esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work
The gathered data, fundamentally the latency, allowed them to identify of different operators, although using all available features increased the accuracy of the classifier. In addition, the quality of experience of web services has been experimentally measured with probes by collecting user traces, some examples being [19; 20]. Moreover, during the last few years, there has been a growing interest in developing tools that allow the capture of packets from the radio layers to deepen the influence of the configurations at this level. In [21], a software tool that allows access to the scheduling information by decoding the LTE control channel, OWL, was introduced. This work showed a one-day data collection campaign: radio network temporary identifier (RNTI) data-rates and MCS (modulation and coding scheme) assignments were shown. Later, Ref [22] presented a complete open source solution (Mobil Insight) capable of obtaining messages from the cellular network protocol stack capable of being installed on any mobile commercial-off-the-shelf (COTS) platform. This software was tested in the present work as a complement to the virtual probe. Other commercial tools available on the market offer similar features to those mentioned above, although they are not open source [23; 24]. It is also worth noting that the aforementioned softwarized environments are giving rise to a renewed interest in benchmarking the possible scenarios that will take place in 5G networks, such as those shown in [25]. This work demonstrates at a computational level the effects of some of the IoT protocols. In terms of social crowds/events analysis, which will focus on the application of vUE probes in the present study, the studies done so far on the impact on the cellular network are limited. The work in [26] focused on optimizing the performance of networks during crowded events. Here, Transport Control Protocol (TCP) metrics, such as round trip time (RTT) and packet loss, are inferred to evaluate network performance. Based on this data, features like dropped calls are reduced to improve Internet user throughput. In this way, two possible solutions for mitigating those problems were proposed: on one hand, radio network parameter tuning and opportunistic connection sharing, and on the other, a selected set of UEs could act as WiFi hotspots for other UEs in their vicinity, thereby reducing the already occupied radio channels. A similar work presented in [27], studied the use of contextual information [28; 29] for cellular network management in social events. Furthermore, a framework for using such contextual information that could be applied to the vUE use case was described. However, unlike the present work, it was not supported by an extensive measurement campaign.
## Appendix A  
The dynamic model of the 2-DoF planner robot shown in Figure 5 is illustrated as follows:  
\[\left\{\begin{array}{l}J_{m1}\tilde{q}_{m1}+f_{m1}(\tilde{q}_{m1})+R_{1}^{- 1}\tau_{e1}=\tau_{m1}\\ K_{1}(R_{1}^{-1}q_{m1}-q_{e1})=\tau_{e1}\\ \tau_{e1}=J_{1}\tilde{q}_{e1}+[m_{2}l_{1}^{2}+m_{2}d_{2}^{2}+2m_{2}l_{1}d_{2} \cos(q_{e2})]\tilde{q}_{e1}+[m_{2}d_{2}^{2}+m_{2}l_{1}d_{2}\cos(q_{e2})]\tilde {q}_{e2}\\ -2m_{2}l_{1}d_{2}\sin(q_{e2})\tilde{q}_{e1}\tilde{q}_{e2}-m_{2}l_{1}d_{2}\sin (q_{e2})\tilde{q}_{e2}^{2}+l_{C2}(\tilde{q}_{e1}+\tilde{q}_{e2})+f_{e1}(\tilde {q}_{e1})\\ J_{m2}\tilde{q}_{m2}+f_{m2}(\tilde{q}_{m2})+R_{2}^{-1}\tau_{e2}=\tau_{m2}\\ K_{2}(R_{2}^{-1}q_{m2}-q_{e2})=\tau_{e2}\\ \tau_{e2}=m_{2}d_{2}^{2}(\tilde{q}_{e1}+\tilde{q}_{e2})+m_{2}l_{1}d_{2}\cos( q_{e2})\tilde{q}_{e1}+m_{2}l_{1}d_{2}\sin(q_{e2})\tilde{q}_{e2}^{2}+l_{C2} \tilde{q}_{e2}+f_{e2}(\hat{q}_{e2})\\ \end{array}\right. \tag{1}\]  
where \(J_{mi}\), \(f_{mi}\), \(q_{mi}\), and \(\tau_{mi}\) are the rotor inertia, friction, angle, and drive torque of motor i (i = 1,2); \(R_{i}\), \(K_{i}\), \(f_{ei}\), and \(q_{ei}\) are the reduction ratio, stiffness, friction, and angle of joint i; \(J_{1}\) is the inertia of link 1; \(m_{2}\) is the mass of link 2; \(l_{1}\) is the length of link 1; \(d_{2}\) is the centroid position of link 2; and \(J_{e2}\) is the inertia around the centroid of link 2. The meaning of the parameters in \(f_{mi}\) and \(f_{ei}\) is given in Equations (5) and (6).  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Parameter** & **Preliminary Value** & **Tuned Value** & **Parameter** & **Preliminary Value** & **Tuned Value** \\ \hline \(J_{m1}\) (10\({}^{-5}\) kg\(\cdot\)m\({}^{2}\) ) & 4.928 & 4.761 & \(\hat{q}_{e1}\) (r/min) & 5.002 & 3.837 \\ \(J_{m2}\) (10\({}^{-5}\) kg\(\cdot\)m\({}^{2}\)) & 4.928 & 4.835 & \(\sigma_{e1}\) (10\({}^{-5}\)) & 9.504 & 5.039 \\ \(\tau_{sm1}\) (10\({}^{-4}\) N\(\cdot\)m \(\cdot\)m \(\cdot\) & 7.003 & 6.812 & \(\tau_{e2}\) (10\({}^{-2}\) N\(\cdot\)m) & 3.300 & 3.808 \\ \(\tau_{vm1}\) (10\({}^{-9}\) N\(\cdot\)m \(\cdot\)min/r) & 1.802 & 2.1 & \(\tau_{e2}\) (10\({}^{-2}\) N\(\cdot\)m) & 4.005 & 4.056 \\ \(\tau_{sm2}\) (10\({}^{-4}\) N\(\cdot\)m) & 3.013 & 3.424 & \(\hat{q}_{e2}\) (r/min) & 4.200 & 3.723 \\ \(\tau_{em2}\) (10\({}^{-9}\)) & 2.301 & 2.048 & \(\sigma_{e2}\) (10\({}^{-5}\) N\(\cdot\)m\(\cdot\)min/r) & 8.200 & 6.090 \\ K\({}_{1}\) (N\(\cdot\)m/rad) & 3742.893 & 4155.417 & \(J_{1}\) (10\({}^{-2}\) kg\(\cdot\)m\({}^{2}\)) & 6.742 & 6.451 \\ \(K_{2}\) (N\(\cdot\)m/rad) & 4640.958 & 5255.644 & \(m_{2}\) (10\({}^{-2}\) kg\(\cdot\)m\({}^{2}\)) & 1.649 & 1.615 \\ \(\tau_{c1}\) (10\({}^{-2}\) N\(\cdot\)m) & 4.001 & 3.658 & \(d_{2}\) (m) & 0.101 & 0.101 \\ \(\tau_{s1}\) (10\({}^{-2}\) N\(\cdot\)m) & 4.200 & 3.855 & \(J_{2}\) (10\({}^{-2}\) kg\(\cdot\)m\({}^{2}\)) & 1.027 & 1.199 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Identification results of dynamic parameters.  
The dynamic parameters obtained by preliminary identification and tuning with CARLA are shown in Table A1.
## 4 Simulation and Experimental Results  
This section includes the results of both the simulation tests and experimental verification of the PQ-MRAS estimator. The estimator was examined for different conditions of the drive operations, such as: load torque and speed variations (including speed reversals). The rated data of the tested induction motor are summarized in Table A1 (Appendix A). During the simulation and experimental tests, the values of adaptation mechanism coefficients were the same (Appendix B). State variables from the drive, such as: rotational speed, electromagnetic torque, rotor flux and stator current are presented along with internal signals from the PQ-MRAS estimator: active and reactive powers, estimated stator and rotor resistances. Each of the signals and state variables is normalized to its nominal value.
###### Abstract  
The main purpose of this study is to estimate the welding current using the arc light signal emitted during the welding process. Traditionally, welding operators determine this current from the arc light based on their visual perception. This study shows that, using artificial intelligence techniques, welding current can be automatically estimated through arc light and can also be useful for monitoring of the process and detecting its disturbances. For this purpose, initially, a data acquisition system is designed to synchronize the movement of the light sensor with the electrode holder. The electrode welding machine is set to different maximum current levels, and two electrodes with different diameters are used at each level. During the welding process, the arc light and current signals are acquired simultaneously. The obtained data are filtered and aligned by cross-correlation. For the ANFIS (adaptive neuro-fuzzy inference system) model, the arc light is defined as the input and the current as the output. The estimation results of ANFIS are further improved through filtering, shifting, and current-limiting processes. The maximum cross-correlation values for training and testing data are 0.9587, 0.9598, 0.9565, and 0.9323, respectively, while the R-squared values are 0.7033, 0.7640, 0.6449, and 0.5853. Compared with the artificial neural network (ANN) model, it is observed that the ANFIS model provides better prediction results. The results confirm that arc light signals can be effectively used for welding current prediction. Therefore, the proposed approach can contribute to the development of intelligent welding systems and quality welding processes by reducing operator dependency.  
arc welding; welding current estimation; arc welding ANFIS; ANFIS; ANN; arc light 2025  
153824
## 2 Methodology
### The Case Study: San Lorenzo Church in Norcia  
Starting from the assumption that cultural heritage requires continuous monitoring (both ordinary and extraordinary) and updates, for the present research, a case study significant from the point of view of vulnerability, hazards and seismic risks was chosen.  
The San Lorenzo Church in Norcia is a representative example of building damaged by the earthquakes that affected the centre of Italy starting from the summer of 2016.  
This ancient building, probably built on the ruins of a temple, was affected by many earthquakes during the years with the consequence of structural damage to the wooden ceiling in the 1950s. After the last event, on 30 October 2016, which damaged the bell-tower, the Church underwent security measures and required safety structures (Figure 2) of the entire building. It is currently still closed and the surrounding area is classified as a \"Red Area\", identifying every city part damaged by the earthquake and temporary closed to the public for safety reasons.  
Figure 1: Methodological overview of the research.  
Figure 2: The Church of San Lorenzo in Norcia after the damage of the earthquake.  
In order to support the representation of the church with a digital 3D model and in a multi-scale project to monitor the damage and for future reconstruction purposes, an integrated 3D metric survey [33; 34; 35] was performed in September 2017 by the Geomatics group of the Politecnico di Torino.  
The first steps have been based on the measurement of the topographic network in order to georeference all the different set of data. Then, close range and UAV (unmanned aerial vehicles) photogrammetric approach and TLS (terrestrial laser scanning) techniques were applied in order to obtain the whole 3D model of the church. A complete photogrammetric survey has been achieved combining images obtained from terrestrial photogrammetry with oblique and nadiral images from aerial photogrammetry. Photogrammetric data were post-processed with Structure from Motion (SfM) algorithms using the commercial software Agisoft MetaShape Professional (version 1.6.1). For the modelling purposes, the 3D dense cloud derived from the photogrammetric acquisition was considered due to the completeness of the point cloud including the roof geometries. Table 2 shows the acquisition and the post-processing data information of sensors used and point clouds generated.  
After the merging phase of the two 3D dense point clouds the model has been filtered and the context has been excluded, considering only the building geometries. Then, the point cloud has been segmented in three different regions (roof, security elements and walls) (Figure 3), to simplify the modeling phase, and exported in _.rcp_ data format supported by the BIM software used.
# Ultrasound in Telemedicine: A Brief Overview
## 3 Results
### Image Quality  
A quality assessment tool was developed a few years ago to standardize sonographic B-images [26]. As an important feature, it attempts to quantify the sonographer's influence with regard to the final image quality. This method can also be adopted for tele-ultrasound. Furthermore, the examiner's skill, as well as the patient's current state and cooperation, are all known to influence ultrasound imaging. Gallstones, for example, may only be scanned when the patient is in a precise inspiratory position and with extremely specific transducer angles and placements. As a result, even in the absence of tele-transmission, the imaging result is not always consistent. When two examiners look at the same patient, they will almost always produce different visuals and, in some situations, diagnoses. This general weakness naturally also has an influence on the use of ultrasound in telemedicine and must always be taken into account in all applications.  
The latest developments in the field of telecommunication have paved the way for several attempts to test tele-ultrasound with mobile device application [22] or via web-based applications [27]. These studies have shown that commercially available video chat software can transmit high-quality and clinically useful ultrasound images. For nearly every anatomic location assessed in these studies, images obtained by means of this method were non-inferior to images obtained directly from a stationary ultrasound machine. There were also mobile ultrasound apps that went beyond simply projecting live images, with built-in tools to grade the images that study participants took and determined whether they passed or failed.  
Data transfer rates have come a long way since the early stages of telemedicine two decades ago, when devices with a bandwidth of 2 Mbit/s were used successfully for obstetric tele-ultrasound [15]. Nowadays, the transmission of real-time ultrasound video footage to a remote iPhone using inexpensive technology is feasible, as shown in the literature. It can be accomplished without the loss of image quality and a minimal delay and works even with 3G mobile connection [28].
## 4 Results and Discussions  
### Absorption Spectra  
Figure 2 presents the absorbance spectra of each of the studied phytoplankton species. It is possible to observe that the absorbance has two peaks, one at 436 nm and the second one at 675 nm. In order to avoid differences in absorption that arise from different concentration of each species, the results presented in Figure 2 are normalized at 436 nm (absorbance spectra of all species to the same value at this wavelength, i.e., _Nannochloropsis gaditana_ and _Tetraselmis suecia_ spectrum values were multiplied by scale factors in such a way that their absorption at 436 nm has the same value as that of _Isochrysis galbana_) to allow the comparison of the absorbance spectra.  
Figure 1: Filter configuration used in the commercial flow cytometer.  
The absorption spectra corresponding to green algae (_Nannochloropsis gaditana_ and _Tetraselmis suecia_; blue and brown lines in Figure 2) are very similar. The brown algae _Isochrysis galbana_ (yellow line) showed a distinct absorption spectrum as compared with the other species under study.  
Due to the scale normalization, to use the absorbance as an identification criterion, the ratio between the absorbance at two wavelengths must be used to avoid the effect of different concentrations. Table 3 presents the relation between the absorption coefficient at 530 nm and 436 nm. The results of this study show that although phytoplankton species can be grouped by colour by analysing their absorption spectra, the observed differences were not sufficient to allow differentiation, especially when more species were considered.
## 3 Results
### Glenn Group  
The characteristics of the Glenn group are included in Table 1.  
The mean \(\mathrm{E_{median}}\) values determined by SWE in the Glenn group were slightly higher than the controls, with no statistical significance (3.99 kPa vs. 3.91, \(p\) = 0.96). Similarly, \(\mathrm{V_{median}}\) values were not significantly higher in the Glenn group compared to controls (1.16 m/s vs. 1.12 m/s, \(p\) = 0.53) (Table 2, Figure 2).  
When comparing the laboratory results of the Glenn group with the controls, we found statistically significant differences for Fibrotest, Actitest, AST, ALT, ApoA1 and APRI (Table 2, Figure 3).  
The Fibrotest investigation showed a degree of fibrosis in 8 out of 18 patients examined: F0 fibrosis in 10 patients, F1 fibrosis in 4 patients, F2 fibrosis in 3 patients and F4 fibrosis in 1 patient. The Actitest showed an A1 necro-inflammatory result in only one patient, and A0 result in 17 patients. We found a significant positive correlation between the time interval since the Glenn operation and Fibrotest (r = 0.6, \(p\) = 0.0078) and the FIB-4 score (r = 0.508, \(p\) = 0.02), respectively. We obtained a negative correlation between the time interval after the Glenn operation and the thrombocyte count (r = \(-\)0.45, \(p\) = 0.04)--Figure 4. No significant correlation could be found between the time interval since the Glenn operation and either APRI, \(\mathrm{E_{median}}\) or \(\mathrm{V_{median}}\) values determined by SWE. We found a negative correlation between the interval after the Glenn operation and \(\mathrm{E_{median}}\) determined by TE (r = \(-\)0.63, \(p\) = 0.02). Similarly, a negative correlation was obtained between the time after Glenn and \(\mathrm{V_{median}}\) determined by TE, but with no statistical significance (r = \(-\)0.48, \(p\) = 0.3).  
Comparing \(\mathrm{E_{median}}\) values determined in Glenn patients by SWE with the published cutoffs [11], we found that 5 out of 20 patients had \(\mathrm{E_{median}}\) elevated values, whereas 5 had \(\mathrm{V_{median}}\) values over the cutoff. We obtained a positive correlation between \(\mathrm{E_{median}}\)  
Figure 4: Scatterplot showing correlations between various parameters for Fontan and Glenn patients.  
determined by SWE and Actitest (r = 0.48, \(p\) = 0.05) and between V\({}_{\rm median}\) and Actitest (r = 0.81, \(p\) = 0.0002).  
The liver stiffness values determined by TE were elevated in 6 out of 14 Glenn patients examined. The E\({}_{\rm median}\) and V\({}_{\rm median}\) values determined by TE were similar to those obtained by SWE (E\({}_{\rm median}\) 4.14 kPa vs. 3.99 kPa \(p\) = 0.8; V\({}_{\rm median}\) 1.25 m/s vs. 1.16 m/s, \(p\) = 0.25) but we could not obtain a significant correlation between the results determined by the two methods (for E\({}_{\rm median}\) r = 0.1, \(p\) = 0.5, V\({}_{\rm median}\) r = -0.01, \(p\) = 0.97).
# Porous Organic Polymers-Supported Metallocene Catalysts for Ethylene/1-Hexene Copolymerization
## 2 Results and Discussion
### Chain Growth of Ethylene/1-Hexene Copolymer

Table 2: Ethylene/1-hescene polymerization activities, differential scanning calorimetry (DSC) and gel permeation chromatography (GPC) results of polyethylene (PE) from the POPs-supported _n_-BuC\({}_{\text{P2}}\)ZrCl\({}_{2}\)/MAO metallocene catalysts. PDI: polydispersity index. However, compared to PE-1, the molecular weight distribution of PE-3 (Mw/Mn = 2.6) from the POP-3-supported metallocene catalyst with wide pore size distribution seemed to be narrower than PE-1. The results could be explained reasonably by the same diffusion mechanism in different pore sizes. Besides the pore size distribution, the abundance in different pore size distributions might cause the discrepancy in wide pore size distribution POP supports. Actually, the abundance in 1-4 nm of POP-3 is the highest in the three POP supports, so the part of PE-3 with high molecular weight is also the highest in the three copolymers (PE-1/PE-2/P-3), and many more long-chain molecules with Mw above 20 \(\times\) 10\({}^{4}\) are in PE-3 than in PE-1 and PE-2. On the other hand, many more short chain molecules with Mw less than 2000 in PE-3 were observed than in PE-2, which might owe to the large pores in the POP-3. Furthermore, compared to PE-4 obtained from meso-pore silica-supported metallocene catalyst, an obvious shift to the right of the GPC curves of PE-1/PE-2/PE-3 due to the micro-pores and narrow meso-pores (roughly 1-4 nm) in the POP supports was seen, except for the part of low molecular weight, with Mw less than about 2000. The incorporation mechanism of ethylene and 1-hexane by diffusion through different pore sizes of the POPs-supported metallocene catalysts is illustrated in Figure 3. In this case, the nanoporous POP supports or POPs-supported metallocene catalysts--especially in microporous and narrow mesoporous range--would behave like nano-valves which can differently affect the diffusion rate of monomer (ethylene), comonomer (\(\alpha\)-olefin), hydrogen, and chain transfer agent (scavenger TIBA/TEAL), etc., according to their molecule size, while larger meso-pores cannot effectively hold back the diffusion of larger molecules such as 1-hexane and TIBA. Figure 3: Schematic incorporation mechanism of comonomers by diffusion through different pore sizes of the POPs-supported metallocene catalysts during ethylene/1-hexane copolymerization. Figure 2: Curves of the molecular weight distribution of ethylene/1-hexene copolymers. (1) PE-1 obtained from BCZ/MAO@POP-Imetallocene catalyst; (2) PE-2 obtained using BCZ/MAO@POP-2 metallocene catalyst; (3) PE-3 obtained from BCZ/MAO@POP-6 metallocenecatalyst; (4) PE-4 obtained from BCZ/MAO@2408 silicametallocene catalyst.
# Design of Liquid-Air Hybrid Cooling Garment and Its Effect on Local Thermal Comfort
## 2 A general Description of the Designed Cooling Garments and the Control Garment
### The Design Ide of the Cooling Garments and the Control Garment  
The location of the skin surface for heat exchange needs continuous improvement. The torso accounts for 74% of the body's basal metabolism. Thus, it is more reasonable to set the torso as the cooling site according to previous studies [32; 33]. Thus, the design of liquid-air cooling garments in the form of a vast structure was used. Three cooling garments and one control garment were designed and made: aluminum-tube fan cooling (AAL), silicone-tube fan cooling (SAL), silicone-tube fan cooling with inner yarn (YAL), and a control garment (CON) without the cooling sources but the same size and fabrics as the cooling garments. The difference between AAL and SAL is the difference in the tubes. AAL uses aluminum tubes, SAL uses silicone tubes, both use the way of upper and lower splicing, the upper part of the torso is liquid-air, and the lower part is air-cooled. The difference between YAL and the first two garments is that YAL uses the way of inner and outer splicing, the inner layer is liquid-air, the outer layer is air-cooled, and the tube part still uses silicone tubes. The total weight of AAL is 1.16 kg, the total weight of SAL is 1.08 kg, the total weight of YAL is 1.14 kg, and the total weight of CON is 0.15 kg. An illustration of the cooling garments is shown in Figures 2-4.  
Figure 4: The liquid–air cooling garment with inner yarn fabric (YAL).  
Figure 3: The liquid–air cooling garment with silicone tube (SAL).  
Figure 2: The liquid–air cooling garment with aluminum pipe (AAL).
# Deployment Protection for Interference of 5G Base Stations with Aeronautical Radio Altimeters
## 4 Results and Discussion
### Effect of Aircraft Pitch\(\backslash\)Roll Angle  
The interference acceptance angle of the RA antenna is affected by the pitch\(\backslash\)rollangle of the aircraft, which influences the size of the prohibited zone. We define pitch\(\backslash\)roll angle of the aircraft as \(\alpha\backslash\beta\). The depicted scenarios assume that the RA antenna exhibits axisymmetric characteristics. Hence, the effects of a \(30^{\circ}\) pitch (\(\alpha=30^{\circ}\)) and a \(30^{\circ}\) roll (\(\beta=30^{\circ}\)) can be considered almost identical.  
Civil aviation aircraft are unlikely to reach \(\pm 30^{\circ}\) at an altitude of 200 ft. However, assuming a maximum of \(\pm 30^{\circ}\) means the worst-case interference scenario. Figures 18 and 19 provide the results of the prohibited zone with a vertical scan angle of \(10^{\circ}\) (\(\theta=10^{\circ}\)) and aircraft pitch\(\backslash\)roll angles of \(\pm 30^{\circ}\)(\(\alpha\backslash\beta=30^{\circ}\)). The horizontal results are calculated within \(\pm 10\) km. The vertical results are calculated within 15 km. The horizontal and vertical prohibited zones significantly increase at all three altitudes AGL.