esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methodology
### Definition of Archetypes  
To study the seismic response of shear wall buildings located on sloped ground, a set of archetypes was developed to represent the architectural typology typically used for mid-rise residential buildings, following a structural layout commonly employed in Chile.  
The archetypes considered in this research consisted of four nine-story buildings, each structured with reinforced concrete shear walls in both orthogonal directions, and with reinforced concrete beams and slabs. A simple structural configuration was adopted, starting from a symmetric distribution of the walls in both directions. Figure 2 shows a floor plan view of this configuration, along with the coordinate axis system used to define the two orthogonal directions of the building structure. It is important to note that the structural response in both directions was analyzed independently, since different slope inclinations induced changes in the dynamic characteristics in the direction perpendicular to the slope (the x-axis in Figure 2).  
Figure 1: A summary of the FEMA P695 methodology.  
The reference case was a prismatic building with identical floor plans at all levels, as shown in Figure 3. This structure represented the baseline configuration, assumed to be founded on flat ground. From this initial archetype, three additional configurations were derived by varying the slope angle to 15\({}^{\circ}\), 30\({}^{\circ}\), and 45\({}^{\circ}\). In all cases, the building was assumed to geometrically conform to the natural slope of the terrain, meaning it was directly embedded on the hillside without the use of terraces, intermediate platforms, or cut-and-fill operations.  
Figure 3: An isometric view of the archetype founded on completely horizontal ground (0\({}^{\circ}\)).  
Figure 2: A plan view of the typical floor layout of the archetypes studied. The shaded black elements represent the shear walls, while the unshaded elements correspond to the beams.  
It should be noted that the slope of the terrain induced significant modifications in the geometry of the structures. To better illustrate how the terrain inclination affected the configuration of the archetypes, Figure 4 shows the elevation views of the four models. As seen in Figure 4b, the shear walls in the last bay of the archetype, founded on a 15\({}^{\circ}\) slope, were reduced to adapt to the terrain. A similar situation occurred for the archetype on a 30\({}^{\circ}\) slope, though the modifications were more severe, as the slab at the first level had to be reduced due to the removal of the supporting shear walls at the rear of the building.  
Figure 4: Elevation views of the archetypes with slope inclinations of (**a**) 0\({}^{\circ}\), (**b**) 15\({}^{\circ}\), (**c**) 30\({}^{\circ}\), and (**d**) 45\({}^{\circ}\).  
A comparable scenario was observed in the archetype placed on a 45\({}^{\circ}\) slope, where the geometric adjustments were even more substantial, not only due to the elimination of wall segments at certain levels but also because of the consequent reduction in slab areas that could no longer rely on those walls for support.
## 4 Potentials and Challenges
### Comparison of the Concepts  
The concepts presented differ in terms of their design and therefore offer advantages and disadvantages with regard to certain criteria. The criteria defined cover different aspects, while a qualitative and experience based assessment of the concepts can be found in Table 3.  
It can be seen that in total the advantages and disadvantages of both concepts are almost balanced. An improvement in the truss concept can be achieved by the fact that conventional trolleys can also be used. Up to now, a special design has been required which is a significant cost driver. However, this also implies a structural modification so that the height of the hollow sections used must be accordingly reduced. This, in contrast, conflicts with the geometric constraint that the traction means should run in them. Although the box section design offers the advantage of using conventional trolleys, it faces mechanical issues in particular. These include the elastic stability of the segments as well as the weld seams at the joints. From these findings, potential for improvement can be identified. As part of a redesign of the structure, it should be made more robust in terms of its elastic stability. Another approach for improvement is to take suitable design measures to relieve the weld seams at the joints. In addition, the setups of the prototypes are depicted in Figure 18. The upper three figures show the crane bridge in truss construction. Here, Figure 18a,b show a crane bridge with a span width of 12 m and a load capacity of 5 t. On the other hand, the crane bridge depicted in Figure 18c has a span width of 6.1 m and a load capacity of 3.2 t. Figure 18d-f show the crane bridge in box section design. As a first functional model, it has a span width of 3 m and is designed for a load capacity of 3 t.  
For the first prototypes, the structures were also analyzed in FE analyses, and a detailed simulative study was carried out for the box section design in [26] and for the truss design in [25]. This shows that the [PERSON] yield criterion was not exceeded at the critical points of the design and that no gaping occurs at the segment joints due to the applied pretension. Still, the aspect of buckling (i.e., the occurrence of negative eigenvalues in the stiffness matrix) with an increase in the required prestressing force is the design driving factor for the box section design in terms of a later practical use.
## 5 Conclusions  
In this work, kinematics and dynamics were obtained for a proposed CDPR system. An overall workspace was analyzed for the CDPR system considering nonlinear tension. Then, experiments were conducted to identify the dynamic modulus of elasticity based on the DMA method, and the results showed that the dynamic modulus depends on various frequencies. Based on the nonlinear cable model, the kinematics equation that includes the nonlinear model was modified and an overall workspace analysis was carried out for a wrench-feasible workspace at various accelerations. As a result, the CDPR system was shown to have a large workspace. At the outer surface, high cable tension generally appeared at the vertices and edges of the simulation region. Moreover, as the acceleration increased, the whole tension increased, and the maximum tension was achieved at 100 m/s\({}^{2}\). The workspace was satisfactory in the case of the end-effector moving in the \(X\)- and \(Y\)-axis limits from \(-0.3\) to 0.3 m. When the axis ranges were increased from \(-0.4\) to 0.4 m, the cable tensions exceeded the constrained condition, and these positions were eliminated from the workspace.  
Figure 10: The wrench-feasible workspace simulation at the acceleration of 100 m/s\({}^{2}\) when extending the axis ranges: (**a**) front plane of the \(X\)-axis; (**b**) back plane of the \(X\)-axis; (**c**) front plane of the \(Y\)–axis; (**d**) back plane of the \(Y\)–axis.  
**Author Contributions:** Writing--original draft preparation, V.N.D.K.; writing--review and editing, V.N.D.K. and S.-C.H. All authors have read and agreed to the published version of the manuscript.  
**Funding:** The authors acknowledge and thank the Ministry of Science and Technology of the Republic of China for their partial financial support of this study under Contract Number MOST 110-2221-E-992-062.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 4 Experimental Results
### Setting
#### 4.1.2 Implementation  
The Adam optimizer was employed to train our network. We set the initial learning rate (lr) to 0.0005, the epochs to 1000 (halved every 200 epochs), the batch size to 16, and the base filter number to 32. As is the case for SRFBN-S [21], we performed the dual mutual-feedback procedure four times.
# Comparing Application-Level Hardening Techniques for Neural Networks on GPUs
## 2 Background
### Software Solutions for Reliability Enhancement of NNs
#### 2.3.2 Ranger  
Ranger places the HardTanH after each NN layer, assigning proper lower and upper bounds to the activation functions of all layers in the NN [14]. The HardTanH function is an activation function that allows an entry-wise check on the input feature map to be performed. While performing inference on 20% of the training dataset, the upper and lower bounds are computed as the minimum and maximum entries recorded from the activation functions that belong to the original NN architecture. After applying Ranger, if the input value exceeds the upper or lower bound, it will be set to the upper or lower limit accordingly. If it lies within the acceptable range, the value remains unchanged.
# An In-Depth Review of the Weibull Model with a Focus on Various Parameterizations
## 3 Interpretation of the Coefficients
### The Bi-Univocity among the Four Parameterizations When Modeling Only \(\lambda\)
* WEI5 model: \[\exp\Bigl{(}-\beta_{0}-\mathbf{x}_{i}^{\bigtriangleup\top}\boldsymbol{\beta}^ {\bigtriangleup}\Bigr{)}=\exp\biggl{(}\delta_{0}+\frac{1}{\tau}\log\biggl{(}1- \frac{1}{\tau}\biggr{)}+\mathbf{x}_{i}^{\bigtriangleup\top}\boldsymbol{\delta}^ {\bigtriangleup}\biggr{)}.\]In this case, we conclude that \(\delta_{0}=-\beta_{0}-\frac{1}{\tau}\log\Bigl{(}1-\frac{1}{\tau}\Bigr{)}\) and \(\mathbf{\beta}^{\triangle}=\mathbf{\delta}^{\triangle}\). Note that in all cases, \((\mathbf{\delta}^{\top},\tau)\) can be obtained uniquely from \((\mathbf{\beta}^{\top},\
u)\). Theorem 4.2 implies that when only \(\lambda\) is modeled in the WEI, WEI2, WEI3, WEI4, and WEI5 models, all the distributions produce the same PDF. Consequently, model selection criteria, such as the Akaike Information criterion (AIC) and Bayesian information criterion (BIC), yield identical values. When an additional set of covariates, denoted as \(\mathbf{z}_{i}\) (not necessarily identical to \(\mathbf{x}_{i}\)), is considered to model the shape parameter as \(\
u_{i}=h(\mathbf{z}_{i}^{\top}\mathbf{\zeta})\), where \(h:\mathbb{R}\to\mathbb{R}^{+}\), the five parameterizations of the Weibull model define different PDFs. This distinction arises because the relationship \(\lambda_{i}=k(\lambda_{i}^{*},\tau_{i})\) incorporates only the covariates \(\mathbf{x}_{i}\) on the left side of the equation, while both sets of covariates are involved on the right side.
## 5 Discussion and Conclusions
\begin{tabular}{p{142.3 pt} p{142.3 pt}} \hline \hline
**Variable** & **Equation or Value** & **Units** \\ \hline Adjusted production rate & IF THEN ELSE (\"Use DNN?\" = 1, MIN (Capacity, DNN predictions), MIN (Capacity, Judgmental production rate)) & Pcs/Month \\ \hline Backlog & INTEG (Sales orders \(-\) Fulfillment rate \(-\) Lost sales, 0) & Pcs \\ \hline Capacity & INTEG (Capacity augmentation rate \(-\) Capacity normalization rate, Normal capacity) & Pcs/Month \\ \hline Capacity augmentation rate & IF THEN ELSE (Delivery lead time overshoot \(>\) 0, Capacity flexibility/Capacity ramp-up time, 0) & Pcs/Month \\ \hline Capacity flexibility & Maximum capacity \(-\) Capacity & Pcs/Month \\ \hline Capacity normalization rate & IF THEN ELSE (Delivery lead time overshoot \(>\) 0, 0, Excess capacity/Capacity ramp-down time) & Pcs/Month \\ \hline Capacity ramp-down time & 2 (base case); 4 (heuristic adjustment) & Month \\ \hline Capacity ramp-up time & 2 & Month \\ \hline Delay tolerance & 2 & Month \\ \hline Delivery lead time goal & 1 & Month \\ \hline Delivery lead time outlook & IF THEN ELSE (Backlog \(=\) 0, 1, Backlog/Fulfillment rate) & Month \\ \hline Delivery lead time overshoot & MAX(0, Delivery lead time outlook \(-\) Delivery lead time goal) & Month \\ \hline Discrepancy with actual returns & Returns time series (Time/One month) \(-\) Perception of returns & Pcs/Month \\ \hline Discrepancy with actual sales & Order time series (Time/One month) \(-\) Perception of sales & Pcs/Month \\ \hline Discretionary capacity & 0.2 & Dmnl \\ \hline DNN predictions & DNN sales predictions (Time/One month) \(-\) DNN returns predictions & Pcs/Month \\ \hline DNN returns predictions & The result of RNN returns forecast is programmatically fed. & Pcs/Month \\ \hline DNN sales predictions & The result of RNN sales forecast is programmatically fed. & Pcs/Month \\ \hline Excess capacity & MAX(0, Capacity \(-\) Normal capacity) & Pcs/Month \\ \hline FINAL TIME & 96 & Month \\ \hline Fulfillment rate & MIN(Backlog, Inventory)/One month & Pcs/Month \\ \hline INITIAL TIME & 1 & Month \\ \hline Inventory & INTEG (Production rate \(+\) Return orders \(-\) Shipment rate, 0) & Pcs \\ \hline Judgmental production rate & Perception of sales \(-\) Perception of returns & Pcs/Month \\ \hline Lost sales & (Delivery lead time overshoot \(\times\) Fulfillment rate)/Delay tolerance & Pcs/Month \\ \hline Maximum capacity & Normal capacity \(\times\) (1 \(+\) Discretionary capacity) & Pcs/Month \\ \hline Net requirements & Sales orders \(-\) Return orders & Pcs/Month \\ \hline Normal capacity & 55 & Pcs/Month \\ \hline One month & 1 & Month \\ \hline Order time series & Test dataset for actual customer orders. & Pcs/Month \\ \hline \hline \end{tabular}   
**Table A3. ** System dynamics parameters.
# A Survey of Handover Management in Mobile HetNets: Current Challenges and Future Directions
## 4 Handover Decision Techniques
### FLC-Based HO Decision Approaches
Therefore, more research is needed to automate the membership function design and to improve its performance in dynamically changing environments.
# Data Downloaded via Parachute from a NASA Super-Pressure Balloon
## 3 Descent and Recovery
### Release Time and Location  
We attempted to release two DRS capsules nearly simultaneously so they would land close to each other to simplify recovery. Low-latency communications with the payload had been lost, so we were forced to send release commands via Iridium shed. We transmitted the two commands on independent channels. However, commands are queued and arrive after an unpredictable delay.  
Contemporaneous logs recorded on SUPERBIT show that the release commands were received at 12:30:58 and 12:31:25 UT. Taking into account an intentional 30 s delay for the Pi to shut down gracefully, we infer that DRS2 and DRS1 were released at 12:31:28 and 12:31:55 UT, respectively.  
The super-pressure balloon (SPB) host logged GNSS locations approximately once per minute. We interpolated between these to infer the location of SUPERBIT at the momentswhen DRS capsules were released (see Table 1). During the 27 s between release events, superBIT travelled 1.79 km on heading 101\({}^{\circ}\).
# An Algorithm to Find the Shortest Path through Obstacles of Arbitrary Shapes and Positions in 2D
## 3 Materials and Methods
### Eliminating Some Pointless Tangents  
[PERSON] and [PERSON] noted a possible simplification of the L-A graph that results from identifying some of the edges that cannot be part of the shortest path [28]. These are the edges that enter into the convex hull of another obstacle when this convex hull does not intersect any other obstacle or does not contain \(S\) or \(T\). Figure 4 illustrates the situation: the  
Figure 3: Bitangents to gray obstacles with endpoints as black dots.  
Figure 2: [PERSON]’s graph and a path through a field of disks shown as gray shapes.  
lighter gray area represents the part of the convex hull of the obstacle that is not within the obstacle itself. The tangents \(AC\) and \(AD\) that end inside the convex hull, cannot be part of the shortest path as the end goal \(T\) is outside of it. Paths along these tangents would eventually have to come out of the convex hull, resulting in a longer path than one that directly bypassed the obstacle by going directly to points \(B\) or \(E\).  
Thus, it is critical to identify which of all tangents can possibly be part of the shortest path. We correspondingly define two classes of tangents, according to their topological properties. The first class consists of \"obstacle glancing\" tangents. These are rectilinear tangents that, upon arriving at a point \(P\) on the boundary of an obstacle and being prolonged past this point, never penetrate inside that obstacle. All of the tangents shown in Figure 4(a) and the tangents \(T_{1}\) and \(T_{2}\) in Figure 4(b),\(c\) are glancing tangents. We let \(H_{\text{i}}\) represent the convex hull of the obstacle \(O_{\text{i}}\).  
The second class of tangents consists of the \"obstacle penetrating\" tangents. These are tangents that when prolonged past their point of contact with an obstacle \(O\) will penetrate inside of \(O\) farther. All of the tangents not identified as \(T_{1}\) and \(T_{2}\) in Figure 4(b),\(c\) are such tangents. Such tangents may only appear in the shortest path to allow it to go through a passage between two obstacles. Figure 6 illustrates this situation: the three tangents shown are all obstacle penetrating tangents; however, they can be parts of a path segment that passes between \(O_{1}\) and \(O_{2}\). Such cases require special attention.  
[PERSON] and [PERSON] proposed to find the shortest path by using the A* algorithm with the visibility graph defined over quadtrees representing the terrain [28]. The consideration of quadtrees brings the simplification of reducing the shape of obstacles to assemblies of rectangles. However, the construction of quadtrees constitutes a preliminary step that requires appreciable computing resources. The method we propose bypasses the step of constructing a mathematical representation of the terrain by a quadtree or otherwise: it deals directly with a digital image of the landscape.  
Figure 4: Various tangents from point A to an obstacle. Dark gray is the obstacle.  
Figure 5: Tangents inside and outside of the convex hulls.
## 2 Joukowsky Airfoil Function Modification  
The lines of a blade or airfoil are usually determined by a coordinate database to give a series of coordinate point data; then, the image dot matrix is connected with a smooth curve in order to generate an airfoil profile line. In this way, a set of coordinate data only represents one airfoil [23; 24]. The analytic function (Joukowsky airfoil transformation) is used to express the airfoil or blade. When the constant value in the function changes, a new airfoil can be generated, and the geometric meaning of the function parameters is very clear. By adjusting the parameter values, the desired airfoil can be generated, and a reverse design can be realized.  
Draw two circles tangent to point B on a plane. The origin of the coordinate is the center of the big circle O, and the center of the small circle O\({}_{1}\) is on the line OB (Figure 1a). Complex variable analytic function:  
\[\xi=\frac{1}{2}\left[z-\frac{c_{0}^{2}}{z}\right];\;(z=\varsigma+i\eta;\;c_{0}=r _{0}). \tag{1}\]  
Conformal transformation of large circumference lines:  
\[\varsigma=\frac{X_{1}}{2}\left(1+\frac{c_{0}^{2}}{X_{1}^{2}+Y_{1}^{2}}\right); \;\eta=\frac{Y_{1}}{2}\left(1-\frac{c_{0}^{2}}{X_{1}^{2}+Y_{1}^{2}}\right) \tag{2}\]  
\[X_{1}=X-(R_{0}-r_{0});\;Y_{1}=Y. \tag{3}\]  
Substitute the coordinates (\(X\), \(Y\)) of each point on the circumference of the large circle into the formula to calculate the coordinates of each point of the new airfoil (\(\xi\), \(\eta\)) (Figure 1b). The value of \(r_{0}\) determines the chord length; \(R_{0}-r_{0}\) determines the thickness of the blade.  
We thus obtain a symmetrical airfoil with a sharp tail. In order to change the thickness of the trailing edge, we need to improve the Joukowsky airfoil and move the large circle O along the O\({}_{1}\) direction. As shown in Figure 2a, where \(I_{\text{BC}}\) is the moving distance of the big circle, other parameters remain unchanged, and conformal transformation is performed on the large circle again.  
\[X_{1}=X-(1-K)\times(R_{0}-r_{0});\;K=\frac{I_{\text{BC}}}{R_{0}-r_{0}};0\leq K\leq 1 \tag{4}\]  
\[Y_{1}=Y. \tag{5}\]  
Substitute the coordinates (\(X\), \(Y\)) of each point on the circumference of the large circle into the formula again to calculate the coordinates of each point of the new airfoil (\(\xi\), \(\eta\)) (Figure 2b). Adjust the thickness of the trailing edge by adjusting different \(K\)-values. The \(K\)-values selected in this article are 0, 0.2, 0.4, and 0.6.  
Figure 1: Joukowsky airfoil. (**a**) Step I. (**b**) Step II.
# Traffic Signal Optimization for Multiple Intersections Based on Reinforcement Learning
## 4 Simulation
### Scenario 1  
In Scenario 1, performance evaluation of the proposed model and the fixed-time model was performed before comparison with the model without constraints. Figure 3 is a simple road structure used in the evaluation. Two intersections were connected, and the traffic volume is indicated on each lane. The same phase sequence was applied to both intersections. In the fixed-time model, the duration of each phase was calculated sequentially as 14, 56, 13, and 27 s. Therefore, the cycle length was 110 s.  
Figure 4 shows the duration ratio for each phase of the proposed model and fixed-time model. Since the proposed model dynamically responded to realtime traffic flow, the duration of the phase changed with each signal cycle. The average phase duration ratio appeared similar to the optimization result of the fixed-time model. Compared to the fixed-time model, the proposed model reduced the average delay per vehicle from 40 s to 30 s and the average number of stops per vehicle from 2.5 to 2 times.
## Methods
### Evaluation of Metrics  
A correlation analysis was performed, comparing the phenology metrics derived from MODIS VIs and in-situ. Based on the assumption that the metrics estimated from the NEE data can function as ground _truth_ data, the uncertainty associated with MODIS-derived metrics was quantified and assessed by calculating the Spearman rank correlation coefficient (\(r\)), the coefficient of determination (\(R^{2}\)), the Root Mean Squared Error (RMSE) and bias.
## 4 Discussion
### Simulation Result Error and Reliability Analysis  
(1) Error analysis of simulation  
The rock formation and fracture parameters selected for the simulation were obtained from indoor rock block mechanical property tests; many factors are simplified according to the principle of easy control. Therefore, the test conditions are difficult to be fully consistent with the field situation. In the future, in-depth research can be carried out on the basis of accurate testing of rock mechanics parameters to make the research results closer to reality.  
(2) Reliability analysis of simulation  
The material simulation results are compared with the numerical simulation results: the heights of the two water-conducting fracture zone are very close, and the development forms and changing processes of the water-conducting fracture zone are relatively consistent, mutually verifying the reliability of the simulation results.  
Comparing the simulation results with the on-site measurement results, the height of the water-conducting fracture zone and the maximum split mining ratio obtained by the borehole TV and the water injection test are similar to the simulation results, which fully verify the reliability of the simulation results.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Methods** & **Empirical** & **Drilling TV** & **Water Injection** & **Material** & **Numerical** \\  & **Formula** & **Test** & **Simulation** & **Simulation** \\ \hline Calculated height (m) & 39.9–59 & 145–150 & 145 & 100–130 & 120–140 \\ Maximum split & 9.83 & 24.27 & 23.46 & 21.67 & 23.33 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of height of water conducting fissures water-conducting fracture zone.