esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
The waste that is generated from power plants that use oil as a source of fuel contains generally a high percentage of carbon, alkaline-earth metals, and metals like Co, Fe, and Mn [1]. V, Pb, Ni, Cd, and Cr are also present in this fly ash, and for this reason it is classed as toxic waste [2, 3, 4, 5]. Numerous methods for fly ash processing exist in order to reduce the volume and impact of the waste [6]. Fly ash processing can usually be divided into a number of categories, including storage, chemical processing, and thermal processing, like incineration, pyrolysis and gasification [7].  
Conventional incineration or combustion of waste has long since held a negative public perception for being environmentally unfriendly. In comparison to incineration, newer technologies like pyrolysis and gasification are preferred [8]. Plasma treatment differs from conventional thermal conversion methods because of the addition of high density energy into the system by means of the plasma torch or plasma arc [9]. During conventional processes, all the energy used for conversion is taken from the feedstock itself. Plasma energy addition thus allows for increased operating temperatures, higher conversion rates and better control of the overall process. For these reasons, plasma technology becomes a promising technique for the treatment of most toxic wastes, such as contaminated hospital wastes, radioactive wastes, and fly ash [10, 11, 12]. One of the plasma processing inconveniences is the volatility of toxic elements during the treatment. For this reason, the volatility of heavy metals needs be controlled during the plasma treatment of waste.  
During the treatment of wastes by thermal plasma, the concentrations of toxic elements, such as heavy metals and radioelements, in the gas phase were measured using optical emission spectroscopy method [13]. Furthermore, many computer codes were developed to simulate the volatility of toxic elements present in the waste during their plasma treatment. For example, a computer code was developed to simulate the volatility of heavy metals during fly ash treatment using a plasma arc [14]. This same code was used to study the effect of different parameters, such as bath surface temperature, plasma current, and partial pressure of oxygen, on the volatility of radioelements during thermal plasma treatment of radioactive waste [10; 15; 16].  
This study has two objectives; the first one is to treat the waste collected from power plants in Saudi Arabia using our new plasma furnace. This treatment consists of the separation of carbon from metals presents in the waste. The second objective of this work is to control the volatility of toxic elements, such as lead and arsenic, during the treatment using a computer code.
## 2 Background and Related Work
### Ensemble Fuzzing  
The traditional approaches of parallel fuzzing run multiple instances of a single fuzzer in parallel to speed up the fuzzing process. However, this type of method can result in each fuzzing instance having the same characteristics, meaning they tend to cover the same branches of the tested program. As a result, traditional parallel fuzzing is limited in effectively covering those branches that are difficult to reach with a single fuzzing instance.  
In reference [15], it is discovered through experimentation that different fuzzers tend to explore different parts of the target program. Based on this finding, the first ensemble fuzzing framework named EnFuzz was proposed. EnFuzz adopts multiple heterogeneous fuzzers for each parallel fuzzing instance rather than using the same fuzzer across all instances. And it will regularly synchronize all fuzzers' seed corpora. Thus, it can fully leverage the strengths of every fuzzer it uses. The evaluation of EnFuzz shows that the ensemble fuzzing framework, which parallels multiple heterogeneous fuzzers, can indeed cover more branches of the target program than the traditional parallel fuzzing framework.  
Building upon this work, CUPID [34] proposes a combined optimization method for heterogeneous fuzzers used in ensemble fuzzing, further improving testing efficiency. It provides a method for obtaining a combination of fuzzers that are universally applicable to various tested programs through pretraining with every candidate fuzzer singly. The fuzzer combination it offers is more complementary than what is manually provided by EnFuzz, meaning that they tend to explore different program spaces, thereby achieving better overall efficiency.  
However, the ensemble fuzzing frameworks lack the mechanisms for task division. Whether in EnFuzz or CUPID, each paralleled fuzzer adopts the seeds synchronization method similar to that of AFL, causing the seed corpora corresponding to each of them to be highly analogous. Meanwhile, the seed mutation strategies of different fuzzers are also similar. As a result, there is a significant amount of duplicate work in current ensemble fuzzing frameworks, resulting in a reduction in the overall fuzzing efficiency.
## 2 Case Study
### Construction Procedure  
The construction work program of the embankment sites involved work platform preparation, installation of PVDs and the compaction of filler materials. The topsoil was cleaned and removed for the preparation of the work platform. The work platform preparation required the topsoil to be cleaned and removed. The first sand layer cushion of 0.5 m consisted of two layers of uniform sand, with the first layer of compacted sand of 0.3 m, compacted with compacting machines. Prior to the installation of PVDs, 1500 kN/m of axial stiffness was applied to the first layer of sand cushion with the use of a non-woven geotextile. They were installed with triangular patterns on a hollow steel mandrel and were driven into the ground by a stitcher attached to an excavator carrier. The outside and inner diameter of the mandrel were 127 mm and 110 mm, respectively, while the anchor shoes were discs with a diameter of about 127 mm. The spacing of PVDs were such that the centres were 1m apart, while the proposed depth was 15 m. This was aimed to assist the acceleration of the consolidation process by ensuring that groundwater discharge is effective. The second layer of 0.2 m thick sand was placed over the geotextile in order to allow the progression of a consolidation process. The 1m thickness of the embankment was constructed as non-homogenous soil was adopted as filler materials and compacted with compaction machinery. The geometry of the embankment had a crest width of 11 m with a 1V: 1H side-slope ratio.
## 3 Results and Discussion
### The Comparison of CSpredR with Other Prediction Models  
To comprehensively evaluate the performance of CSpredR, we conducted a comparative analysis with existing mRNA subcellular localization prediction methods, DM3 Loc and Clarion. As shown in Figure 4, CSpredR demonstrates superior performance across multiple key metrics. Firstly, CSpredR excels in accuracy, indicating its enhanced precision in predicting mRNA subcellular localization. This advantage stems from the model's optimized design and training process, further validating its efficiency in this field. Secondly, CSpredR outperforms other methods in Average precision. The high Average precision reflects its ability to accurately distinguish true localizations from incorrect predictions, thereby improving the reliability and practicality of the results.  
Additionally, CSpredR also shows advantages in metrics such as One-error, Ranking loss, and Hamming loss. Its low One-error value indicates a reduced misclassification rate, while its performance in Ranking loss and Hamming loss highlights its stability and accuracy in multi-label classification and true label prediction tasks.  
In summary, compared to DM3 Loc and Clarion, CSpredR demonstrates significant performance advantages in accuracy, Average precision, and other key metrics. These results not only confirm the reliability and efficiency of CSpredR but also support its application in bioinformatics and biomedical research.
# On Managing Knowledge for MAPE-K Loops in Self-Adaptive Robotics Using a Graph-Based Runtime Model
## 3 Extending the MAPE Scheme: The DSR
### The DSR as the Place for Coordinating MAPE Loops  
Figure 2 exemplifies how the DSR is employed by the software architecture for coordinating the activity of their agents. The internal coordination of the MAPE loop involving the high-level deliberative module is achieved by annotating in the arcs linking the robot with tasks such as navigating, detecting, or picking up. In the example, all launched tasks were successfully reached. But this is not necessarily true for all trials. In Figure 3, the robot detects the roll container but, then, the agent in charge of detecting people modifies the DSR as several people surround the container. The situation is unsafe, and one analyzer agent annotates as tentative action to abort the picking up task. The procedure describing how this recommendation is generated is detailed in Section 6. But what is relevant here is that the decision maker reacts to this new reality by changing the course of action and aborting the task.  
Figure 2 also illustrates how two loops interacts using the DSR. The picking up task involves several agents as they need to check the roll container position and approach it before picking it up. Checking the roll container position and approaching activities are coordinated using the information in the DSR, and the internal state of this task is also informed to the MAPE loop involving the high-level deliberative module. Thus, if a problem forces the picking up task to be aborted, the information is also managed by the deliberative module for adapting the course of action and, for instance, asks the robot to navigate to a new picking area for picking up a different roll container. Being one of the major novelties of this paper, Section 4 provides more information about how our MAPE loops interact using the information stored in the DSR. In this Section 4, we describe how the MAPE-K framework is organized and how the decision makers synchronize their plans or modify them according to changes in the context.  
Figure 2: Evolution of the DSR for starting a picking up task. The **robot** at time \(t\) has just reaching the **picking area 1** and the decision maker finishes **navigating** and starts the **detection** of the roll container. Different agents interact using the information stored in the DSR for launching and stopping their activities. The DSR at time \(t+5\) includes the **roll container** and shows that the **robot** starts **navigating** for approaching to the container (see text). In the graphs, dark blue and bold text are used to highlight changes from one step to the next.
## 2 Experimental Analysis  
Conventional solid-state methods were utilized to synthesize samples of (Mg\({}_{0.6}\)Zn\({}_{0.4}\))\({}_{1-y}\) Ni\({}_{y}\)TiO\({}_{3}\) (y = 0.01-0.2) from high-purity oxide powders (\(>\)99.9%): MgO, NiO, and TiO\({}_{2}\). Raw MgO powder was fired at 600 \({}^{\circ}\)C/1h to avoid moisture retention due to its hygroscopic characteristic. (Mg\({}_{0.6}\)Zn\({}_{0.4}\))\({}_{1-y}\)Ni\({}_{y}\)TiO\({}_{3}\) (y = 0.01-0.2) compounds were made from mixed oxides stoichiometrically and ground in distilled water for 24 h in a ball mill with agate balls. The mixed solution was dried in the oven and calcined at 1100 \({}^{\circ}\)C for 4 h in a high-temperature furnace. A 3.5 wt% of a 12% PVA solution as a binder (Polyvinyl alcohol 500, Showa) was added into the calcined powder, granulated by sieving through a 100 mesh, and pressed into pellets, 11 mm in diameter and 5 mm in thickness, under 200 MPa pressure. The pellets were sintered at temperatures ranging from 1125 to 1250 \({}^{\circ}\)C for 4 h in the atmosphere. The heating and cooling rates of a high-temperature furnace were both set at 10 \({}^{\circ}\)C/min to obtain high-quality samples.  
The crystalline phases of the calcined powder and the sintered ceramics were distinguished by X-ray diffraction (XRD) using CuKa (\(\lambda\) = 0.15406 nm) radiation with a Siemens D5000 diffractometer (Siemens, Berlin/Munich, Germany). Scanning electron microscopy (SEM; Philips, Amsterdam, The Netherlands) was utilized to observe the sintered surface's microstructure, and an energy-dispersive X-ray spectrometer (Philips) was used to identify the existence of secondary phases. Calculation of lattice parameter was performed with the Rietveld method to fit at least five peaks from measured X-Ray patterns. The apparent densities were measured using the Archimedes method with the three sintered samples under the same process and average measured data to calculate relative densities. The permittivity (\(\varepsilon_{t}\)) and the quality factor (Q) at microwave frequencies were measured by the Hakki-Coleman dielectric resonator method [28; 29]. This method utilizes parallel conducting plates and coaxial probes in TE\({}_{011}\) mode, where TE represented transverse electric waves, the first two subscript integers denote the waveguide mode, and the third integer subscript indicates the order of resonance in an increasing set of discrete resonant lengths. The measurement system was connected to Anritsu network analyzer with model MS46122B. The measurement technique for the temperature coefficient of resonance frequency (\(\tau_{f}\)) is almost the same as that of the quality factor measurement except for using a thermostat. The \(\tau_{f}\) at microwave frequencies was measured, and note change of resonance frequency from 30 \({}^{\circ}\)C to 80 \({}^{\circ}\)C. The following formula can calculate the \(\tau_{\rm f}\) value (ppm/\({}^{\circ}\)C),  
\[\tau_{f}=\ \frac{f_{2}-f_{1}}{f_{1}(T_{2}-T_{1})} \tag{1}\]  
where \(f_{1}\) and \(f_{2}\) mean the resonance frequencies at \(T_{1}\) = 25 \({}^{\circ}\)C and \(T_{2}\) = 80 \({}^{\circ}\)C, respectively.
## 4 Discussion
### Seasonal Variation in GPP, ET, and WUE  
We found a general trend toward higher GPP at both YP and MP during the growing season compared to the non-growing season. The GPP responses were consistent with previous observations [72] and recent modeling studies [73; 74], confirming that climatic conditions during the growing season favor the ecosystem photosynthetic activity, even with a slight reduction in soil water availability. This growing season pattern may be explained by the effect of greater incoming radiation and higher temperature, which stimulate leaf area production during the dry growing period [75], favoring greater CO\({}_{2}\) assimilation rates [76]. At these times of high net radiation, the large amount of radiative forcing induced a high evaporative demand and, thus, high ET rates [18; 19], consistent with our hypothesis. The seasonal dynamics of phenological changes also suggested a possible mechanism for high GPP and ET during the growing season, where leaf area peaked during mid-summer. Additionally, water uptake from deeper soil layers helped prevent photosynthesis from declining during the growing season [77].  
Seasonal WUE behaved differently from that of GPP and ET. Seasonal variation in WUE is complicated and challenging to examine because WUE is a trade-off between water loss and carbon gain, and involves both biotic and abiotic drivers. This complexity suggests that wetland forest ecosystems have distinct seasonal carbon and water fluxes affected by local environmental conditions which vary intra-annually, in addition to differences in forest types and soil water dynamics. This complexity makes it difficult to calibrate WUE in ecosystem models for wetland forests.
## 5 Conclusions  
In this paper, we investigated the relationship between the QAM order, SE, NGMI, and AIR in the PS-CAP VLC system for the first time. Simulation and experimental results demonstrate that with the aid of the proposed scheme in this paper, we can meet the NGMI threshold with the lowest computation complexity. Compared with the highest AIR, the AIR loss proposed in this paper is only 0.1 bpQs. Simulation results show that the NGMI only fluctuates within the range of \(\pm\)0.004 around the preset NGMI threshold. Experimental results show that the NGMI are all above the preset NGMI threshold and only vary within a small scale by subtracting 0.1 bpQs SE. Therefore, the scheme proposed in this paper is feasible for the AIR improvement of the PS signal. Additionally, with the scheme proposed in this paper, a very high AIR without coding redundancy can be achieved at the lowest cost according to the channel SNR, which only has a 0.1 bpQs gap at most, to the largest AIR of the system. Consequently, the scheme proposed in this paper has great potential in standardizing communication systems. In this study, the V\({}_{\text{pp}}\) is adjusted in a small range to suppress the nonlinear distortion. In the future, the AIR-based PS scheme in the nonlinear VLC system needs to be investigated. Besides, the performance of the proposed PS scheme needs to be verified in the multi-input multi-output (MIMO) VLC system as well.  
**Author Contributions:** conceptualization, P.Z.; methodology, P.Z.; software, P.Z., and Y.Z.; validation, P.Z., and F.H.; formal analysis, P.Z., and F.H.; investigation, F.H., and Y.Z.; resources, F.H.; data curation, F.H., and Y.Z.; writing--original draft preparation, P.Z.; writing--review and editing, N.C.; visualization, F.H., and Y.Z.; supervision, Y.Z., and N.C.; project administration, N.C.; funding acquisition, N.C. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the National Key Research and Development Program of China (2017 YFB0403603); National Natural Science Foundation of China (61925104).  
**Conflicts of Interest:** The authors declare no conflict of interest.  
Figure 9: Experimental results at the NGMI threshold 0.92.
## 5 Results and Discussion
### Evaluation of Thermal Mass Flexibility  
In order to evaluate the flexibility that the thermal mass can offer, we used the KPIs proposed by [PERSON] et al., 2017 [PERSON] et al. (2017). The two main KPIs are storage capacity \(C_{ADR}\) and storage efficiency \(\eta_{ADR}\).  
The heat storage capacity of the thermal mass of a building during a DR event can be expressed by the available storage capacity (Equation (1)), while taking into account the building's thermal comfort and varying conditions.  
\[C_{ADR}=\int_{0}^{l_{ADR}}\Big{(}Q_{ADR}-Q_{Ref}\Big{)}dt \tag{1}\]  
where \(\int Q_{ADR}\) is the amount of energy consumed during the demand response implementation and \(\int Q_{REF}\) is the amount of energy consumed in the same period of time, but under the reference control. The difference between the energy consumption is the amount of energy stored in the thermal mass of the building.  
The portion of energy that may be stored in the thermal mass and utilized later for energy flexibility and thermal comfort provision is referred to as the storage efficiency (Equation (2)).  
\[\eta_{ADR}=1-\frac{\int_{0}^{\infty}\Big{(}Q_{ADR}-Q_{Ref}\Big{)}dt}{\Big{|} \int_{0}^{l_{ADR}}\Big{(}Q_{ADR}-Q_{Ref}\Big{)}dt\Big{|}} \tag{2}\]  
where \(\int_{0}^{l_{ADR}}\Big{(}Q_{ADR}-Q_{Ref}\Big{)}dt\) is the heat storage capacity and \(\int_{0}^{\infty}\Big{(}Q_{ADR}-Q_{Ref}\Big{)}dt\) is the difference between the energy consumption of the DR scenario and the reference scenario until the building returns to steady state conditions. Essentially, this KPI calculates the amount of stored thermal energy that is possible to be recovered and used.  
In their research [PERSON] et al. (2017), [PERSON] et al. calculated those two indicators for a residential building and considering stable ambient conditions of 5 \({}^{\circ}\)C and zero solar and internal gains. In our case, we evaluated the average value of the same indicators for several days of the year without considering stable outdoor conditions. [PERSON] et al. used these indicators as a tool to assess a building's flexibility during the design stage. We chose to implement the indexes under real operational conditions in order to better capture the operational flexibility of the building.  
The results for each building are presented in Table 3. We can observe the lower capacity of the reference scenario and at the same time the lower storage efficiency compared to the case A. The increased capacity of case A is due to the lower heating power needed before the DR setpoints are implemented, which leaves more power reserve when the heat pump tries to reach the 23 \({}^{\circ}\)C setpoint. The increased efficiency is mainly attributed to the decreased heat losses of the upgraded building. Compared to the results of [PERSON] et al. (2017), we observed a reduced storage efficiency. This is mainly due to the intermittent schedule of the study building, which leads to cycles of charging and discharging of the heat mass of the building, but also due to the residential building shell's superior thermal properties compared to our building.
## 2 Drilling Data Collection
### Signal Acquisition Method While Drilling  
#### 2.2.1 Sensor Configuration  
The multi-parameter measuring instrument for mining while drilling is an intrinsically safe, portable device specifically designed for use in coal mines. The collection equipment consists of a host unit, a torque sensor, and a wire-type distance sensor. The torque sensor integrates pressure sensors, torque sensors, angle sensors, speed sensors, and vibration sensors within its structure. Real-time measurement data display a variety of parameters, including position, footage, inclination angle, rotational speed, pressure, torque, vibration, and speed.
## 3 Experimental Results
### Influence of Liquid Conductivity  
The periodic current waveforms under different liquid conductivities are shown in Figure 13. The cable defect width is 2.2 mm, the grounding resistance is 30 \(\Omega\), and the cable is immersed in water. The liquid conductivity values are set to 0.2, 1, 3, and 5 mS/cm. From the current waveforms, it can be observed that higher liquid conductivity leads to more pronounced current distortion. When the liquid conductivity is 0.2 mS/cm, the current waveform is approximately sinusoidal. As liquid conductivity increases, the current amplitude rises. However, looking into the detailed waveforms, the current exhibits a notch when the voltage approaches the peak. The higher the conductivity, the larger the current notch will be. When the conductivity reaches 5 mS/cm, the notch duration extends beyond a quarter of a cycle. A distinct double-peak phenomenon is observed for the notch current, characterized by a higher left peak and a lower right peak. This unique phenomenon highlights the significant impact of liquid conductivity on leakage current distortion. The underlying physical process will be further discussed in Section 4.2.  
Figure 14 presents the statistical results of intermittent arc occurrences over one hour under different liquid conductivities. The cable defect width is 1.8 mm, and arcs are generated under water penetration conditions with a grounding resistance of 30 \(\Omega\). When the liquid conductivity is below 0.2 mS/cm, intermittent arcs are rarely observed. As the conductivity increases to 3 mS/cm, the number of arcs rises significantly, reaching approximately 50. However, when the conductivity further increases to 5 mS/cm, the number of arcs remains the same as that at 3 mS/cm. Despite the increase in liquid conductivity, no significant change is observed in the magnitude of arc current, which remains in the range of 6-8 A, nor is there any noticeable change in current shape. These  
Figure 13: Periodic current waveforms under different liquid conductivities in the 3 rd stage of liquid penetration and the 2 nd stage of liquid immersion conditions.  
results indicate that liquid conductivity primarily affects arc generation but has little influence on arc current magnitude.