esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Our Proposed Context-Aware Smart Classroom Architecture
### The Context-Aware Smart Classroom Prototype
#### 2.1.1 Basic Requirements of a Technology-Rich Classroom  
A smart classroom is a technology-rich classroom to support classroom activities, including lecturing, group discussion, presentation, and tele-education, with technology devices. The requirements to build a basic technology-rich classroom are listed as follows:  
1. Basic requirements for a comfortable teaching and learning environment, as shown in Figures 1 and 3. \[\bullet\] Whiteboards or blackboards for traditional lecturing. \[\bullet\] A lectern or a table for teacher. \[\bullet\] Swivel tablet chairs for different lecture style. \[\bullet\] Fans for air circulation. \[\bullet\] Air conditioner for temperature control. \[\bullet\] Lights should be divided into several sets with independent power switch.
2. Basic requirements for teaching with digital lecture material, as shown in Figure 2. \[\bullet\] A general purpose personal computer with popular operating system and software applications installed and Internet connectivity. \[\bullet\] The computer is connected to at least two displays. One for controlling the computer, the other is a large screen for all audience. \[\bullet\] The computer controlling display should have the multi-touch function for teachers to put marks with fingers on the displayed contents during a lecture. \[\bullet\] Microphone for lecturing or presentation. \[\bullet\] An audio speaker which takes both the computer's audio and microphone as a sound input source. \[\bullet\] Video/audio switches for devices such as a notebook computer or DVD player.  
Figure 1: Basic requirements for a comfortable teaching and learning environment: Lights in different areas can be controlled respectively; (**a**) a whiteboard for traditional lecture. Chairs with wheels and a tablet for the convenience of notes taking, using a notebook computer, and group discussion, A lectern for teacher with a computer connected to a touch screen and the projector; (**b**) a projector with motorized screen for lecturing with digital contents. Circulation fans and air conditioner for a comfortable learning environment. Lights in different areas can be controlled respectively.  
Figure 3: Swivel tablet chairs for dynamic seat rearrangement: (**a**) seats can be adjusted dynamically to face toward different interactive boards and (**b**) round table with any amount of seats.  
Figure 2: Basic requirements for teaching with digital lecture material: (**a**) A computer installed in the lecture connected to the projector and a multi-touch screen, the input signal goes to both projector and touch screen; (**b**) a wireless microphone for lecturing. Audio speaker play sounds from both the microphone and the computer.
# Time Series Analysis to Predict End-to-End Quality of Wireless Community Networks +
## 3 Experimental Dataset
### Dataset Analysis
#### 3.2.1 Path Behavior  
Our first study analyzed the temporal evolution of the number of paths (routing entries). As stated before, Figure 2 shows this evolution during a period of eight days. At any instant of time, each source node can see only one path (the one with best quality) for each possible destination node in the network. We can observe that the fluctuation of the number of paths seen by one node is quite significant (about 7% of variation). This observation contrasts with the general assumption in mesh networks that the number of paths is stable. Multi-Point Relay selection, which is usually made by consensus, does not  
Figure 1: Frequency of the OLSR path hops.  
take into account this observed variation and, therefore, the accuracy of finding the best routes can be compromised. Our time-series-based analysis will consider this variation to make accurate predictions of future EtEQ.  
Figure 3 plots the fraction of snapshots over the total for which the path has been present, namely the _Persistence_ of each path. Considering that the snapshots were taken every 5 min, we observed that about 100 paths lie below 80% of persistence, but the remaining 840 paths are very persistent. We noticed that this small number of non-persistent paths can explain the variations in Figure 2. Although the amount of non-persistent paths is significant, a path with very low persistence cannot be useful for predicting the behavior of stable paths. Therefore, taking into account that the dataset represents eight days, for the analysis presented in the following sections, we ignored paths that are persistent fewer than two days in total (persistence below 25%). There are only 57 paths with less than 25% persistence, and the remaining 882 paths have more than 25% persistence. Figure 3 shows that most of these former paths have a great persistence (839 paths have more than 80% persistence).
# An Adaptive Dempster-Shafer Theory of Evidence Based Trust Model in Multiagent Systems
## 6 Conclusions  
Introducing trust into the MASs for decision making eliminates many of the untrustworthy agents and significantly improves decision-making efficiency. In this paper, we proposed a trust estimation model in MASs, and the main contributions can be summarized from the following aspects. First, direct trust addresses uncertainty caused by many factors, and it enables agents to react quickly to unexpected changes in performance. Next, we introduced rational confidence in the received testimony in three aspects: consistency, which reflects the quality of the evidence; credibility, which indicates its reliability according to its previous experience; and certainty of the testimony estimated from the perspective of entropy. In addition, we used a credit mechanism to incentivize agents to share truthful information. These aspects are indispensable for trust estimation in dynamic MASs with deceptive and unstable agents. Simulation results show that: (1) the proposed direct trust has quick intuition regarding agents' different performances, which fits dynamic MASs properly; (2) the entropy-based certainty evaluation is correct and reasonable; (3) the proposed approach is efficient in dynamic trust management in MASs. Simulations in some industrial applications are conducted to show its efficiency. The proposed method also has limitations. We assume that the agent will maintain the same attitude towards all agents. That is, if an agent is honest, it will act honestly with all agents. In addition, we compare our approach with the previous literature separately. Some actual implementation of the cases may also be beneficial. In future work, we will improve this aspect and use the trust model in cloud manufacturing.  
Conceptualization, N.W.; methodology, N.W.; validation, N.W.; investigation, N.W.; resources, N.W.; data curation, N.W.; writing--original draft preparation, N.W.; writing--review and editing, N.W.; visualization, N.W.; supervision, N.W. and D.W.; project administration, D.W.; funding acquisition, D.W. All authors have read and agreed to the published version of the manuscript.  
This work is partially supported by the National Natural Science Foundation of China (Grant No. 61763009) and the Doctoral Scientific Research Foundation of Hubei Minzu University.  
Not applicable.  
The authors declare no conflict of interest.
# Fatigue Performance Test and Numerical Analysis of Composite Girders with CSW-CFST Truss Chords
## 2 Model Test
### Loading Method and Rig  
The loading rig drawing and photo are shown in Figure 6a,b, respectively. The four-point loading method was adopted, in a similar way to the fatigue performance test of composite girders with CSW-ST truss chords reported in [11]. A very stiff load distribution beam was placed parallel to the test model, such that its mid-span coincided with that of the test model. Rubber bearings separated by 2000 mm were arranged at both ends of the distribution beam to ensure that it was in contact with the deck slab of the test model. A 500-kN MTS servo-loading system was used to apply vertical load at the mid-span of the distribution beam.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Component** & \(f_{y}\)**(MPa)** & \(f_{u}\)**(MPa)** & \(E_{8}\)**(GPa)** & \(\mu_{8}\) \\ \hline Chord & 348 & 491 & 204 & 0.3 \\ K-bracing & 361 & 457 & 203 & 0.3 \\ Cross-bracing & 349 & 482 & 199 & 0.3 \\ Horizontal bracing & 355 & 468 & 203 & 0.3 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Mechanical properties of steel.  
Figure 4: Key geometric parameters of corrugated steel web.  
Figure 5: Locations of intersecting welds between corrugated steel web and chord.  
According to [19], the maximum axle weight of the design vehicle is 145 kN. Considering that the overloading phenomenon is prominent in China, and the actual axle weight may be twice the design axle weight [20], for safety considerations, the fatigue load amplitude adopted in the test was taken to be 300 kN.  
Combining the servo-loading actuator and the requirements for the fatigue performance tests, the lower limit of the fatigue load needs to be more than 3% of the maximum dynamic load of the actuator. Hence, the lower limits of the fatigue load adopted in the test were taken to be 50 kN.  
According to the specification of the 500-kN MTS servo-loading system and the value of the fatigue load, the loading frequency was taken to be 3 Hz. The probe of the phased array ultrasonic testing system was fixed in the location with the maximum hot-spot stress to monitor the initiation of the fatigue crack uninterruptedly; meanwhile, the intersecting area between the chord and the corrugated steel webs was inspected using a magnifying glass about every 5000 cycles to verify whether a fatigue crack appeared.  
The evolution of fatigue damage in composite girders with CSW-CFST truss chords with the number of load cycles was initially investigated by taking 50 kN as the load grade and 5 kN/s as the loading rate, approximately every 400,000 cycles, when cyclic loading was paused, and a static test was carried out. After fatigue crack initiation, loading was stopped, and static tests were carried out depending on the propagation process of fatigue cracks.
# A Study on the Remaining Reserves of the Weiyuan Shale Gas Block in Three-Dimensional Development
## 3 Evaluation of Remaining Reserves
### Type of Remaining Reserves  
There are two main types of unutilized reserves in the Weiyuan block: vertical unutilized zones and well network deployment gap zones. The study area of this paper is located in the main built-up production area of Weiyuan, with a high density of primary well networks and mostly wells of the last 5 years, with more mature engineering technology, so the blank area of well network deployment is not taken into account as the main type of unutilized reserve. The thickness of the Wufeng-Longyi 1 subsection ranges from about 35.6 to 53.6 m, with an average of 46.4 m. The target layer for the development of the Weiyuan block is the middle and lower part of the Longyi\({}^{1}\) layer, which ranges from about 1.7 to 7.0 m in thickness, with an average of 4.6 m. The analysis of logging monitoring data shows that the artificial hydraulic fracture seam height is only 10-15 m [19; 20]. The well network is only able to extract and produce part of the shale in the longitudinal direction and it is difficult to produce the shale outside the well network, so the main consideration is the longitudinal residual reserve as the type of unutilized reserve. Residual reserves in this category are the difference between the static geologic reserves of a single well and the well control reserves of a single well.  
The generation of a vertical unutilized zone in Weiyuan is due to the limited extension of the seam height of the current single-layer horizontal wells, which fails to effectively improve the degree of utilization of the development target layer. Therefore, three-dimensional development is proposed as a strategy for vertical unutilized areas to increase the degree of vertical utilization. The three-dimensional shale gas development technology has become a major development model in North America in the Eagle Ford, Permian, and other producing areas [21; 22; 23; 24; 25; 26; 27]. China has also carried out three-dimensional development practice in Zhaotong, Changning, and other blocks, among which Fuling shale gas field has successfully realized industrial application and demonstrated good development results [28; 29; 30; 31; 32; 33; 34]. In the exploration of shale gas development in the Weiyuan block, it is difficult to take care of the entire high-quality shale layer section with a thickness of several tens of meters by only one set of horizontal well networks, and it is necessary to use two sets of well networks, one upper and one lower, to realize the advantages of complementarity. In order to determine whether the Weiyuan block can effectively carry out three-dimensional development of well network deployment, its target layer should be confirmed first.
## 2 Related Works
### Multimodal Fusion Method  
Multimodal fusion usually includes three levels: pixel level [33], score level [13; 20; 34; 35], and feature level [22; 23; 30; 36]. For example, [PERSON] and [PERSON] [13] proposed a score-level fusion function called Holistic Fusion, which considers that the matching score of one modality may be more stable than another. The function results in a similar trend between the total matching score and the matching score of the more stable modality. [PERSON] et al. [20] generated composite images of FV texture and composite images of FV shape to be used as input for the DenseNet [37]. Then, matching was performed using the obtained output matching score. Finally, they performed score fusion under different rules such as weighted product, weighted sum, perceptron, and Bayesian. [PERSON] and [PERSON] [34] used five convolution layers and two fully connected layers to extract the features of each modality and then inputted them into their softmax classifier to obtain the similarity score. They used two score methods, namely, the arithmetic mean rule and the product rule. [PERSON] et al. [35] achieved optimal score-level fusion by boosting and suppressing concurrent classifiers and resolving conflict among discordant classifiers.  
Feature-level fusion can achieve better recognition results than pixel-level and score-level fusion because feature-level fusion preserves more original information [38]. [PERSON] et al. [22] first used the network to extract the features of the FV and FKP images, obtained two 1D feature vectors, and then concatenated the two feature vectors into a full feature vector. [PERSON] et al. [36] proposed a two-channel CNN feature fusion framework. The face image feature and the FV image feature were taken as the input to the self-attention mechanism to obtain their respective attention weights. The vein and face features were combined with their respective attention weights and concatenated to form splicing features. Then the splicing features were convolved to obtain the fused features. [PERSON] et al. [30] used a weighted average to fuse the features of the two modalities through the attention module. They also further fused the shallow feature and deep feature of the network. [PERSON] et al. [23] used a convolutional network to extract the 3D feature tensors of each modality. Next, feature tensors were concatenated in series according to the dimensions of the channels into a total 3D feature tensor, which was then sent to the subsequent network layers for further processing. Compared with these works, we adopt a feature-level fusion strategy fusing features at a deeper level.
## 5 Multi-Objective Reliability Optimization of Suspension Control Arm
### Deterministic Multi-Objective Optimization of Control Arm  
The control arm is expected to have sufficient life without fatigue failure, while having a small mass. Further considering the performance constraints of longitudinal stiffness, transverse stiffness, and first-order natural frequency, the deterministic multi-objective optimization of the control arm can be formulated as:  
\[\left\{\begin{array}{c}\min(m(x),-N(x))\\ find\textbf{ }x=(x_{1},\ldots,x_{2})^{\text{T}}\\ \text{s.t.}|k_{x}(x)-k_{x0}|\leq 0.1k_{x0}\\ |k_{y}(x)-k_{y0}|\leq 0.1k_{y0}\\ |f_{t}(x)-f_{t0}|\leq 0.1f_{t0}\\ \textbf{ }x\in(x_{L},\textbf{x}_{U})\end{array}\right. \tag{21}\]  
where \(x\) is the design variable vector of the control arm; \(m(\textbf{{x}})\) is the control arm mass; \(N(\textbf{{x}})\) is the minimum fatigue life; \(k_{x}(\textbf{{x}})\), and \(k_{x0}\) are the longitudinal stiffness and its initial value, respectively, \(k_{x0}\) = 12,821 N\(\cdot\)mm\({}^{-1}\); \(k_{y}(\textbf{{x}})\) and \(k_{y0}\) are the transverse stiffness and its initial value, respectively, \(k_{y0}\) = 25,974 N\(\cdot\)mm\({}^{-1}\); \(f_{t}(\textbf{{x}})\) and \(f_{t0}\) are the first order natural frequency and its initial value, \(f_{t0}\) = 324.1 Hz; and \(\textbf{{x}}_{L}\) and \(\textbf{{x}}_{U}\) are the lower and upper limits of design variable, respectively.  
The NSGA-II algorithm with a population size of 40 and generation of 100 was applied to solve the optimization of control arm. The Kriging surrogate models were used to calculate the performance indexes in the optimization process. Then, the Pareto front was obtained, as shown in the Figure 12. The optimal design parameters were selected from the Pareto solutions by considering that the mass was minimized as far as possible while meeting the fatigue life requirements. According to engineering practice, the optimization results of design variables were corrected, as shown in Table 4. The corrected design parameters were used to modify the finite element model of the control arm, and its performance indexes were obtained, also listed in Table 4. The mass of the optimized control arm decreased from 3.16 kg to 2.88 kg while its fatigue life was 1.08 \(\times\) 10\({}^{6}\), meeting the design requirement, as well as the other performance indexes.  
Figure 11: Accuracy verification of Kriging models. (**a**) Fatigue life; (**b**) mass.
###### Abstract  
Recently, piracy and copyright violations of digital content have become major concerns as computer science has advanced. In order to prevent unauthorized usage of content, digital watermarking is usually employed. This work proposes a new approach to digital image watermarking that makes use of the discrete cosine transform (DCT), discrete wavelet transform (DWT), dipper-throated optimization (DTO), and stochastic fractal search (SFS) algorithms. The proposed approach involves computing the discrete wavelet transform (DWT) on the cover image to extract its sub-components, followed by the performance of a discrete cosine transform (DCT) to convert these sub-components into the frequency domain. Finding the best scale factor for watermarking is a significant challenge in most watermarking methods. The authors used an advanced optimization algorithm, which is referred to as DTOSFS, to determine the best two parameters--namely, the scaling factor and embedding coefficient--to be used while inserting a watermark into a cover image. Using the optimal values of these parameters, a watermark image can be inserted into a cover image more efficiently. The suggested approach is evaluated in comparison with the current gold standard. The normalized cross-correlation (NCC), peak-signal-to-noise ratio (PSNR), and image fidelity (IF) are used to measure the success of the proposed approach. In addition, a statistical analysis is performed to evaluate the significance and superiority of the proposed approach. The experimental results confirm the effectiveness of the proposed approach in improving upon standard watermarking methods based on the DWT and DCT. Moreover, a set of attacks is considered to study the robustness of the proposed approach, and the results confirm the expected outcomes. It is shown by the achieved results that the proposed approach can be utilized for practical digital image watermarking, and that it significantly outperforms other digital image watermarking methods.  
diper-throated optimization; stochastic fractal search; meta-heuristic optimization; digital image watermarking +
Footnote †: journal: 2022, 12, 10642. [[https://doi.org/10.3390/app122010642](https://doi.org/10.3390/app122010642)]([https://doi.org/10.3390/app122010642](https://doi.org/10.3390/app122010642))
# Ocean Remote Sensing Techniques and Applications: A Review (Part II)
## 2 RS Applications in Ocean
### Ocean Chlorophyll (OCH)
#### 2.6.1 Optical
Consequently, ocean Chl-a observations are restricted to specific optical depths [221].
## 2 System Model
### Effective SINR and Channel Gain  
The symbol received by \(k\)-th user, \(y_{k}\), is given by  
\[y_{k}=\sqrt{\frac{P_{tx}N_{t}}{K}}\mathbf{h}_{k}\mathbf{f}_{k}x_{k}+n_{k}+\sqrt{ \frac{P_{tx}N_{t}}{K}}\sum_{l\
eq k}\mathbf{h}_{k}\mathbf{f}_{l}x_{l}, \tag{15}\]  
where \(\mathbf{h}_{k}\) is the \(1\times N_{t}\) channel vector for \(k\)-th user, and \(\mathbf{f}_{k}\) is the \(N_{t}\times 1\) precoding vector for \(k\)-th user. The last term of (15) is inter-user interference (IUI). Note that an efficient method of channel estimation is very important to reduce the IUI. The effective SINR for user \(k\), \(\gamma_{k}\), can be represented as follows:  
\[\gamma_{k}=\frac{\frac{P_{tx}N_{t}}{K}|\mathbf{h}_{k}\mathbf{f}_{k}|^{2}}{\frac {P_{tx}N_{t}}{K}|\sum_{l\
eq k}\mathbf{h}_{k}\mathbf{f}_{l}|^{2}+N_{0}B}, \tag{16}\]  
where \(N_{0}B\) is the noise power in the given bandwidth \(B\).  
In the ideal case that there is perfect channel state information at the TX and \(N_{t}\gg K\), the effective SINR can be simplified as:  
\[\gamma_{k}\rightarrow\frac{N_{t}}{K}\left(\frac{\rho}{N_{0}B}\right). \tag{17}\]  
where \(\rho=P_{tx}\cdot\zeta\) is the received signal power at RX, \(\zeta\) is the path loss component between TX and RX, and \(N_{0}B\) is the noise power in the given bandwidth, \(B\). Here we can say that \(\frac{N_{t}}{K}\) is the channel gain of the LSAS. It is obvious that if we increase the number of TX antennas or reduce the number of users, we can get better channel gain and/or effective SINR. However, it should be noted that MF precoding does not completely remove the IUI from a practical number of TX antennas. \(\frac{\zeta}{N_{0}B}\) can be normalized to unity, and it is equivalent to the average path loss of 134 dB with 2 GHz carrier frequency [30].