esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
For instance, [PERSON] [14] of the National Marine Technology Center conducted low-power operation design and power management for multi-parameter surface drifting buoys. [PERSON] et al. [15] of Kunming Marine Equipment Research and Testing Center carried out a low-power design for a type of underwater navigation body, abandoning the commonly used isolation, switching, and other large-size, high-power components of the method and adopting a small-size, low-power combination of integrated design solutions. [PERSON] et al. [16] of India reduced the power consumption of a tsunami surface buoy system by configuring distributed batteries. In summary, although the research [1; 8] on low-power design of satellite communication terminals has been carried out, there are still many shortcomings. In this paper, a low-power BTTT is designed by drawing on the research results of previous researchers in the low-power design of satellite communication terminals and ocean exploration equipment. The terminal reduces power consumption from two perspectives: reducing the power consumption of a single communication and decreasing the number of communications. Full consideration is given to the energy consumption generated by the BTTT during the operation of the buoy to realize low power consumption. The objective of this study is to reduce the power consumption of remote communication for the Argo buoy. In this paper, communication management for the BTTT is addressed, encompassing both low-power operation and power management strategies aimed at reducing the power consumption associated with communication processes. Additiontionally, a differential coding-Huffman hybrid compression algorithm is utilized to reduce the volume of data transmitted.
# Deep Learning-Based Portable Image Analysis System for Real-Time Detection of _Vespa velutina_
## 4 Discussion  
Recently, research has been conducted on the automated detection of _V. velutina_ using optical sensors with machine learning by analyzing the wingbeat frequencies of seven species of bees, wasps, and hornets. This research was carried out in an entomological tent in the laboratory, and _V. velutina_ individuals were detected by distinguishing species of bees, wasps, and hornets based on the wingbeat frequency [32]. Previous studies have also used machine learning to predict the prioritization of tasks in sighting reports of _V. velutina_ in situations in which human resources are limited [33]. The previous studies performed auxiliary roles such as the identification of _V. velutina_ and the prediction of task priorities.  
In this study, we developed a portable monitoring system based on deep learning to minimize the damage caused by _V. velutina_ in beekeeping farms. This system can be directly applied in the field as it detects _V. velutina_ objects from image data. Considering that _V. velutina_ appears in various areas of an apiary site, a system was designed to ensure portability and ease of installation. To create a portable system, we utilized an embedded PC (Jetson Xavier NX, Santa Clara, CA, USA), whose performance is worse than that of a regular PC. This system must perform real-time video streaming and display tasks while detecting _V. velutina_ objects. Therefore, we used the YOLOv5s model, a fast 1-stage algorithm, to simultaneously perform localization and classification tasks [22; 23; 24; 25].  
During field testing, only blurred images were obtained for _V. velutina_ that fly fast, and the feature points could not be clearly marked, making detection difficult. In contrast, _V. velutina_ hovering for hunting could be detected with high reliability. Therefore, by acquiring additional image data of fast-flying _V. velutina_ and adding them to the training set of image data, it may be possible to extract the distinguishing features of fast-flying _V. velutina_, which could improve the detection reliability and accuracy.  
Figure 9: Notification app for _V. velutina_ detection: (**a**) Real-time monitoring of image data; (**b**) Alarm for detection of _V. velutina_ (“Wasp was detected”); (**c**) Updated image data, red box area.  
The system is powered by 220 VAC electricity to supply power to the embedded PC, monitor, router, and other components. Therefore, in situations wherein the power supply is difficult, the system must be operated using a high-capacity power bank. To address this issue, a possible solution would be to change the system's operation to have the central PC handle the detection of _V. velutina_ objects, reducing the power consumption of the on-site installed system. This can be achieved by implementing a small-scale solar power generation system.
# Cognitive Frequency-Hopping Waveform Design for Dual-Function MIMO Radar-Communications System
## 2 System Configuration and Signal Model
### Information-Embedding Scheme  
It is assumed that a phase symbol denotes \(B\) bits of binary sequence. During the \(i\)-th radar pulse, the binary sequence that needs to be embedded is used to select phase symbols \(\Omega_{m,q}(\textbf{i}),m=1,\ldots,M,q=1,\ldots,Q\) from a pre-defined constellation of \(K=2^{B}\) symbols. It is considered that the constellation is uniformly distributed between \([0,2\pi]\), which can be expressed as:  
\[\mathbb{C}_{PSK}=\left\{0,\frac{2\pi}{K},\ldots,\frac{(K-1)2\pi}{K}\right\} \tag{7}\]  
The set of PM-based FH waveforms at time \(i\) can be rewritten as:  
\[x_{m}(t,i)=\sum_{q=1}^{Q}e^{i\Omega_{m,q}(i)}e^{j2\pi c_{m,q}\Delta ft}u(t- \Delta t),m=1,\ldots,M \tag{8}\]  
In (8), \(\Omega_{m,q}(i)\)\(\in\)\(\mathbb{C}_{PSK}\). To simplify discussion and implementation, it is assumed that a communication receiver equipped with a single antenna is located at a known direction \(\theta_{c}\). Therefore, the received signal at the output of the communication receiver can be expressed as:  
\[y(t,i)=\alpha_{c}\textbf{a}^{T}(\theta_{c})\textbf{x(}t,i)+n(t,i) \tag{9}\]  
where \(\alpha_{c}\) denotes the channel attenuation coefficient. It is considered that \(\alpha_{c}\) keeps constant during the whole processing interval. \(\textbf{a(}\theta_{c}\textbf{)}\) indicates the steering vector of the transmit antenna array, and \(n(t,i)\) denotes the zero-mean white Gaussian noise with covariance \(\delta_{w}^{2}\).  
It is assumed that the communication receiver has full knowledge of the code \(c_{m,q}\) and the step \(\Delta f\). Hence, the communication received signals (5) are matched-filtered to the orthogonal FH sub-pulses yields:  
\[\begin{array}{ll}r_{m,q}(\textbf{i})&=\int_{0}^{\Delta t}y(t,i)e^{-j2\pi c_ {m,q}\Delta ft}u(t-\Delta ft)dt\\ &=\alpha_{c}e^{-j\pi d_{m,q}\sin\theta_{c}}e^{\Omega_{m,q}(\textbf{i})}+n_{m,q}(i)\\ &\quad m=1,\ldots,M,q=1,\ldots,Q\end{array} \tag{10}\]  
In (10), \(d_{m}\) denotes the distance between the first and the \(m\)-th antennas of the transmit array. The communication receiver has the ability to undo \(e^{-j\pi d_{m}\sin\theta_{c}}\) before it estimates the symbol \(\Omega_{m,q}(i)\). \(r_{m,q}(i)\) is viewed as a phase-rotated and noisy version of the \(m\)-th entry of \(\textbf{a(}\theta_{c}\textbf{)}\). Hence, \(\Omega_{m,q}(i)\) that need to be embedded is estimated as:  
\[\hat{\Omega}_{m,q}(i)=Angle(r_{m,q}(i))-Angle(\alpha_{c})+2\pi d_{m}\sin \theta_{c} \tag{11}\]  
Then, the communication receiver compares \(\hat{\Omega}_{m,q}(i)\) to the predefined \(\mathbb{C}_{PSK}\). \(\Omega_{m,q}(i)\) is restored from \(r_{m,q}(i)\) at the output of the (\(m\),\(q\))-th matched filter. It allows the communication receiver to determine \(\Omega_{m,q}(i)\) and convert \(\Omega_{m,q}(i)\) into the original sequence. The advantages of the communication information embedding scheme have been presented in [44].
## 2 Related Work
### Information Diffusion and Macroscopic Prediction
#### 2.1.3 Deep-Learning-Based Approaches  
Inspired by the recent success of deep learning in many fields, cascade prediction has achieved significant performance gains using deep neural networks. DeepCas [23] is the first method based on graph representation learning to model and forecast the popularity of information cascades. It uses DeepWalk [33] concepts to sample cascaded graphs via random walks.  
Unlike the DeepWalk concepts in DeepCas, Topo-LSTM [24] employs a directed acyclic graph(DAG)-structured recurrent neural network to model diffusion topologies. CasCN [22] leverages GCN and LSTM to extract both structural and temporal information from the cascade graph. By considering a cascade graph as a sequence of sub-cascade graphs, CasCN first learns each sub-cascade's local structure through graph convolutions and then adopts long short-term memory neural networks (LSTM) to model the evolving process of the cascade's structure.  
Deep learning has good predictive power, but deep learning models lack model interpretability due to the \"black box\" nature of neural networks [34]. In addition, the computational cost of deep learning models is considerably greater than that of generative models and feature-based models.
# The Influence of Heat Input on the Surface Quality of Wire and Arc Additive Manufacturing
## 4 Conclusions  
In the current work, the effect of heat input on surface quality of arc additive manufacturing is studied using real-time heat monitoring and 3D scanning data. The key results are drawn as follows:  
(1) In the WAAM process, for different positions on the substrate, the temperature change trend is different, so that the molten pool has different stabilization effects.  
(2) Within a certain range of heat input, with the decrease of heat input, the average width and average height of these specimens show a downward trend.  
(3) In the case of high or low heat input, the average error of the specimen is negative, and the standard deviation and root mean square value reached 1.908 and 1.963, respectively. However, the average value of the error of the specimen with moderate heat input is positive, and the standard deviation and root mean square value are only 1.634 and 1.713, respectively.  
(4) The heat input has a relatively high degree of influence on the surface quality of the wire and arc additive manufacturing specimens, and higher or lower heat input is unfavorable to it.  
Although single-layer, single-pass specimens are the basis of arc additive manufacturing, the industry needs metal parts that are formed once. In the following work, we will combine real-time temperature monitoring and 3D images to study the influence of heat input on the interlayer bonding and surface quality of arc additive manufacturing parts.  
Conceptualization, W.N.; Data curation, J.Z.; Investigation, X.L.; Methodology, X.L.; Software, J.Z.; Supervision, W.N.; Writing--original draft, J.Z.; Writing--review & editing, J.Z. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Institutional Review Board Statement**: Not applicable.  
**Informed Consent Statement**: Not applicable.  
**Data Availability Statement**: Not applicable.  
Figure 8: The degree of deviation of the cross-section and the longitudinal-section of each specimen.
###### Abstract  
As traditional automobiles develop towards new energy vehicles, the noise, vibration and harshness (NVH) performance of automobiles is facing new challenges. Without the cover of the traditional engine noise and inlet and exhaust noise, the high-speed wind noise becomes more prominent. Thus, research on the calculation method of vehicle interior noise in high-speed driving condition is needed. However, vehicle body structure is complex, and the external excitation components are complicated. In order to analyze the method of predicting the vehicle interior noise at high speed, an idealized side mirror model is taken as the research object in this paper and the radiated noise of a panel under the fluctuating surface pressure (FSP) due to the idealized side mirror is studied. The FSP of the panel is first studied by the numerical simulations of incompressible and compressible flow field. For the incompressible flow field, the Corcos turbulent boundary layer (TBL) model is established to simulate the convective component and the boundary element method (BEM) is used to extract the acoustic component. Subsequently, the Corcos model coupling BEM method, the random modal force coupling BEM method and the deterministic modal force coupling BEM method are used separately to calculate the noise of the panel under the FSP. For the compressible flow field, the convective and acoustic component in the fluctuating pressure are separated by the wavenumber-frequency spectrum (WFS) method. The radiated noise of the panel under the FSP is calculated again by using the WFS, the method of random modal force and the method of deterministic modal force, respectively. Then, the computational time of the six methods of incompressible and compressible calculation is compared. Finally, a fast and accurate method of calculating the panel radiated noise under FSP is obtained by comparing the computational accuracy with the experimental results and combining the computational time: the method of incompressible random modal force. This method can be used to quickly and accurately analyze the vehicle interior noise at high speed, and to optimize the exterior protrusions and the vehicle sound package for improving the vehicle NVH performance at high speed.  
fluctuating surface pressure; idealized side mirror; convective component; acoustic component; wavenumber-frequency spectrum; vehicle interior noise
## 3 Results and Discussions
### Low Frequency Mini-Brillouin-Zone Mode
#### 3.2.1 Overview of Experimental Results
Hence, in the following section, we will elaborate on the interpretation of our experimental findings and propose potential mechanisms that facilitate MBZ-center and -edge mode excitation and detection in the DBR of the MQW-SESAM structure.
# SDN Architecture for 6 LoWPAN Wireless Sensor Networks
## 5 Evaluation and Results
### Evaluation Tools
#### 5.1.3 Traffic Generation  
A packet generation module included in the firmware simulates the sensing application. Packets are sent to a server installed in 6 LBR, which receives and sends them back. The purpose is to generate traffic in the data plane, enabling the performance evaluation. The application sends UDP packets every 30 s, with random variation (uniform) of up to 5 s, to reduce collision probability. Packets have a 20-byte payload to prevent the frame size from exceeding 127 bytes, avoiding packet fragmentation. The simulation time is 20 min, allowing the application to send at least 30 messages per node.
# The Odyssey of Entropy: Cryptography
## 3 Entropy and Encryption
### Modeling, Design and Implementation
#### 3.1.5 Modification and Use of Existing Cryptosystems  
Some researchers have modified existing cryptosystems and used entropy (and related concepts) in part of their research. For example, a modified variant of the block encryption algorithm _Blowfish_ was designed by [PERSON] et al. [127] in order to be used in IoT (Internet of Things) platforms. The security of this variant was evaluated using entropy. ElGamal elliptic curves were used to improve the TLS (transport layer security) protocol in terms of entropy [128]. A chaotic image scrambling method based on DES was proposed by [PERSON] et al. [129] that uses image block entropy to select blocks for scrambling.  
Entropy analysis was used in the design of an image encryption system aiming at the reduction of correlation between image blocks [130]. Moreover, entropy analysis has played  
Figure 3: PUF architecture.  
roles in the design of elliptic curve point addition algorithms [131] and unconditionally secure encrypted authentication schemes [132].
# Learning and Comfort in Human-Robot Interaction: A Review
## 2 Teaching and Learning in Human-Robot Interaction  
### Robot Learning from Demonstration  
The robot programming approach has gone through three distinct reforms in the past 60 years. As shown in Figure 1, these robot programming approaches include: teach pendant-based programming, computer-aided design (CAD)-based programing, and robot learning from human demonstrations.  
In the first approach, teach pendants are handheld devices that can be used by humans to program robots directly with predefined tasks via different programming languages, such as RAPID, KRL, and VAL3. Afterwards, the robots are controlled to plan motions step-by-step to accomplish the fixed workflows [14]. However, the teach pendant-based programming approach usually requires the human mastering skillful technical expertise. Therefore, it is time-consuming and not cost-effective, especially for large-scale manufacturing tasks.  
In order to assist users with programming robots in an intuitive way, the CAD-based robot programming approach was developed that allows humans to program robots in 3D manufacturing environments with basic CAD skills. Using the CAD-based programming approach, the robot programs are generated offline and then converted into robot commands for corresponding tasks [15]. This approach improves the efficiency of robot programming to some extent. However, in this approach, the users are still required to master a certain level of programming skills and expertise.  
Figure 1: The robot programming approach has gone through three distinct reforms.  
Robot learning from demonstration (LfD), which is also known as robot programming by demonstration (PbD), imitation learning, or apprenticeship learning, is a paradigm for enabling robots to autonomously perform new tasks [16]. The study of robot learning from demonstration started about 30 years ago. This approach has grown significantly and has become a center topic of robotics, especially in the area of human-robot interaction [8]. Via the approach of robot learning from demonstration, the human partners can program robots easily and greatly extend the robots' capabilities for different tasks without programming expertise [7]. The process of robot learning from human demonstrations is basically divided into two steps: human teaching and robot learning. Numerous methods and technologies, such as force-sensor-based teaching [17], vision-system-based teaching [18], and natural-language-based teaching [19], have been developed and implemented in the human teaching process. Additionally, multiple learning algorithms have also been designed and developed for the robots to extract, learn, and build task strategies from human demonstrations [9]. In the human-robot teaching-learning process, the robot usually learns from the human in a direct or indirect manner. The former is human intuitive teaching toward the robot (e.g., kinesthetic-based teaching) and the latter is the human teaching using some external devices (e.g., via a vision system). Approaches of human teaching and robot learning are detailed as follows.
## 4 Modeling Process and Results
### Landslide Susceptibility Mapping  
After obtaining the LSI of each evaluation unit, ArcGIS 10.7.1 software was used to draw landslide susceptibility maps. The natural breaks method can identify a classification that maximizes the difference between categories, which is widely used in landslide susceptibility mapping [26; 30]. In this study, the natural breaks method was used to divide LSI values into five categories from high to low, representing very high, high, moderate, low, and very low landslide susceptibility levels, respectively. Figure 9a-c show the landslide susceptibility maps obtained by the SI model, the GD-SI model, and the GD-GPR-SI model, respectively. Figure 10 shows the area percentage of each susceptibility class of models.  
Based on the landslide susceptibility maps, high susceptibility areas are approximately distributed along roads and rivers, which is consistent with the distribution of historical landslides. Moreover, most landslides are located in valleys, which are also compatible with the characteristics of landslides in mountainous areas [44; 82]. These observations indicate that the landslide susceptibility maps obtained by the three models are reasonable and reliable as well as prove the validity of the factor analysis results of GeoDetector.  
Figure 10: Area percentage of different susceptibility classes.  
Figure 9: Landslide susceptibility maps: (**a**) SI model; (**b**) GD-SI model; (**c**) GD-GPR-SI model.
## 3 Results and Discussion
### Message Content and Trust
From the responses,it was clear that occupants wanted to know the location of the fire, which egress to take, and roughly the estimated time available for escape. Four interviewees added to this by stating they wanted a clear set of instructions from the detection of the fire until evacuating the building. Some of these instructions could be: evacuate immediately without taking any belongings, use egress emergency stairs instead of main stairs, fire is located on the fourth floor, please wait in your apartment until help arrives, use the second lift to evacuate the building. Interviewees believed that receiving a set of instructions might reduce pressure and allow the evacuation process to be easier. It is worth noting that three interviewees expressed concerns about the time they have to use the egress route that was recommended in the message. As the fire situation in the building could change within seconds, it is important that the message contains a rough estimate of the time occupants have to evacuate the building before it is unsafe. Interviewees who live on higher floors, compared with interviewees living on the first couple or middle floors, wanted to know if the lift could be used when evacuating the building. The responses are understandable, as the evacuation time will be longer for occupants living on higher floors. Having an alternative option to evacuate was also a key requirement raised by all interviewees. They called it a \"Plan B\". One interviewee stated that they would expect a second message following the first message within a couple of minutes explaining what actions needed to be taken following the evacuation. For example, \"meeting at assembly point\", \"move your car\", \"stay as far as possible from the building\". Additional information suggested by interviewees was to know whether firefighters are on the way or in the building. Three interviewees stated that that they also wanted to know whether they should be heading to another floor and waiting for help there. It is clear from the responses that the location of the fire, options to evacuate and alternative options, duration of the time before the route is unsafe to be used, and clear instructions what to do were the main information interviewees believed the message should contain. Although the message should be short, it is required to have enough detailed instructions for faster evacuation. It is worth noting that one interviewee stated that they wanted to know the time when the fire had been detected, for example, the message could include a \"fire has been detected on the 7 th floor at 14:27\". The interviewee believed that knowing the precise fire detection time would reduce stress levels as it would enable the evacuees to estimate the time they have to evacuate the building.