esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 The Proposed NeXtFusion 3D Object Detection Network
### Associating Radar Data to the Image Plane  
The proposed NeXtFusion network utilizes a modified frustum generation mechanism to associate image data with radar data, similar to CenterFusion's approach [36]. This technique leverages the object's two-dimensional bounding box from the camera sensor along with estimations of the object's three-dimensional size, depth, and orientation from the radar sensor. By doing so, a tightly defined region of interest (RoI) is constructed around the object of interest called frustum. This frustum then facilitates the filtering of radar detections. Only radar detections located within the frustum are then considered for association (concatenation) with the camera-detected object.  
Instead of utilizing only one radar detection for each object proposal individually, as described in [36], the proposed NeXtFusion network utilizes a modified mechanism that utilizes an entire cluster of radar detections that fall within the object's designated RoI. This allows the network to make use of collective information within the cluster, resulting in a more robust camera-radar data association because it was observed that the entire radar detection cluster, encompassing its shape, size, and orientation, holds valuable information resulting in an improvement in multi-modal object detection compared to the approach described in [36] which focuses solely on individual detections that only provide information about their specific location and velocity.  
Figure 9 provides a visual representation of the architecture of the proposed NeXtFusion multi-modal (sensor fusion) 3D object detection network. The design of the proposed network incorporates several modifications to the baseline single-modal (camera-based) NextDet [9] architecture. The proposed architecture comprises two CondenseNeXt [25] CNNs integrated into the backbone module to extract feature map representations from data acquired through camera and radar sensors. These feature maps are subsequently transmitted to the neck for feature fusion, facilitated by connections indicated by dashed lines. Furthermore, the design of the network incorporates a strategy called early fusion, where feature maps from cameras and radars are concatenated at an initial stage. This merges information from both sensors (camera for visual details and radar for object distance and presence) to create a richer feature representation for object detection. Thus, by combining these features early on, the network can learn a more robust representation of the environment for object detection.  
Inspired by the work of [37], the design of NeXtFusion incorporates these connections in the network, which aims to improve gradient backpropagation, alleviate gradient fading, and minimize the loss of feature information, especially in scenarios involving small objects in adverse weather conditions, resulting in an improved and robust performance of the proposed object detection network.
## 3 Description of the Environment
### The Observation Space  
The agent's observation space must capture all critical environmental information that an AUV can obtain. Initially, the full-occupancy grid extracted from the electronic navigation chart was provided to the DRL agent; however, this configuration was found to be inefficient due to slow and unstable training progress, prompting the use of a simplified sonar-based representation. Beyond basic navigational data such as the position, heading, and speed, variables describing the agent's location relative to the goal were included. Specifically, the bearing and distance to the target were added. Moreover, to avoid collisions with fixed map objects, the sonar beams were simulated so that the vehicle could scan a \(90^{\circ}\) sector in front of its bow. At time \(t\), the entire observation of the vehicle is represented by an \(n\)-dimensional vector:  
\[\mathbf{o}_{t}=\Big{[}\,\bar{x}\;\bar{y}\;\bar{\theta}\;\bar{\cos(\theta)}\; \sin(\theta)\;\bar{d}_{\text{goal}}\;\bar{\phi}_{\text{goal}}\;\bar{r}_{1}\; \bar{r}_{2}\;\cdots\;\bar{r}_{N}\Big{]}^{\mathsf{T}}, \tag{32}\]  
where  
* \(\bar{x},\bar{y}\in[0,1]\) are the normalized coordinates of the vehicle's position;
* \(\bar{\theta}=\frac{\theta}{2\pi},\quad\theta\in[0,2\pi]\) is the normalized heading (in radians);
* \(\bar{v}=\frac{v}{\max_{\theta}},\quad v\in[0,v_{\max}]\) is the normalized speed of the vehicle;
* \(\cos(\theta),\sin(\theta)\) provide the cosine and sine of the current heading;
* \(\bar{d}_{\text{goal}}=\frac{d}{d_{\max}},\quad d\in[0,d_{\max}]\) denotes the normalized distance to the goal;
* \(\bar{\phi}_{\text{goal}}=\frac{\phi}{360^{\circ}},\quad\phi\in[0^{\circ},360^ {\circ}]\) is the normalized bearing to the goal;
* \(\bar{r}_{i}\in[0,1]\) are the normalized sonar readings for \(i=1,2,\ldots,N\).  
The heading was encoded using \(\sin(\theta)\) and \(\cos(\theta)\) instead of the raw angle \(\theta\). This approach avoids discontinuities at the \(0^{\circ}/360^{\circ}\) boundary, which could otherwise lead to instability in the training process. By mapping the heading to the unit circle, the policy network sees a continuous representation for the vehicle's orientation. An illustration of the observation space is provided in Figure 2.  
A simplified sonar model is adopted. A total of \(n=24\) sonar beams are assumed, uniformly distributed across a \(90^{\circ}\) sector ahead of the vehicle's bow, and thus each beam covers an angle of \(\frac{\pi}{n}\) where \(\alpha=90^{\circ}\). The maximum range of the simulated sonar is \(r_{\max}=500\,\text{m}\). Although industrial multibeam sonars can produce hundreds of range readings, the number of beams was set to \(n=24\) to balance computational efficiency with the fidelity of the environment. In future work, we intend to investigate more detailed sensor models (e.g., with 128 or 256 beams) and potentially use convolutional layers to extract rich embeddings from high-dimensional sonar data. The AUV's speed is constrained to the interval \([0,2.05\,\mathrm{m}/\mathrm{s}]\). Figure 3 shows the concept of these simulated sonar measurements.
## 5 Materials and Methods
### Overview  
Prior research work in the field of device-free sensing is mainly focused on adversarial models trained on a multi-source combined data features to extract domain-invariant representations of all domains and map them onto a new target domain for matching cross-domains with inconsistency problems [27, 41, 59]. At first, it is difficult for an adversarial model to efficiently extract domain-invariant features coming from a diverse range of source domains, and one-on-one mapping of these domains in a common feature space aligned with the target domain is also not convenient. This leads to poor recognition accuracy in many domain shifting tasks. Researchers also employed pre-trained models for transforming domains in device-free HAR in order to minimize the computational cost and training time and also to deal with the training data scarcity issues. However, transfer learning-based approaches are not very effective in reducing the training data proportions, as investigated by the work of [79]. They achieved comparable results to the adversarial models with 500 training data samples using a pre-trained roaming model that was already trained between environments A and B in order to learn quickly between environments A and C. Again, common feature extraction from multiple source domains is not easier using transfer learning-based approaches, especially when the training data samples of a new domain are very limited. Therefore, our proposed model is a combination of a shared pre-trained model connected with many adversarially trained domain-specific sub-networks. The first stage uses the benefits of transfer learning to extract domain-invariant representations of different pairings of multiple sources with a target domain. The second stage uses domain adaptation techniques to align each pair of source and target domains into specific feature spaces and match their distributions. To the best of our knowledge, this is the first attempt to test multi-source domain adaptation with a transfer learning-based method to assess the domain inconsistency problem in human activity classification using device-free sensing.
## 2 Related Work
### QKD Techniques in Hybrid GEO/LEO Scenarios  
The quantum-limited coherent measurements of optical signals were successfully sent from a GEO satellite to the ground station in 2017 [27], indicating that the GEO can implement QKD in principle. In [28], a double-layer quantum satellite network architecture was proposed, where both GEO and LEO satellites were included. In particular, the researchers are planning to launch the higher-orbit quantum satellite [12]. These works have demonstrated the value of further research on the routing and secret-key provisioning schemes in the hybrid GEO/LEO scenario. In this paper, we focus on improving the relatively low efficiency of secret-key provisioning.
## 2 Reinforcement Learning
### General Deep Reinforcement Learning
The DNN model has a predefined number of outputs that correlate with the number of possible actions. The Q-value is computed for each action after every learning iteration. The DNN model is learned to minimize the expected squared error (also known as _loss_) between the predicted Q-value and the target Q-value, as described by the following equation:  
\[L=\mathbb{E}\left[(r_{t+1}+\gamma\max_{a^{\prime}\in A}Q(s_{t+1},a^{\prime})-Q( s_{t},a_{t};\theta))^{2}\right] \tag{3}\]  
Target Q-values are usually computed based on the separate target DNN model, which has the same architecture as the Q-value DNN model. The weights of the target DNN model are updated periodically by copying weights \(\theta^{\prime}\) from the Q-value DNN model (which updates its weights during each learning iteration). Keeping the fixed weights in target DNN model for a predefined period of time ensures a temporally static Q-value target. That eliminates the moving target problem and consequentially provides stabilization of the DQL learning process. Additionally, the described approach reduces drastic oscillations in the control policy design upon small changes of the Q-value [20]. The use of the two DNN models in the DQL framework is commonly known under the term Double DQL. It is shown that Double DQN effectively mitigates over-estimation of rewards in noisy controlled environments such as traffic flows in complex traffic networks, and therefore improves its overall performance [5]. The target Q-value is described as  
\[Q_{target}(s_{t},a_{t})=r_{t}+\gamma Q(s_{t+1},arg\max_{a^{\prime}\in A}Q(s_{t +1},a^{\prime};\theta);\theta^{\prime}). \tag{4}\]  
In Double DQL framework, the output of Equation (4) replaces the \(\underset{a^{\prime}\in A}{\max}Q(s_{t+1},a^{\prime})\) from Equation (3). Another important part of the general DQL is Experience Replay (ER). The ER is used as the _memory buffer_ for storing the experience tuples \(\{s_{t},a_{t},r_{t},s_{t+1}\}\) during the observation phase in DQL learning process. The observation phase starts with the operational work of DQL, while the end of this phase means that the ER is full. At the end of the observation phase, it is possible to sample Mini-Batches from ER. Mini-Batches are used as the learning sets of inputs for the Q-value DNN model. The ER has a predefined size; thus, when the new experience tuple is added, the last stored tuple is removed. Furthermore, many studies use _reward clipping_ procedure which scales and clips the rewards in a specific range (commonly \([-1;+1]\)). The described procedure is done to avoid the boosting of weights when back-propagating [20]. Functional scheme of general DQL can be seen in Figure 1.  
The most commonly used algorithms for the update of DNN weights are based on the first-order gradient optimizers such as Stochastic Gradient Descent (SGD) algorithms [14; 16], and RMSprop [21; 22], but it is possible to include a more advanced optimization method such as ADAM [23] and AdaBound [24]. The weights must be optimized in every learning iteration to induce the minimization of Equations (3).
# Self-Guided Pixel-Wise Calibration for Low-Light Image Enhancement
## 3 Method
### Color Correction Module  
The enhanced images also suffer from low saturation, looking as if they are masked in a thin veil, as illustrated in Figure 2c. Visualizing the histogram of the enhanced image reveals that its pixels are concentrated within a limited range, rather than covering the full dynamic scale ranging between [0, 255]. Moreover, referring to Figure 2d, there are only a few pixels with values less than 20; thus, they could be negligible. However, their presence leads to a compression of the image dynamic range.  
In order to solve the problem of low color saturation caused by the dynamic range compression, the CCM is introduced in the network to generate enhanced images with better visual contrast and more vivid colors as demonstrated in Figure 2e,f. As the main goal of this paper is to achieve a wider distribution of the histogram, covering the full dynamic range [0, 255] is a must. To reach this goal, a shadow threshold is set up. This causes pixel values above the threshold to be rescaled to the range of [0, 255], and values below the threshold will become black. More specifically, the CCM can be defined as follows:  
\[O=\lfloor(I-thr)/(255-thr)\rfloor. \tag{5}\]  
where \(I\) represents the image before color correction and \(O\) is the corrected image, \(\lfloor.\rfloor\) is the floor function, and \(thr\) denotes the shadow threshold. In our experiments, firstly, the pixel values and total number of images are counted, and then the pixel values are sorted from smallest to largest, and the pixel value corresponding to the 0.1 quartile of the entire set is taken as the threshold.  
Figure 2: The first line shows the original low-light image and its histogram, and the second and third lines show a comparison between the results before and after applying the CCM. Parts (**a,b**) show the original low-light image and its histogram. Parts (**c,e**) illustrate visual effects, while parts (**d,f**) display their respective histograms. All the histograms represent what is common to each RGB channel.
## 2 Materials and Methods  
### Site Description (Including Vegetation)  
The study took place in the surroundings of the Puchuncavi-Ventanas industrial complex in the coastal area of the Valparaiso region in central Chile. The primary emission sources operating in the complex are a copper smelter and refinery (400,000 metric tons of refined copper per year) [17] and a thermoelectric coal plant (544 MW) [18]. The soil of this zone has received polluted atmospheric deposition since 1964, mainly metal-rich particulate matter and acid rain due to the SO\({}_{2}\) emissions from the copper refinery. Since 1999, i.e., after 35 years of production, the emissions were reduced significantly thanks to the incorporation of abatement measures required for entry into force of the new state environmental regulations [19]. As a consequence, the soils around the industrial complex now have an acidic pH and high concentrations of trace metals and other elements [10; 11; 20; 21; 22]. Accordingly, the ecosystems associated with these soils have become seriously degraded and eroded, characterized by a bare and sparse vegetation [11].  
Three sampling points were selected for the present study: La Greda (LG) (32\({}^{\circ}\)45\({}^{\prime}\)14\({}^{\prime\prime}\) S-71\({}^{\circ}\)28\({}^{\prime}\)17\({}^{\prime\prime}\) W), Los Maitenes (LM) (32\({}^{\circ}\)46\({}^{\prime}\)20\({}^{\prime\prime}\) S-71\({}^{\circ}\)28\({}^{\prime}\)13\({}^{\prime\prime}\) W), and Valle Alegre (VA) (32\({}^{\circ}\)48\({}^{\prime}\)27\({}^{\prime\prime}\) S-71\({}^{\circ}\)26\({}^{\prime}\)5\({}^{\prime\prime}\) W), located 1.2 km NE, 1.5 km SE, and 6.5 km SE, respectively, from the industrial complex. Additional samples were collected in Cachagua (CH) (32\({}^{\circ}\)34\({}^{\circ}\)52\({}^{\prime\prime}\) S-71\({}^{\circ}\)26\({}^{\prime}\)55\({}^{\prime\prime}\) W), located 20.3 km N from Ventanas. These sites were selected according to previous soil contamination records [10; 22] that may follow an expected concentration gradient pattern. La Greda and Los Maitenes are the nearest to the emission sources, and therefore, they are expected to be the most impacted sites. In contrast, Valle Alegre is located in an intermediate impact position, and Cachagua is the farthest location northwards and so is nearly unaffected by industrial impact (Figure 1).  
The vegetation at these sampling sites corresponds to typical Mediterranean ecosystems with a predominance of grassland and bushes [20]. In the study area, especially the zone about 2-3 km around the emission sources, the vegetation cover decreases gradually until almost disappearing at the bottom of the stack of smelting slag [12].
# Real-Time Eye Diagram Monitoring for Optical Signals Based on Optical Sampling
## 2 Theory of Optical Sampling and Eye Diagram Recovery
Figure 1: Schematic diagram of the proposed eye diagram monitoring method for an optical signal. PC: polarization controller; EPG: electrical pulse generator; MZM: Mach-Zehnder modulator; PD: photodetector; ADC: analog-to-digital converter; BER: bit error rate. Figure 4: Eye diagram recovery algorithm. Figure 3: Eye diagram. Figure 2: Principle of random equivalent sampling to recover the eye diagram. \(T_{X}\): the period of the optical signal under test; \(T_{S}\): the period of the sampling pulse; \(T_{P}\): pulse width of the sampling pulse. Step 1: Set the expected period \(T_{X}\) of the optical signal under test and recover the eye diagram according to the random equivalent sampling described in Figure 2.  
Step 2: Set the quantity \(m\) of the rectangle and the width and height of the \(i\)-th rectangle to be \(w_{i}\) and \(h_{i}\), and obtain the initial eye area \(S\) according to Equation (1). \(S_{0}\) indicates the eye area of the initially obtained eye diagram. Step 3: Increase \(T_{X}\) by \(n\Delta t\), repeat step 2, and obtain \(S_{n}\). It should be noted that \(\Delta t\) is the time resolution for recovering the period of the eye diagram. Step 4: Let \(n=1\), obtain \(S_{1}\) using step 2, and calculate \(Y\)= \(S_{1}-S_{0}\). If \(Y>0\), turn to step 5. If \(Y<0\), turn to step 6. If \(Y=0\), obtain the eye diagram and period \(T_{X}\). Step 5: Repeat \(n=n+1\), obtain \(S_{n}\) using step 2 and calculate \(Y\)= \(S_{n+1}-S_{n}\). When \(S_{n}/256>Y>-S_{n}/256\) (256 corresponds to 8-bit effective bits), obtain the eye diagram and period \(T_{X}\). Step 6: Let \(n=-1\), Repeat \(n=n-1\), obtain \(S_{n}\) using step 2, and calculate \(Y\)= \(S_{n-1}-S_{n}\). Obtain the eye diagram and period \(T_{X}\) when \(S_{n}/256>Y>-S_{n}/256\). According to the recovered eye diagram, more time-domain parameters such as period, time jitter, \(Q\) value, and bit error rate (BER) for the optical signals under test are obtained. The eye width is the period \(T_{X}\) of the optical signal under test. The time jitter can be extracted from the jitter histogram. The \(Q\) value, which is used to describe the signal quality, can be given by [19]  
\[Q=(I_{1}-I_{0})/(\sigma_{1}+\sigma_{0}), \tag{2}\]  
where \(I_{1}\) and \(I_{0}\) are the mean values of the eyelid and eye ground marked in Figure 3, respectively. \(\sigma_{1}\) and \(\sigma_{0}\) are the noise standard deviations of the eyelid and eye ground marked in Figure 3, respectively. The BER can be calculated based on the \(Q\) value as [19]  
\[\text{BER}=1/2\text{erfc}(Q/2^{1/2})\approx\exp(-Q^{2}/2)/[(2\pi)^{1/2}Q]), \tag{3}\]
# A Novel Energy-Efficient Reservation System for Edge Computing in 6G Vehicular Ad Hoc Network
## 3 Methodology
### Interfacing Protocol  
The IEEE 802.11p PHY/MAC protocol with multichannel expansions based on the IEEE 1609 WAVE standard is used in this investigation.
## 4 Conclusions  
Taking into account the analyses carried out, the most favorable scenario for conducting ATPL training in terms of emissions and energy consumption is scenario B. However, in this case, the future pilot only flies 45 h in real conditions, which is only 20% of current practical training. Considering the element of teaching and acquiring piloting skills in the analyses, the most favorable solution is scenario C. With such a solution, the future pilot receives 50% of training (approximately 110 h) in practical conditions and the same amount in simulators. This not only reduces CO\({}_{2}\), NOx, and HC emissions by 20-25%, but also reduces the cost of training by about 23% (taking into account fuel and energy consumption cost). Scenario A is favorable in terms of emissions and training costs but conducting training entirely in simulator conditions could negatively affect flight safety and pilot qualifications. Based on performed analyses, it can be stated that an increase of FSTD use in pilot training should be a significant consideration in European Union green transport politics. The use of simulators during training, e.g., 5 h for the PPL license, should be obligatory, and not just an option, as it is currently. This approach brings many benefits, especially ecologically. Changing the training program and increasing the use of flight simulators can result in a reduction of CO\({}_{2}\) emissions by up to 70%. The energy consumption needed for training using FSTD depends on the advancement of the device used. However, regardless of the type of Flight Simulator Training Device and the value of energy consumed, CO\({}_{2}\) emissions resulting from the production of the necessary energy are much smaller and depend on the energy production methods in a given country, which are also presented in this article. Taking into account the dynamic development of renewable energy sources, CO\({}_{2}\) emissions in many countries will be negligible and will have a positive effect on reducing the impact of aviation on the environment in the training aspect, which is becoming more and more popular as the number of pilots is constantly increasing.  
Figure 8: ATPL integrated training cost depending on source of energy.  
**Author Contributions:** Conceptualization, M.G.-G. and M.G.; methodology, M.M., P.K.-P. and M.G.; software, M.M.; validation, M.M.; formal analysis, M.M. and P.K.-P.; investigation, M.M.; resources, P.K.-P.; data curation, P.K.-P. and M.M.; writing--original draft preparation, M.M. and P.K.-P.; writing--review and editing, M.M. and M.G.-G. visualization, M.M. and P.K.-P., supervision, R.J. project administration, M.M.; funding acquisition, R.J. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available in article.  
**Conflicts of Interest:** The authors declare no conflicts of interest.