esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### SICS Controller in the Server-based Mode (SM)  
The SICS SMM controller always requires an active web browser (client) during runtime, as this is where I/O routing to the devices takes place. This is eliminated with the SISC-SM controller: All required service entities of a SICS controller run exclusively in the server.  
Based on the general structure in Figure 5a, a SICS controller in the server-based mode (SM) is shown in Figure 11.  
Process data communication takes place only between server (cloud) and devices. The client (web browser) has no influence on this communication. In the client, which is temporarily required for operating purposes, there are only HMI proxy objects for operation and visualization of the SICS runtime and router instances.  
The prototype implementation is based on the SMM structure of Figure 10, and it relocates the SISC router to the server. In addition, a SISC SM manager is required in the server, via which the SISC runtime instances must be generated explicitly by means of an administration tool. The simplified implementation structure of a SISC-SM controller is shown in Figure 12. A complete description of the SISC-SM can be found in [14].  
A SISC-SM controller is operated and visualized via a web page. However, this is only temporary if necessary for monitoring the control. The SISC controller runs independently in the server or in a  
Figure 11: SICS controller in the server-based mode (SM).  
Figure 12: Implementation structure of a SICS SM controller at runtime (s—server-based part; c—client-based part).  
cloud after startup. As with the SICS-SMM solution, any number of SISC controller instances can be distributed on any servers, e.g., also running on the local network via Edge or Fog computing.  
Some advantages of the SM solution are:  
* Controllers can run as software instances independent of web browsers distributed on any server system.
* Communication to the devices may be via any TCP protocols (e.g., Modbus TCP or OPC UA), as far as is supported by the servers. The devices do not need a web-enabled communication.
* Different SICS router instances enable dynamic reconfiguration of the control system online without delay.
* The SICS runtime can use the full performance and stability of the server. However, the prototype implementation still has a few disadvantages:
* The configuration of a SICS-SM controller cannot yet be realized completely via an IoT or IIoT platform. There is still a need for an additional administration tool.
* It is basically possible to set up C3x controllers as a smart service, since all SICS-SM instances are also available and manageable as separate service objects. However, the complete SaaS model (software as a service) is much more difficult to implement than in a SISC-SMM solution. Therefore, an implementation is not yet available.
## 3 Results and Discussion
### Flocculation Dependence  
The protein adsorption on GNPs is often controlled by the optical density of the colloidal solution at 580 nm after the addition of 10% NaCl [29]. Typically, optical density increases with increasing protein concentration, reaches a maximum, and begins to decline, reaching a plateau. It is believed that reaching a plateau of flocculation dependence corresponds to the composition of conjugates with maximum binding ability that are necessary for highly sensitive immunoassaysays in noncompetitive formats. We obtained this form of the flocculation curve upon conjugation of Mab 2H2F6 antibodies with GNPs. The preparation of antibodies Mab 2H2F6 did not cause aggregation of nanoparticles (and changes in the optical density of the colloidal solution) by itself. When 10% NCl was added, changes in optical density were observed depending on the concentration of antibodies (see Figure 2A).  
When antibodies Mab 2H3C6 were added to the colloidal solution of the GNPs, a fundamentally different dependence was observed: Nanoparticles were aggregated immediately after mixing with antibodies (Figure 2B). This flocculation did not require the addition of 10% NaCl and reflected the unsuitability of the conjugates for further analytical use due to their instability. Monoclonal antibodies do not always behave this way. As a rule, conjugation of monoclonal antibodies with GNPs produces stable nonaggregating conjugates (as was the case with Mab2H2F6). However, it is known that the aggregation stability of GNPs with monoclonal antibodies is significantly different for different antibody preparations [30]. We thus studied the cause of the aggregation of nanoconjugates with the used Mab 2H3C6 preparation.
# Self-Improved Learning for Salient Object Detection
## 4 Experiment
### Analysis on Design Choices
#### 4.3.1 Updating Strategy of PUM  
To obtained smooth labels in SIT, we need to update the PUM module from the SOD network. A straightforward updating strategy is to update the PUM module only once per epoch, which is similar to the \(\Pi\) model [65]. In this paper, we adopt a momentum-updating manner to integrate models of different steps to the PUM module progressively. From Table 4, we can see that all the momentum-updating strategy settings outperform the epoch-based updating strategy setting. With the momentum coefficient \(\eta\) in the range of 0.9 to 0.9999, the momentum-updating strategy could achieve a stable performance improvement, exceeding the epoch-based updating strategy on average by 1.1% on \(mF\) and 0.8% in \(S_{\alpha}\).  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Updating Strategy** & \(\eta\) & **MAE**\(\downarrow\) & \(mF\uparrow\) & \(S_{\alpha}\uparrow\) & \(E_{\xi}\uparrow\) \\ \hline Epoch Based & — & 0.038 & 0.833 & 0.882 & 0.895 \\ Iteration Based & 0.9 & 0.035 & 0.843 & **0.891** & 0.901 \\ Iteration Based & 0.99 & **0.034** & **0.846** & **0.891** & **0.904** \\ Iteration Based & 0.999 & 0.035 & **0.846** & 0.889 & 0.903 \\ Iteration Based & 0.9999 & 0.035 & 0.841 & 0.888 & 0.902 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The effect of different updating strategies of PUM in our proposed SIT on the DUTS-TE dataset. ‘Epoch-based’ denotes updating \(\theta_{\text{PUM}}\) only once per epoch. ‘Iteration-based’ denotes updating \(\theta_{\text{PUM}}\) by Equation (2). \(\eta\) denotes the momentum coefficient. \(\uparrow\) and \(\downarrow\) indicate that the larger and smaller scores are better, respectively. The best results are highlighted in bold.  
Figure 5: Performance comparison with baseline models and our method on the DUTS dataset. The **first column** shows comparison of precision–recall curves. The **second column** shows comparison of F-measure curves over different thresholds. As a result, our method improve the performance of different baseline models.
## 3 Results and Discussion
### Analysis of Geotechnical Properties  
After treatment as shown in Table 1 and Figure 1, the proportion of coarse-grained soils retained in sieve No. 200 (0.075 mm) was increased compared to initial as well as contaminated soil due to the desorption of oil and organic matters absorbed in the soil particle and even fine-grained soil particles in the form of dust and mist. It was found that the liquid limit and permeability coefficient were almost recovered to the initial state due to the desorption of oil occupied in the pores between soil particles.  
After lubricant-oil contamination (Figure 7a), the cohesion (C) which was initially 14.33 kN/m\({}^{2}\) was increased to 24.04 kN/m\({}^{2}\), and the internal friction angle (\(\mathcal{O}\)) which was initially 41.7\({}^{\circ}\) was decreased to 32.6\({}^{\circ}\) due to the lubrication effect of viscous oil and organic matters contained in the pores between soil particles [39]. However, after thermal treatment, the cohesion was decreased to 10.73 kN/m\({}^{2}\), and the internal friction angle was increased to 44.1\({}^{\circ}\), which is attributed to desorption of oil, organic matters from the lubricant-oil contaminated soil [40; 41].  
Figure 5: (**a**) Initial soil; (**b**) Lubricant-oil-contaminated soil before treatment [TPH 26,600 mg/kg]; (**c**) Cleaned soil after treatment (600 \({}^{\circ}\)C) [TPH 38.2 mg/kg].  
Figure 6: Remediation results: (**a**) Residual TPH concentration and (**b**) Removal efficiency.  
The maximum dry density which was initially 2.02 g/cm\({}^{3}\) was decreased to 1.70 g/cm\({}^{3}\), and optimal moisture content, which was initially 8.0%, was increased to 12.4% after oil contamination. However, thermal treatment had then been restored to the almost initial level of 1.96 g/cm\({}^{3}\) and 9.6% (Figure 7b), which results from the fact that thermal treatment removes the lubrication effect of oil, which makes the soil a looser material [42].
## 2 Materials and Methods
### Data Processing  
The data were processed in two phases using the commercial software ReflexW 10.0 [22]. First, a series of basic processing steps were performed to remove the main instrumental noise. Second, the two stacking strategies were tested on the processed data: a conventional in-line stacking and the new off-line stacking. A combination of the two strategies was also evaluated.  
In the first series of processing steps, we applied only the strictly necessary processing to the data, with the following processing flow:  
* Subtract mean or Dewow. This is a filter separately applied on each trace. Considering all the samples within a moving time range of 4 nanoseconds, this filter subtracts the mean value of the samples from the actual sample value. It was useful to remove instrumental noise that shifted the signal toward positive or negative values, instead of oscillating around 0.
* Move start time. The first nanoseconds of the signal, before the emission of the electromagnetic impulse, are removed. An automatic recognition of the transmitted impulse timing is needed because each channel of the Stream C multichannel system starts to record at a slightly different time. In ReflexW software, the combination of \"Correct max phase\" and \"move start time\" filters performs this task.
* Background removal. This filter calculates the mean trace of the whole radargram and subtracts this trace to each trace. It removes horizontally coherent instrumental noise.
* Subtracting average. This filter acts as a horizontal high pass filter. It is similar to the background removal, but restricted to a moving window of a certain number of traces. We selected a moving window of 50 traces corresponding to about 2 m. This filter is suggested for archaeological studies aimed at finding walls or similar features in [23].
* Gain. An automatically calculated gain function was applied with the \"Energy Decay\" filter of ReflexW to compensate for the intrinsic and geometric attenuation of the signal at depth.  
No migration was performed since it was difficult to recognize well-defined hyperbolae to estimate an appropriate electromagnetic wave velocity. Furthermore, this study aims to recognize the main buried walls in a challenging environment, not to pinpoint the location of small features.  
The time-slices were then finally created by interpolating all the 2D radargrams into a 3D data volume, and then cutting it into horizontal slices. For the comparisons presented in this study, for simplicity sake, we used only the vertically (VV) oriented dipoles. To better visualize time-slices, an envelope filter was also applied to the data. The envelope is a measure for reflectivity strength, which is proportional to the square root of the complete energy of the signal at an instant of time [22].
## Appendix A Unstructured Data Classification and Grading Framework  
The framework for unstructured data classification and grading comprises two modules: text-based data classification and grading, and entity-based data classification and grading. The text-based module determines the domain and type of the text, while the entity-based module identifies specific entities embedded within the text. As depicted in Figure 11, the framework provides information on the domain of the text, the involved text type (such as diplomas, CVs, insurance policies, etc.), and the sensitive entities present in the text (e.g., ID card numbers, email addresses, cell phone numbers, etc.).
## 2 Modal Based Damage Detection: Development & Challenges
### Development of Modal-Based Damage Sensitive Features
#### 2.1.5 Modal Strain Energy  
Modal curvatures form part of many other modal-based DSFs, such as the Damage Index Method (DIM) [59]. The DIM defines modal strain energy as the energy stored in a structure when it deforms in its mode shape pattern [54]. Damage is determined by observed reductions in modal strain, indicating that the structure is incapable of storing as much energy due to a loss of stiffness, as per Bernoulli-Euler beam theory.  
[PERSON] et al. [35] found that modal strains performed better than frequency-based damage indicators for damage localisation when assessed on Finite Element (FE) modelled beams. They also observed that modal strains could reasonably estimate crack size, making it possible to quantify damage. [PERSON] et al. [60] observed that calculated displacement modes and strain modes for a tested plate structure were almost the same; however, strain modes exhibited a higher sensitivity to local structural change. [PERSON] et al. also noted that the obtained strain response for higher modes was not very strong, which would indicate that their damage sensitivity is limited in these modes. As the DIM is based on modal curvatures, it therefore faces the same challenges as the MCM, as the central difference approximation method is required in most cases. Additionally, the DIM also employs curve fitting techniques between sensor locations to obtain continuous strain values; however, this can smooth-over and conceal local damage [54].  
The DIM is presented in Equation (4), where \(\beta_{i,j}\) represents the damage feature value for the \(i\)th mode at location \(j\). The values \(\varphi^{\mu^{\prime\prime}}\) and \(\varphi^{\mu^{\prime\prime}}\) are the curvatures of the undamaged and damaged mode shapes, respectively; \(L\) is the element length; and variables \(a\) and \(b\) are the element limits.  
\[\beta_{i,j}=\frac{\left[\int_{a}^{b}\left(\varphi^{\mu^{\prime\prime}}\right) ^{2}dx+\int_{0}^{L}\left(\varphi^{\mu^{\prime\prime}}\right)^{2}dx\right]}{ \left[\int_{a}^{b}\left(\varphi^{\mu^{\prime\prime}}\right)^{2}dx+\int_{0}^{L} \left(\varphi^{\mu^{\prime\prime}}\right)^{2}dx\right]}\cdot\frac{\int_{0}^{L} \left(\varphi^{\mu^{\prime\prime}}\right)dx}{\int_{0}^{L}\left(\varphi^{\mu^{ \prime\prime}}\right)dx} \tag{4}\]  
The DIM was advanced by [PERSON] et al. [61] to allow the application to be extended to 2-Dimensional (2-D) bending structures and again by [PERSON] et al. [62] for structures exhibiting axial and torsional responses, although these methods require many more sensors and deviate from the original curvature-flexural stiffness relationship (Equation (2)).
## 1 Introduction
In the case of communication technology deployment, studies have categorized MG interactions into two groups: wired and wireless. While RS-232, RS-485, Power Line Carrier (PLC), optical fiber, and ethernet are commonly used wired technologies in MG communications, ZigBee, Bluetooth, Wi-Fi, WiMAX, Global System for Mobile (GSM), 5G/4G/3G/HSPA, LoRa, and satellite communication are wireless ones [15; 16; 17; 18; 19; 20]. Due to wireless technology advantages in comparison with wired technology, such as cost effectiveness, convenient installation, portability, low risk of ground potential issues, and scalability, an investigation into employing this technology in MGMS should be undertaken.
## 3 Proposed Algorithms
### Space and Time Complexity Analysis  
This section provides a space and time complexity analysis of VFDDM. Firstly, VFDDM requires maintaining a sliding window of size \(n\), where \(n\) is a fixed value during the algorithm's operation. Therefore, the size of the sliding window is constant, resulting in a space complexity of \(O(1)\). Additionally, VFDDM requires several registers to store parameters such as the weight factor _diff_, the mean influence factor \(\beta\), the variance \(\sigma^{2}\), and the significance threshold \(\epsilon\). However, the storage of these parameters only requires a fixed number of registers, and the storage space does not increase with the number of window traversals. Thus, the space resources required by VFDDM are constant, with a space complexity of \(O(1)\).  
The sliding window maintained by VFDDM moves 1 instance at a time, assuming there are \(S\) data instances. Each time the window slides, VFDDM operates according to the process outlined in Algorithm 1, with the main time expense being the traversal of the window, which has a time complexity of \(O(n)\). Therefore, the overall time complexity is \(O(n\cdot S)\).
# A Multiobjective Decision-Making Model for Risk-Based Maintenance Scheduling of Railway Earthworks
## 4 Case Study  
The developed model was applied to the Irish Rail network, see Figure 5, to determine the optimal schedule for asset maintenance. Relevant asset metrics were sourced from Irish Rail; the objective was to be accomplished by minimizing annual maintenance costs and user delay costs while maximizing asset reliability.  
The Irish Rail asset database is comprised of 3404 slopes giving details on function, location, average height, slope angle, construction type, drainage, condition scores, damage detail, critical failure mechanism, reliability index and other characteristics. Any assets with a reliability index greater than 2 were deemed to be at low risk of failure and were not considered for maintenance. Similarly, if an asset slope was located at a distance more than twice its height from the track, it was eliminated from the dataset. This left 190 slopes requiring some form of maintenance. The intervention rules described in Section 4 were applied to determine maintenance options, relevant costs, reduced speed, the duration of maintenance, and to estimate the reliability index followed intervention for the asset in question. The related decision attributes, i.e., improvement in reliability index, annual maintenance cost and user costs, were then computed.  
As the objectives are conflicting, MAUT was applied to the case study data with a minimization function. As a result, the slope with the lowest overall utility score contributes most to the realization of the objective. The application of MAUT ranks the slopes (i.e., alternatives) into some order based on how well they meet or tradeoff multiple performance objectives. Whichever slope has the lowest overall utility score is the slope that will benefitmost from maintenance. The following sections outline the quantification process to compute single utility functions, the weighting factors and overall aggregation scores of the alternatives.
## 5 Results
### Performance of the Classifiers on the Imbalanced Corpus  
In this experiment, the objective is to identify the most promising classifier to face the case study. Thus, we compared the five supervised learners through performance metrics. The parameter setting for each classifier is the following:  
* CART: _criterion_ = 'entropy', and _max_depth_ = None. * Complement Naive Bayes: _fit_prior_ = True, _class_prior_ = None, and _norm_ = False. * \(k\)NN: _n_neighbors_ = 10, _weights_ = 'distance', and \(p=2\) (euclidian distance). * Random Forest: _n_estimators_ = 100, _criterion_ = 'entropy', _max_depth_ = None. * SVM: _loss_ = 'hinge', _penalty_ = '12', \(\alpha\) = 0.001, _max_iter_ = 5, _learning_rate_ = 'optimal', \(\varepsilon\) = 0.1, and _tol_ = None. According to the scientific literature, sensitivity, precision, \(F1\)-score, ROC-AUC, and PR-AUC may provide useful information in order to measure the performance of the classifiers on the imbalanced dataset derived from the case study. Table 2 presents, fold by fold, the efficiency of the classifiers in terms of those metrics. After analyzing averages from Table 2, we may state that:  
* The results from SVM were quite encouraging because this classifier ranked best in terms of PR-AUC (namely the most appropriate measure under conditions of class imbalance), and the second-best in terms of precision, \(F\) measure, and ROC-AUC. * Random Forest is also remarkable, because it was the best considering ROC-AUC and \(F1\)-score, and the second-best in sensitivity and PR-AUC. * Although CART obtained the highest recall, it was simultaneously the worst in terms of precision, ROC-AUC, and PR-AUC. * Complement Naive Bayes was only notable in precision, but it was not skillful in recovering instances of the minority class.
# IoT-Chain and Monitoring-Chain Using Multilevel Blockchain for IoT Security
## 1 Introduction
### Challenges
#### 1.2.2 Consensus Requirement  
The consensus algorithm for block generation uses proof of work (PoW) [12], which requires a high amount of CPU operation, or practical Byzantine fault tolerance (PBFT) [13], which requires a certain amount of network communication. For IoT devices, a consensus algorithm that requires substantial computation is not suitable owing to its low performance [14], and a consensus algorithm that requires substantial communication in an IoT network with many nodes is not effective, particularly when using a wireless network.
# VENuS-Derived NDVI and REIP at Different View Azimuth Angles
## 2 Materials and Methods
### Vegetation Indices and Albedo  
To explore the effect of the view zenith angle across the study sites, two VIs were selected, the normalized difference vegetation index (NDVI) [(6)] and the red-edge inflection point (REIP) [(7; 8; 9)]:  
\[\mathrm{NDVI}=\frac{(\rho_{782}-\rho_{667})}{(\rho_{782}+\rho_{667})} \tag{1}\]  
\[\mathrm{REIP}=702+40\left\{\frac{[(\rho_{667}+\rho_{782})/2]-\rho_{702}}{\rho_ {742}-\rho_{702}}\right\} \tag{2}\]  
where \(\rho\) is the relative reflectance of the subscript wavelength expressed in nm. The NDVI is a well-known and frequently used VI that can be calculated from many satellite sensors, while the REIP is less known and rarely used because the four needed bands are not available in many spaceborne sensors. These bands are available in the VEN\(\upmu\)S and Sentinel-2 satellites. The NDVI and the REIP were simulated and discussed for VEN\(\upmu\)S resampled bands to estimate leaf area index by [(8)] and, in the current study, were calculated from space to allow a comparison of their values under different view angles. In the current study, the NDVI was calculated with bands B7 and B10, and the REIP was calculated with bands B7, B8, B9, and B10 (Table 1).  
Additionally, the albedo \(\alpha\) was calculated as follows [(11)]:  
\[\alpha=\frac{\sum_{i=1;i\
eq 5}^{12}B_{i}b_{i}}{\sum_{i=1;i\
eq 5}^{12}b_{i}} \tag{3}\]  
where \(B_{i}\) and \(b_{i}\) are the surface reflectance and the bandwidth, in nm, for band \(i\), respectively. All VEN\(\upmu\)S bands were involved in this process.