esa-sceva/satcom-chunk-collection huggingface dataset:
# Non-Autoregressive Sparse Transformer Networks for Pedestrian Trajectory Prediction
## 2 Related Works
### Human-Human Interactions  
The early works on crowd interaction modeling are based on Social Force [12; 36], which assume that the pedestrians are driven by virtual attractive and repulsive forces for making the future trajectory. Other handcrafted methods [37; 38] have been applied to crowd simulation [39; 40], behavior detection [41], and pedestrian trajectory prediction [42], etc. With the popularity of deep learning in recent years, many approaches based on deep neural networks have been proposed to model social interaction. Distance-based methods [6; 7; 14] computed the geometric relation of the agents and pooled the features of neighbors to obtain the representation of a particular agent. Attention-based methods [10; 15; 16; 17] adopted soft attention mechanism to generate different weights for neighbors. The graph-based methods [8; 9; 18; 19] considered the pedestrians in the scene as graph nodes and modeled social interaction with an adjacency matrix. STGAT [9] and Social-biGAT [18] are models that adopted graph attention networks to model the spatial interaction among the agents. Social-STGCNN [19] constructed a spatial-temporal graph and designed a kernel function on the adjacency matrix to extract spatial interaction. However, previous works commonly model undirected interaction with all the other pedestrians or neighbors within a fixed distance. The needless dense interaction among the agents may have brought deviations from reality. In contrast, we designed a method to capture social interactions with a sparse spatial transformer that integrates the relative distance feature into the multi-head self-attention. It is capable of discovering the salient spatial interaction and finding the exact pedestrians involved in the social interaction.
# Evaluation of Noise-Reduction Techniques for Gas-Turbine Test Stands: A Preliminary Analysis
## 6 Results
### Acoustic Attenuation with Convective Flow Phenomena  
To evaluate the attenuation of an attenuator, it is necessary to consider the convective phenomena caused by the airflow within it. These phenomena, which affect the propagation of sound by modifying the properties of the propagation medium, such as sound velocity and density, can be assessed using Actran software by introducing the flow field, specifically the velocity and density fields.  
In Figure 13, we show the velocity, density, and pressure fields in direct and reverse flow through the domains. The simulations indicate that some domains of variable densities appear due to the flow conditions, which have important effects on sound propagation,  
Figure 11: Averaged insertion loss for the analyzed configurations of the attenuator.  
Figure 12: Acoustic field inside linear and staggered configurations—sound-pressure level [dB]: (**a**) 500 Hz; (**b**) 1000 Hz; (**c**) 5000 Hz.  
with the baffle region acting as an interface with different acoustic impedance. Thus, the direct flow creates a higher-density domain in front of the baffles, where, by applying a direct acoustic field, a higher attenuation than in the static field or the inverse flow condition is obtained. Along this simulation, a flow of 100 m/s (M \(\approx\) 0.3) in the baffle channels was considered to emphasize the variation of densities between distinct domains.  
Figure 14 shows the attenuation graphs obtained after FEM simulations. A higher attenuation in the case of a direct flow condition can be noticed, as compared to static and inverse flows, due to a passage from a higher-density domain to a lower-density one, and also, a frequency shift is revealed for all three cases due to the Doppler effect.
# Monitoring Vehicles on Highway by Dual-Channel \(\varphi\)-OTDR
## 2 Experiment Setup  
The experimental setup of the DC-\(\varphi\)-OTDR is shown in Figure 1. The intention of experimenting the dual-channel structure is to try to resolve the mistaken vibration measurement as occurred in the SC-\(\varphi\)-OTDR caused by signal fading and noise. In the DC-\(\varphi\)-OTDR, the probability of signal fading occurred simultaneously at the same location in both channels may be extremely small. Similarly, the possibility of simultaneously producing the noise-related incorrect results at the same point is very low. For simplicity, a typical heterodyne \(\varphi\)-OTDR is used to set up the DC-\(\varphi\)-OTDR, although new schemes of \(\varphi\)-OTDR would have better performance [19]. In the dual channel system, the two sensing fibers are tied together and share the same transmission path. The laser source is a fiber laser with a narrow linewidth of \(\sim\)3 kHz and frequency drift of less than 20 MHz. The emission wavelength of the laser is 1550 nm and the maximum output power is 20 dBm. The laser light is split into two paths by a 50:50 optical coupler to form two separated SC-\(\varphi\)-OTDR systems. In each SC-\(\varphi\)-OTDR, the light is further divided into two channels by a 90:10 optical coupler: one is the sensing arm and the other is the reference one. The 90% light in the sensing arm is modulated by an acousto-optic modulator (AOM; driven by an arbitrary waveform generator, AWG) to generate probe pulses of 100 ns (corresponding to a spatial resolution of 10 m). The optical pulses are downshifted by 200 MHz from the input laser after the AOM. The rise/fall time of the AOM is 10 ns, typical insertion loss is 2 dB, and the extinction ratio is 50 dB. Then the pulsed light is launched into the sensing fiber via a circulator (CIR). In the reference arm, the 10% light is polarization controlled by a polarization controller (PC) to produce the reference light. The backscattering Rayleigh light reflected from different positions of the sensing fiber propagate backwards via the CIR and will combine with the reference by another 50:50 optical coupler for beating. The beating signal is converted to electrical signal by a 200 MHz balanced photodetector (BPD). The responsivity of the BPD is 0.9 A/W at 1550 nm. Then, the electrical signals output from both SC-\(\varphi\)-OTDRs are processed by I/Q demodulation and sampled by a data acquisition system with a sampling rate of \(\sim\)100 MHz, which means one sample corresponds to \(\sim\)1 m spatial distance. Finally, the vehicle vibrations are analyzed in real time by a computer. With the help of the dual sensing fibers, it is expected to decrease the possibility of wrong judgement for vibration events. For comparison, we also perform vibration measurements by using each SC-\(\varphi\)-OTDR.
## 4 Control Quality Factors and Leading Signals
### Integral Indices and Statistical Properties Used as Control Quality Metrics  
The statistical metrics used in this paper are based on the error calculated as a subtract between the setpoint (STPT) and the process variable (PV). Data were collected with 1 s resolution. Metrics were calculated for one hour (for 3600 samples) and for one day (for 86,400). The following metrics were used in this study:  
* Mean square error (MSE): \[\text{MSE}=\frac{1}{N}\sum_{k=1}^{N}{[x_{i}]^{2}}\] (1)
* Mean absolute error (MAE): \[\text{MAE}=\frac{1}{N}\sum_{k=1}^{N}{[x_{i}]}.\] (2)* Standard moments of normal distribution: \[\text{mean}(x)=\bar{x}=\frac{1}{N}\sum_{i=1}^{N}x_{i},\] (3) \[\text{StdDev}(x)=\sigma_{\text{G}}=\sqrt{\frac{\sum_{i=1}^{N}(x_{i}- \bar{x})^{2}}{N-1}},\] (4) \[\text{Skewness}(x)=\frac{1}{N\sigma^{3}}\sum_{i=1}^{N}(x_{i}-\bar{x})^ {3},\] (5) \[\text{Kurtosis}(x)=\frac{1}{N\sigma^{4}}\sum_{i=1}^{N}\Bigl{\{}(x_{i}-\bar{ x})^{4}-3\Bigr{\}}.\] (6)
* Robust median estimator: \[\text{median}(x)=\bar{x}_{med}=\begin{cases}\frac{x_{\frac{N}{2}:N+x_{\frac{ N}{2}+1:N}}}{2}&\text{if $N$ is even},\\ x_{\frac{N+1}{2}:N}&\text{if $N$ is odd},\end{cases}\] (7)
* Factors of the \(\alpha\)-stable family of distribution functions: \[F_{\alpha,\beta,\delta,\gamma}^{\text{stab}}(x)=\exp\bigl{\{}i\delta x-|\gamma x |^{\alpha}(1-i\beta l(x))\bigr{\}},\] (8) with \[l(x)=\begin{cases}\text{sgn}(x)\tan\bigl{(}\frac{\gamma\alpha}{2}\bigr{)}& \text{for $\alpha\
eq 1$}\\ \text{sgn}(x)\frac{2}{\pi}\ln|x|&\text{for $\alpha=1$}\end{cases}\] (9) \(0<\alpha\leq 2\) is called the stability index, \(|\beta|\leq 1\) a skewness factor, \(\delta\in\mathbb{R}\) a shift coefficient and \(\gamma>0\) a distribution scale. Metric interpretation is presented with the results in Section 5.
## 3 Results and Discussions  
### Economic Analysis: Cost and Benefit  
In order to have a comprehensive overview from different literature reviews, the adoption of a clearly defined measuring system that includes item costs derived from quite different backgrounds is proposed. The unit of measurement to compare the different scenarios is \(\boldsymbol{\xi}\)/tonne of OFMSW that can be treated. Table 1 shows the range of values for each scenario for costs and benefits obtained from the experiences checked in different countries, years, and volume treated, including extreme values when costs and benefits are likely to oscillate. According to the data reviewed, the initial costs of a plant for OFMSW composting (Scenario 1) range from 18.49 to 78.15 \(\boldsymbol{\xi}\)/tonne of waste treated. The main initial costs are land acquisition, civil work construction, fixed and mobile equipment (just for the centralized plant), planning, design, and engineering costs. [PERSON] and [PERSON] studied two different situations for composting organic fraction with a range from 18.49 to 43.7 \(\boldsymbol{\xi}\)/tonne of waste. The first situation, consisted of 30 small-scale labor-intensive plants that used open window composting (plant capacity 6.5 tonnes/day of incoming waste, to a total of 71,175 tonnes of waste/year) and open dumping of the other fractions; the second situation consisted of a centralized plant that also used open window composting and open dumping of the other fractions for about 200 tonnes/day (equivalent to 73,000 tonnes of waste/year) [7]. An analysis of five composting plants using different composting technologies carried out by [PERSON] et al. [8] showed that the initial cost could reach 78.15 \(\boldsymbol{\xi}\)/tonne of waste treated. The different literature studies investigated showed operation and maintenance costs ranging from 13.33 to 25.93 \(\boldsymbol{\xi}\)/tonne of waste per year. [PERSON] and [PERSON], with the same conditions outlined for initial costs, showed that the operation and maintenance costs could range from 13.33 to 25.85 \(\boldsymbol{\xi}\)/tonne of waste per year [7]. In an Italian case study, [PERSON] et al. showed average operation and maintenance costs of 19.84 \(\boldsymbol{\xi}\)/tonne of waste per year for a composting designed plant [8]. A study performed by [PERSON] and [PERSON] showed operation and maintenance costs of 25.93 \(\boldsymbol{\xi}\)/tonne of waste per year [24]. Although both capital and operating costs decreased substantially as the plant scale increased, until leveling off, these could be considered to be the extreme value for the initial analysis. The annual revenue from compost's sales can be highly variable, according to the review done, fluctuating from 5.60 to 29.91 \(\boldsymbol{\xi}\)/tonne. [PERSON] and [PERSON] reported about 28.90 \(\boldsymbol{\xi}\)/tonne of waste per year [7]. However, this revenue can be quite different. For example, [PERSON] et al. showed an income from compost of 5.6 \(\boldsymbol{\xi}\)/tonne of waste treated per year [32].
## 3 Results
### Validation of the LAI Products Temporal Dynamics  
The metaCycle analysis showed that a statistically significant period of 12 months (\(p<0.05\)) is present in the monthly field LAI time series at BNF and in all monthly satellite LAI time series at all sites, except for MODIS 500 m LAI, which showed no significant 12-month periodic characteristics at BNF. Overall, LAI retrieved from GEOV3 300 m showed very close values of the base, phase, amplitude and relative amplitude with the field LAI at BNF (Figure 5). LAI retrieved from GEOV2 1 km showed a lower base and amplitude, similar phase, but larger relative amplitude compared with GEOV3 300 m LAI for most sites and the field at BNF (Figure 5). LAI retrieved from MODIS 500 m product showed the lower base values, but had a high variation of phases with one underestimated phase for the forest at ALF compared with the other two LAI products (Figure 5b). The relative amplitude of LAI retrieved from MODIS 500 m is lower for the forest at GGF and SNF, but higher at HSF compared with the other two LAI products (Figure 5c).  
Figure 4: Distribution of the ratio of EO uncertainty estimates to bias, based on a comparison against in situ LAI measurements. Dashed lines represent the median values and the color represents different EO products. The vertical black line is the reference line where the ratio is equal to 1. A ratio larger than 1 indicates the uncertainty estimate is larger than the bias and is therefore considered robust.
###### Abstract  
Space-air-ground integrated network (SAGIN) is considered an enabler for sixth-generation (6G) networks. By integrating terrestrial and non-terrestrial (satellite, aerial) networks, SAGIN seems to be a quite promising solution to provide reliable connectivity everywhere and all the time. Its availability can be further enhanced if hybrid free space optical (FSO)/radio frequency (RF) links are adopted. In this paper, the performance of a hybrid FSO/RF communication system operating in SAGIN has been analytically evaluated. In the considered system, a high-altitude platform station (HAPS) is used to forward the satellite signal to the ground station. Moreover, the FSO channel model assumed takes into account the turbulence, pointing errors, and path losses, while for the RF links, a relatively new composite fading model has been considered. In this context, a new link selection scheme has been proposed that is designed to reduced the signaling overhead required for the switching operations between the RF and FSO links. The analytical framework that has been developed is based on the Markov chain theory. Capitalizing on this framework, the performance of the system has been investigated using the criteria of outage probability and the average number of link estimations. The numerical results presented reveal that the new selection scheme offers a good compromise between performance and complexity.  
os 2024 13 806 10.3390/electronics13040806
# Fast Face Tracking-by-Detection Algorithm for Secure Monitoring
## 3 Proposed Algorithm
### Tracking  
FFTD uses the KCF algorithm for tracking. Figure 2 shows the tracking module flow and update strategy in a block diagram. If the object is removed from the camera view, the KCF tracker usually fails and never recovers. To overcome the challenge, the detector is introduced. The detection result is used to correct the tracking result of the previous frame, and then the corrected result is used to update the filter model with a certain learning rate in current frame.  
In the first frame, to get the training samples, KCF uses the window (2.5 times larger than the given object) to cycle shift, and saves the samples into the filter model after extracting. Then, the training samples are labeled with Gaussian distribution. The regression coefficient of each sample is generated and stored through ridge regression.  
In later frames, the windows (2.5 times larger than the best result) are cyclic-shifted to obtain the samples. We calculate the similarity between the samples and the target in the filter model. The current result is the largest response sample. Then, the filter model is updated by the result.  
Next, the ridge regression method and update strategy are described in detail.  
Figure 1: The fast face tracking-by-detection (FFTD) framework. A tracker based on a correlation filter finds the best position of the target in each frame. The face detector, which can correct and restart the tracker, detects and locates the faces in images. The discrimination determines when to perform the detector and update the tracking model.  
Figure 2: Tracking module flowchart.
## 3 Experimental Design and Results
### Ablation Experiment
The metrics include precision, recall, and mAP at 50% and 50-90% IoU thresholds. Arrows indicate the change in performance relative to the baseline model. The EMA model shows improvements in precision, recall, and mAP metrics, while the MPDIoU model shows a slight decrease in precision but improvements in recall and mAP. Figure 15: Improved comparison charts. (**a**) Precision graph. This graph shows the precision values over 500 epochs for the baseline model, the model with EMA, and the model with EMA and MPDIoU. (**b**) Recall graph. This graph depicts the recall values over 500 epochs for the three models. (**c**) mAP50 graph. This graph illustrates the mAP at 50% IoU over 500 epochs. (**d**) mAP50-90 graph. This graph shows the mAP across IoU thresholds from 50% to 90% over 500 epochs.
# Advancing Image Spam Detection: Evaluating Machine Learning Models Through Comparative Analysis
## 4 Results
### ROC Curve  
The ROC curve is a graphical representation used to evaluate the performance of a binary classifier. It plots the true positive rate (TPR) against the false positive rate (FPR) at various classification thresholds. A curve that bows towards the upper-left corner indicates stronger model performance, as it reflects a higher rate of correctly identified positives and a lower rate of false alarms.  
As illustrated in Figure 11, models such as ResNet50 and XGBoost produce ROC curves that closely approach the top-left corner, indicating high sensitivity and specificity to highlight excellent classification performance. These curves exhibit a steep rise, reflecting a strong ability to identify spam images while minimizing false alarms. Intermediate models such as VGG16, Logistic Regression, LightGBM, and SVM demonstrate moderate ROC curves, performing better than random forest but with varying degrees of trade-off between true positive and false positive rates. The visual distinction between these curves highlights how effectively each model processes visual features from the image dataset and underscores the impact of model architecture on classification capability.
# Triangular Sierpinski Microwave Band-Stop Resonators for K-Band Filtering
###### Abstract  
Triangular resonators re-shaped with Sierpinski geometry were designed, manufactured, and tested for potential applications in the K-Band. Prototypes of band-stop filters working around 20 GHz and 26 GHz, interesting for RADAR and satellite communications, were studied in a coplanar waveguide (CPW) configuration. Single and coupled structures were analyzed to give evidence for: (i) the tuning of the resonance frequency by increasing the internal complexity of the triangle and (ii) resonance enhancement when coupled structures are considered. The exploited devices were part of the more extended family of metamaterial-inspired structures, and they were studied for their heuristic approach to the prediction of the spectrum using experimental results supported by electromagnetic simulations. As a result, a Sierpinski resonator, not only fed into but also fully embedded into a CPW environment, had a frequency response that was not easily determined by classical theoretical approaches.  
Sierpinski triangle; microwave resonators; frequency tunability; metamaterials +
Footnote †: journal: Physics Letters B  
article
# Damage Detection Using Modal Rotational Mode Shapes Obtained with a Uniform Rate CSLDV Measurement
## 1 Introduction
### Modal Rotational Damage Indicators  
The modal rotational mode shape of the measured plate can be estimated using the normal translational mode shape. The modal rotation of point \((x,y)\) can be expressed by the central finite-difference method as below:  
\[\begin{split}\theta_{x}(x,y)&=\frac{V_{R}(x,y+\delta y )-V_{R}(x,y-\delta y)}{2\delta y}\\ \theta_{y}(x,y)&=-\frac{V_{R}(x+\delta x,y)-V_{R}(x -\delta x,y)}{2\delta x}\end{split} \tag{18}\]  
where \(\delta x,\delta y\) are the distances between two neighboring measurement points along the x and y directions, respectively. When the point locates near the boundary of the scan area or the edge of plate, the forward or backward finite-difference formulation can be used to estimate corresponding modal rotation and expressed as  
\[\begin{split}\theta_{x}(x,y)&=\frac{3V_{R}(x,y- \delta y)-4V_{R}(x,y)+V_{R}(x,y+\delta y)}{2\delta y}\\ \theta_{y}(x,y)&=-\frac{3V_{R}(x-\delta x,y)-4V_{R}( x,y)+V_{R}(x+\delta x,y)}{2\delta x}\end{split} \tag{19}\]  
The modal rotational mode shape of the undamaged state also can be derived using the above formula. Then, the modal rotational damage indicator (RDI) is defined as the changes of modal rotation in both directions, that is  
\[RDI_{x}\ =\ \big{|}\theta_{x}^{U}-\theta_{x}^{D}\big{|},\quad RDI_{y}\ =\ \big{|}\theta_{y}^{U}-\theta_{y}^{D}\big{|} \tag{20}\]  
The superscripts \(U,D\) represent the undamaged and the damaged states, respectively.
# Influence of Hyperparameters in Deep Learning Models for Coffee Rust Detection
###### Abstract  
Most of the world's crops can be attacked by various diseases or pests, affecting their quality and productivity. In recent years, transfer learning with deep learning (DL) models has been used to detect diseases in maize, tomato, rice, and other crops. In the specific case of coffee, some recent works have used fixed hyperparameters to fine-tune the pre-trained models with the new dataset and/or applied data augmentation, such as image patching, to improve classifier performance. However, a detailed evaluation of the impact of architecture (e.g., backbone) and training (e.g., optimizer and learning rate) hyperparameters on the performance of coffee rust classification models has not been performed. Therefore, this paper presents a comprehensive study of the impact of five types of hyperparameters on the performance of coffee rust classification models. Specifically, eight pre-trained models are compared, each with four different amounts of transferred layers and three different numbers of neurons in the fully-connected (FC) layer, and the models are fine-tuned with three types of optimizers, each with three learning rate values. Comparing more than 800 models in terms of F1-score and accuracy, it is identified that the type of backbone is the hyperparameter with the greatest impact (with differences between models of up to 70%), followed by the optimizer (with differences of up to 20%). At the end of the study, specific recommendations are made on the values of the most suitable hyperparameters for the identification of this type of disease in coffee crops.  
deep learning; CNN; coffee rust; transfer learning; DenseNet; Xception; inception Article