esa-sceva/satcom-chunk-collection huggingface dataset:
# Identification, Control, and Characterization of Peristaltic Pumps in Hemodialysis Machines
## 2 Methodology
### System Identification
By employing this method, the transfer function parameters are estimated precisely, ensuring that the dynamic behavior of the peristalic pump is accurately captured, even under varying experimental conditions. The first-order transfer function in the Laplace domain is defined as shown in Equation (3):  
\[G(s)=\frac{k}{\tau s+1} \tag{3}\]  
The objective of the non-linear least squares method is to minimize the sum of the squares of the differences between the observed values and the values predicted by the model. The objective function to be minimized is defined as follows:  
\[S(\theta)=\sum_{i=1}^{n}(y_{i}-f(x_{i},\theta))^{2} \tag{4}\]  
where \(y_{i}\) represents observed RPM values at time \(t_{i}\), while \(f(x_{i},\theta)\) denotes the predicted value as calculated by the model. The latter employs the parameters \(\theta=[k,\tau]\) to describe the first-order transfer function. Finally, \(x_{i}\) signifies the time at which each respective sample was taken. The fitting process is implemented using the Levenberg-Marquardt algorithm [17], which combines the Gauss-Newton and gradient descent methods to  
Figure 3: System dynamics in response to a step input. **Caption:** System response to a 24 V step input, showing the inherent oscillations due to the dynamic characteristics of the peristalic pump. Fluctuations between 72.05 and 74.67 RPM are attributed to transient response and system inertia. estimate the optimal parameters \(\theta=[k,\tau]\). Following the completion of the fitting process, the transfer function is expressed as illustrated in Equation (5):  
\[G(s)=\frac{73.54}{0.0108s+1} \tag{5}\]  
The estimated function provides a satisfactory approximation, as evidenced by the fact that when we multiply \(4\tau=0.0434\)\(s\), the result represents 98.2% of the maximum amplitude. Nevertheless, the accuracy of the transfer function is evaluated using the root mean squared error (RMSE), as detailed in Equation (6):  
\[RMSE=\sqrt{\frac{1}{n}\sum_{i=1}^{n}(y_{i}-\hat{y}_{i})^{2}} \tag{6}\]  
where \(\hat{y}_{i}\) are the values predicted by the model, resulting in an estimation error of 1.897 RPM, as illustrated in Figure 4.
## 4 Measured Results and Discussion  
In this section, the proposed 4 \(\times\) 4 \(\times\) 3 (row \(\times\) column \(\times\) layer) multilayer antenna array is measured and discussed. The reflection coefficient was measured by a vector Network Analyzer (R&S ZVA50, Rohde & Schwarz, Munich, Germany), and the radiation patterns were measured by a compact antenna test range.
## 3 Results
### Relationships between the FCO\({}_{2}\) and Environmental Variables and VIs  
The relationships between FCO\({}_{2}\) and ST and between FCO\({}_{2}\) and SM are further explored. The results indicate that FCO\({}_{2}\) is positively correlated to the ST and SM at the two study areas (Figure 4). The coefficient of determination (R\({}^{2}\)) between FCO\({}_{2}\) and ST is 0.61 (_p_ < 0.001) and 0.63 (_p_ < 0.001) in CHD and GY area, individually. The R\({}^{2}\) between FCO\({}_{2}\) and SM is 0.64 (_p_ < 0.001) and 0.75 (_p_ < 0.001) in CHD and GY areas, respectively. The significant relationships of the FCO\({}_{2}\) with ST and SM illustrate that abiotic variables are the crucial driving factors to affect the dynamics of the FCO\({}_{2}\) in the subtropical forests during the dry season. In addition to the abiotic factors, the biotic factors (e.g., the vegetation indices (VIs) related to plant productivity) also show a significant and positive correlation with FCO\({}_{2}\) (Table 3), suggesting that the biotic factors are other determinants of the variations of the FCO\({}_{2}\) in the subtropical forests. Therefore, based on the relationships between the FCO\({}_{2}\) and environmental variables and VIs, the indices representing the temperature, moisture availability, and vegetation productivity derived from remotely sensed data are considered to modeling the spatial and temporal variability of FCO\({}_{2}\) in this study.  
Figure 2: Temporal variations of the FCO\({}_{2}\) in (**a**) CHD (_n_ = 35) and (**b**) GY (_n_ = 35) area. The embedded figures are based on summarized values of the FCO\({}_{2}\) for the whole dry season, autumn, and winter, respectively. The green quadrilateral represents the average, the gray line in the boxplot is the median, and the black circles are outliers.  
Figure 4: The relationships between the FCO\({}_{2}\) and (**a**) the soil temperature, and (**b**) soil moisture in the CHD (\(n=35\)) and GY (\(n=35\)) areas, respectively.  
Figure 3: Temporal variations of the SM and ST in CHD (**a,b**) and GY (**c,d**), respectively. The embedded figures are based on summarized values of the SM and ST for the whole dry season, autumn, and winter, respectively. The green quadrilateral represents the average, the gray line in the boxplot is the median, and the black circles are outliers.
## 2 Fatigue Strength Assessment of Real Components
### Fatigue Assessment of the Compressor Mounting Bracket  
#### 2.1.1 Background  
This case involves the suspension system of the compressor for pneumatic car suspension. The structure shown in Figure 1 consists of a vibrating mass, functioning as a compressor for the car air suspension system, which is screwed to compressor brackets. To isolate vibrations, the structure is suspended on three vibration isolators. A single isolator includes spring, rubber bearing, and a spring seat, which adopts the function of lower bumper. Additionally, the entire assembly is tightened to a rigid frame that serves as the upper bumper for the rubber bearing.  
During operation, the structure works near or at its natural frequency, resulting in collisions between the rubber bearing and bumpers, even under low levels of excitation. The collisions occur on both sides, involving contact between the rubber bearing and the upper bumper, as well as the lower bumper. As a result, the fatigue life of the compressor brackets is considerably impacted.  
In this research the goal was to use spectral methods for bracket fatigue calculations, which are entirely performed in the frequency domain [9; 10] and are more effective than time domain calculations [11; 12]. An input for spectral methods is FEM frequency response analysis [13] (e.g., [14] Nastran SOL 111 or ANSYS Harmonic Response). Since these are linear simulations from the definition [12; 15], they do not capture the non-linear behaviour of the structure, like collisions, and cannot be used directly for predicting stress history.  
Figure 1: (**a**) Simplified scheme of suspension system of compressor; (**b**) Vibration isolator cross-section.  
To address this limitation, an adaptation was required to include effects of collisions in the stress response of the structure. This issue was first time reported by [PERSON] et al. in (2016). However, the initial method provides very conservative results compared to those presented in this work. Therefore, this study proposes an alternative approach using equivalent effective accelerations to account for the non-linear effects of collisions in linear FEM simulations.
## 2 Train Dynamics Model  
Considering the additional resistance and external disturbances in the process of train fully automatic operation, according to the principle of Newton dynamics, the time-based dynamic model of train single-particle motion can be described as follows:  
\[\left\{\begin{array}{l}\dot{p}(t)=v(t)\\ m\dot{v}(t)=F(t)-g(v(t))-f(p(t),v(t),t)\end{array}\right., \tag{1}\]  
where \(m\) is the total mass of the whole train, \(p(t)\) is the train position, \(v(t)\) is the train speed, \(\dot{v}(t)\) is the train acceleration, \(F(t)\) is the train traction or brake force, \(g(v(t))\) is the basic resistance during train fully automatic operation, and \(f(p(t),v(t),t)\) is the additional resistance and other unknown disturbances that cannot be modeled.  
The basic resistance refers to the resistance suffered by the train when running along the straight track, which is commonly described by the Davis Equation [17].  
\[g(v(t))=a_{0}+b_{0}v(t)+c_{0}v^{2}(t), \tag{2}\]  
where \(a_{0}\), \(b_{0}\), and \(c_{0}\) are the basic resistance coefficients. The coefficients vary with different trains, operating environments, weather conditions, and other factors, leading to uncertainty in the parameters of the train dynamics model. Therefore, this paper introduces an adaptive control mechanism to cope with changes in train model parameters and ensure the control performance of the control system.  
The additional resistance mainly refers to the route resistance affecting the train due to curves, ramps, or tunnels on the running route.  
\[f(p(t),v(t),t)=\omega_{r}+\omega_{i}+\omega_{s}, \tag{3}\]  
where \(\omega_{r}\), \(\omega_{i}\), and \(\omega_{s}\) denote the additional resistance of the route curve, ramp, and tunnel.  
Due to the effect of route curves, ramps, and tunnels, it is impossible to build an accurate model. In this paper, we consider the bounded time-varying dynamics and use RBFNN to approximate the unknown nonlinear additional resistance and unknown disturbance function.  
Define \(x_{1}=p(t)\), \(x_{2}=v(t)\); thus, Equation (1) is rewritten as follows:  
\[\left\{\begin{array}{l}\dot{x}_{1}=x_{2}\\ \dot{x}_{2}=u+g+f(x)\end{array}\right., \tag{4}\]  
where \(x_{1}=p(t)\) and \(x_{2}=v(t)\) are the train position and train speed, \(u=\frac{F(t)}{m}\) is the control input of the FAO controller, \(g=-\frac{g(x_{2})}{m}\) is the basic resistance, and \(f(x)=-\frac{f(x_{1}x_{2},t)}{m}\) is the additional resistance and other unknown disturbances that cannot be modeled.  
**Assumption 1**.: _The dynamics system of train operation given in Equation (4) is an input-state stable system._
# Crescent Microstrip Antenna for LTE-U and 5G Systems
## 6 Simulations and Measurements Results
### Reflection Coefficient \(S_{11}\)  
The base reflectance value was taken to be \(-10\) dB, which means that \(10\%\) of the incident power is reflected, i.e., \(90\%\) of the power is received by the antenna, which is considered good for mobile communication. Figure 7 shows the results of the reflection coefficient as a function of frequency for the proposed antenna. The continuous line shows the results of the simulations obtained in CST Microwave Studio, while the dashed line shows the results of the measurements made for the physical model of the antenna. The proposed antenna has two resonances at \(3.75\) GHz with return losses of \(-13.70\) dB and \(5.17\) GHz with return losses of \(-20.17\) dB for the simulation results, and at \(3.52\) GHz with a reflection coefficient of \(-31.54\) dB and \(5.42\) GHz with a reflection coefficient of \(-23.15\) dB for the measurement results. The antenna has a bandwidth of \(3.00\) GHz, which gives a relative bandwidth of \(68.18\%\).
## 4 Proposed Scheme
### Verification  
Before accepting the message \(M_{i}\) for further processing, the receiver-enrolled vehicle needs to test whether the message-tuple \(\{PPID_{vi},\,M_{i},\,T_{vi},\,\sigma_{vi}\}\) is valid or not. As well, Figure 3 explains the process of the verification phase in detail.  
* When the message-tuple arrives \(\{PPID_{vi},\,M_{i},\,T_{vi},\,\sigma_{vi}\}\), the receiver \(v_{j}\) initially tests the freshness of timestamp \(T_{vi}\) as follows. Assume \(T_{r}\) is the time of arriving and \(T_{v}\) is the time of predefined delay. If \((T_{v}>T_{r}-T_{vi})\), thereafter \(T_{vi}\) is legitimate. Otherwise, the message-tuple \(\{PPID_{vi},\,M_{i},\,T_{vi},\,\sigma_{vi}\}\) is discarded.
* Then, the receiver computes the parameter \(SM_{vi}^{-}=h(M_{i}||PPID_{vi}||T_{vi})\).
* Lastly, the receiver utilizes the message signature \(\sigma_{vi}\) of the message-tuple \(\{PPID_{vi},\,M_{i},\,T_{vi},\,\sigma_{vi}\}\) to verify the message \(M_{i}\), where \(\sigma_{vi}=\mathcal{T}_{SM_{vi}}(Pr_{vi})modP\). The receiver accepts the tuple when the following equation holds. Otherwise, the message is rejected. \[\mathcal{T}_{PPID_{v_{i}},SM_{vi}^{-}}(Pr_{vi})\stackrel{{?}}{{= }}\mathcal{T}_{PPID_{v_{i}}}(\sigma_{vi})\] (3)
## 5 Literature Review by Domain Area
### Urban Planning and Smart Cities  
AR presents exciting possibilities for the fields of urban planning and smart cities. By overlaying digital information in the real world, AR allows for a seamless integration of digital intelligence and analytics within the physical environment. This can facilitate real-time decision-making and public engagement, streamline construction and maintenance processes, and enhance community well-being [88]. For urban planners, AR provides a dynamic tool for visualizing changes in landscapes, infrastructures, and traffic flow, making it easier to evaluate the impact of proposed developments. Meanwhile, smart city initiatives can benefit from AR applications to improve public transportation, emergency services, and citizen engagement [89]. Following are some developments of AR in this domain area:  
* **Virtual Zoning and Land Use Modeling:** AR can make the process of zoning and land-use planning much more interactive and accessible. Using AR apps, planners and citizens alike can visualize different zoning scenarios on-site, thereby promoting more informed discussions and decisions. For instance, you could point your smartphone at an empty lot and see a digital overlay of a proposed building, complete with height, design, and how it interacts with its surroundings [90].
* **Asset Management and Maintenance:** Cities have numerous assets like streetlights, water pipes, and public benches that need regular maintenance. AR can help city workers identify and assess the condition of these assets in real time. Maintenance staff can point a device at an asset and receive immediate information on its maintenance history, required repairs, or even step-by-step repair instructions, thereby improving operational efficiency [91].
* **Public Transport Navigation:** One of the challenges in public transport is navigating complex stations or understanding when the next bus or train will arrive. Augmented reality can improve user experience by providing real-time information through AR glasses or smartphones. For instance, as you enter a subway station, AR could guide you to your platform or even your seat by projecting arrows onto your field of view, while also displaying real-time schedules [92].
## 3 Results and Discussions
### Finger-like Disturbance Phenomenon  
As mentioned above, the \"finger-like\" disturbance was observed during the droplet spreading process. Droplets do not form regular circles when they spread on solid walls. Instead, some prominent disturbances are formed at the periphery, which have been called \"finger\" structures in previous studies. There is no unified theory about the formation mechanism of \"finger\" structures, and some researchers attribute it to the instability caused by the relative motion of the air-liquid interface. [PERSON] [48] suggested that fingers form at the periphery of a droplet because of Rayleigh-Taylor instability, which is caused by the acceleration of the interface between two kinds of fluid with different densities. In addition, surface roughness also has a certain influence. [PERSON] and [PERSON] [49] put forward that the surface roughness caused the instability of the liquid film, thus affecting the splash phenomenon because the \"finger\" disturbance formed the necessary condition for the splash. However, due to the differences in hydrophilicity, surface structure, and droplet properties, the shape and number of \"finger-like\" disturbances in this paper are different from those of previous studies.  
The \"finger disturbance\" observed in this study is weak but has more waves. To eliminate the influence of different initial diameters on the number of fingers, the number of fingers following refers to the total number of fingers at the maximum diameter divided by initial diameters, that is, the number in a unit circumference of a droplet. Figure 8 shows the relationship between the number of \"fingers\" and impact velocity on different roughness surfaces. It can be inferred that roughness has little effect on the number and shape of the \"finger\" disturbance, but impact velocity mainly determines the finger number. This finding is consistent with that of [PERSON] and [PERSON] [36]. R-Square testing was used to explore the relationship between finger number and impact velocity. The R-Square value on four roughness surfaces are 0.838, 0.922, 0.876, and 0.948 in turn, and it is concluded that the number of fingers presents almost linear variation with the impact velocity.
# Improving the Design of MEMS INS-Aided PLLs for GNSS Carrier Phase Measurement under High Dynamics
## 2 Tracking Error Analysis of INS-Aided PLLs  
To analyze tracking errors, MEMS INS-aided PLLs were introduced, implemented in our software GNSS/INS deeply-coupled system. Then, INS-aided PLLs were tested using GPS/INS hardware simulator, and tracking errors caused by aiding information were analyzed and used to guide INS-aided PLLs optimization.
# Sensing and Device Neighborhood-Based Slot Assignment Approach for the Internet of Things
## 5 Simulation Results
### Average Packet Loss Ratio (APLR)  
The average packet loss ratio is the ratio of the number of transmitted packets to the number of packets that are not successfully received by the target device or are lost during communication, in this case, the base station, and it is inversely correlated with the average throughput of the IoT network. The suggested neighborhood-enabled TDMA scheme had a lower minimum APLR ratio than its opponent schemes when deployed in an IoT network according to a comparison of the proposed and current schemes (in terms of the average packet loss ratio, or APLR) provided in Figure 6. Additionally, in an unbalanced cluster environment seen in IoT networks, every server module \(S_{j}\) is constrained to provide time slots that are identical to neighboring or member devices, which is impossible using current methods.  
Figure 4: Slots Waiting time (ms) where member devices are less than or equal to ten (10).
## 2 Literature Review  
The BB84 protocol is a QKD protocol that was proposed by [PERSON] and [PERSON] in 1984 [20]. It is one of the most widely used QKD protocols and is named after the surnames of its inventors and the year of its invention. CRYSTALS-Kyber is another technique used to safeguard messages, especially against newer, more powerful computers that could break traditional encryption methods [21]. It relies on advanced mathematical concepts to create codes that are very difficult for these computers to crack.  
At its core, lattice-based cryptography takes on the complexities of mathematical problems built within lattice structures. These complex problems form the basis upon which the security of lattice-based schemes is established, effectively protecting them from potential attacks from quantum computers [22]. These computers act as an approaching threat to modern digital signature mechanisms as well. CRYSTALS-Dilithium appears as a signature resistant to these quantum attacks and is secure against side-channel attacks as well [23]. As digital communication evolves, a strong and safe method of creating digital signatures is provided by this algorithm, which is at the cutting edge of the evolution at this moment, where the need for digital signatures that are resistant to quantum incidents is urgent. Falcon also appears to be an appropriate choice for safe digital signatures in the rapidly developing area of post-quantum cryptography. With its foundation based on lattice-based cryptography, Falcon addresses an important need for cryptographic algorithms that maintain effectiveness and practicality while dealing with the power of quantum computers [24; 25]. The strength of all three PQC algorithms comes from the complexity of lattice problems, which is a mathematical idea that serves as the core of their security.
###### Abstract  
Precursor signals for earthquakes, such as radon anomalies, thermal anomalies, and water level changes, have been studied in earthquake prediction over several centuries. The atmospheric vertical electric field anomaly has been observed in recent years as a new and valuable signal for short-term earthquake prediction. In this paper, a physical mechanism of the atmospheric vertical electric field anomaly before the earthquake was proposed, based on which the Wenchuan earthquake verified the correctness of the model. Using Monte Carlo simulations, the variation of the radon concentration with height before the earthquake was used to simulate and calculate the ionization rates of radioactive radon decay products at different heights. We derived the atmospheric vertical electric field from \(-593\) to \(-285\) V/m from the surface to \(10\) m before the earthquake by solving the system of convection-diffusion partial equations for positive and negative particles. Moreover, negative atmospheric electric field anomalies were observed in both Wenjiang and Pixian before the Wenchuan earthquake on \(12\) May, with peaks of \(-600\) V/m in Pixian and \(-200\) V/m in Wenjiang. The atmospheric electric field data obtained from the simulation were shown to be in excellent concordance with the observed data of the Wenchuan earthquake. The physical mechanism can provide theoretical support for the atmospheric electric field anomaly as an earthquake precursor.  
atmospheric electric field; earthquake precursors; radon concentration
# Density Functional Theory Study on the Adsorption of Co(II) in Aqueous Solution by Graphene Oxide
## 5 Experimental Verification of Adsorption Properties of GO
### Experimental
#### 5.1.4 Adsorption Experiments  
The 0.02 g of GO powder was added to a 50 mL Co\({}^{2+}\) solution and placed in a constant temperature shock chamber for shock, and the shock frequency was set to 140 min\({}^{-1}\). During the experiment, the atomic absorption spectrometer (flame method) was used to determine the concentration changes of Co\({}^{2+}\) in the solution before and after adsorption under different conditions, and the adsorption capacity was calculated so as to determine the influence of pH, temperature, interfering ions and other factors on adsorption. Adsorption capacity q\({}_{t}\) (mg/g) was calculated using Equation (1):  
\[\mathrm{q}_{t}=(c_{0}-c_{t})\times V/m \tag{1}\]  
where \(c_{0}\) is the initial concentration of Co\({}^{2+}\), mg/L; \(c_{t}\) is the concentration of Co\({}^{2+}\) after adsorption, mg/L; \(V\) is the volume of solution, L; and \(m\) is the mass of the adsorbent, g.