esa-sceva/satcom-chunk-collection huggingface dataset:
# Evaluation of Online Teaching Quality Based on Facial Expression Recognition
###### Abstract  
In 21 st-century society, with the rapid development of information technology, the scientific and technological strength of all walks of life is increasing, and the field of education has also begun to introduce high and new technologies gradually. Affected by the epidemic, online teaching has been implemented all over the country, forming an education model of \"dual integration\" of online and offline teaching. However, the disadvantages of online teaching are also very obvious; that is, teachers cannot understand the students' listening status in real-time. Therefore, our study adopts automatic face detection and expression recognition based on a deep learning framework and other related technologies to solve this problem, and it designs an analysis system of students' class concentration based on expression recognition. The students' class concentration analysis system can help teachers detect students' class concentration and improve the efficiency of class evaluation. In this system, OpenCV is used to call the camera to collect the students' listening status in real-time, and the MTCNN algorithm is used to detect the face of the video to frame the location of the student's face image. Finally, the obtained face image is used for real-time expression recognition by using the VGG16 network added with ECANet, and the students' emotions in class are obtained. The experimental results show that the method in our study can more accurately identify students' emotions in class and carry out a teaching effect evaluation, which has certain application value in intelligent education fields, such as the smart classroom and distance learning. For example, a teaching evaluation module can be added to the teaching software, and teachers can know the listening emotions of each student in class while lecturing.  
artificial intelligence; online education; learning quality; face detection; expression recognition Article
## 4 Results
### Experiment Conditions: Algorithm Parameter Tuning and Neuronal Constraint Handling Training Process
\begin{table}
\begin{tabular}{c c l} \hline \hline
**Algorithm** & **CHT** & **Parameters** \\ \hline \multirow{4}{*}{DE/RAND/1/BIN} & FR & \(CR=0.95\), \(F_{min}=0.13\), \(F_{max}=0.95\) \\  & SR & \(CR=0.91\), \(F_{min}=0.23\), \(F_{max}=0.96\), \(Pf=0.15\) \\  & EC & \(CR=0.89\), \(F_{min}=0.20\), \(F_{max}=0.81\), \(Pg=0.07\), \(Tc=590\), \(Rg=5\), \(cp=6\) \\  & PF & \(CR=0.95\), \(F_{min}=0.17\), \(F_{max}=0.91\), \(\omega_{k}=10,000\) \\ \hline \multirow{4}{*}{GA} & FR & \(CR=0.80\), \(MR=0.06\) \\  & SR & \(CR=0.23\), \(Pf=0.20\), \(MR=0.05\) \\  & EC & \(CR=1\), \(Pg=0.05\), \(Tc=610\), \(Rg=4\), \(cp=8\), \(MR=0.08\) \\  & PF & \(CR=0.83\), \(MR=0.11\), \(\omega_{k}=10,000\) \\ \hline \multirow{4}{*}{PSO} & FR & \(v_{min}=0.0\), \(v_{max}=0.01\), \(C1=1.29\), \(C2=2.04\) \\  & SR & \(Pf=0.69\), \(v_{min}=0.0\), \(v_{max}=0.01\), \(C1=2.22\), \(C2=1.06\) \\  & EC & \(Pg=0.05\), \(Tc=610\), \(Rg=4\), \(cp=8\), \(v_{min}=0.0\), \(v_{max}=0.01\), \(C1=1.10\), \(C2=1.79\) \\  & PF & \(v_{min}=0.05\), \(v_{max}=0.17\), \(C1=2.04\), \(C2=1.06\), \(\omega_{k}=10,000\) \\ \hline \multirow{4}{*}{MUMSA} & FR & \(CR=0.73\), \(F_{min}=0.34\), \(F_{max}=0.78\), \(R=0.87\), \(MR=0.05\) \\  & SR & \(CR=0.81\), \(F_{min}=0.69\), \(F_{max}=0.79\), \(R=0.73\), \(MR=0.08\), \(Pf=0.31\) \\ \cline{1-1}  & EC & \(CR=0.93\), \(F_{min}=0.71\), \(F_{max}=0.99\), \(R=0.75\), \(MR=0.03\), \(Pg=0.08\), \(Tc=257\), \(Rg=3\), \(cp=9\) \\ \cline{1-1}  & PF & \(CR=0.02\), \(F_{min}=0.13\), \(F_{max}=0.78\), \(R=0.04\), \(MR=0.02\), \(\omega_{k}=10,000\) \\ \hline \hline \end{tabular}  
* \(CR\) is the crossover factor, \(F_{min}\) and \(F_{max}\) are the maximum and minimum scale factor limits. \(MR\) is the mutation rate. \(Pf\) is a probabilistic factor in SR. \(cp\) is a parameter to control the speed of reducing relaxation of constraints. \(T_{c}\) is the maximum number of iterations (generations or time) to relax constraints. \(RG\) is the number of attempts to improve a solution, and \(Pg\) is a probabilistic factor to improve a solution using the gradient-based mutation in \(cC\).  
\end{table} 
Table 2: Algorithm parameter setting obtained by an irace package in study case 1.
# A Joint Detection and Decoding Scheme for PC-SCMA System Based on Pruning Iteration
## 1 Introduction
### Organization and Notation  
The remainder Section 2 introduces the PC-SCMA system model. Section 3 provides the detail of the proposed optimization schemes for joint detection and decoding. Computational complexity analysis and BER performance simulation analysis are expounded in Sections 4 and 5, respectively. Finally, Section 6 gives the discussion and conclusions.  
In this paper, lower-case letters \(x\) represent column vectors and upper-case letters \(\mathsf{X}\) denote matrices. The diag(\(\mathsf{x}\)) is the diagonal matrix with the diagonal entries being vector \(x\) and \(\mathbb{C}\) denotes complex numbers. \(x\)-\(cN\) (\(\mu\),\(\delta^{2}\)) indicates that the variable \(\mathsf{x}\) follows the complex normal distribution with mean \(\mu\) and variance \(\delta^{2}\).\(\left\lVert\cdot\right\rVert\) denotes the Euclidean norm of a vector and \(\left\lvert\cdot\right\rvert\) means the absolute value of a scalar. \(\prod\) and \(\prod^{-1}\) represent the interleaving and de-interleaving operation, respectively. \(\varepsilon\)/\(j\) indicates the set \(\varepsilon\) with element \(j\) being excluded.
# Dynamic Information Flow Tracking: Taxonomy, Challenges, and Opportunities
## 6 Discussion and Prospect
### A Flexible Tag-Based System  
For in-core DIFT design, the adopted global security policy configuration registers provide flexible customization for security policies to some extent. However, the global configuration register is not suitable for the actual software context. The security policy needs to be configured frequently at runtime to adapt to different program environments. In some cases, this type of design may incur high false positive rates. That is, the security policy customized by global configuration registers cannot provide fine-grained protection which is suitable for the actual context.  
Therefore, fine-grained tags are designed to express the atomic tag operation, e.g., the tag bit operation, tag fetch and update. The security policy can be expressed using a combination of multiple atomic tag operations. In addition to the flexible expression of tags, the variable length of a tag is considered to support multiple security policies at onetime. At the hardware layer, the tag-related operations are executed in extended processor pipeline stages or added copro processor. For the extended processor pipeline, extra tag-related instructions are executed to finish tag-related operations. Therefore, the extra software performance overhead is introduced. A better solution to this problem is to adopt parallel hardware pipeline stages to process the tag-related instructions instead of extending the original pipeline stages. In the coprocessor, the extra tag-related instructions are counted as nor-operation instructions in the processor pipeline. However, this also influences the performance overhead. A possible solution is to divide the normal instructions and tag-related instructions and place them in different memory locations. Then, the coprocessor can receive the committed instructions from the processor and fetch the tag-related instructions according to the committed instructions.
# AUV Adaptive Sampling Methods: A Review
## 5 Multiple Platforms Networking  
When planning any oceanographic mission, there is always a trade-off between the sensing coverage and resolution. They must be sufficiently adjusted and balanced so as to encompass the mission objective. Depending on the spatial and temporal scale, the number of vehicles involved and/or the sensor payloads need to be decided. Multiple vehicles are most likely to be involved, working as a mobile sensor network, when the mission is to monitor or track features with a high temporal variance or a widespread distribution. When a long-term endurance mission to explore a larger area is expected, a fleet of mobile platforms may be adopted to maximise the coverage without losing resolution. [PERSON] et al. [22] highlighted the importance of relevant spatiotemporal scales on detecting and classifying underwater features using multiple AUVs.
## 6 Proposal of a Layered Communication Architecture and an Application Protocol for MG's
### Network Layer  
The network protocols used for this architecture are based on TCP/IP suite model. To improve the smart capacities of a microgrid, it is mandatory the ability to connect a large number of devices and handle a large volume of data in real time, which must be quickly updated to make decisions in the least possible time. Therefore, for the transfer of data the following are needed: (i) a large bandwidth; (ii) high data rate and (iii) a network layer with fast channel response. To accomplish these requirements, TCP/IP is preferred for interconnecting power systems devices, because it provides high connectivity, high bandwidth and quick response [86]. The interconnection network ensures reliable and secure communication between components by employing an internet protocol (IP) and data traffic remains secure and safe because the transport communications protocol (TCP) is connection-oriented. In addition the IEC 61850 via Ethernet standard suggests the use of TCP/IP for efficient communications among microgrid components [87; 88].
## 3 Results
### Hydrological Control
#### 3.2.3 Groundwater Head Relationship with Basin Elevation  
Groundwater levels display a near linear trend, with ground elevation of R\({}^{2}\) 0.955 (n = 63) that improves to R\({}^{2}\) 0.986 (n = 59) with removal of four low-head outliers (Figure 5b). The relationship indicates that groundwater levels (despite years of data collection varying) are quite predictable from ground surface elevations. This is ascribed to groundwater drainage towards the lake within shallow aquifer(s) beneath the gently declining basin land surface. Groundwater levels at \(>\)700 m elevation in areas of colluvium are very predictable with little data scatter and remain quite predictable at lower elevations in the areas of high-level and terrace deposits. Marked exceptions, the anomalous outlier data points in Figure 5b, could be data errors, but also could be explained by drilling on hill features subject to increased ground elevation error. Overall though, the linear relationship provides an informative tool guiding drilling depths and expected groundwater strike.
## 5 Results and Discussion
### Reliability Analysis  
In this section, the probabilistic reliability analysis was carried out for our proposed AFTCS model. Let \(R\) represents the reliability of each sensor, then the probability of failure of these components follows:  
\[P_{CF}=1-R \tag{64}\]  
where \(P_{CF}\) represents the probability of component failure.  
Without fault tolerance, the engine shuts down due to a fault in any of the four sensors.  
Let \(P_{SF}\) represent the probability of system failure and \(R_{System}\) represents the reliability of the overall system, then we have  
\[P_{SF}=4(1-R) \tag{65}\]  
\[R_{System}=1-P_{SF} \tag{66}\]  
\[R_{System}=1-4\;(1-R) \tag{67}\]  
If \(R\) = 0.9, then \(R_{System}=1-4\;(1-0.9)=0.6\)  
\[R_{System}=60\%\]  
The proposed AFTCS prevents the engine from shutting down in the event of a malfunction in a single sensor. However, if two sensors fail at the same moment, the engine will shut down. Hence,  
\[P_{SF}=\frac{4}{2}C(1-R)(1-R) \tag{68}\]  
\[P_{SF}=6\;(1-R)^{2} \tag{69}\]  
\[R_{System}=1-P_{SF} \tag{70}\]  
\[R_{System}=1-6\;(1-R)^{2} \tag{71}\]  
If \(R\) = 0.9, then \(R_{System}=1-6\;(1-0.9)^{2}=0.94\)  
\[R_{System}=94\%\]  
Thus, the reliability of the system improves by 34% with the proposed AFTCS for sensor faults.  
From the abovementioned MAP estimation results, throttle estimations results, MATLAB simulation results, and reliability analysis, we can say that the proposed methods, and especially the nonlinear regression, are more efficient as compared to other techniques because:  
* The estimated values generated by PSO, GA, and NLR are very close to the actual MAP and throttle values. The standard error of estimation is less than 0.02 for MAP estimation and less than 0.4 for throttle estimation.
* The computational power requirements are much less for these techniques.
* The computational time needed by these techniques is much less, which makes these methods efficient in terms of response time in the case of a sensor fault.
## 2 Materials and Methods
### DNA Amplification  
DNA migration patterns characteristic of all 31 wheat samples, as a result of agarose gel electrophoresis, indicate that only 6 ISSR primers out of 11 presented clear and well-defined bands (UBC808, UBC810, UBC855, UBC857, UBC859 and UBC880). The migration pattern for UBC 808 can be seen in Figure 1, and the results obtained for the other five primers can be found in Supplementary Materials--Figures S1-S5.  
The number of DNA bands per primer varied from 3 (for UBC859 and UBC880) to 8 (for UBC808), with a mean value of 4.83 bands/primer. Most of the primers had a number of polymorphic fragments equal to the number of amplified fragments, except UBC808, which had 7 polymorphic fragments from a total of 8. For each ISSR primer, we calculated the polymorphic information content (PIC) as proposed by [PERSON] et al. [39]. The formula was PICi = 2f\({}_{\rm i}\) (1 \(-\) f\({}_{\rm i}\)), where f\({}_{\rm i}\) is the number of amplified fragments and 1 \(-\) f\({}_{\rm i}\) is the frequency of absent fragments. The polymorphic information content varied from 0.32 for UBC859 to 0.42 for UBC857, with a mean of 0.36. The total number of amplified fragments, fragment length, polymorphism percentage and PIC value can be found in Table 4.  
The analysis of 31 wheat accessions using 6 ISSR primers showed that the mean allele number was 4.83; the minimum number of alleles was obtained for UBC859 and UBC880 (3 alleles), and the maximum number was 8 alleles for UBC808.  
Considering the allele frequency, the average was 0.6, with the highest value for UBC880 (0.77) and the lowest for UBC808 (0.45). The other primers registered the following allele frequencies: UBC810--0.55, UBC855--0.6, UBC857--0.64 and UBC859--0.72. Primers with the lowest number of alleles (3 for UBC859 and UBC880) showed the highest allele frequency (0.72, respectively 0.77). Information about allele number and frequency can be found in Figure 2, and the exact values are shown in Supplementary Materials, Table S2.  
Figure 1: DNA migration pattern of the ISSR marker (UBC808) for all 31 samples of _Triticum_ (2% agarose gel, migration for 130 min, 70 V).
## 4 Security Analysis of the MAPP Protocol
### Resistance to Attacks
#### 4.2.4 DOS Attacks  
Our three proposed authentication methods support the immediate verification of packets. In contrast to key disclosure protocols that allow the receiver to wait till the sender discloses the signing key. The key disclosure protocols allow receivers to buffer a high number of packets until the key is received. Overwhelming the receiver buffer prevents the receiver from verifying the packets and result in message loss. For this, a large buffer size is required for key disclosure protocols, while in our authentication method, a small buffer is required to store a list of pseudo-identities, secret keys, and public keys (\(Lpid_{i},Lsk_{i},Lpk_{i}\)).  
From the previous analysis, we can summarize that our protocol can satisfy a wide range of security analysis and can resist a different type of attacks.
## 2 System Model and Problem Formulation
### Problem Formulation
#### 2.3.3 Determining the Minimum Trust Score Threshold  
The minimum trust score threshold \(\tau_{\text{min}}\) is a critical parameter that ensures the selection of vehicles with high reliability while mitigating the impact of outliers and temporary anomalies. It is dynamically adjusted based on historical trust data or learned heuristics [43]. A common approach sets  
\[\tau_{\text{min}}=\mu+\sigma \tag{10}\]  
where \(\mu\) and \(\sigma\) are the mean and standard deviation of historical trust scores. This approach prioritises vehicles scoring significantly above the norm, ensuring robust and adaptive participant selection. The \(\mu+\sigma\) threshold balances between including the most reliable vehicles and excluding outliers, reflecting the inherently dynamic nature of IoV networks. This method effectively mitigates temporary anomalies that may bias trust distributions. By deriving the minimum trust threshold from historical performance data and statistical methods, the system ensures that selection criteria remain robust and evolve with the network's operational history.
# CubeSat-Based Observations of Lunar Ice Water Using a 183 GHz Horn Antenna: Design and Optimization
## 2 Design Methodology
This process ensures that the resulting smooth-walled antenna meets the desired specifications and delivers improved performance compared to the original corrugated design.
# A Deep Learning Approach for MIMO-NOMA Downlink Signal Detection
## 2 Fundamentals of MIMO-NOMA and the Deep Learning System  
In this section, we describe the fundamental theory of NOMA transmission and the traditional SIC algorithm. Then, we analyze the basic architecture of deep learning. For simplicity, we assumed that the bandwidth is 1 Hz. The channel impairment includes Rayleigh fading channel and the additive white Gaussian noise (AWGN) channel.
# An On-Demand Scheduling-Based MAC Protocol for UW-WiFi Networks
## 4 Design of SDDA MAC Protocol
Additionally, then they make use of the sum of the lengths of all data packets in the sending queue to figure out their respective data transmission delay (\(td_{data\_i}\)) and put \(td_{data\_i}\) (i.e., \(data\_tx\_delay\) in the \(RE\) packet shown in Figure 3) into the \(RE\) packet. When the waiting duration expires, terminal nodes dispatch \(RE\) packets to BS. As long as BS receives an \(RE\), it takes notes on the reception time (\(t_{rec\_RE\_i}\)) and \(td_{data\_i}\) carried in the \(RE\) packet. Instead of plenty of RTS-CTS handshakes, this reservation scheme diminishes the number of control packets by broadcasting a \(TR\), while solving the problem of control packet collisions, hence saving energy, improving channel utilization, and shortening end-to-end delay. The pseudo code of this phase is shown in Algorithm 1. Figure 2: An example of packets transmission scheduling of SDDA MAC protocol. ```
1.for each node ID \(i\), \(1\leq i\leq n\), \(n\) is the number of terminal nodes, do
2. calculate the transmitting time of each terminal node's \(RE\),
3.\(wd_{tx\_RE\_i}=(i-1)\times(pd_{max}+td_{RE})\)
4.endfor
5. write each \(RE\)'s transmitting time information into \(TR\)
6. record \(t_{send\_TR}\) and broadcast \(TR\)
7. switch from the sending \(TR\) state to the waiting \(RE\) state
8. start the waiting \(RE\) timer, the duration of this timer is \(D_{w\_RE}=td_{TR}+(n+1)\times pd_{max}+n\times td_{RE}\)
9.if\(D_{w\_RE}\) doesn't expire, then
10. receive RE, record \(t_{rec\_RE\_i}\), and \(td_{data\_i}\), \(T_{DATA}+=td_{data\_i}\), \(T_{DATA}\) is the transmission delay of all data packets from all terminal nodes
11.else if the number of \(RE\) packets received > 0, then
12. switch from the waiting \(RE\) state to the sending \(TS\) state
13.else
14. go to the step of Line 6
15.endif
```  
**Algorithm 1** The Initial Reservation Phase.
# A 3D Object Detection Based on Multi-Modality Sensors of USV
###### Abstract  
Unmanned Surface Vehicles (USVs) are commonly equipped with multi-modality sensors. Fully utilized sensors could improve object detection of USVs. This could further contribute to better autonomous navigation. The purpose of this paper is to solve the problems of 3D object detection of USVs in complicated marine environment. We propose a 3D object detection Depth Neural Network based on multi-modality data of USVs. This model includes a modified Proposal Generation Network and Deep Fusion Detection Network. The Proposal Generation Network improves feature extraction. Meanwhile, the Deep Fusion Detection Network enhances the fusion performance and can achieve more accurate results of object detection. The model was tested on both the KITTI 3D object detection dataset (A project of Karlsruhe Institute of Technology and Toyota Technological Institute at Chicago) and a self-collected offshore dataset. The model shows excellent performance in a small memory condition. The results further prove that the method based on deep learning can give good accuracy in conditions of complicated surface in marine environment.  
multi-modality sensors; fusion; object detection; unmanned surface vehicle +
Footnote †: journal: Applied Sciences
# Precise Identification of Coal Thickness by Channel Wave Based on a Hybrid Algorithm
## 1 Introduction
GAs have also been employed for the solution of seismological and other geophysical and environmental problems in the present day [29]. A population can be randomly generated as the initial model, under prior constraints defined by the GA with a bigger search space. This is compared with inversion methods, which are based on local linearization wherein only a single individual is used as the initial model. Besides, a GA offers a diversity of solutions in searching spatially, through selection, crossover, and mutation operations. Therefore, the probability of local optimal solutions is greatly reduced. This is why they are increasingly used for geophysical problems. However, the large dimensionality involved in most geophysical optimization problems can reduce the efficiency of the GA, especially when a large computational cost is required to obtain a small accuracy improvement in the later stages of evolution. In this case, the GA tracks the global minimum, but only reaches a point around the optimal value, and has a poor ability in local search. This research introduces a new hybrid approach, GA-SIRT, which combines the best features of GAs' strong global search capability and the SIRT's faster local convergence rate, aiming to obtain the objective function's global optimal solution as quickly and accurately as possible. The GA-SIRT has been employed to obtain a precise coal thickness distribution in the Guoerzhuang coal mine.