esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Performance Metric  
The coefficient of determination (R\({}^{2}\)), Nash-Sutcliffe efficiency (NSE) coefficient, root mean square error (RMSE), and the ratio of the RMSE to the standard deviation of measured data (RSR) were taken into account to examine the predictive capacity of the models, as shown in Equations (3)-(6) [48; 49; 50]:  
\[R^{2}=\left[\frac{\sum_{i=1}^{n}\left(O_{i}-\overline{O}\right)\left(P_{i}- \overline{P}\right)}{\sqrt{\sum_{i=1}^{n}\left(O_{i}-\overline{O}\right)^{2}} }\right] \tag{3}\]  
\[NSE=\frac{\sum_{i=1}^{n}\left(O_{i}-\overline{O}\right)^{2}-\sum_{i=1}^{n} \left(P_{i}-O_{i}\right)^{2}}{\sum_{i=1}^{n}\left(O_{i}-\overline{O}\right)^{ 2}} \tag{4}\]  
\[RMSE=\sqrt{\frac{1}{n}{\sum_{i=1}^{n}\left(P_{i}-O_{i}\right)^{2}}} \tag{5}\]  
\[RSR=\frac{\sum_{i=1}^{n}\left(O_{i}-P_{i}\right)^{2}}{\sqrt{\sum_{i=1}^{n} \left(O_{i}-\overline{O}\right)^{2}}} \tag{6}\]  
where \(n\) is the number of observations under consideration, \(O_{i}\) is the \(i\)th observed value, \(\overline{O}\) is the mean observed value, \(P_{i}\) is the \(i\)th model-predicted value, and \(P\) is the mean model-predicted value.  
R-squared, also called the determination coefficient, describes the change in data as the degree of fit. The normal \"determination coefficient\" range is (0-1). The model is considered to be efficient if the R\({}^{2}\) value is greater than 0.8 and is close to 1 [51]. The NSE is a normalized statistic that controls the relative extent of the residual variance relative to the variance of the data measured [52]. The NSE varies between \(-\infty\) and 1. When NSE = 1, it presents a flawless match among observed and predicted values. Model predictive output with a range of 0.75 < NSE \(\leq\) 1.00, 0.65 < NSE \(\leq\) 0.75, 0.50 < NSE \(\leq\) 0.65, 0.40 < NSE \(\leq\) 0.50, or NSE \(\leq\) 0.4 is graded as very good, good, acceptable, or unacceptable, respectively [53; 54]. The RMSE is the square root of the ratio of the square of the deviation between the observed value and the true value of the number of observations n. The RMSE has a value greater than or equal to 0, where 0 is a statistically perfect fit for the data observed [55; 56; 57]. The RSR is interpreted as the ratio of the measured data's RMSE and standard deviation. The RSR varies between an optimal value of 0 and a large positive value. A lower RSR presents a lower RMSE, which indicates the model's greater predictive efficiency. RSR classification ranges are described as very good, good, acceptable, and unacceptable with ranges of \(0.00\leq\) RSR \(\leq\) 0.50, \(0.50\leq\) RSR \(\leq\) 0.60, \(0.60\leq\) RSR \(\leq\) 0.70, and RSR \(>\) 0.70, respectively [53].
# A Multi-Objective Approach for Optimizing Virtual Machine Placement Using ILP and Tabu Search
## 3 Proposed Methods
### Mathematical Formulation of the Multi-Objective Integer Linear Programming Model
#### 3.1.1 Notations  
We use the following notations and formatting conventions.  
Index Notations  
The indices \(i\) and \(j\) refer to a VM request and a PM, respectively.  
Parameters  
The parameters used in the model capture the core characteristics of the VMs and PMs in the DC.  
Figure 2: An example of a VMP of three VMs over one PM. (**a**) A first example of a VMP of three VMs over one PM; (**b**) A second example of a VMP of three VMs over one PM.  
* \(V=\{v_{1},v_{2},\ldots,v_{N}\}\) represents the set of VM requests received by the DC. Each VM \(v_{i}\) is defined by \[v_{i}=\{c_{i},r_{i},s_{i}\}\] where
* \(c_{i}\) represents the CPU requirements of VM \(v_{i}\);
* \(r_{i}\) represents the RAM requirements of VM \(v_{i}\);
* \(s_{i}\) represents the storage requirements of VM \(v_{i}\).
* \(P=\{P_{1},P_{2},\ldots,P_{M}\}\) represents the set of available PMs in the DC. Each PM \(P_{j}\) is characterized by \[P_{j}=\{C_{j},R_{j},S_{j},\mathcal{E}_{j}^{0},\mathcal{E}_{j}^{1}\}\] where
* \(C_{j}\) represents the initial CPU capacity of \(P_{j}\);
* \(R_{j}\) represents the initial memory capacity of \(P_{j}\);
* \(S_{j}\) represents the initial storage capacity of \(P_{j}\);
* \(\mathcal{E}_{j}^{0}\) represents the average power consumed by \(P_{j}\) when the PM is idle.;
* \(\mathcal{E}_{j}^{1}\) represents the average power consumed by \(P_{j}\) when the PM is busy.
* \(N=|V|\) and \(M=|P|\) are the number of VM requests and the number of PMs available in the DC, respectively.  
Variables  
To define the decision-making aspect of the model, we introduce the following binary variables:  
* The binary variable \(\phi_{ij}\), equal to 1 if VM \(v_{i}\) is hosted by PM \(P_{j}\) and 0 otherwise.
* The binary variable \(\kappa_{j}\), equal to 1 if there is at least one VM hosted by PM \(P_{j}\) and 0 otherwise.  
Table 3 presents an overview of the notations employed in the MOILP model, serving as a convenient reference for each parameter and its role in the optimization framework.  
\begin{table}
\begin{tabular}{c l} \hline \hline
**Symbol** & **Description** \\ \hline \(V\) & Set of VM requests. \\ \hline \(N\) & Number of VM requests. \\ \hline \(P\) & Set of available PMs in the DC. \\ \hline \(M\) & Number of PMs. \\ \hline \(c_{i}\) & CPU requirements of VM \(v_{i}\). \\ \hline \(r_{i}\) & RAM requirements of VM \(v_{i}\). \\ \hline \(s_{i}\) & Storage requirements of VM \(v_{i}\). \\ \hline \(C_{j}\) & CPU capacity of PM \(P_{j}\). \\ \hline \(R_{j}\) & RAM capacity of PM \(P_{j}\). \\ \hline \(S_{j}\) & Storage capacity of PM \(P_{j}\). \\ \hline \(\mathcal{E}_{j}^{0}\) & Power consumption of PM \(P_{j}\) when idle. \\ \hline \(\mathcal{E}_{j}^{1}\) & Power consumption of PM \(P_{j}\) when busy. \\ \hline \hline \end{tabular}
\end{table}
Table 3: Notations used in the MOIP model.
## 3 System Design
### Assistance Data  
The proposed demonstrator utilizes GNSS assistance data to process weak Galileo E1b/c signals. This assistance data is provided in various forms, including a reference date and time, a reference user location, Galileo ephemeris data, Galileo ionospheric data, and the Galileo Coordinated Universal Time (UTC) model, as detailed in Table 2. The receiver obtains the current date and time from the embedded GNU/Linux OS clock, retrieves the reference user location from the GNSS-SDR configuration file, and accesses the Galileo ephemeris data, ionospheric data, and UTC model from Extensible Markup Language (XML) files. These XML files, which contain data about the visible satellites, can be generated by the receiver itself by placing it outdoors and operating it in normal-sensitivity mode.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Signal** & **Operating Mode** & **Receiver Sensitivity** \\ \hline Galileo E1b/c & High-Sensitivity mode & 20 dB-Hz \\ \hline GPS L1 C/A & & \\ GPS L5 & Normal-sensitivity mode & approx. 37 dB-Hz \\ Galileo E5a & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Default receiver operating modes.  
Figure 4: Picture of the high-sensitivity GNSS receiver prototype.  
Table 3 outlines the purpose of the assistance data and specifies the data that is utilized for each designated task. The receiver uses the reference date and time, the reference user location, and the assistance ephemeris data to estimate the Doppler frequency of the received signals and reduce the Doppler search space during acquisition. The reference date and time are also utilized to estimate the transmitted Time of the Week (TOW) in the navigation messages. This estimation is needed because the TOW is essential for performing GNSS basic measurements. However, when tracking severely attenuated signals, the receiver may struggle to reliably demodulate the TOW field of Galileo E1b/c navigation messages due to noise-induced bit errors [36; 37]. Additionally, the assistance ephemeris data, ionospheric data and UTC model are utilized to compute the PVT when the receiver cannot reliably demodulate the navigation messages due to the presence of bit errors.  
The Doppler prediction, the TOW estimation, and the computation of the navigation solutions are explained in more detail in Sections 3.5, 3.8, and 3.9, respectively.
## 2 Preparation and SEY Characterization Methods of MgO Coatings
### SEY Measurement of MgO Coatings  
Figure 2 shows the internal structure schematic diagram of the SEE measurement platform employed in this study. As shown in Figure 2, the facility mainly contains six parts, including electron gun components (including test gun and neutralization gun), collectors (including inner grid, outer grid and collector), voltage source (Keithley 6487) and precision current meter (Keithley 6485), signal amplifier unit (ADA4817-1), oscilloscope (Rigol DS7014), as well as ultrahigh vacuum acquirement system. The facility employs a high precision electron gun (Kimball 3101D) to generate the primary electron beam, and the electron beam density is 80 nA. Size of the beam spot varies as the electron beam energy increases; to be specific, the spot diameter is about 900 \(\upmu\)m when primary electron energy (denoted as \(E_{\rm p}\)) equals to 80 eV, and is about 80 \(\upmu\)m when \(E_{\rm p}\) equals to 1500 eV. In addition, we utilize the signal generator (Rigol DS4102) to produce pulsed signals, rise  
Figure 1: Sketch map of ALD processes for MgO coating preparation.  
time and fall time of the single pulse are 500 ns, and the pulse width is 10 \(\upmu\)s. Surface charge neutralization for dielectric materials is achieved by the neutral electron gun. To be specific, when the surface is positively charged, we use the low-energy electrons (less than 10 eV energy) to irradiate the surface to eliminate the accumulated positive charges. On the contrary, if the sample surface is negatively charged, we utilize the low-energy ions to irradiate the surface to neutralize the accumulated electrons. To determine whether the surface is neutralized, the external oscilloscope with four channels is connected with the sample holder to monitor the sample potential since the induction charges will be produced at the sample bottom when the sample is positively or negatively charged.  
The steps for SEY testing can be briefly described as follows: First, the chamber pressure is pumped below \(10^{-5}\) Pa to meet the environmental requirements of the test. Second, to measure the current of primary electron beam, the metallic sample holder (not the sample) is irradiated by the electron beam; at the same time, an external high-precision current meter is used to measure the sample current (\(I_{\text{SC}}\)) and secondary electron current (remember \(I_{\text{SEC1}}\)). Here, a secondary electron current contains three parts: inner grid current, outer grid current and collector current. Third, the sample is moved onto the sample holder and is irradiated with the same electron beam, and then the secondary electron current (denoted as \(I_{\text{SEC2}}\)) is measured again. After the above tests, SEY of the sample can be calculated by the following formula:  
\[\delta=\frac{I_{\text{SEC2}}}{I_{\text{SC}}+I_{\text{SEC1}}} \tag{1}\]
# Development of Energy-Based Impact Formula--Part I: Penetration Depth
## 3 Development of New Energy-Based Penetration Formula
### Involved Energies in Impact Mechanism
#### 3.2.3 Elastic Penetrated Energy of Concrete Panel (E\({}_{Ep}\))  
When two bodies touch each other, each consumes energy, which creates elastic deformation. Energy occurs when an elastic deformation of a reinforced concrete slab is defined as the elastic deformed energy (\(E_{EP}\)). It follows the Hertzian contact theory [43], neglecting frictional contact energy. [PERSON] [43] was the first to study the mechanics of contact between two elastic bodies and the deformation related to their modulus of elasticity. The elastic penetration depth between a sphere and a half-space is derived as shown in Equations (91) and (92) and Figure 7. The Hertzian contact theory assumed that the space thickness is infinite. However, realistic concrete panels have a particular thickness. The test specimen thickness may be smaller than the required thickness, showing elastic deformation. Given that the maximum strain of concrete is normally 0.002, the elastic deformed energy (\(E_{EP}\)) according to the type of projectile nose can be obtained as Equations (93) to (95) [44; 45; 46].  
\[d_{elastic}=\left(\frac{9F^{2}}{16E^{2}r}\right)^{1/3} \tag{91}\]  
\[\frac{1}{E^{*}}=\frac{1-v_{p}^{2}}{E_{p,dyn}}+\frac{1-v_{c}^{2}}{E_{c,dyn}} \tag{92}\]  
\[E_{EP}=F_{nose}\times\left[d_{elastic}\text{ or }0.002h\ \right]_{\min}\text{ for sphere or hemisphere} \tag{93}\]  
\[E_{EP}=F_{nose}\times\left[\frac{F_{nose}}{2 aE^{*}}\text{ or }0.002h\ \right]_{\min}\text{ for flat nose} \tag{94}\]  
\[E_{EP}=F_{nose}\times\left[\sqrt{\frac{F_{nose}\pi(1-v_{c}^{2})tan\theta}{2E_{c,dyn}}}\text{ or }0.002h\ \right]_{\min}\text{ for conical, sharp, or ogive nose} \tag{95}\]  
where \(d_{elastic}\) is the elastic penetration depth by Hertzian contact theory (mm); \(F\) is the acting force (N); \(v_{p}\) and \(v_{c}\) is [PERSON]'s ratios of a sphere and half-space (projectile and concrete panel in this study, respectively); \(E_{p,dyn}\) and \(E_{cc,dyn}\) are the elastic moduli of the sphere and the half-space under the dynamic loading, respectively; \(E_{EP}\) is the elastic penetrated energy (N-mm); \(F_{nose}\) is the force on the nose of a projectile (\(N\)) that is defined by Equation (79); \(h\) is the thickness of a concrete panel (mm); and tan\(\theta\) is the angle between the plane and the side surface of a conical nose.
# Surround by Sound: A Review of Spatial Audio Recording and Reproduction
## 2 Binaural Recording and Rendering
### Binaural Rendering
#### Rendering by Headphones or Loudspeakers  
Headphones are the natural and most effective way to reproduce binaural sounds. Equalization is commonly applied to make them meet an ideal target response. The original reference was that of a frontal free field (i.e., free-field equalization), but a preference for a diffuse field with random incidence was later proposed (i.e., diffuse-field equalization) [76] and now has been widely adopted by the headphone manufactures. In terms of binaural rendering itself, the recent results show that only presenting the variation of the sound spectrum due to the source position changes can provide the localization cues from the perception point of view [2]. However, from a practical application point of view, compensation is necessary for accurate binaural synthesis over headphones, either through individual measurements [77] or non-individual recordings of the headphone transfer function (HpTF) [78]. The metrics for evaluating the perception of equalized HpTF was recently proposed [79].  
Binaural rendering through loudspeakers is intrinsically affected by the crosstalk and thus requires a pre-processing crosstalk cancellation system (CCS) [5]. This system, however, is sensitive to the listener's head movement or misalignment given limited sweet spot. The research has focused on developing robust CCS for a two-speaker or multiple-speaker setup [6; 80]. The optimal loudspeaker positions for CCS are of interest, among which an effective dual-speaker system called \"stereo dipole\" [81] and the optimal loudspeaker array configuration [82; 83] were developed. Other recent work includes investigating the sound source localization performance provided by CCS [84] and designing CCS with a head tracker based on an online monitoring of ITD [85].
# Measuring Collision Risk in Mixed Traffic Flow Under the Car-Following and Lane-Changing Behavior
## 3 A New Risk Measurement Method Considering Multiple Driving Behaviors
### Proposal of a New SSM
#### 3.1.3 Total Risk Assessment of Vehicle  
The total risk assessment of a vehicle combines longitudinal risk and lateral risk. The calculation of \(R_{\text{total}}(t)\) is given by Equation (12).  
\[R_{\text{total}}(t)=\alpha R_{\text{lon}}(t)+(1-\alpha)R_{\text{lat}}(t) \tag{12}\]  
where  
\(R_{\text{total}}(t)\): The total risk assessment value at time \(t\), where a higher value indicates higher risk.  
\(R_{\text{lon}}(t)\): Longitudinal risk assessment value, reflecting collision risk during car-following behavior.  
\(R_{\text{lat}}(t)\): Lateral risk assessment value, reflecting collision risk during lane-changing behavior.  
\(\alpha\): Weight parameter ranging from \([0,1]\), used to balance the influence of longitudinal and lateral risks.
## 3 Methodology
### Computation of Monthly Land-Water Masks with Data Gaps
#### 3.2.1 Pre-Processing
By averaging all valid pixels, the resulting composite image represents a quasi-averaged surface area at which initial scenes are made with an irregular temporal resolution within a month. Figure 4 shows an example for the image combination for Lake Ray Roberts in April 2016. For this month, three images are available, two from Sentinel-2 and one from Landsat. Figure 4d shows the combined monthly composite. It contains optical images of Landsat-7 from the 2016-04-25 which has a cloud coverage of 27%. The monthly Sentinel-2 composite contains two optical images of Sentinel-2A from the 2016-04-02 (0% cloud coverage) and 2016-04-15 (27% cloud coverage). Both daily Sentinel-2A images are taken from different satellite tracks. This causes additional no-data gaps in the daily scene from Sentinel-2A on 2016-04-02 (Figure 4a) as the satellite stripes the study area on the edge only. Despite the usage of several scenes, clouds, cloud shadows, and snow gaps remain in the resulting composite. One can clearly see that the cloud-free Sentinel-2A image has the largest impact on the resulting combined composite because the other images are covered with clouds. The remaining data gap in the cloud-free image is then filled by combining the other cloud-covered scenes. Therefore, the monthly composite is divided into a cloud-free part on the right and a cloud-covered part on the
## 2 Review of Waveform Inversion Methods for Reconstructing the Dynamic Processes of a Landslide  
Waveform inversion methods for reconstructing the dynamic processes of a landslide can be traced back to [PERSON] and [PERSON] [10, 11]. So far, three waveform inversion methods have been widely used to study the dynamic processes of a landslide. The first one is a frequency-domain inversion method (FIM) proposed by [PERSON] et al. [12]. [PERSON] et al. [13] used FIM with the seismic records of eight broadband seismic stations in Taiwan for the Hsiaolin landslide and obtained a maximum force of about \(5\times 10^{10}\) N. [PERSON] et al. [14] also used FIM with seismic records of six stations for the Akatani landslide in Japan. The variations of frictional coefficients of the Akatani landslide were calculated, and their range was 0.38-0.56.  
The second one is the landslide force history (LFH) inversion method proposed by [PERSON] et al. [7]. LFH is based on the assumption that the block mass becomes decoupled from the sliding surface and induces forces on the slope during the movement of the landslide mass. Seismic waves are generated by this time-varying force acting on the sliding surface. The three-component (east, north, and vertical) force is parameterized as a sequence of partially overlapping isosceles triangles to synthesize a landslide force history. [PERSON] et al. [5], [PERSON] et al. [15], and [PERSON] et al. [16] applied LFH to the Bingham Canyon Mine in the USA, the Oso landslide in the USA, and several landslides in Taiwan, respectively. The LFH method is executed in the time domain. The relationships between the maximum force and surface wave magnitude, landslide mass, and maximum momentum have been investigated through the inversion results by using the LFH method.  
The third method is the point force history (PFH) inversion method proposed by [PERSON] [17], which is executed in the time domain and based on the assumption that a landslide can be simplified as a stationary, constant mass block sliding on a slope. [PERSON] [17] applied the PFH method to the Mount Meager rockslide-debris flow in British Columbia and figured out that the frictional coefficient of the rockslide-debris flow was about \(0.38\pm 0.02\). [PERSON] et al. [18] also used the PFH method for the Oso landslide in the USA and compared the inversion results with the numerical modeling results.  
In summary, despite the operating environments (e.g., time and frequency domains), the three common inversion methods can provide information on the dynamic processes of landslide movement, such as force magnitude, sliding velocity, frictional coefficient, and runout distance. Based on the literature studies, it is found that the PFH can be an efficient and practical method as it is less time-consuming and can be performed with fewer data recorded from seismic stations. In this study, the PFH method was applied to a large-scale landslide, the Taimali landslide. The inversion results were used to reconstruct the dynamic processes of the giant landslide.
## 2 Remarks on the Topology of Microgrid
### Integration of Renewable Energy Sources
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Feature** & **Grid-Connected Mode** & **Isolated Mode** & **AC Operation** & **DC Operation** & **Hybrid (AC/DC)** & **Operation** \\ \hline Connectivity & Connected to the main grid. & Independent. & Compatible with traditional systems. & Efficient for specific systems. & Integration of AC and DC. \\ \hline Flexibility & High, but depends on the main grid. & Limited by local capacity. & High for integrating diverse sources. & Limited by equipment and applications. & High, allows specific use of AC and DC. \\ \hline Efficiency & Moderate, depends on grid. & Variable, depends on ESS. & Moderate due to conversion losses. & High, eliminates conversion losses. & High, optimizes based on source. \\ \hline Challenges & Requires synchronization and stability. & Real-time generation and load management. & Frequency and phase synchronization. & System protection and conversion. & Integration and management of different standards. \\ \hline Applications & Urban areas, grid support. & Remote areas, critical sites. & Traditional systems, existing infrastructure. & PV systems, batteries, EVs. & Modern infrastructure, mixed installations. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Operating modes and types of power supply for microgrids. also outlines the AC and DC operation of a microgrid. Each side has a central operating bus. The DC bus interconnects sources and loads of a DC nature, even allowing the connection of an electric vehicle (EV) as a flexible load. The AC side interconnects traditional AC loads and is interconnected with the local AC power network. A bi-directional DC/AC converter interconnects the DC and AC sides. The following section discusses general applications of microgrids.