esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Controller Design
### The Design of SC-AFNN
#### 3.2.2 SC-AFNN Construction
: According to the condition of Lemma 1, for \(\forall i,j=1,2,...,n\), we have  
\[\theta_{i}^{\mathrm{T}}P_{2}+P_{2}\theta_{i}<0 \tag{20}\]  
\[\theta_{j}^{\mathrm{T}}P_{2}+P_{2}\theta_{j}<0 \tag{21}\]  
Combine (20) and (21)  
\[(\theta_{i}+\theta_{j})^{\mathrm{T}}P_{2}+P_{2}(\theta_{i}+\theta_{j})<0 \tag{22}\]  
Namely, for \(\forall i,j=1,2,...,n\), \(\hat{\theta}_{i}+\hat{\theta}_{j}\) is the stable matrix. The steps to finding the common positive definite matrix \(P_{2}\) are as follows. Figure 4: The structure of Takagi–Sugeno model-based adaptive neural network (TSANN). (1) Find a positive definite matrix \(P_{i}(i=1,2,...,n)\) that makes \(\phi_{i}^{\rm T}P_{i}+P_{i}\phi_{i}<0\). Suppose a positive definite matrix \(Q_{i}\), which satisfies the Lyapunov equation \(\theta_{i}^{\rm T}P_{i}+P_{i}\phi_{i}+Q_{i}=0\). If \(\theta_{i}\) is stable, \(P_{i}\) will be obtained. (2) Verify the existence of \(P_{j}\in(P_{i}\mid i=1,2,...,n)\), which makes \(\phi_{j}^{\rm T}P_{i}+P_{i}\phi_{j}<0\) for \(\forall i\). If \(P_{j}\) exists, \(P_{j}=P_{i}\); if not, return to step (1). There exists a common positive definite matrix \(P_{2}\) that enables \(\phi_{i}^{\rm T}P_{2}+P_{2}\phi_{i}<0\,(i=1,2,\cdots,n)\). The second result is provided below. For the hybrid robot system (1), under Assumption 1[29], when the SC-AFNN model in (19) is applied, the system asymptotic stability will be ensured. Select an appropriate Lyapunov function candidate as follows:  
\[V_{2}(x(t))=x(t)^{\rm T}P_{2}x(t) \tag{23}\]  
Equation (23) satisfies  
(1) \(V_{2}(0)=0\);  
(2) \(V_{2}(x(t))>0\), when \(x(t)\
eq 0\);  
(3) \(V_{2}(x(t))\rightarrow\infty\), when \(\|x(t)\|\rightarrow\infty\). Taking the derivative of (23):  
\[ \begin{split}\dot{V}_{2}(x(t))&=\dot{x}^{\rm T}(t)P_ {2}x(t)+x^{\rm T}(t)P_{2}\dot{x}(t)\\ &=\left(\frac{\sum_{i=1}^{n}\varpi_{i}\dot{\theta}_{i}x(t)}{\sum_ {i=1}^{n}\varpi_{i}}\right)^{\rm T}P_{2}x(t)+x^{\rm T}(t)P_{2}\left(\frac{\sum _{i=1}^{n}\varpi_{i}\dot{\theta}_{i}x(t)}{\sum_{i=1}^{n}\varpi_{i}}\right)\\ &=\frac{\sum_{i=1}^{n}\varpi_{i}x^{\rm T}(t)(\theta^{\rm T}P_{2}+ P_{2}\theta_{i})x(t)}{\sum_{i=1}^{n}\varpi_{i}}\end{split}  \tag{24}\]  
Due to \(\sum_{i=1}^{n}\varpi_{i}>0\) and the conditions of Theorem 2, for \(\forall i\), \(\vartheta_{i}^{\rm T}P_{2}+P_{2}\theta_{i}<0\) and, thus,  
\[\dot{V}_{2}(x(t))<0 \tag{25}\]  
According to [PERSON] stability theorem, the asymptotic stability of the SC-AFNN system is guaranteed.
## 1 Introduction  
Advancements in digital technology have moved diagnosis and treatment planning from a two-dimensional (2D) age into a three-dimensional (3D) age. The innovative digital technique of 3D printing, also known as additive fabrication is more and more popular in medical fields. It has numerous advantages, including cost-effectiveness, improved quality control, speed and accuracy; and reduced labor [1, 2]. In brief, 3D printing is a Computer-Aided Manufacturing (CAM) technique; it is a method that uses 3D Computer-Aided Design (CAD) datasets to produce 3D physical objects by the addition of material layers [3]. Its first medical application was the production of a model of cranial bony anatomy from CT image data in 1990; the constructed model provided full internal anatomical details [4]. Over the past few years, 3D techniques have become a preferred method for many dentits. Simultaneously, an ever-increasing variety of 3D products are becoming available in the fields of prosthodontics [5], oral and maxillofacial surgery [6], dental implantation [7], and, in particular, orthodontics [8, 9, 10, 11, 12, 13, 14]_ENREF_10_ENREF_8_ENREF_9. Orthodontics is keeping pace with the trend of 3D printing. Over the last decade, orthodontists have applied 3D printing to the fabrication of orthodontic appliances, such as customized lingual/labial brackets and arch wires [8, 9], 3D-printed retainers [10, 11], transparent aligners [12], digital titanium Herbst devices [13] and 3D-printed hyrax devices [14]. However, there are still some traditional orthodontic appliances that have yet to be digitalized, such as transpalatal arches (TPAs), Nance palatal arches, molar distalization appliances, maxillary protraction appliances and so on.  
TPAs might be one of the most commonly used intraoral auxiliary fixed appliances in orthodontics [15]. The reason for this is that TPAs have a range of applications, and can be used in anchorage support, vertical control [15; 16], transverse maintaining [15; 17], the correction of bucucally placed maxillary molars [18; 19; 20; 21], molar intrusion [22; 23], molar distalization [24; 25] and even maxillary expansion [26; 27]. However, traditional TPAs have some disadvantages that should not be overlooked, such as complicated workflow (which comprises teeth separation, band try-in, steel-wire bending, welding and so on), contraindications (a large cusp of Carabelli and a short crown), health problems (residual adhesives, gingival injury and gingivitis), patient discomfort (including long waiting times and uncomfortable fitting process). In order to overcome the shortcomings of traditional TPAs, efforts should be made to explore the possibility of manufacturing 3D-printed TPAs (3D TPAs) to refine the TPA techniques used both in clinics and laboratories.  
Therefore, the aim of this article was to describe the process of designing and manufacturing 3D TPAs and to discuss some clinical cases in which 3D TPAs were used. It is hoped that the information provided in this study will fuel future research into 3D printing in orthodontics.
# A Numerical Model for Simulating Ground Motions for the Korean Peninsula
## 5 Conclusions  
In this study, a numerical model was proposed to simulate ground motions in the Korean Peninsula. The model consisted of a stochastic point-source model and a shaping window model developed based on ground motions recorded at 111 stations during the 2017 Pohang earthquake. Conclusions obtained from this study are as follows:  
1. Source, path, and site effect functions were developed for the stochastic point-source model, which reflected the seismological characteristics of the Korean Peninsula.
2. To generate ground motions in the time domain that represents ground motions recorded in the Korean Peninsula, an envelope shape and a duration time function were proposed based on the ground motions recorded at 111 stations.
3. In order to verify the accuracy of the proposed numerical model, residuals measuring the difference in \(PSA(T)\) between recorded and simulated ground motions were calculated for 111 stations. It was observed that ground motions in the Korean Peninsula were simulated accurately using the proposed numerical model, which included proper source, path, and site effect functions.
4. The results of this study reveal the potential of the proposed numerical model to simulate input ground motions for low-to-moderate seismic regions, such as the Korean Peninsula.  
Methodology, S.W.H.; investigation, H.W.J. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Korea Agency for Infrastructure Technology Advancement, grant number 20 CTAP-C152179-02.  
This research was supported by grants from the Korea Agency for Infrastructure Technology (20 CTAP-C152179-02).  
The authors declare no conflict of interest.  
Figure 13: Estimated residuals, mean residual, and slope residual at various periods for the 2017 Pohang earthquake: (**a**) Path effect function used in this study, (**b**) Path effect function by [PERSON] (2003).
## 1 Introduction
**Dly-ACK:** In the Dly-ACK (delay-acknowledgement) mechanism, a receiver sends an acknowledge frame for a group of data frames rather than an individual data frame. This mechanism is a tradeoff between No-ACK and Imm-ACK. Both Imm-ACK and Dly-ACK have adopted a retransmission method to recover the corrupted frames in unsuccessful transmissions [1]. In [2] and some other literature implied-acknowledgment (Imp-ACK) was proposed for bidirectional communication. Implied acknowledgement (Imp-ACK) permits a CTA to be used bi-directionally within a limited scope. During the CAP, Imp-ACK cannot avoid ambiguities between two frames; (a) the frame that is transmitted in response to a frame with an implied ACK request, and (b) the frame that is transmitted independently when the original frame is unsuccessfully received. In this paper we focused only on the three aforementioned acknowledgement schemes as Imp-ACK is currently neither widely accepted in the research literature nor in standard documents [1,2]. Figure 3 shows the different ACK policies in IEEE 802.15.3. **Figure 3.** No-ACK, Imm-ACK and Dly-ACK (Burst Size = 3) : Different ACK Policies in IEEE 802.15.3. All these ACK policies have a large impact on the throughput, delay, and channel utilization of the network and require a detailed study to determine overall performance or channel capacity of the network. In this paper, we present a performance analysis of IEEE 802.15.3 from a protocol architecture's point of view. Furthermore, we show the impact of our improvements in WPAN MAC. In a nutshell, the main contributions of this paper are as follows. * To present an analytical model and performance evaluation study to determine optimization of payload size and ACK policies under error channel conditions. * To introduce a Dly-ACK-AGG policy to improve the performance of WPANs. * To apply payload size adjustment mechanism to improve the WPAN MAC under error channel conditions. * To apply the Improved Backoff (IB) algorithm instead of the Binary Exponential Backoff (BEB) algorithm with CSMA/CA method during the CAP period.
## 3 Experimental Results  
This paper presents a method for detecting vehicles and classifying their types and colors using images captured while driving on highways and city roads in Korea. The road images were acquired using a DashCam camera installed in the vehicle. The acquired images were HD-level, 24-bit color frames with a resolution of 1920 \(\times\) 1080 pixels, and were obtained at a rate of 15 frames per second. The proposed method was tested on a Windows 10 operating system with a 3.2 GHz processor (16 cores up to 5.2 GHz, 30 MB cache, 24 threads), a 4090 GPU (24 GB), 128 GB RAM, 4 TB SSD, and Matlab. The vehicle detection model was trained on 32,500 images, while 156,000 vehicle images were used to train the vehicle color classification model.  
This study aims to improve the reliability of autonomous driving services by selecting and changing driving lanes for optimal lane changes with driving autonomous vehicle. Furthermore, vehicle types and colors are used in the vehicle detection and tracking process. Experiments were conducted on vehicle detection, vehicle type classification, and vehicle color classification in road images. In the vehicle detection step, the pre-trained Resnet-50 CNN extracts features from the input images and detects vehicles using the YOLO v4 deep learning neural network. To classify vehicle types and colors, neural network classifiers were trained by modifying the fully connected, softmax, and classification layers of the pre-trained Resnet-50 CNN to set the optimal network hyperparameters and training options. The Resnet-50 CNN is a neural network designed to classify 1000 classes. For the proposed  
Figure 8: The process of generating augmented data, (**a**) the step of generating augmented data for learning a vehicle type classifier, (**b**) the step of generating augmented data for learning a vehicle color.  
vehicle type and color classification, the fully connected layer of the Resnet-50 CNN was replaced with a new fully connected layer. The number of output values in the new layer was determined by the number of vehicle types and colors to be classified.
## 2 Results and Discussion  
Preparation of Protein Hydrolysates of Swim Bladder and Their HO\(\bullet\) Scavenging Activities  
In the experiment, four proteases, including alcalase, papain, pepsin, and trypsin were used to hydrolyze the proteins of croceine croaker swim bladders, respectively. The degree of hydrolysis (DH) and the HO\(\bullet\) scavenging activities were used to screen the most suitable enzymes for subsequent experiments, and HO\(\bullet\) scavenging activities was expressed as EC\({}_{50}\) (the concentration where a sample caused a 50% decrease of the initial concentration of HO\(\bullet\)) (Table 1). The DH (%) of alcalase hydrolysate was 22.32% \(\pm\) 0.74%, which was significantly higher than those of papain hydrolysate (17.84% \(\pm\) 0.71%), pepsin hydrolysate (19.52% \(\pm\) 0.49%), trypsin hydrolysate (16.21% \(\pm\) 0.37%), and neutrase hydrolysate (21.37% \(\pm\) 0.67%) (\(p\) < 0.05). The EC\({}_{50}\) value of alcalase hydrolysate was 8.85 mg/mL, which was significantly lower than those of papain hydrolysate (11.76 mg/mL), pepsin hydrolysate (13.68 mg/mL), and trypsin hydrolysate (10.02 mg/mL) (\(p\) < 0.05). The result was in accordance with the previous reports that high DH and low MW of hydrolysate made a great contribute to their antioxidant activities including HO\(\bullet\) scavenging activity [15,16]. Proteases digest long protein chains into shorter fragments by splitting the peptide bonds that link amino acid residues. Due to the specificity of enzymes reactions, protein hydrolysates from the same proteins hydrolyzed using different proteases exhibit different DH and bioactivities because the obtained peptides are diverse in terms of chain length and amino acid sequence [5]. Therefore, alcalase hydrolysate (designated as SBP) was selected for further study.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Protease** & **Enzymolysis Condition** & **Degree of Hydrolysis (DH\%)** & **HO\(\bullet\) Scavenging Rate (\%)** \\ \hline
**Papain** & pH 7.0, 60 \({}^{\circ}\)C, 4 h, & 17.84 \(\pm\) 0.71 1  & 34.85 \(\pm\) 1.05 1  \\
**Alcalase** & pH 8.0, 50 \({}^{\circ}\)C, 4 h, & 22.32 \(\pm\) 0.74  & 54.76 \(\pm\) 1.94  \\
**Trypsin** & pH 8.0, 40 \({}^{\circ}\)C, 4 h, & 16.21 \(\pm\) 0.37  & 62.38 \(\pm\) 1.67  \\
**Pepsin** & pH 2.0, 37 \({}^{\circ}\)C, 4 h, & 19.52 \(\pm\) 0.49  & 55.47 \(\pm\) 2.02  \\
**Neutrase** & pH 6.0, 50 \({}^{\circ}\)C, 4 h, & 21.37 \(\pm\) 0.67  & 50.67 \(\pm\) 1.85  \\ \hline \hline \end{tabular}
\end{table}
Table 1: HO\(\bullet\) scavenging activities of protein hydrolysate of scalloped hammerhead cartilage using different proteases (\(c\) = 15 mg protein/mL). All of the values were mean \(\pm\) standard deviation (SD) (\(n\) = 3).
## 2 Materials and Methods
### Data Collection and Procedures  
In the original study, the main researcher collected data using a modified version of WHO STEPwise questionnaire [51] under the three major items: the demographic, behavioural data, and physical measurements. Demographic and behavioural data entailed occupation (i.e., job description and working shifts), age, marital status, education level, smoking and alcohol use, fruit, vegetables, salt consumption, physical activity, diabetes status, and family history of HBP (see Supplementary Material from the original study). The questionnaire was tested for validity and reliability using language translators from English to IsiZulu, content and face validity, and a pilot study; the results of the pilot study were not included in the main study [45].  
The original study further collected physical measurements in a form of blood pressure and anthropometry [45]. Standard procedures were adhered to during measurements [52] using a digital blood pressure monitor, electronic weighing scale, stadiometer for height, and a tape measure for waist and hip circumferences. HBP was defined as the average of three systolic blood pressures (SBP) of \(\geq\)140 mmHg and diastolic blood pressure (DBP) of \(\geq\)90 mmHg, or history of HBP or both, and studied the overall prevalence [53]. Body weight was measured from a participant wearing light clothes, and to the nearest 0.1 kg in a standing position using a calibrated smart D-quip electronic scale, and height was measured to the nearest 0.1 cm by a stadiometer in a standing position without shoes and with closed feet. The waist circumference (WC) was measured at the level of the umbilicus and hip circumference (HC) at the widest part of the buttocks using a non-elastic tape. The body mass index (BMI) was categorized as: underweight (BMI < 18.5 kg/m\({}^{2}\)), normal (18.5-24.9 kg/m\({}^{2}\)), overweight (25.0-29.9 kg/m\({}^{2}\)), and obese (\(\geq\)30 kg/m\({}^{2}\)). However, during data analysis, overweight and obesity were combined, and expressed either as overweight/obesity or obesity. Abdominal obesity was defined as having a WC \(\geq\) 90 cm, WHR \(\geq\) 0.90, and WHtR \(\geq\) 0.5 [54; 55].
## 3 Results and Discussion
### Zeta Potential and Hydrodynamic Diameter  
The results of the zeta potential and hydrodynamic diameter measurements of carboxylated CNC are collected in Table 3. Zeta potential measurements indicate that carboxylated nanoparticles are slightly less charged than sulfated [17]. Spray-dried powdered carboxylated CNC were easier to disperse upon sonication; however, the measured hydrodynamic diameter had large polydispersity. Upon adding 5 mM of NaCl or NaSal to LAE 0.006 wt.%--cCNC 0.3 wt.%, dispersion, the zeta potential was only slightly reduced, whereas the addition of the same concentration of GuaHCl decreased the potential by c.a. 10 mV. A similar decrease could be observed at the addition of 6 M of urea. In the case of NaCl and GuaHCl, the decrease in zeta potential was accompanied by a large (almost twice) increase in hydrodynamic diameter, indicating aggregation. No changes were observed when 5 mM NaSal was added to the dispersion, whereas for 6 mol/L urea, the dispersion turbidity almost disappeared, as seen by the naked eye. Nevertheless, the hydrodynamic diameter of 100 nm, with a considerably lower polydispersity, could be measured. The effect of urea was also checked for 0.015 wt.% LAE concentration. Similarly to 0.006 wt.% of LAE, the urea addition increased the dispersion size from 112 nm to 132 nm but reduced polydispersity from 0.44 to 0.35. The plausible explanation of those changes could be that despite the average size (by intensity) of the dispersion grows, the larger cellulose nanocrystals aggregates are destroyed by the addition of urea since it enhances cellulose solubility [38].  
Figure 3: Foam half-life in the dispersion of LAE 0.006 wt.% and cCNC 0.3 wt.% with the addition of NaCl, NaSal, GuaHCl and urea.  
\begin{table}
\begin{tabular}{c c c} \hline
**LAE 0.006 wt.\%—cCNC 0.3 wt.\% with:** & **Zeta Potential [mV]** &
5 mmol/L & \(-26\pm 3\) & 209 (0.52) \\ \hline \end{tabular}
\end{table}
Table 3: Zeta potential and hydrodynamic diameter of cCNC dispersions of LAE 0.006% and additional compounds.
# Safe and Sustainable Design of Composite Smart Poles for Wireless Technologies
## 2 Materials and Methods  
### Pole Structure  
The pole structure for experimental tests in this study was a hollow pull-winded (specialized pultrusion) composite profile. The reinforcing fiber was a commercial E-glass fiber yarn (4800 tex, filament diameter \(\approx\) 23 \(\upmu\)m, Europe) for the structural layers and ECR-glass for the surfacing mat. Glass fibre reinforced plastic (GFRP) was formed by using polyester (Norsodyne P 46074, Polyt, Italy) as the matrix constituent. The shaft was pultruded by Exel Composites Plc. (Finland) and a lay-up of [0\({}^{\circ}\), 85\({}^{\circ}\), 0\({}^{\circ}\)] was finally applied with nominal layer thicknesses of 2.8, 0.4, 2.8 mm, respectively. The shaft had a constant outer diameter of \(D_{outer}=\) 168 mm and as-received nominal wall thickness of \(t_{wall}=\) 6 mm. The surfacing mat was used due to aesthetic reasons and had its thickness comparable to the standard manufacture deviation in the pole thickness.
# Decentralized Inner-Product Encryption with Constant-Size Ciphertext
## 2 Preliminaries
### Definition of Decentralized Inner Product Encryption
#### 2.3.2 Definition of DIPE  
A decentralized inner product encryption scheme consists of five PPT algorithms: **Setup**, \(\mathbf{AuthSetup}\), \(\mathbf{KeyGen}_{\mathbf{A}_{i}}\), \(\mathbf{Encrypt}\) and \(\mathbf{Decrypt}\). Unlike the single authority construction, in DIPE, the private key of a user is generated by multiple authorities. Each authority \(\mathbf{A}_{i}\) computes a \"partial key \(sk_{i}\)\" of a user using its master secret key and the user's predicate vector. The full private key of a user is \(\{sk_{i}\}_{i=1,\ldots,n}\), where \(n\) is the number of authorities:  
* \(Setup(1^{\lambda})\). An authority in the system or a third party will run the algorithm. Taking as input a security parameter \(1^{\lambda}\), the algorithm outputs a public parameter \(pp\).
* \(AuthSetup(pp,i)\). All authorities will run the algorithm. Taking as inputs a public parameter \(pp\), and a number \(i\), the algorithm outputs a master secret key \(MSK_{i}\) and a public key \(PK_{i}\) of each authority, where \(i\) is the index of authority.
* \(KeyGen_{A_{i}}(pp,MSK_{i},GID,\vec{X})\). All authorities will run the algorithm. Taking as inputs a public parameter \(pp\), a master secret key \(MSK_{i}\), a global identity \(GID\) and a predicate vector \(\vec{X}\), the algorithm outputs a partial key of the private key associated with \(\vec{X}\) generated by \(i_{th}\) authority. Note that the description of \(\vec{X}\) will be included in the partial keys.
* \(Encrypt(pp,\{PK_{i}\}_{i=1,\ldots,n},M,\vec{Y})\). A sender will run the algorithm. Taking as inputs a public parameter \(pp\), all the public keys of each authority \(\{PK_{i}\}_{i=1,\ldots,n}\), a message\(M\) and an attribute vector \(\vec{Y}\), the algorithm outputs a ciphertext \(C\) associated with \(\vec{Y}\). Note that the description of \(\vec{Y}\) will be included in the ciphertext.
* \(Decrypt(\{sk_{i}\}_{i=1,\ldots,n},C)\). A receiver will run the algorithm. Taking as inputs all the partial key of private keys of each authority \(\{sk_{i}\}_{i=1,\ldots,n}\), a ciphertext \(C\) and an attribute vector \(\vec{Y}\), the algorithm outputs a message \(M\) or \(\bot\).  
**Correctness**. For \(pp\gets Setup(1^{\lambda})\), \((PK_{i},MSK_{i})\gets AuthSetup(pp,i)\),  
\(sk_{i}\gets KeyGen_{A_{i}}(pp,MSK_{i},GID,\vec{X})\), \(C\gets Encrypt(pp,\{PK_{i}\}_{i=1,\ldots,n},M,\vec{Y})\), where \(i=1,\ldots,n\), we have that:  
* If \(\langle\vec{X},\vec{Y}\rangle=0\), then \(Decrypt(\{sk_{i}\}_{i=1,\ldots,n},C)=M\).
* If \(\langle\vec{X},\vec{Y}\rangle\
eq 0\), then \(Decrypt(\{sk_{i}\}_{i=1,\ldots,n},C)=\bot\).