esa-sceva/satcom-chunk-collection huggingface dataset:
# Systematic Literature Review of Predictive Analysis Tools in Higher Education +
###### Abstract  
The topic of predictive algorithms is often regarded among the most relevant fields of study within the data analytics discipline. They have applications in multiple contexts, education being an important one of them. Focusing on higher education scenarios, most notably universities, predictive analysis techniques are present in studies that estimate academic outcomes using different kinds of student-related data. Furthermore, predictive algorithms are the basis of tools such as early warning systems (EWS): applications able to foresee future risks, such as the likelihood of students failing or dropping out of a course, and alert of such risks so that corrective measures can be taken. The purpose of this literature review is to provide an overview of the current state of research activity regarding predictive analytics in higher education, highlighting the most relevant instances of predictors and EWS that have been used in practice. The PRISMA guidelines for systematic literature reviews were followed in this study. The document search process yielded 1382 results, out of which 26 applications were selected as relevant examples of predictors and EWS, each of them defined by the contexts where they were applied and the data that they used. However, one common shortcoming is that they are usually applied in limited scenarios, such as a single course, evidencing that building a predictive application able to work well under different teaching and learning methodologies is an arduous task.  
predictive analytics; early warning systems; learning analytics; learning technologies +
Footnote †: journal: Applied Sciences
# A Novel Mobile Communications Authentication Scheme with Roaming Service and User Anonymity
## 3 Proposed Scheme
### Roaming Phase  
Figure 3 shows the roaming phase of the proposed scheme. The steps are detailed as follows.  
**L1.** MU\(\rightarrow\)FA: m\({}_{\rm Ro1}\)[A, U]  
The MU calculates m\({}_{\rm i}\) = (TCert\({}_{\rm MU}\) \(\mid\) \(\rm{sk}_{\rm i+1}\) \(\mid\) \(\rm{Other}\))\({}_{\rm ski}\), mac = H(TCert\({}_{\rm MU}\) \(\mid\) \(\rm{sk}_{\rm i+1}\) \(\mid\) \(\rm{Other}\)), and (m\({}_{\rm i}\) \(\mid\) \(\rm{mac}\) \(\mid\) \(\rm{sk}_{\rm i}\) \(\mid\) \(\rm{I}\) \(\rm{TM}\))\({}_{\rm EC}\) and sends the message {A, U} to the FA, where \(\rm{sk}_{\rm i+1}\) is a session key for the next communication and T\({}_{\rm MU}\) is the current timestamp.  
**L2.** FA: A, U  
The FA calculates aA = abP = EC and decrypts U to obtain m\({}_{\rm i}\), mac, \(\rm{sk}_{\rm i}\), and T\({}_{\rm MU}\). The FA then checks the validity of T\({}_{\rm MU}\). If it is valid, the FA decrypts m\({}_{\rm i}\) by using \(\rm{sk}_{\rm i}\) and calculates mac\({}^{\prime}\) = H(TCert\({}_{\rm MU}\) \(\mid\) \(\rm{sk}_{\rm i+1}\) \(\mid\) \(\rm{Other}\)). If the equation mac\({}^{\prime}\) = mac holds, the FA updates the session key \(\rm{sk}_{\rm i}\) with \(\rm{sk}_{\rm i+1}\) for the next communication.
## 4 Experiment and Analysis
### Comparison with Other Models  
In order to further validate the instance segmentation effect for insulator defects of the proposed method, the proposed method is compared with other state-of-the-art algorithms, mainly comparing the inference speed and average mask accuracy of the model. The results compared with other models are shown in Table 4.  
As shown in Table 4, the proposed algorithm still has a gap compared to other advanced methods in accuracy, but it also achieves an average mask accuracy of 37.1% and achieves the optimal detection speed. Compared with other models, our model is more suitable for task scenarios with certain requirements for real-time performance and accuracy. Furthermore, since the enhancement measures implemented in this paper do not significantly increase the model's scale, its accuracy performance can be further enhanced by introducing a more intricate attention structure and other methodologies. Therefore, this method has certain potential and advantages in the insulator defect instance segmentation scenario.
## 4 Results
### Application to Oxy-Fuel Jet Flame B3
Comparison between experiment and simulation results; Solid line: LES hybrid ESF/FPV with different SFi numbers, Dashed line: Measurements. Figure 10: The figure was moved bellow the paragraph where it was mentioned for the first time, please confirm. Comparison between experimental data for oxy-flame flame B\({}_{3}\) and LES hybrid ESF/FPV numerical results for different numbers of SFi at different axial positions; z/d = 3; 5 and 10. Radial profiles of mean mass fraction of (**a**) O\({}_{2}\) species, (**b**) H\({}_{2}\) species. In general, the results of CO and H\({}_{2}\)O mass fraction at axial positions close to the nozzle using 8 SFi are shifted in Figure 9 toward the axial centerline. Further downstream, the agreement with experimental data is excellent. Similar observations can be made in Figure 10 for the results of O\({}_{2}\) and H\({}_{2}\) species mass fractions with 8 SFi. While slight under-prediction of O\({}_{2}\) at z/d = 10 was recorded, the use of different SFi numbers had a negligibly small impact on the H\({}_{2}\) mass fraction evolution. The instantaneous scatter plots of the temperature distribution at axial position near the fuel nozzle (z/d = 3) were compared with available experimental data in Figure 11. Thereby, the vertical black line drawed in both plots corresponds to the stochiometric mixture fraction value. The maximum reached value of the temperature distribution was 2100 K, which matches the experimental data. All values referring to the extinction locations were properly reproduced numerically. However, the lowest value predicted with the simulation reads 750 K, while experimentally it is 500 K. Furthermore, the mean temperature profile is lightly overestimated near the stoichiometry region comparing to experimental results. Figure 12 compares the PDF calculated using different numbers of SFi with those obtained from measurements at axial positions z/d = 3; 5 and 10. For both sets, the PDF was estimated for a narrow band of the mixture fraction around the stoichiometry where \(\Delta\) = 0.04. In most locations, the shape of the PDF of the temperature could be reproduced only with simulation results using 16 SFi, but slight differences were noticed at both last axial positions where few values of temperature in the range of 1500 K and 1700 K were over-predicted numerically. Nevertheless, the probability of the presence of the temperature range from 1800 K to 2100 K is matching the values obtained experimentally for all positions in a good way.
## 4 Experiments
### Comparative Experiments  
This article selects methods from the text detection field as comparison networks to verify the effectiveness of the text detection network: TextSnake [30], TLOC [4], PAN [31], and DBNet++ [18]. To verify the effectiveness of the proposed overall framework, MTF-STTR, methods from the text erasure field were chosen as comparison networks: EnsNet [27], EraseNet [10], Pix2 Pix [32], and Bain [33].  
As shown in Table 1, the performance of MTF-STTR's text detection network section compared to other text detection networks on the CTW1500 dataset is presented, with bold numbers indicating the best values in the table. From the table, it can be observed that the method proposed in this article achieves improvements of 22.0%, 12.5%, 3.5%, and 2.0% in precision, respectively, compared to TextSnake, TLOC, PAN, and DBNet++. Correspondingly, recall improves by 0.3%, 15.8%, 4.4%, and 2.8%, respectively. The F-score is elevated by 12.1%, 14.3%, 4.0%, and 2.4%, respectively. This indicates that the proposed method outperforms the compared approaches in terms of text detection accuracy, providing evidence for the effectiveness of the text detection network incorporated into the MTF-STTR framework. The substantial improvements in precision, recall, and F-score suggest that the MTF-STTR framework offers a competitive solution in the field of text detection.  
The performance of the various networks on the SCUT-Syn dataset is shown in Table 2, with bold numbers indicating the best values in the table. From the table, it can be observed that the method proposed in this article compares favorably with EnsNet, EraseNet, Pix2 Pix, and Bain. Regarding the evaluation metric MSE, our method differs from EraseNet by only 0.0001, while it surpasses the compared networks in all other evaluation metrics.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Precision** & **Recall** & **F-Score** \\ \hline TextSnake [30] & 67.9 & 85.3 & 75.6 \\ TLOC [4] & 77.4 & 69.8 & 73.4 \\ PAN [31] & 86.4 & 81.2 & 83.7 \\ DBNet++ [18] & 87.9 & 82.8 & 85.3 \\ MTF-STTR & **89.9** & **85.6** & **87.7** \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of various networks on CTW1500 dataset.
## 6 Results
### Evaluation of Network Profitability  
Finally, we analyze the results in terms of network profitability \(R_{total}\) as reported in Figure 12. Positive values indicate that monetary energy saving due to the dynamic usage of SM is higher than the corresponding reparation costs. Interestingly, \(R_{total}>0\) in most of the cases. In particular, the profitability increases when the network load is increased (see e.g., the increasing range of the y-scale across Figure 12a-c). A negative profitability (i.e., a cost for the operator) is observed only in the case when \(AF^{sleep}=0.5\) and \(\chi=5.0\). This is the worst assumption for the usage of SM, i.e., \(AF^{sleep}=0.5\) represents the lowest benefit for the LC lifetime due to putting it into SM, and \(\chi=5.0\) means the highest penalty for the LC lifetime due to the state transition. In this case, the application of SM leads to high reparation costs, which are even higher than the achieved energy savings.
## 2 Related Research
### Position Fingerprinting Method
#### 2.1.1 CNN-Based Location Estimation Method  
CNN (Convolutional Neural Network) simulates the human brain's cognitive process of external information and can extract complex structural features in a large amount of data to establish processing methods [23]. In the literature [24], it was proposed to convert RSSI values into a single vector or matrix to be processed as pixel values in images and to use the features of RSSI values in indoor environments to learn the nonlinear relationship between RSSI and location coordinates.  
Although the CNN-based approach can achieve high accuracy in simulation, the number of fingerprint points used will be thousands to tens of thousands when the environment is large. In other words, it is very costly.
## 1 Introduction
Stand characteristics strongly controlling the modeled carbon fluxes such as standing biomass or phenology [26], can be mapped with an appropriate analysis of RS time-series. This is not feasible for other ecophysiological parameters (e.g. C:N ratios, fraction of available nitrogen in leaves) which are instead assumed to be constant for a given species [10] or even plant-functional type (e.g. evergreen needleleaf forest, deciduous broadleaved forest, etc.) as described in several applications at continental level [12]. This assumption neglects the temporal and within-species variability of such parameters but allows their spatial determination through the use of a land use map. In this paper we present a modeling study conducted at site level which represents the first step toward the analysis of the carbon budget of poplar plantations at regional scale. According to the previous distinction of model parameters in two groups, species-dependent (ecophysiological parameters) and spatially variable (standing biomass and phenological parameters), we propose the following modeling scheme:  
1. determine the ecophysiological parameters exploiting site level EC measurements;
2. determine the spatially variable parameters necessary for modeling poplar productivity over large areas through assimilation of RS data into the optimized BIOME-BGC. More in detail in this paper we describe:  
1. a modified version of BIOME-BGC (named PROSAILH-BGC) which was developed by coupling BIOME-BGC with the vegetation radiative transfer models PROSPECT and SAILH. The aims of this coupling were twofold: _i)_ to improve the description of the radiative transfer regime within the canopy and _ii)_ to allow assimilation of remotely-sensed vegetation indexes time series, such as MODIS NDVI, into the process-based model. 2. an inverse modeling approach developed for the optimization of the key [25] electrophysiological parameters of the PROSAILH-BGC. In this first-step optimization, model parameters were optimized for poplar plantations by inverting the model against EC data measured at the experimental field site. 3. a technique developed for assimilation of MODIS NDVI data into the process model. For this purpose we inverted the PROSAILH-BGC against the MODIS NDVI (second-step optimization) in order to retrieve key drivers [25] of modeled GPP (e.g. start and end of growing season, maximum leaf carbon during the year). 4. the evaluation of model accuracy: daily and yearly GPP modeled after two-step optimization were compared to site observations.
# Low-Latency Bit-Accurate Architecture for Configurable Precision Floating-Point Division
## 4 General Architecture and Main Parts
### Part 1 PRECONFIG  
As presented in Figure 2, PRECONFIG first judges whether exception situations exit after breaking down the two FP inputs _dividend_In_ and _divisor_In_ into three portions: sign, exponent, and mantissa. If either one of the two inputs or both belong to the following exception situations: Zero, Infinity or NaN (Not a Number), the exception signal _Exception_ is then judged to be Zero or NaN depending on _dividend_In_ and _divisor_In_. Specific judgement is illustrated in Table 3.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Parameter \(m\)**} & \multirow{2}{*}{**Parameter \(t\)**} & \multicolumn{2}{c}{**Data Width of LUT Entries**} & \multicolumn{2}{c}{**Guaranteed Bits per Cycle**} & \multirow{2}{*}{**Clock Cycles**} & \multirow{2}{*}{**Achieved Bits per Cycle**} \\  & & & & & & \\ \hline
10 & 2 & 512 & 20 & 6 + 1 = 7 & 120 \\ \hline
10 & 3 & 512 & 29 & 4 + 2 = 6 & 116 \\ \hline
10 & 4 & 512 & 38 & 3 + 3 = 6 & 114 \\ \hline
11 & 2 & 1024 & 22 & 6 + 1 = 7 & 132 \\ \hline
11 & 3 & 1024 & 32 & 4 + 2 Otherwise, the exception signal _Exception_ is set to be Normal. The next is performing Leading Zero Detection onto the mantissas of _dividend_In_ and _divisor_In_ for subnormal checks.  
Two output signals _dividend_Out_ and _divisor_Out_ are the mantissas of _dividend_In_ and _divisor_In_ after subnormal checks with an implicit bit \"1\".  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Input**_Dividend_ & **Input**_Divisor_ & **Exception Signal**_Exception_ \\ \hline NaN & NaN & NaN \\ NaN & Zero & NaN \\ NaN & Infinity & NaN \\ Zero & NaN & NAN \\ Zero & Zero & NaN \\ Zero & Infinity & Zero \\ Infinity & NaN & NaN \\ Infinity & Zero & NaN \\ Infinity & Infinity & NaN \\ \hline \hline \end{tabular}
\end{table}
Table 3: Judgement for Exception Signal.  
Figure 2: General architecture of the proposed division algorithm.  
At last, the exponent difference \(Exp\_out\) and the exclusive-OR value \(Sign\_out\) (if the exception signal _Exception_ is normal) of the inputs _divided_In_ and _divisor_In_ are computed and delivered to NORMALLIZE along with signal _Exception_. The above explanation of the operations in Part1 PRECONFIG can be observed in Figure 2.  
The detailed architecture of Exception Judgement is demonstrated in Figure 3. Exception Cases mainly check whether the input FP number belongs to the following exceptive conditions: Zero (the input's exponent and mantissa are both \"0\"), Infinity (the input's exponent is all \"1\" while mantissa is all \"0\"), and NaN (the input's exponent is all \"1\" but mantissa is not all \"0\").
# HSVNet: Reconstructing HDR Image from a Single Exposure LDR Image with CNN
## 1 Introduction
This is partly because loss functions were simple mean-absolute error (MAE) or mean-squared error (MSE) between the predicted and target images. Therefore, we propose a customized loss function for HSV color space which allows the network to output reliable and natural color. This paper presents the following three main contributions. 1. We propose a deep learning system that can reconstruct a high-quality HDR image (32-bit) from an arbitrary single-exposed LDR image (8-bit) which may include both over- and undersaturated regions;
2. We present a mask function derived from Saturation (\(S\)) and Value (\(V\)) information in HSV color space which can detect both over- and underexposed areas and suppress unnecessary information for each channel;
3. We propose a customized loss function appropriate for HSV color space and choose an optimal loss function by experiments.
## 2 Results and Discussion
### Miscellaneous Eremophila by Endemic Location
#### 2.4.1 Central and South Australian _Eremophila_  
In traveling north from Port Augusta into the Northern Territory to meet Alice Springs, the vegetative communities change moderately. In particular, the endemic distribution of several species is restricted to South Australia, extending only several hundred kilometres north of Port Augusta and Adelaide. This is true for _E. santalina_ (Esan), a species with a slight morphological resemblance to _E. deserti_, and according to the current chemical study, a strong chemical relationship. This species produced >90% methoxymodesert-3-ene in its volatiles profile (Table 11) and yielded at >1% g.g\({}^{-1}\) of wet leaf weight, which is like _E. deserti_.  
In contrast, _E. weldii_ (Ewel) expressed nearly 70% \(\alpha\)-pinene, which is a chemical feature familiar to other species of _Eremophila_. Furthermore, 9-hydroxydendrolasin was tentatively identified as a minor component in _E. weldii_, as well as _E. paisleyi_ (Epai). This furanosesquiterpene was characterised as a major component in hydrodistilled essential oil from _E. arbuscular_ as mentioned earlier and reiterated in Table 11 as a minor component due to the high yield of freelignite. An unidentified furanosesquiterpene was also detected in the profile from _E. hillii_, which also included alloaromadendrene, which is unusual for species of _Eremophila_, and a phenol (2-methoxy,4-vinyl-phenol), which is generally associated with species that produce lignans. This was not investigated in the current study.  
However, it is of interest that _E. paisleyi_ expressed > 80% elemol in its volatiles profile. This is because it was previously speculated that elemol was associated with inhibition of _Sarcoptes scabiei_ by using extracts from _E. dalyana_[3]. These are two out of three species known for the treatment of scabies ith mite in traditional Australian medicine [20; 21]. The third is _E. duttonii_, but this species is widely distributed, and the geographical specificity of therapeutic use has not been clearly elucidated. The specimen of _E. duttonii_ of the current study did not express elemol (Table 4).  
A species that was sampled from the plains surrounding Uluru (NT), _E. gilesii_, was examined as an essential oil (Egi-341; Table 4) and as a solvent extract (Egi-518; Table 12). The essential oil is predominantly made of \(\alpha\)-pinene, myrcene, and limonene, but one specimen also expressed traces of kazahanaenone. The solvent-extracted specimen in Table 12 demonstrates unidentified larger mass compounds in the chromatogram that were not studied further in the current study.
# A Multicomponent Face Verification and Identification System
## 4 Discussion  
Among the tested models, FaceNet emerged as a preferable choice due to its balance between accuracy and inference speed for both face identification and verification tasks. The results of this research are consistent with previous studies that have found the FaceNet model to perform well in face recognition tasks. A study by [74] found that the FaceNet model achieved a higher accuracy than other face recognition models on the YALE face dataset. Also, in [8], FaceNet achieved an accuracy of 99.6% in the Labeled Faces in the Wild (LFW) dataset, as well as the related system achieving a 49 ms runtime. In the same study, several other combinations with FaceNet achieved rates from 79.1% to 95.7% with a runtime from 49 ms to 75 ms. In [46], FaceNet achieved a recognition rate of 97.2% on a masked face dataset [75]. Such results are quite similar to the extracted ones in this research, indicating a homogeneous and stable performance of FaceNet. However, it is worth noting that the performance of face recognition models can vary depending on the dataset and the application scenario. For example, a study by [29] found that the ArcFace model outperformed the FaceNet model in a face identification task on the YouTube Faces (YTF) and LFW datasets. Therefore, it is important to test the models on the specific dataset and application scenario of interest before deploying them. As previously mentioned, the proposed system employs a flexible, modular, secure, and scalable architecture and thus, in this context, enables the deployment of any proper model and not only FaceNet. In [3; 5; 13; 36; 37; 43; 51], several comparative analyses were performed, highlighting the performance of several algorithms/models in various datasets for face recognition.  
However, there are some limitations that should be considered when interpreting the results. In this research the performance of the models was evaluated on only a single dataset, the curated ID vs. Spot dataset. While this dataset is quite representative and consists of face images that are captured in both controlled and uncontrolled environments, the results may not generalize to other datasets or application scenarios. Furthermore, for this research and the implemented model, to achieve satisfactory results, the captured face portrait image must (i) contain the face of one person, (ii) be taken in proper light conditions to ensure that all face features appear properly and also are not occluded (i.e., do not wear masks, glasses, etc.), and (iii) contain a face that is looking straight into the camera's lens and without face expressions and face rotations.