esa-sceva/satcom-chunk-collection huggingface dataset:
# A Simple Direct Method to Obtain Kinetic Parameters for Polymer Thermal Decomposition
## 2 Approach for a New Direct Method
### Application to Decomposition Reactions for Overlapping Steps  
The application of the methodology to multiple decomposition reactions requires the segmentation of the TG results. This requirement implies the definition of the different decomposition reactions pertaining to the polymer by directly analyzing the TG and \(dm/dT\) experimental curves by the user or by an automatized method to define the different reactions.  
One way to automatize the definition of the decomposition reactions is to associate each peak of the \(dm/dT\) curve with a reaction. In the \(dm/dT\), the local maximum defines the points of the different reactions when more mass is released, and the local minimum is the separation between the different reactions. It is necessary to remark that this method separates decomposition by the predominant reaction in each temperature range considering decomposition reactions as consecutives.  
According to this, the second derivative of the mass loss (\(dm^{2}/dT^{2}\)) helps to directly obtain \(dm/dT\) maximum and minimum when \(dm^{2}/dT^{2}\) is equal to 0. This can be used to define material reactions, as shown in the Figures 3 and 4 for the NOMEX. In Figure 3, we  
Figure 2: Location ofintervals for reaction order determination.  
can see two different peaks in the NOMEX \(dm/dT\). The first peak is associated with the first reaction and the second peak with the second reaction, respectively. In Figure 3, we can appreciate that the separation between both reactions is produced at the same temperature of the zero value of \(dm^{2}/dT^{2}\) (vertical grey line). Figure 4 shows the representation of the two considered reactions in the TG curve.  
Once the different reactions considered are defined, the proposed methodology is applied independently to each one of them. To do so, the degree of conversion, \(\alpha\), is calculated for each reaction using Equation (1), as shown in Figure 5.
# Augmented Reality in Surgery: A Scoping Review
## 4 Results
### Surgical Training and Medical Education  
AR is assuming a fundamental and emerging role also for what concerns surgical training and medical education [79; 80; 81; 82; 83]. Its introduction results in providing students with a better anatomic conceptualization and allows surgical simulations to improve their performances [84]. AR ensures the possibility to practice surgeries without risks for the patient, saving the need of a supervisor and consequently reducing costs for the structures [85]. It also provides an increasing acquisition of skills such as speed, ability to multitask, accuracy, hand-eye coordination and bimanual operation. The evolution of this system has led to the use of telemonitoring, where experienced surgeons can train students remotely, and also to take part in consultations among experts located in different countries [86].
# Aerostat-Based Observation of Space Objects in the Stratosphere
## 2 Astronomical Observation Conditions in Stratosphere
### Temperature and Humidity  
The environmental temperature in the stratosphere airspace is in the range of \(-\)70 \({}^{\circ}\)C to \(-\)50 \({}^{\circ}\)C [11]. Above 20 km, the environmental temperature shows an upward trend, which is contrary to the troposphere. In the scientific experiment of a high-altitude balloon flight conducted by the Institute of Space Information Innovation, CAS, on 16 August 2019, in Dachaidan district, Qinghai province, the temperature sensor was placed inside and outside of the pod and worked during the whole flight. This experiment lasted for 29 h in the stratosphere. The temperature inside the pod during the daytime was in the range of 10 \({}^{\circ}\)C to 30 \({}^{\circ}\)C, and the environmental temperature outside the pod was \(-\)50 \({}^{\circ}\)C to \(-\)40 \({}^{\circ}\)C as measured. The lowest temperature inside the pod at night was \(-\)30 \({}^{\circ}\)C, and the lowest environmental temperature outside the pod was \(-\)70 \({}^{\circ}\)C. The temperature inside and outside the pod differed greatly because the application load and supporting equipment in the pod were in the working or standby state, so the heat radiation made the temperature inside the pod significantly higher than the environmental temperature of the pod surface [12].  
Water vapor in the atmosphere does not affect the infrared transmittance of optical astronomical observation and also sets additional requirements for the maintenance of equipment. The site selection of ground astronomical observation stations considers the humidity of the environment. For example, the atmospheric water vapor (Precipitable Water Vapor, PWV) of the Lenghu observation station in Qinghai Province, China, is lower than 2 mm [13] for 55% of the night. A humidity profile of the atmosphere is shown in Figure 5, which was obtained by the Anhui Institute of Optics and Precision Mechanics, CAS, with a meteorological balloon on 5 August 2020 in the Dachaidan district of Qinghai Province. As can be seen from the figure, the water vapor at about 10 km showed a decreasing trend and could not be detected at about 18 km (also reaching the limit of the sensitivity of the humidity detector). It can be seen that the stratosphere atmosphere is very dry, as analyzed in Section 2.1, which brings excellent atmospheric infrared transmittance for astronomical observation in this airspace, which can carry out infrared detection of space debris and NEA.  
Figure 4: (a,b) Vertical distribution of the aerosol quantity density and mass concentration. Color bars indicate local standard time (from 22:00 UTC\(-\)24 day to 1:00 UTC), showing the observation period of POPS from rise to fall. The dashed line indicates the troposphere top calculated from the radio probe measurements. Data source: [10].
# Trade-Off Analysis for Array Configurations of Chipless RFID Sensor Tag Designs
## 2 RCS of Chipless RFID Arrays  
The concept of the backward link in a chipless RFID tag is based on signal scattering, similar to how a radar system operates [13]. Essentially, the RCS of a chipless RFID tag refers to the ratio of the reflected signal power compared to an ideally smooth sphere with a 1 m\({}^{2}\) cross-sectional area. This value is usually represented as m\({}^{2}\) or normalised value dBsm. In simpler terms, the RCS of a chipless RFID tag determines how effectively it can reflect signals to the reader. This is important for maintaining reliable communication between the tag and the reader.  
There are several ways to improve the RCS of chipless RFID tags, as shown in Figure 1, such as using resonators with a higher RCS value, but it shows a slight improvement as expected [14]. In addition, the same resonator can be placed in a non-periodic arrangement to increase the RCS. However, this arrangement is shown to not considerably increase the RCS level [15; 16]. Moreover, 3D lenses can also be used to improve the RCS of the chipless tag, but that improvement is minimal and utilises more resources [17]. Therefore, the most promising way to increase the chipless tag's RCS is to periodically replicate a unit-cell tag or resonator, either linearly or planar array configurations. Although these replications do not increase the tag data encoding capacity, they significantly enhance the detection probability by increasing the RCS level [18].  
In [19], a configuration is presented, where three 13-bit slot ring resonators (SRRs) are organised into a linear 1 \(\times\) 3 array with an inter-element distance of 1 mm, resulting in an enhanced RCS. This arrangement exhibits a 5 dBsm improvement in RCS compared to an individual SRR. Similarly, another work introduces a 3 \(\times\) 3 planar array of crack sensors based on compass-shaped patch resonators. This array design increases the coverage area for crack monitoring and improves the RCS response of the sensor array by approximately 10 dBsm when compared to a single cell [20]. Additionally, in [21], a configuration involving a 4 \(\times\) 4 planar array of 4-bit SRRs is proposed to extend the reading distance and amplify the RCS value by +20 dBsm in contrast to the RCS of a unit cell tag. So far, all the experiments have concentrated solely on improving RCS through array configurations. However, while RCS is enhanced, other critical parameters, such as the Q-factor and spherical detectability, are reduced. Therefore, balancing these parameters to achieve the optimal outcome is essential.
# Polarization Sensitive Imaging with Qubits
## 1 Introduction  
Imaging using entangled photons is increasingly popular. Quantum imaging applications often use pairs of entangled photons with relatively weak photon flux that causes longer image acquisition times. Increasing acquisition speed impairs image quality as quantum correlations become comparable to the background noise. Recent reports have focused on the resilience of quantum imaging to stray light and overall enhancement of image quality and resolution [1; 2]. In [1], this is achieved by combining the signal and the phase profile using entangled photons and spatial light modulators (SLM). Subsequently, the image is reconstructed using coincidence measurements of four projections operators. In this article, we mitigate the noise detriment by using inverse numerical optimization for the density matrix reconstruction and by assuming that the noise in correlation measurements has a Gaussian probability distribution. Similar techniques are used in quantum state tomography reconstruction [3; 4; 5; 6]. Reconstruction of a quantum state and quantum state tomography are of increasing importance in quantum information science. Quantum state tomography is used to describe entanglement of trapped ions [3] and photons. The technique extracts information about the quantum state of a system. Inverse techniques offer significant improvement over the direct quantum tomography approach [4; 5; 6; 7; 8; 9; 10; 11; 12; 13; 14]. Yet, very little work was done to experimentally demonstrate image enhancement and the superiority of the inverse techniques. In this work, we compare images of birefringent samples obtained from the reconstructed density matrix of entangled states between two techniques, direct quantum state tomography (DQST) and inverse numerical optimizationquantum state tomography (IQST). Both are linearly related to a set of measured coincidence rates [4; 5; 6; 7; 8; 9; 10; 11; 12; 13; 14]. A number of inverse algorithms were proposed with faster convergence time [15; 16; 17; 18]. The performance and image enhancement of these algorithms is outside of the scope of this paper. Scanned images are reconstructed from the levels of entanglement and concurrence calculated for each pixel. The localized birefringence alters the relative phase and polarization of the entangled qubits. These variations in turn result in changes in the correlation measurements for projection operators. The correlation measurements are used to reconstruct the density matrix, from which the calculation of the entanglement follow. The changes in the calculated entanglement between two qubits reflect the localized birefringence in the reconstructed image. The density matrix obtained via inverse numerical optimization technique, IQST, reduces experimental noise and provides a positive, semidefinite unit trace matrix [17; 18; 19]. This technique improves overall image quality and detail resolution.
# Optical Element Surface Defect Size Recognition Based on Decision Regression Tree
## 2 The Layout of the MSDI System  
The layout of the MSDI system is shown in Figure 1. The optical element is put on the X and Y stage, and the movement of the stages is controlled by a motion controller. The incident light irradiates on the optical element surface from white light-emitting diode (LED) sources, which are in annular distribution. When there is no defect on the optical element surface, all incident light will be reflected, and almost no light will enter the MSDI system, which leads the collected image to be dark. But when there is a defect on the optical element surface, a part of the incident light will be scattered, and some scattered light will be collected by the charge coupled device (CCD) of the MSDI system. The defect region in the image will be brighter than the dark background region. Thus, the defect is detected. To further obtain defect size from the image, the gray distribution of the defect is extracted, as can be seen in the inset of Figure 1. Different size defects have different gray distribution. For example, the defect whose width is 4 \(\upmu\)m or 8 \(\upmu\)m, its gray distribution has two peaks. Thus, the defect width can be deduced from the distance between two peaks. However, when the defect size approaches to the diffraction limit of the MSDI system, the defect gray distribution has only one peak, as can be seen from the distribution of the defect whose width is 2 \(\upmu\)m, and the defect size cannot be judged by the distance between two peaks.
## 3 Results and Discussion
### Test of X-Ray Tube  
To validate the device simulation herein, an X-ray tube with the designated geometry and dimensions (a 20 mm cathode-anode separation; a 15 mm diameter; a 5 mm focusing cup height; and a Cu anode) was constructed and tested. The focal spot size was estimated experimentally to be roughly \(\leq\)0.8 mm. The deviation from the optimal simulation result might be attributed to the slightly different electric field distribution in the real device with the tilted anode, as shown in Figure 9, or to the non-ideal alignment of the anode relative to the cathode. The most important applications of X-rays are the nondestructive inspection of electronic devices and medical diagnosis. To demonstrate the good resolution of the cathode design herein, X-ray images of a fine test circuit board and a human tooth (Figure 10) were obtained using our X-ray tube at 50 kV. The FE current was 0.076 mA at 50 kV. The image was taken using a film placed behind the objects, which were irradiated by the X-ray tube. The sample to be analyzed was placed in the direction perpendicular to the cathode-anode axis (_i.e._, to the right side of the anode in Figure 9). The source-to-sample and sample-to-film distances were 10 and 20 cm, respectively. The film used for the imaging was Kodak Dental Intraoral D-Speed (Kodak, Rochester, NY, USA), and the exposure time was determined by adjusting the mAs for a gross optical density of about 1.5 on a portion of the film that was not covered by the test pattern. As shown in Figure 10, the fine structures of the test circuit board and the tooth were clearly resolved using our X-ray tube. The image quality is likely to be similar to that achieved by the conventional X-ray imaging technique, as judged by the fine details in the image of the reference phantom.  
Figure 10: X-ray image of a circuit board and a tooth captured using the self-focusing X-ray tube.  
Figure 9: Photograph of the anode and cathode geometry in our X-ray tube; the anode material is Cu.
# Additive Manufacturing in Underwater Applications
## 5 Areas of Application
### Sensors  
Important applications for underwater purposes are different types of sensors, including whole systems such as fish tracking tags (optimizing a surface) [16]. In this area, the use of 3D printing technology helps in manufacturing more precise devices that better fit specific needs, particularly in the case of scientific activities [102]. An example in this area is the work by [PERSON] et al. [79], who designed a new bio-inspired sensor based on the lateral-line system of fish [79]. It was produced using 3D printing technology as a prototype solution. The main purpose of this sensor was to detect underwater flow disturbances [79]. The research confirmed the usefulness of this solution in underwater environments [79].  
Another group of sensors for underwater applications was inspired by phocid seal whiskers [103; 104]. Their main task was to sense an upstream wake. The results confirm the ability of whisker-inspired sensors to detect a high signal-to-noise ratio, indicating its efficiency in long-distance contexts [103]. This makes this invention potentially useful for application in underwater robots, especially in areas of poor visibility [103]. A similar design was presented by [PERSON] et al. [105]. It was based on the pinniped group: they designed their sensors in the form of whiskers to detect and track the vortices inside the water [105]. The whiskers were fully 3D-printed and produced using a PU core and graphene cover in the form of different patterns. The results proved the applicability of this solution for underwater soft robots [105].  
Moreover, not only single sensors are produced by 3D printing technology, but also whole systems--for example, non-invasive electroencephalography (EEG) sensors for zebrafish monitoring for the investigation of some diseases [106; 107]. Thanks to AM, the material cost and fabrication time could be reduced significantly in comparison to the traditional method of manufacturing [106; 107]. Another example in this area is the design of an underwater 3D scanner and its manufacturing with this technology [108].
# Early Basal Leaf Removal at Different Sides of the Canopy Improves the Quality of Aglianico Wine
## 2 Materials and Methods  
### Vineyard, Leaf Removal Treatment and Vinification  
The early leaf removal experiments were carried out in a commercial Aglianico (_Vitis vinifera_ L) vineyard situated in the Apulia region, southern Italy, near Corato, 41\({}^{\circ}\)04\({}^{\prime}\)35\({}^{\prime\prime}\) N 16\({}^{\circ}\)21\({}^{\prime}\)46\({}^{\prime\prime}\) E, 354 m a.s.l., during the 2016, 2017 and 2018 seasons, as detailed in our previous study [24]. In the vineyard, six rows of 50 m were selected to set up a randomized complete block design, with two rows as a block separated by a buffer row. Within every two rows, three sections of 18 vines per plot were tagged and randomly assigned to the leaf removal treatment groups, with 54 vines for each treatment. For each treatment, 3 replicates were performed (18 \(\times\) 3 = 54 vines). The removal of the basal part of the shoot up to the last cluster was manually performed 15 days before flowering. The following early leaf removal treatments were applied:  
* Control (C): no leaf removal, where all basal leaves were retained in each shoot;
* Leaf removal on the south canopy side (S): 100% of fruit-zone leaves on each shoot were removed from the south canopy side;
* Leaf removal on the north canopy side (N): 100% of fruit-zone leaves on each shoot were removed from the north canopy side;* Leaf removal on the north-south canopy side (NS): 100% removal of fruit-zone leaves on both the north and the south side of the canopy.  
For each trial, about 100 kg of grapes were manually harvested and vinified at the experimental winery of Bari University according to the procedure described by [PERSON] et al. [27]. Briefly, the grapes were crushed and de-stemmed with a stainless-steel crusher-destemmer (Enotecnica Pillan, Camisano Vicentino, Italy) and placed in 100 L vertical stainless-steel vats. Potassium metabisulphite (6 g/100 kg), yeast (_Saccharomyces cerevisiae_ var. Bayanus, Mycoferm CRU05, 20 g/100 kg, Ever, Pramaggiore, Italy) and yeast activator (Enovit, AEB, Venice, Italy) were added. Maceration was performed for 9 days with 2 punch-downs per day. Then, free-run wine was recovered by draining, and the grape pomace was gently pressed to recover press-run wine using an 80 L stainless-steel hydropress (Enotecnica Pillan, Camisano Vicentino, Italy). The free-run and press-run wines were blended and raked after 2 weeks to eliminate gross lees. The wines were bottled after 6 months, without any treatment, and analyzed. For each wine, three bottles were taken and each was analyzed in triplicate.
## 3 Materials and Methods  
### Shale Samples  
A total of 40 shale core samples were taken from two wells, X and Y, in the northeastern Sichuan Basin. The main formations in well X are the Dalong Formation, Wujiaping Formation and Maokou Formation; the main layers in well Y are the Wujiaping Formation and Maokou Formation. Sampling depth of well X: 4213.35-4335.0 m; sampling depth of well Y: 3295.6-3355.47 m. Of these samples, all 15 from the Dallon Formation were taken from the X well; 15 in the Wujiaping Formation, including 8 in X wells and 7 in Y wells; 10 from the Maokou Formation including 3 from well X and 7 from well Y; samples totalled 40. The list of samples is given in Tables 1 and 2 below.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sample Number** & **Wells** & **Sampling Depth/m** & **Stratum** & **TOC (\%)** \\ \hline X-1 & & 4213.35 & & 1.23 \\ X-2 & & 4216.25 & & 1.65 \\ X-3 & & 4224.91 & & 2.60 \\ X-4 & & 4229.1 & & 1.63 \\ X-5 & & 4234.69 & & 2.49 \\ X-6 & & 4243.1 & & 2.87 \\ X-7 & & 4244.71 & & 5.03 \\ X-8 & & 4247.13 & Dalong & 8.00 \\ X-9 & & 4250 & Formation & 0.80 \\ X-10 & & 4253.69 & & 5.07 \\ X-11 & & 4261.76 & & 11.20 \\ X-12 & & 4263.09 & & 6.10 \\ X-13 & & 4266.63 & & 7.75 \\ X-14 & & 4268.47 & & 6.55 \\ X-15 & & 4270.39 & & 0.83 \\ \hline X-16 & & 4278.59 & & 7.63 \\ X-17 & & 4280.78 & & 3.92 \\ X-18 & & 4290.4 & & 5.98 \\ X-19 & & 4296.87 & Wujiaping & 0.57 \\ X-20 & & 4297.28 & Formation & 10.90 \\ X-21 & & 4301.53 & & 3.23 \\ X-22 & & 4310.39 & & 4.69 \\ X-23 & & 4314.24 & & 4.41 \\ \hline X-24 & & 4330.48 & & 17.10 \\ X-25 & & 4331.9 & Maokou & 3.60 \\ X-26 & & 4333.75 & Formation & 5.57 \\ \hline \hline \end{tabular}
\end{table}
Table 1: List of samples from Permian X wells in northeastern Sichuan.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sample Number** & **Wells** & **Sampling Depth/m** & **Stratum** & **TOC (\%)** \\ \hline Y-1 & & 3295.6 & & 12.27 \\ Y-2 & & 3301.3 & & 8.76 \\ Y-3 & & 3304 & & 16.98 \\ Y-4 & & 3307.9 & Wujaping & 8.18 \\ Y-5 & & 3308.7 & Formation & 4.78 \\ Y-6 & & 3323.38 & & 6.80 \\ Y-7 & & 3324.38 & & 14.70 \\ \hline Y-8 & & 333
## 5 A Case Study in Engineering Application
### The Effectiveness of the Resilient Recovery Method based on Outer Synchronization
It can be seen that the position of the removed links has different effects on resilient recovery and there are also primary and secondary points in the backbone links. For example, the removed links in network 5 are more important than the removed links in network 3. As Figure 15 shows, removing links belonging to the backbone causes greater damage than removing the other redundant links. Therefore, it is very important to identify the network backbone during resilient recovery.