esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Proposed Control Method
### Nonlinear Disturbance Observer  
The nonlinear disturbance observer proposed by [PERSON] et al. [20] is implemented to estimate disturbance. Assuming that the disturbance in Equation (1) is slowly varying or constant, the time derivative of the disturbance can be expressed as follows:  
\[\dot{d}\approx 0 \tag{7}\]  
Then, the observer error can be defined as follows:  
\[e_{d}=d-\hat{d} \tag{8}\]  
where the \(\hat{d}\) represents the disturbance estimates. The desired disturbance error dynamics is chosen as the 1 st-order system.  
\[\dot{e}_{d}+Le_{d}=0 \tag{9}\]  
where \(L\) (the observer gain) is chosen as the Hurwitz matrix.  
The observer error pursues the above desired disturbance error dynamics if the following disturbance observer dynamics relationship holds  
\[\dot{d}=L\left(d-\hat{d}\right)=-L\hat{d}+L\left(\dot{x}-f(x,u)\right) \tag{10}\]where \(f(x,u)\) is a function of state \(x\) and control input \(u\), already including the model dynamics and control input terms. Defining an auxiliary variable as follows:  
\[z_{aux}=\hat{d}-Lx \tag{11}\]  
\[\hat{d}=z_{aux}+Lx \tag{12}\]  
\[\begin{array}{ll}\dot{z}_{aux}&=\hat{\hat{d}}-L\dot{x}=-L\hat{d}+L\big{(}\dot {x}-f(x,u)\big{)}-L\dot{x}\\ &=-L\hat{d}-Lf(x,u)\\ &=-Lz_{aux}-L^{2}x-Lf(x,u)\\ &=-Lz_{aux}-L(f(x,u)+Lx)\end{array} \tag{13}\]  
Then, the observer error dynamics by solving the differential equation of Equation (9) will become the following:  
\[e_{d}(t)=e_{d}(t_{0})\exp(-Lt) \tag{14}\]  
The observer error will eventually converge to 0, exponentially. Here, the above solution of Equation (14) is derived from Equation (9) using the nonlinear dynamic observer in detail as explained in Appendix B.
# Mitigating Class Imbalance in Network Intrusion Detection with Feature-Regularized GANs
## 4 Experimental Results
### NSL-KDD Results
#### 4.2.2 Overall Accuracy and Macro-F1  
Table 3 presents the overall classification accuracy and macro-F1 scores on the NSL-KDD dataset. Overall accuracy differences among all methods are relatively small, with CWGAN+SNNL achieving the highest value at 76.8%. Other GAN-based methods such as WGAN+SNNL and WGAN-GP+SNNL also show slight improvements over their respective baselines, indicating that the inclusion of SNNL does not negatively impact general classification performance.  
Compared to traditional oversampling methods (SMOTE, ADASYN, ROS), GAN-based approaches yield slightly higher macro-F1 scores, which reflect better class-wise balance. Notably, WGAN-GP+SNNL achieves the highest macro-F1 of 56.7%, an increase of 3.3 percentage points over its base model, WGAN-GP. These results suggest that SNNL  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{3}{c}{**R2L**} & \multicolumn{3}{c}{**U2R**} \\  & **Precision** & **Recall** & **F1-Score** & **Precision** & **Recall** & **F1-Score** \\ \hline SMOTE & 0.930 & 0.097 & 0.150 & 0.477 & 0.097 & 0.150 \\ ADASYN & 0.910 & 0.073 & 0.137 & 0.497 & 0.090 & 0.137 \\ ROS & 0.887 & 0.057 & 0.107 & 0.423 & 0.070 & 0.107 \\ WGAN & 0.980 & 0.106 & 0.168 & 0.790 & 0.090 & 0.154 \\ WGAN+SNNL & 0.980 & 0.110 & 0.200 & 0.795 & 0.095 & 0.170 \\ CWGAN & 0.980 & 0.113 & 0.200 & 0.732 & 0.090 & 0.155 \\ CWGAN+SNNL & **0.983** & 0.112 & 0.200 & **0.817** & **0.102** & **0.175** \\ WGAN-GP & 0.980 & 0.090 & 0.167 & 0.697 & 0.080 & 0.137 \\ WGAN-GP+SNNL & 0.979 & **0.120** & **0.217** & 0.767 & 0.092 & 0.154 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Minority-class performance comparison on NSL-KDD dataset.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Method** & **Accuracy (\%)** & **Macro-F1 (\%)** \\ \hline SMOTE & 76.3 & 53.5 \\ ADASYN & 76.5 & 53.8 \\ ROS & 75.6 & 50.9 \\ WGAN & 76.5 & 54.2 \\ WGAN+SNNL & 76.7 & 55.1 \\ CWGAN & 76.7 & 54.8 \\ CWGAN+SNNL & 76.8 & 55.3 \\ WGAN-GP & 76.0 & 53.4 \\ WGAN-GP+SNNL & 76.7 & 56.7 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Overall accuracy and macro-F1 comparison on NSL-KDD dataset.  
may contribute to improving the discriminative utility of generated samples across all classes, particularly in imbalanced settings.
## 2 Materials and Methods
### Data Analysis  
The significance of differences between mean values was determined using the Mann-Whitney U test. The initial critical probability value was set at \(p<0.05\). Ordination techniques were used to describe the relationships between the groups of water parameters (pH, EC, DO, PO\({}_{4}\)-P, NH\({}_{4}\)-N, NO\({}_{3}\)-N, and Cl\({}^{-}\)) and environmental variables (distance to the river mouth and discharge Q). The length of the gradient determined by Detrended Correspondence Analysis of plant communities was <2 SD, which suggests that principal component analysis (PCA) was the appropriate method [44; 45]. PCA was performed to validate the distribution of water parameters between the studied periods (T+0 and T+6).  
Generalized Additive Models (GAMs) were used to test the importance of environmental variables for treatments. Two different datasets, one for each sampling period (T+0 and T+6), were used in GAM analyses to avoid dependence in the datasets. Eight environmental variables were selected for analysis from the measured parameters. The selected variables included WQ variables (NH\({}_{4}\)\({}^{+}\)-N and NO\({}_{3}\)\({}^{-}\)-N) and a landscape variable (distance to the river mouth). The above parameters were chosen to minimize redundancy in the set of explanatory variables during regression analysis. All variables were log-transformed (log10(x + 1)) to reduce the effect of outliers. GAM calculations were performed with CANOCO software. The variables for the final model were obtained by automatic stepwise selection, and the Akaike Information Criterion (AIC) was used to select the best model with increasingcomplexity (degrees of freedom equal to 1, 2, and 3). The increase in deviance caused by each variable included in the model and obtained by stepwise selection was tested, and only variables that caused a significant increase in deviance were retained in the final model. When overdispersion was detected, the F-test was performed instead of the Chi-square test to determine the significance of the deviance explained by each selected variable [44]. Drop contribution was used to identify the contribution of each explanatory variable, and it was expressed as the decrease in deviance resulting from the elimination of a variable from the final model [44; 45].
## 3 Initial Conditions and Definitions of the Algorithm
### Space Representation  
The presented algorithm uses a regular grid of cells as the exploration space \(S_{real}\) (e.g., Figure 1 with four independent subspaces).  
**Definition 1**.: _Let \(S_{real}=\{s_{\{1,2\}},s_{\{2,1\}},...,s_{\{n,i\}}\}\) represent the space being explored, which consists of various subspaces \(s_{\{n,i\}}\), where \(n\) represents the number of subspaces of the \(S_{real}\) space and \(i\) represents the number of subspace types (e.g., land, water, and air)._  
Every subspace comprises a single type of environment and there is a finite number of the existing environments. The space may also be divided by obstacles, such as walls into subspaces with one or more entrances, that an agent can use to enter the subspace \(s_{\{n,i\}}\).  
**Definition 2**.: _Let there be a set of entrances \(E_{j}=\{e_{j_{1}},e_{j_{2}},...,e_{j_{m}}\}\) to the subspace \(s_{\{j,i\}}\), where \(m\) is the number of entrances of the \(s_{\{j,i\}}\) subspace and \(s_{\{j,i\}}\in S_{real}\), \(j\in<1,n>\), \(E_{j}\exists e_{j_{m}}\)\(\forall\)\(e_{j_{m}}\subseteq E_{j}\subset s_{\{j,i\}}\). The entrance \(e_{j_{m}}\) is a position where the agent may enter the subspace \(s_{\{j,i\}}\)._  
**Definition 3**.: _Let \(S_{real}\) have an entrance \(e_{S_{real}}\), \(e_{S_{real}}\subset s_{\{n,i\}}\) that an agent, who can operate in the \(i\) type of space, may use to enter in its subspace \(s_{\{n,i\}}\)._
## 2 Software-Defined GNSS Receiver
### BeiDou Signal Acquisition  
The main task at the signal acquisition stage is to determine which of the satellites are visible to the user and then, to coarsely estimate the carrier Doppler and the code phase of those visible satellites. Generally speaking, signal acquisition is a three dimensional search, where the satellite PRN number, the carrier Doppler and the code phase are coarsely estimated. An FFT-based signal acquisition technique is implemented in the software receiver.
# A Multi-Organ Segmentation Network Based on Densely Connected RL-Unet
## 3 Experiments  
### Evaluation Metrics and Datasets  
In this paper, the mean Dice-Similarity Coefficient (DSC) and mean Hausdorff Distance (HD) are utilized as evaluation measures. Both of them describe the similarity measures between two sample sets, but DSC focuses more on the segmentation accuracy of the internal filling part while HD is more sensitive to the segmentation boundary. They are defined as follows:  
\[DSC=\frac{2|A\cap B|}{|A|+|B|} \tag{24}\] \[HD(A,B)=max(h(A,B),h(B,A))\] (25) \[h(A,B)=\underset{a\in A}{max}\bigg{\{}\underset{b\in B}{min}\|a- b\|\bigg{\}}\] (26) \[h(B,A)=\underset{b\in B}{max}\bigg{\{}\underset{a\in A}{min}\|b- a\|\bigg{\}} \tag{27}\]  
where \(A\) represents the label diagram of the medical image and \(B\) represents the prediction graph of algorithm segmentation. Equation (24) is bidirectional HD, and Equations (25) and (26) are unidirectional HD from \(A\) to \(B\) and from \(B\) to \(A\), respectively.  
This paper verifies the segmentation effect of RL-Unet on the Synapse abdominal multi-organ segmentation, BraTS2021, ACDC, and BUSI datasets.  
Synapse dataset: The abdominal CT scan dataset consists of 30 cases, focusing on the segmentation of various abdominal organs. Every CT scan consists of 85-198 slices of 512 x 512 pixels. We randomly partition the dataset into 18 scans for training (2212). Validation scans were 12 axial slices and 12 scans. We only segmented eight abdominal organs, including the aorta, gallbladder (GB), left kidney (KL), right kidney (KR), liver, pancreas (PC), spleen (SP), and stomach (SM).  
BraTS2021 dataset: BraTS2021 includes multi-parameter MRI scans from 2000 patients. MRI scans have four modal data sizes: Flair, t1 ce, t1, and t2, each of which is \(240\times 240\times 155\) shared segmentation labels. The dataset was arbitrarily split into a training set, verification set and test set at a ratio of 8:1:1. Methods of data enhancement were pruning, rotation, flipping, Gaussian noise, contrast transformation, and brightness enhancement.  
ACDC dataset: The ACDC dataset comprises 100 patients focusing on the segmentation of the right ventricle cavity, left ventricle myocardium, and left ventricle cavity. The segmentation labels for each case include left ventricle (LV), right ventricle (RV), and myocardium (MYO). We use 70 cases (1930 axial slices) for training: 10 for validation and 20 for testing.  
BUSI dataset: The BUSI (Breast Ultrasound Image) dataset, categorized into normal, benign, and malignant classes, serves as a comprehensive resource for both classification and segmentation tasks in the analysis of breast ultrasound images. For training purposes, only the normal and malignant categories from the BUSI dataset are selected.
## 7 Cross-Layer Design Schemes
### Transport and MAC, Physical Layer Schemes  
Due to energy constraints and protocol simplicity, some low power standards, e.g., ZigBee that overlays 802.15.4, offers only best effort end-to-end delivery of individual packets. This means only limited transport layer functions (packet fragmentation and re-assembly) at the application layer as there is no fragmentation support at the network layer. Other standards that are designed to underpin the higher protocol layers require transport layer functions that overlay a low power MAC/physical to implement a full protocol stack.  
Typically for traditional wireless networks, the standard protocols operating at the transport layer are TCP--_providing connection orientated services to the network_, and UDP--_providing connectionless services_. Overlaying a transport layer protocol such as standard TCP or UDP over a WBAN/WSN structure can be problematic in that each has their own overheads that impact on the energy efficiency of low power networks. For example, transmitting IPv6 packets over 802.15.4 cannot take place without the inclusion of an additional transport layer between IPv6 and the 802.15.4 MAC layer that basically compresses the frame to meet the physical payload size required by the protocol. Standard UDP has no mechanism for reliable communication, therefore cannot be considered in the WBAN context, as there is no method to support QoS. Additionally, as UDP cannot control traffic rate at the transport layer. The only way the traffic can be managed is via connection admission control or end-to-end rate control. These schemes would need to be cross optimized with the physical layer to cater for the variable link capacity.  
Standard TCP can manage congestion avoidance, but as it was originally designed for wired networks, it suffers from performance issues related to interpretation of BER, limited bandwidth, and assumptions that packet loss is mainly caused by congestion. Performance gains can be made by optimizing a congestion control algorithm utilizing physical layer parameters as part of a cross layer scheme.  
As the wireless link is prone to varying channel quality, fading, and interference, transport layer protocols can deliver better performance when optimized to consider the varying link capacity [91]. Cross-layer design solutions operating at the transport and physical layers could, for example, report the channel condition from the physical layer as a new parameter to the transport layer that would enable a protocol to distinguish real congestion from packet loss to make better performance related decisions. There is a direct relationship between the physical/MAC layer and transport layer where adapted cross-layer protocols can deliver higher throughput and minimal end-to-end packet delay. Spectrum sensing mechanisms operating at the lower layers can mitigate the issues relating to neighbor node interference, allowing protocols to sense the spectrum and access the channel in an opportunistic way allowing the transport layer to make more informed decisions concerning congestion and collisions in delivering reliable communications [92].
## 3 Novel Implementation of Audio Encryption Scheme
### Proposed Encryption and Decryption Algorithm  
These are the steps describing the audio encryption algorithm:  
1. In file A', the header bytes of a input audio file A are moved without cryptographic modifications;
2. Using logical XOR operation with the same amount of bytes as the bytes in the sample produced by the pseudo-random generator described above the bytes in the sample are encrypted;
3. Encrypted sample from Step 2 is processed into file A';
4. Repeat Steps 2-3 until the end of input file A is reached;
5. The produced output file A' is the final encrypted audio file.  
For example, for the key set 1: \(x_{1,0}=0.61337047692752\), \(y_{1,0}=0.7315988807484\), \(x_{2,0}=-0.90983014150675\), \(y_{2,0}=0.44066640271424\), \(u_{1}=0.7941\), and \(u_{2}=0.694\); after 200 iterations, we obtained \(x_{1,200}=0.814632\), \(y_{1,200}=-0.854957\), \(x_{2,200}=0.571817\) and \(y_{2,200}=0.398801\). The output byte \(s_{0}=a\oplus b\oplus c\oplus d=219\oplus 138\oplus 246\oplus 191=24\). The first byte \(a_{0}=114\) from the audio sample is encrypted by \(ae_{0}=a_{0}\oplus s_{0}=114+24=106\).  
For security reasons, we presented a few overall rounds of the audio encryption algorithm.  
The decryption scheme is the same as the encryption algorithm because the proposed cryptographic algorithm is symmetric using the same steps and the same key for audio decryption.
# Heat Loss Coefficient Estimation Applied to Existing Buildings through Machine Learning Models
## Materials and Methods
### Case Study Data Acquisition
#### 2.3.2 Pre-Processing Data
In addition, the models, after being trained and tested with 10 different test samples, were validated with three independent samples to prove their efficiency in estimating the HLC of the building:  
* 17/12/2018 07:00
* 21/01/2019 07:00
* 11/03/2019 07:00  
These three samples had to comply with certain restrictions due to the specification of the HLC formula (see Section 2.1) and because of the singularities of the monitoring of the building studied (see Section 2.3.1). The restrictions for considering a period suitable for the calculation of the HLC are [8,44]:  
* **Weekend**: Due to the non-availability of occupation data, the period must be on the weekend when there was no occupancy in the building. * **Cool period**: The average difference between indoor and outdoor temperature must be 10 K or more. * **Cloudy period**: Solar radiation must be low: gains from solar radiation equal to or below 10% of the thermal demand. * **Temperature stability**: The average between indoor and outdoor temperature must be similar at the beginning and end of the period to ensure steady state conditions. Figure 2: Summary of the training and prediction process. On the left, the input variables of the models. On the right, the objective estimations of the Heat Loss Coefficient (HLC).
# Design of Micro-Phasor Measurement System to Identify Phasor of Distribution Transformers
## 3 Compensation of Sampling Signal  
In the micro-phasor measurement system, the 1 PPS pulse signal output from a GPS is adopted as the starting sampling point. However, it is not easy to receive GPS signals in basements. Therefore, a TCXO is adopted to retain the strength of the sampling signals. Notably, the clock of the TCXO may variation due to temperature, humidity, frequency, pressure, and aging. Hence, appropriate adjustment and compensation should be performed to improve the accuracy of measurements [4; 5; 6; 7].  
During compensation, the difference between the offset of the GPS and TCXO generated sampling signals is referred to as gain. This is obtained by averaging n gains that were repeatedly acquired from sampling signals. After the TCXO was triggered by the 1 PPS sampling signals sent from the GPS, the DSP required additional time to complete its programming procedures before it could be started. Therefore, a time difference is produced between the 1 PPS signals and the sampling signals generated after TCXO started. This time difference is referred to as offset \(C_{0}\), and the time difference caused by the required initial programming procedures is referred to as \(\Delta t\). Thus, the actual time difference should be the sum of the offset and the measured time difference. The calculation equation is shown in (4).  
\[t_{real}=C_{0}+\Delta t \tag{4}\]  
where \(t_{real}\) is the actual time difference and \(C_{0}\) the initial offset value. The calculation of compensation value is outlined in Figure 5.  
Figure 4: Framework of the voltage phasor comparison.  
Regarding the compensation values used in adjustment, the first gain was acquired by subtracting offset \(C_{0}\) from offset \(C_{1}\) (notably, \(C_{1}\) refers to a new offset value measured after a certain time interval from the previous value). The second gain was then acquired by subtracting offset \(C_{1}\) from offset \(C_{2}\). This calculation is repeated for n times. All gains were then totaled and averaged to produce \(C_{avg}\), namely the average compensation value. Finally, adding offset \(C_{0}\) to \(C_{avg}\) produced \(V_{comp}\), which is the adjustment compensation value required by the system. Equations (5) and (6) present the calculation of the average compensation value and adjustment compensation value respectively.  
\[C_{avg}=\frac{\sum\limits_{j=1}^{n}\left(C_{j}-C_{j-1}\right)}{n} \tag{5}\]  
\[V_{comp}=C_{0}+C_{avg} \tag{6}\]  
After \(V_{comp}\) is obtained, it could be added to the time difference between the sampling signals generated by the TCXO and the zero-crossing sampling signals to elevate the accuracy of the measured data at each measurement time. With this adjustment and compensation mechanism, the proposed method is tested and confirmed to the standards developed by the Taipower. Equation (7) expresses the measured data after compensation. The principle of TCXO compensation is outlined in Figure 6.  
\[t_{comp}=V_{comp}+\Delta t \tag{7}\]  
Figure 5: Calculation of compensation value.  
Figure 6: Principle of TCXO compensation.
# Realistic Multi-Agent Formation Using Discretionary Group Behavior (DGB)
## 3 Algorithm: Discretionary Group Behavior  
### Overview  
Our simulation can handle both group-group and group-obstacle interactions. There is a preprocessing step for the leader to calculate the best path to its target position using A* pathfinding algorithm [25] due to the simplicity and ability of this method to find the minimum cost solution. In our implementation, this algorithm works in a grid space with \(G_{min}\) units (see Section 4 for more details) as the grid value that makes a smooth motion for all agents while not having large memory consumption. The proper position for each agent to follow, considering the group's shape, will be calculated based on the leader and agents' unit velocity. Meanwhile, in the moving direction, each agent can sense the density of obstacles around itself. If there is no obstacle in front of the agent and if the agent is not in its acceptable area, it will move toward the calculated location by adding two other half-planes toward the right direction, continuously. Finally, if the calculation of density around the agent determines the presence of obstacles, the agent will find its best neighbor which is more near to the leader and will follow the selected subleader until there is no obstacle near it (Figure 1). The stage of the flowchart that evaluates the position of agents depends on their proper locations in the group is simply done by computing the relative position for each agent (subtraction of them) and then, comparing the value with \"T\" as a predefined threshold (see Section 4 for more details). We clarify each step individually.
## 3 Methods
### Patient Recruitment and Clinical Data Collection  
This study is a secondary analysis of neonates with CHD (term and preterm) undergoing brain MRI scans who were recruited as part of a prospective, observational study from two large medical centers, CHLA and CHP. Neonatal CHD cases were prospectively recruited from (1) pregnancies with fetal CHD confirmed with fetal echocardiography, (2) postnatal admissions to the cardiothoracic intensive care unit postoperatively between 2002-2016, and (3) infants with both preterm and term CHD undergoing clinically indicated brain MRI scans at approximately term-equivalent gestational age (GA) were recruited both prospectively and retrospectively in the peri-operative period from 2002-2017. The inclusion criterion was critical CHD defined as a heart defect expected to require corrective or palliative cardiac surgery during infancy. Exclusion criteria for the CHD included documented major chromosomal abnormalities and major congenital brain malformations. Normal referents were recruited (1) from healthy pregnant volunteers and (2) postnatally from a normal newborn nursery. An additional comparison group was included, which was comprised of preterm infants without CHD who were recruited from a high-risk NICU at the same institutions as previously published [21; 52; 53; 54].  
There were 423 participants who met the inclusion criteria for this study: 307 full-term neonates (215 with CHD and 92 term controls) and 116 preterm neonates (65 with CHD born 25-36 weeks GA and 51 preterm control patients born 24-36 weeks GA) (Figure 1). This is the dataset for which we performed the primary analysis of calculating a BDS and comparing the difference between groups.