esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Evaluation of Classification System  
Using a confusion matrix (Table 2), we can evaluate the performance when classifying the 9 disease types and 1 healthy type of tomato leaf. Some parameters, such as accuracy (\(ACC\)), sensitivity (\(SEN\)), specificity (\(SPE\)), positive predictive value (\(PPV\)), and F1 score (\(FIS\)), are also considered in the VGG-19 model.  
In the confusion matrix with the output at the ith layer, the value \(n_{ij}^{T}\) is the number of correctly recognized images, and \(n_{ij}^{F}\) describes the number of images at the \(i\)th layer but recognized to the \(j\)th layer, (\(i\
eq j\)). Furthermore, \(P_{i}\) represents the total number of images at the \(i\)th layer after recognition, \(N_{i}\) describes the total number of images at the \(i\)th layer in the original label, and \(N_{T}\) is the total number of images in the testing dataset. Therefore, the \(N_{i}\), \(P_{i}\), and \(N_{T}\) values are calculated using the following equations:  
\[N_{i}=n_{ii}^{T}+\sum_{j\
eq i}n_{ij}^{F}, \tag{5}\]  
\[P_{i}=n_{ii}^{T}+\sum_{j\
eq i}n_{ji}^{F}, \tag{6}\]  
\[N_{T}=\sum_{i=1}^{C}N_{i}=\sum_{i=1}^{C}P_{i} \tag{7}\]  
From the values of \(N_{i}\), \(P_{i}\), and \(N_{T}\), the \(ACC\), \(SEN\), \(SPE\), \(PPV\), and \(FIS\) values are determined using the following formulas:  
\[ACC=\frac{1}{N_{T}}\sum_{i=1}^{C}n_{ii}^{T}, \tag{8}\]  
\[SEN=\frac{1}{C}\sum_{i=1}^{C}\frac{n_{ii}^{T}}{N_{i}}, \tag{9}\]  
\[SPE=\frac{1}{C}\sum_{i=1}^{C}\frac{\sum\limits_{i\
eq j}n_{ij}}{\frac{C}{C}N_{ i}-N_{i}} \tag{10}\]  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Predicted Classes**} \\ \hline \hline  & & 1 &... & \(i\) &... & \(C\) & \\ \(1\) & & \(n_{11}^{T}\) &... & \(n_{1i}^{F}\) &... & \(n_{1C}^{F}\) & \(N_{1}\) \\ \(\vdots\) & & \(\vdots\) & \(\ddots\) & \(\vdots\) & & \(\vdots\) & \(\vdots\) \\ \(\vdots\) & & \(n_{11}^{F}\) &...  
\[PPV=\frac{1}{C}\sum_{i=1}^{C}\frac{n_{ii}^{T}}{P_{i}}. \tag{11}\]  
\[F1S=2\times\frac{REC\times PRE}{REC+PRE} \tag{12}\]  
The model can be evaluated through Equations (8)-(12), in which the classifier is highly effective when the _ACC_, _SEN_, _SPE_, _PPV_, and _F1S_ are large.
# A Thorough Evaluation of GaN HEMT Degradation under Realistic Power Amplifier Operation
## 5 Experimental Results
### Comparison of the Degradation Obtained at 30 \({}^{\circ}\)C  
Some of the differences between the two investigated stress conditions have been already discussed. Nevertheless, we can now compare the level of degradation achieved by the devices after the stress tests.  
Since the stress conditions are different, we will compare quantities normalized with respect to their initial values, also to compensate for some small process variations always present between samples, although very limited in our case. Therefore, the comparisons shown in Figure 10 are related to the relative percentage variation of various parameters, following the definition in Equation (1):  
\[\Delta X_{\%}=100\cdot\frac{X(t)-X(0)}{X(0)} \tag{1}\]  
where \(X(t)\) represents the generic quantity \(X\) after a time \(t\) from the beginning of the stress experiment (\(t=0\) s).  
From the data reported in Figure 10, the class-E operation produces a higher degradation of the device performance with respect to the class-AB condition in this first experiment. This suggests that the higher drain voltage reached by the class-E load line accelerates the degradation mechanisms. An interesting result confirming this phenomenon can be seen in the different trends of the average drain current (Figure 10b). For the class-E condition, the largest variation of the current appears within the first 20 min, whereas under class-AB the degradation appears more gradual throughout the stress.  
Regarding the leakage gate current, one of the most important indicators of the device degradation, we observed a similar variation for both stress conditions. Here, the class-E condition seems less critical, although the measurements of this quantity were very noisy, and a conclusion based only on this parameter is not clear. This is highlighted also in Figure 11, where we compare the complete I-V characteristics measured before and after the stress measurement. By looking at the \(I_{D}\)-\(V_{D}\) characteristics, the global degradation seems negligible, thus suggesting that the effect of the applied stress is mainly related to the dynamic behavior of the transistors.  
Figure 9: DC control measurements for the 2 MHz class-E condition at 30 \({}^{\circ}\)C: (**a**) \(I_{D}\)-\(V_{G}\) transcharacteristic measured before (black dashed line) and after (red continuous line) the stress, (**b**) saturated drain current at \(V_{G}\) = 2 V versus stress time, and (**c**) gate voltage for \(I_{D}\) = 100 mA/mm versus stress time.
## 3 Efficient Network AOS Comprehensive Multiplexing Algorithm  
### Synchronous Traffic  
#### 3.1.1 Frame Generation Algorithm  
In the packet multiplexing layer, to maintain the temporal characteristics of the messages, an isochronous frame generation algorithm is used. Each virtual channel generates frames at fixed time intervals, with the frame intervals being configured according to the message period and delay requirements, thereby maximizing scheduling efficiency. Assuming that there are \(N_{t}\) virtual channels for synchronous traffic, the frame generation algorithm for any virtual channel \(i\) (\(i\) = 0, 1, 2, \(\ldots\), \(N_{t}\)\(-\)1) can be expressed as follows:\[F_{t,i}=\left\{\begin{array}{ll}1,&\Delta t_{t,i}\geq T_{t,wi}\\ 0,&\Delta t_{t,i}<T_{t,wi}\end{array}\right. \tag{1}\]  
In the equation, \(F_{t,i}\) represents whether a frame is generated for the \(i\)-th VC at the current moment. A value of 1 indicates that a frame is generated, while 0 indicates that no frame is generated. \(\Delta t_{t,j}\) is the time interval between the current moment and the last framing moment for the \(i\)-th VC, and \(T_{t,wi}\) is the framing period threshold for the \(i\)-th virtual channel.
# Graph Theory Approach to the Vulnerability of Transportation Networks
## 1 Introduction  
Globalization forces every business activity to be flexible and free of weaknesses. One of the critical branches is transport, which, together with:  
* Energy;
* Information and communication technology;
* Agriculture (water, food);
* Finance, public and legal order, and safety;  
Constitute the foundations of the activity of societies in developing and developed countries. Not without reason, among others, these branches are recognized by individual countries as critical systems or critical infrastructures [1; 2; 3; 4; 5]. Therefore, addressing the vulnerability of transport networks is the key to reliable operation of transport and logistics systems [6; 7; 8]. This is particularly important in the context of climate change [9; 10; 11; 12; 13] and the need to preserve the business continuity of these networks [9; 12; 14]. It was assumed that the best and most natural way to describe transport systems was a graph network representing the infrastructure of this system and allowing characterization of the traffic flows occurring in this network [15; 16; 17; 18]. One should agree with this because the structure and traffic flow in the transport network reflect the relationships between individual elements of thetransport system in the best way, and thus, determine it [8; 12]. According to the above, one of the most important things in transportation networks' analysis is the finding of the bottlenecks. They impose delays and restrictions in the normal flow of transportation [18; 19; 20; 21; 22]. There are three major types of bottlenecks:  
* Infrastructure bottlenecks--can be the outcome of chronic or temporary conditions (e.g., factors: climate change, traffic expansions, such as a bridge or a port).
* Regulations bottlenecks--can be the results of regulations that delay goods movement for security or safety inspections (in international transport: custom procedures for passengers and freight).
* Supply chain bottlenecks--can be results of specific tasks and procedures in supply chain management (labor availability--work shifts may impose time-dependent capacity shortages; some firms may create bottlenecks on purpose as a rent-seeking strategy since they control key elements of the supply chain; different information exchange protocols can create delays in information processing, and therefore, delays in shipments).  
The article provides parameters characterizing the transport network's vulnerability and algorithms for their determination. The proposed tools were based on the graph theory concepts, particularly domination topics [23; 24; 25]. It should be mentioned that the use of the domination in graphs allows one to:  
* Minimize the distance to the critical points for the citizens under a fixed number of them;
* Reduce the number of base stations needed to support all of its users;
* Select the minimum number of representatives necessary for the overall monitoring of communication networks.
# TrendFlow: A Machine Learning Framework for Research Trend Analysis
## 2 Related Work  
### Text Encoding  
Text encoding plays a critical role in Natural Language Processing (NLP) by enabling computers to comprehend and process textual data effectively. There are several text encoding techniques, ranging from traditional methods such as Bag-of-Words and TF-IDF to more advanced neural word embeddings.  
**Bag-of-Words (BoW)**. The Bag-of-Words model [7] is one of the simplest text encoding techniques. It represents each document as a \"bag\" of its words, disregarding grammar and word order but keeping track of word frequency. The BoW model builds a vocabulary comprising all unique words in the dataset and encodes each document as a vector. In this vector representation, each element corresponds to the frequency of a word in the document.  
**Term Frequency-Inverse Document Frequency (TF-IDF)**. TF-IDF [8] is an improvement over the Bag-of-Words model. It considers both the frequency of words in a document (Term Frequency) and their importance across all documents (Inverse Document Frequency). The idea is to give more weight to words that appear frequently in a document but rarely in the entire dataset, as they are more likely to carry important information. The resulting encoding is a weighted vector that better represents the semantic content of the document.  
**Neural Word Embeddings**. Neural word embeddings, such as Word2 Vec [9; 10] and GloVe [11], are more advanced text encoding techniques that represent words as continuous vectors in a high-dimensional space. These embeddings are trained using neural networksto capture semantic and syntactic relationships between words, resulting in similar words having similar vector representations. In contrast to traditional methods, neural word embeddings can capture the meaning and context of words more effectively. In recent years, neural word embeddings have been used in various NLP tasks, such as sentiment analysis, machine translation, and document classification. One notable application is the Transformer model, which has become the foundation for state-of-the-art NLP models such as BERT [2], a pre-trained language model that learns contextualized word embeddings by processing text bidirectionally.
# Analysis of Heavy Metal Sources in Xutuan Mining Area Based on APCS-MLR and PMF Model
## 3 Results and Discussion
### Analysis of the Potential Sources of the Sediment Heavy Metal Elements
#### 3.4.1 Correlation Analysis  
Correlation analysis can be used to assess the relationships between the contents of heavy metals and deduce their transformation and migration pathways [43; 44]. The obtained results showed very significant positive correlations between Cu and Cr (r = 0.761), Cu and As (r = 0.646), and Cr and As (0.610), as well as significant positive correlations between Cu and Zn (r = 0.540) and Zn and Cr (r = 0.484) (Table 4). These findings suggest that Cu, Cr, Zn, and As might be derived from the same pollution source and were further precipitated in the sediments through co-precipitation or co-adsorption. Previous studies have shown a strong positive correlation (r = 0.85) between Cu and Zn in sediments, indicating that the sources of these elements are similar, and Cd and As are moderately correlated with other metals [45]. On the other hand, Pb in the sediments might be derived from atmospheric deposition and terrigenous inputs. Previous studies showed that the correlation between Pb and heavy metals such as Mn, Cr, and Ni was not statistically significant (\(p>0.05\)) [46]. Hence, the heavy metal elements had different potential sources.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Sediment** & **Cu** & **Cr** & **Cd** & **Zn** & **Pb** & **As** \\ \hline Cu & 1 & & & & \\ Cr & 0.761 ** & 1 & & & \\ Cd & \(-\)0.430 & \(-\)0.179 & 1 & & \\ Zn & 0.540 * & 0.484 * & \(-\)0.327 & 1 & & \\ Pb & 0.247 & 0.017 & \(-\)0.014 & 0.181 & 1 & \\ As & 0.646 ** & 0.610 ** & \(-\)0.170 & 0.413 & \(-\)0.073 & 1 \\ \hline \end{tabular} Note.* and ** indicate significant correlations at the \(p<0.05\) and \(p<0.01\) levels, respectively.  
\end{table}
Table 4: Pearson correlations between the heavy metal contents.
## 4 Results and Discussion
### Near-Field Flow Field Characteristics
#### 4.1.2 Flow Characteristics of Different Configurations  
Figure 9 shows the normalized Q-criterion-based instantaneous iso-surface vorticity for different pantograph diversion strategies, colored by magnitude velocity. As seen in Figure 9, the incoming airflow directly impacts the pantograph surface and then separates, forming a clear separated vortex on the leeward side of the panhead, balance arm, and base frame. When the pantograph is installed on the train roof alone, the airflow first forms a series of small, flocculated vortices on the train surface while in front of the camera. The large vortex is formed on the pantograph surface with a large velocity at the beginning, and its velocity magnitude gradually decreases as the vortex moves backward away from the pantograph. When there is a sinking platform on the roof, the airflow is separated at the front edge of the sinking platform first, and a series of hairpin-shaped vortices are formed inside the platform. Then, the airflow that entered the sinking platform flows through the pantograph, and will separate again due to the interference of the pantograph, but with a different separated vortices shape when the pantograph is installed on the train roof alone. However, the airflow above the sinking platform is similar to case 1, and there are many fragmented small-scale vortices. When the pantograph fairing is installed, the airflow first impacts the leading edge of the fairing, generating a small number of low-speed vortices, and then the disturbed incoming airflow continues to impact the pantograph surface, forming a series of hairpin-shaped large-scale vortices, which then impacts the rear of the fairing. These results are consistent with reference [38]. Compared with the former configurations, a large number of hairpin-shaped separated vortices are generated when the airflow enters the cavity due to the existence of the fairing. The high-speed airflow impacts the rear of the pantograph fairing, generating more separated vortices which are larger in scale, and elongate in the downstream.
## 4 Radiation Pressure-Driven PhC Cavity Sensors
### Future Perspectives for Optomechanical Sensing Based on PhC Cavities  
#### 4.3.1 Nonlinear Optomechanics Enhanced Sensitivity  
In most optomechanical interaction, the solution for optomechanical systems is obtained using an approximately linearized method. However, nonlinear effects are conceptually important and cause bistable behavior in the classical regime, which has a non-negligible effect on the optomechanical system [17,34]. There are two sources of nonlinear behavior in cavity optomechanics. First, the radiation pressure interaction is inherently nonlinear under high-intensity light input. Plenty of theoretical proposals [148, 149, 150] and experimental demonstrations [151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 16  
\[S=\frac{dT}{d\Delta}\frac{d\Delta}{dn}\cdot\frac{dn}{d\alpha} \tag{12}\]  
where \(T=P_{out}/P_{in}\) is the optical transmission and \(d\alpha\) indicates the change of environmental parameter. In most resonators, the derivative of frequency detuning over mode index (\(d\Delta/dn\)) depend on the refractive index and geometry of the cavity and this variation is generally small. Similarly, the transduction of external perturbation to varying mode index (\(dn/d\alpha\)) is also small. Since the last two parts are fixed for a given resonator, alternatively, further increase in the first term \(dT/d\Delta\) can enhance the total sensitivity. Considering that nonlinear optomechanics induced resonance shift, the transmission curve becomes asymmetric. Based on the concept of nonlinear optomechanical sensor, the optomechanical effect is used to engineer the transmission spectrum and increase its slope. Owing to the overall sensitivity is proportional to the slope, it has been demonstrated that dispersive optomechanical coupling can enhance sensitivity by at least two orders of magnitude [160].  
For engineering applications based on nonlinear effects in the optomechanical systems, in addition to introducing the second order optomechanical coupling for enhanced sensitivity in precision sensing for displacement [177; 178; 179; 152], several nonlinear optomechanical models have been developed and analyzed for magnetic field [161; 162], and electrical charge [163; 164; 165; 166; 167] sensing recently. Another proposal concept is the integration of a nonlinear mechanical resonator into cavity optomechanics. For instance, in a doubly coupled zipper PCN cavity, the movable nanobeam can be anchored with a nonlinear mechanical part. Typically, a curved bistable beam bucking could be a possible candidate for this nonlinear mechanical resonator [168]. When the optomechanical coupling occurs, the nonlinear effects from mechanical oscillators give rise to intense optical response and interesting dynamic backaction phenomena.
## 4 Results
### Learning Level of Older Adults According to Their Instructors  
A survey was administered to the instructors involved in the educational process to ascertain their perceptions of the impact of the training on older adults. The survey concentrated on the extent of knowledge acquired by older adults in informatics-related topics and their capacity to apply it in practice (see Section 3).  
Figure 2: Achievement level and comprehension of the tasks.  
The results demonstrate that improvement was perceived to be for 34.43% of the senior cohort fair (_regular_), 40.98% of the seniors enhanced considerably (_quite a lot_), and 11.48% substantially (_a lot_). Conversely, only 12.30% of the students were reported to have had minimal (_a little_) improvement.  
The results were also analysed according to age group. In the cohort of older adults between the ages of 50 and 64, 35.8% of the participants were classified as having experienced a moderate (_regular_) improvement, for 38% of them a notable enhancement was reported (_quite a lot_), and, for 15%, a substantial (_a lot_) improvement was indicated. In the case of the group of older adults over 64 years of age, for 43.6% of the students, the improvement was considered quite significant (_quite a lot_), for 32.7% it was considered fair (_regular_), and for 14.5% it was considered slight (_a little_). Overall, there was a favourable perception of improvement in their informatics literacy for both age groups. However, the older cohort (aged 64 and above) appeared to demonstrate a slightly lesser increase compared to the younger group.  
The gender-based analysis revealed that, according to the supervisors, 29% of the male students were perceived to have had a considerable (_quite a lot_) improvement in informatics skills, while, for female students, this percentage was 21%. When combining categories, 47% of the female participants achieved a significant (_quite a lot_) or fair (_regular_) improvement, whereas the percentage was 45% for males. However, the distribution between both categories shows that the learning was considered greater for male participants (29 vs. 21 in significant improvement).  
Additionally, the supervisors were queried about the necessity for modifications to the exercises or difficulties encountered when transmitting knowledge. While the materials were generally deemed adequate, some adaptations were proposed, including an increase in the font size and the inclusion of additional visual examples. Conversely, these suggestions appear logical given the age of the participants.
## 3 Results and Discussion
Learning by example models usually require a large training set data. Because of the relatively limited number of available scans, leave-one-out cross validation experiments were applied. For each scan the training feature space was recovered from the remaining 23 scans. As noted, the \(k\)-NN classification model depends on the choice of \(k\) and \(h\). Following the formation of the feature-space, these parameters were studied by letting \(k\) vary between 1 and 11 while potential values for \(h\) ranged from 1 to \(k\). The highest accuracy value recorded was ACC = 0.878 when using \(k=9\) and \(h=5\). The corresponding segments' confusion matrix (contrasting the pixels that were presented above) is:  
\[C= \begin{bmatrix}0.1861&0.0936\\ 0.0283&0.6920\end{bmatrix} \]  
All confusion matrices resulting from this experiment (for all \(k\)'s and \(h\)'s) lead to the following receive operating characteristics (ROC) curve [26] in Figure 20. The area under the ROC curve is 0.872, which is evidence for good segment classification. Following the classification examination, the algorithm is tested in its holistic form, including the refinement phase. As a performance metric we use the percentage of correctly recognized tree points (true-positive) and the correctly recognized background points (true-negative) resulting in  
\[C= \begin{bmatrix}0.2154&0.0052\\ 0.0056&0.7738\end{bmatrix} \]  
and ACC = -0.99. This result shows the high level of success of the complete algorithm. Notice that the pixel level classification is significantly higher than the segment level one. This effect is related to small non-tree segments that wear a form of tree segments, mostly because of their small size which is insufficient to obtain a consistent attribute. The refinement phase resolves most of these misclassifications. Examples for classification results on the complete scans are presented in Figure 21, marked on top of the intensity channel. Figure 20: ROC curve created from k-NN classifications with different parameters.