esa-sceva/satcom-chunk-collection huggingface dataset:
# Communication Architecture in Mixed-Reality Simulations of Unmanned Systems
One of the biggest challenges of integrating SWEs and HWEs in an MR simulation is to handle differences in the message transfer among the entities while preserving a high fidelity of the simulations (see Figure 1). The focus of this work is on designing communication architecture that would serve as a communication middleware for MR simulations and allow broadcast and directed message exchange between SWEs and HWEs while providing simultaneous wireless network simulation and movement of the entities in real time. The communication architecture must provide common addressing and message routing processes between SWEs and HWEs, as well as suitable models of media utilization and communication reachability for the MR world. In this paper, all of these problems are addressed within the context of an unmanned aerial system (UAS) as a reference system and practical deployment; however, the presented approach can be applied to other vehicular systems with direct communication among vehicles (vehicle-to-vehicle, V2V). The communication architecture presented in this work has been utilized and verified during the development of different aviation applications within the projects funded by the Czech Ministry of Defence, the U.S. Air Force Research Lab., and the U.S. Army CERDEC [15; 16; 17]. The architecture has been used as a part of an MR simulation framework for the incremental development of complex systems and their verification in high fidelity testing. The rest of this paper is organized as follows. After a discussion of related work, mechanisms of message exchange in MR systems are described in Section 3. Based on them, requirements for MR communication architecture are stated in Section 4. Then, in Section 5, a communication architecture for the MR simulations is proposed. Aspects of the fidelity of this architecture together with the precise communication modeling are addressed in Section 6. In Section 7, a set of experiments are presented to show a similarity in characteristics of the fully deployed hardware communication and  
Figure 1: Visualization of the problem of maintaining high fidelity in communication system models in mixed-reality (MR) simulations. the communication of entities in the MR simulation using the proposed architecture and models. Section 9 concludes the paper.
## 3 Results  
### Three-Ply Phase  
Results of the three-ply phase showed that for all participants, the force measured by the sensor placed at the distal location decreased as the volume increased. This observation is consistent with the expected pattern of variations in the interface force as a result of fluctuations in the volume of the residual limb. Normalized means of peaks of force obtained at the distal location for the four participants of the three-ply phase are shown in Figure 3.  
In two other considered locations, namely the lateral distal and the anterior distal locations, the force measured by sensors followed the expected pattern in all participants except in participant 2. The normalized average peak of force at the medial distal location did not follow the expected pattern in either participant 2 or participant 3. Moving forward with this study, we chose to use the distal location as it was consistent in all participants.  
Figure 2: The data analysis process. The data analysis process consisted of filtering raw force sensitive resistor (FSR) signals, finding peaks of the filtered signals, averaging peak values, converting average values to force, and normalizing the average peaks of force.  
To determine if the normalized mean of distal force peaks changed significantly in response to variations in the volume of the limb, mixed-model analysis was performed on the results of the three-ply phase. In this analysis, the null hypothesis was considered to be the normalized mean of distal force peaks remaining the same in different sock ply conditions. As a result, rejecting the null hypothesis would determine that the normalized mean of distal force peaks changed in response to variations in the volume of the limb, thus proving that there is a relationship between distal force and volume of the limb. This confirms that volume variations can be determined by monitoring the distal force.  
Results of the Shapiro-Wilk test determined normality of residuals (less plies: \(p=0.1415\) and extra plies: \(p=0.8833\)) confirming that assumption of the statistical tests used in this study were not violated. It should be noted that all values for the normal plies condition were similar (100%) due to the normalization step. Results of mixed model analysis rejected the null hypothesis (F(2,6) = 5.1742, \(p=0.0494\)), proving the feasibility of our proposed method.  
Results of the two phases are also shown in Figure 4. This figure shows adjusted means and standard errors as predicted by the model fit to data from all individuals, as repeated measures, of each phase during mixed model analysis [27]. Using the adjusted means of the different sock ply conditions was deemed appropriate for quantitative representation of the results of this study since it isolated the effect of sock plies by eliminating the effect of individual-specific variables, such as body weight and socket fit.  
Figure 3: Results of the three-ply phase. Average peaks of normalized force in the distal location of the prosthetic sockets of the four participants in the three-ply phase.
# Morphological Foot Model for Temperature Pattern Analysis Proposed for Diabetic Foot Disorders
## 3 Results and Discussion
### Foot Models for Variable Range of Shoe Sizes  
The robustness of the final model was tested by creating additional models in which the available shoe sizes were grouped in alternative ranges. First, the most common shoe size was considered, being 37 and 43 for females and males, respectively. Then, a close range was established selecting one size above and below, that is, the female ranged from 36 to 38, whereas the male was between 42 and 44. The overlap measures are displayed in Table 4.  
As can be seen, the performance of these models is quite similar to the final model extracted previously that employed all the available shoe sizes. In fact, according to [PERSON]'s test, no significant differences were detected among the overlap measures corresponding to these female models for any foot. Similarly, for the male models, no significant differences were found in the overlap measures for the left foot. However, regarding the right foot, _DICE_ and _IoU_ were significantly higher for the male model, extracted for a shoe size of 43, as compared to the close range model, extracted using show sizes between 42 and 44 (_p_ < 0.05). Additionally, sensitivity was significantly higher for the model extracted with all shoe sizes in comparison to the one extracted using a close range (_p_ < 0.01). Similarly, the model extracted for a single shoe size presented a higher sensitivity compared to the model extracted using a close range (_p_ < 0.05). _FP_ presented the opposite trend to the one observed for the sensitivity, in which values were higher for the close range model as compared to the single shoe size model (_p_ < 0.05) and the model extracted using all shoe sizes (_p_ < 0.01).  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Foot Model** & **Foot** & **DICE** & **IoU** & **Specificity** & **Sensitivity** & **False Positives** & **False Negatives** \\ \hline Female & L & 97.56 \(\pm\) 0.60 & 95.25 \(\pm\) 1.15 & 99.68 \(\pm\) 0.21 & 97.08 \(\pm\) 0.53 & 2.93 \(\pm\) 0.53 & 0.32 \(\pm\) 0.21 \\ (36–40) & R & 97.54 \(\pm\) 0.48 & 95.21 \(\pm\) 0.92 & 99.80 \(\pm\) 0.08 & 96.55 \(\pm\) 0.98 & 3.45 \(\pm\) 0.98 & 0.20 \(\pm\) 0.08 \\ \hline Male & L & 97.31 \(\pm\) 0.81 & 94.77 \(\pm\) 1.51 & 99.65 \(\pm\) 0.16 & 96.74 \(\pm\) 1.48 & 3.26 \(\pm\) 1.48 & 0.35 \(\pm\) 0.16 \\ (41–45) & R & 97.63 \(\pm\) 0.62 & 95.37 \(\pm\) 1.17 & 99.67 \(\pm\) 0.18 & 97.45 \(\pm\) 0.90 & 2.56 \(\pm\) 0.90 & 0.33 \(\pm\) 0.18 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Overlap measures for the final models, associated with the right and left feet, for female and male groups of subjects with European foot sizes ranging between 36 and 40 and 41 and 45, respectively. Average values and their corresponding standard deviations are expressed as percentages.  
Figure 3: Masks corresponding to the model extracted using all the available female (**a**) and male (**b**) European shoe sizes.
# Named Entity Recognition for Sensitive Data Discovery in Portuguese
## 1 Introduction  
The amount of sensitive information available on the web, as well as in companies and other industries is growing, which consequently urges for a thriving need to filter and process information, so that it can be used for specific purposes and to be able to protect sensitive information and personal data. The vast majority of existing textual data is unstructured, requiring even more processing efforts to extract reliable information [1]. The emergence of obligations for processing unstructured data has been increasing the focus on the advancement of Named Entity Recognition (NER) [2]. However, for languages with fewer resources, such as the Portuguese language, it is still a challenge and the results are still quite inferior when compared to English, for example. This work strives to evaluate these problems focusing on the research, implementation, and evaluation of NER systems for Portuguese, focusing on Sensitive and Personal Data, with the intent to build a reliable solution that can be used by organizations in a real scenario.  
The proposed work aims to transform many of the processes that can be carried out manually and with high cost into automatic processes that can carry out efficiently. It allows organizations tohave confidence in the security of their data and comply with protocols and regulations imposed, as is the case of the General Data Protection Regulation (GDPR) [3]. The main feature of this work is the development of a module based on NLP techniques, focused on named entity recognition for the sensitive data covered by GDPR in unstructured textual documents.  
The process of recognizing sensitive data is still a task that is often carried out manually, respecting certain rules, which implies additional time spent and higher chance for errors and failures. Due to this, there has been a great advance in the application of NLP tasks in the real world. However, despite the advances and the encouraging progress in NER, most of the real systems developed base their classification on the document's metadata instead of classifying the content [4]. If we manage to overcome these limitations, the practical applications in other projects besides DataSense would be countless, and to several markets.  
The NER Component required the development of a system of sensitive information discovery in text documents. The set of textual information to be processed by the component consists of legal documents, contracts, curricula, minutes, etc. For this reason, this work focused on the study of the NER task for sensitive data, as well as in all involving natural language processing tasks, more specifically in text preprocessing techniques, such as Part-of-Speech Tagging. With this study, we have achieved results that allow us to integrate the developed work and the NER Component into a real-world product, the DataSense Project.
## 2 Related Work
### Gaps and Contribution  
The diagnosis of faults for CNC linear axis components is a research domain filled with multiple opportunities to advance the CBM field. The research article was motivated by the following identified gaps:  
* Previous signal segmentation analysis methods were used to improve signal quality and detect operational state changes. However, its application to detect and localize faults such as misalignments is not well explored.
* Misalignment detection studies focused on analyzing the full stroke data and detecting if a misalignment was present. Regardless, these studies did not locate where the misalignment was present in the stroke by analyzing both stroke directions.
* Health monitoring studies in linear axis components, especially the ones reviewed for misalignment, lacked a discussion of how a repair state varies from the baseline state.  
The main contribution from the research work is to provide a CBM tool that applies signal segmentation in vibration monitoring to localize a misalignment and evaluate its effect on the linear axis in both stroke directions. Additionally, to address the identified gaps and demonstrate the capability of the proposed segmentation technique, a right horizontal misalignment was introduced to the linear guide rail and two studies were conducted from it. The studies consisted of applying the segmentation technique and analyzing the vibration signals from both stroke directions (forward and reverse) independently. Ultimately, to build an understanding of how the misalignment changes the vibration signal based on the stroke's direction:  
1. Study A consisted of applying the segmentation technique and analyzing the vibration data from the stroke's forward motion.
2. Study B was conducted to comparatively analyze the collected vibration data after applying the segmentation technique for the reverse motion stroke.  
Lastly, both studies were further enhanced by conducting an analysis on the repair state condition of the linear guide rail after the misalignment. The analysis at the repair state primarily focused on understanding through segmentation if there were any changes to the system.
# An Improved Hybrid Transfer Learning-Based Deep Learning Model for PM2.5 Concentration Prediction
## 2 Proposed Model
### Spatial-Temporal Features Extraction Based on DSTP  
The center site is the site to be predicted, and the best matching site of the center site is determined by the exogenous series correlation method. The main reason to use this DSTP model is that a stable attention weight can be obtained by the DSTP model, which uses a dual-stage attention mechanism in the encoder stage. Thus, temporal and spatial features can be extracted simultaneously [24].  
Given all sites' data, each site contains \(n\) exogenous series and a target series (series to be predicted). Within the window size \(S_{W}\) of the central site collection, the \(k\)-th exogenous series is represented by:  
\[x^{k}=(x_{1}^{k},x_{2}^{k},\dots,x_{S_{W}}^{k})^{\text{T}}\in\mathbb{R}^{S_{W }}. \tag{4}\]  
All exogenous series within window size \(S_{W}\) are represented by:  
\[X=(x_{1},x_{2},\dots,x_{S_{W}})^{\text{T}}\in\mathbb{R}^{n\times S_{W}}. \tag{5}\]  
The target series is represented by:  
\[Y=(y_{1},y_{2},\dots,y_{S_{W}})^{\text{T}}\in\mathbb{R}^{S_{W}}. \tag{6}\]  
In this study, the encoder adopts a two-stage attention mechanism, which aims to study the spatial correlation between the exogenous series of the central site collection, its matching sites' exogenous series and target series. Specifically, the spatial correlation between the exogenous series of the central site collection and the exogenous series of the matching sites is studied in the first stage of attention. In the second stage of attention, the weighted features are studied again, that is, the spatial correlation among the exogenous series of the central site collection, its target series and matching sites' target series. Thus, the two-stage spatial mechanism ensures that the learned spatial correlations are stable. The decoder is a temporal attention mechanism designed to learn the temporal correlation among the encoder hidden state, the target series of the central site collection, and the target series of the matching site.
## 3 Detection of Damage Occurrence
### Time Domain Methods
#### 3.1.1 Output-Only Methods  
In order to identify the structure dynamic parameters from the time domain data, at the early stage several techniques were proposed in the time domain on the basis of the experimentally measured responses of the structure: the [PERSON] method [17], the random decrement method [18], the least-squares complex exponential method [19], and the eigensystem realization algorithm [20]. [PERSON] and [PERSON] [21] used the beat phenomenon to characterize small differences. [PERSON] and [PERSON] [22] reviewed the stochastic system identification methods used to estimate the modal parameters of vibrating structures in operational conditions, including the covariance-drive stochastic subspace identification (SSI-COV) and data-driven stochastic subspace identification (SSI-DATA) methods. Later, [PERSON] et al. [23] compared the SSI-COV with the second-order blind identification (SOBI) approaches to identify the dynamic characteristics of the bridge directly from the vibration measurement. [PERSON] et al. [24] also introduced the recursive stochastic subspace identification (RSSI) technique to identify the natural frequencies and [PERSON] et al. [25] employed the kernel density estimation (KDE) to characterize the correlation between the vibration signatures acquired in the time domain. These methods eliminate the need of measuring or identifying inputs, and thus are valuable tools for identifying modal parameters from time domain measurements. The extracted vibration features and modal properties can then be used for detecting damage occurrence and/or location by comparing the identified modal properties with the original values. Meanwhile, there are methods that directly detect damage based on the measured date only, for example, using the moving time window technique to trace abrupt changes in the structural parameters [26], detecting novelties in the recorded seismic response by using expansion in a basis of bi-orthogonal wavelets [27], detecting damage based on changes in wave travel times between selected sections of a structure [28; 29].
# Integrating Dilated Convolution into DenseLSTM for Audio Source Separation
## 2 Related Works  
Gated residual network (GRN), which is a CNN-based architecture, consists of a frequency-dilated module that extends the receptive field on the frequency axis, a time-dilated module that increases the receptive field on the time axis, and, finally, a prediction module that outputs a mask of the same size as the input [35]. In particular, the GRN architecture is a deeper network that has a wide receptive field along the time axis by arranging several time-dilated modules, and this architecture has shown better performance than vanilla RNN and BLSTM based on a fully connected layer in speech enhancement [35].  
Recently, the multi-scale multi-band DenseNet (MMDenseNet) architecture using densely connected convolutional networks (DenseNet) [36] showed good performance in music source separation [25]. In the DenseNet architecture, since the final output feature map includes the input and the output feature map of each layer, it has the advantage that a lot of information is included in the feed-forward process. In addition, there is an advantage that the gradient vanishing problem can be untangled because an error is simultaneously transmitted to each layer without passing through nodes in the error backpropagation process [36]. MMDenseNet has a multi-scale DenseNet (MDenseNet) architecture in parallel for each divided frequency band. The MDenseNet architecture has a process of obtaining low-dimensional features by repeating a dense block of DenseNet and down-sampling, and then restoring the original size by repeating dense block and up-sampling. For simplicity, the process of representing the input data as a low-dimensional feature is called an encoding process, and the process of restoring the low-dimensional feature obtained in the encoding process to its original size is called a decoding process. Recently, the best performance of the music source separation task is MMDenseNet with LSTM (MMDenseLSTM) [31], which combines BLSTM with CNN-based MMDenseNet [25]. MMDenseLSTM is a CRNN architecture in which a BLSTM architecture is added after several dense block of MMDenseNet. Similar to speech enhancement tasks, the CRNN architecture is currently the best performing architecture.  
In the time domain, end-to-end methods not relying on spectrogram have been successfully applied to audio source separation [37; 38]. There are two drawbacks when the spectrogram is used as input. First, it consumes time to extract the spectrogram and restore it to a signal. Second, the phase information of the mixture used in the restoration process causes distortion. Recently, one of the end-to-end methods, Wave-U-Net, actually did not show good performance in music signal separation [37], whereas the other Conv-TasNet showed very good performance in the speaker separation task [38]. From these previous study results, we took the approach of using spectrogram as and input and will pursue the end-to-end methods in the future research work.
## 3 Results and Discussion
### Thermal Properties  
Figure 5 illustrates DSC thermograms of different pure and composite nanofibers. In Figure 5a thermograms of PLA series of samples are shown. It can be seen that the addition of oils has an influence on Tg compared to pure PLA fibers, increasing it for 4-6 \({}^{\circ}\)C. For crystallization temperature, a slight decrease was recorded in the range of 2-3 \({}^{\circ}\)C, while melting temperatures were not affected by the addition of oils into the PLA fibers. This behavior is in the correspondence with the morphological characteristics of the samples and phenomenon of immiscibility of PLA with oils added. Phase separation of amorphous and crystalline phase occurred within PLA-based fibers, which had a stronger effect on the amorphous domains.  
Figure 5: DSC thermograms of (**a**) PLA-based and (**b**) PVP-based series of samples.  
\begin{table}
\begin{tabular}{c c c} \hline
**Sample Name** & **Maximum Stress (N/mm\({}^{2}\))** & **Elongation at Break (\%)** \\ \hline PLA & 3.82 & 15.97 \\ PVP & 5.77 & 8.77 \\ PLA-5 SB & 6.44 & 31.81 \\ PLA-10 SB & 6.33 & 61.05 \\ PVP-5 SB & 8.61 & 18.39 \\ PVP-10 SB & 3.49 & 13.88 \\ PLA-5 PSO & 5.69 & 33.42 \\ PLA-10 PSO & 6.78 & 44.36 \\ PVP-5 PSO & 9.81 & 17.67 \\ PVP-10 PSO & 1.61 & 7.44 \\ PLA-5 PSOSEP & 3.12 & 10.23 \\ PVP-5 PSOSEP & 8.77 & 26.07 \\ PVP-10 EP & 1.23 & 6.46 \\ \hline \end{tabular}
\end{table}
Table 3: Mechanical Properties of Prepared Samples.  
On thermal properties of PVP fibers (Figure 5b), the addition of oils had plasticizing effect, which is evidenced by the decrease of Tg values from 2 to 5 \({}^{\circ}\)C. Because of the properties of PVP, which is water soluble polymer, fibers absorbed some water during the time of the storage. Endo peak with low intensity was detected at around 80 \({}^{\circ}\)C which corresponds to water evaporation.  
Thermal properties for all prepared samples are given in the Table 4.
# Evaluation of Waste Express Bag as a Novel Bitumen Modifier
## Discussion
### Workability  
The workability of asphalt mixtures is a very broad concept concerning production, transport, laying, and compaction of asphalt mixtures. The workability of bitumen is generally evaluated by Rotational-viscosity (RV). Figure 4 presents the variation in the rotational viscosity of WEB-modified bitumen binders at different dosages and constant temperatures. This figure shows that the WEB modifier can increase the viscosity of the bitumen. Similar to the results of the softening point and rutting factors, the rotational viscosity increased steadily before 6% and showed great enhancement after 8%. The modified binder exhibited higher viscosity with increasing WEB dosage at 135 \({}^{\circ}\)C.