esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Calculation of the Thermal Conductivity: Physical Principles  
Heat is transmitted mainly by three widely studied thermal mechanisms: conduction, convection, and radiation. In the subsurface, the main heat transfer mechanism is conduction [53]. Heat transfer by conduction, in an isotropic medium and under stationary conditions, is described by Fourier's Law. Applying Fourier's Law for one dimension, we obtain Equation (2).  
\[q_{x}=\lambda_{x}\times\
abla T \tag{2}\]  
where  
\(q_{x}\) [W * m\({}^{-2}\)] represents the heat flux.  
\(\lambda_{x}\) [W * k\({}^{-1}\) * m\({}^{-1}\)] stands for the thermal conductivity in the x direction.  
\(\
abla T[\)K * m\({}^{-1}\)] indicates the thermal gradient between the heat source and the heat sink.  
It also can be expressed as follows (Equation (3)):  
\[\
abla T=\frac{dT}{dx}\cong\frac{\Delta T}{x} \tag{3}\]  
Figure 4: Distribution of the temperature and heat flux sensors within the green swale cross-section.  
where  
\(\Delta\)T [K] is the temperature differential.  
\(x\) [m] represents the total thickness of the green swale model.  
Since the values of heat flux and temperature gradient have been registered in the laboratory, the equivalent thermal conductivity was directly computed as it is the constant ratio between the two of them. In addition, the thermal transmittance (U) can be obtained by applying Equation (4):  
\[U[W*k^{-1}*m^{-2}]=q_{x}/\Delta T \tag{4}\]  
Convective heat transfer, on the other hand, occurs across the boundaries between the different layers. If radiative heat transfer is not taken into account, the heat transfer by conduction between two surfaces can be defined as follows (Equation (5)) [54]:  
\[q_{TCC}=k_{TCC}*(T_{c,T}-T_{c,C}) \tag{5}\]  
where  
\(q_{TCC}\) [W * m\({}^{-2}\)] represents the heat flux in the interface.  
\(k_{TCC}\)[W * m\({}^{-2}\) * \({}^{\circ}\)C] stands for the thermal conductivity coefficient at the contact interface. Previous research suggests a value of 2 kW/m\({}^{2}\)\({}^{\circ}\)C [54].  
\(T_{c,T}\), \(T_{c,C}\) [\({}^{\circ}\)C] are the contact point temperatures registered in the target and the contact surfaces, respectively.
# Automatic Brix Measurement for Watermelon Breeding
## 2 Materials and Methods  
The watermelon variety used for the study was ZaoJia 8424, which is bred and released in China. The watermelons were grown in an open field located at a longitude of 28.18\({}^{\circ}\) and a latitude of 113.07\({}^{\circ}\), and were picked on 11 July 2022, 119 days after sowing. A total of 25 watermelons were sampled from a single plot for the experiments. On the day they were picked, as shown in Table 1, the vertical diameter, horizontal diameter, and diameter along 45\({}^{\circ}\) of the watermelons were measured using a lathe slide with an accuracy of 0.05 mm; the weights were measured using an electronic balance with an error within 0.1 g.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**No.**} & \multirow{2}{*}{**Weight/kg**} & **Vertical** & **Diameter** & **Horizontal** \\  & & **Diameter/mm** & **Along 45\({}^{\circ}\)/mm** & **Diameter/mm** \\ \hline
1 & 0.521 & 103.0 & 103.1 & 101.9 \\
2 & 0.689 & 112.1 & 110.5 & 110.7 \\
3 & 0.838 & 121.5 & 120.0 & 120.0 \\
4 & 0.875 & 115.7 & 126.5 & 123.1 \\
5 & 1.088 & 126.6 & 128.6 & 130.0 \\
6 & 1.135 & 128.7 & 127.5 & 127.5 \\
7 & 1.204 & 134.5 & 132.7 & 131.5 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Information about watermelon samples.
## 4 Results  
### Illustrative Study  
For the demonstration and evaluation of the presented concept, the excavation of a 2000 m long metro tunnel with a diameter of 9 m, which is excavated with a slurry shield machine, is simulated. The excavation takes place in gravel-sand soils and passes through three stations that have not yet been excavated. An overview of the tunnel with the areas to be excavated is shown in Figure 9.  
Within the scope of this simulation study, the data of a completed reference project are used. However, certain data underwent minor adjustments, such as the exclusion of a significant accident from the project's data. The primary input data of the simulation are based on data from the preliminary planning, historical data, and engineering assumptions.  
Figure 8: Block definition diagram of the analyzed simulation model.  
Figure 9: Overview of the illustrative tunnel project (Area A: tunnel section between the stations; Area B: excavation of the stations) [34].  
For the replication of the real-time simulation, the data of the reference project, which were known at the respective update time depending on the construction progress, were queried directly by process controlling with the help of API requests. Only the data which were recorded in real time up to that point were used and processed as quasi-real-time input values. In this way, the boundary conditions of a real-time simulation during construction were simulated, and the example could be used to evaluate the developed concept. The process times per ring, i.e., the total ring construction time, were used to study and update the functions. For the advance speed, the average value per ring was used.  
The adjustment of the input data was achieved by integrating the current construction progress into the simulation model and updating the input data for the process durations of the two core processes, excavation and ringbuild, in three steps. Instead of updating the geotechnical parameters to define a new advance speed, here, the advance speed is updated directly. It is recommended to update the geotechnical parameters in a detailed case study in order to be able to predict other aspects, such as tool wear, with the help of the process simulation. The results of the simulation study are generated by MCS to account for the uncertainties of the input data.
## 2 Experimental Apparatus Description  
To perform the experiments, an open-loop experimental apparatus located in a normal environment (1 atm) is used. The experimental apparatus is shown by the photographic view and schematic diagram in Figures 1 and 2. The experimental setup mainly consists of a tubular heat exchanger, augmentation inserts, a data collection method, and measurement devices. The heat exchanger is built of an aluminium conduit with an outside diameter of 5 cm, an inner diameter of 4.5 cm, a thick of 0.25 cm, and a length of 13.50 cm. Figure 3 shows the geometric configurations of the tubular heat exchanger, basket turbulators, and twisted-tapes inserts. The twisted-tapes are made of galvanized iron and have twist ratios of TR = 2, 3, and 6 (w = 1.5 cm, y = 3-9 cm). Five pieces of basket turbulators, which are made from the iron bar, are used in this experiment with a constant distance between them (PR = 150 mm). The basket turbulators are inserted into the heat exchanger in a wall-attached position, and the twisted tapes are tightly fitted into the centre of the basket turbulators. The heat exchanger is Ohmically heated using continuously whining flexible nickel-chrome wire to obtain a boundary condition with a homogeneous heat flux. A voltage regulator is applied to regulate the input power and automatically maintain the heat flux within acceptable limits along with the heat exchanger. Twenty-two calibrated thermocouples (type k) with a temperature range from \(-\)58 to 1298 \({}^{\circ}\)C are used in positions which are eighteen thermocouples installed along the heat exchanger's wall to determine the distribution of the wall temperature. To acquire the bulk air temperature, four thermocouples are fitted in the input and exit sections. Table 1 shows the placement of the thermocouple along the heat exchanger. All thermocouples are linked to a selection switch, which is subsequently linked to a multi-channel digital data logger system. The exterior surface of the heat exchanger was thermally insulated by an asbestos layer and a fibreglass sheet to reduce heat loss.  
Figure 1: Photographic view of the using experimental apparatus.  
Figure 2: Schematic of the using experimental apparatus.
# 5G-Compliant Authentication Protocol for RFID
## 4 Analysis
### Passive Adversaries  
According to 5G, the \(SUPI\) and sequence number (that also leaks information about the frequency of connections) must be protected from eavesdroppers to guarantee the privacy of the subscribers. In the 5G-compliant RFID protocol the sequence number is not involved in the RFID part and the \(SUCI\) is protected during the initialization procedure by using the masking value \(AK1\).  
Theorem 4.1: _A passive adversary cannot distinguish the messages exchanged during the 5G-compliant RFID protocol from random messages with probability better than negligible (in terms of the length \(n\) of the key \(k\))._  
Proof: We first prove the result for the initialization procedure (Lemma 1) and then for the main protocol (Lemma 2). We note that one cannot extract any specific message from a pseudo-random message with probability better than negligible.  
Lemma 1: _A passive adversary cannot distinguish the exchanged messages from random messages with probability better than negligible during the initialization procedure._  
Proof: (Sketch) The messages exchanged during the initialization procedure are:  
\[\langle r_{i},SUPICi,PKC1 Ci,PKC2 Ci,AUTNi,CONFIG\rangle.\]  
We are assuming that \(h1(k,\cdot),h2(k,\cdot),h3(k,\cdot),h5(k,\cdot),h6(k,\cdot)\) are jointly PRF. Note that if we XOR any constant values to one or more of these functions, then we do not loose pseudo-randomness. In particular, for the constants \(SUPI\), \(pk1\), \(pk2\): the functions,  
\[h1^{*}(k,\cdot) = h1(k,\cdot)\oplus SUPI\] \[h2^{*}(k,\cdot) = h2(k,\cdot)\oplus pk1\] \[h3^{*}(k,\cdot) = h3(k,\cdot)\oplus pk2\] \[h5^{*}(k,\cdot) = h5(k,\cdot),\text{ and}\] \[h6^{*}(k,\cdot) = h6(k,\cdot),\]  
are jointly PRF. However, these functions for joint input:  
\[\langle r_{i},r_{i},r_{i},r_{i}\|SUPCi\|PK1 Ci\|PK2 Ci,SUPIi\|AK1i\rangle,\]  
will output the exchanged messages \(\langle SUPICi, PKC1 Ci, PKC2 Ci,AUTNi,CONFIG\rangle\). It follows that they must be pseudo-random.  
Lemma 2: _A passive adversary cannot distinguish the exchanged messages from random messages with probability better than negligible during the authentication process._  
Proof: (Sketch) We use a similar approach. In this case, the messages exchanged are:  
\[\langle r_{s},ARt,AUTNt,RESt,CKt,IKt,AUTN,INTREST,XRES^{*}\rangle.\]The first five (excluding \(r_{s}\)) can be defined in terms of the functions: \(h1,h6,h2,h3,h4\) that are jointly PRF. These functions, on joint input:  
\[\langle r_{s},r_{s}\|ARt,r_{s},r_{s},r_{s}\rangle\]  
will output the five exchanged messages (excluding \(r_{s}\)). \(AUTN\), \(INTRESt\) and \(XRES^{*}\) are 5G-AKA messages generated by \((f1,f5)\), \(h5\) and a KDF that are also pseudo-random and independent. The \(SUPI\) is not involved in the communication between the tag and the reader, and only its randomized asymmetric encryption \(SUCI\) is transmitted by the reader if SN requests it. This uses an independent random number \(n_{e}\). It follows that the exchanged messages are jointly pseudo-random.
## 2 Mission and Satellite Design
### Mission and Satellite  
The S-STEP space segment was optimized for operation in low Earth orbit (LEO) with the required radar performance and technology capability maintained, and it is compatible with a wide range of launchers owing to its compact shape and low mass [27]. The flat-panel shape of the spacecraft efficiently fits the shape of typical launcher faring. The solar array and SAR antenna are displayed in Figure 4a,b, respectively. The majority of the electronic units are accommodated on the rear of the SAR antenna, the length and width of which are 1970 and 1060 mm, respectively. In the nominal altitude of the mission orbit, the SAR antenna off-nadir angle was 23.7\({}^{\circ}\).  
The critical tasks of the S-STEP satellite bus include providing the SAR instrument with adequate energy and precise pointing, as well as maintaining a stable orbit and appropriate thermal conditions. In addition, the spacecraft employs a fully passive thermal-control approach [26].  
The system specifications of the S-STEP satellite are listed in Table 1. The S-STEP microsatellite bus is a low-cost platform that mostly adopts the COTS units in addition to the integrated avionics unit (IAU), attitude control system unit (ACSU), and X-band modulator (X-MOD). The onboard computer of the microsatellite SAR platform was developed as a unit in which the common components of the command and data handling unit, solid-state recorder unit, ACSU, and X-MOD comprising the platform were integrated and shared to form the IAU. The common components include a high-performance FPGA (Xilinx Kintex UltraScale), large-volume memory, and regulators. The IAU onboard computer includes a processor module based on the LEON3 Fault-Tolerant SPARC V8 soft-core processor, which is executed on the FPGA with RTEMS as a real-time operating system.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Specification** & **Value** \\ \hline Mission lifetime & 3 years \\ Total spacecraft mass & 80.3 kg excluding launch adapter \\ Satellite size & 1970 \(\times\) 1060 \(\times\) 200 mm \\ Power & Generation: 340 W (BoL) \\ Storage: 648 W \\ Inter-satellite link & RF (S-band, 20 kbps) \\ Telemetry telecommand (TMTC) & S-band (Up: 32 kbps; Downr: 1 Mbps) \\ Datalink & X-band (1 Gbps) \\ Stabilization method & 3-axis stabilized \\ Pointing accuracy & 0.085\({}^{\circ}\) (3\(\sigma\)) \\ Image acquisition time & 60 s max \\ \hline \hline \end{tabular}
\end{table}
Table 1: S-STEP satellite system specifications [25; 26; 27].  
Figure 4: Mechanical configuration of the S-STEP satellite viewed from the (**a**) solar-panel side and (**b**) SAR-antenna-panel side. Patches appearing in the SAR antenna in (**b**) are schematic representations for understanding. In an actual SAR antenna, the applied inverted patches are invisible.
# HERMES CubeSat Payload Thermal Balance Test and Comparison with Finite Volume Thermal Model
## 3 Test Procedures
### Test Configuration
Once the shield is placed (Figure 11c), all thermocouples are plugged to the TVAC feedthrough connectors. The same goes for the heaters by means of a dedicated D-SUB 50 pin connector at the TVAC interface. Heaters are connected to three different channels with 2 GPS-43043 power supplies ([[https://it.farnell.com/gw-instek/gps-4303](https://it.farnell.com/gw-instek/gps-4303)]([https://it.farnell.com/gw-instek/gps-4303](https://it.farnell.com/gw-instek/gps-4303)) /alimentazione-4-o-p-3a-30v-reg/dp/2748523 (accessed on 2 November 2022)) which are manually set and limited in current. Whenever requested, the power supply is turned on with the values reported in Table 1, and Figure 11d. All TCs used and their positions are reported in Figure 4:1 \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Channel ID** & **Component** & **Wire Color** & **Voltage [V]** & **Current [A]** & **Power [W]** \\ \hline pch1 & PSU & Red & 3.9 & 0.37 & 1.44 \\ pch2 & BEE & Black & 3.9 & 0.37 & 1.44 \\ pch3 & PDHU & Blue & 2.5 & 0.24 & 0.60 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Heaters settings. Figure 12: **(Top)** Radiation shield panels and base plate temperatures, (**Bottom**) temperature of structural components. Inside the pale red bands heaters are on.
## 3 Results and Discussion
### Relationship between Microstructure and Mechanical Property
Meanwhile, accompanied by the phase change during the cooling process after hot rolling, the steel plate eventually presents inhomogeneity of microstructures and mechanical properties in the thickness direction [35, 36, 37]. In this work, the microstructure's inhomogeneity and its effects on mechanical properties in an as-quenched thin specification and high-strength NM450 TP wear-resistant steel was investigated and discussed based on systematic quantitative characterization. The results show that NM450 TP steels are only composed of ferrite and martensite, no residual austenite was found because of the fast cooling rate. Martensite and ferrite grains show directional features due to a certain degree of deformation of prior austenite rolled in the temperature range of incompletely dynamic recrystallization, meanwhile, cooling rate and deformation of prior austenite through the thickness of the steel cause the microstructures' inhomogeneity and the anisotropic mechanical properties. Although the content of martensite and ferrite phases were found to have inhomogeneous distribution through the thickness, the steel still has high strength and good ductility, this is because NM450 TP steel has lower carbon content, and the formed lath martensite is a phase with high strength and good ductility due to its high density of dislocations. In addition, silicon manganese and chromium elements offer the benefit of strengthening the ferrite phase, and extra carbon expelled by the formed ferrite will increase the strength of lath martensite. The results will be helpful for understanding and modifying parameters in the fabricating process of the dual-phase steels and their subsequent processing such as bending and welding processing. The texture examination indicates that NM450 TP steel with high strength might have good formability because of its higher content of the near \(\gamma\) texture, while the anisotropic features of this steel should be concerned in the case of bending, the result will improve the understanding of the subsequent bending investigation and application of the steel. The inhomogeneous morphology of the dual phases can influence the microstructure evolution in the heat affected zone and mechanical properties of the welded joints.
# Blockchain Technology in Healthcare: A Comprehensive Review and Directions for Future Research
## 4 Supply Chain Management
### Blockchain Use Cases in Supply Chain Management  
**Claims and Billing Management**: Healthcare service has its own cost which already makes the whole industry worth a trillion dollars, and it is increasing rapidly [126]. The process of medical billing is an integral part of the healthcare sector. This is because, without billing, a proper service delivery cannot be ensured. This process starts from the time the patient is admitted to the hospital to the time it is checked-out. It involves several steps, such as check-in, confirming financial responsibility, coding and billing compliance, transmitting the claim, and receiving payment from insurance companies [127]. The entire billing scheme can be challenging as some of the fees are entirely covered by the patient's individual health insurance plan, or paid by the patient. One of the main issues in medical billing is excessive billing due to the lack of transparency and trust among doctors, patients, and insurance companies. Claims and billing in the healthcare sector are being continuously abused, but can be resolved or diminished by using a transparent system for every stakeholder. Blockchain can ensure such transparent system keeping everyone engaged in the whole process and removing mistrust among them [126].  
**Quality management:** A drug is considered as counterfeit, if it contains improper ingredients, and traded with the intent to hide or imitate its provenance, authenticity or even effectiveness [128; 129]. Furthermore, products and drug counterfeit greatly influence SCM [130]. Their performance in the pharmaceutical industry are competitive factors that disrupt the efficiency, authenticity and robust profitability in a particular healthcare industry enormously [131]. Customers are often unaware of the exact sources of the products they purchase and consume in a global marketplace [132]. Since this type of drug is malicious for a patient, it is a life-threatening issue around the world [133]. This also causes a threat to the reputation of the original pharmaceutical companies, making drug manufacturers and distributors invest a huge amount of money in countermeasures [128]. The study in [133] found that techniques such as spectroscopic and chromatographic are effective for detecting counterfeit because of their detection of active ingredients and image sample composition. However, they have their limitations as it relies on electromechanical apparatus, which increased the overhead cost. One way to solve these challenges is to have pharmaceutical manufacturers' information, including product serial numbers and package number on the blockchain, whereby pharmaceutical companies, drug manufacturers and customers could verify the authenticity of the data by connecting to the blockchain. This process ensures low-cost quality control, product registration, drug tracking, and drug counterfeit through the entire SCM process [131].
## 4 Experimental Results and Discussion
### Databases Description  
To evaluate the performance of the proposed texture classification method optimized by HALO, three public texture databases were used in the experiment. The first database CGT [37] offers digital pictures of all sorts of materials with the pictures of fabric, wood, metal, bricks, plastic, and these texture images can be used for graphic design and visual effects. In the experiments, 18 homogeneous texture images from the database were as shown in Figure 2, all chosen texture images without any rotation and 10 images for each class were utilized as training samples, while the other 50 images were used as testing samples.  
The second database Kylberg [38] was imaged under only one light setting from one direction on the same distance. Textured surfaces are arranged, such as oatmeals, linseeds, lentils, the texture  
Figure 2: Samples of the 18 categories randomly selected from the CGT database.  
samples with the same category have 12 different angles of rotation with 30 degrees increment. In the experiments, 20 homogeneous texture images from the database were as shown in Figure 3, and images without any rotation were utilized as training samples with the number per category set as 15, and 60 images with other angles of rotation per category were used as testing samples.  
The third database is Brodatz [39] with different background intensities, and the figure below gives an example of 40 different texture features organized into 5 columns. For example, D6 has a black background, whereas D10 has gray and white backgrounds, and D101 is a regular texture, whereas the texture type of D111 is irregular. In the experiments, 30 homogeneous texture images from the database were as shown in Figure 4, all of the images were rotated with a 20\({}^{\circ}\) step, images with 20\({}^{\circ}\) rotation were utilized as training samples with the number set as 6 for each class. The rest of the 48 images were utilized as testing samples.