esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### The Geodynamic Model Examples
#### 3.2.1 Model of Fracturing in the Rock Mass  
As the analysis of the manifestation of geodynamic processes shows, they are expressed in the structural and textural rearrangement of the rock. We will consider these transformations using the example of fracture formation in fractured carbonate reservoirs. At first, vertical cracks appear in the initially layered rock model under the influence of irreversible deformations. Such a model, corresponding to the model shown in Appendix A (Equation (A2)), can be described by the following distribution of local electrical parameters:  
\[\sigma=10X(x)Y(y)Z(z),\]where  
\[X(x)=\left\{\begin{array}{c}0.01,x=[0,0.2]\\ 10,x=[0.02,0.02+h]\\ 0.01,x=[0.02+h,0.04+h]\end{array}\right.;\ Y(y)=\left\{\begin{array}{c}1,y=[0, 0.02]\\ 0.15,y=[0.02,0.04]\\ 0.5,y=[0.04,0.06]\\ 2,y=[0.06,0.061]\\ 0.3,y=[0.061,0.081]\end{array}\right.;\ Z(z)=\left\{\begin{array}{c}1,z=[0, 0.2]\\ 0.1,z=[0.2,2.2]\end{array}\right..\]  
The dependence of the effective tensor of resistivity on macroscale anisotropy is shown in Figure 10.  
The behavior of the graph of the anisotropy coefficient shows that the macro anisotropic characteristics of the rock in terms of electric conductivity depend, and quite strongly, on the presence, thickness, and electric conductivity of cracks. In this case, the macroscale anisotropy coefficients change to  
\[k_{22}^{11}=\sqrt{\frac{\rho_{22}}{\rho_{11}}}=[1.26,0.38];\ k_{33}^{11}= \sqrt{\frac{\rho_{33}}{\rho_{11}}}=[1.29,0.38];\ k_{33}^{22}=\sqrt{\frac{\rho_{ 33}}{\rho_{22}}}=[1.01,1.01]\]  
when the crack opening degree (h) changes from 0 to 3.2\(\cdot 10^{-4}\) m.  
This change is sufficient and permits us to assert that the resistivity macroscale anisotropy tensor is an informative parameter sensitive to modern geodynamic processes. Additionally, we conclude that it is necessary to monitor all elements of the macroscale anisotropy tensor because, as follows from the above calculations, the anisotropy coefficient \(k_{33}^{22}\) varies slightly depending on the crack opening degree. Thus, all the elements of the macroscale anisotropy tensor are specific objects of research in active electromagnetic monitoring. The following model confirms this fact.
## 4 Experiments  
### Scene Description and Data Preprocessing  
1. Donghai Bridge dataset [58]: The Donghai Bridge (Shanghai, China) is 32.5 km long and equipped with more than 200 cameras to support the commercial operation of self-driving trucks in Yangshan Port, as shown in Figure 6. This dataset contains video data during peak and off-peak hours throughout the day. In this experiment, we obtained data from two visual sensors on the Donghai Bridge to realize vehicle trajectory reconstruction. The distance between the visual sensors is 1020 m. The videos were collected during the peak hours of the day and lasted for half an hour, and the traffic flow in the videos was restricted. We have adjusted the debouncing and color contrast to make the videos more suitable for multi-target tracking. We captured 101 sets of vehicle images from two videos to create a dataset for the vehicle re-identification  
Figure 5: The neural network structure of the bidirectional LSTM vehicle trajectory completion model based on graph attention.  
experiment. We also obtained complete short-distance trajectory data from the videos and created a dataset to evaluate the vehicle trajectory completion model.
2. CityFlow dataset [59]: To train the vehicle re-identification model, we used the CityFlow dataset. The dataset is captured by 40 cameras in the traffic surveillance environment. It contains 56,277 bounding boxes in total. The training set comprises 36,935 bounding boxes from the 333 vehicles, and the test set comprises 18,290 bounding boxes from the other 333 identities. The remaining 1052 images are the queries. The dataset contains a variety of vehicles and can be used as training data for the vehicle re-identification model.
3. The Argoverse bidirectional trajectory completion dataset: To train and evaluate the trajectory completion model, we modified the Argoverse motion forecasting dataset [60] and named it the Argoverse bidirectional trajectory completion dataset. The Argoverse motion forecasting dataset can be used to support vehicle trajectory forecasting tasks, providing rich trajectories and lane centerlines collected by a fleet of AVs in Pittsburgh and Miami. The dataset contains 333 K 5 s long sequences. We split it into 200 K for training, 66 K for validation, and 67 for testing. The vehicle trajectories of the Argoverse bidirectional trajectory completion dataset are sampled at 10 Hz. We selected the (0, 2] s and the (4, 5] s as the ground truth and the (2, 4] s for prediction.
## 3 Design of the Proposed HRES beyond Economic Optimization
### Solving Method  
PSO (particle swarm optimization) algorithm is one of the meta-heuristic algorithms used to find the approximate solution in the case of a combinatorial optimization problem [35; 36; 37]. The behavior of this algorithm comes from the characteristic of collective organisms. Under giving the objective function as the searching target, particles move around with information communication in searching space to find the optimal solution. This method, which consists of a constant search of the best solution, moves the particles at a specific speed calculated in each iteration. The expected result is that the particle swarm converges to the best solution. Particle motions are defined by a vector, which represents the velocity of the swarm in each direction. The velocity and position for each swarm are updated according to its experience and the best global particle. PSO was used to find the optimal size of the main components of the proposed HRES. Figure 5 shows the connection between the optimization and the simulation parts. The simulation part can calculate the hourly hydrogen and electricity generation over a year, given the demand load requirement and weather conditions (temperature and global radiation). The fitness values of the PSO algorithm are the installed capacity of each component of HRES such as PV module, fuel cell, electrolyzer, hydrogen tank, and SCWG reactor. The estimated fitness values were checked with the simulation part to ensure that the HRES meets the annual electricity balance. If the goal was not achieved, the PSO algorithm would find another fitness value by updating the velocity and position of each particle, using the following equations:  
\[v_{id}(t+1)=\omega\cdot v_{id}(t)+c_{1}\cdot\phi_{1}\cdot(P_{id}(t)-x_{id}(t) )+c_{2}\cdot\phi_{2}\cdot(g_{id}(t)-x_{id}(t)) \tag{31}\]  
\[x_{id}(t+1)=x_{id}(t)+v_{id}(t+1) \tag{32}\]  
where \(\mathrm{v_{id}(t+1)}\) is particle vector after updating; \(\mathrm{v_{id}(t)}\) is particle vector before updating; and \(\omega\) is inertia weight of vectors, which control the vector size--this value should be selected from 0.5 to 1 [38]. Here, \(\omega\) is set as 0.7. The values of \(C_{1}\) and \(C_{2}\) are set as 1.0 and 0.5, respectively [39].  
Figure 5: The connection between the optimization and simulation parts. Adopted from [40].
# FluoC Spec 2--An Automated Field Spectroscopy System to Monitor Canopy Solar-Induced Fluorescence
## 1 Introduction
These field SIF instruments are important tools for our understanding of the structural and environmental controls of photosynthesis and SIF. For example, [PERSON] et al. [2] showed that the fluorescence yield is higher under cloudy days compared with sunny days. [PERSON] et al. [10] demonstrated that canopy structure is the dominant control of far-red fluorescence (specifically, SIF at 760 nm), while canopy biochemistry, such as the maximum carboxylation rate (Vcmax), and nitrogen concentration (%N) plays a secondary role. [PERSON] et al. [38] analyzed the angular dependence of SIF emissions on a wheat field. [PERSON] et al. [11] found that the SIF yield (SIF/APAR, where APAR is the absorbed photosynthetically active radiation) and light use efficiency (LUE = GPP/APAR) were negatively correlated over the course of a day. These measurements still only cover a small portion of the global biomes, and most of them are located in temperate ecosystems. To facilitate continuous and long-term observations, instruments that can be used by the entire scientific community are essential. Networks of ground-based sensors have been proved to be essential to our understanding of how different ecosystems function and also provide a way of upscaling the results to the regional and global scales with the help of airborne or satellite remote sensing. For example, the FLUXNET is a network of EC towers that provide continuous measurements of land-atmosphere exchanges of CO\({}_{2}\) and water [4]. The SpecNet is a network of optical sensors that collocate with EC towers, aiming to provide information on plant functioning [39]. A network of digital cameras has been established to monitor vegetation phenology, providing both observations of individual trees and a way to link ground observations to the satellite phenology data [40; 41; 42; 43]. Individual sites of SIF measurements have been established in the past few years, but a network that synthesizes the efforts is still lacking. Here, we describe the design, calibration, and field tests of the new version of FluoCSpec, known as FluoCSpec 2. FluoCSpec 2 has a better temperature control, data collection flow, and signal-to-noise ratio. We document the instrument design, calibration, data acquisition, and lab and field tests. Using this instrument, we establish a network of SIF sensors (FluoNet). In the end, we discuss the future directions of field SIF system.
## 3 Results and Discussion
### Antioxidant Activities and Total Phenolic Content in Different Tissues of Aridchoke Genotypes
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{4}{c}{**FRAP (\(\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{ }}}}}}}}} {{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{ }}}}}}}}}}}}}}}}}}\)))**})**} \\ \hline
**Genotype** & **Leaf** & **Stem** & **PFH** & **SFH** \\ \hline Orte 1 F4 P10 & 183.57 \(\pm\) 2.02 \({}^{\text{g}}\) & 381.78 \(\pm\) 1.66 \({}^{\text{b}}\) & 270.23 \(\pm\) 7.91 \({}^{\text{e}}\) & 407.52 \(\pm\) 1.08 \({}^{\text{a}}\) \\ Orte 1 F3 P8 & 216.71 \(\pm\) 1.67 \({}^{\text{f}}\) & 400.26 \(\pm\) 6.02 \({}^{\text{a}}\) & 299.02 \(\pm\) 5.48 \({}^{\text{d}}\) & 321.06 \(\pm\) 0.44 \({}^{\text{c}}\) \\ [PERSON] 1 F17 P3 & 114.26 \(\pm\) 6.13 \({}^{\text{i}}\) & 211.29 \(\pm\) 4.57 \({}^{\text{f}}\) & 144.89 \(\pm\) 2.06 \({}^{\text{h}}\) & 254.76 \(\pm\) 2.09 \({}^{\text{e}}\) \\ \hline [PERSON] 2 F7 P2 & 217.54 \(\pm\) 8.16 \({}^{\text{g}}\) & 519.67 \(\pm\) 5.24 \({}^{\text{b}}\) & 357.56 \(\pm\) 4.21 \({}^{\text{d}}\) & 434.14 \(\pm\) 5.48 \({}^{\text{c}}\) \\ [PERSON] 2 F7 P5 & 156.83 \(\pm\) 2.65 \({}^{\text{h}}\) & 311.87 \(\pm\) 2.90 \({}^{\text{e}}\) & 158.21 \(\pm\) 3.37 \({}^{\text{h}}\) & 352.42 \(\pm\) 1.03 \({}^{\text{d}}\) \\ [PERSON] 2 F15 P3 & 243.34 \(\pm\) 3.09 \({}^{\text{f}}\) & 552.57 \(\pm\) 5.99 \({}^{\text{a}}\) & 219.86 \(\pm\) 0.34 \({}^{\text{g}}\) & 340.80 \(\pm\) 12.81 \({}^{\text{d}}\) \\ \hline Campagnano & 161.12 \(\pm\) 1.92 \({}^{\text{i}}\) & 329.53 \(\pm\) 4.64 \({}^{\text{e}}\) & 443.31 \(\pm\) 6.13 \({}^{\text{b}}\) & 275.42 \(\pm\) 1.36 \({}^{\text{g}}\) \\ C3-13 & 174.31 \(\pm\) 8.06 \({}^{\text{i}}\) & 403.77 \(\pm\) 7.49 \({}^{\text{d}}\) & 422.94 \(\pm\) 2.81 \({}^{\text{c}}\) & 437.94 \(\pm\) 1.12 \({}^{\text{bc}}\) \\ Grato 1 & 236.29 \(\pm\) 2.22 \({}^{\text{h}}\) & 340.55 \(\pm\) 0.01 \({}^{\text{e}}\) & 429.90 \(\pm\) 3.37 \({}^{\text{bc}}\) & 471.59 \(\pm\) 0.23 \({}^{\text{a}}\) \\ Castellammare & 173.60 \(\pm\) 1.40 \({}^{\text{i}}\) & 299.52 \(\pm\) 0.44 \({}^{\text{f}}\) & 311.13 \(\pm\) 6.85 \({}^{\text{f}}\) & 334.68 \(\pm\) 4.25 \({}^{\text{e}}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: FRAP values of leaf, stem, primary and secondary flower head (PFH and SFH) of six different genotypes of Orte 1 (F4 P10, F3 P8, and F17 P3) and Orte 2 (F7 P2, F7 P5, and F15 P3) populations, along with the four reference genotypes (Campagnano, C3-13, Grato 1 and Castellammare). The ANOVA analysis was conducted within each group (Orte 1, Orte 2, and the four reference genotypes) for all tissues analyzed. Different letters indicate statistically significant differences at \(p\leq 0.05\).
# Target-Aware Feature Bottleneck for Real-Time Visual Tracking
## 4 Experiments
### Experimental Settings  
For the experiments, we run our tracking algorithm given the testing split of two benchmark datasets and obtain performance metrics to conduct comparisons with other trackers. When evaluating a performance metric, we use a fixed hyperparameter setting throughout all the sequences in the test split. The large-scale LaSOT [19] benchmark represents a long-term tracking benchmark where it contains mostly long sequences with an average length of 2512 frames, which is equivalent to 83.7 s under a 30 fps setting. It contains 1400 sequences with 70 target object categories, where 280 sequences are used for testing and the remaining sequences are used for training. Along with the bounding box coordinates, annotations of various challenge attributes such as occlusion, out-of-frame disappearances, motion blur, background clutter, etc., are provided. To evaluate a tracker, three performance metrics are used which are the area under the curve (AUC) of the output success plot, center pixel precision plot, and target-scale normalized pixel precision plot. Success plots and precision plots are generated by varying the threshold values of the IoU and center pixel error.  
The large-scale GOT-10k [20] benchmark represents a short-term tracking benchmark where it contains mostly short-term sequences with an average length of 150 frames, which is equivalent to 15 s under a 10 fps setting as stated in the specifications [20]. It contains 10,000 sequences with 563 target object categories, where 180 representative test sequences were chosen for online evaluation. Different from previous benchmarks, GOT-10k aims to evaluate the tracker's generalization performance under a one-shot protocol where the training and test splits have no overlap in the target object categories. For the evaluation metrics, the average overlap (AO) measure for the predicted output and the labeled GT are measured over the test sequences, and the success ratio (SR) using both IoU threshold values 0.50 and 0.75 is measured. We evaluate the trackers using the one-pass evaluation (OPE) setting.
## 2 Literature Review
### Health and Safety Issues  
#### 2.3.1 Health Issues  
A study in British Columbia (Canada) found that consumers who purchase seafood because of its health and nutritional benefits tend to consume seafood more frequently [37]. While these benefits may correspond to different aspects, a different investigation found that in a focus group of European countries (France, Italy, Germany, Greece, Norway, Spain and the UK), almost a third of the discussions towards the health benefits of fish consumption focused on the benefits of Omega 3 [17].  
Some studies highlight that consumers are willing to pay premiums for seafood products including labels or claims supporting health benefits associated with Omega 3 [26; 38; 39]. An investigation found that Canadian consumers were willing to pay premiums for farmed salmon products with increased Omega 3 rather than for products which reduced local or global impacts, suggesting that health benefits were considered more relevant than the environmental performance of the production process, as in this case, the benefits are clearly internalized [26]. Nevertheless, in general, attributes associated to decreasedlevels of contamination or the origin of the product were ranked higher than the health benefits. Similarly, a different study found that consumers were willing to pay a premium for seabream that includes a natural Omega 3 claim; however, other attributes such as the origin, the harvest method (wild or farmed) and the sustainability claim were more important [39]. Interestingly, there were differences when the authors applied the same experiment to retailers, which evidenced that consumers and retailers could value the importance of attributes differently. Moreover, for amberjack in the European context (including Germany, France, Italy, Spain and the UK), another study found that consumers were willing to pay premiums for nutrition claims related to Omega 3 richness and health claims for improved heart function [38]. However, these factors were less valued than others such as the origin, price and the Aquaculture Stewardship Council (ASC) ecolabel.  
Moreover, the literature also highlights the preference for other health benefits different from those related to Omega 3. For earned tuna in the US, an investigation found that a heart-healthy label certified by the American heart association had a higher WTP than the Marine Stewardship Council (MSC) ecolabel and a Bisphenol A (BPA) free product label [23]. In addition, in the southern region of the US, a different study found that parents with children are willing to pay premiums for nutritional and health claims associated to seafood, with nutritional information being more relevant [40]. Curiously, the study also determined that providing the nutrition and health benefits information together did not increase the marginal willingness to pay for seafood products more than providing the information of health benefits or nutrition on its own.
## 1 Introduction  
The knee is the second most frequently injured body site among high school athletes, and anterior cruciate ligament (ACL) tears constitute over 50% of all knee injuries [1,2]. ACL tears affect 68.6 per 100,000 individuals annually, and 125,000-200,000 ACL reconstructions (ACLRs) are performed yearly in the United States [3]. Although the annual cost of ACLR in the U.S. is approximately one billion dollars, it appears to be a cost-effective procedure compared to conservative treatment [1,4]. ACLR restores the knee's native biomechanics and increases both anteroposterior and rotational stability. Moreover, this procedure also addresses concomitant intra-articular lesions, such as meniscal tears and chondral lesions [5, 6, 7]. Although the long-term clinical outcomes of standard intra-articular ACLR techniques have been proven to reliably reduce knee instability, these techniques do not fully restore the anterolateral rotational stability of the knee joint [8, 9, 10]. The reason behind rotatory knee laxity is that ACL injury often results in damage to the anterolateral knee structures (including the anterolateral ligament [ALL]) [11]. The integration of lateral extra-articular tenodesis (LET) with ACLR enhances the anterolateral complex of the knee, prevents persistent postoperative rotational instability, and reduces the risk of graft failure [12,13]. Several studies have reported that LET biomechanically aids in shifting the load from the ACL graft, which subsequently decreases excess anterior tibial translation and rotation [12, 13, 14, 15, 16]. Despite the abundant body of work on ACL identification in magnetic resonance imaging (MRI) and ultrasonography, the literature does not provide much information on postoperative ultrasound findings of the liotibial band (ITB) strip that is harvested during the modified Lemaire technique [17]. Ultrasonography is a non-invasive and cost-effective tool that allows assessment of soft tissue structures, making it particularly suitable for evaluating the ITB strip. The primary objective of this study was to determine the detectability of the ITB strip using ultrasound imaging and to describe its postoperative sonographic appearance in patients undergoing combined ACLR and LET by the modified Lemaire technique. Secondary aims included identifying any correlation between sonographic findings of the ITB strip and clinical outcomes. A better understanding of this subject creates potential opportunities to improve currently used therapeutic algorithms.
## 2 Materials and Methods
### Statistical Analysis  
Statistical analysis was performed using the SPSS 20.0 software (IBM Corporation, Armonk, NY, USA). The Shapiro-Wilks W test was used to test the normality of the distributions. The sample size calculation was done based on pilot measurements, which revealed very small variations (standard deviations ranged between 6 and 15% of the mean value). As the primary endpoint was to test the significance of the differences in loading positions and loading forces and to compare one-, two-, three-, and four-MDI models, with a presumption that the difference in the mean values will be between 15 and 30%, and with alpha set at 0.05 and a power of 80% (beta = 0.2) [30; 31], the calculated sample size varied between 6 and 14 measurements. However, we decided to perform 15 measurements for each loading force and each loading position.  
Mean values and standard deviations were calculated. Boxplot diagrams were also generated. The significance of the differences in the recorded peri-implant microstrains as dependent variables with the loading site and the extent of the applied forces as factors were tested using the MANOVA in each mandibular model: the one-, two-, three- and four-MDI model. The Bonferroni post-hoc test was used. The significance of the differences in the microstrains recorded from the right and left posterior edentulous area under denture saddles as dependent variables with the loading force, loading position and the number of mini-implants inserted as factors were tested using the MANOVA and Bonferroni post-hoc tests.
## 4 Result and Discussion
### Communication Performance
#### 4.2.1 Downlink Analysis
_Validation of the Proposed Heuristic Approach:_ Since the analysis performed for illumination and communication (in Sections 4.1 and 4.2, respectively) revealed that CoIQ-9 provides better performance than CLQ, we now validate that the proposed heuristic design formulae also yields optimum transmitter locations. Using (17)-(19), the optimized coordinates obtained from Algorithm 1 for \(5\times 5\times 3\) m\({}^{3}\) dimensions are:  
\[\begin{array}{l}TP_{0}=\left[ \begin{array}{rrr}0,&0,&1.5\\ TP_{1}=\left[-1.76,&-1.76,&1.5\\ TP_{2}=\left[-1.76,&1.76,&1.5\right]\\ TP_{3}=\left[\begin{array}{rrr}1.76,&1.76,&1.5\\ TP_{4}=\left[\begin{array}{rrr}1.76,&-1.76,&1.5\end{array}\end{array}\right]\right. \end{array} \]  
The resultant transmitter coordinates closely match with CoIQ-9 (see Figure 3), which implies that the proposed design formula provides optimum results. To corroborate the practicality of our method, Table 5 presents a comparison of CLQ and CoIQ for the results obtained against different room dimensions with a fixed number of transmitters. It can be observed that the performance metrics for illuminance and communication, especially the deviation in \(SNR\), have improved substantially in CoIQ. Additionally, with exception of room sizes that are smaller in dimensions than \(5\times 5\times 3\) m\({}^{3}\) (showing comparable average RMS delay spread), the \(D_{rms}\) values in CoIQ are lesser than CLQ and will facilitate relatively better bit rates. Figure 9: SNR histograms for sleeping, sitting and standing positions under low-intensity conditions in CLQ and CoIQ-9 arrangements.