esa-sceva/satcom-chunk-collection huggingface dataset:
# Design Parameters Affecting Rill Swell Events for Block Caving Applications
## 6 Conclusions  
This numerical study analyzed the magnitude of RS events associated with a hang-up release mechanism, modeling different drawpoint geometries and amounts of fine material  
Figure 11: RS event simulation with different rock densities **(a)** plan view of RS event for rock density of 2.0 t/m\({}^{3}\), **(b)** profile view of RS event for rock density of 2.0 t/m\({}^{3}\), **(c)** plan view of RS event for rock density of 3.4 t/m\({}^{3}\), **(d)** profile view of RS event for rock density of 3.4 t/m\({}^{3}\).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{**Density (t/m\({}^{3}\))**} \\ \cline{2-4}
**Parameter** & **2.0** & **2.7** & **3.4** \\ \hline Tonnage & 184 & 257 & 341 \\ Run-out distance towards the north (+; m) & 8.3 & 6.4 & 10.1 \\ Run-out distance towards the south (\(-\); m) & 4.1 & 9.3 & 6.1 \\ Large rocks in production drift? & No & Yes & Yes \\ Deposition angle in direction +, 1 (m) over the floor (\({}^{\circ}\)) & 5 & 8 & 10 \\ Deposition angle in direction \(-\), 1 (m) over the floor (\({}^{\circ}\)) & 5 & 7 & 10 \\ Height of flow (m) & 1 & 1.1 & 1.4 \\ \hline \hline \end{tabular}
\end{table}
Table 8: RS results simulating different rock densities.  
over the hang-up. The numerical method was useful to replicate the RS events studied. Here, it was observed that the effective drawpoint height and the amount of fine material over the hang-up influenced the material flow. Based on the tonnage of fine material available over the hang-up, each drawpoint geometry was associated with a maximum expected RS event. The drawpoint geometry in terms of height and width were found to highly influence RS event magnitude, almost independently of the amount of fine material over the drawpoint. In our model, when the height of the brow beam increased by 50%, the amount of fine material in the RS event decreased by 51.4%. Given these results, the design of drawpoints should be evaluated and adjusted accordingly if there is a risk of RS events. Narrower drawpoints with higher brow beams could significantly decrease the magnitude of an RS event. If there is a potential risk of an RS event due to dry and fine material, the methodology used here can be used to anticipate the magnitude of an RS event.  
Conceptualization, R.C. and C.V.; Methodology, R.C. and C.V.; Software, C.V. and R.G.; Calibration, C.V.; Formal analysis, R.C., C.V. and R.G.; Investigation, R.C. and C.V.; Resources, R.C. and K.S.; Writing--original draft preparation, C.V. and R.G.; Writing--review and editing, K.S. and K.Z.; Visualization, C.V.; Supervision, R.C.; Funding acquisition, R.C. and K.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by CONICYT/PIA Project AFB230001. This research was partially funded by the FONDECYT Regular 1230749. The APC was funded by AGH University of Krakow, scientific subsidy under number: 16.16.100.215.
# Formal Analysis of DTLS-SRTP Combined Protocol Based on Logic of Events
## 3 Logic of Events
### Expansion of Logic of Events Theory
#### 3.4.2. Sequential Combination Rules  
The sequential composition rule ensures the correct combination of sub-protocols. Assuming the post-event of protocol \(Q_{1}\) is \(e\) and the pre-event of protocol \(Q_{2}\) is \(e^{{}^{\prime}}\), then there exists a sequential composition \(\forall e,e^{{}^{\prime}}\in(E(Send)\lor E(Rcv)\lor E(Encrypt)\lor E(Decrypt))\). The events \(e\) and \(e^{{}^{\prime}}\) each contain relevant information and include the atoms \(a\) and \(b\), i.e., \(e\) has \(a\) and \(e^{{}^{\prime}}\) has \(b\). The specific definition of \(e\) has \(a\) is as follows:  
\[\begin{array}{c}\text{e {has }}a\equiv_{def}\\ (e\in E(New)\wedge New(e)\text{ {has }}a)\vee\\ (e\in E(Send)\wedge Send(e)\text{ {has }}a)\vee\\ (e\in E(\text{Re}\,ceive)\wedge\text{Re}\,ceive(e)\text{ {has }}a)\vee\\ (e\in E(Encrypt)\wedge Encrypt(e)\text{ {has }}a)\vee\\ (e\in E(Decrypt)\wedge Decrypt(e)\text{ {has }}a)\vee\\ (e\in E(Sign)\wedge Sign(e)\text{ {has }}a)\vee\\ (e\in E(Verify)\wedge Verify(e)\text{ {has }}a)\end{array} \tag{4}\]  
If the post-event output of protocol \(Q_{1}\) matches the pre-event input of protocol \(Q_{2}\), then the protocols \(Q_{1}\) and \(Q_{2}\) can be sequentially composed, and as a result,  
\[\begin{array}{c}\left(\begin{array}{c}\forall Q_{1},Q_{2}:(Q_{1}\
eq Q_{2} ),\forall e,e^{{}^{\prime}}:(e\in Q_{1}\,,e^{{}^{\prime}}\in Q_{2})\forall a,b: Atom\\ Matching(a,b)\Leftrightarrow Matching(b,a)\wedge\\ Send(e)=Rcv(e^{{}^{\prime}})\lor Sign(e)=Verify(e^{{}^{\prime}})\lor Decrypt(e )=Encrypt(e^{{}^{\prime}})\end{array}\right)\end{array} \tag{5}\]  
**Lemma 1**.: _If the protocol \(Q\) is a sequential composition of protocols \(Q_{1}\) and \(Q_{2}\), and if the following formulas hold for the subjects \(A\) and \(B\) in protocol \(Q\), we can conclude that the invariant set \(\Gamma_{1}\cup\Gamma_{2}\) also holds for protocol \(Q\)._  
\[\begin{array}{c}\left(\begin{array}{c}\forall e:E,\forall A,B:Id,A\in Q_{1 },B\in Q_{2}\\ \forall thr_{1},thr_{2},(inOneof\,(e,thr_{1},bss,A)\wedge inOneof\,(e,thr_{2},bss,B)) \end{array}\right)\Rightarrow Q\supset\Gamma_{1},\Gamma_{2}\end{array}\]
## 2 Atmospheric Spectroscopy
### Direct Absorption Spectroscopy (DAS) Technique  
The simplest realization of laser-based absorption techniques, and therefore well adapted to in situ measurements, is Direct Absorption Spectroscopy. The beam of a tunable laser passes through a gas sample and then is measured with a detector. If the frequency of the emitted light is close to a molecular transition of the gas sample, the light is then absorbed and the transmitted intensity decreases. The concentration of the absorbing species in the gas mixture is then calculated according to Lambert-Beer's law [9].
## 3 Calculation Method of the Backscattered Light of the Taiji Telescope
### Analysis of the Proportion of Scattered Light Energy by the Mirrors of the Taiji Telescope  
Before performing surface roughness and cleanliness analysis, it is necessary to clarify the contribution of each optical surface to scattering. We placed a parallel light source with a wavelength of 1064 nm in front of the telescope's exit pupil and set a fully absorbing target surface (1 mm in diameter) at the exit pupil to receive the backscattered light. At the same time, we also set the corresponding light aiming area. In this paper, the Harvey-Shack model is used to describe the scattering under different surface roughness. The Harvey-Shack model is a common model for BSDF modeling of optical surfaces and is supported by most stray light analysis software [26; 27; 28]. In the analysis of this section, we set the roughness of each optical surface to 5 A.  
The scattering energy analysis results of each mirror are shown in Table 2, and the main scattering paths are shown in Figure 3. We can see that the scattering energy from the M4 and the M3 accounts for a considerable part. Among them, the M4 produces the first-order scattering with the largest energy, while the scattering energy from the M1 is the lowest.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Optical Surface** & **Scattered Energy** & **Percentage** \\ \hline M1 & \(2.27\times 10^{-12}\) & 0.03\% \\ M2 & \(1.35\times 10^{-10}\) & 1.72\% \\ M3 & \(2.42\times 10^{-9}\) & 31.02\% \\ M4 & \(5.25\times 10^{-9}\) & 67.22\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: The proportion of scattered energy of each mirror.
## 4 Results
### Moment-Rotation Curve
As the load increased, the pocket was gradually distorted, and the crack propagated in the welded part. Thus, the specimen with the lateral rotation represented the smallest moment capacity. In comparing the mean values of the maximum moment according to the rotation direction, it was \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Rotation Direction**} & \multirow{2}{*}{**Labels**} & \multicolumn{2}{c}{**Maximum Moment (kNm)**} \\ \cline{3-4}  & & **Mean** & **SD** \\ \hline Downward rotation & DN-1\(-\)DN-30 & 0.934 & 0.038 \\ Upward rotation & UP-1\(-\)UP-30 & 1.942 & 0.117 \\ Lateral rotation & LAT-1\(-\)LAT-30 & 0.321 & 0.019 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Maximum moments of specimens for each rotation direction. Figure 13: Moment–rotation curves divided according to the rotation direction, with references to Eurocode 3.  
found that the maximum moment of the upward rotation increased to 2.08 times that of the downward rotation, but the maximum moment of the lateral rotation decreased significantly to 0.34 times that of the downward rotation. In the connection classifications of Eurocode 3 [19], if the connection's stiffness belongs in the nominally pin connection or rigid connection regions, the connection can be a nominally pin or rigid connection during the design. In the case of the lateral rotation in Figure 13, the connection can be considered a hinge; however, the initial rotational stiffness of the rotation directions other than lateral rotation was such that they are both located in the semi-rigid region. These results mean that it will be difficult to accurately predict the overall safety of system scaffolding if its connections are assumed to be hinges when a structural analysis is performed on a system scaffolding structure. Therefore, to perform an accurate structural analysis on scaffolding structures with pocket-type connections, the connections should be treated as semi-rigid connections when defining the stiffness of the connections' upward and downward rotations. In addition, there are differences in the moment-rotation curves and maximum rotation according to the rotation direction; therefore, individual rotation stiffness models should be used for each rotation direction. On the other hand, depending on the designer's engineering judgment, it may be possible to consider the connection as a hinge instead of a stiffness model in the case of horizontal rotation.
## 5 Electrical Characterization
### Temperature Testing
#### 5.4.1 Response Characteristics  
To gain a comprehensive understanding of the electrical characteristics of the MRAM PUF across different temperatures, we conducted enrollments with 30 MRAM devices at three specific temperatures: 0 \({}^{\circ}\)C, 23 \({}^{\circ}\)C, and 80 \({}^{\circ}\)C. The detailed results are provided in Table 3.  
Upon analyzing the data, several patterns emerged. Firstly, in the low-resistance state, we observed that, as the temperature increased, the responses also increased. Conversely, in the high-resistance state, as temperature decreased, responses decreased.  
Furthermore, we noticed that, as the temperature increased, the variation in the resistance between cells within a single device (intra-cell variation) increased, while the variation in the resistance between different devices (inter-cell variation) decreased. This trend led to a lower inter-cell to intra-cell variation ratio. However, even at 80 \({}^{\circ}\)C, the inter-cell to intra-cell variation ratio remained higher than 20 for both resistive states, which is more than enough for reliable bit generation.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**High-Resistance State**} & \multicolumn{3}{c}{**Low-Resistance State**} \\ \hline
**MRAM Response Statistics** & **0\({}^{\circ}\)C** & **23 \({}^{\circ}\)C** & **80 \({}^{\circ}\)C** & **0 \({}^{\circ}\)C** & **23 \({}^{\circ}\)C** & **80 \({}^{\circ}\)C** \\ \hline Maximum & \(T1\times 1207\) & \(T\times 1203\) & \(T2\times 1.95\) & \(L1\times 1.3807\) & \(L\times 1.3666\) & \(L2\times 1.3226\) \\ \hline Average & \(T1=T\times 1.00375\) & \(T\) & \(T2=T\times 0.9816\) & \(L1\times 1\times 0.9952\) & \(L=T\times 0.8521\) & \(L2=L\times 1.00440\) \\ \hline Minimum & \(T1\times 0.8843\) & \(T\times 0.8914\) & \(T2\times 0.8947\) & \(L1\times 0.8962\) & \(L\times 0.8980\) & \(L2\times 0.8993\) \\ \hline Range & \(T1\times 0.3232\) & \(T\pm 0.3118\) & \(T2\times 0.3001\) & \(L1\times 0.8484\) & \(L\times 0.4676\) & \(L2\times 0.4231\) \\ \hline Average bit-line & \(T1\times 0.005873\) & \(T\times 0.005794\) & \(T2\times 0.005646\) & \(L1\times 0.005331\) & \(L\times 0.0058262\) & \(L2\times 0.005011\) \\ \hline Average intra-cell variation & \(T\times 0.000231\) & \(T\times 0.000231\) & \(T2\times 0.000240\) & \(L1\times 0.000201\) & \(L\times 0.000203\) & \(L2\times 0.000217\) \\ \hline Inter-cell to intra-cell & \multirow{2}{*}{25.44} & \multirow{2}{*}{25.03} & \multirow{2}{*}{22.78} & \multirow{2}{*}{26.55} & \multirow{2}{*}{25.88} & \multirow{2}{*}{23.09} \\ variation ratio & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Relative analog response statistics for 30 MRAM devices at 0 \({}^{\circ}\)C, 23 \({}^{\circ}\)C, and 80 \({}^{\circ}\)C.
# Size Effect on the Acoustic Emission Behavior of Textile-Reinforced Cement Composites
## 2 Experimental Details  
Three series of four samples each were tested in a lab under three-point bending. The composite consisted of a wollastonite-based powder matrix (IPC, CaSiO\({}_{3}\)) mixed with a 1%vf ALESTA HR polymer agent. Thin plates were cast with the hand lay-up method by impregnating 8 layers of randomly chopped glass fiber textile mats reaching a 20% volume fraction. The plates were cast into curved molds with both the length and width fixed at 300 mm. The height was set at 150 mm. The 4-mm thick plates were cured at 60 \({}^{\circ}\)C for 24 h after casting. Then, the plates were cut into samples with varying widths of 25, 50 and 300 mm. After curing at room temperature for at least 24 h more, the samples were tested for bending until failure using an INSTRON testing bench with a load cell capacity up to 10 kN. The test was displacement controlled, and the point of load application in the middle of the beam was moving downward with a constant speed of 0.02 mm/s. This relatively low speed was applied in order to allow the slow development of fractures, avoiding interactions between different fracture sources as much as possible. The sample was confined along both sides (Figure 3a,b).  
Four AE sensors were mounted at the top side of the sample with the use of tape in the configuration illustrated in Figure 3c. A thin layer of vacuum grease between the sensors and the specimens was applied for acoustic coupling. Resonant (150 kHz) R15 sensors, provided by Mistras Group, were used. Three of the AE sensors were positioned at equal distances of 60 mm at one side, while the fourth one was placed at the opposite side of the sample, thus fully covering the fractured zone. The received signals were preamplified by 40 dB, while the low noise environment enabled the application of a quite low and sensitive threshold of 35 dB. The sampling rate was 2 MHz, while the acquisition system was a Micro-II digital AE system from Mistras Group. The signal \"timing\" parameters used to separate successive signals and measure their parameters were as follows: hit definition time = 200 us, peak definition time = 100 us, maximum hit duration = 1 ms and hit lock out time = 800 us. These settings may have influenced the absolute value of the measured parameters and were compatible with the manufacturers' recommendations and our own experience.  
Figure 2: Schematic representation of AE in curved plates with different widths. Corresponding to (**a**) a beam of 25 mm width (**b**) a beam of 50 mm width, (**c**) plate with 300 mm width.
# Comparative Evaluation of Remote Sensing Platforms for Almond Yield Prediction
## 2 Materials and Methods
### Data Collection and Processing  
The data processing workflow consists of four sequential steps (Figure 2). In the first step, the data acquired from various sources is collected, including the agronomic parameters, vegetation indices, and climate data (identified in the following subsections). The second step involves the integration of various features into a dataset comprising 171 features. The third step includes the application of ML regression models, which includes the feature selection process, the selection of ML regression models, and hyperparameter optimization. In the fourth step, the model evaluation is conducted. The details of the four-step approach are provided in the subsequent subsections.
# Recent Advances and Insights into Bromelain Processing, Pharmacokinetics and Therapeutic Uses
## 8 Conclusions  
Bromelain extract is an interesting nutraceutical to be potentially used as an adjuvant in several conditions, including cardiovascular diseases, skin disorders, sport injuries, sinusitis and inflammatory diseases. However, the poor quality of clinical studies and the high cost of bromelain extraction and purification represent the main limitations to the use of these extracts. In this regard, new comprehensive studies are needed to validate the efficacy and safety of clinical applications and long-term RCTs must be designed before therapeutical recommendations. Further investigations of the mechanisms of action are also required. In addition, despite the broad array of studies on the applications of unconventional extraction methods, there is still a lack of guidelines for the processing steps required to obtain the highest yields and enzymatic activity.  
Finally, new studies on the same matrices, comparing classic and non-conventional techniques or their combination, are also needed to optimize extract yield and activity, which may lead to an economically viable product.  
Conceptualization, A.C., S.A. and G.C.; literature search A.C., M.M. and S.L.; writing--original draft preparation, A.C. and S.L.; writing--review and editing, S.A. and G.C.; supervision, G.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the University of Turin and by the Ministry of Science and Higher Education of the Russian Federation: World-Class Research Center, Sechenov First Moscow State Medical University.  
Not applicable.  
This work was carried out under the auspices of the University of Turin (Ricerca Locale 2020) and the Ministry of Science and Higher Education of the Russian Federation (World-Class Research Centers agreement \(N\) 075-15-2020-926).  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Purification Approach** & **Technique** & **Advantages** & **Limits** \\ \hline \multirow{3}{*}{Classic methods} & Centrifugation & \multirow{3}{*}{\(\uparrow\) yield during} & \multirow{3}{*}{\(\downarrow\) enzymatic yields} \\  & Ultrafiltration & & \\  & Lyophilisation & pre-purification steps & \\ \hline \multirow{6}{*}{Unconventional methods} & Gel filtration & \(\uparrow\) selectivity and purity of the & \\  & Ion exchange chromatography & \(\uparrow\) enzymatic yields & \\ \cline{1-1}  & Affinity chromatography & \(\downarrow\) number of processes for & \\ \cline{1-1}  & Aqueous two-phase extraction & final bromelain extract & \\ \cline{1-1}  & Reversed micelle extraction & \(\uparrow\) efficacy of bromelain & \\ \cline{1-1}  & & purification & \\ \hline \end{tabular}
\end{table}
Table 4: Advantages and limits of bromelain purification using classic or unconventional methods.
## 2 Materials and Methods
### Method  
#### 2.3.1 NDVI and VV + VH Backscattering Time Series  
SPOT-5 Bands B2 (Red) and B3 (NIR) were used to compute an NDVI image for each epoch [29]. Based on these NDVI images, it was possible to calculate the average NDVI and the standard deviation values for each crop parcel. SPOT-5 Take-5 time series graphs for each crop type (pasture, maize, soybean and bean) and for each epoch were generated to assess the behavior of each crop throughout the entire growing season using the trend analysis of the NDVI values.  
Likewise, the gamma VV + VH bands were used to determine the mean value for each crop type and for each epoch. After calculating the mean values, they were converted from power scale values into logarithmic scale values to correctly represent dB values using the following equation (Equation (2)):  
\[\gamma_{i}\left(dB\right)=10\,\times\log_{10}\left(\gamma_{i}\right) \tag{2}\]  
where \(\gamma_{i}\) is the gamma-calibrated backscattering coefficient obtained from Equation (1) for each pixel of the SAR images.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**KIBALA (Catofe)**} & \multicolumn{3}{c}{**WAKO-KUNGO**} \\ \cline{2-7}
**Month** & \begin{tabular}{c} **Average Air** \\ **Temperature** \\ **(\(\mathrm{\SIUnitSymbolDegree}\)C)** \\ \end{tabular} & \begin{tabular}{c} **Precipitation** \\ **(mm)** \\ \end{tabular} & \begin{tabular}{c} **Solar** \\ **Radiation** \\ **(\(\mathrm{\SIUnitSymbolDegree}\)M\(\mathrm{\SIUnitSymbolDegree}\)\(\mathrm{\SIUnitSymbolDegree}\)\(\mathrm{\SIUnitSymbolDegree}\))** \\ \end{tabular} & \begin{tabular}{c} **Average Air** \\ **Temperature** \\ **(\(\mathrm{\SIUnitSymbolDegree}\)C)** \\ \end{tabular} & \
\begin{tabular}{c} **Precipitation** \\ **(mm)** \\ \end{tabular} &
\begin{tabular}{c} **Solar** \\ **Radiation** \\ **(\(\mathrm{\SIUnitSymbolDegree}\)M\(\mathrm{\SIUnitSymbolDegree}\)\(\mathrm{\SIUnitSymbolDegree}\)\(\mathrm{\SIUnitSymbolDegree}\)\(\mathrm{\SIUnitSymbolDegree}\))** \\ \end{tabular} \\ \hline Mar & 20.0 & 232.6 & 185.91 & - & 2.0 & 179.81 \\ April & 19.6 & 131.0 & 214.44 & - & 0.0 & 194.50 \\ May & 20.5 & 18.7 & 232.69 & 19.3 & 9.0 & 212.94 \\ June & 18.6 & 1.6 & 212.56 & 19.1 & 222.0 & 204.37 \\ July & 18.9 & 1.8 & 192.75 & 19.9 & 0.0 & 185.23 \\ August & 18.7 & 0.7 & 192.62 & 21.0 & 0.0 & 189.03 \\ September & 19.3 & 10.3 & 210.84 & 21.7 & 2.8 & 188.32 \\ October & 20.4 & 268.9 & 213.92 & 22.0 & 0.0 & 210.08 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Monthly average air temperature, precipitation and solar radiation values for the Kibala and Wako-Kungo stations of the SASSCAL WeatherNet.
# Security and Privacy in Physical-Digital Environments: Trends and Opportunities
## 5 Discussion and Future Directions
### Future Directions
#### 5.2.2 Cloud-Edge-End Orchestrated Secure  
In the physical-digital environments, the gathering of vast amounts of multi-sensory and multimodal information from the real world is a challenge that can be addressed through the orchestration of cloud-edge-end computing. This approach leverages the collaborative and dynamic sharing of computation, communication, and storage resources among various entities to enhance the quality of experience (QoE) for users/avatars and the quality of service (QoS) for MR and Metaverse services.  
Cloud-edge-end computing [44] involves a distributed architecture that spans across cloud data centers, edge devices, and end devices. By leveraging this architecture, it is possible to process data closer to the source, improving the speed and efficiency of data processing. This approach can also help with edge intelligence and user privacy protection by aggregating and processing user private data on edge devices.  
Furthermore, cloud-edge-end computing can help address the challenges of data transmission in the physical-digital environments. With collaborative use of computational resources, it is possible to reduce the amount of data that need to be transmitted, thereby reducing network congestion and latency.