esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Synergy and Challenges of UAV-Assisted THz-VLC Systems
### Challenges in Using VLC for Relay-Destination Links  
The challenges associated with using VLC for relay-destination links are illustrated in Figure 2 and are described below.  
VLC relies heavily on a clear LoS between the transmitter and receiver. Obstacles such as furniture, walls, and even people can block the light path, leading to interruptions in the communication link [23].  
The effective range of VLC systems is typically confined to the immediate vicinity of the light source. This limitation restricts the coverage area and necessitates multiple light sources for broader coverage, increasing complexity and cost [24].  
Figure 2: Challenges in using VLC for relay–destination links. This diagram highlights the primary challenges associated with employing VLC in relay–destination links, including LoS requirement, limited range, interference from ambient light, energy efficiency, and modulation bandwidth.  
Ambient light sources, such as sunlight or other artificial lighting, can cause interference with VLC signals. This interference can degrade signal quality and reduce communication reliability, especially in environments with varying lighting conditions [25].  
Maintaining efficient energy consumption while ensuring adequate illumination and communication quality poses a challenge. Balancing the dual role of lighting and data transmission requires advanced control mechanisms to optimize power usage without compromising performance [4].  
The modulation bandwidth of LEDs used in VLC is limited, restricting data transmission rates. Enhancing the modulation capabilities of LEDs to support higher data rates involves sophisticated engineering and can impact the cost and complexity of the system [23].  
VLC systems are susceptible to eavesdropping and unauthorized access, as the communication medium (light) can easily be intercepted by unintended receivers within the coverage area. Implementing robust security measures to protect data transmission is essential to ensure privacy and prevent data breaches [25].  
Addressing these challenges involves innovative approaches in materials science and communication technology design to create efficient, compact, and lightweight systems suitable for UAV deployment. Although we do not directly tackle these integration issues in this study, we provide a foundational framework and valuable insights that set the stage for future research to tackle these integration challenges, guiding the development of effective solutions.
# Content Management Based on Content Popularity Ranking in Information-Centric Networks
## 4 Performance Analysis
### The Network Topology and the Experimental Procedures  
The proposed schemes for content caching and cache removal were tested in several different network topologies with a random number of server and client nodes and various content in order to evaluate the performance of the schemes. The schemes were implemented using the CCN paradigm. Content request and retrieval experiments were executed in each topology by varying the \(X\)-axis parameters, including the size of the cache repository of the server nodes, the maximum number of content a client node can request to a server node, and the maximum number of clients that can attach to one server node simultaneously. Additionally, the \(Y\)-axis parameters were varied as well as mentioned in the following subsections. Therefore, the resultant graphs show the average of various outcomes from all the different topologies. For example, there were 10 server nodes in one of the network topologies, 100 client nodes of which a maximum of 12 nodes could attach to one server node at a time, and the client nodes were requesting random content from a pool of 30 different forms of content. In another network topology, a maximum of 30 client nodes could request as many as 70 forms of content to a server node, and there were 500 client nodes and 20 server nodes dispersed randomly. The cache size of different servers ranged from 10 Megabytes (MB) to a maximum of 150 MB, and the total number of available content varied from 10 forms of content and up to 90 forms of content in each experiment. Furthermore, the sizes of these content varied from 1 MB up to 100 MB. These content are taken from a total of 150 content, which consists of various video, audio, and text files, and they are all different forms of content from the ones that were available in the cloud server, which were used to measure the values of the labels. The maximum number of client nodes that can request several content from one server node started from 5 client nodes and up to 50 client nodes. The server nodes kept track of several experimental parameters that are given in the following subsections. We simulated these network topologies using NS-3 [37]. A total of 100 simulation results for each of the \(X\)-axis values were gathered and averagedto plot the outcomes of the experiments using the graphs. Table 1 summarizes the network topology configuration.  
Figure 1 shows the depiction of one of the network topologies.
## 4 Optimization Methods  
### Objective Function Formulation  
We developed an objective function to estimate the unknown stiffness and damping parameters by minimizing the sum of squared errors between measured and calculated transmissibility:  
\[\textit{Minimize}\qquad\frac{\int_{f_{1}}^{f_{2}}\left|T_{Exp.}(f)-T_{Model}(f) \right|^{2}df}{\int_{f_{1}}^{f_{2}}\left|T_{Exp.}(f)\right|^{2}df} \tag{25}\]  
\[\textit{Subject to}\quad 20\leq k_{ij}\leq 200\;kN/m,\;\;\textit{and}\;\; 1\leq k_{i}^{T}\leq 4\;kNm/rad\]  
\[10\leq c_{ij}\leq 4000\;Ns/m,\;\;\textit{and}\;\;5\leq c_{i}^{T}\leq 500\; Nms/rad\]  
where \(T_{Exp.}\) and \(T_{Model}\) are transmissibility obtained from experiments and those calculated using Equation (24) for each body segment, while \(f_{1,2}\) is the frequency range of vibration measurements. For our analysis, \(T_{Exp.}\) measured at the head, trunk, thigh, and leg were used as feeding data in the objective function to estimate the unknown stiffness and damping parameters. The parameters were subject to upper and lower bounds obtained from reviewing the published range of values of stiffness and damping in the literature [15; 16; 25; 26; 27]. The specified bounds limit the estimated stiffness and damping coefficients to only positive values and speed up the convergence of the optimization algorithms.
# Adaptive Computation Offloading with Task Scheduling Minimizing Reallocation in VANETs
## 5 Vehicular Adaptive Offloading (VAO)
### Check Reallocation  
Although our scheduling method tries its best to avoid task reallocation, this cannot be perfectly avoided because of the dynamic topology change in VANETs. The following Algorithm 3 shows how VAO handles task reallocation caused by the connection disruption topology change makes.  
```
Input: a set of SVs \(\textbf{V}^{\textbf{S}}\) Output: a set of unfinished tasks \(T\)
1 periodically broadcasts RV's location information
2 if receives a message that \(v_{s}\) are expected to leave the communication range
3 informs all SVs of reallocation event to make them stop data transfer to \(v_{s}\)
4 let \(T\) a set of unfinished tasks allocated to \(v_{s}\)
5 request \(v_{s}\) to transfer the stored data for the tasks
6 endif
7 return\(T\)
```  
**Algorithm 3**check realloc (\(\textit{V}^{\textbf{S}}\))  
In Line 1, an RV \(v_{r}\) periodically broadcasts its location information to SVs, then SVs which are expected to be out of communication range send report messages to \(v_{r}\). In Lines 2 to 6, all SVs are informed of a task reallocation event if any \(v_{s}\) is expected to be out of range. All the SVs stop data transfer to \(v_{s}\) and \(v_{r}\) requests \(v_{s}\) to transfer stored data for the tasks. After that, the algorithm returns the set of unfinished tasks, so Algorithm 1 can reallocate the tasks.  
To filter out the vehicles, Algorithm 2 needs to compare every route of the searched vehicles with that of RV. The time complexity of comparing the routes is \(O\left(\alpha|\textit{V}|\right)\) when the number of the searched vehicles is \(|\mathbf{V}|\). The time complexity of finding the first idle time slot is \(O\) (\(|\mathbf{T}|^{2}|\mathbf{V}|\)). So, the total complexity of the VAO is \(O\) (\(|\mathbf{T}|^{2}|\mathbf{V}|\)).
## 4 Results and Discussion  
### Metrics  
The efficiency of the algorithm was tested for the value of the number of bits embedded in the image \(N\) in the range from 4 to 16,384 bits at various values of the number of bits per one symbol \(n\). For each variant, training and validation of the developed neural network algorithm were carried out together with the selection of the optimal values of hyperparameters and weighting factors. The purpose of selecting the parameters was to obtain an algorithm that would allow the embedding of a specific number of binary signature bits in the image in a transparent and robust manner at the same time. The fulfillment of the conditions was verified during the validation epochs using the PSNR and SSIM metrics for transparency and the BER metric for robustness. The metrics are defined below:  
* Luminance comparison function: \(x\) and \(y\) represent the two images being compared, while \(\mu\) represents the average value. \(C_{1}\) is the stability constant when the denominator is 0, calculated as \(C_{1}=0.01^{2}\): \[l(x,y)=\frac{2\mu_{x}\mu_{y}+C_{1}}{\mu_{x}^{2}+\mu_{y}^{2}+C_{1}}\] (14)
* Contrast comparison function: \(\sigma\) is the standard deviation for a given image, while \(C_{2}\) is a constant value, equal in calculations to \(C_{2}=0.03^{2}\): \[c(x,y)=\frac{2\sigma_{x}\sigma_{y}+C_{2}}{\sigma_{x}^{2}+\sigma_{y}^{2}+C_{2}}\] (15)* Structure comparison function: \(C_{3}\) is a constant whose value in the calculations was assumed to be equal to \(C_{3}=C_{2}/2\): \[s(x,y)=\frac{\sigma_{xy}+C_{3}}{\sigma_{x}\sigma_{y}+C_{3}}\] (16)
* SSIM--structural similarity index: coefficients \(\alpha\), \(\beta\), and \(\gamma\) are weighting factors for each defined function; \(\alpha=\beta=\gamma=1\) was assumed in the calculations: \[SSIM(x,y)=[l(x,y)]^{\alpha}.[c(x,y)]^{\beta}.[s(x,y)]^{\gamma}\] (17)
* MSE--mean square error: \(m\) and \(n\) are the row and column numbers in the image: \[MSE=\frac{1}{MN}\sum_{n=1}^{N}\sum_{m=1}^{M}(x(n,m)-y(n,\ m))^{2}\] (18)
* PSNR--Peak Signal-to-Noise Ratio: in the calculations, the value \(R^{2}=2\) was assumed: \[PSNR=10 log_{10}\frac{R^{2}}{MSE}\] (19)
* BER--bit error rate: the ratio of the number of bits decoded incorrectly \(bit_{err}\) to all decoded bits \(bit_{all}\): \[BER=\frac{bit_{err}}{bit_{all}}\] (20)
## 2 Materials and Methods
### Method  
#### 2.3.1 SIF Phenology Fitting  
A double-sigmoidal function was used to fit the seasonal cycle SIF representing photosynthetic status signals for each county [73]. It has the advantage of retrieving vegetation phenology with noisy data [74; 75], such as OCO-2 SIF. This equation is as follows:  
\[y(x)=a_{1}+\frac{a_{2}}{1+\exp(-d_{1}(x-b_{1}))}-\frac{a_{3}}{1+\exp(-d_{2}(x -b_{2}))} \tag{2}\]  
where y represents the SIF value, x is a given day of the year (DOY); \(a_{1}\) represents the SIF value in the winter; \(a_{2}\) and \(a_{3}\) represent values at the spring and autumn plateau, respectively; \(b_{1}\) and \(b_{2}\) are theDOY mid-points of transitions for spring greenup and autumn browndown, respectively; and \(d_{1}\) and \(d_{2}\) are the corresponding slope coefficients of these transitions.  
Parameters (\(a_{1}\)-\(a_{3}\), \(b_{1}\), \(b_{2}\), \(d_{1}\), \(d_{2}\)) on the right of Equation (2) were fitted using three-year mean SIF in different months by a genetic algorithm [73]. Only counties with seasonal curves of SIF well fitted (R\({}^{2}>0.8\)) were used for further analysis. Finally, there were a total of 218 and 201 counties with SIF data available for maize and soybean, respectively. For all of these available counties, three-year means of EVI and climate data were accordingly calculated for all individual months from May to September.
# Frequency-Domain Characterization of Finite Sample Linear Systems with Uniform Window Inputs
###### Abstract  
We discuss determining a finite sample linear time-invariant (FS-LTI) system's impulse response function, \(h[n]\), in the frequency domain when the input testing function is a uniform window function with a width of \(L\) and the output is limited to a finite number of effective samples, \(M\). Assuming that the samples beyond \(M\) are all zeros, the corresponding infinite sample LTI (IS-LTI) system is a marginally stable system. The ratio of the discrete Fourier transforms (DFT) of the output to input of such an FS-LTI system, \(H_{0}[k]\), cannot be directly used to find \(h[n]\) via inverse DFT (IDFT). Nevertheless, \(H_{0}[k]\) contains sufficient information to determine the system's impulse response function (IRF). In the frequency-domain approach, we zero-pad the output array to a length of \(N\). We present methods to recover \(h[n]\) from \(H_{0}[k]\) for two scenarios: (1) \(N\geq\ \max(L,\ M+1)\) and \(N\) is a coprime of L, and (2) \(N\geq L+M+1\). The marginal stable system discussed here is an artifact due to the zero-value assumption on unavailable samples. The IRF obtained applies to any LTI system up to the number of effective data samples, \(M\). In demonstrating the equivalence of \(H_{0}[k]\) and \(h[n]\), we derive two interesting DFT pairs. These DFT pairs can be used to find trigonometric sums that are otherwise difficult to prove. The frequency-domain approach makes mitigating the effects of interferences and random noise easier. In an example application in radar remote sensing, we show that the frequency-domain processing method can be used to obtain finer details than the range resolution provided by the radar system's transmitter.  
Keywords:discrete Fourier transform; finite sample linear systems; impulse response function; radar remote sensing +
Footnote †: journal:
## 3 Test Results and Discussion  
### Compression Properties  
Figure 3 presents the results obtained for the rubber-sieved calcareous sand mixtures of different rubber contents and initial void ratios. Note that all of the mixtures were prepared at quite large initial void ratios, primarily since the admixture of rubber chips quickly rebounded after tamping and maintained a loose packing state.  
Despite different initial void ratios, the virgin compression curves were originally linear and flat, indicating an elastic response. When the applied vertical load was beyond the yield stress of the mixtures, it is interesting to see that the curves tended to converge in the stress range investigated, with an implication that all specimens were compressed to a unique state not associated with the initial conditions. For comparison purposes, the compression curve of Fujian sand (Chinese standard sand) was also plotted in this Figure. It is clear that the compression curve of the Fujian sand is less steep than that of the rubber-calcareous sand mixtures, whilst the rebound curves of the test specimens are nearly parallel. The above observations strongly suggest that, under otherwise similar  
Figure 3: Compression curves of mixtures: (**a**) Rubber content = 5%; (**b**) rubber content = 10%.  
Figure 2: Test equipment: (**a**) Modified oedometer cell; (**b**) resonant column apparatus (LVDT: linear variable differential transformer).  
conditions, rubber-calcareous sand mixtures are more compressible than Fujian sand, and that most of the deformation of the mixtures is unrecoverable during unloading. This implies that the particles were subjected to strong reorientation during loading, while during unloading, the interlocking of particles prevailed in the present cases of the mixtures with rubber chips.  
Furthermore, in Figure 4, a comparison of the compression curves of specimens with different rubber contents is provided. It is noteworthy that the state where the compression curve tended to merge depended on the rubber content. More specifically, examination of the compression curves shows that the specimens of a 10% rubber content merged at a vertical load of approximately 4 MPa, whereas the specimens of a 5% rubber content merged at 8 MPa. Given the limit range of deformation in the oedometer test, the final void ratio of all tests was approximately 0.8. The observations in Figure 4 imply that the role of rubber chips of varying contents that contributed to the compressibility of the mixture was different. More specifically, when the applied load was higher than the yield stress, both the rubber particles and the calcareous sand were members of force chains. The mixture with a higher rubber content tended to have softer particles in the force chain, and it was easy to buckle under the external load. Accordingly, under the same stress level, the specimen with higher rubber content--i.e., sieved (10)--yielded a larger deformation.
## 2 Materials and Methods
### Methods
#### 2.3.1 Remote-Sensing-Based Indices  
Eight VIs were calculated from the Sentinel-2 data, as shown in Table 2. Three indices did not contain red-edge information, including the EVI [74], NDVI [75], and the near-infrared reflectance of terrestrial vegetation (NIRv [76]). Five of the indices, including one or more of the three Sentinel-2 red-edge bands (CIr, CIg, and MTCI) and two normal deviation indices of the red edge (NDRE1, NDRE2 [36; 77]), were related to red-edge reflectance (band 5 to band 7 of the MSI sensor).
# Investigation of the Influence of Splitter Blades on the Resonance Conditions of Impellers
## 5 Conclusions  
In this paper, the resonance conditions for impellers with splitter blades under the excitation from the impeller-diffuser interaction are proposed. The influence of splitter blades on the resonance conditions is investigated analytically and numerically. The applications of the proposed resonance conditions in identification of the potential resonances, determination of the dangerous engine order of excitation, and the hazard evaluation for excitations with various engine orders are illustrated in the tuned and mistuned cases. Moreover, the significance of the proposed resonance conditions in guiding the compressor design by choosing an appropriate number of vanes is discussed. The potential of the proposed resonance conditions in designing the intentional mistuning is also pointed out. Some main conclusions obtained from the current work are as follows:  
1. A lumped parameter model of the impeller with splitter blades is established for the first time. For describing the traits of the impeller's modes adequately, the RMV should be extracted based on all main and splitter blades rather than sectors, i.e., the blade-based RMV should be used. 2. The blade-based RMV of a tuned mode contains two harmonic components due to the existence of the splitter blades. For the conventionally designed impellers, the RMV of a tuned mode contains only one harmonic component. 3. The proposed resonance conditions consist of two terms: (1) the excitation frequency equals the natural frequency of the impeller; (2) the excitation order matches either harmonic index of the two harmonics. When both terms are simultaneously satisfied, resonance occurs. 4. The potential resonance can be effectively identified by the proposed resonance conditions. It also indicates that different resonance states of the impeller can be reached even for the same tuned mode. This is due to the different amplitudes of the harmonic components of a mode, which is reflected by the different values of \(h\) in the proposed resonance conditions. However, the conventional resonance conditions contain no information on this. 5. The harmfulness of different excitations with various engine orders can be evaluated accurately by the joint use of the spatial harmonic analysis result and the proposed resonance conditions. Analyzing based on the conventional resonance conditions may lead to the misjudgment of the harmfulness of different excitations. So, when evaluating the harmfulness of the excitations with various engine orders for mistuned impellers, the proposed resonance conditions should be used instead of the conventional ones. Conceptualization, K.L. ; Methodology, K.L. and C.Y. ; Software, K.L. ; Validation, K.L. and C.Y. ; Formal analysis, K.L. and C.Y. ; Investigation, K.L. ; Resources, K.L. ; Data curation, K.L. ; Writing--original draft preparation, K.L. ; Writing--review and editing, C.Y. ; Visualization, K.L. and C.Y. ; Supervision, C.Y. ; Project administration, K.L. This research received no external funding.
## 2 Materials and Methods  
### Synthesis of ZIF-8  
There are various routes, such as hydrothermal, solvothermal, rapid synthesis, mechano chemical, etc. Among all these routes, a rapid synthesis route has been adopted to synthesize ZIF-8 due to its easy variation of particle size.  
ZIF-8 was synthesized using a rapid and scalable aqueous synthesis route, chosen for its simplicity, reduced synthesis time, and tunability of particle size. Zinc nitrate hexahydrate (Zn(NO\({}_{3}\))\({}_{2}\cdot\)6H\({}_{2}\)O, 98%) and 2-methylimidazole (MeIM, 98%) were used as the metal precursor and organic linker, respectively. Initially, 1.17 g of Zn(NO\({}_{3}\))\({}_{2}\cdot\)6H\({}_{2}\)O was dissolved in 8 mL of deionized (DI) water, while 22.70 g of 2-methylimidazole was dissolved in 80 mL of DI water in separate beakers. Both solutions were stirred at room temperature for 5 min to ensure complete dissolution, with pH values being monitored and adjusted as necessary.  
The synthesis was initiated by rapidly mixing the two solutions under continuous stirring, maintaining the reaction at room temperature. The milky white dispersion formed was subjected to centrifugation at 7000 rpm for 5 min to separate the ZIF-8 nanoparticles. The precipitate was then washed three times with DI water to remove any residual reactants and impurities. Finally, the purified nanoparticles were dried in an oven at 85-90 \({}^{\circ}\)C overnight to obtain the final ZIF-8 product.  
To investigate the effect of synthesis parameters on particle size, the stirring time was systematically varied between 5 and 60 min, and the pH values were varied from 10.9 to 11.8, respectively. Table 1 details the synthesis conditions for each ZIF-8 sample. This approach allowed for the precise control of particle size, producing ZIF-8 crystals with dimensions ranging from 99.46 nm to 105.7 nm. The process flowchart for the synthesis can be observed in Figure 1. These steps were repeated to synthesize ZIF-8 samples of different particle sizes by varying stirring time from 5 min to 60 min and varying pH values at room temperature [25; 26; 27]. Table 1 represents the ZIF-8 samples prepared with varying pH and stirring time to vary the particle size.  
The rapid synthesis route demonstrated here offers a significant advantage in controlling ZIF-8 morphology and particle size, essential factors influencing CO\({}_{2}\) adsorption performance. This method also provides a scalable and reproducible pathway for synthesizing ZIF-8 with tailored properties suitable for integration into advanced CO\({}_{2}\) capture systems.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Serial Number** & **Samples** & **Stirring Time (Min.)** & **pH** \\ \hline
1. & Z1 & 5 & 11.8 \\
2. & Z2 & 30 & 11.4 \\
3. & Z3 & 60 & 10.9 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Synthesized ZIF-8 samples for varying stirring time (minutes) and pH values.  
Figure 1: Flowchart representing the synthesis of ZIF-8 via the rapid synthesis route.