esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology
### Procedures for Model Construction
#### 3.3.4 Performance Indicators  
The performance of the various models constructed in this study was assessed using performance measures such as variance accounted for (VAF), correlation coefficient (\(R\)), coefficient of determination (\(R^{2}\)) and mean squared error (MSE). These indicators are precisely shown in Equations (29)-(32) as:  
\[\text{MSE}=\frac{1}{p}\left[\sum_{i=1}^{p}(s_{i}-q_{i})^{2}\right] \tag{29}\]\[R=\frac{\sum\limits_{i=1}^{p}{(s_{i}-\overline{s})(q_{i}-\overline{q})}}{\sqrt{ \sum\limits_{i=1}^{p}{(s_{i}-\overline{s})}^{2}}\times\sqrt{\sum\limits_{i=1}^{ p}{(q_{i}-\overline{q})}^{2}}} \tag{30}\]  
\[R^{2}=\left[\frac{\sum\limits_{i=1}^{p}{(s_{i}-\overline{s})(q_{i}-\overline{q} )}}{\sqrt{\sum\limits_{i=1}^{p}{(s_{i}-\overline{s})}^{2}}\times\sqrt{\sum \limits_{i=1}^{p}{(q_{i}-\overline{q})}^{2}}}\right]^{2} \tag{31}\]  
\[VAF=\left(1-\frac{var(s_{i}-q_{i})}{var(s_{i})}\right) \tag{32}\]  
where \(\overline{q}\) represents the mean of the estimated values, \(q_{i}\) represents the estimated values, \(s_{i}\) represents the measured values, \(p\) is the number of observations, while \(\overline{s}\) denotes the average of the measured values.
## 3 Phenology-Based Crop Identification
### Crop Identification Model of Characteristic Ellipses  
The phenology-based crop identification is a combination of phenology and vegetation indexes, which had been effectively applied to multi-year maize classification in the Hetao Irrigation District [11]. This method considered the differences of growth stage and growth condition to identify crops. Phenological metrics represent the growth stage of crops, such as the time of sowing, harvesting, etc. Although the sowing time of the same crop may be different, the growth cycle of the same crop is usually stable. Therefore, the intervals between two phenological phases were applied instead of a single phenological phase to represent the growth stage of crops. We also used the fast growth phase (FGP = _t_max - _t_inf_) as a typical phenological metric as [PERSON] et al. [11]. The NDVI characteristics indicate the growth condition of crops, and three NDVI characteristics (Table 3) were compared to find the most appropriate index. The vegetation growth condition varied among different years due to variation of meteorological conditions. In order to reduce the impact of meteorological conditions on crop growth in different years, it is preferable to normalize the above indexes. [PERSON] et al. [11] normalized each index using the corresponding average value of all farmland pixels. Considering that the spatial distribution and planting structure of crops changed in different years, we also used the average values of grassland pixels and forest pixels in the normalization. The mean values of NDVI characteristics and phenological metrics for farmland, grassland and forest were calculated as the reference values for normalization (Table 4), and the ratio of the above indexes to their corresponding reference values were used as the normalized indexes. \begin{table}
\begin{tabular}{c l l} \hline \hline
**No.** & \multicolumn{2}{c}{**NDVI Characteristics**} & \multicolumn{2}{c}{**Phenological Metrics**} \\ \hline
1 & NDVI\_max, maximum value of NDVI & _t_max, time corresponding to NDVI\_max_ \\ \hline
2 &
\begin{tabular}{l} NDVI\_inf, NDVI value of the left inflection \\ point with maximum growth rate \\ \end{tabular} & _t_inf, time corresponding to NDVI\_inf_ \\ \hline
3 & \
\begin{tabular}{l} \(\Delta\)NDVI, difference between NDVI\_max and \\ NDVI\_inf \\ \end{tabular} &
\begin{tabular}{l} FGP = _t_max \(-\)_t_inf, duration from the left inflection \\ point to the peak point (Fast growth phase) \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Selected NDVI and phenological indexes. The normalized FGP was negatively correlated with normalized NDVI characteristics (Figure 6). Each combination of the normalized FGP with one of the nine normalized NDVI characteristics formed a phenology-based classifier for the identification of maize and sunflower (Figure 6). Consequently, we have nine phenology-based classifiers, the name and details of the ellipses were shown in Table 5.
## 4 Cyber Security Attacks in Vehicular Ad Hoc Networks (VANETs)
### Autonomous Vehicles: Security Measures and Technological Interplay  
Artificial intelligence (AI) and machine learning (ML) technologies have empowered the development and efficiency of autonomous vehicles, catalyzing intelligent decision making and improved security. However, just like vehicle ad hoc networks (VANETs), autonomous vehicles are also vulnerable to various cyber threats. In particular, these vehicles integrate technologies like LiDAR, Radar, GPS, and computer vision, which also become potential targets for cyber attacks. Notable threats include Sybil and replication attacks, which involve spoofing and identity theft, capable of causing critical operational issues [49, 50]. Countermeasures against these threats, some of which were initially developed for VANETs, have been adapted for autonomous vehicles. For instance, digital signatures and time stamps help authenticate messages and data sources [51, 52]. Mechanisms that detect disparities in motion trajectories and, hence, identify potential Sybil attacks, can also be applied in the context of autonomous vehicles [51].  
Additionally, [PERSON] et al. [53] presented a cooperative message authentication protocol (CMAP), which includes the sender vehicle's location data in each safety message. This protocol can be adopted in autonomous vehicles to counter replication attacks, thus significantly improving security [53]. Furthermore, techniques such as the anonymous batch authenticated and key agreement (ABAKA) scheme, which was initially introduced in  
Figure 4: VANET.  
VANETs [50], can be tailored for autonomous vehicles to authenticate multiple requests and generate multiple session keys simultaneously. It aids in quick validation and session key negotiation, thus minimizing transmission overhead and verification latency.  
To tackle the evolving security landscape, the development of advanced intrusion detection systems (IDS) is crucial. By leveraging anomaly detection techniques, autonomous vehicles can quickly detect and react to deviations from normal operations triggered by potential cyber attacks. Finally, secure communication protocols can be incorporated into the vehicle's systems to safeguard sensitive data, thereby further improving the overall security of autonomous vehicles [54, 55, 56].
# Execution Model to Reduce the Interference of Shared Memory in ARINC 653 Compliant Multicore RTOS
## 5 Related Works
Experimental results show that the IPC decreased with the limiting of memory bandwidth, but the throughput increased because the interference due to memory separation was reduced. In Reference [6], the per-core memory bandwidth mechanism of single core equivalence (SCE) was applied to the multicores. Additionally, an adaptive mixed-criticality (AMC) scheduling algorithm was applied, and the interference due to the shared memory was reduced by this stall-aware scheduling. Memory bank-aware methods have also been proposed to reduce memory interference. A method of allocating a bank of memory to each core by applying a page-based kernel-level memory allocator has been studied [12; 46]. These methods implement page allocators by extending Linux's buddy algorithm. The works were conducted by using an Intel Xeon to measure interference when multiple cores were allocated to the same memory bank and when each was allocated to a separate memory bank. When allocated to the divided bank, the memory access latency remained constant even when the number of cores executed concurrently increased. Moreover, when allocated to the same bank, memory access latency increased by about 150 ms. It was shown in Reference [47] that memory contention can be eliminated in avionics systems with a dynamic memory bandwidth level. In this study, the schedule table was configured offline and assigned to each core through interference-sensitive WCET computation and memory bandwidth throttling mechanisms.
# Spatial Distribution and Monitoring of Land Subsidence Using Sentinel-1 SAR Data in Java, Indonesia
## 5 Conclusions  
This study has ascertained the capability of SAR data in mapping LS for massive areas such as Java, Indonesia. It is found that LS in Java not only transpires in major cities, such as Jakarta, Semarang, and Bandung, which are frequently studied and discussed, but has also been occurring in smaller cities, such as Cirebon and Pandeglang, with annual subsidence rates reaching up to 110 mm/year. This study has also identified two new locations affected by LS that were not previously studied: Cianjur and Tegal. Although the LS rates in these regions are relatively lower compared to larger cities, they remain a significant concern. A summary of the lithology and compositions of LS regions and LS probable causes are tabulated in Table 2.  
The deformation caused by LS derived from SAR data shows strong agreement with GNSS measurements, allowing us to ascertain its accuracy and reliability. CORS GNSS measurements not only validate the deformation from SAR but also play an important role throughout the SAR processing steps. This highlights the importance of integrating both methods to enhance the accuracy of LS estimation. The prevalence of LS in industrial areas in Indonesia with thick sediment deposits aligns with previous studies, identifying groundwater overexploitation as the primary cause. LS is also observed in agricultural regions like Brebes and Tegal, where irrigation-related groundwater use may contribute, though detailed causal studies are still lacking. Additionally, the north coast of Java, where most LS cases are identified in this study, requires particular attention due to the severe impacts of subsidence. In conclusion, it is suggested that continuous monitoring of LS in Java using SAR and GNSS measurements should be performed, as land subsidence may emerge in new areas. Conceptualization, T.P.S. and I.G. ; methodology, T.P.S. ; software, T.P.S. ; validation, T.P.S., I.G. and H.Z.A. ; writing--original draft preparation, T.P.S. ; writing--review and editing, I.G. and R.L. ; visualization, R.L. ; supervision, H.Z.A., I.M. and A.P. ; funding acquisition, I.G. and H.Z.A. All authors have read and agreed to the published version of the manuscript. Part of this research was funded by Faculty of Earth Sciences and Technology at ITB (PPMI scheme) grant number 57A/IT1.C01/SK-KS/2024. The data presented in this study were derived from the following resources available in the public domain: InaCORS (Indonesia's Continuously Operating
# DMSO Improves the Ski-Slope Effect in Direct PCR
## 3 Results and Discussion
### Reducing of Ski-Slope Effect by DMSO in Direct PCR
For each color channel, DMSO enhanced the RFU ratio of STR genotyping by \(-\)5.4\(\rightarrow\)+101.8% for blue dye (6-FAM), \(-\)5.5\(\rightarrow\)+38.0% for green dye (VIC), \(-\)3.6\(\rightarrow\)+42.6% for yellow dye (NED), \(-\)5.9\(\rightarrow\)+34.8% for red dye (TAZ), and \(-\)3.0\(\rightarrow\)+74.7% for purple dye (SID), as shown in Table 1. These results indicate that DMSO could be used to rectify the ski-slope effect in direct PCR through increased amplification of large-sized DNA sequences and reduced amplification of small-sized DNA sequences. Figure 4: Reduction of ski-slope effect by dimethyl sulfoxide (DMSO) in direct polymerase chain reaction (PCR). The average relative fluorescence unit (RFU) ratio of short tandem repeat (STR) locus in each color channel was analyzed with the GlobalFiler kit using 50 Korean buccal OC card samples (**A**–**E**). Y-indel and DYS391 were excluded for calculation related to peak height. Data represent mean \(\pm\) standard deviation.
# Fault-Resilient Manufacturing Scheduling with Deep Learning and Constraint Solvers
## 6 Integration Mechanism for Fault Prediction and Scheduling Optimization
### Benefits of the Integrated System  
Integrating deep learning-based fault prediction with SMT-based scheduling optimization offers several key advantages for production line management:  
* Proactive Fault Prevention: The deep learning model predicts potential machine faults before they occur, enabling the SMT solver to adjust job schedules proactively. This reduces the likelihood of unexpected breakdowns and enhances overall system reliability.
* Efficient Scheduling: By incorporating fault probability \(P(M_{i},J_{j},t)\) into the scheduling process, the SMT solver optimizes job assignments to minimize both fault risk and machine downtime. This leads to increased operational efficiency and productivity.  
* Flexibility and Adaptability: The feedback loop allows the system to continuously adapt to real-time changes in machine conditions and job requirements. This flexibility ensures smooth operation even in dynamic and unpredictable production environments.  
```
1:procedureINTEGRATED_OPTIMIZATION(Real-time Machine States \(S(t)\), Jobs \(J\), Constraints)
2:while Production Line Active do
3:\(S(t)\leftarrow\) Acquire real-time machine state data from sensors
4:\(P(M_{i},J_{j},t)\leftarrow\) Predict Fault Probabilities using Deep Learning Model (Fault prediction step)
5: Initialize SMT Solver (Start SMT-based scheduling optimization)
6: Define \(S_{j}\), \(E_{j}\), and \(x_{i,j}\) as SMT Variables for all \(J_{j}\in J\) and \(M_{i}\in M\)
7:for each \(J_{j}\in J\)do
8: Add constraint: \(E_{j}\leq D_{j}\) (Deadline constraint)
9: Add constraint: \(x_{i,j}\leq C_{i,j}\) for all \(M_{i}\in M\) (Machine capability constraint)
10: Add constraint: \(E_{j}=S_{j}+d_{j}\) (Job duration)
11:endfor
12:for each pair of jobs \((J_{j},I_{k})\in J\times J\), \(J_{j}\
eq J_{k}\)do
13:for each \(M_{i}\in M\)do
14: Add constraint: \(x_{i,j}+x_{i,k}\leq 1\lor S_{j}\geq E_{k}+\delta\lor S_{k}\geq E_{j}+\delta\) (Non-overlapping jobs)
15:endfor
16:endfor
17: total_fault_cost \(\leftarrow\) 0
18:for each \(J_{j}\in J\)do
19:for each \(M_{i}\in M\)do
20: total_fault_cost \(\leftarrow\) total_fault_cost \(+\)\(P(M_{i},J_{j},E_{j})\cdot x_{i,j}\) (Fault risk minimization)
21:endfor
22:endfor
23: Minimize total_fault_cost
24:if solver.check() = SAT then
25: solution \(\leftarrow\) solver.model()
26:for each \(J_{j}\in J\)do
27: Assign \(J_{j}\) to \(M_{i}\) where \(x_{i,j}=1\) with schedule \((S_{j},E_{j})\)
28:endfor
29:else
30: Return \"No feasible schedule found\"
31:endif
32:if Model Drift Detected then
33: Retrain or Fine-tune Deep Learning Model with new data (Adaptation step)
34:endif
35:endwhile
36:endprocedure
```  
**Algorithm 1** Integration of Fault Prediction and SMT-Based Scheduling Optimization
## 3 Results and Analysis
### DDI Analysis  
The DDI was estimated for all years using the Albedo-NDVI feature space (Figure 3). To this end, \(k\) values were calculated using \(a\times k=-1\), and the final DDI expressions were obtained for the two phases of the Urat front flag data as reported in Equations (17) and (18).  
\[\text{DDI}_{2010}=6.6445\times\text{NDVI}-\text{Albedo} \tag{20}\]  
\[\text{DDI}_{2020}=10.2041\times\text{NDVI}-\text{Albedo} \tag{21}\]coefficients of 0.96 and 0.93, meeting the requirements for desertification dynamics research in the region. Among them, the Albedo-NDVI model has high production accuracy for moderate, serious, and extremely serious desertification, implying that underestimation of these three desertification levels is minimal. However, the production and user accuracy for weak desertification is extremely low, indicating a high multimetric error in identifying weak desertification areas.  
The validation results demonstrate that the Albedo-NDVI feature spatial method is capable of achieving optimal results in both the extraction of desertification land and the classification of desertification degree with higher accuracy and efficiency than other widely used remote sensing methods for desertification monitoring.
# Application and Evaluation of Energy Conservation Technologies in Wastewater Treatment Plants
## 2 Green Energy Utilized in WWTPs
### Energy Recovery of WW&S
#### 2.3.3 Thermochemical Treatment and Organic Fuel Production
Bio-oil is mainly composed of C, H, N, S, and O, where C is about 70%, H is about 9%. It has a high calorific value and can be used as fuel or raw material for chemical industry production. Bio-char can be used to make adsorbents, catalysts, or industrial materials. Bio-Char also has a calorific value, which is about 5 MJ/kg [93]. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Processes** & **Reaction Conditions** & **Main Products** & **Main Pollutions** & **Reference** \\ \hline \multirow{2}{*}{Inclination} & Auxiliary fuel, & \multirow{2}{*}{\(\backslash\)} & Fly ash, SO\({}_{2}\), SO\({}_{3}\), & \multirow{2}{*}{[62; 87]} \\  & Aerobic atmosphere & & NO\({}_{x}\), Heavy metals & \\ \hline \multirow{2}{*}{Wet air oxidation} & Oxidation, 125–320 \({}^{\circ}\)C, & & \\  & 0.5–20 MPa & & \\ \hline \multirow{2}{*}{Gasification} & Anoxic (non-combustion) & \multirow{2}{*}{Bio-gas} & H\({}_{2}\)S, SO\({}_{2}\), Other & \multirow{2}{*}{[89; 90]} \\  & atmosphere, 1000–1150 \({}^{\circ}\)C & & & \\ \hline \multirow{2}{*}{Hydrothermal liquefaction} & \multirow{2}{*}{250–400 \({}^{\circ}\)C, 4–22 MPa} & Bio-oil, Gas, Water-soluble product & Liquefaction & \\  & & & & wastewater \\ \hline \multirow{2}{*}{Pyrolysis} & Anoxic atmosphere, & Bio-char, Bio-oil, & Pyrolysis & \multirow{2}{*}{[89]} \\  & 400–800 \({}^{\circ}\)C & Bio-gas & wastewater & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of several thermochemical reactions. Compared with incineration and gasification, pyrolysis seems to be the best choice of thermochemical treatment. Pyrolysis has been used as an alternative technology for incineration and is expected to become the main technology in WWTPs in recent years because it has less pollution and higher economic benefits. However, there are some problems in pyrolysis technology, such as high energy consumption of pyrolysis and low fuel quality.
# Research on the Applicability of Transformer Model in Remote-Sensing Image Segmentation
## 3 Experiment and Results  
### Experimental Setup  
In this study, the network is constructed using the Pytorch framework and Python language, on an Intel(R) Xeon(R) Gold 5218 CPU with a GeForce RTX 2080 Ti GPU. The Adam optimizer is used to optimize the training process; the learning rate of each model is the same \(3\times 10^{-4}\); and the number of iterations is set to 40. The learning rate of each model is the same \(3\times 10^{-4}\); the number of epochs is set to 40; and the image size is set to \(224\times 224\).
## 3 Materials and Methods
### Faster-LTNet  
Conventional neural networks usually enhance efficiency by decreasing the number of floating-point operations (FLOPs), but this approach does not always result in substantial latency reduction. Techniques like group convolution (GConv) and depthwise separable convolution (DWConv) are effective in reducing FLOPs, yet they are hindered by low floating-point operations per second due to frequent memory access. The Faster-LTNet aims to design a highly efficient network architecture by minimizing redundant computations and memory access, optimizing computational resource utilization, and enabling more effective spatial feature extraction.  
Faster-LTNet adopts a hierarchical design comprising four stages, each preceded by an embedding layer or a merging layer for spatial downsampling and channel expansion. The embedding layer performs initial downsampling and channel expansion through a 4 \(\times\) 4 convolution, while the merging layer further reduces spatial resolution and increases channel numbers between stages using a 2 \(\times\) 2 convolution. The specific network architecture is illustrated in Figure 2. Each Faster-LTNet block consists of a partial convolution (PConv) [33] and two convolutions, collectively creating an inverted residual block with an expanded channel count in the intermediate layer and a shortcut connection for reusing input features. This design leverages information across all channels, forming a T-shaped convolution structure that emphasizes central features. The T-shaped structure design captures key features while retaining diversity, giving the model greater robustness, making it more robust in the face of changes in data distribution or interference. This capability is particularly important for practical application scenarios, such as maintaining high  
Figure 1: The overall structure diagram of improved RT-DETR.  
accuracy in a noisy background. PConv focuses on extracting local spatial features and significantly reduces FLOPs compared to regular convolutions. To balance computational efficiency and model performance, activation functions and batch normalization (BN) are judiciously placed to preserve feature diversity while reducing latency. Additionally, BN can be fused with adjacent convolutional layers to accelerate the inference process.  
The efficiency of Faster-LTNet is reflected in two key aspects: first, by optimizing FLOPS rather than solely focusing on reducing FLOPs, it achieves both low latency and high throughput; second, its design substantially reduces memory access, making it particularly suitable for 1/O-constrained devices such as mobile and embedded systems. Although Faster-LTNet has demonstrated excellent performance, there remains room for further optimization. For instance, the network's receptive field could be expanded by incorporating global operations, or its performance could be further enhanced by integrating operators such as attention mechanisms and MLPs.
## 1 Introduction
In [21], a data-driven PPC scheme was developed for an unmanned surface vehicle with unknown dynamics. However, the conventional PPC approach only ensures that the system states converge to the preset region as time tends to infinity [22], leading to an unsatisfactory solution for time-limited problems, such as the re-entry mission. Moreover, the input constraints and the optimality are not comprehensively considered in the conventional PPC paradigm. Therefore, the expected performance index cannot be consistently guaranteed and optimized for RLV suffering from poor aerodynamic maneuverability and limited control torques. Motivated by the foregoing considerations, a novel RL-based tracking controller, with fixed-time prescribed performance for RLV subject to parameter uncertainties, external disturbances and input constraints, is investigated. The main contributions and characteristics of the proposed method can be summarized as follows. * An online RL-based, nearly optimal, controller with limited inputs is developed by synthesizing the AC structure and the hyperbolic tangent performance index. In addition, the robustness of the learning-based controller is strengthened by incorporating a super-twisting-like sliding mode control. * Compared with a previous learning-based controller described in [10; 11; 12], in which the system dynamics are required to be known exactly, the proposed control scheme only requires the input-output data pairs of RLV, such that the system dynamics can be completely unknown. * In contrast to existing RL-based control schemes with asymptotic or finite-time convergence [10; 11; 12; 17; 21], the proposed control scheme can ensure that the tracking errors converge to a preset region within a preassigned fixed time. Moreover, the prescribed transient and steady-state performance can be guaranteed. * Comparative numerical simulation investigations show that the proposed method can provide improved performance in terms of the transient response, and steady accuracy with less control effort.