esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Step 4: Scenario Modeling
#### 2.3.2. Intelligent Maintenance Based on Deterioration Modeling (Scenario 5)
The final use case scenario, Scenario 5, is dedicated to the deterioration modeling of the components associated with the case study, i.e., rotor, bearing, and seal. Modeling component deterioration is required to highlight the capabilities of implementing an intelligent maintenance management system, i.e., levels of detection, diagnosis, and prognosis [16]. This paper develops individual loading equations of the components of interest, based on the plot presented by [PERSON] [25]. Since [PERSON] only represents the deterioration curves and not the specific deterioration equations, the associated loading equations presented in Table 8 are replications. The individual loading equations developed are then incorporated into the \"Loading model\" (shown in Figure 5) and are simulated and optimized to fit the designed lifetime presented by [PERSON] [25] using Anylogic, as depicted in Figure 6. As seen, the deterioration curves highlight the entire deterioration process from when the specific component starts operating until a fault is present at the designed lifetime. The deterioration curves also demonstrate that the component deterioration propagates differently. Clearly, this affectsthe opportunities of detecting, diagnosing, and predicting the future behavior of the associated component deterioration process. For example, the deterioration of the seal appears with a steeper slope in comparison to the two other components, which thus reduces the opportunities of performing intelligent maintenance as it is more difficult to detect and predict the occurrence of seal deterioration. In contrast, the deterioration curve of bearing introduces the gentlest slope, which increases the opportunities to perform intelligent maintenance as it is possible to detect and predict the occurrence of bearing deterioration at an early stage. It is important to highlight that this research adopts a detection and prediction level of 70% for Scenario 5. The main justification of this selection is based on recommendations from the literature [31] and experts in the field.
## 4 Performance Metrics and Evaluation  
All the models proposed in this study are implemented using Tensorflow, Keras, and Scikit-learn. The number of epochs run for the model is 100, with a batch size of 64. The models were trained and tested on the dependent features of satellite images and the HURDAT2 data. The data are split into 70% for training, 20% for testing, and 10% for validation. To evaluate the performance of the models, the metrics used are mean absolute error (_MAE_), mean absolute percentage error (_MAPE_), mean squared error (_MSE_), and root  
Figure 10: Architecture of VGG 19.  
mean squared error (_RMSE_). In the below specified equations, the predicted output and the actual values are represented.  
\[MAE=\frac{1}{N}\sum_{i=1}^{N}(\overline{x_{i}}-x_{i}) \tag{10}\]  
\[Relative\ RMSE=\frac{\sqrt{\left(x_{p}-x_{t}\right)^{2}}}{\frac{n-1}{\overline{ x_{p}}}} \tag{11}\]  
\[MSE=\frac{1}{N}\ \sum_{i=1}^{N}(\overline{x_{i}}-x_{i})^{2} \tag{12}\]  
\[RMSE=\sqrt{\frac{1}{N}\sum_{i=1}^{N}(\overline{x_{i}}-x_{i})^{2}}. \tag{13}\]  
The proposed models are evaluated based on the above metrics given in Equations (10)-(13). The performance metric used for classification is the confusion matrix, which describes about four categories of data, i.e., true positives (_TP_), true negatives (_TN_), false positives (_FP_), and false negatives (_FN_). The success-failure ratio is determined by the confusion matrix.  
When the speed of the wind increases and exceeds a certain threshold value, then severe weather events cause serious damages to human lives, property, and crops. The performance metrics computed for the damage prediction are precision, recall, \(F\)1 score, false alarm rate (_FAR_), probability of detection (POD), and critical success index (_CSI_), as given in Equations (14)-(18).  
Precision: It is the ratio of actual positive identifications to the total actual hailstorm or hurricane and no hailstorm or no hurricane.  
\[Precision=\frac{TP}{TP+FP} \tag{14}\]  
Recall: It is the ratio of the actual positive identifications to the summation of total positive and false positive values.  
\[Recall=\frac{TP}{TP+FN} \tag{15}\]  
\(F1\) score: It is the measure of the test's accuracy and is calculated from the precision and recall.  
\[F1\ Score=2.\frac{precision\text{-}recall}{precision+recall}=\frac{TP}{TP+1/2( FP+FN)} \tag{16}\]  
For the critical success index (CSI), also called the threat score (TS), a measure of categorical forecast performance is computed as  
\[CSI=\frac{FP}{TP+FP} \tag{17}\]  
The false alarm rate (_FAR_) is also called the probability of false detection and is computed as  
\[FAR=\frac{TP}{TP+TN+FP+FN} \tag{18}\]  
The prediction results of all the models are presented in the subsequent section.
# Equivalent Modeling and Verification of a High-Steepness and Lightweight Elliptical Aluminum Mirror
## 5 Finite Element Analysis
### Gravity Deformation Analysis  
Since the designed application of the mirror described in this paper is in the space environment, the mirror processed in the ground environment will cause the space degradation of the surface shape. In order to predict this degradation, we generally apply a gravitational acceleration of 1 g to the mirror on the ground to evaluate the surface shape change from the mirror into space. The shape of the mirror we studied is elliptical and has no rotational axis of symmetry, so self-weight deflection in three directions needs to be analyzed.  
By removing the rigid body displacement and angle, the surface shape fitting is carried out using MATLAB software(MathWorks, Natick, MA, USA). The analysis result of the mirror is better than 35 nm (RMS), as shown in Figure 10, which meets the performance requirements of the mirror.  
The previous equivalent modeling method is constructed in the vertical direction of the optical axis. The reason for this is that the gravity deformation of the medium- and large-aperture mirror in the vertical direction of the optical axis is more difficult to control. Therefore, ensuring the minimum gravity deformation of the mirror in the vertical direction of the optical axis is the main optimization goal of the design. In the finite element analysis of gravity deformation, we focus on the vertical deformation of the optical axis. The design value of the equivalent gravity deformation for the plate with a high steepness is 31.64 nm (1/20 \(\lambda\), \(\lambda\) = 632.8 nm), and modeling error and finite element analysis error are within 10%, as shown in Table 2. Gravity analysis results show that the equivalent model has a high precision.  
Figure 9: Final design structure of mirror: (**a**) lightweight schematic of mirror back; (**b**) schematic diagram of mirror back trimming.
# Feature Extraction and Recognition of Chinese Mitten Crab Carapace Based on Improved MobileNetV2
## 1 Introduction
[PERSON] and others improved the ResNet101 residual network to extract the features of Chinese mitten crabs, using these features to calculate their similarity with authentic images for verification against a registered database, achieving individual crab traceability with an accuracy rate of 92.1% [20]. This method introduces a new technical approach to recognition technology. However, due to its large model size and computational demands, it requires substantial computational resources and training time, making it less suitable for resource-constrained environments. Considering the issues mentioned above, as well as the potential applications and scalability of Chinese mitten crab traceability, this paper proposes a lightweight individual recognition method based on the image features of Chinese mitten crabs. This method aims to enhance the traceability of Chinese mitten crab products and combat counterfeit products. The main contributions of this study are summarized as follows:  
1. A Chinese mitten crab image recognition dataset is constructed, containing data for 122 individual Chinese mitten crabs and 64,050 annotated images. 2. A method for recognizing Chinese mitten crab carapace features based on MobileNetV2 is proposed, utilizing a lightweight model structure to effectively improve the accuracy and efficiency of crab recognition. 3. A coordinate attention mechanism is introduced to the module, enhancing the model's ability to extract detailed features of the crab carapace. 4. The Additive Angular Margin Loss (ArcFace) is introduced to train the model, enhancing the intra-class compactness and inter-class variability of the extracted features. The rest of the paper is organized as follows. Section 2 describes the data collection process, data augmentation, carapace recognition process, and the methods. Section 3 presents the experimental setup and analyses used to evaluate the proposed model, including the model training results, ablation experiments, quantitative comparisons with other advanced algorithms, and generalization testing of model feature extraction. Finally, the discussion and conclusions are presented in Sections 4 and 5, respectively.
## 3 Results and Discussion
### Anti-Adhesion Effect of Fir Honeydew Honey  
FHH at concentrations from 1.5625 to 50 mg/mL in MH broth and milk significantly reduced the adhesion of _C. jejuni_ NCTC 11168 on the polystyrene microtiter plate after 24 h of incubation. As shown in Figure 2, the reduction was stronger when the concentration of FHH in the MH broth was higher, with complete reduction at 25 and 50 mg/mL. The same was observed in milk, where concentrations of 12.5, 25 and 50 mg/mL completely reduced the adhesion of _C. jejuni_ NCTC 11168 on the polystyrene surface (\(p<0.05\)).  
FHH had a lower effect on the adhesion of _C. jejuni_ K49/4 on a polystyrene microtiter plate after 24 h incubation in MH broth compared to the effect on _C. jejuni_ NCTC 11168. Only concentrations of 25 and 50 mg/mL significantly reduced the adhesion of _C. jejuni_ K49/4 in MH broth (Figure 3). FHH had a similar effect on the adhesion of _C. jejuni_ K49/4 on the polystyrene surface of the microtiter plate after 24 h incubation in milk compared to  
Figure 1: Transmission electron microscopy images of _Campylobacter jejuni_ NCTC 11168 of samples not treated (**A**) and treated with fir honeybee honey at concentrations of 12.5 mg/mL [1\(\times\) minimal inhibitory concentration (MIC)] (**B**) and 25 mg/mL (2\(\times\) MIC) (**C**). Samples were scanned after 24 h of incubation at 42 \({}^{\circ}\)C in microaerobic atmosphere in Ringer solution, where fir honeybee honey was not (**A**) or was added (**B**,**C**).  
Figure 2: Normalised adhesion of _Campylobacter jejuni_ NCTC 11168 to a polystyrene surface after 24 h incubation in Mueller Hinton (MH) broth (**A**) and milk (**B**) under microaerobic atmosphere at 42 \({}^{\circ}\)C. Cells were exposed to fir honeybee honey at a concentration from 1.5625 to 50 mg/mL. The adhered cells in the microtiter plates were suspended in phosphate buffered saline (PBS) by ultrasonic bath and their concentration was determined via the plate counting method. The CFU is given per mL of PBS. Normalised mean values (control = 1) + standard deviation are given.  
the effect on _C. jejuni_ NCTC 11168, where concentrations of 6.25, 12.5, 25, and 50 mg/mL completely reduced the adhesion of _C. jejuni_ K49/4 (_p_ < 0.05).  
Our results showed that the adhesion on the polystyrene surface of both strains was lower in milk compared to MH broth. Even lower concentrations of FHH reduced the adhesion of _C. jejuni_ on the polystyrene surface in milk. The reason for the higher anti-adhesion effect in milk can include milk proteins, which can form a conditioning layer and prevent bacteria from adhering to the polystyrene surface. Also, the ionic composition of the suspending medium can influence the attachment of the cells to the abiotic surface [57]. To overcome the limitations of this research, further studies will include more bacterial strains that will be tested on several food models.
## 2 Radially Polarized Laser Beams
In mathematical terms it reads  
\[P = \int_{0}^{2\pi}\int_{0}^{\infty}\mathbf{S}_{av}\cdot\hat{\mathbf{ e}}_{z}rdrd\theta \tag{3a}\] \[= \frac{1}{2\mu_{0}}\int_{0}^{2\pi}\int_{0}^{\infty}\mathrm{Re}\{ \tilde{E}_{r}\tilde{B}_{\theta}^{*}\}rdrd\theta \tag{3b}\]  
Accordingly, the power associated with the lowest-order RPLB is [36]  
\[P=\frac{\pi A_{0}^{2}}{2\eta_{0}k_{0}^{5}z_{0}^{3}}\exp(-2k_{0}z_{0})[1-2(k_{0 }z_{0})^{2}+2k_{0}z_{0}\sinh(2k_{0}z_{0})-\cosh(2k_{0}z_{0})] \tag{4}\]  
where \(\eta_{0}=\mu_{0}c\) is the impedance of free space. In the paraxial limit:  
\[\lim_{k_{0}z_{0}\gg 1}P\simeq\frac{\pi w_{0}^{2}}{4\eta_{0}}\exp(1)E_{0}^{2} \tag{5}\]In the limit where \(k_{0}z_{0}=0\), the power defined at Equation (4) is identically zero. It corresponds to the case where an RPLB is focused uniformly over \(4\pi\) steradians [37,38]. In an ideal setup, the inward and outward energy flows would then be perfectly balanced, resulting in a null Poynting vector. In the weak focusing limit (\(k_{0}z_{0}\gg 1\)), RPLBs appear as the characteristic doughnut-shape profile shown in Figure 1(a). In this case, most of the electric energy is concentrated in the transverse component (\(W_{e}\propto|\mathbf{E}|^{2}\simeq|E_{r}|^{2}\)). Nevertheless, as the beam spot size is decreased, part of it is transferred to the longitudinal component \(E_{z}\) that is maximum at \(r=0\). The dark center then gradually disappears and, eventually, the longitudinal component of the electric field dominates [see Figure 1(b)-(c)]. In the limit of extreme focusing (\(k_{0}z_{0}\to 0\)), most of the electric energy is concentrated at the center of the beam that now appears as a bright symmetric spot whose diameter is smaller than the dominant laser wavelength [see Figure 1(d)]. This particular transition from paraxial to subwavelength focusing, where rotational symmetry of the beam profile is preserved, was predicted [25,28,30,33,39] and observed [27,40,41,42,43] by different authors. Because of their field symmetries and strong axial longitudinal electric field component, RPLBs are appealing for electron acceleration. \[\tilde{E}_{z} = \frac{4}{3}A_{0}\left[j_{0}(k_{0}\tilde{R})+j_{2}(k_{0}\tilde{R} )P_{2}(\cos\tilde{\theta})\right]\exp(-k_{0}z_{0})\]0 Transverse distribution of the average electric energy density \(W_{e}=\epsilon_{0}|\mathbf{E}|^{2}/2\) of a focused RPLB at beam waist. The energy density associated with the individual electric field components is shown to emphasize the contribution of the longitudinal field when the beam spot size is comparable to or below the wavelength. In (**a**) \(k_{0}z_{0}=350\); (**b**) \(k_{0}z_{0}=10\); (**c**) \(k_{0}z_{0}=5\); and (**d**) \(k_{0}z_{0}=1\). Insets show the intensity distribution (the scale is that of the corresponding transverse coordinate axis).
# New Approaches in Dynamic Metrics for Lighting Control Systems: A Critical Review
## 2 Review  
### Static Metrics  
The Daylight Factor (DF) is defined as the ratio of the illuminance value at a point within an interior space to the exterior illuminance value under a specific overcast-sky condition [30]. Said ratio can be expressed as follows (1):  
\[\text{DF}=\frac{\text{Ei}}{\text{Ee}} \tag{1}\]  
This metric is considered one of the oldest daylighting indicators--dating back to the early 20 th century--developed within the hygienist movement to improve lighting conditions in densely populated urban housing and industrial buildings. For decades, it served as the de facto standard for justifying access to natural light. Conversely, while DF maintains a foothold in some regulatory frameworks, there has been a clear shift in certifications such as LEED v4.1 [31] and WELL v2 [32], which emphasize dynamic, climate-based metrics like sDA or mEDI.  
Its calculation considers the window and room geometry and properties such as the surface reflectivity of the materials. However, the window orientation, solar position, and weather conditions are not taken into account, since the calculation scenario considers only an ideal overcast sky [33]. A static luminance distribution allows designers to determine the potential illuminance at a given point in relation to an overcast sky, focusing on the characterization of architecture. The Daylight Factor remains useful for analyzing daylight availability in various architectural elements, including windows [8; 34; 35], atriums [36], courtyards [37], and skylights [38].  
Although originally conceived to ensure minimum hygienic and sanitary standards for natural lighting in occupied spaces (such as homes, schools, and offices), the DF can also serve to estimate the peak energy consumption resulting from electric lighting, considering an overcast sky as one of the most conservative scenarios. While this assumption generally holds true, certain scenarios such as north-facing windows under clear-sky conditions have been proven to be more adverse [39], making it inadequate to set a threshold for the minimum electric lighting requirement. Moreover, as it ignores the dynamic luminous distribution of skies beyond overcast conditions and the actual illuminance required for visual tasks [40], the Daylight Factor (DF) does not accurately reflect electric lighting energy usage, especially in locations with predominantly sunny skies.  
While the DF remained the most widespread metric for assessing the potential of natural light inside buildings until 2006, since then it has progressively been replaced by dynamic metrics that more accurately account for the variability of natural lighting, driven by improvements in computational power and its increased accessibility.
## 4 Fractional-Order Controller and Fractional-Order Disturbance Observer Design
### Heading Controller Design  
The state space model of the yaw axis can be obtained by defining the state variables as \(x_{7}=\psi\) and \(x_{8}=\dot{\psi}\) in (18) as follows:  
\[ \begin{array}{l}\dot{x}_{7}=x_{8},\\ \dot{x}_{8}=\frac{l_{7}-l_{8}}{l_{2}}\dot{\phi}\dot{\phi}+\frac{l}{l_{2}}u_{ \psi}+\tau_{4\psi}\\ =f_{8}+g_{8}u_{4}+\tau_{4\beta},\end{array}  \tag{73}\]  
where \(f_{8}=\frac{l_{x}-l_{y}}{l_{2}}\dot{\phi}\dot{\theta}\), \(g_{8}=\frac{l}{l_{7}}\), \(u_{4}=u_{\psi}\), and \(\tau_{4\beta}=\tau_{4\psi}\). The tracking errors are defined as \(e_{7}=x_{7}-x_{7d}\) and \(e_{8}=x_{8}-\xi_{71}\). Define the fractional-order filtered error surfaces including the tracking errors as  
\[\sigma_{7}=D^{\gamma_{1}}e_{7}+D^{\gamma_{2}-1}k_{7}^{2}(b_{71}e_{7}+b_{72}sig^{1/ 2}(e_{7}))-\xi_{71}, \tag{74}\]  
\[\sigma_{8}=D^{\gamma_{1}}e_{8}+D^{\gamma_{2}-1}k_{8}^{2}(b_{81}e_{8}+b_{82}sig^{1/ 2}(e_{8}))-\xi_{81}, \tag{75}\]where \(k_{j}>0\), \(\kappa_{il}>0\), \(b_{j1}>0\), and \(b_{j2}>0\), \(i=1,2,j=7,8\), are constants. In the fractional-order filtered error surfaces, the dynamics of the compensating signals \(\xi_{ji}\), \(j=7,8\), \(i=1,2\), are expressed as  
\[D^{\overline{7}_{1}}\xi_{71}=-\beta_{71}\xi_{71}+\xi_{72}+\zeta_{71}-\alpha_{7 }-\lambda_{71}sig^{1/2}(\xi_{71}), \tag{76}\]  
\[D^{\overline{7}_{1}}\xi_{72}=-\beta_{72}\xi_{72}-\xi_{71}-\lambda_{72}sig^{1/2 }(\xi_{72}), \tag{77}\]  
where \(\beta_{71}>0\), \(\beta_{72}>0\), \(\lambda_{71}>0\), and \(\lambda_{72}>0\), are constants. Furthermore, the output of the tracking differentiator \(\xi_{71}\) is obtained from the tracking differentiator dynamics described in Theorem 3 as  
\[D^{\gamma_{1}}\zeta_{71}=\zeta_{72},\]  
\[D^{\gamma_{1}}\zeta_{72}=-\Lambda_{7}^{2}[c_{71}\sinh(r_{71}\varphi_{71}(\xi_ {71}-\alpha_{7}))+c_{72}\sinh(r_{72}\varphi_{72}(\xi_{72})/\Lambda_{7})], \tag{78}\]  
where \(\varphi_{71}(\zeta_{71}-\alpha_{7})=\varepsilon_{7}sig^{1/2}(\xi_{71}-\alpha_ {7})+(1-\varepsilon_{7})sig^{(2+\varphi_{7})/2}(\zeta_{71}-\alpha_{7})\), \(\varphi_{72}(\zeta_{72})=sig^{1/2}(\xi_{72})\), \(0<\varepsilon_{7}<1\), and \(0<\upsilon_{7}<1\) are constants. Taking the time derivative of (74) and (75) applying \(D^{-\gamma_{1}}\) to both sides of the resulting equation, we obtain  
\[ \begin{array}{c}D^{\overline{7}_{1}}\omega_{7}=\dot{e}_{7}+D^{\overline{7}_ {2}}k_{8}^{2}(b_{71}e_{7}+b_{72}sig^{1/2}(e_{7}))-D^{\overline{7}_{1}}\xi_{71} \\ =e_{8}+\zeta_{71}-\dot{x}_{74}+D^{\overline{7}_{2}}k_{8}^{2}(b_{71}e_{7}+b_{72 }sig^{1/2}(e_{7}))\\ +\beta_{71}\xi_{71}-\xi_{72}-\zeta_{71}+\alpha_{7}+\lambda_{71}sig^{1/2}(\xi_{ 71})\\ =e_{8}-\dot{x}_{84}+\alpha_{7}+\alpha_{7},\end{array}  \tag{79}\]  
\[ \begin{array}{c}D^{\overline{7}_{1}}\omega_{8}=\dot{e}_{8}+D^{\overline{7}_ {2}}k_{8}^{2}(b_{81}e_{8}+b_{82}sig^{1/2}(e_{8}))-D^{\overline{7}_{1}}\xi_{82 }\\ =f_{8}+g_{8}u_{4}+\tau_{48}-D^{\overline{7}_{1}}\xi_{72}+D^{\overline{7}_{2}}k _{8}^{2}(b_{81}e_{8}+b_{82}sig^{1/2}(e_{8}))+\beta_{82}\xi_{82}+\xi_{81}+ \lambda_{82}sig^{1/2}(\xi_{82})\\ =f_{8}+g_{8}u_{4}+\omega_{8}+\tau_{48},\end{array}
## 3 Results
### Performance and Biomechanical Variables during the 6-Week Training Period
#### 3.7.2 Biomechanical Variables  
The SR in G-MS and G-ME increased, while SR in G-CON decreased in week 6 compared with week 1 (group \(\times\) time interaction, \(F_{(10,105)}\) = 2.21, \(p\) = 0.02, \(\eta_{p}\)\({}^{2}\) = 0.18 [large], Figure 6). Moreover, the swimmers in G-ME managed to increase their SR to a higher extent in week 6 (\(F_{(14,147)}\) = 1.80, \(p\) = 0.04, \(\eta_{p}\)\({}^{2}\) = 0.15 [large], Figure 6).  
The SL was similar among the G-MS, G-ME, and G-CON during the 6-week training period (Figure 7). In addition, all groups increased their SL in weeks 4 and 5 compared with week 1 (\(F_{(5,105)}\) = 5.01, \(p\) = 0.01, \(\eta_{p}\)\({}^{2}\) = 0.19 [large], Figure 7). The G-MS increased the SL between week 4 and week 1 by 2.4 \(\pm\) 4.5%, compared with 5.4 \(\pm\) 6.6% and 3.1 \(\pm\) 6.6% increments in the G-ME and G-CON, respectively (Figure 7). The G-MS increased the SL by 1.8 \(\pm\) 4.1% between week 5 and week 1 compared with 5.6 \(\pm\) 6.8%, and 2.1 \(\pm\) 5.9% increments in the G-ME and G-CON, respectively (Figure 7).  
Figure 5: Performance time changes in the 4 \(\times\) 50 m training set (panel (**a**)) and decrement score (DS, panel (**b**)) during the 6-week training period. G-MS: group of maximum strength, G-ME: group of muscular endurance, and G-CON: control group. * \(p\) <0.05, performance changes in weeks 4, 5, and 6 compared with week 1 (panel (**a**)), and decrement score changes in week 4 compared with weeks 1 and 2 (panel (**b**)).  
Figure 6: Stroke rate changes in the 4 \(\times\) 50 m training set during the 6 weeks of training period in the three groups of swimmers participating in the study. G-MS: group of maximum strength, G-ME: group of muscular endurance, and G-CON: control group. # \(p\) < 0.05, between G-MS and G-ME compared with G-CON.  
The SI was similar among the G-MS, G-ME, and G-CON during the 6-week training period (Figure 8). In addition, in all groups, the SI increased in weeks 4, 5, and 6 compared with weeks 1, 2, and 3 (\(F_{(5,105)}=10.03\), \(p=0.01\), \(\eta_{p}{}^{2}=0.32\) [large], Figure 8).  
The G-MS showed higher %\(\Delta\) values of the performance time compared with G-CON between week 6 and week 1 (\(F_{(2,21)}=3.66\), \(p=0.04\), \(\eta_{p}{}^{2}=0.26\) [large], Table 7). However, the corresponding %\(\Delta\) values of the SR, SL, and SI were similar between groups (\(p>0.05\), Table 7).  
Figure 8: Stroke index changes in the 4 \(\times\) 50 m training set during the 6 weeks of training period in the three groups of swimmers participating in the study. G-MS: group of maximum strength, G-ME: group of muscular endurance, and G-CON: control group * \(p<0.05\), weeks 4, 5, and 6 compared with weeks 1, 2, and 3 for all groups.  
Figure 7: Stroke length changes in the 4 \(\times\) 50 m training set during the 6-week training period in the three groups of swimmers participating in the study. G-MS: group of maximum strength, G-ME: group of muscular endurance, and G-CON: control group. * \(p<0.05\), weeks 4 and 5 compared with week 1 for all groups.