esa-sceva/satcom-chunk-collection huggingface dataset:
# Joint Task Offloading and Power Allocation for Satellite Edge Computing Networks
## 4 Resource Management Algorithm  
Inspired by the IPDD algorithm, we propose a new resource management algorithm called DB-IPDD, which features a triple-loop structure. In the inner loop, we optimize primary variables by solving the AL sub-problem. In the middle loop, we reallocate the resources based on Theorem 1. In the outer loop, the dual variables and penalty parameters are updated. The algorithm terminates when the errors of the equation constraints become sufficiently small or the maximum number of iterations is reached.  
We introduce auxiliary variables \(W_{k,i}\) and \(V_{k,i}\) to replace coupling variables \(\alpha_{k,i}x_{k,i}\) and \(P_{k,i}x_{k,i}x_{k,i}\), respectively, and incorporate the equality constraints into the objective function as penalty terms. After that, a new objective function is constructed as follows.  
\[P_{1}:\min_{\mathcal{X}}\sum_{i=1}^{N}\max_{1\leq k\leq M}t_{k,i }+P_{\rho}(\mathcal{X}) \tag{36}\] \[s.t. 0\leq\alpha_{k,i}\leq 1,\quad k\in\mathcal{K},i\in\mathcal{T}.\] (37) \[\sum_{i=1}^{N}r_{k}\alpha_{k,i}D_{i}C_{k}^{2}\leq\mathcal{E}_{2}\] (38) \[\sum_{i=1}^{N}r_{m}C_{0}^{2}D_{i}\alpha_{0,i}+\sum_{i=1}^{N}\sum _{k=1}^{M}\frac{P_{k,i}\alpha_{k,i}D_{i}\kappa_{k,i}}{B}\leq\mathcal{E}_{1}\] (39) \[\sum_{i=1}^{M}P_{k,i}+r_{m}C_{0}^{3}\leq\mathcal{P}_{m} \tag{40}\]  
where \(P_{\rho}(\mathcal{X})\) can be shown as  
\[P_{\rho}(\mathcal{X})= \frac{1}{2\rho}\sum_{i=1}^{N}\sum_{k=1}^{M}(t_{k,i}-\frac{D_{i}s} {C_{0}}\alpha_{k,i}-\frac{D_{i}}{B}W_{k,i}+\rho\lambda_{k,i}^{1})^{2}+\] \[\frac{1}{2\rho}\sum_{i=1}^{N}(t_{0,i}-\frac{D_{i}s}{C_{0}}\alpha_ {0,i}+\rho\lambda_{0,i}^{1})^{2}+\] \[\frac{1}{2\rho}\sum_{i=1}^{N}\sum_{k=1}^{M}(V_{k,i}-P_{k,i}\kappa _{k,i}\alpha_{k,i}+\rho\lambda_{k,i}^{2})^{2}+\] \[\frac{1}{2\rho}\sum_{i=1}^{N}\sum_{k=1}^{M}(W_{k,i}-\alpha_{k,i} \kappa_{k,i}+\rho\lambda_{k,i}^{3})^{2}+\] \[\frac{1}{2\rho}\sum_{i=1}^{N}\sum_{k=1}^{M}(\kappa_{k,i}\log_{2}( 1+\frac{P_{k,i}\mu_{0}}{\sigma^{2}d^{2}})-1+\rho\lambda_{k,i}^{4})^{2}. \tag{41}\]  
The overall procedure is outlined as follows.
## 7 Results  
To study the impact of different traffic origin-destination demand (OD) levels, the calibrated traffic rates are multiplied by OD Scaling Factors (ODSFs) ranging from 0.25 through 1.0 at a 0.25 increment, which produces four traffic demand levels (0.25, 0.50, 0.75, 1). For each of these four traffic demand levels, we used nine different market penetration rates of the probe-connected vehicles, \((1,2,5,10,20,25,50,75,100)\). Thus, we ran 36 scenarios using the IDEAL communication configuration, and then reran the same 36 scenarios with the realistic C-V2X V2I communication modeling. The next subsections present and analyze the results at the different traffic demand levels.  
We evaluate the C-EEDR application using the IDEAL and C-V2X communication using the following measures: the average fuel consumption, the average travel time, and the average stop delay per vehicle.
## 1 Introduction
Although modeling approaches offer a valuable tool for assessing long-term and large-scale WUE, they may introduce errors due to uncertainties within the models themselves [30]. Conversely, remote sensing can capture GPP and ET across extensive spatial and temporal scales. Numerous regional and global studies have utilized MODerate resolution Imaging Spectroradiometer (MODIS) GPP andET to analyze spatio-temporal variations in WUE. This approach provides a balanced alternative between observations from eddy covariance and model simulations [31, 32, 33]. Additionally, cloud computing platforms like Google Earth Engine (GEE) facilitate access to long-term datasets, enabling studies on WUE variability at large scales [4, 32]. The farming-pastoral ecotone of Northern China (FPENC), functioning as a transitional zone between the agricultural southeast and pastoral northwest, is situated within the arid and semi-arid regions of China [34]. Historically, the FPENC has been characterized by its ecological vulnerability, particularly to drought conditions stemming from water scarcity [35, 36]. The impact of climate change has become increasingly evident over the past half-century; Northern China has experienced a notable rise in temperature. This rapid warming has exacerbated soil water consumption in the FPENC. Meanwhile, precipitation in the region has shown varying trends of increase over the past decade [34, 37]. Human activities, particularly change in land use/land cover (LULC), have influenced precipitation patterns, evapotranspiration, and consequently alter ecosystem functions [38]. The Chinese government, therefore, has been carrying out a series of environment restoration projects, such as the Grain for Green Project. Hence, it is crucial to investigate the discrepancy of WUE among various vegetation types in the FPENC within the backdrop of climate change. However, the spatio-temporal variations of WUE and its controlling mechanisms remains insufficiently explored [39, 40, 41]. Utilizing MODIS GPP and ET products from 2003 to 2022 with the spatial resolution of MODIS GPP and ET is 500, alongside biophysical variables' data and land cover information, this study investigated the characteristics of WUE variation in the FPENC over the past two decades with the support of the GEE platform. Additionally, the resilience of various ecosystems in the FPENC to drought was examined on a regional scale. The objectives are as follows: (1) to analyze the extent and spatio-temporal variations of WUE across the FPENC; (2) to evaluate the regulation mechanisms of biophysical variables on the WUE of each vegetation; (3) to uncover the resilience of various vegetation types to drought in FPENC. Our findings aim to enhance the understanding of WUE in the FPENC and help supply strategies for alleviating drought stress in arid regions.
# Minimizing the In-Cloud Bandwidth for On-Demand Reactive and Proactive Streaming Applications
## 5 Conclusions  
In this paper we proposed cloud streaming bandwidth optimization techniques. For unpopular video cases, we presented a new variant of the Slotted Stream Tapping (SST) protocol called Share All SST (SASST). We analytically proved that SASST reduces the storage node bandwidth by almost 30% and 20% compared to stream tapping (ST) and SST, respectively. However, Dyadic further reduces the bandwidth by almost 10% compared to SASST while being harder to implement, as shown in [8]. We discussed how SASST could be smoothly turned on a proactive protocol to also serve popular videos. To minimize the bandwidth, we proposed two LP formulations to obtain the optimal segments to channel schedules for known and unknown time horizons. We showed how the optimal schedule for the unknown time horizon case could be repeated infinitely to serve new incoming requests. We called the new broadcasting protocol based on these optimal schedules the New Optimal Proactive Protocol (NOPP). As it is optimal, it outperforms the rest of the proactive broadcasting techniques of the literature in terms of the consumed server and network bandwidth. In the future, we plan to bring the proposed techniques into practice, which requires studying in depth the possible ways to change segment-based streaming protocols, such as HLS, to minimize the steaming bandwidth in a multicast enabled network.  
Conceptualization, A.G. and L.H.; methodology, A.G.; software, A.G.; validation, A.G., B.B.Y. and M.K.; formal analysis, A.G., L.H. and M.K.; writing--original draft preparation, A.G.; writing--review and editing, A.G., M.K. and B.B.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Deanship of Scientific Research at King Saud University through research group no. RG-1439-001.  
The authors declare no conflict of interest.
## 2 Methods and Data
### Pseudo-Measurement Generation
#### 2.2.3 Pseudo-Measurement Uncertainty Calculation  
State estimation determines the maximum likelihood network state, based on the reliability of measurements. Since measurements with a lower level of uncertainty are considered with a higher weight, it is essential to set the uncertainties realistically.  
Uncertainty was calculated for each scenario as the mean of relative difference between the scenario consumption and the reference dataset consumption.  
\[u_{\text{sc}}=\frac{1}{LT}\sum_{l=1}^{L}\sum_{t=1}^{T}\frac{P_{\text{sc}}^{l}(t )-P_{\text{ref}}^{l}(t)}{P_{\text{ref}}^{l}(t)} \tag{6}\]  
where  
* \(u_{\text{sc}}\) is the uncertainty of scenario sc;
* \(P_{\text{sc}}^{l}(t)\) is the power consumed by load \(l\) at time \(t\) according to scenario sc for odd weeks;
* \(P_{\text{ref}}^{l}(t)\) is the reference power consumed by load \(l\) at time \(t\). These values are the original smart metered consumption values of even weeks. The obtained values are seen in Table 2. There are two orders of magnitude difference between the uncertainty of residential and controlled loads, which is due to the intermittent nature of controlled consumption. This is explained, for example, by the fact that a  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Boundaries of the Datasets (Aggregated Electricity (AE) in kWh)** & **Number of Datasets** \\ \hline AE \(\leq\) 2000 & 167 \\
2000 \(<\) AE \(\leq\) 5000 & 104 \\
5000 \(<\) AE \(\leq\) 10,000 & 29 \\
10,000 \(<\) AE & 60 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Cluster boundaries and number of datasets in each cluster.  
significant proportion of consumers have water boilers, and their behavior is difficult to estimate.
# Synchronization of Acoustic Signals for Steganographic Transmission
## 4 Technique Development and Implementation  
The methods of signal synchronization in conjunction with the procedure of data embedding and extraction described in the Section 3 should allow for hidden data transmission in the selected telecommunications channel. The first method Monotonic Phase Correction and the second Direct Spread Spectrum of synchronization, consist of the construction of the synchronizing signal and adding it to the steganographic signal. The third method Pattern Insertion Detection consists of inserting a synchronizing marker into the speech signal preceding the steganographic transmission. The fourth and last method Minimal Error Synchronization, on the other hand, consists of the appropriate preparation of steganographic information.
# Ensemble Learning-Based Coronary Artery Disease Detection Using Computer Tomography Images
## 2 Materials and Methods  
In this study, the authors built a CAD detection model using the EL technique. The EL technique is a prominent strategy integrating numerous base models to increase overall prediction performance. In contrast to straightforward ensemble methods such as bagging and boosting, stacked generalization combines the benefits of multiple base models to produce a more robust and accurate predictive model. A meta-model learns to efficiently balance the contributions of each base model, which may capture different elements of the underlying patterns in the CCTA images. Medical image classification relies on RF [38], CatBoost [39], and Microsoft's Light Gradient Boosting Machine (LightGBM) [40] due to their robust performance, interpretability, and ability to handle complex data. LightGBM is an efficient and fast gradient boosting system [40]. A histogram-based learning strategy is employed to expedite training by dividing continuous feature values into discrete bins. As an EL technique, RF constructs multiple decision trees and integrates their findings. The generalizability and robustness of results are enhanced using the ensemble technique. Compared to other models, CatBoost typically requires limited hyperparameter modification, making it user-friendly for medical image analysis with various features. LightGBM is scalable and handles massive datasets, making it ideal for medical imaging applications with multiple images and features. Therefore, the authors were motivated to apply CatBoost, LightGBM, and RF models in the proposed study for classifying the CCTA images.  
Figure 1 presents the proposed methodology for classifying the CCTA images. Initially, a 3D CNN technique generates the straightened MPR volume in a longitudinal view of vessels. The CCTA image quality is improved using pre-processing techniques. In order to classify the CCTA images, the authors construct a CNN-based feature engineering model using the MobileNet V3 model's weights. The features are converted into a one-dimensional array using a flattened layer. CatBoost and LightGBM models are employed to classify the features, and outcomes are stacked into a single layer. Finally, the authors apply the RF model to categorize the CCTA images into normal and abnormal classes.
## 2 Developing Zero-Cement Binder  
One way towards zero cement binder can be through the alkali activation technique. [PERSON] et al. [1] studied the potential feasibility of the use of thermally pre-treated carbonate-high illite clay (LCR) as a precursor to produce alkali-activated paste. For that purpose, different activating solution concentrations (4, 6 and 8 M NaOH), with an alkaline solution/clay ratio of 0.55, were used. In terms of cost and technical performance, the paste with 6 M NaOH solution can be considered an optimum concentration. Additionally, an aluminium-enriched C-S-H (C-A-S-H) gel of paste with a higher concentration (e.g., 6 M NaOH) is less carbonated than that of the lower concentration. Furthermore, the output of this study showed that the LCR can be considered a potential material to be used in alkali-activated material with acceptable mechanical strength (20 and 30 MPa at 2 and 28 days, respectively).  
[PERSON] et al. [2] enhanced the fracture properties of alkali-activated mortar made with slag by using steel microfibers. The effective crack model, work-of-fracture method and double-K fracture model were considered to evaluate the fracture properties of the mortar. This study concluded that the addition of 10-15% of the precursor's mass in microfibers is an optimum percentage to enhance the fracture properties of the alkali-activated mortar. For example, the fracture toughness and fracture energy values of the mortar increased by up to 50% and 250% with the addition of the steel fiber of 20%, respectively. In addition, the compressive strength increased by up to 40% with the incorporation of the steel fiber of 10%.  
Ecotoxicological and Chemical Characteristics of the Non-Conventional Materials Used to Replace Cement and Natural Aggregates  
Concrete can be produced with low environmental impact and cost by incorporating some non-conventional binders and/or aggregates. Nevertheless, it may still not be considered a sustainable product due to its ecotoxicological issues. Thus, a study [3] evaluated the ecotoxicological potential of using fly ash (FA) and recycled concrete aggregates in concrete. In order to consider all aspects that relate to the toxicity, chemistry (non-metallic and metallic parameters) and ecotoxicology (battery of tests with the luminescent bacterium _Vibrio fischeri_, the freshwater crustacean _Daphnia magna_, and the yeast _Saccharomyces cerevisiae_) of eluates obtained from leaching, tests of raw materials and concrete containing FA and/or recycled concrete aggregates (RCA) were evaluated, and then compared to the limit values given by standards (non-hazardous or non-hazardous). The output of the results shows that, in terms of chemical characterization, although FA as a raw material can be classified as hazardous to landfill, concrete containing FA is classified as a non-hazardous material due to the solidification effect. In terms of ecotoxicity, although FA and RCA presented no or low levels of potential ecotoxicity, concrete with a high volume of FA and RA was classified as clearly ecotoxic, mainly due to _Daphnia magna_ mobility.
## 6 The Evaluation of Unmatched Receiving of ACE-BOC
### Performance Evaluation
#### Tracking Performance under WGN  
To study the code tracking performance under WGN, Figure 3 shows the equivalent Gabor bandwidth of ACE-BOC(15,10,[1,1,3,3]) under the aforementioned three receiving strategies. IMR and BLR receives the lower sideband, Q-channel signal component \(s_{\text{LQ}}\). The receiving configuration can be referred to Table 1.  
Different from the code tracking error Equation (28), which is also used to evaluate the code tracking performance, the equivalent Gabor bandwidth decoupled from the integration duration, SNR, interference strength, making the evaluation of the signal less affected by the specific working scenario of receivers.  
As shown in Figure 3, when the receiving bandwidth exceeds 24.2 MHz, matched receiving shows a notable advantage over the IMR and BLR. The equivalent Gabor bandwidth of IMR is slightly larger than BLR, because the IMR uses a square waveform subcarrier which is matched with the received signal, while BLR uses a sinusoidal waveform subcarrier which employs only the energy on the 1 st harmonics of the subcarrier frequency.  
From the results shown in Figure 3, useful information can be obtained to guide the receiving strategies. First, it should be noted that when the receiving bandwidth is about 35 MHz, there is no need to apply the more complicated IMR, as BLR can achieve competitive performance with IMR. Also, when the receiving bandwidth can be increased to obtain better ranging accuracy, the increment should refer to the slope of curves shown in the Figure 3. For instance, when the original bandwidth is 25 MHz for BLR, increasing 10 MHz bandwidth brings a notable promotion of 1.08 MHz in Equivalent Gabor bandwidth. While, when the original bandwidth is 35 MHz, the benefit in Equivalent Gabor bandwidth by increasing 10 MHz of bandwidth is only 0.214 MHz.
## 4 Discussion
\(F_{2}\)—force measured at the level of the top of the head without a gravitational moment of the feet, \(F_{3}\)—force measured at the level of the top of the head with a gravitational moment of the feet, \(I_{2}\)—body height without feet, \(h_{CoG\_4}\)—distance from the axis of the upper ankle joint to the centre of gravity of the feet, \(W_{4}\)—weight of the feet. the feet of living people and determine their percentage share in the total body weight. It may also be applied to estimate the weight of other body segments, such as the head. Accurate determination of the centre of gravity has both theoretical and practical relevance. A higher centre of gravity of a human body during tilting on the axis of rotation in the upper ankle joint results in greater rotational moments than previously assumed. A greater gravitational moment means that greater muscle force is required at a constant lever arm length to generate a muscle moment that balances the gravitational moment. This insight forms the basis for revising rehabilitation and training protocols based on muscle force calculations. This may also be relevant when analysing load distribution on soldiers' bodies, including the effect of backpacks or carried equipment. Up to now, rehabilitation and training methods have been developed largely intuitively or without taking into account that the foot is a support platform for the tilting rest of the body [19; 20]. Many scientific reports are important in studying the shifting of the centre of gravity in the context of medical therapies. For example, monitoring the distance between the \(CoM\) and the limits of foot support allows for assessing balance and the risk of falling, especially in older people [21]. It has been noted that training to limit lateral swings of the centre of mass (\(CoG\)) can improve gait functions and the results of tests such as the TUG or Berg Balance Scale in patients with, for example, spinal cord injury [22]. Analysis of the centre of mass trajectory in patients, e.g., after hip replacement, helps to adjust rehabilitation protocols, taking into account gait asymmetry and compensations. Our revised approach can significantly enhance the precision of rehabilitation design, sports training, and human load analysis during movement. The limitation of our approach is that these are not high-precision measurements, because they are based on basic measuring devices and a system that is difficult to measure with high accuracy. Modern technologies enable precise real-time tracking of the body's centre of gravity, allowing for dynamic and personalized training feedback. Based on our observations, this approach could significantly enhance the precision of future studies on human body dynamics. Apart from that, future efforts can focus on developing simulation software based on collected data to improve calculation accuracy helpful, for example, in planning surgical procedures to increase the rotational strength of muscles, e.g., in patients with muscle weakness.
# Modeling and Performance Analysis of mmWave and WiFi-Based Vehicle Communications
## 4 Simulation Results
However, in an NLoS scenario, as depicted in Figure 7, the AT for WiFi bands experiences a slight decrease as the transmission distance increases, whereas mmWaves exhibit a significant decrease with the increase in distance between vehicles. Furthermore, it can be inferred that mmWaves initially demonstrate higher throughput, particularly with a wider frequency band, compared to WiFi. Conversely, WiFi exhibits greater resilience to long distances. Figure 5: STP vs. transmission distance in an NLoS scenario. Figure 6: Average throughput vs. transmission distance in an LoS scenario.