esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Real-Building Case Study
### Missing Data Description of Selected Sensors
\[Z=\frac{R-\frac{2n_{1}n_{2}}{n_{1}+n_{2}}-1}{\sqrt{\frac{2n_{1}n_{2}(2n_{1}n_ {2}-n_{1}-n_{2})}{(n_{1}+n_{2})^{2}(n_{1}+n_{2}-1)}}}, \tag{2}\]  
The calculated Z-score indicates the randomness of a series of data: under a 10% significance level, the Z-score over 1.65 indicates non-randomness of data. The results of missing data randomness quantification by Z-score by sensor and time indicator are shown in Table 3. By comparing the calculated Z-score, missing data in sensors such as \"chws_control/chw_flow\" and \"peco_meter/elec_usage_chiller\" are more systematically missed, while sensors such as \"ahu1/htg_stm_vlv_1_3\" and \"ahu1/stat_press\" are less systematically missed. To compare the average of Z-scores among the time indicators, the missing mechanism seems less random and more correlated with hour of day. In other words, the missing data in most sensor are missed under some hourly pattern. It is worth mentioning that this paper will not study the truth of the mechanism of missing data in each sensor but only describe general randomness using Z-score. Moreover, the randomness test is not part of the framework, but it will help readers to better understand the characteristics of missing data for each sensor in the case study. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**No** & **Sensor Name** & **Number of Missing Data** & **Maximum Hole Size** \\ \hline
1 & peco\_meter/elec\_usage\_chiller & 466 & 2 \\
2 & chws\_control/chw\_flow & 512 & 2 \\
3 & chws\_control/chws\_temp & 439 & 1 \\
4 & chws\_control/delta\_press & 505 & 2 \\
5 & chws\_control/cw\_delta\_press & 236 & 2 \\
6 & chws\_control/cwr\_temp & 446 & 1 \\
7 & chws\_control/cw\_vlv\_fdbk & 531 & 3 \\
8 & ct\_control/ct\_fan\_status/trend\_log & 426 & 1 \\
9 & ahu1/ra\_temp/trend\_log & 466 & 1 \\
10 & ahu1/econ & 269 & 2 \\
11 & ahu1/chw\_valve & 315 & 3 \\
12 & ahu1/ra\_humidity & 446 & 2 \\
13 & ahu1/sfan\_amps & 334 & 2 \\
14 & ahu1/htg\_stm\_vlv\_1\_3 & 118 & 2 \\
15 & ahu1/stat\_press & 118 & 1 \\
16 & ahu1/pht\_temp & 236 & 2 \\
17 & vv-1-b-1/air\_flow/flow\_input & 216 & 3 \\
18 & vv-1-b-1/da\_temp/trend\_log & 571 & 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Missing data description of the selected sensors.
# Correlation Mapping of Perfusion Patterns in Cutaneous Tissue
###### Abstract  
Perfusion patterns of cutaneous tissue represent a valuable source of information about the state of the patient's cardiovascular system and autonomic nervous system (ANS). This concept aims to observe the perfusion changes in the foot sole in two healthy individuals and two subjects affected by diabetes mellitus (DM). We use photoplethysmography imaging (PPGI) to monitor cutaneous perfusion changes. This method, in contrast to conventional contact photoplethysmography (PPG), allows the monitoring of skin perfusion with spatial distribution. We use a machine vision camera and an illumination system using the green light. To induce the perfusion changes, we perform an experiment in the form of a deep breathing test (DBT). The experiment consists of three stages, with the middle stage being the DBT. To evaluate spatial perfusion changes, we use a normalized measure of the correlation of PPGI signals with a reference PPG signal obtained from the foot's little toe. This method also increases the signal-to-noise ratio (SNR). Subjects with DM shows different patterns of tissue perfusion changes compared to healthy subjects. The DM subjects show increased perfusion after DBT compared to the pre-DBT state, whereas in healthy subjects, the tissue perfusion does not reach the level of the pre-DBT phase. This work can be considered as proof of concept in developing a non-contact and non-intrusive monitoring system that allows a different view of microcirculatory damage in patients with diabetes mellitus, focusing on its spatial distribution.  
correlation; deep breathing; diabetes mellitus; mapping; photoplethysmography imaging; perfusion
# Improved Adaptive Successive Cancellation List Decoding of Polar Codes
## 3 Improved Segmented CRC-Based Adaptive SCL Algorithm
### Improved Algorithm Based on Segmented Adaptive SCL
However, in the decoding of the second part, when \(L=L_{\max}\), the decoder directly outputs the final decoding result \(\hat{u}=\left[\hat{u}_{1}^{t_{1}},\hat{u}_{1}^{t_{2}}\right]\) if the decoding result of \(\hat{u}_{2}^{t_{2}}\) does not pass CRC. Figure 4: SAD-SCL decoding process. Step 4: Since there are some error bits in this decoding result, the SC algorithm is directly used for the decoding of the second part. Assume the result of the decoding is \(\hat{u}_{2}\), then the final output decoding result is \(\hat{u}=\left[\hat{u}_{1}^{t_{1}},\hat{u}_{2}\right]\). Considering the FER performance of the algorithm, the proposed algorithm can be further improved. If the first part of decoding fails to pass CRC with \(L=L_{max}\), then the whole codeword will be in error. When the decoding is terminated, the number of error frames can also be counted. Therefore, the termination of the second part of decoding will not lose FER performance. So, the second part of decoding is unnecessary in this situation. This decoding can be terminated to avoid wasting resources. This can reduce the complexity of decoding without loss of performance. The modified algorithm decoding process is shown in Figure 5.
# Experimental Development of Composite Bicycle Frame
## 3 Experimental Results and Discussion
### Structural Strength Test Results
#### 3.3.1 Influence of Frame Simplification  
In the first step, the influence of frame simplification from frame specimen 0 to a simple triangle in the case of frame specimens 1, 2, 4, 5, 6 and 7 was evaluated. The question was whether the standard ISO case (specimen 0), where the frame is attached to the ground using rear dropouts, was equivalent to the simplified case, where the frame is attached using the bottom bracket tube (BB case, specimens 1, 2, 4, 5, 6 and 7). For both cases, the loading using the equivalent test fork was identical. Strain gauges were installed on all the tested frames near the head tube (DTF1, 2 and TTF1, 2 in Figure 4). These strain gauges were placed on the top and bottom surfaces of the tubes and connected using the quarter-bridge measurement scheme, from which the bending strain component was calculated. The dependence of the measured mechanical strain on the loading force of the fork is plotted in Figure 14a for the DTF location and in Figure 14b for the TTF location. A linear flexibility response of the frame was found, with values of \(-0.91\) um/m/N compared to \(-1.02\) um/m/N (12% increase) at the DTF location. At the TTF location, values of \(-0.66\) um/m/N and \(-0.69\) um/m/N were found (an increase of 4.8%). This corresponds to the supposedly increased portion of load transfer by the simplified frame triangle, while the DT transfers a bigger part of the load. The simplification of the load case scenario from the mounting by using the rear dropouts to the mounting, which is using the bottom bracket, is viable. The upper and lower frame tubes are strained slightly more; however this is still on the safe side.
# Fault ride-Through Control Strategy for Variable Speed Pumped Storage Unit with Full-Size Converter
## 2 Structure and Control Strategy of FSC-VSPSU
### Power Generation and Pumping Control Strategies
The characteristic equation of the water pump in Figure 3, namely the relationship among the water head, water flow, and speed of the motor, can be approximated using the quadratic polynomial shown in Equation (2):  
\[H_{d}=a_{0}\omega_{g}^{2}+a_{1}\omega_{g}|Q_{d}|+a_{2}Q_{d}^{2} \tag{2}\]  
where, \(a_{0}\), \(a_{1}\), and \(a_{2}\) are the fitting coefficients.
## 1 Introduction
### Evaluation Indicators of Lateral Stability  
#### 2.2.1 Sideslip Stability  
Vehicles will be subjected to lateral force when driving on curve segments, especially in adverse weather with wind and rain. If the vehicle drives through the curve at a pretty high speed, the grip of the wheels may dramatically decrease, thus resulting in the drift of the vehicle and large lateral sideslip [23, 41]. Figure 2 shows the vertical view of a dynamic model, while the vehicle drives on curve segments. \(F_{\rm{syl}}\) and \(F_{\rm{syr}}\) denote lateral forces exerted on the left and right tires of \(s\) th axle (\(s=1\), 2). \(V_{\rm{w1}}\), \(V_{\rm{w2}}\), \(V_{\rm{w3}}\), and \(V_{\rm{w4}}\) are the velocities of four wheels, respectively. \(V_{\rm{vb}}\) is the forward velocity of the vehicle body. \(\alpha_{1}\), \(\alpha_{2}\), \(\alpha_{3}\), and \(\alpha_{4}\) denote the slip angles of front and rear wheels, which are defined as angles between \(V_{\rm{wi}}\) (\(i=\)1, 2, 3, 4) and wheel direction, respectively. \(\beta\) denotes the slip angle of vehicle body, which is defined as the angle between \(V_{\rm{vb}}\) and longitudinal axis. \(\delta_{\rm{s}}\) is the steering angle of the front wheel.  
Figure 1: Technical framework of this study.  
In addition, the superposition of lateral force caused by lateral wind and the centrifugal effect will significantly increase the lateral acceleration (LA), resulting in the vehicle laterally deviating from the original trajectory and increasing sideslip risks. It was specified that the LA for ordinary cars shall be less than 0.4 g.  
Slip angle (SA) of the vehicle body is the angle its velocity vector at the center of gravity makes with the longitudinal axis (\(X\) axis) of the vehicle, which is also used to characterize the risk of side slip, as described by [42]:  
\[\beta=\arctan(v_{y},v_{x}) \tag{1}\]  
where \(v_{y}\) and \(v_{x}\) denote the lateral and longitudinal velocity of vehicle body, respectively. The safety threshold for slip angle is \(5^{\circ}\)[43].
# Water Level Simulation in River Network by Data Assimilation Using Ensemble Kalman Filter
## 2 One-Dimensional Hydrodynamic Model of a River
Thus, the following linear equations, expressed in increments, are obtained as follows:  
\[\left\{\begin{array}{l}a_{1i}\Delta Q_{i}+b_{1i}\Delta Z_{i}+c_{1i}\Delta Q _{i+1}+d_{1i}\Delta Z_{i+1}=e_{1i}\\ a_{2i}\Delta Q_{i}+b_{2i  
where, when \(i=0\),  
\[\left\{ \begin{array}{l}EMT(1,1)=1,\ EMT(1,2)=0,\ EMT(1,3)=0\\ EMT(2,1)=0,\ EMT(2,2)=1,\ EMT(2,3)=0\end{array} \right. \tag{10}\]  
and when \(i>0\),  
\[\left\{ \begin{array}{l}EMT(2i+1,1)=a^{\prime}_{1i}EMT(2i-1,1)+b^{\prime}_{ 1i}EMT(2i,1)\\ EMT(2i+1,2)=a^{\prime}_{1i}EMT(2i-1,2)+b^{\prime}_{1i}EMT(2i,2)\\ EMT(2i+1,3)=a^{\prime}_{1i}EMT(2i-1,3)+b^{\prime}_{1i}EMT(2i,3)+c^{\prime}_{ 1i}\\ EMT(2i+2,1)=a^{\prime}_{2i}EMT(2i-1,1)+b^{\prime}_{2i}EMT(2i,1)\\ EMT(2i+2,2)=a^{\prime}_{2i}EMT(2i-1,2)+b^{\prime}_{2i}EMT(2i,2)\\ EMT(2i+2,3)=a^{\prime}_{2i}EMT(2i-1,3)+b^{\prime}_{2i}EMT(2i,3)+c^{\prime}_{ 2i}\end{array} \right. \tag{11}\]  
For a river with \(m\) reaches, the number of cross sections is \(m+1\). Based on Equation (7), we can obtain  
\[\left\{ \begin{array}{l}\Delta Q_{m+1}=a\Delta Z_{1}+b\Delta Z_{m+1}+c\\ \Delta Q_{1}=d\Delta Z_{1}+e\Delta Z_{m+1}+f\end{array} \right. \tag{12}\]  
where  
\[a=EMT(2m+2,1)-\frac{EMT(2m+1,1)*EMT(2m+2,2)}{EMT(2m+1,2)};\;b=\frac{EMT(2m+2,2)}{ EMT(2m+1,2)};\]  
\[c=EMT(2m+2,3)-\frac{EMT(2m+1,3)*EMT(2m+2,2)}{EMT(2m+1,2)};\;d=-\frac{EMT(2m+1,1)}{ EMT(2m+1,2)};\]  
\[e=\frac{1}{EMT(2m+1,2)};f=-\frac{EMT(2m+1,3)}{EMT(2m+1,2)} \tag{13}\]  
According to the above equations, we can see that the flow increment at the first and last cross-sections of the river can be expressed in terms of the increment of the water level. Therefore, the corresponding equations using the non-increment method can be obtained by expanding Equation (12). The equations are:  
\[\left\{ \begin{array}{l}Q^{l+1}_{m+1}=aZ^{l+1}_{1}+bZ^{l+1}_{m+1}-aZ^{l}_{1}- bZ^{l}_{m+1}+Q^{l}_{m+1}+c\\ Q^{l+1}_{1}=dZ^{l+1}_{1}+eZ^{l+1}_{m+1}-dZ^{l}_{1}-eZ^{l}_{m+1}+Q^{l}_{1}+f \end{array} \right. \tag{14}\]  
In addition, in this study we adopted a three-stage joint solution method based on a large sparse matrix solver to solve the river network. The method divides the solution of the equations into three levels--the micro-segments, river segments, and nodes--and calculates and back-substitutes them step by step, in order to compress the size of the matrix and improve the calculation speed.
## 4 Numerical Results  
As described in Section 2, the current numerical method consists of the sequential applications of the Eulerian and Lagrangian approaches. In Sections 4.1 and 4.2, the effects of grid resolution and turbulence models on the tip-vortex flow predicted using the RANS solver are presented. In Section 4.3, the reconstruction of tip-vortex flow is detailed. In Section 4.4, the initial nuclei distribution used for the simulations is explained. Finally, the tip-vortex cavitation inception and noise are predicted and compared with the measured data in Section 4.5.
# Machine Learning Techniques for Non-Terrestrial Networks
## 6 Machine Learning Techniques in NTN
### ML for Protocol Stack Layers
#### 6.2.3 Network Layer  
Finding the optimal path between two or more nodes of a network can be challenging if highly varying network conditions, such as overloaded routers, malfunctions or network outages, have to be considered. Traditional routing algorithms have to deal with a heavy computation load to assure good end-to-end transmission performance. Moreover, obtaining good local performance does not imply that the global transmission performance improves, too. In NTNs, the routing problems it is even more difficult due to the heterogeneous nature of the network. In [49], the authors proposed a supervised learning approach, which improves the routing performance in terms of signaling overhead, throughput and per hop delay compared to the traditional Open Shortest Path First (OSPF) protocol. Simulation results proved that the ML approach outperforms OSPF in the three metrics. More specifically, this technique lowered the signaling overhead by 70%, increasing the throughput of 2% and reducing the hop-delay of 90%.
# CommuSpotter: Scene Text Spotting with Multi-Task Communication
## 3 Text Spotter with Communication  
Scene text spotting mainly consists of text detection and recognition tasks, as shown in Figure 1. For the text detection task, the image is fed into a CNN-based network to extract feature maps, which are then pooled by \(MaxPooling\) and \(Softmax\) layers for the coordinate prediction of text locations and classification of text detection. The metrics include accuracy, recall, and F-score of text classification, as well as location overlap with the ground truths. Due to challenges in scene text variations, detection often contains many false positives, leading to incorrect recognition based on those detections. Text recognition relies on RNN networks that sequentially process the feature maps to predict each word character in text instances. For a maximum length of 25 for each word, each character is predicted and grouped to form the final recognition results. The metrics include recognition accuracy, recall, and F-score for every word.
# WELDMAP: A Photogrammetric Suite Applied to the Inspection of Welds
## 2 Methods
### Defectology Analysis  
Imperfections are anomalies that occur in the welded joint. They are considered as a flaw when, due to their magnitude or location, they could compromise the integrity and/or functionality of the part and/ or cause the failure of the joint. The generation of accurate and reliable photogrammetric point clouds models allows the application of different analyses oriented to the extraction of characteristics and the detailed evaluation of the state of the bead (Figure 2). In particular, the defectology analysis was developed considering four different types of defects (references in [2]) and the limits established in the international standard [3]:  
* _Excess weld metal_ (\(H\)) (ref. 502): it is related to the height of the bead over the plaque, \(H\). The following criterion (B level) is established: \(H\geq 1\) mm + 0.1 \(\times\)\(T\), \(T\) being thewidth of the bead (max h = 0.5 mm). If this value exceeds 10% of the bead width, a defectology warning will be generated.
* _Linear misalignment_ (_d_) (ref. 5071): it is related to the different heights between plaques, \(d\). The following criterion (B level) is established: \(d\geq 0.1\times t\), \(t\) being the thickness of the plaque. If this value exceeds 10% of the plaque thickness, a defectology warning will be generated.
* _Angular misalignment_ (_\(\delta\)_) (ref. 508): it is related to the different orientations between plaques, \(\delta\). The following criterion (C level) is established: \(\delta\left[n_{1}-n_{r}\right]\geq 2^{\circ}\), being \(n_{1}\) and \(n_{r}\) the normal vector of plaque left and right, respectively.
* _End crater pipe_ (_dz_) (ref. no. 2025): it consists of the detection of holes or depressed areas in the weld beam, _dz_. The most efficient way to detect them is by analysing the transversal profiles resulting from the weld, establishing a threshold of _dz_ < 0.2 \(t\), \(t\) being the thickness of the plaque, with a maximum of 2 mm (D level).  
All these defects were analysed using cross-sections along the weld, using the axis of the bead as a reference. Although the software allows the assessment of the weld from an automatic point of view, the photogrammetric point clouds allow for inspecting the main metric characteristics of the weld (quantitative analysis), as well as its visual appearance (qualitative analysis). For instance, the existence of possible friction-affected areas and/or corrosion zones can also be evaluated from a qualitative point of view.
## 2 Study Area and Datasets
### Determination of Input Remote Sensing Data
#### 2.2.2 Land Surface Temperature  
Using the aforementioned land surface parameter of surface emissivity and the atmospheric parameters including upwelling and downwelling radiance and transmittance, the atmospheric correction method based on the radiative transfer equation is then applied to estimate the land surface temperature [43]. The following expression of the simplified radiative transfer equation was applied to the thermal infrared images:  
\[L_{sensor,\lambda}=B_{\lambda}(T)\varepsilon_{\lambda}\tau_{\lambda}+L_{ \lambda}^{\uparrow}+(1-\varepsilon_{\lambda})L_{\lambda}^{\downarrow}\tau_{\lambda} \tag{5}\]  
where \(L_{\text{sensor},\lambda}\) is the radiance at the top of atmosphere (TOA; the value after radiation calibration) in Wm\({}^{-2}\cdot\)sr\({}^{-1}\)um\({}^{-1}\); \(\varepsilon_{\lambda}\) is the land surface emissivity; \(B_{\lambda}(T)\) is the radiance emitted by a blackbody at temperature \(T\), where \(T\) is in \(K\) and \(B_{\tau}(T)\) is in Wm\({}^{-2}\cdot\)sr\({}^{-1}\cdot\)um\({}^{-1}\); \(L_{\lambda}^{\uparrow}\) and \(L_{\lambda}^{\downarrow}\) are the atmospheric downwelling and upwelling radiances in Wm\({}^{-2}\cdot\)sr\({}^{-1}\cdot\)um\({}^{-1}\), respectively; and \(\tau_{\lambda}\) is the atmospheric transmittance. Atmospheric radiative transfer models (e.g., LOWTRAN and MODTRAN codes) are often used to calculate \(\tau_{\lambda}\), \(L_{\lambda}^{\uparrow}\) and \(L_{\lambda}^{\downarrow}\) if simultaneous atmospheric profiles are available.  
The land surface temperatures retrieved from the ASTER TIR data with pixel resolution of 90 m, were compared to that calculated from the upwelling and downwelling long-wave radiation measured by four-component radiometer in the tower sites or calculated from the measured air temperature and relative humidity while the flux tower without four-component radiometers in the study area during the HiWATER-MUOEXE. The results indicate that the ASTER land surface temperatures for the selected overpass dates (DOY 167, 176, 192, 215, 224, 231, 240, 247 and 256) are in acceptable agreement with the ground observations having an average bias of -1.12 K and an average root mean square error (RMSE) value of 2.25 K.
# Protection of Oats against _Puccinia_ and _Drechslera_ Fungi in Various Meteorological Conditions
## 4 Discussion
coronata_ was recorded after the application of s.c. from the chemical group of strobilurins, an increase in the yield from 17 to 27% was obtained (yield increase from 0.69 to 0.78 t \(\times\) ha\({}^{-1}\)), depending on the sowing date and variety. According to research [19], in conditions of weak infection with pathogenic fungi in the cultivation of the Belinda variety, in which treatments with active ingredients from the chemical group of triazoles were applied, analogously to our own research, near the BBCH 63 stage, the yield increased by 5.9% (yield increased by 0.3 t \(\times\) ha\({}^{-1}\)). The author also observed that the application of fungicides from the triazole chemical group in the above-mentioned stage resulted in statistically higher yields compared to treatments performed in stage BBCH 40-44. The conditions occurring in the temperate climate zone (cool temperatures and high moisture) are suitable for the development of the fungi _D. avenue._ The occurrence of the pathogen was confirmed in regions where rainy and cool weather conditions occurred during sowing and the initial stages of development [41]. The optimum temperature for the pathogen's development is approximately 20 \({}^{\circ}\)C, with a minimum temperature of 2-3 \({}^{\circ}\)C, while the mycelium can survive at 14 \({}^{\circ}\)C for at least 56 days under lab conditions [42]. The authors noted that climatic conditions unfavorable for the development of oats may affect the limitations in the development of the pathogen _D. avenue._ In years that are characterized by a moderate occurrence of climatic anomalies, the development of the pathogen seems to be undisturbed. Stress conditions for plant development may affect the variable level of intensity of the pathogen occurrence. Extremely unfavorable conditions for the development of oats (2016) were reflected in the low intensity of the pathogen's occurrence. However, this did not affect the efficacy of the fungicides used in the field experiments. In relation to the causal agents of _P. coronata_, unfavorable climatic conditions for the development of oat plants (low Sielianinow coefficient) influenced the intensity of oat crown rust. In other research [43], the author suggested that the occurrence of rust was preceded by the occurrence of dry and warm weather. In our own research, the highest level of infection with _P. coronata_ was observed in the season of 2016 (extremely unfavorable conditions for oat plants). These observations indicate that oat rust's incidence is potentially linked to climate instability. This is also reflected in studies that have investigated the impact of climate on the occurrence of diseases in agricultural crops [44; 45].