esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Background
### Nuisance Attribute Projection  
Nuisance attribute projection was initially used in speaker recognition systems to reduce noise and interference information among channels improving the recognition performance of the system. In fact, the NAP algorithm constructs a projection matrix to reduce the distance of different channels, languages and other features under the same speaker in the projection space. Correspondingly, these nuisance attributes are load, rotating speed and noise in fault diagnosis.  
Consider a data matrix \(\textbf{F}=~{}[f_{1},f_{2},\ldots,f_{n}]\) with \(n\) vectors, where \(f_{i}\) is feature vectors of \(N\)-dim. Different \(f\) stands for different working conditions. For the same fault, when the feature vectors \(f_{i}\) and \(f_{j}\) of its two working conditions, the distance between them in the feature space is defined as:  
\[\delta=\left\|f_{i}-f_{j}\right\|^{2}. \tag{4}\]  
Figure 1: The typical structure of LeNet-5 convolutional neural network (CNN) model.  
The subtraction of feature vectors in different working conditions of the same fault can eliminate the same information and retain the different information. In order to weaken the different information between working conditions, the high-dimensional matrix \(\mathbf{P}\) of \(N\times N\) is defined to minimize \(\delta\):  
\[\delta_{P}=\left\|\mathbf{P}\cdot f_{i}-\mathbf{P}\cdot f_{j}\right\|_{2}. \tag{5}\]  
For multiple signals of the same fault, their working conditions may be the same or different. When these signals are in the same condition, it is not necessary to extract the different information. While their conditions are different, it is necessary. Therefore, define the weight matrix \(\mathbf{W}\), where the matrix element is  
\[\mathbf{W}_{ij}=\left\{\begin{array}{ll}1&workingcondition(f_{i})\
eq working condition(f_{j})\\ 0&workingcondition(f_{i})=workingcondition(f_{j})\end{array}\right.. \tag{6}\]  
Then, the \(\delta_{P}\) can be redefined as:  
\[\delta_{P}=\sum_{i,j}W_{ij}\left\|P\cdot f_{i}-P\cdot f_{j}\right\|^{2}. \tag{7}\]  
Thus, NAP algorithm can be regarded as the following optimization problem:  
\[PE=\underset{\mathbf{P}}{argmin}\sum_{i,j}W_{ij}\parallel P\cdot f_{i}-P\cdot f_{ j}\parallel^{2}. \tag{8}\]  
The traditional method to solve Equation (8) is to transform it into solving eigenvectors of the following eigenproblem [21]:  
\[\mathbf{F}(\mathbf{W}-diag(\mathbf{W}))\mathbf{F}^{\mathrm{T}}v=\lambda v. \tag{9}\]  
Figure 2: The structure of the proposed method.  
The matrix \(\mathbf{P}=\mathbf{I}-\Omega=\mathbf{I}-\sum\limits_{i}\omega_{i}\omega_{i}{}^{\text{T}}\) with \(\|\omega_{i}\|=1\) and the \(\omega_{i}=\mathbf{F}v_{i}\). \(I\) is a length column vector of all ones. The eigenvector \(v\) of \(N\)-dim obtained from Equation (6) is the related to the working conditions. Rank all eigenvalues and select the larger ones corresponding eigenvectors to estimate the nuisance subspace.  
In this paper, the objective function Equation (8) is directly put into the CNN solved by gradient descent iteration. More details are shown in Section 3.
# An Approach to Incorporating Implicit Knowledge in Object Detection Models
## 3 Problem Formulation and Preliminaries
### Modeling Implicit Knowledge  
In the above discussions, we explored the implicit knowledge embedded within the error term of the model and analyzed the parts of object detection models where information loss is prone to occur, along with potential causes. In this section, we will outline how to incorporate this implicit knowledge into the model.  
In the transformer model [6; 26], the self-attention mechanism captures the dependencies between different positions, whereas the introduction of the _cls_token_ directs the model's focus to the entire sequence, thereby better capturing global semantic information. The _cls_token_ serves as a special token for input sequences and is typically used to represent the semantic information of the entire input sequence or as input for classification tasks. It is transformed into a vector representation through an embedding layer and passed as part of the input sequence to the transformer encoder. The _cls_token_ itself is not directly related to the classification target but serves as a learned special representation during the model's design and training processes, providing contextual information and semantic representation for the overall sequence. Constructing a structure similar to _'cls_token'_ in object detection models enables the extraction of implicit knowledge obtained during the sampling process or influenced by model structural factors.  
Suppose that there exists a set of parameters, \(z\), that interact with the model and that aid in capturing the complex patterns that exist in the data but are not captured by the model, thereby storing the implicit knowledge, \(\mu\), present in the data. These parameters should be capable of storing and processing various types of information, dynamically updating and modifying during the training process. They should adjust their content and structure dynamically based on different input data and task requirements.  
We believe that this set of parameters can be structured in the form of a dictionary and applied to the model. However, the exact mechanism of their influence is currently unknown. Therefore, we temporarily use the symbol \"\(\otimes\)\" to denote the operational relationship between explicit and implicit knowledge, as follows:  
(6)  
which can be represented as  
(7)  
We use \(g_{\varphi}\) to represent the implicit knowledge gained by the model under the influence of parameter \(z\). We attempt to use the features obtained from sampling in the object detection model as the \"keys\" of the dictionary and set multiple sets of mutually independent parameter vectors as the \"values\" of the dictionary, storing task-related implicit knowledge.
## 4 Discussion
Overall, the higher correlation between _RFD_ and _EE_ indicates that \(MW_{c}\) is the predominant chemical parameter in determining the encapsulation outcome, rather than the _HSP Distance_. This suggests that controlling \(MW_{c}\) is crucial for achieving the desired encapsulation results within the molecular structures included in this study. To further understand the influence of monomer solubility on the capsule, it would be valuable to test monomer differences with higher differences in _HSP Distance_. Similarly, in future studies, it would be worth evaluating perfume leakage over time, as the HSP distance can strongly influence this parameter since the more the perfume is soluble in the wall membrane, the more effortlessly it would permeate through. Lastly, solubility parameters beyond the _HSP Distance_, such as solubility computed via COSMOtherm [39], should be considered for further research to broaden the understanding of monomer-perfume interactions beyond _HSP Distance_. The relationship between all variables and _On-Fabric delivery_ is not linear; instead, a quadratic model is more fitting, attributed to the intricate dynamics of perfume microcapsule delivery to fabrics during washing. This complexity is due to unpredictable factors such as capsule deposition and retention [40]. The deposition and retention of capsules are influenced by the surface affinity between the capsule wall and the fabric, which involves steps not thoroughly investigated in this study. Hence, the correlation appears more intricate than a linear relationship. Figure 7 underscores that _RFD_ is the primary parameter correlating with _On-Fabric delivery_. It implies that washing cycles, encompassing both low-speed and high-speed rotations [41], profoundly impact the mechanical integrity of the capsules. Hence, the mechanical robustness of the capsules directly influences _On-Fabric delivery_. The data reveals a parabolic relationship between _RFD_ and _On-Fabric delivery_, suggesting an equilibrium between capsule resilience and perfume discharge. An increasing relationship for _RFD_ values lower than 6.0 \(\times\) 10\({}^{1}\) N/mm is observed, while beyond the 6.0 \(\times\) 10\({}^{1}\) N/mm threshold, the relationship changes to decreasing. Stronger capsules are believed to resist rupture better and remain intact under the mechanical stress of the washing machine. Their increased strength enables them to withstand rigorous conditions, avoiding premature rupture and maintaining structural integrity. As a result, more capsules can be deposited onto the fabric surface without breaking, leading to a linear rise in _On-Fabric delivery_. However, beyond the optimal balance point of 6.0 \(\times\) 10\({}^{1}\) N/mm, further increases in rupture force may not necessarily result in a proportional increase in the number of deposited capsules. Excessive increases in capsule strength can hinder perfume release: indeed, when the capsules become excessively strong, they may be less prone to rupture even under normal usage conditions, thus limiting the release of the encapsulated perfume.
# Efficient Pruning of Detection Transformer in Remote Sensing Using Ant Colony Evolutionary Pruning
## 4 Experimental Results and Analysis  
To validate the effectiveness of the method, we conducted extensive experiments using multiple DETR-like models, including DETR, Deformable DETR, and DINO models. During the experiments, we used the Ubuntu 18.04 operating system, Nvidia Titan RTX as the GPU, Intel core i7-9700k as the CPU, Python3 as the programming language, and PyTorch as the deep learning framework.
# Dynamic and Energy Efficient Cache Scheduling Framework for IoMT over ICN
## 3 Materials and Methods
### Cache Routing and Replacement Policy  
The caching technique characterizes the replication technique used to spread duplicates of the content in the cache. Replication techniques have two measurements. Content-based replication, which depends on attributes of the content and settles on caching choices, e.g., pick popular content to store and ignore others. Node-based replication depends on the qualities of the topology, e.g., picking more important nodes for storing the content. Both content-based and node-based replication are resource optimization approaches. Both techniques attempt to designate storage space to attain maximum advantage, e.g., increased cache-hit-ratio. The replication techniques explored in the context of ICN are all node-based since the content-based technique can work on a large Internet scale. There exists several caching approaches, i.e., Leave Copy Everywhere (LCE), Leave Copy Down (LCD), Bernoulli random caching, random choice caching, Probabilistic Caching (ProbCache), centrality-based caching, and hash-routing [40; 41; 42; 43]. In the IoT over ICN network, caching nodes can be configured using one of the state-of-the-art cache eviction policies, i.e., Least Recently Used (LRU), Least Frequently Used (LFU), First in First Out (FIFO), and random. This research utilizes the LRU cache eviction policy because it is the most widely used cache replacement strategy. At an instance when new content is required to be pushed into the cache, it eliminates the least recently asserted content request. This replacement strategy is proficient for line speedup activities because both search and substitution replacement can be performed in consistent time, i.e., \(O(1)\). Nonetheless, its efficiency degrades under the Independent Reference Model (IRM) supposition because of the likelihood that the current requested content does not rely upon previous content requests.
## 5 Discussion and Future Challenges
### Challenges of Integrating Blockchain and IoT
_Transaction Efficiency:_ There is a major issue with the transaction speed of a blockchain when integrating with IoT. It is universally acknowledged that IoT integration slows down systems. IoT systems can generate massive amounts of data promptly, while the pace of a blockchain may not be able to catch up. Popular blockchain systems, e.g., Bitcoin, can only process seven transactions per second. Ethereum, on the other hand, is faster, and can process around 30 transactions per second. Ethereum 2.0 has promised to increase the speed up to #100,000 transactions per second. On the other hand, several studies have addressed this issue, e.g., by helping to estimate the transaction throughput of the blockchain, increasing transaction throughput, etc. [139; 140]. Nevertheless, the speed of transaction processing is still likely to be insufficient, especially in large-scale IoT applications. _Law and policy:_ Despite the blockchain method leading to numerous positive consequences for society, lawmakers and authorities have come under fire. Blockchain has run into a number of legal problems because of a lack of legal analysis [141]. A detailed understanding of blockchain processing will be essential to support the design and implementation of legal regulations governing blockchain activities.
## 4 Blockchain-Based Lightweight Point Cloud Data Authentication Framework
### Structure of the Hybrid On-Chain and Off-Chain Storage  
In general, a 3D model construction needs multiple segmented point clouds. and each point cloud segment \(PC_{i}\) may have a large data size and demand privacy preservation. Thus, it is impractical to directly store point clouds in a transparent Blockchain for data authentication. To ensure efficient and privacy-preserving data storage and sharing, we adopted a hybrid on-chain and off-chain storage structure in the data authentication framework, as shown in Figure 2b. In the point cloud data collection stage, the meta-data of the point cloud segments are saved in the DDS, while the raw data are managed by private servers. The meta-data \(MD_{i}\) contain the data configuration (e.g., server address and properties), which is relatively small regardless of the size of the original data. In addition, an audit proof consists of the integrity of the authenticator of a point cloud segment and a signature singed by a data owner, which are byte strings with a small length. Therefore, the small size of the meta-data can greatly reduce the communication cost in the verification process. Furthermore, the meta-data are encrypted and then saved on the DDS, and only authorized users are allowed to query and decrypt the meta-data. It is promising to protect the privacy of data owners without exposing sensitive information on the Blockchain and DDS.  
In our Swarm-based DDS, each of the stored meta-data has a unique Swarm hash as the addressable reference to the actual data storage, and any change of the stored data will lead to an inconsistent Swarm hash. Therefore, recording the Swarm hash on an immutable distributed ledger provides the non-tamperability property of the meta-data on the DDS. To verify the data integrity of a large point cloud file, the Swarm hash of meta-data \(MD_{i}\) is considered as a digest \(D(i)\), which is located on a leaf of the Merkle tree. Then, we use such an ordered list of digests to construct a binary Merkle tree \(MT\_root=BMT(D(1),D(2),...D(N_{m}))\), where \(N_{m}\) is the number of meta-data. Modifying digests or changing the sequential order will lead to different root hash values \(MT\_root\) of the Merkle tree. Therefore, \(MT\_root\) is also stored on the distributed ledger as the data integrity proof of the entire file. In the data verification process, a data user can query digests from the Blockchain and then in parallel validate the integrity of the segment data. Then, it can easily reconstruct the Merkle tree of the digests and obtain \(MT\_root^{\prime}\). Finally, the data integrity of the entire point cloud file can be efficiently verified by comparing \(MT\_root^{\prime}\) with \(MT\_root\) on the distributed ledger.
# Quantum LDPC Codes Based on Cocyclic Block Matrices
## 3 Quantum Codes Based on Cocyclic Quasi-Jacket Block Matrices
### Cocyclic Matrix Derived from Jacket Block Matrices
#### 3.2.2 Quasi-Jacket Block Matrices with Size \(4p^{m}\) by \(4p^{m}\)  
Furthermore, to obtain the more general length of code, we define a matrix  
\[\mathcal{Q}_{\alpha}\triangleq\left[\begin{array}{cc}I&O\\ p^{h}&I\end{array}\right],\quad\mathcal{Q}_{\beta}\triangleq\left[\begin{array} []{cc}I&P^{-h}\\ O&I\end{array}\right]. \tag{39}\]  
It is easily checked that  
\[\mathcal{Q}_{\alpha}^{2}=\mathcal{Q}_{\beta}^{2}=I_{2p},\quad\text{and}\quad \mathcal{Q}_{\alpha}\mathcal{Q}_{\beta}+\mathcal{Q}_{\beta}\mathcal{Q}_{\alpha }=I_{2p}. \tag{40}\]  
Furthermore, in terms of the basic properties, we consider matrix  
\[\mathcal{Q}\triangleq\left[\begin{array}{cc}\mathcal{Q}_{\alpha}&\mathcal{ Q}_{\beta}\\ \mathcal{Q}_{\beta}&\mathcal{Q}_{\alpha}\end{array}\right]=\left[\begin{array} []{cccc}I&O&I&P_{p}^{-h}\\ P_{p}^{h}&I&O&I\\ I&P_{p}^{-h}&I&O\\ O&I&P_{p}^{h}&I\end{array}\right], \tag{41}\]  
and its inverse matrix  
\[\mathcal{Q}^{-1}=\left[\begin{array}{cc}\mathcal{Q}_{\beta}& \mathcal{Q}_{\alpha}\\ \mathcal{Q}_{\alpha}&\mathcal{Q}_{\beta}\end{array}\right]=\left[\begin{array} []{cccc}I&P_{p}^{-h}&I&O\\ O&I&P_{p}^{h}&I\\ I&O&I&P_{p}^{-h}\\ P_{p}^{h}&I&O&I\end{array}\right]\] \[=\left[\begin{array}{cc}O&I\\ I&O\end{array}\right]\times\left[\begin{array}{cc}\mathcal{Q}_{\alpha}& \mathcal{Q}_{\beta}\\ \mathcal{Q}_{\beta}&\mathcal{Q}_{\alpha}\end{array}\right]=\mathcal{T}_{4p} \times\mathcal{Q}. \tag{42}\]  
It is obvious that the constructed matrix \(\mathcal{Q}\) is also a quasi-Jacket matrix which can be seen as a \(4p\times 4p\) binary cocyclic matrix. It means that a quantum code with a length of \(4p\) may be obtained. Similarly, the recursive relationships may be used to obtain the large-length block matrices with a larger size \(4p^{m}\) by using the Kronecker product, i.e.,  
\[\mathcal{J}_{4p^{m}}=\mathcal{J}_{4}\otimes\mathcal{J}_{p^{m}},\quad\text{for }\ m\geq 2, \tag{43}\]  
such that the more general case of the generator matrix with a long size is achieved. In a similar manner, the obtained large-size matrix also satisfies the conditions of self-orthogonal and commuting like the process before proving the case of size \(4^{m}\). We take two matrices based on parameters \(h_{1},h_{2}\) in \(\mathcal{P}_{e}=\{\omega,\omega^{3},\ldots,\omega^{p-2}\}\) or in \(\mathcal{P}_{o}=\{\omega^{2},\omega^{4},\ldots,\omega^{p-1}\}\) as \(G^{x}\) and \(G^{z}\), respectively. It is evident that the obtained matrices commute each other.
# Experimental Research into the Ultrasonic P-Wave Velocity of Coal Slime Based Backfill Material
## 3 Orthogonal Test Schemes and Test Results
### Ultrasonic Experimental Design  
The material prepared at the room temperature of 25 \({}^{\circ}\)C was poured into a standard triplet mold measuring 70.7 mm \(\times\) 70.7 mm \(\times\) 70.7 mm. After initial setting and demolding, the specimens were placed in a standard YH-60B curing box to be cured for 28 d. After 28 d, the specimens were removed to measure their ultrasonic p-wave velocities, followed by measurement of the uniaxial compressive strength and uniaxial shear strength. The ultrasonic p-wave velocities were measured using a HICHANCE HC-U81 concrete ultrasonic detector, and the strength was tested on an MTS electro-hydraulic servo-motor machine.  
A substantial body of other relevant studies [28; 29; 30] has demonstrated that, in the testing of cohesive materials, pressure-waves exhibit faster propagation speeds and possess relatively greater penetration depth compared to SH waves and SV waves. Therefore, we selected pressure-waves as the focus of our current research.  
The ultrasonic p-wave velocity was measured using the following method: a standard specimen was put between two probes coated with the coupling agent. One of the probes was used as the pulsed acoustic emission source and the other as the pulsed acoustic receiver. Results of the received pulses were displayed on the screen of a microcomputer equipped with an ultrasonic detector. After finishing all tests, the computer was used to process the measurement data. If the long axis along the height of the specimen is L and the arrival time is t, then the ultrasonic p-wave velocity V in the specimen is given by L/t, as shown in Figure 12. Each specimen was tested three times and the arithmetic mean was adopted as the final test value. The ultrasonic tester parameter settings are shown in Table 8. The test results are summarized in Table 9.
# A Compact Quad-Element UWB-MIMO Antenna System with Parasitic Decoupling Mechanism
## 4 Results and Discussions
### Isolation/Decoupling Performance  
The simulated and measured isolation/decoupling performance for all relative placements are shown in Figure 7. Isolation better than 20 dB is achieved in the entire band for all configurations. Two distinct isolation mechanisms, stub loading in the ground plane as well as parasitic decoupling element provide significant mutual coupling reduction among antennas. Since the diagonally placed elements 1, 3 and 2, 4 are spatially apart so they have minimum coupling even without the use of decoupling structure. However, the strong coupling between antenna 1, 2 and 3, 4 are greatly suppressed by use of dumbbell shaped decoupling structure. The high frequency coupling in across elements 1, 4 and 2, 3 are reduced remarkably by employing the parasitic decoupling structure.
# Phase Center Corrections for BDS IGSO and MEO Satellites in IGb14 and IGSR3 Frame
## 4 Results and Validation
### Validation
#### 4.4.2 Coordinates
This demonstrates that great accuracy was achieved for the BDS-only solution, and it will be improved with more BDS satellites used for positioning. For the GPS-R3 solution, the mean scale parameter was about +0.021 \(\pm\) 0.101 ppb with respect to the IGSR3 solutions, whereas the scale bias was about +0.226 \(\pm\) 0.175 ppb between the BDS-FR3 solutions and IGSR3 frame. The bias up to +0.226 ppb clearly demonstrates that this inconsistency between PANDA and IGS antenna model was assimilated into the BDS PCO estimation. However, it was still within the confident level, as the standard derivation was about 0.175 ppb. In the bottom subplot of Figure 7, the scale bias of BDS-IGS with respect to IGSR3 frame is also plotted to demonstrate the scale inconsistency between BDS-released and Galileo-released PCO values. It is clear to show that the scale derived from BDS and Galileo-released PCOs is not consistent, and the bias reach +1.854 \(\pm\) 0.191 ppb. Moreover, the reduction of scale bias for BDS solutions in IGb14 as well as IGSR3 shows the good consistency of PCO/PCC estimates for BDS with other systems of GPS/GLONASS antenna offsets is achieved.
# Gamification in IT Service Management: A Systematic Mapping Study
## 4 Results
### RQ1. What Issues of ITSM Process Implementation Have Been Addressed through Gamification?
#### (d) IT Service Transition Processes  
The only study that dealt with IT service transition was [P06]. The objective of this work was to present and evaluate a gamification approach for learning IT service transition processes. The results of the application case carried out provided a preliminary evidence that mixing business process management and gamification can accelerate the learning process.
# Full Real-Time Positioning and Attitude System Based on GNSS-RTK Technology
## Appendix A Configuration of the Prototype  
### Microcontroller Configuration  
The microcontroller and its peripherals are configured with the following parameters (Table 1):  
For UART3, which sends the attitude and position estimation to the external devices, a speed of 115,200 bps was used, since it is compatible with most industrial equipment.