esa-sceva/satcom-chunk-collection huggingface dataset:
# Evaluating the Energy-Saving Potential of Solar Dedicated Ventilation Systems Based on ADRC
## 3 Accurate Control and Analysis Based on ADRC
### Control Logic of the Proposed Solar Dedicated Ventilation System Based on ADRC  
The control logic of the proposed solar dedicated ventilation system is necessary to realize accurate control. The logic controls the indoor temperature by using the ADRC to adjust the opening of the electric control valve of the solar thermal system, the opening of the electronic expansion valve of the dedicated ventilation system, and the compressor frequency. The regulating process involves primary and secondary regulation for accurate control. The control strategy regulates the opening of the electronic expansion valve and the frequency of the compressor in the primary adjustment and the opening of the electric control valve of the solar thermal system in the secondary adjustment based on the indoor conditions. If the indoor temperature is higher than the setting temperature, the ADRC can be applied to increase the opening of the electronic expansion valve and adjust the compressor frequency to meet the indoor temperature requirement. The ADRC can also change the opening of the electric control valve of the solar thermal system to achieve accurate control. Similarly, if the indoor temperature is lower than the setting temperature, the ADRC can be applied to decrease the opening of the electronic expansion valve and adjust compressor frequency, and then change the opening of the electric control valve of the solar thermal system. The temperature setting range is an accurate scope. When the indoor temperature does not fall inside the accurate temperature range, the ADRC will adjust the proposed system to the proper temperature range.  
Besides, a delay must be set for the indoor temperature change and regulating process on the flow process chart (for example, regulating the opening of the electric control valve of the solar thermal system and of the electronic expansion valve and compressor frequency of dedicated ventilation system by ADRC). A flowchart of the control system is shown in Figure 3.  
Figure 3: Flowchart of the control system.  
Consequently, stability and accuracy can be obtained by this proposed precision control system.
## 4 Results
### Simulation of TOC Reflectance  
We assessed the performance of the RTMo model in retrieving vegetation properties by: (1) using the goodness-of-fit measured between the simulated and observed TOC reflectance; and (2) comparing the retrieved with the measured vegetation properties (Section 4.3). Figure 3 illustrates the goodness of fit using COR and NSE, and provides a spectral comparison between the observed mean TOC reflectance (by HLS and airborne) and the simulated TOC reflectance (by the RTMo model) for all 14 days during the vegetation growing season. We found that the NSE was close to one on each day, indicating a perfect match between the simulated and observed TOC reflectance. Strong positive correlations between simulations and observations were indicated by a COR value close to one. These showed that the RTMo model was able to successfully reproduce the observed HLS and airborne TOC reflectance. We presented the observed and simulated TOC reflectance curves for the selected Julian days in Figure S2.
## 4 Simulation and Analysis
### Sinusoidal Steering Combined with Braking Conditions  
Under this condition, the initial speed is set to 60 km/h, the road adhesion coefficient is set to 0.85, the brake master cylinder pressure is set to 0.3 Mpa, and the sinusoidal steering angle is input in Carsim. The corresponding input of the observer is shown in Figure 8, and the corresponding comparison of the algorithm results is shown in Figure 9.  
Figure 8: Sinusoidal steering combined with braking conditions: (**a**) observer input, including front wheel angle and four-wheel speed; (**b**) vehicle trajectory.  
Figure 9: Simulation results of vehicle state under sinusoidal steering combined with braking conditions: (**a**) longitudinal velocity; (**b**) lateral velocity; (**c**) yaw rate; (**d**) probability of each IMM’s model.  
As shown in Figure 9a, various algorithms can maintain effective tracking of longitudinal vehicle speed, with the IMM-SCKF algorithm having higher accuracy. In the lateral vehicle speed observation results in Figure 9b, all three algorithms have some errors due to simultaneous braking in vehicle steering, but the IMM-SCKF algorithm can quickly switch to adjust the probability of each model and can maintain a small error output with the reference value. All three algorithms have high accuracy in yaw rate observation, but IMM-SCKF has a small estimation error.  
To further evaluate the estimation effectiveness of the two algorithms, the root mean square error (RMSE) was used for quantitative analysis and is calculated as follows:  
\[\text{RMSE}_{k}(i)=\sqrt{\frac{1}{M}\sum_{l=1}^{M}\left(x_{k}^{(l)}(i)-\hat{ x}_{k|k}^{(l)}(i)\right)^{2}} \tag{52}\]  
The calculated RMSE index are shown in Table 2.  
Based on the data in Table 2, the IMM-SCKF algorithm has lower RMSE values under both operating conditions, according to the above indexes. The IMM-SCKF algorithm has higher observation accuracy than the SCKF and UKF algorithm, and the state observer of the IMM-SCKF algorithm can better adapt to noise uncertainty and can always follow the model output with smaller errors. As can be seen from Table 3, the SCKF algorithm has lower computational complexity and shorter simulation time than the UKF algorithm, while the IMM-SCKF also shows better computational performance, the actual consumption of simulation time is the shortest under various operating conditions, and the algorithm has better real-time performance while ensuring accuracy.
## 3 Results
### Geopolymer Characterization
#### 3.2.5 Microstructural Analysis
The samples of the first series, containing MK where biochar was added, absorbed and released more moisture as the percentage of biochar increased, because biochar had a strong absorbent capacity. The second context simulated an outdoor environment (temperature and humidity data were provided by the weather station of the department). The test showed a progressive increase in weight as a function of humidity. In the first context, the second series samples containing IBA were characterized by greater capacity of absorption and release of humidity. This result was obtained from the synergistic effect of biochar's properties and intrinsic characteristics of IBA. In the second context samples showed a correlation between the changes in humidity of materials and the water content. In both compositions there was a delay in the mechanism of absorption of moisture due to (a) positioning of the sample in the atrium, which is not a completely an external environment, but simulates one, and (b) the diffusive motion of the molecules of gaseous water within the open porosities is not immediate. This behaviour can enhance the regulatory effect on the environmental humidity. [PERSON] et al. (2017) found that the addition of biochar improved cement hydration, which was attributed to moisture regulation effect. This conclusion can be related to biochar's capacity to hold water in its porous structure, gradually releasing it for hydration. Additionally the initial decrease in water absorption shown in Figure 12, when biochar was added to the MK/IBA series, can be related to the water content of biochar. To conclude, analysing all the obtained results, it can be observed that the properties of biochar were maintained in the prepared geopolymers. In particular, lightness, water absorption and humidity exchange capacity. All these characteristics are related to nature of biochar which has high absorption capacity corresponding to six times its weight, according to literature values. This capacity also influences the water absorption, which is high, notwithstanding that addition of biochar leads to a decrease in porosity (from 47% for metkaolin and 42% for biochar geopolymer), because it plays the role of filler in the matrix, as was observed in the SEM images. The effect of IBA without biochar was both high porosity and high water absorption and the filler effect of biochar was more evident in this series than in the metkaolin one. Figure 14: Absorption and release of humidity of MK + 70%biochar samples in an outdoor environment and 50% MK_50% IBA + 70%biochar samples in an outdoor environment.
## 3 Methodology
### Objectives and Methodology for Regional Deployment
Step 3: Evaluate the objective function of each population (mean SPDOP) of visible satellites in addition to the sub-objective of the distance between the nearest GSs in the same population. Step 4: Sort the population according to the ranking of min mean (SPDOP) and distance between GSs. Step 5: Apply the main loop of evolutionary algorithms (Appendix A) with all operations of selection, crossover and mutation of individuals (or GSs). Step 6: After each crossover and mutation operation, check whether the GS is in a regional area. Step 7: If any ground station is outside the regional area, move it to the nearest regional land. Step 8: Evaluate the objective function of each population (mean SPDOP) of visible satellites in addition to the sub-objective of the distance between the nearest GSs in the same population. Step 9: Sort the population based on the ranking of the min mean (SPDOP) and the distance between GSs. Step 10: Check the number of iterations (generation), and if it does not reach the max iterations go to Step 5; else, end the algorithm. ```  
**Algorithm 2**Optimal deployment method for local distribution of GSs. Both algorithms (i.e., for global or regional deployment) are similar when the number of GSs is fixed. When the number of GSs is considered as a decision variable, the number of variables of the optimization problem is no longer fixed and instead depends on the number of GSs used in each population. For each iteration, the number of variables changes from an individual solution to another due to the crossover and mutation operators of the evolutionary algorithm. These changes must be checked at each iteration in order to fit the value of the number of GSs used to the number of coordinates generated. If the number of GSs is considered as a variable in the regional deployment problem, the first algorithm (of global deployment) could be used. Both algorithms are similar when the number of GSs used is fixed a priori. If not, the number of variables is also not fixed and, therefore, the main operators of the evolutionary algorithm become difficult to handle.
# Coverage Area Decision Model by Using Unmanned Aerial Vehicles Base Stations for Ad Hoc Networks
## 4 Performance Analysis of the UAV-BS
### Experiment Results of the Coverage Area Decision Model for the UAVs-BS  
Coverage is always a challenging part of cellular networks. To address the coverage issue in cellular networks, unmanned aerial vehicles (UAVs) solutions have been adopted where the UAVs are working as a base station (BS). The proposed coverage area decision model for UAVs (CADM-UAVs) is evaluated in this section to check its performance to improve the ground network connectivity and provide high data rate services. The different simulation experiments are conducted to assess the performance of the proposed model. The proposed model is evaluated using network simulations for better coverage and decision making in terms of network delay, data throughput coverage, and spectral efficiency.
## 4 Experimental Results and Discussions
### Experimental Details  
The experimental platform is a PC with a 3.4 GHz CPU, 8.0 GB RAM, and Windows 10 operating system. The proposed method was implemented by Python 3.6 based on the Pytorch framework, accelerated by two NVIDIA GeForce GTX 2080 Ti GPUs with 11 GB GPU memory, CUDA10.0, and cuDNN7.0.  
The proposed SSD7-FFAM network was trained using Stochastic Gradient Descent (SGD) from scratch with a Gamma value of 0.1. The momentum parameter and weight decay were chosen as 0.9 and 0.0005. In the training process, the batch size we used was 32, which can be adjusted according to the capacity of the GPU used. Unlike SSDs that use backbone networks, all convolution layers of SSD7-FFAM were initialized with the \"Xavier\" method [40]. The max iteration was set to 120 k. The initial learning rate was set to 0.001 and decreases by 10% at 80 and 100 k, respectively. For the training procedure, making the training model and the test model have similar input sizes has been proven to be the best way to train a multi-scale object detection network [41]. In these experiments, all networks' input image size was set to 300 \(\times\) 300 \(\times\) 3. SSD7-FFAM uses the NMS with a Jaccard overlap of 0.45 per class and a confidence threshold of 0.01. We maintained the top 200 detections per image. To further limit the number of predictions to parse, we set top_k to 5 in the testing phase. Other experimental settings were the same as the original SSD.
# Improvement of Perception Layer Routing Protocol with Static Nodes in IoT-Based Microgrids
## 3 System Models
### Algorithm Improvement under Static Nodes
#### 3.3.2 Optimal Cluster Head Ratio  
The cluster head ratio \(p\) in the LEACH protocol is an important parameter. Before the network is initialized, the value of \(p\) is already set in advance according to the network scale and other factors. When the LEACH protocol repeats the establishment of clusters in units of rounds, the cluster head ratio \(p\) always remains unchanged. However, each time the location of the base station, the scale of the network, and the deployment scenario will change, the value of \(p\) will be reconsidered. At the same time, during the operation of the network, the number of surviving nodes also changes...at all times. As the static cluster head ratio \(p\) is no longer suitable for these changes in the network, the paper aims to design a dynamic cluster head ratio \(p\) which varies with the number of surviving nodes. At the same time, the optimal number of cluster heads in the network can be calculated on the premise that the total energy consumption of the network is the lowest:  
\[K_{opt}=\sqrt{\frac{N}{2\pi}}\sqrt{\frac{\xi_{fs}}{\xi_{mp}}}\sqrt{\frac{M}{ \text{d}_{\text{tongs}}^{2}}} \tag{11}\]  
where \(N\) denotes the number of nodes in the network, the area where the nodes are distributed is a rectangle of \(M^{*}M\), \(\xi_{fs}\) and \(\xi_{mp}\) are the energy model parameters related to the LEACH protocol, and \(d_{\text{tongs}}^{2}\) is the square of the distance between the cluster head and the base station. Therefore, the optimal cluster head ratio \(p\) can be expressed as:  
\[p=\frac{K_{opt}}{N}=\sqrt{\frac{1}{2\pi N}}\sqrt{\frac{\xi_{fs}}{\xi_{mp}}} \frac{M}{d_{tongs}^{2}}. \tag{12}\]  
It can be seen from Formula (12) that the improved value of \(p\) is correlated with \(N\), the number of nodes in the network, and changes dynamically with \(N\). In this way, the dynamically changing value of \(p\) can be more adapted to the changes in the network. \(d_{tongs}^{2}\) denotes the square of the distance from the base station to the cluster head. In the actual network operation, the distance between the base station and the cluster head has to be repeatedly calculated in each round, which will add to the algorithm complexity. However, in the paper, the expectation of the square of the distance from the base station to the node as the value of the parameter to reduce the computational complexity. Therefore, when a node in the network runs out of energy and loses its function, the system will mark such a node and update the value of the number of nodes, \(N\), and calculate the new optimal cluster head ratio \(P\) value, and broadcast it to each node.
## 6 Security Evaluation with ProVerif
### Key Enrollment Procedure  
We modeled a set of cryptographic primitives \(F_{c}\) that are used in our proposed key protection system (see the ProVerif code in Listing 1).  
Figure 3: User authentication procedure using either the salt server (with \(salt^{1}\)) or the mobile device (with \(salt^{2}\)) in the proposed system.  
```
Functions: F_c={HASH, KDF, ENC} HASH(data)= hashed value. KDF(data, salt, HASH, iteration)= hashed value. ENC(Encryptionkey, data)= ciphertext.
```  
In the salt server \(s\) or mobile device \(m\), a random salt value (i.e., either \(salt^{1}\) or \(salt^{2}\)) is stored along with the user's ID and password. The corresponding ProVerif expression is provided in Listing 2.  
```
Store(\(id_{u}\), HASH(\(pass_{u}\)), \(salt^{1}\)or \(salt^{2}\))
```  
In the device \(d\), the user's sign key \(sk_{u}\) is stored after being encrypted with a key-value derived from the user's password and random salt value. The corresponding ProVerif expression is provided in Listing 3.  
```
\(k^{1}\)=KDF(\(pass_{u}\), \(salt^{1}\), HASH, 10000) \(k^{2}\)=KDF(\(pass_{u}\), \(salt^{2}\), HASH, 10000) StoreENC(\(k^{1}\), \(sk_{u}\)), ENC(\(k^{2}\), \(sk_{u}\)), \(cert_{u}\)
```  
**Listing 3** Procedure performed on device \(d\) for the key enrollment.  
For these settings, the proposed system can successfully defeat an attacker who is unable to compromise both the user device \(d\) and salt server \(s\) (or the mobile device \(m\)), simultaneously. For example, if only \(d\) is stolen, the attacker can only obtain the ciphertext of the user's sign key \(sk_{u}\), but neither the user's password \(pass_{u}\) nor a salt value \(salt^{1}\) (or \(salt^{2}\)). Therefore, it is infeasible for the attacker to obtain \(sk_{u}\) from the ciphertext. Similarly, if only \(s\) or \(m\) are stolen, the attacker can only obtain the user id \(id_{u}\), the hash value of the password HASH(\(pass_{u}\)), and a salt value \(salt^{1}\) (or \(salt^{2}\)). This knowledge is also insufficient for obtaining \(sk_{u}\) from the ciphertext.
## 3 Results and Discussions
### Validating HHV Correlation for Torrefied Biomass Using Proximate and Ultimate Analysis  
A study by [PERSON] and [PERSON] [54] reviewed the published literature for predicting the HHV of raw and torrefied biomass using ultimate and proximate analysis, as demonstrated in Equations (7) and (8), respectively, and concluded that the ultimate analysis-based correlations are more accurate than the proximate-based correlation. This study compared four correlations for predicting HHV using proximate and ultimate analysis, which is illustrated in Table 3. Equations (5) and (6) were used for predicting the HHV using proximate analysis ([PERSON] et al. [55]) and ultimate analysis ([PERSON] et al. [56]). The correlations used for comparing the predicted HHV with experimental HHV of this experiment are as follows:  
\[\mathrm{HHV1}=0.3543\mathrm{FC}+0.1708\mathrm{VM} \tag{5}\]  
\[\mathrm{HHV2}=(3.55\mathrm{C}^{2}-232\mathrm{C}-2230\mathrm{H}+51.2\mathrm{CH} +131\mathrm{N}+20,\!600)\times 10^{-3} \tag{6}\]  
\[\mathrm{HHV3}=0.1846\mathrm{VM}+0.3525\mathrm{FC} \tag{7}\]  
\[\mathrm{HHV4}=32.7934+0.0053\mathrm{C}^{2}-0.5321\mathrm{C}-2.8769\mathrm{H} +0.0608\mathrm{CH}-0.2401\mathrm{N} \tag{8}\]  
Figure 8: Chemical exergy of sewage sludge as a function of the torrefaction residence time.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c c c c c} \hline \hline \multicolumn{1}{c}{**Tenetfication Residence**} & \multicolumn{12}{c}{**290 °C**} & \multicolumn{1}{c}{**300 °C**} & \multicolumn{1}{c}{**300 °C**} \\ \cline{2-13} \multicolumn{1}{c}{**Time  
It can be concluded from Table 3 that the correlation that best fits with the actual HHV of sewage sludge in this study was the correlation developed by [PERSON] and [PERSON] [54] using ultimate analysis. With other correlations, high estimation errors can be seen. Most of the correlations that are developed to date are concentrated only for lignocellulosic biomass. However, using the same correlation for sewage sludge, a non-lignocellulosic biomass showed accuracy in the predicted data. The correlations proposed by [PERSON] and [PERSON] [54], based on ultimate analysis, fit well with the HHV obtained in this study. However, a varied range of non-lignocellulosic biomass must be carried out in order to draw an accurate prediction of HHV using ultimate and proximate analysis. Further study is required for detailed validation of the correlations using non-lignocellulosic biomass, such as digested and undigested sludge, and chicken litter, in order to have a good prediction band in different types of non-lignocellulosic biomass.
# A Diffusion Modeling-Based System for Teaching Dance to Digital Human
## 4 Implementation of Digital Human Dance Movement Teaching System
### Results Analysis of Digital Human Dance Teaching System
#### 4.3.1 Music-Driven Generation of Dance Performance Tests  
We input a piece of random music into all four models. The results are illustrated in Figure 15. We conducted comparative experiments with three existing dance movement teaching systems: A (ChoreoMaster), B (TM2D), and C (AlxDance). Analysis of Figure 15 reveals that the dance movements generated by our system are notably more diverse and optimized in terms of naturalness and fluency. They better align with the natural movement characteristics of human arm joints. Additionally, the dance movements produced by this system exhibit a high degree of harmony with the music, resulting in improved synchronization of rhythm, intensity, and melody. This enhances the interaction between the music and the dance movements, thereby reflecting the aesthetic quality of the dance.  
Figure 14: Teaching Modern Dance Moves to Digital Human.  
Figure 15: Comparison of dance sequences generated by different methods.  
In Figure 16, we used the same music to generate three different types of dance sequences: Breaking, Locking, and Pop. Breaking dance clips typically feature fast ground movements (such as spins and windmills) and complex actions that showcase strength and agility. Locking is known for its precise and intricate movements, as well as the locking and unlocking of the body. Common movements include quick arm swings, leg bounces, and exaggerated facial expressions. Pop is characterized by rapid and precise muscle contractions and relaxations. Typical movements include chest fluctuations, arm bounces, and sudden stops of the feet. As can be seen from the figure, each dance sequence displays the characteristics of its respective dance style.  
To verify the matching of rhythm and intensity features between music and dance, we compared their respective rhythm and intensity characteristics, as shown in Figure 17. We divided the music into 10 different segments, performed rhythm analysis on each segment, and calculated the matching percentage. Although the rhythm fluctuation curves do not completely overlap, the trends and fluctuation amplitudes of the curves are very similar, indicating a high degree of matching in rhythm features.  
The matching degree of music and dance intensity features is shown in Figure 18. Within the [100, 150] interval, the intensity feature curves fit closely and completely overlap, indicating a high degree of matching between the intensity features of music and dance.  
Figure 16: Different types of dance sequences.  
Figure 17: Rhythm characteristics.