esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Dataset Preparation and Experimental Setup
Several image examples from the dataset are depicted in Figure 1, while Figure 2 presents a statistical overview of the dataset. As shown in Figure 1, there are a variety of images in our dataset with dark floors. Figure 2 shows slightly imbalanced classification categories. The motivation behind gathering more samples for the \"cat/dog food\" category primarily stems from the significant interest shown by owners of robotic vacuum cleaners. Through the identification and separation of pet-related items, these owners would like to guarantee the proper collection and recycling of pet food. This strategy improves the effectiveness of recycling initiatives, reduces the risk of contamination, and aligns harmoniously with the broader objective of decreasing the environmental impact linked to pet care. The increased quantity of samples within the dog/cat food category aimed to enhance the AI models' capability to acquire expertise in identifying and discerning the distinct characteristics linked to this category. \begin{table}
\begin{tabular}{c c} \hline \hline
**Object Category** & **The Number of Samples in Our Dataset** \\ \hline rice & 2158 \\ cat/dog food & 5606 \\ sunflower seed shell & 3414 \\ red bean & 2205 \\ soybean & 2305 \\ green bean & 2179 \\ cat litter & 3014 \\ millet & 2161 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Our dataset comprises eight distinct categories of indoor suctionable objects intended for classification and recycling purposes. Figure 1: Illustrative samples from each category in the generated dataset.
# Hierarchical Optimization Method for Energy Scheduling of Multiple Microgrids
## 5 Case Studies
### Internal Prices of the MMG  
For the convenience of the following analysis, we provide the optimal prices in the MMG under the condition of market equilibrium as shown in Figure 5. It can be seen that that during the periods of 0:00-8:00 and 18:00-24:00 the internal buying and selling prices were equal to those of the power grid because the PV generation of each MG was very small, or even 0, and the internal load demand was relatively large. While all MGs are in the purchasing state, the EMO cannot improve its own benefits by adjusting the internal prices. During the period of 8:00-18:00, the internal buying price was always higher than that of the power grid, and the MG could sell more electricity by adjusting load demand to maximize operation utility. During the periods of 11:00-12:00 and 13:00-16:00, the internal sale price was lower than the selling price of the power grid, and the MG could increase energy consumption to reduce the purchasing cost of the MG and create more economic benefits.  
The above results show that the EMO could only adjust the internal electricity price during the period of 8:00-18:00, when PV generation is relatively strong. During the daytime, it could promote energy trading in the MMG, and realize the improvement of the operation efficiency of each MG. Next, we analyze the benefits of the MGs and the EMO in combination with the internal prices proposed in this paper.
# Systematic Analysis of the Hydrogen Value Chain from Production to Utilization
## 2 Hydrogen Production Technologies
### Thermochemical Processes
#### 2.1.4. Gasification  
Gasification is a thermochemical process that uses heat and chemical reactions to convert carbonaceous solid fuel, in the presence of a specific gasification agent, into a synthetic gas, composed of carbon monoxide and hydrogen. Coal, heavy hydrocarbons, and biomass are frequently used as feedstocks. The entire process to obtain the desired hydrogen is composed of three phases, namely gasification, gas cleaning and upgrading, and hydrogen separation [30], as represented in Figure 6. Using coal as the feedstock results in the production of gray carbon, as a high content of CO\({}_{2}\) emissions is released [33]. When biomass is used as the feedstock, green hydrogen is produced, since it is considered that all CO\({}_{2}\) generated is absorbed by plants through the photosynthesis process, producing new biomass and making this process net zero [32]. Current research on biomass gasification focuses on assessing the effects of the reactor's operating conditions, such as the temperature, diameter, residence time, gasifying agent, and ash effect on the quality and characteristics of the final products [34].
## 4 Case Study
### Simulation Modeling
#### 4.1.2 SCWT Sensor Fault  
EnergyPlus provides 16 fault models (such as coil fouling and various sensor faults) to simulate the impact of faults occurring in sensors, controllers, and equipment within HVAC systems. In this study, the TemperatureSensor:Offset:ChillerSupplyWater fault model was utilized because it focuses on faults in the SCWT sensor. This fault model allows the user to directly input the sensor fault schedules and offset values. The measured SCWT, supply chilled water flow rate, and cooling capacity of the chiller are calculated when this fault model is applied. The measured SCWT is expressed as follows:  
\[T_{scw,f}=T_{scw,a}-\Delta T, \tag{7}\]  
where \(T_{scw,f}\) represents the SCWT measured by the faulty sensor (\({}^{\circ}\)C), \(T_{scw,a}\) represents the actual SCWT (\({}^{\circ}\)C), and \(\Delta T\) denotes the sensor fault offset (\({}^{\circ}\)C). The supply chilled water flow rate is expressed as follows:  
\[Q_{scw,f}=\frac{q_{ccha}}{\rho\cdot C_{p}\cdot\left(T_{rcw,f}-T_{scw,des} \right)}, \tag{8}\]  
where \(Q_{scw,f}\) represents the supply chilled water flow rate in the case of a faulty sensor (m\({}^{3}\)/s), \(q_{cch,a}\) represents the actual cooling capacity of the chiller (W), \(\rho\) represents the density of water (kg/m\({}^{3}\)), \(C_{p}\) represents the specific heat of water (J/kg\(\cdot\)K), \(T_{rcw,f}\) represents the chiller return water temperature in the case of a faulty sensor (\({}^{\circ}\)C), and \(T_{scw,des}\) denotes the supply chilled water set-point temperature (\({}^{\circ}\)C). The cooling capacity of the chiller is expressed as follows:  
\[q_{cch,f}=\rho\cdot Q_{scw,f}\cdot C_{p}\cdot\left(T_{rcw,f}-T_{scw,f}\right), \tag{9}\]  
where \(q_{cch,f}\) represents the cooling capacity of the chiller in the case of a faulty sensor (W).
# AA-HMM: An Anti-Adversarial Hidden Markov Model for Network-Based Intrusion Detection
## 6 Measurement Metrics
### Efficiency Matrix  
Cost matrix is a good metric for evaluating the NIDS because it can directly reflect the security (protection) level established by the models deployed in an operational environment. However, the common values, such as true positive, true negative, false positive, and false negative, have not been published and cannot be calculated given the published metrics. Therefore, referring to the definition of cost matrix, an alternative metric called Efficiency Matrix (EM) is defined as below, which evaluates the security level by calculating the precisions and recalls of benign and malicious samples, separately.  
In Table 2, the matrices \(\mathrm{E_{11}}\), \(\mathrm{E_{12}}\), \(\mathrm{E_{21}}\), and \(\mathrm{E_{22}}\) are the efficiency values of Precision (B), Recall (B), Precision (M), and Recall(M), respectively. So, the efficiency of the evaluated model should be calculated by:  
\[\mathrm{Efficiency(Model)}=\mathrm{Precision(B)}\times\mathrm{E_{11}}+\mathrm{ Recall(B)}\times\mathrm{E_{12}}+\mathrm{Precision(M)}\times\mathrm{E_{21}}+\mathrm{Recall(M)}\times \mathrm{E_{22}} \tag{5}\]  
The EM should be interpreted in a different manner from the cost matrix: the higher the efficiency, the better the performance. The pre-defined value \(\mathrm{E_{xy}}\) varies between scenarios (applications). For evaluating NIDS, since misclassifying malicious samples is more costly than benign samples and the recall is a better metric than the precision in terms of reflecting the protection level provided by the model, the four efficiencies should maintain the following relationship:  
\[\mathrm{E_{22}}>\mathrm{E_{21}}>\mathrm{E_{12}}>\mathrm{E_{11}} \tag{6}\]
## 6 Example Solution and Analysis
### Distribution Route Optimization  
This study focused on enhancing the grey wolf algorithm to partition customer nodes and optimize transportation routes. The resulting optimization divided 46 customer nodes into seven regions for more efficient distribution. Visualizations of the route optimization for each region and the iterative optimization process were created using the algorithm. Furthermore, a set of examples consisting of 202 samples was added to provide evidence for the effectiveness of the algorithm. The figures illustrating the route optimization and iteration diagrams for each region can be found in Figure 9.  
The specific distribution van or refrigerated truck travels to all the customer nodes and finally heads back to the pharmaceutical factory. For example, the distribution order of region 1 is as follows.  
Region 1: 0-7-6-4 5-3-1-2-19-9-0;  
Thus, the carbon emissions and the comprehensive cost of the logistics process are the lowest. Moreover, according to the total demand of each region, different distribution methods are selected, and the specific delivery methods are shown in Table 5. The comparison of the new and old transportation methods is shown in Table 6.  
Figure 9: Path optimization and algorithm iteration graph.  
By comparing our dual-method distribution scheme with the traditional refrigerated truck distribution, there is a significant reduction in both comprehensive and carbon emissions, which decreased by 7.73% and 34.86%, respectively.
# Propospheric Attenuation in GeoSurf Satellite Constellations
## 2 GEO Probability Distributions of Rain Attenuation at the Reference Sites
In Figure 2, we show the probability distributions of rain attenuation predicted for the slant path to a GEO satellite along the meridian of the site, by using the ITU\(-\)R rain rate probability distributions as input to the models and its rain height [29]. The elevation angle at each site is reported in Table 1. For comparing, at a glance, the rain attenuation exceeded at the same probability level, the scales are kept fixed for all figures. Figure 1: Annual probability distributions (%) of rain attenuation exceeded in the slant path at Spino d’Adda to a geostationary satellite (elevation angle 37.7\({}^{\circ}\)) on the meridian, at 39.6 GHz and a circular polarization, calculated by the indicated prediction models, using, as input, the rain rate probability distribution recorded locally, with rain rate expressed in millimeters per hour, in the years 1993\(-\)2002. The solid red line refers to using the full SST [17], whose input is the full set of rain rate time series recorded in the same period of time, whose probability distribution is used as input to the models. Of course, each site, because of the local climate, shows very different results. However, we must comment that, excluding the full SST, which is very reliable, all other models give predictions within too large a range to be really useful in link design, as a few more dBs in the link margin come at a cost. For example, at 0.1%, the full SST predicts 20.5 dB in Spino d'Adda (Figure 1), but the predictions range from 17 to 26 dB, a large range for any cost-effective design. In Figure 2, we notice similar large ranges at 0.1%: from 9 to 18 dB in Robledo, from 20 to 27 dB in Holmdel, from 8 to 15 dB in Pasadena, and from 12 to 19 dB in Canberra. However, the results reported in Figures 1 and 2 are useful for our exercise, because in the next section, we compare them with those obtainable in paths with a 90\({}^{\circ}\) elevation angle (GeoSurf), i.e., at the local zenith. This just a first and rough approach to studying the tropospheric attenuation in GeoSurf constellations because a more significant comparison should be done with the slant paths in LEO constellations--with elevation angles largely variable--a task beyond the scope of this paper, except for a single case, as we show in Section 4. Figure 2: Annual probability distributions (%) of rain attenuation exceeded in the slant paths directed along the local meridian to a Geostationary satellite at 39.6 GHz and a circular polarization in: (**a**) Robledo (elevation angle 42.8\({}^{\circ}\)): (**b**) Holmdel (elevation angle 43.3\({}^{\circ}\)), (**c**) Pasadena (elevation angle 50.3\({}^{\circ}\)), and (**d**) Canberra (elevation angle 49.0), predicted by using the ITU\(-\)R [28; 29] rain rate probability distributions.
# Dissimilarity Space Based Multi-Source Cross-Project Defect Prediction
## 3 DM-CPDP Approach
### Dissimilarity Space Construction  
To construct a CPDP model in dissimilarity space, we represent the sample by the pairwise dissimilarity between the sample and the prototype set \(R\) obtained from the target set \(X_{T}\). \(R=\{p_{1},p_{2},\ldots,p_{r}\},R\in T\). In dissimilarity space, the set is represented as:  
\[D(X,R=(p_{1},p_{2},\ldots,p_{r}))=\left[k\big{(}x_{i},p_{j}\big{)}\right]_{n \times r} \tag{1}\]  
Each instance is represented as a r-dimensional dissimilarity vector. \(k(x_{i},p_{j})\) represents the dissimilarity between the _i-th_ sample in data set \(X\) and the _j-th_ prototype sample in prototype set \(R\). This paper uses the density-based clustering method to select the cluster center as the prototype set, and uses the arc-cosine function to measure the pair-wise dissimilarity between samples.  
Figure 1: The framework of the multi-source cross-project defect prediction method based on dissimilarity space (DM-CPDP).
## 1 Introduction  
With the development of industry and agriculture, ever-increasing volumes of soil have been polluted by the discharge of mining and the massive use of pesticides and chemical fertilizers. Heavy metal pollution has become a global problem, posing environmental and health risks. Lead (Pb) and cadmium (Cd) are the two most universal inorganic pollutants in mining sites [1, 2], and they are often found to be naturally associated with zinc (Zn) and copper (Cu) due to mining activities [3, 4, 5, 6, 7, 8].  
Phytoremediation has been recognized as a type of environmentally safe and efficient technique to rehabilitate heavy-metal-contaminated soils [2; 9]. Phytoremediation is mainly divided into two categories: phytoextraction and phytostabilization; the former utilizes hyperaccumulator plants to remove heavy metals from the soil, while the latter uses some tolerance plants with special abilities to transform and immobilize heavy metals in the soil or plants to prevent their migration [10; 11]. Thus, the selection and utilization of appropriate plants are crucial steps for the successful applications of both phytoextraction and phytostabilization programs.  
_Macleaya cordata_ (Wild.) is a perennial herb in the family Papaveraceae, which contains various bioactive alkaloids [12], and this plant has been approved by the European Food Safety Authority (EFSA) as a safe plant for the manufacture of feed additives [13; 14]. Meanwhile, this plant was found in tailing areas, with fast growth rates, large biomass, and huge taproots, making it a good candidate species for studying phytoremediation. _M. cordata_ had been studied for the phytoextraction of uranium- and molybdenum-contaminated soil [15; 16]. _M. cordata_ has also been reported to show a good ability to accumulate mercury, cadmium, zinc, and manganese [16; 17; 18; 19], but little is known about the absorption and transport mechanisms of this plant relative to these heavy metals. Moreover, previous studies on _M. cordata_ usually adopted soil substrates with complex components [14; 16; 17; 18; 20], which makes it quite difficult to explore the underlying mechanisms [20]. Hydrogenic experiments that can eliminate the disturbance of soil substrates are usually conducted to unravel the response mechanisms of plants to toxic materials [21; 22; 23].  
The objectives of this study were (1) to investigate the differences in the tolerance of _M. cordata_ under varying concentrations of Zn, Cu, Cd, and Pb in solutions; (2) to compare the effects of metal absorption and transport in _M. cordata_ exposure to Zn, Cu, Cd, and Pb treatment; and (3) to analyze the changes in metal accumulation in the _M. cordata_ exposed to two or more heavy metals under hydroponic conditions.
# Experimental Validation of Ellipsoidal Techniques for State Estimation in Marine Applications
## 5 Estimation Results
### Localization with Heading Measurement  
An enhancement of the estimation quality becomes possible as soon as the interval data for the measured heading are additionally included in the estimation procedure. The corresponding results are shown in Figure 8a, where no significant differences between the direct use of the measured heading intervals in the system matrix of the state-space representation in (55) and its replacement by the estimated heading bounds are visible.  
Note that, in both Figures 7 and 8, the measurements are assumed to be available at each sampling time instant of the discrete-time system model, where measurements of all four state variables are used in Figure 8, in contrast to the specified subsets in Figure 7.  
Figure 7: Localization of the DDboat A without heading measurement. (**a**) Pure position measurement. (**b**) Position and velocity measurement.  
A reduction in the measurement frequency is analyzed in Figure 9. There, it becomes obvious that the use of measured heading information in the evaluation of the system matrix in (55) leads to much tighter state enclosures than the use of purely predicted heading bounds. Here, it has been assumed that the duration between two subsequent measurements corresponds to \(3T_{8}\). The fact that the use of the measured heading information in the evaluation of the system matrix during the state prediction step significantly reduces the width of the predicted state bounds gives rise to the following practical recommendation if low-cost sensors and computing hardware are used: to be able to reduce the sampling frequency of the GPS, while still obtaining sufficiently tight position bounds for the vessel after the prediction step under consideration of large external disturbances as in this paper, it is reasonable to provide compass-based heading information with higher sampling frequencies, even though their tolerance bounds may not be especially tight.  
Moreover, event-triggered state estimation procedures can be implemented in future work on the basis of the widths of the predicted vessel position, which request new state measurements as soon as the volume of the predicted ellipsoids exceeds a user-defined threshold value.
###### Abstract  
Conductive nanomaterials affect anaerobic digestion (AD) processes usually by improving methane production. Nevertheless, their effect on anaerobic communities, and particularly on specific trophic groups such as syntrophic bacteria or methanogens, is not extensively reported. In this work, we evaluate the effect of multi-walled carbon nanotubes (MWCNT) on the activity of two different anaerobic microbial communities: an anaerobic sludge and a river sediment. Methane production by anaerobic sludge was assessed in the presence of different MWCNT concentrations, with direct methanogenic substrates (acetate, hydrogen) and with typical syntrophic substrates (ethanol, butyrate). MWCNT accelerated the initial specific methane production rate (SMPR) from all compounds, with a more pronounced effect on the assays with acetate and butyrate, i.e., 2.1 and 2.6 times, respectively. In the incubations with hydrogen and ethanol, SMPR increased 1.1 and 1.2 times. Experiments with the river sediment were performed in the presence of MWCNT and MWCNT impregnated with 2% iron (MWCNT-Fe). Cumulative methane production was 10.2 and 4.5 times higher in the assays with MWCNT-Fe and MWCNT, respectively, than in the assays without MWCNT. This shows the high potential of MWCNT toward bioenergy production, in waste/wastewater treatment or ex situ bioremediation in anaerobic digesters.  
**Keywords:** methane; syntrophic degradation; multi-walled carbon nanotubes; anaerobic sludge; sediment +
Footnote †: journal: Applied Soft
## 3 Experimental Results Analysis  
### Effect of Loading Rate and Inclination Angle on the Axial Stress-Strain Curve  
As shown in Figure 6a-f, the relationship curve between axial stress component and axial strain of coal samples under different loading rates and inclination angles is obtained by the method described in Section 2.2. To clearly describe the characteristics of the axial stress component and axial strain curve, Figure 6g shows the typical axial stress and strain curve of the sample in this experiment. According to the characteristics of curves, they can be divided into two types. When the inclination angle of samples within is 0\({}^{\circ}\)-10\({}^{\circ}\), the curve can be divided into four stages under any loading rate: the initial compression stage, near-linear elastic stage, nonlinear crack growth stage and post-peak instantaneous drop stage; brittle failure characteristics are observed, and this curve belongs to the type I curve. When the inclination angle is within 15\({}^{\circ}\)-25\({}^{\circ}\), the curve can be divided into four stages: initial compression stage, near-linear elastic stage, nonlinear crack growth stage and post-peak softening and attenuation stage; plastic deformation are observed, and this curve belongs to the type II curve. Overall, the pre-peak sections of type I and II curves are similar, but the post-peak sections are different. For the characteristic curve of type I, when the peak strength of the sample is reached, the post-peak curve shows the characteristics of rapid drop and large modulus reduction. This indicates that the internal microcracks of the sample have penetrated and extended to the surface of the sample, and a macroscopic crack has been formed, leading to the complete failure of the sample. For a characteristic type II curve, obvious strain softening appears at the post-peak stage, showing plastic deformation  
Figure 5: Schematic diagram of crack mode and acoustic emission (AE) characteristic parameters.  
characteristics. Due to the influence of inclination effect, the friction effect around the crack in coal increases, leading to the inhibition of the sliding of macro failure surface and the low residual strength. At this point, the sample still has a certain bearing capacity after being damaged. Thus, the mechanical property and deformation behavior of coal samples under the coupled compression-shear loading is highly correlated with the inclination angle [40; 41]. Figure 6 shows the typical axial stress-strain curve of coal samples in this experiment.  
As shown in Figure 6, the loading rate and inclination angle not only have a great impact on the post-peak of axial stress-strain curve of coal samples, but also have a significant impact on its mechanical properties such as peak compressive strength and elastic modulus. This is probably caused by the shear stress component under the coupled compression-shear loading, which will be studied in detail below.  
Figure 6: _Cont._
# An IoT-Based Encapsulated Design System for Rapid Model Identification of Plant Development
## 3 Encapsulated Design Plant Growth Devices
### Climate Parameter Regulation
#### 3.1.4 Soil Moisture and Air Humidity  
Water is delivered to the soil by pumps and valves to each of the four zones individually and controlled by corresponding hysteresis controllers based on the information gathered from the electrical conductivity sensors placed in the soil, as depicted in Figure 2. Valve on/off durations transform the water flow in the tubes that supply water to the soil from within of the central tower. In the plant area of the encapsulated design, the humidity control loop consists of a humidity sensor, an ultrasonic humidifier, and a corresponding fan that distributes the created mist to the leaves. The set-point of 10-100% humidity is achieved by a PI controller for fan speed.