esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Simulation and Experimental Results
### Experiment Results
Since these three sets of echo data originate from the same THz radar system, the larger the non-uniform sampling interval, the more uneven the obtained non-uniform sampling data. The selected first set of data exhibits a relatively regular interval between the positions of the sampled points, and it has the smallest non-uniform sampling interval. The second and third sets of data were obtained using random sampling, but the third set is based on sparser raw echo data, resulting in larger non-uniform sampling intervals compared to the other two sets of data. For the proposed algorithm, the wavenumber spectrum interval \(\Delta k=1/(x_{N_{m}}-x_{1})\times N/(N-1)\), and the model parameters are calculated as shown in Table 5. Figures 12, 13 and 16 show the imaging results for these three sets of non-uniformly sampled data. In Figure 16, the regions where substantial differences exist  
Figure 14: Pedestrian crossing scene optical image. Figure 13: (a) airplane model optical image; (b) RDA; (c) AR-RDA; (d) TR-RDA; (e) BPA. among the three algorithms are marked by red boxes. We evaluate the imaging quality using image entropy, and the results are presented in Table 6. The experimental results indicate that, compared to the other three non-uniform sampling image reconstruction algorithms, the image focusing effect obtained by using the traditional RDA algorithm to process the non-uniform sampling echo data is poor. As shown in Figure 12c-e, all three algorithms yield satisfactory imaging results for the first set of data with denser sampling points. However, for the other two sets of data with larger non-uniform sampling intervals, AR-RDA does not yield satisfactory results, as evident from the noticeable blurring in Figures 13c and 16a. Furthermore, within the red box region on the right side of Figure 16c, the imaging results of BPA exhibit certain geometry distortions as a result of the non-uniformity of the sampling positions. It can be seen that the experimental results are consistent with the simulation results, both of which validate the effectiveness of the proposed algorithm in this paper. Compared to the other two algorithms, the proposed algorithm possesses a unique advantage of simultaneously balancing accuracy and computational complexity. \begin{table}
\begin{tabular}{c c c c} \hline
**Algorithm** & **AR-RDA** & **TR-RDA** & **BPA** \\ \hline First set of data & 2.1339 & 2.0129 & 1.8183 \\ Second set of data & 2.3617 & 2.1484 & 1.7142 \\ Third set of data & 12.8971 & 12.8715 & 12.8710 \\ \hline \end{tabular}
\end{table} 
Table 6: Image entropy of imaging results from three algorithms. Figure 15: Interval size between non-uniform sampling point positions of two sets of data.
## 4 Calibration of the Camera Array
### Pairwise Calibration
The matrix was also calculated with regard to the convention such that coordinates of points in an image rises in the Y dimension from the top to bottom unlike in the commonly used Cartesian coordinate system. **left camera**: Images from a left camera with corresponding images from a central camera were flipped around y-axis. Therefore,  
\[W_{3}= \begin{bmatrix}-1&0&2c_{x}\\ 0&1&0\\ 0&0&1\end{bmatrix}  \tag{6}\]
**bottom camera**: In case of using a stereo camera consisting of a bottom camera and a central one images were rotated \(90^{\circ}\) counterclockwise. \[W_{4}= \begin{bmatrix}0&1&0\\ -1&0&2c_{x}\\ 0&0&1\end{bmatrix}  \tag{7}\]  
Stereo matching algorithms process pairs of images \(I_{P}\) which were obtained by applying calibration transformations on images \(I_{R}\). Thus, points in a disparity map obtained as a result of using matching algorithms refer to points of a transformed central image as this image is regarded as a reference one in every considered pair of images. The central image is differently transformed in every considered image pair. In order to acquire disparity maps whose points correspond to points of a raw central image, it is necessary to perform transformations reversing in disparity maps the calibration process. These reverse transformations including flipping and rotating images described earlier. Maps obtained after reverse transformations can be further processed. Further processing can result in merging maps obtained from individual image pairs in order to acquire a map which has a higher quality than constituent maps. This is a subject of EBCA merging methods described in [2; 3]. If ground truth is available then results can be compared in order to estimate the quality of stereo matching.
# Optimization of the Weighted Multi-Facility Location Problem Using MS Excel
## 2 Materials and Methods
### Evolutionary Method Excel-Solver  
The evolutionary algorithms (EAs) are subset of Evolutionary Computations and belongs to a set of modern heuristics-based search methods. Due to the flexible nature and robust behavior inherited from Evolutionary Computation, it is an efficient means of problem-solving for widely used global optimization problems. It can be used successfully in many applications of high complexity [21].  
In this work, the results were obtained using an evolutionary method. This method is available in the Solver add-on and is intended for solving a non-continuous problem. It uses genetic algorithms to find \"good\" solutions to continuous optimization problems. It relies in part on random sampling. It is a nondeterministic method that can provide different solutions in different runs.  
According to [22], evolutionary algorithms can be described by means of the following main features.  
In the first step, a random selection of samples is performed. The evolutionary algorithm maintains a population of candidate solutions. Only one (or several, with the same goals) of them is the \"best\", but other members of the population are \"role models\" in other areas of the search space where a better solution can be found later. The evolutionary algorithm regularly makes random changes or mutations in one or more members of the current population, resulting in a new candidate solution (which may be better or worse than existing members of the population). The evolutionary algorithm attempts to combine elements of existing solutions in order to create a new solution with some features of each \"parent\". Elements of existing solutions are combined in a crossover operation. In the end, the evolutionary algorithm performs a selection process in which the \"most suitable\" members of the population survive and the \"least suitable\" ones are excluded. In a limited optimization problem, the term \"fitness\" depends partly on whether the solution is feasible (i.e., whether it meets all the constraints) and partly on the value of its objective function. The selection process is a step that leads the evolutionary algorithm to ever better solutions.  
The algorithm can be presented in this pseudocode notation.  
START  
Generate the initial population  
Compute fitness  
REPEAT  
Population  
Mutation  
Crossover  
Selection  
Compute fitness  
UNTIL population has converged  
STOP  
Convergence values, mutation frequency, base file size, random number, and maximum time without enhancement can also be set for this method. What the effect is of changes in these parameters on the calculation is not stated; therefore, default values were left for all calculations. The default values of parameters are listed in Table 1.  
In the attached file (the link for download is in Section 1), the method is described in detail on the example of an MFLP-W task with 25 points and 5 centers.
# The Inactivation of Microscopic Fungi in Bakery Products Using Hurdle Technology--A Case Study
## 2 Materials and Methods
### Bakery Recipes and Process Parameters  
Basic test samples were prepared according to the standard recipes used at the company. The composition of the dough and process parameters remain the plant's secrets. These samples were used to conduct MAP storage trials with or without ethanol in the package. Laminated foil with an ethylene vinyl alcohol copolymer (EVOH) layer was used in the packaging process. EVOH has excellent barrier properties for oxygen, water vapor, and a variety of other gases and volatiles.  
Influence of the Composition of the Gas Mixture Used in MAP Technology and the Use of Ethanol on the Development of Microscopic Fungi in Bakery Products  
Test samples of four products, ciabatta (product A), a wheat hot dog (product B), a graham hot dog (product C), and a wheat roll (product D), were packed in three different modified atmosphere conditions (70% CO\({}_{2}\)/30% N\({}_{2}\); 100% CO\({}_{2}\); and 100% N\({}_{2}\)). Ethanol was also added to some variants at concentrations of 1.5, 4.5, and 9%. The dose of ethanol added to the packaging was calculated in relation to the weight of the products in the packaging. Ethanol was injected into the package manually just before sealing the package.  
MAP-packed products were stored at room temperature for seven (product A) or five weeks (products B, C, and D).  
Three packages of each variant were observed macroscopically every 2-4 days for potential yeast or mold growth. If so, contamination was confirmed by microscopic observations.  
In each variant, three packages with no alcohol added were also checked for the residual oxygen content at weekly intervals using an O\({}_{2}\)/CO\({}_{2}\) OxyBaby gas analyzer (WITT--Gasetechnik GmbH & Co, Witten, Germany) (Table A1). The maximum allowable percentage of residual oxygen inside the packages was set at 0.5%.
# Multi-Templates Based Robust Tracking for Robot Person-Following Tasks
## 3 Method
### Scene Analysis Module
#### 3.2.2 Instance Segmentation  
The density of the response map is estimated by the GMM; however, the GMM components are not discriminative in instances. [PERSON] [28] adopted the Chinese whispers algorithm to solve clustering problems using undirected and weighted graphs, which can further facilitate segmenting the GMM into instance-aware mixtures.  
We define \(G=(V,E)\) as a graph with nodes \(v_{i}\in V\) and weighted edges \((v_{i},v_{j},w_{ij})\in E\). The adjacent matrix \(\mathcal{W}\) of graph \(G\) is a square matrix, where the entries \(w_{ij}\) denote the weight of the edges between \(v_{i}\) and \(v_{j}\). Since the segmentation is conducted on probability densities, we use Kullback-Leibler divergence (KL divergence) as the metric to set up the weights of the graph edges.  
Given two components \(f=\rho_{f}\mathcal{N}(\mathbf{\mu}_{f},\Sigma_{f})\) and \(g=\rho_{g}\mathcal{N}(\mathbf{\mu}_{g},\Sigma_{g})\) of a mixture, according to the definition, their KL divergence is given by  
\[D_{KL}(f,g)=\rho_{f}\bigg{(}D_{KL}\Big{(}\mathcal{N}(\mathbf{\mu}_{f},\Sigma_{f}),\mathcal{N}(\mathbf{\mu}_{g},\Sigma_{g})\Big{)}+log\frac{\rho_{f}}{\rho_{g}} \bigg{)}, \tag{3}\]  
and a closed-form solution is derived as  
\[D_{KL}\Big{(}\mathcal{N}_{f},\mathcal{N}_{g}\Big{)}=\] \[\frac{1}{2}\Bigg{\{}\log\frac{\left|\Sigma_{g}\right|}{\left| \Sigma_{f}\right|}-n+\text{tr}\Big{(}\Sigma_{g}^{-1}\Sigma_{f}\Big{)}+\Big{(} u_{g}-u_{f}\Big{)}^{T}\Sigma_{g}^{-1}\Big{(}u_{g}-u_{f}\Big{)}.\Bigg{\}}\]  
We take the GMM components as the nodes in \(G\). If the KL divergence of two nodes is greater than a threshold, an edge is established and the reciprocal of its divergence is set to the corresponding position in the adjacent matrix \(\mathcal{W}\). Then, the algorithm iteratively segments by grouping nodes that have the maximum mutual weights.  
Figure 3d presents an example of an outcome of applying instance segmentation. The GMM components are segmented into four clusters, where each cluster corresponds to a potential object in Figure 3a.
# Enhanced Effect of Phytoextraction on Arsenic-Contaminated Soil by Microbial Reduction
## 3 Results and Discussion  
### Composition of Iron-Sulfur-Reducing Flora  
The iron-sulfur-reducing flora obtained through artificial enrichment was dominated by Desulfovibrio, Escherichia-Shigella, Paraburholderia and Achromobater, as depicted in Figure 1. Among them, Desulfovibrio, an acknowledged sulfate-reducing bacteria (SRB), had strong sulfate-reducing ability [25] and exhibited the highest relative abundance, suggesting that enrichment was achieved. Desulfovibrio plays an irreplaceable role in the biotransformation of sulfate by converting low molecular weight substrates (lactic acid, acetic acid, ethanol hydrogen, etc.) to acetate as an incomplete oxidation product or carbon dioxide as a complete oxidation product to reduce sulfate [26]. Previous studies have shown that SRB plays an important role in the biogeochemical cycling of arsenic, and the sulfur and iron reduction processes mediated by SRB are considered to be an important mechanism affecting the transport and transformation of arsenic in anaerobic environments [27]. In addition to sulfate, sulfate, thiosulfate and elemental sulfur, SRB can also use nitrate, nitrite, iron and other compounds as electron acceptors.
# Ultra-Low Interfacial Tension Foam System for Enhanced Oil Recovery
## 3 Results and Discussion  
### Foaming Properties and Oil-Liquid IFT of Surfactants  
To evaluate the foaming ability, foam stability, and IFT of a single surfactant foaming system, solutions of each surfactant (100 mL) of various concentrations (0.05, 0.1, and 0.15 g/L) were stirred at 7000 rev/min for 3 min to generate foam. The resulting foam properties and oil-liquid IFT reduction capabilities are listed in Table 7.  
Figure 2: Flow chart of core-flooding experiment. Among the anionic surfactants, SO showed weak foaming properties due to poor solubility. The cationic surfactant CTAB possessed strong foaming capacity but weak foam stability. SDS and AES showed both strong foaming capacity and good foam stabilization effects. Amphoteric surfactants (THPB, DHPB and 3-SDB) exhibited good foaming properties and a high FCI. As the hydrophobicity of the hydrophobic group increased, the surfactant molecules distributed themselves closer to the gas-liquid interface. Compared with other surfactants, THPB, DHPB, and CTAB exhibited stronger hydrophobicity due to their longer alkyl chain, resulting in better foaming capacity. The foam stability was dependent on the liquid viscosity and the compactness of surfactants' arrangement on the interfaces. Thus, the same surfactant exhibited better foam stability at high concentration. Similar to the foaming properties, the oil-liquid IFT of the surfactant solution was mainly influenced by the strength of the hydrophilic chain. A stronger hydrophobic chain led to stronger bonding between surfactant molecules and water molecules, improving the ability of the surfactant to reduce the IFT. Therefore, solutions of the surfactants with stronger hydrophilic chains, including SO, CTAB and THPB, had a lower oil-liquid IFT.
## 3 Results
### Modal Identification by RAR  
Recent advances in remote sensing techniques confirm the possibility of extract the modal shapes with the use of RAR in high-rise buildings [28]. The greater displacements shown by these types of structures simplify the extraction. To investigate the possibility of estimating the modal shapes of the studied structure using RAR, the following assumptions were considered: (1) the RAR measures the movement of the structure at different elevations on each Rbin, (see Figure 8a); (2) the motion recorded from the different Rhins is synchronized and (3) the relative displacement between the RAR and the base of the structure is negligible.  
Figure 20a shows a cycle of 0.4 s (from 60.25 to 60.65 s) from the history of the displacement measured by each of the bins for a frequency of 2.49 Hz, corresponding to the first identified mode of the \"_La Vina_\" building. For this, a Butterworth band pass filter was used between 2.490 and 2.492 Hz. The five different time instants are represented by the circles. Figure 20b shows the five displacement instants of each of the bins. The movement of the base has been set to zero. Due to the resolution of the Rhins of the RAR, it is difficult to assign a measurement point to each of the Rhins, and so the vertical axis is not exactly related to the height of the building. It can be seen that the motion is similar at the different measured points and that the amplitude of the motion varies with each of the bins. Noticeably, similar motion patterns can be observed among these Rhins. This is because, once the period has been isolated by filtering, the corresponding displacement-time histories are associated with the motion of points at different heights (see Figure 8) of the facade of the building, due to, in this case, the first mode of vibration, so that, under adequate conditions and highly accurate measurements, even the modal shapes can be observed. Further, Rbin41 and Rbin42 present the higher displacements, and this is maybe related to the better SNR that those Rhins presented. The motion is likely related to the projection in the RAR LOS of the first modal shape of the building. More research on the use, resolution and capabilities of the radar is necessary. Further, a comparison with other techniques, for example, a vertical array of accelerometers, at the different levels of the structure could validate the results presented herein.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Mode** & **Type** & **f (Hz)** & **T (s)** \\ \hline
1 & Rotational & 2.49 & 0.40 \\
2 & Combined translational & 2.79 & 0.36 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Modal results of the _“La Vina”_ building.
## 1 Introduction
His findings indicated that low-cost masonry bricks costing 6.4 times less than conventional fired clay bricks could be manufactured using these waste materials without the need for Portland cement. Further, [PERSON] et al. [31] examined the compressive strength of unfired bricks made from coal combustion residues such as fly ash. The bricks produced were lightweight, demonstrated good mechanical resistance, and posed no health threats. The design specifications suggest that to achieve a compressive strength of 7-8 Mpa (1015-1160 psi), a 10% inclusion of cement is required. Consequently, ratios of 10% and 15% by weight of cement were explored to assess if a reduction in cement use could still meet the 1000 psi strength requirement [32]. This was corroborated by [PERSON] [33], who determined an optimal mix achieving a strength of 15.15 MPa (approximately 2200 psi), with a cement ratio of 10.91%, significantly exceeding the required strength of 1000 psi (6.89 Mpa). Based on these findings, we will test the idea that stabilized dredged materials from the Southeast Texas waterways can be used to produce non-fired bricks for building construction. According to Section 14.7.4.11 C of the New Mexico Building Code (2009), the average strength of cured adobe bricks must be at least 300 psi (2068 kPa), with no individual specimen falling below 250 psi (1724 kPa) [34]. Similarly, the International Building Code (2003), in Section 2109.8.1.1, requires that five adobe units be tested with the same minimum average strength of 300 psi and no individual unit having a strength below 250 psi (1724 kPa) [35]. Previous research by [PERSON] [1] demonstrated that a combination of 7% hydrated lime (HL) and 14% fly ash (FA) failed to meet the specifications outlined by the New Mexico Code. However, further findings [1; 36] indicated that a FA/HL ratio between 2 and 3 yielded the most significant strength improvements. Taking these findingsinto consideration, we produced bricks made from DM, a FA/HL ratio of 2.3, and the addition of 10, 15, and 20% Portland cement (PC) based on the dry weight of the mix. Our objective was for the bricks to achieve a compressive strength of at least 1000 psi (6895 kPa), thereby surpassing the requirements set by both cited codes.
# IoT Intrusion Detection Taxonomy, Reference Architecture, and Analyses
## 4 Standards, Paradigm Uptake and Inherent Vulnerabilities
### IoT Standards  
Several criteria (i.e., standards) to baseline IoT architectures have been proposed by standards organizations, such as ITU Telecommunication Standardization (ITU-T) [56], and related open-standards organizations (e.g., advancing network functions virtualization (NFV) and software-defined networking (SDN)). Intel's involvement in these open-standards organizations includes 3 GPP, Cloud Native Computing Foundation, European Telecommunications Standards Institute, Linux Foundation Edge, Open Network Foundation (ONF), Open RAN Alliance (O-RAN Alliance), Internet Engineering Task Force (IETF), and Open Container Initiative (OCI) [57]. Such standard initiatives aim at facilitating interoperability, simplifying development, easing implementation, and identifying both functional and non-functionable weaknesses in the IoT systems. Figure 3a illustrates the functional view of the IoT standard proposed by ITU-T [56]. The architecture consists of four layers: the (1) device, (2) network, (3) service and application support, and (4) application layers.
# General Moving Object Localization from a Single Flying Camera
## 7 Discussion
### Wind Interference  
Wind is the most critical and typical factor which affects the accuracy of the algorithm directly. The presence of wind can change the position of the drone in its flight. A tail wind will move the drone further along its axis, and a head wind will slow it down or even move it backwards. This is significant, because it changes the baseline between the camera positions. Figure 20 shows the situation when a sudden wind affects the drone's position and attitude while it is still hovering.  
This situation can be mitigated if the wind speed or direction is known--e.g., from a wireless anemometer. We can find the component of the wind in the direction of the drone's flight and then add or subtract it from the drone's actual speed. This will allow us to compute a multiplier for the distance travelled by the drone so as to compensate for the effect of the wind. Another alternative is to make use of positioning systems such as GPS or WiFi positioning to provide the correction needed due to the wind. However, such positioning systems can be quite slow, so this alternative will only work for very slow-moving or stationary objects.  
Figure 19: Depth ambiguity due to the almost constant speed of the drone.
## 3 Preliminaries
### Building \(V\) Coordinates  
These coordinates are similar to the ones in VCap [6], JUMPS [7] or VCost [9]. Several nodes, \(L_{1},,\ldots,L_{k}\) with \(k\geq 3\), in the network are distinguished as _landmarks_. Each landmark broadcasts a beacon in the network incremented at each hop. From it, an arbitrary node \(x\) knows its virtual coordinate vector \(V(x)=(h_{1},\ldots,h_{k})\) where \(h_{i}\) is the hop-distance between \(x\) and \(L_{i}\). Figure 1(a) shows an example of how nodes are assigned virtual coordinates. We suppose \(3\) landmarks: nodes \(10\), \(9\) and \(14\). Every node thus has a \(3\)-dimensional vector as coordinates constituted by the number of hops between itself and every landmark. For instance, node \(0\) can reach Landmark \(1\) (node \(10\)) in \(2\) hops, Landmarks \(2\) (node \(9\)) in \(4\) hops and Landmark \(3\) (node \(14\)) in \(3\) hops. Its virtual coordinate is thus \(V(0)=(2,4,3)\). The distance used on these virtual coordinates is \(d_{V}\) where \(d_{V}(u,u^{\prime})\) is the Hamming distance from node \(u\) to node \(u^{\prime}\) on \(V\) coordinates (\(d_{V}(u,u^{\prime})=\sum_{i=1}^{M}|h_{i}(u)-h_{i}(u^{\prime})|\)). For example, on Figure 1(a), the distance \(d_{V}(0,8)\) between node \(0\) (\(V(0)=(2,4,3)\)) and node \(8\) (\(V(8)=(4,1,3)\)) is \(d_{V}(0,8)=|4-2|+|1-4|+|3-3|=2+3+0=5\).  
Obviously, using only these coordinates does not guarantee delivery since the node coordinates are not unique (_i.e._, several nodes may have the same virtual coordinates) and thus do not identify a single node. This is for example the case for nodes \(6\) and \(15\) on Figure 1(a) which are both labeled with \((4,2,4)\).