esa-sceva/satcom-chunk-collection huggingface dataset:
# Reversible Data Hiding in Encrypted Image Based on Multi-MSB Embedding Strategy
## 2 Proposed Method
### Image Encryption  
Actually, to construct the encrypted image, there are three steps: auxiliary information generation, image rearrangement, and image encryption. Firstly, the auxiliary information is generated which is needed at the decoding phase. Next, the pixels are rearranged to produce a rearranged image. Last, the rearranged image is encrypted to generate its final version.  
1) Auxiliary information generation: In order to correctly extract data and restore the original image losslessly during the decoding phase, auxiliary information is essential, including: threshold \(T\) (8 bits), parameter \(m\) (3 bits), the number of pixels in \(I_{W}^{s*}\): \(S\) (16 bits), and Location map \(LM\). \(LM\) is compressed losslessly by arithmetic coding to reduce its size, and its compressed version is denoted as \(L_{clm}\) and the size of \(L_{clm}\) is \(l_{clm}\). A parameter \(n\) (16 bits) is required to record the value of \(l_{clm}\) for extracting the auxiliary information in advance. And the sequence of parameters in auxiliary information is as follows: \(T\), \(m\), \(S\), \(n\), \(L_{clm}\). Consequently, the total size of the auxiliary information is (43+ \(l_{clm}\)) bits.  
2) Image rearrangement: As Figure 2a shows, the black pixels occupy a quarter of the image; hence, we arrange these black pixels on the top quarter of the image in raster scan order, and the different types of pixels belonging to \(I_{W}\) are arranged by the order of \(I_{W}^{s*}\)\(\rightarrow\)\(I_{W}^{s*}\)\(\rightarrow\)\(I_{W}^{c}\) after \(I_{B}\). Finally, the pixels in \(I_{ED}\) are arranged on the end of image. Figure 3 is a rearranged vision of the original image which is denoted as \(R\).  
Figure 3: Rearranged vision of the original image.  
3) Image encryption: In order to prevent the original image content leakage, it is encrypted with the encryption key \(K_{e}\), which is generated by encryption algorithm RC4 [40] due to its security and efficiency. Let \(R_{l}\)(\(i\),\(j\)) be the \(l\)th (\(l\) = 1, 2,..., 8) bit of \(R\)(\(i\),\(j\)); then:  
\[R_{l}(i,j)=\bigg{|}\frac{R(i,j)}{2^{l-1}}\bigg{|}mod\ 2 \tag{8}\]  
where \(|\cdot|\) is a floor function, and each encrypted bit \(E_{l}(i,j)\) can be calculated by  
\[E_{l}(i,j)=R_{l}(i,j)\oplus p_{l}(i,j) \tag{9}\]  
where notation \(\oplus\) denotes the exclusive-or operation, and \(p_{l}(i,j)\) is the standard stream cipher generated by the encryption key \(K_{e}\). Then the encrypted pixel value \(E\)(\(i\),\(j\)) can be calculated by:  
\[E(i,j)=\sum_{l=1}^{8}E_{l}(i,j)\times 2^{l-1} \tag{10}\]  
After that, the encrypted image \(E\) can be obtained.
# Long-Range Wireless Radio Technologies: A Survey
## 2 Long-Range Wireless Technologies
### Mobile Band-Based Technologies
#### 2.1.3 Extended Coverage GSM IoT (EC-GSM-IoT)  
Extended Coverage GSM IoT (EC-GSM-IoT) is also specified in 3 GPP release 13. In contrast with eMTC and NB-IoT, EC-GSM-IoT is based on eGPRS technology (a.k.a. 2.75G) and not LTE (4G). EC-GSM-IoT could be considered to be the equivalent of LTE-M in the GSM spectrum, as it mainly brings range and power efficiency improvements. Bandwidth per channel is of 200 kHz, for a total bandwidth of 2.4 MHz. It offers a data rate of 70 or 240 kbps depending on the modulation used (GMSK or 8 PSK).  
While this technology could be extremely useful for already existing and deployed IoT applications that use the GSM network, it is getting much less attention than its two aforementioned counterparts. This is because some MNO are planning to decommission their GSM network soon [14]. On top of that, no operational EC-GSM-IoT network is available for use today.
# A Model to Measure U-Learning in Virtual Higher Education: U-CLX
## 4 The U-CLX Model Proposal
### The Mathematics under the U-CLX Model
#### 4.2.1 U-CLX Model Equation  
The U-CLX Model equation for calculating the level of U-Learning is based on the calculation of the volume of a 4-dimensional hypersphere in R4. It measures ubiquitous learning based on the volume of a hypersphere, in which there are four variables with minimum and maximum restrictions. Therefore, the maximum value is the maximum level of ubiquitous learning, and the minimum value is the minimum level of ubiquitous learning.  
The mathematical basis of the model is based on the following premises: there are four (4) variables (Time, Place, Medium, and Context), and all the variables have the same weight or value. Therefore, all the variables have the same level of development; they are all positive. The concepts of point, line, segment, and hyperplane are all contained within the hypersphere in the model.  
For this reason, the calculations and measurements inside the hypersphere correspond to Ubiquitous Learning, or U-Learning, in the U-CLX Model.  
The U-CLX Model's four dimensions for measuring U-Learning are: any time (T), any place or space (P), any device or medium (M), and any context or reality (C), which are mutually related. The dimensions are the variables of the U-CLX model and, at the same time, are the points in the hypersphere:  
* \(x\): is the Time (T), which is a plane of the form P1 = (v1; 0; 0; 0)
* \(y\): is the Place (P), which is a plane of the form P2 = (0; v2; 0; 0)
* \(z\): is the Medium (M), which is a plane of the form P3 = (0; 0; v3; 0)
* \(w\): is the Context (C), which is a plane of the form P4 = (0; 0; 0; v4)  
The points of the four dimensions form coordinates in the hypersphere, and these points can be used to define the radius (\(r\)) of the hypersphere in R4 (2).  
\[r^{2}=(x-x_{0})^{2}+(y-y_{0})^{2}+(z-z_{0})^{2}+(w-w_{0})^{2} \tag{2}\]  
The spherical coordinates of the U-CLX Model correspond to  
\[x=\rho\ \sin(\varphi)\ \sin(\varnothing)\cos(\varnothing) \tag{3}\]  
\[y=\rho\ \sin(\varphi)\ \sin(\varnothing)\sin(\varnothing) \tag{4}\]  
\[z=\rho\ \sin(\varphi)\ \cos(\varnothing) \tag{5}\]  
\[w=\rho\ \cos(\varphi) \tag{6}\]  
If \(\rho=1\), the above expressions correspond to a hypersphere of 4D dimensions.  
The U-CLX model calculates the ubiquitous learning level through the volume equation of the hypersphere in R4, and is given by the Equation (1), and  
\[r=\sqrt{x^{2}+y^{2}+z^{2}+w^{2}} \tag{7}\]  
\[r^{2}=x^{2}+y^{2}+z^{2}+w^{2} \tag{8}\]  
where  
* \(x\) is the Time T
* \(y\) is the Place P
* \(z\) is the Medium M
* \(w\) is the Context C
* Whose conditions are:  
\[0\leq x,y,z,w\leq 100 \tag{9}\]  
Scaling the U-CLX Model equation, it is essential to assume that the maximum values of the four dimensions (T; P; M; C), i.e., (\(x\); \(y\); \(z\); \(w\)) = 100. The equation must be scaled inorder to calculate the maximum level of ubiquitous learning or U-Learning; for this reason, the following equations and solutions must be used.
## 4 Overview of GNSSs
### BeiDou
#### 4.4.2 BeiDou Space Segment  
BeiDou's regional satellite navigation system consists of three groups of satellites: GEO, IGSO, and MEO. The constellation includes 44 satellites: 7 in geostationary orbits (GEO), 10 in 55\({}^{\circ}\) inclined geosynchronous orbits (IGSO), and 27 in medium earth orbit (MEO). In addition, eight satellites (four in medium earth orbit, two in geostationary orbit, and two in inclined geosynchronous orbit) are undergoing testing or launch. The constellation is designed to provide worldwide coverage. The lifetime of the satellites is eight years. The platform and navigation payload of all three types of satellites are essentially similar, but there are differences in the equipment of each GEO satellite [88].
# A New Approach to Estimating the Path Loss in Underground Wireless Sensor Networks
## 1 Introduction
The TDR extracted permittivity values are then applied to the Modified-Friis model to give an approach that does not require laboratory analysis of the soil at the location of the WUSN. This alternative approach is also then compared to the existing approaches and the RF attenuation measurements obtained from field trials. Figure 1: Schematic of a typical WUSN for pipeline monitoring.
# Artificial Intelligence and Employment: A Delicate Balance Between Progress and Quality in China
## 4 Empirical Results and Analysis
### Baseline Regression
While aggregate results indicate a modest negative effect of AI adoption on employment quality (\(-0.0000355\)**), regional heterogeneity reveals a dual reality: automation displaces low-skilled roles in underdeveloped areas (e.g., central China's manufacturing sectors) while simultaneously fostering high-skilled opportunities in technologically advanced regions (e.g., Shanghai's AI-driven industries). This bifurcation underscores that AI's labor market consequences are not uniform but shaped by local industrial structures, human capital, and policy frameworks--a critical departure from one-size-fits-all predictions prevalent in the existing literature. Furthermore, the analysis empirically validates that policy interventions, such as reskilling subsidies and targeted urbanization strategies, can neutralize displacement risks, as hypothesized in H3. For instance, eastern cities' resilience is evidenced by the significant positive coefficient for urbanization (\(+0.0656932\)*** in Table 4), where investments in education and tech infrastructure have enabled workers to transition into AI-augmented roles. This finding not only confirms the moderating role of policy but also provides actionable insights for governments seeking to align AI adoption with sustainable employment. By contextualizing AI's impacts within regional socioeconomic ecosystems, this research reframes technological disruption as a governable process, contingent on proactive governance rather than an inevitable force--a novel contribution to both development economics and AI policy scholarship. The Breusch-Pagan and Wooldridge tests confirm the absence of heteroscedasticity and autocorrelation, respectively, supporting the validity of standard inference. Residuals approximate normality (Shapiro-Wilk \(p=0.12\)), aligning with the Central Limit Theorem given our large sample size (n = 1.755). To further mitigate minor heteroscedasticity, we re-estimated the model with clustered robust standard errors (Table 3), which yielded nearly identical coefficients (\(-0.0000348\) vs. \(-0.0000355\)), underscoring result stability.
## 3 System Model
### Threat Model and Assumptions
#### 4.1.2 Authentication and Encrypted Transmission
Node A verifies that the NodeID and the public key sent by Node B satisfy the correspondence. Verify that the signature is correct using the public key of node B. Generate a new elliptic curve private key \(dA\), compute \(HA=dA*G\). Compute  
Figure 7: The workflow of public message transmission. symmetric key \(S=dA*HB\) for DES encrypted transmission, generate \(IV\) vector at random. Sign \(HA\) and \(IV\) using the private key and send the data with the signature result to node B. 4. Node B uses the public key of node A to verify that the message signature from node A is correct and calculates the symmetric key \(S=dB*HA\) used for DES encrypted transmission. At that time, node A and node B have the same DES encryption key \(S\) and \(IV\) vector, then use this key to encrypt the request to establish a channel command to send to node A. 5. After receiving the data, node A decrypts the data using \(S\) and \(IV\). The data to be communicated can be sent to node B. The encrypted communication channel is thus established.
###### Abstract  
The broad point spread function of the _NuSTAR_ telescope makes resolving astronomical X-ray sources a challenging task, especially for off-axis observations. This limitation has affected the observations of the high-mass X-ray binary pulsars SXP 15.3 and SXP 305, in which pulsations are detected from nearly overlapping regions without spatially resolving these X-ray sources. To address this issue, we introduce a deconvolution algorithm designed to enhance _NuSTAR_'s spatial resolution for closely spaced X-ray sources. We apply this technique to archival data and simulations of synthetic point sources placed at varying separations and locations, testing the algorithm's efficacy in source detection and differentiation. Our study confirms that on some occasions when SXP 305 is brighter, SXP 15.3 is also resolved, suggesting that some prior non-detections may have resulted from imaging limitations. This deconvolution technique represents a proof of concept test for analyzing crowded fields in the sky with closely spaced X-ray sources in future _NuSTAR_ observations.  
Keywords:image deconvolution; image resolution; point spread function (PSF); X-ray astronomy; X-ray binary pulsars (XRBs); NuSTAR X-ray telescope _Article_
# Weigh-in-Motion Method Based on Modular Sensor System and Axle Recognition with Neural Networks
## 3 Results and Discussion in Axle Recognition
### Training Process
#### 3.1.2 Standardized Processing of Data  
The vehicle was separated according to the output signal of the light curtain separator, and the corresponding weighing signal of each vehicle was obtained. To ensure the consistent scaling of the model's input signals and to enhance the training stability, the vehicle weighing signal was first normalized according to (1).  
\[x^{\prime}=\frac{x-\min(x)}{\max(x)-\min(x)} \tag{1}\]  
where \(x\) is the weighing signal, and \(x^{\prime}\) is the signal after being normalized.  
And then signals were standardized using a z-score as described in (2).  
\[x^{\prime\prime}=\frac{x^{\prime}-\mu}{\sigma} \tag{2}\]  
where \(\mu\) is the mean of the signal and \(\sigma\) is the standard deviation of the signal, and \(x^{\prime\prime}\) is the signal after being standardized.
## 3 Results and Discussions
### Robocasting of Lattice, Dense and Bi-Layer Parts
#### 3.1.3 Dense Parts  
Representative images of the dense samples after sintering are presented in Figure 5. The dense samples were cylinders with regular structures and precise dimensions, in good agreement with the CAD design. With the average heights of \(13.88\pm 0.15\) and \(11.67\pm 0.09\) mm in the dried and sintered conditions, respectively, the samples exhibited \(15.90\pm 0.05\%\) linear shrinkage during the sintering (the values are the average of at least 10 samples). Compared to the lattice samples, they had lower sintering shrinkage, in agreement with the results published by [PERSON] et al. [62] who showed that the sintering shrinkage of HA scaffolds has a reverse relationship with their infills. As listed in Table 2, density and porosity measurements provided \(8.7\pm 1.6\%\) open porosity. The density of solid in these parts was comparable to the porous parts (95%TD) but the presence of open porosities decreased the average apparent density to 2.69 g/cm\({}^{3}\). As can be seen in Figure 6, these open porosities stemmed from the separation of the adjacent filaments during the post-printing processes. It seems that these filaments did not squeeze well into each other during printing resulting in spaces between them in the final structure. These flaws were more pronounced at the center of the samples than in peripheries.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Scaffold Type** & \begin{tabular}{c} **Strut Size** \\ **(\(\upmu\))** \\ \end{tabular} & \begin{tabular}{c} **Pore Size** \\ **(\(\upmu\))** \\ \end{tabular} & \begin{tabular}{c} **Apparent** \\ **Density (g/cm\({}^{3}\))** \\ \end{tabular} & \begin{tabular}{c} **Density of Solid** \\ **Part (g/cm\({}^{3}\))** \\ \end{tabular} & \begin{tabular}{c} **Relative Density** \\ **of Solid (\%)** \\ \end{tabular} & \
\begin{tabular}{c} **Total** \\ **Porosity (\%)** \\ \end{tabular} &
\begin{tabular}{c} **Open** \\ **Porosity (\%)** \\ \end{tabular} \\ \hline Lattice & 341 \(\pm\) 7 & 275 \(\pm\) 8 & 2.06 \(\pm\) 0.10 & 2.97 \(\pm\) 0.01 & 95 & 34.1 & 30.7 \(\pm\) 3.4 \\ Dense & - & - & 2.69 \(\pm\) 0.20 & 2.95 \(\pm\) 0.01 & 95 & 13.9 & 8.7 \(\pm\) 1.6 \\ Bi-layer & 619 \(\pm\) 21 & 1040 \(\pm\) 31 & 2.19 \(\pm\) 0.10 & 2.99 \(\pm\) 0.01 & 96 & 29.9 & 26.8 \(\pm\) 2.6 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Physical characteristics of different 3D structures.  
Figure 5: Representative dense samples, showing well-constructed cylinders with regular dimensions.
# Rapid Assessment of Rice Quality Traits Using Low-Cost Digital Technologies
## 2 Materials and Methods
### Colour, pH, and Texture Measurement  
The physicochemical quality of the cooked rice was measured to obtain the color, texture, and pH of samples in triplicates. The Nix Pro color sensor (illuminant: D65, observer angle: 10\({}^{\circ}\); Nix Sensor Ltd., Hamilton, ON, Canada) was used to determine the L (lightness), \(a\) (red/green), and \(b\) (blue/yellow) color components of the cooked rice samples. Additionally, a pH meter, CT-6021A (Shenzhen Ke Dida Electronics Co., Ltd., Shenzhen, China), was used to determine the pH of the cooked rice samples. An amount of water was added to the sample (4 g) and mashed to form a slurry solution before measuring the pH. Ten grams of cooked rice samples were used to determine the rice texture (T) using a digital penetrometer (GY-4, China) equipped with an 8 mm diameter cylindrical probe to obtain the hardness of the rice.
## 4 Results and Evaluation  
To verify the accuracy and reliability of the proposed approach, a systematic approach was employed to validate the results of each component of the proposed RS. The first use case comprises two distinct components: the numerical and the categorical. To assess the numerical component, the validation process utilizes the MAE and the RMSE metrics. Both metrics were employed based on the criterion that their values should not exceed 10% of the expected values derived from the standard principles of Passive House construction. The criterion for evaluating these values is that they should be equal to or lower than 10% of the anticipated range of values for these variables. The establishment of these ranges is as follows in Table 4.  
In the categorical component, the validation process involved assessing the accuracy of the proposed RS by utilizing a confusion matrix to calculate the number of correct predictions. The accuracy of the proposed RS was determined by examining the frequency of accurate predictions made. To discuss the results of RSs from both use cases, this section is divided into two major portions for the two use cases. In this section, results along with evaluation metrics are discussed, and also, justifications for the selection of the ML algorithms were made.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Building Envelope Elements** & **Acceptable Range** \\ \hline Wall U value & 0.1–0.25 \\ Roof U value & 0.07–0.2 \\ Wall insulation thickness & 0.2–0.3 \\ Roof insulation thickness & 0.025–0.035 \\ Window U value & 0.5-0.6 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Acceptable ranges for selected building envelope elements.  
Figure 4: User flow (second use case).
# A Deep Learning Model to Forecast Solar Irradiance Using a Sky Camera
## 3 Methods  
The method to forecast solar irradiance, which is comprised of the two steps, is presented in this section. In the first step, the cloud cover is calculated from sky images, and then the future cloud cover is predict using LSTM. In the second step, the GHI is forecasted by applying the predicted cloud cover for the solar radiation model as the input data.
## 4 Classification into RPN Groups by Analysis of Risk Priority Number (RPN)
### Measures for Improvements to Higher-Level RPN Groups  
As a result of the FMEA, the RPN group C can be advanced to group B by improving the failure occurrence of the joint and heel block bolt, excluding the rails among the turnout components, crossing rail, switch toes, and joint, and heel block bolts.  
As a result of the RPN analysis, the crossing baseplate and switching bolt of group B could be improved to group A. However, the occurrence score was low in the case of the wooden tie; this makes it difficult to improve from this aspect. Furthermore, the analysis showed that there was an improvement effect only in the maintenance of the current state.  
As shown in Figure 17, the improvement measures are as follows: implement the use of locknuts to prevent the loosening of bolts; adjust the baseplate size to secure the clamping force; and increase the quantity of screw spike connections. The analysis showed that when the clamping force of the baseplate is secured, the damage quantity of the baseplates and screw spikes can be reduced.  
Additionally, the performance improvement of the baseplate not only indicates the advancement of the RPN group of the baseplate, but also reduces damage to the rails, wooden ties, screw spikes, and e-clips, which are other components that may be affected by the performance improvement of the baseplate. Therefore, it is expected that the occurrence score can be reduced. By improving the performance of the baseplate, it is possible to secure the clamping force and bearing capacity for fixing the positions of the wooden sleeper, baseplates, and rails. Furthermore, as the area of the baseplate increases, the load applied on the wooden sleeper is distributed. As a result, the performance improvement of the baseplate can decrease the occurrence of failures by reducing the number of damages of the crossing baseplate, wooden sleeper, and fastening system.  
Considering the effect of advancement from one RPN group to the next, the final RPN group can be advanced to the B group by improving the occurrence and sensitivity, as shown in Table 10.  
As is shown in Table 10, the improvement of the baseplate can affect the components of the wooden sleeper turnout system, such as the baseplates, wooden sleeper, and the fastening system, such as the screw spike and rails, which can increase the overall fastening force. Therefore, the components that can be upgraded from Group C to Group B can be upgraded by over 30% in five components (among the seven components) excluding rails. In existing urban railways, maintenance is typically concentrated on the rails. However, in this study, components with potential risks were classified into groups based on the FMEA analysis, and a plan to improve turnout maintenance was proposed by applying risk groups for damage reduction.  
Figure 17: Improvement in baseplate design: (**a**) before improvement of the baseplate and (**b**) after improvement of the baseplate.