esa-sceva/satcom-chunk-collection huggingface dataset:
# Dynamic Relief Items Distribution Model with Sliding Time Window in the Post-Disaster Environment
## 1 Introduction
A fuzzy-based distance matrix is generated at each time slot that is applied for route selection and vehicle routing. This fuzzy distance is calculated based on road condition, road traffic load and the number of turns in the route. The fuzzy variable gives a weighted fuzzified distance between locations and, hence, a fuzzified distance matrix is used to find the shortest path between two points. 2. The priority index of each disaster region is calculated based on casualty level and wait time for relief items. The casualty level is defined based on the number of people severely affected and the initial wait time is set to zero for each disaster region. The priority index is calculated as the weighted sum of these individual priorities. At each time slot, the casualty level is updated along with the wait time for receiving relief items for each disaster region and, hence, the priority index is updated in each time slot. 3. The re-optimisation of distribution schedule with sliding time window based on the time-varying updates of the disaster impact, relief items demand and other resource information over a period. In the presented RID model, the uncertainty of disaster impact, update in disaster information, fuzzified distance matrix and priority index are combined into a single dynamic RID model to consider the aforementioned dynamic conditions. This dynamic RID model generated relief items distribution schedules based on supply points information,the disaster region's causality information and also road conditions. These considerations in the model make the model as close to a realistic representation as possible of humanitarian supply chain management after a disaster as a smart city application. The performance measure of the model is analysed in terms of its applicability to reflect realistic implementation in the humanitarian supply chain. The performance evaluation shows that the proposed dynamic RID model reflects very close realistic distribution tasks covering many post-disaster relief operation management aspects compared with the similar distribution model used for the selected case study [14]. This also makes the decision-maker to implement relief items distribution operation more effectively in disaster environment. The rest of the paper is organised as follows: In Section 2, the state of the art in RID is discussed. In Section 3, the solution approach for the dynamic RID model is presented. In Section 4, computational experiments and results are presented. In Section 5, the comparative performance analysis of dynamic RID models is described. In Section 6, an observatory conclusion has been presented. Finally, in Section 7, discussion and directions for future work have been presented.
## 3 Results and Discussion
### Parametric Investigation of UV-A/TiO\({}_{2}\) Process
#### 3.2.1 The Effect of UV-A Radiant Power per Volume Unit  
A crucial parameter examined in this study was the UV-A light dose applied per liter of treated solution, as it significantly affects the performance of heterogeneous photocatalysis [31; 32; 47]. Four different values of radiant power per unit volume (or UV-A light doses) were tested: 2.13 W / L, 4.26 W/ L, 6.39 W/ L, and 8.52 W/ L. Remarkably, only the case with the lowest radiant power failed to completely degrade all of the available SDX concentration within the specified time. To compare the efficiency of the remaining options, the percentage TOC removal was used as a criterion, under the condition of complete SDX removal within one hour of operation. At a UV-A light dose of 4.26 W/ L, mineralization reached a level of 77%, while it seemed to stabilize at about 85% in the tests with higher light doses. To compare these results and determine the optimal conditions, the Electrical Energy per Order (EEO) value was calculated using Equation (1) [48]. The corresponding results are summarized in Table 1. Taking into consideration the factor of developing an energetically sustainable method and excluding the 2.13 W/ L option due to incomplete SDX degradation, the UV-A dose of 4.26 W/ L turns out to be the most efficient option used in all experiments discussed in the following sections.  
\[EEO=\frac{Energy\ (\mathrm{kW})\times Time\ (\mathrm{h})/Sample\ Volume\ (\mathrm{m}^{3})}{\mathrm{log\ \frac{TOC_{0\ \mathrm{min}}}{TOC_{60\ \mathrm{min}}}}}\ (\frac{\mathrm{kWh}}{\mathrm{m}^{3}}), \tag{1}\]
# Tunable RF Filters Based on Liquid Crystal for Space Applications
## 3 Qualification Tests and Results
### Qualification Test Sequence  
Table 2 provides a detailed overview on the relevant qualification test sequence, including test parameters for each step.  
The test steps have been performed according to this table from top to bottom, major results are presented in the following subsections.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Test Step** & **Test Parameter, Test Load** & **Label for RF Measurement** \\ \hline \multirow{2}{*}{**Initial functional test**} & Measurement of RF performance at ambient temperature to establish baseline performance & IFT \\ \hline \multirow{2}{*}{**Mechanical vibration test**} & Load applied in x- and y-axis (parallel to mounting plate in Figure 8): per axis 9.0 g for 180 s & - \\  & Load applied in z-axis: 16.9 g for 180 s & \\ \hline \multirow{2}{*}{**Post vibration test**} & Measurement of RF performance at ambient temperature to verify integrity of the filter & PVT \\ \hline \multirow{2}{*}{**Mechanical shock test**} & Load applied: 3 shocks in each axis up to 1600 g at 10 kHz & - \\ \hline \multirow{2}{*}{**Post shock test**} & Measurement of RF performance at ambient temperature to verify integrity of the filter & PST \\ \hline \multirow{2}{*}{**Thermal-vacuum test**} & In vacuum & \\  & 3 thermal cycles –35/+75 \({}^{\circ}\)C & TT0: 23 \({}^{\circ}\)C (before first cycle) \\ \multirow{2}{*}{**Thermal-vacuum test**} & 8 thermal cycles –10/+65 \({}^{\circ}\)C & TV2, 3, 4: +65 \({}^{\circ}\)C, \(-10\) \({}^{\circ}\)C, 23 \({}^{\circ}\)C \\ \multirow{2}{*}{Plateaus for RF measurements at 23 \({}^{\circ}\)C (beginning \\ (on last cycle) \\ \end{tabular} } & \multirow{2}{*}{(on last cycle)} \\  & and end), and at \(-10\) \({}^{\circ}\)C and +65 \({}^{\circ}\)C (last cycle) & \\ \hline \multirow{4}{*}{**Power handling test**} & In vacuum at +65 \({}^{\circ}\)C & \multirow{4}{*}{A, B: begin, end of first application of 0 dBm} \\  & Permanent application of nominal RF power to the filter of 0 dBm, RF performance measurement & \\  & immediately and after 60 min & 0 dBm \\ \multirow{2}{*}{**Power handling test**} & Permanent application of overdrive RF power of & \\  & 20 dBm, RF performance measurement & \\  & immediately and after 60 min & \\ \multirow{2}{*}{**Power handling test**} & Permanent application of RF power to the filter of & E, F: begin, end of second application of 0 dBm \\  & 0 dBm, RF performance measurement & \\  & immediately and after 60 min & \\ \hline \multirow{2}{*}{**Final functional test**} & Measurement of RF performance at ambient temperature to verify integrity of the filter & FFT \\ \hline \multirow{2}{*}{**EMC, ESD**} & Listed in this table for completeness, details beyond this article & - \\ \hline \hline \end{tabular}
\end{table}
Table 2: Qualification test sequence.
# Power Grid Reliability Evaluation Considering Wind Farm Cyber Security and Ramping Events
## 4 Attack Scenarios
### Worst-Case Attack of Wind Turbines  
Considering the most severe possible outcome that occurs in a power system, the worst-case attack in this paper was designated as the one which trips a WT on a vulnerable moment of a power system and leads to the worst power imbalance. In order to locate the vulnerable moment, an index of active power margins has been proposed in [16]. The power system takes a higher risk for load shedding when the active power margin stays at lower level during the attack. However, the index does not consider the output of wind power. As the attack target, the running state of the WT should be monitored on the evaluation of the vulnerable moment. Only when the output of the WT is high is the impact of tripping attack is obvious. In this paper, the index was further extended as:  
\[V_{a}(t)=P_{gen}(t)+P_{w}(t)-P_{load}(t) \tag{5}\]  
where \(V_{a}(t)\) is the active power margin of power system at time \(t\). \(P_{gen}\) and \(P_{w}\) are the conventional generation capacity and the total output of wind farms, respectively. \(P_{load}\) is the total load power.  
The power system suffers the greatest loss with a minimum value of \(V_{a}(t)\) when the tripping attack is launched. The estimation of the tripping moment based on Equation (5) requires the attacker to accurately handle information such as the total conventional generation capability, the wind power output, and the load demand. Though the rationality of the worst-case setting is controversial, the accuracy of load forecasting needs a strict data foundation, and the schedule of power dispatch is challenging to obtain. From the perspective of system reliability, the worst case exposes the greatest impact of cyber-attacks in a conservative way. In this paper, the active power margin prediction was assumed to be obtained by the attackers.
## 3 Materials and Methods
### The Gilbert-Elliott Channel Model
The probability of being in steady state \(G\) (\(P_{GG}\)) and the probability of being in steady state \(B\) (\(P_{BB}\)) are dependent on their mean sojourn times and are computed using (10):  
\[P_{GG}=\frac{T_{G}}{T_{G}+T_{B}}\text{ and }P_{BB}=\frac{T_{B}}{T_{G}+T_{B}} \tag{10}\]  
The number of bit errors per unit time is known as the mean bit error rate (BER) (M\({}_{\text{BER}}\)) and can be obtained by using the following equation:  
\[M_{BER}=(P_{GG}\times P(G))+(P_{BB}\times P(B)) \tag{11}\]  
Figure 4: Gilbert–Elliot channel model. It is assumed that \(G\) state is error-free, meaning that \(P(G)=0\). All of the bits of the frame are likely to be transmitted correctly in the good state. The good state is considered error-free because it represents a period of time when the channel is in favorable condition with low error probability. This assumption allows the model to show a burst nature of the transmission channel by focusing on capturing the characteristics of the bad state where errors are more likely to occur. This simplification provides a reasonable approximation for communication system analysis [43]. The probability of being in a good or bad state is dependent on \(M_{BER}\)[44]. The GE model is used to introduce bit errors in transmitted bitstreams, assuming that the occurrence of bit errors is independent of each other. It calculates the steady state probability, transition probability, and bit error rate at each state. In our study, all of the bits in the frames that are in state \(G\) are supposed to be transmitted without any error. This reduces the computational cost of the traditional GE model.
## 3 RPM Path Planning Method Based on Probabilistic Roadmap and Heuristic Rule
### RPM for Solving the Optimal Control Path Planning Problem
Then, the terminal state can be expressed as follows:  
\[x\Big{(}\tau_{f}\Big{)}\approx X(\tau_{N})=X(\tau_{0})+\frac{t_{f}-t_{0}}{2} \sum_{i=1}^{N}\omega_{i}f(X(\tau_{i}),U(\tau_{i}),\tau_{i}) \tag{21}\]  
where \(\omega_{i}\) is the Gaussian integral weight, \(\omega_{i}=\frac{2}{1-\tau_{i}^{2}}\Big{[}\dot{P}_{N}(\tau_{i})\Big{]}^{2}\). Similarly, the boundary constraints and process constraints can be discretized as:  
\[\Phi\Big{(}X(\tau_{0}),t_{0},X(\tau_{N}),t_{f}\Big{)}=0 \tag{22}\]  
\[h\Big{(}X(\tau_{i}),U(\tau_{i}),\tau_{i},t_{0},t_{f}\Big{)}\leq 0 \tag{23}\]  
Overall, the optimal control problem of PMCU pushing path planning can be transformed into a NLP problem and solved by sequential quadratic programming (SQP).
## 2 Literature Review
### From Pure Manual to Semi-Automated Traditional Evaluations of Teaching Behaviors in PE Classrooms
#### 2.1.1 Purely Manual Evaluation Stage  
During this initial stage, evaluators drew on their accumulated teaching experience and professional knowledge to design appropriate evaluation tools for manually annotating and analyzing teaching behaviors observed in PE classrooms. [PERSON] (1983) daptated Flanders' Interaction Analysis System(FIAS) into the Cheffers' Adaptation of Flanders' Interaction Analysis System(CAFIAS) for analyzing three types of behaviors reflecting the classroom learning atmosphere, teaching style, and teaching effectiveness: teachers' speech, students' speech, and silence or chaos [5]. [PERSON] (1989) designed the Observational Recording Record of Physical Educator's Teaching Behavior (ORRPETB) tool for analyzing three aspects of teacher behavior: classroom time allocation, teacher-student interactions, and teaching behaviors [6]. [PERSON] et al. (2018) designed the Need-Relevant Instructor Behaviors Scale (NIBS) to assess the frequency and intensity of teachers' behaviors in meeting students' emotional, competence, and autonomy needs [7]. However, because these methods relied solely on manual evaluation, they were subject to limitations such as subjectivity, small sample sizes, and time-consuming coding processes.
## 2 Digital Twin in Literature
### Digital Twin Description in Literature
[11] reported that more than half of the papers they reviewed were describing and/or studying 'Digital Model' or 'Digital Shadow' despite authors claiming it to be Digital Twin. [PERSON] et al. [2] also found the similar result that there is more literature on 'Digital Model' or 'Digital Shadow' than on 'Digital Twin' (see Section 3.3 for Digital Model' and 'Digital Shadow'). There are other concepts such as 'Product Avatar' or 'Digital Thread' which are also sometimes interchangeably used or get confused with DT. Just like 'Digital Twin', the term 'Digital Thread' was also emanated from the aerospace industry, where it was used to describe an integrated system engineering process used for managing the entire process digitally. It included 3D CAD models, model-based engineering, BoM, manufacturing processes, assembly logistics, delivery systems, etc. [50]. Digital Thread has also been described as the communication framework that consolidates the asset's data and allows seamless data flow; in other words, Digital Thread provides the right information at the right time to the right place [31]. Therefore, as compared to DT, which is a real-time virtual representation of its physical twin, Digital Thread is just the record of the information on the physical twin throughout its lifetime [60]. The major difference between Product Avatar and DT comes from the fact that they have been derived from two different research lines and thus have different capabilities and purpose; a Product Avatar is a digital counterpart of a smart product that lets its user use the attributes and services of that smart product for its entire lifecycle [9]. Due to the presence of a plethora of definitions in the literature, there is no consensus of what can actually be described as DT or not. Thus, giving academics and businesses permission to use the term DT loosely and conveniently to their needs creates confusion between different terminologies related to the digitalization of industries. A sudden influx in the number of publications related to 'Digital Twin' also indicates that interest is increasing exponentially, as it happens with any new promising technology. In order to simplify the confusion around different terminologies used to describe DT, a definition of DT is proposed here that can be applied irrespective of the industry or its application:\"A Digital Twin is a dynamic and self-evolving digital/virtual model or simulation of a real-life subject or object (part, machine, process, human, etc.) representing the exact state of its physical twin at any given point of time via exchanging the real-time data as well as keeping the historical data. It is not just the Digital Twin which mimics its physical twin but any changes in the Digital Twin are mimicked by the physical twin too.\"_
## 4 Results
### Accuracy Assessment  
The different classification workflows were compared based on their accuracy metrics. Figure 11 reports the OA for the 6 maps that were obtained. The binary maps capturing only the presence and absence of tree cover have the highest accuracy, with an OA higher than 95%. For the classification representing the forest type, the \"window\" approach, wherein the spatial context is taken into account, outperformed the \"pixel\" approach in terms of OA (87% versus 85%). With respect to the OA, the difference between the RF and the ETC classifier was not important. When looking at the PA and UA, a greater difference between the two classifiers was visible, especially between the RF and ETC classifiers in regard to the \"window\" approach. Lower values of PA were observed for the ETC classifier with reference to the 80-100% forest classes indicating higher omission errors for those classes compared to RF.  
Figure 9: Resulting maps from the three classification workflows.  
Figure 11: _Cont._  
Figure 10: Features’ importance for two classifications in the “pixel” approach.  
The second part of the accuracy assessment evaluated the discrimination of single trees within the 98 samples that were labelled class 21 (Figure 12). We saw that the different models captured any type of tree cover class (including class 21) in about half of the samples that were identified as class 21 (Figure 13). The window classification model outperformed the other models, as it gave a more accurate representation of the tree cover percentage in those samples that were labelled as having a 1-10% tree cover. However, the analysis highlights that the discrimination of single trees is still a challenge when using satellite imagery at a 10 m spatial resolution.
# Dynamic Shortest Paths Methods for the Time-Dependent TSP
## 3 A Branch-and-Price Algorithm
### Valid Inequalities
#### 3.4.5 Cycle Inequalities  
While it is possible to generate variables for the (TDTSP-A) according to \(k\)-cycle-free paths, solutions of the LP relaxation generally do not possess a \(k\)-cycle-free path decomposition. We adapt the cycle inequalities introduced in [15] for the SDTSP to the TDTSP, cutting off solutions based on the elimination of \(k\)-cycles. Consider a path  
\[P=(a=(u,u_{1},\theta),a_{1}=(u_{1},v_{1},\theta_{1}),\ldots,a_{k}=(u_{k},v_{k},\theta_{k}))\]  
in \(D^{\mathcal{T}}\) satisfying the following properties:  
1. The arcs \(a_{1},\ldots,a_{k}\) form a \(k\)-cycle \(C\) not containing \(s\).
2. The arc \(a\) enters the cycle \(C\), that is, \(u\
otin\{u_{1},\ldots,u_{k}\}\).  
Let \(T\) be a tour entering the cycle \(C\) via arc \(a\). Since \(T\) is \(k\)-cycle-free, it can contain, at most, \(j\leq k-1\) of the arcs in \(C\), leaving the cycle via an arc \((u_{j},w,\theta_{j})\) with \(w\
eq u_{j+1}\). It also holds that \(w\) cannot coincide with \(u_{\ell}\) for \(\ell<j\) or \(u\) (otherwise \(T\) would contain a cycle of length \(\leq j\)). Based on these observations, the cycle inequality associated with \(P\), defined as  
\[x_{u_{1}v_{1},\theta_{1}}\leq\sum_{j=1}^{k-1}\sum_{\begin{subarray}{c}(u_{j},w,\theta_{j})\in A^{T}\\ w\
otin\{u,u_{1},\ldots,u_{j+1}\}\end{subarray}}x_{u_{j}v,\theta_{j+1}},\]  
is valid for (TDTSP-A). We separate these inequalities using a simple heuristic: Based on \(x^{*}\) we compute a decomposition into paths \(P_{1},\ldots,P_{k}\subseteq\mathcal{P}\). For each path \(P_{i}\), we determine whether it contains a \(k\)-cycle, adding the corresponding inequality if necessary.  
Note that cycle inequalities were reexamined in [37], leading to the stronger class of Time-Dependent Cycle Inequalities (TDCIs) for the SDTSP. The techniques required to derive these inequalities are, however, specific to the SDTSP and do not generalize to the TDTSP.
# Simulation of Calibrated Complex Synthetic Population Data with XGBoost
## 2 Synthetic Data Generation
### Synthetic Data Generation with XGBoost  
#### 2.3.1 General Comments on XGBoost  
Extreme Gradient Boosting (XGBoost) is a highly sophisticated distributed gradient boosting algorithm. XGBoost builds many consecutive decision trees to correct errors in previous trees. There are two main types of decision trees: classification and regression trees. XGBoost works for both regression and classification, and both types are implemented in various software products, such as the R package XGBoost (see [20]).  
Boosting is an ensemble method with its origin in computer science, which is opposite to the statistical approach of bagging (bootstrap aggregating) used in random forests. The general idea is to boost the performance of weak learners, which are slightly better than random chance, by weighting and combining them. Gradient boosting is the formulation of the boosting approach as gradient descent optimization with an arbitrary differentiable loss function. It then minimizes the loss by iteratively adding regression (or classification) trees, also known as CART (Classification and Regression Tree). Since overfitting is a massive problem in regression trees, ref. [20] proposed adding a regularization term. This regularization is similar to the regularized greedy forest (RGF) of [21] and favors smaller and more predictive trees. In addition to the first regularization method, ref. [20] included two other regularization techniques: shrinkage developed by [22] and feature (column) subsampling used in random forest. The latter improves the computation time and prevents overfitting. Shrinkage scales newly added weights, and thus decreases the dependency on individual trees. Furthermore, ref. [20] introduced an evaluation function to measure the quality of a tree structure, which is similar to the tree impurity function used in decision trees and random forest. Ref. [20] added more improvements to standard tree boosting.