esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Discussion

Table 11: Student’s \(t\)-test findings for the 3D-FD versus other methods on dataset 2a. When relating to the results obtained in the motor imagery reference paper [14], which featured our MI pipeline, traditional feature extraction techniques such as common spatial patterns (CSPs) and Riemannian Geometry-based approaches have been widely used for motor imagery classification. The latter still face some limitations, like handling low signal-to-noise ratios and capturing complex, non-linear dynamics characteristics of EEG signals. For example, CSP is highly sensitive to noise and primarily optimized for binary classification, making it less suitable for multi-class scenarios similar to our use case. Yet, the ER-based features employed, fractal dimensions (Katz, Petrosian, Higuchi), wavelet analysis, and higher-order spectra (HOS), are designed to handle these types of issues, underlining the superior robustness, as shown by the remarkable performance gained in this study. Our mixed ER/MI approach using three-dimensional fractal dimensions (Katz, Petrosian, and Higuchi) reached an exact accuracy of 79.04% for LinearSVM, which is even higher than the top-of-the-line results acquired, regardless of CSP (73.70%) or Riemannian (75.47%) [14]. Moreover, the current findings align with the emotion recognition findings adopted from [7], as the latter stated that the best overall accuracy was achieved using the three-dimensional fractal dimension feature set. In this study, fractal dimensions showed promising results when applied to the motor imagery span. Furthermore, when compared with the latest method used in the MI field, the TWSB method from [45], our results reached approximately the same accuracy of 79.1%. The current research presents novel findings, specifically the greater accuracy of fractal dimension features, including Katz, Petrosian, and Higuchi. It also highlights the improved performance of LinearSVM compared to GSVM, CART, and SVM with a polynomial kernel. As fractal dimension features are robust to noise and capture notable structural patterns within the EEG signals used, this leads to an enhancement in the system's ability to classify MI tasks with subtle signal differences. This makes this custom pipeline distinctive and underlines a unique contribution to the classification improvements of the motor imagery field with feature sets from the emotion recognition field. In future research, more fractal dimension algorithms can be introduced to our embraced approach. By combining methods other than the Katz, Petrosian, and Higuchi algorithms, different groupings might yield different outcomes. By extending our mixed pipeline, comparing the latter might influence the overall accuracy precisely when different fractal dimensions are used with the four performance classifiers utilized in the current research: GSVM, CART, LinearSVM, and SVM with a polynomial kernel. This extension aims for even higher accuracy and better-quality average success rates.
# Dynamic Vertical Mapping with Crowdsourced Smartphone Sensor Data
## 5 Conclusions  
This paper describes our novel framework for the dynamic mapping of the vertical characteristics of a building. The proposed method makes use of a new sensor available in the latest smartphones (the last from 2017), and the barometric sensor, which indicates the ambient pressure and manages uncertain sensor data collected from crowdsourcing. The method estimates the altitude of the collected data with the use of the barometric formula. For achieving this, we introduce a novel approach for the extraction of the reference pressure at the OITransition of the user, which is identified through sensor fusion. More specifically, the GPS uncertainty, the magnetic disturbances and the ambient light are taken into consideration for identifying the transition, while the proximity sensor is also used as a supportive sensor. We faced an unsupervised classification problem, in which the number of floors--or the number of clusters--as well as the altitude--or the label of each class--for each floor were unknown. To resolve this problem, a clustering analysis technique called the elbow method and the popular K-means clustering algorithm were used. Finally, we propose a way to map these characteristics by enhancing the standards of the CityGML, enabling it to carry information about the vertical characteristics of a building in lower LoDs.  
Although it has been demonstrated in the paper that our approach can work with any barometric sensor (Section 4.1), as the offset between different barometric sensors is stable, our approach has been extensively evaluated only for the Samsung Galaxy S6 (Samsung Galaxy S6, 2017).  
Additionally, we noticed that when a significant delay follows the OITransition and precedes ascending to different floors, the vertical localization error increases. This is due to the long-term instability of the ambient air pressure. The same happens when there is lack of data from one floor. It is very likely that such data will not be taken into consideration in the clustering analysis and finally in the clustering phase. This will result in a missing floor in the final model.
A method is then proposed to detect the reference tilt based on the inverted pendulum's position, and a technique is suggested for calibrating the center of mass through thrust and tilt angle modulation. The reference leveling is controlled by a piezoelectric actuator, and the tilt drift is confirmed through experimentation. To address the potential issue of reference leveling, a two-stage torsion pendulum in series is suggested to achieve the separation of gravity and thrust. In summary, this paper systematically examines the microgravity coupling problem of the torsion pendulum through theoretical analysis, numerical simulation, and experimental verification. It also introduces compensation methods for reference leveling and centroid calibration, which can be used as a reference for measuring micro thrust based on the torsion pendulum.
## 3 pLEO Time Scale Development  
The following insights may have been previously detailed in [13]. The success of pLEOT hinges on establishing an accurate, robust, and stable time scale. Once this foundational time scale is in place, all local clocks within the pLEO constellation will be synchronized to it, known as pLEOT.  
In established GNSS like GPS, ground monitoring networks meticulously determine correction values for each satellite clock in relation to the system time, such as GPST or UTC. These timing offsets are then uploaded to the satellite constellation and broadcast to terrestrial users through navigation messages. It is important to note that an anomaly in any single satellite clock can adversely affect the performance for all terrestrial users. Consequently, ensuring robust and reliable timing on each satellite is absolutely essential.  
For the pLEO constellation, where a stable onboard timing reference is not readily available, the stability of onboard timing can be significantly improved by utilizing a clock ensemble technique that leverages the strengths of various clocks. Different clock types excel with distinct averaging intervals. Furthermore, integrating multiple clock types into a mixed ensemble not only enhances the stability of the time scale but also strengthens its resilience, as all clocks of the same type provide vital active redundancies. This approach underscores the commitment to delivering reliable and precise timing solutions for the future.  
The stability of an ensemble clock is fundamentally characterized by the Overlapping Allan Deviation (OADEV) [14]. By employing a finite set of phase measurement data [15], one can accurately estimate the OADEV, which is critical for selecting various diffusion coefficients that reflect the covariance matrix of the process noise. These coefficients are essential as they encompass diverse fundamental noise types, including white frequency noise, random walk frequency noise, etc. Ultimately, a reduction in the diffusion coefficients of an individual clock indicates superior performance, leading to more precise predictions.  
Moreover, the integration of different clock types within a mixed ensemble through a Kalman filter approach is not only feasible but also highly advantageous [16]. This method allows for the creation of a time scale that capitalizes on the unique strengths of each clock, resulting in a composite clock solution that consistently outperforms the best clock in  
Figure 4: Local quadratic regulators for the ICaC.  
the ensemble [17; 18]. As illustrated in Figure 5, an operational architecture is proposed for a pLEOT time scale, applicable to any pLEO constellation. This innovative design maximizes the potential of all participating platforms, existing satellite communications, and inter-satellite measurements, paving the way for effective onboard implementations of pLEOT. Each platform can operate with its local low-noise oscillator, complemented by a numerically controlled oscillator signal synthesis, ensuring exceptional performance and accuracy.
## 3 Results and Discussion
### Plugging Performance  
#### 3.2.1 Walnut Plugging Performance  
Walnut was able to seal a maximum 2.0 mm fracture width in either the simulated situation of no shearing or the situation with a high shearing effect. Thus, the material's plugging efficiency was not materially affected by the minimum size degradation seen during testing. The fluid loss during testing with no shearing and with high shearing was 15 and 19 mL, respectively. The fluid loss volume increase was because of the size degradation and the absence of some of the larger particles, which adversely affected the bridging mechanism, as shown in Figure 5.  
The original walnut dry phase D\({}_{50}\) was 2060 \(\upmu\)m, while the D\({}_{50}\) after degradation was 1972 \(\upmu\)m. Therefore, the newly developed particle size selection criterion is based on the original D\({}_{50}\) of the dry phase given in Equation (2). It states that the median particle size of walnut over the fracture width size must be equal to or greater than 1.0 to plug the fracture.  
\[\frac{\text{D}_{50}}{\text{Fracture width size}}\geq 1.0 \tag{2}\]
## 2 State of the Art
### Workflow-Driven BIM-Based Collaboration  
Use cases and recurring workflows in BIM context are modeled with the industry standards IDM and BPMN [3; 16; 17; 18]. The IDM is an international standardized framework to model the information delivery in BIM-based projects [16]. The BPMN is a standardized non-construction-specific comprehensive framework for modeling business processes of all kinds [19; 20]. The IDM framework is well researched for the application of the information delivery [17; 18; 21] and the specification of information requirements in the construction industry [22]. The relevance of IDM and BPMN in the renovation phase of buildings was also investigated by [PERSON] et al. [23]. The authors presented these two frameworks as an essential method for openBIM workflows during the reuse of a building. This study revealed that both the interoperability of software solutions and the systematization and structuring of interactions between participants and the information exchange process lead to a higher level of collaboration. Hence, all phases of the building life cycle can benefit from the application of IDM and BPMN, which have a key role for successfully establishing openBIM in construction projects.  
Fundamental research on increasing interoperability between software applications, such as design and structural analysis, through the use of BPMN was conducted by [PERSON] and [PERSON] [24]. The information delivery using BIM is defined in ISO 19650 [8], providing workflows for requesting and delivering information at certain points during the project delivery and asset operation. Applied to CDEs, workflows are proposed providing access rights to the data in certain status types [25]. The status types are Work in Progress, Shared, Published and Archived and the respective workflows, which transfer data from one status to another, require an approval procedure in each case [8]. These workflows are  
Figure 1: Proposed methodology for developing the toolchain framework.  
implemented in several CDEs and are also considered in the specification for openCDE DIN SPEC 91391 [9].
## 3 Results and Discussion
### Characterization of the Raw Hydrolysates
#### 3.2.1 Sugar Composition and Hydrolysis Efficiency
Given the mild conditions employed for hydrolyzing the lignocellulosic materials (dilute acid), the hydrolysis efficiency of cellulose (GluR) was below 30% for all materials (Figure 3). This result is explained by the high crystallinity and polymerization degree of cellulose that requires harsher conditions for an efficient hydrolysis [PERSON] and [PERSON] (2018). In contrast, the hydrolysis of hemicellulose was considerably more efficient for most materials. The highest hemicellulose hydrolysis efficiency was reached for BSG, where the xylan and arabian fractions were almost completely converted into xylose and arabin, respectively, with XylR and AraR values of 91.0 \(\pm\) 1.8% and 98.7 \(\pm\) 2.5% (Figure 3B). These values are similar to the ones reported by [PERSON] et al. [PERSON] et al. (2018), who demonstrated the recovery of 95% of the BSG's xylan as xylose and 96% of the arabian as arabin, under similar hydrolysis conditions. High efficiency was also achieved for the hydrolysis of xylan's fraction of grape stalks (99.6 \(\pm\) 0.8%) and sawdust (67.8 \(\pm\) 0.3%) (Figure 3E,F). Similar results (XylR of 89.9%) were reported by [PERSON] et al. [PERSON] et al. (2018). For the remaining lignocellulosic materials, i.e., banana peels, corn cobs, and grape pomace, lower hydrolysis efficiency values were obtained for XylR (<60%) (Figure 3A,C,D).
## 4 Evaluation
### Results of Simple and Stationary Scenarios
In general, the relaxed binary solution yields better results than its binary counterpart; however, this is not the case in this work. This is because, in this study, fractional association and channel assignment are not allowed by assumption and thus, the real-valued solutions must be converted to binary. For this, a simple rounding solution has been applied, which degrades the quality of the solution. It is interesting that even when there is only a single user on the network, the amount of allocated power varies depending on the considered solution. Both Proposed and SSF/opt outperform the rest since they are essentially the same when there is a single PU on the network. In the case of R&R/opt, when the VU is located in such a way that its distance to some RSUs is almost the same, it results in real-valued solutions. The following simple rounding and random tie-breaking approach leads to deviation of the solution from the optimal. Figure 5 shows the association fairness in terms of Jain's fairness index. As expected, Util&LB/opt tries to maximize the fair distribution of VUs among RSUs with weight \(1-\alpha\), and thus its load balancing performance outperforms the rest. Overall, as the number of VUs increases the load balancing index increases as there is only a single channel per RSU. It is interesting to see that when there are four and five VUs on the network, Proposed does not increase the fairness, indicating that NOMA is being exercised although there are five RSUs available. It may happen in the cases that the unutilized RSUs are far away from the RSUs and thus, Proposed has decided to switch to NOMA for reducing the aggregate power consumption. The above discussion can also be verified by Figure 6, which shows the ratio of the NOMA channels to the entire channels available. Util&LB/opt triggers NOMA communication in the most delayed manner when there are five VUs on the network. On the other hand, other approaches including Proposed activate NOMA earlier in order to minimize the power consumption. Figure 5: Mean association fairness performance for a simple, stationary configuration with a single channel with respect to the number of VUs on the network. The optimal power-minimization formulation results in the least power consumption. The relaxed counterpart, generally yields a better performance due to the relaxed search space, since we have used the naive binary recovery algorithm in this study the solution quality degrades. Despite the difference in formulation, both SSF/opt and Util&LB/opt have shown some similar behaviors, suggesting that the low complex SSF/opt could be a reasonable replacement for Util&LB/opt. The fairness index and the ratio of NOMA channels directly do not indicate the power minimization performance especially when few devices are available.
# Effect of Near Ultraviolet Radiation on Varroa Destructor Using Digital Holographic Interferometry
## 2 Materials and Methods
### Sample Collection  
Varroa destructor specimens were donated by the Secretaria de Agricultura y Desarrollo Rural (SADER). Sampling was conducted during the spring of the year 2023, ensuring representation across different developmental stages of the mite. Mites were carefully collected using the [PERSON] bee wash method (following NOM-001-ZOO-1994 [17], last modified in 1998) to avoid causing stress or damage to the specimens. Special care was taken to select mites that exhibited typical features of a healthy population. The careful collection and handling of specimens were essential steps in obtaining reliable data on the effects of UVA irradiation. In total, 35 samples were donated for studies about the Varroa mites, and in this study, five specimens were used.
# Detection and Localisation of Abnormal Parathyroid Glands: An Explainable Deep Learning Approach
###### Abstract  
Parathyroidoid scintigraphy with 99 mTc-sestamibi (MIBI) is an established technique for localising abnormal parathyroid glands (PCs). However, the identification and localisation of PCs require much attention from medical experts and are time-consuming. Artificial intelligence methods can offer an assisting solution. This retrospective study enrolled 632 patients who underwent parathyroid scintigraphy with double-phase and thyroid subtraction techniques. The study proposes a three-path approach, employing the state-of-the-art convolutional neural network called VGG19. Images input to the model involved a set of three scintigraphic images in each case: MBI early phase, MBI late phase, and 99 mTc04 thyroid scan. A medical expert's diagnosis provided the ground truth for positive/negative results. Moreover, the visualised suggested areas of interest produced by the Grad-CAM algorithm are examined to evaluate the PG-level agreement between the model and the experts. Medical experts identified 545 abnormal glands in 452 patients. On a patient basis, the deep learning (DL) model attained an accuracy of 94.8% (sensitivity 93.8%; specificity 97.2%) in distinguishing normal from abnormal scintigraphic images. On a PG basis and in achieving identical positioning of the findings with the experts, the model correctly identified and localised 453/545 glands (83.1%) and yielded 101 false focal results (false positive rate 18.23%). Concerning surgical findings, the expert's sensitivity was 89.68% on patients and 77.6% on a PG basis, while that of the model reached 84.5% and 67.6%, respectively. Deep learning in parathyroid scintigraphy can potentially assist medical experts in identifying abnormal findings.  
deep learning; explainable artificial intelligence; parathyroid glands; hyperparathyroidismicism 2022, 15, 455. [[https://doi.org/10.3390/a15120455](https://doi.org/10.3390/a15120455)]([https://doi.org/10.3390/a15120455](https://doi.org/10.3390/a15120455))
## 4 Simulation Results
### Number of Alive Nodes  
We study the number of alive nodes over several rounds. In Figure 4, when the initial energy is 0.25 J, we simulated the number of alive nodes for numerous rounds for GB-PEDAP, TBEEP, GSTEB, and PEDAP. The execution rounds of the first node death for GB-PEDAP, TBEEP, GSTEB, and PEDAP are 751, 572, 486, and 339, respectively. The execution rounds of the last node death for GB-PEDAP, TBEEP, GSTEB, and PEDAP are 1441, 1259, 1215, and 1092, respectively. Therefore, GB-PEDAP can extend the network's lifetime effectively.
# Implementation and Evaluation of a Serious Game for Working Memory Enhancement
## 2 Theoretical Background
### Research Goals  
As mentioned, the aim of this study is to examine the effectiveness of an SG in WM training. In other words, the primary research goal (RG1) is whether WM can be increased by following an intervention program with this particular SG. Since the game was designed and built from scratch, based on SG principles and WM training strategies, the main question is whether this design fulfilled the main goal.  
The second important research goal (RG2) is to investigate the usefulness and enjoyment of the game itself, since commitment and consistency in daily sessions were crucial factors. Additionally, the depth of the game, in other words the size of the grid and the maximum path length, needed to be defined in order to adjust the game and avoid both too easy and too difficult levels.
## 2 Results of Literature Analysis
### Systematic Process  
The literature search showed AM applications in various industries that include aviation [17; 24; 27; 34; 35; 54; 56; 57; 59], manufacturing and logistics [5; 9; 18; 20; 28; 38; 63], automotive [15; 25; 32; 39], military [7; 11; 22; 37; 55], medicine [16; 52], electronics [31; 64] and petroleum [41]. However, most of the literature provides research which is not related or specified to any particular industry sector [3; 6; 10; 14; 19; 23; 26; 29; 33; 36; 40; 42; 43; 45; 46; 48; 49; 50; 51; 53; 58; 59; 60; 61; 62; 65; 66]. The review shows that AM has significant impact and growth in SC networks, largely in the aerospace industry. Quantitative analysis refers to research articles involving various types of quantitative and analytical studies. Qualitative analysis refers to literature reviews, scenario analysis, empirical studies, surveys, and case studies related to AM and SCs. Scenario  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Authors** & & **Subject Areas** \\ \hline  & ✓ & Additive Manufacturing \\
[3; 5; 6; 9; 10; 11; 12; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36; 37; 38; 39; 40] & ✓ & Sparse Parts Management \\  & ✓ & Supply Chain \\ \hline  & ✓ & Additive Manufacturing \\
[8; 41; 42; 43; 44; 45; 46; 47; 48] & ✓ & Additive Manufacturing \\  & ✓ & Supply Chain \\ \hline  & ✓ & Additive Manufacturing \\
[4; 7; 49; 50; 51; 52; 53; 54; 55; 56; 57; 58] & ✓ & Additive Manufacturing \\  & ✓ & Sparse Parts Management \\ \hline  & [14; 59; 60; 61; 62] & ✓ & Additive Manufacturing \\ \hline \hline \end{tabular}
\end{table}
Table 1: References along with their dealt topics.  
Figure 1: Review process flowchart.  
analysis can be considered using a quantitative model. The review shows that the majority of the literature focuses on the quantitative analysis of SC in AM. The classification of the analysis method is given in Table 2. Empirical studies focus on examining the impact of the AM on the inventory performance and technical feasibility of digital spare parts, whereas scenario analysis aims to compare different SC configurations and models adopting AM. Quantitative analyses are mainly conducted in the contexts of resource allocation, inventory management, and dual sourcing.