esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Valorization Technologies for Avocado-Derived Biomass through Obtaining Bioactive Compounds
### Phenolic Compounds

Table 5: Extraction methods applied to recover the phenolic compounds from avocado waste, total phenolic content (TPC) (g gallic acid equivalents/100 g), and antioxidant activity, determined by the redox equivalent antioxidant capacity (TEAC) and ferric reducing antioxidant power (FRAP) (mmol TE/100 g). In addition to the peel and stone, a recent study evaluated the chemical composition of the seed coat, considering it also has important content of phenolic compounds. To perform the characterization of the avocado stone and the stone peel, [PERSON] et al. [84] used the accelerated solvent extraction and liquid chromatography coupled to quadrupole-time-of-flight mass spectrometry for characterization. They reported the presence of phenolic acids, derivatives of phenolic alcohols, and flavonoids, including catechins, procyanidins, and other polar compounds, such as organic acids. Their results also revealed that the seed coat showed a significantly higher antioxidant activity than the whole stone, in addition to a higher content of organic acids and flavonoids, with the exception of the quinic acid [84]. Furthermore, using ultrasound-assisted extraction, antioxidant extracts from avocado leaf were obtained. In these samples, phenolic acids, flavan-3-ols, flavones, and flavonoids, among others, were characterized [89]. As a summary, Table 6 depicts the phenolic components characterized in avocado waste, including the phenolic compounds. Despite the fact that there are discrepancies between some of these studies as to whether the peel or stone have a higher concentration of phenolic compounds and capacity antioxidant, all agree that the extracts of these by-products have a greater antioxidant capacity and a higher concentration of phenolic compounds than the avocado pulp. The recovery of these phenolic compounds from avocado waste represents a new alternative to obtain raw materials of biological origin for the food industry thanks to their natural antioxidant properties [90]. Considering that flavonoids are not only powerful antioxidants but also they present other positive effects against various diseases, such as cancer, neurodegenerative, or cardiovascular diseases, the extracts from avocado waste are of great interest for medicinal purposes [86; 93]. Moreover, [PERSON] et al. [91] showed that the avocado stone exhibited in vitro antihelmitic activity against third-stage larvae of _Haemonchus contortus_, with epicatechin and quercetin being some of the active constituents.
## 2 Experiment Setups
### Mean Wind Pressure Coefficient at a Wind Incidence Angle of 45\({}^{\circ}\)  
For a typical oblique wind incidence angle of 45\({}^{\circ}\), Figure 7 shows the wind pressure coefficient \(C_{p}\) measured on the windward and leeward surfaces under various rib extensional depths. It can be found that large positive pressures appear on the upwind area of the windward surface, but these decay rapidly and become negative on the downwind area of the windward wall. The negative pressure area enlarges with the increase in the extensional depth. This may be attributed to the contribution of local recirculation caused by the downwind vertical ribs with a large depth, leading to an earlier air separation in the wake area. Figure 7 also indicates that increasing the rib extensional depth has a slight effect on the distribution of the negative wind pressure on the leeward surface.  
Figure 6: Mean wind pressure coefficients at a height of 55 m on the front, side, and back surfaces (\(\theta=0^{\circ}\)).  
Similarly, for this oblique wind incidence angle, the wind pressure coefficient \(C_{p}\) at the middle height of 55 m on the windward and leeward faces is shown in Figure 8. It shows that the positive wind pressures on most measuring points of the windward surface are quite similar between different rib cases while a sharp increase in the negative wind pressure was found on the downwind area (e.g., measuring tap 24) as the rib depth increased. Negative wind pressure appeared on an earlier measuring point when using a larger rib depth. This is consistent with the finding of Figure 7. However, on the leeward face, the negative wind pressure shows a moderate increase (e.g., within 10% from the smooth surface to the largest rib depth case) with the increase in the rib extensional depth while that on the model with a \(b/B\leq 2\%\) is quite similar.
# Self-Training Can Reduce Detection False Alarm Rate of High-Resolution Imaging Sonar +
## 4 Results
### Experimental Settings
#### 4.1.2 Model Preparation  
In the field of object detection, the existing mainstream algorithms are mainly divided into two-stage detection networks represented by R-CNN and Faster R-CNN, and one-stage detection networks represented by SSD and the YOLO series. Two-stage detection networks primarily generate corresponding regions of interest through a region proposal strategy, then locate and classify the bounding boxes, and finally obtain the results. On the other hand, one-stage detection networks treat the detection task as a regression problem, using deep convolutional networks to achieve the function of mapping from raw image input to object localization and classification output. Therefore, in terms of real-time performance, one-stage detection networks outperform two-stage detection networks.  
Considering the high requirements for model real-time performance when unmanned platforms perform underwater detection tasks, this paper selects the one-stage YOLO series model to conduct experiments based on the proposed low false alarm detection strategy. Drawing on the work of other researchers, this paper chooses the YOLOv8 model, which has been widely used in object detection research in recent years, and trains it using three strategies: training from scratch, \"pre-trained-fine-tune\", and the strategy proposed in this paper (SSL is also mentioned but not explicitly categorized as a separate strategy here for brevity). The detection performance of the model is then evaluated on the test set.
# Analysis of Weighted Factors Influencing Submarine Cable Laying Depth Using Random Forest Method
## 2 Materials and Methods
### Method
#### 2.3.2 BP Neural Network  
A backpropagation neural network (BPNN) is a type of multilayer feedforward artificial neural network that is trained using the backpropagation algorithm [28]. A BPNN consists of an input layer, hidden layers, and an output layer. Each layer contains multiple neurons, and the neurons in adjacent layers are connected through weighted connections. The training objective of the network is to adjust the weights and biases to minimize the error between the network's output and the desired target [29; 30].  
The training process of the BPNN consists of two phases: forward propagation and backward propagation.  
1. Forward Propagation: During the forward propagation phase, input signals pass through the hidden layers layer by layer, eventually producing the output values of the output layer. For the neuron in the layer, its input value is calculated as follows: \[z_{j}^{(l)}=\sum\
olimits_{i}\omega_{ji}^{(l)}a_{i}^{(l-1)}\,+b_{j}^{(l)}\] (10)  
Here, \(\omega_{ji}^{(l)}\) represents the weight from the \(i\) neuron in the \(l-1\) layer to the \(j\) neuron in the \(l\) layer, \(a_{i}^{(l-1)}\) represents the output value of the \(i\) neuron in the \(l-1\) layer, and \(b_{j}^{(l)}\) represents the bias term of the \(j\) neuron in the \(l\) layer.  
Then, the neuron's output value is calculated using an activation function such as sigmoid or ReLU:  
\[a_{j}^{(l)}=f\left(z_{j}^{(l)}\,\right) \tag{11}\]
2. Backward Propagation: During the backward propagation phase, the error is first calculated for the output layer:  
\[\delta_{j}^{(L)}=\left(a_{j}^{(L)}-y_{j}\right)\cdot f^{\prime}\left(z_{j}^{(L )}\,\right) \tag{12}\]  
where \(L\) represents the output layer, \(y_{j}\) represents the desired target value, and \(f^{\prime}(z_{j}^{(L)})\) represents the derivative of the activation function at \(z_{j}^{(L)}\).  
Then, the error is computed layer by layer from the output layer to the input layer:  
\[\delta_{j}^{l}=\left(\,\sum\
olimits_{k}\omega_{kj}^{(l+1)}\delta_{k}^{(l+1) }\,\right)\cdot f^{\prime}\left(z_{j}^{(l)}\,\right) \tag{13}\]Finally, the weights and biases are updated based on the errors:  
\[\omega_{ji}^{(l)}=\omega_{ji}^{(l)}-\eta\delta_{j}^{(l)}a_{i}^{(l-1)} \tag{14}\]  
\[b_{j}^{l}=b_{j}^{l}-\eta\delta_{j}^{l} \tag{15}\]  
where \(\eta\) is the learning rate.  
A BP neural network employs gradient descent or other optimization algorithms (such as Adam and RMSProp) to update weights and biases, aiming to minimize prediction error.
# NDVI or PPI: A (Quick) Comparison for Vegetation Dynamics Monitoring in Mountainous Area
## 3 Results
### Vegetation Indices Time-Series  
The raw NDVI time-series (Figure 4) are particularly noisy during the winter and spring, especially for coniferous trees. For deciduous trees, the metrics derived from the double logistic function indicate a mean NDVI of 0.54 over the entire period, with minimum and maximum values of 0.22 and 0.82, respectively. Coniferous trees exhibit a mean NDVI of 0.59, with corresponding minimum and maximum values of 0.23 and 0.80. Grasslands show a lower mean NDVI of 0.34, with a range from 0.06 to 0.56. Shrublands have a slightly higher mean NDVI of 0.40, with minimum and maximum values of 0.04 and 0.77. All vegetation classes exhibit a marked seasonal cycle.  
For the PPI results (Figure 5), the raw data also display noise, albeit to a lesser extent, particularly in the winter and spring. The metrics derived from double logistic functions show mean PPI values of 0.52 for deciduous trees, 0.35 for coniferous trees, 0.43 for grasslands, and 0.41 for shrublands. The paired minimum and maximum values (min/max) are 0.01/1.50 for deciduous trees, 0.06/0.96 for coniferous trees, 0.01/1.13 for grasslands, and 0.01/1.54 for shrublands.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Land Cover Classes** & **Area [km\({}^{2}\)]** & **Proportion [\%]** & **Mean Altitude [m]** \\ \hline Deciduous trees & 484 & 9.3 & 1387 \\ Coniferous trees & 722 & 13.8 & 1558 \\ Grasslands & 1176 & 22.5 & 2093 \\ Shrublands & 133 & 2.5 & 2103 \\ Other & 2713 & 51.9 & 2444 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Vegetation classes with corresponding area, proportion of Valais territory, and altitude mean.  
Figure 3: Vegetation distribution in Valais (projection: CH1903+/LV95).  
Figure 4: NDVI time-series per vegetation classes (top) with raw data (grey) and the double logistic function derived (colored). The bottom chart depicts all double logistic functions.  
Figure 5: PPI time-series per vegetation classes (top) with raw data (grey) and the double logistic function derived (colored). The bottom chart depicts all double logistic functions.
## 2 Materials and Methods
### Data
#### 2.2.2 Sentinel-2  
The Copernicus Sentinel-2 (S2) mission [63] comprises twin satellites, S2A and S2B, in orbit since June 2015 and March 2017, respectively, carrying the Multi-Spectral Instrument (MSI) that samples 13 spectral bands with a spatial resolution ranging from 10 to 60 m, with a swath width of 290 km, and a temporal resolution of about 5 days. All scenes are delivered in 100 \(\times\) 100 km\({}^{2}\) grid tiles in the UTM/WGS84 projection.  
In this study, we used all the available scenes from the beginning of the mission (i.e., 30 July 2015) to 31 December 2021. The images were processed following the workflow described in [PERSON] et al. [67]: first extracting the cloud cover with the Fmask (function of mask) processor [68], then, performing the atmospheric and topographic correction with Sen2 Cor [69]. Finally, the Snow Cover Extent (SCE) is extracted through the revised-Let It Snow (rLIS) algorithm, which exploits the approach firstly proposed by [PERSON] et al. [32] and then modified by [PERSON] et al. [67]. rLIS, which is specifically aimed at improving the discrimination between snow and clouds in mountain areas, is a tree algorithm based on thresholds on the normalized difference snow index (NDSI) and the red and the shortwave-infrared (SWIR) reflectance, and on a Digital Elevation Model (DEM). Primary, it identifies the snowline elevation using conservative thresholds, and then detects the snow cover using lower conservative thresholds and the snowline elevation, under which the snow presence is excluded. Furthermore, in this case, no data and cloudy pixels, retrieved by Fmask, were masked out.
# Route Optimization for Open Vehicle Routing Problem (OVRP): A Mathematical and Solution Approach
## 3 Materials and Methods
### Solution and Analysis of Results
#### 3.4.1 Heuristic Algorithms  
Greedy (H.1)  
Greedy heuristics have a short-term selection approach that does not take into account long-term consequences. It is based on the premise that locally optimal decisions will lead to a globally optimal solution. This approach is notable for its conceptual simplicity and computational efficiency, taking advantage of the optimal substructure present in various problems, where local solutions can be combined to generate a global solution. The general procedure of H.1 is shown in Algorithm 1.  
\begin{table}
\begin{tabular}{c l l} \hline \hline
**Type** & **Symbol** & **Definition** \\ \hline \multirow{3}{*}{Sets} & N & Set of nodes, includes clients and the repository. \\ \cline{2-3}  & ARC & Set of arcs, represents the connection between node \(i\) and \(j\). \\ \hline \multirow{3}{*}{Parameters} & \(d_{\text{n}}\) & Demand of each client. \\ \cline{2-3}  & \(qq\) & Vehicle capacity. \\ \cline{2-3}  & \(c_{\text{ij}}\) & Cost of traveling from one node to another. \\ \hline \multirow{3}{*}{Variables} & \(x_{\text{ij}}\) & Binary variable equal to 1 if the arc is used and 0 otherwise. \\ \cline{2-3}  & \(u_{\text{n}}\) & Continuous variable for subtour elimination. \\ \hline \hline \end{tabular}
\end{table}
Table 2: The definition of symbols.  
```
1:Input: List of vehicles (vehicles), List of customers (customers), Vehicle capacities (Q), Demand per customer (q), Distance matrix (distance)
2:Output: Dictionary of routes (routes)
3: Initialize an empty route dictionary.
4: Initialize a list of available vehicles (available_vehicles) from vehicles.
5: As long as customers are not empty and vehicles are available:  a. Select the first available vehicle (vehicle_available)  b. Initialize the current vehicle capacity (current_capacity) with Q[vehicle].  c. Start a new route with the vehicle in the depot (current_route = [0]).  d. Initialize the distance traveled (distance_traveled) to 0.  e. As long as current_capacity > 0 and customers are not empty  i. Find the nearest customer (nearest_closest_customer) to the last stop in current_route.  ii. If the demand of the nearest_closest_customer (q[nearest_customer]) is less than or equal to the current_capacity: q[nearest_customer].  equal to the current_capacity:  1. Deduct demand from current_capacity 2. 2. Add the distance to distance_traveled 3. Add nearest_closest_customer to current_route  4. Remove nearest_customer from customers  iii. Otherwise, break loop  f. Store completed route (routes[vehicle] = current_route)  g. Delete vehicle from available_vehicles
6:Return routes
```  
**Algorithm 1** The pseudo-code of Greedy (H.1) algorithm
# Geolocation, Calibration and Surface Resolution of CYGNSS GNSS-R Land Observations
## 3 Calibration of GNSS-R Land Reflections
### Level 1 Reflectivity Calculation  
The surface footprint returning power to the receiver is significantly altered in the case of coherent reflection, as explored in Section 4. In these conditions the surface is most appropriately described using the surface reflectivity rather than BRCS, where reflectivity (\(\Gamma\)) is the standard definition used in remote sensing [20]. Under these conditions, the reflection process is modeled using the Friis transmission equation:  
\[P^{coh}=\frac{\text{Y}G^{R}\lambda^{2}\Gamma}{(4\pi)^{2}(R_{R}+R_{T})^{2}} \tag{25}\]The key difference with the diffuse bistatic equation [11]) is the scaling by the total path length squared as opposed to the squares of the separate transmit and receive path lengths, as the signal reflects and does not diffusely scatter at the surface. The above equation can be rearranged into an expression of surface reflectivity as  
\[\Gamma(\tau,f)=\frac{P^{coh}(4\pi)^{2}(R_{R}+R_{T})^{2}}{\text{Y}G^{R}\lambda^{ 2}} \tag{26}\]  
where \(P^{coh}(\tau,f)\) is the level 1a DDM of received power in Watts. Considering the smaller reflection area, it is often useful to consider only the maximum power pixel in the DDM, representing the power level received within a small area near the estimated geolocation point, such that,  
\[\Gamma_{max}=max[\Gamma_{\tau,f}] \tag{27}\]
# Biomechanics of Punching--The Impact of Effective Mass and Force Transfer on Strike Performance
###### Abstract  
Effective mass, the portion of an athlete's mass contributing to a punch, is a key biomechanical factor influencing punching strength in boxing. This study examines its relationship with punch mechanics, impulse dynamics, and body composition, identifying techniques that maximize effective mass and enhance force transfer efficiency. Thirty trained male boxers performed jab, cross, lead hook, and rear hook punches while punching force and limb acceleration were measured using an AMTI MC12-2K force plate and Noraxon Ultium EMG sensors. Effective mass was calculated as the ratio of peak force to fist acceleration at impact. Statistical analysis compared punching techniques and examined correlations with body composition and training experience. Straight punches (jab, cross) exhibited significantly higher effective mass than hooks (KW-H = 235.24; \(p<0.001\); \(\eta^{2}=0.468\)), despite hooks generating greater peak forces. Cross punches had the highest effective mass (31.17 \(\pm\) 16.20 kg), followed by jabs (30.39 \(\pm\) 15.09 kg). No significant correlation was found between effective mass and body composition or training tenure, suggesting technique is more critical than absolute body mass. These findings highlight the importance of optimizing linear punch mechanics and impulse-to-acceleration synchronization in training to enhance effective mass transfer and striking performance.  
boxing; biomechanics; effective mass; punching force; kinetic analysis Article
## 3 Results and Discussion
### Effect of 30 Kc19\(\alpha\) on Cell Growth and Viability  
EPO-CHO cells were seeded into 6-well culture plates and incubated with a serum-containing complete medium for 48 h. Then, EPO-CHO cells were treated with 30 Kc19\(\alpha\) in the complete medium for a further 24 h. To explore the effect of 30 Kc19\(\alpha\) on the growth and viability of EPO-CHO cells in a serum-free culture condition, the medium was replaced with serum-free DMEM/F12 containing the same concentration of 30 Kc19\(\alpha\). During incubation with a complete medium-containing serum, the viability of cells was maintained at over 95% and the percentage of viable cells increased exponentially. However, after the medium was changed to serum-free DMEM/F12, the rate of viable cell growth gradually decreased. Subsequently, the percentage of viable cells started to decrease. Our previous studies have demonstrated that the addition of the recombinant 30 Kc19 protein (6.5 \(\upmu\)M) or the expression of the _30 Kc19_ gene has no impact on the viability or the percentage of viable EPO-CHO cells in a serum-free culture [23; 24]. As in the case of 30 Kc19, significant alterations in viability and viable cell concentration were not observed when 30 Kc19\(\alpha\) up to 16 \(\upmu\)M was added to the medium (Figure 3 and Supplement Table S1).  
Figure 1: Construction of recombinant 30 Kc19\(\alpha\) and its expression in _E. coli_ in comparison with 30 Kc19: (**a**) construction of _E. coli_ expression vectors pET-23a/30 Kc19\(\alpha\) and pET-23a/30 Kc19; (**b**) Western blot results; (**c**) sodium dodecyl sulfate-polyacrylamideamide gel electrophoresis (SDS–PAGE) of 30 Kc19\(\alpha\) and 30 Kc19 expressed in soluble form. M, protein maker; S, soluble cell lysate; E, proteins eluted from Ni-NTA affinity chromatography column; D, finally dialyzed protein; (**d**) comparison of production yields of 30 Kc19\(\alpha\) and 30 Kc19 after chromatography-mediated purification and dialysis.  
These results indicate that, similar to the full-length 30 Kc19, 30 Kc19\(\alpha\) has no ability to regulate the intracellular apoptotic pathway induced by serum deprivation.
## 3 Problem Formulation  
In this section, we formulate the problem of Targeted Propagator Discovery and Targeted Customer Discovery. The Targeted Propagator Discovery aims to find the set of users who will generate more people to visit the target POI in the future. Moreover, the aim of Targeted Customer Discovery is to find the set of users who will visit the target POI in the future.  
We first present the notations. Let \(G_{1}=(U,E_{1})\) be a social network, where \(U\) is the set of users and \(E_{1}=\{e_{ij}=(u_{i},u_{j})\},u_{i},u_{j}\in U\). Also let \((u,p,t)\) represent a check-in record, which means user \(u\) visits POI \(p\) at the time \(t\). We denote a _check-in network_ as \(G_{2}=(V,E_{2})\), where \(V=U\cup P\), \(P\) is the set of POIs and \(E_{2}=\{e_{up}=(u,p)\}\), where \(u\in U\) and \(p\in P\).  
**Problem 1 (Targeted Propagator Discovery, TPD):** Given an LBSN containing social network \(G_{1}\) and check-in network \(G_{2}\), a target POI \(p\) and the current time \(t_{c}\), the goal of TPD is to find the \(k\) users who will generate more people to visit POI \(p\) in the time period \([t_{c},\infty]\).  
**Problem 2 (Targeted Customer Discovery, TCD):** Given an LBSN with social network \(G_{1}\) and check-in network \(G_{2}\), a target POI \(p\) and the current time \(t_{c}\), the goal of TCD aims to find a set of users who will visit POI \(p\) in the time period \([t_{c},\infty]\).