esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Algorithm Theory and Method
### Threshold and Similarity Proportion of Each State  
#### 2.3.1 State Thresholds for Historical Equipment  
According to the clustering results, the time series data in the different states of historical equipment can be obtained. The probability distribution of the different states and the corresponding state thresholds can also be obtained.  
Kernel density estimation is a nonparametric probability density estimation method that is mainly applied to directly reflect the distribution of characteristic parameters of different states. In the actual production process, the probability density function of the collected data is often unknown, and the specific distribution form cannot be determined, so the kernel density estimation method is adopted to analyze the distribution law that is not known in advance. In this data processing approach, the data to be analysed are one-dimensional data, and the one-dimensional kernel density estimation equation is adopted:  
\[\hat{f}(x)=\frac{1}{n}\sum_{i=1}^{n}K\left(\frac{x-x_{i}}{h}\right) \tag{8}\]  
where \(h\) is the bandwidth for \(h>0\), and \(K\) is a non-negative function called the kernel function. Here, the Gaussian function is used as the kernel function, namely, \(K(x,h)\propto\exp\left(-x^{2}/2 lt^{2}\right)\); \(\hat{f}(x)\) is the kerneldensity estimation of density function \(f(x)\). Bandwidth H can be selected as the minimum of the mean integrated square error (MISE).  
\[h_{M}=\min\left\{E\left[\int_{-\infty}^{+\infty}\left(f(x)-f(x)\right)\right]^{2 }dx\right\} \tag{9}\]  
The initial threshold value of the alarm is defined as \(\omega_{0}\) when the value of the probability distribution function reaches 99%.
## 4 Conclusions  
An experimental study was conducted to investigate the magnetic field effect on the forced convective heat transfer in a tube according to the magnetic field strength of water-ethylene glycol-based Fe\({}_{3}\)O\({}_{4}\) nanofluid and Fe\({}_{3}\)O\({}_{4}\)-MWCNT hybrid nanofluid. It is confirmed that the convective heat transfer coefficient increased as the concentration of the nanofluid and the magnetic field strength increased. Moreover, the convective heat transfer coefficient and pressure drop increased due to the effect of the external magnetic field. When the magnetic field strength was varied from 0 to 750 G, the maximum convective heat transfer coefficients were observed for the 0.2 wt% Fe\({}_{3}\)O\({}_{4}\) and 0.1 wt% Fe\({}_{3}\)O\({}_{4}\)-MWNCT nanofluids, and the improvements were 2.78% and 3.23%, respectively. In addition, when the strength of the magnetic field was 750 G, the convective heat transfer coefficient of the 0.2 wt% Fe\({}_{3}\)O\({}_{4}\) and 0.1 wt% Fe\({}_{3}\)O\({}_{4}\)-MWCNT nanofluids exhibited 24.6% and 25.9% improvements on average, respectively, when compared with the base fluid. The average pressure drops for the 0.2 wt% Fe\({}_{3}\)O\({}_{4}\) and 0.2 wt% Fe\({}_{3}\)O\({}_{4}\)-MWNCT nanofluids were 4.73% and 5.23%, respectively. Furthermore, it was observed that the convective heat transfer coefficient of 0.1 wt% Fe\({}_{3}\)O\({}_{4}\)-MWCNT hybrid nanofluid was 5% higher than that of 0.2 wt% Fe\({}_{3}\)O\({}_{4}\) nanofluid, although it had a lower concentration. This was because of the complex aspects of blocking heat transfer owing to the aggregation of Fe\({}_{3}\)O\({}_{4}\) nanoparticles generated by the external magnetic field and the large amount of aggregates generated on the inner wall of the tube. Therefore, to increase the convective heat transfer of the Fe\({}_{3}\)O\({}_{4}\)-MWCNT hybrid nanofluid using an external magnetic field, the dispersion stability, concentration of the nanofluid, and the magnetic field strength should be optimized.  
Conceptualization, A.L. and C.V.; methodology A.L. and H.C.; validation, H.C. and A.L.; formal analysis, A.L.; investigation, A.L. and C.V.; resources, H.C.; writing--original draft preparation, A.L. and C.V.; writing--review and editing, H.C.; visualization, A.L.; supervision, H.C. All authors have read and agreed to the published version of the manuscript. This work was supported by a National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (No. 2020R1A5A118153) and by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2020R1A6A3A13072352). The authors declare no conflict of interest.
## 4 Conclusions  
The characterization of a spray is not merely acquiring information on the size and velocity of its droplets in several locations from single-point measurement techniques, or in several planes from 2D- or 3D-measurement techniques. Although it is essential to acquire enough information, as explored here through an information theory approach, the clarity of the research question associated with the spray characterization is very much like the clarity in the display and analysis of data. Therefore, a well-defined research question is what guides the kind of spray characterization performed. In this article, we review the statistical language used in spray characterization, and:  
* the differences between organizing spray data using probability histograms or histograms of probability density;
* how to choose the number of classes in histograms;
* the different kinds of probability histograms considering the number of droplets, their area, or the liquid volume, and corresponding moments;
* a method based on the excess entropy from information theory to assess if there is enough information for post-processing;  
Figure 10: Polydispersion degree given by the normalized Shannon entropy, \(H_{n}\), and spray heterogeneity degree given by the volume-weighted standard deviation, \(SD_{v}\) [\(\blacktimes\)], for the planes of \(Z=10\) mm (**top**) and \(Z=20\) mm (**bottom**).  
* the physical meaning of fitting mathematical probability distribution functions, namely the Log-Normal, Gamma, and Weibull, to spray data;
* and introduce, for the first time, the notion of Drop Size Diversity with its polydispersion and heterogeneity degrees quantified by the normalized Shannon entropy and volume-weighted standard deviation, respectively.  
Finally, the topics explored on spray characterization are applied to nanofluid sprays to explore the effect of introducing a surfactant and nanoparticles on the spray structure and dynamic characteristics.  
Conceptualization, investigation, M.O.P.; resources, A.S.M. and A.L.M.; writing-original draft preparation, M.O.P.; writing-review and editing, M.O.P and A.S.M.; project administration, A.L.M.; funding acquisition, A.S.M. and A.L.M. All authors have read and agreed to the published version of the manuscript.  
[PERSON] would like to acknowledge project No. 030171 funded by LISBOA-01-0145-FEDER-030171/PTDC/EME-SIS/30171/2017, and project UTAP-EXPL/CTE/0064/2017.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
CTAB CetylTrimethylAmmonium Bromide DSD Drop Size Diversity PDI Phase-Doppler Interferometer
# QuietPlace: An Ultrasound-Based Proof of Location Protocol with Strong Identities
## 6 QuietPlace Communication Specification
### Proof Creation  
#### 6.1.1 Meeting  
When the user wants to obtain a new proof, they begin by broadcasting a Prover Request (PR) message.  
\[\text{PR}=C(P,r_{\text{P}})\ |\text{L}_{0}|\text{K}_{0}|\text{t}_{1}|CA \tag{1}\]  
where  
P: the prover's identity  
r\({}_{\text{P}}\): nonce generated by the prover  
L\({}_{0}\): prover's location with highest accuracy (10 characters plus code, represents a 14 m \(\times\) 14 m square)  
K\({}_{0}\): the seed for the location chain  
t\({}_{1}\): prover's current timestamp  
Figure 4: A sequence diagram of QuietPlace.  
CA: CA's identity (i.e., hostname)  
Each witness checks that their location expressed as a 10-character plus code matches the one reported by the prover, by checking the reported area'square' and the neighboring ones. The \(14\times 14\) m area is sufficient for the range of ultrasound messages. Also, each witness checks that the prover's timestamp t\({}_{1}\) is valid. The witness will use the identity with which they are registered with the prover's selected CA and reply with a Witness Presence (WP).  
\[\text{WP}=\text{C}(\text{W}_{i},\text{r}_{\text{wi}})\mid\text{n}_{\text{i}} \tag{2}\]  
where  
\(W_{\text{i}}\): identity of witness i  
\(r_{\text{wi}}\): nonce generated by the witness i  
\(n_{\text{i}}\): nonce  
The prover begins capturing video and constructs Start (S) message  
\[\text{Start}=\text{C}(\text{P},\text{r}_{\text{p}})|\text{K}_{\text{n}}|t|\text {h}(\text{f}_{1}) \tag{3}\]  
where  
\(\text{K}_{\text{n}}\): the head of the location chain  
t: current timestamp  
\(\text{h}(\text{f}_{1})\): hash of the first frame of video  
The prover broadcasts the following message:  
\[\text{hsStart}=\text{Start}|\text{h}\text{S}_{\text{kp}}(\text{Start}) \tag{4}\]  
Each witness now checks that \(\text{K}_{\text{n}}\) is a valid location chain for the \(\text{L}_{0}\), \(\text{K}_{0}\) in PR and that t is a valid timestamp. If the above are valid, the witness saves the timestamp t\({}_{\text{Src}}\) at which it received Start.
# Carbon, Water, and Light Use Efficiency Under Conservation Practice on Sloped Arable Land
## 2 Materials and Methods
### Estimation of Net Primary Productivity, Gross Primary Productivity, and Carbon Use Efficiency  
For the estimation of net primary productivity (NPP) and gross primary productivity (GPP), we followed the approach described in [38] and the STICS model.  
Net primary productivity (NPP) was calculated as the daily change in the total carbon content (TCC) of both the aboveground and belowground plant biomass [39]. Biomass was multiplied by the carbon content coefficient:  
\[\textit{TCC}=\textit{aboveground biomass}\times\textit{0.42}+\textit{roots biomass}\times \textit{0.38} \tag{2}\]  
Belowground NPP was derived using a constant coefficient based on shoot-to-root ratios and harvest indices. Various approaches exist, such as the method described in [38], which incorporates a root + rhizodeposition coefficient: 0.59 for maize; 1.5 for winter wheat before the maximal rate of leaf growth (end of the juvenile phase); 0.43 between the maximal rate of leaf growth and flowering; 0.33 after flowering; and 1.5 for cover crops  
Figure 1: Schematic representation of the experimental design (2021–2023), showing crop rotations and tillage directions for each treatment. Plots 1 and 2 represent conventional tillage (CT) performed up and down the slope, while Plots 3 and 4 represent minimum tillage (MT) with contour tillage and a cover crop (barley) incorporated as green manure between wheat and maize. Arrows indicate tillage direction. Each plot followed a specific crop sequence to reflect practical rotations.  
terminated before booting. However, in this study, we applied a coefficient of 2.46, as proposed by [40].  
\[GPP=NPP+AR \tag{3}\]  
where \(GPP\) is gross primary productivity (kg C m\({}^{-2}\)), \(NPP\) is net primary productivity (kg C m\({}^{-2}\)), and \(AR\) is autotrophic respiration (kg C m\({}^{-2}\)).  
Autotrophic respiration (AR) was calculated following the procedure described in [38; 41]:  
\[AR=GR+MR \tag{4}\]  
where \(GR\) is growth respiration and \(MR\) is maintenance respiration.  
Growth respiration (GR) represents the CO\({}_{2}\) emitted as a byproduct of growth processes. Based on the amount of glucose required to synthesize 1 g of plant material, ref. [41] proposed a coefficient of 0.28 (within the range of [0.25, 0.29]).  
\[GR_{i}=(GPP_{i-1}-MR_{i})\;*\;0.28 \tag{5}\]  
\(GR\) was calculated on day \(i\) as a function of GPP on day \(i-1\)[36].  
Initial values GPP1 and GR1 are null, for each crop, before sowing.  
\[MR=(aN+b)*Q_{10}^{\frac{T-25}{10}}*W \tag{6}\]  
\[AR=0.28*GPP+(1-0.28)*(aN+b)*Q_{10}^{\frac{T-25}{10}}*W \tag{7}\]  
where \(Q_{10}\) is the temperature coefficient of respiration, equal to 2; \(AR\) is the aboveground autotrophic respiration; \(a\), \(b\) are the empirical coefficients, with values of 2.16 and \(-0.66\), respectively; \(T\) is the temperature (\({}^{\circ}\)C); \(N\) is the nitrogen content of the plant tissue (%); \(W\) is the aboveground biomass (kg m\({}^{-2}\) day\({}^{-1}\)) [38].  
Carbon use efficiency was calculated as [42]:  
\[CUE=NPP/GPP \tag{8}\]
## 4 The Blueprint Concept
### Fundamental Blueprint Structure  
We define Blueprint as a uniform abstract description of a cloud service offering that abstracts away from all specific technical details and complexities to facilitate the cloud application developers with the selection, customization and composition of cross-layered cloud services across various vendors. A blueprint has the following fundamental structure, as illustrated in Figure 3:  
* **Basic properties**: Some basic description properties of a blueprint including its unique ID, ownership, release date, version info, _etc_.
* **Offering**: The description of one or many cloud service offerings including their names, functionalities, signature interfaces, interaction protocols, elasticity offerings, and QoS offerings.
* **Implementation Artifacts**: The description of the artifacts that implement the offerings. Implementation artifacts may include the binary files, configuration files or some deployable web packages.
* **Resource Requirements**: The description of the required cloud resources including their QoS requirements. The resource requirements are needed to deploy the implementation artifacts.  
* **The Virtual Architecture (VA)**: this part is specified by the application developers to capture the desired virtual architecture in terms of the interdependencies and interconnections between the offerings, implementation artifacts and resource requirements across blueprints.
* **Policy**: describing the policy constraints that may not be violated by any element (the offering, implementation artifacts, resource requirements) of a blueprint.
## 1 Introduction
The literature [17] proposed a robust fixed-order frequency domain controller design method to maximize the tracking performance with specified stability margin in multiple operating points. In order to suppress ultra-low frequency oscillation, the most effective strategy is governor parameter optimization to improve the system damping within the ultra-low-frequency band. The literature [18] put forward the design principle of the damping control index of ULFO. These studies can provide references for the suppression of ultra-low frequency oscillation. For low-frequency oscillation suppression, the DC modulation power required is small, and it is generally not necessary to have multiple DC simultaneous modulation. However, for ultra-low-frequency oscillation, because of the different oscillation energy, it may require multiple DC modulation at the same time. Methods of using two kinds of DC to suppress the ultra-low-frequency oscillation of the system have become a new problem, but there is some research investigating this. In this paper, a multi-DC modulation coordination strategy for suppressing ultra-low frequency oscillations is proposed. Firstly, the mechanism of suppressing ultra-low frequency oscillation in hybrid DC modulation system is analyzed from the perspective of energy, and the calculation method of system energy in practical engineering is given. Finally, a deep deterministic policy gradient (DDPG) algorithm is used to optimize the selected DC modulation parameters.
## 3 Materials and Methods  
As described in previous work [28], the integration of artificial intelligence algorithms into digital transformation approaches involving tools such as data analytics for physical asset monitoring and management was only a question of time, and many governmental agencies and commercial initiatives have been launched for exploiting the information collected by road damage acquisition systems and stored digitally into databases in various ways.  
The rationale for doing so is twofold, as depicted in Figure 4. First, to leverage the recent advances made in several disciplines (artificial intelligence and Internet of Things mainly) for conceiving novel platforms to facilitate the labor of road inspectors during the inspection on a day-to-day basis. For instance, the computer vision algorithms discussed in Section 2 can run on edge devices (either smartphones or mounted in drones or cars) for implementing lightweight mobile measurement systems that can be used as well as gateways for sending georeferenced info to the cloud. Second, this information can be exploited for creating large datasets or digital representations of a road or street conditions (but in a streamlined fashion as metadata, instead of images), from medium to large cities, effectively creating what is known as \"digital twins\" (Figure 3b).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Damage Type** & **Description** & **Class Name** & **Num Images** \\ \hline \multirow{4}{*}{Crack} & \multirow{2}{*}{Linear} & Car wheel mark & D00 & 2678 \\  & & Cement joint & D01 & 3789 \\  & & Zebra crossings & D10 & 742 \\  & & Cement or asphalt construction joint & D11 & 636 \\ \cline{2-3}  & Alligator & Partial, overall pavement & D20 & 2541 \\ \hline \multirow{2}{*}{Other damages} & \multirow{2}{*}{Hutting, bump, peeling or pothole} & D40 & 409 \\  & & White lane wearing & D43 & 817 \\ \cline{1-1}  & & Zebra cross wearing & D44 & 3733 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Asphal damage classes IDs used in [25] and their corresponding definitions.  
Figure 4: Architecture of a prototypical road damage acquisition and physical asset management system based on artificial intelligence (AI) and big data technologies.  
The information can be aggregated over time for bridging the digital transformation gap that is currently playing the road inspection process. These platforms could help to reduce the burden for the municipalities and federal government, with the benefit of providing actionable insights about their assets. Platforms with these capabilities have emerged as commercial solutions (RoadBotics [26]), but their service is more oriented towards a general assessment of the street qualities.  
In this paper, we focus on the creation of the dataset and training the AI algorithm in charge of performing the road damage detection and identification; the road damage acquisition system is already in use as a mobile app (similar to the one on the left side of Figure 4). The other components of the system will be described in a subsequent work as they are currently under development.
# A Novel Feature Selection Based on VMD and Information Gain for Pipe Blockages
## 2 Materials and Methods
### Information Gain-Based Selection of Effective IMF Components  
The method operates on the following principle: Given a sample set \(D\) and continuous attributes \(a\), assuming that \(a\) have \(n\) different values on \(D\), which are sorted from small to large and are represented by \(\big{\{}a^{1},a^{2},\ldots,a^{n}\big{\}}\). The values of adjacent attributes are set as \(a^{i}\) and \(a^{i+1}\), while the division results generated by \(t\) are identical when any value in an \([a^{i},a^{i+1}]\) interval is taken. Thus, for continuous attributes \(a\), the median point \(a^{i}+a^{i+1}/2\) of the interval \([a^{i},a^{i+1}]\) is assigned as the candidate partition point. The partition point \(t\) can divide the given sample set \(D\) into subset sums \(D_{t}^{-}D_{t}^{+}\), of which \(D_{t}^{-}\) represents the samples with the value range of attributes \(a\) not greater than \(t\), while \(D_{t}^{+}\) denotes those samples with the value range of attributes \(a\) greater than \(t\). The relevant computational formula for information gain is [33]:  
\[Gain(D,a)=\max\;Gain(D,a,t)=\max_{t\in T_{a}}Ent(D)-\sum_{\lambda\in\big{\{}- \,,+\}}\frac{\Big{|}D_{t}^{\lambda}\Big{|}}{|D|}Ent(D_{t}^{\lambda}) \tag{3}\]  
where \(\text{Gain}(D,a,t)\) denotes the information gain of the given sample set \(D\) following the dichotomy based on the partition point \(t\). According to the following steps, the partition point that makes \(\text{Gain}(D,a,t)\) the maximum is selected:Step 1: The given sample set \(D\) is computed.  
Step 2: For each attribute \(a\), i.e., component, the information gain \(\text{Gain}(D,a,t)\) and partition point \(T_{a}=\left\{\frac{d^{i}+d^{i+1}}{2}|1\leq i\leq n-1\right\}\) are computed.  
Step 3: After selecting out the maximum value \(\text{Gain}(D,a,t)\), the corresponding component is chosen as the root node of the decision tree. The sample-set is split into two parts according to the computed partition point, where the samples greater than \(t\) are denoted by \(D_{t}^{+}\), and the samples less than or equal to \(t\) are denoted by \(D_{t}^{-}\).  
Step 4: The remaining components are deemed as the dataset of the previous node, and the component with the largest information gain is chosen as the non-leaf node split by the root node.  
Step 5: For each component, Steps 3 and 4 are repeated if the information gain value is greater than the given threshold.  
Step 6: The component selection is terminated and completed if the maximum information gain is less than a given threshold. The information gain-based component selection method allows accurate extraction of the components that contain the majority of blockage features.
# Security Assessment of Agriculture IoT (AIoT) Applications
## 2 Materials and Methods
### The System Operation States
#### 2.2.1 Cybersecurity Assessment Key Questions  
An assessment process should always follow the same procedures. This is important because a cybersecurity assessment must be repeated periodically to identify new types of vulnerabilities and to improve the system against new attack vectors. This enables successive evaluations to be compared.  
* What is the System under Consideration (SuC)?
* Based on the SuC definition, what are the borders of the system?
* Which interfaces reach from the outside into the SuC?
* What are the assets of the system?
* Which assets are not part of the analysis?
* Which assets are outside of the SuC?
* What are the potential threats?
* What are the security leaks and vulnerabilities of these architectures?
* What are the existing hardware vulnerabilities?  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Main-State (Activity)** & **Substates (Activity)** \\ \hline \multirow{4}{*}{Production} & Component selection \\  & Design (HW, SW) \\  & Manufacturing \\  & Firmware Installation \\  & Component testing \\ \hline \multirow{4}{*}{Installation} & Software installation \\  & Component and system Integration \\  & Security key transfer and initialization \\  & System testing \\ \hline \multirow{4}{*}{Operation} & Data reception \\  & Data processing \\ \cline{1-1}  & Data transmission \\ \cline{1-1}  & Renew security keys \\ \cline{1-1}  & System backup \\ \hline \multirow{4}{*}{Decommissioning} & System/component reset \\ \cline{1-1}  & Disposal \\ \cline{1-1}  & Complete data deletion (data management) \\ \hline \hline \end{tabular}
\end{table}
Table 1: System operation states.  
* What are the existing software vulnerabilities?
* What are the existing network vulnerabilities?
* Which attack vectors must be assumed for the defined SuC?
* What are the impacts of security attacks for the different parties and situations (loss of values, loss of trust, and loss of reputation)?