esa-sceva/satcom-chunk-collection huggingface dataset:
# Opportunities and Challenges of Smartglass-Assisted Interactive Telementoring
## 1 Introduction  
The number of computing devices that people encounter in their daily life is increasing. Beyond the typical form factor of a desktop computer, computing devices are becoming smaller, more abundant, and ubiquitously available. The vision of \"ubiquitous computing\" is more apparent today due to the myriad of connected everyday appliances known as the Internet-of-Things (IoT). Both ubiquitous computing and IoT emphasize environment-centric sensing and service provision through ambient intelligence. On the other hand, wearable computing complements the aforementioned environment-centric view with an intimate first-person view realized by always on, always accessible, and always connected wearables. Rapid advancements in ubiquitous computing and wearable computing have established a viable infrastructure to connect people over long distances to neutralize the barriers of physical locations. The recent COVID-19 pandemic has accelerated the use of this type of technology [1, 2] in forms of remote collaboration, working-from-home, teleconferencing, online education, and metaverses enriched with various configurations of digital twins (DTs) [3], augmented reality (AR), virtual reality (VR), mixed reality (MR), and extended reality (XR).  
There are numerous reports and surveys supporting the wide spread and adoption of wearables, XR, and metaverses in the foreseeable future. According to BCG and Motor Intelligence, XR markets are growing and will reach 30.7 billion USD in 2021 and 300 billion USD by 2024, respectively. Another report by the IDC forecast that consumer spending on AR and VR technology is approximately 6.36 billion USD, accounting for 53% of global AR and VR spending in 2020. A survey published in February 2021 by ARtillery Intelligence highlights that enterprise AR glasses hardware and software revenue will rise to 2.05 billion USD in 2021 and 11.99 billion USD by 2024, respectively. XR industry experts forecast that such immersive technologies will have a great impact on the healthcare and medical sector [4].  
In this paper, we explore the opportunities and challenges of smartglass-assisted interactive telementoring and its applications. Specifically, our contribution is threefold, as follows:  
1. First, we elicit requirements and trends by reviewing recent telementoring studies;
2. Second, we define a terminology \"smartglass-assisted interactive telementoring (SAIT)\" to refer to specialized use cases of telementoring and elaborate on its distinguishable characteristics;
3. Third, we identify opportunities and challenges of SAIT applications through five illustrative and descriptive scenarios.
## 3 Results and Discussion
### Integration of Renewable Energy Sources and Hybrid Microgrid Configurations
#### 3.2.3 Control and Coordination of Hybrid Microgrids  
Reliable control and coordination strategies are essential for the stable operation of hybrid AC/DC microgrids under varying generation and load conditions [39; 43; 66]. Both centralized and decentralized control approaches have been explored, with decentralized architectures providing greater scalability and resilience, particularly in systems with high renewable penetration [70; 72]. Advanced control strategies based on hierarchical frameworks integrate multiple control layers, ranging from primary voltage and frequency regulation to higher-level optimization of energy dispatch and economic performance [75; 76]. The implementation of predictive control models and AI-based algorithms is also gaining prominence, enabling real-time optimization and adaptive control in complex hybrid systems [49; 77]. Ensuring the safe and stable operation of smart microgrids is a critical concern, particularly as system complexity increases with the integration of distributed energy resources, intelligent controllers, and communication networks. Operational safety involves coordinated protection schemes, fault detection and isolation, and control redundancy to prevent cascading failures. Stability is also influenced by the dynamic behavior of power electronics and the responsiveness of control systems under disturbances. Moreover, with the increasing use of digital communication and AI-based control, cybersecurity has become an essential aspect of operational resilience. Recent studies have emphasized the need for fault-tolerant control architectures, secure communication protocols, and real-time monitoring systems to guarantee reliable operation under both normal and abnormal conditions [85].  
Nonetheless, achieving seamless coordination among diverse energy sources, storage systems, and controllable loads remains a key challenge. The development of interoperable communication protocols and standardized control interfaces is critical to facilitate effective interaction between heterogeneous system components and to enhance the reliability and efficiency of hybrid microgrid operations [22; 48; 61].
## 3 Proposed Architecture  
The implementation of a real-time IoT-based monitoring system for oil pipelines is an emerging area. In real-time monitoring, the role of wireless communication protocol is crucial, because the sensory data need to transmit reliably and effectively. Generally, in many studies, the researchers have implemented the 2.4 GHz-based Zigbee and LoRa communication protocol separately for obtaining the sensory data from the oil pipelines. However, the implementation of the ZigBee communication protocol is only for short-range communication, so it is somewhat challenging to deploy multiple ZigBee sensor nodes for establishing a connection. The failure connection of a single sensor node during the establishment of the connection leads to complete connection failure. In case, the implementation of the LoRa network in every single node raises the infrastructure cost. To overcome all these challenges, a hybrid architecture is proposed for oil pipeline monitoring by integrating 2.4 GHz-based ZigBee, LoRa, and Wi-Fi communication and it is shown in Figure 2.
## 2 Experiment 1
### Results  
For each condition, data distribution was assessed using the Shapiro-Wilk test (please refer to Supplementary Materials S1-T1 for the data). We then compared the selected running speed to the reference running speed using in each case either a \(t\)-test when data was normally distributed or a Wilcoxon signed rank test. For all four tests, the alpha level was corrected for multiple comparisons using Bonferroni correction (i.e., 0.05/4 = 0.0125). For all conditions, the selected running speed was not significantly different from the reference running speed after correction.  
We then tested whether the selected running speed was different based on the optical flow condition. Since data was not normally distributed, a [PERSON] rank sum test for repeated measures was used. No differences were found for the selected running speed in the four conditions \(\left[\chi^{2}(3)=3.1,\ p=0.3765\right]\) (see Figure 2). Because we did not find any significant difference between the means (i.e., we could not reject H0), we also computed the Bayes factor. The Bayes factor was 0.37, a value which can be interpreted as an 'anecdotal' evidence in favour of H0 [27].  
Lastly, we tested whether the duration of the selection phase was different based on the optical flow condition. Data was not normally distributed, so a [PERSON] rank sum test for repeated measures was used. Here again, we did not find any significant difference between optical flow conditions regarding the duration of the running phase \(\left[\chi^{2}(3)=1.084,\ p=0.7809\right]\) (see Figure 3). As for the previous analysis, we computed the Bayes factor, which was 0.11, thereby constituting a moderate to strong evidence in favour of H0 [27].  
Figure 2: Running speed selected as matching the reference running speed in different optical flow conditions. The bars represent the means and the error bars the 95% confidence interval of those means.
# TinyGS vs. SatNOGS: A Comparative Analysis of Open-Source Satellite Ground Station Networks
## 5 Discussion
### Comparison of the Number of Members  
The number of SatNOGS members on 29 July 2023 was based on the number of sign-ups on the Libre Space Community, since SatNOGS is a project of the Libre Space Foundation. Initially, determination of the number of SatNOGS members was intended to be undertaken through the number of accounts created on the SatNOGS network, but taking into account that this information is not publicly available, it was decided to use the previous reasoning. As a result, the number of SatNOGS members will be approximate. In turn, the number of TinyGS members is based on the number of members available on the web application [23].  
The number of sign-ups in the Libre Space Community is approximately 2500, so the number of members in SatNOGS is less than or equal to this. Taking into account that the number of SatNOGS created up to 29 July 2023 was 2841 (considering the number of GSs created so far from the SatNOGS Network API), the number of SatNOGS members should be close to the number of sign-ups.  
As for TinyGS, the number of members is approximately 4400.
## 5 Use Cases
### Analysis Using Graph Neural Networks
#### 5.7.3 Architectural Insights  
The enhanced GCN architecture revealed several key findings:  
* Feature importance: room pricing (ADR) and lead time emerged as crucial predictors.
* Temporal patterns: strong seasonal effects captured through graph structure.
* Market segmentation: clear differentiation between city and resort hotel patterns.
* Regional effects: significant spatial correlations in cancellation behaviors.  
The graph-based analysis provides additional validation of the theoretical framework's ability to integrate several advanced techniques to capture complex relationships in hospitality data. The GNN implementation demonstrates the framework's extensibility to advanced sophisticated approaches while maintaining interpretability through attention mechanisms and feature importance analysis.
## 2 Materials and Methods
### Analysis of Visible and Thermal Imaging Data  
Visible and IR videos of the subjects' faces were simultaneously recorded during the driving experiment at an acquisition frame rate of 30 Hz and 10 Hz, respectively.  
Given the availability of computer vision algorithms for visible videos, in the present study, visible imaging was used as reference for tracking facial landmark features. The purpose of the visible tracking was to transfer the visible facial landmark features tracked to the thermal imagery, estimating the geometrical transformation between the two imaging optical devices.
# DRL-Based Dynamic Destroy Approaches for Agile-Satellite Mission Planning
## 2 Problem Description
### Mathematical Formulation  
1. Constraint Conditions
2. Conflict constraint: A satellite can only perform one mission per observation. There is no crossover between any two tasks, as the optical sensor cannot perform two observation tasks at  
Figure 1: Agile satellite observation. Agile satellites with three-axis maneuvering capabilities must schedule a considerable number of remote sensing missions within a limited time frame. Targets beyond the observation range remain undetectable and are therefore considered invalid.  
the same time. \(\forall u,v\in\{1,2,\ldots,n\}\) and \(u\
eq v\): \[[t_{u}^{s},t_{u}^{r}]\cap[t_{v}^{s},t_{v}^{e}]=\emptyset\] (1)
2. Task execution uniqueness constraint: In this paper, we do not consider repetitive tasks. Each task can just be observed at most once in all satellite observation periods: \[x_{i}=\sum_{j=1}^{|\mathbf{O}\mathbf{W}_{i}|}x_{i}^{j}\leq 1,\forall r_{i}\in R\] (2) \(\mathbf{O}\mathbf{W}_{i}\) is the execution time window set for task \(r_{i}\). We define the \(x_{i}^{j}\) as a binary decision variable indicating whether the \(j\)-th execution time window of \(\mathbf{O}\mathbf{W}_{i}\) associated with task \(r_{i}\) is selected or not. It means that for each task we can choose only one execution time window to fulfill its observation.
3. Satellite-attitude maneuver time constraint: The actual observation time of the task \(r_{u}\) must be within a visual time window of the task: \[w_{u}^{s}\leq t_{u}^{s}\leq t_{u}^{e}\leq w_{u}^{e}\] (3)  
\[t_{u}^{e}=t_{u}^{s}+t_{u}^{d} \tag{4}\]  
Then, we can obtain the satellite-attitude maneuver time constraint between \(r_{u}\) and \(r_{v}\):  
\[t_{u}^{e}+t_{uv}^{r}\leq t_{v}^{s} \tag{5}\]  
The transition time \(t_{uv}^{r}\), as defined in [19], is approximated using a piecewise linear function. The time interval between two scheduled tasks must exceed the transition time required for the satellite attitude maneuver. In the subsequent specific experiments, the transition time is set as in [35].
## 2 Introduction of pH Value Regulation System and Analysis of Regulation Process
### Analysis of Liquid Fertilizer pH Regulation Process
#### 2.2.1 Objective Function and Design Variables
 \tag{10}\]  
The pH equation can be derived from Equations (1)-(10) as:  
\[\sum\limits_{i=1}^{n}a_{i}\left(\left[H^{+}\right]\right)x_{i}+\left[H^{+} \right]-\frac{K_{w}}{\left[H^{+}\right]}=0 \tag{11}\]  
When \(i\) is an acid:  
\[a_{i}\left(\left[H^{+}\right]\right)=-\frac{m_{i}+\left(m_{i}-1\right)\frac{ \left[H^{+}\right]}{K_{an_{i}}}+\cdots+\frac{\left[H^{+}\right]^{m_{i}-1}}{K_ {an_{i}}K_{ab_{i}}\cdots K_{an_{i}}}}{1+\frac{\left[H^{+}\right]}{K_{an_{i}}} +\cdots+\frac{\left[H^{+}\right]^{m_{i}-1}}{K_{a_{i}}^{2}K_{ab_{i}}\cdots K_{ an_{i}}}+\frac{\left[H^{+}\right]^{m_{i}}}{K_{a_{i}}^{2}K_{ab_{i}}\cdots K_{an_{i}}}} \tag{12}\]  
When \(i\) is a base:  
\[a_{i}\left(\left[H^{+}\right]\right)=\frac{n_{i}[H^{+}]^{n_{i}}+\left(n_{i}-1 \right)\frac{K_{w}}{K_{an_{i}}}[H^{+}]^{n_{i}-1}+\cdots+\frac{K_{w}^{n_{i}-1} \left[H^{+}\right]}{K_{b_{2}}K_{ab_{i}}\cdots K_{bn_{i}}}}{\left[H^{+}\right] ^{n_{i}}+\frac{K_{w}}{K_{bn_{i}}}[H^{+}]^{n_{i}-1}+\cdots+\frac{K_{w}^{n_{i}- 1}\left[H^{+}\right]}{K_{b_{2}}K_{ab_{i}}\cdots K_{bn_{i}}}+\frac{K_{w}^{n_{i}} }{K_{b_{1}}K_{b_{2}}\cdots K_{bn_{i}}}} \tag{13}\]  
Since the whole pH neutralization process can be considered as the neutralization of a strong acid and a weak base, thus  
\[a_{1}\left(\left[H^{+}\right]\right)=-1 \tag{14}\]  
\[a_{2}\left(\left[H^{+}\right]\right)=\frac{\left[H^{+}\right]}{\left[H^{+} \right]+\frac{K_{w}}{K_{b}}}=\frac{1}{1+\frac{\left[OH^{-}\right]}{K_{b}}} \tag{15}\]  
The above analysis leads to the fact that the pH equation can be rewritten as:  
\[-x_{1}+\frac{1}{1+10^{pK_{b}+pH-14}}x_{2}+10^{-pH}-10^{pH-14}=0 \tag{16}\]  
where \(pK_{b}=-\log K_{b}\), \(x_{1}\), \(x_{2}\) are the total ionic concentrations of acid and base in the fertilizer mix, respectively. This equation is the static model of pH neutralization. As can be determined from Equation (16), the control process of pH described by this static equation is strongly nonlinear.
## 3 Database of Water Content and Hyperspectral Information
### Analysis of Hyperspectral Information
#### 3.3.1 Geometric Correction  
Geometric correction is intended to correct errors (geometric distortion) that occur in the process of representing the 3D Earth on a 2D plane. This is the task of converting hyperspectral data so it is located in an appropriate actual location, corresponding to pixel coordinates of satellite images and geographical coordinates on the ground. As shown in Figure 9a, the image captured from the drone shooting path is converted to include coordinates, as shown in Figure 9b.
# Artificial Intelligence-Based Autonomous UAV Networks: A Survey
## 2 Background and Preliminaries
### Interference Management  
Interference management is one of the key considerations of any wireless communications. In UAV communications, interference also plays a big role in determining the system performance. Many network researchers have explored the effect of interference on UAV network performance. Most recent research studied interference management for UAV networks either by considering UAVs as the extension of cellular networks or by pairing them with power control. In [36], a path planning protocol focusing on latency and interference for ground networks was studied for UAV networks. In this paper, the interference mitigation technique based on game theory is investigated, and a learning algorithm using the deep reinforcement approach is proposed. Each UAV acts as a player and decides on its path while minimizing the latency and interference for the ground network. Another similar study was conducted in [37]. This study also utilizes game theory, but this time, the goal is to maximize the energy efficiency of the UAVs while minimizing the latency and interference for the ground network. A study based on the approaches of using UAVs with cellular networks is investigated in [38]. In this study, the UAVs are used to assist in the integrated access and backhaul (IAB) cellular in-band networks and reduce interference at various levels of networks. The problem is formulated as a maximization problem for the network sum targeting various network factors and features. The authors claim that the results achieved in this study outperform many existing techniques.  
When it comes to interference management, power control is another feature that cannot be ignored. To lower the interference, a machine-learning approach is proposed in [39]. In this approach, the idea is to manage the overall network interference by controlling the UAVs/drone power and location. The proposed framework uses K-means and affinity propagation. The simulation results and discussions provided in the paper are promising for managing network interference. Another study conducted by [PERSON], [PERSON] et al. [40] addresses the same problem but a bit differently. Here, the authors have tried to manage the interference by dividing the problem into two sub-problems, i.e., finding the optimal transmit power for a given UAV trajectory and then also considering the best trajectory for the fixed transmit power. This helps in finding the optimum solution for the overall network. [PERSON], [PERSON] et al. [41] address the same problem in a bit different way. The authors focus on not only power control to minimize the interference but also consider UAV clustering to address the problem. The solution is designed as a game to maximize the sum rate of the whole system.
## 5 Conclusions and Future Work  
In this paper, we have presented an analytical procedure of calculating the node non-isolation probability based on a generalized radio link model in wireless sensor networks. The non-isolation probability is the upper bound of one-connectivity probability. We use a combination of analytical and simulation-based methods to study the impact of various parameters on the connectivity behavior. The results can be applied for practical design and simulation in wireless _ad hoc_ and sensor networks. For example, given the environmental and node configuration, we can determine the minimum density needed to achieve a one-connected network or less strictly, form a giant component, according to different requirement of QoC.  
Several issues remain for future work in this area. First, this paper only focus on the connectivity in the spatial domain, it is also interesting to study the temporal dynamics of wireless connectivity. For more challenging dynamic environment, statistics of time-varying links are required; second, the giant component size needs further analytical study. It is important if an explicit expression could be derived for the giant component probability or largest component size; third, this paper assumes the node distribution follows homogeneous Poisson point process. However, in many real-world scenarios, the nodes are in an inhomogeneous distribution. [PERSON] _et al._ in [32] has proposed an algorithm to create random inhomogeneous distribution. We believe the more generalized distribution model will lead to more convinced analytical and numerical results. Fourth, it will be interesting if we incorporate the context of MAC layer, say IEEE 802.15.4, so that connectivity is more information based than pure physical link. Finally, _k_-connectivity is a more generalized measure of global connectivity and provides more redundancy for routing choice. In other words, one-connectivity is just a special case for k-connectivity. Some analysis has been conducted in [33], but still need further investigation. The procedure may find some similarities, but the result is of greater generality.
# Automatic Detection Method of Sewer Pipe Defects Using Deep Learning Techniques
###### Abstract  
Regular inspection of Sewer pipes can detect serious defects in time, which is significant to ensure the healthy operation of sewer systems and urban safety. Currently, the widely used closed-circuit television (CCTV) inspection system relies mainly on manual assessment, which is labor intensive and inefficient. Therefore, it is urgent to develop an efficient and accurate automatic defect detection method. In this paper, an improved method based on YOLOv4 is proposed for the detection of sewer defects. A significant improvement of this method is using the spatial pyramid pooling (SPP) module to expand the receptive field and improve the ability of the model to fuse context features in different receptive fields. Meanwhile, the influence of three bounding box loss functions on model performance are compared based on their processing speed and detection accuracy, and the effectiveness of the combination of DIoU loss function and SPP module is verified. In addition, to address the lack of datasets for sewer defect detection, a dataset that contains 2700 images and 4 types of defects was created, which provides useful help for the application of computer vision techniques in this field. Experimental results show that, compared with the YOLOv4 model, the mean average precision (mAP) of the improved model for sewer defect detection are improved by 4.6%, the mAP can reach 92.3% and the recall can reach 89.0%. The improved model can effectively improve the detection and classification accuracy of sewer defects, and has significant advantages compared with other methods.  
sewer inspection; CCTV; spatial pyramid pooling; DIoU; YOLOv4
## 4 Results and Discussions  
### Implementation Details  
The ResNet-50 [21] with FPN [34] is used as the backbone network of ACA-Net. The NEU-DET dataset is a small dataset which is not enough to support the training of complex neural networks. Therefore, the ResNet-50 is pretrained on ImageNet [46] dataset. All of the surface defect detectors are trained on a Tesla V100 32G GPU with 2 images. Stochastic gradient descent (SGD) is used to train with a learning rate of 0.002, momentum as 0.9 and weight decay of 0.0001. We train 36 epochs in the end-to-end manner, and decrease the learning rate at the 28 th and 34 th epochs. The models are trained by the MMDetection toolkit [47].  
The accuracies of all detectors are evaluated by the COCO [48] standard metric average precision (AP). The speed of each detector is evaluated on frame per second (FPS), i.e., the number of images that a detector can process per second. The IoU strategy in [25] is employed to determine the positive and negative samples from the anchors. The \(AP\) is evaluated at a different IoU threshold (\(IoU\in[0.5:0.95]\)) over all kinds of surface defects. It can be defined as  
\[AP=\frac{1}{K}\sum_{n=1}^{K}\int(P_{n}(R_{n}))dR_{n} \tag{15}\]  
where \(R_{n}\) denotes the recall for class \(n\), \(P_{n}\) is the corresponding precision, and \(K\) is the number of classes. The precision (\(P\)) and recall (\(R\)) are defined as follows  
Figure 6: Defect images in NEU-DET. The yellow box is a groundtruth of a defect. (**a**) Crazing. (**b**) Inclusion. (**c**) Patches. (**d**) Pitted surface. (**e**) Rolled-in scale. (**f**) Scratches.  
\[P =\frac{TP}{TP+FP} \tag{16}\] \[R =\frac{TP}{TP+FN} \tag{17}\]  
where \(TP\), \(FP\), and \(FN\) denotes the number of true positive samples, false positive samples, and false negative samples, respectively. Table 1 shows all of the evaluation metrics in out experiment.