esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Example Analysis  
### Overview of the Simulation System  
An actual distribution network in a region of Gansu Province, China was used as an example to verify the model and algorithm proposed in this paper. The simulation system is shown in Figure 6. The waste-to-energy power stations were connected to nodes 19 and 23 in the system, and the maximum output power of the waste-to-energy power station was 600 kW. The micro energy grid of the agricultural park was connected to node 11, and the maximum exchanged power between the distribution network and the micro energy grid was 500 kW. The rated voltage of the lines was 10 kV, and the impedance of the lines is shown in Table 1. The simulation platform was MATLAB 2014a. The improved GWO algorithm was used to solve the model in this paper. The parameters of the algorithm were as follows: the grey wolf swarm size was \(N=20\), and the maximum number of iterations was 50. The power-supply equipment in the micro energy grid of the agricultural park included PV power, wind power, microturbines, gas-fired boilers, heat-recovery boilers, lithium-bromide absorption-type refrigerators, battery storage, heating and cooling storage tanks, and air-source heat pumps. The price of gas was 0.35 RMB/kW\(\cdot\)h. The price of the power grid was determined using the peak and valley electricity prices issued by the Gansu Provincial Development and Reform Commission, as shown in Table 3. The related environmental pollution parameters were provided in Reference [41]. The equipment parameters of the micro energy grid are provided in Tables 4 and 5 [7,41]. Load curves  
Figure 5: Result comparison of test function 1 **(a)**; function 2 **(b)**; function 3 **(c)**; function 4 **(d)**; function 5 **(e)**; and function 6 **(f)** with five types of algorithms—the particle swarm optimization (PSO) algorithm, the invasive weed optimization (IWO) algorithm, the GWO algorithm, the improved GWO algorithm, and the GWO/evolutionary population dynamics (GWO-EPD) algorithm. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Storage Equipment**} & \multicolumn{2}{c}{**Maximum Efficiency of Combined**} & \multicolumn{2}{c}{**Charged State of Combined**} & \multirow{2}{*}{**Lifespan/a**} & \multirow{2}{*}{**Capacity/(0/kW-b)**} & \multirow{2}{*}{**Initial Investment**} & \multirow{2}{*}{**Maintenance**} \\  & **Electricity Heating and Cooling** & & & & & & **Cost/(RMB/AW-b)** & **Cost/(RMB/AW-b)** \\ \cline{2-7}  & **Charging** & **Discharging** & **Maximum** & **Minimum** & & & & \\ \hline Battery storage & 0.2 & 0.2 & 0.9 & 0.2 & 5 & 1000 & 1100 & 0.03 \\ \hline heating and cooling storage tank & 0.2 & 0.2 & 0.9 & 0.1 & 10 & 600 & 1200 & 0.02 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Storage equipment parameters of the micro energy grid.
## 3 Results and Discussion
### Intracellular Polysaccharide Synthesis and Composition
IPS composition during fungi cultivation seemed to increase or decrease depending on strain and agitation imposed (Table 3) and in all cases, glucose was the most abundant individual sugar identified and quantified in the produced polysaccharides (40-70%, w/w). Mannitol was found in equal or smaller quantity than glucose (20-41%, w/w), whereas fructose was detected in relatively small concentrations (up to 10%, w/w). The only exception was the cultivation of _M. rotunda_ on agitated culture of M-25 where mannitol was detected in the highest quantity (43%, w/w), following glucose (37%, w/w) and fructose (20%, w/w). Similarly with the data of this study, glucose was the most abundant carbohydrate in the mycelium of _M. esculenta_[29; 60] and other mushrooms; _G. applanatum_[29; 58], _P. pulmonarius_[29; 61] and _Agrocybe aegerita_, _F. velitpies_[29]. Likewise, [PERSON] et al. [29] indicated that mannitol was produced in significant amounts in _M. esculenta_, compared to other species that produced more fructose. Glucose was the principal monosaccharide detected in the mycelia of _Morchella_ species regardless of the carbon source utilized, sucrose or molasses. Furthermore, with an increased C/N ratio, glucose percentage decreased and fructose percentage increased, those of mannitol decreased in static cultures and increased in agitated cultures.
# Smart Roads for Autonomous Accident Detection and Warnings
## 3 Proposed Accident Alert Light and Sound System
A node can be powered from existing light poles on the roads or highways; otherwise, solar panels can be deployed. The basic functions of the AALS system are accident detection from outside vehicles (i.e., from the roadside) and alerting oncoming vehicles about an accident by blinking a light and making a sound. The AALS system communicates with the next node through EDWSN, a wireless communication protocol that uses two HC-12 modules with low power consumption. It functions only when an accident is detected by the AALS or when the reset button is pressed. When an accident happens, certain events occur. For example, when brakes are applied at high speed, a squealing sound is produced; when a vehicle hits another vehicle, it produces a loud sound that can be heard from a distance. If glass breaks, it produces an audible sound; if a vehicle is burning, the temperature of the environment rises, and smoke is produced; when a vehicle suddenly stops in the middle of the road, it becomes an obstacle for other vehicles. By considering all these factors, a foolproof system is available for accident detection. The steps of the proposed algorithm for a node in the system are shown in Figure 1.
# Beyond the Horizon, Backhaul Connectivity for Offshore IoT Devices
## 3 Results  
Results for predicted path loss using P.E based simulation tool, measured average path loss with additional slow fading and expected availability and capacity are shown in this section.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Constant Parameters** & **Value** \\ \hline Frequency & 5.8 GHz \\ Transmit Power & 30 dBm \\ Transmitter antenna Gain & 20 dBi \\ Receiver Antenna Gain & 20 dBi \\ Insertion Loss & 2 dB \\ Bandwidth & 20 MHz \\ \hline \hline \end{tabular}
\end{table}
Table 2: Constant parameters and values of Wi-Fi equipment installed.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Availability (\%)** & **Fade Margin (dB)** \\ \hline
90 & 3 \\
95 & 5 \\
99 & 7 \\
99.9 & 10 \\
99.99 & 12 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Fade margin for MIMO based Rayleigh fading model.
# Influence of Open Differential Design on the Mass Reduction Function
## 5 Optimization of Differential Transmission  
### Topological Optimization and Determination of Design Parameters  
The amount of the obtained maximum equivalent stress in the ring gear submodel (Figure 10) is lower than the allowable stress and a significant part of the submodel has much lower values of stress leaving the possible to apply the method of topological optimization for the determination and selection of the general design parameters of the ring gear and eventually lower the mass of submodel. The selected parameters represent the input parameters for the RS analysis. The topological optimization is performed in ANSYS Workbench and results in the form of a presentation of submodel volume that has to be retained, as shown in Figure 13a, but the obtained irregular geometry is not suitable for production using standard gear manufacturing processes. With the geometry thus obtained by taking care that the entire part that must be retained is included in the geometry, a new ring gear geometry suitable for production was created and later used to assess the individual influence of design parameters on overall mass using RSM (Figure 13b).  
The resulting ring gear geometry is defined by giving three additional dimensions (_a_, \(b\) and _h_) as parameters (Figure 14). The parameterization of the presented model was  
Figure 12: Stress and displacement distribution of the drive shaft: (**a**) distribution of equivalent von Mises stress in MPa; (**b**) total deformation in mm.  
Figure 13: Ring gear geometry: (**a**) geometry obtained by the topological optimization method; (**b**) corrected geometry.  
performed in Ansys SpaceClaim 2022 software [44], allowing for the change of desired parameters in parametric analysis.  
The determination of the design parameters of the drive shaft results from a numerical analysis for the previous Section 3.3, which showed that the amounts of the equivalent stresses in the central part of the drive shaft are very small (Figure 12a). Therefore, it is possible to apply the structural solution of the hollow shaft and determine the design parameter in relation to the inner radius of the driving shaft (Figure 15).  
The final selection of the general design parameters to be analyzed using the RSM is shown in Table 8. The selected initial and final values of the solution interval of the design parameters were determined from the analysis of the stress results obtained by the numerical calculation, the topological optimization and the geometry of the CAD model obtained by the calculation of the design elements of the differential transmission according to the standard [35].
## 2 Methodology
### Performance Evaluation Metrics  
By processing the input parameters included in ML algorithms, four different performance criteria are used to evaluate the accuracy and validity of the values of the estimation data of the output parameters. Three of the four measurement criteria represent the margins of error of the ML algorithms, while one represents the correlation coefficient (R\({}^{2}\), percentage of accuracy). It is desirable that the RMSE, MSE, and MAE values, which represent the margins of error, are small and the accuracy coefficient is high to verify the validity of the results obtained from the ML algorithms. The lower the MAE, RMSE, and MSE values of the performance criteria of ML algorithms, the better the algorithm prediction performance [54]. The mathematical equations for the performance criteria are provided below [55]:  
Root mean square error (RMSE):  
\[RMSE=\sqrt{\frac{\sum_{i=1}^{n}\bigl{(}x_{o}-\widetilde{x}_{p}\bigr{)}^{2}}{n}}, \tag{1}\]  
Mean squared error (MSE):  
\[MSE=\frac{1}{n}\sum_{i=1}^{n}\left(x_{o}-\widetilde{x}_{p}\right)^{2}, \tag{2}\]  
Mean absolute error (MAE):  
\[MAE=\frac{1}{n}\sum_{i=1}^{n}\bigl{|}x_{o}-\widetilde{x}_{p}\bigr{|}, \tag{3}\]  
Correlations coefficient (R\({}^{2}\)):  
\[R^{2}-1=\sum_{i=1}^{n}\biggl{[}\frac{x_{o}-\widetilde{x}_{p}}{x_{o}-\overline{ x}_{p}}\biggr{]}^{2}. \tag{4}\]  
where \(n\) represents the data volume of the data set (observations) used for the testing and training phases, and the observation values are symbolized by \(x_{o}\). The symbols of \(\widetilde{x}_{p}\) and \(\overline{x}_{p}\) signify the value of prediction and the mean of actual values, respectively [56].  
A comparison of the measurement ratios of the absolute measurement error was provided using RMSE, MAE, and AE calculations to evaluate the adequacy of the models. These values estimate the relative error and accuracy if the ML models are assessed on  
\begin{table}
\begin{tabular}{l l} \hline \hline
**NN Model** & **Parameters** \\ \hline Hidden layers & 100 \\ Activation & ReLu \\ [PERSON] & [PERSON] \\ Alpha & 0.0001 \\ Max iterations & 200 \\ Replicable training & True \\ \hline \hline \end{tabular}
\end{table}
Table 5: The parameters of NN algorithm.  
different samples with different values. In addition, the R values of the ML models were calculated, and the degree of closeness between the prediction data and the actual data was measured.
# Assessing the Security and Privacy of Baby Monitor Apps
## 6 Discussion & Conclusions
### Limitations  
A relevant part of the presented results was obtained by intercepting and decrypting protected network communication using a proxy. Since MitM attacks on properly secured TLS connections result in connection errors due to failed certificate checks, we disabled certificate checks for the privacy analysis part. We did so by patching app instances using Objection, which alters the application. Alteration of the application may threaten the validity of our investigation, as the behavior of the application may change in response. We compared patched and non-patched monitoring sessions, and to the best of our knowledge, no negative influence could be determined. Moreover, patching Android applications is a widely used technique that has also been used in previous similar work [51].  
A weak point of our methodology is the small sample size of only four Android applications. However, published studies of niche applications with few alternatives offered also used a sample of similar size [22], and therefore, we see the fact that all apps have more than 500,000 downloads as a mitigating factor.  
Since we did not attempt to reverse engineer encoded or obfuscated data transmissions, we did not consider this data exchange in our evaluation. Therefore, we may have missed registering privacy leaks in these circumstances.
## 4 Experimental Results
### Application  
#### 4.7.1 Experiment Platform  
To validate the flexibility and practicality, we apply our model to a real-world data encryption scenario. In this application, we use the computer system to test the performance of cost time and accuracy in data encryption application. This test system contains a 64-bit CPU with Intel(R) Core(TM) i7-9750H, and a USB camera with a resolution of 640 \(\times\) 480. a face image is captured by the USB camera then the captured face data and plaintext are entered in the test model to verify its performance. We can utilize the PC display to observe the test result.
## 5 Experimental Setup
### Model Performance Evaluation Experiment  
We design model performance evaluation experiments on six datasets, monitoring the changes in metrics such as AUC, AP, loss, and Top@K across training epochs, in order to analyze the model's performance optimization process.  
As shown in Figure 6, both AUC and AP increase with the number of epochs. The rise in AUC reflects the improved ability of the model to distinguish between positive and negative samples, while the increase in AP indicates improved accuracy in predicting positive samples. Both metrics demonstrate improvements in the model's learning and prediction accuracy. At the same time, loss decreases with epochs, meaning the deviation between model predictions and true labels reduces, indicating better fitting of the model.  
As shown in Figure 7, Top@1 to Top@20 increase horizontally with the increase in epoch, indicating that the model's prediction accuracy for each predefined ranking position continues to improve, allowing for more precise determination of the results at the corresponding ranks. At the same time, its vertical increase with epoch reflects that the model's overall prediction capability across the entire ranking range has been comprehensively enhanced, with the result quality at all ranking positions being optimized.  
Figure 5: Performance evaluation metrics of GEVEHGAN and its derived models on the NYC and TKY datasets. (**a**) Shows the AUC scores of GEVEHGAN and its derived models on the NYC and TKY datasets; (**b**) displays the average precision scores of these models on the two city datasets; (**c**) presents the TOP@10 hit rate of the models on the NYC and TKY datasets. Through these detailed metric comparisons, we can gain a deeper understanding of the performance differences of GEVEHGAN and its variants in different data environments.  
Figure 6: The change trends of AUC, AP, and loss with respect to epoch for GEVEHGAN on six datasets. (**a**–**f**), respectively, show the improvement in AUC and AP, and the decrease in loss, with the increase in training epochs on the IST, JK, KL, NYC, SP, and TKY datasets.
## 4 Discussion
### Ecology of Mesopelagic and Bathypelagic Phages  
Bacteriophage from the aphotic zone were almost all classified as unknown, and shared the least similarity with previously known phage (Figure 2), suggesting that the diversity of both mesopelagic and bathypelagic phage is still underrepresented in current databases [50; 51; 52]. The neat separation of mesopelagic and bathypelagic communities into distinct clusters is in accordance with previous analysis on the global virome, that found similar separation at least in temperate and tropical latitudes [53].  
Marine deep ecosystems are characterized by cold temperatures and the limited bioavailability of nutrients to sustain a high microbial metabolism. The genomic content revealed a high number of putative transporters and enzymes related to the de novo synthesis of nucleotides (AIR synthase). The expression of virally-encoded transporters may enhance the uptake of nutrients by the host, to later be used as building blocks for the de novo synthesis of nucleotide and ultimately the assembly of new phage [42]. These results emphasize the potential of viruses to enhance microbial nutrient scavenging, especially in habitats where nutrients are a limiting factor for microbial growth and metabolism.  
The relative increase of integrase genes with depth suggests an increase in the potential for a lysogenic lifestyle. This result is strengthened by the comparable trend in the integrase gene depth distribution for both the virome database (Figure 6c) and the total genes from the microbial database (Supplementary Figure S3). A lysogenic lifestyle could be preferable in a situation where the host growth or metabolism is either compromised or slowed down, like deep ecosystems, where there is a possible limitation of resources for virion production. Moreover, host genome size has also been linked with an increase in bacteriophage lysogenic lifestyle [54]. Environments that have less constraints in organism genome size could be more permissive with the accumulation of exogenous DNA, like prophage, and thus favor a bacteriophage lysogenic lifestyle [55]. Deep microbes are indeed usually characterized by a larger genome size [56].
## 4 Research Activities  
This section provides a brief overview of 6G research activities. Several 6G activities have been commenced globally by industrial organizations and governments whose objectives include formulating and defining 6G technology and then re-adjusting the outline in addition to the business model of wireless systems. The United States FCC has proposed allocating the 95 GHz to 3 THz spectrum to be used for 6G research, thereby setting the US as the pacesetter in the 6G research race. Other entities have begun concrete research efforts in 6G networks. Some of the notable names include the Finnish consortium through their 6 Genesis Flagship Program and the Terabit Bidirectional Multi-user Optical Wireless System (MU-OWS) for 6G LiFi which commenced in early 2019. The 6G research race from academia can be said to have started in March 2019, when the first 6G Wireless Summit was held in Levi, Finland [13]. Apart from the aforementioned efforts, other mini workshops and conferences have been conducted globally to examine the prospect of 6G, including the Huawei 6G Workshop, the Wi-UAV Workshop of Globecom 2018, and the Carleton 6G Workshop. A research group based on the EU's Terranova project is now working toward a reliable 6G connection with 400 Gbit per second transmission capability in the THz spectrum [19]. LG Electronics also announced the foundation of a 6G research center at the Korea Advanced Institute of Science and Technology, Daejeon, South Korea. Samsung launched its 6G research in June 2019. SK Telecom announced a collaboration with Nokia and Ericsson for 6G research in 2019. In late 2018, China's Ministry of Industry and Information Technology declared its goal of leading the wireless communication market in the 2030s by expanding research investment in 6G. In addition, an EU-Japan project under Horizon2020 ICT-09-2017 funding called \"Networking Research beyond 5G\" also investigated the possibility of using the THz spectrum from 100 to 450 GHz. Table 2 summarizes the research initiatives into 6G communications. Moreover, IEEE launched the IEEE Future Network with the tagline \"Enabling 5G and beyond\" in August 2018. The ITU-T Study Group 13 also established the ITU-T Focus Group Technologies for Network 2030 in its aim to understand the service requirements for future networks by 2030 [19].
## 3 Building Load Type
### Classify Based on Energy Type
#### 3.3.1 Power Load  
Given the fluctuations in electricity prices during peak and off-peak hours, the ability to predict peak loads in advance is crucial for avoiding unnecessary additional costs [110]. Accurate predictions of energy demand, along with an understanding of anticipated peak loads, not only help ensure the reliability of power supply but also effectively reduce electricity costs for consumers. However, due to the variations in energy consumption characteristics among buildings in different regions, building accurate load prediction models remains a challenging task [111].  
To improve the accuracy of load forecasting, commonly used strategies involve adopting systematic loss methods such as neural networks [112], SVR [113], and their derivative techniques [114]. The utilization of deep reinforcement learning for short-term load predic  
Figure 10: An example of load demand forecasting for residential buildings [104].  
tion can effectively address issues related to high temporal correlation and high convergence instability [115].  
Short-term building energy consumption forecasting is a significant topic in the energy market and the operation of smart grids. Existing forecasting methods include sequence-to-sequence long short-term memory networks (S2S LSTMs), seasonal autoregressive (SAR), multi-output support vector machines (MO-SVMs), multi-layer perceptron (MLP), and clustering-based approaches [116]. These methods each have their own advantages in improving the accuracy of short-term load forecasting, providing powerful tools for energy management and optimization.  
With the popularization of electric vehicles, their charging load has become an emerging and non-negligible part of the building's power load. In various building scenarios, such as residential areas, commercial buildings, and public parking lots, the charging demand for electric vehicles is increasing day by day. Compared with traditional power loads, the charging load of electric vehicles has strong randomness and uncertainty. On the one hand, the charging time and charging amount of electric vehicles depend on factors such as the travel habits of vehicle owners, the battery capacity of the vehicles, and the remaining power. On the other hand, the simultaneous charging of a large number of electric vehicles may cause a sudden surge in power demand in a local area within a short period, putting great pressure on the power grid.  
To accurately predict the charging load of electric vehicles, researchers have attempted to adopt a variety of methods. For example, a probability model based on user behavior analysis. By collecting and analyzing the charging behavior data of a large number of vehicle owners, it predicts the probability of electric vehicles accessing the power grid and the charging power at different times. And a real-time monitoring and prediction system combined with smart grid technology. It uses sensors and communication networks to obtain the charging status information of electric vehicles in real-time and then accurately estimates the future charging load.