esa-sceva/satcom-chunk-collection huggingface dataset:
# DD-FoG: Intelligent Distributed Dynamic FoG Computing Framework
## 4 Proposed Algorithms of Monitoring and Control
### Service Time Providing through Device Selection (SWARM-Based Algorithm)
At the same time, some of the parameters are evaluated at the level of comparisons with limit values, for example, the amount of allocated logical RAM required for the microservice to work. For optimization, parameters were defined that describe fog-node in terms of ensuring quality of service. Globally, the task is as follows: it is necessary to maintain the service time by choosing of the fog-node to which the microservice needs to be migrated. Thus, the minimized fitness function looks like this:  
\[T=\sum_{i=0}^{n}w_{i}TimeSlot_{i} \tag{5}\]  
Where \(T\) is a total parameter, \(W_{i}\) is the weight of the 1-st parameter, \(TimeSlot_{i}\) is the parameter value, \(n\) is the total number of parameters. In this article will used the following two parameters: \(TimeSlot_{1}\) (network delay), \(TimeSlot_{2}\) (microservice request processing time). For each of the parameters, weight (\(W\)) is 0.5. Time parameters are measured in milliseconds (ms). In general, the fitness function is:  
\[T=0.5 TimeSlot_{1}+0.5 TimeSlot_{2} \tag{6}\]  
The first parameter is determined via a time tracker in the framework. The second parameter is sent by the device--the processing time of the task. **5. Framework Modeling**  
Regarding the considered framework algorithm (Figure 7) in the previous paragraph, this paper considers two algorithms: determining the centers of user congestion (user devices) and searching for more free fog-nodes to which the necessary microservices will be migrated. For a practical study of the proposed framework and the two algorithms under consideration, the following use case was developed, based on the city model. This model includes: two high-rise office buildings, a fast food cafe on the first floors of the buildings, an intersection.
## 5 Optimization Analysis of Temporary Dismantling Based on Determined Parameters
### Surface Subsidence Analysis  
To better analyze the suitable distance of the removal length of the center diaphragm wall in the station, the optimization was carried out based on the length range provided by the design institute. Four lengths were selected to analyze the removal length of the center diaphragm wall, so as to obtain a more appropriate removal length of the center diaphragm wall. The ground settlement under four schemes, namely 6 m, 12 m, 18 m, and 24 m, was compared, respectively, to determine the optimal removal length of the center diaphragm wall. The surface settlement curve is shown in Figure 10.  
It can be seen from the surface settlement curve that, during the removal of the center diaphragm, the surface was symmetrically distributed to both sides with the center diaphragm as the center, and the surface settlement was distributed in a \"V\" shape. All the maximum surface settlement values were on the tunnel axis. When the removal length of the center diaphragm of the station was 6 m, the maximum surface settlement value was 4.9 mm, which was 6.65 mm, 9.75 mm and 14.31 mm when the removal length of the station center diaphragm was 12 m, 18 m and 24 m, respectively. It could be found that, with the increase in the removal length of the center diaphragm, the maximum surface settlement gradually increased. When the center diaphragm was removed for 6 m, the minimum surface settlement occurred, and when it was removed for 24 m, the maximum surface settlement occurred. This is because the stress conversion of the main pilot tunnel of the station after excavation is relatively stable when the removal length of the center diaphragm is small, while when the removal length of the center diaphragm is too long, the stress conversion of the main pilot tunnel of the station after excavation is severe, which may lead to the instability of the structure, thus causing a greater settlement of the ground surface. It can be seen from the surface settlement curve that the removal length of the center diaphragm had a great impact on the surface settlement, which is not enough to be judged only by the surface settlement, so a further analysis is needed.
## 4 Experimental Results
### Angular Velocity Estimation  
We used our sample videos recorded in DB3, to evaluate the average accuracy of the angular velocity estimation for the proposed system. Our algorithm is loaded by all these 3600 images/video but for better visualization, obtained results for just 200 consecutive images are plotted in Figure 14. The achieved results for different models with different precision (0.99, 0.93, 0.78, and 0.60) are compared in this figure. In addition, in Figure 14b, the DenseNet implementation result with _accuracy_ = 99% is given. As we can see in this figure, our proposed method is able to extract a mono tone and soft signal that precisely indicate the frequency of the wind turbine rotation. Table 5 makes a  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model Name** & **Database Size** & **Image Size** & **Epochs** & **val\_loss** & **val\_acc** \\ \hline DenseNet & 10,000 & (32, 32) & 10 & 0.1085 & 0.9912 \\ DenseNet & 1000 & (32, 32) & 10 & 0.7901 & 0.9421 \\ SqueezeNet & 1000 & (32, 32) & 10 & 0.6931 & 0.5000 \\ SqueezeNet & 1000 & (32,32) & 20 & 0.6931 & 0.5030 \\ SqueezeNet & 1000 & (32, 32) & 100 & 0.6931 & 0.5000 \\ SqueezeNet & 1000 & (64, 64) & 10 & 0.6931 & 0.4970 \\ SqueezeNet & 1000 & (480, 480) & 10 & 0.6931 & 0.5020 \\ ResNet50 & 1000 & (32, 32) & 10 & 0.2938 & 0.9268 \\ InceptionV3 & 1000 & (32, 32) & 10 & 0.1757 & 0.9573 \\ InceptionV3 & 1000 & (32, 32) & 100 & 0.2599 & 0.9360 \\ InceptionV3 & 1000 & (32, 32) & 50 & 0.1987 & 0.9543 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of four pertained classification approaches for Blade and Non-Blade binary classification problem.  
Figure 13: Accuracy and loss graphs for ResNet50 for Blade/Non-Blade classification. (_image size_ = 32 \(\times\) 32, _database size_ = 1000, _epoch_ = 10). (**a**) The loss (**b**) The accuracy.  
comparison between estimated wind turbine velocity (\(\hat{V}_{B}\)) and true one (\(V_{B}\)) (in RPM) for our proposed model and [6]. It clearly indicates our new algorithm can achieve about 99.05% accuracy.
## 3 Hotline-Based Reliable WSN Topology
### Inter-Gateway Communication  
The gateways are connected using high-speed Ethernet cables. Gateways are supposed to exchange certain information that would be required for routing and monitoring the state of the links. Our sensor network has been divided into clusters but overall network can be considered as one autonomous system (AS). We therefore require a protocol configured in gateways that can handle cluster to cluster (Intra-AS) communication. We propose to employ the widely-used Open Shortest Path First (OSPF) protocol for inter-gateway communication. We choose OSPF because of two main reasons: 1) In distance vector routing protocols, each router does not possess information about the complete network topology and consequently there is a slow convergence problem [29]; 2) OSPF works over IP and has a richer set of extensions and added features [29] as compared to other link state and distance vector routing protocols.  
Figure 5: Border nodes can communicate to each other directly.  
Each gateway has information saved about nodes of the complete network in form of a gateway network table. Every time a node is added or removed from a particular cluster, an update is sent to the default gateway. The default gateway accordingly updates the gateway network table. An update message is eventually sent to all the gateways of the network connected via the hotline. This completes one cycle of the update mechanism. This process is already supported in OSPF by the database synchronization mechanism [29]. The synchronization process begins as soon as the gateway attempts to bring up the adjacency. The database is described by each gateway by sending a series of Database Description packets to its neighbors. As gateways are connected in a bus network, via hotline, all the gateways will synchronize their databases.  
Gateways are also deployed with a monitoring mechanism. Specifically, gateways are aware of the health of neighboring gateways and the corresponding links. This monitoring mechanism helps to ensure the connectivity of the gateways and eventually ensuring the availability of the hotline. This mechanism is supported by the OSPF Hello protocol. The neighbor relationship is maintained with the help of the Hello Protocol. It is also responsible of ensuring the bidirectional communication between neighbors. Hello packets are sent periodically on all router interfaces [29].  
In summary, gateways are configured with dual functionality. They are deployed with two different protocols. One protocol (AODV) is used for intra-cluster or cluster-node communication, the other protocol (OSPF) is used for inter-gateway communication.
## 2 Sensor Calibration  
### Camera Parameter Calibration  
#### 2.1.1 Constant-Focus Optical Path in Line Laser Vision Sensors  
Traditional line laser vision sensors are limited by the insufficient depth of field of standard cameras during measurement. Once the overall structure is adjusted and fixed; they can collect measurement points that meet detection requirements only near the reference plane. This is because, without changing the lens (with a fixed focal length f), the detection points need to satisfy the following constraints:  
\[\frac{1}{f}=\frac{1}{a_{t}}+\frac{1}{b_{t}} \tag{1}\]  
where \(a_{t}\) and \(b_{t}\) are the object distance and image distance of the \(t\)-th detection point, respectively.  
Traditional laser vision sensors have a limited focusing range, as shown in Figure 1. Due to constraint condition (1), only the clear image point \(P_{0}^{\prime}\) corresponding to the point \(P_{0}\) on the reference plane can be projected onto the image plane. The clear image points \(P_{1}^{\prime}\) and \(P_{2}^{\prime}\) corresponding to of the measured points \(P_{1}\) and \(P_{2}\) fall outside the image plane. The farther the measured points are from the reference plane, the more blurred the imaging points become. This greatly limits the high-precision detection range of traditional laser vision sensors, especially in detection scenarios that require a large depth of field, such as the field of thick plate welding.  
To address the aforementioned issues, a novel and effective solution is to tilt the photosensitive element such that the image points \(P_{1}^{\prime}\) and \(P_{2}^{\prime}\) corresponding to the measurement points \(P_{1}\) and \(P_{2}\) are also focused on the image plane. When the tilt of the photosensitive element adheres to the Scheimpflug principle--which dictates that the laser light plane, lens plane, and photosensitive element (image plane) intersect at a single line--a constant-focus optical path is established [27]. Consequently, all visible measurement points on the light plane of the line laser vision sensor can be accurately focused, as illustrated in Figure 2. Geometrically, the conditions required for the line laser vision sensor to establish a constant-focus optical path are as follows:  
\[a_{0}tan\ \alpha=b_{0}tan\ \beta \tag{2}\]  
where \(a_{0}\) and \(b_{0}\) represent the object distance and image distance at the reference plane \(P_{0}\), respectively; \(\alpha\) is the angle between the laser screen and the principal optical axis of the lens; and \(\beta\) is the tilt angle of the photosensitive element.  
Figure 1: Imaging principle of traditional line laser vision sensors.  
Figure 2: Principle of imaging of laser vision sensor with constant-focus optical path.
## Performance Evaluation of Multi-Channel Wireless Mesh Networks with Embedded Systems  
**[PERSON], [PERSON] * and [PERSON]**  
Department of Ubiquitous IT, Division of Computer & Information Engineering, Dongseo University, Busan, 617-716, Korea; E-Mails: [EMAIL_ADDRESS] (J.H.L.); [EMAIL_ADDRESS] (W.K.T.)  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS], Tel.: +82-51-320-1760; Fax: +82-51-327-8955.  
_Received: 15 December 2011; in revised form: 2 January 2012 / Accepted: 2 January 2012 / Published: 5 January 2012_  
**Abstract:** Many commercial wireless mesh network (WMN) products are available in the marketplace with their own proprietary standards, but interoperability among the different vendors is not possible. Open source communities have their own WMN implementation in accordance with the IEEE 802.11s draft standard, Linux open80211s project and FreeBSD WMN implementation. While some studies have focused on the test bed of WMNs based on the open80211s project, none are based on the FreeBSD. In this paper, we built an embedded system using the FreeBSD WMN implementation that utilizes two channels and evaluated its performance. This implementation allows the legacy system to connect to the WMN independent of the type of platform and distributes the load between the two non-overlapping channels. One channel is used for the backhaul connection and the other one is used to connect to the stations to wireless mesh network. By using the power efficient 802.11 technology, this device can also be used as a gateway for the wireless sensor network (WSN).  
**Keywords:** FreeBSD; NanoBSD; multi-channel; wireless mesh network; embedded system; PCEngines alix; 802.11s; static routing; dynamic routing
# Automatic Detection of Multilevel Communities: Scalable, Selective and Resolution-Limit-Free +
## 2 Method
### On the Hierarchical Ravas-Barabasi (RB) Networks
The multiresolution modularity tunes the resolution through a parameter \(\gamma\):  
\[Q_{\gamma}=\sum_{\mathcal{G}}\left[\frac{k_{in}^{\mathcal{G}}}{2M}-\gamma \left(\frac{k^{\mathcal{G}}}{2M}\right)^{2}\right]; \tag{4}\]  
Here, \(M\) represents the total number of edges within the whole network; \(k_{in}^{\mathcal{G}}\) and \(k^{\mathcal{G}}\) represent the in-degree and total degree of community \(\mathcal{G}\), and the modularity is summed over all communities. In Supplementary Figure S5, we exhibit the plateaus detected by \(Q_{\gamma}\) for three RB networks. Only for the smallest RB125 network, \(Q_{\gamma}\) detects all three community levels. For larger networks such as RB625 and RB3125, \(Q_{\gamma}\) detects no more than two levels of communities for each of them. Therefore, our method outperforms previous multiresolution methods on deep hierarchical networks--as we will suggest in the Discussion, the scalability of our community fitness function plays an important role.
# Inductive Thermography as Non-Destructive Testing for Railway Rails
## 2 Inductive Thermography Measurements  
### Description of the Laboratory Setup  
The principle laboratory setup consists of an induction generator with an induction coil and an infrared camera (see Figure 1). Two setups for measuring railway rails were used: a static setup where equipment and specimen stayed in place and a scanning setup where the rail was moved at constant speed during measurement. A more detailed description of the two measurement setups is given in Section 6.3.  
In the static setup a generator with a power output up to 10 kW was used. A short heating pulse of 0.1-0.2 s was applied to the rail sample through a Helmholtz coil, as is shown in Figure 1. The scanning setup was carried out by using a 5 kW generator with a linear coil. Both generators worked in the excitation frequency range of 100-200 kHz.  
An infrared camera (IRCAM Veloc 1310 k SM) recorded the surface temperature of the specimen during and after the heating pulse. This cooled IR camera with an InSb detector has 1280 \(\times\) 1024 pixels, it is sensitive in the range of 1.5-5.1 \(\upmu\)m and its noise equivalent temperature difference (NETD) is less than 25 mK. In full window mode it can record 180 images/s, but by using the binning mode and merging four pixels to one, the recording frequency increases up to 600 images/s. The IR camera and the induction generator were controlled and synchronized by a PLC. The recorded IR sequence was evaluated on a PC by software based on MATLAB, developed by the Chair of Automation of the University of Leoben.
# Cloud-Based Single-Frequency Snapshot RTK Positioning
## 3 Experiment Setup and Results
### Results and Discussions
#### 3.3.5 Positioning Accuracy  
The output coordinate errors are plotted in Figures 11 and 12 to show the accuracy performance of SRTK when carrier phase ambiguities are fixed. These results are generated using 100 ms of integration time and 31.8 MHz signal bandwidth under 5 km baseline distance.  
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline  & **31.8 MHz** & **25.44 MHz** & **21.2 MHz** & **15.9 MHz** & **12.72 MHz** & **10.6 MHz** & **7.95 MHz** & **6.36 MHz** \\ \hline \multirow{2}{*}{**40 ms**} & 636 & 508.8 & 424 & 318 & 254.4 & 212 & 159 & 127.2 \\  & 87.14 & 92.95 & 87.14 & 89.21 & 93.36 & 88.38 & 82.16 & 78.84 \\ \hline \multirow{2}{*}{**60 ms**} & 954 & 763.2 & 636 & 477 & 381.6 & 318 & 238.5 & 190.8 \\  & 90.46 & 91.29 & 87.97 & 90.46 & 90.04 & 88.38 & 86.72 & 82.99 \\ \hline \multirow{2}{*}{**80 ms**} & 1272 & 1017.6 & 848 & 636 & 508.8 & 424 & 318 & 254.4 \\  & 94.19 & 94.19 & 92.95 & 94.19 & 90.87 & 90.46 & 90.87 & 87.55 \\ \hline \multirow{2}{*}{**100 ms**} & 1590 & 1272 & 1060 & 795 & 636 & 530 & 397.5 & 318 \\  & 96.27 & 95.85 & 95.85 & 96.68 & 95.02 & 95.02 & 94.19 & 91.25 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Snapshot data size (1 st value, in kB) and SRTK fix rate (2 nd value, percentage) for different integration times and bandwidths at 15 km baseline  
Figure 10: SRTK fix rates in relation to the snapshot sizes at 15 km baseline.  
Figure 11 shows a small common bias in the solutions, respectively, \(-0.11\) cm, 0.93 cm and 0.48 cm for East, North and Up directions. These biases might be a result of the inaccuracies in the ground truth, which was computed by averaging RTK-fixed solutions from a closed-loop receiver. The corresponding standard deviations are 0.29 cm, 0.41 cm and 0.59 cm. These results prove that centimeter level accuracy can be achieved with just a brief interval of signal collected by a GNSS front-end.  
Finally, the horizontal errors are also presented in Figure 12; as can be seen, the position points are generally located inside a 2 cm \(\times\) 2 cm region with a calculated Circular Error Probable (CEP) of 1.012 cm, as denoted by the red circle. The Root Mean Square (RMS) horizontal error is 1.066 cm and the 3D-RMS error is 1.309 cm. The 95-percentile horizontal (denoted by the green circle) and 3D errors were 1.619 cm and 1.921 cm, respectively.  
Figure 11: East (blue), North (orange) and Up (green) errors of SRTK fixed solutions under 5 km baseline.  
Figure 12: Horizontal positioning errors in East and North under a 5 km baseline.
###### Abstract  
As is known to us all, head loss affects the water transmission process, especially under the vibration condition. However, the detailed mechanism of the vibration effect on head loss was unclear, and most studies only focused on the pipeline property and friction itself. In this study, the vibration effect on the head loss mechanism was explored by wettability measurement and Materials Studio (MS) simulation. Iron casting, steel, polyvinyl chloride (PVC), and polyethylene (PE) were chosen as the representative pipeline materials. Different pipelines materials showed different effects on the water drops, and the static contact angles and dynamic contact angles were different. The molecular dynamic simulation results indicated that the water drops showed different interaction energy with pipelines' surfaces, which was the main reason for the head loss. The pipelines' roughness influences the wettability of pipelines, which cause the head loss. The roughness of different pipelines followed the rule: \(\text{Roughness}_{\text{iron\,casting}}>\text{Roughness}_{\text{steel}}>\text{Roughness}_{\text{PVC}}>\text{Roughness}_{\text{FE}}\). The vibration influences the surface roughness, and this fact influences the corresponding fluid flow property, which was widely studied in the previous study. Moreover, the MS results indicated that the vibration affected the solid wettability, which was in accordance with the experimental results, and the vibration altered the internal energy between water drops and pipeline surface. In the end, an economic evaluation was conducted, and the different pipelines' operating costs were compared.  
w +
Footnote †: journal:  
In a previous study, the different frequency effects on the vibration effect have been widely studied [23; 24; 25; 26; 27]. [PERSON] [17] et al. studied the surface wettability alteration by mechanical vibrations at low ultrasonic frequencies. The corresponding wettability was independent of the frequency. In addition, the corresponding relationship between the vibrating surface and the surface wettability was analysed.  
Therefore, the aims of this work were as follows: (i) to explore clean/sewage water drops' static contact angles and the dynamic contact angles of water drops onto iron casting, steel, PVC, and PE surfaces; (ii) to study the water drops' behaviour on iron casting, steel, PVC and PE surfaces from molecular perspective by MS simulation; (iii) to explore how the water flows onto the pipeline's surface; and (iv) to study the relationship between wettability and head loss.
## 2 Materials and Methods
### Statistical Analysis  
The statistical analysis of the data in this research was carried out using the IBM SPSS Statistics program for Windows, version 25.0 (IBM Corp.: Armonk, NY, USA), with a significance level of \(p<0.05\). Results for the quantitative variables (i.e., functional fitness components, anthropometry, age) are presented through measures of central tendency (mean and standard deviation); qualitative variables (i.e., sex; degree of overweight or obesity) are presented using percentages and frequencies. To verify the normality of the data, the Kolmogorov-Smirnov test was used. First, descriptive statistics (mean and its standard deviation) were calculated for each dependent variable examined. Second, the Chi square test was performed to check whether the groups (Experimental group/Control group) were equivalent with respect to the sex and degree of obesity or overweight of the participants, and an independent samples t test to check the equivalence between groups in age and anthropometry. After 6 months of intervention, a three-factor ANOVA was performed (time \(\times\) group \(\times\) degree of overweight or obesity) using time as a repeated measures factor, [i.e., Time (pre-test vs. post-test), Group (Control group vs. Experimental group) and Sex (man vs. woman)] to analyze the possible main effect of these factors on the functional physical fitness variables and their interaction using the Bonferroni statistic. The effect size was calculated in terms of eta squared (\(\eta^{2}\)).