esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Cyber Security Attacks in Vehicular Ad Hoc Networks (VANETs)
### Vehicle-to-Pedestrian (V2P) Network and Its Implications  
As we navigate an increasingly digitized world, the ubiquity of smartphone usage among both drivers and vulnerable road users (VRUs) has grown exponentially. To mitigate the potential hazards associated with this trend, particularly in contexts where traffic is a primary concern, it is essential to develop novel approaches for enhancing road safety. One such strategy involves the implementation of collision prediction algorithms, capitalizing on pedestrian-to-vehicle (P2V) and vehicle-to-pedestrian (V2P) communication technologies [68]. [PERSON] et al. presented a sophisticated example of this approach, proposing an algorithm that uses global positioning system (GPS) data and magnetometer readings from a pedestrian's smartphone in conjunction with sensor data from an autonomous vehicle to predict potential collision scenarios.  
Figure 5 provides a schematic representation of this innovative algorithm's functionality. It highlights the method by which the algorithm calculates the potential angle of collision, thereby informing preventative measures.  
Upon the prediction of a possible collision, the system alerts both the pedestrian and the autonomous vehicle, enabling them to take necessary evasive action and replan their routes accordingly. This immediate response facilitates the prevention of accidents, enhancing the safety of all road users.  
While V2P communication is an emerging area of exploration, its potential for improving road safety cannot be underestimated. Nonetheless, given its nascent stage, it requires substantial research and development to ensure its robustness and security. Future aspirations of achieving a fully integrated Internet of Things (IoT) are reliant on the resolution of current security vulnerabilities. As such, it is crucial that further advancements in this field be underpinned by an unwavering commitment to ensuring the security and safety of all connected devices. By doing so, we can progress towards realizing the full potential of V2P communications, contributing significantly to the development of emotionally intelligent autonomous vehicles.
## 3 Numerical Simulation and Experimental Analysis
### Crack Depth  
#### 3.3.1 Numerical Analysis  
It is researched in this subsection that how the depth of transverse crack affects the propagation characteristics of the elastic waves. Mode III is adopted here for the conclusion of Section 3.1. The non-uniform shaft is still the same one but with different crack depths as listed in Table 1. Similarly, substituting all the parameters into Equation (3), the transfer matrix is then derived. In addition, the numerical method is adopted to obtain the numerical results of the propagation characteristics of the elastic waves, the numerical results are shown in Figure 10.  
As can be seen in Figure 10, the numerical results derived at different depth are signed by different symbols and colors, while the real line and the dotted line of the same color are a pair of conjunct propagation constants. The stopbands are not directly identified in the figure for conciseness, instead, they are listed in Table 5.  
It can be seen from Table 5 that there are three stopbands for each crack depth. The three stopbands vary with the crack depth: the bandwidths of all the three stopbands become wider with the increase of the depth of the transverse crack, while the central frequencies of the three stopbands become lower and lower with the increase of the crack depth.
# Visual and Haptic Guidance for Enhancing Target Search Performance in Dual-Task Settings
## 2 Related Works
### Multimodal and Visual-Haptic Display Design  
Navigating through a target search task within a visually complex and cluttered scene can be challenging due to the dense information space. Strategically deploying multiple modalities could enhance user awareness and task performance. Existing research indicates that integrating multiple sensory channels, such as visual and tactile feedback, can improve users' perception of their surroundings and enhance operational accuracy [20]. When detecting moving objects, multimodal feedback can increase target awareness, with tactile feedback proving particularly useful [49].  
Multimodal interactions have also been extensively studied in tasks such as visual search and positioning, particularly involving audio and visual modalities [50; 51; 52; 53]. These interactions not only enhance the precision of positioning tasks but also ensure that the user's visual field remains unaffected during high-precision operations such as surgery [54; 55].  
Interestingly, cross-modality can induce sensory interaction effects. For instance, the perceived direction of auditory motion can be influenced by visual motion [56]. Consequently, in practical applications, designers must consider potential sensory cognitive conflicts arising from cross-modality interactions, such as the competition between auditory and tactile senses [57; 58], or auditory and visual senses [59].  
In addition to sensory interaction effects, visual and haptic feedback can play unique roles in enhancing target selection guidance. For example, users could leverage haptic feedback to determine the direction of a target within a potential area and then utilize visual information to pinpoint the target [60]. Similarly, a two-step guidance feedback strategy could employ auditory cues to initially provide a vertical location, followed by indicating the horizontal direction of the target [61].  
The burgeoning body of research on multimodal feedback, particularly studies involving visual and haptic modalities, has provided valuable insights on enhancing user performance in complex environments (as summarized in Table 1). However, a more nuanced understanding is needed when these modalities are deployed in dual-task target search scenarios. Moreover, the design and interplay between different modalities during multi-tasking remains under-explored, such as the effectiveness of dynamic visual guidance and haptic guidance for different tasks. Accordingly, there is a pressing need to deepen our understanding of visual and tactile multimodal feedback in the context of multi-task target search scenarios. This stream of research not only advances theoretical understanding but also promises practical implications for designing more user-friendly interfaces and systems.
## 7 Comparison of the Feasibility of Using a Hybrid SPV/WTG system vs. a Diesel Generator
### Diesel Generator  
The DG needed approximately is 4 kW, which computed as (maximum LTE-macro BS, AC and DC load 1.1 kW) divided by (DG efficiency 30% [7] \(\times\) converter efficiency 95%):  
1. IC cost: the DG IC cost is $2640 (size 4 kW \(\times\) cost $660/1 kW). However, the fossil fuels not sustainable and expensive, and the price go up continuously.
2. O&M cost: the annual cost for the maintenance and operation of the DG amounted to $4680 (without counting the cost of fuel transport). A breakdown of this cost, (i) $438 for DG maintenance per year based on a DG maintenance cost of $0.05/h \(\times\) annual DG operating hours 8760 h; and (ii) a fuel cost of $4242, based on the diesel price of $1.04/L [36]) multiplied by the a total diesel consumption of 4079 L per year, which computed based on a specific fuel consumption of 0.388 L/kWh \(\times\) annual electrical production of the DG of 10,512 kWh/year (DG capacity size 4 kW \(\times\) DG efficiency 0.3 \(\times\) 24 h \(\times\) 365 days/year).
3. Replacement cost: mobile operator may need to change the DG every 3 years, which means at least three times during the life of the project. Thus, the total DG replacement cost is 3 \(\times\) (size 4 kW \(\times\) cost $660/1 kW), equal $7920 at least.
4. NPC cost: the total NPC include IC cost $2640 + O&M cost $46,800 + Replacement cost $7920, equal $57,360 over the project lifetime (10 years); without counting the cost of fuel transport, which adds further to this cost.
5. GHG emissions: According to [37], the CO\({}_{2}\) emissions of diesel fuel are 2.68 kg/L. Hence, the total annual CO\({}_{2}\) emissions are 10,931 kg, computed based on the specific annual diesel consumption of 4079 L multiplied by 2.68 kg CO\({}_{2}\)/L.
# Improving Lossless Image Compression with Contextual Memory
## 3 PAQ8 PX Algorithm for Lossless Image Compression in Detail
### General Aspects  
Compressing a file goes through four main stages: preprocessing, model prediction, context mixing, and probability refining. An optional pre-training phase can be activated via command line parameters. The pipeline for image compression has been described schematically in Figure 1.  
The preprocessing phase is also split into three parts. At first, it searches through the file to be compressed for known stream types. Based on these types, different models are activated for the second stage of compressing. For example, it searches for image (1 bpp, 4 bpp, 8 bpp, 8 bpp grayscale, 24 bpp, 32 bpp, png 8 bpp, png grayscale 8 bpp, png 24 bpp, png 32 bpp), jpeg, gif, text, audio (8 and 16-bit mono and stereo), exe, base64, zlib streams, file containers, and others. After this stage, an optional transform phase is applied for certain stream types such as text, where an end of line transform can be applied, or EXE, where certain instructions are replaced with others. The transform phase is then applied in reverse and if the result matches the original stream, the transform is kept.  
In the case of images, the preprocessing phase extracts the file header, which is compressed separately, and the byte stream containing the pixel values of the image. The width and the bit depth of the image are extracted from the header and the width is passed on to the image model selected by bit depth.  
The model prediction and context mixing phase happen consecutively. Probabilities of individual bits from the input stream are predicted by many specialized models. All the probabilities are combined into one probability via the context mixing algorithm. The output probability is refined using a network of adaptive probability maps. The final prediction is used to encode the bit from the stream using a binary arithmetic coder. The algorithm is symmetrical, meaning that both the coder and the decoder do the same operations ending up with the same final probability. The decoder uses the probability to decode the bit from the compressed stream.
# Short-Term Traffic State Prediction Based on Mobile Edge Computing in V2X Communication
###### Abstract  
Real-time and reliable short-term traffic state prediction is one of the most critical technologies in intelligent transportation systems (ITS). However, the traffic state is generally perceived by single sensor in existing studies, which is difficult to satisfy the requirement of real-time prediction in complex traffic networks. In this paper, a short-term traffic prediction model based on complex neural network is proposed under the environment of vehicle-to-everything (V2X) communication systems. Firstly, a traffic perception system of multi-source sensors based on V2X communication is proposed and designed. A mobile edge computing (MEC)-assisted architecture is then introduced in a V2X network to facilitate perceptual and computational abilities of the system. Moreover, the graph convolutional network (GCN), the gated recurrent unit (GRU), and the soft-attention mechanism are combined to extract spatiotemporal features of traffic state and integrate them for future prediction. Finally, an intelligent roadside test platform is demonstrated for perception and computation of real-time traffic state. The comparison experiments show that the proposed method can significantly improve the prediction accuracy by comparing with the existing neural network models, which consider one of the spatiotemporal features. In particular, for comparison results of the traffic state prediction and the error value of root mean squared error (RMSE) is reduced by 39.53%, which is the greatest reduction in error occurrences by comparing with the GCN and GRU models in 5, 10, 15 and 30 min respectively.  
intelligent transportation system; short-term traffic state prediction; V2X communication; mobile edge computing; neural networks +
Footnote †: journal: Applied Sciences
# Viscosity Prediction of Different Ethylene Glycol/Water Based Nanofluids Using a RBF Neural Network
## 2 Basic Theory of a RBF Neural Network  
Benefiting from the inspiration of the human brain's structure and activity mechanism, many different artificial neural networks have been developed for different purposes including classification and regression. In the fields of curve-fitting and nonlinear predictive modeling, the RBF neural network proposed by [PERSON] and [PERSON] [27] can exhibit a good ability because of its high accuracy and stability [28].  
Figure 1 presents the basic structure of a typical three-layer RBF neural network. The input and output layers respectively correspond to the dendrite and synapse of biological neurons, which are used to mathematically describe the modeling object. The hidden layer, similar to the function of the cyton, plays a role of intermediation to process the input-output information and deliver it to the output layer. The connections between different layers are established through a series of artificial neurons and weights.  
Theoretically, the modeling process of the RBF neural network is to solve the mapping from \(X^{n}\) to \(Y^{q}\) (\(n,q\geq 1\)) in Euclidean space. Assuming that the input vector of the RBF neural network is \(X\), the response of the \(k\)th neuron in the output layer (\(y_{k}\in Y^{q}\)) can be obtained by using the following linear weighting function [29].  
\[y_{k}=\sum\limits_{j=1}^{m}\omega_{jk}R_{j}(X),\quad(k=1,2,\cdots,q) \tag{1}\]  
where \(\omega_{jk}\) is the connection weight between the \(j\)th hidden layer neuron and the \(k\)th output layer neuron. \(m\) and \(q\) are the numbers of neurons in the corresponding layer, respectively.  
Different from many other ANNs, the response of the RBF neural network's \(j\)th hidden layer neuron is usually determined by the RBF. When it selects a Gaussian function, the corresponding \(R_{j}(X)\) can be defined as,  
Figure 1: A typical three-layer RBF neural network.  
\[R_{j}(X)=\exp(-\frac{\|X-c_{j}\|^{2}}{2\sigma_{j}^{2}}),\quad(j=1,2,\cdots,m) \tag{2}\]  
where \(\|\|\) is the Euclidean distance between the input vector \(X\) and the \(j\)th neuron center \(c_{j}\). \(\sigma_{j}\) is the width of the \(j\)th neuron.  
Analyzing Equations (1) and (2), it can be easily found that the key of RBF neural network training is how to determine \(\omega_{jk}\), \(c_{j}\), and \(\sigma_{j}\). In the past few decades, different unsupervised and supervised algorithms have been developed to solve this problem [30]. In this study, the network parameters are updated by using an orthogonal least squares (OLS) approach, of which the minimizing function is shown in Equation (3). More detailed information about OLS can be found in [31].  
\[\text{min}J=\sum_{k=1}^{q}\left(|y_{nk}-y_{dk}|^{2}\right) \tag{3}\]  
where \(y_{nk}\) and \(y_{dk}\) are the network output and desired output of the \(k\)th output layer node, respectively.
## 2 Materials and Methods
### Analysis of Remote Sensing Ecological Index (RSEI) Change Trends  
The Mann-Kendall mutation test was applied to the time-series _RSEI_ of the Shendong mining area from 1990 to 2023. The initial step involved constructing an X-rank sequence, denoted as sk, derived from the _RSEI_ data. Subsequently, a statistic was defined based on the assumption of randomness in the time series. The expression for the \(U\!F_{K}\) statistic is as follows:  
\[U\!F_{K}=\frac{s_{k}-E(s_{k})}{\sqrt{Var(s_{k})}} \tag{14}\]  
In the equation, \(E(s_{k})\) and \(Var(s_{k})\) represent the mean and variance of \(s_{k}\), respectively. \(U\!F_{K}\) generates the characteristic curve of \(U\!F\), which is then subjected to a confidence test to determine if there is a significant trend of change. A \(U\!F_{K}\) > 0 indicates an upward trend in the _RSEI_ sequence, while lower values indicate a downward trend. UF follows a standard normal distribution given the significance level \(\alpha\). Using a significance level of \(\alpha\) = 0.05, the critical value of UF0.05 is 1.96. When a significance level of 0.05 is used, the curves of UF and UB are represented in the same graph. UF > 0 indicates an upward trend in the sequence; conversely, it indicates a downward trend. When the _RSEI_ mutation begins, or when the curves of UF and UB intersect within the critical line, it indicates the _RSEI_ mutation point.  
The Theil-Sen model was applied to examine the spatial trend characteristics of _RSEI_ and climate factors. The Theil-Sen model is expressed as follows:  
\[\beta=Median\bigg{(}\frac{x_{j}-x_{i}}{j-i}\bigg{)} \tag{15}\]  
where \(\beta\) represents the slope, and i and j denote specific years. A \(\beta\) value > 0 indicates a positive trend in the _RSEI_/climate factor time series. Conversely, a \(\beta\) value < 0 indicates a negative trend in the _RSEI_/climate factor time series.  
The coefficient of variation (Cv) reflects the relative fluctuation of changes in _RSEI_. A higher Cv value indicates greater disturbance intensity and increased instability in the _RSEI_. The Cv is calculated as follows:  
\[C_{v}=\frac{\sqrt{\frac{1}{n-1}\sum_{i=1}^{n}\left(RSEI_{i}-RSEI_{mean}\right) ^{2}}}{RSEI_{mean}} \tag{16}\]  
where \(C_{v}\) represents the coefficient of variation of _RSEI_ changes, \(n\) is the length of the time series, \(RSEI_{i}\) represents the _RSEI_ value for year \(i\), and \(RSEI_{mean}\) is the multi-year average of _RSEI_ from 1990 to 2023.
# SNA: Sensitivity-Aware Neural Architecture Adaptation for Uniform Quantization
## 4 Experimental Results  
In this section, we conducted experiments to verify the effectiveness of the proposed hypothesis. We investigated the relationship between the network size and sensitivity on ResNet-50 [39] and EfficientNet-B2 [37]. Respectively, ResNet-50 represents insensitive redundant models, and EfficientNet-B2 represents sensitive compact models. To evaluate the effectiveness of our method, we quantized the networks with each architecture uniformly to W3A8, W4A8, and W8A8 under different memory constraints and multipliers. The results are shown in Table 1 and in later sections.
# Fast Format-Aware Fuzzing for Structured Input Applications
## 2 Background
### Header and Trailer  
We describe two common types of fields, _header_ and _trailer_, within the structured input, using the TIF file format and the corresponding parsing code in the real-world software optipng.  
Applications in the present computer systems generate and parse a large number of structured input all the time [16], and most of them store information as a long binary string in a chunk manner [8]. We refer to such software as structured input applications. Typically, some fragments hold the essential metadata such as the file signature, version [17], length, checksum [18], type [19], and so on. We refer to these short, but metadata-intensive fields as _header_ fields, and those long, but lacking metadata fields as _trailer_ fields. The control information stored in the _header_ guides the parser on how to obtain the recorded information from the _trailer_ and affects its execution path. Changing the contents of the bytes in the _header_ will have more potential to affect the execution of the target program than those in the _trailer_ during fuzzing.  
Tagged Image Files (TIF) contain graphic bitmap content and can be handled by image readers and writers easily. Figure 1 presents a TIF file internally consisting of three types of chunks, Image File Header (IFH), Bitmap Data, and Image File Directory (IFD) [20]:  
* **Image File Header (IFH)**: The first 4 bytes of the IFH are the signature of one TIF file, and the last four indicate the offset of the IFD chunk. For example, \"62 D6 00 00\" means that the IFD starts with 0 xD662. The IFH belongs to _header_ field.
* **Bitmap Data**: Image data in the.tif file are placed closely after the IFH, and each byte stores the RGB color or just the compressed data. Bitmap Data belong to the _trailer_ field.
* **Image File Directory (IFD)**: The location of the IFD is specified by the last 4 bytes within the IDH, with a length of \(n\) bytes, and \(n\) is far greater than \(8+m\). Most of the key control information such as the image height, width, compression method, bit rate, photometric interpretation, etc., is concentrated in the IFD. The IFD belongs to the _header_ field.  
Listing 1 shows how the signature within the IFH and the contents within the IFD are validated. The first 4 bytes of the IFD will be compared with the buffer containing \"49 49 2a 00\" in Line 6. Once this check fails, the function returns 0, and the application exits later to avoid further misreading of the data.  
Like the example shown in Listing 1, although tiny in length compared with the _trailer_ fields, the _header_ fields contain more critical metadata and impact the execution of the target application more.
## 3 Results
### The Results of the FEM Simulation
#### 3.1.2 The Results of the Lateral Offset Case  
Based on the FEM model as shown in Figure 5, the dual tracking receivers have 1 m lateral offset, while \(d_{1}\) is 3 m, \(d_{2}\) is 4 m. Therefore, the magnetic field measured by tracking receivers, shown as Figure 7, are the radiated magnetic field of the transmitting coil on the line ((1, 3, \(-4.5\)), (1, 3, 4.5)) and the line ((1, 3.5, \(-4.5\)), (1, 3.5, 4.5)) in meter. In a similar manner as the normal case, \(B_{y}(z)\) follows the characteristics of origin symmetry while \(B_{z}(z)\) follows the characteristics of axisymmetric. Furthermore, the coils used for measuring \(B_{z}(z)\) can be omitted by the MFSI method.  
In the figure, due to the existence of the lateral offset, there are obvious differences between the measured magnetic field \(B_{y1}\) and the original magnetic field \(ActualB_{y0}\) in the amplitude and the position corresponding to the maximum amplitude. If the lateral offset  
\begin{table}
\begin{tabular}{c c c} \hline
**DOF** & \(z\) & \(y\) \\ \hline MFSI method (cm) & \(-0.9\) & 298.12 \\ True value (cm) & 0.0 & 300.00 \\ Error (cm) & \(-0.9\) & \(-1.88\) \\ Error (\%) & - & \(-0.62\) \\ \hline \end{tabular}
\end{table}
Table 1: The tracking error of the algorithm in the normal case.  
Figure 6: The radiated magnetic field of the transmitting coil in the normal case. \(B_{y1}\), \(B_{x1}\) and \(B_{y2}\). \(B_{z2}\) are \(y\)-axis and \(z\)-axis components of the magnetic field on the line ((0, 3, \(-4.5\)), (0, 3, 4.5)) and ((0, 3.5, \(-4.5\)), (0, 3.5, 4.5)) in meters, respectively. \(integrationB_{y1}\) and \(integrationB_{y2}\) are the integrals of \(B_{y1}\) and \(B_{y2}\).  
is ignored and \(B_{\rm y1}\) are used directly like the traditional method, there will definitely be errors in the positioning results.  
Referring to (12) and (15), the lateral offset \(w\) and the distance between the PIG transmitter and the tracking receivers can be calculated by the MFSI algorithm. Furthermore, the tracking errors are shown in Table 2. In the table, the tracking error of the DOF \(x\) is 3.17 cm and DOF \(y\) is \(-\)2.48 cm, which are both much lower than the error of 20 cm in [30]. The tracking error of the DOF \(z\) is \(-\)0.10 cm, which is also much lower than the error of 14.3 cm in [30] and the error of 40 cm in [27].