esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Validation Results
#### 3.2.2 In Vivo Dataset  
Since the true SO\({}_{2}\) values for the in vivo dataset are unknown, the performance of the proposed algorithm was validated by its ability to yield differences in the SO\({}_{2}\) predictions for the at-rest and the occlusion states that are statistically significant. Figure 8 shows the resulting mean-predicted SO\({}_{2}\) values and one-standard-deviation error bars for these two states over all numbers of selected wavelengths. For all but the \(k=3\) experiment in the ideal case, the separation between the mean values for the at-rest and occlusion states is statistically significant, as determined via a two-tailed independent sample \(t\)-test (MATLAB's _ttest2_ function) with a confidence level of 95%. However, there is noticeable fluctuation in the mean values for \(k<20\).  
Table 4 lists the time-averaged and student-averaged SO\({}_{2}\) estimates for the at-rest and occlusion states, for both the ideal and the convolved cases, and compares these values with values from the recent literature. The SO\({}_{2}\) estimates from this study are thus consistent with those of other studies for both the at-rest and occlusion conditions. However, we do note that there is considerable discrepancy between the ideal and convolved cases, with the convolved case yielding consistently lower at-rest SO\({}_{2}\) values and higher occluded SO\({}_{2}\) values. This discrepancy between the two cases is inconsistent with the findings with the Monte Carlo dataset discussed in the previous section. Without additional physiological data such as the true SO\({}_{2}\) values and epidermal thicknesses, it is not possible to determine the exact cause of this discrepancy.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Ideal**} & \multicolumn{2}{c}{**Convolved**} \\ \hline
**k** & **At Rest** & **Occlusion** & **At Rest** & **Occlusion** \\ \hline
3 & 92.59 \(\pm\) 1.94\% & 92.53 \(\pm\) 1.96\% & 87.28 \(\pm\) 2.17\% & 84.20 \(\pm\) 2.82\% \\
5 & 81.06 \(\pm\) 5.60\% & 8.71 \(\pm\) 4.63\% & 68.27 \(\pm\) 2.86\% & 13.64 \(\pm\) 3.93\% \\
7 & 69.03 \(\pm\) 4.57\% & 13.37 \(\pm\) 5.64\% & 65.86 \(\pm\) 3.37\% & 3.64 \(\pm\) 2.49\% \\
11 & 91.14 \(\pm\) 3.34\% & 19.24 \(\pm\) 5.36\% & 73.51 \(\pm\) 3.15\% & 7.67 \(\pm\) 3.66\% \\
15 & 72.35 \(\pm\) 5.34\% & 7.16 \(\pm\) 3.90\% & 75.84 \(\pm\) 2.16\% & 27.45 \(\pm\) 3.01\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Time-averaged and student-averaged mean and standard deviation of SO\({}_{2}\) estimates for the at-rest and occlusion conditions in this study (top) compared with values from recent literature (bottom).  
Figure 8: Mean prediction values and one-standard-deviation error bars for the in vivo dataset for the different numbers of selected wavelengths. Values for the subjects in the at-rest state are shown in blue and for the occlusion state in red. Results are given for (**a**) the ideal case and (**b**) the convolved case.
# On the Use of Transformer-Based Models for Intent Detection Using Clustering Algorithms
## 3 Materials and Methods
### Sentence Embeddings  
Both classical embedding methods, such as Word2 Vec, and recent transformer-based models operate at the word or sub-word level, meaning that the output representations are for words or sub-words and not sentences. To attain a fixed representation for sentences, a usual approach is to take the word embeddings' mean (mean-pooling) [33; 34; 35]. However, maximum (max-pooling) and minimum (min-pooling) pooling are also possible. Hence, the three pooling approaches were examined. The concatenation between the three pooling methods was also evaluated.  
Although transformer-based models can attain the best state-of-the-art results in numerous supervised NLP tasks, it is unclear if they can deliver helpful out-of-the-box sentence embeddings. Recent studies on this topic concluded that the [CLS] embeddings, provided by a pre-trained BERT model, do not offer optimal embeddings for similarity tasks. Such evidence points to the need for assessing the embeddings' quality for tasks that include clustering, ranking purposes, and information retrieval. In fact, averaging the token embeddings of a sentence also leads to poor performance on similarity tasks, with older embedding creation methods, such as GloVe, outperforming BERT.  
To address this issue, it was proposed to retrain a BERT model with a natural language inference dataset using a Siamese structure. This dataset requires the model to predict the truthfulness of a given hypothesis (neutral, entailment, or contradiction) based on a given premise [22]. Afterward, the model was fine-tuned by feeding pairs of sentences one by one in a Siamese fashion. Then, pooling was employed, and a SoftMax classifier was used on top.  
A Sentence Evaluation (SentEval) toolkit can evaluate the quality of sentence embeddings on a range of tasks, including sentiment analysis, paraphrase detection, and sentence similarity. It achieves this by taking embeddings generated by any model and using them as features to train a logistic regression classifier, which can then be used to assess the performance of the features. SBERT was evaluated by SentEval, reaching state-of-the-art performance and surpassing preceding standard sentence encoders, including InferSent and the Universal Sentence Encoder (USE) [36].  
Since SBERT and Sentence-RoBERTa (SRoBERTa) were shown to be capable of attaining state-of-the-art results, they were also examined in this work. Though both models were fine-tuned using domain-independent data, this work postulates that they would transfer properly for conversational clustering, outperforming their pre-trained versions.
## 5 Evaluation Results
### Results and Discussion
Figure 5: Reward comparison of different DRL agents: (a) DQN; (b) DDQN; and (c) DDPG-KRP. Figure 6: Performance comparison of different DRL agents for (a) bandwidth allocation. (b) PDR. The average delay is given by the average value for 1000 TTIs. Figure 8 compares the average delay values between the three DRL agents considering the complete system with all two services. The mathematical formulation of the average delay is not easy. Thus, the average delay is calculated based on a queue maintained in the BS, according to Equation (18). Figure 8 shows the better performance of the DDPG-KRP compared to the other algorithms. We have also applied a \(t\)-test to verify if the difference between the delay and throughput results of Figure 8 for each approach is zero. We have found that the test does reject the null hypothesis at the 1% significance level. Figure 9 depicts that for the cellular wireless network, the increase in the number of D2D pairs causes an increase in the energy efficiency once increased throughput due to the spectrum reuse. To achieve good EE performance, devices must be able to communicate with each other using as little power as possible. This is a challenge for D2D communication because it often requires more transmission power than traditional communication methods [10]. The EE metric considered in this paper is given by Equation (17). In Figure 9, the DDPG-KRP shows the best result in terms of the EE. Figure 8: Average latency for each agent considering the two services. Figure 7: Performance comparison of different DRL agents for throughput and average delay in packet transmission: (a) URLLC slice throughput and (b) video slice throughput. Figure 10 shows the spectral efficiency (SE) value when varying the signal transmission power of the D2D pairs from 0 to 1 W. The remaining parameters stay the same. The results of Figure 10 are obtained by considering all two services. The SE metric is the ratio of the throughput to the allocated bandwidth. Figure 10b shows that the SE of the D2D pairs increases with the increasing signal transmission power. Accordingly, the highest value is achieved by the DDPG-KRP. However, in Figure 10a, the transmission signal power of the D2D pair should be increased in a controlled manner because it interferes with the UEs and degrades their performance requirements. The allocation of inappropriate resources and transmission power to the D2D pair introduces excessive interference, which could impair UE communication.
## 5 Conclusions  
This paper proposes a novel approach for PQD detection and classification. At the first stage, the proposed algorithm takes advantage of VMD to decompose the obtained signal into a series of intrinsic mode functions. Subsequently, the statistical features, such as RMS, standard deviation, and multiscale moments, are extracted from each IMF. Subsequently, a two-stage heuristic feature selection algorithm is introduced to rank the dominant features based on permutation entropy and the Fisher score. Finally, the reconstructed feature vectors are imported into a multiclass SVM classifier for training to identify the different types of PQD signals. The artificial dataset of the PQD signals is utilized for the model training and developing in order to improve the robust of the PQD classifier. Subsequently, the trained model is tested on the real experimental setup. Several analysis methods, such as confusion matrix and receiver operating characteristic (ROC) curve, are imported and analyzed the performance of the trained classifier model. Afterwards, the trained model is tested on the real microgrid platform to verify the effectiveness of the proposed algorithm.  
The conclusions of this research are summarized, as follows. Signal preprocessing is obligatory at the first stage. The VMD process is beneficial for both signal de-noising and PQD detection. The permutation entropy analysis had the ability to identify useful IMFs that were obtained from VMD, which is vital in the feature selection process. By identifying the dominant features, the accuracy and the computational efficiency are both improved. Particularly, permutation entropy combined with the Fishe score showed better performance in ranking the relevant features. According to the real experimental data analysis, the proposed approach showed enough flexibility under different noisy environments, either for PQD detection sensitivity or specificity.  
**Author Contributions:** Each author contributed extensively to the preparation of this manuscript. [PERSON] and [PERSON] designed the experiment; [PERSON], [PERSON] and [PERSON] performed the experiments; [PERSON], [PERSON] and [PERSON] analyzed the data; and [PERSON] wrote the paper.  
**Funding:** This work is supported by the National Basic Research Program of China (973 Program) (Project Name: Comprehensive Control and Cooperative Optimization of Multi-energy Flow in Industrial Zone Park. No. 2017 YFA0700300), National Natural Science Foundation of China (Project Name: Research on fault recognition and diagnosis technology for wind turbine gearboxes based on product test data. No. 51275453), and National Natural Science Foundation of China (Project Name: Research on the Hierarchical Collaborative Control Method of the Dynamic Coupling Force/Displacement for the Compliant Macro-Micro Gripping System. No. 51805276.  
**Acknowledgments:** We wish to thank [PERSON] and [PERSON] for advice on experiment analysis of this study.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Framework Design and Deployment
### Proposed Framework  
Figures 2 and 3 collectively illustrate the key components, interactions, and workflow of the proposed FL framework, emphasizing its integration of blockchain technology, XAI, and digital twin systems in IIoT environments. These figures provide a visual representation of how the framework addresses critical challenges, such as data privacy, security, and scalability.  
Figure 2: Workflow of the proposed FL framework, showcasing how anonymized data from IoT sensors are processed using XAI and blockchain technology for secure and collaborative global model training and deployment within a digital twin system.  
Figure 2 highlights the high-level architecture, showing how anonymized data from IoT sensors are processed at the edge devices using privacy-preserving techniques like HE. The framework performs local model training on encrypted data, ensuring that the raw data remain secure throughout the process. These locally trained models are aggregated into a global model through the FL process, with blockchain ensuring the integrity and authenticity of the updates. The figure further illustrates the use of blockchain as a decentralized and immutable ledger for recording model updates and facilitating secure collaboration between edge devices and digital twins. The integration of XAI enhances the interpretability of the global model, fostering trust and usability in industrial settings.  
Building on this, Figure 3 provides a detailed workflow of the framework, presenting a step-by-step view of its processes. The workflow begins with model initialization on the blockchain, where the initial model parameters are securely recorded to ensure transparency and integrity. Next, local model training occurs on edge devices using HE, allowing computations to be performed on encrypted data. The updates from edge devices are then aggregated using federated averaging, with XAI integrated to analyze feature importance and refine the global model. The aggregated global model is validated and recorded on the blockchain in the blockchain recording and validation stage, ensuring only authenticated updates are included. Finally, smart contract execution automates the deployment of the validated model to edge devices if predefined quality standards are met.  
Together, these figures convey the multi-layered approach of the proposed framework, demonstrating how blockchain, FL, and XAI converge to enable secure, scalable, and privacy-preserving model training and decision-making processes. By providing both a high-level architectural view (Figure 2) and a detailed workflow (Figure 3), the figures ensure clarity and relevance, bridging the conceptual design and practical implementation of the framework.
## 3 Results and Discussions
### Mineral Content  
The fermentation process of the cassava root flour improved the bioavailability of essential minerals such as calcium, magnesium, iron, and zinc. Calcium content, essential for bone health and enzymatic functions, increased from 32.50 mg/100 g (control) to 41.35 mg/100 g at 48 h [36]. Magnesium, a crucial cofactor for enzymatic reactions, showed a gradual increase from 21.81 mg/100 g to 26.12 mg/100 g, indicating improved availability. The iron content increased from 5.42 mg/100 g (control) to 6.82 mg/100 g at 24 h, stabilizing afterwards, highlighting fermentation's role in enhancing iron solubility and reducing inhibitory compounds, and the zinc content, which plays a vital role in immune function and enzyme regulation, increased steadily from 1.74 mg/100 g to 2.62 mg/100 g after 48 h of fermentation. The increase in the mineral content of the naturally fermented cassava root flour may be attributed to phytate reduction, as previously demonstrated in Table 1, which reduces mineral chelation and enhances bioavailability [37]. Also, cassava's microbial metabolism and structural breakdown during fermentation increase mineral extractability and solubility [12]. This finding is in line with the work of [PERSON] et al. [38], who reported an increase in zinc content of fermented cassava root.  
The findings from this study demonstrate that natural fermentation is an effective technique for improving cassava root flour's nutritional quality. The progressive acidification during fermentation, enhanced protein digestibility, increased vitamin C levels, and significant reduction in anti-nutrients highlight its potential as a sustainable bio-processing approach. The observed trends suggest that longer fermentation times (48 h) may be optimal for maximizing nutritional benefits. The enhancement of phenolic compounds, antioxidant activity, and mineral content in fermented cassava flour suggests its potential use in functional foods and dietary supplements, especially for populations at risk of micronutrient deficiencies.
## 2 Materials and Methods
### Data Validation and Statistics  
Statistical methods were validated according to the ICH Guidelines and implemented on three different days. All of the analytes presented in this work are in the acceptance range specified by the manufacturer, whilst most analytes are within clinical reference ranges, as shown in Table 4.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{4}{c}{**Concentration Levels for the Internal Quality Control**} \\ \cline{2-5}  & \multicolumn{2}{c}{**Lower Level**} & \multicolumn{2}{c}{**Upper Level**} \\ \hline \multirow{2}{*}{**Element**} & **Obtained Value** & **Acceptance** & **Obtained Value** & **Acceptance** \\  & **(\(\mu\)g L\({}^{-1}\))** & **Range (\(\mu\)g L\({}^{-1}\))** & **(\(\mu\)g L\({}^{-1}\))** & **Range (\(\mu\)g L\({}^{-1}\))** \\ \hline Al & 7.50 & 6.00–9.00 & 100.00 & 85.00–115.00 \\ V & 0.13 & 0.10–0.15 & 23.10 & 18.40–27.70 \\ Cr & 7.70 & 6.20–9.30 & 30.40 & 27.70–33.20 \\ Mn & 0.33 & 0.26–0.40 & 9.30 & 7.40–11.20 \\ Fe & 4.20 & 3.40–5.00 & 61.00 & 48.80–73.30 \\ Co & 0.19 & 0.15–0.23 & 12.80 & 10.20–15.30 \\ Cu & 26.00 & 24.00–28.00 & 61.00 & 48.80–73.30 \\ Zn & 171.00 & 136.00–205.00 & 1179.00 & 984.00–1374.00 \\ As & 97.00 & 77.00–116.00 & 198.00 & 173.00–223.00 \\ Se & 10.50 & 8.40–12.60 & 66.20 & 51.90–80.40 \\ Mo & 17.00 & 13.60–20.40 & 73.80 & 68.10–79.50 \\ Cd & 0.62 & 0.49–0.74 & 4.70 & 3.70–5.60 \\ Sn & 2.40 & 1.90–2.80 & 46.00 & 36.80–55.30 \\ Ba & 3.60 & N/A & 21.50 & N/A \\ Tl & 0.11 & 0.10–0.11 & 8.40 & 6.87–10.00 \\ Pb & 1.51 & 1.20–1.81 & 78.40 & 70.40–86.40 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Internal quality control evaluation.  
A validation of the methods was carried out to determine performance and comprised of precision in series (CV in %), accuracy, limits of detection (LOD) and limits of quantification (LOQ). Precision was assessed using the coefficient of variation and accuracy by analyte recovery determination.
## 1 Introduction
These methods enable the multi-frame integration of target energy, thereby enhancing the target detection performance. It is noteworthy that these inter-frame integration methods do not account for the RA of moving targets, assuming that the range walk within each frame is less than one range unit. Consequently, the performance gain from intra-frame integration is constrained by the target's residency time within a single range unit. However, to our knowledge, these methods are predominantly applied in radar signal processing. Research addressing RA and low signal-to-noise ratios (SNRs) in DFRC OFDM signal processing remains sparse in the open literature, forming the primary focus of this study. In this paper, we propose joint intra-frame coherent integration and inter-multi-frame coherent integration for the DFRC OFDM signal processing of moving weak targets with RW during the observation time. The main steps are summarized as follows. (1) Intra-frame integration along the symbol axis is introduced to integrate energy within one OFDM frame, enabling the extraction of the speed information of targets within a single frame. (2) A multi-frame hybrid coherent integration approach is proposed, combining intra-frame integration across different OFDM frames with incoherent methods across subframes. This method leverages the integration gain from intra-frame and inter-frame coherent accumulation to enhance the SNR and improve the detection performance of the DFRC OFDM system. (3) The paper provides the formulation, reasoning, and analysis of various performance metrics for the proposed DFRC OFDM algorithm, including the communication bit error ratio and output response probability of detection at different SNRs. Detailed numerical experiments are presented to demonstrate its effectiveness. The remainder of this paper is organized as follows. Section 2 presents the DFRC OFDM transmit and receive signal model. Section 3 details intra-frame and inter-frame coherent integration, along with the modified generalized Radon transform. Section 4 provides a performance analysis of the methods discussed. Section 5 includes the experimental results and SNR analysis. Conclusions are drawn in Section 6.
## 3 Classification Method
### Deep Classification  
In this section, we give the details of the CNN-based deep classification. Three types of architectures, including AlexNet [19], Visual Geometry Group Network (VGGNet) [20], and Deep residual network (ResNet) [21], were adopted for classification.  
These architectures are often used for object recognition. AlexNet was used as it has been proposed. Note that VGGNet uses 16 layers of network and that ResNet uses 18 layers of network structure.  
The CNN learning method was conducted using a stochastic gradient descent method, and the learning rate schedule was obtained using simulated annealing. The ImageNet pre-trained weights [22] were used as initial weights to accelerate the convergence of learning.  
As explained in Section 2, we captured mosquito images with a simple white background. Images with the little-noised simple background can lead to overfitting images for training and a declined generality.  
To suppress overfitting, the training images were transformed by the following data augmentation steps.  
* Rotation: Rotate the image by angle \(\theta\). Angle \(\theta\) varies randomly in the range from \(-\theta_{r}\) to \(\theta_{r}\). Here, the limit value \(\theta_{r}\) is set to be \(45^{\circ}\).
* Brightness change: Randomly change the brightness of the image. Change rate \(\alpha\) is chosen in the range from \(-\alpha_{r}\) to \(\alpha_{r}\). Here, the limit value \(\alpha_{r}\) is set to be \(40\%\).
* Contrast change: Randomly change the contrast of the image. Change rate \(\alpha\) is chosen in the range from \(-\alpha_{r}\) to \(\alpha_{r}\). Here, the limit value \(\alpha_{r}\) is set to be \(40\%\).
* Saturation change: Randomly change the saturation of the image. Change rate \(\alpha\) is chosen in the range from \(-\alpha_{r}\) to \(\alpha_{r}\). Here, the limit value \(\alpha_{r}\) is set to be \(40\%\).
* Hue change: Randomly change the hue of the image. Change rate \(\alpha\) is chosen in the range from \(-\alpha_{r}\) to \(\alpha_{r}\). Here, the limit value \(\alpha_{r}\) is set to be \(25\%\).  
The limit values, \(\theta_{r}\) and \(\alpha_{r}\), were determined a priori by reference to the default ones used in the iNaturalist Competition 2018 Training Code. It is noteworthy that the number of training images was constant since the transformed image was replaced by the original one.  
An example of the data augmentation is depicted in Figure 6. The variation of training images increases in terms of color, brightness, and rotation fluctuation based on the aforementioned processing.
## 3 Memristive Analog Design
### Memristive RF Circuits  
New designs for wireless devices with multiple bands and standards require the reuse of RF chains to minimize their size and power consumption. Memristive components can be used in RF-programmable circuits in multi-band radios. These components maintain their resistance values even in the absence of power, rendering them ideal for RF-programmable circuits. In addition, memristive components offer the advantage of adjusting their resistance values in real time, allowing for the dynamic reconfiguration of RF circuits. This flexibility enables seamless adaptation to changing frequency bands and standards without needing physical modifications or additional components. The following are some examples of memristor-based RF circuits published in the literature. These RF circuits demonstrate the potential use of memristive components in various applications.