esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 SSC and LRR Clustering with LLE Regularization
### The Proposed LLE-SSC and LLE-LRR Models  
LLE is a classic manifold learning method for dimension reduction, which can discover non-linear structure in high dimensional data by exploiting the data local geometry by linear reconstruction. Additionally, LLE maps its input data into a single global coordinate system of lower dimensionality and does not involve local minima by generating highly non-linear embedding operation. For the given dataset \(\mathbf{X}\), LLE is to find the optimal weight matrix \(\mathbf{W}\) to minimize the following sum error of the data samples which are linearly represented by their neighbours:  
\[\min_{\mathbf{W}_{ij}}\sum_{i=1}^{n}\|\mathbf{x}_{i}-\sum_{\mathbf{x}_{j}\in N (\mathbf{x}_{i})}\mathbf{W}_{ij}\mathbf{x}_{j}\|_{2}\text{ s.t. }\sum_{\mathbf{x}_{j}\in N(\mathbf{x}_{i})}\mathbf{W}_{ij}=1, \tag{8}\]  
where \(N(\mathbf{x}_{i})\) is the neighbourhood of \(\mathbf{x}_{i}\), and \(\mathbf{W}_{ij}=0\) if \(\mathbf{x}_{j}\
ot\in N(\mathbf{x}_{i})\). For convenience, the above objective function can be formulated as matrix form as follows:  
\[\sum_{i=1}^{n}\|\mathbf{x}_{i}-\sum_{\mathbf{x}_{j}\in N(\mathbf{x}_{i})} \mathbf{W}_{ij}\mathbf{x}_{j}\|_{2}=tr(\mathbf{X}(\mathbf{I}-\mathbf{W})^{T} (\mathbf{I}-\mathbf{W})\mathbf{X}^{T}). \tag{9}\]  
To this minimization problem, there exist several efficient solutions, such as the method in [24]. Once obtaining the solution, denoted by \(\mathbf{W}^{*}\), which is regarded as the matrix encoding the manifold structure of the data, we can use \(\mathbf{W}^{*}\) to construct the regularization for SSC and LRR. The argument is that the learned manifold property of the data \(\mathbf{X}\) is also valid to its sparse or low rank representations \(\mathbf{Z}\). In other words, if we replace \(\mathbf{X}\) with \(\mathbf{Z}\), the error in (8) will be still minimal as they share the same manifold structure.  
If we assume  
\[\mathbf{L_{M}}=(\mathbf{I}-\mathbf{W}^{*})^{T}(\mathbf{I}-\mathbf{W}^{*}), \tag{10}\]  
then we can obtain the LLE regularization as follows:  
\[\underset{\mathbf{Z}}{\text{min}}tr(\mathbf{Z}\mathbf{L_{M}}\mathbf{Z}^{T}). \tag{11}\]  
Integrating the above regularization with SSC and LRR models, we naturally obtain our final LLE-SSC and LLE-LRR models as follows:  
\[\underset{\mathbf{Z}}{\text{min}}\|\mathbf{Z}\|_{1}+\frac{\lambda_{1}}{2}\| \mathbf{X}-\mathbf{X}\mathbf{Z}\|_{F}^{2}+\lambda_{2}tr(\mathbf{Z}\mathbf{L_{ M}}\mathbf{Z}^{T}), \tag{12}\]  
\[\underset{\mathbf{Z}}{\text{min}}\|\mathbf{Z}\|_{*}+\frac{\lambda_{1}}{2}\| \mathbf{X}-\mathbf{X}\mathbf{Z}\|_{F}^{2}+\lambda_{2}tr(\mathbf{Z}\mathbf{L_{ M}}\mathbf{Z}^{T}). \tag{13}\]  
In practice, to avoid numerical instability, \(\mathbf{L_{M}}\) is generally added a disturbed value to enforce \(\mathbf{L_{M}}\) to be strictly positive definite, i.e., \(\mathbf{L_{M}}\) is replaced by \(\mathbf{L_{M}}=\mathbf{L_{M}}+\varepsilon\mathbf{I}\) where \(\mathbf{I}\) is the identity matrix and \(0<\varepsilon\ll 1\), the same as that in [14].
## 5 Results
### Validation and Case Studies  
The validation results showed a strong correlation between the extended remaining life predicted by the DNN model and the life calculated through FEA simulations. For various LSP parameter settings, the relative error between the DNN-predicted extended RUL \(N_{\text{DNN}}\) and the FEA-calculated extended RUL \(N_{\text{FEA}}\) remained consistently low, ranging from 2.5% to 5.8%. This indicates that the DNN model accurately captured the effects of different LSP parameters on the bearing's fatigue life. When the optimized LSP parameters were applied, the FEA results showed an increase in the bearing RUL; there was an increase in \(N_{\text{FEA}}\) of 30.31%. The DNN predictions closely matched these results; The \(N_{\text{DNN}}\) value was increased by 29.33% compared to the DNN outcomes. Figures 9 and 10 show the FEA simulation under different LSP parameters.  
Figure 8: Comparison of different optimization algorithms.  
Figure 9: FEA simulation under different parameters.
## 4 Results
### Starting
#### 4.5.1 Query Initiation  
_Query initiation_ refers to a strategy that participants began their search tasks with creating a query. The log data showed that 88.3% of the cases included at least one search query statement with the first six search tactics. A web search engine is the most frequently utilized source for this type of strategy. The following series presents the typical sequence of tactics and an example (Table 11) of this type of search strategy:  
\(Lead\)\(\rightarrow\)\(AccF\)\(\rightarrow\)\(Creat\)\(\rightarrow\)\(EvalR\)\(AccF\)\(\rightarrow\)\(EvalI\)  
In addition, a variant related to this strategy was observed. In one case, a participant applied a learning tactic (Lrn) to get started with learning how to use the functions of Google Map in finding some satellite pictures of Rome (S2). When users begin a search with unfamiliar systems or system features, they might need to learn how to make good use of the system or system features to get started.  
The following presents the pattern of a variant of starting with a query initiation strategy:  
\(Lead\)\(\rightarrow\)\(AccF\)\(\rightarrow\)\(Creat\)\(\rightarrow\)\(EvalR\)\(AccF\)\(\rightarrow\)\(Lrn\)  
\begin{table}
\begin{tabular}{c c} \hline
**Tactic** & **Activity** \\ \hline \multirow{3}{*}{_Lead_} & “_so I am going to search and see if they might have a different growing habit so I am going to search for just right now, butterfly bush and see what I can get...and now I am at Google_” \\  & “types up here www.google.com” \\ \cline{1-1}  & [query: butterfly bush] \\ \cline{1-1}  & [clicks 3 rd result: Buddleja - Wikipedia the free encyclopedia]... \\ \cline{1-1}  & [clicks to Wikipedia] \\ \cline{1-1}  & [\(All\) right, this did not give me any info that I did not know...” \\... &... \\ \hline \end{tabular}
\end{table}
Table 11: Query initiation strategy.  
_Query initiation_ strategy was the dominant starting strategy. Participants expressed their reasons for the selection of this strategy: (1) Comprehensive coverage: \"_I feel that starting off with a broad Google search will lead me to many articles, websites and resources relating to my search topic.\"_ (S3); (2) Ease of Use: \"_I know from past experiences that Google is fairly easy to use\"_ (S5); (3) Familiarity/past experience: \"_Keyword search is what I usually use and I am usually successful in my search\"_ (S10); and (4) Efficiency: \"_I use a search engine to do a quick search to see what's out there\"_ (S20). After starting with _query initiation_ strategy, participants frequently switched to one of the exploration strategies such as _iterative exploration_ and _whole site exploration_. This type of transition occurred in about 71.7% of all cases. _Query initiation_ strategies were used to find and access relevant items. At the same time, participants also applied exploration strategies as an alternative approach when they could not find relevant items by using _query initiation_ strategy.
## 2 Literature Review
### Advancements in In Situ Process Monitoring for Resin 3D Printing  
#### 2.8.1 Optical and Thermal Imaging During Layer Deposition  
In situ process monitoring utilizing optical and thermal imaging technologies has emerged as an essential advancement in enhancing the quality assurance of resin printing processes. The incorporation of real-time imaging enables the immediate detection of defects during layer deposition, allowing corrective measures to be implemented in real time [63; 70].  
Research has demonstrated that employing these technologies significantly reduces the occurrence of print failures while simultaneously improving the overall dimensional fidelity of printed components [33; 34]. Continuous data stream analyses facilitate dynamic adjustments to printer settings, improving process yield and ultimately enhancing the reliability and robustness of 3D printing workflows [27; 52].
## 1 Introduction
The listening test was undertaken internationally over the Internet in realistic or semi-realistic conditions; that is, in an environment typical for ordinary listeners listening to binaural music recordings using headphones (with uncontrolled electro-acoustic conditions). Head-tracking systems and the technique of individualization of head-related transfer functions (HRTFs) were not used in this study as most of the ordinary listeners of binaural audio content still do not exploit such facilities. Each listener was requested to classify 30 binaural music excerpts, randomly drawn from the repository of 1560 recordings. In total, 1560 binaural excerpts were classified by the listeners. The same task was also undertaken automatically, exploiting four machine learning algorithms--convolutional neural network (CNN), support vector machines (SVM), extreme gradient boosting framework (XGBoost), and logistic regression (Logit)--allowing for a comparison of the classification accuracy reached by the human listeners with those obtained using the machine audition algorithms. To the best of the authors' knowledge, this is the first study aiming to undertake such a comparison. The paper is organized as follows. The next section provides the background of this study. Section 3 describes the corpus of the binaural music recordings used both in the listening test and in the experiments with the automatic classification of spatial audio scenes. Sections 4 and 5 elaborate on the methodology and the results obtained using the listeners and the machine learning algorithms, respectively. The outcome of the comparison and the drawn conclusions are discussed and summarized in the final two sections.
# A Review of Early Injection Strategy in Premixed Combustion Engines
## 1 Introduction  
Due to the advantages of better fuel economy, durability, reliability, and high specific power output compared to gasoline engines, diesel engines are widely used not only for heavy-duty vehicles such as trucks, construction machines, and generators, but also for light-duty ones including passenger cars [1; 2; 3].  
However, the well-known trade-off law of nitrogen oxides (NOx) and soot emissions for conventional diesel combustion makes it difficult to reduce NOx and soot simultaneously while maintaining a high level of thermal efficiency [4]. In addition, the exhaust emitted from diesel engines, especially soot emission, has been proved to have undesirable effects on human health [5; 6; 7; 8; 9; 10]. To solve the problem, many advanced combustion technologies have been studied. HCCI and PCCI combustion have proven to be promising strategies.
## 2 Study Area and Data  
### Experimental Area  
The experimental area in this study is shown in Figure 1, and the experiments were conducted at the Xilinhot SAR satellite calibration and validation site of the CASPLOS_CCSI project. This site is located in Xilinhot, Inner Mongolia, and the underlying surface is a typical grassland with krylov needlegrass and leymus chinensis as the predominant species. The site was built to achieve a long period of monitoring of satellite performance and satisfy the needs of multiple SAR imaging modes, by means of fixed calibrator that can be remotely controlled, supplemented by a flexible calibrator, which was in the construction stage. In this time, the experimental site was concentrated in the Xilinhot Climate Observatory, as shown by yellow line in Figure 1, which had a flat terrain and was a vast area with an altitude of about 1090-1160 m. In this site, the types of surface coverage included grassland, rivers, low buildings, etc., and scattering characteristics of the background features were homogeneous and stable without interference from a strong target or electromagnetic signal, which can effectively reduce the interference from background clutter [12].
# Ground Control System Based Routing for Reliable and Efficient Multi-Drone Control System
## 3 GCS-Routing Design
### Complexity Analysis  
In terms of scalability, low cost route update is an important requirement for routing protocols. Common PRPs periodically issue and exchange HELLO messages, whereas GCS-routing does not issue  
Figure 8: Supposed situation for proof of Theorem 1.  
HELLO messages. Routing table management for GCS-routing also has much lower overhead than other protocols in respect of the number of route update messages. Let us assume a network composed of \(N\) randomly deployed nodes. Route update cost can be measured in terms of communication complexity, i.e., the number of messages required to perform a protocol operation [35].  
Suppose destination sequenced distance vector routing (DSDV) [36], one of the most widely used PRPs, is applied to the network. DSDV has two types of route update messages. One immediately updates drastically changed routes, and the other periodically updates the overall route. Communication complexity of the former case is \(O(N)\), since a route update message would be forwarded by all nodes in the network in the worst case. Communication complexity for the periodical route update is much larger. In a network where every node periodically broadcasts its own route table, each table would be forwarded \(N\) times in the worst case. Therefore, communication complexity is \(O(N^{2})\).  
In contrast, OLSR [18] chooses one or more nodes among neighboring nodes to be multipoint relay (MPR) nodes, and topology control (TC) packets are transmitted by MPR nodes only. Thus, OLSR reduces the number of packet transmissions for route update. However, in the worst case, all OSLR nodes except the end node are MPR nodes, hence communication complexity for route update in OLSR is also \(O(N^{2})\), similar to DSDV.  
On the other hand, consider GCS-routing applied to the same network. Two types of route update messages are used in GCS-routing for initialization and route maintenance. The worst case is when all node route information is updated, which is identical to initialization. As discussed above, GCS generates a tree to issue route update messages, where GCS is the root. GCS then issues each aggregated route update message to corresponding subtree, and each drone repeats the operation. Therefore, every node in the network receives the aggregated route update message one time only. Thus, communication complexity is \(O(N)\), significantly lower than DSDV or OLSR, and hence more efficient.
# Special Equipment Safety Supervision System Architecture Based on Blockchain Technology
## 2 Requirement Analysis
### Role Analysis
#### 2.1.2 Inspection Agencies  
China's special equipment safety supervision has implemented a dual-track system that combines safety supervision with performance inspection, which means that inspection agencies play an important role in safety supervision systems. According to laws and regulations, the user of special equipment should submit a periodic inspection request to the special equipment inspection agency one month before the expiration of the validity inspection period [38]. The special equipment that has not been inspected regularly, or fails to pass the inspection, shall not be used continuously. After receiving the requirements of periodic inspection, the inspection agency should conduct safety performance inspection and energy-efficiency tests on the equipment in accordance with the requirements of safety technical specifications. Sharing inspection results with users and supervisors is another important task for inspection agencies.
## 4 Website Interface Architecture
### JavaScript Front-End Logic
#### Algorithm Implementation  
The implementation of the DietNerd algorithm combines real-time processing with database retrievals:  
1. **Question Submission:** When a user submits a question, the question is first checked for validity using the check_valid endpoint, defined in Section 4.1.
2. **Database Check:** If valid, the system checks if an answer already exists in the database using the db_get endpoint, defined in Section 4.1.
3. **Similar Questions:** If no exact match is found, the system performs a similarity search to suggest questions already answered by our system to the user's question using the sim_search endpoint in Section 4.1, and offers these questions as options.
4. **Real-time Processing:** If the user requests a new answer for their question: * A session ID is generated and returned to the client. * The process_query endpoint (Section 4.1) is called and the answer generation workflow starts.  
The client establishes an SSE connection to receive updates, using the SSE endpoint in Section 4.1.
* The FastAPI back-end processes the question asynchronously, sending progress updates to the client.
* **Result Delivery:** The final answer, along with references and citations, is sent to the client and displayed to the user.  
This workflow achieves efficient handling of both new and previously answered questions, providing a responsive user experience while managing server resources effectively.
## 3 Results
### Effect of Parameters on Extraction Efficiency
#### 3.4.4 Effect of Desorption Temperature and Time  
The temperature and time of desorption are important factors that influence the efficiency of the SPME process. To ensure the complete transfer of analytes from fiber coating to the GC column, the GC inlet system was operated in the splitless mode. The effect of the desorption temperature (GC inlet) was studied in the range of 175-205 \({}^{\circ}\)C. As shown in Figure 5D, the extraction efficiency increased from 175-200 \({}^{\circ}\)C and remained almost constant afterward. Thus, 200 \({}^{\circ}\)C was chosen as the optimum desorption temperature. Then, the effect of desorption time was investigated in the range of 2-4 min at 200 \({}^{\circ}\)C. Therefore, regarding the obtained results in Figure 5E, 3 min was selected as the optimum desorption time to achieve the total desorption of all analytes with no carryover effect.
## Appendix A Qualitative Analysis of Hurst Exponent and Entropy: Case-by-Case Description
### Landoh (2012)  
The Landoh malaria time series (Est Mono district in Togo, 2012) [25] reveals a consistent upper trend in malaria incidence in the form of a mild increase in the 36-month moving average (\(I_{36}\)) over the whole time series, ranging from the initial \(\sim\)18 cases _phy_, to a peak at \(\sim\)30 cases _phy_ in month 72.  
Consistent with the steady upper trend in \(I_{36}\) during the entire time series, we find a declining trend in the 36-month moving average of the Hurst exponent (\(H_{36}\)) from a peak of \(\sim\)0.85 at month 47, to an all-time-low value of \(\sim\)0.55. Along with this downward trend in \(H_{36}\), there is a consistent increase in entropy in the form of a 36-month moving average (\(S_{36}\)), during the entire time series, despite its undulatory behavior. In the Landoh time series, the behaviors of \(H_{36}\) and \(S_{36}\) are reasonably well correlated with the behavior of \(I_{36}\) in time--see Figure 15.
## Theory for the Local Wavenumber Approach
### Acquisition of the Average Wavenumber  
A spatial window is used for reducing the magnitude of the side lobes. It should be noted that the spatial window size is at least twice the wavelength of the A0 mode in this paper. The centered coordinate of this spatial window is located at \((x_{m},y_{n})\). Under this circumstance, the process of obtaining the average wavenumber is clearly illustrated as below.  
First, a short space transform is conducted in order to retain the spatial information and reduce the magnitude of side lobes in the wavenumber domain. Specifically, the wavefield data is multiplied by a spatial window to produce the windowed dataset \(\widetilde{U}_{mn}(x,y,t)\). The spatial window is non-zero for only a short period in space. The Hanning window \(W_{xy}\) is usually adopted [30].  
\[\widetilde{U}_{mn}(x,y,t)=\widetilde{U}(x,y,t)W_{xy} \tag{6}\]  
\[W_{xy}=\left\{\begin{array}{ll}0.5\Big{[}1+\cos(2\pi\frac{r}{D_{x}})\Big{]}& \text{if }r\leq D_{x}/2\\ 0&\text{otherwise}\end{array}\right. \tag{7}\]  
where \(r\) and \(D_{x}\) can be expressed as:  
\[r=\sqrt{\left(x-x_{m}\right)^{2}+\left(y-y_{n}\right)^{2}} \tag{8}\]  
\[D_{x}\geq 2\lambda \tag{9}\]  
Here, \(\lambda\) denotes the wavelength of the Lamb wave. \(D_{x}\) is the window length. These signals processed with the spatial window are transformed into the spatial-frequency-wavenumber domain by using 3D FFT.  
\[\widetilde{U}_{mn}(k_{x},k_{y},f)=F_{3D}[\widetilde{U}_{mn}(x,y,t)] \tag{10}\]  
The weight of \(\|k\|\) must be considered before calculating the local wavenumber \(k_{mn}\), as presented in the following equation:  
\[k_{mn}(f)=\frac{\sum\limits_{k}\left[\left|\widetilde{U}_{mn}(k_{x},k_{y},f) \right|^{2}\|k\|\right]}{\sum\limits_{k}\left|\widetilde{U}_{mn}(k_{x},k_{y},f )\right|^{2}} \tag{11}\]  
where \(\|k\|\) is the Euclidean norm of the 2D wavenumber range [31]. The average wavenumber \(\overline{k_{mn}}\) over the selected frequency band is calculated because a real signal usually contains a certain bandwidth instead of the singular frequency component. The average wavenumber of each point can be obtainedby changing the centered coordinate of this spatial window and repeating the above steps. The flow chart of the local wavenumber approach is shown in Figure 3.  
\[\overline{k_{mm}}=\frac{1}{N}\sum_{i=1}^{N}k_{nm}(f_{i}) \tag{12}\]
# Thrust Measurements on the High Efficient and Reliable Vacuum Arc Thruster (HERVAT)
## 3 Experimental Results  
In the following, thrust measurements as well as measured arc energies for different pulse energies with and without magnetic field are presented. All the thrust measurements for each energy configuration were performed at the thruster pulse repetition rate of 10 Hz and averaged over eight consecutive thrust measurements.
## 4 Discussion
### Generalization Analysis  
To verify the generalization ability and robustness of our model, the model trained on Dataset 1 was tested on Dataset 2. Figure 6 compares the generalization performance of our model with the three most advanced models. After five rounds, in terms of generalization capability analysis, our method shows significant advantages. Our model still shows the highest Top-1 accuracy, performing best among all models. Additionally, according to the loss curve, our model has better stability.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Models**} &
\begin{tabular}{c} **Average** \\ **Runtime (MS)** \\ \end{tabular} & **Params(M)** & **GFLOPs** & **Input Size** & **Model Size** \\ \hline ResNet152 & 52.34 & 58.2 & 23.20 & 224 \(\times\) 224 & 222.8 \\ ViT & 14.84 & 58.1 & 22.57 & 224 \(\times\) 224 & 330.3 \\ Swin Transformer & 32.39 & 19.6 & 5.96 & 224 \(\times\) 224 & 108.2 \\ Ours & 23.53 & 21.9 & 6.70 & 224 \(\times\) 224 & 108.3 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Detailed parameters of the four models.  
Figure 5: SHAP interpretability analysis diagrams of the four models for rocks. color intensity represents the strength of correlation; darker shades of red indicate stronger positive correlations, while darker shades of blue denote stronger negative correlations.