esa-sceva/satcom-chunk-collection huggingface dataset:
# Trigonometric Inference Providing Learning in Deep Neural Networks
## 3 Methodology
### Forward and Backward Inference  
#### 3.1.1 Forward Inference  
Let \(w\) denotes the model weights and \(a\) activations of an arbitrary layer. Note that we use subscript \(j\) to indicate a middle layer, and \(i\) denotes the previous layer of \(j\). Equation (3) provides the standard forward inference and Equation (4) indicates the activation calculation of our proposed trigonometric inference.  
\[a_{j}=\sum_{j}w_{j}\sigma(a_{i})+b_{j} \tag{3}\]  
\[a_{j}=\sum_{j}\sin(w_{j})\sigma_{sine}(a_{i})+b_{j} \tag{4}\]  
where \(\sigma(x_{j})\) is an activation function and \(\sigma_{sine}(x_{j})\) is our newly proposed sine-based activation function for trigonometric inferences. A sine function has been studied as an activation function in previous studies. In 1999, [PERSON] et al. [25] found that a multiplayerperceptron with sine as the activation learns faster than one with sigmoid activation on certain tasks. As a significant difference, the sine is periodic, whereas common activation functions are monotonic. [PERSON] et al. [26] found that sine activation functions perform reasonably well on current datasets, and the network ignores the periodic nature of sine functions. After training, only the central region at near zero of the sine activation function was used. [PERSON] et al. [27] used automatic search techniques to discover new activation functions. They then found that \(min(x,\sin(x))\) activation can achieve a performance comparable to that of ReLU for image classification. [PERSON] et al. [28] proposed a sine-activated algorithm for a multi-input function approximation that can efficiently obtain a relatively optimal structure.  
Based on previous findings, herein we propose our sine-based activation. Rather than directly applying the activation function as \(\sin(x)\), we combine the ReLU activation and sine activation because this combination has two merits: First, ReLU excludes negative outputs and, therefore, the computational burden is considerably reduced. In addition, regulating the activations to only positive values can lessen the poor influence caused by the periodicity property of the sine activation. In Section 5, we show that this rectified sine activation performs much better than a pure sine activation for trigonometric training. Equation (5) gives our activation function, and from Figure 3, we know that \(\sin(x)\) and \(tanh(x)\) are extremely similar for small values.  
\[\sigma_{sine}=\left\{\begin{array}{ll}0,&\text{if }x<0\\ \sin(x),&\text{else}\end{array}\right. \tag{5}\]
## 3 Transmission Protocol Configuration and Resource Allocation Scheme
### Transmission Protocol Configuration
The transmission power \(P_{0}^{l}\) of HAPS on LP-ABS can be determined by the following two methods. The first approach is that the expected accessible throughput of HAPS and GBS is related to \(P_{0}^{l}\) and \(\Phi^{l}\). \(P_{0}^{l}\) and \(\Phi^{l}\) can be determined by maximizing expected throughput, i.e. \[\left\{\Phi^{l},P_{0}^{l}\right\}=\underset{\text{argmax}}{\sum}\sum_{j\in B }C_{j}\Big{(}\Phi^{l},P_{0}^{l}\Big{)} \tag{18}\]  
Another method is to solve the corresponding cell utility maximization problem under the condition of a given power \(P_{0}^{l}\). By continuously updating the utility value of the cell between HAPS and GBS, the power \(P_{0}^{l}\) is updated to determine the optimal solution to maximize the network utility. The determination of power \(P_{0}^{l}\) in this resource allocation scheme considers specific traffic flow allocation, so the network utility may be higher than that determined in the first approach. However, the second method will bring some additional signaling overhead. Therefore, this paper adopts the first method to determine \(P_{0}^{l}\).
## 5 Discussion
### The Limitations of This Study  
However, there are also inherent limitations in this research. Firstly, the input parameters of the downscaling model only included MODIS NDVI and LST data, without incorporating meteorological and soil condition data. In future research, it is imperative to assess the influence of different input parameters on the model's output results. Secondly, drought is a complex phenomenon influenced by various interconnected factors encompassing the atmosphere, vegetation, and soil. Consequently, it becomes imperative to integrate multi-source remote sensing data while modelling drought and establishing indices. Lastly, for effective drought monitoring in China where farmland often exhibits a fragmented state, higher resolution remote sensing products are indispensable.
# Challenges of Using the L-Band and S-Band for Direct-to-Cellular Satellite 5G-6G NTN Systems
## 3 Materials and Methods  
In 2021, 3 GPP released a technical report, TR 38.821, titled \"Solutions for NR to Support Non-Terrestrial Networks (NTN)\". This report provides the necessary parameters for simulating NTN networks that offer 5G services [12]. Its main objective is to define the essential features and adaptations required for the New Radio (NR) protocol to operate in non-terrestrial networks, with a particular focus on satellite access. With the release of 3 GPP Release 17, non-terrestrial networks were officially incorporated into the 3 GPP ecosystem. The primary NTN bands defined for this release are band 255 (UL: 1626.5-1660.5 MHz/DL: 1525-1559 MHz), and band 256 (UL: 1980-2010 MHz/DL: 2170-2200 MHz). The specifications of Release 17 aim to support New Radio (NR)-based satellite access deployed in FR1 bands, which operate below 6 GHz. This enables the provision of global service continuity for handheld devices. Additionally, it will also support NB-IoT- and eMTC-based satellite access to cater to use cases in sectors such as agriculture, transport, and logistics [13]. Figure 1 illustrates a typical scenario depicting the direct-to-cellular NTN composition. In 2022, ITU-R published a report called ITU-R M.2514 [14], which is titled \"Vision, Requirements, and Evaluation Guidelines for Satellite Radio Interface(s) of IMT-2020\". The purpose of this report is to establish a vision and define the requirements and evaluation guidelines for the satellite component of IMT-2020. It covers various aspects such as use cases, application scenarios, capabilities, and system requirements. The characteristics outlined in this report align closely with those provided in TR 38.821 by 3 GPP. The report includes baseline configuration parameters that are essential for analyzing and simulating candidate satellite radio interfaces for 5G. These characteristics provide the necessary parameters for simulating link budgets and traffic models and conducting an interference analysis. As our focus is on an interference analysis in which the candidate satellite 5G NTN system acts as a source of interference, we will only use the relevant characteristics from the table that pertain to simulating 5G satellite interferences. Table 2 in Report ITU-R M.2514 presents these parameters. It is worth noting that 3 GPP TR 38.821 contains nearly identical parameters. While these parameters are specified for the S-band, it is mentioned that the carrier frequency of 2 GHz is merely an indicative value. Therefore, in our study, we assume that the characteristics for the L-band will be similar. Considering that the propagation difference between the downlink of the n255 and n256 bands is approximately 3 dB, we have adjusted the downlink satellite EIRP (equivalent isotropic radiated power) for the L-band accordingly. The orbital parameters, as well as the number of satellites, are not specified in the typical characteristics because they will largely depend on the types of services that are not specified.
## 4 Discussion and Conclusions
Even when the ratio between the EL signal and the background signal is below 0.2%, FFT can provide high-quality results by analyzing signal transmission at the modulation frequency. The tested waveforms have produced similar images, with square wave modulation showing slightly better results in term of SNR. However, all waveforms delivered sufficiently high-quality outcomes and similar qualitative quality. Future work will focus on two key points: analyzing image quality under a wide range of irradiance levels and other weather conditions; and integrating the proposed electronics or their variations into devices such as module-level solar optimizers or micro-inverters, in order to enable luminescence measurements under high irradiance conditions in a more economically feasible manner. Conceptualization, A.R.-P., J.I.M.-A. and V.A.-G.; methodology, A.R.-P.; software, A.R.-P.; validation, A.R.-P., A.Z.V.-B. and L.H.-C.; formal analysis, A.R.-P.; investigation, A.R.-P.; resources, A.R.-P.; data curation, A.R.-P.; writing--original draft preparation, A.R.-P.; writing--review and editing, V.A.-G., A.L.Z.-L. and L.H.-C.; supervision, V.A.-G. and L.H.-C.; funding A.R.-P., V.A.-G. and L.H.-C. All authors have read and agreed to the published version of the manuscript. **Funding:** This research was funded by MCIU with grant number FPU21/04288; by MCIU / AEI/ 10.13039/501100011033, FEDER, EU with grant number PID2023-148369 OB-C43 (DETECCION-FV-N); and U.T.E. Duques de Soria.-ARCOR, S.L.U. Y Hermanos Rubio Grupo Constructor HERCE, S.L. with grant reference DETECTA-PV-UVa. **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** The data that support the findings of this study are openly available under the DOIs 10.5281/zenodo.15095306 and 10.5281/zenodo.15095552. **Acknowledgments:** The authors acknowledge the support provided by the Thematic Network 723 RT0150 \"Red para la integracion a gran escala de energias renovables en sistemas electricos (RIBIERSE-CYTED)\" financed by the call for Thematic Networks of the CYTED (Ibero-American Program of Science and Technology for Development) for 2022. **Conflicts of Interest:** The authors declare no conflicts of interest.
# Dynamic Voltage Optimization Based on In-Band Sensors and Machine Learning
## 3 Case Studies and Issues for Conventional CVR
### Case Studies: Non-Optimal Results
#### 3.1.5 Jacksonville Electric Association (JEA)  
JEA realized a significant decrease in load using voltage reduction in 2003. By implementing a 5% reduction in voltage, JEA reduced load by more than 65 MW from the peak of 3166 MW. JEA reported no customer complaints during this voltage reduction event, which allowed all customers to receive electric service and averted rolling blackouts during an unprecedented demand peak [24].
## 2 Related Works
TBDP defines the AUV auxiliary network as the end-edge-cloud system, in which the underwater sensor system including the sensor node and the communication gateway node is defined as the end system, the AUV and the water surface sink node are defined as the edge system, and the shore-based data center is defined as the cloud environment [43; 44]. Two-way prediction algorithms are set between the end system and the edge system, and between the edge system and the cloud environment, to compress the transmission data demand. Although using an AUV as a method of underwater data acquisition has certain advantages, the real-time performance of data acquisition is still insufficient compared with that of the fixed 3D underwater sensor network. In addition, the prediction algorithm proposed by TBDP has room for improvement in prediction accuracy, applicability, and mobility for different application requirements. Therefore, on the basis of the above research, in order to improve the communication efficiency of the fixed 3D underwater wireless sensor network, it is necessary to carry out research on the adaptive data transmission algorithm that comprehensively considers business applications and the transmission environment. The algorithm realizes data compression or preprocessing based on edge prediction, and realizes the selection of relay nodes and the control of data transmission accuracy based on reinforcement learning. Underwater environment monitoring includes the monitoring of the underwater temperature, salinity, pressure, PH value, dissolved oxygen, NH4-N, and other time series data, and various applications have great differences in terms of the quantity, accuracy, frequency, speed, and reliability of data transmission. However, in this paper, only the underwater temperature monitoring is taken as an example to illustrate the system framework and algorithm. Other types of monitoring applications can be improved in the system framework in combination with the application characteristics.
# Short-Term Travel Demand Prediction of Online Ride-Hailing Based on Multi-Factor GRU Model
## 4 Result Analysis
### The Comparison of Predicted Results
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Model** & \multicolumn{3}{c}{**The Improved GRU Model**} & \multicolumn{3}{c}{**GRU Model**} \\ \hline
**Index** & **RMSE** & **MAE** & **MAPE** & \(\mathbf{R^{2}}\) & **RMSE** & **MAE** & **MAPE** & \(\mathbf{R^{2}}\) \\ \hline Test I & 314.69 & 440.47 & 0.30 & 0.88 & 748.21 & 889.46 & 0.42 & 0.72 \\ Test II & 348.93 & 467.59 & 0.33 & 0.86 & 774.91 & 908.87 & 0.46 & 0.66 \\ Test III & 302.73 & 423.28 & 0.28 & 0.89 & 768.65 & 895.23 & 0.44 & 0.68 \\ Test IV & 359.61 & 482.84 & 0.34 & 0.85 & 759.82 & 890.94 & 0.44 & 0.68 \\ Test V & 320.17 & 456.21 & 0.31 & 0.88 & 740.93 & 901.64 & 0.43 & 0.70 \\
**Average** & **329.23** & **454.08** & **0.31** & **0.87** & **758.50** & **897.23** & **0.44** & **0.69** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Predictive evaluation index comparison. Figure 9: Comparison of predictions for a rest day. According to Figure 10, \"GRU+\" represents the improved GRU model proposed in this paper. It can be seen that the prediction accuracy of the GRU model is higher than that of the RNN and LSTM models when the data sample size and training times are less, and the prediction accuracy of the proposed improved GRU model is also much higher than that of the other models. Based on the above analysis, it can be concluded that the improved GRU model proposed in this paper has good advantages in short-term online ride-hailing travel demand prediction, and the prediction accuracy of the weighted GRU model is greatly improved compared with the ordinary GRU model.
# Background Subtraction for Moving Object Detection in RGBD Data: A Survey
## 6 Comparisons
### Comparisons on the SBM-RGBD Dataset
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Method** & **Rec** & **Sp** & **FPR** & **FNR** & **PWC** & **Prec** & **F\({}_{1}\)** \\ \hline \multicolumn{7}{c}{Bootstrapping} \\ \hline RGBD-SOBS & 0.8842 & 0.9925 & 0.0075 & 0.1158 & 2.3270 & 0.9080 & 0.8917 \\ RGB-SOBS & 0.8023 & 0.9814 & 0.0186 & 0.1977 & 4.4221 & 0.8165 & 0.8007 \\ SRPCA & 0.7284 & 0.9914 & 0.0086 & 0.2716 & 3.7409 & 0.9164 & 0.8098 \\ AvgM-D & 0.4587 & 0.9861 & 0.0139 & 0.5413 & 7.1960 & 0.6941 & 0.5350 \\ Kim & 0.8805 & 0.9965 & 0.0035 & 0.1195 & 1.5227 & 0.9566 & 0.9169 \\ SCAD & 0.8997 & 0.9940 & 0.0060 & 0.1003 & 1.8015 & 0.9319 & 0.9134 \\ cwisardH+ & 0.5727 & 0.9616 & 0.0384 & 0.4273 & 8.1381 & 0.5787 & 0.5669 \\ MFCN & **0.9866** & **0.9985** & **0.0015** & **0.0134** & **0.2286** & **0.9885** & **0.9876** \\ \hline \multicolumn{7}{c}{ColorCamouflage} \\ \hline RGBD-SOBS & 0.9563 & 0.9927 & 0.0073 & 0.0437 & 1.2161 & 0.9434 & 0.9488 \\ RGB-SOBS & 0.4310 & 0.9767 & 0.0233 & 0.5690 & 16.0404 & 0.8018 & 0.4864 \\ SRPCA & 0.8476 & 0.9389 & 0.0611 & 0.1524 & 4.3124 & 0.8367 & 0.8329 \\ AvgM-D & 0.9001 & 0.9793 & 0.0207 & 0.0999 & 2.0719 & 0.8096 & 0.8508 \\ Kim & 0.9737 & 0.9927 & 0.0073 & 0.0263 & 0.7389 & 0.9754 & 0.9745 \\ SCAD & **0.9875** & 0.9904 & 0.0096 & **0.0125** & 0.7037 & 0.9677 & 0.9775 \\ cwisardH+ & 0.9533 & 0.9849 & 0.0151 & 0.0467 & 1.1931 & 0.9502 & 0.9510 \\ MFCN & 0.9859 & **0.9977** & **0.0023** & 0.0141 & **0.4272** & **0.9893** & **0.9876** \\ \hline \multicolumn{7}{c}{DepthCamouflage} \\ \hline RGBD-SOBS & 0.8401 & 0.9985 & 0.0015 & 0.1599 & 0.9778 & 0.9682 & 0.8936 \\ RGB-SOBS & 0.9725 & 0.9856 & 0.0144 & 0.0275 & 1.5809 & 0.8354 & 0.8935 \\ SRPCA & 0.8679 & 0.9778 & 0.0222 & 0.1321 & 2.9944 & 0.7850 & 0.8083 \\ AvgM-D & 0.8368 & 0.9922 & 0.0078 & 0.1632 & 1.6943 & 0.8860 & 0.8538 \\ Kim & 0.8702 & 0.9968 & 0.0032 & 0.1298 & 0.9820 & 0.9433 & 0.9009 \\ SCAD & 0.9841 & 0.9963 & 0.0037 & 0.0159 & 0.4432 & 0.9447 & 0.9638 \\ cwisardH+ & 0.6821 & 0.9949 & 0.0051 & 0.3179 & 2.4049 & 0.9016 & 0.7648 \\ MFCN & **0.9870** & **0.9986** & **0.0014** & **0.0130** & **0.2134** & **0.9741** & **0.9804** \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Average results of various background subtraction methods for each category of the SBM-RGBD dataset (Part 1). In boldface, the best results for each metric and each category.
## 2 Study Area and Datasets
### Data Collection  
The ancient Muyubao landslide was reactivated by the impoundment of the Three Gorges Reservoir in September 2006. A landslide monitoring system consisting of twelve GPS survey monuments was installed on the landslide mass (see Figure 2 for GPS monument locations) to monitor landslide movement. Nearly 13 years of monitoring data from October 2006 to October 2018 were acquired. According to the monitoring data, the maximum landslide displacement occurred at ZG291 with a cumulative displacement of 2437.36 mm. The landslide displacement at ZG291, reservoir level in the Yangtze River, and rainfall intensity are shown in Figure 3. As shown, the Muyubao landslide exhibits step-like deformation. Sharp increments of displacement occur mainly from November to March, with the reservoir level decreasing from 175 m to 165 m.  
Figure 3: Time series of landslide displacement at ZG291, reservoir level, and rainfall intensity during the monitoring period from October 2006 to October 2018.  
Figure 2: Location and geological profile of the Muyubao landslide, Three Gorges Reservoir area.
# Safety Assessment of the Cover-and-Cut Method Under Blasting Vibration Induced by Tunnel Excavation
## 4 Vibration Analysis of Subway Tunnel Construction
### Trend and Regression Analysis of Repeated Blasting Vibrations at Vulnerable Points  
Based on the cloud map and vibration measurement results, it can be observed that the location most affected by the blasting vibrations is near measuring point 11 on the central column. This is identified as the vulnerable point of the structure, with the highest vibration velocity recorded at 1.537 cm/s. Due to the central part being subjected to both roof pressure and vibration impacts, the pressure and vibration effects are significant. Repeated vibrations may cause the supporting structure of this vulnerable point to exceed its safety limits. To ensure safety, further research will be conducted to assess how many blasting vibrations might exceed the structural capacity of the central column. The specific trend is shown in Figure 14.  
After six blasting events, irregular changes in the peak vibration velocity at the monitoring points were observed. However, from the fourth blast onward, a sustained gradual increase was observed, followed by a smooth trend, all remaining below 1.537 cm/s. Therefore, the peak vibration velocities caused by cyclic blasting remained below the tunnel construction safety standard of 2.5 cm/s.  
Figure 14: Regression relationship between peak particle velocity (PPV) at point 11 and blasting frequency.  
The regression equation for the blasting vibration speed at the same monitoring point is as follows:  
\[F(x)=0.0025\times x^{5}-0.05\times x^{4}+0.36\times x^{3}-1.24\times x^{2}+1.9 \times x+0.43 \tag{7}\]  
Regression analysis of the blasting vibration changes at the same monitoring point shows that a relationship between tunnel structure and blasting frequency can be established, which clarifies the overall trend of vibration velocities at the monitoring points. The polynomial regression equation between unit blasting vibration velocity and blasting frequency is given by Equation (7), with a confidence level exceeding 0.95. The results indicate that the relationship between tunnel structure damage and blasting frequency follows a cubic polynomial pattern. After eight blasts, although no damage occurred, the peak total blasting vibration velocity increased. Therefore, optimizing the existing blasting plan is imperative.
# Low Power Wide Area Network, Cognitive Radio and the Internet of Things: Potentials for Integration
## 6 Benefits of CR-LPWAN
### Benefits to IoT Application Areas
#### Smart Agriculture  
Smart agriculture could involve deploying sensors on foraging animals in order to monitor and keep track of their movement and health conditions [103]. Since these animals often transverse long distances, CR-LPWAN systems could provide white space transmission to achieve longer range communication. Most importantly, CR-LPWAN systems would allow these sensors to transmitat different frequency bands to avoid interference particularly when they forage faraway to uncharted terrains.
# Deep Reinforcement Learning for Resource Management on Network Slicing: A Survey
## 6 Resource Scheduling
### Remarks  
RL and DRL have been applied successfully to perform resource scheduling in NFV, RAN, and optical networks disjointly, as shown in Table 5. RL-based approaches [90; 91; 92; 94] used algorithms such as Q-learning, QV2-learning, and QVMAX2-learning to schedule packets in service function chains as well as in radio and optical network slices to minimize cost, delay, and packet drops primordially. DRL-based approaches [89; 95; 96] employed algorithms such as DQN and A3C to schedule resources to 5G-RAN and UAV network slices while optimizing network utilization, SLA satisfaction, QoE meeting, and dealing with scalability issues of RL-based approaches. As the worst-case complexity of Q-learning and DQN was introduced in the early sections of this paper, here we show the complexity of the A3C algorithm [89]: \(O((N*(1/N_{u})*T_{c}*M*(\sum_{i=0}^{L_{e}}u_{i}{}^{a}*u_{i}{}^{(i+1)}+\sum_{j =0}^{L_{e}}u_{i}{}^{c}*u_{i}{}^{(j+1)}))\) where \(N\): number of neurons, \(N_{u}\): number of CPU threads used to train the algorithm, \(T_{c}\)number of training steps, \(M\): number of slices, \(i\) and \(j\): number of units in the \(i\)th and \(j\)th layer of the DNN, and \(u_{a}\) and \(u_{c}\): number of units of the actor-critic network.  
Unfortunately, few works (e.g., [90, 91, 94]) have investigated the scheduling of physical and virtual links to connect RAN, CN, and edge networks to guarantee the QoS of slices and providers' profit or investigated resource scheduling NSL from an end-to-end perspective (like [93] did). Indeed, most research has focused on proposing scheduling approaches for 5G-RAN, disregarding the importance of TN, CN, and edge networks for realizing NSL. Since NSL is an end-to-end concept, more sophisticated solutions in multitenant environments involving elements from RAN, TN, CN, and edge networks are necessary to make 5G/6G network slices a reality. As in Table 5 only [89] used a distributed RL/DRL-based approach, novel solutions should consider multiagent, noncentralized, and online RL/DRL to handle the dynamism of 5G/6G networks and avoid making decisions with incomplete network views; the network overhead and consensus protocols are pivotal to assessing the feasibility of such solutions. Furthermore, the mentioned table corroborates and highlights the necessity to advance the RL/DRL-based scheduling approaches to operate with datasets containing real traces and evaluate them in real or testing networks. The need to obtain NSL solutions for other vertical application domains such as immersive media and remote surgery is also noteworthy.