esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Materials and Methods
\begin{table}
\begin{tabular}{c c c c} \hline
**Specimen** & **R-030** & **R-031** & **R-033** \\ \hline Type of load & \begin{tabular}{c} In-phase tension \\ and torsion \\ \end{tabular} & \
\begin{tabular}{c} 90° out-of-phase \\ tension and torsion \\ \end{tabular} &
\begin{tabular}{c} 45° out-of-phase \\ tension and torsion \\ \end{tabular} \\ \hline Load ratio & \(\text{R}_{\text{L}}=\text{R}_{\text{T}}=-1\) & \(\text{R}_{\text{L}}=\text{R}_{\text{T}}=-1\) & \(\text{R}_{\text{L}}=\text{R}_{\text{T}}=-1\) \\ \hline Measured SIF range & \(\Delta K_{l}\), \(\Delta K_{ll}\), \(\Delta K_{lll}\) & \(\Delta K_{l}\), \(\Delta K_{ll}\), \(\Delta K_{ll}\) & \(\Delta K_{l}\), \(\Delta K_{ll}\), \(\Delta K_{ll}\) \\ \hline \end{tabular}
\end{table} 
Table 1: Applied loading settings and assessed SIF for every sample. Figure 4: Schematics for data used (**a**) input format needed, and (**b**) steps used in the implementation. the curvature effect as well as not getting into the near zone which would capture plastic strains. Furthermore, the fields must be aligned with the crack growth direction to comply with [PERSON] premises. However, other factors like rugosity, trapped debris (which could enhance opening mode), and crack flank interlocking (which could impede sliding modes) are naturally taken into consideration. The COD model assumes that points A and B, from Figure 1, are \(\pm\)180 degrees from the CTL [25], so Equation (1) is simplified to Equation (14). \[ \begin{array}{l}K_{I}=E\frac{\Psi_{A}-\Psi_{B}}{4}\sqrt{\frac{2\pi}{\tau}}\\ K_{II}=E\frac{\Psi_{A}-\Psi_{B}}{4}\sqrt{\frac{2\pi}{\tau}}\\ K_{III}=E\frac{\Psi_{A}-\Psi_{B}}{4(1+\
u)}\sqrt{\frac{2\pi}{\tau}}\end{array}  \tag{14}\]  
where \(E\) is the elastic modulus, and \(\
u\) is the Poisson ratio. Therefore, with the nonlinear effects restrictions in mind, Figure 5a shows the AOI for specimen R-031 (90\({}^{\circ}\) out-of-phase loading), Figure 5b for specimen R-033 (45\({}^{\circ}\) out-of-phase loading), and Figure 5c for specimen R-030 (in-phase loading) as yellow squares of about 1 \(\times\) 1 mm. Moreover, the superimposed red dots represent the location where displacements were obtained for the COD model, Equation (14) whereas the green dots represent the crack tip. It is important to note that the displacement fields must be aligned according to the axis described in [PERSON] model; this is the positive \(X\) axis coinciding with the crack growth direction. Furthermore, a COD clip gauge must be placed on the slit, and due to the nature of the crack growth, it may not give displacements that represent the stress field for the cases shown in Figure 5a,b.
## 2 Materials and Methods
### Universal GNSS Channel Validation
#### 2.2.5 GNSS Test Scenarios
In order to synchronize onto the frame and to overcome its Forward Error Correction (FEC) encoding with a \(\
icefrac{{1}}{{2}}\) ratio, a pattern composed of the common 8-bit preamble followed by the satellite-specific 6-bit PRN is used: Of the resulting 28 encoded symbols, the last 16 are not affected by the unknown data from the previously broadcasted frame [44]. These are then used to locate the beginning of a frame at an offset of 12 symbols. Once, the navigation data is obtained, the L2 CL offset can be assessed prior to merging it with the L2 CM stream, achieving a 3 dB gain with a TMBPSK match filter approach. This multiplexing requires a clock with twice the rate, _i.e._, 1.023 Michip/s, which is then divided down to 511.5 kchip/s for the codes generation. The same 2 ms integration time is preserved in order to keep integrating over an integer number of chips for each of L2 CM and L2 CL codes.
## 2 Development of Proposed Controller
### Proposed Complex-Order PI-PD Controller  
Complex-order PI-PD (COPI-PD) control is an advanced version of PID control that incorporates the principles of fractional and complex arithmetic. It employs complex orders \(\lambda+j\beta\) in the integral and \(\mu+j\gamma\) in the derivative terms to deliver more precise control responses, particularly in complex systems. The complex orders enables engineers to fine-tune the integral and derivative terms, enhancing control performance for systems with nonlinear dynamics. By adjusting \(K_{p}\), \(K_{i}\), \(K_{d}\), and orders, engineers can customize COPI-PD controllers to meet the specific requirements of a system and achieve superior control performance. The parameters that requires tuning are \(\lambda+j\beta\) and \(\mu+j\gamma\), which can be achieved using a trial-and-error method to meet the set point and minimize process disturbances. The COPI-PD controller's block diagram is shown in Figure 2, followed by its controller, which is given as  
\[U(s)=\bigg{(}K_{p}+\frac{K_{i}}{s^{\lambda+j\beta}}\bigg{)}E(s)+\bigg{(}1+K_{ d}s^{\mu+j\gamma}\bigg{)}Y(s),\;0<\lambda,\beta,\mu,\gamma<1. \tag{5}\]
# Decision Support Application for Energy Consumption Forecasting
## 2 Material and Methods
### Data Sources  
One of the most important facts for the forecasting algorithms to present a better behavior is the data that are used as the inputs of the methods. All the decisions that these algorithms make are based on this data. Two types of variables are used in the proposed strategies in this work which are the energy consumption and the environmental temperature. As the application is implemented to predict the electricity consumption of the building N of GECAD facilities located in Porto, Portugal, the electricity consumption of this building is used as well as the environmental temperature of the related place which is same as the environmental temperature of the Porto city.  
The electricity consumption is collected from the SQL server of the GECAD. This SQL server has a database which stores the electricity consumption of the building from 5 energy meters by a time interval of 10 seconds. Every energy meter includes the electricity consumption of a specific part of the building divided into three variables. These three values are the consumption of the electrical sockets, lights, and air-conditioning system of the building. As this application only uses the total energy consumption of the building by a time interval of 1 hour and in order to reduce the time of the data collection from database, has been created a new database in the GECAD SQL server where an agent calculates the average of the total electricity consumption of the building from the main database by a time interval of 5 minutes and stores this data in to the new database. Having this database reduces the execution time of the application and makes the process of data collection less intricate.  
The Environmental temperature values are collected from the Weather Underground site [37]. All the Meteorological information of the city of Porto ate available in this site. This information is calculated by the ISEP meteorological station [38].
## 3 Results and Discussion
### Coupling Analysis at Water Mixing and Variable Excess Air Coefficient Conditions
#### 3.3.2 Sensitivity Analysis  
In many research fields, sensitivity analysis is applied, and the reaction rate of components \(i\) is derived from mass action law:  
\[\frac{d\mathcal{C}}{dt}=f(\mathcal{C};K) \tag{14}\]  
\[\mathcal{C}(0)=\mathcal{C}_{0} \tag{15}\]  
\[\beta_{ij}(t)=\frac{\partial\mathcal{C}_{i}(t)}{\partial K_{j}} \tag{16}\]  
The higher the sensitivity, the more important the elementary reaction \(j\) to the component \(i\). The specific sensitivity results are shown in Figure 9, corresponding to the starting point of the second stage reaction.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multicolumn{1}{c}{**X**} & \multicolumn{1}{c}{**H**} & \multicolumn{1}{c}{**O**} & \multicolumn{1}{c}{**OH**} & \multicolumn{1}{c}{**HO\({}_{2}\)**} & \multicolumn{1}{c}{**H\({}_{2}\)**} & \multicolumn{1}{c}{**H\({}_{2}\)O\({}_{2}\)**} \\ \hline \multirow{4}{*}{Y} & NO & 0.0075 & 0.8586 & 0.8917 & 0.0316 & 0.7789 & 0.3368 \\  & N\({}_{2}\)O & 0.1474 & 0.9940 & 0.8602 & 0.7699 & 0.9383 & 0.0857 \\  & NO\({}_{2}\) & 0.0887 & 0.9128 & 0.9789 & \(-\)0.5744 & \(-\)0.9639 & \(-\)0.6662 \\ \hline \hline \end{tabular}
\end{table}
Table 3: SRCC matrix of free radicals mole fraction and maximum reaction rate of NO\({}_{x}\).  
Figure 8: Result of Student’s t significance test for SRCC (confidence level \(\alpha=0.05\)).  
The positive sensitivity coefficient indicates that the elementary reaction affects the generation of free radicals, and the negative sensitivity coefficient indicates that the elementary reaction affects the consumption of free radicals. The mutual influence increases with the absolute value of the sensitivity coefficient increases. From Figure 9, it is found that elementary reaction #1, #9, #13, #16 and #20 can promote or inhibit the generation of free radicals OH, O, HO\({}_{2}\) and H\({}_{2}\) than others, but the elementary reaction #16 is more important. At the same time, OH and O show a consistent sensitivity to different elementary reactions. HO\({}_{2}\) and H\({}_{2}\) show a consistent sensitivity to different elementary reactions. Whereas the sensitivity of OH, O and the sensitivity of HO\({}_{2}\), H\({}_{2}\) are antagonistic.
# Electronic Nose Analysis to Detect Milk Obtained from Pasture-Raised Goats
## 4 Conclusions  
In our study, E-nose has proved to be a reproducible method for routine quality control analyses by correctly authenticating milk from pasture-fed goats. Using linear discriminant analysis to process E-nose data, Pasture milk has been discriminated from Stall milk with a correct discrimination rate of 88%. Pasture milk achieved a significantly higher intensity for \"grassy\" and \"sweet aromatic\" odour descriptors and the E-nose sensors that have best discriminated these milk samples were W5S, W2W and W1W. Terpene compounds have qualitatively characterised the milk from pasture-fed goats and could be chemical markers of pasture milk. The evidence from this study implies that E-nose can be a useful and simple device for the rapid control of the genuineness of raw milk in order to protect producers and consumers of dairy products from potential fraud, especially if the production of milk must comply with a production specification. Further investigation would be recommended to better understand the link between terpene compounds and type of grazed forage crops as well as their contribution to the different response intensities of the E-nose sensors.  
Conceptualization, R.T.; methodology, R.T., P.I. and A.G.; formal analysis, A.B. and A.G.; investigation, A.B., P.I. and S.C.; resources, R.S.; data curation, A.B.; writing--original draft preparation, A.B.; writing--review and editing, A.B. and A.G.; visualization, A.B. and A.G.; supervision, R.S. and A.G.; project administration, R.T. and R.S.; funding acquisition, R.T. and R.S. All authors have read and agreed to the published version of the manuscript.  
This work was supported by Campania Region, Italy (Project Code: PSR Campania 2014/2020, Misura 19--94250203448 \"Nobili Clientari\").  
No ethical approval was required for this study since the animals were reared under actual farm conditions, and no distress, pain suffering, or lasting harm was caused to them. The animals were managed in compliance with the European requirements concerning the protection of animals used for scientific purposes (Dir. 2010/63/UE), as implemented by the Italian legislation (DL n. 26, 4 March 2014).  
Not applicable.  
Data Availability Statement:Data supporting the conclusions of this article not already presented in the tables and figures will be available from the corresponding author (A.G.) upon reasonable request.  
Thanks are due to the staff of the farm \"I Moresani\" (Casal Velino, SA, Italy) for help with the care of the animals.  
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.
# Protocol-Specific and Sensor Network-Inherited Attack Detection in IoT Using Machine Learning
## 2 Related Work
### SN-Ihterited Attacks in RPL-Based IoT  
SN-inherited attacks are named so because they inherit the attacking mechanisms from the WSNs and optimize them to target the IoT networks. Another factor is the presence of foundational base of sensor networks in an IoT infrastructure which contributes towards the instigation of SN-inherited attacks in IoT. In an SN-W attack, two malicious nodes cooperate to create a tunnel [19] between each other and entirely or selectively transmit the network traffic maliciously through a poor routing path, rather than sending it through the original route. The attacking nodes establish a fake link between two nodes, which is apparently fast with low latency [20], to target attack the network. The goal is to disrupt the routing mechanism by misguiding the victim nodes and exhausting the network resources. An SN-W attack is capable of instigating other attacks in the network, such as selective forwarding, packet dropping, black hole, grey hole, denial of service [21], and Sybil attacks [22]. The attacker(s) sniffs, eavesdrops, and replays the data packets, which consequently impacts the overall network performance. Therefore, in this paper, this attack from the SN-inherited category is selected for detection along with the PS-R attack from the protocol-specific category of RPL attacks.  
There are three methods of wormhole creation, which include (1) packet relay, (2) packet encapsulation, and (3) out-of-bound link. In the packet encapsulation method, malicious nodes use a path, which is originally meant for sending regular data, and create a logical tunnel by encapsulating the data packets [10]. This is done to hide the hop counts from other nodes present on the tunnel's route. In the packet relay method, one or more illegitimate nodes send packets or control messages between two legitimate nodes, which are located far from each other, to mislead them into being close neighbors [23]. Usually, this is done by transmitting packets without updating the hop count [10; 24]. In the out-of-bound strategy, a wormhole is created by utilizing a wired or wireless link that is out of the network boundary to create a tunnel between the external attacker and an internal malicious node.
# Pico-Sat to Ground Control: Optimizing Download Link via Laser Communication
## 2 Physical Components and Methods
### FSO Communication--The Ground Station's Side
#### 2.3.3 Prediction
\[K=P_{k}A^{T}(AP_{k-1}A^{T}+R_{k})^{-1} \tag{8}\]  
\[P_{k+1}=P_{k}-KAP_{k} \tag{9}\]  
The new position \(X_{k+1}\) is then calculated:  
\[X_{k+1}=AX_{k}+K(Z-AX_{k}) \tag{10}\]  
This final equation calculates the satellite's location according to the prediction and the measurement values. This equation gives more weight to the predicted value when the Kalman gain is small, which means that in such cases, the system relies less on the measurements. **A note on TLE-based tracking:** Recall that the orbit of a satellite is represented using TLE (two-line element); whenever an accurate TLE is available, it can serve as a single source of tracking. Yet, in many cases, nano-satellites are hard to detect and identify and therefore it might take several weeks to obtain a reliable TLE. Moreover, the expected accuracy of nano- and pico-satellites is relatively low, as their orbit tends to drift faster than larger satellites. In such cases, the proposed trajectory Kalman filter can be used.
# Automated Data Acquisition in Construction with Remote Sensing Technologies
## 3 Standalone Remote Sensing (RS) Technologies
### Bluetooth, Infrared, and Ultrasound  
Bluetooth technology has recently become more popular with the introduction of Bluetooth low energy (BLE) technology in 2010. In contrast to Wi-Fi, the battery usage on the client side is very low for BLE technology which can enable it to work up to three to four years with only a single small battery. However, the fluctuation in the strength of the received signal is a challenging issue when using these sensors. Saying that, due to the inherent nature of the system, it is highly recommended for accurate positioning. Besides, it is incorporating well with other techniques such as filtering techniques and motion sensors [31]. These technologies can also be used to prepare a congestion map which shows the areas onsite with more accident risk or activity conflicts, by information acquired from workers and vehicles behavioral patterns [19].  
Infrared is electromagnetic radiation at a wavelength longer than red light. Infrared wireless technology can be used for short and medium range communications and control purposes, however, unlike radio-frequency (RF) wireless links, it cannot pass walls. Ultrasound, on the other hand, is usually implemented by using special microphones in a room. In fact, items that need to be tracked have a tag ID with a speaker attached. While the sound is stopped by the door, wall, or window but it is not stopped by other physical objects in the room such as people. These sensory systems need hardware infrastructure for signal communication and they have scalability limitations. Actually, they are rather focused on high accuracy than ease of deployment. Having said that, it requires a line-of-sight due to its limitation to penetrate in materials [31].
# Overview of the Research Status of Intelligent Water Conservancy Technology System
## 3 Status Quo of Key Technologies of Smart Water Conservancy
### Digital Twin Platform Technology
#### 3.4.1 Data Backplane Construction  
At present, problems such as classification inconsistency and format incompatibility exist in the construction of data baseboards, leading to poor generality and data fusion challenges [56]. Therefore, the establishment of high-precision multi-fusion data baseboards has become a key issue at present. One study [57] integrates tilt photogrammetry, airborne LiDAR, multi-beam sounding and other means. The three-dimensional model of an integrated watercourse is successfully constructed. An airborne LiDAR point cloud is used to improve the accuracy of the oblique photography model, and a multi-beam system is used to display underwater scenes in detail. In addition, another study [58] uses three-dimensional mapping technology to simulate the construction of digital twin baseboards, realize elastic update of scenes, and establish an early warning mechanism through satellite and UAV remote sensing, which innovatively solves the shortcomings of multi-source data generation, update and management, and provides key technical support for intelligent water conservancy.
# Analysis of Total-Forms of Cyanotoxins Microcystins in Biological Matrices: A Methodological Review
###### Abstract  
Microcystins (MCs) are cyclic heptapeptide toxins produced by many cyanobacteria. Microcystins can be accumulated in various matrices in two forms: a free cellular fraction and a covalently protein-bound form. To detect and quantify the concentration of microccystins, a panel of techniques on various matrices (water, sediments, and animal tissues) is available. The analysis of MCs can concern the free or the total (free plus covalently bound) fractions. Free-form analyses of MCs are the most common and easiest to detect, whereas total-form analyses are much less frequent and more complex to achieve. The objective of this review is to summarize the different methods of extraction and analysis that have been developed for total forms. Four extraction methods were identified: MMPB (2-methyl-3-methoxy-4-phenylbutyrate acid) method, deconjugation at basic pH, ozonolysis, and laser irradiation desorption. The study of the bibliography on the methods of extraction and analysis of the total forms of MCs showed that the reference method for the subject remains the MMPB method even if alternative methods and, in particular, deconjugation at basic pH, showed results encouraging the continuation of the methodological development on different matrices and on naturally-contaminated samples.  
Keywords:cyanotoxins; microccystins; total toxins content; method of analysis; MMPB method; ozonolysis; deconjugation at basic pH; laser irradiation desorption +
Footnote †: journal: _toxins_
## 3 Case Study
### Results and Discussion
#### 3.4.3 Comparison of Tunnel Reliability Based on ABGPR, Kriging, and XGBoost
Figure 9: Comparison of tunnel reliability based on ABGPR, Kriging and XGBoost. ABGP-based reliability indices of monitoring lines AB and AC or AD and AE are not changed. Due to the symmetry of monitoring lines AB and AC or AD and AE, the reliability indices of monitoring lines AB and AC or AD and AE should be the same, but there are some changes in their Kriging-based results. In this research, three GPR combined kernel functions were developed and compared to the basic kernel functions to determine the best GPR kernel function for the reliability analysis of deep soft rock tunnels. The Adam algorithm was also used to optimize the surrogate model's hyperparameters. Although GPR is a non-parametric model like the Kriging method, the difference between GPR and the Kriging method is that the former assumes that the random field is a Gaussian process and provides a complete distribution of the test sample; the latter assumes that the random field is an inherently stationary process and provides its optimal unbiased estimate for the test sample. Moreover, the Kriging model, as an interpolation model, is very sensitive to noisy data. Furthermore, XGBoost cannot directly identify the interaction between data features at all, and there is a risk of inaccurate predictions if the feature distribution of the prediction set is different from that of the original training set. Therefore, among the ABGPR method, the Kriging method, and the XGBoost method, the ABGPR-based \(R2\_\)score is the largest, the ABGPR-based MAPE is the smallest, and the fluctuation in the ABGPR-based calculation results is also the smallest. The advantages of the proposed method are that its prediction error is the smallest and its prediction ability is the strongest among the ABGPR method, the Kriging method, and the XGBoost method. It should be noted especially that if the data point number is insufficient or unevenly distributed, it may lead to inaccurate Kriging interpolation results. In addition, if the assumption that data have spatial correlation is not valid, the Kriging interpolation results may be affected. The XGBoost model may overfit, especially when the dataset is small or there are many features. And, for completely missing features, the XGBoost model may not be able to effectively learn the importance of these features, thereby affecting the performance of the model.
## 1 Introduction
Next, based on the results of the mirror facet installation measurements, a three-rotation alignment method (adjusting three support bolts in turn) and two-rotation alignment method (adjusting two of the three support bolts) was investigated and a corresponding mathematical model was established (in Sections 4 and 5). The advantage of the two presented mirror facet alignment method is that the adjustment value and adjustment order of each support bolt can be determined prior to the alignment of the mirror facet, which can therefore provide quantitative adjustment information to the operator. The methods were verified by a numerical simulation and an experimentally using a metal facet alignment. The errors and ways to improve the methods in relation to the accuracy of the mirror facet alignment are discussed (in Section 6). Finally, future work aimed at using the real-time monitoring and alignment system is briefly discussed (in Section 7).