esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Rotation (Figure 5, Table 4)
\begin{table}
\begin{tabular}{l l c c c c c c c c c} \hline \hline
**Resorption (mm)** & & **0** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** \\ \hline Rotation & Structure & & 799.80 & 803.35 & 806.90 & 810.45 & 814.00 & 833.10 & 852.20 & 871.30 & 890.40 \\
0.6 N/60 gf & \multirow{2}{*}{Tooth + bracket} & \multirow{2}{*}{a} & 88.60 & 156.90 & 225.20 & 293.50 & 361.80 & 427.17 & 492.54 & 557.91 & 623.30 \\ Stress & & & m & 88.60 & 224.70 & 360.84 & 496.96 & 633.10 & 675.15 & 717.20 & 759.25 & 801.30 \\ Amount & & & c & 177.70 & 291.55 & 405.40 & 519.25 & 633.10 & 652.90 & 672.70 & 692.50 & 712.30 \\ In each & & & C & 799.80 & 803.35 & 806.90 & 810.45 & 814.00 & 833.10 & 852.20 & 871.30 & 890.40 \\ Component & & & s1.00 & 144.52 & 208.00 & 271.56 & 335.10 & 389.20 & 443.30 & 497.40 & 551.50 \\  & & & m & 81.00 & 178.00 & 275.15 & 372.15 & 469.20 & 535.60 & 602.00 & 668.40 & 734.80 \\  & & & c & 195.20 & 297.20 & 399.30 & 501.20 & 603.20 & 605.47 & 607.75 & 610.02 & 612.30 \\  & & & C & 650.00 & 657.32 & 664.65 & 671.97 & 679.20 & 693.17 & 707.05 & 720.92 & 734.80 \\  & & & s & 71.84 & 136.25 & 200.06 & 265.07 & 329.48 & 376.99 & 425.40 & 472.01 & 519.52 \\  & & & m & 71.84 & 159.55 & 247.27 & 334.99 & 422.70 & 489.96 & 557.21 & 624.47 & 691.72 \\  & & & c & 169.46 & 267.67 & 365.89 & 464.10 & 562.31 & 566.96 & 569.62 & 573.27 & 576.92 \\  & & & C & 169.43 & 174.34 & 179.24 & 184.15 & 189.65 & 186.04 & 182.42 & 178.81 & 175.19 \\ Bone & & & s & 76.74 & 84.65 & 92.57 & 100.48 & 108.39 & 119.02 & 129.64 & 140.26 & 150.88 \\  & & & m & 76.74 & 84.65 & 92.57 & 100.48 & 108.39 & 119.02 & 129.64 & 140.26 & 150.88 \\  & & & c & 306.88 & 322.78 & 338.66 & 354.55 & 370.45 & 378.43 & 386.42 & 394.42 & 402.41 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Maximum stress average values (KPa) produced by orthodontic forces in tooth structure and dentine–cementum. Figure 5: Rotation—Tresca stress display in dental tissues subjected to 0.6 N for 0, 4, and 8 mm bone loss: (**A**)—dental tissues’ structure, (**B**)—tooth with bracket, (**C**)—tooth without bracket, (**D**)—dentine component, (**E**)—dental pulp and NVB, (**F**)—PDL, (**G**)—bone with alveolar socket.
# Impact of Climate Change on the Major Eco-Hydrological Parameters in the Dongting Lake Basin
## 3 Results and Discussion
### Contributions of Long-Term Changes to GPP and ET
#### 3.3.2 Extreme Climate Indices with Significant Impacts on GPP and ET  
TNn, TNx, TXn, TXx, DTR, TX10P, TX90P, TN10P, TN90P, RX1 day, and RX5 day were used as independent variables, while GPP was used as the dependent variable for stepwise regression analysis. Following the automatic identification of the model, TNx, DTR, TX90P, TN90P, RX1 day, and RX5 day were the only variables that remained in the final model, as shown in Figure 14. The R2 value was 0.906, indicating that TNx, DTR, TX90P, TN90P, RX1 day, and RX5 day could explain 90.6% of the variation in GPP. The model passed the F-test (F = 334.096, \(p\) = 0.000 < 0.05), indicating that the model was valid. The model equation was: GPP = \(-0.606+0.221\times\) TNx + \(0.174\times\) DTR \(-0.027\times\) TX90P \(-0.032\times\) TN90P + \(0.043\times\) RX1 day \(-0.012\times\) RX5 day.  
Research has shown that the net primary productivity of vegetation in the Dongting Lake Basin is strongly influenced by climate extremes, and there are differences in the effects of different extreme climate factors on the net primary productivity of vegetation [34]. The present study shows that the heat index in the extreme temperature index had a relatively large effect on GPP in the Dongting Lake Basin, which could be ascribed to the heat sensitivity of vegetation.  
ET, as the dependent variable, was subjected to stepwise regression analysis (Figure 15). After the automatic identification of the model, a total of three terms, TNn, TN10P, and RX5 day, were finally left in the model. The R2 value was 0.902, implying that TNn, TN10P, and RX5 day could explain 90.2% of the causes of change in ET. The model passed the F-test (F = 650.233, \(p\) = 0.000 < 0.05), indicating that the model was valid. The model equation was: ET = 14.756 + 2.462 \(\times\) TNn + 0.329 \(\times\) TN10P + 0.171 \(\times\) RX5 day.  
Figure 14: Stepwise regression model of extreme climate indices and GPP in Dongting Lake Basin. (** indicates significant correlation).  
During the dormant and early growing seasons, extreme cold weather prevents evaporation. However, during the peak growing season, excessive precipitation provides sufficient moisture for surface evaporation [35]. In the present study, the results show that the extreme precipitation index representing heavy rainfall had a positive effect on ET, which could be ascribed to moisture provided by extreme precipitation events. However, in the study of this article, it was shown that the increase in precipitation was unfavorable to the growth of ET. It indicated that in Dongting Lake Basin, sudden extreme rainfall events and increased precipitation in the average climate state have not same effect on ET. Sudden heavy rainfall events make regional moisture increase which is favorable for ET growth. However, prolonged rainfall made the regional air moisture higher, which was detrimental to the growth of ET.
# Polar Codes and a M-ary Modulation-Based OFDM-PLC System
## 5 Performance Simulation and Results Analysis  
In order to evaluate the proposed OFDM-PLC system based on polar code encoding, this section conducts simulations and presents simulation results. In the simulation, different noise models, and different high-order modulation methods are compared, and the transmission performance of polar codes in OFDM-PLC systems is studied in different code rates and noise scenarios.
## 3 Data and Methodology
### Topography and Bathymetry  
Except for the Sardinia case study, the information on topography used in this work comes from a very high-resolution DTM, available through the National Geoportal, whose realization has been funded by the Italian Ministry of Environment and Energy Security within the framework of an extraordinary plan for environmental remote sensing. The DTM is based on the results of a Lidar survey at 2 m of resolution performed in 2010, which covered the 1 st and 2 nd order river courses (hierarchical order given in the IGM river catalogue) of the continental Italian territory and coastal areas [100]. The model employs the WGS84--EPSG:4326 geographic projection and has been widely used in the past decade [33; 100; 101]. For the Sardinia test case, a DTM at 5 m of resolution was instead used, derived from a Lidar survey on the coastal zone and inland urban centres owned by the Regional Administration of Sardinia. In the present study, 33,102,778 topographic cells were used to analyse the vulnerability of coastal areas to RSLR. To better represent the underwater environment, a bathymetric dataset was also used as cartographic background. For most of the Italian Coast, bathymetric data were provided by the National Ministry for Environment and Energy Security (Table 1; [102]).  
The accuracy of the digital terrain model is a relevant issue as the planimetric accuracy of the LIDAR dataset is about \(\pm\)15 cm on the vertical component. Nevertheless, in situ surveys indicate that the real accuracy can be higher. An example is the work carried out in the Circeo National Park [103], where the calibration of the LiDAR dataset was performed using DGPS measurements. These studies suggest that accuracy is higher on the emerged beach and lower on the vegetated foredunes and on the submerged beach [93; 94].
# Self-Supervised Keypoint Learning for the Geometric Analysis of Road-Marking Templates
## 3 Methodology
### Self-Supervised Two-Stage Training Strategy  
We propose a novel two-stage self-supervised training strategy designed to effectively learn robust keypoints on structured templates without requiring manual annotation. This strategy utilizes the synthetic template dataset exclusively for both training stages. Starting with clean, geometry-based template images in the first stage enables the effective monitoring and adjustment of the learning process, using controllable factors, such as the template mask and known affine transform parameters, thereby providing confidence before attempting more complex scenarios.  
**Stage 1: Template Pre-training.** In this initial stage, the network is trained exclusively in the synthetic dataset of augmented template images described in the section on the synthetic template dataset. These templates are generated from vector graphics of the five road-marking classes considered, with heavy data augmentation applied using the Albumentations library [28]. The augmentation includes various photometric transformations (e.g., brightness, contrast, noise, random posterize, random solarize, random adjust sharpness, and random autocontrast) and geometric transformations (e.g., Elastic-Transform, rotation, scale, shear, and translate), with specific probabilities, as detailed in the experimental setup. Horizontal and vertical flips are specifically excluded due to the potential for changes in the class identity for certain road markings.  
The primary goal of this stage is to enable the network to learn the fundamental geometric structure of each template class and associate it with the \(K\) keypoints. Training is performed for 300 epochs, using the Adam optimizer [32] with a learning rate of 0.0001 and a batch size of 32. The total loss in Stage 1 is a weighted sum of the specific self-supervised loss components (\(L_{equiv}\) for rotation and affine transformations, \(L_{mask}\), \(L_{peak}\), and \(L_{repulsion}\)), as detailed in Sections 3.5 and 3.6.  
**Stage 2: Template Fine-tuning.** After pre-training on templates for 300 epochs, the model weights are used to initialize the network for the second stage. In this stage, the network is fine-tuned using the Adam optimizer [32], with a fixed low learning rate of 0.00001 and a batch size of 32. The training runs until the model converges or the loss stops improving significantly. The training is performed in the synthetic template dataset, using the same augmentation strategy as in Stage 1.  
This stage aims to refine the features learned in Stage 1, ensuring robustness to geometric variations present in the augmented templates and optimizing the keypoints for accurate alignment between the templates and their transformed counterparts. Crucially, losses requiring template-specific mask information, such as the inside mask loss (\(L_{mask}\)), are not used in this stage, focusing instead on geometric consistency and alignment utility.
# In-Vehicle Data for Predicting Road Conditions and Driving Style Using Machine Learning
## 9 Conclusions  
In this study, an ML system is proposed to solve three categorization problems; road surface conditions, road traffic conditions and driving style. Decision trees, random forests and SVM were implemented in Python. In-vehicle CAN data were used to train and test the algorithms.  
Random forests showed the best accuracy, precision, recall and F-score for all the classifications. The nature of the features and the amount of the training dataset is what gives an algorithm the advantage over another. From the results, we can conclude that random forests is the best algorithm to predict road surface conditions, road traffic conditions and driving style.  
Feature importance of the algorithms was analyzed using permutation feature importance algorithm. It was noticed that decision trees and random forests have almost the same feature importance rank. SVM showed different feature importance rank. For example, SVM showed a high rank for longitudinal acceleration for road surface detection, while decision trees and random forests showed a low rank for this feature. Features ranking can help eliminate the low-ranked features to reduce system complexity, while maintaining the ML system performance.  
Finally, this work shows that vehicle network carries rich information that can be analyzed and classified using ML to provide useful applications. In-vehicle data with traditional ML algorithms can provide a system with high accuracy and inexpensive implementation compared to more complex ML systems.  
[PERSON]-r. developed the detection system overview and defined the system elements. He also did the ML algorithm implementation in Python. [PERSON]-r. contributed to the related work and conclusion. [PERSON] contributed to data analysis, evaluation metrics, features ranking, discussion and general paper review and corrections. [PERSON] wrote the introduction, contributed to the related work section, results, discussion and conclusion. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The used dataset in this work is available on the Kaggle website under the title of Traffic, Driving style, road surface conditions: [[https://www.kaggle.com/datasets/gloseto/traffic-driving-style-road-surface-condition](https://www.kaggle.com/datasets/gloseto/traffic-driving-style-road-surface-condition)]([https://www.kaggle.com/datasets/gloseto/traffic-driving-style-road-surface-condition](https://www.kaggle.com/datasets/gloseto/traffic-driving-style-road-surface-condition)), The data was accessed on 18 July 2022.  
Our gratitude to the people who prepared the dataset and made it available in Kaggle.  
The authors declare no conflict of interest.
programs aimed to move the industry to an innovative format with a new, I4.0, technological order [6]. Both programs have two directions: \"digitalization of the existing economy\" and \"building the digital industry of the future\" [7]. Despite the existence of various support programs, the position of many enterprises does not change. According to the Ministry of Investment and Development survey, over 80% of manufacturing and 60% of the mining industry are at the level of the Second Industrial Revolution [8]. One of the reasons for this could be a misunderstanding of the goal and need of I4.0, which translates into a negative attitude towards the introduction of new technologies.  
This paper aims to look at I4.0 from the perspective of stakeholders. In addition, the study considers the impact of the COVID-19 pandemic, which exposed many hidden issues. Overall, the following research objectives were identified within the framework of the study:  
RO1: To analyze the current state of development of I4.0 in Kazakhstan using primary and secondary data sources;  
RO2: Through the conducted interviews and their further analysis, to identify the I4.0 awareness level of Kazakhstan companies, main critical factors, including challenges, that companies face when implementing I4.0 projects, required enablers such as technologies or other tools, established partnerships, and impact of I4.0 on the performance of the organization;  
RO3: To present future perspectives of stakeholders about I4.0 development in Kazakhstan and their companies in particular;  
RO4: Based on the findings from the literature review and conducted interviews (secondary and primary data) to perform a SWOT analysis (strength-weaknesses-opportunities-threats);  
RO5: As an outcome of the study, to provide recommendations for the successful I4.0 realization to the governmental organizations and to businesses.  
The paper is organized as follows: Section 2 presents an in-depth literature review to elaborate on I4.0 characteristics and the current situation in Kazakhstan. Moreover, Section 2 identifies the research gap. Section 3 introduces the methodology of the paper, including interview data collection and analysis procedures. Section 4 presents primary findings, where empirical findings from stakeholders' interviews will be classified and analyzed. Section 5 presents a discussion of the results, summarizing the findings of the study in the SWOT table, and providing recommendations. Finally, the conclusions section gives an overview of the completed ROs and presents the limitations of the research and future studies.
# A Review of Counter-UAS Technologies for Cooperative Defensive Teams of Drones
## 4 Neutralization Systems
### Electronic Neutralizers
#### 4.1.5 High-Power Electromagnetics and Lasers  
High-power electromagnetics can be used to create beams of electromagnetic energy over a broad spectrum of frequencies, in a narrow- or a wideband way, causing a range of temporary or permanent effects on electronics of targeted drones. According to the classification shown in [9] they can be categorized in two classes: narrowband electromagnetics (also referred as high-power microwaves, HPM), which include high power on a nearly single-tone frequency, and wideband electromagnetics, which have short pulses in the time domain and the energy distributed over a wide band. HMP requires very high power on a single frequency. Consequently, the determination of the effective frequency, which causes malfunctions in the drone to be attacked, is a key factor.  
High-power electromagnetics must be directed precisely towards the target to be effective, otherwise lethality is significantly reduced and some devices may still function after their use. Hence, the assessment of the neutralization effectiveness after a shot is also an issue. An HPM-type device manufactured by Raytheon is illustrated in [98].  
Lasers used as mitigators are capable of disabling or destroying an m-drone. As described in [9], an electrolaser ionizes the path to the drone and emits an electric current down the conductive track of the ionized plasma. Lasers can be categorized into low-power or high-power lasers [9]: low-power ones can be used to neutralize some sensitive sensors of the drone (for example, electro-optical sensors); high-power ones (operating at megawatts) can be a real weapon, able to burn part of the drone and destroy it. For both categories, accurate aiming is required, which implies sufficient time to track the target. Laser weaknesses are represented by the need for high technological development for their implementation (for the high-power lasers), sensitivity to weather conditions, accurate pointing and tracking time. A laser-based neutralizer manufactured by Boeing is illustrated in [99].  
Both high-power electromagnetics and high-power lasers are a strong interdiction measure, typically used in a military context. In a civil environment, they cannot be a viable option, especially in crowded areas, due the risk of the uncontrolled drone crashing or of triggering the deployment of dangerous payloads. Nor are they suitable for airports and the surrounding space, due to the collateral hazard to aviation operations [5].  
Finally, based on limited information available on the market about high-power electromagnetics and high-power lasers, we can easily deduce that they always show large size and weight and require a high power supply. Therefore, they can be mainly integrated into terrestrial platforms (typically they are mounted on tracks) and are not suitable for low-altitude platforms like mini drones.
## 4 Results and Analysis  
This section will discuss the results processed for the Ublox receivers [located at two locations and the Time Lab at FP2]. A difference-correction algorithm will be introduced to correct the errors of a Ublox receiver. For all the experiments conducted for this section, the Septentrio receiver is connected to the external time reference provided by the FP2 lab (as described in Section 2).
# Coordinating Entrainment Phenomena: Robot Conversation Strategy for Object Recognition
###### Abstract
The explicit request strategy focused on how to handle the ambiguity of references and decreased the ambiguity through additional information by users. In contrast, some previous studies have suggested a strategy that changes ambiguous reference behavior into a clear one without explicit request. One concept leading to the strategy is entrainment. In human-human communication, humans curb such vast lexical choices and elicit terms through the phenomenon of entrainment; when humans are talking, they tend to adjust with an addressee such styles as lexical expressions [10; 11; 12], syntax [13], body movement [14; 15; 16], etc., and this phenomenon is known as entrainment, also known as alignment. The finding of entrainment inspired researchers of automatic speech recognition (ASR) improve the systems performance [17; 18; 19; 20]. [PERSON] and [PERSON] proposed a strategy that changed ASR speaking styles and aligned users' speaking styles, shouting and hyper articulation that decreased ASR performance, with the desired one for the performance [19]. While such studies focused on speech information in the field of ASR, they did not treat gesture information, such as pointing gestures that is useful information  
Figure 1: A robot recognizes an object to which a user referred. to recognize an indicated object. It remains unknown how to incorporate lexical and gestural entrainment to the robot conversation strategy for the indicated object recognition, and whether the strategy improves the recognition performance. In this study, we utilize the stance of using entrainment and propose a novel robot conversation strategy incorporating entrainment phenomenon, we call the proposed strategy implicit entrainment, to improve the performance of indicated object recognition, which implicitly clarifies users' indications through human-robot interaction. Related studies using explicit request argued that explicit request was useful to decrease the ambiguity of referencing; however, it remains unknown which strategy, implicit entrainment and explicit request, more effectively clarifies the ambiguity of referencing and improves performance. Additionally, social robots should select a more appropriate strategy by considering not only the recognition performance but also the user's impression of the interaction. Even though the performance might be increased by either strategy, such performance improvement becomes non-appropriate if the users feel discomfort during interaction with a robot by the selected strategy and vice versa. In this study, we will compare the two types of strategies, implicit entrainment and explicit request, and discuss the better conversation strategies in the context of indicated object recognition.