esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Post-Classification Filtering  
The application of the median filter resulted in marginal improvements of the Overall Accuracy and the Kappa Coefficient and of the User's and Producer's Accuracies for most habitat types (Table 4). The User's Accuracy for macroalgae and the Producer's Accuracy for rocks showed the most notable improvements (+4.1% and +2.4%, respectively), while decreases were observed only in two instances, namely for the User's Accuracies of the rocks (\(-3.1\%\)) and shell beds (\(-0.6\%\); Table 4).
# Flowering Index Intelligent Detection of Spray Rose Cut Flowers Using an Improved YOLOv5s Model
## 2 Materials and Methods
### DIoU-NMS Algorithm  
NMS is a post-processing algorithm used in YOLOv5 for object detection, primarily to eliminate redundant detection boxes. Traditional NMS employs IoU as the evaluation metric, relying solely on the overlap area between two boxes to discard redundant detection boxes. However, in images of rose bouquets, two roses with the same degree of openness may be closely adjacent, causing NMS to erroneously classify one of the detection boxes as redundant and subsequently remove it. To address this, the improved DIoU-NMS algorithm is used as the post-processing method for object detection. This algorithm not only considers the overlap area of the detection boxes but also incorporates the distance between the center points of the two boxes as an evaluation metric. For pairs of boxes with a high IoU value, if there exists a significant distance between their center points, they are regarded as distinct objects and thus retained rather than deleted. The formula for the DIoU-NMS algorithm is as follows:  
\[\widetilde{s}_{i}=\left\{\begin{array}{l}s_{i},IoU-\mathcal{R}_{DIoU}( \mathcal{M},B_{i})<\varepsilon\\ 0,IoU-\mathcal{R}_{DIoU}(\mathcal{M},B_{i})\geq\varepsilon\\ \mathcal{R}_{DIoU}=\frac{d^{2}}{c^{2}}\end{array}\right. \tag{7}\]  
where \(\widetilde{s}_{i}\) is the updated confidence score, \(s_{i}\) is the initial confidence score, \(\varepsilon\) is the pre-set threshold, \(\mathcal{M}\) is the box with the highest confidence score, \(B_{i}\) represents other overlapping boxes with \(\mathcal{M}\), d is the Euclidean distance between the center points of the two boxes (represented by the red line segment in Figure 5), and c is the diagonal length of the smallest enclosing area (represented by the blue line segment in Figure 5).
## 4 Conclusions  
The conducted research indicates that the application of PEF treatment did not improve the freeze-drying process of insect biomass and failed to yield microbiological quality suitable for food purposes. Notably, only fungi displayed increased susceptibility to PEF treatment compared to bacteria, resulting in a reduction of 1.9 to 2.6 log cycles. The utilization of pulsed electric field (PEF) treatment prior to freeze drying of insect biomass induced subtle modifications in protein and fat content. Notably, only _T. molitor_ larvae subjected to higher PEF energy input (20 kJ/kg) exhibited a reduction in protein content. Moreover, microtomography images of freeze-dried _H. illucens_ and _T. molitor_ larvae reveal distinct structural changes based on species and pre-treatment. For example, _T. molitor_ larvae exhibit minimal internal structural deformation except for reduced exoskeletal fibrous density after 20 kJ/kg pre-treatment. However, the differences in rehydration and hygroscopic characteristics for both larvae species were not apparent across the studied samples. Samples subjected to higher PEF energy input displayed a slightly higher ability to absorb moisture from the ambient environment. Moreover, the color of the PEF-treated larvae was significantly darker for _H. illucens_, and similar for _T. molitor_ compared to the untreated samples. Also, both species presented lower values of a* and b* color parameters which influence the higher total color difference (\(\Delta\)E). Nevertheless, to explain all the changes occurring during insect biomass processing, further studies and more information are needed.  
Conceptualization, R.B., A.W. and M.N.; methodology, R.B., K.R., K.P., A.W. and M.N.; software, R.B.; validation, R.B., K.P. and K.R.; formal analysis, R.B.; investigation, R.B., K.P. and K.R.; resources, O.P. and S.S.; data curation, R.B.; writing--original draft preparation, R.B., K.P., K.R., A.W. and M.N.; writing--review and editing, O.P., S.S., A.W. and M.N.; visualization, R.B. and M.N.; supervision, M.N. All authors have read and agreed to the published version of the manuscript.  
The research for this publication was carried out with the use of research equipment purchased as part of the \"Food and Nutrition Centre--moderransation of the WULS campus to create a Food and Nutrition Research and Development Centre (CZiZ)\" co-financed by the European Union from the European Regional Development Fund under the Regional Operational Programme of the Mazowieckie Voivodeship for 2014-2020 (project no. RPMA.01.01.00-14-8276/17).  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.
## 3 Debonding Detection Experiment Subjected to Different Attachment Method  
The roller device and the re-attachable device shown in Figure 1 were tested on two different composite structures to evaluate the performance of the two devices. In Figure 2, the two test specimens made for this study, Structure A and B. Structure_A was created by attaching two glass fiber epoxy plates of size 150 mm \(\times\) 150 mm with two different thicknesses of 0.2 mm and 0.4 mm are shown. For adhering to the composite plates, a commercial epoxy (quick-set Epoxy, Loctite, Dusseldorf, Germany) was used where the specimen was left for 48 h to ensure full curing. Only half of the area was adhered to search for differences in impedance signature as this would indicate that the proposed idea works. For both the roller device and re-attachable device, 10 impedance measurements were acquired from the bonded area and debonded area for Structure_A. With the roller device, it was simply placed on top of the specimen for measuring impedance signature and moved to another random location for another measurement until 10 signatures were acquired for both bonded and debonded areas (resulting in 20 impedance signatures). For the re-attachable device, double-sided tape was used to achieve temporary attachment between the device and the composite plate to acquire 10 impedance signatures for both bonded and debonded areas. Then, this test was conducted identically for Structure_B, where the top composite plate thickness was changed to a thicker plate of 0.4 mm to evaluate the performance of the roller device subjected to a different thickness.
## 3 Path Planning for Mobile Robots
### Local Path Planning  
Global path planning can plan the global optimal solution of the route, but it needs to know the accurate information of the environment in advance. The intelligent robot platform integrates the obstacle information into the environment map in real time through its own environmental sensors, when the driving environment of the intelligent robot changes, such as when an unknown obstacle appears. Then, the driving path of intelligent robot platform is replanned by the local path planning method. This is to avoid obstacles  
Figure 4: The smoothing process of third-order B-spline curve.  
and complete local obstacle avoidance. After completing the local obstacle avoidance, the intelligent robot platform continues to drive according to the global optimal solution.  
The dynamic window algorithm can realize trial path planning and real-time obstacle avoidance. In the running process of the smart car, there are no groups of velocity spaces (\(v_{t}\), \(\omega_{t}\)), and the feasible trajectory of the robot can be simulated within the window region time \(t\). At the same time, the feasible trajectory is evaluated according to some metrics, so as to select the speed corresponding to the optimal trajectory to the mobile platform. In view of the existence of several feasible simulated motion trajectories in the velocity space, this paper calculated the evaluation function combined with the global path planning information to ensure that the final local path planning is the global optimal path. The designed evaluation function is as follows:  
\[\begin{array}{c}G(v,\omega)=\mu\cdot goal(v,\omega)+\beta\cdot d(v,\omega) \\ +d(v_{m},\omega_{m})+\lambda v_{e}(v,\omega)\end{array} \tag{17}\]  
where \(goal(v,\omega)\) represents the angle between the vector pointing to the goal and the vector connecting the starting point and the current position; \(d(v,\omega)\) represents the vertical distance between the static obstacle and the current trajectory pointing to the target; \(d(v_{m},\omega_{m})\) represents the vertical distance between the unknown obstacle and the current pointing target trajectory; \(v_{e}(v,\omega)\) represents the evaluation function of the current speed pointing to the target trajectory; and \(\mu\), \(\beta\), \(\phi\), and \(\lambda\) are expressed as weighting coefficients.
## 3 Case Analysis
### Evaluation and Analysis of Model Prediction Results
#### 3.4.1 Model Evaluation Metrics  
To evaluate the performance of the model, this study selects Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Symmetric Mean Absolute Percentage Error (SMAPE) as evaluation metrics. Their calculation formulas are as follows:  
\[\text{RMSE}=\sqrt{\frac{1}{n}\sum_{i=1}^{n}\left(y_{i}-\hat{y}_{i}\right)^{2}} \tag{11}\]  
\[\text{MAE}=\frac{1}{n}\sum_{i=1}^{n}\left|y_{i}-\hat{y}_{i}\right| \tag{12}\]  
\[\text{SMAPE}=\frac{1}{n}\sum_{i=1}^{n}\frac{y-\hat{y}_{i}}{(|y|+|\hat{y}_{i}|)/ 2}\times 100\% \tag{13}\]  
In the formula, \(y_{i}\) and \(\hat{y}_{i}\) represent the true value and the predicted value of the \(i\)-th sample, respectively; the value range of RMSE and MAE is [0, +\(\infty\)]. The closer the value is to 0, the smaller the prediction error of the model is, and the better the performance is. The value range of SMAPE is [0, 200%], and the closer it is to 0, the better the model prediction effect is.
## 3 Geotechnical Guidelines and Practices for the Design of Integral Bridges
### Canadian Practices  
The construction of the integral bridges in Canada started in 1970 [55]. Most of the provinces in Canada have their own design guidelines for the design of integral bridges (for e.g., Alberta, Ontario, and Manitoba). In Alberta, both integral and semi-integral bridges are used in practice and discussed in their design guidelines [56]. Alberta limits the span of its integral bridge typically to 100 m for the IAB and 60 m for the SIAB, with skew angle less than 20\({}^{\circ}\). Alberta sub-categorised the abutments for SIAB: (a) semi-integral abutment with pinned connection, (b) semi-integral abutment with sliding bearings, and (c) semi-integral abutment with partial backwall. For the semi-integral abutment with pinned connection, there is a pin connection between superstructure end and abutment, and only a single line of H-pile is recommended for such type of abutment. Where a single line of H-pile is not applicable (such as stiff soil condition and skewed bridges), a semi-integral abutment with sliding bearings with the double row H-piles or stiff piles is recommended. A distinguishing type of semi-integral abutment, the so-called \"semi-integral abutment with partial backwall\", is similar to the semi-integral abutment with sliding bearings but with partial height backwall. Unlike others, in a semi-integral abutment with partial backwall, the approach slab slides over the partial backwall, which helps to reduce the earth pressure behind the end of the superstructure due to cyclical movements. Specifications regarding the geometry requirements of the components of the integral bridges (e.g., size of the abutment, length of the wingwall and depth of the girder) are advised to minimise the earth pressure on the abutment and abutment foundation. Although a comprehensive design of integral bridge with pile foundation is recommended, for example, detailed design analysis of the pile with the development of the plastic hinge, detailed information for the careful design of the abutment pile foundation has not been discussed. Furthermore, it is advised to design the abutment for the at-rest earth pressure.  
Ontario has limited the total bridge span length to 100 m, and skew less than 20\({}^{\circ}\) for both IABs and SIABs. In addition, it has limited the height of the abutment to 7 m and length of the wingwall to 6 m to reduce the effect of soil pressure on the abutment [57]. SIABs are advised to be used only if IABs are not appropriate (e.g., total length of the pile required for foundation is less than 5 m). Piles less than 3 m in length are generally not economical, and, in that case, SIAB with spread foundation, or the use of caissons should be investigated. Integral abutments are normally designed for the active earth pressure.
## 4 Conclusions  
Using ANSYS Workbench, this study delved into the influence of both the hole position relative to the crack tip and stress ratios on fatigue crack propagation. The analysis concentrated on evaluating fatigue life cycles and the equivalent range of stress intensity factors in a modified compact tension specimen, considering a range of stress ratios.  
The investigation presented in this study led to the following significant conclusions:  
* The presence of a hole in the geometry has a direct influence on the crack, causing it to adjust its path in relation to the hole's location. Consequently, the crack may either change its direction and propagate into the hole, or it might be momentarily diverted by the hole before continuing to grow until it moves away from the hole;
* As the stress ratio increased, there was a corresponding decrease in the equivalent range of stress intensity factors;
* Through varying the stress ratios from 0.1 to 0.9 with a step of 0.1, this study examined the influence of different loading conditions on the fatigue life cycles of materials. It was observed that reducing the stress ratio resulted in a decrease in fatigue life, indicating a shorter lifespan for materials subjected to cyclic loading;
* By carefully incorporating stress ratios into both design and operational practices, it becomes possible to optimize fatigue performance and extend the lifespan of mate  
Figure 15: X-directional deformation for CTS002–CTS05.  
rials. The interplay between stress ratios and fatigue life cycles stands out as a vital consideration in engineering design;
* The number of cycles to failure exhibits an inverse relationship with the level of X-directional deformation. This highlights the importance of managing and minimizing deformations to enhance fatigue resistance and longevity of components and structures.  
The author extends his appreciation to the Deputyship for Research and Innovation, Ministry of Education in Saudi Arabia for funding this research work through the project number ISP-2024.  
Not applicable.  
All relevant data are contained in the present manuscript.  
The author declares no conflict of interest.
###### Abstract  
In the present study, the preparation of nanocapsules using the coaxial electrospray method was investigated. Poly(styrene-co-acrylonitrile) (SAN) was used as a shell material and coconut-oil-based alkylcd resin (CAR) as a core. Chemical structure, thermal stability, and morphology of nanocapsules were characterized by Fourier transform infrared (FTIR) spectroscopy, thermal gravimetric analysis (TGA), and field emission scanning electron microscopy (FE-SEM), respectively. In addition, the formation of the core-shell structure was approved by transmission electron microscopy (TEM) and FE-SEM micrographs of the fractured nanocapsules. Furthermore, differential scanning calorimetry tests (DSC) were carried out to investigate the reactivity of released healing agents from the nanocapsules. The prepared nanocapsules were then incorporated into the epoxy resins and applied on the surfaces of the steel panels. The effect of capsule incorporation on the properties of the coating was evaluated. The self-healing performance of the coatings in the salty and acidic media was also assessed. The FTIR results revealed the presence of both shell and core in the prepared nanocapsules and proved that no reaction occurred between them. The morphological studies confirmed that the electrosprayed nanocapsules' mean diameter was 708 \(\pm\) 252 nm with an average shell thickness of 82 nm. The TGA test demonstrated the thermal stability of nanocapsules to be up to 270 \({}^{\circ}\)C while the DSC results reveal a successful reaction between CAR and epoxy resin, especially in the acidic media. The electrochemical impedance spectroscopy (EIS) test results demonstrate that the best self-healing performance was achieved for the 2 and 1 wt.% nanocapsules incorporation in the NaCl, and HCl solution, respectively.  
self-healing coatings; coaxial electrospray; encapsulation; corrosion resistance; coconut-oil-based alkylcd resin; epoxy resin +
Footnote †: journal: Applied Physics Letters
# Design Procedure for Motion Profiles with Sinusoidal Jerk for Vibration Reduction
## 3 Parameter Design for Sinusoidal Jerk Profile
### Minimum-Time Solution with Actuator Limits
In this case, the motion profile belongs to Type 2, as shown in Figure 3a, where the constant velocity segment disappears (i.e., \(T_{3}=0\)). The peak velocity is determined using Equation (6) as \(V_{peak}=A_{peak}(T_{1}+T_{2})\). In addition, if \(T_{1}=\sqrt{\frac{\pi V_{\text{max}}}{2J_{\text{max}}}}\), this implies that \(T_{1}\) is determined by the velocity limit. The motion profile is classified as Type 3, as shown in Figure 3b. The constant acceleration segments vanish due to the tight limit on the velocity (i.e., \(T_{2}=0\)), and \(T_{3}\) can then be  
Figure 3: Different types of sinusoidal jerk motion profiles: (**a**) Type 2; (**b**) Type 3; (**c**) Type 4.  
computed using Equation (14). The peak acceleration (\(A_{\text{peak}}\)) is lower than its maximum allowable value (\(A_{\text{max}}\)) and is obtained using \(A_{peak}=\frac{V_{peak}}{T_{1}}\). Finally, if \(T_{1}=\sqrt[3]{\frac{\pi|D|}{2I_{\text{max}}}}\), then \(T_{1}\) is determined by the required displacement. The motion profile belongs to Type 4, as shown in Figure 3c, where only the sinusoidal jerk segments exist. Both the constant acceleration and velocity segments vanish due to the short moving distance (i.e., \(T_{2}=0\), \(T_{3}=0\)). The peak acceleration and velocity are calculated using Equations (4) and (6) as \(A_{peak}=\frac{2I_{peak}T_{1}}{\pi}\), \(V_{peak}=\frac{2I_{peak}T_{1}}{\pi}\). The solutions for the four profile types, along with the corresponding evaluation conditions, are summarized in Table 1.
## 4 Problem Formulation
### Mathematical Model
#### 4.2.3 Constraints  
(1) Core Capacity Constraint  
\[C_{FS}\times|F|\leq C_{core} \tag{6}\]  
(2) Routing and Core Selection Constraints  
\[1\leq c_{\tau_{k}}^{p,l_{ij}}\leq|C|\;\forall\tau_{k}\in\textbf{R} \tag{7}\]  
\[\sum_{\alpha_{n}\in\textbf{C}}\gamma_{\tau_{k},l_{ij}}^{\tau_{k}}\leq 1\; \forall\tau_{k}\in\textbf{R},l_{ij}\in p \tag{8}\]  
\[\sum_{f_{x}\in\textbf{F}}\beta_{\tau_{k},f_{x}}^{k_{x},d_{k},l_{ij},\alpha_{n} }\bigg{|}_{\tau_{k}=d_{k}}=0\;\forall\tau_{k}\in\textbf{R} \tag{9}\]  
\[\sum_{p\in P_{k}}\alpha_{k}^{p}=1\;\forall\tau_{k}\in\textbf{R} \tag{10}\]  
Equation (6) indicates that the available bandwidth of each link cannot exceed the capacity of each core. Equation (7) states that the selected core index for each request does not exceed the total number of cores. Equation (8) ensures that any connection request \(\tau_{k}\) could only select at most one core on the selected link \(l_{ij}\). Equation (9) ensures that the source and destination nodes of the generated request \(\tau_{k}\) cannot be the same. Equation (10) means that each request \(\tau_{k}\) can only select one path from its candidate path set \(\textbf{P}_{\tau_{k}}\) for use.  
(3) Spectrum Allocation Constraints  
\[f_{\tau_{k},start}^{p,l_{ij},\mu_{\tau_{k}}}=f_{\tau_{k},start}^{p,l_{ij},\mu_ {\tau_{k}}}=\ldots=f_{\tau_{k},start}^{p,l_{\mu_{k}},\mu_{\tau_{k}}}\;\exists c _{km}\in p,1\leq m\leq n \tag{11}\]\[\sum_{\begin{subarray}{c}r_{k}\in\mathbf{R}\\ r_{k}\in\mathbf{F}\end{subarray}}\delta_{r_{k},r_{k}}^{s_{k}d_{k}d_{k}j_{c}s_{n}} \leq 1\ \ \forall f_{x}\in\mathbf{F} \tag{13}\] \[1\leq f_{r_{k},start}^{p_{k}j_{c}s_{n}}\leq|F|-T_{k}+1\ \ \ \forall r_{k}\in\mathbf{R} \tag{14}\]  
where \(n\) in Equation (11) denotes the number of links in path \(p\). Equations (11)-(13) ensure the spectrum consistency, continuity, and non-conflict under core switching, which has been explained in Section 3.3. Equation (14) indicates that the FSs occupied by each request \(r_{k}\) must be within the entire available spectrum domain.  
(4) Inter-core Crosstalk Constraint  
\[XT_{r_{k}}=\frac{\sum\limits_{\begin{subarray}{c}r_{k},j_{c}s_{n} \in\mathbf{F}\\ r_{k},start\end{subarray}}^{p_{k}j_{c}s_{n}}+T_{k}+GF-1}{T_{k}+GF}<\Theta_{m} \tag{15}\]  
Equation (15) guarantees that the crosstalk value of each connection request \(r_{k}\) on its selected optical path is less than the preset crosstalk threshold \(\Theta_{m}\).
## 3 Use Case: Watson Analytics as AaaS to Identify HF Patients Analyzing Only the ECG Signal
### Medical and ECG-Analysis Background
#### 3.2.2 Heart Failure Diagnosis Methods  
The \"ESC (European Society of Cardiology) Guidelines for the diagnosis and treatment of acute and chronic heart failure\" [25] make explicit that the diagnosis of HF is a complicated process because symptoms are often similar to those of other diseases. In addition, symptoms specifically related to HF (i.e., orthopnoea and paroxysmal nocturnal dyspnoea) are less common. Monitoring of symptoms and signs may be useful to evaluate the effectiveness of the therapy, but for HF diagnosis physicians need to get objective evidence of a structural or functional cardiac abnormality by instrumental examination.  
The ESC Guidelines clearly state which tests are required for a comprehensive diagnosis:* Echocardiiogram: it provides immediate information about the volumes of the atrial and ventricular chambers, and in particular about the ejection fraction.
* Electrocardiogram: it provides information about the rhythm of the heartbeat and possible faults in the electrical signal transmission (atrioventricular block etc.).
* Natriuretic Peptides: they are another important marker of HF. The examination consists in the analysis of blood concentration of the BNP (B-type Natriuretic Peptide) or NT-proBNP (N-terminal pro-B type), hormones secreted in abnormal amounts when the heart is diseased or the load on any chamber is increased.
* Chest X-ray: this examination is often more useful to identify lung diseases that cause symptoms similar to HF. However, this examination may show pulmonary venous congestion or edema in a patient with HF.
* Other laboratory tests: the guidelines indicate a multitude of laboratory parameters that may be related to HF, including biochemical (sodium, potassium, creatinine) and hematological tests (hemoglobin, hematocrit, ferritin, Leucocytes, and platelets). Also thyroid hormone is important because it can have an impact on HF.  
ESC Guidelines also report a complex graph showing the algorithm that manages HF diagnostic decisions (a sequence of given tests related to the symptoms).  
In this complex scenario, some of the mentioned useful parameters for HF diagnosis are not appropriate for a home telemonitoring context. One of the aims of this study is to establish in which cases the sole ECG-HRV analysis could be appropriate to perform a preliminary and early diagnosis of HF.