esa-sceva/satcom-chunk-collection huggingface dataset:
# Reversible Data Hiding for DICOM Image Using Lifting and Companding
## 3 Lifting Based DWT Transform  
The lifting scheme is a technique for both designing fast wavelets and performing the discrete wavelet transform. The technique was introduced by [PERSON]. Lifting-based filtering consists of a sequence of very simple filtering operations for which alternately odd sample values of the signal are updated with a weighted sum of even sample values, and even sample values are updated with a weighted sum of odd sample values as described by Equations (1) and (2) [27; 28].  
\[y(2n+1)=X_{ext}(2n+1)-\frac{X_{ext}(2n)+X_{ext}(2n+2)}{2} \tag{1}\]  
\[y(2n)=X_{ext}(2n)+\frac{y(2n-1)+y(2n+1)+2}{4} \tag{2}\]  
Figure 1: Basic file structure of DICOM image.  
Figure 2: Detail of DICOM data set and data element structures. Source ([PERSON] [26]).  
where, the notation \(X_{ext}\) representsas extended input. The notation \(y\) is the output signal and \(a\) indicates the largest integer not extending 'a'. The lifting based DWT is done by filtering operation on the row and column of the host image. It uses both high-pass and low-pass filter. It is seen that the process results in double the number of samples. So, the output from each filter is then down sampled by two that keeps the sample rate constant [29].  
From Equations (1) and (2) it is also cleared that lifting based DWT creates correlation among the sample values. To validate the above argument, we calculate the entropy of different sub-bands for both traditional DWT and lifting based coefficients [30]. The average entropy values for different sub-bands of lifting based DWT are \(LL=7.26\), \(HL=5.24\), \(LH=5.42\), \(HH=5.40\), and for normal DWT, the values are \(LL=10.41\), \(LL=8.30\), \(LH=6.78\), \(HH=8.62\). The lesser values of entropy for lifting show that there exists a large correlation between the wavelets coefficients. In other words, there is low randomness and high similarity in the lifting based wavelets coefficients. Data hiding scheme takes the advantages of this redundancy to embed a large number of watermark bits for a given embedding distortion that helps in designing a better reversible process [30]. Moreover, lifting provides integer coefficients after transformation that ultimately results in the complete elimination of watermark bits at decoder compared to traditional DWT. For more information regarding various benefits of lifting operation over conventional DWT, interested readers may consult [31]. The lifting based DWT structure is shown in Figure 3a [30]. The number of levels of wavelet decomposition is application dependent. One level of decomposition is demonstrated in the present experimentation. The lifting based DWT decomposition using 'harr' transform is shown in Figure 3b.
# Incorporating Artificial Intelligence Technology in Smart Greenhouses: Current State of the Art
## 2 Artificial Intelligence Technologies for Smart Greenhouses
### Current AI Technologies for Agriculture  
The AI technologies available for smart greenhouses and precision agriculture are diverse, ranging from ecological bio-inspired algorithms, such as Adaptive Neuro-Fuzzy Inference System-Particle Swarm Optimization (ANFIS-PSO), ANFIS-GA (ANFIS with Genetic Algorithm) and ANFIS-ACO (ANFIS with Ant Colony Optimization), swarm intelligence algorithms (Artificial Bee Colony (ABC), Flower Pollination algorithm (FPA), Firefly algorithm, Krill Herd algorithm, and genetic optimization algorithms) [64] to UAV (drones) pesticide application and robotic systems for harvesting [9], irrigation, soil treatment, seeding, fertilizer application, seeding, and other tasks [1; 2]. The current body of knowledge linked the demand for robotics, UAVs, and bio-inspired algorithms in agriculture to distinct factors. On the one hand, scholars argued that the growth was catalyzed by the need to enhance yields and operational efficiency and reduce costs [8; 29; 33]. In line with [5; 29; 33], [PERSON] et al. reported a 10% improvement in tomato yields with the iGrow intelligent greenhouse system and 92% higher profits [18]. On the other hand, AI assimilation in smart agriculture was triggered by natural events, including higher levels of pesticide resistance and forest fires, which compromised agricultural production [65; 66]. The following sections critiqued the two worldviews to yield better insights into the favorable factors and barriers to AI-system adoption in smart greenhouses and precision agriculture at large.
# Performance Analysis of a Novel TCP Protocol Algorithm Adapted to Wireless Networks
## 3 Results
### Wireless Link Emulation
#### 3.1.2 \(\overline{\theta}\) Evaluation  
We did tests with the Iperf tool to obtain the \(\overline{\theta}\) values under different error probabilities. Each test consisted in establishing a TCP connection and to perform a data transference for 1 minute.
## Abbreviations  
The following abbreviations are used in this manuscript:  
SAR synthetic aperture radar  
GMTI ground moving target indication  
DOF degree/rohedoms  
AMF adaptive matched filter  
ABORT adaptive beamformer orthogonalrejection test  
MHT multiple hypothesis testing  
MDV minimum detectable velocity  
ATI along track interferometric  
DPCA displaced phase center antenna  
TSX TerraSAR-X  
TDX TanDEM-X  
SCNR signal-to-clutter plus noise ratio  
SBRAS SpaceBore Radar Advanced Simulator  
i.i.d. independent and identically distributed  
ML maximum likelihood  
STAP space time adaptive processing  
RCS radar cross-section  
GLRT generalized likelihood ratio test  
ACE adaptive coherence estimator  
ROC Receiver Operating Characteristic  
Pd probability of detection  
Pfa probability of false alarm  
LRT likelihood ratios test  
Pc probability of correct classification  
InSAR interferometric synthetic aperture radar  
DEM digital elevation model
# A Topology Map Generation Algorithm for Optimal Path Finding for Image-Based Maps
## 2 Related Works  
### Path-Finding Algorithms  
A* is a very effective and popular algorithm for pathfinding due to its excellent performance in real-world applications compared with the Dijkstra algorithm [17].  
A* is similar to the Dijkstra algorithm [18]. However, one difference is that it assigns a heuristic estimate \(h(n)\), a rank value that estimates the best path through the vertex for each vertex x [19] and defines an evaluation function for each vertex to find the optimal path from the starting vertex to the target vertex. The evaluation function \(f(n)\) for this is as follows:  
\[\textbf{{f(n)}}=\textbf{{g(n)}}+\textbf{{h(n)}} \tag{1}\]  
\[\textbf{{g(n)}}=\text{the weight of one of the connected nodes} \tag{2}\]  
\[\textbf{{h(n)}}=\text{the weight of the heuristic to be used by A*} \tag{3}\]  
where \(\textbf{{g(n)}}\) is the path weight from the starting vertex to vertex n, and \(\textbf{{h(n)}}\) is the heuristic value from vertex n to the target vertex. The heuristic value of A* in this paper uses the distance between the starting and destination vertexes.  
From the starting vertex, the algorithm evaluates the connected vertex by \(\textbf{{f(n)}}\) and adds it to the Open List. The node with the smallest cost among the nodes added to the Open List is added to the Close List, and this process is repeated for the selected node  
Figure 1: Navigation System Architecture for AR HMD.  
until the destination node is selected and added to the Close List. If the procedure cannot be continued because the Open List is empty, the algorithm determines that there are no possible routes to find.
## 3 Performance Evaluation Methods
### Moving Distance  
In practical applications, the UAVs usually have limited battery energy and cannot fly a very far distance. The most significant energy consumption in the actual deployment is the mobility. In such a case, the normalized moving distance traveled by the UAVs can reflect the energy consumption of the whole network system with respect to one 3D deployment scenario. Here we introduced the moving distance as one measurement of system deployment performance.  
Set \(S_{0}^{i}\) be the initial position of the sensor node \(i\) and its position during the whole deployment process are \(S_{1}^{i},S_{2}^{i},S_{3}^{i},\ldots,S_{t-1}^{i},S_{t}^{i}\). Therefore, the displacement distance of node \(i\) can be obtained as  
\[D_{i}=\sum_{j=1}^{t}d(S_{j-1}^{i},S_{j}^{i}) \tag{15}\]  
where \(D\) is the Euclidean distance between the positions calculated from the adjacent time steps. In such a case, the average moving distance and corresponding variance of all nodes in a WSN are  
\[\bar{D}=\frac{1}{N}\sum_{i=1}^{N}D_{i}=\frac{1}{N}\sum_{i=1}^{N}\sum_{j=1}^{t} d(S_{j-1}^{i},S_{j}^{i}) \tag{16}\]  
and  
\[\sigma^{2}=\frac{1}{N}\sum_{i=1}^{N}(D_{i}-\bar{D})^{2}. \tag{17}\]
# Centralized vs. Decentralized Cloud Computing in Healthcare
## 7 Results
#### 7.1.1 Benefits of the Use of Centralized Cloud Computing in Healthcare  
In the healthcare industry, it is crucial to have accurate, easily accessible, and secure information. Centralized cloud computing transforms how medical data are managed, shared, and utilized [60]. This innovative approach merges vast computational resources, medical records, and applications within centralized data centers, creating an environment in which healthcare providers can use digital technologies to enhance therapeutic outcomes and operational efficiency [55]. Centralized cloud computing in healthcare centralizes patient data, streamlines access, and enables comprehensive patient care [55]. It also provides frameworks for secure data exchange, safeguarding patient information, and enabling timely decision making [59]. Centralized cloud environments promote standardized data formats, enhancing communication between healthcare systems and facilitating seamless data exchange throughout the healthcare ecosystem [75; 76]. One significant benefit of centralized cloud computing is that it allows healthcare providers, practitioners, and patients to access and exchange health information anytime, anywhere [50]. This promotes fast response times for the access and retrieval of healthcare data, enhancing the accessibility and availability of patient data, facilitating better care coordination, and engendering informed decision making [57]. Moreover, by centralizing healthcare data in the cloud, different information sources and healthcare organizations can be integrated, offering a comprehensive perspective of an individual's health trajectory [60]. These integrated data enhance the continuity of care and enable healthcare providers to enhance the precision of diagnosis and treatment recommendations. Additionally, the cloud infrastructure provides scalability, enabling healthcare organizations to scale their data storage and computing resources up or down, according to their needs [52]. This approach yields economic efficiencies by minimizing spatial infrastructure demands and attenuating ongoing operational expenditures [77]. Digital infrastructure vendors deploy multilayered safeguarding protocols to ensure health information privacy and preserve data integrity. These mechanisms encompass cryptographic systems and granular access management frameworks [78].
# Energy Efficiency in Short and Wide-Area IoT Technologies--A Survey
## 5 Open Challenges
### Open Challenges for LPWANs  
In the LPWAN context, and specifically for LoRa/LoRaWAN, methods relying on the Channel Activity Detection show how the SF plays an important role for energy consumption. The correct assignment of SF values is thus key for the energy efficiency of LoRaWAN systems, and thus requires further investigation towards improved solutions.  
In the context of cloT technologies, the literature show that the main methods used to increase energy efficiency in LTE-M are based on DRX and on random access enhancements.  
In the DRX case, several methods proposed to assign an urgency weight to each node, which determines the transmission order across nodes. A drawback with the urgency weight is related to the fact that a node could be wrongly assigned a value that is lower than the threshold value. Hence, that node is marked as non urgent node, while it might actually have urgent information to transmit, and should have been thus labeled an urgent node. This will cause the node to lose the opportunity to transmit before all the other nodes increasing its time delay and energy consumption. Another important open point, requiring for further studies is the DRX cycle length definition, since too long DRX cycles have a negative effect on delay and consumption of LTE-M devices.  
Considering the techniques based on random access enhancements, simulation results show that the energy efficiency can be improved but Therefore, the optimization of the random access procedure plays a crucial role towards energy-efficient LTE-M networks.  
Sleep/wake-up methods based on PSM/(e)DRX mechanisms have been largely proposed for NB-IoT. They lead to beneficial effects in NB-IoT energy efficiency; however, several aspects require further investigations.  
While using (e)DRX-based algorithms, the usage of the DR-SC mechanism [153] negatively affects the network performance during multicast transmissions, while the DR-SI scheme is not compliant to NB-IoT standards.  
The majority of the routing techniques are based on the optimal relay selection. A drawback is that when using optimal relay selection, The process of finding optimal relays requires high computational efforts from the network and thus increases energy consumption. Therefore it is important to find novel solutions allowing for direct communication between devices and the base station, and also making possible the selection of relays across devices already in connected mode.  
In the case of the resource allocation techniques, the proposed methods are based on the prediction of resources allocated in uplink, in order to pre-assign the required radio resources and eliminate the scheduling request procedure. Wrong predictions result in wrong resource assignment and increased energy wasting. Hence, it is important to propose novel methods for accurate prediction in order to optimally exploits these methods and achieve higher energy efficiency.
# CONE: A Connected Dominating Set-Based Flooding Protocol for Wireless Sensor Networks
## 2 Related Work
### Tree Growing Algorithm
#### 2.4.1 Greedy Variation  
The tree growing algorithm iteratively adds nodes to the DS until it is connected. This algorithm colors nodes either in white, grey, or green in order to indicate the node's state. White nodes are non-dominators. Grey nodes are also non-dominators, but they are adjacent to at least one dominator. Green nodes are dominators. Algorithm 1 shows the tree growing algorithm in pseudo-code.  
```
1: initially color all nodes in white
2: choose a node \(m\) with the largest number of white neighbors
3: color node \(m\) in green and the white neighbors of node \(m\) in grey
4:while white nodes exist do
5: choose a grey node \(n\) with the largest number of white neighbors
6: color node \(n\) in green and the white neighbors of node \(n\) in grey
7:endwhile
```  
**Algorithm 1** Greedy tree growing algorithm.  
When all nodes are colored in green or grey, the algorithm has successfully constructed a CDS, represented by the set of green nodes. Figure 7 provides an example of the greedy tree growing algorithm. The advantage of this algorithm is that it keeps the set of dominators connected until a CDS has been constructed. The disadvantage of the algorithm is that choosing only grey nodes to become green nodes in Line 5 of Algorithm 1 results in a larger CDS for some topologies. Choosing both white and grey nodes to become green nodes would result in a smaller CDS in less rounds, as shown in Figure 8.  
Figure 7: Tree growing algorithm. The dominators are colored in green. The non-dominators that are adjacent to at least one dominator are colored in grey. (**a**) The algorithm arbitrarily chooses a node with the largest number of white neighbors. The chosen node is colored in green and its neighbors are colored in grey. (**b–f**) The algorithm arbitrarily chooses a grey node with the largest number of white neighbors. The chosen node is colored in green and its white neighbors are colored in grey. The algorithm stops in (**f**) because each node is colored in either green or grey.
# Analysis of Power Allocation for NOMA-Based D2D Communications Using GADIA
## 3 5G Overview
### Challenges and Research Directions  
NOMA-based RAN affords a powerful and promising platform for achieving future application requirements. Various essential issues persist in thoroughly assessing the gains that can potentially endure from such an architectural integration. The following Table 2 addresses the fundamental open problems toward the design of NOMA-based RAN.
# A Novel Deep Learning Segmentation and Classification Framework for Leukemia Diagnosis
###### Abstract  
Blood cancer occurs due to changes in white blood cells (WBCs). These changes are known as leukemia. Leukemia occurs mostly in children and affects their tissues or plasma. However, it could occur in adults. This disease becomes fatal and causes death if it is discovered and diagnosed late. In addition, leukemia can occur from genetic mutations. Therefore, there is a need to detect it early to save a patient's life. Recently, researchers have developed various methods to detect leukemia using different technologies. Deep learning approaches (DLAs) have been widely utilized because of their high accuracy. However, some of these methods are time-consuming and costly. Thus, a need for a practical solution with low cost and higher accuracy is required. This article proposes a novel segmentation and classification framework model to discover and categorize leukemia using a deep learning structure. The proposed system encompasses two main parts, which are a deep learning technology to perform segmentation and characteristic extraction and classification on the segmented section. A new UNET architecture is developed to provide the segmentation and feature extraction processes. Various experiments were performed on four datasets to evaluate the model using numerous performance factors, including precision, recall, F-score, and Dice Similarity Coefficient (DSC). It achieved an average 97.82% accuracy for segmentation and categorization. In addition, 98.64% was achieved for F-score. The obtained results indicate that the presented method is a powerful technique for discovering leukemia and categorizing it into suitable groups. Furthermore, the model outperforms some of the implemented methods. The proposed system can assist healthcare providers in their services.  
blood cancer; leukemia; segmentation; deep learning; categorization; UNET; neural network; DSC; WBC +
Footnote †: journal:  
Article
###### Abstract  
This study focuses on the successful fabrication of a cost-effective strain sensor using exclusively additive manufacturing Fused Deposition Modeling (FDM) technology, enabling fast on-site production, which is particularly advantageous in maritime settings, reducing downtime, and supporting a circular economy approach by minimizing inventory needs and environmental footprint. The principle of operation of the developed device is based on the piezoresistive characteristics of a carbon nanotube (CNT)-enriched building material, from which the main sensing element consists. The prototype exhibited reliable piezoresistive properties, and a clear correlation was observed between the thermal treatment of the printed piezoresistor and the resulting gauge factor, linearity, and hysteresis. Its robustness, simple design, and single-step manufacturing process, together with its ability to be integrated into the readout circuitry through standard soldering, enhance its reliability and durability. The key advantages of the proposed device include its low cost, simple design, and rapid remote production.  
additive manufacturing: 3D printing; 4D printing; FDM technology; sensors; spare parts; marine industry; strain gauge; gauge factor Article
# A Survey of Deep Learning for Electronic Health Records
## 5 Deep Learning Applications in EHR
### Privacy Preservation
#### 5.4.1 Data De-Identification  
The patient's personal health information is frequently included in medical data. HIPAA requires that all released clinical data be stripped of sensitive information such as the patient's name, social security number, and information about the hospital, region, and time, making public EHR data difficult to publish. There are additional bills in the works in the United States to secure patients' personal information and prevent it from being misused. Despite the fact that rigorous legislation can ensure the confidentiality of patient information, data processing will almost certainly require a lot of manual interaction.  
The early automatic data de-identification techniques [101; 102; 103] used were implemented by designing corresponding algorithms for PHI or other specific private data, which lacked generalization capability and required a lot of human work in the early stage. [PERSON] et al. [104] proposed a de-identification algorithm with some generalization capability. However, the algorithm is still based on heuristic pattern matching with dictionary lookup, which is general enough but far from enough to support the development of open medical data.  
As a result, [PERSON] et al. [77] presented an automatic system for clinical text identification, which would eliminate the need for time-consuming manual intervention. Bi-LSTM, as well as character and word embedding, make up the framework. This method is currently quite advanced, according to the investigation. Furthermore, for automatic identification, the integrated method of conditional random fields can be used. Meanwhile, in the field of recognizing possibly recognizable named entities in clinical literature, [PERSON] et al. [78] investigated the combination of multiple RNN variants with various word embedding models and found that all RNN variations outperform the typical baseline approach.  
[PERSON] et al. [79] presented the first recurrent neural network with an attention mechanism based on [77]. The network further enhances the de-identification ability by stacking recurrent neural networks and adding the attention mechanism, which is about 0.03% higher than [77] in the F1-score. However, the number of parameters of the model increases dramatically and is three times higher than [77], reaching 110,000,000.