esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Matrix Factorization Based Editing Expert Recommender System
### Estimating Feedback Ratings by Performing Matrix Factorization  
In this step, we try to infer the unknown feedback ratings of \(F\) since it is necessary to know user's opinion on every editing expert to find the optimal expert for the user. MF decomposes a feedback rating matrix with null values into a user matrix, which represents the latent factors of the users, and an editing expert matrix, which represents the latent factors of the experts. Then, a feedback rating matrix that is closest to the real one can be obtained through an optimization process by estimating the values in the user matrix and the editing expert matrix, and the unknown values in the original feedback rating matrix can be filled [25]. In other words, MF is adopted to build an estimated feedback rating matrix denoted by \(\hat{F}\), whose element \(\hat{f}_{i,j}\) is not null for all \(i,j\). Specifically, \(\hat{F}\) is approximated by multiply the two low-rank matrices obtained by factorizing \(F\) as shown in Equation (9).  
\[\hat{F}\approx RQ^{T}, \tag{9}\]  
where \(R\in\mathbb{F}^{n_{k}\times k}\) and \(Q\in\mathbb{F}^{n_{e}\times k}\) is latent user and expert feature matrices, whose row vectors \(r_{i}\) and \(q_{j}\) represents the \(k\)-dimensional latent feature vectors of \(u_{i}\) and \(e_{j}\), respectively. The parameter \(k\) controls the rank of the factorization and indicates the dimension of latent space for representingthe characteristics of users and editing experts [7]. \(k\) must be a positive integer and smaller than the minimum of \(n_{u}\) and \(n_{e}\)[12].  
MF maps both users and editing experts to a \(k\)-dimensional latent feature space, and \(\hat{f}_{i,j}\) is modeled as the inner products of feature vectors, \(r_{i}\) and \(q_{j}\), in the space as described in Equation (10).  
\[\hat{f}_{i,j}=r_{i}\cdot{q_{j}}^{T}. \tag{10}\]  
\(\hat{F}\) is estimated by minimizing the objective function in Equation (11).  
\[L=\sum_{i=1}^{n_{u}}\sum_{j=1}^{n_{e}}l_{i,j}(f_{i,j}-\hat{f}_{i,j})+\lambda( \sum_{i=1}^{n_{u}}\parallel r_{i}\parallel^{2}+(\sum_{j=1}^{n_{e}}\parallel q _{j}\parallel^{2}), \tag{11}\]  
where \(l_{i,j}\) is an indicator which is 1 if \(u_{i}\) has rated \(e_{j}\), and 0 otherwise. \(\lambda(\sum_{i=1}^{n_{u}}\parallel r_{i}\parallel^{2}+(\sum_{j=1}^{n_{e}} \parallel q_{j}\parallel^{2})\) is a regularization term for preventing overfitting, and \(\lambda\) is a parameter for controlling the strength of the regularization [26]. Equation (11) can be solved by using the stochastic gradient descent, and details are presented in Reference [11].
## 6 Conclusions  
Edible coatings applied to fresh apples may extend their shelf life, and the use of appropriate components together with them may also have antimicrobial or MAP-enhancing effects. The use of coating is in line with the assumptions of a mild technology that does not require specialised equipment but is still not used on a large scale. Phenolic compounds have antioxidant and antimicrobial properties, and by introducing them into the biopolymer matrix, they can act as an active ingredient whose gradual release affects the product, improving its quality. The use of film-forming solutions on the surface of climacter fruit, i.e., ripening after harvest, has an impact on slowing down the fruit ripening process and thus reducing their waste. For this reason, food coating complies with the circular economy approach, which assumes, among other things, that the activities carried out in the agri-food sector are to reduce food waste, thus affecting the protection of the natural environment and preventing the depletion of natural resources. The requirements for protective coatings for apples should be focused on many aspects. First of all, low-cost materials are desired, including waste-based resources such as pectin from fruit or vegetable pomace or agricultural by-products. Good miscibility between the components, biopolymers, solvents, plasticisers, additives, or other functional substances is necessary to obtain a smooth and continuous structure. Thus, their compatibility is also important. When combined with the storage conditions, those factors may result in shelf life extension or retention of the postharvest quality of apples. The designed protective coatings should be effective regardless of the variety of cultivation techniques.  
In conclusion, further research is needed to ensure the cost-effectiveness of edible coatings and to expand their application scope. Biopolymers have the potential to completely change the future of food packaging and preservation practices through further research and development. Despite the great benefits, there is still a problem related to the scale of application of edible coatings and the production costs. By using modern production systems, significant progress can be achieved in packaging and preservation for the food industry, and edible coatings can be adopted as the standard for environmentally friendly food packaging. Furthermore, research should focus on improving the performance of biopolymers under different environmental conditions in order to maintain reliability throughout the supply chain.  
M.M.: Writing--original draft, Investigation, Formal analysis, Data curation, Resources, Methodology. S.G.: Conceptualisation, Writing--review and editing, Visualisation, Validation, Project administration, Funding acquisition, Supervision. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
## 4 Discussion
First, mechanical ventilation and additional insulation were not cost-effective solutions, despite the large share of saved energy. In addition, heat pumps provide the best economic performance, whereas PVs are especially recommendable due to the achievable reduction of global cost and energy demand. However, neither of the papers evaluate single actions in the same manner as proposed in this paper. A comparable approach that considers environmental impact, instead of global cost, is presented in [34]. The method ranks thermal insulation and finds that there are several solutions significantly reducing energy demand, but those are also characterized with different environmental impacts. Association of the proposed method with a more comprehensive Multi-Objective Parametric Analysis (MOPA) [35] can also be drawn. The MOPA method is divided in three stages, which are different in essence from the stages presented here; however, the approach is generally similar. Although MOPA is applied to lightweight addition instead of the whole building, the first stage consists of analyzing components separately and then performing analysis for obtaining optimum design parameters. The third stage deals with the analysis of renewable energy resources. Indeed, MOPA is a parametric methodology that provides a much wider range of solutions. On the contrary, those solutions stem from the parameter variation, rather than real-life products, as is the case in this method. The proposed method can be used as template for guiding the building owner through the tasks, activities, and decisions needed for the selection of a renovation plan to reach the NZEB standard. Furthermore, consultants and energy-saving companies could also apply it to compose renovation scenarios. Currently, the method accounts for energy and economic indicators only, but it can be expanded to account for additional indicators describing CO\({}_{2}\) emissions, life cycle analysis, comfort, etc.
## 3 Observability of the Navigation System
### Observable Degree Analysis
This conclusion is obviously correct because if we use an identical factor for all the state variables, the effect of the adjusting factor will modify the state variables at an equal level. The state variable with a high observable degree will be incorrect and the filter accuracy will be decreased. In the previous section, we have proposed a relative observable degree for each state variable. Meanwhile, the prediction of each state variable is closely related to the observable degree. For this reason, a criterion for defining the adaptive adjusting factor based on the relative observable degree is proposed. The definition of \(\mathbf{\psi}_{k}\) can be expressed as  
\[\mathbf{J}_{k}=\left(\overline{\mathbf{X}}_{k}-\mathbf{X}_{k}\right)^{ \mathrm{T}}\mathbf{P}_{k}^{-1}(\overline{\mathbf{X}}_{k}-\mathbf{X}_{k})+\sum \limits_{i=k}^{k+m}\left(\mathbf{Z}_{i}-\mathbf{H}_{i}\mathbf{X}_{i}\right)^{ \mathrm{T}}\mathbf{R}_{i}^{-1}\left(\mathbf{Z}_{i}-\mathbf{H}_{i}\mathbf{X}_{i}\right) \tag{11}\]2  
where the subscript \((i)\) represents the \(i\)-th diagonal element and \(\mathbf{\eta}_{k}\) which donates the observable degree in \(k\)-th moment is defined in Equation (18). \(\phi(\mathbf{\eta})\) is a mapping function which represents the relationship between the observable degree and the adjusting factor. In the adjusting process, since the original state estimations are biased under weak observable degree, in order to reduce the bias, the adjusting factor should be well designed. For example, when the observation component has a high observable degree, the estimation should rely more on the observation information. In opposite, the estimation should rely more on state prediction.
# Numerical Evaluation of Residual Stress Influence on SIF in CT Specimen
## 2 Initial Data for FEA  
The preparation of specimens, mechanical properties, and all the experiments were carried out at Atomic Energy Research Center (CEA Saclay), France [17].  
The tests were made using the CT specimen machined from a pre-strained 316 L stainless steel plate. The plate was pre-strained at about 40% in the T (transverse) direction (see Figure 1). Pre-straining was made by rolling in RD direction (rolling direction). TRD (transverse rolling direction) is a perpendicular direction to RD. Due to rolling at a high strain level, the otherwise straight plate was curved, where one surface of the plate became concaved and the other--convexed. The plate thickness after rolling was \(\sim\)20... 21 mm and the machined CT specimen's thickness was 15 mm.  
Figure 1: Pre-strained plate with CT specimen.  
The same plate was used for residual stress measurement performed by Bristol University [17] using their developed deep hole drilling (DHD) technique [18]. The measurement technique consists of four steps. Initially, a small diameter hole is drilled through the thickness of the measured specimen. The second step is the accurate measurement of the diameter of this hole at many depths and angular points. After that, the material around the hole is freed coaxially using a coring tool. The last step consists of the measurement of the hole diameter at the same measurement points as in the second step. The measured distortion of the diameter is later used for the determination of residual stress. Measured residual stress profiles are shown in Figure 2. The figure shows the residual stress distribution along the thickness of the plate in RD and TRD directions. The distance of 0 mm is on the convex surface of the plate (see Figure 3) and the distance of 20 mm is on the opposite (concave) surface. The measurements show the higher residual stresses are in TRD, the same direction CT specimen tension, and crack opening is simulated. Therefore, residual stresses in TRD will be taken into account in this analysis.  
Figure 3: Location and orientation of the tensile specimen in the plate.  
Figure 2: The profiles of measured residual stresses.  
The tensile testing of 316 L stainless steel was made at 325 \({}^{\circ}\)C. The location and orientation of the tested tensile specimens in the plate are shown in Figure 3. Two columns with six specimens in each column in different plate depths were prepared. In total, twelve 2 mm thickness specimens were tested. The averaged material properties are as follows [17]:  
* Modulus of elasticity E = 176,360 MPa,
* [PERSON]'s ratio \(\
u\) = 0.3,
* yield stress Rp0.2 = 655 MPa,
* cinematic stress hardening H = 20 GPa,
* thermal expansion coefficient \(\alpha\) = 1 \(\times\) 10\({}^{-5}\)/\({}^{\circ}\)C.
## 4 Discussion
### Uncertainties in Reference Data and Evaluation Methods  
Although we utilized the EC observation as reference data to validate six satellite-based LE products at site scale, the large uncertainties still exist in the LE evaluation framework. First, the EC observations errors and energy imbalance. According to previous studies, ground-observations from the EC sites have an error of approximately 10-30% [5; 25]. The typical EC observations errors may lead to uncertainties for evaluating LE products. Importantly, EC method suffers energy imbalance and [PERSON] reported that EC method cannot observe large eddies, which will cause H + LE \(\
eq\) Rn \(-\) G [66]. Although we correct the LE using the method proposed by [PERSON] et al., this method is based on limited understandings of the nature of the energy imbalance and errors resulting from the unclosed energy problem remain unclear [50; 67]. Second, the limited EC flux tower sites in our study. There are only six flux tower sites available in the Haihe River basin to validate LE products. These EC flux tower sites are sparsely distributed and covered with limited lands cover types. Therefore, limited EC flux tower sites in the Haihe River basin will lead to large uncertainties in LE evaluation. Third, the spatial scale mismatch. The spatial footprint of EC observation is approximately 100-300 m and the spatial resolution of six satellite-based LE products is more than 500 m [60; 68]. Inaccurate representations of the EC footprint may result in large uncertainties in LE evaluation.  
On the other hand, the WBET were used as reference data to validate six satellite-based LE products at basin scales. The WBET was generally based on the assumption that the total water storage change (TWSC) can be negligible over multi-year scale [41; 69]. In fact, TWSC may not equal to zero over the multi-year scale when there is sustained climate change and/or human activities impacts on the water cycle [70]. Studies have showed that TWSCs vary greatly due to glacier and snow melting in headwaters of rivers, reservoir storage, and groundwater withdrawal for agricultural depletion, etc. [70; 71; 72]. The neglection of TWSC is projected to lead to uncertainties in the derivation of WBET. The GRACE mission provides TWSC observations, yet its coarse spatial resolution hampers its application in regional water budget estimation. To data, downscaling GRACE TWSC data using a forward algorithm may be the solution for estimating regional water budget in near future [73].
## 1 Introduction
These hybrid designs can integrate many energy sources and efficiently substitute batteries in IoT applications, relieving technicians from the need to replace batteries often and expensively [25]. The subsystem is designed to efficiently gather and convert many energy sources into usable electrical energy [26]. This study explores MIMO technology in energy harvesting, emphasising using multiple antennas for enhanced output voltage and efficiency, spatial multiplexing, filtering, and diversity [27; 28]. It introduces a novel approach by integrating MIMO RF-EH with PSCs, addressing the challenges of low output voltage. The proposed energy harvesting system, Figure 3, combines MIMO with multiple energy sources across different frequency bands, improving IoT capabilities and reducing dependence on traditional energy sources. This hybrid wireless RF-perovskite system ensures reliable, continuous power and offers lower maintenance costs and deployment flexibility. It is well-suited for IoT applications such as environmental monitoring, wearables, and intelligent infrastructure. Additionally, it provides a sustainable IoT power solution using a hybrid multi-radio frequency system and cost-effective thin-film solar cells optimised for indoor use [22] in smart cities.
## 3 Development of an HS-ANN Hybrid Model
### HS-ANN Hybrid Model  
To find the initial weights of the ANN model that lead into the global minimum of the error function, in general, a Monte Carlo simulation is performed, that is, the training is repeated many times with different initial weights. The Monte Carlo simulation, however, takes a great computational time. In this section, we develop an HS-ANN model in which the near-global optimal initial weights are found by the HS algorithm.  
The HS algorithm consists of five steps as follows [32].  
_Step 1. Initialization of the algorithm parameters_  
Generally, the problem of global optimization can be written as  
\[\begin{array}{ll}\text{Minimize}&f(\mathbf{x})\\ \text{subject to}&x_{i}\in\mathbf{X}_{i},\ i=1,\ 2,\...,\ N,\end{array} \tag{10}\]  
where \(f(\mathbf{x})\) is an objective function, \(\mathbf{x}\) is the set of decision variables, and \(\mathbf{X}_{i}\) is the set of possible ranges of the values of each decision variable, which can be denoted as \(\mathbf{X}_{i}=\{x_{i}(1),\ x_{i}(2),\...,\ x_{i}(K)\}\) for discrete decision variables satisfying \(x_{i}(1)\ <\ x_{i}(2)\ <\ \cdots\ <\ x_{i}(K)\) or for continuous decision variables. In addition, \(N\) is the number of decision variables and \(K\) is the number of possible values for the discrete variables. In addition, HS algorithm parameters exist that are required to solve the optimization problems: harmony memory size (HMS, number of solution vectors), harmony memory considering rate (HMCR), pitch adjusting rate (PAR) and termination criterion (maximum number of improvisation). HMCR and PAR are the parameters used to improve the solution vector.
# A Hybrid Unequal Clustering Based on Density with Energy Conservation in Wireless Nodes
## 2 Literature Review
Table 1 shows a summary and comparison of some of the clustering algorithms. The proposed protocol is an improvement of the HUCL protocol and the method is reviewed in the following sections. This improvement is due to the fact that in our protocol is a novel hybrid unequal clustering which is performed by a simple efficient algorithm for selecting CH node based on density, energy level, and distance to the BS. It also proposed a new mechanism by assisting the CH with intracluster data transmission as well as improved intercluster data transmission using layered mechanism. The main objective is to provide a high-precision clustering protocol to balance the energy consumption, decrease the overhead, substantial increase of networks' lifetime, and finally, to improve existing methods.
## 5 Methods
### Selection of Eligible Studies and Data Extraction  
Two paired investigators ([PERSON] and A.T.) independently screened all titles and abstracts records gathered from literature review to identify potential eligible studies and then evaluated full-text manuscript to determine the final included ones. Any disagreements about eligibility were resolved by discussion between the two investigators until a consensus was reached. We included only RCTs and prospective comparative studies with twenty or more participants, reporting the outcomes of interest and with a minimum of three months follow up. Non-English articles, single-arm studies, retrospective evaluation, meeting abstracts, case reports, editorial commentaries and systematic or narrative reviews were excluded. Reference lists of relevant and recent systematic reviews were manually reviewed to identify supplementary studies of interest.  
The intervention whose efficacy was to be assessed was botulinum toxin type A injection into any pelvic structure to treat CPPS. All CPPS subtypes were included. Control groups could include best clinical practice as suggested by international guidelines, placebo or no treatment. Additional inclusion criteria comprised age of participants > 18 years, presence of patient's assessment before and after the treatment with botulinum toxin and complete data about outcomes of pain/quality of life, voiding dysfunction and adverse events/side effects.  
All data extracted from the included studies were recorded in an electronic database. Collected data included main author and year of publication, country of origin, subtype of CPPS investigated, number, sex and age of participants, dose of BTX-A and number and location of injection administered, duration of symptoms, prior therapies received by patients enrolled in the studies and outcomes measured. The primary outcome was the improvement in pain.
## 4 Economic Analysis and Detailed Turbine Overview
### Cost of Maintenance  
On top of the economic loss due to the downtime of the turbines, there were contributors which have to be included, such as the cost of maintenance. In the SCADA event codes, the 220 is activated when the technician shuts down the turbine manually to perform the repair other repairs. However, these events were not counted as it cannot be clearly distinguished which of these 220 events were also in relation with the 216 types of maintenance. Based on the interview with the site technicians, if the failure has appeared for more than 5 min it was most likely to be fixed by a technician. The required technician will be counted from the event appearance. In real life, this can take couple of minutes before it is communicated to the technicians on site. After the code is cleared, an extra average of 12 min will be added to give time to finish the work. The following calculation--shown by Equation (2)--will be used to add extra cost required by the technicians.  
\[TT_{C}=TLc\cdot T, \tag{2}\]  
where \(TTc\) is the Total Technician Cost, \(TLc\) is the Technician Labor Costs and \(T\) the time, which is estimated as the sum of 216 duration if higher than 10 min plus the time of the event multiplied by a factor of 0.2.  
In B13, there were 134 events in total and 32 of them were fixed by a technician. In C01, there were 75 events and 23 have been fixed by a technician and in C13, there were 84 events and there were 18 taken care by a technician (Figure 8). For these works, additional labor cost will be added. The additional technician cost value for B13 Turbine was $3425 for the total investigated period, for C01 Turbine was $2745, and for C13 $3110 (Table 6).  
Figure 8: Histograms of 216 events—Turbines (B13), (C01), and (C13).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Turbine** & **216 (Filtered)** & **Event Count** & **Corr. (hrs)** & **Labor** & **Result** \\ \hline Turbine B13 & **108** & 32 & 0.2 & \$30.00 & \$3425 \\ \hline Turbine C01 & **87** & 23 & 0.2 & \$30.00 & \$2745 \\ \hline Turbine C13 & **100** & 18 & 0.2 & \$30.00 & \$3110 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Technician costs for the selected wind turbines.  
In the summary (Table 6), the total 15 months of loss can be seen. From Table 5 it was visible that the amount of losses are not consistent for each quarter, hence the value would not be precise for long-term forecasting (10/20 years) and should be only used with caution for mid-term (5 years) (Table 7).  
Since it has been estimated the forecast for 60 months and we usually know that a wind farm lifespan is 20 to 25 years, it can be easily calculated that the total cost only for the selected wind turbines could reach at the high costs for the wind turbine owners. This, at a global scale could influence the market and a reinvestment of the saved amounts could boost the American wind turbine industry.