esa-sceva/satcom-chunk-collection huggingface dataset:
# Face Mask Detection on Photo and Real-Time Video Images Using Caffe-MobileNetV2 Transfer Learning
###### Abstract  
Face detection systems have generally been used primarily for non-masked faces, which include relevant facial characteristics such as the ears, chin, lips, nose, and eyes. Masks are necessary to cover faces in many situations, such as pandemics, crime scenes, medical settings, high pollution, and laboratories. The COVID-19 epidemic has increased the requirement for people to use protective face masks in public places. Analysis of face detection technology is crucial with blocked faces, which typically have visibility only in the periocular area and above. This paper aims to implement a model on complex data, i.e., by taking tasks for the face detection of people from the photo and in real-time video images with and without a mask. This task is implemented based on the features around their eyes, ears, nose, and forehead by using the original masked and unmasked images to form a baseline for face detection. The idea of performing such a task is by using the Caffe-MobileNetV2 (CMNV2) model for feature extraction and masked image classification. The convolutional architecture for the fast feature embedding Caffe model is used as a face detector, and the MobileNetV2 is used for mask identification. In this work, five different layers are added to the pre-trained MobileNetV2 architecture for better classification accuracy with fewer training parameters for the given data for face mask detection. Experimental results revealed that the proposed methodology performed well, with an accuracy of 99.64% on photo images and good accuracy on real-time video images. Other metrics show that the model outperforms previous models with a precision of 100%, recall of 99.28%, f1-score of 99.64%, and an error rate of 0.36%. Face mask detection was originally a form of computing application, but it is now widely used in other technological areas such as smartphones and artificial intelligence. Computer-based masked-face detection belongs in the category of biometrics, since it includes using a person's unique features to identify them with a mask on.  
deep neural network; face detection; face mask classification; Caffe-MobileNetV2 model; transfer learning; feature extraction +
Footnote †: journal: Medical Image Analysis
## 3 Results
### Information Quality
#### 3.2.1 Visual Information  
he comparison of the simulations of sun hours, radiation and the UTCI (Universal Thermal Climate Index) produced by Ladybug and ENVI-met show significant differences between the simulated values. For instance, Ladybug indicates the lowest radiation of \(109\) W/m\({}^{2}\), whereas ENVI-met indicates only \(69.4\) W/m\({}^{2}\) (see Figure 3). These variations might be a result of different calculation methods, as the results of Ladybug are based on the visual scripting method developed by the user, and ENVI-met has a built-in formula. However, these results can be compared visually when evaluating sun hours and shaded areas (Figure 4) and the most radiation-exposed areas (see Figure 3). Both programs show the lowest radiation levels in the shaded areas around buildings and trees. CitySim can produce visual information on radiation and sun hours; however, the accuracy of this information depends on the triangulation of the surfaces and the model. In the objective experiments, CitySim generates the least informative simulations regarding climate adaptation planning. Furthermore, Figure 5 illustrates UTCI values that are highly complex. Therefore, ENVI-met can produce more accurate simulations than Ladybug. Moreover, in other papers [18; 19; 20] ENVI-met is introduced as accurate and trustworthy software that is often used to verify visual scripts in Ladybug.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Requirement** & **ENVI-Met** & **Ladybug** & **CitySim** & **GreenScenario** & **AST** \\ \hline
**BREEAM** & & & & & \\ \hline
**Rainwater assessment** & & & & + & + \\ \hline
**Flood risk assessment and management** & & & & & + \\ \hline
**Microclimate analysis** & + & + & + & & \\ \hline
**Green infrastructure analysis** & + & & & + & + \\ \hline
**Ecological value analysis** & & & & + & + \\ \hline
**LEED** & & & & & \\ \hline
**Floodplain analysis** & & & & + & + \\ \hline
**Rainwater analysis** & & & & + & + \\ \hline
**Green infrastructure analysis** & + & & & + & + \\ \hline
**Impact on heat island** & + & + & & & + \\ \hline
**Ecological analysis (habitats)** & & & + & + \\ \hline
**DGNB** & & & & & \\ \hline
**Climate change analysis** & & & & & + \\ \hline
**Water and soil analysis** & & & & + & + \\ \hline
**Life cycle costs** & & & & & \\ \hline
**Environmental impact** & + & + & + & + & + \\ \hline
**Rainwater management** & & & & + & + \\ \hline
5p. & 3p. & 2p. & 10p. & 13p. \\ \hline \hline \end{tabular}
\end{table}
Table 4: The compliance of different software tools with sustainability requirements (source: authors).  
Figure 4: Comparison of the simulations of sun hours: (**a**) ENVI-met; (**b**) Ladybug (source: authors).  
Figure 3: Comparison of radiation simulations: (**a**) ENVI-met; (**b**) Ladybug (source: authors).
# Sentiment Interaction Distillation Network for Image Sentiment Analysis
## 4 Experiment
### Qualitative Analysis  
To verify that our model can indeed perform a better visual basis after distilling knowledge from object branches, we plotted the saliency maps of the images from EmotionROI. As shown in Figure 5, we observe that the \"Full model + Distill\" can focus on key regions in the emotion stimulus map better than the corresponding \"Global Branch only\" part. For the first image, \"Full model + Distill\" pays major attention to the strawberry as well as the cat, while \"Global Branch only\" focuses on the strawberry only. In the second image, \"Full Model + Distill\" put its attention on the lighting and surrounding  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{**FI**} & \multirow{2}{*}{**Flickr**} & \multirow{2}{*}{**EmotionROI**} & \multicolumn{4}{c}{**Twitter I**} & \multirow{2}{*}{**Twitter II**} \\ \cline{4-4} \cline{6-7}  & & & & **Twitter I-5** & & **Twitter I-4** & & **Twitter I-3** \\ \hline LCH [35] & - & - & 64.29 & 70.18 & 68.54 & 65.93 & 75.98 \\ GCH [35] & - & - & 66.53 & 67.91 & 97.20 & 65.41 & 77.68 \\ SentiBank [31] & - & - & 66.18 & 71.32 & 68.28 & 66.63 & 65.93 \\ DeepSentiBank [36] & 61.54 & 57.83 & 70.11 & 76.35 & 70.15 & 71.25 & 70.23 \\ \hline VGGNet [30] & 70.64 & 61.28 & 72.25 & 83.44 & 78.67 & 75.49 & 71.79 \\ PCNN [33] & 75.34 & 70.48 & 73.58 & 82.54 & 76.50 & 76.36 & 77.68 \\ \hline Yang [7] & 86.35 & 71.13 & 81.26 & 88.65 & 85.10 & 81.06 & 80.48 \\ Wu [8] & **88.84** & 72.39 & 83.04 & 89.50 & 86.97 & 81.65 & 80.97 \\ Ours & 88.71 & **73.59** & **84.01** & **89.86** & **87.04** & **83.31** & **81.11** \\ \hline \hline \end{tabular}
\end{table}
Table 1: Sentiment classification performance on the FI, Flickr, EmotionROI, Twitter I, and Twitter II datasets. The best results are indicated in bold.  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{**FI**} & \multirow{2}{*}{**Flickr**} & \multirow{2}{*}{**EmotionROI**} & \multicolumn{4}{c}{**Twitter I**} & \multirow{2}{*}{**Twitter II**} \\ \cline{4-4} \cline{6-7}  & & & & **Twitter I-5** & & **Twitter I-4** & & **Twitter I-3** \\ \hline Global Branch Only & 83.05 & 70.12 & 77.02 & 84.35 & 82.26 & 76.75 & 76.99 \\ Full Model + Concat & 88.12 & 72.31 & 83.62 & 89.24 & 85.19 & 81.25 & 80.59 \\ Full Model + Distill & **88.72** & **73.59** & **84.01** & **89.86** & **87.04** & **83.31** & **81.01** \\ \hline \hline \end{tabular}
\end{table}
Table 2: The model performance comparison across image datasets.  
areas, while the \"Global Branch only\" is attention-diffused. Similarly, \"Full Model + Distill\" focuses the weights more accurately on the Ferris wheel, and \"Global Branch only\" only focuses on two key regions. This further demonstrates that our proposed sentiment graph as well as the knowledge distillation mechanism endow the model with better visual grounding capabilities.
# Experiment on Flexural Fatigue Performance of Precast Bridge Deck Joints with Loop Connections
## 6 Conclusions  
The flexural fatigue performance of different precast bridge deck joints with different loop connections were analyzed. Some conclusions can be drawn as follows:  
1. The failure mode of the loop connection joint under flexural fatigue loading is similar to that under flexural static loading.
2. In the static loading process after different durations of fatigue-loading cycles, no obvious stiffness degradation occurred in different specimens. Specimens with different types of loop connection joints can meet the requirement of 200 \(\times\) 104 fatigue-loading cycles.
3. The fatigue load has no obvious effect on the joint with loop connections; all kinds of loop connection joints can still maintain their mechanical property requirements under corresponding application conditions.  
Conceptualization, K.H. and Y.Z.; methodology, X.S.; software, C.Z.; validation, C.Z., X.S. and Y.Z.; formal analysis, K.H; investigation, C.Z.; resources, K.H.; data curation, Y.Z.; writing--original draft preparation, Y.Z.; writing--review and editing, C.Z.; visualization, K.H.; supervision, X.S.; project administration, X.S.; funding acquisition, K.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Science and Technology Project of Experimental Research and Application on the Shear and Fatigue Performance of Reinforced Concrete Joint with Loop Connections (Grant No. JKKJ-2020-42).  
Not applicable.  
All the data are available within the manuscript.  
Thanks are extended to the anonymous reviewers whose suggestions improved this manuscript.  
Conflicts of Interest  
[PERSON] was employed by Anhui Transportation Holding Group Co., Ltd., [PERSON] was employed by Shanghai Bridging Engineering Consulting Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
# Opportunistic Multi-Technology Cooperative Scheme and UAV Relaying for Network Disaster Recovery
## 6 Performance Evaluation
### Cooperative Multi-Tier Data Relaying  
#### 6.1.1 Methodology and Setup  
We assess the performance of the proposed multi-tier data relay as in Algorithm 1. For comparison purposes, we present the performance of other two schemes, namely baseline and static, along with our proposed solution.  
* _Baseline approach._ It considers every node as a cluster, namely, each node is responsible to switch on all the necessary network interfaces to transmit its own data. Such a scheme provides no collaboration among nodes. In fact, all nodes are exposed to a maximum energy expenditure, which leads to fast battery depletion. Consequently, the chances of a node to keep in contact with search and rescue teams for long periods of time are subject to such a limitation.
* _Static approach._ The nodes collaborate among each other to relay their data through the tiers. For such a purpose, in the \(n_{1}\) and \(n_{2}\) tier, nodes are organized into clusters and only one _responsible_ node per cluster relays data to the upper tier. Consequently, the other cluster members do not need to switch on the next communication interface, hence mitigating their energy consumption. The CHs are selected based on the initial information on the energy budget of the nodes: the node with the highest available energy level in the cluster becomes the head. Such a node is then responsible to transmit the data of all the cluster members to the next tier. The status of such a node remains invariant over time until its energy fully depletes, which leads to selecting a new CH. Although the static approach provides collaboration among the nodes, it puts the energy expenditure burden on the _static_ CHs only.  
The disaster scenario consists of a varying number of nodes (survivors) randomly distributed over an urban area of 10 by 5 kilometers. Each survivor is equipped with a mobile device (e.g., smartphone) provided with three network interfaces: Bluetooth, WiFi and cellular, with transmission range of 100 m, 200 m, and 500 m correspondingly. By assumption, Bluetooth, WiFi and cellular consume 50 mW, 70 mW and 120 mW respectively [25; 26]. Moreover, each node is assigned a random initial energy level in the range of [10 kJ,20 kJ]. Such parameters are summarized in Table 3.  
This work focuses on demonstrating how multi-tier architecture can be helpful to relay rescue messages during disasters. Simulations do not focus on the communication channel model and suppose that nodes in the highest tier can communicate with the UAV when they enter in the transmission range of each other. Evaluating the transmission range and the loss probability mainly impacted by the obstacles separating UAV and nodes would be very interesting to investigate in future works. We believe that such an assumption would not impact the overall network communication scheme.
###### Abstract  
It is well known that multipath effect remains a dominant error source that affects the positioning accuracy of Global Navigation Satellite System (GNSS) receivers. Significant efforts have been made by researchers and receiver manufacturers to mitigate multipath error in the past decades. Recently, a multipath mitigation technique using dual-polarization antennas has become a research hotspot for it provides another degree of freedom to distinguish the line-of-sight (LOS) signal from the LOS and multipath composite signal without extensively increasing the complexity of the receiver. Numbers of multipath mitigation techniques using dual-polarization antennas have been proposed and all of them report performance improvement over the single-polarization methods. However, due to the unpredictability of multipath, multipath mitigation techniques based on dual-polarization are not always effective while few studies discuss the condition under which the multipath mitigation using a dual-polarization antenna can outperform that using a single-polarization antenna, which is a fundamental question for dual-polarization multipath mitigation (DPMM) and the design of multipath mitigation algorithms. In this paper we analyze the characteristics of the signal received by a dual-polarization antenna and use the maximum likelihood estimation (MLE) to assess the theoretical performance of DPMM in different received signal cases. Based on the assessment we answer this fundamental question and find the dual-polarization antenna's capability in mitigating short delay multipath--the most challenging one among all types of multipath for the majority of the multipath mitigation techniques. Considering these effective conditions, we propose a dual-polarization sequential iterative maximum likelihood estimation (DP-SIMLE) algorithm for DPMM. The simulation results verify our theory and show superior performance of the proposed DP-SIMLE algorithm over the traditional one using only an RHCP antenna.  
Keywords:dual-polarization; LHCP; short delay multipath; MLE +
Footnote †: journal: Sensors
## 3 Results and Discussion
### HYSPLIT Back Trajectory Analysis  
Wind direction and wind speed can have a strong influence on observations of local emission sources. For such observations, the study of back trajectories of the observed air masses might be interesting. Some instructive case studies were found during this study, where high aerosol loads were observed and back trajectory analyses were performed. Figure 8 presents the true colour images (RGB) for fog and clouds, MODIS AOD and air masses movements explored by back trajectories analysis on respective days. In RGB, white colour represents clouds and snow along with the greyish tone for fog distribution over the selected scenes. High AOD levels were also observed over the regions classified as fog on both days of 13 December 2013, and 23 January 2015. To study the reason for these high AOD loadings, the Hybrid Single Particle Lagrangian Integrated Trajectory Model-4 [93; 94; 95] was used. The model was run for reanalysis of back trajectories for 96 h, at 50, 200 and 500 m above ground level by selecting a location in Lahore city (black star in all maps). It was found that air masses calculated at three different heights were coming from India. Air masses propagated over the densely populated and industrial regions of Uttarakhand, western parts of Uttar Pradesh, Haryana, New Delhi and the Punjab before entering Pakistan. Almost similar transport patterns were observed for both days, which indicates transboundary air pollution as an important factor contributing to high aerosol loading observed over the city of Lahore, Pakistan [39; 40].  
Figure 8: Shows spatial distribution of AOD overlaid on RGB image obtained from MODIS observations on 13 December 2013 (in left panel) and on 23 January 2015 (in right panel). Back trajectory analyses were also performed on both occasions for 96 h back in time at three different altitudes of 50 (green), 200 (blue) and 500 (red) m above ground level.  
Moreover, CALIPSO overpasses over the Pakistan were used to further identify the aerosol layer, types and clouds by using the vertical feature mask on both days. Results are shown in Figure 9a,b. Both maps indicate low-lying aerosol layers (orange colour) as a dominant feature on both occasions of 13 December 2013 and 23 January 2015.
# Predicting Models for Local Sedimentary Basin Effect Using a Convolutional Neural Network
## 5 Basin Amplification Characteristics Prediction
### Optimize Input Parameters to Build Prediction Models
#### 5.3.3 Effect of Dip Angle  
The significant difference in hazard location between symmetrical and asymmetrical configurations in the first two sections reflects the effect of the inclination angle. In this section, we only modify the inclination angle to perform sensitivity analysis, and each working condition is presented in Table 10. Regarding the amplification factor, as shown in Figure 19a, the effect of the inclination angle is not obvious, and there is only a positive correlation within a certain range. In the case of asymmetry, through the analysis of  
Figure 17: Max value of amplification factor and hazard location from different shapes. (**a**) Amplification factor. (**b**) Hazard location.  
Figure 18: Max value of amplification factor and hazard location from different shear wave velocities. (**a**) Amplification factor. (**b**) Hazard location.  
working conditions 2 and 7, as well as working conditions 5 and 8, it is apparent that the amplification factor is closer to the symmetrical case of a large dip angle. In terms of hazard location prediction, as shown in Figure 19b, when the dip angle is less than 80\({}^{\circ}\), the hazard location is closer to the center of the basin as the dip angle increases. Similarly, in the case of asymmetry, by analyzing working conditions 2 and 7, as well as working conditions 5 and 8, we can see that the hazard location, in this case, is closer to that in the case of symmetry of a large dip angle.  
The analysis in the above subsections shows that the geometric and media factors considered in this paper have important effects on the peak surface amplification coefficients of the basin, which were necessary parameters for predicting the amplification characteristics of the basin and validating their reasonableness as input parameters of the neural network.
# Blockchain-Assisted Adaptive Reconfiguration Method for Trusted UAV Network
## 3 System Models
### Threat Model  
The UAV network is an ad hoc mission network, which operates in an unknown and non-secure environment. The exploitability of wireless communication and the mobility of UAV nodes make UAV networks face more challenges than ordinary MANETs. The threats that cause the UAV network to fail to accomplish its mission are not to be ignored.  
* Environmental Threats: The UAV network mission execution environment is complex and variable, which may be a distress rescue site or an enemy-occupied area on the battlefield. The interference of unknown terrain topography leads to frequent partitioning of the UAV network and rapidly changing topology information, increasing the overhead of network routing reconfiguration. Multiple anti-drone techniques can not only disrupt communications, but can even cause direct drone damage. UAV networks are thus required to be scalable, adapt to network scaling caused by the environment, and dynamically sense the departure and joining of network nodes.
* Cyber Attacks: The hostile environment and the openness of wireless communication are the security of the UAV network is vulnerable. External malicious nodes can launch a variety of network attacks, such as DOS attacks, black hole attacks, can collude wormhole attacks, etc. These attacks verify to weaken the availability and scalability of the network. Link layer attacks can achieve intrusion hijacking of legitimate nodes.  
Figure 1: Blockchain-assisted reconfigured trusted network system model for UAVs.  
Internal attacks launched by nodes compromised with legitimate identities can cause more damage to the network. Therefore, in addition to establishing an authentication environment to prevent unauthorized access, the UAV network should also have the ability to detect node trustworthiness in real time and isolate untrustworthy internal nodes from the mission network in a timely manner.
* Selfish Nodes: Due to their reduced energy, nodes only receive messages and do not forward them out of self-protection. Although such uncooperative zombie nodes do not launch harmful attacks, they interfere with the network routing configuration and generate invalid communications, wasting energy and reducing the overall performance of the network. The system should also have the ability to identify and tag them for isolation.