esa-sceva/satcom-chunk-collection huggingface dataset:
# Real-Time, Content-Based Communication Load Reduction in the Internet of Multimedia Things
## 4 Results
### Evaluation Parameters
#### 4.2.2 Delays in Sending Video  
The goal of the proposed method has been to find an efficient way of reducing data transmission for low-resourced devices in an IoMT. Therefore, as the word 'efficient' implies, a method should not only save data but also other resources including time. Figure 8, shows line plots representing average time delay in seconds for all five test videos using the proposed method. These values show by how much the subsequent response is delayed, that is, how much time it takes for the next complete frame to reach the receiver. Frames are sent in chunks and these chunks are ultimately combined as one frame at the receiver end. Delays are calculated for a complete frame, when all of its chunks are received and combined, their average for complete video are given in Figure 8. Figure 9 can be viewed in reference to Figure 8, as it reports the sender ROM processing time component in delay at the receiver. However, there is no clear correlation with delay, which indicates that other delays at the sender and some transmission delay has an impact on the delays.  
Figure 8: Average delays during the reception of each consecutive frame at the receiver comparing the proposed method with the All Frames method (plotted as gray bars).  
The time taken for each complete frame to reach the receiver is affected by the overall processing overhead (not just that for ROM identification), which comes with ROM identification and extraction, the amount of data sent, and network conditions. Hence, results show variations for all test videos. The average delay for the first video sequence, Walk, is roughly equal for the proposed method and the All Frames method. For Walk, significant changes occur between consecutive frames and, for the majority part of the video, a ROM is extracted. Therefore, processing overhead and the consequent delay in transmission of the data contributes to the delay at the receiver. For Ground, the delay difference between the two methods is prominent, as the proposed method results in much less delay (on average). This is due to the reason that, for most of the test video frames, motion is not found to be significant and, therefore, the processing part of ROM identification is skipped and empty data is sent instead. Exercise and Football again show comparatively lower delays for the proposed method, when for Exercise less data makes up for low processing overhead, hence, low delay. However, for Duval Street, the trend changes and comparatively longer delays are evident, which are a result of network conditions and processing overhead. Overall, delays in data reception are low for the proposed method and are not significantly higher than the ALL Frames method.
# Hollow-Core Optical Fibers for Telecommunications and Data Transmission
## 5 Fibers with Anti-Resonant Guidance
### Kagome Fibers  
Fibers of this type (Figure 2) feature three sets of flat, equally spaced walls intersecting at 60\({}^{\circ}\) angles (Figure 2a). Kagome fibers have been researched since 2002 [41], but achieving low loss and purely single-mode propagation proved difficult, partly due to scattering at numerous \"nodes\" or intersections of walls, as in HC-PBGFs [18]. It is also difficult to make a Kagome fiber with the desired flat walls intersecting at a 60\({}^{\circ}\) angle, as--unlike a hexagonal pattern in cladding of PBGF or round capillaries in NANF--such structures are not naturally created by or retained under the influence of surface tension when the fiber or \"cane\" is drawn. Figure 11 shows the extent of the deformation experienced in typical conditions.  
Figure 9: Example of arrangement of low-loss bands (“windows”) in anti-resonant fiber, shown in frequency domain (**left**) and wavelength domain (**right**). High-loss bands between windows are centered around resonant wavelengths. Loss components other than CL not shown.  
The lowest loss reported in 2018 for Kagome fiber was 8.5 dB/km at 1080 nm [43]. Hybrid Kagome fibers with an added \"negative curvature\" layer of tubes or half-domes (Figure 2b,c and Figure 11b) were developed, exhibiting good single-modedness [44; 45], and loss reduced to 1.6 dB/km at 1050 nm in 2021 [45]. However, the NANF and DNANF fibers presented in Section 5.3 have lower loss, down to 0.17 dB/km [13] and a wider bandwidth.
## 3 Engineering Geological Investigations
### Wedge Analysis  
The stability of drift in fractured rock mass is mainly affected by the natural occurrence of structural plane, mechanical properties, rock quality, in situ stress, groundwater conditions, and other factors. Therefore, the failure forms of engineering rock mass also show diversity in macroscopic expression forms. Among the failure modes of engineering  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Q Rating** & **RQD** & \(\mathbf{J_{n}}\) & \(\mathbf{J_{r}}\) & \(\mathbf{J_{a}}\) & \(\mathbf{J_{w}}\) & **SRF** \\ \hline \(-915\) m & 99.47 & 12 & 3 & 3 & 0.66 & 5 \\ \(-945\) m & 97.4 & 15 & 3 & 0.75 & 1 & 2 \\ \(-960\) m & 99.65 & 15 & 3 & 2 & 1 & 20 \\ \hline
**RMR Rating** & **UCS** & **RQD** & **Spacing** & **Condition** & **Water** & **Orientation** \\ \hline \(-915\) m & 5.85 & 19.84 & 20 & 14 & 1 & \(-12\) \\ \(-945\) m & 6.63 & 19.11 & 5.43 & 21 & 13 & \(-5\) \\ \(-960\) m & 6.61 & 19.99 & 6.29 & 12 & 7 & \(-2\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Discontinuity values for Q ratings and RMR ratings.  
Figure 5: Contour plot and rosette plot of the study area.  
rock mass, wedge failure is one of the most common. The intercutting of structural planes forms many rock blocks. Three-dimensional wedges of pyramidal shape are formed by the intersection of three or more joint planes on the excavation boundary of drifts. Under the action of the gravity of the block and the mechanical properties of the structural plane, the wedge will slip or even collapse when disturbed by excavation, which is manifested as roof fall of the drift surrounding the rock failures. Unwedge is a geotechnical software for analyzing the wedges formed by structural discontinuities and underground excavations and visualizing the model in 3D. Unwedge calculates the safety factor of potential wedges and analyzes the influence of the support system on the stability of the wedge. Unwedge software was used to identify wedges in the study area and analyze their safety factor, making the drift support more targeted. The parameters of drift and the critical joint sets obtained from the engineering geological investigation were input to Unwedge software for generating the wedge, then for designing the wedge support. Unwedge automatically calculates the wedge stability analysis whenever data are entered or modified. The detailed parameters of the wedges are  
All floor wedges and the lower right wedge located in the \(-915\) m drift were stable. Other wedges were assumed to be potentially prone to failure due to the factor of safety (FS) being lower than 1.5. In order to stabilize these wedges, rock bolts of 2.2 m length or 4.4 m length and 100 mm thick shotcrete were installed during the excavation. A wedge that is too small in size, such as \(-915\) m 8# wedge, can fall down manually and then be supported. During the routine excavation, it is necessary to identify potential wedges using the Unwedge software to ensure construction safety.
# On the Optimal Calculation of the Rice Coding Parameter
## 1 Introduction
### Contributions  
The main contributions of this article are the following:  
1. A closed-form expression for the estimation of the optimal Rice parameter in \(O(N)\) time, where \(N\) is the number of input elements to encode. In contrast to previous works, this estimation applies regardless of the probability distribution of the input data (see Section 3.2);
2. An algorithm for the calculation of the optimal Rice parameter in \(O(L_{max}\cdot N)\) time (see Section 3.4), where \(L_{max}\) is the minimum number of bits needed for natural coding of the largest integer;
3. An algorithm for the partitioning of the \(N\) input elements into sub-sequences yielding a better compression ratio in \(O(L_{max}\cdot N^{2})\) (see Section 5);
4. Two heuristics for the fast estimation for the partitioning of the input sequence in \(O(L_{max}\cdot N)\) (see Section 5.4).  
In order to evaluate the performance of the algorithms, the proposed algorithms are further verified and supported with collected data by a real sensor network in Section 4 for the first two contributions in the list above, and again later in Section 6 for the last two contributions in the list above.
## Materials and Methods
### Methods
#### 2.2.2 Local Weighted Linear Regression (LWLR)  
The LWLR method is an advanced version of the multiple linear regression (MLR) model developed by [PERSON] et al. [44]. LWLR is able to improve MLR performance significantly. To illustrate the LWLR model, consider the following model:  
\[z_{mk}=\alpha_{ko}+\sum_{m=1}^{M}\alpha_{km}x_{km}+\varepsilon_{k} \tag{3}\]  
In the above model, \(z_{mk}\) is a dependent variable that can be calculated based on at least two independent variables (\(x_{k}\)). \(\alpha\) is the regression coefficient calculated by the least-squares (LS) method, \(M\) is the number of data, and \(\varepsilon\) is the random error.  
In the LWLR method, a weight function describes the relationship between input and output data. The fitness function of the LWLR model can be expressed by the following equation [44; 45; 46]:  
\[F=\frac{1}{2M}\sum_{m=1}^{M}w_{m}(z_{om}-z_{m})^{2} \tag{4}\]  
where \(w\) is the regression weight, \(z_{o}\) is the observed data, and \(z\) is the data obtained from the model. The above equation can also be expressed in the form of the following matrix:  
\[F=(X\alpha-Z)^{T}W(X\alpha-Z) \tag{5}\]  
By solving the above equation for \(\alpha\), we obtain  
\[\alpha=\Big{(}X^{T}WX\Big{)}^{-1}X^{T}WZ \tag{6}\]  
where \(X\) is the matrix of input training dataset, \(W\) denotes the weight matrix, and \(Z\) is the vector of data obtained from the model. A kernel function can be used instead of the weight matrix in the LWLR model [47; 48]. In the present study, the RBF function was used as the kernel in the LWLR model. The RBF kernel equation is defined as follows:  
\[w_{ik}=\exp\Big{(}-\mu(x_{i}-x_{k})^{2}\Big{)}a \tag{7}\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Models** & \multicolumn{2}{c}{**Setting of Parameter**} \\ \hline  & Function set & +, -, \(\times\), \(\div\), \(\bigvee\) power, sin, cos \\ \cline{2-3}  & Population size & 300 \\ \cline{2-3}  & Mutation frequency \% & 85 \\ \cline{2-3}  & Crossover frequency \% & 50 \\ \cline{2-3}  & Number of replication & 10 \\ \cline{2-3}  & Block mutation rate \% & 20 \\ \cline{2-3}  & Instruction mutation rate \% & 20 \\ \cline{2-3}  & Instruction data mutation rate \% & 60 \\ \cline{2-3}  & Homologous crossover \% & 90 \\ \cline{2-3}  & Program size & 64–256 \\ \hline LWLR & \(\bullet\)\(\mu=4\) & \\ \hline KStar & \(\bullet\) Global blend = 30 \\ \hline BRT & \(\bullet\) Function: “Bag”, Learning cycles = 50, MinLeafSize = 1 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The characteristics and setting parameters of proposed AI-based approaches.  
where \(\mu\) is a positive number as a kernel variable and \((x_{i}-x_{k})\) is the difference between point \(i\) and \(k\)[49]. It should be mentioned that the main setting parameter of LWLR model can be optimized by a trial and error procedure.
## 1 Introduction
### The 15-Min City and Walkable Life Circles for Older Adults  
The \"15-Min City\", as a pivotal practical theory in New Urbanism, provides urban planners with a theoretical framework to evaluate and enhance urban accessibility [34]. Existing research on spatial identification and measurement methodologies primarily focuses on accessibility and proximity. Mainstream studies employ census tracts [35], postal code zones [36], or regular geographic grids [37] as basic analytical units. These studies often construct idealized pedestrian service radii by extracting geometric centroids, emphasizing facility types [38] and walkability capacity [39] at the service coverage areas. Derived from the 15-Min City concept, the concept of \"15-min walkable life circles for the senior\" has emerged as a research frontier in sustainable urban planning.  
Furthermore, traditional accessibility measurement methods, such as the Gaussian Two-Step Floating Catchment Area (2 SFCA) method and multi-level search radii, have been further developed in 15-Min City studies [40; 41]. Notably, recent studies increasingly prioritize spatial equity for vulnerable groups. Due to reduced walking capacity and tolerance distances among older adults, research confirms a substantial contraction effect in their effective walking thresholds [42].  
To address the growing aging population, scholars have integrated the 15-Min City model with elderly walking capacity to explore its applicability and benefits for older adults. [PERSON] et al. [11] investigated the suitability of the 15-min CLC (Community Life Circle) for older adults, analyzing facility accessibility across different walking life circles in Suzhou. [PERSON] et al. [42] assessed elderly-friendly 15-min walkable life circles, identifying 500 m as the maximum comfortable walking distance for seniors. Their work underscores the importance of incorporating elderly walking capacity into planning to ensure accessible services within reasonable distances. Similarly, [PERSON] et al. [43] examined the current status of elderly care facilities and usage patterns in Xi'an, integrating life circle theory with the diverse needs of older adults.  
In-depth exploration of the 15-Min City at varying scales, particularly from the walkability perspective of mobility-constrained populations, is critical for Chinese cities. Such research can guide urban planners and policymakers in formulating equitable strategies. This study evaluates the accessibility of elderly care facilities and their ancillary services under 5-, 10-, and 15-min walking thresholds, focusing on the elderly pedestrian perspective.
# Resource Allocation for Cognitive LEO Satellite Systems: Facilitating IoT Communications
## 2 System Model
### Channel Model  
The satellite channel fading consists of two parts, including multipath propagation and shadow effect. The shadowed Rice model can effectively describe the two parts of fading effect, and has been widely applied in various frequency bands such as the UHF-band, L-band, S-band, and Ka-band channel analysis [41]. Specifically, the satellite channel fading coefficient between the satellite and the \(m\)-th user is given by  
Figure 1: Cognitive LEO Satellite Communication System.  
\[\tilde{h}_{m}=A\exp(j\psi_{m})+Z\exp(j\phi_{m}) \tag{1}\]  
where \(\psi_{m}\in[0,2\pi)\) is the stationary random phase and \(\phi_{m}\) is the deterministic phase of the line-of-sight (LOS) component. \(A\) and \(Z\) are both independent stationery random processes. Specifically, \(A\) is the amplitude of the scatter component following Rayleigh distributions and \(Z\) is the amplitude of LOS component following the Nakagami distribution, which is given by  
\[\begin{split} p_{A}(a)&=\frac{a}{b_{0}}\exp\left( \frac{-a^{2}}{2b_{0}}\right),a\geq 0\\ p_{Z}(z)&=\frac{2m^{m}}{\Gamma(m)\Omega^{m}}z^{2m- 1}\exp\left(\frac{-mz^{2}}{\Omega}\right),z\geq 0\end{split} \tag{2}\]  
Considering the atmospheric effects, the \(m\)-th user overall satellite channels can be expressed as  
\[h_{m}=C_{L}\tilde{h}_{m} \tag{3}\]  
where \(C_{L}=\lambda/(4\pi\sqrt{d_{0}^{2}+d_{m}^{2}})\) denotes the free-space path loss coefficient with wavelength \(\lambda\), the height of LEO satellite \(d_{0}\), and the distance \(d_{m}\) between the centers of the LEO satellite coverage area and \(m\)-th user.  
Based on the above channel model, let \(h_{u}\) denote the channel coefficient from the \(u\)-th legacy satellite user to the LEO satellite and \(f_{k}\) denote the channel from the \(k\)-th IoT device to the LEO satellite. All of the channels \(h_{u}\) and \(f_{k}\) for \(u=1,\cdots,U\) and \(k=1,\cdots,K\) satisfy the expression of (3).
## 4 Methods
### Bandwidth-Efficient Streaming  
In our system, there are two aspects that need to be considered for bandwidth efficiency: (i) the size of network packets sent from the drone, and (ii) the frequency of transmission. We follow a similar philosophy mentioned in [8] to control the frequency of transmission. The drone sends data to the cloudlet only if the onboard image classification model detects an object of interest (e.g., faces in our application). Furthermore, we can reduce bandwidth usage by sending compressed features instead of raw video frames since the network packet containing feature maps is smaller than the raw video frames. In our approach, we encode the feature maps as PNG images. The packet sent by the drone consists of extracted features and meta-data about the video stream (e.g., frame dimensions).
## 3 Results and Discussion
### Ecosystem Water Use Efficiency Model Development
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**F (eWUE)** & **AIC** & **R\({}^{2}\)** & **R\({}^{2}\) Adj** & & \multicolumn{3}{c}{**Coefficients**} \\ \hline NDVI & 29.0 & 0.41 & 0.38 & 0.281 & 3.312 & & \\ VPD & 31.1 & 0.35 & 0.32 & 2.420 & \(-\)0.065 & & \\ P & 35.3 & 0.21 & 0.17 & 1.336 & 0.010 & & \\ ETo & 31.6 & 0.33 & 0.30 & 2.601 & \(-\)0.006 & & \\ Tair & 18.2 & 0.64 & 0.62 & 3.629 & \(-\)0.098 & & \\ NDVI, VPD & 17.8 & 0.67 & 0.64 & 1.121 & 2.988 & \(-\)0.058 & \\ NDVI, P & 20.1 & 0.64 & 0.60 & \(-\)0.047 & 3.398 & 0.011 & \\ NDVI, Eto & 19.9 & 0.64 & 0.60 & 1.276 & 2.920 & \(-\)0.005 & \\ NDVI, Tair & 2.6 & 0.84 & 0.82 & 2.360 & 2.398 & \(-\)0.083 & \\ NDVI, VPD, P & 17.6 & 0.71 & 0.66 & 0.691 & 3.134 & \(-\)0.039 & 0.005 \\ NDVI, VPD, ETo & 19.8 & 0.67 & 0.62 & 1.143 & 2.979 & \(-\)0.053 & 0.000 \\ NDVI, VPD, Tair & 2.2 & 0.85 & 0.83 & 2.680 & 2.234 & 0.030 & \(-\)0.113 \\ NDVI, P, ETo & 17.8 & 0.70 & 0.65 & 0.714 & 3.115 & 0.007 & \(-\)0.003 & \\ NDVI, P, Tair & 3.6 & 0.84 & 0.82 & 2.066 & 2.515 & 0.002 & \(-\)0.074 & \\ NDVI, ETo, Tair & \(-\)2.4 & 0.88 & 0.86 & 2.709 & 2.202 & 0.005 & \(-\)0.134 & \\ NDVI, VPD, P, ETo & 19.4 & 0.71 & 0.64 & 0.741 & 3.111 & \(-\)0.025 & 0.006 & \(-\)0.001 & \\ NDVI, VPD, P, Tair & 1.4 & 0.87 & 0.84 & 2.303 & 2.369 & 0.041 & 0.004 & \(-\)0.109 & \\ NDVI, VPD, ETo, Tair & \(-\)0.4 & 0.88 & 0.85 & 2.681 & 2.215 & \(-\)0.005 & 0.005 & \(-\)0.133 & \\ NDVI, P, ETo, Tair & \(-\)2.1 & 0.89 & 0.86 & 2.390 & 2.327 & 0.003 & 0.005 & \(-\)0.125 & \\ NDVI, VPD, P, ETo, Tair & \(-\)0.2 & 0.89 & 0.86 & 2.403 & 2.318 & 0.008 & 0.003 & 0.004 & \(-\)0.127 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Water use efficiency linear regression models with each corresponding calibration set. Akaike information criterion (AIC), regression coefficient (R\({}^{2}\)), adjusted regression coefficient (R\({}^{2}\) Adj) and model coefficients. VPD (hPa), P (mm), Eto (mm), Tair (\({}^{\circ}\)C). \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **eWUE** & **NDVI** & **VPD** & **P** & **ETo** & **Tair** \\ \hline eWUE & 1 & & & & & \\ NDVI & 0.63 & 1 & & & & \\ VPD & \(-\)0.55 & \(-\)0.05 & 1 & & & \\ P & 0.48 & 0.06 & \(-\)0.61 & 1 & & \\ ETo & \(-\)0.56 & \(-\)0.08 & 0.93 & \(-\)0.57 & 1 & \\ Tair & \(-\)0.73 & \(-\)0.18 & 0.88 & \(-\)0.66 & 0.90 & 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Pearson correlation coefficient between variables. The real potential of this parametrization of eWUE is that Equation (1) can be used for the operational spatio-temporal estimation of GPP in almond orchards from remote sensing ET information. This allows distributed maps of GPP over large areas to be generated and, hence, monitored for agro-environmental purposes.
## 2 Microgrid Hierarchical Control Group  
It is significantly challenging to identify which control method is adequate, and most commonly, the application predominantly depends on the microgrid's traits. The fundamental control objective in a microgrid topology is to ensure reliable and flexible operation, while maintaining close regulation of the microgrid's voltage and frequency as compulsory targets. In general, there are two main state-of-the-art control principles for a microgrid: centralized and decentralized. A centralized control relies on a set of data collection in a dedicated central controller. It continuously monitors and controls the computation taking action at a single central point for the relevant units via low-bandwidth communication between the central controller unit and the controlled groups [14; 15; 16]. In contrast with the centralized approach, the decentralized control requires that all DG units and the ESS have a local controller and operate independently based just on local measurements [17; 18; 19]. A valid argument between those controls can be attained within the hierarchical control scheme. In this approach, three control levels are distinguished: primary, secondary, and tertiary [20; 21], as depicted in Figure 2 with their respective responsibilities. The primary and secondary controllers are responsible for the islanded mode, while the tertiary control mainly serves and is managed at the grid side.  
Figure 2: Hierarchical control with respective obligations for each level.
## Materials and Methods  
### Materials  
#### 2.1.1 Field Investigation  
A tunneling project in Pahang state, Malaysia, was used to extract the data used in this study. Additional information regarding the field study can be found in [PERSON] et al. [3]. Three tunnel boring machines (TBMs) were used to excavate 35 km of the tunnel, and drilling and blasting techniques were used to excavate the rest of the tunnel [3]. Although most of the excavated rocks consisted of granite (based on the mentioned techniques), there were metamorphic and some sedimentary rocks in the geological units. The research team collected a total of 120 granite block samples from the tunnel face at different tunnel distances and several locations, and the tests were performed by transferring these block samples to the rock mechanics laboratory. Then, the procedure suggested by the ISRM [35] was applied to prepare the block rock samples for each planned test. Laboratory tests--including UCS, point load, density, the Schmidt hammer, BTS, and p-wave--were planned and conducted on the samples in the experimental program. Then, to model this study, the obtained results were considered. As mainly suggested by the literature, the BI values were calculated as BI = UCS/BTS, and then set as the output. The related inputs of the model included the p-wave velocity (V\({}_{\text{p}}\)), point load strength index (Is\({}_{50}\)), dry density (D), and Schmidt hammer rebound number (R\({}_{\text{n}}\)). In Figures 1 and 2, BTS and UCS tests were conducted on the samples and their failures, respectively.  
In this study, 85 data points were collected to model the BI; 75% (64 data points) of the data was allocated for the training dataset, with the rest for the testing dataset. The descriptive statistics of all features and target variables are tabulated in Table 1. The skewness ([0.116, 0.7339]) and kurtosis ([\(-\)0.76, 0.3369]) range of variables demonstrate that both criteria fall in an acceptable range ([\(-\)2, 2]) [36; 37]. Thus, it can be inferred that all datasets have a fairly normal distribution, which is a good indication for modeling rock brittlen  
Figure 1: Failure of a sample under a BTS test [3].  
The predictive and target parameters for decreasing the computational cost and complexity of prediction, normalized in range of [0, 1], are expressed in the following formula:  
\[x_{nor}=\frac{x-x_{min}}{x_{max}-x_{min}} \tag{1}\]  
where the \(x_{nor}\) is the normalized value and \(x_{max}\), \(x_{min}\), and \(x\) are the maximum, minimum, and original values of the modeling dataset, respectively.