esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Database Architecture and Quality Control Implementation  
The data from the lidar array was maintained on a Microsoft (MS) SQL Database Server. The database was stored on an externally attached high performance Dell RAID storage platform and backed up at regular scheduled intervals to disk and tape media. Data was transferred to the main database server via the standard File Transfer Protocol, either by manual transfer or automated scripting techniques. Upon successful transfer the data was then parsed onto the main MS SQL database. For reasons of security and data integrity, the MS SQL database was not directly accessible for data downloads or interrogation by end users. Secure database views determined by user ID were mirrored from the main MS SQL database to a separate MySQL database. This was accessed by user login ID and secured via an SSH connection through the University of Strathclyde's firewall, see Figure 11.  
There were four tables of data within the database containing node definition, ZephIR, Windcube and meteorological mast data. Each lidar was assigned a node number against which information about the installation such as latitude and longitude of installation, installation height AMSL, type of device (ZephIR, Windcube, metmast), scan heights, filtering and verification, _etc_. was stored.  
In order to upload data on to the database a user was required to log on to an FTP server with a node number specific login ID. Each node had a secure directory allocated and the user uploaded the data files into the relevant directory. A node specific login ID allowed access only to data belonging to that single node, access to additional node data was only allowed on the explicit instruction of the project coordinator or relevant node data owner.  
The SQL database created a transaction log (LogID) when data was uploaded and parsed into the database which contained node number, filename, login ID, and date and time of upload. This LogID  
Figure 11: Database structure and security implementation.  
was used to tag the data for all future reference and allowed any single piece of data to be traced back to its original raw data file upload.  
From the node number the format of the input data was known and the data was parsed into the data tables in a standard format for each lidar type. Data fields which contained data which signified poor data, such as 999,999, in the case of the Windcube, were replaced with null characters. Time and date stamping was also modified to follow the standard SQL date and time format.  
Data integrity was checked by taking a random single time sequence of data or group of time sequenced data, and comparing the database data stored with the data sequence from the original uploaded raw CSV data file.
# Non-Uniform Fusion Tree Generation in a Dynamic Multi-Sensor System
## 5 Evaluation and Discussion
### Unsynchronized Start Time  
This section presents the performance of the NU-FTG for the varied \(\Delta T\) in the uniform distribution of targets. The HEED and FTG do not support unsynchronized start times of radars; thus, we did not execute experiments for HEED and FTG. The parameters for the simulation environment are configured as follows:  
* \(N_{tgt}=1200\),
* \(\Delta T=0,\ 10,\ 20,\ 30,\ 40,\ \text{or}\ 50,\ \text{and}\)
* \(P_{r_{1}}=P_{r_{2}}=P_{r_{3}}=P_{r_{4}}=0\%\)  
We executed an experiment for NU-FTG. The experiment consists of six sequences with different \(\Delta T\): 0, 10,..., or 50 s. A radar starts the NU-FTG at a randomly selected time between 0 and \(\Delta T\) seconds. Figure 6 shows the results of this experiment. The first sequence, whose \(\Delta T\) is 0, is the synchronized case. In the synchronized case, all nodes start the NU-FTG at around the same time. The other sequences are unsynchronized cases. The average number of collected tracks and the standard deviation of unsynchronized cases is calculated. As shown in Figure 6, the number of collected tracks of the synchronized case is 902.36, which is within one standard deviation from the average number of collected tracks of the unsynchronized cases. This result shows that the performance of NU-FTG in the unsynchronized cases are similar to the synchronized case, and we can infer that the NU-FTG can guarantee its performance, even though the start time of nodes are not synchronized.
###### Abstract  
Modern agriculturality should limit its degrading impact on the soils, the natural environment, and the climate. No-tillage soil cultivation technologies, which have been in use for many years and are constantly being improved, are a good example of these actions; although, in-depth studies on their impact on the soil microbial community are currently scarce. The aim of our study was to evaluate the effect of cultivation technology on the soil bacterial community to assess differences that can be reflected in the environmental and agricultural functionality, identifying possible bacterial species with ecological properties. In this context, the composition of bacterial communities (at the phyla, order, class, and species levels) was evaluated under different conditions, such as conventional tillage (CT) (plophing), reduced tillage (RT) (stubble cultivator), strip tillage (ST), and no-tillage (directowing on stubble and fallow buffer zone of the experimental field), in a horse bean plantation. Metagenomic methods (next generation sequencing technology, NGS) were used to determine the percentage of individual operational taxonomic units (OTUs). Our study showed that no-tillage cultivation technologies, mainly strip and no-tillage methods, had a positive effect on microbiological communities. In fact, key species related to soil fertility and crop yield, such as _Gemmatomonas aurantiaca_ (a microorganism that reduce nitrous oxide, N\({}_{2}\)O in soil) and _Aeromicrobium ponti_ (a beneficial species for the soil environment, essential for the proper functioning of the crop agroecosystem), increased in reduced cultivation technologies. These species can determine soil fertility and crop yields, and therefore, they are very important for sustainable and even regenerative agriculture. Further studies of soil samples collected from other crop plantations under different cropping systems may indicate beneficial microbial species that are important for soil fertility.  
**Keywords:** biodiversity; the abundance of microorganisms; next generation sequencing; soil properties; _Legumes_
# Adaptive Data Synchronization Algorithm for IoT-Oriented Low-Power Wide-Area Networks
## 1 Introduction
* _exchanged data amount_: sometimes, when the communication is underway, some part of the data sent by the transmitting party may be already received at the receiving side due to not perfect synchronization. That information will be redundant and useless, and furthermore its transmission will lead to a waste of power. This occurrence is typical in remote file synchronization scenarios, where specific protocols are used to make the data transmission between two parties limited only to the new information. By doing so, the data traffic is reduced, optimizing the number packets to be exchanged and thus providing energy saving. Finally, the complexity requested by file processing and synchronization impacts on the device energy consumption, therefore the mechanisms for data communication must be not only efficient but also computationally feasible. LoRa, Sigfox, NB-IoT and the other solutions used in LPWANs have different characteristics, therefore to manage the power consumption in the context of network access requires methods specifically tailored to the considered technology and application. On the other hand, data traffic optimization is independent from the network framework, so it would be possible to design some communication protocols providing good performance in the IoT environment in a more general way. The main contribution of this work is therefore to consider well-known architectural models for IoT interconnection through LPWANs such as the ones discussed in [19] and provide a solution to reduce the data to be exchanged in the system for file synchronization purposes. A possible framework that this approach can refer to is e-health where on one side reliable file synchronization is needed, and on the other side both the energy consumption and the network load have to be reduced ([20; 21]). Taking into account those concerns, we propose an adaptive file synchronization algorithm, particularly tailored to IoT applications, allowing:  
* Data traffic optimization, to avoid the network overload;
* Energy saving, since reducing the amount of data to be transmitted allows the IoT device to limit power consumption. It is worth noting that the proposal here identified can be applied independently to every low-power technology. The rest of the paper is organized as follows. Section 2 reports the state of the art regarding file synchronization algorithms. In Section 3 the cloud-based network model under investigation is introduced, reporting the framework for the file synchronization procedure. Section 4 describes the proposed adaptive file synchronization algorithm, while Section 5 concerns the IoT devices power consumption analysis. Simulation results are shown and discussed in Section 6. Finally, Section 7 draws conclusions.
## 4 Results
### Structural Equation Modeling (SEM)  
The SEM was used in order to evaluate the proposed model, and it was found to have an acceptable fit to the data (\(\chi^{2}\) = 817.331, \(df\) = 235, \(\chi^{2}\)/\(df\) = 3.478, \(p\) < 0.001, NFI = 0.917, IFI = 0.939, CFI = 0.939, TLI = 0.928, and RMSEA = 0.080) [80]. All seven hypotheses were statistically significant at a \(p\)-value of less than 0.05. The results indicated that performance expectancy (\(\beta\) = 0.370 and \(p\) < 0.05), effort expectancy (\(\beta\) = 0.268 and \(p\) < 0.05), social influence (\(\beta\) = 0.152 and \(p\) < 0.05), facilitating conditions (\(\beta\) = 0.288 and \(p\) < 0.05), and perceived risk (\(\beta\) = \(-\)0.183 and \(p\) < 0.05) have significant effects on attitude, which supports Hypotheses 1, 2, 3, 4, and 5. Moreover, attitude positively affected intentions to use (\(\beta\) = 0.797 and \(p\) < 0.05) and word-of-mouth intentions (\(\beta\) = 0.748 and \(p\) < 0.05), which supports Hypotheses 6 and 7. The hypothesis testing results are provided in Table 4.
## 2 Materials and Methods
### Data Obtained by Experiment and Their Formal Interpretation
#### 2.2.3 Correlation Analysis and Regression Dependencies Testing
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Polynomial** & **Dependence** & _RMSE_ [**MPa**] & \(R^{2}\) [**-**] & \(R^{2}\) [**-**] & _DWstat_ [**-**] \\ \hline
1 st degree & \(p=f(t)\) & 0.1599 & 0.3594 & 0.3193 & 0.2270 \\
1 st degree & \(\Delta\sigma_{t}=f(\Delta p)\) & 3.1542 & 0.9459 & 0.9430 & 1.4101 \\
1 st degree & \(\Delta\sigma_{m}=f(\Delta p)\) & 2.7721 & 0.9275 & 0.9235 & 1.0932 \\
2 nd degree & \(p=f(t)\) & 0.0814 & 0.8445 & 0.8237 & 0.6489 \\
2 nd degree & \(\Delta\sigma_{t}=f(\Delta p)\) & 2.8428 & 0.9585 & 0.9537 & 1.9585 \\
2 nd degree & \(\Delta\sigma_{m}=f(\Delta p)\) & 2.4280 & 0.9474 & 0.9413 & 1.6015 \\
3 rd degree & \(p=f(t)\) & 0.0539 & 0.9363 & 0.9226 & 1.4088 \\
3 rd degree & \(\Delta\sigma_{t}=f(\Delta p)\) & 2.9300 & 0.9586 & 0.9508 & 1.9636 \\
3 rd degree & \(\Delta\sigma_{m}=f(\Delta p)\) & 2.4870 & 0.9481 & 0.9384 & 1.6571 \\ \hline \end{tabular}
\end{table} 
Table 4: Parameters of correlation analyses of proposed polynomial models of individual regression dependencies \(p=f(t)\), \(\Delta\sigma_{t}=f(\Delta p)\) and \(\Delta\sigma_{m}=f(\Delta p)\). \begin{table}
\begin{tabular}{c c c c} \hline
**Name** & **Tested** & **Null** & **Alternative** \\
**of the Test** & **Property** & **Hypothesis \(H_{0}\)** & **Hypothesis \(H_{1}\)** \\ \hline Anderson-Darling & normality & normality & a significant departure \\ Goldfeld-Quandt & homoscedasticity & homoscedasticity & from normality \\  & & non-correlation & autocorrelation (zero coefficient of autocorrelation) & heteroscedasticity (the presence of autocorrelation) \\ \hline \end{tabular}
\end{table} 
Table 3: Basic data of the three test procedures applied.
# HEAD Access Control Metamodel: Distinct Design, Advanced Features, and New Opportunities
## 3 Access Control Models
### The Development Stages
#### 3.2.1 Combining AC Models  
With the continuous technology upgrades and distributed computing environments, the presence of security threats also increases. This imposes the need to find hybrid AC models by combining features of two or more AC models to allow defining more AC policies and enhance the process of access management [5]. Several hybrid AC models are proposed in the literature that combine features of RBAC and ABAC, for example, [16, 17, 18]; DAC, MAC and RBAC, for example, [19]; MAC and RBAC, for example, [20]; and many other hybrid models. Figure 2 illustrates the idea of hybrid models.
# Experimental Evaluation of a LoRa Wildlife Monitoring Network in a Forest Vegetation Area
## 3 System Architecture
### LoRa Field Packet Generator
#### 3.2.1 Key Requirements  
The first requirement emerging from the description of the experimental setup is a transportable packet generator capable of stand-alone operation for easy field deployment. The packet generator includes a display and a set of buttons to implement a simple user interface for controlling the settings and operations without a PC connection. The user interface should be able to access the selection of LoRa physical layer key parameters (SF, BW, CR, frequency) and the software has to implement routines to generate bursts of LoRa frames, with the ability to choose, in compliance with Duty Cycle legal limits, how many packets to send, and with which interval. The device must also be able to switch between the EU433 MHz and EU868 MHz for easy comparison. All these requirements are necessary for easy field deployment. Our LoRa field packet generator differs from the off-the-shelf solution due to its portability, open source abilities, fully configurable, easy switch between the 433 MHz and the 868 MHz frequency bands. Our device is also protected by an enclosure with an internal protection 65 (IP65) rating.
## 4 Precise Orbit Determination (POD)
### Orbit Validation
#### 4.2.1 Orbit Overlap Comparisons
All orbit-related parameters were solved in batch mode, and ambiguity fixing for such a short period had little effect on the results for the entire arc, and it was not long enough to improve the orbit precision of MEOs significantly. The performance of BeiDou-2 GEOs was almost unaffected because the ambiguity remained real-valued.
# Experimental Analysis of Plantar Fascia Mechanical Properties in Subjects with Foot Pathologies
###### Abstract  
Plantar Fascia (PF) is a fibrous tissue that plays a key role in supporting the foot arch; it can be affected by several pathologies that can alter foot biomechanics. The present study aims at investigating the mechanical behavior of PF and evaluating possible correlations between mechanical properties and specific pathologies, namely diabetes and plantar fibromatosisids (Ledderhose syndrome). PF samples were obtained from 14 human subjects, including patients with Ledderhose syndrome, patients affected by diabetes and healthy subjects. Mechanical properties of PF tissues were evaluated on three samples from each subject, by cyclic uniaxial tensile tests up to 10% of maximum strain and stress relaxation tests for 300 s, in hydrated conditions at room temperature. In tensile tests, PF exhibits non-linear stress-strain behavior, with a higher elastic modulus (up to 25-30 MPa) in patients affected by [PERSON] syndrome and diabetes with respect to healthy subjects (elastic modulus 10 \(\div\) 14 MPa). Stress-relaxation tests show that PF of patients affected by [PERSON] syndrome and diabetes develop more intense viscous phenomena. The results presented in this work represent the first experimental data on the tensile mechanical properties of PF in subjects with foot diseases and can provide an insight on foot biomechanics in pathological conditions.  
pluraler fascia; plantar fibromatosisis; diabetes; mechanical testing; viscoelasticity +
Footnote †: journal: Applied Sciences
# Research on Damage Localization of Steel Truss-Concrete Composite Beam Based on Digital Orthoimage
## 5 Damage Identification Based on Image Matrix Similarity
### Damage Identification  
#### 5.2.1 Single Damage Test Beam Image Matrix Similarity Analysis  
Under 250 kN load condition, the lower edge curve of the test beam without damage and one rod damage (No. 7 rod) is extracted according to the previous method, and after noise reduction, the pixel coordinates of the lower edge curve form the image coordinate matrices \(D_{0}\) and \(D_{1}\). Then, the Euclidean distance of the homologous image points of the two image matrices is calculated using the Equation (6), whose calculation process is shown in Figure 40, and further the similarity \(sim(D_{0},D_{1})\) distribution (Figure 41) is obtained by amplifying the damaged signal according to the Equation (7). To facilitate the view of the damage recognition effect, only the image matrix similarity results for the truss vertical rod regions are shown in Figure 41.  
From Figure 41, the similarity peaks at vertical bar #7, indicating that damage is more likely to occur here than at other bars. By connecting the maximum similarity at each vertical bar, the similarity envelope of the test beam in the single damage condition can be obtained (Figure 42). The similarity envelope gives a more concise and obvious indication of the damage location.  
Figure 42 shows that the image matrix similarity analysis method works well for single damage identification of the test beam and can accurately locate the damage location.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Load** & \multirow{2}{*}{**Load**} & **Deflection** & **Deflection** & **Deformation** & **Error** \\
**Conditions** & & **Gauge Location** & **Gauge Values** & **Curve Values** & \(\frac{|R_{1}-R_{2}|}{R_{1}}\)\% \\  & & & \(R_{1}/\)mm** & \(R_{2}/\)mm & \(R_{1}/\)mm \\ \hline \multirow{9}{*}{C\({}_{2}\)} & & S1 & 0.655 & 0.634 & 3.11 \\  & & L/8 & 4.155 & 4.184 & 0.70 \\  & & L/4 & 6.479 & 6.609 & 2.02 \\  & & 3L/8 & 9.246 & 8.929 & 3.42 \\  & & L/2 & 10.583 & 10.464 & 1.12 \\  & & 5L/8 & 9.524 & 9.807 & 2.98 Figure 41: Single damage identification results of test beam.  
Figure 40: The calculation process of Euclidean distance in Equation (6).
## 2 Case Study and Data
### Nadir  
To provide the pseudo-nadir dataset, supposed to be representative of what is a current pre-SWOT observational altimetric dataset, the groundtracks of 4 altimetric missions (TOPEX/Poseidon, Geosat, Jason-1 and Envisat) picked up from the 2003 constellation, are used to interpolate the NATL60 simulation from 1 October 2012 to 29 September 2013, thereby covering a whole year of data. A Gaussian white noise with variance \(\sigma^{2}=(4\cdots 9)\) cm\({}^{2}\) is then added to the interpolated NATL60 simulation by the SWOTsimulator tool to mimic a noise with a spectrum of error consistent with global estimates from the Jason-2 altimeter [9].  
As the space-time interpolations will focus on a daily-basis temporal resolution, we also built nadir pseudo-observations with an additional strategy by accumulating observations over a time window \(t_{k}\pm d\) days centered at time \(t_{k}\) in order to increase the daily nadir spatial sampling. As in [5], we investigated the responses of the different interpolation techniques when parameter \(d\) was either set to 0 or 5, corresponding to time windows of respectively 1 and 11 days. For clarity, let it be precisely when \(d=0\)--see Figure 2a--that the time window is one-day long and the nadir observations are collected during the specific day to map. Figure 2b,d is intentionally provided with one-day lag to illustrate how SWOT information moves across the domain over time.
# The Effect of Task Complexity on Time Estimation in the Virtual Reality Environment: An EEG Study
## 2 Methods
### Procedure  
Figure 2 presents the procedure of the experiment. Before the experiment, the participants had completed an online survey with screening questions about their demographic information and previous VR experiences. The experiment lasted about 105 min per participant. At the beginning, each participant was asked to sign a consent form and read a brief set of instructions. Participants were informed that if they experienced any discomfort such as motion sickness, they were free to withdraw from the study immediately. Each participant then performed a practice trial with the VR headset to become familiar with the VR jigsaw game. Participants were informed that in the main study, they would be asked a time estimation question verbally each time they finished a jigsaw puzzle. After a participant completed their practice trial, we set-up the Epoc Flex EEG device. The Cz, Fz, and Pz electrodes were attached to the participant's head with electric gel.  
Each trial during the main experiment required the participants to put every jigsaw piece in the right place on a blank tray with a white border, as shown in Figure 1. To pick up a jigsaw piece, the participants needed to target the jigsaw piece using the virtual laser pointer and then press the trigger button on the controller. Then, they kept holding the button and moved the controller to drag the jigsaw piece to the location they wanted to. The participants only needed to release the button to place the jigsaw piece. The participants were instructed to gently move their necks and wrists and avoid moving other body parts to minimize the artifacts caused by bodily movement on the EEG signal recording. After completing each trial, the participants were instructed to estimate the time, in seconds, that the trial had taken them by answering a question: \"How long do you think you spent on the previous jigsaw game?\" Before moving on to the next trial, the participants also assessed  
Figure 1: (a) The low-complexity task with 8 pieces of the VR Jigsaw game; (b) the high-complexity task with 18 pieces of the VR Jigsaw game.  
Figure 2: The diagram of the experiment (duration of each step is approximate).  
their subjective workloads using the NASA-TLX questionnaire [47]. Each participant gave their answers verbally while wearing the VR headset. There was a one-minute break between trials. The participants were instructed to play the game at a comfortable pace. During the experiment, participants' brain signals, time estimations, actual completion times, and NASA-TLX scores were recorded.