esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
### Aged Hydrocarbons  
Spilled hydrocarbons start degrading aerobically, and the more easily degradable compounds, such as aliphatic hydrocarbons, break down over time, whereas the fraction of more stable molecules such as aromatics, increases. Former industrial, polluted sites (known as \"brownfields\") can continuously pollute the surrounding environment [16] from seeping reservoirs, and natural degradation is slow. There are different techniques that can be applied to deal with such hydrocarbon contaminations. Excavating and incinerating the soil ex situ in a treatment plant is a common, but costly, approach, particularly when the affected soil volume is large. Classic soil remediation methods include \"_ex excavation, solidification, vitrification, electrokinetic, soil washing, flushing and exidoreduction_\" and \"_have shown to be effective in small areas but they need special equipments, are labor intensive, energy-cost and highly-expensive_\" [17]. There exist several in situ techniques for hydrocarbons based on oxidation [18]. In general, a problem with aged hydrocarbons is their high viscosity, which makes dispersants ineffective [19].  
Phytoremediation, the use of plants and their associated microbiota to remove, contain, or render harmless environmental contaminations, has been shown to be effective for hydrocarbon-contaminated soils [13; 17].  
Biological methods for in situ hydrocarbon degradation are bioremediation [20; 21; 22] biostimulation [23] and bioaugmentation [24]. \"_Bioremediation is based on the capacity of microorganisms to degrade organic pollutant compounds, such as hydrocarbons_\" [25]. Bioaugmentation \"_is defined as the addition of pre-grown microbial cultures to perform a specific remediation task in a given environment_\" [26]. Biostimulation consists of the activation of native soil microorganisms through the addition of nutrients. Moisture and nutrient availability are among the most critical factors limiting oil biodegradation [26]. One established technique for the remediation of hydrocarbon-contaminated soil is landfarming. It is an on-site technique. \"_Landfarming involves the degradation of soil hydrocarbons through the activation of natural microorganisms by the incorporation of inorganic fertilizers, water and periodic filling to mix and aerate the soil. The presence of larger number of appropriate microorganisms and the synergistic effect of fungi and bacteria is a key to successful bioremediation_\" [27].  
Another possibility is advanced oxidation, e.g., with the oxidants hydrogen peroxide, Fenton reagents, potassium permanganate, and sodium persulfate [28].  
For an overview of hydrocarbon-remediation techniques, see [29].
# The Importance of Context Awareness in Acoustics-Based Automated Beehive Monitoring
## 3 Results and Discussion  
### Urban Sound Effects  
As urban apiaries burgeon, common ambient sounds, such as car horns and train rail squealing, will become nuances for audio-based hive monitoring applications. As such, it is crucial to understand the impact that these sounds can have on the extracted audio features. For our study, sounds of a nearby train passing by was a common noise that contaminated the audio recordings. Figure 6, for example, shows the spectrogram measured from a 15-minute audio recording when the train was not passing by (subplot a), as well as when the train passed by (subplot b). As can be seen, the train rail squealing caused a significant disturbance in the audio around 64 Hz and higher frequencies such as 250 Hz, which is crucial for beehive dynamics.  
Next, we explore the effects that such urban sounds may have on MFCCs. Figure 7 shows the histograms of the distribution of the first two coefficients (subplots and and b, respectively) across time instances in which the train sound was present versus absent. As can be seen, the train rail squealing sound has a substantial effect on the distribution of the MFCCs; in the case of the first coefficient, it increases the value by around 50 db. Such effects may result in erroneous decisions by automated systems that rely on the first three mel-frequency cepstral coefficients, such as the systems described in [30; 31; 3]. Figure 8 depicts the temporal changes seen in the first MFCC when the train passes by (see subplot b around the 5 min time). For systems that rely on average MFCCs over time, such increases will likely result in errors.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Layer** & **Description** \\ \hline Convolution & 64 filters, kernel size = \(3\times 3\), strides = \(1\times 1\) \\ Batch normalization & momentum = 0.99, gamma = 1.0, epsilon = 0.001 \\ Max-pooling & pool size = \(3\times 3\), strides = \(2\times 2\) \\ Dropout & 0.25 rate \\ \hline Dense & 128 units \\ Batch normalization & momentum = 0.99, gamma = 1.0, epsilon = 0.001 \\ Dropout & 0.5 rate \\ \hline Dense & 1 unit \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._Lastly, Figure 9a,b depict the DWT detail (D1-D10) and approximation (A10) components extracted from the clean and noisy segments, respectively. As can be seen, the detailed coefficients are particularly affected by this urban noise source, and would likely result in errors for systems that rely on DWTs, such as the ones described in [29; 31].  
Figure 6: Spectrograms of cases where urban noises (e.g., train rail squealing sounds) are (**a**) not present and (**b**) present.  
Figure 7: Comparison of the changes in distribution of the (**a**) first and (**b**) second mel frequency cepstral coefficient with and without the presence of train rail squealing sounds.
## 2 Materials and Methods
### Case Study of the Selected Area: The Skikda Region
#### 2.2.2 System Components
This approach helps maintain frequency stability during load fluctuations and enables the inverter to operate in grid-forming mode, generating voltage and frequency references in off-grid conditions and thereby ensuring a stable power supply to all connected loads without relying on an external grid or batteries. To address the reactive power needs of the pump's inductive motor, we included power factor correction--either through the motor drive or with a dedicated capacitor bank or static reactive compensator--to avoid excessive reactive power consumption, energy losses, or voltage drops. These enhancements maintain a focus on renewable integration while improving the system's technical feasibility and reliability, particularly in applications such as irrigation, where service continuity is essential.
## 7 Simulation Environment
### Network throughput calculation  
The Network throughput is defined as that the maximum packets received at particular time period. Figure 13 compares the throughput performance of the nodes for various protocols namely GA-LEACH, MW-LEACH, CSOGA, and MWCSGA. For this scenario, the simulation experiment is carried using 100 nodes. The throughput performance of MWCSGA protocol is 679.82 Kbps. Whereas the earlier protocols such as GA-LEACH, MW-LEACH, and CSOGA protocols produce energy efficiency as 190.53 Kbps, 354.37 Kbps,  
Figure 11: End to end delay calculation.  
Figure 12: Packet drop calculation.  
and 551.35 Kbps, respecitvely. According to analyzes it proves that the performance of MWCSGA protocol performs better when compared with the others.
## 2 Materials and Methods
### Model Scenarios
#### 2.2.1 Near-Critical Point Behavior  
Given the challenges posed by the near-critical point conditions, special care was taken to evaluate how well each machine learning model captured the phase behavior in this region. Near the critical point, small changes in pressure or temperature can cause dramatic shifts in phase behavior, making this a particularly challenging test for machine learning models. The models' ability to accurately predict the onset of the two-phase regions and the correct composition of each phase was closely examined.  
2.2.2 Phase Behavior Modeling and Validation  
The performance of the machine learning models was compared against a physical model using the Peng-Robinson equation of state for the phase equilibrium calculations. The predicted phase compositions and tie-lines were validated against the results from the physical model. The evaluation criteria focused on accuracy in standard and near-critical conditions.  
Figure 1: Model scenarios: (**a**) Direct Model, (**b**) Hybrid Model 1, and (**c**) Hybrid Model 2.
# Analysis of Subharmonic Oscillation and Slope Compensation for a Differential Boost Inverter
## 7 Conclusions  
This paper has focused on the subharmonic oscillation boundary leading to bubbling phenomenon in a single-phase DC-AC differential boost inverter with a linear resistive load. This work has provided a comprehensive study of the system and stability problems of the system were discussed in order to determine stabilizing parameter space. This facilitates convenient selection of parameter values to avoid distortion due to subharmonic oscillation instability in some intervals of the sinewave voltage reference. Therefore, the results are useful for practical design of DC-AC inverters to ensure a stable operation and hence maintain a high power quality and ensuring low and acceptable values of THD. By using time domain waveforms computed from the circuit-level switched model of the system, it was shown that the differential boost inverter could exhibit subharmonic oscillation instabilities at the fast switching scale. Stable and unstable zones of operation, critical parameter values and stability boundaries have been determined. Floquet theory combined with quasi-static approximation has been used resulting in accurately locating the critical values of the system parameters. The theoretical predictions are in perfect agreement with the results obtained from numerical simulations performed on the circuit-level switched model of the inverter. The methodology presented in this study can be applied to other inverter topologies.  
Figure 10: Critical phase angles in [\({}^{\circ}\)] defining the SO interval in terms of the proportional gain \(k_{p}\) for different values of the ramp amplitude \(V_{M}\).  
**Author Contributions:** Conceptualization, A.E.A.; methodology, A.E.A. software, A.E.A.; validation, M.A.-N.; formal analysis, A.E.A.; investigation, A.E.A.; resources, M.A.-N.; data curation, R.H.; writing--original draft preparation, A.E.A.; writing--review and editing, M.A.-N. and M.H.; visualization, M.A.-N., R.H. and M.H.; supervision, M.A.-N.; funding acquisition, M.A.-N. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work has been sponsored by the Spanish Agencia Estatal de Investigacion (AEI) and the Fondo Europeo de Desarrollo Regional (FEDER) under grants DPI2017-84572-C2-1-R. [PERSON] and [PERSON] acknowledge financial support from the Researchers Supporting Project number (RSP-2020/150), King Saud University, Riyadh, Saudi Arabia.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## Results and Discussion  
### Headspace Composition  
To analyse the headspace composition, solid-phase microextraction was used (HS-SPME). A more complementary headspace profile was attained using two fibres of different polarities: divinylbenzene/carboxylate/polydimethylsiloxane (DVB/CAR/PDMS) and polydimethylsiloxane/divinylbenzene (PDMS/DVB). In the FrDV headspace (HS-FrDV), 90.99% of VOCs were identified in total with DVB/CAR/PDMS fibre and 91.74% with PDMS/DVB fibre. In the DrDV headspace (HS-DrDV) with both fibres, 100% of VOCs were identified. Aromatic compounds were dominant in both fresh and air-dried HS samples--55.25% (DVB/CAR/PDMS fibre) and 55.74% (PDMS/DVB fibre) in HS-FrDV and 77.44% (DVB/CAR/PDMS fibre) and 69.32% (PDMS/DVB fibre) in HS-DrDV. Benzaldehyde was the most abundant in fresh samples (26.07%; 27.34%) and decreased 2.7-3.7 times after the drying, and the second-most abundant compound was aromatic aldehyde 2-phenylbut-2-enal (24.20%; 21.72%), which could not be found in HS-DrDV(it probably evaporated). On the other hand, benzyl alcohol was the most abundant in air-dried samples (57.92%; 52.48%) (Table 1), as was found in the research of other algae after drying [11; 12]. The second-most abundant group of compounds belonged to aliphatic compounds: unsaturated hydrocarbons in HS-FrDV (30.77%; 29.28%) and saturated hydrocarbons in HS-DrDV (16.43%; 22.85%). C\({}_{11}\)-hydrocarbons (well-known pheromones) dicyclopterpene C\({}^{\prime}\) (8.65%; 9.34%) and dicyclopterpene D\({}^{\prime}\) (7.78%; 7.12%) with the highest abundance in
## 4 Experimental Results
### SACT Statistic Review
With DMU48 the mean is between the median and the mode indicating that the frequency distribution is close to a symmetrical distribution without taking the atypical value of 3988 (see Figure 15). With DMU50 the mean is greater than the median and the mode but with very close values, indicating that the frequency distribution is close to a symmetrical distribution without taking the atypical value of 3842 (see Figure 15). The shortest time for TA benchmarks was for TA22 and the longest time was for TA30. The time for DMU benchmarks with the smallest relative error was for DMU08 and the time \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Problem**} & **Optimum Units of Time** & **Better Units of Time** & **Worse Units of Time** & **Mean Units of Time** & **\%RE** & **\(\sigma\)** & **t Sec** & **Median** & **Mode** \\ \hline LA36 & 1268 & 1268 & 1281 & 1275 & 0 & 6.1 & 371.6 & 1278 & 1268 \\ LA37 & 1397 & 1397 & 1399 & 1397 & 0 & 0.9 & 230 & 1397 & 1397 \\ LA38 & 1196 & 1196 & 1245 & 1216 & 0 & 20.6 & 55 & 1218 & 1196 \\ LA39 & 1233 & 1233 & 1237 & 1234 & 0 & 1.8 & 54 & 1233 & 1233 \\ LA40 & 1222 & 1222 & 1234 & 1227 & 0 & 3.4 & 2245 & 1228 & 1229 \\ TA01 & 1231 & 1231 & 1231 & 1231 & 0 & 0.0 & 328 & 1231 & 1231 \\ TA02 & 1244 & 1244 & 1244 & 1244 & 0 & 0.0 & 501 & 1244 & 1244 \\ TA03 & 1218 & 1218 & 1223 & 1221 & 0 & 2.3 & 4373 & 1221 & 1223 \\ TA04 & 1175 & 1175 & 1175 & 1175 & 0 & 0.0 & 301 & 1175 & 1175 \\ TA05 & 1224 & 1224 & 1231 & 1229 & 0 & 2.8 & 2218 & 1230 & 1230 \\ TA06 & 1238 & 1238 & 1240 & 1239 & 0 & 0.8 & 5090 & 1239 & 1239 \\ TA07 & 1227 & 1227 & 1228 & 1228 & 0 & 0.4 & 99 & 1228 & 1228 \\ TA08 & 1217 & 1217 & 1224 & 1220 & 0 & 3.0 & 1986 & 1218 & 1218 \\ TA09 & 1274 & 1274 & 1281 & 1277 & 0 & 3.6 & 1433 & 1274 & 1274 \\ TA10 & 1241 & 1241 & 1253 & 1245 & 0 & 4.6 & 3830 & 1244 & 1244 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: SACT results, medium benchmarks JSSP (\(15\times 15\)). with the largest relative error was for DMU49. The shortest time for YN benchmarks with the smallest relative error was for YN3 and the longest time with the biggest relative error was for YN2. Figure 16 presents the average of 5 tests of the relative error obtained for \(15\times 15\) problems. It can be noted that the maximum RE occurs in LA38 and was not greater than 1.7. The most difficult problem was LA40, however 5 trials show that the average RE was not greater than 0.5. For the other \(15\times 15\) problems, the maximum average RE is in the range \(0\leq\text{RE}\leq 0.6\). The problems for which it was easiest to find the global optimum were TA01, TA02, and TA04. In all the tests, they had RE = 0, which is why they are not present in Figure 16.
## 2 The Reliability Based Rescue Resource Scheduling Model
### Analysis of Travel Time and Reliability
#### 2.2.1 Link Travel Time and Intersection Delay  
For a road section, its travel time is mainly affected by link traffic flow and intersection delay. The travel time \(t_{mn}\) of rescue vehicles on a link mainly includes two parts: the travel time of vehicles passing through link \(e_{mn}\) under free-flow conditions, \(t_{mn}^{0}\), and the delay time on the link \(e_{mn}\) due to vehicle speed fluctuation, \(\xi_{mn}\). Thus, the travel time of a link can be expressed as the following:  
\[t_{mn}=t_{mn}^{0}+\xi_{mn} \tag{1}\]  
where delay time \(\xi_{mn}\) is a random variable that is related to the average speed of vehicles, vehicle density and time headway.  
Rescue vehicles have priority when passing through the intersection, but they may face delay due to the influence of social vehicles. Intersection delay time \(d_{v}\) can be divided into two parts--the dissipation time of vehicles driving in front of rescue vehicles and the time of rescue vehicles passing through the intersection [35]:  
\[d_{v}=d_{v}^{0}+\psi_{v} \tag{2}\]  
where \(d_{v}^{0}\) refers to the time when rescue vehicles pass through the intersection without delay, and \(\psi_{v}\) represents the dissipation time of other vehicles driving in front of rescue vehicles, which is related to dissipation speed, queuing length and driving distance of the queuing vehicles. Since the arrival rate of traffic flow at the intersection is a random distribution, \(\psi_{v}\) can be considered as a random variable.
# Combining Contrastive Learning with Auto-Encoder for Out-of-Distribution Detection
## 2 Related Work
### OOD Detection
#### 2.1.2 Unsupervised Methodology  
Unsupervised OOD detection methods include generative-based methods, contrastive learning models, and other self-supervised learning strategies. A notable observation highlighted by [29] is the challenge faced by generative models like the variational auto-encoder (VAE), auto-regressive models, and flow-based methods, which tend to assign unexpectedly high likelihood scores to OOD samples. To alleviate the problem in generative models, [30] employs a likelihood ratio to model the background and foreground information when semantically irrelevant information is added to the input distribution. The authors of [31] found a strong tie between the OOD samples' complexity and likelihood values. In the cases where a simple OOD sample is fed into the model, the universal compressor assigns a high probability to it and effectively corrects the high likelihood, wrongly given by the learned model. [PERSON] and [PERSON] [32] proposed a novel frequency-regularized learning framework for OOD detection, which incorporates high-frequency information into training and guides the model to focus on semantically relevant features. [PERSON] et al. [33] modified the training objective for generative models by incorporating the approximate mass, which smooths the learned distribution in the vicinity of training in-distribution data. In recent research, self-supervised learning (SSL) approaches have emerged. [PERSON] et al. [34] trained the model to learn representation by predicting image rotations, which function as an auxiliary task in SSL. [PERSON] et al. [35] introduced the integration of SSL techniques to enhance model robustness and uncertainty estimation. This was achieved by combining SSL methods with various auxiliary tasks [36]. Furthermore, [PERSON] and [PERSON] [37] leveraged unsupervised learning to maximize the discrepancy between two parallel classifiers when exposed to OOD samples. [PERSON] et al. [17] took advantage of an extra small pool of labeled OOD samples to train its deep semi-supervised OOD detector (deep-SVDD) based on the idea that the entropy of the latent distribution for normal data should be lower than that of the anomalous distribution. [PERSON] et al. [38] proposed a principled yet simple approach to estimating KL-divergence to detect OOD samples according to the KL-divergence measure. Unsupervised OOD detection methods continue to evolve and hold significant promise in addressing the challenges of identifying and handling OOD samples in diverse applications.
# Risk Assessment for Linear Regression Models in Metrology
## 3 Results and Discussion
### Graphical Risk Analysis
#### 3.1.2 Behaviors of Global Consumer and Producer Risk along the Guard Band Axis
1.3 Behavior of Global Consumer's and Producer's Risk with the Changes in Measurement Uncertainty \(\upmu\) of a Future Inspection Process  
The behavior of the global producer's risk \(\mathrm{R_{P}}\) and the global consumer's risk \(\mathrm{R_{c}}\) across all models were tested for three different assumed values of measurement uncertainty \(\upmu\) of a futured inspection process: for \(\upmu=\upmu_{0}/2\), \(\upmu=\upmu_{0}\), and \(\upmu=2\upmu_{0}\). With the increase in measurement uncertainty \(\upmu\), the global consumer's risk \(\mathrm{R_{C}}\) also increases (Figure 11). Additionally, with the increase in the measurement uncertainty value \(\upmu\), the producer's risk \(\mathrm{R_{P}}\) also rises (Figure 12). Figure 11: Behaviors of global consumer’s risk along the moderate scale with the changes in measurement uncertainty \(\upmu\), \(\mathrm{r}=1\): (**a**) Model M1; (**b**) Model M2; (**c**) Model M3; (**d**) Model M4. Figure 12: Behaviors of global producer’s risk along the moderate scale with the changes in measurement uncertainty \(\upmu\), \(\mathrm{r}=1\): (**a**) Model M1; (**b**) Model M2; (**c**) Model M3; (**d**) Model M4. It is evident that, in addition to the range of the tolerance interval to the occurrence of anomalies in the behavior of the global producer's risk \(\mathrm{R_{P}}\), measurement uncertainty \(\mathrm{u_{m}}\) also has an impact. Deviation from the parabolic curve becomes more pronounced (Figure 12c,d). Graphs can completely alter their behavior, as is the case for model M2. When \(\mathrm{u_{m}}=2\mathrm{u_{0}}\), the graph of the global producer's risk \(\mathrm{R_{P}}\) is a parabola with a downward opening (Figure 12b). For \(\mathrm{u_{m}}=2\mathrm{u_{0}}\), the regression line is not outside the tolerance interval or the acceptance interval in any of the mentioned cases. To avoid anomalies in the behavior of the risk curves for \(\mathrm{R_{P}}\), it is necessary to expand the ranges of tolerance intervals. For model M2, this range should be set to at least \(\Delta\Gamma=4.4\mathrm{u_{0}}^{\mathrm{CUM}}\); for model M3, to \(\Delta\Gamma=7.4\mathrm{u_{0}}^{\mathrm{LRA}}\); and for model M4, to \(\Delta\Gamma=7.7\mathrm{u_{0}}^{\mathrm{LRA}}\). This prevents anomalies in the graphs for all values of \(\mathrm{r_{k}},\mathrm{k}=1,2,\ldots,\mathrm{n_{3}}\). According to equation (32), the expression for conformance probability \(\mathrm{p_{C}}\) is independent of the measurement uncertainty \(\mathrm{u_{m}}\) of a future inspection process. Therefore, the graphs for conformance probability for all tested values of measurement uncertainty \(\mathrm{u_{m}}\) are as shown in Figure 6, and for the 3D case, as shown in Figure 10.