esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experimental Setup  
### Materials  
Water (W) with a resistivity of 18.2 M\(\Omega\) cm was obtained through a Milli-Q 185 Plus system from Millipore (Watford, UK). Ethylene glycol (EG) and propylene glycol (PG), both with a 99.5% mass purity, were purchased from Honeywell (Seelze, Germany) and Sigma-Aldrich (St. Louis, MO, USA), respectively, and \(n\)-tetradcane (\(n\)-C\({}_{14}\)) and \(n\)-hexadecane (\(n\)-C\({}_{16}\)) were acquired from Alfa Aesar (Haverhill, MA, USA), both \(n\)-alkanes with mass purities of 99%. MgO nanoparticles were provided by IoLiTec (Heilbronn, Germany), mass purity of 99%, with a pseudo-spherical shape (nominal size diameter by manufacturer of 35 nm, 45 \(\pm\) 3 nm by TEM [34], 39.01 nm by XRD [35]) and a certain level of polydispersity (polidisperity index of 0.32 [34]). Further characterization of these nanoparticles was accomplished in previous studies [34,35].  
The ethylene glycol:water 50:50 vol% mixture (EG:W 50:50 vol%) was prepared by weighing specific amounts of each component to achieve the required volume fraction. MgO/\(n\)-C\({}_{14}\) nanofluid were prepared at 1, 5, and 10 wt% by dispersing MgO nanoparticles within \(n\)-C\({}_{14}\) in an ultrasonic homogenizer Bandelin Sonopuls HD 2200 (Berlin, Germany) with a sonication time of 30 min. All samples were weighed in a [PERSON] analytical balance model CPA225 (Gottingen, Germany) with an uncertainty of 0.01 mg.
# Adaptive Deterministic Vibration Control of a Piezo-Actuated Active-Passive Isolation Structure
###### Abstract  
With the improvement of the performance of optical equipment carried by on-orbit spacecraft, the requirements of vibration isolation are increasing. Passive isolation platforms are widely used, but the ability to suppress the low-frequency deterministic vibration disturbance is limited, especially near the system's natural frequency. Therefore, an active vibration control strategy is proposed to improve passive isolation performance. In this paper, a Youla parameterized adaptive active vibration control system is introduced to improve the isolation performance of a piezo-actuated active-passive isolation structure. A linear quadratic Gaussian (LQG) central controller is first designed to shape the band-limited local loop of the closed-loop system. Then, the central controller is augmented into a Youla parameterized adaptive regulator with the recursive least square adaptive algorithm, and the Youla parameters (\(Q\) parameters) can be adjusted online to the desired value to suppress the unknown and time-varying multifrequency deterministic vibration disturbance. In the experiment, the residual vibration with respect to the combination of multiple frequencies is effectively suppressed by more than 20 dB on average, and a quick response time of less than 0.3 s is achieved when the deterministic residual vibration changes suddenly over time. The experimental results illustrate that the proposed adaptive active vibration control system can effectively suppress the low-frequency deterministic residual vibration.  
adaptive vibration control; unknown and time-varying; deterministic vibration; Youla parameterized; piezo-actuated structure
###### Abstract  
The Global Navigation Satellite System (GNSS) observations with global coverage and high temporal and spatial resolution, provide abundant and high-quality Earth-ionosphere observations. By calculating the total electron content (TEC), estimations from GNSS observables global and regional ionosphere TEC morphology can be further investigated. For the multiple constellation case, the numbers of ionosphere pierce points (IPP) has increased tremendously, and it is worth studying the features of the GNSS derived TEC under geomagnetic storms to show the benefits of multiple constellation measurements. With the Multi-GNSS Experiment (MGEX) observation data, ionosphere TEC responses to the geomagnetic storm on the 22 June 2015 were well studied. TEC perturbations were discovered, accompanied by ionosphere irregularities concentrating in high and middle latitudes. Through analysis of multi-GNSS observations, the Rate of TEC Index (ROTI) perturbations were proved to be generated by the geomagnetic storm, with simultaneous behaviors at different local times around the world, also indicating ionosphere scintillation. The ionosphere spatial gradient was also discussed with two short baseline MGEX sites; the maximum ionosphere gradient of 247.2 mm/km was found, due to ionosphere irregularity produced by the storm. This research has discussed ionosphere responses to geomagnetic storms with multi-GNSS data provided and has analyzed the availability of multi-GNSS observations to investigate ionosphere irregularity climatology. The proposed work is valuable for further investigation of GNSS performances under geomagnetic storms.  
Keywords: ionosphere TEC; multi-GNSS; MGEX data; ionosphere gradient +
Footnote †: journal: Remote Sensing, 10, 666
# A Two-Stage Optimization Model for Airport Stand Allocation and Ground Support Vehicle Scheduling
## 4 Modeling
### The Stand Allocation Problem Considering Turnaround Resource Constraints
#### 4.2.1 Objectives of the First-Stage Model
To enhance the robustness of stand allocation and reduce aircraft waiting time on the apron, some flights are allocated to remote stands. Previous studies often used the gate utilization rate as an optimization objective. In practice, the number of ferry vehicles required for different aircraft types at remote stands varies, and the driving distance from the remote stand to the terminal stand also differs. Therefore, efficiently utilizing remote stands is equally important in stand allocation problem. Optimizing for the shortest ferry vehicles driving time to and from remote stands can indirectly reduce the number of flights allocated to remote stands, particularly larger aircraft, this also aligns with the rule of preferentially assigning large aircraft to contact stands. \[min\ Z_{2}=\sum_{i\in F}\sum_{g\in G}x_{ig}S_{i}^{F}v_{g} \tag{2}\]  
3. Minimizing the Sum of the Squares of the Idle Times. The objective function (hereinafter referred to as idle time) is shown in Equation (3). Minimizing the sum of the squares of the idle times can, on the one hand, effectively improve the utilization rate of all stands, avoiding the overuse of some stands while others remain relatively idle, thereby enhancing the efficiency of airport stand resources. On the other hand, it can increase the robustness of the stand allocation scheme. By allowing for idle time at stands, the system can better accommodate potential flight delays or early arrivals, thus reducing the sensitivity of the allocation plan to flight schedule changes and ensuring safety while mitigating delays. \[min\ Z_{3}=\sum_{i\in F}\sum_{j\in F}\sum_{g\in G}l_{ij}^{2}y_{ig} \tag{3}\]
## 1 Introduction
They investigated the effect of biocatalyst/substrate feed mass ratios and the waste oil quality and reported final conversion of 46%-47% when using biocatalyst/oil mass ratio of 3% and 5%. [PERSON] et al. [24] conducted enzymatic transesterification of oils with combi-lipases as biocatalyst and supported by an ultrasound system. Authors reported a biodiesel yield of 70% when using waste oil and 90% using soybean oil, which suggested that ultrasound technology and enzyme mixtures could be a promising route to reduce costs of biodiesel production. [PERSON] et al. [25] evaluated the use of two low-cost feedstocks such as WFO and wastes of beef fats to produce biodiesel via both chemical and enzymatic transesterification and reported a richer FAME biodiesel with the biological method. [PERSON] et al. [26] analyzed the feasibility of biodiesel production from hybrid oil using bio-support beads immobilized with lipase from _Pseudomonas cepacia_. They obtained a biodiesel yield of 78% under optimized conditions of 24 h, enzyme loading of 10% \(w\)/\(w\) and temperature of \(50\pm 1\)\({}^{\circ}\)C.  
In this work, the feedstock for biodiesel production is a mixture of WFOs collected from local restaurants in East Colombia (Cucuta), which are reused several times and have low fatty acids composition (49.19%). Due to the frying conditions and reusability, there are different substances in WFO formed as result of frying process mainly condensation, oxidation and hydrolysis products that may affect lipase action in enzymatic transesterification. In this sense, it is challenging to obtain high biodiesel yield from low quality fat source. In order to reach relatively high biodiesel production,it was used a combined lipase system from _Candida rugosa_ and _Thermomyces languinosus_. The effect of enzyme concentration, reaction time, pH of reaction media and temperature were evaluated to estimate optimum operating conditions. Physicochemical characterization of biodiesel was performed to determine if such product is suitable for application in diesel engines meeting national and international standards.
## 6 Conclusions  
Real-time transmissions in URLLC require the system to meet hard delay constraints. To address this, we put forth a cross-layer transmission leveraging variable-length coding and truncated channel inversion over parallel channels. In this way, we formulate the system as a two-dimensional Markov chain, where we have packet queue buffering bits poised for encoding and a symbol queue buffering encoded symbols set for transmission. By adopting the embedded Markov chain and focusing on instances when the symbol queue is empty, we simplify our derived Markov chain into a one-dimensional Markov chain, with the data queue length being the system state. Then, we propose an effective gradient descent-based algorithm to reduce the average power consumption, where a well-performing initial point is obtained by a heuristic algorithm. Moreover, a trade-off between the average power and the hard delay constraint is obtained. The numerical results show that our algorithm can meet the hard delay constraint and outperforms conventional algorithms.  
Conceptualization, W.C. and Y.-J.A.Z.; formal analysis, Y.L.; methodology, Y.L. and X.Z.; software, Y.L.; writing--original draft, Y.L.; writing--review and editing, X.Z., W.C., and Y.-J.A.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China under Grant No. 62401140.  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; nor in the decision to publish the results.
## 3 Colloidal Silica Transport Through Soil Stratum
### Transport Mechanisms of CS in Liquefiable Sand  
CS transport, which is a complicated physical process, is determined by several internal and external factors. Since CS has already been mixed with an electrolyte prior to injection, the transport of CS is significantly influenced by the time-dependent gelation process and is a more complicated course than a simple colloid transport. Previous to gelation, CS behaves like a Newtonian fluid. CS transport is quite similar to a simple colloid delivery. When gelation is motivated, CS becomes a non-Newtonian fluid. Viscosity increases continuously and gradually become the dominating factor that controls the transport of CS [49; 64; 65]. On the other hand, porous media with higher hydraulic conductivity presents larger porosity, which is more convenient for CS delivery than that with lower porosity (hydraulic conductivity). Therefore, viscosity and soil type are the fundamental internal factor that could influence CS transport. Moreover, some external factors, such as the injection rate, could also have a significant effect on the transport of CS. The influencing mechanisms of soil type, CS viscosity, and injection rate on CS delivery are reviewed and summarized as follows.
## 3 Research Methods and Parameters
### Modelling Sequence  
The modeling order of the model changes as the segments are assembled. The assembly sequence of segments is as shown in Figure 6. Taking the third segment assembly model as an example, displacement constraints are first imposed on both sides of the looped segment. The back push surface and the global gravity is activated according to the working conditions to initialize the segment's positional information. Next, the thrust of the jack on the face of the two segments that have been assembled is activated. Before the radial or tangential component force of the jack is activated, a certain longitudinal thrust is applied to form a certain contact between the gasket and the surface of the ring-shaped segment to reduce the relative slip. To maintain the boundary conditions of the ring segment, the radial or tangential component force of the jack can be added as required. Finally, the jack thrust and error component of the third segment are activated, and the component force is consistent with that of the other two segments. By adjusting the component force of the jack accordingly, the response of the segment under different assembly errors is simulated.  
Figure 5: Assembly error of segment: (**a**) inward deflection; (**b**) outward deflection; (**c**) lateral deflection. (Take the assembly of one segment as an example.)
# CF-mMIMO-Based Computational Offloading for UAV Swarms: System Design and Experimental Results
## 3 UAV Formation Control Technologies
### Separation  
For local repulsive forces, a simple semi-spring model is employed, using a linearly distance-dependent central velocity term. Interaction begins only within a maximum range \(r_{0}^{rep}\), beyond which repulsion is ignored.  
\[\mathbf{V}_{ij}^{rep}=\left\{\begin{aligned} & p^{rep}\cdot\left(r_{0}^{rep}-r_{ij} \right)\cdot\frac{r_{i}-r_{j}}{r_{ij}},r_{ij}<r_{0}^{rep}\\ & 0,otherwise\end{aligned}\right., \tag{5}\]  
where \(p^{rep}\) is the linear gain of the repulsive force, \(r_{i}\) and \(r_{j}\) denote the coordinate vectors of agent \(i\) and \(j\) in the global coordinate system, respectively, and \(r_{ij}=\left|r_{i}-r_{j}\right|\) is the distance between agent \(i\) and \(j\). The total repulsive force on agent \(i\) from all neighbors is  
\[\mathbf{V}_{i}^{rep}=\sum_{j\
eq i}\mathbf{V}_{ij}^{rep}. \tag{6}\]  
Equation (5) describes the ideal noiseless reception, whereas Equation (6) introduces realistic impairments, including interference and thermal noise. The relationship between them is direct: Equation (6) is a practical extension of Equation (5).
# YOLOv4-MN3 for PCB Surface Defect Detection
## 2 Methodology
### Proposed YOLOv4-MN3
#### 2.2.1 YOLOv4-MN3 Architecture  
YOLOv4 employs the cross-stage partial network (CSPNet) in Darknet53 to construct a CSPDarknet53 backbone. CSPNet partitions the feature map of the input layer into two parts and then merges them through the proposed cross-stage hierarchy for the purpose of enriching gradient combination [24]. However, CSPDarknet53 suffers from high memory consumption, with 29 convolution layers and 27.6 million parameters. Replacing CSPDarknet53 by a lightweight model with fewer parameters while preserving its detection accuracy is a worthy attempt.  
For this study, a cost-effective detector YOLOv4-MN3 was developed, in which the CSPDarknet53 in YOLOv4 was replaced by the lightweight network MobileNetV3. MobileNetV3 utilizes depthwise separable convolution to construct feature maps for each layer. The main convolution process is composed of two parts, as given in Figure 2. The first part is depthwise convolution, and it introduces a filter for each input channel and conducts convolutions for each pair of filter and feature map separately. Its second part is pointwise convolution, and it convolutes a \(1\times 1\) filter to channels output from the depthwise convolution for the purposes of increasing or decreasing the depth of a feature map. If the convolution input and output are 16 and 32, respectively and the filter size is \(3\times 3\), then the parameters of standard convolution and depth separable convolution are \(16\times 32\times 3\times 3=4608\), \(16\times 3\times 3+32\times 16\times 1\times 1=656\) respectively, which indicates that depthwise separable convolution can successfully minimize the number of parameters.  
Figure 2: Depthwise separable convolution.  
In order to improve the detection accuracy, MobileNetV3 introduces the squeeze and excitation attention module into the bottleneck of MobileNetV2, the basic unit (Bneck) of MobileNetV3, given in Figure 3. Meanwhile, MobileNetV3 modifies the Swish activation function to improve detection accuracy. Experimental results verify the effectiveness and superiority of MobileNetV3 because it can achieve high detection speed and accuracy simultaneously [25]. The MobileNetV3 has MobileNetV3-Small and MobileNetV3-Large--two versions, used according to the depth of its layers. MobileNetV3-Large is employed in YOLOv4-MN3 for a balance of accuracy and speed, based on some initial experimentation. The architecture of YOLOv4-MN3 is given in Figure 4, and only the first 15 Bnecks of MobileNetV3-Large are used to extract three different (52 \(\times\) 52, 26 \(\times\) 26, 13 \(\times\) 13) spatial resolution feature maps, which are directly matched to the input size of the YOLOv4 neck network.
# Research on Crack Width Measurement Based on Binocular Vision and Improved DeeplabV3+
## 2 Crack Region Segmentation Algorithm
### Loss Function Modification  
In the obtained crack images, the pixel area occupied by the vast majority of cracks is smaller than the background area. This situation will lead to the imbalance of positive and negative samples in the training process of the algorithm, cause the weight shift, and lead to a poor crack segmentation effect. Based on this situation, this paper uses the combination of Dice loss and Focal loss to replace the cross-entropy loss function to solve the problem of extremely unbalanced samples in the data set, where the expression of Dice loss is as follows:  
\[Dice\ loss=1-\frac{2\sum_{i=1}^{N}y_{i}y^{\prime}_{i}}{\sum_{i=1}^{N}y_{i}+ \sum_{i=1}^{N}y^{\prime}_{i}} \tag{7}\]  
where, \(y_{i}\) and \(y^{\prime}_{i}\) represent the label value and the predicted value of pixel \(i\) respectively, and \(N\) is the total number of pixels.  
The Focal loss is expressed as follows.  
\[Focal\ loss=-\alpha(1-p_{t})^{\beta}\log(p_{t}) \tag{8}\]  
where, \(\alpha\) is used to adjust the ratio of positive and negative sample loss, and the weight of the background region in the loss function of the model can be reduced by setting the value of \(\alpha\), which is set as 0.5 in this paper. \(\beta\) is an adjustable factor, which is used to improve the emphasis of the algorithm on the training of difficult samples for crack extraction. In this paper, 2 is taken, and \(p_{t}\) represents the probability that the predicted pixel is a crack.  
Finally, the loss function in the DeeplabV3+ model is improved as follows.  
\[Loss=Dice\ loss+Focal\ loss \tag{9}\]  
Figure 6: Efficient Channel Attention.
## 4 Monetising Museum Images: NFTs, Image Licensing and Copyright
### NFTs and Copyright  
A common concern amongst sceptics is whether the intellectual property rights of the images featured in NFTs are also part of the trade and therefore museums would risk losing image licensing revenue by venturing in crypto collectibles. Although that is a field that is still being shaped, by general consensus, when it comes to intellectual property rights, thetrade of crypto collectibles largely mirrors real-world art trade unless otherwise specified. By copyright law, when one purchases a physical painting, they only buy ownership over the tangible asset; the copyright of the artwork, including any commercial rights, remains with the artist, unless that is specifically covered in writing and signed [99]. [PERSON], a law professor at Penn State university, in her study on digital collectibles in relation to copyright, concluded that copyright law \"does not specifically identify digital or digitized works as copyrightable subject matter\" [100], because it applies equally \"to physical embodiments and those requiring the aid of a machine\" and therefore, an NFT \"contributed by an author that contains at least a minimum amount of creativity is capable of receiving copyright protection\" [100]. Therefore, from a legal standpoint, it appears to be clear that NFTs would be treated like tangible paintings when it comes to copyright. It must be noted that legislation differs by country, however with regards to copyright law in particular, it could be argued that it shares great similarities across different countries and regions.  
Examining how intellectual property rights of NFTs are enforced on the platforms where crypto collectibles are traded, different approaches can be identified. When the creators of CryptoKitties invented NFTs [49], they also published an accompanying license, i.e., the \"Nifty License\" (nftlicense.org, accessed on 18 October 2021), which allows collectors some commercial reuse of the asset associated with the NFT. However, that does not appear to be adopted elsewhere beyond CryptoKitties. Indicatively, the leading platform Foundation.app (where [PERSON] sold his first NFT raising 5.5 million USD for the Freedom of the Press Foundation [101]), clarifies in its terms of service that collectors are able to trade the NFT, i.e., the token, but they do not obtain any commercial rights on the asset the NFT represents [102]. The \"Nifty License\" demonstrates an intent for crypto collectibles to push the boundaries and introduce new norms to the art trade in relation to artwork copyright ownership. However, at present, it is commonly accepted amongst creators, as well as buyers of NFTs, that, unless otherwise specified, the trade of crypto collectibles does not involve or impact the intellectual property rights of the asset NFTs are associated with in any way.
## 3 Synthetic Test
### Layout of Survey and Time-Lapse Model  
The simulated survey layout is depicted in Figure 3. The center of the transmitting source is situated at 6000 m in the x-direction, with a length of 1000 m and a power supply current of 10 A, indicated by the red line in the figure. There are 25 survey lines, each spanning 1200 m and comprising 25 sites per line, spaced 50 m apart, totaling 625 sites, as denoted by the black dots in the figure. The simulated observations span 18 frequencies ranging from 8192 to 0.1 Hz (8192, 4096, 2048, 1280, 640, 320, 160, 80, 64, 32, 16, 8, 4, 2, 1, 0.5, 0.25, 0.1).  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Parameter** & **1t** & **2t** & **3t** & **Beta** & **Lambda** \\ \hline Test 1 & 2\% & 8\% & 5\% & 10 & 0 \\ \hline Test 2 & 2\% & 8\% & 5\% & 10 & 0.1 \\ \hline Test 3 & 2\% & 50\% & 5\% & 10 & 0.1 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Testing data noise levels and inversion parameters.  
Figure 2: Flow of time-lapse electric field inversion algorithm.  
In the dynamic model of the time-lapse model containing high and low resistivity anomaly blocks, 1t, 2t, and 3t time steps are selected for CSAMT forward and time-lapse electric field inverse testing. The relationship between the scale of the resistivity anomalies and the site positions at three time steps is illustrated in Figure 4.  
In Model O, within a background stratum of 100 \(\Omega\cdot\)m, a model is constructed featuring a high-resistivity block of 2000 \(\Omega\cdot\)m with a continuously decreasing scale and a low-resistivity block of 10 \(\Omega\cdot\)m with a continuously increasing scale, as illustrated in Figure 4. Nx, Ny, and Nz represent the number of grids employed for the purpose of discretization. The forward calculation of three time-step models employs the identical grid discretization. The background medium is omitted in the figure, with only the varying subsurface media displayed.  
Figure 4: Relationship between the time-lapse model’s resistivity anomaly blocks and the positions of the sites.  
Figure 3: Schematic diagram of the layout.