esa-sceva/satcom-chunk-collection huggingface dataset:
# Development of a Datalogger for Submarine Glider: Integration of Fault-Tolerant Software Layers
## 3 Results
### Results for the Writing of the NAND Flash Unit  
The data transmission from the sensor monitoring system sends an average of 128 B/s. If the transmissions were to be carried out at 1280 baud, which would be the minimum transmission speed to be able to perform lossless storage, then the microcontroller would remain working for the entire period of time. That is why the transmission is done at a standard speed of 115200 baud, so that the microcontroller has long waiting periods. This allows to use the low power mode (LPM) that is activated while the DMA is waiting for data by UART.  
To find out the behavior of the datalogger in a limit situation, the transmission speed was 115200 baud and without breaks. For the write process that is related to the NAND flash memory chip with serial data transmission, the state diagram was presented in  
Figure 13: Datalogger printed circuit board.  
Figure 11. The process of data writing in the NAND flash unit was carried out every 255 bytes, therefore a random write process for the NAND flash memory unit is employed. The first random write was realized on the address columns that are defined in the interval 0-254. The second random write process was defined in the interval 255-509, while the third random write goes from 510 to 764 and so on until the last write goes from 1785 to 2039.  
The DMA UART receive operation was performed in parallel with the encoding operations, writing to NAND flash, writing the last page accessed from NAND flash, and waiting for the next command. In order for the information to be stored without loss, the period time of the operations performed by the CPU is required to be less than the RX data reception operation performed by the DMA module. The mean of the time periods in the operations were obtained, and the summary of times for NAND flash writing taking the data by serial at 115200 baud is presented in Table 4 and in Figure 14. It can be seen that  
\[t_{COD}+t_{NF}+t_{LA}+t_{LPM}\leq t_{RX} \tag{15}\]  
Where \(t_{COD}\) is the encoding time by the BCH code, \(t_{NF}\) is the time for the basic sequence of NAND flash writing operation, \(t_{LA}\) is the period of time to storage the address of the last page accessed, and \(t_{LPM}\) is a timeout of the data by the UART receiving port. The sum \(t_{COD}\), \(t_{NF}\), and \(t_{LPM}\) must be less than the reception time by UART \(t_{RX}\). The period of time for writing in the memory flash of the microcontroller is \(t_{LA}=1.32\)\(\mu\)s and represents a very small period of time compared to the times \(t_{COD}\), \(t_{NF}\), \(t_{LPM}\) and \(t_{RX}\).  
There is a Low-Power Mode time \(t_{LPM}\) in which the microcontroller sleeps until an interruption UART is performed. In Figure 14, it can be seen that every time 255 bytes are joined, a partial writing of the NAND Flash page is performed. The partial writing had to be done because if it was sent in a single 2040 packet, the sum \(t_{COD}+t_{NF}\) would be greater than \(t_{RX}\) and it would not store in real-time.
## 3 Results and Discussion
### Levels of Heavy Metals in Flamingo Tissues
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Tissue** & **Location** & **Cd** & **Hg** & **Cu** & **Pb** & **Zn** & **Reference** \\ \hline Liver & Pétrola Lake & 0.304 \(\pm\) 0.549 & 0.534 \(\pm\) 0.461 & 34.6 \(\pm\) 28.5 & 0.160 \(\pm\) 0.115 & 382 \(\pm\) 257 & This study \\ Muscle & Pétrola Lake & 0.040 \(\pm\) 0.089 & 0.088 \(\pm\) 0.06 & 24.0 \(\pm\) 19.7 & 0.025 \(\pm\) 0.014 & 56.2 \(\pm\) 23.7 & This study \\ Fat & Pétrola Lake & 0.007 \(\pm\) 0.005 & BLD & 1.87 \(\pm\) 0.69 & 0.075 \(\pm\) 0.044 & 14.0 \(\pm\) 5.1 & This study \\ \hline Liver & Dofana & na & na & na & 0.885 & 203.4 & [98] \\ Liver & Dofana & 0.01 \(\pm\) 0.02 & na & 79.9 \(\pm\) 13.3 & 0.06 \(\pm\) 1.5 & 192 \(\pm\) 80 & [99] \\ Liver & Ebro Delta & 0.019 \(\pm\) 0.011 & na & na & 0.138 \(\pm\) 0.071 & na & [100] \\ Liver & Ebro Delta & 0.55 \(\pm\) 0.12 & na & 56 \(\pm\) 11 & 149 \(\pm\) 58 & 403 \(\pm\) 45 & [95] \\ Liver & Carmargue & 0.9 & 4 & 100 & 2.0 & 600 & [97] \\ Liver & MSRM Park & 1.30 \(\pm\) 0.31 & 3.83 \(\pm\) 2.95 & na & 313 \(\pm\) 48.1 & na & [96] \\ Muscle & Dofana & na & na & 14.3 \(\pm\) 3.24 & 0.05 \(\pm\) 0.03 & 53.7 \(\pm\) 66.4 & [99] \\ Muscle & Carmargue & 0.04 & 2 & 60 & 0.09 & 100 & [97] \\ Muscle & MSRM Park & 0.15 \(\pm\) 0.03 & 0.59 \(\pm\) 0.47 & na & 3.36 \(\pm\) 0.75 & na & [96] \\ \hline \end{tabular}
\end{table} 
Table 2: Mean (\(\pm\)SD) concentrations of heavy metals in collected in _Phoenicopterus roseus_ samples (mg/kg dry weight), and comparison with other locations. BLD: below the limit of detection; na: not available. other wetlands within the Petrola-Corral-Rubio-La Higuera Saline Complex. Although this has become progressively restricted in recent decades, flamingos can create craters in soft sediments by treading with their feet when feeding [104]. This exposes the pellets buried in the sediment, which can then be ingested [105]. Pb concentrations are of particular concern because this element may negatively affect chick body composition as well as microbial diversity and composition [10]. The highest mean value of Zn in flamingo tissues occurred in liver (382 mg/kg dw), considerably higher than the mean values from muscle and fat (56.2 and 14.0 mg/kg dw, respectively) (Table 2). Liver values were within the range of those reported by [98] in Donana National Park (203.4 mg/kg dw) or [95] in the Ebro Delta (403 mg/kg dw). Regarding Zn in muscle samples, our data are close to those reported by [99] in birds from Donana National Park (53.7 mg/kg dw). Zinc is an essential element required for feather formation and is toxic only at very high levels [106]. Recent studies link it beneficially to gut homeostasis in birds [10]. Nonetheless, Zn concentrations in tissues are weak indicators of exposure in birds [107]; long-term exposure is required for bioaccumulation in flamingos [108]. In sediments, Zn is more bioavailable than Pb [109]. Based on our results, the values from sediments (Table 1) and flamingo tissues (Table 2) are within the range reported in other studies, suggesting no contamination by Zn in Petrola Lake.
# Offloaded Computation for QoS Routing in Wireless Sensor Networks
## 5 Theoretical and Experimental Evaluation
### Complexity of Algorithm 1: Computation of Non-Dominated QoS Paths
#### 5.1.2 Main Loops and Path Processing  
* _While Loop (Line 13):_ Runs until all paths are evaluated. Since paths do not revisit nodes, a path can contain at most \(|V|-1\) nodes. Thus, the While loop executes at most \(O(|V|)\) times.
* _For Loop (Line 15):_ Iterates over all nodes, contributing another \(O(|V|)\) factor.
* _Path Comparisons (Lines 17-30):_
* Each node stores at most \(k\) paths.
* Every adjacent edge is processed, bounded by \(O(|V|)\).
* The number of path comparisons is \(O(k^{2})\) per node.  
Thus, the worst-case total number of path comparisons is  
\[O((|V|-1)^{2}k^{2}|V|)=O(|V|^{3}k^{2}) \tag{1}\]  
_Final complexity of Algorithm 1:_  
\[O(|V|^{3}k^{2}) \tag{2}\]  
This complexity arises due to nested loops and path comparisons involving \(O(k^{2})\) operations per edge and node.
# A Novel Deep Learning Framework for Intrusion Detection Systems in Wireless Network
## 3 Proposed Strategy
### The Threshold  
We set a threshold on the traffic detection model's output to determine whether the attack is significant according to the proportion of malignant packets in the total number of packets after output. In a real context, because every attack needs a specific duration to successfully harm the system, for example, DDoS, the botnet army needs time to send sufficient requests to bring down the server. This time can be up to 30 s, depending on the robustness of the server's DDoS defenses.  
Some minor attacks that last for a fraction of a moment or occur separately are not major threats to the system and can, therefore, be ignored. A greater priority is to focus on major attacks that occur consecutively and last for a very long period, as this is evidence of an incoming attack. We believe that a reasonable threshold to consider the current IP traffic as anomalous is 60%, which means the traffic flow contains less than 60% normal packets and more than 40% harmful packets in total. This threshold assists in decreasing the amount of work in the system since it allows the system to ignore the insignificant anomalous traffic flow and contributes to improving the prediction accuracy. Figure 9 illustrates the way the IDS decides whether the IP packets are an anomaly or normal by using the threshold.  
Figure 8: The MAE comparison in the case of different look-back steps (the left graph) and different steps ahead (the right graph).  
Figure 7: The training result of the regression models.  
\[Traffic\;behavior=\begin{cases}Normal,&\text{if}\;\frac{\sum_{T =t-1}^{t-1}X_{Normal}^{(T)}}{100}\geq Threshold\\ Anomaly,&\text{if}\;\frac{\sum_{T =t-1}^{t-1}X_{Normal}^{(T)}}{100}<Threshold\end{cases} \tag{19}\]
# A Tuning Method for Speed Tracking Controller Parameters of Autonomous Vehicles
## 1 Introduction  
With the rapid development of artificial intelligence and automation technologies, autonomous vehicles have become a key research focus in the field of intelligent transportation. These vehicles are expected to transform our perception of mobility by providing safer, more efficient, and more convenient transportation options. Among the numerous challenges faced by autonomous driving, vehicle motion control, particularly speed control, plays a crucial role. Accurate speed control is essential for ensuring smooth driving, enhancing safety, and improving passenger comfort. However, despite significant advancements in areas such as path planning and obstacle detection [1; 2; 3; 4; 5; 6; 7; 8], research specifically focused on speed tracking control remains relatively limited.  
Speed control is not only crucial for responding to constantly changing road conditions but also directly affects energy consumption and operational stability. In fact, precise speed control is one of the key challenges in the development of autonomous driving technology. Traditional PID (proportional-integral-derivative) controllers are widely used in industrial control systems due to their simplicity and effectiveness [9; 10; 11; 12; 13]. However, the performance of PID controllers heavily depends on the correct tuning of parameters, which is oftencarried out manually and based on empirical methods [14; 15; 16; 17; 18; 19; 20]. In complex and dynamic driving environments, this reliance on experience can prevent the controller from achieving optimal performance.  
In recent years, although numerous studies have introduced intelligent optimization algorithms into the design of controller parameters within industrial control systems [20; 21; 22; 23; 24; 25; 26], to date, no research has specifically applied optimization algorithms to determine vehicle speed controller parameters. Based on this gap, this study proposes a genetic algorithm-based PID parameter optimization method tailored for speed control in autonomous vehicles, applying it within the field of intelligent driving. This method addresses the limitations of traditional manual tuning by implementing an automated optimization process that can adapt to changing road conditions. Experimental results indicate that this approach significantly improves speed control accuracy, reduces error, and enhances system responsiveness. These findings demonstrate that our method provides an effective solution for real-world autonomous driving applications, offering greater stability, efficiency, and adaptability in complex driving environments.  
The second part of this study introduces the construction of the longitudinal PID controller for the vehicle, the third part presents PID controller parameter optimization, the fourth part provides the simulation results and discussion, and the fifth part contains the conclusion.
# Mining Road Traffic Rules with Signal Temporal Logic and Grammar-Based Genetic Programming
## 3 Background: Signal Temporal Logic
It is defined recursively as follows:_  
\[\rho(\top,\mathbf{x},t) =+\infty\] \[\rho(\mu,\mathbf{x},t) =y(\mathbf{x}(t))\quad\text{where }\mu\equiv y(\mathbf{x}(t))\geq 0\] \[\rho(\
eg\varphi,\mathbf{x},t) =-\rho(\varphi,\mathbf{x},t)\] \[\rho(\varphi_{1}\wedge\varphi_{2},\mathbf{x},t) =\min(\rho(\varphi_{1},\mathbf{x},t),\rho(\varphi_{2},\mathbf{x},t))\] \[\rho(\varphi_{1}\mathsf{S}_{[t_{1},t_{2}]}\varphi_{2},\mathbf{x},t) =\sup_{t^{\prime}\in[t-t_{1},t-t_{2}]}\bigg{(}\min\bigg{(}\rho( \varphi_{2},\mathbf{x},t^{\prime}),\inf_{t^{\prime\prime}\in[t^{\prime},t]}\rho( \varphi_{1},\mathbf{x},t^{\prime\prime})\bigg{)}\bigg{)}.\]  
_Similar to the Boolean semantics, the Once and Historically temporal operators are defined based on Since._  
The sign of \(\rho(\varphi,\mathbf{x},t)\) provides the link with the standard Boolean semantics. It holds that \(\rho(\varphi,\mathbf{x},t)>0\) if and only if \(\mathbf{x}\models\varphi\), while \(\rho(\varphi,\mathbf{x},t)<0\) if and only if \(\mathbf{x}\
ot\models\varphi\). The case \(\rho(\varphi,\mathbf{x},t)=0\), instead, is a borderline case, and the truth of \(\varphi\) cannot be assessed from the robustness degree alone. In practical settings, systems are monitored for a given, finite amount of time, and, as a result, trajectories have a limited time span. We define as \(|\mathbf{x}|\) the length of a trajectory, i.e., its number of samples. For the sake of this study, monitoring an STL formula \(\varphi\) over a trajectory \(\mathbf{x}\) is constrained to \([0,|\mathbf{x}|-1]\subset\mathbb{T}\), a subset of the time domain. When talking about temporal formulas, the _necessary length_ concept is of importance [20]. The necessary length of a formula \(\varphi\) (let it be \(\|\varphi\|\)) is defined recursively as:  
\[\|\mu | =0\] \[\|\
eg\varphi\| =\|\varphi\|\] \[\|\varphi_{1}\wedge\varphi_{2}\| =\max(\|\varphi_{1}\|,\|\varphi_{2}\|)\] \[(\mathbf{x},t)\models\varphi_{1}\mathsf{S}_{[t_{1},t_{2}]}\varphi_{2}\ \Leftrightarrow\ \exists\ t^{\prime}<t\ :\ ((\mathbf{x},t^{\prime})\models\varphi_{2}\ \wedge\ \forall\ t^{\prime\prime}\in[t^{\prime},t\big{[}:(\mathbf{x},t^{\prime\prime}) \models\varphi_{1}),\]0Intuitively, the necessary length is the shortest trajectory length such that \((\mathbf{x},t)\models\varphi\) is well-defined. For example, the formula \(\varphi_{1}\mathbb{S}_{0,10}\varphi_{2}\) cannot be evaluated on trajectories shorter than 10 (assuming that \(\|\varphi_{1}\|>10\) and \(\|\varphi_{2}\|>10\)), since this would imply looking at a future that is not part of the trajectory.
# Biometric-Based Human Identification Using Ensemble-Based Technique and ECG Signals
## 1 Introduction
### Major Contributions  
The main contribution of the paper lies in the development of a novel cardiac biometrics system for human identification using deep learning (DL) approaches, specifically focusing on electrocardiogram (ECG) signals. The key contributions can be summarized as follows:  
1. We chose ECG as the biometric modality for human identification. This decision provides a unique and challenging set of physiological data for authentication, different from traditional biometric methods.
2. We proposed a novel ensemble-based fine-tune approach that combines VGG16 pre-trained transfer learning (TL) and Long Short-Term Memory (LSTM) architectures to extract deep features from preprocessed ECG signals.  
Figure 1: A visual example of biometric-based human identification using ECG signal processing and machine learning.  
3. The paper presents a well-defined preprocessing phase for the ECG biosignal to remove noise and ensure data quality. Additionally, you developed a transformation phase that converts the 1-D ECG signals into 2-D spectrogram images. The feature extraction step using the ensemble DL technique is a crucial part of the process, as it extracts discriminative features for human identification.
4. We employed a LightGBM boosting classifier to recognize humans based on the extracted features. This classifier likely enhances the performance of the system and contributes to achieving high accuracy, sensitivity, specificity, and AUC.
5. The developed cardiac biometrics system has practical applications in various life domains where secure authentication is crucial. These applications may include healthcare, finance, access control, and other sensitive areas.  
Overall, the paper contributes a comprehensive approach to human identification using cardiac biometrics, combining deep learning techniques with a unique biometric modality. The experimental results and comparison with existing systems demonstrate the superiority of your proposed approach, making it a valuable contribution to the field of biometric authentication.
# An Efficient Single-Anchor Localization Method Using Ultra-Wide Bandwidth Systems
## 2 Preliminary  
Consider a scenario where a single anchor serves \(N_{\text{a}}\) single-antenna agents at the same time. The anchor is equipped with an \(M\)-element circular antenna array and the antenna spacing is \(d\). The anchor position is known while the agent positions are determined by communicating with the anchor through UWB signals. The anchor is denoted as the node 0, and the index set of the agents is \(\mathcal{N}_{\text{a}}\triangleq\{1,2,\cdots,N_{\text{a}}\}\). Without loss of generality, we denote the position of the anchor as \(\mathbf{p}_{0}\triangleq[0,0]^{\text{T}}\) and the clock drift is denoted as \(\xi_{0}\). The positions and clock drifts of the nodes are denoted as \(\mathbf{p}_{i}\triangleq[x_{i},y_{i}]^{\text{T}}\in\mathbb{R}^{2}\) and \(\xi_{i}\), respectively. According to the IEEE 802.15.4 standard [45], the relationship between the true time \(t\) and the estimated time at the node \(i\), i.e., \(t_{i}\), can be modeled as  
\[t_{i}=\xi_{i}t+b_{i},\,i=0,1,\ldots,N_{\text{a}} \tag{1}\]  
where \(\xi_{i}\) and \(b_{i}\) are the clock drift and the clock offset between the estimated time and the true time, respectively.  
We next introduce the signal model and the proposed SAL scheme.
## Data Presentation
### Calculation of the Ionosphere Gradient  
The ionosphere gradient is defined as the ionosphere delay between two adjacent points per kilometer and is usually analyzed for ionosphere influence on GBAS performance [33]. The two adjacent points are close enough to share the same visible satellites. During geomagnetic storms, the ionosphere can be significantly disturbed and a large spatial gradient can be generated. A gradient as much as 400 mm/km is considered a threat to GBAS, as previous work revealed [34]. Reference [35] found gradients as large as 413 mm/km that can be modeled as a plain semi-infinite wavefront. In South America, a large number of gradients exceeding ICAO's threat model was reported, with a maximum value of 800 mm/km, referred to in Reference [36]. The causes of the ionosphere gradient can be multiple, such as traveling ionosphere disturbance and ionosphere irregularities. In Thailand, the ionospheric delay gradient can vary from about 28 to 178 mm/km, while 20 to 100 mm/km is frequently observed during the plasma bubble occurrences, according to Reference [37]. Therefore, the analysis of the ionospheric gradients can be a good criterion to characterize the ionospheric TEC spatial variations, detecting the inhomogeneity of the ion density and further indicating the ionosphere irregularity. The ionosphere gradient value is greatly influenced by elevation, thus, for physical modeling, the elevation angle should be selected above 30 degrees. In this work, the ionosphere gradient is calculated by Equation (13), where \(sTEC_{r,1}\) and \(sTEC_{r,2}\) are TEC estimations by Section 2.1, measured from two adjacent points (sites) in meters. \(d_{1,2}\) is the distance between the two points. \(f_{L1}\) denotes the L1 band frequency of GNSS, typically 1575.42 MHz.  
\[slope=\frac{40.3}{f_{L1}^{2}}\frac{STEC_{r,1}-STEC_{r,2}}{d_{1,2}}[\text{mm}/ \text{km}] \tag{13}\]  
The overall procedure of the ionosphere gradient calculation is concluded as follows:  
1. The TEC calculation using raw GNSS carrier phase measurements, as Section 2.1 described.
2. The TEC slips detection method of Astfayeva is implemented [38]; TEC slips are calculated by the difference of two adjacent epochs of calibrated slant TEC values; epochs with intervals of 30 s. \[TEC_{slips}=STEC_{jit}-STEC_{ij(t-1)}\] (14)
3. Removing the TEC slips larger than the threshold for ionosphere gradient calculation; the threshold is set to be 2 TECU in the middle latitudes, 3 TECU in the low latitudes and equatorials (refer to Reference [38]).  
* Calculating the ionosphere gradient with Equation (13).
* The post-processing check for large gradient values, to exclude the gradients strongly influenced by cycle slips.
# Nonlinear Effect and MAI Impact on SAC-OCDMA System Based on 2D Multi-Diagonal Code and Laser Array
###### Abstract  
A new architecture for Spectral Amplitude Coding Optical Code Division Multiple Access (SAC-OCDMA) system based on two Dimensional Multi Diagonal (2D-MD) codes named 2D-MD SAC-OCDMA and utilizing a laser optical source is proposed for Long-Reach Passive Optical Network (LR-PON). In this work, a computer simulator tool is used, for the first time, as a SAC-OCDMA simulation set-up utilizing the unique combination of a coherent laser array and 2D-MD codes. In addition, the system performance is addressed numerically by taking into account Multiple Access Interference (MAI), optical coherent source noise, first, second and third order fiber dispersion, nonlinear effects and photo-detector noise. Simulation results indicate that for a single user (i.e., without considering MAI), the system can operate at a maximum bit rate of 55 Gb/s over 250 km of Single Mode Fiber (SMF), with a Bit Error Rate (BER) below \(10^{-9}\) (Q-limit = 15.5 dB), when only first order fiber dispersion is considered. However, including the effects of second and third order fiber dispersion as frequency domain parameters, results in a reduction of the maximum bit rate to 40 Gb/s, while maintaining a Q-factor above the Q-limit under the same transmission distance. Furthermore, we demonstrate that the proposed architecture extends the SMF transmission reach up to 600 km and 480 km, when considering linear and nonlinear effects, respectively. Finally, we show that our proposed 2D-MD SAC-OCDMA system outperforms existing solutions presented in the literature for LR-PON configuration, in terms of both aggregate bit rate and transmission reach.  
LR-PON; SAC-OCDMA; MAI; SMF; nonlinearity; Kerr effect; Raman effect; laser array; security +
Footnote †: journal: Applied Sciences