esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
To identify and diagnose the latent leakage faults of key pneumatic units in the Chinese standard Electric Multiple Units (EMU) braking system, a multi-source information fusion method based on Kalman filtering, sequential probability ratio test (SPRT), and support vector machine (SVM) is proposed. The relay valve is taken as an example for research. Firstly, Kalman's state estimation function is used to obtain the innovation sequence, and the innovation sequence is input into the SPRT model to help recognize latent leakage faults of the relay valve. Using this method, the problem of the incomplete training set of the traditional SPRT method due to the change of the braking level and the vehicle load is solved. Secondly, the eight time-domain parameters of the relay valve input and the output pressure signal are extracted as fault characteristics, and then input to the support vector machine to realize the internal and external leakage fault diagnosis of the relay valve, which provides a reference for maintenance. Finally, this method is verified by the fault simulation data by quickly identifying latent leakage faults and diagnosing the internal and external leakage at a fault recognition rate of 100% by SVM under small sample conditions.  
Chinese Standard EMU; pneumatic unit; latent leakage faults; multi-source information fusion +
Footnote †: journal: Applied Sciences
# Beyond 100G: All-Optical Processor for High-Capacity Access\(\sim\)Networks
###### Abstract  
We propose a novel approach to mitigate the limitations of high-speed Passive Optical Networks (PONs) by introducing an all-optical processor. This solution addresses the escalating demand for higher data rates and improved performance in future access networks. The all-optical processor leverages optical signal processing to enhance system efficiency and reduce power consumption compared to traditional electrical methods. Specifically, we explore the processor's dual functionality in performing all-optical equalization and chromatic dispersion compensation. Our research includes a comprehensive analysis of the processor's design, operational principles, and system validation through extensive simulation studies, demonstrating significant improvements in signal quality and overall network performance. The results indicate that the all-optical processor not only relaxes the DSP and power requirements but also outperforms the more sophisticated digital counterpart methods.  
passive optical networks (PON); optical signal processing; chromatic dispersion compensation; optical equalization; digital signal processing (DSP); machine learning optimization Article
# Microbially Induced Desaturation and Carbonate Precipitation through Denitrification: A Review
###### Abstract  
Microbially induced carbonate precipitation (MICP) has been proposed as a sustainable approach to solve various environmental, structural, geotechnical and architectural issues. In the last decade, a ubiquitous microbial metabolism, nitrate reduction (also known as denitrification) got attention in MICP research due to its unique added benefits such as simultaneous corrosion inhibition in concrete and desaturation of porous media. The latter even upgraded MICP into a more advanced concept called microbially induced desaturation and precipitation (MIDP) which is being investigated for liquefaction mitigation. In this paper, we present the findings on MICP through denitrification by covering applications under two main titles: (i) applications solely based on MICP, such as soil reinforcement, development of microbial self-healing concrete, restoration of artwork and historical monuments, and industrial wastewater treatment, (ii) an application based on MIDP: liquefaction mitigation. After explaining the denitrification process in detail and describing the MICP and MIDP reaction system occurring through denitrification metabolism, the most recent advances in each potential field of application are collected, addressing the novel findings and limitations, to provide insights toward the practical applications in situ. Finally, the research needs required to deal with the defined challenges in application-oriented upscaling and optimization of MICP through denitrification are suggested. Overall, collected research findings revealed that MICP through denitrification possesses a great potential to replace conventionally used petrochemical-based, labour intensive, destructive and economically unfeasible techniques used in construction industry with a bio-based, labourless, low-carbon technology. This worldwide applicable bio-based technology will facilitate the sustainable development and contribute to the carbon-emission-reduction.  
Keywords:nitrate reduction; nitrogen gas; calcium carbonate; liquefaction mitigation; self-healing concrete; ground improvement +
Footnote †: journal: Applied Sci. 2021, 11, 7842. [[https://doi.org/10.3390/app11177842](https://doi.org/10.3390/app11177842)]([https://doi.org/10.3390/app11177842](https://doi.org/10.3390/app11177842))
## 4 Mathematical Model and Optimization Framework CFD Numerical Simulation Setup
### Genetic Algorithm Design  
The Genetic Algorithm used in this study incorporates selection, crossover, mutation, generation gap, and elitism strategies.  
Selection is performed via the roulette wheel method, where the selection probability of individual i is  
\[p_{i}=\frac{f_{i}}{\sum\limits_{j=1}^{N}f_{i}},i=1,2,...,N \tag{4}\]  
Cumulative probability is computed as  
\[\mathrm{c_{i}=\sum\limits_{j=1}^{i}p_{i^{\prime}}c_{0}=0,c_{N}=1} \tag{5}\]  
Random numbers \(\mathrm{r_{k}\sim U(0,1)}\) are used to select individuals satisfying  
\[\mathrm{c_{i-1}<r_{k}<c_{i}} \tag{6}\]* Crossover uses a two-point strategy, randomly selecting two positions, q1 and q2, in the chromosome and exchanging the gene segments between them to generate new offspring.
* Mutation introduces random perturbations into selected genes to maintain population diversity and avoid premature convergence. The mutation probability decreases over generations, enhancing stability and convergence speed.
* Generation gap is applied to preserve population diversity.
* Elitism guarantees the best-performing individual of each generation is directly carried into the next generation without undergoing genetic operations, ensuring optimal solutions are retained.
# Ab Initio Study of Martensitic Transformation in NiTiPt High Temperature Shape Memory Alloys
## 4 Conclusions  
In the present paper we explored the strong dependence of the martensite start temperature \(M_{\rm s}\) on alloy composition in ternary Ni-Ti-Pt shape memory alloys by means of density functional theory. Based on the crystal structure optimizations of Ti\({}_{50}\)Ni\({}_{50-x}\)Pt\({}_{x}\) (\(x=0\), 6.25, 8.33, 10.42, 12.5, 18.75, 25) shape memory alloys, Pt additions substituted for Ni in NiTi with a supercell approach are calculated. From the results obtained in the present work the following conclusions can be drawn:  
The lattice parameters \(a\), \(b\), \(c\) and supercell volume of Ti\({}_{50}\)Ni\({}_{50-x}\)Pt\({}_{x}\) alloys increase with the increasing of Pt content. Since the number of valence electrons of Pt is equal to that of Ni, the larger atomic radius of Pt compared to Ni results in a bigger supercell volume.  
As the calculated results of formation energies presented, for Pt \(>\) 15 at.%, the monoclinic B19\({}^{\prime}\) martensite crystal structure becomes unstable and an orthorhombic B19 crystal structure is formed, resulting from a gradual destabilization/stabilization of B19\({}^{\prime}\)/B19, which is in excellent agreement with the experimental data.  
The computational results of the total energy differences \(\Delta E\) between parent and martensite phases are as follows: for Pt \(<\) 10 at.%, the substitution of Pt for Ni has relatively little effect on \(\Delta E\) (with a minimum at 6.25 at.% Pt), which shows only a moderate dependence of \(\Delta E\) on alloy composition; for Pt \(\geq\) 10 at.%, the \(\Delta E\) increase linearly with increasing Pt content, which brings about the dramatical enhancement of \(M_{\rm s}\).  
Conceptualization, X.Y. and J.S.; methodology, X.Y.; writing--original draft preparation, X.Y.; and writing--review and editing, X.Y. and J.S. Both authors have read and agreed to the published version of the manuscript. This research was funded by the National Natural Science Foundation of China (NSFC, grants no. 51371017 and no. 11864047), and the Natural Science Foundation of Technology Department (QKHLH [2015]7021). Not applicable. Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: The data presented in this study are available on request from the corresponding author. Conflicts of Interest: The authors declare no conflict of interest.
## 3 System Model and Problem Formulation
### Evaluation Indicators  
The coverage rate and redundancy rate are the indexes used to evaluate the effect of coverage optimization, and the moving distance is an important index to measure energyconsumption. Therefore, we use these three metrics to evaluate the effectiveness of the proposed method, as shown below.  
Coverage rate (COVR): Network coverage depends on the effective coverage area of the sensor. In a region of interest of size \(L\times H\), the coverage rate can be expressed as:  
\[COVR=\frac{A_{E}}{L\times H}=\frac{\sum_{i=1}^{n}A_{i}}{L\times H} \tag{5}\]  
where \(L\times H\) represents the area of the ROI, and \(A_{E}\) represents the sum of the effective coverage area of all sensors.  
To comprehensively evaluate the performance of the algorithm proposed in this paper, we used a covering upper bound (CUB) calculation scheme from the method in reference [33], as follows:  
\[CUB=\frac{\text{max}A_{E}}{L\times H} \tag{6}\]  
where \(\text{max}A_{E}\) represents the theoretical maximum effective coverage area of the stationary and mobile sensors.  
Redundancy ratio (REDR): The ratio of the overlapping coverage area of the sensors in the network to the coverage area of the sensors can be expressed as:  
\[REDR=\frac{\sum_{i=1}^{n}A_{O}}{nA} \tag{7}\]  
where \(n\) represents the number of sensors, \(A\) represents the coverage area of a single sensor, and \(A_{O}\) represents the sum of overlapping coverage areas.  
Coverage efficiency: This reflects the resource utilization and monitoring quality of the network. Its definition is the ratio of the effective coverage area to the sensing area of all nodes. The larger the value of coverage efficiency, the higher the resource utilization of the network and the smaller the redundant coverage of nodes. The following formula can express the coverage efficiency:  
\[C_{e}=\frac{A_{E}}{n\times(\pi\times R_{\text{s}}^{2})}=\frac{\sum_{i=1}^{n}A _{i}}{n\times(\pi\times R_{\text{s}}^{2})} \tag{8}\]  
Moving distance: We use moving distance to evaluate the energy consumption during node deployment optimization. We use three indicators to comprehensively measure the performance of our method in terms of moving distance. The three indicators are defined below, namely: total moving distance (TMD), maximum moving distance (MMD), and uniformity of moving distance (UMD):  
\[D_{T}=\sum_{i=1}^{n}d_{i} \tag{9}\]  
\[D_{\text{max}}=\text{max}\{d_{i}\} \tag{10}\]  
\[D_{U}=\sqrt{\sum_{i=1}^{n}\left(d_{i}-\frac{D_{T}}{n}\right)^{2}} \tag{11}\]  
where \(n\) represents the number of sensors and \(d_{i}\) represents the sensor moving distance.
# Q-MeaMetaVC: An MVC Solver of a Large-Scale Graph Based on Membrane Evolutionary Algorithms
## 3 A MVC Solver Based on MEA--Q-MaeMetaVC
### Bipartite Graph Solving Algorithm
#### 3.4.4 Selection and Cytolysis Operator  
The original definition of the cytolysis operator is to lyse the worst membranes in some populations, while the selection operator will amplify the best membranes in some populations. Q-MeMetaVC optimizes this concept. The cytolysis operator of Q-MeMetaVC will lyse part of the membrane to make it jump out of the local optimum. For other membranes, refer to one part of the reference object set in the best membrane to add part of the selection.  
As shown in Algorithm 13 Q-MeMetaVC divides MP into multiple groups, each composed of three M membranes. The CytolysisAndSelection algorithm will try to make the three membranes in each group search in three directions. The membrane with the highest fitness is good enough, so the cytolysis operator (Algorithm 14) is used to discard min(maxReConstruct, min(\(\left\lvert\text{M}\right\rvert,\left\lvert\text{V}_{i}\right\rvert- \left\lvert\text{M}\right\rvert)/2\)) elements to destroy its structure (line 3). The membrane with the second highest fitness uses the selection operator (Algorithm 15) to expand the \"good\" information (line 5, bestPart represents the set of elements with the highest fitness in the bestMem membrane). The membrane with the lowest fitness will expand the \"bad\" information and try to search from the opposite direction (line 6, worstPart represents the set of elements with the lowest fitness in the bestMem membrane). Such a design will expand the search space of Q-MeMetaVC and avoid excessive similarity between membranes to a certain extent.  
```
Input: Population C\({}_{i}\).MP, maximum dissolved amount maxReConstruct; Output: population; # each group Group\({}_{i}\) in C\({}_{i}\). MP executes concurrently
1 bestMem = the membrane with the highest fitness in Group\({}_{i}\); # Each membrane Mx in Group\({}_{i}\) executes in parallel
2 ifFit(M\({}_{x}\)) is the largest in Group\({}_{i}\) then
3 Cytolysis(M\({}_{x}\),min(maxReConstruct,min(\left\lvert\text{M}_{x}\right\rvert- \left\lvert\text{V}_{i}\right\rvert-\left\lvert\text{M}_{x}\right\rvert)/2));
4 else ifFit(M\({}_{x}\)) is the second largest then in Group\({}_{i}\):
5 Selection(M\({}_{x}\), bestMem, bestPart);
6 else Selection(M\({}_{x}\), bestMem, worstPart);
7 repairIndividual(M\({}_{x}\), bestMem );
8 return C\({}_{i}\).MP;
```  
**Algorithm 14**Cytolysis operator  
The cytolysis operator is shown in Algorithm 14; the M membrane will randomly discard dropCount elements.  
```
Input: M membrane, dissolved amount dropCount; Output: M membrane;
1 \(i=1\);
2 for\(i\leq\) dropCount do
3 v = a random element in M;
4 M\(-\)v;
5 i++;
6 return M membrane;
```  
**Algorithm 15**Cytolysis operator  
The selection operator is shown in Algorithm 15. The M membrane will refer to an element set part and add elements v (fitness(v, M)! = 0), which the M membrane does not have and which are not completely unable to adapt (lines 1-3).
## 1 Introduction
[16] demonstrates the potential of LLMs in assisting healthcare providers in the diagnosis of obstructive sleep apnea (OSA) by comparing the responses of a generative pre-trained Transformer and those of experienced otolaryngologists. In this paper, we present a system to support individuals in improving their sleep quality via the machine learning (ML)-based forecasting of future sleep quality and large language model (LLM)-based provisioning of context-aware behavior change tips through a smartphone application. In order to increase users' trust, our system is supported by an LLM-based algorithm to explain the reasons behind the predictions and tips; these explanations are provided in natural language to the user through the app interface. In fact, leveraging the language generation capabilities of LLM, our system can articulate the underlying factors and reasoning behind each behavioral tip in a clear and understandable manner, helping to bridge the gap between the complex ML models used by the system and the user's understanding and trust. The transparency provided by the cooperation of ML and LLM in our system may also enhance users' engagement, enabling them to make informed decisions and take proactive steps toward enhancing their sleep health. Technically, our system continuously collects data from the user's smartphone about activities (physical activities and conversations), usage modalities, location, and environmental conditions (light level, noise, types of sound). The system integrates the raw data and extracts features of interest on a daily basis. The feature vectors are used by a random tree regressor to forecast the next night's sleep quality. The model of the random tree regressor is trained at the server side in order to avoid the disclosure of sensitive data to third parties. Considering the decision path of the ML model, an LLM-based algorithm is used to infer the contextual factors that lead to the prediction. If the system forecasts poor sleep quality, the smartphone application provides context-aware behavior change tips. Moreover, to increase the user's trust and to enhance their adherence to the provided recommendations, the LLM-based algorithm provides explanations for the predictions and tips. We have developed a working prototype of our system and performed experiments regarding both sleep quality forecasting and the utility of context-aware tips together with their explanations. Experiments with real-world data show that the predictions of our sleep quality forecasting algorithms are correlated with the ground truth. Moreover, a user study indicates the potential utility of our system and the increase in trust given by the LLM-based module. The rest of the paper is structured as follows. Section 2 examines the state of the art in pervasive healthcare systems for sleep. Section 3 presents the architecture and algorithms used in our system. Section 4 illustrates our experimental evaluation, the user study, and the achieved results. Section 5 discusses the results, limitations, and possible improvements of our work. Finally, Section 6 concludes the paper.
## 3 Dual-Band Doherty Power Amplifier
### Dual-Band Design Improvements
New technology elements were used in the designs of [49; 50], where micro-electro-mechanical switches (MEMS) were used to select the operating dual-band of quarter wave transmission line, where the switches move the operation center frequency from one band to another depending on the state of the switches. This technique depends on the reliability of switches and their load power limitations. At the same time, this technology suffers from slow switching speed and high actuation voltage in addition to temperature sensitivity and dielectric charging problems [51]. In [52], the combiner section was simplified by matching both amplifiers to 70 \(\Omega\) as illustrated in Figure 13, at the same time, the load impedance was still 50 \(\Omega\), in this case, however, a mismatch between the load and the matching networks will be introduced (\(-15\) dB return loss) in the cost of simplifying the combiner. Further work of reconfigurable as well as concurrent dual-band DPAs was described by [53] where the reconfigurability had been achieved by using switches. The proposed design of digital DPA in [17] targeting 850 MHz and 1.7 GHz bands depends on a 3-coil current-mode combiner which is used as a power combiner of 4-way. The transformer is maintained to reduce the ground parasitic and minimize the mismatch between the two PAs. At the peaking power, both PAs are working, and the currents at the two primary coils are in-phase so that the impedance seen by each amplifier is 50 \(\Omega\). However, the load impedance seen by each PA stays the same when the output power is backed off if the two PAs are controlled synchronously, so that, in [17], both amplifiers are controlled independently in order to perform load modulation. At the same time, a switched capacitor is used because of its good performance in terms of linearity and efficiency. Another attempt targeting mm-wave was done by [54]. A simulation and detailed passive design are discussed in this paper for mm-wave: the lower band was 24 GHz and the upper bad was 28 GHz with a bandwidth of 1 GHz. A recent work using digital dual-input power was done by [55] targeting a dual-band Doherty power amplifier, where the difference of the reactance impedance that the transistor needs to see at the input side for both bands is eliminated by using a short-circuited shunt stub. Moreover, the load modulation of the designed circuit was improved by using different drain voltages, where 22 V and 36 V were used for both the main and the auxiliary amplifier, the difference in drain voltages helped the main amplifier to be more efficient at the back-off and more current to be got from the auxiliary amplifier, where the load modulation can be improved. Figure 13: Load modulation proposed by [52].
# Evaluating Commercial Electrical Neuromodulation Devices with Low-Cost Neural Phantoms
## 4 Discussion
### Limitations  
Several limitations must be acknowledged in this study's implementation. The primary limitation was the low efficiency (and thus, low sensitivity) of the recording system. While the electrodes were physically fixed, their positions constantly shifted. Only a small portion of the recording probe (a metal cone with a diameter of 0.7 mm) was exposed to the material at a given time. Constant shifting between positions changed how much possible contact was measured between the immediate material and the electrode. Noise during recording caused rapid shifts, as did the slow sampling frequency of the system. Another problem was the continued deformation of the soft phantom materials, which shifted when recording electrode positions across repeated measurements. An issue with the relatively small number of electrodes is the limited resolution of electrical activity in each smaller volume. Another issue was the lack of comparison with high-resolution simulations or live trials. The current model was limited to electrodes, whereas other phantoms support multimodal sensors. However, these shortcomings are amenable to possible iterative improvements.
# On the Analysis of Coverage Feedback in a Fuzzing Proprietary System
## 4 Results  
In this section, we explain and analyze the detailed results of our experiment. For the experiment, we first gathered 44 unique memory bugs by running OSS-Fuzz fuzzers (libFuzzer harnesses) in our private infrastructure similar to ClusterFuzz. Once we found such bugs, we utilized them as the ground-truth source for our evaluation. To evaluate the efficacy of coverage feedback in reproducing such bugs in 44 libFuzzer stubs, we modified libFuzzer's behavior by modifying the LLVM-15 source code. For our evaluation platform, we use an Ubuntu 22.04 64-bit server operating system based on an AMD Ryzen-9 5900 CPU architecture, which supports up to 24 cores. Also, we note that we use the -workers=5 option (five parallel evaluation) for all libFuzzer harness testing and iterate the evaluation twice to calculate the average. Table 1 is the summarized result of our evaluation. The Default column indicates the fuzzer execution count for reproducing the same bug using the original libFuzzer. Partial indicates the fuzzer execution count for reproducing the same bug based on the partially modified (using coarse-grained coverage feedback information) libFuzzer. No Cov indicates the fuzzer execution count for reproducing the same bug with the modified libFuzzer for discarding the coverage feedback information. In the No Cov case, the number of fuzzer execution counts is omitted if bug reproduction fails even after running the fuzzer for a sufficiently long time (48 h) and also executing the fuzzing iteration at least twice the original count for discovery. The Seed column indicates the existence of the initially given test data set for mutation in fuzzing. If no initial seed is given, libFuzzer automatically generates a series of random byte sequences for its test input data. The evaluation table shows a discernible relationship between the No Cov fuzzer's result and the Seed. It is noteworthy that the initial seed is manually crafted by the open-source developers and thus contains crucial information regarding data structure and format. The evaluation results based on our test data set suggest that the influence of the initial seed is significantly greater than the detailed mechanism of the coverage feedback