esa-sceva/satcom-chunk-collection huggingface dataset:
# UAV Dynamic Object Tracking with Lightweight Deep Vision Reinforcement Learning
## 3 Preliminaries
### Scope and Constraints  
The scope of this research project is primarily focused on developing a lightweight DRL-based method for tracking target objects using multirotor UAVs, which possess the ability to hover in place [26]. Other UAV types that do not support vertical take-off and landing (VTOL) or hovering are not considered in this paper.  
During the development and testing phases, the method was implemented and evaluated within a simulation environment. The simulation environment gives us immediate access to critical data, including segmentation maps and depth images. The use of a simulated environment also helps to minimize potential risks during the development process.  
However, this project is constrained by the fact that real-world testing has not been conducted within its current scope. The primary focus has been on the development and evaluation of the proposed method in a simulated environment, and the transition to real-world testing presents a range of additional challenges that may impact the system's performance. These challenges may include factors such as variations in lighting, environmental conditions, and potential discrepancies between the simulated and real-world data. We try to extend the project's scope in the future to encompass real-world testing of the developed method. This expansion would provide valuable insights into the algorithm's robustness and adaptability in practical settings, further validating the effectiveness of the DRL approach for UAV tracking tasks.  
The minimum resource requirements that can be suitable for running the proposed method on UAVs are (these requirements are equivalent to the resources on the simulated UAVs used in our experiments):  
* Onboard processor: a low-power, energy-efficient processor with a minimum of two cores, such as the ARM Cortex-A series (e.g., Cortex-A53 or Cortex-A72), to handle real-time data processing and decision making during the operation.
* Onboard memory (RAM): a minimum of 2 GB of RAM to store the DRL model (approx. 1 GB), manage input data processing, and facilitate real-time decision-making.
* Storage: sufficient onboard storage capacity (e.g., 8 GB or larger) to accommodate the DRL model, input data buffers, and other essential software components.
* Camera system: a depth-sensing camera system compatible with the UAV's processor and capable of providing the required depth images for the DRL method. The segmentation camera is only needed when we want to utilize the best-proposed method. Otherwise, we employ a YOLOv4-tiny in our alternative proposed method for UAVs that are not equipped with a segmentation camera.
* Software environment: compatibility with the required software libraries and frameworks for running the DRL model on the onboard processor (e.g., TensorFlow Lite, PyTorch Mobile).
# SDR Proof-of-Concept of Full-Duplex Jamming for Enhanced Physical Layer Security
## 2 Background
### SCS-HK Adapted Scheme  
The ICS-HK and SCS-HK coding schemes [29; 30] rely on interleaving/scrambling the information with a random key, followed by encoding them together with an LDPC code. Some of the bits of the resultant codeword are punctured before the transmission to guarantee that only the legitimate receiver having a better channel can correctly recover the message. As shown in [37] adaptive PLS may be achieved with the ICS-HK and SCS-HK techniques by adjusting the number of punctured bits according to the channel conditions between [PERSON] and [PERSON], assuming the existence of a feedback channel through which [PERSON] can inform [PERSON] of his reception conditions.  
The SCS-HK scheme [29] was adapted to be used in this prototype, where the modifications were mainly in the type of scrambler used (additive rather than multiplicative) and how a packet is found (a simple verification after the header being decoded through a Reed-Solomon code). The first modification was performed to tackle the self-synchronizer capability of the multiplicative scrambler which grants information retrieval even without the correct seed/key. The latter was to maximize the throughput by removing the correlation operation which is computationally heavy and replace it with a bit-level comparison with a known word after being recovered with an error correction code.  
The SCS-HK adapted scheme architecture is represented in Figure 2. Firstly, the desired binary data for transmission \(M\) is scrambled using the additive scrambling (because it is not self-synchronizing, thus it is required to have the correct key to descramble it, or all the frame is lost) with a random key \(K\) resulting in \(S[M]_{K}\). This scrambled data is then appended to \(K\) resulting in \(K+S[M]_{K}\) and then encoded using the LDPC encoder \(E\{K+S[M]_{K}\}\) which results in the packet's payload \(E_{p}\) punctured with a pattern \(X\). Then, a header \(H\) with a known sequence is added, so that it can be recognized on the receiver side to know that the incoming payload results into a packet formed by \(P=H+E_{p}\). Finally, data is modulated with quadrature phase shift keying (QPSK) and pulse shaping is performed by applying the root raised cosine filter, before being transmitted with the SDR. Through the use of scrambling and puncturing of the key, this scheme aims to increase the degradation of the wiretap channel to [PERSON], while causing limited impact on the legitimate channel to [PERSON].
# Comparing Commit Messages and Source Code Metrics for the Prediction Refactoring Activities
## 5 Threats to Validity  
**External Validity:** This refers to the generalizability of the treatment outcomes. The first threat relates to the commits that are extracted only from open source Java projects. Our results may not generalize to commercially developed projects or to other projects using different programming languages.  
**Construct Validity:** This refers to the extent to which a test measures what it claims to be measuring. Since our approach heavily depends on commit messages, we used well-commented Java projects when performing our study. Thus, the quality and the quantity of commit messages might have impacts on our findings.  
**Internal Validity:** This refers to the extent to which a piece of evidence supports the claim. Our analysis is mainly threatened by the accuracy of the Refactoring Miner tool because the tool may miss the detection of some refactorings. However, previous studies [48; 53] report that Refactoring Miner has high precision and recall scores (i.e., a precision of 98% and a recall of 87%) compared to other state-of-the-art refactoring detection tools.
## 3 Methods
### Self-Attention Mechanism  
The self-attention, also called intra-attention, can be viewed as an extension of the attention mechanism with the ability to reduce external information dependency and model dependencies within the input data [37]. In recent years, deep learning models incorporating the self-attention mechanisms demonstrated improved performance in different applications, including machine translation and image description generation [37; 38; 39]. For instance, the authors in [40] demonstrated that the attention mechanism improves the capacity of both the generator and the discriminator (i.e., neural network models) to capture the long-range dependencies in the feature maps. One of the key properties of the self-attention concept consists of its flexibility to be employed in any layer that represents  
Figure 1: Schematic representation of attention layer.  
a data sequence like a time series, which enhances the internal input structure's learning by concentrating on the relation within observations of the same sequence. Notably, the key concept of self-attention is generating weights (termed score) between observation in position \(i\) and \(j\) of input data sequence \(\mathcal{X}\) as [37]:  
\[\varepsilon_{ij}=\frac{(\mathbf{W_{a}}\mathcal{X}_{i})^{T}(\mathbf{W_{a}} \mathcal{X}_{i}^{j})}{\sqrt{d}}. \tag{5}\]  
The weight of the self-attention, \(\mathbf{W_{a}}\), is obtained in the training stage. In (5), the division by \(\sqrt{d}\) is employed to make the convergence faster. High weights are an indicator of high relevance, whereas low weights indicate lower relevancy. The weights, \(\mathcal{E}_{ij}\), are then passed via a _softmax_ function. The normalization of the weights can make them be seen as a probability (the sum of weight values is 1).  
\[\mathcal{A}_{ij}=softmax(\mathcal{E}_{ij})=\frac{\exp(\mathcal{E}_{ij})}{\sum_ {j}\exp(\mathcal{E}_{ij})}. \tag{6}\]  
The output of the self-attention unit is given by,  
\[O_{i}=\sum_{j=1}^{n}\mathcal{A}_{ij}(\mathbf{W_{a}}\mathcal{X}_{i}). \tag{7}\]  
This output effectively enhances the extracted features' quality and explicitly describes the internal correlation of the input data. Of course, self-attention quantifies the level of relevance between the actual observation and any other observation previously seen in the sequence.
## 4 Results and Discussion
### Case Study 2
#### 4.2.2 Application of RTO  
The objective function was evaluated using the proposed RTO calculation with same parameters elucidated from delineation 1. The optimal values of TMS gained are given in Table 8, which shows that the RTO algorithm gives ideal and best values and that it optimized the total operating time to optimal values. The optimal values ensure that the relay will take the least possible time in the system for a fault at any area. The time taken by Relay 1 to work is the base for a fault at point A and will set aside the most noteworthy time for a relay at point C. This is alluring in light of the fact that, for a fault at point A, relay 1 is first to work while, for a fault at point C, Relay 6 should get first chance to work if it fails. Relay 3 should take over tripping action and, if Relay 3 in a similar manner fails to work, then Relay 1 ought to expect control over the tripping action. The objective function value achieved over the span of the simulation for the best applicant arrangement in every cycle is shown in Figure 9. This demonstrates that the convergence is speedier and obtained a satisfactory value in fewer iterations. The optimum value of the objective function is observed to be 11.93 by considering
# Investigation of THz Absorptive Signatures in Opioids
## 2 Materials and Methods  
### Methods by Combining Modeling and Experiment  
We applied both modeling and experimental methods to study absorption signatures (\"lines\") of opioids. The modeling effort involved first-principle molecular dynamics (MD) simulations with software, such as Gaussian [2; 18]. The input of Gaussian simulation was customized to include the structural features of opioid molecules. Gradient-corrected correlation functional b3 lyp was applied to calculate the exchange and correlation energies in density functional theory (DFT) Hamiltonians, and 6-31G(d) was chosen as the Gaussian-type basis set.  
The modeling results are used as guidance for the experimental studies of absorption lines and also provide an atomic-scale view on how the characteristic vibrational modes behave.  
A 780-nm fiber-based frequency-domain spectrometer (Model PB7200, Emcore Corp., Alhambra, CA, USA) was used for the experimental studies. The spectrometer was based upon the photomixing of one tunable wavelength laser and one fixed wavelength laser. Its range spanned from 0.2 to 1.7 THz. And its resolution was set at 1 GHz. Because the spectrometer was designed for field tests having neither a vacuum chamber nor a nitrogen box, the experiments were conducted at ambient conditions. Water vapor absorption lines were present, some of them very strong in the THz region.  
We made the following assumption for the experimental studies. When various materials are mixed together, their individual vibrational modes remain undisturbed. In other words, the absorption signatures from \"carrier\" materials and pharmaceutical opioids can be differentiated from their joint spectra.
# Enhanced Integrated Satellite-Terrestrial NOMA with Cooperative Device-to-Device Communication
## 3 Satellite-Aided NOMA with Cooperative D2D Communication
### SANOCO-D2D Operation
Thus, the maximization problem (17) is modified, as a maximum weighted matching problem in graph theory and specifically perfect, because each user will match with exactly one other user, which can be solved optimally by the Edmonds algorithm [22] in polynomial time. The Edmonds algorithm can be implemented in time \(O(|V|^{3})\)[23, 24], and faster implementation with time complexity \(O(|V||E|\log|V|)\) was given in [25]. Algorithm 1 shows the procedure that is followed in order to create the maximum weighted matching graph. ```
Input: Total number of UEs \(N\); Result:\(G=(V,E)\)for\(i\gets 1\) to \(N\)do
1 Add vertex \(v_{i}\) to \(G\);
2\(V=V+v_{i}\);
3 end for
4 for\(i\gets 1\) to \(N\)do
5 for\(j\gets i+1\) to \(N\)do
6 Connect vertices \(v_{i}\) and \(v_{j}\) with and edge \(e_{i,j}\) in \(G\);
7 Set initial weight of the edge \(e_{i,j}\) to \(w_{i,j}=0\);
8\(E=E+e_{i,j}\);
9
10 end for
11
12 end for
```  
**Algorithm 1**Graph creation. Regarding the operations that should be performed during the execution of Algorithm 1, at first, the total number of users \(N\) is given as an input and the vertices of the graph which represent the UEs in the system are created. Thus, for each UE\({}_{i}\) with \((1\leq i\leq N)\) a corresponding vertex \(v_{i}\) is created and added to the graph \(G\) and to the set of vertices \(V\). After that, each vertex of the graph should be connected with all the other vertices in the graph that is not already connected. For this reason, the algorithm visits the vertex \(v_{i}\) with \((1\leq i\leq N)\) and connects this vertex with all the other vertices \(v_{j}\) of the graph with \((i<j\leq N)\) via a properly labeled edge \(e_{i,j}\) with initial weight \(w_{ij}=0\). Additionally, the new edge \(e_{i,j}\) with weight \(w_{i,j}\) should be added to the set of edges \(E\) of the graph. When the edge creation procedure terminates, Algorithm 1 gives as an output the graph structure that will be used for this specific number of UEs \(N\) in order to update the weights of each edge and solve the maximum weighted matching problem using the Algorithm 2 and the Edmonds algorithm. In this way, SANOCO-D2D can model all the possible pair of users that are able to be formed and served only from the satellite via NOMA or via NOMA and D2D, when the D2D link does not experience failures, and at the same time, it is characterized as profitable compared to standalone satellite NOMA. Note that Algorithm 1 is executed once at the beginning for a specific number of users and also it is possible to save the structure of the graph for different numbers of users and re-load it whenever the number of users in the system changes. The steps of SANOCO-D2D for maximizing the sum rate of the system are given in detail in Algorithm 2.
# State of the Art in LP-WAN Solutions for Industrial IoT Services
## 2 Limitations on Existing IIoT Solutions
### Short-Range Connectivity
While in urban or suburban areas this should not be a problem, in remote locations it could be difficult or, at least, expensive because these areas usually lack a preexisting infrastructure that could provide Internet access [12]. Additionally, special equipment such as bridges is needed for different reasons. Firstly, these nodes are employed as intermediate points between the backhaul network and the edge-nodes due to the limited coverage range of the latter. Besides, all the collected data need to be gathered and formatted before sending it to the storage servers. When talking in terms of Big Data, accomplishing an accurate dimensioning of the bandwidth and the temporal storage needs of these devices is not a trivial task. For all these reasons, other approaches based on long-range technologies have also been employed for deploying IIoT services.
# A Novel Perturbation Consistency Framework in Semi-Supervised Medical Image Segmentation
## 1 Introduction
The effectiveness of our method is demonstrated through experiments on the public ACDC dataset. Our approach surpasses existing semi-supervised segmentation methods and several doodle-supervised segmentation methods with comparable annotation costs. The primary contributions of this paper are:  
* We introduce a novel semi-supervised medical image segmentation model called hybrid contrast learning match (HCLM). This model integrates a weak-to-strong consistency framework along with a contrastive learning framework employing specialized sampling methods. Coupled with the low training cost of the lightweight model, this combination achieves segmentation results with relatively high accuracy. * We propose a novel strong perturbation method called hybrid copy-paste perturbation (HCP). This method involves cropping a portion of a labeled image and pasting it onto an unlabeled image, creating a new training sample. * We introduce a specialized sampling-based contrastive learning framework. Integrating contrastive learning involves incorporating weakly perturbed branches from the weak-to-strong consistency framework into the training process. We employ the semantic grouping contrastive sampling (SGCS) method to reduce variance, facilitating faster model convergence while alleviating memory burdens. * The experimental results demonstrate that our method achieves a DICE accuracy of 90.6% on the ACDC dataset at a 7% annotation rate. These results surpass those of existing similar methods, validating the effectiveness of our approach for this task. This paper is structured as follows: Section 2 analyzes related work on semi-supervised learning and comparison learning. Section 3 details the specific implementation of our HCLM framework. Section 4 evaluates the segmentation performance through a comprehensive experimental analysis. Finally, Section 5 concludes the paper.
# Time-Division Color Holographic Projection in Large Size Using a Digital Micromirror Device +
## 2 Methods
### Unveven Color Correction  
CGHs generated using the method outlined in Figure 3 should be binarized before displayed on DMD. However, this study binarized CGHs after the original image was divided, so the relative brightness information between each CGH is lost. Thus, uneven colors occurred in the reconstructed image. Figure 6 displays the reconstructed image using the optical system in Figure 5. Figure 6a,b show the original color image and the optical reconstruction image with color unevenness, respectively.  
Figure 4: Reconstruction of CGH using the time-division reconstruction method.  
Figure 5: Optical system using a DMD.  
Figure 6: Color original image **(a)** and optically reconstructed image with color unevenness **(b)**.  
To correct the color unevenness of each divided block closer to its original brightness, we increase the projection time when the divided block is darkened. Conversely, if the divided brock is brighter, the projection time is shortened to eliminate color unevenness. Therefore, the projection time of each divided block is adjusted to perform time-division reconstruction. The projection time used is determined from the average brightness value of the original blocked images. The method is shown in Figure 7 and consists of three steps.  
In Figure 7a, the average brightness values of each divided block of the original image are calculated. In Figure 7b, an arbitrarily divided block is defined as a reference (the blue rectangular indicates the reference block), and the ratio of the average value per divided block to the reference block is calculated. In Figure 7c, multiplying the ratio by the hologram display time of the reference block, the projection times of each divided block can be determined and set in the DMD. The reference block should be set so that the projection time of each block does not exceed the performance of the DMD. The calibration of the projection times is required for each new image projection. However, the calibration is not complex and time-consuming.  
Figure 8 shows the optical reconstruction image after adjusting the projection time and correcting the color unevenness. Compared to Figure 6b, the color unevenness was eliminated and confirmed the effectiveness of this method.  
Figure 8: Optical reconstruction image with color unevenness corrected. The brightness and contrast were adjusted to make the reconstructed image clearer.  
Figure 7: Calculation of projection time: (**a**) calculate the average brightness value, (**b**) calculate the ratio of the average values relative to the reference, and (**c**) calculate the projection times for each division block. The blue rectangle indicates the reference block.
## 4 Experiments and Results
### Results and Discussion
#### 4.3.1 Generalist Model (GM)
According to the International Society of Hypertension, blood pressure is considered hypertensive when systolic and diastolic values are in the ranges of \(\geq\)149 and \(\geq\)90 mmHg, respectively. For binary classification assessing whether the predicted values indicated a clinical picture of hypertension, the Normotensive and Hypertensive classes in Figure 6 were binarized, revealing the confusion matrix and relevant metrics. Despite binarization, both models achieved the same F1-Score metric for the Hypertensive and Normotensive classes. NeuBP, with fewer parameters, demonstrated superior results in precision and sensitivity metrics. However, generalist models with holdout validation per person exhibit poor performance due to the signal specificity of PPG and ABP for each individual, akin to human fingerprints, making it a challenging problem to solve with this approach alone. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Target**} & \multicolumn{2}{c}{**NeuBP**} & \multicolumn{2}{c}{**Spectro-Temporal**} \\ \cline{2-7}  & **Systole** & **Diastole** & **Systole** & **Diastole** & **Systole** & **Diastole** \\ \hline Standard deviation & 23.16 & 11.68 & 11.13 & 5.23 & 13.28 & 6.56 \\ \hline Mean & 119.0 & 59.0 & 123.51 & 61.17 & 119.11 & 59.93 \\ \hline MAE [mmHg] & - & - & 17.69 & 8.90 & 18.12 & 8.78 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Arithmetic mean, standard deviation and absolute mean error in the GM experiment. A more effective strategy for handling signal specificity involves specialist models. These models, with data split based on a single individual, adjust their weights relative to that individual. The subsequent evaluations provide results for specialist model scenarios. Figure 5: Confusion matrix of hypertension classes achieved with values predicted by NeuBP and Spectro-Temporal model. The highlight values represents the best values reached between models. Figure 6: Confusion matrix of the values predicted in the GM experiment in relation to the true values in the binary classes. The highlight values represents the best values reached between models.