esa-sceva/satcom-chunk-collection huggingface dataset:
# Fuzzy Graph Learning Regularized Sparse Filtering for Visual Domain Adaptation
## 3 Methodology
### Fuzzy Graph Learning for Domain Alignment
In this paper, we exploit the top-\(k\) (\(k=10\) in this paper) nearest neighbors for target domain. \[W^{tt}_{ij}= \begin{cases}1,&if\ X_{j}\in\mathcal{N}_{10}(X_{i})\\ 0,&otherwise\end{cases}  \tag{7}\]  
where \(\mathcal{N}_{10}(X_{i})\) means the aggregation of \(X_{i}\)'s top-10 nearest neighbors in target domain. Construction of \(W^{st}\) and \(W^{\text{Is}}\): To build the fuzzy graph to model the cross-domain similarity, we first compute the class centroids \(\overline{x}\) of source samples, then the probability \(p(y|\hat{x}^{i}_{t})\) of target sample \(\hat{x}^{i}_{t}\) belongs to class \(y\) can be computed based on the Euclidean distance to each centroid. Finally, \(W_{st}\) indicates the similarity from source to target samples, then for a certain source sample \(\hat{x}^{i}_{s}\) and target sample \(\hat{x}^{i}_{t}\), if they belongs to the same category, they should have a strong connection and vice versa. Since we utilize soft labels here, it can be interpreted as: if the target sample \(\hat{x}^{i}_{t}\) has a big probability of possessing the same label of the source sample (noted that the source sample \(\hat{x}^{i}_{s}\) and its label \(y^{i}_{s}\) are all available), they should have a strong connection. The pseudo code of cross-domain affinity graph construction is given in Algorithm 1, and we can obtain \(W_{ts}\) by \(W_{ts}=W^{T}_{st}\) directly. ```
Input:\(\hat{x}_{s},\hat{x}_{t},y_{s},c\) for\(i=1,2,\cdots,c\)do \(\overline{x}^{i}=\frac{\sum_{j=1}^{d_{s}}\hat{x}^{i}_{s}(y^{i}_{s},i)}{\sum_{j= 1}^{d_{s}}\hat{y}(y^{i}_{s},i)},\ \ \delta(y^{i}_{s},i)= \begin{cases}1,&if\ y^{i}_{s}=i\\ 0,&otherwise\end{cases} \)
1 end for
2 Compute target probability labels. for\(i=1,2,\cdots,n_{t}\)do \(p(y|\hat{x}^{i}_{t})=\frac{exp(-||\hat{x}^{i}_{t}-\overline{x}^{i}||)}{\sum_{j= 1}^{c}exp(-||\hat{x}^{i}_{t}-\overline{x}^{i}||)}\)
3 end for
4 Construct fuzzy affinity graph \(W_{st}\) for\(i=1,2,\cdots,n_{s}\)do for\(j=1,2,\cdots,n_{t}\)do \((W_{st})_{ij}=p(y^{i}_{s}|\hat{x}^{i}_{t})\)
5 end for
6 end for return\(Y\), \(C_{test}\)
```  
**Algorithm 1**Cross-domain affinity graph (\(W_{st}\)) construction.
phases remain imperfect in the project lifecycle, with the implementation process regarded as a \"black box\" in practice [7].  
With the development of information technology in the architecture, engineering, and construction (AEC) industries, building information modeling (BIM) with rich information has facilitated the acceleration of high-quality construction projects [8]. In this context, limited risk identification-related assisting platforms that adopt building information models have been studied [7; 9]. Risk reasoning and identification can be supported to promote decision-making by extracting specific construction information from BIM. However, existing platforms are generally limited in terms of information integration and exchange during the automatic safety risk identification of deep excavation in practice. On the one hand, proprietary geometric modeling kernels and data storage formats which constitute the semantic interoperability of these platforms with other software applications can be hampered in practice [10]. On the other hand, inefficient integration of dynamic construction information on sites and risk-related information on information models challenge practical risk identification. Existing research efforts mainly concentrate on data processing and function module design for the development of an assisting platform, with few focusing on information integration and exchange [11]. The Industry Foundation Classes (IFC) standard is a popular neutral and open data exchange format for describing building and construction industry data [12]; thus, it is adopted in this research with the advantage of information sharing and exchange in practice [9; 13].  
Compared to the traditional way, this research focuses on the method of automatic construction risk identification, aiming at the entire deep excavation process. Information integration and exchange for safety risk identification of deep excavation are explored in this research to address the above research gap by using a BIM-based central database linking the dynamic construction process and risk-related information in project planning. Therefore, this research aims to achieve the requirements for information integration and exchange by developing a semantic industry foundation classes (IFC) data model based on a central database of Building Information Modeling (BIM) in the dynamic deep excavation process. Firstly, safety risks related to deep excavation are analyzed, and construction information required for risk identification is summarized. Then, a semantic data model applied IFC definitions is established, identifying the relationships among required information, involved relationships (i.e., logical relationships and constraints among risk events, risk factors, construction parameters, and construction phases) arising during the dynamic construction process, and BIM elements. The latest version of the IFC standard (i.e., IFC4) is extended to meet the information description requirements. It is hoped that this study can provide a guidance for automatic risk identification in construction management.
## 3 Results and Discussion
### Proximate Composition and pH Values of Coated Chicken Liver Samples
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Sample** & **Moisture (\%)** & **Protein (\%)** & **Fat (\%)** & **Ash (\%)** & **pH** \\ \hline
**FC** & 58.89 \(\pm\) 0.40 \({}^{\rm c}\) & 20.54 \(\pm\) 0.28 \({}^{\rm a}\) & 15.51 \(\pm\) 0.19 \({}^{\rm b}\) & 2.17 \(\pm\) 0.07 & 6.67 \(\pm\) 0.04 \({}^{\rm c}\) \\
**FTH** & 58.35 \(\pm\) 0.57 \({}^{\rm b}\) & 20.58 \(\pm\) 0.30 \({}^{\rm a}\) & 15.98 \(\pm\) 0.41 \({}^{\rm b}\) & 2.21 \(\pm\) 0.09 & 6.53 \(\pm\) 0.02 \({}^{\rm a}\) \\
**FTU** & 59.15 \(\pm\) 0.34 \({}^{\rm c}\) & 20.71 \(\pm\) 0.16 \({}^{\rm a}\) & 15.59 \(\pm\) 0.29 \({}^{\rm b}\) & 2.22 \(\pm\) 0.03 & 6.76 \(\pm\) 0.02 \({}^{\rm a}\) \\
**OC** & 56.53 \(\pm\) 0.23 \({}^{\rm a}\) & 21.50 \(\pm\) 0.18 \({}^{\rm b}\) & 4.51 \(\pm\) 0.05 \({}^{\rm a}\) & 2.29 \(\pm\) 0.03 & 6.61 \(\pm\) 0.01 \({}^{\rm b}\) \\
**OTH** & 57.50 \(\pm\) 0.45 \({}^{\rm a}\) & 21.59 \(\pm\) 0.13 \({}^{\rm b}\) & 4.68 \(\pm\) 0.06 \({}^{\rm a}\) & 2.32 \(\pm\) 0.03 & 6.55 \(\pm\) 0.02 \({}^{\rm a}\) \\
**OTU** & 58.53 \(\pm\) 0.51 \({}^{\rm b}\) & 21.73 \(\pm\) 0.21 \({}^{\rm b}\) & 4.56 \(\pm\) 0.04 \({}^{\rm a}\) & 2.33 \(\pm\) 0.01 & 6.65 \(\pm\) 0.01 \({}^{\rm b}\) \\ \hline \hline \end{tabular} Results are given as mean \(\pm\) standard error. a–d: Different letters used in the same column indicate that the difference between the mean values is statistically significant (\(p<0.05\)). FC: deep-fried sample group containing salt, pepper, and cumin, FTH: deep-fried sample group containing salt, pepper, cumin, and thyme, FTU: deep-fried sample group containing salt, pepper, cumin, and turmeric, OC: oven-cooked sample group containing salt, pepper, and cumin, OTH: oven-cooked sample group containing salt, pepper, cumin, and thyme, OTU: oven-cooked sample group containing salt, pepper, cumin, and turmeric.  
\end{table} 
Table 2: Proximate composition and pH values of coated chicken liver samples. observed that the pH value of samples that were cooked using both methods decreased with the inclusion of thyme in comparison to those of the control samples (_p_ < 0.05). Additionally, the highest pH value was detected in the deep-fried turmeric-added sample. Similar to the present study, [PERSON] et al. [48] detected that the pH value of the turmeric-added beef meatball sample was higher compared to the control sample and the sample containing BHA, a synthetic antioxidant. In addition, a study found that the pH of cooked chicken meatballs was not significantly affected by the different levels of turmeric [49].
## 4 Discussion
### Input Importance
Unlike the GRU, the LSTM can fine-tune the contents of its memory through the output gate, meaning that the predictive strength of the LSTM, in this context, is derived from how it applies importance explicitly to each input rather than long-term patterns. This idea is further supported when considering the attributions of the LSTM (Figure 5). The LSTM demonstrates the least number of negative attributions, and they are all localised towards the end of the network meaning these inputs are critical in reducing the magnitude of the prediction whilst considering relatively few inputs. Consequently, the LSTM is considering fewer data points of relatively high importance to adjust the prediction down to the correct level. Interestingly, while the 92 nd cell confers most of the information to the prediction, it also is the cell that contains most of the negative attributions, excluding the open feature. This result implies that the LSTM functions by primarily considering the opening price, at the 92 nd cell, to raise the prediction whilst considering the remaining features to create an upper ceiling to the prediction. The GRU, much like the LSTM, displays the ability to learn long-term patterns; however, it displays a more simplified architecture [43]. Notably, the GRU in this study demonstrates evidence of learning long-term patterns as it displays consistently higher errors throughout the sequence (Figures 4-6). Further supporting the notion of memory in GRUs, the results demonstrate that the GRU uniquely places greater attention on early network inputs. Given that GRUs combine the input and forget gate, creating the update gate, GRUs are only able to act on the entire contents of their memory when remembering past information [43]. Subsequently, GRUs demonstrate less precision in forgetting past information compared to LSTM. Therefore, the results suggest the GRU model relies on remembering more past information while the LSTM leverages its precision in forgetting past information to achieve comparable levels of accuracy. Interestingly, the GRU assigns increased importance to the early cells (0-2), suggesting the use of attention in the GRU in making predictions. This attention shows that the GRU has learned that the start of a financial quarter confers more information to the future prediction than what occurs during the middle portion. Furthermore, when considering the signs of the attributions, it is clear that most of the attributions increase the prediction, with an initial few inputs serving to lower the prediction (Figure 5C). The positioning and magnitudes of negative attributions suggest that the initial inputs, where the GRU places attention, serve as a fine-tuning mechanism. In contrast, the LSTM remembers less information while considering a few high importance features to modulate prediction. Interestingly, the magnitudes of the first and last attributions in the region of negative attribution are smaller than those in the centre. This observation suggests that the GRU specifies the regions that reduce prediction when the magnitude of the attribution drops below a specific threshold.
## 1 Introduction
Future work will focus on inaccurate mode frequency and mode damping, the accuracy of analysis can be further improved by conducting satellite mode experiments and a finite element model check. The integrated modeling method proposed in this paper has broad application prospects, strong applicability to optical remote sensing satellites, and good engineering and practicality. It can be used to simulate and evaluate micro-vibration problems under more complex imaging modes in the future, and it can provide a reliable reference for the design of satellites micro-vibration vibration isolation and suppression. Section 2 introduces the integrated modeling method and the modeling method of each subsystem. Section 3 shows simulation results when the imaging mode is set to adjust the attitude and control stable push-broom imaging. Section 4 presents a ground verification experiment, this section elaborates on the experimental conditions and procedures and compares the results. Sections 5 and 6 are the discussion and the conclusion of the research.
# Improved Particle Swarm Optimization for Sea Surface Temperature Prediction
## 3 The Proposed DSL Method  
Our method involves three main algorithms, DTW, SVM, and LSPSO, and we have dubbed it DSL for short. Given an SST sequence \(F=F_{1},F_{2},\ldots,F_{|F|}\) (whereby \(|F|\) is the length of the sequence \(F\)), our aim is to predict the SST for the subsequent five days. Figure 1 shows the flow chart of our SSTP algorithm based on the similarity measure.  
The main steps of DSL are as follows: (1) Read the SST sequence \(F\), generating a reference pattern and n analog patterns, and storing the information in the tuples T. (2) Calculate the DTW distances of the reference pattern and each analog pattern separately, and sort them in ascending order according to the DTW distance; the first k modes can be regarded as analog patterns similar to the reference pattern. (3) The obtained analog patterns are used as the input of the SVM model, and the corresponding SST of the following five days in the sequence \(F\) are used as the output of the SVM model; an SVM model with predictive ability is obtained through training. (4) The reference pattern is used as the input to the SVM model, generating a 5-day prediction of the SST. Materials and Methods should be described with sufficient details to allow others to replicate and build on published results. Please note that publication of your manuscript implicates that you must make all materials, data, computer code, and protocols associated with the publication available to readers. Please disclose at the submission stage any restrictions on the availability of materials or information. New methods and protocols should be described in detail while well-established methods can be briefly described and appropriately cited.  
Figure 1: Flow chart of SSTP algorithm based on similarity measure.  
The reference pattern represents the current SST trend, while the analog pattern is the historical SST trend. In the process of generating the reference and the analog patterns, the PL and IS are uncertain; so before using DSL to predict SST, it is necessary to use LSPSO to optimize the model parameters and obtain the appropriate PL and IS.
## 4 Signals in the WBAN  
Many new methods are being developed for low power DSP processing of physiological body signals. These allow some pre-processing to be done directly on a sensor node, so that only useful data needs to be transmitted instead of the whole raw signal. Some examples for EEG data preprocessing on a sensor node are presented in [23, 24]. EEG data can be preprocessed in order to extract events such as when a seizure happened, the type of seizure, and how long it lasted. Other work that shows how an ECG signal can be preprocessed to output the respiration rate is presented in [25]. The ECG is used to extract \"common\" values such as heart rate, and heart condition. Other studies suggest that similar values can be extracted from an SPO2 sensor in addition to the main measurement of oxygen saturation extraction [26]. Therefore, data can be preprocessed to get the actual physiological quantities at the sensor, where the sensors can also be \"smart\" and notify the coordinator if some values go above or below defined thresholds, or some event occurs. Also, the sensor can measure multiple quantities with a certain accuracy, but, more importantly, one quantity (pulse for example) can be measured using different sensors.  
Therefore, sensors and medical devices with an added microprocessor and wireless connectivity can be split into three groups:  
1. _Sensors that stream data_ They have high sample rates and are used for data transfer on demand (ECG, EEG, EMG, SPO2, _etc._).
2. _Sensors that are used for constant patient monitoring_ They usually have very low sample rates, and in general would be sleeping for extended periods of time, but regularly waking up, measuring some value and sending it. Examples of those would be temperature, glucose level, _etc_.
3. _DSP enabled sensors_ Sensors that can transfer data on demand, but can also perform local DSP and send values, such as the sensors in category 2. For example, ECG or SPO2 sensors can stream data, but can also give the status for heart rate or respiration rate.  
Figure 2: Wake Up Receiver Application.  
The sensors from groups 2 and 3 can generate an event if a value passes a predefined threshold.  
Protocols intended for medical applications would deal mostly with notifications and actual information on the extracted parameters as well as the random \"events\", instead of transfers of large quantities of data to the central processing system. But, they would also have to stream data occasionally on demand. Therefore, we will analyse the proposed WUR in medical settings as a method of keeping these networks responsive to the random events and signals as well as to cater for higher volume data transfer.
## 1 Introduction
This finding eliminates the need for GPS trajectory data to determine the effectiveness of such interventions and eases the process of conducting such experiments from the viewpoint of personal information protection. The remainder of this paper is organized as follows. Section 2 delves into related previous studies, including tourist behavior studies using GPS trajectory data and intervention studies using mobile applications. In addition, this section describes the advantages of the present study. Section 3 describes the design and implementation of the smartphone application to provide real-time traffic estimation. It also presents methods for field experimentation to demonstrate that actual behavior can be inferred using an application operation log. Section 4 describes the experimental results and discussion. Section 5 presents our conclusions, limitations of the study, and future work.
## 5 Discussion and Conclusions  
### Discussion  
The inversion analysis and material parameter optimization of a high earth-rockfill dam during construction periods was carried out in this paper. Firstly, the Duncan-Chang EB constitutive model and conventional inversion analysis method were introduced and the step-by-step inversion analysis method, a new inversion analysis solution for construction periods, was proposed in this article. Secondly, the validation of the step-by-step inversion analysis solution was checked by a simple inversion analysis based on the results of forward analysis. Thirdly, the inversion analysis for the material parameters of the bottom of a high earth-rockfill dam was carried out using the verified step-by-step analysis method and conventional analysis method. The material parameters were obtained and the relevant vertical compression values were compared. Finally, the material parameters were optimized by studying the trend of material parameter \(K\). The results calculated by the optimized material parameters were displayed and compared with other results.  
This work focused on the improvement method for the inversion analysis of earth-rockfill dam during construction periods. The original step-by-step inversion analysis method and the variable parameter constitutive model were put forward and verified in this study. The step-by-step inversion method assumes that material parameters vary with construction, which is significantly different from conventional inversion methods assuming that parameters remain unchanged during construction in articles such as [5,6]. The variable parameter constitutive model comes from the study of the material parameter \(K\)'s change trend. The idea of studying the trend of material parameters from monitoring data distinguishes the variable parameter constitutive model from other modified models in papers such as [21].
## 5 Simulation Results and Discussion
### Study Limitations and Future Research  
The proposed methodology performs independent minimization of the selected indicators. This approach allows the DSO to prioritize a specific indicator based on its needs and the availability of relevant data. However, using the cost indicator is a practical challenge when applying this methodology. In some cases, the DSO may lack data on the unit monetary value of unserved energy (\(c_{i}\)), as compensation for supply interruptions is not implemented within the regulatory framework.  
The cost indicator, applied with a fixed unit monetary value of unserved energy per consumer type, independent of interruption duration, is intended for use in developing countries where detailed data on interruption costs by consumer type may not be available. While the proposed methodology is based on a fixed \(c_{i}\), making it less applicable to advanced networks with compensation schemes that account for interruption duration, this limitation can be addressed by extending the algorithm.  
For more complex systems, future research could focus on optimizing a composite objective function that accounts for the sum of all three indicators, normalized relative to their baseline values. This approach could be refined by introducing weighting factors to adjust the influence of each normalized indicator. Furthermore, a methodology could be developed to help the DSO manage these weighting factors.  
Given the rapid expansion of prosumer integration in recent years, particularly in developing countries, future research could focus on reliability assessment that accounts for the presence of prosumers in the distribution network. Additionally, the influence of energy storage systems on reliability could be further examined. A potential research direction is the integration of machine learning with a Monte Carlo simulation to improve reliability assessment by enabling more efficient analysis of prosumer-driven uncertainties in distribution networks.