esa-sceva/satcom-chunk-collection huggingface dataset:
# Empirical Evaluation of TLS-Enhanced MQTT on IoT Devices for V2X Use Cases
## Abbreviations  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} ACE & Authentication and Authorization in Constrained Environments \\ ADAS & Advanced Driver Assistance System \\ AES & Advanced Encryption Standard \\ ABE & Attribute-Based Encryption \\ BSM & Basic Safety Message \\ CA & Certificate Authority \\ C-ITS & Cooperative-Intelligent Transport System \\ CoAP & Constrained Application Protocol \\ CAM & Cooperative Awareness Message \\ CPU & Central Processing Unit \\ C-V2X & Cellular V2X \\ DENM & Decentralized Environmental Notification Message \\ DES & Data Encryption Standard \\ DTLS & Datagram Transport Layer Security \\ ECC & Elliptic Curve Cryptography \\ ECDHE & Elliptic Curve Diffie-Hellman Ephemeral \\ GPS & Global Positioning System \\ IBC & Identity-Based Cryptography \\ IoT & Internet of Things \\ MIN & Multi-Identifier Network \\ MQTT & Message Queuing Telemetry Transport \\ M2M & Machine-to-Machine \\ OBU & On-Board Unit \\ OTA & Over the air \\ QoS & Quality of Service \\ ROS & Robot Operating System \\ RSA & Rivest, Shamir, and Adelman \\ RQ & Research question \\ RSS & Resident Set Size \\ RSU & Road-Side Unit \\ SpaT & Signal Phase and Timing \\ TLS & Transport Layer Security \\ TCP/IP & Transmission Control Protocol/Internet Protocol \\ VM & Virtual machine \\ V2I & Vehicle to Infrastructure \\ V2N & Vehicle to Network \\ V2P & Vehicle to Pedestrian \\ V2V & Vehicle to Vehicle \\ V2X & Vehicle to Everything \\ WiFi & Wireless Fidelity \\ WSN & Wireless Sensor Network \\ \end{tabular}
## 3 Deep-Learning-Empowered Security Solutions for IoT Systems
### Preventing Denial of Service (DoS) and Distributed Denial of Service (DDoS) Attacks  
It is believed that the IoT is a \"Land of Opportunities for DDoS Attackers\" [80]. The number of attacks on IoT infrastructure has dramatically increased on a never-before-seen scale. Mirai, one of these attacks, nearly brought the Internet to a halt by using common household items as bots to carry out DDoS attacks against a number of different corporations. Babycams, printers, and webcams were among the affected items. Other bots that resemble Mirai in [9]. Because of the sophisticated method that was used to spread throughout the IoT network, the Mirai malware family caused significant damage to Internet services. On the other hand, due to the wide variety of designs, developing a unified method to defend against DDoS attacks in the numerous IoT platforms is extremely difficult.  
Significant research accomplishments have been produced through the application of a variety of strategies to resist DDoS attacks in the IoT. For instance, [PERSON] et al. [81] proposed a deep-learning-based DDoS attack detection approach using LSTM and GRU. Compared to conventional ML models, their proposed system achieves superior results. Using deep learning methods such as a stacked autoencoder and stacked restricted Boltzmann machine (RBM), [PERSON] et al. [82] created an NIDS that detects intrusions based on anomalies in network traffic. [PERSON] et al. [83] proposed a novel framework for detecting botnets on TCP/UDP/IP packet flows that makes use of deep neural networks (DNNs) and ladder networks. It presents superior results when compared to traditional methods of detecting botnets. [PERSON] et al. [84] similarly adopted deep learning to mitigate both DoS and DDoS attacked in the IoT. Likewise, [PERSON] [63] employed a multilayer perception (MLP) approach to detect DoS attacks in a common IoT platform, e.g., a sensor network. Both shallow and deep learning methods were investigated in [64]. The authors used probability estimates based on shallow learning methods to foretell intrusions. They tested a convolutional LSTM deep neural network and found that it performed significantly better than the shallow learning techniques they had previously used.  
[PERSON] et al. [85] developed a source-based method to defend against DDoS attacks in fog computing. Their proposed work employs LSTM to identify legitimate and malicious packets by analyzing the patterns in the time series data. Similarly, [PERSON] et al. [86] combined DNN and LSTM to detect unknown DoS and DDoS attacks. [PERSON] et al. [87], with the aim of reducing computation consumption, developed a lightweight and usable CNN for DDoS detection called LUCID. CNN properties were used to detect whether traffic flows were attacked or safe. Their results demonstrate the viability of their proposed method for DDoS attack detection in operational settings with limited resources.
## 5 Model Architectures and Training
### Model Architectures
#### 5.1.1 Baseline Convolution-LSTM  
As a simple baseline architecture, a naive convolution-long short-term memory (Conv-LSTM) model was implemented (see Figure 6). It consists of three subsequent convolutional units, each with a 2D convolution, maxpooling, and an ReLu layer, followed by three LSTM [44] cells with varying hidden state and input size, processing temporal context over the image time series. Following this, the Conv-LSTM implemented here has to be distinguished from a convolutional LSTM, in which the convolutions replace specific gate functions in the LSTM cell itself [45].  
In order to do so, the fundamental core of the LSTM cell consists of a memory cell \(\mathbf{c_{t}}\), accumulating temporal information and being subject to manipulation--including access, addition, and deletion of data (see Figure 6, LSTM cell). This is implemented by several self-parameterized controlling gates, such as an input gate \(\mathbf{i_{t}}\), which controls new information flow \(\mathbf{x_{t}}\) to the memory, and the forgot gate \(\mathbf{f_{t}}\) in order to delete past information from \(\mathbf{c_{t-1}}\). Finally, the output gate \(\mathbf{o_{t}}\) controls which way information propagates from the last cell state \(\mathbf{c_{t}}\) to the cell's final hidden state \(\mathbf{h_{t}}\). Each gate function itself is realized by a respective nonlinear activation function in the form of either a sigmoid function \(\sigma\) or the hyperbolic tangent. The mathematical model can be described as follows, where \(\circ\) denotes the Hadamard product, the matrices \(\mathbf{W}\) contain the respective, learnable edge weights and \(\mathbf{b}\) the according bias [44; 45]:  
\[\mathbf{i_{t}} =\sigma(\mathbf{W_{xi}}\mathbf{x_{t}}+\mathbf{W_{hi}}\mathbf{h_{t -1}}+\mathbf{W_{ci}}\circ\mathbf{c_{t-1}}+\mathbf{b_{i}}) \tag{13}\] \[\mathbf{f_{t}} =\sigma(\mathbf{W_{xf}}\mathbf{x_{t}}+\mathbf{W_{hf}}\mathbf{h_{t -1}}+\mathbf{W_{cf}}\circ\mathbf{c_{t-1}}+\mathbf{b_{f}})\] \[\mathbf{c_{t}} =\mathbf{f_{t}}\circ\mathbf{c_{t-1}}+\mathbf{i_{t}}\circ\tanh( \mathbf{W_{xc}}\mathbf{x_{t}}+\mathbf{W_{hc}}\mathbf{h_{t-1}}+\mathbf{b_{c}})\] \[\mathbf{o_{t}} =\sigma(\mathbf{W_{xo}}\mathbf{x_{t}}+\mathbf{W_{ho}}\mathbf{h_{ t-1}}+\mathbf{W_{co}}\circ\mathbf{c_{t}}+\mathbf{b_{o}})\] \[\mathbf{h_{t}} =\mathbf{o_{t}}\circ\tanh(\mathbf{c_{t}})\]  
For the final classification (Class) or regression (Reg) task, a multilayer perceptron (MLP) with two linear layers is utilized.  
Figure 6: General architecture of the simple, implemented convolutional LSTM.
## 2 Study Area and Resources
### Resources
#### 2.2.2 Landslide Conditioning Factors
Among the soil factors, surface soil taxonomy has a significant influence on landslides through its effects on water retention and permeability, the stability of the soil cover, and its interaction with lithology. Consequently, it is considered one of the primary contributors to landslide occurrences [42]. In this study, the OpenLandMap USDA Soil Taxonomy Great Groups were selected and visualized using OpenLandMap.org. Among the environmental factors, the NDVI serves as an indicator of vegetation cover, illustrating the extent of vegetative presence within a specified area. Vegetation mainly reduces the damage of water flow to the slope through the root stabilization of its rhizomes, thereby helping to prevent landslides [43]. NDVI data were sourced from the MOD13Q1 Version 6 dataset with a time resolution of 16 days. Various land cover types exhibit distinct soil mechanical and hydrological characteristics, which influence slope stability and, thus, landslide development. As a result, land cover types were frequently chosen as a focus for investigating landslide susceptibility [44]. Land cover type data were sourced from the MCD12Q1 Version 6 dataset with a time resolution of 1 year. Landslides are primarily triggered by precipitation, which facilitates the infiltration of rainwater into soil and rock formations. This infiltration alters the physical properties of these materials, thereby diminishing their resistance to sliding. When the shear strength of the soil is insufficient to resist gravitational forces or other external stresses, the stability of the slope is compromised, leading to landslides [38]. Precipitation data were analyzed using the ERA5-Land Monthly Aggregated-ECMWF climate reanalysis dataset with a temporal resolution of monthly average data [45].
# Experimental Test of Continuous Wave Frequency Diverse Array Doppler Radar +
## 4 Data Collection III: Moving Targets
### Theoretical Frequency Domain Response  
We use the Fourier relationships [50]  
\[\begin{split} e^{\pm j\omega_{0}t}&\Leftrightarrow 2\pi \delta(\omega\mp\omega_{0})\\ \mathscr{F}(e^{j(\omega_{0}t\pm\theta)})&=e^{\pm j \theta}\mathscr{F}(e^{i\omega_{0}t})\end{split} \tag{12}\]  
to find the expected frequency response at the receiver. Since the Fourier transform is a linear operation, we can take the Fourier transform of each individual term in Equation (8), then sum the individual responses, to obtain the overall response of the system. This is expressed mathematically as  
\[\begin{split}\mathscr{F}\big{[}F_{R,bb}(t;R,\theta)\big{]}& =\mathscr{F}\bigg{[}\sum_{n=0}^{N-1}e^{i\omega_{n}t}\cdot e^{-j \theta_{n}}\bigg{]}\\ &=\sum_{n=0}^{N-1}\mathscr{F}\bigg{[}\ e^{i\omega_{n}t}\cdot e^{- j\theta_{n}}\bigg{]},\end{split} \tag{13}\]  
where \(\omega_{n}=2\pi(n\Delta f+\
u_{0})\) and \(\theta_{n}=2\pi f_{0}(R_{n}+R_{r})/c\)). Using the Fourier relationships from Equation (12) in conjunction with Equation (13), the total response is then  
\[\mathscr{F}(\omega)=2\pi\sum_{n=0}^{N-1}\delta(\omega-\omega_{n})\cdot e^{-j 2\pi\theta_{n}}. \tag{14}\]  
Note that Equation (14) assumes no stationary clutter exists in the receive echo from the target. In practice, stationary mainlobe clutter will exist at each radiated frequency, with the target Doppler response following Equation (14).  
Figure 11: Photograph of system for drone test in Data Collection III.
# A Metal Detector for Clip Location Tracking of Stomach and Colon Cancer during Laparoscopic Surgery
## 5 Conclusions  
This paper presents a detection method of malignant tumor in the stomach and colon during laparoscopic cancer surgery. Determining the extent of excision for gastric and colon malignancy in the cavity is difficult. Nonetheless, after installing the clip around the tumor, the method of locating the clip using a detector and extracting the tumor is effective.  
Because the proposed clip-detector uses magnetic field coupling based on metal (clip)-detection technology, tissue penetration is easy. Furthermore, the amplitude of magnetic coupling has a relatively high gain at 2.27 V (@11.33 kHz). Therefore, if the detector detects the clip installed in the tumor, an alarm is generated from the speaker at 2.27 V (frequency: 11.33 kHz). In this manner, it is possible to speedily locate the tumor based on the alarm sound in the operating room.  
The designed clip detection method has no such side effects as the conventional ink tattoo method, indocyanine green fluorescence staining method, and autologous blood maker method, and it can speedily locate the tumor, reducing the burden on surgeons and patients. Because the clip-detector uses ferrite and coil, it is inexpensive and easy to manufacture. Furthermore, the clip-detector using ferrite and coil can be mass-produced owing to the low unit price. Because the clip-detector uses an endoscope, it is expected that this study will not only enable efficient surgical laparoscopic surgery, but also stimulate the endoscopy-specific medicine market in the future.  
Design and simulation; K.Y., analysis and supervisor; [PERSON] and [PERSON]. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Ministry of Science and ICT (MSIT), Korea, under the Information Technology Research Center (ITRC) support program (IITP-2021-2017-0-01630) supervised by the Institute for Information and communications Technology Promotion (IITP), G-ABC FRD2019-11-02(3), and by the research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded (NRF-2020R1F1A1076839).  
The experiment was an animal test from the \"experimental animal center of KNOTUS (Songdo Research Center, Incheon, Republic of Korea)\" KNOTUS-IACUC-20-KE617 through the permission of animal institutional review board (IRB) at animal ethics commission on approval of 5 November 2020.  
Not applicable.  
The data presented in this study are available upon request from the permission corresponding author. The data are not publicly available because of privacy and ethical restrictions.  
[PERSON] and [PERSON] equally contributed to the work. [PERSON] and [PERSON] are the co-corresponding authors. This research and animal test was supported by the Scaleup Challenge Lab at Inha University.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
# A Secure IoT-Based Authentication System in Cloud Computing Environment
## 4 Security Analysis
### User Anonymity  
The attacker's use of user anonymity means that the user \(U_{i}\) cannot be identified through the messages in the communication session [43]. In our authentication phase, \(U_{i}\)'s identity \(ID_{i}\) is protected by a hash function \(D_{2}=h(r_{u}\|PID_{i}\|ID_{cs})\oplus ID_{i}\). Therefore, if an attacker wants to obtain \(U_{i}\)'s identity, he/she must compute \(h(r_{u}\|PID_{i}\|ID_{cs})\). However, he/she cannot acquire the \(r_{u}\) because he/she does not have the secret key \(x\) of \(CS\) to derive \(r_{u}\) from \(D_{1}=A_{i}\oplus r_{u}\), where \(A_{i}=h(PSID_{i}\|ID_{cs}\|x)\). Even if the attacker is a legal user, he/she still cannot obtain \(h(r_{u}\|PID_{i}\|ID_{cs})\) by adopting the strategy shown in Section 2.2.2. Therefore, the attacker cannot identify \(U_{i}\)'s identity; furthermore, it shows that our proposed scheme has user anonymity.
### Lorenz Waveforms for Time Series Prediction  
Time series prediction algorithms predict future values of a given signal based on past observations. The ability of these algorithms to accurately predict the behavior of chaotic systems is a key test of their effectiveness. Importantly, the seemingly conflicting attributes of deterministic but entropic waveforms enable their use as a benchmark for testing time series prediction algorithms because they exhibit complex and unpredictable behavior. Solutions to the chaotic systems such as the Lorenz equations are well-known for their sensitivity to initial conditions. As a result, small perturbations of that constitute similar observations between two chaotic waveforms at an early portion of a time series will impose deviations between the initially similar waveforms observed later in time. This chaotic nature makes Lorenz waveforms ideal candidates for testing time series prediction algorithms. Lorenz waveforms are scientifically reproducible but their long-term predictions provides a challenging problem that requires sophisticated techniques that fit the scope of many machine learning efforts.  
There are two primary advantages to using chaotic Lorenz waveforms in time series predictions. First, the determinism of chaotic Lorenz waveforms offers repeatable, rigorous, and standardized benchmarks that pair easily with clearly described dynamical structures such as the location of the system's fixed points. Second, the Lorenz system is well-defined and widely studied. The ubiquity of the Lorenz system allows research comparisons that can clearly illustrate relative performance of different algorithms by means of the same, standardized test data. Thus, strengths and weaknesses of these algorithms can be evenly considered, compared, and discussed. Within these evaluations, these waveforms enable researchers to study various factors on the algorithm performance in relation to algorithmic parameters, training data length, and even the presence of noise. The systematic study of these types of factors results in two important capabilities: (1) the analysis of parameter impact on algorithm performance, and (2) new insight into both limitations and strengths of various approaches to time series predictions problems.  
However, one significant issue with chaotic waveforms involves the quantification of time-series prediction beyond the short, initial predictable regions of the time series. Determinism does allow for computationally taxing numerical methods to provide quantitative (albeit approximate) evaluation for piece-wise sections of data. This approach is sub-optimal when compared to many standard signal construction techniques [26] but is necessary due to the lack of closed-form solutions for trajectories produced by the Lorenz system [14].
# Yeast Extract Affecting the Transformation of Biogenic Tooeleite and Its Stability
## 3 Results and Discussion
### The Role of Yeast Extract during the Formation of Tooleite and As Stabilization Mechanism
#### 3.3.2 Effect of Yeast Extract on Tooeleite Stability  
The As solubility of the As-bearing precipitates is one of the important parameters that must be assessed. The concentration of As leached from precipitates decreased from 13.8 mg/L to 7.2 mg/L when 0.10% yeast extract was introduced into the system. The average particle size and specific surface area of the former were 10.84 um and 67 m\({}^{2}\)/g, but those of the latter were 5.12 um and 71 m\({}^{2}\)/g, respectively (Figure 9). Figure 7(a-1,b-1) shows that the particles prepared in absence of yeast extract were obviously agglomerative, which was consistent with the results of a larger particle size. The results showed that the specific surface area of biogenic tooeleite changed slightly, while the average particle size of tooeleite prepared in the absence of yeast extract was nearly two-times larger than that of tooeleite prepared in the presence of yeast extract. These results may be due to the different morphologies of the particles. With more hollow structures appearing, the surface roughness of particles increased in tooeleite without yeast extract. Therefore, the rougher surface of the tooeleite without yeast extract increased the specific surface area similar to that of the tooeleite with yeast extract, although the particle size varied greatly. In addition, it has been reported that the hollow structure of particles leads to a relatively higher As leaching concentration [14, 57]. This is the reason that both external diffusion and internal diffusion exist in the leaching reaction of the precipitates with hollow structures. Here, it can be suggested that yeast extract is related to the change in the morphology of the precipitates, and the lower As leaching concentration is attributed to the distinct mineral morphology with less hollow structure.  
Figure 9: Particle size and specific surface area of biogenic tooeleite obtained with or without yeast extract (YE).  
The elemental composition of tooeleite is shown in Table 1. When the yeast extract was added or not, the Fe/As of the products became different. In the system without yeast extract, the Fe/As ratio of the precipitate was 2.17, which is obviously above the theoretical value of the tooeleite molecular formula: 1.50. This illustrated that there was amorphous iron oxide in the final product except tooeleite. In addition, the molar Fe/As ratio of the resulting product in the system with yeast extract was 1.60, which was much closer to 1.50. Based on these results, yeast extract could contribute to purer tooeleite formation, which is favorable for the stability of tooeleite.
## 6 Case Study
### Passenger Demand and Parameter Settings  
The passenger demand data come from AFCSs, and the total number of passengers in the study period was 14,704. The OD flow and numbers of people entering and leaving at each station are shown in Figure 13. The scale of the outer circle of the chord graph is the number of passengers entering and leaving at each station, the second circle is the station indicator color, and the third circle is the cumulative number of passengers entering and leaving at each station. Taking station 5, YZQ, as an example, the gray histogram is the cumulative number of leaving passengers, the time axis is the center of the circle pointing in the circumferential direction, the red histogram is the cumulative number of entering passengers, and the time axis is the circumferential point to the center of the circle direction. The chord of the inner circle represents the OD flow, its color indicates the origin station, and its width indicates the flow. It can be seen that commuters from station SJZ in the central urban area of Beijing to WYJ, RJDJ, RCDJ, and TJNL in the Beijing Economic and Technological Development Regions represent the main flow. The actual timetable is shown in Figure 14. The solid lines denote the 12 optimized trains, and the dotted lines denote the front and posterior boundary trains, with two of each. The double line denotes that the train formation is four, the color denotes the train loading rate, and the number next to the line is the train type and number. The number of train types in this case is \(K=3\), and the boundary trains belong to type 4. The train formation is four, six, or eight, where the ratio of motor to trailer is 4:0 (e.g., Guangzhou Metro Line 4), 4:2 (e.g., Beijing Subway Line 4), or 6:2 (e.g., Beijing Subway Line 6). The other parameters of the model and algorithm are listed in Appendix C.  
Figure 12: Locations of Line L in the Beijing Subway network.