esa-sceva/satcom-chunk-collection huggingface dataset:
# Potential and Limitations of Open Satellite Data for Flood Mapping
## 2 Description of the Study Areas
### February-March 2015 Flood in Ebro Valley (Spain)  
The area is located in the middle of the Ebro River course in the Aragon region in (NE Spain; Figure 1A). Here, the alluvial plain is located in a 5-10 km-wide valley. The land use of the floodplain is primarily cultivated land, urban areas (Zaragoza, with approximately 600,000 inhabitants and other minor towns) and natural vegetation along the riverbanks. The floodplain is affected by periodic floods, particularly in the winter and spring seasons with several extreme events [(47)]. In the February-March 2015 period, several events of heavy rainfall occurred in the upper and middle basin of the Ebro River [(48)]. Consequently, the middle Ebro valley near the city of Zaragoza was affected by four different inundations. We focused our analysis on a section of approximately 60 km along the Ebro River from the villages of Alagon to El Burgo de Ebro. The most severe flood occurred approximately 2-5 March 2015, when the Ebro River reached a maximum discharge of approximately 2600 m\({}^{3}\)/s in Zaragoza gauge (E_Za in Figure 1A). Here, the flooding threshold is approximately 1500 m\({}^{3}\)/s, and the mean discharge of the Ebro River for March is 400 m\({}^{3}\)/s. The flood severity was estimated at an approximately 10-year return interval [(48; 49; 50)]. Large cultivated areas, some small settlements and linear infrastructures were inundated, resulting in severe damages [(51)]. The activation of Copernicus EMS EMSR-120 [(52)] allowed the automatic mapping of the flooded areas (delineation maps) using RADARSAT-2 data. In our study, we used Landsat-8, MODIS-Aqua/Terra, Proba-V and Sentinel-1  
Figure 1: Location of the study areas. Ebro River valley near Zaragoza, Spain, affected by the Ebro River flood in March 2015 (**A**); Po River basin, Italy, affected by flood in November 2016 with a focus on the plain around Alessandria town (**B**) and at the plain at south of Turin (**C**); area around Arahal village, Sevilla, Spain affected by a flash flood in November 2017 (**D**). The elevation and hillshade are retrieved by using a digital elevation model downloaded from the webGIS service.  
data for the mapping of flooded areas with the support of a DEM-based water-depth. The flood maps derived from remote sensing data were validated by using inundation maps and river discharge data available on the geoportal of the Ebro River Basin Authority [53].
# Multilayered Network Model for Mobile Network Infrastructure Disruption
## 7 Brief Discussion about Future Steps of Proposed MNM Model
### Energy Consumption of Proposed MNM Model  
A lot of the research activity today is focused on research into energy consumption in the routing process for either wireless networks or multilayered networks such as [44; 45; 46]. The energy is also considered in terms of UAV management [47; 48]. In our paper, energy is also considered in different areas. For example, in a DRONET network, the presence of a central point (dock) is considered, which is responsible for UAV management, charging of drained UAVs, and performing energy-intensive operations, such as clustering. Energy is also considered in interlayer communication (Section 3.5), where using of light protocols such as UDP, CoAP or EXI is advised.  
In the WSN network, the sink routing model is considered, so it is possible to assume, those nodes near the sink node connected to the MANET layer will be asked to forward packets more frequently. To address this problem and also lower the traffic load and energy consumption, the WSN sink node will forward only urgent data. In the DRONET layer the reactive protocol is suggested since periodic updates can be energy-demanding on limited UAVs resources. Also, urgent data are transferred to the DRONET layer only if the AP is not presented in a particular MANET subnet.  
Although energy consumption in MNM is considered, it is nevertheless not evaluated in this paper. Energy consumption in MNM is a however important issue and its evaluation is part of future research, which needs to address multiple areas from network design to management of nodes and routing protocols.  
Figure 24: The average time needed to cover all WSN sensors and WSN gateway sensors.
# ABACO: A New Model of Microalgae-Bacteria Consortia for Biological Treatment of Wastewaters
###### Abstract  
Microalgae-bacteria consortia have been proposed as alternatives to conventional biological processes to treat different types of wastewaters, including animal slurry. In this work, a microalgae-bacteria consortia (ABACO) model for wastewater treatment is proposed, it being calibrated and validated using pig slurry. The model includes the most relevant features of microalgae, such as light dependence, endogenous respiration, and growth and nutrient consumption as a function of nutrient availability (especially inorganic carbon), in addition to the already reported features of heterotrophic and nitrifying bacteria. The interrelation between the different populations is also included in the model, in addition to the simultaneous release and consumption of the most relevant compounds, such as oxygen and carbon dioxide. The implementation of the model has been performed in MATLAB software; the calibration of model parameters was carried out using genetic algorithms. The ABACO model allows one to simulate the dynamics of different components in the system, and the relative proportions of microalgae, heterotrophic bacteria, and nitrifying bacteria. The percentage of each microbial population obtained with the model was confirmed by respirometric techniques. The proposed model is a powerful tool for the development of microalgae-related wastewater treatment processes, both to maximize the production of microalgal biomass and to optimize the wastewater treatment capacity.  
microalgae; bacteria; modelling; wastewater treatment; nutrients; photobioreactor 2021  
A Note: MDPI stays neutral
# Low-Complexity Channel Codes for Reliable Molecular Communication via Diffusion
## 3 Results and Discussion
### Experimental Results  
To add validation to our simulation results, we performed a macro-scale MC experiment using TCH codes. The experimental setup did not intend to exactly mimic an in-body diffusion-based MC scenario, nor was the intention to repeat the same tests and comparisons that were already accomplished in the simulations. This evaluation was carried out by testing the channel without channel correcting codes and by testing the channel with a TCH(16,10) code. We chose to work with the TCH(16,10) code because it has a relative high coding rate while still maintaining good error capabilities. In this experiment we used Manchester pulses, for better channel robustness, combined with BCSK modulation. For transmitting a bit, we define two moments: a first moment that starts at the beginning of the symbol and goes to \(T_{s}/2\), and a second moment that goes from \(T_{s}/2\) to \(T_{s}\). To transmit the bit-0, we start by sending a base solution during the beginning until halfway of the first moment, followed by a period until the end of the first moment when nothing is released to the channel. In the second moment, an acid solution is sent to the channel until halfway of the second moment followed by a period until the end of the symbol where nothing is  
Figure 8: BER comparison for all the considered channel codes, using OOK modulation.
# _remote sensing_
###### Abstract  
In order to accomplish the calibration and validation (Cal/Val) of altimeters, the Wanshan calibration site (WSCS) has been used as a calibration site for satellite altimeters since its completion in August 2019. In this paper, we introduced the WSCS and the dedicated equipment including permanent GNSS reference stations (PGSs), acoustic tide gauges (ATGs), and dedicated GNSS buoys (DCB), etc. placed on Zhi'wan, Wai'ling'ding, Dan'gan, and Miao'Wan islands of the WSCS. The PGSs data of Zhi'wan and Wai'ling'ding islands were processed and analyzed using the GAMIT/GLOBK (Version 10.7) and Hector (Version 1.9) software to define the datum for Cal/Val of altimeters in WSCs. The DCB was used to transfer the datum from the PGSs to the ATGs of Zhi'wan, Wai'ling'ding, and Dan'gan islands. Separately, the tidal and mean sea surface (MSS) corrections are needed in the Cal/Val of altimeters. We evaluated the global/regional tide models of FES2014, HAMTIDE12, DTU16, NAO99b, GOT4.10, and EOT20 using the three in situ tide gauge data of WSCS and Hong Kong tide gauge data (No. B329) derived from the Global Sea Level Observing System. The HAMITDE12 tide model was chosen to be the most accurate one to maintain the tidal difference between the locations of the ATGs and the altimeter footprints. To establish the sea surface connections between the ATGs and the altimeter footprints, a GPS towing body and a highly accurate ship-based SSH measurement system (HASMS) were used to measure the sea surface of this area in 2018 and 2022, respectively. The global/regional mean sea surface (MSS) models of DTU 2021, EGM 2008 (mean dynamic topography minus by CLS_MDT_2018), and CLS2015 were accurately evaluated using the in situ measured data and HY-2A altimeter, and the CLS2015 MSS model was used for Cal/Val of altimeters in WSCS. The data collected by the equipment of WSCS, related auxiliary models mentioned above, and the sea level data of the hydrological station placed on Dan'gan island were used to accomplish the Cal/Val of HY-2B, HY-2C, Jason-3, and Sentinel-3A (S3A) altimeters. The bias of HY-2B (Pass No. 375) was \(-16.7\pm 45.2\) mm, with a drift of 0.5 mm/year. The HY-2C biases were \(-18.9\pm 48.0\) mm with drifts of 0.0 mm/year and \(-5.6\pm 49.3\) mm with \(-0.3\) mm/year drifts for Pass No. 170 and 185, respectively. The Jason-3 bias was \(-4.1\pm 78.7\) mm for Pass No. 153 and \(-25.8\pm 85.5\) mm for Pass No. 012 after it has changed its orbits since April 2022, respectively. The biases of S3A were determined to be \(-16.5\pm 46.3\) mm with a drift of \(-0.6\) mm/year and \(-9.8\pm 30.1\) mm with a drift of 0.5 mm/year for Pass No. 260 and 309, respectively. The calibration results show that the WSCS can commercialize the satellite altimeter calibration. We also discussed the calibration potential for a wide swath satellite altimeter of WSCS.  
s 2024161087. [[https://doi.org/10.3390/rs160610871](https://doi.org/10.3390/rs160610871)]([https://doi.org/10.3390/rs160610871](https://doi.org/10.3390/rs160610871))
# A Novel Digital Signature Scheme for Advanced Asymmetric Encryption Techniques
## 4 Analysis
### Attacks Aiming to Recover the Private Key from the Public Key  
In our proposal, the modulus \(n\) is public. The proposed scheme is based on a Factorization Problem (FB), where the adversary has to factorize \(n=p\times q\) to obtain \(p\).  
If there is a successful attack, the adversary calculates the secret key \(k\) from \(pk\), where  
\[k=pk\mod p \tag{10}\]  
After calculating \(k\), the adversary can extract the secret number \(r_{2}\) from \(r\) since \(r_{1}\) is public, where  
\[r=(pk-k)\div p \tag{11}\]  
Then, \(r_{2}=r\div r_{1}\). Now, the adversary has the whole private key \((k,r_{2},p)\) and can easily produce a valid signature.  
Therefore, secure prime numbers must be used; a prime number \(p\) is called safe if \(p=2\times p^{\prime}+1\), such that \(p^{\prime}\) is also a prime number. In FB, a size of \(p\) and \(q\) that equals 1024 bits is considered secure. To achieve a valid decryption, a message \(m\) to be encryptedmust be less than the private key \(k\), so the size of \(k\) usually depends on the used encryption technique and the plain text range.
# A Real-Time Cotton Boll Disease Detection Model Based on Enhanced YOLOv11n
## 4 Results and Analyses
### Impact of Different Lightweight Networks on Model Detection Performance  
To validate the effectiveness of the C3K2-PConv structure, this study employs YOLOv11 as the baseline and substitutes its feature extraction network with several mainstream lightweight models for comparative analysis. The selected models comprise ShuffleNet, EfficientNetv2 [21], and MobileNetV4 [22]. This approach aims to comprehensively assess the enhancements afforded by the C3K2-PConv structure. By maintaining identical parameters except for the backbone network, we compare their impacts on training performance. The experimental results are shown in Table 3.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Attention Mechanisms** & **Precision/\%** & **Recall/\%** & **[EMAIL_ADDRESS]\%** & **[EMAIL_ADDRESS]:0.95\%\%** \\ \hline EMA & 90.7 & 81.9 & 84.7 & 60.2 \\ SE & 90.2 & 77.6 & 82.1 & 57.4 \\ CA & 87.1 & 78.5 & 80.9 & 55.1 \\ ECA & 90.9 & 79.6 & 83.1 & 59.3 \\ GAM & 88.3 & 71.5 & 79.6 & 58.9 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparative analysis of different attention mechanisms.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Models** & **Parameters/M** & **FLOPs/G** & **Model size/MB** & **[EMAIL_ADDRESS]\%** & **[EMAIL_ADDRESS]\%** & **FPS** \\ \hline ShuffleNet & 1.7 & 4.1 & 3.8 & 78.9 & 55.4 & 86 \\ EfficientNetv2 & 3.5 & 3.6 & 7.3 & 82.9 & 56.9 & 71 \\ MobileNetV4 & 4.0 & 6.5 & 8.4 & 84.8 & 59.3 & 65 \\ C3K2-PConv & 2.5 & 6.1 & 5.3 & 84.4 & 60.8 & 79 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of different lightweight feature extraction backbone networks.  
As shown in Table 3, compared to ShuffleNet, the C3K2-PConv increases the number of parameters and FLOPs by 0.8 M and 2.0 G, respectively, but the [EMAIL_ADDRESS] and [EMAIL_ADDRESS]:0.95 improve by 5.5 and 5.4 percentage points, and the frame rate increases by 3 frames. Compared to EfficientNetv2, C3K2-PConv reduces the number of parameters by 1.0M, improves the [EMAIL_ADDRESS] and [EMAIL_ADDRESS]:0.95 by 1.5 and 3.9 percentage points, reduces the model size by 2.0 MB, and increases the frame rate by 11%. In comparison with MobileNetV4, the [EMAIL_ADDRESS] of C3K2-PConv decreases by 0.4 percentage points, but its number of parameters and FLOPs decrease by 1.5 M and 0.4 G, respectively, while the [EMAIL_ADDRESS]:0.95 increases 1.5 percentage points and model size reduces by 3.1 MB, and the frame rate increases by 14 frames. Although C3K2-PConv has an increase in parameter count and model size compared to ShuffleNet, it shows a significant improvement in both mAP and frame rate. Furthermore, compared to EfficientNetv2 and MobileNetV4, C3K2-PConv outperforms in most metrics, with a significant increase in frame rate. The results demonstrate that the C3K2-PConv structure proposed in this study is effective in improving the model's detection accuracy while reducing computational overhead.
# Privacy-Preserving Data Aggregation with Dynamic Billing in Fog-Based Smart Grid
## 4 Our Proposal
### Billing
#### 4.7.3 Bills Generation  
1. Upon receiving packet \(\mathcal{T}^{\prime}_{AC}\) from AC, the SP checks if the following equation holds: \[e(\prod\limits_{j=1}^{n}\sigma_{AC^{\prime}_{ij}},g)=\prod\limits_{j=1}^{n}e (H(ID_{AC}\parallel ID_{SM_{ij}}\parallel U_{ij}\parallel B_{ij^{\prime}} \parallel T_{AC^{\prime}}),pk_{AC}).\] (6)
2. If Equation (6) is satisfied, the SP uses its private key to calculate \(g_{n=1}^{\sum\limits_{i=1}^{t}c_{ij,n}\cdot p_{m}}\) as follows: \[g_{n=1}^{\sum\limits_{i=1}^{t}c_{ij,n}\cdot p_{m}}=\frac{B^{\prime}_{ij}}{(U_{ ij})^{X_{SP}}}.\] (7) After obtaining \(g_{n=1}^{\sum\limits_{i=1}^{t}c_{ij,n}\cdot p_{m}}\), the SP can obtain \(\sum\limits_{m=1}^{t}c_{ij,n}\cdot p_{m}\) using the Pollard lambda method [35]. Then, using the decoding function in [6], the SP can obtain the user's bill _Bill_ for a billing cycle and charge the user according to \(Bill\).
## 2 Introduction to Related Theories  
### Convolutional Autoencoder Network  
A convolutional autoencoder network (CAEN) is a reconstructed network that can be regarded as an improvement to and combination of an autoencoder model and a convolutional neural network [22]. Compared with the fully connected structure used in traditional autoencoders, CAEN can better complete unsupervised learning feature extraction with robustness. CAEN's constituent units belong to the encoding of one-dimensional convolution and the decoding of one-dimensional deconvolution. Suppose that the input data is \(X=[x_{1},x_{2},\cdots,x_{n}]^{T}\). For the encoding process, first initialize \(k\) convolution kernels, each of which consists of weight parameter \(w_{k}\) and bias \(b_{k}\) of the convolution layer, and generate \(k\) convolution maps \(h\). The calculation is  
\[h^{k}=f\Big{(}xw^{k}+b^{k}\Big{)} \tag{1}\]  
where \(f(\cdot)\) is the activation function. For the decoding process, a one-dimensional deconvolution layer is used to reconstruct this intermediate layer and finally generate the reconstructed output, which can be described by Equation (2):  
\[\widetilde{x}=f\Big{(}h^{k}\widetilde{w}^{k}+\widetilde{b}^{k}\Big{)} \tag{2}\]  
where \(\widetilde{w}\) is the weight from the middle layer to the deconvolution layer and \(b\) is the bias of the deconvolution layer.
## 5 Numerical Results
### The Effect of Rooftop-Mounted BSs  
Finally, we highlight the effect of rooftop-mounted BS on the UAV blockage probability. To this aim, Figure 8 shows the effect of rooftop-mounted mmWave BSs on the UAV blockage probability for UAV altitude \(h_{U}=150\) m, BS height \(h_{A}=5\) m, street width \(l=20\) m, mean building height and standard deviation \(E[H_{B}]=30\) m and \(\sigma[H_{B}]=10\) m, block width and length of \(b_{w}=b_{l}=100\) m. By analyzing the presented data, one may observe that mounting BSs on rooftops allows us to greatly reduce the BS blockage probability. More specifically, adding just three rooftop-mounted mmWave BSs to the considered area allows for the reduction of the UAV LoS blockage probability by multiple times. Recall that in the considered deployment the deployment area is \((b_{l}+l)M_{V}\times(b_{w}+l)*M_{H}\approx 1.44\times 10^{6}\) m\({}^{2}\), implying that the density of rooftop BS is just \(\approx\)2 \(\times 10^{-6}\) BS/km\({}^{2}\). Specifically, by comparing the horizontal and vertical distances between lines in Figure 8, we observe that in terms of UAV LoS blockage probability, adding additional BS at the rooftop is equivalent to deploying 10 more ground-mounted mmWave BSs. This value is affected by system parameters and environmental characteristics of the deployment and may vary between six and twelve.  
Figure 8: The effect of the rooftop-mounted BSs.  
Figure 7: UAV blockage probability as a function of street and building block widths. (**a**) Various street widths; (**b**) Various building block width.
## 5 Experimental Results
### Sensitivity Roll-Off Characterization  
The sensitivity roll-off in Figure 10 was calculated from the measured PSFs. The sensitivity roll-off of the SD-OCT system using the S-TFL was better than that of the S-ADL. The SD-OCT system using the S-ADL had an imaging depth of 0.75 mm at a sensitivity roll-off of -3 dB, and the S-TFL-based SD-OCT systems had an imaging depth of 0.89 mm at the same sensitivity roll-off. At a sensitivity roll-off of -6 dB, the imaging depth was extended to 1.16 mm for the S-ADL and 1.33 mm for the S-TFL. When we considered a sensitivity roll-off of -10 dB, we found imaging depths of 1.63 mm and 1.85 mm. Considering the results of the roll-off measurement, we can confirm that the aberration correction of the imaging lens was a critical factor in increasing the imaging depth of the SD-OCT system.  
Figure 10: Sensitivity roll-off of the spectral domain optical coherence tomography (SD-OCT) system according to the OPD when using the S-ADL and the S-TFL.  
Figure 9: PSF measurement results according to OPD. PSFs obtained using (**a**) the S-ADL and (**b**) the S-TFL
# Real-Time Conveyor Belt Deviation Detection Algorithm Based on Multi-Scale Feature Fusion Network
## 4 Discussion
### Objective Evaluation Results  
In this section, the results of FCN-8s, the proposed method, UNet and Deeplab v3 in pixel accuracy, testing accuracy, testing speed and training time are compared. As can be seen from Table 1, our method achieves the best results in terms of pixel accuracy and testing accuracy. FCN-8s cannot measure the testing accuracy for the serious breakpoint, while the result of UNet is not fine. Deeplab v3 has a longer training time due to the more complex network. Considering the inference time, the proposed method significantly outperforms the others by reducing the parameters of the depthwise separable convolution significantly, while the testing speed of FCN-8s, UNet and Deeplab v3 are too slow to meet the requirements of real-time detection in an industrial field. In a few words, our approach has the following advantages compared with other methods:  
(1) The proposed method has a high pixel accuracy without any post-processing operations.  
(2) The simple training and fast testing of the proposed method are advantageous to practical industrial application.  
(3) When there are objects on the belt, our method can automatically repair the occluded with avoidance of interference.
## 4 The Proposed Scheme: LDAKM-EIoT
### Pre-Deployment Phase
#### 4.1.2 Registration of Cloud Servers  
The \(TA\) picks a distinct unique identity \(ID_{CS_{j}}\) for each cloud server \(CS_{j}\) and proceeds to calculate its pseudo identity as \(RID_{CS_{j}}=h(n\ ||ID_{CS_{j}})\) and \(ACS_{j}=h(K_{EN-CS_{j}}\ ||ID_{CS_{j}}\ ||ID_{EN}\ ||RTSC_{CS_{j}})\) where \(RTSC_{CS_{j}}\) is the registration timestamp for \(CS_{j}\) and \(j=1\), \(2\), \(\cdots n_{c}\), \(n_{c}\) are the number of cloud servers placed initially in the IoT network, while \(K_{EN-CS_{j}}\) is a 1024 bit shared secret key of the edge node and cloud server \(CS_{j}\) generated by the \(TA\). Next, the \(TA\) stores the credentials \(\{RID_{CS_{j}}\), \(ACS_{j}\), \(ID_{CS_{j}}\), \(h(\cdot)\}\) in the database of \(CS_{j}\) before its placement in the network.
## 1 Introduction
In this article, we propose a clustered routing algorithm, namely the location and energy-aware k-means clustered routing (LE-KCR) algorithm, which applies K-means technology in light of both the located site and the remaining energy of each node. The proposed LE-KCR algorithm adopts the gap statistical method to determine the optimal cluster quantity, and clusters the underwater nodes via the k-means algorithm before selecting a head for each cluster by regarding its located site, remaining energy, and distance to the sink node. The K-means algorithm is a typical distance-based clustering algorithm, which takes distance as the similarity evaluation standard. Such an algorithm usually regards obtaining clusters with similar distance and independence as the final solution. In previous algorithms, cluster heads were mostly selected according to the position of the cluster centroid. However, in this paper, more attention is given to the relative distance between the cluster head and other nodes. In addition, given the inaccessibility of some nodes to the whole underwater sensor network resulting from the limited transmission range of their clusters, the dual-hop routing technique is adopted for the edge nodes. We organize the rest of this article as follows. In Section 2, the employed system model is introduced. In Section 3, the details of the proposed location and energy-aware k-means clustered routing (LE-KCR) algorithm is analyzed. In Section 4, the performance of the proposed LE-KCR algorithm is investigated before the article is summarized in Section 5.