esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Opportunistic networks are becoming a solution to provide communication support in areas with overloaded cellular networks, and in scenarios where a fixed infrastructure is not available, as in remote and developing regions. A critical issue, which still requires a satisfactory solution, is the design of an efficient data delivery solution trading off delivery efficiency, delay, and cost. To tackle this problem, most researchers have used either the network state or node mobility as a forwarding criterion. Solutions based on social behaviour have recently been considered as a promising alternative. Following the philosophy from this new category of protocols, in this work, we present our \"FriendShip and Acquaintanceship Forwarding\" (FSF) protocol, a routing protocol that makes its routing decisions considering the social ties between the nodes and both the selfishness and the device resources levels of the candidate node for message relaying. When a contact opportunity arises, FSF first classifies the social ties between the message destination and the candidate to relay. Then, by using logistic functions, FSF assesses the relay node selfishness to consider those cases in which the relay node is socially selfish. To consider those cases in which the relay node does not accept receipt of the message because its device has resource constraints at that moment, FSF looks at the resource levels of the relay node. By using the ONE simulator to carry out trace-driven simulation experiments, we find that, when accounting for selfishness on routing decisions, our FSF algorithm outperforms previously proposed schemes, by increasing the delivery ratio up to 20%, with the additional advantage of introducing a lower number of forwarding events. We also find that the chosen buffer management algorithm can become a critical element to improve network performance in scenarios with selfish nodes.  
opportunistic networks; machine learning; friendship; selfishness; routing +
Footnote †: journal: Sensors
# Lightweight Transformer Network for Ship HRRP Target Recognition
## 3 Experimental Results and Analysis
### Model Complexity Analysis  
The complexity of the model is intuitively reflected by the number of parameters and the amount of computation. The number of parameters and the amount of computation for six different model are shown in Table 4.  
From Table 4, it can be seen that the Transformer model combined with the local RNN module and GLTs, is about the same as the RNN model in terms of parameters, compared to the Transformer model, which has only a quarter of parameters, and moreover, only a fifth of parameters of the Transformer model combined with the local RNN module. In terms of computation, the Transformer model combining local RNN modules and GLTs is about the same as the RNN and LSTM models, much smaller than the Transformer and the Transformer model combined with local RNN module, achieving the best recognition rate using fewer parameters and computation. Model complexity is proportional to the number of parameters, so combined with the local RNN module and GLTs, the lightweight Transformer model achieves a win-win situation between complexity and accuracy.
## 4 Results
### Spatial Autocorrelation Analysis of Vegetation Dynamics in the ARB
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Independent Variable** & \multicolumn{1}{c}{**Mean**} & \multicolumn{1}{c}{**Standard**} & \multirow{2}{*}{**Minimum**} & \multirow{2}{*}{**Maximum**} \\  & & & & **Deviation** & & \\ \hline \multirow{4}{*}{Climate changes} & Precipitation (Pcp, mm) & 565.977 & 100.521 & 289.969 & 800.870 \\  & Mean temperature (Tavg, \({}^{\circ}\)C) & 0.046 & 2.931 & \(-\)6.253 & 6.051 \\  & Maximum temperature (Tmax, \({}^{\circ}\)C) & 6.489 & 2.499 & 0.310 & 12.159 \\  & Minimum temperature (Tmin, \({}^{\circ}\)C) & \(-\)6.263 & 3.394 & \(-\)12.900 & 0.627 \\ \hline \multirow{4}{*}{Anthropogenic activities} & Proportion of forest area (Forest) & 0.460 & 0.324 & 0.000 & 0.997 \\  & Proportion of pasture area (Pasture) & 0.181 & 0.234 & 0.000 & 0.988 \\  & Proportion of wetland area (Wetland) & 0.049 & 0.081 & 0.000 & 0.542 \\  & Proportion of crop area (Crop) & 0.253 & 0.231 & 0.000 & 0.887 \\  & Proportion of residential area (Residential) & 0.014 & 0.035 & 0.000 & 0.500 \\  & Proportion of water area (Water) & 0.032 & 0.101 & 0.000 & 1.000 \\  & Proportion of range area (Range) & 0.011 & 0.065 & 0.000 & 1.000 \\ \hline \multirow{4}{*}{Hydrological processes} & Surface runoff (SURQ, mm) & 8.941 & 6.423 & 0.000 & 31.384 \\  & Groundwater flow (GWQ, mm) & 2.532 & 3.332 & 0.000 & 32.949 \\ \cline{1-1}  & Lateral flow (LATQ, mm) & 0.087 & 0.154 & 0.000 & 1.288 \\ \cline{1-1}  & Actual evapotranspiration (ET, mm) & 33.769 & 7.152 & 14.279 & 76.573 \\ \cline{1-1}  & Amount of water stored in the soil profile & 59.386 & 27.181 & 0.000 & 136.027 \\ \cline{1-1}  & Snowmelt (SM, mm) & 5.052 & 3.034 & 0.000 & 20.291 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Statistical results of period-average explanatory variables for four periods at the sub-basin scale.
# Annual Gross Primary Production from Vegetation Indices: A Theoretically Sound Approach
## 4 Results  
### H1 Hypothesis Testing  
Annual PAR images for the eight years (see Section 3.1) and a coefficient of variation image were calculated (Figure 3a). The coefficient of variation (mean value equal to 0.026) shows the inter-annual variability of the annual PAR throughout the period 2005-2012, which is less than 5%, in the entire study area. Thus, the annual PAR can be considered practically constant. Its variation is ca. 50% lower than that corresponding to the annual mean NDVI and EVI (Figure 3b,c). Annual-mean VI and reference annual GPP series (not shown) present rather similar spatial patterns. A further analysis has  
Figure 2: Flowchart of GPP\({}_{\text{annual}}\) estimation using the PAR-weighted VI (being VI = NDVI, EVI).  
been carried to assess the effects of both the daily PAR, NDVI and EVI inter-annual variability on the reference annual GPP.  
The results of the correlations GPP/PAR (Figure 3d) show that only the 25% of the pixels present a _p_-value \(\leq\) 0.05, (i.e., statistically significant, with mean R\({}^{2}\) = 0.62), whereas 64% of the pixels show a _p_-value \(\leq\) 0.05 for the correlation GPP/NDVI (R\({}^{2}\) = 0.73) and 65% for the GPP/EVI (R\({}^{2}\) = 0.74). Therefore, the NDVI (and EVI) inter-annual variations play a significant role in the GPP inter-annual variations. As the daily PAR series of all eight years show similar characteristic seasonal patterns, hypothesis H1 is accepted. That is, we assume that annual PAR is practically constant (as compared with NDVI and EVI) and each pixel can be characterized by a representative PAR\({}^{\circ}_{i}\) series. The annual representative PAR (\(\Sigma\)PAR\({}^{\circ}_{i}\)) coincides with the average PAR shown in Figure 1b.
# Low-Latency QC-LDPC Encoder Design for 5G NR
## 5 Results and Discussions
### Evaluation Results
Although this design is 40% higher than [21] in terms of resource utilization, the proposed encoder is suitable for full-size \(Z\) and includes CRC calculation. In [19; 21], ASIC synthesis is performed for different lifting sizes, and five and nine encoders are implemented respectively. Thus these encoders can only work under the specified \(Z\) size. Moreover, Refs. [19; 20; 21; 22] do not include CRC calculation. Therefore, the work implemented in this paper has more flexibility for various 5G application scenarios. The entire calculation process of data channel coding in the 3 GPP standard has also been implemented. Consequently, the design of this paper is a low-latency hardware structure with complete encoding functions. From the implementation results, the dynamically configurable architecture designed in this paper can directly perform full \(Z\)-size encoding operations; it can be seen that this design can be applied to different submatrix sizes. The design has significant silicon area efficiency and encoding throughput, and the design can be applied to a variety of code lengths and code rates.
# Coverage Optimization of WSNs Based on Enhanced Multi-Objective Salp Swarm Algorithm
## 5 Results and Discussion
### WSNs Coverage Optimization Analysis
#### 5.2.1 Boxplot Comparison Analysis  
In this section, a box plot is used to compare the accuracy and consistency performance of solution sets obtained by three different algorithms. In Figure 7, \(f_{1}\), \(f_{2}\), and \(f_{3}\) represent network coverage optimization, node utilization, and network energy balance, respectively.  
The results from the boxplot evaluation reveal that, in terms of coverage, NSGA-II and EMSSA outperform MSSA. For node utilization, EMSSA performs well, while NSGA-II exhibits the poorest performance. In the context of network energy balance, EMSSA demonstrates relatively good performance, while MSSA performs the worst. In summary, under the condition of meeting the predetermined coverage requirements, the EMSSA algorithm utilizes fewer working nodes and maintains a more balanced network consumption, resulting in a longer network lifetime. In other words, NSGA-II and MSSA do not perform well under the deployment model considered in this chapter, while EMSSA shows relatively better performance.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Number of nodes & 200 \\ Population size & 60 \\ \(T\) & 300 \\ \(\alpha\) & 0.2 \\ \(\beta\) & 2 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Simulation Experiment Parameter Settings.  
Figure 7: Boxplot of Optimal Solution. (a) network coverage. (b) node utilization. (c) network energy balance.  
From Figures 8-10, it can be observed that EMSSA achieves higher coverage with fewer nodes for different predefined coverage thresholds. Additionally, compared to the coverage deployment solutions obtained by the MSSA and NSGA-II algorithms under the same coverage threshold, the EMSSA algorithm achieves a more evenly distributed deployment of sensor nodes.  
The non-dominated solutions obtained by each algorithm for different predefined coverage thresholds are presented in Table 5 as follows.  
Ensuring network connectivity, lower values of node utilization and network energy balance indicators at the same coverage threshold correspond to better network performance. Observing Table 5, it is evident that when the predefined coverage threshold is set at 90%, EMSSA achieves higher coverage with fewer nodes and a more even distribution of nodes. At a predefined coverage threshold of 95%, EMSSA achieves a 7% reduction in node utilization, significantly reducing the use of redundant nodes, and lowering the overall network energy consumption. When the predefined coverage threshold is set at 100%, EMSSA reduces node utilization by 12% and appropriately extends the network's lifespan. These experimental results indicate that this coverage control strategy effectively reduces the use of redundant nodes, ensuring coverage requirements while maintaining balanced energy consumption.
# Binomial Number System
## 5 Conclusions  
The BNS with the binary alphabet uses binomial coefficients as bit weights. The binomial numbers generated by the BNS divide into the first and second classes, characterized by 0 or 1 in the LSB of the binomial code. Each class is divided into groups with binomial codes of the same code length within the groups and different code lengths between the groups, differing by one bit. Compared with natural PNS, the advantage of binomial codes is the property of error detection and practical uses in information systems for data transmission and processing. Based on the BNS, digital devices, such as binomial decoders and counters, are effectively developed, which have the property of detecting errors. The binomial code has the data compressing property and allows the generation of CW binomial codes. Binary binomial codes have a novelty compared to known PNS and known binomial codes of different classes. The scientific novelty of this paper lies in the generalization of scientific results in the field of BNS and the method of obtaining them.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**No**} & **Binomial** & **CW Binomial** & **No** & **Binomial** & **CW Binomial** \\  & **Codewords** & **Codewords** & **Codewords** & **Codewords** \\ \hline
0 & 00 & 001111 & 8 & 10111 & 101110 \\
1 & 010 & 010111 & 9 & 1100 & 110011 \\
2 & 0110 & 011011 & 10 & 11010 & 110101 \\
3 & 01110 & 011101 & 11 & 11011 & 110110 \\
4 & 01111 & 011110 & 12 & 11100 & 111001 \\
5 & 100 & 100111 & 13 & 11101 & 111010 \\
6 & 1010 & 101011 & 14 & 1111 & 111100 \\
7 & 10110 & 101101 & & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: CW binomial codewords with parameters \(n=6\), \(k=4\).  
**Author Contributions:** Conceptualization, O.B. and S.M.; methodology, O.B.; validation, O.B., S.M. and V.B.; formal analysis, O.B., S.M. and V.B.; investigation, S.M.; resources, O.B.; data curation, V.B.; writing--original draft preparation, O.B. and S.M.; writing--review and editing, O.B., S.M. and V.B.; visualization, S.M.; supervision, V.B.; project administration, V.B.; funding acquisition, S.M. All authors have read and agreed to the published version of the manuscript.  
This work has been supported by the European Regional Development Fund within the Activity 1.1.1.2 \"Post-doctoral Research Aid\" of the Specific Aid Objective 1.1.1 \"To increase the research and innovative capacity of scientific institutions of Latvia and the ability to attract external financing, investing in human resources and infrastructure\" of the Operational Programme \"Growth and Employment\" (No. 1.1.1.2/VIAA/3/19/421).  
Not applicable.  
**Conficts of Interest:** The authors declare no conflict of interest.
# Hybrid Artificial Protozoa-Based JADE for Attack Detection
## 5 Case Study: Application of APO-JADE for Attack Detection
### Hyper Parameter Tuning and Configuration  
Hyperparameters play an instrumental role in delineating the architecture of a neural network and presiding over its training dynamics. In our study, while the foundational architecture of the Deep Recursive Artificial Neural Network (APO-JADE) remains invariant, the task of ascertaining the optimal values for the hyperparameters is entrusted to the APO-JADE algorithm. This approach is intended to fine-tune the network for enhanced attack detection precision.  
The hyperparameters under consideration include the learning rate, number of epochs, momentum, batch size, and dropout rate. We provide a comprehensive elucidation of these parameters, which is followed by their optimal values as determined by the hybrid PSO algorithm across different datasets. The optimization of these hyperparameters is pivotal for the effectiveness of the neural network in accurately detecting attacks, ensuring that the model is not only robust but also sensitive to the nuances of the data it processes.  
The optimization of these hyperparameters is achieved using APO-JADE algorithm. This method combines the adaptive behavior of Artificial Protozoa with the efficiency of the JADE algorithm to iteratively search for the optimal combination of hyperparameters. Each candidate solution within the population adjusts its position in the search space based on its experience and the collective experiences of neighboring candidates, emulating the dynamic adaptability of protozoa.  
Given the distinct characteristics and variability of the datasets employed--DS2 OS, UNSW-NB15, and ToN_IoT--the optimal set of hyperparameters often varies across different problems. The APO-JADE algorithm is designed to dynamically explore the hyperparameter space, adapting to the unique features and demands of each dataset. For instance, the DS2 OS dataset, with its focus on Industrial Internet of Things (IIoT) security, requires different hyperparameter settings compared to the UNSW-NB15 dataset, which is centered on traditional cybersecurity threats, or the ToN_IoT dataset, which is tailored for IoT/IIoT environments.  
The APO-JADE algorithm is executed separately for each dataset, with its parameters--such as population size, inertia weight, and cognitive and social coefficients--fine-tuned to the specific characteristics of the dataset. This process results in hyperparameters that are not only tailored to the particularities of each dataset but are also optimized to enhance the neural network's performance in detecting cybersecurity threats across diverse contexts.
## 3 Background
### Power Sub-System Overview
#### 3.2.2 Power Storage  
The CubeSat design guide does not permit batteries to be charged upon launch, they must be fully discharged and only activate 30 min after release from the P-POD [1]. Therefore \"Primary\" pre-charged batteries typically used over short mission durations are not permitted for this design. Secondary rechargeable batteries therefore must be utilized, fed by the power generation source which is usually a solar cell. Secondary batteries are commonly made using Nickel-Cadmium (NiCad), Nickel-Hydrogen (NiH\({}_{2}\)), Lithium Iron (Li-ion) and Lithium Polymer (Li-Po) [23]. The type of battery selected depends upon the application. Criteria such as energy density, life cycle, reliability, and cost must all be considered. Figure 8 shows power energy densities of various battery types.  
Typically, batteries with a high energy density would be selected, as they are able to store a large capacity per unit mass; however, this incurs an ever-increasing build cost causing selection to be influenced by available budget. Lithium Iron and Lithium Polymer represent cutting-edge technologies in the field of energy storage [23] and are constantly under development, recently used in hybrid/solar powered vehicles on Earth. Figure 9 provides off-the-shelf comparison of battery cell energy densities of various products.  
Figure 8: Battery Energy Densities [29].  
Figure 7: Typical Solar Cell Efficiency [23].  
Typically, secondary batteries are unable to power all operational CubeSat components during the eclipse phase of orbit. Many non-critical components will enter a low power period during this phase. If a design alteration is permitted, some CubeSat designs have incorporated primary batteries to release solar panels booms for example, to allow the secondary batteries to begin storing electrical energy [3].
## 2 Background
### Data Integration of BIM and GIS  
The construction of BIM shape/attribute information modeling based on the existing blueprint consists of 3D BIM data modeling incorporating facility maintenance data and 2D architectural plan. If the Geographic Information System (GIS) information is added to this BIM information, the 3D BIM data will be augmented with accurate locational information. Based on this adjustment, GIS and BIM information can be stored into a master database, which can be used for precise maintenance and repair work during the facility management (FM) phase, even for the complex FAB facility. Moreover, any data generated during this process can be archived and stored in the data library for future use, thus creating a virtuous circle of data usage. In order to build the aforementioned system, data should be managed in accordance with the FM system. Finally, data mapping system-based various data, such as locational information, spatial information, attribute information, equipment/facility information, historical track record information, etc., are managed in terms of object and space, thus building the database system, which in turn is saved as an integrated database constantly being used for facility maintenance work. Figure 3 graphically shows BIM and GIS-based working system configuration that can be applied to FM work for FAB facility.  
Figure 3: BIM and GIS-based working system configuration diagram.
## 2 Emerging Applications of 6G
### Machine Learning and Artificial Intelligence (AI)  
Artificial intelligence and machine learning have gained their prominence in various industries, such as financial, healthcare, security, agriculture, education, and retail, due to their inherent ability to analyse current data, determine patterns, and make future predictions. For example, in the healthcare sector, these attributes can help clinicians to predict hereditary diseases and take precautions to overcome such diseases. To perform such activities, it is vital that the communication standards support high data rates and high-speed data processing. Especially when it comes to AI technology employed in industries such as healthcare and security, low latency and the reliable transmission of data are extremely paramount, as the predictions made by the AI systems depend on the network performances.
## 5 Common Systems
### Timing and Synchronization
#### 5.1.9 Laser Arrival Monitor  
The LAM is one example of a client that directly uses the pulses from the OMO. The Gun-LAM (next to the photocathode, including beam propagation through laser chain and transfer line) employs a novel scheme, where the current generated by the UV pulses impinging on a photodiode directly drives an electro-optic intensity modulator acting on the optical reference. Thus, arrival time is directly encoded onto the amplitude of the reference pulse train. A balanced photoreceiver in combination with a fast analog-to-digital converter (ADC) decodes this information shot-by-shot, limited by the ADC sampling rate. Expected temporal resolution is low tens of fs. A laser arrival time jitter of around 40 fs (rms) at the gun would lie within the tolerance budget to ensure that FEL beam fluctuations stay within intrinsic fluctuations at 200 pC as well as for 10 pC electron beam charge mode [199].  
The experiment LAM is based on a spectrally resolved cross correlator [127]. The incoming IR pulses from the experiment laser and the reference pulses (stretched and chirped) mix in a nonlinear crystal and their relative timing results in wavelength change that is read out with a spectrometer. A resolution of 5 fs (rms) should be achievable for this application. The tolerable laser jitter at the Experimental Stations is 25 fs (rms).  
For the LAM at the electron gun, which is still close to the source of the reference generation and distribution, the link will initially be realized with a few tens of meters of temperature stable fiber with low drift (3 fs/m/K and 0.4 fs/m/%rH). The LAM at the experimental stations will initially get a pulsed link prototype.