esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
### Geomorphological Features Mapping Using OBIA  
The results of the geomorphological features mapping are shown in Figure 6. The pre-event result shows the original Hitara riverbed with water (Figure 6a). The post-event results (Figure 6b for 2018 and Figure 6c for 2019) show the landslide, the landslide-induced changes in the watercourse, and the landslide-dammed lake. The reference map from NLSI [77], which was used for comparison, is shown in Figure 6d. The water flowing out of the dammed lake found a new route through a lava field towards the south and then merged with the Stekka river, resulting in more water flowing in this riverbed than prior to the landslide. However, the upper part of the connection between the lake and the Stekka river is not easily distinguishable in the Sentinel-2 image taken in August 2019, probably due to less water flowing there at the time of the image acquisition one month later in the year 2019 (July 2018 versus August 2019). It seems that the Hitara river partly passes through the landslide and appears as a spring in the river bed downstream, i.e., approximately 2.5 km downstream from the lake and approximately 1 km downstream from the closest part of the landslide deposition. This is confirmed by the manually created reference data from NLSI [77]. The landslide area was estimated to be approximately 2000 ha in 2018 and 2019. Slight differences between the landslide areas likely result from variations in the segmentation. We estimated a lake area of 58 ha in 2018 and 47.1 ha in 2019. The reference values for the lake area are reported to be approximately 47 ha by [PERSON] et al. [45], but, the lake area from the shapefile provided by NLSI is 39.8 ha [77]. A comparison of our results with these numbers reveals an overestimation of the OBIA result for the year 2018. This could be associated with a high moisture content near the lakeshore and shallow water areas with high sediment load, which introduces uncertainty in the segmentation and classification [17]. The lower value for 2019 is probably due to the warm summer in 2019, which caused the discharge of some rivers to be lower for a few weeks [85].  
The maximum width of the landslide deposition is approximately 1.5 km, and the overall runout length of the landslide is approximately 2.3 km. These results are in line with the numbers reported by [PERSON] et al. [45].
## 3 Optimizing the UCSP Using POGA-DP
### Time Slot Scheduling Based on Genetic Algorithms
#### 3.2.1 Chromosome Encoding  
To clearly illustrate the complex relationships among different factors in the course scheduling problem, the encoding method for chromosomes is shown in Figure 1. The figure shows a summary view of the solution for administrative classes, where \(S_{1},S_{2},\ldots,S_{n}\) represent the solutions for the 1 st, 2 nd,..., nth administrative class. \(S_{i}\) displays the detailed course allocation for each of the 25 time slots for an administrative class, with each time slot indicating one of the five teaching periods on the five working days of the week. \((e_{1},k_{1})\) represents the course schedule for administrative class \(e_{1}\) during time slot \(k_{1}\), \((e_{1},k_{2})\) represents the course schedule for administrative class \(e_{1}\) during time slot \(k_{2}\), and so on. Each time slot includes information about the corresponding administrative class, assigned teaching class, assigned course, assigned teacher, and the specific time slot.
## 3 Results and Discussion
### Delay
#### 3.2.2 Computational Time  
A runtime speedup of up to 15\(\times\) is observed in Figure 13 using the \(I_{eff}\)-based delay model to calculate the inverter delay and output slew compared with MC SPICE simulations. The proposed method only requires a single-point HSPICE DC or transient simulation to obtain the nominal value of the current and delay when the process variation is 0. Compared with the conventional Monte Carlo simulation, the prediction method reduces the number of simulations and obtains a higher computational efficiency. The solution to the delay prediction coefficient for any number of samples only needs to be performed once, which means that the model has a better performance in terms of computational speed with a large number of samples. Experiments show that when the number of samples is 1000, the time spent in the coefficient calculation of the prediction model is proportional to the prediction order. The improvement in prediction accuracy with n = 2, with respect to that with n = 1, is about 3-5\(\times\), while the required computational time increase is about 1.5-2\(\times\). When the prediction order is 4, the calculation time used for the coefficient calculation is 0.291 s, and the time spent on delay prediction is 0.089 s. It can be seen that the coefficient calculation of the prediction model accounts for the main runtime. The runtime of Monte Carlo simulation using HSPICE is 12.97 s and the prediction model with n = 4 resulted in a 21\(\times\) reduction in computation time compared with Monte Carlo simulation. Detailed statistical results of delay H-L are summarized in Table 5.  
Figure 12: MAPE comparison of the inverter delay and slew prediction under (**a**) PHIG1 variation (**b**) L variation in the subthreshold region (0.3 V). Figure 13: Computational time comparison between the prediction model and 1000 MC SPICE simulations in terms of the prediction order in the subthreshold region (0.3 V) with (**a**) L variation (**b**) PHIG1 variation (**c**) TSI variation. The relationship between the delay and the process parameter changes with the supply voltage. The front gate function PHIG1 contributes most to the nonlinearity of the delay, and we take PHIG1 as an example. At the same time, we compared the delay characteristics of different cells. The nominal values of underlying process parameters for inverters with different drive strengths are given in Table 6. As shown in Table 7, we performed additional experiments and selected three different voltage regions of sub-threshold region (0.25 V), near-threshold region (0.5 V) and above-threshold region (0.8 V) to compare the accuracy of the model when the prediction order is 1 and 2.
## 1 Introduction
### Contributions Organisation  
We propose the Energy-Aware Framework (Figure 2) to address the aforementioned limitations by analysing a bus passenger ridership dataset using multi-tier Self-Organising HetNet with one MC and nine SCs in Central London location [5]. The main focus is to anticipate the passengers mobility behaviour travelling on a bus who are passing through a HetNet architecture so that concerned SCs become artificially intelligent and autonomous. This Artificial Intelligence (AI) would be used to formulate a novel ES optimisation problem through mobility management for proactive SCs scheduling and offloading satisfying QoS requirements. The following are the summarised contributions of our research:  
1. As a building block of novel Energy-Aware Framework, we develop a spatiotemporal mobility prediction framework by analysing a statistical K-Nearest Neighbour (KNN) model which would modernise ES conventional limitations.
2. A novel method of passengers future location estimation is proposed to map the next cell spatiotemporal Handover (HO) based on the idea of landmarks using multiple \(K\) values in KNN model and a detailed comparison.
3. Another novelty of this proposal is that, based on the future cell load information and CIOs as optimisation variables for load balancing among SCs, a proactive ES optimisation problem is formulated to reduce power and energy consumption by switching off lightly loaded, idle or  
Figure 2: Network model comprising one MC and nine SCs (Data Base Sations, DBS) in HetNets deployment with CDSA. The Energy-Aware Framework with bus passenger ridership dataset as an input.  
underutilised HetNet SCs. Intelligence in load balancing would exploit specifically lightly loaded SCs to be switched off while satisfying QoS.
4. Based on the information achieved from mobility management of passengers ridership and ES awareness, a novel scheme for CO\({}_{2}\) reductions is also quantified.
## Materials and Methods
### Image Analysis
#### 2.3.2 Digital Image Analysis  
Visual image analysis raised the assumption of bedform movement in the upper flats of the island of Norderney, therefore a spatio-temporal analysis of bedform positions was performed using the extensive time series of satellite images collected during this study. To extract relevant markers from the SAR data, bedform positions were determined by detection of the water-covered troughs according to the method proposed by Ref. [19] which is based on textural analysis combined with an unsupervised classification: For comparison with the complete set of TerraSAR-X data, the images were re-sampled at their highest common resolution, a pixel size of 1.25 m. Speckle reduction was performed by edge-preserving Frost and Median filtering and followed by a textural analysis calculating Gray Level Co-occurrence Matrix (GLCM) statistical parameters (variance, homogeneity, and mean) according to Ref. [29]. From the resulting feature images, the water-covered troughs were derived by means of unsupervised ISODATA classification. Image processing was carried out using ERDAS imagine (2013-2015) and ENVI 4.7 software. The classification output was vectorized and imported into ESRI ArcGIS 10.1 for further analysis. The correct assignment of classes was verified by regularly collected in situ data combined with visual interpretation of the SAR images.
## 3 The Third Industrial Revolution (TIR) and Energy Internet
### Real-Time Polling
First, QIRs are pulses that can be transmitted instantaneously and at significantly higher frequencies. Second, QIRs carry incremental information about the queue, whereas REPORTs carry its entire size. Therefore, the OLT needs to keep track of the queue size by adding up QIRs until the arrival of the next REPORT. QIRs enable ONUs to reduce the waiting time of frames prior to their reporting. In addition, they enable the OLT to increase the granted transmission windows on a shorter notice, hence significantly reducing the total frame delay. Unlike thread-specific REPORTs in MTP, the generation of QIRs in RTP does not trigger GATE messages. Figure 6 shows simulation results for comparing the average delay performance of MTP and RTP with that of the aforementioned benchmark IPACT as well as the so-called double phase polling (DPP), which has recently been found to give the best delay-throughput performance among single-thread polling mechanisms in next-generation long-reach PONs [53]. Note that in Figure 6 the offered load is normalized to the capacity of a 100 km range 1G-EPON, _i.e._, 1 Gb/s. We observe that MTP achieves only a somewhat lower delay than IPACT as the multiple threads tend to degenerate to a single thread. That is, one thread carries the bulk of the requested grants and the other threads send only small grants or empty REPORTs. DPP provides some delay reduction compared to IPACT and MTP, if the offered traffic load is low to moderate; for high loads the DPP delay approaches the IPACT delay. Note that RTP achieves the smallest delays across all loads compared to the other polling frameworks. Note that RTP leaves the passive ODN untouched, but requires an additional wavelength channel for out-of-band signalling. Ideally, this out-of-band signalling wavelength may reuse the U-band (1,625-1,675 nm), which was approved in ITU-T recommendation L.66 for in-service maintenance of PONs. In-service monitoring of NG-PON's fiber infrastructure without disturbing ongoing services is expected to become increasingly important in order to avoid the OPEX and large service restoration times of offline troubleshooting. Furthermore, note that the aforementioned OC-enabled ONUs can be realized by using low-cost fiber Bragg gratings (FBGs) for remote encoding, reflection, and modulation, which are completely unpowered components and thus help maintain the passive nature of NG-PONs [54]. For further information on RTP and OC-enhanced NG-PONs we refer the interested reader to [55].
# Fault-Resilient Manufacturing Scheduling with Deep Learning and Constraint Solvers
## 3 System Design and Scheduling Problem Definition
### Problem Formulation  
We define the scheduling problem, emphasizing the need to balance fault reduction, deadline compliance, and resource optimization in a dynamic production environment. The system-wise inputs and outputs are:  
* Inputs: -Machine States: Real-time data such as temperature (\(T\)), vibration (\(V\)), operating hours (\(H\)), and tool wear (\(W\)) provide insight into machine conditions and fault risks.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Symbol** & **Definition** \\ \hline \(M=\{M_{1},M_{2},\ldots,M_{n}\}\) & Set of machines in the production line \\ \(J=\{J_{1},J_{2},\ldots,J_{m}\}\) & Set of jobs to be scheduled \\ \(C_{i,j}\) & Indicates whether machine \(M_{i}\) can perform job \(J_{j}\) \\ \(d_{j}\) & Duration of job \(J_{j}\) \\ \(D_{j}\) & Deadline of job \(J_{j}\) \\ \(S_{j}\) & Start time of job \(J_{j}\) \\ \(E_{j}\) & End time of job \(J_{j}\) \\ \(x_{i,j}\) & Binary variable indicating the assignment of job \(J_{j}\) to \\  & machine \(M_{i}\) \\ \(P(M_{i},J_{j},t)\) & Probability of fault for machine \(M_{i}\) executing job \(J_{j}\) at time \(t\) \\ \(T(M_{i},t)\), \(V(M_{i},t)\) & Temperature and vibration of machine \(M_{i}\) at time \(t\) \\ \(H(M_{i},t)\), \(W(M_{i},t)\) & Operating hours and tool wear of machine \(M_{i}\) at time \(t\) \\ \(S(t)\) & Real-time machine state variables at time \(t\), including \\  & \(T(M_{i},t)\), \(V(M_{i},t)\), \(H(M_{i},t)\), \(W(M_{i},t)\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Quick reference guide to symbols and definitions.  
* Job Characteristics: Information about tasks, their durations (\(d_{j}\)), deadlines (\(D_{j}\)), importance (\(I_{j}\)), and task dependencies guides scheduling decisions.
* Predicted fault probabilities (\(P(M_{i},J_{j},t)\)) for each machine-job pair and time step.
- Optimized schedules, including task-to-machine assignments and start/end times, while respecting task dependencies and deadlines.
# An Overview of Material Extrusion Troubleshooting
## 3 Troubleshooting of ME Parts
### Deposition-Associated Issues
#### 3.2.6 Dimensional Accuracy  
The measurement of the printed object differs from the CAD model. For example, the holes may be in the wrong size; parts do not align or do not fit together. Lack of dimensional accuracy creates a fitting problem while assembling other parts of the model. Inaccurate or incorrect dimensions could be due to incorrect measurements in the CAD model, not adhering to design rules, layer height issues, issues with the belt movement or loose pulley, and/or material shrinkage. Solutions include the following:  
1. Check the physical and working unit of measurements of the CAD model especially when using the STL file format. Ensure that correct and identical unit selection (mm or cm) is used for the CAD and slicing software.
2. Set proper tolerances such as 0.5 mm clearance for fixtures like male and female connectors, screws, holes and snap fits.
3. Test the printer accuracy by printing a cube of 50 mm \(\times\) 50 mm. Keep a consistent height for all print samples. Use a digital caliper to check that the measurements are equal. If not, measure the top 20 layers which should be equal to 20 mm. If this is still correct, but the overall height is wrong, then it is likely that the first few layers are causing the issue. Investigate the height of the extruder. If the extruder height is 1 mm from the print platform, but the printed layer height is  
Figure 9: Inconsistent extrusion of filament.  
2 mm, then this could be happening because there is too much filament being laid down on the first layer. Recalibrate the printer, run through first layer calibration with a suitable layer height (refer to 3.1.2: (1) Incorrect built platform levelling).
4. Recalibrate X-Y axes. Check if all belts and rails are tight and screwed into place. If the printed hole looks oval, this could be due to a loose belt or slight misalignment on the X and Y- axis (refer to 3.2.7: (1-2) Improper axis movement and (3) Loose pulley).
5. A 1-mm reduction in size could be due to material shrinkage after cooling. Calculate the percentage of error and increase the scale of the print to compensate for the shrinkage.
# Topology and Parametric Optimization-Based Design Processes for Lightweight Structures
## 2 Types of Structural Optimization
### Level Set Method  
In this paper, the LSM was chosen for the TO due to its effectiveness and simplicity in the post-processing [19]. In addition to that, it can be mesh-independent and does not suffer from checkerboard discontinuities [20]. [PERSON] and [PERSON] [21] introduced first the mathematical background of the level set method, which was later used effectively by [PERSON], [PERSON] [22] and [PERSON], [PERSON] [19] as an alternative topology optimization technique. The Eulerian shape parametrization of a structure is represented using a Level Set function, \(\varphi(x)\) with the following formulation [2,20,22]:  
\[\varphi(x)\left\{\begin{array}{rl}&>0\;\forall\;x\in\mathit{solid},\\ &=0\;x\;\forall\;\;\in\mathit{boundary},\\ &<0\;\forall\;x\in\mathit{void}.\end{array}\right. \tag{1}\]  
An illustration of this level set function, as well as its design domains, is depicted in Figure 2.  
The topology optimization of the structures is attained by solving the Hamilton-Jacobi equation:  
\[\mathrm{d}\varphi/\mathrm{d}t+\mathrm{V}\times|\
abla\varphi|=0 \tag{2}\]  
where:  
t: Pseudo-time  
V: Speed function of \(\varphi\)(x) change  
Figure 2: The level set function and its domains adapted from Jia, Beom [20].  
Hence, the optimization problem is formulated as:  
\[\text{minimize:}\] (u, \[\varphi\] ) \[= \int_{\Omega}\text{F}(\text{u})\times\text{H}(\varphi)\times\text{d}\Omega, \tag{3}\] \[\text{s.t.:}\] (4) \[:\text{u}\mid_{\text{r}\text{d}}=\text{u}_{0},\text{ }\forall\text{ v}\in\text{U},\] (5) \[:\text{V}=\int_{\Omega}\text{H}(\varphi)\times\text{d}\Omega \leq\text{V}_{\text{max}} \tag{6}\]  
In terms of energy bilinear form \(\alpha\)(u,v,\(\varphi\)), (5), (6), and (7) can be formulated as:  
\[\alpha\] (u, \[\
u\] ) \[= \int_{\Omega}\text{E}_{\text{ijkl}}\times\varepsilon_{\text{ij}} \left(\text{u}\right)\times\varepsilon_{\text{kl}}\left(\text{v}\right)\times \text{H}(\varphi)\times\text{d}\Omega, \tag{7}\] \[\text{L}(\
u,\varphi)=\int_{\Omega}\text{P}\times\text{v}\times \text{H}(\varphi)\times\text{d}\Omega+\int_{\Gamma}\tau\times\text{v}\times \delta(\varphi)\times|\
abla\varphi|\times\text{d}\Omega\] (8) \[\text{V}(\varphi)=\int_{\Omega}\text{H}(\varphi)\times\text{d}\Omega \tag{9}\]  
where:  
\(\delta\)(x): Dirichlet function  
F(u): Structure volume by means of a continuous auxiliary function  
u: Displacement field in the space U  
H(x): Heaviside function  
v: Volume field of volume V  
Eijkl: Elastic tensor  
\(\varepsilon_{\text{ij}}\): Strain tensor  
p: Displacement  
u\({}_{0}\): Prescribed displacement  
L(v,\(\varphi\)): Linear form of the load  
V(\(\varphi\)): Volume of the structure  
\(\tau\): Boundary tractions  
\(\Omega\): Design space  
\(\Gamma\): Partial design space  
\(\Gamma_{\text{d}}\): Partial boundary  
Here, the Level Set topology optimization of the three presented examples was implemented at the Workbench ANSYS finite element analysis software.
# Revisiting Problem-Solution Co-Evolution in the Context of Team Conceptual Design Activity
## 2 Materials and Methods
### Measures and Visualisations
Since the added-up proportions of ASE always make up 100% (\(p_{\text{A}}+p_{\text{S}}+p_{\text{E}}=100\%\)), only two measures are needed to characterise an activity (the third measure can be deducted, i.e., \(p_{\text{E}}=100\%-p_{\text{A}}+p_{\text{S}}\)). If the triangular proportion visualisation is utilised, the two measures are embedded in the vector, which is anchored in the centre of the triangle (Figure 3). The measures correspond to the vector's endpoint distance from the triangle centre of gravity (vector length \(r\)) and the vector's direction (angle \(\delta\)), as shown in Figure 3. If the distance \(R\) from the centre of gravity to the corners of the triangle is conceptualised as equal to 1 (or 100%), then the vector length \(r\) ranges from 0 to a maximum of 1 in the triangle corners, whereas the direction angle \(\delta\) can be any angle. Furthermore, suppose the angle \(\delta\) is defined clockwise from the vertical axis, as shown in Figure 3. In this case, the relations between the triangular visualisation variables and the proportions of ASE design operations can be defined as shown in Equations (1)-(3). The equations reveal that, in the case where the vector length is zero, the proportion of all three design operations is equal to 1/3:  
\[p_{\text{A}}=1/3-(2/3)\cdot r\cdot\sin\,(\delta\text{ - }30^{\circ}), \tag{1}\]  
\[p_{\text{S}}=1/3+(2/3)\cdot r\cdot\cos\,(60^{\circ}\text{ - }\delta), \tag{2}\]  
\[p_{\text{E}}=1/3\text{ - }(2/3)\cdot r\cdot\cos\,(\delta). \tag{3}\]  
The proposed visualisation does not only enable intuitive and straightforward characterisation of the activities' nature in terms of ASE but can also be used to describe the change in ASE proportions as a function of time passed during the activity (e.g., by sampling different parts of the activity). Moreover, additional layers of information can be added by colour coding the proportion instances within the triangle in order to display, for example, the problem/solution ratio, as well as the probabilities of particular state-transition sequences. Different use cases for the proposed triangular proportion visualisation are presented across the results section. Figure 3: Triangular proportion visualisation of analysis, synthesis and evaluation transitions using two visualisation variables: distance from the centre of gravity \(r\) (maximal distance is \(R=1\)) and angle \(\delta\) (0–360\({}^{\circ}\)).