esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methods  
### Speaker Test  
We conducted a speaker test to investigate whether and how bird vocalizations can be localized in a forest environment using azimuth and elevation angles. The experiment was conducted at Nagano park, Kawachinagano, Osaka, Japan on 3 December 2018 (Figure 1). Figure 2 shows a schematic diagram of two experimental setups. In Experiment 1, we placed a loudspeaker on a tripod (height = 1.3 m). A 16-ch microphone array DACHO (WILD-BIRD-SONG-RECORDER; SYSTEM IN FRONTIER Inc., Tokyo, Japan) was also placed on a tripod. The array was specifically developed for bird observations in the field. It consists of 16 microphones, arranged within an egg-shaped frame, which is 17 cm in height and 13 cm in width. It records using a 16-channel, 16 bit, 16 kHz format. Recorded raw data are stored in SD cards and can be exported in wave format for further analysis. One can schedule a recording by preparing the time settings in a micro-SD card. See [18] for more detail and an example of using this microphone array in open fields. We changed the distance between the loudspeaker and the microphone array from 0 to 65 m, with an interval of 5 m, by moving the microphone array along a straight path. This is because the maximum length of the ridge that could be considered straight was 65 m around the loudspeaker. Within this distance, a spacing of 5 m was chosen as it was sufficient to measure the effect of the difference in loudspeaker height and the horizontal difference between the array and the loudspeaker.  
We replayed a sound file containing four vocalizations of Scaly Thrush (_Zoothera dauma_) at each location as shown in Figure 3. The distance between the loudspeaker and the microphone was 30 m (Experiment 1). In this figure, four vocalizations of the replayed songs were localized successfully, and at the same time, other sound sources were localized around 1 and 8 s. This species is known to sing this type of songs mainly at night. In this experiment, we adopted this vocalization as the playback sound, to simulate observations of such nocturnal vocalizations, which are not easily observed by other methods such as video recordings.  
In Experiment 2, we attached the loudspeaker on a tree, 6.55 m above the height of the microphone array. This is because it was the maximum height at which we could  
with small patches of broad leaf trees. We placed a DACHO on a path around a patch of broad leaf trees as shown in Figure 4.  
Recording was conducted from 4 April to 7 May 2018. Common bird species actively vocalized during the breeding season here. In particular, Blue-and-white Flycatchers (_Cyanoptila cyanomelana_) tend to sing, advertising their territories, on top of tall trees in their territories. We focused on a 1000-s recording from 8:00 AM on 3 May (Figure 5), where such a typical pattern of bird vocalizations was observed.
# Ground-Based Cloud Image Segmentation Method Based on Improved U-Net
## 2 Materials and Methods
### Encoder  
The designed encoder module in this paper consists of four dilate blocks and four down-sampling processes; each dilate block contains two dilated convolutions and one ASPP, as shown in Figure 2.  
Dilated convolution is a special type of convolution operation. In standard convolution, the elements of the convolution kernel are arranged consecutively, while in dilated convolution, gaps are inserted between the elements of the convolution kernel. This increases the receptive field of dilated convolution without directly increasing the size of the kernel, allowing it to capture features over a larger range, thereby avoiding the issue of information loss during down-sampling [20,21]. The cloud structure in the image spans multiple scales, including large-scale cloud clusters and local small cloud clusters. In order to further enhance the ability to extract cloud image features, this paper adds ASPP between the two dilated convolutions in each layer of dilate blocks. ASPP [20] was proposed to combine semantic information from different receptive fields without losing contextual information, thereby improving segmentation accuracy.  
Therefore, this paper combines dilated convolution with ASPP, replacing traditional convolution layers with dilated convolution as the encoding module. This approach retains the fine structure of cloud images and reduces the loss of cloud layer information, while capturing a larger range of contextual information, completing the feature extraction of shallow cloud images. Additionally, by embedding ASPP between two layers of dilated convolution and setting convolution kernels at different dilation rates, multiple scales of cloud features are extracted in parallel. This allows the encoder to simultaneously capture large-scale background information and small-scale detail features, adapting to the complex multi-scale structure of cloud images.  
The encoder accepts a 128 \(\times\) 128 \(\times\) 3 RGB cloud image as input, and after feature extraction using four layers of dilate blocks, the output size is 8 \(\times\) 8 \(\times\) 128. The bottleneck  
Figure 2: The main components of the dilate block.  
layer, located at the bottom of the network, receives the output from the encoder and operates through two convolutional layers to effectively filter and compress the information, allowing the most important features to be retained and passed on to the decoder part.
# An Interoperable Component-Based Architecture for Data-Driven IoT System
## 3 Component-Based IoT Architecture  
The illustration of the IoT architecture is shown in Figure 1. Through the adoption of the component-based design method, we defined five major types of classification for devices and services that interact under the proposed architecture: (i) central control system component; (ii) end devices component; (iii) data-driven feedback model component; (iv) external data input component; and (v) software application component. The abstract representations of these classified components are provided to efficiently create and manage the services of each component. Based on the classification, the interface protocols and ways of interacting between each component are determined. The design of the architecture adopted mobile edge computing (MEC) technology that greatly reduces the latency by placing the computational power at the edge to meet the real-time needs of IoT applications [35].  
To perform specific tasks independently or corresponding to other components, the components need to expose their services in a homogeneous way. Hence, the proposed IoT architecture should be  
\begin{table}
\begin{tabular}{l c c c} \hline \hline  & **Data-driven Function** & **System** & **System** \\  & **Availability** & **Interoperability** & **Reusability** \\ \hline DIAT [15] & Yes & No & Yes \\ Condense [14] & No & No & Yes \\ Atlas CEB [16] & Yes & No & Yes \\ [PERSON]’s model [33] & Yes & No & No \\ [PERSON]’s model [34] & No & No & No \\
2 EA [31] & Yes & No & No \\ FIFu [12] & No & No & No \\ MsM [32] & No & Yes & Yes \\ IoT-CANE [11] & No & No & No \\ IoT Manager [18] & No & Yes & Yes \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparisons of different IoT architectures.  
Figure 1: The components of the proposed IoT architecture.  
able to accommodate arbitrary components and their operating behaviors, while standardizing the interconnection protocol for each type of components, especially dealing with low-level hardware. We intend to design an IoT platform architecture that is able to support and accommodate the interoperations of devices operating under different OS. Unlike systems that require all the devices to have the same OS for easier system maintenance and management, our proposal focuses on enabling quick and easy \"plug-and-play\" of devices or IoT systems for the instant prototyping with only the data format and communication protocol to be standardized.
## 2 Materials and Methods
### System's Software Programming and Workflow  
#### 2.2.1 NIR and RGB Cameras' Calibration  
The geometric calibration of both cameras was conducted by assuming a pinhole camera model and a frontal parallel arrangement. This calibration process involved the utilization of a known-size asymmetric checkboard pattern consisting of 22 \(\times\) 15 squares, with each side measuring 25 mm. The checkboard pattern was supported by a wooden flat surface, which was securely mounted on a robotic platform (Figure 10b, Appendix A). The entire setup was positioned at a distance from the cameras' lenses that approximately matched the expected maximum height of the potted lettuce in the final application, ranging approximately 35 to 70 cm. Next, a total of 15 pairs of images were taken, with each pair consisting of a left and right image. These images were captured from different 3D positions and orientations, ensuring that the inclination of the checkerboard did not exceed 35 degrees relative to the plane of the cameras during each image pair capture. The images were saved in an uncompressed format, such as a PNG. The resulting dataset consisted of 30 images, which were then processed using the stereo camera calibration tool in MatlabTM, Computer Vision ToolboxTM. This processing allowed for the calculation of various parameters related to each camera's intrinsic parameters, distortion coefficients, and extrinsic camera parameters. These parameters included the radial and tangential distortion, intrinsic matrix, focal length, principal point, and mean reprojection error. The NIR camera's translation and rotation matrices, as well as the fundamental and essential matrices, were computed in relation to the RGB camera. These computed values were then compared to the manufacturer's datasheet (Arducam, OV2710 Dual-lens module) [54] whenever possible. Table A2 of Appendix A presents some of the noteworthy parameters that were calculated. Accurately determining the pose (position and orientation) of the NIR camera with respect to the RGB camera is crucial for performing NDVI calculations,  
Figure 8: In (**a**), the simplified electronics schematic of the part’s interconnections with the Raspberry Pi is shown, and in (**b**), the designed custom PCB Raspberry Pi shield is shown. The PCB was designed to be one-sided. The two red lines correspond to short-wire bridges between the top and bottom copper layers. Two wires were soldered for that purpose during our custom fabrication of the circuit. In (**c**), a top view shows the Raspberry Pi fitted in the carriage together with the designed Raspberry Pi shield that houses all the electronic parts.  
especially considering that both fixed cameras have almost identical technical specifications. The proper alignment of the RGB and NIR images is essential to ensure the integrity of the data obtained, even though NDVI calculations are not the focus of this study. Ultimately, the resulting overall mean reprojection error of 0.17 pixels was deemed acceptable for the current scenario.
# Machine Learning Enabled Performance Prediction Model for Massive-MIMO HetNet System
## 3 Performance Prediction Model
### Step 1: Data Preparation  
In order to accurately predict the performance metrics for an arbitrary network configuration, the supervised learning model requires a quality dataset that is a collection of instances with input attributes and a leveled output. For the given configuration, the input attributes are \(N_{macro},R_{macro},N_{pico},R_{pico},P_{TD}\) and the output is (PMs; either \(CP\) or \(ASE\)). The data set is created by running a realistic simulation for a massive MIMO HetNet system with various combinations of network parameters. For a given set of network parameters (input attributes), the simulation is carried out to evaluate the numerical values of the performance metrics given in Equations (13) and (14). The values of the network parameters and the simulated result of the performance metrics constitute a single instance of the dataset.  
In each round of simulation with new input attributes, two-tier cellular networks (K = 2) were simulated, comprising macro-cells and pico-cells, where the BSs of each tier were massive-MIMO enabled. The threshold (Th) of the SINR value in the CP calculation was taken as 10 dB. To capture the stochastic nature of the network topology, for each network configuration, the network was simulated 200 times and the results were averaged out. Each time, the cellular network was deployed on a torus of (10 \(\times\) 10) km\({}^{2}\). To capture the worst-case scenario, the spatial locations of the BSs stations were modeled using two independent PPPs, with the node densities of two layers related as \(\lambda_{pico}=3\lambda_{macro}\). The topology of a typical tow-tier simulated network where the spatial locations of BSs are modeled with two independent PPPs is shown in Figure 2.  
For every network topology, the test user was considered to be static, located randomly in the torus. The user was associated to a single BS of any tier that promised the highest average received signal strength as the servicing BS. The signals from all other remaining base stations were considered to be interference. The stochastic channel fading property was captured by simulating each network topology for 1000 coherence time periods with uncorrelated channel coefficients, and the expected values of the outputs were considered.  
The simulation was carried for 1450 combinations of these network parameters. The ranges of values of all parameters are given in Table 1.  
The outputs of the simulation were coverage probability \((CP)\) and area spectral efficiency \((ASE)\) in bits/sec/Hz/km\({}^{2}\). The output of each combination was recorded as a single instance in the dataset. The prepared dataset was used for model learning and testing.
###### Abstract  
Mobile Ad hoc Networks (MANETs) are intended to work without a fixed framework and provide dependable interchanges to ground vehicles, boats, airplanes, or people and structure a self-mending process that will empower persistent correspondences in any event, when at least one of its nodes are debilitated or briefly expelled from the system. Notwithstanding, MANETs demonstrate themselves to be progressively harder to create for enormous systems with hundreds or thousands more nodes than initially envisioned. In our proposed technique, the node switches its communication mode depending on the connectivity of the adjacent nodes. The transmission power of each node will be calculated with the help of two major scenarios i.e., tree scenario and zone scenario. The autonomous clustering of the nodes among the tree and the zone scenario will be channelized by a comparison of the transmission power (residual energy) among the nodes. The inter and the intra communication of the node is also discussed in the paper. The result will be carried out by the simulation work in various perspectives, such as checking the percentage level of malicious nodes, traffic density, transmission power, and the longevity of nodes.  
ad hoc networks; cluster; self-mending; traffic density; transmission power Article
# Design of Multi-Modal Ship Mobile Ad Hoc Network under the Guidance of an Autonomous Ship
## 3 Ship Ad Hoc Network Construction
### Design of Network Mode
#### 3.2.1 Intercommunication Mode  
This mode adopts a network topology as shown in Figure 2, which is suitable for ships in the seas far away from the coast, waterways, fishing areas, anchorages, and other special sea areas. Here, there are limited ships to meet, and communication is usually carried out for taking collision avoidance actions. Therefore, stable and high-quality communication is required. In network topology, there are many paths between nodes, collision and blocking can be reduced, and local failures do not affect the whole network. In this mode, ships in the network can communicate with any node in the network according to their needs. According to the characteristics of application scenarios, we name this mode an intercommunication mode. In such a network, the problem to be solved is how to send messages in the network.  
As one of the important research directions of ad hoc network technology, a routing protocol is responsible for the decision-making of message dissemination. According to the different topologies of the network, the routing protocols of an ad hoc network can be divided into plane routing protocol and hierarchical routing protocol [28; 29]. For the plane routing protocol, in the plane structure, there will be multiple paths between the source node and the destination node. All nodes are equal in status and they have identical functions. Plane routing protocols include all active routing protocols and on-demand routing protocols. The difference between them is the difference in route discovery strategies. In the active routing protocol, each node in the network should periodically exchange routing information with other nodes and each node should save the routing table [30]. When  
Figure 2: Network topology of intercommunication mode.  
the network topology changes, the nodes broadcast the routing update information in the whole network, so that each node can continuously obtain the network information.  
It is more typical to have Dynamic Source Routing Protocol (DSDV). The on-demand routing protocol is only created by the source node when routing is required, so the topology and the content of the routing table are established on demand. The routing is maintained during the communication process and will not be maintained after the communication is completed [31]. Common ones are Dynamic Source Routing Protocol (DSR) and Ad Hoc On-Demand Distance Vector Routing (AODV) [32]. We can choose the appropriate routing protocol according to the characteristics of the routing protocol and the needs of navigation.
# Wavelet-Based Analysis of Motor Current Signals for Detecting Obstacles in Train Doors
## 4 Simulation Results
This limitation can be addressed by refining the thresholds and rules to account for a broader range of obstruction profiles. Figure 11: Soft-obstacle scenario. (**a**) W9 and peak amplitudes; (**b**) simulation output; (**c**) force between doors and motor current signal. The proposed method processes only particular frequency bands of current signal where obstacles are detected and filters out other frequency bands. This reduces the false detections, and it improves reliability and accuracy by using the combination of norm and peak in a frequency band signal. However, this method requires more computational power. Traditional approaches process the entire current signal in time domain and detect the peak, which contains more noise and may lead to false detections. Other existing solutions use sensitive-edge sensors. These are expensive and sensor failure can lead to potential injuries.
## 3 Results and Discussion
### X-Ray Diffraction (XRD) Analysis  
The diffractograms of neat PBAT, alum sludge, and their composites with varying concentrations of alum sludge are presented in Figure 6. The neat PBAT sample exhibits distinct diffraction peaks at 16.3\({}^{\circ}\), 17.4\({}^{\circ}\), 20.3\({}^{\circ}\), 21.6\({}^{\circ}\), 23.2\({}^{\circ}\), and 24.8\({}^{\circ}\) 20, corresponding to (011), (010), (110), (100), and (111) crystalline diffraction planes [38], respectively. These peaks are superimposed on a broad amorphous background, confirming the semicrystalline nature of PBAT. In contrast, the alum sludge sample does not display any diffraction peaks, indicating its amorphous or low-crystallinity nature, which is consistent with observations from previous studies.  
The incorporation of alum sludge into the PBAT matrix does not shift the positions of the diffraction peaks, indicating that the crystalline structure of PBAT remains unaffected by the addition of alum sludge. However, as the concentration of sludge increases in the composites, a noticeable reduction in peak intensity can be observed. This decrease suggests a reduction in the overall crystallinity of the composite materials, which mirrors the observations from the DSC analysis. The decline in crystallinity can be attributed to the disruption of the polymer chain arrangements, as discussed in the previous section. Since alum sludge is amorphous, it likely hinders the ordered packing of PBAT chains, thereby reducing the crystalline regions within the composite matrix.
# An Effective Scheduling Algorithm for Coverage Control in Underwater Acoustic Sensor Network
## 1 Introduction
At present, most of these achievements are for the design of land-surface wireless sensor networks. Underwater sensor networks have some common problems, as summarized below. 1. In the research object, the system model does not consider the water environment factors in practical application, and cannot meet the energy-optimization requirements of the underwater sensor network. 2. The autonomy of network nodes is insufficient, and there is a lack of intelligence in the process of energy-optimization of nodes. To solve the above problems, this paper proposes an energy-saving coverage-scheduling scheme to optimize UASNs. We studied the node sleep-wake scheduling mechanism based on a memetic algorithm, closed the redundant nodes in the network after node deployment, and optimized the UASN's topology. It is worth noting that the coverage area of a redundant node usually overlaps with that of its neighbor nodes. Switching a redundant node to sleep mode can reduce network overhead. Further, an effective node wake-up scheme is also a problem considered in this paper. With the operation of the network, the energy of some nodes is depleted, which can easily cause network cavities. The wake-up scheme will activate one or more nodes in an affected neighborhood to maintain high coverage. The ESACC algorithm achieves maximum coverage and extends the entire network life cycle with a minimum number of active nodes. This study theoretically analyzed and evaluated the performance of the algorithm through experiments. The results show that the algorithm is superior to the CMSS [6] and LEACH-Coverage-U [11] algorithms in maintaining high coverage and a long lifetime. The rest of this paper is organized as follows: Section 2 describes the preconditions and models related to the ESACC algorithm. Section 3 provides a detailed description of the issues studied in this paper and the proposed ESACC algorithm. Section 4 describes our experimental results and compares them with different methods. Conclusions are given in Section 5.