esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
### XRD Analyses  
Figure 4 presents the X-ray diffraction (XRD) patterns of three ZIF-8 samples (ZS1, ZS2, and ZS3) synthesized under varying conditions. The XRD peaks observed at 10.3\({}^{\circ}\), 12.7\({}^{\circ}\), 14.7\({}^{\circ}\), 16.4\({}^{\circ}\), 18.0\({}^{\circ}\), 19.3\({}^{\circ}\), 22.1\({}^{\circ}\), 24.6\({}^{\circ}\), 25.6\({}^{\circ}\), 26.7\({}^{\circ}\), 30.6\({}^{\circ}\), and 32.3\({}^{\circ}\) correspond to specific planes ((0 0 2), (1 1 2), (0 2 2), (0 1 3), (2 2 2), (1 2 3), (1 1 4), (2 3 3), (2 2 4), (2 2 3), (0 4 4), and (2 3 5), respectively) within the crystal structure of ZIF-8. Each of these reflections is characteristic of the ZIF-8 based on the sodalite-type topology, as commonly reported in the literature.  
The identification of these specific peaks, particularly the most intense reflections at lower angles (such as 10.3\({}^{\circ}\), 12.7\({}^{\circ}\), and 14.7\({}^{\circ}\)), provides strong evidence for the successful growth of ZIF-8, confirming the maintenance of its sodalite structure. The presence of these peaks, with no additional reflections, rules out the formation of other phases, ensuring phase purity and the correct formation of ZIF-8.  
The XRD patterns were analyzed utilizing X-pert High Score software (version 4.9) and indexed using the standard JCPDS number. Figure 5 shows the XRD patterns for the ZIF-8-based cellulose air filter (ZCAF), illustrating the integration of ZIF-8 into a cellulose matrix. The patterns here are a composite of the peaks corresponding to both ZIF-8 and the cellulose pulp. The cellulose peaks, indexed using the standard JCPDS number for pulp (00-056-1718), appear alongside the ZIF-8 peaks, demonstrating that the two components coexist without any chemical reactions altering their individual structures.  
Figure 4: XRD patterns of ZIF-8.  
The key insight here is the maintenance of the structural integrity of ZIF-8 within the cellulose matrix. The XRD patterns show no evidence of new peaks or significant shifts, which would suggest interaction or reactions between ZIF-8 and cellulose. This suggests that the integration process has been successful in embedding ZIF-8 into the cellulose without compromising the crystalline nature of either component. The distinct phases of both ZIF-8 and cellulose pulp were prominently observed, indicating no reaction occurring between these constituent phases.  
This information supports the potential use of ZCAF as a functional material in applications such as CO\({}_{2}\) capture, where maintaining the properties of ZIF-8 is crucial for performance. The presence of both ZIF-8 and cellulose peaks also suggests a well-dispersed composite material, which is important for the effectiveness of the filter in real-world applications.
# Smartphone Sensors in Motion: Advancing Traffic Safety with Mobile Technology
## 3 Results  
Brake deceleration measurements (see Table 3) were carried out on a single road using a single vehicle on different types of surfaces. The experiment involved braking on a dry surface, where the objective was to achieve maximum braking deceleration. Driving tests were conducted at four different speeds (30, 50, 70, 90 km/h). Speeds of 30 and 50 km/h were chosen to simulate an urban environment where lower speeds lead to shorter braking distances and slightly lower deceleration values. These tests were designed to verify that the smartphones employed can record data with sufficient accuracy for analytical purposes. The speeds of 70 and 90 km/h simulated driving on roads outside a municipality or on roads with higher speed limits, which typically increase deceleration values and lengthen braking distances. To ensure the highest possible accuracy of the measurements made, four trials were performed for each speed.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & & \multicolumn{3}{c}{**XL Meter**} & \multicolumn{3}{c}{**Mate 20**} & \multicolumn{3}{c}{**Redmi 5G**} & \multicolumn{3}{c}{**11T**} \\
**No.** & **Velocity** & **Time** & **Distance** & **b** & **b\({}^{-}\)** & **b\({}^{-}\)** & **b\({}^{-}\)** & **b\({}^{-}\)** & **b\({}^{-}\)** & **b\({}^{-}\)** & **b\({}^{-}\)** \\
**[km/h]** & **[s]** & **[m]** & **[m/s\({}^{2}\)]** & **[m/s\({}^{2}\)]** & **[m/s\({}^{2}\)]** & **[m/s\({}^{2}\)]** & **[m/s\({}^{2}\)]** & **[m/s\({}^{2}\)]** & **[m/s\({}^{2}\)]** & **[m/s\({}^{2}\)]** \\ \hline
1 & 26.95 & 0.76 & 2.81 & 9.97 & & 10.88 & & 10.76 & & 11.16 & \\
2 & 25.01 & 0.62 & 2.20 & 10.97 & & 10.58 & & 10.53 & & 10.85 & \\
3 & 24.76 & 0.68 & 2.27 & 10.42 & & 10.51 & & 10.23 & & 10.57 & \\
4 & 25.13 & 0.72 & 2.44 & 9.99 & & 10.85 & & 11.15 & & 10.49 & \\
5 & 44.70 & 1.20 & 7.51 & 10.26 & & 10.48 & & 10.46 & & 10.53 & \\
6 & 44.14 & 1.21 & 7.23 & 10.40 & & 10.30 & & 10.28 & & 10.81 & \\
7 & 44.71 & 1.22 & 7.51 & 10.27 & & 10.78 & & 10.43 & & 10.06 & \\
8 & 45.04 & 1.24 & 7.68 & 10.19 & & 10.11 & & 10.66 & & 9.97 & \\
9 & 60.49 & 1.63 & 13.52 & 10.44 & & 10.71 & & 10.65 & & 10.52 & \\
10 & 65.82 & 1.77 & 15.94 & 10.49 & & 10.77 & & 10.52 & & 10.55 & \\
11 & 63.04 & 1.68 & 14.47 & 10.60 & & 10.41 & & 10.58 & & 10.74 & \\
12 & 63.58 & 1.68 & 14.69 & 10.62 & & 10.68 & & 10.65 & & 10.37 & \\
13 & 79.96 & 2.12 & 23.55 & 10.47 & & 10.86 & & 10.87 & & 10.82 & \\
14 & 79.93 & 2.10 & 23.17 & 10.64 & & 10.82 & & 10.37 & & 10.72 & \\
15 & 80.73 & 2.16 & 23.90 & 10.52 & & 11.16 & & 10.48 & & 11.07 & \\
16 & 82.01 & 2.19 & 24.36 & 10.65 & & 10.60 & & 10.87 & & 10.24 & \\  & & & & & & & & & & \\ \multicolumn{7}{c}{Average} & 10.43 & & 10.66 & & 10.59 & & & 10.59 & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Processed XL Meter measurements.  
Figure 8: View in Diagram software.
## 3 Key Internet of Things Scenarios
### Smart Health  
New healthcare services leveraging the IoT in hospitals can include patient positioning, continuous monitoring of vital signs, such as respiration and heartbeat, nursing applications, remote control of infusion pumps and robotic assistance. In some cases, these mMTC services can be deployed in conjunction with uRLLC services, such as remote ultrasonography and remote surgery. Some IoT services may require the exchange of operation and control data transfer of high-resolution medical images and videos from cameras and medical instruments, with data rates between 1 and 20 Mbps in both up and down links (eMBB requirements) and latency values in the range 20-100 ms. In the cases of services not strictly related to emergency management, such as the remote monitoring of patients, latency requirements can be relaxed. In these cases, thanks to its excellent responsiveness, serverless computing can represent an efficient way to deploy healthcare services across the entire edge-to-cloud continuum. In fact, the instantiation of specific functions, such as alert mechanisms for medical personnel, can be activated promptly by specific events, such as the receipt of health data records.
# A Total Crop-Diagnosis Platform Based on Deep Learning Models in a Natural Nutrient Environment
## 2 Related Works  
### IoT Protocol  
IoT requires low-power, high-speed communication. Existing communication protocols such as IP, HTTP, and TCP are not suitable for IoT. The following paragraphs describe current studies of the protocols and gateways for IoT communication.  
Zigbee is for low-data rate, low-power applications and is an open standard. This, theoretically, enables the mixing of implementations from different manufacturers, but in practice, Zigbee products have been extended and customized by vendors and are thus plagued by interoperability issues. In contrast to Wi-Fi networks, used to connect endpoints to high-speed networks, Zigbee supports much lower data rates and uses a mesh networking protocol to avoid hub devices and create a self-healing architecture [7].  
In the [8], this study aims to integrate named data networking (NDN) with Zigbee to give NDN a better support for IoT applications that are known to require wireless sensing/actuating abilities, mobility support and low power consumption. They propose to leverage the strengths of NDN and Zigbee, combining them to make a new step towards IoT application development. In real terms, they propose an implementation that allows NDN communication over the Zigbee protocol acting as a layer 2 support for NDN. Since our implementation can run on any Linux distribution with an NDN module, we believe that it will allow NDN to target a larger set of devices and gateways, and cover more IoT applications.  
In reference [9], they present a mobile matrix (\(\upmu\)Matrix), a routing protocol that uses hierarchical IPv6 address allocation to perform any-to-any routing and mobility management without changing a node's address. In this way, device mobility is transparent to the application level favoring the Internet of Medical Things (IoMT) and the Social Internet of Things (SIoT) implementation and broader adoption. The protocol has a low memory footprint, adjustable control message overhead, and achieves optimal routing path distortion. Moreover, it does not rely on any particular hardware for mobility detection (a key open issue), such as an accelerometer.
# Dynamic Behavior Analysis and Stability Control of Tethered Satellite Formation Deployment
## 4 Stable Deployment Laws of Tether Release Rate and Tether Tension Control
### Tether Release Rate Control
#### 4.1.2 Fixed Angular Velocity \(\theta^{\prime}\)  
The system is assumed to rotate steadily and uniformly in the direction of the in-plane angle, \(\theta=\omega v+\theta_{0}\), where \(\omega\) is the angular velocity, and \(\theta_{0}\) is the initial value. \(\theta^{\prime}=\omega\) and \(\theta^{\prime\prime}=0\) are substituted into Equation (26), which can be written as follows.  
\[\frac{l^{\prime}(v)}{l(v)}=\frac{e\sin v}{1+e\cos v}-\frac{3\sin[2(\omega v+ \theta_{0})]}{4(\omega+1)(1+e\cos v)} \tag{28}\]  
The log ratio of tether length can be obtained by applying integration.  
As shown in Figure 7, tether length amplitude is positively related to \(e\), and the system requires longer tethers to achieve stability control. The results above can be used to guide TSS system attitude control. When tether release conditions satisfy Equation (28), TSS systems can operate at a fixed angular velocity, \(\theta^{\prime}\).
# A Multi-Domain Model for Variable Gap Iron-Cored Wireless Power Transmission System
## 3 Mutual Induction Model: The Dynamic Aspects
### Intra-Winding Equivalent Capacitance  
Another important parameter that needs to be characterized for a better description of the phenomenon is the equivalent intra-winding capacitance. The best schematization of a single coil is represented by a series between the equivalent high-frequency AC resistance of the conductor and an inductor, which are connected in parallel with the self-capacitance of the coil. Thus, this R-L-C circuit allows us to define the natural frequency of the stand-alone coil. In detail, [PERSON] et al. in [26] propose a clever formulation for the estimation of the equivalent capacitor both for cored and non-cored coils.  
The final stray capacitance \(C_{s}(n)\) formulation for an \(n\)-winding cored coil is obtained through a combination of the turn-to-turn capacitance \(C_{tt}\) and the turn-to-core capacitance \(C_{lc}\).  
The turn-to-turn capacitance presents a dependence on the electrical parameters of the material expressed under the form of relative permittivity \(\epsilon_{r}\), and the constitutive geometric characteristic of the coil, such as \(D_{o}\) and \(D_{c}\), which represent the diameter of the conductor including the insulator material and not, respectively.  
\[C_{tt}=\epsilon_{0}l_{t}\left[\frac{\epsilon_{r}\theta^{*}}{\ln\frac{D_{o}}{ D_{c}}}+\cot\left(\frac{\theta^{*}}{2}\right)-\cot\left(\frac{\pi}{12}\right)\right] \tag{18}\]  
\[\theta^{*}=\arccos\left(1-\frac{\ln\frac{D_{o}}{D_{c}}}{\epsilon_{r}}\right) \tag{19}\]  
\[C_{s}(n)=\frac{C_{tt}}{2+\frac{C_{tt}}{C_{s}(n-2)}}+C_{tt} \tag{20}\]  
Due to the presence of the term \(C_{s}(n-2)\) that represents the stray capacitance at \(n-2\) turns, for the mathematical implementation of the above-reported Equations, the formulation of the stray capacitance at \(n=2\) and \(n=3\) is defined in Equation (21) and Equation (22), respectively.  
\[C_{s}(n=2)=2C_{tt} \tag{21}\]  
\[C_{s}(n=3)=\frac{3}{2}C_{tt} \tag{22}\]
# A Review on Design Considerations and Connection Techniques in Modular Composite Construction
## 2 Conceptual Background  
### Types of Precast Systems  
In prefabricated construction, precast systems are crucial in influencing contemporary building practices. Among these, precast beams and columns are fundamental components characterised by their one-dimensional form. These elements are produced in specialised off-site facilities, ensuring precision and quality. They are then transported to the construction site where they are assembled, providing essential support and structural integrity to various building designs [28, 29].  
Similarly, precast slabs and walls serve as critical two-dimensional panels that facilitate the creation of expansive and functional spaces within buildings. These components undergo rigorous quality checks in controlled, off-site environments before being transported for on-site assembly [30]. Their role is integral in modern architectural designs' floor and wall systems, highlighting their importance in enhancing construction efficiency and ensuring consistent quality [31, 32].  
Regarding three-dimensional construction, precast concrete modules represent a sophisticated evolution in prefabricated building techniques. These modules are meticulously prefinished in dedicated factories, integrating extensive automation to enhance precision and efficiency [33]. Upon transportation to the construction site, they are assembled into permanent structures. This method is regarded as the pinnacle of prefabricated construction due to its high precast ratio and streamlined assembly process [34].  
Table 1 presents various levels of precast systems that are currently in practice. The levels range from raw materials and individual components to sophisticated modules and fully integrated building systems, reflecting the evolution and complexity of precast construction techniques. Understanding these varied precast systems lays the groundwork for an extensive examination of how modular construction measures against alternative precast methods, especially regarding efficiency, cost-effectiveness, and structural soundness. This comparative assessment will clarify each strategy's distinct benefits and possible drawbacks, offering a well-rounded review of contemporary prefabrication techniques.
## 2 Sliding Mode Control Law of the DFIG System
### Sliding Mode Control of GSC  
The circuit scheme of GSC is shown as Figure 3. Similar to RSC, in order to control the active power and reactive power directly, let us define the state variables of the GSC \(x_{\text{ge}}=[x_{\text{sep}}\ x_{\text{geq}}]^{\text{T}}\) as  
\[\mathbf{x}_{\text{ge}}=\mathbf{x}_{\text{g}}^{*}-\mathbf{x}_{\text{g}} \tag{9}\]  
where, \(\mathbf{x}_{\text{g}}=[P_{\text{g}}\ Q_{\text{g}}]^{\text{T}};\mathbf{x}_{\text{  
\[\mathbf{f}_{\rm g}=\left[\begin{array}{c}\omega Q_{\rm g}-1.5u_{\rm g}^{2}\\ \omega P_{\rm g}\end{array}\right]+\frac{R_{\rm g}}{L_{\rm g}}\mathbf{x}_{\rm g}\mathbf{ d}_{\rm g}=-1.5\left[\begin{array}{cc}\zeta_{\alpha}&\zeta_{\beta}\\ -\zeta_{\beta}&\zeta_{\alpha}\end{array}\right]\mathbf{i}_{\rm g\alpha\beta} \tag{11}\]  
where, \(\mathbf{f}_{\rm g}\) is the known disturbance; \(\mathbf{d}_{\rm g}\) is the unknown disturbance pulsating at the frequencies of \(2\omega\) and \(6\omega\).  
The method followed is similar to that described in Section 2.2. Thereby, the GSC's sliding surfaces \(\mathbf{S}_{\rm g}=[\mathbf{S}_{\rm gp}\ \mathbf{S}_{\rm gq}]^{\rm T}\) are defined as following  
\[\mathbf{S}_{\rm g}(s)=G_{\rm gs}(s)\mathbf{x}_{\rm ge}(s)\ G_{\rm gs}(s)=\left(1+ \frac{k_{\rm gs}}{s}+\sum_{i=2,6}\frac{sk_{\rm gi}}{s^{2}+2\varepsilon_{\rm c }(i\omega)s+(i\omega)^{2}}\right) \tag{12}\]  
where \(k_{\rm gs}\) is the integral coefficient; \(k_{\rm gi}\) (\(i\) = 2, 6) is the resonant coefficient; \(\varepsilon_{\rm c}\) is related to cutoff frequency.  
According to the condition of the equivalent control, viz. \(d\mathbf{S}_{\rm g}/dt=0\), the equivalent controls of GSC are derived as  
\[\mathbf{u}_{\rm geq}\big{(}\mathbf{x}_{\rm g},t\big{)}=-L_{\rm g}\mathbf{B}_{0}{}^{-1} \left[\mathbf{f}_{\rm g}+k_{\rm gs}\mathbf{x}_{\rm ge}+\frac{d\mathbf{x}_{\rm g}^{*}}{dt}\right] \tag{13}\]  
According to control law, the switching controls of GSC are  
\[\Delta\mathbf{u}_{\rm g}(\mathbf{x},t)=-L_{\rm g}\mathbf{B}_{0}{}^{-1}k_{\rm gu}sat\big{(} \mathbf{S}_{\rm g}\big{)} \tag{14}\]  
where, \(\mathbf{k}_{\rm gu}=[k_{\rm gu1}\ 0;\ 0\ k_{\rm gu2}]\); \(k_{\rm gu1}\) and \(k_{\rm gu2}\) are positive control gains in GSC.  
Then the voltages to be applied to GSC are  
\[\mathbf{v}_{\rm g}\big{(}\mathbf{x}_{\rm g},t\big{)}=\mathbf{u}_{\rm geq}\big{(}\mathbf{x}_{ \rm g},t\big{)}+\Delta\mathbf{u}_{\rm g}\big{(}\mathbf{x}_{\rm g},t\big{)} \tag{15}\]
# Graphene Nanoplatelets Impact on Concrete in Improving Freeze-Thaw Resistance
## 3 Results and Discussion
### Mass Change  
Concrete mass could be reduced due to subject to F-T actions. The main reason of mass reduction was involved in the concrete corners due to damage caused by freeze-thaw cycles. Figure 6 shows the mass loss ratio \(\Delta m_{n}\) after different cycles.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Cycles**} & \multicolumn{6}{c}{**GNP Additives**} \\ \cline{2-7}  & **0\%** & **0.02\%** & **0.05\%** & **0.1\%** & **0.2\%** & **0.3\%** & **0.4\%** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Visual observations of GNP concrete at different F-T cycles.  
Figure 6: Mass loss ratio of concrete versus F-T cycles.  
Although in some F-T cycles (before 50 cycles), the value of the mass loss reduction was small, this phenomenon could be explained as the higher durability of the specimen (such as GC0.05 and GC0.1) against F-T cycles. In freeze-thaw cycle test, mass loss can be used as an index to measure material damage. According to the Chinese standard GB/T 50082-2009 [23], the mass loss of concrete more than 5% means that the material has been destroyed by rapid F-T, and the test can be stopped. From Figure 6, we can see that \(\Delta m_{n}\) rise gradually for all samples with the cycles continue. After 200 cycles, the maximum value of \(\Delta m_{n}\) is 5.42% for GC0, followed by 2.06%, 1.96%, 2.19%, 2.72%, 2.92%, and 3.06% for GC0.02, GC0.05, GC0.1, GC0.2, GC0.3, and GC0.4, respectively. This implies that GC0 has been destroyed; therefore, the maximum number of experimental cycles we designed was 200. Moreover, the maximum value of \(\Delta m_{n}\) of modified concrete implied that GNP has a noticeable ability to keep matrices together; this may be attributed to the smaller size of GNP which could refine the internal pore structure of concrete [12]. It is noteworthy that the mass loss ratio of the concrete samples containing 0.05% GNP is the smallest during the F-T cycles.
## 3 Results and Discussion
### MDT Solutions from Different DTU1-Based MSS  
The selection of the MSS is of great importance to MDT modeling. In this case, the difference of the inputs of the OIM was the selection of MSS; therefore, the parameters such as the filtering radius of calculating the large-scale MDT and the prior MDT to estimate the variance and correlation function of the MDT were not considered. Although the accuracy of MSS in the open sea can reach the centimeter level [6], the accuracy of the MSS reduces rapidly near the coastal zone due to the defects of satellite altimetry. In this study, the DTU15 MSS and DTU18 MSS were used for MDT modeling, and the error distribution of the two MSSs is shown in Figure 6a,b, in which it can be seen that the error of DTU18 MSS was significantly smaller than that of DTU15 MSS, especially in the region around the coast. Table 6 gives the statistical information of the error of the MSS in the study area, and the accuracy of the DTU18 MSS improved by about 1 mm relative to the DTU15 MSS. The MDT solutions derived from the combination of the DIRR6 GGM and the DTU15 MSS, DTU18 MSS are shown in Figure 7a,b, and are denoted as DTU15 DIRR6-MDT and DTU18 DIRR6-MDT, respectively. It is shown that the two MDTs were similar to each other; thus, the reference MDT was used to perform the comparison, and the differences between the MDTs and the reference MDT are shown in Figure 7c,d. The statistical results of the differences are shown in Table 7 (with the mean differences removed), from which we can see that the RMS of the difference between DTU18 DIRR6-MDT and reference MDT was 0.7 mm smaller than that of DTU15 DIRR6-MDT, which indicated that the improvement of MSS could not significantly improve the quality of the final MDT. The reason is that the improvement of the DTU18 MSS focused on the coastal area with respect to DTU15 MSS, but the improvement was limited in the other area.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**MDT Solutions** & **Min** & **Max** & **RMS** \\ \hline DTU18 DIRR4-MDT & \(-434.6\) & 292.6 & 82.4 \\ DTU18 DIRR5-MDT & \(-389.1\) & 284.8 & 79.4 \\ DTU18 DIRR6-MDT & \(-383.3\) & 270.2 & 77.0 \\ DTU18 TIMR4-MDT & \(-446.6\) & 281.1 & 83.2 \\ DTU18 TIMR5-MDT & \(-402.5\) & 272.7 & 78.0 \\ DTU18 TIMR6-MDT & \(-376.6\) & 269.2 & 77.5 \\ \hline \hline \end{tabular} Note: the mean values of the misfit between the estimated MDTs and the reference MDT model are removed.  
\end{table}
Table 5: Statistics of the differences between the reference MDTs and the estimated MDT (units: mm).  
Figure 6: The error distribution of (**a**) DTU15 MSS; (**b**) DTU18 MSS.