esa-sceva/satcom-chunk-collection huggingface dataset:
# Characterization of Inter-System Biases in GPS + BDS Precise Point Positioning
## 2 Methods
\begin{array}{l}p_{f}^{T,s}(i)=\rho^{T,s}(i)+c[\hat{\Delta}t_{r}^{T}(i)-\hat{ \Delta}t^{T,s}(i)]+\mu_{f}^{T,t}r_{,1}^{s}(i)+T_{r}^{s}+\epsilon_{P}(i)\\ L_{f}^{T,s}(i)=\rho^{T,s}(i)+c[\hat{\Delta}t_{r}^{T}(i)-\hat{\Delta}t^{T,s}(i)]- \mu_{f}^{T,t}r_{,1}^{s}(i)+T_{r}^{s}+\lambda_{f}^{T}\hat{N}_{f}^{T,s}+\epsilon _{L}(i)\end{array}  \tag{5}\]  
We select the estimable GPS receiver clock \(\hat{\Delta}t_{r}^{G}\) as reference, then the GPS/BDS ISB can be denote as  
\[ \begin{array}{l}ISB^{G,C}=\hat{\Delta}t_{r}^{C}-\hat{\Delta}t_{r}^{G}\\ =(\overline{\Delta}t_{r}^{C}-\overline{\Delta}t_{r}^{G})+(\frac{\mu_{r}^{C}}{ \mu_{2}^{C}-1}b_{r,1}^{C}-\frac{1}{\mu_{2}^{C}-1}b_{r,2}^{C})-(\frac{\mu_{2}^{ G}}{\mu_{2}^{C}-1}b_{r,1}^{G}-\frac{1}{\mu_{2}^{C}-1}b_{r,2}^{G})\\ =(\Delta t_{r}^{C}-\Delta t_{r}^{G})+(\Delta D^{G}-\Delta D^{C})+(\frac{\mu_{ 2}^{C}}{\mu_{2}^{C}-1}b_{r,1}^{C}-\frac{1}{\mu_{2}^{C}-1}b_{r,2}^{C})-(\frac{ \mu_{2}^{G}}{\mu_{2}^{C}-1}b_{r,1}^{G}-\frac{1}{\mu_{2}^{C}-1}b_{r,2}^{G}) \end{array}  \tag{6}\]  
where \(\overline{\Delta}t_{r}^{G}\) and \(\overline{\Delta}t_{r}^{C}\) respectively represent the GPS and BDS receiver clock offset which absorb the satellite clock datum. \(\Delta D^{G}\) and \(\Delta D^{C}\) are GPS and BDS satellite clock datum, respectively. For the reason that the receiver and satellite clock parameters are linear dependent, a reference of clock offset must be set for each system [26]. In the meantime, the variation of the reference will be absorbed in the receiver clock offset. Therefore, the ISB obtained by PPP contains the offsets of inter-system satellite clock datums and the receiver-code biases. The state vector \(X\) of GPS/BDS undifferenced and uncombined PPP can be written as  
\[X=[ \begin{array}{cccc}x&\hat{\Delta}t_{r}^{G}(i)&T_{r}^{s}&T_{r,1}^{G,s}(i) &\hat{T}_{r,1}^{C,s}(i)&\hat{N}_{r,f}^{G,s}&\hat{N}_{r,f}^{C,s}\end{array} ] \tag{7}\]
# Evaluation and Comparison of Cortisol Levels in Saliva and Hair among Dental Students
###### Abstract  
Cortisol has frequently been used as a stress marker, and the variation in cortisol levels in saliva and hair, as well as between males and females, has been reported. This study aimed to evaluate and compare the level of cortisol variation in hair and saliva among dental students of both genders. After giving written agreement, 151 students (79 males and 72 females) participated in the study. Saliva and hair samples were collected at two time intervals with a gap of three months from the same set of participants. Saliva and hair samples were utilized to measure the levels of cortisol using an enzyme-linked immunosorbent assay (ELISA). Each participant's salivary and hair cortisol levels and demographic information, including age and gender, were recorded. To examine group comparisons, two-tailed Student's \(t\)-tests were used (\(\alpha<0.05\)). The comparisons of salivary and hair cortisol levels showed significant difference (\(p<0.05\)) at two time intervals. Genderwise comparisons of the salivary and hair cortisol's levels showed significant differences (\(p<0.05\)) for male students, while, for female students, the comparisons showed non-significant differences (\(p>0.05\)). Comparisons between the cortisol levels of the two specimen collections at the two different time intervals indicated substantial variations (\(p=0.000\)). Results confirm the variations in the cortisol levels in the saliva and hair samples. Cortisol concentrations in hair and saliva at the two times points varied. Significant variations between the male students salivary and hair cortisol levels and non-significant differences between the cortisol levels for the female students at the two time points were found. Cortisol levels in the selected subjects' saliva and hair samples varied.  
cortisol; saliva; hair; salivary cortisol; hair cortisol; cortisol levels Article
## 3 Experimental Results and Discussion
### Detailed Experimental Unfastening Procedure  
The detailed flowchart of the proposed unfastening strategy is shown in Figure 22. In Figure 22, parameters are defined and calculated in the context of the motion control of a robotic system, which clearly shows the use of these parameters and the whole control process. In Figure 22, the left column shows real actions of a robotic system, including image collection using a camera and motions of the robot and unfastening tool. The real actions were controlled by the parameters obtained by computing modules of PC, which are presented in the right column in Figure 22. The modules in the right column denoted imaging processing in HALCON and parameter calculations programmed in the C# environment. The left column provided images as input of the right column.  
Figure 21: A hexagonal screw-removal platform.  
For the proposed strategy, the working efficiency could be improved by setting the high motion speeds for the unfastening tool and the robotic end effector. However, high speeds could easily cause mechanical vibration, which could further severely affect the stability of thread engagement and reduce the thread quality of the workpiece during the unfastening operation. Therefore, in this experiment, the torque values obtained under different values of the rotational velocity \(w\) were collected by sensors mounted on the tool and used to analyze the effect of the motion speed on the unfastening quality.
## 4 Numerical Results and Discussion
### Diversity Order Performance  
In Figure 2, we show the diversity order of each level of water turbidity when considering small air bubbles and large air bubbles when considering the same UOWC system. Firstly, in the presence of small air bubbles, the diversity order exhibits an exponentially increasing trend as the concentration of antacid grows, demonstrating a proportional relationship between the two variables. For large air bubbles, we observe that at the lowest level of antacid, i.e., 3.6 mg/L, the diversity order decreases slightly compared with tap water, i.e., 0 mg/L. It may be attributed to the unique and singular impact of partial and total light blockages caused by large bubbles. This effect diminishes at higher antacid concentrations with a more significant collection of scattered photons, as detailed in [22]. For this reason, after the initial singularity in the diversity order of the large air bubble scenario, both graphs provide a coherent and valuable insight into the exponential growth of diversity order in relation to water turbidity.
# Multitask-Based Trajectory Planning for Redundant Space Robotics Using Improved Genetic Algorithm
## 3 Improved Genetic Algorithm
### Mutation
#### 3.2.3 IGA Work Mechanism  
Based on the above analysis, the IGA work mechanism is outlined in this part. Two-hundred individuals are randomly generated, where three parts of each chromosome are encoded according to Section 3.2.1. After this, the iterative loop starts. First, the fitness value of each chromosome, i.e., the value of the objective function, is computed after decoding. The chromosome with a minimum fitness value is denoted as the elite in each generation, which is saved directly for the next iteration. Second, the terminal condition is verified, where the maximum number of iterations is defined as 500. If the terminal condition is not followed, genetic operators, including reproduction, crossover, and mutation are successively executed as per Section 3.2.2. After this, the updated chromosomes, together with the saved elite, are denoted as the initial chromosomes for the next iteration. Once the terminal condition is satisfied, the loop stops, and the optimal solution is exported. Figure 10 shows the IGA flowchart.
# A Chaotic System with Infinite Equilibria and Its S-Box Constructing Application
## 1 Introduction  
Systems with chaotic behaviour have been discovered and studied for many years [1; 2; 3; 4]. In general, there are countable equilibrium points in conventional chaotic systems [5]. By applying a systematic examination, [PERSON] found 18 simple chaotic flows with one or two equilibrium points [6]. The well-known Lorenz system has one saddle and two unstable saddle-foci [1]. Similarly, other typical chaotic systems with three equilibrium points are Chen system [7], Lu system [8], Yang systems [9; 10] and so on [11]. Dynamical analysis and electronic circuit design of a chaotic system with four equilibrium points were reported in [12]. [PERSON] and [PERSON] proposed a chaotic system with five equilibrium points [13]. Furthermore, a method to construct chaotic systems with any preassigned number of equilibria was presented by [PERSON] and [PERSON] [14].  
Recently, researchers have shown an increased interest in chaotic systems with an infinite number of equilibrium points such as systems with a line equilibrium [15], systems with circular equilibrium [16], or systems with square equilibrium [17], etc. It is noted that memristor-based systems often have a line of equilibrium points [18]. It is interesting that [PERSON] et al. have found nine discrete chaotic systems with one-line equilibria and applied them for image encryption [19]. Chaotic systems with infinite equilibria and their applications should be further investigated.  
It is now well established from a variety of studies that chaos is useful for designing S-Box, which is the vital nonlinear confusion part in encryption algorithms [20; 21; 22; 23]. Nonlinear chaotic algorithm for designing substitution-boxes was presented [24]. S-Box generation algorithms were designedwith chaotic maps [20; 25], the Lorenz system [21; 26], or the chaotic scaled Zhongtang system [27], [PERSON] and [PERSON] developed S-boxes using a time-delay chaotic system [28]. In addition, a new eight-term chaotic system with two equilibrium points was introduced and employed in a novel S-Box [23]. Creating S-Box structures based on chaotic systems with infinite equilibria is an attractive topic. Previous research has established that S-Box is the only nonlinear part using in block encryption algorithms. S-Box is considered as the most fundamental structure of block encryption algorithms [29; 30; 31; 32]. As a result, a well-designed S-Box structure makes the encryption resistant against various attacks. The application of S-Boxes in cryptography is reported for wireless sensor network [33], image encryption scheme [34], color image watermarking [35], and copyright protection [36].  
The aim of this work is to study a chaotic system with an infinite number of equilibrium points and its S-Box generating application. In Section 2, we introduce the system and its dynamics. Circuit implementation of the system is presented and experiential results are reported in Section 3. In Section 4, a new S-Box generation algorithm is proposed. Finally, Section 5 concludes our work.
# A Novel Anomaly Behavior Detection Scheme for Mobile Ad Hoc Networks
## 3 Related Work
[39] proposed a heterogeneous, cluster-based secure routing scheme that offers trust-based secure networks for attack detection--such as wormholes and blackholes instigated by the existence of malicious nodesin MANETs. The simulation outcomes show that the proposed model works effectively for spotting malicious nodes efficiently. The efficiency of the proposed approach is 96% for malicious node detection; it is also 10% more efficient in terms of energy consumption. [PERSON] et al. [40] state that clustering is a key routing technique used to reduce energy consumption and, in their work, they propose and evaluate energy-efficient cluster head selection for fault-tolerant routing to reduce single-link failure in MANETs; the simulation results show that the proposed model could implement improved fault tolerance and extend the lifetime of the network. [PERSON] et al. [41] propose an algorithm called trust-and-energy-based ad hoc on-demand distance vector; this isolates malicious nodes by dynamic calculation of the trust and energy values of the nodes in the topology, and enhances the routing performance of the AODV algorithm--specifically, the packet delivery ratio and average end-to-end latency. [PERSON] et al. [42] detected selfish nodes through their proposed approach, and quantitatively examined the influences of node selfishness triggered by energy depletion in MANETs in terms of packet loss rate, round-trip delay, and throughput. [PERSON] et al. (2018) developed a high-power-saving management system for MANETs via the use of means clustering; they achieved reduced power and energy loss compared to other methods, such as transmission and direct communication protocols; the experimental results suggest that the proposed scheme is suitable to decrease energy dissipation in comparison to other protocols, such as transmission and direct communication protocols. [PERSON] et al. [PERSON] et al. (2018) discussed resource constraints in MANETs, and presented limited energy and system lifetime as challenges faced by MANETs; they give an account of protocols such as the highest degree clustering algorithm (HDCA) and lowest identifier clustering algorithm (LIDCA) under three headings of throughput, network lifetime, and packet delivery ratio; their proposed novel clustering algorithm's simulation results show that it outperforms HDCA and LIDCA in terms of network lifetime and offers effective energy distribution. [PERSON] et al. [PERSON] et al. (2018) proposed a form of red deer algorithm (RDA)-based energy-efficient QoS routing for MANETs called RDA-EQRP. RDA finds the shortest path from a source to a destination while preserving energy, in addition to supporting reliability, bandwidth, static resource capacity, quality, and delay; simulation results show that the proposed RDA-EQRP consumes less energy in MANET routing.
## 8 Algorithm Used to Achieve QoE/QoS  
Finally, in Figure 5, we present the flowchart of the process to allow other researchers to implement the proposals based on other network parameters. Figure 5 is explained as follows:  
Figure 4: MOS vs. Packet Loss and Jitter. Basketball model.  
Step 1: Researchers decide to either record their own videos (step 1A) or select videos from a public database (Step 2).  
Step 3: Select the desired type of model (experimental design). Upon selecting the type of mathematical model, researchers will be able to implement the experimental design.  
Step 4: Select the number and type of QoS parameters to include in the model. Other types of network parameters may also be used.  
Step 5: Execute video treatment according to the available resources. If hardware and software resources are available, researchers can implement a test (Step 5A) to introduce video impairments. If only software resources are available, simulation tools may be used (Step 6).  
Step 7: Select a method to assess the video QoE. Subjective or objective methods may be selected.  
Step 8: Execute the method and data collection. The experiments are executed with the number of executions defined in Step 3.  
Step 9: Conduct the data analysis using statistical tools.  
Step 10: A preliminary model is obtained, which can be evaluated for performance.  
Step 11: Select the type of performance evaluation for the proposed model. Subjective (Step 11A) or objective methods (Step 11B) may be used.  
Step 12: Performance threshold. Performance factors, such as PLCC and SROCC, are analyzed. If the selected performance metric is greater than or equal to 0.5, the researchers have obtained the final version of the model (Step 13). Otherwise, the procedure must be repeated from Step 3.  
Figure 5: Algorithm to obtain QoE/QoS models.
## 4 Analysis of Research Variables
### DL ReTrans Rate  
As mentioned above, DL-SCH is a shared transport channel for transmitting user data in the DL direction. If the connection between the eNodeB and the user equipment is not established in one attempt, a reattempt is undertaken to send the packet over this channel or the retransmission, usually based on the modulation and coding scheme determined during the initial transmission. The data packets are sent in _Transport Blocks_ (TB), and the block size is determined by the number of bits sent within one _Transmission Time Interval_ (TTI) lasting 1 ms. If retransmissions by signal modulation types are taken into account, the value of the retransmission rate can be calculated according to the following expression:  
\[\text{DL Retrans Rate}=\frac{\text{QPSK.TB.Retrans}+16\text{QAM.TB.Retrans}+64 \text{QAM.TB.Retrans}}{\text{QPSK.TB}+16\text{QAM.TB}+64\text{QAM.TB}+\text{ QPSK.TB.Retrans}+16\text{QAM.TB.Retrans}+64\text{QAM.TB.Retrans}}\times 100 \tag{2}\]  
where the meanings of the individual variables that appear in the expression (2) are given in Table 3, and they are, in addition to _DL ReTrans Rate_, used as independent variables in the predictive model.  
Figure 4: RRC connection setup procedure (adapted from [41]).
## 4 Discussion
### Variability in Control Indicators  
HbA1c is a marker of the average blood glucose concentration that is linearly related to the risk of vascular complications. HbA1c is very useful for the diagnosis of diabetes. Its value is increasingly discussed as a treatment objective, because the average glucose and blood glucose range is becoming critical [48]. Although the HbA1c level is continuously associated with CV risk, different guidelines have recommended HbA1c < 7% as the cut-off point for establishing recommendations for managing T2D [1; 4]. Analysis of electronic HbA1c records helps to predict increased emergency department visits and hospital admissions [49]. In Navarra, a cross-sectional study showed that women were less likely than men to achieve HbA1c control targets (59% vs. 61%). Patients under 65 years of age had worse control than the older age groups [27]. Data from our study agree with these results [45; 47]. Intra- and inter-GPP variability in the treatment of diabetes could not be explained by differences in the guidelines for the treatment of diabetes. The guidelines were the same throughout Navarra [31]. All GPPs should follow them, but the degree of adherence to these guidelines could change.  
The main finding of our study was that patients associated with a GPP in the worst control cluster of HbA1c \(\geq\) 9% had a higher likelihood of having a HbA1c level \(\geq\) 9% by 73%. This is a cause for concern because of the inequity involved.
# A Graph-Based Approach to Recognizing Complex Human Object Interactions in Sequential Data
## 3 Materials and Methods
### Key Point Detection  
After extracting the human silhouette, 12 key human body parts are identified. For this purpose, the first step is to convert the human silhouette into a binary silhouette whose image skeleton is then obtained. In the binary image, the foreground is black and the background is white. The process of skeletonization keeps reducing the foreground until no more pixels can be removed. The skeletal remnant is then used to identify key points. For image skeletonization, a graph-based technique called the image foresting transform (IFT) [44] is used. The IFT defines a minimum-cost path forest in a graph, whose nodes are the image pixels and whose arcs are defined by an adjacency relation between pixels. The cost of a path in this graph is determined by an application-specific path-cost function, which usually depends on local image properties along the path--such as color, gradient, and pixel position. The roots of the forest are drawn from a given set of seed pixels. For suitable path-cost functions, the IFT assigns one minimum-cost path from the seed set to each pixel in such a way that the union of those paths is an oriented forest, spanning the whole image. The IFT outputs three attributes for each pixel: an optimum path from the root, the cost of that path, and the corresponding root. The path attribute can be used to find an image skeleton.  
Figure 3: Results of image segmentation. (**a**) Original image, (**b**) super-pixels obtained using quick shift, (**c**) merged super-pixels, (**d**) extracted human–object pair.  
Seven key points are obtained from the nodes marking the start and end positions of various paths in the obtained skeleton. These points are identified as the head, left hand, right hand, upper torso, bottom torso, left foot and right foot. Using the obtained seven points, five additional key points are also found, namely, the neck, left elbow, right elbow, left knee, and right knee. The method of finding these additional points is simple: the mid-point of any two key points is calculated, and a point on the contour lying closest to the obtained mid-point is stored as an additional key point. The mid-point \((x_{m},y_{m})\) of two existing points \(j\) and \(k\) is calculated using Equation (3). Each step of the process is shown in Figure 4.  
\[(x_{m},y_{m})=\left(\frac{x_{j}+x_{k}}{2},\frac{y_{j}+y_{k}}{2}\right) \tag{3}\]
## 4 Discussion
### Reliability  
Both systems seem to be reliable measurement systems for quantifying FPA. The baseline test-retest reliability for the PSW was excellent (ICC = 0.97), and for the IMUs it was considered to have good reliability (ICC = 0.89). When outliers were included, the PSW test-retest ICC equaled 0.90, which indicates good reliability; the inclusion of outliers did not affect the test-retest ICC for IMUs. From this data, it can be concluded that both systems have a high reproducibility, when walking with a natural FPA. Previous research showed a similar test-retest ICC of 0.98 [19] for FPA during normal walking on the GAITRite(r), a pressure sensitive walkway similar to the Zeno(tm) walkway that was used in this study. Another study found a lower test-retest reliability for FPA measured with the GAITRite(r) in older adults (aged between 76 and 87 years), ICC = 0.71 (right foot) and ICC = 0.82 (left foot), compared to young adults (aged between 22 and 40 years) ICC = 0.88 (right foot) and  
Figure 2: Scatterplot of mean FPA against the difference in FPA between the two systems of all gait types combined. Difference in FPA between IMUs (Opal sensors) and the PSW (Zeno(tm) Walkway) is displayed on the y–axis, and the mean FPA for Opal and Zeno(tm) is displayed on the x–axis. The red line represents the mean difference. A linear regression line (\(y=-0.79+0.17*x\)) is fitted to the dots.  
ICC = 0.94 (left foot) [8]. This may be the result of higher gait variability in older adults and should be considered when using these systems for other populations.  
After excluding outliers, the SEM for the PSW is smaller (0.96\({}^{\circ}\)) than for the IMUs (1.62\({}^{\circ}\)), but the latter can still be considered acceptable, particularly in a clinical environment. The SEM is the amount of error that can be considered as measurement error. The MDC indicated that, consequently, IMUs are not suitable for precise measurements smaller than 5\({}^{\circ}\), with the PSW not being suitable for measurements smaller than 3\({}^{\circ}\). The MDC is described as the least amount of change that is not the result of measurement error [13]. However, it should be noted that not every step is identical, so part of this MDC is explained by physiological differences. A t-test showed a significant difference between the two measurement systems for toe-in -and toe-out gait.
###### Abstract
* Since the DRL-based solution is model-free, it does not depend on the network size and a large amount of training data. Unlike the supervised learning technique, there is no need to execute an optimal strategy to design a massive training dataset. This makes the proposed DRL-based optimization scheme less complex and scalable for large-scale systems. * We provide numerical simulations to assess the performance of the proposed scheme. Simulation results indicate that the proposed DQL approach significantly improves the optimization of transmit strategies. The remainder of this paper is structured as follows. A review of related works is presented in Section 2. Section 3 introduces the system model and problem formulation using a non-cooperative game model. In Section 4, we present our proposed DQL-based resource allocation approach in detail. Section 5 provides the numerical simulations and discussions, followed by a conclusion in Section 6.