esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Data-Driven Performance Assessment and Fault Early Warning of Marine Diesel Engine
###### Abstract  
To enable proactive prediction of marine diesel engine failure time and root causes, thereby reserving sufficient time for maintenance, this study proposes a data-driven multi-algorithm integration framework for performance assessment and fault early warning in marine diesel engines. By integrating the SSD (steady-state detection) algorithm, a data-driven CLIQUE clustering algorithm was chosen for automatic multi-parameter high-dimensional running condition partitioning. This innovative approach overcomes the limitations of traditional single-parameter approaches or dimensionality reduction techniques, significantly enhancing state classification accuracy. The improved classification results subsequently increase the reliability of Mahalanobis distance as a performance indicator for marine diesel engine condition assessment. Finally, the cumulative anomaly method combined with the Yamamoto test was employed for anomaly detection analysis, enabling precise identification of fault occurrence time and establishing an effective early-warning mechanism. The study demonstrates that this technique effectively characterizes the overall performance of marine diesel engines and captures their performance degradation features. Implemented on a 6 RT-flex82T marine diesel engine dataset, the method achieved precise prediction of fault occurrence time with early warnings, providing approximately 20 days advance notice for maintenance planning. Furthermore, comparative analyses with existing studies revealed its superior capability in pinpointing the anomaly to the jacket cooling water outlet temperature of cylinder #2. These results confirm the method's effectiveness in both performance assessment and fault early warning for marine diesel engines, offering a novel approach for intelligent maintenance of shipboard equipment.  
Keywords:performance assessment; data-driven; high-dimensional running condition partitioning; mutation detection; fault early warning +
Footnote †: journal: Applied Sciences
## 5 System Sizing  
The system size was designed based on the customer's electrical profile during the year 2019. The peak sun hours of 2019 were 4.4 h on average based on Table 1. The total yearly electrical consumption was 3,194,184 kWh. The building falls under the 'C Tariff' with an electricity price during peak hours of MYR 0.365/kWh, an off-peak price of MYR 0.224/kWh, and an MD of MYR 45/kW.  
In order to calculate the system size, we obtained the MD point of every month of in 2019 and calculated the average point, which was 505 kW.  
\[\text{Size of PV system}=\text{MD}\times 75\%=505\text{ kW}\times 75\%=378.75\text{ kW}\cong 380\text{ kW} \tag{6}\]  
The allowed PV system size to connect to the utility grid is 380 kW, which is 16% of the total electricity demand. The annual global solar irradiation of the location of the Building A was measured using the PVsyst program, producing a value of 4.4144 kWh/m\({}^{2}\). The average ambient temperature was 27.4 \({}^{\circ}\)C. Table 4 shows the monthly global solar irradiation and the output of a one kW PV system at that location.  
After sizing the system according to 75% MD, the PV system production could only cover 380 kW of electricity demand. While this is seen lower compared to the 2671.2 kW PV plant where electricity demand was met 100%, this still could reduce the electricity bill and cost saving, particularly since the MD for electricity is recorded during the peak hours.
# A Performance Analysis of VANETs Propagation Models and Routing Protocols
## 4 Performance Metric and Simulation Setup
### Simulation Parameters Settings  
A proper selection of routing mechanisms with propagation models can enhance the performance in V2I and V2V communication. Based on varying numbers of nodes and simulation times, routing protocols and propagation models from the perspective of throughput, end-to-end delay, packet delivery ratio, average routing throughput, and MAC/PHY overhead is examined. The parameter settings for the scenario taken for simulation are depicted in Table 3.
# Power Grid Load Forecasting Using a CNN-LSTM Network Based on a Multi-Modal Attention Mechanism
## 3 Methodology
### Multi-Head Self-Attention Model  
Self-attention is a technique that allows the model to \"pay attention\" to the relationships between different parts of the input data. Intuitively, it acts like an intelligent \"highlighter\" that can automatically identify and emphasize the most important parts of the input data for the current task. For example, in power grid load forecasting, the load data may include many time points and features, but not all time points or features are equally important for the prediction. The self-attention mechanism calculates the relevance between each part of the data and others, dynamically assigning different weights to each part, allowing the model to focus more on the information that has a greater impact on the prediction. This mechanism is especially suitable for handling long-sequence data or complex non-linear relationships, as it can capture long-range dependencies in the data, not just local or short-term patterns. In this way, self-attention helps the model better understand the inherent structure of the data, thus improving prediction accuracy and robustness. The Transformer model can efficiently handle long sequence data; as such, it performs exceptionally well in tasks like grid load forecasting, which involve long-term dependencies. The Transformer model employs the Self-Attention Mechanism, which allows for parallel processing of the entire input sequence. This parallel computation capability significantly improves the speed of model training and prediction, making it suitable for processing large-scale grid data and meeting real-time requirements. Therefore, we use the multi-head self-attention mechanism to enhance the model's global modeling capability  
Figure 2: The structure diagram of LSTM.  
and capture long-range dependencies between features. The multi-head self-attention module is shown in Figure 3 and is represented as follows:  
\[f_{out}=BN(MHSA(LN(f_{in})))+f_{in}, \tag{10}\]  
where \(LN\) represents Layer Normalization, BN stands for Batch Normalization, and MHSA stands for multi-head self-attention. The time series data first undergo layer normalization (LN), then the data are reshaped into feature vectors. These feature vectors are used as the original input features, which are further standardized through batch normalization and then undergo a linear transformation to generate the query (Q), key (K), and value (V). To further compress the temporal dimension and extract global information, the key (K) and value (V) are first subjected to average pooling after generation. Subsequently, the query (Q) along with the pooled key (K) and value (V) are fed into the multi-head self-attention (MHSA) mechanism, which generates the attention output. The output of the self-attention mechanism is then added to the original input features through a residual connection. This approach not only preserves the original input information but also alleviates the vanishing gradient problem, making the model easier to train.
## 3 Analysis of Test Results
### Rock Mechanical Properties Under the Interaction of Multiple Factors
#### 3.3.2 Factor Interactions on Peak Stresses
However, under a high number of freeze-thaw cycles, the rock specimen has been severely damaged, and the compaction effect of the confining pressure is limited, resulting in a smaller increase in peak stress. In addition, under low confining pressure conditions, the increase in the number of freeze-thaw cycles resulted in a 55.2% decrease in peak stress, while under high confining pressure conditions, the increase in the number of freeze-thaw cycles resulted in a 40.9% decrease in peak stress. Under low confining pressure, the lack of constraints on crack expansion exacerbated the deterioration of the rock mass due to the frost heaving effect caused by freezing and thawing. However, under high confining pressure conditions, the confining pressure effectively constrained the expansion and penetration of cracks, resisting the structural damage caused by the frost heaving force during the freeze-thaw cycle. In summary, high-strength grouting materials can inhibit the attenuation of peak stress caused by freeze-thaw cycles, while a high level of freeze-thaw cycles inhibits the reinforcing effect of grouting materials. Similarly, high confining pressure can inhibit the reduction in peak stress caused by freeze-thaw cycles, while a high number of freeze-thaw cycles also inhibits the strengthening effect of confining pressure. This indicates that there is an obvious interactive inhibition mechanism between grouting materials and the number of freeze-thaw cycles and between the number of freeze-thaw cycles and confining pressure at high levels. However, when each factor is at a low level, the inhibitory mechanism  
Figure 12: Visualization of the interaction effect between the number of freeze–thaw cycles and confining pressure on peak stress. of the interaction has less of an effect. This shows that in the harsh environment of low confining pressure and high freeze-thaw cycles, attention should be focused on improving the frost resistance of the rock mass. Measures such as optimizing the grouting material and applying confining pressure to strengthen the material can be used to minimize the impact of strength attenuation on structural safety.
## 4 Solution of the Fiber Optical Repeater
### System Infrastructure and Link Budget
#### 4.1.6 R4/4A in Subway Tunnel for Traffic Data Signal Coverage Canalization  
* R4 subway tunnel entrance to tunnel, R4A subway tunnel entrance to tunnel, and Point Of Interconnection + 1-7/8\({}^{\prime\prime}\) leakage coaxial cable:  
Equations for the practical analysis of the radio frequency signal coverage area from the Point Of Interconnection through a 1-7/8\({}^{\prime\prime}\) coaxial cable to the N/S tunnel intersection exit connected via 1-7/8\({}^{\prime\prime}\) leakage coaxial cable can be written as Equation (18):  
\[P_{o(RU)}=P_{t(eNB)}-L_{POI}-L_{CC} \tag{18}\]  
Here, \(P_{o(RU)}\) is the output power of RU to the leakage coaxial cable, \(P_{t(eNB)}\) is the output power of eNB, \(L_{POI}\) = the insertion loss of POI, \(L_{CC}\) = the coaxial cable loss (\(\sim\)183 m), and \(P_{o(RU)}\) = 33 - 9 - 7 = 17 (dBm). Subsequently, we generate Equation (19):  
\[L_{TOT}=P_{o(RU)}-SS_{req} \tag{19}\]  
Figure 20: SCE system network plan and calculation of link budget for D2D architecture.  
Here, \(L_{TOT}\) is the total of maximum allowed loss and \(SS_{req}\) is the signal strength requirement (\(-85\) dB), \(L_{TOT\,(-85\,\text{dB})}\) =16 \(-\) (\(-85\) dB) = 101 dB, which can be written as Equation (20):  
\[L_{LL}=L_{TOT}-L_{F}-L_{S}-L_{C}-L_{W} \tag{20}\]  
Here, \(L_{LL}\) is longitudinal loss, \(L_{F}\) is feeder loss (5 dB), \(L_{S}\) is splitter loss (3.5 dB), \(L_{C}\) is coupling loss (66 dB), L\({}_{W}\) is window loss (12 dB), giving \(L_{LL}\) = 101 \(-\) 5 \(-\) 3.5 \(-\) 62 \(-\) 12 = 22 dB.  
* Interconnection exit to R4 tunnel and interconnection exit to R4A tunnel: A practical analysis of the signal coverage area of the intersection exit RU connected to 1-7/8\({}^{\prime\prime}\) leakage coaxial cable is as follows, according to Equation (20): First, intersection exit to R4 inside of tunnel: \(P_{o(RLI)}\) = 27 dBm, splitter \(\times\) 2 (1:4). 1-7/8\({}^{\prime\prime}\) leakage coaxial cable is 480 m. \(L_{TOT\,(-85\,\text{dB})}\) = 27 \(-\) (\(-85\) dB) = 112 dB \(L_{LL}\) = 112 \(-\) 4 \(-\) 7 \(-\) 66 \(-\) 12 = 23 dB. Second, intersection exit to R4A inside of tunnel: 1-7/8\({}^{\prime\prime}\) leakage coaxial cable is 600 m. \(L_{LL}\) = 112 \(-\) 4 \(-\) 1 \(-\) 66 \(-\) 12 = 29 dB
# Estimation of Chlorophyll Fluorescence at Different Scales: A Review
## 3 Chlorophyll Fluorescence Detection near the Ground
### Passive Chlorophyll Fluorescence Measurement
#### 3.2.1 Spectral Fitting Method (SFM)  
The canopy radiance can be expressed as the combination of the fluorescence contribution (SIF) and the reflectance radiance [85]:  
\[L(\lambda)=SIF(\lambda)+\rho(\lambda)\frac{E^{S}(\lambda)}{\pi} \tag{1}\]  
In the selected bands of interest, such as the oxygen absorption bands or Fraunhofer lines, the least squares fitting technique is used to estimate the fluorescence and reflectance (assuming the reflectance and fluorescence are functions of wavelength), and the mathematical equation is expressed as Equation (2), and the variables are defined in Table 1:  
\[\text{min}\sum{(L_{measure}(\lambda)-SIF(\lambda)-\rho(\lambda)\frac{E^{g}(\lambda )}{\pi})}^{2} \tag{2}\]  
Compared with other methods, the SFM method exploits more bands and a continuous spectrum to improve the signal-to-noise ratio (SNR) and is used to retrieve chlorophyll fluorescence from the FLEX-like simulate data [86] and FLEX/FLORIS simulated data [84]. The SFM method is developed to reconstruct the full SIF spectrum. [PERSON] et al. proposed the SpecFit method to obtain the full SIF spectrum, and he selected a different combination of Gaussian, Lorentzian, and Voigt profiles to model the SIF emission peaks, and obtain the SIF spectrum by using the cubic spline fitting method which minimizes Equation (2) [83; 87]. 2.2 Fluorescence Spectrum Reconstruction (FSR) Method and Advanced Fluorescence Spectrum Reconstruction (aFSR) Method  
In Equation (1), [PERSON] et al. believed that reflectance and fluorescence can be expressed by Taylor polynomials at absorption lines [81]:  
\[ \begin{split} SIF(\lambda)&\approx SIF(\lambda_{0} )+\frac{dSIF(\lambda_{0})}{d\lambda}\cdot(\lambda-\lambda_{0})+\frac{1}{2} \cdot\frac{d^{2}SIF(\lambda_{0})}{d\lambda^{2}}\cdot(\lambda-\lambda_{0})^{2} \\ &=b_{0}+b_{1}\cdot(\lambda-\lambda_{0})+b_{2}\cdot(\lambda- \lambda_{0})^{2}\\ \rho(\lambda)&\approx\rho(\lambda_{0})+\frac{d\rho( \lambda_{0})}{d\lambda}\cdot(\lambda-\lambda_{0})+\frac{1}{2}\cdot\frac{d^{2} \rho(\lambda_{0})}{d\lambda^{2}}\cdot(\lambda-\lambda_{0})^{2}\\ &=b_{3}+b_{4}\cdot(\lambda-\lambda_{0})+b_{5}\cdot(\lambda- \lambda_{0})^{2}\end{split}  \tag{3}\]  
Combining Equations (1) and (3), the canopy radiance can be expressed as follows [82]:  
\[L(\lambda)=(\lambda-\lambda_{0})^{2}\cdot\frac{E_{g}(\lambda)}{\pi}\cdot b_{ 5}+(\lambda-\lambda_{0})\cdot\frac{E_{g}(\lambda)}{\pi}\cdot b_{4}+\frac{E_{g }(\lambda)}{\pi}\cdot b_{3}+(\lambda-\lambda_{0})^{2}\cdot b_{2}+(\lambda- \lambda_{0})\cdot b_{1}+b_{0} \tag{4}\]  
in which, there are six unknown parameters. Through sampling at five absorption lines (H\(\alpha\) 656 nm, O\({}_{2}\)-B 687 nm, water vapor 719 nm, O\({}_{2}\)-A 761 nm, water vapor 823 nm), the unknown parameters are determined, then SIF radiance at absorption lines are obtained. In the second step, SIF data simulated by the SCOPE (Soil Canopy Observation, Photochemistry and Energy fluxes) model is used to generate
## 2 Modeling of Residual Life Prediction for Electrical Connector
### Evaluation and Parameter Estimation Algorithm of HSMM
#### 2.5.2 Parameter Re-estimation Algorithm  
According to the observation sequence \(O\) = {\(\varrho_{1}\),... \(\rho_{\text{T}}\)}, we can modify the parameters of HSMM \(\lambda=(\pi\), \(A\), \(B\), \(P_{i}(d))\) to maximize \(P(O|\lambda)\) using a parameter re-estimation algorithm. Considering the randomness of intermittent failure data, it is necessary to use data from multiple electrical connectors to train an HSMM. The derivation of the parameters' re-estimation algorithm for multiple observation sequences is as follows.  
Let \(L\) observations be \(O^{(l)}=O_{1}^{(l)},\cdots,O_{T_{l}}^{(l)}\), \(l\) = 1,..., \(L\). assuming that each observation sequence is independent of each other, the probability of \(O\) given the model \(\lambda\) can be expressed as follows:  
\[P(O|\lambda)=\prod_{l}^{L}P(O^{(l)}|\lambda) \tag{14}\]  
Since the re-estimation algorithm is based on the frequency at different times, the re-evaluation algorithm for the \(L\) training observation sequences can be modified as follows:  
\[\begin{array}{rl}\overline{a}_{ij}&\triangleq\frac{\sum\limits_{l=1}^{L}trans -counts(i,j,l)}{\sum\limits_{l=1}^{L}state-counts(i,l)}\\ &=\frac{trans-counts(^{A})+trans-count(^{B})}{state-counts(^{A})+state-counts ^{B}}\\ \end{array} \tag{15}\]  
\[\begin{array}{rl}\overline{b}_{jk}&\triangleq\frac{\sum\limits_{l=1}^{L}vect -counts(k,j,l)}{\sum\limits_{l=1}^{L}state-counts(j,l)}\\ &=\frac{vect-counts(^{A})+vect-counts(^{B})}{state-counts(^{A})+state-counts ^{B}}\\ \end{array} \tag{16}\]  
\[\begin{array}{rl}\overline{p}_{i}(d)&\triangleq\frac{\sum_{l=1}^{L}durationvect -counts(d,i,l)}{\sum\limits_{l=1}^{L}state-counts(i,l)}\\ &=\frac{durationvect-counts(^{A})+durationvect-counts^{B}}{state-counts ^{A})+state-counts^{B}}\\ \end{array} \tag{17}\]  
In Equations (15)-(17), _trans-counts (B)_, _vect-counts (B)_, _state-counts (B)_ and _duration vect-counts (B)_ denotes the transition number, vector number, state number and resident vector number.
# Ten Years of Experience with Scientific TerraSAR-X Data Utilization
## 2 TerraSAR-X Proposals, Scientific Orders and Literature Review  
### TerraSAR-X Proposals  
The access to TerraSAR-X data for scientific purposes is granted via a proposal mechanism. TerraSAR-X data are usually exploited by a team of scientists headed by a PI, with partners acting as Co-Investigators (Co-Is). In total, 955 Principle Investigators from 617 institutions in 55 different countries submitted requests for TerraSAR-X products until the end of June 2017. Figure 1 shows countries of the institution with which the PIs were affiliated.  
Please note that the location of the investigator's affiliated institution but not the PI's nationality is considered. A further 1396 Co-Is from 1062 institutions in 79 countries collaborate with the PIs in the related projects.  
Some of the PIs handed in several proposals so that in total 1636 proposals were submitted until the end of June 2017. A keyword scan was applied to all of the 1636 proposals in order to investigate which user community uses TerraSAR-X data for what purposes. Here, 55 thematic and 25 technique-related keywords were considered. On that basis, we assigned every proposal to one of the following six areas of research (=sphere):  
* Anthroposphere (e.g., urban areas, infrastructure, mining, vulnerability studies).
* Biosphere (e.g., agriculture, forestry, grassland, wetland).
* Cryosphere (e.g., glacier, snow, permafrost, sea ice).
* Hydrosphere (e.g., sea conditions, ocean current, tidal flats and coastal areas, soil moisture, hydrological cycle).
* Geosphere (e.g., earthquakes, volcanos, landslides, soil conditions).
* Methods and techniques (e.g., new image analysis and processing techniques, preparation of new imaging modes).  
Note that a proposal can just belong to one sphere so that the sum of proposals over all spheres is 100 percent. Each sphere is subdivided into characteristic thematic fields. Thus, one keyword may occur multiple times within title and keyword list. These keywords are used to characterize the corresponding research and application more specifically. Here, the number of keywords used within the respective sphere is given as a percentage, though the sum is not necessarily 100 percent. A more detailed analysis of the proposals is provided in Sections 3.1-3.7, which are supplemented by references to published results.
# Integrated Photodetectors Based on Group IV and Colloidal Semiconductors: Current State of Affairs
###### Abstract  
With the aim to take advantage from the existing technologies in microelectronics, photodetectors should be realized with materials compatible with them ensuring, at the same time, good performance. Although great efforts are made to search for new materials that can enhance performance, photodetector (PD) based on them results often expensive and difficult to integrate with standard technologies for microelectronics. For this reason, the group IV semiconductors, which are currently the main materials for electronic and optoelectronic devices fabrication, are here reviewed for their applications in light sensing. Moreover, as new materials compatible with existing manufacturing technologies, PD based on colloidal semiconductor are revised. This work is particularly focused on developments in this area over the past 5-10 years, thus drawing a line for future research.  
photodetector; semiconductor; microphotonics; group IV; colloidal systems +
Footnote †: journal: International Journal of Modern Physics
# Incremental Formula-Based Fix Localization
## 5 Results of the Experiments
### Result of RQ4: Real Software Bugs  
This experiment is to evaluate the capability of AgxFaults on real bugs in large and complex projects. Table 3 has details of the projects and characteristics of the bugs that are used in this study. Columns _\"Name_ and _\"LOC\"_ in the table show the project name and the number lines of the Java code in these projects. Columns _\"Bug ID\"_ and _\"Description\"_ show the unique id that identifies the bug and a description of each bug. Columns in the _\"Patch size\"_ section of the table show the complexity of the patch written by the developer to fix the bugs. Specifically, columns _\"Add\"_, _\"Del.\"_, and _\"Edit\"_ show the number of lines that the developer has added, deleted, and edited to fix the bugs. Table 4 shows the results of our method for each bug in the benchmark. Column _\"#MFC_ shows the number of angelic fix candidates that AgxFaults found for a failing test case. All these generated MFCs are feasible. Column _\"#Susp. Lines\"_ shows the number of distinct lines reported in the list of MFCs. Column _\"Found actual fault?\"_ describes whether the reported lines contain the actual faulty statements or not (_\"yes/no\"_). Column _\"Exam lines\"_ shows the number of lines of code that the developer needs to examine to identify the first fault location. _\"Solver time\"_ and _\"Run time\"_ shows the time spent on SMT solver and the total running time of AgxFaults for each buggy program. Figure 10: Formula solving time with increasing loop unwinding bounds. Figure 9: Total runtime of different methods on the TCAS programs. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Bug ID** & **\#MFC** & **\#Susp. Lines** & **Found Actual Fault?** & **\#Exam Lines** & **Solver Time (ms)** & **Run Time (ms)** \\ \hline Chart5 & 6 & 7 & yes & 3 & 470 & 3167 \\ \hline Chart7 & 4 & 4 & yes & 4 & 15(s) & 19(s) \\ \hline Chart18 & 3 & 3 & yes & 3 & 458 & 5749 \\ \hline Chart22 & 1 & 1 & no & 2 & 220 & 3509 \\ \hline Codec16 & 6 & 3 & yes & 5 & 58 & 2237 \\ \hline Codec18 & 48 & 12 & yes & 12 & 2938 & 7870 \\ \hline Comp24 & 31 & 14 & yes & 3 & 9370 & 26(s) \\ \hline Comp27 & 31 & 17 & yes & 2 & 29(s) & 63(s) \\ \hline Comp6 & 4 & 4 & yes & 2 & 46 & 1586 \\ \hline Csv2 & 2 & 2 & yes & 1 & 5 & 1419 \\ \hline Csv8 & 4 & 4 & yes & 1 & 202 & 2369 \\ \hline Lang14 & 1 & 1 & yes & 1 & 69 & 1772 \\ \hline Lang21 & 8 & 1 & yes & 1 & 2897 & 6534 \\ \hline Lang22 & 1 & 1 & no & 3 & 25(s) & 29(s) \\ \hline Lang30 & 11 & 8 & yes & 5 & 166 & 2397 \\ \hline Lang31 & 39 & 13 & yes & 1 & 851 & 5757 \\ \hline Lang40 & 13 & 4 & yes & 4 & 445 & 6444 \\ \hline Lang58 & 4 & 4 & yes & 1 & 157 & 5907 \\ \hline Math94 & 2 & 2 & yes & 1 & 57(s) & 59(s) \\ \hline Math97 & 2 & 2 & yes & 1 & 722 & 2512 \\ \hline Mock11 & 1 & 1 & yes & 1 & 4 & 1743 \\ \hline Mock21 & 2 & 2 & no & 3 & 5 & 1725 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Fault localization results of AgxFaults for real bugs.