esa-sceva/satcom-chunk-collection huggingface dataset:
# A Vision of 6 th Generation of Fixed Networks (F6G): Challenges and Proposed Directions
## 6 Conclusions  
The 6 th generation of fixed networks is about to offer humankind new services and exert a profound impact on all aspects of human activity. In this vision article, we first categorized key F6G services and shed light on their main requirements, to be met concurrently:  
Figure 39: High-level representation of our vision on the I4.0 inter- and intra-factory connectivity.  
Low latency, a high bit rate, and ample connectivity. Next, we examined the main building blocks of the network to be developed using low-cost and low-power-consumption techniques. In particular, acknowledging that electronics is approaching its limits and that all-optical techniques will be increasingly called 'to the rescue', we explained how our proposed direction for adopting an optical-DAC is fully aligned with and supports the trend of improved energy efficiency and enhanced performance. Moreover, we proposed directions including three strategies to increase the rate of transmitted data in an optical system: The use of more spectrally efficient modulation formats, full exploitation of the physically available amplification bands of SMF, and the adoption of SDM. The implementations of each of these strategies, or a combination thereof, depend on the network domain--e.g., metro vs. core--and target constraints on cost, footprint, and power dissipation. Further, we discussed the two classes of optical switches featuring ultra-fast (enabling Time-Sensitive services) vs. ultra-high capacity (enabling use-cases that require switching very high-volume traffic) along with their requirements. Finally, we proposed potential solutions and directions based on the development of multi-granular waveband-selective flexibly switched optical add/drop multiplexing nodes, respectively.  
Conceptualization, methodology approach development, editing, writing, and reviewing: M.N., D.M.M., R.M., K.P., D.U., K.M., C.P. and I.T. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The data discussed in this article have been referenced at the corresponding points.  
The authors declare no conflict of interest.
# Adaptive Multi-Modal Ensemble Network for Video Memorability Prediction
## 3 Proposed Methods
### Video Representation  
ResNet [51] made a name for itself and influenced the direction of deep learning in academia and industry. Research shows that the depth of the network is an essential factor in achieving good results. However, the gradient dispersion/explosion becomes an obstacle to the training of the deep network, leading to the failure of convergence. The ResNet model provides better performance of the network to make up for this disadvantage, but also becomes a relatively advanced network model. Considering the feature redundancy and other problems brought by the direct use of the network to extract video features, such as C3D features, we adopt the end-to-end Resnet3D model by feedback process to directly predict the video memory score, which can achieve better prediction effect and make full use of video information. The experimental results also prove our guess. Based on this, we fine-tune the ResNet3D model [6] to use an end-to-end approach to predict video memorability scores rather than extracting features directly for video features.  
Based on previous studies, we chose the ResNet3D model with 34 residual blocks and replaced the classification layer of the last layer with the full connection layers to obtain the memorability scores. Compared with the ResNet3D model fine-tuned by [PERSON] et al. [5], we added two hidden layers and one output layer in the full connection layer, with 100 neurons in the first hidden layer, ten neurons in the second hidden layer and output in the output layer. In the training stage, the loss function was adjusted from the \(L1\) loss function to the MSE loss function, and the optimization algorithm was adapted from the Adam algorithm to the SGD algorithm. Given a video instance \(\mathbf{V}_{i}=(v_{i},i_{i},t_{i})\), output values of the trained model were selected to obtain with the fine-tuned ResNet3D model instead of extracting the feature \(v_{i}\) directly. For example, let us define \(\gamma_{v}\) as the parameter of the video branch, the learned video-output \(\mathbf{V}\mathbf{o}_{i}\) of video \(i\) can be represented as follows:  
\[\mathbf{V}\mathbf{o}_{i}=f_{v}(v_{i},\gamma_{v}) \tag{1}\]  
where \(f_{v}\) represents the function of video features \(v_{i}\), which is used to obtain the memorability score of the video, and \(\mathbf{V}\mathbf{o}\in\mathbb{R}^{n\times 1}=\{\mathbf{V}\mathbf{o}_{i}\}_{i=1}^{n}\).
## 5 Case-Study: Resource Fitting
### Experiment 1: Comparison of Native Performance of ML Backends
#### 5.1.1 Training Round Duration and Model Accuracy  
Firstly, we investigate if there is a significant variance in the duration of training rounds and the accuracy of the ML model, depending only on the dataset and ML backend. Figure 5a shows that both TensorFlow and PyTorch have comparable accuracy on the MNIST dataset, while PyTorch outperforms TensorFlow by 5% on the CIFAR-10 dataset. However, the average round duration varies significantly between the two ML backends and is an aspect for consideration (Figure 5b). Despite the 5% accuracy gain, training the MobileNetV2 model on PyTorch has a round duration of slightly over 5 min, while TensorFlow takes up to 140 s. Similarly, TensorFlow outperforms PyTorch, in terms of training round completion, by a large margin (around 60 s) for the CNN model on the MNIST dataset. Hence, _the absence of a dominant implementation across all cases, highlights the need to finely tune the ML backend and model architecture on a per-case basis, in order to improve the duration and accuracy of the FL pipeline.
## 2 Related Work and State-Of-The-Art Technologies
### State-Of-The-Art Technologies
#### 2.2.1 Microservices-Based Architecture  
A microservice-based architecture is an architectural style that puts its emphasis on dividing and designing an entire application as a set of loosely coupled, lightweight, independent services [23; 24; 25]. Each independent service can be independently developed, tested, deployed, monitored, scaled and even implemented in different programming languages [23; 24; 26]. As the conclusion of [27; 28; 29], compared to the monolith architecture, a microservice-based architecture addresses the following concerns:  
* increased resilience due to independent development and deployment of services
* fast
* better code quality
* easier debugging and maintenance
* increased productivity
* improved scalability
* freedom (in a way) to choose the implementation technology/language
* continuous delivery  
Therefore, a microservice-based architecture can be a very convincing architecture style for setting up a workflow environment with the aim of loose coupling independent runnable tasks (e.g., arbitrary execution environments). Additionally, by utilizing a microservice-based architecture, the whole presented framework built as a set of separate microservices can be developed, debugged, tested, versioned, deployed and scaled easily, quickly and flexibly.
## 3 Case Study
### Training the Network by Using Frequency-Domain Data  
#### 3.2.1 Training Process of the Neural Network  
By using the same set of 12.8 s vibration signals in Section 3.1.1, a Fourier transform was performed on the Channel 8 data. After the transform, Channels 1-6 were used as the test predictor variables (XTest2) and Channels 7-8 as the response sequence (YTest2), while keeping the other parameters consistent with those in Section 3.1.1, and the LSTM network was used to predict the incomplete sequence that lacked data from Channels 7 and 8. The prediction results from the training network for the missing data from Channel 7 are shown in Figure 16.  
Figure 14: Comparison of vibration level at the one-third octave from Channel 7.  
Figure 15: Relative error of the one-third octave spectrum from Channel 7.  
The user-specified parameters in the above neural network are optimized to maintain the model's training accuracy and speed. The optimization results are as follows:  
Effect of Epoch on Model Accuracy  
As shown in Figure 17, when the epoch value is less than 20, the RMSE value drops rapidly. The decline rate of the RMSE value slows down with the continuous increase in epoch value, and the RMSE region stabilizes when the epoch value exceeds 50. Therefore, 50 is selected as the epoch value for this model by considering the computational accuracy and efficiency.  
Figure 16: Training results from Channel 7 using processed data.  
Figure 17: Effect of different epochs on model accuracy using frequency-domain data.  
Effect of Batch Size on Model Accuracy  
An appropriate batch size selected in an LSTM can increase the model's accuracy and reduce the model's training time. As shown in Figure 18, the value of the RMSE shows a trend of first decreasing and then increasing with the increase in batch size in this model. When the batch size is selected as 250, the value of the RMSE is the minimum, and so 250 is used as the value of the batch size for this model.  
Effect of the Number of Hidden Units in the LSTM Layer on Model Accuracy  
When the number of hidden units is less than 30, the RMSE of the training model drops rapidly, as shown in Figure 19. When the number of hidden units is up to 50, the RMSE value is the minimum. As the number of hidden units continues to increase, the RMSE value tends to increase slowly. Therefore, 50 is selected as the value of the number of hidden units in the LSTM layer for this model.  
In general, the model built with frequency-domain data achieves faster iterative efficiency in the training process than that built with time-domain data. The overall time spent on training is shorter, with lower losses and RMSE, MEA, and R2 values.  
Figure 19: Effect of different hidden units on model accuracy using frequency-domain data.  
Figure 18: Effect of different batch sizes on model accuracy using frequency-domain data.
## 4 Discussion
### Quality of the Interaction and Areas for Improvement  
Furthermore, as this is a novel study on the use of a social robot as a teacher in the Tanzanian context, we have presented shortcomings of the technology, as experienced and perceived by the teachers. Most of the weaknesses emanate from the contextual uniqueness of Tanzania. For example, factual incorrectness in the feedback provided to pupils, language code-switching, and at times unintelligible responses were all exclusively experienced in the context where the language had to be Swahili. Thus, these can be attributed to the prevailing insufficiency of Swahili training data in AI models. Nevertheless, it is noteworthy that, while these shortcomings have a critical impact, their occurrences were minimal. Similarly, the issue of mispronunciation in the speech generated by Microsoft Azure Speech Services (Text-to-Speech) is likely also attributable to Swahili being a low-resource language.  
Though crucial, the issue of perceived complexity in setting up the system is temporary, as what we demonstrated was still a prototype and not market-ready. This was also noted by teachers in other studies, demanding that the tutoring systems are plug-and-play [13]. Moreover, latency in feedback from the robot, which was stated to significantly impact the learners' interest in the sessions, is a critical issue that needs to be resolved. This is in line with other studies that seek to manage the latency in conversations to enhancehuman-robot interactions [29; 32; 33]. In our context, the latency was largely caused by the low bandwidth and unstable Internet connection, especially in rural areas.
###### Abstract  
A novel method was proposed for the determination of vitamin C (VC) using an infrared camera combined with the iodine-turbidimetric method. Based on the redox between VC and iodine, the residual iodine was measured using the turbidimetric method with an infrared camera to obtain VC content. The light emitted by the infrared light-emitting diode (LED) was absorbed and scattered when it penetrated the residual iodine suspension. The transmitted light was captured by the infrared camera to form a digital image and the responding color components and grayscale values were obtained. The obtained color components and log-grayscale were fitted to the VC concentration, and the fitted relation expressions were used to measure the unknown VC solution. A VC measuring device equipped with an infrared camera and processing software was designed to obtain the color components corresponding to the images of the iodine suspensions. Compared with the spectrophotometry, the method based on the color component of brightness had a higher accuracy for measuring the VC standard solution. For VC measurements in tomatoes, nectarines, and VC tablets, our proposed method was highly consistent with spectrophotometry. Therefore, this method could potentially be implemented in the determination of VC in fruits and tablets, or other foods.  
food analysis; analytical method; vitamin C; iodine suspension; infrared camera; image processing +
Footnote †: journal: Applied Sciences
# Dynamic Detection and Recognition of Objects Based on Sequential RGB Images
## 5 Conclusions  
LSMNet and MVRFFNet were proposed in this study for the OCIS and ZSFGR3D subtasks that are involved in the complex LROC task. Experiments were conducted on an OCIS dataset and a fine-grained recognition dataset about objects on a conveyor, respectively. LSMNet could achieve a recalling accuracy close to YOLACT on large and middle objects, while its computing speed on an NVIDIA GTX1660 TI GPU was 25 fps, which is much faster than YOLACT's 80 fps. MVRFFNet performed much better than traditional metric learning methods in the retrieval task by mapping the features into a redundancy-free feature space.  
Future works will concern three aspects. First, we are going to collect more samples that match the actual situation in Figures 9-11, each sample of which only contains one object that does not cover the case of crowdedness and occlusion. A more comprehensive analysis of the performance of MVRFFNet will be carried out. Second, we will extend the pix2 pix architecture to multiple output branches and establish an LSM for each category, LSMNet would be suitable for multiple class instance segmentation tasks. The third is to find contours in a new manner that can transmit the supervised information of detection to improve the performance of LSMNet on small objects. The last task is to combine multiview features and semantic attribution together to improve the performance further. The motivation is that the multiview registered features serve as the attribution of GZSL setting in MVRFFNet, and high-level semantic attribution has not been utilized yet.  
Conceptualization, data curation, K.Z.; methodology, S.D.; project administration, W.L.; software, Z.Y. All authors have read and agreed to the published version of the manuscript.  
This work is supported by National Natural Science Foundation of China (62002053), the Guangdong Basic and Applied Basic Research Projects (2019A1515111082, 2020A151110504), Fund for High-Level Talents Afforded by University of Electronic Science and Technology of China, Zhongshan Institute (417 YKQ12, 419 YKQN15), Social Welfare Major Project of Zhongshan (2019B2010, 2019B2011, 420S36), Achievement Cultivation Project of Zhongshan Industrial Technology Research Institute (419N26), the Science and Technology Foundation of Guangdong Province (2021A0101180005), and Young Innovative Talents Project of Education Department of Guangdong Province (419 YY04).  
Not applicable, the study does not report any data.  
The authors declare no conflict of interest.
# A Channel Measurement-Based Listen-Before-Talk Algorithm for LTE-LAA and WLAN Coexistence
###### Abstract  
To support the coexistence of long-term evolution (LTE)-license-assisted access (LAA) and wireless local area network (WLAN) in unlicensed bands, the load-based listen-before-talk (LB-LBT) scheme has been developed, incorporating channel sensing and backoff functions similar to those used in WLAN. In the LB-LBT scheme, the contention window size and clear channel assessment (CCA) threshold of the LTE-LAA eNodeB (eNB) significantly influences its transmission probability and the interference caused by concurrent WLAN transmissions outside the CCA range. However, most existing LB-LBT schemes use fixed contention window sizes and CCA thresholds, irrespective of the channel congestion status. To address this limitation, in this paper, we propose a channel measurement-based LBT (CM-LBT) scheme to enhance overall system throughput while ensuring fairness between LTE-LAA and WLAN systems. Our proposed CM-LBT scheme adaptively adjusts the contention window size and CCA threshold of LTE-LAA eNB in the LB-LBT scheme, according to the current channel access activities of LTE-LAA and WLAN systems. Through simulations, we evaluate the performance of our proposed CM-LBT scheme against existing LBT schemes by assessing the throughput of LTE-LAA and WLAN systems, as well as the fairness between them, using a reward function.  
l 2025 14 37
# Unveiling Biological Activities of Marine Fungi: The Effect of Sea Salt
## 2 Materials and Methods
### Antibacterial Activity of Fungal Extracts  
The dried crude extracts from the mycelia and culture media were tested for their antimicrobial activity against the same six bacteria mentioned above using the disk diffusion method described by the National Committee for Clinical Laboratory Standards (NCCLS) [39]. Bacterial suspensions were prepared from 24 h-old fresh bacterial cultures and adjusted to turbidity of 0.5 McFarland. Mueller-Hinton agar (Oxoid, Hampshire, England) plates were inoculated with a sterile swab and the plates were allowed to dry. The dried crude extracts were dissolved in sterile water to a concentration of 500 mg/mL. Then, 10 \(\upmu\)L of the dissolved extract was pipetted to 6-mm diameter sterile filter circular paper disks (Liofilchem, Roseto degli Abruzzi, Italy). Discs with sterile water were used as negative controls and commercial discs containing amoxicillin (10 mg/disc) and tigecycline (15 mg/disc) (Oxoid, England) were used as positive controls. After 15 min at room temperature, the plates were incubated at 37 \({}^{\circ}\)C for 24 h. The diameter of the inhibition zone was measured in millimeters and the assay was carried out three times for each strain.
## 4 Tool Application to Evaluate Pollutant Emissions
### Carbon Balance Method
#### 4.2.1 Demonstration of the Carbon Balance Method
To obtain the dry exhaust gas volumetric flowrate, the water contribution \(\dot{V}_{H_{2}O}\) must be deducted as follows:  
\[\dot{V}_{exh,dry}\;=\;\dot{V}_{exh}\;-\;\dot{V}_{H_{2}O}. \tag{26}\]  
The volumetric flowrate for CO and HC can be evaluated through the single components concentrations as follows:  
\[\dot{V}_{CO}\;=\;\dot{V}_{exh}CO_{concentration}\;\;;\;\dot{V}_{HC}\;=\;\dot{V}_{ exh}HC_{concentration}. \tag{27}\]  
On the other hand, as regards the volumetric flowrate for H\({}_{2}\)O and CO\({}_{2}\), the following formulae must be used:  
\[\dot{V}_{H_{2}O}=\frac{\dot{m}_{comb\;air,wet}Comb\;air_{cont\;H_{2}O}H_{2}O _{malor\;volume}}{H_{2}O_{malor\;mass}}+\frac{\dot{m}_{fuel}Fuel_{mix\;cont\;H_{2 }O}H_{2}O_{malor\;volume}}{2H_{atomic\;mass}} \tag{28}\]  
\[\dot{V}_{CO_{2}}=\frac{\dot{m}_{comb\;air,wet}Comb\;air_{cont\;CO_{2}CO_{2\; molaror\;volume}}}{CO_{2\;malor\;mass}}+\frac{\dot{m}_{fuel}Fuel_{mix\;cont\;C\;CO_{2\; molar\;volume}}}{C_{atomic\;mass}} \tag{29}\]  
These equations are based on the principle for which water and carbon dioxide are contained also in the combustion air; therefore, to consider it, a first addend, whose factors are described below, is necessary:  
* \(\dot{m}_{comb\;air,wet}\;[\rm kg/h]\) is the wet mass flowrate of the combustion air;
* \(Comb\;air_{cont\;H_{2}O}\) and \(Comb\;air_{cont\;CO_{2}}\) are the concentration of water and carbon dioxide in the combustion air, respectively;
# Evaluation of Reinforcement and Deep Learning Algorithms in Controlling Unmanned Aerial Vehicles
## 7 Conclusions  
Autonomously flying UAVs can no longer continue to use traditional controllers such as PID due to tuning, stability, and flexibility issues. However, new reinforcement and deep learning methods are currently showing better control and movement strategies in autonomous UAV flights.  
In this work, the simulation and performance evaluation of learning algorithms such as Q-learning, SARSA, and DQN was presented. These algorithms have been evaluated under a combination of positions (X, Y, and Z direction desired positions), optimizers (RMSProp, Adadelta, SGD, and ADAM), and reward functions (Euclidean distance and its MSE). From the evaluation, DQN with the Adadelta optimizer using MSE has shown the best performance in flying drones from the initial to the desired position.  
In the future, we plan to investigate the performance of other deep network and neural network learning algorithms under environments that involve obstacles and complicated destinations and to introduce a complex reward function that is more suitable for the autonomous UAV flight.  
Figure 8: DQN and Adadelta optimizer with steps/episode varying for a total of 500 episodes using MSE reward function.  
Figure 7: DQN and different optimizers with 100 steps/episode for a total of 500 episodes using MSE reward function.  
**Author Contributions:** Conceptualization, methodology, validation, and writing--original draft preparation, Y.Z.J. and Y.W.N.; supervision, project administration, funding acquisition, B.K. and M.A.; software, resources, visualization, M.T.R.K. and R.P.; writing--review and editing, validation, S.H.A.S. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was supported by the Bisa Research Grant of Keimyung University in 2020 (No. 20200195).  
**Institutional Review Board Statement:** Not Applicable.  
**Informed Consent Statement:** Not Applicable.  
**Data Availability Statement:** The data used in the experimental evaluation of this study are available within this article.  
**Conflicts of Interest:** The authors declare no conflict of interest regarding the publication of this manuscript.