esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experimental and Simulation Results Comparison
The highest stresses occur in the simulations and experiments at the site of strain gauge 1 or node 1377. Figure 11 gives a comparative diagram of the strains at the site of strain gauge 1. Based on Figure 11, it can be concluded that the difference in the strain values is almost negligible in the double-tooth and triple-tooth meshing, while the strain values for the single-tooth meshing differ considerably. Namely, in the case of the triple meshing, the strain values obtained by numerical analysis are slightly lower compared to the values obtained experimentally. The lowest strains occur at the site of strain gauge 5 in the experimental analysis, while in the numerical analysis, the lowest strains occur at strain gauge 6. In the numerical analysis, the strain values are much higher at the sites of strain gauges 4 and 5, while at the site of strain gauge 6, the stress values are lower. This is not the case with other strain gauges. This inconsistency may be caused by improperly glued strain gauges or inhomogeneity of the cycloid disc material, among other reasons. The deviations between the numerical and experimental analysis results for the strain gauge 1 range from 3% to about 15%. Therefore, it can be concluded that the numerical results are valid and that the numerical model was well developed. The deviation between results probably occurred because the von Mises stress is considered instead of the stress component. The results deviation is at appropriate values.
## 7 Conclusions  
In this work, an IIoT architecture based on smart contract-based BCT and ML was proposed for smart manufacturing applications. The architecture is composed of five layers including sensing, network/protocol, transport enforced with BCT components, application and advanced services (ML and BCT data and cloud services) layers. A comparative study was carried out with contributions from the literature to position our approach in terms of architectural design, smart contract data structure and application of smart contract in the manufacturing field. To illustrate the potential benefits of our architecture, a data-driven study based on the TON_IoT dataset was investigated. The performance metrics of ML classifiers against common attacks in an industrial context were considered with two scenarios. A first scenario considered a data-driven analysis without BC by taking into account ML classifiers to classify the common attacks in IIoT in terms of accuracy, precision, sensitivity and MCC metrics. A second scenario used an advanced framework based on a BC smart contract data structure, smart contract code and rules for sensor access control along with ML classifiers in the context of IIoT to detect DoS, DDoS, injection, XSS, scanning and brute force attacks. It was demonstrated in our experiments that by implementing a framework based on the second scenario, the number of DDoS, injection, brute force and XSS attacks were reduced considerably. The DDoS, brute force, injection and XSS attacks are reduced by a factor of 34.98%, 25.93%, 37.90% and 46.57%, respectively. An improvement of 0.1% and 1.8% was also observed for the MCC metric regarding RF and NB classifiers to classify DDoS attacks. Finally, the MCC value is increased by 0.1%, 0.2% and 1.3% for SVM, RF and NB classifiers to classify brute force attack.  
Conceptualization, H.M. and A.J.; software, H.M.; validation, D.T.; formal analysis, A.A.; investigation, A.A.; resources, D.T.; data curation, H.M.; writing--original draft preparation, H.M.; writing--review and editing, H.M. and D.T.; visualization, A.A.; supervision, D.T.; project administration, A.J. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
# Fractional-Order LQR and State Observer for a Fractional-Order Vibratory System
## 2 Background
### Controllability of Fractional-Order Dynamical System  
Next, a linear fractional-order non-autonomous system can be given as follows:  
\[\left\{\begin{array}{rl}&D^{\alpha}\mathbf{x}(t)=\mathbf{A}\mathbf{x}(t)+\mathbf{B}\mathbf{u}(t),\ \ \mathbf{x}(0)=\mathbf{x}_{0}\\ &y(t)=\mathbf{C}\mathbf{x}(t)\end{array}\right., \tag{8}\]  
where \(\alpha\) designates the non-integer order of differentiation, \(\mathbf{x}(t)\) is the state vector, \(u(t)\) is the input, \(y(t)\) is the output, \(\mathbf{A}\) is the system matrix, \(\mathbf{B}\) is the input matrix, and \(\mathbf{C}\) is the output matrix.  
For an arbitrary initial state \(\mathbf{x}(0)=\mathbf{x}_{0}\), a time \(t_{f}>0\), and a desired value \(\mathbf{x}_{f}\), if an input \(u(t)\) that enables a solution to satisfy \(\mathbf{x}(t_{f})=\mathbf{x}_{f}\) exists, then the system described by Equation (8) is controllable. In addition, the propositions shown below are equivalent [25; 26].  
Figure 1: Imaginary axis (red line) and negative part of real axis (blue line) on \(\omega\)-plane (\(\alpha=\frac{1}{2}\)).  
1. A pair of matrices \((\mathbf{A},\ \mathbf{B})\) is controllable.
2. The rank of the following controllability matrix \(\mathbf{N}_{c}\) is \(n\): \[\mathbf{N}_{c}=\Big{[}\mathbf{B}\ \mathbf{A}\mathbf{B}\ \cdots\ \mathbf{A}^{n-1}\mathbf{B}\Big{]}.\] (9)
# Simulation of Gross Primary Productivity Using Multiple Light Use Efficiency Models
## 2 Materials and Methods  
### Study Area  
The Heihe River Basin is the second-largest inland river basin in China and is located in the northwest arid region of China. The elevation ranges from 5300 m upstream to 900 m downstream. It contains diverse landscapes, such as glaciers, permafrost, alpine steppe/meadow, and forests in the upstream and irrigated croplands, riparian vegetation, wetlands, and gobi desert in the middle stream and downstream [12]. An EC measurement network was established in 2013 in the Heihe River Basin [13; 14]. The flux between the ecosystem and the atmosphere and ancillary meteorological variables were measured in the dominant vegetation types of this region. The measured data was collected from five sites from 2014 to 2015. The detailed information of these sites is listed in Table 1, while the spatial distribution of these sites is shown in Figure 1. The Arou site and the Dashalong site are located in the upstream, and the vegetation is the alpine meadow. The Daman site and the Shidi site are located in the middle stream, and the vegetation is maize (cropland) and reed (wetland), respectively. The Sidaoqiao site is located in the riparian zone of the downstream, and the vegetation at this site is tamarisk.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Site Name** & **Land Cover** & **Latitude** & **Longitude** & **Elevation (m)** \\ \hline Arou & Grassland & 38.0473 & 100.4643 & 3033 \\ Dashalong & Grassland & 38.8399 & 98.9406 & 3739 \\ Daman & Cropland (Maize) & 38.85551 & 100.3722 & 1556 \\ Shidi & Wetland & 38.97514 & 100.4464 & 1460 \\ Sidaoqiao & Shrubland & 42.0012 & 101.1374 & 873 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Information of the Eddy Covariance (EC) sites in the Heihe River Basin.  
Figure 1: Distribution of the study sites in the Heihe River Basin.  
The EC measured 10 Hz data was processed using despike, coordinate rotation, frequency response correction, and WPL correction to obtain the half-hourly data [15]. Then the half-hourly EC data was gap-filled and partitioned into the GPP and ecosystem respiration using the REddyProc package ([[https://cran.r-project.org/web/packages/REddyProc](https://cran.r-project.org/web/packages/REddyProc)]([https://cran.r-project.org/web/packages/REddyProc](https://cran.r-project.org/web/packages/REddyProc)) (accessed on 10 January 2019)) [16] in the R environment. The measured air temperature, downward shortwave radiation, relative humidity, soil moisture, and GPP daily value were used to force or validate the LUE model. The MODIS reflectance at these sites was extracted from the MOD09A1 collection 6. The normalized difference vegetation index (NDVI), the enhanced vegetation index (EVI), and the land surface water index (LSWI) were calculated from the MODIS reflectance data at these sites.
## 2 Materials and Methods
### Impulsive Manoeuvres  
Impulsive manoeuvres are orbital transfers in which brief firings of on-board thrusters change the magnitude and direction of the satellite velocity vector instantaneously. It is assumed that during an impulsive manoeuvre, the position of the spacecraft is considered  
Figure 1: Orbital coordinate system used for satellite motion analysis. Created based on [4]. (**a**) Representation of a satellite moving in an Earth orbit with indications of radial, normal, and “along-track” directions at a selected point of the orbit. (**b**) Directions of engine alignment during orbital manoeuvres.  
to be fixed; only the velocity changes. The key parameter in impulsive manoeuvres is the change in the velocity of the spacecraft, designated as \(\Delta\)v. It is a vector, which means that both its magnitude and direction may be modified. The value of \(\Delta\)v is closely linked to the weight of the consumed fuel (\(\Delta\)m), which is described by the following equation:  
\[\frac{\Delta m}{m}=1-e^{\frac{\Delta v}{k_{\mathrm{SP0}}}}, \tag{1}\]  
\[\Delta v=\ln\biggl{(}1-\frac{\Delta m}{m}\biggr{)}I_{\mathrm{SP0}}, \tag{2}\]  
where m is the weight of the spacecraft before burning, g\({}_{0}\) is standard acceleration of gravity on sea level, and I\({}_{\mathrm{sp}}\) is specific impulse of the propellant [7].  
As suggested by Equation (1), the required weight of the propelling material during the use of an engine of a defined specific impulse I\({}_{\mathrm{SP}}\) shows an exponential relationship with the change in velocity. The \(\Delta\)v coefficient refers to the total change in velocity that is necessary to perform the given manoeuvre. Certain manoeuvres require a series of impulses to achieve the intended goal. In such cases, the total value of \(\Delta\)v is a sum of the values of the absolute changes in velocity required for each of the impulses. This means that, regardless of whether the manoeuvre involves accelerating, slowing down, or changing the direction, every one of these actions required a specific amount of fuel that must be consumed in order to perform the manoeuvre [7].
# Assisting Drivers at Stop Signs in a Connected Vehicle Environment
## 5 Performance Evaluation  
In this section, we aim to study the performance of the proposed protocol. We first try to determine the affecting parameters. Then, we measure the protocol's effectiveness in reducing accident rates and increasing the throughput of road intersections controlled by stop signs. Different typologies of road intersections are investigated in this study (i.e., T-intersection, Y-intersection, and X-intersection). These typologies are tested while considering to contain the main road and, in all ways, stop scenarios. Three main parameters are evaluated for each topology: breaking distance, accident rate, and the throughput of the investigated road intersection. These parameters are compared for the selected scenarios where the proposed protocol is used to assist the drivers (assist-used) and when it is absent (i.e., assist-absent). The accident percentage is computed in this work based on the possibility that two conflicted vehicles are met at the road intersection controlled by the stop sign. We assume that 20% of the vehicles arriving at the road intersection are not stopped in the assist-absent scenario.  
Table 1 illustrates the used parameters in each tested scenario. SUMO [26] was used to generate the mobility scenarios at the assumed road intersection. The tested experiments used different traffic densities (i.e., very low, low, medium, high, and congested). NS-2 [27] was used to implement the proposed protocol. NS-2 is a discrete event simulator targeted at networking research. It can be used to support simulation for network-based applications. In these experiments, we assume that all vehicles are following the recommendations of the assistance protocol in the assist-used scenario.  
\begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **Value** \\ \hline Simulators & NS-2/SUMO \\ \hline Transmission range (m) & 250 \\ \hline \end{tabular}
\end{table}
Table 1: Simulation parameters.
## 5 Future Work
### Exploration of Other Applications of CMLs  
In operation, CMLs are not only affected by the atmospheric environment but may also suffer from plant, animal and human activities. [PERSON] et al. [182] reported that the presence of birds along the path of CMLs causes additional attenuation and suggested the use of CMLs to monitor migratory birds. And, [PERSON] et al. [183] discovered that a cellular network signal strength of 2.4 GHz is related to the presence of vegetation in the path, and, if present, as well as to the water content of the vegetation and the distance the signal passes through the vegetation. Thus, the links between CMLs and arbitrary microwave receivers can be used to estimate vegetation characteristics. Furthermore, in [159], artificially caused occlusion of objects in the path was found to lead to a drift in the attenuation of the CMLs. It would be interesting to monitor these phenomena using CMLs. The variation character of the attenuation due to these phenomena could be studied in the future to enable the identification and observation of these phenomena.
## 4 Combined with NB-LDPC Codes over GF(7)
In addition, the initial probability of the \(l\)-th received symbol to be symbol \(a\), denoted as \(f_{l}^{a}\), are calculated using the symbol likelihood information, which can be written as  
\[f_{l}^{a}=\frac{P(y_{l}\left|x_{a}\right.)}{\sum\limits_{i=0}^{6}P(y_{l}\left| x_{i}\right.)}=\frac{\exp\left[-\frac{(y_{l}-x_{a})^{2}}{2\sigma^{2}}\right]}{ \sum\limits_{i=0}^{6}\exp\left[-\frac{(y_{l}-x_{i})^{2}}{2\sigma^{2}}\right]}. \tag{27}\]  
Moreover, the complexity analysis of the NB-LDPC coded 7-QAM scheme is carried out in two aspects, and the benchmark scheme is the combination of 8-PSK (or 8-QAM) and NB-LDPC codes over GF(8). The first aspect is on the symbol demapping complexity at the receiver. With the same soft demodulation methods, each received symbol in the NB-LDPC coded 7-QAM scheme needs to calculate the likelihood information of seven-dimension vector, while the symbol likelihood information for each 8-PSK (or 8-QAM) symbol is an eight-dimensional vector. Therefore, the computation complexity for demapping is similar. The second aspect is on the complexity of decoding. The BP decoding algorithm can be used for both types of NB-LDPC codes. Due to the same decoding algorithm is used, the decoding complexity is similar, though some more efficient processing schemes can be performed on the NB-LDPC codes over GF(8) [35].
## 4 Method  
Usually, the neural network model takes 32-bit single-precision floating-point numbers as parameters. The floating-point number consists of three parts: symbol (one bit), exponential (eight bits), and tail numbers. The pipeline of floating-point addition and subtraction includes: (1) Checking the 0 operands. If the number of operations involved is zero, the operations directly return the results. (2) Comparing the size of two order codes and completing the alignment for the addition or subtraction of the tail number. (3) The results are rounded and standardized. When the network size is large, it requires a lot of memory and computational resources to conduct the floating-point operation. Differently,  
Figure 3: Architecture of the clipping-based post-training quantization method.  
the low-precis fixed-point operation could save memory and compute, because it computes the summation directly; it is needless to conduct alignment before computing. Network quantification is based on this idea. It transfers the number from floating points to fixed points by discretization of the weight parameters of the network. It effectively compresses the storage space of network parameters and reduces the amount of calculation. Specifically, it uses a low-precision way to store and calculate network parameters. Usually, the neural network uses 32-bit floating-point number to store all the parameters and calculation results of the network. If a small amount of network performance is sacrificed, the data are mapped to a smaller numerical range by using the quantitative method, which can greatly reduce the number of parameter storage and the memory space occupied by calculation. By adopting a reasonable low-precision method, only a small part of network precision can be lost while the model volume is greatly compressed.  
To reduce the model and solve the excessive accuracy loss caused by traditional quantification methods, we propose a post-training quantization method combined with a clip during training. In addition, a fully connected layer compression method based on singular value decomposition is conducted to minimize storage consumption. Furthermore, we propose a new loss function to improve the accuracy and avoid the performance drop during quantization. These methods have achieved good results in the following experiments in Section 4, and their mechanism will be sequentially introduced as follows
# Optimization of Machining Parameters for Corner Accuracy Improvement for WEDM Processing
## 3 Experiment and Discussion
### Effect of Parameter on Corner Error
#### 3.1.6 Effect of Flushing Pressure (WA) on Corner Error  
The WA parameter was significant for the 30\({}^{\circ}\) and 90\({}^{\circ}\) corners due to the \(p\)-value which was less than 0.05. However, the \(p\)-value was larger than 0.05 for the 60\({}^{\circ}\) corner which indicated that it was not significant. This might be because the top and bottom fluid jet were symmetrical, so the flow influence would cancel each other, and the erosion products were not flushed as a consequence [31]. Table 11 shows the experimental results of corner machining for different flushing pressure (WA) parameter settings. It can be seen that when the flushing pressure (WA) increased from 2 to 4, the corner error slightly increased from 0.089, 0.035, 0.023 mm to 0.109, 0.037, 0.025 mm for the 30\({}^{\circ}\), 60\({}^{\circ}\), and 90\({}^{\circ}\) corners, respectively. The change of corner error was relatively small for the 60\({}^{\circ}\) and 90\({}^{\circ}\) corners. Regarding the machining time, there was no significant effect of the flushing pressure on machining time.  
The Pareto chart shown in Figure 3 illustrates the importance of the parameters and their interaction effects on the output response of corner error and machining time. The values for each parameter effect are represented by the horizontal columns in the Pareto  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & & \multicolumn{2}{c}{**WT Parameter (Code)**} \\
**Corner Degree** & & **Corner Error (mm)** & \\ \cline{2-4}  & **10** & **12** & **14** \\ \hline
30\({}^{\circ}\) & 0.102 & 0.090 & 0.086 \\
60\({}^{\circ}\) & 0.036 & 0.034 & 0.025 \\
90\({}^{\circ}\) & 0.029 & 0.021 & 0.017 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Wire tension (WT) vs. corner error.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & & \multicolumn{2}{c}{**WIA Parameter (Code)**} \\
**Corner Degree** & & **Corner Error (mm)** & \\ \cline{2-4}  & **2** & **3** & **4** \\ \hline
30\({}^{\circ}\) & 0.089 & 0.098 & 0.109 \\
60\({}^{\circ}\) & 0.035 & 0.036 & 0.037 \\
90\({}^{\circ}\) & 0.023 & 0.024 & 0.025 \\ \hline \hline \end{tabular}
\end{table}
Table 11: Flushing pressure (WA) vs. corner error.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & & \multicolumn{2}{c}{**SV Parameter (Code)**} \\
**Corner Degree** & & **Corner Error (mm)** & \\ \cline{2-4}  & **36** & **38** & **40** \\ \hline
30\({}^{\circ}\) & 0.092 & 0.094 & 0.097 \\
60\({}^{\circ}\) & 0.031 & 0.035 & 0.042 \\
90\({}^{\circ}\) & 0.017 & 0.018 & 0.024 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Servo voltage (SV) vs. corner error.  
chart. It can be seen that all the parameters such as ON, OFF, OV, SV, WT, WA, and their interaction, cross the red lines that indicate all the parameters and their interaction were significant at the 95% confidence level (\(\alpha\) = 0.05). Therefore, all these parameters need to be considered to evaluate the effect on corner error and machining time.
# FLINT: Flows for the Internet of Things
## 5 Evaluation
### Levels of Interoperability
#### 5.1.3 Semantic and Platform Interoperability  
FLINT is well suited to provide both semantic and platform interoperability. For example, imagine a LPWAN sensor network with the following requirements:  
* _Low-end_ sensors must be manageable through the LwM2M protocol. Preferably, their packets should be compressed using the SCHC compression and fragmentation standard.
* For proprietary sensors, an adapter must run as a digital twin in the FLINT platform. This can be done by converting their proprietary format using the Virtual Device Manager (VDM) [29].
* Data from the LwM2M server must be delivered to a LDP. Hence, the LwM2M ontology must be mapped to RDF. This can be done using the RML.  
FLINT's modular and extensible architecture makes this easy; Figure 7 shows the configuration. Data coming from the LPWANs are delivered either to the SCHC router or the VDM. Both adapters send requests to the LwM2M server and deliver responses to the _Mapper_ adapter. The LwM2M adapter subscribes to the event stream of the LwM2M server. The RML adapter on its turn maps the LwM2M ontology to the Semantic Sensor Networks (SSN) ontology. The result is published to a LDP.  
Figure 6: Mobility and queue management in a FLINT system. This diagram shows how packets travel through a FLINT system configuration. Time moves downwards. The central element is a _Mapper_ adapter. Packets are distributed from transmitting devices to the destination. Packets in the downward direction are queued and dequeued according to the device’s interfaceType.  
Figure 7: FLINT configuration that provides semantic and platform interoperability. Packets from the LPWANs are delivered to the LwM2M server. Data from the LwM2M server are mapped to an ontology that matches the semantics of the LDP.  
The above configuration shows how two incompatible platforms can be interconnected through FLINT. An adapter is dedicated to match their semantics and thereby provides _semantic and cross-platform interoperability_. Once cross-platform interoperability is achieved, _cross-domain interoperability_ can be enabled by adding adapters that integrate platforms from heterogeneous domains.