esa-sceva/satcom-chunk-collection huggingface dataset:
# Time Series Prediction of Sea Surface Temperature Based on an Adaptive Graph Learning Neural Model
## 3 Experiments  
### Data Set  
We adopted NOAA's earth system research laboratory provided by the physical sciences, NOAA and high resolution SST data of the high resolution ocean surface temperature data set (OISST) as the source of experiment data ([[https://www.esrl.noaa.gov/psd/](https://www.esrl.noaa.gov/psd/)]([https://www.esrl.noaa.gov/psd/](https://www.esrl.noaa.gov/psd/)) accessed on 4 May 2022). The data set covers the mean daily, weekly, and monthly sea surface temperatures of the global ocean from September 1981 to the present, which is updated over time. The daily mean SST data set (4018 days) with a spatial resolution of 0.25\({}^{\circ}\times\) 0.25\({}^{\circ}\) from January 2008 to December 2013 in the Bohai Sea and the South China Sea was selected as the data set for the experiment in this paper. We first preprocessed SST data from the Bohai Sea (135 points) and South China Sea data sets (2307 points), and then divided the data sets into training data sets and test data sets as shown in Table 1 for daily mean SST prediction. Firstly, the Bohai sea data set is trained and tested. Then, in order to verify the robustness of the model, this paper also carries out comparative experiments of six models on the South China Sea data set.
# Methodology for Conceptual Navigational 3D Chart Assessment Based on Eye Tracking Measures
## 2 Materials and Methods
### Stimuli  
At the start, the research area was defined, specifically, a cell of the 2D navigational map. The selected cell encompasses the range of the harbour in Swinoujscie, Westpomeranian Voivodeship, Poland (Figure 6).  
The 2D and 3D Chart Scenarios  
Based on the selected cell of the navigational map, a conceptual 3D navigational map was developed using ArcGIS Pro software (version 3.4.0.). The same area allowed for subsequent comparisons between maps. The assumption for the transformation from 2D to 3D was to minimally adjust the standardised map into three-dimensional space. Existing map symbols were preserved but presented in 3D space. Additionally, surface objects, such as buildings and forested areas, were represented at Level of Detail 1 (LoD1) according to  
Figure 5: Example of a map scenario for the task: “Find the north cardinal buoy on the next slide and click on it”. Description in English: text row 1—Task 3: Find the north cardinal buoy on the next slide and click on it, text row 2—Legend, text row 3—Pillar buoy, text row 4—North cardinal mark.  
Figure 6: Location map of Swinoujscie (marked with a red dot) in Poland (**left** side) and map of the area of the navigation map cell, Swinoujscie harbour (**right** side).  
the CityGML standard [59]. The conceptual map also included three-dimensional models of lights.  
The conception was based on keeping the relation with standardised 2D maps; we did not aim at this stage of research to develop a ready-to-use product but only test its effectiveness. Such conception can be treated as one option among many. Maps for test scenarios were selected, along with waterways, to cover various types of symbols. Figure 7 illustrates the locations with borders of the 2D and 3D maps for the test series. The spatial range of the 2D and 3D maps were similar, which allowed us to compare the same cartographic content. The reason for choosing these cells was the possibility to relate research results to previous research by using computer-based 3D navigation charts [58]. Finally, 20 maps in each series were prepared for the 2D and 3D charts. The maps in the series include various cases with simulated content similar to real conditions, which continuously change the map contents.  
The preparation of twenty test scenarios was intended to simulate one stage of the navigation process--the assessment of the navigation situation using an electronic map. In a real-world setting, this task involves the watch officer analysing the content of the map, visually interpreting the types and positioning of objects, and assessing the safety of the ship's conducting on this basis.
## 2 Materials and Methods
### Ants
#### 2.5.1 Feasibility D-MAS  
The FAs were implemented to reduce the usage of processing resources and time, to partially cope with R1 and to minimize unnecessarily traveled distances and time, so as to address R4. They facilitate the search of the EAs for an optimal route.  
A CA's feasibility D-MASs module is initialized at MRSA's request via a direct message. To re-address R1, the module of each CA is executed only once, considering the static positions of the CAs and their real-world counterparts. Feasibility pheromones do not evaporate (thus, we may rather use the term feasibility information). Feasibility information (FI) exists in the GSN during the lifetime of the system. Before an MRSA deploys the EAs, it tries to find an FI originating in the MRSA's destination. If there is no FI deployed by the destination CA, an MRSA requests the CA to deploy FAs by a direct message. Such one-shot deployment of the FAs for each crossroad, and the persistence of the FI, reduce the time and the usage of processing resources.  
Figure 6: An example of the activation types.  
Figure 7: Ant colony ID.  
The task of the FAs is to spread FI about its CA to all other CAs. Each FI contains a road sign with the ID of the physical node from which an ant arrived and the cost (composed of the aggregated time and distance) of its traveled route. When an FA is deployed, it clones itself and the clones are placed at all possible virtual nodes. During their journey, their clones are placed at all virtual nodes, except the node they came from. The life cycle of an FA ends, when FI is already dropped at the node and contains a lower cost. The use of the FAs reduces the number of EAs that are required, as well as their complexity.  
Each physical node (in the virtual environment) contains a pheromone structure called the pheromone container (PhC). The structure is managed by the CA. PhC holds the IPs and the FI. It also contains a function that ensures the evaporation of the IPs.  
In order to re-address R1, a data structure that holds FI was implemented as a binary search tree (BST); see Figure 8. The main advantage of the BST is the fast look-up (O (log n) time complexity on average), addition, and removal of items.  
The key values in the feasibility BST are represented by the names of the CAs, which own the FI. The values of the BST consist of the FI, and they may contain up to four \"pairs of road signs and costs\" (PsRC). When dropping the FI, the FA inserts the number of the hops traveled into it.
# Advances and Challenges in IoT-Based Smart Drug Delivery Systems: A Comprehensive Review
## 6 Regulatory Frameworks  
Regulatory frameworks and guidelines for ensuring data security in IoT-based drug delivery systems vary across different countries and regions. Some notable examples include the general data protection regulation (GDPR) in the European Union, which sets guidelines for data collection and processing, and the United States Food and Drug Administration (FDA) guidelines, which address cybersecurity considerations for medical devices [81]. The National Institute of Standards and Technology (NIST) provides cybersecurity guidelines applicable to IoT devices, while the International Medical Device Regulators Forum (IMDRF) offers guidance on cybersecurity for medical devices. The European Medical Device Regulation (MDR) and Underwriters Laboratories (UL) also provide requirements and standards for ensuring the security of IoT-based systems. The threats faced by IoT drug delivery systems include data breaches, unauthorised access, and the potential compromise of patient information. To overcome these threats, manufacturers and stakeholders must implement robust security controls, encryption mechanisms, access controls, and regular software updates to mitigate vulnerabilities and protect the confidentiality, integrity, and availability of data. Ongoing monitoring, compliance with regulatory frameworks, and staying informed about emerging threats and best practices are crucial for maintaining data security in IoT-based drug delivery systems.
# Optimal Routing for Time-Driven EH-WSN under Regular Energy Sources
## 2 Energy Consumption Model  
There are two types of duty-cycled MAC protocols in sensor networks, namely synchronous and asynchronous. Synchronous protocols are based on TDMA and thus represent an extreme case of duty-cycling, since nodes are only active during the specific timeslots devoted to transmit or receive. This results from the fact that any transmitter and its receiver wake up at the same time. However, such protocols require tight synchronization among nodes, and exhibit significant limitations in terms of scalability and adaptiveness to changing traffic conditions. These disadvantages make asynchronous protocols more attractive, at the expense of more energy consumption and some throughput degradation.  
In asynchronous communication, nodes have their duty cycles completely decoupled, as it is shown in Figure 1. Thus, two general mechanisms have been proposed in the literature in order to link a transmitter that has data to send with its receiver: Low Power Listening (LPL) and Low Power Probing (LPP). In LPL, the responsibility of the task is shifted to the transmitter, which uses a duty period to initially send a long preamble or a burst of advertisement packets in order to warn the receiver that it has pending data. It can also send a repetitive sequence of the data packet itself. Upon waking up and detecting the preliminary signalling or the sequence of data packets, the receiver stays awake until the transmission process is completed, meaning that a full data packet has been correctly received. Examples of implementations of LPL are X-MAC [15], Aloha with preamble sampling [16], B-MAC [17], and BoX-MAC-1 and BoX-MAC-2 [18]. In contrast, in LPP [19], it is the receiving node that periodically sends small packets called beacons or probes, to announce that it is awake and ready to receive data. A node willing to send a packet turns its radio on and waits for a probe. Upon receiving a probe from the intended destination, it sends an acknowledgment and, subsequently, the data packet. The most representative LPP protocols are RI-MAC [20] and A-MAC [21].  
As stated above, both synchronous and asynchronous mechanisms are part of the class of duty-cycled MAC protocols, as in both cases nodes use non-activity periods to switch to sleep mode in order to save energy. However, usually the term duty-cycled MAC protocol refers to the asynchronous version, which is the most extended implementation [22]. This is the focus of the present paper.
# Stream: Spatial Trajectory Enhanced Attention Mechanism for Abnormal UAV Trajectory Detection
## 3 System Design
### Attention Mechanism
```
Input: Input feature map \(X\) Output: Attended feature map \(X^{\prime}\) Initialize: Trajectory Convolutional Layer: Conv2D\({}_{\text{trajectory}}\); Context Convolutional Layer: Conv2D\({}_{\text{context}}\); Dynamic Weights Layer: Dense\({}_{\text{dynamic}}\); Spatial Attention Convolutional: Layer: Conv2D\({}_{\text{spatial}}\); forachtraining iterationdo \(T\leftarrow\text{Conv2D}_{\text{trajectory}}(X)\); /* Extract Trajectory Features */ \(C\leftarrow\text{Conv2D}_{\text{context}}(X)\); /* Extract Contextual Background Features */ \(F\leftarrow\text{concatenate}(T,C)\); /* Concatenate Trajectory and Context Features */ \(D\leftarrow\text{Dense}_{\text{dynamic}}(F)\); /* Compute Dynamically Weighted Features */ \(A\leftarrow\text{Conv2D}_{\text{spatial}}(D)\); /* Generate Spatial Attention Map */ \(X^{\prime}\gets X\odot A\) /* Apply Spatial Attention to the Input Feature Map */  end for return\(X^{\prime}\)
```  
**Algorithm 1** Spatial Trajectory Enhanced Attention Mechanism (STEAM)
# Wireless Control Combining Myoelectric Signal and Human Body Communication for Wearable Robots
## 1 Introduction
(4) HBC is advantageous for miniaturizing the transmitter (Tx) by using one electrode for both detection and transmission of myoelectric signals (as described in Section 3). Therefore, HBC is very well suited for wearable robot control compared to other short-range wireless communication technologies. In this paper, focusing on the use of myoelectric signals for wearable robot control, we present a wireless control method for wearable robots which combines myoelectric signal detection and HBC. Section 2 describes the developed HBC module structure. Section 3 describes how to share the detection electrode and transmitting electrode for Tx module \begin{table}
\begin{tabular}{l c c c} \hline \hline  & **Bluetooth** & **UWB** & **HBC** \\ \hline Frequency & 2.4 GHz & 3.1–10.6 GHz & 10–60 MHz \\ Bandwidth & \(\sim\)1 MHz & \(\sim\)500 MHz & \(\sim\)50 MHz \\ Transmission speed & Not fast & Very fast & Fast \\ Transmission loss & Large & Very large & Small \\ Transmission mechanism & Radiation & Radiation & Electric field coupling \\ Antenna & Need & Need & Not need \\ Sharing of electrodes & Not possible & Not possible & Possible \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of Bluetooth, UWB, and HBC for wireless transmission on the human body. Figure 1: Concept of a wearable robot hand controlled using myoelectric signals. miniaturization, and Section 4 describes the electrode structure suitable for the receiver (Rx) module. Section 5 describes the communication performance of the HBC module and experimental result for driving a robot hand based on myoelectric signals. Lastly, Section 6 concludes this paper.
## 3 Materials and Methods
### Forecasting Models  
We used the most popular models (SARIMA, Prophet, LSTM, and the proposed SLSTM) in our experiment. Their features and implementation specifications are summarized as follows.  
Figure 5: In the (**top**) plot, the blue and orange lines are the raw data and the data after removing outliers, respectively. The (**bottom**) plot shows that the missing values (blue line) of the orange line are interpolated; a part of the bottom plot is also enlarged and shown.
# Field Data Forecasting Using LSTM and Bi-LSTM Approaches
## 2 Materials and Methods
### Modeling and Pattern Selection  
#### 2.3.1 Proposed model  
In this section, we describe the design methodology of our LSTM-based soil moisture forecasting model. Both LSTM and Bi-LSTM are used in our model. Our design (see Figure 3) was developed through a trial phase in which we tested various model architecture settings such as layer count, size, and so on. Our model has 14 inputs, which are the environmental parameters. Following the first layer is a stack of 4 pairs of LSTM and Dropout layers. A dense layer of 12 units is used to encode the feature pattern of the input data and an output prediction unit is the prediction of the soil moisture.  
It is worth noting that some the outcomes of the SupplyLedger Project (The SupplyLedger Project www.supplyledger.qa, accessed on 6 December 2021) were used in the development of the LSTM model.
# An Efficient Task Autonomous Planning Method for Small Satellites
## 1 Introduction
The simulation results show the efficiency and the effectiveness of the proposed approach to determining a suboptimal tour for multi-goal motion planning in complex environments cluttered with obstacles. [PERSON] [23] proposed a generic approach for the integration of vehicle routing and scheduling, and motion planning for a group of autonomous guided vehicles, and their method realized the time-optimum and collision-free paths for all autonomous guided vehicles. These papers give us some inspiration for the autonomous task planning of small satellites. This paper will discuss and analyze the difficulties in the time-dependent and resource-dependent task planning of small satellites, as well as put forward the task planning model based on various constraints. On the basis of the above literature, we will propose a rolling-planning framework of on-board autonomous task planning and an improved genetic algorithm for rolling planning, in order to meet the need of real-time tasks. The rest of the paper is organized as follows. The second part will propose the model of the task planning and specify the corresponding restriction conditions. Then, the framework and algorithms to solve this problem will be presented in the third part. In the fourth part, the feasibility and effectiveness of the proposed method will be verified by an example. Finally, the conclusion is given in the fifth part.
## 5 Results and Discussion
### Workpiece Surface Condition Analysis  
In order to confirm the effects of the non-toxic magnetic abrasive compound on the surface finishing of orthopedic Ni-Ti wire, SEM microimages were used. Figure 8 shows the SEM images of the Ni-Ti wire workpieces before and after finishing under the optimal conditions. Before the finishing process, many grooves, scratches, and uneven patches were visible, and the surface roughness (Ra) value was 0.13 \(\upmu\)m (Figure 8a). After the MAF process with grapeseed oil at 1000 rpm for 180 s was completed, all of the initial defects were completely removed, and the surface's condition was smooth and clean (Figure 8b). The final surface roughness (Ra) value was 0.04 \(\upmu\)m. This confirmed that grapeseed oil can be used as the processing oil for high-precision finishing of Ti-Ni wire material.  
Figure 6: Surface roughness (Ra) vs. finishing time according to the oil used (1000 rpm).  
Figure 7: Surface roughness (Ra) vs. finishing time according to the oil used (2000 rpm).
# Propagation Attenuation Maps Based on Parabolic Equation Method +
## 3 Propagation Attenuation Map
### Exemplary Results  
To check the developed algorithms, two positions of the TX have been proposed (see Figure 3). One of them is located in a lowland area (\(52.51\,\,{}^{\circ}\!N,\,\,18.48\,\,{}^{\circ}\!\)E), the other-in a hilly terrain (\(51.22\,\,{}^{\circ}\!N,\,\,15.55\,\,{}^{\circ}\!\)E). For each transmitting point located in the center of the map in Figure 3, terrain profiles are determined for the propagation directions (i.e., azimuths), which are shown by rays marked in red. In this case, the elevation maps obtained based on DTED2 [46; 47], i.e., with an average resolution \(\Delta R=30\) m, are illustrated in the background.  
Figure 4 presents PEM-based electromagnetic field distributions obtained for yellow rays (see Figure 3) representing the terrain profiles at the azimuth \(\alpha_{0}=0^{\circ}\) in two analyzed areas. In this case, the terrain profiles are shown as white shapes below the field distributions. In Figure 5a, we depict the exemplary level equal to the receiving antenna height, \(h_{R}=2\) m, above the analyzed lowland terrain profile \(\mathbf{TP}(x)\) from Figure 4a. The path loss read from the determined field distributions at the receiving antenna height above the terrain profile is the basis of the created PAM matrix. Figure 5b presents exemplary path losses versus terrain profile radius (i.e., length) for the two analyzed profiles and field distributions from Figure 4.  
Figure 4: PEM-based electromagnetic field distributions for exemplary terrain profiles from (**a**) lowland and (**b**) hilly areas.  
Figure 3: DTED-based elevation maps for two selected TX locations in (**a**) lowland and (**b**) hilly areas for which terrain profiles are calculated.  
The path loss values versus terrain profile radius, \(\mathbf{L}(D)\), for analyzed receiving antenna height, \(h_{R}\), and calculated field distributions (e.g., see Figure 5b) are written into the PAM matrix in the cells corresponding to the analyzed azimuth, \(\alpha_{n}\). In this way, the sparse PAM matrix, \(\mathbf{PAM}(j,k)\), is created. It is the basis for determining the full PAM matrix by applying the interpolation process described in Algorithm 2. The final result of the developed algorithms is illustrated in Figure 6. In this case, we show PAMs (i.e., full matrices) for two selected TX locations (see Figure 3) in lowland and hilly areas, respectively. These PAMs were obtained based on 360 terrain profiles with a step \(\Delta\alpha=1^{\circ}\). The PAM quality depends on the density of profiles on the basis of which it is generated. On the other hand, we may see the differentiation of the obtained PAMs depending on the terrain and TX position for which they are determined.
## 2 Results and Discussion  
### Effect of Microwave Pretreatment on Oil Extraction Yield  
Oil extraction yield is one of the key indexes to evaluate the production efficiency of oil from oilseeds. MW treated and untreated peanut samples were cold-pressed to study the effect of MW pretreatment on oil extraction yield. The initial oil content of peanut was 48.90 \(\pm\) 0.30%. The oil extraction yield of untreated peanut was 57.21 \(\pm\) 1.51%. As can be seen from Figure 1, the increasing microwave pretreatment time significantly (\(p\) < 0.05) increased the oil extraction yield. For the 1, 2, 3 min MW-treated peanut, the oil extraction yield increased to 64.93 \(\pm\) 0.58%, 68.16 \(\pm\) 0.59% and 73.93 \(\pm\) 0.21%, respectively. Five minutes of MW treatment reached the maximum oil extraction yield (76.52 \(\pm\) 0.10%), which was 33.75% greater compared to the control. The results are consistent with previous studies of the MW pretreatment effect on the extraction yield of rapeseed oil, palm oil, soybean oil, rice bran oil, cottonseed oil, and Chilean hazelnuts oil [9; 10; 11; 12; 13; 14; 20; 22; 23]. With 2-10 min MW pretreatment on 10.5% moisture rapeseed, the oil extraction efficiency by cold pressing gradually increased from 39.21% to 53.73% [23]. The oil extraction efficiency by solvent extraction increased with longer irradiation time (up to 3.5 min), and the optimum extraction conditions resulted in a cottonseed oil extraction efficiency of 32.6% [13]. MW pretreatment to oilseeds improved the oil yield of both extraction methods (mechanical pressing and solvent extraction). Microscopic studies on the MW-treated oilseed structure changes showed the MW resulted in protein denaturation which could cause damages in the lipoprotein membrane surrounding individual lipid bodies. These changes promote the passage of oil from the cell membrane, which leads to improving the oil release efficiency during the extraction procedure [9; 14; 22].