esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
MSIMP is classified in detail from the dimensions of time, space, and task planning and management; in addition, the specific applications of MSIMP in the four phases of pre-processing, task planning, execution, and feedback replanning, considering the related disciplines and various application areas, are introduced in detail. A specific example of forest fire monitoring is used for concrete description. Then, research on MSIMP based on SM-EMOEAs is analyzed in terms of both MSIMP modeling and optimization algorithms. The mathematical model of MSIMP is introduced from four aspects: the target mission set, satellite resource set, time window set, and evaluation set. In addition, the currently most commonly used traditional optimization algorithms are classified into two categories--deterministic algorithms and heuristic algorithms--to provide a detailed overview of the traditional optimization algorithms based on MSIMP. We also discuss the strengths and weaknesses of traditional optimization algorithms in solving the MSIMP problem. A detailed overview of the classical expensive multi-objective evolutionary algorithms (EMOEAs) and a discussion of the process of EMOEA-based MSIMP are provided. We summarize the main ideas, advantages, and disadvantages of the most commonly used surrogate models from three categories: statistical models, function approximation models, and machine learning models. Then, the surrogate model is selected according to the actual situation in the MSIMP problem, and the EMOEA framework based on the surrogate model is further explored. The improved SM-EMOEAs for MSIMP are discussed in terms of surrogate model improvement, adaptive strategy improvement, and the diversity maintenance and quality assessment of the solutions. The improvements provided by four types of surrogate models--namely, the global surrogate model, local surrogate model, bi-layersurrogate model, and aggregation model--are summarized. Then, the improved adaptive strategies, such as the adaptive sampling strategy, multi-group co-evolutionary strategy, and domain knowledge strategy, are further summarized. Finally, the solution diversity maintenance methods and solution quality assessment are discussed. The remainder of this paper is organized as follows. Section 2 introduces the basics of MSIMP and analyzes the MSIMP constraints. Section 3 describes the MSIMP problem and introduces MSIMP based on traditional optimization algorithms. Section 4 presents the flowcharts of the expensive multi-objective evolutionary algorithms based on MSIMP and the surrogate model used to assist the EMOEAs, and explores the expensive multi-objective evolutionary algorithms based on the surrogate model. Section 5 describes the MSIMP method based on the improved SM-EMOEAs in detail, in terms of the improved surrogate model, the improved adaptive strategy, and the diversity maintenance and quality assessment of the solutions. Section 6 discusses the SM-EMOEAs and SM-EMOEA-based MSIMP literature and concludes this work by providing future research directions.
# Efficient Gamma-Based Zero-Reference Deep Curve Estimation for Low-Light Image Enhancement
###### Abstract
We propose a novel curve formula, the Gamma-based light-enhancement (GLE) curve, specifically tailored for low-light image enhancement tasks, which executes independent pixel-wise transformations and conducts distinct parameter estimation for each of the three RGB channels. This formula achieves pixel brightness mapping with a single application, thereby avoiding the parameter redundancy and computational slowdown associated with multiple iterations. In contrast to Zero-DCE and Zero-DCE++, which require eight iterations for curve application, our GLE curve is applied only once, resulting in a significantly faster computational speed and enhanced efficiency. 2. We introduce a new channel attention mechanism, i.e., GCA. The GCA module retains only the essential channel weight allocation process, simplifying the computationally intensive squeeze-and-excitation operations that can impede speed. We redesign the network architecture to fully leverage the GCA module. The proposed model boasts a significantly reduced parameter count of merely 8000, in stark contrast to the 80,000 parameters of the Zero-DCE model. This streamlined architecture, comprising only basic convolutional layers, activation layers, and the GCA module, ensures both effective enhancement and high execution efficiency. 3. Our proposed method not only achieves low-light image enhancement results comparable to or even better than those of the state-of-the-art methods in terms of various objective evaluation metrics but also boasts the fastest execution speed. Consequently, our model demonstrates broader application prospects, particularly in scenarios requiring real-time processing and deployment on resource-constrained devices.
## 5 Conclusions  
This work demonstrates the effectiveness of FMCW radar technology integrated with a GDD for imaging applications, particularly in detecting subtle variations in object depth and internal structures. Experiments with various metal shapes, including flat and projected \"H\" shapes, showed sensitivity to boundary contours and depth differences, with calculated range differences closely matching theoretical predictions. The 24.1 mm range discrepancy in the 3D \"H\"-shape setup aligned well with the 25 mm projection, validating the system's accuracy.  
However, the system's spatial resolution limitations were evident when resolving fine details in objects. The system reliably distinguished objects separated by 8 mm but struggled with finer spatial details, highlighting the need for improvements in the optical setup, including larger imaging mirrors. Minor deviations in OPD and beat frequency analysis were within acceptable bounds.  
Despite these limitations, the findings demonstrate the potential of FMCW radar with a GDD for low-cost, high-sensitivity 3D MMW imaging applications, providing a foundation for future improvements in resolution and signal integration. In conclusion, the GDD-based MMW imaging system holds great potential for applications in security screening, offering enhanced detection capabilities in complex and cluttered environments. Additionally, its robustness in harsh environmental conditions, including sandstorms and extreme temperatures, positions it as a reliable alternative to traditional MMW systems.
## 2 Literature Review
### Environmental Effects on Resin-Printed Part Performance
#### 2.3.3. Long-Term Mechanical Property Decay in Varying Service Environments  
The long-term mechanical property decay of resin-printed parts under varying service conditions is a multifaceted challenge. Factors such as thermal cycling, chemical exposure, and prolonged mechanical loading can all lead to the deterioration of printed resins. Studies reveal that post-curing processes are inadequate for completely addressing the mechanical weaknesses that emerge from environmental stressors [17; 49].  
Notably, experimental evaluations have underscored that repeated mechanical loads lead to fatigue failure behaviors in certain photopolymer systems, necessitating a reevaluation of service life expectations for 3D-printed applications. Research into alternative crosslinking agents and fillers is being pursued to enhance resilience against such decay mechanisms, effectively informing the design of more robust and reliable resin formulations [23; 45].
## 4 Finite Element Analysis of Temperature Distribution Characteristics in HSLLRBs
### Temperature Field Analysis Model  
#### 4.2.1 Contact and Heat Flux  
The working temperature of rolling bearings depends on the type and flow of lubricating oils and the materials of the bearing. The working temperature distribution of the bearing can be calculated by inputting the heat conductivity parameter of the bearing material, lubricant parameter, heat flux, and heat convection coefficient into ANSYS Workbench.  
The augmented Lagrangian multiplier method is used to define the contact between the roller and ring raceway, in which the roller is defined as the rigid target surface and the ring raceways are the flexible contact surface by using the surface contact.  
The friction heat is loaded by the heat flux, as shown in Figure 7.  
The heat flux q applied on the contact surface can be given as:  
\[\text{q}=\left\{\begin{array}{ll}\text{I}\times\text{H}/\text{A}&\text{t}_{ \text{i}}<\text{t}<\text{t}_{\text{i}}+\text{dt}\\ 0&\text{t}_{\text{i}}+\text{dt}<\text{t}<\text{t}_{\text{i}+1}\end{array}\right. \tag{24}\]  
Here, I is the heat distribution coefficient, H is the contact surface friction power loss, and t is action time. The results obtained by [PERSON] et al. (2018) showed that the value of I is 0.5.  
The heat source action cycle T can be given as:  
\[\text{T}=\frac{2\pi}{\text{N}|\omega_{\text{c}}-\omega_{\text{n}}|} \tag{25}\]  
The heat source action time dt can be obtained as follows:  
\[\text{dt}=\frac{2\text{b}_{\text{n}}}{\text{D}_{\text{m}}|\omega_{\text{c}}- \omega_{\text{n}}|} \tag{26}\]
## 3 Results and Discussion  
### Pore Structure Alterations Characterized by NMR Response  
One of the meanings of the low-field NMR \(T_{2}\) distribution in fluid physics is the pore size distribution of the core. The \(T_{2}\) distribution provides pore-size-distribution information [23]. In rock pores, the \(T_{2}\) relaxation time of fluid can be expressed as [24; 25]:  
\[\left(\frac{1}{T_{2}}\right)_{total}=\left(\frac{1}{T_{2}}\right)_{S}+\left( \frac{1}{T_{2}}\right)_{D}+\left(\frac{1}{T_{2}}\right)_{B} \tag{1}\]  
where: \(\left(\frac{1}{T_{2}}\right)_{total}\) is the total relaxation time; \(\left(\frac{1}{T_{2}}\right)_{S}\) is the fluid transverse relaxation time caused by surface relaxation; \(\left(\frac{1}{T_{2}}\right)_{D}\) is the fluid transverse relaxation time caused by diffusion relaxation under a gradient magnetic field; \(\left(\frac{1}{T_{2}}\right)_{B}\) is the transverse relaxation time of the fluid in a free state.  
An important feature of the rock surface relaxation is related to the specific surface of the rock. A larger specific surface area of the rock (which refers to the ratio of pore surface area to pore volume in the rock) yields a smaller \(T_{2}\) relaxation time, and vice versa. Therefore, the rock surface relaxation can be expressed as [26]:  
\[\frac{1}{T_{2}}=\rho\times\frac{S}{V} \tag{2}\]  
where \(\rho\) is the relaxation rate of the rock surface and \(S/V\) is the ratio of the pore surface area to fluid volume.  
If we assume that the pore radius is related positively to the throat radius, Equation (2) becomes [27]:  
\[\frac{1}{T_{2}}=\frac{\rho}{r}\times F_{s} \tag{3}\]  
where \(F_{s}\) is the geometric factor and \(r\) is the aperture. Based on Equations (1)-(3), the lateral relaxation time is proportional to the pore radius r of the core. Therefore, the \(T_{2}\) distribution can reflect the pore radius distribution. A longer relaxation time has a larger corresponding hole radius, and vice versa.
# A Simple and Robust Equalization Algorithm for Variable Modulation Systems
## 3 Proposed Equalization Algorithm for VM System  
### VM-DDLMS Algorithm  
The modulated frame header and the modulated pilot block are similar to QPSK mapping, which can be regarded as known information from the PL frame and used as the training sequence of the equalization algorithm. Therefore, an improved equalization algorithm based on training sequences suitable for VM systems called VM-DDLMS is proposed in this paper. Only when the input signal is the training sequence is the signal valid; the error signal is then calculated, and the filter coefficients are updated. The input signal in the data block is corrected by the trained filter.  
According to Equations (3) and (8), CMA and MCMA error signals depend on the correction parameters of the current modulation mode. When the operating mode is switched in VM systems, the correction parameters are changed accordingly, so the CMA and MCMA are not optimal for VM systems. In addition, the performance of CMA and MCMA has not improved based on the training sequence. Therefore, the DDLMS algorithm is improved for VM systems based on the training sequence in this paper. The structure of the VM-DDLMS algorithm is shown in Figure 3. The filter coefficients are updated according to the input signal and error signal where the adjudicator only needs to make the QPSK symbol decision. The QPSK decision output can be expressed as:  
\[\left\{\begin{array}{rl}re(y(k))=\left\{\begin{array}{rl}0.7071,&re(\hat{y }(k))\geq 0\\ -0.7071,&re(\hat{y}(k))<0\\ im(y(k))=\left\{\begin{array}{rl}0.7071,&im(\hat{y}(k))\geq 0\\ -0.7071,&im(\hat{y}(k))<0\end{array}\right.\end{array}\right.,\end{array} \tag{13}\]where \(y(k)\) is the input of the adjudicator, and \(\hat{y}(k)\) is the decision output. The cost function of the VM-DDLMS algorithm can be expressed as:  
\[J(k)=\frac{1}{2}E\Big{[}\left(\hat{y}(k)-y(k)\right)^{2}\Big{]}, \tag{14}\]  
The VM-DDLMS error signal can be expressed as:  
\[e(k)=\left\{\begin{array}{cc}\hat{y}(k)-y(k),&\quad enable=1\\ e(k),&\quad enable=0\end{array}\right., \tag{15}\]  
where the _enable_ signal is valid, it indicates that the data are the frame header or pilot, and the formula for updating the filter coefficients of the VM-DDLMS algorithm can be expressed as:  
\[e(k)=\left\{\begin{array}{cc}\hat{y}(k)-y(k),&\quad enable=1\\ e(k),&\quad enable=0\end{array}\right., \tag{16}\]  
Considering all factors, \(\mu=0.001\).
# A Novel Cloud Approach for Connected Vehicles
## 5 Evaluation  
In this section, we compare the NDN, RENE, VCDN, VCDNoCAM, and GeoVCDN approaches based on the models proposed in the previous section. These comparisons are done on two performance indicators: network overhead and data freshness. These comparisons aim to confirm and evaluate the benefits of our approach, compared to the existing methods, at the network level. Furthermore, to validate the proposed analytic framework, we develop an ad hoc simulator that takes into account some aspects of vehicular communication and the mobility of the vehicles. Finally, we tested NDN, RENE, and GeoVCDN on the shortest pathfinding use case in a smart city. This aims to validatethe benefits of our approach at the application level. Let us remind the reader that our approach is composed of three versions: the first one (VCDN) groups a set of data as one set of metadata, the second one (VCDNoCAM) does the same and also includes ICN exchanges in the CAMs, and the third one (GeoVCDN) extends the second one by adding the V2V communications. In this section, we propose, for each parameter, a table in which every approach is compared using a specific value of these parameters. Expected values are obtained by applying the equations defined in Section 4.3 in the model described in Section 4.1 considering the mobility as described in Section 4.2. Observed values are the ones obtained with our simulator.
# Secure K-Means Clustering Scheme for Confidential Data Based on Paillier Cryptosystem
## 8 Performance Analysis
### Discussion on Practical Constraints  
While the proposed protocol demonstrates practical scalability and accuracy, we acknowledge several operational constraints:  
* Encryption Overhead: Paillier encryption introduces substantial computational overhead (modular exponentiation) and large ciphertext sizes (hundreds of bytes), affecting bandwidth usage.
* Resource Requirements: Due to the volume of homomorphic operations and ciphertexts, sufficient CPU and memory are essential, especially for high-dimensional data.
* Threat Model Assumptions: The protocol assumes that the computing parties (e.g., cloud providers) are non-colluding. In a collusion scenario, privacy guarantees may be compromised unless enhanced with zero-knowledge proofs or verifiable computation.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline  & **This Paper** & [21] & [22] & [23] \\ \hline Parties & \(>\)2 & \(>\)2 & 2 & \(>\)2 \\ User online operation & \(\times\) & ✓ & ✓ & ✓ \\ Encryption algorithm & [PERSON] & Chen’s Multi-Key FHE & Paillier & YASHE \\ \hline \hline \end{tabular}
\end{table}
Table 6: Literature comparison.
# An Incremental Local Outlier Detection Method in the Data Stream
## 3 Results
### Scalablity
As shown in Figures 7 and 8, both on the standard Gaussian distribution and uniform distribution synthetic datasets, the proposed CLOF method had excellent efficiency compared to the kLOF and the CNN_WIUS methods; for example, in Figure 7a-c, the updating time of kLOF, CNN_WIUS, and CLOF was 5.7, 8.9, and 5.9 s respectively when data dimension \(D=2\) and sliding window width \(N=5000\); the updating time of kLOF, CNN_WIUS, and CLOF was 7.6, 10.8 and 5.9 s respectively when data dimension \(D=6\) and sliding window width \(N=5000\), and the updating time of kLOF, CNN_WIUS, and CLOF was 9.4, 12.6, and 5.9 s respectively when data dimension \(D=10\) and sliding window width \(N=5000\). These results indicated that the proposed CLOF method had comparable efficiency with the state-of-the-art kNN-based methods, the proposed incremental update strategy could improve the efficiency of CNN-based outlier detection methods, and this strategy reduced the sensitivity to data dimension D compared to the kLOF and CNN_WIUS methods. Similar results were also obtained for the uniform distribution synthetic datasets, as shown in Figure 8.  
Figure 7: The time for updating the local outlier factor on standard Gaussian distribution synthetic datasets: (**a–c**) the time at \(k=5\) and \(D=2\), 6, 10 respectively; (**d–f**) the time at \(k=10\) and \(D=2\), 6, 10 respectively; (**g–i**) the time at \(k=20\) and \(D=2\), 6, 10 respectively. A clear relationship among the efficiency of kLOF, CNN_WIUS, and CLOF methods was CLOF > kLOF > CNN_WIUS when the sliding window width N was large enough. However, as shown in Figure 9, by analyzing the update time when \(N\) took a small value, there was a significant intersection of the time curves of the kLOF and CLOF methods; that is, the efficiency of kLOF was higher than that of CLOF. For example, the updating time of kLOF was 0.002, 0.009, and 0.271 s when \(k\) = 5, \(D\) = 6, and \(N\) = 100, 500, and 1000 respectively, and that of CLOF was 0.040, 0.041, and 0.230 s when \(K\) = 5, \(D\) = 6, and \(N\) = 100, 500, and 1000 respectively, which caused a significant intersection in the time curves of the kLOF and CLOF methods. Therefore, the proposed incremental update strategy consumed larger amounts of calculation resources than that of the direct update when the sliding window width \(N\) was small, and the proposed method could better reduce the amount of calculations when updating the local outlier factor with large amounts of data. Figure 8: The time for updating local outlier factor on uniform distribution synthetic datasets: (**a–c**) the time at \(k\) = 5 and \(D\) = 2, 6, 10 respectively; (**d–f**) the time at \(k\) = 10 and \(D\) = 2, 6, 10 respectively; (**g–i**) the time at \(k\) = 20 and \(D\) = 2, 6, 10 respectively.
# A Comparative Analysis for Air Quality Estimation from Traffic and Meteorological Data
## 2 Materials and Methods
### Experiments
Specifically, we computed the Root Mean Squared Error (RMSE), which better represents the actual error on the test set and the Symmetric Mean Average Percentage Error (sMAPE), which is an error-based measure expressed in percentage and therefore unbound from any value range. Formally these measures can be expressed as in Equation (2). \[RMSE=\sqrt{\frac{1}{N}\sum_{i=1}^{N}\left(y_{i}-\hat{y}_{i}\right)^{2}}\ \ \ \ \ \ sMAPE=\frac{1}{N}\sum_{i=1}^{N}\frac{\left|y_{i}-\hat{y}_{i}\right|}{\left|y _{i}\right|+\left|\hat{y}_{i}\right|} \tag{2}\]  
We precise that our current formulation for the sMAPE does not correspond to a percentage, but we kept a simpler \([0,1]\) range, where \(1=100\%\) error rate, to improve readability. Completed and evaluated the regression problem, the AQI estimation performance still needs to be investigated. For this task, only a subset of the pollutants need to be taken into consideration, which typically consist of Nitrogen Dioxide (\(NO_{2}\)), Ozone (\(O_{3}\)) and particulate matter, in most cases referring to \(PM_{10}\). Regarding the AQI, the European Environment Agency (EEA) defined a common metric, named Common Air Quality Index (CAQI), which is described by five different thresholds and computed on the pollutants listed above [31]. A subset of the complete table, including global indices and thresholds for individual pollutants are reported in Table 5.  
The CAQI can be computed with both hourly and daily concentrations. In order to compute the global index, the maximum value among pollutant concentrations must be taken. Since our data contained hourly concentrations for every pollutant except particulate matter, we computed the hourly version using the daily mean of \(PM_{10}\), using the ad hoc index provided by the CAQI. In order to evaluate the regression estimates through the AQI, we first computed the ground-truth indices using the real pollutant trends in the test set, then applied the same transformation to the estimated time series produced by the trained models. We defined the latter task as a standard multi-class categorization problem, employing the F1-score as metric for its robustness against unbalanced classes.
# Diverse Decoding for Abstractive Document Summarization
## 4 Experiment
### Experiment Results
#### 4.2.1 DPBS Evaluation  
In Table 1, we report the diversity statistics calculated according to the number of distinct n-grams produced by the standard beam search and the DPBS method. These two methods are applied to the same test set with the same beam size of four. As discussed in the previous section, the diversity statistics present how diverse the parallel generated sequences are. The standard beam search achieved 0.1714 (Distinct-1), 0.3627 (Distinct-2), and 0.4344 (Distinct-3). The DPBS exceeded this with 0.1953 (Distinct-1), 0.4476 (Distinct-2), and 0.6389 (Distinct-3). Distinct-1 measures how different the multiple generated sequences are at the single-word level, and Distinct-2 and Distinct-3 are more about the phrase level. The results in Table 2 show that DPBS had improvements on both sides. As the diversity was calculated using distinct n-grams divided by sequence length, the total number of the generated distinct n-grams of the method was considerably bigger than these figures suggest. To have an intuitive view of the diversity provided by our DPBS method, we used a sample news article from the test set and obtained the decoding outputs by DPBS and beam search. The results are show in Figure 4. Due to the original article being too long, we do not show it here. We used the human-written summary to have an overview of its content. As shown by this example, our DPBS effectively increased the diversity without much harm to the readability. We also provide the different decoding results of a battery of articles in this paper's Supplementary Material.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline  & **Distinct-1** & **Distinct-2** & **Distinct-3** \\ \hline Standard Beam Search & 0.1714 & 0.3627 & 0.4344 \\ DPBS & 0.1953 & 0.4476 & 0.6389 \\ \hline \hline \end{tabular}
\begin{tabular}{l} **Gold Summary:** international fellowship of christians and jews has brought 600 jews to israel since december. the margin family is among them ; their home in eastern ukraine was bombed. \\ \hline \hline  
Figure 4: Example from the CNN/Daily Mail test dataset showing the outputs generated by both DBPS and standard Beam Search (BS).