esa-sceva/satcom-chunk-collection huggingface dataset:
_Applied_  
_sciences_  
_Drill Bit Deformations in Rotary Drilling Systems under Large-Amplitude Stick-Slip Vibrations_  
**[PERSON] 1, [PERSON] 1\({}^{\copyright}\)**, [PERSON] 2 and [PERSON] 3,*\({}^{\copyright}\)**  
1 Laboratoire Energtique-Mecanique & Ingenieries (LEMI), Universite M'Hamed BOUGARA de Boumerdes, Boumerdes 35000, Algeria; [EMAIL_ADDRESS] (I.K.); [EMAIL_ADDRESS] (S.B.)  
2 Laboratoire d'automatique appliquee, Faculte des Hydrocarbures et de la Chimie,  
Universite M'Hamed BOUGARA de Boumerdes, Boumerdes 35000, Algeria; [EMAIL_ADDRESS]  
3 School of Engineering, University of Greenwich, Kent ME4 4 TB, UK  
* Correspondence: [PERSON]ADDRESS]  
Received: 29 August 2020; Accepted: 15 September 2020; Published: 18 September 2020  
**Featured Application: This research work can be applied to provide a preventive maintenance planning and strategy of drill bit in rotary drilling systems under harmful torsional vibrations during the drilling process.**  
**Abstract:** In oil and gas industry, rotary drilling systems are used for energy exploration and productions. These types of systems are composed of two main parts: mechanical and electrical parts. The electrical part is represented by rotating motor called top drive; however, the mechanical part of the system is composed of tool string with many pipes, at the bottom end of these pipes the bit is attached to cut the rock during their contact. Since the bit is in a direct contact with rock characteristic variations, it can be under risk for heavy damage. The latter is principally caused by the fact that the rock-bit interaction term is highly nonlinear and unpredictable. In literature, many mathematical models have been proposed for rock-bit interaction, but they do not reflect the dynamic of the bit under vibrations since torsional and axial vibrations are strongly coupled and synchronized with it. In industrial development, the design of drill bit has faced many improvements in order to overcome these vibrations and mitigate unpredictable phenomena. Even though, the practical use of these drill bits confirmed that there are still many failures and damages for the new designs; moreover, bits' virtual life become shorter than before. The objective of this study is to analyze the drill bit deformations caused by the stick-slip vibration phenomenon which is characterized by high-frequency high-amplitude in rotary drilling systems. The obtained results were validated through a case study of MWD (measurement while drilling) data of well located in a Southern Algerian oil field.  
**Keywords:** oil and gas industry; rotary drilling systems; drill bit deformation; torsional vibration; large-amplitude vibrations
## 2 Materials and Methods
### Datasets
#### 2.2.2 SIF Data  
GOSIF is an OCO-2-based Sun-induced fluorescence product provided by the Earth Systems Research Center at the University of New Hampshire ([[http://data.globalecology.unh.edu/data/GOSIF_v2/](http://data.globalecology.unh.edu/data/GOSIF_v2/)]([http://data.globalecology.unh.edu/data/GOSIF_v2/](http://data.globalecology.unh.edu/data/GOSIF_v2/)), accessed on 20 October 2024). GOSIF offers greater spatial continuity and broader coverage than the directly measured SIF data, making it a more accurate and reliable indicator of vegetation dynamics. In this study, monthly GOSIF data from 2000 to 2020 were used, with negative values as noise was adjusted to zero.  
Figure 1: Study area of the Xinjiang arid region in northwest China (Vegetation is classified as follows: Forest (red), high coverage grassland (HCG, >50%, dark green), moderate coverage grassland (MCG, 20–50%, medium green), and low coverage grassland (LCG, 5–20%, light green). The ecological regions (R1–R5) are delineated with different hatching patterns, and meteorological stations are marked with red dots. The map was created using the standard map approved by the Ministry of Natural Resources of China (review number GS (2024) 0650). The base map provided by the Ministry of Natural Resources was used without any modifications. Similarly, all other maps in this study were created using standardized methods and remain unaltered.
## 4 Experimentation and Analysis
### Qualitative Results  
#### 4.2.1 Ablation Experiments  
Ablation studies were conducted to verify the effectiveness of the DMR-GCN framework and its components. The experimental results are shown in Table 1 and Figure 9a. The base model (MR-GCN) performed mediocrely on all metrics, indicating its limitations in capturing vehicle lane-changing behavior and complex interaction patterns. After adding the dynamic multilevel relationship graph (DRG), the accuracy and recall of the model significantly improved from 0.85 and 0.84 to 0.87 and 0.86 for CLAL and CLAR, respectively; from 0.91 to 0.93 for Map; and from 0.90 to 0.92 for F1. These improvements suggest that the DRG is effective in capturing complex dynamics.  
Introducing the enhanced temporal interaction graph generation method (ETIG) improves the model's ability to capture complex interaction patterns significantly, with CLAL and CLAR reaching 0.88 and 0.87, respectively, and Map and F1 reaching 0.94 and 0.93. Additionally, graph attention's dynamic neighborhood matrix updating mechanism (GAMU) further improved dynamic relationship capturing and feature processing, achieving CLAL and CLAR scores of 0.86 and 0.85 and Map and F1 scores of 0.92 and 0.91, respectively.  
The model's performance significantly improved when combined with the dynamic multi-level relational graph (DRG) and enhanced temporal interaction graph generation method (ETIG) components. Adding these components boosted the model's accuracies on continuous left-lane alteration (CLAL) and continuous right-lane alteration (CLAR) to 0.89 and 0.88, respectively. At the same time, the mean average precision (Map) and F1 scores  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Architecture** & **Classes** & **CLAL** & **CLAR** & **LCL** & **LCR** & **OVT** & **Map** & **Recall** & **F1** \\ \hline MR-GCN & 0.85 & 0.84 & 0.83 & 0.82 & 0.80 & 0.91 & 0.89 & 0.90 \\ MR-GCN + DRG & 0.87 & 0.86 & 0.85 & 0.84 & 0.82 & 0.93 & 0.91 & 0.92 \\ MR-GCN + ETIG & 0.88 & 0.87 & 0.86 & 0.85 & 0.83 & 0.94 & 0.92 & 0.93 \\ MR-GCN + GAMU & 0.86 & 0.85 & 0.84 & 0.83 & 0.81 & 0.92 & 0.90 & 0.91 \\ MR-GCN + DRG + ETIG & 0.89 & 0.88 & 0.87 & 0.86 & 0.84 & 0.94 & 0.92 & 0.93 \\ MR-GCN + DRG + GAMU + ETIG & 0.89 & 0.88 & 0.87 & 0.86 & 0.84 & 0.94 & 0.92 & 0.93 \\ \hline \hline \end{tabular}
\end{table}
Table 1: DMR-GCN framework ablation experiments.  
increased to 0.94 and 0.93. These enhancements validate the effectiveness of the DRG and ETIG components in capturing and predicting complex traffic dynamics.  
Following integrating all components, the model showed robust performance across all metrics, with CLAL and CLAR scores of 0.89 and 0.88 and Map and F1 scores of 0.94 and 0.93. This demonstrates the components' effectiveness in enhancing the model's understanding and prediction of complex traffic scenarios. In conclusion, integrating these components significantly improved the MR-GCN framework's capacity to handle intricate interaction patterns and various traffic scenarios.
# Scalable Name Lookup for NDN Using Hierarchical Hashing and Patricia Trie
## 4 Proposed Name Lookup Scheme
### Lookup Algorithm  
Whenever an interest name is given for lookup, the name lookup engine extracts and processes each component as starting from the first level. The hash code of an extracted component locates an entry in the hash table. Each hash table entry has the pointer to a Patricia trie, which is provided for overflow area. Thus, it needs to search the Patricia trie to find a matching result. The search result consists of a face number and a pointer to the next level hash table. If the Patricia search succeeds, the found face is recorded as the potentially best result, and lookup proceeds with a next level component. The lookup process stops at the level where the search fails, and the recorded last result becomes the final result, i.e., the longest matching result. As an example, suppose /a1/b1/c1 is given as an interest name, and the given set of prefixes is /a1/b1, a1/b1/c2. Then, the lookup stops at the third level, because c1 is not matched with c2 at this level. Consequently, the best result becomes /a1/b1, which is the last matching prefix.  
The whole lookup procedure is described in Algorithm 1. Additionally, Algorithm 2 shows how to search a Patricia trie. When a component string is given, a specific bit that is designated by _bitpos_ is extracted. According to the bit, the left or the right child is traversed, and the procedure is repeated until a leaf node is reached.  
```
1:\(name\) that is an Interest name
2:\(tab\) = root_hash_table
3:\(best\_result\) = default_face
4:forever do
5:\(c\) = next_component(\(name\))
6:\(h\) = hash(\(c\))
7:\(aux\) = Search_Patricia(\(tab[h]\), \(c\))
8:if\(aux\) is not null and \(c\) == \(aux\)\(\rightarrow\)key then
9:\(best\_result\) = \(aux\)\(\rightarrow\)face
10:\(tab\) = \(aux\)\(\rightarrow\)next
11:else
12:return\(best\_result\)
```  
**Algorithm 1** Algorithm for Name Lookup in FIB  
```
1:\(t\) = \(root\_hash\_table\)
2:\(best\_result\) = default_face
3:forever do
4:\(c\) = next_component(\(name\))
5:\(h\) = hash(\(c\))
6:\(aux\) = Search_Patricia(\(tab[h]\), \(c\))
7:if\(aux\) is not null and \(c\) == \(aux\)\(\rightarrow\)key then
8:\(best\_result\) = \(aux\)\(\rightarrow\)face
9:\(tab\) = \(aux\)\(\rightarrow\)next
10:else
11:return\(best\_result\)
```  
**Algorithm 2** Search Algorithm for [PERSON]  
```
1:\(t\) = null then
2:return null
3:for\(t\) is not a leaf do
4:\(b\) = GET_BIT(\(c\), \(t\)\(\rightarrow\)bitpos)
5:\(t\) = \(t\)\(\rightarrow\)child[\(b\)]
6:return\(t\)\(\rightarrow\)\(aux\)
```  
**Algorithm 3** Search Algorithm for [PERSON]
# An Adaptive Data Rate Algorithm for Power-Constrained End Devices in Long Range Networks
## 4 LoRa Network Performance Evaluation
### ED Power Consumption Model  
#### 4.2.1 ED Hardware Design  
The LoRa ED is a fundamental component of the LoRa network, responsible for collecting sensor data and uploading them to the NS. Its hardware design must meet the requirements for multiple sensor interfaces, power supply options, and low power consumption. The ED uses the STM32F103 RCT6 microcontroller (MCU) (manufactured by STMicroelectronics, Geneva, Switzerland) as the main controller and Semtech's SX1262 RF chip (manufactured by Semtech Corporation, Camarillo, CA, USA) as the communication module. The device is compatible with UART and RS-485 interfaces, making it suitable for a variety of sensors. A physical diagram of the ED hardware is shown in Figure 4.  
Figure 4: Physical diagram of ED hardware.  
Figure 3: Network throughput variation with different SFs when CR = 4/5 and TP = 17 dBm.
# Mapping [PERSON] Using Sentinel-1 SAR Time Series in Camargue, France
## 2 Materials
### SAR Data  
Since rice cultivation in Camargue takes place between May and October of each year, we focus our analysis of SAR data only on this period. For this reason, thirty-one S1 images obtained from the Sentinel-1A (S1A) and Sentinel-1B (S1B) satellite constellation operating at the C-band (frequency = 5.406 GHz, wavelength \(\sim\) 6 cm) were downloaded for the period between 08 April 2017 and 29 October 2017. The 31 S1 images used were acquired in the Interferometric Wide Swath (IW) imaging mode with the VV and VH polarizations. In addition, the images were generated from the high-resolution Level-1 Ground Range Detected (GRD) product with 10 m \(\times\) 10 m pixel spacing. These Sentinel-1 images are available via the Copernicus website ([[https://scihib.copernicus.eu/dhus/#home](https://scihib.copernicus.eu/dhus/#home)]([https://scihib.copernicus.eu/dhus/#home](https://scihib.copernicus.eu/dhus/#home))). To calibrate the S1 images (radiometric and geometric correction); the Sentinel-1 Toolbox (S1 TBX) developed by the ESA (European Spatial Agency) was used. The radiometric calibration aims to convert the digital number values of the S1 images into backscattering coefficients (\(\sigma^{\circ}\)) in a linear unit. The radiometric accuracy of the Sentinel-1 SAR backscattering coefficient is approximately 0.70 dB (3\(\sigma\)) for the VV polarization and 1.0 dB (3\(\sigma\)) for the VH polarization [26]. The aim of the geometric correction is to ortho-rectify the S1 images using the digital elevation model of the SRTM \"Shuttle Radar Topography Mission\" at 30 m spatial resolution. It is good to mention here that the study area is relatively flat with no high slope areas. A multi-temporal speckle filter, given in Equation (1), was then applied to the 31 calibrated S1 images to reduce the speckle noise in each SAR image [27]. Finally, a temporal series of 31 calibrated S1 images were obtained in the WGS-84 geographic coordinate system (World Geographic System 1984).  
\[J_{k}(x,y)=\frac{\langle I_{k}(x,y)\rangle}{M}\sum_{i=1}^{M}\frac{I_{i}(x,y)}{ \langle I_{i}(x,y)\rangle} \tag{1}\]  
where \(J_{k}(x,y)\) is the radar intensity of output image k at pixel (x, y), \(I_{i}(x,y)\) is the radar intensity of input image i at pixel (x, y), and \(\langle I_{i}(x,y)\rangle\) is the local mean intensity of input image I at pixel (x, y).  
Figure 2: NDVI time series of 11 crop types in the study area.
## 2 Principles and Technical Architecture of the Satellite Navigation Signal Authentication
### Incremental Capability  
Navigation signal authentication technology will bring a new service to the GNSS, which neither improves the accuracy nor augments the integrity and continuity, just focuses on improving the anti-spoofing capability of GNSS civil signals to provide users with more credible PNT services. Signal authentication is a system-side anti-spoof technology which can resist generative spoofing. The orange part in Figure 7 represents the incremental capability.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Protocol** & **Cryptographic Algorithm** & **Calculated Amount** & \begin{tabular}{c} **Authentication Information** \\ **Truncation** \\ \end{tabular} & \
\begin{tabular}{c} **Key Distribution** \\ **Requirements** \\ \end{tabular} &
\begin{tabular}{c} **Key Length under** \\ **the Same Security** \\ **Level** \\ \end{tabular} \\ \hline TESLA [28, 29, 30, 31] & Hash, HMAC & Small & Yes & Yes & Short \\ \hline DS [18, 27] & DS & Big & No & No & Long \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of TESLA and ECDSA.  
Figure 6: Satellite navigation signal authentication architecture.  
Anti-spoofing method  
Anti-spoofing capability  
According to the GNSS cheating attacker type, it is divided into generated spoofing and meaconing. The anti-spoofing effect of the satellite navigation signal authentication is detailed, as shown in Table 4[17].  
Generated spoofing means that the attacker generates a spoofing signal with the exact same structure as the real GNSS signal [39], which utilizes the known vulnerabilities of the civilian signal ICD to generate a false GNSS spoofing signal and broadcast it to the target receiver. The prerequisite for satellite navigation signal authentication is that the spoofing attacker cannot break the cryptographic algorithm, so that the authentication message/spreading code cannot be forged. Therefore, satellite navigation signal authentication can solve the generative spoofing attack to civilian users.  
Meaconing means that the attacker receives the navigation signal [40], performing proper delay and power amplification on the real GNSS signal, and then broadcasts the meaconing signal to the target receiver. The meaconing does not change the message and spreading code, so the satellite navigation signal authentication effect is not good for this method.  
Figure 7: Ability of satellite navigation signal authentication technology.  
In addition to the above two common spoofing methods, Security Code Estimation and Replay (SCER) [41] has also been proposed in recent years. This method is to receive the real signal and estimate the encrypted or authenticated message in real time as much as possible. Then, the encrypted or authenticated message in the signal is reassembled and sent. SCER predicts the authentication message based on the security code estimation method, which is effective for security codes with a low symbol rate (navigation message), but less effective for security codes with a high symbol rate (spreading code).
## 3 Multimodal Fusion Network Based on Attention Mechanism
### Multimodal Fusion Module
#### 3.2.1 Textual Double-Embedding and Self-Attention Module  
For text sentences in rumor detection, recurrent neural networks with a certain amount of structure are usually used, such as LSTM and GRU layers for representation learning to build sentence models [28; 29]. However, recurrent neural networks do not capture well the internal information words in different dependencies between positions. They are also costly in terms of time, especially when dealing with long sentences: the attention mechanism is more focused on capturing the internal dependencies between words than the recurrent network. To use the attention mechanism, which uses key-value pairs to query the dictionary, it is possible to reconstruct the information based on the similarity of elements [30], as shown in the following equation:  
\[Attention(Q,K,V)=softmax(\frac{QK^{T}}{\sqrt{d_{K}}})V \tag{3}\]  
where \(d_{K}\) denotes the dimension of the key. Inspired by TUDE (Transformer with Untied Positional Encoding) [31], we included sentence information in the relational modeling of each word pair to reduce the loss of semantic information.  
Specifically, we combined the embedded words and sentences in the following way. Word information could ensure that the TDEDA model chose the right keywords to interact with visual features in the process of feature understanding and further inferred whether the tweet was rumor information or not. The purpose of marking with WordPiece was to process the text information into continuous word information and to facilitate the presentation and extraction of word information and sentence information through a self-attention embedded representation, as shown in Figure 2.  
\[\alpha_{ij}=\frac{1}{\sqrt{2d}}(\hat{t}_{i}W^{Q})(\hat{t}_{j}W^{K})^{T}+\frac{1}{ \sqrt{2d}}(SU^{Q})(SU^{K})^{T} \tag{4}\]  
\[t_{i}=\sum_{j=1}^{n}\frac{exp(\alpha_{ij})}{\sum_{j=1}^{n}exp(\alpha_{ij}^{ \prime})}(\hat{t}_{j}W^{V}) \tag{5}\]  
where \(W^{Q},W^{K},W^{V}\in\mathbb{R}^{d_{i}\times d_{i}}\) denotes the matrix of learnable items of \(t_{i}\). \(U^{Q},U^{K}\in\mathbb{R}^{d_{i}\times d_{i}}\) denotes the moment of learning items of the \(S\) matrix, and \(d_{i}\) is the dimension of the text feature space. Then, we obtained the double-embedding representation of the rumor text, \(T=(t_{1};t_{2};\ldots;t_{n})\in\mathbb{R}^{n\times d_{i}}\). By this method, the internal dependencies between words and the information of sentences were encoded to represent the semantics of the text more effectively.
## 1 Introduction
The travel duration of the leakage source signals from the leak point to either sensor was extracted from the identified propagation channel. This paper proposes a modified generalized cross-correlation (GCC) location function, which can compensate for the weakening effect of the different propagation paths on the leakage-induced signals. After the multilayer perceptron neural networks (MLPNN) is trained by the distances between the impact points and the sensors and the energy ratios of two impulse response signals measured by two sensors, it can obtain the relationship between the signal energy ratios and the vibration excitation location, and thus can locate a pipeline leak. This location method can be used to locate a leak in a pipe made of isotropic materials, such as metallic, plastic, etc., whose elastic modulus, Poisson's ratio and density of which should be constant. In this paper, the impulse response signals are generated by the collision between an impulse hammer and the pipe. Although the proposed method is applicable for pipes with variable operating pressure, this study only deals with the location of a single leak source when the gas pressure inside the pipe is constant. The rest of this paper is organized as follows. Section 2 introduces the modified GCC location method and wave speed estimation based on modal analysis of gas-leakage-induced guided waves. Section 3 introduces the principle of the leak location method based on the AE signal energy attenuation and the MLPNN. Section 4 presents the experimental setup, data collection and the signal processing for leakage location. Section 5 analyzes the experiment results. Section 6 presents the study conclusions.
## 5 Discussion
### Parameter Discussion of Superpixel Number  
In this section, the effect of the number of original superpixels is investigated. The numbers of training and test samples are selected to be the same as in the aforementioned experiments on the SAR1, SAR2, and SAR3. According to the size of the image, the number of original superpixels for SAR1 is selected from 2500 to 18,500 with a step size of 1000. The number of original superpixels for the SAR2 is selected from 59,500 to 67,500 with a step size of 500, while the number of original superpixels on the SAR3 is selected from 1500 to 21,500 with a step size of 1000.  
Figure 10 illustrates the OA values of the proposed MAKC method under different original superpixel numbers on all three real SAR images. For SAR1 in Figure 10a, it can be seen that when the number of superpixels is 10,500, the classification accuracy of OA reaches the optimum, marked with a red rectangle, and when the number of original superpixels is selected as other values, OA decreases. This is mainly due to the fact that when the number of original superpixels is fewer than 10,500, the spatial information of different categories may be contained in one superpixel, resulting in a decline in classification accuracy. On the other hand, when the number of original superpixels is higher than 10,500, the size of each superpixel will decrease, so that spatial information in a large homogeneous area of the SAR1 image cannot be fully utilized for classification. In addition, the same situation can also be seen on SAR2 in Figure 10b and SAR3 in Figure 10c with the optimal number of original superpixels being 64,500 and 4500, respectively.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **Non-Spatial** & **AP** & **GLCM** & **WL** & **MAKC** \\ \hline AA & 0.7173 & 0.864 & 0.7081 & 0.7306 & 0.8747 \\ OA & 0.7867 & 0.872 & 0.8568 & 0.8589 & 0.901 \\ Kappa & 0.46 & 0.65 & 0.6 & 0.6 & 0.7 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Classification results on SAR1 with TN = 10.  
Figure 8: Classification result (in OA, AA, and Kappa) comparison between MAKC and MAKC without spatial information, with different numbers of training samples. This is demonstrated on (**a**) SAR1 image, (**b**) SAR2 image, and (**c**) SAR3 image. TN = n means the number of training samples is equal to n.  
Note that in Figure 10c, the number of training samples is large, and the accuracy value is stable and high; conversely, the accuracy value fluctuates greatly and is low. The reason for this is that the number of training samples is large, the extracted discrimination features are more, and the recognition ability is improved, so the classification results change little. At the same time, with different numbers of training samples, the best number of original superpixels for the SAR3 image is the same, which demonstrates the robustness of the proposed method.