esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
In this study, initially, we verified that the noise level and similarity of the thoracic CT image obtained from the MASH phantom are most efficiently improved by the FNLM denoising algorithm as compared with the conventional denoising filters, and then we applied the FNLM denoising algorithm to the practical thoracic CT image to demonstrate the feasibility of the proposed algorithm in the clinical field. In addition, we measured the time resolution to compare the FNLM and NLM denoising algorithms. Thus, it was demonstrated that computations could be effectively simplified by vectorization. Consequently, we proposed a solution to address the disadvantages of low-dose examination and conventional denoising filters. Thus, we expect that the application of the FNLM denoising algorithm can contribute to the acquisition of high-quality images for the early diagnosis of lung cancer with as little exposure as possible. Figure 9: (**a,b**) are signal intensity profiles of the 5-year-old phantom images applied the Wiener filter and the FNLM denoising algorithm, respectively. The graphs represent the signal intensity of the yellow lines in the images. The boxes represent the denoising performance of the Wiener filter and the FNLM denoising algorithm. The circles represent the sharpness of the images. However, the results reported in this study may vary according to some conditions of the utilized images. According to the study of [PERSON] et al., because the reconstruction algorithms affected the quality of the image, the results reported in this study could be affected when the different reconstruction algorithms were applied to the image [36]. Then, the results may vary depending on whether artifacts such as star-like artifact, beam hardening artifact, and ringing artifact, are present or not in the image. Because the artifacts affect the geometric structure of image, the FNLM denoising algorithm based on the Euclidean distance may show varied results.
###### Abstract  
Evaluating dynamic loads in real time is crucial for health monitoring, fault diagnosis and fatigue analysis in aerospace, automotive and earthquake engineering among other vibration related applications. Developing such algorithms can be vital for several safety and performance functionalities. Therefore, over the past few years the identification of dynamic loads has attracted a lot of attention; however, little literature on the online identification can be found. In this paper, we propose an online-identification method of structural dynamic loads so that the dynamic load is evaluated in real time and while the system response is still being measured. This is achieved by significantly improving the identification efficiency while retaining a high accuracy. The proposed method which is based on Kalman filter, is introduced in detail for a finite as well as an infinite number of degrees of freedom. Starting from an initial guess of the state vector we evaluate the error covariance, which then helps to identify the value of the excitation force using a weighted least square method and minimizing the covariance unbiased estimation. This is repeated at certain time intervals i.e., time steps where the state vector is updated in real time as acceleration measurements are updated. The feasibility of the method is validated using numerical simulations and an experimental verification where a detailed LabVIEW (National Instruments Ltd.) implementation is provided.  
online identification; kalman filter; inverse problem; real-time identification; dynamic load; LabVIEW
## 2 Materials and Methods
### Experimental Procedure and Laboratory Installation  
The anaerobic digestion experiments were conducted in batch mode using laboratory stand BioReactor Simulator provided by BPC Instruments (Lund, Sweden). This device consisted of 6 anaerobic reactors, each with an active volume of 1.8 L. The reactors were placed in a water bath to keep the adopted temperature.  
Each digester was provided with a mechanical agitator working on a cycle of 5 min and 25 min of mixing and rest, respectively. Prior to the experiments, all reactors were purged of inert gas to achieve anaerobic conditions. The volume of generated biogas was continuously monitored through the system provided by BPC Instruments.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Parameter** & **Unit** & **IPW** & **MSS** & **OW** & **Inoculum** \\ \hline COD & g/L & 464 \(\pm\) 7.8 & 47.4 \(\pm\) 6.2 & 10.9 \(\pm\) 1.2 & \\ sCOD & g/L & 449.8 \(\pm\) 12.3 & 2.48 \(\pm\) 0.04 & & \\ TS & g/kg & 989.5 \(\pm\) 33.1 & 43.4 \(\pm\) 3.7 & 940.3 \(\pm\) 23.4 & 24.45 \(\pm\) 5.8 \\ VS & g/kg & 979.6 \(\pm\) 25.1 & 34.0 \(\pm\) 4.1 & 874.5 \(\pm\) 60.1 & 14.37 \(\pm\) 3.7 \\ VFA & mg/L & 121 \(\pm\) 8.9 & 715 \(\pm\) 21.4 & 470 \(\pm\) 14.2 & \\ TA & mg/L & 4375 \(\pm\) 56.3 & 735 \(\pm\) 47.3 & 30.1 \(\pm\) 8.9 & \\ pH & & 3.52 \(\pm\) 0.5 & 5.62 \(\pm\) 0.7 & 4.94 \(\pm\) 0.9 & 7.36 \(\pm\) 2.1 \\ TP & mg/L & 19.6 \(\pm\) 5.4 & 474 \(\pm\) 44 & 16.3 \(\pm\) 2.3 & \\ TN & mg/L & 1542 \(\pm\) 30.1 & 1701 \(\pm\) 36 & 90.1 \(\pm\) 9.8 & \\ N-NH\({}_{4}\)\({}^{+}\) & mg/L & 2850 \(\pm\) 17.2 & 102 \(\pm\) 10.1 & 36.1 \(\pm\) 5.8 & \\ P-PO\({}_{4}\)\({}^{3-}\) & mg/L & 11.2 \(\pm\) 1.4 & 265 \(\pm\) 8.9 & 12.3 \(\pm\) 3.2 & \\ Phenols & mg/L & 1.3 \(\pm\) 0.4 & 7.4 \(\pm\) 1.1 & 64.1 \(\pm\) 4.5 & \\ D-limonene & ppb & nd & nd & 312.7 \(\pm\) 11.3 & \\ \hline \end{tabular}
\end{table}
Table 1: The composition of materials applied in this experiment (average values with standard deviation are shown).  
Each reactor was supplied 1.4 L of inoculum and 0.4 L of the main component e.g., MSS. In this research, four experimental series with differing feedstock configurations were provided:  
* S1--control series, mono-digestion of MSS;
* S2--two-component co-digestion of MSS and 1.5 g of OW;
* S3--two-component co-digestion of MSS and 1.0 g of IPW;
* S4--three-component co-digestion of MSS, 1.0 g of IPW, and 1.5 g of OW.  
The adopted doses of the co-substrates were established on the basis of substrate-to-inoculum ratio to achieve the highest methane production. In this study, this ratio varied between 0.68 and 0.72. In turn, the total organic load varied from 13.6 to 14.4 g VS/L depending on the feedstock composition.  
This batch anaerobic digestion was performed under mesophilic conditions (37 \({}^{\circ}\)C) and lasted 21 d. The experiments were repeated three times under the same operational conditions.
# A Framework for Fall Detection Based on OpenPose Skeleton and LSTM/GRU Models
## 2 System Architecture and Method
### OpenPose to Retrieve Human Skeleton  
OpenPose is a supervised convolutional neural network based on Caffe for real-time multi-person 2D pose estimation developed by Carnegie Mellon University (CMU) [25]. It can realize posture estimation of human body movements, facial expressions, and finger movements. It is suitable for single- and multiple-user settings with excellent recognition effect and fast recognition speed.  
The OpenPose algorithm calculates the human skeleton through Part Affinity Fields (PAFs) and Confidence Map. First, the feature map is obtained through the VGG-19 model. Then, the two-branch multi-stage CNN network is used to input the previously calculated feature map. The first branch is a set of 2D Confidence Maps for predicting joint parts, and the second branch is PAFs, which are used to predict limb parts. Finally, the confidence map and PAFs are analyzed through a greedy algorithm to generate 2D skeletons for the characters in the figure.  
As shown in Figure 3, there are 25 joint points in the human body estimated by OpenPose, with Point 0 indicating the nose, 15-18, the right and left eyes and ears, 1, the neck, 2-7, the right and left shoulders, elbows and wrists, 8, the center of the hip, 9-14, the right and left hips, knees and ankles, and 19-24, the left and right soles, toes and heels.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Dataset** & **Non-Fall Event (Group)** & **Fall Event (Group)** \\ \hline UR Fall Detection Dataset & 240 & 240 \\ Fall Detection Dataset & 330 & 330 \\ Mix Dataset & 570 & 570 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The dataset composition.  
Figure 2: Dataset device location. (**a**) UR Fall Detection Dataset device location; (**b**) Fall Detection Dataset device location.  
Figure 4 shows the joint points captured by OpenPose. It can be seen that this method can identify the joint points of a human body in a complex environment, especially in a dark ambient environment, as shown in Figure 4b. An \(M_{3\times 25}\) matrix is generated for each frame, which includes the \((x,y)\) coordinates of the 25 joint points and the prediction score of the joint point. The higher the score, the higher the accuracy of the joint point position.
# Fronthaul Design for Wireless Networks
## 3 Fronthaul Design Methodology
### Network Planning Algorithm
#### 3.2.1 Network Costs Analysis  
With respect to the total costs of the network, there are two main aspects to take into consideration: the costs of each RRH-BBU link and the costs of a BBU. Accordingly, the network total costs are given  
Figure 2: Flowchart of the link design algorithm.  
by the sum of the global costs of the RRH-BBU links plus the costs of the total number of BBUs used in the network.  
The costs of each RRH-BBU link can be obtained with the link design algorithm. It is important to notice that when dealing with the acquisition of equipment, as is the case when designing the fronthaul network, some vendors might be able to provide substantial discounts on their prices for purchases involving larger amounts of equipment. Accordingly, these discounts could make one technology preferable to another, cost-wise. Nevertheless, even though this is an important consideration, it is extremely difficult to quantify these discounts as constraints within the algorithm in a universal manner. Therefore, it was assumed that the unitary price of the equipment remained unaltered for large quantities, which means that the global costs of the RRH-BBU links considered by the network planning algorithm can be regarded as an upper limit for this type of cost; nonetheless, please bear in mind that since the source code of this algorithm is freely available online, it can be modified by anyone, namely by a system designer, in order to incorporate other features such as particular costs' computations according to specific terms of the vendors.  
It is worth pointing out that when deciding to add or remove a BBU from the network, the BBU costs play a major role when determining the total costs of the network. More specifically, if the BBU costs were considered negligible, the optimal solution (although unrealistic) would be given by the total number of BBUs equaling the number of RRHs, with colocated placements, as this would mean close to zero distances between the RRHs and the corresponding BBU, thus yielding the minimum possible costs regarding RRH-BBU links. Accordingly, one of the virtues of the network planning algorithm is that the BBU costs are not disregarded when determining the optimal number of BBUs to be deployed for a certain environment.
# Investigation of Data Quality Assurance across IoT Protocol Stack for V2I Interactions
## 1 Introduction
### Consistency  
Refers to preserving coherence and uniformity of data throughout the data circle. It involves maintaining data integrity, a consistent format, meaning, and structure during transmission between sending and receiving nodes. Consistency is crucial for accurate interpretation and reliable utilisation by intended recipients. A beneficial use-case example in V2I is a beacon message broadcasting where homogeneity in broadcast messages is essential to relay important/emergency communications. As observed by [PERSON] et al. [PERSON] et al. (2018), spatial consistency is fundamental in supporting the targeting and effective distribution of beacon messages to vehicles within the proximity of infrastructure elements, ensuring seamless communication in V2I scenarios.
## 4 Discussion
### Forests Contributed Most to the Global NEP IAV  
Our estimated NEP showed an increasing trend from 1982 to 2016, which was also illustrated by previous studies [9; 30]. Large interannual variability was found from 1982 to 2016, with the largest negative NEP anomaly being \(-2.32\) Pg C in 1998 when the Northern Hemisphere suffered a widespread drought [31]. Furthermore, the largest positive NEP occurred in 2013, with a NEP anomaly of 1.62 Pg C when the climate condition was suitable for plant growth [28].  
Our study also indicated that forests have larger NEP IAV than other vegetation types. This finding is consistent with the results that NEP IAV can be mainly attributed to ecosystems with larger productivity [2], and the global terrestrial carbon sink is mainly controlled by highly productive lands [26; 32]. The carbon cycle through Earth's forests is about 69 Gt each year [33; 34].  
The study illustrated that the contribution to global NEP IAV decreased from the equator to the poles and the tropics region, and EBF especially plays a vital role in regulating the global NEP IAV. The finding agrees with the previous finding that tropical forests dominate the variability of land carbon uptake [16; 35]. The tropics are the dominant term in the exchange of CO\({}_{2}\) between the land and the atmosphere, and tropical forests account for 70% of the gross carbon sink in world forests on average [36]. This result emphasizes the important role of the tropics, especially EBF, in regulating the magnitude of the global carbon sink and playing a dominant role in the global NEP IAV. Therefore, preserving tropical ecosystems should be a global priority to mitigate anthropogenic CO\({}_{2}\) emissions [37]. This suggests that more action should be taken with tropical forests, especially EBF conservation, for climate mitigation in the future.
# A Simple Model for Estimating the Hydraulic Conductivity of Unsaturated Soil
## 2 Methods  
### Soil-Water Interaction Mechanisms  
Two distinct soil-water interaction mechanisms exist in unsaturated soil: capillarity and adsorption. The capillarity results from the difference between the inner water pressure and the outer air pressure (as caused by the surface tension), and the adsorption stems from four interactions involving the water film and the soil particles: electric double layer, van der Waals, and the surface and cation hydration potentials [31]. The electric double layer and van der Waals potential are commonly known as ionic-electrostatic and molecular potential, respectively [25]. The residual suction is generally regarded as the boundary between the capillarity and adsorption. Accordingly, the soil-water characteristic curve (SWCC) and the hydraulic conductivity curve can be divided into the capillary and adsorptive dominant regions [26; 32]. Furthermore, due to the different shapes of the capillary water, the capillary dominating region can be separated into the boundary effect zone and the transition zone [33]. Figure 1 shows the soil-water interaction mechanisms, a typical soil-water characteristic curve and a hydraulic conductivity curve.  
When the suction is smaller than the air-entry value, almost all pores in the soil are water-filled, and the soil falls within the boundary effect zone. In the boundary effect zone, the hydraulic conductivity is only slightly reduced, as compared with the saturated one. As the suction further increases, the filled water in the large pores becomes discharged and replaced by air, a large amount of pendular water appears between the soil particles, and the soil enters the transition zone. Many previous studies have shown that the hydraulic conductivity in the transition zone decreases almost linearly with the suction in the log-log scale [11; 25; 31]. As the suction exceeds the residual one, the water in the soil mainly exists in the form of an adsorptive water film, and the adsorption will dominate the soil-water interactions in this range. In this adsorptive dominant region, the thickness of the water film decreases with increasing suction, and the hydraulic conductivity is recognized to be decreasing linearly with increasing suction in the log-log scale as well [26; 29; 30]. In addition, when the suction is smaller than the transition suction \(\Psi_{f}\), the hydraulic conductivity is entirely dependent on the capillary flow [31].
# Evaluation of Waste Express Bag as a Novel Bitumen Modifier
## Discussion
### Low Temperature Performance  
The stiffness value and the m-value are two key indicators to evaluate the low temperature performance obtained from the bending beam rheometer (BBR) test. Higher stiffness values mean more low-temperature cracking. According to the BBR result, the WEB additives had almost no negative effect on the low temperature performance. From some perspectives, it even improved the low temperature performance. In contrast to the SBS modified bitumen, the WEB-modified bitumen only had an effect on stiffness while the SBS modified bitumen could improve both the stiffness and m-value significantly [26]. Compared with PE, the WEB additives had a stronger improvement on stiffness, while the effect on the m-value was similar.  
As shown in Table 4, the normal pen 60/70 bitumen met the AASHTO specifications until it reached \(-6\)\({}^{\circ}\)C. At the same time, the WEB-modified bitumen also met the AASHTO specifications until it reached \(-6\)\({}^{\circ}\)C. The incorporation of the WEB additives slightly decreased both the creep stiffness and m-value and they both tended to decrease with an increase in the dosage of the WEB modifier. As a consequence, the WEB-modified bitumen can be used more efficiently in regions where the lowest temperature is over 0 \({}^{\circ}\)C.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Binder Type** & **Temperature** & **J\({}_{\text{nr0.1}}\)** & **J\({}_{\text{nr3.2}}\)** & **J\({}_{\text{nr-diff}}\)** & **R** & **Traffic Level** \\ \hline
**Units** & **(\({}^{\circ}\)C)** & **(kPa\({}^{-1}\))** & **(kPa\({}^{-1}\))** & **(\%)** & **(\%)** & **/** \\ \hline Pen 60/70 & 58 & 3.977 & 4.768 & 19.9 & \(-4.4\) & S \\
2\% WEB & 64 & 1.591 & 2.011 & 26.4 & \(-10.6\) & S \\
4\% WEB & 64 & 0.692 & 1.083 & 56.5 & \(-0.4\) & H \\
6\% WEB & 64 & 0.522 & 0.496 & \(-5.1\) & \(-6.9\) & E \\
8\% WEB & 64 & 0.027 & 0.164 & 500 & 36.4 & E \\
10\% WEB & 64 & 0.059 & 0.161 & 173.8 & 7.5 & E \\ \hline \hline \end{tabular}
\end{table}
Table 3: Multiple stress creep recovery (MSCR) test results with DSR at 64 \({}^{\circ}\)C.
## 3 Materials and Methods
### Fusion of Perception Data Based on Ship ST-TF  
#### 3.4.1 Preprocessing of Visual Trajectory Data  
Due to factors such as ship navigation, camera shake, errors in bounding box size during manual annotation, and interference from the horizontal plane background, there may be small positional discrepancies between the detected bounding box size and the actual outline of the target ship. This can lead to incorrect estimation of the image coordinates for tracking the target ship, which subsequently affects the accuracy of visual localization estimates. To address these challenges effectively, we employ Kalman filtering to preprocess the position coordinates of the visual estimates, ensuring a smoother and more reliable trajectory. Kalman filtering is able to effectively suppress noise in measurements and processes, and better adapt to changes in the actual system, thereby improving the accuracy and stability of position estimation.
###### Abstract  
The immune system homeostasis relies on a tight equilibrium of interconnected stimulatory and inhibitory signals. Disruption of this balance is characteristic of autoimmune diseases such as systemic lupus erythematosusous (SLE). Aside from activating the classical complement pathway and enhancing pathogens and apoptotic cells phagocytosis, C1q has been recently shown to play an important role in immune modulation and tolerance by interacting with several inhibitory and stimulatory immune receptors. Due to its functional organization into collagen-like (CLR) and globular (GR) regions and its multimeric nature, C1q is able to interact simultaneously with several of these receptors and locally congregate pro- and anti-inflammatory signals, thus modulating the immune response. Leukocyte associated immunoglobulin-like (lg-like) receptor 1 (LAIR-1), a ubiquitous collagen receptor expressed in many immune cell types, has been reported to interact with the CLR of C1q. In this study, we provide new insights into the molecular and structural determinants underlying C1q/LAIR-1 interaction. Recombinant LAIR-1 extracellular Ig-like domain was produced and tested for its interaction with C1q. A molecular dissection of C1q combined with competition assays reveals that LAIR-1 interacts with C1q's CLR through a binding site close but different from the one of its associated C1r2s2 proteas tetramer. On the other side, we identified LAIR-1 residues involved in C1q interaction by site-directed mutational analysis. All together, these results lead to propose a possible model for C1q interaction with LAIR-1 and will contribute to the fundamental understanding of C1q-mediated immune tolerance.  
immune tolerance; immunoglobulin-like receptor; complement C1q; collagen-like region Article
# Predicting Shot Accuracy in Badminton Using Quiet Eye Metrics and Neural Networks
## 4 Methodology
### Limitations  
We acknowledge several limitations in our methodology:  
* The laboratory setting may not fully replicate match conditions.
* The sample size, while adequate for our analyses, may limit generalizability.
* The cross-sectional design does not allow for an assessment of long-term effects or learning.  
This methodology provides a more detailed account of our research process, including participant selection, data collection techniques, experimental procedures, and analytical approaches. It offers a comprehensive overview of how we integrated eye-tracking technology, biomechanical analysis, and machine learning to investigate the relationship between Quiet Eye, motor performance, and shot accuracy in badminton.