esa-sceva/satcom-chunk-collection huggingface dataset:
# Real-Time, Content-Based Communication Load Reduction in the Internet of Multimedia Things
## 4 Results
### Video Quality Metrics
#### 4.3.1 Structural Similarity (SSIM) Index  
SSIM is used for the purpose of identifying degraded quality or loss in image information. The index's value lies between 0 and 1, where near 0 means little to no similarity and 1 or near to 1 means more similarity. It was introduced [65] to represent the response of the human visual system more closely than other objective measures of video quality, without the cost and overhead of subjective testing. Herein, the SSIM index is computed for complete videos. An original frame of a video is compared with the one reconstructed at the receiver after ROM substitution. Results have also been computed for those frames which are not sent due to less motion and compared with the previous frame displayed at the receiver. This is done to show how many dissimilarities exist between actual frames at the sender and those received or displayed at the receiver. Results in Figure 11 showing  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Proposed Method** & **All Frames** \\ \hline
**Video Sequence** & **fps** & \\ \hline Walk & 11 & 12 \\ Ground & 6 & 3 \\ Exercise & 19 & 12 \\ Dval Street & 6 & 9 \\ Football & 17 & 10 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of fps for the proposed method and the All Frames method.  
Figure 10: Bit rates with and without encryption for five evaluation videos.  
average SSIM for all five video sequences. Average SSIM is within a range of 0.96 to 0.98 showing very few dissimilarities.  
Though, SSIM, along with PSNR, is generally employed by researchers as a way of comparing their results, other video quality metrics have been surveyed and evaluated, such as in Reference [66] or Reference [67]. Unfortunately [68], it is difficult to make conclusions based on such comparative studies because the test images, testing procedures and applications differed. Ideally, subjective assessment is required to assess video quality. Unfortunately, managers of video streaming normally do not have access to a panel of viewers, owing principally to: time restrictions; and the difficulty of assembling a suitable set of viewers. However, objective subjective ratings can approximate the results of subjective testing with a high degree of correlation. For example, the Video Quality Experts Group (VQEG) Full-Reference Television (FR-TV) Phase II tests [69] for VQM [70], resulting in Pearson linear correlation coefficients (PCCs) with difference Mean Opinion Score (MOS) from subjective tests, of above 0.9 (out of a maximum of one). Following the VQEG evaluation procedure SSIM also resulted in correlations well above 0.9 and in Reference [71] outperformed four other models on the authors' still image database. Since the original SSIM presentation [72], a number of refinements have also occurred. These give good confidence that SSIM along with VQM are excellent objective measures of MOS. However, the computation overhead resulting from VQM is high, which is why SSIM is generally preferred by researchers to SSIM.
# Recent Advances in Non-Traditional Elastic Wave Manipulation by Macroscopic Artificial Structures
## 2 Elastic Metamaterials
### Elastic Hyperlens
The overdot denotes a time derivative and \(<\!>\) denotes the ensemble average. Note that \(\mathbf{S}_{eff}\) and \(\mathbf{\hat{S}}_{eff}\) are called Willis coupling tensors. Willis coupling requires coupling between strain and momentum fields or stress and velocity fields (Equation (10)). Several approximations and simplifications to the general approach of Willis media have been published [117, 118, 119, 120, 121, 122, 123]. In fact, most studies of Willis coupling are more active in the acoustics [124, 125, 126, 127, 128, 129, 130] than in elastodynamics, but these developments in acoustics indicate that Willis metamaterial for elastic waves in solid may become practical. Recently, Willis metamaterial for flexural waves has been reported [131]. Based on a cantilever bending resonance, effective bianisotropy in a Willis metamaterial for flexural waves was designed and experimentally demonstrated.
# Research on Intelligent Fault Diagnosis of Rolling Bearing Based on Improved Deep Residual Network
## 4 Experimental Verifications
### Performance Comparison  
We did a comparison for the designed method with the modified CNN, SVM, KNN and DPBN. The results are shown in Table 5. The modified CNN gets an accuracy of 98.2%. The accuracy achieved using KNN is 91.9%. The accuracy of Support vector machine (SVM) is 94.1%. The accuracy of the DPBN is 92.3%. The comparison confirms that the improved ResNet method designed in this paper conducts better performance than the methods such as DPBN, SVM, and KNN and the existing CNN.  
This has been possible for the reason of the GAP involved in the improved ResNet. The GAP assures fewer amount of training parameters are needed and keep away overfitting troubles to training data. This GAP realizes deep learning and guarantees excellent defect identification result even if the data is unseen.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Diagnosis**} & \multicolumn{2}{c}{**The Improved ResNet**} & \multicolumn{2}{c}{**The Improved CNN**} & \multirow{2}{*}{**SVM**} & \multirow{2}{*}{**KNN**} & \multirow{2}{*}{**DPBN**} \\  & 228.53 s & 229.53 s & 2.264 s & 0.049 s & 327.09 s \\ Testing time & 0.193 s & 0.198 s & 0.829 s & 1.322 s & 0.141 s \\ \hline
**Fault category** & \multicolumn{5}{c}{**Accuracy \%**} \\ \hline H0 & 100 & 100 & 100 & 96.25 & 100 \\ F1 & 100 & 100 & 96.72 & 78.00 & 92.86 \\ F2 & 98 & 92.01 & 92.01 & 86.31 & 93.55 \\ F3 & 100 & 98.36 & 98.31 & 90.15 & 98.36 \\ F4 & 98 & 98.36 & 100 & 97.00 & 100 \\ F5 & 100 & 98.33 & 93.25 & 92.85 & 66.59 \\ F6 & 100 & 96.77 & 95.24 & 96.59 & 96.08 \\ F7 & 100 & 100 & 87.32 & 100 & 100 \\ F8 & 100 & 100 & 100 & 100 & 100 \\ F9 & 100 & 98.31 & 78.59 & 81.69 & 75.56 \\ Averages & 99.6 & 98.2 & 94.1 & 91.9 & 92.3 \\ \hline \hline \end{tabular}
\end{table}
Table 5: A comparison of the proposed method Vs existing methods.  
Figure 9: Confusion matrix demonstrating classification performance using testing data.
## 3 MG Overview and Experimental Test Bench  
### MG Overview  
The SSWT studied in this work is intended to be integrated in a DC MG applied to tertiary buildings. Figure 1 illustrates the topology of a DC MG. This MG consists of a small WT and photovoltaic panels as renewable energy sources. Storage is added to deal with the intermittence of power generated by this type of energy. A utility grid is also connected to the MG in order to ensure the exchange of power. All these components are connected to a common DC bus through their appropriate converters. The power system of this MG supplies a DC load (the building's electrical appliances) which is directly connected to the DC bus through a capacitor \(C_{DC}\) in order to stabilize its voltage. In this context, the DC power system is chosen thanks to its many advantages. Examples of these benefits are the fact that the DC MG could efficiently integrate many DC native power generators (photovoltaic or fuel cells), and that the majority of tertiary buildings' electrical appliances can be DC supplied [3].  
The MG is supervised by an energy management strategy which controls each physical element by an independent controller. This strategy aims to ensure the power balance between power production and power consumption [30]. Therefore, the SSWT must be driven to provide the maximum power and ensure more benefits. However, when the power provided by the WT source exceeds the needs of the MG, this could destabilize the entire system and risk damaging components, especially when they reach their physical limits. Thus, limiting the power provided by the WT source is a solution to keep the power balance and prevent any critical operating situations. In this case, LPPT mode is activated by the SSWT.
## 3 Application Results
### Data Preprocessing  
In order to establish a platform for conducting the DDMs for streamflow modeling, this section provides some preliminary steps of data normalization as one of the preprocessing methods. The goal of normalization is to change the values of numeric columns in the dataset to use a standard scale, without distorting differences in the ranges of values. There are two primary advantages of data normalization, increased consistency (reducing the possibility) and easier object-to-data mapping (normalized data schemas are closer conceptually to object-oriented schemas), according to which data needs to be normalized for DDMs.  
In the present research, firstly, streamflow data of Dizaj and Tapik stations were normalized by using Equation (3), and data standardization was calculated by Equation (4). Table 3 shows the skewness of the data before and after the normalization. Before normalization, the skewness of the Baranduzchai and Nazluchai stations was 2.055 and 2.822, respectively. Using Equation (3), parameter C was extracted by trial and error; thus, the mentioned parameters were 0.714 and 0.268 for Baranduzchai and Nazluchai, respectively. Then, after the normalization process, skewness was obtained as \(-0.000187245\) and \(0.000861473\) for Baranduzchai and Nazluchai, respectively.
# Elliptic Curve Signcryption-Based Mutual Authentication Protocol for Smart Cards
## 6 Analysis of Security Functionalities of ECSSP-SC
### Analysis of Security Attributes of ECSSP-SC
#### 6.1.6 Anonymity  
The presented ECSSP-SC satisfies user anonymity, since the identity of the \(USR\) cannot be revealed from any of the two messages transmitted in the protocol. From any component of the first message \(\{c,T,w,\ T_{U}\}\), the identity of the \(USR\) cannot be obtained because \(c\) is obtained by encrypting the \(P_{U}\) with key \(K\), which is computed secretly. Moreover, according to Assumption 3, the attacker cannot break ciphertext \(c\). As per Assumption 4, the attacker cannot deduce \(r\) from \(T\) since \(T=rG\). Moreover, an attacker fails to deduce any secret from \(w\) since it is the division of \(v\) and \(r\). Similarly, the attacker will fail to deduce any secret from the message \(\{A_{S}.T_{S}\}\), since they need to break the cipher \(A_{S}\), which is infeasible as per Assumption 3.
# A Multi-Scale Contextual Information Enhancement Network for Crack Segmentation
## 4 Experiments
### Datasets and Metrics  
We verify the effectiveness of our method on three publicly available crack datasets: DeepCrack-DB (proposed in [25]), CFD (proposed in [37]), and CCSD [53]. There are 537 images in the dataset DeepCrack-DB with a resolution of \(544\times 384\). These images contain various cracks of roads, walls, bridges, and so on. The images are relatively clear, but the background environment is complex with many kinds of disturbing factors. We split the dataset DeepCrack-DB randomly in a ratio of 8:2, resulting in 429 images in the training dataset and 108 images in the test dataset. The dataset CFD has 118 images taken from road surfaces with a resolution of \(480\times 320\). The images are blurred and the cracks are very thin, making segmentation more difficult. We remove some images from the dataset because they have obvious errors in the annotation. The remaining images are randomly partitioned in a ratio of 8:2, resulting in a training dataset with 89 images and a test dataset with 23 images. There are 458 high-resolution images in the dataset CCSD and we resize them to the resolution of \(512\times 384\). These images are taken approximately 1 m away from the surfaces and the concrete surfaces have variation in terms of surface finishes (exposed, plastering, and paint). We split the dataset CCSD randomly in a ratio of 8:2, resulting in 366 images in the training dataset and 92 images in the test dataset.  
Additionally, random image augmentation methods are adopted to each image during the network training, including methods such as rotations, flips, and brightness shifts. Some of the images of the three datasets are shown in Figure 6.  
We select intersection over union (IoU), precision, recall, and F1 score as the evaluation metrics. Their precise definitions are:  
\[IoU=\frac{TP}{TP+FP+FN} \tag{6}\]  
\[Precision=\frac{TP}{TP+FP} \tag{7}\]  
\[Recall=\frac{TP}{TP+FN} \tag{8}\]  
\[F1-Score=2\times\frac{Precision\times Recall}{Precision+Recall} \tag{9}\]  
where \(TP\) denotes the number of true positives, \(FP\) denotes the number of false positives, and \(FN\) denotes the number of false negatives. Considering that the images in dataset CFD are blurred and there are transition regions between the crack pixels and the non-crack pixels in the subjectively labeled ground truth, the two pixel points around the labeled crack pixels are also considered as \(TP\). It should be noted that this evaluation method has been commonly used in other studies [24,27,37,54].
## 3 Methodology
### BFRS Representation Based on the Weighted Clustering
#### 3.6.2 Representation Using Weighted Clustering  
With the refinement process, the detected result \(BFRS_{refined}\) is converted to each segment \(BFRS_{group}\), which is composed of consecutive points \(BFRS_{group}\{b_{1},b_{2},\cdots,b_{n}\}\). However, the BFRS is the specific position, such as the speed breaker or pothole, so the next step is to distinguish the segments \(BFRS_{group}\) from each other and convert the consecutive segment into a specific object location \(BFRS_{obj}\), as depicted in Figure 6b. The operations are as follows:  
1. Compute the difference index of each detected sampling point based on the index orders from the original recordings.  
Figure 6: The BFRS representation from segment to position. (**a**) BFRS detected by CNN. (**b**) Refined and segmented as each cluster group. (**c**) Represented by weighted cluster center.  
2. Segment detected sampling points into each group \(BFRS_{group}\) if the index difference is within the threshold value \(d\), as denoted in (15). \[BFRS_{group}=\text{Cluster}\Big{(}BFRS_{refined},d\Big{)}\] (15) 3. Each detected sampling point in \(BFRS_{group}\) is composed of the specific coordinate \(P_{i}(x,y)\) and the related probability \(BFRS_{prob}^{i}\); hence, points in each \(BFRS_{group}\) can be further integrated into a single point \(BFRS_{obj}\) (depicted in Figure 6c) based on the weight of \(BFRS_{prob}^{i}\), as denoted in (16). \[BFRS_{obj}=\text{Weight}\Big{(}BFRS_{group},BFRS_{prob}\Big{)}\] (16)  
In (15), there is a key parameter d that segments the consecutive detected sampling point into each group, and it can be selected depending on the data quality of the original recordings. Usually, d can be set as 3 if too many noisy samplings are not included. While in (16), the coordinate of \(BFRS_{obj}\) in each \(BFRS_{group}\) is automatically computed based on each detected sampling point and the related \(BFRS_{prob}\) (depicted as the graded green color in Figure 6c), and no other parameters are needed.
# Damage Detection in Beam Bridges Using Quasi-static Displacement Influence Lines
## 5 Conclusions  
In this study, a method based on quasi-static displacement ILs is proposed to detect damage in the superstructure of a beam bridge. The following conclusions are drawn:  
(i) Based on a previous method [25], an improved method based on the correlation coefficient between quasi-static displacement ILs is proposed and numerical and experimental results show that this method is sensitive to damage in the superstructure of a beam bridge. Furthermore, similar to the previous method, the proposed damage feature is independent of inconsistency in the loading conditions before and after damage. Therefore, the proposed method is suitable for detecting the damage of beam bridges using quasi-static displacement ILs.  
(ii) Considering that only a few quasi-static displacement ILs are obtained for actual bridges, we reconstructed the Hankel matrix of the damage feature. The results of numerical and experimental examples show that the new form of the Hankel matrix of the damage feature is beneficial for damage detection and it is robust to the effects of measurement noise.  
(iii) A new metric for evaluating the damage feature is proposed. Unlike the previous method [25], the calculation of this metric does not need to be repeated for each measured point of displacement in one load test; thus, the efficiency of damage detection is improved dramatically.  
(iv) Unlike the method based on the difference between displacement ILs before and after damage, a certain threshold is adopted in this study to resist the effects of measurement noise on the damage determination.  
(v) For the practical application of the proposed method, the measured data, obtained by the first quasi-static load test, have to be utilized to build the data model for reference state, therefore, it cannot detect the damage of bridge for the first load test.  
Figure 15: Comparison of the damage detection performance between the traditional method and the proposed method under different loading conditions before and after damage: (**a**) measured displacement ILs; (**b**) difference between the measured displacement ILs obtained by the traditional method; (**c**) metric of the damage feature obtained by the proposed method.  
**Author Contributions:** All authors discussed and agreed upon the idea and made scientific contributions. [PERSON] wrote the original paper; [PERSON] reviewed and modified the paper.  
**Funding:** This study is supported by the National Key Research & Development Program of China \"Strategic International Cooperation in Scientific and Technological Innovation\" Key Special Project of 2016 Annual Project (Grant No: 2016 YFE0202400) and the Ministry of Housing and Urban-rural Development of China (Grant Nos: 2016-K4-068 and 2015-K5-014).  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Simulation
### Simulation Results
As seen in Table 7, it was confirmed that total UPE (2 drms) is basically determined by the product of Total GDOP and receiver observation errors (2 \(\sigma\)), as indicated in Equation (30), with additional errors due to systematic errors, while the minimum UPE is determined by systematic errors when receiver observation errors are small (such as 0.4 m), as discussed in Section 2.3. It was also confirmed that the MDPO algorithm can provide a position accuracy of several tens of meters to a few hundred meters with 95% probability (2 drms) within a one-minute observation, depending on the receiver observation errors.
###### Abstract  
Mechanical performance monitoring of civil infrastructure using fiber Bragg grating (FBG) sensors has received significant public attention in recent years. However, there is currently little research on the mechanical performance monitoring of piles used in high-piled wharts in coastal ports during pile driving using the FBG sensor technique. Based on the properties of precast prestressed concrete piles used in high-piled wharts in coastal ports and servicing seawater environments, and the benefits of FBG sensors, the mechanical performance monitoring for precast prestressed concrete piles used in a newly-built high-piled wharf in the Tianjin Port of China is devised and deployed with the FBG sensor technique. To conduct performance monitoring of the precast prestressed concrete pile, a state-of-the-art FBG strain sensor, which is less thermosensitive and does not require temperature compensation, was used to monitor the strain status of different locations of the pile. In one pile, three of this kind of strain sensor were set near the head, middle and tip of the pile, and one FBG angle sensor was set near the head of the pile to measure the dip angle of the pile. During the testing, data were recorded for all of the details of the pile driving process. According to the data analysis, it is clear that the compressive strain at the middle of the pile during driving is larger than that near the head and tip of the pile. Therefore, the middle of the prestressed concrete pile is the key location that should be preferentially monitored during pile driving. Meanwhile, when the hammer impacts the pile continuously, the obvious tension strain at the tip of the pile increases and the maximum dynamic tension strain reaches 56 \(\mu\)e, which approaches the tension ultimate strain. This occurs because the frictional resistance of soil is small in the middle of the pile when the tip meets the significant supporting soil layer. This study can provide a reference for the mechanical performance monitoring deployment of precast prestressed concrete piles used in high-piled wharf structures in coastal ports.  
p 2017 24 March 2017; 26 April 2017; Published: 10 May 2017
## 2 Materials and Methods
### Problem Formulation  
For this study, we formulated the primary objective to be the early prediction of AKI as a binary classification task. Patients who developed AKI according to the KDIGO criteria were labeled as the positive class, while patients who did not develop AKI were labeled as the negative class. Samples were patient EHR records, with a series of vital sign values with time stamps. Each patient had a unique patient ID, \(i\), and unique admission ID, \(j\), for every ICU admission.  
Formally, \(y=f(F)\) was our target function. Our goal in early AKI prediction was to learn a function \(f(F)\) that maps a set of features \(F\) to predict the binary outcome of \(y\in\{-1,1\}\), where \(y\) is equal to 1 if the patient has a positive AKI label and \(y\) with non-AKI labels is indicated by \(-1\). Patient \(i\) was admitted to the ICU with \(j\) as their unique admission ID, while \(t\) denotes the timestamps. Time gap is the time between AKI onset  
Figure 1: Data cohort workflow.  
and how many hours in advance before the onset do we want to predict the onset event without available data during the gap but using the data from feature window instead. In our study, we denote time gap as \(T_{TP}=[0,48]\) h before the onset. Feature window \(T_{FW}=[24,48]\) is the number of hours before AKI onset, and we collect data with in this time frame. The collected data during this \(T_{FW}\) time is being used to predict if the patient will have AKI onset.  
Our main objective was to study the relationship between AKI onset prediction with different time gaps and feature windows using the vital sign feature engineering framework we developed. Specifically, we considered the following two time ranges:  
1. Time gap \(T_{TP}\) variation: The time gap from AKI onset to 48 h \(T_{TP}=[0,48]\) before onset, using a feature window \(T_{FW}=24\) of 24 h. In another word, we conducted a research where the time gap \(T_{TP}\) rolling from 0 h before onset to 48 h before onset. Coupled with.
2. Feature window \(T_{FW}\) variation: The feature window size from 24 h to 48 h before onset, with a time gap of 24 h.  
Figure 2 illustrates the AKI prediction task, with a sequential EHRs problem set-up, using a feature window of 24 h versus 48 h with a time gap of 24 h. Non-AKI patients with a feature window of 24 h were randomly selected [17]. Table 1 shows the symbols used in this study.