esa-sceva/satcom-chunk-collection huggingface dataset:
# A Comprehensive Survey of Masked Faces: Recognition, Detection, and Unmasking
## 5 Masked Face Methods
### Masked Face Recognition Approaches
#### 5.3.3 Deep Learning-Based Masked Face Approaches
Through enhancements in model performance, generalization, and robustness, along with improved data utilization, this study offers promising prospects for strengthening security systems, especially in domains like public safety and healthcare. [PERSON] et al. (2018) introduced two methodologies for detecting and identifying masked faces using a single-camera setup. The first method employed a single-step process utilizing a pre-trained YOLO-face/YOLOv3 model. Conversely, the second approach involved a two-step process integrating RetinaFace for face localization and VGGFace2 for verification. The results from experiments conducted on a real-world dataset exhibited robust performance, with RetinaFace and VGGFace2 achieving impressive metrics. Specifically, they attained an overall accuracy of 92.7%, a face detection accuracy of 98.1%, and a face verification accuracy of 94.5%. These methodologies incorporated advanced techniques such as anchor box selection, context attention modules, and transfer learning to enhance the efficiency and effectiveness of detecting masked faces and verifying identities. [PERSON] et al. (2017) introduced an innovative method to improve masked face recognition performance by integrating the Embedding Unmasking Model (EUM) with established face recognition frameworks. Their approach incorporated the Self-Restrained Triplet (SRT) loss function, enabling the EUM to generate embeddings closely resembling those of unmasked faces belonging to the same individuals. The SRT loss effectively addressed intra-class variation while maximizing inter-class variation, dynamically adjusting its learning objectives to ensure a robust performance across various experimental scenarios. Leveraging fully connected neural networks (FCNN), the EUM architecture demonstrated adaptability to different input shapes, thereby enhancing its versatility. Rigorous evaluation of multiple face recognition models and datasets, including both real-world and synthetically generated masked face datasets, consistently revealed significant performance enhancements achieved by the proposed approach.
## 5 Feasibility and Evaluation
### Architectural Comparison
#### 5.5.6 Monitoring Scope  
EDAS can monitor any event source as long as it is accessible and can communicate the events that it generates. This makes the monitoring scope of EDAS much broader as compared to traditional mechanisms that only monitor a specific concern.
## 4 Results
#### 4.1.5 Model Training  
The publicly available Kinetics-400 dataset, which encompasses 400 action categories, was used to train the model [25]. From this dataset, 50 categories considered relevant to classroom teaching behaviors (e.g., \"teacher demonstration\" and \"student interaction\") were selected. The training configuration comprised a batch size of 16, an initial learning rate of 0.001 (adjusted using cosine annealing), the Adam optimizer, and 50 epochs. Feature fusion techniques (e.g., weighted averaging of Red-Green-Blue (RGB) and optical flow features) were applied to enhance the accuracy of classifications. Data augmentation methods (e.g., random flipping and cropping) were used to improve the model's generalizability. The model achieved an accuracy rate of 92% for the test set, with a consistency of 95% relative to manual annotations (Table 2).  
Application of the CogVLM2-Video Model-Based System for Evaluating Teaching Behavior in PE Classrooms  
The video lesson example of \"Basketball Give-and-Go (Cut) Cooperation\" at Level V (Grade 10 of senior high school) was imported into the system for data collection, analysis, and deconstruction. The following characteristics of this lesson were derived.  
.2.1 High Information Entropy, Low Redundancy: Stimulating Classroom Vitality and Eco-Constructivism  
Precise calculations using intelligent algorithms revealed that the information entropy of this lesson was 3.202 bits, indicating considerable diversity in teaching behaviors. The teacher employed a variety of instructional strategies, thereby avoiding monotony and significantly enhancing student engagement and autonomy. The redundancy rate was 0.180, suggesting minimal repetitive behaviors, which enabled the teacher to manage classroom time efficiently and focus on critical knowledge points.
# Study of Information Dissemination in Hypernetworks with Adjustable Clustering Coefficient
## 4 Simulation Results and Analysis
### The Results of Information Communication Theory Analysis
#### 4.2.2 The Impact of Hypernetwork Scale  
Figure 6 displays information propagation curves of the density of I-state nodes with respect to time, for various scales of the hypernetwork. The following parameter values were utilized: \(m_{0}=6\), \(m_{1}=3\), \(m_{2}=3\), \(m=3\), \(\beta=0.1\), \(\gamma=0.1\), and \(p=0.6\). The curves demonstrate that the system takes the same time and reaches the same density of I-state nodes in the steady state, regardless of the hypernetwork scale. This result suggests that the hypernetwork scale has a minor impact on information propagation. Consequently, for the following simulation experiments, the number of nodes was set to \(N=1000\).  
Figure 5: Comparison of theoretical analysis and simulation results of informed node density \(\rho\) in the steady state.  
Figure 6: Simulation results are displayed for various scales of the hypernetwork.
# Dynamic Controllability of Processes without Surprises
## 5 Sudden Termination Constellations and Patterns
### Sudden Termination Constellation and Pattern STP-3  
We define STP constellation STP-3 (see Figure 6) as follows: a contingent activity \(C\) and a semi-contingent activity \(S\) with constraints \(ubc(C.s,S.e,w)\) and \(lbc(C.e,S.e,v)\) as above are in an STP, iff there is no way to schedule the start of \(S\) and the start of \(C\), such that the value for the end of \(S\) can be fixed, when \(S\) starts.  
**Definition 9** (Sudden Termination Pattern 3): _Let \(P\) be a dynamically controllable process. Let \(S\) and \(C\) be activities in \(P\), with the duration constraints \(d(S,S.d_{min},S.d_{max},sc)\) and \(d(C,C.d_{min},C.d_{max},c)\). Let \(ubc(C.s,Se,w)\) and \(lbc(C.e,S.e,v)\) be constraints in \(P\)._  
\(S\) _and \(C\) are in a sudden termination pattern STP-3 iff \(\forall S.s,C.\)\(\forall S.d_{min}\leq S.d\leq S.d_{max}\)\(\exists C.d_{min}\leq C.d\leq C.d_{max}:\)\(S.s+S.d>C.s+w\), or \(S.s+S.d<C.s+C.d+v\)._  
In the following we derive conditions to check whether an STP-3 might occur for a given pair of activities \(S\) and \(C\).  
**Theorem 5**: _Let \(P\) be a dynamically controllable process. Let \(S\) be a semi-contingent activity, and \(C\) be a contingent activity in \(P\), with duration constraints \(d(S,S.d_{min},S.d_{max},sc)\) and \(d(C,C.d_{min},C.d_{max},c)\). Let \(ubc(C.s,S.e,w)\) and \(lbc(C.e,S.e,v)\) be constraints in \(P\)._  
_A sudden termination of \(S\) cannot occur, iff \(C.d_{max}+v<S.e-C.s\leq w\) holds in \(P\)._  
Proof.: Please refer to Appendix A.2.  
**Theorem 6**: _Let \(P\) be a process. Let \(S\) be a semi-contingent activity, and \(C\) be a contingent activity in \(P\) with duration constraints \(d(S,S.d_{min},S.d_{max},sc)\) and \(d(C,C.d_{min},C.d_{max},c)\). Let \(ubc(C.s,S.e,w)\) and \(lbc(C.e,S.e,C.d_{max}+v)\) be constraints in \(P\)._  
_A sudden termination of \(S\) cannot occur, if the constraints \(lbc(C.s,S.e,C.d_{max}+v)\) holds in \(P\)._  
Proof.: The constraints express exactly the condition described in Theorem 5. Hence in a process model \(P\) including these constraints \(S\) and \(C\) cannot be in an STP-3.
# Preliminary Results of Ship Detection Technique by Wake Pattern Recognition in SAR Images
## 3 Results
Finally, the merit indexes \(F_{v}\) and \(F_{t}\) can be estimated for the pixel identified by the yellow dot. Figure 4: Steps of the candidate wake identification. The first level of validation of the wake presence requires the evaluation of the distribution of the merit index \(F_{w}\), estimated over the SAR image. Figure 6 shows the statistical distribution of the \(F_{w}\) along with the bell-shaped distribution fitting the data (shown as red line) and the merit index value corresponding to corresponding to \(\mu+2\sigma\) (shown as vertical black line). Results confirm that (i) the pixels belonging to the sea lead to a bell-shaped distribution, and (ii) the pixels belonging to the wake satisfy Equation (4). Indeed, it is interesting to note that pixels showing merit index values larger than the vertical black line are the ones belonging to the wakes. Such pixels are shown as full yellow circles in Figure 7, whereas all the pixels in which the wake identification phase is applied are shown as empty yellow circle. Figure 5: Candidate wake identification results. (**a**) Tile identified around the pixel under analysis, i.e., yellow dot in Figure 2, (**b**) Radon domain applied to the tile and identification of the tough/pair related to wake a, b, and c, (**c**) reconstructed wake. Figure 6: Wake index statistical distribution. Red line is the bell-shaped distribution fitting the data. The vertical black line is the merit index value corresponding to \(\mu+3\sigma\). Figure 7: Results of first validation phase. Pixels satisfying Equation (4) are shown as full yellow dots. The second level of validation aims to limit the false detection rate, discarding the wakes, whose presence is not validated in neighbor pixels. This is the case of the false detection in Figure 7. To this end, the score-based approach is implemented, and the results are shown in Figure 8a. White, blue, and red identify pixels crossed by 1, 2, and 3 wakes, respectively. Since each candidate pixel implies one reconstructed wake, the component due to the false detection is also shown in Figure 8a as a white line. Then, since it is due to a false detection, no wake presence is identified in neighbor pixels and, hence, it can be discarded by the score logic approach. The effectiveness of the proposed technique is confirmed by the results shown in Figure 8b, which shows pixels crossed by at least one wake.
## 2 Materials and Methods  
The \(RE\)FeO\({}_{3}\) (\(RE\) = Ce, Nd) precursors were prepared by a solid-state reaction. The CeO\({}_{2}\), Nd\({}_{2}\)O\({}_{3}\), Fe\({}_{2}\)O\({}_{3}\) and Fe powders of 99.9% purity were used as raw materials. The powders were weighed and ball-milled for 24 h with zirconia balls in ethanol. After drying in an oven at 75 \({}^{\circ}\)C, the powder mixture was pelletized with a uniaxial press and then heat-treated at 850-1050 \({}^{\circ}\)C for 6 h in Ar. After the heat treatment, the pellets were crushed into powder using a mortar. To make a homogeneous mixture, the powders were mixed with H\({}_{3}\)BO\({}_{3}\) powder, which is the boron source of the 2-14-1 phase, ball-milled for 24 h and then dried in an oven. Calcium granule particles with a purity of 99% were added to the powder mixture at a 1:1 wt.% ratio, and, then, the whole mixture was pressed into a green compact with a diameter of 1 cm using the uniaxial press. For the R-D process, the pressed compacts were heat-treated at 1050 \({}^{\circ}\)C for 6 h in Ar, which was followed by furnace cooling to room temperature. The final products were crushed into powder and then washed with deionized water and acetic acid several times to remove Ca and CaO. Finally, the washed powder was dried at 120 \({}^{\circ}\)C for 30 min in vacuum. The preparation process of \(RE_{2}\)Fe\({}_{14}\)B magnetic powder through the modified R-D method is illustrated in Figure 1.  
The phase analyses were carried out using X-ray diffraction (XRD, D8 Advance, Bruker Corporation, Billerica, MA, USA) with Cu K\(\alpha\) radiation. The magnetic properties were measured at room temperature using a vibrating sample measurement system (VSM, VSM-7410, Lake Shore Cryotronics Inc., Westerville, OH, USA), and the microstructure and particle size were observed by a field emission scanning electron microscope (FE-SEM, MERLIN Compact, Carl Zeiss AG, Oberkochen, Germany).
## 5 Discussion  
### Contribution  
In the study, we designed collection tools to crawl hazardous-chemical-accident information. Additionally, we proposed a rule template and Bert-BiLSTM-CRF based model (RT-BBC) to construct a NER model specifically for the Chinese hazardous chemicals domain. With RT-BBC, the named entities such as reason, scene, and loss that not considered  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Entity Type** & **Precision** & **Recall** & **F1** & **Support** \\ \hline Date & 97.20\% & 96.47\% & 96.83\% & 1140 \\ Time & 87.37\% & 69.36\% & 77.33\% & 710 \\ Place & 81.37\% & 66.53\% & 73.20\% & 1870 \\ Chemical & 81.01\% & 58.01\% & 67.61\% & 810 \\ Person & 76.38\% & 81.86\% & 79.02\% & 2370 \\ Organization & 77.33\% & 74.30\% & 75.78\% & 1790 \\ Reason & 38.89\% & 29.79\% & 33.73\% & 470 \\ Loss & 58.49\% & 62.00\% & 60.19\% & 500 \\ Scene & 44.44\% & 16.00\% & 23.53\% & 250 \\ \hline micro avg & 77.57\% & 71.30\% & 73.83\% & 9910 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Recognition results for 9 types of entities.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Model** & **Precision** & **Recall** & **F1** \\ \hline RT-BBC\({}_{\text{LSTM}}\) & 53.67\% & 52.74\% & 52.43\% \\ RT-BBC\({}_{\text{Bi-LSTM}}\) & 59.19\% & 59.50\% & 58.46\% \\ RT-BBC\({}_{\text{Bi-LSTM-CRF}}\) & 67.33\% & 60.71\% & 63.62\% \\ RT-BBC (ours) & 77.57\% & 71.30\% & 73.83\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Overall entity recognition results of the four models.  
in other models can now be identified. As described in Section 4.3.2, the experimental results based on real data show that our model is superior to the compared models in terms of entity recognition precision and F1. In a word, this work provides a NER model for the field of hazardous chemical accidents, and provides new ideas for the field analysis of hazardous chemical accidents. At the same time, it has laid a solid foundation for the next step of identifying the relationships between hazardous chemical accident entities and building a knowledge graph of hazardous chemical accidents.
## 3 Results and Discussion
### Self-Healing Performance
#### 3.1.2 Repair Effect of Electrical Damage
In other words, the repair agent in the microcapsule can begin to repair the electrical tree at the early stages of its propagation. Therefore, when the doping amount of the microcapsule is appropriate, the polyethylene insulating composite is fully capable of repairing electrical damage, such as electrical trees. For a long time, electrical trees have been regarded as irreversible, permanent damage [34]. In this paper, the electrical tree formed by the faults/contaminants in polyethylene could be prevented/delayed by the microcapsule. The microcapsule could not only attract the electrical tree, and inhibit its propagation process, but also repair the tubules of the electrical tree effectively. It has good application value in reducing the harm of electrical damage.
# Effects of Grout Compactness on the Tensile Behavior of Ground Splice Sleeve Connectors
###### Abstract  
The quality of grouted sleeve has a significant influence on the performance of the sleeve splice. Incompactness of the infilled grout is inevitable in sleeve grouting. To investigate the tensile behavior of grouted splice sleeves due to different grout compactness, monotonic tensile tests on grouted splice sleeve connectors were performed at grout compactness of 100%, 90%, 70%, and 50%, respectively. The bond-slip analytical model of rebar-grout was deduced by fitting the tensile test data, and the formula for the tensile capacity of the grouted splice sleeve was proposed in the paper. The results show that the tensile strength of the splice sleeve reduces as the grout compactness decreases. It was found from the experiment that the calculated values of tensile capacity are in good agreement with the experimental values. The proposed formula can be adopted in determining whether reinforcing remedies or re-grouting should be taken in the case of incompact grout in grouted splice sleeve connectors.  
grout compactness; precast concrete; grouted splice sleeve connector; tensile capacity; bond-slip analytical model Article
## 3 Integration of Volunteered Geographic Information
### Management of Volunteered Data  
For integration into the Marine Information System (MIS), the app and the WhiteBox device are complemented by a suitable back-end, whose main features and components are described in this section. Namely, a dedicated service unit in the MIS manages the volunteered data by controlling the information sent by volunteers through text messages (SMS) and received by means of a mobile extension of the MIS, i.e., a specific hardware component dealing with communication through the cellular network. The data-flow is shown in Figure 3.  
Figure 3: The service unit in the Marine Information System (MIS) dedicated to the management of Volunteered Geographical Information. The service unit takes care of storing received data in the operational database of the MIS.  
Figure 2: The main screen of the ARGO Sentinel, available at Google Play.  
In particular, a GSM listener has been set up for managing the communication with an external GSM modem connected via a Universal Serial Bus (USB) interface to the mobile extension of the MIS. The GSM listener is constituted by the open source Gammui SMS Daemon [25], a program that periodically scans the GSM modem for received messages and stores them in a predefined storage. The Gammui SMS has been configured for storing the received messages in the inbox table of a local Database (DB). For parsing and processing volunteered data, a Java scheduler procedure checks every thirty seconds if a new message has been stored by Gammu in its local DB. When a new message is found, this is processed by a Java class, which stores the information into the MIS. Two kinds of messages are currently managed, namely the messages issued via the ARGO Sentinel app and via the WhiteBox device. For each of them, the payload of the SMS, the sending phone number and the timestamp are passed to a parser, which decodes the message, extracting the location where the message has been issued. Finally, the parsed data are stored in a suitable table in the operational geo-enabled DB of the MIS and associated with a particular volunteer as identified by the phone number. Figure 4 shows how an alert reported by a volunteer using ARGO Sentinel appears through the web interface of the MIS.
# Intelligent Video Surveillance Platform for Wireless Multimedia Sensor Networks
## 4 Design Implementation
### Distributed Source Sending Rate Control  
The rate control increases the video quality of several videos sent through the network. Source nodes should control their rates to reduce the packets loss due to buffer overflow. To avoid buffer overflow, sensor nodes should reduce their sending rate if buffer increases to stage that network cannot maintain. We present a method in which each source node maintains its data sending rate and periodically updates its data sending rate according to its neighbor's congestion level. Each node has a unique IP address and can establish a routing path from itself to the sink through multi-hop communication.  
In the beginning, the sink node broadcasts the assigned maximum transmission rate of its child through feedback mechanism. Then, the sink's child broadcasts and calculates the maximum transmission rate to its child again. After a while, every node will have a maximum transmission rate. The goal of this feedback mechanism is to maximize the quality of the played streams. The feedback  
Figure 5: Buffer states for congestion detection.  
process analyses the occupancy at each node and sends feedback information to the sender node. The sender node gets the feedback information, analyses it, and makes required adjustments to its transmitted streams. The idea is to modify the quantity of transmitted data according to the feedback information: when the network appears to be heavily loaded, the quantity of transmitted data is reduced; when the network appears to be lightly loaded, the quantity of transmitted data can be increased again. In this way, the source sending rate can be adjusted more efficiently and accurately.  
We use \(\beta\) to denote the falling speed of the sending rate at each time when a state turns to its next slower one. Therefore, the sending rate at node \(n\) can be expressed as (3), where \(S_{n}\) represents minimum sending rate of node, and \(S_{N}\) represents normal sending rate of state.  
\[S_{n}=\beta S_{N} \tag{3}\]  
The normal sending rate, minimum sending rate, and \(\beta\) can be obtained from the experiments.
# Numerical Simulation of Natural Gas/Hydrogen Combustion in a Novel Laboratory Combestor
## 2 Materials and Methods
### Mathematical and Physical Models
#### 2.2.5 Boundary Conditions  
The mass flow rates of air and fuel were specified at their respective inlets (see Table 2). The velocity direction at the swirler exit was aligned with the inclination angle of the swirling vanes. A turbulence intensity of 10% was assumed at the inlets. The combustor operates at atmospheric pressure and the inlet air and fuel temperatures were set to 300 K for all studied cases. The species mass fractions of the fuel at the inlet were determined according to the considered hydrogen content, assuming that the natural gas is entirely constituted by methane. A pressure outlet boundary condition with zero-gauge pressure was applied at the exit section.  
The laws of the wall were used to define the boundary conditions at the walls for the velocity and energy equations. The size of the control volumes adjacent to the walls was chosen to guarantee that \(y^{+}\) marginally exceeds 30, so that the corresponding grid nodes lie in the logarithmic layer, allowing for the use of the wall functions. The temperature of the cylindrical wall, as well as the temperatures of the other surfaces facing the interior of the combustion chamber, was set to 900 \({}^{\circ}\)C, based on measurements acquired using a thermocouple embedded in the wall. The temperature of the annular surface at the burner exit, between the fuel tube and the annular swirler, was set to 300 K.  
To optimize computational efficiency, the computational domain was restricted to a sector of 45\({}^{\circ}\) of the combustion chamber, taking advantage of the geometrical periodicity. Periodic boundary conditions were applied to the axial planes delimiting the computational domain.
## 7 Experimental Results
### CPU Utilization Bound
Consequently, the curves in both graphs look similar. The bottom line which represents the proposed optimal threshold approach has a utilization bound close to 0.7. It, therefore, proves the claim that the proposed approach works well for any priority system. With this utilization, the input tasks can be scheduled on a static-priority system like RM and a dynamic-priority system like EDF. The static threshold and harmonization produce a utilization bound close to 1, and thus it means that these approaches can also be employed in normal scenarios. As it is discussed in many places, this paper addresses more dynamic context like IoT and Industry 4.0, the following two approaches take specific attributes that are needed in these contexts. Harmonization suffers from the scalability as this approach allows a period of only specific discrete values. On the other hand, the static approach does not have as such constraints, but it can produce a very high overhead at times, which results in increasing the memory footprints of the system drastically.