esa-sceva/satcom-chunk-collection huggingface dataset:
# On Construction of a Campus Outdoor Air and Water Quality Monitoring System Using LoRaWAN
## 3 System Design and Implementation
### System Environment  
The system environment includes the hardware and software requirements. About the hardware, the list in Table 1 shows the components needed for the air quality monitoring station.  
For water monitoring, we used three sensors for logging data. In addition to PM\({}_{2.5}\), we apply a Multi-parameter Water Quality Sensor for water quality sampling, which including dissolved oxygen, pH, and temperature sensor:s  
1. Dissolved Oxygen SensorsThere are two methods for measuring dissolved oxygen: the optical method and the amperometry method. The optical method is the more commonly used method [30].
2. pH Sensors The pH value is determined via a pH-sensitive electrode [34]. The activity of hydrogen ions in the solution generates a tiny current, which can be utilized to determine the pH level in the water.
3. Temperature Sensors Temperature sensors perform to a certain extent depending on the voltage across the diode. The resistance of the diode changes in a precise proportion to the change in temperature of the device. When the temperature drops, the resistance decreases, and vice versa for the same reason.  
The list showed in Table 2 are the components needed on the end of the data displaying station, which included a Citrix IBM AMD Opteron (tm) Processor 6172 \(\times\) 24 cores, a RAID10/300 GB/SAS * 4 HDD, and 40 GB RAM. In terms of the software, the operating system used Ubuntu 14.04 LTS 64 bit, ELK (Elasticsearch, Logstash, and Kibana), Grafana, Java, and Open Street Map to build up the system.  
We refer to [24] to set up six monitoring stations on campus; the locations are shown in Table 3.  
\begin{table}
\begin{tabular}{c c c} \hline
**ID** & **Name** & **Quantity** \\ \hline
1 & LoRa Node & 6 \\ \hline
2 & Arduino UNO & 6 \\ \hline
3 & PMS5003T & 5 \\ \hline
4 & Charge and discharge control board & 5 \\ \hline
5 & Solar panels & 5 \\ \hline
6 & Gateway & 1 \\ \hline
7 & GE T6603-5 CO\({}_{2}\) & 1 \\ \hline \end{tabular}
\end{table}
Table 1: Monitoring Station Equipment.  
\begin{table}
\begin{tabular}{c c c c} \hline
**ID** & **CPU** & **RAM** & **HDD** & **NIC** \\ \hline \multirow{3}{*}{1} & AMD Opteron & & \\  & Processor [EMAIL_ADDRESS] & 40 GB DDR3 & 1 TB & 4 GB Ethernet \\ \cline{1-1}  & GHz x24 & & \\ \hline \end{tabular}
\end{table}
Table 2: System Service Equipment.  
\begin{table}
\begin{tabular}{c c c} \hline
**ID** & **Location** & **Description** \\ \hline A & Technology building & Monitor road air quality \\ \hline B & Library & Monitor the library room \\  & learning room air quality \\ \hline C & Language Hall & Monitor air quality on campus \\ \hline D & Restaurant & Monitor the air quality of the sports area on campus \\ \hline E & School gate & Monitor road air quality \\ \hline F & Pasture & Monitor the air quality of industrial areas \\ \hline \end{tabular}
\end{table}
Table 3: The locations of monitoring stations.
# A Dynamic Edge Server Placement Scheme Using the Improved Snake Optimization Algorithm
## 4 Dynamic Edge-Server Placement
### Minimum Movement Distance Based on Linear Programming Problem  
When the positions of multiple dynamic servers are changed during the cost-process solution, the minimum movement distance from the original to the new position must be calculated for all moving edge servers. As shown in Figure 7, which presents the original and new locations of three moving edge servers, each edge server can choose one of the three new locations as its new location; thus, there is a variety of movement possibilities, as shown by the line segments in the figure. At this point, a movement scheme that minimizes the total distance moved by the edge servers must be found. This problem can be regarded as a linear programming problem and can be solved using the interior point method, the core concept of which is that the optimal solution is approached gradually by searching inside the feasible solution. Compared with the traditional step-by-step approach to boundary approximation, the interior point method avoids searching the entire feasible solution space by iterating over the points within the feasible solution; this approach effectively reduces the number of iterations and improves the solving efficiency. As a result, the interior point method is widely used to solve linear programming problems.  
Figure 7: Schematic diagram of minimum travel distance.  
The interior point method can be used to calculate the minimum movement distance between two sets of points. In each iteration, the interior point method gradually approaches the optimal solution by calculating the gradient and Hessian matrix, solving for the Newton step and decrement and implementing line searching and solution vector updating. Finally, the minimum cost of placing edge servers at the current moment is obtained by calculating the minimum movement distance and combining the dynamic edge-server addition and deletion operations. The solution steps are listed in Algorithm 3.  
**Inputs:** Original and new position matrices  
**Output:** Minimum cost  
```
1:while\(i<\) max_iter do
2: Calculate the gradient and Hessian matrix
3:if\(\lambda/2<\) tol then
4: break
5:endif
6:while\(x+t\cdot dx\) is not valid do
7:\(t\gets t\cdot\mu\)
8:endwhile
9:\(x\gets x+t\cdot dx\)
10:endwhile
```  
**Algorithm 3** Minimum Placement-Cost Algorithm Based on Interior Point Methods
# Proximity-Based Adaptive Indoor Positioning Method Using Received Signal Strength Indicator
###### Abstract  
In this paper, we propose a proximity-based adaptive positioning algorithm to address the challenge of positioning errors in indoor localization based on RSSI (received signal strength indicator). When positioning by trilateration, if a receiver is close to one AP, the signals of other APs become rapidly unstable, so positioning accuracy is reduced. Therefore, this paper proposes an algorithm to identify the proximity state with AP and adaptively determine the positioning technique based on this state. The proposed algorithm consists of three steps: RSSI error correction, adaptive location estimation, and post-processing. The RSSI error correction step corrects unstable RSSI. The adaptive location estimation step utilizes a modified proximity technique when identified as close to an AP, employing trilateration otherwise. Finally, in the post-processing step, an efficient filtering algorithm is applied. For the static state experiment, the accuracy of the proposed algorithm is found to be improved by about 28% compared to the method measured using only the trilateration technique applying the RSSI error correction step and post-processing step. The proposed algorithm improved the positioning accuracy of the entire area by improving accuracy in regions with weak signals without additional devices.  
indoor positioning system; location estimation; BLE; RSSI; trilateration; Kalman filter +
Footnote †: journal: Medical Imaging  
1
Footnote 1: email: {joon, j-h.; [PERSON], H-j-; [PERSON], S-k. Proximity-Based Adaptive Indoor Positioning Method Using Received Signal Strength Indicator.
# A Novel Monocular Vision Technique for the Detection of Electric Transmission Tower Tilting Trend
## 2 Contour Extraction of Transmission Tower
### Edge Detection of the Modified Canny Algorithm  
The first step in rod-tower tilt monitoring is detecting and extracting rod towers from a complex background. Image segmentation is a technique and process of dividing an image into several specific and unique regions that closely mimic human perception and extract the target of interest. Choosing the appropriate color space is an important problem in color image segmentation. RGB (Red, Green, Blue) and HSV (Hue, Saturation, Value) are the two common color spaces, and the three indicators of HSV space are independent of each other, making it more intuitive for expressing light and shade, color, and bright color degree, and facilitating the contrast between colors. Therefore, this project is based on tower extraction based on HSV color space.
## 5 Case Study
Therefore, the accurate ultimate deviation of the angle of intersection can be obtained through pose decoupling of the gear axis. Similarly, the pose combinations of ultimate parallel misalignment between two gear axes are M\({}_{11}\)M\({}_{21}\) + M\({}_{33}\)M\({}_{43}\) and M\({}_{13}\)M\({}_{23}\) + M\({}_{31}\)M\({}_{41}\), and the pose combinations of ultimate angle deviation of stagger are M\({}_{12}\)M\({}_{24}\) + M\({}_{34}\)M\({}_{42}\) and M\({}_{14}\)M\({}_{22}\) + M\({}_{32}\)M\({}_{44}\), respectively. Accurate deviations on corresponding DOF can be gained in the same way as the angle of interaction. This decoupling model is also applicable to statistical analysis. It is already known that area coordinate values of TF\({}_{i}\) meet constraint relation as shown in Formula (9), based on which area coordinate values \(\lambda_{2\_1}\), \(\lambda_{3\_1}\), \(\lambda_{4\_1}\), \(\lambda_{5\_1}\); \(\lambda_{2\_2}\), \(\lambda_{3\_2}\), \(\lambda_{4\_2}\), \(\lambda_{5\_2}\); \(\lambda_{2\_3}\), \(\lambda_{3\_3}\), \(\lambda_{5\_3}\) in Formula (20) can be given through sampling method in statistics. Through multiple simulations in Formula (20), namely Monte Carlo simulation, the distribution diagram of four end points M\({}_{1}\), M\({}_{2}\), M\({}_{3}\) and M\({}_{4}\) of gear axis can be obtained in Table 5. \begin{table}
\begin{tabular}{c c c} \hline
**End Point** & **Variation Range of X Axis** & **Variation Range of Y Axis** & **Variation Range of Z Axis** \\ \hline Point M\({}_{1}\) & (14.8815, 15.1185) & (\(-\)0.1185, 0.1185) & (29.99971, 30.00023) \\ Point M\({}_{2}\) & (14.8084, 15.1916) & (\(-\)0.1916, 0.1916) & (45.9996, 46.0002) \\ Point M\({}_{3}\) & (\(-\)0.0685, 0.0685) & (\(-\)0.0685, 0.0685) & (29.99993, 30.00006) \\ Point M\({}_{4}\) & (\(-\)0.08515, 0.08515) & (\(-\)0.08515, 0.08515) & (45.99994, 46.00006) \\ \hline \end{tabular}
\end{table} 
Table 3: Variation ranges of four end points of the gear axis (Unit: mm). The error value of each TF in the corresponding dimension chain can be found for each point in in Table 5. The error value can be the input data in the analysis of product performance.
## 2 Experimental Program  
### Specim Details  
As illustrated in Figure 1, the specimen investigated in this study was part of the precast panel of the steel-concrete composite bridge at the wet joint (Figure 1a). The investigated specimen was composed of two precast bridge panels and one wet joint, as shown in Figure 1b. For the specimen with the conventional wet joint (labeled as S-CWJ), the conventional wet joint had a rectangular shape (Figure 1c), and the interface between the precast bridge panel and wet joint was a flat surface. The specimen with the concave square frustum-shaped wet joint (labeled as S-SWJ) was proposed to enhance the shear capacity of the joint interface. It can be seen from Figure 1b that the proposed wet joint had a concave square frustum shape, and the corresponding precast bridge panel had a convex square frustum-shaped shear key.  
A comparative study was conducted between S-SWJ and S-CWJ to validate the superiority of the concave square frustum-shaped wet joint. For comparison, S-SWJ and S-CWJ were fabricated, and their fabrication process is shown in Figure 2. During fabrication, the two precast bridge panels for each specimen were fabricated firstly, and then the concrete of the wet joint was poured. As shown in Figure 3a,b, all specimens had the same overall dimension. Both S-SWJ and S-CWJ were designed as cuboids with dimensions of \(250\times 250\times 1350\) mm, the dimensions of each bridge panel were \(250\times 250\times 600\) mm, and those of the wet joint were \(250\times 250\times 150\) mm. The detailed dimensions of the shear key in S-SWJ are illustrated in Figure 3b. All specimens were reinforced by steel bars with a diameter of 16 mm and length of 715 mm. A welding method was adopted to connect the two adjacent steel bars within the region of wet joint, and the welding length (i.e., the overlap length of two adjacent steel bars within the region of wet joint) was 80 mm.  
Figure 1: Schematic diagrams of (**a**) bridge panel with convex-shaped shear keys, (**b**) specimen with the concave square frustum-shaped wet joint (S-SWJ)) and (**c**) specimen with the conventional wet joint (S-CWJ).  
Figure 2: Fabrication process of specimens: (**a**) formwork for shear key, (**b**) precast bridge panels for S-SWJ, (**c**) axially aligned bridge panels for S-SWJ and S-CWJ, and (**d**) final specimens.  
In addition, 12 concrete cubes were fabricated to obtain the basic mechanical properties of concrete. Among them, six concrete cubes were fabricated along with the fabrication of precast bridge panels, while the other six concrete cubes were fabricated along with the fabrication of wet joints. In this study, the two kinds of concrete cubes are called precast and cast-in-situ concrete cubes. The concrete consisted of ordinary Portland cement, gravel, and river sand, and the mix proportion is tabulated in Table 1. Note that the same mix proportion of concrete was adopted for the bridge panels, wet joints, and concrete cubes.
# Acoustic Emission Wave Velocity Measurement of Asphalt Mixture by Arbitrary Wave Method
###### Abstract
The micro-structure and macro-pores in rock mass have a significant impact on the propagation characteristics of acoustic waves such as the wave velocity, wave frequency and attenuation [27; 28]. For the measurement method of the rock wave velocity, researchers proposed the correction method of the rock mass integrity index [29], the particle flow model analysis method of the rock wave velocity varying with stress [30] and the sparse sensor network monitoring method of the rock mass surface wave velocity [31]. In addition to rocks, wave velocity characterization and test methods of other materials have also attracted researchers' attention. The material composition, structural parameters and cracks in reinforced concrete slabs all affect the wave velocity and propagation of AE [32], and the AE wave velocity of corroded concrete drops sharply [33]. For composite materials, the differences in structural layers, elastic modulus and density affect the propagation velocity of the AE wave [34; 35]. In the characteristic medium, the AE wave velocity in liquid nitrogen [36] and natural fat [37] has also attracted researchers' attention. To sum up, the research on AE wave velocity is mainly in the field of rock and concrete materials. Although some researchers have begun to explore the application of AE technology in asphalt pavement materials, the research scope mainly concerns the combination of the mechanical test method and the AE monitoring method to characterize the pavement performance of asphalt mixtures, and the exploration of the AE wave velocity is very limited. Compared with rock and concrete materials, the composition of asphalt pavement materials is more complex, and the material properties show typical heterogeneity, which makes the propagation speed and propagation characteristics of AE in asphalt pavement materials more complex and variable. Therefore, exploration of the wave velocity measurement method with fewer influencing factors and less discreteness is helpful to better understand the AE propagation mechanism and material propertiesof asphalt pavement. In this paper, an arbitrary wave (AW) method is proposed and designed to measure the AE wave velocity of an asphalt mixture. Then, the initial AE wave velocity in an asphalt mixture beam is measured by the pencil lead break (PLB) method, the automatic sensor test (AST) method and the AW method. Finally, the dispersion of the initial wave velocity, the time-domain signal and the frequency distribution of AE measured by the three methods are compared and analyzed.
## 7 Conclusion and Future Work  
This paper has presented a hybrid partitioning detection and connectivity restoration algorithm for mission-critical applications of WSANs. Unlike most published schemes, PCR pursues pre-failure planning to distinguish between critical/non-critical nodes and designates an appropriate backup for each critical actor. The pre-designated backup continuously monitors and triggers a recovery in case of primary failure. The correctness and performance of the proposed scheme is validated through formal and simulation techniques respectively. We modeled the WSAN network as a dynamic graph and transformed PCR into a corresponding formal specification using Z notation. This formal specification is analyzed and validated using the Z Eves tool. Moreover, the performance of PCR is validated through extensive simulations. Simulation results have confirmed the effectiveness of PCR compared to contemporary schemes found in literature. In the future, we plan to evaluate the performance of PCR through a testbed network consisting of mobile robots.
# PV-Powered Charging Station with Energy Cost Optimization via V2G Services
## 2 PV-Powered Charging Station with V2G Service
### Stationary Storage  
In order to extend the lifetime of the lithium-ion batteries as a stationary storage method and protect them from overcharging or over-discharging, the maximum and minimum SOC of the storage, \(SOC_{S\_max}\), \(SOC_{S\_min}\), as well as the maximum storage power \(P_{S\_max}\) must be respected as given by (11) and (12) [47; 50]. The SOC of the storage \(soc_{S}\) can be simplified as in [51], neglecting the effects of self-discharge and temperature, and is given by (13) below:  
\[-P_{S\_max}\leq p_{S}(t_{i})\leq P_{S\_max},\;\textit{where}\;p_{S}(t_{i})=p_{ S\_C}(t_{i})-p_{S\_D}(t_{i}), \tag{11}\]  
\[SOC_{S\_min}\leq soc_{S}(t_{i})\leq SOC_{S\_max}, \tag{12}\]  
\[soc_{S}(t_{i})=SOC(t_{0})+\frac{1}{3600\cdot E_{Bat}}\int_{t_{0}}^{t}\;p_{S}(t _{i})dt, \tag{13}\]  
where \(E_{Bat}\) and \(p_{S}\) are the energy capacity (kWh) and power of the stationary storage, respectively.
# Polynomial Cancellation Coded DFT-s-OFDM for Low-PAPR Uplink Signaling
## Appendix A Spectral Shaping Function of PCC-DFT-s-OFDM
(2019) [PERSON]; [PERSON]; [PERSON]; [PERSON] [PERSON] A Comparison of CP-OFDM, PCC-OFDM and UFMC for 5G Uplink Communications. _IEEE Access_**2019**, \(7\), 157574-157594. [CrossRef]
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* [PERSON] (2012) [PERSON] On Instantaneous Power Distributions of Single-Carrier FDMA Signals. _IEEE Wirel. Commun. Lett._**2012**, \(1\), 73-76. [CrossRef]
* [PERSON] and [PERSON] (2018) [PERSON]; [PERSON] A Low Complexity Linear Moving Average Filtering Technique for PAR Reduction in OFDM Systems. _Int. J. Commun. Netw. Syst. Sci._**2018**, _11_, 199-215. [CrossRef]
* Samsung (2017) Samsung. Frequency Domain Spectrum Shaping for DFT-s-OFDM, R1-1703016. 2017. Available online: [https://www.3 gpp.org/ftp/tsg_ran/WG1_RL1/TSGR1_88/Docs/R1-1703016.zip](https://www.3 gpp.org/ftp/tsg_ran/WG1_RL1/TSGR1_88/Docs/R1-1703016.zip) (accessed on 31 October 2019). * [PERSON] (1991) [PERSON] Effect of HPA-nonlinearity on 4-DPSK/OFDM-signal for a digital sound broadcasting system. In Proceedings of the 2 nd European Conf. Satellite Commun. (ECSC), Liege, Belgium, 22-24 October 1991; pp. 179-184. * [PERSON] et al. (2005) [PERSON]; [PERSON]; [PERSON] An Improved ICI Reduction Method in OFDM Communication System. _IEEE Trans. Broadcast._**2005**, _51_, 395-400. [CrossRef]
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## 3 Antimicrobial Peptides (AMPs)
### Application of AMPs Against Foodborne Pathogens and Persisters  
#### 3.2.1 AMPs Used in Food  
Bactericons are small, heat-stable, ribosomally-synthesized AMPs or proteins generated by many bacteria, prominently including lactic acid bacteria (LAB). Due to their characteristics of effectively eradicating foodborne pathogens and persisters as well as being harmless and easily digested in the human digestive tract, bacteriocins and corresponding LAB are traditionally used as food preservers [144]. Unlike other AMPs, most bacteriocins have a relatively narrow spectrum of antimicrobial activity against closely-related bacteria [145]. Normally, there are three categories of bacteriocins-based food preservation strategies: antimicrobial solution with partially purified bacteriocins, bacteriocin-containing fermented products, and bacteriocin-producing microbes [146]. Structurally, bacteriocins can be divided into four groups in which class I-II are peptides smaller than 5 kDa and 10 kDa, respectively, and class III-IV are antimicrobial proteins that are not mentioned in this review.  
Class I bacteriocins, also known as lantibiotics, are composed of post-translationally modified peptides with unusual amino acids like lanthionine [147]. With broad-spectrum and efficient killing activity, nisin is the most famous class I bacteriocin used to improve food safety. Nisin is produced by _Lactococcus lactis_, and acts against Gram-positive pathogens including _L. monocytogenes_ and _S. aureus_ by binding to lipid II and consequently halting cell wall biosynthesis as well as making holes in the membrane [136]. In addition, [PERSON] et al. [128] discovered that nisin inhibits _Bacillus_ spore outgrowth via its perturbing effect on the inner spore membrane. Another class I bacteriocin, lacticin 3147 is used by a veterinarian cooperatively with nisin against mastitis in cows. Besides, lacticin 3147 shows higher _B. cereus_ killing efficiency than nisin [148]. Class II bacteriocins (non-lantibiotics) contain non-modified peptides and are divided into five subgroups IIa-Ile. Among them, Class IIa pediocin-like bacteriocins from several Gram-positive bacteria are majorly used agents to prevent _L. monocytogenes_ by targeting bacterial mannose phosphotransferase [145]. Nisin and pediocin have been approved for commercial use in food, which further led to steadily growing usage of bacteriocins and bacteriocin-producing LAB in the dairy industry to prevent foodborne pathogens [149].