esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Spin and Anomalous Hall Conductivities
The solid circles originate from the DFT calculations described in the manuscript and the dashed lines are the extrapolation results. The theoretical AHC and SHC are obtained from the y intersect of the dashed lines (\(N_{k}\rightarrow\infty\)). The Hall spin polarization \(P^{H}\) and spin polarization \(P^{D}\) calculated from the electronic density of states at the Fermi level for the Co\({}_{3-x}\)Cr\({}_{x}\)Al Heusler compounds are also listed in Table 4, along with the spin-decomposed Hall conductivities (\(\sigma_{xy}^{H\uparrow}\) and \(\sigma_{xy}^{H\downarrow}\)). The Hall spin polarization \(P^{H}\) is infinity in nonmagnetic materials. This is because the charge Hall current is zero in nonmagnetic materials, meaning \(\sigma_{xy}^{H\uparrow}=-\sigma_{xy}^{H\downarrow}\) and, thus, \(P^{H}=\infty\). In the Co\({}_{3-x}\)Cr\({}_{x}\)Al Heusler alloys, the spin-down Hall conductivity is larger than the spin-up Hall conductivity, except for Cr\({}_{3}\)Al. For example, \(\sigma_{xy}^{H\downarrow}\) of Co\({}_{3}\)Al was ten times larger than that of \(\sigma_{xy}^{H\uparrow}\), meaning that the Hall current carriers are mostly spin-down particles. The sign of \(P^{H}\) and \(P^{D}\) was different from those of Co\({}_{3}\)Al, Co\({}_{2}\)CrAl in the \(XA\) structure and Cr\({}_{2}\)CoAl in the regular structure.
# Distinct Signatures of Host Defense Suppression by Plant-Feeding Mites
## 2 Results  
### Defense-Associated Phytohormones  
Consistent with previous studies on tomato-mite interactions [23; 33; 54], higher concentrations of jasmonates and SA were detected after seven days of infestation with _T. urticae_ (Tu) when compared to the non-infested control (C) plants (Figure 1). Note that mites from this _T. urticae_ strain were originally collected from spindle tree (_Euonymous europaeus_), have subsequently been propagated on bean (_Pluseolus vulgaris_) for over ten years and, hence, are not adapted to tomato (see Section 4.2 Mites). Leaflets infested with _A. Iycopersici_ (Al) also contained higher amounts of defense-associated hormones, however the concentrations were consistently about two-fold lower than in the Tu samples. The infestation with _T. evansi_ (Te) resulted in even smaller increases in the amounts of JA-Ile and SA, while no significant changes were detected for the JA-Ile precursors 12-oxo-phytodienoic acid (OPDA) and JA.  
Figure 1: Phytohormone concentrations in tomato (_Solanum Iycopersicum_) leaflets after seven days of infestation with herbivorous mites. Tomato leaves were infested with either a single mite species (_Tetranychus urticae_ (Tu), _Tetranychus evansi_ (Te), or _Aculops Iycopersici_ (Al)) or two species simultaneously (_T. urticae_ plus _T. evansi_ (Tu+Te), or _T. urticae_ plus _A. Iycopersici_ (Tu+Al)). Non-infested plants served as controls (C). The figure shows the average (+ SEM) amounts of: (**A**) 12-oxo-phytodienoic acid (OPDA); (**B**) jasmonic acid (JA); (**C**) jasmonic acid-isoleucine (JA-Ile), and; (**D**) salicylic acid (SA). Multivariate analysis of variance indicated that the factor “mite-infestation treatment” had a significant effect on the phytohormonal profile (F\({}_{20}\) = 8187; \(p<0.001\)). Different letters above the bars indicate significant differences at a level of \(p\leq 0.05\), after applying a generalized linear model followed by [PERSON]’s Least Significant Difference test. Phytohormone concentrations are presented as nanogram per gram fresh leaf material (ng.g\({}^{-1}\).FW).  
In general, the phytohormonal profiles of dual-infested leaflets were most similar to those of the Tu treatment (Figure 1). Yet, in leaflets infested with _T. urticae_ plus _T. evansi_ (Tu+Te), the JA concentration was intermediate as compared to that of each of the respective single infestations. Similarly, the amount of OPDA in leaflets infested with _T. urticae_ plus _A. lycopersici_ (Tu+Al) was intermediate as compared to OPDA amounts measured in the Tu and Al samples. By contrast, JA-Ile was most abundant in Tu+Al samples, indicative of an additive response.
## 5 Tomato Bioactives as Ingredients in Sun Oils for Skin Care  
### Tomato Bioactive Compounds  
There are different varieties of tomatoes, round, oval, and \"cherry\", with all of them having the same nutritional characteristics as they are an important source of potassium, phosphorus, magnesium, and iron, necessary for the normal activity of nerves and muscles; and vitamins such as A, B, and C (tomatoes are the third source of vitamin C in our diet and the fourth source of vitamin A, thanks to their beta-carotene content). In addition, they contain phytosterols that help maintain cholesterol and folic acid, which helps eliminate homocysteine (an amino acid, whose metabolism depends on the metabolism of B vitamins, especially folic acid). In addition to the carotenoids, with the most predominant being lycopene, they also contain ascorbic acid, as well as important polyphenols (such as flavones and anthocyanins), components with strong anti-cancer properties.  
The combination of different carotenoids or the association of carotenoids with tocopherol (vitamin E) or phenolic acid has simultaneous effects in inhibiting pathological effects of free radicals, which are capable of affecting DNA. It has been evident that lycopene may act as a natural sunscreen, protecting the skin from UV rays [20; 21; 22].  
Tomatoes contain considerable amounts of polyphenols, flavonoids, monoterenoids, sesquiterpenes, and saponins. They contain lycopene as a derivative of a terpenoid compound. Lycopene is a vital antioxidant, which is not naturally produced within the body and the human body requires sources of lycopene in order to fight free radicals. Furthermore, carotenoids like lycopene absorb wavelengths ranging from 400 to 550 nanometers (violet to green light) and thus these compounds are usually deeply colored as yellow, orange, or red, while due to this capacity, lycopene seems to have twice the antioxidant power of other carotenoid antioxidants, such as the beta-carotene. Tomatoes are one of the richest sources of lycopene (compared to other fruits and vegetables) that is the cause of their intense color [21; 23; 24].  
The in vitro and in vivo health benefits of the main tomato-derived bioactives used for photoprotection and skin care are shown in Table 3.
## 2 Materials and Methods
### Wavelet Transform
#### 2.7.5 Calculation of a PAC Measure  
The existing PAC measures have been described and compared in a number of papers [23; 27; 29]. We focused on two often-used parameters: the Kullback-Leibler modulation index (KL-MI) [23; 37; 43] and the mean vector length modulation index (MVL-MI) [7; 26; 39]. The MVL-MI in its raw (unnormalized) form scales linearly with the high-frequency amplitude. Thus, to measure the modulation independently from the high-frequency power, this index is usually normalized by either dividing by the root-mean-square amplitude [55] or using surrogates [7; 26]. We adopted the second approach (described below). The KL-MI is sometimes normalized using surrogates [24; 27; 44] and sometimes not [23; 35; 36]. We explored both options.
## 5 Conclusions and Future Trends  
This paper presents a comprehensive view of the current state-of-the-art in UWSNs by analyzing the current research status of the physical, MAC and routing layers. The interaction of these layers is essential in order to advance the research and development of UWSNs. Hence, each layer must account for the advances of the others and use them to its advantage.  
On the physical layer, further research should be conducted to implement complex algorithms and modulations in low-power, low-cost micro-controllers. These algorithms should try to achieve better bandwidth utilization while reaching further transmission distances in an energy-efficient manner. One line of action can be reducing the bit error rate by developing efficient multipath and Doppler correction algorithms, as well as efficient error correction mechanisms. At the same time, research on applying modulations with high spectral efficiency for UWSN and high bandwidth piezoelectric transducers should be conducted.  
In turn, on the MAC layer, protocols should take advantage of hardware advances, like low-power wake-up mechanisms. Current protocols need to be adapted in order to evaluate them and study their performance, since only the T-Lohi protocol implements this new characteristic. In addition, several MAC protocols require localization and synchronization mechanisms, which are usually not taken into account when evaluating their performance. Further studies need to include this services in order to accurately evaluate their performance.  
Following that, in the routing layer, new protocols should account for intelligent self-configuring techniques in order to handle frequent topology changes and reliable delivery. Mechanisms to handle connectivity loss should be further studied for delay-tolerant networks. On the other hand, time-critical applications need latency bounds, which are difficult to provide in an underwater medium.  
Finally, security is an issue that needs to be tackled at all layers of the protocol stack. Although there are a handful of proposals for WSNs, research on the topic of security for UWSNs is still in its infancy. Preliminary jamming studies have been performed, as well as some promising techniques that are mostly focused at the routing layer.
# An Overview of Indoor Localization System for Human Activity Recognition (HAR) in Healthcare
## 5 Signal Technologies
### Magnetic Field-Based Systems  
This technology is used for low-frequency localization. It is based on a platform with a reference station that radiates a magnetic field and a magnetic sensor capable of receiving the radiated field. It is a system that measures position using the Earth's magnetic field disturbances caused by steel structural elements in a building. Their presence deforms the geomagnetic field in a way that varies spatially but is temporally stable. In known positions, land variations of the geomagnetic field in the indoor environment are recorded to build magnetic maps to be used as fingerprints to identify the positions of an unknown target [61]. It is a method that combines the magnetic field measured by the sensor when the user is in a specific position with the fingerprints present in the magnetic map to estimate the user's position. However, the accuracy of the location of the magnetic fingerprint can be influenced by the smartphone's orientation and affected by geomagnetic storms. In addition, it depends on the accuracy of the localization of the magnetic field, the density of fingerprints, and the quality of the acquisition and maintenance process of magnetic maps. Therefore, the current magnetic field positioning technology is mainly combined with other indoor positioning technologies such as Wi-Fi [62] or inertial sensors (IMU) [63] to improve the accuracy. A significant advantage of this type of system is that it offers high accuracy and is not affected by most obstacles; hence, multipath or non-line-of-sight (LOS) errors are avoided. To these must be added the advantages of achieving safety, reliability, and a low cost without additional infrastructure requirements.  
The system can use the environmental magnetic fields produced by iron inside reinforced concrete structures of modern buildings that create local variation in Earth's magnetic field (geomagnetism). Generally, a nonuniform magnetic field produces different magnetic observations depending on the path used. Therefore, positioning can be determined using fluctuations in these environmental magnetic fields. An optimized compass chip inside a smartphone can sense and record these magnetic variations to map indoor locations [64]. Magnetic fields can be generated by a coil powered by alternating current (AC) or pulses of direct current (DC). Electromagnetic fields can also be used to obtain a position by combining the use of electric fields and magnetic fields. The two sources of fields are static charges that produce electric fields and currents which produce magnetic fields. Oscillating charges produce both magnetic and electric fields.
## 4 Discussion
### Limitations and Further Research
The variable performance across modules indicates areas for potential improvement in the R2S platform. For instance, despite high participation, the relatively lower scores in the knowledge module suggest a need to review and potentially revise the content delivery method for traffic rules and regulations.
## 2 Method for SOC Estimation
### GA-GRU for SOC Estimation  
The gated recurrent unit neural network optimized by genetic algorithm (GA-GRU) is proposed to estimate battery SOC. GRU network has been widely used in many fields for its excellent long-term sequence processing capability. At the same time, it can also solve gradient problems, such as gradient explosion or disappearance. However, it is a challenge to determine suitable network parameters for GRU. In order to improve the network performance, we introduced the genetic algorithm to find the optimal parameters of GRU. The implementation process of GA-GRU is shown in Figure 3. Moreover, the specific steps of GA-GRU are as follows:  
1. Initializing parameters. The number of generations (G) is set to 40. The population size (N) is set to 20. The coding length of each chromosome (L) is set to 8. The crossover probability (p1) is set to 0.5. The mutation probability (p2) is set to 0.01.
2. Data processing. The specific process about data processing is conducted in Section 3.2.
3. Training GRU model and calculating fitness. The mean square error (MSE) is chosen as the loss function of GRU model, and it is also the fitness function of genetic algorithm. The mean square error (MSE) is defined as follows: \[MSE=\frac{1}{n}\sum_{1}^{n}(\hat{y}_{i}-y_{i})^{2}\] (7)  
Figure 2: Flow chart of genetic algorithm.  
* Judging whether the end conditions are met. If satisfied, outputting the optimal result and end. Otherwise, going to the step (3).
* Building GRU model with the optimal parameters from GA.
* Validating the GA-GRU model on the test dataset.  
The GA algorithm in this paper was used to optimize the number of GRU layers and its neurons, as well as the number of fully connected layers (FCL) and its neurons. In addition, the number of GRU layers and FCL layers were randomly selected from the range 1 to 3, and their neurons were randomly selected from the range 32 to 256. The optimized parameters of GA-GRU model are shown in Table 1.
## 5 Conclusions  
In this paper, we proposed a Sink-oriented Dynamic Location Service (SDLS) approach to handle sink mobility. We have investigated the problem of the mobile sink in WSNs as follows: 1) We provided the global grid structure to solve excessive system overhead due to the grid structures of data dissemination points for each source node. 2) We provided an Eight-Direction Anchor (EDA) system that acts as a location service server to prevent intensive energy consumption at the border sensor nodes. 3) We provided a Location-based Shortest Relay (LSR) to efficiently forward (or relay) data from the source node to the sink with minimal path delay. The experimental result demonstrated that the proposed protocol based on EDA and LSR significantly reduces energy consumption and communication overhead.
# A Traceable and Verifiable Tobacco Products Logistics System with GPS and RFID Technologies
## 3 The Proposed Scheme
### System Architecture
* The IDI sends the application to the CA, then the CA requests from the BCC to execute a chaincode. The BCC responds to the IDI with a list of IDs that corresponds to every pack of tobacco products. * The IDI approves the application from the MF and responds a list of IDs to them. * After receiving the IDs, MF produces tobacco products. Every pack of tobacco products needs to send the GPS location, ID, the timestamp of production, and the MF's ID by the MR to the CA, then the information is sent and chained in the BCC. * The MF requests LG to deliver the products to the DI to distribute the tobacco products. * When LG receive the products, they need to scan and send the products' GPS location, ID, timestamp, and LG ID to the CA, then the information is sent and chained in the BCC. * After LG arrive at the DI, the information of the GPS location, ID, timestamp, and LG ID also needs to be sent and chained in the BCC via CA. * DI needs to scan and send the products' GPS location, ID, timestamp, and DI's ID to CA, the information is sent and chained in the BCC. * DI needs LG to deliver the products while the DI sells the tobacco products to the RT. * Once LG receives the products from the DI, they need to scan and send the products' GPS location, ID, timestamp, and LG ID to the CA, then the information is sent and chained in the BCC. * LG delivers the tobacco products to the RT, the information of the GPS location, ID, timestamp, and LG ID also needs to be sent and chained in the BCC via the CA. * To ensure the validity and total amount, the RT requests to scan and send the products' GPS location, ID, timestamp, and RT ID to the CA in the last logistics session, then the information is sent and chained in the BCC. Next, the retailer starts to sell tobacco products to the consumers. * A consumer goes to a retailer to buy tobacco products. The consumer needs to provide their ID for the legal transaction. * RT sends all the transactional information including the CS ID, transaction ID, RT ID, and timestamp to the CA, then the transactional information is sent and chained in the BCC. * The transaction is done between the RT and CS. The tobacco product is traced until this step. * If any party has a dispute or doubts the legality of the tobacco product, the party can submit an arbitration request to the arbitrator. * The details of the tobacco products' logistic records were chained in the BCC, and the AB can retrieve and verify the logistics record from the BCC.
## 3 Experimental Principle and Apparatus  
In order to realize the function of observation in real time during slurry penetration, electrical resistivity can be used to reflect the status of slurry invasion. Bentonite is a kind of clay mineral. When mixed with water, the bentonite particles carrying numerous negative charges will greatly reduce the electrical resistivity of water. Therefore, the slurry penetration can result in the reduction in electrical resistivity of pore fluid in saturated soil, which is a reliable indicator of the concentration of bentonite slurry particles in soil pores. According to the research of [PERSON] [28], the electrical resistivity of the soil and rock can be described by a power function,  
\[\rho\ =\ \alpha\varphi^{-m}S^{-n}\rho_{1} \tag{1}\]  
where \(\rho\) is electrical resistivity; \(\rho_{1}\) is electrical resistivity of pore liquor; \(\alpha\) is an empirical constant that is sometimes called the \"fortuosity constant\" or the \"lithology constant\" [29]; \(\varphi\) is porosity of soil; \(S\) is degree of saturation; \(m\) is cementation exponent, which is related to the degree of cementation of the rock fabric, higher values of \(m\) make the formation factor, and hence the rock conductivity, more sensitive to changes in porosity [30]; and \(n\) is the saturation exponent obtained by experiments. Therefore, in this paper, the relationship between the slurry concentration and soil electrical resistivity can be established by the electrical resistivity test.  
Figure 2 shows the schematic and photograph of the test apparatus used for measuring the soil electrical resistivity. The device consists of a soil sample box, soil resistivity meter, and a computer for continuous data logging during the experiments. It can be  
Figure 1: Schematic layout of a filtration column test. (**a**) Designed by [PERSON] et al. [20; 21]. (**b**) Designed by [PERSON] [25]. (**c**) Designed by [PERSON] [26].  
seen from Figure 2a that the basic operation principle of the soil resistivity meter used in this experiment is Wheatstone bridge [9], and the frequency of electrical resistivity measurements performed with about 0.2 Hz. The dimensions of the soil sample box were 120 mm length, 40 mm width, and 60 mm height. The prepared soil samples were placed in the box and two copper electrode slices (40 mm \(\times\) 80 mm) were installed in the sample box.
## 3 System Principle
### LED Modulation  
Pulse width modulation (PWM) is a very effective technique to control analog circuits by the digital transmission of microprocessors. It is widely used in many fields from measurement and communication to power control and transformation.  
For RGB-LEDs, three pulse signals are generated to control the red, green, and blue diodes of the RGB-LEDs, respectively, using PWM. Then, the three diodes will produce a cumulative color. According to the rolling shutter mechanism of a CMOS sensor, the number of bright stripes on the LED project could be changed by changing the modulation frequency of the LED, and the duty-ratio of bright stripes could be changed by changing the duty-ratio of the pulse. In addition, by controlling the start time of the three pulses that input red, green, and blue diodes in a scheme, the three PWM signals have a phase difference. At the receiver, the captured LED images are separated into three R, G, and B images, and the stripes of three different colors in the same exposure period are different.  
The images of different LEDs modulated by PWM with different frequency, duty-ratio, and phase difference are shown in Figures 4 and 5. Furthermore, their modulated schemes at the emitter are shown in Figure 6.  
In Figure 6, the phase difference and duty-ratio of R, G, and B signals in (a) and (b) are the same but the frequencies are different. In (c) and (d), the three signals have the same frequency and phase difference, but they are different in duty-ratio. Examples (e) and (f) have the same frequency and duty-ratio, but the phase difference is different. The naked eye cannot clearly distinguish the specific differences of these color images in some cases, so we extract the features of LED-IDs through image processing for the convenience of the show, which is shown in Figure 4.  
As for (f) and (g), they are different from others in that the frequency of the R, G, and B signals are different from each other in a scheme that is shown in Figure 5.  
Figure 3: System overview.  
Figure 4: Different LED images captured using a CMOS sensor (1).  
Figure 5: Different LED images captured using a CMOS sensor (2).