esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
Four companies took part in the development of Gummetal for orthodontic therapy with the primary role played by Professor [PERSON] of Kanagawa Dental University in Japan.  
According to Professor [PERSON] [10], Gummetal possesses the following characteristics:  
* Possesses a very low modulus of elasticity--a Young's modulus of approximately 40 GPa., providing high flexibility and superelasticity;
* Exhibits ductility approximately 10 times greater than that of conventional metals, making Gummetal superelastic and easy to bend and handle;
* Enables easy control of orthodontic force, as superelastic deformation represents true elastic deformation without hysteresis;
* Does not deform via a dislocation-free plastic deformation mechanism, and intraoral breakage is rare;
* Is biocompatible and non-toxic, being free of nickel;  
Figure 10: Final models: front, left, and right in occlusion, upper, and lower.  
Has a low coefficient of friction, making it an excellent choice for the sliding mechanism of orthodontic tooth movement.  
[PERSON] et al. compared three types of \(\beta\)-Titanium wires (TMA, Resolve, Gummetal) and found that Gummetal wire is characterized by the lowest bending strength, fatigue limit and elastic modulus. Furthermore, the resilience is the highest [11].  
In the research conducted by [PERSON] et al., Gummetal exhibited similar frictional resistance to cobalt-chromium and nickel-titanium archives. When comparing the frictional properties of different alloys, Gummetal falls in between. It is superior to stainless steel alloy wire and inferior to titanium-molybdenum alloy wire [12]. Considering its low friction, [PERSON] et al. stated that Gummetal is a helpful addition in daily orthodontic practice [13].  
Another study revealed that the frictional resistance forces of Gummetal wire are comparable to stainless steel alloy wire and therefore useful in space closuring with sliding mechanics [14].  
Some physicochemical properties of new and used Gummetal were studied by the X-ray powder diffraction and energy dispersive spectroscopy measurements. The XRD diagrams visible in Figure 11 show that for the new Gummetal sample, only peaks from Ti, Nb, and Ta are visible (20 = 38.5\({}^{\circ}\), 55.7\({}^{\circ}\) and 70\({}^{\circ}\)), while for used Gummetal sample some additional peaks appear at 20 = 30.8\({}^{\circ}\), 41\({}^{\circ}\), 44.8\({}^{\circ}\), 50.4\({}^{\circ}\), and 51\({}^{\circ}\). These additional peaks are results of chemical processes which occur in the patient's mouth.  
According to the EDS measurements, the new Gummetal sample is composed of 62.5% of Ti, 31.5% of Nb, 3.6% of Zr, and 2.4% of Ta, while the used Gummetal sample is composed of 62.4% of Ti, 31.3% of Nb, 3.9% of Zr, and 2.4% of Ta. Additionally, elements such K, O, C, Cl, and P were discovered on the used Gummetal samples.
## 4 Bigram and Traditional Results
### Assessing Water Quality and Potability
#### 4.2.1 Bigram Document Analysis  
The main concepts have been examined in the bigram analysis as displayed in Figure 9. It reveals that water quality is predominantly applied to water pollution, groundwater, and water supply. Among the highlighted algorithms, Decision Trees and artificial neural networks have been identified. Additionally, the sensorization aspect is a crucial point under development.  
Four main groups have been observed in the thematic map analysis. The first group, in light blue, is related to machine learning methods employed for water pollution detection. In close proximity to this group, another group focuses on research related to artificial neural networks applied to water quality monitoring. In the emerging themes quadrant, two groups are observed. The first group is associated with predicting components like sulfides and others related to potability, while the final group is associated with water quality control.  
Lastly, when analyzing the conceptual structure map and the dendrogram in Figure 10, two major groups are observed. The blue group is related to water quality monitoring, linking parameters such as turbidity and consumption times. In contrast, the larger group corresponds to machine learning techniques used to predict or classify water quality or related parameters. Neural networks, support vector machines, and Random Forests are among the highlighted techniques.
## 4 Methods
### Node Aggregation and Update
#### 4.4.2 Residual Connections  
To capture the complex interactions in the user-POI heterogeneous graph and enhance the model's expressive power, this paper adopts a multi-layer propagation structure to explore higher-order information. However, in GNNs, stacking multiple propagation layers may lead to issues such as gradient vanishing or over-smoothing, which can affect the model's training performance. To address this problem, this paper introduces node pre-activation residual connections [69, 70, 71] to alleviate the negative effects of deep networks.  
Residual connections help preserve the uniqueness of node representations and prevent feature loss and reduced distinguishability between nodes caused by deep propagation. By adding residual connections at each propagation layer, the model can effectively avoid over-smoothing of information, making node features easier to learn. The update of node representations by edge type \(m_{1}\) is shown in the following equation:  
\[h_{i,m_{1}}^{(l)}=\sigma\left(\sum_{n\in N_{i}^{m_{1}}}a_{i,n}^{m_{1}(l)}W^{( l)}\left[h_{n}^{(l-1)}\middle|\middle|h_{e_{in}^{m_{1}}}^{(l-1)}\right]+W_{ res}^{(l)}h_{i,m_{1}}^{(l-1)}\right) \tag{13}\]  
User behavior data and the interactions between users and locations often involve multi-level complex dependencies. Residual connections help preserve key information in user behavior and interaction data, allowing the model to retain important details while facilitating deeper learning without the risk of losing critical information during the aggregation process.  
Here, \(l\) denotes the \(l\)-th layer, \(h_{i,m_{1}}^{(l)}\) represents the target node \(i\)'s representation connected by edge type \(m_{1}\) in the current layer, \(\sigma(\cdot)\) is the activation function, \(W^{(l)}\) represents the learnable weight matrix for aggregating the neighboring node representations and edge representations at the current layer, and \(W_{res}^{(l)}\in\mathbb{R}^{d_{l+1}\times d_{l}}\) is the learnable residual connection transformation matrix, which maps the node representation from the previous layer to the current layer's dimension when there is a dimensionality change.
## 2 Background
### Named Data Networking
#### 2.1.2 Named Data Networking Communication Model  
In NDN, the consumer node first sends an interest packet requesting data to the data publisher node using receiver-driven communication. When a node receives interest, it first checks its CS and matches the recently satisfied interest entries and the content names entries. If the data or content names match, the data will be forwarded to the consumer node that requested the data. In the case of no entry being found in the CS table, interest will go to the PIT. If an interest matches the PIT entries, then it will drop the recently requested interest because some other interest with the same request has already been forwarded to the network. If there is no matching entry in the PIT, then, after entering that interest entry, it will go to the FIB where the incoming and outgoing faces forward the interest and data for content retrieval toward the data producer.  
After finding the data from the producer, the producer adds the data into the packet with its name and signature. Producer signatures authorize and secure the content. The producer sends the data packet to the consumer following the trail of the interest packet. Pending interest table entries help the data packet keep track of the path of interest packet. Data centricity, in-network discovery of name, data security, flow balance (hop by hop), data centricity, data immutability, universality, and hierarchical naming, is the main design principles of NDN. A complete forwarding and communication model of NDN is presented in Figure 4.
## 2 Improved Quantum Particle Swarm Optimization
### Improvement for Quantum Particle Swarm Optimization  
The evolution speed of individual particles is adjusted adaptively, and its definition is shown in Equation (32):  
\[ip_{i}(t)=\frac{\mathit{Fitness}(P_{g,j}(t))}{\mathit{Fitness}(P_{i,j}(t))} \tag{32}\]  
Equation (11) can be improved to  
\[p_{i,j}(t)=P_{g,j}(t)+\varphi_{i,j}(t)ip_{i}(t)[P_{i,j}(t)-P_{g,j}(t)] \tag{33}\]  
The evolution speed of an individual particle is determined by the ratio \(ip_{i}(t)\) of the global historical optimal fitness to the individual's historical optimal fitness, which changes dynamically in the iterative process. When the difference between individual optimal and global optimal is large, the ratio \(ip_{i}(t)\) approaches 0. The updated attractive potential well is closer to the global historical optimal position, and the evolution speed is faster. While the difference is small, the ratio \(ip_{i}(t)\) approaches to 1, and the evolution speed slows down.  
The adaptive adjustment is made according to the group dispersion, and its definition is shown in Equation (34):  
\[g\mathit{s}_{t}(t)=\left[\frac{\partial p_{i,1}(t)}{\partial x_{1}X_{i,1}(t) };\ \ \frac{\partial p_{i,2}(t)}{\partial x_{2}X_{i,2}(t)},\ \ \ \cdots,\ \ \ \frac{\partial p_{i,N}(t)}{\partial x_{N}X_{i,N}(t)}\right] \tag{34}\]  
Then, Formula (30) is rewritten as:  
\[X_{i,j}(t+1)=p_{i,j}(t)\pm\alpha\big{|}C_{j}(t)-\big{(}1-gs_{i,j}(t)\big{)}X_{ i,j}(t)\big{|}\ln(1/u_{i,j}(t)) \tag{35}\]  
The group dispersion also changes dynamically and tends to be 1 in the iterative process. Adding the adaptive variation of the group dispersion can ensure particle diversity and improve the algorithm's convergence speed.  
During the iteration, the position range of particles is limited according to the constraints. If the range of the updated particle position exceeds the limit, the particle position is reinitialized, and the value beyond the parameter range is set randomly within the limit. On the one hand, the algorithm avoids searching the invalid position and generating divergence; on the other hand, it resets the parameters randomly so that the population has a higher diversity, avoids the calculation falling into the local optimum, and improves the accuracy of the algorithm.  
On this basis, a natural selection mechanism is added to the algorithm to eliminate the particles with the worst adaptability in the group and replace them with the particles with the best adaptability so as to improve the distribution of the whole particle swarm, improve the accuracy of the algorithm, and accelerate convergence. The improved QPSO algorithm flow is shown in Figure 2, the blue-filled part represents the QPSO algorithm improvement section.
## Appendix D Derivation of the Smoothing Performance Index  
The \(\sigma_{\text{p}}^{2}\) of the \(\alpha\)-\(\beta\)-\(\gamma\)-\(\delta\) filter is directly derived from definitions (1)-(8). \(x_{\text{ts},k}\) is the **true** motion of the constant-jerk target expressed as  
\[x_{\text{ts},k}=x_{\text{ts},k-1}+Tv_{\text{ts},k-1}+\frac{T^{2} }{2}a_{\text{ts},k-1}+\frac{T^{3}}{6}j_{\text{ts},k-1}, \tag{104}\]  
Using the definition of prediction process (1) and (104), the predicted error is calculated as  
\[\sigma_{\text{p}}^{2} =E[(x_{\text{p},k}-x_{\text{ts},k})^{2}]\] \[=E[(x_{\text{s},k-1}-x_{\text{ts},k-1})^{2}]+2 TE[(x_{\text{s},k-1 }-x_{\text{ts},k-1})(v_{\text{s},k-1}-v_{\text{ts},k-1})]\] \[\quad+T^{2}E[(x_{\text{s},k-1}-x_{\text{ts},k-1})(a_{\text{s},k-1 }-a_{\text{ts},k-1})]+\frac{1}{3}T^{3}E[(x_{\text{s},k-1}-x_{\text{ts},k-1})(j _{\text{s},k-1}-j_{\text{ts},k-1})]\] \[\quad+T^{2}E[(v_{\text{s},k-1}-v_{\text{ts},k-1})^{2}]+T^{3}E[(v_ {\text{s},k-1}-v_{\text{ts},k-1})(a_{\text{s},k-1}-a_{\text{ts},k-1})]\] \[\quad+\frac{1}{3}T^{4}E[(v_{\text{s},k-1}-v_{\text{ts},k-1})(j_{ \text{s},k-1}-j_{\text{ts},k-1})]+\frac{1}{4}T^{4}E[(a_{\text{s},k-1}-a_{\text {ts},k-1})^{2}]\] \[\quad+\frac{1}{6}T^{5}E[(a_{\text{s},k-1}-a_{\text{ts},k-1})(j_{ \text{s},k-1}-j_{\text{ts},k-1})]+\frac{1}{36}T^{6}E[(j_{\text{s},k-1}-j_{\text {ts},k-1})^{2}], \tag{105}\]  
This indicates that it is necessary to derive the error variances/covariances of the smoothed parameters to obtain \(\sigma_{\text{p}}^{2}\). Based on the smoothing process (5)-(8), the quantities \(x_{\text{ts},k}\), \(v_{\text{ts},k}\), \(a_{\text{ts},k}\), and \(j_{\text{ts},k}\) can be expressed as:  
\[x_{\text{ts},k} =(1-a)x_{\text{ts},k}+\alpha x_{\text{ts},k}, \tag{106}\] \[v_{\text{ts},k} =v_{\text{ts},k}+\frac{\beta}{T}(x_{\text{ts},k}-x_{\text{ts},k}),\] (107) \[a_{\text{ts},k} =a_{\text{ts},k}+\frac{\gamma}{T^{2}}(x_{\text{ts},k}-x_{\text{ ts},k}),\] (108) \[\sigma_{\text{p}}^{2} =E[(x_{\text{p},k}-x_{\text{ts},k})^{2}]\]0  
By substituting the true motion of the constant-jerk target (104) into these equations, we obtain:\[\sigma_{\text{p}}^{2} =E[(x_{\text{p},k}-x_{\text{ts},k})^{2}]\]1 (23) \[\sigma_{\text{p}}^{2} =E[(x_{\text{p},k}-x_{\text{ts},k})^{2}]\]2 (24) \[\sigma_{\text{p}}^{2} =E[(x_{\text{p},k}-x_{\text{ts},k})^{2}]\]3 (25) \[\sigma_{\text{p}}^{2} =E[(x_{\text{p},k}-x_{\text{ts},k})^{2}]\]4 \[\sigma_{\text{p}}^{2} =E[(x_{\text{p},k}-x_{\text{ts},k})^{2}]\]5 (112) \[\sigma_{\text{p}}^{2} =E[(x_{\text{p},k}-x_{\text{ts},k})^{2}]\]6 (113) \[\sigma_{\text{p}}^{2} =E[(x_{\text{p},k}-x_{\text{ts},k})^{2}]\]7 (114) \[\sigma_{\text{p}}^{2} =E[(x_{\text{p},k}-x_{\text{ts},k})^{2}]\]8 (115) \[\sigma_{\text{p}}^{2} =E[(x_{\text{p},k}-x_{\text{ts},k})^{2}]\]9 (116) \[=E[(x_{\text{s},k-1}-x_{\text{ts},k-1})^{2}]+2 TE[(x_{\text{s},k-1 }-x_{\text{ts},k-1})(v_{\text{s},k-1}-v_{\text{ts},k-1})]\]0  
Because we have assumed \(k\to\infty\), the variances and covariances of the errors do not depend on \(k\).
## 3 Hardware Development
### Power Supply
#### 3.4.2 Voltage Regulator  
The output voltage (4.5 V) of the battery bank is higher than the 4.0 V set to power the MCU and sensors and much higher than the 3.6 V which is the tolerable limit of the ESP module. Moreover, the current spikes demanded mainly by the ESP module can damage components. Therefore, two linear regulators have been used to adjust these voltage levels and to absorb the current spikes, as follows.  
(1) To obtain constant 4.0 V, the Microchip's MIC5205 regulator is used. It is designed especially for hand-held, battery-powered devices, and it includes a CMOS or TTL compatible enable/shutdown control pin. When shut down, power consumption drops nearly to zero. This regulator ensures 150 mA output, ultra-low noise output, high output voltage accuracy, and reverse battery protection, among other features. In addition, it has been very useful to turn \"on\" and turn \"off\" the sensors while they are not in operation by forcing the EN (enable/disable) pin [38].  
The MIC5205 is available in fixed and adjustable output voltage versions in a small SOT-23-5 package. The schematic connection diagram of this regulator, as well as the expressions and circuits for calculating and connecting the peripheral components, can be found in the datasheet.  
(2) To obtain a fixed voltage of 3.3 V to power the ESP, TI's TLV755P voltage regulator has been used. This is an ultra-small, low quiescent current, low-dropout regulator (LDO) that sources 500 mA. The TLV755P requires a 1 \(\mu\)F or greater capacitor on the input pin of the LDO. The input capacitor counteracts reactive input sources and improves transient response and PSRR. This also requires an output capacitance of 0.47 \(\mu\)F or larger for stability. Adding capacitance to the output also achieves the voltage filtering effect [39]. In addition, the use of short-circuit and reverse voltage protection using a N-channel MOSFET circuits were considered. The schematic connection diagram of this circuit is shown in Figure 7.  
Since the two main devices (MCU and Wi-Fi) use different voltage levels, there may be a communication problem as the output signals are of different logic levels. To overcome this, a signal coupling circuit has been implemented using two transistors. One transistor is used as a switch by stepping down the 4.0 V of the MCU to 3.3 V, which is acquired by the ESP module. Similarly, the 3.3 V is stepped up by means of another transistor so that the ESP can communicate with the MCU.  
It is worth mentioning that while the MCU can transmit data both synchronously and asynchronously, the ESP module can only do so asynchronously, and hence, the MCU has been forced to operate in asynchronous mode.  
Figure 7: Schematic connection diagram of the TLV755P voltage regulator.
# Function Optimization and Parameter Performance Analysis Based on Gravitation Search Algorithm
## 2 Gravitational Search Algorithm
### Analysis of Gravitational Search Algorithm  
In GSA, the update of the particle positions is aroused by the acceleration caused by the gravitational force of the particles. The value of gravity determines the size of the particle's acceleration, so the acceleration can be regarded as the search step of the particle position update. Its size determines the convergence rate of the gravitational search algorithm. In the acceleration calculation, the number of particles \(N\) and gravity play an important role, and the mass and gravity of the particles are determined the variety of \(G\). The particle with different mass and gravity has different convergence rate. So it is very important to choose the number of particles and the gravity of the universal gravitation search algorithm.  
The parameter initialization for all swarm intelligence optimization algorithms has important influence on the performance of the algorithms and the optimization ability. GSA has two main steps: one is to calculate the attraction of other particles for their selves and the corresponding acceleration calculated through gravitational, and another is to update the position of the particles according to the calculated acceleration. As shown in Formula (11)-(17), the convergence rate of GSA is determined by the value of Gravitational constant \(G_{0}\) to determine the size of the particle acceleration, and  
Figure 2: Flowchart of gravitational search algorithm (GSA).  
parameters \(\alpha\) to determine the pace of change. In this section, the effect and influence of parameters \(G_{0}\) and \(\alpha\) will be analyzed in detail.
# Multiple-Collision Free-Electron Laser Compton Backscattering for a High-Yield Gamma-Ray Source
## 3 Results
### Measured Spectra of the FEL-Compton Backscattering Gamma Rays Generated at Each Collision Point  
In order to clarify the characteristics of the multiple-collision FEL-Compton backscattering gamma rays, the spectra of the gamma rays generated at each collision point were individually measured during the two-bunch operation. Because the electron bunch collided head-on with one FEL pulse in the two-bunch operation, it was possible to identify the spectrum of the gamma rays generated at each collision point. By arranging the electron bunches asymmetrically in the storage ring, we could select any collision point of the FEL-Compton backscattering [34]. When the bunch-fill pattern was #0 and #5, the collision points were C\({}_{3}\) and C\({}_{-3}\), respectively. In the case of the bunch-fill pattern of #0 and #6, the collision points were C\({}_{2}\) and C\({}_{-2}\), respectively. Figure 4 shows the observed spectra of the FEL-Compton backscattering gamma rays generated at C\({}_{3}\), C\({}_{2}\), C\({}_{-2}\), and C\({}_{-3}\). A full-energy peak with a sharp edge in the high-energy side was observed near 1.2 MeV. The broad humps of lower energy were caused by the escape of gamma rays through Compton scattering inside the small detector without full-energy deposition. The electron-beam currents were 8.7 mA for the bunch-fill pattern of (#0, #5) and 11.0 mA for the bunch-fill pattern of (#0, #6). The FEL powers extracted from a cavity mirror, the transmittance of which was approximately \(1\times 10^{-4}\) at the target wavelength of 1530 nm, were 0.50 mW for the bunch-fill pattern of (#0, #5) and 0.88 mW for the bunch-fill pattern of (#0, #6) in the air, respectively. The yields of the FEL-Compton backscattering gamma rays passing through the lead collimator were \(4.4\times 10^{4}\) photons/s for the bunch-fill pattern of (#0, #5) and \(6.9\times 10^{4}\) photons/s for the bunch-fill pattern of (#0, #6), respectively, in consideration of the efficiency of the measurement system. The collision point C\({}_{2}\) was in the optical klystron, and the yield of the gamma rays generated at C\({}_{2}\) was less than one-tenth of that generated at the other points. The FEL-Compton backscattering gamma-ray spectra simulated via EGS5 code, which can simulate photon transport in any material [34; 38], at the position of the detector are also described in Figure 4. We note that the measured spectra of the gamma rays were in good agreement with the calculated spectra. If the multiple-collision FEL-Compton backscattering gamma rays are described as the superposition of the individual FEL-Compton backscattering gamma rays generated at each collision point, the measured spectrum of the multiple-collision FEL-Compton backscattering gamma rays should be reproduced using the calculated spectra in Figure 4.
## 5 Performance Evaluation
The simulation results illustrate that the average buffer size increases with \(M\), and \(p_{f}\) increases. When \(p_{f}\) is below a certain value, e.g., \(p_{f}=0.15\), the rate at which the average buffer size increases is related to the number of active users. That is, the greater the number of active users, the faster the buffer size increases. In order to compare the throughput performance of our protocol with that of reference [11], Figure 13 shows the throughput performance trends with \(M\) values of 10 and 100, respectively. It can be seen that the \(K\) value has an impact on the throughput performance of the system. The throughput performance of \(K=3\) is better than that of \(K<3\). So, the rationality of the parameters set in Section 2.2 is further proved. Figure 11: Successful transmission probability vs. first transmission probability (Ana. vs. Sim.). Figure 12: Buffer size vs. first transmission probability (Ana. vs. Sim. ).