esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Performance Validation
### Results and Discussion
#### 4.2.3 Case 3  
Let us assume five processes arrived at different times. P1 = [22, 4] and arrived at time = 0, P2 = [35, 1] and arrived at time = 2, P3 = [61, 2] and arrived at time = 4, P4 = [75, 5] and arrived at time = 6, and P5 = [96, 3] and arrived at time = 8. The results of Case 3 are provided in Table 3.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Algorithms** & **Turnaround Time (ms)** & **Waiting Time (ms)** & **Context Switching** \\ \hline RR with fixed time quantum without priority & 111 & 65 & 9 \\ \hline RR with dynamic time slice without priority & 97.3 & 52.6 & 5 \\ \hline AN Algorithm [3] & 100 & 50 & 5 \\ \hline RRAPS & 97.3 & 52.6 & 5 \\ \hline \end{tabular}
\end{table}
Table 1: Analysis of Case 1 between different algorithms.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Algorithms** & **Turnaround Time (ms)** & **Waiting Time (ms)** & **Context Switching** \\ \hline RR with fixed time quantum without priority & 111 & 65 & 9 \\ \hline RR with dynamic time slice without priority & 97.3 & 52.6 & 5 \\ \hline AN Algorithm [3] & 100 & 50 & 5 \\ \hline RRAPS & 97.3 & 52.6 & 5 \\ \hline \end{tabular}
\end{table}
Table 2: Comparison of Case 2 between different algorithm methodologies.  
Table 3 compares the performance of four scheduling algorithms: Round Robin with fixed time quantum without priority (RR with fixed time quantum), Round Robin with dynamic time slice without priority (RR with dynamic time slice), the AN algorithm [3], and RRAPS when applied to five processes that arrived at different times. The experimental results demonstrate that the RRAPS algorithm performed better than the other algorithms, achieving the lowest average turnaround time of 83.3, the lowest average waiting time of 47, and only five context switches. The Round Robin scheduling algorithm with dynamic time slice but without priority had the second best performance, with an average turnaround time of 100.5, an average waiting time of 59.1, and seven context switches. In contrast, the AN algorithm has a turnaround time of 103 and a waiting time of 55, with six context switches. The Round Robin with a fixed time quantum without a priority algorithm performs worst, with the highest average turnaround time of 119.1, the highest average waiting time of 72.7, and 13 context switches.
# FBG-Based Monitoring of Geohazards: Current Status and Trends
## 5 Challenges and Future Trends
This is expected to be a hot research topic in the near future;
6. Establishment of theories for forecasting and early warning of abrupt and progressive geohazards. There have been numerous theories of geomechanical analyses, geohazards evaluation and risk assessment. How to relate the field monitoring data to these theories is an urgent task to solve. The Bayesian updating methodology may be a promising tool in this respect [84; 85];
7. Establishment of guidelines and standards for system deployment and maintenance, especially for harsh monitoring geoenvironments [86]. Standardization of the instrumentation program is key to future advances of FBG monitoring systems. Although abundant knowledge and experience have been gained, for those involved in the instrumentation works, a lack of technical instructions will result in unreliable or unavailable field monitoring data. In the future, the field instrumentation team should be well educated and experienced. Only in this way, can the FBG-based geohazard monitoring technology achieve the full market potential.
## 4 Closed-Loop Control Algorithms
### Control Flow Diagram  
The PID-based closed-loop control algorithms are implemented on the MCU, effectively realizing a digital PID controller. Figure 6 illustrates the program control flow diagram for the developed closed-loop fluxgate sensor. The program begins with initializing the global variable _pid_optim_ to zero, indicating that the optimal positional PID parameters have not yet been identified. Then, the neuronal PID parameters are initialized first for the subsequent invocation of the single-neuron-based self-pre-optimization algorithm to determine the optimal positional PID parameters. Next, the DAC and ADC modules inside the MCU are sequentially activated, i.e., the input and output ports of the PID controller are enabled. Since the initial _pid_optim_ value is zero, the single-neuron-based self-pre-optimization algorithm is executed, where the PID parameters are continuously modified to minimize the steady-state error of the closed-loop control system, i.e., to make the _pid_error_ converge to zero. Once the _pid_error_ converges to zero, the global variable _pid_optim_ is set to 1, and the optimal PID parameters \(K_{p}\), \(K_{i}\), and \(K_{d}\) for the positional PID algorithm are identified. Finally, the digital PID controller consistently applies the positional PID algorithm with the optimal parameters for the closed-loop control of the sensor system.  
The advantage of the above closed-loop control is that after each restart, the sensor must first locate the optimal positional PID parameters for the current system using the single-neuron-based self-pre-optimization algorithm. This approach mitigates the risk that the factory-set PID parameters for the positional PID algorithm become suboptimal for the current system due to changes in the sensor system. Furthermore, the single-neuron-based self-learning approach significantly reduces the hardware system's differentiation requirements, as it dynamically identifies the optimal positional PID parameters based on the unique characteristics of each sensor.
# Improving Structural Power Content Analysis Robustness for Satellite Navigation Applications +
## 2 Structural Power Content Analysis
### Signal and Noise Filtering  
The output of the DAM operation is processed by two comb filters in parallel, one called the signal filter and the other the noise filter. The signal filter has its passbands centered on the signal spectral lines, and its purpose is to measure the average signal power in the output of the DAM operation. The noise filter measures the power in an equivalent bandwidth as the signal filter, but has its passbands centered midway between adjacent signal spectral lines, isolating parts of the spectrum that do not contain any GNSS signal components. The purpose of this filter is to provide a normalization which compensates for gain fluctuations in the receiver signal path, such as would be caused by an automatic gain control (AGC) which adjusts the analog signal amplitude to ensure optimum digitization of the received signal. To reduce the variance in the normalization, the output of the noise filter is averaged in time before being applied to the output of the signal filter.  
The outputs of the signal filter and noise filter can generally be written as follows, where the subscript \(x\)s are replaced by \(s\) and \(n\) for signal and noise filter, respectively:  
\[g_{x}[n]=\sum_{k=1}^{N_{x}}h_{x}[k]y[n-k], \tag{2}\]  
where the filter coefficients are given by \(h_{x}[k]\). For the purposes of the analysis to follow in this paper, no specific filter implementation is assumed at this point, and the two filters have generally different lengths. The output of the signal filter after normalization by the average of the noise filter is then  
\[x[n]=g_{s}[n]/\frac{1}{P}\sum_{p=1}^{p}g_{n}[p]. \tag{3}\]
## 3 Results
### Experimental Data Analysis
#### 3.4.2 Training Loss  
The performance measure of YOLOv9-ARC is composed of three segments: classification loss (cls_loss), bounding box regression loss (box_loss), and distribution focal loss (df_loss).  
Classification loss quantifies the inconsistency in comparison to the object identified by the model class and the actual class label.  
Bounding box regression loss assesses the magnitude of similarity connecting the model's forecasted bounding box and the reference values box, refining the model's effectiveness in localizing and providing a more accurate fit of the predicted bounding box around the target object.  
Distribution focal loss models the distribution of each coordinate, enhancing the accuracy of bounding box regression.  
As illustrated in Figure 7, after roughly training conducted over 10 epochs, tangible improvements in model performance were realized with the help of numerous adjustments to the model parameters. In the next phase, the loss reduction rate progressively diminished and reached a steady state, making the detection process more well-calibrated and productive.  
Figure 6: **Feature map of the dataset.** This diagram analyzes the proportions of real and forged facial images, the distribution of the center coordinates of the forged parts in the facial images, and the aspect ratio analysis of the forged parts.  
Figure 7: **Training loss graph of the model.** The model training loss curve demonstrates the changes in the loss value during the training process, showing how the model improves or converges over time.
# An Efficient Ultra-Tight GPS/RISS Integrated System for Challenging Navigation Environments
## 2 Ultra-Tight GPS/INS Integration and RISS
### Position Equations  
Finally, the horizontal velocity components are converted into geodetic coordinates and then integrated to obtain a horizontal position in latitude and longitude form. The Up velocity is integrated to obtain altitude.  
\[\dot{\varphi}=\frac{v_{n}}{R_{M}+h} \tag{8}\]  
\[\dot{\lambda}=\frac{v_{e}}{(R_{N}+h)\cos\varphi} \tag{9}\]  
\[\dot{h}=v_{u} \tag{10}\]  
where \(R_{M}\) is the Meridian radius of the Earth's ellipsoid. A high-level diagram of RISS mechanization is given in Figure 4.  
Figure 4: 3D RISS Mechanization.
# Modeling and Simulation Tools for Fog Computing--A Comprehensive Survey from a Cost Perspective
## 5 Simulation Tools  
Simulators shed light on existing or prototype systems by imitating their operation. Simulators are used to study the system behavior and understand the factors that affect system performance as it evolves over time. Before applying new technologies to real systems it is salutary to test them using simulation tools. Fog computing trends as the latest extension of cloud computing. Although there is a wide range of simulation tools for cloud computing, they cannot be used as-is for studies in the field of fog computing; they have thus been adapted to meet the new needs. At the same time, novel simulation tools have been proposed and developed, specifically for the fog. In this section, we survey all such tools that are used in the fog computing area. The Edge-Fog cloud simulator [23] is implemented in Python. It is composed of two layers: (i) the outer layer which includes the edge devices and (ii) the inner layer which consist of fog devices. All those devices are connected and form a distributed system. The authors also implement the Least Processing Cost First (LPCF) algorithm, which is used to assign tasks to available nodes. The assignment aims to reduce the processing time and network costs. [PERSON] et al. [35] extend the CloudSim simulator [62] by adding new functionalities and developing a simulation toolkit for fog computing, called iFogSim. iFogSim is one of the most popular tools that can be used to model and simulate IoT and fog environments. It is written in Java and employs the JSON file format to represent physical topologies. iFogSim supports the simulation of entities and services. The communication is based on message passing. It is an event-based simulator that can be used to simulate applications in IoT and fog environments. It provides an environment capable of evaluating resource management policies based on different metrics, such as energy consumption, operational costs, and network congestion. The architecture of iFogSim provides physical, logical and management components. Physical components included fog devices, actuators, and sensors. Logical components represent processing modules and their interaction as a directed graph. User can draw physical elements, define their characteristics and build their topology using a user-friendly GUI. Alternatively, the user can define topologies programmatically using Java APIs. A management component assists in application placement, scheduling and monitoring. Despite its popularity, iFogSim has a few shortcomings: (i) the Java-based implementation introduces version compatibility issues while not
## 2 Materials and Methods
### Imagery and Climate Data Collection
#### 2.2.2 PlanetScope Imagery Data  
This study used the PlanetScope surface reflectance product taken with PS2 and PS2.SD instruments (Planet Labs Inc., San Francisco, CA, USA). This product provides ground sample distance at 3 m per pixel, captured using a four-band frame imager in  
Figure 2: Location of study sites at Kaizu city, Gifu Prefecture, Japan. Shaded fields indicate test fields.  
cluding blue (PS2: 455-515 nm; PS2.SD: 464-517 nm), green (PS2: 500-590 nm; PS2.SD: 547-585 nm), red (PS2: 590-670 nm; PS2.SD: 650-682 nm), and near-infrared (NIR) (PS2: 780-860 nm; PS2.SD: 846-888 nm). The imagery was already orthorectified and geometrically corrected using altitude telemetry and ephemeris data with refined GCPs by the provider to deal with geographical errors [39]. Moreover, all available imagery was already atmospherically corrected by Planet Labs Inc. before they released it in their application programming interface framework. This correction was crucial for the study, since we used spectral reflectance values of the imagery. The PlanetScope satellite constellation is capable of recording the land surface at a daily frequency. In this study, we used PlanetScope scenes of the observation fields from the sowing dates until 60 DAS. However, the terrestrial surface was sometimes covered with thick clouds, which made daily observations difficult. Thus, we tried to download the available cloudless imagery that clearly shows the fields and processed the imagery in the following procedure (Section 2.4) to acquire a daily spectral reflectance value. The imagery was very limited in 2020 because of frequent bad weather during the growing season. The number of PlanetScope scenes used for each field is listed in Table 1.
# Secure Encapsulation Schemes Using Key Recovery System in IoMT Environments
## 2 Related Work
### Multi-Agent Key Recovery  
A single agent key recovery system is associated with service overload and security problems. Therefore, we use a multi-agent (at least two agents) key recovery system. The multi-agents receive a ciphertext that contains a key from the user or the KRC. Later, KRAs send pieces of the key to the KRC to allow the KRC to recover the complete key. However, various attacks and security breaches are possible, and efforts have been made to deal with these issues (Kayas
## 4 Results and Discussion
### Time Series Analysis of Surface Deformation and Groundwater Level
The causes of surface deformation include both natural and anthropogenic factors, with groundwater fluctuations being one of the most critical contributors. In addition to the deformation responses induced by groundwater level changes, the cumulative deformation sequence also contains signals associated with geological conditions, engineering activities, and traffic loads, as well as noise [33; 34]. In summary, quantitatively identifying the dominant drivers of non-stationary cumulative deformation time series is significant for effective land subsidence monitoring, early warning, and advancing the understanding of subsidence mechanisms. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Point ID** & **Longitude** & **Latitude** & **Annual Amplitude (mm)** & **Annual Phase (month)** & **SROCC Coefficient** & **Time Lag (month)** \\ \hline JC01 & 116.33 & 34.48 & 14.17 & 5 & 0.501 & 2 \\ JC02 & 116.20 & 34.44 & 13.09 & 5 & 0.782 & 3 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Surface deformation characteristics and groundwater level lag characteristics of the site. Figure 8: Comparison and verification of 2021–2022 data of hydrological sites: **(a)** groundwater level at JC01; **(b)** groundwater level at JC02; **(c)** cumulative surface deformation at JC01; **(d)** cumulative surface deformation at JC02.
###### Abstract  
Undesirable outputs, such as carbon emissions and loss of property due to traffic accidents, hold great significance for the sustainable development of the transport industry. In this study, we applied a super-efficiency data envelopment analysis model with a slack-based measure (Super-SBM DEA) considering undesirable outputs to measure the integrated transport efficiency (_ITE_) of 31 provinces in China during the period of 2009-2016. Following this, we used a spatial autocorrelation model to test and verify the spatial autocorrelation of the _ITEs_ at the level of the 31 provinces, and further to explore the aggregating features. Finally, considering the spatial effects that emerged, we constructed a \(\beta\)-convergence model to analyze the convergence characteristics of China's _ITEs_ and investigate its conditional factors. The research results show that the average _ITE_ demonstrated a linear growth trend; the effective decision-making units (the _ITE_ value was greater than 1) are only 11 provinces, accounting for about 35% by 2016. The mean of _ITEs_ was also found to present a law of decreasing order of Eastern, Central and Western Zones. However, the Central Zone and Western Zone have a better efficiency improvement trend compared to the Eastern Zone. The Moran's I index was bigger than zero, indicating that the _ITEs_ formed a spatial autocorrelation phenomenon. The Moran scatter plots further showed that the provincial _ITEs_ mainly followed the patterns of high-high, high-low and low-low aggregation. The _ITE_ of the 31 provinces was found to have a clear absolute \(\beta\)-convergence and conditional \(\beta\)-convergence characteristics. Moreover, the level of economic development, household per capita traffic consumption, transport industry scale, technology advancement and transport intensity were all seen to have an important impact on the convergence of integrated transport efficiency. It is hoped that the findings of this study may contribute further insights and practical knowledge to effectively measuring the development level of China's integrated transport efficiency, and to understanding future changes in the _ITE_ gap among Chinese provinces.  
integrated transport efficiency; undesirable outputs; Super-SBM DEA; spatial convergence 2018 8 1698 doi:10.3390/app8091698
# Design of an SVM Classifier Assisted Intelligent Receiver for Reliable Optical Camera Communication
## 2 Support Vector Machine  
The SVM has been considered one of the most widely used and accurate classification algorithms than others after being introduced in 1990 within the machine learning community [27]. Fundamentally, it is a supervised learning algorithm whose main objective is creating an optimal hyperplane between two classes of data set focusing on the training samples even if for a miniature training set. The hyperplane acts as a decision boundary to categorize the data in different classes. In Figure 2, points near the hyperplane, called support vectors, are used to determine the optimized hyperplane. For a given training sample \((x_{1},y_{1}),(x_{2},y_{2}),\ldots,(x_{m},y_{m})\) where class labels are represented by  
Figure 1: Scenario of the intelligent receiving system for OCC.  
\[y_{i}=\{+1,-1\},\forall i\epsilon\{1,2,3,\ldots,m\}. \tag{1}\]  
The optimal separating hyperplane is mathematically formulated as [28]  
\[\theta^{T}x_{i}+b=0, \tag{2}\]  
where \(\theta=[\theta_{1},\theta_{2}\ldots,\theta_{m}]\) is an \(m\)-dimensional vector of weights, and \(x_{i}=[x_{0},x_{1},\ldots,x_{m}]\) is an \(m\)-dimensional input vector, where \(x_{0}=1\) by convention, and \(b\) is called the biasing unit. The optimization problem associated with finding the hyperplane can be expressed as follows:  
\[\frac{1}{2}\|\theta\|^{2}=\frac{1}{2}\theta^{T}\theta, \tag{3}\]  
which is subjected to  
\[\theta^{T}x_{i}+b\geq+1\ \ \text{for}\ \ y_{i}=+1. \tag{4}\]  
\[\theta^{T}x_{i}+b\leq+1\ \ \text{for}\ \ y_{i}=-1. \tag{5}\]  
The final nonlinear decision function can be obtained as follows:  
\[f(x)=sign\left(\sum_{i=1}^{m}\alpha_{i}\Big{(}\theta^{T}x_{i}\Big{)}+b\right). \tag{6}\]  
To generalize the SVM classifier about complex data features, SVM uses a technique called Kernel \(k(x_{i},x)\). The value \(k(x_{i},x)\) corresponds to \(\varphi(x_{i}).\varphi(x)\), which maps linearly non-separable patterns into a higher dimension feature space. The commonly used kernel functions, e.g., linear,polynomial, sigmoid, and radial basis function (RBF), are briefly described in Table 1. Finally, the decision function can be modified as follows:  
\[f(x)=sign\left(\sum_{i=1}^{m}\alpha_{i}(\varphi(x_{i})\cdot\varphi(x))+b \right). \tag{7}\]  
\begin{table}
\begin{tabular}{c c} \hline
**Type** & **Function** \\ \hline Linear & \(x_{T}x_{i}+c\) \\ \hline Radial basis function (RBF) & \(exp\left(\left|-\frac{\left\|x-x_{i}\right\|^{2}}{2\sigma^{2}}\right|\right)\) \\ \hline Polynomial & \((x_{T}x_{i}+c)^{p}\) \\ \hline Sigmoid & \(\tanh((x_{T}x_{i}+c)^{p})\) \\ \hline \end{tabular}
\end{table}
Table 1: Kernel functions for the proposed system.  
Figure 2: Illustration of SVM classifier.
# On Transform Domain Communication Systems under Spectrum Sensing Mismatch: A Deterministic Analysis
## 2 System Models and Modulation Schemes  
### Transmitter Side of the TDCS  
Spectrum sensing is recognized as the first step in the TDCS. The whole spectrum band is divided into \(N\) spectral bins when adopting the TDCS. A spectrum mask vector is used to tag the availability of such spectral bins with \(\mathbf{A}=\{A_{0},A_{1},...,A_{N-1}\}\). The value \(A_{k}\) is set to 1 (or 0) if the magnitude is smaller (or larger) than the given threshold, as shown in Figure 1. It is often considered that all the spectral bins form a mathematical set \(\Omega^{\mathbb{C}}\), where \(\|\Omega^{\mathbb{C}}\|=N_{\mathbb{C}}\) denotes the number of spectral bins that are not occupied, i.e., \(\left\{A_{k}=1,k\in\Omega^{\mathbb{C}}\right\}\). To form the waveform, like noise, a user-specific pseudorandom (PR) poly-phase vector, i.e., \(\mathbf{P}=\left\{e^{jm_{0}},e^{jm_{1}},...,e^{jm_{N-1}}\right\}\), is generated by a phase mapping process [23]:  
\[m_{k}\in\left\{0,\frac{2\pi}{2^{r}},\frac{4\pi}{2^{r}},...,\frac{2\pi(2^{r}-1 )}{2^{r}}\right\} \tag{1}\]  
On the transmitter side, illustrated in Figure 2, the pseudorandom poly-phase vector \(\mathbf{P}\) is multiplied one-by-one with the spectrum availability vector \(\mathbf{A}\) to generate a spectral vector, i.e., \(\mathbf{B}=\{B_{0},B_{1},...,B_{N-1}\}=\mathbf{A}\cdot\mathbf{P}\). A fundamental modulation waveform (FMW) \(\mathbf{b}\) is yielded by performing an IFFT operation on this spectral vector \(\mathbf{B}\) as follows:  
\[\begin{array}{l}\mathbf{b}=\{b_{0},b_{1},...,b_{N-1}\}=\lambda\mathrm{F}^{- 1}(\mathbf{B})\\ b_{n}=\lambda\frac{\sqrt{N}}{\sqrt{N}}\sum\limits_{k\in\Gamma^{\mathbb{C}}} \left(e^{jm_{k}}\right)e^{\beta 2\pi kkn/N}\end{array} \tag{2}\]  
where \(\lambda=\sqrt{N/N_{\mathbb{C}}}\) denotes a scaling factor that ensures the normalized transmission power. To this level, the generation of the smart waveform for dynamic spectrum utilization is ready and cached in a buffer for later modulation processing.