esa-sceva/satcom-chunk-collection huggingface dataset:
# Blockchain-Assisted Adaptive Reconfiguration Method for Trusted UAV Network
## 3 System Models  
According to the discussion in the previous section, existing mobile self-organizing network reconfiguration methods either construct new network routes based on dynamically changing topology information or combine other attributes of nodes, such as location information, reputation, etc. to reduce path blindness during routing. In terms of computation, communication, storage or security, none of the solutions can satisfy the application scenarios of lightweight UAV networks in the context of Byzantine General problem. The adaptive trusted reconfiguration approach for UAV networks recommended in this paper is achieved by a blockchain system deployed on UAV nodes. The blockchain system is a permissioned blockchain for identity authentication based on vector commitment cryptography, which dynamically evaluates the trustworthiness of nodes by using their own state change records as consensus objects. The block system isolates untrustworthy nodes from the network by dynamically aggregating identity vector commitments, periodically recording the global state of the nodes. The drone nodes adaptively reconfigure the trusted hierarchical architecture network through the continuously updated blockchain and construct secure routes based on the blockchain record identity information.  
Figure 1 illustrates the system model of a blockchain-assisted dynamically configurable network.
## 2 Materials and Methods
### Analyses of Dairy Products  
Determination of AFB1 in feed and AFM1 in both raw milk and dairy products was performed by the enzyme-linked immunosorbent assay (ELISA) method using standard validated commercial kits (Tecna, Italy, Code MA220 and MA418, respectively), according to the manufacturer's instructions.  
For determining AFB1, the assay was performed in plastic microwells that have been coated with anti-AFB1 antibodies. In the premixing wells, enzyme-labelled aflat and the standard solutions or samples were mixed and then transferred into the anti-aflatoxin microtiter plate. During the first incubation, free aflatoxin in the standard solution/sample and enzyme-labeled aflatoxin compete for the anti-aflatoxin antibody binding sites on the solid phase. Any unbound enzyme conjugate and aflatoxin molecules were then removed in a washing step. The bound enzyme activity was determined adding a fixed amount of a chromogenic substrate. The enzyme converted the colourless chromogen into a blue product. The addition of the stop reagent led to a color change from blue to yellow. The absorbance was measured by a microplate reader (EZ Read 400, Biochrom, Cambridge, UK) at 450 nm. The color development is inversely proportional to the AFB1 concentration in the sample. Results of analyses were calculated from a semi-logarithmic standard curve.  
For determining AFM1, the assay was performed in plastic microwells coated with anti-AFM1 antibodies. AFM1 standard solutions and samples were added to the microwells. After two incubations, explained in [2], the enzyme converts the colorless chromogen into a blue product during the third incubation. The addition of the stop reagent leads to a color change and is the same as for AFB1, the absorbance was measured by a microplate reader and results were calculated from a semi-logarithmic standard curve.  
All analyses were performed in an ISO/IEC 17,025 accredited laboratory in line with recommendations laid down in EU regulation [14]. This routine method is used daily and fits the purpose for performing lots of samples simultaneously.  
The analytical quality was assured by the use of certified reference materials FAPAS T04252 (animal feed), T04224 and T04201 (maize) with certified AFB1 content, and FAPAS T04120 QC and T04224 QC (milk powder) with certified AFM1 content. The quality of the results was tested through participation in a FAPAS proficiency tests for both AFs achieving Z-score values within acceptable range (\(-2<Z<2\)). Limits of detection for the two AFs were 1 \(\mu\)g kg\({}^{-1}\) (AFB1) and 0.005 \(\mu\)g kg\({}^{-1}\) (AFM1). Limits of quantification for the two mycotoxins were 2 \(\mu\)g kg\({}^{-1}\) (AFB1) and 0.02 \(\mu\)g kg\({}^{-1}\) (AFM1). For further statistical analyses, values below limit of quantification (LOQ) were censored and substituted with a constant value of LOQ/2 as one of recommended methods dealing with left-censored values [30].
# Spontaneous Directional Transportation Surface of Water Droplet and Gas Bubble: A Review
## 4 Spontaneous Directional Transportation of Gas Bubble (SDBT): Methods and Applications
### SDBT Based on Wettability Gradient  
The Janus membrane is a typical surface with a wettability gradient. [PERSON] et al. [110] fabricated a Janus aluminum foil membrane with a water contact angle of 23\({}^{\circ}\) on one side and a water contact angle of 155\({}^{\circ}\) on the other side using laser processing. As shown in Figure 8a, the gas bubble would continuously transport to the superhydrophobic upper surface (superaerophilic surface) when contacting the hydrophilic lower surface (aerophobic surface). In the opposite direction, the gas bubbles were blocked, resulting in SDBT. [PERSON] et al. [111] fabricated a stainless steel mesh with similar properties that enabled the interception, penetration, and collection of underwater gas bubbles. It also could be used for the elimination and collection of gas bubbles in containers. [PERSON] et al. [112] used UV radiation technology to modulate the wettability of the TiO\({}_{2}\)-sprayed copper foam surface. They found that the difficulty of gas bubble penetration depended on the difficulty of their transition from gas-liquid contact to gas-solid contact. When the hydrophilic surface had continuous water film adsorption, the upward penetration of the gas bubble became more difficult. In addition, researchers also fabricated a wettability gradient on non-metallic materials and conducted SDBT on it. [PERSON] et al. [113] fabricated a Janus membrane for the transportation and collection of underwater gas bubbles on a PDMS substrate with an underwater contact angle of 156\({}^{\circ}\) on one side and an underwater contact angle of 0\({}^{\circ}\) on the other side. Inspired by fish scales and lotus leaves, [PERSON] et al. [114] fabricated a wettability gradient on a silicon wafer to achieve the capture and collection of gas bubbles, as shown in Figure 8b. [PERSON] et al. [115] also achieved fast capture, collection, and directional transportation of underwater gas bubbles on a Janus carbon cloth. [PERSON] et al. [116] fabricateda Janus membrane by constructing a wettability gradient on polytetrafluoroethylene (PTFE) using laser processing. On this surface, gas bubbles could transport directionally from the aerophobic side to the aerophilic side of the Janus membrane.
## 2 DTPPF Fault Identification Method
### Method for Identifying Overvoltage Faults of Filter Capacitors  
The harmonic voltage vector of a DTPPF is shown in Figure 2.  
In Figure 2, \(U_{C(h)}\), \(U_{L(h)}\), and \(U_{R(h)}\) are \(h\)-th harmonic voltage RMS of the filter capacitors, ECFR primary reactance winding, and resistors, respectively.  
Figure 2: Harmonic voltage vector of DTPPF.  
The \(h\)-th harmonic voltage RMS on the power supply side is provided by  
\[U_{1(h)}=U_{L(h)}+U_{C(h)}+U_{R(h)}. \tag{11}\]  
The \(n\)-th harmonic inductive reactance and \(h\)-th harmonic inductive reactance are provided by  
\[\left\{\begin{aligned} X_{L(h)}&=j2\pi hf_{0}L_{(h)} \\ X_{L(n)}&=j2\pi nf_{0}L_{(n)}.\end{aligned}\right. \tag{12}\]  
The fundamental inductive reactance is  
\[\left\{\begin{aligned} X_{f0}&=2\pi f_{0}L_{(h)}= \frac{X_{L(h)}}{h}\\ X_{f0}&=2\pi f_{0}L_{(n)}=\frac{X_{L(n)}}{n}.\end{aligned}\right. \tag{13}\]  
From Equation (13), the \(h\)-th harmonic inductive reactance can be obtained:  
\[X_{L(h)}=\frac{h}{n}X_{L(n)}. \tag{14}\]  
Similarly, the \(h\)-th harmonic capacitive reactance of the filter capacitor can be obtained as follows:  
\[X_{C(h)}=\frac{n}{h}X_{C(n)}. \tag{15}\]  
Assuming that the resistance is a constant (\(R_{(h)}=R\)), according to the quality factor \(Q\) in Equation (1) the ratio of \(U_{L(h)}\) to \(U_{C(h)}\) and the ratio of \(U_{R(h)}\) to \(U_{C(h)}\) at the \(h\)-th harmonic can be calculated as follows:  
\[\left\{\begin{aligned} \left|\frac{U_{L(h)}}{U_{C(h)}}\right|= \frac{h^{2}}{n^{2}}\\ \frac{U_{R(h)}}{U_{C(h)}}=\frac{h}{n}\times\frac{1}{Q}=\frac{h}{nQ}.\end{aligned}\right. \tag{16}\]  
Substituting Equation (16) into Equation (11), the relationship between \(U_{1(h)}\) and \(U_{C(h)}\) is obtained:  
\[U_{1(h)}=U_{C(h)}-\frac{h^{2}}{n^{2}}U_{C(h)}+j\frac{h}{n}\frac{U_{C(h)}}{Q}. \tag{17}\]  
The \(h\)-th harmonic voltage and phase of the filter capacitor can be derived from Equation (17):  
\[\left\{\begin{aligned} & U_{C(h)}=\frac{u_{1(h)}}{\sqrt{\left(1-\frac{h^{2}}{n^{2}} \right)^{2}+\left(\frac{h}{nQ}\right)^{2}}}\\ &\tan\theta_{u(h)}=\frac{\left(n^{2}-h^{2}\right)Q}{nH}.\end{aligned}\right. \tag{18}\]  
The filter capacitor voltage RMS is provided by  
\[U_{C(x)}=\sqrt{U_{C(1)}^{2}+U_{C(2)}^{2}+U_{C(3)}^{2}+\cdots+U_{C(n)}^{2}}. \tag{19}\]  
Assuming the rated voltage \(U_{CN(x)}\)of the \(x\)-th filter capacitor,  
the per unit value \(K_{u(x)}\) of the voltage for the filter capacitor can be calculated by  
\[K_{u(x)}=\frac{U_{C(x)}}{U_{CN(x)}}. \tag{20}\]  
From Equation (20), it can be concluded that \(U_{C(x)}\) is \(K_{u(x)}\) times \(U_{CN(x)}\).  
Using \(K_{u(x)}\), it is possible to determine whether the capacitor is overvoltage. When \(K_{u(x)}>1\), the filter capacitor is overvoltage, while when \(K_{u(x)}\leq 1\) and \(U_{C(x)}\leq U_{CN(x)}\) the filter capacitor voltage is normal.  
Filter capacitors can withstand overvoltage for a short period of time. However, if the duration of overvoltage exceeds the allowed value (see Table 1) this can lead to the breakdown of the filter capacitor.
## 3 The Air Quality Fuzzy Comprehensive Evaluation (AQFCE) Paradigm
### Early Warning Module of AQFCE  
Besides air quality evaluations at different time scale, forecasts and early warnings can also be achieved by the AQFCE based on forecast data. In order to prevent air pollution and provide maximum protection for public health in a timely manner, the air pollution early warning module was designed as Table 4, which references documents of the AQI from many countries [7; 22; 23; 39] and emergency plan for severe air pollution of many provinces in China [41; 42; 43; 44]. As an alert and recommendation, it can not only guide people's daily activities but provide decision-making support for governments, such as evaluating whether the air quality of a city reaches the standard, taking mandatory measures to address potential air pollution and so forth. SO\({}_{2}\): Sulphur dioxide is mainly from the combustion of sulphur-containing fuels and can be the precursor of a secondary particle. It can damage the respiratory system of humans and animals, hinder the growth and development of plants and corrode metal materials. NO\({}_{2}\): Nitrogen dioxide is mainly from thermal power plants, transportation and industrial sources and can be the precursor of a secondary particle. Bronchus and lungs will be affected under high concentration. CO: Carbon monoxide is from the incomplete combustion of carbon-containing fuel and can cause serious harm to the nervous and cardiovascular system of humans and animals. O\({}_{3}\): Ozone is from photochemical reaction of nitrogen oxide and volatile organic compound produced by transportation and industry. It is a strong oxidant which can cause coughing, chest discomfort and inflammation of the respiratory tract. Plant and building materials are also injured by it. PM\({}_{10}\) and PM\({}_{2.5}\): Particulate matter is from natural (volcano eruption, sandstorm) and anthropogenic (industrial processes, traffic emission) sources. It can be divided into primary (emitted directly) and secondary (atmospheric chemistry reaction of precursors) particles. PM\({}_{10}\) (d \(\leq\) 10 um) can enter into the upper respiratory tract and has a wide range of health effects involving ones on the cardiovascular system, respiratory system, child growth and mortality. It also reduces visibility by
# Inter-Data Center RDMA: Challenges, Status, and Future Directions
## 4 Existing Representative Works
### BiCC  
BiCC [50] also breaks down the congestion control loop, including the near-source control loop, near-destination control loop, and end-to-end control loop, which leverages two-side DCI switches to manage congestion both at the sender and receiver sides, reducing the control loop to a single DC scale. This approach is illustrated in Figure 6.  
The workflow of near-source control loop is composed of three parts. First, the sender-side DCI switch detects congestion within the sender-side DC. Then, the near-source feedback (e.g., Congestion Notification Packet) is generated and sent back to the sender. Lastly, the sender accordingly adjusts its sending rate based on the feedback to alleviate congestion promptly. Similarly, the end-to-end control loop works following the three main steps of detection, feedback generation, and rate adjustment, but it applies to the receiver-side DC. Specifically, the receiver-side DCI switch detects congestion within the receiver-side DC and sends feedback to the sender, with the sending rate being adjusted by combining this feedback with the near-source feedback.  
The near-destination control loop cooperates with the near-source control loop and end-to-end control loop, and it mainly focuses on alleviating congestion within the receiver-side DC and solving the HoL blocking issue. Specifically, the receiver-side DCI switch uses Virtual Output Queues (VOQs) to manage traffic and prevent HoL blocking. It dynamically allocates VOQs and schedules packet forwarding based on congestion information, ensuring smooth traffic flow.  
The authors implement BiCC on commodity P4-based switches and evaluates its performance through testbed experiments and NS3 simulations, showing significant improvements in congestion avoidance and FCTs. Specifically, BiCC reduces the average FCT for intra-DC and inter-DC traffic by up to 53% and 51%. Additionally, since BiCC is designed to work as a building block with existing host-driven congestion control methods, it has a good compatibility with the existing infrastructures.
## 2 Materials and Methods
### Data Processing  
In this section, we present two different approaches to process the measured data and create an high resolution heat map from it. The first approach is a deterministic algorithm, the LMA, and the second approach is an ML-based algorithm, FCNN. The initial step for both approaches is preprocessing the data. During the preprocessing stage, the initial phase involves data filtration. The data received by the system contain high-frequency noise, as well as a direct current offset originating from the ADC. To address the DC offset, we employ a detrend technique. As for high frequencies, we used a Butterworth low-pass Infinite Impulse Response (IIR) filter with a basic cut-off of 50 Hz. In addition, it is necessary to implement another filter to eliminate any traces of the carrier wave. This is performed using a smoothing procedure, namely, the Savitzky-Golay filter [33]. For the ML approach, there is a second step in the preprocessing. The second step involves generating a two-dimensional magnetic heat map. We define three distinct categories of magnetic heat maps: theoretical magnetic heat maps, magnetic heat maps derived using simulations of measured magnetic field, and magnetic heat maps obtained using the actual experimental setup.  
The purpose of the theoretical and simulated heat maps is to generate data that can be used to train a learning model. This model will predict the intensity and location of the magnetic source in the experimental heat maps.  
Theoretical magnetic heat maps are generated using a dense two-dimensional grid measuring 50 mm \(\times\) 50 mm. The field calculations are performed with a spatial resolution of 1 mm\({}^{2}\) and rely on the Biot-Savart law (BSL).  
The simulated magnetic maps are generated by positioning a network of simulated sensors over a 50 mm \(\times\) 50 mm plane--identical to the theoretical maps, but with a much coarser grid--then computing the magnetic field using BSL only at the simulated sensors locations. The heat map is then computed by a two-dimensional interpolation on the same grid used in the theoretical maps calculations. We have tried various methods of two-dimensional interpolation, including cubic spline and Kriging, all of them leading to very similar results. As previously stated, the inclusion of more simulated sensors in the network will enhance the precision of the simulated magnetic heat map. Obviously, the more simulated sensors in the array, the higher the accuracy of the magnetic heat map. After the preprocessing stage is over, we turn to describe each approach separately:  
Figure 3: System communication network diagram.
# Resource Management for Collaborative 5G-NR-V2X RSUs to Enhance V2I/N Link Reliability
## 5 Improved V2I/N Link Reliability with Proposed Collaborative RSU for Resource Management  
Each RSU configuration can apply various technologies through different and heterogeneous parameters. Therefore, mutual cooperation between RSUs can maximize throughput performance using the proposed methods. Each applicable technology uses dynamic inter-cell interface coordination (ICIC), coordinated scheduling coordinated multi-point (CS-CoMP), and 3D beamforming technologies for cell edge users in BS-type RSUs. In addition, performance improvement is confirmed by applying cell range expansion (CRE) technology at the UE-type RSUs, and analyzes the characteristics of each technology application.
## 3 Results and Discussion
### GC\(\times\)GC-TOF-MS Volatile Metabolome  
In total, 209 volatile compounds identified and semi-quantified by GC\(\times\)GC-TOF-MS are listed in Table 3, including 27 terpen, 11 norisoprenoids (a C\({}_{9}\)-norisoprenoid \(\upalpha\)-isophorone, a C\({}_{10}\)-norisoprenoid safranal, and 9 C\({}_{13}\)-norisoprenoids), 28 benzenoids, 21 aldehydes and ketones, 28 alcohols, 9 acids, 53 esters, 5 volatile phenols, 12 furanoids and lactones, 10 compounds with sulfur, and 5 miscellaneous compounds. As in the case of GC-MS results reported in Table 2, the compounds were sorted in a descending order based on their \(F\)-ratio values within each chemical class. Clearly, GC\(\times\)GC-TOF-MS delivered much more information about the volatile metabolomes of the two investigated wines in terms of the number of compounds identified, owing to its superior separation efficiency, enhanced sensitivity, and clearer mass spectra obtained. On the other hand, particular compounds found in low concentrations by GC-MS, such as \(\upbeta\)-ionone, ethyl cinnamate, and eugenol, were not identified by automated peak detection and spectral deconvolution after GC\(\times\)GC-TOF-MS analysis. This confirmed the fact that targeted analysis and identification using chemical standards is necessary in some cases. It should be noted that the results of GC\(\times\)GC-TOF-MS analysis were semi-quantitative, relative to the internal standard 2-octanol with the detector response factor used equal to one. This means that the obtained values deviated from the actual concentrations to a certain degree depending on the compound [45], but were certainly useful for the comparison of volatile metabolomes and differentiation of the two monovarietal wines. The results obtained by the two instrumental techniques should therefore be considered complementary. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**No.
###### Abstract  
The robotics field of engineering has been witnessing rapid advancements and becoming widely engaged in our lives recently. Its application has pervaded various areas that range from household services to agriculture, industry, military, and health care. The humanoid robots are electro-mechanical devices that are constructed in the semblance of humans and have the ability to sense their environment and take actions accordingly. The control of humanoids is broken down to the following: sensing and perception, path planning, decision making, joint driving, stability and balance. In order to establish and develop control strategies for joint driving, stability and balance, the triple inverted pendulum is used as a benchmark. As the presence of uncertainty is inevitable in this system, the need to develop a robust controller arises. The robustness is often achieved at the expense of performance. Hence, the controller design has to be optimized based on the resultant control system's performance and the required torque. Particle Swarm Optimization (PSO) is an excellent algorithm in finding global optima, and it can be of great help in automatic tuning of the controller design. This paper presents a hybrid H\({}_{\infty}\)/sliding mode controller optimized by the PSO algorithm to control the triple inverted pendulum system. The developed control system is tested by applying it to the nominal, perturbed by parameter variation, perturbed by external disturbance, and perturbed by measurement noise system. The average error in all cases is 0.053 deg and the steady controller effort range is from 0.13 to 0.621 N.m with respect to amplitude. The system's robustness is provided by the hybrid H\({}_{\infty}\)/sliding mode controller and the system's performance and efficiency enhancement are provided by optimization.  
metaheuristic optimization algorithm; particle swarm optimization; engineering design; H\({}_{\infty}\); sliding mode control; triple inverted pendulum; robust control; uncertainty; torque Article Application of Particle Swarm Optimization to a Hybrid H\({}_{\infty}\)/Sliding Mode Controller Design for the Triple Inverted Pendulum System.
## 3 Dataset and Methods  
### Dataset and ML/QSPR Models  
In the previous article, two ML-QSPR models were built for the ideal gas phase standard enthalpy of formation and entropy, based on data from the DIPPR database [15]. These properties will be denoted as enthalpy (H) and entropy (S) in the rest of this article, for simplicity. Each molecule was represented by a vector of 5666 descriptors calculated using the software AlvaDesc from Alvascience [54; 55]. After a series of preprocessing steps, the dataset was finally composed of 1785 molecules \(\times\) 2506 descriptors for the enthalpy and of 1747 molecules \(\times\) 2479 descriptors for the entropy. This dataset will be referred to as the \"preprocessed data\" in the rest of this article. More precisely, data preprocessing was composed of the following steps: elimination of the missing values, elimination of quasi-constant descriptors and elimination of correlations among descriptors. Finally, in this previous article, the number of descriptors was further reduced to 100 via a GA-based dimensionality reduction step to improve the interpretability of the models, in comparison to the initial 2506 or 2479 descriptors (for enthalpy and entropy, respectively). Further reduction of the number of descriptors is possible, but at the cost of prediction accuracy. A large diversity of molecules was also considered to broaden the AD. The best performing models turned out to be Lasso (Least Absolute Shrinkage and Selection Operator) models, based on the results averaged over five different train/test splits and in comparison with the different screened ML models. At last, a hyperparameter optimization step was also performed; however, this will not be considered in this article, as the main focus here is understanding the AD definition (or outlier detection) in a high-dimensional problem.