esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
Based on the experimental results, our proposed automated FMS assessment method utilizing the improved Gaussian mixture model (GMM) achieved a scoring accuracy of 0.8, outperforming the other models. The high agreement between the scoring results and expert scoring (kappa = 0.67) demonstrates the effectiveness of the improved GMM-based method in accurately assessing FMS. In conclusion, the high scoring accuracy and agreement with expert scoring support its value in accurately assessing functional movement patterns. Our study validates the efficacy of the proposed method based on the improved GMM for FMS assessment. Moreover, the successful implementation of depth cameras for FMS assessment holds great promise in fields such as sports performance analysis, rehabilitation programs, and personalized movement assessments. These advancements have the potential to enhance injury prevention strategies and optimize training protocols.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Feature Data before Balance**} & \multicolumn{3}{c}{**Feature Data after Balance**} \\ \hline ID & 1 & 2 & 3 & 1 & 2 & 3 \\ \hline m01 & 0.86 & 0.48 & 0.25 & 0.86 & 0.63 & 0.71 \\ \hline m03 & 0.45 & 0.49 & 0.57 & 0.77 & 0.37 & 0.88  
Further exploration and integration of depth-camera-based FMS assessment methods will contribute to the advancement in these areas, benefiting athlete performance, reducing injury risks, and improving overall well-being.  
Conceptualization, R.H., Y.S. ([PERSON]) and Y.S. ([PERSON]); methodology, R.H.; data curation, R.H. and Q.X.; writing--original draft preparation, R.H.; writing--review and editing, Y.S. ([PERSON]) and Y.S. ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, grant number No.72071018.  
Not applicable.  
The dataset used in this article is from the article \"Functional movement screen dataset collected with two azure kinect depth sensors\", published by [PERSON] et al. [PERSON] et al. have applied for ethics statement, and their numbers are as follows: ethic committee name: Beijing Sport University Experimental Ethics Committee of Sport; approval code: 2021156H; approval date: 25 October 2021. Attached is the ethical statement, so the ethical statement is no longer available in this paper.  
The dataset mentioned in this study can be found at [[https://www.nature.com/articles/s41597-022-01188-7](https://www.nature.com/articles/s41597-022-01188-7)]([https://www.nature.com/articles/s41597-022-01188-7](https://www.nature.com/articles/s41597-022-01188-7)).  
The authors declare no conflict of interest.
# Grousers Effect in Tracked Vehicle Multibody Dynamics with Deformable Terrain Contact Model
## 4 Simulations and Results
### Tracks with Grousers
#### 4.2.1 Sinkage  
On soft sandy terrain 12 mm height grousers completely sank into the soil, due to ground characteristics and machine weight (Figure 18a).  
With 25 mm height grousers the global vehicle sinkage was slightly smaller (Figure 18b), according to Bekker force acting on grousers base area. On compact agricultural soil grousers partially  
Figure 17: Traction force-slip curves.  
Figure 18: Tracks sinkage with 12 mm high grousers on sand (**a**), 25 mm high grousers on sand (**b**), and 25 mm high grousers on compact terrain (**c**). In each figure, only one virtual grouser is represented on each image to figure out the track sinkage.  
sank into the ground, but the latter did not come into contact with track links base (Figure 18c). Figure 18a-c highlight just one grouser for the sake of clarity. However as discussed before, simulations take into account the effects from all the grousers of the tracks.
###### Abstract  
Ready-to-eat fresh-cut apples deteriorate rapidly in visual quality due to browning, leading to consumer rejection and food waste. In addition, minimal processing induces tissue damage and releases organic substrates, which could accelerate microbial growth. The present study evaluated the impacts of alkaline and acidic electrolyzed water (AIEW and AEW) on natural microbial load and bioactive compounds on fresh-cut 'Granny Smith' apples. Minimally processed apples were dipped for 10 min in AEW and AIEW solutions (200 mg L\({}^{-1}\)), packed in PET containers with lids, and stored for 9 days at 2 degC. Overall, fresh-cut 'Granny Smith' apples treated with AEW significantly (\(p<0.05\)) maintained higher total phenolics (99.4 \(\pm\) 4.3 mg GAE L\({}^{-1}\)) and antioxidant capacity (79.5 \(\pm\) 6.5 mg ViCle L\({}^{-1}\)) compared to the non-treated control samples (42.9 \(\pm\) 5.1 mg GAE L\({}^{-1}\), 31.9 \(\pm\) 8.1 mg GAE L\({}^{-1}\), respectively). Similarly, pretreatment with AIEW maintained the highest total flavon content (55.71 \(\pm\) 1.5 mg QE L\({}^{-1}\)) compared to the AEW-treated samples and control (\(p<0.05\)). AEW pretreatment led to a 2 Log and a 1 Log decline in total aerobic mesophilic bacteria and yeasts and moulds, respectively. The best visual quality and highest visual score was maintained by AEW and followed by AIEW. This study further demonstrated the effectiveness of electrolyzed water treatments in minimizing browning and enhancing bioactive compounds in fresh-cut 'Granny Smith' apples.  
food safety; antioxidant capacity; fresh-cut apples; flavonols; total phenolics +
Footnote †: journal: Applied
## 3 Methodology
### Cross-Modal Embeddings and Fine-Tuning of VLMs
Given a query bbx \(b_{q}\), we compute its embedding \(e_{q}\) and compare it against the embeddings of existing tracks as given in Equation (10):  
\[Score(b_{q},e_{i})=cos(W_{v}\cdot e_{q},W_{v}\cdot e_{i}) \tag{10}\]  
The query bounding box is assigned to the track \(i^{*}\) with the highest score as given in Equation (11):  
\[i^{*}=\arg\max_{i}Score(b_{q},e_{i}) \tag{11}\]  
We consequently perform the fine-tuning by minimizing a cross-entropy loss over the track assignments during the training process. The extracted embeddings are processed by a VLM as given in Algorithm 3 to generate rich cross-modal embeddings. These embeddings encode both visual and semantic information, enhancing the model's capacity for accurate object representation. Fine-tuning the VLM further aligns its outputs to the MOT task. ```
0: Set of embeddings \(E_{t}\), Pre-trained VLM (e.g., Florence-2, CLIP)
0: Set of cross-modal embeddings \(V_{t}=\{v_{1},v_{2},\dots,v_{n}\}\)
1:for each embedding \(e_{i}\in E_{t}\)do
2: Extract visual and semantic features \(v_{i}\) using the VLM
3: Add \(v_{i}\) to \(V_{t}\)
4:endfor
5: Fine-tune the VLM on the extracted features to enhance representation for MOT
6:return\(V_{t}\)
```  
**Algorithm 3** Cross-Modal Embeddings and Fine-Tuning of VLMs  
Our approach for generating cross-modal embeddings and fine-tuning VLMs in MOT is illustrated in Figure 5 in detail, which begins with the sequence of previously detected and cropped pedestrians within bbxes that are extracted for each individual. These cropped regions are processed to compute visual features, including keypoints and descriptors, which are structured into a data format containing fields such as \(frame\_id\), \(person\_id\), \(bbx\_coordinates\), keypoints, descriptors, \(overlap\_ratio\), and \(confidence\_score\). For each individual, a textual description is generated in natural language using these features, aligning the visual information with textual inputs required for VLMs. Both the visual and textual data are projected into a shared embedding space via learned projection matrices, enabling cross-modal alignment. The embeddings for each individual are stored as \(V_{t}\) vectors for track management and undergo fine-tuning using a contrastive learning objective to enhance identity association. This process ensures that embeddings of the same identity are brought closer together while those of different identities are separated. The resulting multi-modal embeddings effectively improve track association accuracy by leveraging both visual and textual cues in a unified representation.
# Position Sensing with a Compact 0.1 THz Amplitude Modulated Source
## 4 Discussion and Conclusions  
In this paper, the method and setup for concealed target position monitoring using a THz system is presented. The vertical level height of the obscured target object was successfully determined using an AM THz system, where the laser technology could not or where it had problems. When the resolution of the proposed method is compared with the resolution of a commercially available FMCW method, comparable results are obtained. The resolution of the FMCW sources depends on the bandwidth used, using the equation \(\Delta\)R = c/2B, where \(\Delta\)R is the resolution of the FMCW system, c is the speed of light and B is the bandwidth of the system. For example, the-state-of-the-art FMCW sources with 40 GHz bandwidth have a measurement resolution of \(\pm\)1.875 mm. The results of the resolution of the AM system depend on the measurement setup. With the change in the angle of incidence, the same or better resolution as the FMCW source can be achieved, but for the cost of the smaller measurement range. Furthermore, the AM method is less efficient due to the limited scanning range. The FMCW method has a measuring range of 10 m to 100 m, while the AM method with the proposed setup has a relative range of \(\pm\)10 cm around the reference point.  
Nevertheless, the AM method has the potential to be used in applications where the area of interest is small and the budget of the system is an important factor. An array of sensors is planned to be used in the current scanning system, and future investigations of the proposed measurement method will be carried out.  
Conceptualization, funding acquisition, and supervision, J.T.; methodology, software, formal analysis and writing original draft preparation, T.K.; writing--review and editing, T.K., A.S. and J.T. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. Not applicable. Not applicable. Not applicable. Data Availability Statement: The data used in this article is reported in the paper's figures. The authors thank the Slovenian Research Agency (ARRS) for their support. The authors declare no conflict of interest.
## 2 Methods
### Instantaneous Correlation Coefficient Method for Detecting R Peaks
#### 2.1.1 Dimensionless Heart Beat Model  
The ECG waveform and the normal range of the parameters were obtained from the literature [4; 25]. Heart rate (HR): 60-100 bpm. P wave amplitude: \(0.25\pm 0.05\) mv. Interval: \(110\pm 20\) ms. QRS complex amplitude: \(1.6\pm 0.5\) mv. Interval: \(100\pm 20\) ms. R wave amplitude: \(1.6\pm 0.5\) mv. Q wave amplitude: \(0.25\times\text{R wave}\). T wave amplitude: 0.1-0.5 mv. Interval: \(160\) ms. According to the normal range of a heart beat, the reference heart beat model was established by taking the middle value. The dimensionless R peak and heart rate cycle were adjusted to obtain a dimensionless beat model, as shown in Figure 1.
# Calculation of Main Circuit Steady-State Parameters for Capacitor Commutated Converter System
## 3 Calculation Model and Method of the Main Circuit Parameters  
In this section, the CCC at the inverter side is explained in detail firstly. Then the voltage waveform of the commutation capacitor is drawn and the commutation process of the CCC is derived to give the steady-state mathematical model of the CCC. After applying the proposed selection principles (the limitations of the valve stress and the reactive power consumption), the value of the commutation capacitor is determined. Finally, the calculation formulas of the main circuit parameters at both sides are given. Thus, the theoretical analysis of the LCC-CCC HVDC system is completed.  
Figure 1: Topology of the two-terminal LCC-CCC HVDC transmission system.
## 2 Preliminaries  
### Problem Definitions  
Let \(I\) be an instance of the stable marriage problem. In this paper, we consider only complete preference lists with no ties. The _rank of a woman \(w\) in man \(m\)'s preference list_, denoted \(r_{m}(w)\), is one plus the number of women that precede \(w\) in \(m\)'s list. For a matching \(M\) and a person \(p\), let \(M(p)\) be \(p\)'s partner in \(M\). The _score of \(m\) in \(M\)_ is the rank of his partner in \(M\), _i.e._, \(r_{m}(M(m))\). The _score of \(M\)_ is the sum of the scores of all men, _i.e._, \(\sum_{m}r_{m}(M(m))\). The _man-optimal score of \(I\)_, denoted \(MO(I)\), is the score of the man-optimal stable matching \(M_{0}\) of \(I\).  
Let \(k\) be a positive integer. If \(\tilde{I}\) is an instance obtained by changing the preference lists of at most \(k\) men in \(I\) in some way, then we say that \(\tilde{I}\) is a \(k\)_-neighbor_ of \(I\). Suppose that \(\tilde{I}\) is a \(k\)-neighbor obtained by changing the preference lists of \(\tilde{m_{1}},\tilde{m_{2}},\ldots,\tilde{m_{\ell}}\) (\(\ell\leq k\)) and let \(\tilde{M_{0}}\) be the man-optimal stable matching for \(\tilde{I}\). The _man-optimal score of \(\tilde{I}\) with respect to \(I\)_, denoted \(MO_{I}(\tilde{I})\), is the score of \(\tilde{M_{0}}\) where \(\tilde{m_{1}},\tilde{m_{2}},\ldots,\tilde{m_{\ell}}\)'s scores are measured in terms of their preference lists in \(I\). (Intuitively, their scores are measured in their true preference lists.) We say that \(\tilde{I}\) is _proper_ if each man receives at least as good a partner in \(\tilde{M_{0}}\) as in \(M_{0}\), _i.e._, \(r_{m}(\tilde{M_{0}}(m))\leq r_{m}(M_{0}(m))\) for each \(m\), where \(\tilde{m_{1}},\tilde{m_{2}},\ldots,\tilde{m_{\ell}}\)'s scores are measured in their preference lists in \(I\).  
The problem MAX MAN-OPT IMPROVE(\(k\)) (MMI(\(k\)) for short) is, given a stable marriage instance \(I\), to find a proper \(k\)-neighbor \(\tilde{I}\) of \(I\) such that \(MO(I)-MO_{I}(\tilde{I})\) is maximized. The problem POSITIVE MAN-OPT IMPROVE(\(k\)) (PMI(\(k\)) for short) is to determine if there is a proper \(k\)-neighbor \(\tilde{I}\) of \(I\) such that \(MO(I)-MO_{I}(\tilde{I})>0\).
###### Abstract
Post-ripening, WBC are repacked in plastic bags under vacuum (without brine) or in plastic containers with brine [6]. Tin cans are usually used for the ripening and preservation of WBC, before their sub-packaging in smaller containers. However, in recent years, stainless steel tanks of large capacity have been used more and more, mainly in large cheese factories. More specifically, the use of tin containers (TC) was one of the most suitable for the ripening, packaging, and handling of brine cheeses due to their low cost, light weight per unit area, easy handling, and O2 product protection. Over the years, TC have been partially replaced by other materials, such as plastic and stainless steel. The latter is resistant to corrosion and low temperatures, accepts electro-polishing, and offers the product protection from O2, light, odors, and microorganisms. An advantage of the stainless steel tanks (SST) is their reusability and ease of cleaning, even with a CPI system. Thus, the use of SST containers is constantly increasing and mainly by cheese factories, where a large part of their production is intended for repackaging. Although there are many research studies that refer to the characteristics of WBC matured and preserved in tin or wooden containers, no research to date provide data on the characteristics of cheeses that are ripened and preserved in large capacity stainless steel tanks (SST) (approximately 500 kg). Considering the growing interest in the use of large capacity containers for ripening and preservation, this study aims to investigate the use of stainless steel tanks (SST), and their effect on the quality of WBC, comparing this to those ripened in tin containers (TC). The physico-chemical, microbiological, textural, and sensory characteristics of WBC, made from sheep's milk, were studied during their preservation over 180 days in SST and TC.
# Performance Analysis of Amplitude Comparison Monopulse Direction-of-Arrival Estimation
## 4 Analytic Expression of the MSEs of the Angular Tracking of Amplitude Comparison Monopulse  
In this section, we derive explicit expressions of the MSE in (18). Let \(\theta\) and \(\phi\) denote the true azimuth and the true elevation of \(\bar{\theta}_{\text{T}}\), respectively  
\[\bar{\theta}_{\text{T}}=\left[\begin{array}{c}\theta\\ \phi\end{array}\right]. \tag{21}\]  
In (15), \(\theta^{\prime o}\) and \(\phi^{\prime o}\) denote azimuth and elevation of estimated angle, \(\bar{\theta}^{\prime o}\), respectively:  
\[\bar{\theta}^{\prime o}=\left[\begin{array}{c}\hat{\theta}^{\prime o}\\ \hat{\phi}^{\prime o}\end{array}\right]. \tag{22}\]  
Similarly, \(\theta^{\prime}\) and \(\phi^{\prime}\) denote the azimuth and the elevation of the estimated angle, \(\bar{\theta}^{\prime}\), respectively:  
\[\bar{\theta}^{\prime}=\left[\begin{array}{c}\hat{\theta}^{\prime}\\ \hat{\phi}^{\prime}\end{array}\right]. \tag{23}\]  
Euclidean distance between \(\bar{\theta}_{\text{T}}\) and \(\bar{\theta}^{\prime}\) is given by  
\[d\left(\bar{\theta}^{\prime},\bar{\theta}_{\text{T}}\right)=\left\|\bar{\theta}^{ \prime}-\bar{\theta}_{\text{T}}\right\|_{2}. \tag{24}\]  
The explicit expressions in (29) and (30) are derived in the Appendix A. The expressions in the Appendix A are the results for nonzero-mean measurement noise with \(E[n_{A}]=E[n_{B}]=E[n_{C}]=E[n_{D}]\) = \(\mu\). The results for zero mean measurement noise, \(E[n_{A}]=E[n_{B}]=E[n_{C}]=E[n_{D}]=0\), are presented in the Appendix B.
# High-Performance Computing and Parallel Algorithms for Urban Water Demand Forecasting
## 2 Materials and Methods
### Data Preprocessing Pipeline
#### 2.3.1 Outlier Detection  
Statistical techniques were applied to identify anomalies in the water level readings that could distort model training. Specifically, an Interquartile Range (IQR) filter and a moving standard deviation threshold were used to flag extreme deviations. These thresholds were not selected arbitrarily; instead, they were fine-tuned using the Optuna hyperparameter optimization tool to maximize anomaly detection performance [16].  
By distributing the computation of these statistical metrics across MPI ranks, with each rank analyzing a subset of reservoirs, a significant speedup in outlier identification was achieved. Spark's DataFrame APIs were utilized to efficiently read and slice the dataset for this step, taking advantage of in-memory distributed computation. Any detected outliers were either removed or capped based on domain-informed limits to prevent unrealistic values from biasing the models.
## 2 Survey for Outdoor Agricultural UAV Applications
### Spraying  
One of the most preferred precision agriculture applications lately is the use of UAVs for spraying applications. Agricultural chemical products sprayed on crops are aimed at increasing the yield of the crop and reducing possible plant diseases and pests. However, with unbalanced and excessive use, it has negative effects on both environmental health and human health, causing diseases such as cancer and neurologic disorders [4; 38]. Compared to a fast and unbalanced sprayer, UAVs can reduce pesticide use and maximize plant health and yield [4; 24]. The chemical pesticide is sprayed on the plants, usually with a spraying  
Figure 2: Camera types used in agricultural UAV applications [3]. (**a**) Thermal camera [35]. (**b**) RGB camera [36]. (**c**) Multispectral camera [34]. (**d**) Hyperspectral camera [37].  
system mounted on the UAV. With techniques such as image processing and artificial intelligence, the condition of the soil or plant is predicted, and spraying is performed accordingly. In a sample study on spraying, [PERSON] et al. [39] carried out a low-cost and high-efficiency UAV spraying application for olive and citrus orchards.
## 4 Numerical Results and Discussion
### General Response
Figure 11 shows the maximum temperature of each loading cycle at a couple of locations in the concrete and the soil. The maximum temperature increases with the loading cycles but starts to be converged at a stable value after a sufficient amount of cycles. This convergence is because the maximum temperature loading at the inner surface (\(r\) = 0.1 m) is the same for all the cycles (See Figure 11a). Moving from the center of the pile foundation, more cycles are needed for the maximum temperature to be converged, which again implies the effect of the temperature loading becomes smaller. Figure 12 shows the temperature distribution in different loading cycles at three different times: (a) 7:20 at the beginning of each loading cycle, (b) 19:00 at the middle of each loading cycle, and (c) 7:00 at the end of each loading cycle. As seen in Figure 12a, the first loading cycle shows a different temperature distribution shape than those of other cycles at the beginning of each loading cycle because it is the only cycle not being affected by the residual temperature. For other time hours, the temperature distributions follow similar shapes because of the influence of the input temperature loading within the cycle (See Figure 12b,c). Therefore, the cumulative residual temperature has larger effects on the magnitude of temperature than the shape of distribution along the radius direction. Figure 11: Maximum temperature of each loading cycle in the: (**a**) concrete; (**b**) soil.
## 3 Results and Discussion
### Obtaining and Characterization of Specific Reagents for the LFIA  
AuNPs were prepared by the reduction of chloroauric acid with sodium citrate, as described by [PERSON] [27]. Characterization of AuNPs using TEM showed that the average diameter of spherical non-aggregated particles was \(29.9\pm 7.2\) nm (in total, 55 nanoparticles were analyzed, the minimum and the maximum diameters were 19.2 and 40.3 nm, respectively) and confirmed their suitability for the LFIA [32]. An image of AuNPs is presented in Figure 1A.  
Clones 4E2 cc, 7C3, 1B4, MyoA6, MyoD4, MyoE4, MyoF5, and MyoH9 of monoclonal antibodies that were selected according to the results of the ELISA were conjugated to AuNPs. The selection of conjugation conditions was carried out on the basis of photometric data reflecting AuNPs' aggregation  
\begin{table}
\begin{tabular}{c c c c} \hline
**Immobilized/Biotinylated** & **Detection Limit in the** & **Detection Limit in the** & **Color Intensity of the** \\
**(AuNPs-Labeled) Antibody** & **ELISA of MG, ng mL\({}^{-1}\)** & **LFIA of MG, ng mL\({}^{-1}\)** & **Analytical Zone, \%** \\ \hline
7C3/MyoA6 & 9.2 & 5.0 & 100 \\
7C3/MyoD4 & 9.1 & 24.1 & 75 \\
7C3/MyoE4 & 12.8 & 35.6 & 35 \\
7C3/MyoF5 & 10.8 & 15.6 & 78 \\
7C3/MyoH9 & 13.7 & 10.2 & 79 \\
7C3/1B4 & 14.6 & 19.4 & 53 \\
7C3/4E2 cc & 2.0 & 21.9 & 31/19 \\
4E2 cc/7C3 & 23.0 & 4.5 & 39/25 \\ \hline \end{tabular}
\end{table}
Table 2: Analytical parameters of the enzyme-linked immunosorbent assay (ELISA) and lateral flow immunoassay (LFIA) of MG.  
Figure 1: (**A**) Image of gold nanoparticles (AuNPs) obtained by transmission electron microscopy (TEM); (**B**) Histogram of particle size distribution.  
after the addition of 10% sodium chloride. As an example, the flocculation curve for the MyoA6 clone of antibodies is demonstrated in Figure 2.  
The obtained concentration dependences are characterized by an initial increase in OD, followed by a decline, and reached a plateau at a concentration of antibodies sufficient for a complete monolayer coating of a particle with antibodies (flocculation point). As recommended by [PERSON] [33], the concentrations of antibodies were 10-20% higher than those corresponding to flocculation points, and therefore the surface of AuNPs were stabilized with the protein and the formation of aggregates was prevented. Therefore, for conjugation, 4E2 cc and 7C3 clones of antibodies were used at a concentration of 20 \(\upmu\)g mL\({}^{-1}\); clone 1B4 of antibodies was used at a concentration of 10 \(\upmu\)g mL\({}^{-1}\); and MyoA6, MyoD4, MyoE4, MyoF5, and MyoH9 clones of antibodies were used at a concentration of 8 \(\upmu\)g mL\({}^{-1}\). The excess of unreacted antibodies was removed during the precipitation of the conjugates.