esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
### Centralized Solutions  
Centralized methods involve computing a central system and assigning a feasible conflict-free path to each AGV. These methods can be further divided into A*-based search algorithms, conflict-based search algorithms, and other heuristic algorithms.  
A*-based search algorithms are extensions of the A* algorithm adapted for multi-agent scenarios. Representative algorithms include OD A*, ID A* [6], OD rM* [7], and other improved A* algorithms [8; 9]. These algorithms either simultaneously expand all possible states of all agents at the next time step or construct a k-agent state space by sequentially expanding the states of each agent and then performing searches based on this state space.  
In conflict-based search (CBS) algorithms and their variants [10; 11; 12; 13; 14], search tree nodes do not store agent states directly but instead store the solution (i.e., a set of paths), along with detected conflicts and constraints. The algorithm operates on the following two levels: the low-level search plans individual AGV paths under the given constraints, typically using A* or its variants; the high-level search evaluates solutions based on cost, the number of conflicts, or the number of conflicting agent pairs. Then, it iteratively adds constraints and resolves the problem until a conflict-free solution is found.  
Other metaheuristic algorithms include Ant Colony Optimization (ACO) [15], Genetic Algorithm (GA) [16], Differential Evolution (DE) [17], Artificial Potential Field (APF) [18], Particle Swarm Optimization (PSO) [19], and others. These algorithms generally achieve high solution quality; however, like other centralized methods, they still suffer from poor adaptability to dynamic obstacles, real-time planning, and large-scale AGV environments.
## 1 Introduction
SPT is not recommended for hard geo-materials such as hard soil, soft rock, and highly weathered rock because its penetrability is not sufficient [5]. However, conventionally SPT rather than PMT is used in field practice to estimate the geotechnical properties of weathered granite because of time consuming process and high cost of PMT. Therefore, it is important to suggest the correlation between the SPT and PMT results conducted in weathered granite with N value \(\geq\)50 (i.e., hard residual soil, completely weathered rock, highly weathered rock). Meanwhile, many studies have been conducted on the variation of the engineering properties of rocks due to weathering. The feasibility of representing the engineering properties of a rock by a chemical weathering index has also been suggested through a correlation analysis of the index with the engineering properties (unconfined compressive strength, dry density, shear strength, etc.) [10; 11; 12; 13; 14]. This index quantitatively describes the degree of weathering based on the changes in the chemical composition of the rock constituent minerals. In the present study, empirical equations for determining \(E_{m}\) and \(P_{L}\) based on an SPT-\(N\) value and chemical weathering index were derived through a case study of weathered granite, including highly weathered granite and residual soil, in South Korea. The results of SPTs and PMTs conducted at three sites were used, and the equations of \(E_{m}\) and \(P_{L}\) were derived through the multiple nonlinear regression analysis of the SPT-\(N\) value and chemical weathering index, respectively. The values of \(E_{m}\) and \(P_{L}\) determined by the proposed equations were compared with those obtained in previous studies and by field measurements.
## 3 Results
### Analysis of Differential Volatile Terpenes and Differential Gene Expression  
Based on the differential expression patterns of petal-expressed _R_c_TPSs, the butterfly rose petals sampled on the anthesis day (D1, fresh flower) and day 3 post-anthesis (D3, old flower) were selected for volatile analysis. In total, 24 monoterponoids and 30 sesquiterpenoids were identified (Figure 6a). The levels of almost all monoterpenes emitted from D3 samples were higher than the levels of those from D1 samples, whereas different sesquiterpenes exhibited peak release from D1 or D3 samples. As the VIP value of most volatile terpen was very low, differential metabolites were screened based on \(\mid\) Log\({}_{2}\)FC \(\mid\)\(\geq\) 0.8 and VIP \(\geq\) 0.6. Compared with D1 samples, 14 compounds in the D3 sample including two sesquiterpenes and 12 monoterponoids were upregulated, whereas five sesquiterpenoids were downregulated (Figure 6b).  
Differential gene expression analysis between D1 and D3 samples were conducted. Differential expression of genes involved in the middle and downstream pathway of terpene biosynthesis (short-chain _R_c_T_PTs, _R_c_TPSs, and _R_cNULDX1s_) were screened based on \(\mid\) Log\({}_{2}\)FC \(\mid\)\(\geq\) 0.8 and Q \(<\) 0.05 (Figure 6c). Compared with D1 samples, four _R_c_TPSs and _R_c_NULDX1-1a4_ were upregulated and four _R_c_TPS genes were downregulated in D3 samples. Both _R_c_F_PPS1_ and _R_c_F_PPS2_ were upregulated in D3 samples.  
The relationship between DEGs and differential volatiles was analyzed. _R_c_GDS expression was higher in D1 samples than in D3 samples, which was consistent with the decreased germacrene D emission in D3 samples (40% of D1 samples). Germacrene D was not classified as a differential volatile because of its low VIP value. Increased _R_c_LIN-_N_ERS3, as well as decreased _R_c_LIN and _R_c_LIN-_N_ERS1, were expressed in D3 samples, whereas the linalool emission level was higher in D3 samples (130% of D1 samples). The chirality of linalool could not be detected due to the detection method limitations. Thus, it is difficult to analyze the correlation between linalool and these three genes.  
The emission abundances of geraniol, nerol, and some monoterpenes [myrcene, (_Z_)-ocimene, and (_E_)-\(\beta\)-ocimene] from the D3 samples were 1.3-2.1 times those from the D1 samples, which might be related to the upregulated expression of _R_c_NULDX1-1a4_ in D3 samples. However, the protein sequences of _R_c_NULDX1-1a4_ was identical with other three _R_c_NULDX1-1 genes, so that the qRT-PCR validation of _R_c_NULDX1-1a4_ were not carried out. In order to verify the correlation between the _R_c_TPT expression levels and emission amounts of geraniol, volatile analysis and qRT-PCR were performed between D1 and D3 samples (Figure 6d). The results showed that only _R_c_GPPS showed similar expression trend to emission of geraniol, suggesting that it might be related to the biosynthesis of geraniol.
# Efficient Deep Learning-Based Detection Scheme for MIMO Communication Systems
## 2 MIMO System Model
### Optimal ML Detection  
The optimal ML detection scheme is one of the most common strategies used to evaluate the performance of BER on MIMO systems. The ML detector is a procedure that aims to find the smallest possible distance between the received symbol and all possible products among the estimated channel and the symbol constellation. The optimal ML detection can be defined as  
\[\widehat{s}=\underset{i}{\arg\min}\|\mathbf{r}-\tilde{\mathbf{H}}\mathbf{s}_{i} \|\ \forall i=1,\cdots,M^{N_{\ell}}, \tag{4}\]  
where \(\tilde{\mathbf{H}}\) is the estimated channel matrix at the receiver. \(\widehat{\mathbf{s}_{i}}\) is the set of all possible combinations of symbols \(\mathbf{s}\). ML is an optimal algorithm, but its implementation is difficult due to its exponential computational complexity proportional to \(M^{N_{t}}\) combinations of modulated signals and CSI [35; 36]. Without loss of generality, perfect channel state information (CSI) is assumed.
## 3 Experimental Results  
To evaluate the performance of our proposed scheme, a famous 3D mesh model dataset, Princeton Shape Retrieval and Analysis Group (PSR&AG) [34], was used in this paper. The four models of the dataset, dog, face, spaceship, and car, were analyzed as special cases, shown in Figure 3. The 3D model processing software utilized in this paper is MATLAB R2023b, and the software for 3D model visualization is MeshLab v2022. 02. The encrypted data streams employed are generated using MATLAB's built-in pseudo-random functions. We first evaluated the embedding rate of four test models. The embedding rate mentioned in Equation (10) denotes that the capability of each vertex to carry how many additional bits. The security of the encrypted 3D model and the embedded encrypted 3D model were measured qualitatively and quantitatively. Finally, the proposed scheme was compared with other state-of-the-art schemes to demonstrate the superiority.
# Optimization of DLTS Hinges for the Assembly of the Solar Arrays of a Communication CubeSat
## 4 Results and Discussion
### Optimization Analysis--Taguchi Model  
The Taguchi array design is a well-established statistical method which is used to optimize the design parameters within this experiment. In order to make sure that the experiments are varied, an L9 Taguchi array design with three factors and three levels will be undertaken. The experimental parameters used to conduct the simulation experiments are set out in Table 1; these values will be used to identify which parameters have the greatest effect on the \(\sigma_{\rm v}\) and steady-state moment.
# Improving Bearing Fault Diagnosis Using Maximum Information Coefficient Based Feature Selection
## 4 Experimental Results and Analysis
### Experiments on CUT-2 Datasets
#### 4.2.1. Experimental Setup and CUT-2 Datasets  
In this section, the proposed method was applied on fault datasets from CUT-2. The details of the datasets are shown in Table 5. There were 4 kinds of bearing condition, including 1 normal condition and 3 fault conditions. The 3 fault conditions were inner race fault, outer race fault and baller fault. Two hundred samples were collected for each condition. Each sample contained 1024 continuous data points. Each dataset included 800 samples in total. Through data seeded, 3 kinds of datasets were acquired at 2000 rpm, 2500 rpm, and 3000 rpm. The training process and the testing process were carried out at the same speed. The training set included 560 samples and the testing set included 240 samples.  
Figure 4. Experimental test rig: **(a)** CUT-2 data acquisition system; **(b)** CUT-2 platform.  
Figure 5. Locations of bearing faults: **(a)** outer race fault; **(b)** ball fault; **(c)** inner race fault; and **(d)** combination of parts.
## 4 Multicast Routing Approaches in DTN  
In this paper, we classify some of the recently proposed DTN multicast routing protocols based on the purpose of the routing algorithm suggested in the present study. The implementation of multicast routing protocols in DTN is challenging due to the intermittent nature of DTN as well as the limitations of IoT devices. Multicast in DTN requires efficient data transmission to a group of intended receivers without neglecting the overall performance of the networks. Given the wide variety of DTN applications in different fields, there is no single DTN multicast solution capable of addressing all the multi-faceted limitations of DTN in different situations. Each proposed solution is focused on addressing particular aspects of a given scenario or addresses specific issues in an application. Different multicast routing approaches are studied to understand the design aspect of multicast algorithms. We discuss some of the significant multicast approaches in DTN that have been proposed recently in this section. Figure 4 shows the classification of DTN routing approaches. In this taxonomy, the surveyed multicast DTN routing approaches are grouped into four main concepts: Social-aware, buffer-efficient, energy-aware, and geographical routing strategies under multicast DTN routing approaches. The Unicast DTN routing approaches are outside the scope of this paper but are significant within the comprehensive literature under the DTN umbrella.
# Assessment of Soft Computing Techniques for the Prediction of Suspended Sediment Loads in Rivers
## 2 Methodology
### Wavelet Transform (WT)  
Wavelet transform (WT) analysis seems to be a leading approach, compare to Fourier transform (FT), particularly when analyzing the non-stationary time series [56, 57]. The WT analysis approach is preferred to the FT analysis because wavelets are localized in both time and frequency domains, while the standard FT is only localized in the frequency domain. Owing to this ability of the WT, the changes in the hydrological processes can be analyzed.  
The WT decomposes the data series by transforming it into its subcomponent \"wavelets\", a scaled and shifted version of the'mother' wavelet. Scaling a wavelet indicates the process of amplifying or compressing the data series in time, and it is inversely proportional to the frequency. An amplified wavelet helps to observe the gradually varying changes in adata series, while a compressed wavelet helps to observe swift changes. On the other hand, shifting a wavelet simply means advancing or delaying the onset of the wavelet along the length of the data series. It helps to align a data series to examine a particular feature in the data.  
The present study will not examine a detailed background theory of the wavelet transform. A mathematical summary of WT and the exhaustive literature on its applications are covered in [PERSON] et al. [58].
# A Multiple Access Protocol Based on Gray Forecast for Satellite Network
## 4 Priority Sorting Bandwidth Allocation  
In bandwidth allocation of CFDAMA-GF, a user priority allocation method is considered to set a high priority for users with a high demand. Thus, a priority ranking algorithm is designed. In the algorithm for prioritizing the bandwidth allocation, the parameters are set as follows:  
1. Total number of users in the network: \(N\).
2. The number of users who have reserved slots: \(n\).
3. The number of users who have a reserved slot application and \(for_{slots\_i}>0\): \(k\).
4. The number of slots assigned to the \(i\)-th user by the satellite: \(alloc_{slots\_i}\).
5. The \(i\)-th user's priority number: \(num_{i}\).
6. Total number of data slots for one frame: \(slots\_sum\).
7. The number of remaining data slots after allocation of demand assignment: \(slots\_sur\).  
In the priority allocation stage for the satellite, the satellite initializes the reservation and free allocation tables according to the relevant information in the previous row of frames after the user's request is received shown in Tables 1 and 2. The priority is set and sorted according to the number of reserved timeslots of each user. The greater the number of reserved timeslots is, the higher the priority will be. With a large number of reserved timeslots, the demand of the current user is high, and the priority is high. High-priority user information is then stored at the top of the table.  
Then, Equation (9) is used to calculate the number of remaining slots after the demand assignment is allocated.  
\[slots\_sur=slots\_sum-\sum\limits_{i=0}^{n}(req_{slots\_i}+for_{slots\_i}) \tag{9}\]  
If \(alloc_{slots\_i}\leqslant 0\), indicating that there are no remaining time slots, the satellite assigns weighted allocations to the requesting user according to Equation (10).  
\[alloc_{slots\_i}=(req_{slots\_i}+for_{slots\_i})\times\frac{slots\_sum}{\sum \limits_{i=0}^{n}(req_{slots\_i}+for_{slots\_i})} \tag{10}\]  
If \(alloc_{slots\_i}>0\), indicting that there are remaining time slots, the satellite allocates the demand assignment to each user according to the reservation table from top to bottom. The demand assignment allocation is the number of timeslots allocated by the satellite for which the current user applies. The user information is deleted from the reservation table, and the user information row is simultaneously moved to the lowest end of the free allocation table. This process continues to loop until the demand assignment table is empty. After the demand assignment is allocated, the satellite allocates timeslot from top to bottom to each user in the free allocation table until no remaining timeslots exist. After the timeslots are allocated, the satellite sends the assigned number of time slots to the corresponding user.
## 4 Analysis of the Dynamic Performance of 66 kV Test System
### Three-Phase Line to Ground Fault
#### 4.1.1 Parameter Selection for Washout Filters  
The damping during the transient process is ensured by the washout filters in the reactive power and active power control loops as seen in Figures 3 and 4 respectively. Parameter sensitivity analysis needs to be performed for the washout filters to ensure fast damping of transients. The output of the washout filter for various proportional gains for a step response is depicted in Figure 9. The proportional gain represents values to which the output of the washout filter changes when there is a change in the input. The time constant represents the rate of this change. It can be seen that the output of the washout filter changes proportionally to the gain value specified and comes back to the initial value after a specific time, depending on the time constant mentioned.  
Effect of this property of washout filter is observed in the active and reactive currents at the PCC for a similar three-phase fault as performed in the previous section. Measurements are recorded between 0.45 s to 0.65 s in Figures 10 and 11 for active and reactive currents, respectively to analyze the behaviour of transients. The proportional gain is set as 0.01, 0.03 and 0.05 for comparison, keeping the time constant = 0.01 s for all the three cases. It is observed that faster damping can be achieved for higher proportional gains. It makes sense as the system would try to damp the transients to the maximum possible value in order to have minimum fluctuations and also to limit interactions with other controllers during this period. Thereby, it is worthy to conclude that choosing the right parameters for the washout filter is one among the key factors in this control approach.  
Figure 8: Active and reactive currents flowing to the network following a three phase fault in the middle of the cable.  
Figure 7: Frequency response synthesised by Phase Locked Loop (PLL) following a three phase fault in the middle of the cable.  
Figure 11: Current in q axis following a three phase fault for proportional gains 0.01, 0.03 and 0.05.  
Figure 10: Current in d axis following a three phase fault for proportional gains 0.01, 0.03 and 0.05.  
Figure 9: Output of a washout filter for different proportional gains for a step response.
# An Empirical Study on Software Defect Prediction Using CodeBERT Model
## 4 Approach
### Tokenizing Source Code  
During the compilation of the source code, a normal tokenizer that separates the tokens following grammar rules is sufficient. However, as is usually neglected during compilation, the semantics hidden in texts, e.g., function names and variable names, are often neglected. Therefore, a tokenizer that could extract the text semantics in source code should be used.  
We follow the settings of CodeBERT, which uses a word piece tokenizer. Before the original source code is fed into the word piece tokenizer, code comments are removed, the white spaces at the head and the tail of the source code are removed, the tokens are separated by splitting the white space, and the punctuations are also separated into independent tokens. This paper uses three preprocessing patterns for software defect prediction, which corresponds to the three prediction patterns described in Section 4.2. The first preprocessing pattern (i.e., the traditional pattern) starts with a 
 mark at the beginning and 
 at the end. The second and the third preprocessing pattern (i.e., the sentence and keyword pattern) add an extra 
 mark to separate the source code and the declarative sentence or the keywords. The preprocessed code is then ready for tokenization.  
During the tokenization process, the source code is tokenized using a pre-trained vocabulary file. The uncommon words are separated into several sub-words with higher occurrences in the vocabulary file using a greedy longest-match-first algorithm. For example, the word \"TestCase\" will be separated into \"Test\" and \"##Case\". The \"##\" token marks that the token is a sub-word token. If a token is not found in the vocabulary file, an unknown token, <UNK>, will be used.
# Federated Few-Shot Learning-Based Machinery Fault Diagnosis in the Industrial Internet of Things
## 2 Related Work
### Few-Shot Learning for Fault Diagnosis  
To tackle the challenge of limited fault samples, recent research efforts have centered around few-shot fault-diagnosis techniques, which are generally categorized into three main approaches: data-based, algorithm-based, and model-based. (1) Data-based methods concentrate on improving the training dataset by employing data augmentation strategies, including data generation [21; 22], oversampling [23], and reweighting [24]. (2) Model-based methods constrain the hypothesis space through prior knowledge, such as embedded learning [25; 26] and generative adversarial modeling [27]. (3) Algorithm-based methods primarily utilize prior knowledge to modify the search strategy within the hypothesis space. This category can be further divided into two subcategories: methods that provide good initialization parameters and those that directly train an optimizer to output the search step. Fine-tuning is an effective means of providing good initialization parameters [28], while Model-Agnostic Meta-Learning (MAML) represents a framework for directly training an optimizer to determine the search step. For instance, [PERSON] et al. [11] introduced a few-shot transfer learning approach based on MAML for diagnosing faults in variable conditions. The effectiveness of the proposed method is verified based on a unified one-dimensional convolution network on three public datasets. [PERSON] et al. [29] propose a feature-space metric-based meta-learning model (FSM3) to overcome the challenge of the few-shot machinery fault diagnosis under multiple limited data conditions. FSM3 exploits both the attribute information from individual samples and the similarity information from sample groups. In contrast to FSM3, the model in [30] is trained directly in the data space rather than the feature space. Notably, the approach presented in this study falls within the framework of MAML.  
The aforementioned methods primarily concentrate on addressing the issue of fault diagnosis with limited samples within a single source domain. Although these methods exhibit satisfactory performance within the single source domain scenario, their effectiveness in accommodating multiple source domains requires further validation. Additionally, there is a lack of adequate attention paid to maximizing the utilization of fault information from each source domain to acquire additional prior knowledge and enhance the generalization capability of models in a multi-source domain scenario. Furthermore, most research efforts often overlook considerations related to user privacy and data security.