esa-sceva/satcom-chunk-collection huggingface dataset:
# Multiuser Transmit Precoding Design for Dimming Compatible Visible Light Communications
## 3 Joint Multiuser TPC Design and Dimming Control
 \right]^{T},\] \[\varepsilon_{k} =\sqrt{\frac{u_{k}}{\gamma}+(u_{k}-b_{k}^{2})},\]  
where \(\mathbf{0}_{1\times N_{t}N_{r}}\) is an all-zero row vector of size \(N_{t}N_{r}\). Since the set defined by (31) is a second-order cone (SOC), the design problem of (19) is a quasi-convex problem [23]. In this case, the optimal solution of (19) can be efficiently obtained, e.g., by the bisection methods. Accordingly, the feasibility problem used to search for the optimal solution in the bisection methods is given by:  
\[ \begin{split}\text{find}\ \ \mathbf{p},\\ \text{s.t.}\ \ ||\mathbf{B}_{k}\mathbf{p}+\mathbf{v}_{k}||_{2}\leq \varepsilon_{k}\mathbf{h}_{k}^{T}\mathbf{W}_{k}\mathbf{p},\ k=1,\cdots,N_{r}, \\ \widehat{\mathbf{A}}\mathbf{f}-\mathbf{C}\mathbf{p}\leq\widehat{ \mathbf{d}}_{L},\ \widehat{\mathbf{A}}\mathbf{f}+\mathbf{C}\mathbf{p}\leq\widehat{ \mathbf{d}}_{H},\\ -\mathbf{f}\leq\mathbf{p}\leq\mathbf{f}.\end{split}  \tag{33}\]  
Note that the above problem is SOC programming, which can be efficiently solved by standard convex optimization methods, such as the interior-point method [23]. Finally, the procedure of the proposed joint TCP design and dimming control is summarized in Algorithm 1. ```
1:Initialize \(\gamma_{\text{min}}=\gamma_{1}\) and \(\gamma_{\text{max}}=\gamma_{2}\), where \(\gamma_{1}\) is sufficiently small so that (33) is feasible when \(\gamma=\gamma_{1}\), and \(\gamma_{2}\) is large enough so that (33) is feasible when \(\gamma=\gamma_{2}\). Let \(\rho>0\) be the target precision. 2:while\(\gamma_{\text{max}}-\gamma_{\text{min}}>\rho\)do
3: Set \(\gamma=\left(\gamma_{\text{max}}+\gamma_{\text{min}}\right)/2\). 4: Solve (33). If the problem is feasible, then set \(\gamma_{\text{min}}=\gamma\). Otherwise, set \(\gamma_{\text{max}}=\gamma\). 5:endwhile
6: The optimal TPC vector for the \(k^{\text{th}}\) user is \(\mathbf{p}_{k}^{*}=\mathbf{W}_{k}\mathbf{p}^{\prime}\), where \(\mathbf{p}^{\prime}\) is the last feasible solution to (33). Then, the DC bias required for the desired dimming level can be obtained based on (18). ```  
**Algorithm 1** Joint TPC design and dimming control.
# A Model-Free Online Learning Control for Attitude Tracking of Quadrotors
## 3 Model-Free Online Learning Control Design  
### Control Law Design  
Considering the control objective, we define the attitude synthesis error \(\mathbf{\epsilon}=k_{p}\mathbf{v}_{e}+\mathbf{\omega}_{e}\), where \(k_{p}>0\). It is clear that, as the attitude and angular velocity error converge to a small region, \(\mathbf{\epsilon}\) also converges to a small neighborhood of the origin. Then, the model-free online learning control (MFOLC) law is proposed as follows:  
\[\mathbf{u}(t)=\mathbf{\eta}+\mathbf{L}(\mathbf{t}) \tag{8}\]  
\[\mathbf{\eta}=-\mathbf{K}\mathbf{\epsilon} \tag{9}\]  
\[\mathbf{L}(t)=\mathbf{K}_{L}\mathbf{u}(t-\tau) \tag{10}\]  
where \(\mathbf{\eta}\) is the baseline controller, \(\mathbf{K}=\mathrm{diag}(k_{1},k_{2},k_{3})\), and \(k_{i}\geq 0,i=1,2,3\). \(\mathbf{L}(t)\) is the online learning term, where \(\mathbf{K}_{L}=\mathrm{diag}(k_{l,1},k_{l,2},k_{l,3})\) is the learning intensity matrix, and \(k_{l,j}\geq 0,i=1,2,3\). \(\mathbf{u}(t-\tau)\) is the control input in time \(t-\tau\), and \(\tau\geq 0\) is called a learning interval.  
**Remark 1**.: _Since the ordinary OLC law is_  
\[\mathbf{u}(t)=k_{1}\mathbf{u}(t-\tau)-k_{2}\bigg{[}\mathbf{\epsilon}-\frac{1}{\kappa_{1}} \bigg{(}-\mathbf{\omega}^{\wedge}\mathbf{J}\mathbf{\omega}+\frac{k_{p}}{2}\mathbf{J}(s_{e} \mathbf{I}_{3}+\mathbf{v}_{e}^{\wedge})\mathbf{\omega}_{e}\bigg{)}\bigg{]} \tag{11}\]  
_where \(k_{1}\geq 0\) is the learning intensity, \(k_{2}\geq 0\) is the control gain, \(\kappa_{1}>0\) is the feedforward gain. There are two main differences between the proposed MFOLC and the ordinary OLC. (1) The baseline controller of the MFOLC discards the gyroscopic torque compensation term, which enables the controller to be independent of the inertia matrix of quadrotors and to weaken the control saturates. (2) The control parameters of each attitude axis can be adjusted independently, which enhances the control effectiveness for quadrotors with an asymmetric structure._
# ReSQoV: A Scalable Resource Allocation Model for QoS-Satisfied Cloud Services
## 6 Conclusions  
This paper focuses on the cloud QoS violation rectification process incorporated with scalable resource utilization. The ReSQoV aims to prevent or rectify cloud QoS violations caused by under or over-provisioning events, maintaining resources at a certain level to avoid certain or probable violation conditions. We focused on the estimation of resource utilization to ensure QoS. The ReSQoV is proposed based on the Universal Scalability Law (USL), which helps to predict the service capacity for a specific load. The model coefficients show the contention and coherence quantified values, while the \(p\)-values show the model fitting. As the QoS violation detection decision is Probably Violation and Definitely Violation, the remedial action is triggered, and required resources are added to the virtual machine as vertical scaling. Experiments show that the ReSQoV was able to present the calculation and modeling of serialization and parallelism overheads, which help allocating ample resources to VM so that QoS is not compromised, which is an important aspect of cloud services. The statistical test ANOVA shows the difference between the policy-based and quantified scaling systems due to the generated QoS parameter values and their interactions.  
Conceptualization, H.M.K. and F.-F.C.; methodology, H.M.K.; software, H.M.K.; validation, H.M.K., F.-F.C. and T.T.V.Y.; formal analysis, H.M.K.; investigation, H.M.K., F.-F.C. and T.T.V.Y.; data curation, H.M.K.; writing--original draft preparation, H.M.K.; writing--review and editing, H.M.K., F.-F.C. and T.T.V.Y; visualization, H.M.K.; supervision, F.-F.C. and T.T.V.Y.; project administration, F.-F.C.; funding acquisition, F.-F.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Multimedia University, Malaysia.  
Not applicable.  
The authors declare no conflict of interest.
# State of the Art of Telecommunication Systems in Isolated and Constrained Areas
## 5 The Tethered Balloon Solution
### Balloon Technology: The Physical Structure  
From a technological point of view, these captive balloons will be inflated with helium. Their external envelope will be close to that of the Loon balloon: three PET-PA-PET layers will constitute the balloon's thickness to form beams. The balloon's final structure will be the beams' junction converging on its top and its base. Four cables spaced 90\({}^{\circ}\) apart and  
Figure 16: Artificially intelligent control of the balloon.  
Figure 15: Illustration of the telecommunication coverage for an isolated sector with captive balloons.  
attached to the balloon will roll on a winch system on the ground. This system will deploy the balloon and, of course, lower it back to the ground.
## 2 Materials and Methods
### Instrumentation and Data Collection  
A schematic representation of the utilized line-scan Raman imaging system is shown in Figure 1, with excitation performed using a custom-designed diode near-infrared laser system (OptiGrate Corp., Oviedo, FL, USA) combining the laser beam from 19 emitters to produce a relatively high-intensity laser line. The laser beam emanating from the laser box was passed through a cylindrical lens (\(f\) = 200) and an engineered diffuser (ED1-L4100; ThorLabs, Hans-Boeckler, Dachau, Germany) mounted next to the above lens to obtain homogeneous laser intensity at each point of the laser line. The generated laser line was then projected onto a 785-nm beam splitter placed at 45\({}^{\circ}\) to reflect the laser beam onto the sample surface and act as a filter to mitigate Rayleigh scattering generated during Raman data collection. The dimension of the generated laser line on the sample surface was approximately 1.5 mm \(\times\) 16 mm and laser power \(\sim\)450 mW was measured by a digital power meter (PM100D, ThorLabs, Germany).  
The generated Raman signals were passed through the beam splitter to the sensing unit, which comprised an imaging spectrograph and a CCD camera. An objective lens with a focal length of 22 mm was mounted onto the spectrograph for focus and aperture adjustment. The generated Raman signals were further filtered to remove Rayleigh-scattered photons using two 785-nm long-pass filters and subsequently entered the imaging spectrograph via a slit. A 16-bit CCD camera (iKon-M 934, Andor Technology, South Windsor, CT, USA) was placed in the focus plane of the spectrometer to collect the dispersed signals and create images. During image collection, the CCD was cooled to -65 \({}^{\circ}\)C to reduce the dark current effect. The camera was connected to a computer via USB cables for control and data transfer. The whole system, with the exception of the computer, was installed in a black box to exclude the influence of surrounding light. The laser-line uniformity on sample surface was measured by scanning a Teflon sheet and was about 14 cm.  
The sample holder packed with a given adulterated paprika powder sample was placed on a conveyor belt for line-by-line scanning. In addition to adulterant-containing samples, samples of pure paprika powder and pure adulterants were also prepared as references. To improve the signal-to-noise ratio, the exposure time was set to 1 s and a total of 270 steps, and a step size of 0.15 mm/scan was selected to cover the spatial shape of the sample. Raman data were collected in the wavelength range of 740-1000 nm (corresponding to Raman shifts of \(-\)763 to 2837 cm\({}^{-1}\)) and at a CCD spatial binning of two. The generated Raman images were saved in ENVI format as a 3D hypercube. The dark reference was obtained with the laser turned off and the camera lens covered by a cap, with the obtained dark current subtracted from each collected hypercube to obtain corrected data for further analysis.
# A Survey of Precision Formation Relative State Measurement Technology for Distributed Spacecraft
## 3 Analysis of the Characteristics of Relative State Measurement Technology
### Composite Relative Measurement Technology of Spacecraft Formation
#### 3.6.5 Vision + RF Coupling Measurement Technology  
Vision + RF composite relative state measurement technology is a precision measurement technology that can be applied to the deep space context. One of the current application directions is to use visual sensors as the main navigation method, while the spacecraft releases radio beacons for auxiliary observation. The radio beacon provides distance and speed information after filtering and smoothing and combines this with the sight vector of the target spacecraft to generate relatively accurate relative state measurement information [69]. With a positioning accuracy of m and an altitude measurement accuracy of 0.006\({}^{\circ}\), the method can be used for precise measurements without communication, in cooperative spacecraft failure modes. Another method is to use radio to obtain the relative motion state information of the target spacecraft, in the case of communication and cooperation, and use the visual sensor to measure and obtain the relative altitude information. A filter is further designed to determine the relative position and altitude [70]; the method achieves a positioning accuracy of cm and an altitude measurement accuracy of 0.0001\({}^{\circ}\).
## 2 Data and Methods  
### Solar Wind and Geomagnetic Indices  
In order to detect the solar contribution and coupling to ionospheric variability, solar wind and geomagnetic indices from the OMNIweb database ([[https://omniweb.gsfc.nasa.gov/](https://omniweb.gsfc.nasa.gov/)]([https://omniweb.gsfc.nasa.gov/](https://omniweb.gsfc.nasa.gov/)), accessed date: 20 January 2021) [44] are used along with data from the International Service of Geomagnetic Indices (ISGI) website, which is available at [[http://isgi.unistra.fr/data_download.php](http://isgi.unistra.fr/data_download.php)]([http://isgi.unistra.fr/data_download.php](http://isgi.unistra.fr/data_download.php)) (accessed date: 29 January 2021). There is the use of the solar wind (SW) plasma speed, temperature, pressure, proton density, beta parameter, and density ratio between alpha and proton particle, and also the IMF B\({}_{x}\) component and total Bt IMF, the eastward zonal electric fields (E\({}_{y}\)), the geomagnetic auroral indices AE and Am indices, and the equatorial SYM-H and Dst indices. Here, the symmetric disturbance field in H, SYM-H, is essentially the same as [PERSON]'s hourly Dst index. The main difference is the time resolution, where the effects of the solar wind dynamic pressure variations during geomagnetic storms may be more clearly seen in the SYM-H than in the hourly Dst index. As compared to the SYM-H and Dst indices that measure the intensity of the globally symmetrical EEJ (the \"ring current\"), the Am index is susceptible to any geophysical current system, including magnetopause currents, field-aligned currents, or auroral electrojet. The ASY-D and ASY-H indices represent the range between the maximum and the minimum deviations of the magnetic D and H components, and usually are more suitable to monitor disturbances globally and in the polar regions, similar to the Am index (for a description of various geomagnetic indices, see [45]). Note also E\({}_{y}=-\)V\({}_{x}\)\(\times\) B\({}_{z}\) moves the plasma in the magnetosphere from the tail to Earth and creates the ring current.  
Ref. [46] demonstrated that the merging electric field (E\({}_{m}\)) is a physical quantity that closely correlates with upper atmosphere variations during geomagnetic storms. The E\({}_{m}\) assumes that there is an equal magnitude of the electric field in the solar wind, the magnetosheath, and on the magnetospheric sides of the magnetopause [47]. We calculate this variable as follows:  
\[\mathrm{E}_{m}=\mathrm{v}_{SW}\sqrt{\mathrm{B}_{y}{}^{2}+\mathrm{B}_{z}{}^{2}} \sin^{2}\left(\frac{\theta}{2}\right) \tag{1}\]  
In this equation, B\({}_{y}\) and B\({}_{z}\) are the IMF components, v\({}_{SW}\) is the solar wind speed, and \(\theta\) is the IMF clock angle in Geocentric Solar Magnetospheric (GSM) coordinates.
###### Abstract  
This work aims to measure the thermodynamics of a main cabin ventilation system in a JU-2000E jack-up offshore platform. A three-dimensional (3D) physical model of the ventilation system was established, and the computational fluid dynamics (CFD) software (ANSYS FLUENT) was used to calculate the model thermodynamics. Numerical analysis was performed to investigate the influence mechanisms of the ventilation factors such as ventilation temperature and volume on the ventilation performance. The analysis results demonstrate that (1) top-setting of the exhaust vents is more effective than the side-setting in terms of high temperature reduction, (2) small ventilation temperature and volume can improve the ventilation efficiency, and (3) proper shutdown selection of the backup diesel engine can enhance the ventilation performance. Furthermore, the effect of humidity for the ventilation air was investigated. Lastly, an experimental platform was developed based on the simulation model. Experimental tests were carried out to evaluate the shutdown selection of the backup engine and have shown consistent results to that of the simulation model. The findings of this study provide valuable guidance in designing the ventilation system in the JU-2000E jack-up offshore platform.  
Keywords:main engine cabin; ventilation effect; thermodynamics; CFD +
Footnote †: journal: Applied Sciences
## 2 Materials and Methods  
### Biological Material  
In this study, the native isolate of _B. bassiana_ designated as MA-Bb1, with GenBank accession number MN209825.1, was used. It is maintained at the Agroecology Center of the Institute of Sciences of the Benemerita Universidad Autonoma de Puebla (ICUAP-BUAP) and was isolated from _Sphenarium purpurascens_ in a corn (_Zea mays_) cultivation. The following procedure was employed. _B. bassiana_ was cultivated on Sabouraud dextrose agar (SDA) at a consistent temperature of 26 \(\pm\) 1 \({}^{\circ}\)C for seven days with a photoperiod of 12 h white light and 12 h dark. Subsequently, the culture's surface was gently scraped using a sterile glass rod, and the resulting material was mixed with 100 mL of a sterile 0.01% Tween 80 aqueous solution (Sigma, St. Louis, MO, USA). Then, the concentration of conidia was adjusted to 1 \(\times\) 10\({}^{8}\) conidia/mL by direct counting using a Neubauer chamber. The resultant suspension of 250 mL was stored at 8 \({}^{\circ}\)C until its application.  
The commercial strains of _B. bassiana(r)_ and _M. anisopliae(r)_ used in this study were obtained from the collection of entomopathogenic fungi (CHE) of the National Reference Center for Biological Control (CNCRB), identified under the code keys CHE-CNRCB 497 and CHE-CNRCB 535, respectively (Liquid presentation 250 mL). The viability of the conidia was confirmed by following the protocols described by [PERSON] [28] and [PERSON] et al. [29], as follows. We placed five 10 \(\upmu\)L aliquots of the final conidial suspension in 100 \(\times\) 15 mm Petri dishes, with each formulation replicated in triplicate. After a 24 h incubation period, the number of germinated conidia displaying only germ tubes in the five sampling points was recorded. This value represented the percentage of germination rate for each formulation at the time of inoculation under laboratory conditions.  
Additionally, diatomaceous earth was used, a material composed mainly of silicon dioxide, derived from the fossilized remains of diatom algae. Diatomaceous earth was selected due to its ability to dissolve cuticular lipids, thereby facilitating the penetration through the exoskeleton of the target insects. Furthermore, diatomaceous earth was observed to play a role in contact chemical communication processes, which could improve the efficacy of the entomopathogenic agent [30]. The commercial product was diluted to 10% (\(w/v\)) in distilled water (10 g Diatomin(r): 100 mL water) and shaken for 5 min.
# A Survey of Graphical Page Object Detection with Deep Neural Networks
## 3 Methodologies
### Mask R-CNN  
Mask R-CNN [10] is the extended model of Faster R-CNN [11] with an addition of an extra loss known as segmentation loss. Figure 7 depicts the basic architecture of Mask R-CNN. However, the comprehensive detail about the network can be found at [10]. The graphical page objects present in the document images have very low inter-class variance. An object originally labeled as a table can easily be misinterpreted with a figure or formula. By leveraging the segmentation loss of Mask R-CNN, researchers in the document image analysis community have improved the performance of graphical page object detection systems. This section covers those methodologies.  
[PERSON] et al. [32] published the method for page object detection in document images through employing Mask R-CNN. Their end-to-end deep learning-based system, called Graphical Object Detection (GOD), detects the tables, figures, and formulas directly from the raw input images. The authors propose that there is no need to add extra pre or post-processing steps to solve page object detection. By leveraging the power of transfer learning, the authors have done bench-marking on the well-known datasets of ICDAR-17 POD [5], UNLV [44], and ICDAR-13 [38].  
Figure 7: Explained architecture of Mask R-CNN. Image is obtained from [10].  
A recent end-to-end table detection network called CDeC-Net is introduced by [PERSON] et al. [34]. The system CDeC-Net leverages the novel object detection network Cascade Mask R-CNN based on Cascade R-CNN [12]. The presented article has shown a noticeable improvement in the performance of table detection system across several datasets such as ICDAR-17 POD [5], ICDAR-13 [38], ICDAR-2019, Marmot [45], TableBank [46], PubLayNet [9], and UNLV [44]. After extensive evaluations, the authors have concluded that the network Cascade Mask R-CNN is superior to the previous state-of-the-art table detection systems.
## 3 Results and Discussion
### Volume Expansion and Porosity Control of Carbon Block by CB Addition  
The volume expansion of blocks by the carbonization process was calculated from the bulk density, and the results are shown in Figure 6. As the amount of added CB increased, volume expansion of the carbon blocks decreased. In general, the carbon block was expanded by gases generated from the evaporation of low molecular weight compounds contained in the pitch during the carbonization process [28; 29; 30]. The carbon block with coal-tar pitch (binder pitch) also showed volume expansion, and the volume expansion of C_CB_0 was the highest (38.4%) among all samples. The addition of CB decreased the volume expansion; C_CB_10 showed the lowest volume expansion at 13.1%. The decrease of volume expansion of carbon block, according to CB addition, could be confirmed through two aspects. First, as shown in the results of the thermal properties of carbon blocks, the carbonization yield increased by CB absorbing the low molecular weight compounds in the binder pitch during the carbonization process [26; 27]. These results were due to the oil absorption ability of the CB, and the volume expansion of carbon block could be partially inhibited by preventing the evaporation of low molecular weight compounds. Second, the CB had a specific aggregate structure and this structure could prevent the volume expansion of carbon block by the release of generated gases to the outside [31]. The porosity was calculated using the bulk density and real density of the carbonized carbon block, and the results are shown in Figure 7. The porosity of the carbonized block also decreased according to increasing of CB amount. In the carbonization process, the binder pitch is changed to a dense structure. However, in cases where the generated gas from binder pitch was not released to the outside and remained inside, the generated gas formed pores and the porosity of block increased [32]. CB absorbed low molecular weight compounds of binder pitch during carbonization to reduce generated gas. Also, CB released gas to the outside and suppressed the generation of pores. Therefore, as the amount of CB added increased, the porosity of the block decreased [31].  
The mechanism was defined based on the results of the CB addition to the carbon blocks, and is suggested in Figure 8. The CB could absorb the low molecular weight compounds in the binder pitch during the carbonization process [26; 27]. The properties of CB reduced the amount of gas generated by suppressing the evaporation of low molecular weight compounds (Figure 8b). In general, CB had a unique bundle structure similar to grape clusters, and CB's unique bundle structure could diffuse gas, so CB was mainly applied to gas diffusion electrodes [33; 34; 35]. A pass-way to release gas was created by the gas diffusion effect of CB in carbon block. The created pass-way released evaporated  
Figure 5: The density of carbon blocks according to the added CB amount: (**a**) bulk density and (**b**) real density.