esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Biological Assessment  
#### 2.5.1 Antioxidant Activity  
The antioxidant activity of red _Beta vulgaris_ L. aqueous extract and the prepared solutions of metallic/bimetallic nanoparticles was evaluated via DPPH\({}^{\bullet}\) assay [41]. A solution of DPPH\({}^{\bullet}\) (0.135 mM) was used as a positive control, methanol (80%) was used as a negative control, and ascorbic acid was used as a reference standard. The samples were prepared in a serial dilution using methanol (80%). DPPH\({}^{\bullet}\) (1 mL) was added to each tube and then incubated for 30 min at room temperature. The absorbance of each tube was recorded at 517 nm of wavelength (\(\lambda\)). The exponential curves were plotted for the % DPPH\({}^{\bullet}\) remaining versus the sample concentration to estimate the values of IC\({}_{50}\) in mg/mL. The values of IC\({}_{50}\) were calculated to simplify the comparison between the results of the investigated samples, in which there was an inverse relationship between the antioxidant capacity of the sample with the IC\({}_{50}\) values [42]. The percentages of remaining DPPH\({}^{\bullet}\) were calculated from Equation (1):  
\[\text{Remaining DPPH}^{\bullet}\;(\%)=\left(\text{DPPH}^{\bullet}\right)_{ \text{T}}\;/\left(\text{DPPH}^{\bullet}\right)_{\text{T}=0}\;\times 100 \tag{1}\]  
The total antioxidant capacity was assessed via the phosphomolybdate method to investigate the antioxidant activity of _Beta vulgaris_ L. extract, and metallic/bimetallic nano-solutions. A modified procedure was applied, in which the results were expressed as mgascorbic acid equivalent per 100 g of dry sample [43]. A freshly prepared reagent (0.6 M sulfuric acid, 28 mM sodium phosphate, 4 mM ammonium molybate) was made. The standard reagent (3 mL) was mixed with 300 uL of the tested sample and incubated for 90 min at 95 \({}^{\circ}\)C. The intensity in the blue color was measured at \(\lambda\) = 695 nm. A negative control was prepared by mixing the standard reagent with 300 uL of distilled water instead of the tested sample [44].
# A Novel Battery-Supplied AFE EEG Circuit Capable of Muscle Movement Artifact Suppression
## 2 Related Works: EEG Circuit Design Analog Front-End Architectures
### Double Notch Filter Circuit Design of EEG Analog Front-Ends: Case \"3\"  
This subsection offers an elaborate examination of the structure of a high-performance EEG signal acquisition system. This device integrates a driven-right-leg feedback circuit, differential amplifier circuits, pre-amplification components, and multiple filters. The design incorporates both a pre-amplifier and a two-stage post-amplifier to efficiently amplify the signal. The circuit uses two low-pass filters and two 50 Hz notch filters in order to reduce the occurrence of noise interference present in the raw EEG signal. In the pre-amplification stage, an instrumentation amplifier is used to ensure low levels of noise, minimal changes over time, a high input impedance, and a high CMR. Moreover, the circuit incorporates a post-amplification stage that employs a high-precision chopper-stabilized operational amplifier. Aside from the post-amplification stage, additional filtration is implemented, namely two notch filters and a low-pass filter, to eradicate high-frequency noise resulting from differential interference [49]. A clarifying overview of this architecture is given in Figure 4 below.
# Automatic Detection of Tomato Diseases Using Deep Transfer Learning
## 4 Conclusions  
Tomato is an important mass-produced agricultural product that is susceptible to diseases and the consequent yield loss. The use of deep transfer learning and well-established models showed a great potential in many applications in the literature. In this work, we targeted the identification of tomato diseases from infected leaf images. Using leaf images as input, eleven deep learning models were customized and retrained to identify nine tomato diseases in addition to healthy plants. The models (i.e., DarkNet-53, DenseNet-201, GoogLeNet, Inceptionv3, MobileNetv2,ResNet-18, ResNet-50, and ResNet-101, ShuffleNet, SqueezeNet, and Xception) were compared in terms of six common metrics and training/validation times. Although all models performed well, the DenseNet-201 model produced the best results with values larger than 99% for all metrics. However, the SqueezeNet model trained the fastest, and had the shortest inference time (i.e., 0.50 milliseconds/image).  
The transfer learning approach carries inherent credibility and less complexity. In addition, it does not require explicit image processing nor feature extraction. Thus, it is suitable to be implemented in standalone smartphone applications, which can aid plant pathologists and farmers in quick and effective disease recognition and control. Future work will consider evolving the models by using incremental learning (i.e., improving the model during deployment). Moreover, the same approach can be adapted to identify diseases from tomato fruit images rather than the leaves. This may require 3D deep learning models to cover all sides of the image. In addition, other models or an ensemble of models can be used for solving the same problem. Field testing and commercial availability in the form of ready-to-download applications are promising areas of future activities.  
Conceptualization, M.F.; methodology, M.F. and E.F.; software, M.F. and E.F.; validation, M.F. and E.F.; formal analysis, M.F.; investigation, M.F., E.F. and N.K.; resources, M.F. and N.K.; data curation, M.F. and E.F.; writing--original draft preparation, M.F. and E.F.; writing--review and editing, M.F. and N.K.; supervision, M.F. and N.K.; project administration, M.F.; funding acquisition, M.F. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Support for the article processing fees was provided by Jordan University of Science and Technology, Deanship of Research project number 20210047.  
The authors declare no conflict of interest.
# Analyzing LLAMA3 Performance on Classification Task Using LoRA and QLoRA Techniques
###### Abstract  
Large language models (LLMs), consisting of billions and trillions of parameters, have demonstrated exceptional ability in natural language understanding (NLU) and natural language generation (NLG) tasks. Increases in their numbers of parameters and model sizes have resulted in better performance and accuracy. However, models with such enormous numbers of parameters incur significant computational costs and resources, making them challenging to fine tune and adapt to a specific downstream task. Several parameter-efficient fine-tuning (PEFT) techniques have been proposed to address this issue. This study demonstrates the improvement obtained over the base LLAMA3-8B model using two prominent PEFT techniques: LoRA and QLoRA. We use the sequence classification task of sentiment analysis to conduct the experiments. Additionally, we analyze the effects of hyperparameter adjustments (\(r\) and \(a\)) on the model's performance. We examine the tradeoff between efficiency and memory savings obtained using the quantized LoRA (QLoRA) technique. We also investigate and compare the performance changes of LoRA and QLoRA techniques obtained after adapting to attention layers (query, key, value, and project) to all the linear layers during fine tuning. We report the findings of our work along with limitations and future directions.  
LLaMA3; LLMs; LoRA; QLoRA; large language models; sequence classification; sentiment analysis; Low-Rank Adaption; Quantized Low-Rank Adapters +
Footnote †: journal: Applied sciences  
0000-0001-5000-0000-0000-0000-0000-0  
the ICL to produce poor results compared to those produced with fine tuning. Finally, the order examples and the wording and formatting of the prompts in the ICL can also have unpredictable impacts on the model's performance. Fine tuning overcomes these issues. Further training of these foundation models in the target dataset improves their performance, allowing them to adapt much better to the target domain, vocabulary, and task.  
Unlike pretraining, which uses a self-supervised approach, the fine-tuning model uses a supervised technique that requires a labeled dataset of the downstream task. Additionally, data from a generic domain are used for pretraining, whereas data from specific distributions are used for fine tuning or transfer learning. A fine-tuning technique improves the performance of base models in a specific downstream task. This helps to tailor and adapt to the language models to align with the target domain data. For instance, even if the base language model was not explicitly trained in the token or sequence classification domains, it can be fine tuned to adapt to and perform classification tasks, such as sentiment analysis, name entity recognition, and part-of-speech tagging. Fine tuning enables the model to learn and acquire domain-specific knowledge and vocabulary. It can add desirable behaviors and remove undesirable ones, improving the model's performance in the target task.
# An Efficient Task Autonomous Planning Method for Small Satellites
## 3 Methods
### Algorithm Design
#### 3.2.1 Encoding Method  
The genetic algorithm regards the solution of the problem as an individual or a population through the encoding process. We can get better individuals through the selection, crossover, and mutation process of the initial individual. The encoding forms are shown in Figure 5.  
\(M\) and \(N\) respectively represent the number of time windows and the number of tasks needed to be planned. An individual is a \(2\times M\) matrix composed of integers. The value on the gene bit represents the corresponding time window. The value on the task bit represents the task number corresponding to the time window on the current gene bit. A task may have several time windows that can be selected, that is, a task number may correspond to several time windows. The initial population is generated randomly. For example, if \(M\) equals 5, \(N\) equals 3, it means that the number of time windows is 5, and the number of tasks is 3. An individual \(G\) may be encoded like this:  
\[G=\ \left[\begin{array}{cc}1,\ 3,\ 5,\ 2,\ 4\\ 1,\ 1,\ 2,\ 3,\ 3\end{array}\right]\]  
We can see that task\({}_{1}\) corresponds to window\({}_{1}\) and window\({}_{3}\), which means that window\({}_{1}\) and window\({}_{3}\) are selected by task\({}_{1}\), window\({}_{5}\) is selected by task\({}_{2}\), and window\({}_{2}\) and window\({}_{4}\) are selected by task\({}_{3}\).
# Microalgal Biomass Generation via Electroflotation: A Cost-Effective Dewaring Technology
## 4 Conventional Dewatering Approaches in Microalgal Culture
### Filtration  
Filtration approach is used to separate microalgae, and water, either via physical, biological, or chemical methods, that allow the suspension to pass through specific filters, such as membranes, screens, or microstrainers. The pressure difference across the filters is important to move the fluids, driven by either pressure, gravity, centrifugal, vacuum, or magnetic filtration [55; 64]. There are numerous types of filtration systems, such as vacuum, centrifugal, gravity, floating, and multilayer systems. In recent times, membrane filtration has received ample attention due to its proven effectiveness in microalgae recoveries. The membrane pore sizes are generally classified as ultrafiltration for 0.02-2 \(\upmu\)m, microfiltration for 0.1-10 \(\upmu\)m and macrofiltration for more than 10 \(\upmu\)m pore sizes [2].  
Membrane filtration is suitable for dewatering and harvesting of fragile cells. It has the ability to dewater microalgae at a very low cell density [65], whereas microfiltration and ultrafiltration are effective at dewatering smaller algae [59]. The dewatering of microalgae can be achieved by applying these membranes to the tangential flow filtration system (TFF). The TFF, also known as cross-flow filtration, has a promising potential for microalgae harvesting, where the medium flows tangentially across the membrane with about 70-89% of microalgae biomass recovery [66]. Further, TFF was reported to concentrate _Tetraselmis suecia_ microalgae up to 148 times with only 2.06 kWh/m\({}^{3}\) energy consumption compared to flocculation which consumes up to 357 times more energy at 14.81 kWh/m\({}^{3}\)[66]. However, the benefit of a longer life span of the membrane in dewatering may be achieved if used under low imposed transmembrane pressure and low cross-flow velocity. Furthermore, fouling and membrane replacement also contributes to the high-cost consumption for large-scale recovery of microalgae, which is considered as the major limitation of filtration techniques [55].
# Complete Systematic Error Model of SSR for Sensor Registration in ATC Surveillance Networks
## 7 Results
### Basic Model  
In [6; 7] a simplified bias model is used for range and azimuth without bias in altitude. The biases used in this estimation are the range offset (\(\Delta\rho_{0}\)), the range gain (\(\alpha_{1}\)) and the azimuth offset (\(\Delta\theta_{0}\)). With these three parameters the majority of the error produced by the biases is corrected. With these models, the deviation of the bias errors \(\sigma_{b}\) is reduced up to 286.60 m obtained with Equation (73). In Figure 17a,b two illustrative trajectories are represented. The asterisks mark the measured position  
Figure 16: Real scenario with 227 tracks measured by two radars.  
and the circles mark the corrected position. Both trajectories are allocated in the bottom left corner of the scenario and the raw plots of radar 2 are displaced in azimuth in counter-clockwise direction. The algorithm corrects the plots and displaces the plots towards the correct azimuth. In Figure 17c several trajectories placed in top-right corner (low density traffic area) are represented. Raw plots are aligned and the corrected plots are displaced clockwise. In this scenario is easy to see that the simple model is not good enough to estimate and align the plots of the radars for the whole radar coverage. In this scenario, the trajectories of the Figure 17a,b are separated almost \(180^{\circ}\) in azimuth from trajectories of the Figure 17c. The azimuth dependence of the biases is clearly shown in these figures. This dependence is included in the complete azimuth bias model of Equation (34).
# Sensing and Device Neighborhood-Based Slot Assignment Approach for the Internet of Things
## 5 Simulation Results
### End-to-End Delay QoS Metric  
End-to-end delay is assumed as a vital performance evaluation metric and is described as the total time taken by a packet transmitted from a sender to reach the destination module in the IoT network. Generally, this time is directly proportional to the number of slots, i.e., generated slots for communication, and active devices in the vicinity of a particular server module. As the proposed approach uses server's neighborhood information to develop a more informed slot development strategy, i.e., the TDMA, than the existing  
Figure 5: Evaluation of the proposed and existing methodologies in terms of throughput.  
Figure 6: Evaluation of the proposed and existing methodologies in terms of APDR.  
schemes, this ultimately resulted in a minimum of possible time slots. However, these slots are enough to accommodate all active member devices, preferably those residing in the vicinity of server module, simultaneously. In Figure 7, it is clearly visible that that the proposed scheme successfully achieved the minimum possible delay ratio over the available state-of-the-art approaches in terms of different IoT networks.
## 2 Literature Review
### Related Work
#### 2.3.6 SDN Models  
Recently, Software-Defined Networking (SDN) has gained popularity as a way to overcome traditional system limitations by controlling the network through one sophisticated piece of software. The SDN system will simplify network management and improve programmability by separating control and data planes. Furthermore, the recommended strategy would provide SDN protection that is more precise, efficient, and scalable than current techniques, thereby overcoming the limitations of current techniques. Many businesses use network detection algorithms to identify and organize harmful traffic. However, it is difficult to identify these algorithms in unbalanced datasets [42]. [PERSON] et al. [43] developed a method for detecting network threats in an SDN-Internet of Things environment by using an IDS model-based LSTM algorithm. The algorithm employs T-SNE-based learning, which is derived from DL models over hidden layers. Based on the SDN-IoT dataset, LSTM models achieved an accuracy rate of 97.1%. However, real-time attack detection requires a large number of labeled data. [PERSON] and [PERSON] [44] proposed methods for detecting and identifying SDN attacks as part of the attack detection and identification techniques. The following ML models were employed to detect attacks: DT, RF, and XGBoost. An accuracy rate of 95% was achieved on the NSL-KD dataset. Only one dataset was used, which may need to be updated to include current attacks. [PERSON] et al. [45] demonstrated that deep learning-enabled, SDN-enabled approaches can enhance the effectiveness, scaling, and cost-effectiveness of threat detection within IoT environments. The hybrid model was trained and tested by using the Cu-BLSTM and Cu-DNNGRU classifiers, which achieved 99.87% accuracy. Even though the proposed model was tested on a limited dataset, it has not yet been tested on datasets with different characteristics. For the detection and mitigation of MR-DDoS attacks in an SDN environment, six ML algorithms were employed: RT, REP Tree, SVM, J48, and MLP. As outlined by [PERSON] et al. [46], their method utilizes a modular architecture that is very flexible. MLP has an accuracy rate of 95%. Tests and evaluations were conducted on (CIC) DoS datasets.
## 5 Results and Analysis
### Bit Error Rate and Signal-to-Noise Ratio  
A network link can be examined by evaluating its bit error rate (BER) and signal-to-noise ratio (SNR). For a simple Gaussian noise channel model, a propagated signal attains the noise from the medium through which signal is penetrating and, at the receiver end, the ratio of the original to altered bits is the key indicator of the quality of communication. The SNR for the wireless link in the underground mine is computed as the function of distance, and the expression is given in Equation (42) [38]  
\[SNR(z)=P_{T}+G_{T}+G_{R}-\overline{PL}_{dB}-NP \tag{42}\]  
where \(\overline{PL}_{dB}\) is the distance-dependent propagation loss and \(NP\) is the noise. After computing the SNR for the mine's sub-gallery, the mine LOS segments are evaluated by calculating the BER. A data rate of 4800 bps is used to compute the BER by employing Binary Phase-Shift Keying (BPSK) modulation. Please note that the parameters in Equation (42) are different for each LOS classified link, including path loss and fading. The mathematical equation to calculate BER from SNR using BPSK is stated below in Equation (43)  
\[BER_{BPSK}=Q\Bigg{(}\sqrt{2\frac{E_{b}}{N_{o}}}\Bigg{)}\qquad;\qquad Q(z)=\frac {1}{2\pi}\int_{x}^{\infty}e^{-\frac{y^{2}}{2}}dy \tag{43}\]  
Figure 10 shows the comparative analysis in terms of LOS segments. The SNR is computed using Equation (42), and it can be seen that a distance-dependent path-loss factor exists in SNR expression. The path loss is computed distinctly for each LOS link including fading. Therefore, it is clear that high link distance gives low SNR value, and for low SNR, the BER performance will be obtained with a high error value. It can also be observed that the NLOS link is running with the highest BER, whereas the LOS link is facing a low probability of error and the BER of PLOS is expected to be in the middle of both the links.  
Figure 9: (a) Refraction loss at operating frequency of 2.4 GHz and 5 GHz, (b) Roughness loss at 2.4 GHz and 5 GHz.
# Cross-View Outdoor Localization in Augmented Reality by Fusing Map and Satellite Data
## 4 Results
### Analysis of Model
This points to the model not optimally utilizing the ground-level image and instead using the knowledge of the camera mostly being aligned with the road direction to quickly obtain a good estimation. Figure 4: **Qualitative examples. ** Two success and two failure cases for the KITTI cross-area dataset using our best-performing model. The model obtains the query and reference OSM and satellite data. Here, the OSM data are uncropped and unrotated to show the data available before the preprocessing steps. The white arrow on the reference satellite image shows the starting pose; the black arrow shows the ending pose, and the blue arrow shows the ground-truth pose. The dots show intermediate steps going from white to black over the steps. Figure 5: Percent of samples in the cross-area test set within 1 m/degree for the left graph and 3 m/degree for the right graph over the steps. The best-performing model was used, which utilized OSM data and started at the minimum longitudinal value. In both graphs, the lateral and longitudinal recall rates increased over the steps, while the azimuth recall rate slightly decreased.
# A Deep-Learning-Based Fault Diagnosis Method of Industrial Bearings Using Multi-Source Information
## 4 Performance Evaluation
### Performance Evaluation in the Conflict Resolution of the Multi-Part Model
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Evidence** & \(m_{i}\) **(Normal Status)** & \(m_{i}\) **(Inner Ring Fault)** & \(m_{i}\) **(Outer Ring Fault)** \\ \hline Evidence 1 & \(9.9984777\times 10^{-1}\) & \(4.4219062\times 10^{-11}\) & \(1.5215934\times 10^{-4}\) \\ Evidence 2 & \(7.2511165\times 10^{-13}\) & \(9.8507023\times 10^{-1}\) & \(1.4929717\times 10^{-2}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The basic assignment function \(m_{i}\) of each evidence when there is a high degree of conflict. basic probability assignment \(m_{12}\) (MLMF-CR) is more in accord with the actual situation. In addition, according to the multi-local model conflict resolution strategy proposed in this paper, the final fusion diagnosis result is normal, and the diagnosis result is consistent with the actual running status of the bearing, which shows that the multi-part model conflict resolution strategy proposed in this paper is reasonable and effective. The classic D-S theory improved Yager D-S combination rule (DSNCR), and the improved Deng Yong conflict evidence combination method (ECACE) are used as comparison algorithms to fuse the local diagnostic decisions (basic probability assignment) generated by the two local models. The misjudgment rate of each comparison algorithm is shown in Figure 14. The results show that the misjudgment rate of the MLMF-CR algorithm is much lower than that of the three comparison algorithms. In addition, the operation efficiency of the fusion algorithm is considered comprehensively. The fusion time of D-S evidence theory, DSNCR algorithm, ECACE algorithm, and MLMF-CR algorithm is 0.0026 s, 0.000717 s, 0.109 s, and 0.271 s, respectively. Compared with the other three fusion algorithms, the MLMF-CR algorithm does not have an advantage in terms of fusion time. This is because the decision conflict resolution strategy not only considers the statistical characteristics of the data sample itself but also comprehensively  
Figure 14: Misjudgment rate of each fusion algorithm. Figure 13: Comparison of basic probability assignment before and after assigning trust degree. considers and analyzes the difference in sensitivity of bearing faults in the actual operating environment, resulting in relatively high computational costs.