esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 HydroSAR Product Portfolio and Product Algorithms
### Quantitative HydroSAR Flood Information Products
#### 4.3.1 HYDRO30: Surface Water Extent
The use of expectation maximization rather than Otsu [16] thresholding is a third departure from the work in [12]. We made this switch as we found the radar brightness distributions for many image tiles to be trimodal rather than bimodal. This caused occasional classification errors when using an Otsu classifier, which expects bimodal behavior. Using this dynamically calculated threshold, both the VV and VH bands of the Sentinel-1 RTC30 data were classified into water and no-water classes, and the classification maps derived for VV and VH were combined to create a combined surface water candidate mask. **Step 4: Fuzzy Logic Post-Processing. ** Similar to [47], fuzzy logic post-processing was used to identify and remove false water defections from the surface water candidate mask. Fuzzy rules were established for the (1) radar brightness, (2) HAND elevation, (4) surface slope, and (4) water patch size. The rule set was designed to ensure that dark pixels with low HAND elevations and low surface slopes received higher likelihoods to be true flood pixels [49]:  
1. **RCS**: \( \begin{cases}x_{u,RCS}&=&\tau_{g}\\ x_{l,RCS}&=&\mu_{o^{0}(\tau_{g})}\end{cases} \) with \(\sigma^{0}(\tau_{g})=\) RCS of initial flood candidate pixels;
2. **HAND**: \( \begin{cases}x_{u,HAND}&=&\mu_{HAND(unter)}+3\cdot\sigma_{HAND(unter)}\\ x_{l,HAND}&=&\mu_{HAND(unter)}\end{cases} \);
3. **Surface slope**\(\alpha\): \( \begin{cases}x_{u,\alpha}&=&0^{\circ}\\ x_{l,\alpha}&=&15^{\circ}\end{cases} \);
4. **Area**\(A\): \( \begin{cases}x_{u,A}&=&10 px\\ x_{l,A}&=&3 px\end{cases} \). Our second fuzzy rule deviated from the approach in [47], which used DEM heights rather than HAND. As a hydrologically conditioned variable, we found the HAND to lead to more hydrologically relevant fuzzy weights and fewer rejections of true water pixels. Fuzzy membership functions were calculated for each indicator using Z-shaped activation functions. Membership scores were averaged, and a threshold of 0.45 was applied to remove false detections. **Step 5: Discrimination of Permanent and Flood-Related Water. ** In a final step, we separated perennial water from flood-related surface water using reference data from the Joint Research Centre's global surface water dataset. The initial version of this dataset was produced using Landsat images at a 30 m resolution acquired between 1984 and 2021 [50], and it is currently updated annually. HYDRO30 products were operationally created across the HKH as part of the HydroSAR service. An example of a HYDRO30 product is shown in Figure 5c, and a performance assessment is presented in Section 7.
# A Comprehensive Review of Micro UAV Charging Techniques
## 8 Potential Challenges in UAV Charging Techniques
### Technological Cost and Complexity  
Another concrete challenge in large-scale deployment of laser-powered UAVs is the complexity and cost of manufacturing the entire power transfer system. The technical uncertainties and technological costs of such systems give rise to several concerns about availability and scalability. In our view, this technology is not fully mature yet; thus, further research contributions are required before the large-scale deployment of laser-powered UAVs for civilian applications.
## 3 Proposed System Methodology
### Multi-Person Tracking
#### 3.3.1 Human Silhouettes Verification: Particles Force Model  
We present a robust particles force model for human silhouette verification. First of all, every extracted labeled silhouette was converted into particles, as shown in Figure 5a. We treated all pixels as fluid particles, thus, every extracted silhouette was a collection of many particles, as depicted in the magnified view in Figure 5b. Therefore, in our designed method, each silhouette was represented by a set of particles \(Q=[p_{1}\), \(p_{2}\), \(p_{3}\),..., \(p_{N}]\), where \(N\) is the total number of particles in one silhouette.  
Figure 4: Human silhouette extraction. (**a**) Distance algorithm from the center to two extreme points for every object, (**b**) single silhouette extracted uniquely through labeling, along with its distance graph, and (**c**) a single non-silhouette, along with its distance graph.  
We know from physics that, in solids, particles do not have enough kinetic energy to overcome the strong forces of attraction, called bonds, which attract the particles toward each other. Using this physics phenomenon, we found the force of attraction between particles of every extracted silhouette, as shown in Figure 6:  
For simplicity, we found the force of attraction between only two mutually interacting particles using Equation (10) in all frames from 1 to \(Z\).  
\[F_{i}=\frac{p_{1}p_{2}}{r^{2}} \tag{10}\]  
where \(i\) is in the range [1 \(E\)] with \(E\), representing the maximum number of silhouettes per frame, while \(F_{i}\) is the force of attraction between particle \(p_{1}\) and \(p_{2}\) of the \(i\)th silhouette and \(r^{2}\) is the square of Euclidian distance between particles \(p_{1}\) and \(p_{2}\). After calculating the force between particles of every silhouette in all video frames, we discarded those silhouettes whose force of attraction was static in frame t and frame \(t+1\) using Equation (11):  
\[H_{s}=\left\{\begin{array}{ll}1&\quad\text{if }\frac{dF_{i}}{dt}>0\\ 0&\quad\text{otherwise}\end{array}\right. \tag{11}\]  
Figure 5: The particles force model. (**a**) Particle conversion of every extracted silhouette and (**b**) magnified view of particle conversion.  
Figure 6: Particles force model. (**a**) Interacting force between two particles (**b**) for non-human silhouettes and (**c**) human silhouettes.  
where \(\frac{dF_{i}}{dt}\) represents the change in attraction force between particles of every \(i\)th silhouette, with respect to time between frames \(t\) to \(t+1\). After application of the particles force model, we only retained human silhouettes in each frame, as depicted in Figure 7:
## 2 Epoxy-Based Composites
### Epoxy-Based Composites Reinforced with Natural Fibers  
Bio-composites made from epoxy polymer and natural fibers have a great scope of research for engineering applications such as structural applications. Researchers have continued working with natural fiber and epoxy polymer-based bio-composites over the last decade. The problem with natural fibers is that they cannot be used as reinforcement material due to lignin hydrophobicity [23] and some impurities. However, the alkali-treated fibers can be used as the alkali treatment of fiber removes such impurities [24]. Therefore, the researchers focused on alkali-treated natural fibers to control synthetic fibers, and non-biodegradable waste, to produce epoxy-based bio-composites. Natural fiber composites possess environmental benefits as they are biodegradable. Natural fibers have many advantages: high strength, light weight, water resistance, corrosion resistance, high durability, electrical resistance, fire resistance, good thermal and acoustic insulating properties, and chemical resistance. As natural fiber composites are environmentally friendly, they regain attention over the synthetic fiber. The first used natural fiber composite was straw-reinforced clay for bricks and pottery [18; 25; 26]. NFRCs are either a combination of natural fiber and synthetic resin or natural fibers and bio-resin. Bio-resins refer to resin biodegradability; bio-resins and synthetic resins are in the form of thermoplastic or thermoset resin. Thermoset resin is the most used resin for the structural applications of natural fiber composites. NFRCs are used in aerospace industries [27; 28] and automobile industries [29]. These composites have also been used in biomedical applications such as tissue and bone repair [30].  
Common natural fibers are hemp, jute, nettle, coir, agave, sugar palm (Gomuti), sisal, flax, ramie, and cotton. These fiber composites are gaining importance due to their strength, corrosion resistance, biodegradability, and many more advantages in engineering applications such as automobile, aerospace, structural components, and construction. However, some disadvantages are associated with NFRCs, such as moisture absorption and processing temperature. Due to their unique features, such as low density and a cellular structure, natural fibers provide great acoustic and thermal insulation [31]. However, some of these disadvantages can be improved with fiber treatments before composite fabrication. The fiber orientation and length improve the natural fiber composites' mechanical and physical properties [7; 32]. NFRCs are composites whose mechanical efficiency is determined by the fiber-matrix interface and the stress transfer function, which transfers stress from the matrix to the fiber. Many investigators in several research papers have reported this [26; 33; 34; 35; 36; 37; 38; 39; 40; 41; 42].
# Practical Application of Mesh Opportunistic Networks
## 3 Materials and Methods
### Analysis
#### 3.6.1 Survey  
We conducted a written survey of all the participants in the 10 days after the experiment using the Google Forms platform. The survey included both closed and openquestions. In addition to the basic data provided by the respondent (personal data, participant number and telephone model used), the survey involved questions grouped into thematic areas: one-to-one communication, group communication, and the use of the integrated GPS location in the created communication flow of the Mesh OppNet.
## 2 System Model
Then, the array steering vector of RIS is given by  
\[\mathbf{a}_{\text{t}}(\phi_{RIS})\mathbf{=}\sqrt{\frac{1}{N}}\Big{[}1,e^{j\frac {2\pi}{M}d\sin(\phi_{RIS})},\ldots,\;e^{j\frac{2\pi}{M}d(N-1)\sin(\phi_{RIS})} \Big{]}^{T},\]  
where \(\phi_{RIS}\mathbf{=}\arctan(\frac{H}{\left\lVert\mathbf{s}_{\text{U}}[n]- \mathbf{s}_{\text{G}}\right\rVert})\). The position of EVE is denoted as \(\mathbf{s}_{Ev}=[x_{E},y_{E}]^{T}\). Considering the stability of the system, the UAV flies at a fixed altitude. We denote the horizontal position of UAV at \(n\)th time slot as \(\mathbf{s}_{U}[n]=[x_{U}[n],y_{U}[n]]^{T}\). We denote the position of the GU as \(\mathbf{s}_{\text{G}}=[x_{G},y_{G}]^{T}\)  
Figure 1: The RIS-aided UAV secure transmission model. The channels BS \(\rightarrow\) UAV, UAV \(\rightarrow\) GU and BS \(\rightarrow\) Eavesdropper are denoted by \(w\), \(h_{UG}\), and \(h_{UE}\), receptively. The transmission signals of the BS can be denoted as  
\[\mathbf{p}[n]{=}\mathbf{w}[n]\text{s}, \tag{3}\]  
where \(\mathbf{w}[n]\in\mathbb{C}^{M\times 1}\) is the beamforming vector at \(n\)th time slot and \(s\) is the privacy information transmitted by BS to GU. To reduce the SINR received at the eavesdropper, the average transmit power of the BS is limited by  
\[\|\mathbf{w}[n]\|\leq I,\forall n=1,2,\ldots,N, \tag{4}\]  
where \(I\) is the maximum permitted transmit power. The trajectory of UAV should satisfy the following constraints  
\[ \begin{cases}\mathbf{s}_{u}[1]=\mathbf{s}_{S},\\ \mathbf{s}_{u}[N]=\mathbf{s}_{E},\\ ||\mathbf{s}_{u}[n]-\mathbf{s}_{u}[n-1]||^{2}\leq V\delta,n\in\{2,\ldots,N\}, \end{cases}  \tag{5}\]  
where \(\mathbf{s}_{S}\) and \(\mathbf{s}_{E}\) denote the initial and end positions of UAV; \(V\) denotes the maximum flight speed of UAV; \(V\delta\) represents UAV's maximum flight distance in each time slot. According to the above discussion, we can derive the transmission rate of GU at the \(n\)th slot as  
\[R_{u}=\log_{2}(1{+}\frac{\left\|\mathbf{h}_{UG}^{H}[n]\mathbf{\Phi}[n]\mathbf{W}[n] \mathbf{w}[n]\right\|^{2}}{\sigma_{u}^{2}}), \tag{6}\]  
where \(\sigma_{u}\sim\mathcal{CN}(0,\sigma_{0}^{2})\) is the additive white Gaussian noise (AWGN) at GU; \(\mathbf{\Phi}\) is the phase shift of the RIS. Simultaneously, the EVE also receives the confidential information with rate as  
\[R_{e}=\log_{2}(1{+}\frac{\left\|\mathbf{h}_{UE}^{H}[n]\mathbf{\Phi}[n]\mathbf{W}[n] \mathbf{w}[n]\right\|^{2}}{\sigma_{e}^{2}}), \tag{7}\]  
where \(\mathbf{h}_{UE}=\sqrt{\text{d}_{\text{UE},n}^{-K}}v_{\text{E}}\xi_{E}\xi_{ \text{a}}\mathbf{t}_{\text{a}}(\phi_{\text{BS}})\) is the channel between UAV and EVE.
## 3 System Model
### Computational Model  
#### 3.3.1 Terminal Layer Computing Model  
In the MD model, two kinds of queues are included: the task queue, in which newly generated tasks but not yet assigned decisions are stored, and the task computation queue, which is used to output the computation results of tasks. It is specified that only one task can enter the queue for processing in a time slot.  
The task generated by the \(t\) time slot must wait until the computation queue is free to execute the computation, i.e., wait for the previous task to complete its execution, and its waiting delay \(\tau_{m,\text{wait}}^{t}\) can be expressed by Equation (1).  
\[\tau_{m,\text{wait}}^{t}=\left[\max_{t^{\prime}\in\{0,1,\ldots,t-1\}}l_{m}^{ \text{comp}}\left(t^{\prime}\right)-t+1\right]^{+} \tag{1}\]  
Figure 1: An illustration of EC system with a mobile device and an edge node.  
where \(l_{m}^{\text{comp}}\left(t\right)\) denotes the completion time slot of the task (completing the task or being discarded). When the task is processed in the computation queue, there is a processing delay. Suppose that the processing capacity (bits/s) of the MD is \(f_{m}^{\text{device}}\), then the processing delay \(\tau_{m,exe}^{t}\) of the task in the computational queue can be expressed by Equation (2).  
\[\tau_{m,\text{ }exe}^{t}\ =\ \frac{D_{m}^{t}\rho_{m}^{t}}{f_{m}^{\text{device}}} \tag{2}\]  
Therefore, we can find the completion time slot \(l_{m}^{\text{comp}}\left(t\right)\) of task \(\lambda_{m}(t)\) by the above equation as shown in Equation (3).  
\[l_{m}^{\text{comp}}(t)=\min\bigl{\{}t+\tau_{m,\text{ }wait}^{t}+\tau_{m,\text{ }exe}^{t}\,\quad t+\tau_{m,\text{max}}^{t}\bigr{\}} \tag{3}\]  
\(t+\tau_{m,\text{ }wait}^{t}+\tau_{m,\text{ }exe}^{t}\) denotes the time slot when the task execution is completed and \(t+\tau_{m,\text{max}}^{t}\) denotes the maximum tolerated time slot of the task. The total processing delay \(\tau_{m,MD}^{t}\) of task \(\lambda_{m}(t)\) on the MD can be expressed by Equation (4).  
\[\tau_{m,MD}^{t}=\min\bigl{\{}\tau_{m,\text{ }wait}^{t}+\tau_{m,\text{ }exe}^{t}\,\quad\tau_{m,\text{max}}^{t}\bigr{\}} \tag{4}\]  
At the same time, this paper considers the energy consumption of MD in processing tasks, which consists of two parts: (1) task computation energy consumption expressed as the product of computation power and computation time, and (2) task waiting energy consumption, expressed as the product of waiting power and time. Therefore, the energy consumption \(E_{m}^{\text{device}}\) required for the task to be executed locally is shown as follows.  
\[E_{m}^{\text{device}}\ =\ P_{m}^{\text{ }exe}\ \tau_{m,\text{ }exe}^{t}\ +P_{m}^{\text{wait}}\ \tau_{m,\text{ }wait}^{t} \tag{5}\]
# Adaptable and Automated Small UAV Deployments via Virtualization
## 4 Validation of the Solution
### Ad-Hoc network Routing Alternatives
#### 4.3.2 Simulation Results  
To compare the different protocols, we defined three relevant metrics: the convergence time needed to find a path between two nodes (characterized in fact by the average total throughput, meaning that the less throughput achieved, the longer it takes to converge), the overhead that is generated by the routing protocol (overall total control traffic) and the optimal path usage measured in term of the percentage of packets that are sent through the shortest path. All the configurable parameters and timers for AODV and OLSR are based on the default values indicated, respectively, by [PERSON] et al. (2011) and [PERSON] (2012). After ten simulations (results are the average of all the experiments), we can conclude that both protocols may be a suitable alternative for this scenario. Convergence times, represented by the throughput, are quite similar (slightly better in AODV with failures every 20 min, although OLSR performs better when failures are increased, as shown in Figure 8a). OLSR normally selects shorter paths, as illustrated in Figure 8b, both for the realistic scenario and the worst case scenario. In fact, this is quite reasonablen since OLSR, as RRP, is converging to the optimal path when it is available, as opposed to AODV that maintain the selected path until a failure takes place. As expected, the overhead introduced by AODV (Figure 8c) is smaller than the overhead introduced by OLSR, however, both are negligible compared with the regular service throughput.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Values** \\ \hline Traffic & Constant Bit Rate \\ Transmission Rate & 32 kbps \\ Network Protocol & UDP \\ Simulation Time & 18,000 s \\ Number of SUAVs & 16 \\ Mobility Model & Static \\ \multicolumn{2}{c}{Failures} & Uniform Distribution \(a=0,b=40\) \\ Replacements & Exponential Distribution \(\lambda=1/3\) \\ Simulation Area & 280 m \(\times\) 280 m \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation parameters.  
Figure 7: Example of fails and replacements of one simulation.
## 6 Numerical Results
### Performance in the Asymmetric Networks
In the low \(E_{b}\)/\(N_{0}\) regimes, the proposed schemes can achieve about 2.5 dB higher average received SINR than the convention IA scheme. Moreover, the difference of average received SINR between the conventional and the proposed schemes increases with the rise in \(E_{b}\)/\(N_{0}\). For instance, the average SINR at \(E_{b}\)/\(N_{0}=\) 14 dB is 19 dB, 26 dB and 24 dB for the conventional IA scheme, the (RGS) JSCC-IA schemes with identical delay and the (RGS) JSCC-IA schemes with different delays, respectively. Therefore, simulations validate  
Figure 15: The average received SINR of the conventional IA scheme, the JSCC-IA schemes with identical/different propagation delays and the RGS JSCC-IA schemes with identical/different propagation delays in the (4 \(\times\) 4, 1)\({}^{20}\) asymmetric networks. that the proposed JSCC-IA schemes can improve the overall performance of the networks by increasing the averaged received SINR. To sum up, the proposed JSCC-IA scheme can accomplish better BER and received SINR performances than the conventional IA system, not only in symmetric but also in asymmetric networks. Moreover, the RGS algorithm can search for better grouping combinations and provide further improvement over the non-RGS JSCC-IA scheme in the asymmetric networks.
# OPMSS: Optimal Producer Mobility Support Solution for Named Data Networking
## 4 Formulation Analysis of OPMSS
### Scenario I: Handoff Latency  
The total handoff latency of the Kite solution can be generated when the mobile producer disconnects from CR2 and moves to CR3 within D1. Before the resumption of communication, the consumer must wait for the update signaling to take place after the disconnection and reconnection. The mobile producer exchanges the \(TI\) and \(TD\) with the anchor router or the RS via CR2 to CR1. Then, the consumer's interest packet can be redirected from CR1 to CR3 to the mobile producer. The transmission latency between the consumer, the producer, and router is \(3\times a\) hops, and one between routers is \(3\times b\) hops for sending the \(TI\), \(TD\), and consumer's interest before the resumption of communication after the handoff, which can be formulated in Equation (3). In addition, if the mobile producer moves to D2, the total latency covers additional \(4\times a\) and \(6\times b\) hops, as presented in  
Figure 5: Network analysis model.  
Equation (5). Hence, the numerical formulation of handoff latency for the intra-domain of the Kite solution and OPMSS can be presented in Equations (3) and (4), respectively.  
\[HLat_{Kite}=((\ T Lat_{ul}+TLat_{w})+(2 TLat_{ul}+2\ TLat_{w})) \tag{3}\]  
\[HLat_{opmss}=((\ TLat_{ul})+(2 TLat_{ul}+TLat_{w})) \tag{4}\]  
The total handoff latency of the proposed OPMSS solution can be generated when the mobile producer disconnects from CR2 and moves to CR3 within D1, the same as in the Kite solution. CR3 broadcasts the \(MI\) packet within the domain to locate the mobile producer. The total handoff latency between the consumer, the producer, and router is \(3\times a\), and one between routers is b hops, for sending \(MI\) packet and new interest to the new location of mobile producer; these can be formulated in Equation (4). In addition, if the mobile producer moves to D2, the total latency covers additional \(3\times a\) and \(3\times b\) hops due to \(MI\) broadcasting for both D1 and D2, as presented in Equation (6). Equations (5) and (6) present the numerical formulation of handoff latency for inter-domain as:  
\[HLat_{Kite}=((\ TLat_{ul}+TLat_{w})+(2 TLat_{ul}+2\ TLat_{w})) \tag{5}\] \[+((2 TLat_{ul}+3 TLat_{w})+(2 TLat_{ul}+4 TLat_{w}))\]  
\[HLat_{opmss}=((\ TLat_{ul})+(2 TLat_{ul}+TLat_{w})) \tag{6}\] \[+(((TLat_{ul}+2 TLat_{w})+(2 TLat_{ul}+2 TLat_{w})))\]
# Optical Cable Lifespan Prediction Method Based on Autoformer
## 3 Related Work
### Historical Data Collection and Processing  
The experimental data utilized in this article are extracted from a weather dataset originating from Guangzhou, China, encompassing 13 years of meteorological records from January 2011 to December 2023. This dataset encapsulates key indicators such as daily maximum, minimum, and average temperatures, as well as wind speed. To correlate this meteorological data with corresponding historical cable lengths, the study applied a cable length calculation formula to the weather dataset, thereby deriving the relevant optical cable lengths. As a result, the resultant dataset unites cable length metrics with temporal and meteorological details, provides a comprehensive dataset for the task of predicting the lifespan of optical cables.  
Furthermore, to ensure the integrity of this multifaceted dataset for lifespan prediction, it is imperative to address the issue of data quality. During the data collection and storage phase, errors may occur, resulting in missing or anomalous historical data. Such inaccuracies can severely impact subsequent model training if not appropriately addressed. Therefore, preprocessing to correct these missing and outlier data points is a crucial prerequisite for the model training. To tackle this issue, this study employs the Lagrange interpolation method for estimating and substituting anomalous values, as shown in Equation (13), where \(y_{i}\) represents the dependent variables at each sample point and \(l_{i}(x)\) denotes the Lagrange basic polynomial.  
\[L_{n}(x)=\sum_{i=0}^{n}y_{i}l_{i}(x) \tag{13}\]  
The calculation formula for \(l_{i}(x)\) is represented as  
\[l_{i}(x)=\prod_{j=0,j\
eq i}^{n}\frac{x-x_{j}}{x_{i}-x_{j}}, \tag{14}\]  
Moreover, discrepancies in the scales and ranges of numerical variables represent a prevalent challenge when managing datasets. If these raw data are not properly processed, the performance of the model could be negatively impacted, particularly when starting with low initial parameter values. Under such circumstances, variables with larger values may become dominant, overshadowing the effects of variables with smaller values and affecting the model's training efficiency during the parameter update phase. To alleviate the detrimental effects of scale and range variations on the training pace and model precision, normalizing certain features in the dataset is necessary. The min-max normalization method is applied to rescale the data to a range between 0 and 1. The formula for this transformation is  
\[\hat{x}=\frac{x-x_{min}}{x_{max}-x_{min}}, \tag{15}\]  
where \(\hat{x}\) represents the normalized data value, \(x\) represents the original data value, \(x_{min}\) represents the minimum value of the data feature, and \(x_{max}\) represents the maximum value of the data feature.