esa-sceva/satcom-chunk-collection huggingface dataset:
# On the Security of Offloading Post-Processing
## 2 Quantum Key Distribution
### QKD Post-Processing  
QKD comprises two phases to arrive at a key agreement with strong correctness and secrecy guarantees. First, qubits are randomly generated on one side, transmitted over the optical quantum channel, and measured on the other side to generate the so-called raw key.  
In the second phase, a non-quantum (classical) post-processing protocol is executed to agree on identical keys (correctness) on both ends of the transmission line and to render useless any information a potential attacker could have learned by attacking the transmission phase (secrecy).  
In detail, the steps to extracting a secure key from the raw data of the transmitted quantum bits are as follows:  
1. **Sifting** removes non-relevant information from the raw key (e.g., in conjugate coding protocols, events prepared and measured in different bases are deleted). Additionally, events not received by [PERSON] are discarded in discrete-variable protocols (cf. Section 3).
2. **Error estimation** determines an upper bound on the information leaked to an adversary on the quantum channel and can provide information to optimize the subsequent information reconciliation. Although more advanced methods have been proposed in the literature, this is typically accomplished by cut-and-choose methods. Additionally, the idea of using a confirmation phase to replace error estimation was proposed by [PERSON] [13].
3. **Information reconciliation**, which often uses methods from forward error correction, aims at correcting all errors in the remaining raw key so that the sender and receiver should obtain identical keys. The classical (non-quantum) messages exchanged in this process must not leak information on the final key. Typically, the leakage is tracked and treated during the privacy amplification step.
4. **Confirmation** detects non-identical keys (for which information reconciliation has failed) with a probability close to one. If non-identical keys are detected, then the parties either go back to the information reconciliation step or abort the QKD protocol.
5. Finally, **privacy amplification** eliminates the information leaked during all protocol steps (quantum and classical) from the final key by running a (strong) randomness extraction protocol between the peers.  
All processing steps together enable [PERSON] and [PERSON] to agree on a final key which is \(\epsilon\)-close to an ideal key. Various optimizations of the above key agreement process have been proposed in the past, either for efficiency reasons or implementation aspects, but this generic structure is typically followed in one way or another.
## 3 Computer Simulation of PV Cell and Panel
### Review of PV Simulation Models  
#### 3.2.1 Parameters for the Models  
The single-diode model is commonly used for simulating silicon-based PV cells [8; 19; 20; 21]. In the available literature, depending on the information of the PV cells, different types of models can be found. Basically, we can obtain the required parameters, such as the open-circuit voltage (\(V_{cc}\)) and the short-circuit current (\(I_{sc}\)), from the datasheet of the photovoltaic manufacturer. Additionally, we can assume certain parameters, such as the ideality factor (A), with typical values to establish simulations. In order to improve the accuracy, different parameter identification techniques can be used to estimate more suitable values for parameters [20].
# COVID-19 CXR Classification: Applying Domain Extension Transfer Learning and Deep Learning
###### Abstract  
The infectious coronavirus disease-19 (COVID-19) is a viral disease that affects the lungs, which caused great havoc when the epidemic rapidly spread around the world. Polymerase chain reaction (PCR) tests are conducted to screen for COVID-19 and respond to quarantine measures. However, PCR tests take a considerable amount of time to confirm the test results. Therefore, to supplement the accuracy and quickness of a COVID-19 diagnosis, we proposed an effective deep learning methodology as a quarantine response through COVID-19 chest X-ray image classification based on domain extension transfer learning. As part of the data preprocessing, contrast limited adaptive histogram equalization was applied to chest X-ray images using Medical Information Mart for Intensive Care (MIMIC)-IV obtained from the Beth Israel Deaconess Medical Center. The classification of the COVID-19 X-ray images was conducted using a pretrained ResNet-50. We also visualized and interpreted the classification performance of the model through explainable artificial intelligence and performed statistical tests to validate the reliability of the model. The proposed method correctly classified images with 96.7% accuracy, an improvement of about 9.9% over the reference model. This study is expected to help medical staff make an integrated decision in selecting the first confirmed case and contribute to suppressing the spread of the virus in the community.  
COVID-19; chest X-ray; image classification; domain extension transfer learning; explainable artificial intelligence +
Footnote †: journal: Medical Informatics
# Toward Long-Term Implantable Glucose Biosensors for Clinical Use
## 2 Materials and Methods
### Calibration  
#### 2.3.1 Calibration to Blood Glucose  
Subcutaneous glucose sensors needs calibration of sensor signal-to-glucose concentration (in vitro) and glucose concentration-to-blood glucose (in vivo). Electrical current of amperometric sensors or fluorescence intensity of fluorescent sensors should be converted into information of glucose concentration. Assuming a linear relationship between sensor reading and blood glucose, blood glucose can be calculated from sensor readings. Thus, sensor sensitivity means the calibration factor. After in vitro calibration, in vivo calibration should be followed. Since subcutaneous glucose sensing is indirect sensing for blood glucose, we should have calibration factor to convert glucose concentration in subcutaneous interstitial fluid to blood glucose concentration. Until recent development of factory-calibrated CGM sensors, users conduct in vivo calibration by themselves using finger-picking home blood glucose devices. The in vivo calibration is usually conducted two times a day, reducing errors from sensor and individual variation. Although finger-picking calibration can increase accuracy [28], the process is painful, time-consuming, and sometimes leads to false readings. Therefore, there have been strong motivations to remove the user calibration process.
## 4 Conclusions  
This study developed a sustainable, high-energy-absorbing 2D re-entrant structure by using a polymer composite based on recycled iron powders and biodegradable PLA. Our key findings include the following:  
Figure 11: SEA vs. iron content graph.  
Figure 10: Compressive test of samples, (**a**) pure PLA, (**b**) PLA/5% Fe, (**c**) PLA/10% Fe, (**d**) PLA/15% Fe.  
1. Improved Mechanical Properties: Adding iron powders at 5-10% loading significantly increased both the compressive strength and specific energy absorption (SEA), making the composite far more effective at energy absorption compared to neat PLA.
2. Thermal Stability and Enhanced Crystallinity: While maintaining stable melting and glass transition temperatures, the composite's crystallinity improved due to iron, though onset degradation occurred at lower temperatures due to iron's catalytic effects.
3. Optimal Composition: The 10% iron-filled PLA structures achieved the best balance of mechanical, thermal, and rheological properties, with an SEA of 2.051 kJ/kg, far surpassing the 0.160 kJ/kg SEA of neat PLA.  
The results of this study suggest that the novel recycled iron/PLA composites developed here, with their enhanced compression and energy absorption properties, hold promise for applications in the automotive, biomedical, and construction industries, where sustainability and cost efficiency are essential. Future research could focus on further optimizing the mechanical properties of these composites, thoroughly investigating the distribution of iron particles within the polymer and their impact on compressive strength and energy absorption, while also exploring new material formulations by incorporating multiple fillers alongside recycled iron and biodegradable polymers. These efforts aim to develop sustainable 3D-printable composites using recycled and eco-friendly resources with enhanced properties for broader end-use applications.  
Investigation, S.A.A.B.T. and N.K.; Writing--original draft, S.A.A.B.T. and N.K.; Writing--review & editing, H.F.; Supervision, H.F. All authors have read and agreed to the published version of the manuscript.  
The financial support of NSERC Discovery and Ontario Tech Start-up grants is greatly appreciated.  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to confidentiality nature of material formulations.  
The authors would also like to thank [PERSON] for his contribution on the shared lab facilities.  
The authors declare no conflict of interest.
# A Real-Time Novelty Recognition Framework Based on Machine Learning for Fault Detection
###### Abstract  
New technologies are developed inside today's companies with the ascent of Industry 4.0 paradigm; Artificial Intelligence applied to Predictive Maintenance is one of these, helping factories automate their systems in detecting anomalies. The deviation of statistical features from standard operating conditions computed on collected data is a common investigation technique that companies use. The information loss due to transformation from raw data to extracted features is a problem of this approach. Furthermore, a common Predictive Maintenance framework requires historical data about failures that often do not exist, neglecting the possibility of applying it. This paper uses Artificial Intelligence as Machine Learning models to recognize when something changes in the data's behavior collected up to that moment, also helping companies to gather a preliminary dataset for future Predictive Maintenance implementation. The aim concerns a framework in which several sensors are used to collect data by adopting a sensor fusion approach. The architecture is composed of an optimized software system able to enhance the computation scalability and the response time regarding novelty detection. This article analyzes the proposed architecture, then explains a proof-of-concept development using a digital model; finally, two real cases are studied to show how the framework behaves in a real environment. The analysis done in this paper has an application-oriented approach; hence a company can directly use the framework in its systems.  
predictive maintenance; prognostic; novelty detection; machine learning; fault detection; anomaly detection; condition monitoring; industrial real case study
## 3 Error Measures  
It is very important to find the model with the best prediction effect among many models, and the indexes to evaluate the advantages and disadvantages of prediction models usually include: relative error (RE), root mean square error (RMSE), average absolute error (MAPE) and average absolute error (AAE). The smaller the error value, the better the accuracy of prediction, and the more effective the method. The calculation formula of these four indicators are as follows:  
\[RE=\frac{y_{t}-y_{t}^{*}}{y_{t}}\times 100\% \tag{19}\]  
\[RMSE=\sqrt{\frac{1}{N}\sum_{t=1}^{N}{(\frac{y_{t}-y_{t}^{*}}{y_{t}})}^{2}} \tag{20}\]  
\[MAPE=\frac{1}{N}\sum_{t=1}^{N}{\left|\frac{y_{t}-y_{t}^{*}}{y_{t}}\right|} \times 100\% \tag{21}\]  
\[AAE=\frac{1}{N}(\sum_{i=1}^{N}{\left|y_{t}-y_{t}^{*}\right|})/(\frac{1}{N} \sum_{i=1}^{N}{y_{t}}) \tag{22}\]  
In the formula, \(y_{t}\) represents the actual charge at time \(t\), \(y_{t}^{*}\) denotes the predicted charge at time \(t\), and \(N\) represents the data group.
## 4 Experiments  
The experiments were divided into three phases. Phase one was data acquisition. In this phase three different patterns were planned and executed. The patterns were planned to represent typical hydrographic surveys based on the present unit (Pattern 1) (Figure 6) and the bottom object investigation plan (Patterns 2 and 3) (Figure 6). All the profiles were numbered in accordance with their execution order. Profiles 1 to 10 belong to pattern no. 1, profiles from 11 to 14 belong to pattern no. 2, and profiles from 15 to 19 belong to pattern no. 3. All data were recorded within their typical hardware configuration, meaning that no additional technical rearrangements of the unit were conducted. The unit in this configuration was prepared to undertake surveys based on best knowledge and practice including use of state-of-the-art hydrographic equipment, therefore, all equipment was calibrated, and all GNSS and INS equipment offsets were measured, and data entered into all hardware units.  
Phase two of the experiment concerned logged data filtration and preparation and evaluation of navigation GPS accuracy in the dynamic measurements. To prepare for this evaluation, studies described elsewhere [1] were used.  
The approach used the PL-2000 system (ETRS89/Poland CS2000 zone 6) which afforded the replacement of angular coordinates recorded by the GPS and RTK by Cartesian coordinates (in meters). This conversion (from angular GPS coordinates to Cartesian) allowed the calculations to be simplified and the results to be presented in meters. The PL-2000 coordinate system is Cartesian 2D coordinate system with northing (x) and easting (y) axes with orientations fixed to north, east, and units of measurement in meters.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter Name** & **Specification** \\ \hline Channels & 220 \\ \hline \multirow{5}{*}{Signal tracking} & GPS: L1 C/A, L2E, L2C, L5 \\  & GLONASS: L1 C/A, L2 C/A, L2 P, L3 CDMA \\  & Galileo: 1 BOC, E5A, E5B, E5A!BOC \\  & Beidou B1, B2 \\  & SBAS, QZSS \\  & L-Band OmniSTAR VBS, HP, XP \\ \hline \multirow{2}{*}{Horizontal position accuracy (1 sigma)} & SBAS/DGPS: 0.5 m/0.25 m \\  & PPP: 10 cm \\  & RTK: 0.8 cm + 1 ppm \\ \hline Velocity accuracy & 0.7 cm/s RMS \\ \hline \multirow{2}{*}{True heading accuracy} & 0.09 \({}^{\circ}\) at 2 m baseline \\  & 0.05 \({}^{\circ}\) at 1 0m baseline \\ \hline \multirow{3}{*}{Operating limits} & Altitude: 18,000 m \\  & Velocity: 515 m/s \\  & Acceleration: 11 g \\ \hline \multirow{2}{*}{Time to first fix} & Cold start: \textless{}45 s \\  & Warm start: \textless{}30 s \\ \hline Signal reacquisition & L1/L2/L5: \textless{}2.0 s \\ \hline Max output frequency & 50 Hz \\ \hline \hline \end{tabular}
\end{table}
Table 3: GPS RTK specification.
## 3 Results
### Simulating Experimental Data Using MC
#### 3.3.1 Modeling Phantoms with Inactive Flow  
The correlation transport coefficients in each layer of the simulated phantoms when the pump was inactive (i.e., for flow \(F_{0}\) phantoms), were treated as identical across layers and were each set to the same Brownian flow coefficient \(D_{B}\). Since the fluid in the channel was identical to the liquid media above it, the three-layer model in this case was equivalent to a semi-infinite medium and used \(h=0\). The input value of \(D_{B}\) to the MC phantom tissue model was derived by correcting the Brownian flow coefficient obtained from theoretical fitting--i.e., the retrieved \(D_{B}\) from experimental fits was first multiplied by 0.83 (the correction factor) and then used as input to the MC model. This correction factor was obtained from previously run semi-infinite MC simulations (described in Section 3.1.5 and Figure 7), at the optical transport coefficients of the liquid phantom (\(\mu_{a}=0.075\)/cm and \(\mu_{s}^{\prime}=12\)/cm), at the experimentally employed SDS (0.15 cm).  
Figure 10: Derived flow coefficients obtained from fitting phantom data using Equation (2). (**a,b**) show the retrieved Brownian diffusion \(D_{B}\) and flow speed \(V\), for each phantom. Each line shows fixed channel depth (shown in legend), symbols show mean values and error bars are standard deviations (across the five DCS scans obtained for each phantom). (**c,d**) show relative changes flow vs. relative change in input flow rates, normalized relative to flow phantoms \(F_{1}\). Dashed line in (**c,d**) show \(y=x\).  
Since variations were observed in \(D_{B}\) coefficients across phantoms \(D_{0}F_{0}-D_{3}F_{0}\), (as seen in Figure 10a), the input Brownian coefficients for MC phantom models simulating different channel depths were obtained by correcting \(D_{B}\) from experimentally fitted data from each phantom at \(F_{0}\), at each channel depth. Thus, the \(D_{B}\) coefficient obtained from the four phantoms \(D_{0}F_{0}-D_{3}F_{0}\) were each multiplied by \(0.83\) to create input values to MC simulations (since phantoms had same optical properties and were measured at fixed SDS, they had the same correction factor). Figure 11a-d show representative \(g_{2}(\tau)\) data measured in phantoms (symbols), the corresponding theoretically Brownian flow model fitted using Equation (2) (dashed line), and predicted using MC simulations (solid line), for phantoms \(D_{0}F_{0}-D_{3}F_{0}\), respectively. It is worth noting that the theoretical lines were obtained by fitting the measured data, while the MC data were directly computed (for given inputs). Table 2 lists the values of the input coefficients used in these simulations.
## 2 Fractional-in-Space Parabolic Equation
### Adaptive Forward-Backward Euler Discretization in Time
When choosing a predictive time step, we always aim to increase the previous one by multiplying it by a predetermined factor \(\gamma>1\) as follows:  
\[\tilde{\tau}_{i+1}=\gamma\tau_{i}.\]  
The prognostic solution \(\tilde{\mathbf{u}}^{i+1}\) is then calculated by the forward Euler scheme  
\[M^{L}\frac{\tilde{\mathbf{u}}^{i+1}-\mathbf{u}^{i}}{\tilde{\tau}_{i}+1}+K \mathbf{u}^{i}=\mathbf{f}^{i}. \tag{8}\]  
Here, the approximate truncation error at the prognostic time step is computed in the form  
\[\Psi_{i+1}=\tilde{\mathbf{f}}^{i+1}-M^{L}\frac{\tilde{\mathbf{u}}^{i+1}- \mathbf{u}^{i}}{\tilde{\tau}_{i+1}}-K\mathbf{u}^{i} \tag{9}\]  
where \(\tilde{\mathbf{f}}^{i+1}\) is the right-hand side at the prognostic time \(\tilde{t}_{i+1}=t_{i}+\gamma\tau_{i}\). That is how we obtain  
\[\overline{\tau}_{i+1}=\frac{\delta}{||\tilde{\Psi}_{i+1}||}\gamma\tau_{i}.\]  
Finally, we apply the constraints that the time step \(\tau_{i+1}\) cannot be less than \(\tau_{0}\) and cannot exceed the estimated time step, and so we obtain  
\[\tau_{i+1}= \begin{cases}\tau_{0},\text{ if }\overline{\tau}_{i+1}<\tau_{0},\\ \tilde{\tau}_{i+1},\text{ if }\overline{\tau}_{i+1}>\tilde{\tau}_{i+1},\\ \overline{\tau}_{i+1},\text{ otherwise.}\end{cases} \]  
We also introduce the threshold parameter \(\theta\) and impose the following step change constraint:  
\[\text{ if }\min\left\{\left|\frac{\tau_{i+1}-\tau_{i}}{\tau_{i}}\right|, \left|\frac{\tau_{i}-\tau_{i+1}}{\tau_{i+1}}\right|\right\}<\theta\text{ then }\tau_{i+1}:=\tau_{i}.\]  
This rule allows us to avoid too small relative changes in the time steps. One time step of the forward Euler scheme involves matrix-vector multiplication with computational complexity of \(O(N^{2})\), while the backward Euler scheme requires additionally solving a dense system of linear algebraic equations that has computational complexity of \(O(N^{3})\) in the general case when using LU factorization (or any other Gaussian elimination-type method). Thus, the total computational complexity of the discussed adaptive time-stepping method, if a LU factorization solver is used in the implementation, is \(O(mN^{3})\). The rest of the paper is aimed at improving the performance of the method by using HSS compression instead of LU factorization. In conclusion, we note that, in general, the number of time steps \(m\) will be much smaller with the adaptive stepping scheme than if we use a uniform scheme with \(\tau=\tau_{0}\).
## 2 Challenges for Modeling of Five-Axis Coordinate Measuring Systems
### Determination and Modeling of Other Sources of Errors
#### 2.3.1 The Length of Probe Stylus  
The influence of stylus length for classic CMM measurements is a known subject. However, the impact of stylus length can be different in case of five-axis measuring systems because of changes it introduces to the probing process. The standard ring was measured to assess how different stylus  
Figure 1: Five-axis measuring system and standards used during the experiments.  
length affects the measurements. First, it was mounted in the machine in such a manner that the ring's axis was parallel to the machine \(z\) axis. Three different stylus lengths were utilized, each time the same tip ball was used and the change in length was obtained by adding appropriate extensions. Measurements showed that change in stylus length highly influenced the diameter measurements. Deviation from nominal diameter value, was four-times bigger for longest 42 mm stylus length comparing to 12 mm stylus and reached almost 2 \(\upmu\)m. The influence is not so obvious in case of form measurements where difference between shortest and longest of used styli not exceeded 1 \(\upmu\)m. What is even more important is that the results show that probe errors (in the sense as set forth in [1]) vary slightly with changes in the stylus length. Figure 2 shows the PE values for different styli lengths depending on the changing angle of probe approach \(\alpha\).  
However, such a standard ring arrangement is favorable for the system's accuracy because the head will first rotate around vertical axis, then only the rotations around the horizontal axis are used during measurement which means that all points can be measured practically using the same contact point on the probe tip. The experiment was then repeated for different arrangements of the ring in which each point is measured using different probe deflection and head angles. In this case, less obvious results were obtained. The stylus length influence on measurements of diameter and form error was at similar level, with differences between deviations observed for the shortest and longest styli not exceeding 1 \(\upmu\)m. The observed probe errors showed bigger instability, and their values slightly increased with extending stylus length. This experiment showed that stylus length is a factor that has to be included in the model of a five-axis system, the question is if it can be modeled just by changing stylus length in a kinematic model of probe head or additional efforts, as Probe Error Function (PEF) (defined in [1]) determination for different stylus lengths would be necessary to model that factor influence correctly.