esa-sceva/satcom-chunk-collection huggingface dataset:
# Secured Real-Time Machine Communication Protocol
## 4 Vulnerability Analysis
### Attacker Model  
The attacker is assumed to possess the following capabilities:  
1. **Eavesdropping:** The ability to monitor network traffic to capture sensitive data, including authentication tokens and transmitted messages.
2. **Man-in-the-Middle (MitM) Attacks**: The ability to intercept, modify, or inject packets, potentially disrupting or manipulating communication between the client and server.
3. **Replay Attacks:** The capability to reuse previously captured valid packets to disrupt system operations or gain unauthorized access.
4. **Traffic Analysis:** The ability to infer sensitive data or communication patterns using statistical analysis or machine learning on observed traffic.
5. **Data Injection/Tampering Attacks:** The attacker injects malicious data or modifies legitimate data within the communication stream to disrupt operations or compromise system integrity.
6. **Post-Quantum Attacks:** Access to quantum computing resources capable of breaking classical cryptographic schemes.
###### Abstract  
Nowadays, there is an increasing interest in smart systems, e.g., smart metering or smart spaces, for which active sensing plays an important role. In such systems, the sample or environment to be measured is irradiated with a signal (acoustic, infrared, radio-frequency... ) and some of their features are determined from the transmitted or reflected part of the original signal. In this work, infrared (IR) signals are emitted from different sources (four in this case) and received by a unique quadrature angular diversity aperture (QADA) sensor. A code division multiple access (CDMA) technique is used to deal with the simultaneous transmission of all the signals and their separation (depending on the source) at the receiver's processing stage. Furthermore, the use of correlation techniques allows the receiver to determine the amount of energy received from each transmitter, by quantifying the main correlation peaks. This technique can be used in any system requiring active sensing; in the particular case of the IR positioning system presented here, the relative amplitudes of those peaks are used to determine the central incidence point of the light from each emitter on the QADA. The proposal tackles the typical phenomena, such as distortions caused by the transducer impulse response, the near-far effect in CDMA-based systems, multipath transmissions, the correlation degradation from non-coherent demodulations, etc. Finally, for each emitter, the angle of incidence on the QADA receiver is estimated, assuming that it is on a horizontal plane, although with any rotation on the vertical axis Z. With the estimated angles and the known positions of the LED emitters, the position (\(x\), \(y\), \(z\)) of the receiver is determined. The system is validated at different positions in a volume of \(3\times 3\times 3.4\) m\({}^{3}\) obtaining average errors of 7.1, 5.4, and 47.3 cm in the X, Y and Z axes, respectively.  
Keywords:IR sensors; CDMA; energy measurement; visual light positioning +
Footnote †: journal: Applied
## 3 The Cross Time Slot Partial Interference Alignment
### The Number of Achievable SDoF  
According to References [5; 6], the SDoF is defined as the pre-log factor of the sum rate during a unit time period. However, since the transmission period in our paper is divided into two parts (each part containing one sub-time slot), we define the achievable SDoF as the ratio of the total number of DoF to the number of sub-time slots, i.e.,  
\[SDoF=\underset{SNR\rightarrow\infty}{lim}\frac{R_{\Sigma}(t)}{log(SNR)\sum t }=\frac{\sum N_{s}^{[i,l]}(t)}{\sum t}, \tag{22}\]  
where \(R_{\sum}(t)=\sum_{i=1}^{2}\sum_{l=1}^{2}R^{[i,l]}(t)=\sum_{i=1}^{2}\sum_{l=1}^ {2}log_{2}\big{(}1+SINR_{[i,l]}(t)\big{)}\) and \(N_{s}^{[i,l]}(t)\) represent the sum-rate performance and the number of data streams to UE\([i,l]\) in sub-time slot \(t\), respectively. As can be found from (20) and (21), the antenna configurations at each receiver (i.e., CEU) is bigger than that at the transmitters (i.e., BS and relay) due to the influence from both inter-cell and intra-cell interference. Here, we take the number of transmit antennas \(M\) as an example. In the first sub-time slot, two CCUs are served simultaneously, the number of data streams decoded at each one is \(M/2\), yielding \(M/2\times 2=M\) DoF. Note that we assume the number of antennas configured at each CEU is big enough to decode the received data streams. Hence, in the second sub-time slot, each CEU can also achieve \(M/2\) DoF. Since the two CCUs are also served by the BSs in the second sub-time slot, the achievable SDoF in the whole transmission period can be derived as  
\[SDoF=\frac{M+2*M/2+M}{2}=\frac{3M}{2}. \tag{23}\]
# An Analysis of Satellite Multichannel Differential Code Bias for BeiDou SPP and PPP
## 6 Discussion  
DCB and OSB characterize pseudo-range hardware bias in differential and absolute form, respectively. The CAS offers DCB and OSB products with high stability in timing. However, large numerical variations occur when new satellites are added, and the data quality of the solved observations is poor. The stability of DCB and OSB at the satellite end varies among different types of satellites and over different time periods and is influenced by differences in observation time, geometry, and orbital repetition period and the quality of the solution data. We verified the linear correlation between DCB and the OSB of the CAS from the perspective of numerical analysis, and the difference between different OSB combination values and multichannel DCB was very small. OSB has more flexibility and scalability as a pseudo-range absolute bias parameter. DCB and OSB can reflect satellite hardware performance to some extent and can be used as an evaluation indicator.  
For the positioning impact of the multichannel DCB of the BDS in SPP and PPP, correcting either DCB or TGD can improve the positioning accuracy of SPP and PPP. However, compared to the case of correcting TGD, the improvement in localization performance after DCB correction for dual-frequency SPP is more obvious than that of DCB correction for single-frequency SPP, and the effect of DCB correction for the dual-frequency case will be more obvious considering more frequency information. The contribution of pseudo-range bias correction to PPP is based on the contribution of pseudo-range to PPP. The PPP positioning accuracy of the dual-frequency ionosphere-free model with different combinations of observations differs due to the quality of the observed data, the signal combination factor, and the combination noise. It should be considered that different DCB combinations of pseudo-range bias correction in different signal combinations of PPP also have different degrees of positioning influence. Additionally, the inclusion of DCB affects the estimation of two types of parameters to be estimated (receiver clock and ionosphere-free combined ambiguity), which in turn affects the convergence time of PPP.  
In this paper, only the time-varying characteristics of the OSB and DCB of the BDS provided by the CAS and the positioning effects of DCB on single- and dual-frequency SPP and dual-frequency PPP were analyzed. With the development of DCB and OSB products and the increasing abundance of multi-frequency and multi-constellation GNSS observation data, the application of DCB and OSB in positioning with different analysis centers from multiple systems is also worth exploring. The OSB products can be extended to correct the effect of phase hardware delays in the carrier phase, which is more flexible. Further analysis of pseudo-range and phase OSB products for multiple systems can be performed in the future for applications in PPP and PPP ambiguity resolution.
## 2 Related Work  
In traditional MANETs, all routing processes are implemented directly on nodes, meeting the basic communication requirements of wireless environments [12]. S-DSR [13] represents a novel hybrid security routing system within MANETs, ensuring secure data packet transmission and optimal performance across network nodes. This protocol utilizes trust information from neighboring nodes to select the safest routes for file transmission. [PERSON] et al. [14] proposed a Trust-Optimized Cluster-Routing Algorithm based on Selfish Nodes (SN-TOCRP), adhering to the concept of layered clustering to create node clusters.  
In recent years, the effective reduction in control overhead while enabling flexible network management through controllers has emerged as a crucial challenge in SD-MANETs. [PERSON] et al. [15] have proposed an SD-MANET architecture and designed three centralized communication protocols to meet the demands of active, passive, and layered routing strategies within MANETs, addressing the inherent challenges of the network. [PERSON] et al. [16] introduced a unique routing protocol called Distinct Network Yarning (DNY) within the SD-MANET framework. In this protocol, the controller stores information about the entire SDN system's operational functionalities for congestion control. [PERSON] et al. [17] proposed a Geographic Routing Protocol suitable for VehicularAd-Hoc Networks (VANETs) named SDN-Based Geographic Routing (SDGP). This protocol employs the controller to gather vehicle information within the network, enabling the computation of optimal paths through a network-wide view, thereby facilitating intelligent and secure routing in VANETs. [PERSON] et al. [18] introduced a performance-enhanced routing protocol specifically designed for Infrastructure-Assisted Vehicular Networks. This protocol explores the use of cellular networks to relay control information to the controller in a low-latency manner, exhibiting good availability under limited routing overhead.  
Among all the proposed routing decisions mentioned above, there is a certain degree of reduction in the occurrence probability of issues such as flow table expiration and disconnection between data nodes and control nodes, thereby enhancing routing effectiveness. However, as these issues are tackled, there is a tendency for more control operations to be centralized at the controller, resulting in a rise in control overhead.
## 4 Results and Discussion  
This section showcases the simulation outcomes to illustrate the effectiveness of the three trajectory planning algorithms aimed at energy conservation for AUVs. We study an IoUT environment that has a surface station positioned at the center of the search space, one AUV departing from the surface station, and n _SNs_. Two scenarios are evaluated in the examination, with the _SNs_ positioned within a square area of \(M\times M\times H\) meters. In the first scenario, 13 nodes are utilized, randomly placed within a 0.25 km \(\times\) 0.25 km \(\times\) 0.25 km radius, with a transmission range of 480 m. In the second scenario, three nodes are arranged in a grid network, systematically located within a 0.6 km \(\times\) 0.6 km \(\times\) 0.6 km radius and possessing a transmission range of 25 m. The battery-powered AUV is assumed to move at a constant velocity, with a power output of 100 watts (W), tasked with gathering data from all the _SNs_ within the successful transmission range. Transmitting and receiving data entail a specific amount of energy; each sensor node utilizes 0.3 W for sending and _0.1_ W for receiving. As the AUV reaches the boundary of an \(SN\), the node promptly prepares samples of each data chunk, compiles them into a packet of length \(L\) ranging from _130_ to 220 kbits, and transmits it to the AUV. All simulations were performed by using MATLAB R2021a on a PC with an Intel Core i7 CPU (1.80 GHz), a 1 TB HDD Hard Disk, and 16 GB of RAM.
# Enhanced Gradient-Based Local Feature Descriptors by Saliency Map for Egocentric Action Recognition
## 3 Saliency Map-Based Local Feature Descriptors for Egocentric Action Recognition
### Video Representation  
An egocentric video clip is usually formed by a set of video frames, and each frame is represented as a two-dimensional array of pixels. Thus, each video clip \(\mathcal{V}\) can be viewed as a three-dimensional array of pixels, with \(x\)- and \(y\)-axes representing the plane of the video frame, and the \(t\)-axis denoting the timeline. An egocentric video clip is denoted by \(\mathcal{V}\in\mathbb{R}^{m\times n\times f}\), where \(m\times n\) represents the resolution of each video frame, and \(f\) represents the total number of frames. Histogram-based LFDs use each pair of consecutive frames \(\mathcal{F}r_{j}\) and \(\mathcal{F}r_{j+1}(1\leq j\leq f-1)\) to capture the motion information along the timeline (except HOG, which does not consider the temporal information) and use the neighboring pixels in every frame \(\mathcal{F}r_{i}(1\leq i\leq f)\) to extract the spatial information. In this work, all LFDs, including HOG, HMG, HOF, MBHx, and MBHy, were enhanced by using the saliency map, yielding SM-HOG, SM-HMG, SM-HOF, SM-MBHx, and SM-MBHy, respectively. Briefly, HOG, HMG, HOF, and MBH represent videos using the in-frame gradient only, in-and-between frame gradient, in-frame optical flow and between-frame gradient, and the imaginary and real parts of the optical flow gradient and between-frame gradient, respectively.
## 1 Introduction
Through on-site monitoring and numerical simulation, it was found that the deformation allowance of shallowly buried measurements can be set uniformly. However, for deeply buried tunnels, deformation increases with the slope bias, and recommended deformation allowance values are provided for different slope biases. Additionally, [PERSON] et al. [14] studied the Gushan Tunnel of the Menghua Railway, analyzing the impact of each stage of bench excavation on deformation. Their research specifically investigates how each phase of bench excavation in soft rock tunnels affects the overall deformation. [PERSON] et al. [15] relied on engineering practice to study the deformation and failure mechanism of loess tunnels under rainfall conditions through indoor model experiments, and summarized the stages of tunnel deformation and failure under rainfall conditions. [PERSON] et al. [16], [PERSON] [17], and [PERSON] et al. [18] counted the monitoring data of crown settlement and side wall convergence of thousands of tunnel sections in various strata, identified deformation patterns, and provided recommended deformation allowance values. [PERSON] et al. [19] systematically analyzed the phenomenon and mechanism of loess liquefaction in China, analyzed new theories for improving loess liquefaction, and provided corresponding prevention and control methods. By reviewing domestic and international research, it becomes evident that the deformation of loess tunnels varies with soil types. Under the influence of different surrounding rock classes, tunnels in sandy loess sections experience more pronounced deformation compared to clayey loess and silty loess sections. Sandy loess tunnels are more sensitive to moisture content compared to tunnels in clayey loess and silty loess sections. In conclusion, domestic and foreign researchers have studied deformation allowance values for loess tunnels. However, there is a need for well-defined deformation allowance values for tunnels in different loess soil types, and there is a shortage of quantitative studies on this subject. Therefore, it is necessary to propose the design value of deformation allowance for tunnels in different loess soil types. Using data from 148 tunnel monitoring sections across various loess soil types, and considering factors like surrounding rock conditions, moisture content, and cover depth, this paper provides suggestions for the design of deformation allowance of tunnels in different loess soil types. This offers valuable guidance for future projects with similar designs. This study conducted statistical analysis on the settlement data of the arch crown of loess tunnels in China, investigated the correlation of factors affecting tunnel settlement, and provided recommended values for reserved deformation of tunnels with different loess soil types. The rationality of the recommended values was verified through engineering practice. The research results will help improve the standard for determining the reserved deformation of loess tunnels and provide suggestions for similar projects.
## 3 Methodology  
### Model Architecture  
Aiming at the challenges of complex texture and uneven scale in open-pit mining areas in remote sensing images, as well as the sticky boundaries of extracted mining areas, a Segmentation for Mine (SegMine) model is proposed, and its overall architecture is shown in Figure 1. The SegMine model consists of a Vision Transformer-based encoder and a lightweight attention mask decoder, which are responsible for extracting image features and learning segmentation mapping, respectively. On the encoder side, a multi-scale priori information complementary structure is designed to minimize the error due to the lack of priori information in the image space for Vision Transformer and to mitigate the segmentation error due to the loss of features in the small open-pit mining area. On the decoder side, a lightweight attention masking module is designed to obtain the final segmentation output through masking operations in order to mitigate low segmentation accuracy due to the blurring of mine boundaries as a result of feature upsampling.  
In SegMine, the whole segmentation process consists of three steps: feature encoding, feature decoding, and segmentation computation. First, Vision Transformer containing multi-scale priori information is utilized to encode the features of the target image, and each Transformer block after encoding will output features with global semantic information and rich boundary detail information. Second, the multi-layer attention maps and global semantic features output from Transformer blocks are collected and integrated,  
Figure 1: Overall architecture of SegMine.  
and the segmentation mask and multi-layer global semantic features are obtained using the attention mask decoder. Finally, the global semantic features are multiplied with the segmentation mask in order to obtain high-quality segmentation results. Figure 1 shows the overall architecture of the proposed SegMine model.
# Overview of Tool Wear Monitoring Methods Based on Convolutional Neural Network
## 4 Information Data of Indirect Online Measurement on Tool Wear
### Workpiece Surface Roughness  
In terms of workpiece surface roughness, the surface roughness increases as the tool wear increases or breakages occur. Based on this, the wear or damage condition of the tool can be indirectly inferred. The measurement methods of workpiece surface roughness are divided into two categories. (1) The contact measurement directly obtains the evaluation parameters of surface roughness, which are only suitable for offline measurement. (2) The non-contact optical reflection measurement obtains the relative value of the workpiece surface roughness. Automatic monitoring usually adopts two types: fiber sensor and laser test system [29,101]. [PERSON] et al. [28] carried out finished turning operations on hardened materials, and measured the flank wear of the tool and the surface finish, which varied with machining time under different machining conditions. [PERSON] et al. [29] developed an online surface roughness recognition system for a lathe based on a neural network and sensor system.  
Because the surface roughness feature is mainly used to measure the machined workpiece and cannot be monitored in real time, its application in tool wear online monitoring is limited.  
The installation diagram of a power sensor is shown in Figure 2.
## 3 Experiment Results and Analysis  
### Experiment Data Set  
#### 3.1.1. _Taxis GPS Data Set_  
For the purpose of verifying the performance of the novel algorithm, five GPS data sets were used as shown in Table 3. GPS data sets typically contain vehicle ID numbers, longitude values, latitude values, altitude values, timestamps, GpsSpeed, etc., which are periodically recorded by the onboard GLOBAL positioning system (GPS). Currently, public  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Algorithms** &
\begin{tabular}{c} **Time-** \\ **Consuming** \\ \end{tabular} & **SC** & **SP** & **SSE** & **ACI** \\ \hline RS(60) & 2.518 s & 0.9258 & 265.24 & 277,835.5 & \\ FPSO-GAK(85) & 65.495 s & 0.9365 & 266.79 & 275,294.1 & \\ RS(5000) & 206.853 s & 0.9297 & 266.42 & 271,651.7 & \\ FPSO-GAK(1000) & 803.172 s & 0.9383 & 267.13 & 269,894.1 & 0.2213 \\ \hline \hline \end{tabular} The number within parentheses represents the number of iterations performed by the algorithms.  
\end{table}
Table 2: Average indexes for clustering operations in synthetic dataset.  
Figure 2: The synthetic dataset distribution diagram.  
transport authorities in many cities have accumulated taxi GPS data sets for several years. In this study, we focused on GPS data sets from representative metropolitan areas in several countries, i.e., Aracaju (Brazil), Beijing (China), Chongqing (China), Rome (Italy), and San Francisco (USA). Data set Aracaju is a taxi GPS data set from Aracaju city in Brazil, which belongs to the standard data set in UCI Machine Learning Repository. Data set Beijing (China) is a taxis GPS data set from downtown Beijing, China, which is from the well-known Geolife Trajectories project [51; 52]. Data set Chongqing (China) is a taxis GPS data set from urban area Chongqing, China. Data set Rome (Italy) is a taxi GPS data set from Rome city, which belongs to the data set in Crawdad and contains GPS data of about 320 taxis' trajectories [53]. Data set San Francisco (USA) is a taxi GPS data set from San Francisco, which belongs to the data set in Crawdad and contains GPS data of about 500 taxis' trajectories [53]. The data sets have been cleaned beforehand; just a subset of the original data sets has been extracted, and a few records between 1:00 and 6:00 p.m. have been eliminated.