esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Fractional Sine Chaotic Map and Its Application to Image Encryption and Watermarking
## 5 Analysis of Image Encryption and Decryption
### Key Security Analysis  
#### 5.3.1 Key Space  
Our encryption scheme comprises seven keys \(x_{0},y_{0},b,c,\varpi\) for the chaotic maps and so for the keys with precision \(10^{-14}\), the key space is obtained as \(10^{70}\). For any safe encryption system a key space of \(2^{100}\) is sufficient [48]. Therefore, it is evident that our algorithm has a large enough key space to withstand any attacks.
## 3 Background
### Word Embedding Techniques
#### 3.2.2 Word2 Vec  
Word2 Vec is a word embedding technique introduced by [PERSON] at Google in 2013 [11]. The algorithm is based on a shallow neural network that can be used to embed features in a high-dimensional space. The trained extracted embeddings can be used in several tasks as the words that are similar in context are represented closely together.  
Word2 Vec consists of two algorithms that can be trained to generate the embeddings for words, which are the Common Bag of Words (CBOW) and Skip N-gram. In CBOW, the embedding for a given word is generated by combining the distributed representations of the other words that appear in its context. The context is usually defined as a window where the word to be represented is the middle word, and all others are the context. In Skip N-gram, the model tries to model a given word by trying to predict the neighboring words that would appear in its context, i.e., given an input word, the model tries to predict what the words are that either appear immediately before or after.  
While both of these models are quite effective for generating embeddings, the Skip-gram model is more powerful as it models every single word in the vocabulary. However, due to this, it also incurs a high overhead compared to the CBOW. In our experiments, we use the default model available which is based on the CBOW architecture.  
Since the Word2 Vec similarity is also based on the cosine similarity discussed in the previous section, the experiments are somewhat analogous to the HMM2 Vec experiments. However, Word2 Vec offers some advantages over the HMM2 Vec. The main one is that HMM2 Vec is based on a Markov model of order one, which means it is limited in the context information it may capture compared to Word2 Vec. Another drawback with HMM is the training time, being slower to train for long sequences as in our case.
## 4 Model Result
### MLPNN Model Outcomes  
MLPNN networks belong to a class of supervised learning in artificial intelligence approaches, and their applications in concrete demonstrate an appreciable relationship between forecasting strength with actual strength. The outcome of the concrete prediction via MLPNN with its error distribution of the testing data with a coefficient of correlation R\({}^{2}\) = 0.77 is demonstrated in Figure 10a,b. The MLPNN distribution yielded an average error value of 5.24 MPa for the test set, with a minimum error value of 0.09 MPa and a maximum error value of 19.57 MPa. However, a significant enhancement of R\({}^{2}\) was observed using ensemble bagging and boosting techniques. The MLPNN-bagging algorithm showed R\({}^{2}\) = 0.833 with an average error of approximately 3.94 MPa, as shown in Figure 10c,d. Whereas, MLPNN with boosting techniques produced a higher correlation of R\({}^{2}\) = 0.80 with an average error of approximately 4.42 MPa, presented in Figure 10e,f. Even though the ensemble model trains itself on the weak learner to obtain a strong learner this ultimately produces a better model [82]. The data analysis of the bagging and boosting algorithm models in terms of errors shows that most of the data points of the predicted model had errors below 10 MPa. The bagging and boosting algorithms demonstrated a 92.55 and 94.68 percent enhancement of the model compared to the individual model without ensemble technique. Table 7 presents the results of the statistical analysis of testing data performed using MLPNN ensemble modeling.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Analysis** & **DT** & **DT-Bagging** & **DT-AdaBoost** \\ \hline Average EDT & 4.41 & 3.54 & 2.96 \\ Minimum EDT & 0.036 & 0.006 & 0.057 \\ Maximum EDT & 22.84 & 19.82 & 20.53 \\ Results lies below 10 MPa & 84 & 90 & 90 \\ Results between 10 MPa and 15 MPa & 8 & 3 & 1 \\ Results between 15 MPa and 20 MPa & 1 & 1 & 1 \\ Results between 20 MPa and 25 MPa & 1 & 0 & 0 \\ Total data in testing data & 94 & 94 & 94 \\ Average below 10 MPa & 89.36 & 95.74 & 95.74 \\ Results between 10 MPa and 15 MPa & 8.51 & 3.19 & 3.19 \\ Results between 15 MPa and 20 MPa & 1.06 & 1.06 & 1.06 \\ Results between 20 MPa and 25 MPa & 1.06 & 0 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Statistical evaluation of an individual with ensemble DT model.  
Figure 9: (**a**) DT prediction vs target; (**b**) DT error distribution; (**c**) actual vs prediction of DT-bagging; (**d**) DT-bagging error distribution; (**e**) actual vs forecasted results for DT-boosting; (**f**) error distribution of DT-boosting.
# A Comprehensive Experimental Emulation For OTFS Waveform RF-Impairments
## Conflicts of Interest: The authors declare no conflict of interest. Abbreviations  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l}
5G & Fifth Generation \\
6G & Sixth Generation \\ A/D & Analog-to-Digital \\ BER & Bit Error Rate \\ CCDF & Cumulative Distribution Function \\ CFO & Carrier Frequency Offset \\ CP & Cyclic Prefix \\ D/A & Digital-To-Analog() \\ DC & Direct Current \\ eMBB & Enhanced-Mobile Broadband \\ I/Q imbalance & In-Phase And Quadrature Imbalance \\ ICF & Iterative Clipping And Filtering \\ ICI & Inter-Carrier Interference \\ ISFFT & inverse symplectic finite Fourier transform \\ ISI & Inter-Symbol Interference \\ JRC & Joint Radar Communication \\ LNA & Low-Noise Amplifier \\ LS & Least Squares \\ MIMO & Multi-Input Multi Output \\ mMTC & Massive Machine Type Communications \\ mm-Wave & millimeter wave \\ MP & Message Passing \\ NOMA & Non-Orthogonal Multiple Access \\ OFDM & Orthogonal Frequency Division Multiplexing \\ OTFS & Orthogonal Time-Frequency Space \\ PA & Power Amplifier \\ PAPR & Peak-To-Average Power Ratio \\ PSW & Prolate Spheroida Waveform \\ QAM & Quadrature Amplitude Modulation \\ RF & Radio-Frequency \\ SDR & Software-Defined Radio \\ SFFT & Symplectic Finite Fourier Transform \\ SLM & improved Selective Mapping \\ URLLC & Ultra-Reliable And Low Latency Communications \\ VSA & Vector Signal Analyzer \\ VSG & Vector Signal Generator \\ RCF & Raised-cosine filter \\ LO & Local oscillator \\ HPA & high power amplifier \\ OOB & out-of-band \\ AWGN & additive white Gaussian noise \\ CMOS & complementary metal-oxide-semiconductor \\ VCO & voltage control oscillator \\ \end{tabular}
###### Abstract  
This paper addresses the handover challenge in indoor visible light communication and Wi-Fi heterogeneous networks, proposing an adaptive handover strategy based on user trajectory prediction. Extracting meaningful and important location points from massive trajectory data for clustering, an improved hidden Markov model is used to predict the user's next location by analyzing the patterns of the user's historical mobile trajectory data. The Q-learning algorithm is then used to determine the optimal network handover based on the current network state, while a seamless handover protocol is introduced to ensure successful network transition and uninterrupted data transmission. Compared with the traditional STD-LTE handover scheme, the proposed algorithm can reduce vertical handover rates by up to 32% during fast walking. When indoor user connections increase, the algorithm can maintain high fairness and high throughput when indoor user connections increase, verifying that it is robust in different scenarios.  
visible light communication; Wi-Fi; network handover; user mobility Article
# Performance Analysis of an Improved Gravity Anchor Bold Expanded Foundation
## 4 Response of the Foundation  
### Stability of the Foundation  
The wind turbine is different from general buildings, which belongs to the category of high-rise structures. It is mainly subjected to horizontal load. Slight surface inclination or uneven settlement of the foundation will cause a large horizontal deviation of the wind turbine, resulting in a large eccentric bending moment. When it is serious, it will bring greater security risks to the operation of the wind turbine. For this reason, we used ABAQUS to analyze the inclination and settlement of the improved foundation.  
Figure 10: Constraints of the anchors.  
The bending moment from the upper support structure causes a large load eccentricity, which causes the bottom of the foundation to disengage from the soil. Figure 11 illustrates the finite element calculation results for the flange and foundation. The results show that the displacement field of the flange and foundation varies continuously and the contact pressure between the bottom of the flange and the bottom of the foundation increases gradually in the direction of the bending moment. No disconnection region was found.  
As shown in Figure 11d, three settlement observation points were selected for this simulation, including the left side, center point, and right side. The settlement-step curve of the foundation is shown in Figure 12a. The results show that the settlement of the center point of the foundation reaches 56.7 mm under analysis step 1 (self-weight effect), and the inclination of both the subsoil and the flange occurs in analysis step 6 (bending moment effect). The inclination is defined as the ratio of the maximum vertical displacement difference to the diameter distance. Figure 12b demonstrates the variation in the inclination of the subsoil and the flange. The results show that the inclination of the flange is always greater than the tilt of the lower soil and grows faster. The maximum inclination of the flange is 0.00166, and the maximum inclination of the lower soil is 0.00061, which is the inclination allowed by the Design Code for Wind Turbine Foundations for Onshore Wind Power Projects (NB/T 10311-2019).  
Figure 11: (**a**) Vertical displacement of the flange; (**b**) contact pressure on the bottom of the flange; (**c**) contact pressure on the bottom of the foundation; and (**d**) displacement clouds of the foundation and soil.
# A Vehicle Velocity Prediction Method with Kinematic Segment Recognition
## 3 Methodology
### LSTM Model  
Neural networks have made breakthroughs in the fields of image and natural language processing, among which recurrent neural networks (RNNs) are widely used in the processing and prediction of time series because they can learn the correlation information of the time before and after data. As an important variant of RNNs, LSTM uses three \"gates\" in structural design to control the hidden layer state, which can retain useful information at every moment. In many tasks, LSTM performs better than classical RNNs [33; 34; 35; 36].  
The LSTM cell diagram is shown in Figure 4. In the LSTM cell diagram, \(x\) = [\(x_{1}\), \(x_{2}\), \(x_{3}\),..., \(x_{t+1}\)], \(t\) is the time step, \(x_{t}\) is the input at time \(t\), \(h\) = [\(h_{1}\), \(h_{2}\), \(h_{3}\),..., \(h_{t+1}\)], \(h_{t}\) is the corresponding output at time \(t\), \(c\) = [\(c_{1}\), \(c_{2}\), \(c_{3}\),..., \(c_{t+1}\)], and \(c_{t}\) is the corresponding state of time t memory cells. Cell state is the memory unit of LSTM, carrying all previous state information [34]. Compared with the RNN unit structure module, the LSTM unit structure module is more complex and has more parameters.  
The forget gate determines what information to forget from the cell state, reads \(h_{t-1}\) and \(x_{t}\), and uses the sigmoid function for processing to determine the proportion of information forgotten at a moment in time on the cell state. The calculation formula is as follows:  
\[l_{t}=\text{sigmoid}\left(M_{f}\cdot[h_{t-1},x_{t}]+b_{f}\right) \tag{29}\]  
In the formula, \(M_{f}\) and \(b_{f}\) are the weight matrix and bias vector of this stage, respectively.  
As for the input gate and select memory stages, selecting the memory stage involves selectively memorizing the input and screening out the important information input to the current state. In this stage, \(h_{t-1}\) and \(x_{t}\) are read, and the proportion of new memory \(\widetilde{c}_{t}\) written into memory cell state \(c_{t}\) is determined by the sigmoid function. The relevant calculation formula is as follows:  
\[i_{t}=\text{sigmoid}(M_{i}\cdot[h_{t-1},x_{t}]+b_{i}) \tag{30}\]  
\[\widetilde{c}_{t}=\tanh(M_{c}\cdot[h_{t-1},x_{t}]+b_{c}) \tag{31}\]  
\[c_{t}=l_{t}c_{t-1}+i_{t}\widetilde{c}_{t} \tag{32}\]  
where \(M_{i}\) and \(b_{i}\) are the weight matrix and bias vector of the sigmoid layer, respectively; \(M_{c}\) and \(b_{c}\) are the weight matrix and bias vector of the tanh layer, respectively, and tanh is a hyperbolic tangent function.  
The output gate calculates the proportion of the cell state \(c_{t}\) output information through the sigmoid function, and then multiplies the activated cell state to obtain the final output result.  
\[o_{t}=\text{sigmoid}(M_{O}\cdot[h_{t-1},x_{t}]+b_{O}) \tag{33}\]  
\[h_{t}=o_{t}\text{tanh}(c_{t}) \tag{34}\]  
where \(M_{O}\) and \(b_{O}\) are the weight matrix and bias vector of the output gate, respectively.
# Real-Time Geophysical Applications with Android GNSS Raw Measurements
## 3 Results
### Periodic Dynamic Scenario
\begin{table}
\begin{tabular}{l c c c c c c c} \hline  & & & \multicolumn{2}{c}{**Xiaomi Mi 8**} & \multicolumn{2}{c}{**STONEX S900A**} & \multicolumn{2}{c}{**Xiaomi Mi \(-\) STONEX S900A**} \\ \hline  & **Elevation (\(C\))** & **Azimuth (\(C\))** & **Mean (dB-Hz)** & **Std (dB-Hz2)** & **Mean (dB-Hz)** & **Std (dB-Hz2)** & **(dB-Hz)** \\ \hline
**G02** & 35.8 & 61.7 & 45.25 & 0.93 & 46.65 & 0.54 & \(-1.39\) \\
**G0  
Furthermore, the 3D velocity estimated during a period of kinematic conditions for the vibrating table using both 10 Hz and 1 Hz measurements (Figure 8) demonstrates the capability of raw GNSS measurements from the Xiaomi Mi 8 to follow the periodic motion in analysis. Due to the low frequency at which the table was moving, the increased noise level of variometric solutions obtained with 10 Hz measurements in both static and kinematic conditions could be immediately detected (Figure 7). Aiming at defining the accuracy achievable in the reconstruction of the motion parameters using the Xiaomi Mi 8, a comparison with the 1 Hz solution obtained from the STONEX S900A was carried out for the epochs between 14:17:40 and 14:19:40 GPST on 2 April 2019. Figures 9 and 10 show the trend of 1D velocity and displacements, along with the residuals computed epoch by epoch with respect to the reference solutions, for the kinematic scenario under analysis. Despite the differences between the two GNSS receivers considered in this paper, the analysis of Figure 9 shows a good agreement between the estimated velocities. The main differences between the two solutions were observed close to the peaks in the velocity domain. This behavior could be related to the different \"prediction\" algorithm of carrier-phase measurements implemented within the two different GNSS receivers. In addition to this, the estimated velocity using the Xiaomi Mi 8  
Figure 6: Skylplot containing elevation and azimuth dependance of the C/No value for STONEX S900A (**left**) and Xiaomi Mi 8 (**right**). Figure 7: Time variation of 3D velocity in a local reference frame (East-North-Up)) obtained during the dynamic test. The plot shows variometric solutions for 10 Hz (blue) and 1 Hz (orange) measurements from STONEX S900A and 1 Hz (green) measurements from the Xiaomi Mi 8. SOW: second of the week.
## 2 Materials and Methods
Another researcher occupied the back seat, observing the entire ride and measurement (Figure 4). Eye-tracking measurements are focused on specific indicators that can be measured by an eye tracker: fixations, saccadic movements, and blinks. Fixation refers to the period when the eyes are relatively still, focusing on a specific point of interest. During fixation, visual information is gathered and processed. Fixations typically last around 200-300 milliseconds and allow the brain to extract detailed information from the visual scene [48]. Fixations indicate what a person has noticed, how long they looked at it, and the order in which they viewed different elements in a scene. Saccadic movements, on the other hand, are rapid and involuntary eye movements that shift the gaze from one point to another. These movements allow us to explore the visual environment by redirecting our focus to new points of interest. Saccades occur between fixations and typically last around 20-50 milliseconds. They are crucial for scanning the visual scene and are involved in acquiring new visual information [45]. Saccadic movements measure the speed and accuracy with which the eyes move from one point to the next, providing insight into the user's cognitive and perceptual processes. Blinks are brief closures of the eyelids that occur regularly to keep the eyes moisturized and protected. Blinks are essential for maintaining the health and function of the eyes. During a blink, the visual system is momentarily interrupted, and visual information is not processed. Blinks typically last around 100-150 milliseconds, but their frequency can vary depending on factors such as task demands, attention levels, and individual differences [45]. Blinks are used to gauge a person's attention and engagement with a particular stimulus and can even reveal visual fatigue and distraction. The last stage of the research (sixth step) entailed a confidential interview (questionnaire) and an A/B testing between the researcher and the driver (steps two and three). This took place in a secluded atmosphere, free from any third-party interference. The interviewer used a pre-structured questionnaire, noting down the responses into their laptop. For the A/B testing, the researcher presented the driver with a tablet containing the prepared images which they were required to go through. Upon completion of the interview, the measurement was also concluded and the research progressed to the subsequent driver. Considering the data obtained from the research, the results are interpreted via tables and graphs. The tables provide an in-depth analysis of the examined variables that were gathered using the eye tracker. The graphs illustrate the frequent responses given by the research participants following the rides.
# Multi-Modal Spatio-Temporal Knowledge Graph of Ship Management
## 3 Method
### Multi-Modal Spatio-Temporal Ontology Construction
#### 3.1.2 Definition of Classes and Conceptual Hierarchy  
To improve the semantic expression ability and inductive integration ability of an ontology, it is crucial to define classes and the hierarchical structure between them. This entails determining the parent-child relations between classes and ensuring that the classes possess an appropriate level of generality to encompass and describe a specific range of instances.  
Guided by domain knowledge in ship communication, we identify classes and their hierarchical structure from the more general terms of table names and column names. Initially, we establish preliminary unified concepts and semantic relations for describing ship communication data by defining _Plat_, _Equipment_, _Event_, _Image_, _Video_, _Instant_, and _Env_ classes as shown in Figure 2. Since ships, submarines, and shore stations are all entities carried by communication equipment, they have common attributes and functions in the communication system. In order to maintain conceptual consistency, they are abstracted into _\"Plat\"_ classes. Moreover, in ship communication events, the attribute parameters of communication equipment and the spatial information of the ship will change over time. In the instantiation process, the communication equipment entities and spatial information (environment) entities at different times need strict one-to-one correspondence. Therefore, we design a _\"State\"_[17] class connected with _Plat_ class, which describes time series information, spatial position information, communication equipment information, and other attributes of ships in different times and spaces. Based on the time node of the latest communication event that occurred during the voyage, a _State_ class node is added to the communication subject participating in the event, which connects the spatial position information of the current communication subject and the resource data of various communication equipment installed on the communication entity. As a result, considering the occurrence of communication events, the _\"State\"_ node is added to represent the communication situation of the entity during the corresponding period/moment.  
Based on the above eight core classes, we defined _Ship_, _Submarine_, and _Shore Station_ as subclasses of _Plat_ class as shown in Figure 3. There are six subclasses including _Data Transfer Event_, _Communication Enhancement Event_, _Disconnection Event_, _Connection Device Event_, _Voice Call Event_, and _Message Event_ for the _Event_ class in Figure 4. As can be observed from Figure 5, _Equipment_ class has these five subclasses such as _Communication Repeater Equipment_, _Satellite Communication Equipment_, _Radio Equipment_, _Optical Communication Equipment_, and _Radio Navigation Equipment_, where these subclasses also have lower subclasses, such as _GPS Receiver_ of _Radio Navigation Equipment_ class.  
Figure 2: Core classes in the multi-modal spatio-temporal ontology.
# The Role of Sensory Cues in Collective Dynamics: A Study of Three-Dimensional Vicsek Models
## 2 Methods
### Order Parameters  
Our next step is to define the order parameters that will be used to measure different behavior patterns at the group level.  
We consider the order parameter polarization, which quantifies group alignment. It is calculated as the average linear momentum of the system:  
\[P(k)=\frac{1}{N}\biggl{\|}\sum_{i=1}^{N}\mathbf{v}_{i}(k)\biggr{\|},\]  
where \(\|\cdot\|\) denotes the norm of the vector. Polarization values range from zero to one, where values closer to one indicate aligned groups, and values closer to zero indicate random motion.  
Next, we consider the order parameter cohesion, which measures how particles are spatially arranged in relation to the group's center of mass. At time step \(k\), the center of mass of the group is calculated as \(\mathbf{\chi}(k)=(1/N)\sum_{i=1}^{N}\mathbf{x}_{i}(k)\), and the position of each particle relative to this center is given by \(\mathbf{r}_{i}(k)=\mathbf{x}_{i}(k)-\mathbf{\chi}(k)\). Cohesion is then defined as:  
\[C(k)=\frac{1}{N}\sum_{i=1}^{N}\exp\biggl{[}-\frac{\|\mathbf{r}_{i}(k)\|}{l_{a} }\biggr{]},\]  
where we set the scaling coefficient \(l_{a}=4R\), following the study in [12]. The cohesion ranges from zero to one, where one indicates that all particles reside at the center of mass, and zero indicates that particles are infinitely far from the center of mass. Due to periodic boundary conditions in a finite arena of our simulation, the system cannot achieve a cohesion value of zero.  
The third order parameter is the cluster size, which represents the size of the largest weakly connected component in the interaction graph at a given time [31]. Two particles are part of the same cluster if they are connected by a path of interacting particles.