esa-sceva/satcom-chunk-collection huggingface dataset:
# Applications of Probabilistic Forecasting in Smart Grids: A Review
## 6 Decision Making under Uncertainties
### Stochastic Programming  
A stochastic optimization problem models uncertainties of input data by weighting the decision-making solutions. The weights are selected based on the probabilities of occurrence, considering that each set of input data leads to a single solution. In this way, one will achieve the effects of uncertain input data on the decision-making solution [84]. A simple stochastic programming can be formulated as [6]:  
\[ \begin{array}{c}\underset{x}{\min}\mathbb{E}(f(x,\omega))\\ Subject\ to:\\ g(x,\omega)=0\\ h(x,\omega)\leq 0\end{array}  \tag{15}\]  
where \(x\) is the decision variable and \(\omega\) denotes the scenario. As the formulation states, uncertainties should be modeled in terms of different scenarios. The most common and simple techniques for generating scenarios need to use the inverse of the cumulative distribution function (CDF) or PDFs of the uncertain parameters. Hence, the probabilistic forecasts of the inputs are necessary to develop stochastic programming. However, it would be easier if the random input data have specifically known parametric distribution [6]. This means that parametric probabilistic forecasts are more favored in this sense. Here, if one knows the probability distributions of the inputs, they can achieve the probability distributions of the output data. In power system concepts, an independent system operator or a generation company are proposed to conduct stochastic unit commitment by using stochastic programming [85]. In unit commitment applications, stochastic programming is divided into two-stage and multi-stage problems. Two-stage models consider both day-ahead (hear and now) and real-time (wait and see) commitment decisions. In the day-ahead stage, the conventional generators are dispatched and their commitment decisions are determined while in real-time, uncertain renewable resources and flexible energy resources are dispatched. Regarding the second (real-time) stage, to develop the two-stage problem, one needs the PDFs of renewable generation as well as those of flexible energy resources to build a large number of relevant scenarios for the PDFs of the outputs. Table 6 summarizes some of the most recent studies that considered stochastic programming to find the optimal commitment decisions for their resources. Once we make a decision for the day-ahead stage, decomposition methods are then employed to treat the real-time stage scenarios independently. This approach leads to considerably fewer scenarios, compared to the non-decomposed method [75]. Multi-stage stochastic programming models construct a scenario tree and accordingly try to capture uncertainties in a dynamical way. In multi-stage models, the uncertainties
# DMRVR: Dynamic Milk-Run Vehicle Routing Solution Using Fog-Based Vehicular Ad Hoc Networks
## 2 Related Work
The TCC calculates the shortest path using the density information and the density history at that time of the day as well. The results obtained from the simulation demonstrated that, to get the best route, it is necessary to use the density information stored in a database at the TCC. Despite considering traffic jams, the route is calculated once and it is not changed during the displacement. The RSUs send information from the TCC using LTE, which can be relatively costly in a big city scenario. The authors in [45] proposed an efficient parallel genetic algorithm for the vehicle routing problem. The proposed parallelism is based on multi-core Central Processing Units and many-core Graphics Processing Units. It tries to compare the two processors' power in parallelizing genetic algorithms. The authors propose using the solution in a vehicular cloud computing (VCC) environment, where a vehicle acts as a leader. Through the Internet, the leader communicates with the traffic management that must execute the proposed solution using the information received, resulting in the ideal routing for the entire road network in the city. Despite presenting the use of VCC, the authors do not simulate the vehicular network. Table 1 compares the related works' features. The use of VANETs in logistics scenarios is recent. Five works used some vehicular communication but without using milk-run operation approaches. Three of them used the 802.11p protocol, one used mobile telecommunication networks, and the other one proposed VCC use. None of them are based on fog computing. The other three milk run presented solutions did not use any vehicular communication. Six of these works used different vehicle routing algorithms to plan the routes. Ref. [11] used a genetic algorithm with 3-opt optimization to create the pickup route. Ref. [19] used a time seed-based solution using particle swarm optimization to split the problem into smaller dynamic vehicle routing problems. The Ref. [5] approach considers travel time, avoiding congested highways, and Ref. [10] used a SA to create a pickup route. Finally, Ref. [44] did not specify the used algorithm, and Ref. [45] used a parallel genetic algorithm. Considering milk-run operations, Refs. [5; 10; 11] presented solutions that use dynamic vehicular routing to change routes during displacement. However, in these works, the changes can only occur when the vehicle is at the supplier, not allowing modifications during the tour. These modifications would only be possible if these solutions used a vehicular network. Six works used simulation environments for their evaluation. Nevertheless, only one used a network simulator bidirectionally coupled with an urban mobility simulator aiming to make the most realistic simulations. Our proposed solution (DMRVR) stands out from the related works because it uses a fog-based vehicular network in a milk-run operation to provide dynamic vehicle routing. DMRVR features will be presented in the next section and are included in the last line of Table 1
# Potential of Waste Oyster Shells as a Novel Biofiller for Hot-Mix Asphalt
## Materials and Methods
### Preparation of Filler-Asphalt Mastic Samples  
One-base bitumen, denoted as AP-5 asphalt (penetration grading of 60-70 at 25 \({}^{\circ}\)C), was used in this study. It was obtained from SK Energy Co. Ltd. Seoul, Republic of Korea. The physicochemical properties of AP-5 asphalt are given in Table 1. The mastics were produced following a standardized experimental protocol, optimized in order to obtain homogeneous OSP-asphalt mastics. The samples were prepared using a high shear L5M-A mixer (Silverson Machines Inc., East Longmeadow, MA, USA) at 180 \({}^{\circ}\)C and a speed of 3000 rpm. The OSP contents used were 5, 10, 15 wt % by weight of blend. These percentages were chosen to see closely the impact of a gradual increase in OSP concentration on the attributes of asphalt cement. In preparation, 600 g of the asphalt heated to fluid condition was poured into a 1000 mL stainless steel container. Upon reaching 175 \({}^{\circ}\)C, a preweighed amount of OSP was slowly added to the binder in small quantities of approximately half teaspoon. This was done to ease smooth mixing and prevent agglomeration of the additive. The filler also was introduced into the binder about 2.5 cm above the surface of the bitumen, so that the finest part of the filler would not be lost into the air. Mixing was then continued at 180 \({}^{\circ}\)C for 2 h to ensure good distribution and dispersion of filler particles. Due to the fact that the preparation procedure will lead to the oxidation of asphalt and changes in its properties, it was also applied on the plain bitumen to achieve the same effect.  
After completion, the asphaltic sample was removed from the can and divided into small containers. The blend was cooled to room temperature, sealed with aluminum foil, and stored in a dark chamber thermostated at 25 \({}^{\circ}\)C to retain the obtained morphology. Since pretreatment introduces additional cost, the waste of oyster shell powder was intactly mixed with the binder without any prior treatment. This also allows one to study solely the direct effects of filler on the bitumen performance.
# A Realistic, Flexible and Extendible Network Emulation Platform for Space Networks
## 4 Designs
### Network Topology  
The network topology of the proposed platform can be divided into the lower physical network topology and the upper logical network topology.
## Appendix C Package Assessment Results
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Criteria** & **mStats** & **pointblank** & **sanityTracker** & **skimr** \\ \hline \hline Explicit reference to data quality & & yes & & \\ Based on a data quality framework & & & & \\ Control via GUI & & & & \\ Reproducibility via programming & yes & yes & yes & yes \\ Single function call for output & yes & yes & & yes \\ Report generation (not only console) & & yes & & \\ Input of metadata through functions & & yes & yes & \\ Input of metadata through separate file & & & & \\ Grading of data quality issues & & yes & & \\ Dataset summary/overview & yes & yes & & yes \\ Descriptive summary statistics & yes & yes & & yes \\ Descriptive statistics graphs—univariate & yes & yes & & yes \\ Descriptive statistics graphs—multivariate & yes & & & \\ Handles string properties & & yes & & yes \\ Integrity: Structural dataset error & 1003 & 1001, 1002, 1003, & 1003 & \\  & & 100X & & \\ Integrity: Dataset combination error & & 1004, 1005 & 1004, 1005 & \\ Integrity: Value format error & 1006D & 1006, 1006D, & 1006D \\  & & 1007D, 1007, & & \\ Completeness: Crude missingness & 2001 & 2001 & 2001 & 2001 \\ Completeness: Qualified missingness & & & & \\ Consistency: Range and value violations & 3001D, 3003D, & 3001, 3002, 3003, & 3001, 3003, 3006 & 3001D, 3002D, \\  & 3006D & 3005, 3006, 3007 & & 3003D, 3006D, \\ Consistency: Contradictions & & & & \\ Accuracy: Unexpected distributions & 4001D, 4002D, & 4001D, 4002D, & 4003D, 4004D, \\  & 4003D, 4004D, & 4003D, 4005D, & 4005D, 4006D \\ Accuracy: Unexpected associations & 4007, 4007D, 4008D & 4007D, 4008D, & \\  & 4008D, 4009D & 4009D & \\ Accuracy: Disagreement of rep. meas. & & & \\ \hline \end{tabular} Data quality indicator numbers followed by D mark functionalities which are not included as an indicator, but only as a descriptor. Extending the reference data quality framework, we included three additional codes: the code 100X denotes that the package provides functions to detect unexpected variable names, and 100Y denotes that the package provides functions to detect data format mismatches. The code 400X indicates a functionality to detect loners (i.e., values which occur only once). 2 first published as mosaicQA.  
\end{table} 
Table 10: Data quality assessment capabilities by package (continued).
## 4 Discussion
### Limitations  
The Zung SDS is a creditable and well-justified method of measuring multiple MDD symptoms; however, it is not the only such instrument, and the use of alternative self-report instruments could add some greater validity to the overall results. Additionally, the DSM-5-TR Diagnostic Criteria are complemented by Diagnostic Features, but no current instrument exists that includes all of these. In fact, the tendency has been towards the development and use of briefer lists of MDD symptoms to diagnose MDD, such as the Personal Health Questionnaire-9 [53], but there are reasons from this study for moving towards more inclusive diagnostic instruments that measure all the aspects of depressive behaviour that contribute to the final diagnosis. This study used a'snap-shot' research design, with data being collected at a single time point, whereas individuals' mood states vary across days or weeks, and so repeated sampling of mood states would enhance the generalizability of this research. The sample was composed of volunteers from a single cultural and geographical region; comparative data from other settings could help extend the current findings. EEG does produce precise measures of site-specific electrical activity in the brain, but might be profitably matched with fMRI (for example) to provide a more detailed account of neurocognitive activity. The method for identifying MDD subtypes used in this study might be described as 'a priori' because the subtypes were grouped according to the commonalities between specific MDD symptoms, performed by a panel of clinicians [15]. Another method of identifying subtypes is 'a posteriori' by applying cluster analysis to EEG data and then regressing those data upon MDD symptoms from a scale like the SDS. Neither method has yet shown superiority, and both represent valuable models for defining MDD subtypes. Although some of these limitations do limit generalizability, they do not invalidate these results. Finally, research is always strengthened by testing hypotheses based on previous findings, but that was not possible here due to the lack of studies in this field.
## 3 System Model
### Computational Model
#### 3.3.2 Edge Layer Computing Model  
Since the computational resources of the edge server are much larger than those of the MD, the task can be immediately entered into the computational queue for execution when the task offloaded by the MD arrives, so the waiting delay of the task is not considered on the edge server. For the task \(\lambda_{m}(t)\) generated by device \(m\) in time slot \(t\), it is denoted by \(\lambda_{m,n}(t)\) on the edge server. Assume that the current processing capacity (bits/s) allocated by the edge node for processing the task is \(f_{n}^{EC}\). Then, the processing latency \(\tau_{m,n,\text{ }exe}^{t}\) of the task at the edge layer can be expressed as:  
\[\tau_{m,n,\text{ }exe}^{t}\ =\ \frac{D_{m}^{t}\rho_{m}^{t}}{f_{n}^{EC}} \tag{6}\]  
Similar to terminal layer computing, the completion time slot \(l_{m,n}^{\text{comp}}\left(t\right)\) for edge layer tasks \(\lambda_{m,n}(t)\) can be expressed as:  
\[l_{m,n}^{\text{comp}}\left(t\right)=\min\bigl{\{}t+\tau_{m,n\text{ }wait}^{t}+\tau_{m,n}^{t}\ +\tau_{m,n,\text{ }exe}^{t}\,\quad t+\tau_{m,\text{max}}^{t}\bigr{\}} \tag{7}\]  
The total delay \(\tau_{m,nEC}^{t}\) of task \(\lambda_{m,n}(t)\) on edge server \(n\) can be expressed as  
\[\tau_{m,nEC}^{t}=\min\bigl{\{}\tau_{m,\text{ }wait}^{t}\ +\tau_{m,n}^{t}\ +\tau_{m,n,\text{ }exe}^{t}\,\quad\tau_{m,\text{max}}^{t}\bigr{\}} \tag{8}\]  
where \(\tau_{m,\text{ }wait}^{t}\) denotes the waiting delay in the local model, \(\tau_{m,n}^{\text{tran}}\) denotes the transmission delay, \(\tau_{m,\text{ }wait}^{t}+t_{m,n}^{t\text{ran}}\ +\tau_{m,n,\text{ }eee}^{t}\) denotes the time slot required for a task to be offloaded from the endpoint to the edge server and executed to completion, and \(\tau_{m,\text{max}}^{t}\) denotes the maximum tolerated delay.  
The energy consumption \(E^{\text{edge}}_{m,n}\) incurred when tasks are offloaded to the edge server for execution can be expressed as  
\[E^{\text{edge}}_{m,n}\ =\ P^{\text{wait}}_{m}\ \tau^{t}_{m,\text{ wait}}\ +\ P^{\text{ tran}}_{m,n}\ \tau^{\text{ tran}}_{m,n}\ +\ P^{\text{exe}}_{m,n}\ \tau^{t}_{m,n,\text{ exe}} \tag{9}\]  
where \(P^{\text{wait}}_{m}\ \tau^{t}_{m,n}\,P^{\text{tran}}_{m,n}\ \tau^{\text{ tran}}_{m,n}\,P^{\text{exe}}_{m,n}\ \tau^{t}_{m,n,\text{ exe}}\), respectively, denote the waiting energy consumption, transmission consumption, and edge node computation consumption of the task, respectively.
# Bearing Capacity of a Concrete Grouting Pad on the Working Surface of a Highway Tunnel Shaft
## 1 Introduction
The author has observed in the field that the failure mode of a grouting pad was a punching shear failure, guided by cracking along the peripheral grouting holes, rather than a punching shear failure with overall cracking at the bottom [22]. Thirdly, the treatment of the load on a grouting pad deviates from the actual situation in the field. Even in the case of poorly fractured rock, the peripheral high-pressure grouting holes generate a back pressure that is difficult to apply to the entire bottom of a grouting pad [23]. Therefore, the load range of a grouting pad should be simplified to the vicinity of the grouting holes. Consequently, this paper analyses the failure mode of a grouting pad and establishes a mechanical model under symmetrical loading. Firstly, a unified yield criterion is introduced, and the plastic limit load of grouting pad punching shear failure is derived. Secondly, the rationality of the understanding is verified, and the influence of plastic ultimate load parameters is discussed. Finally, a design method for the plastic limit of slurry cushion thickness is proposed. The research results have been applied to the design of grouting pads for well 2-2 of the Tianshan Shengli Tunnel, one of the longest highway tunnels in the world. This method solves the problem of frequent cracking caused by insufficient bearing capacity in the elastic design of grouting pads. The results of the study can provide a reference for the design and construction of grouting pads in similar shaft projects.
# Phase Shift Design in RIS Empowered Wireless Networks: From Optimization to AI-Based Methods
## 3 Review on Optimization Methods under Continuous Phase Shift
### SDR Method  
To handle the nonconvex modulus constraints, we can introduce a rank-one auxiliary variable \(\mathbf{Q}=\mathbf{e}\mathbf{e}^{H}\). This translates the optimization variable from \(\mathbf{e}\) to \(\mathbf{Q}\), and the objective function changes from \(f\Big{(}\mathbf{x}^{(n)},\mathbf{e}\Big{)}\) to \(f(\mathbf{x}^{(n)},\mathbf{Q})\). To account for the rank-one property of \(\mathbf{Q}\) and the fact that the diagonal elements of \(\mathbf{Q}\) are all 1, we need to add constraints \(\text{rank}(\mathbf{Q})=1\) and \(\mathbf{Q}_{m,m}=1,\forall m\). Then, problem (5) under C1 is equivalent to  
\[\min_{\mathbf{Q}:=\mathbf{0}} f\Big{(}\mathbf{x}^{(n)},\mathbf{Q}\Big{)},\] s.t. \[\mathbf{Q}_{m,m}=1,\ \ \forall m,\] \[\text{rank}(\mathbf{Q})=1. \tag{6}\]  
Notice that the transformed problem is still intractable due to the rank constraint \(\text{rank}(\mathbf{Q})=1\). However, the celebrated SDR method (i.e., removing the rank constraint) can be employed to solve this problem if the cost function \(f\Big{(}\mathbf{x}^{(n)},\mathbf{Q}\Big{)}\) is convex in \(\mathbf{Q}\).  
More specifically, with the remaining constraints \(\mathbf{Q}_{m,m}=1\) for \(m=1,\ldots,M\) being transformed into semidefinite constraints \(\text{Tr}(\mathbf{E}_{m}\mathbf{Q})=1\) for \(m=1,\ldots,M\), where \(\big{\{}\mathbf{E}_{m}\in\mathbb{C}^{M\times M}\big{\}}_{m=1}^{M}\) is a matrix with a single 1 in the \((m,m)^{th}\) position and zero in all other positions, the variable \(\mathbf{Q}\) can be directly updated via the interior point method, which is available in the software package CVX. If \(f\) is not convex, we may add another layer of successive convex approximation (SCA) to convexify the objective function in each SCA iteration with the complexity increased by a factor equal to the number of iterations for SCA. However, since the rank-1 constraint is relaxed, the obtained solution may not be a feasible solution to the original problem (6).  
In general, a feasibility check is used to verify whether the obtained \(\mathbf{Q}\) satisfies the rank constraint. Since the relaxed problem is a convex problem, a closed-form solution for \(\mathbf{Q}\) or explicit expression with respect to \(\mathbf{Q}\) can be derived in its dual domain. Then, the feasibility check can be done by leveraging the ranks of product inequalities technique [44]. If the rank constraint is not satisfied, a Gaussian randomization procedure can be employed to extract a feasible solution [45]. Since the computational complexity order of SDR is \(\mathcal{O}\Big{(}\sqrt{M}(2M^{4}+M^{3})\Big{)}\), it could be too time-consuming for large-scale RISs.
## 4 Simulation Results
### Simulation Parameters  
The simulations are performed using an Intel(r) CoreTM i7-7700 CPU system operating at 3.60 GHz (8 CPUs). The programming software is MATLAB R2018B. In order to achieve reliable results, the simulation results have been averaged over extensive number of random realizations of wireless channels and user drops during simulations. The simulation parameters are shown in Table 1.
# An Anti-Interference Method Based on Energy Residual Searching in GNSS Positioning Applications
## 4 Anti-Interference Method Based on Energy Residual Searching
### Performing Peak Searching on the Energy Residual
4:Step 1: Compute the search sequence \(U\) and the objective function sequence \(G\) by (12) and (13). 5:Step 2: Compute the RMS value \(u_{\text{rms},k}\) of the search points by (14), and insert it into \(U\) in ascending order of the search points. 6:Step 3: Identify the minimum point \(B\) and its neighboring points by (15). 7:Step 4: Update the RMS value \(u_{\text{rms},k}\) of the search point sequence by (16). 8:Step 5: If \(|u_{\text{rms},k}-u_{\text{rms},k-1}|<e|u_{\text{rms},k}|\) is satisfied,Terminate the iteration and select the current RMS value \(u_{\text{rms},k}\) as the peak point \(p\);
9:Step 6: Else,Construct a new search point sequence according to (11) and to Step 2
```  
**Algorithm 2** The overall algorithm structure
## 2 Preliminary Remarks on the Offline Motion Preparation
### Performance Evaluation  
In this work, selected motion preparation algorithms will be evaluated for their performance. Therefore, they are applied to the process show in Figure 1. In our example, the performance depends on two parameters: the amount of data produced by the algorithm and the effort required for offline motion preparation. The required motion accuracy on the TCP has to be maintained in all cases.  
For a good performance, the amount of data has to be small. The overall data quantity results from the total number of coefficients required to describe the whole motion specification for all axes. This number is the sum of all spline coefficients in the characteristic map of both axes, Figure 1c.  
A low preparation effort as an equivalent to the algorithms speed is also important for a high performance. Especially against the background of individualized production and the increasingly rapid changes in the motion specifications, this point is becoming increasingly important. The offline preparation effort is determined in the same way as in [6]. The effort is measured in FLOP (floating point operations). The number of FLOPs depends on the computing operations used. For weighting, the one from [8] is used: 1 FLOP for add, subtract, multiply, and IF; 4 FLOP for divide, square root; and 8 FLOP for exponential, sine, etc. All required operations are summed up to give the computational effort, which is expressed in 1 MFLOP = 10\({}^{6}\) FLOP.  
To evaluate the performance of the algorithms, the 2D curve representation similar to that suggested in [9] is used. In our case, the abscissa is the amount of data calculated with the algorithm, the ordinate is the necessary number of FLOPs. The performance curve is calculated by converting the motion profiles with a specific algorithm and a technically relevant TCP motion accuracy. The resulting data quantity and calculation effort are displayed as a point in the diagram; points of one algorithm for different accuracies form together the performance curve for this algorithm. In the following, a TCP motion accuracy between 1 \(\upmu\)m and 500 \(\upmu\)m is used as an example to evaluate in a qualitative way how the algorithm's performance changes with differing requirements.