esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Rolling-MFO-GM (1,1) Model
### Rolling-MFO-GM (1,1)  
Rolling-MFO-GM (1,1) is much more complex than GM (1,1) and MFO-GM (1,1), in which the parameters of GM (1,1) are optimized by MFO and the rolling mechanism is also introduced to enhance the precision of forecasting results. The steps of Rolling-MFO-GM (1,1) are listed as follows.  
Figure 5: Forecasting procedure of rolling-GM (1,1).  
Step 1: The actual data sequence \(\left\{x^{(0)}(1),x^{(0)}(2),\cdots,x^{(0)}(p)\right\}\) is utilized to build MFO-GM (1,1), and then the optimal parameters \(a\) and \(b\) could be calculated by MFO using Equation (22). Then the forecasting sequence \(\left\{\hat{\chi}^{(0)}(p+1),\hat{\chi}^{(0)}(p+2),\cdots,\hat{\chi}^{(0)}(p+q)\right\}\) can be calculated using Equation (13).
* As the rolling mechanism aims at employing the latest data for forecasting; in this step, MFO-GM (1,1) should be rebuilt using the new practical data sequence \(\left\{x^{(0)}(q+1),x^{(0)}(q+2),\cdots,x^{(0)}(q+p)\right\}\). Then, the parameters \(a\) and \(b\) would be re-optimized by MFO using Equation (23) and the forecasting sequence \(\left\{\hat{x}^{(0)}(p+q+1),\hat{x}^{(0)}(p+q+2),\ldots,\hat{x}^{(0)}(p+2q)\right\}\) can be calculated using Equation (13). \[f=\min\frac{1}{p}\sum_{k=1}^{p}\left|\frac{x^{(0)}(q+k)-\hat{\chi}^{(0)}(q+k) }{x^{(0)}(q+k)}\right|\times 100\%\] (23) Step 3: Repeat Step 2 until all the forecasting data points are obtained.  
The procedure of Rolling-MFO-GM (1,1) is illustrated in Figure 6.
# Tool for Predicting College Student Career Decisions: An Enhanced Support Vector Machine Framework
## Appendix B SVM & BOA  
### Support Vector Machine (SVM)  
SVM has applied to many real-life problems including diagnosis of breast cancer [67], diagnosis of tuberculous pleural effusion [68], analyzing patients with paraquat poisoning [69], prognosis of paraquat poisoning patients [70], forecasting electricity price [71], forecasting electricity spot-prices [72] and predicting [PERSON]'s disease [73]. The principle of SVM is to discover an optimal plane that can maximize the separation of different data. The support-vector is the data point that is closest to the border. When it comes to data processing, the SVM is frequently used as a supervised learning strategy in order to determine the optimal hyperplane that can accurately distinguish between positive and negative samples. The hyperplane can be written as follows, given the data collection \(\mathrm{G}=(x_{i},y_{i}),i=1,\ldots,N,x\in R^{d},y\in\{\pm 1\}\).  
\[\mathrm{g}(x)=\omega^{\mathrm{T}}x+b \tag{1}\]  
Maximization of geometric spacing equals minimization of in terms of geometric comprehension of the hyperplane. In the event of a few outliers, the idea of \"soft interval\" is introduced, and the slack variable \(\xi_{i}>0\) is used. The disciplinary factor \(c\), which represents the capacity to accept outliers, is one of the major factors that can impact the effectiveness of SVM classification. A standard SVM model exists as shown below.  
\[\left\{\begin{array}{rl}&\min(\omega)=\frac{1}{2}\,||\omega\,||^{2}+c\sum_{i= 1}^{N}\xi_{i}{}^{2}\\ &s.t\,\ y_{i}\big{(}\omega^{\mathrm{T}}x_{i}+b\big{)}\geq 1-\xi_{i},i=1,2, \ldots,N\end{array}\right. \tag{2}\]  
in which \(\omega\) is the weight of inertia, \(b\) is a constant.  
By using this method, the lower dimensional data is transformed into higher dimensional data and the optimal classification surface is divided by combining multiple linear techniques. Meanwhile, the SVM changes the linearly inseparable sample set \(\Phi:R^{d}\to H\) nonlinearly. To ensure that the computational results of the sample in the high-dimensional part remain the same as in the low-dimensional, a suitable kernel function \(k\big{(}x_{i},x_{j}\big{)}\) is constructed, with \(\alpha_{i}\) denoting the Lagrange multiplier and Equation (A3) being converted as follows:  
\[\left\{\begin{array}{rl}&Q(\alpha)=\frac{1}{2}\sum\limits_{i=1}^{N}\alpha_{ i}\kappa_{j}y_{i}y_{j}k\big{(}x_{i},x_{j}\big{)}-\sum\limits_{i=1}^{N}\alpha_{i}\\ &s.t\,\sum\limits_{i=1}^{N}a_{i}y_{i}=0,0\leq a_{i}\leq C,i=1,2,\ \ldots,N \end{array}\right. \tag{3}\]The generalized radial basis kernel function is employed in this work, and its formulation is as follows.  
\[k(x,y)=e^{-\gamma||x_{i}-x_{j}||} \tag{10}\]  
where \(\gamma\) is a kernel parameter that specifies the interaction breadth of the kernel function and another factor that is highly significant to the classification performance of SVM.
# Adaptive PI + VPI Harmonic Current Compensation Strategy under Weak Grid Conditions
## 1 Introduction
The proposed adaptive notch filter based three-phase synchronous phase-locked loop (ANF-PLL) introduces a series of adaptive lattice notch filters to improve the grid phase and frequency estimation under the conditions of voltage unbalance, distortion and frequency deviation. The paper also analyzes and discusses the working mechanism of the adaptive lattice notch filter. Finally, the simulation and experimental results indicate that ANF-PLL can maintain high phase and frequency tracking performance under complex conditions of a weak grid. The proposed adaptive PI + VPI resonant current tracking control strategy can maintain high steady-state compensation performance and dynamic compensation performance and has high frequency adaptability.
## 2 Materials and Methods
### Cell Proliferation Assay  
A colorimetric cell proliferation assay using a WST-1 Cell Proliferation Reagent Kit (Sigma-Aldrich, USA) was used to analyze the effect of TQ on cell proliferation in HCT-116 and Jurkat cells. Briefly, the cells were seeded in 96-multiwell plates at a density of 4 \(\times\) 10\({}^{4}\)/well for Jurkat cells and 10\({}^{4}\)/well for HCT-116 cells. After 24 h of incubation, the cells were exposed to different concentrations of TQ for different times. The cell proliferation rate then was evaluated through a rapid WST-1 reagent. After incubation for the different times, 10 \(\upmu\)L of the WST-1 solution was added and incubated for an additional 3 h at 37 \({}^{\circ}\)C. Finally, the absorbance was read at 450 nm with a microplate ELISA reader (ELx800\({}^{\text{TM}}\) Biotech, Winooski, Vermont, USA) and the results were analyzed using the Gen5 software (Biotek, Winooski, VT, USA). The reaction was based on the cleavage of the tetrazolium salt WST-1 to formazan by means of cellular mitochondrial dehydrogenases. The quantity of formazan dye in the medium is directly proportional to the number of viable metabolically active cells. The percentage of cell viability was calculated by assuming control (untreated) samples to be 100% viable.
# A Sparsity-Invariant Model via Unifying Depth Prediction and Completion
## 5 Experiment
### Experimental Results in Real-World Application
#### 5.5.2 Construction of Trainset
* Step 4: Given that the sparse depth map obtained from the iTOF sensor may contain regions with missing depth values due to environmental factors, we generate random polygons in the sparse depth map to simulate these \"black holes\". * Step 5: Considering the randomness in the hardware measurement distance range, we randomly set the maximum value in the sparse depth map and filter out pixels exceeding this depth value. * Step 6: Taking into account hardware noise and external interference, there may be deviations between the sparse depth map obtained from the iTOF sensor and the ground truth. We randomly add depth value offsets to the sparse depth points to simulate this noise. * Step 7: Finally, considering the alignment issue between the color image and the sparse depth points, as well as potential noise near object edges, we randomly offset the positions of sparse depth points to simulate this scenario. We illustrated the results of each step involved in obtaining the sparse depth map in Figure 7. Comparing (c) to (b), there is a slight shift in the positions of each mask point. From (d) to (e), noticeable \"black hole\" regions appear. Observing the discrepancy between (h) and (g), it can be seen that some valid points on the chair back in the foreground have shifted into the background. These results also confirm the accuracy of each step in our process. Figure 7: Sparse depth map generation illustration: (**a**) color image, (**b**–**h**) results of obtaining the sparse depth map at each step. (A dilation operation on the sparse depth map was performed for better visualization.)
# Construction 4.0: A Systematic Review of Its Application in Developing Countries
## 1 Introduction
### Research Rationale  
Research in the subject area of Construction 4.0 is very much an emerging field which focuses on the fourth industrial revolution. It is becoming more well-known not just among members of the academic community but also among those working in the business world. The kinds of digital platforms that Construction 4.0 includes make it easier to share information that may be helpful/necessary to initiate certain activities [8]. This has occurred as a direct result of the revolution's impact on the building industry. Because of this, the automated collection and processing of electronic data for discrete activities within the value chain is now possible. It has become much easier to manufacture building elements, building components, and building furnishings through the incorporation of robotic technologies into the construction sector. These technologies are more frequently known as construction automation technologies. According to [PERSON] et al. [9], integrating Building Information Modelling (BIM) into an information technology (IT) infrastructure makes it easier to go from a reactive strategy to an anticipatory one when controlling events [10]. Previous systematic literature reviews on Construction 4.0 have very much focused on Construction 4.0 technologies per se [11; 12] or have been conducted within the context of decision making [13], BIM [14], deep learning for vision systems [15], or sustainability [16] or from the perspective of impacts on society, environment, or governance [17]. This study has the narrower focus of the application of Construction 4.0 in developing countries--an area less well researched within the literature. Therefore, this paper, which is part of a larger research project, starts to put forth a better understanding of the topic so a framework for the implementation of Construction 4.0 in developing countries can be developed. The first part of this work is to identify the status quo. This is investigated by answering a series of research questions (Section 1.2). Moving forward, this research will allow the construction industry within developing countries to create more efficient production processes and business models and enhance construction value chains [18]. Moreover, as research findingsemerge, specific policy recommendations and implementation strategies tailored to the context of developing countries can be proposed.
# A Review of PCI Express Protocol-Based Systems in Response to 5G Application Demand
## 3 Technology Analysis Using PCI Express, CXL, USB, NVMe, and Security
### NVM Express  
NVM Express follows the PCIe specification to design its products, which allows host software to communicate with a nonvolatile memory subsystem. NVMe reduces the storage access method to memory access and block access. NVMe status can replace traditional storage access methods, which can be implemented in different interconnects through data communication (NVMe over PCIe, NVMe over Fabrics, NVMe over Ethernet, NVMe over InfiniBand, RDMA offload) [23; 29; 30; 31]. This is also based on high-speed Ethernet environments to access resources and storage through Ethernet (RDMA over Converged Ethernet (RoCE)) [23]. Figure 6[23] shows the topology with NVMe connected to a PCIe port. NVMe has two controllers with PCI functions, single root I/O virtualization, multi-path I/O, and namespace sharing [32]. NVMe 1.3 enhanced security features and power management features. The secure storage access method with multiple access was enhanced in NVMe. NVMe 1.4 improves access methods and is cross-interconnected with industry standards to increase performance. NVMe 2.0 enhances NVMe over Fabrics and multiple usages in the subsystem.  
Figure 5: Flex bus layers for CXL [22].
## 4 Application to Model Updating of an In-Service Bridge with Extra-Width  
### Description of Bridge Structure  
The pre-stressed concrete bridge, Hunan Road Bridge in Liaocheng City of Shandong Province in China shown in Figure 4a, a self-anchored suspension bridge with double-tower and double-cable plane, has a span arrangement of \(53+112+53\) m and a total width of 52 m. The main girder with material of C50 concrete has a section of double-lateral box connected by transverse beams with a height of 2.8 m of standard section and a height from 4.3 to 5.6 m of sections in the region of the main cable anchorage. The tower is made of C40, consisting of the main tower and vice tower with a gate-shape, has a height (including the spire) of 45 m over the deck. The two cables are arranged symmetrically with a core-to-core transverse distance of 31.7 m and the rise-span ration of the middle span is 1/5.276. There are 37 pairs of hangers of this bridge, 8 pairs on each of the two side spans and 21 pairs in middle span, with a standard distance of 5 m between hangers and a distance of 6 m between axis of the tower and lateral hangers. The general layout of this bridge is shown in Figure 4b.  
Figure 4: Description of Hunan Road Bridge: (**a**) Image of Hunan Road Bridge; (**b**) Image of Hunan Road Bridge; (**c**) FE model of Hunan Road Bridge.
## 3 Results and Discussion
### Production of CMCase with Shifts in Aeration Rate and Agitation Speed of Pilot-Scale Bioreactor
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{2}{c}{**Conditions and Results**} & \multicolumn{2}{c}{**Without Shift**} & \multicolumn{2}{c}{**With Shifts at 36 h**} \\ \hline \multirow{3}{*}{Conditions} & \multicolumn{1}{c}{Aeration (rpm)} & 180 & 150 & 180 to 150 rpm \\ \cline{2-5}  & \multicolumn{1}{c}{Agitation (vvm)} & 0.50 & 0.95 & 0.50 to 0.95 vvm \\ \hline \multirow{6}{*}{Results} & DCW (g/L) & 5.88 & 3.45 & 5.21 \\  & Cellulase (U/mL) & 785 & 890 & 1212 \\  & \(\upmu\) (h) & 0.059 & 0.044 & 0.052 \\ \cline{1-1}  & \(\upmu_{\text{max}}\) (\(\upmu\)h) & 0.092 & 0.083 & 0.103 \\ \cline{1-1}  & dx/dt (g/L-h) & 0.061 & 0.035 & 0.054 \\ \cline{1-1}  & dp/dt (U/mL-h) & 8.177 & 9.271 & 12.625 \\ \cline{1-1}  & dp/dx (U/g) & 133.50 & 257.97 & 232.63 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of cell growth and production of CMCase by _E. coli_ KACC 91335P in a 100 L bioreactor. Figure 6: Comparison of (**a**) cell growth and (**b**) production of CMCase by _E. coli_ KACC 91335P under the fixed optimal conditions of aeration rate and agitation speed for cell growth (\(\upmu\)), under fixed optimal conditions of aeration rate and agitation speed for production of CMCase (\(\Circinus\)) and with shifts in optimal conditions of aeration rate and agitation speed from cell growth to production of CMCase at mid-log phase (\(\blacktriangledown\)). continuous productivity as well as reduced labor costs [31]. As a strategy of multistage continuous cultures for the higher production of CMCase, the first stage should involve the cell growth of _E. coli_ KACC 91335P and the second stage should involve the production of CMCase. However, the set-up and maintenance of continuous fermentations, including continuous multistage systems, are more difficult than those for batch fermentations. Significant volumes of medium, including products, may be lost when contamination occurs during continuous multistage fermentation [32]. Moreover, the cost for constructing continuous fermentation equipment with multistage bioreactors is more expensive than that for the batch fermentation. The CMCase produced by _E. coli_ KACC 91335P was the mixed-growth-associated product. Without the multistage continuous system, the production of CMCase by _E. coli_ KACC 91335P in the batch system of a 100 L pilot-scale bioreactor was enhanced with shits in optimal conditions of the aeration rate and agitation speed at the mid-log phase.
# Surface Charging on Insulating Films with Different Thicknesses in UPS
###### Abstract  
The conventional view holds that ultraviolet photoelectron spectroscopy (UPS) measurements are not applicable to insulating materials due to interference from charging effects. To avoid surface charging, researchers typically restrict valence band structure investigations to ultra-thin films. However, the UPS spectral performance of ultra-thin films tends to correlate with the substrate characteristics and film thickness, while charging effects, which still unavoidably occur, can also affect the realism of the results. This study systematically investigates the charging effects and valence band structural evolution in SiO\({}_{2}\) insulating films with controlled thickness variations through XPS and UPS depth profiling. By analyzing spectral shifts, surface potential dynamics, and work function variations, three continuous regimes are identified. The results demonstrate that the surface potential undergoes abrupt intensification when exceeding critical thickness thresholds (about 8 nm), a phenomenon governed by substrate resistivity and charge compensation pathways. Conventional work function determination methods remain valid only when the actual effect of the applied bias exceeds the surface potential values. For thicker films, the limited efficacy of negative bias fails to compensate for the spectral shifts caused by surface charging, consequently rendering work function measurements unreliable. These findings provide critical guidance for optimizing UPS measurements and spectral interpretation in insulating films.  
insulating films; UPS; valence band structure; work function +
Footnote †: journal: Applied Sciences  
eurmurm
## 3 Control Approach
According to this definition, the main system equation is expressed as:  
\[\frac{d^{2}}{dt^{2}}(v_{o})=-\frac{R_{f}}{L_{f}}\frac{d(v_{o})}{dt}-\frac{v_{o}}{ L_{f}C_{f}}+\frac{K_{PWM}v_{i}}{L_{f}C_{f}}+n(t) \tag{13}\]  
and in general form, we can re-write this equation to (14). \[\frac{d^{2}}{dt^{2}}(x_{isl}(t))=d\frac{d}{dt}(x_{isl}(t))+f\ x_{isl}(t)+g\ u(t)+n(t) \tag{14}\]  
where the parameters \(d\), \(f\) and \(g\) are determined by comparing (8) and (9). \(n(t)\) is a bounded distortion applied to the system where \(|n(t)|<\sigma\), and \(\sigma\) is a positive constant. Since the voltage tracking error is \(e_{isl}=v_{o}-v_{ref}\), the three order sliding surface could be introduced as (15). \[ \begin{array}{c}S_{isl}(t)=k_{4}e_{isl}(t)+k_{3}\int\limits_{0}^{t}e_{isl}( t^{\prime})dt^{\prime}+\frac{d\left(e_{gc}(t)\right)}{dt}\\ \\ \frac{d}{dt}(S_{isl}(t))=k_{3}e_{isl}(t)+k_{4}\frac{d\left(e_{isl}(t)\right)}{ dt}+\frac{d^{2}\left(e_{isl}(t)\right)}{dt^{2}}\end{array}  \tag{15}\]  
where \(k_{3}\) and \(k_{4}\) are positive constants. In this way, the switching control law is defined as follow:  
\[u=u_{t}+u_{sw}\]  
\[u_{r}=-g^{-1}(fv_{o}+k_{3}g_{gc}-\frac{d^{2}\left(v_{ref}\right)}{dt^{2}}+d \frac{d\left(v_{o}\right)}{dt}+k_{4}\frac{d\left(i_{ref}\right)}{dt}\]  
\[u_{sw}=-g^{-1}\hat{\Theta}(t)sign\left(S_{gc}(t)\right)\]  
where \(\hat{\Theta}(t)=\)1/\(\xi_{isl}\int S_{isl}(t^{\prime})dt^{\prime}+S_{isl}(t)\) and \(\xi_{isl}\) is a positive constant. In order to modify the stability of the controller, these modifications can be applied to the conventional equations:  
\[ \begin{array}{c}E_{i}=E_{i}^{*}+m\left(P_{i}^{*}-P_{i}\right)-m^{\prime} \left(Q_{i}^{*}-Q_{i}\right)\\ f_{i}=f_{i}^{*}-\frac{n}{2\pi}\left(Q_{i}^{*}-Q_{i}\right)+\frac{n^{\prime}}{ 2\pi}\left(P_{i}^{*}-P_{i}\right)\end{array}  \tag{17}\]  
Using low pass filter (LPF) and Laplace transform to Equation (4), and some simplifications considering the feeder impedance effect, it could be derived that:  
\[ \begin{array}{c}\hat{P}_{i}=\frac{\omega_{c}}{s+\omega_{c}}\frac{U}{r_{i}} \left(\hat{E}_{i}\cos(\phi_{i})-\hat{\phi}_{i}E_{i}\sin(\phi_{i})\right)\\ \hat{Q}_{i}=\frac{\omega_{c}}{s+\omega_{c}}\frac{U}{r_{i}}\left(-\hat{E}_{i} \sin(\phi_{i})+\hat{\phi}_{i}E_{i}\cos(\phi_{i})\right)\end{array}  \tag{18}\]  
\[ \begin{array}{c}\hat{E}_{i}=-m\hat{P}_{i}+\frac{nE_{i}X_{feedar}}{r_{i}}\hat {Q}_{i}\\ \\ \hat{f}_{i}=\frac{n}{2\pi}\hat{Q}_{i}-\frac{mX_{feedar}}{E_{i}r_{i}}\hat{P}_{ i}\end{array}  \tag{19}\]  
and finally, the closed loop three order system, equation is achieved in (20). \[ \begin{array}{c}s^{3}+s^{2}\bigg{\{}2\omega_{c}+\frac{U\omega_{c}m\cos(\phi _{i})}{r_{i}}+\frac{U\omega_{c}nE_{i}\sin(\phi_{i})X_{feedar}}{r_{i}^{2}} \bigg{\}}\\ \\ +s\bigg{\{}\omega_{c}^{2}+\left(nE_{i}+m\omega_{c}\right)\frac{U\omega_{c}\cos( \phi_{i})}{r_{i}}+\left(nE_{i}\omega_{c}-m\right)\frac{U\omega_{c}\sin(\phi_{i })X_{feedar}}{r_{i}^{2}}\bigg{\}}\\ \\ +\bigg{\{}\left(\cos(\phi_{i})r_{i}+mU\right)\frac{nU\omega_{c}^{2}}{r_{i}^{2}} +\left(r_{i}^{2}+nUE_{i}X_{feedar}\right)\frac{mU\omega_{c}^{2}X_{feedar}}{r_{ i}^{2}}\bigg{\}}=0\end{array}  \tag{20}\]
###### Abstract  
In recent years, the number of Earth Observation missions has been exponentially increasing. Satellites dedicated to these missions usually embark with payloads that produce large amount of data and that need to be transmitted towards ground stations, in time-limited windows. Moreover, the noisy nature of the link between satellites and ground stations makes it hard to achieve reliable communication. To address these problems, a standard for a flexible advanced coding and modulation scheme for high-rate telemetry applications has been defined by the Consultative Committee for Space Data Systems (CCSDS). The defined standard, referred to as CCSDS 131.2-B, makes use of Serially Concatenated Convolutional Codes (SCCC) based on 27 ModCods to optimize transmission quality. A limiting factor in the adoption of this standard is represented by the complexity and the cost of the hardware required for developing high-performance receivers. In the last decade, the performance of software has grown due to the advancement of general-purpose processing hardware, leading to the development of many high-performance software systems even in the telecommunication sector. These are commonly referred to as Software-Defined Radio (SDR), indicating a radio communication system in which components that are usually implemented in hardware, by means of FPGAs or ASICs, are instead implemented in software, offering many advantages such as flexibility, modularity, extensibility, cheaper maintenance, and cost saving. This paper proposes the development of an SDR based on NVIDIA Graphics Processing Units (GPU) for implementing the receiver end of the CCSDS 131.2-B standard. At first, a brief description of the CCSDS 131.2-B standard is given, focusing on the architecture of the transmitter and receiver sides. Then, the receiver architecture is shown, giving an overview of its functional blocks and of the implementation choices made to optimize the processing of the signal, especially for the SCCC Decoder. Finally, the performance of the system is analyzed in terms of data-rate and error correction and compared with other SW systems to highlight the achieved improvements. The presented system has been demonstrated to be a perfect solution for CCSDS 131.2-B-compliant device testing and for its use in science missions, providing a valid low-cost alternative with respect to the state-of-the-art HW receivers.  
CCSDS 131.2-B; software-defined radio; graphics processing unit; receiver; low-cost; earth observation; new space economy 2024