esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Fundamentals of Ion-Imprinted Polymers
### Components of the IIP
#### 2.3.2 Ligand
In this context, [PERSON] and coworkers synthesized ten polymers for the Cd\({}^{2+}\) ion, nine with chemical imprinting (IP1\({}_{\gamma}\), IP2\({}_{\gamma}\), IP3\({}_{\gamma}\), IP4\({}_{\gamma}\), IP4\({}_{\delta}\), IP4\({}_{\delta}\), IP4\({}_{\delta}\), IP4\({}_{\epsilon}\), IP4\({}_{\epsilon}\)) and one (CP4) as a control polymer, using four functional monomers (M1: allyurea; M2: 1-vinylimidazole; M3: acrylamide; M4: allythiourea) with different stoichiometric ratio metal ion: functional monomer (\(\zeta\) = 1:1; \(\delta\) = 1:2; \(\gamma\) = 1:4; \(\beta\) = 1:8; \(\alpha\) = 1:16 e \({}_{\epsilon}\) = 2:3) [56]. Among the synthesized polymers, polymers containing M4 (IP4) showed a higher adsorption capacity for Cd\({}^{2+}\) ions when compared with the other three monomers, at two pH values (1.00 and 6.00). This behavior was attributed to the nature of the chemical bond, once the Cd\({}^{2+}\) (soft acid) ions have greater interaction with the thiocarbonyl group (C=S) present in M4 (soft base) and less interaction with the oxygen present in M1, M2, and M3, according to the [PERSON]'s theory [61]. Regarding the stoichiometric ratio of the metal ion and the functional monomer, the authors observed that the surface area and adsorption capacity of the IP4 polymer increased when the stoichiometric ratio was 1:1 and 2:3, corresponding to the polymers IP4\({}_{\zeta}\) and IP4\({}_{\epsilon}\), with an adsorption capacity of 3.79 and 2.44 ug g\({}^{-1}\) and imprinting factor of 13.68 and 8.80 at pH 1.00, while 1.71 and 1.72 ug g\({}^{-1}\) with effect imprinting of 2.55 and 2.56 at pH 6.00, respectively. Hence, if the concentration of Cd\({}^{2+}\) ions during synthesis is higher, the chemical imprinting and the adsorptive capacity will be greater too. Those results agree with the study made by [PERSON] and coworkers [52], who synthesized imprinted polymers for Ni\({}^{2+}\) ions and a control polymer (Non-Imprinted Polymers, NIP), using two salts (nitrate (NO\({}_{3}\)\({}^{-}\)) or perchlorate (ClO\({}_{4}\)\({}^{-}\))) as the metal ion source and the functional monomer Vbamp (1-(2-pyridinyl)-N-(3-vinylbenzyl)methanamine) in the stoichiometric ratio 1:2, 1:4 and 2:1 (metal ion: functional monomer, respectively) for the polymer IIPNO\({}_{3}\), and 1:2 for IIPClO\({}_{4}\). In this study, the authors obtained higher adsorption capacity with the polymer IIPNO\({}_{3}\) at pH 4.00 and 7.00 (7.24 and 11.73 mg g\({}^{-1}\), respectively) when the metal ion was in greater proportion than the functional monomer (2:1). In addition, the authors observed that as soon as the concentration of the functional monomer is increasing in the synthesis, the imprinting factor decreases, once the adsorption capacity of the IIPNO\({}_{3}\) in the 1:4 ratio is the same as the adsorption capacity of the control polymer (NIP) at pH 4.00 and 7.00.
# An Adaptive Routing Algorithm Based on Relation Tree in DTN
## 4 Relation Tree
### Maintenance of the Relation Tree
#### 4.1.1 Tree Insertion  
Whenever the root node A encounters a new node B so that the number of encounters between them in a memory cycle reaches M, an insertion operation is performed on the Relation Tree of A. The insertion operation includes:  
1. Add the new node B to become a child of the root node A.
2. Add all nodes in the original Relation Tree of B that are not nodes in the Relation Tree of A and whose encounter times with B exceeds M (nodes in the Relation Tree of B with depth 2 and not in the Relation Tree of A) to become children node of B in the Relation Tree of A.  
If M = 2, when node A and node B meet again in the same memory cycle, the number of times they meet reaches the judgment threshold, as shown in Figure 2. Since nodes C and E with depth 2 in the Relation Tree of B are already in the Relation Tree of A, only node G is added to become a child node of B in the Relation Tree of A.
# A Machine-Learning-Based Software for the Simulation of Regional Characteristic Ground Motion
## 3 Results
### Validation of Methods
#### 3.1.3 Validation of Synthetic Ground Motion  
We used the method in this study to synthesize a ground motion and match it with a record selected from the real ground-motion database to verify the feasibility of the synthesis method and to verify if the matching results are reasonable. One station in the Changing earthquake, i.e., 51 NXT, was arbitrarily selected, and the specific ground-shaking information is as listed in Table 3. The synthesis was conducted based on this method, and the matching effect of the synthesized peak ground acceleration, response spectrum, and duration with the real ground-motion records, is also summarized in Table 3. The matching effect and error distribution of the synthesized ground motion and actual seismic records are illustrated in Figure 5. In Figure 5b, the horizontal and vertical axes represent the error values between the response spectrum and the holding time of the synthetic ground shaking, and between the response spectrum and the target values obtained from the ground-shaking model, respectively. These error values are calculated using Equations (4) and (5). Each scatter in the distribution of the figure represents a set of linear combination coefficients of waves in the optimal solution set, whereas the red scatter represents the finalized combination coefficients under the judging criteria. The synthetic ground motion in Figure 5a,c shows the result of the linear combination of waves obtained using the corresponding combination coefficients of the scatter. Peak ground accelerations and response spectra of the synthetic ground motion are well matched with the actual  
Figure 4: Comparison between the response spectra and durations of Changing ground motions and ground-motion models: (**a**) PGA; (**b**) Sa (T = 0.2 s); (**c**) Sa (T = 1 s); (**d**) \(D_{5-95}\).  
records; more importantly, 90% of the significant duration of the synthetic ground shaking is matched perfectly with the actual records, and the duration of synthetic earthquakes is controlled to the target level of the regional duration prediction model. This method allows the synthesis of ground motion considering constraints of the regional ground-motion prediction model; they can be used for the construction of a regional set and a ground-shaking time field for engineering structure input.
## 2 Materials and Methods
### Synthesis of CdSe-Embedded PPy Nanords  
PPy nanorods containing CdSe nanoparticles were synthesized by a method developed from the well-established templated electropolymerization on AAO substrates (Figure 1) [20,26].  
Specifically, Ag film 100 nm thick was deposited on one side of a commercial (or home-grown) AAO membrane by an ion coater (IB-3, Eiko Engineering Co., Ltd., Ibaraki, Japan). Then, a three-electrode electrochemical setup (BAS 100, BASi, West Lafayette, IN 47906, USA) was configured in which the Ag layer, a Pt plate, and an Ag/AgCl electrode were used as the working, counter, and reference electrodes, respectively. A Teflon cell and an O-ring were used to prevent leakage of the plating solution. First, sacrificial Ag was electrochemically deposited by \(-\)800 mV potential for 5 min to cover defective pores located at the bottom of the AAO membranes. Au was deposited on top of the Ag rods by \(-\)750 mV potential for 30 min. Then, CdSe-embedded PPy nanorods were grown by 1200 mV potential for 2 h using 20 mL of a homemade tetrahydrofuran solution containing 0.1 M of pyrrole, 1 mg of CdSe nanoparticles, and 0.1 M of tetrabutylammonium tetrafluoroborate. Several methods reported to fabricate PPy in aqueous condition with LiClO\({}_{4}\)[34], but we chose the organic solvent to improve quantum yield of QDs [35]. It is important to note that this growth condition is optimized for growing CdSe-embedded polymer nanorods. When the concentration of CdSe was low (0.5 mg or 0.1 mg of CdSe), there was no fluorescence effect of CdSe, and when the concentration of CdSe was higher than 1 mg (2 mg of CdSe), the AAO hole was blocked by CdSe nanoparticles; therefore, CdSe-embedded nanorods were not formed (data not shown). Next, sacrificial Ag and AAO templates were sequentially removed by etchant 3 M HNO\({}_{3}\) and 3 M NaOH, respectively, for 3 h each; the hybrid nanorods from the AAO membrane were dispersed into solution. The nanorods were then rinsed with DI water three times using centrifugation and were transferred to ethanol for improved dispersion on substrates for characterizations. A control sample without the CdSe nanoparticles can be synthesized in a similar manner except for the inclusion of CdSe nanoparticles in the PPy electropolymerization step.
# Reliable Internet of Things: Challenges and Future Trends
## 3 Major Use Cases of IoT
### Internet of Vehicles  
Internet of Vehicles (IoVs) is a major application of IoT in the transportation industry [33; 34; 35; 36; 37; 38]. IoVs involve vehicles that are equipped with wireless transceivers and infrastructure Road Side Units (RSUs) placed at different regions on the road. These transceivers enable communication between vehicles within a neighborhood, thus allowing vehicles to have a spatial map of the traffic. Vehicles can also share the mobility and traffic information with the RSUs, thus extending the range of communication [39; 40; 41; 42; 43].  
In the IoV use case, traffic density is sensed by the number of vehicles within the communication range. RSUs can gather the traffic density information on the road through the vehicles or by evaluating the arrival rate of vehicles within a particular region. Other information that can be sensed includes safety awareness of vehicles, the emergency situation on the road, and traffic congestion.  
IoVs can use IEEE 802.11p or Cellular V2X communications (C-V2X) to disseminate the sensed information to the cloud server located in RSUs or the central infrastructure unit of the city. While IEEE 802.11p uses ad hoc Wireless Local Area Network (WLAN)  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Communication Range (m)** & **Data Rate (Bits per s)** & **Wireless Technology** \\ \hline
100 & 250 k & Zigbee \\ \hline
60 & 1 M & BLE \\ \hline
100 & 54 M & IEEE 802.11 \\ \hline
100–1000 & 1 T & 6G \\ \hline
100–500 & 10 G & Cellular V2X \\ \hline \hline \end{tabular}
\end{table}
Table 2: Selection of wireless technology based on application requirements.  
Figure 1: Challenges of Reliable Data Transmission in IoT.  
-based communications, C-V2X utilizes cellular communications to share and disseminate traffic data information [44; 45].  
In IoVs, the data analytic part involves finding out the useful insights that improve the traffic flow. Based on the traffic density and mobility information, RSUs can send safety-related notifications to the vehicles. In case of an emergency at a particular road, emergency departments such as hospitals and fire units can be informed. Similarly, efficient routes that facilities the drivers as well as reduce congestion on the roads can be evaluated [46; 47].
## 2 Materials and Methods
### Sorted Missing Percentage and Recommended Steps  
The concept of sorted missing percentages was proposed by [PERSON] and [PERSON] [14], who applied a cutoff value of 10% in their study that was related to the APS data [9]. This study used 10% as a cutoff value to construct a control group of attributes. It then explored different cutoff values to construct experimental groups of attribute. Filtering can be called conservative if the percentage of missing data was greater than the cutoff value. For example, by applying a cutoff value of 20% or 30%, fewer attributes but more instances can be selected compared with that of the control group. In contrast, filtering can be called liberal if the percentage of missing data is less than the cutoff value. For example, by applying a cutoff value of 5%, more attributes but less instances can be selected compared with that of the control group.  
Note that this method does not alter the original data. Furthermore, the included instances can be used more flexibly when the maximum attribute space is established under different filtering criteria. This can be considered an advantage because, under the premise of the same maximum attribute space, the assumption of a consistent distribution between the training and test sets can be satisfied. Through comparison, it was established that the attribute space of the group producing the best model performance is also the best configurable attribute space for the considered dataset. The recommended  
Figure 1: Workflow of the machine learning modeling.  
steps for implementing the sorted missing percentage method for the groups can be summarized as follows.  
* Step 1: Sort attributes in descending order of missing percentages.
* Step 2: Set a cutoff value of 10% and select all attributes with less than that 10% of missing values into the control group.
* Step 3: Set different missing percentages as thresholds and use them to define the experimental groups.
* Step 4: Compare the control and experimental groups. Select the group that yields the best model performance and define the best configurable attribute space.
###### Abstract  
A low-cost smart sensor GNSS system has been developed to provide accurate real-time position and orientation measurements on a floating offshore wind platform. The approach chosen to offer a viable and reliable solution for this application is based on the use of the well-known advantages of the GNSS system as the main driver for enhancing the accuracy of positioning. For this purpose, the data reported in this work are captured through a GNSS receiver operating over multiple frequency bands (L1, L2, L5) and combining signals from different constellations of navigation satellites (GPS, Galileo, and GLONASS), and they are processed through the precise point positioning (PPP) and real-time kinematic (RTK) techniques. Furthermore, aiming to improve global positioning, the processing unit fuses the results obtained with the data acquired through an inertial measurement unit (IMU), reaching final accuracy of a few centimeters. To validate the system designed and developed in this proposal, three different sets of tests were carried out in a (i) rotary table at the laboratory, (ii) GNSS simulator, and (iii) real conditions in an oceanic buoy at sea. The real-time positioning solution was compared to solutions obtained by post-processing techniques in these three scenarios and similar results were satisfactorily achieved.  
smart sensor; GNSS system; floating offshore; low-cost sensors; real-time positioning Article  
15 December 2022  
7 January 2023  
2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
# Distributed Online Multi-Label Learning with Privacy Protection in Internet of Things
## 8 Conclusions  
Distributed multi-label algorithms without centralized servers have great potential for application in real-world situations. However, there is still a research gap in implementing these problems. This article proposes a novel distributed multi-label classification learning method without a central server based on the distributed least-squares method. This approach succeeds in building a self-coordinated network structure, thus removing the centralized servers. This allows the potential for privacy data leakage or backdating to be eradicated on the transmission side. Experimental results show that our proposed DOML algorithm is not inferior to existing centralized algorithms in terms of Hamming loss and F1 score, and outperforms traditional centralized algorithms in some datasets. Meanwhile, we find that the DOML algorithm is a constant trade-off between the pursuit of local optimality and convergence with neighbouring nodes' data, which effectively suppresses the overfitting situation. At the current stage, we have implemented distributed online multi-label learning methods under a static network. However, in real situations, the network is often unstable. Future work will include the improvement and experimentation of our proposed algorithm for dynamic networks.  
Conceptualization, F.H.; Methodology, F.H.; Software, F.H.; Formal analysis, F.H.; Investigation, F.H. and N.Y.; Resources, D.Y.; Data curation, F.H.; Writing--original draft, F.H.; Writing--review & editing, F.H. and D.Y.; Visualization, F.H.; Supervision, H.C., W.B. and D.Y. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Figure 7: Micro F1 Score of DOML in a 100-node network with different connectivity and OML with same amount of instances as each node.
# Visual SLAM Mapping Based on YOLOv5 in Dynamic Scenes
## 3 Dynamic Feature Point Rejection  
### Principle of Dynamic Feature Point Rejection  
SLAM systems can be divided into two general categories, which are used for removing dynamic feature points when building maps in dynamic scenes. The first dynamic feature detection method uses geometric constraints and other traditional methods to detect whether dynamic points are detected, using the position change relationship between two adjacent frames. The disadvantages of this approach are inaccurate dynamic feature detection; unavoidable problems, such as failing to detect or incorrectly detecting objects with low dynamics; and large positional estimation errors. The second dynamic feature detection method that is now added to the existing SLAM system uses machine learning and deep learning target detection methods, such as deep neural networks, convolutional neural networks, etc. When using the second method for detecting dynamic targets, the network is first trained to recognize dynamic targets, which enables fast and accurate identification of the desired dynamic targets.  
When the system builds a map, it first uses the YOLOv5 network to perform dynamic object detection on keyframes, labels the detected dynamic objects using dynamic object boxes, and then inputs each frame detected by the network as having dynamic objects into the Tracking thread to reject the dynamic points using the Dynamic Feature Rejection method. Figure 5 shows the overall algorithm framework of this paper, and the gray boxes indicate the three threads of ORB-SLAM2. In this paper, the dashed box is a new thread added to the dynamic object detection algorithm.  
Figure 4: (**a**) YOLOv5 outdoor target recognition results; (**b**) YOLOv5 indoor target recognition results.
## 3 Results and Discussion
### Accuracy Rate for Inference Model
#### 3.3.2 Keras Model  
Table 7 below shows the classification results with the Keras model. Similar to the case of the Caffe model, we aggregated the classification results into Healthy (No DR) and Unhealthy (Mild to Proliferate DR). The input image types to the model are indicated in the table. The model accurately predicted healthy images 97% of the time (specificity or true negative). The best result for images with DR was again observed for the Proliferate DR. The model accurately classified 55% of images as unhealthy (sensitivity or true positive). The least accurate results were with Mild and Moderate DR images. The models inaccurately classified 97% of Mild and Moderate DR images as Healthy. In addition, with the Keras model, the sensitivity increased as the severity of the disease increased. The combined sensitivity for Severe and Proliferate DR was 48%. The overall accuracy of this model was 76.7%. In addition, in this case, we observe that the accuracy is influenced by the higher specificity value and a large number of No DR images compared to other classes. For moderate to proliferate DR, the sensitivity for the Keras model increased significantly compared to the Caffe model. For Proliferate DR images, the sensitivity for the Keras model was 55% compared to 40% for the Caffe model.  
The least accurate results both for Caffe and Keras models were with Mild and Moderate DR images. We believe this is due to the fact that Mild and Moderate DR are the initial stages of retinopathy, and they are less distinguishable from healthy (No DR) compared to Severe and Proliferate DR. The accuracy of the presented models need to be further improved for practical application. Methods that may be used to improve the accuracy include removing low-quality images, using the local spectrum analysis method [39], or enhancing local contrast using contrast limited adaptive histogram [40]. The accuracy can also be improved by optimizing the presented model or considering alternative models such as recurrent neural network (RNN). The ability of RNN to accumulate features or memorize from past inputs can be advantageous in DR image classification [41; 42].
## 3 Results and Discussion
### Compressive Strength  
The literature [15; 35] states that the assessment of the results of aging tests can be made by specifying the compressive strength ratio (CV) in relation to the aging. CV in aging was calculated as a ratio of compressive strength of 0t and 1t foams (Table 6). The choice of this property for the assessment of aging was made due to its importance in the application of PUFs in civil engineering.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Foam** & **L** & **a** & **b** & \(\Delta\)E** \\ \hline W\_0t & 82.8 & \(-\)2.6 & 28.0 & 87.5 \\ C5\_0t & 85.1 & 0.5 & 14.9 & 86.4 \\ C15\_0t & 76.6 & 3.4 & 15.8 & 78.3 \\ WU\_0t & 81.8 & \(-\)3.6 & 27.0 & 86.2 \\ CU10\_0t & 71.0 & 2.7 & 15.9 & 72.8 \\ W\_48h & 71.4 & 8.4 & 27.9 & 77.1 \\ C5\_48h & 70.7 & 6.8 & 27.3 & 76.1 \\ WU\_48h & 58.7 & 15.8 & 35.1 & 70.2 \\ CU5\_48h & 62.6 & 5.9 & 23.1 & 67.0 \\ CU15\_48h & 65.2 & 5.6 & 19.1 & 68.1 \\ W\_1t & 56.4 & 18.2 & 17.3 & 61.8 \\ C5\_1t & 57.3 & 17.4 & 16.1 & 60.1 \\ C15\_1t & 50.8 & 21.6 & 29.2 & 62.4 \\ WU1\_t & 56.3 & 16.0 & 21.3 & 62.3 \\ CU5\_1t & 53.4 & 16.7 & 21.7 & 60.0 \\ CU15\_1t & 57.1 & 13.7 & 18.1 & 61.4 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Foam color measurement results.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Foam** & \(\Delta\)**I (\%)** & \(\Delta\)**V (\%)** & \(\Delta\)**m (\%)** \\ \hline W & \(+\)0.39 \(\pm\) 0.00 & \(-\)1.07 \(\pm\) 0.01 & 2.27 \(\pm\) 0.01 \\ C5 & \(+\)0.00 \(\pm\) 0.00 & \(-\)1.37 \(\pm\) 0.01 & 2.09 \(\pm\) 0.01 \\ C10 & \(+\)0.80A significant decreasing in compressive strength was observed for aged reference foams (W_1t and WU_1t) in relation to the compressive strength of non-aged foams (W_0t and WU_0t). Therefore, these foams had the lowest CV. Only the addition of cinnamon extract increased the value of the CV. This could be related to the cross-linking formation in the foam structure due to the polyphenols present in the cinnamon composition. However, more cinnamon-based filler content in the foam (both for the series with flame retardant C and for the series without flame retardant CU) decreased this coefficient by about 10%.  
The CV ratio exceeded 100% for CU series foams. This meant that the strength of the aged (CU_1t) foams increased in relation to the strength of non-aged foams (CU_0t). In the course of photodegradation (apart from the destruction processes), there were also crosslinking and oxidizing. FTIR studies were shown that the -CH bonds were destroyed (Figure 3). Free radicals could be formed in the CU series, which were not blocked due to the lack of flame retardant (Antiblaze TMCP) as a radical inactivator. The structure could probably was crosslinked. It led to a reinforcement of the foam structure, thus increasing compressive strength [3].