esa-sceva/satcom-chunk-collection huggingface dataset:
# Building Semantic Knowledge Graphs from (Semi-)Structured Data: A Review
## 3 Background
### Data Preprocessing  
Preprocessing of the data is of high relevance, especially since the data may be of poor quality. There are different tasks involved in data preprocessing, including (i) enrichment, i.e., adding additional information to the data, (ii) reconciliation, i.e., correctly matching entities from different sources, and (iii) cleaning, i.e., improving the quality of the data. [PERSON] et al. [42] classify data quality problems in data sources, differentiating between single- and multi-source and between schema- and instance-level.  
Schema-level problems refer to the overall schema for the data source and may affect several data instances at once. Single-source problems are related to the poor schema design and lack of integrity constraints. This includes uniqueness violation, illegal values, violated attribute dependencies, etc. For multi-source, the problems are related to translation and integration between schemas. Schema integration problems may occur when matching entities with respect to naming and structural features. Naming problems may occur when the same object has different names or different objects have the same name. Structural problems may be the difference in data types, different integrity constraints, etc. Instance-level problems refer to the problems happening at instance level in the data. Instance-level problems cannot be prevented at the schema level. Single source problems, at the instance level, may be spelling error, duplicates, etc. Multi-source problems, at the instance level, may be contradicting values for objects, different use of units, different use of aggregation, etc.  
To resolve the mentioned problems, [PERSON] et al. [42] mention several phases, which include data analysis, definition of transformation workflow and mapping rules, verification, transformation, and backflow of cleaned data. Several tools exist for the execution of data cleaning, including spreadsheet software, command line interface (CLI) tools, programming languages, and complex systems designed to be used for interactive data cleaning and transformation [43; 44].
## 2 Throughput Evaluation:  
* **Regular Traffic:** The simulation outcomes in Figure 23 indicate that DSR had the highest throughput at different node speeds due to lower routing overhead. On the other side, AODV showed lower performance at higher speeds due to frequent route discoveries occupying network bandwidth.
* **Irregular Traffic:** Figure 24 indicates that DSR and AODV have comparable performance at lower node speeds. On the other hand, it is clear that both DSR and DSDV protocols can demonstrate improved throughput at speeds exceeding 18 m/s.  
Figure 23: Throughput vs. node speed (regular traffic).  
Figure 22: Energy consumption vs. node speed (irregular traffic).
# A Real Model of a Micro-Grid to Improve Network Stability
## 1 Introduction
Generally, the proposed article describes a smart energy model (SEM) of a micro-grid and defines a management strategy suitable for micro-grid control within the Czech Republic. The SEM to be discussed herein constitutes a hardware implementation of a laboratory smart grid, a smart metering system, and a physical simulator of energy consumption, all materialized by the Laboratory of Automation and Embedded Systems of the Centre for Research and Utilization of Renewable Energy (CVVOZE, Brno, the Czech Republic). The purpose of the SEM is to simulate the different physical states in all types of smart grid networks and to develop new network types and related control possibilities. A major advantage of the discussed model lies in its simplicity and economical character (low initial set-up costs), and it is also important to note that various scenarios, including the demand-response approach, can be effectively tested. Two different levels of control algorithm testing are available: plain software simulation of the energy balance in the grid, and the use of real energy from renewable sources to control the micro-grid stability. The latter option is obviously more time-consuming. In the given context, the article also contributes to the general awareness of micro-grids and their varied applications. The paper is organized as follows: Section 2 describes the overall technical specification of the smart energy model; in Section 3, the actual micro-grid is described; Section 4 defines the strategy of the control centre (CC); and Section 5 presents the conclusions of the study.
# LEACH-MTC: A Network Energy Optimization Algorithm Constraint as Moving Target Prediction
## 2 System Model and Conventional Method
### Nonlinear Motion Model of Target  
In a two-dimensional monitoring area, target motion model is as follows:  
\[\left[\begin{array}{c}x(k+1)\\ y(k+1)\\ \theta(k+1)\end{array}\right]=\left[\begin{array}{c}x(k)\\ y(k)\\ \theta(k)\end{array}\right]+\left[\begin{array}{ccc}\Delta t&0&0\\ 0&\Delta t&0\\ 0&0&\Delta t\end{array}\right]\left[\begin{array}{cc}cos\theta(k)&0\\ sin\theta(k)&0\\ 0&1\end{array}\right]\left[\begin{array}{c}\
u(k)\\ \omega(k)\end{array}\right] \tag{1}\]  
where \(x(k)\) and \(y(k)\) are the coordinates of the target in \(x\) and \(y\) direction at time \(k\), respectively, and \(\theta(k)\) is the angle between the target and the horizontal direction at time \(k\). \(\Delta t\) is the sampling time. \(v(k)\) is the speed of moving target, and \(\omega(k)\) is the angular velocity of moving target at time \(k\). Assuming that the target motion is disturbed by external environmental disturbance noise, above model can be simplified as follows:  
\[X(k+1)=f(X(k))+W(k) \tag{2}\]  
where \(X(k)=[x(k)y(k)\theta(k)]^{T}\) is the state of moving target at time \(k\). \(f(\cdot)\) is the nonlinear state transition function, which is consist in \(x(k)\), \(y(k)\), \(\theta(k)\). \(W(k)\) is the target state Gaussian noise, and it satisfies \(W(k)\sim N(0,Q(k))\). \(Q(k)\) is the noise covariance matrix.
## 5 Parametric Analysis
### Parametric Analysis and Discussion
#### 5.2.2 Coupling Effect of Sediment Thickness and the Radial Unloading of the Surrounding Soil  
The coupling effect of the sediment thickness and radial unloading of the surrounding soil, characterized by the softening range and degree, is involved and the corresponding results are presented in Figures 10-13.  
Figure 11: Coupling effect of the sediment thickness and soil softening range on the velocity response.  
Figure 10: Coupling effect of the sediment thickness and soil softening range on the pile complex impedance.  
Figure 12: Coupling effect of the sediment thickness and soil softening degree on the pile complex impedance.  
Figure 13: Coupling effect of the sediment thickness and soil softening degree on the velocity response.  
Figure 10 shows the coupling effect of the sediment thickness and soil softening range on the pile complex impedance. For a given sediment thickness, the oscillation amplitudes of \(K_{\text{P}}\) and \(C_{\text{P}}\) corresponding to a larger soil softening range are higher. Additionally, the decrease in the resonant frequency of the complex impedance driven by the increase in the sediment thickness becomes more notable as the soil softening range increases. One more point, the decrease in the resonant frequency of \(K_{\text{P}}\) and \(C_{\text{P}}\) caused by the increase in the sediment thickness remains consistent regardless of the soil softening range. Generally speaking, the influence of the sediment thickness is found to be more notable as the soil softening range increases.  
The coupling effect of the sediment thickness and soil softening range on the velocity response is portrayed in Figure 11. A higher soil softening range corresponds to a stronger signal reflected by the pile tip and a more notable weakness of the reflected signal caused by the increase in the sediment thickness. Figure 11 also shows that the delay in the reflected signal attributed to the sediment remains almost the same under different soil softening ranges.  
Figures 12 and 13 present the coupling effect of the sediment thickness and soil softening degree on the pile complex impedance and velocity response, respectively. As the soil softening degree increases, the oscillation amplitude of the complex impedance becomes higher, leading to a more pronounced reflection of the elastic wave by the pile tip. Concurrently, the aforementioned factors representing the pile dynamic response are more notably diminished with the increase in the sediment thickness. It is also noted that the decrease in the resonant frequency of the complex impedance and the delay of the signal reflected by the pile tip remain unaffected by the variation insoil softening degree.
## 3 Results
### Effects of Ultrasonic Vibration on the Fluidity of Cement Paste after CO\({}_{2}\) Absorption
#### 3.4.1 Compressive Strength  
The effects of ultrasonic vibration on the compressive strength of cement-based materials after CO2 absorption are shown in Figure 10. At the age of 3 d, the CO2 AA are 0%, 0.44%, 0.88%, 1.32%, 1.76%, and 2.20% with a corresponding compressive strength of the hardened cement paste of 5.9, 5.4, 5.7, 5.2, 5.6, and 5.3 MPa under mechanical agitation. According to the data, the compressive strength may increase and decrease with the increase of CO2 AA, but the fluctuation amplitude is not very large. At the age of 7 and 28 d, the compressive strength of hardened cement paste is increased to some extent. At 7 d, the compressive strengths are 6.9, 6.7, 6, 6.1, 6.5, and 6.5 MPa, respectively. The variation law of the compressive strength firstly decreases slowly but decreases significantly when the CO2 AA is 0.88%. Subsequently, it increases with the increase of CO2 AA.  
At the age of 28 d, the compressive strengths of the hardened cement paste are 7.4, 7.4, 7.6, 7.6, 7.2, and 7.1 MPa, respectively. In view of the data, the compressive strength changes stably and even presents a slowly increasing trend in the early stages of CO2 absorption as the absorption amount increases. However, the compressive strength decreases to a small extent with the increase of absorption amount, though the variation amplitude is not evident. At different ages, the compressive strength of fresh cement paste does not increase significantly with an increase of CO2 AA. It does change to some extent in the middle, however, it does not change greatly with CO2 AA.  
When the CO2 AA are 0%, 0.44%, 0.88%, 1.32%, 1.76%, and 2.20%, under ultrasonic vibrating agitation, the compressive strengths of the hardened cement paste at 3 d are 6, 5.5, 5.9, 5.3, 5.8, and 5.4 MPa, respectively. In view of the data, the variation law of compressive strength is very close to that under mechanical agitation. At 7 d, the compressive strengths of hardened cement paste are 6.9, 6.7, 6, 6.1, 6.5, and 6.5 MPa, respectively, which then  
Figure 10: Effects of ultrasonic vibration on the compressive strength of cement-based materials after CO2 absorption: (**a**) 3 d; (**b**) 7 d; (**c**) 28 d.  
Figure 9: Mean pore size.  
increases to 7.4, 7.6, 7.5, 7.9, 7.5, and 7.4 MPa at 28 d, respectively. Meanwhile, the compressive strength at 7 and 28 d under ultrasonic vibrating agitation approaches the results under mechanical agitation as the CO\({}_{2}\) AA increases.  
The variation laws of compressive strength under mechanical agitation and ultrasonic vibrating agitation are essentially identical. The compressive strength of hardened cement paste under ultrasonic vibrating agitation is increased to some extent compared with those after equal CO\({}_{2}\) AA at the same age under mechanical agitation. With the increase of CO\({}_{2}\) AA, the compressive strength changes slightly, indicating that ultrasonic vibration influences the compressive strength of cement paste after CO\({}_{2}\) absorption slightly.
## 2 Data and Methods
### Data and Pre-Processing
#### 2.2.4 MODIS GPP Product  
GPP refers to the total organic carbon fixed by organisms through photosynthesis, which is a vital variable of the global carbon cycle. It is one of the most important indicators of ecosystem function. We obtained the Terra/MODIS MOD17A2 8-day GPP data with a spatial resolution of 500 m, which are calculated by the light-use efficiency model (PSN) ([[https://www.earthdata.nasa.gov/eosdis/daacs/lpdaac](https://www.earthdata.nasa.gov/eosdis/daacs/lpdaac)]([https://www.earthdata.nasa.gov/eosdis/daacs/lpdaac](https://www.earthdata.nasa.gov/eosdis/daacs/lpdaac)) (accessed on 2 June 2021)) [33].
# Deep Feature Fusion Based Dual Branch Network for X-ray Security Inspection Image Classification
## 4 Experiments
### Ablation Studies
#### 4.4.3 Loss Weight Setting of Dual Branch Network  
We design a dual branch network by integrating the deep feature fusion model and focal loss function, which reflects the importance of each branch through setting weights (\(\lambda_{1}\) and \(\lambda_{2}\)) of loss on it. After the optimal setting of these two modules is determined, we pick multiple pairs of \(\lambda_{1}\) and \(\lambda_{2}\) for conducting classification performance experiments of dual branch network on SIXray10 subset, whose results show in Table 5.  
Firstly, comparing experimental results row-wise, we find that network performance is better when \(\lambda_{2}\) is larger than \(\lambda_{1}\). Furthermore, investigating weight setting for optimal performance, we notice that network performance reaches the peak when \(\lambda_{1}=1\), \(\lambda_{2}=3\). This implies that, as for the dual branch network proposed in this paper, it is demanding to expand the importance of branch II, i.e., deep feature fusion module, for better network performance.
## 5 Conclusions
### Evaluation of Outdoor UAV Applications  
Recent studies in indoor (greenhouse) and outdoor environments were considered for this survey. It can be easily seen in recent studies that the UAV has a wide variety of application areas in open fields (outdoor) and wide areas. Many experimental studies on crop monitoring, weed detection, remote sensing, spraying, etc., have been widely applied in the open field. Large lands, low-altitude flying of UAVs, high-resolution images and low cost make such applications preferable. Conversely, monitoring by plane or satellite is both difficult and expensive. In addition, various index information about the crop in the field can be obtained by using cameras with different imaging systems with the UAV. This enables precision agriculture practices such as balanced/sufficient irrigation and balanced/sufficient spraying that protect human health and the environment. Thanks to the GPS in outdoor areas, the UAV can be tracked with manual control, and missions can be carried out on desired sites in a wide area. In addition, since location information is available, autonomous missions can be performed by UAVs at certain locations. The results of this research indicate that open field UAV applications can be used on many more agricultural products in the future.
# Satellite Requirements for Observation of Close Proximity Celestial Bodies +
## 1 Introduction  
The celestial bodies of the solar system remain as an unexploited reservoir of many materials and substances of interest for our societies. Nonetheless, large distances and extreme conditions to humans have prevented the obtainment, processing, and utilization of those resources. Among the closest celestial bodies to our planet are Mercury, Venus, Mars, and the Moon. Those, in addition to their shorter relative distance to us, compared to other objects of our solar system, are rocky planets similar to Earth and resource extraction [1, 2] from them seems much more feasible than, e.g., from gaseous planets.  
Table 1 shows the minimum distance from Earth, radius and mass of the major celestial bodies of our solar system. In this table, it is clearly observable that our closest neighbors are the Moon, and then Venus and Mars, at distances around 100 and 140 times, respectively, larger than for the Moon. In addition to the distance, other characteristics make the Moon one of our best options for exploration compared with Venus and Mars; among them, arethe surface conditions (such as temperature and pressure) and the required exit velocity of the celestial body.  
The temperature and pressure of a celestial object is mainly determined by its atmosphere. In the absence of this, the pressure will be close to null and the temperature of the planet will vary according to its exposition to the Solar rays. For our closest neighbors, Venus has remarkably extreme conditions, including a high and almost uniform temperature of around 450 K and a pressure close to 90 atm; those conditions are mainly caused by its dense carbon-dioxide atmosphere.  
On the other hand, the Moon has no considerable atmosphere and thus, pressure is close to 0 atm and temperature varies (from 95 K to 390 K). The temperature of the Moon and its variations were extensively analyzed by the Lunar Reconnaissance Orbiter (LRO), and their results, including diverse maps, are available at [3].  
The escape velocity, \(v_{e}\), of a celestial body, is the minimum speed for leaving the gravitational effect of the celestial object, and it is defined as  
\[v_{e}=\sqrt{\frac{Gm}{R}} \tag{1}\]  
where \(m\) is the mass of the and \(R\) is the radius of the celestial body. From this equation, it is clear that \(v_{e}\) increases as the ratio \(m/R\) augments, and we can infer that it would take much more fuel and oxidizer to leave a celestial body such as Venus (\(v_{e}=10.36\) km/s) compared to Mercury (\(v_{e}=4.25\) km/s), Mars (\(v_{e}=5.03\) km/s), or the Moon (\(v_{e}=2.38\) km/s). From the materials' extraction perspective, it is important to consider the scope velocity since it will determine the amount of required trust for the rockets to leave the celestial body.
# A Comparative Analysis of Wi-Fi Offloading and Cooperation in Small-Cell Network
## 4 Comparison between Cooperation and Wi-Fi Offloading in Small-Cell Network
### Offloading in Co-Located Wi-Fi-Small-Cell BS
The SORE algorithm is applied to all the UEs in the co-located Wi-Fi-SC network. The order of complexity of SORE is \(O((M\times N)(2K)+(M^{2}\times N)(3K))\). ```
1:Input:\(h_{ij}\), \(R_{ji}^{th}\), \(P_{i}^{max}\), \(RB_{total}\), \(L_{i}\)
2:Output:\(R_{j}^{o},y_{ij}^{k}\), \(P_{ij}^{k}\), \(z_{ij}\), \(R_{ij}^{ll}\)
3:\(\tilde{i}\leftarrow\arg\max_{i}\{h_{ij}\}\) first priority \(\forall j\)
4:\(y_{ij}^{k}=1\) without repetition of \(k\)'s \(\forall j\)
5:\(p_{i,j}^{k}=(P_{i}^{max}-\Delta_{\tilde{i}})/N_{\tilde{i}}\)\(\forall i\in I\)
6: Calculate \(R_{j}\)\(\forall j\)
7:\(i^{\prime}\subseteq I_{j}\leftarrow\arg\min\{d_{i,j}\}\) first priority \(\forall j\)
8:while\(L_{i}<L_{total}^{th}\)do
9:for all UEs do
10:if\(R_{j}\ <\ R_{j}^{th}\ OR\ RB_{i}<RB_{total}\)then
11: For All SBSs **Check** if \(\tilde{i}==SBS\)
12:if\(RB_{\tilde{i}}<RB_{total}\)then
13:\(y_{\tilde{i},j}^{k}=1\), increment values of \(M_{j}\), \(N_{\tilde{i}}\), \(RB_{\tilde{i}}\)
14:elseif\(d_{i^{\prime},j}<d_{w}^{th}\) AND \(L_{i^{\prime}}>0\)then
15:\(z_{i^{\prime},j}=1\)
16: Provide rate according to its data rate demand
17:else
18:if\(\Delta_{\tilde{i}}>0\)then
19:\(p_{i,j}^{k}=\Delta_{\tilde{i}}/2\)
20:endif
21:endif
22: Update powers allocation
23: Calculate \(R_{j}^{o}\)
24:endif
25:endfor
26:endwhile
27: Overall Rate is \(\sum_{j}R_{j}^{o}\)
```  
**Algorithm 2** Sequential Offloading Rate Enhancement (SORE)
# Current-Feedback Instrumentation Amplifier Using Dual-Chopper Fill-In Technique
## 3 Measurement Results  
A die photograph of the proposed CFIA is shown in Figure 13. The circuit was fabricated using a 0.18 \(\upmu\)m CMOS process, and the area of the proposed chopper fill-in CFIA circuit is 455 \(\times\) 880 \(\upmu\)m.  
Figure 14 depicts the test board and measurement environment used to evaluate the performance of the proposed CFIA using a printed circuit board.  
The transient result of the proposed CFIA is shown in Figure 15. The proposed CFIA had an ideal gain of 51 V/V. As shown in Figure 15a, an input sine signal of 30 mV\({}_{\text{peak-to-peak}}\) and 1 kHz was amplified to a 1.6-V\({}_{\text{peak-to-peak}}\) sine signal. The chopper clocks of channels 1 and 2 have a 1/4 phase difference, as shown in Figure 15b, and the proposed circuit uses a chopper frequency of 18 kHz.  
Figure 16a represents the measurement results of the proposed CFIA transfer function. The transfer function has an average DC gain of 33.8 dB (49.2 V/V) and a 3-dB frequency of 12 kHz at 30.9 dB, resulting in a gain bandwidth (GBW) of 420 kHz. Figure 16b shows the CMRR measurement results of the proposed CFIA. The average CMRR from 1 Hz to 100 Hz is 100.8 dB.  
Figure 17a presents the measured input-referred noise of the proposed CFIA; at 1 Hz, its value was 53.3 nV/\(\surd\)Hz. For the IMD tone amplitude measurement, 1 kHz and 10 mV sine inputs were used. As shown in Figure 17b, the measured amplitude of the IMD tone a twice the chopper frequency of 36 kHz with a chopper frequency of 18 kHz is \(-\)70 dB.  
Figure 14: Measurement environment of the proposed CFIA with test board.  
Figure 13: Die photograph of the proposed CFIA.  
The measured output and input offset of the proposed CFIA is shown in Figure 18. The output offset measurement result shows that an average offset of 548 \(\upmu\)V is present in the output of the CFIA and has a peak-to-peak value of 451 \(\upmu\)V. Figure 18b shows the result of converting the measured output offset of the CFIA into input offset, which has an input average offset of 11.02 \(\upmu\)V.  
Figure 16: (a) Transfer function and (b) CMRR of the proposed CFIA.  
Figure 17: Experimental (a) input-referred noise and (b) amplitude spectrum of 1 kHz 10 mV sine input for IMD tone measurement.  
Figure 15: (a) Sine wave amplification and (b) chopper clock transient response measurement result for the proposed CFIA.  
The calculated noise efficiency factor (NEF) for the proposed CFIA is 4.4. Table 6 shows a performance summary and comparison of the proposed circuit with other previous works on low-noise CFIAs.
## 4 Analysis of Optimization Calculation and Results
### Optimization Scheme and Result Analysis  
#### 4.2.1 Optimization Scheme  
In order to reasonably evaluate the optimization results of the bi-level multi-objective model, calculations of single-level programming optimization and bi-level programming operation collaborative optimization for the MCS were carried out according to the improved NSGA-III. Then, the optimization results of the two schemes were compared.  
Scheme 1: Multi-objective optimization of single-level programming with the upper programming optimization model;  
Scheme 2: Bi-level programming operation collaborative multi-objective optimization with the upper level programming optimization model and lower-level operation optimization model combined.