esa-sceva/satcom-chunk-collection huggingface dataset:
# Mycoremediation as a Potentially Promising Technology: Current Status and Prospects--A Review
## 3 Bioremediation (In Situ and Ex Situ)  
This is a simple, eco-friendly, long-lasting, and cost-effective way of restoring and cleansing contaminated soil. It entails the natural decomposition of petroleum hydrocarbon pollutants by microorganisms like fungi, bacteria, and yeast that degrade hydrocarbons. Microorganisms with adequate nutrients and optimized limiting factors convert contaminants in soil into nontoxic or simpler compounds, such as water and carbon (IV) oxide, through oxidation under aerobic conditions [87; 88; 89; 90]. In addition, In situ and Ex-situ bioremediation are well-established technologies in contaminated environments (Figure 2) [91]. Before adopting bioremediation, it is critical to evaluate all the limiting elements that can influence the remediation process's effectiveness. It is easier for microorganisms to break down aliphatic hydrocarbons than branched or long-chain hydrocarbons, which are more difficult to biodegradable [92]. Microorganisms that degrade hydrocarbons use carbon compounds for reproduction, growth, and energy.  
Bioremediation utilizing chosen microorganisms to break down petroleum hydrocarbons is gaining interest among researchers. _Pseudomonas_ bacteria efficiently break down petroleum hydrocarbons into simpler compounds [93; 94]. In addition, fungi including _Rhizopus_, _Fusarium_, and _Penicillium_ have been identified and employed in the bioremediation of sediments and soil contaminated with petroleum hydrocarbons [95; 96]. However, bioremediation has been used for petroleum hydrocarbons since 1940 but gained a lot of attention after the Exvon Valdez oil spill in 1980 [97]. [PERSON] [98] categorized microorganisms participating in bioremediation processes as follows:  
_Aerobic microbes_ are responsible for biodegradation in the presence of oxygen, with _Mycobacterium_, _Rhodococcus_, _Sphingomonas_, _Alcaligenes_, and _Pseudomonas_ being the aerobic bacteria identified for their degrading abilities. It has been observed that these bacteria degrade pesticides and hydrocarbons, including alkanes and polyaromatic compounds. Many of these bacteria rely solely on the contaminant for energy and carbon.  
_Anaerobic_ bacteria degrade without oxygen. Anaerobic bacteria that dechlorinate the solvent trichloroethylene (TCE), chloroform, and PCBs from river sediments are gaining increasing attention.  
_Ligninolytic fungi_, including the white rot fungus _Phanaerochaete chrysosporium_, can break down various toxic or persistent environmental pollutants.  
Figure 2: Different techniques of bioremediation.
# Robust Algorithm for Calculating the Alignment of Guide Rolls in Slab Continuous Casting Machines
## 4 Results and Discussion
### Failure Modes  
As shown in the previous experiments, the random angle measurement errors are the main cause of computational errors. However, the experiments do not show at which error amplitude the algorithms fail. To answer this question, each algorithm was tested with an increasing random angle measurement error until the mean and the 90 percentile of the computational error exceeds the target of 0.1 mm, respectively. The amplitudes of both components of the random roll position errors are kept constant at 0.5 mm. The constant angle measurement error is also kept constant at a value of 0.01\({}^{\circ}\). Due to the zero-mean compensation, the amplitude of the constant angle measurement error has no effect in this case and could be set to an arbitrary value without altering the result. These error amplitudes are identical with Experiment 5 to keep the results, which are shown in Table 3, comparable.  
The results show clearly that only the optimization-based algorithm reaches the desired computational accuracy given the current measurement abilities of SCM. None of the geometric algorithms is able to reach the desired accuracy in the 90 percentile metric at all, which is due to the negative influences of the roll position errors (see Experiments 1 and 2). Even in the case of perfect angle measurements, their 90 percentile computation error stays above 0.22 mm. This influence is also the reason why none of the algorithms is able to keep the maximum computational error under the targeted 0.1 mm. However, this metric is prone to random outliers and the 90 percentile metric is the most significant for the practical application.
# Improving Multi-Label Learning by Correlation Embedding
## 3 The Proposed Method
### Label Correlation Embedding  
Exploiting the label correlation can improve the generalization ability of a model and significantly improve the accuracy of model prediction in multi-label learning [53; 54]. In this paper, we model the label correlation under the second-order strategy.  
First, we calculate the label correlation matrix \(\mathbf{C}\in\mathbb{R}^{q\times q}\) by cosine similarity based on the label matrix \(\mathbf{Y}\in\{0,1\}^{n\times q}\), where \(n\) represents the number of samples, and \(q\) indicates the number of labels. Each element \(C_{ij}\) indicates the correlation between the \(i\)-th and \(j\)-th labels, and it is obtained by Equation (1).  
\[C_{ij}=\sum_{h=1}^{n}Y_{hi}Y_{hij}/\left(\sqrt{\sum_{h=1}^{n}Y_{hi}^{2}}\sqrt{ \sum_{h=1}^{n}Y_{hij}^{2}}\right) \tag{1}\]  
where \(Y_{hi}\) represents the value of the element in the \(h\)-th row and \(i\)-th column of \(\mathbf{Y}\), and \(Y_{hij}\) represents the value of the element in the \(h\)-th row and \(j\)-th column of \(\mathbf{Y}\).  
Second, we decompose the label correlation matrix \(\mathbf{C}\) into a low-dimensional space by graph embedding as follows  
\[\underset{\mathbf{W}}{\text{min}}\quad\frac{\lambda_{1}}{4}||\mathbf{C}- \mathbf{W}^{\mathsf{T}}\mathbf{W}||_{F}^{2}. \tag{2}\]  
For \(\mathbf{W}\), we can utilize it as the model coefficient to construct a multi-label classifier. In this paper, we first construct a linear model for multi-label classification as follows  
\[\underset{\mathbf{W}}{\text{min}}\quad\frac{1}{2}||\mathbf{X}\mathbf{W}- \mathbf{Y}||_{F}^{2}+\frac{\lambda_{1}}{4}||\mathbf{C}-\mathbf{W}^{\mathsf{T} }\mathbf{W}||_{F}^{2}+\frac{\lambda_{2}}{2}||\mathbf{W}||_{21}, \tag{3}\]  
where \(\mathbf{W}=[\mathbf{w}_{1},\mathbf{w}_{2},\dots,\mathbf{w}_{q}]\in\mathbb{R}^ {d\times q}\), \(\lambda_{1}\) and \(\lambda_{2}\) are non-negative weight parameters. The \(\ell_{21}\)-norm regularization term is imposed on \(\mathbf{W}\) to ensure the sparsity, which can select discriminative features. In addition, \(\ell_{21}\) norm has been confirmed to be robust to outliers and noise [55].  
Figure 1: The learning framework of MLLCE.  
Previous studies mainly constrain the correlation between labels on the model coefficient matrix or the output by manifold regularization [35; 36]. Different from previous studies, we directly model the pairwise label correlations by graph embedding, and the structure of label correlation will be well kept in \(\mathbf{W}\).
## 8 Speaker Direction and Signal Obstruction
### Experiment Method  
Positioning was done in the same test lab as in the previous section. However a different set of four check points (P1,P2,P3,P4) was used this time. The first check point (P1) is located precisely in the center of the room and each next check point falls along a straight line to one of the corner mics at 1 m intervals. At each of the four check points the user stood in eight different locations around the check point while holding the phone directly over the check point (Figure 6). In positions B, D, F, H the user would stand in the direct line-of-sight between the phone and one of the microphones. These positions are referred to as \"blocked\". In positions A, C, E, G the user stands in such a way as to not block the path to any microphone. These positions are referred to as \"direct\".  
In each of the eight positions the phone was held in three different tilt orientations: speaker facing upwards (up), facing the user (back), and facing away from the user (forward). In each orientation the ultrasound signal was produced 10 times, which is treated as one sample. Altogether for this experiment 960 signals were sent, making 96 samples.  
Figure 6: Locations where user stands around each check point. Grey circle in centre indicates check point, over which the smartphone is held. The 8 silhouettes marked A–H show a top view of the user standing with their back facing outwards in all cases.
# Study on Multi-Objective Optimization-Based Climate Responsive Design of Residential Building
## 3 Multi-Objective Optimization Platform Set-Up  
### Definition of Objective Functions  
Building climate responsive design aims to ensure the comfort of the building's thermal environment while reducing building energy consumption and life cycle costs as much as possible. Therefore, the thermal environment comfort model, building energy demand model and building life cycle cost model are the three important aspects of climate responsive design. To some extent, these objective functions are both interrelated and conflicting. The basic parameter settings of these three objective functions for building climate responsive design are defined as follows:  
(1) Thermal environment comfort  
The international standard ASHRAE 55 [24] defines \"thermal comfort\" as: a state of consciousness in which a person expresses satisfaction with the thermal environment, which is affected by differences in personal emotions, individual physiological structures, climate, culture, and society, etc. Thermal comfort is a relative concept. There is no absolute thermal comfort. Comfort is a subjective psychological state. It cannot be determined because it cannot be measured objectively and changes constantly according to various factors. Based on a series of parameter settings, the study uses the PMV model to calculate the annual Discomfort Hours percentage (DH) as an indicator for thermal environment comfort assessment.  
Figure 10: Environmental selection process.  
According to the typical activity levels and clothing thermal resistance values listed in ANSI/ASHRAE STANDARD 55-2013, the research roughly determined the input parameters of clothing thermal resistance values for simulation. The step value changes according to the change of the monthly external average temperature. The metabolic rate is fixed at 1.2 met in the case, which corresponds to the sedentary behavior in residential buildings. In addition, the air speed is set to a very low speed of 0.05 m/s, which is more common in most enclosed indoor environments. The specific input parameters for PMV calculation vary with monthly steps, as shown in Table 2. Table 2 lists only a few fixed values that do not need to be changed in EnergyPlus calculations, namely indoor air speed, clothing thermal resistance, and human metabolic rate. The indoor air temperature, relative humidity and average radiant temperature vary with parametric simulation which needs to be read after calculation by EnergyPlus and cannot be set in advance.
## 3 Methodology
### Preprocessing of Input Layers
#### 3.1.1 Preprocessed Layers of Sentinel-2 Images
\[NDVI=\frac{\textit{Band 8-Band 4}}{\textit{Band 8+Band 4}} \tag{2}\]  
NDWI is an index that is widely used for vegetation analysis by using the difference of spectral characteristics according to moisture content in vegetation [16]. The difference between NIR and SWIR, and the difference between green and SWIR, are the widely used methods [37]. In this study, two types of NDWIs were generated using the difference between green (Band 3) and SWIR (Band 11, Band 12) as follows [39]. \[NDWI_{1}=\frac{\textit{Band 3-Band 11}}{\textit{Band 3+Band 11}} \tag{3}\]  
\[NDWI_{2}=\frac{\textit{Band 3-Band 12}}{\textit{Band 3+Band 12}} \tag{4}\]  
NDre is an index using vegetation red edge band, indicating that the higher the NDre value, the greater the activity of vegetation. In this study, two types of NDre were created using three red edge bands (Bands 5-7) provided by Sentinel-2 [40,41]. \[NDre_{1}=\frac{\textit{Band 6-Band 5}}{\textit{Band 6+Band 5}} \tag{5}\]  
\[NDre_{2}=\frac{\textit{Band 7-Band 5}}{\textit{Band 7+Band 5}} \tag{6}\]  
In addition, texture data were used to visually consider the difference in the forest texture and the reflectivity of forest communities according to the dominant species. In the RGB image, the texture of the image is determined by the distribution of shadows. In the case of the artificial forest of the single-layer structure having the same number of species and ages, the texture is smooth due to the constant height between the trees. Multi-layered natural forests of varying ages and species are roughly textured [18]. It is also considered that the arrangement of canopies is uniform in single-layer forests and uneven in multi-layer forests [18,22]. To take this into account, the PCA, which is a representative dimensional reduction feature extraction technique used in multiband image processing, was used [42]. An image that has undergone the PCA technique has the advantage of showing surface characteristics and spectral information that were difficult to read from the original image [43]. The calculation of the PCA texture maps is conducted by three steps: (1) generation of base map by applying a \(5\times 5\) median filter to the produced PCA image with the moving window technique, (2) subtracting a texture image from the PCA image and applying the root mean square deviation equation, and (3) applying a \(3\times 3\) median filter to the texture image to reduce unwanted noise information. The final PCA texture map was produced by converting the calculated image value into a DB unit [37,44] as follows. \[Texture_{db}(i,j)=-20\cdot\log_{10}(Texture(i,j)) \tag{7}\]
# Accuracy and Precision of Three-Dimensionally Printed Orthognathic Surgical Splints
## 4 Discussion
### Post-Processing Protocols  
This study has been conducted by following the post-processing protocols recommended by the manufacturer. However, there are alternative protocols that have been reported to be more successful in enhancing the degree of conversion, which refers to the extent of monomer-to-polymer conversion during curing, and mechanical properties, thus minimising the dimensional inaccuracies occurring during the printing process.  
Oxygen is reported to have adverse effects on the polymerisation process and studies have been conducted to evaluate the effect of oxygen shielding and temperature on the degree of conversion and mechanical properties of 3D-printed photopolymer products. [PERSON] et al. reported that the highest degree of conversion and flexural modulus was observed for specimens post-cured while immersed in glycerin for oxygen shielding when compared with those post-cured in a medium-low vacuum chamber at 5 \(\times\) 10\({}^{-2}\) Torr or no additional conditioning [26]. In addition, increasing the temperature to 80 \({}^{\circ}\)C further enhanced this effect.  
Another study conducted by [PERSON] had similar findings, emphasising the importance of oxygen shielding to maximise the degree of conversion and flexural modulus [27]. This study revealed that the washing protocol also influences the mechanical properties of the 3D-printed products; washing in IPA showed increased resistance to wear in comparison to the centrifuge and ethanol rinse treatments, whereas centrifugation followed by nitrogen polymerisation exhibited the highest flexural modulus.
## 3 Results
### Calibration
#### 3.1.4 Relationship between Surface Reflectance NDVI and CC  
The results of the regression analysis between surface reflectance NDVI (from the HJ-1A/B images) and CC establishment (using Equation (3)) for Groups A and B are shown in Figure 7. The selected model equations for the regression analysis are summarized in Table 8. The graphs show the best fit with the polynomial function for both groups, as well as for the relationship between NDVI and %CC. The highest R\({}^{2}\) values for the polynomial formula of NDVI to %CC for group A (average surface reflectance NDVI from 26 training pixels and B (average surface reflectance NDVI from 9 training pixels) were 0.94 and 0.86, respectively.  
Besides, using the linear relationship (Pearson's correlation) was operated to determine the relationship between the average NDVI and CC which collected for 17 field-observed dates. Nevertheless, as some images have cloud covered on the experiment plots, therefore those pixels were eliminated (11 images were applied here). There were very strong, positive correlation between NDVI and CC of rice group A (r = 0.860, N = 11, \(p\) < 0.001) and strong positive correlation between two variables on rice group B (r = 0.713, N = 11, \(p\) < 0.001).
# Estimation of River Velocity and Discharge Based on Video Images and Deep Learning
## 2 Theory and Methods
### Improvement of ShuffleNetV2
#### 2.4.1 Delete the Second \(1\times 1\) Convolution and Enlarge DWConv Kernel Size  
Typically, there are two purposes for using \(1\times 1\) convolution before and after DWConv. One is to fuse inter-channel information to compensate for the limitations of DWConv, the other is to perform a dimensional adjustment, which may be a reduction or expansion in dimension. The ShuffleNetV2 network uses two \(1\times 1\) convolutions when fusing channel information, but this task can be accomplished with only one \(1\times 1\) convolution. Therefore, to delete the second \(1\times 1\) convolution in the main branch of downsampling unit and the basic unit can not only preserve the ability to fuse information, but also reduce the calculation amount of the model.  
As can be seen from the distribution of the computational volume of ShuffleNetV2, the vast majority of the computational volume is concentrated on the \(1\times 1\) convolution, with DWConv contributing a relatively smaller portion. To improve the detection accuracy without significantly increasing the computational effort, we change the kernel size of all DWConv from \(3\times 3\) to \(5\times 5\). The padding needs to be adjusted from \(1\) to \(2\) to ensure that the dimension of the output feature maps remains constant in the PyTorch code.
# Automated Vulnerability Exploitation Using Deep Reinforcement Learning
## 4 Methodology
### RL Training and Deployment: DRL
#### 4.3.4 Advantages over Q-Learning  
While Q-learning is a popular RL technique, DRL offers several advantages in this specific application:  
* **Scalability:** Q-learning requires storing Q-values for all possible state-action pairs, making it impractical for large state and action spaces. DRL, using function approximation techniques like NNs, can efficiently handle complex environments with vast state and action spaces like the one encountered in penetration testing. In the case of GV, adding one extra vulnerability doubles the size of the Q-Table, whereas in DRL, the same DQN can handle this complexity without bloating its size, making it much more scalable.
* **Continuous Learning:** DRL agents can continuously learn and improve their performance over time by interacting with the environment. Q-learning typically requires manual updates to the Q-Table (e.g., new states), making it less suitable for dynamic environments where vulnerabilities and available payloads might change. This justifies the improvement in the accuracy of the system when using DRL over Q-Table, given that the learning continues after the deployment phase in DRL.
* **Generalization:** DRL agents can learn from past experiences and generalize their knowledge to unseen situations because of using NNs. This allows the agents to adapt to different vulnerabilities and OS. Q-learning, on the other hand, struggles with generalization and requires retraining for each new scenario. In our future work, we plan to test vulnerabilities that were not used in the training phase. This is the ultimate goal of this line of research.  
By leveraging the strengths of DRL, this approach paves the way for autonomous penetration testing tools that can learn, adapt, and efficiently navigate complex landscapes within the Metasploit framework.  
The two use cases, CouchDB (Section 4.1.1) and GV (Section 4.1.2), were tested using DRL instead of Q-Table. The results are presented and discussed in the next section.
# A Low-Power WLAN Communication Scheme for IoT WLAN Devices Using Wake-Up Receivers
## 2 Background
### Low-Power Wake-Up Receivers Based on On-Off Keying  
Low-power, low-rate transceivers have been proposed primarily for wake-up receivers in sensor networks. Low-power operating wake-up receivers generally employ On-Off Keying (OOK) modulation for simplicity and low-power operation. Thanks to an interferer filter design with simple OOK modulation, the wake-up receiver only consumes several microwatts, which is negligible compared to the power consumption during data transmission and idle listening. The main properties of previously developed wake-up receivers are shown in Table 1.
## 1 Introduction
The different international standardised methods for COD determination do not differentiate between biodegradable and recalcitrant COD fractions [9]. The biological and chemical oxygen demand ratios (BOD/COD), at times used to indicate the biodegradation capacity of organic matter to be broken down, are generally low in influents and effluents of CWs treating the liquid fraction of animal manure [10]. The high influent COD concentrations of approximately 800-1700 mgO\({}_{2}\)/L and 50-100 mgO\({}_{2}\)/L of BOD (1:17 BOD/COD ratio) after the biological treatment of the liquid fraction of animal manure indicate the low biodegradability of the remaining COD [10]. The constructed wetlands treating the liquid fraction of piggery manure together with plant litter generate fractions of recalcitrant or non-biodegradable organic matter which increase COD concentrations [11; 12; 13]. Studies have indicated that CWs have reached BOD and COD removal efficiencies above 80% [2; 10; 14; 15] in Flanders. Several researchers have shown that aerated CWs can potentially enhance the removal of organics and nutrients [4; 16; 17; 18; 19]. Mostly synthetic, urban or landfill leachate wastewaters have been considered for testing. [PERSON] et al. [19] assessed the effect of aeration and effluent recirculation on: (i) organic matter, and (ii) total nitrogen removal of pig manure liquid digestate generated by anaerobic digestion. In this study, the liquid fraction of piggery manure coming from the biological treatment and discharged into a CW was used for analysis. However, the present experimental set-up only considered the effect of aeration and no recirculation of the wastewater. Water was collected and kept in containers where different aeration regimes were applied. The total nitrogen (TN), ammonium-nitrogen (NH4\({}^{+}\)-N), nitrate-nitrogen (NO3\({}^{-}\)-N), COD and BODconcentrations were determined to assess the effect of aeration over time. During the experimental process, three aspects were tested: (i) ammonium volatilisation due to aeration; (ii) the evolution of physicochemical parameters of the wastewater (COD, BOD and nitrogen compounds concentrations); iii) the enhancement of the denitrification process by the addition of an external carbon source. The results were treated through a mixed model procedure and discussed, based on the removal efficiency of the different aeration rates.