esa-sceva/satcom-chunk-collection huggingface dataset:
# Color Visual Secret Sharing for QR Code with Perfect Module Reconstruction
## 3 The Proposed VSS-QR
### VSS-QR Decoder
#### 3.2.3 Binarization  
The grayscale QR code image needs to be binarized to facilitate QR code localization and module sampling in later steps. An advantage of QR code binarization is that we know in _a priori_ that there are two classes of pixels in clean image, black pixels and white pixels. Thus the grayscale image \(\hat{\mathbf{T}}\) should have two peaks in its histogram. With this knowledge, we may utilize [PERSON]'s algorithm to find the best threshold [32].  
Let the histogram of the image \(\hat{\mathbf{T}}\) be \(h(\hat{g})\), where \(0\leq\hat{g}\leq 255\). Given a trail threshold \(\tau\), all pixels can be classified as either background pixels or foreground pixels. Let the mean for background, foreground and entire image be calculated as:  
\[\mu_{b}=\frac{\sum_{\hat{g}=0}^{\tau}\hat{g}\cdot h(\hat{g})}{N_{b}},\ \mu_{f}=\frac{\sum_{\hat{g}=\tau+1}^{255}\hat{g}\cdot h(\hat{g})}{N_{f}},\ \mu=\frac{\sum_{\hat{g}=0}^{255}h(\hat{g})}{N_{b}+N_{f}}. \tag{16}\]  
where \(N_{b}=\sum_{\hat{g}=0}^{\tau}h(\hat{g})\) and \(N_{f}=\sum_{\tau+1}^{255}h(\hat{g})\) are the number of background pixels and number of foreground pixels, respectively. Let \(N=N_{b}+N_{f}\), then the intra-class variance can then be calculated as:  
\[\sigma^{2}(\tau)=\frac{N_{b}}{N}\left(\mu_{b}-\mu\right)^{2}+\frac{N_{f}}{N} \left(\mu_{f}-\mu\right)^{2}. \tag{17}\]  
The optimal \(\tau^{\star}\) can be determined by maximizing this intra-class variance \(\sigma^{2}(\tau)\). Using \(\tau^{\star}\), we obtain the binary QR code image as:  
\[\mathbf{T}^{\prime}[i,\ j]=\left\{\begin{array}{ll}1,\ \ \text{if}\ \hat{ \mathbf{T}}[i,\ j]\geq\tau^{\star},\\ 0,\ \ \text{if}\ \hat{\mathbf{T}}[i,\ j]<\tau^{\star}.\end{array}\right. \tag{18}\]
# Dynamic Torsional Stiffness of Reducers and Its Testing Method
## 2 Dynamic Characteristics of Torsional Stiffness  
### Static Torsional Stiffness  
The reducer's hysteresis is shaped by an interplay of geometric inaccuracies, friction, and elastic deformation. As a result, the hysteresis curve's depiction of the torque-angle relationship incorporates elements like geometric discrepancies and friction [18; 19; 20; 21].  
The output angle curve of the reducer is set as \(\theta_{out}(\tau,\dot{\tau})\), and represented as the rising curve and the falling curve, as shown in Equation (1). The output torque is \(\tau\) and \(\dot{\tau}\) is the loading rate [22]. It is superimposed by \(\theta_{g}(\tau)\) generated by geometric errors, \(\theta_{f}(\tau,\dot{\tau})\) is generated by friction, and \(\theta_{s}(\tau)\) is generated by elastic deformation. \(\theta_{g}(\tau)\) and \(\theta_{f}(\tau,\dot{\tau})\) mainly determine the enclosed area [4], which mainly affects the degree of inclination of \(\theta_{s}(\tau)\)[13; 23]. The relationship between them is shown in Figure 2 and Equation (2). In this equation, R stands for the transmission ratio of the reducer.  
\[\theta_{out}(\tau,\dot{\tau})=\left\{\begin{array}{ll}\theta_{out+}(\tau, \dot{\tau}),&\dot{\tau}>0\\ \theta_{out-}(\tau,\dot{\tau}),&\dot{\tau}<0\end{array}\right. \tag{1}\]  
\[\theta_{out}(\tau,\dot{\tau})=\left\{\begin{array}{ll}\theta_{f+}(\tau,\dot {\tau})+\theta_{s+}(\tau)+\theta_{g+}(\tau)+\frac{\theta_{in}}{R},&\dot{\tau}> 0\\ \theta_{f-}(\tau,\dot{\tau})+\theta_{s-}(\tau)+\theta_{g-}(\tau)+\frac{\theta_ {in}}{R},&\dot{\tau}<0\end{array}\right. \tag{2}\]  
Equation (2) can be expressed in another form, as shown in Equation (3).  
\[\theta_{out}(\tau,\dot{\tau})=\theta_{e}(\tau)+\theta_{f}(\tau,\dot{\tau})+ \theta_{g}(\tau)+\frac{\theta_{in}}{R} \tag{3}\]  
During the torsional stiffness testing, the input end is held fixed, and the input angle \(\theta_{in}\) is set to 0. In this scenario, Equation (3) simplifies to Equation (4).  
\[\theta_{out}(\tau,\dot{\tau})=\theta_{e}(\tau)+\theta_{f}(\tau,\dot{\tau})+ \theta_{g} \tag{4}\]  
At this point, the torsional stiffness \(K_{s}\) of the reducer, calculated based on the test results, represents the static stiffness of the reducer, as shown in Equation (5).  
\[K_{s}=\frac{\tau}{\theta_{out}(\tau,\dot{\tau})}=\frac{\tau}{\theta_{s}(\tau) +\theta_{f}(\tau,\dot{\tau})+\theta_{g}} \tag{5}\]  
Figure 2: Composition of the hysteresis curve.
# DARI-Mark: Deep Learning and Attention Network for Robust Image Watermarking
## 3 Proposed Framework
### Training Details  
The above four networks were encapsulated into an overall framework for end-to-end training. The watermark used in the training phase was randomly generated, and the optimizer used was a stochastic gradient descent (SGD). The specific network training parameters are shown in Table 1.  
Figure 4: Attack layer: simulate four common attacks.  
Figure 5: Extraction network: responsible for watermark extraction.  
The optimization goal of the framework was to minimize the difference between the cover image and the watermarked image and maximize the correct rate of watermarking extraction. Here, structural similarity (SSIM) and binary cross-entropy (BCE) were used as loss functions of the embedding network and the extraction network. Although mean squared error (MSE) and peak signal-to-noise ratio (PSNR) can also represent the difference between two images, they only represent the difference in pixel points at the same location, ignoring information such as the local structure of images. SSIM is a perceptual model that evaluates the difference between images from three aspects: brightness, contrast, and structure, which is more in line with the HVS. The purpose of introducing the attention network in this paper was to generate watermarked images that were more in line with human subjective perception, so SSIM was chosen as the loss function of the embedding network, denoted by \(L_{\text{emb}}\):  
\[L_{\text{emb}}=\text{SSIM}(\mathbf{I},\mathbf{I}_{\text{W}})=\frac{(2\mu_{\mathbf{I}}\mu_{ \text{I}_{\text{W}}}+c_{1})(2\sigma_{\mathbf{I},\mathbf{I}_{\text{W}}}+c_{2})}{(\mu_{ \mathbf{I}}^{2}\mu_{\text{I}_{\text{W}}}^{2}+c_{1})(\sigma_{\mathbf{I}}^{2}\sigma_{ \mathbf{I}_{\text{W}}}^{2}+c_{2})} \tag{1}\]  
where \(\mathbf{I}\) is the cover image; \(\mathbf{I}_{\text{W}}\) is the watermarked image; \(\mu_{\mathbf{I}}\) is the average value of \(\mathbf{I}\); \(\sigma_{\mathbf{I}}\) is the standard deviation of \(\mathbf{I}\); \(\mu_{\text{I}_{\text{W}}}\) is the average value of \(\mathbf{I}_{\text{W}}\); \(\sigma_{\mathbf{I}_{\text{W}}}\) is the standard deviation of \(\mathbf{I}_{\text{W}}\); \(\sigma_{\mathbf{I},\mathbf{I}_{\text{W}}}\) is the covariance of \((\mathbf{I},\mathbf{I}_{\text{W}})\); and \(c_{1}\) and \(c_{2}\) are two metric constants, which were set to \(10^{-4}\) and \(9\times 10^{-4}\) in this paper, respectively.  
The loss function of the extraction network was represented by \(L_{\text{ext}}\), which was defined as:  
\[L_{\text{ext}}=-\sum_{i\in\mathbf{\Omega}}\left[y_{i}\text{log}(\hat{y}_{i})+(1-y _{i})\text{log}(1-\hat{y}_{i})\right] \tag{2}\]  
where \(\mathbf{\Omega}\) is the image domain; \(y_{i}\) is the pixel value of the real watermark at position \(i\); and \(\hat{y}_{i}\) is the pixel value of the extracted watermark at position \(i\).  
The loss function \(L\) of the overall framework was defined as:  
\[L=\lambda L_{\text{emb}}+(1-\lambda)L_{\text{ext}} \tag{3}\]  
where \(\lambda\) is the weight between the two loss functions, which was set to 0.5 in this paper.
## 5 Results
### Petrographic Characteristics
#### 5.1.1 Metadolostone  
Most of the collected samples (TAR1-5, 7, 20, 22, 49, TAP33, 45, TAC1-3, TASC1-6, 8; Table 1) were metadolostones that represented the main lithology outcropping around the archaeological area together with \"Ceroide Limestone\" Mb (not used as a building material).  
Metadolostone samples occur in different facies that can be subdivided in three main groups namely laminated, massive and brecciated dolomites.  
Massive metadolostone (TAR1, 20, 22, 49, TASC1-2; Table 1; Figure 10a) was characterized by the absence of sedimentary structures even if some samples still preserved relics of peloid-rich beds and desiccation fenestrae. The presence of metadolostone samples with intermediate features between laminated and massive, suggested that at least some of the latter may have resulted from a progressive secondary dolomitization/recrystallization that obliterate primary structures. Nonetheless, massive dolomitized carbonate muds completely devoid of structures may exist too. Here millimeter-sized dolomite rhombohedra could be found locally.  
Laminated metadolostone (TAR3, TAR5, TAP33, TAC2, TASC3-6, TASC8; Table 1; Figure 10b) were characterized by the occurrence of thin algal mats (\"stromatolites\" AA) and peloid/pellets or oncoidal-oncolitic-rich beds. Interparticle voids, filled by calcite (fenestral fabric: [80; 85]) were quite common and are probably linked to desiccation cycles, in a sabkha-like environment.  
Brecciated metadolostone (TAR2, 4, 7, TAC1, 3, TAP45; Table 1; Figure 10c), either massive or layered, were characterized by a millimeter to centimeter-sized network of randomly distributed fractures that separate angular metadolostone clasts heterogeneous in size. Fractures were commonly filled by dolomite and/or calcite cement with variable amounts of iron oxides. Syn-sedimentary and tectonic recent breccias can be distinguished from each other on the basis of the isoorientation and the good sorting of the synsedimentary elements and the strongly angular shape and the poor sorting of the tectonically originated fragments.
## 2 Materials and Methods
### Modified Degree of Contamination (\(mC_{d}\))  
[PERSON] [26] presented a modified and generalized form of the [PERSON] [24] equation for the calculation of the overall degree of contamination at a given sampling or coring site as follows: (a) The modified formula is generalized by defining the degree of contamination (\(mC_{d}\) ) as the sum of all the contamination factors (\(C_{f}^{i}\)) for a given set of estuarine pollutants divided by the number of analyzed pollutants; (b) The mean concentration of a pollutant element is based on the analysis of at least three samples; and (c) The baseline concentrations are determined from standard earth materials The modified equation for a generalized approach to calculating the degree of contamination is given in Equation 4.  
\[mC_{d}\,=\,\frac{\sum_{i=1}^{i=n}C_{f}^{i}}{n} \tag{4}\]  
where n is the number of analyzed elements and and \(C_{f}^{i}\) is the contamination factor.  
Using this generalized formula to calculate the \(mC_{d}\) allows the incorporation of as many metals as the study may analyse with no upper limit. For the classification and description of the \(mC_{d}\) seven gradations are proposed as shown in Table 4.  
\begin{table}
\begin{tabular}{l l} \hline \(mC_{d}\) Class & **Modified Degree of Contamination Level** \\ \hline \(mC_{d}<1.5\) & Nil to very low degree of contamination \\ \(1.5\leq mC_{d}<2\) & Low degree of contamination \\ \(2\leq mC_{d}<4\) & Moderate degree of contamination \\ \(4\leq mC_{d}<8\) & High degree of contamination \\ \(8\leq mC_{d}<16\) & Very high degree of contamination \\ \(16\leq mC_{d}<32\) & Extremely high degree of contamination \\ \(mC_{d}\geq 32\) & Ultra high degree of contamination \\ \hline \end{tabular}
\end{table}
Table 4: Different modified degree of contamination (\(mC_{d}\) ) for soil [26].  
\begin{table}
\begin{tabular}{l l} \hline \(C_{d}\) Class & **Degree of Contamination Level** \\ \hline \(C_{d}<8\) & Low degree of contamination \\ \(8\leq C_{d}<16\) & Moderate degree of contamination \\ \(16\leq C_{d}<32\) & Considerable degree of contamination \\ \(32\geq C_{d}<8\) & Very high degree of contamination \\ \hline \end{tabular}
\end{table}
Table 3: Different degree of contamination (\(C_{d}\) ) for soil [24].  
An intrinsic feature of the \(mC_{d}\) calculation is that it produces an overall average value for a range of pollutants. As with any averaging procedure, care must however be used in evaluating the final results since the effect of significant metal enrichment spikes for individual samples may be hidden within the overall average result.
## 4 Discussion
Specifically, if the ALEXI ET is considerably larger than the aggregated ET, DisALEXI-PT will adjust by increasing E. In vineyard and almond systems, where distinct differences are present between the drip-irrigated canopy and non-irrigated soil, this may lead to inappropriately high E values. Conversely, when the ALEXI ET is much lower than the aggregated ET, DisALEXI-PT will adjust by decreasing E, producing reasonable partitioned estimates of E during the growing season. However, should these conditions be met in the spring, when the soil moisture content is larger [78; 79; 80], decreases in E may be excessive. The incorporation of DisALEXI-PM may provide a solution by adjusting E and T more proportionately. Large differences between ALEXI ET and disaggregated ET were not found during the current study. Future work will look to identify ALEXI pixels displaying large biases to disaggregated ET based on surface spatial heterogeneity and/or time of year and test differences between the DisALEXI-PT and DisALEXI-PM approaches. Spatial analysis mirrors patterns found at the field scale, with DisALEXI-PT showing elevated E in comparison to DisALEXI-PM, which indicates E values closer to zero over each site and for most of the surrounding area (Figure 10). Estimates of T are opposite in relation, with DisALEXI-PM producing larger T estimates over each site and over surrounding fields. DisALEXI-PT shows more range in T, particularly for low values. The summation of E and T for both approaches produces analogous ET estimates, with differences of \(\leq\)0.75 mm day\({}^{-1}\) in either direction over the regions shown. Such maps demonstrate the differences in the partitioned E and T between approaches despite the similarities in total ET values. In water-limited agronomic systems where distinct differences are present between the soil (likely dry during the growing season) and canopy (direct application of irrigation leading to full canopies), differentiation between E and T is required for improved irrigation management.
## 4 Field Application
### Application of the Favorable Gas Extraction Height  
We drilled a reasonable number of extraction boreholes in the favorable zone at the 204 working face, where the gushed gas and extracted gas are investigated while advancing this working face. Figure 10 presents the gushed and extracted gas variation as the working face advances from 0 m to 600 m.  
The gas quantity first grows, then tends towards stability as the working face advances, forming two different areas: the growth and stable stages.  
The growth stage ranges from 0 m to 150 m of \(D_{v}\), where the dominated area of the three zones gradually evolves, and the number of valid extraction boreholes constantly increases. Then, the height of the three zones remains unchanged, and the number of valid extraction boreholes no longer increases when the working face exceeds 150 m, during which the gas quantity tends to be stable with an acceptable fluctuation.  
The gushed gas quantity presents an unobvious variation which increases from 5.70 m\({}^{3}\)/min to 8.50 m\({}^{3}\)/min in the growth stage and fluctuates around 9.0 m\({}^{3}\)/min in the stable stage. On the contrary, the extracted gas quantity varies more significantly, leading to a significant increase from 13.90 m\({}^{3}\)/min to 22.60 m\({}^{3}\)/min in the growth stage and a noticeable fluctuation around 23.0 m\({}^{3}\)/min in the stable stage. The extraction rate, dominated  
Figure 10: Relationship between working face production and gas drainage.  
by the gushed and extracted gas quantity, varies dramatically between 68.75% and 75.44%. It meets the essential requirement of gas extraction for safe mining and provides a stable source for gas utilization.
# THz Rectennas and Their Design Rules
## 5 Power Level
### Received Wave Bandwidth
#### 5.2.1 Impact from the Incident Wave  
A dedicated work is the one performed by [PERSON] and [PERSON] [88] and by [PERSON] and [PERSON] [34] where they conducted a study on solar light to identify the coherence of the incident ray in addition to the optimal antenna size to maximize the received power. It might be helpful to mention that [PERSON] and [PERSON] utilize the equal time mutual coherence function (EMCF), which is identified in (24) based on the van Cittert-Zernike theorem.  
\[EMCF=\langle E(r,t)\cdot E^{*}(r^{\prime},t)\rangle_{t}=2\eta\int_{\sigma}I(s )\cdot\frac{e^{ik(r-r^{\prime})}}{r\cdot r^{\prime}}\ ds \tag{24}\]According to the van Cittert-Zernike theorem, the time-averaged product of two fields emanating from a radiating source on two different points of an aperture antenna is given by (24) where: (i) \(\eta\) is the vacuum impedance, (ii) \(I(s)\) is the irradiance of the source, (iii) \(r\) and \(r^{\prime}\) are the coordinates of the different points of the aperture antenna and (iv) \(k\) is the wave vector of the incident wave. The integration is performed over the entire radiating source surface \(\sigma\). [PERSON] and [PERSON] [34] have found significant results by running this integral for monochromatic and wideband sources in the visible range (For waste heat sources, the distance between the source and the rectenna array is very small. When integrating over the radiating surface, the aperture radius values that yield higher EMCF values will be considerably smaller than those obtained for the sunlight as in [PERSON] and [PERSON]'s study [34].). That is, the intercepted power by the antenna is proportional to the aperture area if the aperture radius is much smaller than the average wavelength \(\bar{\lambda}\). However, when the aperture is much larger than the wavelength, the intercepted power by the antenna is independent of antenna size and relies only on the wavelength and the source irradiance. This sets limits how large the aperture should be. A more realistic approach might be reached by integrating the EMCF function over the entire bandwidth after introducing the frequency-dependent terms into (24). This has led to the EMCF curves shown in Figure 10, which shows that the situation is not much different for the wideband nature of light as compared to the quasi-monochromatic cases represented on the same graph.  
From Figure 10, it can be concluded, except for the lowest wavelength \(\lambda=$0.5\,\mathrm{\SIUnitSymbolMicro m}$\), that for any aperture radius below \(100\,\mathrm{\SIUnitSymbolMicro m}\), quasi-monochromatic light is more likely to deliver higher power to this aperture than the complete bandwidth of the solar light. This result has been pronounced also by [PERSON] and [PERSON] [88] suggesting that splitting the whole solar bandwidth into smaller sub-bands would yield higher efficiencies as the case for multi-junction solar cells. This solution can be extended to be applied also to rectennas, as this defect relies merely on the wideband nature of light and has nothing to do with the successor stages.
# Energy Efficiency for Green Internet of Things (IoT) Networks: A Survey +
## 3 Literature Review
### An Overview of WSNs  
At the core of the IoT are WSNs. It is one of the most promising wireless technology systems for enabling IoT networks. WSN contains tens of thousands of nodes connected and communicated with each other using wireless technologies [49]. Such networks involve low cost, low-range, low-power circuits and tiny sensor nodes. The main equipment of each node are a sensing and processing unit, power source, memory and receiver and transmitter unit as illustrated in Figure 5[50].  
These units are [50]:  
* Sensing Unit: is the core component of the WSN and has two functions. First, it senses information from the surrounding physical environment and converts this information into digital data. Second, it forwards the data towards the processing unit.
* Processing Unit: contains a microprocessor with a limited amount of memory. It is responsible for receiving the information from the sensing unit and forwarding the data to the transceiver after necessary processing.
* Communication Unit: combines both a radio transmitter and a receiver, and is responsible for exchanging information with other smart objects in the sensing field.  
Figure 5: Basic components of WSN.  
Power Unit: is responsible for providing power to all other units. The sensor node would die, stop obtaining and/or transmitting data if the power unit stopped working. Therefore, preserving the working life of the power unit by energy conservation becomes an important and challenging issue in WSNs.  
A WSN can have many types of sensors depending on the application, whether terrestrial, underwater, underground, multimedia, or mobile [51]. The main task of the deployed sensors in WSN applications is monitoring and, as stated above can include such diverse fields as meteorology [46], fire prevention [52], flood and earthquake detection and monitoring [48], mapping environmental bio-complexity and studying environmental pollution [53]. WSNs have also been used to observe the activities of animals, birds and insects [54]. Table 1 illustrates an example of WSNs applications [55]. In such applications, the smart objects are positioned over a wide geographical area and non-accessible environments.
## 3 Numerical Analysis
### Discretization and Analysis  
Different types of finite elements were used in the creation of the numerical model, depending on their ability to describe the given issue. The 1D finite elements were used to model the longitudinal reinforcement of the beam as well as its shear reinforcement (stirrups). They represent isoparametric elements with one or two integration points for linear or quadratic interpolation for elements with two or three nodes. The geometry of the finite element is shown in Figure 13.  
For creating the beam, steel supports and the anchor model, finite spatial elements from the \"3D solid\" group were used. These are tetrahedral or brick finite elements. The geometry of these finite elements is shown in Figure 14a,b. Their detailed mathematical description can be found in [26].  
The mesh was generated by the automatic mesh generator implemented as a part of the software's pre-processor. The density of the mesh was variable in the model. The fine mesh (approx. 3 mm) were used for the meshing of the headed anchor and surrounding concrete. The mesh with a maximum element size of 25 mm was used on the meshing of other concrete member areas.  
Numerical model calibration is generally an important and sensitive part of numerical analysis [47; 48]. The calibration of both models (with and without supplementary reinforcement) consisted of the mesh density settings, the contact surfaces definition, as well as the optimal loading steps. The contact between the reinforcement bars and concrete was modelled as a perfect connection. The contact between the anchor and surrounding concrete was modelled by the interface (GAP) material. It allows for the consideration of  
Figure 14: The geometry of the 3D solid elements [26; 46]: (**a**) tetrahedral elements with 4 to 10 nodes; (**b**) brick elements with 6 to 15 nodes.  
Figure 13: The geometry of the 1D element [26].  
cohesion and friction on the headed anchor surfaces. The interface material parameters were defined following recommendations published in [49; 50].  
The anchor was loaded by the displacement increment (0.05 mm) in successive steps. The total number of the required load steps for the model was 90 (model without supplementary reinforcement) or 130 (model with supplementary reinforcement). The standard Newton-Rapson procedure was chosen for the solution process with four different convergence criteria. The iteration limit for one analysis step was 40, and the stiffness was updated after each iteration.