esa-sceva/satcom-chunk-collection huggingface dataset:
# Energy Efficient Data Transmission for Sensors with Wireless Charging
## 3 Transmission in Limited \(K\) Periods
### A Simple Problem with \(C=e\)
In OPT, we can fill the six packets with size 5 into the three batteries, and the total amount of data is 30. However, in Algorithm 1, only the three packets with size \(5+\epsilon\) will be filled in the three batteries, and the total amount of transmitted data is \(15+3\epsilon\). ```
Input: \(S=\{s_{1},s_{2},\cdots,s_{n}\}\) and \(K\), assuming \(\forall s_{i}\leq C\) Output: Transmit a subset \(S^{\prime}\subset S\) of packets to maximize the total amount of data. 1: Sort \(s\) in non-increasing order. Let the ordered sequence be \(s_{1}\geq s_{2}\geq\ldots\geq s_{n}\);
2:for\(j=1\) to \(K\)do
3:\(e=C\);
4:for\(i=1\) to \(n\)do
5:if\(e\geq s_{i}\)then
6:\(trans(i)=j\); //Transmitting packet \(i\) in battery \(j\)
7:\(e=e-s_{i}\);
8:endif
9:endfor
10:endfor
```  
**Algorithm 1**FillDecreasing (S)  
Readers may wonder whether packets should be transmitted in an increasing order. We use a counterexample to show that transmitting packets in an increasing order of their sizes may still have a bad result. Given \(S=\{\epsilon,10\}\), \(C=10\), \(E=10\) and \(K=1\). In OPT, it transmits the packet with size 10, which will deplete the energy, while if transmitting packets in an increasing order of their sizes, only \(\epsilon\) units of energy is used, but \(10-\epsilon\) units of energy will be left unused. This example shows that transmitting packets in an increasing order cannot bound the energy utilization rate. **Lemma 1**. : _When \(K=1\), filling batteries with packets in decreasing order is a 2-approximation algorithm, but filling batteries with packets in another order is unbounded._  
This can be shown from two examples. In the case \(S=\{5+\epsilon,5,5\}\), \(C=10\), \(E=10\) and \(K=1\), filling batteries in decreasing order results in \(\lim\limits_{\epsilon\to 0}\frac{10}{5+\epsilon}=2\). In the previously mentioned example, \(S=\{\epsilon,10\}\), \(C=10\), \(E=10\) and \(K=1\), filling batteries in increasing order results in \(\lim\limits_{\epsilon\to 0}\frac{10}{\epsilon}=+\infty\).
## 3 Application Scenario and System Model
### System Model
#### 3.2.4 Mobile Sensor Model (UAV)  
The UAV instance is considered to have an internal state at any moment of time, which contains two components: a physical state and an engagement state:  
1. _Physical State (PS)_: this state includes information on the UAV's current position, speed, heading angle (\(\psi\)), communication range (\(R_{com}\)), and remaining energy resources.
2. _Engagement State (ES)_: according to the detected threats in the surveillance area and to the respective alarms issued, a UAV can be in one of the following states: _Idle_, _Engaged_ or _Busy_. The first state may occur when a UAV is just patrolling the area, being considered idle and ready to engage in performing a task over a threat informed about by an alarm. The second state occurs when a UAV is engaged in performing a task related to a threat, but it is not performing it yet, e.g., it is still moving towards the location where the threat was detected. The third state occurs when a UAV arrives at the threat location and starts handling the threat, _i.e._, it is performing a task over it, for example tracking it or just confirming it as a threat.  
Received alarms are stored in a queue and then handled according to a first come first serve policy.  
Figure 3 presents the FSM for the UAVs, in which one can observe the possible states and transitions among them. The transition from state 1 (Idle) to state 2 (Engaged) happens when a UAV receives an alarm and assumes the responsibility for handling it.  
Once an engaged UAV reaches the threat location, after being informed about it by an alarm, it transits to state 3 (Busy). When it finishes handling the threat it transits to the Idle state, unless it is engaged also with any other alarm and instead transits back to 2 (Engaged).  
The adopted kinematic model considers that the UAVs move along continuous trajectories with constant speed and with a constrained turning angle [29]. This turning angle may vary after the UAVs fly a given distance, or movement step \(L_{j}\), according to the adopted movement pattern. Figure 4 presents an example in which a UAV turns at point \(A\), to move a distance \(L_{j}\) towards the defined direction until it reaches point \(B\), where it will change its direction again.  
Figure 3: Finite state machine for unmanned aerial vehicles.
# Bistatic Landmine and IED Detection Combining Vehicle and Drone Mounted GPR Sensors
## 2 Methodology
### Ray-Tracing
#### 2.2.2 Implementation  
Usually, many rays are launched from each transmitter for proper illumination of the scenario [11]. However, to reduce the computational time required for multiple rays, only the rays that come from the specular reflection at the air-soil interface and the rays coming from the target are used. The former can be calculated directly using simple geometrical relations. For the latter, we estimate the angles of the incident rays that hit the left and the right sides of the target (i.e., the points \((x_{tg}-\delta_{tg},y_{tg})\) and \((x_{tg}+\delta_{tg},y_{tg})\)). These estimations require first calculating the refraction points at the air-soil interface for those points on the target. Then, many rays are launched between the computed angles. If one of these rays (after its reflection at the target) is closer than a given threshold (\(th\)) to a receiver, it is assumed that ray hits that receiver and it is used to compute \(E_{target}\). A detailed flowchart of the implemented approach is shown in Figure 3.  
Figure 3: Flowchart of the ray-tracing implementation.
## 3 The LBOP-ARIMA Model
### Model Parameter Selection  
#### 3.2.1 Determination of d  
Before establishing the ARIMA model, the data stationarity of the original data series is checked. The autocorrelation function (\(ACF\) value) is described as follows:  
\[ACF(k)=\rho_{k}=\frac{Cov(y_{t},y_{t-k})}{Var(y_{t})} \tag{10}\]  
Figure 6 shows the autocorrelation diagram of the bandwidth occupancy rate of link \(l_{j}\). For a stationary sequence, there are usually two main types of autocorrelation plots: tailing and truncating. Truncation means that the autocorrelation coefficients are all 0 after a certain order delay. The tail indicates that the correlation coefficient varies not only in a non-zero state, but also shows a decaying trend. It can be seen from Figure 6 that  
Figure 5: Effect picture of pre-processing data by the sliding window method.  
the autocorrelation diagram of the sequence is neither tailing nor truncating. The time series downlink bandwidth occupancy rate demonstrates non-stationarity, which means the proposed ARIMA model is required to analyze and predict the link load data.  
The difference calculation for the original sequence is used. The unit root test is adopted to verify the stationarity of the series. The test results obtained from the calculation are shown in Table 4. It can be seen from Table 4 that the absolute values of the T value of ACF of the original sequence are smaller than the absolute values corresponding with the rejection probabilities 1%, 5%, and 10%, which means the hypothesis that the sequence is non-stationary has to be accepted. The absolute values of the T value of the second-order difference series are greater than the absolute values of the 1%, 5%, and 10% levels, which means there is no single root in the second-order difference sequence, and the sequence has the characteristic of stability. Table 4 also shows that after the second-order difference, the acceptance probability is 0, which also proves that the second-order difference sequence is stable. It can be determined that in the ARIMA model, parameter \(d\) can be taken as 2.
# SEAIS: Secure and Efficient Agricultural Image Storage Combining Blockchain and Satellite Networks
## 7 Conclusions  
In sum, this paper proposes SEAIS, a secure and efficient storage scheme for agricultural images combining blockchain and satellite networks. SEAIS can use satellite imaging in remote areas and extreme environments, based on imaging from drones and ground equipment. To ensure data integrity and authenticity during denoising and enhancement of images, we combine homomorphic encryption and zero-knowledge proofs. Image data is stored on IPFS, and its hash and raw images are recorded on the blockchain for immutability and transparency. In the experiments, we evaluate the encryption and decryption times, transmission time, and storage overhead. The results show that SEAIS has stable encryption and decryption rates, with time overhead increasing as image size grows. Also, the transmission time of SEAIS for \((1024\times 1024)\) images is about 21.3% of the traditional ground networks. Meanwhile, the storage method combining IPFS and blockchain reduces blockchain storage costs and enhances scalability.  
In future work, we will implement batch processing, data aggregation, edge computing, and IPFS sharding to enhance the scalability of SEAIS, enabling it to efficiently handle larger datasets and more frequent updates in real-time agricultural monitoring scenarios.  
Conceptualization, L.L.; methodology, H.Y.; software, H.Y.; validation, P.J.; and Z.W.; formal analysis, H.Y.; investigation, H.Y.; resources, P.L.; data curation, H.Y.; writing--original draft preparation, H.Y.; writing--review and editing, P.J. and Z.W.; visualization, P.J. and Z.W.; supervision, L.L.; project administration, P.L.; funding acquisition, P.L. All authors have read and agreed to the published version of the manuscript.  
This work was funded by the National Key Research and Development Program of China, grant number 2022 YFD2100605.  
The data presented in this study are available on request from the corresponding author due to privacy.  
The authors declare no conflicts of interest.  
Figure 4: Agricultural image storage overhead.
## 3 Results and Discussion
### Bread Characterization
#### 3.2.2 Color  
The color of bread is a crucial characteristic that influences the consumers' choices. Figure 4 illustrates the color coordinates of control bread (CB) with NaCl and breads B50 and B100, with _S. perennis_ powder. Regarding bread color, the substitution of sodium chloride by _S. perennis_ powder allowed a more colored and darker crumb with a lower L* parameter and a higher greener (a* negative) and yellow (b* positive) parameters. Comparing B50 with B100, it is also observed that the increase of halophyte powder addition turned the bread color more greenish and yellowish. The increase of crumb color intensity can be attributed to the higher addition of natural pigments such as carotenoids and chlorophyll present in _S. perennis_ powder. Moreover, the higher yellow appearance with the increase of _S. perennis_ addition can be explained by the degradation of pigments during baking, producing brown-colored compounds. This finding is in accordance with the results obtained by [PERSON] el al. [23] with the addition of the same halophyte powder in snacks. The whiteness index of bread control was 69.7 and decreased from 55.2 (B50) to 49.2 (B100) with the increased halophyte powder addition.  
Moreover, the chromaticity (C*) also increased from the control bread (14.22) to bread with the addition of _S. perennis_, being 27.04 and 32.86 to B50 and B100, respectively. The  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **CB** & **B50** & **B100** \\ \hline Total baking loss (\%) & 13.5 & 14.1 & 9.5 \\ Weight (g) & 71.2 \(\pm\) 1.8 & 73.4 \(\pm\) 1.8 & 76.42 \(\pm\) 2.0 \\ Specific volume (cm\({}^{3}\)/g) & 3.24 & 3.50 & 3.66 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Physical characteristics of control (CB), B50 and B100 bread samples.  
overall assessment of the change in colour, measured as total color difference (TCD), with the addition of _S. perennis_ was 15.6 to B50 and 22.2 to B100. Considering these values, it was possible to observe that the addition of Sarcocornia powder introduced a perceptible change in the color of the bread.
## 5 Results and Evaluations
### Latency Analysis  
The delay between the action of a user and the response from the system is known as the latency or the trip time of a data packet. Latency analysis can also be used for performance evaluation; a lower latency means that the system is more responsive and efficient. Figure 13 shows the comparison of the latency of the proposed system with that of [53,54].
# Harnessing Biotechnology for the Remediation of Organic Pollutants in Coastal Marine Ecosystems
## 3 Organic Pollution in Coastal Marine Ecosystems
### Persistent Organic Pollutants (POPs)  
POPs are a class of highly resistant chemical compounds that degrade slowly, hence bioaccumulating in marine organisms and biomagnifying through trophic levels [27]. They pose long-term ecological and human health risks. These pollutants are particularly concerning due to their global transport via ocean currents and atmospheric circulation. The main categories of POPs include polychlorinated biphenyls (PCBs) that are historically used as dielectric fluids in transformers, capacitors, and industrial lubricants. PCBs persist in sediments and accumulate in fatty tissues of marine species, leading to reproductive disorders, immune system suppression, and developmental abnormalities. Dioxins and furans are byproducts of industrial combustion and waste incineration; these toxic chemicals cause endocrine disruption, neurological damage, and carcinogenic effects in marine organisms [28]. Organochlorine pesticides (OCPs) like DDT, aldrin and dieldrin, which were once widely used in agriculture, still persist in marine environments despite bans, affecting predatory fish and marine mammals through biomagnification [29].  
Polycyclic aromatic hydrocarbons (PAHs) are a major subclass of POPs. They are a group of complex organic compounds formed by the incomplete combustion of fossil fuels, biomass burning, and petrochemical spills [30; 31]. They are among the most prevalent organic contaminants in coastal ecosystems due to their presence in crude oil, coal and tar; they are mutagenic and carcinogenic, hence posing serious threats to mankind [31]. PAHs can be classified into two categories: low-molecular-weight (LMW) PAHs (e.g., naphthalene, anthracene), which consist of 2- and 3-ring aromatic structures and are more water-soluble and acutely toxic to marine invertebrates and plankton, and high-molecular-weight (HMW) PAHs (e.g., benzo[a]pyrene, chrysene), consisting of 4- to 6-ring aromatic structures; they are more hydrophobic, persistent and prone to bioaccumulation, and they cause long-term carcinogenic and mutagenic effects in marine species [32].  
PAHs are particularly dangerous in sediment-rich coastal environments where they bind to organic matter and affect benthic organisms [33]. Microbial Succession Theory plays a role in PAH degradation, as certain microbial communities with bacteria such as _Pseudomonas_, _Alcanivorax_ and _Mycobacterium_ species evolve over time to enhance their ability to metabolize these toxic compounds through dioxygenase-mediated pathways,converting them into catechols and ultimately mineralizing them through central metabolic routes like the \(\beta\)-ketoadipate and TCA cycle.
# Nationwide Flood Monitoring for Disaster Risk Reduction Using Multiple Satellite Data
## 3 Results
### Validated Synchronized Floodwater Index  
#### 3.1.1 Time-Series MLSWI Coupled with Water Level  
In order to reduce cloud-error effect particularly during the monsoon period, pixels containing clouds 10 percent or less were selected through pixel-based classification. Then the flood area was validated and modified using time-series MLSWI with water level data from 2015. Figure 3 shows the time-series annual behaviors of average MLSWI calculated by the single use of MLSWI (blue dots) (Figure 2(a)) and water level (Figure 2(b)). The figure also shows the behavior of SfWi (red dots) to compare it with that of water level, based on the data collected at the training sites. The comparison finds that SfWi exhibits two peaks on 10 June and 21 August 2015, when the water level exceeded the flood danger level. The global threshold of MLSWI, which is the MLSWI value at the time of floodwaters exceeding the flood danger level, was optimized to over 0.7 of MLSWI by means of supervised classification, i.e., flooded rice field and river water, based on the samplings at the training sites in the Sirajgan) district. In particular, time-series SfWi was proved by ALOS-2 data to clarify the behavior of floodwater. In Figure 2(a), the blue dots with the lowest MLSWI values at the second peak of the grey zone were confirmed as surface or flood water covered by cloud validating the floodwater pixels of MLSWI in comparison with the water pixels from backscattering of ALOS-2 data.  
Figure 3: Mean annual SfWi change between MLSWI and water level in selected Sirajgan) sites.
# A Reward Population-Based Differential Genetic Harmony Search Algorithm
## 4 Experiments
### Comparative Study on RDEGA-HS and HS Variants
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Function**} & \multicolumn{6}{c}{**HMCR**} \\ \cline{2-7}  & **0.9** & **0.8** & **0.6** & **0.5** & **0.3** & **0.1** \\ \hline Ackley & \(4.26\times 10^{-3}\) & \(8.54\times 10^{-3}\) & \(9.98\times 10^{-3}\) & \(3.41\times 10^{-2}\) & \(9.88\times 10^{-2}\) & 6.54 \\  & \(9.25\times 10^{-4}\) & \(4.51\times 10^{-3}\) & \(5.89\times 10^{-2}\) & \(5.68\times 10^{-1}\) & 1.25 & \(6.66\times 10^{1}\) \\ Griewank & \(2.54\times 10^{-10}\) & \(7.84\times 10^{-8}\) & \(1.54\times 10^{-6}\) & \(5.65\times 10^{-5}\) & \(6.58\times 10^{-4}\) & \(2.58\times 10^{-3}\) \\  & \(7.33\times 10^{-2}\) & \(9.98\times 10^{-2}\) & \(1.54\times 10^{-1}\) & 6.85 & 8.98 & \(4.65\times 10^{1}\) \\ Rastrigin & \(9.91\times 10^{-9}\) & \(7.54\times 10^{-8}\) & \(8.54\times 10^{-7}\) & \(3.69\times 10^{-5}\) & \(9.58\times 10^{-4}\) & \(7.54\times 10^{-1}\) \\  & \(2.54\times 10^{-4}\) & \(8.96\times 10^{-4}\) & \(2.56\times 10^{-3}\) & \(6.66\times 10^{-3}\) & \(2.58\times 10^{-2}\) & \(8.95\times 10^{-2}\) \\ Rosenbrock & \(7.56\times 10^{-2}\) & \(8.75\times 10^{-2}\) & \(2.45\times 10^{-1}\) & \(9.87\times 10^{-1}\) & 2.6 & \(8.74\times 10^{1}\) \\  & \(1.58\times 10^{-1}\) & \(6.87\times 10^{-1}\) & 8.95 & \(6.54\times 10^{1}\) & \(1.26\times 10^{2}\) & \(9.68\times 10^{2}\) \\ Sphere & \(2.54\times 10^{-39}\) & \(8.54\times 10^{-38}\) & \(5.64\times 10^{-35}\) & \(9.87\times 10^{-34}\) & \(6.84\times 10^{-31}\) & \(5.36\times 10^{-29}\) \\  & \(7.84\times 10^{-30}\) & \(5.42\times 10^{-28}\) & \(6.46\times 10^{-28}\) & \(3.38\times 10^{-25}\) & \(5.10\times 10^{-23}\) & \(3.86\times 10^{-21}\) \\ Schwefel & \(5.98\times 10^{-36}\) & \(6.04\times 10^{-33}\) & \(9.40\times 10^{-31}\) & \(3.20\times 10^{-30}\) & \(1.65\times 10^{-28}\) & \(9.69\times 10^{-27}\) \\  & \(2.31\times 10^{-30}\) & \(3.94\times 10^{-29}\) & \(4.48\times 10^{-27}\) & \(9.45\times 10^{-25}\) & \(4.31\times 10^{-23}\) & \(1.41\times 10^{-22}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Effects of HMCR on performance of RDEGA-HS (HMS = 80). has a strong optimization ability. Upon a general review of each algorithm's optimization process for the above six functions, the dimensions of the functions had a significant influence on the optimal value. As the dimensions increased, the optimal value of each algorithm also changed. For example, for the Ackley function, the variable dimensions had a significant influence on each algorithm. In terms of a low-dimensional function, RDEGA-HS and the other algorithms were all able to achieve the global optimum; however, in terms of a high-dimensional function, the RDEGA-HS algorithm still achieved a satisfactory result.
### Greece: The Southern Cluster Demonstrator  
Greece-based pilot project implementation includes the Southern cluster demonstrator. In order to address the unique requirements of TSOs, DSOs, market participants, customers, and regulatory peculiarities, this project also offers a cutting-edge common method for TSO-DSO cooperation for flexibility and shared services.  
The Peloponnese and Crete regions of Greece will gain the development and implementation of the F-platform, a sophisticated forecasting platform that assesses the requirements and flexibilities for congestion management and balancing. The island of Crete has recently been connected to mainland Greece as a result of which it is now a part of the pan-European integrated electrical network (Figure 3). In Crete and the Peloponnese, the high voltage level is currently 150 kV; however, new projects, consisting of new Overhead Lines (OHLs) at 400 kV and new HV substations, have been proposed and are currently being built. Since the Peloponnese region is mountainous and has a high wind capacity, many wind parks have been established despite the current network's capacity being insufficient to accommodate the installation of further wind power. Since diesel power units must be phased out in the next few years due to environmental regulations, the TSO included the AC connectivity with South Peloponnese. A second HVDC (scheduled for operation in the beginning of 2024) will connect Attica with Crete as well.  
In accordance with the OneNet overall design, the ultimate goal of the Southern demonstrator is to prescribe, develop, implement, and evaluate a research and development project in Greece that addresses the balancing and congestion management difficulties faced by system operators in the clean energy era. By utilizing grid services from prosumers, aggregators, suppliers, and producers, the TSO and DSO will cooperate in flexibility and coordination issues. Additionally, they will maximize the use of network assets and big data processing tools for network predictability and observability.  
In order to assess particular approaches and market processes that will eventually be included in the OneNet platform, the Southern demonstrator will use effective algorithms for forecast, technologies for grid observability, algorithms for balancing, and congestion management. The outcomes of the demonstrator will be assessed in order to offer suggestions for upcoming reforms in the market structure and the forthcoming harmonization of an EU wide power market.