esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
In this study, fatigue cracking resistance of ECC under different stress ratios was first studied using the SCB test and DIC technology. Then fatigue cracking resistance of ECC under OSBD pavement working conditions was studied using the ITBF test, and CT and DIP technologies were applied to investigate the internal-crack distribution in ECC specimen. Some conclusions were obtained as follows:  
1. The ECC fatigue cracking propagation process can be divided into three stages, and the crack stable propagating stage (stage II) occupies the main proportion during the total fatigue fracture process. The fatigue fracture process tends to be ductile, indicating that the ECC has a strong working capability with existing cracks.
2. Similar to asphalt mixtures, a logarithmic linear correlation between ECC fatigue cracking lives and stress ratios could be found and fatigue life prediction equation can be established based on this relationship.  
Figure 18: Crack pixel amount in specimens.  
Under OSBD pavement working conditions, the fatigue cracking life of ECC is longer than EAC, presenting better fatigue cracking resistance. Besides, the fatigue cracking process of ECC is slow and tough, so it could keep the load-bearing capability of pavement even after cracks occurring, which is very beneficial in maintenance and repair work.
4. Under OSBD pavement working conditions, multiple cracks propagate simultaneously in ECC instead of a single crack, while some cracks initiate and propagate from pavement inside, instead of top-to-bottom propagation.  
Due to the excellent fatigue cracking resistance of ECC, the ITBF test takes too long. In this paper, only two specimens were tested in parallel. Similar research will be continued in the following study to expand the sample size.  
Furthermore, because of the limited resolution of CT scanned images, it was difficult to observe the micro-cracks and fiber pull-out phenomenon in specimens. The subsequent plan is to carry out Scanning Electron Microscope (SEM) to scanning the fatigue cracked specimens. Combining the respective advantages of CT and SEM, the failure characteristics of specimens can be further analyzed.  
Conceptualization, Y.Z. and F.N.; Formal analysis, L.Z.; Funding acquisition, Y.Z.; Investigation, J.J.; Methodology, J.J.; Resources, F.N.; Supervision, L.Z.; Writing--original draft, J.J.; Writing--review & editing, Y.Z.  
The authors gratefully acknowledge the support of the National Natural Science Foundation of China (NO. 51808115), Cyan and Blue Talent Training Project of the Colleges and Universities in Jiangsu Province, 333 High-Level Talent Training Project of Jiangsu Province, Nanjing Vocational Institute of Transport Technology Science Research Funding (JZ1802), Jiangsu Overseas Visiting Scholar Program for University Prominent Young & Middle-Aged Teachers and Presidents.  
The authors declared no conflicts of interest.
## 3 SAT-Hadoop-Processor  
This section describes the proposed SAT-Hadoop-Processor architecture, explains Hadoop's implementation, and shows how to include cloud computing, especially the OpenStack and parallel programming for RS data ingestion. Figure 1 illustrates the SAT-Hadoop-Processor architecture. We note the steps from RS data acquisition to the last query and access in such a schema as briefly explained in the following subsections.
## 2 Materials and Methods
### MC3-Based Shadow Fine Detection Method  
In addition to rough detection of shadows based on external a priori knowledge, fine detection of shadow can be achieved by discriminating shadows with features extracted from the image itself, which requires the spectral features that distinguish shadows from other dark ground objects.  
The C1C2C3 color space is a form of nonlinear transformation of the RGB color space. [PERSON] et al. [15] improved the C1C2C3 color space by adding near-infrared band information and proposed an MC1C2C3C4 color space with a modified C3 component (MC3):  
\[\begin{split} MC_{1}&=\arctan\biggl{(}\frac{R}{ \max(G,B,NIR)}\biggr{)}\\ MC_{2}&=\arctan\biggl{(}\frac{G}{\max(R,B,NIR)} \biggr{)}\\ MC_{3}&=\arctan\biggl{(}\frac{B}{\max(R,G,NIR)} \biggr{)}\\ MC_{4}&=\arctan\biggl{(}\frac{NIR}{\max(R,G,B)} \biggr{)}\end{split} \tag{4}\]  
The MC3 component has the highest brightness in the shadow region of the image by virtue of the increased weight of the blue component in shadows and the lower shadow reflectance values in the near-infrared band. Figure 8 illustrates the four components of the MC1C2C3C4 space of an image. It can be seen that in the MC3 image, the shadow region has the highest value. The dark vegetation around the shadow has relatively low values, but the difference is not large.  
Only using spectral properties to perform detection makes it especially easy to confuse shadows with dark objects, such as vegetation, water bodies, soil, etc. The image may also have uneven brightness inside the shadow due to the different reflectance of features in the shadow-covered area, which will interfere with the recognition of shadows and lead to shadow misdetection and omission. This problem can be avoided to some extent by combining the topographic factors brought by DEM.
# A Novel Dynamic Transmission Power of Cluster Heads Based Clustering Scheme
## 3 Preliminaries
### HELLO Message  
UAVs obtain information of surrounding nodes for clustering through HELLO message exchange. The structure of HELLO message is shown in Figure 2. The HELLO message contains the following contents:  
* ID: the identification of the UAV node.
* Cluster ID: the identification of the cluster.
* Role: the value can be 0 or 1, where 0 represents CH and 1 represents CM.
* Weight: the weight of a CH candidate in the CH selection phase.
* Speed: the speed of the UAV node.
* Direction: the flying direction angle of the UAV node.
* Position: the position of the UAV node.
# Resonance Fatigue Behaviour of Concretes with Recycled Cement and Aggregate
## 2 Materials and Methods  
### Materials  
#### 2.1.1 Binders  
The concretes were manufactured with two types of cement: (i) 100% Ordinary Portland cement (OPC), class 42.5 R according to EN 197-1 [41], without additions, supplied by Lafarge Holcim Cement, Toledo, Spain, and (ii) a recycled cement (OPC-25 CDW) composed of 75% OPC and 25% recycled ceramic-based waste (bricks, tiles, blocks, etc.) from the crushing and grinding of CDW waste (RWP-CDW). Table 1 shows the chemical composition of RWP-CDW and the resulting OPC-25 CDW. This cement is registered by patent No ES2512065 [42] and can be classified as CEM II/B or CEM IV/A of strength class 42.5 MPa according to EN 197-1 [41]. Similarly, a water-based modified polycarboxylate superplasticiser (SP) admixture with a density of 1.1 g/cm\({}^{3}\) and a solids' content of 20% from BRYTEN NF furnished by FUCHS Lubricantes was used.
# Fatigue Crack and Residual Life Prediction Based on an Adaptive Dynamic Bayesian Network
## 3 Fatigue Crack Propagation Experiment Verification
### Single Adaptive Variable DBN Model
#### 3.1.2 Uncertainty Sources Considerations  
In the DBN, the error analysis of crack propagation is focused on two primary aspects: model errors and measurement errors. The model errors, particularly those arising from the limitations of Paris' law, are identified as the core of the analysis. This includes the intrinsic limitations of the formula and the effects of structural defects on the accuracy of material parameter estimations and equivalent stress calculations, which together lead to deviations in performance evaluation. The measurement errors, such as noise from measuring instruments, irregularities in crack paths and shapes, and non-standard operations during the loading process, also significantly impact the accuracy of the assessment. To address these challenges, the DBN in this study considers the following main uncertainty factors:  
1. Equivalent stress at crack tip.  
The equivalent stress is obtained from the calculation of alternating load, and there are errors in the selection of stress intensity criteria, structural defects, and load observation noise. According to experience, it is assumed that the variance of this error is within 10%, so the normal distribution of 10% variance is taken in this paper.  
2. The error of the parameter \(m\) for Paris' law.  
The parameter \(m\) in Paris' law is influenced by geometric defects in the structure and material properties, leading to significant variability even within the same material and structure [35]. Therefore, in this study, based on the variability observed in experiments, the initial values of several specimens were averaged and subjected to a Gaussian distribution [36]. Subsequently, after applying filtering and DBN inference, the distribution range of the modified samples was determined through kernel density estimation.  
3. Uncertainty of crack length.  
The length of the crack mainly exists in the measurement error of the crack increment in the experiment and the numerical calculation error caused by the non-uniform crack length on the left and right sides as well as the internal crack length. This error is fitted to the binary curve in line with its own trend according to the data, and the fitting variance of the measured data points is taken as 4.6% [37].  
The parameter \(m\), serving as a parameter variable within the DBN, undergoes adaptive updates following each round of filtering, whereas other uncertain elements remain unchanged, manifesting merely as errors within the particle filtering process. An initial measurement error is presumed to be 10%. Due to the incremental growth of the crack per cycle being considerably minor in comparison to the initial crack, each subsequent crack increment incorporates an additional 5% measurement error and system process error.
# The Influence of Ionizing Radiation on Paclitaxel-Loaded Nanoparticles Based on PLGA
## 3 Results and Discussion  
The aim of this research was to develop nontoxic PLGA systems for the controlled release of PTX. The DDSs obtained in this work are intended for biomedical application and therefore require sterilization. For this purpose, the use of ionizing radiation (\(\gamma\)-rays and EB, 25 kGy) as a potential sterilization technique was proposed. The influence of irradiation on the structure and physicochemical properties of polymeric nanoparticles, as well as the kinetic profile of PTX release, was analyzed in detail.
# Captioning Transformer with Stacked Attention Modules
## 5 Results
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Methods** & **BLEU-1** & **BLEU-2** & **BLEU-3** & **BLEU-4** & **METEOR** & **ROUGE-L** & **CIDEr** \\ \hline Google NIC [1] & - & - & - & 27.7 & - & 23.7 & 85.5 \\ Soft-Attention [2] & 70.7 & 49.2 & 34.4 & 24.3 & 23.9 & - & - \\ Hard-Attention [2] & 71.8 & 50.4 & 35.7 & 25.0 & 23.0 & - & - \\ VAE [17] & 72.0 & 52.0 & 37.0 & 28.0 & 24.0 & - & 90.0 \\ Google NICv2 [1] & - & - & - & 32.1 & 25.7 & - & 99.8 \\ Attributes-CNN + RNN [7] & 74.0 & 56.0 & 42.0 & 31.0 & 26.0 & - & 94.0 \\ CNN\({}_{1}\) + RHN [18] & 72.3 & 55.3 & 41.3 & 30.6 & 25.2 & - & 98.9 \\ PG-SPIDE-TAG [19] & 75.4 & 59.1 & 44.5 & 33.2 & 25.7 & 55.0 & 101.3 \\ Adaptive [20] & 74.2 & 58.0 & 43.9 & 33.2 & 26.6 & - & 108.5 \\ \hline NIC (Resnext\_101\_64\(\times\)4d) & 72.4 & 55.6 & 41.8 & 31.4 & - & 53.7 & 100.9 \\ Soft-Attention (Resnext\_101\_64\(\times\)4d) & **73.7** & **57.1** & 43.3 & 32.6 & - & - & 104.6 \\ \hline CT-C1-64a4n6 (Resnext\_101\_64\(\times\)4d) & 71.8 & 55.4 & 41.8 & 31.5 & - & 54.7 & 105.8 \\ CT-C2-64a4n6 (Resnext\_101\_64\(\times\)4d) & 73.3 & 57.0 & 43.6 & 33.2 & - & 55.1 & 107.0 \\ CT-C3-64a4n6 (Resnext\_101\_64\(\times\)4d) & 73.0 & 56.9 & **43.6** & **33.3** & - & 54.8 & **108.1** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The results of the Caption Transformer (CT) model compared with several state-of-the-art methods on standard evaluation metrics: BLEU-1, BLEU-2, BLEU-3, BLEU-4, METEOR, ROUGE, CIDEr. CT-C1-64a4n6,CT-C2-64a4n6,CT-C3-64a4n6 are our proposed models, but with different combination method. In Table 4, we report the results of the different CNN usage. We use the Resnet_152 as the encoder to evaluate the effect of different CNNs. We use the same training settings as the models reported in Table 1. The performances of the models in Table 4 are slightly worse than models in Table 1, but our CT model can also achieve better performance than original NIC and Soft-Attention model under the same CNN. From Table 5, we can find that the CT model is faster per parameter than the LSTM. It is because that the CT model can be trained in parallel and do not have the timing dependence structure.
## 5 Case Studies on the OMF-Enabled Testbed
### Case Study 1: Round-Trip Time Measurements
Finally, the RTT passively estimated by the RTT-tool is compared to the average one of the ten active icmpping and udpping measurements. The varying queuing times simulating the required real-world condition during the measurements were also specified in the experiment description file. Particularly, in the web server machine, the delay of packet forwarding has been controlled using the netem tool which was configured to cause 100 different randomly set queuing delay variations between 50 ms and 400 ms. For each of the randomly set queuing time, the average of the 10 ping measurements was used as a basis for comparison with the SA estimates. Figure 4 illustrates the passive TCP-based SA estimates in comparison with the corresponding active ICMP-based and UDP-based ping measurements for the same end-to-end path between research nodes S and R. Analogously to the methodology used in [27], an SA estimate for the end-to-end connection is categorized as accurate if it is within 5 ms or 10% of the corresponding average active ping measurement. The dashed lines in Figure 4 illustrate this accuracy region. With this error tolerance, it is observed that compared with the results obtained by using icmpping and udpping, the fraction of inaccurate measurements of the RTT-tool is 6% and 9%, respectively. It can be concluded that RTT-tool, though passive, produces comparable RTT estimates to that measured by the two active ping tools.
# Electrochemical Diagnostics of Sprayed Fiber-Reinforced Concrete Corrosion
## Laboratory Tests
### Results and Analysis of the Tests
The mean values of shrinkage strain determined from surface measurements of all B1 and B2 specimens are provided in Figure 11.  
In B2 specimens (with addition of steel fibers), total shrinkage stabilized earlier than in B1 specimens. According to standard [1], a shrinkage measurement taken after 90 days is reliable for assessing shrinkage strains in sprayed concrete. The average strain values determined in that period clearly showed a significantly lower shrinkage in B2 specimens with the addition of randomly dispersed micro-reinforcement steel fibers, reaching \(\varepsilon=0.35\%\), whereas for B1 specimens, the shrinkage was \(\varepsilon=0.48\%\). The addition of 1% of steel fibers to concrete reduced total shrinkage in the analyzed specimens by over 26%. Figure 11: Changes of average shrinkage strain over time. Figure 10: Results of the measurements of the concrete cover resistivity. C specimens were used for testing the compressive strength of concrete according to Reference [45] and calculating the mean strength, the standard deviation, and the coefficient of variation. The specimen compression charts are provided in Figure 12, and the obtained values are summarized in Table 4.  
The obtained values indicate that the addition of 1% of steel fibers improved the compressive strength of the specimens by around 25%. Furthermore, C2 specimens had an approximately 45% lower coefficient of variation, which indicated higher uniformity of those specimens as compared to C1 specimens. Figure 13 shows a scanning microscope image of the concrete sample cut from the concrete cover of an A2 specimen, with the visible fiber section (Figure 13a), as well as the charts from EDS analysis performed for the concrete covering the fiber (Figure 13b) and the fiber itself (Figure 13c). The sample was taken from a depth of about 20-25 mm, near the reinforcing bar. The results of the analysis showed no corrosion products in the structure of the concrete around the fiber, which may suggest that chloride ions had not penetrated into this place. At the same time, the microstructural image shows the absence of cracks, thus confirming good waterproofing of the concrete. Of course, on the surface of the specimens, corrosion products appeared around several of the fiber fragments found there. However, the fiber diameter in relation to the surrounding concrete was so small that the corrosion products formed on the surface did not pose a threat to the main reinforcement. \begin{table}
\begin{tabular}{c c c} \hline \hline  & **C1 Specimens** & **C2 Specimens** \\ \hline Mean breaking force F\({}_{\text{c,cub}}\) [kN] & 1176.83 & 1481.96 \\ Mean compressive strength f\({}_{\text{cm}}\) [MPa] & 52.31 & 65.87 \\ Standard deviation s[MPa] & 1.31 & 0.91 \\ Coefficient of variation \(\
u\) & 2.51 & 1.39 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Results of compressive strength tests of concrete for C1 and C2 specimens. Figure 12: Charts of compressive strength tests of the specimens.
# Adaptive Power-Controlled Depth-Based Routing Protocol for Underwater Wireless Sensor Networks
## 3 The Proposed Protocol
### Detailed Design of APCDBRP
#### 3.3.2 Data Transmission Phase
```
1:\(depth_{node}\leftarrow\) depth in routing table of the node
2:\(depth_{packet}\leftarrow\) depth in packet
3:\(receiverID\leftarrow\) receiver ID in packet
4:\(nodeID\leftarrow\) node ID in routing table
5:\(packetID\leftarrow\) packet ID in packet
6: Q \(\leftarrow\) the queue of pending acknowledgment packets in the node
7: recv(packet)
8:procedureDataTransmission()
9:if\(depth_{packet}>depth_{node}\)then
10:if\(packetID\in\) Qthen
11: Remove the packet from the queue
12:endif
13: drop(packet)
14:else
15:if\(nodeID==receiverID\)then
16: packet.receiverID = routingTable. optimalNextHopID
17: Update other information
18: SendPacket(packet)
19: Set timer T
20: if newTransmitPowerLevel < oldTransmitPowerLevel then
21: ReplyACKPacket();
22:endif
23:endif
24:endif
25:endprocedure
```  
**Algorithm 3**Data transmission phase  
Figure 5 illustrates an example of data forwarding. The source node sends a data packet and sets the next hop of the packet to node N5 based on the routing table. Upon receiving the data packet, node N5 only forwards the packet. Then, it adds the packet to Q, but does not send an ACK packet to the source node. This is because the power level used by node N5 for forwarding the packet is higher than the power level at which the source node sent the data packet. Consequently, the source node can directly monitor the data packet forwarded by N5, thereby confirming the successful transmission of the data packet, updating the information of N5 in the routing table and removing the packet from Q. On the other hand, when node N3 forwards the data packet, it needs to send an ACK packet to node N5. This is because the power level used by N3 for forwarding the packet is lower, and N5 cannot monitor the forwarded packet from N3. Additionally, since the sink node does not forward data packets underwater, it also needs to send an ACK packet. Node  
N4 in the figure receives the data packet sent by N5, but since it determines that it is not the intended destination node, it discards the packet. At the same time, N4 also receives the data packet and ACK packet sent by N3, and updates the routing table based on the information in the packets and the signal strength.
# sRrsR-Net: A New Low-Light Image Enhancement Network via Raw Image Reconstruction
## 3 Methods
### Loss Function  
The loss function of our LIE approach sRrsR-Net is defined based on two aspects: the reconstruction of image pixel details and the quality of generated images. To address the reconstruction of pixel details, we incorporate the \(L_{1}\) loss. Additionally, to enhance the brightness and perceptual quality of the reconstructed images, we employ perceptual loss (\(L_{ggg}\)) as guidance.  
\(L_{1}\), also known as the mean absolute error, is applied to minimize the sum of absolute differences between corresponding pixel values in the enhanced image and the ground-truth image. Its goal is to recover image details effectively. On the other hand, \(L_{ggg}\) relies on features extracted from a pretrained VGG network [34]. The input to the VGG network includes both the image to be enhanced and the ground-truth image. By comparing the feature representations of these two images, the enhancement process is guided to achieve semantic and stylistic similarity. The perceptual loss function is computed as the Euclidean distance between the feature representations of the enhanced and ground-truth images. The overall loss function of sRrsR-Net, \(L_{total}\), is expressed as follows:  
\[L_{total}=\lambda_{1}L_{1}+\lambda_{2}L_{ggg} \tag{10}\]  
where \(\lambda_{1}\) and \(\lambda_{2}\) are weight coefficients. We consider both perceptual quality and reconstruction of pixel details equally important, so \(\lambda_{1}\) and \(\lambda_{2}\) are both set to 0.5.
## 6 Experiment Evaluation
### Limitations  
The main strength of our study is that we put forward a novel prediction method by exploring sleep related features. However, some limitations of this study should be pointed out.  
Firstly, we evaluate the framework using a real polysomnogram dataset that contains 20 stroke patients and 159 healthy individuals. Compared to the percentage of ischemic stroke in the real world, the number of health samples examined in experiments is very small. For example, the prevalence of stroke in China in 2013 was 1114.8 per 100,000 people [87]. We could not obtain enough information of normal controls to further estimate our prediction model and polysomnogram features. Therefore, our experimental setup cannot be indicative of the real situation of ischemia and might have influenced the performance of our model. Nevertheless, by adopting reasonable indicators (i.e., precision, recall and area under the precision-recall curve), we validated the predictive effects of our model and features in imbalanced data and proved the rationality and effectiveness to some extent.  
Secondly, the participants in SHHS were asked to wear a home PSG device to record the occurrences of obstructive sleep apneas (OSA). OSA elicit increases in sympathetic nerve activity with higher ischemic stroke incidents, meanwhile patients with OSA manifest marked increases in blood pressure during sleep [88]. Thus, EEG features might have shown hyperactivity and sympathetic function resulting hypertension or other clinical factors and might not have been totally caused by cerebral ischemia. The correlation between clinical features and EEG features was analyzed by the correlation coefficient and it was found that EEG features were moderately or weakly associated with clinical features. However, the limited number of samples may not be able to fully explore the correlation among them. Further comprehensive association analyses and functional experiments are required in the future.  
Thirdly, CBF is the major cause of ischemic stroke. Although EEG abnormalities are closely tied to CBF, it directly affects the reliability and accuracy of the result because data on CBF levels are not available in our study. Based on a suitable public and real data set, developing a more robust prediction system to study the relationship between stroke and CBF will be one of our future works.