esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Performance Analysis
While our CTMC-based model does not directly account for these factors, it can be effortlessly combined with auxiliary models to incorporate most of them, thereby enhancing its versatility as a tool for evaluating a wide range of performance metrics in hybrid FSO/RF communications. Let us provide the following illustrative examples. For IoT applications with stringent energy consumption requirements, it is possible to estimate the excess energy consumption \(e_{1}\) and \(e_{3}\) in the FSO \(\rightarrow\) RF and RF \(\rightarrow\) FSO states, respectively. These estimates can be derived using statistical methods or computational methodologies tailored to the specific application. For instance, such excess energy consumption may result from an increased number of packet retransmissions compared to the optimal communication scenario. Therefore, the energy leakage can be estimated as follows:  
\[p_{1}e_{1}+p_{3}e_{3}. \tag{26}\]  
One of the most important reliability metrics for IoT devices is the mean time to failure (MTTF), as it quantifies the anticipated operational lifespan, enabling manufacturers and users to project failure probabilities and strategically schedule maintenance or replacements. In power-constrained IoT deployments, a comprehensive understanding of the MTTF is imperative for optimizing device longevity, ensuring uninterrupted functionality, and mitigating downtime-related costs. Let \(B_{C}\) be a battery capacity, quantified in terms of  
Figure 5: Optimal rate of the switching mechanism in hybrid FSO/RF systems. the number of transmitted packets. Let us consider a scenario where ARQ is employed as an error control mechanism to mitigate transmission errors and ensure data integrity. Denote the packet transmission rates in the states of FSO, FSO \(\rightarrow\) RF, RF, and RF \(\rightarrow\) FSO as \(\lambda_{0},\ \lambda_{1},\ \lambda_{2},\) and \(\lambda_{3},\) respectively. Therefore, the MTTF can be estimated as follows [45]:  
\[L\Big{(}w_{f},w_{r},\mu\Big{)}=\frac{\frac{\mu_{f}}{\mu_{r}}+1}{1+\frac{w_{r}+ \mu_{f}}{w_{f}}+\frac{\mu_{f}}{\mu_{r}}\Big{(}\frac{w_{r}+\mu_{r}}{w_{r}}+1 \Big{)}}. \tag{20}\]0  
Let us select the following numerical parameters for the analysis: \(\lambda_{0}=\lambda_{2}\ =\ 1,\ \lambda_{1}\ =\ \lambda_{3},\)\(\mu_{r}\ =\ \mu_{f}=2,\ \text{and}\ \lambda_{1}/\lambda_{0}=\chi,\)\(\chi\in[1;10].\) The battery capacity is defined as 1000 energy units. Figure 6 illustrates the behavior of the MTTF. The expenditure of one unit of energy per unit of time corresponds to an MTTF of 1000 time units. The increase in the number of damaged packets and their retransmissions in states FSO \(\rightarrow\) RF and RF \(\rightarrow\) FSO leads to a rapid degradation of the MTTF. Additionally, the lifetime degrades due to increasing environmental instability, such as the growing mobility of network nodes. However, implementing a switch to the most favorable communication method (FSO or RF) mitigates the impact of mobility.
## 4 Conclusions  
This paper investigated the sensitivity of the superhydrophobic coating range, heating range and power to icing environmental parameters. Based on the results of the tests, the following conclusions were derived:  
1. IPS with a coating prevented runback ice formation and reduced the power consumption by more than 50% compared to IPS with no coatings.
2. IPS is highly sensitive to environment temperature. A designed IPS might not work well when environment temperature is beyond the design point.
3. IPS is sensitive to MVD which determines the area that needs to be protected and is less sensitive to LWC. Nonetheless, increased MVD and LWC at the same time may demand enhancing the IPS's heating power.
4. The droplet impingement area is where the superhydrophobic coating works. The coating cannot promote all the water falling off when situated outside the impingement area.  
Figure 16: Ice formation on the leading edge at a power density of 4 kW/m\({}^{2}\).  
Figure 17: Runback ice forms after droplet impingement limit.  
ment area. Therefore, the superhydrophobic coating needs to cover the droplet impingement area only.
* 5. Sufficient heating power is required to keep the surface temperature of the superhydrophobic coating above the freezing point. Effective heat distribution on the surface is critical to IPS design.  
In general, when applying the new IPS method that combines thermoelectric and superhydrophobic coatings to an aircraft, the design of its protection range requires no additional consideration compared to conventional designs. In terms of energy consumption, it is necessary to ensure that the surface temperature is above 0 \({}^{\circ}\)C or slightly higher for optimal anti-icing performance. However, the addition of superhydrophobic coatings may result in a decrease in the heat transfer efficiency. Thus, the measured and adjustable temperature inside the model needs to be a little higher than the temperature one wants to achieve on the surface of the superhydrophobic coating. Detailed research on the heat transfer process and temperature variations is likely to be necessary in the future.  
Conceptualization, L.Y. and D.Z.; Methodology, L.Y.; Software, Y.W.; Validation, Y.W. and H.Z.; Data curation, H.Z.; Writing--original draft, L.Y.; Writing--review & editing, L.Y.; Supervision, D.Z.; Project administration, D.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key R&D Program of China, grant number 2022 YFE0203700.  
Not applicable.  
We acknowledge the Liaoning Provincial Key Laboratory of Aircraft Ice Protection, Joint Research Center for Advanced Materials and Anti-icing of Tsinghua University (SMSE)-AVIC ARI and the China-Italy Joint Laboratory on Green Aviation Technology for the support for this research.  
The authors declare no conflict of interest.
## 5 Multi-Path Routing Protocol
### Multi-Path Searching Algorithm
The work in [26] proposes resilient routing reconfiguration (R3), which consists of an off-line pre-computation phase and an online reconfiguration phase. By converting topology uncertainty into uncertainty in rerouting traffic, it applies linear programming duality to find the optimal route and protection route.
## 5 Simulation Analysis
### Computational Time Consumption Comparison and Analysis  
In this section, the computational performance and the quality of the results of our method are further compared with other methods. Simulations are performed under  
Figure 10: Comparison of the minimum distance between different methods. (a) the minimum distance between robots, (b) the minimum distance between robot and obstacle.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Method**} & **Our** & **Centralized** & **Soft** \\  & **Method** & **Method [6]** & **Constraint-Based** \\  & **Method** & **DMPC [17]** \\ \hline Minimum distance from obstacles & 0.40 m & 0.38 m & 0.40 m \\ Minimum distance between robots & 0.50 m & 0.51 m & 0.44 m \\ Task success rate & 100\% & 100\% & 93.3\% \\ Average velocity & 0.45 m/s & 0.39 m/s & 0.36 m/s \\ Average standard & & & \\ deviation of velocity & 0.20 m/s & 0.15 m/s & 0.12 m/s \\ Task time & 21.25 s & 22.80 s & 24.20 s \\ Calculation time per horizon & 0.20 s & 1.05 s & 0.18 s \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison between different methods.  
different numbers of robots. The cost is utilized to evaluate the quality of the results and calculation time per horizon is utilized to evaluate the computational efficiency. Since the soft constraint-based DMPC does not guarantee a 100% success rate, our method is compared with the centralized method. The robot operating environment and parameter settings are the same as in Section 5.1. The covariance matrix is set to \(N_{noise}=diag(0.1\text{m},0.1\text{m},0.5\text{deg})\). The results are shown in Figure 11.  
As can be seen from Figure 11, the computational efficiency of our method decreases slightly as the number of robots increases while the computational efficiency of the centralized method decreases rapidly. Figure 11 illustrates that the cost of our method is a little higher than that of the centralized method. According to the experimental data, the computational efficiency of our method is on average 3.50 times higher than that of the centralized method, and the cost is on average 23.9% higher than that of the centralized method.
# Sliding Suffix Tree
## 2 Notation and Preliminaries
### Suffix Tree  
A _suffix tree_ is a dictionary containing each suffix of the text as a key, and its position in the text as its value. The data structure is implemented as a compressed trie. To reduce space, we only store edge labels implicitly, so that each internal node stores the _start index_ of the label on the incoming edge and its _length_, whereas each leaf stores the _position_ of the suffix it represents in the text (see Figure 1). Additionally, each node \(\alpha\) stores a _shortcut to any of the descendant leaves_ denoted by \(leaf(\alpha)\). If \(\alpha\) is a leaf, then \(leaf(\alpha)=\alpha\). During the shift operation of the window, this shortcut will be updated so that it always points to a valid leaf. For example, on Figure 1 the leaf shortcut from the internal node labeled 1:1 could point to a leaf labeled 11, 8, 1, 4, or 6.  
We denote by \(|\alpha|\) a _string depth_ operation of some node \(\alpha\) in a suffix tree, and define it as a sum of all label lengths of edges from the root to \(\alpha\). Consequently, a label is a substring of a suffix starting and ending at the string depth of the originating node and the string depth of the terminating node, respectively. We say a node \(\alpha\)_spells out_ string \(A\), where \(A\) is a concatenation of all labels from the root to \(\alpha\). For example, the only internal node of depth 2 in Figure 1 (node with value 2:3) has a string depth of 4, and spells out a string ABRA.  
Let \(A\) and \(A^{\prime}\) be strings which are spelled out by nodes \(\alpha\) and \(\alpha^{\prime}\), respectively. We define a suffix link as an edge from node \(\alpha^{\prime}\) to \(\alpha\) if \(A=A^{\prime}[2:]\), and denote this by \(\alpha=\text{suffix\_link}(\alpha^{\prime})\). Suffix links in Figure 1 are represented as green dotted lines.
## 2 Literature Review
[43] employed a smartphone's onboard tri-axial accelerometer attached to the wrist and ankle of the subjects to collect data for three emotions: happy, neutral, and anger. They extracted 114 features using principal component analysis (PCA). Different classical machine learning classifiers were trained and they found that the support vector machine (SVM) performed best. They also provided a comparison of emotional identification using wrist versus ankle data. They reported the best classification accuracy of 85% and also showed that the emotions prediction from the ankle sensor is much higher than from the wrist sensor. [PERSON] et al. [19] collected emotion data from smartphones' onboard IMUs for six different emotions: anger, sad, happy, fear, disgust, and surprise. They demonstrated a manually-built feature set that comprised elements from the time, frequency, and wavelet domains. They concluded that time, frequency, and wavelet domain characteristics may be employed to train various supervised learning models. The best classification accuracy for all six emotions is stated to be 86.45%. In the majority of the studies above, the number of classes is limited excluding the study [19] which deals with the case of classifying six activities.
## 2 Physical Model
#### Temperature Dependent Physical Properties  
The mathematical model introduces both temperature dependent \(C_{p}(T)\) and \(k(T)\) functions in order to see the differences in internal nodal heat transfer at the boundary interface. As heat flows outwards across fixed distances, average thermal conductivities between nodes (\(k_{12}\) and \(k_{23}\)), were evaluated through the harmonic mean approximation  
Figure 1: System’s geometry, composed by a piece of Gd and an idealized surrounding. The 1D model assumes a heat flux path that begins inside the MCM and extends outwards along the line that intercepts the control volumes associated to nodal points 3, 2 and 1.  
(Equations (4) and (5)), that correspond to the effective thermal conductivity values at the interfaces between the adjacent control volumes 1 and 2, and 2 and 3, respectively:  
\[k_{12}=\left[\frac{2k_{1}k_{2}}{k_{1}+k_{2}}\right] \tag{4}\]  
\[k_{23}=\left[\frac{2k_{2}k_{3}}{k_{2}+k_{3}}\right] \tag{5}\]  
Temperature dependent thermal conductivity inputs have been adapted from [PERSON] et al. (2019) [29], as shown in Figure 2, where \(k(T)\) values change between 8.4 and 12.6 W m\({}^{-1}\) K\({}^{-1}\). A near-room temperature value of 295 K was used to numerically trigger sudden changes in the material's thermal conductivity, whether the simulated behaviour is increasing or decreasing on temperature. The \(k(T)\) dependencies can be described as:  
1. _kGd_--constant \(k=10.5\) W m\({}^{-1}\) K\({}^{-1}\), equal to the thermal conductivity of Gd at 293 K;
2. _k(T)minus_--decreasing \(k\) while temperature increases;
3. _k(T)plus_--increasing \(k\) while temperature increases.  
The chosen values for \(C_{p}\) and \(\rho\) were adapted from [PERSON] et al. (2008) [36]. While \(\rho\) is kept constant at 7900 kg/m\({}^{3}\) throughout the entire slab's surface (\(\rho_{1}=\rho_{2}=\rho_{3}\)), the chosen specific heat values were linearly interpolated from data shown in Figure 3. The Curie temperature is highlighted with a vertical black line at \(T_{c}=293.4\) K, around which \(C_{p}(T)\) suffers a sharp increase from 174.12 to 302.06 J kg\({}^{-1}\) K\({}^{-1}\), when the material's temperature is decreasing.  
Figure 2: Three distinct \(k(T)\) dependencies: _kGd_—dash blue line; _k(T)plus_—dashed red line; _k(T)minus_—dash black line. Adapted from [PERSON] et al. (2019).
# Generalized Optimality Criteria Method for
## 3 Numerical Comparisons
### Comparison with OCM  
Since GOCM is directly related to OCM, it would make sense to compare the performance between the two. In this section, GOCM is implemented in the 99-line Matlab code. Although GOCM can handle different objectives and multiple constraints, this comparison is purely based on the compliance objective with volume fraction constraint.  
In order to have an objective comparison, we modified the function OC (  
Figure 3 shows the optimization histories for OCM and GOCM for the first 80 iterations. Both methods showed a similar convergence trend, but the OCM showed smooth variation because it enforced the equality constraint at every iteration. The OCM maintained \(g(\mathbf{x})=0\) throughout all iterations, while the GOCM oscillated the positive and negative values until it was stabilized after the 50 th iteration. The Lagrange multiplier was converged to 0.6166 (OCM) and 0.6126 (GOCM). This example shows that GOCM and OCM showed a similar optimization trend. However, the GOCM showed oscillation in early design but converged much faster. In addition, the design updating process of GOCM was 10 times faster than that of OCM.  
Figure 3: Design optimization histories for optimality criteria method (OCM) and generalized optimality criteria method (GOCM).  
Figure 2: Design domain and optimum designs of a cantilevered beam.
## 2 MWC System Model
### Signal Sampling  
The framework of the MWC system sampling section is illustrated in Figure 2. The input signal \(x(t)\) is a sparse multi-band signal. The sparse signal \(x(t)\) contains a large number of zero elements. The support set of \(x(t)\) is defined as the smallest closed set containing all points that are not mapped to zeros. The sub-band number of \(x(t)\) is \(N\), and the bandwidth of each sub-band is not more than \(B_{\max}\). \(f_{NYQ}\) is the Nyquist frequency of \(x(t)\). The frequency span of \(x(t)\) is in the range of \(\mathcal{F}\in\left[-f_{NYQ}/2,f_{NYQ}/2\right]\). The MWC system has \(m\) channels in the analog sampling front-end and each channel will input \(x(t)\). Taking the \(i\)th channel as an example, the first step is to multiply \(x(t)\) with the mixing function \(p_{i}(t)\). \(p_{i}(t)\), which has \(T_{p}\) as its period, can extend the bandwidth of \(x(t)\) to the baseband.  
\[p_{i}(t)=a_{ik},k\frac{T_{p}}{M}\leq t\leq(k+1)\frac{T_{p}}{M},0\leq k\leq M \tag{1}\]  
Figure 1: MWC System Framework.  
where \(a_{ik}\in\{+1,-1\}\), so the Fourier expansion of \(p_{i}(t)\) can be seen as  
\[p_{i(t)}=\sum_{l=-\infty}^{\infty}c_{il}e^{j\frac{2\pi}{T_{p}}lt} \tag{2}\]  
\[c_{il}=\frac{1}{T_{p}}\int_{0}^{T_{p}}p_{i(t)}e^{-j\frac{2\pi}{T_{p}}lt}dt \tag{3}\]  
Therefore, after the mixing function, the Fourier transform of \(x_{i}(t)=p_{i}(t)x(t)\) is as follows.  
\[\begin{split} X_{i}(f)&=\int_{-\infty}^{\infty}x_{ i}(t)e^{-j2\pi f^{t}}dt\\ &=\int_{-\infty}^{\infty}x(t)\left(\sum_{l=-\infty}^{\infty}c_{ il}e^{j\left(2\pi/T_{p}\right)lt}\right)e^{-j2\pi ft}dt\\ &=\sum_{l=-\infty}^{\infty}c_{il}\int_{-\infty}^{\infty}x(t)e^{-j 2\pi(f-l/T_{p})t}dt\\ &=\sum_{l=-\infty}^{\infty}c_{il}X\big{(}f-lf_{p}\big{)}\end{split} \tag{4}\]  
Then, \(x_{i}(t)\) is passed through an ideal low-pass filter with bandwidth \(B_{LPF}\geq B_{\text{max}}\) and sampled at the sampling rate of \(f_{s}\) to obtain the sampled \(y_{i[n]}\). \(f_{s}\) is well below \(f_{NYQ}\). The discrete Fourier transform of \(y_{i[n]}\) is as follows:  
\[\begin{split} Y_{i}(f)&=\sum_{n=-\infty}^{\infty}y _{i[n]}e^{j2\pi fnT_{s}}\\ &=\sum_{l=-L_{0}}^{L_{0}}c_{il}X\big{(}f-lf_{p}\big{)},f\in \mathcal{F}_{s}=\left[-f_{s}/2,f_{s}/2\right]\end{split} \tag{5}\]  
where  
\[L_{0}=\left[\frac{f_{NYQ}+f_{s}}{2f_{p}}\right]-1 \tag{6}\]
# FOXS-GSC--Fast Offset Xpath Service with HexagonS Communication
## 3 Proposed Solution
### Response Message Routing  
The response routing method is used when the hops (relays) necessary to reach the destination node are known. For the FOXS-GSC case, this method is used when a new route for the requesting vehicle is answered. As the service response time is sufficiently fast compared to network topology changes (vehicles moving), the requesting vehicle's response message uses the list of relay nodes (MSG._visited_vehicles_) contained in the request message.  
Figure 5 shows a flowchart describing the protocol's behavior when sending a message from a cloudlet to a vehicle. In stage (A), the protocol receives a message (MSG) created by the cloudlet service (e.g., route response) or a retransmission message where the vehicle that received the message is a relay. At each retransmission of the response message, the protocol removes the last ID from the list and copies its value to the next hop field (MSG._nexthop_vehicle_) (B). Afterwards, it checks whether the next hop is the destination (B). If it is, the message is transmitted and the protocol is finalized. Otherwise, the message is sent (D) and a timer is activated to wait for the retransmission of the message on the next hop (E).  
This methodology is applied due to the likelihood of a transmission failure (e.g., topology change). Thus, the recovery mode is used. The timer is set to 100 ms, which is the time for two-channel changes in the 802.11p protocol. If the message is listened to,  
Figure 5: Flowchart of response message routing.  
the protocol ends. Otherwise, recovery mode is activated (F). Thus, the message is updated, consecutively removing all visited sectors and vehicles from MSG._visited_sectors_ and MSG._visited_vehicles_. After this, the retransmission process is switched to the protocol phase presented in the previous step in Section 3.5. When stage (H) is reached, the flow returns to the beginning and starts again.  
This is possible because the relayed message contains the destination vehicle sector and the fact that the response time is sufficiently fast to ensure that the vehicle is located in the same sector as it was when it sent the original requisition message.  
With the communication protocol defined, the next subsection presents the vehicle routing service that uses this protocol. The vehicle routing service used here is an improvement on the FOXS service [15]. FOXS is a vehicle route suggestion service based on the fog computing paradigm. In its operation, the scenario is sectorized; each sector contains a fog-based infrastructure with storage, computation, and communication capabilities, referred to as a cloudlet. The size of the sectors is based on the communication radius of the cloudlets. Each cloudlet is responsible for acquiring, classifying, and routing the vehicles within its sector.  
Among the main changes we introduce are the use of multi-hop communication between vehicles and infrastructure, the method of acquiring traffic knowledge, and the service's ability to work autonomously when the infrastructure is unreachable.
## 3 Approach  
### Missing Seeding Detection  
#### 3.1.1 Image Preprocessing  
The paddy field environment is diverse and complex, making the detection of missing seedlings susceptible to interference from floating straw, exposed soil, strong light reflection, and machine vibrations. Therefore, image preprocessing is necessary to extract seedling information from the background. Image preprocessing includes grayscale conversion, threshold segmentation, and labeling connected regions [14]. Since the green component of seedlings exhibits significant differences from other areas in the background, the ExG (Excess green) feature value [15] is employed for grayscale conversion. This effectively suppresses background information such as shadows, straw, and soil. The grayscale image is illustrated in Figure 3.  
The Otsu method [16] is utilized to obtain the binary image of seedlings. Due to the influence of the actual shooting environment, the captured images often contain irrelevant information such as the sky, human reflections, and water ripples formed by light [17]. These interferences can create significant noise, as illustrated in Figure 4a, where non-agricultural information generates large isolated pixel blocks. To ensure that the detection of missing seedlings is not affected by this noise, the connected region labeling method [18] is employed to eliminate small connected regions that are not connected to the characteristic region. This ensures complete separation between seedlings and the background, as shown in Figure 4b. As rice seedlings are non-rigid body deformations, there may be gaps between adjacent leaves after image preprocessing. This can result in one seedling having multiple contours and centroids. To address this, a dilation operation [19] is performed to fill small  
Figure 2: Rice seedling samples in different scenarios.  
gaps between adjacent regions. Although the individual seedling morphology expands, it does not affect the centroid position of the seedlings, as shown in Figure 4c.