esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Background  
The use of DOWD as a tracking technique for orbit determination was first investigated in 1977 by conducting covariance analyses to verify its effectiveness [4]. At that time, DOWD was employed to track spacecraft that lacked transponders compatible with the TDRS satellites. This was part of an initiative to track user spacecraft that were without such transponders, and DOWD's efficacy was validated by comparing its results with the FDF's operational Orbit Determination (OD) solutions for the Upper Atmosphere Research Satellite (UARS), a NASA spacecraft that researched the Earth's upper atmosphere [5]. Since then, DOWD has found various applications and is frequently used as an early orbit determination technique when a spacecraft's onboard navigation systems are not fully functional during launch. Additionally, it is used for spacecraft lacking coherent tracking or onboard navigation methods, as well as for spacecraft with non-TDRS compatible transponders or transceivers that are not spread spectrum [6].  
However, an accurate knowledge of the orbits of the TDRS satellites is essential for conducting DOWD measurements. The FDF plays a crucial role in determining and maintaining the orbits of the TDRS constellation. The FDF is prime for analyzing the ground-based tracking data to precisely determine the definitive and predicted position and velocity of each TDRS satellite. The FDF currently relies on two types of data, BRTS and TT&C, to determine the ephemerides of the TDRS satellites. The BRTS measurements often involve tracking through multiple stations and encompass both range and Doppler measurements. In contrast, TT&C solely employs single station range measurements.  
In light of the potential financial implications linked to the replacement of aging BRTS transponders, the FDF undertook a series of analyses on the first generation of TDRSsatellites more than two decades ago. The objective of these analyses was to explore alternative methodologies for TDRS orbit determination that do not rely on BRTS tracking measurements [7]. One of these alternatives involved using only TT&C measurements. The use of TT&C measurements offers a notable advantage over BRTS measurements due to its higher transmitting frequency, which results in significantly less atmospheric refraction of signals (around 50 times less). However, it should be noted that TT&C measurements rely on single-site geometry, which results in a high correlation to the TDRS states. This strong correlation increases the risk of introducing orbit errors if biases associated with TT&C measurements are not adequately accounted for. The objective of this study is to evaluate the reliability of the usage of TT&C-only measurements for determining the orbits of second and third-generation TDRS satellites, as well as its potential effects on the orbits of user spacecraft relying on DOWD measurements.
## 2 Topological Map Construction Based on Regional Dynamic Growth
### Regional Growth Process
In addition, it is not necessary to consider whether the traversal order of the three points is clockwise or counter-clockwise in the calculation process, because it can be cancelled in the subsequent calculations. \[\left\{ \begin{array}{l}x_{g}=\frac{x_{1}+x_{2}+x_{3}}{2}\\ y_{g}=\frac{y_{1}+y_{2}+y_{3}}{3}\end{array} \right. \tag{2}\]  
\[S=\frac{(x_{2}-x_{1})*(y_{3}-y_{1})-(x_{3}-x_{1})*(y_{2}-y_{1})}{2} \tag{3}\]  
Assuming that an irregular convex polygon is composed of \(n\) triangles, in which each triangle has an area of \(S_{i}\) and a center of gravity of \(G_{i}(x_{i},y_{i})\), the integral can be transformed into an accumulation sum by using the formula for calculating the center of gravity of a planar thin plate, as shown in Equation (4):  
\[\left\{ \begin{array}{l}x_{g}=\frac{\iint xdS}{S}=\frac{\sum\limits_{i=1}^{n} x_{i}S_{i}}{\sum\limits_{i=1}^{n}S_{i}}\\ y_{g}=\frac{\iint ydS}{S}=\frac{\sum\limits_{i=1}^{n}y_{i}S_{i}}{\sum\limits_{i= 1}^{n}S_{i}}\end{array} \right. \tag{4}\]  
Then, in any irregular convex \(n\) polygon \((p_{0},p_{1},p_{2},\ldots,p_{n})\) with \(p_{i}(x_{i},y_{i})(i=0,1,2,3,\ldots,n)\) as a vertex, it is divided into \(n\) triangles composed of a center point \(p_{\text{c}}(x_{\text{c}},y_{\text{c}})\) as a vertex and any two points \(p_{i}\). Then, the coordinates of the center of gravity of the polygon are calculated as shown in the following Equation (5):  
Figure 2: Main steps of the regional dynamic growth algorithm. (**a**) Growth state at a certain moment. (**b**) The next moment is grown in a circular expansion manner to obtain adjacent candidate points. (**c**) Remove the unqualified points in the adjacent candidate points. (**d**) Add qualified candidate points to the topological region. \[\left\{ \begin{array}{l}x^{\prime}=\frac{\sum\limits_{i=1}^{n}{(x_{c}+x_{i}+x_{i-1 })S_{i}}}{3\sum\limits_{i=1}^{n}{S_{i}}}\\ y^{\prime}=\frac{\sum\limits_{i=1}^{n}{(y_{c}+y_{i}+y_{i-1})S_{i}}}{3\sum \limits_{i=1}^{n}{S_{i}}}\end{array} \right. \tag{5}\]  
Finally, the principal semi-axial length and the short semi-axis length of the region are calculated by principal component analysis. The pseudo code of the regional dynamic growth algorithm is shown in Algorithm 1. ```
0: The growing point is regarded as a temporary topological vertex, and then all the neighboring points are scanned annularly to select eligible points and add them to the current topological region. 0: Growth point \(p_{c}(x,y)\), maximum growth radius \(R_{\max}\). 0: Central point \(p_{c}^{\prime}\) of the topological region, radius \(R_{\
ew}^{\prime}\) of the topological region.
# A Tiered Control Plane Model for Service Function Chaining Isolation
## 4 Services in the Architecture
### Service Components on the Control Plane
#### 4.2.3 Tiers 2 and 3 BGP Peerings  
The BGP control plane for SFC [10] intends to let every Compute Node announce their virtual services (VNFs) as Service Function Instantiated routes (SFIR). In order to let the network itself calculate the correct SFC paths, the Tier 2 control agent can inject the Service Function Path Routes as a Route Distinguisher (SFPR-RD) and distribute them to the Compute Nodes. This route distribution enables each virtual router agent in the Compute Node to calculate the next hop in the SFC. Hence, the Compute Nodes virtual Tier 2 agent can calculate and change the SFC headers Service Path Index (SPI) field, in order to reflect the next hop in the SFC.  
By introducing an additional layer of Encryption Links (Tier 3), each Compute Node also needs to distribute information about their encryption services. The encryption VNF identities announced from the Compute Node we define as Service Function Instantiated Routes with encryption services (SFIR-E). Accordingly, the controller announces the corresponding SFCs that contain the routing information about the encrypted SFC and its relation with the outer SFC, namely the Service Function Path Routes with Encryption Route Distinguisher (SFPR-E-RD) (Figure 11).  
The main roles of the Tiers 2 and 3 network controller agents, are to translate the rendered SFC into BGP routes. Since BGP also can contain redundant routes, the redundant routes are also calculated and distributed according to what redundant VNFs have been provisioned. An additional functionality of the Tiers 2 and 3 control agents is to serve the encryption setup procedure with location information. The automated Encryption Link procedure is based on pre-provisioned VNF services that are configured to contact a common KMS service in order to exchange their encryption keys. Hence, the route messages of SFPR-E-RD must be populated with encryption information (public keys) by the Tier 3 control agents. The control agent makes sure that the VPN tunnel is set up before the route is announced by polling information from a Key Management Service. In summary, the Tier 3 control plane is responsible for:  
* [noitemsep]
* Distributing route information of how to route the SFCs to other Compute Nodes--this also includes redundant routes,
* Translating the SFC into BGP messages,
* Serving the KMS server with information from BGP, such as identities and encryption keys.
# Laser-Assisted Etching of EagleXG Glass by Irradiation at Low Pulse-Repetition Rate
## 3 Experimental Results
### Analysis of the Irradiation Parameters
Comparable results are achieved for all the explored scan speeds except for the highest one (\(v_{t}\) = 4 mm/s), for which the A.R. is visibly lower, independently of the inscription power. On the other hand, in KOH the etching process seems to have a more complex dependence on the irradiation parameters, and the measured A.R. reaches optimal values only for selected combinations of \(E_{p}\) and \(v_{t}\). For \(E_{p}\leq\) 2.9 \(\upmu\)J, the A.R. of the etched cones increases with the energy, reaching a maximum value of about 80 for \(v_{t}\) = 1 mm/s and \(E_{p}\) = 2.9 \(\upmu\)J. For higher energies, 2.9 \(\upmu\)J \(<E_{p}<\) 6.6 \(\upmu\)J, the A.R. drops and then increases again with the energy, reaching values around 80-85 for \(E_{p}\) = 8.6 \(\upmu\)J and \(v_{t}\) = 1-2 mm/s. The process is indeed very sensitive to the irradiation speed. It is worth noting that, whereas KOH enables higher selectivity than HF, the etching process in the KOH solution is much slower. In fact, in the latter case, the maximum etching rate of the irradiated track observed in our experiments is about 0.5-0.6 \(\upmu\)m/min (the etching rate of the bulk substrate is about 0.008 \(\upmu\)m/min).
# Reducing PAPR with Low Complexity Filtered NOMA Using Novel Algorithm
## 4 Discussion and Conclusions  
The primary concern of the proposed work is to investigate the PAPR of the F-NOMA, which is regarded as the primary contender for 5G radio; the PAPR of SLM for different transmission schemes is also evaluated and analyzed. The PAPR can be reduced to an optimal level by selecting the maximum S and P. A significant outcome of the results is that PAPR in F-NOMA obtained a gain of 1 dB for different QAM transmissions without applying any PAPR minimization algorithms. The SLM with sub-blocks (S) and P is applied to F-NOMA. It is shown that the SLM (S = 4 \(p\) = 4) obtained optimal PAPR and BER performance as compared with the conventional SLM. Further, it is seen that the complexity of SLM is increased with high-order modulation schemes. In future work, the performance of the SLM-based advanced waveforms can be studied for the Rician channel. The complexity and throughput of the Rician channel can be compared with the Rayleigh channel for its high number of sub-carriers and transmission scheme order (S = 256 and 256-QAM).  
Conceptualization, A.K.; methodology, A.K.; software, A.K.; validation, K.R.; formal analysis, G.G.; project administration, H.S. and N.G.; funding acquisition, M.M.; review and editing, M.A.A. and S.H.A. All authors have read and agreed to the published version of the manuscript.  
Taif University Researchers Supporting Project number (TURSP-2020/98, Taif University, Taif, Saudi Arabia).  
The author declare no conflict of interest.
## 2 Computation Model and Methodology
### Thermodynamic Validation for CO\({}_{2}\)-H\({}_{2}\)S Configuration  
The ability of the RFM model to simulate the condensation of CO\({}_{2}\) with impurities needs some validation. After an extensive literature search, we found work on CO\({}_{2}\)-rich streams with H\({}_{2}\)S (95/5% by mole%) as an impurity in the experiments of [PERSON] et al. [17]. These experiments provide a unique set of experimental data consisting of three isotherms. As shown in Figure 2, the lowest isotherm (273.15 K) crosses the dome in the subcritical region, and the highest isotherm (353.15 K) is in the supercritical region, with the intermediate isotherm (323.15 K) close to the critical point of the mixture (95% CO\({}_{2}\) + 5% H\({}_{2}\)S). The predictions plotted in the P-\(\rho\) diagram (see Figure 2) are obtained with the CPA-EoS, one of the EoS available in the IFPEN-Carnot thermodynamic library. A good agreement between the numerical and experimental is obtained, despite the slight deviation (less than 3%) that occurred at high pressure for the subcritical isotherm (273.15 K) in the liquid side.  
Although the validation is not on the mixture of interest (CO\({}_{2}\) + N\({}_{2}\)), for subsequent de Laval nozzle simulations, H\({}_{2}\)S is one of the impurities in CO\({}_{2}\)-rich vapors and having validation of one of the impurities in industrial CO\({}_{2}\) should give more confidence in the following simulations where condensation takes place and in one of the main expected numerical results.  
Figure 2: Isotherms in the P-\(\rho\) diagram for the CO\({}_{2}\)–H\({}_{2}\)S mixture. Predictions obtained with CPA-EoS are compared to the [PERSON] et al. [17] experimental data sets. Experimental results are shown as ‘o’ marker and modeling results as solid lines (colors online). The isotherms follow the trends observed in the experimental data set and have agreement at high pressures and densities.
# Integrated Digital-Twin-Based Decision Support System for
## 6 Case Study Implementation  
To evaluate the effectiveness of the proposed DT framework for relocatable module allocation plans in RMB projects, a case study was implemented using a virtual RMS system. The selected project is a pioneering case in South Korea, marking the first adoption of the RMB technique for temporary schools that can be relocated to multiple locations. Unlike general problem-solving methods, the single-case study approach provides in-depth, contextualized insights into real-world challenges and solutions. This methodology is particularly effective in uncovering intricate details about the data, issues, and practical resolutions in specific situations [62]. In the construction industry, case studies play a critical role by enabling decision makers to base their judgments on objective, evidence-based analyses, thereby influencing stakeholders' adoption of innovative building methods for future projects [63].  
The selection of the RMS system as the representative case is strategic given the growing prominence of the educational sector within the relocatable construction industry. Schools are a key market segment, driven by the increasing need for flexible and rapidly deployable educational facilities to address fluctuating student populations and evolving educational demands. As part of the Korean government's \"Green Smart School\" initiative [64], the RMS aims to modernize educational infrastructure with sustainable, adaptable solutions. The RMS system in this research, developed by the NRB company, exemplifies this approach by providing temporary educational facilities that meet high standards for learning environments. A study by [PERSON], [PERSON] [1] highlights the RMS's success in delivering high-quality educational spaces, achieving notable results in technical innovation, cost efficiency, and lifecycle assessment.  
In this implementation, the DT framework synchronized the types and locations of physical assets--specifically modular classroom units--with their digital counterparts within the DT environment proposed in Section 4. This integration enabled the DT platform to precisely identify and select suitable modules for reuse and to optimize logistical routes for deploying RMS projects. By simulating and analyzing logistical strategies, the DT framework supported efficient module allocation, minimized transportation costs, and ensured alignment with project requirements and site-specific conditions. The virtual assets within the DT platform played a critical role in enhancing the decision-making process. Through real-time synchronization, stakeholders could explore and compare alternative scenarios for module routing and placement. This capability ensured not only that the modules met the technical and functional needs of the project but also that the deployment was cost effective and environmentally sustainable.
# Fuzzy Based Prediction Model for Air Quality Monitoring for Kampala City in East Africa
## 2 Materials and Methods
### Fuzzy Modeling Approach
#### 2.1.2 The Proposed Fuzzy Logic Control Model  
The proposed prediction model is based on fuzzy control model reasoning to predict the Kampala Air Quality Index as a percentage of the given air pollutant status. For the simulations, we used MATLAB R2017b, an environment where the fuzzy toolbox logic controller is embedded.  
The fuzzy control model is designed predict the KAQI based on a set of predefined parameters, which include NO\({}_{2}\), SO\({}_{2}\) and PM\({}_{2.5}\).  
To build the fuzzy logic system, the principle steps are followed as shown in Figure 1. The design steps included during design are as follows: defining the input variables, fuzzyfication, formulation of fuzzy inference rules, defuzzification and model evalutation.
# Comparison of Postural Stability and Regulation among Male Athletes from Different Sports
## 4 Discussion
### Limitations  
As with any research, there are a few limitations of our study worth noting. First, we tested postural control using a static activity. However, an important physical characteristic of divers, soccer players, and hockey players is strong dynamic performance. This may have resulted in test conditions that did not mimic the athlete's respective sport-specific conditions. Our testing protocol was chosen due to its' clinical relevance and to accurately compare previous findings with our results that used a similar methodology. Second, differences in training routines between the athlete groups may have had an effect on the results of our study. Third, and as previously mentioned, the amount of physical training in an individual's respective sport can directly impact their level of postural control. Optimal training programs may be easier for coaches to create when working with individual sports such as diving, compared to team sports such as soccer and hockey, which may also be influenced by the physical requirements of each field-specific position. In team sports, core stability and mobility training are often less frequently used when compared to technical sports. This can lead to imbalances in key muscle groups and an increased risk of injury. Lastly, our groups of subjects had significantly different sample sizes (ice hockey: \(n=16\), soccer: \(n=23\), diving: \(n=11\)), which may result in an overestimation of differences; [27] thereby limiting the interpretation of results. In this context, we did not perform a power analysis to calculate the number of necessary subjects because the number of athletes was limited based on the performance level and the voluntary nature of the study. Therefore, it was a sample of convenience.
# SPME-GC/MS Analysis of Methanol in Biospecimen by Derivatization with Pyran Compound
## 3 Materials and Methods
### Instruments  
Methanol analysis was performed using GC-FID with 6890 GC and 7697A Autosampler from Agilent Technologies (Foster City, CA, USA) equipped with the column of 80/100 0.20 %o CW-1500 Carbopack C 6 ft \(\times\) 1/8 in \(\times\) 2.1 mm SS from Supelco (Bellefonte, PA, USA). The temperature of the autosampler oven and the transfer line was 65 \({}^{\circ}\)C and 120 \({}^{\circ}\)C, respectively. The time for vial equilibration was 5 min, injection duration 1 min, and cycle 4.5 min. The carrier gas used was 99.9999% He at a flow rate of 13.9 mL/min. GC inlet temperature was 200 \({}^{\circ}\)C, and carrier gas was 99.9999% He with a flow rate of 14.7 mL/min. GC oven temperature was maintained at an isothermal state of 120 \({}^{\circ}\)C with a run time of 3.5 min. For FID detector, the flow rate of hydrogen and air was 40 mL/min and 350 mL/min, respectively, and temperature was maintained at 250 \({}^{\circ}\)C. GC/MS analysis was performed using the 7890B GC and 5977B MSD system from Agilent Technologies (Foster City, CA, USA) equipped with DB-5 MS UI capillary column (30 m length \(\times\) 0.25 mm id, 0.25 \(\upmu\)m film thickness, J & W Scientific, Folsom, CA, USA) and carrier gas was 99.9999% He with flow rate of 1.0 mL/min at constant flow mode. The temperature of injector and interface was set to 260 \({}^{\circ}\)C and 280 \({}^{\circ}\)C. The GC oven temperature was at 40 \({}^{\circ}\)C for 3 min, and then heated to 250 \({}^{\circ}\)C at the rate of 10 \({}^{\circ}\)C/min and then maintained for 3 min. The solvent delay time was set to 4.2 min to exclude the effect of the derivatization reagent  
((part of Thermo Scientific) Sunnyvale, CA, USA). In order to prevent column contamination, IonPac AG-11 4 mm guard column from Thermo Scientific (Sunnyvale, CA, USA) was used and separated into IonPac AS-11 4 mm column. For eluent, carbonate/bicarbonate eluent was diluted to 4.5/1.5 mM concentration and analyzed with a conductivity detector by applying a current of 26 mA to Thermo Scientific's ASRS 4 mm suppressor at 1 mL/min of isocratic flow rate.
# Decision Tree-Based Adaptive Reconfigurable Cache Scheme
## 5 Results and Analysis
### Complexity and Overhead  
The computational complexity of training a decision tree (the J48 algorithm) is \(O\) (\(n\cdot m\cdot\log n\)), where \(n\) is the number of samples in the training set, and \(m\) is the number of attributes. The computational complexity of using the decision tree model to determine the optimal associativity (the proposed adaptive control algorithm) is \(O\) (\(d\)), where \(d\) is the max depth of the decision tree.  
The implementation of adaptive reconfigurable cache comes with additional performance and hardware overhead.  
From the performance perspective, the decision-making process of the optimal associativity is independent of the running of the application; thus, the decision-making process does not harm the performance of the application. According to the trained decision tree model, each decision of the optimal associativity takes at most three comparisons to reach a leaf node, and each comparison takes one clock cycle; therefore, the decisions will be made within three clock cycles. However, the tuning of the associativity leads to performance loss from two aspects:  
Figure 15: Waveform of cache write option.  
* Reconfiguration overhead: This is an intuitive performance loss. During reconfiguration, the adaptive reconfiguration controller sends an interruption to the CPU to block the current operating until the cache finishes writing back dirty blocks and flushing. Each reconfiguration takes 500 clock cycles.
* Compulsory cache miss: After reconfiguration, all cache blocks are in an invalid state, and the temporary increase in cache miss rate caused by this increases the AMAT.  
As analyzed in Section 3.2, the performance is related to the reconfiguration period. The performance gains brought by the adaptive tuning of the associativity should be greater than the performance loss caused by the reconstruction so that the overall performance of the system can be improved. From the perspective of hardware overhead, the additional hardware overhead for the real-time adaptive reconfigurable cache comes from two key modules:  
* Performance monitor: To obtain the runtime statistics required for decision-making, three additional 32-bit counters need to be allocated to each core to save the IPC, total memory access, and miss rate parameters of the current reconfiguration period. Therefore, for a multi-core system with 8 cores, a total of 24 additional 32-bit counters are required.
* Adaptive controller: The adaptive controller is implemented by FSM, and the decision of the optimal associativity is realized by a three-level conditional judgment statement.  
The hardware implementation of the related modules is relatively easy. The area of the synthesized hardware circuit is used to evaluate the complexity of the hardware implementation, and the results are shown in detail in Section 5.3.