esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
To address the issues of low efficiency and high omission rates in monitoring workers' compliance with safety dress codes in intelligent factories, this paper proposes the SFA-YOLO network, an enhanced real-time detection model based on a Selective Feature Attention mechanism. This model enables inspection robots to automatically and accurately identify whether the workers' attire meets the safety standards. First, this paper constructs a comprehensive dataset of safety attire, including images captured under various scenarios, personnel numbers, and operational conditions. All images are manually annotated to enhance the model's generalization capability. The dataset contains 3966 images, covering four classes: vast, no-vest, helmet, and no-helmet. Second, the proposed model integrates the SFA mechanism to improve the YOLO architecture. This mechanism combines multi-scale feature fusion with a gated feature extraction module to improve detection accuracy, strengthening the model's ability to detect occluded targets, partial images, and small objects. Additionally, a lightweight network structure is adopted to meet the inference speed requirements of real-time monitoring. The experimental results demonstrate that the SFA-YOLO model achieves a detection precision of 89.3% and a frame rate of 149 FPS in the safety attire detection task, effectively balancing precision and real-time performance. Compared to YOLOv5n, the proposed model achieves a 5.2% improvement in precision, an 11.5% increase in recall, a 13.1% gain in [EMAIL_ADDRESS], and a 12.5% improvement in [EMAIL_ADDRESS]:0.95. Furthermore, the generalization experiment confirms the model's robustness in various task environments. Compared with conventional YOLO models, the proposed method performs more stably in safety attire detection, offering a reliable technical foundation for safety management in intelligent factories.  
target detection; deep learning; Selective Feature Attention +
Footnote †: journal: Applied sciences  
Article
# UAV-Borne Profiling Radar for Forest Research
## 2 FGI-Tomoradar Development
Tomoradar is also equipped with a high-accuracy georeferencing system, which consists of a high-quality global navigation satellite system (GNSS) receiver and a tactical-grade inertial measurement unit (IMU). The georeferencing system measures the helicopter flight trajectory with centimetre-level accuracy, which avoids the problems that HUTSCAT faced. Tomoradar was designed for forest inventory. Thus, the three main requirements for FGI-Tomoradar were as follows: (1) accurate measurement of the backscattering coefficient with good range resolution; (2) multiple polarization capability for comparing measurements from different polarizations; and (3) simultaneously operable multiple polarization, light weight and low power consumption for UAV-based forest inventory tasks.
## 2 Characteristics of Surface Potential Distribution for DC Grounding Electrode
### Introduction of OSDM Model  
#### 3.1.1 Sample Experimental Design  
According to the number of each feeder rod in Figure 2, the distribution current of the DC grounding electrode is defined. Let the distribution current of the kth feeder rod be \(I_{k}\) (\(1\leq k\leq Q\)). \(Q\) represents the total number of all feed rods in the grounding electrode, so the distribution current matrix of all feed rods is:  
\[I=[I_{1},I_{2},I_{3},\ldots,I_{k},\ldots,I_{Q}] \tag{12}\]  
Figure 13: OSDM structure flow chart.  
When the connection between the DC grounding electrode distribution cable and the feeder rod breaks, the distribution current of the broken feeder rod is zero, which further affects the surface potential characteristics of the broken area. The current matrix \(I\) is used as the input of the grounding electrode potential calculation. According to the COMSOL Multiphysics 6.1 finite element simulation, the surface potential distribution of the corresponding DC grounding electrode under the current matrix is calculated. Combined with the schematic diagram of measuring point arrangement shown in Figure 12, the number of measuring points arranged on the surface of the grounding electrode is represented by \(P\), and the surface potential matrix is obtained as follows:  
\[U=[U_{1},U_{2},U_{3},\ldots,U_{P}]^{T} \tag{13}\]  
According to the grounding electrode model shown in Figure 7, the total number of feed rods \(Q\) = 434, and the number of measurement points \(P\) = 98. Five fracture types were selected for the experimental design. Specifically, 217 working conditions are calculated under two fractures, 185 working conditions are calculated under three fractures, 168 working conditions are calculated under four fractures, 174 working conditions are calculated under five fractures, and 146 working conditions are calculated under six fractures. The potential matrix \(U\) of 98 measurement points on the surface of the grounding electrode under each fracture condition is obtained by simulation. Some experimental samples and results are shown in the following table:  
In Table 3, Sample 1 displays the results under normal operating conditions, where the input corresponds to the allocated currents for \(Q\) feeder rods, and the output represents the potential values at \(P\) measurement points. Samples 2 to 217 present test results under the condition of two broken feeder rods. Taking Sample 2 as an example, with feeder rods numbered 1 and 2 being broken, the input currents \(I_{1}\) and \(I_{2}\) are set to 0. The remaining \(Q-2\) feeder rods equally share the total injection current, while the output maintains potential values at \(P\) measurement points. Comparative analysis with Sample 1 reveals that measurement points closer to breakage locations exhibit greater potential variations, whereas those farther from breakage sites demonstrate smaller potential deviations. Similarly, the following samples are the test results under different fracture positions.
## 3 Experimental Results and Discussion
Three models are considered: Equivalent circuit, ANNs, and GRUs. the considered bias condition. For any new temperature, the equivalent-circuit elements are to be extracted from new measurements of the \(S\)-parameters at the desired temperature. Therefore, the number of parameters to be optimized and the need for new measurements, and the corresponding effort for model extraction increase by increasing the number of temperatures we want to develop the model for. In contrast, the behavioral models (i.e., ANN-based and GRU-based models) have temperature dependence included. This means that once the models are developed, to obtain the \(S\)-parameters for any temperature, one should simply find the model response without the need for the measured \(S\)-parameters. Therefore, the optimization of the behavioral model parameters is performed only in the model development phase and at once for the whole temperature range with a limited number of measurements. However, in the case of the equivalent-circuit model, the extraction is to be repeated for every temperature of interest, requiring the corresponding \(S\)-parameter measurements. Finally, it is worth noting that, although better usefulness comes at the cost of a lower prediction capability, the equivalent-circuit model can be seen as physically meaningful feedback for improving transistor fabrication and as the basic brick before moving on to the nonlinear and noise modeling, which is a crucial task for the design of HPAs and LNAs. To illustrate the physical meaningfulness of the obtained model parameters, Table 4 reports the impact of \(T_{a}\) on the transconductance (\(g_{m}\)), the total gate capacitance (\(C_{gg}\)), and the intrinsic unity current-gain cut-off frequency (\(f_{T}\)), which is determined as the ratio between \(g_{m}\) and \(2\pi C_{gg}\). As can be observed, when heating the DUT, the degradation of the electron transport properties causes a substantial reduction in \(g_{m}\) and, in turn, of \(f_{T}\), which are crucial figures of merit for the assessment of the transistor performance. The obtained degradation of \(g_{m}\) with increasing \(T_{a}\) can be foreseen in the experiments by the observed reduction in the magnitude of the low-frequency forward transmission coefficient (\(S_{21}\)) when heating the DUT (i.e., the magnitude of the measured \(S_{21}\) at 200 MHz decreases from 22.57 to 13.84 by increasing \(T_{a}\) from 35 \({}^{\circ}\)C to 200 \({}^{\circ}\)C).
## 1 Introduction
### Detection and Prediction of Episodes of Aggression in Patients with Dementia  
Aggression and agitation behaviors in dementia are difficult to treat in a timely manner for caregivers and mental health facilities. In a systematic umbrella review, monitoring technologies of activities and behavioral symptoms in elderly neurodegenerative disease patients were investigated. Their application, as promising as it is, is met with a variety of challenges due to methodological diversity and the need for systematic measures in developing ethical AI in medicine [14]. Wearable sensor use is investigated to develop an individualized machine learning model for Behavioral and Psychological Symptoms of Dementia (BPSD). The study focused on the development of digital biomarkers, further innovating the approach in digital phenotyping of BPSD [15]. Another work introduced a public dataset based on non-intrusive sensors, i.e., wearables or cameras, to monitor agitation-related physical activity in dementia patients. Their performance showed high accuracy under controlled conditions, but under leave-one-out conditions, a decrease was observed, proving the complexity in the invariance space [16]. Another study presented an efficient computation scheme for real-time detection of aggressive behavior with the use of a wrist accelerometer. The wireless actigraphy system captures behavioral symptom information. Their utility is limited, however, due to limited computational capability [17]. Subsequently, a machine learning-driven classification model from the MediaPipe holistic model has been suggested that can recognize and classify aggressive episodes in real-time. The proof-of-concept study presents an innovative solution with the potential to predict aggressive behavior in patients with dementia before their onset [18].
## 3 System Model and Problem Formulation
### SFC Requests  
The set of IoT-SRs is denoted by \(R\). The \(ith\) IoT-SR is denoted by IoT-SR\({}_{i}\), corresponding to a 7-tuple \((S_{i},D_{i},F_{i},R_{i,p}^{cpu},R_{i}^{bw},R_{i}^{delay},R_{i}^{loss})\). In the 7-tuple, \(S_{i}\) and \(D_{i}\) represent the source node and the destination node, respectively. \(F_{i}=\left\{F_{i}^{1},F_{i}^{2},\ldots,F_{i}^{l}\right\}\) is the set of VNFs requested by IoT-SR \(i\in\mathcal{R}\) in which \(\left\{F_{i}^{1},F_{i}^{2},\ldots,F_{i}^{l}\right\}\) represent the \(\{1 st,2 nd,\ldots,th\}\) VNF requirement, and \(F_{i}\), \(l=|F_{i}|\) is the length of IoT-SR\({}_{i}\), i.e., the total number of VNF instances of IoT-SR\({}_{i}\). \(R_{i,p}^{cpu}\) represent the CPU consumption of VNF \(p\), \(R_{i}^{bw}\) represents the bandwidth consumption of IoT-SR \(i\in\mathcal{R}\), and \(R_{i}^{delay}\) and \(R_{i}^{loss}\) represent the maximum tolerated delay of IoT-SR\({}_{i}\) and the maximum tolerated packet loss rate of IoT-SR\({}_{i}\), respectively. We use a binary variable (\(x_{pc}\)) to indicate whether VNF type \(p\in\mathcal{P}\) is deployed on node \(c\in\mathcal{C}\). Specifically, if there is a VNF instance on the cloudlet node (\(c\in\mathcal{C}\)), \(x_{pc}\) equals to 1 and 0 otherwise. Note that we consider the packet loss rate as 0 when a packet traverses across a VNF, since the performance of software and hardware is increasing.  
Figure 1: A schematic diagram of IoT-MEC networks.
###### Abstract  
The homogeneous turbid medium assumption inherent to the Beer-Lambert's law can lead to a reduction in the shading effect between leaves when non-green vegetation canopies are present, resulting in an overestimation of the fraction of absorbed photosynthetically active radiation (FAPAR). This paper proposed a method to improve the FAPAR estimation (FAPAR\({}_{\text{FVC}}\)) based on Beer-Lambert's law by incorporating fractional vegetation coverage (FVC). Initially, the canopy-scale leaf area index (LAI) of the green canopy distribution area within the pixel (sample site) was determined based on the FVC. Subsequently, the canopy-scale FAPAR was calculated within the green canopy distribution area, adhering to the assumption of a homogeneous turbid medium in the Beer-Lambert's law. Finally, the average FAPAR across the pixel (sample site) was calculated based on the FVC. This paper conducted a case study using measured data from the BigFoot Project and grass savanna in Senegal, West Africa, as well as Moderate Resolution Imaging Spectroradiometer (MODIS) LAI/FPAR products. The results indicated that the FAPAR\({}_{\text{FVC}}\) approach demonstrated superior accuracy compared to the FAPAR determined by MODIS LAI, according to the Beer-Lambert's law (FAPAR\({}_{\text{LAI}}\)) and MODIS FPAR products (FAPAR\({}_{\text{MOD}}\)). The mean absolute percentage error of FAPAR\({}_{\text{FVC}}\) was 48.2%, which is 25.6% and 52.1% lower than that of FAPAR\({}_{\text{LAI}}\) and FAPAR\({}_{\text{MOD}}\), respectively. The mean percentage error of FAPAR\({}_{\text{FVC}}\) was 16.8%, which was 71.6% and 73.4% lower than that of FAPAR\({}_{\text{LAI}}\) and FAPAR\({}_{\text{MOD}}\), respectively. The improvements in accuracy and the decrease in overestimation for FAPAR\({}_{\text{FVC}}\) became more pronounced with increasing FVC compared to FAPAR\({}_{\text{LAI}}\). The findings suggested that the FAPAR\({}_{\text{FVC}}\) method enhanced the accuracy of FAPAR estimation under the presence of non-green vegetation canopies. The method can be extended to regional scale FAPAR and gross primary production (GPP) estimations, thereby providing more accurate inputs for understanding its tempo-spatial patterns and drivers.  
Keywords:light use efficiency model; Beer-Lambert's law; homogeneous turbid medium assumption; non-green vegetation canopy; gross primary production estimation +
Footnote †: journal: Remote Sensing
## 3 Experiments and Results  
### Experimental Setup  
Two different servers were used to conduct the evolution tests (Table 1).  
We restrict the search space of valid architectures for our experiments as follows:  
* Maximum number of fully connected layers was set to 1 in order to reduce the memory and computational cost of training the network during the evolutive design stage. The maximum number of neurons on those layers was kept small for the same reason (8, 16, 32).
* Only convolutions of dimensions (1, 2, 3) were allowed, according to findings in [45] regarding factorized convolutions. The number of features is a set of typical used values in the range [2,256].
* We allowed the use of the two most frequently used types of pooling (max-pooling and average-pooling).
* Regarding the stride of convolutions and poolings, we used 1 to keep spatial dimensions and 2 to allow their reduction at certain parts of the architecture (those places are specified inside the meta-grammar restrictions).
* For the dropout rates, valid values for the drop-rate parameter were set inside the interval [0, 0.5] with increments of 0.1. Here, 0.0 is interpreted as an inactive dropout operator. The higher value 0.5 was set empirically to avoid TensorFlow-Keras warnings obtained for values above 0.5.
* We allowed the use of no normalization operation or Batch Normalization (BN) to let the design process choose where to place the BN operations.
* On all cases during design, the network was trained for its evaluation only for a maximum of 20 epochs (early stop if during 5 there was no improvement on the training metrics).  
For the models labeled with the prefix \"v2\" (version 2), we allowed the following settings (due to higher computational capabilities of the server used for the experiments):  
* Maximum number of FC layers was 3 instead of 1.
* Bigger dimensions for FC layers (up to 256).
* Separable 2D convolutions (\"sepconv\") were used in addition to regular convolutions.
* BN momentum was empirically set to 0.5 (due to average size of training batches caused by GPU number and available memory).  
On all of the experiments, the dataset was partitioned by experiment into train/validation/test subsets with a ratio of 70:20:10. This data partitioning was due to internal intermediate reports being filled with test metrics from that subset. Despite k-fold being used over the whole dataset, for final evaluation at the design step, we used only 90% of the dataset; we report this statistic for full  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Server** & **GPUs** & **CPU** & **RAM** \\ \hline IGN1 & 4\(\times\) Tesla V100 16 GB & 2\(\times\) Intel(R) Xeon(R) Gold [EMAIL_ADDRESS] GHz & 128 GB \\ \hline Cartobot & 2\(\times\) RTX 2080 Ti 11 GB & i7-8700 3.2 GHz & 64 GB \\ \hline \hline \end{tabular}
\end{table}
Table 1: Test servers: hardware specifications.  
disclosure. Tournament selection used size \(k=4\) (_selectionPressure_ took values 0.7 or 1 depending on the experiment).
# An Improved Kernelized Correlation Filter Algorithm for Underwater Target Tracking
## 4 Experimental Results and Analysis
Figure 6.8 Location error precision curves (354 th frame to 434 th frame). Figure 6.9 Center position error curves (354 th frame to 434 th frame). The traditional KCF uses a fixed-size base sample for training when tracking the target. In this way, if the target size is reduced, the background information contained in the base sample will increase, and the introduction of a large amount of background information eventually leads to poor training of the filter. If the target is enlarged, the background information contained in the base sample is correspondingly reduced, resulting in too many positive samples and too few negative samples, which is ultimately not conducive to filter training. The poor training effect of the filter means that it is likely to fail when the filter is used to detect the target in the detection process, and the failure of the detection will affect the subsequent detection and tracking process. Once the error is accumulated, it will eventually lead to tracking failure. For the base sample with a dynamically continuous scale proposed in this paper, when the target scale changes, the base sample also dynamically changes accordingly, which can ensure that the background information contained in the base sample is just right, neither too much nor too little, which in turn can guarantee the training effect of the filter. The traditional KCF adopts a frame-by-frame linear updating strategy in the update of the filter model. However, the model is updated frame by frame, making the updates too frequent, which leads to a large amount of calculation. When the target disappears and appears again, the linear updating strategy will introduce erroneous information to the filter model, due to the accumulation of tracking error, and the error will be continuously amplified, which will eventually lead to the drift of the model and cause tracking failure. The proposed adaptive filter update strategy based on PSR can reduce the amount of computation caused by frame-by-frame linear updating to a certain extent, and the proposed method can automatically avoid updating the model under the adverse circumstances when the tracking deviation occurs, so as to prevent the model from drifting due to the introduction of the erroneous information. Through the above verification and comparative experimental analysis, it is clear that the proposed IKCF has a better tracking accuracy in forward-looking sonar image sequences, and it still has effectiveness and adaptability, to some extent, with targets with varying scales and targets that disappear and appears again. Figure 7.0 Overlap precision curves (354 th frame to 434 th frame).
## 7 Comparison of Projection Density Estimate and Histogram
The error of the histogram \(\tilde{g}^{(n)*}\) relative to the density \(g\) includes deterministic and stochastic components, and it is estimated from below by the value \(\varepsilon_{g}^{(L)*}\):  
\[u(x)\approx u^{\langle L\rangle*}(x)=\sum_{i=0}^{L-1}U_{i}^{*}\hat{\Pi}_{i}(x),\]1  
The results of calculations using Formula (44) for both densities from Examples 1 and 2 are given in Tables 5 and 6, respectively. They should be compared with the rows \"\(*\)\" in Tables 1 and 3. Such a comparison shows the undoubted advantage of projection density estimates using Legendre polynomials. The approximation accuracy when using block pulse functions for \(L=64\) corresponds to the approximation accuracy when using Legendre polynomials for \(n=8\) in Example 1 and for \(n=4\) in Example 2. If the number of block pulse functions \(L\) is doubled, then the deterministic component of error decreases by approximately 2 times, and this conclusion does not depend on the degree of smoothness of the estimated density. The problem of the conditional optimization of the algorithm for obtaining the histogram was solved in [1]. In this problem, optimal parameters satisfy the relationship \(N_{\text{opt}}\asymp L_{\text{opt}}^{3}\) which corresponds to inequality (42). A generalization of this result in the context of stochastic differential equations can be found in [3]. Choosing parameters in this way, the computational error \(\bar{\epsilon}_{g}^{(L)*}\) will be approximately twice as large as its deterministic component \(\bar{\epsilon}_{g}^{(L)*}\), since the conditional optimization of the algorithm for obtaining the histogram assumes the equalization of deterministic and stochastic components. For densities from Examples 1 and 2, to reduce the approximation error using the histogram by 2 times, it is necessary to increase the number of block pulse functions \(L\) by 2 times and the sample size \(N\) by \(2^{3}=8\) times. The projection estimates of densities using Legendre polynomials are more effective in these examples. To reduce the approximation error by 2 times in Example 1, it suffices to increase the projection expansion length \(n\) by \(2^{2/3}\approx 1.587\) times and the sample size \(N\) by \((2^{2/3})^{4}=2^{8/3}\approx 6.350\) times. In Example 2, it suffices to increase the projection expansion length \(n\) by \(2^{2/5}\approx 1.320\) times and the sample size \(N\) by \((2^{2/5})^{6}=2^{12/5}\approx 5.278\) times. In this case, increasing \(n\) and \(N\) implies their subsequent rounding-up.
## 2 System and Channel Model
### Siso Mrr Tow Links  
Assuming OOK with intensity modulation and direct detection (IM/DD), the instantaneous BER (bit error rate) for each examined TOW backward link is given as [20]:  
\[P_{e}(h_{m})=\frac{1}{2}erfc\Big{(}\eta_{m}h_{m}/2\sqrt{N_{0}}\Big{)}, \tag{14}\]where _erfc_(.) denotes the complementary error function ([31], Equation (8.250.4)). By averaging Equation (14) over \(h_{m}\), the ABER, \(P_{e,av}\), is expressed as [22]:  
\[P_{e,av}=\int_{0}^{\infty}P_{e}(h_{m})f_{h_{m}}(h_{m})dh_{m}. \tag{15}\]  
By substituting Equations (11) and (14) into Equation (15) and by then using \(\int_{0}^{\infty}\epsilon^{z-1}erfc(t)dt=\frac{1}{\sqrt{\pi z}}\Gamma\Big{(} \frac{z+1}{2}\Big{)}\), \(Re(z)>0\) ([34], Equation (06.27.21.0132.01)), the corresponding ABER is obtained as:  
\[P_{e,av}=\Big{(}2\sqrt{\pi}A_{0,m}^{\xi_{m}^{2}}\xi_{m}^{2}\Big{)}^{-1}h_{l,m} ^{-2\xi_{m}^{2}}\Big{(}2\sqrt{N_{0}}/\eta_{m}\Big{)}^{\xi_{m}^{2}}\Gamma\Big{[} \Big{(}\xi_{m}^{2}+1\Big{)}/2\Big{]}, \tag{16}\]  
where \(\Gamma(.)\) stands for the gamma function ([34], Equation (06.05.02.0001.01)).
## 4 Experimental Results and Analysis  
### Dataset Characteristics and Setup  
This study evaluates the proposed approach using four representative WiFi sensing datasets, each targeting distinct recognition tasks:  
* **NTU-Fi**: Contains CSI data for six basic human activities (walking, running, sitting, standing, lying, and waving).
* **NTU-Fi-HumanID**: Comprises CSI data from 14 different individuals' walking patterns.
* **UT-HAR**: Includes seven daily activities, including fall detection.
* **Widar**: Features 22 fine-grained hand gestures.