esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Methodology
### Training Algorithm  
The deep reinforcement learning algorithm used is the proximal policy optimization (PPO) algorithm [16] with the actor-critic setup. We adapted the centralized learning, decentralized execution paradigm [12] in which each robot has a copy of the policy \(\pi_{\theta}\) and the value \(V_{\phi}\) networks. Both the policy and the value networks have different sets of weights \(\theta\) and \(\phi\), respectively. Algorithm 1 summarizes the entire data collection and training process. At each time step, each robot receives its observation \(O_{i}^{t}\) and uses its copy of the policy \(\pi_{\theta}\) to generate the action \(a_{i}^{t}\). Each robot collects its data \(\left(O_{i}^{t},a_{i}^{t},r_{i}^{t}\right)\) from the environment, and after the amount of data exceeds a certain threshold \(T^{th}\), it sends the rollouts of data over to a centralized copy of both the policy and the value networks. Then, the gradients of the objective function \(L\) with respect to the centralized policy network weights \(\theta\) and value network weights \(\phi\) are computed. Then, the Adam optimizer [18] updates the weights \(\theta\) and \(\phi\) using the learning rate \(l_{r}\) for \(E\) epochs. After each update, each robot receives a copy of the update weights \(\theta\) and \(\phi\) to start collecting a new batch of data. Thus, the policy and value networks are trained on the experiences collected by all the robots simultaneously. As shown in Algorithm 1, Line 27, the objective function \(L\) is a combination of three objective functions. The first one is the PPO clipped surrogate objective function \(L^{clip}\) with the generalized advantage function (GAE) [19]\(A_{i}^{t}\) as the advantage estimator. The GAE \(A_{i}^{t}\) uses the value estimation obtained from the value network \(V_{\phi}\!\left(O_{i}^{t}\right)\) as the a baseline and \(\lambda\) as the decay parameter. The PPO clipped surrogate objective function \(L^{clip}\) is used to update the policy network. The second objective function \(L^{v}\) is used to train the value network \(V_{\phi}\) to reduce the mean squared error between the returns \(R_{i}^{t}\) and the value estimation \(V_{\phi}\!\left(O_{i}^{t}\right)\). The third objective function is the entropy \(L^{\epsilon}\) of the policy, which is used to ensure that the policy performs sufficient environment exploration over the training period. Gradient ascent is performed on the objective function by performing gradient descent on its negative value \(-L\).
###### Abstract  
Existing nighttime visibility enhancement methods primarily focus on improving overall brightness under low-light conditions. However, nighttime road images are also affected by glare, glow, and flare from complex light sources such as streetlights and headlights, making it challenging to suppress locally overexposed regions and recover fine details. To address these challenges, we propose a Nighttime Road Glare Suppression Network (NRGS-Net) for glare removal and detail restoration. Specifically, to handle diverse glare disturbances caused by the uncertainty in light source positions and shapes, we designed a gated positional attention (GPA) module that integrates positional encoding with local contextual information to guide the network in accurately locating and suppressing glare regions, thereby enhancing the visibility of affected areas. Furthermore, we introduced an improved Uformer backbone named LCAtransformer, in which the downsampling layers adopt efficient depthwise separable convolutions to reduce computational cost while preserving critical spatial information. The upsampling layers incorporate a residual PixelShuffle module to achieve effective restoration in glare-affected regions. Additionally, channel attention is introduced within the Local Context-Aware Feed-Forward Network (LCA-FFN) to enable adaptive adjustment of feature weights, effectively suppressing irrelevant and interfering features. To advance the research in nighttime glare suppression, we constructed and publicly released the Night Road Glare Dataset (NRGD) captured in real nighttime road scenarios, enriching the evaluation system for this task. Experiments conducted on the Flare7K++ and NRGD, using five evaluation metrics and comparing six state-of-the-art methods, demonstrate that our method achieves superior performance in both subjective and objective metrics compared to existing advanced methods.  
low-light image; Unet-transformer; glare suppression; gated positional attention +
Footnote †: journal: Applied sciences  
Article
# An Analysis of ML-Based Outlier Detection from Mobile Phone Trajectories
## 3 Framework for Continuous PoI Detection in Smart Cities  
### Smart Cities and Urban Sensing Background  
A key aspect in the development of Smart Cities or Smart Communities relates to the use of data collected via _cyber-physical systems (CPS)_ installed in specific IoT infrastructures across the city, or data collected from mobile personal devices, such as smartphones. MCS applications, therefore, rely on available IoT infrastructures and on personal CPS to improve people-centric services provided by Smart Cities. MCS is today applied to a wide range of services that enrich the notion of a Smart City, for instance, monitoring of infrastructures (e.g., energy consumption); increased awareness on social behaviour [19; 20]; improvement of traffic patterns [21]; detection of Pols based on user behaviour and user preferences [22]. The use of MCS based on pervasive, opportunistic sensing [20] needs to be seen as a key component of Smart Cities, where collected data can be used to better plan the city, by incorporating personal preferences of users to planned Pols. In the related literature, most work focuses on recommendations for Pols, where Pols are defined in a static way by municipalities, and PoI detection is considered in recommendation engines, e.g., to provide recommendations about potential itineraries around a city [23]. However, MCS brings in the possibility to detect Pols in a passive way, derived from data collected from city infrastructures and users' personal devices. Here, the key aspect is not to provide recommendations based on pre-established points; instead, it is to derive Pols based on the mobility behaviour of users in a city. For this purpose, the next sections start by explaining the proposed architecture and then explain the concept of PoI in the context of our work. Then, debate on how the detection of Pols and outlier detection can be carried out.
# A Survey of Deep Learning for Data Caching in Edge Network
## 3 Deep Learning Outline
### Convolutional Neural Network (CNN)  
As aforementioned, the enormous number of weight parameters pose a significant challenge in the training complexity of FNNs. In order to overcome this drawback, whilst being inspired by the idea of receptive field in biology, CNN is proposed with the structure of stacked convolutional layer and pooling layer, followed by some fully connected layers. The convolutional layer employs sliding convolutional filters to the input vector, and the filter's weights do not change during processing, a.k.a. weight sharing. The pooling layer applies downsampling operation, usually through the maximum or mean pooling. In this respect, both weight sharing and pooling reduce the number of training parameters. It is worth noting that the downsampling operation also takes the risk of dropping some information, which lowers the association between the local and global patterns, especially for the case of info-intensive task (i.e., each element of a matrix or each pixel of an image plays an important role in final object detection). For example, there is no pooling layer in Alpha-Go's neural architecture [44]. Generally, CNN tends to contain deeper layers and smaller convolutional filters, and the structure is likely to be a fully convolutional network [45], reducing the ratio of  
Figure 3: Typical deep neural network (DNN) structures.  
pooling layers, as well as fully connected layers. Taxonomically, CNN belongs to feedforward neural network and is the mainstream deep model in computer vision. The reasons come from that in image recognition: (1) the local pattern makes more contribution in classifications; (2) the same pattern appears in different positions; and (3) downsampling some pixels will not affect the classification. The convolutional layer fits reason (1) and (2), while the pooling layer suits reason (3). CNN is also employed in natural language processing. Including CNN and FNN, one of the limitations of the feedforward neural network is that the output only depends on current input vectors. So, it is hard to deal with sequential tasks.
## 2 Materials and Methods
### Configuration Software  
Figure 1(a) describes the interface software for configuring the P/R parameters. The communication protocol between the P/R device and the software is described in Figure 1(b). This is a fairly simple protocol which is used in a number of devices of the same type. The software was implemented based on Qt Creator 4.9 and compiled in C ++. Table 1 describes the functions of the configurational software for each channel of the P/R device.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Items** & **Function’s Name** & **Descriptions** \\ \hline
1 & Enable/Disable & Enable or disable pulse signal out. \\ \hline
2 & Trigger Source & Select if trigger sources are internal or external. \\ \hline
3 & Mode & Select the mode of the device. There are two modes: \\  & & 1. Pulse/echo mode: the device creates a pulse signal and \\  & & 2. & True receives the echo signal on the same channel. \\ \hline
4 & Trigger Repeat Frequency & Only for the internal trigger source, the device can select to \\  & & repeat the frequency of the internal trigger (frequency from 0.1 kHz to 50 kHz). \\ \hline
5 & Select Gain & Select the gain of the amplifier module (gain from 9 dB to 40 dB). \\ \hline
6 & Voltage Out & Select the voltage out of the pulse signal (voltage out from 40 V to 90 V). \\ \hline \hline \end{tabular}
\end{table}
Table 1: Functions of each channel of P/R software configuration.  
Figure 1: Block diagram of the pulser/receiver (P/R) device.
## 3 Background and Previous Related Work
### IoV and VANET  
IoV is a concept that extends the IoT to the transportation domain, allowing for seamless connectivity and communication between vehicles, infrastructure, and other smart devices [46]. IoV seeks to develop intelligent transportation systems with enhanced safety, traffic efficiency, and vehicle experience. VANETs serve an essential role in the execution of the IoV vision. The IoV incorporates a wider array of applications, devices, and technologies, whereas VANETs are primarily concerned with V2V and vehicle-to-infrastructure (V2I) communication. The contributions of VANETs to the IoV ecosystem are as follows: Communication: VANETs serve as the communication infrastructure for IoV vehicles. They enable V2V and V2I communication, facilitating real-time data sharing and decision making. Safety: By enabling vehicles to share information about their position, speed, and orientation, VANETs enables various safety applications, such as collision avoidance and early warning systems, which are integral components of the IoV [47]. VANETs enable vehicles to share traffic information such as congestion levels, road conditions, and detours, which can optimize traffic flow, reduce travel time, and enhance overall transportation efficiency in the context of IoV. Data collection and analysis: VANETs can facilitate the accumulation of vast quantities of data from vehicles and infrastructure that can be used for real-time monitoring, predictive analytics, and decision making within the IoV ecosystem. VANETs can be incorporated with other IoT systems, such as smart grids and smart cities, allowing for a more comprehensive and interconnected approach to transportation management and urban planning. VANETs are a crucial component of the IoV, as they provide the communication infrastructure required for vehicles and the infrastructure to exchange data and cooperate. VANETs contribute to the development of safer, more efficient, and environmentally friendly transportation systems within the IoV ecosystem as a whole by facilitating efficient communication between vehicles and roadside infrastructure.
## 2 Methods
### Quality Assessment  
Visual assessment is a primary step in assessing the relative calibration quality of images and selecting the bands suitable for analysis. Extending the visual assessment to evaluate the quality of the relative corrected images and recognize and locate the small residual stripes is not new to radiometric calibration or the image processing community. Examples of its use include EO-1 [19], IKONOS [11], and Landsat-8 [15]. For OVS radiometric calibration, we visually assessed and compared the relative calibrated or corrected images. Moreover, profiles drawn at columns across the images before and after correction, in the direction perpendicular to stripes, were used to show the changes in the local brightness levels and reduction in striping.  
Meanwhile, as the relative radiometric calibration should eliminate the variations of response between sensors and subjectively judge the effect of image correction, the \"streaking metrics\" and root-mean-square of the column mean were used in the quantitative analysis to calculate the accuracy of OVS radiometric calibration. The streaking metrics can be used to evaluate the high-frequency response difference of adjacent detectors as follows:  
\[Streaking_{i}=\frac{|\mu_{i}-(\mu_{i-1}+\mu_{i+1})/2|}{(\mu_{i-1}+\mu_{i+1})/2 }\times 100, \tag{13}\]  
where \(Streaking_{i}\) is the streaking metric of the \(i\)th detector in the sensor and \(\mu_{i}\) is the mean value of the image in the \(i\)th detector of the sensor.  
The root-mean-square for the column mean of the image refers to the ratio of the standard deviation of the image column mean to the image mean and can be used to evaluate the low-frequency response difference of all detectors as follows:  
\[RMS=\frac{\sqrt{\frac{\sum_{i=1}^{N}\left(\mu_{i}-\mu_{img}\right)^{2}}{N-1}} }{\mu_{img}}\times 100, \tag{14}\]where \(\mu_{i}\) is the mean value of the image of the \(i\)th detector of the sensor, \(N\) is the number of image columns, and \(\mu_{img}\) is the mean value of the entire image.
# A Survey: Future Smart Cities Based on Advance Control of Unmanned Aerial Vehicles (UAVs)
## 6 Importance of Hybrid Control Techniques
**Contribution of Hybrid Control Strategies**  
* Versatile Performance
* Robustness and Safety
* Multi-Objective Optimization
* Fault Tolerance
* Integration of Perception and Control  
**Technical and Research Aspects of Hybrid Control Strategies**  
* Nonlinear and Linear Control Integration
* Sensor Fusion Techniques
* Adaptation and Learning
* Real-time Implementation
* Experimental Validation  
Hybrid control strategies offer a versatile and robust solution for UAVs operating in smart cities. By combining the strengths of different control approaches, these strategies enable UAVs to navigate through complex urban landscapes, perform diverse tasks, and contribute to the development of smart city infrastructure and services. Careful consideration of mode transitions, control mode selection, sensor fusion, and real-time implementation is essential for successfully applying hybrid control methods to UAVs in smart city contexts [127].
## 1 Introduction
On the other hand, various DL approaches have been used to address the problems in wireless communication and achieve valuable conclusions. [PERSON] has studied the demodulation technology of CNN in orbital angular momentum (OAM) atmospheric laser communications in [16]. [PERSON] shows that RNN and LSTM have some advantages in decoding aliased signals by experiments in [17]. To further improve the network performance and select the most discriminative features, an attention mechanism is introduced into the network to explore the dependencies between features. The authors of [18] proposed a dual attention network (DANet) with a self-attention mechanism to enhance the discriminant of feature representations for scene segmentation, in which a position attention module is proposed to learn the spatial interdependencies of features and a channel attention module is designed to model channel interdependencies. It significantly improves the segmentation by modeling rich contextual dependencies over local features. In the 2022 IEEE ICAIT conference, we proposed a BP neural network to promote the BER performance of signal detection in an atmospheric channel [19]. A BP neural network has the advantages of high self-learning ability and self-adaptive ability. It can learn the mapping rules between input and output data during training process and adaptively memorize such rules by using the network weights. Therefore, the neural network is less affected by the acceleration factor and roll-off factor, which ensures the spectrum utilization of the system at an FTN rate. However, the forgetting of sequence information exists in BP neural networks. Therefore, we employ the LSTM neural network and attention mechanism to overcome the problem of forgetting sequence information. Nowadays, the attention mechanism has become a common data processing method in the DL field and is widely used in various DL tasks, such as natural language processing, image recognition, and speech recognition. Assembling features by assigning larger weights to some'significant' features not only reduces the parameters of the network, but also improves the discriminative power of the features. Therefore, the attention mechanism is introduced into the LSTM network to build an LSTM attention decoder for the signal detection of a pulse position modulation (PPM) and phase shift keying (QPSK) hybrid modulated FTN OWC system to improve the system performance while ensuring spectrum efficiency.
# GNSS Profile from the Greenland Korth Expeditions in the Context of Satellite Data
## 5 Greenland North Expeditions and Satellite Gravimetry
26, is located in the central part of the profile, at an altitude of 2270 m (Figure 5). In order to convert the altitude changes into mass changes one needs some model of the firm density; due to changing physical conditions (especially temperature), in these models there is a large uncertainty. Here, as the firm density we used a rather conservative estimate of 600 \(\pm\) 300 kg m\({}^{-3}\) for Greenland regions above 2000 m [46]. As is shown in Figure 8e, three GNSS height differences agree well with GRACE mass estimates within the uncertainty limits (shown as transparent error bars). Let us also point to the correspondence between the three GNSS steps in panel Figure 8e of gradually increasing size and the trend values (indicated by different colors) at the measurement site in three panels of Figure 8a-c. There are several reasons to show the comparison between the GNSS and GRACE datasets for only one illustrative location. As mentioned at the end of the previous section, virtually all Greenland North Expeditions sites are located in the percolation zone of the ice sheet, except for a very few endpoints. Then, to compare the point-wise GNSS data with the much lower-resolution GRACE data, we applied the 200-km Gaussian smoothing to the GNSS profile. As a result, the local features along the GNSS profile are smeared and combined together. Finally, for a comparison between such an averaged GNSS profile and GRACE monthly gravity fields, a typical point near the middle of the traverse was selected, for which the GRACE trend maps in Figure 8a-c display a different value in each period. Figure 8: Trend in the equivalent water height over Greenland derived from GRACE (no glacial isostatic adjustment (GIA) included, 200 km Gaussian smoothing) for the Greenland Korth Expeditions periods: (**a**) 8/2002–7/2006, (**b**) 8/2006–7/2010, (**c**) 8/2010–7/2015. Panel (**d**): Glacial isostatic adjustment in terms of the equivalent water height. Panel (**e**): Surface density change from satellite gravity and the GNSS data (200 km Gaussian smoothing) at Greenland Korth Expeditions site No. 26 (indicated as a red point in panels (**a**–**c**)).
## 3 Capacity Enhancement by Channel State Information
### Channel State Information  
By computing the singular value decomposition (SVD) of the channel matrix as \(H=U\Lambda V^{\dagger}\), where \(\Lambda=\mathrm{diag}(\sqrt{\lambda_{1}},...,\sqrt{\lambda_{M}})\) and \(U,V\) are unitary matrices. CSI refers to the matrices \(\Lambda\) and \(V\) that can be sent back from the receiver to the transmitter. In this case, \(V\) is used by the transmitter to send correlated symbols vector \(VX_{M}\). The matrix \(\Lambda\) is used to compute the optimal powers allocated to each mode. This can be done by the iterative water-filling algorithm known from MIMO wireless communications [20; 21]. The average channel capacity with channel state information is given by [12; 20; 21]:  
\[C_{CSI}=\mathbb{E}_{\lambda_{i}}\left(\sum_{i=1}^{M}\log\left(1+p_{i}^{*}\frac {\lambda_{i}}{2\sigma^{2}}\right)\right) \tag{6}\]  
\(p_{i}^{*}\) is the optimal power allocated to the mode \(i\) and is given by:  
\[p_{i}^{*}=\mu-\frac{2\sigma^{2}}{\lambda_{i}} \tag{7}\]  
\(\mu\) is a constant that satisfies the total power constraint \(\sum_{i=1}^{M}(\mu-\frac{2\sigma^{2}}{\lambda_{i}})=P_{t}\). This power allocation strategy faces two major limitations. First, the CSI must be provided to the transmitter to send the data before the channel changes. Second, the optimal powers must be updated at the minimum as fast as the channel variations. These constrains cannot be satisfied for the moment for FMF optical systems where the channel dynamics change faster than round-trip delays. For example, in [22], the authors found that for a 26-km two-mode fiber, the channel variations at a wavelength \(\lambda=1550\) nm were of the order of 40 \(\upmu\)s. In this case, the round-trip delay, is equal to 250 \(\upmu\)s which is more than six-times longer than channel variations.