esa-sceva/satcom-chunk-collection huggingface dataset:
# Enhancing Detection of Arabic Social Spam Using Data Augmentation and Machine Learning
## 3 Proposed Framework for Enhancing Arabic Spam Detection
### Data Preparation
#### 3.1.2 Data Labeling  
To construct our spam/non-spam dataset, the extracted tweets were manually annotated as spam/non-spam. Fifteen Arabic annotators helped in this process. To accelerate the process, a website ([[https://tweettag.000](https://tweettag.000) webhostapp.com/login.php]([https://tweettag.000](https://tweettag.000) webhostapp.com/login.php), accessed on 7 January 2022) has been created to allow multiple experts to annotate the selected tweets as follows:  
1. Before starting the annotation process, each annotator was given our definition of spam text as well as some examples of spam content. We asked annotators to carefully read a tweet before evaluating whether it contained spam content (spam class); or did not contain spam content (non-spam class).
2. The 5 k tweets have been split into five chunks, each chunk containing 1000 tweets. After that, every three annotators have worked on the same 1000 tweets separately so that the annotation score (spam/non-spam) is calculated based on the three annotators, as illustrated in the following step
3. Finally, the annotation results for the 5k tweets from the 15 annotators are grouped, such that each tweet has three annotated values, each from a different expert. We marked the tweet as spam or non-spam according to the mode (most occurring value) result. After labeling, we found that the data are imbalanced with 420 spam tweets and 4580 non-spam tweets.
## 4 Experimental Results and Analysis
### Discussion of the Dimension of the Embedding Vector  
The dimension of the embedding vector (denoted as embedding size) is an important factor in the embedding layer, as it can affect the detection performance of the whole steganalysis model. Therefore, to determine a relatively optimal value for the dimension of the embedding vector, this paper conducted a series of experiments with the embedding rate set to 100%. The experimental results are shown in Figure 4. Figure 4 summarizes the experimental outcomes after increasing the embedding size from 10 to 50.  
From Figure 4, it can be concluded that when the embedding size was increased from 10 to 50, the detection accuracy of our method initially increased and then exhibited a decreasing trend across the three steganography datasets. This observation suggests that  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Parameter** & **Value** \\ \hline Embedding dimension & 20 \\ Input dimension of mask-gating layer & 180 \\ Output dimension of mask-gating layer & 720 \\ Hidden dimension of mask-gating layer & 360 \\ Reduction ratio of mask-gating layer & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameter settings.  
Figure 4: Detection accuracies for different embedding sizes across the three steganography datasets.  
there is an optimal embedding size for achieving the highest detection accuracy. Increasing the embedding size allows the model to capture more intricate and fine-grained features in the codewords, which can potentially improve detection performance. However, it is important to consider that increasing the embedding size also results in higher computational costs and potentially increased model complexity. Therefore, there is a trade-off between detection accuracy and computational efficiency when selecting the embedding size. In conclusion, it appears that the relatively optimal value for the embedding size is 20. This value likely strikes a balance between capturing sufficient features for accurate detection while maintaining reasonable computational demands.
## 2 Methodology
### Literature Review  
The literature review was conducted using a systematic approach across several academic databases, including Google Scholar, IEEE Xplore, Web of Science, and ScienceDirect. Keywords used in the search process included 'AI in maritime safety', 'predictive maintenance in shipping', 'AI for risk management in maritime transport', and'maritime AI navigation systems'. Studies were included based on the following criteria: (1) peer-reviewed articles, (2) published between 2010 and 2023, and (3) directly related to the application of AI in maritime safety, risk management, and operational efficiency. Additionally, relevant industry reports and white papers were included where they contributed significant insights into AI applications. Articles were initially screened by title and abstract, followed by full-text reviews to ensure relevance to the research questions (Figure 1).  
\begin{table}
\begin{tabular}{p{85.4 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**AI Application Area** & **Description** & **Key Benefits** & **Examples** \\ \hline Risk Analysis and Management & Identifying potential hazards and predicting risks using data analysis and machine learning. & Enhanced risk prediction and mitigation & Maersk’s AI-Powered Risk Management, DNV GL’s Veracity Platform \\ \hline Crew Resource Management & Enhancing crew training and decision-making through AI-powered simulations and real-time support. & Improved training effectiveness and decision support & Wartsiläs SmartPredict System \\ \hline Hazardous Material Handling & Monitoring and managing hazardous materials using AI systems for compliance and safety. & Increased safety and compliance & ABB AbilityTM Marine Pilot Vision \\ \hline Predictive Maintenance & Analyzing equipment data to predict and prevent failures, reducing downtime and costs. & Reduced downtime and maintenance costs & Kongsberg’s Kognifai Maritime Ecosystem \\ \hline Navigation Systems & Optimizing routes, avoiding collisions, and monitoring environmental conditions with AI. & Enhanced navigation safety and efficiency & Wartsiläs Fleet Operations Solution, ABB AbilityTM Marine Pilot Vision \\ \hline \hline \end{tabular}
\end{table}
Table 1: Overview of AI applications in maritime safety and risk management.
# Wake-Up Receiver-Based Routing for Clustered Multihop Wireless Sensor Networks
## 5 Experimental Setup and Measurement
### Experimental Setup  
In the following, the parameters used during the experimental measurements are listed in Table 1. At this point, we would like to give a note on the hardware used. For Fog Node, we have used Raspberry Pi 4 [35]. As already mentioned, off-the-shelf components were used to build the WuRx sensor node, which consist of the AS3933 as the WuRx, the SPIRIT1 as the transceiver, and the MSP430 as the microcontroller.
## 5 Conclusions  
In summary, we utilized satellite-based SIF and EVI observations to investigate the spatial patterns and seasonal dynamics of vegetation phenology across wet and dry years along the North Australian Tropical Transect. Considerable impacts of drought and wet extremes on the phenology and production of dryland vegetation were revealed by both SIF and EVI, especially in the arid/semiarid interior of Australia. Although EVI exhibited a considerably delayed senescence relative to SIF and GPP, phenological metrics derived from SIF had more variable accuracy in contrast to those derived from the greenness-based vegetation index. In addition, EVI exhibiting stronger correlation with tower-based GPP (\(r^{2}\): 0.47-0.86) can be a superior indicator to track the seasonal and interannual variation in dryland vegetation production than the spatially coarse SIF dataset (\(r^{2}\): 0.47-0.78). In spite of the sparse sampling and high retrieval noises of GOME-2 SIF products, which offset the drawbacks of greenness-based phenology products with a potentially lagged end of the season, spaceborne SIF retrieved from state-of-the-art instruments (such as TROPOMI, OCO-2) has the promising potential of assessing the characterization of the phenology dynamics of dryland ecosystems.  
Conceptualization, S.L. and A.H.; methodology, S.L.; validation, S.L.; formal analysis, S.L.; investigation, S.L.; data curation, S.L. and J.C.; writing--original draft preparation, S.L.; writing--review and editing, A.H., Q.Y., R.Z., and Q.W.; visualization, S.L. and Q.W.; supervision, A.H. and Q.Y.; funding acquisition, Q.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Natural Science Foundation of Fujian Province (2021)01627) and the National Natural Science Foundation of China (41601562).  
All satellite-based data, as well as other datasets in this study are available from the corresponding author upon reasonable request.  
The first author acknowledges the financial support from Chinese Scholarship Council, the Ministry of Education, China. We also would like to thank the opening of flux tower data for public access by the Australian Terrestrial Ecosystem Research Network (TERN, Available online: www.tern.org.au)--OzFlux facility (Available online: www.ozflux.org.au, accessed on 1 October 2020).  
The authors declare no conflict of interest.
## 5 Thermal Analysis and Results Discussion
### Discussion and Summary  
Passive thermal control methods for cryogenic instrumentation, high power payloads and electronics on nano and micro satellite platforms may be a suitable solution, but in this design scenario, it fails to maintain the system interface temperature between 20 to 30 \({}^{\circ}\)C. However, there is a power threshold where a CubeSat will have adequate radiating capability. Hence, active thermal control techniques are recommended to be considered for such high power satellite missions. Active thermal control methods such as Loop Heat Pipes (LHP), mechanically pumped fluid loop systems, are being studied and considered suitable for solving thermal challenges in small satellite platforms. Radiator designs for different satellite configurations were analyzed and compared. A satellite radiator designed for a particular altitude, inclination, and specific orientation with respect to the sun can provide the same performance for other inclination angles only if the radiator panels are kept under the sun pointed solar panels. For this particular 6U design, as long as the BMR is not seeing the partial/full sun or exposed to direct solar flux, it is expected that the change in temperature of the radiator is minimal and within \(\pm\)10 \({}^{\circ}\)C approximately. Radiator mass affects its performance to a larger extent. While keeping the available surface area constant, increasing the thickness increases the mass of the radiator panel fabricated from Al6061-T6. The higher the thickness or higher the mass of the panel, the lower the temperature difference and at the same time the overall radiator performance is increased.  
Surface coatings were analyzed for a 6U satellite with body-mount and deployable radiator configurations. It is observed that the surface coatings greatly enhance the radiating capacity of the radiators for any satellite categories. Proper mounting methods and placement of the radiators are to be chosen very carefully in order to achieve the maximum heat loss into deep space. Most of the satellite radiators are designed to be pointed to deep space rather than at the Earth or the sun. Two 6U satellite configurations discussed in this study assumed that the radiators are pointed to deep space to minimize the environmental heat load. Some satellite missions or payload require specific pointing which could point the radiator to the sun for a few minutes. These special cases are carefully analyzed by calculating the total incoming environmental heat load to the radiator precisely. Radiator surfaces that faces the ram direction of the satellite need to be fabricated with high strength materials or should be thicker in order to deal with micrometeoroid impacts in low earth  
Figure 29: DR Surface coating study.  
orbits and atomic oxygen effects which significantly erode the surface coatings before the end of the mission life.
## 4 Discussion
### Nanopore Structural Parameters  
To further investigate the changes in the nanopore structural parameters of coal, linear fitting was performed between the nanopore parameters of coal and its volatile matter content. The fitting curves are shown in Figure 7. Based on the variations in the pore structural parameters of the samples, the study found that as the volatile matter content of coal increases, the number of pores exhibits a linear decreasing trend (r = 0.951, Figure 7A).  
Figure 6: Surface roughness as a function of volatile matter content; (**A**) Average roughness (R\({}_{\text{a}}\)); (**B**) Root mean square roughness (R\({}_{\text{q}}\)).  
This may be due to the production of a large amount of gas during the increase in volatile matter, which leads to the destruction of the walls of adjacent small pores, causing pore merging and a decrease in the number of pores. Simultaneously, the increase in volatile matter results in the closure of some tiny pores, further reducing the number of pores. Secondly, the average pore diameter exhibits an increasing trend with the increase in volatile matter (r = 0.948, Figure 7B). The increase in volatile matter causes some adjacent pores to merge, leading to an increase in pore diameter. Additionally, small pores are less stable and are more prone to closure during the increase in volatile matter, while large pores are retained or even expanded due to their structural stability. The area porosity exhibits a decreasing trend with the increase in volatile matter (r = 0.738, Figure 7C). When small pores merge into larger pores, the surface area decreases due to changes in geometry, resulting in a reduction in area porosity. Finally, the study found that the shape factor also exhibits a decreasing trend with the increase in volatile matter (r = 0.948, note: corrected from r = 948, Figure 7D). The process of volatile matter increase leads to the escape of gas, making the pore edges rougher and increasing the shape complexity. Thus, coal samples with high volatile matter content have fewer pores and lower area porosity than those with low volatile matter content, while the average pore diameter shows an opposite trend. Meanwhile, the pores in coal samples with high volatile matter are more irregular.  
Figure 7: Variation in Nanopore Structural Parameters with Volatile Matter Content (**A**) Number of Pores; (**B**) Average Pore Diameter; (**C**) Area Porosity; (**D**) Shape Factor.
# Key Recovery for Content Protection Using Ternary PUFs Designed with Pre-Formed ReRAM
## 4 Content Protection with Ternary Unclonable Functions
### Preparation Cycle--Encryption and Delivery of the Digital Files  
As shown in Figure 5, the process of preparing the delivery of a digital file **M** starts with the generation of a set of challenges (**T**, _IDA Access_). **T** is obtained through a random number generator, and _ID Access_ is computed from the look-up table containing an image of the PUF. This allows for the masking of the ternary positions, as well as the parameters such as the electric current that is injected during the key generation. The digital file **M** is encrypted with responses **K**. An example of the protocol that encrypts and transmits the ciphertext to a client device is as follows:  
* Both communicating parties have independent access to a shared password **PW**; number **T** and **PW** are XORed. The resulting stream is hashed with a SHA-3 generating **MD**, which is extended with a SHAKE to generate stream **A** for the m addresses: \(\bigcirc\) **MD**\(\leftarrow\) SHA-3 (**T**\(\oplus\)**PW**) \(\bigcirc\) **MA**\(\leftarrow\) SHAKE(**MD**) \(\bigcirc\) **MA** is pointing at the m addresses of the PUF
* The m-bit long mask is retrieved from _IDA Access_ to hide the addresses containing fuzzy positions. This leaves k positions, k \(<\) m, for response generation from the image of the PUF. The output is the k-long response **K**.
* The digital file **M** is encrypted into ciphertext **C** with **K**, the responses **K** are hashed with a SHA-3 to get **H(K)**, and the mask is XORed with **MD**.
* **T**, **C**, and **H(K)** are transmitted to the client device.  
As presented in Section 2.1, it is important to prevent the opponent from knowing the fuzzy positions, as well as any other parameters. The suggested protocol assumes that **T**, **C**, and **H(K)** can be transmitted through non-secure channels. The information is protected by the password and needs _IDA Access_, as well as the PUF, or its image to disclose the digital file **M**. At this point of the protocol, the client device does not have access to the digital file. As needed, the server can prepare multiple digital files with multiple challenges. In the commercial context and example of the delivery of movies, the service provider can send a stream of files to its customer, each encrypted with its own responses.
## 3 Results and Discussion
### Crystallization Study
#### 3.3.2 Identification and Purity of the Crystals  
The XRD spectra showed that the crystalline phase that precipitated was Na\({}_{2}\)SO\({}_{4}\). Equal crystalline peaks were also found in the literature [19; 25] and in the XRD database by analyzing powdered pure Na\({}_{2}\)SO\({}_{4}\) (Figure 9).  
The crystalline phases that precipitated in the experiments were found to be Na\({}_{2}\)SO\({}_{4}\), which can precipitate in the anhydrous form and as the hydrated forms, Na\({}_{2}\)SO\({}_{4}\)\(\cdot\)10H\({}_{2}\)O (mirabilite) and Na\({}_{2}\)SO\({}_{4}\)\(\cdot\)7H\({}_{2}\)O (heptab hydrate) [25]. Na\({}_{2}\)SO\({}_{4}\) anhydrous can precipitate in different polymorphs where five different phases have been identified (phase I, II, III, IV, and V) [26]. At room temperature, phase V is stable, while phase III is metastable. Phase I and II are high-temperature polymorphs (>270 \({}^{\circ}\)C and >225 \({}^{\circ}\)C) [27]. Phase IV is considered to be metastable, and the phase transition has not yet been well established [26]. The structure of Na\({}_{2}\)SO\({}_{4}\) (III) and Na\({}_{2}\)SO\({}_{4}\) (V) were found to be needle-shaped and prismatic/bipyramidal crystals by the evaporation of solution at 60\({}^{\circ}\)C [26], which corresponds to the structures found in this study. The needle-shaped and prismatic crystals obtained in the MD and  
Figure 8: SEM pictures of the crystals at different magnifications for the membrane distillation/ crystallization (MD/MCr) experiments conducted at different operating conductions.  
MCr experiments were consistent with the Na2 SO4 structures found in the literature [25], where the needles were said to be phase III, and the prisms were said to be phase V. The prismatic structure of Na2 SO4 that was obtained in these experiments was described in the literature [28], and is said to be phase V. In previous studies on the membrane crystallization of sodium sulfate crystallization, other structures such as orthorhombic structures [20; 29] or rectangular/circular crystals [18] were observed, which is slightly different from this study. This can be attributed to the differences in feed composition or operative conditions, which also highlights the potential of controlling polymorph structures by membrane crystallizers through changing operational parameters.  
The purity of the Na2 SO4 crystals was analyzed by ICP (Table 5). The largest percentage of cations in the crystalline structures was Na, which was expected due to the XRD results. The data showed that the crystalline structures contained small amounts of Fe, Mg, Mn, and P, which could indicate that residue wastewater remained with crystals after their separation from the mother liquid, or the possible co-crystallization of other minerals. Although Al was not detected in the supernatant; this was perhaps due to its quantity being below the detection limit of the instrument, which was eventually incorporated into crystal lattice.
# Indoor Positioning Systems Can Revolutionise Digital Lean
## 4 Application to the Monitoring of a Flexible Manufacturing Process
### Purpose of the Project  
The presented study focuses on the monitoring of CNC machines and assembly stations used to produce metal parts for an automotive company. There are five CNC machines, one assembly station, one assembly line and a packaging station. The orders (tasks) move along different paths during the production, depending on the production plan. The project aims to reduce transportation waste, identify the waiting and queueing times, and monitor the cycle times. Due to the changing number and variations of product families, this process is not a one-time activity. One small change in the product architecture can cause changes in the assembly sequence, which can lead to significant performance losses.  
Traditionally, LM masters will detect these 3M (Muda--Mura--Muri) via eye observation, then conduct re-calculation and re-arrangement to find a new optimal point. By using the IPS, the manufacturing activities can be easily tracked and automatized. The positional data from the moving carts are analyzed to identify whether they are not in a pre-defined value-added area (like assembly stations). The extracted cycle times are used to find the potential wastes of human works (changing times, manual work) and focus on these areas, such as defining a standardized digital work instruction that depends on the current position information of the semi-finished product.
## 3 The SOMM Model
### Achieving Flexibility via Virtual Machine
#### 3.3.3 Service Description  
For a client to be able to interact with a service, the service must be well described. To this end, some standard languages like Web Service Description Language (WSDL) have been developed. WSDL is an XML-based language that provides a model for describing Web services. In SOMM, service registries also store service descriptions in WSDL format. However, nodes in SOMM do not support SOAP and HTTP bindings and only support TCP binding in binary format. This is because HTTP and SOAP bindings are text based and sending text messages causes more overhead compared to binary messages and increases the power consumption of nodes.  
Each service in SOMM has four characteristics: service type, endpoint, QoS, and interface. As it is clear, the service type determines the type of service which is limited to the types of services that nodes of networks can provide. Endpoint defines the identifier of node that provides the service. QoS is the quality of service range that the provider node can provide and interface specifies the service operations and required parameters for each operation. Services in SOMM must be accessible from the outside of network; therefore, sink nodes which are the bridges between the network and the outside world, support SOAP and HTTP bindings. Users send their queries to sink nodes using SOAP orHTTP, then, the sink interprets the received queries, connects to the appropriate service registry and asks for the suitable services. Next, the sink extracts the address of server node and redirect the user requests to that node in binary format. Sink nodes act as the interpreters between the network users and network nodes. They convert the SOAP and HTTP messages to binary format and _vice versa_. In this way, services of network will be available over the Internet while controlling the power consumption of network. Figure 4 shows the service description for a simple video streaming service in SOMM. The service is hosted by node 3 of cluster a4 with the QoS range of 3-7 and as it is shown, its interface has three methods: start, stop and pause.
## 3 Transport layer  
The data are continuously collected from sensory devices to establish efficient data analytics mechanisms that extract useful information from the collected data. This layer moves data between the network and application layers through the appropriate wireless interface [82].