esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Material and Methods
### Characterization of Nanoparticles
#### 2.2.7 Drug Distribution in GIT  
Male Sprague-Dawley rats (200-250 g) were fasted for 18-20 h before the commencement of the experiment. Rats were divided into 5 groups with 15 rats in each group. The first group (control) received oral administration of an aqueous solution of sitagliptin. The second to fifth groups received sitagliptin nanoparticles. Oral administration of nanoparticles was performed by diluting 20 mgof nanoparticles in 1.0 mL saline, and injected using a rubber tube without anesthesia. Three rats were sacrificed each after 1, 3, 5, 8, and 12 h of administration. Stomach (Section 1) along with the entire small intestine were isolated and further subdivided into six sections (Sections 2-7; length of each section, 14 cm). The obtained stomach and intestinal sections were then cut open to expose the inner mucosal surface. All nanoparticles located in each part of the section were then harvested using a spatula to scratch the nanoparticles off the mucosa. The mixture was then masked using a homogenizer to extract sitagliptin, and the solution was then left for 24 h before centrifuged at 800 \(\times\) g for 20 min, and the supernatant was then determined using a spectrophotometer.
# Proposed New Analytical Method of Tower Load in Large-Span Arch Bridge Cable Lifting Construction
## 2 Proposed Calculation Method
### The Acting Force of Towing Cable to Tower  
Figure 5 shows the schematic diagram of calculating the forces acting on the tower from the towing cable. The role of the towing cable is to tow a trolley for transportation on the main cable. The towing winch is set at the left bank, and the geometrical layout of the towing cable is shown in Figure 5.  
Figure 5: Schematic diagram of calculating the forces acting on a tower from a towing cable: (**a**) left bank; (**b**) right bank.  
The horizontal and vertical forces on the tower tops at the left and right banks from the towing cable are \(H_{4}\), \(V_{4}\), \(H_{5}\), and \(V_{5}\), respectively, which can be formulated as:  
\[H_{4}=\eta^{2}F_{T}\cdot cos\beta+W_{3}\cdot cos\gamma-(\eta F_{T}+W_{3})\times cos \theta_{1} \tag{27}\]  
\[V_{4}=-\eta^{2}F_{T}\cdot sin\beta+W_{3}\cdot sin\gamma+(\eta F_{T}+W_{3})\cdot sin \theta_{1} \tag{28}\]  
\[H_{5}=\eta^{2}F_{T}\cdot cos\beta+W\cdot cos\gamma+\sqrt{\left(\eta^{2}F_{T} \cdot cos\beta+W\cdot cos\gamma\right)^{2}+\left(\eta^{2}F_{T}\cdot cos\beta+ W\cdot sin\gamma\right)^{2}}\cdot cos\theta_{2} \tag{29}\]  
\[V_{5}=\eta^{2}F_{T}\cdot cos\beta+W\cdot sin\gamma+\sqrt{\left(\eta^{2}F_{T} \cdot cos\beta+W\cdot cos\gamma\right)^{2}+\left(\eta^{2}F_{T}\cdot cos\beta +W\cdot sin\gamma\right)^{2}}\cdot sin\theta_{2} \tag{30}\]  
where \(W\) represents towing force (kN),\(F_{T}\) represents running head force (kN), \(\gamma\) represents towing rise angle (\({}^{\circ}\)), and \(W_{3}\) represents post-traction relaxation tension (kN), which can be formulated as:  
\[W=G\cdot(sin\gamma+\mu\cdot cos\gamma)+2\eta^{2}F+q\cdot\frac{L_{5}^{2}}{8f_{ q}} \tag{31}\]  
\[W_{3}=q\cdot\frac{L_{5}^{2}}{8f_{q}} \tag{32}\]  
\[F_{T}=\frac{W}{\eta^{n}} \tag{33}\]  
\[\gamma=tan^{-1}\frac{f_{1}-L_{1}\cdot\Delta H/\left(L+L_{3}+L_{4}\right)}{L_ {1}} \tag{34}\]  
where \(\mu\) represents the resistance coefficient of the sliding trolley running, \(q\) represents the weight per unit length of the towing cable (kN/m), \(f_{q}\) represents the towing droop (m), \(L_{5}\) represents the post-traction length (m), and \(n\) represents the number of pulleys.
## 2 Theoretical Background of the Uncertainty Theory
### Dependent Chance Programming Model  
The use of a dependent chance programming (DCP) model in SCM seeks to optimize supply chain networks under correlated risks, such as multiple suppliers or transportation times. Recent studies have used DCP models to improve the robustness of production planning and distribution systems by accounting for supply chain uncertainty [6; 20]. Unlike conventional models, which assume independent random variables, DCP considers interdependencies among uncertain parameters--extending traditional deterministic models into stochastic ones. By taking the joint distribution of uncertainties into consideration, DCPhelps improve decision-making under correlated risks. For example, a holistic approach might be needed for both production schedules and delivery times when an interruption in raw material supply affects them simultaneously [20].  
Mathematical Formulation of DCP  
The DCP model optimizes decision variables \(x\) by combining both deterministic and probabilistic constraints. Typically, the objective function to be minimized is a combination of deterministic costs as well as expected costs due to uncertainties.  
\[\text{Minimize }Z=f(x)+\sum_{i=1}^{n}\mathbb{E}[C_{i}(\xi)]\]  
The notation \(f(x)\) is used when discussing deterministic parts of an expression, whereas \(\mathbb{E}[C_{i}(\xi)]\) represents uncertain costs. Probabilistic constraints are necessary to guarantee the operation of the system in cases where it is impossible to determine something precisely. They ensure certain performance levels under uncertainty:  
\[\Pr\{h_{k}(x,\xi)\leq d_{k}\}\geq\alpha_{k},\quad\forall k\]  
where \(h_{k}(x,\xi)\) represents the probabilistic constraints, \(d_{k}\) are thresholds, and \(\alpha_{k}\) are acceptable probabilities that satisfy the constraints.  
The model represents relationships between the parameters using copulas or joint probability distributions. For example, their joint distribution is denoted as \(\Pr(\xi_{1},\xi_{2})\) when \(\xi_{1}\) and \(\xi_{2}\) are dependent uncertain parameters.
## 1 Introduction
The choice of approaches regarding the thematic questions is crucial for successful modelling processes. Many different modelling approaches exist and each of these have a different degree of complexity, requiring different data and application skill levels, and are not equal for system conceptualisation, analysis, prediction or prospection [36; 37]. However, all the approaches provide some level of useful knowledge of agricultural systems and can help decision-making. Most agricultural models were developed from science knowledge for policy development and focus on agricultural sustainability and resilience [38; 39; 40; 41; 42]. Some of the available models group a wide range of indicators, such as the integrated Sustainable Development Goals model (iSDG, [40]), which is customisable for all countries, or the Integrated Valuation of Ecosystem Services and Tradeoffs (InVest), a multi-ecosystem-services modelling platform [43]. Other models only focus on one part of the system, such as crop models, water models, energy models, GHG emission calculators, or climate models. The selection of model thus depends on the application scale, the questions needing answers, system complexity, and data availability. With more than six decades of multidisciplinary contribution to concepts and tools for agricultural systems modelling, the scientific community considers models critical to make informed agricultural decisions [39]. However, today, only few countries seem to have either appropriate agricultural systems models or decision tools to support policy development under disruptive changes (i.e., EU with their CAPRI model, [41]). In New Zealand, but also more widely in other Pacific countries, there is an urgent need for assessing the different pathways and interventions for a sustainable future of the agricultural sector under disruptive changes. Numerical and participative modelling is an option considered by the New Zealand government, and has been requested by the industry and sectoral organisations to gain in global understanding of the future of NZ agriculture and help define efficient long-term policies. As several modelling options are available, there is a need of synthesis knowledge to assist modellers and model users in selecting the most appropriate option for future application. The aim of this paper is (1) to analyse agricultural system modelling approaches and (2) identify available model examples for thematic applications (3) to help address current NZ needs and support policy development. Agricultural system modelling knowledge is synthesised to help stakeholders with model selection and future model development. Agricultural system modelling approaches are analysed and currently available, and freely accessible (non-commercial) models are listed per thematic application. Model processes are described, as well as requirements in terms of skills and data needs, strengths, weaknesses, and the questions each is intended to answer. Discussion focuses on model usability and limitations.
## 5 Surface Auxiliary System  
### RECAS Surface Deployment System  
The RECAS surface deployment system primarily comprises a rotatable drilling mast, an auxiliary tower, a drilling winch, an auxiliary winch, and a base (Figure 7a). The mast is constructed as a single, independent truss with a length of either 1 m or 2 m. Featuring an interchangeable module-splicing structure, the height of the mast can be flexibly adjusted according to the length of the sonde. To precisely monitor cable tension and the initial sonde depth, a load cell and an encoder were installed on the top pulley assembly.  
The drilling winch was specially engineered to rapidly raise or lower the RECAS sonde within a dry hole in the firn layer. It is capable of holding cable up to 200 m in length and operates at a speed ranging from 0 to 240 m/h. To guarantee stability through drilling operations, a robust wooden foundation is constructed beneath the base of the mast, preventing subsidence and tilting. Moreover, a protective tent was set up to shield the equipment from wind and snow, thereby safeguarding the drilling process.  
The rotatable mast offers two primary operational positions: horizontal and vertical (Figure 7b). Along the mast, support blocks were fixed at 1 m intervals. When the mast is in the horizontal position, these blocks can function as workbenches for the disassembly and maintenance of the sonde. At the beginning of the drilling process, the mast is rotated vertically to suspend the sonde. An auxiliary winch and tower, along with a steel wire, are used to erect and lower the mast. Once the mast rotates to the vertical position, two support rods are utilized to connect the mast to the base. The length of the support rods can be adjusted via the double-headed thread located at their lower part. This adjustment mechanism allowed the controlling and gradual elevation of the mast to the vertical position and prevented sudden mast collapse when the center of gravity of the mast passed over the  
Figure 7: Structural diagram (**a**), working process (**b**), and photograph (**c**) of RECAS surface deployment system.  
fulcrum. After the whole sonde has penetrated the ice layer, the mast is laid horizontally, and all surface equipment is sheltered within the tent.
# Securing IPv6 Neighbor Discovery Address Resolution with Voucher-Based Addressing
## 2 Background and Motivation
### Motivating Attack Commonalities  
There exists a glaring commonality between the various NDP attack surfaces that present opportunities for on-path attacks: caching. Regardless of how the cached value is updated or exposed to poisoning, the efficacy of the attack relies solely on making a \"bad\" update to the target's Neighbor Cache. Attacks successfully poisoning a target's Neighbor Cache often then only need to maintain the malicious entries through the normative Neighbor Unreachability Detection (NUD) process. This observation reveals that by merely guarding the cache at the target node, through some form of sender validation or challenge-response authentication, attacks resulting in false updates to (or creations of) Neighbor Cache entries can be mitigated altogether.
## Appendix A Stability
### Stability Condition on Raman Effect  
Again, we consider the wave equation plus the below difference equation for the Raman effect,  
\[(1-\alpha)e_{0}\chi_{0}^{(3)}\frac{\tau_{1}^{2}+\tau_{2}^{2}}{ \tau_{1}\tau_{2}^{2}}\bigg{(}\frac{\partial}{\partial t}\vec{E}(\vec{r},t) \int_{0}^{t}\vec{\overline{g}}(t-\tau)|\vec{E}(\vec{r},\tau)|^{2}d\tau\] \[+\overline{\overline{g}}(t)|\vec{E}(\vec{r},t)|^{2}\vec{E}(\vec{ r},t)\bigg{)}+e_{\infty}e_{0}\frac{\partial}{\partial t}\vec{E}(\vec{r},t)= \frac{\partial}{\partial t}\vec{D}(\vec{r},t)\]  
By applying the operators, we determine the discretized expression:  
\[e_{0}\partial_{t}\vec{E}^{s}\bigg{(}e_{\infty}+\Psi\int_{0}^{s \Delta_{t}}\overline{\overline{g}}(s\Delta_{t}-\tau)|\vec{E}(\vec{r},\tau)|^{2 }d\tau\bigg{)}\] \[+e_{0}(1-\alpha)\chi_{0}^{(3)}\frac{\tau_{1}^{2}+\tau_{2}^{2}}{ \tau_{1}\tau_{2}^{2}}\overline{\overline{g}}(s\Delta_{t})\bar{\Delta}\Big{[} \vec{E}^{s}|\vec{E}^{s}|^{2}\Big{]}=\partial_{t}\vec{D}^{s}\]We calculate the characteristic polynomial replacing equations Equations (A1) and (A2) in the discretized wave equation and in the above expression. Then, we combine both derivation in the same formula,  
\[\Phi(y)=(y-1)^{2}\bigg{(}e_{\infty}+y^{2s-1}\Psi\bigg{(}y\int_{0}^{ \Delta_{t}}\overline{\overline{s}}(s\Delta_{t}-\tau)d\tau\] \[+\frac{\Delta_{t}\overline{\overline{s}}(s\Delta_{t})}{2}(y^{2}+ y+1)\bigg{)}|\widetilde{\overline{E}}_{0}|^{2}\bigg{)}+4y\epsilon_{\infty}v^{2}\]  
Under an approach analogous to the one utilizes in the previous case we have to link the power laser to the medium. That means that the scheme should verify the inequality,  
\[\epsilon_{\infty}\gg y^{2s-1}\Psi\bigg{(}y\int_{0}^{s\Delta_{t}}\overline{ \overline{\overline{s}}}(s\Delta_{t}-\tau)d\tau+\frac{\Delta_{t}\overline{ \overline{s}}(s\Delta_{t})}{2}(y^{2}+y+1)\bigg{)}|\widetilde{\overline{E}}_{0} |^{2}.\]  
We know that \(\int_{0}^{s\Delta_{t}}\overline{\overline{\overline{s}}}(s\Delta_{t}-\tau)d \tau\gg\frac{\Delta_{t}\overline{\overline{s}}(s\Delta_{t})}{2}\), hence, a reasoning similar to the previous section applies and we establish the stability condition \(|\widetilde{\overline{E}}_{0}|<\sqrt{\frac{\epsilon_{\infty}}{D_{f}(1-\pi) \chi_{0}^{(3)}\frac{\epsilon_{\infty}}{\tau^{2}+\frac{1}{2}}}{\tau^{2}}\int_ {0}^{\Delta_{t}}\overline{\overline{s}}(s\Delta_{t}-\tau)d\tau}\). Finally for a reason of normalization we find that \(|\widetilde{\overline{E}}_{0}|<\sqrt{\frac{\epsilon_{\infty}}{D_{f}(1-\pi) \chi_{0}^{(3)}}}\), together with the classic condition \(\Delta_{t}\leq\frac{n_{i}}{c_{0}}\sqrt{\frac{1}{\Delta_{\pi}^{2}}+\frac{1}{ \Delta_{\overline{\overline{s}}}^{2}}+\frac{1}{\Delta_{\overline{z}}^{2}}}\) are the stability conditions in this case. Once again, we observe that stability is related to the medium optical properties as well as the laser intensity when the Raman nonlinear effect is present.
# Numerical and Experimental Analysis of Labyrinth Seals with Rhomboidal Cells
## 3 Experimental Results  
After installing the new seal (Figure 16) in the TA, the experiments were performed by adjusting the temperatures, pressures, and mass flow rates to reproduce the same conditions under which the honeycomb cell had been experimentally tested (Table 8).  
The conditions at the different TA inlets for the rhomboidal cells are shown in Table 11. It is possible to note that only the Active Clearance Control (ACC) temperatures differ  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Temperature (K)** & **M9** & **M11** & **M8** & **T10** \\ \hline THC,num & 729 & 705.2 & 786.2 & 772.8 \\ TRB3,num & 722.9 & 699.5 & 784.4 & 767 \\ \(\Delta\)T & \(-6.1\) & \(-5.9\) & \(-1.8\) & \(-5.8\) \\ \hline \hline \end{tabular}
\end{table}
Table 10: Test 4: Numerical temperature of honeycomb and rhomboidal cells.  
Figure 16: Test Article (TA) stator land with R3 rhomboidal cells.  
Figure 15: Thermocouples placed in the seal region.  
from those previously used. This is because the ACC supply line is not equipped with an electrical heater, and therefore, the ACC temperature is not regulated, but it is simply measured [18].  
For this reason, to evaluate the impact due to lower ACC temperatures, especially in the shroud region (thermoouples M9 and M11), two numerical simulations were performed using the input conditions reported in Table 10.  
The exam of the numerical results revealed a more than limited effect in the shroud region, with a decrease of the temperature of about \(1\div 1.5\) K, which authorizes the comparison between the experimental results obtained with rhomboidal and honeycomb cells.  
Tables 12 and 13 compare the temperatures in the sealing region (see Figure 15) obtained using the two different types of cells.  
The experimental results show that by using the rhomboidal cells, a decrease of the temperature in the region of the labyrinth seal of about \(3\div 5\) K is obtained, which confirms the numerical forecast. If we compare this decrease with the mean temperature recorded in this region, the percentage reduction obtained is less than 1%. This observation allows us to conclude that while the new seal shows a positive effect in reducing the leakage flow, its impact from the thermal point of view is negligible.
## 3 Outcomes
### Users and Reach
In 2016, COMET released eight lessons as part of the US NWS Satellite Foundational Course for GOES-R (SatFC-G, [[https://www.meted.ucar.edu/training_module.php?id=1301](https://www.meted.ucar.edu/training_module.php?id=1301)]([https://www.meted.ucar.edu/training_module.php?id=1301](https://www.meted.ucar.edu/training_module.php?id=1301))), along with the production of other sponsor-requested satellite training lessons. In 2018, COMET released five lessons as part of the US NWS Satellite Foundational Course for JPSS (SatFC-J, [[https://www.meted.ucar.edu/training_module.php?id=1614](https://www.meted.ucar.edu/training_module.php?id=1614)]([https://www.meted.ucar.edu/training_module.php?id=1614](https://www.meted.ucar.edu/training_module.php?id=1614))), in addition to three case exercises and other satellite-focused training. In the first quarter of 2019, COMET released seven satellite-focused lessons: Two of these lessons compose part of a new distance learning course on Frontal Diagnosis while another three serve as core components of a Modifying NWP Output Course. Figure 10: MetEd users can voluntarily report their affiliation when registering in the system. A total of 44% of MetEd learners are affiliated with universities, both within the US and internationally. Learners identifying as “weather enthusiasts” and “other” also represent a substantial number of users, and government sector forecasters and scientists make up other user groups on MetEd. Lessons produced by COMET for the US NWS as part of the SatFC-G course have seen high usage. The full SatFC-G course, hosted on the Cooperative Institute for Research in the Atmosphere (CIRA) Satellite Hydrology and Meteorology (SHyMet) website ([[http://rammb.cira.colostate.edu/training/shymet/](http://rammb.cira.colostate.edu/training/shymet/)]([http://rammb.cira.colostate.edu/training/shymet/](http://rammb.cira.colostate.edu/training/shymet/))), contains 37 lessons and was published as a joint effort coordinated by the US NWS Satellite Training and Advisory Team. The team consisted of trainers from the NWS, COMET, CIRA, the Cooperative Institute for Meteorological Satellite Studies, and the NASA Short-term Prediction Research and Transition Center. This group, which also created the SatFC-J course that provides training for JPSS, worked together for multiple years to plan, produce, and make the full course available to forecasters prior to the GOES-16 satellite launch. These lessons are applicable not just to the US NWS but also to meteorological services in Canada, Central and South America, and the Caribbean. The Satellite Foundational Course lessons are mandatory for US NWS forecasters. In 2017 alone, at least 2500 NOAA staff accessed each of COMET's eight SatFC-G lessons. Usage statistics for these lessons are shown in Figure 12.  
Figure 11: New satellite meteorology lessons published per year (blue) and cumulative number of satellite meteorology lessons (green) on MetEd from 1999 to March 2019. As of early April 2019, COMET offers approximately 120 satellite meteorology lessons on MetEd.
## 4 Performance Evaluation
### Results and Discussion  
The purpose of the performed experimental evaluation is to assess the performance of different AI models in regressing and predicting the values of the ALCOA+ principles by identifying correlations with manufacturing line data. In Table 3, low-level statistical information (maximum, minimum, average, and standard deviation) are shown regarding the values contained withing the analyzed dataset.  
Based on these values, it is important to note at this point that for some of the ALCOA+ principles it was not feasible to extract meaningful patterns in the data. This was the case when the ALCOA+ values did not include any informative content because there was no value range (original, available, consistent and enduring). So, in the experiments presented in this work we only included the ALCOA+ principles that we had meaningful data on, specifically, the attributable, legible, contemporaneous, accurate, and complete principles.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline  & **LSTM** & **GRU** & **ALCOAi** \\ \hline Number of layers & 2 & 2 & - \\ \hline Number of hidden states & 256 & 256 & - \\ \hline Number of encoder layers & - & - & 4 \\ \hline Number of decoder layers & - & - & 4 \\ \hline Number of attention heads & - & - & 16 \\ \hline Dropout & 0.3 & 0.3 & 0.5 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Network hyperparameters identified as optimal for the LSTM, GRU, and ALCOAi models.  
\begin{table}
\begin{tabular}{l l l l l l l l l} \hline \hline
**ALCOA+ Principle** & **I600** & \multicolumn{6}{c}{**I1000**} \\ \hline  & **Min** & **Max** & **Mean** & **Std** & **Min** & **Max** & **Mean** & **Std** \\ \hline Attributable & 25 & 50 & 48.17 & 2.13 & 0 & 50 & 47.8 & 3.34 \\ \hline Legible & 75 & 94 & 83.1 & 3.34 & 79 & 91 & 84.66 & 1.71 \\ \hline Contemporaneous & 62 & 95 & 87.65 & 6.31 & 50 & 94 & 87.45 & 4.62 \\ \hline Original \({}^{\star}\) & 100 & 100 & 100 & 0 & 100 & 100 & 100 & 0 \\ \hline Accurate & 0 & 50 & 37.22 & 6.43 & 0 & 100 & 35.41 & 5.74 \\ \hline Complete & 84 & 98 & 96.76 & 2.85 & 83 & 100 & 97.2 & 1.82 \\ \hline Consistent \({}^{\star}\) & 100 & 100 & 100 & 0 & 100 & 100 & 100 & 0 \\ \hline Available \({}^{\star}\) & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\ \hline Enduring \({}^{\star}\) & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Statistical information for each of the ALCOA+ principles used in the evaluation. The principles with an asterisk did not contain any meaningful data.
# Personalized Privacy Protection Based on Space Grid in Mobile Crowdsensing
## 4 Personalized Privacy Protection
### Personalized Private Space Decomposition Framework Design
#### 4.1.3 Spatial Grid Division Strategy
Personalized dynamic spatial meshing strategy PAG: For personalization, we set the privacy budget percentage \(\alpha\) to a random value. Refer to Algorithm 1 for the specific algorithm. Table 1 presents a description of the special symbols utilized in Algorithm 1, while Figure 3 serves as an aid for comprehending the flowchart of Algorithm 1. According to References [34; 35], we understand that the Laplace mechanism can introduce noise to the spatial index of the dataset, ensuring that the output of the counting query function satisfies the requirements of \(\varepsilon\)-differential privacy.