esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Experimental Measurement
#### 3.2.1 STAGE 1 Verification of Measurement Repeatability--Static Measurement  
The first step was a calibration measurement under static loading to verify the correct functionality of the applied strain gauges. The torque was induced by force applied on the rigid arm at a perpendicular distance of 500 mm from the flange axis (see Figure 2). The force sensor registered the magnitude of the loading force. The sampling frequency was set to 200 Hz.  
Figure 8 shows an example of the time records of the strains registered by four strain gauges. The flange was loaded in two steps with torques of 5 Nm and 10 Nm and then unloaded. The above procedure was repeated five times within one measurement. The aim was to determine the sensitivity of the measurement at low load levels. It should be noted that the considered flange is designed for a minimum operating torque of 28 Nm. In Figure 9, the time records of the strains over the entire working range, i.e., up to a torque value of 200 Nm, are documented.  
Figure 8: _Cont._Figure 8: (**a**) Time record of the strains registered by the strain gauges at 5 Nm and 10 Nm load; (**b**) time record of the torque; (**c**) the time record detail of the strains for one cycle.  
Figure 9: _Cont_.  
Table 2 shows the mean values and standard deviations of the strains registered separately for each of the five loading cycles at different torque levels (5 Nm, 10 Nm and 200 Nm).  
Table 2 shows that the measured values achieved excellent agreement at low and also high load levels. According to the HBM manufacturer (Germany), with a measured value of less than 1500, the measurement error per million cycles is less than 10 [23]. For this reason, the measurement error under cyclic loading, even at the maximum load (200 Nm), will not exceed 1%.  
Table 3 compares the values of shear stresses obtained by numerical modelling and experimental measurements using Relations (1) and (2) at two load levels. The measured values are averaged from five measurements for each of the four strain gauges (see Table 2).  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{
\begin{tabular}{c} **Torque** \\ **(Nm)** \\ \end{tabular} } & \multicolumn{8}{c}{**Strain Measured in the Locations of Applied Strain Gauges**} \\ \cline{2-10}  & \multicolumn{8}{c}{**SG1 (\(\mu\epsilon\))**} & \multicolumn{8}{c}{**SG2 (\(\mu\epsilon\))**} \\ \hline \multirow{2}{*}{5} & \(-22.8\) & \(-22.9\) & \(-23.0\) & \(-23.1\) & \(-23.2\) & \(-23.4\) & \(-23.5\) & \(-23.5\) & \(-23.6\) & \(-23.6\) \\  & \(\pm\) 0.
# Calculation Method of Loose Pressure in Surrounding Rock Mass
## 5 Upper Bound Solution for Loose Pressure in the Surrounding Rock Mass  
### Upper Boundary Solution for Loose Pressure for Extra-Large Section Tunnels  
#### 5.1.1 Basic Assumptions  
Four basic assumptions must be made when the upper bound theory is applied to analyze and calculate the loose pressure of surrounding rock:  
1. The rock-soil mass is regarded as a perfectly elastic-plastic material that obeys the H-B yield criteria.
2. The tunnel arch is under a vertical even load \(q\), and the side walls are under a horizontal even load \(e\). Assuming that \(q\) is proportional to \(e\), the proportional coefficient is the side pressure coefficient \(K\), that is, \(e=Kq\)
3. The influences of the construction process and floor heave are not considered.  
\begin{table}
\begin{tabular}{c c c} \hline
**Factors** & **Formula** & **Correlation Coefficient \(\mathbf{R}^{2}\)** \\ \hline \(H\) & \(L=78.28-67.87\times 0.9935^{H}\) & 0.9986 \\ \(H_{1}=19.15-65.12\times 0.9741^{H}\) & 0.9433 \\ \(B\) & \(L=2.53+1.728\) & 0.8477 \\ \(B\) & \(H_{1}=7.98+1.01B\) & 0.8535 \\ \(\lambda\) & \(L=13.73+132.05\lambda-67.56\lambda^{2}\) & 0.9767 \\ \(H_{1}=-23.74+71.88\lambda-35.18\lambda^{2}\) & 0.9978 \\ \(K\) & \(L=36.66-16.60\ln(K+0.2403)\) & 0.9991 \\ \(H_{1}=17.89+4.49\ln(K-0.1532)\) & 0.8659 \\ GSI & \(L=41.63\times(\text{GSI}-24.45)^{-0.05}\) & 0.8699 \\ \(H_{1}=16.67\times(\text{GSI}-24.47)^{-0.26}\) & 0.8904 \\ \hline \end{tabular}
\end{table}
Table 8: Unitary regression equation.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \multicolumn{8}{c}{**Variance Analysis of Correlation 7**} & \multicolumn{8}{c}{**Variance Analysis of Correlation 8**} \\ \hline
**Source of** & **Sum of Deviation** & **Freedom** & **Mean** & **Source of** & **Sum of Deviation** & **Second** & **Mean** & **F** \\
**Variance** & **Squares** & **Freedom** & **Square** & **F** & **Variance** & **Squares** & **Freedom** & **Mean** & **Square** & **F** \\ \hline Regression & 21391.7 & 6 & 1572.8 & 6.59 & Regression & 23964.4 & 6 & 1160.0 & 7.02 \\ Error & 3149.1 & 4 & 251.3 & N/A & Error & 3645.5 & 4 & 269.7 & N/A \\ sum & 24646.6 & 10 & N/A & N/A & sum & 29764.1 & 10 & N/A & N/A \\ \hline \end{tabular}
\end{table}
Table 9: Variance analysis of multiple regression equation.
# Centralized MPPT Control Architecture for Photovoltaic Systems Using LoRa Technology
## 3 Experimental Setup  
### Overview  
This study emphasizes the utilization of LoRa-based wireless communications aimed at optimizing output power in PV systems. To achieve this objective, an experimental setup was implemented by setting a point-to-point LoRa network using MKR WAN 1300 LoRa modules (Arduino S.R.L., Monza, Italy) connected to the PV system and to a computer that executes MPPT control algorithms in MATLAB R2022b. This is depicted in Figure 7, illustrating the key components. \(G\) represents the solar irradiance, measured in W/m\({}^{2}\), while \(T\) denotes the cell temperature. \(I_{\text{pv}}\) and \(V_{\text{pv}}\) correspond to the current and voltage of the PV module, respectively. Downstream of the DC-DC converter, a resistive load is used to focus on the demonstration of MPPT control through LoRa, although it could be a grid interface, as proposed in Section 2.2. Irradiance, temperature, PV current, and PV voltage serve as inputs to the controller while the output is the duty cycle of the PWM signal applied to the power switch of the DC-DC converter. Figures 8 and 9 show photographic images of the experimental setup. It is physically located at the Faculty of Engineering, Vitoria-Gasteiz.  
Figure 7: Layout of the experimental setup.
## 5 Performance Evaluation of EMOR
### Results
#### 5.2.2 Packet Delivery Ratio (PDR)  
The metric PDR can be defined as the ratio of the packets received by the mobile sink node to the packet sent by the sensor nodes. We have designed two scenarios to perform simulation for the evaluation of PDR. In the first scenario, PDR is evaluated against long radio range \(R_{l}\) of the mobile sink node whereas in the second scenario, the performance of the PDR was measured against network density. In order to set up the simulation environment for PDR, we need to set a benchmark for the packet successfully transmitted and packet dropped due to certain reasons. If the node finds one of its neighboring nodes is in working mode or if the data packet is forwarded between adjacent sensor nodes for which both of the sensor nodes are in working mode, we can consider it as the benchmark of a packet successfully transmitted; whereas if the node cannot find the forwarder due to asynchronous working-sleeping cycle strategy or if the data packet is forwarded between adjacent sensor nodes and one of the node experiences status transition (i.e., working to sleeping mode, thus causing transmission failure), then we will consider it as the benchmark for a packet drop. Figure 9 shows that PDR for POFA [57] increases with the increase in long radio range \(R_{l}\) but for schemes like DCBONC [22], RPRDC [58], ExOR [19] and EMOR, the PDR decreases with the increase in long radio range \(R_{l}\). The reason for the increase in PDR for POFA can be explained in terms of unchanged network density. When \(R_{l}\) increases, more nodes will be part of the data collection process but as the total number of nodes remains the same, it results in the increase of hops for all the sensor nodes in the data collection scope. This increase in number of hops will eventually decrease the success rate of transmission and delivering the sensory data to mobile sink node correspondingly. However, the sensory data will be forwarded to mobile sink node through multiple paths in POFA, thus increasing the possible paths towards mobile sink node and improving the success rate of data transmission and reception. It is evident from Figure 9 that our proposed scheme EMOR incorporates real-time and non-real-time disjoint paths and the performance of both is better than POFA (for long radio range \(R_{l}\) up to 71%). For real-time EMOR, the performance is better than ExOR, RPRDC, and DCBONC throughout the graph as \(R_{l}\) expands due to the reason that the proposed scheme always forwards the sensing data along its optimal path which is based on maximum value of path connectivity. In order to strengthen our basis for improvement in PDR, the link connectivity (in EMOR) depends on several factors like time-frequency parameter, residual energies of sensor nodes, buffer capacity of sensor nodes, and link quality factor between sensor nodes and mobile sink node.
## 3 Functional Roles of Nicotinamide and Pyridoxine
### Absorption, Transport, and TISSUIE Distribution  
#### 3.2.1 Nicotinamide (Vitamin B3)  
Vitamin B3, including both nicotinic acid and nicotinamide, is absorbed primarily in the small intestine, with efficiency depending on dosage and chemical form [24; 109; 116]. At physiological concentrations, absorption occurs through facilitated diffusion via sodium-dependent transporters such as SLC5A8 and SLC2A13, while at pharmacological doses, passive diffusion dominates. After absorption, nicotinamide is distributed systemically via the portal vein and taken up by tissues through equilibrative nucleoside transporters (ENTS) [24; 109; 116]. Within cells, it enters the NAD\({}^{+}\) salvage pathway, where it is converted to nicotinamide mononucleotide (NMN) by nicotinamide phosphoribosyltransferase (NAMPT), then to NAD\({}^{+}\) by NMN adenylyltransferase (NMNAT). This route is essential in skeletal muscle, which has low expression of enzymes required for de novo NAD\({}^{+}\) biosynthesis from tryptophan and thus depends heavily on the salvage pathway to maintain NAD\({}^{+}\) levels and mitochondrial health [32; 54; 85]. Nicotinamide is also converted into NADP\({}^{+}\) by NAD\({}^{+}\) kinase, which is necessary for redox reactions involving glutathione and thioredoxin. Tissue-specific NAD\({}^{+}\) availability depends on the balance between synthesis and consumption by enzymes such as sirtuins, poly(ADP-ribose) polymerases (PARPs), and CD38, all of which are expressed in muscle tissue and increase their activity in response to oxidative stress and inflammation [105; 117; 118].
# A New Control Scheme for the Buck Converter
## 2 Proposed Control Methodology
### Operation Principle  
The detailed operation of the converter is listed as follows:  
1. When the switch \(\mathrm{S_{1}}\) is ON, and the other switch \(\mathrm{S_{2}}\) is OFF.
2. The inductor (L) is in the charging state, and the ON-time of \(\mathrm{S_{1}}\) is controlled by the adaptive \(\mathrm{T_{ON}}\) control block. In the adaptive \(\mathrm{T_{ON}}\) control block, the ON-time is  
Figure 4: Proposed scheme.  
decided by V\({}_{\rm ramp}\) and V\({}_{\rm CTR}\). The V\({}_{\rm ramp}\) is the function of V\({}_{\rm IN}\) and V\({}_{\rm o}\), which replaces the conventional method that requires a current sensor to sense inductor current.
3. When the switch S\({}_{1}\) is OFF, and the other switch S\({}_{2}\) is ON.
4. The inductor (L) is in the discharging state, and the OFF-time of S\({}_{1}\) is controlled by the adaptive T\({}_{\rm OFF}\) control block. In the adaptive T\({}_{\rm OFF}\) control block, the OFF-time is decided by V\({}_{\rm ramp2}\) and V\({}_{\rm CMP}\), both of which are the functions of the V\({}_{\rm o}\). Since the V\({}_{\rm o}\) controls the OFF-time through two paths, the control scheme has good transient response.
5. The operation of the constant frequency mechanism will make the REF_CLK and the FB_CLK equal. The REF_CLK can be set by the user. In this paper, the REF_CLK is set to 1 MHz.
6. The states of the switches S\({}_{1}\) and S\({}_{2}\) are complementary and non-overlapping. The key waveforms in Figure 5 are drawn in Figure 6.
7. In Figure 6, the I\({}_{\rm L}\) is the inductor current. In the steady state, we can find that the I\({}_{\rm L}\) is triangle wave, and the mean is I\({}_{\rm avg}\). When the S\({}_{1}\) is ON (i.e., V\({}_{\rm G}\) is high), the I\({}_{\rm L}\) is linearly rising and the inductor (L) is in the charging phase. On the contrary, the S\({}_{1}\) is OFF (i.e., V\({}_{\rm G}\) is low), the I\({}_{\rm L}\) is linearly falling, and the inductor is in the discharging phase.  
Figure 5: Circuit implementation of the proposed converter.
# Algorithms for Delivery of Data by Drones in an Isolated Area Divided into Squares
###### Abstract  
Drones are frequently used for the delivery of materials or other goods, and to facilitate the capture and transmission of data. Moreover, drone networks have gained significant interest in a number of scenarios, such as in quarantined or isolated areas, following technical damage due to a disaster, or in non-urbanized areas without communication infrastructure. In this context, we propose a network of drones that are able to fly on a map covered by regular polygons, with a well-established mobility schedule, to carry and transfer data. Two means exist to equidistantly cover an area with points, namely, grouping the points into equilateral triangles or squares. In this study, a network of drones that fly in an aerial area divided into squares was proposed and investigated. This network was compared with the case in which the area is divided into equilateral triangles. The cost of the square drone network was lower than that of the triangular network with the same cell length, but the efficiency factors were better for the latter. Two situations related to increasing the drone autonomy using drone charging or battery changing stations were analyzed. This study proposed a Delay Tolerant Network (DTN) to optimize the transmission of data. Multiple simulation studies based on experimental flight tests were performed using the proposed algorithm versus five traditional DTN methods. A light Wi-Fi Arduino development board was used for the data transfer between drones and stations using delivery protocols. The efficiency of data transmission using single-copy and multiple-copy algorithms was analyzed. Simulation results showed a better performance of the proposed Time-Dependent Drone (TD-Drone) Dijkstra algorithm compared with the Epidemic, Spray and Wait, PRoPHET, MaxProp, and MaxDelivery routing protocols.  
drones; network; DTN; mobility schedule; routing algorithms; data delivery Article
## 1 Introduction
### Rationale of the Proposed Research  
Simulated plant hydrology may not be easily reconciled with precipitation and soil hydrology due to the complexity and uncertainty of the latter, especially in peatlands [26; 27], and the limited knowledge of plant hydrological properties in general [14]. Detailed model equations for plant water relations permit calculations of dynamic water fluxes. However, these calculations involve canopy, turgor, and/or osmotic water potential gradients for simulated plant species, constrained by axial resistances or conductances for primary and secondary vascular plant roots or bryophyte rhizoids, radial resistances or conductances from root/rhizoid surfaces to their internal axes, as well as from the surrounding soil to the surfaces of roots or rhizoids. In addition to the complexity of calculations, the required parameters for simulated plant water fluxes are normally taken from the literature or lab studies and may not necessarily reflect the plant properties at the sites of interest; they thereby may need to be adjusted, which increases modelling uncertainty [10]. Furthermore, many of the existing models are technology-demanding, especially if combined for large spatial scale simulations with other research software, e.g., Geographic Information Systems (GISs) [28] or various remote sensing products [29]. Even a single model run may require specialized knowledge of operating systems (such as Linux), Visual Studio, or CMD consoles instead of the more standard Windows or Mac environments. Thus, the computational skills needed for model applications often limit their wide use by researchers with no modelling background.  
To overcome the above limitations, a process-based (PB) model, the _DIMONA_ PB model, was developed for simulating the effects of plant water relations on GPP (Copyright & 2019 [PERSON], Edmonton, AB, Canada). The model introduces a series of modifiers for plant water control on GPP, which affect simulated photosynthesis, plant growth, root/rhizoid respiration, and nutrient uptake. They are especially designed to emphasizesoil hydrological properties that are measured at field sites, extractable by remote sensing, or accessible on designated websites or internet data repositories, rather than using the scarce and laboratory-derived plant-specific parameters. While the hydrological component of the _DIMONA_ PB model were successfully tested in a peatland environment [26, 27], the present study is focused on testing the plant components of the model that simulate GPP. To facilitate its use, the model was embedded within the namesake online ecosystem modelling and research platform _DIMONA_ (Copyright (c) 2019 [PERSON], Edmonton, AB, Canada). _DIMONA_ is an internet-based, easy-to-use, multi-purpose modelling and research platform designed as a web application.
## 5 Discussion
### Uncertainty of Groundwater Potential and Suggestion  
In the terrain and geology of the ACC, groundwater flow of fractured bedrock is complex. To achieve the goal of regional groundwater exploration and sustainability, it is useful to integrate RS and GIS techniques to identify the groundwater occurrence in places where the geological profile of the tested sites may consist of regolith and fractured bedrock aquifers within 40 m depth. However, relying on those parameters of surface information entails some uncertainty. For example, regolith depth changes greatly with variation in terrain, but external collapse deposits may occur and accumulate more groundwater. In addition, an area with a high LD represents a well-developed fracture network under the movement of the ACC, which leads to brittle fractures and open fractures favorable to groundwater flow in metamorphic rock. In contrast, it causes unfavorable conditions of GWPS due to the high number of shear planes or shear zones filled with mud and less open fracture in sedimentary rock. Thus, it is possible to have no groundwater in areas of high groundwater potential, while it is impossible to expend further effort to conduct an entire in-situ investigation. To figure out the key parameters of groundwater potential, the possibility of GWPS in a zone of metamorphic rock is about 62.2%, and except for argillite, those in each rock type composed of metaphoric rock are about 50 to 80% (Table 6). In the geomorphic thematic map considering SD, DD, and TWI, the possibility of GWPS is about 44% to 76% (Table 7). Geological properties may have greater influence on GWPS. Thus, the parameters of HGU, LD, and SMI were implemented in the RS- and GIS-based models with equal rankings, values, and weighting, as well as the topographic parameters of SD, DD, and TWI. The results showed that the accuracy of identifying GWPS reached 84.7%. The parameters of HGU, LD, and SMI derived from hydrogeological mapping and multi-temporal Landsat imagery increased the predicted result. They are important factors to affect the groundwater potential in the ACC. In future work, the proposed method may be applied to other test areas over a large scale. In addition, future work can increase the precision of HGU mapping, LD analysis, DEM models, and RS imagery, as well as the quantity of in-situ data, to illustrate groundwater flow clearly and reduce its internal uncertainty. Therefore, it is suggested that finding the key impact factors initially by integrating RS and GIS data for targeted area is needed on the issue of groundwater exploration.
# Multi-Scale Audio Spectrogram Transformer for Classroom Teaching Interaction Recognition
## 2 Related Work  
### Multi-Channel Audio Spectrogram Features  
The purpose of audio classification is to automatically determine the category to which the sound belongs through the audio signal. Different types of audio have different waveforms with different frequencies and intensities, and acoustic features are used to capture the differences in audio. Earlier studies usually used statistical features such as zero-crossing rate (ZCR), short-term energy (STE) and cut-off frequency to characterize the audio, which required a great deal of a priori knowledge and complex calculations by the researcher. Subsequently, cepstral linear predictive coding (LPCC) and Mel-frequency cepstral coefficient (MFCC) are presented. Of these, MFCC can similarly emulate the processing characteristics of the human ear and is currently the most widely used audio feature [10]. In addition to extracting speech parameters from artificial features, another popular approach is to let neural networks perform feature extraction. For example, features are extracted from Log-Mel spectrograms [37] and 2D speech spectrograms [38]. Single-channel spectrogram features often lack refined characterization of real environment audio in complex audio scenes. In order to compensate for the limitations of single feature expression capability, one can enrich input features and improve the classification performance of the network. In [20], an improved classification of environmental sounds was achieved by extracting log gammatone spectrograms (Log-GTs) with their delta information to construct a 3D audio spectrogram representation. In [39], four-channel audio features were constructed by stitching spectrograms, cochleograms and MFCC with fractal images. Training was performed in an end-to-end approach through the 3D CNN architecture, which improved the accuracy of classification. Currently, multichannel features are used in CNN approaches, but recently Transformer-based methods have not exploited multichannel spectrogram features [25; 40; 41].
# FPGA Implementation of ECT Digital System for Imaging Conductive Materials
## 7 Conclusions  
In this paper, the ECT system is realized and deployed on FPGA architecture. In addition, hardware-software (HW/SW) codesign of the ECT digital processing unit is proposed. The solution of the inverse problem, Iterative Linear Back Projection (ILBP), is implemented on the FPGA to reconstruct the final metal distributions. The design and implementation of the ECT system via FPGA has greatly minimized the development time and reduces the design cycle as well as alleviates remodeling the system in each design cycle. A big improvement has been achieved by utilizing the reconfigurable platform compared to the PC. The new system has small size, high speed as well as low cost. The new architecture was evaluated on the FPGA running at a clock frequency of 50 MHz using Cyclone V. The results have showed that the proposed system just used only about 11% of the FPGA logic gates and 15% of its memory; also the speed has almost been doubled. The results showed that the proposed reconfigurable system is promising.  
**Funding:** This work was supported by the Deanship of Scientific Research at Umm Al-Qura University, Grant No. 43308004.  
**Acknowledgments:** The author would like to thank the Deanship of Scientific Research at Umm Al-Qura University for the financial support (Project No.: 43308004).  
**Conflicts of Interest:** The authors declare no conflict of interest.  
Figure 6: Actual metal distribution and reconstructed tomographic images for different metal objects using an ILBP algorithm.