esa-sceva/satcom-chunk-collection huggingface dataset:
# A Risk-Sensitive Intelligent Control Algorithm for Servo Motor Based on Value Distribution
## 5 Experiment and Analysis  
### Experimental Environment and Setting  
The Python-based Gym Electric Motor (v.0.1.1) is an open-source simulation platform that offers a straightforward algorithmic interface for reinforcement learning agents. It provides a favorable condition for researchers to develop and test reinforcement learning control algorithms, as the interaction between servo motor and agent can be simulated conveniently [32].  
As shown in Table 1, six common tasks provided by Gym Electric Motor are selected to verify the effectiveness of the proposed risk-sensitive intelligent control method.  
Two different deep reinforcement learning algorithms are selected: the twin delay depth deterministic strategy gradient (TD3), and the depth deterministic policy gradient (DDPG). Meanwhile, two different feature extraction methods are also taken into account: the difference value input (DVI), and state input without special processing (SI). Therefore, the four comparison methods are obtained by combining the reinforcement learning algorithm and feature extraction method with each other, which are termed as DDPG-DVI, DDPG-SI, TD3-DVI, and TD3-SI, respectively. In addition to these methods, our previous method (RESAC) [33] is also used to compare the performance.  
The strategy network of the proposed risk-sensitive intelligent control method contains two hidden layers. For the value network, there are two hidden layers in the coding part and there is one in the integrated part. Each hidden layer has 512 neurons and the ReLU activation function is used. The strategy network's input length is set as 20 and the output length corresponds to the number of duty cycle dimensions corresponding to each environment. The number of quantiles in the probability distribution is set to be 32. When the positions of the quantiles are input into the neural network, the number of input neurons of the value network is 64, and the cos function is used as the auxiliary embedding function. The positions of the quantiles are randomly generated by a uniform distribution. The final output layer has 32 neurons, where each output represents one of the quantiles on the distribution.  
The temperature coefficient is chosen as 1. The total number of training time steps for each control task is 1 million. The batch size is set as 256, and the samples are uniformly randomly sampled from the capacity of the experience buffer, whose capacity is 200,000. The Adam optimizer [34] is used to optimize network parameters for gradient descent, and the learning rate of the optimizer is set to 0.0003. The discount reward factor is 0.99. The update ratio is set to 0.005 when soft updating parameters for the target network.
# A System for Controlling and Monitoring IoT Applications
## 6 Obtained Results  
Control tests have been performed to ensure the full management and different aspects of this system. Also, different tests have been performed with a simple script in order to see the behaviour with respect to temperature variation, resource consumption and power consumption. Regarding the consumption of the CPU, the generated processes of LED control (apache2 and Python), the activated processes when a data request is sent by the sensors (apache2 and Python) and the webcam process activation (mjpg_streamer) were taken into account. One of the most remarkable pieces of data is that when the webcam is active, the consumption of the CPU rises to 53.4% compared to a maximum of 17.8% without the webcam. With respect to the total power consumption, the Raspberry Pi demands 15 W including a necessary USB hub. Compared with the average power consumption of a normal laptop of around 65 W, the use of this system for this kind of installation leads to a power consumption saving of 76.9%.  
Figure 3: Devices and services involved in the communication.
# At the Nexus of Blockchain Technology, the Circular Economy, and Product Deletion
## 3 Framework and Propositions
### Macro: Circular Economy at the Institutional Level
#### 4.1.3 Secondary Materials Markets  
Substituting primary components with materials that are acquired from secondary markets can effectively support circular economy principles. Blockchain technology can provide a distributed platform for trading secondhand materials and products. Improving information transparency and verifiability allow network participants to sell and buy their wastes and used materials and products in secondary markets. Amazon and eBay are examples of current secondary markets. The presence of real-time information regarding the veracity and status of the used materials and products can further boost circular economy efforts.  
This information can also denote the feasibility of replacing materials from a primary market to a secondary market. Reducing material costs can provide financial resources for extending the product life cycle and increasing material efficiency and durability. Quality and performance of the secondary materials are traceable on a blockchain. This information can further address the potential and opportunities for developing new products that incorporate secondary materials, meet the green initiatives, and maximize circularity. Knowing this information can help delete products that do not meet the necessary circularity criteria.  
Disintermediation is another advantage provided by blockchain that can cultivate secondary market activities by connecting buyers and sellers without any intermediaries and reduce the costs of transactions. Those materials or products that cannot be managed with fewer intermediaries may result in more costly and complex systems, causing the deletion of these products from further consideration.  
Accurate and updated information about the secondary materials and markets can ameliorate product deletion analysis and decision making. Blockchain-based information can provide more accurate reusability and recyclability of materials and products information. Transparent market pricing and costing information on secondary materials may also provide information to determine which products are more feasible or should remain in a portfolio. Products that demonstrate poor performance in the secondary market or incorporate materials that are not replaceable by used items might be candidates for deletion. Agents and business entities can make their deletion efforts more profitable and leverage the circular economy by selling their wastes and marketing the inventory of deleted products on a blockchain platform.
# Design and Analysis of Pattern Reconfigurable Antenna Based on RF MEMS Switches
###### Abstract  
The research on reconfigurable antennas has some disadvantages such as low working frequency and large size. This paper presents a Ka-band patch antenna with pattern reconfigurability using RF MEMS switches. The antenna contains one main patch, two sub-patches, two parasitic patches, and two RF MEMS switches. By controlling the states of the RF MEMS switches, the antenna can achieve three different radiation patterns (\(-8^{\circ}\), \(0^{\circ}\), and \(+8^{\circ}\)) at 35 GHz. The pattern analysis was based on the electric field vector addition method. The analytical, simulated, and measured results agree well with each other. Due to its compact and thin structure of 3.7 mm \(\times\) 5.2 mm \(\times\) 0.4 mm, this antenna can be applied in fields such as satellites, smartphones, etc.  
RF MEMS switch; pattern reconfigurable antenna; the electric field vector addition method +
Footnote †: journal: Electronics  
0
## 4 Experimental Results and Discussion
### Skeleton Curves  
The skeleton curve of the tests was obtained by connecting the peak points of the first cycle at each loading level in the hysteresis curves [43], as shown in Figure 10. The curves were basically symmetrical, indicating that the system presented similar mechanical behaviors in both loading directions. Since no serious damage occurred to the wall panels during the entire loading process, and only the energy dissipaters yielded and dissipated input energy, the lateral strength of the system continued to increase as the loading drift grew. The characteristic states during positive loading are shown in Table 3, with the yield states obtained by linear fitting of the skeleton curves, and the peak point of curves were taken as the ultimate limit states since the specimen never shows significant damage. According to the stipulation in the Chinese seismic design code [33], the maximum story drift of concrete shear wall-frame structures in the plastic state should never exceed 1%. As shown in Figure 10, the lateral strength of the specimen did not degenerate at the 2.5% drift level, indicating that the system had favorable deformability for practical buildings.  
The inflection points of the skeleton curves for all three tests appeared at the same loading drift, which verified that the SEDPs in all tests yielded at the 0.67% drift level, as described above. However, the lateral strength of Test RW1-1 was the highest before the SEDPs yielded because it was the first test of the specimen, and the bond of the infill grout between the wall panels and the foundation element had to be overcome. Comparedwith Test RW1-1, Test RW1-2 showed a higher lateral strength at the same corresponding loading drift due to the function of the MBSFs. The overall trend of the Test RW1-3 curves was similar to that of Test RW1-2, but the lateral strength remained relatively lower than the corresponding strength of Test RW1-2 for the reasons mentioned above.  
The ratio of ultimate drift to yield drift was taken as the ductility factor. Comparing the ductility factors of the three tests, it was shown that the ductility of RW1-3 was relatively weaker than that of RW1-2 due to the non-setting of Belleville springs for MBSFs. The ductility of RW1-1 was optimal, due to the lowest number of dissipaters and the lack of dissipative bars, which indicated that the restraining effect of the dissipative bars on the concrete elements reduced the deformation capacity of the structure. Meanwhile, the ductility factor decreased gradually with the growing loading sequence, which demonstrated that the cumulative structural damage weakened the ductility performance.
## 5 Conclusions
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline  & \multicolumn{3}{c}{**Accuracy**} & \multicolumn{3}{c}{**Precision**} & \multicolumn{3}{c}{**Recall**} & \multicolumn{3}{c}{**Specificity**} & \multicolumn{3}{c}{**F1**} \\ \hline
**Model \({}^{*}\)** & **1** & **2** & **3** & **1** & **2** & **3** & **1** & **2** & **3** & **1** & **2** & **3** & **1** & **2** & **3** \\ \hline Proposed & 99.9 & 99.3 & 99.8 & 100 & 99.6 & 99.9 & 99.9 & 99.6 & 99.8 & 100 & 99.7 & 99.9 & 99.8 & 99.6 & 99.8 \\ RFC & 18.3 & 15.2 & 23.6 & 13.8 & 10.8 & 17.9 & 12.9 & 9.4 & 15.3 & 29.1 & 21.7 & 31.4 & 15.6 & 10.0 & 16.3 \\ SVC & 46.2 & 46.0 & 45.9 & 44.7 & 43.8 & 46.2 & 51.5 & 25.6 & 69.6 & 33.7 & 66.8 & 23.6 & 47.1 & 32.3 & 55.6 \\ \hline \multicolumn{10}{c}{\({}^{*}\) 1: 1\% inflation factor and 8 h time window. 2: 1\% inflation factor and 1-day time window. 3: 2\% inflation factor, and 1-day time window.} \\ \end{tabular}
\end{table} 
Table 9: Metrics scores of the three classifiers in three indicative instances.
## 4 Experimental Verification
### Precision of the FNN Model  
The predicted results and real pointing errors obtained from the actual telemetry data are shown in Figure 10. The Pearson correlation coefficients between the predicted results and actual telemetry data were 99.85% and 98.42%.  
Based on the above analysis and discussion, the error distribution of the calculated pointing error using the proposed algorithm is shown in Figure 11. As can be seen from Figure 11, the median calculation error of the elevation, azimuth, and combined pointing error is 2.0448 \(\upmu\)rad, 13.1691 \(\upmu\)rad, and \(-9.3123\)\(\upmu\)rad, respectively, while 75% of the error is within 50 \(\upmu\)rad. Since the field of view is 600 \(\upmu\)rad, the pointing error can meet the requirement.  
Figure 10: Verification of pointing error: (**a**) in azimuth; (**b**) in elevation.  
Figure 9: Trajectory of scanning step.
## 3 Materials and Research Methods
### Requirements
#### 3.1.1 Key Functional Requirements for the System  
* Encrypted communication between devices and servers (IoMT and web server).
* Information integrity checking implementation for message verification.
* Information availability, authentication of users and devices, and non-repudiation functionality incorporation.
* Salting of the stored hashes to add the next level of security.
* Secure storage of data.
* Self-healing and no data loss during a network failure.
# Dark-Center Based Insulator Detection Method in Foggy Environment
## 4 Experimental Results and Analysis
### Target Detection Experiments
#### 4.2.2 Evaluation Indicators  
In this paper, the mean average accuracy (\(mAP\)), the number of frames per second transmitted (\(Speed\)), and the total number of parameters (\(Params\)) are used as the evaluation indexes of the algorithm detector accuracy and detection speed and model size, respectively. In the target detection task, \(mAP\) is the mean value of the average accuracy (\(AP\)) for each type of target, as shown in Equation (19).  
\[mAP=\frac{1}{N}\sum_{i=1}^{N}AP_{i} \tag{19}\]  
where \(N\) is the number of categories, and \(i\) denotes a particular category. The \(AP\) for a particular category \(i\) is calculated as follows:  
\[AP_{i}=\int_{0}^{1}P(R)dR \tag{20}\]  
where \(P(R)\) is the mapping relationship between precision (\(P\)) and recall (\(R\)), which is often represented by a \(P\)-\(R\) curve, and the area of the region below the curve is the \(AP\) value for that category. Precision and recall are calculated as follows:  
\[P_{i}=\mathit{TP}/\left(\mathit{TP}+\mathit{FP}\right) \tag{21}\]  
\[R_{i}=\mathit{TP}/\left(\mathit{TP}+\mathit{FN}\right) \tag{22}\]  
where \(\mathit{TP}\) denotes the number of samples where both the detection category and true label are \(i\); \(\mathit{FP}\) denotes the number of samples where the detection category is \(i\), and the true label is not \(i\); and \(\mathit{FN}\) denotes the number of samples where the detection category is not \(i\), but the true label is \(i\).
## 2 Materials and Methods
### Proposal of Musculoskeletal Models for Pregnant Women
#### 2.3.3 Supplementing Body Weight during Pregnancy  
The other characteristic change during pregnancy is weight gain. This change was determined following two previous research works regarding the musculoskeletal models of pregnant woman and inertial parameters of the lower trunk of pregnant women [17; 29]. This research pertained to musculoskeletal models of pregnant woman during the last trimester. [PERSON] et al. added 0.95 kg to the breasts, 3.325 kg to the segments of the abdomen, arms, and legs, and 6.175 kg to the abdominal area around the uterus, for a total gain of 10.45 kg during pregnancy. A proportionate amount of weight was thus added to each segment of the participant in the current study. For example, based on the participant's own weight gain, 0.818 kg was added to the breasts, 2.864 kg to the segments of the abdomen, arms and legs, and 5.318 kg to the abdominal area around the uterus of the participant, who had gained 9.0 kg during pregnancy. In particular, the addition of mass in the abdominal area around the uterus required meticulous care because it is strongly related to a shift in the center of gravity. However, it is extremely difficult to measure the center of the uterus mass gained in pregnancy. Hence, the addition of mass to this area was decided putatively, by reference to previous research on the inertial parameters of the lower trunks of pregnant women  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Spinal Level**} & \multicolumn{2}{c}{**Data from Previous Research [28]**} & \multicolumn{2}{c}{**Data from a Participant of This Study**} \\ \cline{2-5}  & **Girth (m)** & **Depth (m)** & **Girth (m)** & **Depth (m)** \\  & **(Measured Value)** & **(Measured Value)** & **(Measured Value)** & **(Calculated Value)** \\ \hline L2 (A) \({}^{\dagger}\) & 0.865 & 0.266 & 0.840 & 0.258 \\ L4 (B) & 0.936 & 0.289 & 0.905 & 0.279 \\ L5 (C) & 0.938 & 0.290 & 0.910 & 0.281 \\ \hline \hline \end{tabular} \({}^{\dagger}\) (A)–(C) are correspondence to the (A)–(C) in Figure 4.  
\end{table}
Table 2: Values used to determine enlarged rectus abdominis.  
Figure 4: Musculoskeletal model of an average woman (**a**) and a pregnant woman (**b**). (A)–(C) express the points that were used to decide the wrapping points of the rectus abdominis and are correspondence to the (**A**)–(**C**) in Table 2  
[29]. Thus, the location of the center of mass of the lower trunk segment was estimated at 10.5 cm below the lower edge of the tenth rib and 15.35 cm forward from the posterior end of the lower trunk segment. In this way, mass was added to specific points of the musculoskeletal model. Figure 5 shows each point of additional mass in the model for pregnant women. The red circles indicate the points where mass was added.
# Experimental Investigation on a Throttleable Pintle-Centrifugal Injector
## 2 Design of the Throttleable Pintle-Centrifugal Injector  
_Design of the Pintle-Centrifugal Injector_  
The throttleable pintle-centrifugal injector was designed to enhance throttling precision and combustion efficiency in hybrid rocket engines. It integrates the pintle's deep throttling ability with centrifugal swirl injection for improved atomization. The pintle adjusts the annular slit area for variable flow rates, while tangential apertures create a swirl to enhance oxidizer mixing with solid fuel. This addresses traditional injector limitations, like poor low-flow atomization, while leveraging hybrid propulsion benefits such as safety and control. Design parameters were optimized for pressure drop, spray uniformity, and structural integrity, tailored to hybrid motor needs.  
As delineated in Figure 1, the pintle-centrifugal injector is an assembly comprised of the injector panel, the injector's upper cap, the pintle mechanism, and the dynamic sealing system. Incorporated within the pintle, beneath and central to the injector panel, are conical surfaces configured at uniform angles. The axial displacement of the pintle modulates the gap between these conical surfaces, thereby varying the cross-sectional area available for injection. This dynamic adjustment enables the regulation of the pressure differential across the injector for diverse flow rates. Furthermore, the dynamic seal serves a critical function, ensuring a hermetic interface between the pintle and the injector panel throughout the pintle's range of motion, precluding any potential fluid leakage.  
As delineated in Figure 2, the pivotal structural parameters of the throttleable pintle-centrifugal injector designed for empirical evaluation are systematically enumerated. Within this figure, the variable \(s\) signifies the traversal range of the pintle mechanism. The count of tangential apertures within the injection panel is denoted as \(N\), and \(D_{t}\) signifies the internal diameter of these orifices. Moreover, \(D_{inj}\) represents the diameter at the pintle's nadir, cooperating with the injection panel's internal diameter \(D_{a}\), constitutes the annular injection slit. The pintle adjusts the annular slit, formed by the injection panel's internal diameter (\(D_{a}\)) and the pintle's nadir diameter (\(D_{inj}\)). At a pintle stroke of \(s=0\) mm, the slit is closed, ideally sealed, and opens progressively as s increases. Additionally, the angular disposition of the injection channels is indicated by \(\theta\). A comprehensive tabulation of these parameters is provided in Table 1. The size and scale of our injector were constrained by the experimental capabilities of our laboratory and the requirements of a standard engine test article, leading to the selection of the current dimensions, with studies  
Figure 1: Diagram of the pintle-centrifugal injector.  
conducted to investigate the effects of varying tangential hole sizes. Furthermore, the actualized physical assembly of the throttleable pintle-centrifugal injector and the pintle apparatus is depicted in Figure 3.
## Materials and Methods
### Materials and Apparatus
(**a**) The operation of the interdigital sensors was in resemblance to a planar parallel-plate capacitor where the electric field lines would penetrate through the material under test (MUT) that is kept in contact or proximity to the sensing area. (**b**) The field penetrates through the sample flowing through the microfluidic channel to determine their responses at different frequencies. The response was studied with different concentrations to determine the characteristics of the samples at different frequencies. The concentration of the MUT will determine the \(\varepsilon_{\mathrm{r}}\). Therefore, the capacitance (C) will be dependent on the \(\varepsilon_{\mathrm{r}}\) if the geometry is set. By measuring the relationship between Reactance \(X\) and the frequency, we can get the capacitance \(C\), therefore determining the concentration.
# Coordinated Optimization of Feeder Flex-Route Transit Scheduling for Urban Rail Systems
## 4 Solution Approach
### Departure Interval Setting Stage  
When determining the departure interval, it is essential to account for the characteristics of the bus schedule and develop a practical and well-structured plan. Although the departure interval is derived from the model, it does not directly represent the optimal solution. Since the departure interval is relatively fixed for buses, the system cost must be evaluated iteratively to determine the most suitable interval. Based on relevant standards and empirical data, the upper and lower limits of the departure interval are established. The system cost associated with each possible departure interval is then calculated and compared to identify the optimal departure interval for the vehicle. The pseudo-code for the specific solution algorithm is shown in Algorithm 1.  
```
Input:  Lower limit of departure interval \(H_{\text{min}}\), upper limit of departure interval \(H_{\text{max}}\), departure interval \(H_{\
u}\), where: \(H_{\
u}=H_{\text{min}}\), \(\ldots\), \(H_{\text{max}}\), \(\
u=1\), \(2\), \(\ldots\), \(n\) Output:  Optimal departure interval \(H^{\star}\), Optimal system cost \(U^{\star}(H^{\star})\) Step 0  Take the initial departure interval \(H_{1}=H_{\text{min}}\), let the optimal departure interval \(H^{\star}=H_{1}\), calculate the current system cost \(U(H_{1})\), let the optimal system cost \(U^{\star}(H^{\star})=U(H_{1})\); Loop:  While \(\
u<n\) Step 1  Based on the current departure interval, obtain a new vehicle departure interval \(H_{\
u}\) + 1,and judge the constraint condition, if \(H_{\
u+1}\) does not satisfy the constraint condition, repeat Step 1; Step 2  Calculate the system cost \(U(H_{\
u+1})\) under departure interval \(H_{\
u+1}\), if \(U(H_{\
u+1})<U^{\star}(H^{\star})\), accept \(H_{\
u+1}\) as the new optimal departure interval, \(H^{\star}=H_{\
u+1}\), Otherwise, retain the original optimal departure interval \(H^{\star}\); Step 3  If termination condition \(H_{\text{tr}}=H_{\text{max}}\) is satisfied, then output optimal departure interval \(H^{\star}\) and optimal system cost \(U^{\star}(H^{\star})\), termination routine, Otherwise, repeat Step 1.
```  
**Algorithm 1** Departure interval setting algorithm