esa-sceva/satcom-chunk-collection huggingface dataset:
# Design and Implementation of LoRa Based IoT Scheme for Indonesian Rural Area
## 3 Design and Implementation
### The LoRa Physical Layer  
LoRa uses frequency chirp modulation with varying frequency over time to decode information. This is known as chirp spectrum modulation. Due to the linearity property of the chirp, the frequency offset in the server and client are equivalent, eliminated by the decoder. This advantage property allows the frequency offset, reaching 20% of the bandwidth without impacting the decoding process [29]. In this LoRa modulation scheme, several parameters are open to customization. These parameters include bandwidth (BW),  
Figure 3: Client–server communication flow.  
Figure 2: Client architecture.  
the spreading factor (SF), and code rate (CR). LoRa uses the logarithm in base 2 for chirp in the spreading factor. It influences the bitrate modulation, resistance to noise, and decoding easily. In this type modulation, the chirp rate will depend on the bandwidth parameter, as the chirp rate is proportional to the bandwidth. Equation (1) formulates the relation between the chirp rate and the bandwidth.  
\[T_{s}=\ \frac{2^{SF}}{BW} \tag{1}\]  
where _Ts_ represents the duration of package transmission, _SF_ is the spreading factor, and _BW_ is the bandwidth. This formula represents that the increasing spreading factor will eventually divide the span of the frequency of chirp by 2 and multiply the duration of package transmission. Moreover, the bit rate at any given spreading factor is proportional to the bandwidth, also doubling the transmission rate. Moreover, the code rate (_CR_) is also open to adjustment. Equation (2) formulates the customizable _CR_ in LoRa modulation.  
\[R_{b}=SF\ \times\ \frac{BW}{2^{SF}}\ \times\ CR \tag{2}\]  
where \(R_{b}\) is the bit rate. The Equation (2) allows us to compute the bit rate (\(R_{b}\)) in LoRa modulation.
# Airspace Geofencing and Flight Planning for Low-Altitude, Urban, Small Unmanned Aircraft Systems
## 3 Definitions and Algorithms  
The term airspace geofence was formally defined in [37] to support a common framework for airspace volume reservation in UTM. Our work follows this definition:  
**Definition 1**.: _A Geofence \(g=\{n,v[],z_{f},z_{c},m,h[]\}\) is a volume defined by a list of \(n\) vertices in the horizontal plane \(v=[(x_{1},y_{1}),(x_{2},y_{2}),\cdots,(x_{n},y_{n})]\), where \(n\geq 3\), and an altitude floor \(z_{f}\) and ceiling \(z_{c}\). The volume is defined relative to a set of home locations, \(h_{i}=(x_{i},y_{i},z_{i},t_{i})\), where \(h[]\) is a list of length \(m\geq 2\). Lateral home positions can be represented as latitude/longitude pairs \((\phi_{i},\lambda_{i})\) or locally referenced Cartesian coordinates \((x_{i},y_{i})\). \(z_{i}\) is the altitude of the home location above Mean Sea Level (MSL). \(t_{i}\) is the activation time for home location \(i\) where \(1\leq i\leq m\). \(t_{m}\) is the deactivation time for geofence \(g\). For consistency, Cartesian coordinates and altitudes are defined in meters and activation/deactivation times are in seconds._  
This data structure supports geofence types: static, durational, and dynamic. A _static geofence_ has a permanent fixed home location \(h[\,]\) and typically surrounds physical obstacles such as buildings or sensitive areas (i.e., no-fly zones). A _durational geofence_ is active over a finite time interval with a fixed home location \(h[\,]\). A _dynamic geofence_ is active over a specific time interval; its _home_ location can move over time.  
The following simplifying assumption is made in this paper to facilitate path planning and eliminate the need for traffic deconfiction.  
**Assumption 1**.: _One aircraft (e.g., UAS) is allocated to each local geofence volume. No other UAS is permitted to cross into this volume. UTM efficiency therefore relies on minimizing each reserved geofence volume and its duration._  
Dynamic airspace volumization for geofencing will enhance safety by wrapping a UAS in an airspace volume that assures separation from other traffic. The below subsections describe our geofencing algorithm pipeline for UTM, where flight plans are designed with keep-in/keep-out geofencing volumes on a low-altitude airspace map. Three-dimensional trajectory keep-in geofence volumes safely wrapping UAS flight paths are described in Section 3.1, keep-out geofence construction for a low-altitude urban map is described in Section 3.2, and geofence-based path planning solutions are illustrated in Section 3.3.
## 3 Materials and Methods
### DEA Method  
DEA is an empirical, comparative, and non-parametric method employed for the efficiency measurement. The fundamental roles of DEA are to contrast the efficiency of the decision-making unit under observation, \(DMU_{o}\) (\(o=1\), \(2\), \(3\)... \(n\), where \(n\) is the number of DMUs) to the efficiencies of all other \(DMU_{j}\) (\(j=1\)... \(n\)) within the production possibility set (PPS) according to the resources they use (inputs \(X=(x_{1j},x_{2j},\ldots,x_{mj})\)) and the outcomes they produce (outputs \(Y=(y_{1j},y_{2j},\ldots,y_{sj})\)), and to estimate the production frontier (production function). The objective function of the DEA mathematical model maximizes the efficiency; it returns the optimal values of the weights assigned to each input and output under the principle that a given DMU is able to perform better if another DMU can carry out this function, assuming that the inputs and outputs are identical for all DMUs [34; 35]. An input-oriented CCR model can be expressed as shown in Equation (1) and modeled in Figure 2.  
\[\begin{split} Max\sum_{r=1}^{s}& u_{r}y_{r}\\ S.t\sum_{i=1}^{m}& v_{i}x_{io}=1\\ &\sum_{r=1}^{s}u_{r}y_{rj}-\sum_{i=1}^{m}v_{i}x_{ij}\leq 0\ \ \ j=1,2,\ldots,n\\ u_{r}\geq 0&\ \ \ \ r=1,2,\ldots,s\\ v_{i}\geq 0&\ \ \ \ i=1,2,\ldots,m\end{split} \tag{1}\]  
where \(s\) is the number of outputs and \(m\) is the number of inputs.  
Figure 2: DMU inputs and outputs.
# Globally Conditioned Conditional FLOW (GCC-FLOW) for Sea Clutter Data Augmentation
## 3 Experiment Settings
### Complexity  
Table 1 summarizes the complexity of the GCC-FLOW in terms of number of parameters, training time, and generation time. To avoid overfitting, the model is trained with an early stop patience of 20, resulting in different training times depending on the dataset. The experiments were performed under identical GPU conditions on a single NVIDIA RTX 3090. The generation time is used to describe the time required to generate a sequence of length 512.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{2}{c}{**GCC-FLOW**} & \multicolumn{1}{c}{**Training**} & \multicolumn{1}{c}{**\# of Params**} & \multicolumn{1}{c}{**Generation**} \\ \hline
**Global** & **Training Data** & **Time** & **\# of Params** & **Time** \\
**Condition** & **Training Data** & **Time** & **(per 512 Samples)** \\ \hline \multirow{4}{*}{Fixed} & Dartmouth & \multirow{2}{*}{2.6 h} & \multirow{4}{*}{71 k} & \multirow{4}{*}{1.17 s} \\  & high sea state & & & \\ \cline{1-1} \cline{3-5}  & Dartmouth & & & \\ \cline{1-1} \cline{3-5}  & low sea state & & & \\ \cline{1-1} \cline{3-5}  & Grimbsy & & & \\ \cline{1-1} \cline{3-5}  & 11 th range of \#155 & & & \\ \hline Variable & Dartmouth & \multirow{2}{*}{18.5 h} & \multirow{2}{*}{1.17 s} & \multirow{2}{*}{1.17 s} \\  & high and low sea state & & & \\ \end{tabular}
\end{table}
Table 1: Complexity analysis of GCC-FLOW.  
Figure 3: Architecture of coupling networks in GCC-FLOW.
# On the Design of a Network Digital Twin for the Radio Access Network in 5G and Beyond
## 2 NDT Architecture for the RAN
### Service Mapping Models  
The _service mapping models_ includes different models for representing the various elements and functionalities in the RAN. A modular design is devised to improve the programmability of the network services and the agility of operation and deployment. The models are fed by the data in the data repository to update their parameters according to the real RAN behaviour. The different service mapping models interact between them and are divided into two main types: basic and functional models, which are described in the following subsections, indicating some considerations on their design.
## 6 Space-Segment Optimization Aspects
### Constellation Maintenance
#### 6.6.6 Space Debris  
When designing a satellite constellation, one should also address potential space-debris-related problems. A high number of satellites at a certain orbital altitude increases the risk of those satellites colliding with each other. An orbital satellite collision can generate a cloud of debris, which can render even more satellites nonfunctional and can degrade the space-segment functionality. A collision with satellites belonging to other owners can also lead to liabilities with heavy economic consequences. Even when collisions can be avoided by avoidance maneuvers, frequent maneuvering will increase operational cost and will require in-orbit consumables. Therefore, the collision risk and the required avoidance maneuvers should be estimated for the space segment.  
To assess the collision risk, typically, an annual collision probability with a large space object along with orbital lifetime is estimated. The estimation requires an estimate of the satellite projection area, the orbital parameters of the satellite, and the satellite launch date. The collision risk analysis is usually performed using a predictive model database of space objects and space debris. The same database is required for collision avoidance maneuver budget estimation. For example, the European Space Agency (ESA) provides a MASTER (Meteoroid and Space Debris Terrestrial Environment Reference) database and a DRAMA (Debris Risk Assessment and Mitigation Analysis) software package for this task. The space-segment optimization for space debris avoidance is a complex task, which cannot be easily integrated into the general optimization tasks addressed in this paper, and it is omitted in the current framework.
## 3 Quality of Service
### Evaluation of the Selected Specifications
#### Medium Access Control Mechanisms  
Each standard uses TDMA as medium access control mechanism (Table 3). However, TDMA is not the main medium access scheme in the ZigBee standard, which uses primarily CSMA/CA, but can also provide guaranteed timeslots in its beacon mode. Even if all specifications make use of TDMA access mechanisms, some differences between them can be observed. First of all, the timeslot length can be configured in the ISA100.11a, ZigBee and 802.15.4e FA MAC standards, while it is fixed in the others standards. Adapting the timeslot length to the needs of the applications allows taking into account  
\begin{table}
\begin{tabular}{c|c|c|c|c} \cline{2-5}  & \multicolumn{4}{c}{Real-time requirements} \\ \cline{2-5}  & None & Soft & Hard & Isochronous \\ \hline \hline Cycle duration & & greater than 10 ms & 1 ms to 10 ms & 250 \(\mu\)s to 1 ms \\ \hline Jitter & & & & less than 1 ms \\ \hline Application examples & Maintenance & Process automation & Control & Motion control \\  & Diagnosis & Data acquisition & Machine tools & \\ \hline \end{tabular}
\end{table}
Table 2: Industrial automation applications classified according to their real-time requirements [35].  
the specific characteristics and optimizes the timeslot duration. Without such optimization, time can be lost between two successive slots if the slot is longer than the data to send. Configuring the length of the timeslots is thus an important feature to allow for optimizing the real-time support. However, all timeslots inside each superframe have the same length that limits a real adaptation to the requirements of individual applications, as only the overall traffic mix can be optimized. Another feature is the selection of the type of the timeslots, which can be either shared or dedicated. Most of the selected specifications offer both kinds of timeslots. Only the WISA specification uses exclusively dedicated timeslots. The choice between dedicated and shared timeslots is made difficult by the trade-off between real-time support and optimized utilization of the medium. Indeed, the dedicated timeslots are assigned to one particular device only. If this device wants to send commands or measurements, the data are transmitted immediately within the reserved timeslots. Otherwise the timeslot is lost and other devices wanting to transmit information may be constrained to wait longer. In case of shared slots, the devices have to compete to access the medium. The medium utilization is thus optimized because slots can be utilized by the stations in need of bandwidth, but the data transmission cannot always be immediate due to random back-off mechanisms for example.
# Interactive Exploration of Virtual Heritage by Means of Natural Gestures
## 1 Introduction
When taking into account various age gaps and various technological backgrounds of museum visitors, the willingness of users to use such technological solutions and, thus, their acceptance by the users, is questionable. Our work lies in the field of enhancing cultural heritage-related activities in museums by means of VR software solutions, while adjusting the experience to current limitations regarding social interactions. We wish to offer users the possibility to explore (or experiment) the history contained in the artifacts displayed in the museum beyond their present physical appearance, offering an unforgettable experience mediated by modern interactive technologies. We aim to deliver a sense of touch of the artifacts that are usually encased in glass boxes or may by fragile or too big, altogether, to be exposed in a museum setting. Our solution makes use of natural hand gesture-based interaction, in a desire to reduce physical contact among different persons to a minimum, thus following pandemic recommendations of social distancing and limited contact. In this study, we present our experience with developing a VR system that uses natural interaction with the artifacts in a museum by hand gestures. We explore several research questions (RQ), in order to assess the degree of usability and usefulness of the presented approach:  
* Research Question 1 (**RQ1**): _How is natural interaction by means of hand gestures perceived by the users?_
* Research Question 2 (**RQ2**): _Do visitors consider that the gesture-based VR system contributes to the attractiveness of the museum?_
* Research Question 3 (**RQ3**): _Are visitors satisfied with using the VR system implemented in the museum?_
* Research Question 4 (**RQ4**): _Does the interaction with the system spark user interest towards VR/AR applications?_  
To investigate the opinions of users with respect to the research questions, we conducted a user study with 137 participants among the museum visitors, during a period of 6 months. The paper is organized as follows. First, we provide an overview of some recent works in the field of VR applications and cultural heritage. The next section is devoted to the detailed description of the design and functionality of the VR systems implemented in the two museums. We take special emphasis on explaining the navigation metaphor. We aim to assess the usability and usefulness of the developed systems, as they result from our study involving a quantitative research methodology. We evaluate the perceived ease of use and usefulness of the developed VR systems, and debunk the skepticism that surrounds the acceptance of new technologies among certain age groups.
# Handover Management for Drones in Future Mobile Networks--A Survey
## 3 Connectivity Requirements for Drones
### High-Accuracy Positioning and Seamless Connectivity  
Drones flying in multiple levels of airspace require precise localization and seamless connectivity, which are both required for network planning and implementation. A secure connection and extensive network coverage ensure seamless connectivity while the drones are flying autonomously. Covering a wide range of altitudes and maintaining reliable communication is a significant challenge for 4G/5G cellular networks. Sixth Generation integrates radar technology for high-accuracy localization and positioning. The development of dynamic maps and 3D positioning in the sky using a variety of high-tech sensors allows for high-accuracy positioning of drones. Multi-Level Networks (3D) composed of ultra-dense heterogeneous networks in 6G can boost the number of connected drones in high-density ecosystems by roughly \(10^{7}\) devices/km\({}^{2}\), which is 10 times higher than the connection density in 5G. A standardized, high-quality, and reliable cellular connection with extensive 6G coverage provides robust, cost-effective, and seamless connectivity beyond visible LoS. The high-capacity backhaul connectivity provided by the high-speed OWC system allows for the transmission of massive amount of drone traffic data.
## 3 Results and Discussion
### Losses Due to Evaporation and Drift (Re)  
The component Re had low variability and little effect on total water loss, even with very high ETo and wind speed (Table 6). This result was due to the low wind drift (speed \(\leq\) 3 m s\({}^{-1}\)) and to the similarity between the average of the evaporation losses in the three catch cans placed at the base of the pivot (2%) and the losses calculated through the Re equation. However, wind speed was found to be the variable that best explained evaporation and drift losses, with abrupt changes in wind speed and direction also affecting those losses [38].  
The sprinkler used in the test has a grooved deflector plate that, coupled with a wobbling action, emits droplets of 2 to 4 mm in diameter (a situation that is not considered in Equation (6)); these droplets are barely sensitive to the action of wind and evaporation [37]. The authors also evaluated a sprinkler with two types of plate, smooth and coarse serrated (droplet size of 0.3 to 5 mm), and determined evaporation and drift loss of 0.7 to 1.4% for a smooth plate and 0.4 to 0.6% for a serrated plate at equal operating pressure to those used in this work but under higher wind speed conditions (6 m s\({}^{-1}\)) [39].
## 5 Critical Analysis  
By analyzing the previous review papers, and performing a co-occurrence and timeline view analysis of keywords, the authors found that the image recognition algorithm based on deep learning is receiving more and more attention from researchers. Therefore, it was decided to conduct further research on the road crack recognition algorithm based on deep learning technology, and a critical analysis of the related papers was conducted to further discover the related knowledge of the method. In the previous section, 72 papers that focused on deep learning-based road crack recognition were screened. Excluding the papers that did not provide the detailed process of realizing the research method, 65 papers were finally screened. The authors grouped these 65 papers based on the types of computer vision techniques used in them. The authors then analyzed the 65 papers based on the problem statement, methodology, and results. The following issues were raised. Q1. What deep learning method does a paper use? Q2. What backbone does the deep learning method use? Q3. What deep learning framework does a paper use? Q4. What datasets does a paper use? Q5. What concrete surface does a paper consider? Q6. What loss function does a paper use? Q7. What optimizer does a paper use? Q8. What annotation tool does a paper use? Q9. What performance levels does a paper reach? The answers to these questions are summarized in Tables 13-15 for papers in various categories. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Ref** & **Method** & **Backbone** & **Framework** & **Dataset** & **Surface** & **Loss Function** & **Optimizer** & **Annotation Tool** & **Performance** \\ \hline
[14] & DCNN & VGG-16 & Keras & LTPP & Asphalt pavement & - & Adam & - & & Accuracy = 90\%, Precision = 90\%, Recall = 90\%, F1-score = 90\% & & Accuracy = 80\% \\ \hline
[50] & CNN & AlexNet & PyTorch & Own collection & Asphalt pavement & - & MBGD & - & - & Accuracy for longitudinal cracks = 79.2\% \\ \hline
[51] & CNNs & VGG-16 & - & GSV & Asphalt pavement & Cross-Entropy & Adam & Manually & Accuracy = 97.2\% \\ \hline
[8] & CNN & EfficientNet-B4 & - & Own collection & Asphalt pavement & Cross-Entropy & Adam & - & Accuracy = 99.32\% \\ \hline \hline \end{tabular}
\end{table} 
Table 13: Summary of deep learning techniques for crack classification. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Ref** & **Method** & **Backbone** & **Framework** & **Dataset** & **Surface** & **Loss Function** & **Optimizer** & \begin{tabular}{c} **Annotation** \\ **Tool** \\ \end{tabular}  & **Performance** \\ \hline
[42] & CNN & - & MATLAB & Own collection & \begin{tabular}{c} Asphalt \\ pavement \\ \end{tabular}  & - & \begin{tabular}{c} - \\ - \\ \end{tabular}  & \begin{tabular}{c} SGDM \\ \end{tabular}  & \ \begin{tabular}{c} Labeler \\ \end{tabular}  &
\begin{tabular}{c} FMR = 0.0\% \\
# Two-Dimensional Full-Waveform Joint Inversion of Surface Waves Using Phases and Z/H Ratios
## Appendix A  
_Limited-Memory BFGS Algorithm_  
The main algorithm is: given an initial model m\({}_{0}\), objective function f, diagonal scaling D, memory value l, and stopping threshold \(\delta>0\), the L-BFGS algorithm is as follows:  
1. Evaluate \(f_{0}=f(m_{0}),g_{0}=\
abla f(m_{0})\);
2. Set \(p_{0}=-g_{0},k=0\);
3. If k \(>0\), compute p\({}_{k}\) from recursion, below;
4. Compute \(\alpha\)k by line search and set \(m_{k+1}=m_{k}+\alpha_{k}p_{k}\);
5. Evaluate \(f_{k+1}=f(m_{k+1}),g_{k+1}=\
abla f(m_{k+1})\);
6. Set \(s_{k}=m_{k+1}-m_{k},y_{k}=g_{k+1}-g_{k},k=k+1\);
7. Repeat (3-6), stopping immediately when \(g_{k+1}^{T}g_{k+1}<\delta\);Recursion (1) Set \(q=g_{k},i=k-1,j=min(k,l)\); (2) Perform j times: \(\lambda_{i}=\frac{s_{i}^{\gamma}q}{y_{i}^{\gamma}s_{i}},q=q-\lambda_{i}y_{i},i=i-1\); (3) Set \(\gamma=\frac{s_{i}^{\gamma}1_{y_{k-1}}}{y_{k-1}^{\gamma}y_{k-1}},r=\gamma Dq,i=k-j\); (4) Perform j times: \(\mu=\frac{y_{i}^{\gamma}r}{y_{i}^{\gamma}s_{i}},r=r+s_{i}(\lambda_{i}-\mu),i=i+1\); (5) End with result \(p_{k}=-r\); L-BFGS's relatively modest memory usage rests on the fact that if k is the current iteration number and l is the memory value, then vector pairs prior to [sk\(-\)l,yk\(-\)l] are no longer needed and can be removed from storage. The scaling factor \(\gamma\), which accounts for differences between the true Hessian and the approximation thereto, is essential to the good performance of the algorithm.