esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Analog Image Signal Relay Transmission (AISRT)  
The analog video/image has been commonly applied in TV broadcast systems and surveillance equipment since it has some advantages such as more precise resolution and much easier realization. In this section, firstly, a fundamental composite video/image signal format is explained, then a basic optical system for AISRT has been established. The system for AISRT is demonstrated in Figure 2. The experimental verification on this system has been conducted which will be discussed in Section 6.
## 3 Interoperability Issues
### HBIM and GIS Integration: Semantic and Technical Interoperability  
The main aim of the present research regards the conversion phase of the model from HBIM to GIS verifying the geometries and the attributes structure. In this regard, two different tests have been considered. In the first case a commercial software was chosen in order to test the possible geometric, semantic, georeferencing data loss from BIM to GIS data formats; secondly, an open source solution has been used to carry out the same test. In this last case, standard data formats have been considered, therefore the use of a data format conversion software has been necessary.
# Variation Trends of Fractal Dimension in Epileptic EEG Signals
## 2 Data and Methods
### Higuchi Algorithm  
Higuchi algorithm Higuchi (1963) is based on the length measurement of signals \(L(k)\). Taking \(k\) sampling points as the unit, \(D\) satisfies:  
\[L(k)\propto k^{-D} \tag{1}\]  
For signal \(X=x(1),x(2),x(3),\ldots,x(N)\), \(k=k_{min},\ldots,k_{max}\), rebuild \(k\) new time series: \(X_{k}^{m}=x(m),x(m+k),x(m+2k),\ldots,x(m+\lfloor\frac{N-m}{k}\rfloor\cdot k)\), \(m=1,2,3,\ldots,k\), among them: \(m\) is the initial point, \(k\) is the interval, \(\lfloor~{}\rfloor\) denotes the Gauss' notation and both \(m\) and \(k\) are integers. For \(k\) reconstructed new sequences, calculate the length of each sequence \(L_{m}(k)\):  
\[L_{m}(k)=\frac{1}{k}\cdot\left(\sum_{i=1}^{\lfloor(N-m)/k\rfloor}\mid x(m+i \cdot k)-x[m+(i-1)\cdot k]\mid\right)\cdot\frac{N-1}{\lfloor(N-m)/k\rfloor\cdot k} \tag{2}\]  
\(N\) is the total number of signal, and \(\frac{N-1}{\lfloor(N-m)/k\rfloor\cdot k}\) is the normalization correction factor. Take the average length of \(k\) sequences with the same interval as the signal length \(L(k)\) corresponding to interval \(k\), namely:  
\[L(k)=\frac{1}{k}\sum_{m=1}^{k}L_{m}(k) \tag{3}\]  
\[\ln L(k)\propto-D\ln k \tag{4}\]  
Given that each signal series \(X(k)\) and the corresponding series length \(L(k)\), fitted in the double logarithmic coordinates, \(D\) is the opposite slope of the signal. The accuracy of Higuchi algorithm is generally high under various noise levels when \(k_{max}\) is in 40 and 50 based on the previous literature [PERSON] (1963); [PERSON] (1963), and \(k_{max}=42\) is used in this study.
## 3 Natural Compounds Inhibiting De Novo Fatty Acid Synthesis and Modification
### Green Tea Extracts with a Galloyl Moiety Inhib FASN by Competing with NADPH  
FASN is a multifunctional enzyme with seven catalytic activities. Among them, the \(\beta\)-ketoacyl reductase (KR) domain and enoyl reductase (ER) domain catalyze NADPH oxidation [62]. EGCG could inhibit the enzymatic activity of FASN in vitro through competing with NADPH to bind the KR domain [29, 63]. Based on structural similarity, EGCG likely exerted this function as a competitive inhibitor of NADPH. Importantly, unlike EGCG, other green tea extracts without the galloyl moiety exhibited much-reduced activity in inhibiting FASN, suggesting that this structural feature of EGCG played a critical role in its inhibitory activity [64].  
Several well-known FASN inhibitors also can bind to the KR domain of FASN, such as cerulenin and a synthetic inhibitor C75. However, they have been demonstrated to act as non-competitive inhibitors due to their covalent inhibition of the ketoacyl synthase [25]. Thus, the effects of this type of irreversible inhibitor are different from the reversible inhibition of FASN by green tea extracts. Actually, the reversible inhibition of EGCG may be one of the major reasons for its lack of adverse effects when exerting its activities, including modulation of fatty acid synthesis, maintenance of lipid metabolism balance, and prevention of metabolic disorders.
# A Geographically Weighted Random Forest Approach to Predict Corn Yield in the US Corn Belt
## 3 Results
### Model Performance with Different Sets of Input Features
#### 3.2.3 Gross Primary Production  
We further trained the six machine learning models using GPP data only. The time-series GPP data were used an independent variable to predict corn yield. We report the R\({}^{2}\) and RMSE of the models in Figure 7. The derived R\({}^{2}\) ranges from 0.553 to 0.839. The R\({}^{2}\) and RMSE of GWRFR are 0.839 and 0.974 MT/ha, respectively. The results also illustrate that GWRFR outperforms other five models such as MLR, PLSR, SVR, DTR, and RFR by 0.144-0.286 in terms of R\({}^{2}\). MLR and PLSR have a low performance (R\({}^{2}\) = 0.553 and RMSE = 1.625 MT/ha). GWRFR improves yield prediction by 0.144 in terms of R\({}^{2}\) and 0.367 MT/ha in terms of the RMSE when compared with its non-spatial version, RFR (R\({}^{2}\) = 0.685 and RMSE = 1.341 MT/ha). The predictive performance of all machine learning algorithms including GWRFR is lower when compared with that of the models trained with full length features and VIs; however, the performance of GWRFR decreases only by 0.01 in terms of R\({}^{2}\) and 0.03 MT/ha in terms of RMSE. The scatterplots of the observed and predicted yield values are also presented in Figure 7. As compared to previous feature sets, more under- and over-estimations can be observed in the scatterplots; however, GWRFR can still perform better than other models.
# Improved Temporal Fuzzy Reasoning Spiking Neural P Systems for Power System Fault Diagnosis
## 2 ITFRSNP System
### Actual Case Analysis
#### 4.2.2 Case 2  
A fault occurs at \(\rm B_{5}\). \(\rm B_{5}\) operates, and then \(\rm CB_{51}\) trips, but \(\rm CB_{53}\) fails to operate; thus, \(\rm Lp_{91}\)s operates and \(\rm CB_{91}\) trips. The alarm messages are briefly described as \(\rm B_{5}(0)\), \(\rm CB_{51}(29)\), \(\rm Lp_{91}\)s(525), and \(\rm CB_{91}(550)\). The fault diagnosis process is as follows:  
1. \(\it CBS=\{\rm CB_{51},\rm CB_{91}\}\) can be obtained from the alarm message. Traverse \(CBS\) and search for suspected faulty components. There are a subset \(COM_{51}=\{\rm B_{5},\rm T_{1},\rm B_{4},\rm L_{CXI},\rm B_{CXI},\rm L_{CXI},\rm L_{CXI}\}\), a subset \(COM_{91}=\{\rm B_{9},\rm L_{CXI},\rm B_{5},\rm T_{1}\}\), and a total set \(COMS=\{\rm B_{5},\rm T_{1}\}\).
2. \(\rm B_{5}\) is selected for fault diagnosis. The \(\mathbf{\alpha}_{p(11)}^{0}\), \(\mathbf{T}_{p(1)}^{0}\), \(\mathbf{\alpha}_{p(12)}^{0}\), and \(\mathbf{T}_{p(12)}^{0}\) of the fault diagnosis model for the \(\rm CB_{51}\) and \(\rm CB_{52}\) subnets are as follows. \[\left\{\begin{array}{l}\mathbf{\alpha}_{p(11)}^{0}=[0.9833,0.2,0.2,0.8564,0.2,0,0,0,0]^{\rm T}\\ \mathbf{T}_{p(11)}^{0}=[[29,29],[29,29],\varnothing,[0,0],\varnothing,\varnothing, \varnothing,\varnothing]^{\rm T}\\ \end{array}\right.\] (38) \[\left\{\begin{array}{l}\mathbf{\alpha}_{p(12)}^{0}=[0.2,0.2,0.75,0.9913,0.2,0.7,0,0,0]^{\rm T}\\ \mathbf{T}_{p(12)}^{0}=[\varnothing,\varnothing,[550],[0,0],\varnothing,[525,525], \varnothing,\varnothing,\varnothing]^{\rm T}\\ \end{array}\right.\] (39)
3. The diagnosis results for the \(\rm CB_{51}\) subnet are \(\alpha_{p10(11)}=0.9873\) and \(T_{p10(11)}=[-40,-10]\). The diagnosis results of the \(\rm CB_{52}\) subnet are \(\alpha_{p10(12)}=0.5567\) and \(T_{p10(12)}=[-15,15]\). The diagnosis results of \(\rm B_{5}\) are \(\alpha_{(1)}=0.7915\) and \(T_{(1)}=[-15,-10]\), and its measured value \(\eta_{(1)}=0.8540\), which is greater than 0.6. Thus, \(\rm B_{5}\) is a faulty component. 4. Similarly, the measured value of \(\rm T_{1}\) is 0.2363, and \(\rm T_{1}\) is a non-faulty component.
# An Electric Load Simulator for Engine
## 5 Results and Discussion
### Experimental Results  
The test platform was established to test system performance, which is shown in Figure 14. The platform was made up of the control computer, load actuator, drive actuator, displacement sensor, force sensor, power supply, charge amplifier, and DSP controller. The control computer received signals from DSP controller and monitored the operations of the electric load simulator in real time. DSP controller collected feedback signals of the sensors, output PWM (Pulse Width Modulation) control signals, and realized the operation of the control algorithm. The power drive circuit adjusted the voltage of the load actuator according to the PWM signals. Then, the current in the actuator coil was controlled to achieve the control requirements.  
The output force of the electric load simulator should reproduce the target force by adjusting the control parameters of the PID controller continuously. The comparison chart of the experimental results and target force is shown in Figure 13. The rise time, peak time, and overshoot of the electric load simulator were 0.9 ms, 1.1 ms, and 24.5%, respectively. The output force of the electric load simulator ranged from 0-308 N. During the opening process of the exhaust  
Figure 13: Comparison chart of the output force.  
the output force and target force was kept below 5% FSR. The dynamic load accuracy was kept below 2% FSR after 5 ms. Experimental results showed that the output force of the electric load simulator could follow the variation of the target force and meet the requirements of the dynamic load accuracy. Compared with the simulation results, the response of the experimental force had a delay of 1 ms. This was mainly caused by the disturbance of current and displacement to the output force in the actual actuator system.
## 1 Introduction
### Properties of AgNPs against Microorganisms and Fungi  
[PERSON] et al. [19] report highly promising antimicrobial, antiviral, and anticancer properties from using AgNPs, including antibacterial and antifungal activity against _Salmonella typhi_, _Staphylococcus aureus_, and the fungus _Aspergillus flavus_. According to laboratory testing, the bactericidal activity against _Staphylococcus aureus_ and _Escherichia coli_ presented by the AgNPs could be related to a high concentration of the nanoparticles; however, very high concentrations could cause toxicity [20,21]. Other microorganisms, such as _Pseudomonas stutzeri_, have shown resistance and accumulation against AgNPs [22].  
AgNPs have been demonstrated to be an effective antimicrobial agent against multiple pathogenic microorganisms, such as the fungus _Aspergillus flavus_, with positive outcomes being reported for biological synthesis in the laboratory [23]. AgNPs obtained via biosynthesis using the fungus _Aspergillus melleus_ have shown antibacterial and cytotoxic activity against _S. aureus_ and _E. coli_, which creates a solid background for antifungal pharmaceutical and cosmetic applications based on biosynthesized AgNPs [24]. An example of a cosmetic antifungal application is fluconazole combined with AgNPs, which is effective against fungi such as _Candida albicans_, _Phoma glomerata_, and _Fusarium semitectum_[25].  
AgNPs have shown properties that inhibit the growth of different types of fungi, including _Aspergillus fumigates_, _A. niger_, _A. flavus_, _Trichophyton rubrum_, _Candida albicans_, and _Penicillium_ spp. According to the current hypothesis, AgNPs alter the permeability of the cell membrane, therefore causing cell death by producing reactive oxygen species (ROS) and free radicals, which cause denaturalization and damage to the nucleic acids, proton pump, and cell wall, as well as peroxidation of lipids [26].  
Reported studies using AgNPs against fungi from the _Trichophyton_ family have shown efficient antifungal and antimycotic capacities. [PERSON] et al. presented an article about an _Allium sativum_ extract and AgNPs with a reported growth inhibition of 94% against _T. rubrum_[27]. The AgNPs presented a concentration-dependent antifungal activity against fungal infections caused by _T. rubrum_[28]. [PERSON] et al. [29] reported antifungal activity against the fungus _T. mentagrolytes_ in concentrations from 20 to 100 ugL\({}^{-1}\). Other studies combining photodynamic therapy with AgNPs [30] and AgNPs-decorated zinc oxide [31] were also demonstrated to be highly effective at inhibiting the growth of fungi from the _Trichophyton_ family.
## 2 Optical Measurement Setup  
We propose a simple optical measurement method for the simultaneous sensing of turbidity and chlorophyll \(a\) concentration of the lake water. Basically, we obtain these two quantities with a single measurement setup by measuring the output light intensity, which is transmitted through the sample water. However, the physical origin of the output light to be detected is different when turbidity and chlorophyll \(a\) concentration measurements are taken. In the case of turbidity sensing, we detect the scattered light intensity caused from the suspended tiny pollutant particles in the water. In other words, the stronger the scattered light intensity is, the higher the degree of pollution will be. On the other hand, in the case of chlorophyll \(a\) concentration sensing, we detect the fluorescent light intensity emitted from chlorophyll \(a\) molecules. Chlorophyll \(a\) molecules emit fluorescence with a wavelength of around 685 nm when an excitation with a wavelength of around 450 nm is applied [9; 10]. If the suspended aquatic phytoplankton is rich, then chlorophyll \(a\) concentration is high and the fluorescence intensity becomes strong.  
A schematic diagram of the optical measurement setup is shown in Figure 1. In the optical measurement setup, we utilized low cost LEDs and photodiode (PD) as an incident light source and a detector of the transmitted light intensity, respectively. The small water sample bottle (diameter; 24 mm, height including lid; 52 mm) is put into the socket and fixed. The socket was fabricated by a 3D printing technique. As shown in Figure 1, in the socket, we prepared 5 holes for 2 LEDs and 3 PDs to investigate an optimal measurement position. However, generally, we use 2 LEDs such as a red and a blue LED. These 2 LEDs and 1 PD are sufficient to measure two quantities in real time.  
In the experiment, we used the red LED with a typical wavelength of 624 nm (OS5 RKA3131A, OptoSupply, Hong Kong, China) and the blue LED with a typical wavelength of 470 nm (OSB56A3131A, OptoSupply) as incident light for turbidity sensing and an excitation light for chlorophyll \(a\) fluorescence, respectively. The PD (S7183, Hamamatsu Photonics, Hamamatsu, Japan) with a spectral response range of 300-1000 nm was adopted. The diameter of the LEDs, and the area including the burr of the PD are 3 mm and 4.3 \(\times\) 4.6 mm\({}^{2}\), respectively.  
An equivalent circuit diagram of our optical measurement setup is shown in Figure 2. The power supply voltage (Vcc), the current control resistor (R1) for the LEDs, and the bias resistor (R2) for photovoltage measurement are 5 V, 0.984 k\(\Omega\), and 9.89 k\(\Omega\), respectively. In the experiment, a socket with the sample is placed in a box to prevent the influence of environmental light. A view of the experiment is shown in Figure 3. The measurement system is assembled based on the equivalent circuit shown in Figure 2. Moreover, in the experiment, we performed turbidity and chlorophyll \(a\) concentration sensing experiments individually to demonstrate feasibility.
# Physical-Chemical and Thermal Properties of Clays from Porto Santo Island, Portugal
## 1 Introduction  
Minerals have been used in the pharmaceutical and cosmetic industries and for therapeutic purposes [1], and most of these are clays and clayey minerals, known as healing clays [2]. One of those is bentonite, which is a special type of clay mainly composed of smectite [3]. Bentonite is an aluminium phyllosilicate clay, but it is distinguished from other clays, as it has high cation exchange capacity, high specific surface area and alkaline pH values (>7). Among therapeutic applications, the external use of bentonites is highlighted, known as pelotherapy, for osteopathic rheumatic disorders treatment, post-traumatic processes and sprains [2, 4], or even in dermocosectics and dermopharmaceuticals, due to the following characteristics: consistency and adhesiveness, high specific heat capacity, low cooling rate, high cation exchange capacity and finer and more regular grain, which results in a pleasant sensation [5, 6]. For instance, a high cation exchange capacity is a way of compensating the human body with the supply of some vital chemical elements that are present in minerals [4, 7]. Furthermore, a high specific surface area enables higher adsorption of toxins, impurities, bacteria and viruses [1, 8].  
On Porto Santo Island, these green-coloured clays composed of fine-grained particles (<63 um) and whose most abundant mineral is smectite are one of the most characteristic geological materials on the island and are of scientific, touristic and educational interest [9, 10, 11, 12, 13]. The occurrence of several bentonite deposits is a source of resources that are used locally in the construction of Porto Santo's typical parlour houses, in wine clarification and in medical geology applications.  
However, there is a shortage of recent research about the textural, chemical, mineralogical, physicochemical, thermal, rheological and toxicological characterization and suitability of these clay materials from Porto Santo Island for pelotherapy applications. The thermal parameters, such as cooling rate and specific heat capacity, have an important role in clay materials' behaviour as therapeutic agents [14; 15]. Bentonites have the potential to retain heat while the therapeutic treatment occurs, which enables a greater effect on musculoskeletal diseases [2; 15].  
Therefore, the main goal of this research is to assess the potential of seven residual soil samples from Porto Santo Island for therapeutic and dermocosmetic treatments through empirical analysis. For this purpose, the following objectives have been defined: (1) to characterize the textural properties, (2) to characterize chemical and mineralogical composition, (3) to characterize thermal properties, (4) to compare the values obtained to previous research and pre-established parameters in the literature and (5) to identify the best bentonite outcrop with the most suitable characteristics for the proposed treatments.
# Secure and Transparent Space Exploration Data Management Using a Hybrid Blockchain Model
## 4 Implementation and Experimental Analysis
### Attack Scenarios and Mitigation Strategies  
Attack Scenario 1: Cross-chain synchronization attack. An attacker could exploit vulnerabilities in the cross-chain interoperability protocol during data synchronization between public and private blockchains by altering or intercepting the transmitted hash data or synchronization messages, leading to incorrect data being recorded on the public blockchain. To mitigate this risk, a robust digital signature-based authentication mechanism should be enforced, requiring all cross-chain messages to carry digital signatures verified through smart contracts. Additionally, duplicated verification checks involving original data hashes and Zero-Knowledge Proofs should be employed at the receiving blockchain to ensure data integrity.  
Attack Scenario 2: Insider attack via unauthorized access. Authorized users within the organization might misuse their privileges to improperly access or leak sensitive data stored on the private blockchain. To prevent such insider threats, a role-based access control system combined with multi-signature verification processes should be implemented, necessitating multiple approvals for accessing highly sensitive information. Furthermore, real-time monitoring systems should be established to detect abnormal access patterns instantly, allowing immediate suspension of suspicious accounts and conducting thorough security investigations. Table 5 summarizes the described attack scenarios and mitigation strategies.
## 3 Results
### Effectiveness of Urban Green Spaces  
#### 3.1.1 Cooling Effects of UGSs for Each Level of SD Based on the Same TP  
Considering the SD based on the same TP, when meteorological conditions were less affected by solar radiation, the effects of UGSs were as follows. In all observed TPs, the cooling effect was smallest in the SVF range of 0.0-0.2 with high SD: \(-\)0.836 \({}^{\circ}\)C (at 9:00), \(-\)0.661 \({}^{\circ}\)C (at 11:00), \(-\)0.505 \({}^{\circ}\)C (at 13:00), \(-\)0.936 \({}^{\circ}\)C (at 15:00), and 0.312 \({}^{\circ}\)C (at 17:00). On the contrary, the cooling effect is highest in the SVF range of 0.8-1.0, which is the range with low SD: \(-\)4.208 \({}^{\circ}\)C (at 9:00), \(-\)7.426 \({}^{\circ}\)C (at 11:00), \(-\)9.749 \({}^{\circ}\)C (at 13:00), \(-\)6.863 \({}^{\circ}\)C (at 15:00), and \(-\)3.833 \({}^{\circ}\)C (at 17:00). Therefore, as the SVF increased at all TPs, HICS effectiveness also tended to increase (Table 4). On the same note, when meteorological conditions were affected by solar radiation, the cooling effects of UGSs were as follows. In all observed TPs, the cooling effect was smallest in the SVF range of 0.0-0.2 with high SD: \(-\)0.025 \({}^{\circ}\)C (at 9:00), \(-\)1.208 \({}^{\circ}\)C (at 11:00), \(-\)1.678 \({}^{\circ}\)C (at 13:00), \(-\)0.775 \({}^{\circ}\)C (at 15:00), and \(-\)1.072 \({}^{\circ}\)C (at 17:00). Conversely, the cooling effect is highest in the SVF 0.8-1.0 range, which is the range with low SD. However, at 17:00, the highest cooling effect is shown in the SVF range of 0.6-0.8: \(-\)11.839 \({}^{\circ}\)C (at 9:00), \(-\)13.704 \({}^{\circ}\)C (at 11:00), \(-\)13.328 \({}^{\circ}\)C (at 13:00), \(-\)8.224 \({}^{\circ}\)C (at 15:00), and \(-\)5.050 \({}^{\circ}\)C (at 17:00). Therefore, at all TPs except 17:00, a larger SVF was associated with a larger cooling effect (Table 4).
# Continuous Camera-Based Premature-Infant Monitoring Algorithms for NICU
## 5 Results
### Top-Level Classification Block  
A 12-h-long video set of the clinical trial was selected for system-wide top-level classifier evaluation. The protocol for the selection was to cover all available classes with as many possible transitions between them as the recordings allowed, all different camera-angles, and different infants. In total, this set contained approx. 5.5 h quiet and visible baby, 1.5 h of extensive motion, 3 h of care periods, 1.5 h empty incubator and half an hour various degraded image quality sequences (such as dark or saturated view, UV lamp treatment). During model validation and optimization, 5-fold cross-validation is used. Under these conditions, the Top-Level Classifier architecture provided 97.9% sensitivity (true positive rate), 97.5% specificity (true negative rate) and 98.1% F1 score (harmonic mean of precision and sensitivity) in the formerly listed main event classes (Figure 7).  
Dynamic and static analysis provide complementary information about the scene, e.g., respiration-like waveform is detected, and the infant is visible. As a result, we found that the combination of analysis (see Figure 8) greatly enhanced the precision of the system, resulting in accurate detection of the infants' movements on one hand, and indicator items of care/cure in the incubator on the other hand.  
The execution time of feature extraction is in the range of 150-190 ms, object detection by YOLOv3 detector is 740-850 ms, classification network inference is 5-10 ms, all executed once in every 3 s. These values are measured on a i5 processor-powered, conservative setup, using a single processor with no GPU acceleration enabled, with the implications that such a system could run on relatively low-end computers or even on embedded systems.