esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Step 5: Identify Loss Scenarios  
In the STPA method, the \"Identify Loss Scenarios\" step comes after identifying the UCAs. The main goal of this step is to figure out how each UCA could happen and lead to potential losses or accidents.  
To accomplish this, we look at each UCA and think about the possible reasons or scenarios that could cause it to occur. We consider things such as:  
* What could go wrong with the control loop components (controllers, actuators, sensors, etc.)?
* What might cause the controllers to have incorrect or incomplete information about the system?
* How could the control actions be applied in the wrong way or at the wrong time?  
By brainstorming these potential scenarios, we develop a better understanding of the risks associated with each UCA. We document these scenarios and use them to develop more specific safety requirements and constraints for the system (Table 5).  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**UCA ID** & **Hazardous Causal Factor** & **HCF ID** & **Loss Scenarios** \\ \hline \multirow{4}{*}{
\begin{tabular}{} \end{tabular} } & Absence or delay in decision-making & \multirow{2}{*}{HCF1-N-1-1} & The OMS administrator is absent, or there is a delay in approving commands, GCS control commands may not be delivered. \\  & & & The GCS Administration Module does not operate normally due to hardware or software problems, and GCS control functions may not be provided. \\  & & & GCS control commands may not be delivered due to communication disruptions or data transmission failures between OMS and GCS. \\  & & & The GCS Administration Module may stop or intermittently operate due to a power outage or unstable power supply. \\  & & & The OMS administrator may fail to issue necessary commands due to a lack of concentration or responsibility. \\  & & & The OMS administrator may not be able to issue appropriate commands due to a lack of situation assessment or decision-making ability. \\  & & & The GCS Administration Module may not issue control commands due to non-reception or loss of status information from the GCS. \\  & & & The GCS Administration Module may not issue necessary control commands due to failure to process or analyze received GCS status information properly. \\  & & & GCS control commands may be incorrectly generated if external information such as weather, geography, or flight restriction zones is inaccurate or not updated in real time. \\ \hline \end{tabular}
\end{table}
Table 5: Identify Loss Scenarios (Partial list due to space constraints).
# Green Shipping--Multifunctional Marine Scrubbers for Emission Control: Silencing Effect
## 2 Materials and Methods
### The Case Studies: Experimental Tests and Numerical Analyses  
In this work, two geometries (Figure 6) were analysed: a simple expansion chamber and a model-scale scrubber.  
Figure 4: Example of acoustic mode shapes in circular ducts.  
Figure 5: Schematization of the acoustic FEM model with duct modes.  
The expansion chamber represents one of the simpler reactive mufflers, and its TL can also be easily calculated analytically with the following equation:  
\[\text{TL}=10\log_{10}\!\left(1+\frac{1}{4}\left(h\!-\!\frac{1}{h}\right)^{2} \sin^{2}(kl)\right)\ \, \tag{5}\]  
where \(l\) is the expansion chamber length (500 mm in the present case), \(k\) is the wavelength, and \(h\) is the expansion ratio between the cross-sectional area of the chamber and the inlet/outlet pipe (12.7 in the present case), assuming the same diameter for the inlet and the outlet pipe [26]. Moreover, for a simple expansion chamber, the frequencies corresponding to the minima (\(f_{min}\)) and the maxima (\(f_{max}\)) of the TL curve can be calculated using the following equations, where \(n\) is the number of the considered harmonics [27]:  
\[f_{\min}=n\frac{c}{2l}\ \ \ n=0,\ 1,\ 2,\ \ldots, \tag{6}\]  
\[f_{max}=\frac{c}{4l}\!+\!n\frac{c}{2l}\ \ \ n=0,\ 1,\ 2,\ \ldots, \tag{7}\]  
The analytical expression was used to assess both the experimental set-up and the numerical model.  
Then, a model-scale scrubber with perforated plates was tested. The perforated plates of the model-scale scrubber (Figure 6b) are 1 mm thick, with holes featuring a diameter of 6 mm. The spacing of the holes is 9 mm. The water at the bottom of the model-scale scrubber, which is generated by the water spraying on the perforated plate in the chemical processing of the exhaust gases to reduce emissions, was only considered numerically. The experimental set-up with the impedance tube was not designed to test the prototype with water inside.  
During the experimental tests, the room temperature and the pressure were 15 \({}^{\circ}\)C and 101,325 Pa, respectively. The same environmental conditions apply to the numerical models  
Figure 6: Geometry of (**a**) expansion chamber and (**b**) model-scale scrubber; dimensions in mm.  
for air inside of the chamber and the water at the scrubber bottom. Table 1 summarizes the characteristics of the fluids.
# Adaptive Computation Offloading with Task Scheduling Minimizing Reallocation in VANETs
## 5 Vehicular Adaptive Offloading (VAO)
### Task Scheduling  
The proposed task scheduling method has two important features: first, it utilizes the DAG to consider the precedence relationships between tasks, and, second, it selects only nearby vehicles whose routes are similar to the route of RV as SVs, in order to reduce the  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Notation** & **Meaning** \\ \hline \(D\) & a directed acyclic graph \\ \(V\) & a set of searched vehicles \\ \(V^{\delta}\) & a set of service vehicles \\ \(v_{r}\) & a requesting vehicle (RV) \\ \(v_{s}\) & a service vehicle (SV) \\ \(v_{min}\) & an SV that can provide the minimum EFT for a task \\ \(T\) & a set of the tasks \\ \(t_{i}\) & a task \\ \(t_{exit}\) & an exit task \\ \(\chi\) & a matrix of a task schedule \\ \hline \hline \end{tabular}
\end{table}
Table 2: The notations and meanings.  
chance of task reallocation. As mentioned earlier, a task schedule \(\chi\) is the most important factor affecting performance, since a long task schedule means a long execution time. In general, the more reallocations that occur, the longer the task schedule is.  
```
Input: a given DAG \(D\), a set of tasks \(T\) in \(D\), a set of searched vehicles \(V\) Output: task schedule \(\chi\)
1 compute average processing and communication cost
2 calculate \(\textit{rank}_{u}\) of every task in \(D\) in an upward direction, starting from an exit task
3 let \(\textit{T}^{\prime}\) be the list of tasks in \(\textit{T}\), sorted in non-increasing order by \(\textit{rank}_{u}\)
4\(\textit{V}\leftarrow\{v_{i}|v_{i}\in V\text{ and its route overlaps the route of }v_{r}\text{ by more than }\alpha\text{ blocks}\}\)
5 while there exist tasks in \(\textit{T}\)do
6\(t_{i}\leftarrow\) the first task in \(\textit{T}^{\prime}\)
7 for\(v_{k}\) in \(\textit{V}\)do
8 calculate the EFT (\(t_{i}\), \(v_{k}\)) with insertion-based scheduling
9 endfor
10 allocate \(t_{i}\) to the minimum EFT vehicle \(v_{min}\)
11 remove \(t_{i}\) from \(\textit{T}^{\prime}\)
12 endwhile
```  
**Algorithm 2**task schedule (\(\textit{T},\textit{V}\))  
Lines 1 to 2 compute the average cost of searched vehicles and the \(\textit{rank}_{u}\) of every task in an upward direction from an exit task. Line 3 sorts the tasks in non-increasing order by \(\textit{rank}_{u}\). Then, in Line 4, out of the searched vehicles, only vehicles whose routes overlap the route of \(v_{r}\) by more than \(\alpha\) blocks are filtered into a new set of vehicles \(V^{\prime}\). Only vehicles in \(\textit{V}\) are considered as a candidate SV that can provide the minimum EFT for each task in subsequent lines. Line 6 gets the first task in the sorted list \(\textit{T}^{\prime}\) into \(t_{i}\). In Lines 7 to 9, \(v_{r}\) calculates the EFT of \(t_{i}\) on every \(v_{k}\) in \(\textit{V}\) with insertion-based scheduling. Line 10 allocates \(t_{i}\) to the vehicle that can provide the minimum EFT. Lines 6 to 11 are repeated while there exist any unallocated tasks.
## 3 Spatial Audio Reproduction Techniques for Soundscape Design
### Perceptual Reconstruction of Sound  
Perceptual reconstruction techniques for spatial audio aim to replicate the natural listening experience by generating sufficient audio cues to represent the physical sound. Binaural technique, according to [PERSON] [72], is defined as \"a body of methods that involve the acoustic input signals to both ears of the listener for achieving practical purposes, e.g., by recording, analyzing, synthesizing, processing, presenting and evaluating such signals\". Reproduction through this technique has two parts: one is the synthesis and rendering portion of the signal, and the other is the playback system. Head related transfer functions (HRTFs) are used to describe the change in sound spectrum due to the interaction of sound waves with listener's body, head and pinna [59]. The synthesis and rendering of binaural audio is usually realized by convolving a dry sound source with the HRTF at a particular direction. For accurate reproduction, personalized binaural rendering using individualized HRTFs is required [73]. There are various techniques to accurately measure or synthesize the individualized HRTFs [74; 75]. Recently, [PERSON] et al. [76; 77] proposed a fast and continuous HRTF acquisition system that incorporates the head-tracker to allow unconstrained head movements for human subjects. Binaural technique is used by soundscape researchers to playback the recorded sound with sufficient spatial fidelity. [PERSON] et al. [54] used it for their study with the aim of finding the underlying and primary components of soundscape perception. They used an artificial head (Bruel & Kjaer Type 4100) for binaural recording and headphone (Senheiser HD600) for playback.  
Binaural reproduction can also be realized over a pair of loudspeakers. Transaural audio is the method used to deliver correct binaural signals to ears of a listener using speakers (mostly in a stereo setup). However, there are some challenges to this approach, namely effective crosstalk cancellation and limited sweet spot range. Several solutions to these problems have been described in the literature, e.g., [78]. Often, double transaural approaches [78] are used to make sure that the front back confusion is minimum. However, this method has rarely been used in soundscape studies. [PERSON] et al. [79] compared the quality of spatial audio reproduction between transaural, ambisonics and stereo methods. They used the Ircam default decoder to playback different auditory scenes from both indoors and outdoors [66], such as road traffic noise, a car interior, music concert, etc. Two experiments were performed, one for overall spatial quality evaluation and the other for localization accuracy. The results from the study indicate that ambisonics provide good immersion but poor localization for a sound scene. Stereophony and transaural techniques, on the other hand could be useful in the case where precise localization is required but do not have good immersive spatial sound.
# Modeling of Satellite-to-Underwater Integrated FSO-PON System Using NOMA-VLC
## 1 Introduction
### Contributions  
The contributions of this paper are explained as follows:  
* Although the hybrid FSO-PON system is a promising solution for 6G long-haul networks, a satellite-to-underwater-based hybrid FSO-PON using a NOMA-VLC system still has not been addressed in terms of data privacy or security scenarios in the recent literature;
* A NOMA-VLC system has been utilized with a hybrid FSO-PON system employing MNZCC OCDMA code, where the justifiable secure communication is carried over PON, FSO and VLC links, simultaneously;
* The broadcasting information transmission in water undergoes turbulence and scattering with absorption losses. Thus, to attract more insightful recommendations about the suggested system, three distinct ocean water types, namely clear, coastal and turbid harbor, are studied;
* Additionally, the effect of UOWC VLC link losses, fiber impairments like nonlinearities, noise, attenuation, dispersion etc., are also measured through numerical and simulation analyses;
* The designed model is verified through a comparison of its performance with existing works.  
The novelty behind this work is to design a satellite-to-underwater optical communication model, which permits a platform for developing Internet access to various satellite-to-land-to-undersea devices distinct communication environments and channels facing difficult and vital challenges. For this, a hybrid PON-FSO system using a VLC for the long-haul as well as high bandwidth has been proposed. The designed model's performance is measured under the impact of different fiber and water impairments with different OCDMA codes. The numerical and simulation results show that the proposed design offers >100 Gbps data rate, >100 km fiber transmission distance and enhances the communication security from satellite-to-underwater. It helps to fulfil long-haul, high-speed, mobile, secure and cost-effective desires of connected devices in distinct oceans on earth. Furthermore, it offers potential applications that support many end users, long-haul transmission with a high transmission rate and security in numerous satellite-to-underwater scenarios.  
In this paper, the suggested system design is illustrated in Section 2. Section 3 presents the numerical analysis of the suggested design. Section 4 shows the results along with the discussion and the conclusion follows it in Section 5.
# Object Detection-Based One-Shot Imitation Learning with an RGB-D Camera
## 3 Methodology
### Motion Policy Network  
A depth map is chosen as the input of the motion policy network. The position change of the end-effector \(\mathbf{P}\) and the state of the gripper \(\mathbf{G}\) are made up of a motion action, which is the outputs of the policy network. The state of the gripper is a Boolean variable and the position change of the end-effector is a continuous variable. Therefore, the output layer of the position action is linear function and sigmoid function is used for the gripper state action. Detailed structure of the motion policy network is shown in Figure 4. The depth map of the environment is first processed by the trained encoder network and then these feature blocks are fed into a convolutional layer to compress their dimensions. Output of the convolutional layer is reshaped into a vector and concatenated with the  
Figure 3: The structure of the object detection network.  
pixel coordinates of the keypoint that obtained by the object detection network. Two fully-connected layers are followed. With different output layers, the position action and the gripper action can be acquired. The activation function of these two dense layers is ReLU function and the dropout layer is also used between each dense layer to avoid overfitting. For continuous output, the mean square error is normally used as the loss function and cross-entropy function is the loss function of Boolean variable. Losses of the position change of the end-effector and the state of the gripper can be formulized as:  
\[\mathcal{L}_{p}=\left\|\mathbf{P}-\mathbf{P^{\prime}}\right\|_{2}^{2} \tag{5}\]  
\[\mathcal{L}_{g}=-\mathbf{G_{L}}log(\mathbf{G})-(1-\mathbf{G_{L}})log(1-\mathbf{G}) \tag{6}\]  
\[\mathcal{L}_{T}=\lambda_{4}\mathcal{L}_{p}+\mathcal{L}_{g} \tag{7}\]  
where \(\mathcal{L}_{p}\) and \(\mathcal{L}_{g}\) are losses of position changes and the gripper states respectively. \(\mathbf{P}\) is the labeled position change and \(\mathbf{P^{\prime}}\) is the predicted value. \(\mathbf{G_{L}}\) is the labeled gripper state and \(\mathbf{G}\) is the output of the network. Parameter \(\lambda_{4}\) is the weight of loss of position change to increase the effect of this term.
## 1 Introduction
There have been significant advances in this regard in colloidal synthesis, which have enabled the use of nanoparticles as \"artificial atoms\" to build novel materials that integrate the size- and shape-dependent characteristics of individual nanoparticles while also generating unique combined properties via their interactions when organized into clusters [25]. The solvothermal synthesis of clusters stands out, due to the relatively simple steps and setups required and the low energy requirements, and despite the limited scalability and absence of options for direct observation of the reaction process. Clusters of transition metal oxides such as Fe\({}_{3}\)O\({}_{4}\)/\(\gamma\)-Fe\({}_{2}\)O\({}_{3}\), MnFe\({}_{2}\)O\({}_{4}\), CoFe\({}_{2}\)O\({}_{4}\) and ZnFe\({}_{2}\)O\({}_{4}\) have been successfully produced using this method, which allows for an effective tuning of the size of both clusters and the nanoparticles that compose them [26; 27], taking into account a two-stage growth process: initially, the nucleation of nanocrystals and, subsequently, the consistent and controlled aggregation into larger nanostructures [20]. These aggregates or clusters of nanoparticles can offer alternative ways to increase thermal efficiency, given the different arrangement of the nanoparticles that create a particular solid-liquid interface through which heat transfer is promoted, whose extension is also tuned in the final nanofluid when considering different average size or concentration of clusters. This research reports on the synthesis of clusters of cobalt ferrite nanoparticles with customized average sizes (from 97 to 192 nm) via the solvothermal method. The formation of the spinel cobalt ferrite magnetic phase was confirmed by X-ray diffraction (XRD), Raman spectroscopy (RS) and vibrating-sample magnetometry (VSM). The effective adjustment of the average size of the clusters was checked by transmission electron microscopy (TEM), while the specific surface area (SSA) of the clusters was examined by nitrogen physisorption. Moreover, these clusters were dispersed in different base fluids, with the purpose of studying the influence of this particular solid-liquid interface created between the magnetic material arranged in clusters and the fluid under consideration in the thermal conductivity of novel nanofluids.
## 1 Introduction
* In order to evaluate the effectiveness of this algorithm, extensive simulation experiments were conducted in this research. Taking into account factors, such as lighting conditions and urban structure, we also selected a dataset from the urban area of Melbourne, Australia, for further experiments. The results show that compared to the existing technology, this scheme is able to significantly improve coverage and the fairness index while extending the lifetime of the system. The rest of the paper is organized as follows. Section 2 reviews the relevant work related to our research and describes the differences in comparison with the work presented here. Section 3 introduces the system model and outlines problem statement. Section 4 provides a detailed introduction to our algorithm and conducts a complexity analysis. Section 5 presents our experimental content, which encompasses comparative experiments with several typical algorithms currently in use. Finally, Section 6 summarizes our research and provides prospects for future studies.
# Service Interface Translation. An Interoperability Approach
## 3 Service Interface Translation
### Proposed Solution  
In order to solve the interoperability issues at the service level, a new methodology has been designed. The proposed solution is based on the aforementioned requirements. The interface mismatch between two systems can be solved via the generation of an interface translator instance. The new interface bridges both interfaces, providing a secure and successful path for data exchange.  
The generated instance (translator) has an interface capable of understanding the data from the consumer. When the provider response is received, it is translated and sent to the consumer. The translation algorithms are based on the service contract information of both systems.  
The use of this intermediate translation instance allows seamless communication. Details about the translation, the generator system, and the generated interfaces are provided in the following sections.
# Fast Equipartition of Complex 2D Shapes with Minimal Boundaries +
## 6 Experimental Evaluation
### Evaluation Metrics  
Based on the formulation of the 2D-SEP problem, we have compared the performance of _SEP-PSO FBC_, _SEP-FBC_, _SEP-RG_, and _SEP-ILS_ on the normalized total intrinsic boundary length \(NL(R)\) (see Section 3). For a given dataset, we also compute \(Pr(m/NL)\), where \(m\) is a method in \(\{\)_SEP-PSO FBC_, _SEP-FBC_, _SEP-RG_, _SEP-ILS_\(\}\). \(Pr(m/NL)\) is defined as the percentage of shapes of the datasets where the method \(m\) outperforms the others under the \(NL\), defined as follows:  
\[Pr(m/NL)=\frac{\sum_{S\in D}H(NL_{m}(S)-max_{n\
eq m}NL_{n}(S))}{|D|} \tag{10}\]  
where \(H\) is the unit step function, \(|D|\) denotes the number of shapes of dataset \(D\), and \(NL_{m}(P)\) is the \(NL\) metric of method \(m\) given the shape \(S\). This also means that the value \(100\%-\sum_{m}Pr(m/IoU)\) gives the percentage of images for which there is no clear winner method. In addition, we study the computational efficiency of all methods by measuring the Average Execution Time (\(AET\)) per image for different values of \(N\).  
Furthermore, in this work, we use the circularity (\(Circ(S)\)) and solidity (\(Sol(S)\)) shape descriptors [37] to show that the used datasets consist of quite different shapes and to measure how the performance of the proposed methods is affected by the complexity of the shapes. Circularity (\(Circ(S)\)) is defined by the ratio of the area of the shape \(|S|\) multiplied by the product \(4\pi\) divided by the square of the perimeter of the shape (\(Per(S)\)). The circularity of a circle is one (maximum value), and, the lower it goes, the less circular it is or has a more complex outer boundary.  
\[Circ(S)=\frac{4\pi|S|}{Per^{2}(S)} \tag{11}\]  
Solidity (\(Sol(S)\)) is defined by the proportion of pixels in the convex hull that are also in the region. Solidity describes the extent to which a shape is convex or concave. The solidity of a completely convex shape is one; the farther the solidity, the greater the extent of concavity in the structure. Figure 10(a) shows the circularity and solidity relative frequency histogram for the LEMS and MPEG-7 datasets, highlighting the diversity of shapes found in both datasets.