esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Comparison of Accelerometer Measurements between ADHD and Control Groups  
In this study, we compared the measurements of variance and ZCR obtained using the accelerometer between children with ADHD and controls. All average variance values for the three axes were higher in children with ADHD than in the controls. Significant differences in the average variance values were observed across the three axes, namely \(x\) (4.849 \(\pm\) 3.173 vs. 3.071 \(\pm\) 1.566, \(p=0.001\)), \(y\) (5.149 \(\pm\) 3.061 vs. 3.209 \(\pm\) 1.395, \(p<0.001\)), and \(z\) (4.866 \(\pm\) 2.440 vs. 3.096 \(\pm\) 1.280, \(p<0.001\): Table 3). Figure 3 presents differences in the variance in the accelerometer measurements between a child with ADHD and the matched control.  
Similarly, significantly higher ZCR values were noted across the \(z\)-axis of the accelerometer (0.067 \(\pm\) 0.030 vs. 0.051 \(\pm\) 0.024, \(p=0.006\)) in children with ADHD than in the controls (Table 3).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & & **ADHD** & **Control** & \(p\)**Value** \\ \hline \multirow{3}{*}{Variance} & Gx & 0.200 \(\pm\) 0.052 & 0.174 \(\pm\) 0.050 & 0.021 \\  & Gy & 0.093 \(\pm\) 0.029 & 0.075 \(\pm\) 0.020 & 0.001 \({}^{*}\) \\  & Gz & 0.094 \(\pm\) 0.026 & 0.080 \(\pm\) 0.024 & 0.012 \\ \hline \multirow{3}{*}{ZCR} & Gx & 0.421 \(\pm\) 0.010 & 0.415 \(\pm\) 0.011 & 0.005 \({}^{*}\) \\  & Gy & 0.414 \(\pm\) 0.014 & 0.404 \(\pm\) 0.018 & 0.003 \({}^{*}\) \\ \cline{1-1}  & Gz & 0.401 \(\pm\) 0.016 & 0.389 \(\pm\) 0.021 & 0.004 \({}^{*}\) \\ \hline \hline \end{tabular} \({}^{*}p<0.05/6=0.008\).  
\end{table}
Table 2: Comparison of the gyroscope measurements between the attention-deficit hyperactivity disorder (ADHD) and control groups.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & & **ADHD** & **Control** & \(p\)**Value** \\ \hline \multirow{3}{*}{Variance} & Ax & 4.849 \(\pm\) 3.173 & 3.071 \(\pm\) 1.566 & 0.001 \({}^{*}\) \\  & Ay & 5.149 \(\pm\) 3.061 & 3.2093 \(\pm\) 1.395 & \(<\)0.001 \({}^{*}\) \\  & Az & 4.866 \(\pm\) 2.440 & 3.096 \(\pm\) 1.280 & \(<\)0.001 \({}^{*}\) \\
## 1 Introduction
The specific objectives were to compare the model performances (1) between the temperate steppe and alpine meadow and (2) between the drought and non-drought years in the temperate steppe. The grasslands in northern China are ideal for comparisons of satellite-based GPP models among vegetation subtypes. First, they play an important role in both the global and China's national carbon budget due to their large area of coverage (33% of the total land in China) [40; 53; 54]. The northern China grasslands have been estimated to store 3.3%-10.1% of global grassland carbon [40; 55] and approximately 7.1% of Chinese terrestrial carbon [56]. Second, the vegetation growth of temperate grassland and alpine grassland, the two major grassland types in northern China [57], are mainly controlled by water availability [58; 59] and temperature [58; 60], respectively. Third, the temperate grassland has experienced frequent extreme drought events during the past decades [61; 62], and more severe droughs due to decreasing precipitation are projected in the next 30-90 years (e.g., the Palmer Drought Severity Index is projected to decrease by 0-7) [63; 64]. Finally, the Chinese Terrestrial Ecosystem Flux Observation and Research Network (ChinaFLUX) [65] provides continuous carbon flux data in different grassland types, facilitating model comparisons among different grasslands and years. Our analyses will provide important insights for future efforts to improve satellite-based models in predicting terrestrial GPP at both regional and global scales.
## 1 Introduction
Furthermore, disturbances in iron levels or iron deficiency not only affects the production of hemoglobin but also impair the synthesis of iron-containing proteins, such as cytochromes, myoglobin, catalase, and peroxidase [17]. However, there is limited data and often contradictory information on oxidative and antioxidant defenses in IDA [18]. The primary pathophysiology of IDA is reduced hemoglobin production, which impairs the ability of blood cells to carry oxygen [19]. Furthermore, in ID states enzymes of the antioxidant defense system may be functionally defective, leading to an imbalance of free radicals and increase oxidative stress [19]. It has been reported that IDA is associated with increased oxidative stress, and that treatment of IDA can enhance antioxidant capacity [20]. The inhalation of H\({}_{2}\)S gas or injection of aqueous H\({}_{2}\)S donor solutions has been shown to have a variety of biological effects in humans [3]. Various studies have explored the antioxidant properties of H\({}_{2}\)S and its impact on oxidative stress in different contexts [3]. However, there is a scarcity of research specifically investigating the interaction between H\({}_{2}\)S and low ferritin levels concerning oxidative stress. Therefore, the main objective of this study was to evaluate the oxidative status of individuals with low ferritin levels and investigate the influence of prolonged exposure to naturally occurring H\({}_{2}\)S on their oxidative status. The study aimed to assess several oxidative stress parameters, including ferritin levels, total oxidative stress (TOS), total thiol, endogenous plasma H\({}_{2}\)S, lipid hydroperoxides (MDA), and total antioxidant capacity. The investigation involved both low- ferritin levels and normal individuals who had been exposed to H\({}_{2}\)S emissions over an extended period of time.
## 4 The Control Correction
Values of \(c_{i,l}^{(p+1)}\) are calculated by minimizing \(M^{\alpha}(c_{i,l}^{(p)},\alpha^{(p)})\);
2. \(\alpha^{(p+1)}=\alpha^{(p)}\gamma\), \(0<\gamma<1\);
3. If the specified accuracy of \(M^{\alpha}\) is achieved, then stop. Assuming that \(u_{1,i}\) are found exactly, by the successive approximations approach, we can establish the following theorem under additional smoothness conditions for a function \(F_{1}\). Theorem 1: _Let the vector function \(F_{0}\) be such function that the system in (2) is flat and \(u_{0}(t)\) is the open-loop control in problem (2) obtained by means of algorithm [9; 10], the regularization parameter \(\alpha=O(\varepsilon^{k})\), where \(k\geq 2\), \(F_{1}\) is twice differentiable of all variables, the coordinates \(u_{1,j}\) are found exactly. Then, there is such a perturbation parameter \(\varepsilon_{0}>0\) thatfor all \(0<\varepsilon\leq\varepsilon_{0}\)_  
1. \(\|x(t_{f},u_{0}(t_{f})+\varepsilon u_{1}(t_{f}))-x_{f}\|=\theta_{1}=o(\varepsilon)\)_,_
2. \(\|x(t_{f},u_{0}(t_{f})+\varepsilon u_{1}(t_{f}))+\varepsilon\omega(t_{f})-x_{f} \|=\theta_{2}<\theta_{1}\)_, if_ \(\|\omega(t)\
eq 0\|\) _for all_ \(t\in(t_{0},t_{f})\)The proof is carried out similarly to the scheme in [14] for smooth perturbations of the optimal control problem. Note that during the practical use of the theorem, for greater accuracy of the final condition approximation, it is necessary to match the regularization parameter \(\alpha\) to the accuracy of the solution of the regularized problem. If a terminal control error is unacceptable for a given real-world problem, then auxiliary control \(\omega(t)\) may be used to compensate for it. In that case, \(\omega(t)\) is found as a solution in Problem 2, i.e., it minimizes  
\[e^{\prime}e\rightarrow\min_{d_{i,l}},\]  
where residual has the form  
\[e=x(t_{f})-x\Big{(}t_{f},u_{0}(t)+\varepsilon(u_{1}(t)+\omega(\tau(t),d_{i,l}) )\Big{)},\quad i=1,\dots,r,\quad l=0,\dots,k_{\omega},\]  
and controls \(u_{0}\) and \(u_{1}\) are already known. Let us note that \(\omega(\tau(t),d_{i,l})\) is represented in the form of series (15), the number of members of which is determined by the parameter \(k_{\omega}\geq 0\).
# A Study on the Normalized Delineation of Airspace Sectors Based on Flight Conflict Dynamics
## 1 Introduction
Voronoi diagrams in the spatial sectioning equipartition well match the demand of airport area for sector division; the core problem of using this method for airspace sector division is to select a suitable method to obtain the Voronoi-diagram-generating elements so as to obtain the airspace sector scheme. Based on this, [PERSON] used airports, waypoints, and intersections as the center points to generate Voronoi diagrams in the controlled airspace [14]. [PERSON] used the spectral clustering method to based on the static structure of the airspace to build a weighted graph model, which is divided into convex polygons with the same control loads to generate a defined sector-division scheme [15]. [PERSON] used a spectral clustering algorithm to bisect the airspace, followed by a weight-balancing algorithm to balance the weights of the obtained subgraphs [16]. [PERSON] et al. improved the K-means algorithm by introducing weighting factors and capacity margins to determine reasonable cluster centers, and used the Voronoi diagram partitioning idea to achieve sector division [17]. The above methods are based on the fixed static route structure, and the clustering algorithm is selected or the generating elements are obtained directly according to the fixed position, and the control sector-delineation scheme is obtained with the goal of balancing the controller load. In order to better fit the actual airspace structure, this paper considers obtaining the historical air-traffic condition data of military airspace, quantifying the comprehensive index of the flight conflict network, clustering the air situation to obtain the Voronoi diagram generator, and then realizing the goal of balancing the control load. In view of the above problems, this paper proposes a control-sector planning method based on a mean shift clustering algorithm. Firstly, based on the flight-conflict network, six network topology indicators are comprehensively considered to distinguish the sector idle time segment and busy time segment. Secondly, EM clustering is used to obtain the distribution of aircraft node-area centers, and the clustering method of the mean shift algorithm is used to obtain the node-area centers for the clustering center. Finally, the clustering center is used as a generating element using the Voronoi diagram method to obtain the new sector planning scheme.
# Salient Semantic Segmentation Based on RGB-D Camera for Robot Semantic Mapping
## 4 Experiments
### The Performance in the Testing Set Data
#### 4.2.5 Speed and Performance Comparison  
We also compared the real-time performance of our proposed salient semantic segmentation with the related Highly Cited Models in semantic segmentation. We pay attention to the real-time performance and manually label the 100 BDD100k images' ground truth used for salient semantic segmentation testing. The real-time of our proposed method in Table 7 is the performance of the proposed method in high-resolution images and general GPU speed. Similarly, FPS can be improved by using a lower image input resolution and a more powerful GPU. In theory, it could reach 33 FPS under the condition of image size 224 \(\times\) 224 and Titan GPU. Figure 9: In the same coordinate system, we set the RGB’s three channels to 255, where the coordinates are identical to the salient objects’ coordinates in grayscale images. The salient objects’ coordinates in grayscale images are where the gray values are greater than 200. Here the threshold of 200 is set according to the imaging effect of the salient objects in the grayscale image (Confidence: Left person 0.951, Right person 0.969). We manually labeled the 100 BDD100k testing part's ground truths for salient semantic segmentation testing. The saliency detection of these images has a clear contour, as shown in Figure 7. Other classical semantic segmentation algorithms used for comparison are based on the ground truth of the 100 images with semantic segmentation annotations from the BDD100K testing part for the three-class objects' test calculations. To compare the quantitative performance of our method and the related highly cited algorithms, we used three commonly used performance measures to illustrate. The relevant experimental details were as above in Table 9. In order to facilitate horizontal comparison, we made the same experimental arrangement as much as possible. These semantic segmentation neural networks are trained based on 224 BDD100K images (randomly selected from the training part with semantic segmentation annotations of BDD100K). The training segmentation targets are also three types of objects. The image 10K datasets in BDD100K can be used for semantic segmentation training and testing, and the per picture has 5.9 cars, 5.8 poles, and 1.8 traffic signs on average. BDD100k image contains more objects and types. Although there are only 224 images, it contains more training information than the dataset used in our method. Our training dataset differs from BDD100k because our algorithm only needs object recognition training and does not require semantic-level annotation. The salient objects in DUTS and Cars are imaging larger, there is only one kind of object in an image, and the details of contour features are detailed. It is easy to implement salient object detection and carry object recognition training. Through the comparison, we can know that our proposed algorithm is easier to
# Two-Wire Power Line Microwave Communication Using Low-Loss Modes
## 3 The Spectral Solution
### A Simple Solution  
[PERSON], [PERSON] and [PERSON] [25] have developed a simplified solution for the total current in the single conductor perfectly conducting the case that is valid for \(z/(k_{0}a^{2})>>1\). It is  
\[\hat{I}^{CA}(z)\cong\frac{-jV}{\eta_{0}}\ln\bigg{[}1+\frac{2\pi j}{(\ln(2z/(k _{0}a^{2})-\gamma_{\epsilon}-j3\pi/2)}\bigg{]}e^{-jk_{0}z} \tag{52}\]  
It was shown in [22] that (52) is a very good approximation of the total current (i.e., continuous spectrum plus the SG mode) induced on a conductor with typical conductivity.  
Hence, it was used as a simple approximation for the total current on a lossy wire. It will be shown in the next section that it is reasonable to use (52) for the common mode current in the two-wire case if the wire radius \"\(a\)\" is replaced by the geometric mean radius of the two wires, \(\sqrt{ad}\). The notion that this is reasonable can be shown by examining (49) and noting that a major contribution to the common mode current is the same as that for the single wire case in [22] but with \"\(a\)\" replaced by \(\sqrt{ad}\).  
It will also be demonstrated shortly that the quasi-TEM mode contribution to the differential mode is the dominant contribution away from the immediate vicinity of the source. Its residue (using small argument expansions for the Hankel functions in (35) is  
\[\hat{l}^{dA}(z)=\frac{2\pi V}{\eta_{0}\ln(d/a)}e^{-\hat{\jmath}_{IQTEM}z} \tag{53}\]  
Hence, a reasonable approximation for the total current on the two wires is as follows  
\[\hat{l}_{1/2}(z)=\hat{l}^{CA}(z)\pm\hat{l}^{dA}(z) \tag{54}\]  
where the plus (minus) sign is for the current on wire one (two).
# Vision Systems for a UR5 Cobot on a Quality Control Robotic Station
## 2 Experimental Setup of the Robotic Station  
As part of the research work, a robotic quality control station equipped with a UR5 cobot and an original vision system with dedicated software was developed (Figure 1 (red dotted line) and Figure 2). The task of the robotic station is to measure a selected group of semi-finished products and segregate them based on defined quality criteria. The developed quality control station also cooperates with neighboring cells equipped with SCARA i4L-550 robots and the AMR LD90 mobile robot (Figure 1, green dotted line). Thus, it complements the technological stack implementing the geometric analysis of semi-finished products.  
The designed robotic station, in cooperation with neighboring stations (Figure 1), performs the following tasks:  
* Transporting semi-finished products from the storage area of the OMRON SCARA i4-550L station and, after completing the vision-based analysis process, placing them in the local storage area of UR5.
* Handling elements from the local warehouse area of the UR5 cobot to the LD-90 mobile robot, where the elements go back to the beginning of the robotic line.
* Handling the collision zone occurring in the area of collecting elements from the SCARA robot and UR5 cobot.  
The robotic stand which was designed and constructed (Figure 1(1)) is equipped with the following (Figure 1 (red dotted line) and Figure 2):  
* Cobot UR5 CB2 with the piCOBOT vacuum ejector from Piab. It is a compact and exceedingly powerful vacuum pump unit.
* Mako G-125B GigE camera (CCD, 1292 \(\times\) 964, frame rate: 30 fps, ADC: 14 bit, PoE) with the Computar lens (focal length: 16 mm, iris: F1.4-F16).
* Savio CAK-01 USB webcam (CMOS, 1920 \(\times\) 1080, frame rate: 30 fps).
* Illumination system (backlight) for the Mako camera. In the case of a Savio CAK-01 camera placed on the robot, a fluorescent lamp lighting system installed in the room was utilized.
* PC platform: Intel Core i7 4702 MQ, 16 GB RAM, NVIDIA GeForce GT740M; OS: Windows 10 22H2.  
Figure 1: Communication diagram for integration of robotic station UR5: 1—quality control station; 2—cobot station cooperating with other robots (stationary i4-550L, mobile LD-90) from the robotic line.
Distinct from other fusion strategies, we generate weight maps through a deep neural network (i.e., UNet) under the guidance of a multi-objective mixed loss function in an unsupervised manner, which allocates and determines the optimal pixel-level weights to each pre-enhanced image based on the no-reference metric value of the fused images. Note that the strategy of utilizing random input \(\mathcal{Z}\) as network input facilitates the generation of optimal pixel-level weight maps across different scenarios, significantly boosting its robustness. In addition to pixel-level weighting, we also incorporate global weighting coefficients for the pre-enhanced images, creating a mixed weighting mechanism that ensures the optimal enhancement of the fused image. 3. To the best of our knowledge, there are multiple objective evaluation metrics for assessing the enhancement performance of LLIEs, and each of these metrics has its own emphasis. To provide a more concise and comprehensive assessment of the various LLIE methods, we have devised a novel scoring system to evaluate their overall performance. Within this scoring system, an optimal combination pattern of LLIE methods (i.e., Night and PairLIE) was selected, which outperforms others comprehensively. Note that our method has potential scalability. In the future, if new high-performance low-light enhancement methods emerge, they can still be incorporated into the proposed fusion framework to achieve better enhancement effects, verified through our scoring system. The organization of this paper is as follows: In Section 2, a comprehensive review is provided for the selected methods, which are chosen based on their complementary characteristics. These methods generate pre-enhanced images that serve as target images integrated into our fusion framework. Section 3 focuses on the backbone network used in the fusion stage. It covers the structure of the network, the processing of the generated image, and the composite loss function applied in the optimization process. Section 4 provides detailed information about the experimental dataset, settings, and the results obtained from comparing our approach with other LLIE methods. This comparison includes both classical methods and SOTA LLIEs, allowing for a comprehensive evaluation of our method. Finally, in Section 5, a comprehensive summary of our work is presented, highlighting the key contributions and findings.
# Guineensine: Isolation, Synthesis, and Biological Activity
## 3 Total Synthesis and Reactions
### Total Synthesis
#### 3.1.3 The [PERSON] et al. Approach (2011)  
[PERSON] et al. employed a Julia-Kocienski reaction for the construction of the styryl double bond. According to the authors, this was the key step to their synthetic route (Scheme 3) [56].  
The intermediate sulfone **45** was prepared from the commercially available thiol **43** by a Mitsunobu reaction with the alcohol **42** followed by oxidation with ammonium heptamolybdate and H\({}_{2}\)O\({}_{2}\). As with the Wittig reaction, Julia olefination allows you to select which alcohol will be converted into the sulfone. The other reactant would be used as an aldehyde. [PERSON] et al. abandoned the alternative option of using THP-protected decanediol-derived sulfone and piperonal early due to very low yields.  
At this point, there is a controversial issue regarding the synthesis of product **46**. [PERSON] et al., in their total synthesis publication [57], reported that they tried to prepare **46** using the Julia-Kocienski reaction, but without success due to poor \(E/Z\) selectivity (1:1.7), even though the yields were high. This inconsistency was attributed to the reaction conditions. Taking into account that the experimental protocols were not included in the initial publication, [PERSON] et al. supposed that [PERSON] et al. actually followed the Barbier protocol. In this case, first the sulfone is mixed the aldehyde followed by the addition of the base, as opposed to the premetallation technique, where the sulfone is firstly mixed with the base and the addition of the aldehyde follows. This variation alters the selectivity of \(E/Z\), often in favour of the \(E\)-alkene [58]. Whether these conditions ultimatelylead to the product **46** with satisfactory \(E/Z\)-selectivity remains to be seen when a research team repeats the reaction under Barbier conditions.  
Protected alcohol **46** was easily converted to the corresponding aldehyde **48** by acid deprotection of the hydroxyl group and oxidation using 2-iodobenzoic acid (IBX). The useful dienoate **40** was successfully accessed from **48** by an efficient approach. First, Corey-Fuchs alkynylation and carboxylation with \(n\)-BuLi and ethyl chloroformate of the intermediate dibromoalkene **49** provided alkynoate **50**[59]. Next, using the Rychnovsky variation in the Trost isomerization with PPh\({}_{3}\) and PhOH [60,61], ester **40** was obtained in excellent yield. Finally, **40** was converted to isobutylamide in two steps. The authors do not mention any technique for the purification of the final product.  
[PERSON] and co-workers' methodology gives access to guineensine more efficiently than the previously reported methods but in more steps, employing an interesting synthetic approach.
## 2 System Description
### Models of Main Devices
#### 2.2.1 Chtp  
CHP receives gas as input and converts it into electricity and heat output, generating CO\({}_{2}\) emissions in the process. The output of heat is its primary function, and it follows the principle of'setting power with heat' [23]. The constraints of CHP are as follows:  
\[P^{E}_{CHP,t}\leq P^{E}_{CHP,max}-\alpha_{CHP_{1}}P^{H}_{CHP,t} \tag{1}\]  
\[P^{E}_{CHP,t}\geq P^{E}_{CHP,min}-\alpha_{CHP_{2}}P^{H}_{CHP,t} \tag{2}\]  
\[P^{E}_{CHP,t}\geq\alpha_{CHP_{3}}P^{H}_{CHP,t} \tag{3}\]  
\[G_{CHP,t}=\beta_{CHP}P^{H}_{CHP,t} \tag{4}\]  
\[C_{CHP,t}=\gamma_{CO_{2}}G_{CHP,t}=\gamma_{CO_{2}}\beta_{CHP}P^{H}_{CHP,t} \tag{5}\]  
where \(P^{E}_{CHP,max}\) and \(P^{E}_{CHP,min}\) refer to the maximum and minimum of electricity generated by CHP, while the coefficients \(\alpha_{CHP_{1}}\), \(\alpha_{CHP_{2}}\) and \(\alpha_{CHP_{3}}\) denote the electrical and heat conversion efficiency. In addition, \(\beta_{CHP}\) represents the gas consumption efficiency of CHP, while \(\gamma_{CO_{2}}\) is used to denote the efficiency of CO\({}_{2}\) generation from the combustion of \(CH_{4}\). Equations (1)-(3) describe the constraints on the relationship between electrical and heat power in CHP. Equation (4) and Equation (5) represent the constraints for the gas consumption and carbon emissions of CHP, respectively.