esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Example Analysis
### Determining the Comprehensive Weight  
Equation (23) is used to obtain the comprehensive weights of each evaluation index as shown in Table 7 and Figure 6. Figure 7 is a weight map of each evaluation index.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Secondary Indicators** & **Scheme 1** & **Scheme 2** & **Scheme 3** & **Scheme 4** & **Objective Weight** \\ \hline Average service availability index (B1) & 0.5480 & 0 & 1 & 0.8292 & 0.0842 \\ System average interruption duration & 0.5480 & 0 & 1 & 0.8292 & 0.0842 \\ index (B2) & & & & \\ Initial investment cost (B3) & 0.8566 & 1 & 0 & 0.2560 & 0.1093 \\ Operation and maintenance cost (B4) & 0.8566 & 1 & 0 & 0.2560 & 0.1093 \\ Network loss cost (B5) & 0 & 1 & 0 & 0.6667 & 0.1899 \\ Voltage offset (B6) & 0.5 & 0 & 0.5 & 1 & 0.0923 \\ Frequency deviation (B7) & 1 & 0 & 1 & 1 & 0.0766 \\ Harmonic distortion rate (B8) & 0.5 & 0 & 0.5 & 1 & 0.0923 \\ Emissions of polluted gases (B9) & 1 & 0 & 1 & 1 & 0.0766 \\ Land occupation area (B10) & 0.7773 & 1 & 0 & 0.5243 & 0.0853 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Standardized data and objective weights.  
Figure 4: The subjective weight of each evaluation indicator.  
Figure 5: The objective weights of each evaluation indicator.
# Diagenesis of Deep Low Permeability Reservoir in Huizhou Sag and Its Influence on Reservoirs
## 4 Results
### Diagenetic Processes  
Diagenesis, which exerts strong control on the evolution of pore structure and the alteration in the physical and chemical properties of elastic rock, is controlled by a variety of chemical, physical, and biochemical interactions [46; 47; 48; 49]. Currently, an important driving force for researching deep reservoir diagenetic studies in the Paleogene Emping and Wenchang Formations is the efficient exploration and exploitation of the deep and ultra-deep reservoirs (exceeding 3500 m) in the Pearl River Mouth Basin [39]. The low-permeability sandstone reservoirs in Huizhou Sag of the Pearl River Mouth Basin have undergone various diagenesis processes [38]. However, the main diagenesis types are still compaction, cementation, and dissolution, which significantly influence the reservoir quality (Figure 6).
# Evaluation of Viscoelastic and Rotational Friction Dampers for Coupled Shear Wall System
## 3 Shake Table Test of Six-Story Coupled Wall System with Damper
### Experimental Test Results of the Six-Story Frame
#### 3.5.2 Results Under Sinusoidal Waves  
Figure 17 shows the maximum displacement response under sinusoidal wave excitations of 50 gal and 100 gal, comparing the performance of three configurations: the frame with shear wall (Shear Wall), the frame with shear wall and an RF damper (Shear Wall + RF), and the frame and shear wall and a VE damper (Shear Wall + VE). As shown in Figure 17, the story displacement under sinusoidal waves indicates that both the RF and VE dampers significantly reduce displacement compared to the shear wall. Under the 50 gal sinusoidal wave, the RF damper reduces displacement by approximately 42%, while the VE damper achieves a 62% reduction. Under the 100 gal excitation, the VE damper reduces displacement by 37%, whereas the RF damper achieves a 25% reduction.  
Figure 18 presents the maximum acceleration response, demonstrating that the damped configurations substantially reduce peak accelerations. Under the 50 gal excitation, the RF damper reduces acceleration by 35%, whereas the VE damper provides a reduction of about 41%. Under the 100 gal excitation, the VE damper reduces acceleration by 18%, whereas the RF damper achieves a reduction of 26%. These results highlight the efficacy of friction and VE dampers in enhancing structural performance by reducing both displacement and acceleration under dynamic loading conditions.  
Figure 17: The maximum displacement under sinusoidal waves: (**a**) 50 gal, (**b**) 100 gal.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**White Noise Intensity (gal)** & \begin{tabular}{c} **Shear Wall Natural** \\ **Frequency (Hz)** \\ \end{tabular} & \
\begin{tabular}{c} **Shear Wall + RF Natural** \\ **Frequency (Hz)** \\ \end{tabular} &
\begin{tabular}{c} **Shear Wall + VE Natural** \\ **Frequency (Hz)** \\ \end{tabular} \\ \hline
40 & 1.48 & 2.43 & 2.32 \\ \hline
80 & 1.07 & 1.98 & 1.71 \\ \hline
120 & 1.07 & 1.77 & 1.48 \\ \hline
160 & 1.07 & 1.77 & 1.48 \\ \hline
200 & - & 1.48 & 1.48 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Natural frequency of the 6-story frame under the white noise waves.
# An Accelerated Method for Protecting Data Privacy in Financial Scenarios Based on Linear Operation
## 5 Discussion
### Model Acceleration
#### 5.1.3 Batch Processing  
As shown in Table 6, since the communication cycle is often longer than the server's computation cycle, the server's computation power can be improved by combining batches to reduce model latency. The user's data is encrypted and packaged for transmission as a batch stream so that one batch is computed on the server while multiple batches are transmitted, thus alleviating the mismatch between transmission and computation cycles. The comparison between before and after optimisation is shown in Table 8.  
As shown in the table above, batch processing can greatly increase the degree of parallelism in the execution of this method, thereby increasing the utilisation of the GPU computation core and reducing the impact of communication, ultimately reducing the latency of the communication phase by 38.6%.
# Dynamic Data Filtering of Long-Range Doppler LiDAR Wind Speed Measurements
## 4 Results  
For the validation and comparison of the new proposed dynamic filtering approach in Section 2.3, we applied all described filters on the data of the three measurement campaigns from Section 3. The influence of filtering on the data availability and the velocity error regarding the ultrasonic anemometer from the offshore campaign are shown in the following. Moreover, the behaviour of the velocity error will be discussed.
## 4 Results and Discussion
### Discussion  
#### 4.5.1 Influence of Risk Level \(\varepsilon\)  
The availability of wind power is uncertain. Therefore, there is some risk that the system reserve capacity cannot surmount the prediction errors of wind power. The probability of system imbalance  
Figure 5: System load and wind power utilization.  
caused by too little system reserve capacity is the risk level. With different risk levels, this optimization model produces different results. To test the influence of the risk level, several levels of risk are tried in the model with both SLs and HLs involved in the optimization. The iteration number is \(S\times M=5\times 5\). The results are shown in Table 8.  
As the risk level increases from 0.05 to 0.5, the total wind power utilization increases, and the cost of system operation decreases. This is reasonable because a lower risk level corresponds to a more restrictive chance constraint. Correspondingly, the generation of thermal units is decreased, which results in a decrease in total cost. An extreme case is \(\varepsilon=1\), in which the chance constraint can be neglected. In such a case, the optimal wind power utilization is larger than that at any other risk level; furthermore, the cost of system operation is the smallest.
## 3 Proposed Tracking Algorithm
### Improved Tracking Algorithm
In the Kalman filter, \(\mathbf{r}_{k}=\mathbf{Z}_{k}-\mathbf{H}_{k}\mathbf{X}_{k,k-1}\) represents the innovation vector, and its covariance matrix is \(\mathbf{N}_{k}=\mathbf{H}_{k}\mathbf{P}_{k,k-1}\mathbf{H}_{k}^{T}\). If the signal is not affected by environmental factors, such as refraction, reflection, and multipath effect, it can be considered that \(\mathbf{r}_{k}\) obeys the Gaussian distribution with the zero mean. If the influence of the external environmental factors leads to the decrease in the positioning accuracy or abnormal positioning values, it can be considered that \(\mathbf{r}_{k}\) follows the Gaussian distribution with the mean value of \(\mathbf{Z}_{k}-\mathbf{Z}_{k}^{T}\), where \(\mathbf{Z}_{k}^{T}\) is the actual location. Based on the innovation vector and its covariance matrix, the test information is constructed as \(\Delta\mathbf{r}_{k}=\mathbf{r}_{k}^{T}\mathbf{D}^{-1}\mathbf{r}_{k}\), and it is used as a condition to judge whether the positioning result is abnormal; it is defined as follows:  
\[ \begin{cases}|\Delta\mathbf{r}_{k}|\leq m&\text{Locate normal}\\ |\Delta\mathbf{r}_{k}|>m&\text{Locate abnormal}\end{cases}  \tag{32}\]  
where \(m\) denotes the threshold value, which is determined through multiple static tests at different points. Huber functions are used to construct adaptive factors as follows:  
\[\alpha_{k} \begin{cases}1&|\Delta\mathbf{r}_{k}|\leq m\\ \frac{\Delta\mathbf{r}_{k}}{m}&|\Delta\mathbf{r}_{k}|>m\end{cases}  \tag{33}\]  
The robust estimation of positioning can be achieved by replacing \(\mathbf{R}_{cv,k}\) in the gain matrix with \(\alpha_{k}\mathbf{R}_{cv,k}\). The absolute value of \(\Delta\mathbf{r}\) is compared with the prior threshold information. When the absolute value of \(\Delta\mathbf{r}\) is greater than the prior threshold, the positioning result is considered abnormal. In this case, the robust factor of the constructed measurement noise is used to update its covariance matrix to achieve robust filtering performance and to improve the positioning accuracy. The Sage-Husa filter can estimate and modify the statistical characteristics of the system noise in real-time so as to improve the filter accuracy further and has a certain anti-interference ability. Therefore, combined with the improved Sage-Husa filter, the covariance matrix of the system noise can be estimated in real-time as follows [24]:  
\[\begin{split}\mathbf{Q}_{cv,k}=(1-\beta_{k})\mathbf{Q}_{cv,k-1}+\beta_{ k}\big{[}\mathbf{K}_{t}\mathbf{r}_{k}\mathbf{r}_{k}^{T}\mathbf{K}_{k}^{T}+\mathbf{P}_{k}-\mathbf{F}_{k-1} \mathbf{P}_{k-1}\mathbf{F}_{k-1}^{T}-\\ 2\mathbf{K}_{k}\mathbf{H}_{k}\mathbf{P}_{k,k-1}\mathbf{H}_{k}^{T}\mathbf{K}_{k}^{T}-2\mathbf{K}_{k} \alpha_{k}\mathbf{R}_{cv,k}\mathbf{K}_{k}^{T}+\mathbf{P}_{k,k-1}\mathbf{H}_{k}^{T}\mathbf{K}_{k}^{ T}+\mathbf{K}_{k}\mathbf{H}_{k}\mathbf{P}_{k,k-1}\big{]}\
# Enhancing Photovoltaic Power Predictions with Deep Physical Chain Model
## 2 Methodology and Resources
### Review of Open Data Sources
#### 2.1.1 Dataset Selection  
The choice of the dataset is crucial for developing forecast models of photovoltaic generation. In this work, we have selected the PVOD ensemble [8] due to multiple reasons, explained below. First, unlike others like SOLETE [18] and the Chinese [19], PVOD includes both LMD and NWP, fundamental for hybrid forecasting methodologies. Despite the acquisition cost of NWP forecasts, their presence increases the applicability and relevance of our study. Second, the PVOD set provides relevant metadata, offering a contextual framework for data interpretation, similar to other datasets in this respect. This metadata is important for models that consider the specificities of solar stations. Third, although the Californian set [17] is complete, the additional data may limit its applicability for costs. PVOD focuses on more accessible data, increasing the applicability of models based on it. Finally, PVOD has a Python toolkit, facilitating data analysis and forecasting method development. In summary, we have selected PVOD for its practicality, data accessibility and useful tools, trusting that it will provide a solid foundation for developing robust photovoltaic forecasting methodologies.
## 4 Location Estimation-Based Congestion-Aware Routing Protocol (LECAR)--Detailed Proposal
### Routing Decision Process
#### 4.3.9 Packet Forwarding Interval  
When the UAV itself has the lowest \(FC\) value, the UAV waits for a packet forwarding interval (\(P_{interval}\)) period and starts the packet forwarding process again. The packet forwarding process is described in the following subsection. The \(P_{interval}\) primarily considers that the packet should be sent after traveling a certain distance and is defined as follows:  
\[P_{interval}=\frac{1}{v\times R_{rate}}, \tag{12}\]  
where \(R_{rate}\) is the retry rate with a value of 0.004 m\({}^{-1}\), indicating that the custodian tries to send the packet after traveling every 250 m.
# Physical and Chemical Methods to Assess Performance of TPO-Modified Asphalt Binder
## 4 Discussion
### DSR Testing Comparison  
After blending 2, 4, and 8% TPO, standardized physical properties were determined by DSR testing for unaged, short-term aged, and long-term aged blends. DSR testing was performed to determine the high and intermediate PG temperatures. The high PG indicates the blend's resistance to putting, a common asphalt failure during the beginning of service life at elevated temperatures. The high PG depends on the highest temperature where the parameter G*/sin\(\delta\) is greater than or equal to 1.0 kPa (unaged) or 2.2 kPa (short-term aged). Both unaged and short-term aged samples were considered for the high PG. The intermediate PG indicates a blend's resistance to fatigue during the end of service life at intermediate temperatures. The intermediate PG is determined after short-term (RTFOT) and long-term aging by the PAV and depends on the lowest temperature when the parameter G*-sin(\(\delta\)) is less than 5000 kPa. The grades are summarized in Table 2. Adding tire pyrolysis oil at 8% made the binder softer and decreased the high PG, meaning the blend had a higher susceptibility to rutting failure. Additionally, adding more oil did not change the intermediate temperature, suggesting the binder did not retain the softening  
Figure 13: NMR results of long-term aged blends.  
effect of the oil to have better fatigue resistance. Therefore, according to the DSR standard testing, adding TPO at or above 8% would result in a blend that is more susceptible to rutting and has no enhancement in fatigue resistance.
# Reliability Model Based Dynamic Multi-Level Trust Analysis
## 1 Introduction
Therefore, in addition to trust and distrust, there is also a type of information that expresses trust that is uncertain or unfamiliar. Distrust should be distinguished from neutrality, and at the same time be used as a description of the trust relationship between users like trust. In addition, propagation and combination calculation methods for distrust and neutral are also different from those for trust. Trust can be transmitted, but distrust cannot be propagated [18; 25]. Therefore, in the trust model, distrust and uncertainty cannot be dealt with in the same way as trust; they should be handled separately. Dynamicity is one of the properties of trust, which can change frequently with new experiences [26]. New experience is important, but its impact on trust can be neither too fast nor too slow. The dynamicity of trust can be formalized through trust update functions [27]. Reasonable dynamic changes can effectively curb the damage of malicious nodes to trust evaluation. Most of the researches on dynamic trust are for continuous or binary trust [27; 28; 29; 30; 31; 32]. In fact, it is more practical to do dynamic trust and distrust aggregation in multi-level. In view of the above problems, this paper proposes a multi-level trust model that takes into account distrust and uncertainty. This model combines the characteristics of continuous trust model and discrete trust model, which not only can facilitate data collection, but also can relatively accurately describe the trust of each level. At the same time, distrust and uncertain propagation and combination calculations are considered to complete accurate mapping of user trust descriptions in the real world. Based on the new trust model, we extend the MDD-based method of [16] for trust evaluation, which addresses the non-independent trust transfer path problem during the MDD model generation process. Section 2 presents related work on trust modeling and evaluation for OSNs. Accordingly, we describe our trust model and the dynamic rules of trust relationships in Section 3. In Section 4, in order to validate our main thought, we conduct a series of experiments to analyze the performance of our algorithm. After a discussion of the results in Section 5, we conclude this study and provide some directions of future work in Section 6.
# MALDI-Based Mass Spectrometry in Clinical Testing: Focus on Bacterial Identification
## 8 Pitfalls in Bacterial Identification by MS  
Bacteria with high homology at the genetic level cannot be differentiated and are distinguished by their biochemical properties [76]. Similarly, _Salmonella typhi_, _Salmonella paratyphi_ A, and _Salmonella_ spp. cannot be differentiated and are all identified as _Salmonella_ spp. by MS. Other examples of pairs of species that cannot be distinguished by MS include _Bacillus cereus_ and _Bacillus anthracis_, as well as _Yersinia pestis_ and _Yersinia pseudothevellosis_. Bacterial identification by MS is based on matching with the MALDI mass spectrum of the species (strains) listed in the database. If the species is not listed in the database or is a new species, such an organism will not be identified and instead must be classified by another method. One of the pitfalls of bacterial identification is that identification by mass spectrometry involves a matching of the mass spectra of the bacteria (strains) listed in the database; therefore, bacteria that are not listed in the database cannot be identified. Thus, if a species cannot be identified, it must be identified using another method. However, if the species to be identified is not listed in the database, the score value of a related species is high, and if the species is presented at species level, it is an erroneous identification.
## 2 Islanding Detection Methods
### Passive Methods
#### 2.1.1 Over under Voltage/Over under Frequency Method (O/UV or O/UF)  
In O/UV or O/UF methods, standard protective relays are connected to the grid to detect abnormal conditions such as Islanding that happen during the operation of the main utility network. The protective relays engage to prevent the PV inverters from supplying electricity to the nearby loads if the frequency value and voltage amplitude at PCC exceed the limits. The difference in active and reactive power between the load and the DG just before the switch opens to create the island affects how the system behaves when the utility grid is cut off. If the difference of active power between DG and load (AP) does not equal zero, a change in voltage amplitude will be observed and detected to prevent islanding. However, if the reactive power difference (\(\Delta\)Q) between the load and the DG is not zero, there will be a phase shift in the load voltage, which will deviate from the inverter current frequency and cause the DG to be disconnected [4].  
Such methods are simple and cost-effective, because both voltage and frequency are considered. Different standards, including IEEE and IEC, determine the maximum and minimum voltage and frequency. When the system parameters cross this limit, the system will operate as an islanding condition within a time range from 4 ms to 2 s which makes this method appropriate for systems with some certain power imbalance [5; 6].
# Hierarchical Parallel Evaluation of a Hamming Code
## 2 Preliminaries
### Parallel Processing Model  
We assume a hierarchical model of parallel processors working in several layers. For a given layer \(i\), with \(i>0\), only half of the processors that were active in the previous layer \(i-1\) will be used, and each processor will act on a pair of processors of the previous layer. More specifically, we are given \(n\) processors \(p_{0}\), \(p_{1},\ldots,p_{n-1}\), where now we assume that \(n=2^{m}\) is a power of two. Note that the number of processors \(n\) is the number of bits we wish to process, including the leading \(0\)-bit for ease of description.  
In an initialization phase we call Step \(0\), the input string is assigned to the processors--one bit per processor. In fact, half of the processors--say, those with even indices--are only used in this initialization, and do not perform any work in the whole process. We thus need actually only \(\frac{n}{2}\) working processors, and use the others only to allow a consistent description of all the steps.  
In Step 1, each of the \(\frac{n}{2}\) processors indexed by odd numbers works in parallel on the data stored in two adjacent processors of the previous step: \(p_{1}\) working on the data in \(p_{0}\) and \(p_{1}\), \(p_{3}\) working on the data stored in \(p_{2}\) and \(p_{3}\), and generally \(p_{2k-1}\) working on the data in \(p_{2k-2}\) and \(p_{2k-1}\), for \(k=1,\ldots,\frac{n}{2}\). The work to be performed by each of these processors will be described below.  
In Step 2, each of the \(\frac{n}{4}\) processors whose indices are \(1\) less than multiples of \(4\) are used, and each of them is applied in parallel on the data stored in two adjacent processors of the previous stage. That is, we apply \(p_{3}\) on the data of \(p_{1}\) and \(p_{3}\), and in parallel \(p_{7}\) on the data in \(p_{5}\) and \(p_{7}\), and generally, \(p_{4k-1}\) will be working on the data in \(p_{4k-3}\) and \(p_{4k-1}\), for \(k=1,\ldots,\frac{n}{4}\).  
Continuing similarly with further steps will yield a single operation after \(\log n\) iterations, as represented schematically in Figure 1, for \(n=8\). Note that compared to a sequential approach, the overall work is obviously not reduced by this hierarchical scheme, since the total number of applications of the procedure on block pairs is \(\sum_{i=1}^{\log n}\frac{n}{2}=n-1\), just as if it were done by sequential evaluation. However, if we account only once for operations that are executed in parallel, the number of evaluations is reduced to \(\log n\), which should result in a significant speedup.