esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### Up-Scaling of the Resolution  
We have used a model called a \"super-resolution generative adversarial network\" (SRGAN) to evaluate the effects of higher resolution. The loss function in this model consists of two components: adversarial loss and content loss. Adversarial loss aims to produce realistic images that resemble the original, while content loss makes sure that the generated image has the same features as the low-resolution original. The loss function incorporates both adversarial and content loss using a perceptual loss function. A discriminator network, trained to differentiate between high-resolution images generated and true photorealistic images, drives the solution towards the manifold of natural images through adversarial loss [92].  
In SR-GAN, a generator network is trained to learn the mapping between LR and HR images. The generator network is a convolutional neural network (CNN) that takes an LR image as input and produces an SR image as output. A discriminator network is also trained to distinguish the SR image generated by the generator from the HR image. The generator and discriminator networks are trained simultaneously in an adversarial manner. The generator network is trained to minimize the difference between the SR image and the HR image, while the discriminator network is trained to maximize this difference.  
The adversarial loss function used for this purpose is typically the binary cross-entropy loss function, given by:  
\[L_{adv}=-(y\times log(D(x_{hr}))+(1-y)\times log(1-D(G(x_{lr})))) \tag{6}\]  
where \(G(x_{lr})\) represents the generated SR image, \(D(x_{lr})\) represents the output of the discriminator network for the HR image, and \(y\) is the label (1 for real images and 0 for generated images).  
Additionally, a Mean Square Error (MSE) loss function is also used to measure the difference between the generated image and the original image:  
\[L_{mse}=\frac{1}{n}\times\sum(x_{hr}-G(x_{lr}))^{2} \tag{7}\]  
where \(x_{hr}\) represents the original HR image and \(x_{lr}\) represents the LR input image. These two loss functions are combined to create a total loss function, which is used to train the generator network. An optimizer algorithm such as Adam, SGD, or RMSprop is used to adjust the parameters of the generator and discriminator networks during training.  
The hyperparameters of SR-GAN are summarized in Table 3. The performance of SR-GAN was evaluated on several publicly available datasets. The results show that SR-GAN was able to produce SR images with a significant improvement in quality compared to We implemented the SRGAN tensorflow model used in [92] and trained it on our commercial dataset of VGG-masking face and the CASIA masking face dataset to further improve the resolution of the images and obtain a better generalization and performance of the model.
## 3 Enhanced SoC-Balancing Strategy with Saturation
### Design of a Dual-Loop Controller  
The selection of controller parameters is vital to implement a control scheme. In fact, it directly influences the stability and dynamic performance of energy storage systems. Compared with other balancing methods, the main difference of the proposed strategy is the voltage loop after CDRs are introduced. A simplified control diagram of the \(k\)-th unit is outlined in Figure 12. Typical PI regulators are applied for both voltage and current control. In parallel applications, the transfer functions from \(d_{k}\) to \(i_{k}\), \(G_{i4k}\) and from \(i_{k}\) to \(u_{\text{dc}}\), \(G_{i7k}\) are formulated as  
\[G_{i4k}=\frac{u_{ik}(RCs+2)}{\left[L_{k}CRs^{2}+L_{k}s+mR(1-d_{k})^{2}\right] (1-d_{k})} \tag{36}\]  
\[G_{i7k}=\frac{mR(1-d_{k})^{2}-sL_{k}}{(1-d_{k})(RCs+2)} \tag{37}\]  
Figure 11: Ideal curves under conditions of battery degradation: (**a**) currents in parallel applications, (**b**) currents in series applications, (**c**) SoC in parallel applications, and (**d**) SoC in series applications.  
Figure 12: Simplified control block diagram for \(k\)-th unit.  
As \(u_{\rm{\delta}k}\) varies little during discharge, it is considered to equal 12 V, which is the nominal battery voltage.  
The adopted controller parameters are shown in Table 2. A bode diagram of the open current loop is given in Figure 13a, and \(w_{\rm{c}}\) is the cut-off frequency. The stability margin of the inner loop is at least 83\({}^{\circ}\) and \(w_{\rm{c}}\) is at least 370 rad/s when \(R\) ranges from 3 \(\Omega\) to 1.5 \(\Omega\). The upper and lower limits of the CDR are 0.66 and 0.00024, which correspond to 33 A (\(i_{\rm{satu}}\) in parallel applications) and 0.012 A (1 LSC in Inequation (10)). There are usually deviations between the real and nominal resistances. Similar phenomena also occur for the inductance and capacitance in actual converters. In addition, the CDR of each unit gradually varies until SoC balance is achieved. Therefore, the abovementioned cases were all considered when choosing \(k_{\rm{pv}}\) and \(k_{\rm{iv}}\). Sufficient stability margins are effective solutions to mitigating these issues. For conventional applications, the phase margin is usually designed to be no less than 45 degrees. In order to ensure stable operation under CDR variations and mismatches of circuit parameters, the phase margin is enhanced to no less than 70 degrees in this paper. In Figure 13b, the magnitude and phase response curves under several boundary conditions are depicted. The expected phase margins are acquired. Although the voltage-loop \(w_{\rm{c}}\) is low, it is acceptable, as the guaranteed system stability is a prerequisite for balancing control.  
As for series applications, the values of the control parameters are the same. The procedures for controller design are similar and have been omitted for convenience.
## 4 Factors Affecting the Performance of AC  
### Raw Materials  
Biowaste with high carbon content is generally suitable for the production of AC. The factors such as low ash (inorganic), high carbon content, low degradation, economy, safety, low volatility, low moisture content, and high density may be considered while choosing the raw material to obtain activated carbon with a high degree of porosity. Coconut shells and olive stones are most widely used commercially, because of their volatility, hardness, and high density [43]. In the last decade or so, waste tyre-derived AC has also been used for various applications such as electrode materials in energy storage devices, hydrogen storage, and removal of heavy metals such as lead, cadmium, copper, zinc, chromium, cadmium, manganese, and arsenic from the wastewater [44].
## 8 Simulation Setup and Results
### Performance Analysis  
2.1 Test: 1 Packet Throughput, PDR, and E2E Delay with Different Vehicles Mobility and Increasing Malicious Nodes  
In this part, we first carried out performance analysis among our proposed scheme TER, original AODV, and AASR. Under different vehicles mobility and an increasing number of malicious nodes, the performance of these three schemes varies over time. From Figure 6, it is clear that TER outperforms the other two routing schemes, i.e., AASR and AODV. As vehicles reaches up to 10 m/s  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Examined Protocol** & **TER** \\ \hline Simulation time & 100 (s) \\ Number of nodes & 50 \\ Simulation area & 1000 m \(\times\) 1000 m \\ Movement model & Random way point \\ Vehicle speed & 0\(\sim\)10 m/s \\ Transmission range & 250 m \\ Physical link bandwidth & 11 Mb \\ Traffic type & CBR/UDP \\ Packet size & 512 bytes \\ Connection rate & 4 pkt/s \\ Pause time & 5 s \\ Routing Attacks & Black Hole, On-off attack \\ Number of malicious nodes & 0\(\sim\)25 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Simulation parameters in trust enhanced routing.  
our scheme still have 50% of throughput, see Figure 5(a). In second Figure 5(b), as half of nodes become malicious (25 nodes) our scheme TER can achieves 45% packet throughput, AASR gives 35%, and AODV gives 30%. So it favors the TER scheme under these two scenarios.  
Second, Packet loss is also an important metric to check any communication network performance. In Figure 5(c), our proposed scheme has less packet loss drop. Since AODV takes no additional actions, so it has a high packet loss of 75% at \(V=10\) m/s. AASR spend some time on initial cryptographic processing, so packet loss is 70% at \(V=10\) m/s. TER minimizes this effect by using trusted information exchange among neighbor vehicles. TER has minimum packet loss of 55% at \(V=10\) m/s. Also, when we have half of the nodes malicious, i.e., (25 nodes are malicious), the performance of these three schemes varies. AODV has 65% of packet loss, AASR has 55% of packet loss, and TER has 45% of packet loss, which is minimum among these schemes, see Figure 5(d).  
Third, E2E delay is a vital metric for measuring any network performance, especially for a delay-sensitive network such as VIoT. In this analysis, we compared our proposed scheme TER with  
Figure 6: Packet throughput, PDR, and E2E delay with different vehicles mobility and increasing malicious nodes.  
AASR and AODV. AODV has E2E delay of 150 (ms) at \(V=10\) m/s. However, AASR spends time in the route discovery and making some initial security processing, so it has a high E2E delay of 190 (ms) at \(V=10\) m/s. TER minimizes this effect by using trusted information exchange among neighbor vehicles. TER has E2E delay of 130 (ms) at \(V=10\) m/s, see Figure 5e. Similarly, in the presence of 50% malicious nodes, TER has a minimum E2E delay of 50 (ms). TER outperforms the other two routing schemes as AODV and AASR have E2E delay of 110 (ms) and 90 (ms) respectively, see Figure 5f.
## 3 Results  
### Analysis of Spatial and Temporal Changes in Snow Cover and NDVI  
#### 3.1.1 Variations in Snow Depth  
As shown in Figure 4, the variations in the snow depth over the entire plateau surface during 1981-2020 exhibited small but significant fluctuations with a multi-year rate of change of \(-\)0.016 mm/a. The sharpest fluctuations occurred between the 1980s and 1990s, with the highest snow depth in 1986, followed by a clear decreasing trend. From the 1990s to the mid-2010s, the fluctuations were smaller, but a clear decrease was observed, although a significant increase occurred in 1995 until a peak was reached around 1998, before a subsequent decrease. After 2016, another significant increase was observed, and this continued until 2020. Spatially, the areas with decreasing trends in the multi-year snow depth accounted for 56.5% of the entire plateau area during the study period, with significant decreasing trends in the Karakorum Mountains, western Kunlun Mountains, Nyenchen Tanglha Mountain, Tanggula Mountains, and Zongwulong Mountains, where the slope of the one-dimensional regression was \(-\)0.05 and above. The snow depths in the central, eastern, and southwestern areas of the plateau exhibited an increasing trend, including the areas surrounding the plateau surface. Significant increases were found in some areas of the central \"Sanjiangyuan\" region (The source of the Yangtze River, Yellow River and Lancang River), but the overall area was relatively small at only 10.9%. The multi-year snow depth in the highlands was more significant but with a decreasing trend.
# Energy Efficient Dynamic Symmetric Key Based Protocol for Secure Traffic Exchanges in Smart Homes
## 3 The Proposed Scheme  
The proposed network model is made up of the Smart Home Devices (SHDs), the Smart Home Owner (SHO), Mobile Device (MD), the Trusted Authority (TA), and the wireless network. Here, wireless connectivity can be the internet which facilitates SHO and SHDs interactions. The SHOs utilize software controls in their MDs to access SHDs services. On the other hand, the TA acts as the Home Gateway (HG), as shown in Figure 1.  
The remote user commands are transmitted to the HG before being forwarded to the SHDs. In this arrangement, the HG manages all SHDs and is assumed to be a trusted entity with slightly higher memory, energy, and computation capabilities compared with SHDs. The communication link between the remote user and the HGs are assumed to be insecure. The MDs can be smartphones, laptops, or tablets. Table 2 presents the notations deployed here and their descriptions.  
Before the onset of the packet exchanges between the SHDs and SHOs, the MDs and SHDs must be registered at the TA, as outlined in Section 3.1 below. Thereafter, Authentication and Key Agreement (AKA) procedures are executed, as evidenced in Section 3.1. The block diagram in Figure 2 gives a summary of these steps.  
Figure 1: Network model.  
As shown in Figure 2, the chronology of events includes registration, authentication, and data exchange between the smart home device and the smart home owner's mobile device. During registration, the trusted authority generates and distributes the security parameters to the smart home device and the mobile device. To accomplish this, secure communication channels are utilized. During the authentication process, these security parameters are deployed to derive some security tokens used to validate the authenticity of the communicating parties. After successful authentication procedures, the smart home and mobile device can trust one another and commence data exchange.
# A Review of Adsorbents for Heavy Metal Decontamination: Growing Approach to Wastewater Treatment
## 6 Factors Affecting the Adsorption Process
### Commercially Available Adsorbents
#### 6.9.3 Activated Carbon
The batch method was used to study the adsorption capacity of the resultant adsorbent. It was found that adsorption value increased progressively with an increase in pH and the highest absorption was observed at pH 6.0 for lead and copper and 7.0 for mercury in the pH range 2.0-9.0. Experiments were conducted at a different initial concentration range from 25 to 100 ppm using 2.0 g/L activated carbon. It was found that removal efficiency increased with time and equilibrium was achieved after 3 h with a constant value of adsorption. Regeneration of adsorbent was done through 0.1 mol/L HCl and the adsorption-desorption cycle could be used three timesfor extraction. The removal ability of the resulting activated carbon followed this pattern: Pb\({}^{2+}\), Hg\({}^{2+}\), and Cu\({}^{2+}\). Activated carbon from pecan nutshell was used for the elimination of Zn (II), Cd (II), Ni (II), and Cu (II), from a binary system of metal ions [279]. [PERSON] et al., examined exhausted activated carbon for the extraction of heavy metal ions; the exhausted activated carbon was collected from a biological activated carbon water treatment plant of southern China [280]. It was found that activated carbon had a great sorption value of 95% and 86% for Pb\({}^{2+}\) and Cd\({}^{2+}\) at very little concentration of 200 ppb, respectively. Phosphoric acid impregnated activated carbon (DPAC) was synthesised from the seeds of date pits via single-step activation. Phosphoric acid is introduced into the seeds of date pits through 12-hour soaking [281]. After filtration, seeds were carbonised in a muffle furnace under continuous nitrogen flow. This process was done at 650 \({}^{\circ}\)C for 120 min. DPAC was utilised to remove lead ions with the maximum sorption capacity, which was around 101.35 mg/g at pH 6, in 30 min at 30 \({}^{\circ}\)C. The adsorbent was regenerated with hydrochloric acid and can be reused four times with a 10% reduction in lead ion extraction effectiveness. Iron oxide and iron oxide-activated carbon were prepared by the co-precipitation process for the extraction of Cr\({}^{6+}\), Cu\({}^{2+}\), and Cd\({}^{2+}\) by using the batch method [282]. Different parameters affect the sorption rate such as pH (2.0,6.0), temperature (25 \(\pm\) 1 \({}^{\circ}\)C), stirring rate 180 rpm, a dose of adsorbent (50 mg/10 mL), initial metal ions concentration (50 mg/L), and time for adsorption (3 h) was studied. Iron oxide nanoparticles were reported to achieve adsorption equilibrium for Cr\({}^{6+}\), Cu\({}^{2+}\), and Cd\({}^{2+}\) after 60, 120, and 30 min, respectively. The extraction value of Cr\({}^{6+}\) by activated carbon-iron oxide nanoparticles was obtained as 95.5% within 45 min after which no change in adsorption value occurs. Likewise, Cu\({}^{2+}\) and Cd\({}^{2+}\) removal efficiency was reached at 85.5%, 96.5% by the iron oxide/activated carbon nanoparticles within time periods of 90 min and 120 min.
# Mechanical Activation of Smectine-Based Nanocomposites for Creation of Smart Fertilizers
## 1 Introduction
### Soil Column Leaching Experiments  
The nutrient release behavior of the smectic-urea composites was conducted by soil column leaching experiments [45; 46; 47; 48]. Before testing, the total N content of each nanocomposite was determined by the Kjeldahl method. A bulk soil sample was collected from 0 to 20 cm deep surface layer from an experimental farm at Tomsk in Western Siberia. The soil was dried at room temperature after cleaning roots of plants and coarse materials (using a 2 mm sieve). The dry soil (80 g) was separately mixed with composites (65 mg) and traditional urea (39 mg) at a dose of 380 mg N\(\cdot\)kg\({}^{-1}\) dry soil. Nitrogen content in composites was 30 mg. Care was given to make the mixture homogenous. The resultant mixture was taken into a PVC tube with a 100-mesh screen at the bottom (inner diameter 7 cm and height 25 cm). The bottom of the column was fitted with a [PERSON] filter paper #42. A PVC cap at the bottom of the column, with numerous small openings, prevented the loss of soil from the column. The surface of each soil column was covered with quartz sands to minimize the loss of volatiles. The experiment was carried out in a completely randomized design with five treatments, including three varieties of smectite-urea composites, with urea and one control devoid of composite/urea. Each treatment was repeated three times.  
Throughout the experiment, the soil water content was maintained at 75% field capacity by weighing after 1, 4, 7, 14, 21, 28, 42, and 56 days. An amount of 200 mL of deionized water was added slowly into each soil column, and the leachates were collected in a 50 mL conical flask. Then, the leachates were filtered and stored at \(-\)24 \({}^{\circ}\)C the chemical analyses. The potassium (K\({}^{+}\)), ammonium (NH\({}_{4}\)\({}^{+}\)), and nitrate (NO\({}_{3}\)\({}^{-}\)) contents, as well as the pH, were determined by AutoAnalyzer 3 ([PERSON], Hamburg, Germany). Cumulative leaching amounts were calculated to evaluate the nitrogen release behavior of the prepared composites. The final values of cumulative leaching were determined by averages of three repetitions.
# Some Applications of ANN to Solar Radiation Estimation and Forecasting for Energy Applications
###### Abstract  
In solar energy, the knowledge of solar radiation is very important for the integration of energy systems in building or electrical networks. Global horizontal irradiation (GHI) data are rarely measured over the world, thus an artificial neural network (ANN) model was built to calculate this data from more available ones. For the estimation of 5-min GHI, the normalized root mean square error (nRMSE) of the 6-inputs model is 19.35%. As solar collectors are often tilted, a second ANN model was developed to transform GHI into global tilted irradiation (GTI), a difficult task due to the anisotropy of scattering phenomena in the atmosphere. The GTI calculation from GHI was realized with an nRMSE around 8% for the optimal configuration. These two models estimate solar data at time, t, from other data measured at the same time, t. For an optimal management of energy, the development of forecasting tools is crucial because it allows anticipation of the production/consumption balance; thus, ANN models were developed to forecast hourly direct normal (DNI) and GHI irradiations for a time horizon from one hour (h+1) to six hours (h+6). The forecasting of hourly solar irradiation from h+1 to h+6 using ANN was realized with an nRMSE from 22.57% for h+1 to 34.85% for h+6 for GHI and from 38.23% for h+1 to 61.88% for h+6 for DNI.  
solar irradiation; estimation; forecasting; meteorological data; short time step +
Footnote †: journal: Applied Physics Letters B
# Flow Control of Radial Inlet Chamber and Downstream Effects on a Centrifugal Compressor Stage
## 2 Methodology  
### Flow Configuration  
The research object of this paper is the radial inlet chamber coming from the inlet stage of a multistage centrifugal compressor, which is an industrial product of SBW (Shenyang, Blower Works Group Co., Ltd., Shenyang, China). The working fluid of the compressor is air, and its mass flow rate at the design condition is 4.29 kg/s. Figure 1 shows the schematic diagram of the radial inlet chamber (from \(in\) to 0) and the first stage of the centrifugal compressor (from 0 to \(out\)). As a typical structure for a radial inlet, this chamber mainly consists of an intake channel, a spiral channel and an annular convergent channel, where \(b_{e}\) and \(r_{c}\) are the width and radius of the spiral channel, respectively, \(w_{e}\) is the intersecting width of the intake channel and the spiral channel and \(b_{I}\) is the inlet width of the annular convergent channel. The major geometric parameters of the original radial inlet chamber and the first stage of the compressor are listed in Table 1.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Radial Inlet Chamber** & **Size** & **Compressor Stage** & **Size** \\ \hline Inlet diameter \(D_{in}\) & 460 mm & Inlet diameter of impeller \(D_{0}\) & 298 mm \\ Intersecting width \(w_{e}\) & 590 mm & Outlet diameter of impeller \(D_{2}\) & 400 mm \\ Width of spiral channel \(b_{e}\) & 220 mm & Outlet width of impeller \(b_{2}\) & 40 mm \\ Radius of spiral channel \(r_{c}\) & 480 mm & Number of impeller blades \(Z\) & 17 \\ Inlet width of annular channel \(b_{I}\) & 110 mm & Outlet diameter of diffuser \(D_{4}\) & 641 mm \\ Inlet diameter of annular & 440 mm & Outlet width of diffuse \(b_{4}\) & 37.3 mm \\ channel \(D_{I}\) & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Major geometric parameters of the radial inlet chamber and the compressor stage.
# Efficient V2V Communications by Clustering-Based Collaborative Caching +
## 4 Simulation Evaluation
### Comparison Methods and Evaluation Metrics  
We compared the proposed method with the following three methods. 'Pop' in a method name indicates that popularity inference is used in the method.  
* V2V communication using basic CCN [2] without clustering (BCCN-Pop): A vehicle on a communication path caches the content on receiving a data packet, while the cache buffer for a vehicle off the path is not exploited.
* V2V communication with clustering enabled but collaborative caching disabled (C-BCCN-Pop): Here, clusters are constructed for forwarding packets in the same way as in the proposed method.
* V2V communication with clustering and collaborative caching but without content popularity inference (C-CoCach [9]): In this method, cache management is based on the Least Recently Used (LRU) policy.
* ECV+ method [3] in which each vehicle decides the cache probability based on channel usage.  
A vehicle sends a beacon periodically in both the proposed and comparison methods. A beacon only contains the sender's position and velocity in BCCN-Pop, while it includes the information for constructing and managing clusters in C-BCCN-Pop, C-CoCach, and C-CoCach-Pop. The simulation also evaluates the impact of overhead for managing CMs' caches.  
We measured the median latency of data packets, channel usage, the success ratio of receiving requested content, and the cache hit ratio as metrics to evaluate the performance improvement of the proposed method.