esa-sceva/satcom-chunk-collection huggingface dataset:
# Chronic Periodontitis and Acute Respiratory Infections: A Nationwide Cohort Study
## 1 Introduction  
Chronic periodontitis (CP) is one of the most common chronic inflammatory diseases. Severe localized or generalized CP affects >10% of the global population and is a major public oral health problem [1, 2]. CP progressively and pathologically destroys the tooth-supporting soft and hard tissues, including the cementum, periodontal ligament, and alveolar bone, and may ultimately cause tooth loss [3]. Although previous epidemiologic and cohort studies have suggested that CP is a unidirectional risk factor for lifestyle-related non-communicable diseases, such as cardiovascular disease, hypertension, diabetes mellitus, rheumatoid arthritis, osteoporosis, erectile dysfunction, and cancer, there is increasing evidence indicating that the association between CP and major systemic diseases is bidirectional [4, 5, 6, 7].  
Several epidemiologic and etiologic studies have suggested a potential association between CP and acute and chronic respiratory infections such as pneumonia, acute bronchitis, lung abscess, and chronic obstructive pulmonary disease [8, 9]. Moreover, previous systematic reviews have confirmed a positive causal association between CP and respiratory infections [10, 11].  
Accumulation of oral pathogens, especially _Fusobacterium mucleatum_, associated with CP may increase the risk of exacerbation and hospitalization in patients with respiratory infections [8]. These infections possibly share the direct bacterial-respiratory pathwayand the same proinflammatory cytokines or products, such as tumor necrosis factor-\(\alpha\) (TNF-\(\alpha\)), interleukin (IL)-1, IL-6, and IL-8 [12]. Major immunoinflammatory cells and inflammatory mediators from the mucosal epithelial cells of the upper and lower respiratory tract make the respiratory tract more susceptible to infection and development of severe respiratory disease [12].  
While research on the association between CP and chronic respiratory infections, particularly chronic obstructive pulmonary disease, has steadily progressed over recent decades, relevant studies between CP and acute respiratory infections are scarce and scattered [13,14]. Consequently, the epidemiological association between CP and acute respiratory infections remains unclear and requires further research. Therefore, the goal of the present study was to evaluate the associations between CP and acute respiratory infections by analyzing a population-based longitudinal database from the National Health Insurance Service--National Sample Cohort (NHIS-NSC) in South Korea.
# Environmental Impacts on Hardware-Based Link Quality Estimators in Wireless Sensor Networks
## 5 Environmental Impacts on Hardware-Based LQEs
### Environmental Impacts on Mapping Models between Distance and \(PRR\)
#### 5.1.2 Environmental Impacts on Mapping Models between Distance and PRR  
Using Equation (5), the theoretical model between communication distance and \(PRR\) in three environments was plotted, as shown in Figure 5. As the background noise is easy to be got, the measured noise power of the corresponding environment was used as \(P_{n}\) in the corresponding model. It can be seen from Figure 5 that the theoretical model is basically in agreement with the measured data in the runway. However, it is quite different from the measured data in the other two environments. Even in the artificial lawn which channel condition is closer to the runway, the relationship between communication distance and \(PRR\) is inconsistent with the theoretical model. Taking a closer look at Figure 5, it can be found that there are some differences between the theoretical curves in three environments. This is caused by the random variable \(X_{\sigma}\) in Equation (5). Although the theoretical curve obtained for each run will be a little different, the pattern of the theoretical curves is not changed and it does not affect the above conclusions. Using Equation (7), the fitting model between communication distance and \(PRR\) in three environments was plotted, as shown in Figure 6. From Figure 6, it is obvious that this model is also basically in agreement with the measured data in the runway. It is also quite different from the measured data in the other two environments.  
To describe the environmental impacts on these mapping models quantitatively, RMSE of the estimated \(PRR\) and real \(PRR\) in the three environments was calculated, as shown in Table 3. It can be seen that even the smallest RMSE is as high as 0.2862. This indicates that it is not accurate to estimate \(PRR\) using communication distance. On the other hand, RMSEs in the artificial lawn and corridor are much higher than those in the runway when using the mapping model constructed in the runway, no matter whether the model is a theoretical model or a fitting one. Compared with the runway, RMSEs of the theoretical model in the artificial lawn and corridor increase by 57.48% and 85.32%, respectively. Meanwhile, RMSEs of the fitting model in the artificial lawn and corridor increase by 37.95% and 40.31%, respectively. That is to say, the mapping model between communication distance and \(PRR\) constructed in a specific environment cannot be directly used in other environments.  
Figure 4: Communication distance vs. \(PRR\) in different environments.  
Figure 5: Effects of the theoretical model in different environments.  
Figure 6: Effects of the fitting model in different environments.
# Mobile Robotics in Agricultural Operations:
## 2 Materials and Methods
### Definition of Planning Attributes
#### 2.2.2 World Model  
Word maps refer to the internal representation of the operating space by a mobile robot [26]. In this context, workspace representations refer either to the use of a pre-generated map (along with the characteristics of any involved entities) or the creation of a new map. Research in the field of mobile robots recognizes three (3) types of world maps, namely: (i) metric maps; (ii) topological maps; and (iii) semantic maps.  
In a metric map, the world representation regards the configuration of the environment and the location of any entities that affect robot self-localization. To that end, either Cartesian or grid-based world models are applied. Cartesian models consist of discrete geometric primitives such as points, lines, or polynomial functions. These primitives are connected either with a local or a global two-dimensional or tree-dimensional coordinate system [20; 27]. In grid-based models the workspace is decomposed into an evenly-spaced grid and each entity corresponds to a region within the environment [28].  
Moreover, topological maps use graphs to represent world environments [29], thus allowing the execution of navigation tasks. Specifically, the nodes of a graph represent locations, obstacles or landmarks (such as doorways). In addition, arcs connect the nodes in case a direct relation exists between them; the arcs denote a \"calibrated\" parameter that represents the cost (e.g., distance) for the safe transition of a mobile robot between nodes.  
Finally, the need for robots to execute high-level tasks in human-involved environments motivated the introduction of semantic maps in the related technical literature [30]. Specifically, semantic maps use meta-information to provide robots with a range of inference capabilities for efficient navigation, object detection, human-robot interaction, etc.
## 2 Data Preparation
### Data Collection  
The internal defects dataset in this research comes from the inspection records of railway lines in Nanchang, China. Figure 2 demonstrates how the rail defect detector was used to collect images of internal defects and the correspondence between B-scan images and actual defects.  
For data processing, one of the most important conditions is acquisition devices and their parameters that determine the resulting quality. GCT-8C rail defect detector, a kind of small hand-pushed digital rail ultrasonic defect detector that has a total of nine detection channels: one \(0^{\circ}\) channel, two \(37^{\circ}\) channels, and six \(70^{\circ}\) channels ensuring sufficient view angle, used for detection. Each of the channels corresponds to a specific color (customized) and detection area, forming an image with complex colors but a regular layout. The main technical parameters of the instrument are shown in Table 1, and the setting of color and detection area of different channels are shown in Table 2. These channels run with a rate of 60 Hs per second along rail and display in 2 forms: ultrasonic A-scan image and B-scan image. In them, ultrasonic A-scan image is mainly used for on-site equipment status adjustment and monitoring due to the large data amount. In contrast, the ultrasonic B-scan image, which has a small amount of data, stores and further analyzes railway conditions. Furthermore, B-scan images could intuitively reflect the location of the defects, which is beneficial for the operator to identify and locate them. Also, compared with traditional images, ultrasonic B-scan images are not likely to be affected by light or shadows, and the resolution is fixed, depending on the minimum scanning distance of the rail inspection vehicle, which avoids objects of the same category with different features due to different resolutions. In this study, the collected continuous long B-scan images were cut and extracted to establish a dataset containing 200 samples, and each sample has a pixel size of 1325 pixels \(\times\) 346 pixels.  
However, the established B-scan image dataset has some drawbacks: the data is extremely sparse, lacks features, and is always noisy. Due to the characteristics of B-scan data, the corresponding image processing methods are also specific, and to address these drawbacks, pre-processing methods are introduced in the next steps.  
\begin{table}
\begin{tabular}{l l} \hline
**Classification** & **Parameter** \\ \hline Channel Number & 9 \\ Swipe speed/(\(\mathrm{Hz}\).\(\mathrm{s}^{-1}\)) & 60 \\ Probe operating frequency/\(\mathrm{Hz}\) & 400 \\ Repetition detection frequency/\(\mathrm{Hz}\) & 400 (per channel) \\ Detection range/mm & 0–200 \(\pm\) 4 (43 kg/m and 50 kg/m rail); 0–250 \(\pm\) 5 \\  & (60 kg/m and 75 kg/m rail) \\ Horizontal linearity error & \(\leq\)2\% \\ Vertical linearity error & \(\leq\)5\% \\ \hline \end{tabular}
\end{table}
Table 1: Main technical parameters.  
Figure 2: The structure of GCT-8C rail defect detector.
# Mobility-Enabled Edge Server Selection for Multi-User Composite Services
## 5 Simulated Experiments and Analysis
### Experiment Results and Analysis  
In this part, we first present the simulation results on the impact of different number of resources, the number of service sin each composing service, and the number of users. The superiority of the proposed algorithm was verified by comparing our algorithm with two baseline approaches. Then, we analyzed how the round-trip and the service downtime generated by service migration impacted the total waiting time in MEC.
## 1 Introduction
This sensor detects the hardness, surface geometry, and incipient slip of objects by using an internal camera to detect deformations caused when a transparent rubber layer coated with a reflective membrane comes into contact with the object. However, due to its mechanisms, the sensor is limited in terms of how small it can be manufactured. Furthermore, the sensor lacked the sampling rate to be used in robotics. Above all, as the aforementioned sensors were developed with a focus on vibrations and force modalities, none are able to detect temperature, which is necessary to measure the inherent thermal conductivity of an object. On the other hand, the BioTac (SynTouch LLC, Los Angeles, CA, USA) fingertip sensor [13; 14; 15] is also able to measure thermal conductivity. However, the BioTac sensor struggles to directly obtain information regarding an object's friction coefficient, which is measured using horizontal direction forces. Hence, our research team developed a robot fingertip module that can discriminate textures of objects based on a tactile sensor with multimodal (3-axis force and temperature) sensing capabilities to detect various tactile modalities. The multimodal tactile sensor that was applied to the module had a single-cell structure comprised of four U-shaped strain gauges based on boron-doped single crystalline silicon, which enabled the sensor to measure 3-axis forces. In addition, a thin serpentine-shaped gold wire was designed to simultaneously detect the temperature of the module. However, temperature sensing capabilities alone are insufficient to detect thermal conductivity, which is related to the flow of heat. As such, we integrated a heater based on enamel copper wire (Elektrisola Korea Co., Ltd., Siheung-si, Korea) to the module to maintain the module temperature above room temperature. This enabled the flow of heat when the module came into contact with an object that had been placed at room temperature. Therefore, the multimodal tactile sensor mounted on the module exhibited excellent sensitivity by detecting vertical (Z-axis) forces as small as 0.5 gf. Moreover, the sensor showed a low hysteresis error and repeatability error of less than 3% and 2% in the vertical force measurement range of 0 gf to 100 gf, respectively. In terms of horizontal direction (X- and Y-axes) forces, the sensor was able to detect forces as small as 20 mN. As such, the sensor could detect 3-axis forces with an average cross-talk error of less than 3%. In addition, the sensor demonstrated its multimodal sensing capability by exhibiting a constant degree of change according to thermal stimuli applied to the module over a temperature range of 23 \({}^{\circ}\)C to 35 \({}^{\circ}\)C.  
Furthermore, the robot fingertip module was mounted on a motorized stage to enable both static contact and dynamic contact between the module and the object. Based on the sensor measurement results from both forms of motion, four tactile modalities (i.e., hardness, friction coefficient, roughness, and thermal conductivity) were calculated to discriminate the texture of the object.
## 2 Artificial Neural Networks
### Types of Artificial Neural Network
#### 2.2.2 Recurrent Neural Networks  
In recurrent neural networks (RNNs) the information goes back and forth as can be seen in Figure 5a, for this reason, they are also called feedback networks. In these networks, the connections between nodes form a directed cycle, where at least one path leads back to the initial neuron. In this type of network there are different types of structure [86]:  
* Hopfield network: each neuron is completely symmetrically connected with all other neurons in the network. If the connections are trained using Hebbian learning, then the Hopfield network can function as a solid memory and resistant to the alteration of the connection. Hebbian learning involves synapses between neurons and their strengthening when neurons on both sides of the synapse (input and output) have highly correlated outputs [87] as shown in Figure 5b. There is a guarantee in terms of convergence for this network [88].
* Elman network: this is a horizontal network where a set of \"context\" neurons is added. In Figure 5c the context units are connected to the hidden network layer fixed with a weight. The subsequent fixed connections result in the context units always keeping a copy of the previous values of the hidden units, maintaining a state, which allows sequence prediction tasks [89].
* Jordan network: these are very similar to [PERSON]'s networks. However, context units feed on the output layer instead of the hidden layer.  
Figure 5: Recurrent neural networks structure: (**a**) Simple structure of a recurrent network, (**b**) Hopfield network structure, (**c**) Elman network structure.  
RNN is distinguished from a FFNN by the presence of at least one feedback connection. FFNNs do not have the intrinsic ability to process temporary information. There are two important considerations about why recurrent networks are viable tools for modeling: inference and prediction in noisy environments. In a typical recurrent network architecture, the activation functions of the hidden unit are fed back each time step to provide additional input. That is, the recurrent networks are built in such a way that the outputs of some neurons feed back to the same neurons or to the neurons in the previous layers [86]. Feedback from hidden units allows filtered data from the previous period to be used as additional input in the current period. This causes the network to work not only with the new data, but also with the past history of all entries, as well as their leaked equivalents. This additional filtered input history information acts as an additional guide to assess the current noisy input and its signal component. By contrast, filtered history never enters a FFNN. This is where recurring networks differ from a FFNN. Second, since recurrent networks have the ability to maintain the past history of filtered entries as additional information in memory, a recurrent network has the ability to filter noise even when the noise distribution can vary over time. In a FFNN a completely new training must be carried out with a new data set containing the new type of noise structure [78].
## 4 Experiment and Results
### The Optimization of the Total Execution Time  
Our experiment ran the ingestion software in four different VMs, as detailed in Table 4, with an Internet bandwidth of 1 GB/s. Commonly, the pre-processing of the RSBD takes a long execution time. From Figure 12, we remark that the download phase took approximately eight minutes in standard mode and around six minutes when the parallel tools were applied. This time could be optimized more by speeding up the Internet  
Figure 11: The screenshot of the output from Hive.  
bandwidth or/and switching the Internet Protocol (IP) from the Transmission Control Protocol (TCP) to the User Datagram Protocol (UDP).  
The decompressing and the orbit filter execution time requires 20 min in standard mode and only about 10 min in parallel. The conversion is the lengthiest process, reaching about 50 min in standard and less than 10 min with a parallel algorithm. The subset needs more than an hour in standard; however, it requires only 15 min parallelly. In conclusion, the extraction script takes an average of 20 min. In contrast, it takes only three minutes with the parallel approach.  
The developed scripts are optimized by reducing database connections, such as selections and insertion requests; thus, the network traffic economizes. Removing unused loops by breaking the loops by conditions is also essential to reduce CPU and RAM consumption. Besides, discarding the collections lists, arrays, and vectors after each file processing reduce RAM utilization.  
Scaling the input and output operations by only filtering datasets of interest accelerates the execution time and makes more free memory available. Finally, reducing the number of reads and write processes surely adjusts the HDD and CPU performance. In this testing, and according to Figure 13, the total pre-processing time of 55 GB of RSBD takes more than nine hours in the standard mode Before Optimization (BO). However, it requires less than four hours in a traditional approach and within an optimized code. On the other hand,  
Figure 12: The execution time of the standard and parallel pre-processing phases.  
we pre-processed the same input size within an optimized code and in parallel in only 34 min. Accordingly, optimizing the code and integrating cloud and parallel programming techniques optimized the total execution time by 90%. Thus, this number could be reduced more using the power function \"y\" when the instance capacities (VCPU, RAM, HDD) are larger, such as a super-computer. The processing speed-up will grow when the cluster capacity is extended by adding extra VMs, reaching a plateau equal to a speed-up-max. The speed-up-max = 600/SFET, where SFET is the Single File Execution Time. In our study, the average SFET is 5 min; thus, the speed-up-max is 120 times based on the power equation shown in Figure 13 with the green color. It is worth mentioning that the speed-up factor will reach a plateau after a certain number of VMs due to the overhead in communications. The maximum number of VMs used to reach a plateau is roughly 27 VMs.
## 3 Scheduling Analysis
### Linear Programs for Solution
Constraints (58)-(60) come from Proposition 4, and we can obtain \(\omega_{i1}\) and \(\psi_{4}\). For scheduling problems, to satisfy practical application requirements, computational efficiency is a very important issue. By the above-proposed methods, a schedule is obtained by a linear program if it exists. It is well-known that a linear program can be solved in polynomial time, implying that the proposed method is efficient and scalable. This Section analyzes the temporal properties of the four scheduling strategies (OBS, OHTS, TBS, and THTS) in multi-space process modules with RTCs. It presents the mathematical models and equations for calculating the cycle time and robot task sequences for each strategy. The analysis focuses on optimizing robot-waiting time and task scheduling to achieve minimal cycle time while meeting the RTCs. Linear programming models are developed to determine feasible and optimal schedules for maximizing the throughput. It should be noted that, with LPs 1-4, by applying different strategies, the obtained results may be different due to different parameter combinations. Thus, given a group of parameters, we need to apply LPs 1-4 and select the best schedule.
# A Low-Loss Circuit with High-Pass Low-Pass Broadband Flat Negative Group Delay Characteristics
## 2 Circuit Design and Analysis with High-Pass and Low-Pass Negative Group Delay
### Low-Pass Flat Negative Group Delay Circuit
#### 2.2.6 Low-Pass Negative Group Delay Circuits with Different \(L_{3}\) Values  
When the values of \(C_{1}\), \(R_{1}\), \(L_{1}\), \(R_{2}\), and \(L_{2}\) in the circuit remain unchanged, as shown in Table 8, and \(L_{3}\) is sequentially changed to 0.3 nH, 1.3 nH, and 2.3 nH, we observe the changes in the circuit transfer function and group delay. The circuit results in these three cases are shown in Table 13 and Figures 25 and 26. Through observation, it is found that,  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \(L_{2}\) **(nH)** & \(\tau\) **(ns)** & \(FBW\) **(MHz)** & **IL (dB)** \\ \hline
0.9 & \(-\)0.162 & 84 & \(-\)1.938 \\
3.9 & \(-\)0.168 & 85.5 & \(-\)1.938 \\
5.9 & \(-\)0.172 & 86.5 & \(-\)1.938 \\ \hline \hline \end{tabular}
\end{table}
Table 12: Circuit results with different \(L_{2}\) values.  
Figure 23: Group delay results for different \(L_{2}\) values.  
as the value of inductance \(L_{3}\) increases, the group delay of the high-pass circuit at 0 MHz decreases. As the value of inductance \(L_{3}\) increases, the negative group delay also decreases. The degree of change in the circuit insertion loss is not significant, but, as the frequency increases, the insertion loss increases with the increase in inductance \(L_{3}\).