esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Wetting Resistance of the Coating
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Membrane**} & **Coating** & **Mass Fraction in** & \multicolumn{2}{c}{**\(\theta\) (\({}^{\circ}\))**} \\ \cline{3-5}  & **Components** & **Coating Solution** & **Surface 1** & **Surface 2** \\  & & & **(Coated Side)** & **(Uncoated Side)** \\ \hline Uncoated & - & - & 27 \(\pm\) 6 \({}^{1}\) & 28 \(\pm\) 4 \({}^{1}\) \\
1 & PFD solution & 5 wt. \% & 117 \(\pm\) 1 & 90 \(\pm\) 5 \(\rightarrow\) 62 \(\pm\) 5 \({}^{2}\) \\
2 & PFD solution & 10 wt. \% & 118 \(\pm\) 1 & 96 \(\pm\) 1 \(\rightarrow\) 73 \(\pm\) 6 \({}^{2}\) \\
3 & Ak/T/D/BTFO2N & 5 wt. \% & 100 \(\pm\) 3 & 100 \(\pm\) 1 \(\rightarrow\) 85 \(\pm\) 5 \({}^{2}\) \\
4 & Ak/T/D/BTFO2N & 30 wt. \% & 110 \(\pm\) 1 & 83 \(\pm\) 2 \({}^{1}\) \\
5 & V/Mc/F13 & 5 wt. \% & 109 \(\pm\) 1 & 110 \(\pm\) 1 \\ \hline \hline \end{tabular} Legend: \({}^{1}\) Droplet wets the membrane within 2 min; \({}^{2}\) Contact angle after 0.5 s \(\rightarrow\) Contact angle after 2 min.  
\end{table} 
Table 5: Contact angle. To improve the coating homogeneity, coating system 5 (V/Mc/F13, 5 wt. %) was selected for spray coating application. The single component systems (1 and 2) are not sufficiently stable (see Section 3.4), whereas the Ak/T/D/BTFO2N system (3 and 4) requires a higher amount of silica components to achieve the same contact angle (Table 5). This spray coating process ensures an improved homogeneity compared to the bar coater system. The coating was applied in two ways: (a) only on surface 1, resulting in a membrane with a hydrophobic and a hydrophilic side and (b) on both sides of the membrane, producing a membrane with two hydrophobic sides (Table 6). Membrane 6, coated on surface 1 only shows a slightly lower liquid entry pressure compared to membrane 7, coated on both sides.
# An Analysis of Cloud Security Frameworks, Problems and Proposed Solutions
## 3 Cloud Security Frameworks
### Iso 27017:2015
#### 3.3.1 Security Controls for Cloud  
ISO 27017 specifies a set of security rules that organizations may use to safeguard their information assets in a cloud computing environment. These rules are intended to handle the specific risks and problems that cloud services present. ISO 27017 specifies the following important security controls in Table 7: \begin{table}
\begin{tabular}{c c} \hline \hline
**Process** & **Tools and Techniques** \\ \hline Understand the cloud deployment models and service models & Cloud Service Provider assessment tools: CAIQ, CCM \\ \hline Prioritize security and privacy requirements & Risk assessment tools: Nessus, Qualys and for framework: NIST Risk Management Framework, FAIR \\ \hline Protect data in transit and at rest & Encryption tools: SSL/TLS protocol \\ \hline Implement strong identity and access management controls & Identity and access management (IAM) solutions (e.g., Okta, Azure Active Directory, AWS IAM) \\ \hline Security controls implementation & Network Security Tools: IDPS, Wireshark Secure configuration management tools: Chef, Ansible \\ \hline Continuous Monitoring and Logging & Security information and event management (SIEM) systems (e.g., Splunk, Elastic SIEM) Log management and analysis tools (e.g., LogRhythm, Graylog) Threat intelligence platforms (e.g., ThreatConnect, Recorded Future) \\ \hline Incident Response and Recovery & Incident Response tools: IBM Resilient, Service Now, EnCase, Volatility \\ \hline Compliance and Governance & RSA Archer, MetricStream \\ \hline Personnel training and education: & Security awareness training: KnowBe4, SANS Simulation tools: Cofense, Sophis \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Tools for NIST.
# Comparing GRACE-FO KBR and LRI Ranging Data with Focus on Carrier Frequency Variations
## 4 KBR Dual-One Way Ranging (DOWR)  
In the KBR, the microwave radiation with carrier frequencies \(\
u^{\text{K/Ka}}_{A/B}\) on satellite A and B is produced by upconversion of the USO base frequencies \(f_{\text{A/B/USO}}\) using fixed integer multipliers (see Table 1). Two phase measurements \(\varphi^{\text{K/Ka}}\) are performed per satellite, one at the K- and another at the Ka-band. These are combined into  
\[\varphi^{\text{K/Ka}}_{\text{DOWR}}(t) :=\varphi^{\text{K/Ka}}_{\text{B}}(t)-\varphi^{\text{K/Ka}}_{ \text{A}}(t) \tag{43}\] \[=\varphi^{\text{K/Ka}}_{\text{B}}(t)-\varphi^{\text{K/Ka}}_{ \text{B}}(t-\Delta t^{\text{K/Ka}}_{\text{BA}})+\Phi^{\text{K/Ka}}_{\text{A }}(t)-\Phi^{\text{[PERSON]/Ka}}_{\text{A}}(t-\Delta t^{\text{[PERSON]/Ka}}_{\text{AB}})\] (44) \[\approx\
u^{\text{K/Ka}}_{\text{B}}(t)\cdot\Delta t^{\text{K/Ka}} _{\text{BA}}(t)+q^{\text{K/Ka}}_{\text{B}}+\
u^{\text{K/Ka}}_{\text{A}}(t) \cdot\Delta t^{\text{K/Ka}}_{\text{AB}}(t)+q^{\text{K/Ka}}_{\text{A}}, \tag{45}\]  
where \(\Delta t^{\text{K/Ka}}_{\text{AB}}(t)\) describes the light propagation time from satellite A to B at the respective K- or Ka frequency band at a reception time \(t\). The propagation time can be expressed as  
\[\Delta t^{\text{K/Ka}}_{\text{AB}}(t)=\Delta t^{\text{K/Ka}}_{\text{media,AB} }(t)+\Delta t_{\text{AB}}(t)=\Delta t^{\text{K/Ka}}_{\text{media,AB}}(t)+ \Delta t_{\text{LTC,AB}}(t)+\Delta t_{\text{inst}}(t), \tag{46}\]  
where the first term mainly describes the effect of the ionosphere on the propagation time, which is proportional to the squared frequency (\(\Delta t^{\text{K/Ka}}_{\text{media,AB}}\propto 1/\
u^{2}_{\text{A}}\)). The second term on the right-hand side is the light-time correction [45]  
\[\Delta t_{\text{LTC,AB}}(t):=\Delta t_{\text{AB}}(t)-\Delta t_{\text{inst}}(t) \tag{47}\]  
and the third term is the desired instantaneous range rescaled to light propagation time  
\[\Delta t_{\text{inst}}(t):=\left|\vec{r}_{\text{A}}(t)-\vec{r}_{\text{B}}(t) \right|/c_{0}. \tag{48}\]  
One can easily show with the previous definitions that the linear combination [11]  
\[\rho_{\text{DOWR}}(t):=c_{0}\cdot a^{\text{K}}\cdot\frac{\varphi^{\text{K}}_{ \text{DOWR}}(t)}{\
u^{\text{K}}_{\text{A}}+\
u^{\text{K}}_{\text{B}}}+c_{0} \cdot a^{\text{Ka}}\cdot\frac{\varphi^{\text{Ka}}_{\text{DAWR}}(t)}{\
u^{ \text{Ka}}_{\text{A}}+\
u^{\text{Ka}}_{\text{B}}} \tag{49}\]  
with \(a^{\text{K}}=-9/7\) and \(a^{\text{Ka}}=16/7\) is a so-called ionosphere-free combination where the \(\Delta t_{\text{media}}\) terms vanish. This equation is usually employed with constant frequencies \(\
u^{\text{K/Ka}}_{\text{A/B}}\) in the denominator (cf. Section 3.1) and on daily segments in the regular KBR processing. It is reasonable to assume that the four phase measurements \(\varphi^{\text{K/Ka}}_{\text{A/B}}(t)\) are debiased such that the phase is zero at the initial epoch \(t=0\) of the time-series in order to remove the arbitrary constant.
## 3 Semantic Service Description and Context-Based Orchestration
### Service Registration and Semantic Service Repository  
The efficient discovery of services provided by field devices depicts the basis for a context-based orchestration for control of manufacturing processes. To this end, mechanisms for the registration and advertising of services on the network level as well as for the capability-based search and discovery of services are needed. For the automatic registration of devices and their services, we make use of the discovery mechanism of DPWS (Device Profile for Web Services) [37], which specifies a reduced web service stack (e.g., WS-Discovery, WS-Eventing) designed for the implementation of web services on resource-constrained devices such as embedded systems. Our approach is based on the implementation of DPWS services on microcontrollers, which are connected to the respective field devices. In addition to the web service implementation and its syntactic interface definition, the semantic description of the service (as discussed in Section 3.3) is stored on these microcontrollers as an OWL file. As soon as a field device is available, it broadcasts a hello message on the network, which is recognized by a java program (Service Monitor) running on a server (step **a** in Figure 5). This program can also send an initial probe message to find all services currently available. The Service Monitor retrieves the semantic description of the service and passes it to the Repository Manager (step **b** in Figure 5), which extracts all information about the service and stores it in the Semantic Service Repository (step **c** in Figure 5). We implemented the Semantic Service Repository based on the OWLIM triple store, which can be used to store and retrieve mass data in an efficient way. In this repository, information about a service is represented as RDF triples. The Repository Manager not only extracts the necessary information from the device ontologies and OWL-S descriptions of a service, but also provides high-level methods for adding and removing services to or from the Semantic Service Repository based on SPARQL queries.
# Semi-Automatic Method of Extracting Road Networks from High-Resolution Remote-Sensing Images
## 2 Materials and Methods
### Methodology  
In remote-sensing images, roads have obvious shape characteristics, including high aspect ratio, small width changes, small curvatures, and T'-, 'Y'-, and '+'-shaped intersections, etc. The radiation characteristics of the roads are also obvious, such as consistent gray values, obvious edges, and uniform textures, etc. As shown in Figure 2, based on the characteristics of the road, the road network extraction method is composed of two parts: interactive single-road extraction and road network generation.  
Single-road extraction is performed in four steps. First, a single growing point on the road is selected manually to acquire the road width automatically. Several seed points are further selected and a working area of the algorithm is generated at the same time according to the road width. Second, the skeleton of the road is obtained by region growth, morphological closing operations, and image thinning. Third, a tracking algorithm is used to vectorize the skeleton. Then, the optimized road segments are obtained through simplification and deletion of overly short segments. Finally, the endpoint modifications and road connections are processed to optimize the road segments further, and the broken road segments are connected into a complete road vector.  
After obtaining a series of road vectors one by one, the road network generation algorithm is performed. First, complete vectorized centerlines are generated by intersection connection. After that, the road network is formed through the buffer algorithm.
# Feature-Model-Based In-Process Measurement of Machining Precision Using Computer Vision
## 2 Measurement Algorithm
### Image Preprocessing
#### 2.1.2 Weighted Median Filter  
Compared to traditional median filter methods, weighted median filtering considers the weights of the pixels when calculating the image's median filter, resulting in a smoother outcome. Weighted median filters have certain advantages in image processing [28]. They can retain the edge information of the parts and have a good removal effect on different types of noise. The principle can be summarized as follows:  
First, for a given pixel in the image, a \(3\times 3\) window is used to determine its neighborhood size. The size of the neighborhood directly affects the smoothing effect, and larger neighborhoods can achieve better smoothing results. However, if the neighborhood is too large, this can lead to blurred loss of edge information, making the output image blurred. Secondly, pixels within the neighborhood are sorted by brightness values to obtain an ordered sequence. Then, Formula (3) is used to calculate the weight of each pixel, utilizing the distance from the center pixel as the weight. The weighted median function can be expressed as:  
\[\text{Med}_{w}(I_{\text{in}}(x,y))=median_{i,j}\{I_{\text{in}}(x+i,y+j)\cdot w (i,j)\} \tag{3}\]  
where \(I_{\text{in}}(x,y)\) is the pixel value of the input image at position \((x,y)\), \(I_{\text{out}}(x,y)\) is the pixel value of the output image at position \((x,y)\), and \(\text{Med}_{w}\) is the weighted median function, which calculates the weighted median of the pixel values in the input neighborhood.  
Finally, the weighted median is calculated based on pixel weights, using the median as the new value for that pixel. The weighted median is used as the output pixel value. The pixel formula processed can be expressed as:  
\[I_{\text{out}}(x,y)=\text{Med}_{w}(I_{\text{in}}(x,y)) \tag{4}\]  
where \(i\) and \(j\) are the coordinate offsets of pixels in the neighborhood, and \(w(i,j)\) is the weight of the corresponding pixel.  
Figure 2: Bilinear interpolation calculation: (a) original pixel image; (b) bilinear interpolation of the pixel image.
# Smart Healthcare Network Management: A Comprehensive Review
## 5 Reference Architecture for Smart Healthcare System Network Management
### Resource Management Layer
#### 5.4.5 Security Management  
It enables the management of security, which is required for all management functional areas and TMN transactions.
# A Dynamic Service Placement Based on Deep Reinforcement Learning in Mobile Edge Computing
## 1 Introduction
### Motivation and Challenges  
An illustration of the dynamic service placement problem is shown in Figure 1 to represent the unique challenges under this problem. (i). Since there are no restrictions on the locations of services, where these services are placed so that they can reach better utilization on the physical resources of edge servers include the aspects on the computing, communication, and storage in MEC is nontrivial. For example, suppose that the computing capacity of edge server \(m_{2}\) in area 2 is much higher than others with lower storage. When the movement trajectories of users overlap with the areas nearby \(m_{2}\), the services thatcorrespond to these users are expected to be placed at a server that is close to them and has better performance. However, it is obvious that the available storage capacity of \(m_{2}\) cannot satisfy the requirements of all users. Therefore, how the system can deal with the services that attempt to migrate over requesting high computing capacity with limited storage resources is important. (ii). The services serve users one-to-one, and the activities of users are erratic. It is nontrivial to find an efficient strategy that adapts the erratic movements by considering minimizing the total delay under the cost constraint. As shown in Figure 1, we suppose that users in areas 1, 3, and 4 are on the move at time slot \(t\). One of the simple solutions to maintain performance is to migrate services in order to follow users, which produces lower latency. However, frequent service migration will bring additional traffic load in the backhaul network and higher operational costs. Therefore, it is challenging to deal with the services that can realize dynamic adaptation with low latency under limited cost.
## 3 Numerical Experiments and Discussions
### Performance Index
#### 3.3.1 Availability  
To evaluate the availability improvement provided by the vision system, the HPL/VPL is computed during the approach and landing phase. The availability of these two methods is calculated and compared  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Performance Requirement** & **APV-I** & **LPV-200** & **APV-II** \\ \hline HAL & 40 m & 40 m & 40 m \\ VAL & 50 m & 35 m & 20 m \\ HA (95\%) & 16 m & 16 m & 16 m \\ VA (95\%) & 20 m & 4 m & 8 m \\ TTA & 10 s & 6.2 s & 6 s \\ \hline \hline \end{tabular}
\end{table}
Table 1: The performance requirements for aviation.  
Figure 7: The standard deviation of VP error with different image resolution and DE. (**a**) The standard deviation of VP with different image resolutions with 1 pixel DE; (**b**) The standard deviation of VP with different DE on a 300 dpi image.  
with the service levels in Table 1. If the HPL exceeds the HAL or the VPL exceeds the VAL, the integrity is said to be unavailable for operation. The HPL/VPL curves of one sortie are shown in Figure 8. The results show that with the aid of vision system, the HPL decreases from 41 m to 12-26 m, and the VPL decreases from 56 m to 22-40 m, and higher image resolutions can generate less protection level of the integrated system. The reason is that the proposed VA-RAIM provides navigation measurements which are integrated with the GPS measurements to improve availability. Furthermore, since the line-of-sight of vision system is below the aircraft, the vision measurements can improve the geometrical configuration and decrease the protection level. During approach and landing operations, with an accurate feature detection result on a high resolution image, the VA-RAIM can improve the availability performance for APV-I and LPV-200 applications. However, as the performance requirement is very stringent for APV-II, the method needs to be further investigated in the future.
## 3 Design of the Stress Wave-Based Communication System
### Software Design
#### 3.3.1 Main Program  
The main program handles the following tasks: (1) the initialization of system and peripherals; (2) data reception; (3) stress wave emission; (4) signal return. The flow chart of the main program is shown in Figure 9.  
The initialization is the first step to implementing various functions in the hardware circuit and maintaining the stable operation of the system. After entering the main program, the watchdog is shut down and the system clock is initialized, then the external crystal oscillator is selected as the system clock. As for the initialization of the ports, the I/O port pins are configured according to requirements. Their pins are configured with the I/O mode and assigned to the digital peripherals (serial ports, timers, etc.) by the crossbar configuration register in order of priority. The initialization of the serial ports is mainly about the selection of the working mode, the use of baud rate generator, the setting of baud rate and the interruption itself.  
The port pin P0.7 is used to implement the acoustic signal emission in the program. The device emits two types of acoustic signals, single pulse and multi-pulse signals respectively. The single pulse refers to a pulse signal generated by a high instantaneous voltage within 25 \(\upmu\)s. The multi-pulse signal refers to a series of pulses that are encoded with the specific coding format, whose shape and amplitude are the same as those of the single pulse. To ensure accuracy, the general-purpose timer, T0 with timing of 25 \(\upmu\)s, is selected to cooperate in the generation of pulse signals. The requirements of sending the single pulse, bit '0' and bit '1' are distinguished by the query mode.
## 3 Theoretical Analysis and Simulation Model of Temperature Error  
This paper proposes an error correction method for the temperature measurement process in near space through theoretical analysis of Joule heat, radiation heat, and aerodynamic heat generated by the temperature sensor during operation. The method was further validated through simulations using COMSOL and ANSYS software. The simulation results provided an error model, optimizing the sensor structure as much as possible. Error compensation of the actual detection data can lead to higher measurement accuracy. We can use this error correction method to perform in situ temperature measurements in near space.
# Review of Substrate Integrated Waveguide Circuits for Beam-Forming Networks Working in X-Band
## 6 Conclusions  
A review of some SIW components designed for DVB-S applications has been presented. The main goals of the proposed solutions have been the cost of the devices, which should be as low as possible, and the size of the devices, which should be as small as possible. The first goal has been satisfied by using a Taconic RF-35 substrate.  
Despite those constraints, the performances obtained by the synthesized devices are very good, and some improvements to the proposed solutions have been also illustrated.  
The proposed devices can be easily modified to work at other frequencies by simply shrinking or enlarging them. As very general-purpose devices, they also can be used for other applications.  
Conceptualization, investigation, and methodology, G.V., D.M., A.M., M.F., F.P.; writing--original draft preparation, G.V.; writing--review and editing, D.M., A.M., M.F. and F.P.; supervision, F.P.; project administration, F.P.; funding acquisition, A.M., M.F, F.P.  
This research was funded by the Italian Ministry of University (MIUR), grant number PON01_01224 on development of Substrate Integrated Waveguide circuits for ITC applications.  
The prototypes shown in this review were built by Somacis S.p.A., Via Jesina, 60022 Castelfidardo (AN), Italy. Author would like to thank Dr. [PERSON] for the realization of the empty substrate integrated waveguide prototypes.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
## 4 Experimental Study
### Setting Algorithm Parameters  
As it is generally known, the performance of metaheuristic algorithms is greatly affected by the values of the algorithm parameters. To investigate the statistically significance of the parameter values of the algorithm, we performed an analysis of variance (ANOVA) on the obtained experimental results. In this study, 4 key parameters were considered, and each parameter contained 3 alternative value levels. Specifically, the population size included 3 levels, namely 100, 150, and 200; the inertia weight \(w\) included 3 levels, namely 0.7, 0.8, and 0.9; the constant \(c\) includes three levels, namely 0.3, 0.5, and 0.7; the function \(g\) in Equation (17) can choose three crossover operation modes, namely SPX, PMX, and OX [32]. The result of the ANOVA is shown in Table 4.  
In Table 4, \(SS\) means the sum of squares for the factor, which reflects the degree of variation between sample means. The abbreviation df means the degree of freedom of the factor. Here, df = k - 1, and k is the number of value levels of the factor. \(MS\) means the mean square between groups for the factor, and MS = SS/df. The label \"F\" is the test statistic in the ANOVA, which is equal to the ratio of the mean square between groups and the mean square within groups. The greater the value of F, the greater the difference between the groups, or in other words, the more significant the influence of this factor on the results.  
According to Table 4, since the \(p\)-value (sig.) is less than the significance level (0.05), the above four parameters all have significant impact on the experimental results. Taking the relative percent difference (RPD) of the makespan as the metric, Figure 5 shows the corresponding means plot and the least significant difference (LSD) intervals of inertia weight \(w\) at the 95% confidence level for the RPD metric. When the inertia weight \(w\) is set to 0.8, the value of the RPD metric is the smallest, and thus its value is set to 0.8. Similarly, the value of population size is set to 200, while the value of constant \(c\) is set to 0.5, and the function \(g\) is set to be the SPX mode.  
\begin{table}
\begin{tabular}{c c c c c} \hline  & **Sum of Squares (SS)** & **df** & **Mean Square (MS)** & **F** & **Sig.** \\ \hline A. population size & 0.049 & 2 & 0.025 & 4.235 & 0.019 \\ B. inertia weight \(w\) & 0.059 & 2 & 0.030 & 5.111 & 0.009 \\ C. constant \(c\) & 0.053 & 2 & 0.027 & 4.577 & 0.014 \\ D. function \(g\) & 0.041 & 2 & 0.020 & 3.510 & 0.036 \\ \hline \end{tabular}
\end{table}
Table 4: ANOVA results for the obtained results of RPD.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Instance** & **No. of Operations** & **No. of Machines** & **No. of Assembly Levels** \\ \hline
1 & 18 & 4 & 3 \\
2 & 33 & 4 & 3 \\
3 & 86 & 5 & 4 \\
4 & 92 & 5 & 5 \\
5 & 125 & 6 & 5 \\
6 & 148 & 8 & 5 \\
7 & 164 & 8 & 5 \\
8 & 170 & 9 & 5 \\
9 & 176 & 9 & 6 \\
10 & 185 & 10 & 6 \\ \hline \end{tabular}
\end{table}
Table 3: Characteristics of test instances.