esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Unilateral Strength Ratio  
The results of our analysis did not reveal a significant effect of the observed main factors on the unilateral ratio in the target groups. In the ACL group, the H:Q ratios in the injured and non-injured legs were almost the same (IL: 61.55 \(\pm\) 16.17%; NL: 61.89 \(\pm\) 13.68%). However, in the HAM group, the difference was more than 3.5%. An H:Q ratio of 55-64% is considered a healthy limit in soccer players [22]. The H:Q ratio is a commonly used test for detecting injury risk. However, it appears to be an insufficient predictor without a combination of other injury predictors [56]. The authors of [56] found the biggest problem with this method to be that it is used in a fatigue-free state, but most hamstring injuries and anterior cruciate ligament (ACL) injuries occur in a state of fatigue. Football-specific fatigue disrupts the mechanics of neuromuscular feedback, and the time between the onset of the response to a stimulus of muscle activity and the onset of strength is prolonged [10]. Furthermore, fatigue causes a temporary decrease in muscle strength and negatively affects the movement pattern of the lower limbs [57]. This leads to a deterioration of the dynamic stability of the knee joint from a neuromuscular point of view, which may increase the risk of ACL injury [10]. On the other hand, the conventional H:Q ratio can be a good predictor of recurrent ACL injuries in combination with other methods [58]. In disagreement with our study results, [PERSON] and [PERSON] [37] conducted several meta-analysis prospective studies in which they analyzed the isokinetic strength of the knee muscles as a risk factor for hamstring injury, showing that the high strength of the quadriceps relative to the hamstring was a significant risk factor for lower limb injuries (SDM = 0.43, 95% CI: 0.05-0.81, \(p=0.03\)). [PERSON] et al. [21] found a statistically significant relationship between an H:Q ratio below 50.5% and the risk of acute posterior thigh muscle injury in professional soccer players (OR = 3.14; 95% CI: 1.37-2.22), based on concentric mode testing at angular speeds of 60\({}^{\circ}\cdot\)s\({}^{-1}\) and 240\({}^{\circ}\cdot\)s\({}^{-1}\).  
In spite of the existence of the abundant literature dedicated to the relationship between strength asymmetry and injury, it remains controversial. Studies on the etymology of sports injuries must take into account the multifactorial nature of sports injuries by including as many relevant risk factors as possible and using a multidimensional statistical approach [59].
Deep Learning Segmentation of Satellite Imagery Identifies Aquatic Vegetation Associated with Snail Intermediate Hosts of Schistosomiasisiasis in Senegal, Africa  
[PERSON]  
1 Hopkins Marine Station, Stanford University, Pacific Grove, CA 93950, USA; [PERSON]_ADDRESS] (A.J.C.); [PERSON]ADDRESS] (K.T.); [PERSON]DDRESS] (I.J.J.); [PERSON]DRESS] (S.H.S.); [PERSON]_ADDRESS] (G.A.D.L.)  
[PERSON]  
1 Hopkins Marine Station, Stanford University, Pacific Grove, CA 93950, USA; [PERSON]_ADDRESS] (A.J.C.); [PERSON]ADDRESS] (K.T.); [PERSON]DDRESS] (I.J.J.); [EMAIL_ADDRESS] (S.H.S.); [PERSON]_ADDRESS] (G.A.D.L.)  
[PERSON]  
1 Hopkins Marine Station, Stanford University, Pacific Grove, CA 93950, USA; [PERSON]_ADDRESS] (A.J.C.); [PERSON]ADDRESS] (K.T.); [PERSON]DDRESS] (I.J.J.); [PERSON]DRESS] (S.H.S.); [PERSON]_ADDRESS] (G.A.D.L.)  
[PERSON]  
1 Hopkins Marine Station, Stanford University, Pacific Grove, CA 93950, USA; [EMAIL_ADDRESS] (A.J.C.); [EMAIL_ADDRESS] (K.T.); [EMAIL_ADDRESS] (I.J.); [EMAIL_ADDRESS] (S.H.S.); [EMAIL_ADDRESS] (G.A.D.L.)  
[PERSON]  
2 Department of Computer Sciences, Stanford University, Stanford, CA 94305, USA; [EMAIL_ADDRESS] (J.L.)  
[PERSON]  
7 Institute of Human-Centered Artificial Intelligence, Stanford University, Stanford, CA 94305, USA; [EMAIL_ADDRESS] (J.L.)  
[PERSON]  
8 Institute of Human-Centered Artificial Intelligence, Stanford University, Stanford, CA 94305, USA; [EMAIL_ADDRESS] (J.L.)  
[PERSON]  
9 Institute of Human-Centered Artificial Intelligence, Stanford University, Stanford, CA 94305, USA; [EMAIL_ADDRESS] (G.A.D.L.)  
[PERSON]  
10 [PERSON]  
11 Hopkins Marine Station, Stanford University, Pacific Grove, CA 93950, USA; [PERSON]_ADDRESS] (A.J.C.); [PERSON]ADDRESS] (K.T.); [PERSON]DDRESS] (I.J.); [PERSON]DRESS] (S.H.S.); [PERSON]_ADDRESS] (G.A.D.L.)  
[PERSON]  
12 Department of Earth System Science, Stanford University, Stanford, CA 94305, USA; [EMAIL_ADDRESS] ([PERSON]
semantic segmentation. A deep learning model (U-Net) was built to automatically analyze high-resolution satellite imagery to produce segmentation maps of aquatic vegetation, with a fast and robust generalized prediction that proved more accurate than a more commonly used random forest approach. Accurate and up-to-date knowledge of areas at highest risk for disease transmission can increase the effectiveness of control interventions by targeting habitat of disease-carrying snails. With the deployment of this new framework, local governments or health actors might better target environmental interventions to where and when they are most needed in an integrated effort to reach the goal of schistosomiasis elimination.  
deep learning; U-Net; semantic segmentation; schistosomiasisomiasis; aquatic vegetation; neglected tropical diseases; Senegal River Basin
## 3 Distance-Aware Replica Adaptive Data Delivery Scheme
### Data Delivery Algorithm  
By receiving hello messages, each node maintains a dynamic neighbor list in which it can acquire the delivery probability of every neighbor within its communication range. Take \(n_{i}\) for example, let it have a data message \(M\) need to delivery, and its neighbor set be \(\sum=\{\Psi\text{Z}\ |\ 1\leq\text{Z}\leq\text{Z}\}\), where Z is the neighbor number of \(n_{i}\). Firstly \(n_{i}\) looks for the neighbor with a highest delivery probability, denoted as \(n_{m}\), in set \(\sum\).  
Figure 2: Pseudo-code of the data delivery algorithm.  
If the delivery probability of \(n_{m}\) is greater than that of \(n_{i}\), then \(n_{m}\) is just regarded as the next hop; or else, there is no proper next hop at present, and the routing algorithm ends. Secondly, \(n_{i}\) checks whether the ticket of \(M\), denoted as \(ticket_{M}\), is equal to 1. If so, then \(n_{i}\) directly transmits \(M\) to \(n_{m}\), routing decision completes. Otherwise, \(i.e.,ticket_{M}\geq 2\), \(n_{i}\) generates a replica \(M^{\prime}\) for message \(M\), and set \(ticket_{M^{\prime}}\) as \(\left\lfloor{ticket_{M}}/{2}\right\rfloor\). Node \(i\) forwards \(M^{\prime}\) to \(n_{m}\). At last, \(n_{i}\) updates \(ticket_{M}\) as\(\left\lceil{ticket_{M}}/{2}\right\rceil\)and then puts \(M\) into its routing queue. Detailed pseudo-code is shown in Figure 2.
## 3 Results and Discussion
### Flavor Compound Distribution in Commercial Beverages  
The distribution pattern of VOC flavors proved to be different for each type of commercial beverage (Figure 1). Except for the carbonated lemon drink, ester compounds were the majority of flavors in all other three samples. In the apple juice sample, ester compounds were detected in 64%. Alkene compounds were the next predominant class of compounds (18%), followed by aldehydes and terpenes/terpenoids, which together amounted to 18% (Figure 1a). In the commercial tea, esters represented 57% of the total flavor compounds identified. Terpenes/terpenoids were also detected as a high percentage (29%), while alkenes represented only 14% (Figure 1b). A high percentage of esters were also detected in the flavored sparkling water (53%), while alkene and terpene/terpenoid compounds together were up to 20%. Unlike the other samples, alcohols and aldehydes were identified, totaling a significant percentage of up to 27% (Figure 1d). A totally different pattern was observed in the case of the carbonated lemon drink (Figure 1c). Terpenes/terpenoids were the majority of compounds identified in this sample, representing a percentage of 86%, with a difference of up to 100% being taken up by aromatic compounds.  
Figure 1: Percentage distribution of VOC flavors in (**a**) AJ, (**b**) F, (**c**) S and (**d**) CC samples.
## 4 Result
### NMR Experiment and Its Fractal Characteristics
#### 4.3.2 NMR Fractal Dimension  
The NMR fractal dimension of shale samples derived from the Funing Formation in the Gaoyou Sag is depicted in Figure 8. The curve demonstrates a clearly discernible two-stage pattern. The fractal curve can be partitioned into two distinct segments, delineated by the \(T_{2C}\) cut-off value. The segment featuring relaxation times less than \(T_{2C}\) represents bound-fluid pores, while the segment with relaxation times exceeding \(T_{2C}\) represents movable-fluid pores. By linear fitting of the two parts, the bound-fluid-pore fractal dimension (D\({}_{\rm Min}\)) and the movable-fluid-pore fractal dimension (D\({}_{\rm Max}\)) (Table 3) are obtained. The observed high values of the fitting degrees suggest that NMR fractal analysis can be employed as a viable method for characterizing the pore structure. It can be seen from Table 3 that D\({}_{\rm Min}\) is between 0.5795\(\sim\)1.3813 (the average value is 0.8110); D\({}_{\rm Max}\) ranged from 2.9592 to 2.9793 (mean 2.9651).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Sample ID** & **Total Porosity/\%** & **Porosity of Movable Fluid/\%** & **Bound Fluid Porosity/\%** & **Permeability/mD** \\ \hline L-1 & 7.68 & 3.78 & 3.90 & 0.06777 \\ L-2 & 6.41 & 3.63 & 2.78 & 0.05983 \\ L-3 & 4.28 & 1.10 & 3.18 & 0.00083 \\ L-4 & 5.34 & 1.98 & 3.36 & 0.00586 \\ L-5 & 5.21 & 2.80 & 2.41 & 0.02072 \\ L-6 & 5.13 & 3.53 & 1.60 & 0.06981 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Physical parameters of shale calculated based on the NMR experiment.  
Figure 8: NMR fractal dimension curves of the Funing Formation shale in the Gaoyou Sag.
## 3 Results and Discussion  
### Effect of the Mass Ratio of Fe(0) to ATP  
Figure 1 shows the Ni\({}^{2+}\) removal efficiencies of different materials. The Ni\({}^{2+}\) removal efficiencies of the nZVI@ATP composites exceeded those of the pure ATP at different Fe-to-ATP ratios. The removal efficiency of nZVI@ATP for Ni\({}^{2+}\) first increased and then decreased with an increasing iron-ATP ratio and was the highest at the iron-ATP ratio of 2:1 (reaching 71.93%). This finding demonstrates that the material at this particular ratio possessed the highest number of active sites. This characteristic ensures an adequate number of active sites and effectively prevents a decline in adsorption efficiency caused by issues such as nZVI agglomeration. A further increase in the Fe-to-ATP ratio resulted in a gradual decrease in adsorption performance, probably caused by the insufficient number of effective active sites due to the decreasing Fe\({}^{0}\) content. Easy agglomeration and oxidation are factors impeding the full exploitation of the adsorption performance of pure nZVI. Notably, when the ATP was used for loading, the nZVI agglomeration phenomenon considerably improved and more active sites were involved in the reaction to improve the adsorption performance of nZVI@ATP on Ni\({}^{2+}\). Because the best adsorption performance of the composites on the Ni\({}^{2+}\) was achieved at an iron-ATP of 2:1, the adsorption performance and mechanism were studied based on this ratio in subsequent experiments.
## 2 Materials and Methods
### Coagulation/Flocculation and Sedimentation Tests  
The coagulation/flocculation tests were performed in a jar test floc control (PoliControl, Sao Paulo, Brazil) by adding 40 mL of each extract coagulant solution to 300 mL of microalgal suspension collected from the photobioreactor (PBR). The experiments were conducted in triplicate for each coagulant dosage (3.5, 5.9, and 8.2 g L\({}^{-1}\)) and for each eluent, with a total of 18 assays. After adding the coagulant, the suspensions were subjected to rapid mixing at 150 rpm for 3 min and a slow mixing step at 30 rpm for 15 min. The stirrer was then switched off, and the flocs were allowed to settle undisturbed for 30 min. Additionally, a control test was performed in the same conditions but without adding a coagulant. The optical density at 750 nm was monitored in three stages: at the beginning of the experiment, before the addition of coagulant, (OD\({}_{750}\))\({}_{1}\); after coagulation/flocculation and before settling, (OD\({}_{750}\))\({}_{2}\); and in the supernatant after settling (OD\({}_{750}\))\({}_{3}\). The monitorization of the optical density allows the measurement of the light absorbed by the sample. The harvesting efficiency was calculated as represented in Equation (4) [27]. Considering that the pH of the eluent solutions could influence the harvesting efficiency, it was of paramount importance to evaluate the hydrogenionic potential before, during, and after coagulation, flocculation, and sedimentation tests. The initial pH of the culture was adjusted to 7.0, and at the end of the 8 d of cultivation, the pH was 8.5. The acid and alkaline coagulant solutions that were adopted as eleuents for the adsorption of the coagulant agent of the cactus had a pH of 5.5 and 12.5, respectively. The coagulant solutions to the microalgal suspension caused a shift in the culture's pH from 8.5 to 7.9 and 9.1, respectively.  
\[\text{Harvesting efficiency}\ (\%)= \frac{(\text{OD}_{750})_{1}-(\text{OD}_{750})_{3}}{(\text{OD}_{750})_{1 }}\times 10 \tag{4}\]  
After settling, the sedimentation kinetics was evaluated using a 250 mL measuring cylinder to observe the height of the flakes falling over time. In this last test, the decanting kinetics of the flakes was verified, generating a curve of the surface height versus the decanting time of the flakes. A linear adjustment was performed for each curve to determine the average speed of sedimentation, which corresponds to the symmetrical of the curve slope.
## 3 Mathematical Model and Solution Algorithm  
### Problem Description  
#### 3.1.1 Problem Analysis  
Consider an urban rail transit line with \(N\) stations, each end of the line being connected to a depot. The set of up stations is \(S^{(u)}=\{1,2,\ldots,N\}\) and the set of down station is \(S^{(d)}=\{N+1,N+2,\ldots,2N\}\). The full-length route trains use the stations at both ends of the line as the originating and final stations. The short-turn route trains operate in certain sections, and the locations of the return stations are known. The up short-turn route trains run from station 1 to station \(m\) and returns, and the down train originates from station \(2N+1-m\) and ends at station \(2N\), as shown in Figure 1. After the rolling stock departs from the depot, it performs various train tasks before returning to the depot. The depot to which each rolling stock belongs is not fixed; hence, the departure and return depots may vary. This flexibility allows the rolling stock to operate regularly on different routes.
## 2 Materials and Methods
### Method
#### 2.3.2 VHI  
VHI is a remote sensing index that combines both vegetation and temperature data. This integration enables VHI to simultaneously reflect changes in moisture and temperature within a given region, making it an effective tool for monitoring drought conditions across various time scales and geographic areas [19; 59]. VCI [60], derived from the NDVI, is used to assess vegetation health [13]. TCI [19], calculated from LST, reflects land surface temperature and is widely employed in drought assessment and monitoring [61; 62]. In this study, VHI is employed to evaluate the severity of drought conditions in the YRB, where a lower VHI value indicates more severe drought conditions and a higher value signifies less severe drought. The formula for calculating VHI is as follows [17]:  
\[VCI_{i,j}^{k}=\frac{NDVI_{i,j}^{k}-min\Bigl{(}NDVI_{i}^{k}\Bigr{)}}{max\Bigl{(} NDVI_{i}^{k}\Bigr{)}-min\Bigl{(}NDVI_{i}^{k}\Bigr{)}} \tag{6}\]  
where \(NDVI_{i,j}^{k}\) is the monthly NDVI at a given pixel \(k\), month \(i\), and year \(j\), and \(max\Bigl{(}NDVI_{i}^{k}\Bigr{)}\) and \(min\Bigl{(}NDVI_{i}^{k}\Bigr{)}\) are the maximum and minimum value of NDVI in that same month \(i\) over the growing seasons from 2001 to 2021 [17; 63]. The \(VCI_{i,j}^{k}\) value ranges from 0 to 1. Higher VCI values indicate healthier vegetation conditions for the current month, while lower VCI values suggest poor vegetation health [60].  
\[TCI_{i,j}^{k}=\frac{max\Bigl{(}LST_{i}^{k}\Bigr{)}-LST_{i,j}^{k}}{max\Bigl{(} LST_{i}^{k}\Bigr{)}-min\Bigl{(}LST_{i}^{k}\Bigr{)}} \tag{7}\]  
where \(LST_{i,j}^{k}\) is the monthly LST at a given pixel \(k\), month \(i\), and year \(j\), and \(max\Bigl{(}LST_{i}^{k}\Bigr{)}\) and \(min\Bigl{(}LST_{i}^{k}\Bigr{)}\) are the maximum and minimum value of LST in that same month \(i\) over the growing seasons from 2001 to 2021 [17; 63]. The \(TCI_{i,j}^{k}\) value ranges from 0 to 1. Higher TCI values indicate more favorable temperature conditions for vegetation health during the current month, while lower TCI values suggest less favorable temperature conditions, possibly leading to thermal stress or drought conditions [61; 62].  
\[VHI_{i,j}^{k}=\alpha\times VCI_{i,j}^{k}+(1-\alpha)\times TCI_{i,j}^{k} \tag{8}\]  
where \(\alpha\) is the contribution of VCI to VHI, and \(1-\alpha\) is the contribution of TCI to VHI. The drought degree of the VHI index is divided into 5 grades, as presented in Table 2.
## 3 Analysis of Accuracy and Performance
### Analysis of VLF Amplitude and Phase Characteristics
#### 3.2.5 The Influence of the Change in Collision Frequency on the Phase  
The collision frequencies were amplified by 20 times and 10 times, respectively, and compared with the standard parameters to study the influence on the phase of the electromagnetic wave when the collision frequency was amplified and reduced. The phase-variation curve is shown in Figure 12. Figure 12 shows the variation curve of the Er phase value with the vertical distance when the vertical electric dipole source is placed on the ground under different collision frequencies.  
When the collision frequency is amplified, as the amplification factor increases, the abnormal situation of the phase becomes more obvious. The phase becomes different from 60 km above the ground. When the collision frequency is amplified by 20 times, the height at which abnormalities occur increases accordingly, and abnormalities occur at 73 km.  
Figure 11: (**a**) The electric-field intensity curves when the electron density is amplified compared with the standard situation; (**b**) the phase curves when the electron density is amplified compared with the standard situation.  
Figure 12: The phase curve when the collision frequency is amplified compared with the standard situation.
# A Deadline-Aware Scheduling and Forwarding Scheme in Wireless Sensor Networks
## 5 A Deadline-Aware Scheduling and Forwarding Scheme  
In order to solve the problem in Equation (3), we need to obtain the distribution function of E2E delay of a packet in the WSN. Since E2E delay is the sum of one-hop delays, as in Equation (1), we estimate the one-hop delay distribution. Nodes are deployed randomly with a uniform distribution, and each relay node makes a forwarding decision independently. In a dense network, each node has a similar number of neighboring nodes. Note that one-hop delay random variables can be considered to have the same distribution function. Therefore, in order to approximate the E2E delay distribution, we apply the central limit theorem for a long path, _i.e._, the E2E delay is approximated by the normal distribution function where the mean and variance of E2E delay are the sum of the mean and variance of one-hop delays, respectively.  
In this section, we first describe the proposed delay-constrained forwarding algorithm and then approximate the E2E delay distribution using the duty cycle interval and network parameters. Then, we show that with the given network parameters, it is possible to adjust the duty cycle interval to satisfy the delay bound with a desired delay-constrained success ratio.
## 8 Cross Layer Optimization
RRA scheme first minimizes the end-to-end transmission delay in the network while using minimum data generation rate, and then an optimal transmission radius is calculated in this phase. Then by using this derived transmission radius, the data generation rate can be adjusted to increase the amount of gathered data. Therefore, the cross layer framework of RRA scheme can be summarized into four steps: 1) choosing the optimal transmission radius for sensor nodes at the physical layer, 2) constructing multiple routing paths by using multipath routing protocol like TPGF in the routing layer, 3) selecting the suitable paths from the found paths by the routing protocol at the transport layer, and 4) adjusting the data generation rate of source nodes in the physical layer. Another cross layer design combining network and MAC layers for QoS enhancement in WMSNs is presented in [84]. At network layer, the proposed framework is aiming to find near-optimal paths satisfying the application-specific QoS requirements based on using Multi-Objective Genetic Algorithm (MOGA), while at MAC layer the routing information is used in the MAC algorithm, which is based on CSMA/CA, for QoS-based packet classification and automatic adaptation of the contention window.
## 4 Experiments and Analysis
### Comparison of Small Object Test Results
Corresponding to the second situation, none of the objects detected by classical YOLO satisfies the IoU condition in images 6, 12, 16, 17, 24, 30, 31, 41, 42, 50, 62, 65, 71, 77, 78, 79, 84, 94, and 96, and the same is true of the objects detected by Multi-view YOLO in images 4, 17, 33, 36, 50, 78, and 94; by classical YOLOv2 in images 50, 77 and 97; by Multi-view YOLOv2 in images 18, 50 and 63; and by classical SSD in images 26, 29, and 50. However, Multi-view SSD successfully retrieved objects in all images. When the above results are combined, classical YOLO has twenty-nine zero points, Multi-view YOLO has nine zero points, classical YOLOv2 has seven zero points, Multi-view YOLOv2 has three zero points, classical SSD has nine zero points, and Multi-view SSD has no zero points. Therefore, the multi-view methods demonstrate better retrieval abilities for small objects than the classical methods do.