esa-sceva/satcom-chunk-collection huggingface dataset:
# Packaging Design Image Segmentation Based on Improved Full Convolutional Networks
## 4 Results
### Performance Validation and Analysis of ISS Model  
This study first conducted experiments on a superpixel-assisted ISS model with FCN. Table 2 illustrates the environmental configuration and parameter settings for the experiment.  
There is a relatively limited dataset of packaging design in ISS and computer vision. Therefore, this study selected a public dataset for ISS, namely the CamVid dataset, as  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Experimental Configuration** & **Detailed Parameters** & **Configuration Item** & **Detailed Parameters** \\ \hline Operating system & 64 bit-Ubuntu 20.04 & Initial learning rate & 0.0001 \\  & 10 GB GeForce RTX 2080 Ti & Learning rate adjustment & Halve the learning rate after every 10 epochs of iteration \\ Graphics card (GPU) & (Nvidia, Santa Clara, CA, USA) & Memory  
an alternative dataset for preliminary performance testing. This dataset contains driving scene images collected by the University of Cambridge in the U.K. The original FCN, deep semantic segmentation network, and contour aware edge network were selected as comparison algorithms and named M1-M3, respectively. The research proposed a superpixel-assisted ISS model based on FCN, which was named M0. The accuracy and false-positive rate of each method were compared. The results are shown in Figure 8.  
In Figure 8a, M0 consistently outperformed the other three algorithms in terms of accuracy. Among the average results obtained from 10 experiments, M0 achieved an average accuracy of 96.84%, while M2 had the highest average accuracy among the comparison methods: only 90.42%. In Figure 8b, the average false-alarm rate of M0 was only 2.78%, while the average false-alarm rates of comparison methods M1-M3 were 8.14%, 5.94%, and 5.62%, respectively. From this, the ISS model proposed had higher accuracy and a lower false-alarm rate. This shows that the superpixel technology played a key role in small target recognition and fine segmentation, effectively avoiding the problem of small target loss caused by over-pooling in traditional methods. With the help of superpixel technology, the model could better retain details, reduce false positives, and improve the overall performance.
# Steady State Response of Linear Time Invariant Systems Modeledby Multibond Graphs
## Appendix C Proof of Theorem  
Firstly, given an LTI system modeled by a multibond graph in an integral causality assignment (MBGI), this MBGI contains multiport storage elements that represent the state variables. Furthermore, these elements can have an integral causality assignment determining linearly independent state variables \((\underline{x})\) and the linearly dependent state variables \(\left(\underline{x}_{d}\right)\), and a derivative causality is assigned. In order to prove that the state matrix \(A\) associated with(20) and is invertible, some given properties are necessary. Property 1 [37]. The order \(n\) of a model is equal to the number of \(I\) and \(C\) elements in integral causality when a preferred integral causality is assigned to the bond graph model. Property 2 [38]. (a) The bond graph rank \(q\) of the state space \(A\) matrix deduced from the bond graphs is equal to the number of \(I\) and \(C\) elements in derivative causality when a preferred derivative causality is assigned to the bond graph model. (b) The number \(k=n-q\) of structurally nummmodes is equal to the number of \(I\) and \(C\) elements, which have to stay in integral causality when a preferred derivative causality is assigned to the bond graph model. From the state equation for a bond graph model described by [37]:  
\[\overset{\bullet}{x}(t)=Ax(t)+Bu(t)\]  
with \(x(t)\in\Re^{n}\) and \(u(t)\in\Re^{p}\). Property 2 (a) can be interpreted by [37]:  
\[x(t)=A^{-1}\overset{\bullet}{x}(t)-A^{-1}Bu(t)\]  
If some of the \(I\) and \(C\) elements do not accept a derivative causality assignment, this means that the \(A\) matrix is not invertible and then not of full rank. The second part of Property 2 corresponds to the writing of the characteristic polynomial of the \(A\) matrix as [37]:  
\[P(s)=\det(sI_{n}-A)=s^{k}\Big{(}s^{q}+a_{q-1}s^{q-1}+\cdots+a_{1}s+a_{0}\Big{)}\]  
The point of view is structural because we detect \(k\) structurally null modes, but not the cases where \(a_{0}\) could be null. Hence, a multibond graph in a derivative causality (MBGD) can be obtained. This MBGD has all the multiport storage elements in a derivative causality assignment, and using Lemma 2, (25) can be written as:  
\[\left[ \begin{array}{c}\underline{x}(t)\\ \underline{x}_{d}(t)\end{array} \right]=\left[ \begin{array}{cc}\mathbf{A}_{11 }^{*}&\mathbf{A}_{12}^{*}\\ \mathbf{A}_{21}^{*}&\mathbf{A}_{22}^{*}\end{array} \right]\left[ \begin{array}[ ]{c}\overset{\bullet}{\underline{x}}(t)\\ \underline{\underline{x}}_{d}^{*}(t)\end{array} \right]+\left[ \begin{array}{c} \mathbf{B}_{1}^{*}\\ \mathbf{B}_{2}^{*}\end{array}
## 2 Materials and Methods
### The proposed Motion Analysis System
#### 2.1.2 Parameter Calculation
For example, the elbow flexion/extension angle is calculated using the frame coming from the camera that has a lateral view of the person, while in case of abduction, the frame coming from the frontal camera is considered, as reported in Table 1.  
To evaluate which camera view is more convenient to measure the elbow flexion/extension angle, the extent of the shoulder abduction angle is considered: if it is less than 45\({}^{\circ}\), the elbow flexion/extension angle is estimated considering the frames from the lateral camera. Otherwise, data provided by the frontal camera are used. Finally, the system can estimate whether the person is rotating his/her head in respect to the shoulders or not, considering a proportion between the distance from the ear to the eyes (CMU keypoints 16-14 for the right side and 17-15 for the left side), and the length of shoulders (euclidean distance between CMU keypoints 2-5). A reference threshold for this calculated ratio is empirically estimated due to the lack of literature. Currently, this solution has been applied only when a person has an orientation between \(-30\) and \(+30\) degrees with respect to the camera (i.e., in a frontal position). A similar approach has been considered to detect a torso rotation, calculating the proportion between the segment from the left to the right shoulder (CMU key points 2-5), and the pelvis one (CMU key points 8-11).
# Robust JND-Guided Video Watermarking via Adaptive Block Selection and Temporal Redundancy
## 3 Proposed Method
### General Architecture
#### 3.1.6 Step 6: Watermark Extraction and Voting  
The final stage of the proposed framework is responsible for accurately retrieving the embedded watermark from a potentially distorted or attacked video sequence. This step must be robust to quantization noise, frame loss or temporal reordering, transcoding, and post-processing distortions. To achieve this, we employ an inverse QIM decoding process coupled with majority voting across temporally distributed redundant embeddings.  
Assuming that the watermark was embedded into the DCT coefficient \(C^{\prime}(u^{*},v^{*})\), the decoder extracts each bit by checking the parity on the quantization bit:  
\[\hat{w}_{i}=\left\{\begin{array}{ll}0,&\text{if }\text{ mod }\left(\left\lfloor\frac{C^{ \prime}(u^{*},v^{*})}{\Delta}\right\rfloor,2\right)=0\\ 1,&\text{otherwise}\end{array}\right. \tag{13}\]where \(\Delta\) is the same quantization step used during embedding; the modulo operation maps the coefficient to its associated binary label. The decoding assumes synchronization with the embedding locations, which is maintained through consistent block selection and frame alignment from earlier stages.  
Given that each bit \(w_{i}\) was embedded across \(r\) different keyframes (see Step 4), we extract a set of candidate values \(\{\hat{w}_{i,1},\hat{w}_{i,2},\ldots,\hat{w}_{i,r}\}\). To eliminate the effect of distortions, or loss, we apply majority voting:  
\[w_{i}=\left\{\begin{array}{ll}1,&\text{if}\ \sum\limits_{j=1}^{r}\hat{w}_{i,j}>r/2\\ 0,&\text{otherwise}\end{array}\right. \tag{14}\]  
This approach guarantees correct decoding even in the presence of up to \(\lfloor(r-1)/2\rfloor\) bit errors per symbol, akin to a \((r,1)\) repetition code.  
In environments with high noise levels or under heavy compression, the decoding performance can be enhanced by adjusting the QIM threshold in accordance with the statistics of the recovered coefficients:  
\[\Delta^{*}=\underset{\Delta}{\text{argmax}}\ Consistency(\hat{w}) \tag{15}\]  
where \"Consistency\" can be measured via entropy, correlation with known pilot bits, or Hamming stability over time [41]. This step consolidates the watermark recovery process, ensuring that individual bit errors do not propagate, bit-level synchronization is preserved across frames, and recovery is possible even under temporal and spatial distortions. It complements the perceptual embedding, adaptive quantization, and redundancy strategies by closing the loop with a robust and statistically sound extraction mechanism.
## 3 Results and Discussion
### Risk Assessment Calculation  
The total concentrations of chromium, manganese, and bromine in goose breast meat (regardless of skin and treatment) were Cr--1.46 mg/kg, Mn--1.41 mg/kg, and Br--5.35 mg/kg. The Estimated Daily Intake (EDI) for each element was calculated using the following equation:  
\[\text{EDI}=\frac{\text{C-IR}}{\text{BW}}\]  
where C--concentration of target element in food (mg/kg), IR--intake rate (kg/day), BW--body weight (kg).  
To calculate the EDI value, it was assumed that the consumption of goose meat in Poland is 0.3 kg/year/person (-0.00082 kg/day), and the average body weight of an adult is 70 kg. The calculated values are shown in Table 6.  
Next, the Hazard Quotient (HQ) for each element was calculated using [83]:  
\[\text{HQ}=\frac{\text{EDI}}{\text{RfD}}\]  
where EDI--Estimated Daily Intake, RfD--Reference Dose = 0.003 mg/kg BW/day for Cr, 0.1 mg/kg BW/day for Mn, Cr and Mn or ARfD for Br (Acute RfD = 0.4 mg/kg BW/day for Br) [15; 84; 85].  
The calculated HQ values are shown in Table 6. All HQ values were well below one, indicating no health risk associated with the consumption of goose meat. The HQ for iodine was not calculated because the RfD was not established.  
The Total Hazard Quotient (THQ), summarizing the combined exposure to all three elements, was  
\[\text{THQ}=\text{HQ}_{\text{Cr}}+\text{HQ}_{\text{Mn}}+\text{HQ}_{\text{Br}}=0.057+0.00017+0.00016\ \approx 0.0061\]  
Since THQ < 1, the combined exposure to chromium, manganese, and bromine from goose breast meat does not pose a health risk for Polish consumers.
## 4 Discussion of Results
### Toughness in Flexure  
The flexural toughness (\(T_{b}\)) was derived from the flexural load-vs.-displacement responses previously shown and obtained from three-point bending tests, EN 1015-11 [53], adapted according to [PERSON] et al. [41]; see Figure 10. The energy absorbed by the specimens during the fracture process in flexure is represented by the area under the load vs. deflection curve. According to [PERSON] et al. [37], the load-vs.-deflection response depends on (i) the specimen size; (ii) the loading configuration (midpoint versus third-point loading); (iii) type of feedback control variable used during testing (load, load-point deflection, cross-head displacement, etc.); (iv) and loading rate. In order to minimize the effects of load configuration and specimen size, the normalization of the energy absorption capacity is required. This can be achieved by using the normalised flexural toughness factor ratio, \(Re_{(J150)}\), presented in Equation (7), where for each specimen the flexural toughness factor (\(FT\)) (see Equation (6) and Figure 18) is normalized with respect to the \(MOR\) value presented in Equation (2) [64; 65]. The deflection \(\delta_{Tb}\), adopted to calculate \(T_{b}\) and \(FT\), was limited to \(1/150\) of the distance between supports; see Figure 18.  
\[FT=\frac{T_{b}\times L}{\delta_{tb}\times b\times d^{2}} \tag{6}\]  
Figure 16: Toughness ratio for RH 60 and 85 curing conditions: (**a**) \(\text{TR}_{c,3}\); (**b**) \(\text{TR}_{c,5}\).  
(7)  
The obtained results, presented in Figure 19, show that \(FT\) has increased with the fibre fraction. For the reference mixture, the \(FT\) value varied between 0.1 and 0.3 N/mm\({}^{2}\), for RH 60% and 85%, respectively. These values increased about 10 and 16 times when 1.00% of  
Figure 17: Relation between compressive strength and toughness ratio, TR\({}_{c3}\) and TR\({}_{c5}\) for RH60 and RH85.  
Figure 18: Definition of flexural toughness ratio [65].  
fibres was added to both mixtures. The ratio \(Re_{(l/150)}\), obtained for the fibre volume fraction of 1.00%, showed a variation between 4.9 and 6.8 times relative to the reference mixture.  
Figure 20 shows the relation between the flexural strength and the toughness ratio in flexure obtained for L/150 deflection. The results showed an increase in the toughness ratio in flexure with the increase in the flexural strength. This strong correlation is indicative of the higher ductility of NHL-FRMs with higher strength in flexure, in this case because the fibre reinforcement contributed both to increase flexural strength and to the improvement in the post-peak behaviour in flexure.  
Figure 19: Toughness values for RH 60% and 85% curing conditions: (**a**) flexural toughness (FT); (**b**) flexural toughness factor Re(l/150).  
Figure 20: Relation between flexural strength and toughness ratio.
## 4 Simulation and Experimental Verification
### Quadratic Optimization Best Value Verification
#### 4.2.2 Fatigue Performance Verification  
The fatigue life under the three conditions was analyzed, and the calculated life distribution diagrams for conditions 1 and 2 are shown in Figure 26, with a fatigue life of more than 1.0 \(\times\) 10\({}^{7}\), which tends to an infinite life. The life distribution diagram of condition 3 is shown in Figure 27, which reaches 5.142 \(\times\) 10\({}^{6}\) and satisfies the corresponding standard requirements. The position of the minimum life under condition 3 is also the element node-218,253.
## 2 Materials and Methods
### Characterization of Silver NPs  
Nanoparticles are generally characterized by their size, shape, surface area, and dispersity [24; 25]. The usual techniques which used to characterizing NPs are as follow: Ultraviolet-visible spectroscopy,FSEM, TEM, FTIR and XRD [11]. For the purposes of this study, synthesized AgNPs were characterized using UV-visible spectroscopy. The biologically reduced brown color solution mixture was recorded as a function of the reaction time on a Shimadzu Lambda UV mini-1240 instrument (from 250 to 800 nm) operating at a resolution of 1 nm [11]. The reaction mixture of seed extracts and AgNPs was subjected to centrifugation at 8000\(\times\) g for 15 min, and, according to the aforementioned process, the resulting pellet was washed three times with deionized water and filtered. An aliquot of this filtrate was used for FTIR, XRD, FESEM and TEM analysis [26].  
Detection of possible biomolecules absorbed on the synthesized AgNPs involved FTIR analysis, which was recorded by its scanning in the range 350-4000 cm\({}^{-1}\) at a resolution of 4 cm\({}^{-1}\) in potassium bromide pellets [13]. For XRD studies, synthesized AgNPs were coated on an XRD grid, and the spectra were recorded using an Italian APD 2000 X-ray generator operating at a voltage of 40 kV and a current of 30 mA with Cu K\({}^{-1}\) radiation. The surface morphology and size distribution of AgNPs were characterized using JEOL JSM 6700F (FE-SEM) and Transmission Electron Microscopy (TEM) measurements from a TECHNA 110-Philips instrument. The purity and elemental composition of the AgNPs were analyzed using a RONTEC EDX-spectroscopy system model Quan Tax 200, Germany.
## 5 DSM Using DGs and ESS  
DSM is the systematic energy management in the case of using DGs and ESS. Using DSM can have a lot of benefits to industry, residents, nations, and the globe, which is shown in Figure 7. DSM can be implemented by using distributed energy resources such as solar, wind, waste-to-energy, etc. DSM generally involves load shape modification by applying different optimization techniques [27, 28, 29]. This modification is carried out by the significant DSM component, which is the load duration curve (LDC). LDC offers a general and analytical idea of off-peak hours and peak hours. Six techniques are used in load shaping, which are discussed below and in Figure 8.  
_Peak Clipping_: This technique is used to reduce the peak demand at peak hours. Effective use of this method can reduce the chances of establishing new generating stations. Generation from DERs also helps in balancing load and can reduce the peak demand. Figure 5: Different processes of waste-to-energy. Figure 6: Electric vehicle as a source of energy. * _Valley Filling_: This technique is set to rebuild the load during off-peak hours, which helps reduce tariffs. Charging electric vehicles at off-peak hours to work as V2G at the time of need is a possible example of valley filling. * _Load Shifting_: This is based on shifting load from peak hours to off-peak hours. * _Load Reduction_: This strategy is based on using energy-efficient equipment to reduce load demand. Rooftop solar installation in residential areas can reduce the load overall, which is an example of this technique. * _Load Growth_: Building up the load at the time of reduced load conditions or in off-peak hours. This technique is an example of charging ESS or EVs at non-peak times or during non-peak days. * _Flexible Load Shaping_: The rearrangement of LDC according to the conditions. WEC system generation is an example of this method. Figure 8: DSM techniques. Figure 7: Benefits of using DGs and ESS in DSM. The discussed DSM objectives can be achieved by integrating the DERs described in earlier sections in a forecasted manner [29]. The reason for using all these renewables is as simple as the UN's Sustainability Goal of cleaner energy. Different types of available DERs and possible DSM techniques are discussed in Table 1, which is a new and innovative way of expressing the information in this paper.
# Fault Detection Method for Wind Turbine Generators Based on Attention-Based Modeling
## 3 Experimental Analysis
### Description of Comparison Methods  
To fully validate the effectiveness of the proposed algorithm, this paper used four fault detection models that have emerged in recent years as comparison methods: (1) EMD-GRU (gated recurrent unit with empirical mode decomposition); (2) LSTM (long short-term memory network); (3) CNN (convolutional neural network); (4) FFT (fast Fourier transform).
## 1 Introduction: Acoustic Wave Transducers as Passive Cooperative Targets
Furthermore, using a piezoelectric substrate for converting the electromagnetic to acoustic waves offers the opportunity of making the best out of the anisotropy of the substrate: varying the acoustic wave propagation direction allows for varying dependence of the acoustic velocity (and hence returned signal delay) with environmental conditions. Various characteristics of a cooperative target property can be investigated: RADAR cross-section (i.e., returned magnitude), resonance frequency, echo time of flight, quality factor, etc. The former isa poor selection due to being excessively sensitive to the link budget and antenna impedance on both ends of the communication channel. Frequency, along with its reciprocal time, is the physical quantity measured with the highest accuracy and reproduced most easily with excellent references available in compact and low power formats. Practically, the time delay of a periodic signal is measured as a phase and hence as the integral of the frequency. Selecting the measured quantity is not solely a matter of transducer design; both (wideband, high timing resolution) delay line and (narrowband, high frequency resolution) resonators are readily designed using classical acoustic transducer geometries. Selecting the quantity under investigation and hence the sensor architecture is a matter of the systems engineering approach, including interrogation system design, intended application conditions, possibly existing hardware and, not least, compliance with regulations if commercial applications beyond the laboratory investigation are considered. The paper is organized as follows: first, we briefly review the surface acoustic wave transducer architecture, focusing on the reflective delay line we will be using throughout these experiments. The suitability of this sensor architecture to the pulsed mode GPR electronics is then developed, with an emphasis on the antenna characteristics' dependence on the environment and how RADAR and sensor spectral characteristics must match. The wireless interrogation of the passive transducer acting as a cooperative target sensor is then demonstrated in a relevant environment with a measurement range well beyond 1 m when the sensor is buried in homogeneous gravel or sand. GPR timebase drift is demonstrated as the factor limiting the detection limit, and the source of this drift is identified as being caused by the stroboscopic timebase generator. A digital electronics solution to this drift issue is demonstrated, in a setup suitable to modify existing commercial GPR units in order to meet stability requirements for sensor interrogation.