esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimentation
### Database Used  
The first employed database was Karolinska-directed emotional faces (KDEF) [42] which contains seventy different subjects (thirty-five male and thirty-five female) with five different pose variations labeled with seven basic expression categories. Here, we used only 1210 samples as training sets, whereas 1213 samples were used as testing sets, as only these samples were available from the license agreement downloaded site. Figure 7 shows some examples from this database.  
The second employed database is the GENKI database [65], which is composed of 4000 facial images that have been labeled as two classes: (i) happy and (ii) non-happy. Additionally, for this database, two thousand images were randomly selected as training sets, while the remaining two thousand images were considered for the testing set. Some examples of this database are shown in Figure 8.  
The Extended Cohn-Kanade (CK+) [66] is our third database, which is composed of 593 video sequences from 123 subjects captured between the ages of 18 to 50 years. Here, only 309 image sequences were labeled with six basic expressions. During the experimentation, we randomly split this database into the training and testing sets. Figure 9 presents some image samples from the CK+ database.  
Figure 7: Some image samples from the KDEF database.  
Our fourth database was Static Facial Expressions in the Wild (SFEW) [26], which was created from the AFEW video database by selecting the keyframes based on facial point clustering. The challenging SFEW dataset contains 700 images which were divided into the training set (346 images) and testing set (354 images). This database has seven facial expression classes, namely afraid, anger, disgust, happiness, neutral, sadness, and surprise. Figure 10 presents image samples from the SFEW database.  
Figure 8: Some image samples from the GENKI database.  
Figure 10: Some image samples from the SFEW database.  
Figure 9: Some image samples from the CK+ database.  
Table 4 presents the detailed descriptions of the KDEF, GENKI, CK+, and SFEW facial expression databases. The objectives of the selection of these databases were (i) to obtain expressions that were common and generic in people; (ii) to ensure that the other expressions used in the affecting computing research areas were composed of a mixture of these basic facial expressions; and (iii) to ensure that the good recognition system for these expressions would be very beneficial for several real-world applications, such as in e-Healthcare frameworks,in the social Internet of Things (IoT), and in emotion AI in business organizations.
## 3 Results
### Assembly and Expression Analysis  
The alignment was used to assemble the transcripts through StringTie, which also calculates the read counts and fragment per kilobase of transcript per million map reads (FPKM) value corresponding to every assembled gene/transcript. Finally, the count matrix was made from the StringTie output of every sample as count matrix is the recommended input for the differential-expression analysis [23]. The read count matrix of comparison-I and comparison-II had six samples and nine samples, respectively, which were used for further analysis in iDEP 9.1. In expression analysis, genes with 0.5 minimal counts per million (CPM) in at least one library were selected and taken for the clustering and heatmap analysis. Heatmaps were generated using 1000 most differentially expressed genes in both the comparisons (comparison-I and comparison-II). Hierarchical clustering within the heatmaps clustered the PTG and PNTG groups in separate clusters in both the comparisons (Figures 2 and 3). In differential expression analysis, a total of 1951 and 130 genes were found to be up-regulated in comparison-I and comparison-II, respectively (Table D1 and Table D2). Similarly, 2396 and 859 genes were found to be down-regulated in comparison-I and comparison-II, respectively (Table D1 and Table D2). Different plots such as MA plot, volcano plot and scattered plot were also constructed from the DEGs in both the comparisons (Figures 2 and 3).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sr. No.** & **SRA IDs** & **Good Reads \%** & **Alignment Rate** \\ \hline
1 & SRR8581331 & 98.79\% & 98.10\% \\
2 & SRR8581332 & 99.70\% & 98.63\% \\
3 & SRR8581333 & 99.60\% & 98.54\% \\
4 & SRR8581334 & 99.71\% & 98.46\% \\
5 & SRR8581335 & 99.68\% & 98.39\% \\
6 & SRR8581336 & 99.59\% & 98.53\% \\
7 & SRR8581337 & 99.71\% & 98.45\% \\
8 & SRR8581338 & 99.71\% & 98.45\% \\
9 & SRR8581339 & 99.66\% & 98.56\% \\
10 & Average all sample & 99.57\% & 98.45\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Preprocessing and alignment results of all nine samples.  
Figure 2: Heatmap and plots of differentially expressed genes in comparison-I (**A**) Heatmap build with top 1000 differentially expressed genes, (**B**) volcano plots with differentially expressed genes (DEGs), (**C**) MA plot with DEGs and (**D**) scattered plots with DEGs).
# An Acoustic Feature-Based Deep Learning Model for Automatic Thai Vowel Pronunciation Recognition
## 7 Discussion
This indicates that utilizing a variety of data dimensions and designing, collecting, and verifying data are very helpful for creating high-quality input data for speech recognition models. The automatic CAPT uses the CNN model for Thai vowel speech recognition. It solves problems such as lack of expertise, complexity, time-consuming processes, and nonreal time processes. Deep learning is applied to an ASR recognition model to recognize the learner's pronunciation. The system uses raw speech input data and uses MS acoustic features along with CNN to extract the distinctive features of the vowels and classify them. After that, the vowel derived from the classification is compared with the vowel selected by the learner. If the comparison matches, the learner's pronunciation is correct. To increase the preciseness of pronunciation, linguists use the acoustic phonetics method for phonetic analysis. In general, those research works necessitate the hand-crafted extraction of formant1 (F1) and formant2 (F2), which presents significant challenges due to the large amount of data and speaker acoustic variation. This work differs from previous studies that used Praat with phonetics principles in the analysis of pronunciation [12; 13; 14; 15; 16; 17]. In general practice, linguists usually employ the F1 and F2 using Praat. Then the F1 and F2 of native and nonnative speakers are plotted using Microsoft Excel, Python, or R programming language. After that, the graphs are compared to find the differences between the native speakers and the learner; differences mean the pronunciation is incorrect. These normal steps are a multistep rather than real-time approach, and they require someone with expertise in Praat or someone who can program in R or Python language. Those traditional methods by linguists may be complex for others, whereas this research provides a very uncomplicated method. It works in real time and can solve those problems. Moreover, the technique can be utilized for developing CNN models in a similar domain by applying various strategies, optimizing the neural networks, and adjusting the hyperparameters for improved performance in learning assist systems in the future.
## 2 Evaluation Method
### Source Analysis of Uncertainty
(5) Uncertainty caused by environmental factors  
The temperature, humidity, temperature spatial and temporal gradient, vibration, dust, and other environmental factors may have impacts on the measurement results. During CMM dimensional measurement, temperature compensation is usually needed. If the temperature changes, the CMM grating ruler and workpiece thermal expansion coefficient will all cause uncertainty through the process of temperature compensation. The evaluation of measurement uncertainty should consider all factors that may affect the measurement results and focus on the specific measurement tasks. Based on the above analysis, it can be concluded that the sources of CMM task-oriented measurement uncertainty have the following characteristics: Wide sources and rich types, interconnection between sources of uncertainty, difficulty in quantifying sources of uncertainty and close correlation between sources of uncertainty and measurement tasks. Because of the complexity and quantification difficulty of the sources of uncertainty, it is a key problem to establish an uncertainty evaluation model which can fully reflect the influence of the sources of uncertainty and their relationship on the measurement results.
# Compressed Communication Complexity of _Hamming Distance_
## 3 Compressed Communication Complexity of _Hamming Distance_
### On the Sizes of Non-Self-Referencing LZ77 Factorization of Suffix
#### 3.1.2 Upper Bound for \(\zeta(n)\)
This procedure is based on a folklore algorithm (cf [18]) that concatenates two given AVL-trees of height \(h\) with \(O(h)\) node rotations. In the concatenation procedure of AVL-grammars, \(O(1)\) new productions are produced per node rotation. Therefore, \(O(h)\) new productions are produced in the concatenation operation. Suppose we have the AVL-grammar of a string \(S\) of length \(n\). It contains \(\mathsf{avl}(S)\) productions and the height of its parse-tree is \(h=O(\log n)\) since an AVL-tree is a balanced binary tree. For any proper suffix \(S^{\prime}=S[i..]\) of \(S\) with \(1<i\leq n\), we split the AVL-grammar into two AVL-grammars, one for the prefix \(S[1..i]\) and the other for the suffix \(S[i..n]\). We ignore the former and concentrate on the latter for our analysis. Since split operations on a given AVL-grammar can be performed in a similar manner to the aforementioned concatenation operations, we have that \(\mathsf{avl}(S^{\prime})\leq\mathsf{avl}(S)+a\log n\) for some constant \(a>0\). Now it follows from Lemma 3, Lemma 4, Lemma 5, and that the size \(\mathsf{cn}\) of the C-factorization of any string is no more than the number of productions in any SLP generating the same string [10], we have  
\[\mathsf{zn}(S^{\prime})\leq\mathsf{cn}(S^{\prime})\leq\mathsf{avl}(S^{\prime })\leq\mathsf{avl}(S)+a\log n\leq a^{\prime}\mathsf{cn}(S)\log n\leq 2a^{ \prime}\mathsf{zn}(S)\log n\]  
where \(a^{\prime}>0\) is a constant. This gives us \(\mathsf{zn}(S^{\prime})/\mathsf{zn}(S)=O(\log n)\) for any string \(S\) of length \(n\) and any of its proper suffix \(S^{\prime}\). Since the size \(\mathsf{zn}(S)\) of the non-self-referencing LZ77 factorization of any string \(S\) of length \(n\) is at least \(\log n\), the next corollary is immediate from Lemma 6:  
**Corollary 2**. : _For any string \(S\) and its proper suffix \(S^{\prime}\), \(\mathsf{zn}(S^{\prime})/\mathsf{zn}(S)=O(\mathsf{zn}(S))\)._
# BSTCA-HAR: Human Activity Recognition Model Based on Wearable Mobile Sensors
## 3 Methods  
### BiLSTM  
LSTM is an RNN that solves the problem of the difficulty RNN models face in handling long-term dependent data [25]. RNN networks outperform CNNs in feature extraction from sequential data by using gated units to control memory forgetting and updating while selectively retaining long-term and short-term information. In our proposed model, we employ bidirectional LSTM for enhancing temporal pattern recognition from sequential data. Each layer comprises 64 memory units, with the input disseminated to distinct gates--the input gate, the forget gate, and the output gate--that orchestrate the functioning of each memory cell, governing the flow of information. By leveraging its ability to consider both past and future information simultaneously, bidirectional LSTM has better ability to understand the contextual information in sequential data. BiLSTM consists of two independent LSTM networks; one processes the sequence in chronological order, while the other processes it in reverse chronological order. Their outputs are then combined and passed to subsequent layers to capture bidirectional dependencies in the sequential data.  
LSTM consists of a set of recurrently connected memory units, each of which contains one or more cell structures. Each cell structure retains or forgets data information through different gates. The activation formulas for each LSTM unit are described as follows:  
1. Historical information deemed unnecessary or irrelevant is discarded by the forget gate, as shown in Equation (1). \[f_{t}=\sigma\Big{(}W_{f}^{*}h_{t-1}+W_{f}*x_{t}+b_{f}\Big{)}\] (1)
2. The information retained from the previous time step along with the current input information jointly serve as the updated state of the input gate, as shown in Equations (2) and (3). \[i_{t}=\sigma(W_{i}^{*}h_{t-1}+W_{i}^{*}x_{t}+b_{i})\] (2) \[\widetilde{C}=W_{i}^{*}h_{t-1}+W_{c}*x_{t}+b_{c};C_{t}=f_{t}^{*}C_{t-1}+i_{t} ^{*}\widetilde{C}_{t}\] (3)
3. The current state information is output by the output gate, as shown in Equations (4) and (5): \[o_{t}=\sigma(W_{o}^{*}h_{t-1}+W_{o}^{*}x_{t}+b_{o})\] (4) \[h_{t}=o_{t}*\tanh(C_{t})\] (5) where \(W_{i}\), \(W_{c}\), \(W_{f}\), and \(W_{o}\) are the corresponding connection weights, while \(b_{i}\), \(b_{c}\), \(b_{f}\), and \(b_{o}\) are the corresponding biases. The variable \(f\) represents the forget factor determined by the forget gate at time \(t\), \(\sigma\) is the Sigmoid function, \(C_{t}\) corresponds to the updated value of the cellular state at the moment \(t\), and \(h_{t}\) corresponds to the output value of the current neural unit.
## 4 Result
### Time-Series Deformation Analysis of Typical Landslide Hazard Areas  
Based on the interpretation results from ascending and descending orbit SAR imagery, one representative landslide hazard site was selected from each dataset for time-series deformation analysis. Using InSAR-derived displacement data from multiple observation points, the temporal deformation characteristics of the hazard zones were evaluated.  
Landslide Hazard Point A was identified through the comprehensive interpretation of ascending orbit SAR data. Four deformation observation points were selected within this area for time-series analysis. As shown in Figure 8a,b, significant surface deformation occurred in the northern part of the delineated area during 2023, with the maximum cumulative displacement reaching \(-36\) mm. Notably, visible landslide tension cracks were observed along the rear edge of the northern slope. In contrast, the southern portion exhibited minimal displacement and remained relatively stable throughout the year.  
Landslide Hazard Point B was interpreted from the descending orbit SAR data. Similarly, four deformation observation points were selected for time-series analysis in this region. The results indicate that, between 2022 and 2023, the rear edge of the landslide-prone area experienced substantially greater displacement than the frontal accumulation zone, with a maximum cumulative deformation of \(-52.3\) mm. The deformation was observed to gradually propagate outward from the central portion of the rear edge. Given the continuous deformation trend, it is recommended that this area will be subject to regular monitoring and hazard reassessment, as illustrated in Figure 8c,d.
# QoS-Based Pattern Recognition Approach for Web Service Discovery: Ar_WSDS
## 1 Introduction
Its process consists of decomposing each QoS parameter to be analyzed by dynamic recognition modules until the desired WS is achieved for the composition required by a user. Such processing analyzes the characteristics of the entities (patterns) to be assigned to a category type. The main contributions of this paper are as follows:  
First, we establish a theoretical framework for the implemented strategy, which emphasizes pattern recognition through the specification of a supervised learning classifier. It uses a variable selection segmentation process based on the analysis of QoS parameters. The WS classifier creates a mechanism for the hierarchization or WS ranking. The mechanism calculates the service score according to the QoS parameters registered by the user for each service and the composite service. Thus, this ranking mechanism offers a range of quality solutions, where the user chooses the most appropriate services according to the quality they wish to obtain through their composition. Second, we develop a formal specification of web services at the atomic and composition levels, representing information entities representing the patterns and signals to be recognized. In this process, a simple approach is offered to decompose the problem of combinatorial nature into a search and selection strategy of services based on dynamic recognition modules. The latter feature is provided when the system can create modules from the requirements of a user. Finally, a computational strategy based on a component architecture is defined. The strategy independently and dynamically develops analysis modules for each non-functional requirement of the composition. A mechanism for the categorization of WSs using QoS levels is finally established. Our method bases its computational strategy on selecting the servicesrequired for a composition, thus establishing different solution pathways (SPs) or sets of services involved in the composition. They are categorized with different metrics, offering the system a more comprehensive range of searching and selecting. The organization of the remainder of this paper is as follows:  
* Section 2 discusses research on the selection of web services related to the recognition approach. * Section 3 outlines the formalization of the process of composition, web services, and QoS parameters, represented through signals and patterns by Ar_WSDS. * The Ar_WSDS method is presented in Sections 4 and 5, specifying the recognition module concept and strategies, the system model, and the proposal of the algorithm solution. * In Sections 6 and 7, the experimental phase is presented and discussed, using a data set to compare the proposed system to another recognition method with similar heuristics to analyze the developed algorithm's performance. The paper is concluded in Section 8.
# Path Planning and Real-Time Collision Avoidance Based on the Essential Visibility Graph
Although the application of the velocity obstacles concept is shared with other papers [64; 65; 66; 67; 68], the applicability with obstacles of a general shape and the efficient use of the visibility graph concept coupled with [PERSON] paradigm in finding the optimum flyable path place the proposed approach a further step ahead in the field of aircraft collision avoidance algorithms; finally, the further implementation of the visual flight rules, compliant with the International Civil Aviation Organization (ICAO) Annex II Right of Way rules, makes our proposed collision avoidance strategy suitable for future applications in hybrid scenarios where both manned and unmanned aircraft share the airspace. The article is organized as follows: In Section 2, the geometrical approach based on [PERSON] car is recalled, aimed at providing smooth trajectories compliant with the aircraft minimum turn radius constraint; in Section 3, a graph based approach based on the Essential Visibility Graph (EVG) is presented, with the proof of the optimality of the resulting path. The extension of such a method to dynamic scenarios with the implementation of the ICAO Right of Way rules is presented in Section 4. Finally, numerical results are shown and discussed in Section 5.
# Traffic Aware Scheduler for Time-Slotted Channel-Hopping-Based IPv6 Wireless Sensor Networks
## 3 Related Work
### Adaptive Approaches
#### 3.2.1. Paas  
The parameterized slot scheduling for adaptive and autonomous TSCH networks (PAAS) [9] is a solution that finds a compromise between RBS and SBS Orchestra. The implementation is based on SBS Orchestra, and in the initialization phase, it allocates for each node one Tx cell and a number of Rx cells equal to the number of children. If the measured traffic load is lower than the computed threshold, nodes use the same cell to listen to multiple children, reducing the number of Rx cells. The information needed to adapt the schedule is sent via RPL DIO messages. This scheduler maintains the same reliability and delay performance as SBS Orchestra but lowers energy consumption. The PAAS scheduler ensures better reliability and delay performance than RBS Orchestra, but leads to higher energy consumption. Its shortcoming is that it allocates first the Tx cell which might lead to lost packets if the node starts to transmit before the corresponding Rx cell is allotted.
## 5 Evacuation Modeling
### Queue Formation and Egress Model  
Based on the HRR and the positions of the fire source along the tunnel, different FDS+Evac simulations were performed. In the case of unidirectional traffic flow, the number of vehicles in the queue upstream of the fire was estimated assuming that: (i) queued vehicles stop without overtaking the burning vehicle; (ii) the vehicles queue up filling both lanes; (iii) the first vehicle in the queue is located at 10 m from the posterior surface of the burning vehicle; (iv) each vehicle maintains a safety distance of 2 m from the preceding vehicle in the queue. According to these assumptions, when the burning vehicle is at 145 m from the entrance portal (i.e., portal A), the traffic queue is formed by 32 vehicles, 66 vehicles when the burning vehicle is at 280 m from portal A, 100 vehicles when the burning vehicle is at 420 m from portal A, 138 vehicles when the burning vehicle is at 570 m from portal A, and 174 vehicles when the burning vehicle is at 710 m from  
Figure 1: View of the tunnel simulated by means of FDS.  
portal A. Figure 2 represents a schematic layout of queued vehicles upstream of the fire source for the one-way tunnel investigated.  
The number of users potentially at risk within the tunnel (i.e., the number of people trapped upstream from the burning vehicle) depends on the number of cars, HGVs, and buses in the queue, and the number of people present in each type of vehicle. In this respect, the occupancy rate of cars, HGVs, and buses were assumed to be equal to 1.7 [27], 1, and 30 people, respectively. The average occupancy rate considering all vehicles in the queue (i.e., including cars, buses, and HGVs) was computed to be equal to about 2.1. Obviously, by multiplying the aforementioned number of queuing vehicles by 2.1, we estimated the following number of people escaping upstream of the fire: 68, 139, 211, 291, and 367 users when the burning vehicle is located at 145 m, 280 m, 420 m, 570 m, and 710 m, respectively.  
The initial position of each user that starts to walk for reaching a safe place, at the moment of fire starting or during the fire growth phase, is assumed to be next to his own vehicle.  
It was also assumed, when the burning vehicle is located at 145 m, 280 m, and 420 m, that all users (evacuee) evacuate from the entrance portal (i.e., portal A). Instead, when the burning vehicle is located at 570 m and 710 m from (the) entrance portal A, escaping users use both the mentioned portal A and the emergency exit located in the middle of the tunnel length. Table 3 shows the total number of escaping users in the direction of (the) portal A and emergency exit, respectively.