esa-sceva/satcom-chunk-collection huggingface dataset:
# Video over DSL with LDGM Codes for Interactive Applications
## 2 Channel Codes  
This Section is a review of some available channel codes from the points of view of latency, computational complexity, error correction capability, and other implementation factors.
## 2 Mathematical Optimization Model  
To understand how a STATCOM-supplied reactive power is formed into electrical grids, consider the schematic representation of a STATCOM presented in Figure 1. In this representation, we observe that the STATCOM is a device composed of a power electronic converter and a small energy storage device (battery or capacitor) that is connected to the grid using a transformer [12]. The connection of the STATCOM is shunt, and the possibility of dynamically controlling the reactive power with lagging or leading power factors is dependent on the converter and the linear/nonlinear control technique applied on it [31].  
In the following optimization model, we assume that the controller is applied on the STATCOM to reach the desired reactive power output (i.e., the reference). In this sense, the proposed optimization model to site and size STATCOMs in power systems can be considered as the tertiary control scheme, as, once the location of the STATCOMs is defined, the optimization model defines the desired reactive power curve that each STATCOM must follow to minimize the total grid energy losses. The complete optimization model is formulated below.
## 4 Algorithm for Energy and Fuel Consumption Minimization
### Overlap of DP Solutions of Road Stretches  
Depending on the variables considered in the dynamic programming and its discretization, the execution time of the algorithm can be very high. This is due to the exponential increase in the number of possible solutions created in each iteration. The basic optimization algorithm presented in [35] takes 49 seconds to find the speed profile of a trip of only 20 km, but this runtime increases linearly if a longer route is considered for fuel consumption optimization. Therefore, new ways must be found to achieve results that can be evaluated in real time or at least give the driver tips for optimal speed on each section with a low significant delay.  
In [66] simulations are performed of how long the road stretch taken into account should be in the dynamic programming strategy and indicated that fuel consumption savings tend to be constant with distances of over 2 km.  
Considering this fact, an evolution of the DP algorithm is presented, consisting in overlapping two partial speed profiles. If the length of these sections and the overlaps are long enough, it is found that the speed profile created by overlapping partial optimal speed profiles closely resembles the profile of the entire trip. Figure 2 explains the method used.  
With D being the length of the partial speed profiles and nD the total trip length, an optimal speed profile is calculated for the road stretch between 0 and 2D starting with the vehicle speed at the point of distance 0 and ending with a zero speed at the point of distance 2D. This profile is used as a solution for the stretch between 0 and D, setting the speed at point D as the initial speed for the following DP algorithm execution. From point D, the second partial speed profile is obtained in a new DP algorithm run that finishes at point 3D. In this case, a valid solution is taken between D and 2D, and so on. The union of all partial speed profiles calculated provides the approximate optimal speed profile for the whole trip.  
The final selection of D depends on two factors: (1) differences between approximate solution and optimal one should be small; (2) the overlapping distance should not be very long because optimal speed updates considering real driving conditions are performed every D stretches and those updates should be quite frequent. Tests have been made varying overlapping distances for a trip of 20 km, and the results are shown in Table 2. Therefore, we have chosen overlap stretches of 5 km, longer than the 2 km stretchesproposed in [66], because this value provides a short computational time and results accuracy is significantly improved.  
This process makes the DP algorithm be performed every D meters, so it is possible to adjust the weighting factors in each run to ensure that the destination is reached on time taking into account the deviations from the optimal speed profile and modifications in speed limits or the appearance of unforeseen traffic congestion situations.
## 2 Materials and Methods
### Data Adjustment Techniques
#### 2.2.1 Surface Response Methodology  
The results of the 36 experiments are used to create a model through RSM, in order to identify the relationship between the conidiospores production and the two control variables (percentage of straw humidity and the culture time).  
The data are fitted using the command stool from the statistic toolbox of Matlab(r) 8.1 version (MathWorks(r), Natick, MA, USA). The response variable, representing the amount of produced conidiospores, is expressed as an equation that depends on the values of each factor that contributes to the process. The resulting equation is based on the quadratic model of the surface response (Equation (1)).  
\[C=\beta_{0}+\sum_{i=1}^{k}\beta_{i}x_{i}+\sum_{j=2}^{k}\sum_{i=1}^{j-1}\beta_{ ij}x_{i}x_{j}+\sum_{i=1}^{k}\beta_{ii}x^{2}+e \tag{1}\]  
where \(C\) represents the number of conidiospores, \(\beta_{0}\) is the constant term in the equation, \(\beta_{i}\) is the coefficient of the linear term, \(\beta_{ij}\) is the coefficient of the interaction variables term, \(\beta_{ii}\) corresponds to the coefficient of the quadratic term, \(x_{i}\) and \(x_{j}\) are independent variables, and \(e\) denotes the observed error in the response model.  
An analysis of variance (ANOVA) is utilized to validate the fitting of the obtained RSM model and the statistical significance of the regression coefficients. The interaction between independent and response variables is analyzed through a contour graphic of the RSM.
# Some Critical Insights into the Synthesis and Applications of Hydrophobic Solid Catalysts
## 3 Characterization of Hydrophobic Catalyst
### Characterization Hints for Solid-liquid Interfaces  
In the previous subsection, the importance of assessing the hydrophobic degree of a solid catalyst surface has been underlined, and some possible methods with direct or indirect approach have been listed and discussed. However, studying and characterizing the solid surface-liquid interfaces could be a key element to have a better comprehension of adsorption-desorption mechanism in solid-liquid catalysis. Some reviews have already covered this topic [68; 69]. In this paragraph, some techniques will be highlighted with these purposes.  
X-ray reflectivity studies could be addressed to understanding how water meets hydrophobic surfaces at a microscopic level. High-energy X-ray microbeams have been used to penetrate to the hydrophobic interface and to measure hydrophobic gap size between water and silicon wafer functionalized with a layer of octadecylthriclorosilane, a typical silane used to hydrophobized surfaces. The size could be obtained by fitting the reflectivity data versus the spatial resolution represented by the maximum momentum transfer of the experiment [70].  
Sum frequency generation spectroscopy has been used as a tool to deeply discern the explanation of macroscopic contact angle measurements at a molecular level. [PERSON] et al. used this technique to investigate water organization at silica surface identifying weakly hydrogen-bonded water OH groups. Results demonstrated that hydrogen atoms of these OH groups are pointing toward local hydrophobic sites, namely, oxygen bridges of the silica. Consequentially, an increase in the macroscopic contact angle corresponds to an increased density of these molecular hydrophobic sites, stated by an increase in these OH groups [71]. Water orientation near silica surface can be discerned using also nuclear magnetic resonance spectroscopy exploiting the orientation dependence of the nuclear magnetic \({}^{1}\)H-\({}^{1}\)H dipolar interaction [72].  
Atomic force microscopy could be utilized even to directly map the interfacial energy between a liquid and a solid building a model to clarify the mechanism ruling the image formation. This model shows also that the energy dissipated during the experiment is related to the interfacial energy [73].
# Conservative Interference Injection to Minimize Wi-Fi Sensing Privacy Risks and Bandwidth Loss
## 5 Proposed Contention Model for Defence Against Wi-Fi Sensing
### Empirical Validation
#### 5.3.1 Experimental Setup  
The experimental setup consists of a CSI sensing apparatus surrounded by interference devices as illustrated in Figure 10.  
As shown by the red arrows, the CSI sensing apparatus is surrounded by 10 interference devices at 1 m distance from Rx. In each trial, we collect CSI by transmitting Wi-Fi signals from Tx to Rx, as shown by the black wavy line, for a duration of 60 s, and we compute the average gradient (\(\frac{d_{\text{CSi}}}{d_{\text{i}}}\)) of the time series to estimate the average CSI data  
Figure 10: Experimental setup for validating analytical model.  
Figure 9: CSI snapshot rate under varying packet lengths.  
rate. We first conduct a trial with none of the interference devices (D\({}_{n}\)) operating, and we conduct 9 subsequent trials with each interference device iteratively switched on. When switched on, the interference devices propagate ping packets at the saturation rate using the sudo ping -f command.
## 1 Introduction
Existing simulation models show low amplitude, but these fail to reveal the mechanisms of severe faults in high-voltage equipment. This paper proposes a simulation model of operating overvoltage due to a dropping pantograph based on the pantograph-catenary arc and variable capacitance considering the influence of pantograph-catenary capacitance on the operational overvoltage's amplitude and steepness. The credibility of the model is proven by making a comparison between simulation and test results. The variation law of overvoltage under different dropping speeds or catenary phases is investigated. Formation Mechanism of Pantograph-catenary Arc and Arc Reignition Overvoltage 2.1. Pantograph-catenary Arc Development Process  
The pantograph-catenary arc is an electric spark phenomenon that occurs when the carbon contact strip of an electric locomotive disconnects from the catenary [12]. As shown in Figure 1, there are three stages during the arc development process. At the moment of the pantograph dropping, the voltage difference between the pantograph and catenary increases rapidly. The air gap between them is broken and then enters the arc burning stage. A bright channel can be seen between two contacts. The temperature of the channel is extremely high, reaching approximately 4000-5000 K within microseconds [13]. As the pantograph dropping process continues, the arc channel elongates and narrows. Simultaneously, the arc resistance increases and the conductivity decreases. The arc entersan unstable state and is easily interrupted by external interference. Finally, the insulation state between the two contacts is restored and leads to arc extinction.
# Advanced Scale-Propeller Design Using a MATLAB Optimization Code
## 2 Materials and Methods  
### Propeller Theory--Geometry  
#### 2.1.1 Airfoil Geometry  
An airfoil is a 2D cross-sectional shape of an object that, when moved through a fluid, produces an aerodynamic force. The most common application of airfoils is on the wings of fixed-wing aircraft, however, they are also used on propellers. Among others, the main objective when selecting an airfoil is to create the highest lift to drag ratio. The most suitable airfoil will depend on the flow conditions the airfoil will be operating in. Using online tools such as JavaFoil [27] or QProp [28], existing airfoils can be analysed, and new airfoils can be created and analysed. Figure 3 depicts a NACA0012 in JavaFoil, which is one of the most common airfoils. As can be seen, the airfoil is symmetric (it has no camber). This can be deduced from the '00' digits in the airfoil name. The airfoil has a thickness to chord ratio of 12% (hence the '12' digits in the airfoil name).  
However, most airfoils used on propellers are cambered, as symmetric airfoils require either high angles of attack or high flow speeds to produce significant  
Figure 3: NACA0012 airfoil profile (JavaFoil, 2024) [27].  
lift to drag ratios. The NACA3414 airfoil, as seen in Figure 4, is a cambered airfoil. It has 3% maximum camber (signified by the '3' digit), with the maximum camber occurring at 40% chord (signified by the '4' digit), and a thickness to chord ratio of 14%.
## 4 Applications
### Post-Disaster Recovery and Emergency Rescue  
In the aftermath of natural disasters or sudden emergencies, certain affected areas may become inaccessible due to complex geographical conditions, restricted transportation, or the threat of secondary hazards, significantly impeding the execution of rescue operations. UAVs, with their flexible deployment, high mobility, and remote operation capabilities, can play a critical role in such scenarios. As aerial platforms, UAVs can swiftly conduct reconnaissance missions over disaster-stricken regions, capturing high-resolution images, videos, and environmental data to construct 3D [148; 149] models of the affected areas. This enables rescue teams to make informed decisions and formulate effective action plans. Additionally, UAVs can be equipped with emergency communication devices (e.g., LTE/5G BS) or payload delivery systems to provide communication support or deliver essential supplies to stranded individuals. The literature [150] refers to several examples of UAV applications in disaster responses in Japan, including those in Hiroshima (2018 landslide), Okayama (2018 flooding), and Kanagawa Prefecture (2019 flooding and landslide).
# Nanoarchitectures in Management of Fungal Diseases: An Overview
## 4 Nanocarriers in Management of Fungal Infections
### Nanoemulsions  
Nanoemulsions (NEs) are the transporters of drug particles with colloidal molecular arrangements in submicron sizes (10-1000 nm) [75]. [PERSON] et al. developed sulconazole-loaded nanoemulsions (SCZ-NEs) for the improvement of antifungal activity and transdermal permeation. The optimized formulation exhibited a greater permeability (1.7-fold) and inhibition rate (20.4 \(\pm\) 2.5 mm and 23.5 \(\pm\) 2.4 mm) against _T. rubrum_ and _C. albicans_, respectively. Therefore, SCZ-NEs effectively solved the poor solubility issues, promoted skin permeation, and improved the antifungal activity of SCZ. So, the percutaneous administration of SCZ-NEs can be helpful for the treatment of fungal infections of the skin induced by _T. rubrum_ and _C. albicans_[76].  
[PERSON] et al. demonstrated the potential of the NB-201 nanoemulsions against human-pathogenic fungi. It was observed that NB-201 significantly decreased the fungal growth at the site of infection and displayed an improved curing property in terms of inhibition activity against the development of other fungal pathogens, like _A. fumigatus_, _Cryptococcus_ spp., and Mucorales. Due to their nature, there are the least chances of developing drug resistance, which results in a novel treatment for skin infections or fungal wounds [77].
## 4 Case Study of Lateral Distance Measurement  
### Static Testing  
During the static test, the situation in the tunnel is simulated, the experimental environment is set in the corridor, the LiDAR is placed on the horizontal ground, the algorithm result value is measured by LiDAR, and the results are measured by steel tape. The experimental results of multiple measurements are shown in Table 1. As can be seen from the table, in these measurements, the results of the algorithm have centimeter-level accuracy, the fluctuation range is within 1 cm, and lateral positioning results with centimeter-level accuracy in the tunnel can be obtained.
## 2 Materials and Methods
### Phenotype Microarrays  
Metabolic characteristics of selected strains were determined using phenotype microarrays (Omnilog, Biolog, Hayward, CA, USA). Bacteria were grown on Biolog Universal Growth Agar with 5% sheep blood, and bacteria were harvested and inoculated into Phenotype Microarray (PM) plates according to the manufacturer's instructions [40]. PM plates 1-10 were used to assess the phenotype characteristics of strains RM15596, RM17366, RM18387, RM18411, RM18433, RM18465, RM19068, and RM20875 using pre-configured 96-well plates (Biolog). PM plates 1-10 measured growth resulting from 950 different compounds to test for metabolism of carbon sources (PM-1 and PM-2), nitrogen and peptide nitrogen sources (PM-3, PM-6, PM-7, and PM-8), phosphorus and sulfur sources (PM-4), nutritional supplements (PM-5), osmotic and ionic effects (PM-9), and pH effects (PM-10) [40]. Phenotypes for each strain were determined in three replicate experiments and analyzed using Data Analysis v. 1.7 software (Biolog). Data were expressed as Omnilog Units (OUs), which are a spectrophotometric-based measure of metabolism based on the intensity of a redox dye in each well of the PM plates. The range of OUs is 0-500. Each plate contained a negative control well to serve as a background value. Results for strain replicate experiments were compared to ensure reproducibility, and then strains and serovars were compared with each other using the Data Analysis v. 1.7 software. The background values were subtracted from the experimental OU values, and averages and standard deviations were calculated in the Data Analysis v. 1.7 software. The OU values of the wells for each compound were compared between serovars. For comparison purposes, the average OUs for each well were differentiated into three levels: \(<\)20 OUs, indicating little to no utilization; 20-100 OUs, indicating moderate levels of utilization; and \(>\)100 OUs, indicating high levels of utilization. Levels such as these have been used for phenotype microarray analysis by other investigators [41; 42].
# Hypergraph-Clustering Method Based on an Improved Apriori Algorithm
## 2 Related Theoretical Foundations
### Apriori of Association Rules  
Association rules [27] are mainly used to find rules of the form \(A\Longrightarrow B\) from a given dataset. This process can be decomposed into two sub-processes: determining frequent itemsets and establishing association rules. Apriori, as a classic association rule algorithm, generates frequent itemsets based on the prior principle and extracts high-confidence rules with only one item in the consequent based on confidence pruning. (In this paper, according to the dataset characteristics, we used the trial-and-error sampling method to determine the size of the support threshold). The concrete algorithm steps are as follows:  
1. Scan the dataset \(L\), calculate the support of each item, discard items that do not meet the support threshold, and generate one-dimensional frequent itemsets.
2. Iteration: generate two itemsets through the one-dimensional itemsets, discard items that do not meet the support threshold, and generate two-dimensional frequent itemsets.  
3. Repeat, establish \(k\)-dimensional candidate itemsets, discard items that do not meet the support threshold, and generate \(k\)-dimensional frequent itemsets.
4. Prune each \(k\)-itemset; count and retain the association rule itemset that satisfies the set confidence threshold.  
Let \(I\) be a set of \(N\) different items, denoted as \(I=\{i_{1},i_{2},\ldots,i_{N}\}\), referred to as itemset. The length of an itemset is the number of items it contains. A \(k\)-itemset represents an itemset with a length of \(k\), also known as dimension. \(T=\{t_{1},t_{2},\ldots,t_{n}\}\), is a transaction set, where each transaction \(t_{i}\) is a set of items, \(t_{i}\subseteq I\). An association rule has the following form: \(A\Longrightarrow B\), where \(A,B\subset I\) and \(A\cap B=\varnothing\). For a given itemset \(X\subset I\), the number of transactions containing \(X\) is called the support count of itemset \(X\), denoted as \(\sigma(X)\), which is defined as \(\sigma(X)=|\{t_{i}|X\subseteq t_{i},\ t_{i}\in T\}|\). Therefore, the support of the rule \(A\Longrightarrow B\) is defined as \(support(A\Rightarrow B)=\sigma(A\cup B)/n\). The confidence is defined as \(confidence(A\Rightarrow B)=\sigma(A\cup B)/\sigma(A)\); the higher the value of confidence, the greater the credibility of \(B\) inferring the appearance of \(A\). In order to avoid ignoring the support of the itemsets in the rule posterior, the definition of lifting degree is introduced as  
\[lift(A\Rightarrow B)=\frac{confidence(A\Rightarrow B)}{support(B)} \tag{1}\]  
\(lift(A\Rightarrow B)\) reflects the degree of influence of the occurrence of \(A\) on the occurrence of \(B\) in the hyperedge \(e_{i}\), and is a correlation metric. If the _lift_ degree \(l>1\), \(A\) and \(B\) are positively correlated; if \(l=1\), \(A\) and \(B\) are independent of each other and there is no correlation; if \(l<1\), \(A\) and \(B\) are negatively correlated.
# A Systematic Literature Review of LoRaWAN: Sensors and Applications
## 3 Results
### Applications of LoRaWAN
#### 3.1.1 Agriculture  
Agriculture is a sector of special importance due to the food demands of the growing population. In this context, different works have been developed to monitor various aspects that are required for agriculture, ranging from soil quality to crop monitoring. In [9], a remote crop monitoring system was developed using cost-effective sensors that measure parameters such as pH, temperature, and humidity; in this work, the gathered data are sent to a The Things Network (TTN) server. In [10], a prototype based on LoRaWAN was proposed that allows measuring the humidity, temperature, and electrical conductivity of the soil.  
On the other hand, in [13], the importance of security in the field of agriculture was emphasized by implementing a hardware secure module (HSM). This module made it possible to encrypt the secret keys and store the encrypted keys on a Raspberry Pi. The decryption of these keys is only possible if there is access to the HSM.  
In [11], the use of wireless underground sensor networks (WUSNs) was proposed, which provide coverage of a 7 km radius with a depth of up to 50 cm. The results indicate that WUSNs are not scalable if message-delivery-confirmation packets are required. When the nodes were buried 50 cm, the coverage distance was up to 160 m. Additionally, in [12], an air-quality monitoring system was implemented. In this work, nine gateways were placed in six different locations; it was shown that a packet reception could be obtained from 72.4% of the messages sent. The implementation of 100 air monitoring devices and four additional gateways is proposed to test the scalability of this system.
# A Ku-Band Bi-Directional Transmit and Receive IC in 0.13 \(\upmu\)m CMOS Technology
###### Abstract  
This paper presents a Ku-band transmit and receive IC in 0.13 \(\upmu\)m CMOS technology for mobile satellite communication beamforming systems. A Ku-band transmit and receive IC is composed of a bi-directional amplifier, a 6-bit phase shifter, and a 6-bit digital step attenuator. The precise trimming bits are implemented in the phase shifter (2.8\({}^{\circ}\)) and digital step attenuator (0.5 and 1 dB) for the amplitude and phase error correction. The phase variation range of the phase shifter is 360\({}^{\circ}\) with a phase resolution of 5.625\({}^{\circ}\). The attenuation range of 31.5 dB with an amplitude resolution of 0.5 dB is achieved. The gain of 2-5 dB and the input/output return losses of \(>\)10 dB are achieved from 12 to 16 GHz. The chip size is 2.5 \(\times\) 1.5 mm\({}^{2}\) including bonding pads. The DC power consumption is 216 mW.  
Keywords:Ku-band transmit and receive IC; phased array antenna; CMOS +
Footnote †: journal: Applied