esa-sceva/satcom-chunk-collection huggingface dataset:
# Analysis of the Relationship between Scintillation Parameters, Multipath and ROTI
## 5 Conclusions  
Six days of data under strong scintillation from three stations, respectively located in Brazil and Antarctica, are utilized to research on the relationship between MP and ROTI and scintillation indices. The relationship in the temporal and spatial domain is evaluated with a series of data science techniques, including 2D map comparison, Pearson CC, SSIM and variograms. The propagation patterns of all parameters are demonstrated to be similar. According to the discussion, it can be concluded that:  
1. A relationship between MP, ROTI and scintillation exists in both temporal and spatial domain
2. An integration between MP and ROTI can more completely represent scintillation than a single parameter. Precisely, MP and ROTI can reflect different types of scintillation. For equatorial data, MP is more correlated with S4 while ROTI is more relevant to \(\sigma_{\varphi}\). For high latitude data, it is possible for both MP and ROTI to be correlated with \(\sigma_{\varphi}\).
3. The propagation patterns of MP, ROTI and S4, \(\sigma_{\varphi}\) are similar, which can contribute to the prediction of scintillation with MP and ROTI.
4. MP is correlated with scintillation only when scintillation is active while ROTI correlates with scintillation during both quiet and intense periods.  
In the future, a broader network with more data should be utilized to investigate the relationship with more comprehensive characteristics of MP, ROTI, S4 and \(\sigma_{\varphi}\). Furthermore, more detailed studies on the possibility of separating the effects of real multipath and the effects of scintillation in MP data is needed that could include detailed station characterizations.  
[PERSON] and [PERSON] provided the initial idea and conception; [PERSON], [PERSON] and [PERSON] verified the feasibility of the method. [PERSON] and [PERSON] conceived, designed and performed the experiments; [PERSON], [PERSON] and [PERSON] and [PERSON] helped with the analysis of the data. [PERSON] and [PERSON] and [PERSON] wrote the paper; [PERSON] and [PERSON] helped with the writing. All authors have read and agreed to the published version of the manuscript.  
This work is supported by Ningbo Science and Technology Bureau under Commonwealth Research Programme with project code 2019C50017. We acknowledge the University of Nottingham Ningbo China Faculty of Science and Engineering for provision of a PhD scholarship to [PERSON] as well as further support through a new researchers grant to [PERSON] and [PERSON]. The authors would like to thank [PERSON] and [PERSON] on behalf of INGV for providing the Antarctica data. Monitoring stations from the network were deployed in the context of the Projects CIGALA and CALIBRA, both funded by the European Commission (EC) in the framework of the FP7-GALILEO-2009-GSA and FP7-GALILEO-2011-GSA-1a, respectively, and FAPESP Project Number 06/04008-2.  
The authors declare no conflict of interest.
## 3 Quality of Service
### Reliability Support  
Industrial WSNs are located in spaces, where equipment moves, conditions change and interference perturbs the communication. Mechanisms, such as space and frequency diversity as well as acknowledgements (Table 4), are thus required to protect the wireless networks from these disturbances [37].
# On Construction of a Campus Outdoor Air and Water Quality Monitoring System Using LoRaWAN
## 3 System Design and Implementation
### Real-Time Data Collection Service  
Real-time data are important for the assessment of campus air quality, as shown in Figure 3. We use Java programs to capture these real-time data from the monitoring sensors and then store the data in the MySQL database by the Open street map and ELK Stack [36; 37; 38].
## 3 Experimental Results and Discussion
### Dataset
#### 3.1.2 Conversion of the Dataset  
The format of the YOLO dataset is stored in the form of a text file, and the labeling file in the PCB dataset is stored in XML format. Therefore, before training with the YOLO  
Figure 8: Example of dataset defect information (missing holes).  
Figure 7: Dataset example.  
Figure 9: Enhanced defect distribution information.  
model, the label files in XML format are converted to the text file format required by YOLO so that the model can correctly read and parse the information required for the target detection task.
# Controlling Conditions of the One-Dimensional Consolidation Test on Peat Soil
## 7 Conclusions  
This study investigated the influence of temperature, biological oxidation, and sample height on the one-dimensional consolidation characteristics of peat soil under different load levels. Based on the results, the main findings and conclusions can be summarized as follows:  
(1) The small range fluctuation of ambient temperature can lead to significant changes in the peat consolidation rate, and when there is a higher amount of organic matter content, then there is a more significant the effect. In order to ensure that the one-dimensional consolidation test results can truly reflect the in situ tests, during the process of the test, especially when the loading time is long, the ambient temperature should be controlled to be the same as the in situ temperature.  
(2) It has been verified that the decomposition of organic matter that is caused by environmental disturbances after soil samples are taken out of the ground can be inhibited by soaking the samples in a 1% thymol solution t during the one-dimensional consolidation test.  
(3) The influence of the sample height on the consolidation strain of peat soil is different from that of mucky soil. The strain of peat soil at 30 mm height is greater than or equal to that of samples of other heights, while the strain of mucky soil with a sample height of 20 mm is the largest. Therefore, the use of a sample with a height of 30 mm is suggested when conducting a one-dimensional consolidation test on peat samples.  
The TN was used to characterize changes in the organic matter content in peat soil in this study. Although this method has been widely used in agromomy, this was the first application in geotechnical experiments. This provides a new method that can be used to study changes in the organic matter in peat soil and to determine its engineering characteristics; however, the standardization of its operation process needs further study. On the other hand, the solvent in the thymol solution used in this study was ethanol. During the test process the solubility will be reduced due to the volatility of ethanol, and some thymol crystals will be separated out. However, the results show that the volatilized solution still has the effect of inhibiting microbial decomposition. In order to be more conducive to environmental protection, future studies should focus on determining the optimal concentration of the thymol solution.  
Conceptualization, R.F.; data curation, B.P. and R.F.; formal analysis, B.P.; funding acquisition, R.F.; investigation, B.P. and R.F.; methodology, B.P. and R.F.; project administration, R.F.; resources, L.W. and Y.S.; supervision, R.F.; writing--original draft, B.P.; writing--review and editing, B.P. and R.F. All authors have read and agreed to the published version of the manuscript. This paper was supported by The National Natural Science Foundation of China (51778048).  
Not applicable.  
All of the data and models used in this study are available from the corresponding author upon request.  
The authors declare no conflict of interest.
## 1 Introduction
We derived the theoretical analysis in this study and reached some beneficial statistics of the estimation scheme. Based on these obtained analytical results, it can be found that the CFO estimate criterion is equivalent to discovering the frequency that achieves the minimum residual channel energy (CRE). Secondarily, we efficiently reduced the implementation complexity of the proposed CFO estimator by means of developing a simplified scheme without using the exhaustive CFO search method. Such a scheme allows the estimator to need less amount of computation during the data process than that required by a conventional line search approach. In spite of providing a lower complexity than that of a conventional technique, the alternative scheme operates at the expense of a little amount of estimate accuracy loss. Therefore, we further applied an iteration technique to compensate the estimate accuracy loss, which results from the necessary approximation in the analytical process of the estimate statistics. The organization of the rest of the paper is as follows. Section 2 depicts the mathematical models for the received data while considering the presence of CFO and provides an essential review of the conventional estimate approach presented by [PERSON] [19]. In Section 3, we first propose an improved search-based CFO estimator while assuming the channel order is not reached. Next, an iterative algorithm is presented to reduce the computational complexity of the proposed estimator. To have a clear insight of how much complexity can be saved, numerical comparison of the computational complexities among all of the concerned schemes is also included in this section. In Section 4, we provide the computer simulation results to demonstrate the performance advantages of the proposed schemes over [PERSON]'s approach. Finally, the conclusion is reported in Section 5. The notations used in this paper are defined as followed: vectors (matrices) are typed with boldface letters. \(\mathbf{I}_{n}\), \(\mathbf{O}_{m\times n}\) and \(\mathbf{0}_{n}\) represent an \(n\times n\) identity matrix, an \(m\times n\) all-zero matrix and an \(n\times 1\) all-zero vector, respectively. \(\mathbf{D_{a}}\) is a diagonal matrix with the diagonal entries taken from the vector \(\mathbf{a}\). \((\cdot)^{*}\), \((\cdot)^{T}\), \((\cdot)^{H}\) and \(\text{Tr}\{\cdot\}\) the complex conjugate, transpose, Hermitian transpose and trace operations, respectively. The \((i,j)\)-th entry of matrix \(\mathbf{A}\) is denoted by \([\mathbf{A}]_{ij}\). In addition, \(\Re\{\cdot\}\), \(E\{\cdot\}\) and \(\|\cdot\|\) denote the real part of a complex number, the taken ensemble average and the Frobenius norm operator, respectively.
# Design and Application of Secret Codes for Learning Medical Data
## 4 Results

Table 2: Comparison of Construction Methods. **Proof. ** By the definition, the length of each codeword is clearly \(N_{1}N_{2}\), and the size of the code is \(l_{1}l_{2}\). For each support of \(x_{1,i}\), all the indices of support of \(x_{2,j}\) can be combined. Thus, the weight of \(y_{i,j}\) is equal to the product of the weights of \(x_{1,i}\) and \(x_{2,j}\). Assume that \(x_{1,i_{1}}+x_{1,i_{2}}=x_{1,i_{3}}\) for \(1\leq i_{1}\), \(i_{2}\), \(i_{3}\leq l_{1}\) and \(x_{2,j_{1}}+x_{2,j_{2}}=x_{2,j_{3}}\) for \(1\leq j_{1}\), \(j_{2}\), \(j_{3}\leq l_{2}\). Then, it is clear that \(y_{i_{3},j_{3}}=y_{i_{1},j_{1}}+y_{i_{2},j_{2}}\) for any combination of \(1\leq i_{1}\), \(i_{2}\), \(i_{3}\leq l_{1}\) and \(1\leq j_{1}\), \(j_{2}\), \(j_{3}\leq l_{2}\), which implies that \(C_{D}\) is linear. Note that for any \(1\leq i_{1}\), \(i_{2}\leq l_{1}\) and \(1\leq j_{1}\), \(j_{2}\leq l_{2}\), the supports of \(y_{i_{1},j_{1}}\) and \(y_{i_{2},j_{2}}\) are not included in one another if \(i_{1}\
eq i_{2}\) or \(j_{1}\
eq j_{2}\) due to the properties of \(C_{1}\) and \(C_{2}\). \(\square\)  
**Example 2**. : _Assume that there are two codes: \(C_{1}\) of length \(31\) and \(C_{2}\) of length \(63\). Furthermore, suppose the weight distribution of \(C_{1}\) be given by \((d_{1},d_{2},\ldots,d_{k})\) for weights \((w_{1},w_{2},\ldots,w_{k})\), and the weight distribution of \(C_{2}\) by \((d_{1}^{\prime},d_{2}^{\prime},\ldots,d_{l}^{\prime})\) for weights \((w_{1}^{\prime},\ldots,w_{l}^{\prime})\). Then, by Construction \(D\), we can obtain a code of length \(1953=31\times 63\). The weight of a codeword is the product of two weights from \(C_{1}\) and \(C_{2}\)._  
It may be possible to use Construction D in the case that computing resources are enhanced and the number of devices is significantly increased. However, Construction D extends the size and weight from the component codes, cases where \(N_{1}\) and \(N_{2}\) are relatively prime are very rare for previously known constructions. Thus, finding primary constructions for minimal codes for more general parameters should be a future research topic. Furthermore, as shown in Example 2, the parameters of the codes are increased significantly. Therefore, it would be possible to apply Construction D to a case in which the resources are also increased considerably. Table 2 summarizes the characteristics of the resultant codes from Constructions A-D.
# Application of Machine Learning in Electromagnetics: Mini-Review
###### Abstract  
As an integral part of the electromagnetic system, antennas are becoming more advanced and versatile than ever before, thus making it necessary to adopt new techniques to enhance their performance. Machine Learning (ML), a branch of artificial intelligence, is a method of data analysis that automates analytical model building with minimal human intervention. The potential for ML to solve unpredictable and non-linear complex challenges is attracting researchers in the field of electromagnetics (EM), especially in antenna and antenna-based systems. Numerous antenna simulations, synthesis, and pattern recognition of radiations as well as non-linear inverse scattering-based object identifications are now leveraging ML techniques. Although the accuracy of ML algorithms depends on the availability of sufficient data and expert handling of the model and hyperparameters, it is gradually becoming the desired solution when researchers are aiming for a cost-effective solution without excessive time consumption. In this context, this paper aims to present an overview of machine learning, and its applications in Electromagnetics, including communication, radar, and sensing. It extensively discusses recent research progress in the development and use of intelligent algorithms for antenna design, synthesis and analysis, electromagnetic inverse scattering, synthetic aperture radar target recognition, and fault detection systems. It also provides limitations of this emerging field of study. The unique aspect of this work is that it surveys the state-of the art and recent advances in ML techniques as applied to EM.  
electromagnetics; antenna; machine-learning; DoA; object detection; 5G technology +
Footnote †: journal: Electronics Electronics
## 1 Introduction  
Impact loads on structural elements may result from, e.g., vehicle collisions, falling objects, or projectiles. The civilian design provisions generally use quasi-static loads with durations much longer than the structure's natural period, while impact loads instead have durations in the range of, or less than, this period. The relatively short duration of impact loads results in a different response as wave propagation, inertia, and strain-rate effects become significant, as described in [1]. The activation of significant inertia forces results in a different deflected shape during impact loading compared for during equivalent quasi-static loads, which as argued in [2] results in a higher shear force-to-moment ratio. This higher shear force-to-moment ratio may induce a brittle failure mode as the beam becomes dynamically shear-critical, although it was statically flexure-critical. This may occur for any general impulsive load which excites significant inertia forces, as shown experimentally in, e.g., ref. [3] for beams subjected to severe dynamic air-blast loads. The transition from brittle to ductile failure during impulsive loads results in lowered energy absorption capabilities.  
It was experimentally shown by [4] that statically flexure-critical RC members instead showed severe diagonal shear cracks during high-velocity impact loading. The diagonal cracks formed under the impact zone with an angle of approximately 45 degrees, a damage pattern referred to as a shear plug. Diagonal cracks similar to those observed for shear-critical beams during quasi-static tests formed alongside the shear plug for beamscontaining low shear reinforcement amounts. These were less steep diagonal cracks initiating closer to the supports that become horizontal at the top before propagating to the impact zone. During impact loading, it is therefore possible to initiate global shear failure (static flexural shear failure) and local shear failure (shear plug) for statically flexure-critical RC beams. The same conclusions are reported in the review of the impact response of RC beams, presented in [5]. The local shear failure was explained as an effect of only part of the beam resisting the impact force by its inertia in the early stages of the response, where the stress wave propagation has a significant influence. It was experimentally shown in [6] that this transfer from global shear failure to local shear failure under the impact zone occurred as the striker velocity increased, resulting in less impact force duration and higher maximum amplitude.  
While local and global shear damage modes during impact loads have been investigated extensively, there is not much research about their effect on the residual static and dynamic shear capacity. For this aim, a high-velocity initial impact test first initiated severe shear damage on RC beams. The beams were later tested quasi-statically for their residual static capacity and by sequential impact tests for the residual impact capacity. Both were conducted for beams with various amounts of shear reinforcement.
# Physicochemical and Microbiological Changes Associated with Processing in Dry-Cured Tuna
After thawing, the tunas are decapitated and eviscerated, and then washed with drinking water. Progressively, the tunas are manually cut by specialized people following a traditional process called \"ronqueo\", which results in two upper (black) and two lower (white) joins, with the skin and bones removed. The four joins are subsequently cut into strips no thicker than 5 cm and categorized by fat level. Then, the strips are covered in sea salt for a period ranging from 18 to 36 h. The salting step has the capability to reduce water activity (a\({}_{w}\)) in a short time and inhibits the growth of spoilage microorganisms due to the high content of chloride ions proving toxic to most microorganisms [15]. The salt also causes important osmotic dehydration, which removes water from the product and modifies its properties by brine salting [16]. This standardized processing step is necessary to increase the stability of the final product. The next step involves washing the salted strips of loins to remove excess salt from the muscle tissue and soaking them in water for around 7 to 9 h. This is performed to achieve the optimum salt concentration. The loins are then compressed, forcing an additional loss of fat and water, and finally the tuna strips are dried on a horizontal surface for at least two days and then hung for a period ranging from 15 to 21 days under controlled microclimatic conditions (T 16-17 \({}^{\circ}\)C; RH 60-70%). This step is largely responsible for the characteristic smell, flavor, and color of the product, modifying the final characteristics of the dry-cured tuna, including changes in the lipid fraction [17]. Both salting and drying steps contribute to the stability of the final product. The dry-cured tuna products are finally vacuum-packed in transparent plastic bags. It is traded under cold conditions (4-6 \({}^{\circ}\)C) to preserve its physical, chemical, and organoleptic characteristics. The different steps of the traditional _mojama_ elaboration process are essential to improve the stability and flavor of the product. Thus, the salting process inhibits unwanted reactions in the fish and promotes the desired taste, while the drying process enhances the preservation effect of the salt. Overall, the combination of these two steps is important to produce a high-quality _mojama_ that can be enjoyed for a longer period without compromising its texture and taste. The purpose of this study was to evaluate the changes in physicochemical, biochemical, and microbiological parameters during the traditional salting-drying process to elaborate tuna _mojama_.
## Nomenclature  
\(P_{grid}\)Grid supply power  
\(P_{building}\)Building demand power  
\(P_{EV}\)Charging/discharging power of \(EV\)  
\(P_{ESS}\)Charging/discharging power of \(ESS\)  
\(P_{PV}\)Generation power of \(PV\) output  
\(B_{EV}\)Battery capacity of \(EV\)  
\(B_{ESS}\)Battery capacity of \(ESS\)  
\(d_{max}\)Maximum range of \(EV\)  
\(d_{trip}\)Trip distance of \(EV\)  
\(\eta\)Charging/discharging power rate efficiency  
\(t_{a}\)Arival time  
\(t_{d}\)Departure time  
\(t_{stay}\)Stay time  
\(SoC_{i}\)Current SoC of \(EV\)  
\(SoC_{trip}\)Required SoC for trip  
\(SoC_{ESS}\)SoC of \(ESS\)  
\(ts\)Time slot size  
\(\alpha\)Coefficient of \(EV\)  
\(\beta\)Coefficient of \(ESS\)  
\(\gamma\)Coefficient of \(PV\)  
\(C\)Total charging/discharging cost  
\(C_{elec}\)Electricity cost  
\(N_{t}\)Required number of charging slots for trip  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Peak Power** & **Percentage of** & **Charged Cost** & **Discharged** & **Number of EVs** \\  & **Consumption** & **Peak Load [\%]** & **[KRW]** & **Cost [KRW]** & **Participation** \\ \hline Scenario 1 & 208 kW & 26.92 \(\uparrow\) & 202,294 & NA & 100 \\ Scenario 2 & 192 kW & 20.83 \(\uparrow\) & 162,915 & NA & 100 \\ Scenario 3 & 145 kW & 04.60 \(\downarrow\) & 19,090 & 22,456 & 25 \\ Scenario 4 & 133 kW & 12.50 \(\downarrow\) & 49,990 & 63,829 & 63 \\ Scenario 5 & 130 kW & 14.47 \(\downarrow\) & 74,319 & 109,050 & 71 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparison of simulation results.
# Force-Based Self-Organizing MANET/FANET with a UAV Swarm
## 4 A Novel Self-Organizing Approach to Drone-Based MANETs  
We propose a self-organizing approach to enable a swarm of drones to dynamically and adaptively deploy a MANET and manage its topology to optimize LQM.  
In this section, we first discuss the mechanism that allows drones to determine their direction of movement (see Section 4.1). We then discuss how the overall system uses information about the individual nodes' stress levels to iteratively deploy additional drones into the swarm (Section 4.2.1) and, conversely, how under utilized nodes can be reassigned within the swarm (Section 4.3) or retired entirely and recalled to the base (Section 4.2.2).