esa-sceva/satcom-chunk-collection huggingface dataset:
# An Intelligent Warning Method for Diagnosing Underwater Structural Damage
## 2 Methodology
### Learning Steps  
1. Initialization: Set the [0,1] random value as the initial connection weight between the input neuron and the output neuron. A set, \(S_{j}\), of outputting neighboring neurons is selected, wherein \(S_{j}(0)\) represents a set of neighboring neurons of neuron \(j\) at time \(t=0\), and \(S_{j}(t)\).
2. Set the input of the neural network: Make the sample feature parameters into the following matrix and input them to the SOM network: \[X(n)=[x_{1}(n),x_{2}(n),x_{3}(n)\cdots x_{N-1}(n),x_{N}(n)]^{T}.\] (3)
3. Calculate the Euclidean distance: Input layer neurons, \(i\), into mapping layer neurons', \(j\), available Euclidean distance, \(d_{ij}\), indicated as: \[d_{ij}=\|X-W_{j}\|=\sqrt{\sum_{i=1}^{n}{[x_{i}(t)-w_{ij}(t)]^{2}}}.\] (4) In the equation, \(w_{ij}\) is the weight of the input layer neuron, \(i\), to the mapping layer neuron, \(j\). \(W_{j}\) is the connection weight of the neuron, \(j\), on the mapping layer.
4. Obtain the winning neuron: The position of the winning neuron can be obtained by calculating the minimum Euclidean distance between the input vector and the weight vector. When the input vector is \(X\) and the winning neuron is denoted by \(c\), the formula is expressed as: \[\|X-W_{c}\|=\min_{i}\|X-W_{i}\|,\ i=1,2,3\cdots M-1,M,\] (5) where \(x\) is the input vector and the winning neuron is labeled \(c\). \(W_{c}\) is the weight of the winning neuron, \(c\). \(W_{i}\) is the connection weight of the neuron, \(i\), on the mapping layer.
5. Adjust weight: The connection weight of the input neuron and all neurons in the competition neighborhood are corrected by Equation (6): \[\Delta w_{ij}=w_{ij}(t+1)-w_{ij}(t)=\eta(t)[x_{i}(t)-w_{ij}(t)].\] (6) Among them, \(t\) is the continuous time, and the learning rate at time \(t\) is \(\eta(t)\). \(\eta(t)=\frac{1}{t}\) or \(\eta(t)=0.2(1-\frac{t}{1000})\). The value range of \(\eta(t)\) is [0,1].
6. Determine whether the output result meets the expected requirements: If the result meets the previously set requirements, then end; if not, return to step (ii) to continue.  
Figure 2: The Self-Organizing Mapping (SOM) network model.
# Review of Image Augmentation Used in Deep Learning-Based Material Microscopic Image Segmentation
###### Abstract  
The deep learning-based image segmentation approach has evolved into the mainstream of target detection and shape characterization in microscopic image analysis. However, the accuracy and generalizability of deep learning approaches are still hindered by the insufficient data problem that results from the high expense of human and material resources for microscopic image acquisition and annotation. Generally, image augmentation can increase the amount of data in a short time by means of mathematical simulation, and has become a necessary module for deep learning-based material microscopic image analysis. In this work, we first review the commonly used image augmentation methods and divide more than 60 basic image augmentation methods into eleven categories based on different implementation strategies. Secondly, we conduct experiments to verify the effectiveness of various basic image augmentation methods for the image segmentation task of two classical material microscopic images using evaluation metrics with different applicabilities. The U-Net model was selected as a representative benchmark model for image segmentation tasks, as it is the classic and most widely used model in this field. We utilize this model to verify the improvement of segmentation performance by various augmentation methods. Then, we discuss the advantages and applicability of various image augmentation methods in the material microscopic image segmentation task. The evaluation experiments and conclusions in this work can serve as a guide for the creation of intelligent modeling frameworks in the materials industry.  
computer vision; image processing; data augmentation; material microscopic image segmentation; deep learning +
Footnote †: journal: Medical Image Segmentation  
**Copyright** © 2023 by the authors. Liconse MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
# Bridge Health Monitoring Using Proper Orthogonal Decomposition and Transfer Learning
###### Abstract
Bridge FE models subjected to real train loadings measured during passages over the actual bridge under simulated damage scenarios were used to validate the TL approach for bridge damage identification. JDA coupled with a linear kernel, herein referred to as JDA-kernel, was implemented to map between POMs of the two bridge models. The derived relationship and the Kernel Nearest Neighbor (KNN) approach were then implemented to obtain POM labels for a target bridge with unknown labels. The resulting JDA-kernel approach for damage detection and intensity identification was evaluated using three scenarios: known damage intensity (DI) and unknown damage location (DL); known DL and unknown DI; and unknown DL and DI.
## 2 Materials and Methods
### Trajectory Accuracy
In summary, to balance the computational efficiency and localization accuracy, we made a trade-off in the number of levels in the feature pyramid based on previous research and innovatively introduced principal direction information and Pareto optimal solutions to ensure that localization accuracy was not decreased. The experimental results align with expectations.
## 5 Simulation Results and Discussion  
In order to examine the feasibility and performance of the developed OECS, the simulations of OECS were conducted in the HIL system of Spoonbill UAV. Three flight maneuvers, level flight, descending, and climbing, were adopted to perform the simulations. Moreover, the influence of the wind effect was considered to validate the reliability of the developed system. The largest magnitude of wind was set to 8 m/s and the wind direction was set to be a headwind in the simulations.
## 3 Results
### Effect of Age  
In subsequent analyses, DPOAE levels and HTs were analysed in terms of average values over the SF range (1-8 kHz) and over the EHF range (10-16 kHz).  
For each group, DPOAE response levels were correlated negatively with age, as can be seen in Figure 4. The correlations were weak to moderate. Even for the G16 group, the DPOAE response level decreased with age, despite the fact that hearing levels were within normal limits up to 16 kHz, and all ears presented generally high levels of DPOAEs. The effect of age was stronger for DPOAEs in the 10-16 kHz range compared to the 1-8 kHz range, with correlation coefficients of \(-0.37\) and \(-0.27\), respectively. Only for the Gim group in the 10-16 kHz range was there no significant correlation between DPOAEs and age (\(p=0.14\)); nevertheless, the tendency for DPOAEs in that range to decline with age remained (\(r=-0.27\)).  
Similarly, HTs also increased with age, as shown in Figure 5. The correlations between HT and age were significant for all groups for EHFs (10-16 kHz), and for groups G16 and G8 for SFs (1-8 kHz), with Gim close to significance and having a similar trend to the other groups. For SFs, the correlations were weak to moderate, while for EHFs correlations were moderate to strong. The effect was especially pronounced for G8, where the correlation coefficient for EHFs reached 0.76.  
As both DPOAEs and HTs seem to be significantly influenced by age, correlation coefficients with adjustment for other variables were calculated, and the results are shown in Table 1. Here, the data for groups G16 and G8 were pooled together. HT in the SF range correlated significantly with DPOAEs in that range. However, there was no significant correlation with DPOAEs in the EHF range. HT in the EHF range correlated significantly with DPOAEs in the SF range, as well as with DPOAEs in the EHF range. HT in the EHF range correlated weakly with DPOAEs (\(-0.22\), \(-0.17\)), but strongly with age (0.78).  
Figure 4: Average DPOAE response levels for 1–8 kHz and 10–16 kHz as a function of age for all three groups (G16, G8, and Gim). Linear fits to the data are shown (lines), along with correlation coefficients (\(r\)) and significance (\(p\)).
## 4 Experiments
### Case Study
The question also mentions that [PERSON] has 5 fewer stamps than [PERSON]. \\  & Therefore, the number of stamps [PERSON] has can be expressed as \(\chi-5;4\). Now let’s substitute the number of stamps [PERSON] has \(\chi=15;5\). The number of stamps [PERSON] has is: \(\chi-5=15-5=10;6\). Therefore, [PERSON] has 10 stamps; Correct \\  & **PROP:** \\  & 1. [Type: General, Relation Set: ([PERSON] = 15 * stamps, [PERSON] = Ming - 5 * stamps], Answer: \(\text{Li = 10 * stamps}\)]; \\  & Final Answer: [PERSON] has 10 stamps. Correct \\ \hline \multirow{7}{*}{**AWP case 2**} & There are 15 kg of apples, 20 kg of bananas, and 20 kg of rice in the store. Divide all the fruits equally into 5 boxes. How many kg of fruits should be put in each box? \\ \hline \multirow{7}{*}{**Output**} & **ZhangAlg:** \\  & Equation: \({}^{\text{`(15+20+20)}}/5^{\prime}\), Answer: 11 Incorrect \\  & **LiuAlg:** \\  & 1. Calculate the total number of kg of fruit: 15 kg of apples, 20 kg of bananas, total fruit = apples + bananas = 15 + 20 = 35 kg 2. Determine the number of boxes and the average amount of fruit in each box: the number of boxes is 5 boxes, the average amount of fruit in each box = total amount of fruit/number of boxes; 3. Calculate how many kg of fruit should be put in each box: the amount of fruit that should be put in each box = total amount of fruit/number of boxes, the amount of fruit that should be put in each box = 35/5 = 7 kg. 4. Therefore, each box should have 7 kg of fruit, so that all the fruit can be divided equally into 5 boxes. Correct \\ \cline{2-2}  & **PROP:** \\  & 1. [Type: Part-Whole, Relation Set:[apples = 15 * kg, bananas = 20 * kg], Answer: \(\text{fruits}\) = 35 * kg]; 2. [Type: Rate, Relation Set: [Total (fruits) = 35 * kg, Amount (fruits) = 5 * boxes], Answer: [Rate (fruits) = 7 * (kg/box)]; \\  & Final Answer: 7 kg in each box. Correct \\ \hline \multirow{7}{*}{**AWP case 3**} & There are 36 students in the morning, of which girls account for (5/9) of the total number of students. Later, a few boys came. Now girls account for (2/5) of the total number of students. Later, how many boys came? \\ \hline \multirow{7}{*}{**Output**} & **ZhangAlg:** \\  & Equation: \({}^{\text{`36*}}(5/9)/(1-(2/5))-36^{\prime}\), Answer: \(\text{`-2.666\
# A Review of Machine Learning and IoT in Smart Transportation
## 2 Machine Learning and IoT in Smart Transportation
### ML and IoT Applications in Smart Transportation
#### 2.2.3. Lights  
An important part of a smart city, which we consider a part of the smart transportation services, are the Smart Street Lights (SSL). Smart lights can reduce energy consumption and offer dynamic operation and manageability.  
An SSL implementation based on the IoT infrastructure is implemented in [69]. Street lights obtain smart characteristics by adding a light sensor, an IR sensor, GPS and a wireless communication module. In this way the lamps can be aware of crowded areas and adapt their light intensity dynamically, making areas with dense population safer and also saving energy. The GPS can help a centralized system to monitor the lamps, location and state, and speed up maintenance processes in case of a broken lamp. The communication of the SSL and the management system is based on the NB-IoT network. The management system is based on Fog nodes, that collect data from a collection of lamps and check their state on a regular basis. Apart from the automated procedures that the SSLs provide, they can be managed remotely through the implemented management platform.  
A similar but more simplistic approach for smart lights is presented in [70]. In this implementation, a raspberry pi is used as a micro-controller, connecting the lamp with a light sensor, an IR sensor, and an IR led. The light sensors will detect sun rise and set and will trigger the lamp with on/off signals. Additionally, the lamps are able to detect bypassing cars or pedestrians, and switch the lamps dynamically to reduce energy consumption.  
Finally, in [71] the authors propose a smart lighting system, where each lamp post will be a WiFi hotspot that will enable to transfer a different kind of collected information to a central web server. The lights will dynamically switch on/off or dim depending on the surrounding environment to enable significant cost reduction. Lamp posts will be also equipped with cameras and environment checking sensors to ensure people safety during emergencies and measure environmental conditions accordingly and enhance the capabilities of a regular lamp post.
## 4 Results  
### Rainfall Data  
Rainfall data recorded over the monitoring period are shown in Figure 8, while their most important statistics are summarized in Table 1.  
Figure 8: Rainfall data recorded by the Sara rain gauge over the examined period: (**a**) daily and cumulative rainfall (the rainiest phases are highlighted); (**b**) cumulative deviation from mean monthly rainfall along with monthly rainfall.  
Both daily and cumulative rainfall levels followed trends consistent with the local hydrological regime, revealing seasonal cyclic variations with alternating drier and wetter periods. The rainiest phases generally coincide with autumn and winter months, whereas the driest ones coincide with the summer season (Figure 8a). This is more clearly visible from the pattern of the cumulative deviation of monthly rainfalls from the monthly mean for the entire period (Figure 8b).  
The most pronounced positive variations, namely where monthly rainfalls were greater than the mean, typically occurred in October and November or in January and February. However, the cumulative rainfall amounts, along with the monthly and daily average values, reveal that 2016 and the second half of 2015 were wetter than 2017 (Table 1). The total rainfall measured in 2017 was far lower than the local mean annual rainfall (i.e., 1100 mm) and this was chiefly correlated to a dry period covering most of the spring and summer seasons. It can be noted that the rainiest months were October in 2015 (338.4 mm), followed by February in 2016 (335.6 mm) and December in 2017 (218.6 mm). Similarly, the maximum daily rainfall levels were recorded in February 2016 (130.4 mm), December 2017 (107.0 mm), and October 2015 (87.8) and correspond to the most important rainstroms that occurred during the monitoring period (Table 1 and Figure 8a).  
The rainfall events that occurred in October 2015 and December 2017 were characterized by rainfall concentrated over no more than 2-3 days that were followed or preceded by periods, ranging from 10 to over 20 days, with little or no rain (Figure 8a). Conversely, the rainy phase of February 2016 was characterized by two main rainstroms separated by a 20-day wet period with accumulated rainfall levels equalling 108.8 mm over 11 rainy days; also, rainfall continued for some days after.
## 2 Experimental Method and Results
### Overall Net Cavity Dispersion Dependence  
Figure 10 shows the overall characteristics of the developed fiber laser output as a function of net cavity dispersion \(D\). The green area shows the condition in which mode-locking was obtained only when the spectral filter was not used. The orange area shows the condition in which mode-locking was not obtained. Almost transform-limited short soliton pulses were obtained in the anomalous dispersion region for both film A and B. It is interesting to note that the output power was larger when the spectral filter was used. Around the zero-dispersion region, stable mode-locking was obtained when film B was used. In the normal dispersion region, dissipative soliton pulses with high output power and a broad spectral width were obtained. The highest output power, up to about 80 mW, was achieved in the strong normal dispersion region when the spectral filter was used. Mode-locking was not obtained for film B. The observed behaviors were similar to those shown in Figure 9 in [15] for a Tm-doped fiber laser. As mentioned before, the pumping efficiency was \(\sim\)3 times larger for the Tm-Ho co-doped fiber laser, compared to that of a Tm-doped fiber laser, and an efficient operation was obtained.  
Figure 8: Characteristics of output pulses when \(D\) = +0.0503 ps\({}^{2}\) and spectral filter was not used, (**a**) optical spectra, (**b**) autocorrelation trace, (**c**) pulse train, (**d**,**e**) RF spectra of (**d**) fundamental frequency and (**e**) wide range.
## 5 Signal Acquisition Approaches
### Electrical Geodesic Sensors  
These sensors are specifically designed to measure electrical activity on the scalp and are widely used in EEG studies for their ability to monitor activity across the entire scalp [98]. Electrical geodesic sensors utilize a geodesic sensor net to provide even coverageand reliable scalp contact. Typically, these sensors incorporate a large number of electrodes, enhancing the resolution and accuracy of the EEG data. They find extensive application in fields like cognitive neuroscience and clinical diagnostics, proving especially effective in high-density EEG recordings.
## 3 Description of the Environment
### Motivation for Off-Policy RL  
Although various on-policy methods, such as Proximal Policy Optimization (PPO) and Trust Region Policy Optimization (TRPO), can be applied to continuous control tasks, they were found to be less suitable under the given constraints. In preliminary tests using the 6-DOF AUV simulator and an episode limit, PPO and TRPO converged more slowly, reaching a lower than 65% success rate after 600,000 steps. In contrast, off-policy algorithms such as SAC, TD3, and TQC exceeded 90% success in 600,000 steps.  
Focus was paid to off-policy RL for three reasons:  
* **Sample efficiency:** Off-policy algorithms reuse past experiences more effectively thanks to large replay buffers, which is crucial for computationally expensive AUV simulations.
* **Faster convergence:** Experiments showed that the on-policy methods generally required more interactions to attain similar performance levels.
* **Practical constraints:** Given the limited training budget and high-fidelity vehicle dynamics, off-policy approaches balanced exploration and exploitation more efficiently.
## 5 Future Lines of Research  
More systematic reviews that integrate advances in understanding sense of presence across disciplines are needed to update the state of the art on conceptualized dimensions, measurement and associated factors. Quantitative meta-analyses are also necessary to synthesize the quantifiable effects of different environmental and technological variables on sense of presence. Future research could also qualitatively assess the varying definitions and theoretical models proposed, identifying points of convergence to develop integrative frameworks.  
Although this systematic review provides important evidence on sense of presence in virtual environments, it also reveals several research gaps and key challenges that need to be faced to comprehensively advance this field. One pending task is to achieve a more unified conceptualization of sense of presence, integrating dimensions such as spatial presence, social presence and copresence, as well as differentiating the frameworks used to examine these dimensions from a global concept of presence (e.g., overall sense of presence). Further empirical work is needed to determine the relationships between the dimensions. Similarly, more robust explanatory models are needed to understand the cognitive and neurological processes underlying each type of presence.  
Another important challenge is to identify further objective measurements of sense of presence, such as facial expressions, physiological responses and emerging behavioral patterns. This would help to explain aspects of presence not accessible through conscious self-reports. This will require not only greater application of existing measures, but also the design of new, viable objective indicators usable in virtual environments. It will also be key to broaden the environmental variables and spatial attributes studied in relation to sense of presence, to examine the precise effects of lighting, acoustics, spatial distributions and materials. Furthermore, more comparative studies are needed on the differential impact of the various immersive technologies.  
Identifying specific design principles for virtual environments that optimize spatial presence is another core challenge. This would enable them to be more effectively applied in architecture, urban planning and related disciplines. Finally, more multidisciplinary approaches must be pursued, integrating design research, experimental work, computational modeling, cognitive neuroscience and associated fields. Only through comprehensive integration can an integral understanding of sense of presence be achieved.
## 3 The Basic LRMA Based Data Collection Method  
In this section, we formulate the basic LRMA model for data collection in WSNs from the low rank theory [13]. For a wireless network with \(N\) sensor nodes, the \(T\) rounds data collection will form a matrix of measurements \(\mathbf{X}\in R^{N\times T}\). As shown in Figure 1, at each data collection round, the LRMA based method randomly selects a subset of sensor nodes for data sensing. Thus the measurement matrix is generally incomplete, _i.e._, only the positions corresponding to the selected nodes offer valid sensing data. Here we adopt a mask operator \(\mathcal{A}(\cdot)\)[13] to represent the subset sampling procedure:  
\[\mathcal{A}(\mathbf{X})=\mathbf{M}. \tag{1}\]where \(\mathbf{M}\) is an incomplete matrix with only a sparse set of elements with valid sensing values at relevant positions. For the sake of clarity, the operator \(\mathcal{A}(\cdot)\) can be specified as an element-wise matrix product as follows [13]:  
\[\mathcal{A}(\mathbf{X})=\mathbf{Q}\odot\mathbf{X}. \tag{2}\]  
where \(\odot\) denotes the Hadamard product of two matrices _i.e._, \(\mathbf{M}(i,j)=\mathbf{Q}(i,j)\mathbf{X}(i,j)\). \(\mathbf{Q}\) is a \(N\times T\) matrix defined as by the following form [14]:  
\[\mathbf{Q}(i,j)=\begin{cases}1,&\text{if the $i$th node has valid sensing data at the $j$th time slot}.\\ 0,&\text{otherwise}.\end{cases} \tag{3}\]  
Generally, the measurements from one node are continuous in time domain, which shows temporal correlation. On the other hand, the sensing data from adjacent nodes have similar values or the sensing data of a node can be represented by the measurements of its neighboring nodes, which shows spatial correlation. These spatio-temporal correlations bring the low rank property of the measurement matrix \(\mathbf{X}\). According to the low rank matrix completion theory [12, 13], it is highly possible to recover a low-rank matrix from a subset of its entries, generally a random subset with enough elements. Thus, we can recover \(\mathbf{X}\) by solving the following optimization problem:  
\[\begin{split}\mathbf{X}^{*}=&\operatorname*{argmin} \limits_{\mathbf{X}}\quad\text{rank}(\mathbf{X}),\\ &\text{subject to}\quad\mathcal{A}(\mathbf{X})=\mathbf{M}.\end{split} \tag{4}\]  
This is the basic LRMA (B-LRMA) model [12, 13] for data collection. In the representation of B-LRMA model, there is no constraint on the rows or columns continuity of \(\mathbf{X}\). That is, the intrinsic properties of the measurements, the spatial and temporal correlations, are not represented in this model. So to get better recovery result of \(\mathbf{X}\), we propose a correlated spatio-temporal LRMA model. It will be described in detail in the next section.
# Satellite Remote Sensing of Precipitation and the Terrestrial Water Cycle in a Changing Climate  
Vincenzo Levizzani  
1  
Elsa Cattani  
1