esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Forces Induced by Acoustic Wave and Streaming Flow
### Acoustic Radiation Force on the Targets
If we use sound pressure to describe the force of particles in the sound field, \(\overset{\rightarrow}{F}_{r}\), can be found as [10; 41]:  
\[-\overset{\rightarrow}{F}_{r}=\left\langle\int_{S_{r}}\mathrm{P}_{2}\overset{ \rightarrow}{n}\mathrm{d}S_{r}\right\rangle+\left\langle\int_{S_{r}}\rho \Big{(}\overset{\rightarrow}{n}\overset{\rightarrow}{v}_{p}\Big{)} \overset{\rightarrow}{v}_{p}\mathrm{d}S_{r}\right\rangle \tag{2}\]  
\[p_{2}=\frac{1}{2}\kappa_{f}\Big{\langle}\left|p_{1}\right|^{2}\Big{\rangle}- \frac{1}{2}\rho\Big{\langle}\left|\overset{\rightarrow}{v}_{p}\right|^{2} \Big{\rangle} \tag{3}\]  
where the integration surface \(S_{r}\), which encloses the particle, can be chosen arbitrarily, and \(\overset{\rightarrow}{n}\) is the normal of that surface. Typically, for inviscous fluids, \(p_{2}\) can be derived from first-order terms similar to Equation (1). These equations provide a straightforward model of the relationship between acoustic field force and acoustic stream. A specific range should be found so that these principles can be fully used. Particle size here is a critical factor. When the radius of the particles \(R_{p}\) is much smaller than the wavelength \(\lambda\) (\(R_{p}\ll\lambda\)), the traveling wave force \(F\) scales with the sixth power of the radius (\(F\propto R_{p}^{6}\)); when \(R_{p}\gg\lambda\), \(F\) scales with the second power of the radius (\(F\propto R_{p}^{2}\)) due to the tremendous scattering force. A strong nonlinear relationship exists when \(R_{p}\) and \(\lambda\) have the same order of magnitude (\(R_{p}\sim\lambda\)). For this kind of device, although small particles provide the optimal control conditions (we usually control the frequency to achieve the relationship between the wavelength and the particle radius ratio of \(\kappa=2\pi R_{p}/\lambda\)), the force, in this case, is too small, resulting in lower efficiency. Many studies have confirmed that only when the particle diameter approaches that of the acoustic wavelength is the force substantial enough to generate significant particle translations, where the nature of the scattering of sound waves from the particle interface transitions from isotropic (Rayleigh scattering state) to anisotropic-dominate (Mie Regime) [42; 45]. This characteristic determines that similar operations can only be performed at very high frequencies (\(\sim\)10 \(\upmu\)m particles correspond to \(\sim\)100 MHz in a fluidic environment).
## 2 LTE
### LTE Threats  
IMSI refers to the unique ID requested from each user when the net administrator registers the user to the service, and this value refers to the unique number of identifications saved in the USIM in the user device [14].  
Yet, when \"initial attach for UE\" is carried out, in the \"ECM connection establishment\" process, the UE transmits IMSI to MME in plain text. The IMSI transmitted in plain text is transmitted to MME through a number of eNB, which has a vulnerability in which it is leaking to an attacker through malicious eNB. In addition, for a user tracking an attack using the leaked IMSI, a device tracking attack and a privacy abuse attack may take place.  
RNTI, the unique ID to differentiate UE from eNB, and GUTI, used instead of IMSI after a series of the process, also, are transmitted in a variety of initial attach processes in plain text, so that the same vulnerability and the threat of attack of IMSI may occur [6; 7; 9; 25].  
An attack that could allow an attacker to use the stolen identification parameter is shown in Figure 4. UE constantly transmits location information to the eNB and MME; an attacker can track the user using a leaked IMSI. Furthermore, the attacker can make a DoS attack using a leaked IMSI and GUTI, which is used for requesting RNTI [26; 27]. LTE threat model and its process are shown in Figure 4.
# Adaptive Clutter Intelligent Suppression Method Based on Deep Reinforcement Learning
## 5 Experiment and Result Analysis
### Filter Performance Analysis in Simulated Clutter Environments  
Firstly, the clutter with different clutter center frequencies is generated, which is divided into clutter region 1 and clutter region 2, and the Gaussian random noise power is 0 dB, which is distributed at all range cells. Then, two range cells are selected in clutter region 1 and clutter region 2 to add the simulation target signal with an SCNR of \(-10\) dB. A total of four sets of experiments are performed. The experimental parameters and simulated clutter parameters are shown in Tables 2 and 3. The clutter suppression effects of the MTI method, the traditional eigenvector method, and the method proposed in this paper are tested, with all of the above using the third-order FIR system. Figure 3 shows the radar echo data plot before clutter suppression. The radar echo data image before clutter suppression is shown in Figure 3 below.
# Cooperative Power-Domain NOMA Systems: An Overview
## 4 Open Issues and Challenges
### NOMA in Mobile Edge Computing (MEC)  
IoT devices typically have limited memory and processing power. In order to sustain an IoT-enabled network, some of the more complex application tasks of tremendous interest can be managed at a central location. There are still numerous difficulties in transferring to the MEC servers. The first is that many IoT devices may use the same MEC servers, making it difficult to guarantee that these servers have the computational capabilities necessary to complete operations quickly. Second, the battery life of IoT devices is limited. Task computation locally on the device and data transfer to a MEC server both require energy and may cause the battery to discharge quickly. To ensure timely completion of the computing activities and efficient use of the batteries of the IoT devices, real-time applications require the optimization of resource allocation in MEC-based networks. A resource allocation issue that maximises downlink sum rate of NOMA users has been put forth in [211]. Later, [PERSON]. [211] has evaluated an adaptation that jointly optimises the resource allocation and power allocation. Because NOMA permits several users to access the same subcarrier concurrently, using NOMA results in a greater system-weighted sum-rate performance in both works than identical systems that use the OMA scheme. Hence, NOMA in MEC is one of the future research challenges in academia and industry.
## 4 Conclusions and Outlook  
### Conclusions  
In this paper, active feedback control is used to solve the low-frequency sound insulation problem of the double-layer plate system. Based on the theoretical model of the coupling system of the elastic plate structure and water-filled acoustic cavity, a four-terminal parameters transfer matrix is introduced to achieve active feedback control. The theoretical model of the sound insulation system with a double-layer plate structure and active feedback control strategy is established for the water-filled acoustic cavity. The average energy input is taken as the evaluation index of the active sound insulation effect of the system, and its calculation method is derived. The effect of different feedback parameters on the average energy transfer characteristics is analyzed by MATLAB numerical calculations using an active sound insulation system of a double-layer plate with four supports as an example. The following conclusions are drawn.  
1. Based on the proper assignment of feedback parameters, the six feedback control methods of absolute displacement, velocity, and acceleration and relative displacement, velocity, and acceleration can all produce significant effects on the low-frequency sound insulation of the system, and can achieve controllable peak shaving and frequency reduction of the resonance peaks.  
Among these six feedback parameter control methods, only the absolute acceleration feedback control method has a more obvious effect on the high-frequency sound insulation of the system, while the other feedback control methods are not beneficial or even harmful to that.
3. Among these six feedback parameter control methods, the amplitude-frequency response of average energy input in the system with the absolute acceleration feedback control method is the most sensitive.
4. The overall sound insulation effect under the absolute displacement, velocity, and acceleration feedback parameter control methods is better than that under the relative displacement, velocity, and acceleration feedback parameter control methods.
## 2 Materials and Methods
### Data Preprocessing  
Spatial downscaling was first applied to align 1 km MODIS CWP data with 5 km MODIS CTH datasets, and then the AHI observations were collocated with the CTH and CWP retrievals derived from the MOD06 products through spatial and temporal matching. Since the MOD06 CWP retrieval algorithm relies on visible spectrum measurements, only daytime infrared channel observations were selected to ensure physical consistency. Therefore, the collocated AHI-MODIS datasets during different years are used to generate the training (4707 samples from 2016), validation (496 samples from 2018 at 10-day intervals), and testing datasets (524 samples from 2017 and 765 samples from 2022 at 10-day intervals for comparison with CloudSat-CALIPSO, while 336 samples from 2025 for comparison with EarthCARE), respectively. To facilitate fast convergence during training, input features were normalized to the [0, 1] range via min-max transformation (Equation (1)), where \(x_{min}\) and \(x_{max}\) denote the minimum and maximum values observed in the training dataset, respectively. To handle missing values in the MODIS data, we implemented a two-step preprocessing strategy. First, all not-a-number (NaN) values were replaced with a placeholder value of \(-1\), which is critical to ensure numerical stability while preventing NaN propagation during the forward and backward passes of the CNN. The value of \(-1\) was specifically chosen because it lies outside the [0, 1] range of our min-max normalized data. Second, to ensure the model learned only from valid physical measurements, these placeholder locations were masked out and excluded from the loss function calculation during training. \[x_{norm}=\frac{x-x_{min}}{x_{max}-x_{min}} \tag{1}\]  
Subsequently, the normalized training, validation, and testing subsets were generated through a patch sampling approach. The data were divided into 128 \(\times\) 128-pixel patches with an overlap of 4 pixels between adjacent patches, generating 55,635 training subsets and 5922 validation subsets. This patch sampling approach can improve computational efficiency without reducing data features [28]. The summary of the datasets used in this study is presented in Table 2. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sensors** & **Products** & **Variables** & **Spatial Resolution** \\ \hline MODIS & Collection 6.1 MOD06 & Cloud top height & 0.05\({}^{\circ}\) (5 km) \\  & & Cloud water path & 0.01\({}^{\circ}\) (1 km) \\ \hline CloudSat-CPR, & & & \\ CALIOP & 2B-GEOPROF-LIDAR & Cloud top/base height & 1.4 \(\times\) 1.7 km \\ \hline EarthCARE-CPR & CPR\_TC\_2A & Cloud top/base height & 1 \(\times\) 1 km \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._
# Effect of Austenitizing on the Microstructure and Mechanical Properties of Gray Cast Iron
## 4 Conclusions  
By comparing the microstructures and mechanical properties of the as-cast specimen and the specimens treated at different austenitizing temperatures, the following conclusions are drawn.  
(1) As the austenitizing temperature increases during the austempering process, the matrix of the gray cast iron used in coal mine explosion-proof equipment gradually changes from being mainly composed of pearlite to being mainly composed of ausferrite, and the proportion of ausferrite gradually increases with the rise in austenitization temperature.  
(2) When the austenitizing temperature is 850 \({}^{\circ}\)C, the tensile strength of the gray cast iron for coal mine explosion-proof equipment treated by austempering can reach 332 MPa. Compared with the as-cast specimen, the tensile strength is increased by 78.6%, the plasticity is improved by 51.8%, and the wear depth is reduced from 250 \(\upmu\)m to 2 \(\upmu\)m.  
(3) As the austenitization temperature increases, the wear mechanism under a 20 N load transitions from adhesive wear and oxidative wear in the as-cast state and for At 810 to abrasive wear and oxidative wear for At 850 and At 900. However, an excessively high austenitization temperature will lead to an increase in the residual stress of the casting and deformation of the graphite structure, which will affect the wear performance of the material.  
Investigation: [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON]; resources: [PERSON], [PERSON] and [PERSON]; writing--original draft: [PERSON] and [PERSON]; data curation: [PERSON] and [PERSON]; formal analysis: [PERSON]; writing--review and editing: [PERSON]; supervision: [PERSON]. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Key Research and Development Program of China (Grant No. 2022 YFF0605300) and the Key Laboratory of Special Machine and High Voltage Apparatus (Shenyang University of Technology), Ministry of Education (Grant No. KFKT202106).  
Not applicable.  
**Data Availability Statement:** The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
**Conflicts of Interest:** Authors [PERSON], [PERSON], and [PERSON] were employed by the company \"Fushun CCTEG Inspection Center Co., Ltd.\". The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as potential conflicts of interest.
## 2 Localization in IIoTs
### Indoor Positioning System, IPS  
IPSs have enabled various navigation applications that highly require the instant location of a person and any objects in real time, uniformly localizing the mobile device or objects in an indoor environment [39; 40]. It has opened up various new possibilities in industrial, consumer IoT markets and healthcare. In industrial environments, there is a growing interest in IPSs for logistics and manufacturing to ensure the precise navigation of automatic robots, tracking personal tools and equipment in large warehouses and factories. In such environments, device movement can be potentially dangerous, so automatic device tracking has become a fundamental aspect of Industry 4.0 for safety [41; 42]. Additionally, there is an increasing legal requirement for the continuous tracking of coal miners in underground mines due to the increasing number of disasters with many fatalities. These positioning systems can help to connect front-end workers and improve safety [43]. In the consumer IoT market, real-time and reliable location information can digitalize and optimize virtually every aspect of asset and data management for efficiency and security in manufacturing operations and working spaces [44]. Context-aware location-based marketing is undergoing a great change and shows potential improvements in both sales and profits side in e-commerce [45]. This type of marketing strategy enhances the shopping experience in real time by considering the buyer's social profile, shopping history, feedback requirements, spending pattern, history of navigation, online behavior, and so on. Finally, IPSs are attracting attention in healthcare due to the need to improve service quality, such as monitoring biomedical equipment location or guiding patients in crowded hospitals [46]. For example, in the case of emergency patients, doctors can track the safety and mobility of patients instantly. Other applications, such as map construction and route planning, are well suited for IPSs [47]. The map construction is a fingerprinting-based localization approach that consists of an offline stage and an online stage. Fingerprints are in the form of radio frequency (RF) signal strength, which is collected and stored in the offline stage. The location pair consists of exhaustive records of the serviced area. For the online stage, location estimation is estimated by comparing the collected testing records to the stored training fingerprints. Furthermore, route planning for intelligent localization and navigation systems is another extremely useful technology with both military and commercial applications. For standard route planning algorithms, it generates a minimum cost route based on a predetermined cost function. The beneficial opportunities for IPSs are summarized in Figure 1.
# Point-Graph Neural Network Based Novel Visual Positioning System for Indoor Navigation
## 6 Conclusions  
In this study, we proposed a novel VPS method for determining the position and pose of an object, estimated using deep learning, by matching it with the position and pose of an object in a predefined space. Initially, we estimated the position and pose of an object by learning the 3D point cloud obtained from the LiDAR camera through deep learning. Subsequently, the position and pose of the object in a predefined space were matched to determine the user's position. This confirms that the proposed system can ensure high location accuracy and direction estimation in a well-established space with structured voxels. The proposed system uses a down-sampled point cloud similar to a mobile device-based environment that can precisely acquire 3D data when GPS information is insufficient. Moreover, it enables deep learning-based visual positioning using fixed objects in an indoor environment. In summary, it is a decision system that can provide customized augmented reality content to users and also aid in indoor navigation.  
Owing to the characteristics of augmented reality, which predominantly operates using mobile devices, it is necessary to investigate the work schedule of mobile GPUs to minimize the camera power consumption rate of mobile devices or the use of CPU and GPU. Thus, we intend to develop a system that complements deep learning object recognition and simultaneous localization and mapping (SLAM) for indoor navigation and augmented reality in the future.  
Conceptualization, T.-W.J.; Methodology, T.-W.J. and C.-S.J.; Software, T.-W.J. and C.-S.J.; Investigation, T.-W.J., C.-S.J.; and S.-C.K.; Writing--Original Draft Preparation, T.-W.J.; Writing--Review & Editing, K.-D.J. and S.-C.K.; Supervision, K.-D.J.; Project Administration, K.-D.J. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Code and dataset will be made available on request to the first author's email with appropriate justification. The public site for each dataset is as follows. RGB-D Dataset 7-Scenes: [[https://www.microsoft.com/en-us/research/project/rgb-d-dataset-7-scenes/](https://www.microsoft.com/en-us/research/project/rgb-d-dataset-7-scenes/)]([https://www.microsoft.com/en-us/research/project/rgb-d-dataset-7-scenes/](https://www.microsoft.com/en-us/research/project/rgb-d-dataset-7-scenes/)) (accessed on 9 August 2021).  
This work was supported by Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korean government (MSIT) (No.2020-0-00192, AR Cloud, Anchor, Augmented Reality, Fog Computing, Mixed Reality).  
The authors declare no conflict of interest.
## 1 Introduction
Considering the challenges of massive data collection in IoT scenarios described earlier, in this article, we focus on modeling and evaluating the performance of three Frame Slotted ALOHA (FSA) variants named FSA-ACK (FSA with ACKnowledgements), FSA-FBP (FSA with FeedBack Packets) and DFSA (Dynamic FSA). We have selected these FSA variants because they are typically used in such scenarios due to their simplicity, which eases implementation in constrained environments. In particular, we model each protocol variant using an AMC (Absorbing Markov Chain) and derive closed expressions to determine the average transmission delay and energy consumption of both the network gateway and end-devices. We then use computer simulations to check the validity of the models and compare the average delay and energy consumption of each protocol variant depending on the network size. Results show that the mathematical model is accurate and outline the benefits of using DFSA with respect to FSA-FBP and FSA-ACK in this scenarios. Last but not least, it is important to mention that the work presented in this article is an extension of the work presented in [18; 19]. The mathematical model to derive the delay and energy consumption of the described protocols, as well as the methodology used to validate the obtained results, are similar. However, in [18] we compare FSA-FBP with CTA (Contention Tree Algorithm) and in [19] we compare the FSA-FBP with RSFA (Reservation FSA). In contrast, in this paper we compare the three FSA variants with respect to each other and provide metrics to optimize their performance. Hence, we believe that this work is novel and can be useful for researchers and practitioners aiming to select and optimize the operation of a FSA-based protocol for massive data collection in IoT scenarios, where scalability, reliability and low-power requirements are key to success. The remaining parts of this article are organized as follows. Section 2 provides an overview of FSA-ACK, FSA-FBP and DFSA frame structure and protocol operation. Section 3 presents the analytic model of the FSA-ACK, FSA-FBP and DFSA protocol variants using an AMC, which allows to calculate the average number of contention frames, as well as to obtain the average delay and energy consumption for all network device types. Section 4 presents the protocol evaluation based on computer simulations that we have conducted to validate the analytic models and compare its average delay and energy consumption. Finally, Section 5 concludes the article.