esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Works
### Industrial Control Device Fingerprinting Extraction Methods
#### 2.1.2 Content of ICS Device Fingerprint Extraction  
Different fingerprints serve various purposes: hardware fingerprints identify device models, software fingerprints distinguish manufacturers, and behavioral fingerprints detect network attacks. Selecting appropriate features based on task requirements is essential.  
(1) Hardware Fingerprint  
Hardware fingerprints involve identifying device characteristics through hardware information analysis. Obtaining superficial information like CPU model or MAC address is challenging in industrial settings. Instead, analyzing timing characteristics from network traffic helps extract deeper hardware fingerprints. As illustrated in Figure 2, devices process network data packets through hardware modules like network interface cards, main memory, CPU, cache, and data bus. Differences in these components affect the timing characteristics of data packets. By analyzing these timings, fingerprint features can be extracted. A representative feature is the Cross-Layer Response Time (CLRT), referring to the time from sending the request to receiving the response across layers.  
(2) Software Fingerprint  
Software fingerprints are derived from operating systems, drivers, and applications. These characteristics include operating system instruction sets, software protocol stack features, driver interfaces, and system service types and versions. While some require internal access, software protocol stack features are accessible externally. As shown in Figure 3, differences in protocol stack implementations by manufacturers manifest in fields like the TCP/IP stack, with features like TTL, field increment value, and TCP receive window size.  
Figure 2: Process for creating data packets on the device hardware.  
(3) Behavioral Fingerprint  
Behavioral fingerprints describe regular communication patterns of devices, including periodicity, traffic features, content features, and connection modes. These features depend on both the devices and the specific network environment and protocols. Methods for extracting these features operate at the TCP/IP level and the industrial control protocol level.  
At the TCP/IP level, [PERSON] and [PERSON] [20] used network telemetry to extract communication characteristics for identifying unauthorized devices. [PERSON] et al. [21] proposed a PU learning-based intrusion detection method addressing high-dimensional and correlated data in ICSs. At the protocol level, [PERSON] et al. [22] proposed a signature-based intrusion detection system analyzing key fields of the Modbus protocol to detect cyberattacks using predefined signatures. [PERSON] et al. [23] proposed a hybrid model leveraging predictable communication patterns between field devices. However, these methods often depend heavily on the modeling environment, affecting applicability and accuracy across different scenarios.
# Predicting Location of Tweets Using Machine Learning Approaches
## 4 Methodology
### Location Prediction Using Tweet Text and Home Location  
In the first experiment, we aimed to predict the geo-location of tweets based on the tweet text and the home location stated in the user's profile. We generated text features using the TF-IDF method and the home location feature. The TF-IDF method calculates the importance of a word in a particular class by taking into account the frequency of the same word in other classes (see Equation (1)). This enables us to determine the significance of different words in terms of location prediction performance.  
\[tf(t_{i},c_{k})=\frac{f_{c_{k}}(t_{i})}{\sum_{j\
eq k}f_{c_{k}}(t_{j})}\]  
\[idf(t_{i},c)=\log(\frac{|c|}{|\{c_{t_{i}}\in c:t_{i}\in c_{t_{i}}\}|})\]  
where \(tf(t_{i},c_{k})\) is the term frequency of the word \(t_{i}\) in class \(c_{k}\), which can be calculated by dividing the same term frequency \(f_{c_{k}}(t_{i})\) in that class by the total number of words in the class. \(idf(t_{i})\) is the inverse document frequency. It can be calculated by the log of the total number of classes by the number of classes that contain the term \(t_{i}\).  
\[tf\text{-}idf(t_{i},c_{k},c)=tf(t_{i},c_{k})\cdot idf(t_{i},c) \tag{1}\]  
In addition to the tweet text, we also utilized the home location in this experiment. However, we acknowledge that the home location information can be noisy or even missing in many cases, as a large number of users may not state their real location. For instance, we encountered users who stated their home location as \"Earth\" or even \"Milky Way\", causing the detection of their real location to be more challenging.
## 2 Technical Architecture and Solution
### RAG Integration
#### 2.2.1 Indexing Pipeline  
The indexing pipeline presented in Figure 1 is a critical component of our system, and it is responsible for processing and indexing documents into the vectorized database, ensuring that the data are efficiently stored and readily retrievable, enhancing the overall performance and usability of the system.  
The first step in the indexing pipeline is setting up the document store. In our implementation, we use a vectorized database to store document embeddings. This database is configured to use cosine similarity, which is particularly effective for comparing the document's vectorized representations. The setup includes defining the environment and securing access through an API key, ensuring efficiency and security. In addition, to promote the reproducibility and the modularity of our framework, different database solutions can be used (e.g., Astra, Chroma, Elasticsearch, Milvus, MongoDBAtlas, Neo4j, OpenSearch, Pgvector, Qdrant, and Weaviate).  
Once the document store is configured, the next step is preprocessing the documents. Preprocessing involves several sub-steps to prepare the documents for indexing. Initially, the documents are cleaned by removing empty lines, unnecessary whitespace, and extraneous headers and footers. After the data cleansing, the documents are split into smaller chunks to facilitate more efficient processing and retrieval. This splitting respects sentence boundaries and includes a sliding window approach to maintain context between chunks.  
After preprocessing, the documents are ready for embedding. Each document chunk is transformed into a vectorized representation using an advanced embedding model in this step. For our framework, we used text-embedding-ada-002 [30], but this is again under each preference, using various models imported from Hugging Face, OpenAI, Anthropic, and others. These embeddings capture the semantic meaning of the text, making it possible to perform effective similarity searches later.  
The indexing pipeline is designed to be modular, allowing each component to be easily modified or replaced as needed. This flexibility ensures that the system can adapt to different data sources and requirements, maintaining high performance and scalability to support various e-government applications. The main components mentioned above are presented in Figure 1.
# Implementation of a Topology Independent MAC (TiMAC) Policy on a Low-Cost IoT System
## 4 The Proposed System
### Low-Cost Devices  
As already mentioned, each device has to be low-cost and in this sense, industrial motes cannot be used because of their high cost. To achieve the low-cost goal, each node has been assembled using three essential elements. The first element is the core part of the node and is represented by an Arduino prototyping platform [18]. The rest of the elements are two RF modules operating at 433 MHz and are used for transmission and reception purposes. In Figure 2, one of the nodes comprising the system is shown. All three mentioned elements are installed, along with an LCD screen for experiment monitoring and debugging purposes.  
Regarding the Arduino prototyping platform, it is an open-source and low-cost electronics platform, is easy to use, and capable of hosting various components (e.g., the RF modules considered in this paper). There are plenty of Arduino board designs utilizing a variety of microprocessors and controllers. The particular board used for this paper is the Arduino Uno Rev. 3. The latter is a board that is built on top of the ATMEL ATMEGA328 microcontroller with a clock speed of 16 MHz and 2 KB of SRAM [31].  
As for the wireless communication part of the devices, it is handled by short-range RF modules operating in the license-free _Industrial, Scientific and Medical_ (ISM) Band of 433.050-434.790 MHz. This particular band is free licensed to users for amateur and radio-location services [32]. Two different components are used to realize transmission (Tx) and reception (Rx), i.e., RWS-371-V1.0.3 [19] and TWS-BS-V1.0.3 [20], respectively. As already mentioned, these modules co-operate with Arduino and the wiring applied to them is schematically illustrated in Figure 3.  
Figure 2: A device of the considered system enhanced with an LCD screen.
## 4 Results
### Machine Learning-Based IDS on the Cloud without FS Method
#### 4.1.5 MLP-Based IDS Implementation on Cloud without FS Methods  
Four models of MLP are tested for IDS implementation on the cloud without using FS techniques. Model1 had 50 neurons in the hidden layer, Model2 had 150 neurons in the hidden layer, Model3 had 250 neurons in the hidden layer, and Model4 had 400 neurons in the hidden layer. The results of the MLP models are described in Table 4. The results indicate that MLP Model3 outperformed the other MLP models with an accuracy of 93.90%, a precision of 0.84, a recall of 0.81, and an F1-score of 0.82.
## 1 Introduction  
Rutting and fatigue cracking can be considered the main distresses of flexible pavements. Besides reducing the safety and comfort of roadway users, it results in a need for increased vehicle maintenance.  
In the search for better performance of asphalt coatings in relation to these problems, besides a higher strength to reduce other defects, the use of modified asphalt mixtures is one option available. In this context, asphalt mixtures modified with polymers have been applied successfully to road engineering since the 1970s, when they were first used in Europe [1]. In recent years, with the advent of nanotechnology, modification with the use of nanomaterials has also gained the attention of the scientific community. Studies carried out with asphalt nanocomposites have demonstrated the good performance and potential of these materials in the paving sector [2; 3; 4; 5; 6; 7; 8; 9; 10; 11; 12]. More recently, the behavior of asphalt binders and mixtures modified with polymers and nanomaterials have been investigated. The results obtained in this line of research have also been positive [13; 14; 15; 16; 17; 18; 19; 20; 21]. However, most studies have been limited to the binders, and there is a need for more investigations that consider the mixtures, taking into account the interaction between the binder, the granulometry, and the aggregate characteristics. This is important and should be considered because the total composition of the asphalt mixture will be in contact with traffic and weather during the pavement's lifespan, although the binder characteristics have a prominent role.  
Thus, considering the high performance of mixtures modified with the addition of polymers and the potential for the application of nanomaterials to road engineering, the aim of this study was to carry out a comparative analysis of the rheological behavior and resistance to permanent deformation and to fatigue of four different asphalt mixtures: (1) a reference mixture, produced with a conventional asphalt binder (CAP 50/70: Petroleum Asphalt Cement with a penetration range between 5.0 and 7.0 millimeters) [22]; (2) a mixture with binder modified by nanoclay (3% NC), produced in laboratory [22]; (3) a mixture with binder modified by the polymer styrene-butadiene-styrene (SBS 60/85, with a minimum softening point of 60 \({}^{\circ}\)C and a minimum elastic recovery of 85%), produced industrially [23]; and (4) a mixture with binder modified by nanoclay and SBS (3% NC + 2% SBS), also produced in the laboratory [24]. This analysis was carried out through complex modulus and fatigue tests (4 PB, in the four-point bending apparatus) and permanent deformation tests (in the LCPC (Central Laboratory of Bridges and Roads/Laboratoire Central des Ponts et Chaussees) traffic simulator). Besides evaluating the behavior of the complex modulus and the phase angle of the mixtures with variations in the test load frequency and temperature, the rheological study allowed the prediction of the resistance of asphalt mixtures, especially with regard to rutting.
# Deep Learning-Based Detection Algorithm for the Multi-User MIMO-NOMA System
## 2 System Model
### SIC Receiver  
The fundamental concept of the SIC receiver is to detect corresponding symbols in descending order based on signal amplitude. Therefore, the SIC receiver initially sorts the amplitude of each user's signal during the detection process. In the uplink NOMA scenario, the BS detects symbols for each user in descending order, considering the product of the channel gain and power for user \(i\). Typically, each stage of the SIC receiver employs a linear detection algorithm such as zero-forcing (ZF) [31] detection. Assuming the received signal is \(Y\), the symbol gain matrix is \(H\), the actual symbol sent by the sending end is \(X\), and the estimated symbol at the receiving end is \(X\). The objective function of the ZF detection algorithm is \(\min L(x)=|Y-H\hat{X}|_{F}^{2}\), in which  
\[\begin{split} L(x)&=|Y-H\hat{X}|_{F}^{2}=(Y-H\hat {X})^{H}(Y-H\hat{X})\\ &=Y^{H}Y-Y^{H}H\hat{X}-\hat{X}^{H}H^{H}Y+\hat{X}^{H}H^{H}H\hat{X} \,.\end{split} \tag{6}\]  
The derivative form of the objective function can be denoted as  
\[\frac{d(L(X)}{d\hat{X}}=-2H^{H}Y+2H^{H}H\hat{X}=0\,. \tag{7}\]  
Then, we can establish the linear relationship between the received symbols and the symbol estimation at the receiving end. Specifically, the weight matrix of the ZF detection algorithm can be performed as  
\[\hat{X}=(H^{H}H)^{-1}H^{H}Y=W_{ZF}Y\,. \tag{8}\]  
Assuming the original superimposed signal at the receiving terminal is \(Y\), the symbol gain for the \(i\)-th user is \(H_{i}\), and the estimated symbol value for the \(i\)-th user is \(\hat{s}_{i}\). In the \(n\)-th level detection, the superimposed signal can be denoted as  
\[Y^{(n)}=Y-\sum_{i=1}^{n-1}H_{i}\hat{s}_{i},1\leq n\leq N\,. \tag{9}\]  
Through \(N\)-level symbol detection and interference cancellation, the SIC receiver can obtain symbol estimates for all \(N\) users.  
The SIC receiver boasts low computational complexity. However, relying on a linear detection method, it may encounter error propagation issues in case of an error. In the uplink NOMA system, the constrained transmission power of terminals makes it challenging to achieve significant power differences through user matching and power control strategies, leading to interference between users. Additionally, the near-far effect between users and the BS in the uplink scenario exacerbates the situation. The signals of these users are superimposed on the same resource block, potentially resulting in irregular superimposed constellations. Consequently, due to the linear detection algorithm used by the SIC receiver at each detection level, errors may arise in detecting signals within irregular constellation diagrams. As a result, the BER can be significantly higher than that of the downlink scenario. To address these shortcomings, we propose the application of a DNN on the SIC receiver.
## 3 RS Applications in Ocean
### Ocean Wave Height (OWH)
#### 3.3.1 Gnss-R  
Various GNSS-R techniques using either single or double antennas, as well as direct or reflected signals, have been proposed for OWH estimation. The signal arrival time was also used for OWH estimation in [PERSON] et al. [159]. The recorded SNR ratio was first used for OWH measurement by [PERSON] et al. [160] in 1995. Later, [PERSON] et al. [161] and [PERSON] et al. [162] successfully showed the robustness of the proposed method in [160] for OWH estimation. Moreover, [PERSON] et al. [163] used a GNSS Wave Glider (GNSS WG) through a 13-day experiment to measure OWH in the North Sea. Their results showed that GNSS WG could address the challenges of coastline-based tide gauges for OWH measurement, and the temporal and spatial resolution limitations of radar data.
# Terminal Distributed Cooperative Guidance Law for Multiple UAVs Based on Consistency Theory
## 5 Numerical Simulation
### Verification of Upper Level Distributed Coordination Strategy
#### 5.3.2 Chain Network Topology  
Taking the chain network topology as an example, the simulation curves of the flight trajectory, the leading angle, the time-to-go and the acceleration command of each member of the formation are shown in Figure 10.  
As can be seen from Figure 10, the initial time-to-go of the three UAVs is different, namely 30.29 s, 37.97 s, and 22.96 s. However, after mutual coordination, they gradually become consistent in about 8 s. In the later stage, the cooperative guidance law degenerates into the UAV's own control item, and finally the saturation attack on the target is carried out simultaneously in 34.44 s. The leading angle and acceleration command converge to 0 at the terminal moment.  
Figure 9: Simulation results of cooperative attack under ring network topology.
## 4 Conclusions  
This paper presented an analysis of TFR for different earthing designs and arrangements, taking into account the deployment of EEC. Three earthing designs were considered and simulated in the CDEGS environment, adopted from the current practice of the power utility, to improve the transmission line performance. The significant contribution of this work consists in the information, crucially required by the power utility, concerning the TFR values related to each design and the percentual improvement made by deploying EEC. This quantification of information is required for choosing the most technically and financially viable approach to earthing design to be deployed in TFR reduction and thus in improvement of the overall transmission line performance. As highlighted earlier, this work is significant because this 500 kV line is considered critical in Malaysia. Therefore, a much stringent requirement was put in place, especially on the TFR value that needs to be maintained. Even 1 \(\Omega\) improvement can be translated into tens of thousands of Malaysian Ringgit worth of investment and several ohms of reduction will certainly help the power utility to significantly cut down their operational expenses.  
In conclusion, the study confirmed that characteristics of the soil profile and the earthing arrangement design play an important role in the earthing system. The application of EEC surrounding the electrodes is a convenient, simple and easy way to reduce High TFR can lead to an outage of the transmission tower, especially on backflashover, and it is well known that the higher the soil resistivity, the higher TFR of that particular area [48].  
Conceptualization, M.Z.A.A.K., M.O., M.S.A.R. and U.A.U.A.; methodology, N.A.F.M.N.; validation M.Z.A.A.K.; formal analysis, N.A.F.M.N., M.S.M.N.; investigation, N.A.F.M.N., N.H.N.A. and N.H.Z.; writing--original draft preparation, N.A.F.M.N.; writing--review and editing, N.A.F.M.N.; supervision M.Z.A.A.K., M.O., M.S.A.R. and U.A.U.A.; project administration, M.Z.A.A.K., M.O., M.S.A.R. and U.A.U.A.; funding acquisition, M.Z.A.A.K., M.O., M.S.A.R. and U.A.U.A. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Universiti Tenaga National through UNITEN Bold Grant and URND for RA Scheme. Special thanks to Tenaga Nasional Berhad (Grid Maintenance) team for their kind support on the data.  
Not applicable.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Earthing Design with EEC**} & \multicolumn{3}{c}{**\% Reduction of TFR**} \\ \cline{2-4}  & **Tower T42** & **Tower T48** & **Tower T50** \\ \hline Design C & 20.45 & 16.3 & 17.86 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Percentage reduction of TFR for selected towers.
## 3 Results and Discussion
### Influence on VOOs Quality and Composition
#### 3.2.2 Pigments  
Oil pigments, chlorophyll and carotenoids, influence the colour of olive oil and play an important role in oil oxidative stability [50]. However, these are not one of the basic quality parameters of oils included in EU standards [36].  
Results from our study illustrated in Figure 2a showed that carotenoid content decreased in oils obtained from irrigated trees. Decrease of carotenoids content after irrigation has been reported by other authors [9; 51]. Differences among irrigation treatments were not found during our investigation period of two years (Figure 2b). Similarly, [PERSON] et al. [46] found no clear difference among irrigation treatments (100%, 70%, 50%, 25% of full irrigation) for oil pigments across three different seasons. Thus, water reduction in summer has no effect on pigments in the olives at harvest time and consequently the content of pigments in the obtained oils.  
The effect of irrigation on chlorophyll content was different across our two year period of investigation. In 2020, irrigation was noted to result in a decrease in chlorophyll content with no differences among irrigation treatments; however, in 2019, irrigation did not affect the content of chlorophyll in the olive oils (Figure 2b).  
[PERSON] et al. [18] reported that chlorophyll contents extracted from the less-watered olive fruits were higher compared to those from the more-watered fruits, concluding that several factors could influence chlorophyll content, such as the effectiveness of the chloroplast breakage during the grinding of the fruits as well as the level of activity of the enzymes (chlorophylases, peroxididases, lipoxygenases) which are influenced by irrigation and could destroy chlorophyll in the oil. Contradictory results regarding chlorophyll content after irrigation have also been found in the literature. For example, [PERSON] et al. [9] reported a decrease in chlorophyll content from irrigated Chemlali oils comparing to rainfed samples, while other authors reported increases in chlorophyll after irrigation [51; 52].  
Figure 2: Pigment content: (**a**) carotenoids and (**b**) chlorophyll of virgin olive oils from Coratina cv. olive trees grown under different watering regimes during two crop seasons. Results are expressed as mean values \(\pm\) standard deviation of three independent repetitions. Mean values labeled with different lowercase letter within the same harvesting year are statistically different (Tukey’s test, \(p<0.05\)). Watering regimes: C—rainfed conditions; T\({}_{1}\)—deficit irrigation (the usual producer’s practice); T\({}_{2}\)—deficit irrigation acquired by SAN technology in respect to phenological stages; T\({}_{3}\)—irrigation with 100% of evapotranspiration (ETc) level.  
[PERSON] et al. [49] did not find differences in chlorophyll in oil from rainfed treatment and waste water irrigated treatments (20 and 40% EtC) of Chemlali olive trees in Tunisia.