esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion  
### Empirical Equation and ANN Algorithms  
This section compares the relation between the deflection and dynamic stiffness based on the test results to the equation proposed by [PERSON] et al. [10], and the artificial neural network modeling using three different data regression algorithms. Equation (14) shows the proposed equation. The proposed equation uses the dynamic stiffness after 30 min after loading, the applied load, the elastic modulus, and the material coefficients. However, the ANN system considers the following seven attributes for the training data set: applied load, density, elastic modulus at early loading stage, dynamic stiffness after 30-min loading, shape, deflection, and dynamic stiffness.  
\[DS=DS_{30}+\frac{3}{P}e^{(\frac{AC_{1}}{3}-C_{2}\frac{P}{2})} \tag{14}\]  
where \(DS\) is the dynamic stiffness (MN/m\({}^{3}\)), \(DS30\) is the dynamic stiffness 30 min after loading (MN/m\({}^{3}\)), \(P\) is the applied load (N), \(E\) is the elastic modulus (MPa), and \(\Delta\) is the deflection (mm).  
Various nearest feature vectors of distance-weighted k-nn, tree depth and nodes were tested to obtain the minimum Root Mean Squared Error (RMSE) of k-nn regression, regression tree, and MLP Regression, respectively. Figure 5 plots the test results of the three data regression algorithms. The \(X\)-axis and \(Y\)-axis represent the number of variables and the calculated RMSE results, respectively. In the case of the k-nn regression algorithm, the RMSE was the smallest when the value of weighted k-nn was 3. In the case of the regression tree and MLP regression, the RMSE was the smallest when the tree depth was 10 and the number of nodes was 8, respectively.  
Table 2 shows the correlation coefficient, Root Mean Square Error (RMSE), Relative Absolute Error (RAE), and Root Relative Square Error (RRSE) of the algorithms used in this study. Each error can be calculated using the following equations.  
\[RMSE=\sqrt{\frac{1}{n}\sum_{i=1}^{n}(p_{i}-y_{i})^{2}} \tag{15}\] \[RAE=\frac{\sum_{i=1}^{n}\lvert p_{i}-y_{i}\rvert}{\sum_{i=1}^{n} \lvert\overline{p_{i}}-y_{i}\rvert}\] (16) \[RRSE=\sqrt{\frac{\sum_{i=1}^{n}(p_{i}-y_{i})^{2}}{\sum_{i=1}^{n} (\overline{p_{i}}-y_{i})^{2}}} \tag{17}\]  
where \(n\) is the number of data set, \(p_{i}\) is the predicted value, \(y_{i}\) is the actual value, \(\overline{p_{i}}\) is the mean value.  
The correlation coefficient and the errors of the proposed equation by [PERSON] et al. [10] were also calculated for comparison. Since the average correlation coefficient of the three algorithms was 0.9843, the three models are confirmed to well reflect the data of the training set. The correlation coefficient of the ANN models was about 9.5% more than that of the proposed equation by [PERSON] et al. Also, when the ANN models were used, the RMSE decreased by 54.4% and the relative errors decreased by an average of 92.0%. Table 2 confirms that when the ANN algorithms were used, the accuracy was significantly improved Tables 3 and 4 show the calculated weight values between the (input layer and hidden layer) and (hidden layer and output) for dynamic stiffness prediction.
## 3 Results and Discussions
### Phenolic Compounds
Four phenolic acids were identified, namely \(p\)-coumaric acid, o-coumaric acid, cinnamic acid and ferulic acid. Cinnamic acid was the most abundant phenolic acid, and its content was significantly higher in oils obtained at VNFC (24.0 mg/kg of oil), whereas, \(p\)-coumaric acid was significantly higher (\(p\)-value = 0.0241) in Moncorvo oleaster oils (10.7 mg/kg of oil). In general, the obtained results are in agreement with those obtained for Algerian oleaster oils [7], and also similar to the reported by [PERSON] [38] for cultivated traditional Portuguese cultivars (cvs. Galega Vulgar and Cobrancosa). Oleaster oils possess a considerable amount of phenolic compounds, with the mean contents in the range of 600 to 750 mg/kg of oil. Again, the obtained values were higher to those observed by [PERSON] [7] for Algerian oleaster oils, and for Portuguese-cultivated olive varieties [38]. These findings are quite relevant since different works previously demonstrated the importance of phenolic compounds on the sensory characteristics, resistance to oxidation and positive health effects of olive oils, being in this case oleaster oils also a good source of these compounds. Oleaster Oils Discrimination According to the Geographical Location Based on Their Physicochemical Profiles  
As previously discussed, the Portuguese oleaster oils studied showed physicochemical profiles (fatty acids, tocopherols, sterols and phenolic compounds) with similarities with those of oleaster oils from other countries (e.g., Algeria and Tunisia), as well as of cultivated olive trees (from Portugal and other countries). However, as also pointed out, for some chemical minor compounds (i.e., less abundant ones), significant statistical differences were detected among the three populations evaluated. Thus, LDA coupled with the SA algorithm (LDA-SA) was implemented to identify the chemical compounds that could be further used as chemical markers for each population studied (i.e., Alijo, Moncorvo and VNFC locations) allowing discriminating the oleaster oils according to the geographical origin of the wild olive trees (Figure 1). The results show that a LDA-SA model with two linear discriminant functions (explaining 99.72% and 0.28%, respectively) could be established based on the contents of five chemical compounds (i.e., eicosenoic fatty acid; SFA; triterpen alcohols (erythroidi + uval); hydroxytyrosol acetate; and cinnamic acid). The established model enabled the correct classification of 100% of the oils according to the location for the original grouped data. Additionally, for the LOO-CV procedure, a predicted correct classification rate of 100% was also obtained. These results clearly illustrated that, besides their relevance for health or sensory positive sensations, these chemical compounds and their contents may be further used as chemical location markers for oleaster oils.
# Two-Dimensional Modelling Approach for Electrokinetic Water Transport in Unsaturated Kaolinite
## 3 Results and Discussion
### Influence of the Electrode Positioning Type
#### 3.1.2 Electric Potential Gradient Sensitivity  
For the sensitivity analysis, three additional simulations were carried out by fixing the void ratio at 0.9 and applying electric potential gradients of 5, 10, and 50 Vcm\({}^{-1}\). The initial water content of the soil was the same in the three cases (0.25, see Table 1); therefore, the initial degree of saturation for each simulation was 0.736. Figure 6 shows the saturation degree for the three simulated cases.  
Figure 5: 2D maps of the degree of saturation evaluated at two times, 0 and 86,400 s (1 d), and three void ratios, 0.7, 0.9, and 1.2, when the electrodes were inserted into the soil corresponding to the configuration presented in Figure 1b. An electric potential gradient of 1 Vcm\({}^{-1}\) was applied.  
In the electrolyte reservoir scenario, it can be observed that as the applied electric potential gradient increased, the process of saturation of the soil discussed above was accelerated from the anolyte reservoir zone. This shows that the contribution of the electroosmosis process to the net water transport is more significant. In the corresponding scenario with direct insertion of the electrodes, the effect is not as relevant. Although a higher electric potential gradient generates a higher electroosmotic flux, in this case, the redistribution of the water content within the soil produces liquid pressure gradients and consequently gives rise to fluxes opposite to the electroosmotic ones. Therefore, the effect of an increase in the electric potential gradient is counterbalanced in this type of system.
## 6 Experiments and Results
### Scenario  
In this paper, we use a CCTV scenario as shown in Figure 7 where CCTV cameras continuously stream video footage to a central server for monitoring and storage. Each CCTV camera, acting as a UE, connects to its respective CCTV application server, i.e., the AF. To ensure the security and integrity of the data being transmitted, the communication between the CCTV cameras and the server is conducted through the Ua* protocol with the AKMA TLS 1.3 PSK. In more detail, when the CCTV system starts, it initiates an AKMA TLS 1.3 PSK handshake with the server. This handshake performs mutual authentication and key exchange, thereby establishing a secure session based on the selected AKMA TLS 1.3 PSK mode: 0-RTT, PSK-(EC)DHE, and PSK-only. The reason we select this CCTV scenario is that, since CCTV cameras are typically installed outdoors, network connectivity can be unstable due to environmental factors (e.g., weather). As a result, reconnection performance is critical in the event of a disconnection. Accordingly, in this paper, we measure performance by applying various packet loss scenarios.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Configuration Items** & **Parameter** \\ \hline VM & Oracle VM VirtualBox \\ OS & Ubuntu 20.04.6 LTS, 64 Bit \\ OpenSSL Version & 3.2.1 30 January 2024 \\ Virtual Memory & 8 GB \\ CPU & Intel® CoreTM i5-13600K \(\times\) 5 Core \\ \hline \hline \end{tabular}
\end{table}
Table 4: Experiment setup.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Security Requirement** & **PSK-Only** & **PSK-(EODHE)** & **0-RTT** \\ \hline Confidentiality & & \\ Integrity & & \\ Mutual Authentication & & \\ Secure Key Exchange & & \\ Perfect Forward Secrecy & \(\times\) & & \\ Defense against replay attack & \(\times\)1 & \(\times\)1 & \(\times\)1,2 \\ \hline \hline \end{tabular} \({}^{1}\) Signaling-level replay attack; \({}^{2}\): Data-level replay attack; \(\bigcirc\): Support; \(\bigtriangleup\): Limited support; \(\times\): Not supported.  
\end{table}
Table 3: Security comparison of the AKMA TLS 1.3 PSK modes.
## 4 Challenges to Developing a Remote Sensing Based EHA System
### Uncertainties in Ecosystem Health Indicators
#### 4.4.1 Optical Remote Sensing  
Although selecting appropriate optical images with suitable spatial, temporal, and spectral resolution is expected to yield better estimation of health indicators [27], the accuracy is hindered by the fact that optical data are only sensitive to top of canopy in dense vegetated environments [151], and the spectra is highly influenced by the existence of NPV, BSC, and bare soil in sparsely vegetated areas [60; 152; 153] (Figure 2).  
The presence of NPV, BSC, and bare soil affect spectral indices derived from optical remote sensing data and designed for the estimation of biophysical variables [153], such as LAI, green vegetation biomass, and fractions of Absorbed Photosynthetically Active Radiation (fAPAR) which are important attributes of EHA. LAI, fAPAR could be overestimated for ecosystems with randomly distributed sparse PV and NPV mixtures, while underestimated for dense mixtures due to the effects of NPV [153]. NPV accounts for similar amount of variations in spectral indices including NDVI and Modified Soil-adjusted Vegetation  
Figure 2: Spectral response curves of dead vegetation, green vegetation, bare soil, and dry moss as dominated BSC (samples were collected from grasslands National Park, Canada and their spectra were measured in laboratory with an ASD Spectroradiometer).  
Index (MSAVI) [154] as green vegetation in semiarid mixed grassland. To reduce the influence of NPV, a hyperspectral index, litter-corrected ATSAVI (L-ATSAVI) was developed [140].  
The presence of BSC can increase NDVI values by as high as 0.30 units in semiarid environments, which may result in overestimation of ecosystem productivity and misinterpretation of vegetation dynamics [60, 70]. Comparatively, effects of bare soil on spectra have been extensively investigated since early 1970s [155, 156]. The exerted influence on the spectra significantly affects NDVI and further affect LAI estimation [152] and green vegetation cover with the largest errors in grassland and shrubland areas [157]. Thus, many efforts were made to develop vegetation indices (e.g., a soil-adjusted vegetation index (SAVI, [152]) and a modified soil adjusted vegetation index (MSAVI, [158]) to minimize the soil brightness effects. Minimizing bare ground effects significantly improve the \(r^{2}\) value by 0.23 in estimating N in semiarid shrubland using HyMap hyperspectral data [78]. However, little research has been conducted to study the total effects of NPV, BSC and bare soil on vegetation indices and to evaluate the effects on the determination of single EHA attribute (e.g., LAI and productivity _etc._) and to further identify their effects on a comprehensive EHA.
###### Abstract  
Since 23 June 2020, BDS-3 has been entirely operated and obtained the ability of global PNT (Positioning, Navigation, and Timing) services. Afterward, real-time Precise Point Positioning (PPP) service is available in China's surrounding areas via BDS-3 PPP-B2b signal. However, such a real-time PPP service cannot maintain the high accuracy and continuity of positioning solutions in challenging environments, such as urban environments. For that, we carried out a model by integrating between-satellite single-differenced (BSSD) PPP, a low-cost Inertial Navigation System (INS), and an odometer via an extended Kalman filter. The performance of this integration model was assessed with vehicle-borne data. Results demonstrated that (1) the position RMS (Root Mean Square) of BSSD PPP are 64.33 cm, 53.47 cm, and 154.11 cm. Compared with BSSD PPP, about 31.2%, 23.3%, and 27.3% position improvements can be achieved by using INS. Further enhancements of position RMS benefiting from the odometer are 1.34%, 1.41%, and 1.73% in the three directions. (2) Anyway, the accuracy of BSSD PPP/INS/Odometer tightly coupled integration is slightly higher than that of undifferenced PPP/INS/Odometer integration, with average improvement percentages of 7.71%, 3.09%, and 0.27%. Meanwhile, the performance of BSSD PPP/INS/Odometer integration during the periods with satellite outages is better than the undifferenced PPP-based solutions. (3) The improvements in attitudes from an odometer are more significant on heading angle than the other two attitudes, with percentages of 25.00%. (4) During frequent GNSS outage periods, the reduction in average maximum position drifts provided by INS are 18.01%, 8.95%, and 20.74%. After integrating with an odometer, the drifts can be furtherly decreased by 25.11%, 15.96%, and 20.69%. For attitude, about 41.67% reduction in average maximum drifts of heading angles is obtained.  
Keywords:real-time Precise Point Positioning (RT-PPP); Inertial Navigation System (INS); odometer; PPP-B2b service; tightly coupled integration +
Footnote †: journal: remote sensing
## 3 Evaluation and Results
### Results
#### 3.3.2 RF Power Penalty  
First, we measured the optical powers at several (marked) points A-E (Figure 11) along the path of the optical signal propagation and present them together with the RF power setting at the output (point Z) of the VNA in Table 1.  
The careful reader will notice the excessive drop in the optical power after the MZM (point B). This is due to the lower-than-typical bias voltage that was employed to achieve the small-enough CSR for the wavelength locking to have worked.  
To determine the performance of the RoF link, we examined the optical power spectrum of an OSSB signal with a varying SBSR and the RF transfer functions (TF), i.e., the \(s_{21}\) measurement, for several (mis)alignments of the carrier wavelength \(\Delta\lambda=\pm n\cdot\delta\lambda\) with a discrete step of \(\delta\lambda=0.01\) and \(n=0,\;1,\;2,\;3\ldots 10\). Note that when the carrier wavelength is misaligned, the SBSR deteriorates and the PP at the intended RF, visible on the TF, intensities.  
In the Table 2, we show pairs of graphs--referring to the optical power spectrum of the transmitted OSSB signal and the corresponding RF TF of the RoF link shown in Figure 11--for the first five misalignments from the optimal to shorter wavelengths.  
The following graph (Figure 16a) combines all the RF TF measurements for the redshifted and blue-shifted wavelengths. If we now acquire the values at a critical frequency \(f_{0}\), where without the MRR there would have been an extreme power penalty \(\;PP(f_{0})\rightarrow-\infty\;\text{dB}\), we obtain the following graph (Figure 16b). It relates the PP deterioration to the wavelength misalignment. Given the dither evaluated at \(\varepsilon_{\lambda}\approx 0.0038\;\text{nm}<0.01\;\text{nm}\), we can conclude that \(\Delta PP(f_{0},\Delta\lambda)|_{|\Delta|<\varepsilon_{\lambda}}<2\;\text{dB}\).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Point** & **A** & **B** & **C** & **D** & **E** & **Z** \\ \hline \(P_{\text{opt}}\;\text{[dBm]}\) & 3.6 & \(-\)17.1 & 9.6 & \(-\)5.0 & \(-\)8.8 & N/A \\ \hline \(P_{\text{RF}}\;\text{[dBm]}\) & N/A & N/A & N/A & N/A & 13 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Power measurements at several points along the optical path and an RF power setting at the output of the VNA.  
Figure 15: Oscillogram showing voltages at several control points of the CLC system.
# An Energy-Efficient and Robust Multipath Routing Protocol for Cognitive Radio Ad Hoc Networks
## 4 Energy-Efficient and Robust Multipath Routing
### Route Discovery
In Figure 4, nodes B and G drop the RREQ packets with the same sequence number #1 as the RREQ packet that they have already forwarded. This helps maintain the concept of disjoint and loop-free paths, which is crucial in multipath routing. The destination responds to only the RREQs received within the time set in the timer at the destination. All RREQs received after the time specified are dropped automatically. Figure 4: Multipath routing path discovery. Figure 3: Routing table structure. S: Source; D: Destination; SN: Sequence Number; AHC: Advertised Hop Count.
# Factory Logistics Improvement: A Case Study Analysis of Companies in Northern Thailand, 2022-2024
## 4 Results and Case Study
### Warehouse and Inventory Management
#### 4.2.2 Warehouse Layout Design  
This case study examines a medium-sized beverage production factory serving over 100 OEM customers. The product range varies significantly in size, shape, material, and packaging to meet diverse customer requirements. High stock levels, particularly of raw materials like empty bottles, are maintained due to the nature of beverage OEM operations and economies of scale. However, the warehouse lacked proper management, with no Standard Operating Procedures (SOP) or Warehouse Management System (WMS) in place. Consequently, stocks were disorganized, leading to high inventory holding costs, low inventory accuracy, and significant dead stock.  
To address these issues, the following solutions were implemented:  
1. Redesign of warehouse layout based on product category and significance using Pareto/ABC classification.
2. Determination of stock levels for A-level raw materials using Safety Stock (SS) calculations.
3. Introduction of visual control concepts to enhance warehouse operations (as illustrated in Figure 4).  
These interventions resulted in a 16.74% reduction in inventory holding and carrying costs.  
Figure 4: Designed visual control board.
## 3 Ground Landing Station Design
### Battery swapping Mechanism  
The battery swapping mechanism is implemented by a rack, coupling gear, and pinion. The rack is on the bottom surface of the battery carriage while the pinion (82 mm diameter) is placed on the base and is directly actuated by a NEMA 32 stepper motor. The pinion also interfaces with a coupling gear and another pinion. This mechanism assures that the battery holder is moved from the receiver to the slots of the battery storage plate. Figure 3 illustrates the design of the battery swapping mechanism. The power consumption of the NEMA 32 stepper motor is supplied by a GLS energy battery source of 8000 mAh 2S2P, independent of the UAV battery. The consumption rate is 35.33 mAh, considering a period of 1 min per battery replacement with a typical current consumption of 2.12 A.  
Figure 3: Battery swapping mechanism design: (**a**) rack, (**b**) coupling gear, and (**c**) pinion.  
Figure 2: Battery coupling system: (**a**) holder, (**b**) receiver and (**c**) payload.
# Development and Simulation of Motion Control System for Small Satellites Formation
## 7 Multi-Agent Control and Consensus Algorithm
### Consensus-Based Satellite Formation Control Law  
The multi-agent satellite formation control law can be obtained by combining expressions (30) and (34). It should be noted that (34) is a control law for a single coordinate. Let us introduce the following notation: \(n\) is a number of agents in the formation (number of satellites); \(i=1,\ldots,n\) is the agent number; \(\mathbf{e}_{i}=\left[\begin{array}{cccc}e_{xi}&e_{yi}&e_{zi}&e_{xi}&e_{yi}& e_{zi}\end{array}\right]^{\mathsf{T}}\) is the error vector of \(i\)-th agent. Then the control law of agent \(i\) can be written as:  
\[\begin{split}\bar{u}_{xi}&=-K_{px}e_{xi}-K_{dx}e_{xi}-\sum_{j=1} ^{n}a_{ij}\big{(}\gamma_{0}\big{(}e_{xi}-e_{xj}\big{)}+\gamma_{1}\big{(}e_{xi} -e_{xj}\big{)}\big{)},\\ \bar{u}_{yi}&=-K_{py}e_{yi}-K_{dy}e_{yi}-\sum_{j=1}^{n}a_{ij} \big{(}\gamma_{0}\big{(}e_{yi}-e_{yj}\big{)}+\gamma_{1}\big{(}e_{yi}-e_{yj} \big{)}\big{)},\\ \bar{u}_{zi}&=-K_{pz}e_{zi}-K_{dz}e_{zi}-\sum_{j=1}^{n}a_{ij} \big{(}\gamma_{0}\big{(}e_{zi}-e_{zj}\big{)}+\gamma_{1}\big{(}e_{zi}-e_{zj} \big{)}\big{)}.\end{split} \tag{38}\]  
Another variant of multi-agent control of the formation of satellites can be obtained based on (25). To do this, let us introduce the matrix of coefficients \(\mathbf{K}=\left[\mathbf{k}_{x}\quad\mathbf{k}_{y}\quad\mathbf{k}_{z}\right]^ {\mathsf{T}}\), where \(\mathbf{k}_{x},\mathbf{k}_{y},\mathbf{k}_{z}\in\mathbb{R}^{1\times 3}\), and vector \(\mathbf{y}=\left[\begin{array}{cccc}y_{x}&y_{y}&y_{z}\end{array}\right]^{ \mathsf{T}}\). In new notations, (25) can be written as  
\[\begin{split}\bar{u}_{x}&=-\mathbf{k}_{x}\mathbf{y},\\ \bar{u}_{y}&=-\mathbf{k}_{x}\mathbf{y},\\ \bar{u}_{z}&=-\mathbf{k}_{x}\mathbf{y}.\end{split} \tag{39}\]  
Figure 3: Examples of communication graphs of 8 and 12 agents.  
Expressions (39) represent control vector components for a single agent. For the multi-agent control case, it is possible to employ the consensus algorithm for first-order systems since the combined output includes derivatives, and the zero-dynamics [31] of the system is stable. Then the control law based on the passivity of \(i\)-th agent can be written as:  
\[\bar{u}_{xi} =-\mathbf{k}_{x}\mathbf{y}_{i}-\gamma_{0}\sum_{j=1}^{n}a_{ij}(y_{ xi}-y_{xj}), \tag{40}\] \[\bar{u}_{yi} =-\mathbf{k}_{y}\mathbf{y}_{i}-\gamma_{0}\sum_{j=1}^{n}a_{ij}(y_{ yi}-y_{yi}),\] \[\bar{u}_{zi} =-\mathbf{k}_{z}\mathbf{y}_{i}-\gamma_{0}\sum_{j=1}^{n}a_{ij}(y_{ zi}-y_{zj}).\]