esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Methods
#### 2.2.3 Analysis of Spectral Data  
(1) Each spectrum was converted into 1557 data points at 3.86 cm\({}^{-1}\) intervals and saved in an Excel spreadsheet. The data were then analyzed using TQ Analyst 9.4.45, OPUS 7.0, and Matlab 2012a.  
(2) Various spectral preprocessing techniques, such as standard normal variate (SNV), multiple scatter correction (MSC), first derivative (FD), and second derivative (SD), were evaluated to reduce noise and enhance the signal-to-noise ratio. The optimal preprocessing method was chosen based on a comparative analysis [30].  
(3) Synergy interval partial least squares (siPLS) [31] was utilized to divide the pre-processed spectral data into 10-24 evenly spaced subintervals. Partial least squares (PLS) models were then constructed using combinations of 2-4 subintervals. The leave-one-out method was utilized for cross-validation. The subintervals that minimized the root mean square error of cross validation (RMSECV) were selected as the spectral subintervals most strongly correlated with YCXY tea quality.  
The RMSECV was calculated using the following formula:  
\[\text{RMSECV}=\sqrt{\frac{\sum_{i=1}^{n}\left(y_{i}^{\prime}-y_{i}\right)^{2}} {n}} \tag{1}\]  
Here, \(n\) represents the number of samples in the calibration set, \(y_{i}\) denotes the true value for sample i, and \(y_{i}^{\prime}\) represents the predicted theoretical value for sample i based on the calibration set.  
(4) Principal component analysis (PCA) was performed on the selected optimal spectral subintervals, where the number of principal components served as input variables, and YCXY tea quality was the output variable. A near-infrared spectral prediction model for YCXY tea quality was developed using the Jordan-Elman backpropagation artificial neural network (BP-ANN) (Figure 2). The model's performance was assessed using the determination coefficient of cross-validation (Rc\({}^{2}\)), determination coefficient of prediction (Rp\({}^{2}\)), root mean square error of cross-validation (RMSECV), and root mean square error of prediction (RMSEP). Higher Rc\({}^{2}\) values and lower RMSEP values indicate better predictive performance [32].  
Figure 2: Structure of Jordan–Elman nets backpropagation artificial neural network.  
The RMSEP was calculated using the following formula:  
\[\text{RMSEP}=\sqrt{\frac{\sum_{i=1}^{n}\left(y_{i}-y_{i}^{\prime}\right)^{2}}{n}} \tag{2}\]  
Here, \(n\) represents the number of samples in the prediction set, \(y_{i}\) denotes the true value of sample i, and \(y_{i}^{\prime}\) indicates the predicted value of sample i in the prediction set.  
The R\({}^{2}\) was calculated using the following formula:  
\[\text{R}^{2}=1-\frac{\sum_{i=1}^{n}\left(y_{i}^{\prime}-y_{i}\right)^{2}}{ \sum_{i=1}^{n}\left(y_{i}^{i}-\overline{y}\right)^{2}} \tag{3}\]  
Here, \(y_{i}\) and \(y_{i}^{\prime}\) represent the true and predicted values of sample \(i\), respectively, and \(\overline{y}\) denotes the average true value of all samples.
## 3 E-barter Case Study
### System Implementation with Model Transformations and Delta Code Development
#### 3.2.2.1 Delta Code for MAS Part of the E-barter System  
The codes containing the negotiation logic of the Customer agents are critical to the system. These codes are located mostly in the plans of the agents, such as ProposalEval, NegotiationProposal, and PerformNegotiation, where most of the delta codes are added. In Listing 3, the delta code for the reasoning method of ProposalEval plan that allows an agent to evaluate proposals, is shown.  
In this Listing, the templates of communicating messages in Lines 1, 9, 13, and 18-20 are generated and the other lines are added by the developer as delta code. In ProposalEval plan, there is a lower limit and an upper limit for the ratio between offered and needed products of each agent desiring a deal. In Lines 7 and 11, if the incoming bid is below the lower limit or there is an epsilon (0.0001) difference with the previous incoming bid, then the proposal is accepted by the agent. On the other hand, if the offer is above the upper limit, the agent will refuse the offer, as shown in Lines 12-14. If the incoming bid remains between the lower and upper limits, the Customer agent sends a new proposal to the agent which the initial proposal came from (Lines 15 to 20).  
Figure 6: Bartering capability in JACK editor.  
3.2.2.2 Delta Code for SWS Part of the E-barter System  
In this case, study, each service has an ontology to help matchmaking. As there are 2 semantic web services developed for the E-barter system, we have two ontologies called electronic devices and food. These ontologies are used to demonstrate affinity relations in directing appropriate customer agents to appropriate services for barter processing. In the process of matching between the customer needs with services, each semantic web service uses its own ontology.  
In the generation procedure, a structure is generated from the system model for each ontology. These structures are extended to develop the complete ontologies for the services. Part of the ontology developed for the food service is depicted in Figure 7. In this ontology, there are 4 product categories under the basic food node. These are the fruit, diary, vegetable and meat categories. These categories are divided into subcategories within themselves to obtain a tree structure in which the closeness relation can be established semantically.
# Deep Learning for Point-of-Care Ultrasound Image Quality Enhancement: A Review
## 2 Materials and Methods
### Categorization According to Quality Enhancement Aspects  
Papers published on quality enhancement in ultrasound were further grouped based on the specific distortions addressed, which are particularly relevant to POCUS imaging, namely, (1) spatial resolution; (2) contrast; (3) texture or detail enhancement; and (4) noise. The definitions of these quality enhancement aspects as implemented in this review are further specified in Table 1.  
Given the multifaceted nature of distortions in handheld ultrasound and the necessity for real-time quality enhancement, this review focused on deep learning algorithms simultaneously addressing multiple quality enhancement aspects. However, these quality enhancement aspects can be closely related. For instance, this can include the presence of noise reduces image contrast and resolution, thereby affecting edges and fine details [27; 28]. Therefore, improving the quality of the ultrasound image by addressing one or more of the quality enhancement aspects should be specifically described and evaluated through a suitable performance metric.  
Furthermore, studies were also included if they reported on the process of mapping low-quality images to high-quality reference images. This had to be achieved by obtaining ultrasound images that naturally showed a disparity in quality as a result of differences in the capture process, such as a different number of piezoelectric elements or the number of plane waves used, and not by artificially inducing quality reduction or improvement. Consequently, this led to the identification of a final category: (5) general quality improvement. The articles addressing either multiple quality enhancement aspects or quality improvement in general were selected for further descriptive and quantitative assessment.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{284.5 pt}} \hline \hline
**Quality Enhancement Aspect** & **Definition** \\ \hline
1. Spatial resolution & The ability to differentiate two adjacent structures as being distinct from one another: either parallel (axial resolution) or perpendicular (lateral resolution) to the direction of the ultrasound beam [29]. \\ \hline
2. Contrast resolution & The ability to distinguish between different echo amplitudes of adjacent structures through image intensity variations [29]. \\ \hline
3. Detail enhancement of structures & Enhancement of texture, edges, or boundaries between structures. \\ \hline
4. Noise & Minimization of random variability that is not part of the desired signal. \\ \hline
5. General quality improvement & Mapping low-quality images to high-quality reference images, where the quality disparities are inherent to differences in the capture process and not artificially induced. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Definitions of quality enhancement aspects.
## 3 Experimental Results and Analysis
### Microanalysis Results for the MICP-Treated Soil
#### 3.5.2 XRD Analysis of the Soil Composition
Calcium carbonate is generated in the soil primarily within the first 14 days, which is consistent with the changes observed in the physical and mechanical soil parameters such as the unconfined compressive strength. Additionally, microbially induced carbonate generation is the mechanism underlying the use of MICP for soft soil reinforcement.
## Appendix B Algorithm of Time Allocation for the Proxy Nodes  
Algorithms B1 and B2 present the pseudocode of the proxy nodes time allocation. As described previously in Section 4.4, the allocation is transformed into the problem of _maximal coverage of line segment_, a variant of the famous \"Knapsack problem\" [20]. Suppose there are \(n\) key stationary durations (\(ksd^{\prime}s\)), and they are projected on the time line of epochs (x-axis ), so there are \(2n\) points at \(ps\). For each point, the algorithm calculates its maximal expected amount of data and node assignment through function \(getAmount\). We denote \(K\) as the number of slots within a data gathering round, and each slot could be assigned to \(z\) proxy nodes at most, then the calculation space is \((1+z)^{k}\). So the overall time complexity is \(O(2n*z^{K})\). Yet some conditions cuts down the solution space: (1) \(K\) is usually small, e.g., smaller than 10; (2) a proxy node could only be assigned to exactly one time slot; and (3) \(z<<n\), and most slots are covered only by a few key durations.
# A Study of Network Intrusion Detection Systems Using Artificial Intelligence/Machine Learning
## 8 Discussion
### Future Trends
#### 8.3.2 Solution to Complex Models  
Deep learning methods are finding more and more used in recent research and this results in a growth of the complexity of the models. One solution to this growth problem is to use a cloud platform for its computing power. Another solution is to use a high-performance GPU platform. However, these solutions are expensive. Therefore, the use of new feature extraction methods to optimize the dataset that will be used during the training process can be an interesting compromise solution. Using such methods will reduce the complexity of the model while optimizing the accuracy of the solution.
# Radiometric Cross-Calibration of GF-1 PMS Sensor with a New BRDF Model
## 2 Satellites, Test Sites, and Datasets
### Test Sites  
The two test sites are used in cross-calibration in this paper, which are the Dunhuang test site and Golmud test site. The Dunhuang test site is an on-orbit radiometric calibration test site for visible/near infrared sensors in China. It has been used for calibrating Chinese satellites since the late 1990s. The Dunhuang test site is large, flat, and homogeneous, located 15 km west of Dunhuang city. It is located at the coordinates 40.1\({}^{\circ}\)N and 94.4\({}^{\circ}\)E and at an elevation of 1253 m above sea level. The area of the Dunhuang test site is about 30 km \(\times\) 30 km. There is low probability here of interference from clouds, and low aerosol loading, except during the sandstorm periods in spring. The ground is made up of sand and small black stones with no vegetation.  
The Golmud test site is another flat and homogenous test site located 60 km west of Golmud city, Qinghai Province, China. The latitude and longitude of Golmud test site are 36.38\({}^{\circ}\) N and 94.23\({}^{\circ}\) E and the altitude is 2894 m. The ground spectrum and atmospheric characteristics of Golmud test site such as aerosol optical thickness and water vapor column are unknown as no ground experiment was carried out at this site before. The homogeneous area of Golmud test site is about 15 km \(\times\) 15 km. Figure 2 shows two Landsat 8 OLI images over the Dunhuang test site and Golmud test site. The red square boxes in these two images are core test areas with the area of 1.5 km \(\times\) 1.5 km. An assumption was made that the ground parameters of Dunhuang could be used for Golmud (although at very different elevation and acquired at different times or even days), and the uncertainty due to that assumption will be addressed later in the paper.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Parameters** & **Sensor** & **Blue** & **Green** & **Red** & **NIR** \\ \hline \multirow{3}{*}{Band number} & PMS & 1 & 2 & 3 & 4 \\  & MODIS & 3 & 4 & 1 & 2 \\  & OLI & 2 & 3 & 4 & 5 \\ \hline \multirow{3}{*}{Center wavelength (nm)} & PMS & 494.590 & 554.112 & 665.214 & 823.394 \\  & MODIS & 466.800 & 557.153 & 643.380 & 856.551 \\  & OLI & 482.588 & 561.332 & 654.605 & 864.571 \\ \hline \multirow{3}{*}{Spectral range (nm)} & PMS & 450–520 & 520–590 & 630–690 & 770–890 \\  & MODIS & 459–479 & 545–564 & 620–670 & 841–876 \\  & OLI & 450–515 & 525–600 & 630–680 & 845–885 \\ \hline \multirow{3}{*}{Solar Irradiance (W/m\({}^{2}\)/sr)} & PMS & 1958.80 & 1822.90 & 1520.60 & 1071.41 \\  & MODIS & 2054.65 & 1832.33 & 1576.19 & 972.48 \\ \cline{1-1}  & OLI & 2004.59 & 1820.74 & 1549.50 & 951.71 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Band number, center wavelength, spectral range, and solar irradiance of PMS, MODIS, and OLI.  
Figure 2: Landsat 8 OLI image over two test sites. The image of Dunhuang test site is imaged on 12 August 2014, and the image of Golmud test site is acquired on 26 February 2014. (**a**) Dunhuang test site; (**b**) Golmud test site.
## 2 Materials and Methods
### Check-Up Measurements
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{3}{*}{**Battery No.**} & \multirow{3}{*}{**String No.**} & \multicolumn{3}{c}{**Mono-Block Voltage (in V)**} & \multicolumn{3}{c}{**70 Hz Impedance (in mf)**} \\ \cline{3-8}  & & **With** & **OCV after** & **Deviation** & **With** & **Without** & **Deviation** \\  & & **Current** & **Charge** & & **Current** & **Current** & **Deviation** \\ \hline
2-014 & 2 & 13.66 & 12.98 & 0.68 & 49.91 & 49.72 & \(-\)0.19 \\
2-018 & 2 & 13.67 & 13.01 & 0.66 & 33.23 & 33.91 & 0.67 \\
2-024 & 2 & 13.94 & 12.97 & 0.97 & 35.73 & 35.66 & \(-\)0.08 \\
3-004 & 3 & 13.63 & 13.03 & 0.60 & 35.08 & 35.69 & 0.61 \\
3-008 & 3 & 13.76 & 12.98 & 0.78 & 43.83 & 43.09 & \(-\)0.73 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Results of charge current influence measurement on impedance registered by LEM Sentinel 2. Charge currents at time of measurement were: 10.4 mA for string 2 and 12.4 mA for string 3. Batteries came from float charge into pause phase and voltage relaxation was assured. Absolute deviations can differ if batteries have a discharge history or at different charge current rates. It has been shown by [PERSON] et al. [10] that measurements with superimposed DC-currents in discharge direction are more robust and deliver more reliable results over the lifetime than 0 DC measurements or spectra with superimposed charge currents. Therefore, the values of the spectra with superimposed discharge current will be mainly utilized for this work (see also Appendix A, Figure A1). Nevertheless, for the comparison of measurement devices, the single values for the modulus \(\mid\)Z\(\mid\) at 1 kHz with 0 DC is compared to the Hioki device as these measurements were also performed under no load. Different to other tests in literature check-up tests did not include a capacity test. The authors wanted to avoid the introduction of any other aging mechanisms such as cyclic wear of the electrodes (active mass loss or additional sulfation) so that the impedance development is solely influenced by the operation under float charge.
## 2 Related Work  
### A Brief Review of the Geodesic DGGS  
The following introductory paragraphs are intended to acquaint the reader with the concepts associated with the Geodesic DGGS.  
Figure 2: Geodesic dome structure.  
A Discrete Global Grid (DGG) consists of a set of regions that form a partition of the sphere \(\mathbb{S}^{2}\), where each region has a single point contained in the associated region. Each combination of a region and point is called a _cell_. A series of increasingly finer-resolution DGGs composes the DGGS. If the grids are defined consistently using regular planar polygons on a surrogate surface, the _aperture_ of a DGGS is defined using the ratio of the areas of a planar polygon cell at resolutions \(k\) and \(k+1\)[18]. The aperture of the system reflects the relationship between consecutive levels of the grids. Regular hierarchical relationships between DGGS resolutions play an important role in creating efficient data structures [19; 20]. Two types of hierarchical relationships, _congruent_ and _aligned_, are commonly used [6].  
Consider \(C^{k}\) to be a cell region and \(Cp^{k}\) to be a cell point of a cell at resolution \(k\).  
* A DGGS is _congruent_ if and only if \[Cr^{k}=\cup_{j}Cr_{j}^{k+1},\quad j=1,2,\cdots\]
* A DGGS is _aligned_ if and only if \[Cp^{k}\subseteq Cp^{k+1}\] If a DGGS does not have these properties, the system is defined as _incongruent_ or _unaligned_.  
In [PERSON]'s study [6], the construction of the Geodesic DGGS was treated as a series of design choices that were, for the most part, independent. The following five design choices fully specify a Geodesic DGGS:  
1. A base regular polyhedron;
2. Polyhedron orientation;
3. Subdivision scheme;
4. Transformation;
5. Choice of cell points.
## 2 Results and Discussion
### Review of the Bioactivity and Acute Toxicity of Main Absorbed Constituents
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**No.** & **Compounds** & **LD50 (oral) \({}^{\star}\)** & **Distribution \({}^{\text{b}}\)** & **Content \({}^{\star}\)** \\ \hline
**M20** & _dl_-Camphor & mouse, 1310 mg/kg [35] & P (FE, UR, BL, HE, LU, LI, KI, ST, IN) & 0.11\% \\  & & & D (FE, UR, LL, KI, ST, IN) & \\ \hline
**M37** & Estragole & mouse, 1250 mg/kg [36] & P (BL, BR, HE, LU, SP, LI, KI, ST, IN) & 0.28\% \\  & & & D (nd.) & \\ \hline
**M44** & Borneol & mouse, 1059 mg/kg [35] & P (FE, UR, BL, SP; KI, ST, IN) & 0.12\% \\  & & & D (FE, UR, KL, ST) & \\ \hline
**M64** & safrole & mouse, 2350 mg/kg [33; 36] & P (FE, UR, BL, BR, HE, LU, SP, LI, KI, ST, IN) & 23.74\% \\  & & & D (FE, UL, SP, LI, KI, IN) & \\ \hline
**M79** & Methyleugenol & rat, 1179 mg/kg [33] & P (FE, UR, BL, RP, HE, LU, SP, LI, KI, ST, IN) & 4.69\% \\  & & & D (FE, UR, SP, LI, KI, ST, IN) & \\ \hline
**M104** & Piperonal & rat, 2700 mg/kg [37] & P (UR, BL) & 0.07\% \\  & & & D (UR, BL) & \\ \hline
**M112** & \(\beta\)-Asarone & mouse, 418 mg/kg [8] & P (BL, LI, KI, ST) & 0.04\% \\  & & & D (ST) & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Acute toxicity and distribution of the compounds identified in the blood of powder-treated group. Based on the above discussion, we speculate that the toxicity of ARR powder might be derived from the combined effect of a large number of such compounds which can be absorbed with low or very low contents and have similar metabolic characteristics with the main absorbed constituents, rather than a single or a few compounds, and an additive toxic effect might be the mechanism. Further research on the toxic constituents of ARR is necessary.
## 5 Discussion  
In this study, as in other studies in the literature on the same topic, there are several limitations that affect the success of the proposed approach. While the proposed methods have shown promising performance compared to those in the existing literature, there are inherent challenges that constrain further improvements.  
The first and most significant limitation is related to the dataset. Although the number of images used to train the model is substantial, the number of unique patients included in the dataset is limited. Therefore, this restriction reduces the generalization capability  
Figure 10: Visualization of the t-distribution with critical regions and the paired t-statistics for each model compared against the ResNet baseline.  
Figure 9: Paired t-statistics and \(p\)-values of models compared to the ResNet.  
of the model, and the training data may not sufficiently capture the variability in larger populations. To prevent data leakage, images obtained from a patient are not included in both the training and test sets. However, this patient-based split introduces other challenges. In k-fold cross-validation, images in the test sets consist of entirely different patients in each fold. Hence, this strategy is effective in preventing data leakage, but it leads to increased variance across folds due to differences in brain anatomy, age, and disease characteristics Secondly, the heterogeneity in brain tissue among patients, along with variations in age, gender, medications, disorder types, and disease episodes, further amplifies this variance and decreases the performance of models.  
Another critical limitation is the overfitting issue due to the relatively small dataset. In order to overcome this problem, various techniques such as L2 regularization, dropout layers, and careful hyperparameter tuning are employed to reduce overfitting.  
One of the limitations in this study is the restricted application of data augmentation techniques. While data augmentation is an effective method for enhancing model generalization, the augmentation techniques must remain medically meaningful. These requirements limit the augmentation methods that can be applied.  
Finally, one of the most important challenges encountered in this study involves the Grad-CAM explanations. Grad-CAM visualizations sometimes yield misleading results, particularly when determining the most meaningful bounding boxes. Bounding boxes are selected based on a threshold applied to the Grad-CAM scores, and regions above the threshold values are considered significant regions. However, this threshold was determined through trial and error, and this can cause ambiguity in some cases. For instance, as shown in Figure 11, there may be multiple bounding boxes with comparable importance. While the red box surpasses the threshold and is selected, the green box, which may also be significant, is excluded. Determining which region would enhance model performance remains a challenging problem.