esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Results and Analysis
### Time Complexity Analysis  
The time-consuming steps of the proposed algorithm mainly include the polarimetric parameter calculation, sampling region selection, port water extraction, and ROI recognition. For the parameter calculation, if the size of the image is \(n\times n\), the time complexity is \(O\left(n^{2}\right)\), since each pixel only needs to perform several additions and multiplications. For the sampling region selection, if the window size is \(N\), the time complexity is \(O\left(N^{2}n^{2}\right)\). For the port water extraction, if the size of the image is \(n\times n\), the layer of scale is \(k\), and the time complexity of the image pyramid construction is \(O\left(kn^{2}\right)\). However, all pixels in the image plane can be computed in parallel during the three steps of parameter calculation, sampling region selection, and port water extraction. Therefore, the time complexity of these three steps is low if all pixels are computed in parallel. The computation time of the ROI recognition steps is trivial as only PDF fitting and connection region extraction are computed. The computation times of each step are listed in Table 6, where the testing platform is Matlab v9.5, and the CPU is Intel Xeon at 3.6 GHz with 16 GB RAM. The times of the polarimetric parameter calculation are 49.8 s, 37.8 s, and 22.0 s with the Zhanjiang, Fujian, and Berkeley data, respectively. The total times are 101.6 s, 80.8 s, and 55.5 s, respectively. Compared with the proposed method, the time-consuming steps of the comparison method include the water-land segmentation post-processing and jetty scanning, but not the step  
Figure 12: Performance comparison of the proposed method with the jetty scanning method in the Zhanjiang, Fujian, and Berkeley data, where the detected targets are marked with a red box, and the ground truth is marked in a green box: (**a1–c1**) the water and land segmentation results; (**a2–c2**) the port detection of the proposed method in the Zhanjiang, Fujian, and Berkeley data; (**a3–c3**) the water–land segmentation results obtained by the process of small area removal; (**a4–c4**) the port detection of the jetty scanning method; (**a1–a4**) Zhanjiang; (**b1–b4**) Fujian; (**c1–c4**) Berkeley.  
of water-land segmentation. The total times are 447.93 s, 123.9 s, and 101.35 s, respectively, using the three datasets. The total time of the proposed method is less than that of the comparison method. The time complexity of the proposed method is low because accurate coastline detection and sophisticated geometric feature extraction are not needed.
## 2 Principle
### STBC-Assisted RLS Algorithms
By utilizing the STBC scheme, we may choose different combinations, and apply one or more of received copies to correctly decode the received signal, and, thus, attain a better performance. Meanwhile, the STBC is able to accomplish an RLS-level performance with a much lower computational complexity, because no matrix inversion is essential during the recursion. Instead of adding a training sequence to each data block, only a few  
Figure 8: The architecture of an STBC-aided RLS equalizer for MDM receivers [29]. Figure 7: The space and time allocation for the space–time block-coding (STBC)-aided recursive least squares (RLS) algorithm [31]. training blocks are added in the beginning, and the channel variations are tracked at the decision-directed stage in an attempt to reduce the overall system overhead [33].
## 3 Results
It can be speculated that those morphological aspects arise from the machining procedures and do not have any role in the microcracks and fracture propagation. Figure 5: SEM micrographs showing the distortion of spirals of EOF instrument. (**A**) Stretching of the spiral at \(\times\)20 magnification. (**B**) Particular of image A acquired at higher magnification (\(\times\)250), in which an alternation of the spiral can be noted. Figure 3: SEM images of debris on instruments surface. (**A**,**B**) EOF instruments, in which are shown several debris on the entire surface of the instrument. (**C**) OR instrument, in which are shown sporadic debris on the surface. (**D**) OR instrument, in which is shown an oily spot on the surface. Surface alterations such as microfractures and pitting were equally found in all brands, with no statistically significant differences among them (\(p\) > 0.05) (Figure 10). Figure 8: SEM images representing the moderate flattening of cutting edges of OR instrument after 4 simulated clinical uses at three different magnifications (from left to right respectively, of \(\times\)100, \(\times\)250 and \(\times\)500). (**A–C**) cutting edge of OR new instrument. (**A1–C1**) the same cutting edge of the OR instrument acquired after 4 simulated clinical uses. A moderate flattening can be appreciated at \(\times\)250 and \(\times\)500 magnification (**B**,**B1**,**C**,**C1**). Figure 9: SEM images representing the imperceptible flattening of cutting edges of R25 Blue instrument after 4 simulated clinical uses at three different magnifications (from left to right respectively, of \(\times\)100, \(\times\)250 and \(\times\)500). (**A–C**) cutting edge of R25 Blue new instrument. (**A1–C1**) the same cutting edge of the R25 Blue instrument acquired after 4 simulated clinical uses. Figure 10: SEM micrographs of metal defects, microfractures and pitting of the 4 used brand instruments at different magnifications. **(A)** tip of a used EOF instrument, in which some grooves and metals defect can be noted at \(\times\)500 magnification. **(B)** EOF used instrument, in which some grooves and metals defect can be noted at \(\times\)500 magnification. **(C,C1)** microfractures (red circles) noted on the cutting edge of a used WOG instrument at two different magnifications, respectively, \(\times\)1500 and \(\times\)3000. **(D,D1)** sever disruption of the cutting edge of a used R25 Blue instrument with the association of microfractures (\(\times\)250 and \(\times\)500 magnifications). **(E,E1)** pittings showed on a used OR surface at two different magnification \(\times\)250 and \(\times\)500 (blue circles). Regarding the debris accumulated consequentially to the instrumentation, the OR showed the highest values (\(p<0.05\)), with an incidence of 5 at the tip and at 4 and 8 mm from it and an incidence of 2 at 12 mm from the tip, followed by R25 Blue and then EOF and WOG.
## 4 Case Study
To further verify the vibration control effect of the optimized TMD designed based on the AFSA, in this section, considering that the frequency of pedestrians is usually around 2.00 Hz, a situation in which a 700 N person walks, runs and jumps on the middle of the bridge at 2.00 Hz will be simulated. According to [46; 47; 48; 49], a vertical force can be simulated considering of three different frequencies. Figure 4: The vertical mode shape of the pedestrian bridge. Figure 5: The comparisons of structural dynamic amplification factors under different frequency harmonic excitations. (**a**) Excitation frequency ratio from 0.5 to 1.5; (**b**) Local amplification figure. The comparisons of the structural response under 2.00 Hz walking, running and jumping excitations are shown in Figure 6. Note that the unit of acceleration \"gal\" means \"1 cm/s\({}^{2}\)\". It is presented in Figure 6 that the peak accelerations of structure under walking, running and jumping excitations are 6.24 gal, 25.17 gal and 26.90 gal, respectively, in the situation without TMD, while the peak accelerations are 3.54 gal, 15.48 gal and 14.39 gal, respectively, in the situation with TMD, and the corresponding peak acceleration reductions of TMD are 43.27%, 38.50% and 46.51%, respectively. Consequently, the vibration control effect of an optimized TMD system based on AFSA is quite obvious. Therefore, under the same design guideline, the maximum permissible pedestrian number is larger in the situation with TMD.
# Extraction of Novel Bioactive Peptides from Fish Protein Hydrolysates by Enzymatic Reactions
## 4 Novel Peptides Derived from Enzymatic Hydrolysis of Fish Discards
### Antiproliferative Peptides  
Bioactive peptides with antiproliferative activities are considered novel prospects utilized to develop cancer drugs, considering the minimized side effects and cost. Antiproliferative peptides inhibit the growth of cancer cells in various ways, including disrupting the cytoplasmic membrane through micellization, inducing apoptosis, and interacting with the gangliosides on the cell surface. [PERSON] and [PERSON] [74] discussed the antiproliferative peptides derived from fish protein hydrolysates and their development strategies. The authors indicated different approaches to isolating and purifying these enzymes and new approaches to the characterization of the peptides. However, most of the analyses were carried out in vitro and need confirmatory results through in vivo analysis.  
Bioactive peptides, especially those from fish hydrolysates, have the capacity to lessen oxidative stress due to reduced reactive oxygen species (ROS), which in turn inhibits genetic alterations such as mutation and chromosomal aberrations, which are typically important in carcinogenesis. A study conducted on the antiproliferative activity of protein hydrolysates from fish byproducts tested human colon and breast cancer cells [76]. It was revealed that FPH from the skin, bones, head, and viscera from different species significantly inhibited the growth of cancer cells. Furthermore, the authors suggested that the isolation of the responsible peptides for growth inhibition should be carried out and integrated into food supplements. [PERSON] et al. [77] studied the antiproliferative and antioxidative activities of cuttlefish (_Sepia pharonic_) protein hydrolysates, and it was revealed that the FPH inhibited the growth of the tested cancer cells (MDA-231 and T47D), with growth inhibition of 78.2 and 66.2%. The work of [PERSON] et al. [78] focused on _Cyclina sinensis_ protein hydrolysates (CSP) for the production of a novel peptide with antiproliferative pentapeptides that can induce the apoptosis of prostate cancer cells. The authors further stated that the developed hydrolysates from _C. sinensis_ significantly inhibited the growth of DU-145 cells. Furthermore, the peptides from CSP may represent a therapeutic and nutraceutical agent for treating prostate cancer patients. These novel discoveries in finding the solutions to curing various cancer diseases are very interesting, and derived peptides have a high potential to be utilized in these treatments. In this regard, it can be noted that there is evident proof that aquatic and marine resources are good natural sources for medicinal purposes.
# Adaptive Dynamic Disturbance Strategy for Differential Evolution Algorithm
## 3 An adaptive Dynamic Disturbance Strategy for Differential Evolution Algorithm
### Disturbance Mutation Strategy  
Evolutionary algorithms are prone to fall into premature phenomenon in the late iterations. For the DE algorithm, at the later stage of the iteration, the differences between population individuals are gradually narrowed, and the diversity of populations is drastically reduced, thus forming the \"aggregation\" phenomenon. If at this time populations findthe global optimal solution, it has no impact on the algorithm; if it the local optimal solution is found, it is not conducive to the further exploration of the population, and cann't find the global optimal solution. To describe this state, the following precocious definition is given:  
Definition 1 variance of population fitness is shown as follows:  
\[\sigma^{2}=\frac{1}{N}{\sum_{i=1}^{N}}{\left|{f_{i}-f_{avg}}\right|^{2}} \tag{12}\]  
Among these, \(f_{i}\) is the current individual fitness value, \(f_{avg}\) is the average fitness value of the individual and \(N\) is the number of populations. From the equation, we can see that the smaller the value of \(\sigma^{2}\), the more the population gathers together easily. Otherwise, it is in a stage of random search.  
Definition 2 population precocious cardinality, if \(\sigma^{2}<\det\) and  
\[f_{best}(g)>\delta \tag{14}\]  
Then we can say that the individual is premature at the g-th generation.  
Among these, \(f_{best}(g)\) is the optimal value for the g-th generation, det is the variance threshold value and \(\delta\) is precision value. This algorithm setting \(\det=1\times 10^{-6}\), \(\delta=1\times 10^{-6}\).  
If there is a precocious phenomenon in the g-th generation, a new mutation operation is carried out on individuals, and the mutation strategy is as follows:  
\[x_{i}(g)=\mu x_{\text{r},j}(g)+(1-\mu)x_{best}(j)+\beta(x_{\text{r},2,j}(g)-x_ {\text{r},3,j}(g)) \tag{15}\]  
\[\beta=F(1+0.5\eta) \tag{16}\]  
Among these, the weighting coefficient \(\mu\) is the same as Equation (8), and \(\text{r}1,\text{r}2,\text{r}3,i\) are a random number of interval [1,N]. N is the number of populations, and \(j\) is a random number in interval [1,D], \(x_{best}(j)\) is the optimum of the i-th population, while \(\eta\) is the random variable submitted to \(Gauss(0,1)\).  
From Equation (15), we can see that the new mutation strategy comprises two parts: the \(\mu x_{\text{r},1,j}(g)+(1-\mu)x_{\text{best}}(j)\) part is weighted by random and optimal individuals, and use the information of the optimal individual to guide other individuals to evolve in the direction of optimization; while the \(\beta(x_{\text{r},2,j}(g)-(x_{\text{r},3,j})(g))\) part uses Gaussian perturbation vector differences to randomly generate perturbation mutates in order to accelerate individuals to jump out of the local optimum and improve the overall search ability.
## 2 Related Work
ZP is based on partitioning the hot-spot storage responsibility among larger number of sensors, while ZPR is based on replicating the hot-spot in sensor neighbouring the hot-spot area. KDDCS proposed in [4] is also based on a K-D tree-like DIM so the construction process of K-D tree and the process of mapping an event to a bit-code are similar to those in DIM. However, in KDDCS, the refinement of regions in the formation of the K-D tree has the property that the numbers of sensors on both sides of the partition are approximately equal. As a result of this, K-D tree of KDDCS will be balanced, while the K-D tree of DIM is unbalanced so there will be no orphan regions. Also, regardless of the geographic distribution of the sensors, the ownership of events will uniformly distributed over the sensors if the events are uniformly distributed over the range of possible events. As described earlier, the bit-code for an event in DIM means a geographic location, so DIM can use GPSR routing to transmit the event to an owner node. However, the K-D tree in KDDCS splits the sensor network region so as to distribute sensor nodes equally. This means that the bit-code for sensor nodes and events cannot represent the geographical locations. Therefore, KDDCS presents a modification of GPSR routing, namely Logical Stateless Routing (LSR), for the routing of events from their generating sensors to their owner sensors, that is competitive with the GPSR routing used in DIM. Figure 2 shows a K-D tree for the same sensor network of Figure 1 built by KDDCS. In this figure, there is no orphan zone and the K-D tree is well balanced. KDDCS also has a load balancing algorithm called K-D Tree Re-balancing (KDTR). The re-balancing algorithm guarantees load balance even if  
the event distribution is not uniform. If a sensor node detects a hot-spot, it changes the split lines to re-balance the sub-tree. For example, in Figure 2, if the load of N2 exceeds a certain threshold value, it changes the split line from Y = 1.5 to Y = 1.2 and moves some events from N2 to N3. Dynamic balanced data-centric storage (DBAS) [6] presents a cooperative strategy between the base station and the in-network processing. It takes advantage of the resources of the base station to optimize the storage. Furthermore, DBAS is not based on the DIM scheme, so it does not have to maintain complex index structures like K-D trees. The re-balancing operation of DBAS is easy because no data needs to be transferred to other sensor nodes. However, in DBAS, sensor nodes must cooperate with the base station to maintain data centric storage.
# UVsq-Sat NG, a New CubeSat Pathfinder for Monitoring Earth Outgoing Energy and Greenhouse Gases
## 3 Description of the Uvsq-Sat NG Space-Based Mission
### Calibration and Validation--Cal/Val
#### 3.2.1 ERSs for Ongoing ERB Measurements from Uvsq-Sat and Inspire-Sat7  
A first pre-launch absolute calibration of each ERS will be performed using the LATMOS BX-500 blackbody, which provides a broad field of view that enables sensor calibration. The LATMOS blackbody, which has a diameter of 57 mm, an emissivity of 0.95, and a minimum distance of approximately 40 mm from the sensors, will be used at various temperature set-points incremented at 5 K, ranging from 323 K to 343 K.  
The pre-launch absolute calibration of each ERS in the satellite will also be conducted using the BB 3200 pg blackbody of Physikalisch-Technische Bundesanstalt (PTB, Braunschweig, Germany) as the primary standard [25; 26]. The BB 3200 pg blackbody, with an aperture area of 111.4 mm\({}^{2}\) and a minimum distance of about 700 mm from the Uvsq-Sat NG proto-flight model, will be operated at various temperatures ranging from 2800 K to 3150 K.  
Another ground test will be conducted at LATMOS using an Intra Solar Tracker (OWEL GmbH, Davos, Switzerland), which is a mechanical system that aims to follow the path of the Sun with an angular resolution of 0.038\({}^{\circ}\). The satellite will be integrated into a container that will be attached to the solar tracker mount. The objective of this test is to perform a calibration of the ERSs using the Sun as a source. During this test, calibrated pyranometers will also be used to obtain reference values according to international standards. For this purpose, we will use two ground-based pyranometers, namely, the Kimo SAM30 (operating in the range of 400 to 1100 nm, unidirectional, measuring from 1 to 1300 Wm\({}^{-2}\)) and the Kipp & Zonen SMP6-V (covering 270 to 3000 nm, with a 180\({}^{\circ}\) field of view, measuring from 0 to 1600 Wm\({}^{-2}\) with an uncertainty of \(\pm 20\) Wm\({}^{-2}\)).  
Based on all ground tests conducted via LATMOS on its ERSs, it is anticipated that the pre-launch absolute uncertainty for OSR and OLR will be around \(\pm 3\) Wm\({}^{-2}\) (1 \(\sigma\)).
## 3 Methods
### The Multi-Level Location Models for Emergency Shelters
#### 3.1.2 Location Model of Fixed Emergency Shelters  
The location selection of a fixed emergency shelter should be based on the full coverage of the requirement points in the coverage area and minimize the total cost. The construction process of this model includes the minimum number of facilities required to cover all the requirement points in the studied area by minimizing the facility point model; on the basis of the minimum number of facilities identified, the total cost of the facility configuration is minimized by impedance settings. Specific mathematical models are expressed as follows [8,10].  
**Minimizing the facility point model (MPM)**  
\[MPM:min\;z=\sum_{j\in L}x_{j} \tag{1}\]  
\[s.t.\;\sum_{j\in V_{i}}x_{j}\;=1\;,\;\forall i\in D \tag{2}\]  
\[D=\{D_{i}|i=1,2,\ldots,n\},n\in N\star \tag{3}\]  
\[V_{i}=\left\{i\middle|d_{ij}\leq r\right\} \tag{4}\]  
\[x_{j}=\left\{\begin{array}{cc}1,if\;the\;facility\;point\;L_{j}\;is\;identif \;fed\;for\;emergency\;point\\ 0,\;otherwise\end{array}\right.,\;\forall j\in L \tag{5}\]  
\[L=\left\{L_{j}\middle|j=1,2,\ldots,m\right\} \tag{6}\]where \(D\) is the set of demand points. \(L\) is the set of candidate facility points. \(n\) is the number of demand points. \(m\) is the number of candidate facility points. \(V_{i}\) is the set of candidate service facilities for demand point \(i\). \(r\) is the maximum service radius of the fixed emergency shelters and is set it to 2 km in this study. \(d_{ij}\) is the parameter of the distance from point \(i\) to point \(j\).  
Equation (1) is used to minimize the number of facilities. Equation (2) ensures that each demand point \(i\) is covered by only one facility point. In this study, each facility point can be used as an emergency shelter. Equation (3) is an integer constraint of \(x_{j}\). Equation (4) is the set of candidate service facilities for demand point \(i\), and the basic condition for its satisfaction is that the parameter of the distance from point \(i\) to point \(j\) is less than the effective emergency distance. Equation (5) refers to the range and condition of \(x_{j}\). Finally, Equation (6) is an integer constraint of \(L_{j}\).  
**Minimized impedance model (MIM)**  
\[\textit{MIM}:min\ z=\sum_{j\in L}C_{j}x_{j} \tag{7}\]  
where \(C_{j}\) is the travel cost to reach each emergency point. In this paper, we use the travel time (unit: min).  
The minimized impedance model is obtained by the minimization of the facility point model. By increasing the cost of travel, the site selection of the facilities is considered efficient, while taking into account the fairness. Besides, Equation (7) demonstrates that the total travel distance of the demand points, which are covered by the emergency point, to the emergency point \(j\) is minimized.
## 4 System Model  
The proposed WSN System model is given in Figure 2.  
Figure 2: System model.  
Wireless Sensor Networks consist of sensor nodes, cluster nodes, a base station, and a user. SNs have limited computing capabilities, limited energy, and limited storage capacity. The SNs perform the detection function and transmit it to their leader CN. The CNs receive the transmitted data and forward them to the BS.  
In the proposed system model, there are four different actors. These are the Sensor Node (SN), Cluster Node (CN), Base Station (BS), and User (U).  
SN: It refers to the sensor nodes in the environment. These nodes transmit the values they perceive from the environment to the CN of which they are members. Processing capacities and energies are limited.  
CN: It refers to the Cluster Node. It transmits the data sent by the SNs in its cluster to the BS. Processing capacities and energies are limited in CNs as SNs because CNs act as routers between the SN and BS.  
BS: It refers to the Base Station. It receives data transmitted by CNs. These data are also processed on the blockchain network. The BS is considered unlimited in terms of energy, memory, and processing capacity.  
U: It refers to the end user. It can monitor the WSN by connecting to the network whenever it wishes.  
There are three different communications in the WSN: SN-CN, CN-BS/BCN, and U-BS/BCN. These communications are bidirectional.  
Implementing blockchains in the WSN is a major challenge, given resource-constrained devices. Because blocks are held at each node, the larger the block size, the longer the consensus mechanism will be, and the greater the need for storage. Except for the BS in the WSN, SNs or CNs have insufficient capacity to hold these blocks. Even if CNs are strengthened with additional hardware, considering that the number of nodes in WSNs is excessive, the cost will increase. In this case, it is not suitable for the nature of the WSN. Therefore, the model in Figure 2 has been proposed considering that it is suitable for the WSN. As seen in Figure 2, the storage of the BC takes place at the BS level.