esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Accuracy and F1-Score of Self-Acquired Dataset  
To both evaluate the performance of the proposed framework and validity of the self-acquired dataset, accuracy, and F1-score are compiled in Tables 2-4 for pair 1, pair 2, and pair 3 accordingly. Similar to Table 1, the variation between F1-score and accuracy in Tables 2-4 is not considerable, mostly under 4%. Moreover, the mean values of the two metrics are not different from each other, indicating the stability of the method as well as the data. It can be observed that there are subjects (\"ID01\", \"ID02\", \"ID06\", \"ID08\", \"ID11\", \"ID13\" and \"ID16\") achieving high classification rates, except for pair 1 of subjects \"ID06\" and \"ID08\", approximately 85 to over 90%. The DFBCSP--FmRmR has an equal efficiency to its variants. Although it does outperform other methods, we can conclude that the proposed framework is effective in both datasets. Furthermore, the aforementioned subjects in this dataset have the potential to use a practical BCI system, while others are affected by the \"BCI illiteracy\" phenomenon. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Subject**} & \multicolumn{6}{c}{**LH-RH**} \\ \cline{2-9}  & \multicolumn{2}{c}{**FBCSP**} & \multicolumn{2}{c}{**DFBCSP—Fisher**} & \multicolumn{2}{c}{**DFBCSP—mRmR**} & \multicolumn{2}{c}{**DFBCSP—FmRmR**} \\ \cline{2-9}  & **Acc** & **F1** & **Acc** & **F1** & **Acc** & **F1** & **Acc** & **F1** \\ \hline  
Mean \(\pm\) SD & \(64.74\pm 11\) & \(0.64\pm 0.12\) & \(78.29\pm 15.75\) & \(0.78\pm 0.15\) & \(77.81\pm 15.72\) & \(0.77\pm 0.17\) & \(79.03\pm 16.23\) & \(0.79\pm 0.17\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Accuracy and F1-score of four methods on BCI Competition’s dataset for three pairs. Likewise, subjects in the self-acquired dataset, except for subjects \"ID 01\", \"ID 04\", \"ID 07\", and \"ID 10\", elicit a similar pattern of having discrimination for pair 2 and pair 3 better than pair 1. For the four methods, the difference in the mean values is from 2-5%, and it is not as significant as in dataset IIa. Surprisingly, the mean and SD values of this dataset (Tables 2-4) are almost equivalent to those of dataset IIa (Table 1), which ranges from 70-80% (mean) \(\pm\) 10-15% (SD). The results above further verify the validity and similarity between the two datasets as well as the consistency of the proposed framework. However, it is of great importance to notice that FBCSP has an equal efficiency to the other three models in most subjects, unlike in dataset 2. This outcome is inconsistent with studies [40; 65], in which only BCI Competition was used. We hypothesize that this inconsistency might be due to the differences between the two datasets. As mentioned in the previous section, the number of trials in the BCI competition dataset is only 72 per class while that of the self-acquired dataset is over 200 trials per class for each subject. Therefore, we decided to analyze models in each measurement for each subject for a more balanced
## 2 Materials
### Sentinel-2 Optical Data  
Sentinel-2 (S2), which comprises optical satellites, was deployed by the European Space Agency (ESA) in 2015 and 2017 as two constellations, S2A and S2B. Operating in tandem, these constellations offer a revisit time of 5 days. S2 provides optical images that encompass 13 different bands, with bands 4 and 8 specifically employed in calculating the normalized difference vegetation index (NDVI). The high temporal resolution of the S2 satellite enabled the acquisition of 15 cloud-free optical images for plot P1 in 2017, 19 images for plot P2 in 2018, 21 images for plot P3 in 2019, and 17 images for plot P4 in 2020 throughout the maize cropping seasons (between April and October). For each reference plot, the images were downloaded from the Theia website ([[https://www.theia-land.fr/](https://www.theia-land.fr/)]([https://www.theia-land.fr/](https://www.theia-land.fr/)), accessed on 9 November 2022), which offers ortho-rectified level-2 products corrected for atmospheric effects using the Level-2A operational processor. The time span covered by the S2 images aligned with that of the S1 acquisitions for each reference plot and year. The processor employs algorithms for scene classification and atmospheric correction. These optical images were used to calculate the NDVI values by averaging the values of all the pixels at the plot scale for each S2 date (Figure 4). The NDVI values serve as input for estimating the SSM at the plot scale (S2 MP (Sentinel-1/Sentinel-2 Moisture Product) described in Section 3.1). Furthermore, for each grid cell, the average NDVI value was obtained specifically for bare soil pixels exhibiting low vegetation cover, as discussed in Section 3.1.2, to estimate the SSM at the grid scale.
## 2 Framework  
### Data Collection  
#### 2.1.1 Energy Retrofitting Strategies  
The general description of retrofitting is \"the strengthening, upgrading and adding extra equipment to a building\" [19]. For a more specific definition, energy retrofitting is the combination of energy-saving measures that are added to the building to make operational and physical changes, which improve the performance by reducing energy consumption. Several energy retrofitting measures can improve building performance at different levels. These measures can be categorized into four groups:  
* Energy-efficient equipment measures;
* Heating and cooling demand reduction measures;
* Renewable energy technologies;
* Human behavior.  
The selection of multiple retrofitting measures is called a retrofitting strategy or a retrofitting plan [20]. One of the most informative sources where decision makers can obtain different retrofitting measures is international energy retrofitting programs for existing buildings, for instance, LEED v4.1, which is developed by the U.S. Green Building Council [21]; BRECAM [22]; and ENERGY STAR, which is backed by the U.S. Environmental Protection Agency (EPA) [23]. In addition, various types of energy saving measure are identified from books, papers, market experts, architects, contractors, etc.  
In the following, example measures are presented [20; 24]:  
* Energy-efficient equipment measures: Used to improve the energy performance of the building by reducing the energy consumption of the electrical and mechanical equipment. These measures include but are not limited to:
* The replacement of the fuel source for heating and cooling;
* The replacement of lights with energy-efficient ones;
* The use of economy air recycling ductwork;
* The upgrade of the HVAC system;
* The replacement of the electrical equipment with energy-efficient ones.  
Heating and cooling demand reduction measures: Used to improve the building envelope to reduce cooling and heating systems' energy consumption. The components of the building envelope are the roof, walls, windows, and doors, which are considered as thermal barriers between interior and exterior environments. These measures include but are not limited to:  
* Thermal insulation for walls and roofs;
* The use of energy-efficient windows, such as a low-E window and double-glazed and triple-glazed windows;
* Sealing of cracks around windows and doors;
* Planting on the roof;
* The use of external and internal shading for fenestrations.  
Renewable energy technologies: Used to produce heat and electricity from renewable sources. The renewable energy technologies include the following:  
* Solar thermal system;\(\bullet\) Solar photovoltaic/thermal system;  
\(\bullet\) Geothermal power system;  
\(\bullet\) Wind power systems.  
\(\checkmark\) Human behavior measures: Used to monitor and control the use of energy. This might include the use of energy metering, monitoring, and control units, for instance, installing a programable thermostat to control heating and cooling.
## 3 Results
### Distribution Identification
#### 3.2.2 Multivariate Distribution Identification  
Since all the marginal distributions are scaled t distributions, the multivariate distribution associated with each stock market index and its related financial markets should be a multivariate scaled t distribution. Many techniques are available for a parameter estimation of multivariate distributions. Batch approximation algorithm can be identified as a better algorithm for parameter estimation with reference to the literature [54]. Moreover, optimization methods were used to estimate the parameters of multivariate scaled t distribution. To examine the complexity of related optimization problems, both local and global optimization were applied. The \"fmincon\" function in Matlab software  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Financial Index** & **Location Parameter (\(\mu\))** & **Scale Parameter (\(\sigma\))** & **Shape Parameter (\(\upsilon\))** \\ \hline AORD & 0.00035585 & 0.0056227 & 3.58061 \\ GSPC & 0.00063819 & 0.0053566 & 2.87595 \\ ASPI & 0.00007404 & 0.0032934 & 2.29447 \\ Oil & 0.00013971 & 0.0077000 & 2.65188 \\ Gold & \(-\)0.00063982 & 0.0181407 & 3.64656 \\ Cocoa & 0.00004466 & 0.0092006 & 3.46337 \\ AUD-USD & 0.00003763 & 0.0047788 & 4.48998 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameter estimates of fitted scaled t distributions.  
was used as a local optimization technique, and a global optimization algorithm DSO in global and non-smooth optimization (GANSO) library [35; 36] was used as the global optimization technique in the study. Comparison of three above mentioned techniques was carried out, and the results provide evidence that the global optimization method is essential for significant improvement of parameter estimates. The conviction is that obtaining a multivariate distribution with high quality is really challenging.  
The number of potential input variables and the number of parameters to be estimated with respect to multivariate scaled t distributions associated with three stock indices, AORD, GSPC, ASPI, and their related inter-markets are summarized in Table 3.  
Local optimization method and global optimization method were used to estimate the parameters associated with three multivariate distributions of three stock indices under study with their related inter-markets. Only the maximum function values obtained are presented in Table 4 as it is difficult to present all parameter estimates here.  
Table 4 illustrates that the global optimization technique provides better parameter estimates for all three multivariate distributions related to three stock indices and their related inter-markets. Therefore, global optimization technique was considered for further analysis.
## 4 Time-Frequency Analysis for Feature Extraction  
Time-frequency analysis (TFA) techniques enable the simultaneous representation of PD signals in the time and frequency domains, which is crucial to capture the transient nature of PD signals and differentiate between multiple PD sources. TFA represents one of the most powerful tools in modern signal processing, bridging the gap between time-domain and frequency-domain representations. TFA methods are vital for analyzing transient and non-stationary characteristics by integrating temporal and spectral dimensions. A time-frequency representation (TFR) aims to show how the frequency content of a signal evolves over time, shown in Table 5, transformed into 2D images and input into a CNN for automatic feature extraction and classification. This study uses key TFA methods to explore PD signals, each of which provides unique advantages in identifying traits of time and frequency.
# A Brief Review on Challenges in Design and Development of Nanorobots for Medical Applications
## 2 Challenges in the Design and Development of Nanorobots
### Monitoring
#### 2.5.1 Optical Tracking  
The first and most commonly used method is optical tracking [117]. Using optical tracking for localization of nanorobots requires precise calibration of the system [118]. Some advantages of optical tracking systems is real-time observation, having simple equipment, low cost, and having high spatial resolution. However, 3D localization of microrobots using optical tracking is a challenge. The image can easily interfere with by noise or any contamination in the workspace. It means the optical tracking systems can only be used in environments that are transparent. These systems have been used for localization of nanorobots with promising results [119].
# Development of Analytical Model for Bonding of CFRP Rod in Concrete Subjected to Cyclic Loads
## 6 Analytical Approach
### Modelling Elements  
The selection of appropriate modelling elements in FEM analysis is crucial. The bond behaviour between the CFRP rod and concrete is partially geometry dependent. The chosen elements in this study are the CFRP rod acting as a bar element and the concrete, which is represented using rectangular elements. Since the behaviour of bottom concrete with embedded CFRP is similar to the top concrete with embedded CFRP, only one of the concretes (either top or bottom concrete) is considered for analysis.  
The type of element and its geometry can influence the type of meshing, matrix and integration equations, number of nodes, and so on. Plane strain analysis permits strains only in the plane. Therefore, plane strain is a suitable analysis to be used in thick structures of CFRP and concrete elements. Plane strain also provides restraining in the lateral direction and stabilises the element. Figure 19 depicts the conversion of a Three-Dimensional (3D) element to a Two-Dimensional (2D) element. This approach aligns with the procedure employed in numerical studies, where solid elements represent the concrete and CFRP rod, while cohesive elements represent a thin surface as the contact surface between CFRP and concrete elements (Figure 20). Each element's characterisation distinguishes its behaviour from the others.  
Figure 19: Conversion of 3D 22-node Concrete Specim To 2D 6-node Specim.
# Estimation of Compressible Channel Impulse Response for OFDM Modulated Transmissions
## 4 Numerical Experiments and Performance Analysis
### Simulation Parameters
During the simulation, the following channel estimation methods were analyzed (with the labels used in figures' legends and the rest of the paper for the sake of clarity):  
* --full CSI available;
* --classic LS estimation method [7];
* --the basic DFT-based method. The LS impulse response limited to \(M\) samples [9];
* --noise energy-controlled DFT-based method with individual assignment of impulse response coefficients [14];
* --residual error-controlled DFT-based method with impulse response reconstruction in groups (the authors' modification proposed in [13]);
* --the modification proposed in this article.
## 6 Field Trials: The Pilot Evaluation
### Results  
The main insights presented in this section relate to: performance, interaction factors, test duration, interaction mode.  
The system proved to be robust, with an acceptable technical performance rate: 24 patients out of 25 completed the test, which means that there was one failure out of the 25 tests made. Moreover, as explained above, the AUSUS analytical framework was used for the evaluation of performance indicators. After the robot interaction session, during a semi-structured interview based on a questionnaire, patients were asked to evaluate the interaction aspects on a 5-point Likert scale ranging from 1 (strongly disagree, lowest score) to 5 (strongly agree, highest score).  
The different aspects of interaction that were evaluated received encouraging scores (Figure 6): an average above 4 out of a total of 5 (learning; flexibility; perceived usefulness; social acceptability; robot physical aspect; robot perception; safety), and above 3 out of 5 for the more complex aspects of human-robot interaction (concentration; emotions).  
With the exception of three patients, whose test durations were below or above average (9 min 10, 15 min 35 and 14 min 21), the average duration of the tests was 10 min (12 tests) or 11 min (10 tests) (Figure 7).  
Concerning the interaction mode, the remote control emerges as the preferred mode of interaction (Figure 8). This preference confirms the intermediate hypothesis that emerged from the user tests in phase 2 of the project, namely the need for an interaction mode, other than voice and touch, that would be easier and more accessible for older users.  
Finally, all patients reported being satisfied by having participated in this study. In line with the living lab approach adopted throughout this project, especially the participatory dimension, this feedback is insightful for our future research.  
Figure 5: Different camera angles to capture and analyze the activity.
# Analyzing TCP Performance in High Bit Error Rate Using Simulation and Modeling
## 4 Analysis and Validation of Results
### Amount of Packet Retransmitted  
It is expected that a link with a packet drop probability \(p_{drop}\) will retransmit at least \(n\times p_{drop}\) packets. This assumes that only the dropped packets need to be retransmitted. Table 7 shows the calculated number of packets drops and the actual retransmissions sent by the _FTP_ server from Table 5.  
The packet retransmissions should be at least as high as the number of calculated packet drops. A packet dropped because of a bit error needs to be retransmitted to finally transmit the whole file. Thus, at least the dropped packets need to be retransmitted. A higher amount of retransmission might be because of protocol mechanisms.  
While the Table 7 shows that the retransmission count is within the range of the expected packet drops, it also shows that the observed retransmission count is higher, and in one case lower, than the calculated value. We expect that the reason for the difference is in the implementation of the dropping algorithm in Riverbed Modeler and how it tries to achieve a certain drop probability. This assumption is based on the observation that a modification of the _FTP_ download offset (when the download should start relative to the simulation start) also changed the amount of retransmission the _FTP_ server sends.
## 1 Introduction
Therefore, this study aims to fill this gap by combining the flexibility of the Interval-Valued Type-2 Fuzzy TOPSIS (IVT2F-TOPSIS) method with quantitative accuracy, offering distinct advantages. In this research, the IVT2F-TOPSIS method is presented as an innovative approach to evaluate international dispute resolution alternatives under a fuzzy environment. IVT2F-TOPSIS provides the ability to manage both primary and secondary uncertainty levels. This ability offers a significant advantage in managing complex and fuzzy decision environments. Thus, IVT2F-TOPSIS combines traditional TOPSIS with interval-valued Type-2 fuzzy sets. This integration allows capturing a broader range of uncertainties in the decision making process and improves decision making with uncertain or incomplete information. Therefore, such abilities and advantages make IVT2F-TOPSIS an appropriate tool for interdisciplinary work in different fields, including international law and engineering practice. In this context, IVT2F-TOPSIS provides efficient evaluation and ranking of alternative solutions in areas that need detailed decision-making processes, such as international dispute resolution. IVT2F-TOPSIS addresses the evaluation and ranking of alternative solutions by quantitatively modeling subjective criteria when applied to international dispute resolution methods. IVT2F-TOPSIS helps decision-makers compare or evaluate dispute resolution methods, such as negotiation, mediation, conciliation, resolution through international organization, arbitration, and international jurisdiction under determined criteria such as cost-efficiency, impartiality, and binding nature. By applying the IVT2F-TOPSIS framework, decision-makers can assess each alternative resolution method's relative effectiveness and select the most appropriate alternative for complicated or complex international disputes. The IVT2F-TOPSIS framework provides a quantitative approach and advances practical and reliable international dispute evaluation capabilities. Therefore, this research introduces an IVT2F-TOPSIS method to enhance decision-making in international dispute resolution. IVT2F-TOPSIS provides a quantitative structure to evaluate resolution methods and offers international legal practitioners a valuable tool to manage uncertainty. The results of this research show the importance of integrating MCDM methods to contribute to more reliable and effective decisions in legal and interdisciplinary contexts. This research is structured as follows: the relevant literature review, in terms of international dispute resolution methods and interval-valued fuzzy logic, are presented in Section 2. Section 3 provides the proposed IVT2F-TOPSIS methodology, and the application of IVT2F-TOPSIS in international dispute resolution evaluation is presented in Section 4. The managerial and practical implications of the proposed approach and the validation of IVT2F-TOPSIS are shown in Section 5. Finally, this research is completed by presenting the conclusions and future work directions in Section 6.
# Molecular Techniques and Target Selection for the Identification of _Candida_ spp. in Oral Samples
## 3 Molecular Identification of _Candida_ spp.
### Microarray  
Microarrays consist of thousands of DNA sequences attached to a solid surface. They allow the detection of the presence of genomic DNA regions or the quantification of the expression of genes. The low number of studies using microarrays to identify _Candida_ species is notable. The high cost per sample of a single experiment, when compared with sequencing, may be a factor that led to the disuse of microarray for species identification. On the other hand, microarrays depend on specific sequences, and therefore whole genome or RNA sequencing have clear advantages when compared to microarray technologies.  
Microarrays can be applied not only to species identification but also to strain typing with high levels of specificity, sensitivity, and throughput capacity. In terms of molecular typing, microarrays were used to identify and obtain different sequence variants of specific DNA sequences. Oligonucleotide probe sequences for the identification of different _Candida_ spp. [89; 90] are in Table S4.  
Microarrays were used in the identification of _Candida_ spp. from clinical samples, mostly blood [58; 91]. [PERSON] and colleagues used the arrayed-primer extension technique (APEX) in which the direct labeling of PCR products is not required. This technology combines the advantages of Sanger dideoxy sequencing with the high-throughput potential of microarrays [92]. The experiment led to the correct species identification, including of the highly related _C. parapsilosis_ complex. The microarray was tested for its specificity with reference strains and blind clinical isolates [58].  
The major advantages of gene chip technology are its miniature size, high performance, and process automation. The process of optimization is long to ensure stable, specific, sensitive, and reproducible results. The discrimination between specific and unspecific signals may be a challenge in a mixture analysis, as is the case, for instance, of cross-hybridization [93].