esa-sceva/satcom-chunk-collection huggingface dataset:
# Dynamic Routing in Flying Ad-Hoc Networks Using Topology-Based Routing Protocols
###### Abstract  
The ever-increasing demand for flexible and portable communications has led to a rapid evolution in networking between unmanned aerial vehicles (UAVs) often referred to as flying ad-hoc networks (FANETs). However, due to the exclusive characteristics of UAVs such as high mobility, frequent topology change and 3D space movement, make routing a challenging task in FANETs. Due to these characteristics, designing new routing protocols for FANETs is quite difficult. In the literature study of FANETs, a variety of traditional ad-hoc networking protocols have been suggested and tested for FANETs to establish an efficient and robust communication among the UAVs. In this context, topology-based routing is considered the most significant approach for solving the routing issues in FANETs. Therefore, in this article we specifically focus on topology-based routing protocols with the aim of improving the efficiency of the network in terms of throughput, end-to-end delay, and network load. We present a brief review of the most important topology-based routing protocols in the context of FANETs. We provide them with their working features for exchanging information, along with the pros and cons of each protocol. Moreover, simulation analyses of some of the topology-based routing protocols are also evaluated in terms of end-to-end delay, throughput and network load the using optimized network engineering tools (OPNET) simulator. Furthermore, this work can be used as a source of reference for researchers and network engineers who seek literature that is relevant to routing in FANETs.  
U 18 June 2018; Accepted: 9 August 2018; Published: 14 August 2018
# Enhanced and Combined Representations in Extended Reality through Creative Industries
## 4 Results and Discussion
### Discussion
#### 4.2.2 Advanced Cultural Heritage Representation  
The proposed representation could be applied during visits to physical cultural heritage spaces (RQ3). As this solution offers the possibility to observe the non-visible areas of the exhibits, such as their internal structure hidden behind their external surface, it could be used extensively in museums. Exhibits that are in advanced deterioration because of time and environment can be made accessible to the public by providing information about their internal structure through the combined digital representation. Accordingly, complex artifacts that are exhibited in glass cases, where any physical interaction with visitors is forbidden, can communicate with the public through this approach. Also, the case of exhibits related to burial remains is a category of cultural heritage objects that could benefit from the complex representation of the proposed methodology.  
Metaverses and virtual worlds are digital spaces that can adopt this methodology, as they rely on game engines, 3D graphics, and immersive technologies. The proposed solution could be applied during the virtual visit of the user to cultural heritage sites or when studying complex representations in virtual laboratories (RQ3). In addition (RQ4), the solution could be optimized and extended by incorporating explainable artificial intelligence to provide additional real-time information related to the objects [102], as well as to assist with interaction usage. Furthermore, an extension that could be added to the proposed solution would be the possible integration of haptic devices for enhanced users' immersion when interacting with the combinatorial representation. In this way, they could feel the texture of the individual elements through haptic interactions, while at the same time, they would also strengthen the inclusion of people with impaired vision [94; 108].
# Model Mediation to Overcome Light Limitations--Toward a Secure Tactile Internet System
## 2. Haptic Communications  
Humans have five main senses; four of them are dedicated with centralized parts in the human's body. While, the sense of touch is distinct, since it is distributed over the entire human's body [42]. Our bodies contain main active tactile senses as the hands with different sensing sensitivities. Our human skin can distinguish four main sensation modalities; heat, cold, touch, and pain. Other sensations (e.g., vibration, roughness, softness, and moisture) can be characterized as a combination of two or more modalities from the four defined main sensation modalities [43].  
The human can physically interact with a remote environment and controls a remote object by means of haptic communication. Haptic data are the information represents the human senses or the human sensory information [44]. The haptic data consists of two main senses: tactile and kinesthetic. The human skin employs mechanoreceptors that produce tactile inputs which allow the tactile perception of the surfaces [45]. Human muscles, joints, and tendons employ mechanoreceptors that generate kinesthetic inputs responsible for limb movement and position [46]. These two main mechanoreceptors relay sensory information to the human's central nervous system in the form of neural signals. We have an overall perception based on the gathered information from human audiovisual modalities with the neural signals [47]. Human interactions with others or with surrounding objects in the environment occur based on the involvement of tactile and kinesthetic senses.  
Haptic communications can be classified into two main categories based on the type of the human interaction with the remote environment. The first category is the passive haptic communication networks, which refer to the haptic-based networks designed only for exploring remote environments and distributed objects in these environments [37].  
This category mainly considers the perception rather manipulation of objects and surfaces in remote environments. This type represents an open loop haptic communication. The other category is the active haptic communication networks, which contains networks developed for both perception and manipulation of objects and surfaces in remote environments. Based on the application, the remote environment can be real or virtual [48]. Tactile Internet will provide the medium for the transmission of haptic information side by side with the audio and video data with the required latency and reliability constraints.
## 2 Preliminaries and Problem Statement
### Path Planning Layer  
As the next step of the scheduling layer, the path planning layer consists of the path estimation stage and the path planning stage. The role of each stage is as follows:  
1. In stage 1, the Dubins paths bridging the initial position of the UAV and each position of the predefined mother aircraft trajectory are generated based on a sample distance. Given the boundary constraints, the Dubins path was proven to be the shortest curvature-bounded path with the minimum flight time [42] and it has been applied extensively in path planning [43,44]. Combined with the above discussion for the recovery time windows, we can obtain the maximum recovery time window with the Dubins path. As shown in Figure 2, the path estimation results are passed back to the scheduling layer to calculate the recovery time windows.
2. In stage 2, the path with the expected length for each UAV is planned based on the recovery position and recovery path length. Only the Dubins path connecting the initial position and recovery position of a UAV is selected from stage 1 of this layer to construct the path homotopy. Then, the homotopic path planning approach is employed to generate a path with the expected length for the UAV. In the end, the related control parameters of the ideal path are transmitted to the control system for path tracking.  
Overall, it can be seen from Figure 2 that stage 1 of the path planning layer is firstly executed depending on the information from the airborne sensors in the recovery planning framework. Then, the recovery scheduling layer runs according to the path estimation results. Next, stage 2 of the path planning layer is executed to generate a path with the expected length for each UAV, based on the recovery position and recovery path length obtained in the recovery scheduling layer. In the end, the trajectory control parameters are transmitted to the control module of each UAV.
# Optimizing Agricultural Data Analysis Techniques through AI-Powered Decision-Making Processes
## 3 IoT ML Models
### Data Collection and IoT  
In the preceding section, we reviewed several studies related to AI-driven decision-making processes in machine learning. This section delves deeper into the various methodologies used for data collection and the application of machine learning models. Collecting and analyzing sensor data from numerous IoT devices is essential for smart agriculture. The IoT enables data from sources to be communicated, organized, and primarily transmitted to cloud-based centers via the network. Currently, numerous environmental factors are reducing food production, and farmers must adopt new IT technologies on their farms to achieve the most productive crops. In smart agriculture, other relevant information, including weather, soil, and water obtained from the crops, is typically collected and analyzed using machine learning algorithms. The built models can identify solutions that utilize specific IoT sensors and sensor technology for assessing weather conditions and soil quality. Additionally, models could be improved to monitor crop development, using robots for harvesting and weeding. IoT-based data collection methods can be summarized as follows: (a) weather stations, (b) crop monitoring sensors, (c) soil sensors, (d) intelligent irrigation systems, (e) innovative agricultural machinery, (f) smart greenhouses, (g) drone technologies, and (h) data collection platforms.  
From the standpoint of precision agriculture, the IoT data acquired and managed can be invaluable in enhancing efficiency across various agricultural sectors. Data-driven farms can also aid in eradicating plant diseases, analyzing farm soils, and boosting crop productivity. According to [20], work has been carried out on IoT technology for remote sensing and agriculture applications. Data collection enabled by IoT optimizes resource usage and enhances farm efficiency, contributing to precision agriculture. In another recent study [21], the authors developed a data collection model using the ZigBee wireless sensor network, covering all aspects of crops, to establish a new agricultural model.  
To guarantee effective and reliable data collection and transmission, the hardware utilized in the data collection system incorporates a diverse number of sensors (including temperature and soil moisture sensors), microcontrollers (such as Arduino and Raspberry Pi), and communication modules (like Wi-Fi, LoRa, and Zigbee). This comprehensive approach enables the successful implementation of machine learning models. One of the important modules is ZigBee, which stands out as a robust wireless communication protocol with diverse applications, especially in scenarios requiring low power usage and strong network topology. It is capable of facilitating extensive mesh networks and is adaptable for various IoT applications in smart farms.
## 3 Study Data and Processing
### Data Processing  
The spectral response curves of the FY-2G visible channel and MODIS red channel are shown in Figure 5. The solid line in the figure represents the FY-2G visible channel (0.550\(\sim\)0.750 \(\upmu\)m), and the dashed line represents the red band (0.620\(\sim\)0.670 \(\upmu\)m) of MODIS. For the difference between spectral channels, the reflectance spectral data of 156 surface types in the U.S. Geological Survey (USGS) spectral database ([[https://crustal.usgs.gov](https://crustal.usgs.gov)]([https://crustal.usgs.gov](https://crustal.usgs.gov)), (accessed on 19 August 2023)) were selected to convolve with the MODIS red band and FY-2G visible band spectral response functions to obtain the corresponding channel reflectance. The two reflectance data points are linearly fitted to obtain the corresponding linear conversion expression. Figure 6 shows the fitting curves and fitting errors of the two reflectivities. The spectral correction formula between the two bands is  
\[\rho_{FY-2G_{visible}}=0.8920\cdot\rho_{MODIS_{md}}+0.0175 \tag{18}\]  
The spatial resolution of the FY-2G visible light band is 1.25 km, and the spatial resolution of the MODIS red band is 0.25 km. Since the spatial resolution of the visible light band of FY-2G is inconsistent with the spatial resolution of the corresponding band of MODIS, it is necessary to resample the observation data of MODIS to make it consistent with the spatial resolution of the observation data of FY-2G.  
Before land surface albedo inversion, satellite multiangle observation data need to be geometrically corrected to be projected onto a designated projection plane, and then the image is clipped according to the vector data in the Hefei area to obtain the observation data in the Hefei area. Finally, cloud removal processing is required, deducting the cloudy pixels to obtain completely clear sky pixels.  
Figure 4: Hefei’s geographical location and vegetation covering type.
# Multivariate Relationship in Big Data Collection of Ocean Observing System
## 2 Materials and Method
### The EMODnet Dataset  
The _EMODnet_ (European Marine Observation and Data Network) [20] is a long-term marine data initiative begun by DG MARE in 2009, created with the aim of making marine data easily accessible, interoperable, and free from restrictions on use [21]. The _EMODnet_ Chemistry portal describes marine data until 2018, acquired from research cruises and monitoring activities in Europe's marine waters and global oceans. Each cruise (or monitoring activity) represents a subset of marine measurements for specific locations or temporal periods, possibly gathered with their own specific sampling and analytical methodologies. Standard Quality Check procedures are applied to harmonize and validate the dataset [18]. The Mediterranean Sea _EMODnet_ dataset consists of a collection of 101.526 samples, originating from 74 data providers distributed among 18 countries. The collected data range in longitude from \(-5.92\) W to \(36.19\) E and in latitude from \(31.19\) N to \(45.77\) N, guaranteeing a good coverage of the whole Mediterranean area. The parameters included in each sampling include the date, geolocation, temperature, salinity, and oxygen. Moreover, when available, these samples can contain _macronutrients_ such as nitrates (NO\({}_{3}^{-}\)), phosphates (PO\({}_{4}^{3-}\)) silicates (SiOH\({}_{4}\)); _carbonate system variables_ such as the total alkalinity (A\({}_{T}\)), and also chlorophyll-a.
## 4 Experiments
### Comparison with Previous Methods
#### 4.3.1 Short-Term Prediction  
Short-term prediction refers to predicting human poses within a 400 ms range, with a prediction interval of 40 ms. Table 2 shows the MPJPE for all actions at various time-steps, comparing our method with the previous approaches. The bolded and underlined numbers denote the best and second-best results, respectively. It can be seen that our method outperforms existing SOTAs in most actions and time steps. Specifically, at the 160/320/400 ms time steps, our method's MPJPE is, on average, 9.1%/12.2%/10.9% lower than that of the best-performing method (AMHGCN).  
For periodic actions (e.g., Walking, Walking Together), these types of actions exhibit pronounced periodic patterns (such as gait cycles), resulting in higher prediction errors. For instance, in the 400 ms prediction for Walking, our model achieves an MPJPE of 60.4 mm--much higher compared to Traj-GCN. This indicates that dynamic information (joint torques) does not always enhances the model's ability to perceive motion trends.  
For complex interactive actions (e.g., Walking Dog, Greeting), the prediction error for Walking Dog is relatively high (MPJPE = 49.5 mm at 400 ms) due to the interaction between the person and an external object (the dog), which increases the uncertainty of the motion. However, our model still achieves a 44.5% reduction compared to AMHGCN, thanks to the dynamic branch capturing sudden changes in torque (such as abrupt joint torque shifts during pulling actions). In the Greeting action, the rapid swing of the arms results in significant short-term error fluctuations, but the dynamic information, integrated via the KD-fused mechanism, suppresses error accumulation.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**No.** & **Symbol** & **Description** & **Value** \\ \hline
1. & xyz\_scale & Scale factor of input motion & 1/100 \\
2. & k\({}^{d}\) & Number of GCN layers of SS-MGCN in Kinematic Net & 2 \\
3. & k\({}^{k}\) & Number of GCN layers of SS-MGCN in Dynamics Net & 10 \\
4. & C\({}_{1}\) & Output channels of SS-MGCN & 32 \\
5. & C\({}_{\text{out}}\) & Output channels of Kinematic/Dynamics Net & 192 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Key parameters of our model.
## 1 Introduction
### Study Region  
Our region of interest within the Niger Delta covers just over 5200 km\({}^{2}\) of land and about 3500 km\({}^{2}\) of permanent water bodies (Figure 1), encompassing the coastal portion of Rivers State in Nigeria, with the capital Port Harcourt located to the North, and the urban settlements of Bonny Island to the Southeast and Bille situated slightly East. The areas surrounding Bille and Bonny Island have extensive mangroves, with the primary economic activities of the communities in these areas concentrated on fishing in this part of the Delta. The geographic area is also the location of several major oil fields, with a high density of active oil wells and associated petrochemical infrastructure, including the Bonny Oil Terminals which are the main oil export point on the Atlantic coast for transportation of crude oil out of the Niger Delta to importing countries in the West. Indeed, Rivers State itself is one of the major oil-producing states in the Niger Delta, producing \(\sim\)20% of Nigeria's estimated daily production of 1.476 million barrels of crude oil per day [22]. Many of the largest global petrochemical corporations are or have been active in the region including Shell, Gulf Oil, Agip, Mobil and Chevron since the 1960s.
# Optimal Design of One-Sided Exponential Adaptive EWMA Scheme Based on Median Run Length
## 7 Discussion  
This paper addresses the limitation of the one-sided exponential AEWMA scheme in simultaneously monitoring different magnitudes of process shifts. Through an analysis of the RL distribution for both upper- and lower-sided schemes, we observed that the RL distribution is skewed. To mitigate this bias, we proposed using the MRL as a statistical performance indicator for the monitoring scheme.  
An optimal design procedure based on MRL was developed to enhance the performance of the one-sided exponential AEWMA scheme. This procedure ensures more balanced and robust detection of both small and large shifts, thereby improving overall scheme sensitivity. Additionally, the performance of the one-sided exponential AEWMA scheme, one-sided exponential EWMA scheme, and one-sided exponential Shewhart scheme, all based on MRL, is compared. Finally, the proposed one-sided exponential AEWMA scheme based on MRL is illustrated through two case studies: monitoring failure intervals in OLED production and detecting increases in coal mine explosion frequencies.  
In conclusion, the proposed one-sided exponential AEWMA scheme based on MRL provides a robust solution for monitoring process shifts of varying magnitudes, outperforming traditional monitoring schemes in many scenarios. Future research could extend this approach to other types of processes and distributions, focusing on the robustness of the AEWMA scheme across different distributions. Investigating its performance under non-exponential distributions and exploring its applicability in diverse industries would further validate its effectiveness and broaden its utility.  
Conceptualization, Y.Q. and Q.X.; methodology, Y.Q.; software, Z.W.; validation, Z.W. and Q.Z.; resources, Y.Q.; writing--original draft preparation, Y.Q.; writing--review and editing, Q.X.; Visualization, G.J.; supervision, Q.Z.; project administration, Z.W.; funding acquisition, Y.Q., Q.Z., Z.W. and G.J. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Natural Science Foundation of the Jiangsu Higher Education Institutions of China grant number 22 KJB110012, 23 KJD120002, 24 KJB520005.  
Data Availability Statement: Data is contained within the article.  
The authors declare no conflicts of interest.  
Figure 11: Upper-sided exponential Shewhart scheme with \(\widehat{\theta}_{0}\) for the coal mine explosions.
# Transformer-Based Distillation Hash Learning for Image Retrieval
## 3 Method
### Training  
**Loss function for teacher model:** there are two losses used to train the teacher model, namely metric ranking loss based on triplet samples and quantified loss. The metric ranking loss function is used to make the similarity between positive sample pairs greater than the similarity between negative sample pairs:  
\[L_{Triplet}=\sum_{n}[S(I_{n},I_{n}^{+})-S(I_{n},I_{n}^{-})+\delta]_{+} \tag{6}\]  
where \([x]_{+}=max(0,x)\), \(S()\) is the cosine similarity between sample pairs, \((I_{n},I_{n}^{+})\) are positive sample pairs and \((I_{n},I_{n}^{-})\) are negative sample pairs. The metric ranking loss would treat the sample pair of \(S(I_{n},I_{n}^{-})+\delta>S(I_{n},I_{n}^{+})\) as a valid sample pair and increase the penalty, while for the sample pair of \(S(I_{n},I_{n}^{-})+\delta\leq S(I_{n},I_{n}^{+})\) is considered to satisfy the desired goal and is therefore ignored. By training with this loss, the cosine identity of the negative sample pair will be guaranteed to be at least \(\delta\) greater than the cosine identity of the positive sample pair.  
The quantization loss in our approach is mainly to constrain the network output pseudo hash codes where the code values are as close to 0 or 1 as possible, as follows, which penalizes the network if the output of a neuron is close to 0.5:  
\[L_{Quan}=-\sum_{n}\frac{1}{b}(\hat{\mathbf{h}}_{n}-0.5\mathbf{1})^{T}\cdot( \hat{\mathbf{h}}_{n}-0.5\mathbf{1}) \tag{7}\]  
where \(\mathbf{1}\) represents a vector of ones of length \(b\). During training for the teacher model, we weight the two loss components \(L_{Triplet}\) and \(L_{Quan}\) by factors \(\lambda_{1}\) and \(\lambda_{2}\), respectively. Therefore, the overall loss for the teacher model is: \(L_{Teacher}=\lambda_{1}L_{Triplet}+\lambda_{2}L_{Quan}\).  
**Loss function for student model.** The student model uses the same backbone as the teacher model and both sides share the same backbone parameters. We fix the backbone parameters when training the student model. The subsequent structure is mapped to the same \(b\) bits as the output of the teacher model by a fully connected mapping layer and activated with a sigmoid function. Relative entropy is used here as a distillation loss function to measure the distance between the two model distributions. Assuming that, for any sample \(n\), the hash code output by the teacher model is \(\hat{\mathbf{h}}_{n}^{tech}\) and the hash code output by the student model is \(\hat{\mathbf{h}}_{n}^{stud}\), the distillation loss is  
\[L_{distill}=\sum_{n}p_{n}\cdot(\log p_{n}-\log q_{n}) \tag{8}\]  
\[\log