esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Experiment Description
### Measuring Equipment
#### 2.2.1 Thermal Imaging System  
As a part of the laboratory test, a FLIR T640 thermal imaging camera was used to detect conveyor belt mistracking on the return pulley of the belt conveyor. With a resolution of \(640\times 480\) and a field of view of \(45\times 34^{\circ}\), it allows temperature measurement in the range from \(-40\) to \(2000\)\({}^{\circ}\)C. The camera can capture both IR and RGB photos (separately or simultaneously), as well as record IR videos [44]. The FLIR T series is adapted to work in industrial conditions, including the specific conditions prevailing in mines. View of the camera mounted on the stand can be seen in Figure 3c.  
Figure 3: Measuring equipment: (**a**) overall view on the used equipment, (**b**) camera angle during measurement, (**c**) thermal camera used for measurements, (**d**) RGB camera used for measurements.
## 5 Discussion  
The LSTM-based SM forecast model relies on satellite-driven data, soil texture, topography, and climate. Therefore, as the predictions are conducted for different conditions, we investigated the prediction performances for land cover classes, biomass variations based on the NDVI calculated from the Sentinel-2 satellite, climate classes, and soil texture.
## 3 Results and Discussion
### Stage II
#### 3.2.2 Particles Morphology  
Particles morphology was influenced the most by the FR and IAT (Figure 3). Increasing the FR enabled us to obtain more spherical and regular particles with smooth surfaces. Generally, more particles of larger dimensions were observed with increasing the FR, as they were characterized with a diameter exceeding 20 \(\upmu\)m, when the FR was the highest for every tested variant. This relationship was noted for all of the tested IAH and IAT, which underlines the importance of the FR in producing powders of desired morphology. Increasing the FR most probably led to a more intense water evaporation, thus it affected lower outlet air temperatures ensuring more significant cooling effect on the particles. In consequence, this phenomenon resulted in more spherical particles with smooth surfaces as there was less stress caused on the particles' surface. However, despite the significant stress, it should be highlighted that particles can be smooth but at the same time they will become hollow underneath their surface [20,21].  
The effect of IAT on the size of particles could be observed--with increased IAT the increase in the particles' size regardless of IAH was noted (Figure 3). More particles of a larger size (exceeding 40 \(\upmu\)m) were present when powders were produced at the highest IAT (120 \({}^{\circ}\)C). This phenomenon resulted from rapid water evaporation, which led to formation of the particles' morphology at the very early stage of drying, resulting in a larger size of the particles. However, the phenomenon of the locking point must be acknowledged, as it determines the moment at which this skin can first be visually recognized. Lower drying temperatures can lead to particles with a more wrinkled morphology, as the locking point happens later compared to more elevated drying temperatures [22]. Nonetheless, this phenomenon was not observed in this study.  
Figure 2: Powder recovery (\(R_{p}\)) after MD spray drying at different inlet air temperature (80–120 \({}^{\circ}\)C), inlet air humidity (0.1–10 g/m\({}^{3}\)) and feed rate (0.16–0.83 mL/s) (Stage II); blue areas on the figure indicate the experiments, where not-evaporated water was observed inside the drying chamber. The gradient of blue, orange and red color indicate the increasing inlet air temperature, while blue color demonstrates the lowest level, orange the middle level and red the highest level of inlet air humidity.
# Indoor Microclimate Analysis of the San Panfilo Church in Tornimparte, Italy
## 3 Results
### Continuous Monitoring
#### 3.2.5 Monthly Averages  
The average values of temperature were computed for each month as mean of daily data for both outdoor and indoor (Figure 13). The annual trends show that, in general, the temperature below the portico is always higher than one at the meteorological station; moreover, in general, indoor temperature in the apse is higher than the temperature below the portico except during May, June and July. The scatter diagrams (Figure 13b,c) between indoor and outdoor monthly temperatures show the typical behavior of ancient and thick walls [8] absorbing heat during the summer and releasing it during the winter. In San Panfilo church, the internal heat sources are the people and the lighting system.  
Figure 13: (**a**) Temperature monthly averages inside the church (red), below the external portico (black) and at the S. Vito meteorological station (blue); (**b**) scatter diagram with inside and outside (portico) monthly temperature; (**c**) scatter diagram between inside and outside (meteorological station) monthly temperature.
# Statistical Analysis of Acoustic Emission in Uniaxial Compression of Tectonic and Non-Tectonic Coal
## 4 Analysis and Discussion
### Waiting Time Analysis of AE  
The Figure 5 shows the probability density distribution of waiting times. Waiting time is the difference between the two starting times of adjacent hits (see Figure 2c). The adjacent hits can be grouped as defined: \(\delta_{j}=t_{j}-t_{j-1}\), with j labeling only the events with energy larger than a given threshold energy. P(\(\delta\)) is the waiting time distribution function and indicates the probability to observe a waiting time for a threshold energy. The waiting time distribution result showed in Figure 5 is normalized by its average \(<\)\(\delta\)\(>\). After normalization, all waiting time distributions P(\(\delta\))\(<\)\(\delta\)\(>\) collapse together [23,30]. The waiting time distribution between non-tectonic and tectonic coal show obvious difference. For non-tectonic coal, all data in a single curve show double power law behavior. The power law exponents of small and large waiting time arguments are close to 1 and 2, respectively, which agrees well with previous studies about brittle materials failure, like sandstone [23], goethite [31], and even earthquake catalog [19]. For tectonic coal, only the small exponent value has been observed. The small distribution exponent associated with small waiting time, which was linked with the granular-like structure of tectonic coal, and it is consistent with the energy analysis above.  
Figure 4: (**a**) probability density distribution of absolute energy for non-tectonic and tectonic coal, and fitting by the power law function, (**b**) the power law exponent estimated from maximum likelihood curve, with error bar, and dashed curve is from the mixing maximum likelihood estimation.
## 2 Optical Angle Nano-Positioning Mechanism Configuration
### Statics Analysis  
The straight circular flexure hinge, characterized by high precision, stable adjustment, and wear resistance, is illustrated in Figure 4. Within this figure, \(r\) represents the circle radius of the flexure hinge, \(h\) stands for the height of the flexure hinge, \(t\) denotes the thickness between the hinges, and \(b\) indicates the width of the flexible hinge.  
Figure 4: Optical angle nano-positioning mechanism.  
Figure 3: Schematic of the motion mechanism under the pseudo-rigid body assumption.  
The adjustment mechanism consists of eight straight circular flexible hinges, each identical in design and function. A force analysis is performed on one of these flexible hinges. Assuming the left end of the flexible hinge is fixed, the relationship between the angle \(\alpha_{z}\) around the \(z\)-axis and the external torque \(M_{\alpha}\) is established as follows:  
\[M_{\alpha}=k_{\alpha}a_{z}=\frac{Ebr^{2}}{12c}\alpha_{z} \tag{3}\]  
where \(E\) is the elastic modulus of the material, and:  
\[\left\{\begin{array}{l}c=\frac{2s^{3}(6s^{2}+4s+1)}{(2s+1)(4s+1)^{2}}+\frac {12s^{4}(2s+1)}{(4s+1)^{2}}\arctan\sqrt{4s+1}\\ s=\frac{r}{1}\end{array}\right. \tag{4}\]  
The PZT actuator's thrust output, \(F\), is entirely transformed into the flexible hinge's elastic potential energy, \(V\). Consequently, the total energy input of the mechanism is as follows:  
\[V=\int\limits_{\Delta}F\mathrm{d}\Delta \tag{5}\]  
where \(\Delta\) is the displacement corresponding to the angular displacement \(\varphi\) of the flexible hinges pushed by the PZT actuator. The total potential energy \(U\) of the mechanism can be obtained using the energy method:  
\[U=\Sigma\frac{1}{2}k_{\alpha}\varphi^{2} \tag{6}\]  
Furthermore, based on energy conservation, we have the following:  
\[M\varphi_{2}=Fl\varphi_{2}=\Sigma\frac{1}{2}k_{\alpha}\varphi^{2} \tag{7}\]  
\[F=\frac{k_{\alpha}}{2l\varphi_{2}}\Sigma\varphi^{2} \tag{8}\]
## 2 Materials and Methods
Then, the software inside the ultrasound machine calculated the strain ratio value, given by the deformability of the lesion in comparison to the fat tissue, thus providing semi-quantitative information (Figure 2b). Figure 1: Mass overview with BI-RADS score provided by “SmartBreast”. Figure 2: (**a**) A hypoechoic lesion with irregular margins and vascular internal signs at color Doppler evaluation, classified as BI-RADS 5 by human operator. (**b**) Semi-quantitative elastography showing a value of strain ratio (4.07) suggestive of stiff lesion. (**c**) The same lesion, showing intermediate values at 2D SWE evaluation (21.95 kPa) and (**d**) high values at pSWE analysis (122.55 kPa). (**e**–**h**) “SmartBreast” assessment, suggestive of malignancy. At histological evaluation, it was a B5 lesion (infiltrating ductal carcinoma). Subsequently, the lesion was evaluated using the 2D SWE technique. The investigator positioned the transducer so that it remained stable and perpendicular to the lesion without any pressure while maintaining only slight contact with the skin in order to minimize compression artifacts. The patient was instructed to maintain a short inspiratory apnea to avoid respiratory motion artifacts. A mechanical impulse from the probe generated shear waves in the breast tissue, and their propagation was analyzed in a real-time color map. In this technique, the impulse coming from the transducer generates a downward displacement of the tissue parallel to each grid, and the speed of propagation of the shear waves can be measured (m/s). Breast lesion stiffness was assessed by placing an ROI in the lesion and was expressed in kPa. The ROI was placed in the lesion so as to include as much tumoral area as possible, avoiding the loss of signal due to artifacts (Figure 2). The pSWE assessment was based on the same physical principle of 2D SWE single-point stiffness was measured at different points of the lesion instead of an area. Each patient was asked to hold their breath, and the operator positioned the transducer perpendicular to the lesion without any pressure, waiting for the machine to take from 5 to 10 measurements at different points of the breast nodule in order to obtain a representation of the stiffness, expressed in Kpa, through maximum, minimum, and median values. The pSWE evaluation also included a quality bar that indicated the correct execution of the same (Figure 2d). The histopathological examination was considered the gold standard of reference; therefore, all breast lesions underwent core-needle biopsy and/or surgery. Ultrasound-guided biopsy was performed under local anesthesia with a 14-gauge needle (Precisa, HS), and at least three tissue samples were collected for each breast lesion [15]. Every biopsy sample was placed in formalin and analyzed by a pathologist specializing in the diagnosis of breast cancer (with at least 15 years of experience). The pathologist was blinded to the ultrasound results.
## 2 Methods and System Setup
### Feature Extraction and Two-Stage Activity Recognition Method  
To predict falls as early as possible to ensure that subsequent protection steps can be taken and for detailed functional assessment needs, we proposed a two-stage activity recognition method in which a probabilistic neural network (PNN) is first trained on routine activities. This network identifies  
Figure 4: The spatial distribution of normal gait.  
potential falls and passes their data to a supervised SVM-RBF-KNN algorithm that can classify falls and abnormal gaits.  
The feature extraction procedure involves extracting one element (\(x_{i}\)) from three sets of sensing data (photoelectric sensor data (\(P_{i}\)=[\(P_{1}\),\(P_{2}\), \(P_{3}\),\(P_{4}\),\(P_{5}\)]), tension sensor data (\(T_{i}\)=[\(T_{1}\),\(T_{2}\),\(T_{3}\),\(T_{4}\)]), and accelerometer sensor data (\(A_{i}\)=[\(A_{1}\),\(A_{2}\),\(A_{3}\)])) during a sampling period (\(t_{s}\)). Then, the three elements make up the input data (\(X_{i}\)) that describe the features of patient activity during rehabilitation training. The sensor sampling period in our experiment is \(t_{s}=20\) ms.  
Since accelerometer values change dramatically during falls or abnormal gaits, they are often used to predict abnormal conditions. Since the angle of acceleration is related to the sensor's orientation when worn, it is generally not used for prediction. To eliminate the accelerometer gravity effect and avoid complex threshold comparisons from every direction (\(A_{x}\), \(A_{y}\), and \(A_{z}\)), we employed the signal magnitude vector (SMV) algorithm [31] to discriminate between abnormal gaits and falls during normal gait; the SMV algorithm is the most frequently employed algorithm for distinguishing motion:  
\[A_{sem}(SignalMagnitudeVector)=\sqrt{\left(A_{x}\right)^{2}+\left(A_{y}\right)^{ 2}+\left(A_{z}\right)^{2}} \tag{1}\]  
To predict falls, the value of the tension sensor is significant. In particular, the value of the tension sensor can help make a sensitive determination of a flexible fall. The combination of the photoelectric sensor and the acceleration pull force is also valid for abnormal gait determination.  
The tension sensors, which are located between the robot and the patient's waist in different directions, can reflect the force characteristics between the robot and the patient, such as the force direction and the uniformity and continuity of target movement. We adopt d (T\({}_{1}\), T\({}_{4}\)) and d (T\({}_{2}\), T\({}_{3}\)) to represent the forces in the left and right directions, respectively, and d (T\({}_{3}\), T\({}_{1}\)) and d (T\({}_{4}\), T\({}_{2}\)) to represent the frontward and backward force directions, respectively. We adopt T\({}_{\text{svm}}\) to predict falls.  
\[d(T_{1},T_{4}) =T_{1}-T_{4} \tag{2}\] \[d(T_{3},T_{2}) =T_{3}-T_{2}\] (3) \[d(T_{3},T_{1}) =T_{3}-T_{1}\] (4) \[d(T_{4},T_{2}) =T_{4}-T_{2}\] (5) \[T_{sem} =T_{1}+T_{2}+T_{3}+T_{4} \tag{6}\]
## 2 Data from the Full-Scale Test  
The experiment included two full-scale segmental joint specimens designated as SP-0 and SP-1, referring to the unreinforced and reinforced segmental joint specimen, respectively, in order to represent a typical joint configuration in a subway shield-tunnel-lining structure.  
Both specimens were assembled by two segments with a central angle of 23\({}^{\circ}\). The internal and external diameters of the segments were 5500 mm and 6200 mm, respectively, and the width and thickness of the segments were 1200 mm and 350 mm, respectively. Two 5.8-grade M30 bent bolts (center-line-arc radius 380 mm, arc length 530 mm) were used to assemble the segments to form the joint specimen.  
Figure 1 shows the stainless-steel corrugated plate (SSCP) reinforced segmental joint specimen (SP-1). The segments and bent bolts used in the strengthened segmental joint specimen were the same as those used in the unreinforced segmental joint specimen. Chemical anchors made of 2205 stainless steel with a diameter of 16 mm were used to fix SSCP on the segmental joint, and holes with a diameter of 20 mm were punched on SSCP. The ends of the joint specimens were erected on two steel components and their bottoms were supported by two rollers, which means they were simply supported. No preload was applied to the unreinforced specimens (SP-0).  
The segmental joints of tunnels in service undergo stress before reinforcement, and reinforcement does not bear load until the load continues to develop or segmental material degrades. Therefore, the joint is actually under secondary stress when the reinforcement starts to share the load. In order to simulate realistic reinforcement conditions, on the full-scale test, the secondary stress state of the specimen was achieved by applying a vertical load \(\mathrm{F_{k}}\), corresponding to the load of the elastic-deformation limit point of unreinforcedspecimen (SP-0) on the segmental joint before reinforcement. When the load was lower than F\({}_{\text{k}}\), the nut of chemical anchor, which connected the specimen (SP-1) and SSCP, was not tightened; hence, SSCP did not share the load borne by the specimen. In other words, the reinforcement was applied by tightening the chemical-anchor bolt nut when the load reached F\({}_{\text{k}}\) (see Figure 2).
# Congestion Control and Traffic Differentiation for Heterogeneous 6 TiSCH Networks in IIoT
## 3 The Proposed Congestion Control Mechanism  
In this section, we describe the congestion control framework used to achieve load balancing in 6 TiSCH networks. We first present a congestion detection and control method, then we describe a mechanism to distribute congestion information between neighbor nodes. Finally, we introduce the proposed Trickle timer reset strategy.
## 2 Materials and Methods
### Simulation Settings  
Echo data were simulated by Filed II to evaluate the frame work we proposed based on the quality of the images it generates, and to compare it with DAS method and compressive beamformer in the temporal domain. Lateral resolution and contrast were the metrics to assess the image quality in this part. The metrics could be better calculated and analyzed in this part, because noise and other experimental interference could be ignored easily with the simulated data.  
The numerical phantom was generated by Field II, including backscattering points and circular regions to calculate the lateral resolution and contrast. The two circular hypoechoic areas with the diameters of 5 mm and 6 mm in the phantom represented the anechoic cyst, and the two high-brightness circular areas with the same diameter simulated high-density parts in ultrasound imaging. There was a column of backscattering points  
Figure 4: Diagram of wavenumber domain compressive beamforming.  
arranged longitudinally and a row of scatterers arranged horizontally in the phantom. The longitudinal scattering points were used to analyze the lateral resolution of different depths.  
The specifications of the research linear array transducer manufactured by Verasonics (L11-4v, Verasonics Inc., Redmond, WA, USA) were used to simulate the echo data. This is a linear array transducer with 128 elements, whose central frequency is 6.25 MHz. The pitch of transducer L11-4 is 0.3 mm and the width of each element is 0.27 mm. The transmitted signal was sampled at a rate of 50 MHz. There were 6066 samples recorded for each scanline and the image depth was 92.4 mm. The data processing was conducted using a MATLAB program (Mathworks Inc., Natick, MA, USA).
# Can OCT Angiography Be Made a Quantitative Blood Measurement Tool?
## 1 Introduction  
The microcirculation comprises a network of blood vessels that delivers oxygen and nutrients to surrounding tissues, removes waste products and heat, and otherwise supports tissue viability [1; 2; 3]. Red blood cells (RBCs) are the main carriers of oxygen in blood. \"Optical Coherence Tomography Angiography (OCTA)\" is a term for the specialized Optical Coherence Tomography (OCT) scanning protocols and post-processing algorithms that mainly enhance the motion contrast of red blood cells (RBCs) in OCT images to selectively highlight these vessels. By enabling the visualization of cell-perfused vasculature without an exogenous contrast agent, OCT angiography has generated enormous interest in ophthalmology [4; 5; 6; 7; 8; 9; 10; 11], gastroenterology [12; 13], cancer biology [14; 15], and neuroscience [16; 17] over the past decade. It has been particularly useful in studying diseases where the microvascular morphology or presence of perfusion changes over time. However, with few exceptions [18; 19; 20], the majority of published studies have used OCT angiography _qualitatively_,primarily as a means of visualization. Here, we review the relevant basic hemodynamic principles, fundamentals of OCTA, categories of OCTA scanning protocols, and classes of OCTA algorithms. We argue that a rigorous and model-based relationship between hemodynamic parameters, light scattering theory, and measurement observables [21] in OCT angiography will pave the way towards more _quantitative_ imaging of hemodynamics by OCTA and related methods, with the potential to enhance all applications.
# A Guaranteed Approximation Algorithm for QoS Anypath Routing in WMNs
## 4 Simulation Experiment and Performance Evaluation
### Experiment and Analysis of HMAA Algorithm Operation Efficiency  
At present, there is no literature on the MCOAP problem. The existing intelligent algorithms such as the genetic algorithm and the ant colony algorithm are almost solely concerned with the optimal solution, thus its consuming time increases exponentially with the increase in network scale, which is not suitable for WMNs with strong time-varying characteristics. In this section, the MAP algorithm [16] and the deformation of the SAF algorithm [17] are used to the OMCAP problem, thus the HMAA, MAP and SAF algorithms are compared in this experiment. The Waxman model [20] is used to generate a network called RNET with 500 nodes. The RNET network is set up according to the above-described protocol. Three QoS constraints, source node \(s\) and destination node \(t\) are randomly generated. Three algorithms are used to find the anypath routing and record the running time, respectively (run 10 times to find ATC according to Formula (26)). The ATC of the three algorithms is recorded 20 times and shown in Figure 3.  
\[\text{ATC}=\frac{\text{Total time consuming}}{\text{Number of running}} \tag{26}\]It can be seen from the records shown in Figure 3 that: 1 In the RNET network with 500 nodes, regardless of the source node and the destination node, the overall consuming time of the three algorithms is kept within the range [11,16], and the running time is all within 16 ms; 2 When the source node \(s\) and the destination node \(t\) are the same, the running time of the three algorithms to find anyh routing is compared: the running time of HMAA is in the middle, MAP [16] is about 5% more, and SAF [17] is about 15% less.  
The experimental results show that: 3 MAP is an anyhputing algorithm to OMCAP, which has the same time complexity as HMAA to MCOAP. SAF with a single metric also has the same time complexity. Therefore, the running time trends of the three algorithms are consistent. 2 MAP is to find the approximate solution where K = 3, which is slightly more than that of HMAA finding the approximate solution where K-1 in the actual calculation. SAF is a single metric anypath routing algorithm, which is equivalent to the case of K = 1, and the running time is relatively short in practical calculation. According to the simulation experiment data shown in Figure 4, HMAA proposed in this paper is evaluated by analyzing the principle and operation mechanism of the three algorithms. For WMNs with strong time-varying characteristics, the optimal solution to the MCOAP problem can be found in a reasonable time.  
Figure 4: Experiment on real WMNs network. (**a**) the NTT network topology. (**b**) Anypath \(P_{\rm s}^{*}\) found by HMAA.  
Figure 3: Running Time vs. the number of running for MAP, HMAA and SAF.