esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Geological and Tectonic Context  
The Rif Cordillera belongs to the Tethys Alpine Orogenic Belt and together with the Betic Cordillera constitutes an arcuate orocline, which meets at Gibraltar and surrounds the Alboran Sea. The Rif is formed by three structural elements belonging to different paleogeographic domains [10; 33; 34] (Figure 1b), namely the following:  
* Internal zones, also called the Alboran Domain, correspond to an ancient continent that exists off Africa, in the western Tethys. It consists of a turn of three tectonic complexes: the Sebtides, Ghomarides, and the Calcareous Dorsal [10; 35].
* Flysch units that develop on transform zones between the Afro-European margins and the Alboran Domain; these flysches are grouped into two stratigraphic units, one Mauritanian and the other Massylian [36; 37]. The Mauritanian flysch includes the Tsiirene flysch, of the Late Jurassic-Early Cretaceous age, and the Beni Ider flysch, of the Middle Paleogene-Burdigalian age. The Massylian flysch includes the Early Cretaceous and Numidian flysch of Paleogene-Burdigalian age [38].
* External zones, which represent the Tethyan margin of the African plate; this domain consists mainly of terminal Cretaceous parautochthonous terrains represented by the Tangier-Loukous units and Triassic-Tertiary parautochthonous-to-autochthonous carbonate and siliciclastic terrains represented by Mesorifain and pre-Sahelian units [38; 39].  
In the Eastern Rif, the Mesorifain units of Tamsamane are directly in contact with the foreland. Thus, the internal foreland has a Rifian affinity, whose Mesozoic formations show stratigraphic and metamorphic similarities with Mesorifain formations, such as the massifs of Kabdana, Beni Bou Ifroure, Gareb, and Cap des Trois Fourches (Figure 2) [8; 40; 41; 42]. In contrast, their equivalent of the Outer foreland is the Beni Snassene Massif, without Alpine metamorphism, which is shown with the highlands, Atlasic affinities (Figure 2) [43; 44; 45].  
These Mesozoic series outcrop only at the level of the reliefs where they are uncontormably overlaid by deposits of the Lower and Middle Miocene and form isolated mountain massits that emerge in the regional topography [12; 46; 47]. These geological formations are shown here as a relative para-substratum surmounted by a complex arrangement of lowland plains occupied by Upper Miocene formations considered as post-nappe deposits [48; 49]. The shape of these contrasting and compartmentalised structures is clearly related to neetectonic deformations [50].  
The post-nappe basins infill starts with a thick series of Tortonian conglomerates and marks that only outcrop at the foot of the mountain ranges and show significant lateral variations in thickness [17; 48]. Indeed, these formations are differently distributed and are partially cached below the Messinian marks and finally the Pliocene-Quaternary detrital deposits. Their development occurred in isolated basins induced by subsidence phenomena controlled by brittle deformations.
# A High-Dynamic-Range Optical Remote Sensing Imaging Method for Digital TDI CMOS
## 3 Image Enhancement Algorithm of Entropy-Maximized Adaptive Histogram Equalization (EMAHE)
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**TDI Stage**} & **Evaluation** & **Original** & **Enhanced** & **Enhanced by HE** & **Enhanced by Algorithms** & **Enhanced by Algorithms** & **Enhanced by EMAHE 1** \\ \hline \multirow{2}{*}{5} & entropy & 5.17 & 5.09 & 5.46 & 5.48 & 5.50 \\ \cline{2-6}  & average & \multirow{2}{*}{2.94} & \multirow{2}{*}{4.03} & \multirow{2}{*}{6.04} & \multirow{2}{*}{5.99} & \multirow{2}{*}{6.78} \\  & gradient & & & & & \\ \hline \multirow{2}{*}{13} & entropy & 4.51 & 4.48 & 4.51 & 4.48 & 4.54 \\ \cline{2-6}  & average & \multirow{2}{*}{5.14} & \multirow{2}{*}{5.52} & \multirow{2}{*}{6.96} & \multirow{2}{*}{7.11} & \multirow{2}{*}{7.95} \\  & gradient & & & & & \\ \hline \hline \end{tabular}  
* TDI, time delay integration; histogram equalization; EMAHE, entropy-maximized adaptive histogram equalization.  
\end{table} 
Table 1: Comparison of the entropy and average gradient between different enhancement methods.
## 2 Material and Method
### Participants  
Undergraduate and graduate participants were recruited from a four-year research university located in the southwestern region of the United States using announcements placed in the electronic announcement system or postings within approved departments. A sampling of (N = 66) undergraduate students were randomly assigned to one of the two conditions. Thirty-three participants were assigned to the desktop screening environment (four students did not complete the research activities, which reduced our participants to N = 62). In contrast, thirty-three were assigned to the VR instructional environment. None of the students had majored in biology or biochemistry. However, all of them had some elementary knowledge about DNA molecules. Although participants differed in the level of education and prior experience about the subject, the focus of the current research was on participants' spatial understanding rather than role learning.  
Table 1 summarizes the demographic characteristics of the participants in this study. There were 30 male participants (48.4%) and 32 female participants (51.6%). The participants also identified themselves as a freshman, sophomore, junior, or senior (_n_ = 40, 64.5%) and college graduate, some graduate work, or postgraduate degree (_n_ = 22, 35.5%). The majority of students were 18-29 years old (87.1%). The rest were 30-49 (12.9%).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Treatment 1**} & \multicolumn{2}{c}{**Treatment 2**} & \multicolumn{1}{c}{**Total**} \\  & \multicolumn{1}{c}{**Desktop Screening**} & \multicolumn{1}{c}{**VR**} & \multicolumn{1}{c}{**Total**} \\ \hline Participants & Demographic & \(n\) = 29 & \(n\) = 33 & N = 62 \\ \hline Age & 18–29 & 26 & 28 & 54 \\  & 30–49 & 3 & 5 & 8 \\ \hline Gender & Male & 14 & 16 & 30 \\  & Female & 15 & 17 & 32 \\ \hline  & Freshman & 11 & 4 & 15 \\  & Sophomore & 9 & 4 & 13 \\  & Junior & 2 & 7 & 9 \\ Level of education & Senior & 2 & 5 & 7 \\  & College graduate & 1 & 6 & 7 \\  & Some postgraduate & 1 & 4 & 5 \\  & Postgraduate degree & 3 & 3 & 6 \\ \hline  & Average & 12 & 12 & 24 \\ Computer skills & Somewhat high & 12 & 14 & 26 \\  & Very high & 5 & 6 & 11 \\  & Somewhat low & & 1 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Participants Demographic Frequencies on Gender and Level of Education.  
In addition, participants also rated their computer skills. Collectively, participants self-reported computers skills to be average (\(n=24\), 38.7%), somewhat high (\(n=26\), 41.9%), very high (\(n=11\), 17.7%), and somewhat low (\(n=1\), 1.6%).
## 2 Materials and Methods
### PTR-ToF-MS Measurement  
Headspace measurements were performed with a commercial PTR-ToF-MS 8000 apparatus from Ionicon Analytik GmbH (Innsbruck, Austria) in a standard configuration (V mode). The ionization conditions were as follows: 500 V drift voltage, 110 \({}^{\circ}\)C drift temperature, and 2.80 mbar drift pressure resulting in an E/N ratio of 130 Townsend (1 \(\mathrm{T}\mathrm{d}\) = 10\({}^{-17}\) cm\({}^{2}\) V\({}^{-1}\) s\({}^{-1}\)). Sample handling, headspace flushing and sampling were carried out using an autosampler (MPS MultiPurpose Sampler, Gerstel, Germany) specially adapted for PTR-ToF-MS [34]. The autosampler moved the sample from the incubation tray to the temperature-controlled purging site, connected to the PTR-ToF-MS inlet. Dynamic headspace analysis took place for 60 s with an acquisition rate of one mass spectrum per second between \(m/z\) 15 and 349. Due to the high ethanol concentration, argon was added to the inlet system at a flow rate of 120 sccm, with the total flow rate of the system at 160 sccm. This prevented primary ion depletion and the formation of ethanol clusters that might affect the final quantification of volatiles [35]. The argon flow rate was controlled by a multi-gas controller (MKS Instruments, Inc., Andover, MA, USA). After measurement, the vial was moved back to the same position as the incubation tray, and the cycle was repeated on the following sample. During fermentation, the measurement was repeated every 6 h to monitor the fermentation process.  
Deadtime correction, internal calibration of mass spectral data, and peak extraction were performed according to previously described procedures [36; 37]. The peak intensity in ppb/v (parts per billion per volume) was estimated using the formula described in the literature [38]. The formula uses a constant value for the reaction rate coefficient (k = 2.10\({}^{-9}\) cm\({}^{3}\) s\({}^{-1}\)).  
Systematic errors can arise due to various factors, such as the use of a constant reaction coefficient, humidity, and fragmentation. However, in most cases, the error associated with measuring the absolute concentration of each compound is less than 30% and can be corrected post-analysis [36]. Certain mass peaks, such as those associated with isotopologues of \({}^{13}\)C, \({}^{18}\)O, and \({}^{27}\)S, as well as water and ethanol clusters, were excluded from the dataset to minimise errors. Tentative compound identification was conducted by comparing the measured mass to the theoretical mass in the literature (Table 2). SPME-GC/MS was employed in conjunction with PTR-TOF-MS to confirm the tentative identification of compounds through the comparison of their mass spectra and chromatographic retention times.
# Decentralized Consensus Protocols on \(SO(4)^{N}\) and \(Tso(4)^{N}\) with Reshaping
## 3 Consensus on \(SO(4)^{n}\) with Reshaping
### Illustrative Examples
#### 3.3.3 Nine-Agent Ring Graph  
Now, we reconsider the case of \(N=9\) agents with the undirected ring graph in Figure 1b. The initial conditions are defined in Equation (44), and the simulation is conducted using the reshaping Case 1 and Case 2 protocols.  
Figure 3: Simulations on \(SO(4)^{N}\) for (**a**) Base case protocol on a 5-agent ring graph and (**b**) Base case protocol on a 9-agent ring graph. Initial conditions are chosen within the region of attraction of the consensus manifold. Both plots demonstrate consensus.  
Similar to the five-agent base case converging to non-consensus equilibria in Figure 4a, the nine-agent Case 1 reshaping fails to destabilize the non-consensus equilibria, resulting in a steady-state value of the relative PRA \(\theta_{ijk}\) in Figure 5. This principal rotation angle \(\theta_{ij1}\) converges to the separation \(\theta_{s1}=2\pi/9\) of the non-consensus balanced equilibrium. This is a result of the fact that \(\theta_{ijk}<\arg\max_{\theta\in[0,180]}f_{1}(\theta)=50^{\circ}\). As described in Section 3.1, the Case 1 reshaping algorithm fails to destabilize the non-consensus equilibria. Since stable non-consensus equilibria remain, the resulting consensus configuration is only locally stable and not AGAS stable.  
However, reshaping Case 2 successfully destabilizes the non-consensus equilibria. Figure 5b shows that all \(\theta_{ijk}\) associated with the relative configurations converge to zero, signifying that consensus has been achieved. This is expected since \(\theta_{ijk}=40^{\circ}>\arg\max_{\theta\in[0,180]}f_{2}(\theta)=35^{\circ}\).  
The results of the nine-agent ring graph simulation confirm the conclusions in [20] for consensus on \(SO(3)^{N}\). Case 2 feedback reshaping destabilizes non-consensus equilibria for a nine-agent ring graph and ensures the almost global stability of the consensus subspace.
# Impact of Preheating Temperatures and RAP Characteristics on the Activation of RAP Binder
## 3 Materials and Methods
### Methods  
In order to study the activation of RA binder under different influencing factors, a preheating and compaction procedure was used in this study to produce specimens with 100% RAP. It is believed that as the activation degree of RA binder increases, more RA binder will participate in strengthening the bond between RAP particles, and the strength of specimens will also increase. On the other hand, the activated RA binder will become softer and its viscosity will decrease, improving the compaction and resulting in a denser and stronger specimen. Therefore, the volumetric and mechanical properties of specimens were used to indicate the activation of RA binder. The methods used in the study are detailed as follows.
## 5 Towards a Standardized Framework for Digital TPM Implementation  
The purpose of this study is not limited to the bibliometric and thematic mapping of digital TPM literature. Rather, it aims to extract and consolidate the constitutive elements of a standardized implementation framework for TPM4.0. In a rapidly evolving digital industrial environment, the absence of a common reference model leads to inconsistent practices, limited scalability, and uneven performance outcomes, particularly among SMEs, which often lack the resources for extensive technological integration. Establishing a shared standard would provide a coherent structure to guide organizations in aligning technological innovations with the historical pillars of TPM while preserving its foundational philosophy of collective responsibility and continuous improvement.  
Based on the analysis in the previous section, a robust framework must integrate four complementary dimensions: (i) technological choices and integration logic; (ii) methodological deployment stages; (iii) organizational readiness and workforce involvement; and (iv) performance evaluation using standardized metrics. These dimensions constitute the backbone of a practical, adaptable, and scalable reference model for digital TPM implementation.
# Demand Forecasting in Supply Chain Using Uni-Regression Deep Approximate Forecasting Model
## 1 Introduction
### Motivation and Contribution  
The motivation for this research stems from the inherent challenges and crucial role of demand forecasting in contemporary supply chain management. Accurate demand forecasting is pivotal for organizations to optimize their resources, streamline production processes, and maintain an efficient inventory. Traditional demand forecasting models often encounter various difficulties in capturing the intricate temporal patterns and external factors influencing demand. Recognizing the limitations of existing methods, this research seeks to contribute to the field by leveraging advanced machine learning techniques.  
This research proposes a novel integration of NARX models and recurrent BiLSTM networks for demand forecasting. Autoregressive models, like NARX, utilize historical information to predict future values, but they may not fully exploit intricate patterns within supply chain data. By integrating NARX models with recurrent biLSTM networks, a more comprehensive forecasting tool can be created that combines the strengths of capturing temporal dependencies and nonlinear patterns. This integration aims to enhance forecasting capabilities to adapt to the complexities of modern supply chains. The main contribution of the research is as follows,  
* Uni-regression deep approximate forecasting model: The contribution of the unigression deep approximate forecasting model could bring a transformative perspective to the predictive modeling landscape. While the existing models, such as the recurrent BiLSTM Network and NARX model, excel in capturing historical patternsand accounting for external factors, the introduction of the uni-regression model introduces a new dimension. Uni-regression, by embracing a deep approximate forecasting approach, likely enhances the capacity to grasp intricate relationships within the supply chain data. Its unique ability to approximate complex patterns might offer an advantage in capturing nuanced demand dynamics, especially in scenarios where traditional models may fall short. The fusion of insights from the uni-regression model with those derived from the BiLSTM and NARX models could potentially lead to a more comprehensive and accurate demand forecast, contributing to the overall improvement in forecasting accuracy and reliability within the dynamic processes of supply chains.  
The manuscript follows a specific organizational structure. In Section 2, there is a discussion about existing works, their approaches, and challenges. Section 3 explains the effective demand forecasting methodology using a uni-regression deep approximate forecasting model. Section 4 outlines the functioning of the uni-regression deep approximate forecasting model. Section 5 presents the results obtained from applying the proposed demand forecasting model, and Section 6 concludes the manuscript.
## 2 Material and Methods
### Surface Characterization
#### 2.6.1 Sample Preparation for Surface Analyses  
Different kinds of samples were tested. Obourg and Ciply Chalk samples (Figure 5) were prepared by slicing cylindrical samples cored from large blocks, with a diameter of 25 mm and a thickness ranging from 2 to 15 mm. The samples were subsequently saturated with epoxy resin, in order to remove the porosity and avoid water absorption during contact angle measurement. For this purpose, the rock samples were left immersed in epoxy resin in an oven at 60 \({}^{\circ}\)C for about 7 h. This method ensures a homogeneous saturation and excellent resin penetration. After the complete curing of the epoxy, the sample surface composed of chalks grain and resin was polished with silicon carbide paper from grit 120 to 4000.  
A second kind of sample consists of single calcite crystals (Figure 5), with no particular preparation carried out on it. The calcite cleavage plane is defined as (104) according to the hexagonal setting and the axial convention based on the structural unit-cell of the calcite (described by the two orthogonal axial lengths a = 5 A and c = 17 A) [14].  
Figure 5: Cylindrical rock plugs of Ciply Chalk (**bottom left**) and Obourg Chalk (**bottom right**), and pure single calcite (**top**) used for surface characterization. The scale is given by the ruler in mm.
## 3 Case Study
### Comparison of Various Aeration Orifice Dimensions  
Dimensions and shape of the aeration orifice are defined by the equivalent diameter (\(D_{aorf}\)) and discharge coefficient of the aeration orifice inlet/outlet (\(C_{in/out,aorf}\)) and have a direct effect on the air pressure and the surge tank water level (STWL) during the transient process.  
For the analyzed case, the value of the discharge coefficient is set to 0.9. Considering the possibility to modify the cross-sectional area value of the orifice (Equation (10)), the effects of the various values of the discharge coefficient for the round and symmetrical aeration orifice can be neglected. Due to the aeration orifice diameter modification, the discharge coefficient was not defined as the main parameter of the researched SPST. Although the variation-induced effects can be neglected, the actual effects should not be overlooked for the SPST application. The correct real value of the discharge coefficient should be defined depending on the aeration orifice shape. Since the isothermal behavior of the air is to be expected, the polytropic exponent value was set to 1. Various dimensions of the aeration orifice diameter have been researched and their influence is presented in Figures 5 and 6 for cases of both load rejection and simultaneous start.  
Figure 4: STWL of the throttled chamber and semi-pneumatic throttled chamber surge tank for (**a**) load rejection and (**b**) simultaneous start of both turbines.  
In the case of load rejection of both turbines, the absolute air pressure ranges from the atmospheric pressure (\(D_{aorf}=3\div 15\) m) to 270 kPa (\(D_{aorf}=0\) m).  
For the case of simultaneous start, the absolute air pressure varies from the atmospheric pressure (\(D_{aorf}=3\div 15\) m) down to 65 kPa (\(D_{aorf}=0\) m).
## 5 Discussion and Analysis  
In order to validate the obtained results confidently, it is important to compare the results of HHO algorithm in both cases with earlier research results that used the exact microgrid simulation model but with different optimization algorithms, such as fast surrogate-assisted particle swarm optimization (FSAPSO) and several modified PSO algorithms [31]. Table 5 shows the obtained results of the HHO algorithm along with results of other previously proposed algorithms in literature. Number of objective function evaluations (OFE) carried out till best solution is reached is indicated where it is equal to 1\(\times\) number of agents \(\times\) iterations. Additionally, the number of algorithm iterations needed till best solution is reached is indicated in the table along the trials number. Although there is a lack in information in [31], it can be noted that the proposed HHO algorithm successfully reaches lower operating costs with lower iterations. It is suggested as future work to take into consideration other constraints such as PV lifetime and power degradation, and battery state of health and power degradation, where some modifications can be made to power constraints, mentioned earlier in Table 1, accordingly, to give more realistic results especially for long term simulations.  
Figure 5: Convergence curves for the HHO algorithm for (**a**) Study Case 1 sample, (**b**) Study Case 2 sample.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Case**} & \multirow{2}{*}{**Method**} & **Number of Agents** & **Best Solution** & \multirow{2}{*}{**OFE’s Needed**} & **Iterations to** & **Trials** \\  & & **Agents** & **Obtained** & & **Converge** & **Carried Out** \\ \hline \multirow{8}{*}{Case 1} & PSO [31] & - & 277.3237 & Up to 16,000 & - & 20 \\  & FSAPSO [31] & - & 276.7867 & Up to 16,000 & - & 20 \\  & CPSO-T [31] & - & 275.0455 & Up to 16,000 & - & 20 \\  & CPSO-L [31] & - & 274.7438 & Up to 16,000 & - & 20 \\  & AMPSO-T [31] & - & 274.5507 & Up to 16,000 & - & 20 \\  & AMPSO-L [31] & - & 274.4317 & Up to 16,000 & - & 20 \\  & **Proposed** & **50** & **270.413** & **Up to 7500** & **150** & **20** \\  & **HAO** & GA [31] & - & 162.9469 & Up to 16,000 & - & 20 \\  & PSO [31] & - & 162.0083 & Up to 16,000 & 1000 & 20 \\  & FSAPSO [31] & - & 161.5561 & Up to 16,000 & 750 & 20 \\  & CPSO-T [31] & - & 161.0580 & Up to 16,000 & - & 20 \\ Case 2 & CPSO-L [31] & - & 160.7708 & Up to 16,000 & - & 20 \\  & AMPSO-T [31] & - & 159.9244 & Up to 16,000 & - & 20 \\  & AMPSO-L [31] & - & 159.3628 & Up to 16,000 & - & 20 \\  & **Proposed** & **50** & **75.7596** & **Up to 8000** & **160** & **20** \\  & **HHO** & \multicolumn{1}{c}{} & & & & \\ \end{tabular}
\end{table}
Table 5: Comparison of the obtained simulation results for the MG—Case 1 & 2 [31].
## 3 Results  
### Wearing-Off Data Collection  
Fitness tracker data and wearing-off data were collected for 30 days from 23 February until 24 March 2021. The participants recorded their wearing-off periods using a customized FonLog application. Participant 1 collected a total of 227 wearing-off periods, while participant 2 reported 44 wearing-off periods. The average duration of wearing-off for participant 1 was 87.432 min (\(\sigma=77.943\) min); on the other hand, participant 2 had an average duration of wearing-off of 25.295 min (\(\sigma=34.512\) min). Over the 30-day collection period, participant 1 reported at least one wearing-off every day. Meanwhile, participant 2's reporting was more sparse than participant 1; there was even no report on the third week from participant 2. Figures 3 and 4 present the distribution of wearing-off data for both participants.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Predicted Wearing-Off (1)** & **Predicted On (0)** \\ \hline
**Actual Wearing-Off (1)** & True positive (TP) & False negative (FN) \\ \hline
**Actual On (0)** & False positive (FP) & True negative (TN) \\ \hline \hline \end{tabular}
\end{table}
Table 7: Confusion matrix for wearing-off and on of anti-PD medicine.  
Figure 3: Distribution of reported wearing-off over the 30-day collection period.