esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Development and Results of the Multi-Party Dialogue Management
### Development of the Knowledge Expander
To mitigate this problem, we increased the number of generated training instances and employed post-processing to filter out examples that were too similar, using the Levenshtein distance with a threshold of \(lev(a,b)\geqslant 0.9\), thus enhancing the diversity and relevance of the training dataset. A manual check of the training examples showed that some phrases were unusual. Although this generated sentences similar to those used by children or non-native speakers, it led to errors when translated into other languages. For example, the English sentence \"Are you able to understand me?\" was incorrectly translated into German as \"Horst du meine Ansprache?\" (\"Do you hear my speech?\"), which was then erroneously expanded as \"Kannst du meinen Vortrag horen?\" (\"Can you hear my lecture?\"). To address this, we found that better results were achieved by translating the manually created seed sentences first and then expanding them in the target language, rather than expanding the original sentences and translating the entire set. This approach ensured that the nuances of each language were better captured during the expansion process. It is evident that, when using the knowledge expander for translations and responses, manual corrections are necessary to ensure quality. Addressing these challenges is crucial to ensure accurate and consistent interaction with the social robot.
## 3 Results and Discussion
### Catalyst Intermediate Species and Oxidation Mechanisms
An overcrossing band at 2360-2340 cm\({}^{-1}\), growing in intensity alongside the reaction temperature, is ascribed to the stretching vibration of C=O. Meanwhile, the bands at 1680-1640 cm\({}^{-1}\) are associated with the stretching vibration of the C=C bond, which indicates the appearance of the banding vibrations of alkene. The band located at 1378 cm\({}^{-1}\) belongs to the COO- of acetate species, while the one located at 1338 cm\({}^{-1}\) is associated with the COO- of formate species. The bands at 1250-1000 cm\({}^{-1}\) can be ascribed to the stretching oscillation of the C-C of 1,2-DCE. In conclusion, the reaction path of 1,2-DCE over the prepared noble metal-based catalysts could be conducted in several of the main reaction routes of these typical noble metal materials. First, the vibration of C-H (-CH/-CH\({}_{2}\)/-CH\({}_{3}\) groups), COO- (acetate), and C=C (alkene) can be found in the Ru/\(\gamma\)-Al\({}_{2}\)O\({}_{3}\) sample, and thus the main reaction paths of the Ru/\(\gamma\)-Al\({}_{2}\)O\({}_{3}\) catalyst are reaction paths 1 and 4. The vibration of COO- (acetate), C-H (-CH/-CH\({}_{2}\)/-CH\({}_{3}\) groups), and C=C (alkene) can be observed for the Rh/\(\gamma\)-Al\({}_{2}\)O\({}_{3}\) material, and thus the main reaction paths of the Rh/\(\gamma\)-Al\({}_{2}\)O\({}_{3}\) material are reaction paths 1 and 3. In the Pt/\(\gamma\)-Al\({}_{2}\)O\({}_{3}\) catalyst spectrum, the vibrations of C-H (-CH\({}_{2}\) group), C-H (-CH/-CH\({}_{2}\)/-CH\({}_{3}\) groups), COO- (acetate), and C=C (alkene) are obvious, and thus the reaction paths 1, 2, and 4 are the main reaction paths of Pt/\(\gamma\)-Al\({}_{2}\)O\({}_{3}\) material. The vibration of C-H (-CH/-CH\({}_{2}\)/-CH\({}_{3}\) groups), COO- (acetate), and C=C (alkene) can be found with the Pd/\(\gamma\)-Al\({}_{2}\)O\({}_{3}\) catalyst, so reaction paths 1 and 2 can be observed from the process of Pd/\(\gamma\)-Al\({}_{2}\)O\({}_{3}\) material. The vibration of C-H (-CH\({}_{2}\) group), C-H (-CH/-CH\({}_{2}\)/-CH\({}_{3}\) group), COO- (acetate), and C=C (alkene) can be observed on the Ir/\(\gamma\)-Al\({}_{2}\)O\({}_{3}\) material surface. The vibration of C-H (-CH\({}_{2}\) group), C-H (-CH/-CH\({}_{2}\)/-CH\({}_{3}\) groups), COO- (acetate), and C=C (alkene) can be observed on the Au/\(\gamma\)-Al\({}_{2}\)O\({}_{3}\) material surface. Finally, the catalyst of Ir/\(\gamma\)-Al\({}_{2}\)O\({}_{3}\) follows reaction paths 1, 2, and 4, as does Au/\(\gamma\)-Al\({}_{2}\)O\({}_{3}\).
## 2 Site Description and Techniques for Data Analyses
### Statistical Treatment of Data: Principal Component Analysis and Linear Discriminant Analysis  
Multivariate statistical techniques were used to grossly discriminate between the main sources acting on the port city by ensuring that the tested dataset had a suitable number of objects. The analyses were carried out using the R statistical software (www.r-project.org (accessed on 1 March 2021)) and an auto-scaling procedure.  
Principal Component Analysis (PCA) was applied to a dataset of daily PM\({}_{2.5}\) samples (objects). The PCA performs orthogonal linear combinations of the auto-scaled variables, generally by using the correlation matrix. Linear combinations are called principal components scores, while their coefficients are referred to as principal component loadings. The Principal Components are obtained according to the maximum variance criterion, i.e., each successive component is an orthogonal combination of the original variables, so that it covers the maximum of the variance not accounted for by the previous components. The strength of the PCA is that it is based on the data collection evolving in a specific site (receptor). Moreover, it does not require an a priori knowledge about the sources [38; 39; 40]. Therefore, this is the most suitable technique to discriminate between different sources acting on a site according to meteorological/climatological conditions.  
The Linear Discriminant Analysis (LDA) was applied to a dataset of daily PM\({}_{2.5}\) samples (cases), considering the two prevailing wind direction observed as categories (or groups) and 20 chemical components of PM\({}_{2.5}\) as variables. LDA identifies which variables allow to discriminate between two or more groups, determining how these groups can differ as concern the mean of a variable and then using this one to classify new cases. As happens in PCA, in LDA the first discriminates function considered supplies the most discrimination between groups, the second supplies second most, and so on [39; 41; 42]. In our work we performed the LDA in order to evaluate groups separation. The LDA results are generally shown by confusion matrix which contains how many errors, and which types arise in resubstitution. When there are K groups, the confusion matrix R is a K-by-K matrix with R(i,j) = the number of observations of group i that the classifier predicts to be of class j.
## 3 Challenges and the State of the Art Progresses
### Architectural Choices behind Performance and Security Concer
#### 3.3.5 SQL vs. NoSQL Data Stores  
On-chain storage has a limited capacity, thus we have to resort to off-chain mechanisms to store large amounts of data. There are two main options of databases, namely SQL and NoSQL, or relational databases and non-relational databases.  
A relational database is the more rigid, structured way of storing data. The data stored in a relational database have to be structured in a very organized way. ACID (Atomicity, Consistency, Isolation, and Durability) compliancy is ensured by a relational database, which protects the integrity of data and reduces anomalies. Although a relational database is less scalable and does not support high traffic volume compared with a non-relational database, it fits for a more consistent dataset that does not grow rapidly. The popular relational databases include MySQL, Oracle, IMB DB2, Sybase, MS SQL Server, Microsoft Azure and PostgreSQL.  
A non-relational database is document-oriented and distributed, which is more suitable for unstructured data from the web and can include sensor data, social sharing, personal settings, photos, location-based information, online activity, usage metrics, and more. The non-relational databases gain popularity for their speed and scalability, and fit for datasets that have large volumes and have little to no structure. There are four types of non-relational databases: key-value model, column store, document database, and graph database. MongoDB, Apache's CouchDB, HBase, Oracle NoSQL and Apache's Cassandra DB are the popular non-relational databases.
## 5 Limits of CCTA  
There are several limits of the CCTA. Respiratory motion artifacts are a frequent issue in image acquisition that can reduce image quality. The presence of arrhythmias or other technological factors might also cause problems with electrocardiographic gating during picture acquisition. In some cases, there may be technical problems such as partial exclusion of the coronary arteries from the field of vision [41; 90]. Besides that, coronary artery motion artifacts, non-coronary calcium (for instance calcified thoracic lymph nodes, pleural or pericardial calcifications, mitral annular calcification), and artifacts from adjacent metallic prostheses are potential issues that can cause CAC calculation and other CT parameters assessment to be inaccurate [38]. Moreover, radiation exposure is also a limiting factor. However, radiation doses may be further reduced using low tube potential (<100 kVp) and high-pitch helical acquisition, when appropriate, and consequent heart rate lowering [167].
# Restorative Space Analysis by Jaw Motion Tracking Using a Template in Completely Edenulous Patients
## 2 Materials and Methods
### Attaching Target Materials to the Template and Performing Anterior jaw Movement Tracking
Multiple vertical heights can be selected according to the required increase in the restorative space (Figure 5A). If the clinician plans to fabricate an implant-retained overdenture, the presence of an adequate restorative space for incorporating attachments can be determined by designing a fixed restoration, removable restoration, or attachment structure arbitrary using the CAD software (Exocad DentalCAD, Exocad, Germany) (Figure 5B). The maxillary and mandibular stereolithography files with the desired vertical dimension can be exported and then used to design trial dentures with the aid of the denture design module of the 3D CAD software (3 Shape Dental System, 3 Shape, Copenhagen, Denmark). When designing a trial denture, the clinician can adjust the anterior tooth arrangement by referring to or supplementing the lip support and aesthetics of the anterior tooth from the previous denture (Figure 6). The clinician can make trial dentures that have various designs and vertical heights. Figure 4: The jaw motion data of the old denture state implemented in Figure 2 converted into motion data in the complete edentulous arch. (**A**) Closed mouth. (**B**) Tracking movement while opening the mouth. (**C**) Extracting the maxillary and mandibular meshes of the desired height in the tracking trajectory. Figure 5: (**A**) By measuring the distance between the mandibular anterior teeth and the maxillary anterior edentulous ridge, one is able to ascertain the appropriate vertical dimension. (**B**) A bar attachment can be placed in the restorative space to evaluate whether it is adequate. The designed trial dentures were printed using a 3D printer (Form3, Formlabs, Somerville, MA, USA) and 3D printing resin (NextDent C&B, 3D Systems, Soesterberg, the Netherlands) (Figure 7A). The clinician can place each trial denture in the patient's mouth and then select the most appropriate denture for further treatment (Figure 7B). The clinician can use the trial denture to select an appropriate vertical height and restorative space, and then complete the temporary denture and provide it to the patient. For the determined vertical dimension and restorative space, the clinician can select the final prosthesis with an implant-supported fixed dental prosthesis, an implant overdenture, or a conventional fully removable denture that is consistent with that vertical dimension.
## 3 The Proposed Scheme
### Concrete Construction
**Re-Encrypt:** After receiving the ciphertext \(ct\) from the DO, the PS first utilizes its private key \(\beta\) to re-encrypt the parameter \(C\) in ciphertext as  
\[C^{*}=C^{\beta}. \tag{11}\]  
The re-encrypted ciphertext \(ct^{*}\) is formed by (C\({}^{*}\), \(C^{*}\), \(\{C_{i}\}\)). Then the re-encrypted ciphertext \(ct^{*}\) along with the access policy (\(M_{l\times n},\rho\)) and index \(\textit{IX}(\mathbf{kw})\) will be stored by the CS. **Trapdoor:** The DU can utilize the keyword search key \(q_{id}\) to generate a trapdoor for ciphertext search. Let \(k\mathbf{w}\) be the searched keyword and \(\mathbf{w}\) the attribute set of DU. First, the DU computes  
\[T_{q}(k\mathbf{w})=(g^{H(k\mathbf{w})})(q_{id}{}^{-u}), \tag{12}\]  
\[L^{\prime\prime}=L^{-u}, \tag{13}\]  
\[\{K^{\prime}_{x}=K_{x}{}^{-u}\}_{x\in\mathbf{w}}. \tag{14}\]  
Then, the trapdoor \(T^{\prime}_{k\mathbf{w}}=(T_{q}(k\mathbf{w}),L^{\prime\prime},\{K^{\prime}_{x}\}_{x \in\mathbf{w}})\) and the attribute set \(w\) are transferred to the PS. When the PS receives (\(T^{\prime}_{k\mathbf{w}},\mathbf{w}\)) from the DU, the PS computes the parameter \(L^{\prime}\) as  
\[L^{\prime}=(L^{\prime\prime})^{\beta-1}. \tag{15}\]  
Then, the modified trapdoor \(T_{k\mathbf{w}}=(T_{q}(k\mathbf{w}),L^{\prime},\{K^{\prime}_{x}\}_{x\in\mathbf{w}})\) and the attribute set \(\mathbf{w}\) are delivered to the CS. **Test:** After receiving (\(T_{k\mathbf{w}},\mathbf{w}\)), the CS checks all ciphertext index data (\(ct^{*}\), \(\textit{IX}(\mathbf{kw})\)) and the embedded access policy (\(M_{l\times n},\rho\)). Suppose that \(\mathbf{w}\) fulfills the access policy (\(M_{l\times n},\rho\)) of some ciphertext \(ct^{*}\). Let \(\{\mu_{i}\}_{i\in\textbf{I}}\) be a set of constants outputted by the Recon algorithm of LSSS. Then the CS further computes  
\[Q_{ct}=\Pi_{i\in\textbf{I}}[e(C_{i},L^{\prime})e(C^{*},K^{\prime}_{\rho(i)})]^ {\mu_{i}}, \tag{16}\]  
\[k_{k\mathbf{w}}=e(C^{*},T_{q}(k\mathbf{w}))/Q_{ct}. \tag{17}\]  
and verifies whether \(\textit{IX}(\mathbf{kw})\) satisfies  
\[\{k_{i}=[e(g,g)^{d}]^{s}\cdot e(g,H(k\mathbf{w}_{i}))^{s}]_{k\mathbf{w}_{i}\in\mathbf{kw}}, \tag{9}\]0  
If Equation (18) holds, we know that \(k\mathbf{w}\in\mathbf{kw}\) and the CS will transmit (\(ct^{*},Q_{ct}\)) to the PS.
# A Comprehensive Analysis of Various Tokenizers for Arabic Large Language Models
## 4 Experiments
### Datasets
#### 4.3.3 Sentiment Analysis (SA)  
In the SA task, the model's objective is to identify the sentiment of input text data, which can be positive, negative, or sometimes neutral. For this task group, we used six datasets. Two datasets, HARD and ASTD, consist of mixed text in MSA and DA. One dataset, BRAD, consists only of MSA. The other two datasets, Mawqif (briefly described in the NLI and SD task groups) and ArSarcasmV2 (briefly described in the SaD task group), consist of DA. Descriptions of the remaining four datasets are provided below:* Hotel Arabic-Reviews Dataset (HARD): HARD is a large dataset composed of customer reviews of various hotels that were written in both MSA and various DA forms [60]. The corpus contains 409,562 reviews collected from Booking.com. The dataset contains 2 versions: balanced and unbalanced. The the balanced version contains 105,698 reviews annotated with either positive or negative. In this paper, both versions were included in the experiments.
* Book Reviews in Arabic Dataset (BRAD): BRAD is a large dataset of Arabic book reviews [61] collected from goodreads.com. It contains 510,600 reviews associated with a rating on a scale of 1 to 5 stars for sentiment analysis.
* Arabic Sentiment Tweets Dataset (ASTD): ASTD is a collection of Arabic posts collected from Egyptian Twitter accounts [62]. It contains approximately 10,000 tweets.
# Structural Damage Detection Based on One-Dimensional Convolutional Neural Network
## 2 Formulation of Analytical Model  
In this paper, a structure damage detection method based on one-dimensional convolutional neural network has been proposed. The overall framework of the method is shown in Figure 1. First, a three-layer framework model was built by ABAQUS. Damage was simulated by decreasing the stiffness of the column. Then, the displacement data of each node was collected under the environmental excitation. The data was transformed into the interlayer displacement, which formed a damage dataset. The dataset was entered into the proposed convolutional neural network to identify the location and degree of structural damage. In order to verify the identification effect of convolutional neural networks, three datasets (single damage dataset, multiple damage dataset, and damage degree dataset) were generated.  
Figure 1: The overall framework.
# The Spotting Distribution of Wildfires
## 1 Introduction
### Ignition of Fuel Beds by Firebrands
These fuels were ignited under varying values for fuel moisture, ambient windflow, bulk density, and fuel arrangement (or loading). In terms of firebrands, the authors examined pine cones, Eucalyptus bark, acorns and twigs, and assessed their likelihood to cause ignition in terms of the fuel bed properties just mentioned. The general results of [46] are that grasses present higher flammability risk compared to tree and bush litters, pine litter is more ignitable than hardwood litter, and an increase of the fine fuel moisture and bulk density decreases the time, but not necessarily the likelihood of ignition. Finally, firebrand type and state (_i.e._, glowing or flaming) are the most important determinants of ignition. The glowing or flaming state of a firebrand had already been qualitatively discussed in a number of investigations (e.g., [5; 7; 9]). A rigorous analysis of Eucalyptus firebrands by [PERSON] [9], and pine or fir firebrands by [PERSON] [44], confirms the results of [46] that flaming firebrands present greater risk of ignition. The time-to-burnout of flaming was investigated from a theoretical perspective in [31]. Flaming ignition most likely plays a more important role in short-range spotting, where \"re\"-flaming is possible upon landing, and is important to consider [9]. The results of [47] suggested that there are three firebrand groups which are important for spotting. These include: heavy firebrands with the ability to sustain flames, which are efficient for long-distance spotting (e.g., pine cones, cylindrical brands); light firebrands with high surface area-volume ratios are effective for short-range spotting (e.g., Eucalyptus or pine bark plates). Light firebrands with low surface-volume ratios fall somewhere in the middle of the other two classes. Finally, experiments using the Commonwealth Scientific and Industrial Research Organization's (CSIRO's) Pyrotron combustion wind tunnel have further quantified the role of fuel moisture on ignition probability [48]. Further quantitative work, as discussed in these final two paragraphs, is underway and will soon be employed by fire management in assessing fire danger.
###### Abstract  
Recently, high frequency bands (above 6 GHz) have attracted more attention for the next generation communication systems due to the limited frequency resources below 6 GHz. To reveal the influence of frequency on propagation channels, channel characterization results at 14.6 and 3.6 GHz bands based on measurements in an indoor scenario and in a reverberation chamber are presented. The measurement results indicate minimal differences in path loss exponents, shadow fading standard deviation, root-mean-square (RMS) delay spread and coherence bandwidth for the two frequency bands, while the path loss at 14.6 GHz band is clearly larger than that at the 3.6 GHz band. Furthermore, the underlying factors that influence the channel characteristics are investigated. It is found that the RMS delay spread is independent of the frequency in the scenario where free space propagation and/or reflection are the main mechanisms. Measurements in the reverberation chamber verify this inference.  
Keywords:propagation measurement; comparative analysis; indoor scenario; wideband characteristics; reverberation chamber +
Footnote †: journal: Applied Sciences
## 2 Background and Related Work
### DShot Protocol and ESC Communication  
#### 2.1.1 Protocol Architecture  
DShot represents a paradigmatic change from analog PWM to digital ESC communication, enabling precise timing control and embedded error detection [15; 16]. The protocol employs 16-bit frames transmitted at speeds from 150 kbps (DShot150) to 1.2 Mbps (DShot1200). Each frame contains an 11-bit throttle value ranging from 0-2047 where 0 indicates motor stop and 1-2047 represents the active throttle range, followed by a single telemetry request flag and a 4-bit CRC checksum for error detection [17]. As illustrated in Figure 2, the detailed structure of the frame and the signal encoding characteristics define the precise timing requirements that form the foundation of our ML-enhanced timing correction approach.
# Image Scanning Microscopy to Investigate Polycomb Protein Colocalization onto Chromatin
## 2 Materials and Methods
### Confocal Fluorescence Microscopy  
Measurements were carried out by a confocal Zeiss LSM 900 with Airyscan (Carl Zeiss, Jena, Germany). Samples were viewed with a 63\(\times\) Apochromat oil-immersion objective (NA = 1.4). The pinhole size was set to 1 airy unit (AU) for the green acquisition channel. Then, 512 \(\times\) 512 pixel images were acquired line by line in the three channels (blue, green, and far-red, acquisition in this sequence) by setting the pixel dwell time to 1.52 ms and taking 4 averages per line. Cells were imaged at the focal depth maximizing the nuclear section on the image plane. The spectral windows of the acquisition channels were: (i) blue (Hoechst 33342): \(\lambda_{\rm ex}\) = 405 \(\lambda_{\rm em}\) = 420-500 nm; (ii) green: \(\lambda_{\rm ex}\) = 488 (Alexa488), \(\lambda_{\rm em}\) = 500-560 nm; and (iii) far-red (Alexa647 and Abberior StarRed): \(\lambda_{\rm ex}\) = 640, \(\lambda_{\rm em}\) = 650-700 nm. Image processing was carried out by the open-source software Fiji (NIH, Bethesda, USA, 2022) [21]. Intensity detection of green and far-red nuclear fluorescencein multicellular images was performed by applying a preliminary segmentation of single nuclei from the blue (Hoechst) channel, according to the following algorithm:  
1. Blurring (Gaussian, 1 pixel standard deviation) of the blue channel image.
2. Background subtraction routine of Fiji (rolling ball radius: 50 pixels) of the blue channel image.
3. Thresholding [22], filling holes and watershed separation of the blue channel image to yield an 8-bit mask of single nuclei.
4. Particle analysis routine of Fiji of the green and red channels by using the mask of single nuclei.