esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Field Data Processing  
### Preprocessing for Raw Field Data  
Raw field data will inevitably be affected by prominent noise events (e.g., mechanical construction), which makes it impossible to satisfy the assumption of a diffuse wavefield. Therefore, in order to reconstruct passive surface waves better, we first used the noise or signal removal algorithm based on SS-CWT proposed by [PERSON] et al. (2015) to preprocess the field data and separate the prominent noise events. Figure 3a shows an original field record of 90 s on which several relatively prominent noise events (red arrows) are  
Figure 1: Location of the passive seismic survey line. (**a**) Location of mining area where data were collected (blue square); (**b**) geological structure of the mining area. The red dotted line P18-P18\({}^{\prime}\) indicates the passive seismic survey line.  
Figure 2: Typical ambient noise data recorded by a geophone in the survey area.  
distributed. After noise and signal separation, the amplitude of the separated noise records was relatively evenly distributed (as shown in Figure 3b). Figure 3c shows the removed records containing prominent noise events (red arrows).  
Power spectral density (PSD) plots are useful for visualizing variations in the frequency content of ambient data over time [33]. In order to further show the application effect of the noise separation algorithm in preprocessing the field data, we also calculated the PSD plots (as shown in Figure 4) corresponding to the three noise records in Figure 3.  
Figure 4a shows the PSD plots of the original field data, on which a prominent high-energy event (white dotted box) can be seen. After noise separation, as shown in Figure 4b,  
Figure 4: PSD plots. (**a**) Raw field data. (**b**) Separated noise records. (**c**) Removed records. The white dotted boxes indicate prominent noise events.  
Figure 3: (**a**) Raw field data of 90 s. (**b**) Separated noise records. (**c**) Removed records. The red arrows indicate prominent noise events.  
the high-energy event in the white dotted box was attenuated, and the PSD of the whole record was evenly distributed. Figure 4c shows the PSD of the removed noise record, which was occupied by three prominent high-energy events (white dashed boxes). According to the results of Figures 3 and 4, the noise or signal removal algorithm based on SS-CWT could effectively remove the prominent noise events in the original passive seismic data.
# Blockchain Technology: Emerging Applications and Use Cases for Secure and Trustworthy Smart Systems
## 4 Applications and Use Cases of Blockchain
### Smart Property and Public Value
#### 4.5.4 Asset Management  
Asset management involves multiple parties where each party is required to keep the transactions. While keeping the same transaction in different places can make the entire process inefficient and prone to errors. To make matters worse, asset management might also involve cross-border transactions, adding more complexity to verify the transactions. Such issues can be dealt with a distributed ledger where each party can have a copy of the entire transactions and get updated about each transaction using cryptographic communications [45]. This improves the efficiency and reduces the cost as there would be no intermediary to verify the transactions.
# An Adaptable Train-to-Ground Communication Architecture Based on the 5G Technological Enabler SDN
## 4 Simulation Scenario
### Applications
#### 4.4.1 Train Monitoring  
In this application, the OBE periodically informs the CCE about the current state of process variables according to a publisher-subscriber paradigm. It works on top of Message Queuing Telemetry Transport (MQTT) [16], a machine-to-machine (M2M) oriented protocol that is specified for low-latency communications and which runs over TCP. In the train, the OBE collects data from the sensors and publishes it on different topics in the CCE. Each topic represents a variable, for example, the temperature of a component. Then, the CCE's subscriber entity receives all the information published on the topics of interest--the ones that it is subscribed to. This application produces an uplink overall data rate of 20 kbps per variable.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Feature** & **Use Case A** & **Use Case B** \\ \hline Average speed (\(\overline{v}\)) & 72 km/h & 72 km/h \\ Average rail traffic density & 1–2 trains/h & 1–2 trains/h \\ WiFi & IEEE 802.11g (2.4 GHz) & IEEE 802.11g (2.4 GHz) \\ LTE & Band 1 (2100 MHz) & Band 1 (2100 MHz) \\ MPTCP & ✗ & ✓ \\ Data forwarding method & Legacy switches with STP & SDN application (DisPaC) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of use cases.
## 4 Experiment
### Dataset  
Datasets consisting of 82 CT scans were gathered, including 36 scans from patients diagnosed with intracranial hemorrhage, categorized into intraventricular, intraparenchymal, subarachnoid, epidural, and subdural types. Each patient's CT scan comprised approximately 30 sections, each 5 mm thick. The patients' mean age and standard deviation were 27.8 and 19.5, respectively. Out of the total patients, 46 were male and 36 were female. For every non-enhanced CT scan section, two radiologists documented the bleeding type and whether bleeding or fractures were present. Additionally, the radiologists outlined the ICH region in each section. During data collection, Siemens Medical Solutions' syngo software was utilized to read the CT DICOM file, and two videos (AVI format) were saved using brain (level = 40, width = 120) and bone (level = 700, width = 3200) windows. A custom MATLAB tool was implemented to read the AVI files and execute annotations. Concurrently, the generated masks were mapped and saved as NIfTI files for 75 subjects. Figure 8 displays a sample from our dataset, with the top row representing the grayscale CT image of intracranial hemorrhage and the bottom row depicting the corresponding ground truth image, where black areas indicate the background and white areas signify the bleeding lesion.  
Since the data contained a significant number of non-ICH images, an additional data cleaning step was performed to select only regions containing bleeding. Following data cleaning, a total of 318 images (512 \(\times\) 512) were available for use, all containing labeled images. For the dataset, not only were data cleaning, image preprocessing, and other tasks conducted, but data augmentation was also implemented. Due to the original dataset's limited size of 318 training images, deep learning was insufficient, resulting in overfitting. Random rotation, flip, scale, contrast transformations, and noise disturbance were applied simultaneously to images and labels for data enhancement. Figure 9 illustrates the general data augmentation process. Post data augmentation, the dataset expanded approximately 10-fold, generating a total of 3880 images, which were divided into training, test, and validation sets at an 8:1:1 ratio.
# Enhancement of Sentinel-2A Images for Ship Detection via Real-ESRGAN Model
###### Abstract  
Ship detection holds great value regarding port management, logistics operations, ship security, and other crucial issues concerning surveillance and safety. Recently, ship detection from optical satellite imagery has gained popularity among the research community because optical images are easily accessible with little or no cost. However, these images' quality and quantity of feature details are bound to their spatial resolution, which often comes in medium-low spatial resolution. Accurately detecting ships requires images with richer texture and resolution. Super-resolution is used to recover features in medium-low resolution images, which can help leverage accuracy in ship detection. In this regard, this paper quantitatively and visually investigates the effectiveness of super-resolution in enabling more accurate ship detection in medium spatial resolution images by comparing Sentinel-2A images and enhanced Sentinel-2A images. A collection of Sentinel-2A images was enhanced four times with a Real-ESRGAN model that trained PlanetScope images with high spatial resolution. Separate ship detections with YOLOv10 were implemented for Sentinel-2A images and enhanced Sentinel-2A images. The visual and metric results of both detections were compared to demonstrate the contributory effect of enhancement on the ships' detection accuracy. Ship detection on enhanced Sentinel-2A images has a mAP50 and mAP50-95 value of 87.5% and 68.5%. These results outperformed the training process on Sentinel-2A images with a mAP value increase of 2.6% for both mAP50 and mAP50-95, demonstrating the positive contribution of super-resolution.  
ship detection; satellite images; object detection; YOLO; super-resolution; Real-ESRGAN; PlanetScope; Sentinel-2A
## 1 Introduction
### Research Purpose  
This study aimed to set up a defect detection system to accurately distinguish whether defects exist in PCB based on universal standards in the industry, as well as to identify the categories and locations of the defects so that the inspection accuracy can be improved and the false alarm rate can be decreased. During the study, a self-learning system was built based on deep learning methodologies. The system was trained to automatically start the image processing and calculate the parameters required for deductive reasoning in an iterative process. As the parameters could be updated constantly in an iterative process, inspection standards could be established without a complete product defect definition.
# Measuring Method of Node Importance of Urban Rail Network Based on H Index
## 2 Preliminaries
### Node Importance Measurement Index Based on Passenger Flow of Urban Rail Stations  
The passenger flow of the station is obtained by adding the passenger flow of each direction connected to the station. The calculation formula is as follows: \(\omega_{i}=\sum_{a\in\Gamma(i)}f^{a}(i,a)\), \(\Gamma(i)\) is the set of neighbor nodes of the node \(i\); \(a\) is the \(a\)-th node connected to node \(i\), \(a=1,2,\cdots,m\),\(f^{a}(i,a)\) is the passenger flow between node \(i\) and node \(a\).
# Carbon Dots with Up-Conversion Luminescence as pH Nanosensor
## 1 Introduction
It should also be noted that CDs' UCL with different surface functionalization has not yet been studied, although it is well known that the surface properties of nanoparticles significantly affect their properties, primarily colloidal and optical [26; 27]. Despite the contradictions in the results of studying the properties of CDs' UCL, there are many examples of successful application of CDs with UCL in various fields. The main application of CDs with UCL is the optical visualization of nanoparticles in cancer cells and biological tissues [9; 28], in a multiphoton confocal microscope [18], for photothermal therapy of cancerous tumors [12]. It was shown that CDs' UCL could be detected in tissue from a depth up to 1800 \(\upmu\)m [21], which is an important result from the point of view of CDs applicability in biological imaging. The authors of [22] suggested using CDs with UCL for thermosensing. Due to the fact that some substances quench UCL upon interaction with CDs, it was proposed to use CDs with UCL as nanosensors for hypochlorite [29], hydrogen sulfide [30], and Cu\({}^{2+}\) ions [31]. CDs with UCL can be used as a laser amplification medium in low-threshold diode-pumped lasers [32]. While pH nanosensors on the basis of CDs' Stokes luminescence have been actively developed [8; 33; 34], there are almost no publications devoted to the CDs' UCL dependence on pH. At the time of writing, we managed to find only one paper, in which such dependence was shown: intensity of UCL (emission with the maximum around 640 nm upon the excitation at 690 nm with pulse laser) for the studied CDs was shown to significantly change in cells upon their incubation at buffers with different pH (from 4 to 8), at tumor/normal muscle mice tissue (pH 6.5-6.8 against pH 7.4), and in zebrafish injected with CDs in buffers of different pH (from 4 to 8). Unfortunately, while this publication presents a dependence on the Stokes luminescence spectra for CDs on pH in wide region 0.5-9 (excitation at 573 nm), the same was not the case for their UCL [35]. The advantage of this nanosensor, based on UCL is that excitation of up-conversion luminescence does not lead to autolumin-nescence of the biological medium. This article presents the results of studying the dependence of the intensity of UCL on the wavelength and on the power of the exciting radiation for different functionalized CDs synthesized by the hydrothermal method. The features of CDs' UCL with a functionalized surface were studied for the first time. The possibility of using CDs with UCL as a pHnanosensor is shown. For the first time, the accuracy of determining the pH of an aqueous solution and biological tissue using the CDs' UCL spectra was estimated.
## 4 Convergence Analysis
That is, \(\left|a+e^{j\beta}\Gamma_{i}B_{i}C_{i}(K_{i}+\varphi_{i})[(e^{j\omega}-1)(e^{ j\beta}-A_{i})]^{-1}\right|>\left|K_{i}\Gamma_{i}B_{i}C_{i}[(e^{j\omega}-1)(e^{j \beta}-A_{i})]^{-1}\right|\) for all \(i=1,2,\cdots,n\), \(\beta\in[-\pi,\pi)\), \(\omega\in[-\pi,\pi)\), and \(\omega\
eq 0\), when \(a\geq 1\). We denote \(f_{i}(a)\) as  
\[f_{i}(a)=\left|a(e^{j\omega}-1)(e^{j\beta}-A_{i})+e^{j\beta}\Gamma_{i}B_{i}C_{ i}(K_{i}+\varphi_{i})\right|^{2} \tag{29}\]  
Note that \(\left|a+\frac{e^{j\beta}\Gamma_{i}B_{i}C_{i}(K_{i}+\varphi_{i})}{(e^{j\omega}- 1)(e^{j\beta}-A_{i})}\right|^{2}-\left|\frac{K_{i}\Gamma_{i}B_{i}C_{i}}{(e^{j \omega}-1)(e^{j\beta}-A_{i})}\right|^{2}=\left|\frac{1}{(e^{j\omega}-1)(e^{j \beta}-A_{i})}\right|^{2}f_{i}(a)\) for all \(a\geq 1\), \(\beta\in[-\pi,\pi)\), \(\omega\
eq 0\). So, \(\left|a+\frac{e^{j\beta}\Gamma_{i}B_{i}C_{i}(K_{i}+\varphi_{i})}{(e^{j\omega}- 1)(e^{j\beta}-A_{i})}\right|^{2}-\left|\frac{K_{i}\Gamma_{i}B_{i}C_{i}}{(e^{j \omega}-1)(e^{j\beta}-A_{i})}\right|^{2}>0\) as long as \(f_{i}(a)>0\) for all \(a\geq 1\), \(\beta\in[-\pi,\pi)\), \(\omega\in[-\pi,\pi)\), and \(\omega\
eq 0\). Further, we can show that  
\[ \begin{split} f_{i}(a)&=2(1-\cos\omega)(1+A_{i}^{2 }-2A_{i}\cos\beta)a^{2}\\ +2a\Gamma_{i}B_{i}C_{i}(K_{i}+\varphi_{i})[\cos\omega-1-A_{i}\cos \left(\omega-\beta\right)+A_{i}\cos\left(\beta\right)]+(\Gamma_{i}B_{i}C_{i})^ {2}(2K_{i}\varphi_{i}+\varphi_{i}^{2}).\end{split}  \tag{30}\]  
Using the conditions of Theorem 1, we can prove \(f_{i}(a)>0\) for all \(a\geq 1\), \(\beta\in[-\pi,\pi)\), \(\omega\in[-\pi,\pi)\), and \(\omega\
eq 0\) (see Appendix of [17]). Then the eigenloci of \(\lambda[F(\omega,\beta)]\) for all \(\beta\in[-\pi,\pi)\), \(\omega\in[-\pi,\pi)\), and \(\omega\
eq 0\) does not enclose the point \((-1,j0)\). The modules of all roots of (25) are smaller than units. That is, the system achieves a consensus asymptotically. Multiply \(\mathbf{z}-1\) on both sides of Equation (21) and take the limit  
\[\lim_{\mathbf{z}\to 1}[\mathbf{z}(\mathbf{z}-1)I+zH(z)[\mathcal{L}^{\prime}( \mathbf{z})+\Phi]](\mathbf{z}-1)\mathbb{E}_{z}(\mathbf{z})=0 \tag{31}\]  
According to the final value theorem, we get  
\[[zH(z)[\mathcal{L}+\Phi]]E_{z}(\infty)=0 \tag{32}\]  
As the previous certificate, \(det[zH(z)[\mathcal{L}+\Phi]]\
eq 0\). Thus, \(rank[zH(z)[\mathcal{L}+\Phi]]=n\). Based on Sylvester inequality, we get \(rank[zH(z)[\mathcal{L}+\Phi]]+rank(E_{z}(\infty))\leq n\). Thus \(rank[E_{z}(\infty)]=0\) and the final value of \(E_{z}(k)\) is \(\mathbf{0}_{n}\). Theorem 1 is thus proved. When considering random communication packet dropout, the ILC algorithm can converge using the compensation method we proposed. When considering the random packet loss, the ILC algorithm can use this proposed compensation method to converge. Theorem 1 gives the convergence condition of learning gain design and proves that the convergence condition is independent of communication packet loss. According to the final value theorem, the error of formation tracking converges to zero as the number of iterations goes to infinity.
## 3 Methodology
### Sentiment Analysis Model Improvement
#### 3.3.1 Bert  
Figure 5 shows the processing data structure of the BERT model (the model pretraining layer), in which cleansing data from the second stage (data preprocessing) are first transmitted into the third stage (left side of Figure 4). The preprocessed text K will obtain three vector representations: token embedding, position embedding, and segment embedding. These vector representations will be added element-by-element to generate the embedded segment \(\{[cls],Tok1,Tok2,\ldots,TokN\}\). In turn, it is used as input to the BERT encoding layer and is packaged as \(\{E_{cls},E_{1},E_{2},\ldots,E_{n}\}\). Second, the input sequence is pretrained by L-Transformer network layers to refine the token-level features layer by layer. The final word vector \(T^{n\text{=m}}=\{T_{cls},T_{1},T_{2},\ldots,T_{n}\}\), containing the contextual semantics, is obtained, where the dimension of the word vector is defined as m and the sentence length is defined as \(n\).  
The algorithm for calculating this model is shown in Equation (2), where \(Q\) and \(K\) in Equation (2) are the feature vectors for calculating the attention weight, \(V\) is the input  
Figure 5: The structure of BERT.  
feature vector, and _Attention_() is the corresponding weight multiplied by \(V\) according to the degree of attention.  
\[head_{i}=Attention(QW_{q^{\prime}}^{i},KW_{k}^{i},VW_{v}^{i}) \tag{2}\]  
Equation (3) is calculated by first calculating a single attention mechanism \(head_{i}\) (\(i\in(1,n)\)). Second, multiple self-attentive mechanisms are computed in parallel, and then, the output is obtained by multiplying the randomly initialized matrix \(W\) by \(n\) groups and finally by a linear transformation.  
\[Multihead=concat(head_{1},head_{2},\dots,head_{n})W \tag{3}\]  
The model separates the text in the opinion dataset word by word and generates dynamic vectors, which not only avoids ambiguity caused by word separation but also solves the problem of a word having multiple meanings by combining it with its contextual sense.
# Intelligent Deep-Q-Network-Based Energy Management for an Isolated Microgrid
## 4 Results and Discussion
### Performance under Various Conditions
#### 4.4.2 Scenario 2  
The second scenario aimed at demonstrating the performance of the proposed DQN approach with the operation of the diesel generator. Similar to the previous case, Figure 12 shows the PV and WT productions and the load demand, while Figure 13 demonstrates the performance of the DQN- and CD-based methods. No renewable energy was available at the beginning. The level of SOC was 45%, and the amount of hydrogen in the tank was 10%. Thus, the diesel generator was forced to operate when power deficit occurred.  
From 0 to 4 time-steps, since no power was available from PV and WT, the battery discharged to its lower limit of 30%. The fuel cell supplied the power demand from 5 to 7 time-steps, resulting in the reduction of the hydrogen level. Since the power deficit persisted, the diesel generator turned on from 6 to 8 time-steps. The battery was charged fully from 9 to 15 time-steps when more power was produced by PV and WT. Similarly, extra power was used by the electrolyzer to generate hydrogen. After that, the battery discharged from 23 to 34 time-steps, and charged to its upper limit by step 38. The diesel  
Figure 12: Load demand, PV, and WT power in Scenario 2.  
Figure 13: Comparison between the DQN and CD methods in Scenario 2.  
generator and the fuel cell remained off due to no consumption. The operating time of the diesel generator under both methods was 2 h. However, the proposed DQN method only consumed 353 L, while the CD-based method consumed 492 L.
## Methodology
### Overview of the New Simulation System CCTSim
#### 2.2.3 Scoring  
The scores of travel plans are used to measure the actual performance of the plans, and they represent the basis of the replanning module. The CCTSim scoring function is loosely based on the MATSim utility function. However, it does not contain activity components, and some changes are made to the travel-related components.  
The utility of a travel plan \(U_{p}\) is divided into two parts, which represent going to work (\(U_{P-GTW}\)) and getting off work (\(U_{P-GOW}\)).  
1. The going to work travel plan \(U_{P-GTW}\) is computed as the travel utility \(U_{\text{Travel}}\) plus the arrival utility \(U_{\text{Arrival}}\): \[U_{P-GTW}=U_{\text{Travel}}\ +\ U_{\text{Arrival}}\] \[U_{\text{Travel}}=\left\{\begin{array}{l}(T_{Empirical}\ -\ T_{Real})\ +\ ( \frac{T_{\text{Empirical}}}{T_{Real}})^{a},\ T_{Empirical}>T_{Real}\\ (T_{Empirical}\ -\ T_{Real})\ -\ (\frac{T_{Empirical}}{T_{Real}})^{-a},\ T_{Empirical}<T_{Real }\end{array}\right.\] (1) \[U_{\text{Arrival}}=\left\{\begin{array}{l}b\cdot(t_{Arrival}\ -\ t_{O})\ +\ 100,\ t_{Arrival}<t_{O},\ b>0,\ t_{O}=9:00\,\text{a.m.}\\ c\cdot(t_{Arrival}\ -\ t_{O}),\ t_{Arrival}>t_{O},\ c<0,\ t_{O}=9:00\,\text{a.m.} \end{array}\right.\] where \(T_{Empirical}\) is the empirical travel time; \(T_{Real}\) is the real travel time; \(t_{Arrival}\) is the arrival time; and \(t_{O}\) is the obligatory arrival time, which is set to 9:00 a.m. \(a,b,c\) are three parameters, for which the default values are 4, 5/3 and \(-12\) in the simulations described here.
2. Because there is no obligatory arrival time when getting off work, the getting-off-work travel plan \(U_{P-GOW}\) is computed as the travel utility \(U_{\text{Travel}}\): \[U_{P-GOW}=U_{\text{Travel}}\] \[U_{\text{Travel}}=\left\{\begin{array}{l}(T_{Empirical}-T_{Real})+( \frac{T_{\text{Empirical}}}{T_{Real}})^{a},\ T_{Empirical}>T_{Real}\\ (T_{Empirical}-T_{Real})-(\frac{T_{Empirical}}{T_{Real}})^{-a},\ T_{Empirical}<T_{ Real}\end{array}\right.\] where the variables have the same meanings as in Equation (1), above.  
Note that the travel utility takes the absolute and relative differences between both the empirical and real travel times into account. In addition, the performance of a given plan is better if the arrival time is close to the obligatory arrival time, rather than the earlier the better. However, the arrival time cannot be later than the obligatory arrival time. A severe late arrival penalty is assigned to travel plans that do not satisfy this constraint.