esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results & Discussion
### Volatile Composition  
Analysis of volatile compounds was performed by SPME GC/MS targeting to evaluate the volatile characteristics of wines produced by the two wild type yeast strains and compared with the commercial one (Figure 6). In line with the more limited increase in VA, Sb media were characterized by considerably higher production of higher alcohols and ketones compared to commercial and Sa media (Figure 6a,b). According to the results, it was observed that the lower the nitrogen content, the lower the production of higher alcohols in wine. However, as far as Sb strain is concerned, the opposite was observed. A possible explanation for this could be a better utilization of valine by this strain that intensifies the production of fused alcohols [10; 42; 53; 54]. The Sa and commercial strains produced similar contents of higher alcohols regardless of nitrogen content. Moreover, the Sb strain produced 1.9-fold higher amounts of alcohols and 5.6-fold higher amounts of ketones compared to the commercial strain; however, no significant differences were observed between Sa and the commercial strain. These results highlight the variations that might occur in the metabolic pathways of different yeast strains. The increased production of higher alcohols by the Sb strain evinces a less efficient uptake of nitrogen, as previously reported [55].  
Furthermore, the Sb strain produced less fatty acids and esters compared to the other two strains, probably as a defensive mechanism to toxic'metabolic waste' (Figure 6c,d) [55]. Lower concentrations of fatty acids could be linked to lower concentrations of acyl-coenzyme A (CoA), one of the factors that is directly related to ester production [56]. Deficient amounts of acyl-CoA could also result in lower concentrations of acetyl-coenzyme A (CoA), suppressing the TCA cycle in the Sb strain, as has been previously documented. Reduced activity of enzymes involved in the synthesis (alcohol acetyltransferases I and II) and hydrolysis (esterases) of esters in Sb media could be responsible for the insufficient ester production compared to the commercial and Sa wines. Lower expression of _ATF1_ and _ATF2_ genes encoding alcohol acetyltransferases I and II and overexpression of the _IAH1_ gene encoding ester degrading enzyme could be another possible explanation [56]. In addition, lower concentrations of esters in Sb wines could be linked to leucine consumption [57]. As far as the different initial YAN contents are concerned, the three strains showed different patterns of ester and fatty acid production. In media with 150 mg N/L, the commercial and Sa strains resulted in lower amounts of fatty acids and higher amounts of esters compared to the wines containing 250 mg N/L. For the commercial strain, ester production was proportional to initial YAN.  
Figure 5: Production of glycerol by _S. cerevisiae_ in fermentations with (**a**) commercial, (**b**) Sa and (**c**) Sb strains. Red and black lines correspond to samples fermented with low and high initial concentrations of YAN, respectively.
# Platelet-Rich Fibrin: A Viable Therapy for Endodontic-Periodantal Lesions? A Preliminary Assessment
## 4 Discussion
The use of PRF as a material for the regenerative periodontal therapy of endodontic-periodontal lesions showed good results in all the cases which lead to reductions in the probing depths. So far, there is not a gold standard procedure in the treatment of endodontic-periodontal lesions. Other therapies that may be taken into account are open flap debridement (OFD) alone, OFD in combination with enamel matrix derivate, OFD in combination with semantic bone or OFD in combination with PRF and synthetic bone. [PERSON] and collaborators have mentioned in their systematic review that root canaltreatment in combination with synthetic bone was a widely used approach [45]. The use of PRF in other regenerative techniques showed promising and valid results as reported by [PERSON] and [PERSON] [36; 37]. [PERSON] et al. showed a statistically significant result comparing coronally advanced flap alone (CAF) with CAF + PRF, in terms of recession reduction and gingival thickness. [PERSON] et al. investigate the use of PRF in function defects showing statistically significant data for PD and CAL. Our review had several limitations. One of them would be the limited number patients with endodontic-periodontal lesions treated using PRF in the protocol scheme. Also, there was a short follow-up of one year after the periodontal regenerative therapy. Another drawback was that the preparation of PRF procedure that was not the same in the studies included. Moreover, other limitations are the heterogeneity of the data collected, poor quality of the studies included (e.g., case reports) and the missing of a recognized guidelines and clinical protocols for the treatment of endodontic-periodontal lesions with PRF. Another aspect that needs to be considered is the anatomical variable that in this review was assessed. The anatomical structure of molars and premolars is different to the single rooted teeth and thus the regenerative procedure might be less reliable [46]. The systematic search through four databases and precise data extraction represents strengths of our paper. Another strength of our research is represented by the diagnosis of endodontic-periodontal lesions and the benefits and the limitations of using PRF. Moreover, it is the first study that collects and describes data regarding the use of PRF in endodontic and periodontal lesions. Nevertheless, the absence of randomized clinical trials in which PRF was used, makes these data a preliminary result from the literature.
# Zero-Day Threat Mitigation via Deep Learning in Cloud Environments
## 6 Discussion
### Structural Limitations of the Model and Dataset  
The inability to classify malicious files by type or malware family in this study is due to a combination of technical and methodological factors. First, the MVT model was designed with a binary classification architecture, using an output layer that distinguishes only between benign and malicious files. This design prevents the model from capturing distinctive patterns associated with specific malware families. Second, the MaLeX dataset used in this research provides only binary labels, without detailed information on the category or behavior of the malware (e.g., ransomware, trojan, worm, etc.). This limits the possibility of training a multiclass or multicategory model without a prior data curation and re-labeling process.  
Furthermore, implementing a model capable of identifying specific malware types or families would require a dataset with detailed and balanced class annotations, as well as an architecture adapted for multiclass classification. This would involve redesigning the output layer, adjusting the loss function, using class-specific metrics, and applying imbalance-handling techniques. Therefore, this granular level of analysis falls outside the scope of the present study and is proposed as a priority direction for future work to increase the model's practical applicability in real-world cybersecurity scenarios.
# The Implementation of Precise Point Positioning (PPP): A Comprehensive Review
## 2 Error Sources in PPP
### Code Biases
Although there may be an arbitrary offset between the broadcast and IGS parameters, it should not impact the final positioning solution as it is absorbed into the receiver clock error. It is also worth mentioning that the DCB files currently adopt the latest RINEX observation code convention [51], e.g., C1C/C1W/C2W for GPS C1/P1/P2 codes, respectively. Each GNSS employs its timing system in multiconstellation PPP, introducing bias components that represent the delays between the systems. These biases are characterized  
Figure 1: Impact of DCB corrections on PPP convergence utilizing two hours of static data from the YELL station on 20 July 2023: (**a**) without DCB corrections, (**b**) \(DCB_{\text{P1-C1}}\) corrections applied. by the Inter-System Bias (ISB) and are typically estimated for each constellation as a time offset relative to GPS [52; 53]. The Inter-Frequency Bias (IFB) is a code bias that exists in GLONASS legacy signals. The GLONASS legacy signals adopt the Frequency Division Multiple Access (FDMA) technique, whereby each satellite transmits signals at a slightly varying frequency from the other satellites. In contrast, other GNSSs are not affected by these biases, since they operate on consistent frequencies using the Code Division Multiple Access (CDMA) technique. These IFBs in GLONASS are satellite-dependent receiver biases that are not easily isolable or absorbed into other parameters. Therefore, because code biases influence the initial PPP convergence, GLONASS IFBs are generally neglected [46].
# Non-Iterative Cluster Routing: Analysis and Implementation Strategies
## 3 Methods
### Cluster Routing Implementation  
#### 3.3.1 Weighting Computation  
Feature maps were extracted from the original image through a convolutional layer to form primary capsules. This layer is a convolutional capsule layer with \(\mathbf{C}\) channels of convolutional capsules. Each primary capsule comprises \(\mathbf{D}\) convolutional units with a 3 \(\times\) 3 kernel and a stride of 1. To create a vote, cluster routing utilizes a weight matrix for element-wise multiplication with a capsule. Capsules within the same channel can share weights to produce a vote, where each capsule has dimensions of 1 \(\times\) 1 \(\times\) D. However, in cluster routing, the surrounding neighborhood is included to increase the receptive field for producing a vote, resulting in the weight dimension of a capsule becoming 3 \(\times\) 3 \(\times\) D.  
To handle the weight multiplication process, we leverage an optimized function in PyTorch named conv2d. There are two approaches to produce votes from \(\mathbf{C}\) separate channels in the primary capsule layer.  
First, a single conv2d layer can be used to generate votes from all \(\mathbf{C}\) channels, employing group convolution through the conv2d function in PyTorch. The groups parameter in conv2d controls the connections between inputs and outputs; for example, if groups \(=1\), all inputs are convolved to all outputs. In cluster routing with \(C=3\), we can use groups \(=3\) to consider three convolutional layers side by side, creating three capsule channels. Each layer sees one-third of the input tensor and produces one-third of the output, which are subsequently concatenated.  
Second, the feature maps can be segmented into individual channels, with each channel employing its own conv2d layer to generate votes. It is important to note that each channel utilizes a distinct conv2d layer for the generation of votes, which is currently implemented in the code.  
In iterative routing [1,4], the routing weights are computed through an iterative process. The weight update frequency can have significant effects on the performance [1]. However, in our non-iterative approach, the weight only needs to be computed once per epoch based on the agreement vectors. Hence, the weight update frequency does not impact the performance. In fact, without iteration, our method can reduce the training time to approximately 15%.
## 1 Introduction
The time-step size of the method is not limited by the size of the spatial grid, so it can overcome the influence of thin-layer structures in the ionosphere on the algorithm's computing efficiency and has the advantages of high computational accuracy and fast computing speed. The method can realize three-dimensional modeling analysis of VLF electromagnetic-wave propagation in the ionosphere. In addition, it can also provide an important numerical analysis tool for studying the phase and polarization characteristics of VLF electromagnetic waves propagating in magnetized plasma ionosphere.
# Application of the Supercapacitor for Energy Storage in China: Role and Strategy
## 2 Technology Characterization  
Compared with conventional secondary batteries, electrochemical supercapacitors exhibit a long cycling life and high power density but with low energy density as a high-frequency energy storage device [2]. Table 1 describes the advantages and disadvantages of supercapacitors compared with several common batteries [3,4].  
According to the conversion mechanism of electrochemical energy storage, supercapacitors can be classified as double-layer capacitors, pseudo capacitors, and hybrid capacitors [5]. Figure 1 shows several supercapacitor structures.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline \multicolumn{1}{c}{**Energy Storage Devices**} & \multicolumn{2}{c}{**Advantages**} & \multicolumn{2}{c}{**Disadvantages**} \\ \hline \multirow{4}{*}{Supercapacitor} & 1. & Fast charging speed (less than 10 s) & 1. & Electrolyte leakage is likely to occur \\  & 2. & Long cycle life (up to 500,000 cycles) & 2. & Low energy density \\  & 3. & High power density (300 W/kg–5000 W/kg) & 3. & High use cost \\  & 4. & Wide temperature range (\(-40\)\({}^{\circ}\)C\(\!\!
## 3 The Proposed Model
### Parameter Optimization: DWO Algorithm
A clear precision-recall inspection of the DWOML-RWD approach on the test dataset is portrayed in Figure 8. The figure exemplifies that the DWOML-RWD methodology resulted in enhanced values of precision-recall values under all classes. A brief ROC analysis of the DWOML-RWD approach on the test dataset is portrayed in Figure 9. The results imply that the DWOML-RWD method displayed an ability to categorize distinct classes on the test dataset. Figure 8: Precision–recall analysis of DWOML-RWD approach. Figure 6.0 ROC curve analysis of DWOML-RWD approach. Table 3 presents the ransomware detection results of the DWOML-RWD model compared with other ML models. These results indicate that the DWOML-RWD model displays better performance than the other methods [22]. Figure 10 provides a comparative \(sens_{y}\) and \(spec_{y}\) inspection of the DWOML-RWD technique with other existing approaches. The figure reveals that the DWOML-RWD model showed improved performance in all aspects. For instance, based on \(sens_{y}\), the DWOML-RWD approach gained enhanced \(sens_{y}\) of 99.40%, whereas the Adaboost-M1, bagging, ROF, RF, and DT methodologies resulted in lower \(sens_{y}\) values of 94.60%, 93.80%, 96.90%, 99.21%, and 98.12%, respectively. Figure 11 provides a comparative FNR and FPR inspection of the DWOML-RWD algorithm with other existing approaches. The figure reveals that the DWOML-RWD model shows improved performance in all aspects. For instance, based on FNR, the DWOML-RWD approach attained a minimal FNR of 0.60%, whereas the Adaboost-M1, bagging, ROF, RF, and DT models resulted in increased FNR values of 5.40%, 6.20%, 3.10%, 0.79%, and 1.88, respectively. Additionally, based on PNR, the DWOML-RWD approach gained a minimal PNR of 0.60%, whereas the Adaboost-M1, bagging, ROF, RF, and DT techniques resulted in  
Figure 6.1 \(Sens_{y}\) and \(Spec_{y}\) analysis of DWOML-RWD approach with existing algorithms. Based on \(spec_{y}\), the DWOML-RWD method gained enhanced \(spec_{y}\) of 99.40%, whereas the Adaboost-M1, bagging, ROF, RF, and DT algorithms resulted in lower \(spec_{y}\) values of 95%, 96.50%, 97.40%, 98.70%, and 98.51%, respectively. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Methods** & **Accuracy** & **Sensitivity** & **Specificity** & **FNR** & **FPR** \\ \hline DWOML-RWD & 99.40 & 99.40 & 99.40 & 0.60 & 0.60 \\ AdaBoost-M1 & 96.13 & 94.60 & 95.00 & 5.40 & 5.00 \\ Bagging & 99.00 & 93.80 & 96.50 & 6.20 & 3.50 \\ Rotation Forest & 96.24 & 96.90 & 97.40 & 3.10 & 2.60 \\ Random Forest & 98.81 & 99.21 & 98.70 & 0.79 & 1.30 \\ Decision Tree & 97.68 & 98.12 & 98.51 & 1.88 & 1.49 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparative analysis of DWOML-RWD technique with existing algorithms. increased PNR values of 5%, 3.50%, 2.60%, 1.30%, and 1.49, respectively. From these results, it was confirmed that the DWOML-RWD model assured enhanced performance.
# Performance Limits of GNSS Code-based Precise Positioning: GPS, Galileo & Meta-Signals
## 6 GNSS Meta-Signals
### Generalized AltBOC
Note from [PERSON] et al. [32] that most of the energy of \(SC_{P,8,SSB}(t)\) and \(SC_{P,8,SSB}^{*}(t)\) is located in the fundamental harmonics, \(F_{sub}\) and \(-F_{sub}\), respectively. On the other hand, the energy of \(SC_{P,8,SSB}(t)\) and \(SC_{P,8,SSB}^{*}(t)\) is located in the harmonics \(-3F_{sub}\), \(5F_{sub}\) and \(3F_{sub}\), \(-5F_{sub}\), respectively.
# Concept Drift Detection Based on Deep Neural Networks and Autoencoders
## 4 Experiment
### Datasets
Inspired by this approach, this study adopts a similar strategy of embedding drift points to assess the accuracy of drift detection algorithms on real-world datasets. Different strategies were employed for inserting drift points into real-world data with unknown drift benchmarks. When inserting a single drift point, we divided the data into three equal parts: the first one-third is used for pretraining, and this portion of the data remains unchanged. For the remaining two-thirds of the data, the first one-third is left unprocessed to ensure consistency with the distribution of the old concept (i.e., the first one-third). For the final one-third, we use a random function to select a random column and assign its feature values to random numbers between 0 and 1. Details of the datasets can be found in Table 3.  
To insert multiple drift points into real-world data, the dataset is divided into five equal parts: the first one-fifth is used for pretraining and remains unchanged, while the second one-fifth also remains unchanged. In the remaining three-fifths, a random function selects a different feature column for each portion (without repetition) and replaces its values with random numbers between 0 and 1. Details on multi-drift data can be found in Table 4.
## 2 Adaptive R Estimation
### RMNCE Theory  
If there are two redundant measurements for the same signal with uncorrelated zero-mean white noises, the variances of the noises can be estimated just based on the measurement differences.  
**Theorem**.: _Assuming that \(Z_{1}\)(k) and \(Z_{2}\)(k) are independent redundant measurements of a signal \(Z\)(k) from two systems, the measurements can be modeled as [27]:_  
\[Z_{i}(k)=Z(k)\ +\ S_{i}(k)\ +\ V_{i}(k),i=1,2 \tag{3}\]  
_where, for the measurement system \(i\), \(S_{i}\)(k) is the unknown measurement system error and \(V_{i}\)(k) is zero-mean white noise at time epoch \(k\). The first-order-self-difference (FOSD) \(\Delta Z_{i}\) and the second-order-mutual-difference (SOMD) \(\Delta Z_{12}\) are defined as:_  
\[\left\{\begin{array}{l}\Delta Z_{1}(k)=Z_{1}(k)-Z_{1}(k-1)\\ \Delta Z_{2}(k)\ =\ Z_{2}(k)\ -\ Z_{2}(k\ -\ 1)\\ \Delta Z_{12}(k)\ =\ \Delta Z_{1}(k)\ -\ \Delta Z_{2}(k)\end{array}\right. \tag{4}\]  
_If \(S_{i}\)(k) is stable over a short period, namely:_  
\[S_{i}(k)\ -\ S_{i}(k\ -\ 1)\ \approx\ 0 \tag{5}\]  
The estimate of variance is immune to measurement system errors and can be determined without any knowledge of the real measurement by applying FOSD and SOMD.
3. The noise variances of redundant measurements can be estimated simultaneously.  
In practice, because the statistical characteristics are stable over a relatively short period, a sliding window [27] can be employed to derive the autocorrelations in Equation (14). The sliding window width can be empirically set to 30-60.
## 1 Introduction
Thus, the RF model has great potential for \(\text{FCO}_{2}\) retrieval by combining with the RS data and field measurements at the site and large scales [15]. Therefore, against the above-mentioned background, the objectives of this study are to (1) explore the temporal changes of \(\text{FCO}_{2}\) in a subtropical forest during the dry season, (2) analyze how biotic and abiotic factors affect \(\text{FCO}_{2}\) during the study period, (3) use RS data to build an RF model and test its ability to estimate \(\text{FCO}_{2}\) at the local scale, and (4) apply the optimum RF model to map the spatiotemporal variation of \(\text{FCO}_{2}\) at a large forested watershed and to investigate how the topographical factors regulate the spatial variation of \(\text{FCO}_{2}\). Toward these ends, we first conducted field measurements of \(\text{FCO}_{2}\) in a typical subtropical forest located in Guangdong province of southern China. In specific, we quantified the spatial-temporal dynamics of the \(\text{FCO}_{2}\) in the dry season of September to January of the next year. Further, an RF model based on in-situ \(\text{FCO}_{2}\) measurements and RS data was developed, evaluated and eventually used to estimate forest \(\text{FCO}_{2}\) in the dry season. The results from this study can improve our understanding of soil \(\text{CO}_{2}\) flux in the subtropical forests and the proposed RF method coupling with RS data is a useful tool for monitoring \(\text{FCO}_{2}\) in subtropical forested regions.
## 3 Results and Discussion  
### Structure and Morphology of Bi\({}_{2}\)Se\({}_{3}\) Nanoplates  
Figure 1 is the XRD pattern of as-prepared Bi\({}_{2}\)Se\({}_{3}\) nanostructures. All the diffraction peaks in the XRD pattern match well with the Bi\({}_{2}\)Se\({}_{3}\) rhombohedral lattice phase (JCPDS: 89-2008), and the estimated lattice constants are a = 0.42 nm and c = 2.87 nm. The six main peaks can be easily indexed to (015), (1,0,10), (1,0,15), (205), (0,2,10) and (1,1,15) planes of Bi\({}_{2}\)Se\({}_{3}\) rhombohedral lattice phase [(38,39)].  
Figure 2a is SEM image of Bi\({}_{2}\)Se\({}_{3}\) nanoplates, which shows the uniform hexagonal morphology in large scale. Figure 2b is low magnification TEM image of single Bi\({}_{2}\)Se\({}_{3}\) nanoplate, which has a typical flat surface and sharp edges. These Bi\({}_{2}\)Se\({}_{3}\) nanoplates have size of 7.50-10.0 \(\upmu\)m, which is large enough to be observed in the optical microscopy for Raman measurement. The SAED pattern shown in Figure 2c exhibits a hexagonal symmetry diffraction spot pattern and indicates the single-crystal nature of Bi\({}_{2}\)Se\({}_{3}\) nanoplates. Figure 2d is high-resolution TEM (HRTEM) image [(40,41)]. The plane spacings were measured to be 0.35 nm. Raman spectrum of single Bi\({}_{2}\)Se\({}_{3}\) nanoplate at room temperature is shown in Figure 2e. Three Raman optical phonon modes can be identified as A\({}_{1g}^{1}\) at 68 cm\({}^{-1}\), E\({}_{g}^{2}\) at 127 cm\({}^{-1}\), and A\({}_{1g}^{2}\) at 170 cm\({}^{-1}\). The peak positions are consistent with the previously measured bulk crystalline Bi\({}_{2}\)Se\({}_{3}\)[(41)].  
Hydrothermal method is the chemical reaction in solution at low temperature (below 250 \({}^{\circ}\)C). It is a simple while powerful method to obtain desirable crystal materials. By using the hydrothermal method, various structures, include 3D bulk materials, 2D nanoplates, 1D nanowires and 0D nanoparticles, can be synthesized controllable no whether their layer or non-layer nature. The present data indicates that the crystallinity of hydrothermal synthesized Bi\({}_{2}\)Se\({}_{3}\) was compared to that of Bridgman method, which was known for the fabrication of single crystal with high crystal quality. Compared with ref [(36)], we reported the Bi\({}_{2}\)Se\({}_{3}\) nanoplates with large size and uniform morphology, which may be used as integrated chip and have potential application in photonics and electronics nanodevices, and our results were based on the single nanoplate that ensured the accurate Raman shift and lattice dynamics.  
Figure 1: XRD pattern of as-prepared Bi\({}_{2}\)Se\({}_{3}\) nanostructures.