esa-sceva/satcom-chunk-collection huggingface dataset:
# Green Shipping--Multifunctional Marine Scrubbers for Emission Control: Silencing Effect
## 2 Materials and Methods
### The Case Studies: Experimental Tests and Numerical Analyses
#### 2.2.1 Transmission Loss Experimental Measurements  
The TL measurement was performed using an impedance tube featuring a diameter of 45 mm. The two-load technique explained in Section 2.1.1 was adopted, and the two loads were reproduced using a closed and an open termination (Figure 7).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \(\mathbf{c}\) (m/s) & \(\mathbf{\rho}\) (kg/m\({}^{3}\)) & \(\mathbf{\mu}\) (Pa/s) \\ \hline Air & 340.0 & 1.225 & \(1.78\times 10^{-5}\) \\ Water & 1448.9 & 999.1 & \(1.14\times 10^{-3}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Air and water characteristics used in the numerical model.  
Figure 7: Experimental set-up of the (**a**) expansion chamber and the (**b**) model-scale scrubber with the closed (1) and the open (2) terminations.  
The speaker emits a sine sweep with the following parameters: duration 10 s, frequencies range 50-5000 Hz, and variable amplitude between 0.05 V and 0.40 V.  
The signals were acquired by two PCB Piezotronics 378C10 microphones connected to a data acquisition device NI USB 4431, National Instruments, Austin, TX, USA. Three measurements were performed for each set-up configuration, moving microphone 2 to the locations 3 and 4 while keeping microphone 1 in its location (see Figure 3).  
The acquired traces were elaborated upon in order to obtain the TL values using the Main_TL software developed by Materiacustica s.r.l.  
The distance between microphones 1-2 and 3-4 is equal to 30 mm, which is in accordance with the standard ISO 10534-2 [21], which fixes the following limit to the distance \(s\) between the microphones:  
\[s<0.45\ \frac{c}{F}\, \tag{8}\]  
where \(F\) is the maximum considered frequency.  
Moreover, the distance between the sample and microphones 2 and 3 is greater than the 1-2 tube diameters suggested in the literature [28].  
The ISO standard [21] also provides a frequency limit in order to ensure plane wave propagation:  
\[f<0.586\ \frac{c}{D}, \tag{9}\]  
where \(D\) is the diameter of the largest pipe in the structure. In our case, the frequency limit is about 1700 Hz and 2300 Hz for, respectively, for the expansion chamber and the model-scale scrubber.  
The experimental tests include three repetitions for each geometry and microphone location; the results are reproducible, as the standard deviation between the tests' mean curve and the single experimental curve is less than 1%.  
In this study, a bushing with O-rings (Figure 8) was realized to insert the pipes of the tested prototypes inside of the impedance tube. This solution ensures the airtightness of the system, facilitating the assembly of the setup but also creating discontinuity along the pipe that influences the measurements. The effects of this geometry setup on the TL will be analyzed using the FEM model in Section 3.1.2.
# nmODE-Unet: A Novel Network for Semantic Segmentation of Medical Images
## 2 Related Works  
### Deep Neural Networks in DR Lesion Segmentations  
In the past decade, some studies focusing on the recognition of lesions in DR fundus images have shown promising results.  
[PERSON] et al. [14] proposed a CNN architecture called \"Deep Membrane Systems\", which incorporates an encoder and a decoder. This system utilizes FPN [15] and ResNet101 [16] as the backbone for Mask R-CNN [17], and employs two distinct branches for detection and segmentation. It combines both semantic segmentation and edge detection tasks. \"Deep Membrane Systems\" combine the advantages of multiple network structures and can effectively identify lesions of various sizes and shapes in DR fundus images. Experimental results from three challenging DR image datasets have demonstrated the excellent performance and robustness of this method. [PERSON] et al. [18] proposed L-Seg, which utilizes a multi-scale feature fusion approach. L-Seg employs VGG16 [19] for feature learning. In L-Seg, a side feature extraction module is connected after each group of convolution layers, and feature maps are combined through weighted fusion. Otherwise, they proposed an improved multi-channel loss function to address the imbalance between the background and lesions in DR fundus images. The experiments on three public datasets have demonstrated the effectiveness of L-Seg combined with the improved multi-channel loss in DR segmentation tasks. [PERSON] et al. [20] proposed a feature reassembly method named M2 MRF. M2 MRF effectively mitigates the potential loss of information about small lesions during the downsampling process. They replaced the bilinear interpolation and repeated stride convolution layers in HRnetV2 [21] with M2 MRF to create a variant of HRNetV2. Experimental results demonstrate that the HRnetV2 variant utilizing M2 MRF exhibits superior performance and generalization capabilities. The experiments on three public datasets have demonstrated the effectiveness of L-Seg combined with the improved multi-channel loss in DR segmentation tasks.  
While CNN-based methods have made significant advancements in image segmentation, the task of segmenting DR lesions in fundus images still remains challenging.
## 3 Controller Design  
The coaxial HAUV is an underactuated 6-DOF motion system with four control inputs: \(\tau_{v}=\begin{bmatrix}\tau_{vp}^{\mathrm{T}}&\tau_{v\Theta}^{\mathrm{T}} \end{bmatrix}^{\mathrm{T}}\), \(\tau_{vp}=\begin{bmatrix}0&0&u_{1}\end{bmatrix}^{\mathrm{T}}\), \(\tau_{v\Theta}=\begin{bmatrix}u_{2}&u_{3}&u_{4}\end{bmatrix}^{\mathrm{T}}\). According to the control logic of the coaxial twin-rotor UAV, the controllers are designed for the two systems, respectively, to form a cascade control system. The control scheme frame is shown in Figure 3. A disturbance observer-enhanced adaptive sliding mode controller (DOASMC) was designed for the outer loop to achieve accurate position tracking. The inner loop, which is a feedforward proportion differentiation (PD) controller enhanced by a disturbance observer, was designed.  
After the dynamic equation is converted into the inertial coordinate system, the control input of the position subsystem under the inertial system is \(\tau_{p}=M_{\eta 1}^{-1}I_{1}^{-1}\tau_{vp}\), where \(M_{\eta 1}=J_{1}^{-\mathrm{T}}M_{11}J_{1}^{-1}\). In order to simplify the design of the control system, \(\tau_{p}=\left[\tau_{x}\quad\tau_{y}\quad\tau_{z}\right]^{\mathrm{T}}\) is defined as a virtual control law. The variation in the attitude angle of the coaxial HAUV causes the total lift to produce a horizontal component in the inertial coordinate system, which allows the coaxial HAUV to produce a horizontal maneuver. \(\tau_{p}\) is the reason for the horizontal maneuver of the coaxial HAUV.  
If the desired \(\psi_{d}\) value is given, the total lift force and the expected attitude angle of the attitude subsystem can be obtained.  
\[\left\{\begin{array}{l}u_{1}=\tau_{vp}(3),\tau_{vp}=J_{1}^{\mathrm{T}}M_{\eta 1}\tau_{p}\\ \theta_{d}=\arctan\left(\frac{\tau_{x}\cos\psi_{d}+\tau_{y}\sin\psi_{d}}{\tau_ {z}}\right)\\ \phi_{d}=\arctan\left(\frac{(\tau_{x}\sin\psi_{d}-\tau_{y}\cos\psi_{d})\cos \theta_{d}}{\tau_{z}}\right)\end{array}\right. \tag{32}\]
## 3 Results and Discussion
### System Heat Exchange Performance Analysis  
In the above experiments, the heat exchange changes with the refrigerant filling rate under five different air inlet temperatures, and water inlet temperature conditions are taken respectively. The heat exchange values at the optimal refrigerant filling rate are taken from the above results. Since the air volume flow rate (1800 m\({}^{3}\)-h\({}^{-1}\)) at the evaporator side and the cold water flow volume (1.71 m\({}^{3}\)-h\({}^{-1}\)) at the CDU side remain unchanged, conditions 1-5 can be described as the air inlet temperature and CDU of the evaporator The temperature difference of inlet water is 18-28 \({}^{\circ}\)C under five working conditions. Under the optimum filling rate, the change of heat exchange of the backplane heat pipe system with the temperature difference is shown in Figure 10 below.  
It can be seen from Figure 10 that under the optimal refrigerant filling rate, when the air volume flow rate at the evaporator side and the water flow at the condenser side remain unchanged, the heat  
Figure 10: Change of heat exchange with temperature difference between inlet air of evaporator and inlet water of CDU.  
Figure 9: Temperature of refrigerant inlet and outlet in evaporator.  
\[NTU_{e}=\frac{U_{e}A_{e}}{m_{en}c_{p,a}} \tag{6}\]  
\[Q_{e}=m_{en}c_{p,a}(t_{e,ain}-t_{e,aout}) \tag{7}\]  
It can be obtained from Equation (5) above:  
\[t_{e,aout}=-\varepsilon_{e}(t_{e,ain}-t_{e})+t_{e,ain} \tag{8}\]  
Substitute Equation (8) into Equation (7) to obtain:  
\[Q_{e}=\varepsilon_{e}m_{en}c_{p,a}(t_{e,ain}-t_{e}) \tag{9}\]  
Calculate the condenser in the same way:  
\[\varepsilon_{c}=\frac{t_{c,inout}-t_{c,win}}{t_{c}-t_{c,ain}}=1-\exp(-NTU_{c}) \tag{10}\]  
\[NTU_{c}=\frac{U_{c}A_{c}}{m_{en}c_{p,w}} \tag{11}\]  
\[Q_{c}=m_{en}c_{p,w}(t_{c,aout}-t_{c,win}) \tag{12}\]  
It can be obtained from Equation (10) above:  
\[t_{c,inout}=\varepsilon_{c}(t_{c}-t_{c,win})+t_{c,win} \tag{13}\]  
Substitute Equation (13) into Equation (12) to obtain:  
\[Q_{c}=\varepsilon_{c}m_{en}c_{p,w}(t_{c}-t_{c,win}) \tag{14}\]  
The simultaneous Equations (9) and (14) can be obtained:  
\[t_{e}=t_{c}=\frac{\varepsilon_{a}m_{en}c_{p,a}t_{e,ain}+\varepsilon_{c}m_{en }c_{p,w}t_{c,win}}{\varepsilon_{a}m_{en}c_{p,a}+\varepsilon_{c}m_{en}c_{p,w}} \tag{15}\]  
By combining Equation (9) with Equations (14) and (15), the heat exchange prediction formula of the micro-channel backplane heat pipe system is obtained as follows:  
\[Q=\frac{\varepsilon_{a}m_{en}c_{p,a}\varepsilon_{c}m_{en}c_{p,w}(t_{e,ain}-t_ {c,win})}{\varepsilon_{a}m_{en}c_{p,a}+\varepsilon_{c}m_{en}c_{p,w}} \tag{16}\]
## 4 Context Reasoning
### Proposed Hybrid Reasoning Process
#### 4.2.1 Middleware Layer  
Reasoning starts as soon as the first raw contextual data are collected, which includes three steps: pre-processing, sensor fusion and context inference [39]. Pre-processing and sensor fusion can be enhanced with fuzzy logic, which transforms numerical data into meaningful literal categories, thus addressing uncertainty. The _uncertainty module_ works in parallel with the context acquisition procedure increasing the quality and expressiveness of sensed data. The output is still considered low-level context.  
The _ML module_ includes all the ML models, which are built to solve specific CA problems. Unsupervised learning techniques can facilitate the preprocessing step by performing the detection of outliers or unusual measurements, while supervised learning techniques excel at identifying missing values [82]. Pretrained ML models can also tackle specific problems, such as activity recognition. The ML Module receives low-level data and either enhances them or delivers high-level information, working independently of the other modules.  
The _inference engine_ tackles the core domain logic, i.e., the rules and relations which are valid in the domain. They process low-level and high-level data and produce high-level data which are meaningful inside the IoT ecosystem. Ontologies and/or rules can be used to perform the inference, depending on the requirements and limitations of the domain. The selection of the technique does not affect the architecture or the function of the other modules but determines the performance, expressiveness, scalability and validity of the  
Figure 7: Architecture of context reasoning.  
inference engine. The choice is highly affected by the domain, while hybrid solutions are applicable as well.
## 5 Results and Discussion
### Performance Evaluation against Metric-Based Detector
First the mobile robot runs normally where it moves from the start point (blue point) along the green trajectory until \(t=t\_k\) at which kidnapping begins. At random time \(t=t\_k\) the mobile robot is kidnapped, and it is observed whether the proposed QS-2 DDTW is able to detect the robot kidnapping at \(t=t\_k\). This process will be repeated few times under variation of independent predictors before analyzing the performance of the detection algorithm based on LR technique and ROC. A ROC of the true positive rate (TPR) and false positive rate (FPR) for obtained data of classification technique are shown in Figure 13. As it can be seen from Figure 13 that the proposed QS-2 DDTW performed better than the Metric-Based Detector technique in predicting the kidnapping. LR is useful for situations in which it can predict the presence or absence of a characteristic or outcome (in this case is the kidnapping event) based on a set of predictor variables. Another experimental case study for robot kidnapping is shown in Figure 14. The map shown in Figure 14 is the same as the map described previously, the differences are the trajectory of the robot and type of relocalization process. The mobile robot initially moves along the green trajectory from the start point. When it reaches the kidnapping point at \(t=t\_k\), the robot is jammed in that place until it performs the relocalization process. Then it continues along the green trajectory until the desired locatio  
Figure 12: Case study of the kidnapping scenario at \(t=t\_k\). Figure 7:0 Case study of the kidnapping scenario at \(t=t\_k\). repeated few times before the performance of the detection algorithm is analyzed based on LR technique and ROC. Figure 15 shows a ROC of kidnapping scenario with relocalization process. In the experiment with QS-2 DDTW, the kidnapping was successfully detected in which the proposed method successfully distinguished normal conditions around kidnapping points from the kidnapping event at the kidnapping points with low dependency of relocalization process. However, the detection performance of QS-2 DDTW in the experimental case study is clearly not the same as in the simulation as nonuniform Gaussian noise distribution cannot be guaranteed to follow the Gaussian distribution in a real environment, resulting in low accuracy of SLAM and the proposed algorithm.
# Enhanced Photocatalytic Activity of Iron-Doped ZnO Nanowires for Water Purification
## 1 Introduction
The photocatalytic activity of the ZnO:Fe NWs for the organic pollutant degradation was investigated using three organic dyes, commonly used in textile, food-processing, and pharmaceutical industries, in aqueous solution: methylene blue (MB), methyl orange (MO), and azorubine (also called acid red 14--AR14). The ZnO NWs (undoped and Fe-doped), synthesized by low-cost hydrothermal method with a high aspect ratio, offer a large surface to volume ratio. The detailed degradation mechanism has been proposed for each dye in our previous work [14]. The morphology of both the un-doped and Fe-doped ZnO NWs was characterized using a scanning electron microscopy (FEG-SEM, NEON 40 ZEISS, Berkochen, Germany) operating at 10 kV accelerating voltage. The photocatalysis process was followed by an UV-Vis spectrophotometer (Lambda 35, Perkin Elmer, Waltham, MA, USA) by periodically monitoring the absorption spectra of the organic dye solution during the photo-degradation process. Photoluminescence spectroscopy (PL) was used to characterize both undoped and Fe-doped ZnO NWs in order to understand the photocatalytic efficiency enhancement mechanism.
# Formal Matters on the Topic of Risk Mitigation: A Mathematical Perspective
## 3 Formalizing Risk Mitigation
### Mitigation as a Minimization Process  
From a mathematical perspective, risk mitigation can be considered a minimization process: given the set \(\mathcal{C}\) of all possible configurations  
\[\mathcal{C}=\left\{(T,P,B,Q):T\in\mathcal{T},P\in\mathcal{P},B\in\mathcal{B}(P ),Q\in\mathcal{Q}(P,B)\right\},\]  
we might be interested in finding, for example, the lowest risk conceivable in the region  
\[\inf_{C\in\mathcal{C}}R(\Omega,C),\]  
and whether or not this can actually be achieved, i.e., if there exists \(\tilde{C}\in\mathcal{C}\) such that \(R(\Omega,\tilde{C})=\inf_{C\in\mathcal{C}}R(\Omega,C)\) (note that, in this case, the infimum is an actual minimum in the mathematical sense). This can be important to determine the optimal land-use planning in a \"virgin\" territory, but also to determine what can be expected \"at best\" by any risk mitigation process.  
When given an initial configuration \(C_{0}\in\mathcal{C}\), risk mitigation would imply studying the subset of configurations with lower risk:  
\[\mathcal{C}_{<0}=\left\{C\in\mathcal{C}:R(\Omega,C)<R(\Omega,C_{0})\right\}\]  
and possibly look for some \(C\in\mathcal{C}_{<0}\) that is \"optimal\" in some mathematical sense (possibly in relation to \(C_{0}\) itself, as we shall see momentarily).
# Deep Learning at the Mobile Edge: Opportunities for 5G Networks
## 3 Deep Learning Techniques
### Reinforcement Learning  
_Reinforcement Learning (RL)_ has been lauded in the last decade for training ML systems to outperform humans, culminating in DeepMind's development of a winning machine even in the complex and large game space of Go [22; 36]. RL is incredibly powerful because these training steps for the model do not require any prior knowledge of the rules of the games played, but rather, they optimize the model for future rewards using an \"agent\" who makes observations about its environment (pixels, game state, sensor inputs, etc.) and the rewards (points, coins, closeness to end goal) received in response to the actions (turning, moving, shooting, etc.) it makes. The agent determines which action to perform based on a \"policy\", the output of a neural network trained based on \"policy gradients\". In RL, discount rates can be applied to increase or decrease the importance of immediate versus projected long-term rewards when determining the optimal next action.  
Q-learning is a subset of RL where models are built on Markov Decision Processes, stochastic processes where the next state is independent of previous state changes [37]. Q-values estimate the optimal state-action pairs and selects the action with the maximum reward value [36]. Q-learning is promising because even with imperfect or no information on the underlying Markov Decision Process, the transition-probabilities, the agent explores the states, rewards, and possible action pairs through greedy exploration policies that favor exploring unknown and high-reward regions. The optimal decision policy for actions to take can then be obtained with very little prior information. The agent then takes the action with the maximum Q-value calculated by the policy. Deep Q-learning networks can be used to model situations with large state spaces (e.g., Go) using DNN to avoid feature engineering to train the policy and a set of replay memories to continue using information learned in previous steps [36].  
Deep Q-learning networks (DQN) are especially adaptable to open issues within the 5G sphere. Mobile networks are increasingly dynamic where the number of apps, users, and topology of thenetwork have become increasingly ad-hoc. The ML systems used to approximate solutions for these networks must be equally flexible. DQNs could be applied here because they discover new optimal policies after observing additional situations without requiring the model to be completely retrained. Several teams have already used deep Q-learning to address ad-hoc mobile edge computing vehicular networks for 5G [30].
## 5 Material Uses
### Dolomite  
Dolomite is a widespread rock-forming mineral that is the elementary ingredient of sedimentary rock, dolostone, metamorphic rock, and dolomitic marble. It forms calcium magnesium carbonate with a chemical composition of CaMg (CO\({}_{3}\))\({}_{2}\). Limestone-containing dolomite was known as dolomitic limestone. Dolomite or dolostone is a carbonate sedimentary rock that contains more than 50% of the mineral dolomite by weight. Dolomite may precipitate from aqueous solutions (sandstones with dolomitic cement). Some dolomitic rocks are the so-called primary dolomites that were formed in lagoons where dolomite directly precipitated out of seawater, but such dolomites are much scarcer than previously believed. Few elementary dolomite deposits have been discovered from the Holocene age (the last 12,000 years). Seemingly, the elementary dolomites were common in the past, but this premise is difficult to confirm or deny, due to later diagenetic (processes affecting sediment after deposition) overprinting of the original material [25].  
However, there is doubt that dolomitic rock formation is dominated by elementary dolomite deposits, because laboratory experiments have shown that dolomite never precipitates from aqueous solutions under atmospheric conditions (1 atm pressure, temperature <60 \({}^{\circ}\)C). When dolomite rock is replaced by pristine limestone, there are too many pore spaces. However, this exegesis concerning the higher porosity of dolomite rock has been resumed.  
According to a paper published by the Geological Society of London, limestones become non-porous through compaction and cementation, whereas dolostones resist compaction and retain much of their porosity [26; 27]. Dolostones are generally employed for construction purposes. They are crushed and sized for use as an aggregate in asphalt and concrete, a road foundation material, rip-rap, railroad ballast, or even fill. It is also calcined in the cement industry and cut into blocks of specific sizes known as \"dimensional stone.\" In addition, the reaction of dolomite with acid increases its applicability. It is used for acid neutralization in the chemical industry, stream restoration projects, and as a soil conditioner [28].