esa-sceva/satcom-chunk-collection huggingface dataset:
# Time-Series Prediction of Long-Term Sustainability of Grounds Improved by Chemical Grouting
## 2 Methodologies
### Machine Learning Predictive (MLP) Model  
Machine learning is a technique or method for predicting what data will come next by following a set procedure [42, 43, 44, 45]. Figure 3 shows the working steps for the machine learning predictive (MLP) model. The basic steps of the model are as follows: learning the overall picture of machine learning, learning the individual methods, learning how to evaluate the predictions, and trying to use machine learning for specific tasks. The machine learning method in this study employed the support vector machine (SVM). The SVM [45, 46, 47, 48, 49] is a two-class classification machine learning method with supervised learning. Supervised learning is a type of machine learning method in which the machine is given a problem with an associated answer and learns them by adjusting the parameters in the program so that the answer calculated by the program is similar to the prepared answer.  
The SVMs can handle a variety of problems using two methods, namely, the kernel method (the data on a plane can be divided not only by straight lines, but also by curves for various problems) and the margin maximization method (the margin is the region bounded by two hyperplanes that linearly separate two data, and the maximum-margin hyperplane is the hyperplane that lies halfway between them), both of which can be used to determine a straight line that will successfully divide the input data.  
For the hard margin, \(\|w\|\) is minimized and subjected to Equation (7).  
\[y_{i}(w^{\intercal}x_{i}-b)\geq 1\text{ for }i\ =\ 1,\ \dots\dots,\ n \tag{7}\]  
Figure 2: State-space representation (SSR) model.  
where \(\left(w^{T}x_{i}-b\right)=0\) represents the hyperplane for a dataset of \(n\) points (\(x_{i}\), \(y_{i}\)),..., (\(x_{n}\), \(y_{n}\)), \(w\) is the normal vector of the hyperplane, \(b/\|w\|\) determines the offset of the hyperplane from the origin along the normal vector, and the superscript \(T\) denotes the conjugate transpose.  
For the soft margin, Equation (8) is minimized.  
\[\lambda\parallel w\parallel^{2}+\frac{1}{n}\sum_{i=1}^{n}max(0,\;1-y_{i}\;(w ^{T}x_{i}-b)) \tag{8}\]  
where the parameter \(\lambda>0\) determines the trade-off between increasing the margin size and ensuring that \(x_{i}\) lies on the correct side of the margin.  
In summary, the support vector machine (SVM) is a machine learning model that is capable of learning the fluctuation patterns of the input data, and when it recognizes patterns of the up and down values in the data, it can construct a machine learning model to predict the next movement.
## 2 Design Procedure
### Passage from member to storey (\(\alpha_{s}{\rightarrow}\alpha_{F}\)).
Therefore, \(F_{ad}\) can be alternatively expressed as a function either of \(F_{D}\):  
\[F_{ed}=F_{e}+F_{D}=F_{B^{\prime}}+F_{D}=\frac{F_{D}}{(\alpha_{F}-1)}+F_{D}=F_ {D}\frac{\alpha_{F}}{(\alpha_{F}-1)} \tag{9}\]  
or of \(F_{e}\), by substituting (8) in (9):  
\[F_{ed}=\alpha_{F}F_{e} \tag{10}\]  
By referring to the response cycle in Figure 2 and relevant parameters, the following expressions of \(E_{D}\) and \(E_{e}\) are derived:  
\[E_{D}=4F_{D}d_{dmax} \tag{11}\]  
\[\xi_{eq}=\frac{E_{D}}{4\pi E_{e}} \tag{5}\]0  
By substituting (11) and (12) in (5), the following \(\xi_{eq}\) expression is deduced:  
\[\xi_{eq}=\frac{E_{D}}{4\pi E_{e}} \tag{5}\]1  
By introducing (8) and (9), (13) becomes:  
\[\xi_{eq}=\frac{E_{D}}{4\pi E_{e}} \tag{5}\]2  
which allows directly quantifying the equivalent viscous damping ratio demanded to obtain the targeted elastic response up to the MCE, simply as a function of the reduction factor \(\alpha_{F}\). Moreover, by solving (5) to express \(E_{D}\) as a function of \(\xi_{eq}\) and considering (10), the following relation is obtained:  
\[\xi_{eq}=\frac{E_{D}}{4\pi E_{e}} \tag{5}\]3  
4. Evaluation of the energy dissipation capacity of the dampers, \(E_{D}\), and selection of the devices with the nearest mechanical characteristics. The basic design objective of reaching an elastic response of the most critical member(s), and thus of the relevant storey, implies that the maximum storey drift in retrofitted conditions, \(ID_{max}\), is constrained below the corresponding elastic limit \(ID_{B^{\prime}}=ID_{e}\) displayed in Figure 1b. \(ID_{max}\) is given by the sum of \(d_{dmax}\) and the interstorey drift contribution provided by the braced structure. The latter is normally small, because relatively stiff structures--i.e., with \(T_{1}\) below about 0.8 --are dealt with, as mentioned above, and also because the bracing system produces in any case a stiffening effect, although limited by the special installation layout of the FV spring-dampers. In view of this, in order to quickly pre-estimate the energy dissipation capacity of the devices, which is a function of \(d_{dmax}\)--otherthan of \(F_{D}\)--\(d_{max}\) is set as equal to \(ID_{e}\) at this stage. Based on this assumption, the energy dissipation capacity to be assigned to the FV devices on the considered storey is drawn from (11):  
\[\xi_{eq}=\frac{E_{D}}{4\pi E_{e}} \tag{5}\]4  
In order to express \(E_{D}\) as a function of structure-related terms only, (8) is substituted in (16), and gives:  
\[\xi_{eq}=\frac{E_{D}}{4\pi E_{e}} \tag{5}\]5  
Relation (15) can be equivalently adopted to this aim:  
\[\xi_{eq}=\frac{E_{D}}{4\pi E_{e}} \tag{5}\]6  
The sizing process of the spring-dampers is concluded--for each storey--by selecting from the manufacturer's catalogue [48] the device with the nearest energy dissipation capacity to the \(E_{D}\) value estimated by (17) or (18) and a stroke approximately equal to \(ID_{e}\).
# Mitigating Distractor Challenges in Video Object Segmentation through Shape and Motion Cues
## 4 Results
### Ablation Study
As can be seen from the figure, compared to STCN [2] the feature vectors of the distractor objects of our method are more discretized. Figure 9: The figure illustrates the results of our ablative experiments on the shape-based refinement module. It can be observed that the shape-based refinement module effectively refines the mask, enhancing the segmentation accuracy in the object-edge regions. The orange box indicates the areas refined by our module.
## 6 Conclusions  
In this paper, we primarily investigate the challenges associated with applying OFDM-IM technology in vehicular networks, particularly its inability to fully adapt to the complex characteristics of vehicular networks in practical usage and the limitations of the evaluation criteria. Considering the complex and dynamic nature of real-world vehicular networks, we first establish an OFDM-IM transmission model based on a specific vehicular network scenario and formulate an energy efficiency maximization equation. The energy efficiency is influenced by the spectral efficiency, BER, and energy consumption. We propose a vehicular network OFDM-IM communication optimization method based on user requirements and scenario adaptability. This approach flexibly allocates the weights of the three factors through fuzzy logic according to different user requirements and scenarios. The simulation results demonstrate that this method enables the selection of the optimal parameters in various vehicular network scenarios according to different user requirements, thus optimizing the communication performance.  
Conceptualization, X.R. and Y.W.; methodology, Y.W.; software, X.R.; validation, X.R., L.Z. and M.N.E.K.; formal analysis, X.R. and Y.W.; investigation, Y.W.; resources, L.Z. and M.N.E.K.; data curation, X.R.; writing--original draft preparation, X.R. and Y.W.; writing--review and editing, L.Z. and M.N.E.K.; visualization, X.R.; supervision, L.Z.; project administration, X.R. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China under Grant 62371381.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflicts of interest.
# An Optimized Hierarchical Cluster Formation Approach for Management of Smart Cities
## 3 Proposed Approach
### Energy Model  
Like many works that are aimed at extending the durability of WSNs, this research uses the first-order radio model [7] as an energy model. The energy for transmission (\(E_{T}\)) of a unit of data and the energy for receiving (\(E_{R}\)) a unit of data are expressed by the first-order radio model in Equations (1)-(3).  
\[E_{T}(d)=\left\{\begin{array}{ll}E_{ele}+E_{amp}*d^{2}&if\ d<d_{0}\\ E_{ele}+E_{amp}*d^{4}&if\ d\geq d_{0}\end{array}\right. \tag{1}\]  
\[E_{R}=E_{ele} \tag{2}\]  
\[\mathrm{d}_{0}=\sqrt{\frac{efs}{efl}} \tag{3}\]  
where \(E_{ele}\) represents the power of the transmitter electron of a unit of data, \(E_{amp}\) signifies the power of the transmitted amplification energy of a unit of data, and (_efs_) and (_efl_) are the short-distance amplification energy and long-distance amplification energy, respectively.  
In the ICSI model, the energy of \(CH_{i}\) is consumed mainly by three definite energy-consuming periodic tasks. These tasks are receiving data from sensor nodes, the aggregating of the received data, and the sending of the aggregated data to the \(BS\). The consumed energy that can be expended every round is formulated by Equation (4):  
\[E_{CH_{i}}=\sum_{j=1}^{M}E_{R_{ij}}+(E_{i}^{G}+E_{T_{BS}}) \tag{4}\]  
where \(M\) is the cluster size of \(CH_{i}\), \(E_{R_{ij}}\) is the energy of \(CH_{i}\) required to receive the sensed data from cluster member j, \(E_{i}^{G}\) represents the energy of \(CH_{i}\) required to aggregate the received data, and \(E_{T_{BS}}\) is the transmission energy of sending the aggregated data to the BS. The residual energy (\(RE\)) of \(CH_{i}\) is obtained after each round using Equation (5):  
\[RE_{CH_{i}}=RE_{CH_{i}}-E_{CH_{i}} \tag{5}\]  
On the other hand, each sensor node (\(SN_{j}\)) is also responsible for a set of periodic energy-consuming tasks. These tasks are sensing data from its environment and sending the sensed data to the \(CH\) (sink). The consumed energy that can be expended every round by each \(SN_{j}\) is expressed as Equation (6).  
\[E_{SN_{j}}=E_{T}\big{(}d_{CH_{i}}\big{)}+E_{j}^{s} \tag{6}\]  
where \(E_{j}^{s}\) is the energy consumed by each \(SN_{j}\) to collect data from its surroundings, while \(E_{T}(d_{CH_{i}})\) is the transmission energy from each \(SN_{j}\) to the corresponding \(CH_{i}\). Thus, the residual energy of any \(SN_{j}\) in each round is expressed by Equation (7).  
\[RE_{SN_{j}}=RE_{SN_{j}}-E_{SN_{j}} \tag{7}\]
## 3 Selective Redundancy Removal
### Scenario and possible applications  
A unidirectional covert flow, resulting from a web server on the sender side and a web browser on the receiver side, can be used to transmit information between two end points, or disseminate information if the communication pattern is one-to-many. If the steganogram is split across multiple pages, the sender and the receiver(s) must agree on a prearranged sequence of web pages to reconstruct the message, although the order in which these pages are accessed is inconsequential. In another scenario, consisting in an exchange of e-mails in HTML format, the communication is bidirectional.  
Possible applications include the exfiltration of small amounts of data, circumventing for example measures to limit freedom of speech and cut down on the free circulation of information. In the one-to-many scenario, an application can also be the covert coordination of a number of agents. The proposed method can be adapted to various operating environments. For example, a specialised version of the many-to-one scenario can be devised so as to reduce the resource requirements at the transmitting side, concentrating all the computation at the receiving side. This is especially attractive in resource-constrained platforms such as those typical of wireless sensor networks. Applications of the general framework should not suffer from the limitations due to the particular nature of HTML optimization and would thus cover the entire spectrum of covert communication.
## Materials and Methods
### Characterization of Surface Modified HNTs  
The surface functional groups of the modified and unmodified HNTs samples were confirmed by Fourier transform infrared spectroscopy (FTIR, Vertex 70v (Bruker, Karlsruhe, Germany)). The sample waiting for test was pressed with potassium bromide (KBr) at a weight ratio of 1:100 forming a tablet. Then this tablet was analyzed in a mode of scanning range of 4000 to 500 cm\({}^{-1}\) with 32 scans at 4 cm\({}^{-1}\) resolution.  
X-ray photoelectron spectroscopy (XPS, ESCALAB 250 XI (Thermo Fisher Scientific Co., Ltd., Shanghai, China)) was operated using a monochromatic Al K\(\alpha\) source (1486.7 eV) with power of 150 W. All spectra were obtained with a pass energy of 100 eV and the C 1s and N 1s regions were highlighted with high resolution scans.  
X-ray diffraction (XRD, D8 Advanced (Bruker, Madison, WI, USA)) was carried out to analyze the HNTs structure and crystallite performances with a Cu K\(\alpha\) radiation source of 1.5406 A, and theta scans were collected ranging from 10\({}^{\circ}\) to 70\({}^{\circ}\) with a step size of 5\({}^{\circ}\)/min.  
The morphologies of the HNTs samples was observed in field emission scanning electron microscopy (FE-SEM, FEI Quanta FEG 650 (Thermo Fisher Scientific Co., Ltd., Waltham, MA, USA)) at an accelerating voltage of 10 kV. Au layer was decorated on the samples surfaces before observation.  
Transmission electron microscopy (TEM, JEM-2100F (JEOL Ltd., Tokyo, Japan)) was used to obtain the high resolution morphologies of single nanotubes at an acceleration of 80 kV. The samples were dispersed in ethanol followed by sonication. The diluted colloidal solution was dropped onto a copper grid and dried for imaging.  
Water wettability were measured on a video-based contact angle meter (DSA100 (Kruss Gmbh, Hamburg, Germany)) at 25 \(\pm\) 1 \({}^{\circ}\)C and 50 \(\pm\) 10 %RH. Contact angle (CA) test: a droplet of 3 \(\upmu\)L deionized water was mildly put on the samples surfaces and recorded by a CCD camera at a rate of 24 frames s\({}^{-1}\). In addition, to verify the resultant superhydrophobic HNTs surfaces, the sliding angle (SA) was tested according to the method reported in the Reference [34]. A droplet of 10 \(\upmu\)L deionized water was released on the samples surface, and then the platform was tilted gently until the droplet began to roll. The critical angle of the incline surface was recorded as sliding angle. Each sample was tested six different regions to eliminate any error.
## 2 Materials and Methods
### Technical Indicators
#### 2.2.1. Linear Models  
* **Linear Regression (LR)**: The most fundamental of linear models, LR [25] fits a straight line through a series of points by minimizing the sum of squared errors between its targets and predictions. LRs are sensitive to outliers and correlated features. Nevertheless, as they are one of the primary ML models used for DAM forecasting, we include them in our examination.
* **Huber Regression (HR)**: Extending LRs, HR [26] is a linear model robust to response variable outliers. Unlike LR, HR optimises both an absolute and squared loss function, reducing the impact of outliers. To switch between loss functions, HR uses an epsilon hyperparameter. Despite implementing an enhanced optimisation procedure, HR remains sensitive to explanatory variable outliers and correlations.
## 2 Materials and Methods
### Feature Extraction and Selection
#### 2.5.1 Original Band Characteristics  
The multispectral raw spectral bands completely represent the original features of the images. In this study, INPHO software was used to generate orthophotos of the study area with a resolution of 0.2 m, containing four raw spectral bands (blue, green, red, and near-infrared).
# Memetic Chains for Improving the Local Wireless Sensor Networks Localization in Urban Scenarios +
## 5 Memetic Algorithm Chains for the Node Location Problem
### MA-VND-Chains Pseudo-Code  
The structure of the proposed MA is presented in the following pseudocode:  
Figure 3 details the algorithm procedure for optimizing the node distribution. As shown in Figure 3, the optimization process starts with the initialization of a random population and their corresponding LS Chains.  
Once created the initial population, the MA takes place. In this algorithm, a standard GA optimization is combined with a LS intensification, with the novelty of the implementation of LS Chains along a degree of memory into the optimization.  
Moreover, the introduction of these LS Chains serves as an additional source of knowledge for the problem into the LS, thus improving the optimization procedure. However, these LS Chains are effective when the associated individuals remain unaltered after the LS procedure. Hence, the memory of the intensified individuals that have been modified in the crossover or mutation algorithms is reinitialized after these functions take place.  
Furthermore, once the LS criteria is fulfilled, the LS procedure starts by selecting two sets of candidates for the intensification. The first set is composed of elitist individuals, while the second set consists of a selection of the most diverse individuals. Through the evaluation of the intensity \(I_{STR}\) of these candidates, a final set of LS individuals is obtained, based on their convenience for the LS intensification.  
Therefore, once selected the optimal individuals for the LS, the VND LS takes place. After every iteration of the intensification, the LS chains of the given individuals are updated with the actual step size of the Variable Neighborhood Search (VNS).  
Moreover, the LS fitness evaluation consists of a pseudo evaluation of the fitness of the selected individuals. This evaluation is based on the LOS/NLOS distance measurement of the links of the positioning signal among the target and the given nodes, based on the Ray Tracing Algorithm presented in [48]. This pseudo evaluation achieves a lower complexity while obtaining analogous results to those of the main fitness function.  
However, once the LS optimization finishes, the intensified individuals are reevaluated under the main fitness value, which considers other relevant factors such as clock errors, temporal uncertainties and geometric factors. Once evaluated, the individuals that show an increase in their fitness value replace their initial candidate before the LS. Nevertheless, the individuals that performed worse than their initial fitness value keep their initial properties.  
Nonetheless, the LS Chains of every intensified individual are updated with the corresponding fitness value improvement or failed intensification for future intensity evaluations.
## 5 Conclusions  
In this paper, we evaluated the accuracy of the GeoSLAM Zeb Horizon SLAM scanner for the creation of a digital model of a bridge construction. Comparing the data with reference data from a high-accuracy terrestrial scanner and a total station, we yielded very good accuracies of the SLAM cloud across the entire bridge. Although SLAM data were significantly more noisy than data acquired using the static scanner, smoothing using the MLS method significantly improved the noise (although certain details, such as sharp edges, were not preserved). For a 130 m long bridge, a global accuracy of 5 cm or better and local accuracies (evaluated using local ICP transformation of MLS-processed cloud as good as 5 mm) were achieved. Considering the speed and ease of scanning as well as the length of the bridge, the GeoSLAM Zeb Horizon results were very good, and using this scanner could be preferable to static scanners for applications such as creating digital twins, BIM, and their further use.  
It should be strongly pointed out that this technology is definitely not suitable for improving the construction process nor supporting the construction/structural rectification of the bridge. For this purpose, it is necessary to use a precise total station, enabling the measurement of specific points with high accuracy.  
Considering that a new generation of SLAM handheld scanners such as Emesent Hoovermap ST-X or Faro Orbis are already available, which have a significantly better measuring head (distance precision measurement enhanced to 1 cm (standard deviation) and number of channels to 32) and a new processing software, which can bring significantly better overall accuracy and lower noise in the data and therefore better capture of details, future research following this study could focus on comparing the previous and new generation of SLAM scanners and their measurement results.  
Conceptualization, R.U. and M.S.; methodology, R.U.; software, M.S.; validation, L.K., P.B., and J.B.; formal analysis, T.S.; investigation, J.B.; resources, T.S.; data curation, J.B.; writing--original draft preparation, R.U.; writing--review and editing, M.S.; visualization, M.S.; supervision, L.K.; project administration, R.U.; funding acquisition, R.U. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Technology Agency of the Czech Republic--grant number CK0300168, \"Intelligent methods of digital data acquisition and analysis for bridge inspections\" and by the Slovak Research and Development Agency under contract KEGA grant no. 003 TUKE-4/2023 and 011 TUKE-4/2024.  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to size of the data.  
The authors declare no conflicts of interest.
# Transimpedance Amplifier for Noise Measurements in Low-Resistance IR Photodetectors
## 3 Materials and Methods
In the case of \(Q_{n1}\), too, the highest value is obtained at the lowest frequency of interest because of the flicker noise component introduced by the JFETs and by the operational amplifier \(OA_{2}\). At the minimum frequency of interest (1 Hz), assuming a typical value of \(R_{D}\) of 1 k\(\Omega\) and a worst-case scenario in which \(R_{R}\) is limited to 10 k\(\Omega\) (\(R_{R}/R_{D}=10\)), with \(S_{I}=1\times 10^{-18}\) V\({}^{2}\)/Hz [14] and \(S_{eI2}=1\times 10^{-16}\) V\({}^{2}\)/Hz, we obtain \(Q_{n2}\approx 0.28\). If the bias conditions are such that a feedback resistance of \(R_{R}=100\) k\(\Omega\) can be used, \(Q_{n2}\) is reduced to about 0.12, a value essentially set by the noise contribution coming from the JFETs. Overall, therefore, under the same conditions explored in the introduction (DUT resistance on the order of 1 k\(\Omega\)), a value of \(Q_{n}\) is obtained that is a small fraction of 1 (from 0.18 to 0.24, depending on the value of the feedback resistance). This is to be regarded as an excellent result in consideration of the fact that, when investigating the flicker noise, the thermal noise generated by the DUT can be regarded as part of the background noise of the system (i.e., the flicker noise must be much larger than the thermal noise for a reliable characterization). Therefore, obtaining a value of \(Q_{n}\) that is a small fraction of 1 means that we are operating quite close to the ideal conditions under which no excess noise will be introduced by the amplifier. The value of \(Q_{n}\) obtained above is relative to the minimum frequency of interest (1 Hz). As the frequency increases, the background noise decreases because of the reduction of both the impedance of the coupling capacitances and the flicker noise contribution introduced by the active devices. As can be deduced from Equation (18), if \(R_{D}\) decreases significantly, \(Q_{n}\) increases and the background noise of the system becomes relevant with respect to the thermal noise introduced by the DUT.