esa-sceva/satcom-chunk-collection huggingface dataset:
# Wideband E\({}_{00}\)-E\({}_{10}\) Silicon Mode Converter Based on 180 nm CMOS Technology
## 3 Results and Discussion
Compared with the simulated results in Figure 4, the E\({}_{10}\) mode conversion shows good matching to experimental results. However, the measured mode crosstalk deviates from the theoretical expectations apparently. This partly results from the unavoidable fabrication errors. Due to the rules of 180 nm CMOS technology, compromises have to be made in the waveguide layout design. The change in waveguide dimensions compared to the ideal size inevitably affects the phase difference between the two branch arms. In the  
Figure 10: Measured optical loss induced by the input and output grating couplers. Figure 9: Experimental test system with vertical grating couplers. MMI coupler, the phase difference error will lead to the deviation of mode interference from theoretical expectations. Not all the E\({}_{00}\) mode was converted to E\({}_{10}\) mode. Partial E\({}_{00}\) mode power remains at the output, which deteriorates the mode crosstalk within the interested wavelength range. This effect first degenerates with the increment of the wavelength, then becomes serious again at longer wavelengths. To demonstrate the proposed mode converter more clearly, performance comparisons between the reported E\({}_{00}\)-E\({}_{10}\) mode converters and this work are shown in Table 1. All referential experimental results are based on the SOI technology. The inversely designed mode converter shows the lowest mode crosstalk [29], while the mode converter, based on the slot waveguide, has the smallest size and the lowest insertion loss [30]. The photonic crystal waveguide-based converter exhibits balanced performances [31]. In comparison, the proposed work shows the moderate insertion loss and mode crosstalk. Though the largest size, the broadest bandwidth and lower E\({}_{10}\) mode insert loss promise favorable potentials in wideband operation. \begin{table}
\begin{tabular}{c c c c c c} \hline
**Ref.** & **Footprint (\(\mu\)m\({}^{2}\))** & **Insert Loss (dB)** & **Crosstalk (dB)** & **BW (nm)** & **Structure** & **Materials** \\ \hline
[29] & \(2\times 2\) & \(2.2\) & \(-16.2\) & \(40\) & Inversely design & SOI \\
[30] & \(0.8\times 1.2\) & \(<\)1.2 & / & \(50\) & Slots waveguide & SOI \\
[31] & \(6.3\times 3.6\) & \(2\) & \(-12\) & \(43\) & Photonic crystal waveguide & SOI \\ This work & \(3.3\times 79.3\) & \(0.4\)–\(2\) & \(>\)\(-10.3\) & \(>\)\(60\) & PS + MMI & SOI \\ \hline \end{tabular}
\end{table} 
Table 1: Comparison the proposed silicon E\({}_{00}\)-E\({}_{10}\) mode converter with reported works. Figure 11: Measured transmission spectrum of fabricated mode converter in 1540–1600 nm. Figure 12: Measured E\({}_{00}\)-E\({}_{10}\) conversion efficiency of fabricated mode converter in 1540–1600 nm.
## 2 Concept of \"Quantum Filigran\" Based on Strong-Coupling
### Quantum Filigran  
The principle of the filigran used for authenticating banknotes is common knowledge. In summary, when we submit the banknote to a specific optical excitation (illumination at normal incidence), a specific response is induced at the level of the filigran (the image of the filigran becomes visible if we observe it in transparency along the axis joining it to the source). It is not easy to counterfeit such filigrans, which differ from usual 2D images by the fact that they are not visible under grazing illumination. Ultimately, it is this specific response of the filigran that explains why it is a useful tool for authentication. Our goal, roughly expressed, is to conceive quantum filigrans based on the specific optical response of well-chosen fluorophores, in the quantum strong-coupling regime. The scheme of principle of our idea is depicted in Figure 1: a solution containing fluorophores is placed inside a tunable Fabry-Perot cavity; a fluorophore is resonantly excited by a laser source and absorbs a photon at a wavelength of 440 nm. A part of the energy is lost in an initial non-radiative process, followed by a radiative quantum jump at wavelengths of 530 \(\pm\) 25 nm.  
This frequency splitting is the signature of quantum strong coupling which is ensured by placing the fluorophore in a cavity resonant at 530 nm (for more details, see Section 4). The single photons emitted one at a time by the fluorophores are filtered in frequency around one of the two emission peaks and collected in a typical fluorescence device consisting of a single photon detector coupled to an acquisition table. The statistical distribution of the arrival times of the photons emitted by the fluorophore is well-approximated by a double exponential distribution. As a last resort, this distribution is, as explained in Section 4), the signature of the presence, at the level of the fluorophore, of two competing non-radiative processes, a short one and a long one. This specific response of the fluorophore (single photon emission, plus frequency splitting and frequency shift, plus specific temporal statistics) is extremely difficult to clone by classical methods; it will play the role of a quantum filigran in our proposal.
# New Hybrid Techniques for Business Recommender Systems
## 6 Experiment 1: Strengths and Weaknesses of Recommenders  
The goal of our first experiment was to identify a recommendation technique that performs well for different query verbosity values [51]. The results of Experiment 1 are shown in Table 2. Note that the verbosity refers to the absolute number of solution elements that the query contained.  
From the results, we can see very clearly that the collaborative filtering algorithms obviously suffer too much from their inability to accommodate contextual knowledge. Their performance is substantially worse than that of the other recommenders.  
Regarding those, we observed that the performance of the CBR recommender is better than the graph-based recommender; however, there is no improvement in the performance of the CBR recommender above a certain query verbosity. Thus, one can see that retrieving a single case is restrictive for the recommendations since only a limited number of elements are available which in turn creates a recall problem. The performance of the graph-based recommender, on the other hand, steadily improves as more elements are added to the query. In order to enable the CBR recommender to stretch its (better) performance to any size of the query, we repeated the leave-one-case-out evaluation by retrieving more number of most relevant cases. With the top two retrieved cases, the performance of the CBR recommender improved further, however, again only up to a certain query verbosity. By retrieving more and more cases, it was possible to overcome the recall problem and achieve a steady improvement in the performance of the CBR recommender, similar to the graph-based recommender. Nonetheless, one can observe that retrieving more cases also introduces more noise, consequently decreasing the overall performance of the CBR recommender. Thus, increasing the number of retrieved cases seems to be neither the optimal nor a generic solution to the recall problem of the CBR recommender because of its severe precision-degrading effect.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Query Verbosity** & **User-Knn** & **Item-Knn** & **Graph-Based** & \multicolumn{4}{c}{**CBR**} \\  & & & **Top 1** & **Top 2** & **Top 3** & **Top 5** \\ \hline
0 & - & - & 0.408 & 0.773 & 0.783 & 0.773 & 0.747 \\
5 & 0.487 & 0.420 & 0.566 & 0.777 & 0.805 & 0.774 & 0.714 \\
10 & 0.497 & 0.416 & 0.  
Overall, to achieve an optimum performance, the CBR recommender needs to be configured to retrieve a low number of the most-relevant cases. However, if a customer needs a solution with more elements than are available in the (small number of) retrieved cases, the CBR recommender fails to expand its range of recommendations. The graph-based recommender, on the other hand, can leverage the whole range of elements available in the case base and hence seems to be a better solution for increasing recall without adding too much noise. We, therefore, see a benefit in combining the graph-based and CBR recommendation techniques using a hybrid strategy.
## 7 Conclusions  
In conclusion, this paper introduces the CROWDMATCH mechanism as a novel solution to challenges in crowdsourcing. Specifically, our research presents an innovative approach that empowers crowdworkers to strategically select suitable crowdsourcers for information contribution, considering incentives (i.e., rewards), information availability, and information collection costs, while accounting for decisions made by other crowdworkers.  
Figure 6: Comparative evaluation: (**a**) average crowdsourcers’ utility and (**b**) average crowdworkers’ utility under the (i) Accurate CROWDMATCH mechanism, (ii) Highest Reward, (iii) Random Matching, and (iv) Stochastic Learning Automata (SLA) models.  
The development of the Approximate CROWDMATCH mechanism, grounded in matching theory principles, eliminates externalities from crowdworkers' decisions, allowing for individual utility maximization and simultaneous benefits for crowdsourcers. Building upon this, the Accurate CROWDMATCH mechanism incorporates coalition game-theoretic principles, refining the matching process to consider externalities, converging to an optimal and Nash-Individually stable matching. The unique contributions of this research include the introduction of the CROWDMATCH system model, the formulation of utility functions for the crowdworkers and crowdsourcers, and the proposal of two distributed matching mechanisms. Comprehensive simulation results demonstrate the operational advantages, scalability, and superior performance of the CROWDMATCH mechanism over different methods in empowering crowdworkers in crowdsourcer selection within large-scale crowdsourcing systems based on numerical evaluations.  
Our current and future work includes the refinement and optimization of the CROWDMATCH mechanism to accommodate dynamic and evolving contexts, such as fluctuating incentive structures or varying information availability. Additionally, the integration of machine learning techniques could enhance the mechanism's adaptability by learning from historical matching patterns and continuously improving its decision-making process. Exploring the application of CROWDMATCH across diverse domains and real-world platforms would provide valuable insights into its versatility and effectiveness in different contexts. Furthermore, investigating the implications of incorporating fairness and diversity considerations into the matching process could contribute to the development of more inclusive and equitable crowdsourcing systems.  
Conceptualization and writing, A.A. and R.K.; methodology and supervision, E.E.T. All authors have read and agreed to the published version of the manuscript.  
The research of [PERSON] was partially supported by the UNM Research Allocation Committee award, and the NSF Awards #2219617 and #2319994.  
Data Availability StatementData are contained within the article.  
The authors declare no conflicts of interest.
# An Effective Ensemble Approach for Preventing and Detecting Phishing Attacks in Textual Form
## 3 Proposed Approach
### Feature Extraction  
It is necessary to extract the features from the textual dataset since it contains two unique features:  
* Email.
* Label.  
To achieve this goal in our experimental investigation, we utilized the feature hashing (FH) technique. FH is a method that, without any linguistic analysis or preprocessing, turns unique tokens into integers. As an example, let us look at a string of short phrases like \"I loved this book, I hated this book, I love books\". FH builds an n-gram dictionary internally. In the aforementioned example, the list of bigrams would include phrases like \"This book\" (frequency: 2 times), \"loved\" (frequency: 1 time), \"love\" (frequency: 1 time), and so on. Once the aforementioned dictionary is created, FH assigns hash values to the terms and determines the utilization of each feature.  
For every row of text data, the module generates a set of columns, where each column represents a hashed feature. A value of 0 in a column indicates the absence of the hashed feature in the corresponding row, while a value of 1 signifies its presence. The advantage of using feature hashing lies in its ability to represent text documents of varying lengths as numeric feature vectors of equal lengths, thereby achieving dimensionality reduction. On the contrary, if the text columns were used in their original form, they would be treated as categorical feature columns with numerous distinct values. Additionally, having the outputs in numeric form enables the utilization of various machine learning techniques, such as classification, clustering, and information retrieval. The use of integer hashes instead of string comparisons during lookup operations also leads to the faster retrieval of feature weights.  
In our experiment, we utilized N-grams with a parameter value of 2, resulting in a large dataset comprising 65,539 hashed features. To effectively manage this high-dimensional dataset and prioritize the most predictive variables pertinent to phishing detection, we implemented chi-squared feature selection methodologies suitable for categorical data within classification frameworks. This particular feature selection algorithm evaluates the correlation between each feature and the label variable, thereby facilitating the identification of features that possess the most significant discriminatory capabilities. After the application of this technique, we successfully decreased the feature count to 50, thereby enhancing computational efficiency while preserving the most salient features. Furthermore, the selected features--including word frequency patterns, the presence of specific keywords, and the structure of emails--are paramount in differentiating phishing emails from legitimate communications, as they encapsulate essential characteristics of phishing attempts. By concentrating on these features, we ensure that the model not only enhancesits performance but also addresses the fundamental elements of phishing behavior, thereby rendering detection more precise and pertinent to practical applications.
## 3 Methods
### Spatial Autocorrelation
#### 3.1.1 Univariate Spatial Autocorrelation
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Spatial Autocorrelation** & **Calculation Formula** & **Remarks** & **References** \\ \hline global Moran’s I & \(I=\frac{n\sum\sum\sum\limits_{i}w_{ij}\left(x_{i}-\overline{x}\right)\left(x_{ j}-\overline{x}\right)}{\left(\sum\sum\limits_{i}^{n}w_{ij}\right)\sum\left(x_{i}- \overline{x}\right)^{2}}\) & The range of \(I\) is \(\left[-1,1\right]\), \(I<0\) indicates negative & \\ global Moran’s I & \(I=\frac{n\sum\sum\sum\limits_{i}^{n}w_{ij}\left(x_{i}-\overline{x}\right) \left(x_{j}-\overline{x}\right)}{\left(\sum\sum\limits_{i}^{n}w_{ij}\right) \sum\left(x_{i}-\overline{x}\right)^{2}}\) & spacial autocorrelation, \(I>0\) indicates positive & [4; 6; 7; 8] \\  & & spacial autocorrelation, and \(I\) tends to \(0\) indicates & \\  & & spatial random distribution. & \\ \hline global Geary’s C & \(C=\frac{\left(n-1\right)\sum\sum\limits_{i}^{n}w_{ij}\left(x_{i}-x_{j}\right)^{ 2}}{\left(2\sum\sum\limits_{i}^{n}w_{ij}\right)\sum\left(x_{i}-\overline{x} \right)^{2}}\) & The range of \(C\) is \(\left[0,2\right]\)\(C>1\) indicates indicates negative spatial autocorrelation, \(C<1\) indicates positive spatial autocorrelation, and \(C\) tends to \(1\) & [5; 6; 8] \\  & & positive spatial autocorrelation, and \(C\) tends to \(1\) & \\  & & indicates spatial random distribution. & \\  & & \(G<\) mathematical expectation (ME) indicates low & \\  & & value clustered, \(G>\) ME indicates high value & \\  & & clustered, and \(G\) tends to ME indicates spatial & \\  & & random distribution. & \\ \hline local Moran’s I & \(I(i)=\frac{\left(x_{i}-\overline{x}\right)}{\overline{S}^{2}}\frac{n}{j}w_{ij} \left(x_{j}-\overline{x}\right)\) & \(Z(I(i))<0\) indicates negative spatial & \\  & & autocorrelation, \(Z(I(i))>0\) indicates positive & \\  & & spatial auto-correlation, and \(Z(I(i))\) tends to \(0\) & \\  & & indicates spatial random distribution. & \\  & & \(Z(C(i))<0\) indicates negative spatial & \\  & & indicates spatial random distribution. & \\  & & autocorrelation, and \(Z(C(i))>0\) indicates positive & \\  & & & indicates spatial random distribution. & \\  & & & \\  & & spatial autocorrelation, and \(Z(G(i))\) tends to \(0\) & \\  & & indicates spatial random distribution. & \\ \hline local Getis–Ord’s G & \(G(i)=\left(\frac{n}{j}w_{ij}x_{j}\right)/\sum\limits_{i}^{n}x_{j}\) & \(Z(G(i))<0\) indicates negative spatial & \\  & & autocorrelation, \(Z(G(i))>0\) indicates positive & \\  & & spatial autocorrelation, and \(Z(G(i))\) tends to \(0\) & \\  & & indicates spatial random distribution. & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Spatial autocorrelation statistics.
###### Abstract  
Background: The efficiency and safety of primary open-angle glaucoma with high-frequency deep sclerotomy (HFDS) combined with cataract surgery has to be investigated. Methods: Right after cataract surgery, HFDS was performed ab interno in 205 consecutive patients with open angle glaucoma. HFDS was performed with a custom-made high-frequency disSection 19 G probe (abee tip 0.3 \(\times\) 1 mm, Oertli Switzerland). The bipolar current with a frequency of 500 kHz is applied. The nasal sclera was penetrated repetitively six times through the trabecular meshwork and consecutively through Schlemm's canal. Every time, a pocket of 0.3 mm high and 0.6 mm width was created. Results: Mean preoperative intraocular pressure (IOP) was 24.5 \(\pm\) 2.1 mmHg (range 21 to 48 mmHg). After 48 months, the follow up average IOP was 15.0 \(\pm\) 1.7 mmHg (range 10 to 20 mmHg). Postoperative IOP has been significantly reduced compared to preoperative IOP for all studied cases (\(p<0.001\)). After 48 months, the target IOP less than 21 mmHg reached in 84.9%. No serious complications were observed during the surgical procedure itself and in the postoperative period. Conclusions: HFDS is a minimally invasive procedure. It is a safe and efficacious surgical technique for lowering IOP combined with cataract surgery.  
glaucoma; ab interno; minimally invasive glaucoma surgery; cataract surgery; high-frequency deep sclerotomy; intraocular pressure
# Acceptance Model for Mobile Building Information Modeling (BIM)
## 4 Proposed Mobile BIM Acceptance Model
### Research Hypotheses
#### 4.2.1 External Variables for Mobile BIM Acceptance
Personal innovativeness was adopted as an external variable to evaluate the use of technology [15; 35]. **Behavior control** is defined as an external variable related to the environment in which the tool is used. Behavior control factors are divided into internal assessment items and external assessment items. The internal assessment items refer to those inside the organization, whereas the external assessment items refer to those outside the organization. The external variables related to the behavior control include pressure and support. First, pressure is defined as compulsory factors in the environment. It is similar to the subjective norm in TAM2. Subjective norm is the awareness of how people who are important to themselves will feel about doing certain things. It is adopted as an external variable in various literature reviews [21; 28; 33; 35; 36; 38; 39]. Next, support is defined as factors that help users to use their skills more efficiently. Internal support is defined as the assistance within an organization from the organization's policies, bosses, or colleagues in selecting an action. For example, providing software in an organization, providing free education, and providing incentives. Internal pressure is defined as a pressure that the organization gives to an individual, such as the organization's policies, bosses, or colleagues, in selecting an action. For example, compulsory policies in an organization, pressure from superiors or colleagues, and so on. External support is defined as assistance from outside an organization through policies, systems, etc. in selecting an action. For example, there are government economic benefits, free government education, etc. External pressure is defined as the pressure from outside the organization from the framework, contract, and relationship in selecting an action. For example, there is forced pressure from the owner, compulsory selection under the contract, and so on. **Organization innovativeness** is defined as the organization's active psychological attitude or capacity to introduce new information technology. Organization innovativeness adopts an external variable to assess the use of technology in an organization as a factor from the characteristics of the BIM and IS success model [15; 31; 32]. **Collective efficacy** is defined as the belief that an organization will be able to achieve successful results through action. Collective efficacy also adopts an external variable to assess the use of technology in an organization from the characteristics of the BIM and IS success model [15; 31; 32].
# Improved U2 Net-Based Surface Defect Detection Method for Blister Tablets
## 5 Experiments and Analysis of Results
### Training Parameter Settings  
The model was trained for 350 epochs; as a result, batch_size was 4, the initial learning rate was 0.001, \(\gamma\) was 0.75, and the Adam optimizer was chosen. The image size of the collected blister sheet data is input into the model and adjusted to 512 \(\times\) 512 width and height.
## 3 Results and Discussion
### TPC, TFC, and Antioxidant Activity of SWAP and Fortified Wheat Breads
In general, when considering both phenolic fractions, free and total, the antioxidant capacity of the samples, regardless of the assay applied (ABTS, DPPH, FRAP), can be almost entirely attributed to the TPC and TFC content since very high positive correlation (greater than 0.866, \(p<0.01\)) were found among these parameters. The results of the two-way ANOVA (Table 2) revealed the significance of the independent variables (plant waste and level) and their interaction (plant waste \(\times\) level) on the dependent variables (TPC, TFC, ABTS, DPPH, and FRAP) of the breads. It seemed that the type of plant waste, the level of supplementation, and their interactions affected the amount of total TPC, TFC, ABTS, DPPH, and FRAP. On the contrary, there was no significant effect of waste plant species on mean values of free TPC (\(p=0.090\)) and SWAP supplementation level (1% and 2%) on the antioxidant capacity of free phenolic compounds present in breads when estimated using FRAP assay (\(p=0.059\)). In addition, it seems that the interaction factor (plant waste \(\times\) level) has no effect (\(p>0.05\)) on free TPC, TFC, and their antioxidant capacity, as evaluated using all three ABTS, DPPH, and FRAP methods. Generally, breadmaking negatively affected mostly the free fraction of phenolics, flavonoids, and their antioxidant capacity. The breadmaking process decreased the free TPC, TFC, and antioxidant capacity of the supplemented bread samples, except for the TPC and TFC of bread with SPE that showed an increase (TPC: 15.1 and 13.6%, TFC: 4.4 and 32.5%, for