esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Characteristics of SACP System with Activated-Carbon-Based Conductive Mortar
### Results and Discussions
#### 3.2.2 Four-Hour Depolarization Test  
Four-hour depolarization tests were carried out after operating the SACP system for 60 days. One of the most widely adopted methods for concrete structures is the 100 mV depolarization criterion in NACE RP0169 [33]. Note that this 100 mV does not include IR drop. Since we poured fresh water on top of the specimen, the IR drop was negligible. Figure 11 shows the 4 h depolarization measurements. The general mortar specimen had a depolarization potential of 430 mV while increased depolarization potentials of 526, 546, and 739 mV were measured for the specimens with admixture ratios of 5, 10, and 15%, respectively. All the specimens had much higher depolarization potentials than the 100 mV criterion since they were exposed to fresh water, and the zinc mesh anode was close to the rebar. However, all conductive mortar specimens had higher depolarization potentials than the general mortar specimens. In addition, the higher the amount of admixture ratio, the higher the depolarization potential (or the higher the CP performance). In places with wet-dry cycles, such as tidal and splash zones, the depolarization potential will be low with the SACP system. A better depolarization potential is essential for these zones, and the proposed conductive mortar specimens can improve the efficiency of the SACP system.  
Figure 11: Depolarization potential measurement.  
Figure 10: Current density variations for 60 days.
# The Impact of Cloud Versus Local Infrastructure on Automatic IoT-Driven Hydroponic Systems
## 4 Discussion
### Future Research Directions  
In light of the study's conclusions, future research directions include the development of hybrid solutions that combine the advantages of cloud infrastructure with those of local infrastructure. Such a solution would analyze the integration of benefits from both approaches to create an optimized system.  
Additionally, the authors propose several studies in the field of hydroponics, including:  
* Analysis of multiple ML algorithms for modeling the hydroponic system from the perspective of pump activation. This research will examine both local ML-NET-specific algorithms and pre-implemented Azure ML algorithms. The study will also present the specific costs associated with the Azure ML Studio service;
* Optimization of chemical processes using pH predictions and anomaly detection in hydroponic measurements. This direction aims to improve the efficiency and reliability of hydroponic systems through advanced predictive techniques;
* Automatic water flow control systems in hydroponic systems with IoT infrastructure. Developing and testing systems capable of dynamically adjusting water flow based on real-time data;
* Study of environmental variability's impact on plant growth in hydroponic systems. Investigating how fluctuations in environmental conditions affect crop development and yield;
* Prediction of water requirements for hydroponic systems dedicated to specific plant cultures for operational cost analysis using intelligent prediction tools. This research will focus on optimizing resource allocation and reducing costs through data-driven insights.  
By exploring these future research directions, the authors aim to delve deeper into modern technologies within the context of hydroponic systems. They believe such systems will be widely developed at the urban area level as cities continuously expand.
# Automatic Classification of Foot Thermograms Using Machine Learning Techniques
###### Abstract  
Diabetic foot is one of the main complications observed in diabetic patients; it is associated with the development of foot ulcers and can lead to amputation. In order to diagnose these complications, specialists have to analyze several factors. To aid their decisions and help prevent mistakes, the resort to computer-assisted diagnostic systems using artificial intelligence techniques is gradually increasing. In this paper, two different models for the classification of thermograms of the feet of diabetic and healthy individuals are proposed and compared. In order to detect and classify abnormal changes in the plantar temperature, machine learning algorithms are used in both models. In the first model, the foot thermograms are classified into four classes: healthy and three categories for diabetics. The second model has two stages: in the first stage, the foot is classified as belonging to a diabetic or healthy individual, while, in the second stage, a classification refinement is conducted, classifying diabetic foot into three classes of progressive severity. The results show that both proposed models proved to be efficient, allowing us to classify a foot thermogram as belonging to a healthy or diabetic individual, with the diabetic ones divided into three classes; however, when compared, Model 2 outperforms Model 1 and allows for a better performance classification concerning the healthy category and the first class of diabetic individuals. These results demonstrate that the proposed methodology can be a tool to aid medical diagnosis.  
diabetic foot; thermography; machine learning; classification; clustering; computer vision +
Footnote †: journal: _Algorithms_  
0000-0001-801-7000-0001-8017-0000][PERSON]
## 2 Data
### Ship AIS Data  
The Automatic Identification System (AIS) is a widely applied monitoring and communication system used to automatically broadcast and receive ship dynamic, static, voyage, and safety information to realize ship identification. In regard to the \"Oriental Star\" accident case, AIS information of the accident is applied to analyze the causal relationship between typical accidents and weather.
# Virtual Reality for Spatial Planning and Emergency Situations: Challenges and Solution Directions
## 4 Results
### Evaluating Applications  
A total of 8 out of the 15 papers evaluated their application using multiple methods, with some papers being more extensive than others. The main factors that were evaluated were usability and presence. One or both of these were evaluated by seven of the papers. Only [37] deviated from this, whereby the authors evaluated the application based on the results their target group achieved over time, and they measured performance during 5 months of using the application.  
[PERSON] and [PERSON] [30] evaluated usability by means of interviewing their participants about the experience. Their answers were coded and grouped into three aspects: interface and control, experience of the virtual environment and the process of wayfinding. [PERSON] [33] used boxplots for the questionnaire and coding of answers to open questions to evaluate both usability and presence. Likewise, [16] also measured usability but achieved it using the AHP weighting method and fuzzy comprehensive evaluation. Similarly, [34] performed an evaluation but used a confirmatory factorial analysis on four components.  
[PERSON] et al. [31] designed their questionnaire based on [39] to measure presence. Lastly, [PERSON] et al. [20] measured simulator sickness using the simulator sickness questionnaire (SSQ) and presence using the presence questionnaire (PQ). The SSQ is typically employed when testing VR-based applications to measure the levels of nausea and motion sickness, and the PQ assesses the feeling of presence in virtual environments. All of the selected papers evaluated one or more aspects of their application. However, none of them evaluated their application as extensively as [22] did. First, the content validity was measured by comparing the results from the virtual environment to results from other studies that have been conducted in a real environment. The results were compared in three levels of metrics that are used for evaluating wayfinding behavior: decision-making, task performance and observation behavior. Besides the content validity, usability and presence were measured using the face validity questionnaire, the SSQ, the system usability scale (SUS) and the PQ.
# Accelerometers in Monitoring Systems for Rail Vehicle Applications: A Literature Review
## 1 Introduction
### Types of Accelerometers
#### 1.3.5. Optical  
The optical accelerometer's operation principle is based on the seismic mass effect, with an optical fiber cantilever beam as the spring-mass system. This permits the registration of the seismic mass's instant position relative to the sensor's housing through the light conveyed by this fiber [10; 11; 12].
## 2 Materials and Methods
### Algorithm Retrieval  
An atmospherically corrected multispectral image contains reflectance data in different bands centered at certain wavelengths. An idea of the bands to be used can be obtained from the spectrum of the element under study, which will provide its absorption and reflectivity characteristics. These bands can be used, by themselves, or combined by some arithmetic operation, to obtain a correlation between the field data and the data from the satellite sensor [52; 53].  
With the Spectral Index (SI) assessment toolbox in the Automated Radiative Transfer Models Operator (ARTMO) package [54], all possible band combinations of different formulations can be defined and evaluated. This tool allows the transformation of spectral observations into useful estimates. The basic principle is to correlate mathematical combinations of reflectance measured at different wavelength ranges, with biophysical variables of interest. Simple ratio (SR = R1/R2) and normalized difference (ND: (R2 - R1)/(R2 + R1)) formulations as well as various curve fitting functions (linear, exponential, power, logarithmic, and polynomial) have been used to obtain all two-band reflectance (Ri) combinations according to these formulations. Implementing a cross validation (CV) tool, a portion of the database was used to train the model while another portion was used to validate, and then all combinations were calculated. Thus, ARTMO selects the best index, calibrating and validating all data. ARTMO provides the results and their correlation matrix ordered according to their average value of determination coefficient R\({}^{2}\). All infrared simple bands have also been correlated with the biophysical variables of interest (Figure 4).  
Figure 4: Algorithms developing flowchart.  
The process for the setting-up of the algorithms was developed using all data of TSM, POM and PIM. Once we had three data sets, algorithms were calibrated with a regression between the field data, and band combinations using S2-MSI spectral bands, finding the best fitting function. Then, the algorithms were validated adjusting a linear regression between field and estimated data, using as goodness, the determination coefficient (R\({}^{2}\)), the root mean-square error (_RMSE_) and the normalized root-mean-square error (_NRMSE_).  
\[RMSE=\sqrt{\frac{\sum_{i=1}^{N}\left(x_{i}^{estimated}-x_{i}^{measured}\right)^{ 2}}{N}} \tag{1}\]  
\[NRMSE=\frac{RMSE}{\sigma(Y)} \tag{2}\]  
where \(x_{i}^{estimated}\) is the predicted value, \(\chi_{i}^{measured}\) is the observed value, \(N\) is the sample size and \(\sigma(\gamma)\) is the highest predicted value.
## 1 Introduction
Precisely, the HDELC algorithm first reconstructs the original feature space, establishes the feature space reconstruction matrix through the two thresholds of the mean value of information entropy and the screening factor of feature attributes, splits the original feature space, and obtains the optimal feature space scale. Based on the optimal feature space scale, PCA is used to downscale the original feature space to obtain a new high-dimensional feature reconstruction space. The high-dimensional feature reconstruction space is defined as the set of solid feature spaces divided by the average information entropy and the set of optimal weak feature spaces preserved by the screening coefficients. The scale of this feature space is the dimension to which the PCA downscaling needs to be reduced. The feature subspace is a smaller set of features chosen from the original feature space; the set of feature subspaces after dimensionality reduction is used as the best feature subspace and as the feature set for subsequent subspace integration work. Secondly, based on the Stacking model [16], the feature space is divided into several mutually exclusive non-intersecting feature subspaces to determine the optimal number of classifiers in the integrated system to achieve the goal of the overall integration model: \"good but different\". \"Good but different\" means that by assigning different feature subspaces to different classifiers, the performance of the integrated model is improved while ensuring the diversity of classifiers in the integrated system. The contributions of this paper are twofold. Firstly, it focuses on exploring helpful information in the feature space of high-dimensional data and proposes a high-dimensional feature space reconstruction strategy based on the feature space (partition) selection matrix; secondly, a selective subspace ensemble algorithm is designed to optimize the ensemble model and ensure model accuracy and diversity. The rest of the paper is organized as follows. Section 2 introduces some preliminary knowledge of integration learning in previous work related to high-dimensional data ensemble models; Section 3 details the specific process of the high-dimensional data ensemble classification algorithm (HDELC algorithm) based on the reconstruction and ensemble of high-dimensional feature space; Section 4 evaluates the performance of our proposed model through comparative experiments and ablation experiments; and Section 5 summarizes the work of this paper, provides a summary future work and the outlook of the future work.
## 3 Results and Discussion
This may be explained by the higher spatial autocorrelation of GPP after the fires, i.e., the lower and higher GPP values were grouped in larger and well-defined spatial clusters. For all periods under analysis, before and after the fires, regressions were statistically significant with a \(p\) value lower than 2.2 \(\times\) 10\({}^{-16}\), for a 95% confidence interval. Analysis of the residuals (see Figure S1) for the linear regressions was according to the following assumptions: homogeneity of variance, normality, independence, and Cook's distance. The four assumptions for residue validation were fulfilled. Homogeneity of variance and independence presented a random distribution. The Q-Q plot showed close to normal distribution residuals. [PERSON]'s distance was always \(<\)0.5; thus, no sample influenced the residuals. Figure 6 shows the spatial distribution of GPP in the study area before and after the fires of 2003, 2017 and 2019. Before the fires, the study area mostly comprised a continuous patch, nonfragmented shrubs, _Eucalyptus globulus_ Labill., and _Pinus pinster_ Ait. [28]. This area presented very high GPP values, with averages of 5.4 and 5.3 gC/m\({}^{2}\)/day in 1 July 2002 and 15 June 2017, respectively, thus showing good efficiency in the carbon-capture process. Before the 2019 fire (29 May 2019), the average GPP was 4.7 gC/m\({}^{2}\)/day, showing capacity for the rapid regeneration (in less than 2 years) of shrub and _Eucalyptus globulus_ Labill. After the 2003 and 2017 wildfires, the affected areas presented an average GPP of 1.2 gC/m\({}^{2}\)/day, showing that nonindigenous forests were not very resilient to fires. [PERSON] et al. [56] stated that forests that had burnt down are now more vulnerable to the loss of the organic carbon. This carbon, which is stored at the top 5 cm layer, can be easily mobilized by water erosion after the first rains. Figure 5: Linear regression between CO\({}_{2}\)flux and GPP in KgC/m\({}^{2}\)\(\times\) 10\({}^{-4}\) before and after the wildfires of 2003, 2017 and 2019. After the 2019 fire, the burnt area had an average GPP of 2.1 gC/m\({}^{2}\)/day. This increase in GPP when compared to previous years can be explained by the fact that this area was unaffected by fires for a period of 30 years. So, this older forested area was more resilient to fire. GPP values before the fires were in accordance with the average value between April and December 2018 of 1.25 kgC/m\({}^{2}\) (4.6 gC/m\({}^{2}\)/day) estimated by the Satellite Application Facility on Land Surface Analysis (LSA SAF) of the European Organization for The Exploitation of Meteorological Satellites (EUMETSAT) [57]. Regarding the total area under study, GPP was reduced to 1.4, 1.5, and 0.6 GgC/day, respectively, for the wildfires of 2003, 2017, and 2019.
## 4 Conclusions  
Based on the results presented in this study, it can be concluded that pretreatment with _Q. saponaria_ saponins prior to the application of disinfectants led to more effective elimination of the spoilage bacteria _Asaia_ spp. in both planktonic and biofilm form. The best results were obtained using two antimicrobial agents with different mechanisms of action: saponins as a natural detergent and peracetic acid as an oxidizing agent. These findings are of great interest not only with a view to increasing the antibacterial force of disinfectants, but also in terms of protecting the natural environment. _Q. saponaria_ saponins are natural, environmentally friendly compounds [39]. High concentrations of PAA and QAC, despite their biodegradability, can have a significant effect on the biological processes of wastewater treatment [40; 41]. Therefore, the benefits of reduced doses of antimicrobials in cleaning and disinfection processes are measurable in not only economic but also environmental terms.  
Figure 4: Effect of biofilm eradication by peracetic acid (PAA) (0.250% \(v/v\)) in combined action with _Q. saponaria_ extracts (QE) (1% \(w/v\)). (**A**) Six-day biofilm of _Asaia_ spp.; (**B**) biofilm eradication by QE and PAA treatment. Bar represents 20 μm.
# Influence of Manufacturing Process Modularity on Lead Time Performances and Complexity
## 3 Methodological Framework  
To verify the working assumption that process modularity could positively impact MLT and the complexity of assembly process structures, the following methodical procedure was elaborated in this order, as shown in Figure 1.  
A necessary precondition to explore the relationships is the ability to measure the system properties, i.e., modularity and complexity, in an appropriate way. For this purpose, the most competitive indicators were identified from which the best ones were subsequently selected.
## 1 Introduction  
Experimental investigation of structures is the most important source of information about their real behavior and reliability. It is mainly the monitoring of structures during their lifetime utilising specialized sensors in bridges [1] and traffic structures [2, 3], and experimental testing of selected structural members [4, 5] or structural joints and anchors [6, 7]. However, it is necessary to supplement monitoring and experimental investigation with other forms of analysis in many cases. These are done due to the necessity to acquire additional knowledge, but often also for capacity, technical or economic reasons.  
The experimental investigation of the anchorages and precast concrete structural joints is an essential part of the concrete structures' development. It primarily includes the full-scale beam-to-column joints tests [8, 9], cyclic load tests of the connections [10] or experimental investigation of the precast concrete panel joints [11]. In the case of the anchorages, the load-bearing capacity of cast-in-place and post-installed fastenings are mostly investigated [12, 13]. Experiments are usually focused on the determination of the most crucial parameters of the anchorage design, e.g., the size of the anchor, the thickness of the concrete members [14], the position of the anchor in the concrete member [15], the influence of embedment depth [16] or the global loading of the concrete members [17]. It is possible to supplement the test programs via performed numerical analyses [18, 19, 20, 21, 22, 23] based on the finite element method and obtain specific information that is very difficult to measure or observe during the experiment (e.g., a detailed description of the failure mechanism and crack propagation detection). Moreover, the finite-element method is able to combine with neural network-based models [24; 25].  
In our research of progressive short-headed anchors in reinforced concrete members, a series of numerical analyses were elaborated to supplement experimental results. Performed simulations can clarify the real behavior of anchorage in more detail. A numerical model was created to simulate the load-displacement behavior of progressive short-headed anchors acting in cooperation with the supplementary reinforcement. This type of anchorage is becoming popular and often used in modern precast building structures due to their compactness and relatively high resistance.  
After evaluating the available software options, the model was created in ATENA 3D software, which is narrowly specialized in the modelling of reinforced concrete structures [26]. ATENA (Advanced Tool for Engineering Nonlinear Analysis) is software that offers a variety of material models and purposes. Moreover, it is based on non-linear fracture mechanics principles, making it possible to simulate concrete structures' real behavior, including concrete cracking, crushing, and reinforcement yielding [27]. The simulations performed by this software suitably complemented the experimental program.
## 2 System Description
### Pilot Insertion  
The embedding of data in the DFC system involves the insertion of uniformly spaced pilot symbols within each data matrix \(\mathbf{X}[i]\), where \(N_{p}\) pilots are inserted. Each data matrix consists of \(s_{p}+L_{p}\) pixels per column, divided into \(N_{p}\) groups, with each group containing \(B\) adjacent vertical pixels. In each group, the first pixel is dedicated to transmitting the pilot signal. Thus, the DFC data matrix can be represented as  
\[\mathbf{X}[i]=\begin{bmatrix}x_{\{1,1\}}^{p}&x_{\{1,2\}}^{p}&\cdots&x_{\{1,s_{ q}+L_{q}\}}^{p}\\ x_{\{2,1\}}&x_{\{2,2\}}&\cdots&x_{\{2,s_{q}+L_{q}\}}\\ \vdots&&\ddots&&\vdots\\ x_{\{m,1\}}^{p}&x_{\{m,2\}}^{p}&\cdots&x_{\{m,s_{q}+L_{q}\}}^{p}\\ \vdots&&\ddots&&\vdots\\ x_{\{s_{p}+L_{p},1\}}&&\cdots&x_{\{s_{p}+L_{p},s_{q}+L_{q}\}}.\end{bmatrix}, \tag{7}\]  
where \(B=(s_{p}+L_{p})/N_{p}\), and \(s_{p}+L_{p}=N_{p}+N_{i}\). Here, \(N_{i}\) denotes the total number of information symbols per column in the data matrix.  
Figure 3 illustrates the insertion of pilot symbols and data symbols into the frequency domain sub-image, where the pilot symbols are shown to be uniformly inserted in each column. The DFC symbol modulation on the \(l\)th pixel can be expressed as  
\[\mathbf{X}[i]=X(mB+l)=\begin{cases}X^{p}(m)&l=mB+1\\ \text{data pixel}&l=\text{elsewhere}^{\prime}\end{cases} \tag{8}\]  
where \(l=1,2,\ldots\), \(L\) and \(X^{p}(m)\) represents the \(m\)th pilot symbol.