esa-sceva/satcom-chunk-collection huggingface dataset:
# Advancing the State of the Fog Computing to Enable 5G Network Technologies
## 5 Overview of Fog Computing  
The term 'Fog computing' is formed by '[PERSON]' that was later picked up by 'Cisco' and is widely accepted by everyone. As described earlier, it is an extension of CC as well as services to edge network that allows other applications to run in proximity with the user's device [39]. Due to this characteristic, service request latency is cut down, and QoS is improved, resulting in Quality of user experience. The help of fog computing bridges the continuum of cloud to things by communicating, controlling data from data sources. These will in-turn, enables faster processing of data as well as lowering network cost. Moreover, it supports many applications, especially time-critical (sub-millisecond reaction time) applications that need rapid latency. For the emerging Internet of Everything (IoE) applications that demands predictable latency, computing is a necessity for big analytics. Points of data collection in Fog computing are densely distributed geographically. Hence, a fourth axis geo-distribution is added to state-of-the-art Big Data dimensions (volume, variety, velocity, and veracity) [39].  
Fog computing may not be as complicated as it sounds. At the network edge, software developers either write or port IoT applications on Fog nodes. The Fog nodes ingest data from the user or IoT devices from the network edge, which is the closest to the Fog nodes. Different types of data are directed to optimal areas where the analyses are needed. Time-sensitive data is analyzed on Fog nodes that are closest to data source [7]. Moreover, centralized aggregated node clusters are analyzed for data that can wait for seconds or minutes for the action to be taken. For data analysis and storage purposes, data with a lesser time sensitivity is sent to the cloud. A typical example is the data sent periodically from Fog nodes to the cloud for Big data analysis or historical storage purposes.
## 2 Theory
### PIRNN Velocity Model Inversion  
This section describes the implementation of seismic inversion based on PIRNNs, using the inversion of velocity parameters as an example. The process begins by specifying an initial velocity model, which is incorporated as a trainable parameter within the RNN network [22]. Next, an objective function \(J_{D}\) is defined to quantify the discrepancy between the forward-modeled seismic data and the observed data. The L2 norm is chosen to measure this difference:  
\[J_{D}=\sum_{N_{s}}\sum_{T}\lVert d_{cal}-d_{obs}\rVert^{2}. \tag{8}\]  
Here, \(N_{s}\) and \(T\) represent the total number of seismic sources and time steps in forward modeling, respectively. \(d_{cal}\) denotes the shot-gather record obtained through forward modeling using the SGFD RNN operator based on the velocity parameter \(v\), while \(d_{obs}\) represents the observed data, i.e., the actual record. The objective of FWI is to minimize the defined objective function. Within the deep-learning framework, gradient-based optimizers such as SGD or Adam can be utilized to update the velocity parameters by leveraging gradients computed during the backpropagation process. In this context, calculating the gradient of the objective function \(J_{D}\) with respect to the velocity parameter \(v\) is critical. [PERSON] et al. [22] derived the gradient formula for the objective function with respect to the velocity parameter in the framework of wave propagation simulation using the second-order acoustic wave equation and RNNs. Their findings demonstrated that this full-waveform inversion process is equivalent to traditional FWI. Subsequently, [PERSON] et al. [20] validated the effectiveness of this method using the Marmousi velocity model.  
In the neural network framework, the relationships between variables are inherently recorded. During the backpropagation process, gradients are computed via automatic differentiation, and parameters are updated using optimizers. Notably, the automatic differentiation mechanism in RNNs leverages the chain rule to accelerate the backpropagation process. However, this approach demands substantial memory to store the hidden states of each unit, which in this context corresponds to the entire wavefield information, including pressure and particle velocity in the \(x\) and \(z\) directions. To address this memory constraint, this study proposes a dual-PIRNN network architecture. Transferring the loss from the original-scale model to a downscaled model facilitates inversion operations on the downscaled model, significantly reducing GPU resource consumption. The specific network design and underlying principles will be elaborated in the next section.  
Figure 1: PRINN Forward Modeling Workflow.
# Ball Tracking Based on Multiscale Feature Enhancement and Cooperative Trajectory Matching
## 3 Methods
### Heuristic Compound Sampling Strategy
#### 3.2.1 Tiling  
Tiling [32] divides images with larger resolutions into smaller images by dividing them into multiple overlapping patches. The small patch generated by tiling sampling will be used as a new image without scaling. The relative size of the ball in the patch will be larger than that of the entire image after scaling, thereby reducing the difficulty of detection. In order to obtain the global features of the image, we will also use the original image as training data. The purpose of dividing patches with overlapping is to retain the objects at the boundaries of each patch and prevent the integrity of the patch edge objects from being lost due to cropping. Specifically, \(N\) represents the number of input frames, \(w\) and \(h\) represent frame width and height, and \(r_{w},r_{h}\) represent the sampling reduction factor of \(w\) and \(h\). The number of samples after tilling is \(N*r_{w}*r_{h}\). The patch width \(w_{tiling}\) and height \(h_{tiling}\) obtained by sampling are calculated by Equations (8) and (9).  
\[w_{tiling}=\frac{4w}{3r_{w}+1} \tag{8}\] \[h_{tiling}=\frac{4h}{3r_{h}+1} \tag{9}\]
## 2 Improved Ensemble Empirical Mode Decomposition  
### Ensemble Empirical Mode Decomposition  
The EEMD method is an empirical mode decomposition method assisted by noise. By adding N times of random white noise to the signal, the new signal is decomposed by N times of EMD, and the integrated average of each order IMF is achieved to improve the effect of mode mixing in IMF. The EEMD algorithm can be described [28]:  
1. Generate \(x^{i}[n]=x[n]+w^{j}[n]\), where \(x[n]\) is the original signal and \(w^{i}[n](i=1,\cdots,I)\) are different realizations of added white noise.
2. Each \(x^{i}[n](i=1,\cdots,I)\) is fully decomposed by EMD and get their modes \(IMF_{k}^{i}[n]\), where \(k=1,\cdots,K\) indicates the modes.
3. Assign \(\overline{IMF}_{k}\) as the k-th mode of \(x[n]\), obtained as the average of the corresponding \(IMF_{k}^{i}\): \[\overline{IMF}_{k}[n]=\frac{1}{I}\sum_{i=1}^{I}IMF_{k}^{i}[n]\] (1)
## 1 Introduction
Further studies are required to investigate ecosystems' carbon budgets, especially the long-term variations of GPP, Re, and NEP [27; 39; 61] and the reasons for these variations [60; 61; 62; 63; 26]. It will benefit us to make effective policies to achieve the Dual Carbon Goals, along with air pollution reduction. Satellite remote sensing provides useful information to study carbon balance and its related issues over large space. Satellite solar-induced fluorescence (SIF) as a by-product of photosynthesis and an effective proxy for plant photosynthesis has been applied to calculate GPP for ecosystems [64; 65] and to monitor plant stress and growth states [66; 67]. Through correlation analysis with satellite and in situ GPP, the SIF product shows good performance in estimating GPP [68]. This study made further progress based on our previous ones [40; 41] and applied our developed empirical models to investigate long-term variation features of carbon balance in the Qianyanzhou forest region. More introductions about EC measurements and their uncertainties, model simulations, and their uncertainties of GPP, Re, and NEP/NEE are reported in studies [40; 41]. As an important carbon sink, a subtropical coniferous forest in China was chosen for this study. Our aims were to study the long-term variations of GPP, Re, and NEP over 15 years, as well as their variation patterns and mechanisms, and then propose suggestions for achieving win-win effects in dual carbon goals, reducing air pollutants, and slowing down climate warming. Therefore, we (1) estimated GPP, Re, and NEP values in 2003-2017 with the applications of our previously developed empirical models of GPP, Re, and NEP (EMGPP, EMRe, EMNEP) and the use of the observed solar radiation and meteorological variables, (2) further evaluated the performances of EMGPP, EMRe, and EMNEP, (3) studied long-term variations of GPP, Re, and NEP during 2003-2017 and analyzed the reasons between their simulations and observations, as well as providing some beneficial recommendations, (4) investigated the reasons for their variations due to their driving factors, (5) investigated (a) GPP under different atmospheric conditions and which condition is the best to improve carbon storage in the forests and (b) is there any way to store more carbon in the plants and improve air quality? Then, (6) quantified the relationships between GPP and SIF and applied them to estimate GPP.
## 4 Danger Theory (DT) Based Approach
### First Level Detection
#### 4.1.2 Frequency of Mobile Node Meeting  
The clone threats can be traced via the frequency that the mobile nodes meet each other. The action of the mobile nodes are distributed into two groups: (i) Ordinary scenario \(A\) (i.e., no clone node) and (ii) anomalous scenario \(A^{*}\) (i.e., existence of clone).  
\[Z(t)=\begin{cases}\text{Normal }(A)&\text{if }\lambda<\zeta\\ \text{Abnormal }(A^{*})&\text{if }\lambda\geq\zeta\end{cases} \tag{1}\]  
where lambda(\(\lambda\)) characterizes the mobile node meeting (i.e., frequency) and random threshold stetted value represented by Zeta (\(\zeta\)). The mathematical expression demonstrates that there could be a chance of the presence of a clone when the stetted threshold is beyond the frequency of the mobile node meeting. The equation indicates \(A(i)\) as the normal situation, while \(A^{*}(i)\) as the abnormality (i.e., clone mobile node), that comprises the entire \(i\)th counters. Consider the detection probability rate in the form of \(Z(i)\) as below,  
\[P_{r}(A^{*}(i))=\sum_{i=1}^{k}Z(i) \tag{2}\]  
Let \(\lambda\), for clone detection depend upon mobile node meeting frequency can be expressed as,  
\[\lambda=\begin{cases}\lambda_{S}=P(R=1|A^{*})&\text{if IDs with same key}\\ \lambda_{D}=P(R=0|A^{*})&\text{if IDs with different key}\end{cases} \tag{3}\]  
where the clone existence conditional probability is either the similar identity with similar private key is \(P(R=1|A^{*})\) and similar identity and different key in MWSNs can be defined as \(P(R=0|A^{*})\). Hence, the replica mobile node can be expressed by:  
\[P(R=1|A^{*}:\mu_{i})=\sum_{i=1}^{k}Z(i) \tag{4}\]  
The number of counters cab is shown by \(\mu_{i}\) while i represents the value from 1 to \(k\). By solving this expression and extracting the sum of conditional probability by,  
Figure 3: Mobile node encounter to other node at various time intervals.  
\[1-\prod_{i=1}^{k}(1-P(R=1|\mu_{i})\quad\text{if }R=1 \tag{5}\] \[\prod_{i=1}^{k}(1-P(R=1|\mu_{i})\quad\text{if }R=0 \tag{6}\]  
By computing the above mathematical expression (5) and (6), the clone detection probability can be calculated as below,  
\[\lambda_{S}=P(R=1|A^{*}:\mu_{i})=1-\prod_{i=1}^{k}(1-P(R=1|:\mu_{i}) \tag{7}\]  
\[\lambda_{D}=P(R=0|A^{*}:\mu_{i})=\prod_{i=1}^{k}(1-P(R=1|:\mu_{i}) \tag{8}\]  
\(\lambda\geq\zeta\) represents the clone node presence in the MWSN, where \(\zeta\) represents the threshold analyzed by the network operator.
# An Application of Instantaneous Spectral Entropy for the Condition Monitoring of Wind Turbines
## 5 Results
### Signal #2  
Figure 13 reports the results for the second, longer signal. \(\beta=40\) and \(\gamma=3\) were set for this further study. One can notice that except for some tracts where the rotational speed was slightly higher than the average, the normality model--defined over five consecutive acquisitions--was validated almost everywhere on both the two validation sets (corresponding to, respectively, 14 months and immediately before the gearbox replacement). This indicates that the process has a certain level of robustness for relatively similar rotational speeds. Nevertheless, in some segments (more markedly in tract #1489, belonging to the second validation set, and to a lesser extent in #1487 and #1588, from the same set), three false positives were encountered. This can be explained since in binary classification, fine-tuning the classification algorithm parameters generally induces an increment in both the true positive and the false positive rates.  
Immediately after the gearbox replacement, for a comparable wind speed, the ISE showed a noticeable decrease (as already described in the previous subsection). This behaviour was confirmed in the second test set, i.e., after more than seven months from the gearbox replacement. One can notice how some elements of the training and validation datasets have almost the same rotational speed but different ISE--e.g., tract #1588, the last  
Figure 12: Boxplot of the CWT execution time. (**a**): as a function of \(\beta\). (**b**): as a function of \(\gamma\).  
Figure 13: Resulting ISE (signal #2). Values for. \(\beta=40\) and \(\gamma=3\).  
one of the second validation set, and #2021, the first one of the second test set, correspond, respectively, to 785.45 and 786.29 cpm. This seems to indicate that the different output is not linked to any input variation but rather to structural changes.
## 4 Brillouin Optical Correlation Domain Analysis: BOCDA
### Overview of Performance Improvement Schemes for BOCDA
#### 4.3.3 Temporal Gating Scheme to Elongate Measurement Range  
Multiple correlation peaks are generated by the FM along the sensing fiber periodically; then, the sensing fiber length is inherently limited to the interval between the neighbor correlation peak positions in the basic BOCDA system. By using a rather low FM frequency, the range can be elongated. However, the spatial resolution has to be limited in this case, which is indicated by equations (2) and (3). Then, to elongate the measurement length of the fiber, a \"temporal gating scheme\" has been proposed and developed, in which the pump lightwave is modulated in its intensity to have the pulsed shape with the width equal to the correlation peaks' interval [53; 54]. The BOCDA output is also gated with the same width to select only one correlation peak position. Then, the total measurement range is elongated. This scheme is shown in the next section in detail.
# Comprehensive Energy Demand Response Optimization Dispatch Method Based on Carbon Trading
## 6 Case Analysis
### The Impact of Integrated Energy Demand Response on Carbon Emissions  
The electrical, gas, and thermal response loads in Scenarios 2 and 3 are shown in Figure 7.  
Figure 4: Operation plan of each unit under Scenario 1.  
Figure 5: Operation plan of each unit under Scenario 2.  
Figure 6: Operation plan of each unit under Scenario 3.  
For the electric load, due to the influence of the new energy output, the system power load moves to the peak period of the new energy output. For gas load, since there are more surplus new energy sources at night, and the _P2G_ equipment can reduce the carbon emissions of the system, the overall gas load of the system is moved to the peak period of new energy output.  
Firstly, from Figures 3 and 5-7, it can be seen that there is a phenomenon of partial wind and light abandonment during the period of 11:00-13:00 and 23:00-24:00 when the new energy units are in full force. However, after considering the demand response, an IES motivates electric load users to reduce their electric load or transfer the load demand when new energy is small. This reduces the electricity load demand at that moment or transfers it to the new energy time. At the same time, the gas load is transferred to the moment when new energy is released, providing sufficient space for the consumption of new energy. Secondly, since the carbon emission intensity of coal-fired units is greater than that of gas turbines, the carbon emission intensity of wind turbines and photovoltaic units is much smaller than that of gas turbines. Therefore, when dispatching, the new energy units will give priority to the output, and the coal-fired units will maintain the minimum technical output, and the gas turbine will perform peak-shaving scheduling to reduce the carbon transaction cost of the system. Finally, although the cost of demand response is temporarily greater than the cost of carbon emissions, it is in line with the development law of the energy industry to exchange a smaller economic cost for the reduction in carbon emissions.
# Active Thermography for the Detection of Sub-Surface Defects on a Curved and Coated GFRP-Structure
## 4 Results and Discussion
### Detectability of Sub-Surface Defects in Front Perspective
The thermal conductivity perpendicular to the stacking of the layer is weaker because of the low thermal conductivity of the epoxy resin used to connect the single layers. In order to detect the thermal effects of the defect at the surface, the heat  
Figure 7: Results of test series 2 (ROI B, \(t_{\text{exc}}=25\,\text{s}\), \(t_{\text{rec}}=15\,\text{s}\), \(k=89\,\text{mm}\), (**a**) surface temperature of the whole camera FOV, (**b**) result for the reference method without filtering at \(t_{\text{rec}}=0\,\text{s}\), (**c**) reference image with filtering, automatic defect detection and calculation of the CNR, (**d**) result for the PCA-based method without filtering, (**e**) PCA-based image with filtering, automatic defect detection and calculation of the CNR. must propagate perpendicular to the fiber direction. Propagation perpendicular to the fiber direction happens more slowly than propagation lateral to the surface. Therefore, the heat is dissipated laterally more prominently than it can dissipate to the surface, thus making surface temperature differences less pronounced or even insignificant. In case of CFRP, these thermal effects are more pronounced, since the thermal conductivity depends even more on the actual fiber direction [17]. In isotropic materials such as steel, there is no directional dependence of the thermal conductivity [35]. The investigations have shown that sub-surface defects located in a coated test specimen that closely emulates the real rotor blade leading edge can be detected by means of long-pulse thermography with a depth-to-diameter ratio of up to 1.04. Here, the detectability of the defect depends mainly on the defect depth in addition to the defect diameter. False detections occur mainly due to the inhomogeneous material. When it comes to the application of the measurement method to a rotor blade leading edge in situ, it is probably difficult to distinguish between an actual defect and a material inhomogeneity such as a resin accumulation on the basis of the thermogram, since the location of a defect on the rotor blade is unknown. How this can be dealt with will have to be shown when the measurement method is actually tested in the open field.
## 1 Introduction
By incorporating data augmentation techniques, the number of images in the dataset was expanded to 16,400, offering substantial support for future research in related fields. 2. To construct a lightweight detection model, this paper innovatively proposes the Faster-C2f (F-C2f) module and the RepHead detection head. The network structure is redesigned, utilizing the lightweight network EfficientNet-B0 as the Backbone of LTSCD-YOLO, and a lightweight Cross-Scale Feature Fusion Module was adopted in the Neck part. By fusing features of different resolutions across scales, the detection performance is enhanced. Focal-EIoU was adopted as the model's loss function to obtain more accurate predicted bounding boxes, optimize the training process, and improve the model's performance on typical satellite component detection tasks. The following parts of the article are arranged as follows: Section 2 briefly introduces the YOLOv8 model and details the improvement strategies proposed in this paper. Section 3 describes the construction of the experimental dataset. Section 4 introduces the experimental details. Section 5 presents the backbone network comparison experiments, ablation experiments, comparative experiments with different models, visualization experiments, generalization experiments, and validation experiments. Section 6 is the discussion. Section 7 is the conclusions.
# Camera-Based Vital Sign Estimation Techniques and Mobile App Development
## 4 Discussion  
### Limitations of Proposed Method  
The proposed method has several limitations, such as the lack of consideration for motion artifacts, the empirically fixed amplification scale for color difference signals, the limited accuracy of the SpO2 estimation equation, the uncertainty regarding the appropriate frequency bands for bio-signal detection, and the insufficient size of the database. In this study, large subject movements in front of the camera were restricted, and the amplification scale for the color difference signals extracted from the detected facial region was determined empirically. In addition, the proposed SpO2 estimation equation has not been thoroughly validated due to the limited database. Moreover, the frequency bands used for detecting HR, SpO2, and RR differ among researchers, and the optimal bands for various recording environments and video qualities have not yet been sufficiently investigated.
## 4 Results and Discussion
### Qualitative Comparison
#### 4.4.2 Comparison of the Saliency Segmentation Results with Other Models  
To further verify the superiority of the proposed method, we also compared the contour extraction results of the underwater image with the saliency image as the input of several classic models. To test the robustness of the proposed method, we only selected low-quality saliency images (inhomogeneous local intensity and incomplete saliency information) for the comparison experiments. As shown in Figure 7, the segmentation results of LBF are severely affected by the initial contour curve and are disturbed by the inhomogeneous regions inside the target. The LGIF model can avoid the influence of the initial contour curve but cannot extract complete contour information, as shown in the green dotted region in Figure 7(2). The LIF model can extract the target contour relatively completely, but it easily falls into the local optimum and is also affected by the initial contour curve. The RMPAM model avoids the local optimum error, but it also has the problem that the contour information cannot be extracted completely, as shown in the green dotted region in Figure 7(2). Our method can effectively avoid the local optimum and supplement the missing contour information through the original image. Hence, the results of our method are more accurate and complete than other methods.  
Figure 6: Comparison of our method with [PERSON]’s method and [PERSON]’s method [20]. (**a**) Comparison of our method with [PERSON]’s method. (**b**) Comparison of our method with [PERSON]’s method. The first row is the original underwater images; the second row is the segmentation results in [PERSON]’s method [15] and [PERSON]’s method [20]. The third row shows the results of our method.