esa-sceva/satcom-chunk-collection huggingface dataset:
## Appendix C
_Choice of Data Structure_: Arrays are selected for their simple storage and easy access to a collection of elements. _Complexity Reasoning_: Complexity arises when there is a need to quickly obtain or confirm the existence of an item, especially if the index is known. * _Step 5: Identifying Available Information Why This Step_: This step probably requires accessing a certain collection of information sources just one time. _Choice of Data Structure_: Arrays are ideal for storing and accessing a group of data sources in a sequential manner. _Complexity Reasoning_: Accessing an element in an array directly by index has a time complexity of \(\Theta(1)\). * _Step 6: Adjusting Mission Objectives Why This Step_: Decisions are typically made by considering up-to-date information and are often made through a single decision-making procedure. _Choice of Data Structure_: The selection of a data structure is based on the requirement for fast access either by key (hash table) or by index/order (array). _Complexity Reasoning_: The operation of modifying or retrieving data requires a fixed amount of time, independent of the data structure's size. * _Step 7: Revisiting Directory List Why This Step_: This indicates a solitary action of revisiting or examining the list, potentially to make a decision or obtain data. _Choice of Data Structure_: Arrays are suitable for reviewing stored objects since they provide sequential access. _Complexity Reasoning_: Accessing or iterating through an array to review entries is considered a constant time operation for a single access due to its complexity. * _Step 8: Adjusting Based on Revisited Information Why This Step_: Involves modifying decisions based on information that has been previously reviewed or revisited, typically involving a single update or choice. _Choice of Data Structure_: Choose between an array or hash table based on whether the adjustment is related to an order or specific key-based access. _Complexity Reasoning_: Executing a single modification or choice requires a consistent amount of time, denoted as \(\Theta(1)\). * _Step 9: Iterative Procedure Why This Step_: Each iteration requires a logarithmic number of steps because of the operations performed in a balanced binary search tree. _Why Balanced Binary Search Tree_: Balanced binary search tree is chosen for its efficiency in sorting and retrieval activities that scale logarithmically with the number of items. _Complexity Reasoning_: Complexity arises from the fact that insertion, deletion, and search operations in a balanced binary search tree have a logarithmic time complexity. However, the worst-case scenario of O(n log n) takes into account repeated actions on all items. _Total Overall Complexity_  
_Overall Complexity Reasoning:_ The overall complexity is determined by the most complex operation in the set, which involves an iterative method with a balanced binary search tree. This operation helps in calculating the algorithm's time complexity and space needs.
## 3 Evolution Process and Parametric Study
### Parametric Study  
For further clarification of the results and of the operation of the proposed antenna, the impact of key design parameters on the scattering parameters was studied using CST Microwave Studio. Figure 11 shows the results of four separate studies on four design parameters. It is important to note that each parameter was varied individually while the others were kept constant. Figure 11a demonstrates that the resonant frequency of the TE\({}_{101}\) mode at 28 GHz can be adjusted by changing length L1 of the QMSIW cavity. The resonant frequency decreases as L1 increases. Figure 11b,c reveals that the resonant frequencies at 38 GHz and 0.13 THz are linked to the overall lengths of Slot 1 (LS1) and Slot 2 (LS2), respectively. An interesting finding is that the resonant frequency of the QMSIW cavity at 28 GHz does not change during the tuning of the two slots, which highlights the effectiveness of the proposed slot topology, as the slots are designed in a way that does not impact the fundamental cavity mode at 28 GHz. Finally, the total length of the open loop can be changed by altering either the gap width (g) or the stubs' length (Lt). For simplicity, only the effect of gap width is shown in Figure 11d. This confirms that the main radiator at 5.5 GHz is the resulting open-loop antenna.
# Vision-Based Concrete-Crack Detection on Railway Sleepers Using Dense U-Net Model
## 4 Results and Discussions
### Qualitative Results  
In this subsection, we present the qualitative analysis of some representative images from the test set, as demonstrated in Figure 4. The first column of Figure 4 displays the actual railway sleeper images; the second column displays the corresponding ground truth images; and the last two columns display the segmented results by the U-net model and the Dense U-net model, respectively.  
To understand the detailed segmentation performance of the model more clearly, some portions of the images are highlighted by a green circle. We can see that both the original U-net model and the Dense U-net model predict the cracks on the railway sleeper images quite well. However, if we go for a detailed inspection, in the first, second, third, and eighth images, some tiny cracks are detected by a lesser intensity of the pixels. In contrast, the Dense U-net model predicted higher pixel intensity in more detail. In the case of the fourth image, the U-net model could not predict a more significant portion of the tiny cracks when the railway sleeper is black-colored; in contrast, the Dense U-net model predicted more effectively even in a noisy environment. From the fifth picture, it can be viewed that the original U-net model missed multiple narrow portions of the cracks, but the modified U-net model predicted them well. Both of the models were segmented accurately from the sixth, seventh, and tenth images. In the case of the ninth image, the original U-net model mistakenly predicted a non-crack portion as a crack, but the Dense U-net model avoided that portion efficiently. These experimental results show that our proposed Dense U-net model is more accurate than the conventional U-net model for predicting the cracks on the railway sleeper images.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **Accuracy (\%)** & **Precision (\%)** & **Recall (\%)** & **F1-Score (\%)** & **IoU (\%)** & **Dice Loss (\%)** \\ \hline U-net & 99.10 & 89.43 & 79.93 & 84.41 & 73.03 & 2.96 \\ \hline Dense U-net & 99.17 & 88.53 & 84.63 & 86.56 & 76.31 & 2.93 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison between original and Dense U-net results.  
Figure 4: _Cont._
## 3 Lateral Stability Tracking Control of Designed Rollator
### Lateral Controller Development  
Figure 7 exhibits the MPC controller structure for lateral trajectory tracking. MPC scheme is mainly composed of a trajectory error function, system constraint and optimization objective function [35]. The error equation is the mathematical description of the tracking control system and the basis of the control algorithm. System constraints include rollator actuator constraints, control smooth constraints and stability constraints. The design of the objective function is based on the stability and rapidity of trajectory tracking.
## 2 Related Studies and Backgrounds  
### Related Studies  
Data analysis tools, such as Python, MATLAB, R programming, and application development tools, such as C, C++, C#, and Java are being studied for interoperability with development languages. Representatively, there is Common Object Broker Architecture (CORBA) [15].  
CORBA is an interface protocol that allows software components written in different languages and running on different platforms to interact with each other. The method calling method between different languages uses Object Request Broker (ORB) [16]. In the early days, various studies were conducted by using ORB to call methods between Java and C++ languages. With the development of distributed computing with the improvement of high-performance PCs and networks that provide high bandwidth, research has been conducted to secure interoperability using CORBA in a distributed environment [17].  
CORBA can easily integrate between different languages by calling methods. However, the calling method is a calling technique and combining methods developed by each language. In this way, different languages cannot connect to each other and execute methods of different languages. For example, while developing a method in Java, it is necessary in C++ language. You cannot use a method in Java.  
A technology that allows different languages to connect and directly execute functions and commands of different languages has been studied. Representatively, there are rJava and Matlabcontrol.  
rJava can execute R programming functions and commands by interlocking with R programming in Java [18]. These studies are being conducted to secure interoperability between java and R Programming. Interoperability research using rJava uses Java's graphical user interface (GUI) to overcome sophisticated graphic work, which is a drawback of R programming [19]. Representatively, this study graphically shows data analyzed using R Programming using JavaFX [20].  
Matlabcontrol can execute MATLAB source code by connecting to MATLAB engine in Java. Representatively, research has been conducted on remote and virtual laboratories that remotely connect to the server where MATLAB is installed and execute MATLAB commands using Java and MATLAB interface, Matlabcontrol.  
Research on the linkage between application development tools and data analysis tools has been conducted. However, until now, research on applying analysis tools developed in different languages is insufficient. Hence, research is needed to execute the desired algorithm by applying the source code developed with various analysis tools to the system.
## 3 Evolution of Wireless Communication Technology (1G-6G)
### Third Generation (3G) Technology  
The Third Generation Partnership Project (3 GPP) was formed in 1998 to provide a standardized frequency across the globe for mobile networking, enabling high data rate services such as video calls, navigation, and interactive gaming. It is based on the International Mobile Telephone (IMT)-2000 standard and was first made available in Japan by the NTT DoCoMo in 2001. The IMT-2000 focused on providing a wider coverage area, improving the QoS, and making services available to users irrespective of their location [29]. One of the requirements of the IMT-2000 was to have a minimum speed of 200 Kbps for a network to be 3G. The third generation has introduced wireless technology such as video conferencing and video downloading with an increased data transmission rate at a lower cost. It increased the efficiency of the frequency spectrum by improving the audio compression during a call, allowing more simultaneous calls in the same frequency range.  
Third generation technology evolved between 2000 and 2010 to provide Universal Mobile Telecommunications System (UMTS)-based networks with higher data rates and capacities. In particular, High-speed Downlink Packet Access (HSDPA) was deployed (also referred to as 3.5G), which is a packet-based data service providing downlink data rates 8-10 Mbps. To provide services for applications that require high data rates such as interactive gaming, High-Speed Uplink Packet Access (HSUPA) was introduced (referred to as 3.75G) which enabled an uplink data transmission speed of 1.4-5.8 Mbps. However, IP telephony, 3D videos, and High Definition (HD) mobile TV were not supported by the 3G technology, leading to the foundation of the 4G network generation.
## 1 Introduction
Additionally, the proposed CFT-MF system doesn't require foundation reinforcement work to support the weight increase when RC structures with non-seismic details that have weak foundations are applied, as stated in the next chapter. In the present study, a full-scale two-story test structure modeled from existing RC structures with non-seismic details in Korea was subjected to pseudo-dynamic testing. The effects of the CFT-MF system, when applied to existing RC structures, was examined and verified, especially as to its seismic retrofitting performance, i.e., restoring force characteristics, stiffness reinforcement, and seismic response control. In addition, based on the pseudo-dynamic testing results, a restoring force characteristics model was proposed to implement non-linear dynamic analysis of structures retrofitted with the CFT-MF system (i.e., the test structure). Finally, based on the proposed restoring force characteristics, non-linear dynamic analysis was conducted, and the results were compared with those obtained by the pseudo-dynamic tests.
# Investigation of Microseismic Characteristics of Rock Burst Based on Fractal Theory
## 3 Fractal Theory
### Calculation Process  
The distribution range of similar microseismics compared with the actual working conditions must be accurately delineated and an ellipsoidal model established. The long axis length is \(X\), the middle axis is \(Y\), and the short axis is \(Z\). Based on the model parameter scaling, a sub-model is established with time \(t_{n}\) as the variable, and its model parameters are \((X_{t_{n}},Y_{t_{n}},Z_{t_{n}})\). The number of microseismic events within each model is counted, and the relevant index is calculated according to Equation (8).  
\[\mu(X_{t_{n}})=\frac{S_{t_{n}}}{S^{*2}} \tag{8}\]  
where \(S^{*}\) is the total number of microseismic events within the model range, and \(S_{t_{n}}\) denotes the number of microseismic events within the submodels constructed at different time periods, \(n=1,2,\ldots n\), the correlation index is calculated in the range of the space-time domain \(\mu(X_{t_{n}})\).  
Linear fitting of the set of data points (\(\log X_{t_{n}},\log S_{t_{n}}\)) is performed using the least-squares method, as shown in Equation (9):  
\[k=\lim_{x\to n}\frac{\log\mu(X_{t_{n}})}{\log X_{t_{n}}} \tag{9}\]  
When \(k\) satisfies self-similarity, the microseismic time fractal dimension \(D_{f}\) of the null domain can be derived, as shown in Equation (10).  
\[D_{f}=|k| \tag{10}\]  
Based on the above, the calculation process is simplified as follows: the rock burst incubation and occurrence process based on the real-time monitoring by the microseismic system, the total number of microseismic events within the total time course \(T\), and the number of microseismic event even pairs within the \(t\) time course are counted based on the instantaneous-type rock burst conception and occurrence process recorded by the microseismic monitor. The correlation index \(c(t)\) within the time course \(t\) is calculated using Equation (8).  
\[c(t)=\frac{2N(t)}{N(N-1)} \tag{11}\]  
With lgt as the horizontal coordinate and lgc(\(t\)) as the vertical coordinate, a right-angle coordinate system is established, and a linear fit is performed. Slope \(D_{t}\) is the similarity dimension of the microseismic time distribution.  
\[D_{t}=\lim_{t\to T}\frac{\lg c(t)}{\lg(t)} \tag{12}\]  
If the fitted straight line has a good linear correlation, it indicates that the rock microsystem fractures are generated in time with a fractal distribution relationship. The value of the slope \(D_{t}\) is the similar fractal dimension of the temporal distribution of microseismic events. Therefore, the temporal fractal dimension can be calculated using the linear slope method, where the slope value represents the temporal fractal dimension of the microseismic events. This method enables the calculation of the fractal dimension using microseismic events obtained from the monitoring process.
## 13 Non-Traveling Wave (NTW) Method Based on Voltage Levels  
This method is particularly applicable to high impedance faults or the faults closer to converter stations. In this method, decrease in the voltage is observed over a long interval of time. The algorithm is explained in Figure 24. Because of longer intervals of time, this method is referred for backup protection of non-traveling wave method based on rate of change of voltage or current. Selection of time delays and longer time intervals are made carefully in order to avoid operation of protection scheme during abnormal transients or load switching [110].  
In this method, setup for fault identification is made with multiple levels and deeper voltage depressions with shorter response time [110]. Otherwise, this method may fail with the failure of primary protection.  
Accuracy of this method is not questioned because of non-engagement of telecom infrastructure and multiple voltage depression levels. Therefore, it is suitable for backup protection.  
Figure 24: Non-traveling wave (NTW) method based on level of dc voltage detection.
# LoRa Mobile-To-Base-Station Channel Characterization in the Antarctic
## 2 Materials and Methods
### Mobile Transceiver  
At the mobile side, the LoRa transceiver is mounted in a case, which is placed on top of a snowmobile. The transceiver is connected to an omnidirectional sleeve-dipole antenna for the 868 MHz band, positioned on top of the case. For the 434 MHz band, a similar antenna is mounted on the structure of the cargo frame (see Figures 2 and 3). The mobile LoRa unit is controlled by a Raspberry Pi board [13] via a USB interface. Correct operation of the Microchip Lora module is guaranteed down to \(-40\)\({}^{\circ}\)C, but the Raspberry Pi module contains components that are not certified at this low temperature, making the device unsuitable for permanent Antarctic science experiments. During this campaign, the Raspberry Pi was installed inside the vehicle, where the temperature stays within operational range during the whole expedition. The power for this setup was delivered by a USB battery pack, charged by the vehicle's power system.  
A Python control script running on the Raspberry Pi transmits packets every five seconds, on alternating frequency bands. Each packet contains a unique serial number. Synchronously, a GPS unit in the vehicle records the location and a time stamp. The LoRa transmission parameters are summarized in Table 1. Note that the default coding rate 4/5 was preferred over the more robust 4/8 rate, since the former provides a better trade-off between transmit power required by the energy-constrained nodes and communication reliability.  
Figure 2: Mobile setup with omnidirectional antennas for the 434 and 868 MHz bands on the 4 \(\times\) 4 vehicle.  
In general, the tabulated values are the default configuration parameters of the development board, resulting in a data rate of 292.97 bps, transmitted at the maximum available power. The spreading factor produces a link budget of 151 dB and a receiver sensitivity of \(-137\) dBm in ideal conditions [14]. In addition, a cyclic redundancy checksum (CRC) guarantees error-free packets, as only those packets received with a correct CRC will be recorded by the transceiver chip and result in an SNR measurement.
## 3 The Proposed Model
### Adversarial Attack Modeling  
Two types of adversarial attacks are introduced in the training and testing phases to examine the performance of the suggested model against such attacks. Poisoning adversarial attacks are introduced in the training phase, whereas evasion-based adversarial attacks are introduced in the testing phase. This adversarial attack can be defined as an input engineered to cause misclassification in the DL algorithms. Recently, adversarial DL has gained great popularity in healthcare applications, due to the limitations of the current DL models. For example, an adversary might insert new adversarial data into a healthcare DL model to falsely classify a hypothyroid patient [27]. Furthermore, in the context of medical image processing, the researchers reported various adversarial attacks against the DL model that were intended to modify results by inserting noise and causing the misclassification of a benign mole as malignant with high confidence [28; 29]. In the previous section, the DL model was applied to detect normal and abnormal behaviorsregarding the patient's vital signs. The following sections describe the types of adversarial attacks that have been adopted in this work.  
1. Poisoning Attack: This type of attack occurs during the training phase of the proposed model, as shown in Figure 1. In this attack, an adversary manipulates the training data to compromise the entire learning process. Modification, logic corruption, and data injection techniques can be used by an adversary to manipulate the training data. These capabilities allow an adversary to influence the proposed model's overall learning process and cause it to misclassify test results, which might result in the inappropriate treatment of a patient.
2. Evasion Attack: In evasion attacks, the adversary attempts to deceive the model by mounting adversarial attacks during the testing phase, as shown in Figure 1. Such an adversary does not impact the training data, but they may access the proposed model to gain sufficient information. Consequently, they attack the proposed model and manipulate it so that it will misclassify the patient's condition. An adversary may use the fast gradient sign method (FGSM) attack or the basic iterative method attack (BIM) to perform evasion attacks.
3. Fast gradient sign method of attack (FGSM): This method uses the gradient of the underlying model. The original input is manipulated by subtracting or adding a small error in the direction of the gradient, with the intent of altering the behavior of the learning model.
4. Basic iterative method of attack (BIM): The BIM is an extension of the FGSM attack. The BIM attacks are repeated multiple times, using a small size, and are clipped. After each iteration, the result is clipped to guarantee the level of perturbation [30]. Both FGSM and BIM attacks are used to evaluate the robustness of the proposed model in this paper.
# MZAP--Mobile Application for Basketball Match Tracking and Digitalization of Endgame Reports
## 6 MZAP Android Application--Adoption and the Effects of Digitalization  
The results presented in this chapter were obtained through a survey administered to basketball officials. Of the 293 officials contacted, 271 completed the survey, corresponding to an adoption rate of 92.5%. This figure aligns with the number of MZAP application installations recorded on the tracking server, as the application is not distributed via Google Play and is instead installed manually.  
Officials who did not participate in the survey stated several reasons for their non-adoption of the MZAP application: a preference for traditional paper-based match records, limited use of mobile devices, opposition to digitalization, or a refusal to disclose their rationale. A subset of officials expressed reluctance toward the system due to their familiarity with the paper-based process and a lack of trust in the new digital platform. Although these individuals have adopted the application, their mistrust has led them to work in teams wherein one official records data on paper while the other uses the mobile device, allowing both systems to run in parallel.