esa-sceva/satcom-chunk-collection huggingface dataset:
# The Influence of Hard Protection Structures on Shoreline Evolution in Riohacha, Colombia
###### Abstract  
Over the past 50 years, coastal erosion has become an increasingly critical issue worldwide, and Colombia's Caribbean coast is no exception. In urban areas, this challenge is further complicated by hard protection structures, which, while often implemented as immediate solutions, can disrupt sediment transport and trigger unintended long-term consequences. This study examines shoreline changes in Riohacha, the capital of La Guajira Department, over a 35-year period (1987-2022), focusing on the impacts of coastal protection structures--specifically, the construction of seven groins and a seawall between 2006 and 2009--on coastal dynamics. Using twelve images (photographs and satellite) and the Digital Shoreline Analysis System (DSAS), the evolution of both beaches and cliffs has been analyzed. The results reveal a dramatic shift in shoreline behavior: erosion rates of approximately 0.5 m/year prior to the interventions transitioned to accretion rates of up to 11 m/year within the groin field, where rapid infill occurred. However, this sediment retention has exacerbated erosion in downstream cliff areas, with retreat rates reaching 1.8 \(\pm\) 0.2 m/year. To anticipate future coastal evolution, predictive models were applied through 2045, providing insights into potential risks for infrastructure and urban development. These findings highlight the need for a strategic, long-term approach to coastal management that considers both the benefits and unintended consequences of engineering interventions.  
coastal erosion; land management; urban areas; prediction models Article
## 2 Results and Discussion
### Amino Acids Involved in CoA Biosynthesis  
Four amino acids known to be involved in CoA synthesis (Figure 3) were selected for studies to determine their effect on ester production in MGJM. Valine is associated with CoA generation as it is converted to 2-oxoisovalerate, a precursor of pantotheanate [12]. Arginine is the precursor for the biosynthetic production of spermine [13], from which \(\beta\)-alanine is synthesised by _S. cerevisiae_ via polyamine degradation. \(\beta\)-Alanine then acts as the intermediate for pantothenic acid biosynthesis [13]. The later steps of CoA synthesis require the reaction of pantotheanate with cysteine and ATP (Figure 3).  
Since grape juice contains various chemical compounds including pantothenic acid and other precursors that could affect ester syntheses, it is necessary to eliminate these factors when studying the role of these four amino acids in ester production during fermentation. Using a chemically defined MGJM containing a simple mixture of sugars and nutrients (excluding nitrogen sources) can imitate winemaking conditions and is more suitable when gauging the effects of changing one or more variables. However, supplementing model musts with certain amino acids at different levels will also contribute to  
Figure 2: **(A)** Ethyl nonanoate and ethyl dodecanoate concentrations in model wines produced after the addition of increasing amounts of acyl-alanine conjugates to the MGJM. **(B)** Ethyl nonanoate and ethyl dodecanoate concentrations in model wines produced after the addition of increasing amounts of acyl-isoleucine conjugates to the MGJM. Histograms represent the mean values (n = 3) and error bars show standard errors. Different letters (e.g., a, b, c or a’, b’) denote significant differences between treatments at \(p<0.05\) using ANOVA followed by the Games-Howell _post hoc_ test. Note that the y-axis is in log scale.  
differences in total nitrogen contents, which can influence ester synthesis during fermentation (e.g., [37]). Hence, total yeast assimilable nitrogen (YAN) was normalised across the experiments by the addition of inorganic ammonium in the form of ammonium chloride (NH4 Cl). It was also necessary to eliminate pantothenic acid from the normal MGJM as it is through this pathway that we hypothesised that the amino acids may have an effect on ester production.
# Optimal Opacity-Enforcing Supervisory Control of Discrete Event Systems on Choosing Cost
## 5 Scenario 1: Secret \(K\) Is Opaque with Respect to \(L(G)\) and \(\Sigma_{a}\)
### Case 1: \(\overline{K}=L(G)\)
From the opacity of \(L(G)\), we can find sub-language \(L=\{aebgte,aebgbt,aebgb,eabgt,eabg,eabeg,eabegt\}\), whose strings cannot be distinguished with the secret in \(\underline{K}\). For language \(L\), we offer the following computational process: We take \(aebg\in\underline{K}\), and then we have \(\theta(aebg)=ag\) and \(H_{1}=[aebg]=\{eabg,eabeg,aebgb\}\). We take \(aebgt\in\underline{K}\), and then we have \(\theta(aebgt)=ag\) and \(H_{2}[aebgt]=\{eabgt,eabegt,aebgte,aebgbt\}\). So, quotient set \(L/[\cdot]=\big{\{}\{eabg,eabeg,aebgb\},\{eabgt,eabegt,aebgbt,eabegt,aebgbt\} \big{\}}\) is a partition of \(L\). For \([aebg]\), we can compute the choosing cost of the suffix of the non-secret string out of \(\overline{K}\) in the first stage (seen in Function 1 of Algorithm 4). If \(s=eabg\), we have \(\epsilon\in\overline{K}\) and \(V_{\epsilon}(eabg)=5.126\). If \(s=eabeg\), we have \(\epsilon\in\overline{K}\) and \(V_{\epsilon}(eabeg)=5.1256\). If \(s=aebgb\), we have \(aebg\in\overline{K}\) and \(V_{\epsilon}(b\sigma)=0.0002\). For coset \([aebgt]\), we can similarly obtain the following in the second stage: If \(s=eabgt\), we have \(\epsilon\in\overline{K}\) and \(V_{\epsilon}(eabgt)=5.126\). If \(s=eabegt\), we have \(\epsilon\in\overline{K}\) and \(V_{\epsilon}(eabgt)=5.1256\). If \(s=aebgte\), we have \(aebgt\in\overline{K}\) and \(V_{aebgt}(e)=0.00005\). If \(s=aebgbt\), we have \(aebg\in\overline{K}\) and \(V_{aebg}(bt)=0.0002\). Based on \(H_{1},H_{2}\) and the choosing cost out of \(\overline{K}\) above, a weighted directed diagram shown in Figure 8 is constructed using Algorithm 5 calling Algorithm 6. In the diagram, every node denoted by \(\odot\) is shown as a fraction. For the fraction, its numerator is non-secret string \(s=s_{i}\sigma_{i+1}\cdots\sigma_{i+k}\) in \(\cup_{j=1}^{2}H_{j}\), and its denominator is \(V_{\mathrm{S}_{i}}(t)\), where \(s_{i}\in\overline{K}\) and \(s_{i}\sigma_{i+1}\
ot\in\overline{K}\). To show the weight between nodes of adjacent stages, some weight is given as follows by Definition 5: \(V_{eabg}(t)=0,V_{eab}(egt)=0.0056,V_{eabg}(t)=0\), \(V_{eab}(gt)=0.006,V_{aebgb}(t)=0\). By Algorithm 5, label \(\mathit{Label}_{s}\) and minimal choosing cost \(V_{\min}(s)\) of a path from initial node \(t_{s}\) to current node \(s\) are computed in Table 1. In Table 1, we have \(\mathit{Label}_{t_{s}}=\{t_{s},aebgb,aebgbt,t_{t}\}\) for ending node \(t_{t}\). From Line 16 of Algorithm 3, we know that the shortest path is \(t_{s}\to aebgb\to aebgbt\to t_{t}\). So, it holds that \(\min V(L(f/G)-\overline{K})=0.0002\). Since \(V(\overline{K})=1.726\) (by Algorithm 1) is finite, \(V(L(f/G))=1.726+0.0002=1.7262\). So, in Line 17, we have \(L(f/G)=\{\overline{t},abtf,aebgt,aebgbt\}\) shown in Figure 9. In Figure 9, it is verified that \(V(L(f/G))=1.7262\) by Algorithm 1. Figure 8: A weighted directed diagram.
# Comparing Sentinel-2 and Landsat 8 for Burn Severity Mapping in Western North America
## 2 Materials and Methods
### Study Region and Field Data
The GFCC estimates tree cover using regression tree modeling of Landsat surface reflectance and temperature data using 250 m tree cover estimates derived from Moderate Resolution Imaging and Spectroradiometer (MODIS) imagery as training data. This filtering resulted in a final dataset of 912 CBI plots.
# Mapping Aquaculture Ponds for the Coastal Zone of Asia with Sentinel-1 and Sentinel-2 Time Series
## 3 Data and Methods
### Auxiliary Data
#### 3.2.2 Administrative Boundary Data  
Administrative boundary data was collected and processed to match statistical data and the pond aquaculture vector data (derived from Earth observation data) to geographical units. The geodatabase released by GADM provides comprehensive and detailed spatial data on administrative areas for all countries and their sub-divisions (ADM0-country level, ADM1-province level, ADM2-district level). The downloaded data was extracted from the GADM database in version 2.8 (April 2020).
## 5 Two-Stage Iterative Algorithm Design
### Two-Stage Iterative Optimization Algorithm  
Algorithm 2 describes the details of the two-stage iterative optimization algorithm, which iteratively optimizes the two subproblems until the objective value converges or the maximum number of iterations is reached, thereby obtaining the suboptimal solution for optimization problem \(\mathcal{P}1\).  
```
1:Initialize \(i=0\), \(\boldsymbol{\eta}^{(i)}\), \(\boldsymbol{\beta}^{(i)}\), the convergence threshold \(\rho\) and the maximum number of iterations \(I\);
2:repeat
3: Solve the task offloading subproblem for the given \(\boldsymbol{\beta}^{(i)}\) in Algorithm 1 to obtain the solution \(\boldsymbol{\eta}^{(i+1)}\);
4: Solve the task offloading subproblem for the given \(\boldsymbol{\eta}^{(i+1)}\) to obtain the solution \(\boldsymbol{\beta}^{(i+1)}\);
5: Set \(i=i+1\);
6:until the objective value converges within the threshold \(\rho\) or the maximum number of iterations \(I\) is reached.
```  
**Algorithm 2** Two-Stage Iterative Optimization Algorithm
# Research on Localization Algorithms Based on Acoustic Communication for Underwater Sensor Networks
## 2 Related Knowledge
### Routing Technique  
Routing techniques are the guarantee for the reliable and efficient communication between the anchor nodes and the unknown sensor nodes [15]. The design of routing techniques in UWSNs may face more difficulty than the terrestrial environment. The mobility of the node with the water currents may lead to less reliability. In addition, the high propagation delay may decrease the effectiveness. Due to the difference, numerous valuable techniques of WSN cannot be directly employed into UWSNs. Therefore, many new techniques need to be redesigned and verified by using experiments. Various applications are limited because only the acoustic communication is suitable for UWSNs. Fortunately, efficient routing techniques can decrease the power consumption and improve the reliability of UWSNs. In this field, plenty of routing techniques have been reported [18; 19; 20; 21; 22; 23]. Some survey papers have concluded routing techniques [7; 14; 15]. The authors in [7] classify protocols based on the energy-efficiency and reliability and consider protocols devoted to ameliorate energy-efficiency and reliability. The authors present various protocols and compare them in detail, while in [15], the authors describe the routing protocols in two aspects, namely, the cross-layer design routing and the non-cross-layer design routing. Different from the taxonomy of [15], the authors in [14] discuss these routing techniques based on localization, routing, and reliability. Three papers have collected numerous papers and made a detailed comparison. Therefore, we will not introduce the context of those routing techniques that can be found in the three papers.
# An Improved Lightweight Model for Defect Detection on Paths in Images
## 2 Materials and Methods  
### YOLOv8 Network Architecture  
The YOLOv8 algorithm is a significant update released by Ultralytics in 2023, building upon previous versions of YOLOv5, and further enhancing flexibility and performance. The YOLOv8 can be divided into YOLOv8n, YOLOv8s, YOLOv8m, YOLOv8l, and YOLOv8s, depending on the network depth and width. This study employs the RDD2022 [15] China dataset, which is of moderate size. The backbone and neck design are inspired by the ELAN architecture concept introduced in YOLOv7 [16], improving the C3 module from YOLOv5 and introducing the C2f structure for feature extraction at different scales, a modification that significantly enhances gradient flow. In addition, the model architecture has been finely optimized for models of different sizes, with the number of channels meticulously adjusted. Figure 1 illustrates the architecture of the baseline YOLOv8s model, which serves as the foundation for our proposed improvements. The neck layer adopts an FPN (Feature Pyramid Network) [17] + PAN (Path Aggregation Network) [18] structure, enhancing the fusion capability of features and optimizing the integration of semantic and spatial features through up-sampling and down-sampling techniques. The detection head has been updated from the original coupled structure to the widely adopted decoupled head, separating the classification and detection tasks. Additionally, the design shifts from the Anchor-Based approach of YOLOv5 to an Anchor-Free design. The YOLOv8 algorithm uses a dynamic allocation strategy, referencing TOOD's TaskAlignedAssigner [19]. For loss computation, the classification branch utilizes Binary Cross-Entropy (BCE) loss, while the regression branch applies Distribution Focal Loss (DFL) and the CIoU loss function. These loss functions collectively act on the prediction of classification scores and regression coordinates.  
Figure 1: Schematic representation of the structure in YOLOv8. This figure illustrates the standard components, including the C2f modules in the backbone and neck, and the conventional Conv blocks for down-sampling.
## 5 Methodology
### Preliminary Design Phase: Waste Management and Selective Demolition Planning within a IMM Approach  
During the preliminary design phase, the methodology proposes the creation of a digital material inventory, i.e. a digital database, for waste audits to plan and manage CDW streams and the selective demolition activities of existing facilities. A general scheme of the methodology is presented in Figure 4.  
Figure 4: General scheme for waste audits during the preliminary design phase within a IMM approach.  
Figure 3: General scheme of the model’s objectives and instruments.  
In order to collect the information needed to define the digital material inventory, close-range photogrammetry was considered. The output of close-range photogrammetry are points clouds realized from the images collected on site. This technology enables 3D measurements and the remote analysis of the images to identify materials and elements.  
Data collected through field surveys are the basis for the definition of the digital material inventory, i.e. the output of the waste audit. The digital material inventory allows the organization and management of a higher quantity of data by going beyond the basic level of information of a standard inventory of materials [5]. The proposed digital material inventory structure is shown in Table 2, and includes:  
* Basic information, including material estimation and description;
* Detailed information, including material types and European Waste Codes (EWC);
* An improvement of the basic levels of reporting hazardous and non-hazardous materials by considering three levels of reporting: (a) hazardous, (b) non-hazardous and recyclable, and (c) non-hazardous and reusable on site. The three levels enable an easier definition of waste management strategies through the prior identification of recyclable and reusable materials;
* The identification of recyclable materials and authorized recycling plants in the proximity of the project site to minimize the carbon emissions for waste transportation, hence promoting a circular economy approach at the local scale [25].  
The digital material inventory is then used to define proper selective demolition activities and their related costs. Consequently, a more reliable estimation of the costs for the demolition phase can be included in the total budget for the subsequent design and call for tenders phases. In addition, selective demolition plans can be defined considering actual waste quantities and components.  
The evaluation of the methodology applied to the case study was performed by comparing the selective demolition costs calculated through the digital material inventory with the costs of the parametric non-selective demolition of the same buildings.
## 2 Materials and Methods
### Ionospheric Data and Methodologies  
The ionosphere has been investigated in terms of its plasma electron density and the magnetic field's observations from two different satellite missions: China Seismo Electromagnetic Satellite (CSES) and the European Space Agency ESA _Swarm_ three-identical satellite constellation. CSES is a multi-payloads satellite mission primarily dedicated to searching for possible pre- and co-seismic effects of earthquakes in the world [54]. For this purpose, it is equipped with several instruments to detect possible particle bursts, variations of electric and magnetic fields, and the plasma property and composition of the ionosphere. Such a combination of instrumentations is not only precious for studying earthquakes and other natural hazards like volcano eruptions but also for more general geophysical studies, like elaborating a model of Earth's magnetic field ([PERSON] et al. [55]). The _Swarm_ mission is the \"state of the art\" for measuring the Earth's magnetic field and its evolution [56]. The mission has been in orbit for more than eight years, providing excellent results such as the detection of oceanic currents [31], understanding of the geomagnetic field, and even its variation in quasi-real time as an acceleration of movement of geomagnetic poles [57].
# Multi-Connectivity-Based Adaptive Fractional Packet Duplication in Cellular Networks
## 3 Multi-Connectivity in Cellular Networks  
Any end user equipment (UE) in the form of a cell phone, mobile tablet, laptop computer, mobile hotspot, wireless sensor, etc. is considered to have multi-connectivity (MC) when connected to more than one base station (BS) simultaneously. Most often, this is dual connectivity (DC) with only two connections to two BS at a time. These two independent RF connections could be between the BS of the same technology or between two different technology BS, such as LTE, 5G NR, UMTS and WiFi, in conjunction with the multi-radio-access technology (MRAT) standards.  
MC is also highlighted as a crucial URLLC facilitator because of its adoption to spatial diversity, in which many connections serve the UE from geographically dispersed places. In addition, time and frequency diversity are also integral to MC. Time diversity could be achieved by the means of re-transmissions and error-correction methods adhering to the delivery within the expected time interval. If in the same band, coherence bandwidth is used to separate the frequencies of various signals, or numerous frequencies are mixed for a single transmission. This may also be accomplished using carrier aggregation where data is separated over different fading channels.
## 2 Materials and Methods
### Analysis of Turbidity and Coagulation Activity  
A portable turbidimeter (Milwaukee turbidimeter) was used to determine turbidity. Coagulation activity was determined via jar test. A total of 500 mL of synthetic turbid water with various turbidities and pH was poured in a beaker and mixed for 3 min at 250 rpm followed by 15 min of 30 rpm. Various coagulant dosages ranging from 26-223 mg/L were added for each beaker. The suspension was then left for 30 min. After that, samples were collected from the top of the beaker (5 cm from the water surface) and turbidity was measured. Control coagulation (blank) was prepared at the same conditions but without the addition of coagulant. Coagulation activity was calculated using Equation (1).  
\[\text{Coagulation activity}\ (\%)=\frac{\text{Final Turbidity Blank}-\text{Final Turbidity Sample}}{\text{Final Turbidity Blank}}\times 100 \tag{1}\]
## 4 Simulation Results and Discussions
It can be seen that the proposed algorithm outperforms the other four algorithms significantly, and its NMSE is below \(10^{-2}\) when SNR is 0 dB and approaches \(10^{-3}\) when SNR is 20 dB. Among the comparative algorithms, the tensor decomposition-based algorithm approaches the performance of the proposed algorithm at \(SNR>15\) dB, albeit with a higher computational complexity. Meanwhile, the embedded pilot algorithm and two-step modified OMPFR algorithm exhibit performance saturation in the medium-to-high SNR range, indicating their limited capability to suppress IDI caused by fractional Doppler in multipath  
Figure 2: Comparison of metric functions between the basis function-related algorithm [13] and the proposed algorithm when SNR = \(-6\) dB. scenarios. These results validate the superior balance between estimation accuracy and robustness achieved by the proposed algorithm under dynamic LEO channel conditions. Figure 5 shows the BER performances of the proposed algorithm, the embedded pilot algorithm [8], the two-stage basis function correlation algorithm [13], two-step modified OMPFR algorithm [16], and tensor decomposition-based channel estimation [18]. When BER is \(10^{-3}\), the obtained SNR gain of the proposed algorithm is about 7 dB compared with the two-stage basis function correlation algorithm, and only 1 dB SNR loss compared with  
Figure 4: NMSEs of channel estimation vs. SNR for the proposed algorithm, the embedded pilot algorithm [8], the two-stage basis function correlation algorithm [13], two-step modified OMPFR algorithm [16], and tensor decomposition-based channel estimation [18]. Figure 3: MSEs of Doppler shift estimation vs. SNR for the proposed algorithm, the two-stage basis function correlation algorithm [13], two-step modified OMPFR algorithm [16], and tensor decomposition-based channel estimation [18]. the perfect channel estimation. It is also seen that the tensor decomposition-based algorithm can achieve a bit error rate below \(10^{-3}\) under high SNR conditions. The two-step modified OMPFR algorithm demonstrates a marginally better performance than the two-stage basis function correlation algorithm. In contrast, the embedded pilot algorithm exhibits the worst performance due to its limitations in resource efficiency and susceptibility to interference. The computational complexity of the proposed algorithm is \(O(MNlog_{2}(MN)+\hat{P}(QN+Q))\). The computational load of the proposed algorithm for converting time-domain signals to the DD domain requires only 20 \(\upmu\)s on modern spaceborne DSPs (e.g., TMS320C6678), and each iteration involves approximately 6400 complex multiplications, which takes about 0.2 ms on a 1 GHz processor. Therefore, if \(\hat{P}=10\), the entire synchronization process completes about 2 ms on LEO satellite processors. This is practically acceptable for real-world LEO satellite processors.