esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Engine Performance
#### 3.2.2 COVimep  
Figure 7 demonstrates the coefficient of variation of the indicated mean effective pressure (COVimep) of all test fuels for 200 working cycles according to various idling engine speeds. COVimep is considered an important parameter for checking the combustion stability (cycle to cycle variation) of different fuels. The coefficient is defined as the ratio between the standard deviation of the indicated mean effective pressure (IMEP) over many consecutive cycles divided by its mean value [30]. During combustion of the internal combustion engine, due to the charge mixture composition (fuel and air), the change of pressure and temperature in-cylinder, and other factors, the change of COVimep is different. Generally, the higher the COVimep, the more unstable the engine is. Many researchers have pointed out that the COVimep of an internal combustion engine should not exceed 10% for stable combustion and operation [31; 32]. As shown in Figure 7, the COVimep of most blended fuels is higher than that of diesel fuel. On the whole, the average COVimep of CPO10, CPO30, and CPO50, respectively, increased by 60.87%, 8.99%, and 13.38%, compared with that of diesel. This may be due to the higher viscosity and density and the low calorific value of palm oil, resulting in slightly unstable engine combustion compared with diesel. However, it is worth noting that the COVimep of all blended fuels is lower than 3%, indicating that this CRDI diesel engine can burn palm oil blends well and without large cyclic variation under these three idling conditions.
# Quantitative Assessment and Grading of Hardware Trojan Threat Based on Rough Set Theory
## 2 System Model and Assessment Formulation
### Weight Calculation and Quantitative Assessment
#### 2.2.3 Significance of Attribute  
The significance of attributes enables us to evaluate attributes by assigning a real number from the closed interval [0,1] that expresses the importance of an attribute in an information system. The significance of an attribute \(a\) can be evaluated by measuring the effect of removing the attribute \(a\in A\) from the attribute set \(A\).  
For any attribute \(a\in A\), we define its significance, \(\text{sig}_{A-\{a\}}(a)\), for \(A\) as follows:  
\[\text{sig}_{A-\{a\}}(a)=I(A)-I(A-\{a\}) \tag{4}\]  
In particular, when \(A=\{a\}\), \(\text{sig}(a)\) represents \(\text{sig}\phi(a)\):  
\[\text{sig}(a)=sig\phi(a)=I(A)-I(\phi)=I(\{a\}) \tag{5}\]  
where \(\text{U}/IND(\phi)=\{U\}\) and \(I(\phi)=0\).  
The significance of \(a\in A\) is measured by the changes in information content caused by the removal of \(a\) from \(A\). If and only if \(\text{sig}_{A-\{a\}}(a)>0\) is \(a\in A\) indispensable in \(A\) while \(\text{Core}(A)=\{\forall a\in A|\text{sig}_{A-\{a\}}(a)>0\}\).  
In an information system \(S=(U,A,V,f)\) and \(C\subseteq A\), for any attribute \(a\in(A-C)\) we define its significance, \(\text{sig}_{C}(a)\), as follows:  
\[\text{sig}_{C}(a)=\text{sig}_{(C\cup\{a\})-\{a\}}(a)=I(C\cup\{a\})-I(C) \tag{6}\]  
The significance of \(a\in(A-C)\) is measured by the changes in information content caused by the adjunction of \(a\) to \(C\).
## 2 Experimental Section
### Method
#### 2.3.2 Linear Regression Equations of Thermodynamic Parameters  
In addition, the physicochemical thermodynamic equations were used for the prevailing intermolecular interactions between the amino acid taurine (TAU) and amine tyramine (TYR) with D-glucose in aqueous media. The enthalpy (\(\Delta H\)) and entropy (\(\Delta S\)) of surface formation are evaluated by the fitting of surface tension (\(\gamma\)) versus temperature data at constant concentration, from a linear regression equation. The coefficient of a linear regression equation, (d\(\gamma\)/dT)c,p slope, was used to estimate the following equation [35]:  
\[\Delta H=\gamma-~{}\text{T}\left(\frac{\text{d}\gamma}{\text{dT}}\right)_{C,P} \tag{6}\]  
where \(\gamma\) is the surface tension of the aqueous mixture, T is the temperature and coefficient, (d\(\gamma\)/dT)c,p is the slope obtained from the regression equation. The entropy (\(\Delta S\)) was estimated by applying the following thermodynamic relation [35]:  
\[\Delta S=-\left(\frac{\text{d}\gamma}{\text{dT}}\right)_{C,P} \tag{7}\]  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Parameters** & \(\gamma_{\text{CMC}}\) & \(\pi_{\text{CMC}}\) & **CMC** & **CMC** & \(-\Gamma\) & **A\({}_{\text{m}}\)** \\
**T/(K)** & **(mNm\({}^{-1}\))** & **(mNm\({}^{-1}\))** & **(mol\(\cdot\)kg\({}^{-1}\))** & **(mol\(\cdot\)kg\({}^{-1}\))** & **(10\({}^{3}\) mol\(\cdot\)m\({}^{-2}\))** & **(10\({}^{-5}\) nm\({}^{2}\))** \\ \hline
298.15 & 55.95 & 2.45 & 0.02998 & 50.14 & 8.7831 \(\pm\) 0.098 & 1.8903 \\
303.15 & 53.33 & 2.37 & 0.03019 & 44.81 & 7.7199 \(\pm\) 0.091 & 2.1507 \\
308.15 & 50.59 & 2.41 & 0.03035 & 37.41 & 6.3405 \(\pm\) 0.092 & 2.6186 \\
313.15 & 47.70 & 0.80 & 0.03048 & 33.56 & 5.5971 \(\pm\) 0.097 & 2.9663 \\
318.15 & 44.81 & 0.89 & 0.03062 & 32.36 & 5.3122 \(\pm\) 0.094 & 3.1255 \\ \hline \end{tabular}
\end{table}
Table 1: Surface parameters, \(\gamma_{\text{CMC}}\) (surface tension of solution at CMC), \(\pi_{\text{CMC}}\) (the surface pressure), CMC (critical micelle concentration), \(\partial_{\gamma}/\partial\)log\({}_{10}\) C (value of slope), \(\Gamma\) (the surface excess concentration), and \(A_{m}\) (the minimum area per head group) of aqueous tyramine with D-glucose solutions at different temperatures.
## 2 Materials and Methods
### Study Area and Remote Sensing Data  
In this study, we used the moderate resolution imaging spectroradiometer (MODIS), precipitation estimation from remotely sensed information using an artificial neural network climate data record (PERSIANN-CDR), and global precipitation measurement (GPM) integrated multi-satellite retrievals for GPM (GPM IMERG) to calculate various drought indices. Through the MODIS satellite, the LST (MOD11A1), NDVI (MOD13A3), actual evapotranspiration (AET; MOD16A2), and albedo (MCD43B2) data from 2001 to 2019 were collected (Table 1). To obtain the precipitation data, we used the PERSIANN-CDR data from 1983 to 1997 that was generated by the center for hydrometeorology and remote sensing (CHRS) at the University of California in Irvine; the data were obtained before the tropical rainfall measuring mission (TRMM). The TRMM data from 1998 were utilized, and among many data, the gridded TRMM3B42 data were collected until 2014 (at the end of TRMM's life), which was provided by the National Aeronautics and Space Administration (NASA) [32]. Following 2014, we used data from GPM IMERG that obtained data until 2019 to calculate the meteorological drought index [33]. Among the GPM IMERG data, the data after the last four months of the calibration were used to enhance the reliability of the precipitation data. Due to the different spatial and time resolutions of the collected data, the spatial resolution was set at \(1\times 1\) km and the time resolution was considered to be monthly, which is consistently reprocessed. The main areas of this study were the five major rivers of the Korean Peninsula, and we analyzed 849 sub-basins (Figure 1).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Product** & & **Resolution** & **Data Period** \\ \hline \multirow{4}{*}{MODIS} & MOD11A1 & Land Surface Temperature & 1 km, daily & \\ \cline{2-5}  & MOD13A3 & Vegetation Indices & 1 km, monthly & 2001–2019 \\ \cline{2-5}  & MOD16A2 & Evapotranspiration & 0.5 km, 8 days & \\ \cline{2-5}  & MCD43B2 & Albedo & 1 km, 8 days & \\ \hline PERSIANN-CDR & PERSIANN-CDR & Precipitation & 25\({}^{\circ}\), daily & 1983–1997 \\ \hline TRMM & TRMM3B42 & Precipitation & 25\({}^{\circ}\), 3 h & 1998–2014 \\ \hline GPM & GPM IMERG & Precipitation & 10\({}^{\circ}\), 30 min & 2015–2019 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Remote sensing (RS) data used in this study.
## 2 Mathematical Models of the 4 WS System
### The Rear Wheel Electrohydraulic System Model  
The rear wheel electrohydraulic system was used to realize the vehicle's rear wheel rotation based on electrohydraulic proportional control technology, and its schematic diagram is shown in Figure 5. Firstly, the controller sends a current to the proportional electromagnetic valve. Secondly, the spool of the proportional electromagnetic valve is moved. Thirdly, the hydraulic oil flows through the proportional electromagnetic valve to drive the steering cylinder's motion. Finally, the steering cylinder drives the rocker arm and the pull rod to rotate the tire.  
The relationship between the output current \(I\) of the controller and the spool displacement \(y\) of the proportional electromagnetic valve is shown as follows.  
\[K_{m}I=M_{j}\frac{d^{2}y}{dt^{2}}+B_{y}\frac{dy}{dt}+K_{1}(y+y_{0}) \tag{13}\]  
where \(M_{j}\) represents the spool quality of the reversal valve; \(B_{y}\) denotes the viscosity damping coefficient of the spool; \(K_{1}\) is the spring stiffness; \(y_{0}\) represents the pre-compression of the spring.  
The load flow equation of the proportional electromagnetic valve is shown below.  
\[Q_{L}=K_{qh}y-K_{ph}P_{L} \tag{14}\]  
Figure 4: The driver–vehicle closed-loop system.  
Figure 5: The schematic diagram of the rear wheel steering system.  
where \(Q_{L}\) is the load flow; \(K_{qh}\) denotes the zero flow gain of the spool; \(K_{ph}\) represents the pressure-flow rate of the spool; \(P_{L}\) is the load pressure.  
The mathematical model of the hydraulic cylinder is shown as follows.  
\[\begin{array}{c}Q_{L}=A_{c}\frac{dy_{g}}{dt}+C_{L}P_{L}+\frac{V_{e}}{4\beta_ {e}}\frac{dP_{L}}{dt}\\ A_{C}P_{L}=M_{t}\frac{d^{2}y_{g}}{dt^{2}}+B_{t}\frac{dy_{g}}{dt}+K_{L}y_{g}+F_ {L}\end{array} \tag{15}\]  
where \(A_{c}\) is the equivalent area; \(y_{g}\) represents the piston displacement; \(C_{L}\) is the leakage coefficient; \(V_{e}\) is the equivalent volume; \(\beta_{e}\) denotes the elastic modulus; \(M_{t}\) is the total mass; \(B_{t}\) denotes the viscous damping coefficient; \(K_{L}\) denotes the spring stiffness of the load; \(F_{L}\) is the external load force.  
The rear wheel angle by means of equivalent linearization method is shown below.  
\[\delta_{r}=k_{r}y_{g}. \tag{16}\]  
Based on Laplace transform, the transfer function of the rear wheel electrohydraulic system can be shown as follows.  
\[G_{act}(s)=\frac{\delta_{r}(s)}{I(s)}=\frac{K_{r}K_{m}\frac{K_{qh}}{\Delta_{c }}}{s\Big{(}\frac{s^{2}}{\omega_{h}^{2}}+\frac{2K_{h}}{\omega_{h}}s+1\Big{)} \big{(}M_{j}s^{2}+B_{y}s+K_{1}\big{)}}. \tag{17}\]  
In the rear wheel electrohydraulic system, the hydraulic cylinder is a major influence on the dynamic response. Therefore, the equation mentioned above can be expressed approximately as follows.  
\[G_{act}(s)=\frac{K_{r}K_{m}\frac{K_{qh}}{\Delta_{c}}}{K_{1}s\Big{(}\frac{s^{2 }}{\omega_{h}^{2}}+\frac{2K_{h}}{\omega_{h}^{2}}s+1\Big{)}}. \tag{18}\]
## 3 Results and Discussion
### Case Studies
#### 3.2.5 Non-Dimensional Maximum Beams' Static Deflection--Influence of Constituent Materials  
The influence of the constituent materials of the honeycomb auxetic FGM, more specifically the ceramic materials used (Table 3), is analyzed in the present case. The beams studied have a length-to-thickness ratio of 20, and the re-entrant hexagonal cells are characterized by \(\eta 3=0.05\). The non-dimensional maximum deflections, given as \(\vartheta=\frac{E_{\pi}+\upsilon\cdot\hbar^{3}}{10^{9}p_{z}a^{2}}\), are presented in Table 8.  
Figure 14: Non-dimensional maximum static deflection of Al-WC FGM honeycomb auxetic beams with a/h = 20 within the range of angles \(\theta=-75^{\circ}\) to \(\theta=0^{\circ}\)  
Figure 15: Non-dimensional maximum static deflection of Al-WC FGM auxetic beams with a/h = 20, within the range of angles \(\theta=0^{\circ}\) to \(\theta=75^{\circ}\)In addition to the relative stiffness character of the beams built with the different materials, the results achieved identify two main trend profiles, one presented by the auxetic FGM Al-WC and Al-ZrO\({}_{2}\) and the other presented by the auxetic Al-SiC and Al-Al\({}_{2}\)O\({}_{3}\). The first two materials confer upon the beams a non-dimensional maximum deflection that progressively increases from the \(-75^{\circ}\) angle to the \(15^{\circ}\) angle and then starts decreasing. The second material, where the constituent materials of the beam present very different Poisson ratios, provides the beam's maximum non-dimensional deflection profiles as an oscillating trend that is more visible in the Al-Al\({}_{2}\)O\({}_{3}\) case.
# Enterprise Security for the Internet of Things (IoT): Lightweight Bootstrapping with EAP-NOOB
## 7 Implementation
### Architecture
We use Node.js for the implementation. * A database. We use an SQLite database for storing information about user accounts and IoT devices. * An EAP server. We use hostapd [54] for implementing the EAP-NOOB server side components. It interacts with the web server through the shared database. * A RADIUS server. In addition to functioning as an EAP server, hostapd also has a built-in RADIUS server for receiving RADIUS encapsulated EAP messages. Firefly motes in our implementation are the EAP peer devices. They form an IEEE 802.15.4 mesh network and use RPL for routing. Furthermore, one of the motes is connected over USB to a laptop and serves as a controller device, a RPL border router, and an EAP authenticator. It provides Internet connectivity to all other motes after they are authenticated and ensures that new unauthenticated devices are only allowed limited connectivity, mainly to send EAP packets towards the server. As with any other typical EAP deployment, the communication between the authenticator and the EAP server must be protected. For this, we use RADIUS [28] with a shared key. Although this configuration only needs to be performed once, average users can not be expected to do it on their own. In Section 9, we will discuss how this process can be made less burdensome. Successful EAP-NOOB authentication results in two session keys at the server and the peer--MSK and EMSK. MSK is further used to derive a third key, PMK, which is also delivered to the authenticator (controller) over RADIUS. This key is used to protect the CoAP communication between the peer device and the controller. Developers can freely choose an appropriate security protocol to use with the PMK after the secure bootstrapping phase is complete. One suitable candidate is Datagram Transport Layer Security (DTLS) [55] protocol, which is, for example, used by IKEA smart light bulbs [56].
# Resilience Evaluation of Multi-Path Routing against Network Attacks and Failures
## 2 Background and Related Work
### SD-WAN: Software Defined WANs  
SDN continues to attract both industry and research communities as a modern paradigm for the management and operation of computer networks. SDN is changing how computer networks are managed by providing a centralized network management. SDNseparates the control plane of the network with different devices providing a centralized view of the network from the data plane [35]. Conceptually, SDN supports large-scale network control with simple operations and is a new method for preventing network-based attacks [36].  
SD-WAN is the combination of a group of network technologies (overlay, performance routing, and firewalling) that uses software to make a WAN more intelligent, flexible, and easier to manage. The key benefits of a SD-WAN are flexibility and intelligence. SD-WAN is an overlay that enables the use of different underlays, i.e., the Internet, MPLS, VPLS, and LTE. For customers building a SD-WAN over the Internet, they can choose their best partner in a location-by-location manner. This also means that customers can mix the underlays and inherit their key benefits, such as the reliability of MPLS and the low cost of Internet access. SD-WAN solutions have been designed from scratch with the application being the key object to manipulate. Thus, monitoring, prioritization, and routing use applicative parameters in their policies and are fully orchestrated. SD-WAN policies, parameters, and topologies are centrally managed in real-time.  
The quality of SD-WAN depends on the underlays: for multinational customers looking for savings on their access budget, and thus considering the low-cost last-mile of the Internet, the user experience may be impacted. Because SD-WAN cannot control what happens between selected Internet service providers (ISPs), customers may even lose connections between sites for minutes or even hours. Furthermore, SD-WAN solutions are not interoperable, and customers are vendor-locked. SD-WAN is a growth relay for equipment vendors, system integrators, and service providers and is a way for the cloud service providers to get closer to their customers. Typically, SD-WANs are operated using intra-domain routing protocols.
## 5 Discussion and Conclusions
### BERT Prediction Results  
(1) The experimental results of the BERT system are almost identical to the test results of the saved model.  
The experimental results with the BERT system show the highest correct rate of 91.2%, with the saved BERT number bert_30714. Two hundred pieces of news data in the non-training set collected from various websites are additionally collected to be fed into the BERT training model, and the average hit rate of 91.13% is almost the same as the experimental results. This is possibly because the semantics-based BERT system uses a bidirectional transformer to predict the hidden Token and predicts the downstream tasks for the model, learning the relationship between sentences, to result in almost the same experimental results as the test result of the saved model.  
(2) The BERT system has a better true news hit rate for mainstream news platforms than the fake news hit rate for content farms.  
The experimental results with the BERT system reveal the true news hit rate of six mainstream news platforms, as follows: Apple Daily, 90.5%; China Times, 90.5%; Liberty Times, 100%; United Daily News, 96.5%; EToday, 89.0%; and CNA, 100%, where the highest hit rate is 100% and the lowest hit rate is 89.0%. The fake news hit rate of six content farms shows mission-tw.com with 98.5%, no noho.net with 79.0%, kknews.cc with 85.0%, BuzzHand with 89.0%, qiqi.news with 83.0%, and Global Military with 88.5%, where the highest hit rate is 98.5% and the lowest hit rate is 79.0%.  
To further analyze the above data, it is discovered that the BERT system presents a better true news hit rate of mainstream news platforms than the fake news hit rate of content farms. It may be because the BERT system is semantics-based, and the news content expression of true news authors on mainstream news platforms obviously outperforms authors of content farms regarding semantics.
## 2 Dataset and Experiments  
Sentinel-1 is a satellite mission developed by the European Space Agency (ESA) for Earth observation through radar imaging, and the sensor is equipped with a C-band SAR. It plays a pivotal role in monitoring and managing various environmental and geological applications [20]. Sentinel-1 offers all-weather, day-and-night imaging capabilities, making it an invaluable tool for applications such as disaster management, agriculture, forestry, and tracking changes in land and ocean surfaces [21]. Sentinel-1 can acquire many types of data, including multi-temporal SAR image sequences [22], SAR image mosaics [23], and SAR image fusion [24]. Among them, the multi-temporal SAR image sequence contains SAR images acquired in the same region at different times [25]. This enables the long-term monitoring and observation of a specific area, extracting change information from it.  
The PAZ satellite, launched on 22 February 2018, is owned and operated by Hisdesat. PAZ operates in the same orbit of the twin satellites TerraSAR-X and TanDEM-X, and it has the same ground strip and acquisition mode. The three satellites work together as a high-resolution SAR satellite constellation. PAZ is equipped by a side-looking X-BandSAR using the active phased array antenna technology with an operational instantaneous bandwidth up to 300 MHz. It has been designed to be very flexible and is able to operate in a wide array of configurations depending on the desired image performance.  
This paper utilizes Sentinel-1 GRD products and PAZ-1 products as the experimental dataset. Table 1 contains the parameters of the Sentinel-1 dataset, and Table 2 contains the parameters of the PAZ-1 dataset.
# BeiDou Geostationary Satellite Code Bias Modeling Using Fengyun-3C Onboard Measurements
## 3 Results  
Using BDS measurements from IGS MGEX stations, [PERSON] and [PERSON] [8] modeled the biases in MEO and IGSO satellite passes with a linear piece-wise function of elevation. Because GEO satellites remain nearly stationary as seen from a ground receiver, the similar biases are almost constant and removed when averaging the MP time series of an ambiguity arc. Due to the Fengyun-3C satellite, the problem can be overcome with the onboard BDS data. Good observation coverage can be achieved over the entire elevation and azimuth angle ranges, even for IGSO and MEO satellites.