esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Beam Bending Vibration and FE Models
### 3D FE Frequency Convergence Test  
A convergence test is performed to ensure that the 3D FE model is adequately meshed. It is here given for the damaged beam, with a damage at midposition with a width of 2 mm and a depth of 2.5 mm. The beam properties are given in Table 1. The convergence test is carried out by changing the mesh densities. Using the five mesh standard parameters proposed in COMSOL Multiphysics: Finer, Fine, Normal, Coarse, Coarser, the first seven bending natural frequencies are computed. Note that these standard mesh parameters correspond to a number of DOFs varying from 22,000 to 2300 respectively, using tetrahedral elements, and that we rely on COMSOL Multiphysics to adequately mesh the damage area, as shown in Figure 2b. In a convergence study shown in Figure 3, the relative error on the natural frequency (with respect to the \"Finer\" mesh configuration) is determined. It is shown that the relative error for the \"Fine\" mesh is below 0.5%. Therefore, it can be concluded that the results are converged for both \"Fine\" and \"Finer\" mesh parameters and the frequencies are independent on the mesh size. For this reason, the \"Finer\" mesh is used in the rest of the study to build a damage library and to estimate geometry damage parameters.
## 5 Discussion
### Limitations and Future Directions
In the event that the intrusion detection system (IDS) initiates the processing of a packet prior to the completion of the store-and-forward operation, there is a possibility that the IDS may not possess complete access to the entirety of the packet header. Consequently, this may result in partial analysis and the potential failure to recognize intrusion attempts. Another concern pertains to the phenomenon of out-of-order packets. In the context of dynamic networks, it is possible for packets to be received in a non-sequential sequence as a result of fluctuating paths and delays [58]. In the event that an intrusion detection system (IDS) encounters the processing of packets in a non-sequential manner, there exists the possibility of misinterpreting the chronological sequence of events or experiencing afailure in detecting coordinated attacks. This study focuses exclusively on the analysis of store-and-forward operations, in contrast to the comprehensive scanning of the full transmission session. The inherent dynamism of store-and-forward processes presents significant issues for intrusion detection systems. In order to achieve precise and efficient detection, it is imperative to formulate tactics that specifically target the timing differences, packet fragmentation, and other information gaps that may arise from these activities. In dynamic network circumstances, the utilization of intrusion detection systems (IDSs) enables the attainment of a complete and dependable analysis of packet headers.
# Enhancement of Subcooled Flow Boiling Heat Transfer with High Porosity Sintered Fiber Metal
###### Abstract  
We conducted experimental research using high-porosity sintered fiber attached on the surface, as a passive method to increase the heat flux for subcooled flow boiling. Two different porous thicknesses (1 and 0.5 mm) and one bare surface (0 mm) were compared under three different inlet subcooling temperatures (30, 50 and 70 K) and low mass flux (150-600 kg\(\cdot\)m\({}^{-2}\cdot\)s\({}^{-1}\)) using deionized water as the working fluid under atmospheric pressure. The test section was a rectangular channel, and the hydraulic diameter was 10 mm. The results showed that the heat flux on porous surfaces with a thickness of 1 and 0.5 mm increased by 60% and 40%, respectively, compared to bare surfaces at \(\Delta T_{\text{sat}}=40\) K at a subcooled temperature of 50 K and mass flux of 300 kg\(\cdot\)m\({}^{-2}\cdot\)s\({}^{-1}\). An abrupt increase in the wall superheat was avoided, and critical heat flux (CHF) was not reached on the porous surfaces. The flow pattern and bubble were recorded with a high-speed camera, and the bubble dynamics are discussed.  
porous; subcooled flow boiling; fiber metal; CHF; boiling heat transfer; sintered fiber metal; high porosity
# Subsampling Algorithms for Irregularly Spaced Autoregressive Models
## 2 Background
### Modeling Irregularly Spaced Time Series  
Irregularly spaced (or unevenly spaced) time series occur in many domains including astronomy, biomedicine, climatology, ecology, environment, finance, geology, etc. For instance, high-frequency financial transactions typically occur at irregularly spaced time points within a trading day, as each trade is recorded. Within a trading day, the elapsed times (durations) between consecutive trades are not the same for any selected stock. These times also vary between different stocks. In any given time interval (say, one hour), transactions of a stock may occur rapidly, separated by short durations, or occur slowly with longer durations. Since methods that are used for modeling and forecasting regular time series [10] are not useful for analyzing irregular time series, modified approaches have been developed.  
A recent approach for analyzing irregularly spaced time series is the gap time autoregressive (AR) model in (1), which was discussed in [4] for modeling house prices. In this model, the higher the value of \(d_{j}\), the less useful \(y_{j-1}\) becomes for explaining and predicting \(y_{j}\). [PERSON] et al. [5] described AR type models for stationary and nonstationary irregular time series. Similar models were used in [11] and [1] to model and forecast future values of irregularly spaced astronomical light curves from variable stars, for which [PERSON] et al. [12] proposed a bivariate model.  
[PERSON] and [PERSON] [13] proposed the autoregressive conditional duration generalized autoregressive conditionally heteroscedastic (ACD-GARCH) model for analyzing irregularly spaced financial returns, employing the computationally cumbersome Generalized Method of Moments (GMM) approach for estimating parameters. [PERSON] et al. [14] proposed a GARCH-type model for irregularly spaced time series by discretizing a continuous time stochastic volatility process, thereby combining the advantages of the ACD-GARCH model [13] and the ACD model [15]. [PERSON] et al. [16] described a continuous version of the GARCH (i.e., the COGARCH) model for irregularly spaced time series, while [17] proposed a multivariate local-level model with score-driven covariance matrices for intra-day log prices, treating the asynchronicity as a missing data problem. Recently, [6] extended the gap time modeling idea of [4] to construct useful time series models to understand volatility patterns in irregularly spaced financial time series, considering the gaps as random variables. An alternate stochastic volatility model treating the gaps as fixed constants was discussed in [18].  
Most methods for analyzing long irregularly spaced time series can be computationally challenging. Subsampling methods can help us obtain estimates in a computationally feasible way.
## 2 Methodology
### Model Validation
#### 2.2.4 Results  
Figure 5 shows a comparison between the simulation and experimental results. According to the obtained results, the S1 sensor closest to the hydrogen leak hole responds 6 s after the leak and maintains a concentration of 0.1% thereafter. The sensor responds sequentially as hydrogen leaked from the experimental facility sends discharge through the ventilation opening. In accordance with the sensor position, sensor S4 responded at 33 s after the leak, whereas S6 responded at 73 s after the leak. Therefore, the response time varies based on the sensor position.  
In addition, we confirmed that hydrogen moves along S1 through S4 after the leakage because of the low density of hydrogen's physical characteristics. By comparing the overall hydrogen concentration results, we confirmed that the experimental and numerical analysis results predicted similar concentrations with a difference of approximately 15%.  
Figure 4: Grid independence test (H = 0.735 m, Time = 60 s).
## 3 Methods
### Spatial Autocorrelation Analysis  
In this study, spatial autocorrelation analysis was performed using ArcGIS pro software to investigate the spatial distribution characteristics of CES in urban parks in Macau. The analysis aimed to identify spatial patterns within CES evaluation data. Specifically, spatial relationships among urban parks were assessed across various CES dimensions. Specifically, Z-scores were calculated to detect significant clustering or dispersion among urban parks. Spatial analysis tools were applied to clearly identify distribution patterns of CES dimensions among urban parks. The results provided empirical support for park management optimization.
# Forgetful Forests: Data Structures for Machine Learning on Streaming Data under Concept Drift
## 5 Results  
This section compares the following algorithms: forgetful decision tree, forgetful random forest with bagging, forgetful random forest without bagging, Hoeffding Tree [8, 18], Hoeffding adaptive tree [12], iSOUP tree [9], train once, and adaptive random forest [14]. The forgetful algorithms use the hyperparameter settings from Section 4 on both the real datasets and the synthetic datasets produced by others.  
We measure time consumption, the accuracy and, where appropriate, the F1 score.  
The following settings yield the best accuracy for the state-of-the-art algorithms with which we compare:  
* Previous papers [8, 18] provide two different configurations for the Hoeffding tree. The configuration from [8] usually has the highest accuracy, so we will use it in the following experiment: \(split\_confidence=10^{-7}\), \(grace\_period=200\), and \(tie\_threshold=0.05\). Because the traditional Hoeffding tree cannot deal with concept drift, we set \(leaf\_prediction=Naive\)\(Bayes\)\(Adaptive\) to allow the model to adapt when concept drift happens.
* The designers of the Hoeffding adaptive tree suggest six possible configurations of the Hoeffding adaptive tree, which are HAT-INC, HATEWMA, HAT-ADWIN, HAT-INC NB, HATEWMA NB, and HAT-ADWIN NB. HAT-ADWIN NB has the best accuracy, and we will use it in the following experiments. The configuration is \(leaf\_prediction=Naive\)\(Bayes\), \(split\_confidence=0.0001\), \(grace\_period=200\), and \(tie\_threshold=0.05\).
* The designers provide only one configuration for the iSOUP tree [9], so we will use it in the following experiment. The configuration is \(leaf\_prediction=adaptive\), \(split\_confidence=0.0001\), \(grace\_period=200\), and \(tie\_threshold=0.05\).
* For the train-once model, we will train the model only once with all of the data before starting to measure accuracy and other metrics. The train-once model is never updated again. In this case, we will use a non-incremental decision tree, which is the CART algorithm [7], to fit the model. We use the setting with the best accuracy, which is \(criterion=gini\), and no other restrictions.  
The designers of adaptive random forest provided six variant configurations of adaptive random forest [14]: the variants \(ARF_{moderate}\), \(ARF_{fast}\), \(ARF_{PHT}\), \(ARF_{noBkg}\), \(ARF_{stdRF}\), and \(ARF_{maj}\). \(ARF_{fast}\) has the highest accuracy in most cases that we tested, so we will use that configuration: \(\delta_{w}=0.01\), \(\delta_{d}=0.001\), and \(learners=100\).
### Construction of Varying Phase Difference in the Longitudinal Direction  
With reference to the generation of VOF based on phase modulation, vector beams with varying polarization during propagation in free space can be achieved by constructing a longitudinally varying phase difference between two superposed beams. To achieve this configuration, Bessel beams based on linear phase modulation are commonly used.  
An ideal Bessel beam can be described by  
\[E(r,\phi,z)=A_{0}\exp(ik_{z}z)J_{n}(k_{r}r)\exp(\pm il\phi) \tag{2}\]  
where \(J_{n}\) is an \(n\)th-order Bessel function, \(k_{r}\) and \(k_{z}\) are the radial and longitudinal components of the wave vector. The wave number \(k=\sqrt{k_{z}^{2}+k_{\perp}^{2}}\), where \(k_{\perp}\) denotes the transverse component of wave vector, including the radial and azimuthal components. Generally, the Bessel beam can be generated with the help of an axicon [39; 40; 41], or using computer holographic techniques [42; 43; 44; 45], to add a linear phase modulation at the initial optical field. The phase modulation can be described as  
\[\varphi(r)=\exp(-ik_{r}r)\exp(\pm il\phi) \tag{3}\]  
This phase modulation leads to a gradual convergence and produces an approximately nondiffracting region of \(z_{\rm max}=r_{\rm max}k/k_{r}\), where \(r_{\rm max}\) is the maximum radius of the phase mask. If \(l=0\), an approximately zeroth-order Bessel beam is generated at the finite nondiffracting region. As the wave number can be deemed to be a constant, the longitudinal component of the wave vector \(k_{z}\) varies with the transverse component. If two orthogonal-polarized Bessel beams, \(\mathbf{E}_{1}\) and \(\mathbf{E}_{2}\), are generated by the initial phase modulations with different radial wave vectors, a dynamic phase difference will appear during propagation in free space which can be expressed as  
\[\delta(z)=(k_{z2}-k_{z1})z \tag{4}\]  
Therefore, the vector Bessel beam (VBB) with spatially varying polarization during propagation is generated  
\[\mathbf{E}=\mathbf{E}_{1}+\mathbf{E}_{2}=E_{1}\mathbf{e}_{1}+E_{2}\exp[i\delta (z)]\mathbf{e}_{2} \tag{5}\]
## 3 Results and Discussions
### Structure Activity Relationship (SAR)  
The cautious investigation of the relation between structures and anticancer activities data of the test compounds reveals the following assumption about SAR: (i) \(N\)-methyl-3-aryl indazoles **5a-5j** show higher activity than 3-aryl substituted indazoles derivatives **3a-3j** against the tested cell lines HCT-16 and MDA-MB-231; (ii) additional, slight enhancement of the cytotoxic activity of compounds **5a-5j** over **3a-3j** can be attributed to the presence of the methyl group; (iii) the presence of electron withdrawing fluoro substitution at 4 th position of the phenyl ring **5c** is responsible for its superior anti cancer activity. In addition, electron releasing hydroxy group **5i** and \(N\),\(N\)-dimethylamide group **5j** at para position of the phenyl ring exhibited good anti-cancer activity. Hence, from the results of above anticancer activity, it can be concluded that diverse structural requirements are essential for a compound to be active against different cancer targets.
# Machine Learning Reconstruction of Wyrtki Jet Seasonal Variability in the Equatorial Indian Ocean
## 2 Data and Methods
### Methods
The root mean square error (\(RMSE\)) is a commonly used metric for evaluating model performance. It measures the fit between model estimates and true values. The formula for calculating the \(RMSE\) is shown in Equation (3). \[RMSE=\sqrt{\sum\limits_{i=1}^{n}{{({y}_{i}}-{{\hat{y}}_{i}})}^{2}} \tag{3}\]  
A smaller \(RMSE\) indicates that the model's predictions are closer to the observed values, reflecting higher reconstruction accuracy. \(RMSE\) is a crucial tool for model evaluation and is widely used in various model validation and comparison studies. The coefficient of determination (\(R^{2}\)) is used in this study to assess the correlation between the model-reconstructed and OSCAR-observed current speeds, as shown in Equation (4). \[R^{2}=1-\frac{{\sum\
olimits_{i=1}^{n}}{{({y}_{i}}-{{\hat{y}}_{i}})}^{2}}{{ \sum\
olimits_{i=1}^{n}}{{({{\hat{y}}_{i}}-{\overline{y}})}^{2}}} \tag{4}\]  
Generally, \({\overline{y}}\) represents the mean value of OSCAR-observed current speed, a higher \(R^{2}\) (closer to 1) indicates a better-fitting model with more accurate reconstruction results. In order to calculate the sea surface wind stress curl, the zonal and meridional components (\(\tau_{x}\) and \(\tau_{y}\)) of the wind stress are derived using the volume equation [66]:  
\[\tau=\left({\tau_{x},\tau_{y}}\right)=\rho C_{D}v(\mu,v) \tag{5}\]  
where \(\mu\) and \(v\) represent the east and north surface wind speed at 10 m. \(\rho\) is the density of the air and the drag coefficient \(C_{D}\) depends on the height of the wind measurement. Stokes theorem is then applied to obtain the vertical component of _curl_\(z(\tau)=\
abla\times\tau\), namely the sea surface wind stress curl. Given the significant impact of input variable selection on model performance, nine distinct combinations of sea surface parameters were employed as input features for both XGBoost and LightGBM models to estimate the zonal velocity of the WJ in the equatorial Indian Ocean. In this study, the performance of the XGBoost models was evaluated using statistical metrics, including the root mean square error (\(RMSE\)), mean absolute error (\(MAE\)), and the coefficient of determination (\(R^{2}\)).The validation of OSCAR surface velocity data with RAMA data, the XGBoOST machine learning model algorithm flow chart, the correlation analysis between input variables and OSCAR data, and the feature importance analysis are presented in Appendix A.
# Demand Flexibility: A Review of Quantification Methods, Models, and Required Data
## 3 Results
### Data Inputs
#### 3.5.3 Building Characteristics and Models  
Researchers frequently included data regarding building characteristics such as heat capacity or square footage [3; 14; 15; 17; 24; 28; 31; 32; 33; 34; 35; 36; 37; 38; 51; 60; 62]. Building operating characteristics can be used to illustrate building responses to flexibility activities. For example, understanding the characteristics of building thermal capacity directly affects flexibility estimations related to heating, ventilation, and air conditioning (HVAC) operations.  
Other commonly used types of data relate to building occupants. Naturally, the presence and number of occupants in a building determine how they may directly or indirectly interact with energy systems. Direct interaction could be conducted through appliance and lighting usage, whereas indirect interaction can be viewed in terms of body heat emissions that can increase the heat load within a confined space. Occupancy data was included in [32; 36; 59; 69]. This can be collected using data from occupancy sensors, which may raise privacy concerns. Alternatively, occupancy can be estimated based on non-intrusive load monitoring and load disaggregation, where detecting the usage pattern of certain appliances can indicate the number and/or types of occupants present, e.g., different age groups. Other studies used regional population statistics [10; 32; 54], which may indicate certain trends or behavioral patterns in energy consumption.  
A few studies informed their calculations through novel datasets similar to building characteristics, including benchmarking data [68], building locations [38], and regional building stock statistics [12; 35; 59; 60]. Each dataset provided broad statistics regarding the building stock of a region under study.
###### Abstract  
In our experiment, the effect of electrical and CO\({}_{2}\) stunning on pork meat quality attributes was studied. A total of thirty PIC337 female pigs were allocated to two equal groups which were stunned electrically (50 Hz, 210 V, 2.6 A, 15 s) or by CO\({}_{2}\) (85 V% CO\({}_{2}\), 15 V% O\({}_{2}\), 90 s) and slaughtered at a commercial slaughterhouse. For 24 h post mortem, the carcass m. gluteus medius (GM) was used to evaluate meat quality attributes such as pH, color, hardness, water holding capacity (WHC), and total pigment content, and meat classification was also carried out. The CO\({}_{2}\) stunning resulted in muscle with significantly lower pH (_p_ < 0.01) and poorer water holding capacity (_p_ < 0.05) compared to electrical stunning. The GM of pigs stunned electrically showed significantly increased lightness (L*) and redness (a*) (_p_ < 0.05) compared to CO\({}_{2}\)-stunned. The time course of development of rigor mortis was similar for both stunning methods. Pale, soft, exudative (PSE) or dark, firm, dry (DFD) meat defects were not observed. Based on total pigment content, stunning methods did not significantly affect the amount of removable blood.  
p 2024 14 10410 10.10  
electrical parameters, it causes instantaneous but reversible loss of consciousness [13]. During 'head-body' stunning, current passes through the brain and heart. Similar to 'head-only' stunning, two electrodes are placed on the head and a third one on the chest. The electrical current disrupts the electrical activity of the brain causing membrane depolarization or hyperpolarization of the neurons inducing epileptic seizure and unconscious [14]. After the electric shock, neurological and metabolic states are normalized and consciousness returns.  
Early studies pointed out that electrical stunning leads to physiological stress in pigs causing bone fractures and hemorrhages due to strong muscle contraction [15; 16]; furthermore, rapid decrease in pH and WHC compared to CO2 stunning was also observed [9; 17; 18]. In the case of CO2 stunning, the occurrence of PSE meat defects and petechiae reduced and meat quality markedly improved [8; 10; 19]. Electrical stunning may increase the rate of glycogen metabolism causing accelerated acidification and leading to PSE meat [20]. The stunning method may affect meat quality such as color, texture, and WHC as main attributes of meats that determine their suitability for further processing and storage and furthermore consumer acceptance as well. These are also important from an economic point of view regarding losses due to undesirable color changes, early deterioration, or due to weight loss during cooling [21].  
In our study, the meat quality attributes of pork from pigs stunned electrically were evaluated and compared with those of pigs stunned by CO2. The experiment hypothesized that the incidence of meat defects resulting from electrical stunning could be reduced using CO2. Furthermore, pH as an indicator of meat quality is modeled in relation to temperature allowing us to predict meat defects.