esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Discussion and Conclusions
* The form of the proposed PIPO control algorithm has the form of a second-order low-pass filter and has a phase of 90-degree at the filter frequency. Therefore, active damping can be created while minimizing spillover to other modes. The proposed PIPO controller can be implemented by using either an analog circuit or a digital controller. The theoretically proven PIPO control algorithm for a single-degree-of-freedom system can be easily extended to a multi-degree-of-freedom system, thus simultaneously suppressing many natural modes. * The SIMO PIPO control algorithm can control many natural modes with a single displacement sensor. However, the stability condition of the SIMO PIPO control algorithm needs to be checked before practical application. * The efficacy of the PIPO control algorithms is proved experimentally using a single-degree-of-freedom model and a two-degree-of-freedom model. * Using logarithmic decrement, the damping factors of the uncontrolled and controlled SDOF system were obtained. Numerically, we can increase the damping factor from 0.010 to 0.107 for the SDOF system. In the case of the experimental SDOF system, we can increase the damping factor from 0.033 to 0.168. In the case of the numerical MDOF system, we can increase the damping factor from 0.007 to 0.068. In the experiment, we can increase the damping factor of the MDOF system from 0.021 to 0.111. Both numerical and experimental results show the efficiency of the proposed control system and PIPO algorithm. * The serial active type is structurally simpler than the parallel active type. * If the proposed control system is applied to a vibration isolation table, it is possible to control vibrations that were difficult to precisely control with pneumatic control relatively easily. * However, compared to the parallel active system, the serial active system must withstand the static load when installed in the vertical direction. Not applicable. No new data were created or analyzed in this study. Data sharing is not applicable to this article. The authors declare no conflict of interest.
# Green IoT: A Review and Future Research Directions
## 5 Energy-Efficient and Eco-Sustainable Wireless Sensor Networks
### Energy Harvesting, Batteries, and Wireless Charging Techniques
#### 5.3.3 Wireless Charging Techniques
Recent studies have shown that multi-hop energy transfer is possible [63], which presents new opportunities for designing wireless charging protocols, cooperative energy systems, and energy-efficient routing.
## 3 Results
### Indoor Setup
Data refer to means \(\pm\) standard deviations (n = 3). Different letters highlight significant differences between groups (\(p\leq\) 0.05) found using a one-way ANOVA. (Figure 7c). For a report of the ANOVA analyses, see Table S1 in the Supplementary Materials. P concentration in biomass also changed over time, reaching a value of around 2% on day 10 and decreasing after that day. The percentage of N in biomass also fluctuated around values of 7% (Figure 7d). Considering the differences between the initial and the final values of nitrogen in the medium and taking into account the amount absorbed by the algae (about 1.3% of the N initial content), approximately 50% of the nitrogen was removed from the medium by day 20 (Figure 7e). During the 20 days of experiments, the pH of the medium decreased gradually, as shown in Figure 7f. Figure 7: (**a–c**) Nutrient concentrations (N-NO3\({}^{-}\), N-NH4\({}^{+}\) and P-PO4\({}^{3-}\); mg L\({}^{-1}\)) evolution during the 20 days of the indoor experiment. Data refers to means \(\pm\) standard deviations (n = 3). Different letters highlight significant differences between groups (\(p\leq\) 0.05) found using a one-way ANOVA. (**d**) Trends in N and P concentrations in biomass (n = 1). (**e**) Estimate in N mass balance at 20 days of experiment. (**f**) pH trends in culture media (n = 1). As reported in Table 3, along with a reduction in COD, BOD\({}_{5}\), and total suspended solids, almost no residual _E. coli_ was found at the end of the experiment.
# Optimal CNN-Hopfield Network for Pattern Recognition Based on a Genetic Algorithm +
## 6 Results and Discussion
### Implementation Details  
#### 6.1.1 Pretrained CNN LeNet5-Like Model  
We used a modified LeNet5 as a pretrained CNN model (trained on the Noisy MNIST Dataset) for feature extraction. LeNet5 [19] is a simple model composed of two convolutional layers, two average pooling layers, and three fully connected layers. The LeNet5 architecture is a popular network that is known to work well on digit classification tasks. This model gives a high accuracy on MNIST datasets; nevertheless, the model suffers from the problems of both high variance, which represents the overfitting, and high bias, which represents underfitting of the model. In order to minimize both of them and handle the noisy version of MNIST, some changes in the architecture need to be made:  
* Maxpooling instead average pooling for reducing variance;
* Data augmentation to enhance the accuracy;
* A batch normalization layer after every set of layers (convolution + maxpooling and fully connected) to stabilize the network;
* Addition of dropout layers with a hyperparameter of 40% after the pooling layers;
* Addition of some connected layers;
* Addition of two more convolution layers with the same hyperparameters, and the number of filters in the convolutional layers was significantly increased from 6 to 32 in the first two layers and 16 to 100 in the next two layers to handle bias.  
This new model was trained on 60,000 training images (split into a training and validation set) for the three versions of noisy MNIST using the stochastic gradient descent algorithm; the loss function used was the categorical cross-entropy. The total number of trainable parameters was 324,858, of which 200,514 parameters belonged to the last three FC layers (61.72% of the parameters were in the three FC layers). The activation function used was Relu (rectified linear units). In our CNN-RNN architecture and during the training phase, we chose a single representative pattern for each class. It was calculated by averaging the brightness of the pixels from the class-specific feature set; therefore, we only stored 10 patterns instead of 60,000 training images.  
The features were pooled from the second maxpooling layer before the fully connected dense layers of the modified LeNet-5, which had 1 \(\times\) 1600 dimensions. These 10 feature vectors were normalized and binarized with a threshold value of \(p_{th}=86\), then distributed among the networks.  
Figure 10: Image samples from MNIST dataset with motion-blur noise.
# High Precision Indoor Positioning System for Mobile Asset Management and Safety +
## 2 Methodology
### Testing  
Testing was conducted to check the robustness, modularity and efficiency of the system. System performance testing plays a vital role in finding accuracy. Mainly, synchronizing the server side with the hardware was a nail-biter, as it involved several platforms and their syntax and dependencies. Framing through state-of-the-art techniques facilitates in developing a system that is to be tested rigorously.
# Comprehensive Study of IoT Vulnerabilities and Countermeasures
## 2 Preliminary Materials
### Commonly Used Protocols
#### 2.7.15 Advanced Message Queuing Protocol (AMQP)  
The Advanced Message Queuing Protocol (AMQP) is a reliable and versatile M2M binary protocol. It offers two levels of QoS for the delivery of messages, uses TCP as a transport protocol, and uses TLS/SSL and SASL for security, making it a good fit for high-bandwidth, reliable, and secure networks [52]. It supports various messaging patterns, including request/response, publish/subscribe, and transactions (allowing multiple messages to be sent and received as a single unit of work) and topic-based publish-and-subscribe messaging (allows messages to be published to topics so that subscribers can receive messages that are relevant to them) [53].
## 4 Performance Evaluation of Protection Algorithm
### Protection Performance Under Different Fault Types, Locations, and Resistances  
Different fault types mainly include four types of internal or external faults, such as phase-A-to-ground faults, phase-B-to-phase-C faults, phase-A-and-B-to-ground faults, and three-phase short-circuit faults. Fault locations are, respectively, 5 km, 20 km, 40 km, 70 km, and 90 km away from the PV station. Different fault resistances are 0 \(\Omega\), 30 \(\Omega\), 50 \(\Omega\), 70 \(\Omega\), and 90 \(\Omega\) in case of earth faults, and 0 \(\Omega\), 30 \(\Omega\), and 50 \(\Omega\) in case of phase-to-phase faults. The identification accuracy of different CNN models under the above fault conditions is shown in Figure 14.  
The identification accuracy of A-CNN is always 100%. The results show that the two-dimensional reconstructed current trajectory protection algorithm based on A-CNN has good performance and is not affected by fault factors such as fault type, fault location, fault resistances, CT saturation, etc. The protection algorithm can operate correctly, which is better than other models.
## 3 Results and Discussion
### Superiority Verification of the Optimized DDARF Program
#### 3.2.3 Stress Comparison
On the other hand, in the DDARF, after the triangular grids are generated, if two grid blocks are in contact, the normal and tangential contact springs are added between them to restrain normal embedding and tangential slip. If the contact spring force exceeds the virtual joint strength, the contact spring is damaged and removed, the two grid blocks can move independently and the virtual joint becomes the real joint. According to the calculated contact force and failure criteria in Equations (1) and (2), whether the contact spring is damaged can be determined. From Equations (1) and (2), it can be seen that, with two adjacent triangle grid blocks, the contact length l is greater, the failure criterion values f\({}_{\text{n}}\) and f\({}_{\text{r}}\) are higher, the spring is less easily damaged and the virtual joint less easily becomes a real joint--that is, the stress is higher when there is a real joint in the rock, which leads to higher crack initiation stress and peak stress.
## 8 Factors Influencing Temperature and Mechanical Properties of PMMA Bone Cement
For instance, vacuum mixing and controlling the temperature during preparation can reduce porosity by minimizing the air trapped within the cement and keeping the temperature of polymerization under control. Using added nanoparticles can also help to modulate the exothermic temperature, but also may reduce the porosity inside the cement. Mixing the cement under vacuum greatly reduces porosity by minimizing the air bubbles trapped within the cement mixture. For example, vacuum pressures of 39 to 86 kPa below ambient pressure have been shown to reduce porosity effectively, yielding a denser cement matrix. Higher vacuum levels generally reduce the residual porosity even further, enhancing the cement's durability. Porosity reductions under these pressures also help minimize the incidence of cracks caused by cyclic loading, which is especially beneficial for the long-term stability of cemented implants [22; 73; 74]. Temperature control during mixing and application is essential. Preheating the metal implant stem to around 44-45 \({}^{\circ}\)C reverses the direction of polymerization shrinkage towards the bone interface, significantly reducing porosity at the cement-stem interface. This process also helps to control the exothermic temperature rise during polymerization, avoiding excessive local temperatures that can damage surrounding tissues. [PERSON] and [PERSON] demonstrated that such preheated samples showed fewer pores and better fatigue resistance compared to those molded without temperature control [73; 74]. Cement shrinkage during polymerization contributes to the formation of porosity and residual stresses that may lead to cracks at the cement-stem interface. Mixing under vacuum also influences shrinkage, with higher vacuum levels yielding shrinkage levels of up to 7-8%, as opposed to 1-2% with hand mixing. Controlling shrinkage is important, as excessive shrinkage can increase stress concentrations within the cement, particularly around voids, leading to premature fatigue fractures under loading [73; 74]. Porosity at the stem-cement interface greatly reduces the fatigue life of the cement. For instance, four-point bending fatigue tests showed that pore-free samples have up to 50% higher fatigue strength compared to those with pores under similar cyclic loading conditions. Reducing porosity is thus for the durability of the cement mantle in vivo [73]. Several studies have shown that an increase in porosity, particularly at the cement-stem interface, can significantly weaken the fatigue strength of the cement [72]. We can safely conclude that the exothermic reaction of bone cement polymerization is closely linked to the development of porosity, a phenomenon that negatively impacts the mechanical and clinical performance of bone cement. Managing the temperatures of the polymerization reaction is essential in ensuring the optimal mechanical characteristics of the cement used for fixation [22; 54].
# Rendering Strategy to Counter Mutual Masking Effect in Multiple Tactile Feedback
## 3 The Proposed Rendering Algorithm
### Generalized Algorithm
In other words, when \(bd<1\), there always exists a unique solution, \(m_{i}\) and \(e_{i}\), for arbitrary real numbers of \(m_{p}\) and \(e_{p}\). However, when \(bd>1\), the regions covered by condition 1 (gray), 2 (blue), and 3 (red) overlapped with the region covered by condition 4 (shaded). In short, if the desired perceived intensity pair, \(m_{p}\) and \(e_{p}\), is located at the overlapped region, then the solution (input intensity pair: \(m_{i}\) and \(e_{i}\)) is not unique. Furthermore, the solution does not exist if the desired perceived intensity pair is located at another region that is not covered by any of four conditions. Thus, given that the perceived intensity should be designed as a positive value, the proposed algorithm is not applicable when \(bd>1\). \begin{table}
\begin{tabular}{c c c c} \hline \multicolumn{2}{c}{**Condition (\(e_{i}\), \(m_{i}\))**} & \multicolumn{2}{c}{**Solution**} & \multicolumn{2}{c}{**Condition (\(e_{p}\), \(m_{p}\))**} \\ \hline \multirow{3}{*}{(1)} & \(e_{i}<-\frac{a}{b}\) & \(m_{i}=m_{p}\) & \(e_{p}<-\frac{a}{b}\) \\  & \(m_{i}<-\frac{c}{d}\) & \(e_{i}=e_{p}\) & \(m_{p}<-\frac{c}{d}\) \\ \hline \multirow{3}{*}{(2)} & \(e_{i}<-\frac{a}{b}\) & \(m_{i}=m_{p}\) & \(m_{p}<-\frac{1}{d}e_{p}-\frac{c}{d}-\frac{a}{bd}\) \\  & \(m_{i}\geq-\frac{c}{d}\) & \(e_{i}=dm_{p}+e_{p}+c\) & \(m_{p}\geq-\frac{c}{d}\) \\ \hline \multirow{3}{*}{(3)} & \(e_{i}\geq-\frac{a}{b}\) & \(m_{i}=m_{p}+be_{p}+a\) & \(e_{i}\geq-\frac{a}{b}\) \\  & \(m_{i}<-\frac{c}{d}\) & \(e_{i}=e_{p}\) & \(m_{p}<-be_{p}-a-\frac{c}{d}\) \\ \hline \multirow{3}{*}{(4)} & \(e_{i}\geq-\frac{a}{b}\) & \(m_{i}=\frac{m_{p}+be_{p}+a+bc}{1-bd}\) & \(m_{p}\geq-\frac{1}{d}e_{p}-\frac{c}{d}-\frac{a}{bd}\) \\  & \(m_{i}\geq-\frac{c}{d}\) & \(e_{i}=\frac{dm_{p}+e_{p}+c+ad}{1-bd}\) & \(m_{p}\geq-be_{p}-a-\frac{c}{d}\) \\ \hline \multirow{3}{*}{(4)} & \(e_{i}\geq-\frac{a}{b}\) & \(m_{i}=\frac{m_{p}+be_{p}+a+bc}{1-bd}\) & \(m_{p}\leq-\frac{1}{d}e_{p}-\frac{c}{d}-\frac{a}{bd}\) \\  & \(m_{i}\geq-\frac{c}{d}\) & \(e_{i}=\frac{dm_{p}+e_{p}+c+ad}{1-bd}\) & \(m_{p}\leq-be_{p}-a-\frac{c}{d}\) \\ \hline \end{tabular}
\end{table} 
Table 4: Exact solutions for four conditions. Note that Condition 4 is subdivided into two cases: \(bd<1\) and \(bd>1\).
# Simulative Investigation of the Radar Cross Section of Wind Turbines
## 3 Results  
### RCS of Nonrotating Components  
Figure 2 shows the RCS of the nonrotating components of both investigated wind turbines. As the tower is rotationally symmetrical, Figure 2 displays the multistatic RCS in the x-y-plane. The black arrow denotes the direction of incidence of the electromagnetic wave. It can be seen that WT v2's tower has a significantly higher monostatic RCS compared to WT v1's tower (i.e., reflection in direction of incidence), while the multistatic RCS is a little smaller. There is no polarization dependence. The differences can be explained by the tapered shape of WT v2's tower, as it is the only significant difference between the two models.  
Figure 2 shows the monostatic RCS of the ensemble of tower, nacelle, and rotor hub. As can be seen, there are distinctive differences to the sides (at \(90^{\circ}\) and \(270^{\circ}\)). WT v1 causes a very strong single peak, which is not as pronounced with WT v2. WT v2 in turn displays a spread of stronger echoes to directions further backwards. This is caused by WT v1's simple box shape, which also causes a strong peak at \(180^{\circ}\) compared to WT v2's rounded teardrop-like shape. Polarization is different, too. While WT v1 shows little polarization dependence if at all, with WT v2, the H polarization shows oscillations in the front hemisphere which are not present in the V polarization.
## 5 Light-Weight Distributed Security Scheme  
In this section, we explain in details the implementation of our proposed lightweight distributed security scheme of key management: Our security protocol provides privacy and integrity against external attacks. It also limits the effect of insider attacks _i.e._, nodes that have been compromised and controlled by an attacker who now possesses all valid keys) since the generated keys are unique and cannot be used in different areas of the network. In addition, our security scheme allows for aggregation processing since cluster heads can decrypt the sent data if necessary and update the corresponding information. This is achieved with the shared keys between the nodes in the cluster. Moreover, our proposed security algorithm supports both authenticated encryption and authentication only services: with authenticated encryption, the data payload is encrypted using an encryption algorithm such as MISTY1 or Skipjack algorithm [27] and the entire packet is authenticated with a message authentication code (MAC) using for example Hash-based Message Authentication Code (HMAC) based on cryptographic hash function or message digest (SHA1 [28]). The MAC is computed over the encrypted data and the packet header. The decryption operation is done only at the sink and at cluster heads during aggregation process (if needed). In authentication only service where the data is not sensitive, our security scheme authenticates the entire packet with a MAC tag, but the data payload is not encrypted. The notations shown in Table 1 are used for establishing formulas of the needed security keys, as described in the following subsections:  
Figure 6: A Cluster Time Intervals for Scheduling Process.
# Damage Identification Based on Adding Mass for Liquid-Solid Coupling Structures
## 5 Damage Identification Experiment of a Liquid Storage Tank
### Damage Identification  
#### 5.4.1 Single Damage  
The approach for determining the additional mass of the liquid storage tank is the same as that described in Section 4.4.2, and the additional mass is found to be 2.1 kg. The frequencies, after mass is added to each substructure, can be obtained from the experimental data. The liquid storage tank with 0.4 m immersion depth is taken as an example, and the frequencies are shown in Figure 25. The horizontal axis represents the vertical direction of the substructure, and H in the legend represents the horizontal direction. It is found that the frequency of substructure \(S_{6,3}\) is significantly lower than that of other substructures at the same height, which indicates that the substructure is damaged.  
When the method described in Section 3 is used to consider the liquid as the attached fluid mass, implementation problems occur. Additional mass can effectively improve the sensitivity of the tank frequencies to local damage. Consequently, damage can be identified directly by the FE model in ANSYS. When the damage of substructure \(S_{6,3}\) is identified with the same scenario as in the experiment, the first natural frequency (see Table 4 ) can be determined with ANSYS selecting the wall thickness of the substructure \(S_{6,3}\) is within 1-5 mm. The frequency evaluated for different thickness values is plotted in Figure 26.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Thickness (mm)** & **1** & **2** & **3** & **4** & **5** & **6** \\ \hline Frequency (Hz) & 72.83 & 80.82 & 83.77 & 86.72 & 91.68 & 96.73 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Relationship between the thickness of substructure \(S_{6,3}\) and first natural frequency of the tank.  
Figure 25: The first natural frequency of the damaged tank with 2.1 kg of added mass.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Experimental** & **FE** & & **Experimental** & **FE** & \\
**Natural** & **First** & **First** & & **Second** & **Second** & \\
**Frequency** & **Natural** & **Natural** & **Error** & **Natural** & **Natural** & **Natural** \\
**Order** & **Frequency** & **Frequency** & **Frequency** & **Frequency** & **Frequency** & **Frequency** & \\  & **(Hz)** & **(Hz)** & & **(Hz)** & **(Hz)** & **(Hz)** & \\ \hline
**0 m** & 146.78 & 143.85 & 2\% & 194.41 & 197.43 & 2\% \\
**0.2 m** & 139.98 & 135.98 & 3\% & 189.38 & 191.53 & 1\% \\
**0.4 m** & 129.87 & 127.95 & 1\% & 177.36 & 173.66 & 2\% \\
**0.6 m** & 114.98 & 11 Figure 26 and Table 4 show that with the increase of the substructure thickness, the first natural frequency of the structure increases gradually. According to the experimentally measured frequency of 81.27 Hz and from Figure 26, it appears that the corresponding wall thickness is 2.71 mm. The damage factor of the substructure computed as 2.71/6 = 0.47, which is close to the theoretical value of 0.5.
## 4 Results and Discussion
### Effect of Flow Distribution on the WSM
#### 4.2.2 Structural Deformation When the Rake Is Operational  
Normalized deformation contour plots of the rake at the inlet velocity of 0.2 m/s are due to the flow distribution around the WSM, whilst the rake is at \(\varphi_{\rm r}=-75^{\circ}\) and \(\varphi_{\rm r}=0^{\circ}\) as, respectively, as shown in Figure 15a,b. Thus, the fluid forces considerably deform the middle region of the rake. Therefore, the deformation shows high magnitudes in these regions. Moreover, the deformation in the case of \(\varphi_{\rm r}=-75^{\circ}\) is due to stress from the fluid below, whereas that for \(\varphi_{\rm r}=0^{\circ}\) is due to stress on the rake from the front.  
Figure 14: Deformation of the sub-screen bars when the sub-screen is fully lowered for (**a**) U = 0.1 m/s (**b**) U = 0.3 m/s and partially lowered for (**c**) U = 0.1 m/s and (**d**) U = 0.3 m/s.  
The deformation of select rake pins is plotted on the FSI boundary of the rake for the inlet velocity of 0.2 m/s for the posterior and vertical positions of the rake, as, respectively, as shown in Figure 16a,b. The legend in the graph shows the selected rake pins of the rake movement mechanism. R1, R6, R12, and R18 represent the 1 st, 6 th, 12 th, and 18 th rake pins of the rake movement mechanism, respectively. 1 represents the displacement of the FSI boundary on the rake pin, with the initial point at the tip of the pin, and I\({}_{\rm r}\) represents the total length of the rake pin. The rake is significantly more deformed in the posterior position than it is in the vertical position. This finding is due to the flow behavior in the gap between the main screen and the sub-screen; that is, as the fluid passes this gap, the velocity of the fluid is increased right before the flow hits the rake, as shown in Figure 10a. However, the fluid interacting with the rake decelerated in the case of a vertical rake due to the passage of the sub-screen, which is located directly in front of the rake. Further, the present 3D study presents the deformation profile across the entire set of rake pins (R1, R6, R12, and R18), as shown in Figure 16. This enables the exploration of deformations across different rake pins, building upon the findings of [PERSON] et al. [7], which focused on the 2D case.