esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review of Transformer-Based Approaches for Image Captioning
## 2 Datasets and Evaluation
### Datasets
The leaderboard [69] has been particularly instrumental as a basis of comparison. The Novel Object Captioning at Scale (Nocaps) [57] is a dataset that enables novel captioning at scale. It consists of 15,100 images each with 11 human-generated captions resulting in 166,100 captions. The images are split into 4500 and 10,600 images for the validation and test sets, respectively. The images are taken from the Open Images object detection validation and test sets. The training data consists of MS COCO image-caption pairs. Open Images has more object classes than MS COCO and so when training is conducted on MS COCO, the test set of Nocaps has object classes not seen during training. Nocaps encourages models to learn a large variety of visual concepts from alternative and diverse data sources; these visual concepts may not be in the actual training data. It \begin{table}
\begin{tabular}{l c c c c c} \hline
**Dataset Name** & **Size (Images)** & **Categories** & **Language Features** & **Primary Domain** & **Human-Annotated** \\ \hline ImageNet [49] & 14.1 M & 21,841 & 14 M image-level labels & IC, OD, VLP & ✓ \\ MS COCO [50] & 300,000 & 91 & 413,915 captions & IC, OD, IS, Cap & ✓ \\ Visual Genome [58] & 108,000 & 33,877 & 5.4 M region descriptions & ARM, D-Cap, VQA, VLP & ✓ \\ CC3M [59] & 3.3 M & 16,000 & 3 M image-text pairs & IC, VLP & ✗ \\ CC12 IM [60] & 12 M & - & 12 M image-text pairs & VLP & ✗ \\ Flickr 8 K [61] & 8000 & - & 40,460 captions & Cap, ISR & ✓ \\ Flickr 30 K [62] & 30,000 & - & 15,8915 captions & Cap, ISR & ✓ \\ Nocaps [57] & 15,100 & 513 & 166,100 captions & Out-of-domain & ✓ \\  & & & & captioning & ✓ \\ VQA [63] & 254,721 & - & 760 K questions & VQA, VLP & ✓ \\ VQAv2 [64] & 265,016 & - & 1.1 M questions & VQA, VLP & ✓ \\ GOA [65] & 113,000 & - & 22 M questions & VR, VLP & ✗ \\ SBU Captions [66] & 1 M & 89 & 1,000,000 & Cap & ✗ \\ OpenImages [51] & 9 M & 600 & 61.4 M image-level labels & IC, OD, ARM, VLP & Partially \\ Objects365 [67] & 600,000 & 365 & 10 M object labels & OD, VLP & ✓ \\ ALT200M [52] & 200 M & - & 200 M image-text pairs & VLP & ✗ \\ WebImageText [53] & 400 M & - & 400 M image-text pairs & VLP & ✗ \\ ALIGN [54] & 1.8 B & - & 1.8 B image-text pairs & VLP & ✗ \\ WebLI [55] & 10 B & - & 10 B image-text pairs & VLP, Cap & ✗ \\ \hline \end{tabular}
\end{table} 
Table 1: The major datasets used in relation to image captioning. For the primary domains: IC is image classification, VLP is vision-language pre-training, OD is object detection, IS is image segmentation, Cap is image captioning, D-Cap is dense captioning, ARM is attribute and relationship modeling, VQA is visual question answering, ISR is image-sentence retrieval. therefore makes possible the design of models which can run much better in the wild with novel object categories.
## Materials and Methods
### Methodologies
#### 2.2.2 The Normal Projection Reflection Reference Surface Correction Method  
The GFRRSCM is based on the assumption that the normal and the radial directions of the specular reflection point are identical, while actually, there is difference between them. Therefore, it is necessary to take the plane perpendicular to the normal as the reflection reference surface to correct the position by the GFRRSCM. The normal of the WGS-84 ellipsoid is taken as the normal here.  
The idea of the radial-normal correction of the S-C Wu method is to calculate the correction based on a geometric relationship built by equal-quantity substitution, iterating until the correction is small, but the positioning accuracy would be affected by the approximation in the substitution [28]. In order to reduce the influence of the approximation, a normal projection correction method is proposed, which positions the specular reflection point by directly solving the spatial geometric relationship between the projection of the normal on the plane and the reflection path. The positioning error caused by the normal-radial difference can be decomposed into the incidence planes \(TSR\) and its vertical plane \(SOK\), as shown in Figure 3, the correction is carried out in these two planes. It has been found through experiments that after the normal correction in \(TSR\), the normal correction is usually small in \(SOK\), therefore, the correction is first applied in \(TSR\) and then in \(SOK\) for ease of calculation. As shown in Figure 3a, the projection of the normal of \(S\) in \(TSR\) is calculated first, then the intersection \(AS\) of the normal reflection reference plane and \(TSR\) is calculated, and finally the position of the specular reflection point on the intersection line is calculated, details are as follows.  
1. The projection of the normal in \(TSR\).  
\(\stackrel{{\rightarrow}}{{\mathbf{S_{\perp}}}}\), is the projection of the normal \(\stackrel{{\rightarrow}}{{\mbox{\boldmath$S_{\
## 3 Results and Discussion
### Relationship Between Sliding Speed and Polishing Cycles  
Figure 5 depicts the variation in COF at different slipping speeds for various aggregates and sizes. This suggests that the DFT results and COF values are sensitive to a variety of factors. CB had consistently higher COF values for all three sizes, both before and after polishing. Rhy, on the other hand, had lower COF levels. While CB retained comparatively greater friction over time, Rhy, especially the coarse size, showed a considerable drop in COF when the polishing cycle was raised to 140,000 cycles. This implies that CB is superior at maintaining frictional properties, but Rhy may lose friction more quickly with prolongedpolishing. The test findings show that CB's strong polishing resistance and rough surface microtexture are directly responsible for this observation.  
The change in MPD for different aggregates and sizes during different polishing cycles in the Three-Wheel Polishing Device is shown in Figure 6. Both aggregates, in different sizes, showed some texture reduction between the initial values and the measurements after 140 K polishing cycles. During the first 30 K cycles of polishing, some samples showed a slight increase and then a decrease, which can be attributed to aggregate particles that are loosely bound and particles with angular but weak structures being worn by the mechanical polishing force of the TWPD tires. Rhy coarse size showed a slight increase in MPD during polishing, which can be attributed to the changing surface texture and angularity of this aggregate during exposure to the polishing process. Between 70 K and 140 K cycles in the TWPD, the surface of most samples became more stable.  
Figure 5: Influence of aggregate type and size on COF at varying speeds.
## 2 Related Works
The image features are extracted from the image frame sequences using CNN, whereas LSTM uses the gate mechanism to maintain vital information. Afterward, the results are compared with existing detection models, including a mixture of probabilistic principal analysis, motion deep net, social force, and dictionary-based models for performance evaluation. The authors of [24] propose a framework that utilizes deep learning techniques to detect abnormal sea surface temperature (SST) events automatically. The framework consists of two main components: a deep convolutional autoencoder and a classifier. The deep convolutional autoencoder is trained on a large dataset of normal SST patterns. The purpose is to learn the underlying features and patterns to detect abnormal events in sea surface temperature, thus monitoring and managing marine ecosystems and predicting extreme weather events. Subsequently, ref. [25] aims to improve the accuracy and efficiency of SST prediction, as well as reduce computational costs by combining two deep learning models: LSTM networks and CNNs. The hybrid approach consists of two main stages: feature extraction and prediction. During the feature extraction stage, CNN extracts spatial features from satellite SST images. The CNN learns to capture patterns and spatial dependencies in the data; therefore, it allows for the encoding of relevant information. The extracted features are subsequently fed into the LSTM networks in the prediction stage. In this case, LSTM networks are capable of capturing temporal dependencies and long-term patterns suitable for time series prediction tasks. In [26], a valuable overview of deep learning approaches is provided for anomalous event detection in video surveillance that serves as a comprehensive resource for researchers and practitioners inclined toward understanding the advancements and current state-of-the-art techniques in this field. Equivalently notable, existing studies have not specifically addressed the labeling of anomalous worker behavior using descriptive text. This study, therefore, presents a model that detects anomalous behavior performed by manufacturing workers based on human-object interaction in videos. In practice, the model combines object detection and human pose estimation using Mask R-CNN (R-CNN stands for Region-Based Convolutional Neural Network), MediaPipe Holistic, long short-term memory (LSTM), and worker behavior description algorithm. Mask R-CNNs and LSTMs are part of the most outstanding practices within deep learning. Mask R-CNN is capable of detecting objects, whereas Me-diaPipe works as a pose tracker. In addition, LSTM is utilized to train the model to recognize possible differences in worker behavior within manufacturing environments. Additionally, as an advancement of previous research [15; 16], this study focuses on using videos to generate descriptive texts and distinguish normal and anomalous worker behavior.
## 4 Results and Discussion
### Analysis with Additional CICIDS2017 and NSL-KDD  
In this section, the effectiveness of the proposed EDRL model, focussing on efficient and accurate attack detection, is assessed with additional datasets such as CICIDS2017 and NSL-KDD. It also supports assessing the generalization ability and determining the suitability of the proposed EDRL model in diverse IDSs. Table 6 deliberates the performance comparison of the proposed EDRL and existing works using CICIDS2017 and NSL-KDD. In the table, it is clearly noticed that the EDRL model has achieved maximum performance in terms of accuracy, precision, recall and f1-score compared to the existing methods. The maximum accuracy, precision, recall, and f1-score values attained by the proposed EDRL model using the CICIDS2017 dataset are 99.93%, 99.78%, 99.57%, and 99.49%. Similarly, the proposed EDRL model has attained maximum accuracy, precision, recall, and f1-score values of 99.42%, 99.32%, 99.12%, and 99.15% using the NSL-KDD dataset. This demonstrates the generalizability and effectiveness of the proposed EDRL model in IDS.
## 7 Production
### Production Rate Calculation  
Reliable production rate estimations are essential for well-performance optimization. [PERSON] and colleagues introduced a novel workflow that integrates a multiphase choke correlation with an equation of state (EOS) to enhance the accuracy of these calculations. The field under study comprises 30 horizontal wells, organized into three-well pads and drilled at varying depths. Initial well testing is conducted to assess productivity and to prevent contaminants like sand and liquids from entering plant facilities [74].  
Choke management is vital for mitigating operational issues such as sand production. Initial choke settings are configured with a 12-14/64\" diameter to facilitate continuous water flow and prevent hydrate accumulation. Adjustments to choke size are incrementally made in order to manage sand output. During the initial 3-4 month clean-up phase, the choke management plan is frequently revised to address challenges like hydrate and sand accumulation, liquid overproduction, and mechanical issues like valve failures. A cross-disciplinary approach involving various engineering teams was deemed essential for overcoming these challenges [74].  
Advanced AI algorithms for gas injection optimization and new workflows for production rate calculations are anticipated to be increasingly important in future operations. [PERSON] et al. developed a methodology specifically for the Vaca Muerta formation, which is particularly useful when a separator is unavailable for production testing. Their approach employs a choke correlation for mass calculation and an EOS for determining water cut and gas to oil ratio (GOR), aiming to bolster the confidence in production rate estimates. The study also emphasizes the role of wellhead chokes in flow regulation and backpressure maintenance.  
Furthermore, the authors explore the thermodynamic properties of fluids in the Vaca Muerta formation, noting the presence of a wide range of hydrocarbon types. In scenarios where pressure-volume-temperature (PVT) analysis is unavailable, baseline PVT data from analogous regions can be employed. The authors validate their methodology by comparing calculated and actual separator test results across multiple wells, claiming a significant reduction in error rates, with discrepancies ranging between 2-5%, based on historical data.
## 2 Literature Review  
### AR for BIM-Based Architectural Design Collaboration  
Augmented reality (AR) is a mixed reality technology that enhances a user's perception of the real world through information provided by computer systems, which superimposes computer-generated virtual objects, scenes, or system prompts on real scenes to achieve the \"enhancement\" of reality [22]. The widely accepted components of AR are 3D registration, the combination of virtuality and reality, and real-time interaction [14].  
Previous research efforts have demonstrated the possibility of combing AR and BIM for architectural design collaboration. [PERSON] et al. conducted an experiment using AR simulation with real-time video played on a laptop, where 3D models were superimposed in an on-site scene to foresee the conditions of the constructed buildings [23]. An auto AR system developed by [PERSON] can visualize 3D BIM models on building sites in live videos using a head-mounted display and position-detection sensors to establish a real-time precise overlay of building models on a real site [24].  
There are several advantages of applying AR technology in design collaboration. First, objects which do not exist in reality can be viewed and analyzed. Using an AR display, users can hold their individual views under their own control, and the displaced information can be adjusted in various forms [25]. For face-to-face design collaboration, AR could improve the information sharing of collaborative physical spaces, providing an interface for intuitive 3D interactive work assisted by computers. AR could also benefit remote collaboration. A study by [PERSON] et al. found that AR offered substantially stronger co-presence and enhanced the immediate perception of communicative interactions [26].  
However, despite the advantages of AR, several limitations have been also found. From a technical perspective, existing AR systems that use a tablet or head-mounted display require real-time tracking technology that relies on sensor detection to enable precise and effective registration while users are in motion. Within this framework, sensor errors may easily occur [18]. Furthermore, users may feel discomfort caused by holding a tablet or wearing a heavy head-mounted device [20]. As existing AR users see an augmented view through their own AR displays, it is difficult to build common reference points between users when representing spatially-located virtual data, which as a result, hinders the common understanding of virtual information compared with a shared physical space [19; 27]. The quality of visualized information is still low due to the technical limitations of existing AR devices. For example, while using a head-mounted AR display (e.g., Microsoft HoloLens), low resolution, distortion, and limited field of view could lead to a false perception of visualized information. Brightness, contrast, and visibility are other screen-related issues that can significantly affect visualization quality [17].
## 4 Data Diversity of UAV
### Overview of UAV Dataset Uses
#### 4.1.4 Environmental Monitoring  
UAVs are used to monitor forest health, wildlife, and water bodies. They provide data for studying ecological changes, tracking animal movements, and assessing the impacts of climate change.
## 2 Building Energy Code
### Building Envelope  
The building energy code evaluates the building envelope by assessing the overall thermal transfer value (OTTV) and roof thermal transfer value (RTTV) of the enveloped area in a building with air conditioning [22]. This value depends on the thermal properties of the material and the envelope composition, both opaque and transparent. The standard sets the calculation methodology based on factors such as the heat transfer coefficient value, window-to-wall ratio, and equivalent temperature. In addition, for transparent materials, the solar heat gain, shading coefficient, and solar radiation are also considered. The OTTV for each side of the wall can be calculated using Equation (1). Concerning the overall value, the weighted arithmetic between the area and OTTV for each side of the building is calculated using Equation (2).  
\[OTTV_{i}=(U_{W})(1-WWR)\big{(}TD_{eq}\big{)}+\Big{(}U_{f}\Big{)}(WWR)\Delta T +(WWR)(SHGC)(SC)(ESR) \tag{1}\]  
\[OTTV=\frac{(A_{w1})(OTTV_{1})+(A_{w2})(OTTV_{2})+\ldots+(A_{wi})(OTTV_{i})}{A_ {w1}+A_{w2}+\ldots+A_{wi}} \tag{2}\]  
where  
\begin{tabular}{l l} \(OTTV\) & = Overall thermal transfer value of the building (W/m\({}^{2}\)) \\ \(OTTV_{i}\) & = Overall thermal transfer value of the considered side (W/m\({}^{2}\)) \\ \(U_{w}\) & = Heat transfer coefficient of opaque wall (W/m\({}^{2,\cdot}\)C) \\ \(WWR\) & = Window-to-wall ratio of considered side \\ \(TD_{eq}\) & = Differential value of the equivalent temperature (\({}^{\circ}\)C) \\ \(U_{f}\) & = Heat transfer coefficient of transparent wall or window (W/m\({}^{2,\cdot}\)C) \\ \(\Delta T\) & = Differential temperature between the interior and exterior of the building (\({}^{\circ}\)C) \\ \(SHGC\) & = Solar heat gain coefficient \\ \(SC\) & = Shading coefficient \\ \(ESR\) & = Effective solar radiation (W/m\({}^{2}\)) \\ \(A_{wi}\) & = Area in each section of the wall section (m\({}^{2}\)) \\ \end{tabular}  
For the roof, the RTTV can also be calculated using a similar equation but substituting the window-to-wall ratio for a skylight-to-roof ratio and the heat transfer coefficient for the roof composition, as expressed in Equation (3). For a building with various roof types using different materials and compositions, the total RTTV can be calculated using a weight equation similar to the OTTV, as expressed in Equation (4).  
\[RTTV_{i}=(U_{I})(1-SRR)\big{(}TD_{eq}\big{)}+\Big{(}U_{f}\Big{)}(SSR)\Delta T +(SSR)(SHGC)(SC)(ESR) \tag{3}\]  
\[RTTV=\frac{(A_{w1})(RTTV_{1})+(A_{w2})(RTTV_{2})+\ldots+(A_{wi})(RTTV_{i})}{A _{w1}+A_{w2}+\ldots+A_{wi}} \tag{4}\]  
where  
\begin{tabular}{c l} \hline \hline
**Type of the Building** & **OTTV (W/m2)** & **RTTV (W/m2)** \\ \hline Group 1 (Office, Education building) & \(\text{OTTV}\leq 50\) & \(\text{RTTV}\leq 10\) \\ \hline Group 2 (Department store, Conventional Hall, Auditorium) & \(\text{OTTV}\leq 40\) & \(\text{RTTV}\leq 8\) \\ \hline Group 3 (Hotel, Hospital, Residential building) & \(\text{OTTV}\leq 30\) & \(\text{RTTV}\leq 6\) \\ \hline \hline \end{tabular}
# A Comprehensive Analysis of Various Tokenizers for Arabic Large Language Models
## 4 Experiments
### Model Pretraining  
The goal of this project is to evaluate the performance of various tokenizers for Arabic LLms. We analyzed the differences between WordPiece (WP), SentencePiece (SP), and Byte-level BPE (BBPE) tokenizers by pretraining three BERT models using each tokenizer (BERT-WP [[https://huggingface.co/faisalq/bert-base-arabic-wordpiece](https://huggingface.co/faisalq/bert-base-arabic-wordpiece)]([https://huggingface.co/faisalq/bert-base-arabic-wordpiece](https://huggingface.co/faisalq/bert-base-arabic-wordpiece)) (accessed on 21 May 2024), BERT-SP [[https://huggingface.co/faisalq/bert-base-arabic-senpiece](https://huggingface.co/faisalq/bert-base-arabic-senpiece)]([https://huggingface.co/faisalq/bert-base-arabic-senpiece](https://huggingface.co/faisalq/bert-base-arabic-senpiece)) (accessed on 21 May 2024), and BERT-BBPE [[https://huggingface.co/faisalq/bert-base-arabic-bbpe](https://huggingface.co/faisalq/bert-base-arabic-bbpe)]([https://huggingface.co/faisalq/bert-base-arabic-bbpe](https://huggingface.co/faisalq/bert-base-arabic-bbpe)) (accessed on 21 May 2024)) while measuring the performance of each model on seven different NLP tasks. The pretraining text corpus underwent a few preprocessing operations using PyArabic [47], a Python library for preprocessing and normalizing Arabic text. The preprocessing steps included only removing diacritics and Arabic letters' extension (Tatweet), without removing any punctuation, symbol characters, or English words. The final corpus size came out to 35.7 GB (29.6 GB from CC-100-AR [48], and 6.1 GB from Wikipedia dump and Al-Shamela library [49] that was already compiled and made public on Kaggle [50]).  
All three models followed the same architecture as the original BERT model, with 12 encoder layers, 12 attention heads per layer, and a hidden layer size of 768 units, and the vocabulary size was set to 50,000 wordpieces. Furthermore, we used the following configurations for pretraining the models: masking 15% of the input tokens, a maximum sequence length of 128, a batch size of 128, and the 'AdamW' optimizer, with a learning rate of \(5\times 10^{-5}\). Finally, all three models were pretrained for 2 epochs (around 2 million steps). Figure 1 provides an overview of the text preprocessing and tokenizer preparation steps, along with the model pretraining procedures.
## 4 Results and Discussion
### Single-Phase Flow Characteristics and Internal Flow Characteristics
#### 4.1.1 Internal Flow under Different Flow Rates
As a result, the liquid separates into two parts under the influence of the leading edge structure of the vaned diffuser. A portion of the fluid separated from the main flow impacts the blade's leading edge of the vaned diffuser, forming a strong backflow between the vaned diffuser and the blade outlet gap. The other part of the fluid flows downstream along the vaned diffuser blades' suction side, exhibiting significant velocity separation. A distinct stagnation vortex forms at the throat region of the vaned diffuser, reducing its flow capacity. As the flow rate increases, the velocity flow angle in the impeller approaches the designed inlet angle, and the flow separation phenomenon within the impeller passage gradually weakens. The fluid at the impeller outlet enters the throat of the vaned diffuser along the blade pressure side, and the backflow phenomenon in the impeller outlet disappears. Figure 11: The nephogram of pressure on the blade-to-blade surface of the inducer under different degrees of flow rates (\(n=7500\) rpm). The refrigerant flows through the divergent guide section formed by the pump casing and motor housing, transforming part velocity energy into pressure energy. Due to the sudden expansion and contraction of the structure in this section, distinct flow vortices occur at the front position and the outlet, as shown in Figure 13. It can be observed that with increasing flow rate, the vortex at the front of the passage gradually moves toward the inlet of the path, and the subsequent streamlines approach uniformity. Under the same working condition, the fluid velocity does not change significantly in this section. The friction loss between the fluid and the wall surface is the primary part of energy loss. At the posterior passage, the cross-sectional flow area forms a pattern of \"constriction-expansion-constriction\", resulting in continuous variation of the fluid velocity. Therefore, the morphology and quantity of the stepped flow vortices in this section exhibit noticeable changes with increasing flow rates. The losses in this section are mainly composed of fluid friction and bend losses.