esa-sceva/satcom-chunk-collection huggingface dataset:
# Addressing Chemophobia: Bridging Misconceptions in Food Chemistry
## 2 Definitions and Origins of Chemophobia
### Historical Roots of Chemophobia
Despite the refutation of this association, some parents still distrust the MMR vaccine. These and many other false or distorted pieces of information about vaccination, amplified during the COVID-19 pandemic, generate distrust, both towards chemical substances and information sources. As a result, many individuals begin to adopt chemophobic behaviors and refuse to vaccinate themselves or their children. Another domain in which chemophobia is frequently observed is that of food additives. These are chemical compounds deliberately incorporated into food products to enhance specific properties such as appearance, palatability, texture, or shelf life [26]. The emergence of chemophobia in this context is often linked to the negative connotation associated with terms like \"additive\", \"artificial\", or \"synthetic\", which tend to evoke distrust among consumers regarding food safety. In contrast, the term \"natural\" generally elicits positive perceptions and is commonly equated with safety, despite the fact that many natural substances are capable of inducing adverse effects, including allergic reactions and intolerances [27; 28]. In the European Union, as well as in other international regulatory contexts, the use of food additives is strictly governed by safety protocols. Each substance must undergo a rigorous scientific evaluation by independent bodies--such as the European Food Safety Authority (EFSA)--before being authorized for use. Approval is contingent on several criteria: demonstrated technological necessity, clear consumer benefit, absence of health risks at estimated levels of exposure, and the lack of viable alternatives [29]. This perception fuels consumer demand for products bearing labels such as \"chemical-free\" or \"no artificial additives\". Such labels are commonly employed as marketing tools aimed at chemophobic audiences. As mentioned above, from a scientific perspective, the term \"chemical-free\" is a misnomer, as all substances--natural or synthetic--are composed of chemicals. The dichotomy between \"natural\" and \"synthetic\" therefore lacks toxicological validity and often misleads rather than informs consumers.
## 4 Results and Discussion
### Spoken Term Detection  
#### 4.1.1 Development Data  
Evaluation system results for the development data are presented in Tables 8 and 9 for MAVIR and RTVE data, respectively. For MAVIR data, results show quite low performance, which suggests the challenging conditions of MAVIR data. For RTVE data, better results are obtained. The data augmentation technique results in better performance for the system that employs DNN for acoustic modelling both for MAVIR and RTVE data. This better performance is statistically significant for a paired \(t\)-test (\(p<0.001\)) over the system without data augmentation.  
This could be due to the more varied acoustic and background noise conditions used to train the DNN. However, when using SGMM for acoustic modelling, data augmentation gains are not so clear. This could be due to the fact that data augmentation works well with robust learning schema (e.g., neural network-based approaches) as DNNs. However, when less parameters are involved in the model, as in SGMM-based approaches, data augmentation is not so powerful. The better performance obtained with SGMM over the DNN model without data augmentation could be due to the limited training material used for DNN training, which makes SGMM outperform DNNs.  
For MAVIR data, the best performance obtained by the _Kaldi DNN + DA_ system is statistically significant (\(p<0.001\)) for a paired \(t\)-test compared with the _Kaldi SGMM + DA_and the _Kaldi DNN_ systems and weak significant (_p_ < 0.03) compared with the _Kaldi SGMM_ system. For RTVE data, the best performance of the _Kaldi SGMM_ system is statistically significant (_p_ < 0.001) for a paired _t_-test compared with the _Kaldi DNN_ and the _Kaldi DNN_ + _DA_ systems.  
The DET curves for MAVIR and RTVE data are presented in Figures 3 and 4, respectively. For MAVIR data, the _Kaldi DNN_ + _DA_ system performs the best for a low FA ratio, and the _Kaldi DNN_ system performs the best for a low miss ratio. For RTVE data, the _Kaldi DNN_ + _DA_ system performs the best for a low FA ratio, and the _Kaldi SGMM_ system performs the best for a low miss ratio.
# GERP: A Personality-Based Emotional Response Generation Model
## 3 Experiments and Results
### Ablation Study  
When considering the emotion word percentage (Ratio metric), the proposed sentiment dictionary CSVocab provided the main contribution. When substituted by CEVOD, the Ratio score dramatically decreased from 0.040 to 0.008. When other components were removed, the corresponding Ratio scores also decreased, which revealed the effectiveness of the emotion vector, the beam search, and the beam evaluator component in the insertion rate of emotion words. Similarly, when the beam evaluator was removed, the BLEU representing fluency dropped from 0.082 to 0.137. Meanwhile, the emotion vector and the beam search also made a contribution to fluency. Similarly, the scores of the ablation model dropped when using both the F1-M and F1-W metrics. Especially after removing the beam evaluator, F1-M dropped to 0.137 and F1-W dropped to 0.279, which means that the four components helped the model express emotions more accurately. Among the human evaluations, GERP performed more prominently. Models lacking the corresponding  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{4}{c}{**Automatic Testing**} & \multicolumn{4}{c}{**Human Evaluation**} \\ \cline{2-9}  & **Ratio** & **BLEU** & **F1-M** & **F1-W** & **FC** & **EE** & **EC** & **BM** \\ \hline GERP & **0.040** & **0.082** & **0.162** & **0.361** & **15.1** & **10.5** & **8.2** & **5.1** \\ S2S-N & 0.006 & 0.081 & - & - & 3.8 & 5.4 & - & - \\ S2S & 0.010 & 0.023 & - & - & 6.5 & 4.3 & - & - \\ ECM & 0.003 & 0.088 & 0.161 & 0.289 & 4.2 & 3.2 & 1.8 & - \\ SGAN & 0.008 & 0.085 & - & - & 6.9 & 4.2 & - & - \\ Ran & 0.040 & 0.066 & 0.140 & 0.334 & - & - & - & 1.9 \\ \hline \(-\)Vec & 0.032 & 0.076 & - & - & 6.1 & 2.5 & - & - \\ \(-\)B Sea & 0.024 & 0.073 & 0.146 & 0.305 & 4.7 & 2.7 & 3.4 & - \\ \(-\)Div & 0.036 & 0.062 & 0.137 & 0.279 & 6.7 & 3.0 & 4.2 & - \\ +Ex & 0.008 & 0.099 & 0.156 & 0.361 & 2.8 & 5.2 & 5.7 & - \\ \hline \hline \end{tabular}
\end{table}
Table 4: The experimental result of GERP and the comparison models. The results of GREP are bolded.  
components suffered in terms of fluency, emotional expression, the accuracy of emotional expression, and personality fit. The results showed that the integration of sentiment vectors, beam search, the proposed beam evaluator, and CSVocab can improve the overall performance of GERP.
## 3 Results and Discussion
In this discussion, we take into account indirect solid phase growth model, and therefore casting solidifies in the range from liquidus temperature to reaching of the eutectic temperature. Below the eutectic temperature, for which \(f_{s}=1\), only the cooling process takes place. With reference to the co-occurring phenomena, we take into account the changes in temperature and the solid phase fraction in the discussion of the obtained results. Temperature range is between 960 K and 530 K for figures with temperature field, and solid phase fraction range is between 0 and 1 for figures with the solid phase fraction field. We use so-called cooling curves to study the temperature changes over time for a selected location in the casting. The solidification process is the most intricate in the central zone of the casting and between the two cores, as here the solidification occurs at the slowest rate. We select the node in this zone (i.e., node 1336) to showcase the thermal behavior and plot in Figure 6. Initially, we see only cooling that is proceeded by the solid phase formation accompanying the latent heat release. After 136.5 s, we record the end of the solid phase formation with \(f_{s}=1\). After that time, we see only cooling. We plot the corresponding temperature field in Figure 7. Because of the most complex interplay between cooling and solid phase formation in the central zone, we choose this location to perform the verification. In particular, we use cooling curves and the rate of solid phase growth at node 1336 for three time steps (after 65.5, 75, 95 s). Figure 6: Finite element mesh in analyzed system. We analyze the computationally efficient of the used mixed time partitioning method. In particular, we focus on answering two main questions. First, we ask how much we can gain by using the mix of various methods for different parts of the computational domain. We study various combinations of methods from two basic families of time steppers (explicit vs. implicit and its combination). Then we ask the question about computational gains due to applying sub-cycles. Finally, we focus on the accuracy of the solution. To the best of the author's knowledge, this is the first computational study of casting's solidification that involves mixed time partitioning methods.
## 5 Conclusions  
We propose an adaptive pre-distortion scheme for VLC OFDM that takes into account the nonlinear characteristics of LEDs. In the proposed scheme, to reduce the computational resources required at the transmitter in conventional nonlinear compensation methods, the nonlinear characteristics of the LED are estimated at the receiver, and the resulting information is fed back via FBI. Compensation is achieved at the transmitter by multiplying the transmitted signal by the inverse function of the estimated information. Two estimation methods are introduced, namely LSM and MBM. In addition, a method that incorporates FSS is integrated into the proposed scheme to address channel distortion in real-world environments. Simulation results under AWGN conditions confirm that both proposed estimation methods improve the bit error rate performance. Moreover, LSM offers lower computational complexity and attains high estimation performance even with a reduced number of reference signals when compared with MBM. Simulations in a Rician fading environment indicate that LSM maintains robust nonlinear estimation performance despite variations in the K-factor and that the incorporation of FSS further enhances BER performance. Finally, simulations conducted under realistic lighting conditions demonstrate favorable BER performance, thereby confirming the practical viability of the proposed scheme.
## 3 Data Processing & Methodology
### Receiver Clock Offset w.r.t GPS Time
#### 3.1.1 Ublox Data Processing  
The Ublox records data in ubx format. It stores information that is useful in deriving the Refsys values. The flowchart in Figure 4 shows the general steps involved in getting the Refsys values from the raw ubx data. The ubx data are first converted into the receiver independent exchange (Rinex) observation and navigation files by using the rtklib software (version 2.4.3), which is freely available for use [28]. The Rinex observation files are then converted to the CGGTTS format by using the rin2 cggtts program. The program is distributed by BIPM for non-commercial purposes [24].
# Dual Histogram Equalization Algorithm Based on Adaptive Image Correction
## 4 Analysis of Algorithm Results
### Evaluation Results
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Image** & **HE** & **BBHE** & **CLAHE** & **BPLHE** & **RSIHE** & **ESIHE** & **MMBEBHE** & **AICHE** \\ \hline Scene 1 & 9.7051 & 28.2200 & 13.4224 & 32.8191 & 27.2202 & 20.3642 & 30.8145 & 33.1691 \\ Scene 2 & 11.0532 & 18.8172 & 12.1940 & 17.7522 & 18.7340 & 22.4839 & 23.4246 & 31.9793 \\ Scene 3 & 7.6270 & 13.9420 & 11.5169 & 22.6969 & 10.3618 & 12.1808 & 23.5157 & 27.3033 \\ Scene 4 & 9.6131 & 13.2657 & 17.2237 & 11.5263 & 21.4071 & 20.5795 & 14.7804 & 20.5795 \\ Scene 5 & 8.7144 & 14.1542 & 17.9418 & 11.8057 & 21.8398 & 18.4647 & 14.3072 & 23.1845 \\ Scene 6 & 9.2463 & 12.6208 & 10.5965 & 11.0304 & 10.7381 & 12.3334 & 9.8361 & 12.7580 \\ Scene 7 & 17.4424 & 27.7650 & 11.7832 & 30.9384 & 22.9990 & 30.1157 & 26.4948 & 31.3333 \\ Scene 8 & 9.0751 & 25.2620 & 19.9886 & 10.4082 & 25.1913 & 16.6951 & 9.2755 & 21.3871 \\ Scene 9 & 21.2349 & 24.7633 & 6.8659 & 26.1318 & 21.5225 & 27.5689 & 23.6395 & 29.8354 \\ Scene 10 & 8.813 & 23.4973 & 20.3784 & 9.4208 & 22.6432 & 16.3077 & 9.1325 & 25.8520 \\ \hline Average value & 11.2524 & 20.2307 & 14.1911 & 18.4529 & 20.2657 & 19.7093 & 18.5221 & **25.7381** \\ \hline Standard deviation & 4.43957 & 6.34223 & 4.46301 & 9.02647 & 5.60078 & 5.89009 & 7.96312 & 6.37072 \\ \hline \hline \end{tabular} Note: The top averages produced by the compared algorithms are marked in bold font.  
\end{table} 
Table 2: PSNR of different algorithms. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Image** & **Original** & **HE** & **BBHE** & **CLAHE** & **BPLHE** & **RSIHE** & **ESIHE** & **MMBEBHE** & **AICHE** \\ \hline Scene 1 & 6.6795 & 6.2585 & 6.4329 & 7.3721 & 6.6075 & 6.4146 & 6.5399 & 6.4329 & 6.6100 \\ Scene 2 & 6.9335 & 6.6133 & 6.5720 & 7.4798 & 0.8336 & 6.6434 & 6.7688 & 6.5831 & 6.7754 \\ Scene 3 & 6.4832 & 6.0482 & 6.0529 & 7.2479 & 6.1414 & 5.9924 & 6.1180 & 5.9747 & 6.2263 \\ Scene 4 & 5.8144 & 5.7052 & 5.6333 & 6.6725 & 5.7407 & 5.7453 & 5.7456 & 5.6913 & 5.7850 \\ Scene 5 & 5.5746 & 5.0785 & 4.9332 & 6.3464 & 5.0707 & 4.8746 & 5.1037 & 5.0313 & 5.1829 \\ Scene 6 & 6.5849 & 6.2653 & 6.2540 & 7.2336 & 6.3560 & 6.2520 & 6.3487 & 6.2918 & 6.3663 \\ Scene 7 & 7.4980 & 7.2396 & 7.2908 & 7.9450 & 7.4068 & 7.2882 & 7.3781 & 7.3107 & 7.4299 \\ Scene 8 & 6.0384 & 5.1384 & 5.4682 & 6.3106 & 5.5317 & 5.4245 & 5.3574 & 5.5863 \\ Scene 9 & 7.6653 & 7.4882 & 7.4852 & 7.8956 & 7.5763 & 7.5508 & 7.4800 & 7.5963 \\ Scene 10 & 6.0335 & 5.0 Based on the evaluation results, the AICHE algorithm proposed herein has good image enhancement effects in all three working conditions; it can protect the average brightness of the image and reduce the merging of gray levels on the basis of improving the image contrast, and it has good robustness. However, as shown in Table 5, the computational time of the AICHE algorithm is relatively long.
## 6 Discussion  
Through the analysis of simulation signals and experimental signals, we know that the RS-LOD method inherits the strengths of the LOD method, such as fast calculation speed, no obvious end effects, stable decomposition results, and much slighter mode mixing phenomena etc. At the same time, the rational spline function with a pole parameter \(p\) is introduced to overcome the problems of MOC component poor smoothness and distortion. For different signals, the optimal local mean curve can be obtained flexibly by adjusting the pole parameter \(p\), reducing the rounding error, and obtaining the optimal component. However, the optimal selection range of pole parameter \(p\) of rational spline function is estimated through multiple numerical experiments, i.e., the selection of pole parameter \(p\) is subjective and lack of adaptivity. Therefore, how to determine the optimal pole parameter \(p\) automatically is a problem that needs further study. In addition, other interpolation methods [29; 30] can also be considered to improve the decomposition effects in the future.
# Improved Video Anomaly Detection with Dual Generators and Channel Attention
## 3 Method
### Constraint Function
The maximum constraint \(G_{max}\) can be obtained by Formula (14):  
\[G_{max}=max(\lambda_{gc}L_{gc}+\lambda_{sc}L_{sc}+\lambda_{opt}L_{opt}) \tag{14}\]  
where \(\lambda_{gc}\), \(\lambda_{sc}\), and \(\lambda_{opt}\) are the weights of gradient constraints \(L_{gc}\), strength constraints \(L_{sc}\), and optical flow constraints \(L_{opt}\), respectively. In terms of real frames, the minimum constraint \(G_{min}\) is used to narrow the distance between the reconstructed real frames and the real frames. At the apparent level, the inter-frame distance and the intra-frame distance corresponding to the reconstructed real frame and the real frame are shortened, and at the motion level, the optical flow similarity between the reconstructed real frame and the real frame, respectively, and the real frame at the previous moment is improved. The minimum constraint \(G_{min}\) is shown in Formula (15):  
\[G_{min}=min(\lambda_{gc}L_{gc}+\lambda_{sc}L_{sc}+\lambda_{opt}L_{opt}) \tag{15}\]  
Note that the maximum and minimum constraints function can well reduce the problem that the generation ability of the generative confrontation network is too strong, which leads to the perfect reconstruction of abnormal frames. The maximum constraints function is mainly used to increase the distance between the pseudo anomaly frame and the reconstructed pseudo anomaly frame so that the reconstruction generator can recognize the anomaly in advance. At the same time, it makes the reconstruction generator unable to reconstruct similar frames when reconstructing abnormal frames. The minimum constraints function is mainly used to reduce the distance between the original frame and the reconstructed frame so that the reconstruction generator can well learn the normal frame distribution and reconstruct the normal frame.
# Reversible Data Hiding Scheme in Homomorphic Encrypted Image Based on EC-EG
## 3 Proposed Scheme
### Data Extraction
#### 3.6.2 Extract the Embedded Data from Decrypted Image  
As shown in Figure 5f,g, when the receiver only has the private key \(sk\) of the image owner, a directly decrypted image with hidden data is obtained from \(M(P_{i,j}^{{}^{\prime\prime}})=M(C_{i,j}^{{}^{\prime\prime}})+r_{i,j}pk-skr_{ i,j}G\), where \(C_{i,j}^{{}^{\prime\prime}}\) is the ciphertexts with hidden data and \(P_{i,j}^{{}^{\prime\prime}}\) is the corresponding plaintexts with hidden data. According to the method of room reserving, the directly decrypted image is divided into three parts of \(A_{D},B_{D},C_{D}\), and \(M(S_{w})=M(P_{t}^{{}^{\prime\prime}})-M(P_{r}^{{}^{\prime\prime}})\) can be obtained by plaintext subtraction in the target pixel pair of the square grid, where \(P_{t}^{{}^{\prime\prime}}\) and \(P_{r}^{{}^{\prime\prime}}\) are the target pixels and reference pixels in the directly decrypted image, respectively. Finally, we can obtain additional data with the method in Steps 3 and 4 in Section 3.6.1.
# A Software-Defined Networking Framework to Provide Dynamic QoS Management in IEEE 802.11 Networks
## 2 Contention Window Adaptation Mechanism
### The Proposed Mechanism  
In this work, we propose DEDCA (_Dynamic Enhanced Distributed Channel Access_) mechanism, an alternative solution to response to dynamic QoS requirements in 802.11 network based on the definition of Terminal Categories (TC). By default, all the terminals are classified as _normal stations_, using default backoff values. When a terminal requests a punctual and time-limited more demanding QoS requirement it becomes a _requesting station_. The _requesting station_ will modify its \(CW_{min}\) value to increment the channel access opportunity and, consequently to obtain more bandwidth, at the expense of other terminals that give some bandwidth up to the _requesting station_. These last terminals, that also modify their \(CW_{min}\) value to decrement their channel access opportunities, are called _giving terminals_.  
With the goal of determining how to modify the contention window size of the _giving_ and _requesting stations_, a mathematical model has been developed. At this point, it is important to stress that we are not interested in finding a general purpose mathematical model of shared networks using the backoff mechanism. On the contrary, we are looking for a simple and easily programmable model that offers a tool to manage the QoS of these networks. Concretely, we will find a scalar called _gain_ (\(G\)), which will show us how the bandwidth is spread out among the wireless terminals.  
In this paper, we present a particular implementation of this proposed mechanism. As presented in Section 3, we propose to make use of the _Software Defined Networking_ (SDN) technology to define a framework where DEDCA mechanism can be applied in order to give a response to dynamic QoS requirements in 802.11 network.
## 6 Controller Designs
### Outer Feedback Control Loop
The corresponding decoupled Youla controller is:  
\[M_{Y}(i,i)=\frac{M_{T}(i,i)}{M_{p}(i,i)}=\frac{1}{gain(i)}\frac{{{\omega_{n}} ^{2}}}{(s^{2}+2\zeta{\omega_{n}}s+{\omega_{n}}^{2})}\frac{\left(\tau_{in}s+1 \right)^{3}}{\left(3\tau_{in}s+1\right)},\ ie(1,2,3,4,5,6) \tag{57}\]  
Following Equations (43)-(45), the final coupled Youla, closed loop, sensitivity, and observer transfer function matrices are computed as:  
\[Y_{MIMO\_outer}^{linear}e\)\(\mathbb{R}^{6\times 9}=U_{R}M_{Y}U_{L} \tag{58}\]  
Figure 11: Feedback loop with linearized model. \[T_{yMIMO\_outer}^{\text{\emph{linear}}}e\ \mathbb{R}^{9\times 9}=G_{pMIMO\_outer} ^{\text{\emph{linear}}}\cdot T_{MIMO\_outer}^{\text{\emph{linear}}} \tag{59}\]  
\[S_{yMIMO\_outer}^{\text{\emph{linear}}}e\ \mathbb{R}^{9\times 9}=1-T_{yMIMO\_outer}^{ \text{\emph{linear}}} \tag{60}\]  
\[G_{CMIMO\_outer}^{\text{\emph{linear}}}e\ \mathbb{R}^{6\times 9}=Y_{MIMO\_outer}^{ \text{\emph{linear}}}\cdot\left(S_{yMIMO\_outer}^{\text{\emph{linear}}}\right)^{-1} \tag{61}\]  
This controller is based on a linearization about a fixed point \(\vartheta^{0}\), and is therefore valid only within a neighborhood of that configuration. As the actual joint angles \(\tilde{q}(t)\) deviate from \(\tilde{q}^{0}\), the mismatch between the linearized model (Equation (51)) and the true nonlinear model (Equation (50)) grows. To address this, an adaptive control strategy is proposed, illustrated in Figure 12. This method updates the controller in real time using the current estimated joint configuration. First, joint angles \(\tilde{q}\) are estimated from the current image coordinates:  
\[\tilde{q}=\mathcal{F}^{-1}\left(\widehat{p_{image}},\ Pa,\ P^{O}\right) \tag{62}\]  
Here, \(\mathcal{F}^{-1}\) is the inverse of the nonlinear eye-in-hand model, which includes both coordinate transformation from the image to the end-effector frame and the inverse kinematics of the robot. Using \(\tilde{q}\), the current Jacobian and updated SVD-based decomposition are recalculated each time step. The Smith-McMillan form and corresponding controller \(G_{CMIMO\_outer}^{\text{\emph{linear}}}\) are then reconstructed via Equations (58)-(61), yielding an adaptive Youla controller that remains effective across varying joint configurations.