esa-sceva/satcom-chunk-collection huggingface dataset:
# Developing Quantum Trusted Platform Module (QTPM) to Advance IoT Security
## 5 Evaluation
### Results  
#### 5.4.1 Time Efficiency and Memory Usage  
Speed and memory usage are essential in computer science, especially in algorithmic complexity. Seemingly infinitesimal time can significantly affect an algorithm's efficiency. Different sets of random numbers are generated and used for comparing the performance among Xorshift PRNG, Mersenne twister PRNG, and IDQ QRNG. The system class in Java is leveraged to measure the elapsed time in nanoseconds, while the runtime class helps calculate the total memory usage in bytes.  
Figure 4 demonstrates that QRNG takes significantly more time to generate random numbers, which is expected due to the complex process of harnessing quantum randomness through photon counting. In contrast, PRNGs generate numbers using deterministic mathematical formulas, allowing them to produce outputs much faster. While QRNG provides true randomness, its higher computational cost makes PRNGs more efficient for applications requiring high-speed random number generation. To mitigate latency impact,  
the configurable fallback mechanism and entropy caching can be adopted, which allows the system to temporarily revert to PRNGs or use pre-fetched QRNG data when low-latency performance is critical.  
Figure 5 compares the memory usage of QRNG, Mersenne twister, and Xorshift on different data sizes. QRNG exhibits significantly lower memory consumption because it generates truly random numbers without maintaining an internal state, unlike PRNGs, which require storage for previous values to ensure high-quality randomness. The Xorshift result for \(10^{3}\) is omitted because its memory usage fell below the Java Virtual Machine (JVM)'s measurement resolution and was indistinguishable from baseline noise. At this scale, the algorithm's register-based operations and JVM optimizations left no detectable heap allocation, unlike at larger quantities. Mersenne twister has the highest memory usage due to its large state array, while Xorshift, though more lightweight, still requires state storage. As data size increases, PRNGs consume more memory, whereas QRNG remains more efficient, making it ideal for low-memory environments like IoT systems.  
While the higher latency of QRNG compared to PRNG is anticipated, our results quantify the performance trade-offs involved and confirm that QRNG integration remains feasible for IoT applications, which do not require continuous real-time entropy. This validation is important for assessing the readiness for deployment in constrained environments.  
Figure 4: Time efficiency results of PRNGs and QRNG with different quantities of generated random numbers.  
Figure 5: Memory usage results of PRNGs and QRNG with different quantities of generated random numbers.
## 3 Results
### Equation for Error Prediction--Peak Values  
Linear fit equations and R\({}^{2}\) values (goodness of fit) are presented in Table 1 for peak values.  
Therefore, for an initial sensor orientation with zero tilt but 18\({}^{\circ}\) twist, an expected error of 1.3\({}^{\circ}\) can be predicted for movement in the sagittal plane and of 0.8\({}^{\circ}\) in the frontal plane. Furthermore, if a sensor is mounted with 25\({}^{\circ}\) tilt and 25\({}^{\circ}\) twist, a predicted error of 7.1\({}^{\circ}\) for movements in the sagittal plane can be expected. Predictive modeling estimated a 3.2\({}^{\circ}\) increase in peak sagittal plane error for every 10\({}^{\circ}\) increase in tilt and twist, and a 2.3\({}^{\circ}\) increase in frontal plane error for every 10\({}^{\circ}\) increase in pitch and twist.  
Figure 4: Root mean square error (RMSE) box plots of frontal plane bending. (**A**) Accelerometer RMSE compared to motion capture for increasing twist offsets. (**B**) Accelerometer RMSE compared to motion capture for increasing pitch offsets. (**C**) Accelerometer RMSE compared to motion capture for increasing twist and pitch offset together. (**D**) Corrected-method RMSE compared to motion capture for twist offsets. (**E**) Corrected-method RMSE compared to motion capture for pitch offsets. (**F**) Corrected-method RMSE compared to motion capture for twist and pitch offsets together.
# Resource Indexing and Querying in Large Connected Environments
## 2 Motivating Scenario
### Needs & Challenges  
This scenario shows several needs to be considered:  
* Need 1--Query types: users need to detect events, while considering different urgency levels (e.g., a wildfire is more urgent than requesting information for data analytics) in their queries;
* Need 2--Device capacity-based querying: users needs to send their queries to optimal (indexed) devices in a way that reduces network usage and provides the best possible response;
* Need 3--Location-based querying: users need to issue a query from any place in the environment. They also need to retrieve data from a specified area/zone (e.g., obtain temperature data from the left side of the forest), from an unspecified area/zone, i.e., based on a target objective (e.g., obtain alarming high-temperature readings), or based on a combination of both (e.g., obtain alarming high temperature readings from the left side of the forest).  
However, as presented in Figure 1, some issues might affect the aforementioned data retrieval queries: (i) IoT devices cannot store the entire environment's information, since they have limited capacity, in addition to the vastness of the environments; (ii) different deployment densities and coverage issues make spatial querying more complex. Some zones may be covered by devices, while others might not; and (iii) depending on the user's request, the user may require a response at all costs, consuming high resources due to the importance level of the query. To manage the aforementioned issues, different challenges should be addressed:  
* Challenge 1: How to consider the capacity of the devices in each index, in order to optimize network lifecycle and querying?
* Challenge 2: How to maximize the index's coverage by considering covered and uncovered zones?
* Challenge 3: How to adapt the indexing scheme to the different types of queries?
## 3 Results and Discussion
### Regulatory Aspects for 3D-Printed Medical Implants  
The field of 3D-printed medical implants is evolving, as is the regulatory landscape, although not uniformly worldwide. The producers of the implants reviewed in this study were from the US, the UK, and China, and, in each clinical situation, the use of their products was governed by distinct regulatory frameworks, with specific challenges and approval pathways.  
In the EU, the Medical Device Regulation (MDR) (EU 2017/745) [58] came into effect in 2021. According to the MDR, 3D-printed implants are classified as high-risk medical devices (Class III). This classification requires rigorous testing of the titanium alloy's material properties, the mechanical stability, and the biocompatibility of the implant. Furthermore, comprehensive clinical trials are necessary to demonstrate safety and effectiveness before approval. One of the challenges in Europe is navigating the complex and time-consuming approval processes, especially for custom-made implants, which often require clinical data specific to the customized design.  
In China, the National Medical Products Administration (NMPA/CFDA) regulates 3D-printed medical devices, including implants [59]. For example, [PERSON] et al. [21] noted that the spherical 3D-printed titanium implant complies with CFDA standards (ID: YY/T 0640-2016). Similar to the EU, 3D-printed medical devices produced in China are classified based on risk levels, with custom 3D-printed implants typically falling into the Class III category, indicating the highest risk level.  
In the US, the Food and Drug Administration (FDA) regulates 3D-printed implants through the Center for Devices and Radiological Health (CDRH). In 2017, the FDA published comprehensive guidance on 3D printing in medical devices [60], focusing on device design, manufacturing controls, and validation. Custom 3D-printed implants are also classified as Class III devices. Although the FDA acknowledges the need for flexibility in assessing 3D-printed products and has adapted existing frameworks to accommodate the unique aspects of additive manufacturing (AM) implants, patient-specific devices still require tailored clinical studies to demonstrate safety and efficacy, adding to the cost and complexity of approval.  
In the UK, the Medicines and Healthcare products Regulatory Agency (MHRA) oversees 3D-printed devices [61]. While there are commonalities with the EU's MDR framework, producers must navigate both the EU and UK's regulatory pathways if they wish to market implants in both regions.
## 3 Application Examples
### Growing Oilseed Rape (Brassica napus L.) in Sterile Hydroponics  
Ten surface-sterilized seeds of a rapid cycling variety of _Brassica napus_ were placed into bioreactors filled with half-concentrated MS medium [60] without sucrose and operated in airlift mode. The plants grown at 24 \({}^{\circ}\)C for three weeks have not shown any chloroses or other negative effects of cultivation in restricted space and at high air humidity [62; 63] (Figure 3).
# An Unambiguous Delay-And-Multiply Acquisition Scheme for GPS L1C Signals
## 3 Results  
### New DM Acquisition Schemes for GPS LIC Signal and the Unambiguous Property  
#### 3.1.1 Choosing the Optimal Delay \(\delta_{0}\) for the GPS DM Signal  
The signal \(S_{DM,\delta}\)\((t)\) in Equation (8) can be expressed as Equation (10) using some simple notations as Equation (11):  
\[\begin{array}{rl}s_{DM,\delta}\ (t)=&d[3C_{PP,\delta}(t)SC_{P}(t)SC_{P}(t- \delta)\pm\sqrt{3}\ C_{DP,\delta}(t)SC_{D}(t)SC_{P}(t-\delta)\\ &\pm\sqrt{3}C_{PD,\delta}(t)SC_{P}(t)\ SC_{D}(t-\delta)+C_{DD,\delta}(t)SC_{D }(t)\ SC_{D}(t-\delta)]\end{array} \tag{10}\]  
where:  
\[C_{PP,\delta}(t)=C_{P}({\rm t})C_{P}({\rm t}-\delta),\ \ \ \ \ \ C_{DP, \delta}(t)=C_{D}({\rm t})C_{P}({\rm t}-\delta),\]  
\[C_{PD,\delta}(t)=C_{P}({\rm t})C_{D}({\rm t}-\delta),\ \ \ \ \ \ C_{DD, \delta}(t)=C_{D}({\rm t})C_{D}({\rm t}-\delta). \tag{11}\]  
Equation (10) has two \(\pm\) symbols, and the same sign occurs simultaneously, that is, \((+)\)\((+)\) or \((-)\)\((-)\), since \(D(t)C_{OL}(t-\delta)\approx\ D(t-\delta)C_{OL}(t)\).  
Figure 4a is the auto correlation function (ACF) of \(\sqrt{3}SC_{P}(t)\), which shows that it has peaks at \(\delta=33n\)-chips. Figure 4b shows the ACF of \(SC_{D}(t)\), and it has peaks at every \(\delta=0.5n\)-chip. The cross correlation function (CCF) of \(\sqrt{3}SC_{P}(t)\) and \(SC_{D}(t)\) shows a similar result to Figure 4b, and it also has peaks at every \(\delta=0.5n\)-chip. Thus \(\delta_{0}=33n\)-chip will be candidate for the optimal delay.  
Considering the condition of \(\cos(2\pi f_{IF}\delta_{0})=\pm 1\), the optimal delay \(\delta_{0}\) in the GPS L1C DM signal is chosen as Equation (12):  
\[\delta_{0}=\begin{cases}33T_{0},&n=1\\ \frac{k\pi}{2\pi f_{IF}},&k=1,2,3,\ \cdots\end{cases} \tag{12}\]  
The optimal delay \(\delta_{0}\) in Equation (12) is long compared with the Galileo case [18; 19; 20] and thus the Doppler frequency effect cannot be ignored in Equation (9). The value \(d\) becomes \(d_{0}\) with condition (12):  
\[d_{0}=\frac{c}{4}\cos(2\pi(f_{IF}+f_{D})\delta_{0})=\frac{c}{4}\cos(k\pi+2\pi f _{D}\delta_{0})=\pm\frac{c}{4}\cos(2\pi f_{D}\delta_{0}). \tag{13}\]When \(\cos(2\pi f_{D}\delta_{0})=0.5\), we obtain that \(f_{D}=5167\) Hz, which is the maximum Doppler frequency when a GPS receiver moves at the speed of 198 km/h. This calculation comes from the assumption of a circular orbit of the GPS satellite [3]. As is well-known, the Doppler frequency is slowly varying and the average rate of change of the Doppler frequency is 0.54 Hz/s for the stationary receiver [3], which is 0.567 Hz/s at the speed of 198 km/h. Thus \(d_{0}\) in Equation (13) depends on the Doppler frequency, but can be treated as a constant during the acquisition process since the coherent integration time \(T_{c}=10\) ms for the GPS L1C signal.  
The limitation that the proposed DM method which will be given in the following section can be used only for low speed due to the Doppler frequency can be solved by using combined L1 and L1C signal acquisition, which will be given in Section 3.4.
## 3 Results and Discussion
### Experimental Correlation between UCS and \(\mathrm{v_{P}}\) in This Study  
Obtained \(\mathrm{v_{P}}\) values were correlated with obtained UCS using simple and multiple regression analyses. All test results were used to generate empirical equations able to predict limestone UCS beginning from the independent variables (\(\mathrm{v_{P}}\), \(\mathrm{\rho_{sat}}\), and \(\mathrm{n}\)). Four types of function models were tested: linear, potential, exponential, and logarithmic. The equation of the best fit, \(R^{2}\), \(F\)-values, and \(p\)-values (significance) were obtained for each regression analysis.
# Detecting Cracks in Aerated Concrete Samples Using a Convolutional Neural Network
## 2 Materials and Methods  
### Characteristics of the Analyzed Building Material  
In construction, various building materials and products from them are used. The main building materials in industrial and civil construction are cement, concrete, brick, stone, wood, lime, sand, and glass. Materials scientists are constantly improving these materials, giving them new characteristics necessary for specific building needs. Therefore, aerated concrete is a universal material related to cellular concrete, used for the construction of both non-load-bearing and load-bearing walls. For many years, builders have chosen it as a reliable, easy-to-use, and process material for various jobs. Due to its unique structure, aerated concrete has a number of special physical and technical properties. This is a high quality, warm, and environmentally friendly material. It has all the advantages of concrete, but it is easy to process and use. Therefore, it is often used to build walls of a complex shape. A feature of aerated concrete blocks is the precision of their dimensions and the correctness of their shape. Because of this, laying them is very easy and fast. The roughness of the surfaces of the blocks facilitates their subsequent processing. An important feature of the material is its excellent sound and heat insulation properties, fire safety, and environmental friendliness [49; 50].  
The external characteristics of aerated concrete are an important source of information in determining its strength characteristics. The quality of aerated concrete determines the uniformity of distribution, the equality of volume, and the closeness of the pores, as well as the absence of cracks. Defects in aerated concrete blocks occur when the production technology is violated, when one or another number of components in the mixture for blocks is incorrectly supplied, and technological failures in the operation of the autoclave. There are cases of defective batches of blocks at factories with a good reputation and consistently high quality of manufactured materials and products. Therefore, careful checking of suppliers or dealers is recommended when purchasing building materials, as such manufacturing defects can cause too much damage for the builder. Vertical, horizontal, and microcracks in aerated concrete blocks indicate the poor quality of the building material. In the future, when using defective block stone, shrinkage cracks may occur.  
Figure 1 shows a crack in an aerated concrete sample. Identification of damage of this kind is one of the main criteria for the visual inspection of blocks in production and transportation.  
Figure 1 shows crack-damage resulting from the improper transportation of products. To automate the process of detecting defects on aerated concrete samples and minimizing the influence of the human factor, developing an intelligent algorithm that allows the detection of defective products is proposed.
# Numerical Analysis on the Performance of a Heat Pump Powered by Surface Water Freezing
## 2 The Heat Pump System and Its Numerical Model
### Evaporator Model  
Based on ref. [27], the simulation model of the shell-tube direct solidification heat evaporator is built as shown in Figure 2, which has 4 tube processes in total including 64 copper tubes. In order to prevent blocking in the evaporator, the tube diameter is 10 mm and the tube pitch is 80 mm [27].  
When the low-temperature water flows into the evaporator, its sensible heat is firstly released, and then its latent heat follows and the ice is formed, including fixed ice attached to the tube wall and the flowing ice in the water. Reference [27] shows that a larger water flow can take more flowing ice. Under stable operating conditions in the evaporator, the heat released by the low-temperature water \(Q_{\mathrm{d}}\) is equal to the heat absorbed by the refrigerant \(Q_{\mathrm{rev}}\), which can be calculated by Equation (1) and Equation (2), respectively.  
\[Q_{d}=Q_{s}+Q_{l}=A_{\mathrm{ev}}l_{\mathrm{ev}}h_{d}(t_{\mathrm{d1}}-t_{i})+ IPF_{s}\times m_{g}\times r \tag{1}\]  
\[Q_{\mathrm{r,ev}}=m_{\mathrm{r}}\big{(}h_{\mathrm{ev,gg}}-h_{\mathrm{ev,in}} \big{)}+m_{\mathrm{r}}\big{(}h_{\mathrm{ev,out}}-h_{\mathrm{ev,gg}}\big{)} \tag{2}\]  
By inputting boundary conditions including freezing time, evaporation temperature, superheat degree, inlet water temperature, inlet water velocity, and freezing characteristics on the water side into the model, simulation calculation of the direct freezing heat evaporator is carried out. The heat exchange amount of the freezing heat evaporator and the refrigerant flow required by heat pump system can be obtained. In the calculation, the flowing ice fraction is assumed to be 20%, 25%, and 30% of ice corresponding to the inlet water velocity of 0.1 m/s, 0.3 m/s, and 0.5 m/s, respectively.
## 2 Materials and Methods
### System Model
#### 2.1.2 Communication Model  
1. User devices to UAV  
Computing tasks generated by mobile user devices cannot be performed on the local device and must be uploaded to the UAV covering the device. The position of user device \(m\) is shown in Equation (6). The distance calculation formula between user \(m\) and UAV \(n\) is shown in Equation (7).  
\[\omega_{m}(t)=[x_{m}(t),y_{m}(t),0]^{T} \tag{6}\]  
\[d_{mn}(t)=||w_{n}(t)-w_{m}(t)|| \tag{7}\]  
Figure 1: System Model.  
The channel gain between mobile device \(m\) and UAV \(n\) is shown in Equation (8).  
\[h_{mn}(t)=\frac{g_{0}}{[d_{mn}(t)]^{2}} \tag{8}\]  
where \(g_{0}\) represents the unit power gain. The uplink data transmission rate calculation formula between user \(m\) and UAV \(n\) is shown in Equation (9).  
\[R_{mn}(t)=\frac{B_{u}}{M_{n}(t)}log_{2}[1+\frac{h_{mn}(t)P_{m}}{\sigma_{u}^{2}}] \tag{9}\]  
Here, \(B_{u}\) is the uplink bandwidth, \(M_{n}(t)\) represents the number of user devices covered by UAV \(n\) at time slot \(t\), \(P_{m}\) refers to the transmission power of user device \(m\), and \(\sigma^{2}\) is the additive white noise power of each UAV.  
2. UAV to EC  
The fixed coordinates of edge server \(k\) are \(\omega_{k}=[x_{k},y_{k},0]^{T}\). The distance formula between UAV \(n\) and edge server \(k\) can be expressed as:  
\[d_{kn}(t)=||\omega_{n}(t)-\omega_{k}|| \tag{10}\]  
The channel gain calculation formula between UAV \(n\) and edge server \(k\) is shown in Equation (11). When transmitting data between UAV \(n\) and edge server \(k\), interference caused by other UAVs sending data is not considered. The data transmission rate calculation is shown in Equation (12).  
\[h_{kn}(t)=\frac{g_{0}}{[d_{kn}(t)]^{2}} \tag{11}\]  
\[R_{nk}(t)=B_{k}log_{2}[1+\frac{h_{nk}(t)P_{n}^{t}(t)}{\sigma_{e}^{2}}] \tag{12}\]  
Here, \(B_{k}\) is the bandwidth resource pre-allocated to UAV \(k\) by the edge server, \(P_{n}^{t}(t)\) represents the transmission power of UAV \(n\) in time slot \(t\), and \(0\leq P_{n}^{t}(t)\leq P_{max}\), where \(P_{max}\) is the maximum transmission power of UAV \(k\), and \(\sigma_{e}^{2}\) represents the additive Gaussian noise power at the edge server location.
## 1 Introduction
The parameters rating can be based on the geological interpretations in tunnels during its construction, or by core logging during exploration. Through time, the _RMR_ and \(Q\) systems have been updated so as to increase their trustworthiness for tunnel support. Along with tunnel support, these systems are also used to calculate the rock masses' geomechanical properties, the stand-up time for the tunnel and support load. Although these systems were developed, refined and updated for tunnel support design, these classification systems also have applications in \begin{table}
\begin{tabular}{c l c c c l} \hline \hline
**S. No.** & **Classification System** & **Abbreviation** & **Applications** & **Year** & **Authors [References]** \\ \hline
1 & Rock load & - & Tunnels & 1946 & [PERSON] [5] \\
2 & Stand-up time & - & Tunnels & 1958 & [PERSON] [13] \\
3 & Rock quality designation & _RQD_ & General & 1964 & Deere [14; 15] \\
4 & Rock structure rating & _RSR_ & Tunnels & 1972 & [PERSON] et al. [16] \\
5 & Rock mass rating & _RMR_ & tunnels & 1973 & [PERSON] [8] \\
6 & Tunneling quality index & \(Q\) & Tunnels & 1974 & [PERSON] et al. [9] \\
7 & Geological strength Index & _GSI_ & general & 1995 & [PERSON] et al. [12] \\
8 & Rock mass Index & _RMi_ & General & 1995 & [PERSON] [11] \\
9 & Rock tunneling quality index & \(Q_{\text{TBM}}\) & TBM tunnels & 1999 & [PERSON] [17] \\ by TBM excavation & \multirow{2}{*}{\begin{tabular}{c} 10 \\ 11 \\ 12 \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} Continuous rock mass rating \\ 12 \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} _CRMR_ \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} General \\ 12 \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} 2003 \\ 2006 \\ 2014 \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} [PERSON] and [PERSON] [18] \\ [PERSON] et al. [19] \\ [PERSON] et al. [7] \\ \end{tabular} } \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The rock mass classification systems used in the design of tunnel. various engineering projects like mining [26; 27; 28; 29; 30; 31; 32], slopes [10; 33; 34; 35; 36; 37], dam foundations [38] and _TBM_ (Tunnel Boring Machine) tunnels [17; 19; 39]. The _RMR_ and \(Q\) systems have changed with time to reveal the apparent impact of several rock-mass factors on the stability of excavation. The focus of this review is on the historical progress and alteration of the two systems through rock-mass characterization and support recommendation with a particular emphasis on tunneling. The advanced versions of _RMR_ and \(Q\)-system classification and the scope of its application in the field of tunneling are specified. The use of these systems in tunneling other than support recommendation is also discussed. The introduced changes have perhaps improved the applicability of _RMR_ and \(Q\) systems in the field of tunneling but they have still limitations. The limitations of the two systems for use in the field of tunneling are also reviewed.