esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Costs Evaluation  
For resource-limited small-scale producers, it is important to make use of low-cost accessible technologies for cheese processing and microbiological treatment [52]. It is expected that, in the near future, nonthermal technologies will be the best affordable alternative to improve food safety and allergenic properties [33]. The irradiation treatment cost is calculated based on the density of the sample to be treated and maximum dosage to be applied. The consignment for this study cost was US $31.20 for 14.5 kg (sum of 250 g and 500 g cheese pieces). The cheeses were vacuum-sealed in polyethylene bags, and the packaging costs US $0.59 for one bag (20 \(\times\) 30 cm). Polyethylene bags with low permeability to gases are normally used, and samples should not be exposed to oxygen during the treatment to reduce the chances of oxidation that might result in off flavours. It is important to note that smaller packages of <500 kg tend to be relatively expensive;  
Figure 3: Microbial counts of artisanal pecorino-style cheese of pieces of 250 and 500 g at the 5.0-kGy irradiation treatment (\(n\) = 24). ACC: Aerobic colony counts; _E. coli_: _Escherichia coli_.  
Figure 2: Microbial counts at the 0.0-kGy and 5.0-kGy irradiation treatments of artisanal pecorino-style hard cheese (\(n\) = 24). Different superscripts indicate a difference (\(p\) < 0.05). ACC: Aerobic colony counts; _E. coli_: _Escherichia coli_.  
the cost decreases when a bigger consignment is treated (1000 kg). As of August 2018, in South Africa, the minimum cost for each consignment stack of 1 m \(\times\) 1.2 m \(\times\) 1.5 m in height and maximum weight of 1000 kg was US $27.13 (US $0.027/kg), excluding vat. Small-scale producers can work as cooperates so as to have the maximum weight of 1000 kg and benefits from the irradiation low costs. According to the above figures working with a maximum weight of 1000 kg, the cost of packaging bags and the irradiation treatment gives an estimate of US $0.62 per kilogram of cheese. The evaluated costs for the treatment are relatively affordable for small-scale producers in case they produce cheese suspected or known to be contaminated [52]. The cheese products should be treated in their final packaging to prevent further contamination and limit packaging costs.
###### Abstract  
Pedestrian trajectories are crucial for self-driving cars to plan their paths effectively. The sensors implanted in these self-driving vehicles, despite being state-of-the-art ones, often face inaccuracies in the perception of surrounding environments due to technical challenges in adverse weather conditions, interference from other vehicles' sensors and electronic devices, and signal reception failure, leading to incompleteness in the trajectory data. But for real-time decision making for autonomous driving, trajectory imputation is no less crucial. Previous attempts to address this issue, such as statistical inference and machine learning approaches, have shown promise. Yet, the landscape of deep learning is rapidly evolving, with new and more robust models emerging. In this research, we have proposed an encoder-decoder architecture, the Human Trajectory Imputation Model, coinedHTIM, to tackle these challenges. This architecture aims to fill in the missing parts of pedestrian trajectories. The model is evaluated using the Intersection drone the inD dataset, containing trajectory data at suitable altitudes, preserving naturalistic pedestrian behavior with varied dataset sizes. To assess the effectiveness of our model, we utilize L1, MSE, and quantile and ADE loss. Our experiments demonstrate that HTIM outperforms the majority of the state-of-the-art methods in this field, thus indicating its superior performance in imputing pedestrian trajectories.  
Keywords:human trajectory; autonomous vehicle; encoder-decoder architecture; deep learning; inD dataset +
Footnote †: journal: Applied sciences
# Estimation of Aircraft-Dependent Bumpiness Severity in Turbulent Flight
## 3 Experiments and Analysis
### Severity Estimation of Aircraft Bumpiness  
In this section, a special section of QAR data was chosen for bumpiness severity estimation of different aircrafts. Figure 13 shows the horizontal flight path from ZBAA to ZGGG. With the recorded QAR data, the wind-based EDR indicators are described by the color gradient. It can be found that in Zone 1, near 36.2\({}^{\circ}\) N (North latitude), 114.7\({}^{\circ}\) E (East longitude), there was moderate turbulence, while in Zone 2, near 30.1\({}^{\circ}\) N,113.6\({}^{\circ}\) E, the aircraft would experience severe turbulence. The one-minute EDR values are shown in the two sub-windows.  
Figure 12: Aircraft maneuvers effects on Eddy Dissipation Rate (EDR) and bumpiness severity estimation.  
Among the 880 flight segments, there are 12 different kinds of aircraft type, of which the Boeing 737, Boeing 787, and Airbus 330 aircraft are in the majority. The bumpiness severity of these three aircraft types was analyzed. The average Take-Off Weight (TOW), average Mach number, and grid refinement of the three aircraft types are shown in Table 3.  
According to the computing flow shown in Figure 7, the bumpiness severity of the above three aircraft types is shown in Figure 14. It can be found that different aircraft would experience different bumpiness severity in the same turbulence field. With the increase of aircraft TOW, the bumpiness severity decreases. In addition, further experiments show that with the increasing cruising airspeed, the bumpiness becomes more intense. According to estimated bumpiness severity indicator beforehand, the pilot can decide whether to fly through the turbulence field or to deviate from the severe turbulence.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameters** & **B737** & **B787** & **A330** \\ \hline Average TOW & 68.6 t & 184.3 t & 209.8 t \\ Average Mach number & 0.78 & 0.84 & 0.83 \\ Grid on wing & 40 \(\times\) 20 & 40 \(\times\) 24 & 44 \(\times\) 20 \\ Grid on horizontal tail & 12 \(\times\) 6 & 14 \(\times\) 8 & 16 \(\times\) 8 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Related parameters of three aircraft types.  
Figure 13: The estimated EDR on Beijing-Guangzhou airway.  
The bumpiness severity estimation is based on in-situ EDR indication, which is updated once a minute. Thus, the estimated severity would change with the EDR indication per minute. Benefitted by the generated isotropic turbulence components and rigorous algorithm derivations, the estimated value gives a credible bumpiness severity indication per minute.  
It should be noted that if the computing results of target aircraft are saved into a table beforehand according to different aircraft mass and airspeed, the bumpiness severity of target aircraft can be estimated by looking up the table with in-situ EDR value. In addition, the bumpiness severity of the aircraft that is about to fly through the turbulence field can also be predicted.
# Soil and Rockfill Dams Safety Assessment for Henan Province: Monitoring, Analysis and Prediction
## 5 Discussion
### Results and Accuracy Evaluation of MAF-SSA-LSTM Model
\[RMSE(y_{i},\,\hat{y})\,=\sqrt{\frac{1}{m}\frac{m}{\sum_{1}^{m}\left(y_{i}\,- \,\hat{y}\right)^{2}}} \tag{9}\]  
\[MAE(\,y_{i},\,\hat{y})\,=\frac{1}{m}\sum_{1}^{m}\Bigg{|}\,y_{i}\,-\,\hat{y} \Bigg{|} \tag{10}\]  
\[MAPE(y_{i},\,\hat{y})\,=\frac{1}{m}\sum_{1}^{m}\frac{\big{|}\,y_{i}\,-\,\hat{y }\big{|}}{\big{|}\,y_{i}\big{|}} \tag{11}\]  
where \(y_{i}\) is the smoothed true deformation data, \(\hat{y}\) is the predicted values, and \(m\) is the number of predicted data. The RMSE measures the accuracy of the model's predictions, the MAE provides an assessment of prediction errors, and the MAPE evaluates the model's overall accuracy. The smaller the calculated evaluation value, the better the prediction performance and the higher the prediction accuracy. From Figure 17a,b, it can be observed that after comparing with InSAR deformation monitoring values, the results show that the MAF-SSA-LSTM model has an RMSE of 1.526 mm, MAE of 1.447 mm, maximum absolute error of 2.44 mm, and minimum absolute error of 0.07 mm. Additionally, Figure 17c shows that the predicted results are consistent with the deformation trend as monitored by the InSAR technique. This indicates that the MAF-SSA-LSTM model effectively predicts deformation. Furthermore, the MAPE of 2.22% shows the reliability of the MAF-SSA-LSTM model's prediction accuracy, further demonstrating the feasibility of the model and providing accurate predictions for disaster warnings.
# Optimization of Machining Parameters for Corner Accuracy Improvement for WEDM Processing
## 3 Experiment and Discussion
### Effect of Parameter on Corner Error
#### 3.1.3 Effect of Open Circuit Voltage (OV) on Corner Error  
From Tables 3-5, it can be seen that the OV parameter was significant, which was indicated by the \(p\)-value being less than 0.05. Additionally, the results showed that the OV\({}^{2}\) term provides secondary contribution to the 30\({}^{\circ}\) corner. Meanwhile, the interaction between the OV and ON terms provide secondary contribution to the 60\({}^{\circ}\) corner. Table 8 shows the experimental results of corner machining for different open circuit voltage (OV) parameter settings. It was seen, when the open circuit voltage (OV) parameter changed from 9 to 11, that the corner error was almost the same. When the OV parameter changed from 11 to 13, the corner error slightly increased. Similarly, the machining speed also showed as slight shorter from 44.95 to 38.5 min when the OV parameter changed from 9 to 13.
# Human Action Recognition Based on 3D Convolution and Multi-Attention Transformer
## 3 Transformer and C3D-Based Two-Stream Network Modeling
### C3D Time-Stream Convolutional Neural Network  
Two-dimensional convolutional neural networks work well for feature extraction on single frames but do not capture the temporal information well when faced with video-based tasks, hence three-dimensional convolutional neural networks are proposed. Compared to 2D convolutional neural networks, 3D convolutional neural networks can model temporal information better. The C3D convolutional structure is shown in Figure 2.  
Figure 2a shows the application of a two-dimensional convolutional network to a video image sequence frame, and after the convolution operation, the output is a two-dimensional feature map, which loses the time sequence information. Figure 2b shows that the three-dimensional convolutional network is subjected to a convolution operation, and the output is a three-dimensional feature map that retains the time information of the video image sequence frame. Using the C3D network for feature extraction in the temporal dimension  
Figure 1: Improved Two-Stream Network Based on C3D and Multi-scale Transformer.  
Figure 2: C3D Convolutional Structure. (**a**) Two-dimensional convolution in multi-frame images; (**b**) Three-dimensional convolution in multi-frame images; (**c**) C3D network structure.  
enables better extraction of temporal features of continuous optical flow images. The C3D network structure is shown in Figure 2c, which employs five convolutional layers, five pooling layers, two fully connected layers, and a softmax classifier. All layers use 3 \(\times\) 3 \(\times\) 3 convolutional kernels; the first pooling kernel is a 1 \(\times\) 2 \(\times\) 2 structure and the rest of the pooling kernels are 2 \(\times\) 2 \(\times\) 2. The C3D network model has a simple structure and trains the model quickly for better extraction of temporal features.
## 2 Materials and Methods  
### Sample Preparation  
Graphene (G) was grown by chemical vapor deposition (CVD) from mixtures of CH\({}_{4}\):H\({}_{2}\) = 100:0-50 sccm gases at a temperature of 900 \({}^{\circ}\)C and at a total pressure of 4 Torr. The samples were then cooled at a rate of \(\approx\)2 \({}^{\circ}\)C min\({}^{-1}\) in 1 Torr of H\({}_{2}\)[16].  
Copper (Cu) foils of 30 \(\upmu\)m thickness from Alfa Aesar (Ward Hill, MA, U.S.) and 300 nm Ni/300 nm SiO\({}_{2}\)/Si obtained by sputtering were used as substrates. They were pre-annealed at 400 \({}^{\circ}\)C in ultra-high vacuum (UHV) for nickel and copper oxide desorption and then heated to 900 \({}^{\circ}\)C in 1 Torr of H\({}_{2}\) for recrystallization. As verified by Raman spectroscopy analysis, the graphene grown on Cu was a monolayer (1L), while the graphene on Ni was a non-homogeneous distribution of monolayer, bilayer, and multilayer graphene [11].  
After graphene growth, the samples were transferred under vacuum in a Veeco GEN II molecular beam epitaxial system where Ga nanoparticles (Ga NPs) were deposited onto the graphene under UHV conditions. The nanoparticles were deposited at room temperature with a constant Ga flux equivalent to 82 monolayers (MLs) per minute as determined using a thin film approximation. This process assured very clean interfaces between the Ga NPs and the graphene, as well as between the graphene and the underlying metallic layer.  
For comparison, Ga NPs were also simultaneously deposited on 1 ML graphene grown on Cu and transferred by the tape method [15] on a glass substrate to be used as a control sample for the plasmon resonance of the Ga NPs.
# Inter-UAV Routing Scheme Testbeds
## 4 Findings and Discussion
### Stationary/VANET Mode  
In stationary/VANET mode, the data from the MPU6050 gyroscope/accelerometer module may be utilized to form packets of a given length, defined by the protocol's structure. In this mode, the modules shall function independently from an external flight controller. With no connection to a flight controller or an embedded flight control RTOS task, the module can still implement routing and be used as a means of evaluation thereof. This may prove to be a useful implementation for non MANET-specific ad hoc routing schemes. The advantages stemming from the usage of a compartmentalized device and a standard form factor are still there, though exploited to a lesser degree. Usage of this mode is also a possibility for the evaluation of routing schemes in nonaerial ad hoc networks.
###### Abstract  
Low-latency and robust beamforming are vital for sustaining signal quality and spectral efficiency in emerging high-mobility 5G and future 6G wireless networks. Conventional beam management approaches, which rely on periodic Channel State Information feedback and static codebooks, as outlined in 3 GPP standards, are insufficient in rapidly varying propagation environments. In this work, we propose a Dominance-Enforced Adaptive Clustered Sliding Window Regression (DE-ACSW-R) framework for predictive beamforming in O-RAN Split 7-2x architectures. DE-ACSW-R leverages a sliding window of recent angle of arrival (AoA) estimates, applying in-window change-point detection to segment user trajectories and performing both Linear Regression (LR) and curvature-adaptive Support Vector Regression (SVR) for short-term and non-linear prediction. A confidence-weighted fusion mechanism adaptively blends LR and SVR outputs, incorporating robust outlier detection and a dominance-enforced selection regime to address strong disagreements. The Open Radio Unit (O-RU) autonomously triggers localised MUSIC scans when prediction confidence degrades, minimising unnecessary full-spectrum searches and saving delay. Simulation results demonstrate that the proposed DE-ACSW-R approach significantly enhances AoA tracking accuracy, beamforming gain, and adaptability under realistic high-mobility conditions, surpassing conventional LR/SVR baselines. This AI-native modular pipeline aligns with O-RAN architectural principles, enabling scalable and real-time beam management for next-generation wireless deployments.  
O-RAN; 5G; beamforming; angle of arrival; radio management 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Vol. 2, No. 2, pp. 1-10. External Links: Document Cited by: SS1.
## 3 Material and Methods  
### Synthetic Aperture Radar Interferometry (InSAR): Techniques, Potentials and Limitations  
Since the first studies that used radar interferometry to map differential displacements over agricultural fields [42], synthetic aperture radar (SAR) interferometry, or InSAR, has become a useful remote sensing technique to study surface deformations [43]. Different InSAR techniques were used in the past to retrieve spatial and temporal deformation of slow slope deformations [14; 20; 21; 22].  
Data from numerous spaceborne synthetic aperture radar (SAR) missions with different wavelengths are available to retrieve surface deformation by radar interferometry, and the basic principles of this differential remote sensing technique are reviewed for instance, in [PERSON] et al. 2000 [44], [PERSON] et al. 2000 [11], while examples of geological engineering applications can be found in [PERSON] and [PERSON] 2006 [13]. The major limitations for this technique are the following factors:  
1. Phase ambiguity: The differential phase of an interferogram is measured as a fraction of the wavelength, and a deformation field is mapped in the range between \(-\pi\) and \(\pi\) radians. At this point, the phase is generally named wrapped phase, and in deforming areas, a spatial pattern called interferometric fringes can often be observed [45]. Switching from one end of the spectrum to the other is also commonly called phase-jump. Resolving this phase-ambiguity to obtain absolute values requires a process that is called phase unwrapping and can be solved numerically by different approaches [46; 47];
2. Decorrelation: Especially in rural or densely vegetated areas, decorrelation of the interferogram can happen [48]. It is mainly due to surface changes between two acquisitions (temporal decorrelation), which can result from high deformation rates, rapid vegetation growth, snow cover or the long period of the interferogram. Decorrelation may also occur if the distance of the sensors between two acquisitions (known as perpendicular baseline) is large, which is called baseline decorrelation. If coherence is low, unwrapping will also be more problematic [49];
3. Phase noise: Even if the interferometric phase is coherent, it could contain unwanted residual noise due to the differential phase generated by DEM errors, atmospheric phase delay, and orbital inaccuracies [49; 50; 51].  
Several multitemporal techniques, such as persistent scatterers interferometry [10; 17; 52], small baseline techniques [9; 10; 11; 12; 13; 14; 15; 16; 17; 18], or hybrid approaches [53], were developed to address the problems of decorrelation and estimate different error terms of the phase. More recently, various authors [8; 22; 25] have shown that traditional two-pass interferometry can be successfully used to investigate gravitational slope movements thanks to shorter satellite revisit intervals and/or improved radar image quality.
# Scanning Electron Microscopy Techniques in the Analysis of Gunshot Residues: A Literature Review
## 3 Results
### Judicial Implications and the Role of Expert Testimony  
In forensic investigations, the findings from SEM-EDS analysis carry significant weight in judicial proceedings. The proper interpretation and presentation of these results are essential to avoid the misrepresentation or miscommunication of scientific evidence. The forensic expert's role extends beyond the laboratory to the courtroom, where they must explain complex analytical processes and their conclusions in a manner that is both scientifically accurate and comprehensible to non-specialist audiences, such as judges and jurors.  
Judicial scrutiny of forensic evidence has intensified in recent years, with courts demanding greater transparency, validation, and standardization of forensic methods. While SEM-EDS is widely regarded as a reliable method for GSR detection, the forensic expert must clearly articulate the limitations of the analysis, particularly regarding the potential for false positives and the influence of environmental factors. Furthermore, the expert should be prepared to address alternative hypotheses and demonstrate how the findings support or refute these scenarios. In cases where GSR evidence plays a pivotal role, the expert's testimony can influence the outcome of the trial, making it imperative to present balanced, well-supported conclusions based on validated methodologies.
# A Completion Method for Missing Concrete Dam Deformation Monitoring Data Pieces
## 1 Introduction
### Literature Reviews
This paper analyzes the composition, characteristics, and contamination of the concrete dam deformation monitoring information. A multi-value missing data completion method using BP mapping of spatial adjacent points is proposed to improve the accuracy on analysis and pattern prediction of concrete dam deformation behaviors. The proposed method is validated by a case study.
## 4 Results
### Forrester  
The Forrester function results are shown in Figure 10 for both MF SPCE and MF kriging. The acronyms in this and following figures should be interpreted as follows: HF is using high-fidelity data alone with either PCE or ordinary kriging surrogate models whereas HFS and HFSK are employing SPCE and SPCE-Kriging surrogates, respectively. MF2 implies a multi-fidelity surrogate (either PCE or kriging) using data from the highest fidelity level and in this case fidelity level 2. Again, MF2S and MF2 SK are instead employing SPCE and SPCE-Kriging surrogates, respectively. Since for MF kriging the number of low-fidelity points can be arbitrary, the starting amount is given. In this case, as 30 LF (that is all the starting HF locations plus latin hypercube sampled locations subject to a distance constraint). The number of LF training points for the MF PCEs is thirty levels higher for the Forrester function (i.e., 63 LF points are used when using 15 HF points). For the MF SPCE models, various orders for the polynomials were combined with several values of \(\lambda\) to find the combination with the lowest root-mean-square error (RMSE). The resulting best values for HF only and MF3 are shown in Table 3. Values for MF2 and MF4 are somewhat similar and are thus omitted here. One can infer that SPCE performs better than PCE up to a point and that MF models perform better than HF models, except for MF2, where the  
Figure 9: \(x_{1}(t=6)\) when \(1\leq k_{1},k_{2}\leq 4\) with HF and LF results shown in red and blue, respectively.  
LF model is just too expensive. Kriging performs similarly to PCE when using only a few training points but then falls behind. SPCE-Kriging performs similarly to ordinary kriging.
# Study on the Performance of Foam Concrete Prepared from Decarburized Fly Ash
## 2 Experimental Program
### Experimental Methods
#### 2.3.3 Insulation Property of Foam Concrete  
The thermal insulation property of the foam concrete is characterized according to its thermal conductivity. The thermal conductivity test is carried out concerning GB/T10294-2008 \"Thermal insulation--Determination of steady-state thermal resistance and related properties--Guarded hot plate apparatus\". Specimens with dimensions of 300 mm \(\times\) 300 mm \(\times\) 35 mm were prepared. The specimens were maintained under standard conditions until 26 d. They were taken out and dried in an oven to a constant weight for thermal conductivity testing. The hot plate temperature was set to 35 \({}^{\circ}\)C and the cold plate temperature to 15 \({}^{\circ}\)C. The automatic detection time is 90 min, and the preliminary estimate is 60 min. The thermal conductivity is calculated according to Equation (1), and the average value of the group is taken as the final result.  
\[Kc=\frac{\phi\times d}{S\times\triangle T} \tag{1}\]  
where \(Kc\) is the thermal conductivity (W/m-k); \(\Phi\) is the average thermal conductivity of the specimen after completion of the test (J/s); \(d\) is the specimen thickness (mm); S is the test area of the specimen (m\({}^{2}\)); \(\Delta T\) is the temperature difference between the hot plate and cold plate (\({}^{\circ}\)C).