esa-sceva/satcom-chunk-collection huggingface dataset:
# Student Behavior Recognition in Classroom Based on Deep Learning
## 3 Framework of Student Behavior Detection
### Framework of Student Behavior Detection  
The detection framework proposed in this paper aims to provide an efficient solution for detecting student behavior in classrooms by integrating advanced object detection and posture recognition technologies.  
Specifically, the SBD model proposed in this paper first adds the CA attention module to the Backbone of YOLOv5, replaces all convolutional modules with attention module structures, and provides richer semantic information for the prediction layer. Then, the model enhances small target detection accuracy through the fusion of deep and shallow features. In particular, the model extracts student behavior regions to serve as input for the OpenPose network, which is utilized for pose recognition. By eliminating background interference, the model significantly improves the detection of student behaviors.  
As the basic framework in Figure 2 shows, our approach centers on two core modules to achieve student behavior detection in the classroom. First, we integrate an attention mechanism into the YOLOv5 model, forming a new network called YOLOv5-A for feature extraction in SBD. By replacing the original convolutional layers with a self-attention mechanism, the model can better focus on target areas, thereby improving the detection accuracy of small objects, such as student gestures and actions. This mechanism enhances the semantic information density of feature maps, allowing the model to maintain high accuracy even in occluded and complex backgrounds. Second, we utilize the high-quality features extracted by YOLOv5-A as inputs for the OpenPose network, replacing the original VGG-19 backbone. This substitution reduces computational demands and alleviates issues related to vanishing gradients and performance degradation in deep convolutions. The integration significantly minimizes the impact of background noise on detection accuracy and focuses on learning skeletal features to enhance the precision of human pose recognition.
## 2 MIP Application  
### Environmental Monitoring  
The increasing contaminants in the environmental water is an alarming issue [18]. Environmental monitoring can be carried out on environmentally hazardous materials such as dyes [19], persistent organic pollutants [20], pesticide residues [21], mycotoxins [22], heavy metals, and antibiotics [23]. The presence of these contaminants can cause harmful effects on public health, either directly or indirectly [24]. Environmental matrices that can be polluted by pollutants include air, soil, atmosphere, sediment, and flora and fauna [25; 26]. The pre-treatment sample aims to eliminate matrix interference so that only the target analyte is obtained [27]. MIP is commonly used as a sorbent in pre-treatment samples in samples with complex matrices because of its selected properties compared to conventionalorbents [26].  
One of the applications was presented by [PERSON] et al. [28] that analyzed ten macrolide drugs (spiramycin, clarithromycin, erythromycincin, tulathromycin, midecamycin, roxithromycin, josamycin, kitasamycin, tilmicosin, and azithromycin) in environmental water. MIP with functional monomer methacrylate acid and tylosin as a template was used as a sorbent in the molecular imprinted solid-phase extraction (MI-SPE) pre-treatment technique and then analyzed using the LC-MS/MS method. As a result, MI-SPE showed a high recognition ability of macrolides. The mean percentage recovery of macrolides at four spiked concentration levels was 62.6-100.9%, with intra-day and inter-day relative standard deviations below 12.6%.
## 3 Establish an EFLP Model and Solution Method
### Notations and Proposed Assumptions  
#### 3.1.1 Notations  
* \(I\): The set of emergency demand point \(i\) coordinates, \(i\in I\).
* \(J\): Set of coordinates of alternative emergency facility site \(j\) for RDGT, \(j\in J\).  
* \(d_{i}\): When an emergency occurs, the emergency demand point \(i\) coverage distance requirements.
* \(d_{j}\): The scope of emergency rescue services that can be provided by the emergency facility site \(j\) for the RDGT.
* \(d(i,j)\): Distance between emergency facility site \(j\) and emergency demand point \(i\).
* \(C_{j}\): Capacity of emergency facility site \(j\) for RDGT, i.e., emergency service capacity;
* \(\Omega\): The degree of conservatism of decision makers in making location decisions, which we named safety parameter/conservatism. The larger the safety parameter, the more conservative the decision maker is and the more resilient to the risk of demand fluctuations.
* \(k\): Maximum number of emergency demand points \(i\) to be covered.
* \(\beta_{i}\): The uncertainty set of the demand of emergency demand point \(i\), \(\left(\widehat{\beta}_{i}-\beta_{i}\right)^{T}\sum^{-1}\left(\widehat{\beta}_ {i}-\beta_{i}\right)\leq\Omega^{2}\), where \(\beta_{i}=(\beta_{i,1},\ldots,\beta_{i,N})\), i.e., the demand is the product of the quantity of dangerous goods and the amount of unit rescue resources. The unit rescue resources is the number of rescue resources required for a unit of dangerous goods in the event of an emergency. \(\widetilde{\beta}_{i}\) is the nominal value of \(\beta_{i}\).
* \(a_{i,t}\): The amount of \(t\) type of hazardous materials in case of emergencies at the emergency demand point \(i\).
* \(y_{i,t}\): Number of rescue resources required for the \(t\) type of DG per unit.
* \(X_{,j}\): the jth column of the decision variable \(X\).
* \(h\): The upper limit of service recipients at the emergency facility site \(j\).
* Decision variables
* \(X_{i,j}\): 0-1 decision variable, 1 if emergency facility point is selected, 0 otherwise.
###### Abstract  
Successful transmission of online multimedia streams in wireless multimedia sensor networks (WMSNs) is a big challenge due to their limited bandwidth and power resources. The existing WSN protocols are not completely appropriate for multimedia communication. The effectiveness of WMSNs varies, and it depends on the correct location of its sensor nodes in the field. Thus, maximizing the multimedia coverage is the most important issue in the delivery of multimedia contents. The nodes in WMSNs are either static or mobile. Thus, the node connections change continuously due to the mobility in wireless multimedia communication that causes an additional energy consumption, and synchronization loss between neighboring nodes. In this paper, we introduce an Optimized Hidden Node Detection (OHND) paradigm. The OHND consists of three phases: hidden node detection, message exchange, and location detection. These three phases aim to maximize the multimedia node coverage, and improve energy efficiency, hidden node detection capacity, and packet delivery ratio. OHND helps multimedia sensor nodes to compute the directional coverage. Furthermore, an OHND is used to maintain a continuous node- continuous neighbor discovery process in order to handle the mobility of the nodes. We implement our proposed algorithms by using a network simulator (NS2). The simulation results demonstrate that nodes are capable of maintaining direct coverage and detecting hidden nodes in order to maximize coverage and multimedia node mobility. To evaluate the performance of our proposed algorithms, we compared our results with other known approaches.  
wireless multimedia sensor networks (WMSNs); optimized occlusion-free viewpoint and multimedia coverage; mobility; coverage; energy efficient hidden node detection +
Footnote †: journal: Sensors
# First Retrieval of Sea Surface Currents Using L-Band SAR in Satellite Formation
## 3 LuTan-1 Interferometric Experiment
### Reference Data and Sea Surface Height Data  
Owing to the hybrid baseline configuration of LuTan-1, it is sensitive to both sea surface velocity and sea surface height. To obtain accurate sea surface velocity, the sea surface height component should first be removed, and the derived velocity inversion results should then be compared to reference data. In this study, both the sea surface velocity and height reference data were sourced from the Copernicus Marine Environment Monitoring Service (CMEMS), with the product ID being \"GLOBAL_ANALYSISFROECAST_PHY _007_022\" [31]. The spatial resolution of the CMEMS data is 1/12\({}^{\circ}\) Lat/Lon, with a temporal resolution of 6 h. For sea surface velocity, CMEMS provides the velocity components in the true north and true east directions, which should be transformed into the radar line-of-sight direction to validate the current velocity results derived from LuTan-1. Regarding the sea surface height, the CMEMS provides data that are referenced to the Earth Gravitational Model 1996 (EGM96) geoid. The EGM96 geoid-based heights should be corrected by adding the geoid height anomaly to convert them into World Geodetic System 1984 (WGS84) ellipsoid-referenced heights. The reference sea surface height and velocity data from CMEMS are shown in Figure 5b,c, respectively. Note that there are significant differences in both temporal (hours vs. milliseconds) and spatial resolution (kilometers vs. meters) between the CMEMS data and the results obtained from LuTan-1. These discrepancies hinder complete agreement between the absolute measurements of the inversion results and reference data.
# Industrial IoT Monitoring: Technologies and Architecture Proposal
## 2 Technologies for Industrial IoT
### IWSN Standards
The routing mechanisms available in this network are the same as the ones in WirelessHART. Additionally, in the infrastructure (i.e., backbone) side, ISA100.11a uses 6 LowPAN, thus allowing for direct communication between external IP devices and ISA100.11a devices. Lastly, WIA-PA supports a hierarchical topology that uses star and mesh, or star-only topology. In the case of the mesh topology, the network operates using routers and gateways, while in the star topology the network is composed of routers and field/handheld devices. At this level, field devices are cluster members that acquire sensor information and send it to the cluster heads (routers). Then, the cluster-heads form the mesh network. Each routing device in the network shares its neighbour information with the network manager, and then the network manager computes and shares the static routes. For each pair of devices that want to communicate, at least two routing paths are assigned. The transport (TP) layer is responsible for providing host-to-host communication services between applications. As can be seen in Table 1, only WirelessHART and ISA100.11a implement data transport functions at this layer, supporting different service level agreements. Optionally, ISA100.11a offers end-to-end security at this layer. In contrast to ISA100.11a and WirelessHART, WIA-PA provides different service level agreements at the application sub-layer (APS), and not at the transport layer. The service-level agreements available in each standard are presented in Sections 4 and 4.2. Last but not least, ZigBee does not support any transport layer functionality nor does it support traffic differentiation at the APS sub-layer. Moreover, in ZigBee, fragmentation, reassembly, and device discovery are implemented at the APS sub-layer. The application (APP) layer is the layer at which the connection between legacy systems and IEEE 802.15.4-based systems takes place [9; 18]. At this layer, there are two important options that can be identified. WirelessHART uses a command-oriented approach; alternatively, ISA100.11a, ZigBee, and WIA-PA use a more flexible object-oriented approach. Object-oriented approaches are more flexible than command-oriented approaches because they allow for protocol translation by mapping attributes from one protocol to the other. As for native applications, ZigBee supports the ZigBee profiles; WirelessHART supports the HART protocol; WIA-PA supports native protocols like Profibus, FF, and HART; last but not least, ISA100.11a supports the ISA100.11 application protocol.
## 3 Hydrothermal Crystal Growth
### Recent Results For Sesquioxide Laser Materials
#### 3.1.3 Dy:YAG and Dy:Lu\({}_{2}\)O\({}_{3}\)  
For The Suppression of Parasitic Oscillations and Amplified Spontaneous Emission  
We are also investigating hydrothermally-grown Dy:YAG and Dy:Lu\({}_{2}\)O\({}_{3}\) crystals with a view towards their use as potential parasitics and amplified-spontaneous-emission (ASE) absorbers. The measured absorption cross-section data are shown in Figures 42 and 43 for Dy:YAG and Dy:Lu\({}_{2}\)O\({}_{3}\) respectively. Dy:YAG is an interesting laser material that has received attention recently as a relatively rare example of a direct transition (\({}^{4}\)F\({}_{9/2}\)-\({}^{6}\)H\({}_{13/2}\)) in the yellow spectral region at 583 nm [124]. Previously, Sm\({}^{3+}\), Co\({}^{2+}\), Co\({}^{3+}\), Cr\({}^{3+}\), and Cr\({}^{4+}\) in YAG have been evaluated as parasitic and ASE absorbers for ceramic laser materials at the Nd:YAG wavelength, 1064 nm [125]. Our interest is in developing parasitic and ASE absorbers that are effective at various Yb wavelengths around 1030 nm, and that are effective at room temperature and cryogenic temperatures. In particular, we are interested in absorbers that are effective and can be epitaxially-grown on the outside surfaces of Yb:YAG and Yb:Lu\({}_{2}\)O\({}_{3}\) laser elements. For Dy:YAG, the absorption cross-sections at 1030 nm are rather small, 6 \(\times\) 10\({}^{-23}\)cm\({}^{2}\) at 298 K, and 9 \(\times\) 10\({}^{-23}\) cm\({}^{2}\) at 80 K. Even for large Sm dopings, as a parasitics and ASE absorber this material seems marginal at best. In the case of Dy:Lu\({}_{2}\)O\({}_{3}\), however, the results are more promising, with the absorption cross-sections at 1033 nm are 6 \(\times\) 10\({}^{-22}\) cm\({}^{2}\) at 298 K, and 2 \(\times\) 10\({}^{-22}\) cm\({}^{2}\) at 80 K. These cross-sections are roughly an order of magnitude lower than for Sm:YAG at 1064 nm, however, larger Sm doping densities can make this crystal useful at room and cryogenic temperatures. It is however likely that for Yb:YAG, either Cr\({}^{4+}\):YAG or Co\({}^{3+}\):YAG may be better choices since the absorption cross-sections in those materials, both of which may be epitaxially grown on Yb:YAG using the hydrothermal method, are much larger at 1029 nm. A similar conclusion may be reached if Cr\({}^{4+}\) and Co\({}^{3+}\) are doped into Lu\({}_{2}\)O\({}_{3}\).  
A feature of Figures 42 and 43 worth noting is that the absorption cross-sections around 1300 nm are large, and Dy:YAG can be an effective suppressor of ASE from the 1319 and 1338 nm transitions in Nd:YAG. Similarly, Dy:Lu\({}_{2}\)O\({}_{3}\) can suppress the 1359 nm transition in Nd:Lu\({}_{2}\)O\({}_{3}\). At 946 nm, both Dy:YAG and Dy:Lu\({}_{2}\)O\({}_{3}\) have marginal absorption.  
Figure 42: Absorption cross-section as a function of wavelength for a hydrothermally-grown Dy\({}^{3+}\):YAG crystal at 80 K (blue) and 298 K (green).
## 3 Results  
### Phase Composition  
First, the phase compositions of all samples were assessed using X-ray diffraction (XRD), as displayed in Figure 2. The obtained results confirm the amorphous structures of all MBG specimens, except 5.75 mol% Ag-doped MBG. With the higher Ag concentration (the 5.75 mol% Ag case), the XRD pattern exhibited the diffraction peaks at 38.2, 44.4, 64.6, and 77.6\({}^{\circ}\), which correspond to the (111), (200), (220), and (311), respectively, diffraction maximum in silver (Ag, PDF#87-0720), and (116), (314) for silver oxide (Ag\({}_{2}\)O), PDF#46-1476). Hence, for the Ag-doped MBG powders, increasing the silver concentration changed from the amorphous structure to crystalline phases, whereas the cerium-doped MBG powders did not.
# Automatic Bridge Crack Detection Using a Convolutional Neural Network
## 3 Experimental Results and Discussion
### Experimental Results
#### 3.1.2 Model Comparison With or Without Atrous Convolution  
An atrous convolution with an atrous rate of 2 was introduced in the last three convolutional layers of the network, replacing the maxpooling layer. The goal was to avoid an image resolution degradation and loss of edge detail information due to continuous pooling and down-sampling. In order to prove the validity of the atrous convolution, the atrous convolution was replaced with the maxpooling layer and the standard convolution with a stride of 2, respectively. They were tested on the crack dataset. And the experimental results are shown in Table 2.
## 5 Experimental Results and Cases Analysis  
Section 3 developed a multi-objective mathematical model for raw material blending based on production requirements and assumptions, while Section 4 proposed the improved IMOAHLO algorithm to solve this model. Building on these foundations, this section integrates the model input variables and constraints defined in Section 3 with the algorithmic workflow presented in Section 4 to perform a comparative analysis of optimization algorithm performance on real-production data, thereby evaluating IMOAHLO's performance with three multi-objective algorithms: the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) [14], Multi-Objective Human Learning Optimization Algorithm (MOHLO) [8], and Speed-Constrained Multi-Objective Particle Swarm Optimization Algorithm (SMPSO) [15].  
These four multi-objective optimization algorithms are deployed on the RHF production line computers and run in parallel with the manual schemes in the blending system. The optimization algorithms update the recommend formulation schemes on a fixed schedule and display the results on the human-machine interface. Field operators review the current RHF operating conditions and the fitness values from each algorithm's formulation scheme to decide whether to update the active blending plan. All computed blending proposals are stored in a historical database for future reference. Due to the huge amount of data, 100 on-site proposals of formulation schemes under different conditions were selected from the historical database during November to December 2024, along with the corresponding chemical composition and raw material inventory levels.  
Figure 8: The design of IMOAHLO.  
To ensure the optimality of the results, all algorithms used the same calibration parameter method. Apart from the solving process, all algorithms employed identical encoding, decoding, and other operations to ensure a fair evaluation of their performance. The termination condition for all algorithms was set to stop after the same number of iterations with the same population size. The implementation and execution were carried out on the line computer running the Windows 10 operating system with MATLAB R2023b. The computer is equipped with an Intel Core i7-7700 HQ CPU @ 2.80 GHz processor with 8 GB of RAM.
## 3 Results and Discussion
### Effect of CSS on the Shelf Life of Cooked Chicken Burger Samples
#### 3.2.3 Sensory Analysis  
Panellists involved in this analysis were asked to try samples T\({}_{0}\) and T\({}_{72}\) of all formulations on separate days without knowing what they were eating, with the objective of evaluating the presence and the intensity of WOF markers and possible perceptions of extraneous flavours in cooked burgers after refrigerated storage. Descriptors were carefully explained, especially those difficult to associate with food, such as \"cardboard-like\" or \"paint\". In Table 5 are reported all the average values for burger samples tasted immediately after cooking (T\({}_{0}\)) and after 72 h of refrigerated storage (T\({}_{72}\)). T\({}_{0}\) samples of all formulations did not show significative differences, and panellists were not able to trace significant variability from control. Statistical analysis did not show significance between samples even if, looking at the average values, some considerations can be made. The score reported for the bitter descriptor rose to 2.25 for the 3% CSS formulation, while CTRL was 1.25 and CSS 1.5% was 1.87. CSS addition did not influence the average score for all descriptors, and, in most cases, they were the same as CTRL or very near to it. CSS seemed to influence the perception of cooked meat odour rising from 1.75 in CTRL, to 2.87 in 1.5% addition, to 2.75 in 3% addition. This is one of the few situations in which 1.5% received higher scores than 3%. Astringency also registered an increase from CTRL to the 1.5% CSS addition of 3%: from 1.62 to 1.75 to 2.37, respectively. Generally, all the descriptors for all formulations received increased scores, but some significant differences were traced via the statistical analysis or can be noted from the direct comparison among the average scores. Only two descriptors for CSS formulations were significant (\(p<0.05\)): cooked meat odour and bitter. Cooked meat odour was mitigated, mainly by CSS addition. For this descriptor, the score decreased from 4 (CTRL) to 2.75 (1.5%) and 2.62 (3%). This limitation can be seen neither as negative nor positive. If the cooked meat odour can be directly linked with positive sensations, we do not know the considerations of each panellist regarding cooked meat. Nevertheless, from the explanation of each descriptor and the training, these lower scores do not directly show a positive thing. For better discrimination, a comparison with the roasted descriptor can be made; although not significant, it received lower scores than cooked meat.
## 4 Application of the Proposed Method  
To demonstrate the effectiveness of the proposed BCM method, a TO problem for a simple two-dimensional (2D) beam was considered with three loading conditions: Load Case 1 (LC1), Load Case 2 (LC2), and Load Case (LC3), as shown in Figure 3. Here, concentrated loads were used in LC1 and LC2, whereas a uniformly distributed load was used in LC3. The magnitude of the distributed loads in LC3 is given by the load sum \(F_{3}\), and the load distribution (\(q_{3}\)) can be found from the following relationship:  
\[F_{3}=q_{3}\cdot\frac{l}{3}, \tag{17}\]  
where \(l\) is the length of the entire design domain; and \(l/3\) is the range in which the distributed load is applied to the lower edge of the design domain. Usually, when considering a compliance minimizing problem, a volume constraint is used, which is set to 50% of the domain (\(f=0.5\)). The material is assumed to have [PERSON]'s modulus \(E=1\) and [PERSON]'s ratio \(\
u=0.3\).  
The problem was formulated based on the flowchart given in Figure 1 using the SIMP approach. MATLAB software (version R2019a) was used to develop the code, and the SLP method was employed to solve the optimization problems for simplicity. To use the SLP method, the cost function derivatives must be identified alongside the cost function values themselves. The derivatives of the compliance functions (cost functions) are then used to estimate them as linear functions, which is a fundamental step in the SLP method [65]. For FE analysis, the design domain was discretized using square 2D elements. A mesh of 120 \(\times\) 40 (120 elements in the horizontal direction and 40 elements in the vertical direction) was used for this example.  
Two case studies were conducted with different correlations of force magnitudes under different loading conditions to observe the performance of the proposed BCM method and, in particular, how it manages problems with different roles of objective and constraint functions. The calculation results are as follows.