esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Circular Index  
Overall circular index (CI) results tend to be high (>0.85) for the assessed bio-based fertilizers, as can be seen in Figure 3. All the values are relatively high, which conveys that overall, the targeted bio-based fertilizers attain a high circularity level. The uncertainty calculated is below 1.00% for all the cases, representing a high data accuracy, according to the GHG protocol's data accuracy rating. However, any determinant conclusion should always be based on a specific comparison between relevant scenarios.  
The results obtained for the CI allow to rank the assessed products by \"circularity\" levels. For this indicator and under the sector constraints, \"circularity\" is linked to two ratios: (1) virgin non-renewable inputs to total inputs and (2) waste to total outputs. Let us analyze how these two factors influenced the results of each of the five targeted products:  
* Algae scored the highest CI result by having low virgin non-renewable consumptions (<0.1% of total inputs) and no waste generation.
* Spent mushroom substrate, for its part, also has low virgin non-renewable consumption (<5% of total inputs) and low waste generation (<2% of total outputs).
* Feather meal, at the third position, has low virgin non-renewable consumption (<9% of total inputs) and no waste generation.
* Wood vinegar, being below the average, although having low waste generation (<1.6% of total outputs), has a non-negligible consumption of virgin non-renewable materials (23.4% of total inputs), caused mostly by ethylene consumption during the condensation stage.
* Compost owes its position at the bottom of the ranking to the non-negligible waste generation (28.4% of total outputs), which reduces its CI, although it has a low consumption of virgin non-renewable materials (<0.09% of total inputs).  
In all cases, the energy consumption had little influence on the main results, not altering the trend imposed by the material consumption and waste.  
Through the calculation process, including the methodological adaptations described in Section 2.2, it became evident that the original CI has limitations when directly applied to bio-based fertilizers. One of these is the consideration of product lifetime, which, unlike in other types of products, cannot be meaningfully assessed for fertilizers, as they are applied only once to the soil. This finding, in turn, led to the development of the nutrient slow-release index (c.f. Section 3.4).  
The results, in turn, highlight three CBE aspects of bio-based products: virgin non-renewable material consumption, waste generation, and energy consumption (although the latter having little effect on the indicator result). These findings partly confirm the assertion that bio-based products tend to have a higher level of circularity when compared to non-bio-based ones [62; 63].
# Frequency Selective Surfaces: Design, Analysis, and Applications
## 2 Applications of FSSs
### IRSs for Cellular Networks  
One of the most critical applications of IRSs is within cellular networks, particularly in densely populated urban environments where buildings and other physical structures can obstruct signal propagation. An IRS has the unique ability to \"reflect\" and direct wireless signals into areas that would otherwise suffer from poor coverage. For example, an IRS placed on the exterior of a building can reflect signals from a base station to users located in areas where direct line-of-sight transmission is impossible, effectively solving the problem of blind spots [98].
## 3 Results
### Results of Measurements and Correlation Analysis
#### 3.2.3 Results of SVR Model Prediction  
To determine the modeling parameters of SVR, grid searching and five-fold cross-validation were used to select a suitable combination of \(p\) and \(g\). Figure 14 shows the results of the SVR model parameters. Based on the minimum \(MSEC_{V}\), we preferentially selected the values of \(p\) and \(g\), which were 0.25 and 0.64, respectively, for cohesion, and 32.4 and 0.20, respectively, for internal friction angle.  
The optimized p and g were used to construct the prediction models of cohesion and internal friction angle for the calibration set, and the validation set was used to test the models. The results are illustrated in Figure 15.  
As shown in Figure 15a, the predicted results of soil cohesion are \(R_{C}2\) = 0.85, \(RMSE_{C}\) = 4.31, \(R_{V}2\) = 0.83, and \(RMSE_{V}\) = 5.60. Both \(R_{C}2\) and \(R_{V}2\) are greater than 0.80, indicating that the SVR model is successful in predicting cohesion. For the internal friction angle (Figure 15b), the predictive performance indicators in the SVR model for \(R_{C}2\), \(RMSE_{C}\), \(R_{V}2\), and \(RMSE_{V}\) are 0.87, 1.78, 0.86, and 2.40, respectively. \(R_{C}2\) and \(R_{V}2\) are greater than 0.85, showing strong predictive performance.  
Figure 14: Parameter optimization of SVR. (**a**) Cohesion; (**b**) internal friction angle.  
Figure 13: Soil shear strength parameters prediction of PLSR models. (**a**) Soil cohesion; (**b**) soil internal friction angle.
# Quasi-Static Sliding Wear Analysis of 3D Rough Surface Considering Changes in the Point of Contact
## 3 Result and Discussion
### Comparison between Three Wear Models
In the truncation model, the fact that the point at which the slope of RMS roughness becomes smaller coincides with the point at which the number of contact asperities sharply increases indicates that an error occurs from the spherical assumption as in the single asperity wear problem described above, and the effect is increased and accumulated as the number of contact asperities increases. In addition, in the wear analysis process, the actual profile with a higher value than the spherical-fitted profile is cut, and therefore, wear occurs in more nodes than the number of nodes that should have been worn in the spherical shape. This causes the RMS roughness to decrease more rapidly. In addition, under the assumption of contact between the sphere and the rigid plane, 1.5 times the average pressure becomes the pressure at the peak point. Since the pressure gradient in the actual shape is flatter than the  
Figure 5: 2D profile of unworn and worn surface: (**a**) Truncation model; (**b**) deterministic fixed model; (**c**) deterministic quasi-static model. pressure gradient of the rigid plane-sphere contact, the contact pressure on the worn rough surface is overestimated with the Hertz equation. The truncation model has the advantage of being simple and fast, but it results in unavoidable problems. Figure 7 presents a contour map showing the surface change in the wear analysis of the same Gaussian surface as that in Figure 5 from the truncation model. Since the peaks and valleys are uniformly distributed in the beginning, the intersection point of the rigid body plane and the surface profile can be well defined, and there is no big problem in finding the radius by fitting the asperity with a sphere. In each cycle, the peaks are all replaced with the same height. As the wear progresses, the number of nodes with the same height value increases and the flat area becomes wider. As this area becomes wider, the radius estimation error increases, and the accuracy of the truncation model decreases. In addition, the evenly distributed flat area gradually expands and merges with another area in the vicinity to form an irregular shape that makes it difficult to specify the center of the asperities used in the fitting process. For this reason, we considered that the accuracy of the truncation model decreased as the wear progressed, and the analysis was performed until the worn area out of the total area reached 30%. In the case of the Gaussian surface used in Figures 5-7, the analysis was stopped after 210 cycles. Figure 6: RMS roughness vs. wear cycle in the truncation model, deterministic fixed model, and deterministic quasi-static model. Figure 7: Surface contour as the wear cycle progresses in the truncation model.
## 2 Analytical Approach for the Joint Rotational Behavior
### Equilibrium Equations  
As shown in Figure 4, the height of the composite compression zone (\(l\)) is evenly divided into \(n\) sections, and the strains at the two edges are expressed as \(\varepsilon_{e1}\) and \(\varepsilon_{e2}\). Since the composite strain distribution presents linear characteristics, the strain in the center of each section \(i\) can be obtained as follows:  
\[\varepsilon_{i}=\varepsilon_{e1}+\frac{\varepsilon_{e2}-\varepsilon_{e1}}{n}(i -0.5) \tag{11}\]  
The stress of each section \(i\) (\(\sigma_{i}\)) can be given by the stress-strain relationship of the composite material.  
\[\sigma_{i}=\biggr{\{}\begin{array}{cc}0&(\varepsilon_{i}\leq 0)\\ \sigma(\varepsilon_{i})&(0<\varepsilon_{i}\leq\varepsilon_{u})\end{array} \tag{12}\]  
According to the equilibrium condition of the joint axial force and bending moment, we obtain the following:  
\[\sum_{i=1}^{n}\left(\sigma_{i}\frac{Bl}{n}\right)-nF_{b}-N=0 \tag{13}\]  
\[\sum_{i=1}^{n}\left(\sigma_{i}\frac{Bl}{n}\left(\left(b+c-\frac{H}{2}\right)- \frac{l}{n}(i-0.5)\right)\right)+nF_{b}\left(\frac{H}{2}-h_{b}\right)-M=0 \tag{14}\]  
From the joint deformation distribution presented in Figure 4, the following equation is obtained:  
\[\frac{2\varepsilon_{e1}l_{c}-2\varepsilon_{e2}l_{c}}{l}=\frac{2\varepsilon_{ e1}l_{c}+\Delta_{b}}{h_{c}+l-h_{b}} \tag{15}\]  
where \(\Delta_{b}\) is the joint deformation at the bolt position. As shown in Equation (5), the bolt force (\(F_{b}\)) can be calculated by using the following:  
\[\varepsilon_{b}=\frac{\Delta_{b}}{L_{b}} \tag{16}\]  
\[F_{b}=F_{0}+A_{b}\sigma_{b} \tag{17}\]  
where \(L_{b}\) is the effective length of the bolt, and \(A_{b}\) is the cross-sectional area of each bolt.  
Based on the above equations, \(\varepsilon_{e1}\) and \(\varepsilon_{e2}\) can be calculated. Then the joint rotation angle can be presented as the following equation:  
\[\theta=\frac{2\varepsilon_{e1}l_{c}-2\varepsilon_{e2}l_{c}}{l} \tag{18}\]  
Figure 4: Modeling approach.  
Since the concrete contact force at the joint edge was not considered in this study, the joint deformation at the compression edge should meet the following conditions:  
\[\Delta_{e}=2\varepsilon_{e1}l_{c}+\theta h_{a}<\Delta_{e}^{0} \tag{19}\]  
\[\Delta_{e}=2\varepsilon_{e1}l_{c}+\theta h_{a}<\Delta_{e}^{0} \tag{20}\]  
where \(\Delta_{e}\) is the joint deformation at the compression side for the sagging moment scenario, \(\Delta_{e}^{0}\) is the initial gap between the two concrete segments at outside edges, \(\Delta_{i}\) is the joint deformation at the compression edge for the hogging moment scenario, and \(\Delta_{i}^{0}\) is the initial gap between the two concrete segments at the inside edges.
# A Novel RFID Authentication Protocol Based on Reconfigurable RRAM PUF
## 4 Protocol Details
### Certification Phase  
(a) The reader sends an authentication request. After the tag receives the authentication request, it reads the ID and K stored in the memory and sends the ID \(\oplus\) K as the response information to the reader;  
(b) For each tuple <ID', K\({}_{i}\)>, the reader calculates ID' \(\oplus\) K\({}_{i}\) and if it matches ID \(\oplus\) K,the tag is successfully recognized. If it does not match, the reader enters the next round of search, that is, to each tuple <ID', K\({}_{i-1}\), S> continue to calculate ID' \(\oplus\) K\({}_{i-1}\). If it matches ID \(\oplus\) K and S = 0, the tag is successfully identified and the protocol enters the next stage. Otherwise, the reader no longer responds and terminates the protocol;  
(c) The reader generates a random number C and sends it to the tag as a Challenge. To prevent replay attacks, C here should avoid duplicate numbers, which can be generated using a TRNG or PUF module;  
(d) After receiving the C, the tag reads the random number seed M\({}_{0}\) and generates a random number M = Ran(M\({}_{0}\)). In order to reduce the area, the M here is generated by using the PUF module, and its detailed generation method is M\({}_{1}\) = PUF(M\({}_{0}\)), M = M1 \(\oplus\) PUF(M\({}_{i}\)). Then calculate R = PUF(C \(\oplus\) M), X = R \(\oplus\) PUF(R), and generate the error correction code H = Gen(R) of R, and finally send M, H, X to the reader. In addition, the random number seed M\({}_{0}\) is updated to M\({}_{1}\) and written to the memory;  
(e) After the reader receives M, Hand X, the decoder using the error correction algorithm recovers R\({}^{\prime}\) = Rep(PUF\({}^{\prime}\)(C \(\oplus\) M), H), and then calculates X\({}^{\prime}\) = PUF\({}^{\prime}\)(R\({}^{\prime}\)) \(\oplus\) R\({}^{\prime}\) using the PUF model. If X and X\({}^{\prime}\) are within the error range, the tag is considered legal, then the  
Figure 3: (a) RRAM PUF configuration process. (b) RRAM PUF reconfiguration process.  
reader generates a random number N, computes Y = PUF(N \(\oplus\) R\({}^{\prime}\)), and sends N and Y to the tag. Otherwise, the protocol is terminated;  
(f) The tag calculates Y\({}^{\prime}\) = PUF(N \(\oplus\) R). If Y\({}^{\prime}\) and Y are within the error range, the reader passes the authentication, the tag updates K = K \(\oplus\) R, and Z = PUF(K \(\oplus\) R \(\oplus\) Y) is sent to reader. Here Z is used as the final acknowledgment signal to detect man-in-the-middle attacks;  
(g) The reader calculates K\({}^{\prime}\) = ID \(\oplus\) K \(\oplus\) ID\({}^{\prime}\), Z\({}^{\prime}\) = PUF\({}^{\prime}\)(K \({}^{\prime}\oplus\) R \({}^{\prime}\oplus\) Y), if Z\({}^{\prime}\) matches Z, then sets S = 1; if Z\({}^{\prime}\) does not match Z or responds timeout means that an error or an attack occurred during the transmission, and S = 0 is set at this time. Finally, the reader updates K\({}_{i}\) = K\({}^{\prime}\)\(\oplus\) R\({}^{\prime}\), K\({}_{i-1}\) = K\({}^{\prime}\). The protocol flow is shown in Figure 4.
## 3 Numerical Simulation Results  
The finite difference method (FLAC3D) was adopted to evaluate the supporting effect of BFRGC in deep high-stress rock roadways. The following assumptions were made before the numerical simulation:  
1. All the rock materials were assumed to be homogeneous and isotropic;
2. The mean buried depth for the deeply buried roadways was generally over 800 m; thus, the self-weight of rocks was considered as the vertical stress on the model, and the lateral pressure coefficient was used to simulate the horizontal tectonic force;
3. The influence of groundwater was not considered.
# Edible Oils as Practical Phase Change Materials for Thermal Energy Storage
## 1 Introduction
Coconut oil also has been mixed with paraffin [36] and fatty acid [37] PCMs to prepare form-stable composite PCMs for TES in buildings. However, the coconut oil PCM studies reported a wide range of values for the latent heat of fusion, \(\Delta_{\text{fus}}H\), and for the specific heat capacity. For instance, the specific heat capacities and \(\Delta_{\text{fus}}H\) measured by the \(T\)-history method [29] had large uncertainties, with a reported value of \(\Delta_{\text{fus}}H\) of 249 J g\({}^{-1}\), significantly higher than \(\Delta_{\text{fus}}H\) of 103 J g\({}^{-1}\) determined by differential scanning calorimetry (DSC) [33; 34]. Furthermore, important properties such as the accurate thermal conductivity of both the solid and liquid phases, the degree of supercooling and information on the thermal stability after several melt-freeze cycles have not been reported for coconut oil. Such information is necessary to evaluate the economic viability and to model the effectiveness of coconut oil as a PCM, as well as to optimize the quantity needed and placement within the space. In the present work, we accurately determined the thermal properties and thermal stability of coconut oil and blended oils products, namely margarine and shortening, to explore whether these edible oil products could be useful PCMs for residential applications. As an illustration, we investigated if these easily accessible and relatively inexpensive products can be integrated by a consumer as TES materials in a residential greenhouse or a garden shed to buffer temperature changes. We determined the thermal properties (latent heat of fusion, \(\Delta_{\text{fus}}H\), and the melting temperature, \(T_{\text{mpt}}\)) and the thermal stabilities after many melt-freeze cycles of two brands of margarine (with different amounts of oil blends in their composition), vegetable shortening, and of both virgin and refined coconut oil. On the basis of the thermal results and cost, we selected refined coconut oil for further thermal characterization: we measured the heat capacity and thermal conductivity as functions of temperature, and degree of supercooling from heating and cooling curves of bulk samples. The measured thermal properties were then used to evaluate coconut oil as a potential PCM for passive heating and cooling of a typical residential greenhouse. We compared coconut oil to water and fatty acid PCMs, in terms of quantity of materials and cost, and also compared coconut oil's cost with the cost of electric heat for the greenhouse during cold nights.
# Dynamic Analysis of Cavitation Tip Vortex of Pump-Jet Propeller Based on DES
## 3 Cavitation and Vortex Dynamics Analysis of Pump-Jet Propeller
### Severe Cavitation at J = 0.73  
#### 3.3.1 Pressure Fluctuation  
Figure 15 depicts the time domain diagram of pressure fluctuation before and after cavitation at TLV monitoring points. By calculation, the advance ratio \(J\) is equal to 0.73 and the cavitation number _\(\sigma\)_ is equal to 3.98 for both non-cavitation and cavitation scenarios. As can be seen in the figure, there is a big difference in the time domain diagram of pressure fluctuation before and after cavitation. It is most evident that after cavitation, the fluctuation amplitude of LP3 and LP4 is much smaller than that of other monitoring points. In addition, the pressure fluctuation amplitude of all monitoring points was increased compared with that before cavitation. LP1 fluctuates irregularly before cavitation, showing that the difference between the peak and bottom is always changing at different times, which change into sine curve after cavitation. The pressure change of LP2 is more disordered after cavitation, and the number of peak and bottom is significantly increased than that before cavitation. The fluctuation amplitude of LP5 before cavitation is basically similar to that of LP3 and LP4, but it has the largest pressure fluctuation amplitude after cavitation.  
Figure 16 shows the pressure fluctuation of TLV monitoring point before and after cavitation at \(J\) = 0.73. First of all, the primary frequency has not changed which is still 9 times the shaft frequency, but the secondary frequency is not all 4 times the shaft frequency. Except at LP3, the secondary dominant frequency of the other four monitoring points is replaced by 0.1 times the shaft frequency. However, the entire secondary frequency is replaced by low multiples of the shaft frequency. Especially at LP3 and LP4, the main frequency is 0.1 times the shaft frequency, while 9 times the shaft frequency becomes the secondary frequency. The amplitudes of LP3 and LP4 in Figure 14b are far less than those of the other three monitor points, and the amplitudes corresponding to the main frequency of LP2 are also smaller than those under the condition of no cavitation. On one hand, the three monitoring points are located in the serious cavitation area, and the fluid is filled with bubbles, so the turbulence fluctuation of water will be weakened. On the other hand, when cavitation occurs, the position of the vortex core will shift compared with that without cavitation. Therefore, LP2-LP4 may be  
Figure 15: Time domain diagram of pressure fluctuation before and after cavitation at TLV monitoring points. (**a**) non-cavitation (**b**) cavitation.  
the vortex core when cavitation occurs, which is filled by bubbles, resulting in the reduced intensity of pressure fluctuation.
## 3 General Framework and Methodology
### The Self-Adaptive-Attention-Prompt-BERT-RCNN Model
#### 3.3.2 Prompts to Learn (Prompt)  
Cue learning is an approach to learning using pre-trained language models (e.g., BERT), and its core idea is to transform downstream tasks such as text categorization into mask prediction problems similar to what language models are good at with the help of specific cue templates. In text categorization tasks based on BERT models, traditional fine-tuning methods directly adapt the model to the specific task, while cue learning directs the model to focus on information relevant to the categorization task while processing the original text by subtly adding cue templates to the input text.  
Taking medical text categorization as an example, if the original input text is \"What's the matter with high squamous epithelial cells in urine test?\", in a standard categorization task, the fine-tuned BERT model may directly output a symptom category label. However, in the cue learning strategy, we append a cue template specific to medical categorization to the input text to form a new input: \"What's the matter with high squamous epithelial cells in urine test, this is a community medical Q&A short text categorization task. The category is: [MASK][MASK][MASK][MASK]\". At this point, the BERT model not only analyzes the original text content, but also predicts the medical category corresponding to the mask position based on the guidance of the cue template when making predictions. This mode of transforming the categorization task into mask prediction allows the model's output to shift from simple labels to specific medical category names, such as \"Urinary Tract Infection\" or \"Kidney Disease\".  
In this way, cue learning prompts the model to focus more on the demands of the current task, improving the accuracy and relevance of the classification. At the same time, cue learning also alleviates the model's reliance on comprehensive fine-tuning, allowing the model to adapt more quickly to new classification tasks (Figure 4).