esa-sceva/satcom-chunk-collection huggingface dataset:
# Green Small Cell Operation of Ultra-Dense Networks Using Device Assistance +
## 4 Online Algorithm: DANCE
### Feedback from UE  
We assume that UEs can send feedback messages as shown in Figure 1. In the SCC, the collected feedback messages constitute the feedback matrix \(\mathcal{A}=[a_{i,j}]\in\mathbb{R}^{I\times J}\). The feedback message can contain merely the connectivity information when using only one bit or can have more detailed information of connectivity, such as the maximum achievable data rate or appropriate adaptive modulation and coding (AMC) when using multiple bits. Thus, \(\mathcal{A}\) has two variations: a matrix \(\mathcal{A}^{(1)}\) is when one-bit feedback is used, and \(\mathcal{A}^{(N)}\) is when \(N\)-bit feedback is used. An element of a matrix, \(a_{i,j}^{(1)}\in\mathcal{A}^{(1)}\) or \(a_{i,j}^{(N)}\in\mathcal{A}^{(N)}\), means that the feedback message from UE \(i\) to AP \(j\) is either one-bit or \(N\)-bit. If a one-bit message is used, \(a_{i,j}^{(1)}\in\{0,1\}\) represents only the connectivity. It is assumed that connectivity exists if \(SINR_{i,j}(\mathbf{x})\) is greater than some threshold. If an \(N\)-bit message is used, \(a_{i,j}^{(N)}\) represents the achievable data rate or the quantized level of AMC. Examples of the UE feedbacks are illustrated in Table 1. We assume that the achievable data rate can be represented by using multiple \(N\) bits. In our study, \(N\)-bit message implies the achievable data rate. A study about the number of bits considering the tradeoff between performance and feedback overhead is beyond the scope of this paper and remains as a future work. However, when comparing to the BP algorithm where the repeated message exchanging is required, the DANCE algorithms only require one-time feedback messages, which significantly suppresses overall signaling overhead.
## 6 Conclusions  
The SEM and LST tests were conducted to explore the effects of freeze-thaw action on the particle characteristics of saturated silt deposited in water environments. Several different impact factors were considered during sample reconstitution and freeze-thaw processes. The conclusions could be drawn as follows:  
1. Different for the only sand particle crushing in conventional soil shearing, freeze-thaw action could probably induce the particles' (60-200 \(\upmu\)m) breakage, and also affect the clay particles' (less than 5 \(\upmu\)m) aggregation and form flocculent structure. The diameters of flocculent structures and broken particles range from 5-60 \(\upmu\)m. With continuous freeze-thaw cycles, this phenomenon could be more obvious. Freeze-thaw action could hardly influence the shape of particles smaller than 40 \(\upmu\)m, and could  
Figure 22: Schematic diagram of soil structure under freeze–thaw action. **(a)** Unfrozen silt; **(b)** silt after initial freeze–thaw cycle; **(c)** silt after long-term freeze–thaw cycles; **(d)** unfrozen sand; **(e)** sand after initial freeze–thaw cycle; **(f)** sand after long-term freeze–thaw cycles.  
make the shape of particles larger than 40 \(\upmu\)m more spherical (especially larger than 100 \(\upmu\)m).
2. Two parameters of particle size index and particle shape index are defined and specifically improved to quantitively evaluate the particle characteristics of silt under freeze-thaw action. They both hyperbolically increase with freeze-thaw cycles. Within 100 times, the influence degree of single freeze-thaw cycle on the particle size/shape of silt becomes smaller and gets more stable.
3. Greater effective confining pressure, lower freezing temperature, greater compaction degree, and higher fine content could aggravate the influence of freeze-thaw cycles on particles' size and particles' shape for silt to a certain extent. From another aspect, the durability of pavements under freeze-thaw cycles could be comprehensively evaluated and controlled by these impact factors.
4. From a perspective of freeze-thaw mechanism on different soil particles, two micro-structural evolution modes of particle characteristics under freeze-thaw action from initial freeze-thaw cycle to long-term freeze-thaw cycles have been inferred. The main distinction is the role of fine aggregate, especially for clay particles.  
Conceptualization, J.Z. and Z.L.; methodology, J.Z. and Z.L.; validation, J.Z. and W.P.; formal analysis, Z.L.; investigation, W.P.; resources, J.Z. and Z.L.; data curation, Z.L.; writing--original draft preparation, Z.L.; writing--review and editing, Z.L.; visualization, J.Z. and Z.L.; supervision, W.P.; project administration, J.Z.; funding acquisition, J.Z. All authors have read and agreed to the published version of the manuscript.  
The research work herein was supported by National Natural Science Foundation of China (No. 41702299) and Foundation of State Key Laboratory of Frozen Soil Engineering (No. SKLFSE201916).  
The authors declare no conflict of interest.
# Sound Source Localization Indoors Based on Two-Level Reference Points Matching
## 3 Two-Level Matching Method for Adjacent RPs Searching  
Usually, empty rooms, halls, corridors or other scenes with open space or few obstacles can be considered to be the typical simple scene for SSL. At the same time, home and office environments, where the positioning space may be separated into relatively independent regions by obstacles such as furniture and walls, etc., can be considered to be the complex scene.  
The sound field characteristics of simple and complex scenes are both analysed through the pairwise correlation of the RPs in the positioning service area. As shown in Figure 2(a1), 72 RPs are uniformly distributed in the square positioning area without obstacles, and as Figure 2(a2) shows, the correlation coefficient of each of the two RPs obviously decreases with the increase in the distance. Take the ratio of correlation coefficient beyond 0.6 into consideration, and as Figure 2(a3) shows, when the distance is within 1.5 m, most cross-correlation values of two RPs are relatively high. However, when the distance is more than 1.5 m, the ratio of the correlation value beyond 0.6 will decrease rapidly along with increases in the distance. In the global scope of the simple scene, the correlation between the RPs is relatively high in the small scope area and will decrease rapidly along with the increase in the distance.  
As shown in Figure 2(b1), 64 RPs are distributed in the square positioning area with 4 desks. According to Figure 2(b2), when the physical distance of the RPs is within 1.5 m, most of the according correlation coefficient can still reach 0.6, which is better than the simple scene with the same distance. However, as Figure 2(b3) shows, when the physical distance is beyond 2 m, the ratio of the correlation value beyond 0.6 shows obvious fluctuations. This is because the correlation coefficient within each sub-positioning service area separated by the physical plane becomes stronger, and the correlation coefficient between different locations becomes weaker.
# Light-Controlled Microbots in Biomedical Application: A Review
## 5 Imaging
### Radiography Imaging  
Radiography imaging is a technique that refers to a variety of studies that need the use of X-ray, gamma-ray, or any other ionizing radiation technology to visualize the internal organs of the body. Radiography has been used for a long time in medical applications, but it is also used in manufacturing industries. In industry, radiography is used to examine the integrity of the specimen and internal structures. All of these radiations are basically electromagnetic radiation. The wavelength determines the difference between distinct types of electromagnetic radiation. X-ray and gamma-ray offer the shortest workable wavelengths, which allows them to penetrate or travel through different materials, including metals. This kind of imaging can be used to visualize microbots in the absence of biological substances. Radiation sometimes makes it incompatible with working with biological samples for an extended period of time.
# Topology and Control Strategy of PV MVDC Grid-Connected Converter with LVRT Capability
## 3 Control Strategy of the Converter
### Current Control Strategy with Active Damping
#### 3.2.1 Model of Current Closed Loop with LC Filter  
The current closed loop of the converter with the LC filter is shown in Figure 9. \(I_{oref}\) is the output current reference. It is generated by the external voltage loop. \(i_{o}(s)\) is the output current sample signal, the err signal between \(I_{oref}\) and \(i_{o}(s)\) is sent to the output current PI regulator \(G_{i}(s)\). The duty ratio \(d(s)\) is the result of the PI regulator. The difference of \(i_{2}(s)\) and \(i_{o}(s)\) is capacitor current \(i_{c}(s)\). \(v_{g}(s)\) is the grid voltage. The difference of \(v_{c}(s)\) and \(v_{g}(s)\) is output inductor voltage \(v_{Lo}(s)\).  
According to Figure 10, the small-signal transfer function from the output of the current compensation loop to the voltage of the current sampling resistor can be obtained as:  
\[V_{o}\left(1-\frac{sL_{in}}{D^{\prime}R}\right)\hat{d}(s)\cdot\frac{1}{D^{ \prime}}=\hat{i}_{2}(s)\frac{L_{in}s}{D^{\prime 2}} \tag{30}\]  
where \(D^{\prime}=\frac{N}{2(1-D)}\).  
Equation (30) is sorted out to be:  
\[G_{id}(s)=\frac{\hat{i}_{2}(s)}{\hat{d}(s)}=\left(1-\frac{sL_{in}}{D^{\prime}R }\right)\frac{V_{o}D^{\prime}}{sL_{in}}\approx\frac{V_{in}}{sL_{in}} \tag{31}\]  
where \(G_{id}(s)\) is the transfer function of output current to duty cycle.  
Figure 10: Small-signal model in boost mode.  
Figure 9: Model of current closed loop with LC filter.  
Figure 11 is the small-signal model in buck mode, the transfer function of \(G_{id}(s)\) is given as:  
\[G_{id}(s)=\frac{\hat{\tilde{t}_{2}}(s)}{\hat{d}(s)}=\frac{M^{2}R_{e}L_{lk}}{M^{2 }R_{e}+L_{lk}}\frac{2(1-M)V_{in}}{DMR_{e}L_{lk}s} \tag{32}\]  
where \(R_{e}=\frac{2L_{lk}}{D^{2}T_{s}}\).  
\[G_{id}(s)\approx\frac{2(1-M)V_{in}}{DMR_{e}s}=\frac{(1-M)DV_{in}}{ML_{lk}f_{s }s} \tag{33}\]  
Formulae (31) and (33) are the transfer function of boost and buck, respectively. We can find that the two controlled objects are integral links or approximate integral links.  
The current loop gain expression in Figure 9 is obtained in boost mode as:  
\[T_{A}(s)=\frac{G_{i}(s)G_{id}(s)}{L_{o}\text{C}_{o}s^{2}+1}=\frac{V_{in}}{sL_{ in}}\frac{G_{i}(s)}{L_{o}\text{C}_{o}s^{2}+1}=\frac{V_{in}}{sL_{o}\text{C}_{o}L_{in}} \frac{G_{i}(s)}{s^{2}+\omega_{r}{}^{2}} \tag{34}\]  
\[\omega_{r}=\sqrt{\frac{1}{L_{o}\text{C}_{o}}} \tag{35}\]  
\(\omega_{r}\) is the resonance frequency. It can be seen from Formula (34) that there is a resonance point in the transfer function and a \(-180^{\circ}\) phase jump occurs at the same time, which causes the converter to be unstable, as shown in Figure 12.
## 2 Methodology
### Numerical Model Validation  
In a simulation study, the validation of the simulation model was the first step of the study to be conducted, one of the most important stages of the work. It verifies the veracity of the results. In the literature, one can find several methods allowing a new model of the simulation to be validated. In our case study, to compare the measured and simulated data, the coefficient of variation or the square root error (RMSE) and the mean bias error (MBE) were calculated, as it is indicated by ASHRAE in its guide14 [28]. In this report, it is indicated that a simulation model can be identified as validated if it meets the following conditions:  
* Hourly MBE between \(\pm 10\%\) and hourly RMSE less than \(30\%\).
* Monthly MBE between \(\pm 5\%\) and monthly RMSE less than \(15\%\).  
The different equations used as a basis for error calculations are mentioned in the ASHRAE guide. In this study, hourly temperature and monthly electricity data were used as a sample obtaining an MBE value of \(8\%\) and an RMSE value of \(5\%\), where, once compared to the values suggested in the ASHRAE14 directive [28] of \(\pm 10\%\) (case of MBE) and \(\pm 30\%\) (case of RMSE), it can be concluded that our simulation is within the range recommended by ASHRAE. In this case, the model used in this research is considered to be validated on the basis of hourly temperature data. Experimental data were collected in 2019.  
In addition, it is also necessary to compare the monthly energy values simulated and measured in these offices. In this sense, the MBE and RMSE values were \(-4\%\) and around \(7\%\), respectively. In the ASHRAE 14 guideline, the recommended monthly MBE limit was between \(\pm 5\%\) and, for RMSE, between \(\pm 15\%\). By consequence, our found values lie within these intervals. This also means that the new model can be validated with the monthly energy data. Some experimental and simulation data are shown in Figure 5.  
In summary, it was concluded that the simulation data was validated with hourly temperature and monthly values of electricity and, now, the new simulation model used in this study was validated.
# Onboard Processing in Satellite Communications Using AI Accelerators
## 2 Onboard AI Processing  
AI techniques for onboard processing must go through two phases: training and inference. Initially, the AI model undergoes the training phase. The objective is to find the optimal model parameters to predict the satellite configuration for the system conditions. Then, the trained model is obtained and used to indicate the system parameters depending on the input data. We propose an architecture (Figure 1) based on training the offline ML model with a training database that describes the system behavior. A model that manages the satellite could be obtained according to the system conditions. The main advantage of this architecture is that onboard processing times and required power consumption are reduced, firstly because the model has been previously trained. However, this architecture depends on the training data and the models used.  
Not all use cases are implementable onboard the satellite. Therefore, in Table 1, we present the analysis and scoring of the KPIs for the applicability of onboard AI implementation use cases. Each use case should be evaluated accordingly.
# Optimal Algorithms for Sorting Permutations with Brooms
## 4 Sorting Permutations with a Double Broom  
A novel polynomial algorithm \(A_{db}\) is designed in this section for optimally sorting permutations using a double broom. A double broom is a double star with their central vertices joined with a path (also known as a _stem_) [24]. Suppose double broom has a total of \(n\) vertices, \(v_{1},v_{2},\ldots,v_{i}\) on the left star, \(v_{i+1},\ldots,v_{j}\) residing on the path (or stem) and \(v_{j+1},\ldots,v_{n}\) on the right star. Note that we consider the central vertices \(v_{i+1}\) and \(v_{j}\) of the stars as path vertices of the double broom.  
Let \(S_{L}\) and \(S_{R}\) be the left and right stars respectively, with corresponding center vertices \(C_{L}\) and \(C_{R}\). Let \(S_{min}^{R}\) and \(S_{min}^{L}\) be the closest star marker residing on the path to \(S_{R}\) and \(S_{L}\) respectively and \(P_{max}\) be the maximum valued unhomed path marker.  
Our algorithm \(A_{db}\), that transforms a given input configuration to a sorted configuration, is as follows:  
1. While \(\exists\) a \(S_{min}^{R}\) marker, efficiently home \(S_{min}^{R}\);
2. While \(\exists\) a \(S_{min}^{L}\) marker, efficiently home \(S_{min}^{L}\);
3. (At this point, only the stem is left) While \(\exists\) a \(P_{max}\) marker, efficiently home \(P_{max}\);
4. Efficiently home the markers of \(S_{R}\) and \(S_{L}\).  
Note that any vertex to the right of \(C_{L}\) (inclusive) will be considered as a path vertex from the perspective of a marker residing in \(S_{L}\). Similarly, any marker to the left of \(C_{R}\) (including) will be a path vertex for markers in \(S_{R}\).  
Our algorithm places each marker at its respective vertex in 10 swaps (refer Figure 5). It is also noteworthy to mention that the happy leaves are swapped in the process, hence abiding to the Happy Leaves conjecture. \(A_{db}\) obeys following interesting properties which we use to analyse the algorithm in the next section.  
1. Swap of two star markers belonging to a same star will never take place on a path edge;
2. Stem marker moves right to its home, swapping smaller markers to the left.
# Parametric Analysis Towards the Design of Micro-Scale Wind Turbines: A Machine Learning Approach
## 3 Results and Discussion
### Tangential Force  
Similarly, the models were used to predict the tangential load midspan of the blade. This tangential force, besides being a structural aspect of design for blade safety and edgewise deflections, is also a key parameter to calculate the generated torque on the rotor which is later used to predict the power output. Table 4 shows the results of the different ML models based on the 70% training set and 30% testing set.  
Like the power coefficient and normal force, ensemble models come out on top of other models in terms of accuracy. This time, the Random Forest Regressor has exceeded the accuracy of Decision Trees by 0.8%. The Random Forest could predict the tangential force with an accuracy of 98.4%, which is also satisfactory and could be used to effectively design a wind turbine structurally and in terms of power with high accuracy based only on blade design parameters and wind velocity.  
For the tangential force as well, the linear models which are the most time efficient could give an accuracy of about 72.2%. This could also be used for preliminary structural design provided that a sufficient safety factor is used. The linear regression model resulted in the formula shown in Equation (9).  
\[\begin{array}{l}F_{T}=-0.46-9.18\times 10^{-5}\theta+6.81\times 10^{-2}V_{w}-1.3 6\times 10^{-4}\beta\\ -51.22\times 10^{-2}T.R.+2.27\times 10^{-2}\ C_{max,P}+2.59\times 10^{-2}\ C_{max,pos} \\ +1.09\times 10^{-2}T_{max,P}\end{array} \tag{9}\]  
The predicted values are plotted against the actual tangential force for the testing set of the data; the residuals are also plotted in density distribution format, shown in Figure 9.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**ML Model** & **R2** & **RMSE** & **Max Error** & **MAE** & **Training Time (s)** \\ \hline Random & & & & & \\ Forest & 0.984 & 0.034 & 0.446 & 0.013 & 0.359 \\ Regression & & & & & \\ Decision Tree & 0.976 & 0.042 & 0.777 & 0.009 & 0.000 \\ Regression & & & &  
Since the accuracy is slightly lower compared to the accuracy of predicting \(\text{C}_{\text{p}}\) and normal force values, the number of points where the predicted value is far from the actual one has increased. However, this is not the general case. The overall performance of the model is satisfactory for accurate predictions, where the accuracy of prediction is 98.4%. The residuals are normally distributed, so the predictions of tangential force can be obtained by cross-validating the training set. Simply repeating the predictions many times and taking the most biased value will result in the most accurate prediction.
## 3 Results and Discussion
### Calorimetric Analysis  
Distillation stillage has a high heating value (HHV) of 20.544 MJ/kg, which is higher than the HHV of wine pomace at 19.741 MJ/kg (Table 5). The HHV measures the total energy content of the fuel, including the energy contained in the water vapor formed during combustion [44; 45]. The higher HHV of distillation stillage indicates that it has a greater overall energy content compared to wine pomace.  
Similarly, the low heating value (LHV) of distillation stillage is 19.109 MJ/kg, compared to the LHV of wine pomace at 18.514 MJ/kg. The LHV represents the net energy  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Sample** & **N (\%)** & **C (\%)** & **H (\%)** & **S (\%)** & **O (\%)** \\ \hline Distillation stillage & 2.57 \(\pm\) 0.301 & 54.928 \(\pm\) 0.531 & 6.725 \(\pm\) 0.251 & 0.153 \(\pm\) 0.032 & 31.862 \(\pm\) 0.948 \\ Wine pomace & 2.494 \(\pm\) 0.055 & 52.099 \(\pm\) 0.469 & 5.669 \(\pm\) 0.105 & 0.043 \(\pm\) 0.012 & 33.337 \(\pm\) 0.541 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Elemental Composition of distillation stillage and wine pomace (dry basis).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Sample** & **HHV (MJ/kg)** & **LHV (MJ/kg)** \\ \hline Distillation stillage & 20.544 \(\pm\) 0.037 & 19.109 \(\pm\) 0.024 \\ Wine pomace & 19.741 \(\pm\) 0.025 & 18.514 \(\pm\) 0.032 \\ \hline HHV—High heating value, LHV—Low heating value. & \\ \hline \hline \end{tabular}
\end{table}
Table 5: HHV and LHV of distillation stillage and wine pomace (air-dried basis).  
content of the fuel, excluding the energy lost as latent heat in water vapor [44]. The higher LHV of distillation stillage suggests that it provides more usable energy upon combustion than wine pomace. The higher values for both HHV and LHV in distillation stillage compared to wine pomace suggest that distillation stillage has a superior energy recovery potential [44; 45].  
The t-test results from the calorimetric analysis reveal that statistically significant differences are found in both the HHV (\(p\)-value equal to 0.000135) and the LHV (\(p\)-value equal to 0.0000249). This suggests that the two materials have distinct differences in their calorific values, which are unlikely to be due to random variation.  
Overall, distillation stillage offers a higher energy yield than wine pomace, as indicated by its higher HHV and LHV. This makes distillation stillage a more efficient choice for energy recovery processes, providing more energy per kilogram of biomass [44]. Wine pomace, while slightly lower in energy content, still presents a viable option for energy recovery, but with a lower energy yield compared to distillation stillage.
## 2 Materials and Methods
### Machine Learning Approaches
#### 2.5.1 Principal Component Analysis  
PCA is a popular approach for translating a high-dimensional set of features into a low-dimensional set. PCA aims for the lowest-dimensional representation of the data while retaining as much information and variance as possible [21]. PCA turns the original space produced by the original dataset into a new space that is a linear combination of the dataset dimensions in order to capture as much variability as feasible. Each dimension developed is referred to as a principal component. The new data coordinates are referred to as \"scores\". Each principal component contributes to the variation in the original data set. The direction of the first principal component is the direction in which the data varies the most.  
The \"loadings\" represent the contribution of each original dimension to the new dimension. The closer the loading value is to +1 (or \(-1\)), the higher the positive (or negative) impact of the feature on this principal component. Loadings are represented by arrows that are at an angle and have a specific length. The angle represents a specific feature's contribution in the direction of the principal component, where it contributes. The length of the arrow represents the strength of the feature's contribution in that direction. A loading plot depicts the strength with which each attribute influences a major component.  
The \"biplot\" is  
of the variability, the successive components explain a moderate portion, and the final components explain only a small portion of the total variability. In this study, the PCA analysis was carried out using Python v. 3.10.8. The package \"matplotlib\" was used to construct statistical plots, while the PCA analysis was implemented using the libraries \"seaborn\", \"sklearn\", and \"bioinfokit\".