esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Materials and Methods
### Mathematical Model Validation  
The most crucial and challenging task after the training and development process of ANN models is the evaluation of their performance, as well their reliability. A number of statistical indices were calculated to assess the performance of the developed FF-ABC-NN model. In particular, we calculated the root mean square error (RMSE) and the mean absolute percentage error (MAPE), where lower calculated values correspond to higher predictive accuracy of the model, as well as the Pearson correlation coefficient (\(R^{2}\)), where the higher the \(R^{2}\) value, the better the fitting between experimental and predicted values. To the best of the authors' knowledge, the RMSE performance index seems to be the most accurate index for the evaluation and ranking of ANN mathematical models, for selection of the optimum and most robust model. Nevertheless, the majority of researchers use the Pearson correlation coefficient for the evaluation of ANN models, usually in conjunction with other performance indexes [74; 75; 76; 77; 78]. The statistical indices used in the present study were calculated using the following, well known and available in the literature, expressions [79; 80; 81; 82; 83; 84; 85; 86; 87]:  
\[RMSE=\sqrt{\frac{1}{n}\sum_{i=1}^{n}(x_{i}-y_{i})^{2}}, \tag{4}\]  
\[MAPE=\frac{1}{n}\sum_{i=1}^{n}\left|\frac{x_{i}-y_{i}}{x_{i}}\right|, \tag{5}\]  
\[R^{2}=1-\left(\frac{\sum_{i=1}^{n}(x_{i}-y_{i})^{2}}{\sum_{i=1}^{n}(x_{i}- \overline{x})^{2}}\right), \tag{6}\]  
where \(n\) denotes the total number of datasets, and \(x_{i}\) and \(y_{i}\) represent the predicted and target values, respectively.
# Summary of Lunar Constellation Navigation and Orbit Determination Technology
## 3 Overview of Single-Satellite Navigation and Orbit Determination Methods
### Astronomical Navigation Technology
further designed an astronomical speed measurement system based on the solar radial velocity and direction vector [39]. Subsequently, the program has been continuously developed and improved, and many scholars have completed a large amount of in-depth and detailed research work [40; 41; 42]. Velocity navigation initially obtains the speed information of the probe. In order to obtain the spatial position of the probe, the speed needs to be further integrated. When the integration time is long, there is a problem of accumulated error. Therefore, although this method is not limited by the probe distance, it is generally used for short-term autonomous navigation in actual exploration missions. There are obvious differences in the navigation sources, sensors used, measured information, navigation principles and performance characteristics of different navigation methods, so there are also differences in applicability. In practice, information obtained by multiple navigation methods is often fused through filtering to form a combined navigation system to further improve navigation accuracy.
# Sugarcane Stem Node Recognition in Field by Deep Learning Combining Data Expansion
## 3 Methodology  
### YOLO v4  
The YOLO network is a one-stage object detection algorithm of a deep learning method that converts the detection problem into a regression problem. Compared with the Faster Region-based Convolutional Neural Network (Faster-RCNN) [22], it does not need a region proposal network, and can directly generate bounding box coordinates and the probability of each category through regression. This end-to-end object detection greatly improves the detection speed. YOLO v4 is the latest algorithm of the YOLO series and is regarded as an improved version of YOLO v3 [23]. Compared with YOLO v3, it adopts Mosaic data expansion in data processing, and optimises the backbone, network training, activation function and loss function, which is faster than YOLO v3, and achieves the best balance between accuracy and speed in these real-time object detection algorithms.  
As shown in Figure 4, the YOLO v4 network uses the open-source neural network framework Centre and Scale Prediction Darknet53 (CSPDarknet53) [24] as the main backbone network for training and extracting image features; then, the Path Aggregation Network (PANet) [25] was used as the neck network to better integrate the extracted features; the head was the same as YOLO v3's method of detecting objects. The main modules of the sugarcane stem node detection model based on YOLO v4 in the complex natural environment were as follows:  
1. Convolution, Batch Normalisation and Mish (CBM) is a convolutional layer, batch normalisation layer and Mish activation function. This module replaced the activation function in Convolution, Batch normalisation and Leaky-ReLU (CBL) of YOLO v3 with Mish, which is the most common module in YOLO v4.
2. CSPDarknet53 is the backbone of YOLO v4, which mainly consists of CBM and Centre and Scale Prediction (CSP). CSP is composed of CBM and the Res module, while the Res module is mainly composed of two CBM modules. Figure 4 shows their specific structure. It can enhance CNN's learning ability by dividing low-level features into two parts and then fusing cross-level features.
3. Spatial Pyramid Pooling (SPP) [26] is a spatial pyramid pooling layer, which mainly converts convolutional features of different sizes into pooled features of the same length.
4. PANet strengthens the entire feature hierarchy through the bottom-up path and uses accurate bottom-level positioning signals to shorten the information path between the bottom-level and top-level features.
## 4 Experiments
### Experimental Setup
#### 4.1.3 Evaluation Metrics  
We evaluated our framework using a comprehensive set of metrics that capture different aspects of system performance. The response effectiveness (RE) measures the system's ability to successfully mitigate security threats, which is calculated as the percentage of successfully handled incidents among all detected threats:  
\[RE=\frac{\text{Successfully Mitigated Threats}}{\text{Total Threats}}\times 100\%. \tag{17}\]  
This metric is particularly important as it directly reflects the system's primary objective of threat mitigation.  
Average response time (ART) quantifies the system's speed in responding to security incidents, measuring the temporal gap between threat detection and response implementation:  
\[ART=\frac{1}{N}\sum_{i=1}^{N}(t_{response}^{i}-t_{detection}^{i}). \tag{18}\]  
Lower ART values indicate better system responsiveness, which is crucial in preventing threat escalation.  
The false positive rate (FPR) evaluates the system's precision in threat identification and response:  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Attack Type** & **Count** & **Avg. Duration (min)** & **Severity Score** \\ \hline DDoS & 5660 & 45.3 & 8.2 \\ Malware & 4740 & 127.8 & 7.5 \\ Phishing & 3900 & 83.2 & 6.8 \\ Insider Threats & 3040 & 156.5 & 7.9 \\ Ransomware & 2660 & 192.1 & 9.1 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Dataset statistics and distribution.  
\[FPR=\frac{\text{False Alerts}}{\text{Total Normal Events}}\times 100\%. \tag{19}\]  
This metric is essential for assessing the practical deployability of the system as high false positive rates can lead to alert fatigue and unnecessary resource consumption.  
System stability score (SSS) provides a holistic measure of a framework's operational efficiency:  
\[SSS=1-\frac{\text{Resource Overhead}}{\text{Maximum Resources}}\times\frac{\text{ System Disruptions}}{\text{Total Actions}}. \tag{20}\]  
This composite metric balances resource utilization with system disruption, where higher scores indicate more stable operation.  
The adaptability index (AI) measures the system's capability to handle novel attacks and evolve its defense strategies:  
\[AI=\alpha\cdot\text{Novel Attack Detection Rate}+(1-\alpha)\cdot\text{ Strategy Evolution Rate}, \tag{21}\]  
where \(\alpha=0.6\) is determined through extensive validation with domain experts. This metric is particularly relevant for evaluating the system's performance against emerging threats.  
In addition, we introduced a new metric: the Real-Time Performance Index (RPI). This index evaluates the framework's ability to handle security incidents within acceptable time limits under realistic network conditions. It is calculated as the percentage of incidents where the response time is below a predefined threshold (e.g., 30 s):  
\[RPI=\frac{\text{Number of incidents with }ART<\text{threshold}}{\text{Total incidents}}\times 1 00\%. \tag{22}\]  
This metric is crucial for assessing the practical deployability of the system in real-time scenarios.
# Mobility Management Based on Beam-Level Measurement Report in 5G Massive MIMO Cellular Networks
## 1 Introduction
### Contribution  
In this paper, we proposed a unific-measurement-report (MR) scheme based 5G mobility management for both beam-level mobility and cell-level mobility. Based on a unific MR, both serving and adjacent cells are involved in intra/inter-cell beam management. In addition, we model a novel finite-state-machine (FSM) for 5G users, which is extended from the user state machine of conventional LTE/LTE-A networks [28]. With this FSM model, we can evaluate performance metrics via Monte Carlo system-level-simulation (SLS) to reflect a practical 5G NR system. We compare our work to previous works such as [18; 25] in terms of performance metrics such as downlink/uplink overhead, 3-dB loss probability, and RLF rates. That is, we compare the mobility performance of our proposed scheme to that of the existing two main beam-reporting schemes in the practical 5G environments. We show that the proposed scheme outperforms previous works in terms of mobility performance such as system overhead, the accuracy of transmitter (Tx) and receiver (Rx) beam-pair (TRP) selection,and radio-link-failure (RLF) rates. The rest of this paper is organized as follows: the system model is described in Section 2. In Section 3, we describe our unific MR-based mobility algorithm. Our improved FSM is proposed in Section 4. The numerical results are shown in Section 5. The conclusions of this work are provided in Section 6.
## 2 Materials and Methods
### Measurement Setup: Requirements and Procedures
If intensity fluctuations are a considerable factor, for instance due to power drifts of the lamp or ambient temperature changes, it is recommended to split a small portion of the excitation light into a low-drift reference detector. The bulk remainder will be used for sample illumination. This way, measured diffuse reflectance intensities can be scaled by the signal of the reference detector. However, it should be taken into account that any additional detector will inevitably increase the overall electronic noise and should hence be used with care. A horizontal alignment of the powder sample has the distinct advantage of eliminating potential contamination of the spectrometer through material gliding off of the (brittle) surface compared to vertically placed samples as it is common in integrating spheres where an additional cover window is often used to keep the powder in place [15; 16]. With the horizontal alignment, the sample remains preserved throughout the measurement and contamination of its surroundings is avoided even without a superfluous cover window. Furthermore, a parallel excitation/emission beam geometry avoids specular reflection losses originating from a slight tilt of the incident light direction with respect to the sample surface normal. This significantly reduces the impact of non-perfectly-Lambertian sample surfaces. To account for this geometry, the beam path requires at least one additional mirror to reflect the excitation beam downwards onto the sample. In an optimized setup this mirror also relays the emission beam towards the detection arm. A realization of this design is presented in Figure 4.  
Diffusely reflected light from the sample is directed into the path of optical detection and is subsequently recorded by a detector within the spectrometer, e.g., a photomultiplier tube (PMT). For simplicity, we will focus on single-channel detection.
## 4 Ablation Study
### Sensitivity of Hyperparameter \(\varepsilon\)  
We also performed an ablation study on hyperparameter \(\varepsilon\), i.e., how much difference between the loss incurred by an unpruned model and the loss incurred by a pruned model is acceptable. We experimented with \(\varepsilon=\{0.3,\,0.35,\,0.4,\,0.45,\,0.5\}\), and the results are reported in Table 7. We observed that if \(\varepsilon\) was set to a value lower than \(0.3\), it did not degrade the performance, but failed to obtain a compact model. On the other hand, if \(\varepsilon\) was set to a value higher than \(0.5\), it failed to converge. As with the above specified values, the results obtained by the proposed approach, including different initialization strategies, look rather similar; we consider \(\varepsilon=0.5\) a proper value, which returned steady performance for different initialization strategies. Therefore, we set \(\varepsilon=0.5\) for all experiments.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Initialization Strategy** & **Compression Ratio** & **Acceleration Ratio** \\ \hline Random & 6.1\(\times\) & 6.7\(\times\) \\ \hline Chaotic & 5.3\(\times\) & 5.2\(\times\) \\ \hline He & 3.7\(\times\) & 2.8\(\times\) \\ \hline LeCun & 3.6\(\times\) & 2.9\(\times\) \\ \hline Xavier & 3.7\(\times\) & 2.8\(\times\) \\ \hline \hline \end{tabular}
\end{table}
Table 6: Compression and acceleration analysis of the proposed approach on TeranusNet.  
Figure 3: Convergence curve of the proposed approach with different weight initialization strategies. We report the loss of the network just after pruning (and before fine-tuning).
# A TDD Framework for Automated Monitoring in Internet of Things with Machine Learning +
## 1 Introduction
Our hypothesis considers that the RM-ODP [7] standard is a relevant reference for the proposition of a Test-Driven Development (TDD) Framework to allow the monitoring of IoT systems considering a structured approach considering business drivers, non-functional requirements, and dynamic evaluation. The TDD [8] is applied with unsupervised machine learning based on IoT deployed modules data to obtain automated tests that better represent real-world constraints. These automated tests could be used by a developer with a TDD approach, so that IoT module problems (e.g., memory limitation) are identified earlier in the development process. The main contributions of this work are:  
* A Test-Driven Development (TDD) tool built with K-Means clustering algorithm with real testbed data to enhance the Testability non-functional requirement of IoT systems under development;
* A framework that considers business drivers and non-functional requirements to support the IoT solution development process;
* Validation of a proposed framework in smart home and remote lab study cases to obtain deployment conditions and monitoring frequency of different IoT device firmware versions. This article is an extended version of our paper published in Intercloud and IoT at 8 th International Conference on Future Internet of Things and Cloud (FiCloud 2021), considering the historical data collection, the addition of the remote lab case study, and the proposition of a generalized framework considering RM-ODP besides the TDD. To the best of the authors' knowledge, it is the first TDD approach for IoT module automatic assessment which uses machine learning based on real testbed data. Some of the related work carried out experiments in testbeds with real hardware devices, but none of them used machine learning, which motivated the novel TDD approach validated with two study cases. The open source materials related to this article are available in the following repository: [[https://github.com/vthayashi/TDD-IoT](https://github.com/vthayashi/TDD-IoT)]([https://github.com/vthayashi/TDD-IoT](https://github.com/vthayashi/TDD-IoT)), (accessed on 30 November 2022). This document is organized as follows: Section 2 presents a structured literature search and with some related work. Section 3 follows with the Research Background of IoT architecture, ISO25010 as a standard for non-functional requirements, the RM-ODP standard, the microservices architecture and the TDD. Our proposed framework is presented in Section 4. Section 5 describes the smart home and remote lab study cases to show how the framework might be applied in the wild to elaborate suggestions related to microservices, cloud and edge architectures, and device management. We discuss and compare our approach with the related work in the Section 6. The final considerations and directions for related work are presented in Section 7.
## 3 The Data and Previous Work  
We based our analysis on Modbus over TCP/IP data collected from an HVAC system at the University of Alabama Huntsville research lab. These data contained both malicious and non-malicious traffic for the various stages of the HVAC operations, that is, auto cool, auto heat, normal, and random. The dataset consists of eight days' worth of data using Modbus over TCP/IP on an HVAC operation plant. Each day's data consist of raw network packet capture converted to a CSV file with both Modbus and TCP/IP and NetFlow features. Attacks that have been simulated in the datasets consist of DoS, MITM, Reconnaissance, overheating, overcooling, displaying fake values, simultaneous heating and cooling, system shutdown, and setpoint changing.  
For this study, the top three malicious activity families that come up in the literature, namely DOS, MITM, and Reconnaissance, were used.  
A review of the literature [16, 25, 26] shows that attacks in industrial systems are broadly categorized as below:  
* Interception--Adversaries are able to intercept communications, a classic example of which is the MITM attack.
* Interruption--Adversaries are able to disrupt and degrade normal operations, a classic example of which is the DOS attack.
* Modification--Adversaries are able to intercept communications and alter instructions and values, a classic example of which is the MITM attack.
* Fabrication--Adversaries are able to inject new or altered messages into the communication stream.  
Reconnaissance almost always acts as a precursor to all these attacks when adversaries are either actively or passively scanning the networks. These led us to choose the three top attacks, namely MITM, DOS, and Reconnaissance, for this experiment.
## 2 Instruments and Methods
### Measurements of the Precipitable Water Vapor (PWV) by Water Vapor Radiometers  
In some cases, the information on the PWV value and its fast variations are of primary interest. They lead to phase shifts and fluctuations, which should be taken into account in radio interferometry. Although such information can be retrieved from the measurements of the opacity in the atmospheric windows described above, a more effective approach is to perform such measurements at frequencies near the H\({}_{2}\)O transitions. Usually, for this purpose, the transitions at \(\sim\)22 GHz and \(\sim\)183 GHz are used. For example, all 12 m ALMA antennas are equipped with the 183 GHz water vapor radiometers [36]. At the Institute of Applied Astronomy of the Russian Academy of Sciences, a dual-band water vapor radiometer operating at \(\sim\)21 GHz and \(\sim\)31 GHz was developed [37]. It is actively used for atmospheric measurements (e.g., [38; 39]).
## 2 Preliminary
### Threat Model  
The threat model involves the understanding of latent threats to the system; it is an important security analysis that needs to be done to determine the method of defense. When the security services system is implemented in the security company, various threats may be encountered. The threats are as follows:  
1. The integrity of data: The data sent by the sender must not be modified or destroyed by other parties so as to guarantee privacy and security [33]. When the receiver receives the message, they are able to verify the completeness of the data to guarantee that the data will not be missed.
2. Untraceable record: Every record that is generated from a security services company is saved in the database; the record can be freely modified by the service provider, and customers that apply the security service are unable to trace their records correctly. An automated and traceable record included at every stage of the case processing is important to various applications for making evidence in the arbitration phase (for example, medical, criminal, smart home, etc.) [34; 35; 36].
3. Message repudiation: A digital signature can ensure the non-repudiation of the message [37]. Every message that sends from the sender should be signed with the sender's private key; it makes the receiver able to prove that the message is definitely from the sender.
4. Third-party arbitration: The responsibility clarification is important when there is a doubt that occurs. In the traditional security service, the client only can doubt the security company, and the company is able to falsify the record to protect the company's reputation. So the third-party fair arbitration is important for clarification [38].
5. Cyber-attacks: There are some cyber-attacks by attackers that must be considered; for example, a man-in-the-middle attack [39], replay attack [40], forgery attack [41], etc.
6. Unstable communication: There is some internet communication between IoT devices, such as the IoT main controller, clients, and security company. The unstable communication will cause data loss and the message will not be completed. Therefore, the stabilization of communication must be taken seriously [42].
7. Side-channel attack: This is a physical security attack. There are various kinds of side-channel attacks, such as Simple Power Analysis (SPA) and Differential Power Analysis (DPA) [43]. This type of attack monitors the power consumption of the electronic device to extract important data like secret keys [44]. [PERSON] et al. demonstrated an ECDSA key extraction from mobile devices with a side-channel attack [45].
# Three Diverse Applications of General-Purpose Parameter Optimization Algorithm
## 5 Application to Face Contour Approximation Model
### Initial Parameter Values  
The initial values of the parameters are obtained from the related keypoint coordinates. For the upper half circle \(C1\), keypoint 27 of _Openpose_ is used for the center, and the Euclidean distance between two keypoints 27 and 16 is used for the radius. For the lower half circle \(C2\), keypoint 33 is used for the center, and the distance between keypoints 33 and 8 is used for the radius.