esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Principle of Operation of the Single Transistor Soil Water Content Sensor
\[\Delta T=T_{f}-T_{0}=\frac{Q_{l}}{4\pi k_{s}}ln\left(t_{f}-t_{0}\right) \tag{4}\]  
where \(T_{0}\) and \(T_{f}\) are the initial and final temperature, measured at times \(t_{f}\) and \(t_{0}\), \(Q_{l}\) is the heat input per unity length of the heater (W/m), and \(k_{s}\) is the thermal conductivity of the soil (W/m K). Therefore, by measuring \(\Delta T\) as a function of time, we can calculate \(k_{s}\) and correlate its value with the soil water content. The initial transient of temperature that occurs immediately after the heat pulse is applied is usually discarded, and the temperature is measured only after \(t_{0}\), which is taken at 1 or 2 s after the instant that the heat pulse is applied. It is important to notice that, although the soil's thermal conductivity depends on its water content, the thermal conductivity is also affected by both the soil's contents of mineral and organic material and its density [17; 18], and before using the sensor in a soil, an experimental calibration procedure in the laboratory is required. In the field, the sensors are used at a maximum rate of one reading per day, but usually only one reading is taken every three or four days.
# CRC-Based Reliable WiFi Backscatter Communication for Supply Chain Management
## 3 CRCScatter Decoder Design  
### CRCScatter Decoder Overview  
The overview of decoding employed by CRCScatter at the receiver is illustrated in Figure 4. The procedure of decoding tag data can be divided into two steps.  
The first step involves reversing the backscatter packet into the original packet. The received backscatter MAC frame consists of the MAC header, the frame body, and the FCS, but the tag only modulates the frame body. The MAC header and the FCS in the original packet are the same as those in the backscatter packet. However, the original data cannot be obtained directly because the frame body has been modified. The IEEE 802.11b protocol specifies the correlation between FCS and the transmitted data in WiFi packets. The CRC reverse decoder utilizes this intrinsic correlation as a constraint in calculation and uses two CRC algorithms to reverse original data from the backscatter packets.  
The second step is to calculate tag data based on backscatter data and original data obtained through the CRC reverse decoder. In 802.11b data transmission, DBPSK modulation is used, whereas BPSK modulation is adopted by the tag. Due to this difference in modulation, the original XOR decoder in HitchHike cannot obtain the correct tag data. To overcome this, CRCScatter employs a decoder that combines XOR operation and differential decoding to accurately calculate tag data.  
In summary, the CRCScatter system utilizes two decoders to compute the original packet and tag data in steps. While the function of the CRC reverse decoder is to retrieve original data from the backscatter packet, the XOR and the differential decoder can obtain correct tag data from original data and backscatter data.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Tag Bit** & **Phase Offset** \\ \hline
0 & 0 \\
1 & 180\({}^{\circ}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Encoding at the tag.
## 2 Importance of Thermal Condition Monitoring
### Solar PV Panels  
The actual operation of solar PV modules, according to numerous characterized experiments, prove that electrical efficiency decreases significantly with the increase in the temperature of photovoltaic cells when exposed to the sunlight [11; 13; 26; 27]. The loss of electrical power can be enormous if there are no provisions for cooling on the module, especially in tropical regions. When sunlight falls on the solar PV cells, part of the radiation converts into electrical energy, but others reflect or convert to thermal energies [28]. The electrical energy may be stored in a battery for later use or transferred directly to the grid, but the thermal energy available on the surface of the module still exists only to be recovered by the air blowing as a cooling medium.  
In situations where the air temperature is high, the cooling becomes ineffective, and thus creates an adverse thermal effect on the PV modules. In developing power efficiency for solar PV installation, the measured data should include solar irradiation, ambient temperature, and wind speeds to prove the power generation commensurate with the efficacy of module surface cooling by the air velocity [27; 28]. The temperatures that are considered critical in the installation of the solar PV module are the cell design temperature and the temperature of the air flow. With these two temperatures, an evaluation of the electrical and thermal performance of the PV modules can be assured, regarding efficiency and power delivery. The increase in PV cell temperature has the effect of reducing the power efficiency proportionally when the reaction of air-flow temperature increases, hence the need to monitor thermal effects against power delivery efficiency [11; 13; 27; 28; 29; 30].
_Article_  
**Micromagnetic Modeling of All Optical Switching of**  
**Ferromagnetic Thin Films: The Role of Inverse**  
**Faraday Effect and Magnetic Circular Dichroism**  
**[PERSON]\({}^{\copyright}\), [PERSON], [PERSON]\({}^{\copyright}\), [PERSON], [PERSON] and [PERSON]\({}^{\ast}\)**  
Departamento de Fisica Aplicada, University of Salamanca, 37008 Salamanca, Spain; [EMAIL_ADDRESS] (V.R.); [EMAIL_ADDRESS] (R.G.); [EMAIL_ADDRESS] (F.G.-S.); [EMAIL_ADDRESS] (M.A.H.); [EMAIL_ADDRESS] (M.Z.)  
\({}^{\ast}\) Correspondence: [EMAIL_ADDRESS]  
Received: 30 January 2020; Accepted: 11 February 2020; Published: 14 February 2020  
**Featured Application: Existing magnetic recording devices require the application of pulsed strong magnetic fields and/or electrical currents. The duration of these typical pulses needed to reverse the initial state at the nanoscale lies in the nanosecond regime, which imposes a limit for the maximum speed of writing operations. Manipulation of the magnetic state by ultrashort laser pulses is interesting for the future development of novel magnetic recording devices. Differently from conventional field or current pulses, the deterministically control of the magnetic state by means of ultrashort laser pulses with duration of a few femtoseconds will constitute a major step for next future of laser induced recording devices. The present manuscript provides novel and efficient numerical methods which allow us to explore the physics behind all-optical switching and domain wall motion observations in ferromagnetic systems with perpendicular anisotropy.**  
**Abstract: There is a lot of experimental evidence of All Optical Switching (AOS) by applying ultrashort laser pulses on ferromagnetic thin films with perpendicular magnetic anisotropy. However, the physical origin behind these processes remains under debate. In addition to the heating caused by the laser pulses, the Inverse Faraday Effect (IFE) and Magnetic Circular Dichroism (MCD) have been proposed as the most probable phenomena responsible for the observations of helicity-dependent AOS. Here, we review the influence of both phenomena by means of realistic micromagnetic simulations based on the Landau-Lifshitz-Bloch equation coupled to the heat transport caused by the laser heating. The analysis allows us to reveal the similarities and differences between both effects. While both mechanisms may lead to the local inversion of the initial magnetic state of a ferromagnetic sample submitted to a train of circularly polarized laser pulses, the Inverse Faraday Effect proves to be more efficient for nucleation and domain wall movement and it reproduces more accurately the different magnetic configurations that the experiments report for different values of the fluence of the laser beam.**  
**Keywords: micromagnetism; helicity-dependent all optical switching; Landau-Lifshitz-Bloch equation; two temperatures model; Inverse Faraday Effect; magnetic circular dichroism**
## 3 Results and Discussion
### Correlations between Abrasion Rate and Compressive Strength  
The abrasion resistance of concrete without steel fibers with a compressive strength ranging from approximately 31 to 91 MPa was investigated. The relationship between the average abrasion rate and compressive strength of concrete is illustrated in Figure 14. The results indicated that the average abrasion rate of concrete increased as the compressive strength decreased. As the compressive strength decreased from 50 to 31 MPa, the average FAR, IAR-45\({}^{\circ}\), and IAR-90\({}^{\circ}\) increased to 0.08, 0.80, and 1.53 per MPa, respectively, and they increased to only 0.04, 0.21, and 0.52 per MPa, respectively, as the compressive strength decreased from 91 to 50 MPa. The result confirms that compressive strength was a crucial factor affecting the abrasion resistance of the concrete without steel fibers. This can be attributed to the fact that increases in the compressive strength of concrete, which consequently  
Figure 12: Impact abrasion rate (IAR) of concrete.  
Figure 13: FAR and IAR of concrete.  
enhanced mortar strength, were comparable to those of the coarse aggregate; both the aggregate and concrete were concurrently subjected to the same abrasive forces in the water-jet test or particle wear test. Additionally, the interfacial bond strengthened, and the equivalent peeling and grinding on the aggregate and mortar enhanced the overall performance of the concrete in terms of abrasion resistance. This finding confirms the results from other studies [5; 7; 16].
# A 3D Vision-Based Weld Seam Extraction Method for Arbitrarily Placed Spatial Planar Weldments
## 2 Improved RANSAC for Plane Segmentation
### Improved RANSAC
#### 2.2.3 Optimization for Inlier Validation and Update Criterion  
In the irregular scenario depicted in Figure 2(d), a correctly segmented plane erroneously includes inliers belonging to other planes, thereby causing interference with the remaining points to be segmented. This situation underscores the potential for a candidate plane to occupy points intended for other planes.  
Rule 3. Inlier validation.  
Besides the distance threshold, the points selected as inliers must have similar normal vectors as the segmented plane. Under this rule, a point \(p_{i}\) is a valid inlier of plane \(S\) only if the following conditions are satisfied:  
\[\left\{\begin{array}{ll}\text{distance}(p_{i},S)<\sigma_{d},\\ \text{arccos}\bigg{(}\frac{|\mathbf{n}_{i}\cdot\mathbf{n}_{S}|}{\|\mathbf{n}_{i}\|\|\mathbf{n} _{S}\|}\bigg{)}<\sigma_{a}.\end{array}\right.\ i\in\{1,2,\ldots,l\}, \tag{9}\]  
where \(\sigma_{d}\) is the distance threshold and  
\[\text{distance}(p_{i},S)=\frac{|Ax_{i}+By_{i}+Cz_{i}+D|}{\sqrt{A^{2}+B^{2}+C^ {2}}}, \tag{10}\]  
for point \(p_{i}=(x_{i},y_{i},z_{i})\) and plane \(S\) representing \(Ax+By+Cz+D=0\).  
Another irregular situation, exemplified in Figure 2(c), arises when the distance between two parallel planes is less than the distance threshold \(\sigma_{d}\). In this case, the central plane, marked as \(S_{3}\) in Figure 3, is prioritized over \(S_{1}\) and \(S_{2}\) to be chosen as the candidate plane, as per the iteration update in Figure 2, which selects a new candidate plane if it has more inliers than the historical best one. While a straightforward solution would be to use a smaller \(\sigma_{d}\), altering such a significant variable in RANSAC could introduce unexpected variability in the output. Hence, there is a need to revise the update criterion accordingly.  
To begin with, we introduce variable \(\mathbf{e}\) as the local segmentation error of a candidate plane:  
\[e=\sum_{i=1}^{l}\bigg{(}\gamma\text{distance}(p_{i},S)+(1-\gamma)\frac{|\mathbf{n }_{i}\cdot\mathbf{n}_{S}|}{\|\mathbf{n}_{i}\|\|\mathbf{n}_{S}\|}\bigg{)}, \tag{11}\]  
where \(l\) is the number of inliers of the current candidate plane \(S\), \(\mathbf{n}_{i}\) is the normal vector at \(p_{i}\) and \(\gamma\in[0,1]\) is the weight to control the significance of distance errors and angle errors. Rule 4. Update criterion.  
For a new candidate plane \(S_{j}\) in the \(j\)th iteration, it outperforms the historical best iteration \(k\) only when  
\[\rho_{j}=\frac{l_{j}}{e_{j}}>\frac{l_{k}}{e_{k}}. \tag{12}\]  
The introduction of the new objective value \(\rho\) provides a refined measure of the priority of a candidate plane, considering not only its number of inliers but also the distance and angle errors. This modification substantially mitigates situations akin to Figure 2(c), where parallel planes may erroneously interfere with each other.
## 3 Results and Discussion
### Real-World Device Tests
In turn, shifts in operating points lead to significant temperature gradients within the structures of nonlinear components. The self-balancing capability of the low-frequency amplifier was verified according to the following procedure. A sinusoidal signal with a frequency of 1 kHz was applied to the input of the system under test loads with a resistance of 8 \(\Omega\). At time \(t=0\), the system was supplied with a nominal voltage of 70 V. The supply voltage was increased in increments of 1 V at 50 s intervals until 80 V was reached, which occurred around 550 s into the test. Then, also at 50 s intervals, the voltage was stepped down by 1 V until a nominal value of 70 V was reached, which occurred at 1000 s. The supply voltage spikes affected both the circuit with BJT transistors and the power follower with MOSFET transistors. A diagram of the test is shown in Figure 15. The system shows a tendency toward a minimum value of \(U_{DC}\) (and therefore, the desirable self-balancing capability) for all IRFP transistor pairs tested. In particular, regardless of the configuration, the system in question maintained \(U_{DC}\) as low as possible despite sudden changes in the supply voltage. No undesirable increases in the value of \(U_{DC}\) associated with temperature changes in the internal structures of the active elements were observed. Abrupt changes of the supply voltage were accompanied by momentary changes in the value of \(U_{DC}\) at load resistances in the range of \(\pm\)1.4 mV, which is easily noticeable on the waveforms over time presented in Figure 15. However, it should be emphasized that between the successive step changes for all the IRFP transistor pairs tested, in the range of supply voltage changes from 70 to 80 V (and consequently for resting current changes ranging from 0.33 to 1 A) and with an increase in operating temperature in the range of 38 to 65 \({}^{\circ}\)C, the circuit spontaneously brought the value of \(U_{DC}\) to within \(\pm\)200 \(\mu\)V. After the cessation of the step changes of supply voltage (at around 1100 s into the test), the value of \(U_{DC}\) reached that recorded at the beginning of the test--for all pairs of IRFP transistors tested and at an operating temperature of approximately 38 \({}^{\circ}\)C.  
Figure 14: Voltage \(U_{DC}\) measured for 15 W of output power at load R0: (a) 140/9140; (b) 140/9240; (c) 240/9140; and (d) 240/9240 transistor pair.
## 3 Results and Discussion
### Continental System  
As a result of the literature review, more than 120 PCM potential candidates were initially found for the CON system, which doubles the number found for the MED system. After the pre-selection procedure, the number of candidates was considerably reduced, so that only 25 PCM were taken into consideration in the decision matrix. The list of all pre-selected PCM candidates for the Continental system is shown in Table 4.  
Figure 3: TGA of the PCM candidates for the MED system. (**a**) savE OM 03; (**b**) RT4; (**c**) RT3 HC_1. Figure 4 shows the results obtained after applying the decision matrix based on the scoring criteria shown in Table 1 and using the weights that correspond to Scenario 1 in Table 2 only to pre-selected PCM with a phase change temperature between 60 \({}^{\circ}\)C and 65 \({}^{\circ}\)C, which seems to be the most suitable for the HYBUILD CON concept. In this temperature range, the commercial product RT64 HC manufactured by Rubitherm [19] obtained the best score, followed by the bio-based PureTemp 63 commercial PCM manufactured by the American company Entropy Solution LLC [25]. The third position in the ranking is occupied by another commercial PCM, TH5860, manufactured by German company Terr Hel [32]. The same as for the MED system, shipment costs and other potential additional costs should also be taken into account if a final decision should be made in a specific case, which might affect the ultimate choice for the PCM to be used.
# Healthy Effects of Pomegranate (_Punica granatum_ L.) in Internal Medicine and Dentistry
## 5 Pomegranate in Internal Medicine
### Cancer
#### 5.2.1 Pomegranate and Breast Cancer  
BC, generally categorized into estrogen receptor (ER) positive and ER negative, is the most diagnosed cancer and the major cause of cancer-related death among females [114]. Many exogenous and endogenous risk factors could affect the onset and development of BC [115]. Among exogenous factors are early menarche, nulliparity, oral contraceptive use, never having or short duration of breast feeding, use of hormone replacement therapy, circadian disruption, and an unhealthy lifestyle (including smoking, alcohol consumption, an imbalanced diet, etc.). Endogenous risk factors, namely genetic factors, such as mutations on breast cancer gene 1 (BRCA1) and BRCA2, only account for approximately 5-10% of all BC incidences. For this reason, a possible strategy capable of counteracting BC carcinogenesis would be to modify the lifestyle and nutritional choices in order to guarantee proper prevention of this type of cancer. Several in vitro studies have highlighted how pomegranate by-products, namely PFE, PJ, and PSO, are involved in the process of breast carcinogenesis, exhibiting the following effects: (i) the anti-proliferative, anti-aromatase, and anti-estrogenic activities [78]; (ii) the regulation of the transforming growth factor beta (TGF-8)/Smads pathway [79]; (iii) the anti-inflammatory effects, through the reduction in pro-inflammatory cytokines and chemokines; (iv) the reduction in vascular endothelial growth factor (VEGF) levels [80]; (v) the downregulation of the expression of the genes involved in the damage of DNA and the estrogen-responsive genes [116]; (vi) and the disruption of ER and Wnt/8-catenin signaling pathways [117].
## 3 Results
### Thermal Properties with DSC  
The DSC analysis results for starches (Figure 2) indicate that cassava starch exhibits a moderate glass transition temperature (50-60 \({}^{\circ}\)C) and low melting temperature (120-130 \({}^{\circ}\)C), ensuring good material plasticity. After modification, these temperatures increase to 55-65 \({}^{\circ}\)C and 125-135 \({}^{\circ}\)C, respectively, with decomposition temperatures rising to 290-310 \({}^{\circ}\)C, making it more thermally stable. However, additional modifications may be required under high humidity.  
Native potato starch demonstrates high glass transition (60-70 \({}^{\circ}\)C) and melting temperatures (140-150 \({}^{\circ}\)C), reflecting its strength. Modification increases these values to 65-75 \({}^{\circ}\)C and 150-160 \({}^{\circ}\)C, with decomposition temperatures reaching 280-310 \({}^{\circ}\)C. While thermally stable and strong, it may require plasticizers for flexibility.  
Corn starch has moderate glass transition (60-75 \({}^{\circ}\)C) and melting temperatures (135-145 \({}^{\circ}\)C), providing elasticity and transparency. Modification improves these properties, increasing temperatures to 65-80 \({}^{\circ}\)C and 140-150 \({}^{\circ}\)C, but further adjustments may enhance moisture resistance.  
Pea starch has lower glass transition (50-60 \({}^{\circ}\)C) and melting temperatures (125-135 \({}^{\circ}\)C), limiting thermal stability. After modification, these rise to 55-65 \({}^{\circ}\)C and 130-140 \({}^{\circ}\)C, improving thermal stability, especially under humid conditions.  
Rice starch is notable for its high melting (130-140 \({}^{\circ}\)C) and decomposition temperatures (285-310 \({}^{\circ}\)C), making it suitable for thermally stable applications. Modification further enhances these properties but may require plasticizers for flexibility. The thermal property results are consistent with the research findings of [PERSON] et al. [20].  
In summary, starch modifications enhance thermal and mechanical properties, broadening their applications in biodegradable films. DSC analysis highlights unique strengths  
Figure 2: Comparison of DSC curves of various starches.  
and limitations of each starch type [42]. Cassava and corn starches offer good elasticity and transparency for flexible films, pea and rice starches provide water resistance and low hydrophilicity, and potato and wheat starches exhibit balanced mechanical properties ideal for durable films [36].
On the other hand, homomorphic encryption (HE) is an alternative solution to the problem that requires no interactivity during computation, thus resulting in significantly lesser communication costs. Even though HE has been applied in a range of machine learning settings such as Logistic Regression [6] and even Convolutional Neural Networks [7; 8; 9; 10], the application of HE to computations that are not well approximated by polynomials such as statistical tests that involve division has been limited due to computationally expensive bootstrapping protocols. Multiparty homomorphic encryption (MHE) has recently been thrust into the spotlight [11] as it strikes a optimal balance between SMPC and HE by limiting interactivity to a computationally lightweight collective bootstrapping protocol that has to be invoked only after the computation has reached a predefined multiplicative depth. Therefore, our main purpose of research in this paper is to optimize the performance and communication efficiency of federated analytics frameworks through cryptographic algorithms and protocols design based on MHE.