esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology
### Model Construction and Evaluation
#### 3.3.2 Model Evaluation  
The evaluation of model performance is a crucial step in constructing DL models. Various metrics must be employed to obtain an accurate and comprehensive evaluation of the model. In binary classification tasks, the most suitable evaluation metrics are derived from confusion matrices. These include the accuracy, f1-measure, recall, precision, ROC (receiver operating characteristic) curve, and AUC (area under the ROC). A higher proximity of these metric values to 1 indicates a greater efficacy of the model in fulfilling its designated objectives. Among the various evaluation metrics, the AUC is commonly employed to assess the performance of landslide susceptibility models. In the ROC curve, the horizontal axis represents the FPR (false positive rate), while the vertical axis corresponds to the TPR (true positive rate). This curve provides a comprehensive assessment of the model's performance by simultaneously evaluating its sensitivity and specificity. The calculation formulas of various evaluation indicators are shown in Table 3.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Metric** & **Formula** & **Remark** \\ \hline Accuracy & \(\frac{TN+TP}{TP+TP+FN+TN}\) & The ratio of accurately predicted landslide sample points to the total number of sample points. \\ \hline Precision & \(\frac{TP}{TP+TP}\) & False detection rate, the ratio of accurately predicted landslide positive sample points to the overall number of predicted positive sample points. \\ \hline Recall & \(\frac{TP}{FN+TP}\) & Missed detection rate, the ratio of accurately predicted positive sample points for the landslide corresponds to the total number of positive sample points identified. \\ \hline F1-measure & \(\frac{2 TP}{TP+TP+FN}\) & The integrated means of precision and recall. \\ \hline AUC & The integral of the ROC curve & Careful consideration of landslide points with the landslide classification ability can overcome sample imbalance. \\ \hline TPR & \(\frac{TP}{TP+TN}\) & The proportion of positive samples classified as positive. \\ \hline FPR & \(\frac{FP}{TP+TN}\) & The proportion of negative samples classified as positive. \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance evaluation indicators for evaluating binary classification DL models.
# Minimum Interference Channel Assignment Algorithm for Multicast in a Wireless Mesh Network
## 3 Minimum Interference Channel Assignment
### Calculating a Channel Separation of All Pairs of Nodes
Let us assume that the physical distance of S and C is 350 m and that of A and B is 420 m. If the channel separation of two arbitrary nodes is 1 then an interference factor is 1.6 at 2 Mbps of network bandwidth based on Table 1. Now, we can compute the interference range by multiplying 1.6 by 250 m (a given transmission range). Hence, the interference range between any nodes is 400 m in this case. This result means that if the distance of two nodes is within this amount of value then they might interfere with each other. Therefore, we should increase the channel separation of the two nodes in order to avoid network interference. Given by the scenario, the channel separation of S and C should be two and that of A and C is one to remove network interference. Finally, MICA can decide the maximum value (=2) as a result of the channel separation of S and A. This procedure can be applied to calculate the channel separation of all pairs of nodes except F, G, and I in the multicast tree shown in Figure 2.
## 4 Experiments and Results
### Experimental Design  
We trained the RSOD datasets and DIOR datasets using Faster RCNN, SSD, YOLOv2, YOLOv3, YOLOv3-SPP, YOLOv4, and DFPN-YOLO in the PyTorch framework and performed data augmentation uniformly for the unbalanced categories of the original datasets. All experiments were performed on four NVIDIA GTX 2080 Ti with 11 GB of RAM, and to ensure the fairness of the comparison experiments, we used stochastic gradient descent [42] (SGD) to optimize the model with a momentum of 0.843 and a weight decay of 0.00036.
# Research on Tbps and Kilometer-Range Transmission of Terahertz Signals
## 3 Enabling Technologies for THz Communication Systems
### Power Amplifiers  
In THz systems, PAs serve as the core engine for overcoming path loss in ultra-high-frequency bands. According to the Friis transmission equation, the PA's output power (P\({}_{\rm t}\)) and antenna gain jointly determine the available link budget. Notably, the path loss term in the Friis equation scales inversely with the square of the distance d and the square of the wavelength \(\lambda\). Given the extremely short wavelength of THz waves, path loss over the same distance is drastically higher than in microwave bands. A 1 mW PA can support a 10 m link in the 1 THz band [104]. Without a PA providing sufficient transmit power, the received signal would be entirely buried in noise, rendering the system inoperable. Thus, PAs are essential for counteracting extreme free-space loss and atmospheric attenuation during THz signal propagation and are critical for compensating the gain limitations of passive components like antennas.  
The PA's output power, efficiency, and linearity directly dictate the feasible transmission distance, communication quality, and system power consumption, often representing the primary bottleneck in THz implementations. Common amplifiers in our experiments include PAs, LNAs, and InP semiconductor amplifiers [105; 106; 107]. Their core function is to deliver significant power gain, requiring both voltage amplification and robust current-driving capability to achieve high output power. However, when input power exceeds a threshold, gain compression occurs, eventually leading to output power saturation. In reference [88], the gain and noise figure of the PA and LNA are illustrated; in reference [108], a total amplifier gain of 99 dB was achieved for a 30 km ultra-long-range THz transmission system. Furthermore, we recently demonstrated a 3000 m wireless link at 312 GHz using  
Figure 12: Ultra-long-distance terahertz wireless transmission system enabled by fusion of multiple antennas: (**a**) experimental setup of the ultra-long-distance D-band transmission system over 30.2 km; (**b**) photo of the transmitter; (**c**) photo of the transmission link; (**d**) photo of the receiver; (**e**) DSP structure at the transmitter side and receiver side; (**f**) BER curve of 125 GHz QPSK signal; and (**g**) BER curve of 128 GHz QPSK signal.  
an InP semiconductor amplifier--marking the first kilometer-range transmission reported above 300 GHz. As shown in Figure 13 of our experimental results, leveraging an InP semiconductor amplifier enabled kilometer-range transmission of terahertz waves under foggy conditions.
# Short-Term and Long-Term Travel Time Prediction Using Transformer-Based Techniques
## 4 Experimental Setup
### Competitive Methods  
To effectively assess the proposed SLIT's capabilities, it is compared with both widely recognized baseline and existing leading methods in the field. These methods are briefly outlined as follows:  
* HA [28]: The HA model employs a traditional time series regression approach by using historical data averages as the basis for its forecasts.
* LSTM [29]: LSTM models, a specialized form of recurrent neural networks, are adept at handling sequence prediction tasks, including the forecasting of travel times.
* DNN [30]: This deep neural network model, consisting of six layers, is designed to tackle a range of traffic-related prediction tasks, such as estimating travel times and analyzing traffic flows.
* DE-SLSTM [22]: This DE-SLSTM enhances the capabilities of traditional LSTM models by focusing on both short-term and long-term historical traffic data dependencies, aiming to improve travel time prediction accuracy.
* MTSMTT [23]: A framework for multivariate time series forecasting that integrates BiLSTM units with attention mechanisms to unveil hidden data patterns for precise forecasting efforts.
* DHM [8]: This DHM integrates the Gated Recurrent Unit (GRU) with the XGBoost algorithm for freeway travel time predictions, employing linear regression for the integration process.
* TFT[24]: TFT leverages Temporal Fusion Transformers to integrate various input types, showcasing its adaptability in predicting freeway speeds under different conditions.
* PASS2S [5]: The PASS2S model embeds an attention mechanism within a sequence-to-sequence LSTM framework, targeting specifically the challenge of long-term travel time forecasting.  
To ensure consistency in our evaluation, we use the test results from [5], aligning our experimental data with their study's findings.
# In-Motion, Non-Contact Detection of Ties and Ballasts on Railroad Tracks
## 3 Methodology
### Sensors
#### 3.1.2 Distance LiDAR Sensors  
LiDAR, Light Detection and Ranging [15], is a remote sensing method that uses a pulsed laser which can be triangulated to measure the distance from the target point to the lens itself [16]. Ties and ballasts have different surface characteristics to LiDAR, with ties featuring a smoother and ballasts having a rougher pattern. This contrast can be detected in motion using downward-facing vertical LiDAR sensors to identify alternating tie and ballast sections.  
Two Keyence IL-600 sensors from Keyence corporation of America (Itasca, IL, USA) were adopted for this study due to their off-the-shelf availability and cost-effectiveness. The Keyence IL-600 is a precise distance sensor with a nominal accuracy of 50 \(\upmu\)m and a response time of 0.33, 1.0, 2.0, or 5.0 ms. The fastest response time ensures enough spatial resolution (up to 1.3 cm) for the sensor to record sufficient samples for each tie (about 16 samples) at speeds of up to 40 mph (\(\sim\)65 km/h). A higher number of samples per tie is needed to raise the confidence in the measured data on each tie, with 3-4 samples potentially being the minimum. The required 200-1000 mm installation range provides sufficient standoff distance for most railroad applications.  
As shown in Figure 3, two sensors, designated as \"right\" and \"left\" sensors, were implemented in line with each other in the lateral direction to enable the collection of data on two parts of the track. Using two sensors allows data to be gathered at two locations on ties and ballasts, reducing the likelihood of false positives and negatives. In addition, having two LiDAR sensors would enable us to develop more sophisticated data analysis methods based on measurements from both sensors. This will be elaborated further in the Analysis section. Although not part of the objectives of this study, the sensors could possibly be used to detect track anomalies, such as sunk, tilted, or bowed ties, by comparing their outputs with each other. For instance, a tilted or bowed tie can be identified from the data by comparing the left and right sensor measurements. If they return different values over a tie, this means that the tie is tilted. The same procedure can be followed for different anomalies.
# Angle of Arrival for the Beam Detection Method of Spatially Distributed Sensor Array
## 4 Experiment Results
In contrast, the spatially distributed sensor array proposed in this study positions the sensors on a 150 mm \(\times\) 150 mm panel, with a total mass of merely 140 g. This design not only simplifies the circuitry but also substantially reduces power consumption, presenting a more compact, lightweight, and energy-efficient solution.
## 4 Performance Evaluation of Protection Algorithm
### Adaptability to Basic Requirements of Relay Protection  
(1) Reliability requires that the algorithm be both safe and dependable. Safety means not refusing to operate, and dependability means no misoperation. The protection algo  
Figure 14: Model performance under various fault conditions. (**a**) Different fault types; (**b**) different fault locations; (**c**) different fault resistances; (**d**) whether CT is saturated.  
Figure 15: Current trajectory images under abnormal conditions. (**a**) Severe CT saturation; (**b**) CT measurement error; (**c**) outliers.  
rithm based on the current trajectory image and deep learning has solved the problem that traditional FDP may incorrectly operate when PV stations are accessed. Its accuracy rate of fault location is 100%, which meets the reliability requirement.  
(2) Selectivity means that the algorithm has the ability to correctly identify fault areas and fault phases. The proposed protection algorithm is a two-terminal measurement protection with an obvious internal and external fault boundary. In addition, the three-phase reconstructed current trajectory images are simultaneously sent to A-CNN for recognition. This method can ensure selectivity and accurately identify the fault phase.  
(3) Speed requires that fault lines should be cut off as soon as possible. For 220 kV high voltage transmission lines, the fault clearing time is no more than 120 ms, and the instantaneous protection action time is less than 30 ms [29]. The proposed protection algorithm adopts A-CNN based on a cross-overlapping data set, which strengthens fault characteristics in the first 10 ms of the data set. This method can correctly identify the fault location at about 8 ms after the fault occurs. At the same time, it uses the next 10 ms as the backup, which not only ensures reliability but also improves the speed of fault location.  
(4) Sensitivity means that the protection algorithm should be sensitive to any internal fault, regardless of power system strength or weakness, fault type, fault location, fault resistance, and other factors. According to the performance evaluation in the first two sections, the proposed protection algorithm can accurately identify faults under the above fault conditions and abnormal conditions such as CT saturation. As such, it meets the sensitivity requirement.
# The Cloud-to-Edge-to-IoT Continuum as an Enabler for Search and Rescue Operations
###### Abstract  
When a natural or human disaster occurs, time is critical and often of vital importance. Data from the incident area containing the information to guide search and rescue (SAR) operations and improve intervention effectiveness should be collected as quickly as possible and with the highest accuracy possible. Nowadays, rescuers are assisted by different robots able to fly, climb or crawl, and with different sensors and wireless communication means. However, the heterogeneity of devices and data together with the strong low-delay requirements cause these technologies not yet to be used at their highest potential. Cloud and Edge technologies have shown the capability to offer support to the Internet of Things (IoT), complementing it with additional resources and functionalities. Nonetheless, building a continuum from the IoT to the edge and to the cloud is still an open challenge. SAR operations would benefit strongly from such a continuum. Distributed applications and advanced resource orchestration solutions over the continuum in combination with proper software stacks reaching out to the edge of the network may enhance the response time and effective intervention for SAR operation. The challenges for SAR operations, the technologies, and solutions for the cloud-to-edge-to-IoT continuum will be discussed in this paper.  
search and rescue; robot operating system; Internet of Things; edge computing; cloud continuum; orchestration; computer vision; wireless sensor networks; cloud robotics; distributed applications +
Footnote †: journal:  
00 2023 by the authors. Liconse MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/)]([https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/))).
###### Abstract  
Background: The aim of this retrospective study was to conduct an in vivo evaluation of the accuracy of surgical guides obtained via 3D printing technology that were used to transfer the 3D software-planned position and axis during palatal miniscrew placement. Methods: Twenty-four Caucasian subjects with permanent dentition underwent a CBCT examination to plan palatal skeletal anchorage using two miniscrews in the anterior palatal arch. A specific software function capable of identifying and displaying all CBCT scans passing through the planned miniscrew axis was used to identify the scan showing the maximum discrepancy between the planned and final miniscrew placement. The maximum insertion angle discrepancy and the maximum linear difference between the head and tip of the miniscrew were measured on the overlaid 3D STL models of the planned miniscrew position at CBCT with the final clinical position of the miniscrew. Results: Descriptive and inferential statistics were performed. On average, there was a discrepancy between the planned insertion axis and the final insertion axis of 2.95\({}^{\circ}\) (SD \(\pm\) 1.13\({}^{\circ}\)), with a 10 mm miniscrew length. Conclusion: Three-dimensional L-printed surgical guides for palatal miniscrew placement show a mean deviation of 2.95\({}^{\circ}\) from the planned position, indicating good but improvable accuracy in placement.  
scan body; palatal miniscrew placement; surgical guides +
Footnote †: journal: Applied sciences
### Structure of the Survey  
The rest of our survey is organized as described in Figure 1. Section 2 provides an overview of DTs in the context of future networks, while Section 3 delves into the applications of GNNs for future networks. In Section 4, we analyze the fusion between GNNs and DTs, which we refer to as a Graph neural network-based network digital twin (GraphNDT), with a focus on their potential use cases. We specifically investigate two prominent scenarios: routing optimization and network slicing management. Finally, Section 5 concludes the review by highlighting promising directions and challenges in this evolving field.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Ref.** & **Contributions** & **GNN** & **DT** &
\begin{tabular}{c} **Networking** \\ **Applications** \\ \end{tabular} \\ \hline
[5] & A comprehensive review of various combinatorial optimization problems on graphs, with a particular emphasis on their applications in the telecommunications domain. & * & x & * \\
[7] & A comprehensive overview of the application of GNN in wireless networks. & ✓ & x & * \\
[4.8] & A comprehensive review of graph-based deep learning models for solving problems in different types of communication networks including wireless, wired, and software-defined networks. & & ✓ \\
[12] & A comprehensive review of recent advances in the application of GNN to the IoT field, including a deep dive analysis of GNN design in various IoT sensing environments. & ✓ & x & * \\
[13] & A survey on traffic forecasting in the intelligent transportation system. & ✓ & x & x \\
[14] & A brief review on the application of DT with 5G networks and beyond. & x & ✓ & ✓ \\
[15] & A comprehensive survey on the digital twin network including the key features, technical challenges, and potential applications. & x & ✓ & * \\
[3] & A review of the potential use cases and scenarios in a 6G communication network where a digital twin could play an essential role. & x & * & ✓ \\
[16] & A comprehensive survey on the benefits of twins for wireless and wireless for twins. & x & ✓ & ✓ \\
[1] & Presentation of the application of GNN for the core components of network digital twins & * & ✓ & ✓ \\  & and coupling a network optimizer with the network digital twins. & & & \\ \hline Ours & A review of DTs, GNNs, and GraphNDTs in innovating the communication networks. & ✓ & ✓ & ✓ \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of related reviews on DT and GNN related topic (✓, x, and * indicate that the topic is totally, not, or partially covered, respectively).
## Methods
### Land Surface Phenology Modeling
#### 3.1.4 Hybrid Piecewise Logistic Model  
The Hybrid Piecewise Logistic Model (HPLM) [22] is an improvement of the widely-used logistic model that formed the basis for the MODIS Land Cover Dynamics product (MCD12Q2) before Collection 6 [34]. During the growing season, plants can suffer from water stress or other impacts leading to a different greenness trajectory compared to one under favorable weather conditions. A key advance in the HPLM was the incorporation of alternative conditions for vegetation growth--favorable or stressed. To determine whether the plant is under favorable or stressed conditions, the two functions of Equation (5) were fitted to the EVI2 time series and the function with a higher agreement index was chosen.  
\[\text{EVI2}=\left\{\begin{array}{l}\frac{\text{c}_{1}}{1+\text{e}^{2}+\text {t}_{1}\text{t}_{1}}+\text{EVI2}_{\text{b}}\\ \frac{\text{c}_{2}+\text{d}\text{t}_{1}}{1+\text{e}^{2}+\text{b}_{2}\text{t}_{1 }}+\text{EVI2}_{\text{b}}\end{array}\right., \tag{5}\]  
where t is time in the day of year (DOY), a is related to the vegetation growth time, b is associated with the rate of plant leaf development, c is the amplitude of EVI2 variation, d is a vegetation stress factor, EVI2b is the background EVI2 value, and the subscripts 1 and 2 refer to parameters for favorable and stressed conditions, respectively. From each fitted model, we derived a suite of 14 variables to be used in LCLU classification, including timings of vegetation growth and corresponding EVI2 values (Table 2). Note that fitted parameter coefficients from the HPLM were not used directly in classification (as with the CxQ) because the EVI2 time series at each pixel were fitted with multiple logistic curves.
# Review of Target Geo-Location Algorithms for Aerial Remote Sensing Cameras without Control Points
## 5 Conclusions  
Aerial cameras mounted on various types of aircraft have unparalleled advantages in mobility and real-time performance. Relying on aerial cameras for high-precision geographic positioning of shooting targets is a hotspot in recent years and beyond. However, the detailed description and analysis of target location technology without control points in aerial remote sensing images is not perfect at present. Aiming at the target positioning problem without ground control points, this paper introduces several typical aerial camera-to-ground target positioning algorithms and analyzes the advantages and disadvantages of each algorithm according to their different mathematical models. Meanwhile, the error factors of the accuracy are briefly analyzed, and the often-ignored error terms that have great influence are summarized in the current positioning algorithm.  
With the rapid development of aerial photogrammetry technology and the continuous improvement of task requirements, various directions emerge in the target positioning technology field. Nowadays, the research of positioning algorithms not only needs to improve the existing mathematical model, but also demands to analyze the error factors ignored in the traditional algorithm. In addition, establishing a corresponding mathematical  
Figure 17: Schematic diagram of moving target positioning result.  
model and selecting an appropriate correction algorithm for correction are also necessary. Moreover, intelligent remote sensing image processing algorithms represented by deep learning have become a new research hotspot. How to introduce related technologies such as target detection and image registration into the field of target positioning will be a research highlight in the future. Solving the inherent defects of traditional target positioning technology through intelligent algorithms will be one of the feasible means to significantly improve positioning accuracy.  
Conceptualization, Y.C. and J.Z.; methodology, Y.C. and Y.Z.; software, Y.X.; validation Y.X.; data curation, P.Q.; writing--original draft preparation, Y.C.; writing--review and editing, H.Z. and Y.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, grant number 62027801.  
Not applicable.  
The authors declare no conflict of interest.