esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Data Analysis and Main Findings
### Reliability Scale  
A reliability test was conducted using the quantitative data to ensure their reliability and the internal consistency of the structures of the proposed research model, which is a basic requirement when adapting a theoretical framework to ensure that abnormal elements are removed. As recommended by multiple meta-analyses [59; 62], this present study used a minimum of 0.75. Table 2 depicts the Cronbach's alpha (\(\alpha\)) of the eight constructs in the UTAUT framework. The \(\alpha\) ranged from 0.601 to 0.880, indicating the internal consistency and reliability of the survey.
# Light-Controlled Microbots in Biomedical Application: A Review
## 4 Aspects of Design
### Manipulation of Microbots
#### 4.1.3 Electrothermoplasmonic Nanotweezers  
In conventional optical tweezers, a high-resolution trapping is difficult to achieve because of the diffraction-limited trapping potential well. Moreover, a high laser power is required to form a stiff trap that can be transported over long distances. To overcome this, plasmonic tweezers came into action. In plasmonic tweezers, surface plasmons are used to create confined hotspots that allow for more precise trapping of diverse nanostructures and materials. This review of plasmonic tweezers has discussed the birth of technology and the uses and limitations of the system [141]. Plasmonic nanotweezers systems can produce a highly confined and stronger electromagnetic field in the region of nanoantennas. This allows one to generate strong optical gradient force, and the intensified electromagnetic field provides a high optical gradient force and confined trapping potential. This allows stable trapping for the nano-level particles. However, this technique has its drawbacks. The trapping largely depends on Brownian diffusion, which can be sluggish for some applications. There is also the possibility of losing dynamic control. [PERSON] et al. proposed a novel nanotweezer technique called Hybrid Electrothermoplasmonic Nanotweezer (HENT) [142]. In a HENT, the plasmonic nanoantenna's intrinsic photo-induced heating is paired with an external AC electric field to produce on-demand large-scale microfluidic flow. The induced microfluidic flow allows suspended nanoparticles to be delivered quickly to a lit plasmonic nanoantenna, and the trapping of nanoparticles takes place in a matter of seconds.
# The Corrosion Failure Mechanism of a Peak Load Boiler in a District Heating System
## 3 Results
### Corrosion Product and Element Analysis
The detected ionic species primarily originate from boiler water treatment chemicals used for pH control, scale inhibition, and corrosion prevention. Sulfate (SO\({}_{4}\)\({}^{2-}\)) is commonly introduced through sodium sulfate (Na\({}_{2}\)SO\({}_{4}\)), which is used in water treatment to regulate pH and minimize scale formation. Chloride (Cl\({}^{-}\)) can result from sodium chloride (NaCl), which may be added for water softening or introduced as an impurity. Nitrate (NO\({}_{3}\)\({}^{-}\)) is often associated with oxygen scavengers or nitrate-based corrosion inhibitors, while phosphate (PO\({}_{4}\)\({}^{3-}\)) is typically added as a scale inhibitor [24; 25; 26]. These findings align with prior studies that highlight the role of mixed-phase oxides in intensifying corrosion susceptibility under environmental and operational conditions [20; 21], further supporting the conclusion that boiler water leakage introduced corrosive ionic species into the convection section, leading to sodium salt deposition, acidification, and localized corrosion. Given that acidic condensation is a critical factor in corrosion acceleration, it is essential to determine whether operating conditions within the convection section facilitate dew point formation. To further investigate this aspect, computational fluid dynamics (CFD) simulations were conducted to evaluate the thermal distribution within the tubes and assess whether localized cooling effects could lead to dew point condensation and subsequent corrosion initiation.
## 2 Related Research  
### AI-Based Network Intrusion Detection System  
Much of the recent research on NIDS has focused on deploying machine learning algorithms and artificial intelligence techniques to improve traditional NIDS systems. [PERSON] et al. [1] used the NSL-KDD dataset and applied machine learning algorithms, including KNN and SVM, to enhance traditional NIDS systems. [PERSON] et al. [2] used the BoT-IoT dataset and principal component analysis (PCA) techniques to obtain the best features and then leveraged machine learning algorithms, including Bayesian classifiers (naive Bayesian), J48 decision trees, and random forests, to detect DDoS attacks on IoT devices. [PERSON] et al. [3] proposed a set of data preprocessing frameworks, using PCA technology to screen features and applying the density-based clustering analysis algorithm (DBSCAN) to eliminate anomalous data. Using the DARPA99 dataset, the study showed that the proposed architecture could effectively improve the accuracy of detecting cyber-attacks. [PERSON] et al. [4] used both the KDD99 and the NSL-KDD datasets, applied information gain technology for feature selection and data preprocessing, and then utilized machine learning algorithms and deep learning algorithms, including the SVM, MLP, and CNN algorithms. The study's experiments revealed that the MLP classifier is the classifier with the highest accuracy rate.  
Our study found few studies discussing the problem of data imbalance in NIDS systems. None of the aforementioned related studies have taken into account that the amount of data is too scarce for some attack types in a network intrusion system, resulting in a significant imbalance in the data distribution of datasets. We found that for some attack types with rare data, if we only focus on applying different classification algorithms and feature selection methods, it is difficult to improve the identification accuracy of Probe, U2R, R2L, and other rare attack types.
# Confidence Learning for Semi-Supervised Acoustic Event Detection
## 3 Experiments and Discussion  
### Dataset and Metrics  
We carried out experiments on the DCASE 2019 Task4 dataset [12], DCASE 2018 Task4 dataset [11]. Since these two datasets contain the same ten sound events, we take the training set of DCASE 2019 as our training set, including synthetic strongly labeled (2045 clips), weakly labeled (1578 clips), and unlabeled (14,412 clips). The validation set (1168 clips) of DCASE 2019 and the evaluation set of DCASE 2018 (800 clips) are used for tests.  
These two datasets consist of 10 classes of sound events in a domestic environment: Speech, Dog, Cat, Alarm/bell/ringing, Dishes, Frying, blender, Running water, Vacuum cleaner, and Electric shaver/toothbrush. The sampling rate is 44,100 Hz. The duration of each audio clip is around 10 s, and multiple audio events may occur at the same time.  
In addition, we select clips with multiple events from the validation set of DCASE 2019 and name the set _polyset_. This set is used to evaluate the performance for the case with two or more acoustics events in a clip. The polyset contains 331 clips.  
Experiments were evaluated with event-based macro-average error rate (ER) with a 200 ms collar on onsets and a 200 ms/20% of the events length collar on offsets. The formula is  
\[ER=\frac{\sum_{i=1}^{c}\frac{I(i)+D(i)}{N(i)}}{c}, \tag{10}\]  
where \(c\) is the number of sound event types, \(I(i)\), the insertion error, represents the number of sound events \(i\) generated by model outputs that do not appear in the clips actually. \(D(i)\), deletion error, is the number of sound events \(i\) in the clips that were not identified by the model. \(N(i)\) is the number of active sound events \(i\) in the reference. A low ER indicates a more accurate SED system. The specific evaluation details can be found in [26].
## 4 Experimental Results and Comparisons
### Experiments on the Benchmark Dataset NEU  
In the surface-defects dataset of Northeast University (NEU) [31], six typical surface defects of hot-rolling steel strip were collected: crazing (Cr), inclusion (In), patches (Pa), pitted surface (PS), rolled-in scale (RS), and scratches (Sc). Each defect contained 300 labeled grayscale images with an original resolution of \(200\times 200\). Figure 2 shows examples of the defective images in NEU. The defects in the same columns belong to the intra-class defects, and the different columns belong to the inter-class defects. In the figure, it is clear that the inter-class divergence is small, but the intra-class divergence varies. However, the appearance of inter-class defects can be different; for example, the scratches may be horizontal, vertical, or slanting. Moreover, intra-class defects may also have similar aspects, such as the rolled-in scale, crazing, and pitted surfaces. In addition, due to the influence of illumination and material changes, the grayscale of the intra-class defect images varies.  
The Inception_v2, which has been successfully adopted in previous studies, is an effective network for defects detection in the NEU dataset. In this case, we still took this network initially to compare and analyze the experimental results. The main advantage of the Inception network is that it changes the serial target detection network into a parallel computing mode, which can reduce the optimization parameters in the deep learning network, reduce the amount of computation, and optimize the computing speed.  
During the experiment, it first randomly initializes the weight parameters of Inception_v2, and then it selects random samples from the sample dataset to pretrain the network. Then the softmax classifier layer is removed from the pretrained model to form a feature-extraction module, and a 2048-dimensional-feature vector is obtained by inputting all samples into the feature-extraction module by category. In order to reduce the amount of computation, PCA is adopted to reduce the dimension of the extracted 2048-dimensional-feature vector to obtain a 256-dimensional-feature vector, and then the 256-dimensional-feature vector is processed according to the method flow in Section 3.  
Extensive experiments were conducted to evaluate the efficiency of the proposed method. We compared four training strategies, which are described as follows:  
* Strategy 1: The model is trained with the standard Inception_v2 architecture and the proposed method with the original NEU dataset.
* Strategy 2: The model is trained with the standard Inception_v2 architecture and the proposed method with the NEU dataset after data augmentation.
* Strategy 3: The model is trained with standard Inception_v2 architecture and the proposed method with the increasing percentage of the noisy labels in training set.
* Strategy 4: The model is trained by the proposed method and recent state-of-the-art methods with the NEU dataset after data augmentation (50% noisy labels in training set).
## 1 Introduction
Typically the sensors used are not part of the standard equipment of a turbine but of a dedicated condition monitoring system (CMS). Therefore, such a system needs to be installed explicitly and this is associated with further costs [11]. Occasionally, such high-resolution operating data are--partly--available from SCADA systems and could be used for the same purpose. As stated earlier, data storage and transfer are associated with expenses. Therefore, for a cost optimization it is necessary to find a trade-off between reducing the amount of data, i.e., signal scope or resolution, and retaining enough information content to support O&M strategies. To illustrate the information loss Figure 1 shows the outcome of temporal aggregations of SCADA data at different time resolutions. Large aggregation intervals in the order of 300 to 600 s are not able to capture all nuances of the signal as local minima and maxima are flattened in the mean value curves. Clearly, this is only an example obtained for the wind speed and one temperature of the transmission shaft bearings, but similar behaviors can be observed for all signals. Nevertheless, the figure gives an idea of the possible effects of temporal aggregation. A thorough study of the phenomena of information loss due to temporal data aggregation in the context of wind turbine SCADA data will be presented throughout this paper. The objective is to provide a deep analysis of the crucial points when aggregating SCADA data and to quantify the span of the information loss phenomenon. We also want to support turbine operators with a framework that allows them to take better decisions in terms of SCADA data storage and aggregation policies. We break the general motivation down into these three specific research questions:  
* Q1: How much information is lost with reduced temporal resolution? * Q2: Do external conditions have an effect on information loss? * Q3: What is the recommended aggregation frequency? This paper is structured as follows: Section 2 provides a review of related work dealing with wind turbine monitoring and the use of high-frequency data in wind and other application sectors. Section 3 details the data set used in this study. Then, Section 4 tackles the key questions of this paper. Each subsection guides through our analytical approaches to answer these question and the corresponding results obtained. A discussion of the salient points derived from this study is also included for each question. Finally, Section 5 draws conclusions and discusses limitations of the study, the most relevant information, and ideas for future work.
# Current Progress in Femtosecond Laser Ablation/Ionisation Time-of-Flight Mass Spectrometry
## 5 Applications
### Material Science  
Many important processes such as corrosion, surface weathering, catalysis or surface contamination need information on the chemical composition of the material. Further, material purity control and contamination are an important task in preparation of metals and alloys, semiconductors, insulators (glasses, ceramics) and layered materials which are used frequently in the semiconductor industry. Again, due to its high sensitivity and multielement detection capabilities, fs-LIMS is an ideal method for such studies.
# Optimization of Multimodal Discrete Network Design Problems Based on Super Networks
## 3 Formulating the Multimodal Discrete Network Design Problem
### The Generalized Cost Formulas of Entering Links and Leaving Links  
(1) Travel time  
In the actual transportation system, entering links \(A^{E,k}\), \(\forall k\in\{r,p\}\) indicate the process of reaching the stations, which mainly include walking time and waiting time; entering links \(A^{E,k}\), \(k=\{c\}\) express the process of reaching the parking lot, which mostly includes walking time. Therefore, it can be concluded that the travel time of the entering links mainly consists of walking time and waiting time. Consequently, the travel time of the entering link can be formulated as:  
\[T^{E,k}_{ij}=t^{E,k}_{walk,ij}+t^{E,k}_{wait,ij},\ \ \ \forall(i,j)\in A^{E,k},k\in K, \tag{2}\]  
where \(t^{E,k}_{walk,ij}\): the average walking time of the entering links and \(t^{E,k}_{wait,ij}\): the waiting time of the entering links. In this paper, it is set that \(t^{E,k}_{wait,ij}=\frac{1}{2f},\forall(i,j)\in A^{E,k},k\in\{p,r\}\), \(f\) is the departure frequency, and \(t^{E,k}_{wait,ij}=0,\ \forall(i,j)\in A^{E,k},k=\{c\}\).  
Meanwhile, in the actual transportation system, leaving links \(A^{L,k}\) (\(\forall k\in K\)) show the process of arriving at the destination, which mostly contains walking time. Hence, the travel time of the leaving links can be described as:  
\[T^{L,k}_{ij}=t^{L,k}_{walk,ij},\ \ \ \forall\ (i,j)\in A^{L,k},k\in K \tag{3}\]  
where \(t^{L,k}_{walk,ij}\) is the average walking time of leaving links.  
(2) Monetary cost  
Since there is no monetary cost on the entering links and leaving links, we set the monetary cost to zero, which is formulated as:  
\[P^{E,k}_{ij}=P^{L,k}_{ij}=0,\ \forall(i,j)\in A^{E,k}\cup A^{L,k},k\in K \tag{4}\]  
where \(P^{E,k}_{ij}\): the monetary cost of entering links and \(P^{L,k}_{ij}\): the monetary cost of leaving links.  
(3) Comfort loss  
Since the trip is very short and the value of comfort loss is very small, we also set the comfort loss to zero on the entering links and leaving links. The equation is as follows:  
\[S^{E,k}_{ij}=S^{L,k}_{ij}=0,\ \forall(i,j)\in A^{E,k}\cup A^{L,k},k\in K \tag{5}\]where \(S^{E,k}_{ij}\): the comfort loss of entering links and \(S^{L,k}_{ij}\): the comfort loss of leaving links.  
(4) Risk reserve time  
According to travel time, so we set the risk reservation time of the entering links as:  
\[R^{E,k}_{ij}=\left(\rho^{E,k}_{ij}-1\right)T^{E,k}_{ij},\ \forall(i,j)\in A^{E,k},k\in K \tag{6}\]  
where \(\rho^{E,k}_{ij}\): the delay parameter of the entering links, and \(\rho^{E,k}_{ij}>1,\ \forall(i,j)\in A^{E,k},k\in K\).  
Similarly, the risk reservation time of the leaving links is formulated as:  
\[R^{L,k}_{ij}=\left(\rho^{L,k}_{ij}-1\right)T^{L,k}_{ij},\ \forall(i,j)\in A^{L },k\in K \tag{7}\]  
where \(\rho^{L,k}_{ij}\): the delay parameter of leaving links and \(\rho^{L,k}_{ij}>1,\ \forall(i,j)\in A^{L},k\in K\).
# 2D/3D Multimode Medical Image Registration Based on Normalized Cross-Correlation
## 2 Materials and Methods
### Methods  
#### 2.2.1 Normalized Cross-Correlation Based on Sobel Operator  
In this paper, the normalized cross-correlation based on the Sobel operator (NCCS) is proposed by combining gradient vector angle with normalized cross-correlation [39].  
The gradient vectors of the reference image \(I_{R}\) and DRR image \(I_{D}\) at pixel coordinates \((x,y)\) can be expressed by \(\
abla I_{R}(x,y)\) and \(\
abla I_{D}(x,y)\), respectively, then the angle \(\tau(x,y)\) between the reference image \(I_{R}\) and DRR image \(I_{D}\) at pixel coordinates \((x,y)\) can be expressed by (2).  
\[\tau(x,y)=arccos\frac{\
abla I_{R}(x,y)\
abla I_{D}(x,y)}{|\
abla I_{R}(x,y)| |\
abla I_{D}(x,y)|} \tag{2}\]  
We can achieve the registration by comparing the angle between the reference image and the DRR image. The smaller the angle between the gradient vectors, the more similar the 2D reference image and the DRR image and the more accurate the registration result between the 2D reference image and the 3D floating image. In order to make the angle between gradient vectors and normalized cross-correlation function have the same positive and negative trend, the weight function shown in (3) is adopted.  
\[\omega(\tau)=\frac{cos(\tau)+1}{2} \tag{3}\]  
The range of gradient vector angle is \([0,\pi]\), and the range of \(\omega\) is \([0,1]\). When the gradient vector angle between the 2D reference image and the DRR image is equal to zero, the registration result is the worst when the two images' gradient vector angle is equal to zero. Then, the sum of the weight functions \(\omega(\tau)\) at all pixels corresponding to the 2D reference image and DRR image can measure the similarity between the 2D reference image and the DRR image [40], and the position relationship between the 2D reference image and the 3D floating image can be measured, as shown in (4).  
\[G(I_{R},I_{D})=\sum_{(x,y)\in(I_{R}\cap I_{D})}\omega(\tau(x,y)) \tag{4}\]  
The final similarity measure \(S1(I_{R},I_{D})\) is obtained by multiplying the gradient term \(G(I_{R},I_{D})\) with the normalized cross-correlation coefficient. As shown in (5), the gradient information is introduced into the term to enhance the rotation sensitivity by \(G(I_{R},I_{D})\). The optimization process based on the Sobel operator is shown in (6).  
\[S1(I_{R},I_{D})=G(I_{R},I_{D})NCC(I_{R},I_{D}) \tag{5}\]  
\[T_{g}=argmax_{T}\ G(I_{R},P(T(I_{M})))NCC(I_{R},P(T(I_{M}))) \tag{6}\]  
\[I_{D}=P(T(I_{M})) \tag{7}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Data** & **Size** & **Spacing (mm)** & **Pixel Range** \\ \hline CT image & 200 \(\times\) 200 \(\times\) 142 & 2 \(\times\) 2 \(\times\) 2 & \(-\)1024\(-\)2976 \\ Analog X-ray image & 256 \(\times\) 256 & 1 \(\times\) 1 & 0\(-\)255 \\ (DRR) & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Related parameters of image registration.
## 3 Cooperative Perception Information-Sharing
### The Effect of Network Performance on Cooperative Perception
#### 3.3.3 Congestion Control
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Authors, Year** & **Key Research Points** & **Remarks** \\ \hline \hline  & & Communication distance and shelter are the main factors that cause the degradation of DSRC communication performance, and selective deployment of roadside equipment can effectively improve DSRC communication performance. \\ [PERSON] et al. 2021 [100] & \
\begin{tabular}{c} Analyzed the impact of communication distance on packet reception rates in LoS and NLoS test scenarios. \\ \end{tabular} &
\begin{tabular}{c} Communication distance has a great influence on the reception rate of data packets. The greater the communication distance, the more serious the loss of packet reception rate. \\ \end{tabular} \\ \hline \hline  & & By predicting data changes and using historical data, the accuracy of data fusion can be improved, and the detection accuracy is nearly 50\% higher than that of lossy networks. \\ \end{tabular}
\end{table} 
Table 8: Summary of the possible impact of network performance on cooperative perception. [PERSON] et al. [113] first explored the impact of packet rate-degraded congestion control protocols on the application-level performance of vehicular networks. The results show that the method of controlling vehicle packet loss can reduce the flow of data packets transmitted to the wireless channel and improve communication performance. However, dropped packets are not transmitted, resulting in the reduced performance of the application and a negative impact on the co-awareness effect. As shown in Figures 10 and 11, the radio performance and application-level performance are analyzed for the PLR at traffic densities of 120 veh/km and 180 veh/km, respectively. If the DCC mechanism is not used, both reactive and adaptive methods can improve radio performance, but the application-level performance decreases. The main reason is that application-level packets are not discarded by DCC, as shown in Figure 10. At a traffic density of 120 veh/km, the radio performance is hardly affected, and the application-level performance still drops a large number of packets due to the reactive method, resulting in application-level performance degradation. Furthermore, there is no benefit to using DCC for the application-level evaluation of the packet delivery ratio (PDR) at medium and low traffic densities, as shown in Figure 11.