esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results  
In this section, we present the results following a structured approach. First, we compare the CNN-based classifiers considering the performance metrics averaged across all classes. Specifically, we analyze the average Precisions, Recalls, and f1-scores, along with the training time, which serves as a measure of the computational load required for each model. From this comparison, we identify the classifier that demonstrates the best performance, considering the four metrics mentioned above.  
Next, we perform the same analysis for the models based on Vision Transformers, identifying the top-performing Transformer-based classifier. Subsequently, we compare the best CNN-based model with the best Transformer-based model. This comparison includes a detailed evaluation of their performance on individual classes using the respective confusion matrices to highlight the strengths and weaknesses. Finally, we assess the classifiers in terms of their average classification time for a single image after training, providing an estimate of their suitability for real-time applications. It is important to note that while CNNs and Transformers can benefit from the presence of a GPU, all experiments were conducted on a desktop computer without a GPU. The computer was equipped with an Intel(R) Core(TM) i5-10500 CPU running at 3.10 GHz and 16 GB of RAM. This approach was chosen to demonstrate the feasibility and performance of the models in a more accessible and widely available computing environment. For the parameters and hyperparameters, we used the standard settings of the models (see Tables 1 and 2). The considered models, provided by the Torchvision library [27], were pre-trained on the ImageNet dataset [10] and fine-tuned for our specific classification task, which involved classifying six classes of volcanic activity. The final layers of the classifiers were modified to match the number of classes in our dataset. The number of training epochs was set to 4 for all models to ensure each model reached a steady state of learning, providing a fair and consistent comparison. All models were trained using the Adam optimizer with a learning rate of \(1\times 10^{-4}\) and the CrossEntropyLoss function. The CNN models employed the ReLU (Rectified Linear Unit) activation function, while the Transformer models utilized the GELU (Gaussian Error Linear Unit) activation function.
# A Study on the Impact of Blue Light and Its Brightness on Long-Term Memory
## 3 Result and Discussion
### Discussion
As demonstrated in this study, it is still uncertain whether brighter light even has a greater positive effect on long-term memory retention. Therefore, the scope of research should be extended to a wider range of light conditions (even brighter light or light of different colors) through future studies. Third, given that this study was conducted under controlled experimental conditions, further verification is required to apply the major findings of this research to real-life settings. Unlike in laboratory conditions, long-term memory may be affected in reality by uncontrollable external variables. Therefore, future studies need to involve more scenarios and environments that are closer to real-life settings rather than laboratory conditions, maximizing the utility of research outcomes. Fourth, the participants were exposed to a given light condition for 30 min in the morning. This experimental setup was derived from previous studies highlighting the contribution of a 30 min exposure to blue light in improving the ability to execute tasks associated with working memory while promoting functional reactions in the brain's dorsolateral and ventrolateral prefrontal cortex, which play a critical role in working memory processes [19; 23]. However, other studies have reported that blue light can also positively affect cognitive abilities, such as memory and alertness, even when the exposure is much longer (0.8-12 h) or shorter (1 min) than 30 min [17; 27]. Hence, future studies should also extend the range of light exposure timings and durations to explore the optimal exposure conditions. Finally, this study evaluated the long-term memory of participants using recall tests of a set of words composed of five randomly chosen English consonants. However, long-term memory may be categorized into several types, including explicit memory and implicit memory, based on its intrinsic nature. Not that long-term memory can be measured in various methods; recall is just one of them. Consequently, it is less appropriate to regard the major findings of this study as fully representing the specific aspects of all types of long-term memory. Furthermore, the effect of light on cognitive ability or working memory, which may affect long-term memory, as well as the long-term ongoing effect of light on long-term memory, are not in the scope of this research. Therefore, future studies should focus on further verifying such relationships to gain a more accurate understanding of the effect of light on memory and properly apply the resulting findings to real-life settings.
# Reversible Data Hiding in Encryppted Images with Extended Parametric Binary Tree Labeling
## 4 Experimental Results  
To evaluate the performance of the proposed scheme, we conducted experiments on security analysis, image quality, embedding capacity and comparison of state-of-the-art schemes. As shown in Figure 13, we chose four standard grayscale images as the test images in the experiment. More, the datasets UCID [28], BOSSbase [29], and BOWS-2 [30] were used in the experiments to verify whether the scheme can be applied to most images.
# Power Amplifier Predistortion Using Reduced Sampling Rates in the Forward and Feedback Paths
## 3 Proposed Predistortion System
### Proposed Predistortion System Description
However, from a computational complexity perspective, it is more convenient to operate the two converters at the same sampling rates to ensure that the two signals used for the predistortion function identification have identical sampling rates and to avoid the digital signal processing overhead associated with re-sampling one signal to match the other signal's sampling rate for time-delay alignment and digital predistortion function identification.
## 2 Materials and Methods
### Hemo Toxicity  
Freshly obtained EDTA human blood (A, Rh+) from blood bank of A. G. Padmavathi Hospital Blood Bank, Puducherry, India was washed three times by centrifugation (2500 rpm for 10 min) in PBS at room temperature (20-25 \({}^{\circ}\)C). Lymphocytes and platelets (Buffy coat) were removed from gradient centrifugation and a 10% red blood cells (RBC) suspension was prepared in PBS (1\(\times\)), aliquots of 2.5 mL of 10% RBC was mixed with 250 and 500 \(\upmu\)m of mangiferin. Chlorhexidine 0.2% was used as positive control. Glutaraldehyde was added to stop the reaction. The supernatant was removed and the liberated hemoglobin in the supernatant was measured spectrophotometrically as absorbance at 541 nm. For negative control 5% DMSO with 10% of RBC suspension making a final concentration of 1% was used. The experiment was performed in triplicate and mean \(\pm\) standard deviation (SD) was calculated as follows: % Hemolysis = (Absorbance sample/Absorbance positive control) \(\times\) 100.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Gene** & **Primer Sequence (5\({}^{\prime}\)-3\({}^{\prime}\))** & **Amplicon Size (bp)** & **Role in Biofilm Formation** \\ \hline _gtfB_ & FP: AGCAAATGCAGCCAATCTACAAT & 95 & Water insoluble glucan production. \\ \hline _gtfC_ & FP: ACGACTTTGCCGTATTGTCA & 107 & Water soluble and insoluble glucan production. \\ \hline _gtfD_ & FP: GTGCCTACACCCAATGCACG & 183 & Water soluble glucan production. \\ \hline _gtfD_ & FP: TOGCACCGAAATGTCTCTTC & 183 & Water soluble glucan production. \\ \hline _gbpB_ & FP: ATGGCCGTTATGACACGT & 50 & Glucan binding protein. \\ \hline _comDE_ & FP: ACAATTCCTTGAGTTCCATCCAAG & 80 & Competence-stimulating peptide involved in quorum sensing mechanism \\ \hline \multirow{2}{*}{16S} & FP: CCTACGGAGGCACAGCTAG & \multirow{2}{*}{100} & \\  & RP: CAACAGAGCTTACGATCCGAAA & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Nucleotide sequences of primers used for relative quantification of expression pattern of genes involved in _S. mutans_ biofilm formation.
# Software Integration of Power System Measurement Devices with AI Capabilities
###### Abstract  
The latest changes on the distribution network due to the presence of distributed energy resources (DERs) and electric vehicles make it necessary to monitor the grid using a real-time high-precision system. The present work centers on the development of an open-source software platform that allows for the joint management of, at least, power quality monitors (PQMs), phasor measurement units (PMUs), and smart meters (SMs), which are three of the most widespread devices on distribution networks. This framework could work remotely while allowing access to the measurements in a comfortable way for grid analysis, prediction, or control tasks. The platform must meet the requirements of synchronism and scalability needed when working with electrical monitoring devices while considering the large volumes of data that these devices generate. The framework has been experimentally validated in laboratory and field tests in two photovoltaic plants. Moreover, real-time Artificial Intelligence capabilities have been validated by implementing three Machine Learning classifiers (Neural Network, Decision Tree, and Random Forest) to distinguish between three different loads in real time.  
Keywords:power quality monitor; phasor measurement unit; smart meter; open-source software; distributed measurement system; photovoltaic plant; load identification; artificial intelligence; neural network; decision tree; random forest +
Footnote †: journal: Applied sciences
## 4 Analysis And Assessment
### Experimental Strategy
#### 4.1.1 Configuration Strategy
From the 39 globally distributed reference stations, we chose five stations (CEDU, DARW, JFNG, KARR, KZN2) which are shown in Figure 1. These stations are mostly concentrated in the Asia-Pacific region where the number of satellites with triple-frequency data is sufficient.
## 2 Materials and Methods
### Methods
#### 2.2.1 Simulating FPAR Based on the PROSAIL Model
As the secondary parameter of the PROSAIL model, FPAR is retrieved by using the relationship of LAI/FPAR (Equation (7)), which is also used in the Monsi and Sacki models [30]. This process ensures that LAI and FPAR maintain good consistency to some extent:  
\[FPAR~{}=~{}1-e^{-K\times LAI}, \tag{7}\]  
where K is the extinction coefficient. Among the appropriate factors, the LAI and the solar zenith angle have a certain influence on the extinction coefficient (K), which is mostly between 0.3 and 1.5 for the plant community under investigation; the value of K is about 0.3-0.5 for grasslands and mostly above 0.7 for herbs and shrubs [30]. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Vegetation Types** & **Clumping Index** & **Vegetation Types** & **Clumping Index** \\ \hline Evergreen Broadleaf Forest & 0.63 & Shrub & 0.71 \\ Deciduous Broadleaf Forest & 0.69 & Herb & 0.74 \\ Evergreen Needleleaf Forest & 0.62 & Shrub & 0.75 \\ Deciduous Needleleaf Forest & 0.68 & Cropland & 0.73 \\ Mixed Forest & 0.69 & Others & 0.87 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Clumping index based on different vegetation types. 2.2.2. The Inversion of FPAR Based on the Look-Up Table  
The LUT algorithm is simple, easy to operate, and has good precision. This algorithm is widely used in the radiation transfer model. In this paper, the canopy reflectance data obtained from the simulation of the model is compared with the reflectance of the pre-processed satellite data, and a look-up table for the reflectivity of different channels (blue, green, red, and near infrared) is established. Finally, the residual optimization parameter matrix (Equation (8)) was computed to obtain the simulated reflectance value with the smallest error, which was used to invert the model parameters:  
\[E\ =\ \min\sqrt{<R_{i}^{jk}-r_{j}^{jk}>^{2}}, \tag{8}\]  
where \(R\) is the surface reflectance from the FY-3C date and \(r\) is the canopy reflectance estimated from the PROSAIL model.
# A Cost-Optimized Model for Dynamic Port Hinterland
## 4 Experimental Design and Results
### Results: Representing the \(H(x,y)|_{(p_{n},\rho_{n})}\)
#### 4.2.5 Quantitative Analysis
\begin{table}
\begin{tabular}{c c c c c} \hline \multirow{2}{*}{**City**} & **Algeciras** & **Valencia** & **Barcelona** & \\  & **Influence** & **Influence** & **Influence** & **Dominant Factor** \\  & **(\%)** & **(\%)** & **(\%)** & \\ \hline Madrid & 32.5 & 45.2 & 22.3 & Rail connectivity and inland transport optimization \\ \hline Zaragoza & 15.3 & 42.1 & 42.6 & Strategic location and multimodal infrastructure \\ \hline Valladolid & 28.7 & 38.9 & 32.4 & Balanced multimodal connections \\ \hline Sevilla & 78.4 & 18.5 & 3.1 & Geographic proximity and established logistics patterns \\ \hline Bilbao & 25.6 & 35.2 & 39.2 & Specialized rail services and Northern Corridor efficiency \\ \hline Valencia & 8.2 & 82.5 & 9.3 & Local port dominance and distribution networks \\ \hline Barcelona & 5.4 & 12.3 & 82.3 & Regional port infrastructure and local market integration \\ \hline Malaga & 72.1 & 23.4 & 4.5 & Southern Corridor efficiency and proximity to Algeciras \\ \hline Murcia & 21.3 & 67.8 & 10.9 & Mediterranean Corridor connectivity \\ \hline Burgos & 27.8 & 41.2 & 31.0 & Northern inland distribution networks \\ \hline \end{tabular}
\end{table} 
Table 10: Port Influence Analysis for Major Spanish Cities. Source: Own elaboration from simulation results. while the more balanced competition in Zaragoza is represented by the mixed coloring, indicating influence from multiple ports. Figure 5 provides a comprehensive visualization of these overlapping influences, where the RGB color model effectively illustrates the complex nature of port competition. The mixed colors appearing in central Spain, particularly around Madrid, demonstrate areas where multiple ports maintain significant market presence. This visualization supports the quantitative findings presented in Table 10, particularly regarding the balanced competition in central regions.
## 3 Information Bottleneck Decoders for Irregular LDPC Codes Using Message Alignment
### Relevant-Information-Preserving Clusterings for Arbitrary Irregular LDPC Codes  
In contrast to regular LDPC codes, irregular LDPC codes are characterized by nodes with various degrees (i.e., the number of incoming messages differs). Thus, the input joint distribution \(p_{d}(x,\mathbf{y})\) for the information bottleneck depends on the node degree \(d\). Consequently, it is not sufficient to design message mappings only for each node type, but for each node type _considering_ the individual node degrees. For ease of notation, we introduce subscripts for the distributions indicating the node degree. That is, \(p_{d}(t|\mathbf{y})\) is the relevant-information-preserving clustering at a node with degree \(d\) found given the input joint distribution \(p_{d}(x,\mathbf{y})\). In density evolution, a code ensemble is considered. That is, instead of a particular irregular LDPC code with a certain parity-check matrix, the connectivity between variable and check nodes is only known on average defined by the degree distribution. To construct the required input joint distributions \(p_{d}(x,\mathbf{y})\), discrete density evolution from [13] needs to be extended to consider the degree distribution of the code ensemble.  
Conventional density evolution is a technique to analyze the error correction performance of the code ensemble. In the conventional density evolution scheme for irregular codes, one tracks _the average_ output distributions \(p_{d}(x,\mathbf{y})\) of the variable and the check nodes. Therefore, the actual output distribution \(p_{d}(x,\mathbf{y})\) for each node degree \(d\) is weighted according to the edge-degree distributions. That is,  
\[p_{d}(x,\mathbf{y})=\sum_{d=2}^{\omega_{\max}}\omega_{d}p_{d}(x,\mathbf{y}), \tag{3}\]  
where \(\omega_{d}\) is from the edge-degree distribution [20].  
In discrete density evolution, a relevant-information-preserving clustering is crucial to inhibit the exponential growth of the possible input combinations. Thus, \(p_{d}(x,t)\) instead of \(p_{d}(x,\mathbf{y})\) has to be tracked in _discrete_ density evolution. We define the average distribution \(p_{d}(x,t)\) as  
\[p_{d}(x,t)=\sum_{d=2}^{\omega_{\max}}\omega_{d}p_{d}(x,t)=\sum_{d=2}^{\omega_{ \max}}\omega_{d}\sum_{\mathbf{y}\in\mathcal{Y}_{d}^{\mathrm{vec}}}p_{d}(t| \mathbf{y})p_{d}(x,\mathbf{y}), \tag{4}\]  
where \(\mathcal{Y}_{d}^{\mathrm{vec}}\) denotes the set of all possible combinations of \(\mathbf{y}\) for a node with degree \(d\).  
Please note that, assuming a trivial straightforward generalization of information bottleneck decoders to irregular LDPC codes, one would stop at this point. In this case, look-ups in node-degree-specific tables \(p_{d}(t|\mathbf{y})\) would replace the node operations, and only integer values cluster indices would be exchanged. However, in the next subsections, we demonstrate that such a design approach is not beneficial. Given these findings, we propose to include an additional integral step in the construction process.
## 3 Proposed Methodology
### Dataset Description  
This research used three datasets to construct a federated deep-learning system for scalable and privacy-preserving DDoS assault detection. These network traffic statistics include benign and DDoS assault situations and are commonly used in cybersecurity research. The IoT23 dataset records network traffic from IoT devices, covering both benign behaviour and threats [28]. This dataset is useful for studying IoT network traffic under normal and malicious settings. UNSW-NB15 [29] and CIC-DDOS2019 [30] are benchmark datasets that simulate current network traffic, including several attack methods, making them useful for analysing statistical and behavioural metrics. The IoT23 Network Attack Dataset focuses on real-time traffic analysis to identify abnormalities like DDoS assaults and offers a diversity of network traffic patterns. Multi-label DDoS detection using these datasets may identify many assault types in distributed systems. Table 2 provides feature explanations for each dataset. The number in the step 3 of figure shows the work flow sequence of analyzing the data.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**SNo.** & **IoT23 Features** & **UNSW-NB15 Features** & **IoT23 Network Attack Dataset Features** \\ \hline \multirow{7}{*}{1} & - & Includes network flow details such as source and destination IPs, ports, and protocols. & - & Statistical features representing traffic metrics like packet count, byte count, and flow duration. & - & Packet-level details capturing anomalies in network traffic, including timestamps, packet size, and protocol. \\  & - & Covers attack types such as Mirai botnet, DDoS, and benign IoT traffic patterns. & - & Time-based features like start time, and connection duration. & - & Includes labelled data for DDoS attacks and benign traffic scenarios. \\  & - & Offers insights into IoT-specific behaviours, both benign and malicious. & - & Behavioral metrics, including TCP flags, retransmissions, and malformed packets. & - & Designed for anomaly detection, with features supporting real-time traffic analysis. \\ \hline \hline \end{tabular}
\end{table}
Table 2: Dataset features overview.  
Figure 1: Proposed FL-based DDoS attack detection framework.
# Improving the Manipulability of a Redundant Arm Using Decoupled Hybrid Visual Servoing
## 2 Methodology
### Image-Based Visual Servoing (IBVS)  
In the IBVS method, the feedback from the image features will be directly used and the image-Jacobian (Interaction matrix \(\mathbf{L}_{i}\)) would be used to relate the pixel velocity to the camera velocity [29]. The Interaction matrix for the \(i\)th feature would be defined as follows [29]:  
\[\mathbf{L}_{i}=\left[\begin{array}{cccc}\frac{f}{Z}&0&-\frac{u}{Z}&-\frac{ uv}{f}&\frac{f^{2}+u^{2}}{f}&-v\\ 0&\frac{f}{Z}&-\frac{v}{Z}&-\frac{f^{2}+v^{2}}{f}&\frac{uv}{f}&u\end{array}\right] \tag{1}\]  
where \(f\) denotes the focal length of the camera and \(\mathbf{s}=(u,v)\) denotes the coordinates of a point in the image plane. Lets consider \(\mathbf{e}_{i}\) is difference between current and desired positions of each feature in the image plane, and \(\mathbf{v}_{cam}=(\mathbf{v}_{c},\mathbf{w}_{c})\) is the camera velocity vector (where \(\mathbf{v}_{c}=(v_{x},v_{y},v_{z})\) is camera linear velocity vector, and \(\mathbf{w}_{c}=(w_{x},w_{y},w_{z})\)) is its angular velocity vector. The exponential decoupled decrease of the error can be obtained when the Interaction matrix at the desired pose is not singular (i.e., \(\mathbf{s}_{i}(t)-\mathbf{s}_{id}(t)=\mathbf{e}_{i}(t)=0\)). Therefore, the appropriate camera velocity vector will be determined using the following control law [29]:  
\[\left[\begin{array}{c}\mathbf{v}_{c}\\ \mathbf{w}_{c}\end{array}\right]=-k_{i}\mathbf{L}_{i}^{+}\mathbf{e}_{i} \tag{2}\]  
where \(k_{i}\) represents a positive proportional gain and \(\mathbf{L}_{i}^{+}\) represents the pseudo-inverse of \(\mathbf{L}_{i}\).  
Figure 2: Schematic diagram of problem domains in visual servoing. The proposed DHVS method reduces the image and robot singularity configurations, and keeps the robot away from the joint limits. In addition, it is less likely to lose the object from the camera FOV, and it is more robust to the camera calibrations than PBVS method (It generates controllable trajectories with a higher amount of robot Jacobian, resulting in better manipulability).
# The Complex Relationship between Virulence and Antibiotic Resistance
## 4 Regulation of Virulence and Antibiotic Resistance
### Environmental Flux Sensing  
A central principle in effective regulation of both antibiotic resistance and virulence is the ability to sense and respond to the bacteria's external environment, specifically, the presence and concentration of ambient antibiotic. The vancomycin-responsive, histidine kinase in _Streptomyces coelicolor_ or the LiaRS cell envelope damage sensing system of _B. subtilis_ exemplifies the importance of this ability [161]. The system in _B. subtilis_ is regulated by the two component system BceRS and its associated ABC-transporter BceAB [161]. Over 200 Bce-like systems have been identified [162]. Transcriptome profiling, which predicts expression patterns of pathogens in the presence of antimicrobial agents, recently identified virulence and resistance mechanisms of _S. aureus_ in the presence of increasing concentrations of ramoplanin. Ramoplanin resistant strains of _S. aureus_ acquired resistance to multiple antibiotics and with corresponding increased resistance regulation of biotin synthesis, the citric acid cycle, riboflavin biosynthesis, peptidoglycan biosynthesis, folic acid biosynthesis, heme biosynthesis and amino acid metabolism [163]. The disruption of metabolic processes then indirectly affects virulence factors such as biofilm formation. Alternatively, the presence of antibiotics can alter regulation of pneumococcal bacteriocins production [164].