esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 A Deep Learning-Based CSI Feedback for New Radio  
To obtain the optimal precoder for MIMO communications, the channel knowledge is required by the transmitter (i.e., the BS). In the frequency division duplexing (FDD) mode, the uplink and downlink transmissions occur in different frequency bands, so there is no channel reciprocity. Thus, the UE must estimate the downlink channel and send this estimation back to the BS. This procedure is the channel state information feedback. The drawback of this system is the unbearable overhead resulting from the higher number of antenna elements, both in transmission and reception, as highlighted in [12]. The only way to adopt the FDD mode for MIMO transmission is to significantly reduce the feedback overhead--machine learning represents one viable solution to address the problem. In [12], the author designed a DL-based CSI sensing and recovery mechanism, named CsiNet. The main difference from previous DL-based investigations was consideration of the recovery stage. The CsiNet model is based on two main modules:  
* Encoder: the encoder generates compressed codewords by learning a proper mapping from the estimated channel matrices (dimensionality reduction). It is implemented by the UE.
* Decoder: the decoder tries to recover the original CSI from the codewords generated by the encoder, learning the inverse mapping. It is implemented at the BS sides, whereas the codewords are received from the UE through a feedback channel.  
In this paper, we extend the CsiNet model for CSI information reduction and recovery in a 5G context, producing a deep convolutional neural network (CNN), named NR-CsilNet. The additional features are intended to widen the applicability of the model to a more practical range of use cases:  
* We sought to make the model compatible with the typical 5G NR MIMO communications, in contrast to the single receiving antenna limitation of [12]. Practically, this introduces a third dimension to the channel matrices due to the different channel coefficients for each receiving antenna.
* We also considered the downlink channel estimation, whereas this additional step was beyond the scope of [12]. This means that, instead of feeding our NR-CsilNet with perfect channel matrices, we provide as input, noisy matrices, estimated through the CSI-RS.  
The CNN is built and trained via Tensorflow's Keras API [48]. To make this DL-based system work with the 5G NR simulator, the traditional feedback reporting blocks are rearranged. A new block imports the pre-trained encoder and uses it to elaborate an encoded version of the channel matrix, previously estimated from the CSI-RS. Then, at the transmitter side, we insert a specular decoding block which imports the pre-trained decoder model and uses the reconstructed channel matrix at its output to determine the optimal precoding vector.
# Classification of OCT Images of the Human Eye Using Mobile Devices
## 1 Introduction
In general, mobile applications do not use artificial intelligence, i.e., deep neural networks. For OCT images, smartphone ophthalmic applications are emerging that can support the diagnostic process. Of course, a smartphone does not have the ability to acquire OCT images on its own, but it has enough computing power and can analyze B-scans saved in the device's memory. The work in [13] presents a mobile application that allows the classification of four types of B-scan (CNV, DME, DRUSEN, NORMAL). The entire image and a relatively simple CNN network consisting of four sets of 2D convolution layers and max-pooling layers were used for classification. The authors also used YOLOv4 tiny to mark pathological changes in the image. The application and the source code are not available. In the context of studies on the development of ophthalmic telediagnostics screening, our article presents a comprehensive study of the possibilities of using different neural network architectures. A publicly available application has also been prepared, which allows the user to precisely check the probability of belonging to five classes of pathological changes using three previously selected models. The B-scan was assumed to be available in the memory of the mobile device or would be taken using a smartphone camera from the screen of the OCT device or a printout received by the patient after OCT imaging. The presented analysis and solutions may be helpful for quick evaluation of OCT B-scans, including those archived on older-generation OCT devices. The main contributions of the authors presented in the article are as follows:  
* a comparison of the effectiveness of six neural network architectures for automatic classification of OCT B-scans of five groups of lesions;
* a comprehensive comparison of the advantages of three solutions for converting neural network models to the constraints of mobile devices;
* source code of mobile app design for OCT B-scan classification with software release on GitHub (version 1.0). The outline of the paper is as follows. Section 2 describes the dataset and neural networks used in the experiments. The advantages and disadvantages of various approaches for NN implementations in a mobile application are discussed in Section 3. This section also includes a description of the mobile application's design. In Section 4, the experiment setup and classification results are presented.
# Evaluation of Green Strategies for Prolonging the Lifespan of Linear Wireless Sensor Networks
## 3 Classification of Energy Management Techniques for Prolonging WSN's Lifetime
### Adopted Taxonomy for Classifying Energy Management Techniques Is WSN  
From surveying existing studies and their taxonomies for classifying energy management techniques for prolonging the WSN's lifetime, we discovered that the lifetime of a WSN can be prolonged by either of the following three ways: reducing the energy consumed by individual sensor nodes or a group of sensor nodes, ensuring a balance in the battery residual energy of the sensor nodes in the WSN, or replacing the energy consumed in the batteries of sensor nodes. From this observation, we were able to develop a taxonomy that categorizes techniques for prolonging the WSN's lifetime into three main categories: techniques that decrease the power consumption of individual nodes or the network (energy conservation techniques), techniques that provide a balanced energy consumption, i.e., balanced residual energy amongst sensor nodes (energy-balancing techniques), and techniques that replace the energy consumed in the batteries of sensor nodes (energy-harvesting techniques). Our adopted taxonomy is presented in Figure 5. The taxonomy presented in Figure 5 applies to traditional WSNs but can also be adapted to LWSNs. While many energy management techniques used in traditional WSNs will be similarly effective in LWSNs, those that rely on topology and routing may be less efficient or even ineffective due to the linear topology of LWSNs. We will analyze these specific cases in detail.  
Figure 5: Taxonomy for classifying WSN lifetime prolongation strategies.
## 4 Results
### Crop Parameters Retrieval Validation  
#### 4.2.1 LAI Retrieval Validation  
The LAI was retrieved from both the original and nadir reflectance measurements using a regression formula based on the RVI, followed by the application of five distinct methods for correcting the angle effect. Subsequently, the accuracy of the retrieved LAI was validated against field survey data. As depicted in Figure 8, except for the Fixed-Landsat algorithm, which significantly underestimated the LAI extraction results, the retrieval accuracy from the remaining four methods saw a marked enhancement. Specifically, these methods demonstrated coefficients of R\({}^{2}\) greater than or equal to 0.81 and RMSE less than or equal to 0.62, which are substantial improvements over the original reflectance metrics (R\({}^{2}\) = 0.76 and RMSE = 0.735). Notably, the Fixed-Jilin-1 method achieved the highest  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & & \multicolumn{2}{c}{**MODIS-Based**} & \multicolumn{2}{c}{**AFX**} & \multicolumn{2}{c}{**VJB**} & \multicolumn{2}{c}{**Fixed-Landsat**} & \multicolumn{2}{c}{**Fixed-Jilin-1**} \\  & & **Mean** & **Std** & **Mean** & **Std** & **Mean** & **Std** & **Mean** & **Std** & **Mean** & **Std** & **Mean** & **Std** \\ \hline \(C_{\lambda}(\theta_{s}=20.33^{\circ}\), & Green & 0.960 & 0.008 & 0.958 & 0.006 & 0.855 & 0.042 & 0.964 & 0.003 & 0.957 & 0.004 \\ \  
accuracy in LAI retrieval (R\({}^{2}\) = 0.82 and RMSE = 0.539). Additionally, the MODIS-based and AFX methods exhibited very similar levels of accuracy in LAI retrieval.  
Figure 5: Green band reflectance validation based on field measured data. The X-axis is the measured reflectance, the Y-axis is the corrected reflectance, the black solid line is the 1:1 line, and the different color scatter points represent the different observation geometry. (**a**) Original, (**b**) MODIS-based, (**c**) AFX, (**d**) VJB, (**e**) Fixed-Landsat, And (**f**) Fixed-Jilin-1.  
Figure 6: Red band reflectance validation based on field measured data. The X-axis is the measured reflectance, the Y-axis is the corrected reflectance, the black solid line is the 1:1 line, and the different color scatter points represent the different observation geometry. (**a**) Original, (**b**) MODIS-based, (**c**) AFX, (**d**) VJB, (**e**) Fixed-Landsat, And (**f**) Fixed-Jilin-1.
# Multi-User Joint Detection Using Bi-Directional Deep Neural Network Framework in NOMA-OFDM System
## 5 Simulation Results and Discussion  
In this section, the simulation results for the proposed Bi-LSTM model of multiuser CE and signal detection in the NOMA-OFDM system are discussed.
## 5 Results and Discussion
### Cell-Level Analysis for the Base Case
This leads to a lower overall temperature of the battery pack in the serial design compared to the parallel design, as shown in the earlier Figure 10b; however, this comes at the cost of cell-cell temperature variation, which is higher in the series arrangement than the parallel arrangement. Figure 11: Cell-level thermal performances: (**a**) voltage of the serial design at middle of riding, (**b**) voltage of the parallel design at middle of riding, (**c**) power of the serial design at middle of riding, (**d**) power of the parallel design at middle of riding. Figure 12b-d show the gradients of SOC, OCV, and SOP of the individual cells at the middle of riding, and the gradients of SOC, OCV, and SOP in the serial design are larger than those of the parallel design. Furthermore, we can observe that the differences between the serial and parallel designs in terms of SOC range and OCV range are minimal. The disparity between the two designs is slightly larger for the SOP range, around a two-fold difference. However, if we continue to examine Figure 12f-h, we notice that, at the end of riding, the differences between the two designs in terms of SOC range, OCV range, and SOP range rapidly expand to 2.36, 2.38, and 2.36 times, respectively. This increase in the differences suggests that, as it progresses towards the end of riding, the serial design experiences much larger variations in SOC, OCV, and SOP among the individual cells, introducing more than two times the level of cell performance variations compared to the parallel design. However, it should be noted that the absolute differences over this drive cycle are minimal. This further implies that the serial thermal design requires a greater level of cell balancing intervention to mitigate the increased thermally induced cell-to-cell inhomogeneities and ensure balanced performance across the battery pack.
# A Predictive Model to Correlate Amino Acids and Aromatic Compounds in Calabrian Honeys
## 4 Conclusions  
In conclusion, this study delved into the multifaceted world of honey. In this study, which represents a feasibility study, a predictive model was built. A chemometric approach was employed to decipher the intricate relationships between aroma variability data and amino acids linked to botanical origin. To build the model, an analysis of 25 honeys of different botanical origins collected in the Calabria region (South Italy) was performed. Through hydrophilic interaction chromatography (HILIC) coupled with mass spectrometry, the amino acid profile was determined. By headspace solid phase micro-extraction (HS-SPME) with gas chromatography mass spectrometry (GC-MS), the aromatic profile was extracted. A robust statical approach was used to determine the Person's correlation between amino acid profiles and aromatic compounds in various types of honey. Finally, various types of honeys, their amino acid profiles, and their aroma characteristics were correlated by PLS analysis. The results revealed a strong linear association between specific amino acids and various classes of aromatic compounds, shedding light on the potential impact of amino acids on honey's aromatic profile. Further investigations can extend this methodology to explore additional honey varieties, thus contributing to a comprehensive understanding of the intricate interplay between amino acids and aroma compounds in this prized natural product.  
Conceptualization, M.R., S.C. and R.D.S.; methodology, S.C. and R.D.S.; software, R.D.S. and S.C.; validation, R.D.S. and S.C.; formal analysis, S.C. and R.D.S.; resources, M.R., A.M. and S.F.; data curation, S.C., R.D.S., S.F. and M.R.; writing--original draft preparation, S.C., R.D.S., A.M. and M.R.; supervision, M.R.; project administration, M.R.; funding acquisition, M.R. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the projects Ministry of University and Research (MUR) CN_0000022 \"National Research Centre for Agricultural Technologies--Agritech\" and by the Research Infrastructure Saf@med-Food Safety platform.  
Data Availability Statement: The data used to support the findings of this study can be made available by the corresponding author upon request.
## 3 Overall Design Architecture
### Video Compression Sub-Application Layer Protocol for Wysn  
Since video requires huge amounts of data in comparison with other kinds of information, reducing its size without interfering video quality decreases bandwidth usage and increases the communication speed. Hence, in the application layer a video compression sub-layer, which consists of a new compression model that differentiates video frames and prioritizes packets, is proposed. This compression sub-layer observes major points of video transmission as far as possible. Also, it provides data with some extra information to be used in other layers. The details of video compression sub-layer are presented in Section 4.
# Model Predictive Control for Six-Phase Induction Machines with Insight into Past Current Errors
## 2 The Six-Phase Induction Motor Drive
Due to the effects of the Clarke transformation in the presented topology, the switching states can be classified in five different groups according to the module of the voltage vectors in the primary subspace: large (\(V_{l}\)), medium-large (\(V_{ml}\)), medium (\(V_{m}\)), small (\(V_{s}\)), and null (\(V_{0}\)) vectors [34; 35]. This classification is represented in Figure 2. An interesting relation can be observed by comparing the modules of the voltage vectors in the \(\alpha\)-\(\beta\) and the \(x\)-\(y\) plane. Particularly, \(V_{l}\) (colored in blue) presents a notable production in the primary subspace, with the smallest production in the secondary plane. This fact makes them especially suitable for use, since a high degree of exploitation of the dc-bus and small copper losses are desirable control traits [4]. For that reason, these switching states are the ones accessible by the control methods studied in this proposal along with \(V_{0}\) (colored in white). Following with the modification of reference frames, each subspace can be projected again to decouple the flux/torque production through the use of the rotation matrix known as Park transformation (5) once the rotor flux angle (\(\theta\)) is obtained by means of the measured speed and estimated slip [36]. Through the use of this transformation, flux production is only related to the \(d\)-component, while the \(q\)-component is linked to torque generation, which eases the implementation of control schemes. \[\big{[}D\big{]}= \begin{bmatrix}cos\theta&sin\theta\\ -sin\theta&cos\theta\end{bmatrix} . \tag{5}\]  
Figure 2: Classification of available control actions in the \(\alpha\)-\(\beta\) and \(x\)-\(y\) planes, with \(V_{l}\) highlighted in blue.
# Comparative Analysis of BERT and GPT for Classifying Crisis News with Sudan Conflict as an Example
## 2 Related Work
### Text Classification by BERT  
BERT has significantly advanced text classification by introducing a novel bidirectional training approach that captures linguistic context more effectively than its predecessors.  
Unlike earlier models that relied on unidirectional context processing, BERT simultaneously considers both preceding and succeeding tokens within a sequence, allowing for a richer understanding of the semantics and syntax [2]. This bidirectional framework enables BERT to disambiguate meaning, recognize contextual nuances, and enhance classification accuracy, making it highly effective for complex NLP tasks [29; 30].  
The ability to capture subtle semantic and syntactic relationships has positioned BERT as a powerful tool for a wide range of domain-specific text classification applications. In legal document analysis, BERT has been employed to classify case laws, contracts, and regulatory texts with high precision, reducing the need for manual annotation [31]. Similarly, in sentiment analysis, BERT effectively distinguishes nuanced opinions in product reviews, social media posts, and customer feedback, outperforming traditional deep learning models [32]. In news classification, BERT has been widely used to classify articles based on themes such as politics, finance, and global conflicts, demonstrating its adaptability across diverse textual datasets [33]. For instance, [PERSON] [34] highlighted how BERT modeling achieves robust accuracy for classifying concise and ambiguous news headlines, showcasing its effectiveness even for short-form news contexts. [PERSON] [35] demonstrated that augmenting BERT with topic models and structural features can further improve classification performance in multi-class media research, a strategy relevant for complex crisis news datasets.  
Comparative studies have shown that BERT consistently achieves higher accuracy scores than traditional machine learning and deep learning models. For instance, research indicates that BERT outperforms Multilayer Perceptrons (MLPs), LSTM networks, CNNs, and even other Transformer-based models like RoBERTa in multi-class text classification tasks [36; 37]. Its pre-training on large-scale corpora, followed by domain-specific fine-tuning, allows BERT to generalize well while also being adaptable to specialized tasks. However, BERT's performance can be limited when training data is scarce or when dealing with overlapping semantic categories. This research explored these limitations through comparative evaluations with GPT-based approaches.
# A Comprehensive Review of Open Caisson Modeling Technology: Current Practices and Future Prospects
## 3 Advances in Physical Modeling Method
### n-g Modeling
#### 3.3.1 Earth Pressure During Excavation Within Granular Soil  
[PERSON] et al. (2013) [67] used two overlapped semi-cylindrical aluminum segments with a length and outer diameter of 200 mm and 80 mm, respectively, to construct a physical model. It was constructed at a scale of 1:75 and tested at 75 g acceleration in a centrifuge. The model has the equivalent size to a 15 m long, 6 m diameter reinforced concrete prototype shaft, as shown in Figure 7. In the model, granular soil was used.  
In order to simulate soil excavation, a multi-gear motor has been used to move the semi-cylindrical segment at a rate of 1.0 mm per 140 s. Two LVDTs have been attached with the semi-cylindrical portions, one at the top and one at the bottom of the shaft, and eight soil pressure transducers were fixed on the exterior of the shaft.  
Figure 6: Schematic of experimental setup [65].  
The tests revealed that for the first level of excavation the earth pressure progressively reduced corresponding to the height of the first segment, unless an active displacement of \(\delta h/H\) = 0.2 \(\times\) 10\({}^{-2}\) was accomplished. Afterward, there was no additional variation in the pressure. Earth pressure dropped until it reached 30% of its original value, after the active displacement state was achieved. However, at the same time, recorded earth pressure on the bottom portion remained constant.  
Furthermore, in the second phase, the movement of the lower segment, the earth pressure declined until it reached 80% of its primary value, once the active displacement was reached. With the displacement of the lower segment, earth pressures in the upper segment area began to increase concurrently until they approached the initial pressure. For a deeper excavation level, during the second stage of the excavation, the arching effect was more effectively produced. From the test, it was revealed that the plastic zone ranges across a distance of 1D and the vertical displacements were noted as 13 mm.
## 3 Results
### Usefulness of TL in CNN Learning  
We applied TL to the deep-learning models to check whether it contributes to the accuracy improvement of the models. After training each model with TL, we evaluated the performance of each model.  
The average accuracy of each model was 97.8% for VGGNet16-TL, 98.9% for ResNet50-TL, and 99.4% for MobileNet-TL (Figure 6a). This is a significant improvement over the accuracy of Figure 3a (93.9% for VGGNet16; 92.2% for ResNet50; 95.6% for MobileNet). The accuracies were derived from confusion matrices (Figure 6b). The confusion matrix shows what each model predicted for the proposed SA, AMC, and AC. The accuracy was calculated using the formula presented in the Materials and Methods section.  
Figure 5: Visualization of learning weighted regions with MobileNet using Grad-CAM and Guided Grad-CAM. (**a**) Weight is assigned to the histological shape; (**b**) weight is assigned only to specific areas; (**c**) weight is assigned to the entire image including the appearance.  
Figure 6: Performance of deep learning models trained by transfer learning (**a**) Accuracy of three deep learning models trained by TL; (**b**) Confusion Matrix of three deep learning models trained by TL; (**c**) Prediction failures: four images (Nos. 1–4) of SA predicted as AC; one image (No. 5) of AMC predicted as SA. TL, Transfer learning; SA, Sinomenium acutum; AMC, Aristolochiae Manshuriensis Caulis; AC, Akebiae Caulis.  
The identification accuracy of the models improved, but prediction errors still occurred in five out of 180 images. The five images are shown in Figure 6c. VGGNet16-TL judged the four SA images Image Nos. 1-4 (Figure 6c, Image Nos. 1-4) as AMC. ResNet50-TL caused a prediction error on two images of an SA (Figure 6c, Image No. 4) and a peeled AMC (Figure 6c, Image No. 5). MobileNet caused only one error for the SA image (Figure 6c, Image No. 4), which led to a common failure for all three models. The SA image had blurred cuts, and displayed several herbs.
# Building Realistic Mobility Models for Mobile Ad Hoc Networks
## 6 Real Shipping Movement Model
### Results and Discussion
#### 6.8.1 Packet Delivery Ratio on Realistic Shipping Mode I  
The differences in the packet delivery ratio between the protocols (DSDV, AODV, and DSR) are minimal (see Figure 12). The conclusion drawn from the realistic model based on PDR would be that any of these three protocols can be used to set up a MANET to connect to South Stack, Elsam Traffic, or Crosby MCA in this scenario with the condition that the radio range is 15 km or more. However, using Snaefell for the base station is not going to be successful unless the radio range is 30 km. Therefore, because a 30-km radio range is considerably less reliable in this situation, it is advisable not attempt to use Snaefell at all.