esa-sceva/satcom-chunk-collection huggingface dataset:
# Bi-LSTM-Based Neural Source Code Summarization
## 3 Related Works and Motivations
### Related Works
CO3 had an effective architecture that consisted of only two Bi-LSTM instances: one for code summarization and the other one for code generation. CO3 gave an 11.9% BLEU-4 in SQL and 8.5% in Python. [PERSON] et al. [27] proposed a model that made use of the encoder-decoder architecture. They used two encoders: one for representing lexical information and the other one for representing the syntactical structure of the code. The model went through three stages: data preprocessing, data representation as an AST, and finally, summary generation. The lexical encoder was an RNN, and the syntactical encoder was a tree-RNN representing the AST. The output from the two encoders was used to generate the code summary using a switch network. They applied their model on a dataset of 588,108 Java code comment pairs extracted from 9714 GitHub projects. They achieved 17.04% BLEU-4. [PERSON] et al. [28] proposed the Hybrid-DeepCom and made use of the structural and lexical information from the Java methods for better code comment generation. They had two encoders; the first one was the code encoder, which was a GRU, and the second was the AST with an SBT traversal encoder. Then they used an attention model to pay attention to words more than others. The authors did not report their results. In [29], the authors presented a transformer-based encoder-decoder model ComFormer. They used byte-pair-encoding to reduce the out-of-vocabulary problem, where the UNK token is produced for words with low occurrence in the code. In addition, they used a simplified version of the SBT. They applied their framework on a code-only input and code-plus-AST input. They considered three approaches for the two inputs case: the joint encoder, the shared encoder, and the single encoder. The joint encoder uses two separate encoders for code and AST inputs. In the shared encoder approach, the code and the AST share the same encoder after a distinct embedding process. In the single-encoder approach, code and AST undergo single embedding and encoding processes. They tested their technique on a dataset of 588,108 Java methods. They found that their framework reached 43.8% BLEU-4 for a code-only input and 48.437% for a code and AST input with a single-encoder approach. In [30], the authors proposed a transformer-based encoder-decoder architecture with three inputs: the code, the AST, and the API. They applied a joint encoder architecture. They conducted their experiments on 137,007 Java methods with their comments collected by [PERSON] et al. in [31]. They compared the transformer-based encoder-decoder to a GRU-based encoder-decoder. They showed that the transformer-based architecture outperforms the GRU-based architecture. They also showed that for both architectures adding AST information decreases the model performance while adding API knowledge increases slightly the BLEU score. Their best BLEU-4 score was 5.28%.
# Object-Oriented Remote Sensing Image Change Detection Based on Color Co-Occurrence Matrix
## 4 Conclusions  
In the study presented in this paper, an object-oriented remote sensing image change detection method based on a color co-occurrence matrix is proposed. It extends the gray co-occurrence matrix and constructs the color co-occurrence matrix by considering the correlation among bands. It can describe not only the texture in a band but also the texture among bands, and the object-level texture can be more conducive to describing the overall  
Figure 13: Performance analysis of comparisons and the proposed algorithm under different change types.  
characteristics. In addition, to avoid the impact of feature information redundancy on the detection accuracy, a cumulative backward search strategy is proposed to filter the features. It can effectively avoid the error filtering caused by the random fluctuation of feature ranking and obtain a stable and representative feature combination. Through the qualitative and quantitative analyses of the detection results of the proposed and comparison algorithms in different resolution remote sensing images, it is found that the proposed method is superior to the other methods in both false detection and missing detection. In the future, the proposed algorithm will be extended to incorporate multiple changing types, which can lay the foundation for mining special change patterns.  
Conceptualization, X.L. and S.G.; methodology, X.L. and Z.Z.; software, Z.Z.; validation, Z.Z.; investigation, Z.Z.; writing--original draft preparation, Z.Z. and X.L.; writing--review and editing, X.L., T.Z., J.C., S.G., L.Z. and L.S.; supervision, X.L. and T.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, grant numbers 42001286 and 42171323; the Fundamental Research Foundation of Shenzhen Technology and Innovation Council, grant number KCXFZ202002011006298; the Fundamental Research Foundation of Shenzhen Technology and Innovation Council (General Program), grant number JCYJ20190806170814498; the Project of the State Council Three Gorges Office on the Investigation and Assessment of the Aquatic Habitat Status of the Important Tributary of Chongqing Reservoir Area (Fuling-Wushan Section), grant number 500002013 BB5200002; and the Strategic Priority Research Program of the Chinese Academy of Sciences, grant number XDA19030301.  
Not applicable.  
No applicable.  
The authors declare no conflict of interest.
## 3 Results  
### Isothermal Torrefaction  
An isothermal torrefaction process was carried out to study wheat straw from room temperature to 250, 275, 300, and 325 \({}^{\circ}\)C. Figure 4 shows the data from the experiment. Figure 4a shows the graphs of the heating curves at a heating rate of 20 \({}^{\circ}\)C/min, while Figure 4b shows the weight data measured at different time intervals of an initial mass of 50 g. The following conditions were used: at 250 \({}^{\circ}\)C, blue line: 41.680 g at 33 min, 37.320 g at 53 min, and 31.380 g at 83 min; at 275 \({}^{\circ}\)C, red line: 37.375 g at 40 min, 32.262 g at 60 min, and 27.150 g at 80 min; at 300 \({}^{\circ}\)C, black line: 30.190 g at 48 min, 25.220 g at 68 min, and 23.470 g at 88 min; and at 325 \({}^{\circ}\)C, magenta line: 23.910 g at 57 min, 23.540 g at 77 min, and 22.480 g at 97 min. The \(m/m_{o}\) ratio was used to normalize weight loss performance, representing the final weight (\(m\)) and the initial weight (\(m_{o}\)). This indicates that there is a close relationship between temperature and weight loss; in other words, the higher the temperature, the greater the weight loss.
# Effective Surface Roughness Impact in Polarimetric GNSS-R Soil Moisture Retrievals
## 3 Materials and Methods  
### Dataset Description  
The data used for this study are a subset of the SMAP [48] and SMAP-R data collected between 1 January 2018 and 31 December 2019. Additionally, SMAP static files [13; 49] have been used. The data and variables used are summarized in Table 1. The product's spatial resolution is 9 km, and the nearest neighbor interpolation has been used to collocate the gridded values into the scattered SMAP-R positions.  
We will use the DEM dataset to compare the outcomes of our study with actual physical data from the surface. We have selected the product with the closest resolution to our incoherently-scattered signal. The generation of SMAP's DEMSTD and DEMSLPSTD products are described in [29]. The static SMAP DEM-derived maps are a post-processed version of the Shuttle Radar Topography Mission (SRTM) DEM product at a 30 m resolution and were upscaled to a 9 km resolution. The GLDAS soil fraction is a land classification based on modeled data from [50].
# Estimation of Energy Storage Requirements in an Independent Power System from an Energy Perspective
## 4 Energy Storage Demand Simulation
### Discussion of Simulation Results  
The scenario in this study represents a theoretical maximum, as during the year's lowest electricity usage period, renewable energy sources might not always be operating at full capacity. Evaluation based solely on this day might lead to an overestimation of energy storage needs, as only a few days throughout the year may require such high levels of energy storage. Future assessments should use more comprehensive data, such as data spanning an entire year, to determine the appropriate proportion of reduced generation and the required capacity of energy storage for a region. Utilizing energy storage power conversion systems (PCS) that can provide synthetic inertia may help reduce the minimum generation amount of traditional power systems, thereby decreasing energy storage requirements and offering potential economic benefits. This approach will be one of the future research directions.
# Comparative Evaluation of Remote Sensing Platforms for Almond Yield Prediction
## 2 Materials and Methods
### Dataset Creation  
The final dataset is created using three different groups: climate data, VIs, and agronomic data. As mentioned in Section 2.2.3, the three datasets were used in parallel with the VIs customized according to the RS platform (Landsat 8, Sentinel-2, and PlanetScope) to compare the performance of each. This information, extracted for each orchard, was then used as potential inputs into the ML regression models. Each of the datasets contained 171 features, corresponding to the average tree age, irrigation, monthly mean daytime temperature (2017-2021), monthly mean nighttime temperature (2017-2021), monthly accumulated precipitation (2017-2021), monthly EVI2 (2017-2021), monthly GRVI (2017-2021), monthly NDVI (2017-2021), and monthly SAVI (2017-2021). To consider the potential effect of alternate bearing (biannual cyclic production patterns), features from the previous year were also included to assess their potential impact on the following year's production. In addition to features, the datasets also included the yield (mean kg per ha) as a target.
# Cyber-Physical Systems for Water Supply Network Management: Basics, Challenges, and Roadmap
## 3 CPS Architecture and Applications
### ZigBee  
ZigBee is based on the IEEE 802.15.4 standard for low-cost and low-power consumption with small to medium data rates [53]. It is designed to operate at 2.4 GHz, 915 MHz, and 868 MHz with data rates of around 250 kbps (for the 2.4 GHz) and 20-40 kbps (for the 868-915 MHz). As may be noticed in Table 1, ZigBee supports different network topologies, such as star and mesh, to connect hundreds to thousands of devices. As a result of its high throughput and low latency [53], this wireless communication technology is suggested for use in energy-efficient sensor-based applications. Its low communication distance capabilities (10-100 m line of sight (LOS)) limits its usage in large-scale applications, such as water distribution network monitoring.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  
3G/LTE/LTE-A & 3 GPP & \begin{tabular}{c} 700; 750; 800; 1900; 2500 MHz \\ \end{tabular} & OFDMA & \begin{tabular}{c} 1 Gbps; \\ 500 Mbps \\ \end{tabular} & Star & \begin{tabular}{c} 1 km (urban); \\ 10 km (mural) \\ \end{tabular} & Low \\ \hline LoRa & Loa Alliance & \
\begin{tabular}{c} 433 MHz (Availa:) \\ 868 MHz (Europe); \\ 915 MHz (North America) \\ \end{tabular} & CSS & 0.3–50 kbps & Star &
\begin{tabular}{c} 100 m (urban); \\ 10 km (mural) \\ \end{tabular} & Low \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of the common short-to-long range wireless telecommunication technologies.  
Wi-Fi is a trademark of Wi-Fi Alliance, designed based on the IEEE 802.11 standard intended for short-range applications. It utilizes 2.4 GHz UHF and 5 GHz super-high frequency in the industrial, scientific, and medical (ISM) band. It provides higher data rates of 54 Mbps (at 2.4 GHz frequency) and 6.75 Gbps (at 5 GHz frequency) [54]. Wi-Fi uses a quadrature phase shift keying (QPSK) modulation scheme, enabling the signal to carry twice as much data using the same bandwidth. Wi-Fi is low-cost and consumes less power.
## 4 ZnO-Based Nanomaterials for Antimicrobial Application in Water Treatment
### Effects of Radiation Types on the Antibacterial Activity of ZnO-Based Nanomaterials  
In the process of photocatalytic antibacterial studies, ultraviolet (UV) light, sunlight, and other visible light are the most common types of radiation. In previous studies, ZnO nanomaterials have always been used for antibacterial testing under UV light due to their high band constraints [173; 174; 175]. From the work of [PERSON] et al. [174], an important conclusion was found that the oxygen vacancy of ZnO crystals enhanced the photogeneration of ROS, and ZnO nanoparticles (NPs) with polar facets exhibited the most significant effect of antibacterial activity under UV light stimulation. Furthermore, [PERSON] et al. creatively combined N-halamine-based materials with ZnO to improve the stability of ZnO's antibacterial performance under UV light. As a simple and effective method for nanoparticle modification, this technique can be further extended to the application of ZnO nanoparticles in other polar substrates for antibacterial functionalization [176].  
Despite the promising antibacterial performance of ZnO-based nanomaterials under UV-driven radiation, photocatalysis using UV-active semiconductors is difficult due to the limited use of the solar spectrum. Simultaneously, significant progress has been made in photocatalysis using visible-light-active heteronarostructured semiconductors due to their simplicity of use, practicality, reproducibility, reliability, and commercialization [177; 178]. Recently, several studies have found that photocatalytic efficiency can be improved by promoting the surface charge transfer reaction of ZnO. In addition, it may also affect the absorption spectrum of many metal oxide nanoparticles including ZnO, which enables the composites to undergo photocatalytic reactions under visible light [179; 180; 181; 182; 183]. The antibacterial activity of ZnO-based nanomaterials driven by visible light is of great significance for the development of photocatalytic antibacterial. Compared with ultraviolet light, visible light is more accessible, which makes visible light photocatalysis one of the hottest research fields. In addition, studies have shown that the ability of ZnO-based nanomaterials to excite ROS under visible light will be greatly improved, which also leads to the improvement of antibacterial ability under visible light [184; 185]. Therefore, the design and preparation of visible-light-driven ZnO-based nanomaterials will become a meaningful research direction in future explorations.
## 3 Results and Discussion
### Interactions with Endothelial Cells
For example, fluoride treatment of magnesium or its alloys leads to the replacement of original oxide film with a thin and more homogeneous layer of MgF\({}_{2}\) with higher polarization resistance. The advantages of newly formed layers are their high density, lower water solubility, and the harmlessness of the release of fluorine ions into the organism. On the other side, it was found from Figure 9 that the absorbance of Mg-OH was larger than that of Mg-HF, suggesting that the growth and proliferation of endothelial cells on Mg-OH were better than that of on Mg-HF. Although the modified surface cannot produce extracellular matrix (ECM) protein for cell attachment and proliferation, the cells can grow well on the hydrophilic surface, as compared to the cells on the hydrophobic Mg-HF surface. In general, cell growth on the hydrophilic surfaces is usually more efficient than on the hydrophobic surface. In this case, cell adhesion can also occur through non-receptor chemical binding, such as electrostatic, ionic-polar interactions, and hydrogen binding to surface functional groups [40]. As shown in the results of ATR-FTIR and XPS,  
Figure 8: The typical fluorescent images of endothelial cells adhered on AZ31B (**a**,**d**), Mg–OH (**b**,**e**) and Mg–HF (**c**,**f**). The cells were cultured with samples for 6 h (**a**–**c**), 24 h (**d**–**f**), respectively. (yellow, nuclei; green, cytoskeleton). a larger amount of hydroxyl can be introduced on the surface and an excellent hydrophilic surface can be achieved. Cell attachment can occur through hydrogen binding between hydroxyl and cells, leading to better cell adhesion. The adhered cells can synthesize and deposit their own extracellular matrix proteins on Mg-OH for further cell proliferation. The hydrophobic Mg-HF surface cannot interact with cells by the non-receptor binding because it was lacking any reactive groups; however, the hydrophobic surface may adsorb the protein from culture medium and thus promote cell adhesion and proliferation. Consequently, the cell proliferation on Mg-HF was a little bit poorer than Mg-OH.
# Zero-Shot Detection of Visual Food Safety Hazards via Knowledge-Enhanced Feature Synthesis
## 3 Methodology  
This section details our proposed framework for the Zero-Shot Detection of Visual Food Safety Hazards. Figure 1 illustrates the architecture of our method, which consists of three primary components: (1) a Food Safety Knowledge Graph that encodes domain-specific knowledge about visual food safety attributes, (2) a Knowledge-Enhanced Feature Synthesizer that leverages this knowledge to generate discriminative visual features for unseen hazard categories, and (3) a zero-shot detector that uses these synthesized features to identify food safety hazards not seen during training.
## 3 Problem Formulation
### Modeling of DNO
#### 3.2.2 Constraints  
(1) Power flow constraint  
According to the power flow calculation [26], the following constraints need to be satisfied:  
\[\left\{\begin{array}{l}P_{i}^{t}-i_{ij}^{t}\left(R_{ij}+jX_{ij}\right)=P_{j} ^{t}\\ Q_{j}^{t}-i_{ij}^{t}\left(R_{ij}+jX_{ij}\right)=Q_{j}^{t}\end{array}\right. \tag{22}\]  
\[v_{j}^{t}=v_{i}^{t}-2\left(R_{ij}P_{ij}^{t}+X_{ij}Q_{ij}^{t}\right)+\left(R_{ ij}^{2}+X_{ij}^{2}\right) \tag{23}\]  
\[P_{ij}\leq p^{\max}. \tag{24}\]  
(2) Voltage and current constraint  
\[V_{i}^{\min}\leq V_{i}^{t}\leq V_{i}^{\max} \tag{25}\]\[\left|I_{ij}^{t}\right|\leq t_{ij}^{\max} \tag{26}\]  
where \(R_{ij}\), \(X_{ij}\) are the resistance and reactance of the branch _ij_; \(I_{ij}^{t}\) is the current of the branch _ij_ at time \(t\); \(P_{j}^{t}\), \(Q_{j}^{t}\) are the active power and reactive power of \(j\) node at time \(t\); \(P_{ij}^{t}\), \(Q_{ij}^{t}\) are the active power and reactive power at the head of the branch _ij_ at time \(t\); \(V_{1}^{t}\) is the voltage of the DN at time \(t\); \(V_{bus}^{t}\) is the bus voltage at time \(t\); \(V_{j}^{t}\) is the voltage of node \(j\) at time \(t\); \(V_{j}^{\max}\) is the upper limit of the voltage at the node \(l\); \(V_{i}^{\min}\) is the lower limit of the voltage at the node \(i\) and \(I_{ij}^{\max}\) is the upper limit of the branch current.
## 5 Simulation Results
### End-to-End Delay  
The end-to-end (E2E) delay can be defined as the elapsed time that an outgoing packet from a node takes to reach the destination following a multihop path. In this case, this is the time taken for a packet from an underwater sensor node to reach the gateway node on the surface. The longer the route is, the higher the E2E delay is.  
The results obtained by simulations for both FAHP and SPRINT-Fuzzy are presented in Figure 11. It can be noted as FAHP has a value of delay in the same order of magnitude (4-6 ms) when compared to SPRINT-Fuzzy but is a little worse in dense networks (400 nodes). The best behavior is observed when the transmission range is high, evident in Figure 11a for a transmission range of 7 km, obtaining a better delay than SPRINT-Fuzzy. This is logical due to a lower value of hops average for that specific case, shown in Figure 9a.  
Table 12 presents the values obtained in the bar graph of Figure 11.  
A second analysis can be performed from these results: FAHP is stable. Despite the network size increasing by a factor of 4 (from 100 to 400 nodes), the average E2E delay is kept in a 4-6 ms interval, although the transmission range is changed between 2 and 7 km.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**No. Nodes**} & \multirow{2}{*}{**Tx Range (km)**} & \multicolumn{2}{c}{**No. Collisions**} \\  & & **Sprint** & **FAHP (\%)** \\ \hline
100 & 4 & 7041.9 & 6786.7 (\(-\)3.6\%) \\
100 & 5 & 25,296.5 & 27,830.4 (+10\%) \\
100 & 6 & 56,056.3 & 56,676.1 (+1.1\%) \\
100 & 7 & 105,135 & 104,748 (\(-\)0.4\%) \\ \hline
400 & 2 & 3680.7 & 3806.33 (+3.4\%) \\
400 & 3 & 130,852 & 135,981 (+3.9\%) \\
400 & 4 & 1,101,770 & 1,016,980 (\(-\)7.7\%) \\ \hline \hline \end{tabular}
\end{table}
Table 11: Statistical comparison for average No. collisions.  
Figure 10: Average number of collisions. Number of nodes: (**a**) 100, (**b**) 400.
# A Ship Trajectory Prediction Method Based on an Optuna-BILSTM Model
###### Abstract  
In the field of maritime traffic management, overcoming the challenges of low prediction accuracy and computational inefficiency in ship trajectory prediction is crucial for collision avoidance. This paper presents an advanced solution using a deep bidirectional long- and short-term memory network (BILSTM) and the Optuna hyperparameter automatic optimized framework. Utilizing automatic identification system (AIS) data to analyze ship navigation patterns, the study applies Optuna to fine-tune the hyperparameters of the BILSTM network to improve prediction accuracy and efficiency. The developed Optuna-BILSTM model shows a remarkable 7% increase in prediction accuracy over traditional back propagation (BP) neural networks and standard BILSTM models. These results not only improve ship navigation and safety but also have significant implications for the development of autonomous ship collision avoidance systems, marking a significant step toward safer and more efficient maritime traffic management.  
traffic safety; trajectory prediction; BILSTM +
Footnote †: journal: Applied Sciences  
15 March 2024  
Revised: 25 April 2024  
Accepted: 25 April 2024  
Published: 27 April 2024
# Interest Flooding Attacks in Named Data Networking and Mitigations: Recent Advances and Challenges
## 5 Applications and Use Cases of NDN
### IFA Defense in IoT Networks (NDN of Things)
#### 5.1.3 Industrial IoT (IIoT)  
In Industrial Internet of Things (IIOT) settings, such as smart factories that use NDN for sensor/actuator communication, these networks might be targeted by attackers to disrupt operations. Industrial NDN deployments sometimes incorporate additional safeguards [49]. Some IIoT proposals suggest segmenting the network, where the NDN traffic of each machine is isolated, so that an attack from one segment cannot clog the PIT of routers serving another segment. It introduced a behavior auditing scheme using blockchain for industrial NDN, primarily to detect misbehavior (including flooding) and record it as immutable [49],in line with the blockchain approaches discussed earlier. Industrial environments may have more computing resources at gateways, allowing for the use of slightly heavier detection algorithms, but the required response time is also tight (to avoid downtime).
## 6 Verification and Results  
This section presents the results of the three verification episodes we introduced in Section 3. This includes (1) a packet forwarding and provisioning episode, (2) a resilience and availability episode and (3) a security integrity episode.