esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review of Hybrid VLC/RF Networks: Features, Applications, and Future Directions
## 5 Challenges and Future Directions for Hybrid VLC/RF Networks
Hybrid VLC/RF networks have a great deal of potential for IoT applications. In the future, these networks are expected to be used to improve connectivity in areas with weak RF signals [191]. Additionally, hybrid networks can overcome indoor positioning challenges by combining the high resolution of VLC with the ubiquity of RF networks. Another area in which these networks can be applied is smart agriculture, where VLC can be used to monitor crop growth and RF can be used for data communication. The integration of VLC and RF technologies can lead to the development of more efficient energy-saving solutions. In summary, we anticipate a bright future for hybrid VLC/RFnetworks in IoT applications in light of their ability to solve many of today's limitations and offer innovative solutions for a wide range of industries [192].
# Privacy Auditing in Differential Private Machine Learning:
## 2 Preliminaries
### Differential Privacy Composition  
Three core properties of differential privacy are defined for the development of suitable algorithms to preserve privacy and fulfil data protection guarantees. They play a central role in understanding the net privacy cost of a combination of DP algorithms. A crucial property of differential privacy is the _composition of differentially private queries_[50; 78] bounds on privacy guarantees.  
**Sequential composition**[2; 79] is the most fundamental, in which a series of queries are computed and released in a single batch. It limits the total privacy cost of obtaining multiple results on DP mechanisms with the same input data. Suppose a sequence of randomized algorithm, \(\{\mathcal{M}_{1}(D),\ \mathcal{M}_{2}(D),\dots,\ \mathcal{M}_{k}(D)\}\), which consists of \(k\) sequential steps, which is performed with the privacy budget, \(\{\epsilon_{1},\epsilon_{2},\dots,\epsilon_{k}\}\), on the same given dataset, \(D\), then the output mechanism is \(G(D)=(\mathcal{M}_{1}(D),\ \mathcal{M}_{2}(D),\dots,\ \mathcal{M}_{k}(D))\), which satisfies \(\sum_{i=1}^{k}\epsilon_{i}\)-DP.  
**Parallel composition**[13] is a special case of DP composition when different queries are applied to disjoint subsets of the dataset. If \(\mathcal{M}(D)\) satisfies \(\epsilon\)-DP and dataset \(D\) is divided into \(k\) disjoint parts, such as \(d_{1}\cup,\dots,\cup d_{k}=D\), then the mechanism that releases all results, \(\mathcal{M}(d_{1},\dots,d_{k})\), satisfies \(\epsilon\)-DP. In this case, the privacy loss is not the sum of all, \(\epsilon\), but rather the \(\epsilon_{max}\).  
**Postprocessing immunity**[80] means that you can apply transformations (either deterministic or random) to the DP release and know that the result is still differentially private. If \(\mathcal{M}(D)\) satisfies \(\epsilon\)-DP, then for any randomised or deterministic function \(g=(\mathcal{M}(D))\) satisfies \(\epsilon\)-DP. Postprocessing immunity guarantees that the output of DP mechanism can be used arbitrarily without additional privacy leakage [81]. Since postprocessing of DP outputs does not decrease privacy [61], we can choose a summary function \(\tau\) to preserve as much information about \(\mathcal{M}\) as possible.
## 3 Payload Design
### Subpayload 2: Star Tracker Attitude and RSO Detection for Unified Space Technologies (STARDUST)  
The STARDUST payload serves as a dual-purpose real-time payload to demonstrate attitude determination and RSO detection on the same compact, WFOV platform. Additionally, the selected components were chosen to demonstrate the viability of using Commercial-Off-The-Shelf (COTS) components in a stratospheric environment. A Raspberry Pi 48 GB was used as the payload's OBC, running the flight software used for image acquisition, attitude determination, RSO detection, and temperature sensor readings [24]. The optical system consisted of an IDS UI-3370 CP-M-GL Rev.2 compact monochrome camera, paired with a Raspberry Pi 16 mm telephoto lens [25; 26]. This configuration was chosen for its small form factor and its wide 40\({}^{\circ}\)\(\times\) 40\({}^{\circ}\) FOV, capturing images at an exposure time of 100 ms and a resolution of 2048 \(\times\) 2048 pixels. A 512 GB SanDisk Extreme microSD was chosen as the board's memory card to ensure adequate storage for any flight duration [27]. Unlike subpayload 1, this system did not use a weather-rated microSD card. By having both weather-rated and non-weather-rated microSD cards on RSONA II, we were able to assess the viability of using a faster non-weather-rated microSD card for future missions. The final component used was a temperature sensor, consisting of an Adafruit MAX31855 (Adafruit, New York, NY, USA) thermocouple amplifier breakout board with an Adafruit type-k glass braid thermocouple [28; 29].
## 4 Conclusions  
In this paper, the efficiencies of the soft computing models ANFIS-BA, ANFIS-WA, MFNN-BA, and MFNN-WA were examined for SSL prediction for the Atrek basin in Iran. The results were compared with single ANFIS and MFNN models and indicated that the ANFIS-BA and ANFIS-WA models had good performances for SSL prediction, being the best and the second-best ranked models, respectively. The ANFIS-BA model outperformed the other models based on statistical indices and uncertainty analysis. The findings of this article show good performances of BA and WA as optimization algorithms to enhance the efficiency of the ANFIS model for SSL prediction. Regarding the outputs,  
Figure 16: Monthly average suspended sediment load.  
future investigations can consider the performance of multiobjective algorithms for improving soft computing models. In addition, the current version of the BA and WA can be modified to have a better performance during the simulation process. The applied models had uncertainty in the simulation, and uncertainty computations showed that the ANFIS-BA had the best results with a lower uncertainty value. However, the evaluation of models can be completed when the models are evaluated in climate change conditions. In addition, the results of the models can be more comprehensive if the results are compared to the outputs of empirical models.  
Conceptualization, M.E.; Formal analysis, M.E.; Investigation, S.G.; Methodology, S.G.; Resources, A.N.A. and A.E.-S.; Supervision, O.K. and A.E.-S.; Validation, G.H.; Writing--original draft, A.N.A.; Writing--review & editing, C.M.F.; H.A.A., and M.K.  
The authors would like to appreciate the financial support received from Bold 2025 grant coded RJO 1043694 by Innovation & Research Management Center (iRMC), Universiti Tenaga Nasional, Malaysia and from research grant coded UMRG RP025A-18 SUS funded by the University of Malaya.  
The authors appreciate so much the facilities support by the Civil Engineering Department, Faculty of Engineering, University of Malaya, Malaysia.  
The authors declare no conflict of interest.
# A High-Precision Active Vibration Isolation Control System: Experimental Study
## 3 Controller Design
### Design of Feedforward Control  
To begin with, let us consider a dynamical MIMO system whose transfer matrix contains the elements in the diagonal line only. One can see that in this system, each output is independently controlled by the corresponding input, or, in other words, the system is totally decoupled. Then, recall the AVIS model given in (3) and rewrite it as follows:  
\[\mathbf{G}(s)=[G_{ii}(s)]\mathbf{H}(s), \tag{4}\] \[\mathbf{H}(s)=\left(\mathbf{I}_{6\times 6}+\left[G_{ii}(s)^{-1}G_{in}(s) \right]\right)\]  
where \(\mathbf{I}_{6\times 6}\) is the identity matrix, \([G_{ii}(s)]\) is the matrix containing the transfer functions in the diagonal line of \(\mathbf{G}(s)\), and \(\left[G_{ii}(s)^{-1}G_{in}(s)\right]\) is the matrix that the element at the \(i\)th row and \(n\)th column is \(G_{ii}(s)^{-1}G_{in}(s)\), \((i\
eq n)\). It is worth noting that Equation (4) exists only if \(G_{ii}(s)^{-1}G_{in}(s)\) is an analytic function and proper for every \(i\) and \(n\)[19]. Subsequently, the system output can be obtained as follows:  
\[\mathbf{Y}(s)=[G_{ii}(s)]\mathbf{U}_{\mathbf{k}}(s), \tag{5}\] \[\mathbf{U}_{\mathbf{k}}(s)=\mathbf{H}(s)\mathbf{U}_{\mathbf{v}}(s)\]  
Here, \(\mathbf{U}_{\mathbf{k}}(s)\) is the control input for the totally decoupled system, i.e., \([G_{ii}(s)]\). In other words, each DoF of the AVIS can be independently controlled once \(\mathbf{U}_{\mathbf{k}}(s)\) is derived. Then, by an inverse of \(\mathbf{H}(s)\), if \(\mathbf{H}(s)\) is invertible, the actuating forces and torques \(\mathbf{U}_{\mathbf{v}}\) can be obtained from \(\mathbf{U}_{\mathbf{k}}\). From Equations (4) and (5), \(\mathbf{U}_{\mathbf{v}}(s)\) can also be computed as follows:  
\[\mathbf{U}_{\mathbf{v}}(s)=\mathbf{U}_{\mathbf{k}}(s)-\left[G_{ii}(s)^{-1}G_{in}(s)\right]\mathbf{ U}_{\mathbf{v}}(s) \tag{6}\]where the last term on the right-hand side is obtained from a straightforward calculation as follows:  
\[\left[G_{ii}(s)^{-1}G_{in}(s)\right]\mathbf{U}_{\mathbf{v}}(s)=\begin{bmatrix}G_{11}(s)^{-1 }G_{13}(s)F_{z}(s)+G_{11}(s)^{-1}G_{15}(s)M_{\theta y}(s)\\ G_{22}(s)^{-1}G_{24}(s)M_{\theta x}(s)+G_{22}(s)^{-1}G_{26}(s)M_{\theta z}(s)\\ G_{33}(s)^{-1}G_{31}(s)F_{x}(s)+G_{33}(s)^{-1}G_{35}(s)M_{\theta y}(s)\\ G_{44}(s)^{-1}G_{42}(s)F_{y}(s)+G_{44}(s)^{-1}G_{46}(s)M_{\theta z}(s)\\ G_{55}(s)^{-1}G_{51}(s)F_{x}(s)+G_{55}(s)^{-1}G_{53}(s)F_{z}(s)\\ G_{66}(s)^{-1}G_{62}(s)F_{y}(s)+G_{66}(s)^{-1}G_{64}(s)M_{\theta x}(s)\\ \end{bmatrix} \tag{7}\]  
with \(\mathbf{U}_{\mathbf{v}}=\begin{bmatrix}F_{vx}&F_{vy}&F_{vz}&M_{\theta x}&M_{\theta y}&M_ {\theta z}\end{bmatrix}^{T}\). In Equation (7), each row of the control input \(\mathbf{U}_{\mathbf{v}}\) is the sum of the corresponding element in \(\mathbf{U}_{\mathbf{k}}\) and two terms computed from two other inputs. Thus, the term in Equation (7) serves as a feedforward control action eliminating the mutual interferences among the system's DoF.
## 4 Results
### Analysis of the Selected Studies
[68] included DTs insights and applied them to animal husbandry--a brief overview of a case study conducted in the Qinghai Meadow regarding the integration of the DTs concept into the routine pasture--so as to create a supply chain for livestock. The fundamental structure of this approach adopts two main flows, of which the first is related to the horizontal communication between suppliers and consumers, which forms the commercial supply chain, while the second is related to the IT operating system, which is structured into three levels: physical, data, and virtual. According to this structure, the data acquired from different types of sensors in the physical layer are forwarded to the data layer, where they are processed, evaluated, managed, and stored. Consequently, the information generated in the data layer is passed to the virtual layer, which exploits AI and ML to process the information further and provide advanced feedback to the physical layer. The scope of this process is to assist in preventing losses due to catastrophic weather conditions and predicting diseases in the life cycle. To this end, alarms are triggered whenever the system detects abnormal behaviors, whatever the animal or the surrounding environmental may be. This solution, as it appears, has not yet presented any evaluation results. Conclusively, since the decision making in the supply chain of livestock production systems has always caused concern, [PERSON] et al. [69] proposed a computer-aided system for controlling and optimizing the supply chain of animal feed deliverance to a number of livestock farms under the IoFEED project. For this, a DT-based approach was implemented at the farm level by integrating sensors to monitor the inventories remotely and provide data that were exploited through a combination of biased-randomization techniques along with a simheuristic framework. The results of this work indicate that the feeding operations of the livestock farms were optimized based on the proposed solution. An overview of the results of the literature review for the recent application of DTs in livestock production systems can be seen in Table 1.  
Based on the results that were generated by analyzing the works that were included in the literature review, some interesting outcomes regarding their distribution according to the year of publication, the type of the contribution (journal article or conference paper), the presentation (or not) of use case results, and the animal species of interest are illustrated as diagrams in Figure 5.
## 4 Results and Discussion
### The Optimal Pareto Front Solution Sets
\begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline
**Design Solutions** & **CSB** & **ULB** & **A** & **B** & **C** & **D** & **E** \\ \hline Insulation type & EPS & XPS & XPS & EPS & EPS & EPS & EPS \\ Thickness [mm] & WallN & 100 & 300 & 220 & 220 & 300 & 280 & 300 \\  & WallS & 100 & 300 & 160 & 160 & 160 & 160 \\  & WallE\({}_{\rm{E/W}}\) & 100 & 300 & 260 & 260 & 260 & 260 \\  & Roof & 200 & 300 & 220 & 220 & 220 & 240 \\  & WinN & Single-plane & W8 & W3 & W7 & W8 & W8 \\  & Wins & Single-plane & W8 & W5 & W4 & W8 & W7 & W7 \\ Shading [mm] & 0 & 1500 & 0 & 0 & 0 & 0 & 0 \\ EC [kWh/m\({}^{2}\cdot\)a] & 131.6 & 51.2 & 62.42 & 58.83 & 54.65 & 54.91 & 54.64 \\ LCCE [kgCO\({}_{2}\)e/m\({}^{2}\)] & 1335.24 & 663.59 & 686.54 & 659.21 & 627.73 & 630.20 & 627.88 \\ LCC [\(\$/\)m\({}^{2}\)] & 165.85 & 149.25 & 114.50 & 98.83 & 144.66 & 134.79 & 135.16 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Top five multi-objective optimization solutions. Figure 9: Optimization results and Pareto front solutions. cycle, this approach significantly reduces life-cycle costs (33.78%) and achieves a marginal reduction in life-cycle carbon emissions (0.66%) while also minimizing the initial investment for renovation materials. In addition, the overhang depth for south-facing shading was inappropriate, aligning with the findings of the sensitivity analysis. The primary advantage of south-facing shading is that it reduces a minor portion of the summer cooling load. However, the overhang depth is excessive, increasing the energy used for lighting and winter heating. Furthermore, this approach may have a detrimental impact on the environment due to the considerable CEF value of the shading material, which does not contribute to reducing carbon emissions. Overall, the results illustrate a balanced approach, where selecting insulation materials, thickness, and window types can be tailored to meet specific objectives, such as minimizing lifecycle carbon emissions, reducing costs, or maintaining high energy efficiency.
## 4 Discussion
### Impact of Bandwidth and Noise on Estimation Variance  
The presence of noise in PIR sensor signals significantly affects TDE variance, making it an essential factor in performance evaluation. Mathematically, the variance behaviors using the two methods are given in Equations (38) and (51). These results confirm that CCF variance increases with B, whereas CCFHT remains stable across different bandwidth conditions. The numerical results also indicated that CCF-based TDE is highly sensitive to noise bandwidth. Specifically, as bandwidth increases, the variance in CCF-based time period estimation also increases significantly (Figure 6). This occurs because CCF variance scales directly with the noise bandwidth, making it unreliable for wideband signals.  
This trend is evident in Figure 6, where it is seen that CCF variance increases dramatically at higher bandwidths. Meanwhile, CCFHT variance remains stable, confirming its ability to suppress noise-related uncertainty.
# Near-Space Wide-Area and High-Resolution Imaging System Design and Implementation
## 2 Wide-Area and High-Resolution System Design
### High-Resolution Camera Design
#### 2.1.3 Thermal Analysis  
When the temperature environment changes, the image quality of the optical system will change due to the influence of temperature defocusing. The reason for this result is that the temperature effect of ultra-low chromatic aberration glass is obvious, making the focal plane position of the system more sensitive to temperature. The continuous zoom lens uses a front motion group for temperature compensation to eliminate the influence of temperature effects. Within the temperature range of +60 \({}^{\circ}\)C to \(-\)45 \({}^{\circ}\)C, the transfer function curve remains basically constant, as shown in Figure 9.  
Figure 8: 106.7 mm analysis result: (**a**) Full color MTF curve; (**b**) Full color dot plot curve; (**c**) Focal length distortion curve.  
Figure 9: Transfer function diagram of 320 mm telephoto after focusing compensation: (**a**) +60 \({}^{\circ}\)C; (**b**) \(-\)45 \({}^{\circ}\)C.
# Evaluating Preprocessing Techniques for Unsupervised Mode Detection in Irish Traditional Music
###### Abstract  
Significant computational research has been dedicated to automatic key and mode detection in Western tonal music, particularly within the major and minor modes. However, limited research has focused on identifying alternative diatonic modes in traditional and folk music contexts. This paper addresses this gap by comparing the effectiveness of various preprocessing techniques in unsupervised machine learning for diatonic mode detection. Using a dataset of Irish folk music that incorporates diatonic modes such as Ionian, Dorian, Mixolydian, and Aeolian, we assess how different preprocessing approaches influence clustering accuracy and mode distinction. By examining multiple feature transformations and reductions, this study highlights the impact of preprocessing choices on clustering performance, aiming to optimize the unsupervised classification of diatonic modes in folk music traditions.  
mode detection; unsupervised learning; preprocessing; folk music Article
# Percolation Threshold of Red-Bed Soft Rock during Damage and Destruction
## 2 Research Contents and Methods
### Percolation Test for Compression Damage of Red-Bed Soft Rock  
#### 2.3.1 Uniaxial Compression AE Test  
The rock sample used is typical of red-bed soft rock: silty mudstone, purplish-red in color, argillaceous and calcareous in cementation, as shown in Figure 2a. In general, the clay minerals of such soft rocks are mainly illite and kaolinite and the XRD chart is shown in Figure 2d. The samples were prepared into cylinders with a diameter \(\varphi\) of 50 mm and a height H of 100 mm. There were three samples in total, as shown in Figure 5a. The test was performed according to ASTM standards [48].  
The microcomputer-controlled pressure testing machine was used as the loading system in the test. The testing machine can control the loading rate by computer, automatically calculate the stress value in the compression process, read the displacement data, and store it to the computer. The AE acquisition and analysis system used was the DS2 series full information AE signal analyzer, which can measure 16 channels simultaneously and reach a maximum sampling frequency of 10 MHz. A USB high-speed data transmission was adopted, with a maximum transmission speed of 384 Mbps. According to the requirements of the test, the number of AE events was recorded to characterize the degree of damage in the rock. Data was collected through a single channel with a frequency of 1 MHz and an amplifier of 40 dB. In order to make the sensor and the sample as close to each other as possible, a coating of special silicone grease coupling agent was inserted between the sample and the sensor, and a rubber band was used to tighten the sample and the sensor. The location of the sensor is shown in Figure 5c. The sampling threshold of the AE instrument was 100 mV. The starting mode of signal acquisition was set externally, primarily to synchronize the start time of data acquisition to the start time of loading. The loading process adopted a displacement control mode, and loaded at a speed of 0.04 mm/s.
## 1 Introduction  
Global Positioning System (GPS) has been intensively used in high precision positioning applications such as attitude determination of unmanned aerial vehicles e.g., [1] and other autonomous vehicles e.g., [2]. GPS positioning is based on the pseudorange and carrier phase measurements from multiple satellites to a GPS receiver. With a temporary or permanent reference station and a rover station, carrier phase based relative GPS biases and errors such as satellite clock and orbit offsets, ionospheric and tropospheric biases can be eliminated or greatly mitigated with differencing techniques at a short baseline between the rover and reference stations. As a result, GPS positioning can achieve centimetre level accuracy. However, positioning accuracy, availability, and continuity can be affected by signal multipath in some difficult environments because multipath errors cannot be removed or mitigated with differencing techniques [3]. The research question of this paper is: can machine learning methods be used to detect Global Navigation Satellite System (GNSS) multipath errors in high-precision positioning? To answer this question, the following two sub-sections briefly introduce GPS multipath error and mitigation as well as relevant machine learning algorithms used to detect GPS multipath errors. The architecture of the proposed model is presented in Section 2. Model validation methodologies and the relevant assessment criteria are given in Section 3. Section 4 describes how the training and validation data are collected. Section 5 explains how the parameters in the proposed models are determined. After presenting the results of simulated and real data in Section 6, a conclusion is reached in the last section.
## 3 Methodology and Simulation Framework
### Simulation Design and Input Data
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Category** & **Applicable Zone** & **Constraint Type** \\ \hline \multirow{4}{*}{Transmission Constraints} & \multirow{2}{*}{NKY-KY} & Maintain total power flow below a stricter limit (1) \\ \cline{2-3}  & & \multirow{2}{*}{JL-CC} & Maintain total power flow below a stricter limit (1) \\ \hline \multirow{4}{*}{Generation Constraints} & \multirow{2}{*}{KY} & Must-run (6) \\  & & Must-run with minimum output (1) \\ \cline{2-3}  & & \multirow{2}{*}{YD} & Conditional shutdown (1) \\  & & Maximum output limit (1) \\ \cline{2-3}  & & \multirow{2}{*}{JL} & Must-run (3) \\  & & Maximum output limit (1) \\ \cline{2-3}  & & \multirow{2}{*}{CC} & Must-run (1) \\  & & Maximum output limit (5) \\ \hline \multirow{2}{*}{YN} & \multirow{2}{*}{Must-run (2)} \\  & & Minimum output limit (2) \\ \hline \hline \end{tabular} Note: Numbers in parentheses indicate the number of individual constraints for each type in the specified zone.  
\end{table} 
Table 4: Summary of generation and transmission constraints. recovery period, thereby applying a mark-up on short-run marginal costs in the price bidding process. 5. Pricing process implementation: PLEXOS provides built-in pricing processes for the ZMP, LMP, and ELMP. However, the SMP, which follows Korea-specific rules, was not built into the PLEXOS. To address this, we implemented Equation (8) from Section 2 and the Korean market rules in a separate Python-based script. This script utilized the generator commitment, dispatch, and other relevant data from PLEXOS to calculate the SMP according to Korean market rules. 6. Transmission losses: To consider transmission losses, we adopted a sequential linear programming (SLP) method to update marginal loss factors [43]. This process iteratively refines the marginal loss factors for each node, reducing the gap between the actual (quadratic model) and modeled losses to 0.001% tolerance. For schemes using the ATC model, which does not incorporate the transmission line resistance, it is not feasible to endogenously calculate losses based on the marginal loss factors. Instead, to minimize distortions in scheme comparisons owing to transmission losses, we added the losses observed in the nodal pricing scheme to the system demand.