esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Two Degrees of Freedom (2-DOF) Model  
The first step of the proposed methodology consists of modelling the soil-foundation structure system with a simplified 2-DOF model (Figure 1), whose cinematics may be described in terms of:  
* [noitemsep]  
conveniently, it is possible to re-write (1) and (2) by merging the kinematic terms together: accelerations (\(\ddot{u_{s}},\ \ddot{u_{g}}\)), velocities (\(\dot{u_{s}},\ u_{g}\)), displacements \(\big{(}u_{s},u_{g}\big{)}\):  
\[m_{g}\ddot{u_{g}}+c_{g}\dot{u_{g}}+(k_{g}+k_{s})u_{g}-k_{s}u_{s}=-m_{g}\ddot{u_{b}} \tag{3}\]  
\[m_{s}\ddot{u_{s}}+c_{s}u_{s}-k_{s}u_{g}+k_{s}u_{s}=-m_{s}\ddot{u_{b}} \tag{4}\]  
and thus, in the matrix form, the equations of motion of the 2-DOF system may written as:  
\[\mathbf{M}\ddot{u}+\mathbf{C}\ddot{u}+\mathbf{Ku}=-\mathbf{M}\mathbf{r}\ddot{u_{b}} \tag{5}\]  
where:  
\(\mathbf{M}=\left(\begin{array}{cc}m_{g}&0\\ 0&m_{s}\end{array}\right)\) is the mass matrix;  
\(\mathbf{C}=\left(\begin{array}{cc}c_{g}&0\\ 0&c_{s}\end{array}\right)\) is the damping matrix;  
\(\mathbf{K}=\left(\begin{array}{cc}k_{g}+k_{s}&-k_{s}\\ -k_{s}&k_{s}\end{array}\right)\) is the stiffness matrix;  
\(\mathbf{u}=\left(\begin{array}{c}u_{g}\\ u_{s}\end{array}\right)\) is the displacement vector;  
\(\mathbf{r}=\left(\begin{array}{c}1\\ 0\end{array}\right)\) is the base vector
## 3 A Trust Anchor for a Home Network's CA
### Trusted Computing and the Trusted Platform Module  
In this paragraph, we give a high-level introduction into aspects of the _Trusted Platform Module (TPM)_ that are most relevant in our context. For further details, please refer to the TCG standards [12; 13; 14; 15].  
A TPM is a low-cost cryptographic chip, which can be embedded into many computing devices like notebooks or desktops. One of the most important abilities of a TPM is to generate, use and manage asymmetric keys. A private key is securely stored inside the TPM's storage and never exposed to the memory of the computing device. Properties of keys can be specified during their creation, e.g., to prohibit that a private key ever leaves the TPM. A program using the keys to encrypt or sign data needs to hand over the piece of data to the TPM and the TPM hands back the encrypted blob or signature. Keys managed by the TPM can be protected against unauthorized usage with an authorization secret (PIN). Every time the user application desires to use the key, this PIN needs to be provided to the TPM.  
During the production process of the TPM-equipped computing device, various credentials are issued by the manufacturer that can be used to prove the compliance of the TPM-chip and other platform components with the TCG standard to other parties. The most important credential is the EK Certificate which is issued by the manufacturer of the TPM for the _Endorsement Key_ of the TPM. The EK can be seen as the TPM's master key. The EK certificate asserts that the EK is resident inside a standard-compliant TPM.  
Trusted Computing guidelines do not allow to use the EK directly to encrypt or sign data. Instead, so-called _Attested Identity Keys (AIK)_ are generated, which can be seen as anonymized aliases for the  
Figure 4: Between home network A and B exists a trust relationship, _i.e._, entities from home network A may access services in home network B (1). The attacker succeeds in stealing the Home Key of Home CA A (2). The attacker now is able to issue a certificate to its own entity (3) and later is able to access and abuse the service within home network B.  
EK. The generation process of an AIK involves an entity called _Privacy Certification Authority (PCA)_ which issues an AIK Certificate. This certificate attests the usage of a genuine TPM.  
An important property of TPM keys is their migratability. Every key in a TPM, except the EK, AIKs and keys signed by one of them, can be specified as migratable. A migratable key can be exported after providing a migration secret. The advantage of a migratable key is the possibility to backup the keying material.
## 4 Hydrocarbon Potential of formations in Malaysia  
### Organic Geochemical Characteristics of Formations in Peninsula Malaysia (PM)  
#### 4.1.1 Source Richness of Formations in PM  
Generally, the formations in Peninsula Malaysia had very good TOC values, ranging from 1.33% to 41.23% wt%, according to the classification of TOC values by [PERSON] [79]. Table 2 provides the classification of TOC values. The Batu Gajah shale formation has the lowest average TOC of 1.33 wt%, which was still good for hydrocarbon generation according to the [PERSON] [79] interpretation of TOC values. The lower the organic content in the rock, the lower its TOC value, and the lower the TOC value, the lower the chance of generating hydrocarbon. The Batu Arang formation and the Penyu basin had the highest TOC values at 41.23% and 15.84 wt%, respectively, as shown in Table 2. The high values of Batu Arang formation and the Penyu basin could be attributed to the formations being primarily composed of coals, and black shales, which contain very high organic content, especially the coals [80; 81; 82]. The other formations, such as Belata, Semanggol, Kroh, and Semantan, had very good TOC values, ranging from 2.21 to 3.32 wt% (Table 2). The formations with the highest TOC values in Peninsula Malaysia were predominantly composed of coals and oil shales which supported claims made by [PERSON], [PERSON] and [PERSON] [83; 84; 85] that these sedimentary rock types contain high amounts of organic matter. Organic richness measures the amount of organic matter in the sediment capable of generating hydrocarbon, not just the amount of organic matter itself [19]. For this reason, it is not advisable to use TOC alone to determine the quantity of hydrocarbon a rock can generate, as suggested by [PERSON] [17]. This is because there are different types of organic matter in sediments, with some generating hydrocarbons while some are also inert and do not generate any hydrocarbon [72]. The increasing maturity of organic matter also causes TOC to decrease [17; 73]. As the organic matter matures, the relative inert kerogen content of the organic matter increases as the reactive organic matter is used up in hydrocarbon generation [73]. Therefore, it is key to combine TOC with other analyses, such as Rock-Eval Pyrolysis to make accurate predictions of TOC [17]. Figure 4 shows the combined analysis of TOC and S2 of the formations in Peninsula Malaysia. According to Figure 4, the Batu Arang formation and Penyu basin had the highest chance of producing hydrocarbons. This was demonstrated by the high amount of organic matter present in the formations, as confirmed by their high S2 values. The hydrocarbon generation potentials of these formations are promising and require further research for economic considerations. The lower S2 values in the Belata, Semanggol, Kroh, and Semantan formations indicated that the organic matter in these formations had already been generated, because S2 stands for the residual organic matter in the formations.
## 2 Literature Review
### Relation between Cybersecurity and Safety in AHS
Nonetheless, [PERSON] et al. [20] suggest that removable near-infrared cut filters and photochromic lenses provide adequate protection from various angles. A recent study addresses the use of machine learning to detect and mitigate remote attacks via a dedicated anti-hacking device [21]. Notably, these attacks sometimes may not require any physical access to the truck, such as attacks with lasers, and their consequences can be critical. Yet, some of these assaults do not require physical access to the truck and their effects may be serious. Concerning autonomous mining, [PERSON] [6] notes that existing AHS standards and literature have always placed a premium on the system's protection aspect, but never on its cybersecurity components. In addition, the proposed study [6] claims that developing a generic threat model for AHS systems is important, which would be applicable to any OEM and mining facility. In that, [PERSON] [6] introduces an under development solution called MM-ISAC, that would align with safety requirements such as ISO17757:2017 and security standards such as ISO/IEC 27000:2016, ISA99. Although this initiative is still in its early stages, we anticipate that the MM-ISAC will collaborate closely with vendors to refine the system specification across all affected areas, including infrastructure, communications, and cybersecurity, in order to come up with a sound threats model. Finally, [PERSON] et al. [22] argue that safety and cybersecurity should be seen in tandem. Currently used risk assessment approaches treat safety and cyber threats as two distinct entities, while in the Industry 4.0 era, a safety risk is also a cybersecurity risk. Consider an AHT and a manual haulage truck; both provide certain safety measures to safeguard and protect the equipment and operators. However, the only safety concern regarding a manual truck is if the operator tampers physically with the truck. On the contrary, an AHT poses the same safety concerns plus a residual risk of it being a CPS connected to an open network (i.e., Wifi). Hence, the cybersecurity risks is a critical factor that might affect the safety of the mine. The bowtie and attack tree analysis are utilized by [22], combining safety and security in risk analysis to generate an exhaustive representation of risk scenarios. Unfortunately, this method would necessitate the collection of qualitative and quantitative data to calculate the probability of safety risks. These data are private and often proprietary to manufacturers as well as not always accessible for analysis, making this study difficult to conduct further studies to confirm or improve the results. [PERSON] et al. [17] propose creating a trust zone to handle sharing such information, but this entails the acceptance of all parties. Table 1 summarizes some proposed solutions and their challenges.
# A Novel Dual-Band Implantable Antenna for Pancreas Telemetry Sensor Applications
###### Abstract  
In this study, a novel implantable dual-band planar inverted F-antenna (PIFA) is proposed and designed for wireless biotelemetry. The developed antenna is intended to operate on the surface of the pancreas within the Medical Device Radiocommunications Service (MedRadio 401-406 MHz) and the industrial scientific and medical band (ISM, 2.4-2.5 GHz). The design analysis was carried out in two steps, initially inside a canonical model representing the pancreas, based on a finite element method (FEM) numerical solver. The proposed antenna was further simulated inside the human body taking into account the corresponding dimensions of the tissues and the electrical properties at the frequencies of interest using a finite-difference time-domain (FDTD) numerical solver. Resonance, radiation performance, electrical field attenuation, total radiated power, and specific absorption rate (SAR), which determines the safety of the patient and the maximum permissible input power and other electromagnetic parameters, are presented and evaluated.  
implantable antenna; PIFA; SAR; biotelemetry +
Footnote †: journal: _etlecom_  
0
Footnote 0: _Telecom_ **2022**, 3, 1–16. [[https://doi.org/10.3390/telecom3010001](https://doi.org/10.3390/telecom3010001)]([https://doi.org/10.3390/telecom3010001](https://doi.org/10.3390/telecom3010001))
## 8 Conclusions  
Developing fast and accurate ways to quantify EAT is an important task that could improve CVD risk assessment, CVD prevention, additional insight into the exact relationship between various CVDs (such as coronary atherosclerosis, metabolic syndrome, and atrial fibrillation), and EAT volume. Methods that are widely used in clinical settings are either inaccurate when using surrogate measures or slow when manually quantifying EAT volume from CT or MRI imaging.  
After analyzing a wide range of methods of automatic EAT and PAT segmentation, ranging from traditional image processing methods like active contours and region growing to more novel approaches like deep learning, we conclude that there is still a need for more accurate EAT and PAT segmentation methods validated on larger and more diverse patient cohorts. Additionally, researching methods for better pericardium delineation could enable better EAT and PAT segmentation, as well as other tasks such as whole heart segmentation.  
While the existing fully automatic methods show promising results and very fast segmentation times, they are still equal to or below inter-observer variability for EAT segmentation when validating on large patient cohorts. On the other hand, some semi-automatic methods perform better than the fully-automatic ones and show potential for clinical use, but might be too impractical to use in large studies. The biggest challenge to overcome when segmenting EAT is the lack of labeled datasets. More publicly available datasets, as well as new techniques in image synthesis, could help overcome this challenge.  
Conceptualization, M.B. and M.H.; writing--original draft preparation, M.B. and M.H.; writing--review and editing, M.B., M.H., I.G. and A.P.; visualization, M.B. and M.H.; supervision, I.G. and A.P.; project administration, I.G. and A.P.; funding acquisition, I.G. and A.P. All authors have read and agreed to the published version of the manuscript.  
This work has been supported in part by Croatian Science Foundation under the Project UIP-2017-05-4968 as well as the Flemish Government (AI Research Program).  
Not applicable.  
The authors declare no conflict of interest.
## 6 Conclusions  
The dissolution is essential to developing good reservoirs in the Es4 formation of the Dongying subbasin. The dissolution porosity related to the high-quality formation in the Es4 comprises several types: intergranular pore, intragranular pore, intercrystalline pore, and moldic pores. Three types of pores, including primary pores, secondary and mixed pores, are developed in the study interval. The intergranular secondary pores encompass the enlarged pore and intergranular normal secondary pores. The fracture pores comprise rock fracture pores, grain fracture pores, and enlarged fractures. The comparison of the upper part and lower part of the Es4 revealed that the Es4s have good reservoir quality compared to the Es4x. Mechanical compaction is the primary factor reducing porosity in the Es4 interval. However, cementation greatly influences porosity, especially in the retrogradation sequence in the Es4x interval. The compaction index (Icp) indicates that compaction is the major factor in porosity reduction in the Es4s and progradation sequence in the Es4x. However, cementation stands as the leading factor in porosity reduction in the retrograde sequence in the Es4x. The clay matrix is higher in the Es4s compared to the Es4x. The Es4s reservoirs are probably deposited in an open to semi-closed system, while the Es4x is in the semi-closed to a closed system. This study demonstrated that for oil exploration in the fourth member of the Shahejie Formation, the Es4s should be prioritized.  
Conceptualization, K.L.K. and L.Q.; methodology, K.L.K.; software, K.L.K. and K.K.A.D.; validation, Y.Y., B.Y.; formal analysis, K.L.K.; investigation, K.L.K. and Y.Y.; resources, L.Q.; data curation, Y.H.; writing--original draft preparation, K.L.K.; writing--review and editing, K.L.K., Y.Y. and K.S.A.; visualization, Y.Q.; supervision, L.Q.; project administration, Y.Y.; funding acquisition, L.Q. All authors have read and agreed to the published version of the manuscript.  
This study was supported by the Natural Science Foundation of China (No.441972099).  
Not applicable.  
The authors declare no conflict of interest.  
Figure 15: Comparison plot of the clay matrix content in the Es4 members. (**A**) Clay-matrix content split by Formation Member and study areas, (**B**) Clay-matrix split by Member in the study area.
## 2 Theory and Experiments
### The Modified KZK Model  
By introducing Fourier Transform, the \(n\)th time derivative of time-dependent acoustic pressure \(p(t)\) could be written as  
\[\frac{d^{n}p(t)}{dt^{n}}=F^{-}\big{\{}(i\omega)^{n}F^{+}[p(t)]\big{\}}. \tag{2}\]  
Here \(F^{+}\) and \(F^{-}\) represents operators of the Fourier transform and its inversion, respectively. As the order number \(y\) is a non-integer, the fractional order derivative is then defined as a convolution [38],  
\[\frac{d^{y}p(t)}{dt^{y}}=\frac{1}{\Gamma(-y)}\int\limits_{-\infty}^{t}\frac{p(t ^{\prime})}{\left(t-t^{\prime}\right)^{\left(1+y\right)}}dt^{\prime}, \tag{3}\]  
where \(\Gamma(\cdot)\) is the Gamma Function, i.e., \(\Gamma(x)=\int_{0}^{\infty}\xi^{x-1}e^{-\xi}d\xi\). The definition in Equation (3) is hence the Riemann-Liouville fractional derivative [42]. Therefore, the fractional order derivative is not only determined by the pressure value at the time point \(t\), but is also related to its history range from \(-\infty\) to \(t\).  
Since the attenuation factor \(y\) is a non-integer in lossy media, the wavenumber \(\widetilde{k}\) could then be considered as a complex, while its squared form in the low-frequency approximation is  
\[\widetilde{k}^{2}\approx\frac{\omega^{2}}{c_{0}^{2}}+2i\frac{\omega}{c_{0}}a_{ 0}\omega^{y}=\frac{\omega^{2}}{c_{0}^{2}}-2\frac{\omega_{0}}{c_{0}}\frac{(-i \omega)^{y+1}}{(-i)^{y}}. \tag{4}\]  
in which \(i\) is the imaginary unit, and \((-i)^{y}\) can be expressed with trigonometric functions as \((-i)^{y}\)=\(\cos(y\pi/2)-i\sin(y\pi/2)\). With the wave number \(\widetilde{k}=\omega/c_{0}+i\omega_{0}(-i\omega)^{y}/\left|\cos(y\pi/2)\right|\), the phase velocity can be calculated as  
\[\frac{1}{c(\omega)}=\frac{\text{Re}\left(\widetilde{k}\right)}{\omega}=\frac{ 1}{c_{0}}+\alpha_{0}\tan(y\pi/2)|\omega|^{y-1}, \tag{5}\]  
where \(\text{Re}(\cdot)\) represents the real part of the complex value.  
Considering the non-integer attenuation factor \(y\) and the dispersion of phase velocity, the classical KZK equation could be modified as  
\[\frac{\partial^{2}p}{\partial z\partial\tau}=\frac{c(\omega)}{2}\
abla_{\bot }^{2}p+\frac{\delta}{2c(\omega)}\frac{\partial^{y+1}p}{\partial\tau^{y+1}}+ \frac{\beta}{2\rho_{0}c(\omega)^{3}}\frac{\partial^{2}p^{2}}{\partial\tau^{2}}, \tag{6}\]  
where the attenuation term and the phase velocity \(c(\omega)\) could be calculated according to Equations (4) and (5), respectively. Consistent conclusions can be found between Equation (6) and the work of [PERSON] et al. [43], which is an extension of an earlier model for ultrasound propagation in power-law media proposed by [PERSON] et al. [44]. In addition, Equation (6) will not hold if \(y=1\). However, in the framework of discussing HIFU propagation problems, the \(y=1\) case is of no importance [37; 38] and is not of interest here.
# Analysis of a Nature-Inspired Shape for a Vertical Axis Wind Turbine
## 4 Conclusions  
The present work proposes an improvement of the Savonius turbine. The Savonius turbine is based on semi-circular blade profiles, while the present work proposes a nature-inspired shape based on the Fibonacci spiral. This shape is presented repeatedly in nature. A numerical model based on CFD was created to compare the Fibonacci and Savonius blade profiles and obtained better results for the Fibonacci shape. An improvement of 14.1% on the average power coefficient and 13.5% on the average thrust coefficient were obtained with the Fibonacci blade profile. After that, the effect of the number of blades, separation gap, aspect ratio, overlap, and twist angle was investigated. The main findings of the present work are:  
Figure 17: Working principle of the Fibonacci turbine with separation gap.  
* The optimum number of blades are two;
* The optimum aspect ratio is seven.
* The optimum overlap/chord length relation is 0.15.
* The optimum separation gap is 0.
* The optimum twist angle is 180\({}^{\circ}\).  
The findings regarding the optimum number of blades and separation gap are in line to the scientific literature about regular Savonius turbines. Nevertheless, regarding optimum aspect ratio, overlap, and twist angle, there is no consensus in the literature.  
For future works, an important aspect is to design a turbine with the most appropriate combination of number of blades, aspect ratio, overlap, separation gap, and twist angle. To this end, optimization models will be employed in future works, such as Multidisciplinary Design Optimization (MDO) algorithms or Artificial Neural Networks (ANN). Another future research will lay on further improve the performance of the Savonius turbine based on the Fibonacci blade profile by means of improvements, such as V-shape deflectors, deflecting plates, curtains, venting slots, guide vanes, several stages, and combinations of Savonius-Darrieus turbines. Once the most appropriate VAWT design is obtained, another necessary area of research will be the economic aspect. Feasibility analysis will be realized in future works to compute the economic advantages of using VAWT in urban environments.  
Conceptualization, J.B.D., J.d.D.R.G., A.C.C., J.T.M., C.G.C. and M.I.L.G.; methodology, J.B.D., J.d.D.R.G., A.C.C., J.T.M., C.G.C. and M.I.L.G.; software, J.B., J.d.D.R.G., A.C.C., J.T.M., C.G.C. and M.I.L.G.; validation, J.B., J.d.D.R.G., A.C.C., J.T.M., C.G.C. and M.I.L.G.; formal analysis, J.B., J.d.D.R.G., A.C.C., J.T.M., C.G.C. and M.I.L.G.; investigation, J.B., J.d.D.R.G., A.C.C., J.T.M., C.G.C. and M.I.L.G.; writing--original draft preparation, M.I.L.G.; writing--review and editing, J.B.D., J.d.D.R.G., A.C.C., J.T.M. and C.G.C.; supervision, J.B.D., J.d.D.R.G., A.C.C., J.T.M., C.G.C. and M.I.L.G. 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.
# Multi-Feature Fusion with Convolutional Neural Network for Ship Classification in Optical Images
## 4 Experimental Results and Analysis
### Classification Results and Analysis  
To validate its classification performance, the proposed method was compared against other state-of-the-art techniques under identical experimental conditions. All of the experiments have been done several times, and we listed the average results of the experiments. The comparison experiments involved many parameters. They were set as follows. Support vector machine (SVM) toolbox selected sklearn. svm. For HOG + SVM, the kernel was set to 'linear'. For LBP + SVM, the kernel was set to 'rbf'. Gamma parameters and penalty coefficients were obtained by grid optimization. The value of the penalty coefficient C was set to 100, and \(\gamma\) was set to 1.