esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Emission Characteristics
#### 3.3.4 Smoke Emissions  
Figure 11 depicts the variation of smoke emissions for all test fuels according to different idling speeds. As shown in Fig. 11, it can be seen that the smoke opacity of all test fuels increases with the increase of engine speed from 750 rpm to 2250 rpm. The smoke opacity of CPO0, CPO10, CPO30, and CPO50 is respectively increased by 23.08%, 29.17%, 25.00%, and 8.57% at 1500 rpm, compared with 750 rpm. The smoke opacity of CPO0, CPO10, CPO30, and CPO50 is respectively increased by 65.38%, 58.33%, 41.67%, and 14.29% at 2250 rpm, compared with 750 rpm. It can be attributed to the fact that the increase in engine speed causes more fuel to be injected into the combustion chamber, which reduces the air-fuel ratio and increases the fuel-rich area. In addition, under low load conditions, the temperature in the cylinder and the flow intensity of the mixture are weak, and the mixture is uneven, resulting in more smoke emissions generation. [PERSON] et al. [34] also found the increase in engine speed causes an increase in smoke emissions. They indicated that the increase in engine speed causes the decrease in volumetric efficiency and the increase in the fuel-air ratio, which leads to the decrease of oxygen availability for soot oxidation and the increase of carbon availability of soot formation, respectively. Moreover, the incomplete combustion due to insufficient time for fuel and air mixing causes an increase in both PM mass and number at high engine speeds. On the other hand, adding an appropriate proportion (e.g., 10 vol.%, 30 vol.%) of palm oil to diesel resulted in a lower smoke exhaust than diesel. At 750 rpm, the smoke opacity of CPO10 and CPO30 is reduced by 7.69% and 7.69% compared with CPO0. At 1500 rpm, the smoke opacity of CPO10 and CPO30 is respectively reduced by 3.13% and 6.25% compared with CPO0. At 2250 rpm, the smoke opacity of CPO10 and CPO30 is reduced by 11.63% and 20.93% compared with CPO0. However, when excess palm oil such as 50 vol.% is added to diesel fuel, the smoke opacity of CPO50 increases by 34.62% and 18.75%, respectively, at 750 rpm and 1500 rpm, compared with diesel. This is because palm oil is an oxygenated fuel. As shown in Table 1, the oxygen content of palm oil is as high as 11.4%. The oxygen in the fuel improves the air-fuel mixing rate, reduces the formation of fuel-rich areas, promotes complete combustion, and thus reduces smoke emissions [37]. On the other hand, the increase in smoke emissions caused by excessive palm oil added to diesel fuel is related to the high viscosity and density of palm oil, which leads to poorer volatility and reduces premixed combustion.  
Figure 10: NOx emissions of all test fuels according to various engine speeds.
###### Abstract  
In the dynamic landscape of 6G and smart cities, visible light communication (VLC) assumes critical significance for Internet of Things (IoT) applications spanning diverse sectors. The escalating demand for bandwidth and data underscores the need for innovative solutions, positioning VLC as a complementary technology within the electromagnetic spectrum. This paper focuses on the relevance of VLC in the 6G paradigm, shedding light on its applicability across smart cities and industries. The paper highlights the growing efficiency of lighting LEDs in infrastructure, facilitating the seamless integration of VLC. The study then emphasizes VLC's robustness in outdoor settings, demonstrating effective communication up to 10 m. This resilience positions VLC as a key player in addressing the very last meter of wireless communication, offering a seamless solution for IoT connectivity. By introducing a freely available open-source simulator combined with an alternative waveform, UFMC, the study empowers researchers to dimension applications effectively, showcasing VLC's potential to improve wireless communication in the evolving landscape of 6G and smart cities.  
visible light communication (VLC); 6G; IoT applications; smart cities; wireless communication; last meter connectivity; open-source simulator; application dimensioning; outdoor VLC communication; efficient lighting LEDs +
Footnote †: journal:  
Article  
Exploring Universal Filtered Multi Carrier Meter Connectivity in 6G: A Street-Lighting-Driven Approach with Enhanced Simulator for IoT Application Dimensioning.
# A Model and Quantitative Framework for Evaluating Iterative Steganography
## 2 A Model for Iterative Steganography  
For this study, a single _iteration_ is defined as a binary vector that is characteristic of specific type of iterative cover. For instance, in video files, this might correspond to a single video frame; for audio files, it could be a time frame used for calculating Fast Fourier Transform (FFT) frequency components; and for network transmissions, it may represent a data packet from a specific network protocol along with associated timing parameters. Depending on the context and type of cover, individual bits within a single iteration can hold varying significance and semantic interpretation. The core assumption of iterative steganography is that, for a specific type of iterative cover, all iterations are homogeneous--identical in their structure, properties, and characteristics critical to the steganographic encoding and decoding algorithms.
## 3 Our Approach
### Problem Formulation  
The exploration environments are two-dimensional grid maps, in which each historical trajectory, frontier waypoint, landmark, and obstacle corresponds to a grid. Then, four-type grids serve as the nodes in the exploration graph described in Section 3.2. Inspired by reference [8], to represent the uncertainty and occupancy probability of every grid, we construct the raster virtual map by Expectation-Maximization (EM) exploration algorithm [25], where each grid represents a virtual landmark with an initial identical error covariance matrix. Meanwhile, A-optimality metric [26] is used to measure the uncertainty of each virtual landmark \(v_{i}\). Then, the uncertainty \(U\) of the whole map is calculated by:  
\[U=\sum_{v_{i}\in V}\text{tr}(\Sigma_{v_{i}}), \tag{1}\]  
where \(V\) represents the set of all virtual landmarks, and \(\text{tr}(\Sigma_{v_{i}})\) represents the trace of each virtual landmark's error covariance matrix \(\Sigma_{v_{i}}\).  
To measure the robot exploration speed, we take the convergence speed. Specifically, we average the exploration rate for 50 tests at each moment and plot the average exploration rate over time.  
In addition, to evaluate the exploration algorithm, we define the following exploration efficiency:  
\[\eta=\frac{\Delta U}{T}, \tag{2}\]  
where \(\Delta U\) is the uncertainty reduction in time \(T\). Therefore, \(\eta\) is used to measure the reduction of map uncertainty per unit time.  
Finally, to compare the mapping accuracy of different methods, we define the following trajectory error:  
\[e=\frac{n_{a}}{n_{g}}\sqrt{\max_{v_{i}\in V_{p}}tr(\Sigma_{v_{i}})}, \tag{3}\]  
Figure 1: The framework of our approach. The grid environment is modeled as an exploration graph consisting of historical trajectories, frontier waypoints, landmarks, and obstacles shown in red, blue, yellow, and black. The red pentagram represents the currently selected target point, and the exploration graph represents the current state. In the training phase, the robot optimizes the network parameters to maximize the desired reward of the whole exploration progress. Then in the testing phase, our approach evaluates the frontier waypoints and selects the optimal one as the target point.  
where, \(n_{a}\) is the total number of exploration steps, \(n_{g}\) is the total number of grids in the map, and \(V_{p}\) is the trajectory nodes. Furthermore, similarly, we take the mean value of the trajectory error for 50 identical test scenarios.  
Our approach adopts the frontier-based exploration scheme with path planning. The exploration problem can be viewed as a sequential decision-making problem, whereby the robot learns to act optimally from changes to the environment that result from our selection of frontier nodes. Thus, our goal is to choose the best target point to maximize \(\eta\), meanwhile, minimize \(U\) and \(e\).
# Quadcopters in Smart Agriculture: Applications and Modelling
## 5 Challenges and Future Directions
### Security Threats
#### 5.4.2 Future Directions  
Future directions could focus on implementing enhanced cybersecurity measures, such as stronger encryption and authentication protocols, and integrating AI techniques to detect and respond to unusual behavior [171, 170]. These countermeasures would improve system integrity and reduce security risks. For instance, the authors in [172] proposed a hybrid machine-learning technique combining logistic regression and random forest algorithms to detect attacks on a quadcopter network. By utilizing machine learning, this approach enhances the security of an IoT-enabled drone, reducing cybersecurity threats and ensuring robust protection. This is particularly beneficial for agricultural missions, such as precision farming and crop monitoring, where sensitive data regarding crop yields must be protected against potential cyber attacks. For example, unauthorized access to these data could lead to significant financial losses or the misuse of information by competitors, underscoring the necessity for robust cybersecurity measures in UAV-based operations within smart agriculture.
## 3 Results and Discussion  
### Mesh Details  
Dairy cows are mainly housed in naturally ventilated dairy barns with large openings, connected directly to the ambient, turbulent weather conditions. This makes the measurements of volume flow rate from these buildings challenging. Hence, a substantial amount of time was spent in the design of representative meshes to increase the accuracy of CFD results. If an optimum distance between the dairy barn and the boundary of the control volume is not sufficiently secured, faster airflow streams could be compared with the natural airflow resulting in unreal airflow around the leeward vent opening [22]. Thus, the size of the CFD domain used during the simulations was chosen in order to ensure that the position of the outer boundaries did not compromise the CFD solution.  
A tetrahedral mesh was built in commercial ANSYS ICEM CFD(r) and tests of different meshes were carried out using this software. Various sizes of tetrahedral meshes were used, and after several levels of previously evaluated refinement, no significant differences (\(p\)-value \(<\) 0.05) in the air velocity and temperature were encountered. Thus, the selected mesh was composed of 162,191 nodes and 683,222 elements (Figure 7). Such a meshing system was found to conform easily to the exact boundary of the scale model. The convergence of the solution was carefully verified by monitoring the transport equation residuals throughout the simulation.  
In the present study, the authors decided to use the standard \(k\)-\(\varepsilon\) model to model the natural ventilation in the CBP. Previous studies [24] have shown that the standard \(k\)-\(\varepsilon\) model predicts reasonably well the airflow patterns in an experimental naturally ventilated livestock building.
## 2 Theoretical Aspects
### Identification Method of Chaotic Characteristics
#### 2.1.2 Largest Lyapunov Exponent  
The Lyapunov exponent determines the chaotic characteristics of the system based on the diffusion of the phase trajectory. Generally, the direction represented by the positive Lyapunov exponent supports the attractors. In contrast, the contraction direction corresponding to the negative Lyapunov exponent contributes to the attractor dimension's fractional part after counteracting the expansion direction's effect. Thus, the positive Lyapunov exponent is a prominent feature of chaos. Suppose \(\lambda_{1}\) as the largest Lyapunov exponent of a system, then the chaotic components of the system can be found if \(\lambda_{1}\) is positive, and its value reflects the chaos degree.  
[PERSON] [14] proposed the small data sets for computing \(\lambda_{1}\). Its basic steps are as follows:  
Choose suitable \(\tau\) and \(m\) to reconstruct the phase space and find the nearest neighbor point \(Y_{i}\) of each \(Y_{i}\) in the phase space. Short separation limitation is as follows:  
\[d_{i}(0)=\min_{\tilde{i}}\lVert Y_{i}-Y_{\tilde{i}}\rVert(\lvert i-\tilde{i} \rvert>p) \tag{4}\]  
where \(p\) is the average period of time series, \(i\) is the vector in space, \(\tilde{i}\) is the vector of the nearest neighbor of the second vector.  
Define the distance of \(Y_{\tilde{i}+j}\) and \(Y_{\tilde{i}+j}\) as  
\[d_{i}(j)=\lVert Y_{\tilde{i}+j}-Y_{\tilde{i}+j}\rVert \tag{5}\]  
where \(j=0,1,2,\cdots,\min\bigl{(}M-i,M-\tilde{i}\bigr{)}\).  
For each \(j\), compute the \(\ln d_{i}(j)\) average as follows:  
\[y(i)=\frac{1}{q\Delta t}\sum_{i=1}^{q}\ln d_{i}(j) \tag{6}\]  
where \(q\) is the number of nonzero \(\ln d_{i}(j)\). The slope of the regression line made by the least square method is \(\lambda_{1}\).
## 2 Related Research
### System Using EMG  
Among biosignals, SEMG stands out as a powerful tool owing to its ability to enable users to actively control their motion intentions, coupled with its short temporal gap from muscle activity to interpretation. [PERSON] et al. [7] conducted research on human-robot interaction systems and highlighted factors, such as muscle fatigue, skin sweat, electrode detachment, electrode movement, and abnormal movements, which can decrease pattern-recognition accuracy in SEMG. Despite these challenges, SEMG exhibits excellent accuracy and robustness, making it a reliable method for recognizing human motion intentions and serving as an important control signal. [PERSON] et al. [8] proposed a framework for hand gesture tracking and virtual object motion decoding using data gloves and EMG. They argued that conventional methods like handheld controllers, buttons, joysticks, trackpads, and triggers are less intuitive compared to using arms and hands, implying that muscle-computer interfaces can facilitate efficient and immersive interactions with VR/AR systems. [PERSON] et al. [9] developed an immersive game interface using a multichannel EMG module with four directionalities and presented an interface that maps each of the four channels and directions to different muscles: extensor digitorum upwards, flexor carpi ulnaris to the right, flexor carpi radialis downwards, and abductor pollicis longus to the left.  
These studies imply that the use of EMG in developing interfaces is practical and beneficial. [PERSON] et al. [8] aimed to present fatigue-free and natural interaction using hand gestures. However, their approach, which involved data gloves equipped with magnetic motion capture sensors, still imposed a limitation, as participants' hands remained in contact with the hardware. Additionally, the system presented by [PERSON] et al. [9] operated only by comparing the acquired values for each channel with a threshold, which limited the number of commands per channel.  
To address these issues, this study proposes a system that allows for the use of bare hands by applying HMD camera-based hand tracking, alleviating the inconvenience associated with hardware attachments.
## 3 The Proposed Fault Diagnosis Methodology
### The Proposed S-ECNN Model
#### 3.3.1 The Embedding Layer  
The embedding layer is a neural network, which can turn positive integers (indexes) into dense vectors of fixed size. It is usually placed at the beginning of a network to transform categorical non-numerical data into a categorical dense vector representation. Afterward, through downstream supervised learning, the categorical dense vectors can be continuously trained and optimized.  
In this paper, we apply the embedding layer to transform an alarm in the form of a code into a vector in the form of real-valued data. Let \(x_{i,k}\) be the \(k\)-th alarm code in the \(i\)-th alarm sequence. An alarm sequence of length \(l\) (padded where necessary) is represented as:  
\[X_{i}=(x_{i,1},x_{i,2},\ldots,x_{i,k},\ldots,x_{i,l})(x_{i,k}\in E), \tag{3}\]  
where \(X_{i}\in A\) is a labeled alarm sequence, and \(E\) is the collection of alarm codes configured in the wind turbine alarm system. The transformed \(n\)-dimensional vector is expressed as \(\mathbf{x}_{i,k}\in R^{n}\). Then, the alarm sequence can be represented as:  
\[\mathbf{X}_{i}=[\mathbf{x}_{i,1},\mathbf{x}_{i,2},\ldots,\mathbf{x}_{i,k},\ldots,\mathbf{x}_{i,l}]^ {\mathsf{T}}, \tag{4}\]  
where \(\mathbf{X}_{i}\in R^{n\times l}\) is the matrix representation of the \(i\)-th alarm sequence.  
The initialization parameters of an embedding layer can be random or assigned by loading an embedding matrix. In this paper, we initialize the parameters by loading the embedding matrix obtained from the pretrained alarm vectors. After loading the embedding matrix into the embedding layer, the embedding vectors will be continuously updated in the training phase using the labeled alarm sequences. Thus, the labeled alarm sequences and unlabeled alarm sequences can be collaboratively applied.
# A New String Edit Distance and Applications
## 2 Restricted Forensic Levenshtein Distance
### Levenshtein Distance Overview  
String edit distances are based on the minimal number of operations required to transform one string into another. This gives a way to measure dissimilarity of objects in the non-Euclidean space of strings, using the notion that two strings that are similar should differ in only a few characters, while two strings that are dissimilar would require many changes to turn one into the other. The edit distance proposed here could be viewed as a generalization of the Levenshtein distance [26], which counts the minimal number of single-character deletions, insertions, and substitutions required to transform one string into another. For example, the distance from CAT to CGT is one, from CAT to CA is one, and from CAT to CATG is two. Other edit distances exist, with different edits allowed. Damerau-Levenshtein considers indels, substitutions, and transpositions [27], while the longest common subsequence metric only looks at indels [28]. Rather than having a common cost of 1, substitution, insertion, deletions, etc. can each have their own unique cost [29].  
The calculation of minimal edit distance is typically done via dynamic programming [30]. The standard dynamic programming solution to Levenshtein will be briefly discussed below because it is crucial to understanding the novel contributions of the proposed algorithm. Examples will be presented to maximize clarity.
# A Review on Local Failure Probability Sensitivity Analysis
## 4 Local RSA in the Standard Normal Space
### Sensitivity through the Design Point of the System
#### 4.1.3 Sensitivity with Other Approximation Methods Inspired by FORM  
The FORM approximation is accurate for linear lsfs only. Sensitivities derived in a SORM context are proposed in [34], when \(\mathbf{s}\) are distribution parameters, \(\mathbf{s}=\boldsymbol{\theta}\). The SORM context makes it possible to build quadratic approximated lsfs, which can be more accurate when \(g\) is nonlinear. The authors in [34] take advantage of the radial distribution of the standard normal inputs to derive another quadratic approximation of the limit state surface.  
The sensitivities are then estimated with the differentiation of this novel approximation. This method is tested for systems of various dimensions (up to \(d=20\)).  
Other approximation methods inspired by the FORM/SORM framework have been derived in the reliability literature. These approximation methods aim to give better results than FORM and SORM in a nonlinear lsf context. We mention here the multi-hyperplane combination method (MHCM), first introduced in [35]. In the MHCM framework, several hyperplanes are employed to approximate the lsf at the vicinity of a design point, in the standard normal space. The sensitivities with respect to the distribution parameters \(\theta\) of the original inputs \(\mathbf{Z}\) in the MHCM context are presented in [36]. Another approximation method presented in [37] is completely independent of the design points' position, as the approximation of the lsf is a hyperplane constructed from a Monte Carlo sample. The sensitivities with respect to the distribution parameters are then computed with this hyperplane.  
In a broader context, the sensitivities computed with these approximation methods generally require very few to none additional lsf evaluation (or its gradient). However, the error of the resulting sensitivity estimate is usually difficult to measure without any reference value [34; 36].
# Convolutional Neural Networks: A Roundup and Benchmark of Their Pooling Layer Variants
## 2 Materials and Methods
### Let the Pooling Begin
#### 2.3.2 Fractional Max Pooling  
Fractional Max Pooling (FMP) [15] is, as its name suggests, a variant of Max pooling, but the reduction ratio can be a fraction as well, instead of an integer. The most important parameter is the scaling factor \(a\) by which the input image will be downscaled, with \(1<a<2\). Considering an input of size \(N_{in}\times N_{in}\), we select two sequences of integers \(a_{i}\), \(b_{i}\) that start at 1, and they are incremented by 1 or 2 and end at \(N_{in}\). These sequences can be either completely random or pseudorandom when they follow the equation \(a_{i}=ceil(a*(i+u))\), where \(a\) is the scaling factor and \(u\) is a number in the range (0, 1). Then, the input is split into pooling regions, either disjoint or overlapping using the respective variant of Formula (1), and the Max value for each region is retained.  
\[P_{i,j}=[a_{i-1},a_{i}-1]\times[b_{j-1},b_{j}-1]\text{ \emph{or} }P_{i,j}=[a_{i-1},a_{i}]\times[b_{j-1},b_{j}] \tag{1}\]  
where  
\(P\): the pooling region  
\(a_{i},b_{i}\): integer sequences according to the FMP algorithm  
According to the writers' experiments, overlapping FMP seems to have better results than the disjoint alternative, while a random choice of the sequences \(a_{i}\), \(b_{i}\) distorts the image, in contrast with the pseudorandom ones. Overall, FMP's performance appears to be better than that of Max pooling.