esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
DFT technology enables pulse-by-pulse real-time spectroscopic measurement and manipulation, making it a unique and emerging tool for ultrafast signal generation and processing, and high-throughput real-time measurements and imaging. This review paper introduces the concept and implementation methods of DFT, and reviews its wide applications in various microwave photonics applications, such as real-time spectroscopy, optical pulse characterization, ultrafast fiber grating sensor interrogation, photonic microwave arbitrary waveform generation, real-time microwave spectrum sensing, and photonic ADC.  
DFT-based microwave photonics systems are usually implemented using discrete fiber-optic and electro-optical components, which makes the system bulky, costly, unreliable, and with high power consumption. A solution to reduce the system cost, weight, footprint size and power consumption is to  
Figure 12: Schematic diagram of DFT-enabled photonic time stretch analog-to-digital conversion (ADC).  
develop chip-scale systems use photonic integrated circuits (PICs). Future work on DFT includes building integrated on-chip microwave photonics systems. One challenge is to develop an integrated dispersive element for chip-scale DFT. Chirped waveguide Bragg gratings can be a good candidate for integrated dispersive element. Efforts will be made to design and fabricate a three-port waveguide circulator to allow the waveguide gratings to work in reflection mode. An alternative solution is to develop a dispersion-engineered waveguide, such as photonic crystal waveguides.  
This work was supported in part by the Natural Sciences and Engineering Research Council (NSERC) of Canada and in part by EU Marie-Curie Career Integration Grant (FP7-PEOPLE-2013-CIG-631883). The author was supported by NSERC through the Vanier Canada Graduate Scholarship programme, supervised by Professor [PERSON] of the University of Ottawa, and the NSERC Postdoctoral Fellowship program, supervised by Professor [PERSON] of the University of California, Los Angeles (UCLA).  
The authors declare no conflict of interest.
# Short Text Clustering Algorithms, Application and Challenges: A Survey
## 3 Components of Short Text Clustering
### Document Pre-Processing in Short Text Clustering  
Document pre-processing plays an essential part in STC because the short text data (original text) are unsuitable to be used directly for clustering. The textual document likely contains every type of string, such as digits, symbols, words, and phrases. Noisy strings may negatively impact clustering performance, affecting information retrieval [51; 52]. The pre-processing phase for STC enhances the overall processing [47]. In this context, the pre-processing step must be used on documents to cluster if one wants to use machine learning approaches [53]. Pre-processing consists of four steps: tokenization, normalization, stop word removal and stemming. The main pre-processing steps are shown in Figure 2.  
According to [54], short texts have many unwanted words which may harm the representation rather than assist it. This fact validates the benefits of pre-processing the document in STC. Utilizing the documents with all their words, including unnecessary ones is a complicated task. Generally, words classified under particles, conjunctions and other grammar-based categories, which are commonly used, may be unsuitable for supporting studies on short text clustering. Furthermore, as suggested by [PERSON] et al. [55], even standard terms such as 'go', 'gone' and 'going' in the English language are created by the  
Figure 2: Main pre-processing steps.  
derivational and inflectional processes. They fare better if their inflectional and derivational morphemes are taken to remain in their original stems. This reduces the number of words in a document whilst preserving the semantic functions of these words. Therefore, a document free of unwanted words is an appropriate pre-processing goal [56; 57].
## 4 Methodology
### Data Encryption: Pre-Processing  
For FHE, we utilized the Concrete-ML library, an open-source, privacy-preserving machine learning framework based on FHE. This library was selected for its robustness and efficiency in handling FHE operations on real-world datasets. The first step in our proposed approach is to preprocess the data before training a ML model on plaintext data using a library such as Scikit-learn.
## 2 Background
In a desert or polar region, spatial cognitive challenges arise due to the absence of landmarks, suggesting that landmarks provide a context for calculating depth, height, position, and direction [20]. In this regard, it seems plausible that the limited and incomplete visual cues in extreme environments affect the accuracy of spatial perception in the same way it does on Earth. In summary, the literature to date suggests that inaccuracies in size and distance judgments under extreme environments are a consequence of the absence of familiar spatial cues. Here we investigate this claim using VR as a platform. Specifically, we explored perception of size (i.e., depth, height, and length) and distance under three separate VR environments differing in their degree of familiar visual cues. The use of a VR platform allowed us to examine the role of landmarks and of other environmental conditions under more controlled conditions than in previous research.
## 3 Vibration Active Control
### Adaptive Algorithms
#### 3.1.3. Filtered-X Least Mean Square (Fx-LMS) Algorithm  
For real-time active noise control applications, the LMS algorithm, due to the long delay, is not the most suitable algorithm, as the noise at the system input can generate instability. As an alternative to this problem, [PERSON] [26] presented as a solution an algorithm that has an estimated signal input, in which the reference signal is adaptively filtered and compared with the input signal, obtaining the estimated signal. The Fx-LMS algorithm is normally used to update the adaptive filter, as shown in Figure 3.  
Analysis of the Fx-LMS convergence with stochastic signals on entry is presented in [34] considering a moving average process. In this case, \(P(z)\) is the primary path plan,  
Figure 3: Block diagram of Fx-LMS algorithm.  
and \(\widehat{\mathbf{S}}(z)\) represents the estimated value of \(\mathbf{S}(z)\), its secondary path plan. Thus, the estimate of \(\mathbf{S}(z)\) is given by  
\[\widehat{\mathbf{S}}(z)=\sum_{q}^{Q-1}\frac{\mathbf{S}_{q}}{Z^{q}}. \tag{22}\]  
The equation that represents the error of the system, in the time domain, can be described as  
\[e(n)=d(n)-\sum_{q}^{Q-1}\mathbf{S}_{q}y(n-q). \tag{23}\]  
Assuming the output \(y(n)=\mathbf{w}^{\top}(n)\mathbf{x}(n)\), as well as in (5), substituting in (23), we obtain an error equation  
\[e(n)=d(n)-\sum_{q}^{Q-1}\mathbf{S}_{q}\mathbf{x}^{\top}(n-q)\mathbf{w}(n-q). \tag{24}\]  
Using a methodology analogous to [PERSON]'s optimal solution, an expression similar to (12) is obtained but with filtered values, given by  
\[\mathbf{w}_{*}=\mathbf{R}_{f}{}^{-1}(n)\mathbf{p}_{f}(n) \tag{25}\]  
where \(\mathbf{w}_{*}\) denotes the optimal vector of weights, \(\mathbf{R}_{f}(n)\) denotes the correlation matrix of filtered entries, and \(\mathbf{p}_{f}(n)\) represents the cross-correlation between the filtered input and the desired signal.  
The expression for the input \(\mathbf{x}_{f}(n)\) is obtained by filtering \(\mathbf{x}(n)\) using \(\widehat{\mathbf{S}}(n)\):  
\[\mathbf{x}_{f}(n)=\sum_{q}^{Q-1}\mathbf{S}_{q}\mathbf{x}(n-q). \tag{26}\]  
The optimal value of the error measured by substituting (25) and (26) in (24), is given by  
\[e_{*}(n)=d(n)-\mathbf{w}_{*}^{\top}(n)\mathbf{x}_{f}(n). \tag{27}\]  
From (27) the weight vector that updates the Fx-LMS algorithm is  
\[\mathbf{w}(n+1)=\mathbf{w}(n)+\mu e(n)\mathbf{x}_{f}(n) \tag{28}\]  
where \(\mu\), as in the LMS, is the convergence step of the algorithm.
# The Effect of Seed Removal and Extraction Time on the Phenolic Profile of Plavac Mali Wine
## 3 Results and Discussion
### Color Parameters  
Table 2 shows Plavac mali wines' color parameter results such as color intensity (CI), color tonality (T), and proportion of yellow (% Yellow), red (% Red), and blue (% Blue) pigments. Even though it is well known that the content of anthocyanins and their profile is responsible for wine color, nowadays it is apparent that copigmentation is a very notable contributor to the color of young red wines. Copigmentation in wine is mainly connected with the interactions between anthocyanins and a variety of organic molecules such as phenolic acids, flavonoids, flavan-3-ols, and even anthocyanins themselves [42; 49]. According to results presented in Table 2, a 49 day maceration period strongly influenced the color profile of wines by moving it towards more yellow pigments that could be explained by a pronounced interaction between phenolic compounds and the formation of new ones. Interestingly, that change was diminished with seed removal, as presented in Table 2, and no significant differences were noted in the red and yellow pigments between seed removal wines (variants D and E) and 21 day maceration wines (variant B). It can be seen that the effect of applied seed removal and maceration time was the same in both years even though the phenolic profile of tested wines differed showing that the initial grape chemical composition was not decisive.
# Survey on Routing Protocols for Vehicular Ad Hoc Networks Based on Multimetrics
## 4 Routing-Protocol Proposals Based on Metrics
### Multimetric-Routing Proposals
#### 4.2.14 VANET Routing Based on Real-Time Road-Vehicle Density  
The basic idea in a routing protocol is to select the shortest route; in VANET, this could be a road with low vehicle density. To improve this, authors in [30] proposed a vehicular routing protocol that considers real-time road-vehicle-density information. Therefore, not only was position information used by vehicles, but also road-vehicle density was calculated. In this way, each vehicle could establish a reliable route to forward the information, as the reader can see in Figure 10.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline  & **Rout Lifetime** & \multicolumn{2}{c}{**Percentage of**} & \multirow{2}{*}{**Rout Select**} \\  & **(Input)** & & **Same-Directional** \\  & & **Vehicles (Input)** & & **Probability (Output)** \\ \hline R1 & Short & Low & Weakest \\ R2 & Medium & Low & Weakest \\ R3 & Long & Low & Weak \\ R4 & Short & Medium & Weakest \\ R5 & Medium & Medium & Weak \\ R6 & Long & Medium & Normal \\ R7 & Short & High & Normal \\ R8 & Medium & High & Strong \\ R9 & Long & High & Strongest \\ \hline \hline \end{tabular}
\end{table}
Table 4: Rule base for fuzzy-control-based ad hoc on-demand distance vector (AODV) routing (FCAR) protocol [40].  
In the proposed routing scheme, each node keeps the Road Information (RI) to store the path-vehicle density computed from the information in beacons. RI of a vehicle is generated when the vehicle enters the road and is updated upon receiving a beacon from a vehicle moving in the reverse direction. Each node estimates its own total quantity of reverse cars (TRC), and the value of the reverse car in its RI to calculate road-vehicle density and send signalling messages containing the calculated TRC value to its one-hop neighbors. It is possible to calculate the TRC value as shown in Equation (17):  
\[TRC=M_{c}\times\frac{R_{d}}{C_{d}} \tag{17}\]  
where \(M_{c}\) is the value of the Reverse Cars field (vehicles moving in the reverse direction), moving distance after entering the road is \(C_{d}\), and \(R_{d}\) the length of the road. The value of the reverse-car field is increased by one when a vehicle receives a beacon. Then, the value of density of cars (DC) is modified as follows:  
\[DC_{n}=R_{TRC}+STRC \tag{18}\]  
\[DC=\alpha\cdot DC_{n}+(1-\alpha)\cdot DC_{n-1}, \tag{19}\]  
where \(R_{TRC}\) is the TRC value of the vehicle, and \(STRC\) the TRC value in the beacon. \(DC_{n-1}\) is the previously calculated \(DC\), and \(\alpha\) the weight value.  
These values are used in the RREP and RREQ schemes, and it finally selects the route with the highest MinDensity.
# An Analysis of Methods and Metrics for Task Scheduling in Fog Computing
## 5 Taxonomy for Task Scheduling Algorithms in Fog Computing
### Problem Solution Techniques
#### 5.2.2 Heuristic-Based Approaches  
Heuristic algorithms offer an alternative optimization technique for task scheduling in fog computing [68]. These algorithms leverage a set of rules to direct the search for an optimal solution, eschewing reliance on a mathematical model. They can assign tasks to  
Figure 7: Category of task scheduling algorithms.  
devices based on various criteria, such as device availability, processing power, and network latency. Heuristic algorithms are well-suited for handling large-scale systems where the computational complexity of mathematical models, such as ILP and MILP, can become impractical. Heuristic solutions are also combined with mathematical programming modeling to determine the optimal solution and satisfy a predefined set of constraints and objectives [18].
# Study on Failure Mechanism of Mudstone Based on Digital Core and Digital Volume Correlation Method
## 5 Analysis of Mudstone Failure Process by Digital Volume Correlation Method
### Analysis of the Evolution Process of Displacement Field
At the peak load 67.5 MPa, there was a large range of large displacement regions in the sample as a whole, and the large displacement regions changed strongly compared with the previous stage, being redistributed and concentrated. There was an obvious boundary between the large displacement region and the low displacement region, and there was an obvious delamination phenomenon in each displacement region of the sample. The regions with smaller displacement values essentially appeared at the junction of the mineral zone, which was consistent with the final failure form of the sample. At this time, the internal microcracks and pores of the sample expanded and penetrated, and the sample was at the critical point of instability and failure.
## 4 Establishment of Simulated HSI Dataset Based on Experimental Measurement
### Establishment of HSI Dataset
In Figure 8, the arrangement direction of mixed pixels radiates outward from pure pixels; that is, for a certain ground object, the pixel with a high abundance value is close to the pure pixel area, and the pixel with a low abundance value is close to the area intersecting with other ground objects. It is worth noting that the pure pixel hyperspectral data in this paper is obtained by ASD. The collection points of ground objects have a randomness, and their texture information was destroyed. In addition, Figure 8 is only for the convenience of observing the classification results, and this figure can be regarded as a HSI that disrupts the order of pixels inside the object. Since the texture and shape information of the ground objects are not used in the pixel-by-pixel classification in this paper, it has no effect on the classification results. Above, the sample reflectivity measured in the laboratory was used as the end member to perform a mixed pixel calculation and then establish a simulated HSI. Machine-learning classifiers require a large number of samples for training, and complete data labels are  
Figure 9: Spectral Characteristics of Hyperspectral Simulation Image Pixels (SNR = 1000): (**a**) vegetation and green coating; (**b**) vegetation and artificial leaves; (**c**) vegetation and camouflage cloth; (**d**) green coating and artificial leaves; (**e**) green coating and camouflage cloth; (**f**) artificial leaves and camouflage cloth. required in the field of hyperspectral remote sensing applications, while manually labeling the HSI dataset is very challenging and requires a lot of work [41]. This method can not only quantitatively analyze the accuracy of samples with different abundance pixels, but also can easily label all samples, thus eliminating the high cost of manual labeling. In the following, the LBI-BPSO proposed in this paper and the IOIF and GA-BPSO mentioned above will be used to reduce the dimension of the simulated his, respectively. Then, according to these DR results, the simulated HSI is classified, and finally, the classification performance analysis of the wavelengths obtained by the three algorithms for ground objects with close spectral characteristics is completed.
## 4 Real-Time Elbow and T-Junction Detection and Estimation (RT-ETDE) Framework
### Elbow Estimation
#### 4.3.2 Elbow Radius Estimation Using Tangent Angle  
The common approach to finding the elbow radius is typically based on the torus or circle fitting, but it is computationally intensive and often inaccurate due to the incomplete coverage of the torus model. To address this limitation, we propose a simple geometric approach based on the tangent of the elbow in the elbow plane, as shown in Figure 9. Let \(T_{E}\) be the intersection between \(X_{S}\) and the larger inner wall of the elbow on the elbow plane, i.e., \(X_{E}Y_{E}\)-plane. The tangent \(t_{E}\) of the elbow at \(T_{E}\) on the elbow plane creates an angle \(\gamma\) with \(X_{S}\). \(\gamma\) is also equal to angle \(\widetilde{S_{E}O_{E}T_{E}}\), and \(\cos\gamma\) can be computed as follows:  
\[\cos\gamma=\frac{R_{E}}{R_{E}+r_{E}} \tag{2}\]  
From this equation, the elbow radius \(R_{E}\) can be computed as follows:  
\[R_{E}=\frac{r_{E}\cos\gamma}{1-\cos\gamma} \tag{3}\]  
To estimate the tangent, we reuse the processing using the band and centroid. We first extract the points from inner outlier points within the \(\delta d_{E}\) distance to the elbow plane. Then, the selected points are classified into \(\gamma\)-bands along the \(Y_{E}\)-axis of the elbow, as illustrated in Figure 10. The centroid of each band is calculated to provide a robust representation of the points within the band. Using these centroids, the tangent is estimated, enabling the computation of the tangent angle \(\gamma\) and the elbow radius \(R_{E}\).  
Figure 8: Elbow direction estimation based on \(X\)-bands and centroids.
## 3 Photonic Technologies
### Photoelasticity
#### 3.1.2 Dental Applications  
Photoelasticity was for the first time used in dentistry by [PERSON] [24] to study different types of orthodontic movements. Late in the 1940s this method was used in the solution of dental problems with regard to structural design by [PERSON] [25], [PERSON], and [PERSON] [26]. The general procedure is to construct a model of the structure to be investigated from what is known as a photoelastic material. The direction and magnitude of the applied forces on the model, the way in which the model is supported, and the shape of the model must be similar to the conditions of the actual structure. The internal stresses in the model will then be similar to those existing in the actual structure regardless of material [26; 27; 28]. Photoelasticity has been used in a wide number of dental biomechanical studies [29]. It has been applied in 2-D and 3-D studies of cavity preparation and dental restorations, to perform stress analysis [30; 31; 32; 33], and it was also applied in the study of endodontic posts [33; 34]. [PERSON] _et al._[34] have performed a comparative analysis of three types of posts regarding design, insertion, length, and ability to transmit forces to supporting structures. This technique has also been used to obtain, quantitatively and qualitatively, the stress pattern distribution from the tooth root to the supporting alveolar bone, helping to give insights in the knowledge of the behavior of dental supporting structures under physiological function [35; 36] and also root-tipping provoked by orthodontic tooth movements [37]. [PERSON] _et al._[38] have used photoelastic methods to determine how the morphology of occlusal surfaces might affect occlusal loading that is transferred to the tooth apex. They founded that distal incline planes of cusps and lingual incline planes or buccal cusps of mandibular posterior teeth carried the greatest occlusal load in normal occlusion. Other studies reported in literature have also evaluated the influence of dental implant design (Figure 2) [39] as also normal dentition, in the load mechanism transfer between teeth or dental prostheses to the surrounding bone [40; 41] and also related to the method of retention of dental prostheses to abutments [42; 43; 44]. The influence of gap values between a fixed framework and implants, placed parallel or angled, in the stress distribution was also investigated [45].  
Figure 2: Isochromatic fringe pattern representing the stress field transmitted from a dental implant to the surrounding bone media [39].  
Another area of application of photoelasticity method was to study dental materials. [PERSON] _et al._[46] have studied the polymerization contraction stress of resin composite restorations in a model class I cavity configuration. In other studies the stress distributions induced by different post materials in combination with two different core materials in endodontic posts, were obtained by the photoelastic method [47; 48; 49; 50].
###### Abstract  
This paper presents a novel, multi-objective scatter search algorithm (MOSS) for a bi-objective, dynamic, multiprocessor open-shop scheduling problem (Bi-DMOSP). The considered objectives are the minimization of the maximum completion time (makespan) and the minimization of the mean weighted flow time. Both are particularly important for improving machines' utilization and customer satisfaction level in maintenance and healthcare diagnostic systems, in which the studied Bi-DMOSP is mostly encountered. Since the studied problem is NP-hard for both objectives, fast algorithms are needed to fulfill the requirements of real-life circumstances. Previous attempts have included the development of an exact algorithm and two metaheuristic approaches based on the non-dominated sorting genetic algorithm (NSGA-II) and the multi-objective gray wolf optimizer (MOGWO). The exact algorithm is limited to small-sized instances; meanwhile, NSGA-II was found to produce better results compared to MOGWO in both small- and large-sized test instances. The proposed MOSS in this paper attempts to provide more efficient non-dominated solutions for the studied Bi-DMOSP. This is achievable via its hybridization with a novel, bi-objective tabu search approach that utilizes a set of efficient neighborhood search functions. Parameter tuning experiments are conducted first using a subset of small-sized benchmark instances for which the optimal Pareto front solutions are known. Then, detailed computational experiments on small- and large-sized instances are conducted. Comparisons with the previously developed NSGA-II metaheuristic demonstrate the superiority of the proposed MOSS approach for small-sized instances. For large-sized instances, it proves its capability of producing competitive results for instances with low and medium density.  
scheduling; metaheuristics; dynamic, multiprocessor open shop; multi-objective scatter search; tabu search +
Footnote †: journal:  
**Algorithms**