esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Collision-Free Hybrid MAC Protocol Based on Pipeline Parallel Transmission
### Multi-Hop Extension  
The proposed method can be easily extended to multi-hop networks. First, divide the multi-hop network into several one-hop clusters. Then allocate the TDMA time slots in each cluster. The proposed time slot allocation method can be performed in each cluster independently, except that when a node belongs to more than one cluster, all nodes in these clusters should be considered in calculating its time slot using Equation (2). The replication computation can also be used to calculate the data packet schedules inside a cluster. However, because nodes without direct links cannot obtain the handshaking result from each other through broadcasting, the nodes belong to multiple clusters can only joint the communication of one cluster. Furthermore, while other nodes can communicate normally, the nodes belong to multiple clusters should stop their communication when they hear any RTS packets from different clusters in the same transmission cycle to avoid mutual interference. The transmission cycle should also be fixed in all clusters for the purpose of synchronizing, which means that in some cases not all the data packets can be sent out in the current transmission cycle as in the single-hop situation. Therefore, the transmission efficiency in the multi-hop network is not as high as that in the one-hop network.  
Figure 6: Applying replication computation to parallel data transmission. (**a**) conventional method; (**b**) replication computation.
# A ResNet1D-AttLSTM-Based Approach for Ship Trajectory Classification
## 2 Methods
### Construction of the ResNet1D-AttLSTM Model  
As shown in Figure 2, the ResNet1D-AttLSTM ship trajectory classification model primarily comprises a feature extraction module based on ResNet1D and AttLSTM, as well as a classification module based on a multilayer perceptron (MLP).  
Figure 2: Architecture diagram of the ResNet1D-AttLSTM model.  
After completing the data preprocessing, the model first organizes the ship trajectory sequences into a five-channel format based on five features of each trajectory point (longitude, latitude, SOC, COG, and time) and inputs them into the ResNet1D module. Through convolutional operations and multiple residual block processes, this module extracts high-level motion features from the sequence. Simultaneously, the ship trajectory sequences are also fed into the AttLSTM module to capture the temporal variation characteristics of the trajectory sequences.  
Finally, the two types of features extracted from the ResNet1D and AttLSTM modules are concatenated and fused. These combined features are then passed through a multilayer perceptron to output the predicted results for trajectory type classification.
# Detection of Forest Tree Losses in Cote d'Ivoire Using Drone Aerial Images
## 3 Results
### Quality Assessment of Maps  
The quality assessment of all the maps reveals that the overall accuracy (OA) is 97% and the kappa index is 0.95 (Table 6). The user accuracies are estimated at 93% for tree loss, 98% for stable forest, and 99% for stable nonforest. Producer accuracies are estimated at 98% for tree loss, 97% for stable forest, and 97% for stable nonforest.
# Domain Adaptation for Satellite-Borne Multispectral Cloud Detection
## Appendix B Additional Results on Ubotica CogniSAT-XE1  
In addition to the values of ACC and FP rate in Table 6, the confusion matrices in Tables 11-18 below, also obtained on the Ubotica CogniSAT-XE1, facilitate the calculation of precision, recall and F1-score. For example, in Table 11, FISH Mask has  
* \(\text{precision}=\dfrac{TP}{TP+FP}=\dfrac{46.8}{46.8+7.2}=0.8667\),
* \(\text{recall}=\dfrac{TP}{TP+FN}=\dfrac{46.8}{46.8+3.2}=0.9360\) (note false negative rate \(=1-\text{recall}\)),
* \(\text{F1-score}=\dfrac{2 TP}{2 TP+FP+FN}=\dfrac{2\times 46.8}{2\times 46.8+7.2+3.2}=0.9000\). \begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{3}{c}{SOURCE ONLY} \\ \hline \hline \multicolumn{3}{c}{**True**} \\ \hline
**Predicted** & **Cloudy** & **Not Cloudy** \\ \hline
**Cloudy** & TP: 50.00\% & FP: 48.00\% \\
**Not cloudy** & FN: 0.00\% & TN: 2.00\% \\ \hline \hline \multicolumn{3}{c}{FISH Mask} \\ \hline
**Predicted** & **Cloudy** & **Not Cloudy** \\ \hline
**Cloudy** & TP: 41.20\% & FP: 1.20\% \\
**Not cloudy** & FN: 8.80\% & TN: 48.80\% \\ \hline \hline \multicolumn{3}{c}{**True**} \\ \hline
**Predicted** & **Cloudy** & **Not Cloudy** \\ \hline
**Cloudy** & TP: 49.60\% & FP: 40.00\% \\
**Not cloudy** & FN: 0.40\% & TN: 10.00\% \\ \hline \hline \multicolumn{3}{c}{**Test**} \\ \hline
**Predicted** & **Cloudy** & **Not Cloudy** \\ \hline
**Cloudy** & TP: 48.40\% & FP: 34.40\% \\
**Not cloudy** & FN: 1.60\% & TN: 15.60\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Confusion matrices supplementing Table 6a for CloudScout-3-S2 evaluated on L9-TEST, without domain adaptation (SOURCE ONLY) and with domain adaptation (FISH Mask, DUA and Tent). \begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{3}{c}{SOURCE ONLY} \\ \hline \hline \multirow{2}{*}{**Predicted**} & \multicolumn{1}{c}{**Clouy**} & \multicolumn{1}{c}{**Not Cloudy**} \\ \hline
**Cloudy** & TP: 50.00\% & FP: 43.20\% \\
**Not cloudy** & FN: 0.00\% & TN: 6.80\% \\ \hline \hline \multirow{2}{*}{**Predicted**} & \multicolumn{1}{c}{**Clouy**} & \multicolumn{1}{c}{**Not Cloudy**} \\ \hline
**Cloudy** & TP: 46.40\% & FP: 1.20\% \\
**Not cloudy** & FN: 3.60\% & TN: 48.80\% \\ \hline \hline \multirow{2}{*}{**Predicted**} & \multicolumn{1}{c}{**Clouy**} & \multicolumn{1}{c}{**Not Cloudy**} \\ \hline
**Cloudy** & TP: 46.40\% & FP: 1.20\% \\
**Not cloudy** & FN: 3.60\% & TN: 48.80\% \\ \hline \hline \multirow{2}{*}{**Predicted**} & \multicolumn{1}{c}{**Clouy**} & \multicolumn{1}{c}{**Not Cloudy**} \\ \hline
**Cloudy** & TP: 46.00\% & FP: 5.20\% \\
**Not cloudy** & FN: 4.00\% & TN: 44.80\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Confusion matrices supplementing Table 6a for ResNet50-8-S2 evaluated on L9-TEST, without domain adaptation (SOURCE ONLY) and with domain adaptation (FISH Mask, DUA and Tent).
# Architecting an Agent-Based Fault Diagnosis Engine for IEC 61499 Industrial Cyber-Physical Systems
## 3 Architecting the Engine
### Diagnostic Points and Telemetry
\begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt}} \hline \hline
**Packet Type Enum** & **Description** \\ \hline SAMPLED\_DATA\_VALUE & A typed data value sent to the agent that has been either captured from an input or an output port on the function block. \\ \hline PASSTHROUGH\_ENABLED & Command received from the agent to switch the DP to its transparent pass-through mode. \\ \hline POLL\_AGENT & The DP is polling the agent, signaling that it is ready to receive a new data value to inject into the function block. The value is returned in a Post-Back from the NIOserver. \\ \hline TRIGGER\_ENABLED & Command received from the agent to switch from its transparent pass-through mode and begin requesting and injecting test data values. Used in conjunction with the GATE\_OPEN and GATE\_CLOSE commands. \\ \hline GATE\_OPEN & Instructs the DP to open the gate to traffic from other function blocks after switching back to its PASSTHROUGH\_ENABLED mode. \\ \hline GATE\_CLOSE & Instructs the DP to close the gate, blocking traffic to and from other function blocks after switching to its TRIGGER\_ENABLED mode. \\ \hline TRIGGER\_DATA\_VALUE & A typed data value received from the agent to inject into the function block input and event ports. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Packet Type Commands used to control and exchange data with Diagnostic Points. Figure 8: Definition of the Data Packet structure used to exchange telemetry. through an entire application, listening, probing, and then moving on to the next section of interest. This improvement was driven by the ATAM evaluation and resulted in a more dynamic and resilient approach that allows the agents to change the type of interventions they choose to make without needing to re-wire additional DPs at runtime. The Reliability quality attribute was addressed by making the DP function blocks fault-tolerant to network issues by ensuring that they manage their own TCP client connections. In the event of a loss of connection, the DP re-establishes a new connection by itself. Since the data packets carry a unique DP instance identifier, the matching \(DiagnosticPoint\) instance in the engine automatically re-routes the packets to and from the correct FIFO packet queue via the new socket. This ensures that all network interruptions are handled asynchronously by the \(NIOserver\) component and the \(DiagnosticPoint\) subsystems. In this sense, the \(DiagnosticPoint\) instance shown in the Agent layer of Figure 5 acts like a Digital Twin of the DP FB within the FB application [81]. Later, this made the creation of the diagnostic scripts the agents use to execute their goals easier to construct since the instance references to the collection of DPs marshaled by the agent do not change in the event of a network issue.
## 3 Proposed Approach
### Class Weights
#### 3.3.4 Transformer-Convolutional Neural Network (Transformer CNN)
In the case of binary classification, the model leverages binary cross-entropy as its loss function, quantifying the divergence between predicted probabilities and actual binary outcomes. In contrast, the multi-class classification model employs categorical cross-entropy, assessing the difference between predicted class probabilities and the true class labels across multiple categories. For performance evaluation, both models utilize accuracy as their primary metric, which reflects the ratio of correctly predicted instances to the total number of predictions made. This metric serves as a straightforward indicator of model performance, illustrating the extent to which predicted labels correspond with actual labels. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **Block** & **Layers** & **Layer Size** & **Activation** \\ \hline \multirow{8}{*}{Shared Structure} & Input block & Input layer &
\begin{tabular}{c} Transformer \\ output \\ \end{tabular} & - \\ \cline{2-5}  & Hidden block 1 & 1D CNN layer & 512 & ReLU \\ \cline{2-5}  & & 1D Max Pooling & 2 & - \\ \cline{2-5}  & & layer & 0.0000001 & - \\ \cline{2-5}  & Hidden block 2 & 1D CNN layer & 512 & ReLU \\ \cline{2-5}  & & 1D Max Pooling & 4 & - \\ \cline{2-5}  & & layer & 0.0000001 & - \\ \cline{2-5}  & Hidden block 3 & Dense layer & 1024 & ReLU \\ \cline{2-5}  & & Dropout layer & 0.0000001 & - \\ \hline NF-UNSW-NB15-v2 & Output block & Output layer & 10 & Softmax \\ \hline CICIDS2017 & Output block & Output layer & 15 & Softmax \\ \hline \hline \end{tabular}
\end{table} 
Table 30: CNN model layers for multi-class classification.
## 5 Simulation Results and Analysis
### Analysis and Comparison of the Proposed Localization Algorithm
#### 5.2.2 Simulation Results in Different Regions
Only at 16% points, BiasRed algorithm is more accurate than the proposed algorithm when \(\sigma_{toa}=1.5\) m. Moreover, the Chan-Ho algorithm is only more accurate at 13% points compared to the proposed algorithm when \(\sigma_{toa}=1.5\) m. When \(\sigma_{toa}=0.3\) m, the proposed algorithm has a better performance than the two algorithms at all points outside the green quadrilateral. In Figure 15a, the proposed algorithm still has a better performance than the two algorithms at all points inside the green quadrilateral when \(\sigma_{toa}=0.3\) m. In the comparison, the two algorithms and the proposed algorithm with \(\sigma_{toa}=1.5\) have their own advantages. BiasRed is more accurate than the proposed algorithm with \(\sigma_{toa}=1.5\) at 89% points and Chan-Ho is more accuracy than the proposed algorithm with \(\sigma_{toa}=1.5\) at 72% points. However, the proposed algorithm with \(\sigma_{toa}=1.5\) is more reliable than the two algorithm, since the maximum RMSE is only 2.613 and is much lower than the maximum RMSE of the two algorithms. Figure 15: CDF of root mean square error (RMSE) different positioning method when \(\sigma_{toa}=1\) m; (**a**) CDF of RMSE of points inside green quadrilateral; (**b**) CDF of RMSE of points outside green quadrilateral. Based on the simulation results at the two groups' points and the three selected test points, the proposed closed-form localization algorithm using multiple TDOAs and single TOA could provide a better positioning accuracy than the two closed-form TDOA-only positioning methods, Chan-Ho and BiasRed, in the indoor distribution system application scenario.
_Algorithms_**2013**, \(6\), 702-725; doi:10.3390/a6040702  
_algorithms_  
**ISSN 1999-4893**  
www.mdpi.com/journal/algorithms  
_Article_  
**New Parallel Sparse Direct Solvers for Multicore Architectures**  
**[PERSON] and [PERSON] ***  
Scientific Computing Department, STFC Rutherford Appleton Laboratory, Harwell Oxford, OX11 0 QX, UK; E-Mails: [EMAIL_ADDRESS] (J.H.); [EMAIL_ADDRESS] (J.S.)  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +44-0-1235-445131.  
_Received: 9 August 2013; in revised form: 16 October 2013 / Accepted: 21 October 2013 / Published: 1 November 2013_  
**Abstract:** At the heart of many computations in science and engineering lies the need to efficiently and accurately solve large sparse linear systems of equations. Direct methods are frequently the method of choice because of their robustness, accuracy and potential for use as black-box solvers. In the last few years, there have been many new developments, and a number of new modern parallel general-purpose sparse solvers have been written for inclusion within the HSL mathematical software library. In this paper, we introduce and briefly review these solvers for symmetric sparse systems. We describe the algorithms used, highlight key features (including bit-compatibility and out-of-core working) and then, using problems arising from a range of practical applications, we illustrate and compare their performances. We demonstrate that modern direct solvers are able to accurately solve systems of order \(10^{6}\) in less than 3 minutes on a 16-core machine.  
**Keywords:** sparse matrices; sparse linear systems; symmetric systems; direct solvers; multifrontal; supernodal; bit-compatibility; Fortran 95; OpenMP; parallel
# Prediction-Based Energy Saving Mechanism in 3 GPP NB-IoT Networks
## 5 Simulation and Results
### Simulation Setup
#### 5.1.1 Simulation Scenario  
We used five application-layer scenarios for performance evaluation as shown in Table 4. Case 1 corresponds to a UE report scenario, such as the periodic metering service [20], where the IoT device reports sensor values to the network using non-IP packets and receives an acknowledgment from the network to complete the session. In case 2, the application server checks the device status and sensor value. As in case 1, non-IP packets are used, but the session is triggered by a network command and finishes with the UE response and consequent network acknowledgment. Cases 3 to 5 have the same scenario as case 2 but employ IP. Cases 3 and 4 use CoAP/DTLS/UDP that is generally used in IP-based IoT systems [21]. Case 3 includes an initial context creation procedure for DTLS connection, and case 4 considers that the DTLS context already exists, but resuming is necessary. Finally, TCP is used in case 5, but it reduces the header size by robust header compression.
## 4 Results
### Modified RFM Value Calculation  
Researchers commonly utilize the RFM model to analyze customer behavior. As part of this study, we introduced two additional variables, customer engagement index (CEI) and periodicity, to enhance the analysis. The RFMCEIP was calculated from two distinct groups: one that focused on customers and one that focused on products. Customer-oriented groups allowed us to understand customer behavior based on their transactions. On the other hand, the product-oriented group concerned how products perform in sales and transactions. We used 1 January 2012 as a dummy research date to facilitate analysis. This approach enabled us to gather meaningful data and draw valuable conclusions from our study.  
Table 2 shows the result of the RFMCEIP calculation for the customer-oriented group. This table also shows the _CEI_ value normalized with uniform distribution from 0 until 1, meaning 0 is the least-buying customer, and 1 is the most-buying customer. For example, for customer 12346.0, the result showed that the recency equaled 348 days, frequency equaled 25 times, monetary equaled USD 170.40, _CEI_ equaled 0.997, and periodicity equaled 60.68 days. According to the results, it had been 348 days since customer 12346.0's last purchase in the store. In the past two years, the customer completed transactions 25 times, with spending equal to USD 170.40. The customer was included in the group of customers who are quite active in transactions, while the average duration between each purchase was 60.68 days.  
Table 3 shows the result of the RFMCEIP calculation for the product-oriented group. This table also shows the _CEI_ value normalized with uniform distribution from 0 until 1, meaning 0 is the least-sold product, and 1 is the most-sold product. For example, for the product 15 cm Christmas Glass Ball 20 Lights, the result showed that recency equaled 76 days, frequency equaled 437 times, monetary equaled USD 3387.15, _CEI_ equaled 0.926, and periodicity equaled 8.36 days. According to the results, it had been 76 days since the Product 15 cm Christmas Glass Ball 20 Lights was last purchased. The product was sold 437 times in the past two years, generating a total income of USD 3387.15. The product was included in the most-sold products in transactions, while the average duration between each purchase was 8.36 days.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \multirow{2}{*}{**Customer ID**} & **Recency** & **Frequency** & **Monetary** & **CEI** & **Periodicity** \\  & **(Days)** & **(Times)** & **(USD)** & & **(Days)** \\ \hline
12346.0 & 348 & 25 & 170.40 & 0.997 & 60.68 \\
12347.0 & 25 & 222 & 554.57 & 0.980 & 19.06 \\
12348.0 & 98 & 51 & 193.10 & 0.994 & 57.55 \\
12349.0 & 41 & 175 & 1480.44 & 0.986 & 18.66 \\
12350.0 & 333 & 17 & 65.30 & 0.997 & 0.00 \\... &... &... &... &... &... \\
18283.0 & 26 & 938 & 1651.60 & 0.932 & 34.97 \\
18284.0 & 454 & 28 & 91.09 & 0.996 & 0.00 \\
18285.0 & 683 & 12 & 100.20 & 0.998 & 0.00 \\
18286.0 & 499 & 67 & 286.30 & 0.992 & 0.00 \\
18287.0 & 65 & 155 & 346.34 & 0.985 & 73.21 \\ \hline \end{tabular}
\end{table}
Table 2: RFMCEIP Customer-Oriented Value Result.
# Solving the Moment Amplification Factor of a Lateral Jet by the Unsteady Motion Experimental Method
## 5 Conclusions  
This paper conducts unsteady motion tests of a lateral jet. By combining jet control with a wind tunnel free-flight test, curves of pitch moment amplification factors (\(K_{M}\)) for the lateral jet versus angle of attack (\(\alpha\)) are obtained. The method used overcomes the  
Figure 19: Curves of \(K_{M}\) vs. angle of attack at \(M=0.6\), jet on 2.0 and 4.0 MPa.  
Figure 18: Moment curves of the revolution body for three jet conditions at \(M=0.6\).  
disadvantage of previous experiments that can only study the unsteady characteristics of a single parameter (unsteady flow field, unsteady aerodynamic force, or unsteady motion) at a time. The new method ensures that the test model can be coupled in real-time similar to an actual air vehicle. In particular, the unsteady flow field, unsteady aerodynamic force, and unsteady motion of the air vehicle caused by the jet can ensure the unsteady real-time coupling of the three parameters, ensuring that the test results are authentic.  
The primary purpose of this paper is to provide a new test technology and to demonstrate how to obtain more useful and abundant test data to understand the impact of the jet on vehicle movement. The following conclusions can be drawn from these tests. These conclusions summarize these tests and provide reference templates for data analysis by researchers. It is believed that similar phenomena will be observed when using this paper's test method for jet research on other aircraft.  
The tests in this paper are carried out for typical test conditions, and the results show that when the pressure is 2.0 MPa, \(K_{M}\) at \(5^{\circ}<|\alpha|<20^{\circ}\) is generally less than 1, indicating that the pitching moment provided by the lateral jet has no pronounced effect.  
When the pressure is 4.0 MPa, \(K_{M}\) at \(5^{\circ}<|\alpha|<16^{\circ}\) is typically greater than 1, even reaching about 2.25 at \(5^{\circ}\), which shows that the lateral jet plays a role in promoting the pitching moment. When \(16^{\circ}<|\alpha|<20^{\circ}\), \(K_{M}\) is less than 1, indicating that the pitching moment contribution by the lateral jet has no significant effect.  
It was further found that \(K_{M}\) decreases slowly with increasing angle of attack. At 2.0 MPa, \(5^{\circ}\) is the critical angle of attack (where \(K_{M}\) crosses 1), while at 4.0 MPa, the critical angle of attack is \(16^{\circ}\). When the angle of attack is above \(30^{\circ}\), the influence of the jet on the pitching moment nearly disappears.  
Conceptualization, F.X.; Data curation, Y.Z.; Investigation, N.C.; Software, L.L. All authors have read and agreed to the published version of the manuscript. This research was supported by the National Natural Science Foundation of China (U21B2054 and 11772317). This research was also supported by the Open Fund of Key Laboratory of Icing and Anti/De-icing (grant no. IADL20200408). The authors declare no conflict of interest. The authors declare no conflict of interest.
# Object Detection Based on Multiple Information Fusion Net
## 3 The Proposed Method  
The framework of proposed multiple information fusion net (MIFNet) is shown in Figure 1. In our MIFNet, the feature map of an input image is first obtained through a feedforward convolutional network (VGG or Resnet). In the next stage, the network feature map is divided into two parts. One is as a part of the input of the first branch and the other is utilized to get the region proposals through RPN and then served as the input of the second branch. In the first branch network (I), a series of operations is performed on the feature map of the entire image to get the scene-level information as the input of scene GRU (Gated Recurrent Unit in III up). In the second branch network (II), the attention mechanism is utilized to establish object relationships adaptively. For the purpose of classifying and regressing regions of interest (RoIs), the second branch network not only utilizes the appearance feature extracted by convolutional layers and the coordinate information of the object, but also the information of all surrounding objects as the input of relation GRU (in III below). In the message passing module (III), scene GRUs and relation GRUs communicate information to each other in order to keep up with new information. In the last stage, we concatenate the information obtained by these two GRUs to refine the position of the corresponding RoI and predict the category of objects.