esa-sceva/satcom-chunk-collection huggingface dataset:
# Analysis and Assessment of a Brushless DC Outrunner Motor for Agriculture Drones Using JMAG
## 2 Propulsion System Design in Agriculture Multi-Rotor UAVs
### Propeller  
In this section, the sizing and aerodynamic analysis of a commercial propeller using CFD, is presented. The numerical computation of propeller torque, thrust, and efficiency marks the beginning of the propulsion system design methodology. The typical thrust-to-weight ratio in commercial agriculture drones goes from 1.6 to 2.0 [10]. This relation provides good drone maneuverability to external disturbances by reaching a hover state close to the half-throttle capacity. The multi-rotor studied in this paper had a nominal take-off weight of 30 kg and a maximum desired thrust of 470.88 N, aiming for a 1.6 ratio. According to [2], the optimal propeller diameter can be sized with Equation (2). The air density is dependent on flight altitude and ambient temperature, while the optimum angular velocity is obtained from the propeller manufacturer and reported in Table 2. The propeller chosen was the T-Motor carbon fiber, 31.2 \(\times\) 10.2, which also fits with the maximum allowable propeller diameter for the UAV.  
\[D_{prop}=\left(\frac{60^{2}T_{max}}{\rho C_{T}N_{max}^{2}}\right)^{\frac{1}{4}} \tag{2}\]  
where \(\rho\) is the air density, \(N\) is the maximum optimum angular velocity, \(C_{T}\) is the propeller thrust coefficient, and \(T_{max}\) is the theoretical maximum thrust.  
Selecting the right propeller for the imposed design requirements and agriculture application characteristics implies finding a propeller capable of efficiently providing the maximum desired thrust of 470.88 N, so that the flight-time objectives are accomplished. For a hexarotor configuration, each propeller must deliver at least 78.48 N. Computational Fluid Dynamics can be used to evaluate the fit of the propeller chosen.
## 5 Final Remarks
### Conclusions and Future Work  
This paper proposes a novel method for solving AWPs by combining the advantages of LLMs and relation-flow, achieving a highly accurate and reliable algorithm.  
First, the paper identifies key weaknesses in LLM-based baseline algorithms, particularly their tendency to generate invalid equations and incorrect calculations due to error propagation in multi-step reasoning. These weaknesses often stem from the direct use of LLMs for problem-solving, which can accumulate errors when reasoning through complex multi-step problems. To address these issues, the proposed method introduces the COS prompting technique. Instead of directly solving AWPs, COS prompts the LLM to decompose the problem into scenes described in natural language, which significantly reduces the chances of error accumulation by enabling more accurate relation extraction and reasoning processes.  
Secondly, the introduction of the Scene-Aware \(S^{2}\) method plays a critical role in enhancing the robustness of the problem-solving process. By extracting relations from the decomposed scenes, this method provides a structured and reliable basis for further reasoning. This method contrasts with relying solely on LLM-generated logic and equations, which can be error-prone when handling complex AWPs. The relation sets serve as a bufferagainst errors by enabling precise reasoning through a symbolic solver, thereby reducing the overall error rate and improving the performance of the solving algorithm.  
Experimental results demonstrate that this integrated method surpasses both advanced LLM-based and relation-flow algorithms in solving accuracy and output reliability, confirming its effectiveness in tackling complex AWPs.  
For future research, this paper proposes three potential directions. First, distill the related knowledge from LLMs to smaller models, thereby reducing the cost of AWP-solving algorithms and facilitating their wider application in education. Second, vectorize the Scene-Aware \(S^{2}\) model pool to reduce the number of models, thereby improving the efficiency of model matching and relation extraction. Lastly, explore the combination of LLM-based and relation-flow approaches for solving diagram-text problems, as existing LLM-based solving algorithms have not yet achieved significant progress on these challenging problems.
## 3 Scaling Laws of Scheduling Gain--Perfect CSI
### Sum Rate Analysis
As \(M\) tends towards infinity, i.e., massive MIMO, the variance of the sum rate for the ZF receiver decreases to zero, while that for the MF receiver increases to a constant value. Therefore, by substituting (17) and (18) into (15), we can observe the following aspects:  
* For the MF receiver, the scheduling gain first increases and then scales with \(\mathcal{O}\left(1\right)\) as \(M\rightarrow\infty\) under perfect CSI. This implies that the user scheduling to maximize the sum rate is still beneficial for massive MIMO systems with the MF receiver. * For the ZF receiver, the scheduling gain decreases with \(\mathcal{O}\left(\sqrt{\frac{1}{M}}\right)\) under the perfect CSI. Therefore, if the ZF receiver is used at the BS, only a limited scheduling gain can be achievable for large \(M\). This implies that the benefit of user scheduling tends to disappear for massive MIMO systems using the ZF receiver.
# Use of Hydrogels to Regulate Orthodontic Tooth Movement in Animal Models: A Systematic Review
## 3 Results
### General Characteristics of the Studies  
All studies used a hydrogel of different polymer-gelatin-based composition [20; 24; 25; 26; 27], chitosan hydrogel [20], polyethylene glycol-polycaprolactone-polyethylene glycol [21], hydroxyethyl cellulose [22], methylcellulose [23] for delivery of active substances: carbonated hydroxyapatite [24; 25], curcumin [20], sclerostin [21], a formulation of RANKL [22], risedronate bisphosphonate [27], epigallocatechin [26] and parathyroid hormone [23], to accelerate orthodontic tooth movement or inhibit the post treatment relapse. Tooth movement was measured with a digital vernier or in the tomographic software depending on each article.  
All eight studies were done on animal models: four on rats [20; 21; 22; 23], two on rabbits [24; 25], one on mice [26], and the last one on guinea pigs [27]. The number of animals used in the experiments of the studies ranged from 20 to 75.  
Of the four studies carried out on rats, the rats weighed between 180-600 g and were 6-20 weeks old. Three of these four studies sought to accelerate orthodontic tooth movement [21; 22; 23] and one sought to inhibit the effect of orthodontic tooth movement [20].  
In the studies with rabbits [24; 25], the animals were 2.5-3 kg in weight and 10-12 weeks of age when evaluating the relapse of orthodontic tooth movement.  
In the study with mice, they sought to prevent the recurrence of orthodontic dental movement by inhibiting the activity of the osteoclasts using 7-week-old mice [26]; in the same way as in the study carried out on guinea pigs, they used models weighing between 500 and 600 g [27].
# Maximizing Efficiency in Energy Trading Operations through IoT-Integrated Digital Twins
## 3 Proposed Architecture
### ESS Power Management for Optimal Energy Trading
```
1:Input: Define the following variables and sets:
2: Set of participating nano-grid houses: \(H=\{H_{1},H_{2},H_{3},\ldots,H_{k}\}\)
3: Energy Storage System (ESS) installed in each house: \(battery=\{battery_{1},battery_{2},battery_{3},\ldots,battery_{k}\}\)
4: Time interval: \(t\)
5: Load demand: \(EL_{k}(t)\) for the \(k\)th nano-grid
6: Photovoltaic power: \(photovoltaic_{k}(t)\) for the \(k\)th nano-grid
7: Self-supplied energy: \(extra_{k}(t)=\min\{EL_{k}(t)\cdot photovoltaic_{k}(t)\}\)
8: Remaining energy in battery: \(remained_{k}(t)\)
9: Energy load: \(rel_{k}(t)\)
10: Energy saved in ESS: \(saved_{k}(t)\)
11: Sold energy: \(sold_{k}(t)\)
12: Particle Swarm Optimization:
13: Initialize particle positions \(X_{i}\) and velocities \(V_{i}\) for \(i=1\) to \(N\) using problem-specific initialization strategies;
14:while stopping criterion not met do
15:for\(i=1\) to \(N\)do
16:if Trade function yields +1 then
17: Update \(\text{remained}_{k}(t+1)=\text{remained}_{k}(t)+\text{extra}_{k}(t)-\text{saved }_{k}(t)\)
18:elseif Trade function yields -1 then
19: Update \(\text{remained}_{k}(t+1)=\text{remained}_{k}(t)-\text{rel}_{k}(t)\)
20:endif
21:endfor
22: Calculate \(\text{extra}_{k}(t)\) representing energy exchanged by the \(k\)th nano-grid at time \(t\)
23: Enforce \(\underline{\sum_{k}\text{extra}_{k}(t)}\in[0,\text{max allowable energy}]\)
24: Calculate \(\underline{\sum_{k}\text{photovoltaic}_{k}(t)}\)
25: Calculate \(\text{extra}_{k}(t)\) using Equation (6)
26: Calculate \(\text{rel}_{k}(t)\) using Equation (7)
27: Enforce \(\underline{\sum_{k}\text{extra}_{k}(t)}\in[0,\text{max allowable energy}]\)
28:for\(k=1\) to \(k\)do
29: Update \(\text{remained}_{k}(t+1)\) according to Equation (8)
30: Calculate \(\text{sold}_{k}(t)\) using Equation (9)
31: Enforce \(-\text{remained}_{k}(t)\leq\text{battery}_{k}(t)\leq 0\) according to Equation (10)
32:endfor
33: Particle Swarm Optimization Update:
34:for\(i=1\) to \(N\)do
35: Evaluate the objective function \(f(X_{i})\) using Equation (9)
36:if\(f(X_{i})\) is better than the personal best of particle \(i\)then
37: Update personal best: \(P_{\text{best},i}=X_{i}\)
38:endif
39:endfor
40: Find the global best: \(G_{\text{best}}=\arg\min f(P_{\text{best},i})\) over all particles
41:for\(i=1\) to \(N\)do
42: Update velocity: \(V_{i}=\omega V_{i}+c_{1}r_{1}(P_{\text{best},i}-X_{i})+c_{2}r_{2}(G_{\text{ best}}-X_{i})\)
43: Update position: \(X_{i}=X_{i}+V_{i}\)
44: Enforce constraints from Equation (10): \(-\text{remained}_{k}(t)\leq\text{battery}_{k}(t)\leq 0\)
45:endfor
46:endwhile
47:Output: Optimal solution \(G_{\text{best}}\)
```  
**Algorithm 1** Optimal Energy Trading using PSO
## 3 Methodology
### In Situ Topography Survey  
To compare the accuracy of various UAV monitoring systems, ground-based RTK measurements were conducted simultaneously with UAV monitoring. During manual measurements, there can be variations in measurement points between different tasks. To mitigate this, feature points were carefully selected based on the embankment's design. A total of 11 measurement sections perpendicular to the shoreline were established, resulting in 166 measurement points. This arrangement was designed to minimize deviations in measurement points between tasks. The positions of these measurement points are illustrated in Figure 6.  
A specific location on the embankment's cement surface was selected as the reference point, which was utilized to calibrate the DSM data gathered by the UAVs. In the same coordinate system, assuming that the longitude, latitude, and altitude of each actual measurement point are x, y, and z, respectively. Using the x and y values of the actual measurement points as marker points on the DSM of the UAV reconstruction, and after obtaining the z' value of the corresponding position, analyzing the statistical law of z' and z, we are able to analyze the terrain reconstruction capability of the UAVs of different systems. Root mean square error, mean absolute error, and correlation (COR) were employed to evaluate the performance of each UAV.  
RMSE (Root Mean Square Error): The square root of the average of the squared differences between the two; the smaller the RMSE, the more the reconstructed DSM matches the real data.  
MAE (Mean Absolute Error): The average of the absolute error between the reconstructed and measured values; the smaller the MAE, the more stable the reconstruction error of the DSM.  
Figure 5: (**A**) the planned route of Matrice 300 in full scanning of the LGTF (**B**)the camera points and point clouds of the LGTF.  
COR: A statistical indicator of the degree of linear correlation between reconstructed and measured values; the smaller the COR, the higher the overall accuracy of the DSM.  
The formulas are as follows:  
\[RMSE=\sqrt{\frac{1}{n}{\sum_{i=1}^{n}}{(z_{i}-{z_{i}}^{\prime})}^{2}} \tag{1}\]  
\[MAE=\frac{1}{n}{\sum_{i=1}^{n}}\mid z_{i}-{z_{i}}^{\prime}\mid \tag{2}\]  
\[COR\left(z,z^{\prime}\right)=\frac{cov(z,z^{\prime})}{\sigma_{z}\sigma_{z^{ \prime}}} \tag{3}\]  
as noted above, \(z_{i}^{\prime}\) is the altitude of each constructed data point, \(z_{i}\) is the altitude of each measured data point, and \(n\) is the number of data points. _cov_ means covariance, and \(\sigma\) represents the standard deviation.  
The study area was further divided into sandy, muddy, and water areas based on the images obtained by the UAVs. Points collected within these zones were classified into two groups: sandy and muddy. This classification aimed to evaluate how coastal geological characteristics impact the precision of the DSMs. By analyzing data from these distinct geological contexts, we can better understand the variability in DSM accuracy, attributing discrepancies to the specific geophysical properties of each area.
## 3 Results
### Method Comparison Study According to ISO 16140-2:2016
#### 3.5.1 Accuracy Profile Study  
Based on the central values (medians) of the ISO reference method \(X_{i}\), and the v-ddPCR alternative method \(Y_{i}\), the deviation of the alternative method from the reference method (absolute bias \(B_{i}\)) was calculated for all samples. No systematic bias was observed for _C. coli_ samples, only a slight bias was noticed for _C. jejuni_ samples, especially for A2 (+0.20 \(\log_{10}\) counts/mL) and A3 (+0.27 \(\log_{10}\) counts/mL). Additionally, the standard deviation of the alternative method \(s_{alt}\) was determined across all samples (0.083 \(\log_{10}\) counts/mL for _C. jejuni_ and 0.112 \(\log_{10}\) counts/mL for _C. coli_). The standard deviation of the reference method \(s_{ref}\) equals to 0.068 \(\log_{10}\) counts/mL. Taking into account the number of biological replicates (\(n=5\)), the upper and the lower \(\beta\)-expectation tolerance interval \(\beta\)-ETI (_Uli_ and _Li_) were calculated for each sample, and they lie within the acceptability limits (\(-AL\) and \(+AL\)) of \(\pm\) 0.50 \(\log_{10}\) counts/mL. All relevant statistical results are provided in Tables 6 and 7. In accordance with ISO 16140-2:2016(E) [30], the alternative method is accepted as being equivalent to the reference method regarding the used raw chicken neck skin.  
Figure 1: Relative standard deviation of repeatability (RSDr) calculated under repeatability conditions for (**a**) _C. jejuni_ and (**b**) _C. coli._ Estimation of LOQ (RSDr value of 25%) for one tested replicate (solid-filled points and line) and for two replicates (non-filled points and dashed line).
## 1 Introduction
* We addressed joint resource allocation and computation-offloading decisions in order to minimize the total computing overhead of MDs, including completion time and energy consumption. The optimization problem was formulated as a mixed-integer nonlinear program (MINLP) problem of NP-hard complexity based on the inclusion of both continuous (resource allocation) and discrete (offloading decision) variables. This problem is highly complex and difficult to solve optimally in polynomial time. To address this, we divided the original problem into two subproblems: computational resource allocation and computation-offloading decisions. * We developed an algorithm using a meta-heuristic based on PSO to solve the optimization problem. PSO can solve large-scale NP problems with polynomial time complexity while ensuring convergence within specified limits [27; 28; 29]. * Numerical simulations showed that the proposed algorithm achieved outstanding performance in terms of total computing overhead relative to several baseline schemes as well as guaranteeing the convergence of our solution. The remainder of the manuscript is structured as follows. In Section 2, we review relevant literature. In Section 3, we describe a model two-tier computation-offloading system in a multi-usermulti-server MEC in a 5G HetNet. In Section 4, we formulate the problem to minimize the total computing overhead of MDs, including completion time and energy consumption. Our proposed PSO-based algorithm is presented in Section 5, and the numerical simulations are described in Section 6. The conclusion is presented in Section 7.
# New Possibilities of Using DEMATEL and ERPN in the New PFMEA Hybrid Model
## 2 Materials and Methods
### Creation of a Hybrid Model  
After the development of the initial state of the PFMEA, a hybrid PFMEA-DEMATEL-ERPN model is created.  
We have proposed three models to solve the research problem:  
1. Conventional PFMEA;
2. Combined PFMEA with the DEMATEL model;
3. Combined PFMEA with the ERPN model.  
For all models, the procedure will be as follows:  
* Monitored criteria--number of defective products, probability of occurrence--O, risk priority number--RPN;
* Proposal of measures for the four most important failure modes;  
With conventional PFMEA for failure modes with the highest RPN;  
With the PFMEA and DEMATEL model for the most significant failure modes according to the basic DEMATEL model;  
In the hybrid model for failure modes with the highest ERPN;Measures will be designed to reduce the number of defective products; regarding reducing the occurrence of O.
* Prediction and estimation of models' functionality based on defined relations in the DEMATEL model; \(\bigcirc\) Estimation of monitored criteria based on four proposed measures for each model; \(\bigcirc\) Based on the results of the digraph of the DEMATEL model, we have proposed a procedure where we show with a bold line the relations between the failure modes of the significant intensity of sizes 5 and 6, which are the values from experts for creating the initial matrix of the direct relation, and we show with a thin line the relations of average intensity (size 3 and 4). Relations of low intensity (size 1-2) will not be displayed. The location of the failure modes in the quadrants of the DEMATEL model digraph will also be crucial.
* The data collection process for each model will take three weeks;
* The average value of the occurrence of defective products for one week is considered reliable for obtaining results and defining random phenomena;
* A comparison of proposed models based on the modification of monitored criteria;
* The economic evaluation of the models is based on the determined costs of _IFC_ and _EFC_ for the elimination of the effect of each mode of failure according to Formulas (14) and (15).  
\[N_{FMi}=n_{n}.FC=n_{n}(IFC+EFC)[\xi] \tag{14}\]  
\[N_{Mi}=\sum_{i=1}^{n}N_{FMi}[\xi] \tag{15}\]  
where \(N_{Mi}\) is the total cost of the model; \(N_{FMi}\) is the cost caused by the effect of the \(i\)-th failure mode; \(n_{n}\) is the number of defective products; and _FC_ is the failure cost.
# Application Research of Improved YOLO V3 Algorithm in PCB Electronic Component Detection
## 3 Methodologies
### K-means++ Clustering Generates Anchor Box  
YOLO V3 uses the concept of anchor boxes when doing bounding box prediction. The meaning of an anchor box is the most likely the object width and height. This is obtained by clustering in advance; for example, when we want to use a pixel to predict an object. Around this pixel, we can predict the shape of an infinite number of objects. It is not a random prediction. It is necessary to find the size of the anchor box that is the most likely to be counted from the labeled data. In Section 2, we showed the schematic diagram of the nine anchor box sizes generated by the original YOLO V3 based on the COCO data set. In order to adapt to the detection target of PCB electronic components, we use the k-means++ algorithm to generate nine and 12 anchor boxes according to the three output ports of original YOLO V3 and the four output ports of the improved YOLO V3. We can get nine anchor boxes of YOLO V3 based on the PCB dataset through the calculation; the nine clusters are: (\(13\times 31\)); (\(21\times 42\)); (\(31\times 15\)); (\(34\times 58\)); (\(51\times 29\)); (\(57\times 98\)); (\(78\times 48\)); (\(150\times 118\)) and (\(255\times 323\)). There are 12 anchor boxes of improved YOLO V3 based on the PCB dataset; the 12 clusters are: (\(13\times 24\)); (\(14\times 34\)); (\(19\times 10\)); (\(24\times 14\)); (\(28\times 54\)); (\(33\times 15\)); (\(35\times 33\)); (\(47\times 23\)); (\(54\times 87\)); (\(69\times 45\)); (\(146\times 118\)) and (\(255\times 323\)). We assign these anchor boxes to the improved YOLO V3. The anchor boxes allocated by the \(13\times 13\) scale prediction output layers are (\(69\times 45\)), (\(146\times 118\)) and (\(255\times 323\)); the \(26\times 26\) scale prediction output layers are (\(35\times 33\)), (\(47\times 23\)) and (\(54\times 87\)); the \(52\times 52\) scale prediction output layers are (\(24\times 14\)), (\(28\times 54\)) and (\(33\times 15\)); and the \(104\times 104\) scale prediction output layers are (\(13\times 24\)), (\(14\times 34\)) and (\(19\times 10\)). Figure 7 shows nine cluster anchor boxes and 12 cluster anchor boxes generated based on the PCB dataset.  
Figure 6: Improved network structure of YOLO V3.
# Physical Layer Security Based on Non-Orthogonal Communication Technique with Coded FTN Signaling
## 6 Conclusions  
This paper proposed coded FTN signaling for physical layer security. To improve the implementation efficiency and BER performance, three coded FTN signaling techniques were proposed, and the BER performances of the three techniques were evaluated according to the baseband modulation schemes. From the simulation results, the proposed scheme can accomplish physical layer security without significant degradation and problems. In addition, it was checked that the eavesdropper could not detect and demodulate the transmitted signal with coded FTN signaling in all three of the proposed techniques. Furthermore, because the performance evaluation of the proposed technique was executed using a practical DVB-S2X system, the reliable operation of the proposed technique was efficiently shown. Unlike existing physical layer security techniques with artificial noise generation, since the proposed technique uses non-orthogonality of FTN signaling, a novel methodology is suggested with the proposed technique.  
Conceptualization, M.-S.B.; methodology, M.-S.B.; software, M.-S.B.; validation, M.-S.B. and H.-K.S.; formal analysis, M.-S.B. and H.-K.S.; investigation, M.-S.B. and H.-K.S.; resources, H.-K.S.; data curation, M.-S.B.; writing--original draft preparation, M.-S.B. and H.-K.S.; writing--review and editing, M.-S.B. and H.-K.S.; visualization, M.-S.B.; supervision, H.-K.S.; project administration, H.-K.S.; funding acquisition, H.-K.S. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the Institute of Information and Communications Technology Planning and Evaluation (IITP) grant funded by Korea Government [Ministry of Science and ICT (MSIT)], South Korea, under the Metaverse Support Program to Nurture the Best Talents under Grant IITP-2024-RS-2023-00254529; in part by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education under Grant 2020R1A6A1A03038540; and in part by the MSIT (Ministry of Science and ICT), Korea, under the ITRC (Information Technology Research Center) support program (IITP-RS-2024-00438007) supervised by the IITP (Institute for Information & Communications Technology Planning & Evaluation).  
The data will be made available by the authors upon request.  
The authors declare no conflicts of interest.