esa-sceva/satcom-chunk-collection huggingface dataset:
# Power Optimization Model for Energy Sustainability in 6G Wireless Networks
### Contributions and Outcomes  
The massive number of battery-powered IoT nodes and connected devices in a massive IoT network suffer from energy exhaustion or battery drain challenges. The future wireless systems require sustainable energy solutions in order to meet the huge energy demands. This paper proposes a practical power optimization model that offers efficient power-utilization and energy-saving capabilities, resulting in improved network performance.  
The novel contributions of the paper are:* A practical power optimization model is proposed which enables the efficient management of power resources. Power optimization refers to the selection of optimum power with which a particular AP transmits to a particular user such that the system efficiency is increased;
* The performance of the proposed model is evaluated through parametric variations in the number of antennas at the AP, the number of APs, and AP deployments for different network operations;
* The impact of spatial correlation and the access to perfect CSI on the network performance is also evaluated for different precoding schemes;
* Two user-scheduling algorithms, namely, minimum distance scheduling (MDS) and maximum channel gain scheduling (MCS), are proposed that assign a set of users to a particular AP. The minimum distance and the maximum channel gain between a user and an AP are considered for selection criteria;
* The performance of the proposed algorithms is evaluated for three power allocation methods, namely, equal power, fractional power, and sum SE maximization power allocation mechanisms, so as to guarantee higher spectral efficiency for all the users in the network.  
Figure 1 presents the communication scenario with dense AP deployment serving a large number of users.
## 2 Materials and Methods
### Participants  
We recruited 23 healthy young adults, 21 healthy older adults, and 15 older adult Tai Chi practitioners from the local community to participate in this study (See Table 1), as part of an ongoing study at the Mobility and Fall Prevention Research Lab at the University of Illinois Urbana-Champaign. The Tai Chi practitioner group had at least 50 practice hours of a modified Yang style (Range = 61-4680 h). All practitioners came from two local Tai Chi groups and practiced in a similar style (i.e., Yang style 24 form or modified Yang style).  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Young Adult (N = 23)**} & \multicolumn{2}{c}{**Older Adult (N = 21)**} & \multicolumn{2}{c}{**Tai Chi Practitioner (N = 15)**} & \\  & **Mean** & **SD** & **Mean** & **SD** & **Mean** & **SD** & \(p\)**-Value** \\ \hline Age (y) & 21.4 & 2.0 & 70.8 & 5.5 & 76.7 & 5.6 & \textless{}0.001 \\ Height (cm) & 170.9 & 11.0 & 171.0 & 10.4 & 168.1 & 9.0 & 0.649 \\ Weight (kg) & 71.5 & 11.5 & 72.0 & 11.9 & 64.2 & 7.0 & 0.073 \\ Foot Size (cm) & 25.9 & 1.7 & 25.9 & 1.6 & 25.5 & 1.4 & 0.654 \\ SOT Score [0–100] & 75.8 & 4.7 & 75.4 & 6.7 & 73.0 & 6.8 & 0.369 \\ SCK Difficulty [0–4] & 0.2 & 0.4 & 1.1 & 0.9 & 0.9 & 0.9 & \textless{}0.001 \\ Brisk Activity/Week & 4.0 & 2.3 & 4.8 & 3.4 & 3.0 & 2.2 & 0.265 \\ FES-I Score [16–64] & 18.0 & 2.1 & 19.9 & 3.2 & 22.7 & 3.9 & \textless{}0.001 \\ \hline  & **Number** & **Percent** & **Number** & **Percent** & **Number** & **Percent** & \(p\)**-Value** \\ \hline Females & 12 & 52.2 & 11 & 52.4 & 9 & 60 & 0.874 \\ \hline \hline \end{tabular}  
* One-way analysis of variance was used for group differences in continuous variables. The chi-square test was used for group differences in the distribution of females. Abbreviation: SOT score, sensory organization test equilibrium score; SCK, stooping, crouching, or kneeling; FES-I, Falls Efficacy Scale-International.  
\end{table}
Table 1: Sample characteristics by group.
## 5 Mapping  
Visual odometry could estimate the camera pose in a short time by direct image alignment, feature matching, and the addition of keyframes and map points to the map at the same time. However,errors inevitably accumulated during the continuous addition of keyframes for map expansion, and the camera pose estimated by visual odometry became unreliable. Therefore, the back-end optimization of SLAM was required to reduce the error for keyframes and map points that were added to the map. Additionally, the 3D map of the warehouse environment needed to be established to meet the requirements for path planning and obstacle avoidance for the forklift operating in the warehouse. Therefore, a 3D grid map based on an octree was constructed according to the point cloud map [36]. The example of the point cloud map and the grid map is shown in Section 6.4.
## 4 Discussion  
### Climate Impacts on Shrubland/Grassland Fuel Load Changes  
Climate condition is the most important driver of fuel load accumulation on shrubland/grassland ecosystems. The most recent report on climate change indicates that North America may have warmer winter and spring temperatures and more spring precipitation [51]. Higher spring precipitation tends to increase the live vegetation production for the shrubland/grassland ecosystems in our study area, which results in higher fuel loads in the summer than years with a dry spring. Higher fuel loads, as predicted by fire model simulations, resulted in larger energy release component and flame length values, and also greater burn area. Thus, it is likely that climate change will bring more extreme fires in the shrubland/grassland region by increasing the fuel load.  
It is very important to be able to assess vegetation condition and status during the spring time frame for fire applications. Biomass production is highest in spring, and high levels of spring biomass set the stage for high levels of burnable fuels later in the year. The eMODIS NDVI data can depict current greenness and brownness conditions but generally fall short at capturing some important ecophysiological processes such as decomposition, at least without additional information. There is also a notable time lag effect between climate and the dead fuel changes. If not removed by grazing or fire, much of the live herb fuel load from one growing season will be converted into dead fuel load toward the end of growing season and carry over to the next year. Thus, a series of wet years followed by a dry year can markedly increase dead fuel loads and thus increase the potential of extreme fires.  
Meanwhile, climate conditions also impact the litter decomposition process that controls the dead fuel changes. This process tends to be difficult to measure solely using remotely sensed data, and we see a clear role for integrating Biome-BGC results with analyses of satellite-based data. Modeling with Biome-BGC and other similar process-based models can provide useful information of dead fuel load dynamics not readily obtainable through satellite remote sensing.
## 4 Discussion of Results
### Learning Architecture  
Recall that the HEVC features are extracted from the HEVC video coding process. Such a process is based on motion estimation and compensation, which is known to make use of previous video frames in the coding of the present video frame. As such, the resultant feature vector of a video frame inherently contains information from previous frames. This justifies the outstanding results obtained using the RF and 1D-CNN architectures which, unlike LSTM networks, lack the ability to maintain information beyond the current frame.  
On the other hand, in SumMe and VSUMM, RFs achieved higher scores compared to the other two learning architectures, implying less content or scene changes in the content of the videos within these datasets. When a video contains many scene changes, LSTMs excel; however, when there are not many changes, RFs can keep up with and exceed LSTMs in terms of classification accuracy.  
The datasets in which LSTM networks performed better, i.e., TVSum and OVP, indicate that the videos contained in them have more temporal variance or scene changes in their content compared to the other two datasets. This can be explained by the way LSTM networks work; they can retain information about older frames or content through their long memory in addition to the recently preceding frames with their short memory.
# LP-Order Time-Frequency Spectra and Its Application in Edge Detection
## Appendix A Theory of HT  
HT [2] is derived from the Fourier transform (FT) [24; 25; 26] and has been used in signal processing and interpretation for many years. A well-known application of HT is seismic complex-trace analysis using instantaneous amplitude, phase, and frequency.  
For a real signal \(x(t)\) in the time domain, its FT results can be expressed as  
\[X(f)=\int_{-\infty}^{\infty}x(t)\cdot\exp[-i2\pi ft]dt \tag{1}\]  
where \(t\) is the time; \(f\) is the frequency; \(i=\sqrt{-1}\); and the HT result (a real signal) of real signal \(x(t)\) can be expressed as  
\[y(t)=\text{Im}\left[\int_{-\infty}^{\infty}Z(f)\cdot\exp[i2\pi ft]df\right] \tag{2}\]  
and  
\[Z(f)=\left\{\begin{array}{cc}2X(f)&f>0\\ X(f)&f=0\\ 0&f<0\end{array}\right. \tag{3}\]  
where \(\text{Im}[\;]\) takes the imaginary part of a complex number.  
Correspondingly, the analytical signal (or analytical representation) of the real signal \(x(t)\) obtained by HT can be expressed as  
\[z(t)=x(t)+i\cdot y(t)=\int_{-\infty}^{\infty}Z(f)\cdot\exp[i2\pi ft]df \tag{4}\]  
And if \(A(t)\), \(\theta(t)\), and \(f(t)\) denote the instantaneous amplitudes, phases, and frequencies of \(x(t)\) or \(z(t)\), respectively, then  
\[\left\{\begin{array}{c}\tan[\theta(t)]=x^{-1}(t)\cdot y(t)\\ f(t)=\frac{1}{2\pi}\frac{\partial\theta(t)}{\partial t}\\ A(t)=\sqrt{x^{2}(t)+y^{2}(t)}\end{array}\right. \tag{5}\]  
and all can be used for edge detection, hydrocarbon detection, and other applications, but with lower noise robustness and resolution.
# Accessible IoT Dashboard Design with AI-Enhanced Descriptions for Visually Impaired Users
## 1 Introduction
### Background and Motivation  
Current IoT dashboard solutions often treat accessibility as an afterthought, if they address it at all. Many dashboards comply only with minimal requirements, offering, for example, a generic alt text for a chart or a basic high-contrast mode, while ignoring deeper issues of navigability and interpretability for screen reader users. This has resulted in a patchwork of incomplete solutions, so-called \"checklist accessibility\" where a few boxes are ticked without delivering a truly usable experience. A visually impaired user might technically be able to access a dashboard's HTML, yet still gain little insight from it due to the lack of descriptive content and proper structuring.  
The imperative to improve this situation is both ethical and practical. Ensuring equal access to IoT data is increasingly important as such data directly affect critical decisions and daily life. For example, if an environmental sensor dashboard indicates poor air quality or a machine dashboard signals an equipment failure, all stakeholders, including those with visual disabilities, should be able to perceive and act on that information. The motivation for our work stems from this need for inclusive design [10] in IoT monitoring tools. Rather than retrofit accessibility features onto an inherently visual dashboard, we advocate designing the dashboard from the ground up with accessibility in mind. This means employing semantic HTML5 [11] elements, ARIA roles [12], and descriptive text as first-class components of the user interface, not as secondary add-ons.
## 2 Materials and Methods
### The Construction of MSWDD2024
#### 2.1.2 The Processing of Data  
The preprocessing of the experimental data was performed using Pix4D software to stitch multiple drone-captured images into orthophotos. The software effectively corrects geometric distortions caused by camera angles and flight heights through automated image registration and geometric correction. It also performs radiometric calibration to compensate for lighting intensity, solar angle, and sensor orientation, ensuring spatial alignment and spectral consistency across the dataset. Subsequently, the generated RGB and multispectral images were concatenated along the channel dimension, resulting in an 8-channel composite image.  
After data preprocessing, a sliding window approach was applied to extract \(64\times 64\) pixel image patches from the orthomosaic (as shown in Figure 2). The \(64\times 64\) patch size was empirically selected to balance classification accuracy and spatial context. Given the ground resolution, each patch covered approximately \(57.3\) cm \(\times\)\(57.3\) cm on the field, which corresponds well with the typical size of disease-affected wheat regions. This size provides a sufficient spatial context for identifying the spectral and textural patterns of disease while minimizing the risk of mixed labels or information dilution. Each patch was annotated based on the field survey data, categorizing it into one of four classes: healthy, stripe rust, yellow dwarf, and others. The \"others\" class was specifically introduced to account for non-crop regions in the imagery, such as soil, weeds, roads, and artificial structures. Including these complex background samples in the classification process enabled the model to better distinguish disease-affected crop areas from irrelevant background patterns, thereby improving robustness under heterogeneous field conditions.  
Finally, the dataset was divided into training, validation, and testing sets in an 8:1:1 ratio, with the data distribution summarized in Table 1. No data augmentation techniques were applied, as the dataset contained a sufficient number of high-quality samples per class.  
Figure 1: Imaging equipment and experimental site.
# Parameter Analysis of Resilient Precast Concrete Beam-Column Joints
## 4 Parametric Analysis of the REDDC
### Influence of the H-Shaped Steel's Thickness  
The effect of varying the thickness of the H-shaped steel in the REDDC on the mechanical performance of the beam-column joints was investigated by comparing models with thicknesses of 3 mm, 4 mm, 5 mm and 6 mm. The hysteresis curves, shown in Figure 11(a), indicate that as the thickness increased, the load-bearing capacity was improved, reaching 42.44 kN, 51.58 kN, 57.44 kN and 59.17 kN (corresponding to REDDC-B1, REDDC-B2, REDDC-B3 and REDDC-B4, respectively). For a 3 mm H-beam, the ultimate load-carrying capacity was reduced by 21.55%, 35% and 39.43% as compared to the H-beams of 4 mm, 5 mm and 6 mm, respectively. Figure 11(b) shows that the 3 mm thick component exhibited a superior energy dissipation performance, which diminished with increased thickness. Additionally, Figure 12 reveals that as thickness increased, the yield area decreased, indicating a reduced energy dissipation capacity.  
For the components with replaceable energy dissipation devices, it was crucial to balance both the energy dissipation capacity and the ultimate load capacity. Thus, a 3 mm thick component is not optimal. A 4 mm thick plate is recommended to ensure both effective energy dissipation and an adequate load-bearing capacity.
# Effect of ElectroSpark Process Parameters on the WE43 Magnesium Alloy Deposition Quality
## 2 Experimental Details  
### Materials and Processing  
In this study, WE43 magnesium alloy electrodes were used for coating homologue substrate. The chemical composition (wt %) of the WE43 alloy is shown in Table 1. The WE43 magnesium alloy substrate was supplied in artificially aged condition as 15 mm thick discs. Bars of 80 \(\times\) 20 mm were cut from those discs. The artificial peak-aging heat treatment involved solubilization at 525 \({}^{\circ}\)C - 8 h, quenching in hot water at 60 \({}^{\circ}\)C and aging in a hot oil bath at 250 \({}^{\circ}\)C for 16 h followed by air cooling. The consumable electrode was a cylindrical rod with a diameter of 2.3 mm and length about of 50 mm.  
The depositions were performed at room temperature using a \"Technocoat MicroDepo Model 150\" machine. Before starting the depositions, each substrate was polished with 800-gri SiC paper in order to remove the oxide films and any surface defects. The WE43 deposits were processed using a hand-held gun and performing multiple-layer depositions always on a same line as shown in Figure 1. After setting the energy value, each layer was deposited by multiple depositions along the substrate surface in OY direction with a constant deposition travel speed. In particular, for each value of the spark energy, 50 subsequent deposition layers were performed following the OZ direction (Figure 1).  
During depositions the electrode rotation speed and discharge frequency (_f_) were kept constant at 1200 rpm and 390 Hz, respectively. In addition, all the depositions were performed in the presence of a  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline  & **Zn** & **Fe** & **Si** & **Cu** & **Mn** & **Y** & **Nd** & **Zr** & **Mg** & **Other** \\ \hline
**WE43** & 0.203 & 0.0071 & 0.005 & 0.009 & \(<\) 0.0001 & 3.963 & 2.388 & 0.409 & Bal. & \(<\) 0.2 \\ \hline \end{tabular}
\end{table}
Table 1: Chemical composition (wt %) of WE43 magnesium alloy.  
Figure 1: Representation of the ESD process.  
constant Argon flow equal to 10 L/min. The two main electrical parameters, voltage and capacitance, were properly varied in order to change the energy input. The spark pulse-energy in joule (J) of each set of parameters given in the Table 2 was determined as follows:  
\[Es=\frac{1}{2}CV^{2}\]  
where C is the capacitance in Farad (F) and \(V\) is the voltage in Volt (V) [31].  
Especially in this study, the effect of five spark pulse-energy values ranging from 0.15 and 1.69 J (Table 2) on the quality of the coatings has been analyzed.
## 3 Testing of the Roots Pump
### Data Measurement  
During the testing, the rotation speed of the Roots pump is from 3000 to 9000 rpm, the pressure difference is from 5 to 30 kPa, and the suction pressure is from 180 and 230 kPa. Regarding the suction temperature, it is about 30 \({}^{\circ}\)C depending on the pressure difference and rotation speed.  
The performance parameters of a Roots pump include volume flow rate, power consumption, volumetric efficiency, and isentropic efficiency. The volumetric efficiency \(\eta_{o}\) is defined as the ratio of the actual volumetric flow rate \(Q\) to the theoretical volume flow rate \(Q_{th}\). A higher volumetric efficiency implies a better performance of the pump. It is calculated by the following equation:  
\[\eta_{o}=Q/Q_{th} \tag{1}\]  
\[Q_{th}=2 ZA_{0}Ln \tag{2}\]  
where \(Z\) is the lobe numbers of the rotor, \(A_{0}\) is the area between two lobes of the rotor and the casing, \(L\) is the rotor length, and \(n\) is the rotation speed of the rotor.  
The isentropic efficiency \(\eta_{is}\) is the ratio of the power consumption with isentropic compression \(N_{is}\) to the actual power consumption \(N\). A higher isentropic efficiency means a more efficient compression process, fewer friction losses in timing-gear and bearings, and higher efficiency of the motor and controller. It is expressed by the equation:  
\[\eta_{is}=N_{is}/N \tag{3}\]  
where \(N_{is}\) is the power consumption with isentropic compression and can be calculated with the equation:  
\[N_{is}=\frac{\kappa}{\kappa-1}P_{s}Q(\left(\frac{P_{d}}{P_{s}}\right)^{\frac{ \kappa-1}{\kappa}}-1) \tag{4}\]  
\[P_{d}=P_{s}+\Delta P \tag{5}\]  
where \(k\) is the isentropic index of the gas, \(P_{s}\) and \(P_{d}\) are the suction and discharge pressure, and \(\Delta P\) is the pressure difference. This equation indicates that the main factors affecting the power consumption of a Roots pump include the type of gas, suction pressure, and pressure difference.
# Intensity Evolution of Cosine-Gaussian-Correlated Schell-Model Pulse Scattered by a Medium
## 2 Theory
\begin{array}{l}W^{(s)}(r\mathbf{s}_{1},r\mathbf{s}_{2},\omega_{1},\omega_ {2})=W_{0}\exp[-\frac{(\omega_{1}-\omega_{0})^{2}+(\omega_{2}-\omega_{0})^{2 }}{2\Omega_{0}^{2}}-\frac{(\omega_{1}-\omega_{0})^{2}}{2\Omega_{r}^{2}}] \exp^{[\frac{\pi i(k_{2}-k_{1})}{\tau^{2}}]}\\ \times\exp^{\left[-\frac{\sqrt{2}m\omega}{T_{c}}\right]}\cosh[b(\omega_{1}+ \omega_{2}-2\omega_{0})]\iint\limits_{D}C_{F}(\mathbf{r}_{1}^{\prime}, \mathbf{r}_{2}^{\prime},\omega_{0})\exp[-i\left(\mathbf{K}_{1}\cdot\mathbf{r} _{1}^{\prime}+\mathbf{K}_{2}\cdot\mathbf{r}_{2}^{\prime}\right)]\mathrm{d}^{3 }\mathbf{r}_{1}^{\prime}\mathrm{d}^{3}\mathbf{r}_{2}^{\prime},\end{array}  \tag{9}\]  
where \(r\mathbf{s}_{1}\) and \(r\mathbf{s}_{2}\) are the position vectors of two observation points (\(\mathbf{s}_{1}\) and \(\mathbf{s}_{2}\) are unit vectors). \(\mathbf{K}_{1}=-k_{1}(\mathbf{s}_{1}\) - \(\mathbf{s}_{0})\) and \(\mathbf{K}_{2}=k_{2}(\mathbf{s}_{2}-\mathbf{s}_{0})\) are the momentum-transfer vectors. \(D\) is the domain that the medium occupies. A quasihomogeneous medium was considered in this paper of which the correlation function of the scattering potential is given by [1; 29; 33]  
\[C_{F}(\mathbf{r}_{1}^{\prime},\mathbf{r}_{2}^{\prime},\omega_{0})=C_{0}\exp \left[-\frac{\left|\mathbf{r}_{1}^{\prime}+\mathbf{r}_{2}^{\prime}\right|^{2} }{8\sigma_{R}^{2}}\right]\exp\left[-\frac{\left|\mathbf{r}_{1}^{\prime}- \mathbf{r}_{2}^{\prime}\right|^{2}}{2\sigma_{r}^{2}}\right], \tag{10}\]  
where \(C_{0}\) is a positive constant, \(\sigma_{R}\) is the effective radius of the medium, and \(\sigma_{r}\) is the correlation length of the media, which must satisfy inequality \(\sigma_{R}\geq\sigma_{r}\). When we substitute from Equation (10) into Equation (9), we can obtain the cross-spectral density of a pulse scattered by a medium [34]:  
\[\begin{array}{l}W^{(s)}(r\mathbf{s}_{1},r\mathbf{s}_{2},\omega_{1},\omega_ {2})=W_{0}C_{0}(2\pi\sigma_{R}\sigma_{r})^{3}\exp[-\frac{(\omega_{1}-\omega_ {0})^{2}+(\omega_{2}-\omega_{0})^{2}}{2\Omega_{0}^{2}}-\frac{(\omega_{1}- \omega_{0})^{2}}{2\Omega_{r}^{2}}]\exp[-\frac{\sqrt{2}m\omega}{T_{c}}]\\ \With the help of unified theory [35; 36], where spectral density, spectral degree of coherence, and spectral degree of polarization can be treated in the same manner, the intensity of the scattered pulse has the following form. \[ \begin{array}{l}I^{(s)}(rs,t)=\frac{W_{0}C_{0}(2\pi o_{0},\sigma_{\tau})^{3}}{ r^{2}\sqrt{\left[2\left(T_{0}^{2}+m+n\right)\right]\left[1+(m-n)\right]\frac{T_{ 0}^{2}+\sigma_{\tau}^{2}}{T_{0}^{2}}}}\exp\left[-\frac{(t-z)^{2}}{2\left(T_{0} ^{2}+m+n\right)}\right]\\ \times\exp\left[-\frac{2\omega_{0}^{2}(m-n)-\mu^{2}\frac{T_{0}^{2}+\sigma_{ \tau}^{2}}{2T_{0}^{2}}}{1+(m-n)\frac{T_{0}^{2}+\sigma_{\tau}^{2}}{T_{0}^{2}}} -\frac{\sqrt{2}m\mu}{T_{\tau}}}\right]\cosh\left[\frac{2\lambda_{0}(m-n)}{ \frac{T_{0}^{2}+\sigma_{\tau}^{2}}{T_{0}^{2}}+m-n}\right]\end{array}  \tag{15}\]