esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Factor Graph Integrated Navigation Framework
### Factor Graph Information Fusion Theory
Therefore, the above equation can be rewritten as [28]:  
\[X^{map}=\underset{X}{\text{argmax}}\underset{i}{\prod}f_{i}(X_{i}) \tag{3}\]  
In the factor graph model, each factor node will correspond to an error function that characterizes the magnitude of the error between the predicted and actual measurements of the navigation system state quantities. The sensor factor \(f_{i}(X_{i})\) can be represented as:  
\[f_{i}(X_{i})=\text{exp}(-\frac{1}{2}\|(h_{i}(X_{i})-z_{i})\|_{\sum i}^{2}) \tag{4}\]  
\[r(z_{i},X_{i})=h_{i}(X_{i})-z_{i} \tag{5}\]  
where \(h(X)\) is the measurement function of the sensor, \(\sum_{i}\) is the noise covariance matrix of the corresponding sensor, and \(r(z_{i},X_{i})\) is the corresponding sensor measurement residual. The maximum a posteriori estimate \(X^{map}\) needs to be obtained by minimizing \(\underset{i}{\sum}\|r(x_{i},z_{i})\|_{\sum}^{2}\), which turns the maximum a posteriori estimation problem of the system state quantities in the navigation system into a nonlinear least squares problem:  
\[ \begin{split}& X^{map}=\underset{X}{\text{argmin}}\bigg{\{}- \log\prod_{i}f_{i}(X_{i})\bigg{\}}\\ &=\underset{X}{\text{argmin}}\bigg{\{}\underset{i}{\sum}\|r(x_{ i},z_{i})\|_{\sum}^{2}\bigg{\}}\end{split}  \tag{6}\]  
Combining the sensor factors and the a priori information, the maximum a posteriori probabilistic solution of the navigation system state variables is derived, so as to realize the fusion of sensor information. For the GNSS/INS integrated navigation factor graph model proposed in this paper, the optimal estimate \(X^{map}\) of the navigation system state can be expressed as:  
\[X^{map}=\underset{X}{\text{argmin}}\left\{ \begin{array}{l}\sum\limits_{k}^{n} \left\|r_{imu}(z_{b(k)}^{b(k-1)},X)\right\|_{\sum\
olimits_{k=1,k}^{im}}^{2} \\ +\sum\limits_{i}^{n}\left\|r_{gnss}(z_{i}^{gnss},X)\right\|_{\sum\
olimits_{i}^ {gnss}}^{2}\\ +\left\|r_{p}-H_{p}X\right\|_{\sum\
olimits_{p}}^{2}\end{array} \right\} \tag{7}\]  
Using unconstrained nonlinear least squares optimization theory to solve Equation (7) allows for obtaining the optimal values of the navigation state variables at each moment. In the equation, \(r_{imu},\sum\
olimits_{i}^{mu}\) represents the measurement residuals and noise covariance matrix of the IMU pre-integration factor, \(r_{gnss},\sum\
olimits_{i}^{gnss}\) represents the measurement residuals and covariance matrix of the GNSS factor, which will be detailed later in the text. \(\left\{r_{p},H_{p}\right\}\) stands for prior information, and \(\sum_{p}\) is the noise covariance matrix of the prior information. Prior factors are used in navigation problems to introduce prior knowledge or measurement information about the state variables, helping the navigation system to more accurately estimate the states.
# Determination of Cycle to Cycle Battery Cell Degradation with High-Precision Measurements
## 1 Introduction
In order to meet these requirements and therefore to keep the influence of the measuring system as small as possible, two things are necessary: On the one hand, a nearly drift-free measuring system with superior precision and a temperature stabilization for the cell under test are required. On the other hand, it is necessary to know exactly the measurement uncertainties that nevertheless occur. During the last several years, a self-built measuring system which fulfills these requirements and whose measurement uncertainties have been investigated in detail was developed of our research group, and its performance regarding the achievable measurement uncertainty is demonstrated in Section 2.1 [27]. Compared to other state-of-the-art solutions, as shown in [22], where precision benttop multimeters and precision current sources were used, the hardware we use is a single device which has a current measurement range that is much larger, is cheaper to manufacture and more compact. The aim of this work is to quickly quantify the cyclic and calendrical aging of the cell as well as the leakage charge loss due to leakage currents from cycle to cycle for laboratory purposes. Therefore, commercially available state-of-the-art cells are measured with high precision in order to obtain almost noise-free high quality measurement data. In this way, even the smallest changes in capacity degradation can be detected, from which the sought calendrical aging, cycle aging, and leakage charge can be calculated. Such a fast quantification of these aging effects between cycles would make it possible to quickly assess whether cells are suitable for certain applications, to compare cells with each other, or to make initial estimates about the cell lifetime in different applications. The measurement setup and the corresponding investigations of the hardware performance are shown in Section 2. The explanation of the measurement procedure and the measurement results are shown in detail in Section 3.
# RFID Technology for Management and Tracking: e-Health Applications
Two technologies can be distinguished: on the one hand, high-frequency (HF) radio frequency identification (RFID) [12] and near-field communication (NFC) [12] systems, in which wireless information transfer between the tags and the reader is based on electromagnetic field coupling [13; 14]; and on the other hand, ultra high-frequency (UHF) RFID systems, in which electromagnetic backscattering is used to send information from the RFID tags to the reader [13; 14]. Table 1 summarizes the advantages and disadvantages of NFC/HF RFID and UHF RFID technologies, also compared to optical systems. Several examples of RFID systems already deployed in hospitals and medical services can be found in the literature [15; 16], some of them highlighted in Table 2. For example, [15] presents a medication administration system which automatically verifies medication and generates the corresponding prescription, aiming to avoid errors related to paper-based procedures. After deploying the application, it was reported that the number of medical mistakes was significantly reduced. The introduction of a RFID system is described by [17] for a better control of medical assets, providing faster response time (as the doctors and nurses spend less time searching for medical devices). Another example of fully operational RFID system for tracking and managing drugs and patients is presented in [18]. In this case, a dual NFC/UHF RFID system is proposed to ensure the detectability and also to make traceability easier. In addition to this, the level of automation with respect to handheld NFC readers is improved. A hybrid infrared/RFID real-time location system (RTLS) is described in [20], providing detection ranges up to 2 m even in non line-of-sight (NLOS) conditions. This system was initially conceived for monitoring and tracking medical items (e.g., to detect those requiring maintenance), and later extended to track nurses when they respond to emergency calls or visit patients. \begin{table}
\begin{tabular}{c c c} \hline \hline
**RFID System/Hospital** & **RFID Technology** & **Connectivity** & **Application** \\ \hline Bon Secours hospitals. & HF RFID (13.56 MHz) & Wireless local area network (WLAN)/hospital network & Location and tracking of medical assets and patients. \\ \hline Multi-purpose hub application. A Coruña & Hybrid system: NFC (13.56 MHz), & WLAN & Management and tracking of patients and medication. \\ hospital, Spain [18]. & UHF RFID (868 MHz). & & Management of resources at a hospital during a mass casualty situation. \\ \hline \multicolumn{3}{c}{RFID-based real-time} \\ location system (RTLS) & Hybrid infrared/RFID system & WLAN/hospital network & Location of medical items requiring maintenance. \\ \multicolumn{3}{c}{} \\ \([20]\). Benjamin Rusell Hospital. Alabama, USA. & system & Nurse-call system \\ \hline Liverpool Hospital. & HF RFID (13.56 MHz) & WLAN/hospital network & Blood supplies tracking and management. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Review of RFID-based systems for management and tracking in hospitals.
# Optimisation of Shrinkage and Strength on Thick Plate Part Using Recycled LDPE Materials
Compared to other popularly used polyolefins materials in past research, studies conducted using LDPE are rather few in number. Since few works have examined this material, it would be of huge advantage to study its performance when it is recycled. In this study, rejected thick plate parts, including the sprue, runner and gating of LDPE materials, were recycled. The recycled materials were divided into virgin:recycled ratios of 70:30 (R30), 60:40 (R40) and 50:50 (R50). Optimisation methods (RSM and PSO) were applied to optimise the shrinkage and strength of the thick plate part of virgin (R0), R30, R40 and R50 materials in simulation studies [31] using Autodesk Moldflow Insight (AMI) 2012, and were validated with experimental works. This study provides guidance to moulding industries on the effect of simulations and experimental works with integrated optimisation methods and the effect of the recycle ratio on shrinkage and on the strength on the moulded parts. The findings benefit mould industries seeking to use recycled materials while reducing the manufacturing cost without compromising on quality. The long-term implication of this study is its contribution to the global effort to sustain the environment.
## 3 Results and Discussion
According to the statistical results, the different surfaces of each cover can be considered as one sample to link the degradation behavior of the cover transmittance with the meteorological parameters. Therefore, the data in Figure 7 were averaged to obtain the time course for the average transmittance of the four covers tested (\(T_{\text{av}}\)%) and plotted in Figure 10 as a function of the monthly averages of the ambient air temperature and relative humidity. Three-dimensional analysis was applied to the results in Figure 10 using the Table Curve-3D software package (ver-4.3, SYSTAT). The best fitting of the data resulted in a full cubic regression polynomial with, unfortunately, a low \(\text{r}^{2}\) value of 0.58 and a high standard error of 10.8%. This was because degradation of plastic film properties depends mainly on the cumulative effects of climatic factors such as temperature, relative humidity, solar radiation flux, etc. This correlation cannot be used to predict the cover transmittance precisely during its service life in an arid climate. Because of the major influence of solar radiation on the degradation of the optical properties of plastic film covers, the time courses of \(T\) (%) estimated for the four covers tested (data in Figure 7) were plotted in Figure 11 against the accumulative solar radiation (\(CSR\)) measured during the exposure time. By fitting these data, a correlation could be obtained with a coefficient of determination (\(\text{r}^{2}\)) of 0.99 and a standard error of 1%. This correlation is given by:  
Figure 2:0  
Equation (5) can be used to predict the transmittance of a 200 \(\upmu\)m thick LDPE-film-covered greenhouse having any shape, oriented in the N-S or E-W direction, during one-year exposure to an arid climate similar to that in the central region of Saudi Arabia. Films with this thickness (200 \(\upmu\)m thick) are commonly used worldwide to cover green \begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Degradation\%**} & \multicolumn{2}{c}{**Orientation**} \\ \cline{2-3}  & **N–S** & **E–W** \\ \hline \multirow{2}{*}{Shape} & Tunnel & 0.079  & 0.087  \\  & Duo-pitched roof & 0.088  & 0.086  \\ \hline \hline \end{tabular}  
Numbers with the same letter indicate no significant differences (\(p\) = 0.95).  
\end{table} 
Table 1: Average of the degradation rates (%) per day for both orientation and shape. Figure 10: Three-dimensional plot for the average transmittance of the four covers (\(T_{\text{av}}\)%) as a function of the monthly averages of the ambient air temperature and relative humidity during the exposure time. houses [5; 6; 7; 8; 14; 15; 16; 17; 18; 19] because their mechanical and physical properties are the most suitable for greenhouse applications.
## 3 Results and Discussion
It should be noted that for both cases, a five-fold increase in outlet surface area resulted in only a three-fold increase in the volume flow rate. It can also be noted that especially for high flowrates (a high outlet area), most of the pressure drop occurs on the way through the staircase. on the relation between a fraction of the pressure drop occurring in the controlled outlet area restrictor and the pressure drop along the whole flowpath is shown in Figure 5. For this parameter, both cases had almost identical behavior despite having different characteristics, as shown on Figure 4. This observation suggests that the relation of pressure drops along the flowpath is independent of the ventilator type and position, and is rather a function of the geometric configuration of the building (this is in accordance with the incompressible internal flow theory) [27]. Another observation is the fact that for a given configuration, when the outlet area is above 1 m\({}^{2}\), the flow resistance contribution of the outlet restriction is below 50%. The last curve shown in Figure 5 is the \(\varphi\), given by Equation (1):  
\[\varphi=\frac{\left(\frac{1}{A_{r}}\right)^{2}}{\left(\frac{1}{A_{r}}\right)^ {2}+\sum\left(\frac{1}{C_{d}\cdot C_{c}}\right)^{2}} \tag{1}\]  
where  
- \(A_{r}\)--area of outlet restrictor,  
- \(A_{c}\)--areas of consecutive restrictions along the flowpath--here taken as three consecutive openings between staircase storeys with 2.4 m\({}^{2}\) each,  
- \(C_{d}\)--relative discharge coefficient, a dimensionless parameter describing how much less effective is the restriction area \(A_{c}\) compared to the outlet restrictor because of secondary losses in the building. The difference between \(\varphi\) values from the experiment and the one from Equation (1) indicates the contribution of secondary pressure losses due to wall friction, stair interaction, and flow direction changes along the flowpath. The proximity of the experimental results to Equation (1) suggests, however, that the flowpath area constrictions were the main sources of the building flow resistance. The practical conclusion to be taken from these observations is that after the outlet area becomes close to the area of flow restrictions along the flowpath inside the building, it is more important to decrease flowpath resistance by  
Figure 4: Flow characteristics (as a function of air flow from average pressure values) of 2 types of positive pressure ventilators for 5 different surface opening positions (P1–P5). limiting pressure drop due to area constrictions in the building or to increase the pressure induced by ventilators, than it is to further increase the outlet surface area.
## 2 Background and Related Technologies
### AloT  
With the rapid proliferation of IoT technologies, edge computing-based approaches have gained significant attention for their ability to reduce data transmission volumes and enhance system efficiency [20]. Traditional centralized data processing methods often suffer from high network loads and latency due to the transmission of large datasets [21]. To mitigate these issues, various techniques have been adopted for data compression and distributed processing, including advanced AI models, federated learning, and classical compression algorithms such as Run-Length Encoding (RLE) and Huffman coding [22; 23].  
For example, federated learning enables local model training on IoT devices by transmitting only essential model updates rather than raw data, thereby significantly reducing communication requirements [24; 25]. Similarly, data compression techniques like RLE and Huffman coding eliminate redundancy, effectively reducing the size of data before transmission.  
In this study, we referenced research conducted by [PERSON] et al. [26] as a strategy to enhance data transmission efficiency and minimize power consumption in an AloT scenario. In that study, RLE and the k-means clustering algorithm (with \(k=5\)) were applied to agricultural remote sensing data in an edge-computing machine learning environment, reducing the total data volume by approximately \(2.4\pm 0.7\%\) relative to the original data.  
Based on the unique constraints of a satellite-based IoT-NTN environment, our study assumes a more aggressive data reduction scenario, targeting a reduction rate of 15%. Accordingly, the initial daily data transmission volume is assumed to be 11.52 MB, and after applying a 15% reduction, the optimized daily data transmission target is calculated to be approximately 1.73 MB. This significant reduction in transmitted data not only decreases transmission delays and conserves energy but is also expected to make the use of LPWAN technology more advantageous in such environments.  
Edge computing further contributes to system efficiency by enabling localized data processing directly on IoT devices, reducing the need for continuous communication with central servers. This approach minimizes network latency, facilitates faster feedback, and increases data handling capacity--particularly in resource-constrained environments such as embedded systems.
# Prediction of Traffic Volume Based on Deep Learning Model for AADT Correction
## 4 Statistical Learning Verification and Machine Learning Model Analysis and Evaluation  
### Statistical Learning Verification Design  
Previous studies have commonly employed a preprocessing process to correct data anomalies before training the deep learning models. This approach, although necessary in the absence of actual values, tends to complicate the analysis process and significantly reduces the processing speed. Moreover, the use of simple interpolation methods during the correction phase can degrade the data quality. In this study, where actual values are available, the predictive power was verified without prior data correction, thereby simplifying the analysis process and enhancing efficiency. This study evaluated the model's performance using certain metrics, such as the mean absolute percentage error (MAPE), the MAE, the root mean squared error (RMSE), and the coefficient of determination (R\({}^{2}\)), and formulated additional data-missing scenarios for analysis. Figure 3 depicts the design procedure for the time series prediction performance verification.  
* Scenario 1: Assumes 10% of the data is missing.
* Scenario 2: Assumes 50% of the data is missing.  
Bayesian optimization was conducted using the GPyOpt 1.2.6 library, thus necessitating the determination of candidate values for each hyperparameter. Values for certain parameters, such as the learning rate and the dropout rate, were established based on the existing literature. Additionally, this study explored three distinct and arbitrarily selected network architecture variations.  
For the coding and implementation of experiments, the Python 3.12.4 programming language and Keras 2.3 were employed. Keras is an open-source project that offers a high-level API that supports the implementation of deep neural networks along with libraries, such as TensorFlow 2.10 and Theano 1.0.0. In this study, Keras was utilized in addition to TensorFlow, thus providing advantages in rapid prototyping and experimental flexibility through its simple API. This API supports modular neural network construction via the  
Figure 3: Design procedure for verifying time series prediction performance.  
integration of various layers, activation functions, loss functions, and optimizers, and it includes provisions for most conventional deep learning components. Despite its broad utility, Keras may present limitations for constructing custom or novel solutions, for which TensorFlow may be a more appropriate choice. Notably, Keras incorporates an LSTM implementation featuring a forget gate, as described by [PERSON] et al. (2000) ([PERSON] et al., 2000) ([PERSON] et al., 2000).
## 3 Results
### Comparison of Cellular Responses between COST-Jet and nspDBD Treatment of Jurkat T Lymphocytes
#### 3.1.2 Mitochondrial Superoxide Production Increases in Response to Plasma Treatment  
Additional experiments were conducted to determine if other biological endpoints were also comparable at these operating conditions. Another cellular function that is known to be affected by plasma is the triggering of intracellular oxidative stress pathways. O\({}_{2}\)\({}^{-}\) production by the mitochondria is an indicator of cellular oxidative stress [36; 37]. To determine if there were differences in the oxidative stress induced by each device, we compared MitoSOX fluorescence in Jurkat cells treated with both plasma sources. As shown in Figure 3, cells treated with the COST-Jet and nspDBD at the same operating conditions as above had similar magnitudes of MitoSOX fluorescence. Cells responded to plasma from both sources by increasing their mitochondrial O\({}_{2}\)\({}^{-}\) production in a dose-dependent fashion, indicating that stress-responsive pathways were being upregulated in each case.  
Figure 2: Viability comparison of Jurkat T lymphocytes treated with nspDBD or COST-Jet (He + 0.6% O\({}_{2}\)). Cells were treated from 30–75 Hz with the nspDBD and 1–5 min with the COST-Jet. Percent viability was calculated relative to cells not exposed to plasma or feed gas. Cell loss was comparable at 2 and 4 min of COST-Jet treatment with 45 and 75 Hz nspDBD treatment, respectively. Viability experiments were repeated once in triplicate. The data plotted represent the mean and standard deviation.
# Bloch-Surface-Polariton-Based Hybrid Nanowire Structure for Subwavelength, Low-Loss Waveguiding
## 2 Geometry of the Proposed Hybrid Waveguide
The key structural parameters of the hybrid waveguide were chosen as \(t_{\rm r}=150\) nm, \(r=100\) nm, respectively. The extreme case corresponding to \(t_{\rm b}=0\) nm is shown in Figure 3a, where the metal nanowire is put directly on the high-index dielectric ridge. It can be noticed that the electric field is tightly confined at the bottom of the metal nanowire, resulting in an ultra-small mode area. Introducing a low-index buffer layer between the metal nanowire and the dielectric ridge could lead to significant field enhancement and tight optical confinement in the thin buffer layer, as can be seen in Figure 3b,c. Although the squeeze of the field inside the thin buffer layer becomes less significant for the larger thickness, the modal attenuation declines correspondingly, bringing the increment of propagation distance. The simple investigation of the modal properties for the HBSPP waveguide demonstrates that by tuning the gap size, the proposed structure offers possibilities for achieving a large propagation distance in conjunction with tight field localization simultaneously. Figure 2: The comparison between the Bloch surface polariton (BSP) mode, the surface plasmon polariton (SPP) mode, and the hybrid mode for p-polarization. Two-dimensional (2D) electric field distributions (**a**,**c**,**e**) and the corresponding one-dimensional (1D) E plot (**b**,**d**,**f**) for the BSP mode, the SPP mode, and the hybrid mode. The structural parameters of the waveguides are chosen as: \(t_{\rm r}=150\) nm, \(t_{\rm b}=10\) nm, \(r=100\) nm.