esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Conclusions  
In this paper, we contribute to filling the gap found in the literature review and opening new avenues of research. First, a distributed approach to calculating egocentric betweenness scores, in VANETs, was presented. To this end, each vehicle regularly broadcasts one-hop messages about its local information among surrounding vehicles. The proposed approach only uses the locally available information to compute the egocentric betweenness score without the need for information of the entire network topology. A set of simulation experiments has been carried out in a real urban centre area in order to investigate the performance comparison of our egocentric approach against the traditional socioeconomic approach in different vehicle traffic densities. The main contribution here is the demonstration that the egocentric approach has a greater similarity regarding the ranking of nodes in relation to the sociocentric approach. It is important to highlight those solutions that employ the egocentric betweenness measure; it is the ranking of the nodes that matters most, rather than their absolute scores. A case study of the egocentric measure as a selection mechanism of the most relevant vehicle for knowledge generation is another contribution of this work. By analysing the results, it is possible to highlight that the use of the egocentric betweenness measure is a viable option as a selection mechanism in VANETs. This is based on the fact that it overcomes two main issues: (i) the reduction of the bandwidth consumption for a distributed information management system; and (ii) the capability of dealing with the issues of highly dynamic topologies. It is worth highlighting that the egocentric betweenness measure as a vehicle selection mechanism opens new avenues of research for VANETs applications, for instance an application that has to choose the best vehicle to direct the information flow in the network.  
As future works, we intend to apply an adaptive beacon rate mechanism by means of the use of the link lifetime estimation in order to reduce the exchange of beacon messages between vehicles.  
Conceptualization, A.T.A.; Software, A.T.A.; Validation, A.T.A.; Formal Analysis, R.I. and R.W.P.; Investigation, R.I. and R.W.P.; Visualization, A.T.A., R.I. and E.R.M.M.; Supervision, E.R.M.M. and L.A.V.; Funding Acquisition, R.W.P.; Writing--Original Draft Preparation, A.T.A.; Writing--Review & Editing, A.T.A, R.I., R.W.P., E.R.M.M. and L.A.V.  
The authors would like to thank the grants 2015/25588-6, 2016/24454-9, and 2018/02204-6, Sao Paulo Research Foundation (FAPESP) and National Council for Scientific and Technological Development (CNPq 401802/2016-7) for the financial support. This work is part of the INCT of the Future Internet for Smart Cities (CNPq 465446/2014-0, CAPES 88887.136422/2017-00 and FAPESP 2014/50937-1).  
The authors declare no conflict of interest.
# Efficient Model-Based Object Pose Estimation Based on Multi-Template Tracking and PnP Algorithms
## 2 System Framework  
Figure 1 shows the framework of the proposed model-based object pose estimation system, which consists of three modules: object recognition, template tracking and 3D pose estimation. The object recognition module aims to detect and recognize the OOI from the input image using a keypoint-based object detection algorithm. For extracting keypoint descriptors of an image or template patch, the Scale-Invariant Feature Transform (SIFT) algorithm with Graphics Processing Unit (GPU) acceleration [24] was employed to improve the robustness and real-time performance of the proposed system. Moreover, a GPU-accelerated multi-resolution keypoint-descriptor matching algorithm [25] was used to detect the OOI by matching the keypoint descriptors between the input image and the multi-template database.  
Given multiple template patches of the OOI, a keypoint-descriptor dataset was created a priori by recording the SIFT descriptors of each template patch of the OOI. When a scene image is captured, the SIFT algorithm is applied to extract the SIFT descriptors of the input image, and the keypoint-descriptor matching algorithm is then used to find the matching points between the scene descriptors and the reference descriptors of the template database. Next, a Random Sample Consensus (RANSAC) algorithm [26] is employed to remove outliers from the keypoint matches, and the template with maximum matching inliers is used to determine the initial position of the OOI in the image using an initial homography estimation method [27].  
When the OOI in the image is detected, the corresponding initial homography parameters and template index (ID) number are sent to activate the template tracking module to track the target in the incoming frames. In this work, the existing HA template tracking algorithm [19] was extended  
Figure 1: System framework of the proposed model-based object pose estimation algorithm.  
to multi-template tracking, and the technical details are presented in the next section. Based on the tracking result, the six degree-of-freedom (6-DoF) pose of the OOI is estimated by the 3D pose estimation module, which is a PnP solver as given in Figure 1 and presented in Section 4.
# Classroom Expression Recognition Based on Deep Learning
## 2 Related Works
### Expression Recognition Methods  
#### 2.2.1. Based on Traditional Expression Recognition Methods  
Traditional expression recognition methods usually focus on extracting expression features, which may be geometric features of the facial region or facial features. [PERSON] et al. (2013) proposed a new island loss (IL) to enhance the discriminative ability of deep learning features. The island loss can enlarge the difference between classes while reducing the intra-class variation. [PERSON] et al. (2014) proposed a chain code-based facial expression geometric feature extraction algorithm. The algorithm encodes the position of feature points on the face with a circular chain code to extract the geometric features of the face shape. In addition, [PERSON] et al. (2015) improved the accuracy of the model and optimized the algorithm time complexity by fusing different geometric feature extraction methods. The limitation of the geometric feature method is that it can only extract static information of expression and cannot handle dynamic changes of expression. Therefore, the geometric feature method is usually used for the recognition of static expression images.  
In the field of face recognition, facial feature methods mainly extract feature vectors by filtering the entire or partial facial image data. [PERSON]. (2016) proposed an expression recognition method based on two-dimensional Gabor filters and sparse representation. The 2D Gabor filter is used to enhance the robustness of changes. Since sparse representation can be used to handle subtleties, the recognition problem is reduced to a sparse approximation problem. However, after being processed by the Gabor filter, the feature dimension is excessive, and the data are redundant. To solve this problem, [PERSON] et al. (2017) proposed an image sequence facial expression recognition method combining Radon transform and Gabor wavelet transform. This method first uses the ellipse parameter method for face detection, then extracts variable features through Radon transform and Gabor transform filters, and finally, uses self-organizing maps and neural networks as recognition engines to measure six basic facial expressions. The recognition accuracy is higher than many algorithms at the time. In 2020, [PERSON] et al. (2020) proposed an expression recognition algorithm based on Gabor filters. The algorithm uses feature extraction methods such as Haar-Cascade classifiers for face detection, Gabor filters and Eigenface API for feature extraction, and has an accuracy of 80.11% when training the model. The results are analyzed and linked to appropriate teaching methods for educators, and intervention measures related to emotion prediction observed in lectures or classes are recommended.  
Figure 1: Sample images of the dataset.
## 3 Results  
### Respiratory Phase Tracking Accuracy  
Figure 3b shows the respiration phase assigned to each gantry frame (labeled \"Answer\") and the respiratory cycle found by applying the proposed method (labeled \"Detected\"). The accuracy was calculated for each patient as a percentage of the total number of frames with a correctly identified phase in a total of 120 frames. The method successfully identified the respiratory phase of six patients with an accuracy of 97.2 \(\pm\) 2.5%. The detailed accuracy of each patient's respiratory measurement is given in Table 1. The total phase deviation was up to 3 (patient 6) over the entire gantry frame, and the phase deviation was at most 1 per gantry frame with a misidentification of the respiratory phase. Compared to the proposed method, the accuracy of the respiratory phase measurement in the elevation-based method was much lower (40.3 \(\pm\) 17.0%), as shown in Table 1. The phase deviation was an average of 21.3 (c.f., 1.3 in the proposed method), resulting in a relatively large phase deviation per gantry frame with a misidentification of the respiratory phase. In addition, the accuracy of the inhale/exhale identification was low (48.3%). Figure A2 in Appendix B shows examples of a difficult situation in which the shape of the diaphragm is poorly identified. This method is able to accurately measure the respiration, even in images where only one diaphragm is visible, or when multiple organs including two diaphragms are overlapped.
## 2 Materials and Methods
### Experimental Design  
A randomized complete block experiment with two temperature levels and two irrigation rates was designed. In total, there were four treatments: R-g: warming of 1.5 \({}^{\circ}\)C with sufficient water supply, R-d: warming of 1.5 \({}^{\circ}\)C with water deficit, N-g: no warming with sufficient water supply, and N-d: no warming with water deficit. All treatments were replicated six times. The two temperature levels were set as warming at 1.5 \({}^{\circ}\)C and non-warming, and the two soil moisture levels as a full water supply (irrigation rate of 45 mm) and water deficit (irrigation rate of 33 mm). An electric infrared heater (model: MRM2420, Kalglo Electronics Co., Inc., Allentown, PA, USA) was adopted for air warming, and comprised an infrared heater and an electronic controller. The infrared heater had a far-infrared heating black-body tube (length of 1.8 m and diameter of 1.8 cm) with a power of 2000 W, an iron bracket, and a white stainless steel reflective cover (length of 2 m and width of 0.2 m). The bracket was fixed in the soil before sowing, and the far-infrared heating tube was suspended using an iron support. The height of the heating tube was adjusted to increase the temperature to around 1.5 \({}^{\circ}\)C. A 24-h continuous warming mode was adopted during the winter wheat growing season, and the effective warming area was 2 m\({}^{2}\). A no-heating treatment was also provided, with a lampshade used to control and reduce the error of the test factors.  
Winter wheat (_Triticum aestivum_ L) of Zhoumai-22 was sown on 25 October 2021, and harvested on 27 May 2022. For the adequate water treatment, the irrigation rate was 45 mm, and irrigation was performed when the average soil moisture in the 0-60 cm soil layer decreased to 70% of the field capacity (70% \(\theta_{\rm FC}\)); for the water deficit treatment, the irrigation rate was 33 mm, and irrigation was performed when the average soil moisture in the 0-60 cm soil layer decreased to 55% of the field capacity (55% \(\theta_{\rm FC}\)). Soil moisture was measured using the gravimetric method. Winter wheat was sown under the condition of sufficient soil moisture (about 75% \(\theta_{\rm FC}\)) and the row spacing was 0.2 m. There were four drip lines in each lysimeter. The drip line had a diameter of 16 mm, an emitter distance of 0.33 m, and a flow rate of 2.2 L h\({}^{-1}\).
## Appendix A Dual-Doppler Lidar Uncertainty Analysis  
An uncertainty analysis on the dual-Doppler lidar methodology was performed according to standard uncertainty propagation [36]. Similar analysis has been conducted by [PERSON] et al. [37] and [PERSON] et al. [32]. The method described here considers both the lidar measurement uncertainty itself and the numerical uncertainty added by MuLiWEA. The following three basic error sources form the starting point:  
1. The uncertainty of the measured line-of-sight wind speed \(e_{v_{LOS}}\).
2. The pointing accuracy for elevation angle \(e_{\delta}\).
3. The pointing accuracy for azimuth angle \(e_{\chi}\).  
Both pointing accuracies are assumed to be 0.11\({}^{\circ}\) throughout the duration of the measurement campaign. The measurement error on the line-of-sight velocity \(e_{v_{LOS}}\) is assessed by taking into account both the lidar measurement accuracy from its user manual \(e_{v_{LOS_{\text{t}}}}=0.2\) m\(\cdot\)s\({}^{-1}\) and the residuals of the matrix operation in Equation (7), which are used to give a numerical error estimate \(e_{v_{LOS_{\text{b}}}}\):  
\[e_{v_{LOS}}=\sqrt{e_{v_{LOS_{\text{g}}}}^{2}+e_{v_{LOS_{\text{b}}}}^{2}}. \tag{1}\]  
The numerical uncertainty propagation of the MuLiWEA formalism is estimated assuming two lidars are synchronised. The two horizontal wind speed components are expressed as function of the radial velocities (see Equation (1)) of the two units (indicated by 1 and 2):  
\[u=\frac{\cos(\chi_{1})\cos(\delta_{1})v_{LOS_{2}}-\cos(\chi_{2})\cos(\delta_{2} )v_{LOS_{1}}}{\cos(\chi_{1})\cos(\delta_{1})\sin(\chi_{2})\cos(\delta_{2})- \sin(\chi_{1})\cos(\delta_{1})\cos(\chi_{2})\cos(\delta_{2})}, \tag{2}\]  
\[v=\frac{\sin(\chi_{1})\cos(\delta_{1})v_{LOS_{2}}-\sin(\chi_{2})\cos(\delta_{2 })v_{LOS_{1}}}{\sin(\chi_{1})\cos(\delta_{1})\cos(\chi_{2})\cos(\delta_{2})- \cos(\chi_{1})\cos(\delta_{1})\sin(\chi_{2})\cos(\delta_{2})}. \tag{3}\]  
The numerical errors \(e_{u}\) and \(e_{v}\) are:  
\[e_{u}=\sqrt{\left(\frac{\partial u}{\partial e_{LOS_{1}}}e_{v_{LOS_{1}}} \right)^{2}+\left(\frac{\partial u}{\partial e_{LOS_{2}}}e_{v_{LOS_{2}}} \right)^{2}+\left(\frac{\partial u}{\partial\chi_{1}}e_{\chi_{1}}\right)^{2}+ \left(\frac{\partial u}{\partial\chi_{2}}e_{\chi_{2}}\right)^{2}+\left(\frac{ \partial u}{\partial\chi_{1}}e_{\delta_{1}}\right)^{2}+\left(\frac{\partial u }{\partial\chi_{2}}e_{\delta_{2}}\right)^{2}}, \tag{4}\]  
\[e_{v}=\sqrt{\left(\frac{\partial v}{\partial e_{LOS_{1}}}e_{v_{LOS_{1}}} \right)^{2}+\left(\frac{\partial v}{\partial e_{LOS_{2}}}e_{v_{LOS_{2}}} \right)^{2}+\left(\frac{\partial v}{\partial\chi_{1}}e_{\chi_{1}}\right)^{2}+ \left(\frac{\partial v}{\partial\chi_{2}}e_{\chi_{2}}\right)^{2}+\left(\frac{ \partial v}{\partial\chi_{1}}e_{\delta_{1}}\right)^{2}+\left(\frac{\partial v }{\partial\chi_{2}}e_{\delta_{2}}\right)^{2}}. \tag{5}\]  
Finally, the absolute error \(e_{V}\) on the absolute wind speed \(V\) is expressed as:  
\[e_{V}=\sqrt{e_{u}^{2}+e_{v}^{2}}. \tag{6}\]
## 4 Methodology
### Pre-Processing  
A critical goal of the proposed work was minimizing computational complexity, making it more suitable for Internet of Things (IoT) applications and real-time systems. One way to minimize the system's complexity is to reduce the input size [49]. The main advantage of 1D magnitudes is the removal of the sensor's orientation effect. Such an effect is noticeable when the sensor is attached to a joint with higher degrees of freedom, e.g., a wrist-mounted smartwatch or smart band. In this context, we proposed computing a 1D magnitude from 3D accelerations and 3D angular velocities, i.e., reducing the input size from 3D to 1D. The magnitude of the three-dimensional accelerations was calculated as follows. Let \(a_{x}\), \(a_{y}\), \(a_{z}\) be 3D accelerations, then the magnitude of the 3D accelerations, \(\widehat{mag_{a}}\), is given as:  
\[\widehat{mag_{a}}=\sqrt{(a_{x}^{2}+a_{y}^{2}+a_{z}^{2})}-9.8. \tag{1}\]  
The gravitational constant, 9.8, was subtracted from the magnitude to remove the effect of gravity.  
Similarly, let \(\omega_{x}\), \(\omega_{y}\), \(\omega_{z}\) be 3D angular velocities, then the magnitude of the 3D angular velocities, \(\widehat{mag_{\omega}}\), is given as:  
\[\widehat{mag_{\omega}}=\sqrt{(\omega_{x}^{2}+\omega_{y}^{2}+\omega_{z}^{2})}. \tag{2}\]  
The \(\widehat{mag_{a}}\) and \(\widehat{mag_{\omega}}\) were separately fed into the deep learning model for training and validation.
# Design of Antibody-Functionalized Polymeric Membranes for the Immunoisolation of Pancreatic Islets
## 3 Results and Discussion
### The Acryl-PEG-Anti-GFP Complex  
The next functionalization step consists in the binding of the antibody to the immobilized antigen followed by the conjugation of Acryl-PEG to the Fc fragment of the antibody via EDC chemistry. Once the directional binding of Acryl-PEG to the antibody was complete, the PEG-Anti-GFP complex had to be detached from the substrate to proceed toward the development of the PEG hydrogel by breaking the Ag-Ab pair. The specific binding between antigens and antibodies involves weak interactions, i.e., ionic bonding, hydrogen bonding, and van der Waals attractions. The strength of Ag-Ab complexes depends on the relative affinities and avidities of the antibodies [29]. To dissociate the Ag-Ab binding and to recover the antibody at a high yield, purity, and stability, an elution buffer containing 0.1 M glycine-HCl pH 2.8 was used. Afterwards, the complex PEG-Anti-GFP was quantified by WB. Panel (a) of Figure 7 reports the WB analysis, while panel (b) shows the relative density percentage of the eluted Acryl-PEG-Anti-GFP complex compared to that of the recovered Anti-GFP and the initial amount of Anti-GFP 200 nM added to the substrate.  
Figure 6: Western blot analysis (**a**) and relative density percentage (**b**) performed on the feeding solution of GFP (light green bar), GFP recovered after incubation (dark green bar), and GFP eluted from the solid support (red bar). The reported values have been averaged over 3 measurements. Error bars represent standard deviation (SD) (*** \(p<0.001\), *** \(p<0.01\)).  
WB analysis suggests that about 46% of the initial amount of Anti-GFP is coupled to GFP immobilized on the functional substrates, whereas only a small percentage (8.3%) was collected after the elution process.  
Unlike the elution of GFP with imidazole that did not require any post treatment, the detachment of Acryl-PEG-Anti-GFP using 0.1 M glycine-HCl pH 2.8 required a fast neutralization of the pH, to avoid acid-induced denaturation [29]. After the pH neutralization, Acryl-PEG-Anti-GPF was washed in PBS and concentrated by ultrafiltration with a 50 kDa centrifugal filter device. This greater number of steps might explain the low efficiency of elution process together with the fact that 0.1 M glycine-HCl buffer pH 2.8 only partially dissociates antigen-antibody complexes [45].  
It is also worth noting that the mass contribution of Acryl-PEG to the eluted PEG-Anti-GFP complex cannot not be appreciated due to the low MW of the polymer (around \(\approx\)3 kDa) as compared to the whole complex (Mw \(\approx\) 180 kDa) (Figure 7a).
# A Survey of Techniques for Discovering, Using, and Paying for Third-Party IoT Sensors
## 2 Survey Methodology
### Identifying and Selecting the Most Relevant Papers  
To identify the most relevant papers on sharing IoT devices, we used a combination of the terms listed in Table 1 to search the digital libraries of Google Scholar, Microsoft Academic, IEEE Xplore, Scopus, Emerald, and ScienceDirect. Please note that the search terms and expressions in Table 1 are the global IoT device sharing objectives and challenges we discussed in Section 1. Other aspects (e.g., power consumption) that are important for sharing IoT devices are out of scope of this paper. To search these digital libraries, we used the search strings in Table 1, combining multiple terms, to keep the search focused on sharing IoT devices. The \"*\" sign in Table 1 refers to a search for various forms of the word. The search on the selected digital libraries of the period between 2010 to 2023 yielded a total of 845 peer-reviewed publications (with duplicates, including journals, conference proceedings, and books). Peer-reviewed publications were used as they provide better quality than non-peer-reviewed ones. We selected the period between 2010 and 2023 as the modern IoT research and applications era started in mid-2010 [11]. After removing duplicates, we reduced the total number of related articles to 233. Next, we filtered this collection by reading the abstract, introduction, and conclusions of each paper. This stage reduced the number of papers to 85. We then filtered the papers based on their number of citations, as this reflects each paper's contribution to and influence on this field of study [8]. To apply a fair citation filtration of recent papers, papers published in the last two years were excluded from the citation filtration, papers published in the last five years had to have at least five citations to be included in the review, and papers published more than five years ago had to have at least 20 citations to be included in the review. This stage removed 35 papers, reducing the total number of papers to 50. We included these 50 papers in the Citation Network Analysis that is discussed next in Section 2.2.  
Figure 1: Structure of our survey methodology which is inspired by Systematic Literature Network Analysis (SLNA).
## 2 Background
### Swarm Simulation  
In IoUT, the safe and secure movement of an underwater vehicle is a key issue. Especially in a swarm of UUVs, where efficient cooperation between agents is a challenging goal, the difficulties that emerge are tremendous, such as the secure movement and interoperability, as well as the secure communications between them. It is well known that science often tries to copy nature, thus, from the observation of flocks of birds, UUV/UAV technology has been developed [12]. The limitations and constraints of moving from nature to science are many, nevertheless significant advancements towards the development of autonomous UUVs operating in a swarm have been already accomplished. The Science Department of the Universita degli Studi Roma Tre proposed a new type of autonomous underwater vehicles (AUVs) swarm and simulated its operation in a diffused environment.  
They have developed an AUV with specific characteristics, without emphasis on robustness, with auxiliary systems such as a camera for taking pictures and classifying fishes or recognizing contamination. They have simulated a swarm of 25 AUVs, studying their movement, interconnection, and performance of their systems, using Matlab/Simulink tool [12]. Having neither a central platform to send further instructions for the operation nor a \"leader\" vehicle, the results obtained were satisfactory. The Naval University of Engineering in China, similarly using Matlab, a dataset from GEBCO (General Bathymetric Chart of the Oceans) for coordination, a grid system, and a fusion algorithm based on PSO (particle swarm optimization) and ACO (ant colony optimization) named PACO, proposed an approach for autonomous UAV path planning, verifying the effectiveness of their approach [13].