esa-sceva/satcom-chunk-collection huggingface dataset:
# Graphol: A Graphical Language for Ontology Modeling Equivalent to OWL 2
## 6 User Evaluation Study
### Objectives of the Study  
The design of our user evaluation study was geared towards the achievement of two main objectives.  
1. Evaluate the difficulty of using the Graphol language for ontology comprehension and editing by users very experienced in conceptual modeling and (in some cases) with basic skills in logics and ontologies.
2. Evaluate the difficulty of approaching and learning the Graphol language for users with only basic knowledge of conceptual modeling and little or no experience with ontologies, both in isolation, and in comparison with another graphical ontology language that is heavily based on a formalism that is already familiar to them.  
In accordance to these two objectives, we identified two groups of test participants, and defined two different types of tasks for each of these groups. Further details of the test participants, of the required tasks, and of the structure of the two tests are given in the remainder of this section. Here, we give a brief sketch of these two tests: the first one included a series of comprehension tasks and of editing tasks on two ontology models represented in the Graphol language; the second one, involving users with limited knowledge of conceptual modeling and ontologies, instead included two sets of comprehension tasks on two ontologies represented in Graphol and in OWLGrEd [29; 49].  
The reason for limiting the comparative evaluation of Graphol with OWLGrEd to the comprehension test with less-skilled users is that the purpose of the test was to verify that the Graphol language could be learned and used by these users with no more effort and difficulty than that needed for another language which is, by its very nature, more recognizable, due to its strong relation to UML class diagrams.. Therefore, it was not in our interest to gauge the effectiveness of Graphol against OWLGrEd, or any other similar visual language, among more expert users. Instead, our test with these users was specifically designed towards measuring their perception of Graphol as a viable candidate for future use in the design of ontologies in real life.  
Furthermore, we have not carried out a comparative evaluation of our language with ontology editors which do not provide solutions for graphic editing and which are based on formal non-graphic languages, such as Protege, given that our main goal is to deal with users who are not necessarily experts in formal languages and ontologies.
# A Lightweight Perceptron-Based Intrusion Detection System for Fog Computing
## 3 Proposed Method
### Phase 1: Preprocessing
#### 3.1.2 Feature Selection  
The process of feature selection is needed in order to simplify the model, achieve better performance, and reduce the training time. The feature selection (or variable selection) is performed by selecting a subset of the most relevant features. There are three main methodologies of feature selection:  
* _Filter method_: It is performed by ranking the features based on their relevance using criteria like correlation, \(\chi^{2}\) or information value.
* _Wrapper method_: It evaluates different subset of features and chooses the one with best performance.
* _Embedded method_: It allows the algorithm to choose the best subset of features and then performs classification. In this work, we choose the filter method due to its low space and time complexity. Mutual information is a common feature selection method based on the information value of a given attribute with respect to the classification. Mutual information is defined as follows [31]: \[MI(V;C)=\ \sum_{e_{v}e_{c}\in\{0,1\}}P(V=e_{v}\,\ C=\ e_{c})\ \log_{2}\frac{P(V=e_{v}\,\ C=e_{c})}{P(V=e_{v})\ P(V=\ e_{v})}\] (8)  
where \(V\) is a random variable that takes values of \(e_{v}\) = 1 if the trace contains \(n\)-gram \(v\) and \(e_{v}\) = 0 if the trace does not contain \(v\). \(C\) is a random variable that takes values of \(e_{c}\) = 1 if the \(n\)-gram belongs to class \(c\) and \(e_{c}\) = 0 if the trace is not in class \(c\). In the context of information theory, mutual information measures how much information an attribute (an \(n\)-gram in this case) contains about the class. The more similarity between the \(n\)-gram and the class distributions, the less information is obtained by considering this \(n\)-gram.
# Damage Detection in Beam Bridges Using Quasi-static Displacement Influence Lines
## 4 Experimental Example
### Brief Description of the Model Bridge  
The entire experimental system in this section consists of three parts, namely, the superstructure of the model bridge, the bridge bearing and supporting frame and the loading vehicle, as shown in Figure 10. The superstructure of the bridge is composed of three T-shaped steel beams (with a Young's modulus of \(2.0\times 10^{11}\) Pa), a detailed drawing of which is shown in Figure 11. The entire supporting system consists of the supported bearing and the steel frame, which are equivalent to bridge piers. The moving vehicle system includes an electronic machine and a steel vehicle, in addition to several steel clump weights (each of which weighs 20 kg) and two aluminum tracks (detailed information is also described in Reference [25]). The two aluminum tracks keep the vehicle moving over the bridge along the designed trajectory and the electronic machine draws the vehicle at a slow uniform velocity.  
Figure 9: Damage detection results: (**a**) \(p\) is equal to 1; (**b**) \(p\) is equal to 7; (**c**) \(p\) is equal to 17.
## 4 Results
### Relationship between LAI and Aboveground Biomass  
The relationship between the field-measured AGB and remote sensing-estimated LAI is shown in Figure 3. Similar correlations were found between LAI\({}_{\text{DAP}}\)-AGB (R\({}^{2}\) of 0.43) and LAI\({}_{\text{ALS}}\)-AGB (R\({}^{2}\) of 0.45), although the correlations are relatively low. There are apparent differences in correlations among forest types (Table 3). The mixed coniferous and broad-leaved forest shows the best correlation between aboveground biomass and derived LAI with R\({}^{2}\) larger than 0.70. For mixed broad-leaved forest, other coniferous trees, and _Eucalyptus robusta Smith_, R\({}^{2}\) is around 0.50 with AGB ranging from 0 to 400 t/ha. However, for Hrevea brasiliensis, there is almost no correlation between derived LAI and aboveground biomass (R\({}^{2}\) = 0.03 for ALS and 0.01 for DAP). Derived LAI of Hrevea brasiliensis is centered between 0 and 3 with aboveground biomass less than 150 t/ha. Since there are only two plots of Chinese fir, the correlation between derived LAI and aboveground biomass is not computed.  
\begin{table}
\begin{tabular}{c c c} \hline
**Coefficients of Determination** & **Canopy Cover** & **LAI** \\ \hline Mixed coniferous and broad-leaved forest & 0.64 & 0.87 \\ Mixed broad-leaved forest & 0.79 & 0.54 \\ Other coniferous trees & 0.94 & 0.66 \\ Eucalyptus robusta [PERSON] & 0.81 & 0.84 \\ Acacia confusa [PERSON]. & 0.89 & 0.82 \\ Hrevea brasiliensis & 0.71 & 0.67 \\ All & 0.80 & 0.76 \\ \hline \end{tabular}
\end{table}
Table 2: Coefficients of determination (R\({}^{2}\)) of fitted curves between DAP-based and the ALS-based estimated canopy cover and LAI under different forest types.  
Figure 3: Correlation between ALS-derived LAI and aboveground biomass (AGB) (**a**), between DAPS-derived LAI and AGB (**b**) for different forest types.
## 2 Design and Methods
### Comparative Analysis  
To evaluate the DL-FEA model's effectiveness, a structured comparative analysis was conducted using multiple assessment methodologies. The methods employed include the following:  
* Accuracy assessment: The DL-FEA model's predictions were compared against traditional FEA results to evaluate stress-strain distributions. Mean squared error (MSE) and correlation coefficients were computed to quantify deviations in predicted deformation patterns.
* Computational efficiency evaluation: The processing times required for both traditional FEA and DL-FEA were recorded and compared. This involved measuring the computation time for multiple loading scenarios and assessing efficiency gains from using the DL-based surrogate model.
* Empirical validation: The DL-FEA model's stress distribution outputs were cross-referenced with published experimental data on acetabular cup fixation. This comparison ensured that simulation outputs aligned with real-world implant behavior and failure mechanisms.
* Robustness and sensitivity analysis: The model's response to variations in input parameters, such as material properties and boundary conditions, was analyzed. Sensitivity testing was performed to determine the stability of predictions under different simulation settings.
## 3 The Signal Existence Verification (SEV) Process for GPS Signal Detection
That is, the original satellite signal information could not be despread out with the wrong code delay information. In reality, the power of the despread sinusoid wave might be close to the noise floor and difficult to observed from the time-frequency plane on the condition of the low received power signal. As discussed in Section 2, the time-frequency method based on the Fourier sine spectrum could therefore provide an effective tool for the low received power detection. A detailed block diagram of the SEV process for a specific PRN (satellite) signal is shown in Figure 6. In addition, the SEV process would be executed after the acquisition process while the acquired SNR of a satellite signal is below and close to the threshold \(V_{t}\). Figure 4: (a) The concept of typical strength GPS signal detection in acquisition process. (b) The concept of low received power GPS signal detection in acquisition process. Figure 3.0 (a) An example of mapping a raw GPS signal into the time-frequency plane by a time-frequency tool. (b) The concept of satellite signal detection by using the time-frequency tool.
# All-Optical Method of Determining Laser Power from the Photomechanical Effect
## 5 Conclusions  
A dye-doped PDMS film was cured over a polycarbonate substrate with a surface relief grating. A method of determining the linear coefficient of thermal expansion was demonstrated by heating the sample and detecting the angular shift of the first-order diffraction beam with a linear CCD array. The manufacturer reported value for the linear coefficient of thermal expansion of undoped PDMS was within the uncertainty of our measurement. Previous studies have shown that a diffraction pattern can be embossed on the surface of thermoplastics under conditions of elevated temperature and pressure [110, 111, 112], which increases the library of materials that can be studied with this method.  
The increase in grating period from photothermal heating by a pump beam was detected from small changes in the diffraction angle of a probe beam. The change in grating period was found to depend linearly on the pump power, and thereby allowing the method to be easily made into a power sensor. Furthermore, the laser power sensor can be integrated into a portable spectrometer with the same linear CCD array. The experimental dimensions used in this study resulted in a \(\sim\)1 mW sensitivity and a resolution of a few percent. The power detection range for sensors based on this method could be adjusted by changing the arm length, even through mirrored reflections, of the diffracted probe beam.  
**Author Contributions:** Conceptualization, N.J.D. and M.C.; methodology, N.J.D. and M.C.; software, N.J.D. and F.S.; validation, F.S., R.T.S., M.C. and N.J.D.; formal analysis, N.J.D., M.C. and F.S.; investigation, F.S., R.T.S., M.C. and N.J.D.; resources, N.J.D. and M.C.; data curation, N.J.D.; writing--original draft preparation, N.J.D., M.C. and F.S.; writing--review and editing, F.S., R.T.S., M.C. and N.J.D.; visualization, F.S., R.T.S., M.C. and N.J.D.; supervision, N.J.D. and M.C.; project administration, N.J.D.; funding acquisition, N.J.D. All authors have read and agreed to the published version of the manuscript.  
This research was funded in part by the Hawaii Pacific University, College of Natural and Computational Sciences, Scholarly Endeavors Program.  
**Infrared Consent Statement:** Not applicable.  
**Data Availability Statement:** Data produced by this work is available at [[https://doi.org/10.17632/4c2](https://doi.org/10.17632/4c2) whwytgg.1]([https://doi.org/10.17632/4c2](https://doi.org/10.17632/4c2) whwytgg.1) (accessed on 18 October 2022).  
**Acknowledgments:** N.D. thanks the educational science laboratory staff ([PERSON], [PERSON], [PERSON], [PERSON], [PERSON] [PERSON]) for helping with supplies and use of small equipment.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
**Sample Availability:** Samples of the Oil Red O-doped PDMS material used in this study are available from N.J.D.
## 2 Chirp Transform Spectrometer  
### Functional Principle of CTS (Chirp Transform Spectrometer)  
Based on the technology of signal expansion and pulse compression, the CTS system can realize high-resolution and real-time spectrum measurements though analog time-frequency transformation. The measured signal \(s(t)\) sent to the CTS system is first mixed with a chirp signal with a certain dispersive constant, \(\mu_{e}\), and time-bandwidth product \((T_{e}B_{e})\) for signal expansion. This chirp signal is a quadratic phase modulated signal, and can be expressed as:  
\[s_{chirp}(t)=e^{-j\pi\mu_{e}t^{2}/T_{e}}\quad 0<t<T_{e} \tag{1}\]  
After the mixer, the modulated expanding chirp signal carrying the information of the input signal \(s(t)\) can be written as:  
\[s_{mix}(t)=s(t)\cdot s_{chirp}(t)=s(t)\cdot e^{-j\pi\mu_{e}t^{2}/T_{e}} \tag{2}\]  
This is known as the signal expansion process of the input signal \(s(t)\). Subsequently, the modulated expanding chirp signal \(s_{mix}(t)\) carrying the information of the measured signal \(s(t)\) will be amplified, filtered and compressed into pulses by a matched filter. Assuming that a precise dispersion characteristic match exists between the signal expansion and compression, the chirp rate of the matched filter \(\mu_{c}\) is opposite to that of the modulated expanding chirp signal \((\mu_{c}=-\mu_{e})\). The compressed signal from the SAW filter is actually the convolution between the compressor and the expanding chirp signal \(s_{mix}(t)\):  
\[s_{compress}(\tau)=\int_{-\infty}^{+\infty}s_{mix}\cdot h(\tau-t)dt \tag{3}\]  
where \(h(t)\) is the impulse response of the SAW filter. Assuming \(\mu_{c}=-\mu_{e}\) and omitting the time duration of the signal expansion and compression, Equation (3) can be rewritten as:  
\[s_{compress}(\tau)=e^{j\pi\mu_{e}\tau^{2}}\int\limits_{-\infty}^{+\infty}s(t) \cdot e^{-j2\pi ft}dt \tag{4}\]  
If the fixed phase \(e^{j\pi\mu_{e}\tau^{2}}\) in Equation (4) is ignored, the compressed pulse \(s_{compress}(\tau)\) from the SAW filter is the Fourier transform of the measured signal \(s(t)\). This process is known as pulse compression. The frequencies and amplitudes of the input signal can therefore be measured by measuring the time distributions and the envelope information of the output pulses [19].
# Enhancing Smart Agriculture by Implementing Digital Twins: A Comprehensive Review
## 3 Reviewing Relevant Technologies and DT Components
### Technologies Involved in DTs
#### 3.1.1 Augmented, Virtual, and Mixed-Reality  
DTs rely on Augmented (AR), Virtual (VR), and Mixed-Reality (MR) technologies. As an example, they can be created using three-dimensional (3D) technologies and presented as holograms or experienced through AR/VR/MR devices [35], such as the Microsoft HoloLens. Using sensors installed in the field, their real-time digital twin can be generated and projected as a hologram in remote locations. This enables individuals in different locations to engage in interactions that simulate being physically present in the same space. Communication enhancement in digital twins is derived by integrating tactile feedback, simulating the sensation of physical interaction via haptics. Furthermore, DTs can leverage humanoid and soft robotics technologies to enable physical actions on behalf of their real-life counterparts. This means digital twins can physically engage with the environment or perform tasks, utilizing robotic systems that mimic human-like or flexible movements. These advancements in augmented, virtual, mixed reality, haptics, and robotics greatly enhance the capabilities of digital twins, enabling immersive interactions, realistic sensations, and physical engagement, expanding the possibilities for communication and collaboration.  
Figure 5: The Architectural Framework for Implementing DTs in Agriculture.
## 2 Materials and Methods  
### Study Area  
According to previous studies, there are seven different climatic zones in China based on geographical and meteorological data [48; 49], which are the arid desert of northwest China (NWC), the semi-arid steppe of Inner Mongolia (IM), the (semi-) humid cold temperate northeast China (NEC), the semi-humid warm-temperate north China (NC), the humid subtropical central China (CC), the humid tropical south China (SC), and the Qinghai-Tibetan Plateau (QTP) [50] (Figure 1). Among these climatic zones, the QTP has the highest average elevation (up to 4000 m), with considerably high UV intensity and relatively low vegetation coverage. The region is mainly composed of mountains, plateaus, river valleys, and basins. The geology is mainly schist, millstone, sandstone, shale, and volcanic rocks, The QTP region has a variety of ecosystem types and mainly grows barley. The NWC generally has longer daylight hours than other climatic zones. The landscape is dominated by mountains, basins, and deserts. The area is sparsely vegetated, and the desert covers a large area. The crops and fruits are of high yield with excellent quality in places where water is relatively abundant. The IM region receives less rainfalls than other regions. The altitude is generally between 1000 and 1200 m. The terrain is high in the south and low in the north, with 80% of the plateau covered by grassland, which is mainly clumped with grasses. The thickness of the grass layer is between 10 and 60 cm. There are seven main lake areas in the region, including the Hetao and Hulunbuir Lakes. The NEC region has long winters and short summers, with a significant amount of snow and a wet climate. The region is dominated by plains and mountains. There are many marshes and thick mounds of earth. The proximity to the Bohai Sea leads to a long rainy season in the NEC region. The region has large areas of coniferous and mixed coniferous forests and rich black soils. Forestry accounts for 14.7% of the country's land. The main food crop is wheat. The NC is a relatively small region of China with less than 1000 m\({}^{3}\) of water resources per capita. Summers are hot and rainy. Winters are cold and dry. Rainfall varies between 400 and 800 mm. The land type is dominated by the great plains and plateaus, with the terrain decreasing from west to east. The soil type is mainly brown loam. The CC and SC regions generally receive heavy rainfalls in spring, where flooding tend to occur during that time period. The CC region has red loamy soils of low fertility. The hills, plains, basins, rivers and lakes dominate. The main crops grown are rice and oilseed rape. The topography of the SC region is mainly hilly and plain. The average annual temperature is 18-24 \({}^{\circ}\)C. Moreover, the area has a large variety of plants, mainly tropical scrub, grassy slopes, and secondary forests. The soil types are mainly brick red loam and russet red loam.  
Figure 1: The geographical distribution of meteorological stations used in this study.
## 4 Modeling of Multi-Parameter Degradation  
### The Copula Functions  
The Copula function is proposed by [PERSON] and is used to study the correlation between multiple variables. [PERSON]'s theorem [PERSON] (1965) pointed out that a joint distribution can be decomposed into \(n\) marginal distributions and a Copula function. This decomposition greatly simplifies the analysis of the correlation problem and the description of the degradation variable and model specification. Based on [PERSON]'s theorem, the expression of the joint distribution function describing the correlation between variables is as follows:  
\[H(x_{1},x_{2},\cdots,x_{n})=C(u_{1},u_{2},\cdots,u_{n};\theta) \tag{17}\]  
where \(H(x_{1},x_{2},\cdots,x_{n})\) is the joint distribution function of the variables \(x_{1},x_{2},\cdots,x_{n}\), \(C(u_{1},u_{2},\cdots,u_{n};\theta)\) is the Copula function with the parameter \(\theta\), and \(u_{1},u_{2},\cdots,u_{n}\) is the marginal distribution function corresponding to the variables \(x_{1},x_{2},\cdots,x_{n}\).
## 3 System Model
### Channel Considerations  
In this work, the multipath channel has been simulated as the ITU Vehicular A [40,42] channel. Delay profiles for the multipath channel have been shown in Table 1.
## 2 CFD Simulations
### Grid Strategy
#### 2.1.2 Gird Division  
Considering that the ICEM CFD and fluent of ANSYS are adopted to perform numerical simulations, we consult the official reference materials and ANSYS suggests that a more realistic far-field distance for external flow field simulation of the computational domain should be 12-20 times the chord length (\(L\)) of the airfoil [22]. Moreover, the calculation resources are limited and a larger far-field distance would result in a larger mesh number, and the maximum number of it in grid independency tests would easily exceed 1,200,000, which is beyond the capacity of the computer and the numerical calculations are difficult. Therefore, in this paper, considering the official suggestion and the limited calculation resources, we establish the computational domain within 20 chords. \(L\) is 1 m and 12\(L\)/16\(L\)/20\(L\) are chosen to establish the C-type mesh topology to minimize the skewness of a near-wall mesh. Figure 2a demonstrates the computational domain for the sharp trailing edge, where the square black dot represents the origin of the coordinate system. The block at the trailing edge is folded, which is highlighted in cyan. Considering that the meshing adjacent to the airfoil surface has a significant impact on the CFD simulation, the block close to the airfoil surface is divided. The \"INLET\" is the input boundary, which comprises lines c, d, and e. The \"OUTLET\" is the output boundary, which comprises line f. The \"AIRFOIL\" is the wall boundary, which comprises lines a and b. The computational domain for the blunt trailing edge is shown in Figure 2b; similarly, the grid refinements region is highlighted in cyan, and unlike the sharp trailing edge, the block at the trailing edge is retained, which is like a \"fishtail\". The \"INLET\" is the input boundary, which comprises lines d, e, and f. The \"OUTLET\" is the output boundary, which comprises line g. The \"AIRFOIL\" is the wall boundary, which comprises lines a, b, and c.
## 5 LPWAN Security Analysis
### Integrity
In a similar LoRaWAN v1.0 replay attack study, the authors in [72] initially analyze the typical approach that attackers used to implement the replay attack specifically as \"replay of join accept message\" and \"harvest of join message\". For its mitigation, the authors proposed an increment in the size of Devnonce and AppNounce value with no repetition. DevNonce is a random number generated by the end node. It is employed for preventing replay attacks and for generating session keys. Likewise, the author in [73] also propose the DevNonce approach of preventing replay attack. The author suggested that network servers should store all DevNonces used in the previous join procedure in order to prevent replay attack. Furthermore, the author hypothetically analyzed DevNonce method especially in a case whereby end nodes are unavailable. In such case, the authors proposed the increment of the size of the DevNonce field to 24 or 32 bits. In another study, the authors in [74] presented a solution for bit flipping attacks in LoRaWAN. The authors proposed a shuffling method that is performed by end devices and it aims at preventing attackers from identifying positions of message field from bit-flipping attacks.