organized as follows: Section 2 describes related literatures. Section 3 details the technical implementation of the ppFDetector. Experimental results using the ppFDetector on the PV panels dataset compared with state-of-the-art anomaly detection models are presented in Section 4. We finally conclude the paper in Section 5.