esa-sceva/satcom-chunk-collection huggingface dataset:
# Guided Semi-Supervised Non-Negative Matrix Factorization
## 1 Introduction  
Understanding latent trends within large-scale, complex datasets is a key component of modern data science pipelines, leading to downstream tasks such as classification and clustering. In the setting of textual data contained within a collection of documents, non-negative matrix factorization (NMF) has proven itself as an effective, unsupervised tool for this exact task [1; 2; 3; 4; 5; 6; 7], with trends represented by topics. Whilst such fully unsupervised techniques bring great flexibility in application, it has been demonstrated that learned topics may not have the desired effectiveness in downstream tasks [8]. In particular, certain related features may be strongly weighted in the data, yielding highly related topics that do not capture the variety of trends effectively [9]. To counteract this effect, some level of supervision may be introduced to steer learnt topics towards being more meaningful and representative, and thus improve the quality of downstream analyses. Semi-Supervised NMF (SSNMF) [10; 11; 12; 13] utilizes class label information to simultaneously learn a dimensionality-reduction model and a model for a downstream task such as classification. Guided NMF [15] instead incorporates user-designed seed words to \"guide\" topics towards capturing a more diverse range of features, leveraging (potentially little) supervision information to drive the learning of more balanced, distinct topics. Topic Supervised NMF (TS-NMF) [16] associates label information to latent topics to achieve better modeling results. Despite a distinction between the supervision information and goals of Guided NMF, SSNMF, and TS-NMF, there are certainly mutual relationships: knowledge of class labels can be leveraged to improve the quality of learnt topics (in terms of scope,mutual exclusiveness, and self-coherence), whilst a priori seed words for each class can improve classification.  
With these heuristics in mind, the contributions of this paper are as follows. We first analyze the characteristics of both classical and state-of-the-art NMF variants for classification and topic modeling tasks. We then propose the Guided Semi-Supervised NMF (GSSNMF), a model that incorporates both seed word information and class labels in order to simultaneously learn topics and perform classification. Our experimental results demonstrate that utilizing both forms of supervision information concurrently offers improvements to both the topic modeling and classification tasks, while producing highly interpretable results.
# Path Planning of Rail-Mounted Logistics Robots Based on the Improved Dijkstra Algorithm
## 3 Results and Discussion
### UWB Positioning Accuracy Test
#### 3.2.2 The Positioning Test  
The positioning test was conducted in an indoor space with dimensions of 200 m \(\times\) 200 m. The coordinates of the four base stations were (0, 0), (0, 200), (200, 0), and (200, 200), and the coordinate unit was a meter. Ten known coordinate points were selected in the above space, 50 data points were tested by UWB for each point, and the mean value was obtained. The test results are shown in Table 4.  
From Table 4, it can seen that the positioning errors of the UWB positioning coordinates in the x-axis and y-axis can be calculated as \(\pm\)0.19 m and \(\pm\)0.17 m, respectively, which meet the requirements of practical applications.
# Mobility-Enabled Edge Server Selection for Multi-User Composite Services
## 5 Simulated Experiments and Analysis
### Experiment Results and Analysis
#### 5.3.5 The Impact of Downtime  
The impact of service downtime generated by service migration on total waiting time is shown in Figure 8. From the three figures, we can see that under the same condition, less service downtime will result in less total waiting time. In Figure 8a, the total waiting time decreased with increasing edge server resources. In this case, increasing the edge server's resources would enable more services to be deployed on edge servers. This means that there may be more service migration so that the impact of the size of the downtime latency on the total waiting time would be more obvious with more edge server resources. In Figure 8b,c, the more services in a single-service composition and more users resulted in more total waiting time. In Figure 8b, more services invoked by each user did not change the number of migrated services, so that the impact of downtime latency was not obvious. In  
Figure 7: Impact of the round-trip on the total waiting time.  
Figure 8c, with the increasing of users, more users were connected to the remote cloud servers. With the increasing of users' number, although the total waiting time increased, service migration would not happen, so that the impact of downtime latency was also not obvious.
## 2 Overview of Whirlpool Hash Function
### Comparison between Whirlpool and SHA-1
#### 2.3.2 Security  
According to the birthday attack theory, assuming the number of possible outputs a hashing function H have is \(2^{\text{m}}\), _i.e._, the length of output is m bits. If the probability of at least two from k random input of H have the same output is larger than 0.5, as a result, \(\text{k}\approx\sqrt{2^{\text{m}}}=2^{\text{m}/2}\). As the output length of Whirlpool is 512 bits, whereas it is 160 bits for SHA-1, \(2^{512/2}\gg 2^{160/2}\), it is obvious that Whirlpool is much more secure than SHA-1 against birthday attack.
## 2 System Description and Its Mathematical Modeling  
### Operation Principle  
Like in two parallel connected DC-DC converters, a differential inverter is constituted of two identical DC-DC converters sharing the same input [11]. However, in contrast to parallel connected converters that share also the output, in the inverter the outputs of the two converters are connected in a differential way. Figure 1 shows the concept of building a differential inverter using two DC-DC converters [15; 16].  
The system under study in this work consists of a differential boost inverter which is obtained by connecting two identical DC-DC boost converters in parallel supplied from common source and feeding a floating voltage load. The current drawn by the input is shared properly between the two boost converters by the action of a Current Mode Control (CMC) scheme using the difference between the two inductor currents of the two converters as will be detailed later. The schematic diagram of a grid-connected DC-AC differential boost inverter fed by a PV generator is shown in Figure 2. The input PV voltage is regulated to the desired MPP voltage which depends on the solar irradiation \(S\) and the temperature \(\Theta\).  
In contrast to the case with resistive load in which a sine wave AC voltage must be generated at the output of the inverter, for grid connected inverter, it is the grid current that must be controlled to be in phase to the grid voltage. Because of their non minimum phase nature, boost converters can be better controlled using multiple loop CMC using inductor currents. Denoting the two connected converters as Converter 1, whose inductor is \(L_{1}\) and inductor current \(i_{1}\), and Converter 2, whose inductor is \(L_{2}\) and inductor current \(i_{2}\) (Figure 2), the operation of the system can be described as follows. Both converters are controlled in a complementary way via a single pulse-width modulation (PWM) scheme so that Converter 2 is phase shifted \(2\pi D\) with respect to Converter 1 at the switching time scale, \(D\) being the operating duty cycle. This implies that when the current \(i_{1}\) reaches its peak value, the current \(i_{2}\) reaches its valley value and vice versa.
# A Distributed Indexing Method for Timeline Similarity Query
## 4 The Structure of DTI-Tree
### The Timeline Similarity Query of DTI-Tree  
In this section, we discuss the query algorithm of timeline similarity as an example. The similarity between two timelines are defined in Equation (1). All the operations of DTI-Tree depend on interval set operations. Hence, the first step is to convert the timeline \(T\) into an interval set \(Q\) using the function _intervalize_ that is listed in Table 3. Then, the overlaps query region of each interval in \(Q\) is calculated by the method that is presented in the reference [9]. Theoretically, we should make a query for each rectangle. But, this results in a lot of repetitive computations and network IO times. Consequently, we combine the resulting rectangles into a rectangle \(R\). Although it broadens the range of the query in some degree, it can significantly reduce network IO times. The next step is to find all of the slave root triangles overlapping with \(R\). Let the TI-Tree in each corresponding slave run overlap query [9] and return all of the interval points in the rectangle \(R\). Group these interval points by the timeline ID and form some local partial timelines. Calculate the partial similarity between each local partial timeline and the timeline \(T\) and return the results to the master. All of the partial similarity values received by the master are grouped by the timeline ID, and the similarity of each complete timeline and \(T\) is obtained using the function reduce that listed in Table 3. At last, the timeline ID with the maximum similarity value will be returned. The Algorithm 5 summaries the process of the similarity query method and Figure 11 shows the main steps.  
```
Input: \(T\): a timeline; Output: the most similar timeline to \(T\) { 1. call the function intervalize to get a set of interval points \(Q=\{Q_{0},Q_{1},\ldots,Q_{k},\ldots,Q_{m}\}\); 2. calculate the overlap query region of \(Q_{k}\), noted as \(R_{k}\); \(R=R_{0}\cup R_{1},\ldots,\cup R_{k},\ldots,\cup R_{m}\); 3. find all the slave root triangles which overlaps \(R\) by the T-Tree in the master node, the result is \(\{\text{RTI}\}\). \(RT_{i}\) is the root triangle of the TI-Tree\({}_{i}\) on the slave node \(S_{i}\). 4. find all the interval points \(P_{i}\) in the rectangle \(R\) by the TI-Tree\({}_{i}\) on the slave \(S_{i}\) separately; 5. execute \(P_{i}\)_groupBy_(timeline ID) on the slave \(S_{i}\) separately, and the result is some partial timelines \(PT_{i}\)=\(\{PT_{i}\}\); 6. execute similarity (\(T\), \(PT_{i}\)) and send the result that are some partial similarity values \(PSV_{i}\) to the master;
```  
**Algorithm 5**DTI-Tree similarity query shown in Figure 11  
Figure 11: Main steps of similarity query.
###### Abstract  
This study aims to detect starch adulteration in dairy products utilizing an artificial neural network (ANN) model. Globally, milk fraud represents a significant challenge to food safety, posing substantial health risks to consumers. In this context, spectral data derived from milk samples with varying starch concentrations were processed using feature scaling and normalization techniques. The ANN model was rigorously trained and validated employing the stratified k-fold cross-validation method, demonstrating exceptional proficiency in detecting starch-adulterated milk samples and effectively differentiating among various starch concentrations. The principal findings indicate that the model achieved 100% accuracy, coupled with high levels of precision, sensitivity, and F1-scores. Future research should explore the application of this model to different types of adulteration and extend its validation on larger datasets. Furthermore, the potential adaptability of this method for other food products and field applications warrants investigation. This study offers valuable insights for milk producers, food safety professionals, and consumers, particularly highlighting the implications for small-scale rural farms, thereby enriching the discourse on food safety within short food supply chains.  
d  
dairy product integrity; advanced detection techniques; machine learning applications; food safety; milk quality analysis Article
## 3 Results and Discussions
### Statistical Characteristics of Acceleration Peaks and RMS(g)  
Figure 10 shows the calculated probability density functions of the RMS(g) data from field measurement and laboratory measurement and each figure shows the calculated kurtosis and skewness values. Side-by-side diagrams help compare data from laboratory tests and field measurements. In the case of random vibration test, the controller operates with Gaussian distribution, but by specifying the kurtosis distribution the suitable controllers can perform the random vibration test in a more realistic way.  
In the case of the calculated distribution for the RMS (g), the kurtosis values in each case were positively peaked. In both the field and laboratory cases the highest kurtosis values were in the 3 rd layer of the stacked unit with a value of 35.85 for field, and a value of 6.54 for laboratory (Appendix C). The increasing tendency for kurtosis value upward in the stacked unit can be seen in Figure 10. These phenomena are in line with the previous papers' results in which the non-Gaussian nature of random vibrations on delivery vehicles were investigated [18; 19; 20; 21]. The skewness values in each case were positive and between the values of 1.62-2.63.
## 3 Experimental Results and Analysis
### Automotive Part Defect Data  
The automotive engine parts produced by the Tianjin engine part factory had a length of only 2.8 cm and a diameter of 1 cm, as shown in Figure 8. The surface defects of the parts included brwise damages and machine marks. In practice, to remove unqualified parts from production, workers on the production line must visually identify millimeter-length surface defects of produced parts using a magnifying glass. However, due to a large number of produced parts on a daily basis, the efficiency of this method of inspection is very low.  
To improve the efficiency of defect detection and enable the factory to realize intelligent detection, first, a camera sensor capable of capturing high-definition images of the produced parts was considered. A 1080P HDMI digital microscope, shown in Figure 9,  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Total Loss** & **Bbox Loss** & **Cls Loss** & **Mask Loss** & **RPN Bbox Loss** &
\begin{tabular}{c} **RPN Cls** \\ **Loss** \\ \end{tabular} \\ \hline A & 0.96 & 0.14 & 0.10 & 0.27 & 0.44 & 0.01 \\ B & 0.75 & 0.14 & 0.09 & 0.26 & 0.25 & 0.01 \\ C & 0.69 & 0.14 & 0.07 & 0.26 & 0.21 & 0.01 \\ D & 0.88 & 0.21 & 0.02 & 0.29 & 0.35 & 0.01 \\ E & 1.32 & 0.20 & 0.02 & 0.29 & 0.80 & 0.01 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Loss comparison of the network models under different anchor scales.  
Figure 8: Automobile engine parts.  
was selected for acquiring high-definition images of the parts. The image sensor was capable of producing three million pixels with a static camera resolution of 1280 \(\times\) 720 and a magnification of 10-220 times. The manual focusing range was from zero to 150 mm.  
To improve the recognition of a defect area, an independently developed light source is used together with the camera sensor to obtain an image acquisition dataset. We design the independently developed light source based on the light emitting diode (LED) powered by a lithium battery. The volume of this light source is 305 mm \(\times\) 49 mm \(\times\) 28 mm, weighing only 200 g, which can be conveniently fixed on the side of the digital microscope. The entire equipment set is shown in Figure 10. Local image acquisition of bruise damage and machine marks was performed on 560 engine parts, and 560 valid images were obtained. The two categories of defects, namely the bruise damage and machine mark, were manually labeled to validate the effectiveness of the proposed method experimentally. The labeling diagram of automotive engine parts data using the labelImg tool is presented in Figure 11.  
Figure 10: Image acquisition equipment for automotive engine parts.  
Figure 9: A 1080P HDMI digital microscope.
# Review of Professionally Applied Fluorides for Preventing Dental Caries in Children and Adolescents
## 1 Introduction
Limited scientific evidence showed a decrease in the odds (OR = 0.66 [0.48-0.90] or no statistically significant difference OR = 0.92 [0.71-1.18], 95% CI) of having fluorosis when fluoride toothpaste use was started after 24 months. Beginning to use fluoride toothpaste after 12-14 months decreased the risk of fluorosis (OR = 0.70 [0.57-0.88]), thus emphasizing the importance of using the appropriate amount of fluoride toothpaste in children [15]. There are self-applied gel formulations of sodium fluoride (1.1% [5000 ppm] sodium fluoride) or stannous fluoride (0.15% [1000 ppm] fluoride) available by prescription for home use [16]. Professionally applied topical fluorides are used by a dental professional in the dental office, and they could be in the form of a gel, varnish, foam, or mouthrinse [17]. These products have a much higher fluoride concentration than self-applied fluorides and have usage restrictions for legislative reasons [18]. Professionally applied fluoride therapy is relatively inexpensive and easy to apply. Dentists have used topical fluorides for a long time to protect the oral health of children and adults, particularly for patients at greater risk [18]. Several factors may increase the caries risk for children and adolescents: poor oral hygiene, active caries, existing restorations, tooth enamel defects, eating disorders, lack of regular professional dental care, active orthodontic treatment associated with poor oral hygiene, high levels of _Streptococcus mutans_, poor diet, undergoing head and neck radiation therapy, abuse of drugs or alcohol, and dry mouth (xerostonia) [18]. Fluoride acts in several ways to protect against tooth decay. As a topical mode of action, low concentrations of fluoride in dental biofilm and saliva inhibit the dissolution of hydroxyapatite crystals and enhance the remineralization of damaged ones. In addition, fluoride reduces the acid production in dental plaque by interfering with the metabolic activity of various bacterial strains that produce acids [19; 20]. High levels of fluoride resulting from the topical fluoride application produce a temporary layer of calcium fluoride-like material on the enamel surface that acts as a fluoride reservoir. The fluoride is released slowly, particularly during acidic episodes produced by variogenic bacteria, and becomes available to remineralize enamel or to interfere with the bacterial metabolism [17]. This review evaluated the effectiveness of professional topical fluoride application on the prevention of dental caries in primary and permanent dentition.
# Linear Vibration Analysis of Shells Using a Seven-Parameter Spectral/_hp_ Finite Element Model
## 2 Theoretical Formulation  
In this work, the 3D geometry is approximated using a continuum shell element; in other words, the shell element \(\mathbf{\Phi}\) presented in Figure 1 is modeled only by means of its mid-surface \(\Omega\) (shaded area). The shell element shown in figure has constant thickness and radii, \(R_{1}\) and \(R_{2}\); however, in reality, thickness can vary. In Figure 2, a discretization of shell's mid-surface using spectral nodes can be observed; in this case, an element with polynomial order \(p=4\) is shown. In both figures, the reference system is Cartesian \((X_{1},X_{2},X_{3})\), and the local system is curvilinear \((\xi_{1},\xi_{2},\xi_{3})\) and it is attached to the mid-surface.  
The position vector \(\mathbf{\underline{X}}^{k}\), shown in Figure 2, describes the mid-surface coordinates of the \(k\)th spectral node. The spectral nodes, which are not equally spaced, are distributed in positions that correspond to the roots of orthogonal polynomial families, in this case, the Legendre polynomials (more detailed information may be consulted in the work of [PERSON] [14]). Additionally, it is necessary to know the unit normal vector at each node, which is denoted by \(\mathbf{\underline{n}}^{k}\). Both vectors are in terms of the reference system. In addition, the thickness at each node is denoted with \(h^{k}\). All these geometric parameters define the spectral element and correspond to the code's input data. In Section 2.1, the approximation of these parameters will be described.
## 2 Methods
### Case 1: High-Latitude Region
#### 3.1.2 FR and VTEC Estimations
Figure 4a shows the geomagnetic intensity at a 400 km height on 1 April 2007, which varies from 0.466 to 0.478 Gauss (1 Gauss = 10\({}^{5}\) nT). Figure 4b,c presents the spatial distributions of VTEC on 1 April 2007 and 17 May 2007, respectively. It is observed that both Figures 3a and 4b show the stripe-shaped ionospheric anomaly in the upper part of images and prominent inhomogeneity across the whole image, whereas Figures 3b and 4c maintain uniformity across the whole image. This result is consistent with GPS-measured ionospheric variation in Figure 2b,c. The maximum magnitudes of Figure 4b,c are 35.9 and 17.8 Total electron  
Figure 4: **Vertical total electronic content** (VTEC) maps inverted from FR angles. (**a**) \(B\) value of the geomagnetic field on 1 April 2007; (**b**,**c**) VTEC maps on 1 April and 17 May 2007, respectively, (**d**) the difference between (**b**) and (**c**). content unit (TECU), respectively. Furthermore, the mean and standard deviations are 14.1 and 6.2 TECU for the VTEC map of Figure 4b, respectively, and 12.2 and 0.37 TECU for the VTEC map of Figure 4c, respectively. The small difference in mean value suggests that the background ionospheric condition is similar at both SAR acquisition times. Figure 4d shows the VTEC differences between two SAR acquisitions. The VTEC differences produce additional ionospheric phase errors on the interferogram. Figure 5 presents the VTEC differences in profile A-A' of Figure 4d, which indicates that the ionospheric disturbance on the interferogram may be severe.
## 2 Materials and Methods
### Data Acquisition and Data Preparation  
A Landsat-8 OLI image on 27 October 2017 and a Shuttle Radar Topographic Mission (SRTM) Digital Elevation Model (DEM) were obtained from United States Geological Survey (USGS) Earth Resources Observation and Science (EROS) data center. The data were preprocessed to develop a land-cover/land-use (LCLU) map; normalized difference vegetation index (NDVI); and subsequent topographic parameters such as slope, aspect, topographic wetness index (TWI), and topographic position index (TPI). Geological layers and fault lines were extracted from the maps published by the Geological Survey of Pakistan in 1993 and 1982, which were further modified from [44]. Other toponyms, such as catchments, roads, and drainages, were extracted from topographic sheets developed by the Survey of Pakistan (SoP). Due to limited ground weather stations in the area, time-averaged monthly precipitation data product (GPM_3 IMERGM) of Integrated MultiSatellite Retrievals for the Global Precipitation Measurement (GPM) mission (IMERG) for the year 2017 was processed to generate the annual precipitation layer.