esa-sceva/satcom-chunk-collection huggingface dataset:
# Information Fusion-Based Deep Neural Attentive Matrix Factorization Recommendation
## 4 Experiments
### Experiment Settings
#### 4.1.2 Evaluation Protocol and Baselines  
Because it is too time-consuming for each user to predict their preference on all non-interactive items, we follow a general strategy [12; 18]. Through applying the sampling method described in Section 4.1.1, the test set composed of 1 positive sample and 99 negative samples is collected for each user, then the user's preference on these 100 items is predicted, after that the prediction rating is ranged from large to small to generate a top 10 ranking list. The performance of recommendation systems is measured by hit ratio (HR) and normalized discounted cumulative gain (NDCG) [35]. HR intuitively evaluates whether the test item is on the top 10 list. NDCG is used to measure the location of the positive test sample in the ranking list.  
We compare IFDNAMF with the following models:  
* **GMF:** GMF is an improved matrix factorization algorithm [7]. The matrix factorization model is generalized by the activation function and the connection weights with incomplete weights of 1 so that the model can model the non-linear second-order interaction between the user and the item;
* **GMF+MLP:** By removing the interaction layer, attention layer, and pooling layer of the IFDNAMF model, the IFDNAMF model can be generalized to the improved GMF model with the multi-layer hidden layer network, referred to as GMF+MLP, which is utilized to demonstrate the effectiveness of deep neural networks to model high-order interactions between the user and the item;
* **Concat:** The concat is the variant of the IFDNAMF model, which is the common method for traditional recommendation models to process the attribute information. This method completes the combination of features of information through the fully connected layer, which ignores the interaction between different feature domains, and lacks pertinence;
* **Sum pooling:** To verify the effectiveness of the method that endows the cross-features with different weights by the attention mechanism, the IFDNAMF model is compared with the model that contains the same network structure with the IFDNAMF model removing the attention layer, referred to as sum pooling, which crosses the features by element-wise product and combines the cross-features by sum pooling operation.  
We implemented the above models and IFDNAMF recommendation model based on Tensorflow. For the neural network model, the model is initialized by a truncated normal distribution with a mean of 0 and a variance of 0.01. The model is optimized by the cross-entropy loss function and the Adam gradient descent algorithm. The batch size and initial learning rate are 256 and 0.001, the activation function of the hidden layer and the output layer are ReLU and Sigmoid, respectively.
## 3 Materials and Methods
### Inclusion and Exclusion Criteria  
Following a thorough screening process, 133 full-text articles underwent assessment for eligibility. To further refine our selection, specific inclusion criteria were implemented, falling within subject categories related to TCO, CAPEX, OPEX, maintenance, CWPs, chillers, HVAC, ML, DL-based forecasting, optimization strategy, and economic analysis of chillers. Alternatively, a thoughtful application of exclusion criteria was implemented, which excluded articles focusing on irrelevant subjects, lacking full-text availability, being out of scope, or providing insufficient details. This systematic approach ensured a higher standard of research validity and reliability.  
The inclusion and exclusion criteria were meticulously applied, resulting in 31 full-text articles that met the criteria [1; 3; 5; 6; 20; 30; 46; 48; 49; 50; 57; 86; 101; 104; 110; 111; 112; 113; 114; 115; 116; 117; 118; 119; 120; 121; 122; 123; 124; 125; 126]. In addition, we identified four additional papers [11; 52; 98; 127] that were not retrieved from our systematic database search but were relevant to our research. These papers, added separately, contributed valuable insights, ensuring the comprehensiveness of our review. For instance, the studies in [52; 98] explored the impact of cooling load and COP uncertainty on chiller TCO and cost calculation strategies for CAPEX, OPEX, and TCO components, which are highly relevant to the guiding theme 1 of this paper. Additionally, Ref. [127] provides valuable insights into AI-based strategies for energy cost savings and the potential benefits of integrating AI with traditional control strategies to enhance model performance. This study helped in addressing guiding themes 2 and 3. Another study [11] focuses on the LCC analysis and calculation of a solar hybrid HVAC system, highlighting key factors that impact LCC and operational cost savings, which align with guiding themes 1 and 3 of our review. Therefore, including these extra papers alongside our systematic search yielded 35 articles, enriching the depth and breadth of our analysis.
## 2 Organophosphorus Pesticides  
Classifications of pesticides are based on origin, target organism, and chemical structure. There are two major groups of pesticides, which are chemical pesticides and bio-pesticides. Pesticides can also be categorized based on chemical structure such as organochlorine pesticides (OCPs), organophosphorus pesticides (OPPs), carbamates, and pyrethroids [12; 13]. In this paper, only OPPs will be briefly discussed, as this review paper is focusing on the extraction methods and sorbents' development for determination of OPPs in food and water samples. Figure 1 shows the general structure of OPPs. R1 and R2 represent alkyl-, alkoxy-, alkylthio-, or amino groups. X denotes the acyl residue (labile fluorine-, cyano-, substituted or branched aliphatic, aromatic, or heterocyclic groups) [14].  
OPPs have been used as plasticizers, stabilizers in lubricating and hydraulic oils, flame retardants, and gasoline additives [15]. OPPs have also been used for medical usage to treat myasthenia gravis and glaucoma [16]. OPPs are extensively used worldwide in agriculture for crop protection [17], as they are the most effective and compelling method to protect plants from pests and have improved crop yields and agricultural productivity. However, critical adverse effects may arise from the uncontrolled use of OPPs in the agriculture industry, which could lead to soil, water, and crop produce contamination [5] and affect human health. The physical properties and toxicity class of several OPPs are presented in  
Figure 1: General structure of organophosphorus pesticides [14].  
OPPs are neurotoxins (nerve poison), by which they inactivate cholinesterase enzyme and disrupt the transmission of nerve impulses [19]. Consequently, a wider and stringent residues' monitoring has gained upswing demand from consumers regarding the environment and food contaminated by OPPs [6]. Therefore, OPPs' residue determination has become a primary focus and is crucial for food and environmental control.  
The risk of possible adverse effect of OPPs on human health prompted authorities to set a guideline on permissible quantity of OPPs' residue to ensure agricultural products are safe for consumption. Regulatory authorities such as the World Health Organization (WHO), European Union (EU), and Codex Alimentarius Commission (CAC) have established the OPPs' maximum residue level (MRL) of agricultural products for consumer reference. The MRL was established to ensure food commodities are safe for human consumption. The European Union indicated that water for human consumption should not exceed 0.1 ug L\({}^{-1}\) for each pesticide and 0.5 ug L\({}^{-1}\) for total pesticides [20]. Table 2 shows the maximum residue limit (MRL) of several OPPs in fruits commonly consumed by consumer as given by the EU Regulations (EC) No. 2020/1085, No. 2017/978, and No. 2015/399 [21; 22; 23].
## 2 Theoretical Analysis
\begin{cases}\chi_{r}\approx\frac{S}{N}\exp\left(-\frac{r_{k}^{2}}{\omega_{a}^ {2}}\right)\sum\limits_{k=1}^{N}\exp\left(-\frac{r_{k}^{2}}{\omega_{a}^{2}}+ \frac{r_{k}^{2}}{\omega_{a}^{2}}\right)\cos\phi_{k}\\ \chi_{i}\approx\frac{S}{N}\exp\left(-\frac{r_{k}^{2}}{\omega_{a}^{2}}\right) \sum\limits_{k=1}^{N}\exp\left(-\frac{r_{k}^{2}}{\omega_{a}^{2}}+\overline{ \Lambda_{k}}\right)\sin\phi_{k}\\ N=\left\{1.09\left(\frac{r_{0}}{D}\right)^{\frac{1}{2}}\Gamma\left[\frac{\phi }{5},1.08\left(\frac{D}{r_{0}}\right)^{\frac{5}{5}}\right]\right\}^{-1}\end{cases}  \tag{14}\]  
\(N\) is large enough so that \(\chi_{r}\) and \(\chi_{i}\) are considered approaching jointly normal distribution. The PDF is shown in Equation (15). \[p_{Xr},\chi_{i}(\chi_{r},\chi_{i})=\frac{1}{2\pi\sigma_{r}\sigma_{i}}\exp\left[ -\frac{(\chi_{r}-\overline{\chi_{r}})^{2}}{2\sigma_{r}^{2}}\right]\exp\left[ -\frac{(\chi_{i}-\overline{\chi_{i}})^{2}}{2\sigma_{i}^{2}}\right] \tag{15}\]  
\(\overline{\chi_{r}}\) and \(\overline{\chi_{i}}\) are the mean value of \(\chi_{r}\) and \(\chi_{i}\), \(\sigma_{r}^{2}\), \(\sigma_{i}^{2}\) represent the variance of \(\chi_{r}\) and \(\chi_{i}\). Appendix A shows the calculation progress. Substituting \(\chi_{r}=\sqrt{\chi^{2}}cos\theta\) and \(\chi_{i}=\sqrt{\chi^{2}}sin\theta\) into Equation (15) and integrating \(\theta\) from 0 to \(2\pi\), the PDF of \(\chi^{2}\) is obtained shown by Equation (16). \[p_{\chi^{2}}=\frac{1}{4\pi\sigma_{r}\sigma_{i}}\int_{0}^{2\pi}d\theta\exp\left[ -\frac{\left(\chi cos\ \theta-\overline{\chi_{r}}\right)^{2}}{2\sigma_{r}^{2}}\right]\exp\left[- \frac{\left(\chi sin\ \theta\right)^{2}}{2\sigma_{i}^{2}}\right] \tag{16}\]  
The modified Rician PDF [31] is a reasonable solution for Equation (16), shown in Equation (17), in which \(\mathrm{I}_{0}(x)\) represents the first class modified Bessel functions. \[\left\{ \begin{aligned} p_{\chi^{2}}\left(\chi^{2}\right)& =\frac{1+\varepsilon}{\chi^{2}}\exp(-c)\exp\left[-\frac{(1+c)\chi^ {2}}{\chi^{2}}\right]\mathrm{I}_{0}\left[2\chi\sqrt{\frac{(1+c)c}{\chi^{2}}} \right]\\ c&=\left[\frac{\sigma_{r}^{2}+\sigma_{i}^{2}+ \overline{\chi^{2}}}{\sqrt{\overline{\chi}^{2}+2\overline{\chi^{2}}}\left( \sigma_{i}^{2}-\sigma_{r}^{2}\right)-\left(\sigma_{i}^{2}-\sigma_{r}^{2} \right)^{2}}-1\right]^{-1}\\ &\overline{\chi^{2}}=\overline{\chi_{r}}^{2}+\overline{\chi_{i} }^{2}+\sigma_{r}^{2}+\sigma_{i}^{2}\end{aligned} \right. \tag{17}\]  
After the Jacobian transformation, the PDF of CE is calculated, given by Equation (18). It has a simple analytic expression and is easily used for further application. \[\left\{ \begin{aligned} p_{\mathrm{H}}(\eta)&=\frac{1 +\varepsilon}{\overline{\eta}}\exp(-c)exp\left[-\frac{(1+c)\eta}{\overline{ \eta}}\right]\mathrm{I}_{0}\left[2\sqrt{\frac{(1+c)cq}{\overline{\eta}}} \right]\\ &\overline{\eta}=\frac{2}{\pi^{2}R^{2}\omega_{i}^{2}}\overline{ \chi^{2}}\end{aligned} \right. \tag{18}\]  
All the algebraic operations above provide the statistical distribution of the CE under the condition of non-Kolmogorov turbulence. With rational assumptions, the PDF follows Rician distribution, which is beneficial to further study.
## 4 Discussion  
In this paper, we proposed a complete signal-processing framework for temporal and spectral analysis of ultrasound CS data. With our method, relevant spectral information is directly extracted from a compressed measurement. This framework is designed for ultrasound CS data that avoids a redundant reconstruction process. This is enabled by selecting an appropriate basis matrix for use in the data acquisition and reconstruction processes. Hence, we compared the performances of two fundamental basis matrices, determining that the DCT matrix offered the best performance for time and frequency domain analysis.  
From our simulations, the proposed framework demonstrated good performance in both temporal and spectral analysis. Based on this, we introduce a novel imaging mode for medical ultrasound applications combining the results of time and frequency domains analysis, since each domain provides information that is inaccessible from the other. We term the resulting combination of a B-mode image and an attenuation coefficient map, shown in Figure 9, a QUS image (Q-mode image). As proposed, the Q-mode image offers a comprehensive representation of the quantitative measurements from frequency-domain medical ultrasound, and the spatial (or anatomical for in vivo case) relationship from the surrounding B-mode image. This thus makes it possible to conclude professional pathological judgments that cannot be provided from exclusive time-domain analysis (i.e., analysis of traditional B-mode images). With the careful selection of ROI, in addition, the combination of spatial information in the time domain and quantitative results in the frequency domain would aid the development of various deep learning applications for medical ultrasound data. As the deep learning application with a combined MRI images exhibited good performance [40], the new category of comprehensive dataset such as the Q-mode image, also makes it possible to obtain promising results for recognition of pathological characteristics which is not shown in a B-mode image alone.  
Figure 9: Representative Q-mode image combining B-mode image of TM phantoms with an attenuation map.  
The concept of the Q-mode image is also flexible, as any combination of results of quantitative analysis can be included with the B-mode image, which is recognized as the most important representation of time-domain information in medical ultrasound applications. Although our demonstration features the attenuation coefficient map as the result of frequency-domain analysis, QUS information such as backscatterer properties, speed of sound, or elasticity, resulting from frequency-domain analysis could be combined with a B-mode image for a new kind of Q-mode image.
## 3 Results and Discussion
### Digester Temperature  
As presented in Figure 2, in the DE BGPs, the monitoring showed a stable working temperature in digesters under thermophilic or mesophilic conditions (42.3 \(\pm\) 1.9 \({}^{\circ}\)C, 49.3 \(\pm\) 1.0 \({}^{\circ}\)C, and 41.9 \(\pm\) 0.2 \({}^{\circ}\)C for DE1, DE2, and DE3, respectively). This is mainly caused by the heat supplied to digesters sourced from the CHP units.  
In the CN BGPs, records showed a relatively low working temperature with high variations (31.3 \(\pm\) 5.1 \({}^{\circ}\)C, 38.0 \(\pm\) 2.2 \({}^{\circ}\)C, 32.0 \(\pm\) 2.2 \({}^{\circ}\)C for CN1, CN2, and CN3, respectively). [PERSON] et al. (2018) in their paper also presented four BGPs in Beijing that had a working temperature of 35 \({}^{\circ}\)C [42]. Due to the lack of CHP units in CN BGPs and stricter environmental protection regulations in Beijing (no raw coal burning since 2017), external sources of energy, such as solar energy and electricity (CN1), biogas combustion and/or electricity (CN2), and geothermal pumps and solar energy (CN3), were used to maintain the temperatures in the digesters during winter. However, the use of such energy sources is costly; therefore, operators only supplied heating for a few hours in the wintertime (less than 12 h) to prevent pipelines and digesters from freezing. Consequently, the low working temperature limited the biogas productivity in CN BGPs.
## Materials and Methods
### Immobilization of Lacase onto Silica and Polyethylenimine Nanoparticles  
The amino-functionalized nanoparticles were then used to perform the immobilization of lacase according to the sorption-assisted immobilization (SAI) protocol [23], where the amino-functionalized nanoparticles and lacase (15 mg mL\({}^{-1}\)) were incubated in phosphate buffer (pH 7, 100 mM) at 4 \({}^{\circ}\)C and 100 rpm for 2 h. Next, glutaraldehyde was added dropwise to the mixture of nanoparticles and lacase, and the solution was incubated for an additional 18 h. The unreacted glutaraldehyde and the excess and unstable bound enzymes were washed away.  
The immobilization procedure for the polyethylenimine-coated magnetic nanoparticles (PEI-mNPs) was identical to the one previously described for silica nanoparticles except for the step of functionalization with APTES (not required here). The enzymatic activity of both NP-lacase conjugates and supernatants was measured in these immobilization processes as well as in the following ones to determine the activity yield, washing loss, and enzyme load. Variable concentrations of glutaraldehyde and lacase activity were used in the immobilization process: 4-8 mmol g\({}^{-1}\) NPs and 0.9-1.88 U mg\({}^{-1}\) NPs, respectively.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Type of Nanoparticles** & **Size (nm)** & **Concentration (mg NPs mL\({}^{-1}\))** \\ \hline Fumed silica nanoparticles (fsNP) & 7 & 59 \\ \hline Silica-coated magnetic nanoparticles & & \\ FeO-2206W (single-core) & \(21.5\pm 2.1\) & 5 \\ S-57 (multi-core) & \(11.8\pm 2.4\) & 10.9 \\ \hline Polyacrylic acid (PAA) magnetic nanoparticles & & \\ FeO-2204W & \(10.1\pm 2.4\) & 20.5 \\ FeO-36 & \(23.1\pm 4.9\) & 16.2 \\ \hline Polyethylenimine (PEI) magnetic nanoparticles & & \\ VOZ-19 & \(10\pm 1.2\) & 56 \\ \hline Non-coated magnetite & \(9.9\pm 1.4\) & 17.4 \\ nanoparticles & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Characteristics of the different nanoparticles.
## 4 Case Studies  
### Brief History and Location of the Structures Taken under Investigation  
#### 4.1.1 The Church of San Nicola in Montedoro  
The church of San Nicola in Montedoro is one of the oldest in the town of Martina Franca in the province of Taranto (Italy) (see Figure 2a,b). The construction of the church presumably dates back to the 14 th century, the period of the Angevin foundation of the city. It is located in the Montedoro district, hence \"San Nicola di Montedoro\" [20]. The church preserves its original structure, despite the internal transformations of the seventeenth century. The structure is characterized by a modest rectangular hall and late medieval architectural elements visible especially on the outside. The simplicity of the external facade is embellished only by the roof with raised pitches that intersect and form two gables with cladding made with the typical \"_chiancarelle_\" (a type of limestone slab). The portal is surmounted by a lunette and a small radial rose window, while on the tympanum of the main facade stands a graceful bell tower (Figure 2c). The interior consists of a single room and has two baroque altars in stone. On the walls, frescoes are visible, painted on two layers (Figure 2d) [1].  
Figure 2: The church of San Nicola in Montedoro: location (**a**,**b**); image of the internal of the church acquired with fisheye lens (**c**); image of the external of the church (**d**).
## 2 Drivers of Photovoltaic Technologies
### Influence of Efficiency on Module Costs  
The maximum theoretical efficiency of a PV cell, \(\eta_{uc}\), depends on the irradiation spectrum and the absorbing materials. For a cell with a single absorbing material, irradiated with the standard AM 1.5 spectral distribution, the maximum theoretical efficiency varies with the material band gap, as shown in Figure 5 (only radiative recombination is considered). If Auger recombination is also considered, the maximum theoretical efficiency for monocrystalline silicon is 29.4% [11].  
Figure 3: The relative changes of area-related costs caused by a 1% increase in module efficiency calculated for Equation (5).  
The actual efficiency of PV modules will always be lower than that of their cells, and their efficiency will be lower than the theoretical maximum. Modern design principles and technologies make it possible to reduce many of the optical and ohmic losses associated with the module array, but the ultimate module efficiency remains less than the theoretical maximum: \(\eta_{um}\leq\eta_{th}\). However, the materials and procedures needed for the module efficiency, \(\eta\), to approach \(\eta_{um}\) increase the production costs, so that the module cost, \(M(\eta)\), increases rapidly.  
This trend can be modelled as \(\eta<\eta_{um}\), as shown in Equation (6):  
\[M(\eta)=B+\frac{C}{\left(\eta_{um}-\eta\right)^{a}}, \tag{6}\]  
where \(B\), \(C\), and \(a\) depend on materials and the technology level. Such a trend can be found in current module prices [12] if an efficiency of 15% is associated with a \"low price,\" an efficiency of 19% is associated with a \"mainstream price,\" and an efficiency of 21% is associated with a \"high price\" module category, as indicated in Figure 6.  
Figure 4: PV modules technological learning curve for crystalline silicon (c-Si) and thin film (TF) technologies.  
Figure 5: Theoretical efficiency limit for AM 1.5 spectrum, with various materials.  
In this approximation, the dependence of the investment cost \(IC\) on the module efficiency would be expressed by  
\[IC=B+\frac{C}{\left(\eta_{um}-\eta\right)^{a}}+O+\frac{D}{\eta} \tag{7}\]  
which can be used to find the optimal solution (as a case study based on detail knowledge of costs structure in the locality). With progress in manufacturing technologies, the optimum efficiency moves to higher levels.
# Functional Outlier Detection by Means of h-Mode Depth and Dynamic Time Warping
## 4 Analytical Test Cases
### Ranking Results  
Finally, another advantage of the methodology presented in this paper is the ability to provide a scalar ranking criterion (\(\theta_{i}\in[0,1]\), \(\forall i\in\{1,...,n\}\)) for a sample of functional data. This is not only useful from a clustering or outlying detection perspective, but also from an exploratory analysis one. In particular, this kind of score can be used to perform sensitivity analysis on the functional data (see an example in Section 5).  
Depth measures are widely used in this setting, but some advanced techniques that have developed in recent years, such as the method presented in Section 4.2.4, which provides an efficient ordering of the data. Naturally, as it happened in the outlier detection setting, a good order measure should be capable of identifying a potential multimodality in the set of data, as well as handling the existence of magnitude, shape, or mixed outliers.  
The ranking experiments are the same ones as the ones performed for outlier detection testing, with one outlier in the sample of 100 curves. The results are presented in Table 3.  
In this case, we can appreciate that more advanced ranking methods such as the one presented here and the one presented in [35] provide a consistent ordering of the functional data. The results provided by these up-to-date methods show that for the sample of 100 curves, the introduced outlier is always found to belong to the 5% more outlying curves of the sample, and is frequently found to be the most outlying in simpler cases such as Model 3. Usual ranking techniques for functional data such as depth definitions fail to clearly identify the more outlying nature of the outlier in the sample, and cannot be reliably used as order measures in such homogeneous packages of univariate curves.  
Finally, we can mention that the better ranking results provided by the Sequential Transformations algorithm for Model 4 with respect to the method presented in this paper can be explained due to the nature of the chosen transformations. In particular, the use of what they call \(\mathcal{D}_{1}\) transformation in their paper, which consists of taking the first-order derivative of the functional data (in their notation, \(\mathcal{D}_{l}[X(t)]=\frac{d^{(l)}X(t)}{dt^{l}}\)), is obviously appropriate in the case of Model 4, where the pure shape outlier differs mainly by the values of derivatives. This means that its superior ranking capabilities for this specific model cannot be generalized for any type of shape outlier, and both methods provide comparable ranking results.