esa-sceva/satcom-chunk-collection huggingface dataset:
# An Audio Copy-Move Forgery Localization Model by CNN-Based Spectral Analysis
## 3 Experiments
### Localization of Audio Copy-Move Forgery  
Unlike the binary classification task, in the subsequent localization task, audio with a localization error less than 0.1 s is considered detected correctly. Figure 7 illustrates the visualization results of locating audio copy-move forgery segments. In the paper, the prediction in Figure 7c is referred to as the similarity region mask. The localization error is calculated based on the similarity region mask.  
Figure 7: Visualization results of localization model.  
The localization error is defined as shown in Equation (7).  
\[\textit{Localization Error}=\frac{\left|\frac{T_{a}+T_{b}}{2}-\frac{T_{a}^{\prime}+T _{b}^{\prime}}{2}\right|+\left|\frac{T_{c}+T_{d}}{2}-\frac{T_{c}^{\prime}+T_{d} ^{\prime}}{2}\right|}{2} \tag{7}\]  
In Equation (7), \(T_{a}\), \(T_{b}\), \(T_{c}\), and \(T_{d}\) represent the start and end times of the forged segment, while \(T_{a}^{\prime}\), \(T_{b}^{\prime}\), \(T_{c}^{\prime}\), and \(T_{d}^{\prime}\) denote the predicted values of the start and end times of the forged segment by the model. The values of \(T_{a}\), \(T_{b}\), \(T_{c}\), and \(T_{d}\) for normal audio are zero. The values of \(T_{a}^{\prime}\), \(T_{b}^{\prime}\), \(T_{c}^{\prime}\), and \(T_{d}^{\prime}\) are obtained by dividing the x-coordinate of the black-white boundary in the mask by the image width and then multiplying by the duration of the audio. Considering the pace of human speech, a localization error of 0.1 s is sufficient to determine whether there are forged segments in the audio.  
It is worth noting that the localization model predicts the confidence of forgery for each position in the mask to identify forgery segments. Due to the increased difficulty in predicting the edges of forgery regions compared to the center, there may be some distortion at the edges of the detected forgery regions in the similarity region mask. When determining whether a specific timestamp (column) represents a forgery segment, it is considered a forgery segment if the white area (pixel value greater than 150) on this column accounts for more than 90%. Therefore, the minor distortions at the edges have minimal impact on the localization results.  
In the subsequent sections of this section, we first optimized the localization model through experiments and then compared it with some existing localization methods in the field, including DFT-PCC [6], C4.5 Tree [7], HIS-MSE-ER [9], and PS-PCC [11]. Finally, we analyzed the impact of different forgery durations on model performance.
## 3 Results and Discussion
### Sensory Analysis  
Instrumental textural (sensory) measurements of a cutting force (using a knife blade as an impression of top front teeth) give us an objective indication about the firmness of a product. In this investigation, when cooked meat balls made entirely from fresh minced pork with varying frozen to fresh minced pork ratios were put up to this instrumental sensory analysis, no statistically significant differences (\(p>0.05\)) between cutting forces were observed (Figure 3). Since there was only a negligible increase in the soluble protein content in between the different frozen to fresh minced pork ratios (Table 1), this led to a similar quantity of intermolecular protein interactions during the cooking of the meat balls and resulted in a similar firmness of the end-products. Sensory firmness analysis of the cooked meat balls performed by the trained panelists confirmed the instrumental findings. The panelists also could not observe the differences in firmness between samples, and their average scores were set right in the middle of an 11-point scale (Figure 4a), irrespective of the ratio of frozen to fresh meat used for the preparation of cooked meat balls.  
Firmness and juiciness are inversely interrelated. The firmer the cooked pork meat, the dryer it gets [29]. The perception of juiciness is also highly dependent on the fat content that is released while chewing the meat products [30]. Since there was no difference in instrumentally measured and sensory perceived firmness, and the fat content was almost identical for all frozen to fresh ratios of minced pork (Table 1), it was surprising that the juiciness was also assessed as not significantly different by the panelists (Figure 4b). Inner cohesion was also rated similarly to firmness and juiciness and in the middle of an 11-point scale (Figure 4c), where 0 meant loose and 11 meant strong inner cohesion. The only sensory parameter that was significantly affected by the addition of frozen meat into the minced pork meat batter was the inner structure of cooked meat balls. The panelist assessed the inner structure of cooked meat balls made entirely of fresh meat as rougher compared to the ones made with the addition of at least 30% of frozen meat (Figure 4d). The higher the  
Figure 3: Cutting force (n = 32) (mean \(\pm\) standard deviation) of minced meat with varying frozen to fresh meat ratios. Different letters indicate significant differences (\(p<0.05\)).  
ratio of frozen meat utilized to produce minced pork, the finer the inner structure of the cooked meat balls was made, as perceived by the sensory panelists.
# An Improved Multi-Sensor Fusion Navigation Algorithm Based on the Factor Graph
## 1 Introduction
How to incorporate these measurements and calculate the optimal navigation solution by fusing all the available information sources has not been fully investigated. In this paper, based on the principle of the probability graph model, a new approach for information fusion in multi-sensor integrated navigation systems is proposed. At first, the structure of the micro unmanned aerial vehicle system is described and the model for the navigation system is built. Then the details about information fusion method based on the factor graph are presented. An experimental system has been built and experimental results have been designed to prove the effectiveness of the proposed method.
## 2 Developments
### SEFI, a QoS-Based Decision Algorithm for VH  
We name the algorithm we have developed for the VH decision phase taking into account the QoS characteristics of the networks SEFI (from \"Weights Combinations Fast **SE**arch by **F**ixed **I**ntervals\"). The algorithm explores the entire space of possible combinations of weights, searching those that satisfy the hard restriction imposed by (2), considering a maximum number of possible networks and a determined number of QoS parameters (NQoS) (Figure 4).  
The generation of these combinations is done from a given precision value (h) and two limits determined by the user, WMIN and WMAX, where WMIN \(<\) h \(<\) WMAX, WMIN \(>\) 0 and WMAX \(<\) 1. These limits depend of the user's profile, in other works, the purpose of the application of the mobile device in WSN. The algorithm has been programmed to perform a fast search thanks to some recursive  
Figure 4: SEFI searches the best combination of QoS weights for different networks.  
functions. In the computation of the exhaustive search we must take into account as key parameters the precision and the number of QoS parameters considered, for a given number of networks; these parameters influence strongly the computational effort.  
Figure 5 shows how SEFI can be integrated in a MS. The number of discovered networks and their characteristics are collected, forming the instance of the problem to be tackled by SEFI. These parameters, together with a set of predefined weight ranges (depending on the role of the MS) and the applied precision (which can be tuned depending on the size of that instance and the WSN application), make up the input to an embedded microprocessor running the SEFI algorithm. This algorithm finally selects the best solution (the network according to the best fitness found) that is used for the VH decision.
## 7 Experimental Data and Model Validation  
The CFD models do not always reproduce data accurately, and there are many sources of errors in the simulation results. Due to these errors, model verification and validation is a research requirement to obtain accurate simulations with quantified uncertainties [41; 42]. The 2012 ASME committee provides a useful validation approach for cases when experimental data are available. Even though no specific metric is imposed, the guide recommends using a single metric to better quantify the difference between the simulation and the experimental data. The metric must measure the difference between the experimental outcomes and the simulation outcomes; it should be equal to zero if the outcomes are identical, and it should provide a coherent numerical value expressing the model's accuracy and adequacy. The area metric method proposed by [PERSON] et al. [43] allows the comparison of the simulation results with the experimental data.  
Table 8 presents the S45 wing parameters and flow conditions allowing the calculation of the aerodynamics coefficients of the lift and drag and the lift-to-drag ratio. Consequently, it would be possible to compare the S45 performances with the wing prototype experimental data.  
The 29 wind tunnel tests results (lift and drag forces) are presented in Table 9, and they show a very good agreement with the simulation values. The wind tunnel tests are sorted by the angular position of the adaptive leading edge. In total, 29 wind tunnel tests were performed, and the two forces of lift and drag were measured by the aerodynamic scale.
###### Abstract
Our method of imputation plays a crucial role in enhancing the dataset in tabular data form, allowing for more accurate and reliable analyses. By filling in the gaps, we enable the application of a broader range of algorithms that require complete data. Thus, we can consider the imputation step an essential and challenging preprocessing task that ensures the data can be effectively utilized across various analytical and predictive applications. In our imputation model, relying on all available features during the imputation process may require more computing power and could lead to inaccurate results [16]. To effectively fill in the missing values for a feature, it is essential to utilize correlated features, aiming for more accurate outcomes. The selection of these correlated features in relation to the target feature is performed using evolutionary optimization algorithms. This paper first compares four widely used optimization algorithms on synthetic datasets to determine which is most effective at selecting correlated features. Furthermore, the study evaluates the proposed approach, utilizing the selected optimizer, against several state-of-the-art imputation methods using common real-world datasets as well as tabular data extracted from video-surveillance systems. In this study, we focus on video surveillance as an application due to its critical importance in security and public safety. Video-surveillance systems play a vital role in monitoring environments, preventing crime, and ensuring the safety of individuals in various settings. However, we have found that these systems often suffer from data loss for a number of reasons. For example, many installations rely on battery-operated cameras that are subject to power outages; rural areas are often subject to power outages; and unstable internet connections can disrupt data transmission. Other potential problems include hardware malfunctions, environmental factors affecting sensor performance, and limitations in the object detection algorithms used. Our method aims to impute tabular data, especially those extracted from video surveillance in smart cities. It is important to note that our approach does not address raw video frame imputation. The challengesassociated with raw video data are significantly different in terms of volume and nature compared to tabular data. By focusing on the imputation of tabular data, we can improve the reliability and usability of information derived from video-surveillance systems and other applications that rely on tabular data. The rest of the paper is organized as follows: Section 2 provides the theoretical background on different imputation methods and their classification, as well as different optimizers. Section 3 presents the proposed imputation method. Section 4 discusses the experimental results. Finally, Section 5 outlines the conclusions of the study and future work.
## 1 Introduction
Based on the results of the techno-economic analysis, it is possible to compile the inventory data needed to carry out an environmental analysis, so that the list of inputs, raw material and energy requirements, and process outputs, comprising products, by-products and emissions, are extrapolated into environmental impacts associated with the entire life cycle of a product or process [24]. This is the aim of the Life Cycle Assessment (LCA) methodology, which highlights the environmental problems associated with the entire value chain of a product or process, avoiding transferring the environmental burden from one environmental compartment to another [25; 26]. There are several references reporting the application of LCA to assess environmental impacts focusing on marine organisms and their compounds [27]. Few LCA studies of cyanobacteria have been conducted, limited to _Arthospira_ spp. and mainly focused on biofuels [28; 29; 30; 31]. In the specific context of pigment production, [PERSON] et al. [32] used LCA as a tool for solvent selection for phycocyanin extraction from _Arthospira platensis_, and [PERSON] et al. [33] described the use of pulsed electric fields as a pre-treatment for phycocyanin recovery. Thus, no information on LCA research on _Cyanobium_ sp. is available. This report includes the environmental assessment of the obtaining of bioactive pigments from cyanobacteria by applying the LCA methodology. The simulation tool SuperPro Designer(r) has been used to carry out the process modelling to obtain the necessary data for the compilation of the life cycle inventories based on the reported laboratory data. Once the environmental profiles have been obtained, it is possible to identify the main hotspots of the process, i.e., the stages and/or materials that contribute most to the environmental loads and subsequently carry out a sensitivity analysis to improve and optimise the evaluated scenario.
###### Abstract  
Lumbar spondylosis (LS) is a common degenerative spine disease that often leads to impaired motor control, sensory changes, and imbalance. The current study aimed to compare the dynamic balance control between patients with LS and healthy controls in terms of inclination angles (IA) and the rate of change of IA (RCIA) of the center of mass relative to the center of pressure (COM-COP motion) during walking and to identify the correlation between dynamic balance and standing balance in patients with LS. Eleven patients with LS and eleven healthy controls performed level walking and static standing in a gait laboratory while their whole-body motion and ground reaction forces were measured to calculate the IA and RCIA. Gait temporal-spatial parameters were also recorded. Correlations between the COP motions during standing balance and COM-COP motions during gait were quantified using Pearson's correlation coefficients (r). In the sagittal plane, the patients increased posterior IA with decreased posterior RCIA during the double-limb support phase of gait and showed decreased anterior RCIA, with small ranges of IA and RCIA during the single-limb support phase (\(p<0.05\)). In the frontal plane, the patients increased medial-lateral ranges of RCIA and medial IA during the double-limb support phase of gait and increased medial RCIA and ranges of IA during the single-limb support phase of gait (\(p<0.05\)). A moderate to strong correlation was found between dynamic balance and standing balance in the patients (\(p<0.05\)). The patients presented a conservative anterior-posterior dynamic balance control but an unstable medial-lateral dynamic balance control during walking, which may be related to the decreased gait speed. The results showed that the greater the postural sway in the patients' standing balance, the more conservative the dynamic balance control in the sagittal plane, and the greater the risk of imbalance in the frontal plane. It is thus suggested that dynamic balance control deviations during gait in patients with LS cannot be deduced solely from standing balance test data, and should thus be monitored via dynamic approaches in clinical applications.  
Keywords:lumbar spondylosis (LS); dynamic balance; standing balance; gait analysis; center of mass; center of pressure +
Footnote †: journal: Applied Sciences
## 3 Results
### Current Environmental Status of Estonian Coastal Areas  
Taking the 75 th percentile of the results in the years 2016-2021 gave the threshold value for deciding whether GES is reached in a coastal area in 2022. The results showed a lot of variation from area to area. The NW Gulf of Riga achieved a GES according to all four indices; however, the threshold values there were much lower than in other areas (PII = 105, CSA = 0.55, CyaBI (in situ) = 0.55, and CyaBI (satellite) = 0.49) (Table 4) and the results are exclusively reliant on satellite-derived data as the NW Gulf of Riga did not have in situ sampling stations before 2022. Out of 16 coastal areas, 5 did not reach GES under any of the four methodologies--the Haapsalu, Hara and Kolga, NE Gulf of Riga, Matsalu, and Parnu pays. Another seven areas reached GES according to only one index. Reaching GES was most difficult according to the CSA Index, under which only the NW Gulf of Riga achieved it (Appendix A, Figure A1).  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Coastal Area**} & \multicolumn{2}{c}{**CSA Index**} & \multicolumn{2}{c}{**CyaBI (In Situ)**} & \multicolumn{2}{c}{**CyaBI (Satellite)**} & \multicolumn{2}{c}{**Phytoplankton Intensity Index**} \\ \cline{2-9}  & **Threshold** & **2022** & **Threshold** & **2022** & **Threshold** & **2022** & **Threshold** & **2022** \\ \hline \hline   Eru-Käsmu Bay & 0.67 & 0.34 & 0.67 & 0.34 & 0.67 & 0.66 & 237 & **173** \\ Haapsalu Bay & 0.73 & 0.33 & 0.73 & 0.33 & 0.78 & 0.17 & 633 & 1256 \\ Hara and Kolga says & 0.84 & 0.52 & 0.84 & 0.52 & 0.83 & 0.64 & 112 & 148 \\ Hitu Shallow & 0.82 & 0.13 & 0.82 & 0.13 & 0.75 & 0.56 & 28 & **15** \\ Kassari-Ounaku Bay & 0.71 & 0.39 & 0.59 & 0.39 & 0.64 & **0.69** & 16 & 16 \\ Kihelkrona Bay & 0.81 & 0.80 & 0.81 & 0.80 & 0.76 & **0.90** & 59 & **0** \\ Gulf of Riga (center) & 0.47 & 0.16 & 0.71 & 0.61 & 0.73 & 0.51 & 142 & **135** \\ Gulf of Riga (NE) & 0.61 & 0.42 & 0.75 & 0.71 & 0.78 & 0.69 & 304 & 312 \\ Gulf of Riga (NW) & 0.55 & **0.68** & 0.55 & **0.68** & 0.49 & **0.84** & 105 & **0** \\ Matsalu Bay & 0.79 & 0.03 & 0.79 & 0.03 & 0.80 & 0.43 & 813 & 1171 \\ Muuga-Tallinna- & 0.71 & 0.54 & 0.73 & 0.62 & 0.69 & **0.77** & 123 & **47** \\ Navra-Kunda Bay & 0.66 & 0.28 & 0.60 & **0.64** & 0.57 & 0.47 & 237 & **155** \\ Pakri bay & 0.67 & 0.21 & 0.67 & 0.21 & 0.77 & 0.60 & 71 & **42** \\ Parnu Bay & 0.60 & 0.04 & 0.79 & 0.51 & 0.64 & 0.05 & 670 & 794 \\ Soela Strait & 0.85 & 0.27 & 0.85 & 0.27 & 0.79 & 0.36 & 0 * & 0* \\ Moonsund Sea & 0.86 & 0.00 & 0.86 & 0.00 & 0.71 & 0.50 & 46 & **27** \\ \hline \hline  & & & & & & & & \\ \end{tabular}
\end{table}
Table 4: Environmental status of the coastal areas in 2022 compared to threshold values for the 4 indices. Results underlined and in bold represent reaching GES.
# A Cooperative Control Method for Excavation Support Robot with Desired Position/Posture
## 3 Cooperative Control Method
### Control Algorithm for Coordinated ESR Position/Posture  
This subject adopts the sliding mode control algorithm design, and then seeks the posture adjustment controller to dominate the stroke of the lateral AHC in the ESR, which realizes the coordination of the ESR relative to the edge space support posture in stages.  
For any nth order MIMO (Multiple Input Multiple Output) system [32],  
\[y^{(n)}=f(y^{(n-1)},\cdots,y,t)+\Delta f(y^{(n-1)},\cdots,y,t)+b(y^{(n-1)}, \cdots,y,t)u+d(t) \tag{34}\]  
where \(y(y\in R^{m})\) denotes the output vector of the ESR system, \(u(u\in R^{m})\) is the input vector of the ESR system, and \(f(f\in R^{m})\) and \(b(b\in R^{m})\) represents the state matrix of the position/posture morphology of the ESR system.  
Convert expression (34) in the form of state equation and write it as:  
\[\left\{\begin{array}{l}x_{1}=x_{2}\\ x_{2}=x_{3}\\ \vdots\\ x_{n}=f(X,t)+\Delta f(X,t)+b(X,t)u+d(t)\end{array}\right. \tag{35}\]  
where \(x_{n}=y^{(n-1)}\), \(X=\left[x_{1}^{T},x_{2}^{T},\cdots,x_{n}^{T}\right]^{T}\).  
By tracking the expected desired position/posture \(X_{d}=\left[x_{1d^{\prime}}^{T},x_{2d^{\prime}}^{T},\cdots,x_{nd}^{T}\right]^ {T}\) in real time, the error vector is expressed as:  
\[E=X-X_{d}=\left[e_{1}\ \ \ \ e_{2}\ \ \cdots\ \ \ e_{n}\right] \tag{36}\]  
The designed sliding mode switching surface is expressed as:  
\[s(X,t)=CE-W(t) \tag{37}\]where \(C=[C_{1},C_{2},\cdots,C_{n}]\), \(W(t)=Cp_{i}(t)\), herein, \(p_{i}(t)=\left\{\begin{array}{ll}\sum\limits_{k=0}^{n}\frac{1}{k}e_{i}(0)^{(k )}t^{k}+\sum\limits_{j=0}^{n}\left(\sum\limits_{L=0}^{n}\frac{a_{iL}}{T^{j-L+n+ \tau}}e_{i}(0)^{(L)}\right)t^{j+n+1}&(0\leq t\leq T)\\ 0&(t>T)\end{array}\right.\)  
The sliding mode controller is expressed as  
\[s(X,t)=CE-P(t)=C_{n}\Big{[}f(X,t)+\Delta f(X,t)+b(X,t)u+d(t)-x_{1d}^{(n)}-p(t)^ {(n)}\Big{]}+\sum\limits_{k=1}^{n-1}C_{k}\Big{[}e^{(k)}-p(t)^{(k)}\Big{]} \tag{38}\]  
The desired position/posture coordination adjustment system controller in ESR is written as:  
\[u(t)=-b(X,t)^{-1}\Big{[}f(X,t)-x_{1d}^{(n)}-p(t)^{(n)}\Big{]}+C_{n}^{-1}\sum \limits_{k=1}^{n-1}C_{k}\Big{[}e^{(k)}-p(t)^{(k)}\Big{]}=-b(X,t)^{-1}\frac{C_ {n}^{T}s}{\|C_{n}^{T}s\|}[f(X,t)+d(t)+k] \tag{39}\]