esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Conceptual Framework for Predicting Oil Palm Deforestation
### Identifying and Assessing Criteria  
In order to interpret deforestation related to oil palm, we defined a set of criteria: distance to oil palm, (min, max, mean), distance to mills (nearest mills' distance), distance to roads, distance to water, distance to oil palm concessions, related alerts, number of patches, distance between patches (mean distance between patches inside the alert), and alert duration, as shown in Figure 2.  
As for the perimeter, area, slope, and elevation criteria, they have been used in several studies. We will not discuss them in detail as they are widely covered in previous studies [53,54]. Figure 2 shows a set of criteria used to interpret deforestation related to oil palm.  
1. Area: This criterion helps us to determine if a given deforestation area is related or not to oil palm, as it is likely for farmers to cut a minimum of 0.5 hectare to establish new oil palm plantations.
2. Distance to oil palm: We defined this criterion to determine how far oil palm plantations are from the deforestation events. The general idea is that it is more likely for farmers to use the deforested patches for plantations that are close to their already-existing plantations.
3. Distance to mills and roads: This criterion is used to determine how far the deforestation event is from the nearest mill or road. In fact, it is likely for farmers to make roads inside their plantations to help them collect oil palm easier and faster as such palm needs to arrive fresh in the mill (generally before 24 h from harvesting).
4. Distance to water: This criterion indicates that it is likely for farmers to use the deforested patches close to water for planting oil palm trees. This is the case especially for smallholders as oil palm requires a lot of water for irrigation, especially in the dry season [55]. In addition, making water pipelines for irrigation is very expensive.
5. Number of patches: The number of deforestation patches inside a given area should be assessed in order to control palm oil deforestation. This number should not exceed a certain limit.  
Figure 2: Defined criteria for predicting deforestation related to oil palm.  
Distance between patches: This criterion indicates that it is more likely for deforested patches close to each other in a given area to turn into oil palm plantations.
* Alert duration: This criterion calculates the duration of the alert in days between the start date and the end date of the detection.  
Criteria are characterized by rates that reflect their spatial variability and weights expressing their influence on deforestation.  
We should notice that this restricted set of criteria assessments does not aim to be complete. A restricted set could allow us to predict deforestation related to palm oil. Future implementations should include other criteria, such as plantations for other crops, logging, and urbanization.
# Research and Implementation of High-Efficiency and Low-Complexity LDPC Coding Algorithm
## 1 Introduction  
LDPC codes, which are linear block codes characterized by sparse matrices, offer performance that approaches the Shannon limit. They possess remarkable flexibility and low error leveling, and exhibit low decoding complexity. LDPC codes have a highly adatable structure that allows for complete parallel operations. Their hardware implementation complexity is also low, enabling high throughput. LDPC codes also hold the potential for high-speed decoding and demonstrate excellent error correction performance in sudden situations. Notably, LDPC codes do not require correlation during encoding and decoding processes. A single LDPC code can be widely utilized across various channels and has undergone rigorous theoretical analysis to ensure its verifiability. In comparison to Turbo codes, LDPC codes exhibit superior performance in terms of good distance, low complexity, and high parallel decoding methods [1].  
Initially proposed by [PERSON] in 1962, LDPC codes, as a superior-performance linear block codes, did not garner significant research attention due to restricted research and implementation conditions at the time. It was only in 1993, when [PERSON] et al. [2] developed the Turbo codes and achieved significant research results, that the LDPC codes were reintroduced to researchers. In 1996, [PERSON] et al. [2] conducted in-depth investigations and analysis on LDPC codes, revealing their superior coding performance, which even surpassed that of Turbo codes, particularly in scenarios involving long code lengths [3].  
The check matrix of LDPC codes is characterized by sparsity, allowing them to overcome the enormous decoding operations in the case of long block codes. Consequently, efficient decoding algorithms can be applied. Additionally, LDPC codes integrate flexible structures, low decoding complexity, complete concurrent operation, and straightforward hardware implementation [4]. This paper, motivated by the advantages of LDPC codes and aiming to address the high computational complexity of standard coding algorithms, proposes a high-efficiency and low-complexity coding algorithm. Unlike traditional rapid coding algorithms, our proposed approach is achieved by analyzing and studying the check matrices. Experimental evaluation demonstrates that the developed algorithm effectively reduces implementation complexity while ensuring desirable coding performance.
# Spectrum Allocation Based on an Improved Gravitational Search Algorithm
## 2 System Model
### Problem Formulation  
Based on the aforementioned graph coloring model for spectrum allocation, we formulate the optimization problem. First, we introduce a commonly used objective, which is maximizing spectrum utilization and it can be expressed as  
\[U=\max_{A\in\wedge_{n,m}}\sum_{n=1}^{N}\sum_{m=1}^{M}a_{n,m}\cdot b_{n,m}. \tag{1}\]  
Besides, we introduce another objective to improve the fairness of the SU accessing the system, which is achieved by minimizing the standard deviation estimation of SU. It can be expressed as  
\[S=\min_{A\in\wedge_{n,m}}\sqrt{\frac{1}{N-1}\sum_{n=1}^{N}(\sum_{m=0}^{M-1}a_ {n,m}\cdot b_{n,m}-\frac{1}{N}U)^{2}}. \tag{2}\]  
Moreover, a channel assignment result should satisfy the collision-free constraint, i.e. if channel \(m\) is being accessed by the \(n\)-th SU, then the \(k\)-th SU cannot access this channel simultaneously, that is  
\[a_{n,m}+a_{k,m}\leq 1,\quad\forall k\in l_{n}^{m},n\in N;m=1,2,\ldots,M. \tag{3}\]  
where \(k\in l_{n}^{m}\) is the set of interfering to the \(n\)-th SU on channel \(m\) according to interference constraints and it comes from  
\[l_{n}^{m}=\left\{k|k\in c_{n,k,m}=1;c_{n,k,m}\in C\right\}\bigcap\{k|k=1,2, \ldots,N\}. \tag{4}\]Combine the (1)-(3) to formulate the problem. In order to reduce the complexity of the problem, we transform the above multiobjective optimization problem into a single objective problem. Let \(E=U-S\) denote the optimization problem which can be expressed as  
\[\begin{split}&\max_{A\in\wedge_{n,m}}E\\ &\text{s.t.}\quad a_{n,m}+a_{k,m}\leq 1,\quad\forall k\in l_{n}^{m},n \in N;m=1,2,\ldots,M,\end{split} \tag{5}\]  
where \(A\) is a valid spectrum assignment allocation, \(\wedge_{n,m}\) is the set of all valid spectrum assignments given a set of \(N\) SUs and \(M\) spectrum bands.
## 4 Proposed Experimental Results and Comparative Performance
### Performance of Short-Term and Long-Term Learning Ensembles  
This section compares the performance of short-term and long-term learning ensembles. The short-term ensemble is reduced from our multi-model framework by only activating the short-term models, namely, FSD and SMLP. The final forecast made by the short-term ensemble is calculated by applying the weighted strategy. Similarly, the long-term ensemble received the forecasts from CLR and LMLP only and combined them by the weighted strategy. The RMSE, MAE, and MAPE performance of short-term and long-term learning ensembles are shown in Table 6. It is seen that the forecasting performance obtained by the long-term ensemble is around the mean performance, which is achievable by the two embedded models (i.e., CLR and LMLP), and so is the performance of short-term ensemble achievable by FSD and SMLP. The implications are that the ensemble combining the forecasting results by two long-term learning models cannot create additional merits in performance improvement. Similar situation applies for the short-term ensemble. However, the short-term models have the potential to promote the performance of the long-term models although the short-term models are outperformed by the long-term models. This observation is validated by the superior performance of various multi-model strategies as shown in Table 7. The multi-model strategies enable the short-term models to compensate the test cases at which the long-term models performs worse. It is also worthy to note that the forecasting capability of the long-term ensemble may stay effective for some years. In our study, it remains effective in the three test years. However, the maximal number of straight years the long-term ensemble is applicable after a training process still needs further verification.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **RMSE** & **MAE** & **MAPE** \\ \hline Long-term ensemble & 7.86 & 5.67 & 0.38 \\ Short-term ensemble & 10.42 & 7.83 & 0.58 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Three-year forecasting performance obtained by short-term and long-term learning ensembles.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **RMSE** & **MAE** & **MAPE** \\ \hline FSD(30) & 10.19 & 7.69 & 0.61 \\ FSD(40) & 11.89 & 9.23 & 0.73 \\ FSD(50) & 10.14 & 7.74 & 0.62 \\
**FSD(60)** & **9.59** & **7.35** & **0.60** \\ FSD(70) & 12.95 & 9.66 & 0.75 \\ FSD(80) & 9.59 & 7.36 & 0.59 \\ FSD(90) & 12.65 & 9.50 & 0.73 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Three-year forecasting performance obtained by various FSDs.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **RMSE** & **MAE** & **MAPE** \\ \hline CLR & 8.94 & 6.29 & 0.43 \\
**LMLP** & **7.82** & **5.71** & **0.38** \\ FSD & 9.59 & 7.35 & 0.60 \\ SMLP & 10.73 & 8.04 & 0.60 \\ \hline Mean & 9.27 & 6.85 & 0.50 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Three-year forecasting performance obtained by various single models.
# Application of Near Infrared Hyperspectral Imaging Technology in Purity Detection of Hybrid Maize
## 2 Materials and Methods
### Data Processing and Modeling Methods
#### 2.3.3 Partial Least Squares-Discriminant Analysis  
Partial least squares-discriminant analysis (PLS-DA) is a discriminant technique based on PLS regression (PLSR). In spectral data analysis, the PLS algorithm is a widely used regression analysis algorithm. The new variable combination is obtained from spectral data through linear transformation. It is especially suitable for the case where there are many variables with multicollinearity, few sample observations, and large interference noise [28].
# Cybersecurity for Blockchain-Based IoT Systems: A Review
## 4 Challenges of Using Blockchain for Securing IoT Networks
### Blockchain Security
#### 4.2.1 Consensus Mechanisms  
Consensus mechanisms are fundamental to the security and integrity of a blockchain-based system, as they determine how transactions are validated and added to the blockchain. However, traditional consensus mechanisms, such as proof-of-work (PoW) or proof-of-stake (PoS), may not be suitable for IoT devices due to their resource-constrained nature. Developing lightweight and energy-efficient consensus mechanisms that can accommodate the limitations of IoT devices while maintaining the security and integrity of the blockchain is a significant challenge [2; 3; 4; 5; 6; 7].
###### Abstract  
The majority of the Earth's surface is covered by water, with oceans holding approximately 97% of this water and serving as the lifeblood of our planet. These oceans are essential for various purposes, including transportation, sustenance, and communication. However, establishing effective communication networks between the numerous sub-islands present in many parts of the world poses significant challenges. Underwater optical wireless communication, or UWOC, can indeed be an excellent solution to provide seamless connectivity underwater. UWOC holds immense significance due to its ability to transmit data at high rates, low latency, and enhanced security. In this work, we propose polarization division multiplexing-based UWOC system under the impact of salinity with an on-off keying (OOK) modulation format. The proposed system aims to establish high-speed network connectivity between underwater divers/submarines in oceans at different salinity levels. The numerical simulation results demonstrate the effectiveness of our proposed system with a 2 Gbps data rate up to 10.5 m range in freshwater and up to 1.8 m in oceanic waters with salinity up to 35 ppt. Successful transmission of high-speed data is reported in underwater optical wireless communication, especially where salinity impact is higher.  
on-off key; salinity; underwater optical wireless communication; polarization division multiplexing +
Footnote †: journal: Sensor and Actuator Networks  
0
Footnote †: journal: Sensor and Actuator Networks  
0
Footnote 0: _Academic Editor: [PERSON]_  
Received: 20 June 2023  
Revised: 6 September 2023  
Accepted: 15 September 2023  
Published: 7 October 2023
## 2 Experimental Setup and Proposed Method
### System Cost  
The total cost of the 645 kWp PV plant installed in December 2022 was USD 410,000, as directly documented by the Engineering Procurement Construction (EPC) company responsible for the installation. In the literature, the general capital cost of a battery system is expressed as approximately USD 1200/kWh [61]. Unit electricity prices in Turkiye are determined by the Energy Market Regulatory Authority (EMRA). In September 2023, the electricity unit price tariff for industrial subscribers was determined as TRY 2.925/kWh from grid to load (USD 0.108/kWh, USD 1 = TRY 26.95 (Turkish Lira)) and TRY 2.507/kWh from PV to grid (USD 0.093/kWh, USD 1 = TRY 26.95). The invoiced net energy price is calculated by adding the distribution cost, energy fund price, and energy consumption tax to the unit energy price. Therefore, the electricity unit price for industrial subscribers has been determined as USD 0.12/kWh and USD 0.139/kWh from PV to grid and from grid to load, respectively [62]. The renewable energy loan interest rate in Turkiye is approximately 10%. According to the data of the Turkish Statistical Institute, when the annual percentage change in the price index between the years 2005-2023 is considered, the annual average interest rate is 14.9% [63]. The O&M cost of the PV system may vary depending on agreements and country to country. It has been stated in the literature that the operation and maintenance cost of these systems is around 1% of the total cost of the project [64]. When calculating the electrical energy produced by the PV system, annual efficiency loss should also be considered. Due to the aging of materials, PV panels lose their efficiency every year compared to the previous year. This value is stated as 0.07% per year in the manufacturer's catalogs. In this study, the determined key input parameters are provided in Table 3.
# Survey of Dense Video Captioning: Techniques, Resources, and Future Perspectives
## 3 Techniques for Dense Video Captioning
### Encoder-Decoder Based Approaches
#### 3.1.4 Hierarchical Encoder-Decoder Models  
Hierarchical encoder-decoder models aim to capture both local and global temporal structures in videos by employing multiple encoders and decoders that operate at different temporal scales. [PERSON] et al. [25] proposed a hierarchical recurrent neural encoder that effectively captures both short-term and long-term dependencies in videos, which are then decoded into captions using an LSTM. This approach highlights the potential of hierarchical models in understanding complex video structures.  
Building on this concept, [PERSON] et al. [26] introduced the Hierarchical Representation Network with Auxiliary Tasks (HRNAT), as illustrated in Figure 5. This framework incorporates auxiliary tasks, such as cross-modality matching and syntax-guiding, to enhance caption generation. The novelty of their approach lies in the shared knowledge and parameters between auxiliary tasks and the main stream, including the Hierarchical Encoder Module, Hierarchical Attention Mechanism, and Language Generation Mechanism. While  
Figure 4: Redrawn hierarchical RNN-based attention encoder-decoder framework for video captioning (adapted from [23]): Input in green, output in blue, recurrent components in red, and sentence generator reinitialization indicated by orange arrow.  
these hierarchical approaches offer a powerful way to model the complex structure of videos and captions at multiple levels of granularity, they can also increase model complexity and computational cost due to the multiple stages of processing involved.
## 2 Materials and Methods
### R Package and Webapp  
All methods described above are wrapped in a R CRAN package called rPET. R is a well-known programming environment that started out for statistical analysis and has now expanded to provide processing tools for a very wide variety of applications, many of which take the spatial domain into account [26]. A new package developed in R, such as in this case, can include dependencies from existing packages, i.e., it can make use of functions and objects that are already implemented in another package in order not to \"reinvent the wheel\". In our implementation, the following packages are used either directly as dependences or indirectly as forked code: (i) _lidR_[27] for 3D point cloud data processing, i.e., it was used to convert a file in LAS/LAZ format [28] to a matrix with 3D coordinates that represent the point cloud of the study area (see Figure 5) and is used as example data; (ii) the _terra_[29] package for processing all 2D raster data, such as the DTM and the creation of the final comfort maps; (iii) _rayshader_[30], from which the basic rayshade code was forked from and modified to enable actual 3D rayshading. The latter code is what maps the sun radiation considering the shadow effect from elements above the ground. The original rayshader code only considers a 3D surface and not a 3D point cloud. The main workload is related to mapping shadows over a 2D regular matrix that represents the positions of people at each node, as reported in Section 2.4.3.  
To wrap all the processing in an interactive webapp, a function called \"solarApp\" is available to launch a web interface that allows the user to map the three comfort indices over the study area and change climate and physiological variables interactively. By forking the rPET package, a user can also tweak the code to define a different study area by just changing two input elements: (i) the DTM and (ii) the 3D point cloud source data. The solar angle is determined by the user or by the time of day and centroid position of the area in geographic coordinates.  
The code is available at [[https://github.com/fpirotti/rPET](https://github.com/fpirotti/rPET)]([https://github.com/fpirotti/rPET](https://github.com/fpirotti/rPET)) (accessed on 20 September 2022) with some examples from [25]. This package is for research and is not expected to be included in the official R repository, but it can be installed directly in any recent R environment by following the directions contained in the readme file of the Github page.
# NB-IoT for D2D-Enhanced Content Uploading with Social Trustworthiness in 5G Systems
## 4 Cooperative Multihop D2D-Based Data Uploading
The second term \(d_{i,j}\) is a binary function taking value 0 if users \(i\) and \(j\) are not in proximity, and value 1 otherwise. A link not meeting the mentioned constraint is represented in Figure 1 as a blocked link. **Data transmission configuration:** For each coalition that is formed, the eNodeB assigns the respective pool of virtual radio resources to the gateway, and transmits all the required information to the UE so that the transmissions can start. The devices in different coalitions are always allocated to orthogonal frequency resources by the scheduler (we consider a _maximum throughput_ scheduler) so that mutual interference is avoided. The configuration of the D2D communications assumes that UE simultaneously transmitting in the same coalition adopts different RBs to avoid any mutual interference (this leads to a worst case analysis and better results can be obtained with enhanced interference management).
# Performance Evaluation of Mobile RPL-Based IoT Networks under Hello Flood Attack
## 6 Performances Analysis
### The Nomadic Mobility Model (NCM)
#### 6.3.2 End-to-End Delay  
Figure 13 conducts a thorough investigation of the E2 ED in a mobile setting using the NCM mobility model, contrasting scenarios with and without the Hello Flood attack. The \(x\)-axis delineates the number of nodes, ranging from 0 to 60, while the \(y\)-axis portrays E2 ED in milliseconds (ms), spanning from 0 to 8 ms. Distinct lines depict different percentages (10%, 20%, 30%, 40%) of malicious nodes, categorized under two conditions: the mobile environment without the Hello Flood attack and with it. Each line is color-coded for clarity and accompanied by corresponding legends. A consistent observation emerges: lines representing \"attacker nodes\" exhibit higher E2 ED compared to those representing \"mobile nodes\" at similar percentages of malicious nodes. This discrepancy underscores the  
Figure 12: PDR without and with attack using NCM.  
detrimental impact of the Hello Flood attack on network performance, particularly in terms of delay. Notably, there is a noticeable trend of increasing E2 ED as the number of nodes rises, particularly pronounced in scenarios with \"attacker\" nodes. In scenarios devoid of malicious nodes, E2 ED remains relatively low. However, the introduction of malicious nodes notably influences E2 ED, peaking when the mobile network is subjected to the Hello Flood attack, especially evident with 60 nodes and a 40% mobile node composition. This observation underscores the disruptive impact of the attack on data transmission efficiency. Overall, the graph offers valuable insights into how the NCM mobility model and the Hello Flood attack affect E2 ED in mobile networks, stressing the importance of considering both mobility patterns and security threats when evaluating network performance.  
Combining the NCM model with the Hello Flood attack causes the E2 ED to increase through various interconnected factors. Firstly, the organized movement of nodes in the NCM model can create congestion points in the network, leading to packet collisions and longer transmission times, thus raising E2 ED. Additionally, the Hello Flood attack floods the network with excessive data, worsening congestion and interference, further delaying packet transmission, and increasing E2 ED. Secondly, the movement patterns of NCM nodes may destabilize routing, resulting in suboptimal routing decisions and increased packet loss, compounded by the disruption caused by the Hello Flood attack on routing protocols. Furthermore, increased network activity from both the NCM model and the Hello Flood attack can lead to contention for network resources, such as bandwidth and processing power, heightening transmission delays and elevating E2 ED. Moreover, the Hello Flood attack can introduce additional processing overhead and network congestion, further impeding packet transmission and increasing E2 ED.