esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review of Small-Scale Vapor Compression Refrigeration Technologies
## 2 Small-Scale Refrigeration Technologies
### Active Technologies
#### 2.2.8 Joule-Thomson (J-T) Cooling  
The J-T effect describes the change in temperature of a gas as it is throttled through a narrow orifice, resulting in a pressure drop at constant enthalpy due to nearly adiabatic conditions. For many gases (although not all), temperature decreases during throttling. Based on this principle, cooling systems have been developed where a high-pressure gas from a compressor or a pressurized container cools down as it circulates through a capillary tube or a porous plug. In some configurations and to increase system efficiency, the cooled gas from the J-T valve is rerouted to a counterflow heat exchanger to precool the incoming high-pressure gas, and then the refrigerant is circulated back to the compressor for repressurization, as can be seen in Figure 9. Depending on the refrigerant composition and pressure level, the resulting cold refrigerant can be a pure gas or a mixture of gas and liquid. The choice of the working fluid depends on the temperature and cooling capacity needs of the specific application. Typical working fluids used in these systems are He, H\({}_{2}\), CO\({}_{2}\), air, and N\({}_{2}\).  
J-T systems often operate at very high pressure (200-500 atm), considerably higher than VCR systems. Therefore, open-cycle configurations using a high-pressure gas cylinder are often used. Cryogenic temperatures can be achieved by using mixed gas refrigerants.  
Figure 8: Schematic of Stirling cooling technology.  
Figure 9: Schematic of a basic closed J-T cycle.  
Small-scale applications of J-T coolers include electronics cooling, cooling of biological samples at laboratories, and cryogenic cooling for spacecraft equipment and space probes, among others. An important feature of this technology is the potential for reaching very low temperatures, even though cooling capacity may be small (a few watts). For example, some military J-T coolers have refrigeration capacity in the range of 0.1-0.5 W at 80 K, while some J-T cryocoolers for spacecraft have 2.5 W capacity at 80 K [41].
# An Effective Off-Grid DOA Estimation Algorithm Using Difference Coarrays with Limited Snapshots
## 5 Simulation Results
### Fewer Sources than Sensors Case for NLA  
The antenna array configuration consists of 8 elements arranged in a nested structure, as depicted in Figure 3. Filled circles denote the selected elements, while crosses denote the discarded elements. The corresponding coarray is a ULA with 39 elements.  
Figure 1: RMSE of the four method versus SNR with 10 snapshots.  
Figure 3: The 8-antenna nested array.  
Figure 2: RMSE of the four methods versus SNR with 100 snapshots.  
To assess estimation accuracy, Figures 4 and 5 present the RMSE of the search-based, EPUMA, SS-MUSIC, and the proposed methods, plotted against the SNR for an NLA configuration where the number of sources is less than the number of sensors, utilizing different numbers of snapshots. We employed the same snapshot quantity as specified in the preceding simulation scenario. The source angles were set as \(\theta_{1}=0.5^{\circ},\theta_{2}=30.5^{\circ}\), \(\theta_{3}=60.5^{\circ}\). For the search-based method, the same search angle was utilized as in Figure 1. It was found that the proposed approach exhibited the best estimation performance, whereas the RMSE of the search-based and SS-MUSIC methods remained constant at a high SNR. The proposed method showed consistent performance advantages over EPUMA, particularly under low-SNR conditions. In summary, it can be seen from example A and example B that the proposed method is applicable to both ULA and NLA.  
than the benchmarks, and the other was attaining CRB when SNR exceeded a certain threshold, demonstrating optimal estimation capability. Overall, the effectiveness of the proposed method for off-grid DOA estimation with NLA in the case of fewer sources than sensors and more sources than sensors was validated, as shown in both example B and example C.
## 3 QCCS Statistical Basis
### Thematic Accuracy Quality Control  
In order to fix the terms to be used, for a particular category \(\Gamma\), we call the class that gives its name the \"main class\", and the \"rest of classes\" are the remaining initial classes in the confusion matrix or new ones after the merging of one or more initial classes.  
We have to put the rest of the classes in an assumed order, which we can establish freely. As an example, we come back to Table 1. In the quality requirements for the _Grassland_ category, the main class is _Grassland_ and, as stated at the end of Section 2, a confusion with _Non-vegetated_ is considered more important than a confusion with both _Woodland_ and _Water_. Therefore, the second class is _Non-vegetated_, and the third class is the new class _Woodland + Water_. So, we work with a multinomial distribution with three classes, \(X=(X_{1},X_{2},X_{3})\) where \(X_{1}\) represents the number of items in the main class that are correctly classified, \(X_{2}\) counts the number of confusions between the main class and the second class, and \(X_{3}\) stands for the number of misclassifications between the main class and the third class.  
According to the previous notation, quality control involves establishing the following quality conditions for each category:  
* The minimum proportion of well-classified items in the main class;
* The maximum possible \(k-1\) misclassification proportions between the main class and the rest of the classes.  
At this point, we have to define when the classification results of a category agree with the specifications given previously. The criteria adopted are that the specifications are not fulfilled when:  
1. The number of correct items in the main class is lower than those expected, or
2. If the number of correct items in the main class is equal to those expected, the number of misclassifications with the second class is greater than the expected, or
3. If the number of correct items and the number of misclassifications with the second class is equal to those expected, the number of misclassifications with the third class is greater than that expected, and so on.  
As a consequence, thematic accuracy quality control implies the proposal of a test for testing the following null hypothesis:  
\[\begin{array}{c}\mathbb{H}_{0}\text{: The classification results agree with the set of}\\ \text{specifications stated in the quality control for each category.}\end{array} \tag{3}\]  
Assuming the QCCS is an independent set of \(k\) multinomial distributions, we can propose 1, 2, or even \(k\) different null hypotheses (one for each category/column), and hence, we can perform up to \(k\) independent tests. In the case of desiring a global hypothesis test, the Bonferroni correction is used. Therefore, this approach allows a more flexible way of understanding and testing thematic accuracy quality control than the classical methods based on the confusion matrix.
## 2 Methodology
### Overview of the Proposed Method  
Figure 4 illustrates the flowchart of the proposed damage identification method, which includes four steps:  
1. Data acquisition and preprocessing: accelerations under different damage scenarios are obtained and normalized.
2. Building database: accelerations of each measuring point obtained under the same damage scenario are stacked into a data matrix with dimensions \(m\times n\), where m is the number of measuring points and n is the sequence length. Then, the data matrix is divided into k blocks with dimensions \(k\times n\); consequently, the number of data samples under each damage scenario expands to k. After the database is built, it is divided into a training set and a validation set.
3. Model construction and training: the framework of the CNN-LSTM model is constructed, and the features extracted by CNN and LSTM are merged in the fully connected layer. Then, the training and validation sets are utilized to train and verify the model, and the model is optimized according to the training results to find a damage identification model with better performance.
4. Damage identification: accelerations under an unknown state are sent to the trained model to locate damage and predict the severity.  
Figure 3: Example of dropout: (**a**) standard feedforward network; (**b**) two hidden units are disconnected.
## 4 Timing and Carrier Frequency Synchronization through Preamble Detection
### Timing and Fractional CFO Estimation
\begin{array}{rl}R_{1,n_{0}}&=\sum\limits_{l=0}^{N/2-1}{(-1)^{l}\Big{(}(-1)^{l }x_{l}+l_{0}e^{2\pi f_{\rm NB}l}\Big{)}^{*}}\\ &\cdot\Big{(}x_{l}+l_{0}e^{2\pi f_{\rm NB}(l-N/2)}\Big{)}\\ &=\sum\limits_{l=0}^{N/2-1}\Big{(}|x_{l}|^{2}+l_{0}x_{l}^{*}e^{2\pi f_{\rm NB} (l-N/2)}\\ &\quad+(-1)^{l}I_{0}x_{l}e^{-2\pi f_{\rm NB}l}+(-1)^{l}I_{0}^{2}e^{-2\pi f_{ \rm NB}N/2}\Big{)}\\ &\approx\sum\limits_{l=0}^{N/2-1}|x_{l}|^{2}\end{array} . \tag{12}\]  
where the cross terms are eliminated after de-scrambling and sum averaging since the NBI and the preamble are non-coherent signals. From (12), importantly, it is observed that the NBI component \((-1)^{l}I_{0}^{2}e^{-2\pi f_{\rm NB}N/2}\) is eliminated after sum averaging due to the scrambling operation. Therefore, it is observed that the SAC result of \(R_{1,n}\) reaches its maximum peak value at the desired timing position \(n_{0}\). Similarly, the SAC branches \(R_{2,n}\) and \(R_{3,n}\) in (7) and (8) at the desired correct timing position \(n_{0}\) are respectively given as  
\[R_{2,n_{0}}\approx-\sum\limits_{l=0}^{N/2-1}|x_{l}|^{2}, \tag{13}\]  
\[R_{3,n_{0}}\approx\sum\limits_{l=0}^{N/2-1}|x_{l}|^{2}. \tag{14}\]  
Hence, \(R_{2,n}\) and \(R_{3,n}\) also reach their maximum peak value at the desired timing position \(n_{0}\). Finally, the accumulated SAC correlation \(R_{c,n}\) as given by (9) is free from the NBI contamination and will also reach the maximum peak value at the desired timing position \(n_{0}\), so the detected timing position \(n_{0}\) estimated by (10) is accurately the desired timing position \(n_{0}\). Based on this analysis, the capability and robustness of the proposed timing method against the NBI is validated. Furthermore, the proposed synchronization method can be easily extended to multi-path channels and the NBI resistant mechanism as derived in (12) and (3) still effectively holds, which is proved in the Appendix A.  
Figure 4: Comparison for timing peaks of three methods.
## 5 Experiments and Results
### Experiments Setup  
This sub-section presents the environmental and experimental settings for all conducted experiments. Where they were implemented using the Matlab R2016b platform over Windows server 2012. In which, the random-access memory (RAM) is 64 GB, the processor is Intel(R) Xeon(R) CPU E5-2609 v4, while the speed of the two processors is 1.70 GHz.  
Initially, the proposed approach is evaluated using 10 folds cross-validation. Since the adopted SSA algorithm is used as wrapper-based feature selection; it used the KELM as a learning algorithm where its hyperparameters (C and \(\gamma\)) were optimized using SSA. The KELM is responsible for assessing the fitness of solutions; therefore, it returns the weighted-sum of (1-accuracy) and the ratio of features, as given by Equation (12).  
The SSA algorithm is a stochastic swarm-based algorithm as mentioned before; hence, diminishing the randomness effect of the algorithm requires executing it multiple times. Herein, (10) independent runs were performed with the maximum number of iterations is 50. In essence, the population size and the number of leaders are controlling parameters of the SSA that play a vital role in escalating the performance of the algorithm or degrade it if not set properly. The following subsections present a sensitivity analysis for selecting the best values of the leaders' ratio and the population size.
## 1 Introduction
[21] were conducted to investigate the feedback effect on CPR performance. Based on the outcomes from these studies, it was suggested that implementing CPR aided real-time feedback systems can improve chest compression technique. Figure 1: The Cardiopulmonary resuscitation (CPR) compressions techniques; (**a**) the Two-Finger (TF) method, and (**b**) the Two-Thumb (TT) method. Currently, no feedback device is available to guide learners through infant CPR performance compared to a number of adult CPR feedback device. This study presents a real-time feedback system to improve infant CPR performance by medical staff and laypersons using commercial infant manikin. The proposed system is based on an IR sensor to analyse the CPR performance obtained with no-feedback and with a real-time feedback system. The aim of this study is to investigate and improve the CPR performance of the rescuer when delivered during simulated infant CPR. The principal contributions of the proposed system are listed as follows: (1) Establishing background knowledge as necessary to be able to evaluate the effects of the real-time feedback system on CPR quality. (2) Propose a real-time interaction system based on an IR sensor to monitor and extract the CPR parameters and improve CPR performance in any environmental settings. (3) Propose developing a real-time feedback system to monitor and improve infant CPR performance for both medical staff and laypersons. (4) Analyze infant CPR performance with no-feedback and with a real-time feedback system and evaluate the CPR parameters delivered within the recommended target line as the main consideration. (5) Analysis of CPR performance based on the effect of using the TF and TT compression techniques and appraise the appropriate and achievable mechanism applied in infant populations for delivering high-quality infant CPR. This paper is structured as follows: Section 2 presents the materials and methods, including participants and experimental setup. Section 3 reports the experimental results of the proposed infant CPR feedback system with comparison to the recommended guideline [12; 13; 14] of chest compression parameters. Section 4 discusses the results. Finally, Section 5 concludes the work presented in this study.
## 4 Experimental Result of the Position Tracking System and Discussion
### DAA(Detection and Avoid) Experiment  
The UWB central frequency band used practically in the development product is 4492.8 MHz, which is included in the technical criteria of the existing frequency band and the changed frequency band. However, changes in radio frequency and UWB-related laws act as unexpected variables in development. The mean power density, including antenna absolute gain, is \(-\)70 dBm/MHz or less. IAT (Interference Avoidance Technology) reduces  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Min/Max**} & \multicolumn{2}{c}{**Measurement Value**} & \multirow{2}{*}{**Moving Average (mm)**} & \multirow{2}{*}{**Kalman Filter [7] (mm)**} & \multicolumn{2}{c}{**Proposed Extended**} \\  & & **(mm)** & & & & & **Kalman Filter (mm)** \\ \cline{2-7}  & **x** & **y** & **X** & **y** & **x** & **y** & **x** & **Y** \\ \hline Min. & 2687.5 & 2673.3 & 2691.2 & 2679.2 & 2693.4 & 2683.2 & 2696.6 & 2686.8 \\ \hline Max. & 2722.1 & 2723.2 & 2709.6 & 2710.9 & 2708.4 & 2709.4 & 2707.4 & 2705.2 \\ \hline l Max.\(-\)Min. l & 34.6 & 49.9 & 18.4 & 31.7 & 15.0 & 26.2 & 10.8 & 18.4 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Measurement data from sensor units and filtering results.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{2}{c}{**Axial Direction**} & \multicolumn{2}{c}{**Ref. Coordinates (mm)**} & \multicolumn{2}{c}{**Mean Value (mm)**} & \multicolumn{2}{c}{**Mean Error (mm)**} \\ \hline \multirow{2}{*}{x axis} & Min. & 2800 & 2760 & 40 \\ \cline{2-5}  & Max. & 3190 & 3191 & 41 \\ \hline \multirow{2}{*}{y axis} & Min. & 2710 & 2730 & 30 \\ \cline{2-5}  & Max. & 3090 & 3053 & 37 \\ \hline \multirow{2}{*}{z axis} & Min. & 1500 & 1321 & 179 \\ \cline{2-5}  & Max. & 1890 & 1985 & 95 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Measurement data of orthogonal reciprocating motion using testbed.  
the amount to less than \(-70\) dBm/MHz within two seconds and avoids it within 2 s if it detects signals from other radio stations greater than \(-61\) dBm during operation.  
Figure 10: Measurement results and error for the reference coordinate (2700,2700) using Moving Average filter (green line), Kalman filter [7] (blue line), and proposed EKF (red line) for (**a**) x-axis and (**b**) y-axis.  
Spectroscopic analysis shows that the BW (Band Width) was wider than the set 499.2 MHz, as well as signals in addition to BW. If signals were emitted from BW other than 3.735-4.8 GHz BW, the UWB radio frequency band was used by adjusting the register value of DW1000. After BW adjustment, we conducted a wire test and anaerobic radiation test through SMA connector wire. The final test conducted through a spectrum analyzer confirmed that it was within the frequency range in the regulation.
## 3 The Algorithm of the Initial Design
### Computational Parameters
#### 3.3.2. Computation of RMS (Root Mean Square) Current of Each Pole of the Machine  
The RMS of current for each pole can be calculated through Equation (5):  
\[I_{rmsp}=\frac{P_{out}}{(p/2)V_{out}cos\varphi} \tag{5}\]  
3.3. Computation of Fundamental Component Amplitude of the Induced Voltage in Each Winding Turn for the Proposed Design  
According to changes of \(k_{l}\) and \(K_{u}\) coefficients, the following modes can be considered for presented angles in Figure 4:  
\[\beta\geq(\theta_{g5}+\xi)/2\rightarrow\left\{\begin{array}{l}\xi\geq \theta_{g5}>\theta_{g3}>\theta_{g1}\\ \theta_{g5}>\xi\geq\theta_{g3}>\theta_{g1}\\ \theta_{g5}>\theta_{g3}>\xi\geq\theta_{g1}\\ \theta_{g5}>\theta_{g3}>\theta_{g1}>\xi\end{array}\right\}\]  
\[\beta<(\theta_{g5}+\xi)/2\rightarrow\left\{\begin{array}{l}\xi\geq\theta_{ g1}\\ \theta_{g1}>\xi\end{array}\right\}\]  
The steps in obtaining fundamental component amplitude of voltage for the first mode are explained here as an example:  
Step one: in this step, the flux linking all winding turns of each pole, which is generated by the permanent magnet, is calculated at different rotor positions (the following positions):  
1. The first position: the entire surface of the magnet across the E-shaped core (\(\varphi_{1}\) flux)
2. The second position: a rotor displacement of \(\theta_{g1}\) compared to the first position (\(\varphi_{2}\) flux)
3. The third position: a rotor displacement of \(\theta_{g3}\) compared to the first position (\(\varphi_{3}\) flux)
4. The fourth position: a rotor displacement of \(\theta_{g5}\) compared to the first position (\(\varphi_{4}\) flux).
5. The fifth position: a rotor displacement of \((\theta_{g5}+\xi)/2\) compared to the first position (\(\varphi_{5}\) flux)  
Step two: in this step, the voltages induced in each winding turn are calculated by derivation from flux diagram, considering rotor position (Equations (6)-(9)):  
\[e_{1}=\frac{4\pi f\times(\varphi_{1}-\varphi_{2})}{p\times\theta_{g1}} \tag{6}\]  
\[e_{2}=\frac{4\pi f\times(\varphi_{2}-\varphi_{3})}{p\times\left(\theta_{g3}- \theta_{g1}\right)} \tag{7}\]  
\[e_{3}=\frac{4\pi f\times(\varphi_{3}-\varphi_{4})}{p\times\left(\theta_{g5}- \theta_{g3}\right)} \tag{8}\]  
\[e_{4}=\frac{4\pi f\times(\varphi_{4}-\varphi_{5})}{p\times\left(\xi-\theta_{g5 }\right)} \tag{9}\]  
Step three: in this step, the fundamental component amplitude of the voltage induced in each winding turn for the proposed design is calculated using Fourier series (Equation (10)):  
\[E_{11 maxp}=\left(\frac{4}{\pi}\right)\times\left(\begin{array}{c}e_{1}\times \left(1-\cos((p\times\theta_{g1})/2))\right)+\\ e_{2}\times\left(\cos((p\times\theta_{g1})/2))-\cos(((p\times\theta_{g3})/2) )\right)+\\ e_{3}\times\left(\cos((p\times\theta_{g3})/2))-\cos(((p\times\theta_{g5})/2) )\right)+\\ e_{4}\times\left(\cos((p\times\theta_{g5})/2))\right)\end{array}\right) \tag{10}\]
# Narrowband Internet of Things (NB-IoT):
## 2 Narrowband-IoT Standard and Releases
### Release 15 Enhancements
#### 2.3.3 Small Cell Support  
To further improve the capacity as well as coverage, in Release 15, NB-IoT supports small cell deployments. The downlink power to be reused for NB-IoT small cells is specified in section 16.2.2 of TS 36.213 [44]. In general, NB-IoT UE is not allowed to transmit more power than the configured maximum power, even if the configured power is lower than UE's maximum capability. This is done to avoid interference.  
On the other hand, to extend the IoT connectivity especially in remote and rural areas for use cases such as agriculture, logistics, and environmental monitoring, NB-IoT is now able to support up to 100 km range. According to Ericsson, this could be achieved with a software upgrade only, without any changes in the existing NB-IoT hardware [45].
# Superpixel-Based Singular Spectrum Analysis for Effective Spatial-Spectral Feature Extraction
## 3 Results and Discussion
### Experimental Result and Analysis
#### 3.4.2 Results from the Pavia University Dataset  
Quantitative results are presented in Table 3. The proposed SP-SSA method still achieved higher classification accuracy and ranked first among all the compared methods, closely followed by the ASMGSSK algorithm. Also, in comparison to EPF, SCMK, R2 MK, 2D-SSA, MSuperPCA, and 2D-MSSA techniques, the average improvement of the proposed approach is over 4.41%, 3.64%, 2.09%, 2.37%, 1.3%, and 1.48%, respectively. For comparison, the top results in the tables are boldfaced. In Figure 12, different classification maps are shown, based on various testing methods applied to the PU dataset.  
According to Figure 12, the classification map for SVM still continues to remain noisy. Both EPF and 2D-SSA can generate a relatively smooth result; however, some significant regions remain undetected (e.g., the detailed areas). The superpixel-based methods (SCMK, R2 MK, ASMGSSK, and MSuperPCA) and SSA-based approach (2D-SSA and 2D-MSSA) offer significantly improved performance, but the proposed 2D-SSA method remains the most promising approach as it outperforms all the compared algorithms.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline
**Class** & **Samples** & **SVM** & **EPF** & **SCMK** & **R2 MK** & **ASMGSSK** & **MsuperPCA** & **2 DSSA** & **2 DMSSA** & **SP-SSA** \\ \hline Asphalt & 6631 & 90.79 & 96.33 & 94.01 & 95.43 & **99.23** & 97.19 & 97.12 & 97.61 & 98.94 \\ Meadows & 18,649 & 99.54 & 98.06 & 99.14 & 99.78 & 99.76 & 99.83 & 99.52 & 99.45 & **99.85** \\ Gravel & 2099 & 53.12 & 80.86 & 83.69 & 90.81 & 91.85 & 89.73 & 89.35 & 90.53 & **94.17** \\ Trees & 3064 & 81.9 & 83.81 & 87.34 & 93.8 & 98.22 & 95.53 & 96.24 & 95.76 & **99** \\ Painted metal sheets & 1345 & 93.04 & **100** & 99.17 & **100** & 99.17 & **100** & 99.47 & 99.46 & **100** \\ Bare Soil & 5029 & 89.64 & 94.15 & 96.46 & 96.81 & 99.6 & 98.01 & 95.76 & 98.52 & **99.74** \\ Bitumen & 1330 & 55.48 & 89.74 & 86.86 & 91.71 & **99.42** & 94.54 & 85.97 & 97.21 & 98.85 \\ Self-Blocking Bricks & 3682 & 87.92 & 91.47 & 90.97 & 90.97 & 94.69 & 93.05 & 89.11 & 92.64 & **95.62** \\ Shadows & 947 & 90.75 & 90.19 & 97.12 & 98.17 & 98.59 & **98.89** & 95.69 & 88.67 & 98.76 \\ \hline
**OA:** & & 90.71 & 94.53 & 95.3 & 96.85 & 98.67 & 97.64 & 96.57 & 97.46 & **98.94** \\
**AA:** & & 82.47 & 91.62 & 92.75 & 95.28 & 97.84 & 96.31 & 94.25 & 95.54 & **98.33** \\
**K:** & & 87.57 & 92.73 & 93.76 & 95.82 & 98.23 & 96.87 & 95.44 & 96.63 & **98.6** \\ \hline \end{tabular}
\end{table}
Table 3: Classification results for the Pavia University Dataset with 2% training for SVM, EPF, SCMK, R2 MK, ASMGSSK, MsuperPCA, 2D-SSA, 2D-MSSA, and SP-SSA algorithms.  
Figure 11: (**a**) Ground Truth Image, Classification Maps of (**b**) SVM (**c**) EPF (**d**) SCMK (**e**) R2 MK (**f**) ASMGSSK (**g**) MsuperPCA (**h**) 2D-SSA (**i**) 2D-MSSA (**j**) SP-SSA for Indian Pines dataset.