esa-sceva/satcom-chunk-collection huggingface dataset:
# Improving Foraminifera Classification Using Convolutional Neural Networks with Ensemble Learning
## 2 Materials and Methods
### CNN Ensemble Learning  
The theory behind ensemble learning is based on a simple idea: by combining different models, it should be possible to produce better and more reliable results. Ensembles can be constructed on four different levels:  
* Data level: by splitting the dataset into different subsets;
* Feature level: by pre-processing the dataset with different methods;
* Classifier level: by training different classifiers on the same dataset;
* Decision level: by combining the decisions of multiple models.  
Ensembles work best when applied to significantly diverse models [20]. In this work, we construct ensembles by applying different pre-processing approaches, detailed in Section 3, for representing the input images as RGB images. The images generated by these approaches are used to train multiple networks whose decisions are finally combined using the sum rule.  
CNNs were first introduced in the 1980s by the French researcher [PERSON] [21], and were shown to perform well throughout the 1990s [22, 23]. In the last decade, however, due to the advent of big data and GPU computing, the performance of CNNs has increased to the point that, in computer vision and image recognition, CNNs are now considered state-of-the-art.  
As with every other type of neural network, the structure of a CNN is divided into three components: an input layer, which, in a CNN architecture, is usually a volume of \(n\times n\times 3\) neurons directly connected to the input of an image's pixels, hidden layers that utilize shared weights and local connections, and a fully connected output layer.  
CNNs take as input three dimensional tensors, most often used to represent images of size \(\mathrm{W}\times\mathrm{H}\times\mathrm{C}\), with the terms being, respectively, the width and height of the image, and the number of channels associated with each pixel, which can hold information such as RGB color values, alpha channel transparency, depth, and more.  
The output of a CNN is given by the final fully connected layer, and it returns a probability or confidence score for each of the possible labels of the classification problem. The label with the highest probability is chosen as the network's final prediction.  
In this work, we use the ResNet50/ResNet18/GoogleNet/MobileNetV2 topologies pretrained using ImageNet, which is fine-tuned further for 20 epochs with a learning rate of 0.001 and a batch size of 30.  
The Vgg16 architecture was also considered for this study, but the lack of residual connections and the large number of hidden layers made the training very slow when compared to ResNet-based architectures, so we decided to omit it from this ensemble.  
This is because the residual connections, shown in Figure 2, allow information from previous layers to carry over to deeper layers, which enables the network to express a much larger function class with less layers when compared to standard deep CNNs [24].  
Figure 2: Residual block from ResNet [24].
## 3 Distributed Jamming System Model for LEO Signal  
### Distributed Interference Scenario  
The distributed interference scenario model is shown in Figure 1, illustrating a jamming scenario consisting of an LEO satellite, a jamming target, and a distributed UAV jamming station. LEO satellites use spot beams to transmit signals at different frequency points to communicate with users, a method that achieves an effective isolation of users in spatial and frequency domains while preventing crosstalk on the same frequency. The position of the LEO is marked as \(\xi_{k}\). Given the fast speed of LEOs and the requirement of real-time interference, \(S_{k}\) only indicates the LEO locations used to serve the corresponding area during the communication timeframe. The point beam emitted by the LEO is denoted by \(L_{k}\). The jamming target at a specific range is located below the jamming source, and its position is labelled as \(\mathbf{U}_{m}\). Each jamming target can receive multiple LEO signals and maintain a 1-way or 2-way communication link. The jamming target receives and decodes the navigation information from the LEO signals. In the downlink transmission process of the LEO star communication system, the communication mode of gaze is commonly used between the LEO star beam and the ground user terminal. The jamming targets receive the LEO's narrow-beam signals through a multi-antenna transceiver system to enhance the reliability and capacity of the communication, and at the same time, it can well suppress interference from other directions. Therefore, by piggybacking the cluster jammers between the LEO star and the target to be jammed, the jamming effect can be significantly improved. The jammer's position is labelled as \(\mathbf{J}_{i}\). Distributed jammers are able to implement full coverage of star signal reception in the jamming area with the distribution of satellite positions for that time period and are capable of small positional movements. When the beam of an LEO passes through the movable area of the jammer under that time period, the jamming system is considered to be able to pick up the LEO signal and strike the corresponding jamming target effectively. To ensure that strategic jamming is implemented instantly, a division of labour between the jammers is required. Jammers are classified as primary and relative jammers. The locations of the primary and relative jammers are noted as \(\mathbf{J}_{ip}\) and \(\mathbf{J}_{ix}\), respectively. The primary jammers have a high processing capacity and only complete the reception and processing of one LEO signal. The relative jammers enable the broadcast of multiple LEO signals. For a distributed jammer model, each jammer can be either a primary or a relative jammer.
## 5 Discussion
### Relationship Between Fires and SPEI
As shown in [62], the temporal evolution of the SPEI exhibits increasing smoothness with longer accumulation periods; thus, at shorter periods, it appears to fluctuate akin to random noise, while at longer periods, it varies smoothly with time exhibiting a certain persistence. Hence, it is plausible that the SPEI at shorter accumulation periods would amplify the influence of smaller cycles in fires, whereas those at longer accumulation periods would primarily drive the larger cycles in fires. The negative correlation between the SPEI (at accumulation periods less than 10 months) and fires (both monthly occurrence and total burned area) is unsurprising, as negative SPEI values signify hot and dry conditions. Therefore, the negative correlation between the SPEI and fires suggests that as meteorological and climatic conditions become hotter and drier in the studied area, the total burned area increases and the frequency of fire occurrences rises. Conversely, humid conditions would restrict the spread and extent of fires. This pattern is characteristic of IMFs modulated by periodicities spanning from 4 months to 5 years. On the contrary, the IMF with the long-term behavior revealsthe most robust positive correlation with SPEIn (n from 30 to 48), indicating that over decadal temporal scales, prolonged periods of hot and dry conditions would counteract the occurrence and spatial extent of fires, while persistent humid conditions would favor them. This could be interpreted in connection with the accumulation of burnable biomass under favorable conditions.
# A Biologically-Inspired Power Control Algorithm for Energy-Efficient Cellular Networks
## 2 System Model  
We consider a cellular system with a finite number of channels (a frequency or a time slot), and the channels are fully reused in every cell. We denote the number of BSs by \(N\). Then, the number of MSs using the same channel (_i.e._, co-channel MSs) becomes \(N\) assuming that each channel is allocated to a certain MS in each cell by an arbitrary channel assignment scheme (for example, the random channel assignment can be used [28]; the resource allocation scheme is beyond the scope of this paper). In general terms, MS \(i\) is served by the BS \(i\) that is closest to it for communication. \(P_{i}\) denotes the transmit power of BS \(i\), and \(n_{i}\) denotes the noise power at MS \(i\). \(G_{ij}\) denotes the channel gain of the communication link from BS \(j\) to MS \(i\). This is illustrated in Figure 1. Then, the SINR of MS \(i\) is expressed as:  
\[\gamma_{i}=\frac{G_{ii}P_{i}}{\sum_{j=1,j\
eq i}^{N}G_{ij}P_{j}+n_{i}},\ \ \ 1\leq i\leq N \tag{1}\]  
From [PERSON]'s theorem, the bit rate of MS \(i\) per unit bandwidth (or spectral efficiency) in bits/second/Hz is given by:  
\[R_{i}=\log_{2}(1+\gamma_{i})\ \ \ b/\mathrm{s/Hz} \tag{2}\]  
In addition, we define the outage probability of MS \(i\) to be the probability that the received SINR \(\gamma_{i}\) falls below the required SINR, \(\gamma_{req}\), as follows:  
\[\mathcal{P}_{i}^{out}:=Pr\{\gamma_{i}<\gamma_{req}\} \tag{3}\]  
Finally, we define the energy efficiency of MS \(i\) as the number of information bits transmitted without error per unit bandwidth and per unit energy in units of bits/Hz/Joule. Because the occurrence of outage induces bit error, the energy efficiency is defined as [11]:  
\[E_{i}:=\frac{(1-\mathcal{P}_{i}^{out})R_{i}}{P_{i}}\ \ \ [\mathrm{b/Hz/J}] \tag{4}\]
# Photodynamic Therapy--An Up-to-Date Review
## 5 Photodynamic Therapy Applications
### Other Applications  
The versatility of PDT is not limited to clinical applications. This technique is valuable for other applications. One example is the removal of biofilms from medical devices. This is highly important, especially because contaminated devices pose a serious threat to human health through the nosocomial infections they can generate. In this respect, PDT can be used to repel biofilm infections from implants, such as prosthetic joint infections and infections caused by ventilator-associated pneumonia biofilms [157].  
PDT can also be involved in developing photoactive fabrics by incorporating PSs triggered by sunlight. Masks, suits, and gloves can be designed in this manner to ensure a sterile, safe, decontaminated outfit for healthcare workers [150]. Similarly, antimicrobial surfaces can be obtained to avoid infectious disease outbreaks within healthcare facilities [158].  
The selective inactivation of pathogens achievable through PDT is useful for environmental water treatments [33]. Microorganisms like Gram-positive and Gram-negative bacteria, viruses, fungi, and parasites can be eliminated from the surface, ground, drinking, and wastewaters, without developing resistance [159].  
Biotechnology is another attractive field for applying the principles of PDT. For instance, inactivating the microbes from fruits' surfaces prolongs their shelf life, PDT being considered a high-efficiency and nonthermal sterilization technology [160; 161]. Another beneficial use is replacing antibiotics treatment of milk by PDT, to reduce bacteria content from dairy products [162].  
The food industry can be improved through PDT, not only by killing microbes in food itself but also by creating better packaging. Packaging materials that inhibit bacterial growth were reportedly fabricated using this sterilization technique [163].
## Discussion
### Phenometrically-Based Versus Spectrally-Based Classification  
Our hypothesis was that phenometrically-based RFC models would be more accurate than the spectrally-based RFC models in vegetated cover types, at least for crops. However, the results showed that the spectrally-based classification yielded slightly higher accuracy metrics (compared to the phenometrically-based classifications) for most classes, including corn and soybean (Table 8). One possible reason for this result is that both spectral and phenometric variables have their own strengths in classification. Compared to spectrally-based RFC models, phenometrically-based RFC models have an advantage of containing the seasonal information or timing of vegetation growth, thus mapping wheat more accurately. However, the phenometrically-based RFC relied on vegetation growth, as represented by the EVI2 time series calculated from the red and NIR bands. On the other hand, spectral variables in spectrally-based RFC models contain far more spectral information from multiple bands and normalized ratios that give the spectrally-based classification an edge in feature separation. The rich information from input variables could help spectrally-based models to perform better for most classes, including some vegetated covers (e.g., corn, soybean, wetland, and grass) (Table 8 and Table S8). Analysis of important variables (Tables 9 and 10) also helps to explain the better performances of spectrally-based models compared to phenometrically-based models. Both CxQ and HPLM RFC classifications were strongly driven by the maximum fitted EVI2, as shown by the consistent appearance of \(\alpha\) as the most important variable in the CxQ RFC models (Table 9) and the appearance of \(\mathrm{PH}_{\mathrm{HPLM}}\) in the top five HPLM RFC models (Table 10). In the study area, all vegetated covers can be classified effectively without seasonal information, except for wheat. A comparison between phenometrically-based and spectrally-based classifications in an area with more complicated cropping patterns (e.g., the wheat-fallow system used in the western Great Plains) might better demonstrate the relative strengths and weakness of these complementary approaches. Nevertheless, the combination of spectral and phenometric variables yielded the most accurate land use/land cover map.
# Two-Step Enzymatic Synthesis of Structured Triacylglycerglycerols from ARASCO+
## 2 Materials and Methods
### Synthesis and Purification of Structured TAG  
Immobilized lipases from _T. languinosus_, _R. oryzae_, and _R. miehei_ were separately assayed as biocatalysts for the esterification reaction between the ethanolysis products of the ARA-enriched TAG fraction from ARASCO and the ethanalysis products from coconut oil. Substrates were used in a 1:30 weight ratio, and the lipase load was 10 wt% of the substrates' weight. The reaction was carried out at 35 \({}^{\circ}\)C in a rotatory evaporator under vacuum (15 mmHg), in darkness, and at 20 rpm for 24 h. The reaction was finished by adding distilled water (2 mL) and _n_-hexane (2 mL) and centrifuging the mixture at 3000 rpm for 5 min, and the upper hexane layer was then collected. Two additional extractions with _n_-hexane were carried out and all hexane extracts were put together. The solvent was evaporated under a nitrogen stream to a final volume of 1 mL. The reaction products in _n_-hexane were fractionated using an open chromatographic column with silica gel and silver nitrate (10:1 _w/w_) (8 g) previously activated at 105 \({}^{\circ}\)C overnight as the stationary phase, as described by [PERSON] et al., eluting with sequential 10 mL volumes of the following mobile phase: 100 mL of _n_-hexane (eluted fractions labeled from 1 to 10), 50 mL of _n_-hexane/acetone 99.5:0.5 _v/v_ (11 to 15), 100 mL of _n_-hexane/acetone 99:1 _v/v_ (16 to 25), 50 mL of _n_-hexane/acetone 98:2 _v/v_ (26 to 30), 50 mL of _n_-hexane/acetone 90:10 _v/v_ (31 to 35), and 100 mL of _n_-hexane/acetone 70:30 _v/v_ (36 to 45) [23].  
A flowchart of the experimental scheme developed in this study is shown in Figure 1.  
Figure 1: A flowchart showing the experimental scheme developed in this study. ARA: arachidonic acid; FAs: fatty acids; FAEEs: fatty acid ethyl esters; MAGs: monoacylglycerollgycerols; TAGs: triacylglycerlgycerols; STAGs: structured triacylglycerlgycerols.
## 5 Results and Discussion
### Fluorophosphate Glasses
The bond breaking reduces the network rigidity with the formation of discontinuities in the structure and attenuates the acoustic wave propagation in these glasses, as is presented in Figure 4.  
In Figure 5, it appears that the experimental longitudinal, shear, and bulk moduli are strongly affected by the transition from the fluoride (ionic) to phosphate (covalent) limit with increasing the PO\({}^{3-}\)/F\({}^{-}\) ratio in the glasses. The variation in the PO\({}^{3-}\)/F\({}^{-}\) ratio leads to atomic volume rise and bulk modulus decrease. The behavior of the moduli is attributed to the variation in the coordination number with increasing the phosphate content in the glasses. In Figure 6a, the variation in the Poisson's ratio with the Sr(PO\({}_{3}\))\({}_{2}\) mole fraction in the fluorophosphate glasses is presented. The low values of the Poisson's ratio imply a diminished coupling of sound waves from one mode of propagation to another. The Poisson's ratio was found experimentally in the range from 0.46 to 0.47. These values indicate a glass network with low cross-linking density. Both bulk and Young's moduli exhibit similar behavior as is observed in Figure 6b. Figure 4: Experimental and calculated longitudinal (**a**) and transversal (**b**) sound velocities corresponding to the fluorophosphate glasses at room temperature. Devitrification was observed for 0.45 \(<\) x \(<\) 0.65 mole fractions of Sr(PO\({}_{3}\))\({}_{2}\). Figure 5: Variation in experimental and calculated longitudinal (**a**), shear (**b**), and bulk (**c**) moduli with mole fraction for the fluorophosphate glasses. The missing points in the 0.45 \(<\) x \(<\) 0.65 region are due to the observed devitrification. Figure 6: Variation in experimental and calculated Poisson’s ratio (**a**) and Young’s modulus (**b**) with mole fraction for the fluorophosphate glasses. The missing points in the 0.45 \(<\) x \(<\) 0.65 region are due to the observed devitrification. The experimental data available in the literature were compared to the simulated data. From Figures 4-6, it seems that the direct comparison reveals an adequate agreement between the experimental and the numerically simulated results demonstrating the ability of the modelling approach proposed in this work to predict the elastic properties of these glasses and providing a fine description of the microstructure.
# Fast-Convergence Reinforcement Learning for Routing in LEO Satellite Networks
## 2 System Model
### Q-routing Algorithm  
Reinforcement learning is one of the areas in machine learning [21]. Inspired by behavioral psychology, it focuses on what actions an agent should perform to maximize cumulative rewards when confronted with a given environment. Reinforcement learning consists of agent, environment, state, action, and reward, where the environment consists of some states. The agent performs an action based on the current state of the environment, after which the environment moves to the next state based on the action performed and provides the agent with a reward value to evaluate the action. In the long run, the agent learns how to perform the optimal action in a given state [22].  
Q learning is a traditional reinforcement learning algorithm; it provides the method by which the intelligence chooses actions in a given state by maintaining a Q-table. Each Q value in the Q-table represents the total benefit of taking a certain action in a certain state. The update of the Q value is mainly realized through the Bellman Equation:  
\[Q(s,a)=(1-\alpha)Q(s,a)+\alpha[r+\gamma\max_{d\in A}Q(s^{{}^{\prime}},a^{{}^{ \prime}})] \tag{1}\]  
where \(s\) represents the current state of the agent, \(a\) is the action performed, \(\alpha\) is the learning rate, \(r\) is the reward value by performing action \(a\) under state \(s\), \(\gamma\) represents the discount factor, and \(A\) is the action space in state \(s^{{}^{\prime}}\); \(\alpha\) and \(\gamma\) both are in the interval \([0,1]\). Therefore, max \(Q(s^{{}^{\prime}},a^{{}^{\prime}})\) represents the max \(Q\) value of state \(s^{{}^{\prime}}\).  
Q routing is the application of the Q learning algorithm to the routing problem. In the Q routing algorithm, each communication node is treated as an agent which can independently learn the forwarding policy and forward packets to the next port [23]. As shown in Table 1, each node maintains a Q-table, which records the Q value of all actions and states. The agent looks up the Q-table based on the destination node of the packet, finds the action corresponding to the maximum Q value, and then executes it, which is a packet forwarding process. Similar to Equation (1), the update of Q-table is as follows:  
\[Q_{i}(s,a)=(1-\alpha)Q_{i}(s,a)+\alpha[r+\gamma\max_{d\in A}Q_{j}(s^{{}^{ \prime}},a^{{}^{\prime}})] \tag{2}\]  
where \(\alpha\) is the learning rate which determines the updating rate, \(\gamma\) is discount factor, and \(i\), \(j\) represent the index of different nodes. This equation is the essence of agent learning.  
Figure 1: LEO satellite constellation.
## 5 Discussion and Limitations
### Improving Evaluation Methods  
We evaluated model performance on the iCliniq dataset, using metrics such as precision, recall, and F1 score, with BERscore as our evaluation method. While BERscore's advantage lies in its ability to assess answers' similarity to human responses, offering a more realistic measure than conventional test questions, its drawback is that it only measures similarity and not specific accuracy. Given the critical nature of medical Q&As, where errors can have severe consequences, we plan to incorporate additional evaluation methods, such as multiple-choice questions, to enhance the fairness and comprehensiveness of model assessments.
# A Comprehensive Review of Mechanisms, Predictive Techniques, and Control Strategies of Rockburst
## 6 Rockburst Prevention and Control
### Rock Support in Burst-Prone Ground
\begin{table}
\begin{tabular}{p{85.4 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Rock Types** & **Location** & **Buried Depth/m** & **Main Prevention and Control Measures** \\ \hline \multirow{2}{*}{Granite} & Ling Long Gold Mine (195) & 650–1100 & Optimize mining sequence, filling mining, improve support, blasting methods, in-situ monitoring \\ \hline \multirow{2}{*}{Mudstone, Salstone, Marlstone, Sandstone} & Erlang mountain Tunnel of Sichuan-Tibet highway (196) & 770 & Surrounding rock monitoring, high-pressure watering, optimization of excavation scheme, combined support \\ \hline \multirow{2}{*}{Sandstone, Slate, Limestone, Marble} & Jinping II Hydrogen Station, Diversion Tunnel (140) & 2525 & Improve stress state of surrounding rock, combined support, construction methods \\ \hline \multirow{2}{*}{Granite, Gabbro Norite} & \multirow{2}{*}{Creighton Mine (34)} & Stress relief by blasting, optimize mining sequence, filling mining, improve support methods, strengthen rock mechanics research, microseismic monitoring \\ \hline \multirow{2}{*}{Quartzite, Pelitic siltstone} & Sunshine Siderite Mine Kellogg, Idaho (113) & 2100 & Limit the number of stopes, single shift operation, microseismic monitoring, diversification of support methods \\ \hline \multirow{2}{*}{Breccia, Granite gneiss} & Strathcona Mine Onaping, Ontario (113) & 2300–2500 & Microseismic system, shotcrete, support system \\ \hline \multirow{2}{*}{Diorite, Andesite Copper Mine (197)} & El Teniente & & Strengthen support, change mining methods \\ \hline \multirow{2}{*}{Dolerite} & Mount Charlotte Gold Mine (198) & 1200 & Improve and strengthen support, strengthen monitoring \\ \hline \multirow{2}{*}{Sandstone, Dolomite, Shale} & Lubin Copper Mine (199) & 600–1000 & Stress relief by blasting, filling mining, control of structural parameters \\ \hline \multirow{2}{*}{Granite} & Diversion Tunnel of Sima Hydropower Station (200) & 700 & Optimize support, adjust geometry of underground cavern section \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Cont.