esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Analysis of the CRB(\(\boldsymbol{\
### Effects of the Signal Modulation on the \(CRB(\boldsymbol{\tau}_{long})\)
This is due to the fact that for a \(h=3\) m, \(\Delta\rho\) is small enough for the two signal components to interfere, and the separability provided by the PRN code's correlation properties acts only partially. This separability is needed to measure the code delay difference between the two components, which is required to unambiguously estimate \(h\). This cannot be done by only using the phase difference information. We would like to highlight that, if during the \(\Delta\theta_{el}\) observed, the \(\Gamma\) coefficient changes significantly, e.g., when the Brewster angle is covered by \(\Delta\theta_{el}\)[21; 61], lower values of the CRB for \(\varepsilon_{r}\) and/or \(\sigma\) can be achieved with shorter \(\Delta\theta_{el}\). From the CRB point of view, if the pdf of the observed signal changes significantly with the variation of the unknown parameters, such as \(\varepsilon_{r}\) and/or \(\sigma\), then it is easier to estimate these parameters from the observed samples, resulting in lower CRB values. Likewise, when the \(\Gamma\) coefficient barely changes during the observation time considered, it becomes harder to unambiguously retrieve the \(\varepsilon_{r}\) and/or \(\sigma\) parameters from the observed samples, thus resulting in higher CRB values. Figure 7 shows that centimeter precision can be theoretically achieved when estimating \(h\) over elevation spans larger than \(20^{\circ}\) for all GNSS signals. The CRB also shows that millimeter precision can be theoretically achieved for the \(\Delta\theta_{el}=[10^{\circ},\ 25^{\circ}]\) in the case of the Galileo E5 signal, i.e., using its full bandwidth. In practice, however, it is unlikely to find real surfaces that are that flat and homogeneous (and with no obstacles), maybe except for very calm water surfaces. Thus, the millimeter precision is unlikely to be achieved by any estimator in a practical scenario, even with the assumptions made in  
Figure 8: Accumulated area within the first Fresnel zone for \(h=3\) m with the variation of \(\theta_{el}\). The right axis shows the displacement of the specular reflection point from its initial location when \(\theta_{el}=10^{\circ}\). The GPS L5 and Galileo E5 signals have slightly different carrier frequencies (1176.45 MHz and 1191.795 MHz, respectively), but both of their wavelengths have been approximated to the same \(\lambda\ =\ 0.25\) m.  
this paper (e.g., perfect knowledge of the antenna radiation pattern, the satellite elevation angle, etc.). The asymptotic behavior observed over long observation times of the CRB of the conductivity \(\sigma\) can be related to the small dependency and change of \(\Gamma\) during the considered elevation spans for the dry ground surface.
## 4 Methods and Materials  
### Hardware  
The sensors are interfaced and powered from the solar panel. All data is gathered at the sink node and is transmitted to the destination via a gateway. The hardware can be operated on a rechargeable Li-Ion 2200 mAH battery. Figure 3 consists of sensor modules, ADC, LoRa modem, and a LoRa helical antenna. The device can be deployed to collect sensor data from the field and transmit it via the Internet to the receiver device through the gateway, which is a customized ATMEGA 328 p board with an inbuilt Wi-Fi module and it is shown in the Figure 4.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Parameters**} & \multicolumn{3}{c}{**Fully Range Free Based Algorithms**} & \multicolumn{3}{c}{**Hybrid Range Based Algorithm**} \\ \cline{2-7}  & _CA_ & _NCA_ & _DV-HoP_ & _ATPA_ & _EATL_ & _FRTL_ \\ \hline Node deployment & Both uniform and random & Random & Random & Both uniform and random & Random \\ \hline Node density & Low & Low & High & High & Low & Medium \\ \hline Existence of obstacle & Yes & Yes & Yes & Yes & Yes & Yes \\ \hline Anchor node presence & Yes & Yes & Yes & Yes & Yes & Yes \\ \hline Range estimation & Computational & Computational & Computational & Computational & Computational & Computational \\ \hline Range combination & Centroid & Centroid & TOA, TDOA & TOA & TDOA & RSSI \\ \hline Localization co-ordinates & RD & 3D & 2D & 2D & 2D & 2D \\ \hline Scalability & Yes & Yes & No & Yes & Yes & Yes \\ \hline Accuracy & Low & Low & Medium & Very High & Very High & High \\ \hline \hline \end{tabular}
\end{table}
Table 2: Localization algorithms (range free vs. hybrid range-based) [40].  
The block diagram (Figure 2) is a depiction of the components used in the entire project. The connections were made so that the main component Raspberry Pi board is connected to the Arduino Uno board through the standard pyfirmata. The remainder of the sensors are connected with both the Arduino and Raspberry Pi models. The digital sensors are attached to the Raspberry Pi, and the analog sensors are connected to the Arduino Uno board. The sensors gather data in real-time. All of the outputs are stored on the Thingspeak server/Blynk on the cloud, and can be accessed at any point in time.  
A circuit diagram of a reference model of the interface connections made in real time is shown in Figure 5. Firstly, the Arduino is connected to the Raspberry Pi using a USB cable. The gas sensor is connected to the Arduino at the analog A0 pin. The ultrasonic sensor is connected to pin no. 9 of the Arduino for trigger, and echo at pin no. 10. DHT11 is connected to the Arduino Uno board. The sensors gather data in real-time. All of the outputs are stored on the Thingspeak server/Blynk on the cloud, and can be accessed at any point in time.  
Figure 4: LoRa-based gateway.  
Figure 3: LoRa end node custom design for agriculture farm.  
connected to pin 2 of the Arduino. Soil moisture is connected to analog pin A3 of the Arduino. The motor pump is connected to pin no. 8 of the Arduino.
## 4 Implementation
### Implementation Results  
Figure 12 shows the architecture adopted for the MIMO C-OOK scheme. Each packet includes the same sub-packets to support the frame rate variation effect. An SN was used to generate the sub-packet's serial number. The data payload of each sub-packet in a contained packet was the same. In reality, SN is divided and managed according to the combination of the transmitter's packet rate and the frame rate of the camera. Undersampling occurs if the frame rate is lower than the transmitter packet rate. Oversampling, by contrast, occurs when the frame rate exceeds the transmitter packet rate. The transmitter packet rate is defined as the number of packets that include various payloads and that are continuously transmitted in a given time (e.g., 60 packets/s). Data packet frames with a smaller number of data sub-packets (DSs) were included in the proposed data frame structure, and each DS included payload data and an SN. The SN included the data packet's sequence information, as well as the payload's identifier. In practice, payload identification is useful since it allows the camera to detect the new payload in the case of oversampling, as well as missed payloads in the case of undersampling.  
Figure 11: The BER curve for the optical OOK modulation.
# Cybersecurity and Medical Imaging: A Simulation-Based Approach to DICOM Communication
## 2 Background
### Service-Object Pair (SOP) Class and Transfer Syntax  
SOP Classes [42] are service objects that encapsulate DICOM image files. An SOP Class defines a specific type of medical imaging or non-imaging service that can be performed on a DICOM object. It represents a combination of a service and an object on which the service can be performed. Popular SOP Classes include, among others [42]:  
* **CT Image Storage (1.2.840.10008.5.1.4.1.1.2):** stores Computed Tomography (CT) images, which are used for diagnostic purposes and provide detailed cross-sectional views of the body.
* **MR Image Storage (1.2.840.10008.5.1.4.1.1.4):** stores Magnetic Resonance Imaging (MRI) images, which are used to visualize internal structures of the body using magnetic fields and radio waves, providing detailed anatomical information.
* **Ultrasound Image Storage (1.2.840.10008.5.1.4.1.1.6.1):** stores ultrasound images; ultrasound imaging uses high-frequency sound waves to generate images of organs and tissues in real-time, and is often used in obstetrics, cardiology, and other medical applications.
* **X-ray Radiography Image Storage (1.2.840.10008.5.1.4.1.1.1):** stores X-ray images; widely used for various medical purposes, X-rays provide two-dimensional images that can help diagnose bone fractures, detect abnormalities, and visualize internal structures.  
The above SOP Classes define the necessary metadata, attributes, and operations required to handle and interpret images of their respective modalities.
# Study on the Improvement of a Collision Avoidance System for Curves
## 1 Introduction  
According to the Korean National Police Agency and the National Highway Traffic Safety Administration (NHTSA), the increase of vehicles on roads is accompanied by a proportional increase in traffic accidents caused by careless driving [1; 2]. Several research and development projects are being conducted on functions such as advanced driver assistance systems (ADAS) around the world to improve drivers' safety and convenience [3]. The autonomous emergency braking (AEB) system is one such representative system. An AEB system analyzes the collision risk with the preceding vehicle and prevents or avoids a head-on collision, which can occur due to the driver's carelessness, by operating the automatic control function at a dangerous moment [4].  
The existing AEB systems usually detect collision risks using obstacle detection sensors, such as cameras, light detection and ranging (LiDAR), and radio detecting and ranging (RADAR). As these sensors have limitations, many researchers have attempted to integrate multi-sensor data fusion technology with vehicle-to-vehicle (V2V) communication [5]. In addition, existing AEB systems only cover limited road environments, such as curves with constant curvatures. The relative distance and the parameters of the AEB algorithm were calculated by the arc length of a certain sector based on the curvature and the angle between the two vehicles [6; 7; 8]. Since the existing AEB systems do not consider the geometrical shape of winding roads, the relative distance is not very accurate.  
Another study analyzed road alignments using a road section segmentation method. Straight sections account for approximately 34% and curve sections account for approximately 65% of all the roads in South Korea. Additionally, approximately 62% of the curve sections are reversed curves consisting of multiple curved radius and directions [9]. Thus, the geometrical complexity of curves should be considered for the application of AEB systems to usual environments.  
This study aims to improve the performance of an AEB system on curve roads. To achieve this aim, this study adopts V2V communication technology to overcome sensor limitations, such as blind spots. It also applies a curvilinear coordinate conversion, which is used for route planning of autonomous driving vehicle, to consider the geometrical complexity of curves [10; 11]. Moreover, this study shows the effectiveness of the proposed AEB system in various curves in comparison with existing collision avoidance systems.  
The remaining part of this paper proceeds as follows. Section 2 explains the AEB system based on the curvilinear coordinate system. In Section 3, a method for transforming the positions of the host and target vehicles is explained. Based on the curvilinear coordinate conversion, this method calculates the time-to-collision (TTC) required by the AEB system to perceive hazards. In Section 4, a simulation is conducted for a curve with a constant curvature and a reversed curve, using the results of Sections 2 and 3. Finally, the conclusions of this study are presented in Section 5.
## 4 Results and Analysis
### Effect of Injection Conditions on Fracture Propagation
#### 4.3.1 Effect of Injection Rate  
To study the effect of the injection rate on fracture growth, the injection rate in Case 1 was varied from 60 L/min to 30 L/min in Case 4, while the other parameters were kept unchanged.  
Figure 19 shows the fracture shape in Case 4, in which the injection rate is 30 L/min when \(t=265\) s and \(t=270\) s. In this case, the hydraulic fracture of Well 3 interacts with the natural fracture at \(t=265\) s, and then 5 s later, it propagates along the natural fracture. Compared with Case 1, the time required for the hydraulic fracture induced by Well 3 to intersect the natural fracture changes from 90 s in Case 1 to 265 s in Case 4.  
Figure 20 shows the bottom-hole pressure over time at different injection rates. Evidently, when the injection rate is 30 L/min in Case 4, the initial bottom-hole pressure is lower compared with that in Case 1. The time required for convergence between the hydraulic and natural hydraulic fractures in Case 4 increases, and more time is required for the sharp drop in the bottom-hole pressure of Well 2. The lowest bottom-hole pressure at an injection rate of 30 L/min in Case 4 does not differ significantly from that observed at an injection rate of 60 L/min in Case 1.  
Figure 19: Hydraulic fracture shape in Case 4. (**a**) \(t=265\) s (**b**) \(t=270\) s.  
Figure 20: Bottom-hole pressure with time for Cases 1 and 4.  
Figure 21 shows the hydraulic fracture area with time under different injection rates. A variation in the injection rate of the fracturing fluid brings about a corresponding change in the hydraulic fracture area over time.  
By analyzing the changes in the pressure at the bottom of the hole and the hydraulic fracture area over time at different injection rates, it becomes evident that a higher injection rate corresponds to increased pressure at the bottom hole during the pressure-holding stage, accelerated growth of the hydraulic fracture area, and faster fracture expansion.
# NOMA-Based VLC Systems: A Comprehensive Review
## 4 Potential Challenges and Open Research Issues
### Impact of Interference  
Some studies concentrate on cooperative NOMA [36] and employs Bluetooth-like short-range communications in the cooperative phase, despite the fact that interference analysis is a generic term in wireless networks. Nevertheless, present wireless personal area network (WPAN) operations provide a severe interference problem for the usage of BT radio in cellular networks. Interference from other BT devices reduces coverage and throughput, disrupts connectivity temporarily or permanently, and makes pairing with other devices challenging for the user during the discovery process. The deployment of channel allocation is influenced by factors such as the amount of interference present in the operating environment, the size of the payload, and the distance between cooperating users. Since NOMA pairs users based on their CSI, a self-organizing scatternet for BT node management needs to be modified so that it may be used in conjunction with NOMA. Even when users' mobility results in the full loss of some of the wireless links, a resilient scatternet should nevertheless provide viable routes between nodes with a high probability. The movability of users also introduces a new level of complexity to the interference problem. Therefore, it will be interesting to analyze the performance of a cooperative NOMA strategy in this dynamically interfering scenario.  
Recently, rate-splitting multiple access (RSMA) has emerged as a promising PHY-layer paradigm for interference management, non-orthogonal transmission, and multi-user communication. RSMA is a universal MA scheme that subsumes OMA, NOMA, SDMA, and multicasting as sub-schemes. For instance, considering multiple-antenna settings, PD-NOMA becomes sub-optimal compared to RSMA [159]. In short, RSMA is a super-set of current MA techniques and therefore always offers equal or better performance. Through exploiting the splitting of user messages along with non-orthogonal transmission of common messages decoded by multiple users and private messages decoded by their corresponding users, RSMA shows the potential to reconcile the two extreme interference management techniques of treating interference as noise and fully decoding interference used in NOMA and SDMA [159]. The flexible framework of RSMA ensures better performance for all levels of interference. In case of strong or weak interference, RSMA automatically reduces to NOMA or SDMA through tuning powers and contents of users' data streams.
## 3 Results and Discussion
### Prediction of WHC Traits Using Key Wavelengths, Texture, and Their Fusion Data  
The PLSR model was also used to establish the quantitative relationship between three different WHC traits, key wavelengths, and texture data, as well as their fusion data. As can be seen from Table 2, the PLSR models established by key wavelengths had comparable predictive ability to the corresponding full spectra PLSR models, but the numbers of input variables were greatly reduced. The predictive ability of the texture data PLSR model alone was not satisfactory, especially for salt-induced water gain. However, the PLSR model based on fusion features had the best model performances in the prediction of drip loss and expressible fluid (Rp = 0.80, RMSEp = 0.80; Rp = 0.56, RMSEp = 2.10). It means that texture data is an effective supplement to spectral feature and the fusion of them can improve the predictive ability of HSI. The unsatisfactory model's predictive abilities for salt-induced water gain by texture data and fusion feature (Rp = 0.07, RMSEp = 24.30; Rp = 0.68, RMSEp = 18.16) might be due to the measurement method of salt-induced water gain, which used a minced sample instead of an intact sample. Compared to the intact sample used for drip loss and expressible fluid, the physical structure of the chicken muscles in the minced samples for salt-induced water gain had been completely destroyed. Thus, the best prediction method for salt-induced water gain used the key wavelength feature (Rp = 0.69, RMSEp = 18.04).
## 6 Conclusions  
In this survey, we have reviewed interference mitigation schemes for WBASNs. Coexistence of WBASNs has been revisited with respect to problems involving interference, and the existing inter-WBASN interference mitigation schemes have been categorized and qualitatively compared. Although many interference mitigation schemes have been reported, there is no dominating scheme that significantly outperforms the others. An analytical tradeoff between network throughput and power consumption in WBASNs is needed in the dynamic channels in WBASNs. Channel dynamics, QoS, delay, reliability, and network lifespan also must be considered with the design in terms of interference. Because the performance and QoS of WBASNs are severely affected by interference, various interference mitigation algorithms will be developed in the future. The cross-layer design of interference mitigation will be one direction. It should be noticed that the QoS requirement has not been fully investigated, and performance in some biomedical applications would be compromised with existing interference mitigation techniques.
## 3 Motivation and Reuse Scenario  
Our proposed framework aims to provide features that are essential for advancing circular economies within supply chains. The material passport (MP) design enables dynamic updates that ensure the complete transparency and traceability of materials from their origin to destruction/decommissioning or reuse, providing a chain of custody that stakeholders can trust. The architecture of our system is built to support seamless interoperability across different data systems, enabling collaboration across diverse stakeholders. The following important questions are considered in this work:  
**Q1:** Is this approach capable of ensuring the tracing and tracking of products and materials through the entire supply chain while also providing updates on their current status within the supply chain?  
**Q2:** Does this method facilitate the accuracy and autonomy of data for all supply chain participants?  
**Q3:** In what ways can the use of MP and its supporting systems help to integrate material information with other critical systems across the network while also supporting scalability as the supply chain's participants grow?  
The implementation and design of our framework aim to achieve the objective of the questions discussed above. (i) **Verifiability:** Storing supply chain information on a typical database does not ensure data security inherently. However, blockchain can provide a solution to this problem by allowing information to be verified without the requirement for a trusted third party. This promotes confidence among participating entities by guaranteeing that the product's integrity is upheld and that it originates from the stated source. (ii) **Privacy:** The information stored on the public blockchain systems is visible to the participating entities in the network. For enhanced privacy, organisations can employ privately managed blockchains, which allow for controlled visibility. When multiple organisations are part of a construction-like project, these individual blockchains need to interact with one another, highlighting the importance of blockchain interoperability. This setup enables each entity to control its private information and blockchain while selectively sharing data with other project participants as necessary. Our framework utilises _parachains_ to facilitate this process effectively. (iii) **Minimum on-chain information:** Storing every detail of a particular product on a blockchain is not practical due to scalability issues. To address this, we opt to keep the data off-chain while retaining a reference on the blockchain for accountability. Our design incorporates the InterPlanetary File System (IPFS), which helps in maintaining data accessibility, eliminating the risk of a single point of failure and safeguarding against data tampering. (iv) **Provenance:** During its life cycle, a product can change hands multiple times. To evaluate its condition and know how long it has been in use, accessing its full history is crucial. To facilitate this, we have established a system for provenance tracking, enabling the tracing of a product's journey back to its source using blockchain technology.
## 4 Results and Discussion
### Machine and Deep Learning
However, as confirmed by the classification times shown in Table 2, deep learning took longer to classify consumers than did conventional machine-learning methods. The next subsection describes the MetaCost savings. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Machine Learning Algorithm** & **Recall** & **Precision** & **F-Measure** & **Time (s)** \\ \hline KNN & 0.951 & 0.951 & 0.950 & 0.3 \\ Decision tree-J48 & 0.918 & 0.918 & 0.918 & 0.6 \\ SVM & 0.929 & 0.928 & 0.928 & 48.6 \\ Naïve Bayes & 0.696 & 0.726 & 0.701 & 0.4 \\ RIPPER & 0.908 & 0.909 & 0.908 & 8.1 \\ MLP & 0.982 & 0.982 & 0.982 & 298.1 \\ CNN & 0.976 & 0.976 & 0.976 & 857.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Classification results of machine and deep learning. Abbreviations: k-nearest neighbor (KNN); support-vector machine (SVM); repeated incremental pruning to produce error reduction (RIPPER); multilayer perceptron (MLP); convolutional neural network (CNN). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Classified as**} \\ \hline
**Real Data** & \begin{tabular}{c} **High** \\ **Consumption** \\ \end{tabular} & \
\begin{tabular}{c} **Medium** \\ **Consumption** \\ \end{tabular} &
\begin{tabular}{c} **Low** \\ **Consumption** \\ \end{tabular} & **Recall** & **Precision** & **F-Measure** \\ \hline High Consumption & **98.75\%** & 1.09\% & 0.16\% & 0.988 & 0.980 & 0.984 \\ Medium & 2.60\% & **96.31\%** & 1.09\% & 0.963 & 0.978 & 0.970 \\ Low Consumption & 0.21\% & 0.63\% & **99.16\%** & 0.992 & 0.987 & 0.989 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Detailed classification results obtained through MLP.