esa-sceva/satcom-chunk-collection huggingface dataset:
# The Vision-Based Data Reader in IoT System for Smart Factory
## 2 Materials and Methods
### Digit Recognition  
Digital digits can be used to represent information or the condition of electricity, water, gas, etc., in factories, houses, and other places. Digital meters or panels with digit numbers are installed to monitor the real-time conditions. Generally, the digit number reading is obtained by operators. The operator watches the panels and writes down the digit number. Unfortunately, reading digits from the panels of meters and storing information by operators are time-consuming tasks and have very high chances of resulting in privacy, integrity, and authentication problems. Automatic reading by IoT supported with computer vision technology can be implemented with a camera attached to capture images of the screen of the control panels and a computation model can be used to detect text in images. It had been addressed with three main steps to accomplish the goal. It included screen detection, digit area segmentation, and digit number recognition [30]. Screen detection is a significant task to crop images of the required region of interest (ROI) from the panel. [PERSON] and [PERSON] [31] proposed an appearance feature-based method to perform handwritten digit recognition. The appearance features were generated by the histogram of oriented gradients (HOGs). [PERSON] et al. proposed a maximally stable extremal region (MSER) approach using blob detection to extract character candidates from a natural scene image with text [32]. Afterward, the HOG feature was also used to perform Chinese word detection in an image by support vector machines (SVMs) [33].  
Recently, some studies had used conventional datasets of digit numbers to train the deep learning algorithms for text recognition tasks [20; 21]. Some previous research results were only focused on analog meters. YOLO v3 was used to detect the ROI, and a deep learning model based on the VGG network performed the digit number recognition [22]. Another deep learning model based on faster R-CNN is used to extract and recognize digits [23]. Most of the previous results were only focused on analog meters but not on a PLC screen. For R-CNN, it really needs high computing power like GPU to train the object detection model. These techniques can successfully detect and recognize digits under some special cases. However, these models are quite expensive and consume high power, such that it makes them almost impossible to run applications on using edge devices for solving various real problems. For example, dynamic training has to be performed on edge devices for solving various real problems in factories. The proposed approach focused on an FPGA development board by Intel. DE10 Lite was used as the hardware platform of the edge devices for training and testing processes.
## 4 Experimental Evaluation  
### Considered Scenarios  
In this paper, we make the common assumption that sensor nodes (IP-based cameras) are static elements. As in our problem there is not any element that evolves with time, we deal with the problem of evaluating the performance of a particular frequency assignment strategy by means of the computation described in Section 2. Please, note that the frequency strategy evolves with time, but it is computed in an offline manner.  
We have taken into consideration three types of WSSN scenarios. In the first case, we have considered 50 APs and 350 (50 \(\times\) 7) cameras; in the second one, 50 APs and 500 (50 \(\times\) 10) cameras; and in the third one, 100 APs and 500 (100 \(\times\) 5) cameras. The position where APs and wireless terminals are located in a plane is random, considering that a wireless terminal is associated with its closest AP. If a sensor (camera) is not in the coverage area of any AP (given by the sensitivity of its receptor, as explained in Section 2.3), we have removed that sensor from the problem. Additionally, if an AP has no associated sensors, it is also removed from the problem. Obviously, as the density of nodes increases, the number of unconnected nodes decreases. For each type of scenario and due to their randomness, we have generated three specific scenarios, so we have studied nine scenarios. Moreover, given the non-deterministic nature of the different algorithms under study, we have run each algorithm ten times in each scenario. Table 1 summarizes the scenarios under study, where we show the number of nodes (APs and sensors) initially deployed and the number of nodes that remain after removing the unconnected nodes (\(\
u\)). Moreover, for the sake of quantifying the density of each scenario, we also show the mean number of interference signals that each node in the network receives (\(\bar{I}\)).
## 4 Simulation
### Simulation Results
#### 4.2.2 Collision Probability Behavior According to Reasoning Period for a Fixed Number of Samples
The simulation results in Figure 15 reveal that when the number of samples required to calculate the occupancy probability changes while the reasoning period remains the same, the lowest collision probability can be achieved by executing a proper reasoning process if the number of samples is in the range of 16-18. In uniform sampling methods, such as the CB and RB methods, the collision probability becomes rapidly enlarged as the number of samples becomes larger. Conversely, in the WCB and WRB methods, when the number of samples increases, there is not a noticeable effect on the performance of the candidate channel reasoning. Furthermore, if the number of samples is insufficiently small or unnecessarily large, the collision probability becomes high, so the reasoning capability decreases and the reliability is low. Specifically, for a long period of the sampling interval, it is not possible to respond quickly to rapid changes in the PU occupation state.
# Fit for What Purpose? NER Certification of Automatic Captions in English and Spanish
## 6 Discussion
### The Purpose of Live Captions and the Caption to Transcription Continuum
At the other end of the continuum are live captions treated as a transcription of oral speech. They prioritise completeness over readability, that is, they present a raw account of what the speaker says, including mumblings, hesitations and false starts. They are thus verbatim and fast (if the original speech is fast), they do not include error correction and they feature a short delay of typically no more than 2-3 s. This type of transcription-like live caption has become widespread around the world since the COVID-19 pandemic and the introduction of automatic captioning in online meeting platforms such as Zoom, Microsoft Teams and Google Meet. Figure 3: Caption to Transcription Continuum for live captioning. It is important to bear in mind that, as in any other continuum, the Caption to Transcription Continuum does not only account for extremes but also for more nuanced and intermediate positions (see Figure 4). For instance, human live captions may be found closer to the caption rather than the transcription pole, but it depends on how and where they have been produced. Respoken live captions in Spain or Belgium prioritise readability over completeness and meet most of the conditions at the captioning end of the continuum. They are heavily edited and condensed, they have a readable speed, and they typically involve delays of 6 s. In the UK and even more so in the US, human live captions are different. They would still be placed towards the left-hand side of the continuum, but they take completeness more into account than live captions in Spain or Belgium, that is, they are more verbatim and faster than the latter. Conversely, automatic live captions are normally positioned on the right-hand side of the continuum. That is the case for the automatic captions produced by Zoom or Microsoft Teams. Yet, the automatic live captions provided by Lexi, and analysed in this paper, are slightly different. They still prioritise completeness over readability and may thus be positioned towards the right-hand side of the continuum, but they are beginning to introduce elements that improve readability, such as the omission of mumblings and hesitations, increasingly accurate written-like punctuation, different ways of identifying speakers and even description of non-verbal sounds (clapping, laughter, etc.) for the benefit of viewers with hearing loss.
## 2 Materials and Methods
### Fourier Transformed Infrared Spectroscopy (FTIR)  
Powdered samples (demineralized and deproteinized products and chitin obtained from the extractions performed on cicada sloughs harvested in 2019 and 2020, commercial chitin, as well as extracted and commercial chitosan) were used for the preparation of KBr pellets (1 mg of the sample with 100 mg KBr). FTIR spectra were collected in a range of 400 to 4000 cm\({}^{-1}\) with a Nicolet 6700 Spectrophotometer, by the accumulation of 64 scans, with a resolution of 4 cm\({}^{-1}\). However, only peaks from 400 to 2000 cm\({}^{-1}\) are shown in the results, since they are important for the calculation of the degree of deacetylation (\(DD\)) and for the investigation of the differences among the samples.  
The \(DD\) was calculated according to the following equation:  
\[DD(\%)=100-DA\% \tag{1}\]  
where \(DA\) is the degree of acetylation. \(DA\) was calculated as proposed by [PERSON] et al. [11] as follows:  
\[DA(\%)=\frac{\frac{A1320}{A1420}-0.3822}{0.03133}\cdot 100 \tag{2}\]where \(A1320\) and \(A1420\) are the absorbance values at 1320 and 1420 cm\({}^{-1}\) obtained by the FTIR spectra. For each type of chitin and chitosan were produced three KBr pellets and the results were presented as mean \(\pm\) standard deviation of three measurements.
# Bioactive Glass as a Nanoporous Drug Delivery System for Teicoplanin
## 3 Results and Discussion  
### Characterization of B-Doped Mesoporous Bioactive Glass  
Figure 1 shows the microstructures of BG and B-doped BG (BG_B100, BG_B75, BG_B50, and BG_B25). In Figure 2a, the BG particles obtained by the sol-gel synthesis process are shown to be small size powders with particles of less than 100 nm in size. In Figure 1b-e, the microstructures of B-doped BG are obviously different than those of BG. The microstructures of BG with less B doping are not like BG; they are coarser and more porous, as shown in Figure 1e. The nanoscaled pores are distributed on the surface, and more rough structures are shown in BG_B100. This is advantageous for drug delivery because BG_B100 has more porous structures for drug adsorption.  
Figure 1: Microstructures of (**a**) bioactive glass (BG), (**b**) BG_B25, (**c**) BG_B50, (**d**) BG_B75, and (**e**) BG_B100.  
The average sizes of particles and PDI index values are shown in Table 1. The average sizes of particles measured by DLS were 436.23 \(\pm\) 23.27, 347.60 \(\pm\) 99.93, 556.60 \(\pm\) 58.84, 431.57 \(\pm\) 18.60, and 361.93 \(\pm\) 17.15 for samples BG, BG_B25, BG_B50, BG_B75, and BG_B100, respectively. The PDI values of samples BG, BG_B25, BG_B50, BG_B75, and BG_B100 were 0.51 \(\pm\) 0.01, 0.50 \(\pm\) 0.03, 0.53 \(\pm\) 0.02, 0.43 \(\pm\) 0.04, and 0.42 \(\pm\) 0.02, respectively. BG and B-doped BG samples contained nanoparticles with mesoporous microstructures. The particle size ranged from around 300 to 600 nm. The particle size range for drug deposition in various body organs via different dosage forms and routes of administration is as follows: transdermal (10-600 nm), intravenous/intramuscular (200-2000 nm), and ocular (100-3000 nm) [25].  
Figure 2 shows the XRD spectra of BG and B-doped BG after immersion in SBF for 0 and 7 days. In Figure 2a, none of the samples show sharp diffraction peaks and BG has an obvious and wide SiO\({}_{2}\) peak with a low intensity. The XRD pattern observed is similar to that shown in a previous study and is characterized by broad diffraction bands [26]. Amorphous nanosized bioglass has the ability to promote apatite deposition under physiological conditions [27]. In Figure 2b, the diffraction peaks could be characteristic of those found in nature, according to Joint Committee on Powder Diffraction Standards (JCPDS) File No. 9432. Crystalline diffraction peaks of apatite were mainly found at 25.9\({}^{\circ}\), 31.8\({}^{\circ}\), 32.9\({}^{\circ}\), 39.8\({}^{\circ}\), and 46.7\({}^{\circ}\) of 20 and were correlated with Miller indices of (0 0 2), (2 1 1), (3 0 0), (1 3 0), and (2 2 2), respectively. Before soaking in SBF, BG was found to have amorphous SiO\({}_{2}\). After soaking for 7 days, the apatite phase was found in all samples, and these BG based materials were demonstrated to be biomimetic in vitro.
## 3 Results and Discussion
### Evaluation of Bread Quality
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Sample**} & **Hardness** & **Springiness** & **Cohesiveness** & **Gumminess** & **Resilience** \\  & **(N)** & **(Adimensional)** & **(Adimensional)** & **(N)** & **(Adimensional)** \\ \hline M\_550 & 6.95 \(\pm\) 0.02 b  & 3.86 \(\pm\) 0.01 a  & 0.9 \(\pm\) 0.01 b  & 6.28 \(\pm\) 0.00 d  & 2.3 \(\pm\) 0.12 d  \\ P1\_550 & 6.37 \(\pm\) 0.03 b  & 5.58 \(\pm\) 0.02 b  & 0.8 \(\pm\) 0.02 a  & 5.19 \(\pm\) 0.00 b  & 1.8 \(\pm\) 0.11 a  \\ P2\_550 & 5.36 \(\pm\) 0.01 a  & 8.42 \(\pm\) 0.03 a  & 0.8 \(\pm\) 0.03 a  & 4.14 \(\pm\) 0.01 a  & 2.3 \(\pm\) 0.14 d  \\ P3\_550 & 5.60 \(\pm\) 0.02 c  & 6.20 \(\pm\) 0.02 c  & 0.8 \(\pm\) 0.03 a  & 5.39 \(\pm\) 0.01 c [FONOTE:c]Footnote c: The data used in this study are given in Table 2.[ENDFOOTNOTE] & 2.5 \(\pm\) 0.02 d  \\ M\_650 & 6.45 \(\pm\) 0.02 b  & 3.68 \(\pm\) 0.03 a  & 0.9 \(\pm\) 0.03 c [FONOTE:c]Footnote c: The data used in this study are given in Table 2.[ENDFOOTNOTE] & 5.54 \(\pm\) 0.00 c  & 2.5 \(\pm\) 0.01 b  \\ P1\_650 & 6.31 \(\pm\) 0.00 b  & 4.83 \(\pm\) 0.00 c  & 0.9 \(\pm\) 0.01 c  & 5.76 \(\pm\) 0.01 c [FONOTE:c]Footnote c: The data used in this study are given in Table 2.[ENDFOOTNOTE] & 3.0 \(\pm\) 0.11 c  \\ P2\_650 & 5.53 \(\pm\) 0.02 a  & 4.12 \(\pm\) 0.01 b  & 0.7 \(\pm\) 0.01 a  & 4.09 \(\pm\) 0.01 a  & 1.7 \(\pm\) 0.10 a  \\ P3\_650 & 6.21 \(\pm\) 0.03 c  & 3.67 \(\pm\) 0.01 a  & 0.8 \(\pm\) 0.03 b  & 5.09 \(\pm\) 0.00 b  & 2.7 \(\pm\) 0.04 b  \\ M\_1350 & 9.35 \(\pm\) 0.01 c  & 2.95 \(\pm\) 0.11 c  & 0.8 \(\pm\) 0.03 b  & 7.52 \(\pm\) 0.10 c  & 1.9 \(\pm\) 0.04 b  \\ P1\_1350 & 9.01 \(\pm\) 0.01 c  & 2.74 \(\pm\) 0.15 c  & 0.8 \(\pm\) 0.02 b  & 6.85 \(\pm\) 0.01 b  \\ P2\_1350 & 8.71 \(\pm\) 0.02 b  & 1.94 \(\pm\) 0.21 b  & 0.8 \(\pm\) 0.03 b  & 6.82 \(\pm\) 0.0 The graph represented in Figure 7 expresses the distribution of bread samples in relation to all quality attributes that described the fundamental rheological characteristics, empirical and textural characteristics. On axis 1, at the distance of origin, are the bread samples obtained from black flour (1350) with the addition of 10% and 15% hemp seed oil. On axis 2 are the white flour bread samples, type 650 and 550 (P3_550 and P3_550), which have a strong positive ratio. Observing distribution on the graph reveals significant correlations between samples P2_550 and P2_650. In conclusion, the addition of oil in the dough influences the rheological and textural properties, depending on the percentage added and the type of flour.
# A Quantitative Study of Axial Performance of Rockbolts with an Elastic-Debonding Model
## 4 Parameter Study and Discussion
### Borehole Diameter  
Boreholes are used to fill rockbolts and grouting material. For full-length anchorage rockbolts, borehole diameter is normally larger than rockbolt diameter [29]. According to technical specifications, the difference between borehole diameter and rockbolt diameter is normally 6-10 mm [27].  
To analyse how borehole diameter affected the axial performance of rockbolts, this article selected five borehole diameters: 22 mm, 23 mm, 24 mm, 25 mm, and 26 mm. Figure 7 plotted the axial performance of rockbolts. Apparently, borehole diameter had no impact on the axial performance of rockbolts. Although the borehole diameter was different, the force-deformation curves were almost identical. For the peak force of rockbolts, they were almost unchangeable, remaining at 213 kN. As for the deformation at peak force, they were almost equivalent, which was around 5.83 mm.  
[PERSON] and [PERSON] [30] tested four borehole diameters ranging from 42 mm to 106 mm. The tested cable bolt had a small diameter of 15.2 mm. The purpose was to study the impact of large borehole diameter on the axial performance of cable bolts. They found that the impact of borehole diameter on the peak force of cable bolts was insignificant. This was agreed by [PERSON] [15]. Specifically, [PERSON] [15] found that in his test scenario, when the thin  
Figure 6: Deformation at the peak force when the rockbolt diameter varied.  
great annulus and thick grout annulus were used, cable bolts reached the same peak force of 151 kN. Therefore, for the borehole diameter impact, this study found consistent results compared with previous experiment results. This further confirmed the accuracy of these analytical modelling results.
# Performance Evaluation of Graph Neural Network-Based RouteNet Model with Attention Mechanism
## 2 Background and Related Work
### RouteNet Model
This update process allows the GRU to adaptively capture sequential patterns and mitigate the vanishing gradient problem commonly encountered in traditional RNNs. The key components of attention include three essential elements: (a) the Query, (b) the Key, and (c) the Value, illustrated in Figure 2. Each of these plays a distinct and significant role in the mechanism. The Query is a vector that represents the current element or area of focus for which the model is attempting to compute an output. This could be, for example, a word in a sentence that the model is currently processing. The Key vectors correspond to each element in the input sequence, with each key being compared against the Query to ascertain its relevance. The Value vectors are associated with the keys and are crucial in constructing the final output once the relevance of each input element to the Query is established. Once the Query and Key vectors are defined, the model calculates the attention scores. This is typically performed using a function like the dot product, where each Query vector is compared with each Key vector. The resultant attention scores are a set of raw numerical values indicating how relevant each key (and by extension, the associated value) is to the query. However, these raw scores are not directly used. Instead, they undergo Softmax Normalization, a crucial step that transforms the scores into a probability distribution. The softmax function ensures that the scores add up to one, turning them into weights that can be applied evenly across the data. This normalization process is key to the mechanism's ability to focus on the most relevant parts of the input sequence. The next step involves the weighted sum, where the model applies the normalized attention weights to the Value vectors. This process effectively combines the values, with the degree of emphasis placed on each value being proportional to its calculated importance. Finally, the Context vector is the output of the Weighted Sum block and encapsulates the aggregated, attention-weighted information from the entire input sequence. The Context vector is a comprehensive representation of the input, filtered through the lens of the current focus or query. The mathematical framework, along with a deeper understanding of the aggregation, normalization, and attention score computation mechanisms within graph attention networks, is detailed in [11]. Figure 1: Gated Recurrent Unit.
# Ontology with Deep Learning for Forest Image Classification
## 3 Dataset  
Given the scarcity of publicly available forest-type image datasets [29], we downloaded 35 images for each class from the internet [30; 31]. Considering that the obtained image dataset was too limited for the proposed model, the geometric transformation data augmentation technique from the scikit-learn library in Python was employed to produce 65 more images for each class in the training dataset and 9 more images in the testing dataset. Hence, the resulting dataset constituted a total of 952 images, from which 800 were set aside for training and 152 were reserved for testing. Table 1 shows the corresponding forest-type image dataset distribution.  
Data augmentation is a technique that artificially increases the image dataset by creating additional modified copies of already existing data. Table 2 depicts the parameter configuration used in this study to perform data augmentation, where the first column represents the set of geometric properties that require fine-tuning, and the second column represents the set value for each geometric property. In this study, class labels were used as categorical data, and the label-encoder function from the scikit-learn library in Python was employed to convert string categorical data into numerical values. Class labels in this study represent image categories such as woodlands, shrubs, sea, orchards, logged forests, grassland, degraded land, and buildings. The class labels were transformed into distinct numerical values between 0 and 7. As presented in Table 3, the first column represents the transformed numerical values, and the second column represents the corresponding class labels. Since images were of different sizes, the resize function from scikit-learn was employed to resize all images to 226 \(\times\) 226 pixels. For each category, 19 images were set aside for testing, and 100 images were reserved for training. Figure 1 shows a sample of the images used in the study.  
\begin{table}
\begin{tabular}{l l l} \hline
**Training Images** & **Testing Images** & **Class** \\ \hline
100 & 19 & Grassland \\
100 & 19 & Woodland \\
100 & 19 & Orchards \\
100 & 19 & Bare land \\
100 & 19 & Logged forests \\
100 & 19 & Degraded land \\
100 & 19 & Sea \\
100 & 19 & Buildings \\ \hline \end{tabular}
\end{table}
Table 1: Forest type image dataset distribution.  
\begin{table}
\begin{tabular}{l l} \hline
**Property** & **Value** \\ \hline rotation\_range & 45 \\ width\_shift\_range & 0.2 \\ height\_shift\_range & 0.2 \\ zoom\_range & 0.2 \\ horizontal\_flip & True \\ fill\_mode & reflect \\ \hline \end{tabular}
\end{table}
Table 2: Data Augmentation Properties.