esa-sceva/satcom-chunk-collection huggingface dataset:
# A Systematic Review of Cybersecurity Risks in Higher Education
## 2 Background and Previous Work
### What Separates HE from Classic Industry?  
Two important tenets of HE are _academic freedom_ and _openness_, both of which are being universal for most of the sector. Academic freedom is defined by Encyclopaedia Britannica as _\"the freedom of teachers and students to teach, study, and pursue knowledge and research without unreasonable interference or restriction from the law, institutional regulations, or public pressure.\"_ While the other tenet, _openness_, is commonly described as an overarching concept or philosophy that is characterized by an emphasis on transparency and collaboration [17; 18]. That is, openness refers to \"accessibility of knowledge, technology and other resources; the transparency of action; the permeability of organizational structures; and the inclusiveness of participation [18]. In practice, this means that academics enjoy intellectual freedom, free from the constraints of short-term deadlines typical for the industry counterpart. Freedomof choice to pursue and research ideas is primarily limited by the ability to secure funding and resources.  
HE serves an essential societal function charged within research, development, and education. Academic research is mostly collaborative and team-work oriented, both cross-disciplinary and multilateral. Autonomy, individuality, and freedom of choice characterize the HE environment, with few restrictions regarding collaboration and knowledge dissemination. These properties differ from industry, where trade secrets are common and often vital to thrive in business. While HE is highly focused on research and development, much research is done for learning and seldom for immediate profit. HE goals tend to be long-term which also leads to a difference in pace between industry and academia. An essential aspect of HE is that research careers are often individual, and the likelihood of receiving recognition for achievements is more significant than in industry.  
In contrast to cybersecurity's emphasis on secrecy, the academic environment thrives upon openness, building on a tradition of trust, information exchange, and discussion [7]. Therefore, typical characteristics of universities are to be open and including, meaning few physical perimeters and that strong access control is uncommon [5]. HE is also characterized by the yearly enrollment of new students and temporary staff and visiting researchers. Faculties often operate autonomous entities and build their own IT networks designed to support research, development, and teaching activities [5; 6]. The networks are often locally managed with a low degree of centralized control [5].
## 2 Theoretical Analysis of the Integral Backstepping Controller and the ELESO
### The Mode of PMSM  
To simplify the mathematical model of the three-phase PMSM in the natural coordinate system, the coordinate transformation includes static coordinate transformation (Clark transformation) and synchronous rotation coordinate transformation (Park transformation) [22]. After the Clark transformation and the Park transformation, the mathematical model of PMSM in the d-q synchronous rotation coordinate system is shown as follows [23]:  
\[\left\{\begin{array}{l}\frac{d\dot{q}_{d}}{dt}=-\frac{R_{s}}{L_{d}}\dot{i}_{ d}+\frac{L_{q}}{L_{d}}n_{p}\omega i_{q}+\frac{1}{L_{d}}u_{d}\\ \frac{d\dot{u}_{q}}{dt}=-\frac{R_{s}}{L_{q}}\dot{i}_{q}-\frac{L_{d}}{L_{q}}n_{ p}\omega i_{d}-\frac{\dot{\psi}_{d}^{\prime}n_{p}}{L_{q}}\omega+\frac{1}{L_{q}}u_{q}\\ \frac{d\omega}{dt}=\frac{3}{2}\frac{\pi}{T}\Big{(}\psi\dot{f}i_{q}+(L_{d}-L_{q} )i_{d}i_{q}\Big{)}-\frac{1}{T}L_{-}-\frac{B}{f}\omega\\ \frac{d\theta_{e}}{dt}=n_{p}\omega\end{array}\right. \tag{1}\]  
where \(u_{d}\), \(u_{q}\) are the stator voltages of the d and q-axis. \(i_{d}\), \(i_{q}\) are the stator currents. \(R_{s}\) is the stator resistance. \(L_{d}\), \(L_{q}\) are d and q-axis inductances. \(\omega\) is the mechanical angular speed of the rotor. \(J\) is rotor inertia. \(B\) is the viscous friction coefficient. \(n_{p}\) is the number of pole pairs. \(\psi_{f}\) is the magnetic flux of the permanent magnet. \(T_{L}\) is the load torque.
## Appendix D Description of General Purpose Mechanisms
### Practical Byzantine Fault Tolerance [144]  
While not the first work to address Byzantine faults, Castro and Liskov [144] first set out to present a _practical_ implementation of a replication algorithm that can withstand such faults. The major improvement that gives it significance to this day is its ability to function in asynchronous environments while providing an acceptable response time. Sybil attack resistance is not of concern for this algorithm. Therefore, permissionless systems making use of it commonly incorporate other techniques to achieve resistance.
## 2 Materials and Experimental Testing  
### Materials  
#### 2.1.1 Earth  
The natural soil used in this study as shown in Figure 1 was collected from Ait Massoud area (31\({}^{\circ}\)43\({}^{\prime}\)54\({}^{\prime\prime}\) N 8\({}^{\circ}\)4\({}^{\prime}\)49\({}^{\prime\prime}\) W) located 14 km northwest of Marrakesh. The earth was extracted from a layer of 1.5 m under the ground to avoid organic content. This soil was selected because it was used in the past by local builders to construct earth houses and it is characterized by its abundance and availability in the area. The granulometric size distribution (GSD) was determined following ASTM standard D422 [37]: clay: <0.002 mm; silt: 0.002-0.08 mm; sand: 0.08-2 mm; and gravel: >2 mm. [PERSON]'s test (the plastic limit, index plasticity and liquid limit) was performed using ASTM D4318-10 [38]. The methylene blue value of this soil was determined according to the NM 13.1.178 standard [39]. The optimum moisture content for the soil was obtained according to the standard Proctor test [40].  
The chemical composition of the soil was obtained by means of X-ray fluorescence (XRF), and mineral composition was determined using X-ray diffraction (XRD).  
The main purpose of the geotechnical characterization of the used soil was to assess its suitability for the production of CEBs.  
The properties of the soil used in this study are shown in Table 1a. The soil was constituted of 9.1% clay, 38.4% silt, 25.7% sand and 26.8% gravel. The Atterberg limit test indicated that this soil had a liquid limit WL = 42% and a plasticity index PI = 18%, which agreed within the recommended zone limit by the French Standards XP P 13-901 [41]. It had a dry density \(\rho\)d = 1.86 g/cm\({}^{3}\) and a blue methylene value MBV (g/100 g) = 1.25. Therefore, the soil could be classified as type A1, in close agreement with the XP P 13-901 standard for the elaboration of compressed earth blocks [41].  
The chemical composition is shown in Table 1b. The results indicate that the soil consisted essentially of silica, aluminum and calcium carbonate. In addition, it also contained significant quantities of carbon (8%) and magnesium (4%) and a small amount of fluorine (0.87%).
## 2 Motivation and Background
### A New Measurement Technique in Space: Active Limb Sounding
The selection of the wavelength pairs remains a trade-off between higher accuracy and sensitivity with larger differential absorption cross sections, minimization of systematic errors through smaller wavelength differences and avoidance of absorption bands corresponding to other gases, and finally depends on the frequencies that can be generated by available laser sources [37]. Extinction measurements from such a system would also allow the detection of very low concentrations of stratospheric aerosol, which are difficult to obtain from a conventional nadir backscatter LiDAR measurement alone, and is therefore outside the capability of the CALIPSO mission [38]. Rayleigh and Mie scattering maintain the polarization ratio within a 2%-3% limit, whilst non-spherical particles or multiple scattering may induce some degree of depolarization [39]. This ratio could be used to determine particle shape, phase or multiple scattering, with particularly high values in the case of cirrus clouds with small optical depths, due to particles such as crystallites or hexagonal ice crystals [40], even if it may only be directly relevant for the nadir backscatter signal. A similar novel active limb-sounding technique has been proposed to ESA [41] as part of the ACCURATE mission (Atmospheric Climate and Chemistry in the UTLS Region and climate Trends Explorer). The mission aims to retrieve atmospheric constituents and line-of-sight winds from occultation measurements between two low earth orbit (LEO) platforms using active microwave and IR signals. In considering the original ACCURATE proposal, the ESA committee noted the mission's potential for high vertical accuracy, global coverage and good absolute calibration. The Alpbach summer school team arrived at the active limb sounding concept independently, but it is these characteristics that we also wish to exploit, with a focus on water vapour in the UTLS and above.
## 4 Surrogate-Based Optimization  
### Method  
The COMSOL model described in Section 2 used to compute the maximum temperature inside the DGR, \(T_{\text{max}}(x)\), was regarded as a high-fidelity model and a black-box function since the maximum temperature cannot be easily predicted given a particular UFC arrangement. The direct optimization of the objective function in Equation (2) to determine optimal repository designs would be computationally expensive due to many high-fidelity model evaluations being required. Hence, we employed surrogate-based  
Figure 4: Examples of the cosine-based shape function for arranging UFC ages between 30 and 60 years in the half placement room model at several values of \(\omega\).  
Figure 5: Examples of the Kumaraswamy PDF-based shape function for arranging UFC ages between 30 and 60 years in the half placement room model at several values of \(a\) with \(b=1.5\).  
optimization, which approximated the objective function with surrogate models that used computationally cheaper functions. Two types of surrogate functions were used, namely polynomial and radial basis function (RBF) surrogate functions. An RBF is a function that calculates the distance of a point from the origin or a specified center.  
The steps of the used surrogate-based optimization are summarized below [17]:  
1. Initially (\(k:=0\)), a set of ten design points, \(S_{0}\), were chosen through Latin hypercube sampling [18]. These were then evaluated using the high-fidelity model to determine their corresponding maximum temperatures.
2. A surrogate model, \(s_{k}\), was fitted to the available data, \(\{(\mathbf{x},T_{\max}(\mathbf{x}))|\mathbf{x}\in S_{k}\}\).
3. A maximin point from evaluated points, \(S_{k}\), was identified through surface-minimum point sampling (using random selection to break ties) and evaluated using the high-fidelity model.
4. The predicted maximum temperature from \(s_{k}\) was compared to the true value from the high-fidelity model. If the difference exceeded 0.5 \({}^{\circ}\)C, the process was repeated from Step 2 after updating \(S_{k}\); otherwise, the optimization converged.  
The toolbox MATSuMoTo provided MATLAB functions for constructing surrogate models using polynomial functions and RBFs [19].
## 1 Introduction
Their 80-day study verified the canopy temperature measurement at an accuracy of \(\pm\)0.62 \({}^{\circ}\)C, and the model developed using the TIR measurements revealed strong correlation between the canopy temperature and plant water use [20]. These near-surface remote sensing systems have demonstrated that continuous optical and infrared measurements at atmosphere-biosphere interfaces would fill the data and knowledge gaps toward understanding the linkage between local- and regional/global-scale phenomena by providing new datasets at fine spatial and temporal scales. If we could integrate these sensing technologies and complementary sensors into a single synchronized system, that would enable a range of simultaneous environmental and ecological measurements and allow for various experiments and modeling of local atmosphere-biosphere interactions. This paper presents a highly equipped hyperspectral remote sensing system, the EcoSpec system, that integrates complementary environmental sensors to simultaneously collect measurements with hyperspectral reflectance of land surfaces. The ultimate goal of this system is to enable investigation of temporal patterns and associations between continuous measurements of hyperspectral reflectance of land surface and continuous meteorological and biological measurements under natural and managed environments (e.g., agricultural fields).
###### Abstract  
This paper presents a microcontroller-controlled closed-loop fluxgate current sensor utilizing digital proportional-integral-derivative (PID) control with a single-neuron-based self-pre-optimization algorithm. The digital PID controller within the microcontroller (MCU) regulates the drive circuit to generate a feedback current in the feedback winding based on the zero-flux principle in a closed-loop system. This feedback current is proportional to the measured external current, thereby achieving magnetic compensation. Although PID parameters can be determined using heuristic approaches, empirical formulas, or model-based methods, these techniques are often labor-intensive and time-consuming. To address this challenge, this study implements a single-neuron-based self-pre-optimization algorithm for PID parameters, which autonomously identifies the optimal values for the closed-loop system. Once the PID parameters are optimized, a conventional positional PID algorithm is employed for the closed-loop control of the fluxgate current sensor. The experimental results show that the developed digital closed-loop fluxgate sensor has a non-linearity within 0.1% at the full scale in the measuring ranges of 0-1 A and 0-10 A DC current, with an effective response time of approximately 120 ms. The limitation of the sensors' response time is found to be ascribed to its open-loop measuring circuit.  
closed loop; digital proportional-integral-derivative; fluxgate current sensor; microcontroller; positional PID algorithm; single-neuron-based self-pre-optimization algorithm; zero-flux principle 2020
## 6 Results and Discussion
### Metamaterial Array Analysis  
In this study, four different sizes of array structures were investigated. The examination was held under consideration of the resonance and different characteristics of the array at different positions. To evaluate the effects of coupling between the unit cell, different layouts of an array need to be described. We call it mirror positioning when one unit cell is positioned as a mirror to its neighbor unit cell. Moreover, we can classify the array evolution into two types: (A) without mirror positioning; (B) with mirror positioning--inspired by Reference [29]. The top and bottom positions of the two-unit cells were used to place them face to face in a mirror positioning array. In Figure 12, two different types of positioning \(1\times 2\) sized arrays are illustrated; Figure 12a indicates the without mirror positioning array (the normal placement); 12b indicates the mirror positioning array where the top unit cell is mirrored horizontally in its place. Another scenario is presented in Figure 12c,d, where a \(2\times 1\) sized array is examined, where 12 c indicates a general positioned unit cell and 12d indicates a transformed unit cell on top position where it transformed to \(180^{\circ}\) in the \(Z\)-axis at its position. The transmission coefficients from that without mirror positioning and transformed positioning yield almost similar results to that of the metamaterial array characteristics. In normal unit cell placement in \(1\times 2\) sized arrays, the resonance frequencies shifted to 2.63 GHz, 5.8 GHz, 8,2 GHz, and 11.8 GHz from 2.4 GHz, 5.6 GHz, 8.93 GHz, and 11.5 GHz, respectively. It is also observed that with mirror positioning, the resonance frequencies in the same sized array shift back to 2.43 GHz, 5.64 GHz, 9.93 GHz, and 11.55 GHz. The same shifting is occurred in the \(2\times 1\) sized transformed positioning array structure. Utilizing this mirror positioning, the \(2\times 2\) and \(4\times 4\) sized metamaterial arrays are formed and shown in Figure 12e,f, respectively. The graph visualization of transmission coefficients of different-sized metamaterial arrays is shown in Figure 13.  
The relevant transmission coefficients of the differently sized metamaterial arrays, including their different positioning of the unit cell where the mirror positioning and transformed positioning array, almost achieved the same resonances. The \(1\times 2\) and \(2\times 1\) normal positioning arrays fluctuated at their desired resonance frequencies, whereas the mirror positioning array in different sizes \(1\times 2\), \(2\times 1\), \(2\times 2\), and \(4\times 4\) display almost the same resonance frequencies. For the proposed structure, we are able to discuss the homomorphism [42] of the design. The transmission coefficient results of the unit cell and different arrays vary slightly due to the coupling effect; however, when applied to the mirror symmetry method in an array, the transmission coefficient results of both are the same due to less coupling effect.
# System for Indoor Comfort and Health Monitoring Tested in Office Building Environment
## 5 Limitations of This Work  
The developed system implemented a limited number of sensors considered relevant for demonstrating the solution. Sensors for occupancy and open/closed doors and windows will be integrated, leading to better data correlation and local pattern prediction. The aim is to obtain a modular and reliable system that can be adapted to different applications and remain affordable at the same time. Another limitation refers to the integration of all data into a single cloud platform that can be easily accessed by any user. Also, additional settings for system automation have to be included in order to empower users to set their personal comfort parameters. Specific equipment like HVAC, humidifiers, dehumidifiers or air purifiers should also be interconnected to the developed IoT system in order to achieve a complete multipurpose commercial solution. The system can also be improved by setting a signalling module that warns the user (by SMS, e-mail or visual and acoustic signals) when the permissible or set thresholds for the monitored parameters are exceeded.  
Thus, the analysis of the registered data using the proposed IoT system for monitoring indoor comfort and health has led to some recommendations and possible future improvements. In this regard, additional sensors may be used for a broader image of occupants' comfort. Hence, adding noise, light and presence sensors may improve the correlation and interpretation of the registered data. Also, monitoring the power and energy consumption of electricity consumers is envisaged in order to improve the overall building efficiency and user comfort. In addition, the IoT system can be further connected to other similar ones in order to collect and analyse large amounts of data. With this measure, wireless sensor networks can be achieved, thus enabling the end-user to make better and even automatic decisions. The system can be extended to integrate more units that measure data from different sites. Thus, units based on Arduino MKR WiFi 1010 or other compatible boards can be integrated via the Arduino IoT Cloud. Up to 100 units can be connected, while the data can be stored for up to one year or linked using an Application Programming Interface.  
Figure 17: Variation in radon over the preliminary testing period.
# Gamification in IT Service Management: A Systematic Mapping Study
## 4 Results
### RQ1. What Issues of ITSM Process Implementation Have Been Addressed through Gamification?
#### (c) IT Service Capacity Management Process  
We have found a single study that introduced a gamification proposal in the context of IT service capacity management process [P02]. The authors of this work presented a conceptual framework for improving ITSM processes that use gamification in the experimentation activity of process simulation models. The application case developed in the context of the IT service capacity management process show that the framework helps service providers to decide what service capacity strategy implementing for improving the service response times and ensuring the service level objectives compliance.
# Optimal Collision-Free Grip Planning for Biped Climbing Robots in Complex Truss Environment
## 3 Computation of Operational Regions for Negotiating Obstacles
### The Mode to Negotiate Obstacles  
Basically, BiCRs have two modes of surmounting obstacles, which are the striding-over mode and the creeping mode, respectively, illustrated with Climbot in Figure 6. Denote \(d_{o}\) as the distance between the _Key Point_ and the gripping member. The maximum distance \(d_{stride\_max}\) for striding-over and the minimum distance \(d_{crep\_min}\) for creeping, can be calculated with the robot's parameters. Obviously, the robot can surmount the obstacle member only with the striding-over mode when \(d_{o}\) is smaller than \(d_{crep\_min}\) as shown in Figure 6a, and with the creeping mode when \(d_{o}\) is larger than \(d_{stride\_max}\) as shown in Figure 6b. In the case of \(\left(d_{crep\_min}<d_{o}<d_{stride\_max}\right)\), the robot can overcome the obstacle with either of these two modes. Further reflecting in the mathematical model (Equation (5)) to compute the operational regions for negotiating obstacles, the applicable mode determines the boundaries of the rotation angle of the \(T_{1}\) or \(T_{3}\) joint of the robot.
# Noise-to-Norm Reconstruction for Industrial Anomaly Detection and Localization
###### Abstract  
Anomaly detection has a wide range of applications and is especially important in industrial quality inspection. Currently, many top-performing anomaly detection models rely on feature embedding-based methods. However, these methods do not perform well on datasets with large variations in object locations. Reconstruction-based methods use reconstruction errors to detect anomalies without considering positional differences between samples. In this study, a reconstruction-based method using the noise-to-norm paradigm is proposed, which avoids the invariant reconstruction of anomalous regions. Our reconstruction network is based on M-net and incorporates multiscale fusion and residual attention modules to enable end-to-end anomaly detection and localization. Experiments demonstrate that the method is effective in reconstructing anomalous regions into normal patterns and achieving accurate anomaly detection and localization. On the MPDD and VisA datasets, our proposed method achieved more competitive results than the latest methods, and it set a new state-of-the-art standard on the MPDD dataset.  
anomaly detection; reconstruction; M-net; residual attention 2023