esa-sceva/satcom-chunk-collection huggingface dataset:
# Log-Based Fault Localization with Unsupervised Log Segmentation
## 2 Materials and Methods  
The proposed fault localization model is designed to identify the log messages related to failures in complex systems through the expansion of existing fault localization techniques. This model is useful when logs from normal execution of the system are available, and the traditional log-based fault localization method produces a large number of rank \(1\) lines when applied to logs from a failed execution of the system. Logs, along with other parts of the code, constantly change, making it difficult for methods based on a long history to operate well. We propose a method that requires only one current set of normal logs and corresponding failed logs, generating a hierarchy of anomalous log lines, which is crucial from a fault localization point of view. As a result, our method overcomes this limitation and can be easily used for new software releases.  
Traditional LBFL methods may return a substantial number of rank \(1\) log templates. When this happens, the user has to manually examine log lines related to log templates with rank \(1\), sorted by timestamp. We propose to use context so that most important log lines can be analyzed first, and they can be seen in meaningful context. The timestamp-based approach simply ranks log lines based on when they occurred, which can be ineffective in large logs with many failures and when a crucial log line is not the first suspicious line. We propose a context-based approach, which, analyzes the surrounding and considers how suspicious lines relates with their context (by calculating average of lines suspicious score). This is particularly useful when logs contain anomalies buried within sequences of normal-looking events.  
Using unsupervised log sequence segmentation, our method focuses more precisely on related log lines, and distinguish between log anomalies that are genuinely related to faults and those that are incidental (e.g., logs from auxiliary processes or logging artifacts). This reduces false positives and makes it easier to localize the real issue.  
Overall, the context-based ranking approach enhances fault localization by prioritizing log lines that are both suspicious and contextually significant, reducing the manual effort needed to identify the root cause of failure.  
Our approach (Figure 1) consists of two stages:* A log-based fault localization framework that identifies the most suspicious log lines in a failed log file,
* Context-based ranking of rank 1 log lines, based on unsupervised log sequence segmentation.
## 3 Modeling Approach
### Multi-Energy System
On the electrical side, the energy balance (Equation (4)) includes the energy generated by PV panels (\(E_{PV}\)) and diesel generators (\(E_{Gen}\)), the energy flows to and from the battery (\(E_{\text{{\it bat,ch}}}\) and \(E_{\text{{\it bat,dis}}}\)), the electric energy that is curtailed (\(E_{\text{{\it Curt}}}\)), the energy supplied to the thermal storage through electric resistance heating elements (\(E_{\text{{\it Res,tot}}}\)), and the fraction of accepted unmet load (\(E_{\text{{\it lost load}}}\))--set at 1%, as for thermal loads. \[ \begin{array}{ll}E_{\text{{\it EL,demand}}}(t)=E_{PV}(t)+E_{Gen}(t)-E_{\text {{\it bat,ch}}}(t)+E_{\text{{\it bat,dis}}}(t)-E_{\text{{\it Curt}}}(t)-E_{ \text{{\it Res,tot}}}(t)\\ \hskip 14.226378 pt+E_{\text{{\it lost load}}}(t)\ \forall t\end{array}  \tag{4}\]  
where \(E_{\text{{\it Res,tot}}}\) is the cumulative energy absorbed by the electric resistance heating elements of all \(n\) thermal classes, as shown by Equation (5). \[E_{\text{{\it Res,tot}}}(t)=\ \sum_{k=1}^{n}E_{\text{{\it Res,class}}}(k,t)\ \forall t \tag{5}\]  
As compared to the original MicroGridsPy model, which includes both linear programming (LP) and mixed-integer linear programming (MILP) optimization modes depending on the way the diesel generator is modeled, the novel MESpy model is based uniquely on a LP configuration. This choice is motivated by the necessity to keep the computational time within a reasonable range while we increase the temporal resolution of the model from the original 1 h to the current 1 min.
## 2 Problem Formulation
### Control, Structure Design and Optimization Problem  
Even though the NCS of power grid suffers DoS attacks, one of the NCS objectives is to enhance the system performance by mitigating the frequency deviation \(\Delta f_{i}\) and minimizing the control cost. To achieve the control objective, the control law \(\varphi_{i}\) should be investigated. Moreover, the design of control structure and its optimization also need to be considered. Since the control structure associates the placement of sensors (in remote terminal unit (RTU)), actuators (in DES) and communication topology, these placements seriously affect the control performance in a large-scale multi-area power system. Generally, the placed resources (RTU, DES, etc.) are expensive.  
In mathematical form, the control design gives a \(\varphi_{i}\) to achieve the system performance. We define \(Q\) shown in Equation (6) as the control cost to measure system performance.  
\[\varphi^{*}=\arg\min_{\varphi}Q, \tag{6}\]  
where \"argmin\" here also denotes the functional minimization; the control structure design aims to select cyber connected area set \(N_{c}(i)\) for \(i=1,\ldots,n\), which is  
\[{N_{c}}^{*}=\arg\min_{N_{c}}Q. \tag{7}\]  
Therefore, as an optimization, it searches a combination of \(\varphi_{i}\) and \(N_{c}(i)\) for \(N_{c}(i)\), \(i=1,\ldots,n\) to minimize the control cost in the worst case of DoS attack with constraint Equation (5) as shown in Equation (8):  
\[\left(\begin{array}{cc}\varphi^{*},&{N_{c}}^{*},&\delta^{*}\end{array}\right) =\arg\max_{\delta}\min_{\varphi,N_{c}}Q, \tag{8}\]  
where \(\varphi{=}\left(\begin{array}{cc}\varphi_{1}&\cdots&\varphi_{n}\end{array}\right)\), \(N_{c}=\left[\begin{array}{cc}N_{c}(1)&\cdots&N_{c}(n)\end{array}\right]\), \(\delta{=}\left[\begin{array}{cc}\delta_{1N_{c}(1)}&\cdots&\delta_{nN_{c}(n) }\end{array}\right]\).  
We assume the attacker knows the information of the designed power system. The attacker can figure out the minimal system performance, when the controller reaches its maximal performance. The details of optimization in a Stackelberg game are discussed in Section 4.
## 2 Experimental Apparatus and Procedure  
### Experimental Apparatus  
Figure 1 shows the schematic diagram of the tested ASHP, which was composed of four main elements spaced in two split units. The indoor FTHX was placed in the indoor unit, while the outdoor FTHX, the EEV and the compressor formed the outdoor unit. The EEV was a DunAn DPFI.3C-B059, and the compressor model was GSD098 CKQA6J76B. The refrigerant was R32 with a charge of 570 g. With a four-way valve changing positions, the ASHP exhibited alternative operations between cooling and heating cycles, indicating that both FTHXs were dual-role and could be used as the evaporator and the condenser in different cycles. The main parameters for the components in the tested ASHP are listed in Table 1. More detailed information about the tested ASHP can be found in previous research [21].  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Item** & **Information** \\ \hline \multirow{4}{*}{Indoor coil} & Tube number & 34, 18 in the 1 st row and 16 in the 2 nd row \\  & Outer diameter & 5 mm \\  & Tube length & 630 mm \\  & Fin pitch & 1.4 mm \\ \hline \multirow{4}{*}{Outdoor coil} & Tube number & 24 in one row \\  & Outer diameter & 7 mm \\  & Tube length & 770 mm \\  & Fin pitch & 1.25 mm \\ \hline \multicolumn{2}{c}{Electronic expansion valve} & DunAn DPFI.3C-B059 \\ \hline \multicolumn{2}{c}{Compressor} & GSD098 CKQA6J76B \\ \hline \multicolumn{2}{c}{Refrigerant} & R32 with 570 g charge \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters and specifications of the ASHP components.  
Figure 1: Schematic diagram of the tested ASHP [21].  
The original FTHXs, however, had reversely fixed circuitries, i.e., the refrigerant flow-path was the same for both the evaporator and the condenser, with a mirrored arrangement. The outdoor FTHX had 24 tubes spaced in a single row. The tube length was 770 mm, and its outdoor diameter was 7 mm. It had a wavy-type fin with a thickness of 0.95 mm and a pitch of 1.25 mm. As shown in Figure 1, the refrigerant flowpath of the outdoor FTHX corresponded to a 4-in-4-out scheme in both the heating and the cooling cycle of the ASHP. This scheme hardly satisfied the opposite circuit number requirements of the evaporator and the condenser and might reduce the overall energy performance of the system [15]. Therefore, circuitry optimization in the FTHX was needed to obtain an optimal flowpath simultaneously in different modes.
# Recent Progress in Multiplexed Single-Photon Sources
## 3 Multiplexed Single-Photon Sources  
As we have described in Section 2, in heralded SPSs, the generation of single-photon pairs in the nonlinear process is accompanied by a finite probability of obtaining more than one photon pair. In addition to using photon-number-resolving detectors for heralding, another way to decrease multiphoton contribution in the heralded signals is to decrease the mean number of the generated photon pairs in the heralded source. Unfortunately, this solution reduces the output single-photon probability, too. This undesired reduction can be compensated by the multiplexing of several heralded sources, and thus, the whole multiplexed system can still be characterized by a high probability of successful heralding, eventually resulting in a higher single-photon probability at the output of the multiplexed SPS. Multiplexing involves routing the signals originating from the particular heralded sources realized in space, time, or in different spectral modes to a single-output mode by using a switching device. Corresponding to the various realizations of the sources of the heralded photons to be multiplexed, spatial, temporal, and spectral multiplexing have been developed in the literature. In the following, we review each of these types of multiplexing.
# A Three-Dimensional Elastoplastic Constitutive Model for Geomaterials
###### Abstract  
The Mohr-Coulomb (M-C) failure criterion has been a popular choice for geotechnical analysis because of its simplicity and ease of use. The fact that the M-C criterion disregards the intermediate principal stress's impact is a significant drawback. As a result, the M-C criterion is only applied to materials under biaxial stress. This paper presents a three-dimensional version of the M-C criterion. The proposed criterion, called the Generalized Mohr-Coulomb (GMC) criterion, considers the intermediate principal stress's effect, in addition to inheriting the original M-C criterion's benefits. We obtained the conditions that the strength parameters must satisfy when the GMC criterion fulfills the \(\pi\) plane's convexity. The GMC criterion can better describe geotechnical materials' strengths under general stress conditions. Based on an implicit algorithm, the user material subroutine (UMAT) of the three-dimensional GMC model was developed in ABAQUS using the Fortran programming language. The established elastoplastic model's validity and the program's accuracy were examined using numerical simulation. Finally, a numerical simulation of a three-dimensional tunnel excavation under various working conditions was performed. The calculation results from the GMC model are precise and have some engineering-related practical significance.  
generalized Mohr-Coulomb criterion; UMAT secondary development; GSPC; constitutive integral Article
## 2 Materials and Methods
### Instrumentation and Condition for LC-MS/MS Analysis  
Sample was analyzed on Thermo Scientific(tm) Vanquish(tm) UHPLC system (Thermo). AC18 column (Thermo Hypersil Gold C18, 100 mm \(\times\) 2.1 mm, 1.8 \(\upmu\)m) coupled to Q Exactive mass spectrometer (Thermo) was used for the separation and identification of metabolites. The mobile phase consisted of 0.1% formic acid in water (A) for positive mode, 5 mM ammonium acetate in water (A) for negative mode and acetonitrile (B) use the following gradient condition: 0 min-1 min, 1% B; 1 min-8 min, 1-99% B; 8 min-10 min, 99% B; 10 min-10.1 min, 99-100% B; 10.1 min-12 min, 1% B. The flow rate was 0.3 mL/min, the column temperature was set as 35 \({}^{\circ}\)C and the sample injection volume was 4 \(\upmu\)L [19; 20]. Mass spectrometric analysis was performed with Q-Exactive mass spectrometer (Thermo Fisher Scientific, Bremen, Germany), using electrospray ionization sources in positive and negative ionization modes. The operating parameters were operated as follows: Positive polarity; spray voltage 4.0 kV (positive) or \(-3.6\) kV (negative), funnel RF lens value at 50, capillary temperature of 400 \({}^{\circ}\)C, the flow rates for sheath gas, aux gas and sweep gas were set to 45, 15, and 0, respectively. Except otherwise noted, data dependent acquisition (DDA) using the Full MS-ddMS2 setup was used. Full MS resolution was set to 70,000, Mass range was set to 100-1500. For MS2 spectra, Resolution was set to 17,500.
## 2 Materials and Methods
### Synthesis and Characterization
\({}^{1}\)H NMR (300 MHz, CDCl\({}_{3}\)): \(\delta\) = 3.78 (s, 6H, OMe), 6.92 (d, \({}^{3}\)(H,H) = 7 Hz, 4H), 7.27-7.30 (m, 4H), 7.59-7.64 (m, 4H), 7.66 (d, \({}^{3}\)(H,H) = 7 Hz, 4H); \({}^{13}\)C NMR (75 MHz, CDCl\({}_{3}\)): \(\delta\) = 55.1 (OCH\({}_{3}\)), 112.2 (CH), 114.3 (CH), 115.9 (C\({}_{\text{quat}}\)), 121.6 (C\({}_{\text{quat}}\)), 121.7 (CH), 124.1 (CH), 124.2 (CH), 130.6 (C\({}_{\text{quat}}\)), 136.9 (CH), 158.7 (C\({}_{\text{quat}}\)), 159.7 (C\({}_{\text{quat}}\)), 161.7 (C\({}_{\text{quat}}\)). \({}^{29}\)Si NMR (80 MHz, CDCl\({}_{3}\)): \(\delta\) = \(-\)22.2 (s). HRMS Calcd for C\({}_{30}\)H\({}_{22}\)O\({}_{4}\)Si(M\({}^{+}\)): 474.1287; Found: 474.1286. Anal. Calcd. for C\({}_{30}\)H\({}_{22}\)O\({}_{4}\)Si(C, 75.92, H, 4.67); Found: C, 75.60, H, 4.39. **6**: To a 25 mL flask was added 5 mL THF, magnesium shavings (72 mg, 3 mmol), 2-bromo-9,9-dibutyl-fluorene (1068 mg, 3 mmol), iodine, and the mixture was refluxed for 3 h. The gray Grignard reagent suspension was slowly transferred portion wise into the THF solution of **3** at \(-\)78 \({}^{\circ}\)C. The brown mixture was gradually warmed to room temperature, and stirred overnight. The solvent was removed, and the residue was purified by column chromatography on silica (hexane/DCM = 5/1) and recrystallized from hexane and DCM to afford white crystals (408 mg, 0.5 mmol, 50% yield). 1H NMR (300 MHz, CDCl\({}_{3}\)): \(\delta\) = 0.54-0.63 (20H), 0.97-1.05 (8H, CH\({}_{2}\)), 1.87-1.93 (8H, CH\({}_{2}\)), 7.28-7.33 (m, 10H), 7.62-7.74 (m, 10H), 7.78 (s, 2H); \({}^{13}\)C NMR (75 MHz, CDCl\({}_{3}\)): \(\delta\) = 13.8 (CH\({}_{3}\)), 23.0 (CH\({}_{2}\)), 26.0 (CH\({}_{2}\)), 39.8 (CH\({}_{2}\)), 55.0 (C\({}_{\rm{quat}}\)), 112.2 (CH), 116.1 (C\({}_{\rm{quat}}\)), 119.8 (CH), 120.1 (CH), 121.5 (CH), 123.0 (CH) 124.1 (CH), 124.2 (CH), 126.8 (CH), 127.7 (CH), 129.0 (C\({}_{\rm{quat}}\)), 129.8 (CH), 130.7 (C\({}_{\rm{quat}}\)), 134.0 (CH), 140.5 (C\({}_{\rm{quat}}\)), 143.7 (C\({}_{\rm{quat}}\)), 150.5 (C\({}_{\rm{quat}}\)), 151.0 (C\({}_{\rm{quat}}\)), 158.7 (C\({}_{\rm{quat}}\)), 160.0 (C\({}_{\rm{quat}}\)). \({}^{29}\)Si NMR (80 MHz, CDCl\({}_{3}\)): \(\delta\) = \(-\)21.3 (s). HRMS Calcd for C\({}_{30}\)H\({}_{22}\)O\({}_{4}\)Si(M\({}^{+}\)): 814.4206; Found: 814.4196. Anal. Calcd. for C\({}_{58}\)H\({}_{58}\)O\({}_{2}\)Si (C, 85.46, H, 7.17); Found: C, 84.96, H, 7.00.
## 3 Results and Discussion
### Total Phenolic and Profile of Phenolic Compounds
#### 3.1.2 Phenolic Acids Profile
Partial hydrolysis of ester bonds connecting phenolic acids with cell wall polysaccharides and  
Figure 1: HPLC chromatogram of the flavonoids and phenolic acids standards at a concentration of 20 \(\upmu\)g/mL. \begin{table}
\begin{tabular}{c c c c c c c} \hline
**Compound** & **Retention** & **Regression Equation** & **Correlation** & **Linearity** & **LOD** & **LOQ** \\  & **Time (min)** & & **Coefficient (r)** & **Range (\(\upmu\)g/mL)** & **(\(\upmu\)g/mL)** & **(\(\upmu\)g/mL)** \\ \hline Gallic acid & 6.95 & y = 58.4571x \(-\) 297.0195 & 1.0000 & 10–80 & 1.00 & 3.04 \\ Chlorogenic acid & 16.73 & y = 27.5882x & 0.9999 & 10–160 & 2.68 & 8.13 \\ Caffeic acid & 17.66 & y = 72.5182x & 0.9996 & 10–80 & 2.93 & 8.87 \\ Rutin & 21.72 & y = 11.6193x & 1.0000 & 2.5–40 & 0.44 & 1.34 \\ Hyperoside & 23.05 & y = 17.9836x & 0.9999 & 2.5–40 & 0.80 & 2.41 \\ Naringin & 26.07 & y = 29.5348x + 11.9937 & 1.0000 & 2.5–40 & 0.32 & 0.97 \\ Rosmarinic acid & 30.01 & y = 31.2072x & 1.0000 & 10–160 & 2.05 & 6.23 \\ Quercetin & 35.12 & y = 16.0402x + 4.8604 & 1.0000 & 2.5–40 & 0.46 & 1.40 \\ Naringenin & 37.38 & y = 86.6723x & 0.9999 & 2.5–40 & 0.65 & 1.96 \\ Kaempfer & 38.51 & y = 22.7602x + 14.4550 & 1.0000 & 2.5–40 & 0.32 & 0.98 \\ \hline \end{tabular}
\end{table} 
Table 3: HPLC method validation parameters at 280 nm. softening of the matrix promote the release of these compounds. However, when phenolic compounds are released, they can be oxidised by polyphenoloxidase and react with cell wall polysaccharides. The interaction of polyphenols with polysaccharides modifies the extractability of these compounds, even though they may partially retain their antioxidant capacity [33].
## 3 Experiment and Results
### Data Source  
The EEG data were collected by the Brain-Computer Interface Lab, Jiangxi University of Technology, from university students (12 subjects: 8 male and 4 female, average age 21.5 years). In the 24 h prior to the experiments, these subjects were to consume no tea or coffee and have 8 h sleep at night before the experiment. The subject was given an operation introduction while an electrode cap was put on his/her head. After the subject was familiar with driving in the road conditions, EEG signal collection began. For a short time of driving, it is difficult to enter into a state of fatigue to produce a reliable and effective EEG. Unfortunately, for a longer period of driving, most participants experience uncomfortable and unpleasant feelings including boredom, testiness and nausea. Therefore, according to previous experience in a fatigue-related experiment, each subject was asked to drive for 40 min without a break before taking a questionnaire to check the status, based on the [PERSON]'s subjective fatigue scale and [PERSON]'s CR-10 scale. The questionnaire results showed the subject was in driving fatigue. The experiments were authorized by Academic Ethics Committee of Jiangxi University of Technology.  
The sample set of the experiment was divided into training sample (400 samples) and test sample (200 samples). With a 32-electrodes Neuroscan data acquisition device, the international 10-20 system was used for the EEG collection protocol. All channel data were referenced to two electrically linked mastoids at A1 and A2, digitized at 1000 Hz from a 32-channel electrode cap (including 30 effective channels and 2 reference channels) based on the international 10-20 system and stored in a computer for the offline analysis [33; 34; 35; 36]. Eye movements and blinking were monitored by recording the horizontal and vertical EOG.  
After the EEG signals were collected, the main steps of data preprocessing was carried out by the Scan 4.3 software of Neuroscan (El Paso, TX, USA, 2003). The raw signals were first filtered by a 50 Hz notch filter and a 0.15 Hz to 45 Hz band-pass filter to remove the noise. We defined two types of state for every subject within the 40-min EEG recordings: the 10-min of EEG signals before the 40-min virtual driving operation was defined as the normal state, and the last 10 min of EEG signals within the 40-min virtual driving operation was defined as the fatigue state.  
Figure 2 shows a comparison between EEG signals on normal state and fatigue state. As can be seen from the figure, EEG signals in the time domain are mixed and disordered, containing a lot of noise data, with the resulting features not being obvious. Therefore, it is necessary to transform EEG signals before extracting features for describing the fatigue state.  
Figure 1: A flowchart to show the operation steps. EEG: electroencephalography; SVM: support vector machine.
# Electrocatalytic Nanomaterials Improve Microbial Extracellular Electron Transfer: A Review
## 3 Electrocatalytic Nanomaterials for Promoting EET
### Heteroatom-Doped carbons
found that the current density of MFCs showed a positive linear correlation with the pyrrolic N due to the enhancement of both DET and MET of _S. oneidensis_ MR-1 [61]. Moreover, they further demonstrated through molecule dynamic simulation that anodic doping with pyrrolic N achieved a more remarkable reduction in the thermodynamic and kinetic resistances of the EET process compared to pyridinic N and graphitic N.  
In addition to increasing the electrode biofilm growth and decreasing the interfacial charge resistance, N doping has been found to significantly promote the electrocatalytic reaction of microbial electron mediators, especially flavins. [PERSON] et al. revealed that a porous N-doped carbon cloth with a high N/C ratio showed an 18 times higher anodic peak current of riboflavin compared to the carbon cloth alone [64]. [PERSON] et al. also demonstrated that both N-doped carbon nanotubes/reduced graphene oxide (N-doped CNTs/rGO) composites [53] and N-doped carbon nanowires [65] could promote a flavin-based interfacial electron transfer in MFC anodes. More importantly, by tuning N doping, the electrode interface could be atomically matched with the flavin molecule, thus achieving strong adsorption and conversion of the diffusive mediators to anchored redox centers (Figure 2). The atomic matching resulted in a short electron transfer pathway in _S. putrefaciens_-inoculated MFCs. In addition to N doping, S and P doping have been adopted to modify the MFC electrode. [PERSON] et al. prepared 3D macroporous carbon foam with high N, P, and S contents as an MFC anode electrode through the pyrolysis of bread as the raw material and found that the co-doping of N, P, and S greatly improved the biocompatibility of  
Figure 2: The proposed effect mechanism of N doping on flavins-mediated EET: (**a,b**) SEM; (**c**) TEM images and (**d**) XPS N1s spectrum of N-doped carbon nanowires; (**e**) the proposed atomic matching catalysis mechanism (with permission from [65]). the resulting electrode and facilitated the enrichment of bacterial biofilm, which led to a 2.57-fold increase in the power density [66].