esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Antibacterialrial Properties  
The antibacterial results of the CNFs 0.1% films against the Gram-positive and Gram-negative bacterial model are shown in Figure 4.  
Figure 4: Antibacterial agar disk diffusion tests performed with the calcium alginate (control) film and the calcium alginate/carbon nanofibers (CNFs0.1%) composite films against the Gram-positive _Staphylococcus aureus_ bacterial model and the Gram-negative _Escherichia coli_ bacterial model. The normalized antibacterial halos (mean \(\pm\) standard deviation) obtained with Equation (1) are indicated in the images.  
Figure 3: Representative titration plaques of bacteriophage T4: antiviral tests of alginate films and alginate/carbon nanofibers films (CNFs0.1%) at 10 min, and 18 and 48 h of contact with the virus.  
Even though viruses and bacteria are completely different [29], these results are in good agreement with the antiviral results shown in Figures 2 and 3, which demonstrate that these CNFs0.1% composite materials possess broad-spectrum antimicrobial activity against the non-enveloped double-stranded DNA virus and the Gram-positive _S. aureus_ bacteria. However, these composite materials were not able to inhibit the growth of the _E. coli_ Gram-negative bacterial model (see Figure 4), probably due to their different membrane structure which renders Gram-negative bacteria more resistant than Gram-positive bacteria [30].
# Compliant Human-Robot Collaboration with Accurate Path-Tracking Ability for a Robot Manipulator
## 1 Introduction
We validate the proposed approach with two experiments including a linear motion test to demonstrate the behavior of the robot to the direction of the external force applied and a general 3D path test. Both tests are performed on a traditional 6-DOF industrial robot equipped with a force/torque (F/T) sensor on its end effector. No dedicated hardware is required. We compare three types of motion--free motion (FM), force-applied motion (FAM), and combined motion with virtual forces (CVF)--to show the performance in both ends of the assistance range spectrum. FM demonstrates the fully assisted case, where no force from the user is needed; FAM is the no-assistance case, where the motion of the robot along the path depends on the force applied by the user; and CVF is the smooth transition between the previous two cases. To achieve the proposed objective of compliant human-robot collaboration with accurate path-tracking, the robot follows a reference path and the tracking error should be small, whether it is subject to external forces or not. Thus, we evaluate the tracking performance of each type of motion by calculating the root mean square error (RMSE). The experiments exhibit not only accurate path tracking with desired compliant behavior but also the applicability of the proposed approach to traditional industrial manipulators with F/T sensors. Therefore, the main contributions of this work are as follows:  
* Development of a dual-loop control structure that achieves accurate path tracking for collaborative tasks while maintaining compliance along the path. * Safe and intuitive human-robot interaction achieved by the outer adaptive admittance control loop. * Equivalent path tracking performance guaranteed by the inner modified SMC loop under various human-robot collaborative scenarios. * Experimental validation with a traditional 6-DOF industrial robot proving the generality of the method implementation. The paper is structured as follows. Section 2 covers the problem formulation and the methodology, including the inner motion control loop and the outer admittance control loop. In Section 3, we describe the experimental setup and tests performed to validate the proposed approach. Additionally, we present the results and analysis of RMSE, which is used to evaluate the tracking performance. Finally, the conclusions of the work are presented in Section 4. Figure 1: Path tracking for human–robot interaction with compliance along a predefined path.
## 4 Numerical Simulation Results and Analysis
### Simulation Setup  
The LES is chosen for the numerical simulation of turbulence in the overlapping grid partitioning process, offering enhanced calculation accuracy. In this study, the inlet velocity is set to 0.833 m/s, the outlet is subjected to a normal pressure gradient of 0, and the far-field boundary is expanded to 10 times the diameter of the target, effectively mitigating wall effects on the flow field. Additionally, the flowing medium is water, and the density and kinematic viscosity are updated accordingly.  
In the simulations, selection of the turbulence model for the LES was carried out using the Smagorinsky-Lilly subgrid-scale model. The semi-implicit method for pressure linked equations consistent algorithm was employed for coupling pressure and velocity. For pressure spatial discretization, the pressure staggering option algorithm was used, while the bounded central differencing algorithm was applied for momentum calculations.  
Table 2 illustrates that as grid resolution improves, the friction resistance coefficient (\(C_{f}\)) of the AUVs approaches convergence. When the number of grids exceeds 2.07 million, the \(C_{f}\) remains relatively stable. Therefore, this article utilizes a 2.07 million grid for numerical simulation.
# Data Hiding and Authentication Scheme for Medical Images Using Double POB
## 3 The Proposed Scheme
### Information Extraction and Lossless Recovery
#### 3.3.1 POBN Algorithm  
The receiver adopts two inverse POB algorithms in order to recover the received Share image, as the two received shares were formed in accordance with the POB number system. Refer to Algorithm 1, also known as the POBN algorithm, for further information. Two rounds of the POBN algorithm are performed. In order to extract the authentication bit from each pixel, the POBN algorithm is first used to convert the POB value to the corresponding POB number. The authentication bit in the 10-bit data is then erased if it is found to be identical to the authentication bit determined by the statistical technique, indicating that the authentication bit is not a valid signature.  
Figure 5: Process flow chart of extracting secret information and image recovery.  
the image has not been tampered with. In the second execution of the POBN algorithm execution, each pixel's hidden information is extracted, the POB value is converted to a ten-POB number after the identity bit is removed, the embedded information is extracted from each POB number, the ten-bit data are converted to eight-bit data, and the encrypted ShareA\({}^{\prime}\) and encrypted ShareB\({}^{\prime}\) are recovered.  
Input: \(n\), \(r\), and \(v\), where \(r\leq n\) and \(0\leq v\leq\binom{n}{r}-1\).  
Output: the POB number \(A=a_{j-1}a_{j-2}\cdots a_{0}\).  
(i) Let \(j=n\) and temp = \(v\).  
(ii) For \(k=r\) down to 1, perform the following:  
1. Repeat {
2. \(j=j-1\);
3. \(p=\binom{j}{k}\);
4. if (temp \(\geq p\))
5. temp = temp - \(p\);
6. \(a_{j}=1\);
7. else \(a_{j}=0\);
8. } until \(\left(a_{j}=1\right)\);  
(iii) If (\(j>0\))  
for \(k=j-1\) down to 0, perform the following:  
\(a_{k}=0\);  
end.  
(iv) Return A.  
**Algorithm 1** Converts the POB value to the corresponding POB number
## 4 Service and Communication Technology Adaptation Method
### Determination of Index Weights Based on the FAHP-Improved CRITIC Combined Weighting Model
#### 4.2.2 Objective Weighting
The larger \(Cj\) is, the larger the combined information carrying capacity of volatility and conflict for the \(j\)-th index is, and the higher the relative importance is. Finally, the CRITIC objective weight of the \(j\)-th index is calculated according to Formula (16):  
\[C_{j}=\sigma_{j}\sum_{i=1}^{m}(1-r_{ij}) \tag{15}\]  
\[w^{c}_{j}=\frac{C_{j}}{\sum_{j=1}^{n}C_{j}}=\frac{\sigma_{j}\sum_{i=1}^{m}(1- r_{j})}{\sum_{j=1}^{n}\left[\sigma_{j}\sum_{i=1}^{m}(1-r_{ij})\right]} \tag{16}\]  
As an objective weighting method, CRITIC considers the volatility of the index itself and the conflict between indexes; however, it does not take into account the amount of information, which can also be an objective factor affecting weights. The Entropy Weight Method (EWM) addresses this by applying the principle of entropy in physics to calculate weights based on the amount of information carried by the index data. Entropy measures the uncertainty of information. The greater the amount of information in the data, the smaller the uncertainty and the smaller the entropy value. EWM is introduced to improve the CRITIC method so that the improved CRITIC method comprehensively considers objective factors such as the volatility, conflict, and amount of information of the index. The improvement process is as follows. To calculate the entropy value, the first step involves normalizing the standard evaluation matrix \(X^{\prime}\)according to the formula below:  
\[p_{ij}=x^{\prime}_{ij}/\sum_{i=1}^{m}x^{\prime}_{ij} \tag{17}\]  
The entropy value of each evaluation index is calculated according to the following formula, where \(k=1/\ln m\) and \(m\) represents the number of alternatives:  
\[e_{j}=-k\sum_{i=1}^{m}\left(p_{ij}*\ln p_{ij}\right) \tag{18}\]  
Entropy values are incorporated as a reference factor into the weight calculation formula of the CRITIC weighting method, where \((1-e_{j})\) represents the amount of information of the \(j\)-th index. The improved objective weighting formula is as follows:  
\[w_{j}=\frac{(1-e_{j}+\sigma_{j})\sum_{i=1}^{m}(1-r_{j})}{\sum_{j=1}^{n}\left[ (1-e_{j}+\sigma_{j})\sum_{i=1}^{m}(1-r_{j})\right]} \tag{19}\]
## 2 Materials and Methods
### Study Design  
This study is a population-based retrospective cohort study based on Taiwan's NHIRD, which covers more than 99% of the entire population [13]. The NHIRD has been described in detail in previous studies [14, 15].  
The patients with choledocholithiasis were selected from two million random samples from the NHIRD between 1998 and 2015 using the Codes of International Statistical Classification of Diseases and Related Health Problems, Ninth Edition (ICD-9) that were recorded during admission.  
The acute cholangitis or choledocholithiasis patients were selected using ICD-9 574-576; a therapeutic ERCP was defined as an endoscopic retrograde cholangiocarcinoma-creatography (ERCP; OP51.10), an endoscopic retrograde cholangiocarcinoma-creatography (ERC; OP51.11), an endoscopic sphincterotomy, or an endoscopic papillary balloon dilatation (EST or EPBD; OP51.8) during the index admission from 1998 to 2015. The patients who previously underwent biliary surgery or biliary treatment (OP51.3) or who had biliary cancer (155.1 and 156) were excluded. The observation period that was selected was from January 1998 to December 2015.  
A total of 104,053 patients with symptomatic choledocholithiasis who underwent therapeutic ERCP were selected from the nationwide population databases of two million random samples. We excluded patients who underwent gallbladder drainage or other gallbladder treatments, who had gallbladder cancer, and patients younger than 20 years of age. The recurrence, mortality, and complication rates were investigated.  
This nationwide, population-based study on the timing of cholecystectomy (CCY) evaluated 22,996 patients who were newly undergoing therapeutic endoscopic retrograde cholangiocarcinomareatography (ERCP) for choledocholithasis, and the patients were obtained from the Taiwan National Health Insurance Research Database (NHIRD) between 1998 and 2015.  
These patients were further divided into three major groups: early cholecystectomy (ELC, inpatient, performed within 7 days), intermediate laparoscopic cholecystectomy (ILC, performed between 7 days and two months after hospital admission), and delayed laparoscopic cholecystectomy (DLC, performed two months after hospital admission).  
The details of this study's design are shown in Figure 1. Table 1 shows the age, sex, comorbidities, hospital classification, economic status, and living area conditions of the entire study population. The recurrence rate, mortality, complications and economic costs were compared between these three groups.  
Figure 1: The flowchart of the study sample selection.
# Emerging Technologies for Advanced Power Electronics and Machine Design in Electric Drives
## 2 Topologies of Electric Drives
### Other Electric Drive Topologies
#### 2.4.3 Intelligent Stator Cage Drives  
In order to supply the electric machine with sufficient electric power even at DC link voltages below 60 V, fairly unusual multiphase topologies have been proposed, such as the intelligent stator cage drive (ISCAD) [116; 117]. The authors propose a novel induction machine with 60 stator slots, each filled with a single aluminium bar. At one axial end of the machine, the bars are fed by 60 integrated half-bridge modules, while at the second axial end, all stator bars are short-circuited and form a single neutral point.  
The main advantages of this approach are the extraordinary high level of integration, the excellent distribution of power between the phases, and the numerous configuration possibilities in terms of the number of stator phases (as a three-phase machine with distributed windings or as a multiphase machine). Since each slot consists of only a single aluminium bar, the inductances are likely to be rather low, which could be a potential challenge for current control.
## 4 Direct Output Current Control Based on Virtual Voltage Compensation under No Backflow Power
### Disturbance Analysis of the VDCC Method  
In the proposed VDCC method, the output voltage, the output current, and the input voltage have drastic influences on the system's stability. Hence, the influences of output voltage, output current, and input voltage on the output voltage need to be discussed.  
According to (42), the output voltage-to-inner phased shift transfer function can be expressed as  
\[G_{d_{1}u_{0}}=\frac{K_{t}U_{\text{ov}}I_{\text{o}}}{\pi U_{0}^{2}U_{\text{in }}\sin(2\pi D_{1})} \tag{45}\]  
Similarly, the input voltage-to-inner phased shift and the output current-to-inner phased shift transfer functions can be derived, respectively as (46) and (47).  
\[G_{d_{1}u_{\text{in}}}=\frac{K_{t}U_{\text{ov}}I_{\text{o}}}{\pi U_{0}U_{\text {in}}^{2}\sin(2\pi D_{1})} \tag{46}\]  
\[G_{d_{1}i_{0}}=\frac{K_{t}U_{\text{ov}}}{-\pi U_{0}U_{\text{in}}\sin(2\pi D_{ 1})} \tag{47}\]  
since the effects of the input voltage and the load disturbances are ultimately reflected in the output voltage. Based on (46), (47), and the small-signal model built in Section 4.3, the input voltage-to-output voltage and the output current-to-output voltage transfer functions can be derived, respectively as (48) and (49).  
\[G_{u_{0}u_{\text{in}}}=\frac{-4\sqrt{2}NRK_{t}U_{\text{ov}}I_{\text{o}}\sin(2 \pi D)}{\pi^{2}Z_{0}U_{\text{in}}I_{\text{o}}(RC_{\text{dc}2}s+1)} \tag{48}\]  
\[G_{u_{0}i_{0}}=\frac{-4\sqrt{2}NRK_{t}U_{\text{ov}}\sin(2\pi D)}{-\pi^{2}Z_{0 }U_{\text{o}}(RC_{\text{dc}2}s+1)} \tag{49}\]  
It can be summarized according to the above analysis that the small signals of the input voltage and the output current will affect the small signals of the output voltage due to the introduction of the compensation loop. Therefore, accurate sampled values of the control loop are needed to ensure control performance.  
Figure 9: Schematic diagram of the proposed VDCC method for fast transient response and no backflow power.
# IoT-Enabled Smart Cities: A Review of Concepts, Frameworks and Key Technologies
## 2 IoT Technologies and Architecture  
The Internet of things has introduced an important paradigm innovation in the communication among digital devices, as well as in the way that they connect with the physical environment [1] and interact with human users. IoT architectures integrate and unify all steps of data sensing/actuating from/to devices, transmitting/receiving messages, data storage, processing, analysis and final exploitation through the use of cloud, fog and edge computation, services and applications. Many different technologies are involved in the development of IoT frameworks. However, general functional architectures have been proposed in the literature that are based on a simplified version of the Open Systems Interconnection (OSI) model, but with different approaches. Some works [1, 6] define a typical IoT framework composed of three layers (from the lowest physical level to the higher levels of abstraction): the perception/sensing layer; the network layer; and the application layer. In other works [7, 8, 9, 10, 11, 12] the former approach is expanded into five layers of architecture, including: the perception/sensing layer; the transportation/network layer; the middleware/processing layer; the application layer; and the business layer. In Figure 1, the three-layer and five-layer IoT architectures are depicted.  
Figure 1: The most common IoT architectures (three-layer and five-layer) retrieved from the literature.  
Different computing paradigms are defined depending on which level of the stack is being considered, from bottom to top: edge computing; fog computing; and cloud computing [13]. In the following, a description of the different layers is provided in terms of the functionalities and involved technologies. The five-layer architecture was considered since it can be viewed as a more detailed version of the three-layer architecture.
# On the Rollout of Network Slicing in Carrier Networks: A Technology Radar
## 4 Network Slicing Technology Radar
As outlined in Section 1, for the position of each solution into this radar, three criteria have been considered: (i) the technological maturity of the solution, which is subjected to the readiness of the standards; (ii) the roadmap of commercial products, which specifies when the features associated with the solution will be available; and (iii) the relevance for the customers, which determine the prioritization of the solution over others. The following sections provide details on these solutions, across the involved subsystems: CN (Section 5), RAN (Section 6), TN (Section 7) and OSS (Section 8).
## 4 Performance of SERENA and TDMA/CA
### General Communications versus _Tree Communications_  
We notice that the number of colors needed for a data gathering application is smaller than for a general application for all the configurations tested. Indeed, coloring for a data gathering application ensures a tradeoff between two opposite trends:  
* the set \(\mathcal{N}(N)\) contains less nodes in a data gathering application than in a general one. Hence, coloring is less constrained in a data gathering application, leading to a smaller number of colors.
* the choice of the color is more restricted with a data gathering application, where any node is obliged to take a color higher than the color of its parent. This tends to increase the number of colors in a data gathering application.  
Figure 15 depicts the benefit in color percentage for all the configurations tested. The benefit is higher than 8% for all configurations except for 81 nodes with densities 8 and 10 and for 100 nodes with a density of 8. This benefit is perceived by the user in terms of smaller end-to-end delays. The benefits brought by data gathering coloring are twice. By restricting the set \(\mathcal{N}(N)\), SERENA uses a smaller number of colors, leading to a smaller active period. By obliging any node to take a color higher than the color of its parent, SERENA allows TDMA/CA to schedule medium access from any node before its parent. Hence, this node can aggregate the data received from all its children before forwarding it to its parent. Thus, in a single cycle, data collected from the sensors will be aggregated and forwarded to the sink. If this coloring rule was not introduced, the time needed by a data originated from a sensor to reach the sink could be equal to a number of cycles equal to the distance in hop number of this sensor to the sink. That would be unacceptable.  
Notice that using a data gathering coloring for a general application would lead to color conflicts, because 3-hop coloring is needed and data gathering coloring is not 3-hop coloring. Hence, tree coloring can only be used when unicast communications are done according to a tree.  
Figure 15: Benefits brought by tree coloring.
## 1 Introduction
By using the characteristics of the jet colormap, we finely segment each CSI amplitude of each subcarrier at a measured moment into three-dimensional RGB (Red-Green-Blue) channel data through the nonlinear transformation of the jet colormap. During the segmentation of a scalar CSI amplitude value of a subcarrier into three distinct data points, we amplify the subtle differences in the CSI data belonging to different classes so that a deep learning model can better distinguish them. In other words, by separating the CSI features among classes more widely in the input layer of a deep learning model, we enhance the classification accuracy of the deep learning model used for estimating the number of people in a monitored region. To verify our approach, we carry out people-counting experiments across three different real-life scenarios and compare the results obtained by our method to those when other conventional preprocessing methods are applied. The first scenario identifies the number of people inside a typical seminar room from outside the room while people are seated or moving around in the monitored room. In the second scenario, we identify the number of people standing in a row in a classroom. In this scenario, a deep learning model is challenged to correctly estimate the number of people when people are overlapped and obscured. The third scenario detects the presence of a person in a T-shaped corridor by changing the position of a person in the part of the corridor that is not visible from the measurement point. In light of the research findings in [15; 21], where various preprocessing methods are classified into a few categories, we choose four representative preprocessing methods for comparison, each of which belongs to different categories. The first method which uses the median feature belongs to time statistics category. Among the methods in the category using the frequency feature, we use the FFT (Fast Fourier Transform) method as the second alternative method. The third method uses the PCA (Principle Component Analysis) and belongs to the dimensionality reduction category. The fourth method, which utilizes the characteristics of CNN, considers the measured CSI as a grayscale image. The experimental results show that, compared to existing preprocessing techniques, the proposed method improves the people-counting accuracy of a deep learning model in all experimental environments even when we use low-cost commercial off-the-shelf Wi-Fi transceivers having only one receive antenna and one transmit antenna. The rest of the paper is organized as follows. In Section 2, we present the related works. We explain the experiment scenarios and the datasets measured in each scenario in Section 3. In Section 4, we detail our CSI amplitude coloring method and discuss the experimental results in Section 5. We conclude the paper with future research directions in Section 6.