esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Weighted Least-Squares Approach to State Estimation
### The Origin of the Non-Equivalence of the two Kalman Filters
Since the generalisations with respect to the standard Kalman filter without correlated noise terms occur in different equations, these algorithms may, in a purely heuristical way, be merged as follows:  
\[\tilde{A}_{t} =A_{t}-S_{t-1}^{(1)}\boldsymbol{\Phi}_{t-1}C_{t-1} \tag{97}\] \[\tilde{Q}_{t} =Q_{t}-S_{t}^{(1)}\boldsymbol{\Phi}_{t}\left(S_{t}^{(1)}\right)^ {\mathsf{T}}\] (98) \[\mathbf{x}_{t|t-1} =\tilde{A}_{t}\mathbf{x}_{t-1|t-1}+S_{t-1}^{(1)}\boldsymbol{\Phi} _{t-1}\mathbf{y}_{t-1}\] (99) \[P_{t|t-1} =\tilde{A}_{t}P_{t-1|t-1}\tilde{A}_{t}^{\mathsf{T}}+\tilde{Q}_{t-1}\] (100) \[\mathsf{V}_{t} =C_{t}P_{t|t-1}C_{t}^{\mathsf{T}}+R_{t}+C_{t}S_{t}^{(0)}+\left(C_ {t}S_{t}^{(0)}\right)^{\mathsf{T}}\] (101) \[\mathbf{K}_{t} =\left(P_{t|t-1}C_{t}^{\mathsf{T}}+S_{t}^{(0)}\right)\mathsf{V}_{ t}^{-1}\] (102) \[\mathbf{x}_{t|t} =\mathbf{x}_{t|t-1}+\mathsf{K}_{t}(\mathbf{y}_{t}-C_{t}\mathbf{x }_{t|t-1})\] (103) \[P_{t|t} =P_{t|t-1}-\mathsf{K}_{t}\mathsf{V}_{t}\mathsf{K}_{t}^{\mathsf{T}} \tag{104}\]It is less obvious how the smoother algorithms for the contemporaneous form and for the future form may be merged, and in this paper, this problem will not be considered further. Here, it should be mentioned that recently [PERSON] and coauthors [19] also presented a generalised recursive filter algorithm for state estimation, which contains the Kalman filters in Equations (12)-(17) and Equations (27)-(34) as special cases. However, for the case \(\mathsf{S}_{t}^{(0)}\
eq 0\), \(\mathsf{S}_{t}^{(1)}\
eq 0\), their recursive filter differs from the generalised Kalman filter in Equations (97)-(104). The exact relationship between the two filter algorithms still needs to be investigated.
## 3 Materials and Methods
### Magnetic Susceptibility Measurement  
#### 3.1.1 Field Magnetic Susceptibility Measurement  
The values obtained from in situ measurements in caves were acquired using the handheld Kappameter KM-7 device [41]. This device finds widespread use in mineral deposit research, borehole cores, and archaeology. Its specifications include a sensitivity of 1 \(\times\) 10\({}^{-6}\) SI units and an operating frequency of 10 kHz. It can withstand temperatures ranging from \(-\)20 \({}^{\circ}\)C to \(+\)60 \({}^{\circ}\)C, which makes the Kappameter KM-7 a versatile tool for field measurements. It is calibrated for the idealized case in which the needle is attached to an absolutely smooth plane confining a half-space filled with a magnetically homogeneous and isotropic medium. Then the displayed value of the susceptibility is the true value. The true value of susceptibility is computed in the microprocessor from the measured, so called apparent, susceptibility.  
The Kappameter KM-7 offers three measurement modes for magnetic susceptibility: individual, scanning, and remote. During field research, the individual measurement mode  
Figure 2: (**A**) The elevation graph highlights the entrance and profile of Sloväcka Cave. (**B**) An enlarged section of the Sloväcka Cave profile with the investigated area highlighted in red. (**C**) The elevation graph shows the complexity of the Crnopac Cave System, with the former Kita Gá\(\check{\text{e}}\)esina Cave part highlighted in red and the entrance location indicated. (**D**) An enlarged section of the former Kita Gá\(\check{\text{e}}\)esina Cave profile, highlighting the sampled locations. The profiles of Sloväcka Cave and the Crnopac Cave System are schematically illustrated with an exaggerated vertical scale.  
was employed to capture individual readings, with the measuring needle acting as the sensor. This measurement process involves three steps. Firstly, the device was positioned at least 30 cm away from the rock (AIR1). Then, the measuring needle was aligned parallel to the rock surface to obtain the sample value. Finally, a measurement was taken again at least 30 cm away from the rock (AIR2), and the obtained sample value was recorded as the measured magnetic susceptibility.
# Efficiency Estimation of Roller Chain Power Transmission System
## 4 Experimental Measurement  
An experiment is performed in order to measure and verify the estimated efficiency. The layout of the experiment is shown schematically in Figure 8. Driving sprocket axes and driven sprocket axes are connected via the test chain. In the driving sprocket axes, an electric motor with a maximum rotational speed of 1700 RPM and a maximum torque capability of 13 Nm provides propulsion. Driving torque from the electric motor is measured through a torque transducer (accuracy \(\pm\) 0.3%), which has a rated capacity of 0-100 kgf-m. In the driven sprocket axes, a friction disk brake that can regulate speed and torque is attached. The torque of the driven sprocket axes is measured with the torque transducer of the same type as that with the driving axes. Support bearings are used in the rotational axes between the electric motor, torque transducers, brake disk, and driving/driven sprocket axes. In order to take into consideration friction loss due to support bearings, friction loss of individual support bearings is measured with varying rotational speeds in the range of 50 RPM-280 RPM. The lateral offset angle between sprockets can be adjusted by moving the driven sprocket axes laterally.  
Efficiency is measured with different operating conditions. The lateral offset angle is 0/0.0174/0.0349/0.0523 rad with an increment of 1 degree, the torque range is 5-9 Nm with an increment of 1 nm, and the rotational speed is in the range of 40-80 RPM with an increment of 10 RPM. Each test takes approximately 20 s and is repeated seven times.
# An Efficient BGV-type Encryption Scheme for IoT Systems
## 2 Background Knowledge
### Ciphertext Encoding  
Similar to the plaintext case, the ciphertext space in BGV [17] is defined by a quotient ring \(R_{q}=\mathbb{Z}_{q}[x]/\Phi_{m}(x)\), where \(q\) is far greater than \(p\). Naturally, we wonder what is the detailed relationship between \(p\) and \(q\). Reference [19] indicates that \(q\) can be the product of a series of prime numbers \(p_{0}\cdots\)\(p_{L-1}\). \(L\) is the depth of the circuit in a leveled homomorphic encryption scheme and a new ciphertext is with respect to \(q_{L-1}\). Each time two ciphertexts multiply, the new ciphertext of the product is mapped from \(R_{q_{i}}\) to \(R_{q_{i-1}}\), where \(q_{i}=\prod_{j=0}^{i}p_{i}\) for \(i=0,\cdots\), \(L-1\). ([19], Section 3.2) introduces the procedure for mapping an element in \(R_{q_{i}}\) to the corresponding element in \(R_{q_{i-1}}\) in the modulus-reduction process. Hence, \(L\) is the upper bound of the multiplication and addition times. As an optimization of keeping ciphertexts and key switch matrices in evaluation (CRT) representation, Ref. [19] also suggests that \(p_{i}=1\) mod \(m\) for each \(i=0,\cdots,L-1\).  
As a toy example of ciphertext encoding, assume \(p_{0}=11\), \(p_{1}=23\), and \(p_{2}=67\). We have \(q_{0}=p_{0}=11\), \(q_{1}=p_{0}p_{1}=11\times 23=253\), and \(q_{2}=p_{0}p_{1}p_{2}=16951\). Let \(10\) be the ciphertext \(ct^{(1)}{\in}R_{q_{1}}\), we can compute its equivalent ciphertext \(ct^{(2)}=(ct^{(1)}{\times}p_{2}){\in}R_{q_{2}}=10\times 67\) mod \(16951=670\). To directly verify the correctness of the reduction, we may map \(ct^{(2)}\) to \(ct^{(1)}\) by multiplying the inverse mapping element \(rp_{1}\) from \(R_{q_{2}}\) to \(R_{q_{1}}\) (\(p_{2}\) and \(rp_{1}\) are mutually inversed when mapping elements between \(R_{q_{1}}\) and \(R_{q_{2}}\)). Here, we directly give that element \(rp_{1}=34\) in the toy example. Please see ([19], Section 3.2) to find the way to compute it. Then we may test \(670\times 34\) mod \(253=10\).
###### Abstract
\(M_{m}\) is the main shock magnitude, and \(M_{P}\) is the precursor magnitude. (**c**) Changes in the cumulative magnitude anomaly (Cumag) over time; see [12] for the definition. Dashed lines show the precursory increase in the seismicity rate in 1998. The protractor translates the Cumag slope into the seismicity rate in magnitude units per year (M.U. yr\({}^{-1}\)). \(T_{P}\) is the precursor time. A study of the \(\Psi\) phenomenon in synthetic earthquake catalogues shed new light on the matter [17]. It was found that, in a synthetic catalogue generated by the earthquake simulator RSQSim [18; 19], two or more equally plausible identifications of \(\Psi\) could be found for individual mainshocks. These identifications presented very different \(T_{P}\) and \(A_{P}\) values, consistent with a hypothetical space-time trade-off. The evidence for the trade-off, whatever its origin, can also be strengthened through applications of the EEPAS model. One example was the EEPAS model fitted with different fixed lead times [20]. The lead time is defined as the time interval between the start of the catalogue and the origin time of a target earthquake. It was found that as the lead  
Figure 3: Scaling relations and 95% tolerance limits derived from 47 examples of \(\Psi\) from four regional earthquake catalogues, taken after [12]. (**a**) Precursor time \(T_{P}\) versus precursory area \(A_{P}\) (\(R^{2}\) = 34%). (**b**) Product of \(A_{P}\) and \(T_{P}\) versus mainshock magnitude \(M_{m}\) (\(R^{2}\) = 75%). Symbols are enlarged and colored as in Figure 2.  
Figure 2: Predictive scaling relations and 95% tolerance limits derived from 47 examples of \(\Downarrow\) from four regional earthquake catalogues, taken after [12]. (**a**) Mainshock magnitude \(M_{m}\) versus precursor magnitude \(M_{P}\) (coefficient of determination \(R^{2}\) = 71%). (**b**) Precursor time \(T_{P}\) versus \(M_{P}\) (\(R^{2}\) = 65%). (**c**) Precursory area \(A_{P}\) versus \(M_{P}\) (\(R^{2}\) = 48%). Enlarged and colored points are for 1990 Weber (blue square), 1968 Puysegur Bank (red square), 1969 E. Hokkaido (blue circle), 2000 W. Tottori (red circle), 1948 Karpathos (blue triangle), 1983 Kefallonia (red triangle), 1966 Colorado D. (blue cross) and 1980 S. Cascadia (red cross). time increases, the mean of the EEPAS time distribution increases, and the variance of the location distribution decreases. The time and spatial scales involved varied by about a factor of two. Here, we aim to further understand the space-time trade-off by fitting the EEPAS time distribution with a fixed spatial distribution and the spatial distribution with a fixed time distribution. In the next section, we review the defining equations of the EEPAS model and then describe the method and data for the present study. Our results show how the space-time trade-off is revealed through constrained fitting of the EEPAS model to the New Zealand and California catalogues. Finally, we indicate by way of a simple New Zealand example how the space-time trade-off might be exploited for improving the performance of medium-term earthquake forecasts.
## 2 Results and Discussion
### Fatty Acid Analysis  
Thermal drying has been reported to have a minor influence on the concentration of fatty acids because the structure of fatty acids remained stable at a high temperature of 325 \({}^{\circ}\)C [35]. In another study, negligible amount of fatty acids was degraded when thermally treated at 90 \({}^{\circ}\)C for 30 min [36]. Therefore, in the present study, the drying temperatures applied could be assumed to possess minor degradative effect. Table 6 shows the identified fatty acids and their relative abundance determined with GC-MS analysis. \(\alpha\)-Linolenic acid was the major fatty acid present, followed by linoleic and palmitic acids. \(\alpha\)-Linolenic acid is an antioxidant, a plant-derived omega-3 fatty acid and is known for its role in mediating cardiovascular disease [37].  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Compound**} & \multirow{2}{*}{**Retention time**} & \multirow{2}{*}{**ANOVA**} & \multirow{2}{*}{**Fresh**} & \multirow{2}{*}{**CPD-VMFD**} & \multirow{2}{*}{**CD at 50 \({}^{\circ}\)C**} \\ \cline{5-6}  & & & & **Concentration (mg 100 g\({}^{-1}\) db)** \\ \hline \(\alpha\)-tocopherol & 25.945 & n.s.\({}^{\text{a}}\) & 106.08 a & 106.32 a & 85.85 a,b \\ Desmosterol & 26.630 & *** & 218.08 a & 37.74 b & 42.23 b \\ Lanosterol & 26.875 & *** & 152.20 a & 19.84 b & 22.33 b \\ Campesterol & 27.575 & *** & 498.76 a & 128.45 b & 132.60 b \\ Stigmasterol & 28.035 & *** & 1001.48 a & 221.47 b & 193.81 b \\ \(\beta\)-sitosterol & 28.950 & *** & 1162.24 a & 282.31 b & 264.62 b \\ \(\beta\)-amyrin & 29.195 & *** & 268.82 a & 2.78 b & 2.31 b \\ Cycloartenol & 30.045 & *** & 67.20 a & 0.00 b & 0.93 b \\ Betulin & 31.250 & *** & 176.62 a & 10.64 b & 12.38 b \\ \hline TOTAL & & 3647.48 a & 809.56 b & 757.07 b \\ \hline CPD—convective pre-drying; VMFD—vacuum microwave finish drying; CD—convective drying.\({}^{\text{a}}\) NS, not significant F ratio (\(p<0.05\)); \({}^{\text{b}}\) Treatment means of the ANOVA test (values are the mean value of three replicates); values followed by the same letter, within the same row, is not significantly different (\(p<0.05\)) according to [PERSON]’s multiple-range test. & \\ \hline \hline \end{tabular}
\end{table}
Table 5: Concentration of phytosterols influenced by drying methods.
# Advancements and Research Trends in Microgrids Cybersecurity
## 2 Cybersecurity in Smart Microgrids  
Smart Grid is the new paradigm for power systems. Even if there is no unique definition, the European Union Commission Task Force for Smart Grids provides the following one: \"A Smart Grid is an electricity network that can cost-efficiently integrate the behavior and actions of all users connected to it--generators, consumers, and those that do both--in order to ensure economically efficient, sustainable power system with low losses and high levels of quality and security of supply and safety. A smart grid employs innovative products and services, together with intelligent monitoring, control, communication, and self-healing technologies\".  
It is hard to evaluate the cybersecurity risk of smart grids due to the huge variety of Information and Communication Technologies that can be used to achieve a wide set of tasks. For example, the National Electric Sector Cybersecurity Organization Resource (NESCOR) identifies six scenarios in the power system where main failures related to cybersecurity threats [7] can happen: Advanced Metering Infrastructure, Distributed Energy Resources (DER), Wide Area Monitoring Protection and Control, Electric Transportation, Demand Response, and Distribution Grid Management. Each technology has its own peculiarities impacting differently on the whole power system. Several papers address the issue to survey the main vulnerabilities and threats of the electrical power system: [8; 9; 10]. Reference [5] discusses the architecture of power systems with a high penetration of DER and related cybersecurity issues and summarizes attack scenarios against DER also considering attack prevention, detection, and response measures specifically designed for DER.  
A Survey on Cyber-Security of Smart Microgrids is proposed in Reference [11], where a sample of recent cybersecurity projects, a review of cybersecurity standards and protocols for power systems, and a classification of attacks with related impact and mitigation strategies are provided. Reference [11] provides a short overview of some technologies that will be further discussed in this paper. Reference [12] addresses the cyber-physical security in power systems by focusing on microgrids and their control structure. Papers including an analysis at a glance of the most promising recent technologies that can improve the cybersecurity of smart microgrids are still missing at the best knowledge of the authors of this paper.
# Provably Secure ECC-Based Anonymous Authentication and Key Agreement for IoT
## 2 Preliminaries
### Network Model  
A typical IoT application is shown in Figure 1. It mainly involves three main components: end devices, routers, and servers. The end devices may be sensors, actuators, cell phones, etc. Routers include gateway nodes, base stations, and routers for relaying and passing messages. In addition, servers are in charge of managing devices and assigning security parameters.  
An IoT system consists of many low-power, resource-limited end devices placed in unattended or open environments and typically connected to open networks. Through these terminal devices, real-time monitoring and control can be implemented remotely. The end sensors collect real-time data such as agricultural environment parameters, power consumption, biomedical data, and machine conditions and then send the data to remote servers. The servers receive and store the collected data, then extract and evaluate the data to provide the appropriate control measures. The end devices carry out control commands that are received from the server. There is a risk of the adversary controlling the communication channels and compromising the secret credentials of servers and end devices.
# Design of a Novel RFID Reader for Oilwell Downhole Applications
## 2 Design of Downhole HDX Reader Hardware  
### Advantages of Half-Duplex Communication Protocols  
The FDX communication protocol is a communication protocol that can send and receive data simultaneously, while the half-duplex (HDX) protocol is a distinctive communication protocol that allows data to be transmitted in both directions but only allows data to be transmitted in one direction at a time. HDX communication technology closes the reader's magnetic field during the return transmission of data. In contrast, FDX requires the reader's magnetic field to be kept open throughout the data transmission process. The HDX system, by closing its magnetic field upon data reception, allows readers to focus on tag signals more intensely, effectively eliminating the disruptive influence of the reader's RF field on these signals. This results in more accurate readings and a significantly reduced energy consumption when compared to FDX. Moreover, HDX adopts frequency-shift keying technology, which not only enhances the electromagnetic interference (EMI) robustness of HDX tags but also enables the reader the capability to accurately discern and differentiate between closely positioned tags.  
In comparison to FDX, HDX streamlines the transmission process by trimming 3 bits from the data header and omitting control bits, thereby reducing the volume of transmitted data from 128 bits to a mere 112 bits. This compaction results in an enhancement of the transmission rate that approximates a doubling in efficiency. Therefore, adopting a HDX protocol instead of the current FDX protocol can help improve the tag recognition performance of the downhole RFID reader.
## 4 Results and Discussion
### Velocity Distribution  
The contours of velocity under different vertical pressures are shown in Figure 7. The airflow characteristics of packed beds are more noticeable at higher inlet velocities. The inlet velocity \(V_{0}\) = 0.6 m/s was chosen for the study. The velocity field distribution was similar under different vertical pressures. In the axial direction of the packed bed, the inlet airflow is in contact with the particles in the front part of the packed region. The particles form a barrier to the airflow, and a zero-velocity region exists at the back of the particles. In the packed region, the local velocity distribution shows a clear difference. In the outlet region, the airflow stagnates and backflows. The velocity near the cylindrical wall is higher than in other regions, with the local maximum velocity near the wall about 9 times the inlet velocity under 250 kPa vertical pressure. The velocity in the packed bed is not just dependent on the porosity size but also on the connectivity of the pore paths [26]. The particle distribution is more dispersed with the increase in vertical pressure, which improves the internal pore connectivity and causes the velocity to increase.  
Consistent with the radial porosity distribution measurement method, the same coaxial cylindrical surface measurement was used to obtain the radial mean dimensionless velocity (\(V_{r}/V_{0}\)) distribution of the packed bed as shown in Figure 8. The distribution of the radial mean dimensionless velocity at different vertical pressures was similar and consistent with the trend of the oscillatory distribution of porosity. The velocity was at maximum at about one-half particle radius from the cylindrical wall, with the local maximum velocity being 2.51, 2.58, 2.63, and 2.74 times the inlet velocity with increasing vertical pressure, respectively. The mean velocity inside the packed bed increases with the vertical pressure, with the mean velocities 0.75 m/s, 0.78 m/s, 0.81 m/s, and 0.86 m/s at vertical pressures of 0, 50, 150, and 250 kPa, respectively.  
Figure 7: The contours of velocity under different vertical pressures at inlet velocity \(V_{0}\) = 0.6 m/s: (a) 0 kPa (b) 50 kPa (c)150 kPa (d) 250 kPa.
## 1 Introduction
From these studies, we can learn that ecosystem WUE depends on the trade-off between GPP and ET, as well as their responses to climate forcing [28; 35]. Existing research has primarily focused on identifying the drivers of interannual variability in regional WUE. With the increasing probability of extreme climate events, the hydrological cycle of ecosystems is likely to be affected, which may influence the carbon-water coupling processes in vegetation, leading to potential extreme variations in vegetation WUE [36; 37]. We know that the variation in WUE is driven by two variables. However, under extreme conditions, it may be driven by a single factor. This driving characteristic of WUE variation under extreme conditions currently lacks systematic research in China. Studies in other regions have shown that extreme warming and extreme drought in the United States will both increase vegetation ET and decrease GPP, with the increase in ET during extreme drought primarily attributedto soil moisture [38, 39]. Site-scale studies on the Eurasian continental steppe have indicated that the increased frequency of extreme precipitation events will regulate the carbon-water cycle between vegetation and soil through net ecosystem CO2 exchange, promoting carbon absorption in the meadow steppe [40]. Moreover, the contributions of the same climatic factors to WUE variations may differ across regions, necessitating further investigation, particularly in China. Therefore, this study aims to (1) identify the dominant factors driving extreme WUE variations in China, (2) explore regional differences in sensitivity to extreme WUE variations across China, and (3) employ a ridge regression model to quantify the contributions of precipitation, temperature, shortwave radiation, and VPD to WUE variations in China. Due to the extreme variations in WUE, these changes are likely not solely driven by a single extreme climate event but rather by a combination of multiple factors. Therefore, our study investigates the changes in WUE in China and the dominant drivers of these changes from the perspective of extreme WUE. This research aims to provide support for developing ecological management policies to enhance vegetation WUE and mitigate the impacts of extreme climate change on the carbon-water cycle of terrestrial ecosystems in future climate scenarios.
## 2 Materials and Methods
### Overview of the Implemented Crop Mapping Procedure  
The block diagram in Figure 2 shows the flowchart designed to map crop types using PRISMA and Sentinel-2 images. Data gathering, including ground truth data collection and satellite image acquisition, is described in Section 2.3. The pre-processing and processing of data is described in Sections 2.4.1 and 2.4.2, respectively. The classification algorithms and the TR/ACC (TRAIN and ACCuracy assessment) procedure are described in Sections 2.5 and 2.6.  
Figure 1: Location of study areas in (**a**) Italy and (**b**) Iran, and field boundary of (**c**) Jolanda di Savoia, northeast Italy, (**d**) Maccarese, central Italy, (**e**) Grosseto, central Italy, and (**f**) MKK southwest Iran.