esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Background  
Cloud security is a branch of computer and network security controlled by privacy-enhancing technologies and governed by a set of policy rules to protect the deployment of data, software applications, and associated services outsourced in the cloud [12]. Common terminologies in the field of security are shown in Table 1. These terminologies are used across all fields when defining the security and privacy of a particular research area. The STRIDE model [13] provides a systematic way of analysing vulnerabilities by providing distinct understanding based on technical knowledge [14]. The STRIDE approach of analysing vulnerabilities is a matching concept to the existing security terminologies, as shown in Table 2. The STRIDE model is an effective way of knowing the impact vector of an attack before its occurrence [15]. This approach has previously been used in the literature for accessing threat capacity in cloud computing. Literature review of cloud computing security and privacy is shown Table 3 and Table 4 respectively. \begin{table}
\begin{tabular}{c c} \hline \hline
**Terminology** & **Definition** \\ \hline Confidentiality & To ensure the accessibility of information to only authorised users. \\ Integrity & Maintaining the completeness and accuracy of every part of information. \\ Availability & Information is accessible to only authorised users. \\ Non-repudiation & Avoid the deniability of one’s actions. \\ Privacy-preserving & Ability to mask identity and Personal Identifiable Information (PII). \\ Accountability & Obligation or willingness to take responsibility for action with a defined set of rules. \\ Auditability & Maintaining a system with relative ease in other to improve its efficiency. \\ Authentication & Establishing the right identity of a user in a system \\ Authorisation & Access to resources is restricted to only authorised personnel \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Security parameter definition. \begin{table}
\begin{tabular}{c c} \hline \hline
**STRIDE Threat** & **Matching Security Parameter** \\ \hline Spoofing & Authentication \\ Tampering & Integrity \\ Repudiation & Non-repudiation \\ Information disclosure & Confidentiality \\ Denial of service & Availability \\ Elevation of privilege & Authorisation \\ \hline \hline \end{tabular}
\end{table} 
Table 2: STRIDE security definition.
# A New Effective Narrowband Active Noise Control System for Accommodating Frequency Mismatch
## 2 Materials and Methods
### NANC System with AR Model  
[PERSON] et al. proposed a second order AR with reference signals to mitigate the effect of the FM; the \(i\)-th channel of reference signal can be expressed as:  
\[x_{ai}(n)=-c_{i}(n)x_{ai}(n-1)-x_{ai}(n-2),\,\,\,n\geq 2 \tag{10}\]  
Figure 2: Levels of residual noise signals for the conventional NANC system (true primary noise frequency: \(\omega_{p}=[0.1\pi\ 0.2\pi\ 0.3\pi]\), reference signal frequency: \(\omega_{r}=[0.101\pi\ 0.202\pi\ 0.303\pi]\), original amplitude: \(a_{p}=[2\ 1\ 1]\), \(b_{p}=[1\ 1\ 1]\), reference signal: \(a_{r}=b_{r}=[1\ 1\ 1]\), the step size: \(\mu=0.005\), the secondary path: M = 11, \(S_{(z)}=\hat{S}_{(z)}\),\(\delta_{p}^{2}=0.1\), 40 runs, (**a**) Conventional NANC system (without FM). (**b**) Conventional NANC system (with 1% FM).  
\[x_{bi}(n)=-c_{i}(n)x_{bi}(n-1)-x_{bi}(n-2),\ \ n\geq 2 \tag{11}\]  
where \(c_{i}(n)\) is the coefficient the of frequency compensation in the \(i\)-th channel, respectively, its initial value is \(-2\cos(\omega_{i})\). The related coefficients could be updated to obey a LMS recursion:  
\[c_{i}(n+1)=c_{i}(n)-\mu_{c_{i}}e(n)[\overset{\wedge}{a_{i}}(n)\overset{\wedge} {x}_{a_{i}}(n-1)+\overset{\wedge}{b_{i}}(n)\overset{\wedge}{x}_{b_{i}}(n-1)] \tag{12}\]  
where \(u_{c_{i}}\) is the updating step size.
## 3 Methodology
### Environment Map Construction  
Obstacles are categorized as either static or dynamic, with distinct representation methods adopted for each type. Figure 2 illustrates the classification of point clouds corresponding to static and dynamic obstacles.  
**(1) Classification of dynamic and static obstacles:** In this paper, a depth camera is employed to obtain the corresponding point cloud data to classify dynamic and static obstacles. First, the 3D information is obtained. When the camera obtains point cloud data, the obstacles are represented as point cloud clusters. The corresponding point cloud cluster data is then clustered into point cloud clustering, and the corresponding center point is obtained from the point cloud clustering.  
After receiving the corresponding obstacle information, the camera continuously obtains environmental images. At this point, the offset of the corresponding clustered point cloud is obtained while obtaining the corresponding image of each frame. As the static obstacle itself does not move, the offset will remain stable within the threshold. However, because of its own motion,the offset of the dynamic obstacle will be larger than the corresponding threshold if it moves.  
**(2) Static Obstacles:** When dealing with static obstacles, LiDAR is used to obtain environmental information about static obstacles. The LiDAR on the mobile robot scans for SLAM information corresponding to the environment. After obtaining the corresponding SLAM information, the data is processed, and voxels are used to represent the corresponding environmental information. Finally, the corresponding environment information is stored in the information octree to represent the corresponding environments in three  
Figure 1: Motion planning framework for unknown dynamic environments: acquiring multi-sensor data (camera/LiDAR) enables dynamic-static obstacle modeling, feeding velocity prediction modules where K-means analysis generates motion forecasts, ultimately driving trajectory optimization for collision-free navigation.  
Figure 2: Classification of dynamic and static obstacle point clouds. Combinations of several solid squares indicate the static obstacles, and blue dashed rectangles indicate the bounding boxes of dynamic obstacles.  
states: occupied, idle, and unknown. After the corresponding environment information is stored in the node, the corresponding environment information is processed to accelerate the traversal of the octree. When the information changes with the environment, the corresponding information begins to be automatically generated and automatically deleted, thus obtaining the maximum number of static obstacles.
## 4 Experiment and Discussion
### Optimization of Cell Temperature  
Both the relaxation rate and signal amplitude of the magnetometer are affected by the cell temperature. By increasing the cell temperature, the atomic density in the cell and signal amplitude increases, but the relaxation rate can also increase [31]. Generally, it is necessary to find the best cell temperature through an experimental method. Under the condition of each cell temperature, the incident light power and RF power are optimized by the method mentioned in Sections 4.2 and 4.3. Theoretically, the noise level does not change with the cell temperature. However, in our experiment, the noise level and the sensitivity at each temperature point are obtained from the optimized light power and RF power. As the optimal optical power is different at different cell temperatures, the noise level is also different.
## 2 Materials and Methods
### Volatile Profile  
Volatile compounds were quantified according to [PERSON] et al. (2015) with some modifications. Between 0.150-0.300 g of freeze-dried samples were weighed and placed into a 40 mL vial with polypropylene cap and PTFE/silicone septa; isoamyl acetate (1000 mg/L) (5 \(\upmu\)L) was added as the internal standard for semi-quantification of compounds. After 5 min at 45 \({}^{\circ}\)C (equilibration time), a 50/30 \(\upmu\)m DVB/CAR/PDMS fiber was exposed to the vial headspace at 40 \({}^{\circ}\)C with continuous agitation (250 rpm) in a magnetic stirrer (IKA C-MAG HS 4, IKA-Werke GmbH & Co. KG, Staufen, Germany). After 45 min of exposure, the fiber was extracted from the vial and placed into the GC-MS injector. The separation and identification of compounds was performed using a Shimadzu GC-MS Nexis GC2030 (Shimadzu Scientific Instruments, Inc., Columbia, MD, USA), equipped with a Sapiens X5 MS column (30 m \(\times\) 0.25 mm \(\times\) 0.25 \(\upmu\)m) (Teknokroma, Barcelona, Spain), and coupled with a mass spectrometer detector (TQ8040 NX triple quadrupole mass spectrometer; Shimadzu Scientific Instruments, Inc., Columbia, MD, USA). Only the single quadrupole acquisition mode was used on the TQ8040 NX (Q3 Scan; scan speed 5000 amu \(\mathrm{s}^{-1}\); mass range 40-400 \(m/\mathrm{z}\); event time 0.100 s). The oven temperature program was as follows: (i) initial temperature of 35 \({}^{\circ}\)C, hold for 5 min; (ii) increment of 5 \({}^{\circ}\)C min\({}^{-1}\) up to 150 \({}^{\circ}\)C, hold for 1 min; (iii) increment of 10 \({}^{\circ}\)C min\({}^{-1}\) up to 280 \({}^{\circ}\)C and hold for 5 min. Helium column head pressure was 47.6 kPa (constant linear velocity mode of 36 cm s\({}^{-1}\)). The injector, ion source, and interface were at 250, 230, and 280 \({}^{\circ}\)C, respectively. Helium was used as the carrier gas, column flow of 1 mL min\({}^{-1}\), with a split ratio of 1:50, and a purge flow of 6 mL min\({}^{-1}\).  
Retention indexes of a commercial alkane standard mixture (Sigma-Aldrich, Steinheim, Germany) were used to identify the compounds, as well as the NIST 17 Mass Spectral and Retention Index Libraries. The identification was considered tentative when it was based only on mass spectral data, and only compounds with spectra similarity > 90% were considered correct hits. The linear retention similarity filter was set at \(\pm\)10 units.
# WAMs Based Eigenvalue Space Model for High Impedance Fault Detection
## 3 HIF Detection in Eigenvalue Space
### High Impedance Fault Detection  
As stated in previous sections, the eigenvalues of each powerline have a distinct drift pattern in the eigenvalue space. These patterns are determined by the components that make up the impedance of the line, combined with the apparent impedance of the load. When foreign agents are introduced during abnormal conditions, such as a resistive element during a HIF, the eigenvalues of the line will drift away from their normal zones of operation. During a fault the eigenvalues of the line drift towards a point in the Eigenvalue Space determined by the characteristics of the fault. The higher the magnitude of the fault, the closer the eigenvalues move to this point in the Eigenvalue Space. For low impedance faults, this drift is quite dramatic, this is analogous to the changes in current magnitude seen during a bolted fault. Although not as dramatic as the changes seen during a bolted fault, the disturbances seen during a HIF are enough to produce a distinguishable deviation from the normal zones. This sensitivity to disturbances allows this solution to identify HIFs with a resistance of over 1 k\(\Omega\). Figure 9 illustrates a case of eigenvalue drift during a fault with a resistance of 10 \(\Omega\), while Figure 10 shows the drift of eigenvalues in response to a fault with a resistance of 100 \(\Omega\).  
Figure 9: Eigenvalue drift during a fault with a resistance of 10 \(\Omega\).
## 4 Holographic Materials for Solar Concentrators
### Silver Halide Emulsions  
Silver halide emulsion is one of the oldest recording materials for holography. It is a fine suspension of microscopic grains of silver halide (usually silver bromide, grain size in the range of tens of nanometres) in a colloid sol, usually consisting of gelatin. Typically, a layer of emulsion with a thickness in the range of 5 to 15 \(\upmu\)m, is coated onto glass or film substrate. The recorded image is then developed by chemical post-processing, allowing multiple holograms recording. Additionally, emulsions show high sensitivity (10\({}^{-5}\) to 10\({}^{-3}\) mJ/cm\({}^{2}\)) and good resolution (greater than 6000 lines/mm).  
Silver halide emulsions were recently used to obtain a panchromatic holographic material for the fabrication of wavelength multiplexed holographic solar concentrators [25].
## 2 System
### Adjustment of Data Obtained from Sensors  
#### 2.2.1 Acceleration Sensor  
The values obtained from the acceleration sensor require offset and coefficient adjustments.  
Offset Adjustment  
Let \(g=9.798\) be the acceleration of gravity in Japan. When the sensor is placed on a plane, the value of acceleration should be \((x,y,z)=(0,0,g)\). In reality, the values obtained  
Figure 1: Position estimation system.  
from the sensor are not \((x,y,z)=(0,0,g)\) due to the presence of errors in either the positive or negative direction. Therefore, it is necessary to set the value of the error and the opposite sign as the offset value. For example, if the measured value is \((x,y,z)=(0.1,-0.2,9.95)\) when placed on a horizontal plane, then the offset value \((x_{offset},y_{offset},z_{offset})\) is as follows:  
\[(x_{offset},y_{offset},z_{offset}) =(0,0,g)-(0.1,-0.2,9.95)\] \[=(-0.1,0.2,-0.152).\]  
The value after the offset adjustment should be \(g\) for the axis pointing in the direction of gravitational acceleration; in fact, however, it may be larger or smaller than \(g\). Therefore, it is necessary to obtain the coefficients by making measurements with each axis of the sensor pointing vertically upward and downward. For example, if the \(x\)-axis is placed vertically upward and downward and the measured values are \(x=-9.514,x=-10.501\), then the coefficient values in the positive and negative directions of the \(x\)-axis \(x_{P_{coeff}},x_{N_{coeff}}\) are as follows:  
\[x_{P_{coeff}} =\left|\frac{g}{-9.514}\right|\] \[=1.030\] \[x_{N_{coeff}} =\left|\frac{g}{10.501}\right|\] \[\coloneqq 0.933.\]
## 3 Industrial Applications
### Railway  
The railway industry has started adopting AM technologies to carry out production and maintenance work. Alstom, a French multinational company, is adopting AM technologies for production of lightweight components [112]. Elsewhere, the MODTRAIN project has been financed by the European Commission to explore the standardisation/modularisation of rail components to reduce production, maintenance, and reliability costs. This has been further continued in a project entitled 'Run2 Rail' in Europe where they are investigating the applications of AM technologies and composite material ap placations within the railway industry [112]. Another example is the initiatives undertaken by Dubai's Road Transport Authority's maintenance team to apply AM technologies with respect to various metro rail assets such as manufacturing AM parts for ticketing. Additionally, railway companies face problems in finding spare parts that have been discontinued or where the manufacturers are no longer in business, and which would be expensive to reproduce using conventional manufacturing [113]. To overcome this problem, the railway sector is now turning to metal AM. For example, Webtec, a global rail and transit manufacturer, is developing metal spare parts with GE's H2 Binder Jet technology. This company aims to use the H2 printer to produce up to 250 rail parts for its production lines by 2025 [114]. Similarly, Deutsche Bahn AG, a German railway company, in collaboration with Siemens produces spare parts for their older fleets [112; 113].  
Based on the above mentioned, it can be concluded that metal AM provides good opportunities for the railway industry. Interested readers may refer to the article published by [PERSON], [PERSON], Coxon, and Napper [112] for current AM applications in the railway industry sector.
## 3 Results and Discussion
### Desirability Function  
The desirability to maximize all studied variables has been summarized in Figure 6. This procedure helped in determining the combination of experimental factors that involve simultaneous optimization of several response variables and was selected for meeting these goals and giving equal importance to all responses. The maximum 'desirability' predicted by the software was obtained in Run 7 with parameters of 40 MPa, 50\({}^{\circ}\) C, and 8% co-solvent. The results were obtained as the extraction yield of 8.78%, TCC of 15.33 mg/g, TC recovery of 62.85%, TPC of 157.16 mg GAE/g, TEAC of 0.31 mmol TE/g, and FAME of 7.19 mg/g. The optimum desirability was obtained at 38.4 MPa, 56.74 \({}^{\circ}\)C, and 8% co-solvent where the optimal results were extraction yield of 12.82%, TCC of 16.96 mg/g, TC recovery of 69.53%, TPC of 114.57 mg GAE/g, TEAC of 0.32 mmol TE/g, and FAME of 8.31 mg/g. Finally, the optimization desirability was determined to be 0.86 and the experiments under the optimum conditions provided values close to those predicted by the statistical model.
# Impacts of Diversity Technique Application in Order to Reduce the Fading in IEEE 802.15.4 Networks
## 2 System Model  
BER of an IEEE 802.15.4 ZigBee transceiver can be defined as [18]:  
\[BER(\gamma)=\frac{1}{2}erfc\Big{(}\sqrt{\gamma\cdot\log_{2}M}\sin\Big{(}\frac{ \pi}{M}\Big{)}\Big{)}-\frac{1}{8}erfc^{2}\Big{(}\sqrt{\gamma\cdot\log_{2}M}\sin \Big{(}\frac{\pi}{M}\Big{)}\Big{)}, \tag{1}\]  
where \(M\) denotes \(M\)-ary signaling, and \(\gamma=\frac{E_{b}}{N_{0}}\) is the received instantaneous SNR (\(E_{b}\)--the bit energy, \(N_{0}\)--the noise power spectral density).  
BER expression in an environment involving interference can be obtained as [18]:  
\[BER_{AWGN}(\gamma)=\begin{cases}\frac{1}{2}erfc\big{(}\sqrt{T\gamma}\big{)}- \frac{1}{8}erfc^{2}\big{(}\sqrt{T\gamma}\big{)},&\sqrt{T\gamma}<3\\ \frac{1}{2}erfc\big{(}\sqrt{T\gamma}\big{)},&\sqrt{T\gamma}\geq 3\end{cases}, \tag{2}\]  
where \(T\) is defined by [18]:  
\[T=\frac{\log_{2}M\cdot\sin^{2}\big{(}\frac{\pi}{M}\big{)}\cdot N_{0}}{N_{0} \cdot\frac{2k}{S_{L_{c}}}\cdot\log_{2}M\cdot\sin^{2}\big{(}\frac{\pi}{M}\big{)} \cdot T_{b}\cdot\sum\limits_{i=1}^{N-1}P_{i}}, \tag{3}\]  
where \(L_{c}\) denotes spreading sequence lengths whose characteristic values are 32 chips, 64 chips, 128 chips, \(k\)--number of bits per symbol, \(T_{b}\)--bit duration, and \(P_{i}\)--the received power level of interference.  
The average probability of error for the fading channel can be computed by integrating over a fading distribution. Therefore, the BER can be achieved by averaging the conditional error probability with respect to the instantaneous SNR, \(\gamma\), as follows [18]:  
\[BER_{Zigbber}=\int\limits_{0}^{\infty}BER_{AWGN}(\gamma)f(\gamma)d\gamma, \tag{4}\]  
where \(f(\gamma)\) is the probability density function (PDF) of \(\gamma\) for the fading channel at the output of the SC or MRC combiner.  
In this paper, two cases of fading are considered--Rayleigh and Nakagami-\(m\)--defined respectively in [19]:  
\[f_{[PERSON]}(\gamma)=\frac{1}{\gamma_{0}}e^{-\frac{\gamma}{\gamma_{0}}}, \tag{5}\]  
\[f_{[PERSON]}(\gamma)=\frac{m^{m}}{\Gamma(m)\gamma_{0}^{m}}\gamma^{m-1}e^{-\frac{m \gamma}{\gamma_{0}}}, \tag{6}\]  
where \(\gamma_{0}\) is the mean value of the received SNR (ratio of the signal energy per bit to noise spectral density) defined as \(\gamma_{0}=\varepsilon(\gamma)=\frac{E_{b}}{N_{0}}\varepsilon(\alpha^{2})\), where \(\varepsilon(\alpha^{2})\) is the mean square value of the Rayleigh/Nakagami-\(m\) distributed random variable \(\alpha\), \(m\) denotes the fading parameter, and \(\Gamma(\cdot)\) represents the Gamma function.
# Fuzzy Q-Learning Agent for Online Tuning of PID Controller for DC Motor Speed Control
## 1 Introduction
Additionally, in [19] a combination of virtual-reference-feedback-tuning and batch-fitted-Q-Learning approach is applied to a Multi input Multi output nonlinear coupled constrained vertical two-tank system for water level control. In this control scheme, firstly the problem is mostly solved by designing a linear stabilizing virtual-reference-feedback-tuning based feedback controller using few input-output samples from the process in a stable operating point. Next, the resulting controller is used to collect many input-state transition samples from the process in a wide operating range. Finally, a batch fitted Q-Learning strategy uses the database of transition samples to learn a high-performance nonlinear state feedback controller for model reference tracking. The main contributions of this paper are as follows:  
* We deploy a Q-Learning algorithm for adapting online the gains of a PID controller with the initial values that arise by the Z-N method. This PID controller is dedicated to control the speed of a DC motor. This supervision scheme is independent of prior knowledge and is not based on the expert knowledge. * In order to cope with the continuous state-action space, a fuzzy logic system is used as fuzzy function approximator. The structure of this paper is as follows: Section 2 provides preliminaries about DC motors, PID controllers, Reinforcement Learning, Q-Learning and fuzzy Q-Learning. Section 3 presents the proposed control scheme. Section 4 presents the experimental results based on the simulated process. Section 5 discusses the experimental results and sketches future work.
# Energy-Efficient Region Shift Scheme to Support Mobile Sink Group in Wireless Sensor Networks
## 3 Efficient Mobility Support for Sink Group Utilizing Region Shift
### Combining Deviated Location Information  
A _pivot_ calculates a center point by combining information of member sinks out of the region until the next data packet reaches the _pivot_. As shown in Figure 6, there are three movements of member sinks. First, \(M_{1}\) gets out of the region and the center point moves from \(C_{1}\) to \(C_{2}\) as much distance as the sink deviates. Next, \(M_{2}\) moves at the time \(t_{2}\). At this moment, \(M_{2}\) knows that the location of the group is the location of 'current group region' so that \(M_{2}\) recognizes being out of the group region. Then, \(M_{2}\) reports its own location to the pivot. However, the pivot ignores the movement report of \(M_{2}\) because it perceives that \(M_{2}\) is not outside of the new region, the circle presented with gray dotted line. After that, \(M_{3}\) deviates, and then the center point also moves to \(C_{3}\). In this way, the new circle could hold all member sinks. In addition, moving direction of the group is adjusted from fragmentary information. If some sinks temporarily have a different direction from that of majority so that the region is shifted towards the direction, many deviation reports from the majority might occur and region is quickly adjusted. Even if all members have different directions from that of DGR, it does not violate mobility support since RSMGP still covers all member sinks as mentioned above in Figure 4.  
Figure 5: An example of a guard region and cache nodes.