esa-sceva/satcom-chunk-collection huggingface dataset:
# Visual-Based Localization Using Pictorial Planar Objects in Indoor Environment
## 1 Introduction
### Object-Based Mapping  
Extracting from a camera view the image pixels corresponding to certain positions in a 3D map is the first step for mapping-based localization. It has two subproblems: what information it can gather from a captured view, and how to map the view information to the actual 3D map.  
Image information identification and extraction can be done by pixel-based methods or object-based methods. Pixel-based methods try to detect significant feature pixels from the camera view to match with features of some images in the image collection. They usually report only the simlarity level between them without using any 3D mapping. In comparison, object-based methods try to identify some actual objects in their views, such as tables and chairs. Each object has a 3D model with an actual size which can be used to build a 3D map. Using real objects' shapes and sizes, object-based methods can derive the distances between a camera and visible objects in the 3D model. As long as a camera can accurately see and identify known objects, these objects' 3D models provide the intermediary knowledge between the camera view and its real world 3D location.  
Object detection and 3D matching are therefore the foundation technologies for object-based localization's accuracy and efficiency. Some earlier projects [4; 5; 6] have studied 3D model objects, such as tables and buildings, as objects for detection, map building and matching. However, 3D object model building and matching have a high computational complexity and are very time consuming. It may be difficult for low-cost IoT devices to support such computations.  
For fast detection and simplified object model building, artificial or computer-generated objects have been designed in some projects [7; 8; 9]. These artificial, easy-to-identify objects (Figure 1a) can be placed in an environment for a camera to easily detect them as references for localization. However, many artistic or high-end indoor places, such as museums, galleries and hotels, may not want to put such artificial objects on their walls. Using natural or artistic objects is more desirable for VBL technologies.  
Our VBL study for natural object identification and mapping proposes to use 2D matching models rather than 3D models, since there are many 2D image matching techniques much more efficient than 3D techniques. Moreover, many natural objects have some planar surface, e.g., books, furnitures and wall-hanging art pieces. We can identify an object using only its planar surface without taking its complete 3D features and model. By using 2D planar object detection and then applying 2D planar to 3D object construction, the 3D position of a carefully modeled object can be produced efficiently as long as its planar surface can be correctly detected and located.
# Development of Operator Theory in the Capacity Adjustment of Job Shop Manufacturing Systems
## 1 Introduction
### Research Work  
In this paper, the RRCF approach is utilized to design controllers for the capacity adjustment of job shop systems with RMTs and analyze the dynamic and stability performance of the system. In the design of the capacity control system, the WIP level is one key performance indicator, which highly influences cost, throughput, and delivery date reliability [30]. Therefore, our aim was to ensure that the WIP of each workstation is on a planned level. However, the complex material flow, couplings, various delays, and disturbances, including transportation delays between workstations, reconfiguration delays of RMTs, and stochastic demands, make this problem more difficult, especially for large-scale companies. To deal with these challenges, a decoupling controller based on operator theory was applied to transform the complex MIMO system into multiple SISO systems to decrease the complexity for a quick response and less involvement with other workstations [30]. Then, the capacity of each SISO system (workstation) was controlled to ensure the WIP on a planned level. Additionally, to verify the effectiveness of the RRCF control system, PID was considered as the benchmark.  
The structure of the paper is organized as follows: In Section 2, the mathematical preliminaries aew introduced. Then, the model of the job shop system with RMTs is described in Section 3. Thereafter, Section 4 concentrates on the capacity control design, and simulation results are shown in Section 5. Finally, the conclusions and an outlook are provided in Section 6.
# Grouped Contrastive Learning of Self-Supervised Sentence Representation
## 5 Further Investigation of GCLSR
### Effect of Weight Decay  
We find that the value of the weight decay influences performance dramatically. We conjecture that the perturbation of model weight can influence contrastive self-supervised training. Therefore, we perform an experiment to investigate it. The results are shown in Table 5.  
The experimental results show that improper weight decay could make the model stop training early, resulting in underfitting and poor performance.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Batch Size/STS** & **STS12** & **STS13** & **STS14** & **STS15** & **Avg.** \\ \hline
64 & **56.35** & 72.56 & 66.59 & 74.73 & 67.56 \\
128 & 56.20 & **72.77** & 66.72 & 75.03 & **67.68** \\
256 & 55.94 & 72.66 & 66.67 & 75.22 & 67.62 \\
512 (ours) & 55.79 & 72.72 & **66.73** & **75.40** & 67.66 \\
1024 & 55.73 & 72.44 & 66.45 & **75.40** & 67.51 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The effect of batch size.  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Model** & **MR** & **CR** & **SUBJ** & **MPQA** & **SST-2** & **TREC** & **MRPC** & **Avg.** \\ \hline Word2 vec embeddings(avg.) & 75.91 & 77.56 & 89.31 & 87.18 & 80.89 & 77.40 & **72.17** & 80.06 \\ BERT\({}_{base}\)(first-last avg.) * & **78.66** & **86.25** & **94.37** & **88.66** & **84.40** & **92.80** & 69.54 & **84.94** \\ \hline GCLSR\({}_{base}\) & 76.78 & 79.02 & 90.21 & 88.35 & 81.77 & 83.00 & 73.28 & 81.77 \\ GCLSR\({}_{base}\)+EDA & 76.56 & 79.55 & 90.54 & 88.54 & 81.66 & 84.40 & 72.99 & 82.03 \\ GCLSR\({}_{base}\)+FransL & 76.61 & 79.52 & 90.41 & 88.74 & 81.27 & 84.00 & 73.04 & 81.94 \\ GCLSR\({}_{base}\)+PWVA & 76.76 & **80.08** & 90.66 & 88.59 & 81.60 & 85.20 & 73.57 & **83.35** \\ GCLSR\({}_{base}\)+PWVA+self-att. & 76.97 & 78.81 & **90.98** & 88.36 & 80.51 & 84.60 & **73.74** & 82.00 \\ GCLSR\({}_{base}\)+EDA+self-att.+GP & 76.90 & 79.58 & 90.57 & 88.50 & **81.82** & **85.60** & 72.75 & 82.25 \\ GCLSR\({}_{base}\)+TransL.+self-att.+GP & 76.98 & 79.32 & 90.41 & 88.54 & 81.16 & 84.00 & 73.28 & 81.96 \\ GCLSR\({}_{base}\)+PWVA+self-att.+GP & **77.69** & 79.87 & 90.89 & **88.99** & 81.44 & 83.80 & 73.39 & 82.30 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The results of transfer tasks. All results are computed with the Spearman correlation. *: results from [21]; the remaining results are evaluated by us.
## 3 Stationary Localization Algorithms
### Centralized Localization Algorithms  
In this section, five centralized localization schemes are summarized: (1) Area Localization Scheme (ALS), (2) Hyperbola-based Localization Scheme (HLS), (3) Sensor arrays based localization approach, (4) an Probabilistic Localization Method (PLM) and (5) Asymmetrical Round Trip based Localization (ARTL) algorithm.
# ODEM-GAN: An Object Deformation Enhancement Model Based on Generative Adversarial Networks
## 3 Approach
### Ground Truth and Loss of the Detector
#### 3.3.2 Regression Modules  
The structure of the regression module is the same as that of the classification module. The difference is that the output layer is a \(3\times 3\) convolutional layer with four filters. The regression module outputs four offset maps of the ground truth and the bounding box. The distance from each pixel to the top, left, bottom, and right of the bounding box is represented by a 4-dimensional vector \(t_{i,j}^{*}=[t_{i,j}^{*},t_{i,j}^{*},t_{i,j}^{*},t_{i,j}^{*}]\). The regression loss is the same as RetinaNet, using smooth \(L_{1}\) loss, as shown in Equation (10).  
\[L_{reg}=\left\{\begin{aligned} &\left|t_{i,j}^{*}-t_{i,j}\right|^{2}*0.5* 0.9,\left|t_{i,j}^{*}-t_{i,j}\right|<1/9\\ &\left|t_{i,j}^{*}-t_{i,j}\right|-\frac{0.5}{0.9},else.\end{aligned}\right. \tag{10}\]  
where \(t_{i,j}\) is the 4-dimensional offset of the output of the bounding box.  
Note that the parameters for classification are shared across each pyramid level, and the parameters for the regression module are also shared across each pyramid level. However, the parameters for classification and regression are independent. The architectures of the classification and regression modules are shown in Table 3.  
The total loss of the ODEM-GAN is shown in Equation (11).  
\[L_{ODEM-GAN}=\frac{1}{N_{cls}}\sum_{l=3}^{7}\sum_{i,j}L_{cls}+\frac{1}{N_{reg} }\sum_{l=3}^{7}\sum_{i,j}L_{reg}, \tag{11}\]  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Name**} & \multicolumn{2}{c}{**Output Size**} & \multicolumn{2}{c}{**Layer**} \\  & & **(Height, Width, Channel)** & **Act.** & **(Filter\_h, Filter\_w, Num, Stride, Padding)** \\ \hline \multirow{4}{*}{classification module} & class\_conv1 & \((8,8,256)\times 64\) & ReLU & \((3,3,256,1,1)\) \\  & class\_conv2 & \((8,8,256)\times 64\) & ReLU & \((3,3,256,1,1)\) \\  & class\_conv3 & \((8,8,256)\times 64\) & ReLU & \((3,3,256,1,1)\) \\  & class\_conv4 & \((8,8,256)\times 64\) & ReLU & \((3,3,256,1,1)\) \\  & class\_output & \((8,8,\text{K})\times 64\) & Sigmoid & \((3,3,\text{K},1,1)\) \\ \hline \multirow{4}{*}{regression module} & regress\_conv1 & \((8,8,256)\times 64\) & ReLU & \((3,3,256,1,1)\) \\  & regress\_conv2 & \((8,8,256)\times 64\) & ReLU & \((3,3,256,1,1)\) \\ \cline{1-1}  & regress\_conv3 & \((8,8,256)\times 64\) & ReLU & \((3,3,256,1,1)\) \\ \cline{1-1}  & regress\_conv4 & \((8,8,256)\times 64\) & ReLU & \((3,3,256,1,1)\) \\ \cline{1-1}  & regress\_output & \((8,8,\text{K})\times 64\) & - & \((3,3,4,1,1)\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Only PF3 is introduced, similarly to in the other levels.  
where \(N_{cls}\) is the total of the positive and negative samples and \(N_{reg}\) is the total of positive samples.
# An Analysis of MPTCP Congestion Control
## 7 MPTCP Congestion Control
### Coupled CC
#### 7.2.2 Ola  
Like LIA, OLIA [7] shares the increased state of CA algorithm among subflows and does not modify TCP-Reno behaviour in the case of a loss. OLIA uses the TCP SS algorithm with a slight modification. If there are multiple paths established, the SS threshold is set to one MSS. When there is a single path, the threshold is the same as for TCP. The modification is to prevent sending traffic over congested paths when multiple paths are available.  
The OLIA increase algorithm is calculated in the following way. Here, \(L\) is the set of subflows with largest _cvond_, \(B\) is the set of subflows which have the highest throughput (best path) but do not have largest _cvond_, and \(N\) is the number of subflows. We assume that _cvond_ is maintained in packets.  
The increase algorithm in (4) uses two terms. The first term adapts a TCP compatible version of Kelly and Voice's [38] increase algorithm to provide resource pooling, fairnessand congestion balancing. The second part provides responsiveness and non-flappiness by using the parameter \(\alpha_{i}\).  
\[\frac{\frac{cvmd_{i}}{RTT_{i}^{2}}}{(\Sigma_{k}\frac{cvmd_{k}}{RTT_{k}})^{2}}\,+\, \frac{\alpha_{i}}{cvmd_{i}} \tag{4}\]  
Parameter \(\alpha_{i}\) is defined as per Formula (5). If any best path with small cvm exists, then \(\alpha_{i}\) is positive for any best path with small windows and negative for the paths with maximum _cvmd_. OLIA re-forwards traffic from fully used paths to paths with available capacity. If all the best paths have the largest _cvmd_ then \(\alpha_{i}\) is set to zero. The reason being that the available capacity on the best paths is already in use.  
\[\alpha_{i}=\begin{cases}\frac{1}{N\times|B\setminus L|}&,if\,i\,\epsilon\,B \setminus L\,\,\
eq\,\phi\\ \frac{-1}{N\times|L|}&,if\,i\,\epsilon\,L\,\,and\,\,B\setminus L\,\,\
eq\,\phi \\ 0&,otherwise\end{cases} \tag{5}\]
## 4 Results and Discussion
### Hydraulic Conductivities of Fracture-Karst Media by Inversion Analysis  
Taking the center of the arch dam as the coordinate origin, the east was the \(x\)-axis, the north was the y-axis, and the vertical direction was the \(z\)-axis. The model area extended about 600 m upstream, 500 m downstream to Longdang River, and 700 m to watershed on both banks (Figure 5). It can be seen from Figure 3, groundwater was discharged into rivers under natural conditions. The groundwater flow direction was almost perpendicular to the river, so it could be treated as a streamline boundary. The watersheds in the two banks were also treated as the second boundary conditions. The Mabie River and Longdang River in the left bank was considered as the first boundary condition. The model area was divided into 2,339,812 nodes and 4,334,022 elements. The discrete graph of the model area is shown in Figure 6.  
Assuming that the permeability coefficients of five karst pipes remained unchanged (Table 6), the permeability coefficient of the impervious curtain was set to 0.00864 m/d. The groundwater levels of observation holes ZK1, ZK2, ZK3, ZK5, and ZM10 were selected as the measured values. The calculated groundwater levels of these boreholes can be obtained by solving the coupled model using the iterative method. Permeability coefficients were adjusted continuously. When the errors between calculated and measured groundwater levels were small, the parameters at this time could be considered as accurate parameters (Table 7). Fitting curves between calculated and measured values are shown in Figure 7.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Number of Karst Pipes**} & \multicolumn{3}{c}{**Main Hydraulic Conductivities (m/d)**} \\ \cline{2-4}  & \(K_{x}\) & \(K_{y}\) & \(K_{z}\) \\ \hline
1 & 4745 & 582 & 363 \\
2 & 5795 & 1602 & 241 \\
3 & 1650 & 442 & 485 \\
4 & 4871 & 1124 & 410 \\
5 & 3368 & 1226 & 537 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Main hydraulic conductivities for karst conduits.  
Figure 6: Finite element mesh of the model area.  
It can be seen from Figure 7 that the calculated water levels fitted well with the measured water level. Generally, the calibrated model can better capture the hydrogeological characteristics of karst and fractured medium in the study area (Figure 8).  
Figure 7: Calculated and measured values of groundwater levels. (**a**) Comparison between calculated and measured groundwater levels contours. (**b**) Fitting curve between calculated and measured groundwater levels.
# Mechanical and Dielectric Properties of Aligned Electrospun Fibers
## 4 Applications of Aligned Fibers
### Influence of Aligned Fibers on Dielectric Properties of Nanofiber [PERSON] et al. (2014) showed that aligned MWCNT/PVA has high dielectric constant, low dielectric loss, high breakdown strength, and high energy density. These properties contribute in applications such as artificial muscles, energy storage, flexible electronics, and sensors [187]. Aligned MWCNT/PVA composite films were prepared by using electrospinning in situ film-forming technique. Additionally, [PERSON] et al. (2012) confirmed the tailoring of dielectric property by controlling the alignment of CNTs [187]. [PERSON] et al. (2012) reported that aligned PVDF had better molecular orientation than its random fiber counterparts. This is because of the smaller diameter of the aligned fibers. These nanofibers have applications in the field of sensors and actuators [188]. [PERSON] et al. (2009) reported that aligned fibers have applications in nanofluidics, superhydrophobic patterning, nanoelectronics,and nanophotonic circuits [189]. [PERSON] et al. (2012) demonstrated the significance of fiber alignment in improving the piezoelectric property, using centrifugal electrospinning. PVDF and PEO have piezo-, pyro-, and ferro-electric properties, and these aligned fibers can provide for applications in actuators, transistors, textiles, and composites [190]. [PERSON] et al. (2017) showed that aligned graphene films improved EMI shielding. Electromagnetic (EM) waves cause interference or device malfunction and also can cause harm to human bodies [191]. [PERSON] et al. (2013) observed that aligned carbon-based fillers enhanced EMI shielding. The alignment produced anisotropic characteristics that achieve enhancement in absorption and reflection performance [192].
# IoT and Blockchain Integration: Applications, Opportunities, and Challenges
## 4 Blockchain Technology
### Overview of Blockchain Network  
As the name suggests, a blockchain network (BCN) [29] is a broad-cast network of computing nodes that maintain a copy of a synchronized storage known as a blockchain ledger. The nodes in a blockchain network operate to persistently maintain data consistency through resistance to retrospective alteration, operation transparency through network consensus, and user privacy through anonymity, among others [16].  
In previous works such as [20; 30; 31], a blockchain is described as an \"immutable\" ledger\"-a series of data/record blocks. Every new block in a blockchain contains a cryptographic reference to the previous block, and so on. Figure 3 shows a simple structure of a block, blockchain, and a blockchain network [30]. In a blockchain network, every new node in the network is a computing platform with storage. Generally, each node in a BCN should maintain its copy of such a ledger. A new block, however, is appended to an existing ledger only after executing a \"well-formed\" transaction(s). A transaction (TXN) is a data structure representing an atomic operation that involves input values and, upon execution, produces an output and changes the state of a system. The term \"transaction\" is frequently misused in the literature on a blockchain network. Technically, a transaction is an atomic operation whose successful execution changes the system's state. For example, consider a transaction statement: _\"Transfer of monetary value \(x\) from [PERSON]'s wallet \(W_{A}\) to [PERSON]'s wallet \(W_{B}\)\"._ Before the transaction is executed, the system state is \(S_{0}:[W_{A}=v_{1},W_{B}=v_{2}]\). After the transaction is verified (that is, \(BALANCE(W_{A})-x>=0\) is true), then the state of the system changes to \(S_{1}:[W_{A}=v_{1}-x,W_{B}=v_{2}+x]\), on the other hand, a query such as \"\(Balance(W_{A})\)\" returns the balance in [PERSON]'s wallet is just similar to seeking information from storage. Usually, a query by itself does not change the system's state.and reaches an agreement on the transaction output among the network nodes. The mechanism that allows network nodes to reach an agreement on a transaction output is a consensus algorithm. Popular blockchains such as Bitcoin and Ethereum use the proof of work (PoW) consensus mechanism [13; 14; 32]. There are several other consensus mechanisms, such as Byzantine fault tolerance, proof of stake (PoS), proof of capacity (PoC), proof of authority, and proof of importance, among others. However, the PoW mechanism is more energy intensive and costly than other energy-efficient mechanisms such as PoS and PoC [30].  
Contrary to [PERSON] et al. [20], blockchain data storage does not prevent forging and tampering with the data; it serves as a deterrence against such activity. However, when implementing blockchain in a decentralized network environment, one can achieve a higher level of data recovery services.
###### Abstract
[40] predicted bearing RUL by first extracting bearing degradation features using BiLSTM, then combining them with a Transformer. [PERSON] et al. [41] predicted bearing RUL by extracting 13 multi-domain features of bearings and using an LSTM network. [PERSON] et al. [42] developed a model integrating gated convolution and temporal convolution. [PERSON] et al. [43] combined vibration signals and mechanism models and used BiLSTM networks. [PERSON] et al. [44] used stacked BiLSTM and attention mechanism. [PERSON] et al. [45] employed LSTM networks and bidirectional GRUs. [PERSON] et al. [46] proposed a dual-channel convolutional network, which incorporates the Transformer algorithm. In the aforementioned literature, although many scholars have employed LSTM or BiLSTM for predicting the RUL of bearings, the predictive performance and applicability of the models still need to be enhanced. In order to address the aforementioned issues, the paper is structured into the following aspects:  
Section 1 describes the research background, current research status, and research significance of the studied content, followed by a summary. In Section 2, this paper employs the Discrete Wavelet Transform (DWT) for denoising the vibration signal, to obtain a pure signal. Features are extracted from the time domain, frequency domain, and time-frequency domain, the sensitivity of each feature is then evaluated using monotonicity, trendability, predictability, and robustness indicators, and the Kernel Principal Component Analysis (KPCA) is utilized for feature fusion and usesthe fused value as the health indicator (HI) for the bearing. This chapter provides the data foundation for predicting the remaining useful life of bearings. In Section 3, the method combines the application of a Doubled Bidirectional Long Short-Term Memory (DBiLSTM) network with an attention mechanism (A-DBiLSTM), describing the principles of the DBiLSTM network and the attention mechanism network, adaptively searching for network hyperparameters using Bayesian optimization algorithm. By using the 3\(\sigma\) (Three Sigma) method and combining the health indicator (HI) obtained in the second chapter, the health stage and failure stage throughout the entire life cycle of the bearing can be determined, and the process for estimating the remaining useful life of bearings can be outlined. This chapter outlines the complete process for predicting the remaining useful life of bearings. In Section 4, experimental validation is conducted on the remaining useful life prediction method for bearings proposed in the third chapter, the proposed remaining useful life prediction method is compared with other methods, the obtained prediction results are analyzed, and its performance is evaluated using evaluation metrics. In Section 5, the main conclusions are drawn, and a summary is provided. The overall technical route is shown in Figure 1.