esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimizing IoT Video Data: Dimensionality Reduction for Efficient Deep Learning on Edge Computing
## 3 Materials and Methods
### Single Frame  
The final dimensionality reduction technique implemented in this study involves using a single frame for the task, thereby discarding the temporal information contained within the video. The selected architecture processes a single frame, utilizing both the environmental context and the bird-specific features captured at that moment to perform the classification. We propose two primary approaches to extract a single frame from the video sequence. The first one is selecting the central frame of the sequence, while the second is computing the mean pixel values across the sequence to generate a representative frame. This selection serves as the first step in dimensionality reduction, reducing the input dimensions from \(16\times 3\times 224\times 224\) to a single frame of \(3\times 224\times 224\), effectively achieving a 16-fold reduction in size.  
Following this initial dimensionality reduction step and drawing on methodologies used in previously proposed architectures, we employed pre-trained models such as 2D CNNs, Vision Transformers, the DINOV2 [60; 61] model, and a Histogram of Oriented Gradients (HOG) [62]. The 2D CNNs utilized include ResNet [63], MobileNet [64], DenseNet [65], and VGG [66], while the implemented Vision Transformer models include ViT [67] and Swin Transformer [68]. The primary objective of this methodology is to leverage CNN kernels and transformer layers to extract meaningful patterns and features from images for classification tasks. The extent of dimensionality reduction achieved depends on the chosen model. For example, using ResNet, which delivers the most promising results among the 2D CNNs, we obtained embeddings of size 2048, achieving a reduction of over 1100 times. Other 2D CNNs produce varying reduction rates based on their internal representation sizes. For Vision Transformers and the DINOV2 model, the embedding size is 768, resulting in a reduction of more than 3100 times in size. HOG features were computed to extract complementary information [69; 70], with these features yielding a reduction size of 1,031,940, corresponding to a more modest reduction of slightly over two times. As in prior methodologies, the extracted representations were combined with an MLP for final classification. Figure 3 illustrates the overall architecture.
## 3 System Model
### Computation Model
#### 3.3.1 Local Computing  
Let \(f_{n}^{l}\) denote the CPU computation capacity of mobile device \(n\). The local completion latency of \(u_{n,k}\) can be given below:  
\[T_{n,k}^{l}=\frac{w_{k}}{f_{n}^{l}} \tag{2}\]  
The computational energy consumption is expressed as:  
\[E_{n,k}^{l}=w_{k}e^{l} \tag{3}\]  
where \(e^{l}\) is the coefficient. According to work [27], \(e^{l}\) can be obtained by:  
\[e^{l}=k(f_{n}^{l})^{2} \tag{4}\]  
where \(k\) is the energy coefficient. We set \(k=10^{-11}\) according to the work [28].
# Random Access Using Deep Reinforcement Learning in Dense Mobile Networks
## 3 Problem Formulation and System Model  
The system model is illustrated in Figure 3. The network model describes a scenario where multiple TRxPs \(\{A_{1},A_{2},\ldots,A_{N}\}\) are deployed to cover a large area. The TRxPs could be femto-cells, pico-cells, macro-cells, or gNodeBs. User devices \(\{T_{1},T_{2},\ldots,T_{K}\}\) requesting access should select one to proceed with the RA step. The users are moving within the network and therefore are constantly hopping among the TRxPs.  
Users can receive a signal from one or more TRxP depending on their position. We do not assume a centralized controller managing the handover because it is unrealistic. In this case, users must identify TRxPs within the transmission range. Once the selection is performed, users participate in contention-based RA. The selection would be performed by using the deep reinforcement learning algorithm proposed in this paper. The TRxPs will rotate RAOs, which are the preambles periodically selected by the users. A four-way handshake or modified one-shot procedure can work with the considered system model.
# Improved Altitude Control Algorithm for Quadcopter Unmanned Aerial Vehicles
## 4 Numerical Simulation, Experimental Results and Discussions
### Experimental Demonstration and Discussions  
In this subsection, we present the experimental results of a step-altitude-reference-tracking flight to evaluate the performance and demonstrate the effectiveness of our new controller in real flight conditions. After arming, the quadcopter stays on the ground and prepares for take-off during the first ten seconds (Figure 14). The altitude reference of 15 m is commanded at time \(t\approx 10\) s leading the vehicle to ascend. It is clear that, initially, the conventional PID controller performance is considerably faster than our proposed controller. However, similarly to the simulation results, our new controller exhibits a superior performance compared with the PID controller when the tracking error becomes smaller. If we look at the inset in Figure 14, it is seen that the response of our proposed controller is about 5 s faster than that of the conventional PID controller. The experimental vertical velocity and acceleration performance (Figures 15 and 16) also prove the stability and the effectiveness of the proposed algorithm.  
Figure 12: Comparison of altitude performances between our new controller and a conventional PID controller for Scenario 2 (tracking a moving altitude reference).  
Figure 13: Comparison of the altitude tracking error convergence between our new controller and a conventional PID controller in Scenario 2.  
Figure 16: Comparison of the experimental vertical acceleration performance between our new controller and a conventional PID controller.  
Figure 14: Comparison of experimental altitude performance between our new controller and a conventional PID controller.  
Figure 15: Comparison of the experimental vertical velocity performance between our new controller and a conventional PID controller.  
The way our new algorithm exhibits the performance promises a smooth system response when the vehicle starts a movement and a fast target approaching speed when the tracking error becomes small. These properties are useful in performing tasks that require high accuracy and smooth performance such as precision landing, obstacle avoidance, and autonomous delivery to high buildings, etc.
## 3 Results and Discussions
### Horizontal Release through the Leakages on the Wall  
#### 3.2.1 Iso-Surface of 4% Male Fraction  
The iso-surface of 4% mole fraction of hydrogen for Model 4 and Model 5 can be seen in Figure 6. Like Model 1, Model 2, and Model 3, the dispersion process for Model 4 and Model 5 consisted of two phases. In the first phase, the time required for the iso-surface of 4% mole fraction for Model 4 and Model 5 to rise to the ceiling was 100 s and 5 s, respectively, and the shape of the concentration envelope was similar to a rectangle. The distance between the leakage position and the ceiling for Model 4 was greater than for Model 5, and thus took a longer time for the dispersion.  
Figure 5: Velocity vectors for Model 1 (left), Model 2 (middle), and Model 3 (right) whilst the envelope of 1% mole fraction of hydrogen was rising to the ceiling (up) and filling the whole ceiling (down) (m/s).  
Figure 6: Iso-surface of 4% hydrogen mole fraction for Model 4 (left) and Model 5 (right).  
In the second phase, the envelope of iso-surfaces of 4% mole fraction extended horizontally along the ceiling. The amount of time required for the envelope of iso-surfaces of 4% mole fraction in the whole diffusion process, including two phases, rising to the ceiling and extending along the ceiling, was 170 s and 70 s, respectively. Therefore, the time required for both Model 4 and Model 5 in the second phase was about 70 s. It is evident that the time for Model 4 and Model 5 was almost equal. The velocity vectors in the z = 0.37 plane for the envelope of iso-surfaces of 4% mole fraction rising to the ceiling and diffusing horizontally is illustrated in Figure 7. The horizontal diffusion velocity along the ceiling was about 0.15 m/s for Model 4 and Model 5, respectively. Therefore, the time for diffusing horizontally along the ceiling for Model 4 and Model 5 was almost equal. The height of the leakage taking place on the wall had little effect on the horizontal diffusion velocity of the iso-surface of 4% mole fraction at the ceiling.
# Oblique Wide-Angle Multi-Sector Metamaterial Absorber for Space Applications
## 2 Design
### Optimization of Sector B  
A second absorber, denoted sector B, is optimized using a genetic algorithm to operate on the angular range \([55^{\circ},\,65^{\circ}]\). For facilitating the manufacture, the honeycomb thickness is chosen constant for all sectors. The optimization thus only affects the geometrical parameters of the FSS. The optimizations results are presented in Figure 5 and the corresponding values of the geometric parameters are given in Table 2.  
Figure 5: Comparison between analytical model and EM simulation of sectorial absorber B for TE polarization (**a**) and TM polarization (**b**).  
Similarly to what is observed for sector A, a good agreement between simulation and model is obtained. A maximum frequency shift of 50 MHz is observed for 65\({}^{\circ}\) in TE, which is probably due to the fact that the model does not take into account high order resonant modes of the FSS. As with sector A, we observe a scan and a mismatch effect, which limits the operational angular range of the design. For both polarizations, the resonance condition at 2.15 GHz is set at 60\({}^{\circ}\) (center of the targeted incidence range). For this frequency, \(G\) is matched to \(Y_{0}^{TE}\) at 55\({}^{\circ}\) for TE and to \(Y_{0}^{TM}\) at 60\({}^{\circ}\) for TM. The absorber complies with the requirement of \(-\)13 dB between 2 GHz and 2.3 GHz (represented by a blue rectangle in the graph).  
Finally, the reflection coefficient with respect to incident angle is represented at 2.15 GHz for sectors A and B and both polarizations in Figure 6. We observe that both sectors are operating in the targeted angular range. Sector A has a reflection coefficient inferior of \(-\)15 dB in [35\({}^{\circ}\), 55\({}^{\circ}\)] and sector B, a reflection coefficient inferior of \(-\)17 dB in [55\({}^{\circ}\)-65\({}^{\circ}\)]. The sector A remains below \(-\)10 dB from normal incidence to 60\({}^{\circ}\). It is interesting to note that in Figure 6, the absorption peaks with respect to angle are more selective for TM polarization than for TE, which is consistent with the angular sensitivity analysis made for Figure 4.
# Protocol for Energy-Efficiency in Networked Control Systems Based on WSN
## 6 Example of Application of the Energy-Efficiency Protocol
### Network Parameters  
In this experiment, it is assumed that the measurement information gathered by each sensor and the control signal associated with each actuator are transported separately, as illustrated in Figure 4.  
The first route \(\mathcal{R}=1\), which has seven nodes (including the source and sink nodes), transports the information referent to \(\bar{y}_{1}(k)=H_{1}(k)\). This route has \(N=6\) jumps, and each one has success probability equal to \(p(\bar{y}_{1})=0.5\). The second route \(\mathcal{R}=2\) transports the fragment \(y_{2}=H_{2}(k)\) and has \(N=4\) jumps and five nodes, considering the source and sink nodes, such that each node has the success probability equal to \(p(\bar{y}_{2})=0.5\). On the other hand, the transmission of the control signal packets \(\bar{u}_{1}(k)\) and \(\bar{u}_{2}(k)\), is made, respectively, by routes \(\mathcal{R}=3\) and \(\mathcal{R}=4\), both having six nodes, considering source and sink nodes, implying \(N=5\) jumps. The success probability for each node of  
Figure 4: Network topology for the experiment presented in Section 6.  
route \(\mathcal{R}=3\) is \(p(\tilde{u}_{1})=0.45\), while for route \(\mathcal{R}=4\) is \(p(\tilde{u}_{2})=0.4\). All the network parameters are summarized in Table 4.  
As proposed in Section 5.5, the following criteria are used to evaluate the Protocol of Energy-Efficiency of the communication network:  
* The global energy consumption (quantified in J/hour) computed by \[E_{GE}=\sum_{\mathcal{R}\in\mathcal{T}_{\mathcal{R}}}\mathcal{E}(E_{T}(p,L,N ))(\mathcal{R})\] (23) with \(\mathcal{E}(E_{T}(p,L,N))\) given in Equation (17);
* The energy consumed per unit of time \(\mathcal{E}|M^{(i)}|\) by the node \(U\) with the highest data traffic (number of transmissions) in the network. The value of \(\mathcal{E}|M^{(i)}|\) can be computed using the methodology proposed in [6] for \(i=1,\ldots,N\), where \(N\) is the number of hops of the route, or using the R Package 'Hopbyhop' [43], as shown in Appendix A.  
It is important to emphasize that the source and sink nodes are neglected in the determination of the critical node (the one with the highest consumption), since it is considered that they are associated with sensors and actuators with their own batteries. Thus, their energy consumption for communication purposes is not relevant. Besides the sampling period \(T_{S}=10\) s, the parameters required for the computation of Equation (17) are information available in actual communication networks which, in this particular example, were obtained from (The actual energy consumption parameters are based on XBee Pro devices [59]) [9] and [13] Section 7.3.1, corresponding to: \(E_{TX}=228\) uJ, \(E_{RX}=208\) uJ, \(E_{SW}=0\), \(\Xi=3600\).
## 4 Discussion  
In this study, the effect of the CAP activated solution on the [PERSON] skin flap was investigated and was compared with the effect of the PRP solution and a single dose of L-NAME. Reactive oxygen and nitrogen species (RONS) in an optimal dose are crucial players in the biological effects of plasmas [27]. The RONS are created by plasma treatment of aqueous solutions, thus forming the so-called short-lived species of nanoseconds to microseconds (e.g., hydroxyl (OH), peroxyl (HOO), superoxide anion (O\({}_{2}\)\({}^{-}\)), and singlet delta oxygen (\({}^{1}\)O\({}_{2}\))) not the case for this study, as they are neutralized until the application, followed by their transition to long lived species. The CAP activated solution contains long chemical species like hydrogen peroxide (H\({}_{2}\)O\({}_{2}\)), nitrite ions (NO\({}_{2}\)\({}^{-}\)), and nitrate ions (NO\({}_{3}\)\({}^{-}\)), as persistent species, and peroxynitrites (ONOO\({}^{-}\)) as secondary species; the key components of the CAP activated solution used in this study are known to be involved as endogenous radicals in many intra- and inter-cellular processes, such us signaling and biological processes [27; 28; 29; 30; 31; 32; 33; 34]. As denoted by [PERSON] et al. [27], a major challenge in plasma medicine remains the control of plasma generated reactive species formed in the liquid in order to have an indication of the therapeutical dose. Long-lived species such as H\({}_{2}\)O\({}_{2}\) and NO\({}_{2}\)\({}^{-}\) are by far the species mostly characterized, followed by NO\({}_{3}\)\({}^{-}\). The same authors [27] determined that the concentration of H\({}_{2}\)O\({}_{2}\) (\(\sim\)1700 umol/L) and NO\({}_{2}\)\({}^{-}\) (\(\sim\)1800 umol/L) when using biocompatible liquids as the plasma activated media in similar conditions has positive effects on the indirect treatment. In addition, the NO\({}_{3}\)\({}^{-}\) concentration was found to be around 2600 umol/L in comparable settings. In this study, the CAP activated solution preserved a variable concentration of RONS as a function of the elapsed time (2 h later) from the direct exposure of saline to administration in the form of a single dose injection into the flap; therefore, the kinetics (depletion and transformation) of RONS in solution is strongly influenced by temperature (room temperature in this case), as also pointed out by a recent study by [PERSON] et al. [35]. It is expected that 2 h after activation, only the long-lived species such as H\({}_{2}\)O\({}_{2}\), NO\({}_{2}\)\({}^{-}\), and NO\({}_{3}\)\({}^{-}\) will remain in a mM concentration range [36; 37]. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Control Group** & **CAP** & **PRP** & **L-NAME** \\ \hline Neutrophil exudation & ++++ & +/\(-\) & + & +/\(-\) \\ Angiogenesis & + & ++++ & ++++ & ++++ \\ Conjunctive tissue & + & ++++ & ++++ & ++++ \\ Necrosis & ++++ & +/\(-\) & + & \(-\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Scoring of the histological findings in control and treated groups at flaps’ level.
###### Abstract  
Coherent optical transceivers offer significant advantages over direct-detect optical transceivers. However, both intradyne coherent transmitters (ICT) and intradyne coherent receivers (ICR) are more complicated and require careful calibration. Traditionally, an ICR is calibrated through heterodyne beating using a single-polarized signal. Active stabilization to maintain a constant state of polarization for the calibration signal is necessary, leading to a relatively complicated setup. We demonstrate a novel method through heterodyne beating using a polarization-multiplexed signal. No polarization control is needed, resulting in a much simpler configuration. The calibration results obtained through the polarization-multiplexed signal match with the results using a single polarized signal. Moreover, this polarization-multiplexed signal can be generated within the intradyne coherent transmitter without using any external components. This innovative technique enables the calibration of a coherent receiver for deployment in the field throughout its lifetime.  
intradyne coherent receiver; intradyne coherent transmitter; coherent communication; optical communication; polarization-multiplexed signal Article
# Ultrasound Elastography in the Evaluations of Tendon-Related Disorders--A Systematic Review
## 4 Discussion
### Discussion
Despite the controversial outcomes, these studies showed that US elastography is an accurate method in the diagnosis of Achilles tendon pathologies, and it can be useful in the evaluation of Achilles tendinopathy, especially when paired with US imaging. Medial and lateral epicondylitis have also been analyzed multiple times [24; 25; 26; 30; 36; 37; 52; 58]. In these cases, the available findings are not controversial and all studies suggest that pathological tendons are softer than healthy structures. Additionally, both SWE and SE are valid imaging techniques for diagnosing medial and lateral epicondylitis with good to excellent sensitivity and specificity. The study by [PERSON] [53] revealed a higher strain ratio in symptomatic tendons, unlike other papers' assumptions. This is probably due to the fact that they selected the subcutaneous fat tissue near the common extensor tendon origin as a reference ROI for calculating the strain ratio. As fat is softer than the tendon tissue, this may represent a convincing explanation for the resulting higher strain ratio [53]. The last series of studies concerns supraspinatus tendinopathy without tears [37; 38; 39; 40; 51]. In these papers, US elastography has been compared to MRI, showing a good to excellent correlation with MRI findings. The results are in agreement with the expectations about tendon softening. The study by [PERSON] [51] has to be mentioned because, in the strain ratio calculation, they used subcutaneous tissue as a reference area in one measure and a gel pad in the other measure. Despite that, the conclusion did not differ from other papers, since MRI tendinosis grade is associated with stiffness assessed using US elastography [51]. Therefore, we can state that US elastography is a useful clinical tool. It allows a quantitative measurement of tissue stiffness, and, thanks to its sensibility, it is able to detect tendons alterations that are not evident in US B-mode evaluation [37]. However, its role in revealing early subclinical alterations is still debated [65]. Nevertheless, US elastography provides real-time feedback in tendon evaluation. However, as reported in the current literature, there is a need to combine US and clinical evaluation [70].
# New Insights into the Biology of the Emerging Tembusu Virus
## 3 Epidemiology  
### Vector  
TMVU was first isolated from a _Culex tritateniorhynchus_ mosquito captured in Kuala Lumpur, Malaysia, in 1955 and has subsequently been described in different _Culex_ species (Table 1). In nature, _Culex_ mosquitoes appear to be the preferential vectors of TMUV as with other bird flaviviruses such as WNV and JEV. It is presently unknown whether _Aedes_ mosquitoes, which are the major vectors of important flaviviruses such as DENV, are also able to transmit TMUV, although a competence study showed that _Aedes albopictus_ can be infected by TMUV [33]. Recently, _Culex tritateniorhynchus_, _Cx. quinquesfasciatus_ and _Cx. pallens_ were shown to be susceptible to TMUV strains isolated in China. However, despite the presence of the virus in the salivary glands of _Cx. pallens_, [PERSON] et al. found that only _Cx. tritateniorhynchus_ and _Cx. quinquesfasciatus_ were able to transmit the virus to ducks [33]. Despite the latter report and evidence for TMUV infection in _Cx. quinquesfasciatus_ under natural conditions [34], a recent laboratory study did not confirm the vector competence of this species for TMUV, contrary to _Cx. tritateniorhynchus_ that may be considered as a major vector. In the same line of thought, _Cx. tritateniorhynchus_ is mainly found in farms and plays a major role in the dissemination of TMUV in the bird population in Thailand [35; 36]. The widespread detection of TMUV in various _Culex_ species in different countries indicates that a more thorough evaluation of the vector capacities of local _Culex_ species should be undertaken. This will allow authorities to assess the capacity of TMUV to spread in areas where _Cx. tritieniorhynchus_ is absent, or present in small numbers, and where other _Culex_ species are endemic. The intrinsic incubation period during which the virus replicates in the mosquito to reach the salivary glands should also be evaluated to estimate the transmission capacity.
# Data Quality Analysis and Improvement: A Case Study of a Bus Transportation System
## 7 Conclusions and Future Work
### Future Work  
While this approach provides a validated solution for improving data quality in bus transportation systems, there are still two limitations that need to be addressed: performance and real-time requirements.  
On one hand, we have implemented the solution and workflow with multiple stages, but there is room for performance improvement. Data skew exists in these stages due to the default partitioning method. To enhance performance, we plan to implement spatial-temporal partitioning and indexing to efficiently organize datasets in the pipeline. On the other hand, the current solution operates in batch-processing mode, which is insufficient for handling real-time data streaming generated in bus transportation systems. Our next step is to implement our solution using stream-processing engines like Spark Streaming and Flink [41]. This will enable the quick transformation of raw datasets with different data quality problems into high-quality datasets.  
By addressing these limitations, we anticipate achieving higher performance and efficiency compared to the current version.  
Conceptualization, S.S. and W.X.; methodology, S.S., W.X. and X.C.; software, S.S.; validation, S.S.; investigation, W.X.; writing--original draft preparation, S.S. and W.X.; writing--review and editing, S.S., W.X. and X.C.; visualization, S.S. and W.X. All authors have read and agreed to the published version of the manuscript. This research was funded by the National Natural Science Foundation of China (NSFC) [Number: 61862066]. Not applicable. Not applicable. Not applicable. Data Availability Statement: The data presented in this study are available on request from the corresponding author. The authors declare no conflict of interest.