esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology
### Model Training and Evaluation  
We also compared training time, resource consumption, and deployment applicability in order to assess the practicality of the two models. Model training for traditional machine learning models, Naive Bayes, Logistic Regression, SVM, and Random Forest, took an insignificant time to train (approximately 8 to 25 min) and the models were trained on the CPU with a few thousand trainable parameters, leading to the wide utility of these models for low-resource or real-time applications with compromised accuracy. CNN and Bi-LSTM-based deep learning models trained on a single NVIDIA A100 GPU required 2 to 3 h of training, and the models had around 250 K to 400 K parameters, which is a good compromise between accuracy and deployment efficiency on edge devices. Transformer-based models, in particular M-BERT, required about 24 h of training on a pair of A100 GPUs with about 110 M parameters, suggesting high accuracy at the expense of resource usage. On the contrary, the LoRA-optimized LLaMA 2 model took approximately 20 h to be trained on dual A100 GPUs but fine-tuned only 8 to 10 million parameters because LoRA is an adapter-based method. It provided a drastic reduction in computational burden (while achieving the best performance) and was a practical and scalable solution suitable for real-world implementations in cloud or HPC systems.
## 3 Proposed Design
### Problem Formulation Using Traffic Patterns
By analyzing the packet transmission process of IEEE 802.15.4, it is noted that CFP and CAP provide inefficient channel utilization for the patient monitoring biomedical sensor devices, especially when the duty cycle mechanism is used [31]. Thus, using equations in Section 3.1 we have DNS after 4 ms including header = 2 (data) + 9 (minimum MAC layer header) + 6 (physical layer header) = 17 bytes. Further, in patient monitoring systems the traffic pattern of sensor nodes is periodic, which means these nodes generate many packets continuously in a limited time. Due to such a traffic pattern, usually a node has multiple packets in queue for transmission; whereas if it gets a successful channel access it can transmit one packet. Therefore, on getting a channel access, a transmission node is under utilizing the available maximum packet size capacity which is 127 bytes for a MAC frame and only transmits up to 17 bytes shown in Figure 1. When the payload size is small, it faces a larger overhead, especially due to pre-defined overheads of the channel access mechanism. Figure 3: Overheads in successful packet transmission [3]. Figure 2: Frame structure of IEEE 802.15.4 [2].
## 2 Materials and Methods
### Data Collection
#### 2.3.1 Condition Assessment  
Data on the condition of the irrigation infrastructure at the selected Agri-Parks were captured and processed using a Condition Assessment Model developed by ARC-NRE/AE (funded by the Gauteng Department of Agriculture, Rural Development and Environment). Data collection involved visual observations and scoring of the visual indicators. The data collection was performed by ARC-NRE/AE engineers. Data collection involved scoring of visual observations of the subcomponents; however, the analysis outcomes generated automatically by the model were at the component level. The model was developed following an Analytical Hierarchical Approach, where irrigators, irrigation equipment suppliers, and experts from the irrigation field were interviewed to help with weighting of factors and subfactors that affect the condition of the irrigation infrastructure. Therefore, the resultant condition index for each component factored in the relative contribution of each subfactor and factor to the condition of a subcomponent. The resultant condition indices for the components were interpreted following Table 1, which was adopted from [23].  
Figure 2: Schematic layout of an irrigation block at each Agri-Park. Dots on the drip lines are the emitter positions along the lateral lines.
# Full-Scale Measurements of Wind Characteristics on a High- Rise Building during Typhoon Sarika
## 3 Wind Characteristics of Typhoon Sarika
### Turbulence Intensity and Integral Scale
#### 3.3.1 Turbulence Intensity
Figure 7c shows that the ratios of the standard deviations between the turbulence components (\(I_{u}\):\(I_{v}\)) obtained by the anemometers are relatively stable when the 10-min mean wind speed is higher than 10 m/s, and the averaged value of ratio between turbulence components (\(I_{u}\):\(I_{v}\)) is 1.34 during the passage of Typhoon Sarika.
# Experimental Evolution of Magnetic Nanoparticle Resistance in _Escherichia coli_
## 2 Materials and Methods
### Determination of Minimal Inhibitory Concentration (MIC) of Magnetic NPs  
Minimum inhibitory concentration (MIC) values were assessed by measuring the growth rates of _E. coli_ K-12 MG1655 by estimating their optical density at 625 nm (OD\({}_{625}\)). OD\({}_{625}\) is an absorbance measurement at a wavelength of 625 nm in an accuSkan Go spectrophotometer (Fisher Scientific, USA). MIC is defined as the lowest concentration of an antimicrobial that will inhibit the visible growth by \(\sim\)90% after 24 h of incubation at 37 \({}^{\circ}\)C compared with a growth control [38]. These values were determined by broth dilutions in a 96-well microtiter plate format consisting of ten concentrations (0-5000 mg/L) in triplicates of magnetite NPs. The plates were incubated at 37 \({}^{\circ}\)C and agitated at 600 rpm in an I2500 Series incubator shaker (New Brunswick Scientific, Enfield, CT, USA). The OD\({}_{625}\) was measured after incubation at 37 \({}^{\circ}\)C for 24 h. The MIC was determined to be 1000.0 mg/L. A sub-MIC value (750 mg/L) was chosen to initiate the selection experiment. This value allowed for the initial growth of cultures without causing their extinction.
## 2 Materials and Methods
### Sound Event Detection Algorithm Design
#### 2.6.3 Performance Evaluation  
The algorithm's performance is evaluated using labeled data sets and performance metrics. Standard metrics include precision, recall, F1-score, and ROC-AUC curve. Additionally, confusion matrices played a crucial role in identifying specific areas where the algorithm tends to confuse categories. For example, false positive and negative analyses revealed that certain sounds, such as \"Voices\" and \"Ring\", are often confused with \"Screams\". These findings allow us to identify improvement areas and adapt our approach to reduce errors [34]. Cross-validation was used to evaluate model performance on unseen data, mitigating the risk of overfitting. After the initial evaluation, optimization iterations are carried out. This involves fine-tuning the model architecture, modifying specific hyperparameters, and applying regularization techniques. A notable aspect is the execution time of the algorithm, which is especially critical for real-time applications.  
It is important to note that some data sets' classes of sound events were unbalanced. To address this challenge, class reweighting, data augmentation, and detection-threshold tuning techniques were applied, thereby improving the detection of minority classes, and reducing biases.
# The Internet of Drones: Requirements, Taxonomy, Recent Advances, and Challenges of Research Trends
## 4 Taxonomy of IoD
### Applications
#### 4.5.1 Smart Agriculture  
IoD based smart agriculture is being pursued to satisfy the growing demand emerging from an environmental change with an ever-increasing population and limitation to natural resources. In general, due to over-reliance on machinery, many farmers have left behind farming jobs leading to a shortage of workers for agriculture industries.  
Innovations such as IoD, cloud, and IoT will help them monitor their fields in real-time to make an efficient decision. In particular, adopting the IoD and wireless sensor network for smart agriculture will add more value and efficiency in monitoring and growing crops for better yields. Furthermore, IoD eases the process by aerial monitoring and smart spraying in crop affected areas [49]. In the era of precision agriculture, remote sensing with IoD and GPS technologies further helps increase the various types of detection in the farming sector, such as drought stress, nutrient status, and weed detection [50].
# Real-Time Seismic Data from the Bottom Sea
## 2 Standalone Broadband Ocean Bottom System
### The Mooring and Inductive Communication System
#### 2.3.2 Numerical Simulations  
The simulations were performed with OrcaFlex 9.3c [13], which is a marine dynamics software implemented by Orcina for the static and dynamic analysis of several offshore systems. OrcaFlex facilitates a fast and accurate analysis of different kind of cables under wave loads and currents and externally forced movements. It is a 3D software of finite elements in a non-linear time domain capable of arbitrarily handling large deflections from the initial condition. OrcaFlex models use orthogonal coordinates with the origin at the sea surface (in the absence of current, wind, and waves). Axis \(x\) and \(y\) are horizontal and \(z\) vertical (negative downwards). Deep sea water (300 m) is considered for the two models (see Table 1). The analyses have been performed for normal and extreme sea conditions (see Table 2). The normal case considers normal sea conditions while the extreme case considers very bad and unusual weather conditions. In order to consider the worst scenario, waves, wind, and current are imposed to have the same direction and sense (\(x\) positive axis) [14].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & & **Model 1** & **Model 2** \\ \hline \multirow{2}{*}{**Line 1**} & type & steel cable & elastomer \\  & length (m) & 120/140/160 & 17/24/28 \\ \multirow{2}{*}{**Line 2**} & type & steel cable & steel cable \\  & length (m) & 200 & 280 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Lines configuration for the different models.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Property**} & \multicolumn{3}{c}{**METEO**} & \multirow{2}{*}{**Units**} \\ \cline{3-4} \cline{5-5}  & & **Normal** & & **Extrem** \\ \hline \multirow{2}{*}{Wave data} & Hs & 1 & 5.7 & m \\  & T\({}_{\mathrm{z}}\) & 3.73 & 8.61 & s \\ \multirow{2}{*}{Wind velocity} & & 3 & 20 & m/s \\  & on surface & 0.20 & 1.20 & m/s \\ \multirow{2}{*}{Current velocity} & at 100 m depth & 0.053 & 0.31 & m/s \\  & at 200 m depth & 0.028 & 0.17 & m/s \\ \multirow{2}{*}{} & on the seabed & 0.022 & 0.14 & m/s \\ \hline \hline \end{tabular}
\end{table}
Table 2: Meteorological conditions tested. A JONSWAP wave type is used. Hs (m) is the significant wave height; T\({}_{\mathrm{z}}\) (s) is the wave period.  
Figure 6: Schematic vertical view of the models.  
Dynamic simulations have been done using an implicit integration method with a fixed step size. The segment length of Line 2 is 1 m whereas in Line 1 it is 0.1 m and 0.2 m. The first segment on the top needs to be smaller when a big ratio of elastomer is considered with extreme conditions. To study temporal behaviour, simulations have been done lasting a long enough time to overpass the transient. With the transient being 600 s, simulations have been run lasting 20,000 s to ensure the overpass. A step size of 0.1 s has been established, sufficient for the proposals of the present work, small enough to generate correct results.
# Birds Detection in Natural Scenes Based on Improved Faster RCNN
## 3 Materials and Methods
### K-Means Clustering Algorithm  
In Anchor-based object detection algorithm, the Anchor is generally designed manually. For example, in SSD and Faster RCNN, nine anchors with different sizes and aspect to height ratios were designed. However, one drawback of manually designed anchors is that there is no guarantee that they will be a good fit for the dataset. If the size of the anchor and the size of the object are significantly different, the detection effect of the model will be affected. [PERSON] [30] suggested using K-means clustering to replace manual design. By clustering the bounding boxes in the training set, a group of anchors which are more suitable for the data set can be generated automatically.  
K-means clustering is a method of vector quantization, originally from signal processing, that aims to partition n observations into k clusters, in which each observation belongs to the cluster with the nearest mean (cluster centers or cluster centroid), serving as a prototype of the cluster. This results in a partitioning of the data space into Voronoi cells. K-means clustering minimizes within-cluster variances (squared Euclidean distances), but not regular Euclidean distances, which would be the more difficult Weber problem: the mean optimizes squared errors, whereas only the geometric median minimizes Euclidean distances. For instance, better Euclidean solutions can be found using k-medians and k-medoids. K-means clustering algorithm is a typical distance-based clustering algorithm [31]. It uses distance as the evaluation index of similarity. It says that the closer two objects are, the more similar they are.  
The bounding box is represented by the vertices in the upper left corner and the vertices in the lower right corner, namely, \((x_{1},y_{1},x_{2},y_{2})\). When clustering the Box, we only need the width and height of the Box as features. In addition, since the size of the pictures in the data set may be different, the width and height of the picture should be used to normalize the width and height of the box, as shown in Formula (1):  
\[\left\{\begin{array}{l}w=\frac{x_{2}-x_{1}}{w_{img}}\\ h=\frac{y_{2}-y_{1}}{h_{img}}\end{array}\right. \tag{1}\]  
where \(w_{img}\) is the true width of the image in the dataset, \(h_{img}\) is the true height of the image in the dataset, w is the normalized width of the image in the dataset and h is the normalized height of the image in the dataset.  
If the Euclidean distance in standard K-means is directly used as a measure, larger box clusters will generate larger squared errors than small box clusters. Since we only care about the IoU of the anchor and box and do not care about the size of the box, it is therefore more appropriate to use IoU as the measure.
## 3 Preliminaries
### Physical Unclonable Function  
We utilize the Physical Unclonable Function (PUF) to assist in securely storing secret parameters within the RSU. PUF is a hardware implementation circuit that utilizes chip characteristics to achieve unique, non-replicable, instance-specific functions [23]. As PUFs are generated during the chip manufacturing process, incorporating multiple random parameters, this guarantees its randomness and uncertainty. It establishes a unique relationship between the challenge \(\mathcal{C}\) and response \(\mathcal{R}\) signals by utilizing process parameter deviations in chip manufacturing, expressed as \(\mathcal{R}=\text{PUF}(\mathcal{C})\), and can be regarded as a one-way function. In simpler terms, each integrated circuit has a distinct PUF, which means that no two circuits produce the same response (\(\mathcal{R}\)) for the same challenge (\(\mathcal{C}\)). This unique combination of challenge and response is referred to as a challenge-response pair.  
Presently, PUF has gained significant usage in delivering essential security services, particularly authentication and key generation, within constrained environments like the Internet of Things (IoT). These contexts often demand a delicate equilibrium between power consumption and security. In numerous applications, PUF primarily serves the purpose of authenticating IoT devices and securely storing cryptographic keys. This is mainly because PUF has the following characteristics:  
* Each physical device can provide a unique challenge-response pair, even when the same input challenge bits are used [24];
* Any attempt to alter the behavior of the device will impact the PUF outcomes and render the PUF inoperable [25];
* PUF can be regarded as a one-way function, which greatly guarantees safety features [26].
# Dynamics Investigation on Axial-Groove Gas Bearing-Rotor System with Rod-Fastened Structure
## 4 Results and Discussions
### Bifurcation Characteristics  
The bifurcation diagram depicts the projections of orbit of the journal center in the \(y\) direction, i.e., the \(y_{1}\) of the vector \(\mathbf{u}=[x_{1},\,y_{1},\,x_{2},\,y_{2},\,x_{41},\,y_{41},\,x_{42},\,y_{42}]^{T}\) in Equation (14). When the rotating speed is at \(\omega=900\)-\(2500\) rad/s, the bifurcation behavior of the rod-fastened rotor at the left bearing station are shown in Figure 5. The rod-fastened rotor system experiences quasiperiodic motion at \(\omega=900\)-\(1050\) rad/s, and inversely bifurcates to periodic motion at \(\omega=1100\) rad/s, and then maintains the stable motion state until \(\omega=1750\) rad/s. When the rotating speed increases to \(1775\) rad/s, the response of the rod-fastened rotor-gas bearing system bifurcates to quasiperiodic motion again, and the amplitude of quasiperiodic motion increases with the amplification of the rotating speed. From the bifurcation diagram, the rod-fastened rotor-gas bearing system maintains a relatively stable operation at \(\omega=1100\)-\(1750\) rad/s.
# Multi-Tier Heterogeneous Beam Management for Future Indoor FSO Networks
## 2 Background and Motivation
### System View  
Figure 1 shows our system vision where multiple user devices (UDs) receive and transmit data to overhead multi-element access points (APs) using a combination of low GHz/microwave transmission from RFSCs, mmWave directional communications, and narrow electronically steered laser sources. The mmWave APs create multiple coverage zones within the broader coverage of the RFSC. The indoor FSO links may be co-located with mmWave APs or configured as sub-zones in the potential coverage range of a mmWave AP. Each wireless technology has unique resolution and range that impacts the corresponding instantaneous and potential coverage (defined in Figures 1 and 2). Along with steering velocity, these characteristics define the time needed to scan a coverage zone. High resolution is ideal in terms of area spectral efficiency; however, resolution is inversely related to the scan time. The multi-tiered design allows for multiple levels of coverage--addressing the indoor FSO signal acquisition through beam refinement as well as providing various access tiers for UDs with different mobility traits. The radio resource control (RRC) manages the various beam configurations and resource allocation in order to bridge between the network and medium access control (MAC) layers.
# E-MQTT: End-to-End Synchronous and Asynchronous Communication Mechanisms in MQTT Protocol
## 2 Related Works
### Mqtt  
MQTT version 3.1.1 [1] encompasses a comprehensive suite of 14 packets, offering support for three distinct QoS levels to ensure reliability. MQTT provides three QoS levels spanning from 0 to 2. These QoS levels effectively dictate the precision with which messages are relayed in communication scenarios involving the publisher and the broker, as well as between the broker and the subscriber. With the ascending QoS level, MQTT extends its support for increasingly precise transmission semantics [52]. At QoS level 0, a published message possesses the potential to be delivered to the receiver (either the broker or the subscriber) at most once. QoS level 1, on the other hand, guarantees that a given message is delivered at least once. Lastly, QoS level 2 is structured to ensure that a message is delivered exactly once via a four-way handshaking process.  
The most recent MQTT version 5.0 [53] added twenty items of new features including user properties, shared subscriptions, payload format description, request-response pattern, topic alias, enhanced authentication, and flow control. Among them, the support of request-response patterns reflects the need for another communication model in the publish-subscribe model. This new feature facilitates request-response communication between the publisher and the subscriber by exchanging information such as response topic and correlation data during the connection and publishing stages. However, this feature still requires two separate publish-subscribe sessions, which is the main cause of communication overhead.
# Machine-Learning-Based Side-Channel Evaluation of Elliptic-Curve Cryptographic FPGA Processor
## 6 Results and Discussion
### Analysis Phase 1--Accuracy without Pre-Processing  
In the first part of phase-1 analysis, the classification accuracy is calculated on the raw-signal feature data set. It is observed that RF gives an accuracy of 79% for LB4. For NB, SVM and MLP, the accuracy is even lower i.e., 52%, 55% and 71%. For LB2 and LB3, the accuracy is less than LB4, as shown in Table 2. These results clearly show that the data cannot be correctly classified due to the large number of features in the dataset.  
In the second part of phase-1 analysis, the classification accuracy is calculated on the proposed processed feature datasets without any pre-processing (i.e., feature selector/extractor) for all three levels of attack (LB2-LB4), as given in Figure 9. Models are trained and tested using the four classifiers SVM, RF, NB, and MLP. It can be seen that, without the pre-processing step, SVM does not perform well for any level of bit classification. However, for the fourth-bit classification, RF gives an accuracy of approximately 90% while NB and MLP give an accuracy of 85% and 88%, respectively. RF and NB perform well for the datasets in which the features are completely independent of each other. These results prove that the features in the signals feature datasets (for fourth-bit location) are independent of each other.  
For bit 2 and bit 3 classification, the maximum accuracy achieved is 71-73% with RF. Both SVM and MLP perform poorly in these cases. The reason for MLP's low performance could be the feature dataset size. For neural-network algorithms, the training data should be huge, roughly a hundred times more than the number of features in each trace/row. It is worth exploring if MLP or any other neural network can behave better if the number of samples is increased for better training. This analysis is out of the scope of this paper and is a future prospect of this particular research.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithm** & **LB4** & **LB3** & **LB2** \\ \hline RF & 79.2\% & 56.9\% & 58.0\% \\ SVM & 55.4\% & 49.3\% & 45.7\% \\ MLP & 71.9\% & 58.7\% & 55.6\% \\ NB & 52.5\%s & 57.0\% & 55.7\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Accuracy for Raw sample analysis.
## 4 Results and Discussions
### Performance with SOC Percentage Equal to 100%.  
In this sub-section, the performance is analyzed during SOC percentage equals 100%. The characteristic waveforms of the load voltage, supply current, DC link voltage, SOC percentage of BESS, PV power \((P_{PV})\), load power \((P_{L})\), wind power \((P_{WT})\), battery power \((P_{butt})\) and power in dump load \((P_{d})\) is presented in Figure 11.  
It is observed that from t = 0.75 s to t = 0.77 s, the total sum of PV and wind turbine power is greater than load power, but the SOC percentage of BESS is lower than 100%. The BESS is charged with excess produced power, and it is charged fully at 0.77 s. The BESS cannot be overcharged, and, therefore, the dump load consumes surplus generated power until generated power equals load demand. In order to maintain the stability of the system during long periods of surplus power generation and less load demands, which results in top-up the BESS, a d  
Figure 11: Performance of the proposed system when SOC percentage is equal to 100%.colors blue, green red represents phase a, b, and c respectively.  
Figure 10: A comparative graphic showing the percentage THD analysis of the proposed system with DWPT.  
DC bus. The operation of the dump load is controlled using a solid-state self-commutated switch. The dump load absorbs power until 1 s. These results prove the robustness of the proposed technique.