esa-sceva/satcom-chunk-collection huggingface dataset:
# Comparison of Image Normalization Methods for Multi-Site Deep Learning
## 2 Methods
### Normalization Methods  
#### 2.3.1 Classical Methods  
The percentile method (Perc) is very simple. We use 5 th and 95 th as the minimum and maximum values for the input of the network. We set any values outside this range to the corresponding minimum or maximum to eliminate outliers.  
The second method is histogram matching (HM), originally developed for brain images [13]. The idea is to extract landmarks from each image and then average them over all images. As landmarks, we chose the 1 st, 10 th, 20 th,..., 90 th and 99 th percentiles of the voxel intensities. The average of the landmarks is used to define a standard histogram. Then, we interpolated the intensities to follow this standard histogram. We used the 1 st percentile as the minimum value for the input of the neural network and the 99 th percentile as the maximum input value.  
The original paper suggests using Otsu thresholding to separate the brain from the background. Instead, we extracted the landmarks from the center volume (measuring \(180\times 180\times 100\,\mathrm{mm}^{3}\)) because this region does not contain background voxels.  
We also tested a combination of the percentile method and histogram matching (PercHM). We first normalized the images using the percentile method and then extracted the landmarks for histogram matching out of those images.  
As the fourth normalization method, we set the mean and standard deviation to a fixed value (M-std). We set the mean to zero and the standard deviation to one.  
Figure 2: Examples of images from the study. Images with the lowest (top row) and highest mean intensities (bottom row) are shown for each modality. The range of intensity values varies largely, except for the ADC, which has a physical interpretation. Some of this variation is due to different acquisition parameters. The T2w image in the top left was acquired with an echo time of 219 ms instead of 110 ms. For the b800 and ADC images, both examples are from the same center, but different scanners.  
The simplest method uses a fixed window (Win). We set the minimum to zero and the maximum to 3000 for the T2w and ADC images and 1000 for the b800 DWIs. We chose these values because nearly all images have a maximum intensity below them.
## 1 Introduction  
High platform station (HAPS) provides extremely broad coverage regions and a powerful line-of-sight (LoS) connectivity to terrestrial user equipment (UE) at the ground. As early as the 1990s, HAPS began to be paid attention to and studied through numerous research perspectives [1].  
Compared with Geostationary Earth Orbit (GEO) satellites that are orbiting at a height of about 36,000 km and Low Earth Orbit (LEO) satellites that are orbiting at a height of about 1200 km, HAPS operates at the stratosphere at heights between 20 and 50 km. Therefore, the Round Trip Time (RTT) of HAPS is much faster than that of GEO and LEO satellites. Furthermore, since HAPS is relatively close to the ground, the power density is approximately one million times that of a GEO satellite and approximately ten thousand times that of a LEO satellite, allowing HAPS to provide high-quality communication services to existing mobile devices [2]. In addition, compared to other systems such as Starlink, which operates at an altitude of 340 km to 550 km [3], HAPS is much less expensive in terms of both the launch and the communication costs. Nonetheless, it does not \"pollute\" the upper layers of the atmosphere with the space waste it creates.  
It is recognized as one of the hot topics for Beyond 5G (B5G) and 6G mobile communications [4; 5; 6; 7; 8]. With the necessary advanced materials and technological leaps, HAPS has been discussed as a viable technique.  
With their potential and with the decrease in the cost of the technology behind it, they are expected to be massively deployed for consumer usage in the coming years as candidates for cellular coverage to provide service or to augment the capacity of other broadband service providers [9].  
[PERSON] et al. [5] and [PERSON] et al. [10] summarized the essential technical aspects of HAPS systems and the current and potential applications of HAPS. In [11], the authors studied the potential HAPS system architectures and deployment strategies in order to achieve global connectivity.  
In [12], [PERSON] et al. studied the possibility of using HAPS to provide high data rate communications simultaneously to a number of trains in motion. According to the estimated and tracked Direction-of-Arrival (DoA) at the HAPS, they can control the parameters of the antenna array at the HAPS to transmit the beam to the UEs.
## 3 Key Technology of the A2S Integrated MIoT
### Cloud/Edge Computing and Caching
#### 3.2.2 Data Caching  
The main function of data caching is to store frequently accessed data on neighboring nodes to reduce transmission latency when data are accessed. Figure 6 shows the pattern of data caching. When a node needs to access certain data, it first checks whether there is a copy of the data locally, and if there is, it obtains it directly from the local source, thus saving transmission time and energy consumption [124]. If there is no local copy of the data, the node queries its neighbors and caches the data locally for future use.  
Due to the complexity of the marine environment and the instability of communication links, DT often faces many challenges, such as bandwidth limitation and transmission delay. In order to solve these problems, the data caching technique is introduced into the A2S integrated system. Many logistics and distribution companies use data caching to optimize the distribution process in A2S integrated systems. They can deploy data cache nodes in different logistics centers and sorting points to cache commonly used logistics data close to data users. This reduces the latency of remote data access and network traffic, thereby improving the efficiency and accuracy of the logistics distribution. In smart city management, data caching is widely used to provide real-time city information and services. For example, a public transportation system in a city can deploy data cache nodes at different stations and vehicles to cache real-time traffic data [125]. When users need to query bus information, they can directly obtain data from nearby cache nodes, reducing the access to the central server and improving the response speed and availability. In A2S integrated healthcare applications, data caches can be used to store and manage medical data. For example, some hospitals or clinics deploy data cache nodes locally to cache  
Figure 6: Data cache mode in the A2S integrated MIoT.  
patients' basic information and medical record data [126]. In this way, when doctors need to access patient data they can quickly obtain it from the cache, improving the efficiency and quality of medical services [127].  
In conclusion, data caching plays an important role in the application of the A2S integrated MIoT which can improve data access efficiency, reduce energy consumption, and provide better support for marine environmental monitoring and related applications.
# Unravelling the Molecular Regulation Mechanisms of Slow Ripening Trait in _Prunus persica_
## 2 Results  
### Slow Ripening Phenotype and Candidate Gene _Nac072_ Expression Profile  
The SR phenotype was recently identified as a monogenic trait incapable of ripening. Figure 1 displays a comparison between NR and SR phenotypes from T1 (37 DASF; days after 1 September) to T7 (120 DASF), representing the period between 1 week after fruit set and harvest time. SR fruit development seems to be stopped in T3 (65 DASF) and skin color changes did not develop even at fruit harvest time in T7 (Figure 1A). Also, this phenotype is still present in the tree a few months after harvest. Differences in other traits between both phenotypes include soluble solid content, fruit weight, and firmness (Figure 1C). A slight difference in soluble solid content can be observed, with a higher amount observed in the SR fruit in T5 and T7, respectively. A significant change in fruit weight starting in T5 was shown, where the NR reached approximately twice the SR weight values. In addition, for firmness, we observed that in T4, no softening process occurred in the SR phenotype compared to the softening process observed in NR fruit (Figure 1C).  
In parallel, the expression profile of the SR candidate gene Prupe.4G186800 (_NAC072_) was assessed in the same evaluation periods to identify the developmental stages to perform the transcriptomic analysis. The results for the NR fruit showed negligible expression of _NAC072_ in T1 followed by a peak of expression in T2 (51 DASF), that decreased immediately in T3, maintaining a low expression level until T7 (Figure 1B). The results for the SR fruit showed no expression of _NAC072_ at any developmental stage.  
The transcriptomic analysis included four development stages selected based on the _NAC072_ expression profile, where T2 seems to be the most critical point to understand the molecular mechanisms by which _NAC072_ controls fruit ripening (Figure 1B). For this reason, T1, T2, and T3 were selected. On the other hand, T7 was selected because it is the developmental stage with significant differences between the NR and SR phenotypes, considering all information presented in Figure 1.
## 3 Simulations
### Generation of ROV Trajectories for Further Analysis
pronounced for the hybrid controller than for the buoy distance-based controller, resulting in a smoother cable delivery speed. **Overall cable shape**. The overall shape of the cable is depicted in Figure 10 for all cable control configurations at two different keyframes of the trajectory, namely keyframe 5, when the ROV finishes its forward motion, and after keyframe 7, when the ROV returns close to the feeder. With the hybrid control, the cable behaves correctly, except for when a slight excess of cable forms a lobe as the ROV moves toward the feeder. The length of free cable is too long with the ROV speed controller. In contrast, the length of free cable is tightly adjusted when using the buoy distance-based controller. When the cable is not controlled, loops appear on the water surface before the ROV starts to move away, as the cable length is far too long. These loops remain throughout the trajectory, generating a significant risk of snagging or entanglement of the cable. Figure 10: Views of the system configuration for di this is not possible since labels are not compatible with tex symbols different cable control modes at two points of the complex trajectory (video available at this link: [[https://www.youtube.com/watch?v=ZC2](https://www.youtube.com/watch?v=ZC2) zStwkdSo]([https://www.youtube.com/watch?v=ZC2](https://www.youtube.com/watch?v=ZC2) zStwkdSo) (accessed on 1 December 2023)). Figure 9: Comparison of the delivery speed for different control modes with a complex trajectory. Conclusions from controller analysis
* Of the three controllers evaluated in the simulation, the ROV speed-based controller is not sufficiently responsive and generates a static error on the buoys' distance that is never corrected. It is therefore not robust to external disturbances. * The control mode based on the distance between buoys is rather smooth, robust, and reactive. * The hybrid control is quite reactive but presents some more pronounced oscillations while remaining rather stable. Hybrid control could possibly be improved by replacing the ROV command speed with the actual ROV speed, which would require the use of more sensors on the ROV, such as a Doppler velocity log (DVL).
## 3 Control Strategy
### Fuzzy PID Coupling Error Control Strategy  
PID controllers are frequently used for vehicle control. However, satisfactory results are difficult to achieve when working conditions and control parameters change. In contrast, a fuzzy logic controller (FLC) does not require a precise mathematical model of the system. This FLC property guarantees stable system operation even if the control parameters and conditions undergo dynamic change [37]. The fuzzy method has the advantages of keeping a simple control structure and avoiding costly sensor use [28; 38]. In this paper, the angular velocity CE is adjusted via control of the in-wheel motor speed. The speed of the in-wheel motor is controlled according to the CE and its change rate. Additional control of the in-wheel motor speed is needed to avoid the interference of multiple random factors. Therefore, developing a controller based on fuzzy logic is an attractive choice, and a fuzzy PID controller was designed for CE control of MMS.  
The CE and its change rate are the two key parameters in the process of synchronous motion adjustment. Therefore, the CE and its change rate are used as fuzzy controller inputs \(E_{c}\) and \(\Delta E_{c}\), and the two-dimensional fuzzy controller is the most suitable type. The fuzzy PID controller structure is shown in Figure 5. Seven fuzzy linguistic terms are adopted for both inputs and outputs: negative big (NB), negative medium (NM), negative small (NS), zero (Z), positive small (PS), positive medium (PM), and positive big (PB). Some basic fuzzy PID control processes are described as follows. When \(E_{c}\) and \(\Delta E_{c}\) are small, larger \(K_{P}\) and \(K_{I}\) and proper \(K_{D}\) should be adopted compared with current parameters, in order to stabilize the steering system. When \(E_{c}\) and \(\Delta E_{c}\) are medium-sized, smaller \(K_{P}\) and proper \(K_{I}\) and \(K_{D}\) are used to reduce steering angle overshoot. When \(E_{c}\) and \(\Delta E_{c}\) are large, larger \(K_{P}\) and smaller \(K_{I}\) and \(K_{D}\) are used to avoid excessive overshoot and expedite the steering response. The membership functions were fine-tuned experimentally based on human experiences of vehicle operation until the system performed acceptably [39]. Figure 6 shows the membership function curves. The fuzzy rules are shown in Table 1. Exact changes in the PID parameters are calculated using the center-of-area method.  
Figure 5: Fuzzy proportional integral derivative (PID) control structure for coupled motion. \(\omega_{i}\) is the input angular velocity of each off-center steering mechanism (OSM). \(\omega_{mi}\) is the angular velocity of each electric wheel. \(\omega^{\prime}{}_{i}\) the output angular velocity of each OSM.
# Blockchain-Based Multiple Authorities Attribute-Based Encryption for EHR Access Control Scheme
## 6 Scheme Analysis
### Performance Analysis
#### 6.3.2 Communication Overhead  
Let \(|G_{1}|\) and \(|G_{2}|\) be the size of the elements in the multiplicative cyclic groups \(G_{1}\), \(G_{2}\), \(|Z_{p}^{*}|\) denote the size of the elements in the domain \(Z_{p}^{*}\), \(N_{attr}\) denotes the number of attributes contained in the parameters, \(N_{u}\) denotes the number of user attributes, \(U\) denotes the number of attributes in the attribute space, \(L_{v}\) denotes the size of the authentication parameters, and \(N\) and \(n\) denote the number of representative users and AAs, respectively. As shown in Table 2, we compare our scheme with existing schemes in terms of public parameters \(GP\), size of DU complete attribute key \(SK\), size of ciphertext \(CT\), and size of outsourced decryption ciphertext \(CT^{\prime}\). During the system initialization phase, our scheme generates a public parameter \(GP\) with a size of \(3|G_{1}|+|G_{2}|\), which is constant compared to the scheme [21] and scheme [6]. In our scheme, the complete attribute private key of DU is \(SK=\{SK_{C},SK_{O},usk\}\), so the length of \(SK\) is \(2|G_{1}|+|Z_{p}^{*}|\), which is constant compared to schemes [6,21,24,26]. The size of the ciphertext \(CT\) in our scheme is \((3N_{attr}+1)|G_{1}|+(N_{attr}+1)|G_{2}|\), and our scheme hides the access policy, so it has a slightly higher communication burden relative to the other parties. Since the encryption algorithm is only a one-time operation, our scheme does not affect the user experience. Moreover, the size of the outsourced decryption ciphertext in our scheme is \(|G_{2}|\), and the size of the outsourced decryption ciphertext sent to DU is shorter compared to scheme [21].  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{1}{c}{**Scheme**} & \multicolumn{1}{c}{**Attribute Authority**} & \multicolumn{1}{c}{**Blockchain**} & \multicolumn{1}{c}{**Policy**} & \multicolumn{1}{c}{**Outsourcing**} & \multicolumn{1}{c}{**Outsourcing**} & \multicolumn{1}{c}{**Smart Contracts**} \\ \hline Qin [21] & Single & \(\times\) & \(\times\) & \(\surd\) & \(\surd\) & \(\times\) \\ TMACS [6] & Multi-Common & \(\times\) & \(\surd\) & \(\times\) & \(\times\) & \(\times\) \\ dec-ABE [7] & Multi-Common & \(\surd\) & \(\times\) & \(\surd\) & \(\times\) & \(\times\) \\ BC-SABE [24] & Multi-Exclusive & \(\times\) & \(\surd\) & \(\times\) & \(\times\) & \(\times\) \\ O3-R-CP-ABE [26] & Single & \(\times\) & \(\times\) & \(\surd\) & \(\times\) & \(\times\) \\ Our scheme & Multi-Common & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Feature Comparison.
## 3 Results and Discussion  
### Characterization of Microcapsules  
The iron content in guinea pig erythrocytes encapsulated by vacuum drying was 4.00 mg Fe/g, a value higher than that obtained in guinea pig erythrocytes encapsulated using the spray drying technique with tara gum and native potato starch matrices (3.30 mg Fe/g) [4] and also greater than the value reported for commercial bovine erythrocytes (2.49 mg Fe/g) [13]. Dried erythrocytes represent a safe and efficient source of heme iron (up to 99%), making their extraction from various animal sources and subsequent encapsulation through drying methods that enhance release and absorption highly recommended [30,31]. The superior performance of vacuum drying is attributed to its low temperature and pressure conditions, which better preserve heme iron by reducing thermal and oxidative damage [11]. In contrast, spray drying, which operates at higher temperatures, may lead to more significant nutrient losses due to degradation and oxidation, explaining the lower iron content obtained [32,33].  
Figure 1a,b show red-colored powders with average particle sizes of 37.60 \(\upmu\)m. Encapsulation was performed within a matrix, characteristic of the vacuum drying technique [11]. EDS analysis confirmed the presence of biopolymers (carbohydrates and proteins), the NaCl used to extract erythrocytes, and iron in the matrix [5]. Iron absorption mainly occurs in the duodenum, where heme iron plays a crucial role in enhancing the absorption of non-heme iron, the consumption of which is significantly lower in resource-poor countries [34]. The recommended daily intake of iron ranges between 10 and 15 mg, and processed erythrocytes represent a promising alternative for food fortification, such as  
gummy candies [35; 36; 37]. Microencapsulation is one of the most effective and recommended techniques for ensuring the controlled release of iron in these products [38; 39; 40; 41].
## 2 Explanation of the Overall System Structure  
A general example of the proposed framework is presented in Figure 1. In this illustration, the plant consists of numerous second-life batteries, each assumed to have different energy capacities. EV batteries are usually replaced with new ones when the battery energy status falls below 70%, and old batteries are sent for recycling. However, with the method presented here, these batteries are used in SLB facilities. As a result of their use in EVs, their capacity values have decreased. The Battery Storage System Manager predicts the 24 h electricity prices for the next day in the day-ahead electricity market. Based on these predictions, it manages energy buying and selling transactions for the next day. The system enables EV owners to earn revenue from their old batteries, and the battery facility can acquire them at a reduced price. Moreover, prolonging the lifespan of these batteries will have a beneficial impact on the environment. In this study, it is assumed that by being in a price-taking position and having a relatively small capacity, the storage plant does not influence the market price.  
This system can be adapted to different electricity markets, but it would require adjustments to local regulations, pricing mechanisms, and market participation rules. Variability in electricity prices, market dynamics, and infrastructure differences, such as grid reliability and storage technologies, may also introduce challenges that need to be addressed.
## 2 Theory and Analysis  
### Probability Hypothesis  
The probabilistic scanning and positioning imaging theory is based on the method of piezoelectric ceramics exciting the plate wave in the sheet materials. It is a method associated with the excitation end and the receiving end. Damage identification can be thought of as the probability of a microcrack appearing at a location in the structure. Because the ultrasonic wave will attenuate during the propagation process, when scanning the damage, the center line of the path from excitation to reception is the direction of propagation of the lamb wave. It is considered that the microcrack has the highest probability of occurrence on the propagation path. The farther away from the propagation centerline, the more severe the ultrasonic attenuation, and the lower the probability of occurrence. The distribution law is consistent with the normal distribution function.  
\[P_{ij}(d)=\frac{1}{\sqrt{2\pi}\sigma}e^{\frac{d^{2}}{2\sigma^{2}}} \tag{1}\]  
where \(d\) is the distance from the coordinate point \(Z_{ij}\) to the center line of the path from excitation to reception. It is considered that the microcrack has the highest probability of occurrence on the paths' center line [11,24]. The scale parameter \(\sigma\) is set to 0.006. The (\(-\sigma\), \(+\sigma\)) interval is approximately equal to the transducer size of 0.013 m.  
As shown in Figure 1a, a scan path can be formed from the excitation probe \(T\) to the reception probe \(R\). In the path, the darker the color, the higher the probability of existence of a microcrack. The lighter the color, the lower the probability of existence. The corresponding three-dimensional probability distribution is shown in Figure 1b. An X\(\times\)Y matrix is created in the scan area, the magnitude of each point representing the imaging index at which the microcrack appears. The scanning surface is formed by a normal distribution function. A plurality of scanning surfaces can cover the area inspected and turn discretization into continuation.