esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods  
### Sample Selection  
The study took place from January 2017 to December 2022. The study participants were selected according to the following inclusion criteria: bilateral class II molar relationship; class II skeletal relationship (ANB greater than 4 degrees) diagnosed by cephalometric analysis; and retruded mandible (SNB less than 78 degrees). The exclusion criteria were: worsening in the harmony of the profile with Frankel's maneuver; tonsil/adenoid hypertrophy, or other obstacles in the upper airways; periodontal diseases; poor oral hygiene; orofacial inflammation conditions; congenital anomalies or endocrine pathologies that could influence the treatment; skeletal anomalies or significant facial asymmetries; obstructive sleep apnea syndrome; previous functional or orthodontic treatment; and lack of compliance on the part of the patient. Informed consent was obtained from the parents of all the patients who entered the study.  
Twenty-one patients were initially screened to be recruited for the study. After excluding 8 participants who did not meet the inclusion criteria, the sample was made up of 13 patients (4 males and 9 females) with skeletal class II malocclusion. At the beginning of the functional orthopedic treatment, the mean age of the subjects was 10 years and 5 months with a standard deviation of 1 year and 2 months.  
The initial documentation included: study models, intraoral and extraoral photographs, and CBCT. CBCT scans were performed before treatment (T0) and approximately after 12.5 months (T1) using the \"NewTom VGi evo\" device. During the scan, the subjects were standing with their chin supported by a craniostat, and the Frankfurt plane was parallel to the floor. The patients were recommended to hold their breath, not to swallow, to keep their teeth in maximum intercuspation, and to touch the palate with the tip of their tongue. Orthopantomography and latero-lateral teleradiography were obtained from the CBCT. These images were useful for completing the orthodontic diagnosis. All the patients then performed additional CBCT (T1) after the beginning of the orthopedic-functional therapy. This study was approved by the Clinical Investigation Ethics Committee of Verona and Rovigo, Italy (protocol number 70252). The procedures were in accordance with the Helsinki Declaration of 1975, as revised in 2000.
# Growing Kratky Basil in Trombe Wall Cavity: Energy Performance
### Research Background
#### 1.2.3 Hydroponics and Kratky Cultivation Systems  
Soilless farming, dating back to ancient civilizations, came into scientific focus during the Industrial Revolution. Advancements in plant nutrition and the discovery of artificial fertilizers (Haber-Bosch process) spurred its systematic research, with [PERSON] pioneering hydroponic farming systems in the 1930s. The availability of plastic and waterproofing materials later in the century made the systems practical enough to become mainstream, as is described in works by [PERSON] and [PERSON] [52; 53]. Current research on the topic is influenced by environmental concerns, with emphasis on reducing the costs and waste with aquaponics, recirculating or organic nutrient solutions, etc. [54; 55]. The convenience and adaptability of the systems have made hydroponics a sustainable and popular option for food production in urban settings [56; 57].  
The Kratky method (used in the current study) is a low-tech hydroponic system introduced in the 1990s by [PERSON] [58]. The system grows plants in individual nutrient-filled jars with no need for automatized watering or root aeration devices. Its simplicity is particularly well-suited for small-scale farming and research, and it has been used to cultivate a variety of edible plants [59; 60]. This method's larger-scale adaptation, the floating raft system, where several plants grow suspended in a layer of plastic or insulation material over a big pool of nutrients, is now widely used for commercial leafy greens production [61; 62; 63; 64].  
Basil plants, the focus of this study, are a very popular commercial and hydroponic crop with many available studies on their yields, transpiration rates, and successful growth in a variety of hydroponic settings [65; 66; 67; 68]. Their adaptability to a variety of conditions,including very hot environments [69; 70], make them a likely candidate to surviving long-term in a Trombe wall cavity.
## 2 Materials and Methods
### PDD Simulation  
Radiation dose distribution assessment is a challenging clinical problem that requires the development of computing technologies to achieve the best clinical outcomes. As a realistic application, we carried out a simulation of the percentage depth dose distribution of a 6 MV photon beam having a field size of \(10\times 10\) cm\({}^{2}\) and characterizing the output of the clinical linear accelerator Varian Clinax iX through a box phantom (\(48\times 48\times 41\) cm\({}^{3}\)) located at 100 cm from the source. The box composition can be any of the studied materials. As such a procedure is time consuming, it was divided into two stages: (i) simulation of the accelerator head concluding in a phase-space file (containing particles type, energy, position, direction,...) and (ii) simulation of the dose distribution within the target. Thus, a schematic view of PDD computation for a linear accelerator photon beam, including the different parts of the head Linac and voxels used for dose scoring within a water phantom, is shown in Figure 1. Moreover, we used the characteristic phase-space file of the Linac provided by the Nuclear Data Section of the IAEA [17] (more than 2 GB size) and a specific interface for Geant4 simulations [18]. We scored the absorbed dose along the principal beam direction using voxels with 1 mm depth. Next, we normalized the obtained data to their maximum value. For the simulation procedure, we used the Geant4 physics list \"emstandard_opt3\" and the global cut-range of 1 mm for photons and electrons. Moreover, we carried out the simulation using a 40 core Dell Precision T7610 workstation equipped with an Intel Xeon E5-2680v2 CPU at 2.80 Ghz and 256 GB RAM and an 8 core Dell Precision M3800 laptop equipped with an Intel Core i7-4702 HQ CPU at 2.20 Ghz and 16 GB RAM, with all of them working under the Linux operating system (Ubuntu 14.04). The statistical error related to the photon detections was less than 2% for every run carried out during this work.
## 3 Results
### Microtexture and Noise  
Figure 6 shows the statistical results of the regression analysis between side-way force coefficient (SFC) and the corresponding tire/road noise.  
As can be seen from Figure 6, there is no noticeable correlation between SFC and noise of concrete pavement. The pavement noise level of some test sections remains nearly unchanged for the same type of concrete pavement texture despite the varying of SFC. The tire/pavement noise level, however, varies greatly with SFC. Table 8 presents the results of an SPSS correlation analysis of descriptive statistics. As can be seen from Table 8, the sample mean of SFC is 57.8, which meets the requirements of the current industry standard. Nonetheless, the standard deviation of SFC is 7.299, indicating a high degree of dispersion.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Statistical Test Parameters** & **Macrotexture/mm** & **OBSI/dB(A)** \\ \hline \multirow{4}{*}{Macrotexture/mm} & Pearson correlation coefficient & 1 & 0.78 ** \\  & Significant (bilateral) & & 0.001 \\  & Sum of squared & 0.186 & 2.97 \\  & Covariance & 0.005 & 0.085 \\  & N & 36 & 36 \\ \hline \multirow{4}{*}{OBSI/dB(A)} & Pearson correlation coefficient & 0.78 ** & 1 \\  & Significant (bilateral) & 0.001 & \\ \cline{1-1}  & Sum of squared & 2.97 & 76.7 \\ \cline{1-1}  & Covariance & 0.085 & 2.19 \\ \cline{1-1}  & N & 36 & 36 \\ \hline \hline \end{tabular}  
* means the correlation was significant at the level of 0.01 (both sides).  
\end{table}
Table 7: Correlation analysis between macrotexture and noise of longitudinal groove pavement.  
Figure 6: Regression analysis between side-way force coefficient (SFC) and tire/road noise.  
The Pearson correlation coefficient and \(p\)-value from the T statistics test are shown in Table 9. Table 9 shows that the Pearson correlation coefficients of two variables is 0.136, which shows that the two variables may have a weak correlation. In another word, the SFC of concrete pavement has no obvious correlation with the noise. The main reason may be that SFC is mainly related to the material properties of pavement, water film thickness, speed, wear, and pollution of pavement surface [20].
### Application of TES Systems in Various Types of Greenhouses  
TES can be applied to various types of greenhouses to improve energy efficiency, optimize temperature regulation, and enhance overall sustainability. The following are some applications of TES in different greenhouse systems [8; 9; 10; 11]:  
* _Freestanding greenhouses with water barrels_: TES can be implemented using water barrels as thermal mass in a freestanding greenhouse. During the day, solar radiation heats the greenhouse and the water barrels, storing the excess heat. During the night, the water barrels gradually release the thermal energy that has been stored, causing a mild heating that keeps the plants at a consistent temperature.
* _Glass greenhouses with underground thermal mass_: Glass greenhouses often lose more heat than plastic film greenhouses. TES can be applied by incorporating underground thermal mass, such as a buried concrete slab or rock bed. To prevent heat loss and preserve a more constant temperature, the thermal mass absorbs and stores extra heat during the day and releases it at night.
* _Polytunnel greenhouses with insulated soil beds_: Polytunnel greenhouses can benefit from TES by creating insulated soil beds within the structure. The soil acts as a thermal mass, absorbing solar heat during the day and providing natural heating at night. Additionally, using insulated soil beds reduces heat loss from the ground.
* _Shade house greenhouses with cool water storage_: TES can manage daytime temperature peaks in shade house greenhouses. Cool water storage systems, such as underground tanks, can store cool water circulated through the greenhouse during hot periods, providing evaporative cooling and reducing the need for additional cooling equipment.
* _Quonset greenhouses with phase change materials (PCMs)_: Quonset greenhouses can benefit from incorporating PCMs as TES. PCMs can store and release large amounts of heat during phase transitions. During the day, the PCM absorbs heat as it changes from a solid to a liquid state, and at night, it releases heat as it changes back to a solid state, maintaining a more stable temperature inside the greenhouse.
* _Lean-to greenhouses with Trombe wall_: A Trombe wall can serve as TES in a lean-to greenhouse attached to an existing building. The thick, south-facing brick wall stores and collects solar heat during the day and gradually releases it into the interior space at night, thus lowering the need for heating.  
TES can be used in various greenhouse systems to improve sustainability, lower energy usage, and more effectively regulate temperature. It allows greenhouse managers to cultivate plants in a more steady and energy-efficient environment, particularly advantageous in areas with seasonal or temperature swings.
## 3 System Model
### Controller Layer  
This layer--considered the core of the QKD network architecture--is primarily responsible for the unified scheduling of resources and the coordination of communication between layers.  
This layer receives keys generated by QKD links in the quantum layer and manages the life cycle of keys, including key generation, destruction, and storage in QKP. The key management layer serves the crucial role of providing keys to the application layer while actively participating in information interactions with the application layer, the control layer, and the quantum layer. It is noteworthy that in our model, we have consolidated the control and key management layers, allocating them collectively as CP.
# DC-DC Buck Converters with Quasi-Online Estimation of Filter Capacitor Equivalent Parameters
## 2 Materials and Methods
### Scheme Based on the Implementation of the Principle from Figure 7a
The values resulting from the experiment are \(\hat{T}_{1}=0.0183244\) s and \(\hat{T}_{2}=0.000690196\) s. From the perspective of Figure 1, for Figure 10(a): \(t_{0}=0.00919\) s, \(t_{1}=0.03855\) s, \(t_{2}=0.3935\) s. The calculation of the characteristics \(\hat{T}_{1,3.291}(t)\) and \(\hat{T}_{2,3.291}(t)\) is turned off when the condition in Equations (10) and (11) is met, resulting in the moments 0.1655 s and 0.3935 s. The time \(t_{0}\) is an arbitrary time according to the series of experiments mentioned below. The time \(t_{1}\), when the capacitor commutes from discharging to charging corresponds to the moment when \(V_{0}\) reaches the value of 0.6 V, so that for the calculation of \(\hat{T}_{2}\) remains available a voltage interval of approx. 2 V. Time \(t_{2}\) corresponds to the moment when \(\hat{T}_{2}\) is obtained. It is observed that the value of \(V_{0}\) from the moment when OP2 provides \(\hat{T}_{1}\) is approx. 2 V. If \(t_{1}\) had been associated with this moment, then for the calculation of \(\hat{T}_{2}\) there would have been a rather small tension interval available, of approx. 1V. Across the 150 experiments, the parameters fell within the ranges in Table 4, corresponding to the average values indicated by (14). Figure 11: C2 capacitor charging/discharging signal in Figure 9 and the result of its processing with PO2: (**a**) The signal \(V_{0}(t)\) on capacitor terminal; (**b**) \(\hat{T}_{1,K}(t)\) and \(\hat{T}_{2,K}(t)\), \(K=3.291\) V characteristics generated by PO2. Figure 10: Frequency characteristics of the filter capacitor C2 of the converter in Figure 9: (**a**) \(C(f)\) characteristic; (**b**) Characteristic \(R_{s}(f)\). According to the frequency characteristics in Figure 10a,b, the equivalent values \(\hat{C}_{e}\) and \(\hat{R}_{se}\) from (14) correspond to frequencies of approx. 55.6 Hz and 59.5 Hz, respectively. \[\hat{T}_{1}=18.474\ \mathrm{ms},\hat{T}_{2}=0.71193\ \mathrm{ms},\ \hat{C}_{e}=202.15\ \mathrm{\mu F},\hat{R}_{se}=0.21054\ \Omega. \tag{14}\]  
To investigate the impact of \(K\)'s assignment errors on the values of \(\hat{C}_{e}\) and \(\hat{R}_{se}\) obtained with PO2, six series of 25 capacitor discharge/charge experiments were performed for six different \(K\) values within the range [3.2836, 3.3036]. The parameter \(\omega_{0}\) of PO2 was set to the values of 7000 s\({}^{-1}\) for the discharge phase, and \(11,000\) s\({}^{-1}\) for the charge phase. The experiments confirmed the aspects revealed by Figure 5 and Appendix A: \(\hat{T}_{2}\) is increased due to the increase of \(K\), \(\hat{C}_{e}\) variations are reduced, \(\hat{R}_{se}\) is pronouncedly increased, and the dispersion intervals of the parameters are reduced.
## 5 Conclusions and Suggestions
### Suggestions  
1. In the measurements on field test sites, the results of this study can enhance researchers' ability to interpret electrical resistivity profiles, but the field data of hydraulics, geology, and drilling of the site are still required, so as to evaluate the practical situation of the strata of the site accurately.
2. According to the findings, the external factors can degrade the resolution capability of ERT. In order to provide correct electrical resistivity profiles, the inverse computation method can be discussed in the future to enhance the resolution capability.
3. It is suggested to build a standard geophysical exploration test field in the future, so as to compare and check different investigation and test methods.
4. The strata are usually heterogeneous, so it is often found in field measurements that when the strata are not homogeneous in the direction normal to the measuring line, there is a 3D effect. The topographical changes should be paid attention to during in-situ measurements.
5. When laying the ground resistance measuring line, the changes in the landforms and reliefs on both sides should be evaded to avoid the boundary effect.  
Conceptualization, C.P.L.; methodology, C.P.L.; modeling Y.C.H.; software, Y.C.H., Y.C.H., and H.S.C.; data analysis Y.C.H.; conclusions, Y.C.H. and Y.C.H.; electrology consultation, H.S.C. All authors have read and agreed to the published version of the manuscript.  
This study has no funding.  
The authors declare no conflict of interest.
## 5 Numerical Simulation
### Design  
To test the performance of the proposed scheme, we used the initial conditions for the system as follows:  
\[\left[\begin{array}{cccc}q_{1}(0)&q_{2}(0)&\dot{q}_{1}(0)&\dot{q}_{2}(0)\\ \end{array}\right]=\left[\begin{array}{ccc}\frac{\pi}{4}&\frac{-\pi}{4}&0 &0\\ \end{array}\right].\]  
The exact dynamic parameter vector could be obtained as  
\[\theta_{d}=[3.34\,\,0.97\,\,1.2\,\,2.5\,\,1.2]\]  
The following initial parameter vector was used in the simulation:  
\[\hat{\theta}_{d}(0)=[1.67\,\,0.485\,\,0.6\,\,0.125\,\,0.6]\]  
The exact kinematic parameter vector was  
\[\theta_{k}=[400\,\,480\,\,500\,\,600]\]  
The initial kinematic parameter vector was  
\[\hat{\theta}_{k}(0)=[200\,\,400\,\,250\,\,300]\]  
Subsequently, the design parameters were chosen as  
\[k_{p}=1,k_{v}=0.1,\,\alpha=10,\,\,\lambda=4\,\,,\Gamma_{1}=0.5\,\,\text{I}_{ 5}\,\,,\,\Gamma_{2}=100\,\,\text{I}_{4}\]  
where \(\text{I}_{n}\) represents an \(n\times n\) identity matrix.  
It is easy to verify that the design parameters satisfy the stability condition in (26).  
The desired trajectories can be expressed using pixels in an image frame:  
\[x_{d}=[x_{d1}\,\,x_{d2}\,\,]^{T}\]  
\[x_{d1}=420+120\,\,cos(4t) \tag{37}\]  
\[x_{d2}=200+150\,\,sin(4t) \tag{38}\]  
For the problem of set-point control, the desired position in pixels was  
\[x_{dp}=[540\,\,200]^{T}\]
## 5 Numerical Results and Analysis
### Anti-Interception Performance Analysis  
The anti-interception performance of our proposed scheme is depicted in Figures 9 and 10, respectively. It is easy to see that the SER performance of the proposed scheme degrades a great deal along with some mismatched parameters. When the mismatch parameter \(\Delta\alpha\) is small, the secrecy performance is almost unaffected, but the secrecy performance of the system declines rapidly with the increase of the \(\Delta\alpha\). When the mismatch parameter \(\Delta\alpha\) widens to 0.5, the SER drops to about 0.5, which means that users cannot obtain any meaningful information. From Figure 10, we can conclude that \(m_{k}\)\((k=1,2,3,4)\) have a similar but richer variation trend. Parameter \(n_{k}\)\((k=1,2,3,4)\) have similar performance to \(m_{2}\). Beside correctly estimating the LUs indices information, the eavesdroppers also need to meet the premise that nine parameters are completely consistent. Therefore, our proposed scheme has very good anti-interception performance even under the condition that the Eves know the signal transformation mode (SM modulation and WFRFT modulation).
###### Abstract  
Traditional model tests for soil and rock materials face challenges in observing the slurry diffusion within the soil mass, hindering the understanding of the relationship between grouting-induced ground deformation and grout diffusion. This study conducts grouting diffusion model tests using a self-developed experimental setup on both ordinary and transparent sand. We investigate cement slurry diffusion patterns, distribution characteristics, and temporal variations in ground uplift displacement during the grouting process. By leveraging a visualization grouting model and non-intrusive displacement measurements, we directly observe and verify the changes in cement slurry diffusion and ground displacement in transparent sand. The results indicate the following: during non-steady grouting in sand, slurry diffusion progresses from low-pressure infiltration to medium-pressure compaction, culminating in high-pressure fracturing: ground uplift displacement curves exhibit a consistent \"step-like\" increase with grouting time, featuring accelerated growth after each step; and visualization tests reveal a strong correlation between grouting pressure, slurry diffusion, and corresponding uplift displacement. Distinct features in the grouting pressure plot align with the acceleration phases of the displacement; at a water-cement ratio (\(w/c\)) of 0.8, the stratum's vertical deformation shows a symmetric \"higher in the middle, lower on the sides\" distribution. As the burial depth decreases, the stratum's uplift displacement tends to flatten horizontally, especially at \(w/c=0.8\) and 1.2.  
grouting; grout diffusion; uplift displacement; visualization; temporal variations 2024 14 3922 1
# Wireless Mesh Networking: An IoT-Oriented Perspective Survey on Relevant Technologies
## 2 Preliminaries on Routing Protocols for Ad-hoc Networks
### Ad hoc On-Demand Distance Vector Routing Protocol  
The AODV protocol is a reactive routing protocol developed for ad hoc networks, which supports unicast, multicast, and broadcast communications. The first phase in the AODV protocol is the discovery: a source node \(S\), which has no entry, in its routing table, corresponding to the address of the destination node \(D\), generates control traffic according to the following steps.  
* The source node \(S\) broadcasts a Route REQUest (RREQ) packet, whose main fields are the destination address and hop counter, to its \(n\) neighbors \(N_{1}\), \(N_{2}\),..., \(N_{n}\).
* Upon reception of a RREQ packet, a node checks if the packet contains routing information related to the destination. If so, it rebroadcasts the RREQ increasing the hop counter. If the node itself is the destination of the packet, it replies to the source with a Route REPly (RREP) message.
* Finally, the source node \(S\) builds its routing table on the basis of the received RREP messages, storing the next-hop to each destination, and uses this information for following transmissions.  
It follows that before starting the communication with the destination node \(D\), the source node \(S\) has to wait for the completion of the ongoing route discovery process.  
One of the key features of the AODV protocol is represented by the _destination sequence number_, used also to have loop-free routes. This number is generated by each node in order to maintain the entries of the routing tables updated and is useful if there are two different routes from a source \(S\) to a destination \(D\): in this case, \(S\) selects the route associated with the largest destination sequence number. Another interesting feature of the AODV protocol is the management of the local connectivity, in order to detect a route break, since nodes' mobility has to be taken into account as well. This mechanism works in the following way: in case a node, within a specific interval, does not highlight its presence to its neighbors through a specific _hello interval_, then, through a RREP message, it broadcasts a special request containing its identity, thus _forcing_ a discovery phase. In detail, if a relay node between \(S\) and \(D\) fails to receive a minimum amount of _hello messages_--denoted as _allowed hello loss_, then a list of all the destinations which are unreachable due to the link loss will be forwarded by a broadcast Route Error (RERR) message by the node upstream of the break (propagating back to \(S\), the latter has the possibility to perform again the route discovery process if it still needs the route). Thanks to these mechanisms, the AODV protocol has the capability of quickly adapting to dynamic link conditions, and can be employed with low processing power devices, as it has a reduced network utilization and memory overhead.