esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion  
### General Quality of Wastewater and Water Resources within Zaraq River Basin  
The influent to Assamra WWTP contained 1550 mg L\({}^{-1}\) of TS, of which 33.5% was in suspended form, i.e., as TSS (Table S3 in SI). Organic matter content (expressed as COD) was high in influent samples (950 mg L\({}^{-1}\)). Comparison of influent and effluent concentrations indicated that Assamra WWTP was efficient in TSS and COD removal (98% and 96%). At all sampling locations, along with ZR, the TSS concentrations were low (<2-40 mg L\({}^{-1}\)) (Table S3), while the TS concentrations were high (840-4600 mg L\({}^{-1}\)). Jerash stream contained the highest TS concentration (4600 mg L\({}^{-1}\)) and had visible white residue. Organic matter content in river water was low (4-30 mg L\({}^{-1}\)) at all sampling locations except Sukhna station, which seemed to have minor contamination with organic matter (110 mg COD L\({}^{-1}\)). A wastewater pumping station is located upstream of Sukhna station, and leakages of wastewater would flow downstream to Sukhna station.
# Graph Theory Approach to the Vulnerability of Transportation Networks
## 2 Materials and Methods  
### General Assumptions  
In this paper, we consider the connected, simple, undirected graph \(G=(V,E)\), where \(V\) is the set of vertices (nodes), and \(E\) is the set of edges (arcs). Sometimes, we use the fuller notation for graph \(G\), where \(V(G)\) and \(E(G)\) denote its vertex-set and the edge-set respectively. The assumption about connectivity is important, because it is fundamental to the functioning of transport or logistics networks. Some basic definitions of graph theory are necessary to understand ways of the description of network graphs. The set of all adjacent vertices to vertex \(v\in V\) in \(G\) is called neighborhood and denoted by \(N_{G}(v)\) or \(N(v)\). The close neighborhood of this vertex is defined as \(N_{G}(v)\cup\{v\}\) and denoted by \(N_{G}[v]\) or \(N[v]\). The other basic parameter for graphs is the degree of vertex \(v\in V\), which is defined as the number of vertices in \(N_{G}(v)\) and denoted by \(deg(v)\). The minimum and maximum degree are defined as \(\delta(G)=min\{x\in V:\ deg(x)\}\) and \(\Delta(G)=max\{x\in V:\ deg(x)\}\), respectively. Moreover, the set of all edges, incident to the vertex \(v\in V\), is denoted by \(I_{G}(v)\). For any set \(A\subseteq F\), neighborhood is given by \(N(A)=\bigcup_{v\in A}N(v)\). The induced subgraph defined on A is denoted by \(G[A]\).  
Regarding the assumption about connectivity in graphs, it is said that two vertices \(u\) and \(v\) in graph \(G\) are called connected if \(G\) contains a path from \(u\) to \(v\). Otherwise, they are called disconnected. A graph is said to be connected if every pair of vertices in the graph is connected. If the graph is disconnected, there is a connected component, which are maximal connected subgraphs of \(G\). Each vertex belongs to exactly one connected component, as does each edge.
# A Matrix-Based Proactive Data Relay Algorithm for Large Distributed Sensor Networks
## 4 Matrix-Based Data Relay Algorithm
### Energy Cost of Transmission  
The energy cost of transmission is composed of two parts: cost of broadcasting, and cost of receiving. The broadcast energy cost of a piece of data is \(Eb\). Before transmission, \(v_{i}\) does not know exactly which sensor can receive this data, and can only estimate the whole receiving energy cost according to the connection probability in matrix \(C_{i}\). The cost of \(v_{i}\) transmitting \(d_{h}\) can be estimated as follows,  
\[Energy(v_{i},d_{h})=Eb+\sum_{v_{k}\in N_{i}}C_{v_{i},v_{k}}\cdot Er=Eb+Er\cdot(C \cdot\overrightarrow{\Lambda}) \tag{12}\]  
Let matrix \(E=[Energy(v_{i},d_{h})]_{1:|L_{i}|}\), which represents the energy cost of \(v_{i}\) for transmitting any data in \(pendingQue_{i}\). Thus, this matrix can be calculated as follows:  
\[E=(Eb+Er\cdot(C\cdot\overrightarrow{\Lambda}))\cdot\overrightarrow{\Lambda}^{T} \tag{13}\]
# Improving the Efficiency of Energy Consumption in Buildings: Simulation of Alternative EnPC Models
## 4 Application to a Case Study
### Photovoltaic Unit Installation
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Company** & **Solution** & **Occupied Roof** & **Annual** & **Initial Investment** & **Payback** & **CO2 Emissions** \\
**Name** & **Type** & **Area (m\({}^{2}\))** & **Savings (EUR)** & **Not Incl. VAT (EUR)** & **Period (Years)** & **Reduction (t/Year)** \\ \hline
**AREAM** & **6 kW** & **45 a 50** & **1111.00** & **10,200.00** & **9.10** & **10.20** \\ \hline
**RC AU-** & **30 kW** & **800** & **6941.78** & **35,410.35** & **5.10** & **17.48** \\
**TOMACAO** & **45 kW** & **800** & **10,277.23** & **52,265.21** & **5.09** & **26.22** \\ \hline
**FACTOR** & **52.7 kW** & **289** & **7541.98** & **40,628.02** & **5.39** & n.a. \\ \hline \hline \end{tabular} n.a.—not available.  
\end{table} 
Table 4: Main results of the energy audits. Table 4 features a summary of the study of economic feasibility and environmental impact studies for the system's installation. The standard cost considered for the photovoltaic system was 1700 EUR/kW, and the prices charged by distributors may be lower than those considered in the AREAM analysis. The CO\({}_{2}\) emission factor for avoided electricity corresponds to 0.426 kg/kWh (according to the ESIN 2018), and the average cost considered for avoided energy was EUR 0.1787/kWh.
###### Abstract  
In this study, a process is developed for the fabrication of buried top-gated graphene transistors with Al\({}_{2}\)O\({}_{3}\) as a gate dielectric, yielding devices that can be suitable for not only flexible electronics but also laser-induced graphene (LIG)-based technology implementations. A new processing option is presented with the use of tetraethyl-orthosilicate (TEOS) as an etch stop for contact via etching of Al\({}_{2}\)O\({}_{3}\). Buried locally gated Al/Al\({}_{2}\)O\({}_{3}\) graphene field-effect transistors (GFETs) are fabricated with Dirac points as low as 4 V, with a metal-to-graphene contact resistance as low as \(\sim\)1.7 k\(\Omega\) - um, and an average hole mobility of 457.97 cm\({}^{2}\)/V-s with a non-uniformity of 93%. Large device variation and non-uniformity in electrical performance are not uncommon for graphene-based devices, as process-induced defects play a major role in such variation. AFM, SEM, Raman spectroscopy, and model fitting indicated that the rough Al/Al\({}_{2}\)O\({}_{3}\) surface was the main factor for the observed device variation. AFM analysis indicated a graphene surface roughness Ra of 16.19 nm on top of the buried Al/Al\({}_{2}\)O\({}_{3}\) gate in contrast to a Ra of 4.06 nm over Al\({}_{2}\)O\({}_{3}\)/SiO\({}_{2}\). The results presented indicate the need to reduce device variability and non-uniformity by improving transfer methods, as well as the use of smoother surfaces and compatible materials. The presented analyses provide a framework with which other researchers can analyze and correlate device variation and non-uniformities while methods to reduce variability are investigated.  
graphene; graphene field-effect transistors; 2D materials; gate dielectric; mobility; Dirac point; fabrication +
Footnote †: journal: Applied Physics Letters
# Numerical Analysis of Fatigue Life of Wind Turbine Blades Reinforced with Graphene Platelets
## 3 Fatigue Life Assessment for Wind Turbine Blades
### Equivalent Stress and Fatigue Life Calculation  
An engineered structure is subjected to combined loads of static and dynamic loads, and it exhibits responses that combine mean stress and alternative loads. However, the S-N curve used for fatigue life assessment is generally derived under conditions with a mean stress of zero. Thus, it has limitations in accurately assessing fatigue life under  
Figure 4: Variation of wind speed and tip-speed ratio (TSR) over 60 min generated using the Weibull distribution.  
Figure 5: Example of a time-domain stress spectrum and its transformation into the frequency domain.  
combined load conditions. The equivalent stress concept was introduced to overcome the limitations, and the Goodman diagram is a representative method that effectively applies the concept. As such, equivalent stress was calculated in this study using the Goodman diagram presented in Equation (8) to consider fatigue behavior under combined load conditions.  
\[\sigma_{eqn}=\frac{\sigma_{a}(\sigma_{eqn}-\sigma_{u})}{\sigma_{m}-\sigma_{u}} \tag{8}\]  
where \(\sigma_{eqn}\) is the equivalent stress corresponding to the equivalent mean stress \(\sigma_{eqn}\), which is set to zero in this study, \(\sigma_{a}\) is the amplitude stress, and \(\sigma_{m}\) is the mean stress, and \(\sigma_{u}\) is the ultimate stress of the material.  
Next, the number of cycles to failure, N, can be calculated using Equation (9) by applying the equivalent stress calculated through the aforementioned method to the S-N curve of the material. Figure 6 shows the Goodman diagram and the S-N curves of the glass fiber-reinforced polymer (GFRP), carbon fiber-reinforced polymer (CFRP), and GPLRCs used in this study. Here, the report by [PERSON] [26] was referred to for the material properties of the GFRP and CFRP. The material properties of the GPLRCs were derived by extrapolating the properties presented in [PERSON] et al. [41]. Table 6 shows the material properties used in this study for fatigue life assessment. Here, the volume fraction of GPLRCs (2.7%) is a value set to the optimal ratio with the most similar mechanical characteristics to the conventional wind turbine blade, which was found through the previous studies [15; 16] of the authors of the present study.  
\[N=\left(\frac{\gamma\cdot\sigma_{eqn}}{\sigma_{u}}\right)^{-b} \tag{9}\]  
where \(N\) is the number of cycles to failure, \(\sigma_{u}\) is the ultimate strength of the material, \(\sigma_{eqn}\) is the equivalent stress, b is the inverse of the slope of the S-N curve, and \(\gamma\) is the safety factor, which was set to 1.38 in accordance with [PERSON] [24].  
Figure 6: Methods for equivalent stress and fatigue life evaluation: (a) Goodman diagram, (b) S-N curves for GFRP, CFRP, and GPLRC.
# Design and Experiment of Variable-Spray System Based on Deep Learning
## 3 Results and Discussion
### Variable-Spray Experiment Results  
#### 3.3.1 Comparative Analysis of Variable-Spray and Constant-Spray Test Results  
Under the speed of 1 m/s, using water-sensitive test paper No. 4 as the sampling point, the parameters of droplets under two spraying modes can be obtained. The results are shown in Figure 11. According to the relevant standard NYT 650-2013, the droplet density should not be less than 30 droplets/cm2, and the coverage rate should not be less than 33%. The coverage of droplets was chosen as the evaluation criterion.  
Table 2 shows that the median volume diameter of variable-spray drops decreases by 138.13 m, and the density of droplets increases by 38.87 droplets/cm2. There is no significant difference in droplet coverage and deposition between the variable and constant sprays under the premise of meeting the evaluation criteria. However, the droplet diameter was refined. The reason for this is that the spraying volume in the constant spray exceeds the actual demand. This excess led to the re-coalescence of atomized droplets after reaching the blade surface, resulting in large droplet sizes. In contrast, variable spraying will not result in excessive spraying, thereby improving the droplet size and spatial distribution of the liquid.  
Figure 10: Linear fit of actual spray volume to theoretical spray volume.  
Figure 12 depicts the comparison of the flow rates of the four nozzles under the variable-spray and constant-spray comparison experiments. The red horizontal line represents the constant spray flow rate, while each curve represents the rate of change of the variable-spray flow for each nozzle. The difference between the red horizontal line and each curve represents the amount of spray saved. Compared to constant spraying, the spray volumes were saved by 47%, 44.5%, 49.4%, and 44%, respectively, with an average spray volume reduction of 46.3%. This represents a 4.3% increase in pesticide savings over the variable spraying in weed detection proposed by [PERSON] et al. [14].
## 5 Result and Discussion
### Results from the Customer Survey  
A customer survey was performed to gather customer feedback concerning the value propositions offered by the H2020 RESOLVD solutions. This section provides key results of the survey, which was answered by 21 DSOs from 10 countries (nine from the EU and one from India). The survey was designed based on the RESOLVD technology, which would be applied within the DSOs which faced multifold challenges in managing LV grids with increasing penetration of distributed generation. For sense and response, the paper considered five technology developers with one pilot DSO. As discussed in the Methodology section, the sense and response methodology required a minimum of three responses for analysis purposes, and for this reason, the proposed method was best suited to this research. Obviously, like other statistical analyses, the greater the number of responses, the better the results and the more minimal the inconclusiveness. As a result of this, similar research with a large number of technology developers would be needed. However, it is also important to understand the challenges of getting large responses in a highly regulated sector, like energy.
## 5 Demonstration of Machine Learning Model
### Failure Mode Prediction  
The method of determining the failure mode based on the relationship between the shear demand and shear capacity curves was compared with the results of the machine-learning-based model for failure mode prediction. Six key input variables were determined based on the structural details of previous experimental study specimens [1; 2; 3], as shown in Table 7. Applying these key input variables to the machine learning model, the C3-1 specimen in [PERSON] and [PERSON]'s study [1] was predicted to exhibit flexure failure, the 3 SLH18 specimen in [PERSON] et al.'s study [2] was predicted to exhibit shear failure, and Specim 1 in [PERSON] and [PERSON]'s study [3] was predicted to exhibit flexure-shear failure. The failure modes determined through the relationship between the shear demand and shear capacity curves agreed with the modes predicted using the machine-learning-based model. This demonstrates the capability of machine learning methods to accurately predict the failure modes of RC columns using only simple information.  
Figure 10: Relationships between shear demand curve and shear capacity curve [1; 2; 3].  
Figure 9: Traditional method predicting column failure mode.
## 2 Materials and Methods
### Identification of HIPS and ABS Using Stationary FTIR Spectrometer  
The pre-identified HIPS and ABS flakes were re-analyzed more thoroughly using a stationary FTIR spectrometer (Thermo Scientific, Nicolet iS10). More accurate identification was necessary due to the similar appearance of the IR HIPS and ABS spectra, which negatively affected the correctness of the results obtained after the analysis using the handheld IR spectrometer.
# An Investigation of the Selection of LEED Version 4 Credits for Sustainable Building Projects
## 2 The Different Needs in the LEED V4 Projects
Other studies have analyzed previously collected data using statistical methods, data mining, or data mining techniques, taking advantage of these resources to build a foundation for predictions and recommendations for future LEED projects. The results of these analyses can also be useful for assessing the effectiveness of new and updated policies from the USGBC. Most of these studies have focused on the selection of LEED certificates with different factors [1; 10; 32; 33; 34; 35; 36] and the correlational relationships between credits [19; 22; 37], and their results can be usefully applied in the early phase of a project where copious information is not yet available [1] and for providing an overview to guide developers' decision-making processes and the policy decisions made by the regulatory authorities. As yet, however, these studies have only analyzed a limited number of attributes (LEED credits) and old data sources from projects implementing previous LEED versions. The current version of LEED, LEED New Construction version 4 (LEED-NC-V4), which replaces LEED 2009 for all newly registered projects, has been significantly updated, and several new credits have been added [1; 32]. This has created a considerable gap for those seeking to apply the findings of the previous studies to new projects. Previous studies also did not perform a comprehensive analysis that encompassed all the credits, thus failing to provide sufficiently detailed information to enable the project team to fully consider all the available options. A study that investigates all the credits that could potentially be awarded in LEED v4 projects is thus necessary to fill this gap and supply useful additional support for project teams. The major changes introduced in LEED v4 consist of the addition of several new criterion groups, an expansion of the prerequisites and credits, and the requirements for some credits [1; 32; 38]. The LEED for New Construction and Major Renovations (LEED-NC) v4 has two extra categories that were not in the previous version, namely, \"integrative process\" (IP) and \"location and transportation\" (LT). LEED v4 now has 12 prerequisites (up from 8) and 43 credits (down from 49); some parts of the healthcare and education criteria have been altered slightly, and the demonstration requirements for the credits in the material category are significantly different. A more detailed review of the differences between LEED v4 and LEED 2009 is available in the USGBC's Summary of Changes document [38].
## 4 Discussion
### AI Applications in Safety Management Across Industries
#### 4.1.4 Transport and Logistics Sector  
AI addresses safety factors in the transport and logistics sector, including traffic accident prevention, railway safety, maritime safety, and public transport safety. Predictive models and machine learning algorithms analyze real-time traffic data to prevent accidents, enhance safety protocols, and reduce operational disruptions. For example, AI-powered IoT systems detect equipment anomalies in railways, optimizing maintenance schedules. Similarly, AI-driven navigation systems in maritime contexts prevent collisions, while enhanced surveillance in public transport improves security and operational efficiency [81].
## 4 Discussion
### Analysis of the Influence of Interior Reflectance (Bright/Dark Coatings)  
Interior reflectance is the most influential parameter affecting \(DA\) and \(CSA\) outcomes. Bright coatings consistently result in superior \(DA\) and \(CSA\) values, with \(DA_{500}\) values ranging from 80% to 87% for WWRs between 25% and 45% and \(CSA1\) values showing increases of 20% to 25% compared to those of dark coatings. In contrast, dark finishes introduce significant variability and lower values, particularly in north orientations and configurations with smaller WWRs.  
To achieve optimal lighting and circadian conditions, bright interior finishes are highly recommended across all orientations and window configurations. Dark coatings should be avoided, particularly in north-facing rooms and in scenarios with lower WWRs, as they result in substantially lower \(DA\) and \(CSA\) values. When dark finishes are unavoidable, increasing the WWR and opting for centered window configurations can help mitigate some of the negative effects, but their performance will still be inferior to that of bright finishes.
# Parameterized Modeling and Planning of Distributed Energy Storage in Active Distribution Networks
## 3 Parameterized Modeling of Optimal DES Planning in Active Distribution Networks (ADNs)  
### Typical Scenario Generation  
By clustering techniques, the method is developed to capture the daily patterns of load demand, wind turbine output and photovoltaic output. With the clustering method, typical scenarios and their occurrence probabilities can be obtained [28]. The purpose of clustering is to group the data objects into multiple clusters in regards to the similarity. The objects within a cluster are similar, whereas the objects of different clusters are dissimilar.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Lead-Acid Battery** & **Li-Ion Battery** & **VRB** \\ \hline Capital cost of power converter (\$/kW) & 50 & 50 & 50 \\ Capital cost of energy storage (\$/kWh) & 125 & 200 & 250 \\ Cycle efficiency (\%) & 90 & 95 & 75 \\ Cycle life (cycles) & 3000 & 5000 & 10,000 \\ Lifetime (years) & 10 & 12 & 15 \\ Maximum depth of discharge (\%) & 70 & 90 & 70 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Typical parameters of distributed energy storage (DES) [7; 10].  
The k-means algorithm is one of the most popular clustering techniques in the fields of mathematical statistics, pattern recognition, machine learning and data mining. It can well reflect the geometric and statistical significance of clustering [29].  
The calculation steps of the k-means clustering algorithm are as follows:  
Step 1: Given the number of clusters \(x\), randomly select \(x\) objects from the cluster data as cluster centers;  
Step 2: According to the principle that the distance from the cluster center is the smallest, the remaining objects are assigned to the corresponding categories;  
Step 3: After all the objects are divided by category, the cluster center is recalculated; the object with the smallest sum of distances to other objects in the same category is determined as the center of the current class;  
Step 4: Repeat Steps (2) and (3) until the cluster center no longer changes, the cluster ends. Then, the clustering result is obtained.  
By the above steps, the typical scenarios for DES planning are generated by the clustering technique, containing the patterns of load demand, wind turbine output and photovoltaic output.
## 4 Network and Service Modeling
### Graph-Based Modeling of the Dynamic Topology
Then, physical node 0 stores data in its memory until time cycle 2, when it processes the information, thus, a layer change occurs. Finally, the processed image is kept in memory until the last time cycle, when data are transmitted to the node \(n_{2}^{4,1}\), associated to the vGS. All the links previously described are shown in the given example. In particular:  
* Transmission links are inserted between couple of nodes in a region of a same layer, in case their distance is short enough to allow for communication; we report the two transmission links \(\varepsilon_{\{1,0\},\{0,0\}}^{0}\) and \(\varepsilon_{\{0,1\},\{2,1\}}^{4}\) in the example of Figure 4;
* Memory links are inserted between nodes related to the same physical node in two different regions of a same layer, representing the data storage during the entire time cycle associated to the originating region; for the sake of clarity, even though this link type is always present in case a node has enough memory to store at least a task, we only report the four involved storing links in the example of Figure 4;
* Processing links are inserted between the same physical node belonging to the same region on two different layers, representing the data processing; again, even though this link type is always present in case a node has enough processing capacity to elaborate at least a task in a time cycle, for the sake of clarity we only report processing link \(\varepsilon_{\{0,0\},\{0,1\}}^{2}\) in the example of Figure 4.  
To summarize, the proposed modeling provides for each satellite to consume energy when it transmits or processes data. Instead, the only contribution to the energy consumption on ground is given by the processing that takes place on ground stations, i.e., by the energy spent to cross processing links associated to the \(vGS\) in the proposed model.