esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methods  
VNIR spectra-based SOM content prediction generally consists of the following three parts. Part 1 is the spectral preprocessing. Spectral reflectance is unavoidably impacted by random noises, baseline drift, and scattering effects because of the influence of error from the experimental instruments and ambient noises; thus, the stability of the constructed VNIR-spectrum-based prediction model is affected [3]. Hence, spectral preprocessing is a prerequisite for the construction of the prediction model, which will be detailed in Section 3.1. In part 2, the representative calibration set is selected using methods such as the MVARC-R-KS method [3], which will be detailed in Section 3.2. Part 3 aims to build an SOM prediction model based on the VNIR spectra, such as by using typical prediction methods and the proposed PLS-MARS method, which will be elaborated in Section 3.3.1. Lastly, in Section 3.3.2, the accuracy of the constructed prediction model is evaluated by several assessment indices, as will be described in Section 3.3.2.
## 4 Discussion of Numerical Results
### Comparison of Low-Frequency and Low-Mass Floors  
Regardless of the reference limit values for performance indicators to verify, the analysis of acceleration output on F#1 to F#4 systems for the examined walking configurations suggests, on one side, a large sensitivity to modelling assumptions, and on the other side a similar structural sensitivity for pedestrian systems with low-frequency and/or low-mass respectively.  
Such an outcome can be noted, for example, in Figure 11, where RMS and peak accelerations from \(f_{p}\) = 1.5 \(\div\) 2 Hz interval are reported as a function of frequency ratio \(f_{1}\)/\(f_{p}\). In this way, it is possible to see that the F#2 and F#4 systems described in Table 1 have comparable acceleration amplitudes, even characterized by markedly different structural parameters (mass and vibration frequency). Furthermore, for the F#1 and F#3 systems with similar mass ratios and vibration frequencies slightly above 8 Hz, the comparative results in Figure 11 have a qualitative and quantitative agreement. Most importantly, it is to note that the F#4 system, differing from F#2, has a high vibration frequency, which is mostly twice the reference threshold of 8 Hz which is recommended for vibration serviceability issues, but very small mass, compared to the occupant and to the other floor configurations.  
For the F#2 and F#4 floor systems, final attention can be spent on the analysis of SMD effects on basic performance indicators for vibration serviceability, namely RMS value and peak of technical interest towards conventional limits for verification. Both these systems are taken into account because the first one, F#2, is a typical slender slab with very small frequency and high mass, while F#4 is representative of a \"new\" glazed solution.  
Figure 11: Numerical results for (**a**) RMS acceleration and (**b**) peak acceleration on F#1, F#2, F#3 and F#4 systems, in terms of SMD (average), as a function of vibration frequency \(f_{1}\) to walking frequency \(f_{p}\) ratio (ABAQUS).  
Figure 12a,b shows the average RMS value as a function of walking frequency under various SMD assumptions and the scatter of standard deviation. It can be noted that the scatter is rather uniform with \(f_{p}\) variations. Also, as previously discussed, the F#2 system is largely sensitive to fp, whilst this effect is less pronounced for the stiffer F#4 system. Most importantly, the comparisons in Figure 12c in terms of standard deviation and percentage scatter from the average of SMD estimates of the selected models give evidence of a rather good agreement for both F#2 and F#4 systems, thus confirming that both low-frequency and low-mass structural solutions should be verified with dedicated tools.  
Figure 12: Sensitivity of RMS acceleration to selected SMD pedestrian models (with \(M=80\) kg) for floor systems: (**a**) F#2 (concrete) and (**b**) F#4 (glass) configurations, with (**c**) trend of percentage scatter for both systems (ABAQUS).
# Statistical Model for Mobile User Positioning Based on Social Information
## 4 Model Assessment  
In this section, the validity of the model created is assessed. First, the data and scenario used for the development of the model are explained. Then, results of users' statistical distribution are shown (referred to as social model). Results are compared with the other two preliminary models using, first, only the E-CID positioning (E-CID model) and, second, using only E-CID and context information (context model). Finally, the computational complexity of the model is evaluated.
## 2 Reservoir Rock Characteristics and Engineering Problems
### Wellbore Instability Problem  
A critical wellbore collapse incident occurred at a depth ranging from 5182 m to 5187 m in Well GS10, which is located in the Gaoshiti-Moxi area of the Sichuan Basin. The target reservoir in this area is the Dengying Formation. The Dengying Formation was designed as the fifth opening section of Well GS10 with a drill bit diameter of 127 mm. This issue arose while using an oil-based drilling fluid with a density of 1.14 g/cm\({}^{3}\), which was initially selected to prevent clay mineral hydration. However, this density decision did not factor in the presence of cracks and solution cavities, leading to mechanical instability. As a remedial measure, the density of the drilling fluid was increased to 1.24 g/cm\({}^{3}\), which effectively resolved the issue, allowing for the successful completion of the drilling operation in this section. Subsequent imaging logging analysis highlighted the extensive development of cracks and solution cavities in the formation. As depicted in Figure 3, notable features included two large cracks with a dip angle of 19.81\({}^{\circ}\) and a maximum width of 10 mm and  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**No.**} & **Compressive** & **Elastic** & \multirow{2}{*}{**Poisson’s Ratio**} & **Confining** \\  & **Strength/MPa** & **Modulus/GPa** & & & **Pressure/MPa** \\ \hline
1 & 143.61 & 33.45 & 0.23 & 0 \\
2 & 264.20 & 46.32 & 0.32 & 50 \\
3 & 415.38 & 53.55 & 0.24 & 60 \\
4 & 174.99 & 41.10 & 0.22 & 0 \\
5 & 402.31 & 48.84 & 0.29 & 50 \\
6 & 390.08 & 50.06 & 0.26 & 60 \\
7 & 197.03 & 36.20 & 0.23 & 0 \\
8 & 358.30 & 49.30 & 0.27 & 50 \\
9 & 383.27 & 50.01 & 0.23 & 60 \\
10 & 253.11 & 38.41 & 0.24 & 0 \\
11 & 261.86 & 49.34 & 0.21 & 50 \\
12 & 391.34 & 49.19 & 0.28 & 60 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Rock parameters of the carbonate rock tested by the triaxial compression tests.  
Figure 2: Rock samples collected from the Dengying Formation: (**a**) Brownish-gray solution cavities in dolomite at a depth from 5480.27 m to 5480.41 m; (**b**) Dark gray dolomite with solution cavities at a depth from 5460.12 m to 5460.22 m; (**c**) Light gray dolomite, at a depth from 4970.32 m to 4970.41 m; (**d**) Blackish-gray muddy dolomite, at a depth from 4959.81 m to 4959.89 m.  
three solution cavities, each with an average diameter of 10 mm. According to the cutting logging result based on the lithologic difference analysis, the maximum width of the crack is bigger than 30 mm, and the largest diameter of the solution cavity is approximately 900 mm. Therefore, the analysis concluded that these developed cracks and solution cavities were the key contributors to the wellbore instability due to the mechanical properties of the carbonate rocks decreasing significantly when the cracks and solution cavities appear in the rock. This indicates that the influence of the distribution characteristics of cracks and solution cavities on the wellbore stability in carbonate formation is worth further studying to optimize the drilling design on the new wells in this area.
# Research on Fault Diagnosis Method for Rotating Machinery Based on Edge Computing
## 4 Fault Diagnosis Method Based on Edge Computing
### Signal Preprocessing
#### 4.1.2 Normalization Processing  
Since the method employed in this study includes a CNN, which is an algorithm optimized through gradient descent, normalization can significantly enhance the training speed and model performance. Furthermore, the temperature data and vibration data collected in this study differ in magnitude, and the data distributions of different features vary considerably. Normalization helps to mitigate these differences [29; 30]. The min-max normalization method is adopted, and its formula is as follows:  
\[\mathrm{X_{norm}}=\frac{\mathrm{X-X_{min}}}{\mathrm{X_{max}-X_{min}}} \tag{10}\]  
where X is the original data, \(\mathrm{X_{min}}\) is the minimum value in the data set, \(\mathrm{X_{max}}\) is the maximum value in the data set, and \(\mathrm{X_{norm}}\) is the normalized data, which fall within the range of \([0,1]\).
# Explainability and Interpretability in Concept and Data Drift: A Systematic Literature Review
## 6 Taxonomy
### Feature Attribution and Game Theory
#### 6.6.3 Multidimensional Class Evaluation  
Table 15 reveals that this category prominently addresses general concept drift, accompanied by substantial coverage of sudden and gradual concept drift, and a few in  
stances explicitly focusing on data drift. The comprehensive drift-type coverage reflects the inherent flexibility of attribution methods, such as SHAP or LIME, in adapting explanations to diverse drift scenarios. The application contexts are notably diverse, encompassing cybersecurity, energy, healthcare, retail, education, IoT, financial services, and process mining. This broad applicability emphasizes how feature attribution methods naturally provide domain-agnostic explanations that translate effectively across varied data and operational contexts. The analysis reveals a balanced distribution between local and global scope, though local explanations slightly dominate. Feature attribution methods inherently focus on pinpointing specific feature contributions, making them naturally suitable for instance-level interpretability, complemented by global insights through aggregated or averaged attributions over broader data segments. The audience primarily comprises data scientists, with limited explicit mention of end-users. Although attribution methods provide intuitive explanations (feature contributions), effective interpretation and validation usually necessitate advanced domain knowledge and analytical expertise. Interestingly, explicit metrics like feature agreement and fidelity-sparsity are cited within this group, suggesting greater methodological maturity regarding the quantitative evaluation of interpretability compared to other classes analyzed thus far. Finally, this category shows substantial reliance on online and hybrid learning modes, illustrating feature attribution methods' adaptability to streaming or dynamically evolving data contexts. The presence of offline methods, albeit less frequent, emphasizes the methods' flexibility in retrospective analysis settings.
## 3 Methodology
### Evaluation Strategy
#### 3.2.2 Exploratory Data Analysis Method  
Two time series may be strongly correlated within a specific period, but the correlation can be weakened during a sub-period. For example, the GM-/OWCM-derived multiyear SM sequences may be well correlated with RFSM due to their strong seasonality; however, the situations may differ within a short period--especially in a rain or irrigation season--because different fitting models, as well as different fitting strategies (e.g., the wavelet transformation), have different abilities in capturing the rainfall and dry-down events. Therefore, sampled correlation R sampled in different sub-periods may also be different. Here, the exploratory data analysis (EDA) method was used to analyze the temporal consistency between the OWCM-/GM-derived SM and RFSM within different sub-time-periods.  
The EDA method was developed by [PERSON] et al. [86], with the initial idea conceived for a methodology to study the correlation between two data series [87]. EDA allows for the representation of every variation of correlation between two series over time. In this work, EDA was utilized to examine the Pearson correlation coefficient variations between the fine-RES SM series of GMs/OWCMs and the original RFSM series during different time windows. The window length is defined as follows:  
\[Length_{(i,j)}=\begin{cases}2\times\left(DOY_{i}-DOY_{j}\right)&DOY_{min}\leq DOY _{j}<DOY_{i}\leq DOY_{middle}\\ 2\times\left(DOY_{i}-DOY_{j}\right)&DOY_{middle}<DOY_{i}\leq DOY_{max},\,2\times DOY _{i}-DOY_{max}\leq\ DOY_{j}\end{cases}, \tag{7}\]  
where \(DOY_{i}\) and \(DOY_{j}\) are the ordinals of the central and beginning days of the selected time series, respectively, while \(DOY_{min}\), \(DOY_{middle}\), and \(DOY_{max}\) are the ordinals of the start, central, and end days of the entire time sequence, respectively.
## 3 Materials and Methods  
This section details the methodology employed in our research, including the experimental design, participant characteristics, data collection procedures, the metrics used to assess team performance and dynamics, and our data analysis approach.  
Building on existing research, our study investigates the impact of real-time emotional feedback on virtual team performance. We conducted a virtual field experiment with 84 participants from MIT (Cambridge, MA, USA), Living Lab from the Institute for the Future of Education at Tecnologico de Monterrey (Monterrey, Mexico), and the University of Applied Sciences of The Hague (The Hague, The Netherlands). Using a Post-test-Only Control Group Design, participants were assigned to either a control group or a treatment group, receiving real-time emotional feedback via custom software (Moody, version 2.0) [60]. This feedback, based on facial recognition technology, aimed to enhance team performance by providing immediate insights into the team's emotional state.  
Over 29 experiments, with 28 yielding analyzable data, participants engaged in a collaborative Mars colony simulation game. The collected data included video and audio recordings, capturing a wide range of emotional and conversational dynamics. We aimed to understand how real-time emotional feedback influences team performance and dynamics during these interactions. By analyzing this multi-modal data with machine learning models, we aimed to predict team performance based on emotional and conversational indicators.  
Our research contributes to the field of virtual team dynamics and performance in three significant ways. First, we demonstrate how real-time emotional feedback can enhance virtual team performance, providing a novel approach to team collaboration. Second, we extend existing research on the role of emotions in organizational behavior by incorporating multi-modal data analysis to capture the nuances of team interactions. Third, we highlight the predictive power of machine learning models in analyzing multi-modal data to forecast team performance, showcasing the potential of advanced analytical techniques to develop more effective virtual collaboration tools.  
By integrating quantitative analysis and model-based investigation, this study aims to provide a solid foundation for our research questions. The insights gained contribute to the academic understanding of virtual team dynamics and offer practical implications for improving team performance in the digital workplace. Our findings underscore the potential of real-time emotional feedback to optimize virtual team interactions, paving the way for future research and innovation in this area.
## 4 Materials and Methods
### Enantioselective GC-MS Analysis of Volatiles in Dried Leaves and Flowers and EO Constituents  
The enantioselective analysis of chiral compounds was performed on the EO constituents and also on volatiles isolated from the dried leaves and flowers to investigate the effects on enantiomeric distribution due to the distillation procedure. The isolation of volatiles from the dried leaves and flowers was carried out by the headspace solid-phase microextraction (HS-SPME) technique. Briefly, a weighted amount of died plant material (1.5 g) was placed in a 15 mL vial capped with a PTFE/silicone septum (Supelco; Sigma-Aldrich) for HS-SPME. The extraction was carried out by exposing a 2 cm, 50/30 \(\upmu\)m DVB/CAR/PDMS fiber (Supelco; Sigma-Aldrich) to the headspace of the plant material for 30 min, while keeping the extraction temperature at 30 \({}^{\circ}\)C by a water bath. At the end of the extraction, the fiber was immediately inserted into the GC split-splitless injection port for the desorption step and the GC run was started. A duplicate extraction of each cheese sample was carried out. GC analyses were performed using the same GC-MS system reported above, equipped with a chiral column of Cyclosil-B (0.25 mm \(\times\) 30 m, 0.25 \(\upmu\)m of film thickness, stationary phase: 30% Heptakis (2,3-di-O-methyl-6-O-t-butyl dimethylsilyl)-\(\beta\)-cyclodextrinin in DB-1701, J&W; Agilent Technologies). The chromatographic conditions were as follows: desorption for 5 min in the GC injector provided with a 0.75 mm glass liner suitable for SPME and operating at 240 \({}^{\circ}\)C in the spitless mode; oven temperature program from 55 \({}^{\circ}\)C (2 min) to 190 \({}^{\circ}\)C at 4 \({}^{\circ}\)C/min, then to 220 \({}^{\circ}\)C (5 min) at 20 \({}^{\circ}\)C/min; helium used as carrier gas at a constant flow of 1.5 mL min\({}^{-1}\) corresponding to a linear velocity of 45 cm s\({}^{-1}\); and MS detector parameters as stated above in Section 4.3. In the case of the analysis of the EO, 1 \(\upmu\)L EO diluted in methanol (1:20 \(v/v\)) was injected and a slit ratio of 50:1 was set. All the other GC-MS conditions were the same as in the analysis of the HS-SPME isolates.  
The identification of enantiomers was accomplished by a comparison with the mass spectra and linear retention indices (LRI) of authentic standards (Sigma-Aldrich). When authentic standards were not available, tentative identification was accomplished by acomparison with information reported in the NIST/EPA/NIH Mass Spectral Library (Version 2.4, 2020), in the literature [57] or kindly provided by Prof. [PERSON] (personal communication). A standard solution of linear alkanes (C7-C30) was run under the same chromatographic conditions as the samples to determine the LRI of the detected compounds. Enantiomeric ratios were expressed as percent peak areas of the sum of the areas of the peaks of both enantiomers.
# Synthetic Transformations and Medicinal Significance of 1,2,3-Thiadiazoles Derivatives: An Update
## 1 Introduction  
The fascinating aromatic 1,2,3-thiadiazole is a structurally active pharmacophore and great interest for researchers due to its versatile and wide array of biological activities in the field of medicine, pharmacology and pharmaceutics. Thiadiazoles occur naturally in four different isomeric forms, having one sulfur and two nitrogen atoms with hydrogen binding domain as presented in Figure 1[1, 2, 3, 4].  
Figure 1: Structures of different isomeric forms of thiadiazole (1–4).  
Thiadiazole scaffold emerges as an interesting structural motif due to the presence of different heteroatoms in its structure and its promising therapeutic activities in the treatment and cure of several diseases. Different substitutions on the thiadiazole nucleus improve the structure-activity relationship (SAR) of thiadiazole hybrid structures to enhance its therapeutic efficacy against a wide variety of pathogens [1; 5]. Thiadiazole scaffolds exhibit a wide range of biological activities including antimicrobial [6; 7; 8; 9; 10; 11], carbonic anhydrase inhibitors [12; 13; 14], antifungal [15; 16; 17; 18; 19], antibacterial [20; 21; 22; 23; 24; 25], systemic acquired resistance [26; 27], insecticidal activity [28; 29], antiviral [30; 31; 32; 33], antioxidant [34; 35], anti-convulsant [36; 37; 38; 39; 40; 41; 42], antihypertensive activity [43; 44], anticancer and antitumor [45; 46; 47; 48; 49; 50; 51], analgesic [52; 53], anti-inflammatory [54; 55; 56; 57; 58], plant activator [59; 60], antidiabetic [61], antileishmanial activity [62; 63; 64; 65] and antituberculosis [66; 67; 68; 69; 70; 71]. The different marketed drugs of thiadiazole moiety (**5**-**14) are given below [26; 72; 73; 74; 75; 76] (Figure 2).  
The bioactive 1,2,3-thiadiazole analogues display excellent and outstanding new profiles of medicinal, pharmacological and pharmaceutical activities [1; 77]. In this review article, the most recent and relevant synthetic approaches and pharmacological activities of 1,2,3-thiadiazole analogues are discussed and can be classified into antiviral, insecticidal, antifungal and anticancer activities.
## 4 Head Node Checkpointing
### Energy Consumption Model  
We use a Markov model to find the minimum number of backup nodes that meets the expected reliability of users and the energy analysis model to determine the optimal checkpointing interval. The model that is designed to represent the energy consumed during the transmission of data is divided into a shallow ocean (depth of water less than 100 m) and a deep ocean (depth of water greater than 100 m) model in UWSNs. In this paper, the energy consumption of the data transmission model is based on the shallow ocean model [19]. Table 1 shows the notations and functions used in this paper to model our system.
# DTN and Non-DTN Routing Protocols for Inter-CubeSat Communications: A comprehensive survey
## 5 Conclusions and Research Questions
These negative acknowledgments will actually produce more traffic in the overall network and, in some cases, there is insufficient time for negative acknowledgments, especially during short encounter periods or when the path between a source and a destination no longer exists. Besides, most of the reviewed routing protocols are unable to differentiate between link failure that is caused by link congestion and the failure caused by the failure of the satellite itself. Therefore, consideration for which QoS management techniques, such as load balancing, dropping policies, and link recovery in the case of satellite failure, can be used to enhance routing performance in the challenging environment is crucial. For the future, a High Data Rate and Low Power Consumption Shortest Path (HDRLP-SP) DTN routing protocol will be designed and implemented for CubeSat networks. In addition, the MEO/LEO Multi-Layered Hybrid Integrated CubeSat System (ML-HICS) architecture will be designed and twonew DTN routing protocols will be proposed accordingly, namely: (i) DTN HDRLP-SP single-copy forwarding-based routing protocol over ML-HICS architecture; and, (ii) DTN HDRLP-SP multi-copy quota-based routing protocol over ML-HICS architecture. Conceptualization, M.A.A.M., R.R. and S.I. ; methodology, M.A.A.M. ; software, M.A.A.M. ; validation, M.A.A.M., R.R. and S.I. ; formal analysis, M.A.A.M., R.R. and S.I. ; investigation, M.A.A.M. ; resources, M.A.A.M. ; data curation, M.A.A.M. ; writing--original draft preparation, M.A.A.M. ; writing--review and editing, M.A.A.M., R.R. and S.I. ; visualization, M.A.A.M. and R.R. ; supervision, R.R. ; project administration, R.R. All authors have read and agreed to the published version of the manuscript. This research received no external funding. The authors declare no conflict of interest.