esa-sceva/satcom-chunk-collection huggingface dataset:
# Estimation of Tibiofemoral and Patellofemoral Joint Forces during Squatting and Kneeling
## 4 Discussion
### Limitations  
This approach presented here delivered reasonable results, but some limitations need to be considered. The tibiofemoral joint was modeled with one degree of freedom only, which is a common issue of analytical knee models [16; 40; 41]. Consequently, neither knee abduction nor torsion moments have to be compensated by the surrounding muscles. In combination with the assumption of reciprocal muscle recruitment, the present model may underestimate the compressive joint force to some extent. The model does not consider side effects from the stress of the knee joint capsule, nor of the medial and lateral ligaments and the cruciate ligaments. It is assumed, however, that these effects do not contribute substantially to the compressive joint force at the tibiofemoral joint, provided full mobility in terms of flexion of the knee joint is guaranteed. The model would otherwise have overestimated the tibiofemoral joint force during knee bends compared to the direct measurements of [PERSON] et al. [34]. However, the results can be expected to differ strongly in the case of persons with limited knee joint mobility. In particular, among patients with endoprosthetic knee joints, the conditions at max knee joint flexion may be completely different and prevent the results from being transferred.  
Owing to the lack of suitable data, the anatomical model characteristics had to be gained by the extrapolation of existing models. The presence of shear forces, in particular, may lead to different knee joint articulation patterns which may have a considerable impact on the muscle force predictions. However, the EMG data indicated low muscular activity overall for deep kneeling and squatting which supports the legitimacy of the model.  
Keeping the pressure-sensitive pad in place for a few exercises and covering the whole contact area of the thigh and calf presented difficulties at times. In a few cases in which the heels came into contact with the buttocks, the pad was too small and thus would have caused an underestimation of the thigh-calf contact force.  
The small sample size of 13 participants might be seen as an issue. However, it was not the goal of the study to estimate the knee joint forces of the total working population or similar. Our study results will only be applicable for people who can easily achieve the final postures of squatting and kneeling. The large differences in the knee joint forces between lowering into or rising from the squat and the different postures of squatting and kneeling can be verified with such a sample size. If there might be undetected significant differences between the different squatting and kneeling positions these would be of marginal relevance from the biomechanical perspective.
## 3 Results and Discussion
### Viscosity Measurements  
Figure 1 shows the results of measuring the apparent viscosity of the high-protein fat-free dairy desserts. Increases in protein concentration increased the viscosity of the tested products. The lowest values of the tested samples were observed in the product with the addition of 5 and 7% WPC80 (100 J and 130.7 J, respectively) and the highest in the product with 13% WPC80 (507.3 J) (\(p<0.05\)). The obtained results and visual observations of our tested products allowed us to conclude that the highest and the lowest values of the tested feature (viscosity) did not resemble the texture of any of the commercial dairy desserts that we wanted our product to be similar to.  
Viscosity of liquid food can be described as a resistance to flow, informally known as 'thickness' [37]. This feature in food products could be affected by many factors such as temperature, hydrocolloid concentration, molecule size and complexity and polymer chain length in foods such as proteins, starches, hydrocolloids or gums [38]. There are not many studies describing the results of research on the effects of whey proteins as a total replacement for fat in the preparation of dairy desserts. Most research focuses on the partial replacement of fats with the addition of described proteins. One example is a yogurt obtained by the thermostat method with the addition of 85% whey-protein concentrate compared to the addition of 35 and 65% WPC, which was characterized by the highest viscosity and an appropriate texture [24]. The longer heating of milk desserts at a temperature of 85 \({}^{\circ}\)C resulted in an increase in viscosity compared to heating in the range of 60-85 \({}^{\circ}\)C. This is due to the gelling properties of whey proteins. During the cooling of WPC desserts from 85 \({}^{\circ}\)C to 27 \({}^{\circ}\)C, a slow increase in viscosity was observed, but the highest increase in this parameter was observed in temperatures of 24-26 \({}^{\circ}\)C [2]. Moreover, the interactions of \(\alpha\)-lactalbumin, \(\beta\)-lactoglobulin and casein during the heat treatment can increase the viscosity of tested products [33].
# The Privacy Flag Observatory: A Crowdsourcing Tool for Real Time Privacy Threats Evaluation
## 3 The Privacy Flag Observatory Architecture
All Privacy Flag subsystems communicate with this database, which contains up-to-date information resulting from the automated security checks and the users' responses. At the same time, the Privacy Flag Observatory publishes online analytics, in text as well as in graphical forms. The goal is to provide useful information to the public and the experts with respect to the security and privacy risks that stem from the adoption of problematic practices--see Figure 2.  
Figure 2: The position and role of the Observatory in the overall assessment processes. Among the main components of the Privacy Flag Observatory are the following: the _Privacy Scoring System_, the _Questions Generation Service_ and the _Knowledge Database_, which accumulates the crowdsourced data provided, voluntarily, by participating users (see also Figure 2). Each time a user visits a site, the distributed agents running on the Privacy Flag Web add-on and the Privacy Flag Server perform, together, several privacy and security checks, without the user's intervention. These checks rely on the Privacy Scoring System which encompasses the _Top 25 Threat Matrix_ created and maintained by the Privacy Flag participants as a \"living document\" which is updated in real time. This living document contains some of the most dangerous privacy and security risks which are described in suitable formats called _threat descriptors_. In Table 1 we see an example of such a threat descriptor for a concrete threat which also contains information as it is stored by the Privacy Flag Observatory. The Privacy Flag Observatory currently handles 25 threats using the appropriate detection mechanisms. In the end, the results of the Top 25 Threat Matrix based analysis are stored, anonymously, in the Privacy Flag Database for further processing, evaluation and visualization. The global trends of the privacy technologies are presented, in graphical and text formats, on the Privacy Flag Observatory and are made available to any interested party. The significant advantage of the Privacy Flag Observatory is that due to its crowdsourcing implementation, it analyzes websites and mobile applications which are popular among'real' users. Therefore, it is much more accurate than other alternatives based on static web indexes or web crawlers that analyze, indiscriminately, any existing website. Thus, the Privacy Flag Observatory can indeed help identify the progress on the deployment of secure cryptographic algorithms and privacy protection mechanisms on _popular_ websites. Additionally, it can pinpoint obsolete and insecure web technologies which are still, unfortunately, in wide use.
# Lung Ultrasound Imaging, a Technical Review
## 3 Ultrasound Multiple-Scattering Characterization
### Model of Pulmonary Edema In-Vivo  
A preliminary in-vivo study was performed in a rodent model of pulmonary edema. After sedation a tracheotomy was performed on 6 rats ventilated with a Harvard rodent ventilator. Anesthesia was maintained with titrated isoflurane. A left thoracotomy was performed in the fourth interspace. Heparin was administered intravenously to prevent clotting during ischemia. An aneurysm clip was applied across the left lung hilum to render the left lung ischemic for one hour to create ischemia. The clip was then removed to allow reperfusion of the ischemic lung; this ischemia-reperfusion injury causes pulmonary edema [49; 50]. The right lung was used as a control. A sternotomy was performed, and the sternal edges were spread maximally to expose both lungs. Ultrasound coupling gel was applied directly onto each lung and the diffusion constant was evaluated. The results are shown on Figure 8 Significant differences were observed between the control and edematous rat lungs. As expected, the D values proved to be higher in the edematous lung, likely because of the longer propagation paths in the fluid filled lung. These results suggest that the diffusion constant could be used for detecting pulmonary edema and demonstrate the proof of concept for using ultrasound multiple scattering to detect pulmonary edema in the lung [48].  
Figure 8: The D values are significantly higher in the edematous lung than in the control lung. ***: \(p<0.005\).  
Figure 7: \(D\) increases when PBS is instilled into the lung, simulating edema ex-vivo. Three readings were taken for each measurement. The variability is demonstrated by the error bars. mean D \(\pm\) SEM.
# Enhanced Matching Game for Decoupled Uplink Downlink Context-Aware Handover
## 4 Proposed Model
### Convergence and Pareto-Optimality Analysis  
In this section, we investigate the execution of Algorithm 1. Particularly, we outline and survey the convergence and Pareto-optimality. In disposing of the investigation in the above subsection, we have:  
**Lemma 1**.: _Regarding the convergence of the dismissing-matching step, Algorithm 1 achieves a stable matching._  
Proof.: The explanation stems from two factors. Firstly, we found that their nature of transmission deployment leads BSs to reach a predetermined number of UE requests in their region, and consequently, the quantity of conceivable dismissing-matching is limited. In addition, only dismissing-matching which entirely increments a player's utility can happen. The second factor is that when all the worst conceivable cases have been dismissed, the dismissing-matching stage ends and every UE (BS) remains related to the most favored BS (UE). Subsequently, no further changes can be accomplished by dismissing among UEs requesting-matching.  
**Definition 3**.: _If there is no Pareto upturn pair in a matching, it implies that this matching is Pareto-optimal. A couple of UE \((n,n^{\prime})\) is considered as a Pareto enhancing pair in a matching \(\mu\) if \(\mu(n^{\prime})\succ_{n}\mu(n)\) and \(\mu(n)\succ_{n^{\prime}}\mu(n^{\prime})\)._  
**Lemma 2**.: _The handover-matching algorithm is Pareto-optimal._Proof.: Admitting that a Pareto upturn (n, n') exists in our last matching \(\mu\) infers that we can shape another matching \(\mu^{\prime}\), where \(\mu^{\prime}=\mu-\{(n,\mu(n)),(n^{\prime},\mu(n^{\prime}))\}\cup\{(n,\mu(n^{ \prime})),(n^{\prime},\mu(n))\}\) to boost our utility function. We take into consideration the utility of the accomplished matching \(\mu\) be U(\(\mu\)) and the utility of the Pareto upturn matching \(\mu^{\prime}\) be U(\(\mu^{\prime}\)). Based on the concept of preference process, it is clear that a UE dependably requests a matching from the most preferred agent BS first. In this way, if n leans towards \(\mu(n^{\prime})\), it has just reached \(\mu(n^{\prime})\) and been dismissed. The same is valid for n'. Consequently, \(U(\mu^{\prime})<U(\mu)\) and (n, n') cannot be a Pareto change combine. Hence, our proposed coordination is Pareto-optimal.
## 3 Deviation Analysis of New Integrated TBM Disc Cutter  
Position deviation of fastening bolts of the new integrated disc cutter system is the main source of the deviation in the connection of the robot end-effector and the disc cutter fastening bolt. Accurate calculation of the position deviation of the fastening bolts is the basis for the structural design of the cutter-changing end-effector. The deviation sources affecting assembly accuracy include the deviation of geometric location and orientation, variation of geometric form and deviation of part location and orientation [23].  
The position deviations of the two fastening bolts of the new integrated disc cutter system mainly come from the following five parts: assembly deviation of the threaded fastening system, \(FR_{b}\); the assembly deviation between the cutter holders and the disc cutter, \(FR_{t}\); the dimensional deviation of the cutter holders and the disc cutter in the direction of  
Figure 1: New integrated TBM disc cutter. 1—Thread fastening system, 2—Cutter holder, 3—Lifting rod, 4—Cutter box, 5—Shift fork, 6—Clamping block, 7—Cushion block, 8—Disc cutter.  
Figure 2: Thread fastening system. 1—Fastening bolt, 2—Bearing cap, 3—Bearing, 4—Shift forks, 5—Clamping block, 6—Cushion block, 7—Bearing.  
the cutter axis, \(A_{0}\); the installation deviation between the cutter holders and the cutter box, \(FR_{h}\); and the welding deviation between the cutter box and the mounting hole of the cutter head, \(N\). Therefore, the position deviations of the fastening bolts can be described as shown in Equation (1).  
\[E=FR_{b}+FR_{t}+A_{0}+FR_{h}+N \tag{1}\]  
The deviation \(FR_{b}\) can be expressed as:  
\[FR_{b}=\begin{bmatrix}u_{1}&v_{1}&w_{1}&\alpha_{1}&\beta_{1}&\gamma_{1}\end{bmatrix}^ {T} \tag{2}\]  
where \(u\), \(v\), and \(w\) are the translation vector parameters of the axes \(x\), \(y\), and \(z\), respectively. \(\alpha\), \(\beta\), and \(\gamma\) are the rotation vectors of the \(x\), \(y\), and \(z\) axes, respectively. The same goes for the following.  
The deviation \(FR_{t}\) can be expressed as:  
\[FR_{t}=\begin{bmatrix}u_{2}&v_{2}&w_{2}&\alpha_{2}&\beta_{2}&\gamma_{2}\end{bmatrix}^ {T} \tag{3}\]  
The deviation \(A_{0}\) can be expressed as:  
\[A_{0}=\begin{bmatrix}0&0&w_{3}&0&0&0\end{bmatrix}^{T} \tag{4}\]  
The deviation \(FR_{h}\) can be expressed as:  
\[FR_{h}=\begin{bmatrix}0&v_{4}&0&0&0&\gamma_{4}\end{bmatrix}^{T} \tag{5}\]  
The deviation \(N\) can be expressed as:  
\[N=\begin{bmatrix}0&0&0&0&\beta_{5}&\gamma_{5}\end{bmatrix}^{T} \tag{6}\]  
In summary, the position deviation of the fastening bolts is:  
\[E=\begin{bmatrix}u\\ v\\ w\\ w\\ \alpha\\ \beta\\ \gamma\end{bmatrix}=\begin{bmatrix}u_{1}+u_{2}\\ v_{1}+v_{2}+v_{4}\\ w_{1}+w_{2}+w_{3}\\ \alpha_{1}+\alpha_{2}\\ \beta_{1}+\beta_{2}+\beta_{5}\\ \gamma_{1}+\gamma_{2}+\gamma_{4}+\gamma_{5}\end{bmatrix} \tag{7}\]
# Predicting Saudi Stock Market Index by Using Multivariate Time Series Based on Deep Learning
## 4 Methodology and Implementation
### Noise Removal from the Dataset  
The exponential window function is a general method for smoothing TS data, known as exponential smoothing (ES). In contrast to the ordinary moving average, which weights previous data equally, exponential functions use weights that reduce exponentially with time. It is a simple process that can be understood and used to make a decision based on the user's existing presumptions, like seasonality. Time-series (TS) data analysis frequently employs ES. One of the various window functions frequently used in signal processing to smooth data is ES, which serves as a low-pass filter to eliminate high-frequency noise.  
[PERSON]'s use of recursive exponential window functions in convolutions from the 19 th century [21] and [PERSON] and [PERSON]'s use of recursive moving averages in their turbulence studies in the 1940s are methods that precede ES [22].  
The result of the ES algorithm, typically expressed as [19], could be viewed as the best prediction of what the next value of [20] will be. The raw data sequence is frequently  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Summary Statistics**} & \multicolumn{4}{c}{**STC**} & \multicolumn{4}{c}{**Mobility**} & \multicolumn{4}{c}{**Zain KSA**} \\ \cline{2-13}  & **Closing** & **Opening** & **Highest** & **Lowest** & **Closing** & **Opening** & **Highest** & **Lowest** & **Closing** & **Opening** & **Highest** & **Lowest** \\ \hline count & 1462 & 1462 & 1462 & 1462 & 1462 & 1462 & 1462 & 1462 & 1462 & 1462 & 1462 & 1462 & 1462 \\ mean & 38.58 & 25.29 & 38.92 & 38.25 & 25.29 & 25.56 & 25.01 & 10.85 & 10.85 & 10.98 & 10.73 \\ std & 7.19 & 7.16 & 7.29 & 7.13 & 7.15 & 7.16 & 7.22 & 7.06 & 2.63 & 2.65 & 2.60 \\ min & 26.15 & 13.38 & 26.40 & 26.06 & 13.38 & 13.40 & 13.50 & 13.23 & 5.98 & 5.60 & 6.06 & 5.90 \\
25\% & 33.08 & 19.49 & 33.37 & 32.64 & 19.48 & 19.51 & 19.76 & 19.23 & 8.76 & 8.76 & 8.81 & 8.70 \\
50\% & 39.51 & 24.46 & 39.87 & 39.31 & 24.46 & 24.41 & 24.66 & 24.18 & 11.56 & 11.56 & 11.69 & 11.41 \\
75\% & 43.67 & 30.65 & 44.04 & 43.22 & 30.65 & 30.64 & 30.94 & 30.29 & 13.12 & 13.10 & 13.28 & 12.94 \\ max & 54.48 & 42.88 & 54.74 & 53.93 & 42.88 & 42.92 & 43.43 & 42.30 & 15.13 & 15.13 & 15.20 & 14.99 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The statistics of the input data were obtained from STC, Mobily, and Zain KSA.  
Figure 3: Using closing, opening, highest, and lowest prices in the multivariate method.  
Figure 2: Using closing prices in the univariate method.  
signified by starting at time (t = 0). The formulas below provide the simplest form of ES when the observation sequence starts at time (t = 0):  
\[S_{0}=X_{0} \tag{6}\]  
\[S_{t}=\propto x_{t}+(1-\alpha)\ S_{t-1},t>0 \tag{7}\]  
where \(\alpha\) is the smoothing factor and 0 < \(\alpha\) < 1.  
Figures 4-6 illustrate the importance of showing a sample of the noise in a dataset. The original data was also plotted postnoise removal (denoised). The following figures show the original and denoised datasets.
## 4 Conclusions  
HSI (874-1734 nm), combined with the PLSR multivariate analysis method and optimal wavelength selection methods (SPA, _Bw_, RF, and CARS), were used to detect and visualize the SO\({}_{2}\) residual content in _Fritillaria thunbergii_ Bulbus. The pixel-wise spectra were preprocessed by WT (Daubechies 8 wavelet function and the decomposition level 3) and MAS (seven smoothing points) for spectra extraction and image visualization. SPA, _Bw_, RF, and CARS selected 15, 21, 24, and 26 optimal wavelengths, respectively. PLSR models using full spectra and optimal wavelengths obtained good performance, with \(r_{c}\) and \(r_{p}\) over 0.9 (except the RF-PLSR model), indicating the efficiency of optimal wavelength selection. The _Bw_-PLSR was applied on a hyperspectral image to form a prediction map, and the prediction map showed good performance with most of the prediction values in the measured SO\({}_{2}\) residual content range. The overall results indicated that HSI could be applied as a useful and efficient technique for detection and visualization of SO\({}_{2}\) residual in _Fritillaria thunbergii_ Bulbus. The results of this study could be helpful to detect residual SO\({}_{2}\) in other Chinese medicines, as well as the quality of Chinese medicines. The pixel-wise prediction for visualizing SO\({}_{2}\) residuals in _Fritillaria thunbergii_ Bulbus would help to develop online, rapid, and real-time visual detection systems for Chinese medicine quality in the future. More importantly, selections of optimal wavelengths carrying useful information related to the quality parameters could significantly improve the model efficiency and robustness.  
This work was supported by 863 National High-Tech Research and Development Plan (2013 AA102301), and Science and Technology Project of Zhejiang Province (2015C37060).  
[PERSON], [PERSON], and [PERSON] conceived and designed the experiments; [PERSON] performed the experiments; [PERSON], [PERSON], and [PERSON] analyzed the data; [PERSON] contributed reagents/materials/analysis tools; [PERSON], [PERSON], and [PERSON] wrote the paper.  
The authors declare no conflict of interest.
## 4 Core Insights and Critical Reflections
### Critical Commentary
#### 4.6.2 Data Integration Potential Risks: The Overlooked Foundation of Interoperability  
Currently, UAV-mounted sensors exhibit significant differences in imaging mechanisms, spatial resolution, spectral composition, and data formats, posing substantial challenges for the integrated processing and efficient utilization of multi-source data. The data integration crisis has emerged as one of the most critical bottlenecks in the field. While the academic community has shown strong enthusiasm for multi-source data fusion, there is a notable disconnect between data fusion and \"data standardization\". Keywords such as \"fusion\", \"multi-sensor\", and \"LiDAR\" appear frequently, reflecting an urgent demand to integrate heterogeneous data sources. However, practical and foundational terms like data standards, interoperability, OGC standards, and metadata are conspicuously absent from the list of high-frequency or high-burst keywords.  
This reveals a fundamental flaw: the research community is often focused on ad hoc integrations while neglecting the construction of scalable and reproducible infrastructure necessary for sustainable data fusion. As a result, many research teams are building isolated \"data silos\" with solutions that are difficult to replicate, compare, or extend for secondary development. Thus, the primary challenge is not merely \"data overload\", but a deeper \"data integration potential risks\" rooted in the lack of shared standards. This severely hinders the capacity for large-scale, cross-institutional, and cross-regional collaborative data analysis. Addressing this crisis is essential to unlock the full potential of UAV-RS data and to enable its meaningful application across disciplines and regions.