esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
\begin{table}
\begin{tabular}{c c c c c c c} \hline \multicolumn{2}{c}{**Load**} & **70\% (CC)** & **90\% (CC)** & **100\%** & **80\% (CV)** & **50\% (CV)** \\ \hline \multirow{3}{*}{\(V_{cp}\)(V)} & cal. & 133.55 & 149.77 & 180.02 & 144.01 & 90.01 \\ \cline{2-7}  & sim. & 139.58 & 157.05 & 179.04 & 143.39 & 89.76 \\ \cline{2-7}  & exp. & 137.5 & 155 & 184.25 & 142.5 & 91.25 \\ \hline \multirow{3}{*}{\(I_{rp}\)(A)} & cal. & 7.85 & 7.85 & 7.85 & 6.28 & 3.93 \\ \cline{2-7}  & sim. & 7.63 & 7.35 & 7.81 & 6.26 & 3.86 \\ \cline{2-7}  & exp. & 7.5 & 7.39 & 7.53 & 6.19 & 3.75 \\ \hline \multirow{3}{*}{\(I_{rs}\)(A)} & cal. & 5.55 & 5.55 & 5.55 & 4.44 & 2.78 \\ \cline{2-7}  & sim. & 5.54 & 5.46 & 5.53 & 4.42 & 2.73 \\ \cline{2-7}  & exp. & 5.18 & 5.13 & 5.12 & 4.18 & 2.69 \\ \hline \multirow{3}{*}{\(f_{s}\)(kHz)} & cal. & 107.84 & 96.15 & 80 & 80 & 80 \\ \cline{2-7}  & sim. & 103.33 & 91.80 & 80 & 80 & 80 \\ \cline{1-1} \cline{2-7}  & exp. & 103.32 & 91.92 & 80.65 & 80.65 & 80.65 \\ \hline \end{tabular}
\end{table} 
Table 5: Comparisons of parameters using Method II. modulation variables and the trade-off will be higher control complexity, which are to be addressed in separated works.
# A Survey of Detection and Mitigation for Fake Images on Social Media Platforms
## 7 Limitations and Challenges
### Deepfakes  
Deepfake images and videos are increasing day by day. Better generators have created natural, realistic images and videos that are very tough to identify. Though much research has been performed to detect them, no generic model exists. Some are specific to videos, and some are specific to images. They are still based on facial expression methods compared to natural expressions. More research to develop a generic model without intrinsic features can be worked on and researched further.
## 2 Materials and Methods  
### Scaffold Fabrication  
The full details of the scaffold preparation protocols can be found in our previous publications [16; 17]. Briefly, the scaffolds were non-woven fiber meshes constructed using polymer micro-fibers produced with spunbonding. The polymer used in the production of fibers was poly-L-lactic-acid (grade 6251D, 1.4% D enantiomer 108,500 MW, 1.87 PDI, NatureWorks LLC). A custom Brabender extruder (19.1 mm (0.75 in.) diameter \(\times\) 381 mm length) was used to pressurize and melt the polymer. A manually circulated collection screen was used to collect a random even layering of fibers. Layers of fibers were stacked and measured until the stack reached a mass of \(9.0\pm 0.1\) g within an area of 162.8 cm\({}^{2}\). The collected non-woven fiber stack then had a 7 cm center cut sheet obtained from it. Finally, using an 8 mm diameter die, discs were punched from the layered fiber sheets. The resulting scaffolds used in culturing were 8 mm diameter and \(\sim\)2.3 mm thickness. Average fiber diameter was measured optically, using a Nikon HFX-II microscope.  
The porous foam scaffolds were prepared using solvent casting/particulate leaching method [21; 22; 23; 24]. Briefly, poly-L-lactic acid (PLLA, 114,500 MW, 1.87 PDI, Birmingham Polymers) was dissolved into chloroform 5% w/v. The solution was then poured over a bed of sodium chloride crystals. Solvent was allowed to evaporate for 24 h. The resulting salt-polymer composite was inserted into an 8 mm diameter cylindrical mold and compressed at 500 psi. During compression, the composite was heated to 130 \({}^{\circ}\)C and held at constant temperature and pressure for 30 min. Using a diamond wheel saw (Model 650, South Bay Technology, Inc.), the resulting composite rod was cut into 2.3 mm thick discs. The discs were placed into deionized water (DIH\({}_{2}\)O) under agitation for 2 days to leach out NaCl.  
Figure 1: The image-based modeling methodology used in this work; scaffolds are scanned in 3D via high resolution \(\mu\)CT, reconstructed in silico, and the resulting geometries are used by the LBM and rLST solvers.  
Entire DIH\({}_{2}\)O volumes were replaced twice per day. Leached discs were then removed from DIH\({}_{2}\)O and placed under vacuum to remove moisture from the scaffolds. The resulting products were 8 mm diameter, 2.3 mm thick discs. Porosity of scaffolds was determined by measuring the solid volume (mass of the scaffold divided by the density of PLLA) and by comparing it to the total scaffold volume (assuming a cylindrical scaffold shape).
# Leveraging Retrieval-Augmented Generation for Swahili Language Conversation Systems
## 4 Experimentation
### Experimental Results
#### 4.3.6 Multi-Turn Coherence Scores Analysis  
Figure 14 presents the comparison of Multi-Turn Coherence scores across various models (MT5, mBART, GPT-2, and GPT-Neo) using two techniques: fine-tuned and Retrieval-Augmented Generation (RAG). The plot uses color coding to represent the coherence score, which indicates the relevance and consistency of the responses generated by the models across multiple conversational turns. From the plot, it is evident that the RAG technique consistently outperformed the fine-tuned technique across all models. Specifically, for the MT5 model, the coherence score showed a substantial improvement from 75.43 in the fine-tuned method to 88.26 with RAG. Similarly, other models such as mBART, GPT-2, and GPT-Neo exhibited increased coherence scores with RAG, confirming its effectiveness in improving multi-turn response consistency. Notably, GPT-2 showed a significant improvement in coherence when switching from fine-tuned to RAG, with the score increasing from 72.39 to 81.24, highlighting the effectiveness of RAG for this model. Furthermore, the plot illustrates that the performance of the fine-tuned models tended to decline as the conversation progressed, particularly from Turn 1 to Turn 5. On the other hand, the RAG technique maintained a relatively stable performance across all turns, emphasizing the technique's ability to generate more consistent and coherent responses throughout the conversation. The enhanced performance with RAG was likely due to its ability to retrieve relevant information from external knowledge sources, which helped the models generate more contextually appropriate and relevant responses. These results underscore the bene  
Figure 13: Performance comparison across models and techniques for Query Performance.  
\begin{table}
\begin{tabular}{l l} \hline
**Swahili Query** & **English Query** \\ \hline Jinsi ya kuboresha matumizi ya lugha? & How to improve language usage? \\ Nini faida za matumizi ya teknololojia? & What are the benefits of using technology? \\ Ni vigezo gani vinavyotumika kupima ufanisi? & What are the criteria used to measure success? \\ Je, ni mbinu gani bora za kujifunza lugha mpya? & What are the best methods to learn a new language? \\ \hline \end{tabular}
\end{table}
Table 9: Swahili query and corresponding English translation for Figure 13.  
fits of using RAG for improving the quality of responses in multi-turn conversations, as it allows for better coherence and relevance over time.
# Adaptive Feedback-Driven Segmentation for Continuous Multi-Label Human Activity Recognition
## 3 Methodology
### Multi-Label Classification Using Modified ResNet18  
In adapting to the complex demands of continuous HAR, we have modified the ResNet18 architecture, traditionally used for single-label image classification, to support multi-label classification. This adaptation is demonstrated in Figure 3 and is crucial for accurately identifying multiple activities from radar data across three key representations--spectrogram, range-Doppler, and range-time--as shown in Figure 4, each reflecting different aspects of the nuanced realities of human behavior.  
* Final Fully Connected Layer: The original network's final fully connected layer was replaced with a layer adapted based on our dataset. This adjustment ensures that the network's output dimensionality is aligned with the task's requirements.
* Softmax Layer: The softmax activation function is replaced, which constrains outputs to a probability distribution across classes, with a sigmoid function. This change allows for independent prediction of each label, being essential for scenarios where multiple activity types may be present.
* Classification Output Layer: The standard classification output layer was replaced by a custom binary cross-entropy loss layer. This new layer is tailored to manage the outputs from the sigmoid activation, optimizing the network for scenarios where each instance may belong to multiple classes.  
The selection of ResNet18 was informed by its demonstrated efficiency in extracting features from various data types, accommodating both time-specific and general data inputs. This versatility makes ResNet18 an optimal choice for our needs, ensuring robust feature extraction capabilities across a wide range of data.  
Each network outputs a probability vector for all activities (using a threshold set at 0.45, based on preliminary experiments), representing the likelihood of each activity occurring within the segment. In the post-processing step, we fuse the outputs from each individual network by taking the mean of these probability vectors. This fusion method ensures a balanced consideration of each modality's predictions, enhancing the reliability and accuracy of the final multi-label classification.  
It is important to note, however, that our system is optimized for a predefined set of activities from the dataset used; adapting to new or unknown activities would require additional training data and corresponding model updates. Moreover, the fully connected layer is specifically tailored to the predefined set of activity labels in our dataset. Consequently, if new classes are introduced, an update to the output layer would be required, necessitating complete retraining of the model to effectively integrate the expanded set of labels
## 4 Deep Learning Methods for HDR Reconstruction
### Transformers for HDR Reconstruction
#### 4.3.2 Attention-Based Methods  
Another approach researchers have adopted is focusing on other aspects of the frames by applying temporal or spatial attention. Unlike alignment blocks, these methods do not seek to align every object in the scene with the reference frame; instead, they emphasize certain regions based on objects from the reference scene. This is achieved by computing attention maps and multiplying that map by the frames where one wants to apply such attention, as illustrated in Figure 9b.  
Examples include CA-ViT [17], which applies special attention to the features extracted from the input images using a single convolutional layer for each. After obtaining feature frames with spatial attention, these features are refined with a Transformer architecture called Context Aware Transformer (CA-ViT). The final HDR image is reconstructed with one final convolution. The model was trained on the Kalantari [15] dataset, and its generalization was verified with images from the [PERSON] and [PERSON] et al. [14] datasets, with ablation studies underlining the importance of the CA-ViT block.  
Similarly, the HDT-HDR [161] architecture employs spatial attention mechanisms in features extracted from the input images by a convolutional layer. While it also concatenates the features with the applied attention, it further concatenates the attention maps as additional features, refining them with a transformer architecture named HDT. The final image is reconstructed with a convolutional layer. As with previous models, it was trained on the Kalantari dataset and validated on images from [PERSON] and [PERSON] et al. [14] for generalization tests.  
PASTA [50] also adopts convolution to extract the input image features, applying temporal attention across frames to identify the relevant features for each one. To further refine these features, the model employs a progressive representation and aggregation network, scaling features down by factors of two up to four times using self-attention with Transformers to focus on specific regions and then upsampling back to the original scale and adding the features at the matching scale. A final convolution merges these features to yield a three-channel HDR image. The authors also propose an alternative architecture designed to reduce parameter counts at some cost in quantitative metrics. Their models were trained on the Kalantari dataset and the [PERSON] et al. [27] dataset. Their results highlight the model's efficiency, measuring memory usage and inference time for test images.
# Distance-Based Resource Allocation for Vehicle-to-Pedestrian Safety Communication
## 3 Network and System Model
### Problem Formulation  
In this section we will define the optimization problem; objective function and the constraints. Traditionally, most problems [9] assign high capacity to the V2I link and high reliability to V2V link. However, our scenario is particularly focused on the V2P links, so our aim is to provide a high capacity and reliable link to the V2P, while providing a minimum threshold SINR to the CUEs:  
\[\begin{array}{c}Max\sum_{v=1}^{V}\sum_{x=1}^{X}E[log_{2}(1+\gamma_{v})],\\ 0\leq P_{c}\leq Pmax^{c}\\ 0\leq P_{v}\leq Pmax^{v}\\ Pr[\gamma_{v}\leq\gamma_{0}]\leq p\\ \gamma_{c}\geq\gamma_{0}\end{array} \tag{4}\]  
Equation (4) shows the objective function while equations (4) shows the constraints. In the next section, we have further decomposed Equation (4) into three different parts for each category of user. While all the CUEs get a uniform power depending on the threshold SINR. The maximum allowed power is 23 dBm. Table 2 summarizes the list of symbols with their respective definitions.
## 4 Discussion
### Uncertainties  
Our study revealed the effects of temperature, precipitation, and their interactions on the variations of NPP in grasslands on the TP, as well as the impacts of changes in temperature, precipitation, and elevation on the sensitivity of grassland NPP. However, it is necessary to acknowledge the uncertainties of the study and therefore the limitations in the perception of the results. To reveal the influence of climatic factors on NPP, meteorological data for the period 2001-2022 were collected from 268 stations. However, these stations are mostly located in the eastern TP, and there is a lack of meteorological stations in the western plateau hinterland. Although the use of a DEM as a covariate eliminated the interpolation error of temperature and precipitation data to a certain extent, the uncertainty is difficult to eradicate due to the vastness of the Tibetan Plateau and the fact that there are other factors affecting climatic factors in addition to the topography. This created questionable authenticity of the interpolated meteorological data in the west. In addition, although previous studies have confirmed the high accuracy and applicability of MODIS NPP data in modeling grassland productivity on the TP, we did not undertake the step of validating the accuracy of the dataset used in this study due to the lack of measured data. The uncertainty in the accuracy of NPP data is somewhat detrimental to the subsequent conclusions about the relationship between NPP and temperature and precipitation. In addition, the SEM in this study well revealed relationships among multiple factors, but inevitably simplified the complex relationships between some of the factors into linear relationships. The SEM helped to identify the macroscopic patterns of vegetation-climate relationships on the TP, but the application of research results at local scales requires further analysis.
## 5 Experimental Study and Discussion  
This section illustrates the proposed retrieval method using an experimental study. In the experiment, there are four cases of aeroengine fault diagnosis in the case base, as listed in Table 2. The case base is stored in ACCESS software, in which each case is represented by five attributes: the aeroengine model, aeroengine category, aeroengine operation state, other anomalies, and aeroengine fault part and fault mode, denoted by \(a_{1}\), \(a_{2}\), \(a_{3}\), \(a_{4}\), and \(a_{5}\), respectively. Here, \(a_{1}\), \(a_{2}\), \(a_{3}\), \(a_{4}\) are categorical attributes, while \(a_{5}\) is a semantic attribute. Note that in this experiment, we do not cover numerical attributes that are relatively simple and easy to handle. New case information is presented in Table 3. In CBR, the existing cases in the case base must be sorted; therefore, the most similar case is selected to solve the new case.
# Beyond 5G Fronthaul Based on FSO Using Spread Spectrum Codes and Graphene Modulators
## 6 Conclusions  
In this paper, a new fronthaul network architecture based on the FSO, the graphene modulator implementation, and spread spectrum coding techniques was proposed, analyzed and evaluated. The newly proposed network architecture employs the FSO instead  
Figure 10: Signal-to-interference-plus-noise-ratio (SINR) versus the number of simultaneous antennas at the remote unit side.  
Figure 9: Signal-to-interference-plus-noise-ratio (SINR) versus the optimum threshold.  
of traditional wired approaches in order to transmit data bits to the network antennas at the RU side. It is important to note that this approach is also viable for regions that lack wired solutions based on fiber or copper wiring. Here, the signals to be transmitted are first modulated with the novel energy-efficient graphene modulator and multiplexed using the OCDMA spread-spectrum technique. An analytical formalism for performance assessment of the proposed network is presented. The formalism accounts for 1D codes used for carrying users' signals, and APD and thermal noises in the receiver, cross-talk, and OOK modulation also account for different configurations of the graphene modulator. The modulator optimization was aimed at reducing its power consumption and was carried out by changing the amount of graphene in the modulator ring resonator by varying it from 25% to 100% in four discrete steps. Numerical results show the new graphene modulator consumes as little as 4.6 fJ/bit at 42.6 GHz when containing 25% graphene. This novel 42.6 GHz modulator not only reduces the overall power consumption, but also reduces its overall cost (the 25% graphene modulator is cheaper to produce than the 100% one). Furthermore, the optimum receiver threshold to minimize the BER performance of the network was also determined. Analytical results show that the best BER performance of all four considered modulator configurations can be achieved just by using 25% of graphene. The new FSO fronthaul network can also simultaneously accommodate up to 32 remote antennas with error-free data transmissions upon using FEC. Such a network solution can be interesting under scenarios such as the provision of secure broadband services, ultra-dense small cell creation, or cell expansions. To the best of the authors' knowledge, this paper is the first report on the design and performance analyses of power-efficient, highly secure FSO networks for use in the future mobile fronthaul.  
Conceptualization, T.R.; methodology, D.N. and A.S.; software, D.N.; validation, D.N., R.N. and A.S.; formal analysis, A.S.; data curation, A.S.; writing--original draft preparation, T.R. and I.G.; writing--review and editing, A.S., R.N., H.M., I.D., K.O., S.H., I.G. and A.J.-N.; supervision, T.R. and A.J.-N. All authors have read and agreed to the published version of the manuscript. This research was partially funded by CAPES and CNPq.  
Not applicable.  
D.N. acknowledges funding from CAPES as Bolsista CAPES/BRASIL.  
The authors declare no conflict of interest.
## 3 Results and Discussion
### High-Temperature Astaxanthin Extraction Using ILs
High temperatures not only induce a build-up of internal pressure via the heating effect but also promote the hydrolytic activity of thermally stable ILs on the algal cell wall. Collectively, these effects can lead to the effective disruption of the algal cell wall structure through physical and chemical means [18,29]. However, as shown in Figure 1, a careful balance should be maintained between the astaxanthin-extracting capability of the IL solvent and the thermal sensitivity of astaxanthin. In addition, given the energy required for the maintenance of a high temperature and the scale-up of the algal biorefinery process, mild temperatures are preferred if high-efficiency astaxanthin extraction can be achieved [9]. Figure 1: Effect of temperature on astaxanthin recovery efficiency from 90-day-old _Haematococcus lacustris_ cysts using four different ethyl-3-methylimidazolium ([Emim])-based ionic liquids (ILs) with thiocyanate (SCN), HSO4, diethylphosphate (DEP), and Cl anions for 2 h. Astaxanthin was recovered by subsequent ethyl acetate (EA) extraction at room temperature (_a._ 28 \({}^{\circ}\)C) for 30 min. As a control experiment, _H. lacustris_ cyst cells were treated only with EA for 2.5 h. The asterisk (*) represents room temperature.
# Mueller Matrix Decomposition and Image for Non-Destructive Testing of UAVs Skin
## 3 Experiments
### NDT of the UAV Skin
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{1}{c}{**The Intensity Image**} & \multicolumn{1}{c}{\
\begin{tabular}{c} **The Images of** \\ \(\lambda_{i}(i=2,3,4)\) \\ \end{tabular} } & \multicolumn{1}{c}{
\begin{tabular}{c} **The Images of** \\ \(\eta_{i}(i=2,3,4)\) \\ \end{tabular} } \\ \hline Contrast & 2.63 & \{4.64,1.52,1.01\} & \{36.21,8.04,28.77\} \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The contrast of Figures 3–5. Table 4 shows the NDT results of the UAV skin in Figure 5. The shear angle (\(sa\)) between wet and warp yarns is used as a measure of deformation related to the bending stiffness [26]. The deformation of yarns in 1, 2, and 3 regions is different. The deformation of region 1 is caused by warp bending, while the deformation of region 2 is mainly caused by the weft yarn. As shown in Table 4, the change of the shear angle in region 1 is the largest since the warp deformation is the largest, while that in region 3 is the smallest without deformation. When yarns are deformed, their distribution density is greater than that of normal yarns. The density of region 1 is greater than that of region 3. From Table 4, the average density is about 20/cm\({}^{2}\). The discussions and results are as follows:  
1. Non-uniform multiple scattering is closely related to the complex structure and nature of the UAV skin. Some traditional MMP methods encounter difficulty due to multiple scattering reduces the contrast and clarity of the original images. However, the proposed UMMMD can measure complex depolarization effects and provide more depolarization information for different polarization states. 2. Mueller matrix images for the NDT of the UAV skin sample are in agreement with the Mueller matrix decomposition results. The contrast and sharpness of the images are improved. 3. The method is sensitive to the non-uniform structure of the UAV skin and exhibits large dynamic ranges of depolarization related to multiple scattering. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Region** & \(sa\) & **Density** & **Deformation** \\ \hline
1 & 65\({}^{\circ}\) & 21/cm\({}^{2}\) & warp yarn \\
2 & 17\({}^{\circ}\) & 20/cm\({}^{2}\) & weft yarn \\
3 & 90\({}^{\circ}\) & 20/cm\({}^{2}\) & no \\ \hline \hline \end{tabular}
\end{table} 
Table 4: NDT results of the UAV skin. Figure 5: The polarization images. (**a**) The image of depolarization coefficient; (**b**) the image of \(\eta_{2}\); (**c**) the image of \(\eta_{3}\); (**d**) the image of \(\mathit{abs}(\eta_{4})\). Therefore, UMMMD is feasible for the NDT of UAVs skin. The given method could be used to characterize key parameters such as the structures, deformation, and defects of UAVs skin. In further research, this method will be used to study the NDT of other composite materials to obtain more extensive applications.