esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methods  
### New Analytic Model for Principal Hugoniot  
Here is a brief summary of the new analytic model, as per Ref. [22]. The new model is based on the following representation of the shock velocity \(U_{s}\) as a function of the particle velocity \(U_{p}\) over the three \(U_{p}\) intervals, which is continuous and smooth from one interval to the next:  
\[\begin{array}{llll}U_{s}&=\,C\,+\,B\,U_{p}\,+\,A\,U_{p}^{2},&0\,\leq\,U_{p} \,\leq\,U_{p}^{*}\,=\,\frac{2\,(c\,-\,C)}{B\,-\,b}\\ U_{s}&=\,c\,+\,b\,U_{p}\,+\,a\,U_{p}^{2},&U_{p}^{*}\,\leq\,U_{p}\,\leq\,U_{p}^ {**}\,=\,n\,U_{p}^{*}\\ U_{s}&=\,-\,\frac{d}{|f|}\,+\,\frac{4}{3}\,U_{p}\,+\,\frac{dU_{p}}{1\,+\,|f|\, U_{p}},&U_{p}\,\geq\,U_{p}^{**}\end{array} \tag{2}\]  
where \(|f|\) stands for the absolute value of \(f\).  
The corresponding expressions for \(P\) along the principal Hugoniot are:  
\[\begin{array}{llll}P&=&\frac{4\rho_{0}\,C^{2}\,\eta\,(\eta-1)}{\{\eta\,-\, \mathrm{B}\,(\eta-1)+\sqrt{[\eta-\,\mathrm{B}\,(\eta-1)]^{2}-4\,A\,C\,(\eta- 1)^{2}}\}^{2}},&0\,\leq\,U_{p}\,\leq\,U_{p}^{*}\\ P&=&\frac{4\rho_{0}\,C^{2}\,\eta\,(\eta-1)}{(\eta\,-\,b\,(\eta-1)+\sqrt{[\eta- \,b\,(\eta-1)]^{2}-4\,aC\,(\eta-1)^{2}}\}^{2},&U_{p}^{*}\,\leq\,U_{p}\,\leq\,U_ {p}^{\mathrm{max}}=\sqrt{\frac{c}{a}}\\ P&=&\frac{4\rho_{0}\,C^{2}\,\eta\,(\eta-1)}{\{\eta\,-\,b\,(\eta-1)-\sqrt{[ \eta-\,b\,(\eta-1)]^{2}-4\,aC\,(\eta-1)^{2}}\}^{2}},&U_{p}^{\mathrm{max}}\,\leq \,U_{p}\,\leq\,U_{p}^{**}\\ P&=&\frac{\rho_{0}}{4|f|^{2}}\,\frac{\eta}{\eta-1}\,\Big{(}\sqrt{12\,d\,\frac {\eta-1}{\eta-4}\,+\,1\,\mp\,1\Big{)}}^{2},&U_{p}\,\geq\,U_{p}^{**}\end{array} \tag{3}\]  
\begin{table}
\begin{tabular}{l c c c c c c c c c c c c c} \hline  & \(Z\) & \(\rho_{0}\) & \(C\) & \(B\) & \(A\times\mathbf{10^{2}}\) & \(c\) & \(b\) & \(a\times\mathbf{10^{5}}\) & \(L\) & \(d\) & \(f\times\mathbf{10^{3}}\) & \(U_{p}^{*}\) & \(U_{p}^{\mathrm{max}}\) & \(U_{p}^{**}\) \\ \hline Pm &which define pressure along the Hugoniot as a continuous and smooth function of compression ratio \(\eta\equiv\rho/\rho_{0}\), where \(\rho\) is density and the subscript \(0\) indicates the initial (unshocked) state. In the last equation of (3), the \"\(-\)\" sign corresponds to \(f>0\) and the \"\(+\)\" sign to \(f<0\). Whereas \(d\) is always positive, it appears that \(f\) is positive for about \(2/3\) of the periodic table and negative for the remaining \(1/3\). In particular,  
In Equations (2) and (3), \(0\leq U_{p}\leq U_{p}^{*}\) corresponds to the low-\(P\) regime, and \(U_{p}^{*}\leq U_{p}\leq U_{p}^{**}\) and \(U_{p}\geq U_{p}^{**}\) to the med-\(P\) and high-\(P\) regimes, respectively. The value of \(U_{p}^{**}\) is found from Equation [22]:  
\[\left[\,c\,+\,\left(b\,-\,\frac{4}{3}\right)\,U_{p}^{**}\,+\,a\,U_{p}^{**2}\, \right]^{2}\,=\,L\,\bigg{(}\,b\,-\,\frac{4}{3}\,+\,2\,a\,U_{p}^{**}\,\bigg{)}, \tag{5}\]  
where [22]:  
\[L\,=\,\frac{875\,Z^{2.4}}{A}\,\approx\,\frac{875\,Z^{2.4}}{2\,Z\,+\,0.006\,Z^{2 }}\,\left(\frac{\text{km}}{\text{s}}\right)^{2}\!\!\!, \tag{6}\]  
where \(A\) is the atomic mass.
# Performance Analysis of a Novel TCP Protocol Algorithm Adapted to Wireless Networks
## 2 Materials and Methods
### TCP Sockets
#### 2.3.3 TCP Header Modifications  
We used one of the TCP header reserved bits for the NACK implementation, as showed in Figure 3. If the NACK bit is \"1\", it means that a \"corrupted\" packet was received, otherwise it corresponds to an ACK. In the same way that ACK header, NACK header has a NACK information field and a NACK number.  
Figure 2: Linux source code architecture.  
The code that defines the TCP header is in the /include/Linux/tcp.h file. Inside the tcphdr structure, we created the nack attribute for the NACK flag creation, and we reduced to three bits the attribute res1 that corresponds to the reserved TCP field. Algorithm 1 shows the tcp.h file modifications implemented.  
```
res1 = 4 bits if little or big endian bitfield is used then define nack = 1 bit res1 = res1 - size(nack) endif
```  
**Algorithm 1** tcphdr structure modifications for the NACK flag insertion  
The TCP flag position in the header is defined inside the tcp_flag_world structure with the TCP_FLAG_FLAGNAME =_cputobe32(0 xXXXXXXXX) format, where \"X\" corresponds to the bit flag position. Hence, we added the TCP_FLAG_NACK = cpu_to_be32 (0x01000000) line in order to assign the NACK flag position in the TCP header.  
Moreover, we added the #define TCPCB_FLAG_NACK 0x100 line inside the tcp_skb_cb structure in order to assign the value that NACK flag takes when it is assigned to a packet. Additionally, it was necessary to increase the flags attribute type from u8 (8 bits) to u16 (16 bits).
## 4 Measurement Quality Analysis
### MQA1: Measurement Stability Analysis  
The measurement noise (_i.e._, pseudorange standard deviation) is output from the receiver tracking loop. The details and the definition of the pseudorange standard deviation can be found at [32; 33]. This value represents the measurement noise for the current data. The following analysis compares the constellation satellite measurement noise for a given receiver. Figure 13 shows the relation between code-phase pseudorange standard deviation and satellite elevation angle for 10\({}^{\circ}\) increments. The mean value and standard deviation of the pseudorange noise were calculated for the two systems. The circles in Figure 13 are the mean values and the vertical bars are the standard deviation values. The same analysis for the carrier-phase pseudorange noise is shown in Figure 14. Compared with GPS, BDS has less noise for all satellite elevation angles for the code-phase measurements. BDS provides more stable measurements for the user. The carrier-phase pseudorange noise for BDS is slightly higher than that for GPS. The differences between the two systems are less than 1 cm for middle to high elevation angles (50\({}^{\circ}\)-80\({}^{\circ}\)). The difference for the remaining angles is around 1 cm. Carrier-phase measurement stability is important for precise positioning. These results indicate that the code and carrier measurements of GPS and BDS have almost the same measurement noise. The statistical results for MQA1 are summarized in Table 9.  
Figure 13: Relationship between code-phase pseudorange standard deviation and satellite elevation angle.
# Remote Low-Cost Differential Isolated Probe for Voltage Measurements
## 3 Probe Test and Validation
### Isolated Voltage Probe as a Remote Autonomous Instrument
#### 3.2.1 Power Arbitrary Function Generator  
Under the same conditions as presented in Section 3.1.3 the data acquired by the Arduino Nano 33 IoT are sent via the MQTT protocol to the Broker.  
A computer subscribed to the same topic collects these data using the Python library mentioned above (Section 3) and processes and represents them using the numpy and matplotlib libraries, respectively. The gain error could be compensated by software within the microcontroller itself or on the PC. The gain value used for the compensation for this test is 1.0840. This compensation must be carried out before the frequency analysis.  
Figure 19 shows the measurements made using the commercial Tektronix P5205 probe in blue, the proposed probe in green, and the result of the gain compensation in yellow, the latter overlapping with the blue signal. The signal shown corresponds to a signal with harmonics 1, 3, 5, and 7, as shown above.  
The results show that the gain error presented by the probe is easily compensated.  
Once the gain error compensation has been performed, the FFT analysis of the signals is carried out. This is performed in a similar way to the FFT using the oscilloscope.  
Figure 20 shows the FFT of the theoretical signal used to generate the arbitrary function generator signal (blue signal), the FFT of the signal measured using the commercial probe (red signal), and the FFT of the signal measured using the proposed remote probe (green signal). This figure shows the frequency range from 0 to 1000 Hz, the range in which the harmonics of the theoretical waveform used with harmonics 1, 3, 5, and 7 are found. We can see in this figure that the values obtained are very similar for both the mathematically generated signal and the signal measurements provided using the arbitrary generator and the proposed remote probe.  
Figure 19: Comparison between commercial and designed probe, with and without gain error compensation.
# Improvement of Wheat Grain Yield Prediction Model Performance Based on Stacking Technique
## 2 Materials and Methods
### Method
#### 2.3.2 Correlation Analysis  
A correlation analysis is a consistency analysis of the change trend in two or more sets of data, and the magnitude of the correlation coefficient is often used to assess whether the relationship between each set of data is close and its degree. The correlation coefficient is obtained by dividing the covariance of two random variables by the standard deviation. The correlation coefficient lies between \(1\) and \(1\). The greater its absolute value, the greater the degree of correlation. When its value is close to \(0\), it indicates that there is no correlation. The calculation method is as follows:  
\[\rho_{X,Y}=\frac{\text{cov}(X,Y)}{\sigma_{X}\sigma_{Y}}=\frac{E(XY)-E(X)E(Y)}{ \sqrt{E(X^{2})-E^{2}(X)}\sqrt{E(Y^{2})-E^{2}(Y)}}, \tag{1}\]  
where \(\rho_{X,Y}\) represents the correlation coefficient, and \(\text{cov}(X,Y)\) and \(\sigma\) represent the covariance and standard deviation, respectively.
## 2 Literature Review
### Inventory Management Using Drones  
As drone technology develops, drones are being used in various fields. For inventory management automation, studies using drones have also been conducted. [PERSON] et al. (2018) proposed a framework that uses the Histogram of Gradient and Local Binary Pattern to extract the feature map of the warehouse and finally detect the two-dimensional location of QR codes by classifying them via a support vector machine (SVM). [PERSON] et al. (2018) proposed the frame stitching method for images taken with the camera mounted on a drone to obtain data by restoring the corrupted context of images taken from the indoor warehouse. [PERSON] et al. (2018) proposed a grid localization framework based on image-processing algorithms, such as thick line detection, using the drone-mounted low-cost camera for large-scale warehouse system automation. [PERSON] (2018) proposed a remote aviation autonomous sensing model that combines radio frequency identification (RFID) sensors with drones for food safety and security. In addition, research has been conducted to estimate the relative position between RFID sensors attached to the product by mounting an RFID sensor on a drone ([PERSON] et al., 2018). For inventory management using drones, [PERSON] et al. (2018) proposed and implemented the generic architecture that scans products along a given movement path. [PERSON] et al. (2018) proposed a cost-friendly solution by using a drone that scans barcodes attached to products and stores product information in the database to regularly manage inventory in warehouses. [PERSON] et al. (2018) estimated the position of a drone using the ArUco marker attached to shelves for book inventory management. [PERSON] et al. (2018) proposed an inventory management model using drones that is effective for exploring shelf systems structure of the warehouse. [PERSON] and [PERSON] (2018) presented a mathematical model and a solution for a multi-user warehouse inventory using drones, relying on its own movement and the pre-planned path. In the field of drone-based inventory management automation, research was conducted based on warehouses where shelves and grids were defined. Therefore, as shown in Figure 2 (left), the position of the inventory was determined by the marker attached to the shelf. However, it is difficult to apply to the warehouse where shelves and grids are not defined, such as a storage yard. [PERSON] et al. (2018) estimated the object 3D position using two pictures taken of four ground markers and one object marker without shelves and grids. However, it is practically difficult to put more than four ground markers in a single picture. In this study, as shown in Figure 2 (right), it was improved to use only one ground marker. Furthermore, the framework has been improved to use the video frame rather than two pictures. In addition, to improve the performance of 3D position estimation, the proposed framework includes outlier frame removal and error correction utilizing continuity of the video frame.
# Mountain Muon Tomography Using a Liquid Scintillator Detector
## 2 Materials and Methods
### Direction Reconstruction
#### 2.3.2 Process of Reconstruction  
For a detected event that is ready to be reconstructed, we fill the time information into the zero-centered arrival time vector \(\textbf{T}_{\text{rec}}\equiv\Big{(}t_{\text{rec}}^{1},\cdots,t_{\text{rec}}^ {N_{\text{PMT}}}\Big{)}\) in exactly the same way as the template event. Then, we define the distance between this event and the \(n\)-th template event as \(d_{n}\):  
\[d_{n}\equiv\|\textbf{T}_{\text{rec}}-\textbf{T}_{n}\|=\sqrt{\sum_{i=1}^{N_{ \text{PMT}}}\big{(}t_{\text{rec}}^{i}-t_{n}^{i}\big{)}^{2}}. \tag{8}\]  
We assume that the smaller the distance \(d_{n}\), the more similar the \(n\)-th template will be to the event. The reconstructed direction is the weighted sum of the momentum directions of the \(k\) templates with the smallest distance \(d_{n_{n}}\):  
\[\textbf{P}_{\text{rec}}=\frac{\sum_{m=1}^{k}W_{n_{m}}\textbf{P}_{n_{m}}}{\sum_ {m=1}^{k}W_{n_{m}}}, \tag{9}\]where the weight is:  
\[W_{n_{m}}\equiv 1/d_{n_{m}}, \tag{10}\]  
and \(n_{m}\) is the index of the template with the \(m\)-th smallest distance. Here, \(k\) is determined to be the value that minimizes the angular resolution, and the resolution is defined as the mean of the included angle \(\Delta\Theta\) between the reconstructed direction and the true direction.
## 3 Results
### Meta-Analysis of the Association between Pain and Physical Function (GRADE: Very Low)  
After excluding three records that did not report physical function [40; 46] or pain [62; 63] in their analysis, the meta-analysis included four studies [37; 43; 44; 45]. Pain intensity was measured with a Numeric Rating Scale (NRS) [37; 43; 44] or a Visual Analogue Scale (VAS) [45], while physical function was evaluated with the Disabilities of the Arm, Shoulder, and Hand (QDASH) questionnaire [37; 44], the AUSCAN [37], the Oswestry Disability Index [45], and the HOOS [43]. Considering the heterogeneity among the tools used to assess function, the signs of the correlation data from [PERSON] et al., 2023 [37], [PERSON] et al., 2022 [44], and [PERSON] et al., 2022 [45] were adjusted to align with the correlation between lower pain and higher function. The pooled results showed that lower pain intensity after intervention was associated with higher physical function, with a marked heterogeneity (r (95% CI) = \(-\)0.41 (\(-\)0.68 to \(-\)0.05); I2 = 94%, Tau2 = 0.19 (0.04 to 1.32); \(p\) = 0.03) (Figure 3). This heterogeneity remained high in the sensitivity analysis that showed a similar association between pain and physical function after excluding one outlier [45] (r (95% CI) = \(-\)0.55 (\(-\)0.67 to \(-\)0.41); I2 = 86%, Tau2 = 0.02 (0.005 to 0.55); \(p\) < 0.01) (Supplementary File S10). Graphical representations are listed in Supplementary Files S11-S13. No publication bias was detected (Egger test, \(p\) = 0.86, Supplementary File S14). Meta-regression analyses identified various factors that can modulate this association: (a) Using an NRS for measuring pain intensity; (b) the participants' BMI; (c) the completion rate and frequency of sessions in both mHealth and control intervention groups; and (d) the number of sessions in the control group (Supplementary File S15). Subgroup analyses revealed a significant heterogeneity between subgroups (Q-test < 0.05), based on the tools used to assess pain or function, study design, and pain location, and a significant heterogeneity within subgroups (Q-test < 0.05), based on the tools used to assess pain or function, the comparator group, study design, duration of symptoms, and type of mHealth (Supplementary Files S16-S23).
## 3 Results
### PANDORA Motivation Model  
As described in Section 2, this model is primarily based on SDT, which asserts that human motivation and behavior are influenced by three fundamental psychological needs: autonomy, competence, and relatedness. Figure 2 represents the PANDORA Motivation Model, illustrating how each need is achieved by the CA.  
**Autonomy** is the psychological need for individuals to feel in control of their own lives and decisions. In order to improve the user's sense of autonomy, PANDORA implements two main aspects: (1) the user is given the initiative to start the conversation with the  
Figure 2: Diagram of the PANDORA Motivation Model aimed at supporting and motivating individuals with [PERSON]’s disease to maintain regular physical activity.  
chatbot to ask for help, and (2) the chatbot explains the exercises in detail, enabling the user to perform them without the assistance of a health professional.  
**Competence** refers to the need for individuals to feel capable of achieving their goals. In this context, this may be addressed by providing positive feedback and encouragement for completed exercises, as well as offering appropriate challenges and progression as users improve.  
**Relatedness** refers to the human need for social connection and a sense of belonging. In this case, this need is addressed by providing social support and encouragement to users, either through direct interactions with the chatbot or by demonstrating that other PwPD are using the ONParkinson platform to engage in exercises. The success of a CA in motivating a user greatly depends on the quality of its verbal interaction, whether written or spoken [48]. The literature review identified several key features to enhance user motivation which are integrated into the proposed motivational model of the CA. These features include:  
* Informing users of the goals they need to achieve [49].
* Sending a personalized message daily [49].
* Displaying a graph of results over time [50].
* Using positive emojis in messages to increase the sender's perceived warmth [51].
* Incorporating both male and female genders for a customized agent to increase bonding and acceptance [50].
* Providing empathic responses according to the user's state of mind [49].
# Federated Learning for Clients' Data Privacy Assurance in Food Service Industry
###### Abstract  
The food service industry must ensure that service facilities are free of foodborne pathogens hosted by organic residues and biofilms. Foodborne diseases put customers at risk and compromise the reputations of service providers. Fluorescence imaging, empowered by state-of-the-art artificial intelligence (AI) algorithms, can detect invisible residues. However, using AI requires large datasets that are most effective when collected from actual users, raising concerns about data privacy and possible leakage of sensitive information. In this study, we employed a decentralized privacy-preserving technology to address client data privacy issues. When federated learning (FL) is used, there is no need for data sharing across clients or data centralization on a server. We used FL and a new fluorescence imaging technology and applied two deep learning models, MobileNetv3 and DeepLabv3+, to identify and segment invisible residues on food preparation equipment and surfaces. We used FedML as our FL framework and Fedavg as the aggregation algorithm. The model achieved training and testing accuracies of 95.83% and 94.94% for classification between _clean_ and _contamination_ frames, respectively, and resulted in intersection over union (IoU) scores of 91.23% and 89.45% for training and testing, respectively, of segmentation of the contaminated areas. The results demonstrated that using federated learning combined with fluorescence imaging and deep learning algorithms can improve the performance of cleanliness auditing systems while assuring client data privacy.  
federated learning; deep learning; FedML; CSI-D; contamination detection; food service 4.0/. +
Footnote †: journal: Applied Sciences
# Analytic Model for U-Nb Liquidus and U-6 Nb Melting Curve
## 2 Theoretical Model for Melting Curve (Liquidus) of a Mixture
The thermodynamic formulation based on the \"specific\" Gibbs energy function \(G=(1-x)\;G_{1}+x\;G_{2}\)[15] leads to the relations  
\[\frac{1}{\rho B_{T}}\;=\;\frac{1-x}{\rho_{1}B_{T,1}}\;+\;\frac{x}{\rho_{2}B_{T,2}} \tag{4}\]  
and  
\[\frac{\alpha}{\rho}\;=\;\frac{(1-x)\,\alpha_{1}}{\rho_{1}}\;+\;\frac{x\,\alpha_ {2}}{\rho_{2}} \tag{5}\]  
which, when divided by each other, result in [15]  
\[\alpha B_{T}\;=\;(1-x)\,\alpha_{1}B_{T,1}\;+\;x\,\alpha_{2}B_{T,2}\;+\;(1-x)\; x(\alpha_{1}B_{T,1}\;-\;\alpha_{2}B_{T,2})\bigg{(}\frac{\rho B_{T}}{\rho_{1}B_{T,1} }\;-\;\frac{\rho B_{T}}{\rho_{2}B_{T,2}}\bigg{)}. \tag{6}\]  
We now consider a binary mixture at temperature equilibrium at the liquidus point \((P,T_{\ell})\) and assume that each of its constituents is described by the thermal EOS of the form (1); therefore, \((P_{\ell,1}\equiv P_{1}(\rho_{1},T_{\ell})\), \(P_{\ell,2}\equiv P_{2}(\rho_{2},T_{\ell})\), \(P_{\ell}\equiv P(\rho,T_{\ell}))\)  
\[\alpha B_{T}\,T_{\ell}=P_{\ell}-P,\;\;\;\alpha_{1}B_{T,1}\;T_{\ell}=P_{\ell,1} -P,\;\;\;\alpha_{2}B_{T,2}\,T_{\ell}=P_{\ell,2}-P.\]  
Then, the use of these three relations in Equation (6) multiplied by \(T_{\ell}\) leads to \((P\) cancels out)  
\[P_{\ell}\;=\;(1-x)\;P_{\ell,1}\;+\;x\;P_{\ell,2}\;+\;(1-x)\;x\;C(P_{\ell,1}-P_ {\ell,2}), \tag{7}\]  
where \(C\equiv\rho B_{T}/(\rho_{1}B_{T,1})-\rho B_{T}/(\rho_{2}B_{T,2})\). Equation (7) is our formula for the liquidus of a binary mixture. Since both \(P_{\ell,1}=P_{\ell,1}(T)\) and \(P_{\ell,2}=P_{\ell,2}(T)\) are assumed to be available, provided that the value of \(C\) is known, this formula gives the value of the melting \(P\) of a mixture at any given melting temperature \(T_{\ell}^{*}\), via \(P_{\ell,1}^{*}=P_{\ell,1}(T_{\ell}^{*})\) and \(P_{\ell,2}^{*}=P_{\ell,2}(T_{\ell}^{*})\). To assign a physical meaning to \(C\), we associate it with the strength of an \"effective\" interaction between the two constituents of a binary mixture. In this sense, \(C\) is an effective \"coupling\" parameter for a pair of constituents of a mixture. In the case of an \(N\)-component mixture, where \(N>2\), a set of \(C_{ij}\) for each of the \(N(N-1)/2\)\(i\)-\(j\) pairs of constituents should be introduced, and Equation (7) should be modified accordingly. Among the properties of these \(C_{ij}\)s are (i) their absolute values, which are of order 0.1-1 and (ii) that they satisfy the antisymmetry condition \(C_{ij}=-C_{ji}\); in our case of a binary mixture, this condition manifests itself in the invariance of Equation (7) under the simultaneous permutation \(1\leftrightarrow 2\), \(x\leftrightarrow 1-x\), and \(C\leftrightarrow-C\). The derivation of the analog of Equation (7) for an \(N\)-component mixture, \(N>2\), goes beyond the scope of this work and will be presented elsewhere.