esa-sceva/satcom-chunk-collection huggingface dataset:
# Recent Developments in Materials Science for the Conservation and Restoration of Historic Artifacts
## 3 Advanced Polymers for Artifact Stabilization
### Challenges in the Application of Advanced Polymers for Cultural Heritage Conservation  
Notwithstanding the previously presented advancements (harvesting properties such as versatility, reversibility, and non-toxicity), the implementation of advanced polymers in conservation is not without its hurdles. Ensuring chemical compatibility between polymers and varied artifact substrates remains a complex task. Incompatible systems can lead to boundary diffusion problems that compromise both the artifact's integrity and that of the polymer treatment itself. The use of synthetic polymers can also negatively influence the artifacts by supporting biodegradation processes. For example, [PERSON] al. [55], in a study performed on the Cathedral of Milan, revealed that synthetic coatings containing poly-laurylmethacrylate and poly-isobutylmethacrylate can induce damage to the monument, turning into an organic substrate favoring biological growth.  
At the same time, while short-term benefits are documented, the longevity of polymer treatments under constant environmental stress is yet to be universally assured. Thus, thorough field testing combined with laboratory assessments is necessary before widespread adoption. Furthermore, justifying investment in high-cost polymer technologies can be challenging for institutions with limited budgets. The cost-efficiency compared to traditional conservation materials remains a debated subject; however, long-term savings from reduced maintenance may offset initial expenditures.  
Looking forward, the trajectory of polymer technology in conservation anticipates an increased interplay between interdisciplinarity and cutting-edge innovation. Future advancements will likely see enhanced synthesis methods leading to hybrid materials that combine the best attributes of polymers with other conservation technologies such as nanomaterials or inorganic composites. The development of smart polymers that adjust characteristics based on external stimuli--such as temperature changes or humidity fluctuations--paves the way for autonomous conservation systems capable of adaptive responses. This could revolutionize how conservators manage artifacts under varying environmental conditions.  
Finally, expanding on educational initiatives through workshops held globally can facilitate broader dissemination of advanced polymer techniques in conservation. This ensures widespread adoption while optimizing conservation outcomes across diverse cultural heritage contexts.
# Entropy-Regularized Federated Optimization for Non-IID Data
## 6 Discussion
### Applicability and Limitations  
Our evaluation focused on label-skew scenarios (UNSW-NB15 and PneumoniaMNIST) and demonstrated ERFO's ability to mitigate statistical drift in such settings. Although ERFO operates purely on gradient-based updates and does not assume any particular data modality, its effectiveness on other federated learning problems remains to be validated. In future work we will evaluate ERFO on diverse benchmarks--e.g., regression tasks, reinforcement-learning federations, Shakespeare next-word prediction, and speech-recognition benchmarks--to confirm its broad applicability.  
It is also important to recognize that ERFO is not a panacea for all FL challenges. ERFO specifically addresses _statistical heterogeneity_ but does not resolve _system heterogeneity_ issues such as variable compute capabilities or communication latencies across clients. Moreover, while ERFO introduces only one additional hyperparameter, \(\lambda\), end users must still choose a suitable value. In our experiments we used  
\[\lambda_{0} =10^{-3}\text{ (decayed to 0 over 100 rounds, UNSW-NB15)},\] \[\lambda =5\times 10^{-4}\text{ (fixed, PneumoniaMNIST)}\]  
placing \(\lambda\) in the \(5\times 10^{-4}\)-\(10^{-3}\) range. As shown by our sensitivity study (Section 5.4), performance is robust throughout this interval; nonetheless, some tuning may be necessary for drastically different tasks or model scales.  
Finally, ERFO's entropy regularization cannot generate information that is entirely absent from any client's data. If a class or feature never appears on any participant, no amount of entropy promotion will enable the global model to learn it. Remedies such as data sharing, pre-training, or external auxiliary datasets lie outside the scope of ERFO. What ERFO does guarantee is that _any_ unique information present on a client is more uniformly incorporated into the global model, reducing the risk of being overwritten during aggregation, as we observed in improved minority-class detection on UNSW-NB15. However, it cannot fill knowledge gaps that do not exist in the federation's combined data.
## 4 Biomedical Applications of ML-Assisted SWIR Techniques
### Overcoming Technological Limitations
The quality of results in these modalities have a direct relation with the length of captured time trace OCT phase signal over each A-line. As such, these modalities typically suffer from a low acquisition rate, which renders them impractical for clinical use. In a study, using an ANN model, SNR of images acquired with PT-OCT using short acquisitions were improved to SNR values normally offered by very long acquisition times [70]. Another interesting potential of ML is in extracting additional information from OCT datasets that are not directly accessible via OCT raw images. In a relevant work, a DNN model employing a GAN architecture synthesized PS-OCT images from raw OCT intensity images, avoiding the need for additional hardware needed to construct a PS-OCT system [68]. Similar to OCT, ML has been shown to enhance performance in various other SWIR modalities. For example, DL classifiers were utilized to improve the detection accuracy of otoscopy for diagnosing middle ear effusions [115]. Middle ear effusions, commonly associated with ear infections, are a prevalent medical issue, particularly in pediatric patients. The traditional diagnostic process often involves invasive procedures, which can be uncomfortable and impractical, especially for children. Leveraging advanced DL models, the system analyzes SWIR images of the ear canal and tympan membrane, identifying specific features indicative of effusion with a specificity and sensitivity over 90%.
## 4 Experimental Results
### YOLOv8-Based Person and Object Detection Results  
ABODA, an open source dataset frequently used to detect abandoned objects, was passed through the keyframe detection phase and then labeled in the YOLO format for human and object detection. Using the labeled data, the YOLOv8 deep neural network was trained with nano-, small, medium, and large sub-architectures to detect humans and objects, such as bags, suitcases, etc., on video frames. Precision and recall mAP changes of the models are presented in the graph in Figure 6. Graphs showing the changes in the Mean Average Precision (mAP50, mAP50-95) metrics of the YOLOv8I model with the best performance on the test data throughout the training and the precision/recall curve are presented in Figure 7. The model outputs presented in Figure 8 show that YOLOv8 architecture produces successful results for object detection in various environments and lighting conditions. In the literature, most studies on abandoned object detection have focused only on object detection in datasets. In the method developed in this study, both  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Video** & \(\mathbf{N_{f}}\) & \(\mathbf{N_{k}}\) & \(\mathbf{N_{a}}\) & \(\mathbf{N_{m}}\) & **CR** & \begin{tabular}{c} **Calculation** \\ **Time (s)** \\ \end{tabular} & \begin{tabular}{c} **Keyframe** \\ **Precision** \\ \end{tabular} & \
\begin{tabular}{c} **Keyframe** \\ **Recall** \\ \end{tabular} &
\begin{tabular}{c} **Keyframe** \\ **F1-Score** \\ \end{tabular} \\ \hline V\_1 & 2189 & 1058 & 1000 & 60 & 51.7\% & 337 & 94.5\% & 94.3\% & 94.4\% \\ V\_2 & 2818 & 692 & 680 & 23 & 75.4\% & 393 & 98.3\% & 96.  
object (bag, suitcase, etc.) and human detection were performed. Real-time, versatile, abandoned object detection was performed by tracking human and object classes in consecutive frames. When the graphs are examined, it is seen that the human class performs at a value of 0.984 mAP, while the object class performs at a value of 0.966 mAP. The fact that the PR curve is above 0.90 shows that the model has a very low rate of producing false positives.  
Indoor/outdoor environments, crowds, different lighting conditions, and partial images pose challenges for object detection in video analysis. With deep learning models, dynamic and static objects can be detected accurately without any preprocessing, such as background subtraction. Due to its features, such as high accuracy, speed, and multiple object detection, the YOLOv8 deep neural network is used for person and object (bag, suitcase, etc.) detection in this study. The model output in which the human and object classes are detected from the sample image provided as the input to the YOLOv8 model is presented in Figure 9.  
Figure 8: Person—object detection model input and output.  
Figure 6: Performance comparison of YOLOv8 deep neural network sub-architectures.  
Figure 7: Mean average precision change curve—precision/recall curve.  
Figure 9: _Cont._
# An Analysis of the Short Utterance Problem for Speaker Characterization +
## 2 Short Utterances as Occluded Utterances
In the best scenario, both problems contain all possible information. Working with faces we have a complete view of the person of interest, including all the face elements (two eyes, the nose, the mouth, etc.). In speaker recognition we have complete information in an utterance that contains traces for any possible phoneme and its coarticulation. As long as the utterance gets longer and longer the complete information condition is more likely to be achieved. In this scenario performance has improved more and more as long as technologies have evolved. Now we focus on short utterances. These contain much less speech, even less than a second. A simple \"Yes/No\" reply to a question can constitute an utterance. Hence short utterances are very likely to lack of phonemes. In face recognition the equivalent scenario is the recognition of partial information, where some parts such as the mouth and nose are not visible. In both cases the missing information exists but it is unavailable. Faces always have a mouth and a nose although sometimes they can be occluded, for example, by a scarf. Regarding speaker characterization, speakers pronounce all the phonemes of a language while talking, although few of them can be missing in a specific utterance. In our hypothesis we also consider the influence of proportion. According to our analogy of face recognition, faces present a fixed set of elements ( ears, nose, mouth, etc.) with a constrained size and located in the face in specific areas. These restrictions are always the same, regardless of the person nor any occlusion. In speaker characterization the situation is slightly different. When utterances get long enough the language imposes restrictions in the phoneme distribution. These restrictions lead to a reference phoneme distribution. The longer the utterance the more its phoneme distribution tends to the reference distribution. However, short utterances contain a much shorter message and thus its phoneme distribution can be severely distorted. In this distortion we must take into account both the missing phonemes and those present but conditioned to the message in the utterance. This distortion may lead to utterances from the same speaker with different dominant phonemes, hence complicating the evaluation. Consequently, the short utterance problem can be interpreted as an occlusion from a complete information scenario. This occlusion may be complete, where long utterances lack from certain phonemes, or partial, in which utterances have their phonemes seen in very different proportions with respect to their counterparts. The available information about the occlusion is important to be aware of. During evaluation we compare how the two speakers pronounce all the phonemes, available or not, so unbalanced information can lead to an unfair comparison.
## 2 System Model
### Beam Hopping Time Plan  
As stated above, the beam hopping time plan (BHTP) [33], which is also named as the beam hopping illumination pattern [2], determines dwell time and illumination order of each beam. Obviously, it is the most important system configuration since beam hopping technique is mainly based on time slicing. Figure 2 demonstrates an example of the beam hopping time plan for seven beams in a cluster.  
As shown in Figure 2, the BHTP consists of multiple beam hopping periods \(\mathrm{Tw}\), which are periodically repeated. The duration of one time slot is \(\mathrm{Ts}\), which is the smallest time resource scale. Thus, \(\mathrm{Tw}=\mathrm{W}\times\mathrm{Ts}\), where \(\mathrm{W}\) represents the number of time slot in one beam hopping period. Moreover, dwell time represents the length of continuous time slots allocated to a beam.
# An Organizational Perspective on Robotic Process Automation Adoption and Usage Factors
## 5 Conclusions, Contributions, Limitations, and Further Research
### Contributions  
The results of the present study provide theoretical and practical contributions. For scholars, it provides a tested and reliable model of the propensity for the adoption and use of RPA, identifying significant antecedents such as Compatibility or Relative Advantage. Thus, the research confirms the utility of the TOE and DOI frameworks for organizational-level studies. The combination of these frameworks can be valuable for other researchers intending to analyze the use of other technologies within organizations.  
Future investigations could consider broadening research to areas beyond the current scope assessment of adoption and usage to explore the impact it has on value creation and company performance.  
For researchers, this article serves as an example of conducting research with a mixed methods approach, considering the combination of a qualitative study based on interviews with experts and a quantitative study that collected data through questionnaires. The integration of approaches provides a more comprehensive and reliable development of theoretical knowledge.  
To ensure successful RPA implementation, managers should consider factors such as company size, industry sector, and level of digital maturity.  
Based on insights from the literature review, the exploratory study, and the confirmatory study, several recommendations were identified: assess the suitability of RPA for the company's processes, i.e., before adopting automation, internal processes should be mapped; ensure compatibility with existing infrastructure and systems; consider employee impact and address resistance to change; define clear metrics to measure RPA success; start with pilot tests before full-scale automation; address regulatory and data security concerns; and ensure continuous support and ongoing optimization.  
RPA adoption should be strategically planned to achieve operational efficiency and maximize automation benefits across different organizational settings.
###### Abstract  
This study aimed to investigate whether running kinematics can be accurately estimated through an artificial neural network (ANN) model containing GPS-based accelerometer variables and anthropometric data. Thirteen male participants with extensive running experience completed treadmill running trials at several speeds. Participants wore a GPS device containing a triaxial accelerometer, and running kinematics were captured by an 18-camera motion capture system for each trial. Multiple multilayer perceptron neural network models were constructed to estimate participants' 3D running kinematics. The models consisted of the following input variables: 3D peak accelerometer acceleration during foot stance (g), stance time (s), running speed (km/h), participant height (cm), leg length (cm), and mass (kg). [PERSON]'s correlation coefficient (r), root mean squared error (RMSE), and relative root mean squared error (rRMSE) showed that ANN models provide accurate estimations of joint/segment angles (mean rRMSE = \(13.0\pm 4.3\%\)) and peak segment velocities (mean rRMSE = \(22.1\pm 14.7\%\)) at key gait phases across foot stance. The highest accuracies were achieved for flexion/extension angles of the thorax, pelvis, and hip, and peak thigh flexion/extension and vertical velocities (rRMSE \(<10\%\)). The current findings offer sports science and medical practitioners working with this data a method of conducting field-based analyses of running kinematics using a single IMU.  
artificial intelligence; accelerometer/s; GPS; IMU; kinematics +
Footnote †: journal: Applied Sciences  
Article
# Multi-Directional Displacement Threshold Energy and Crystal Irradiation Damage Model
## 2 Displacement Threshold
The displacement threshold energy for a given material is not fixed and generally depends on both the direction of atomic displacement and lattice vibrations. Molecular dynamics simulations or first-principles modeling combined with computer simulations enable the determination of displacement threshold energies for different crystal structures and displacement directions. [PERSON] et al., employed molecular dynamics simulations to obtain carbon-atom displacement threshold energies in carbon nanotubes under various displacement directions which is shown in Figure 1 [22]. The threshold energy varies significantly with changes in the displacement direction, ranging from a minimum of 20 eV to a maximum exceeding 100 eV. A higher threshold energy indicates greater difficulty in displacing an atom from that particular direction. In polycrystalline structures, where the crystal orientation distribution is random for each microcrystal, the average displacement threshold energy serves as the boundary.
# Analysis of Fatigue Performance of Spot-Welded Steel T-Profiles Under Cyclic Torsional Loading
## 3 Results and Discussion
### Validation of the Numerical Model  
For the validation of the developed XFEM model, the experimental data presented in ([PERSON], 2005 [14]) were used. The spot-welded assembly was investigated in two different configurations. In the first configuration, the nugget diameter and part thicknesses were 5.5 mm and 1.5 mm, respectively, while in the second configuration, they were 6.5 mm and 2.0 mm. A 480 Nm. cyclic torsional loading was applied to the assembly. Table 2 displays the \(N_{f}\) values obtained through numerical analysis and experimental observation. The extension of \(t\) from 1.5 mm to 2.0 mm led to an increase in failure cycles from 37,290 to 96,000 experimentally; however, these values were 31,967 and 87,446 cycles, respectively, in numerical analysis. A satisfactory comprehension was achieved. Numerical simulations indicate that the initial appearance and propagation of the crack, leading to the eventual collapse of the assembly, occurred in Part 1 around the spot weld connecting Parts 1 and 2 on the lower left side (see Figure 5). This is also in line with the experimentally obtained crack propagation as shown in Figure 6.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & [13,14] & & & [15] & \\ \hline \(E\) **(MPa)** & \(v\) & **TS (MPa)** & \(G_{1c}\), \(G_{2c}\), \(G_{3c}\) **(N/m)** & \(c_{3}\) & \(c_{4}\) \\ \hline
210,000 & 0.3 & 326 & 6500 & \(7.5\times 10^{-8}\) & 1.75 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The material parameters for EN 10,130 low-carbon steel used in the simulations.  
Figure 4: Deformed shape of the spot-welded assembly when \(T\) **(left)** and \(-T\) **(right)** was applied during cyclic torsional loading (for improved visual aid, the deformation was scaled ten times).  
Figure 5: The shape of evolved cracks in the assembly for \(t\) = 1.5 and 2.0 mm upon \(N_{f}\) is reached for each configuration.  
Figure 6: Experimentally observed crack pattern around the spot welds [14] (view from the bottom side of the assembly).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & & & **Experiments [14]** & **XFEM** \\ \hline \(t\) & \(D\) & \(T\) & & \(N_{f}\) & \\ \hline
1.5 mm & 5.5 mm & \(\pm\)480 Nm & 37,290 & 31,967 \\ \hline
2.0 mm & 6.5 mm & & 96,000 & 87,446 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Experimentally and numerically obtained failure cycles for two different cases.
# Detection of Yunnan Pine Shoot Beetle Stress Using UAV-Based Thermal Imagery and LiDAR
## 2 Materials and Methods
### LiDAR Data  
#### 2.3.1 Individual Tree Segmentation from LiDAR  
The UAV-based LiDAR data were obtained from the core area of the affected Yunnan pine forests (including the two plots) using a UAV-mounted system (LiAir 200, GreenVlley Inc., Beijing, China) with a 40-channel Pandar40 laser sensor. The average flight altitude was 70 m above ground level, with a pulse rate of 10 HZ and a maximum viewing angle of 10\({}^{\circ}\) (from channels 2 to 30). The georeferenced coordinate of the LiDAR data was generated in the UTM 50 N zone using Word Geodetic System data. The LiDAR cloud points were classified into ground and above-ground points using the improved progressive TIN densification algorithm in the LiDAR360 software package.  
The canopy height model with a resolution of 0.5 m was derived from the classified point cloud using a point cloud segmentation (PCS) algorithm, and the accuracy assessment of canopu segmentation was evaluated using three parameters, which included precision (p), recall (r), and F-score (F) [30]. The structural parameters of the tree canopy (e.g., locations, heights, crown areas, and volumes) were extracted from the point clouds of  
Figure 2: The correction of the thermal infrared (TIR) data: (**a**) the original TIR gray image with a digital number value, and (**b**) the TIR image in a plot after calibration and emissivity correction.  
individual trees (Figure 3). The TIR images and the LiDAR canopy areas were registered using visible images via ground control points, which were measured by the RTK device.  
\[p=TP/(TP+FP), \tag{3}\]  
\[r=TP/(TP+FN), \tag{4}\]  
\[F=2\times\frac{p\times r}{p+r}, \tag{5}\]  
where TP is the number of detected trees in a plot; FP is the number of trees that are falsely detected; FN is the number of trees that are omitted; and p, r, and F refer to the detection accuracy, the detection rate, and the overall accuracy, respectively.