esa-sceva/satcom-chunk-collection huggingface dataset:
# A Fast Dynamic Dim Target Tracking Approach for UAVs Using Improved Lightweight Siamese Networks
## 3 Materials and Methods
### SiamRPN  
A SiamRPN network is formed by combining two artificial neural network branches with the same structure and the weight value. The two images are fed into two separate branches and go through a series of network structures to extract abstract features, which are then imported into the similarity measure function to obtain a similarity score for the two images. This will measure the difference between them.  
The used SiamRPN is designed with a feature extraction network consisting of a convolutional layer, a nonlinear activation layer and a pooling layer as the branching body, and a large number of samples are used to train this network offline to ensure the feature extraction capability. We imported the manually selected target image \(x\) in the first frame and the search area image \(z\) in the subsequent frames into the feature extraction network  
Figure 2: Fast dynamic dim target tracking approach.  
to obtain the target feature map \(\varphi_{\theta}(x)\) with resolution \(H_{x}\times W_{x}\times m\) and the search area feature map \(\varphi_{\theta}(z)\) with resolution \(H_{z}\times W_{z}\times m\), respectively, where \(H\) and \(W\) are the aspect of the feature map and \(m\) is the number of channels. Then, by designing and learning a similarity measure function  
\[f_{\theta}(z,x)=\varphi_{\theta}(z)*\varphi_{\theta}(x)+b\cdot I \tag{1}\]  
The response map \(f_{\theta}(z,x)\) representing the similarity between the target features and the search region features is obtained, and the similarity between the target template image of the first frame and the search region of the subsequent frames is judged according to the response map, and the position with the highest response value score is the new target position, where \(\theta\) is the network parameter, \(*\) denotes the convolution operation, and \(b\cdot I\) represents the bias term.
## 2 Experiment Approaches
### Machining Tests  
Experiments were conducted on the Feeler U600 (Fair Friend Ent. Co., Ltd, Taipei City, Taiwan) milling machine with a 4-tooth tungsten carbide end mill cutter. The workpiece material is an aluminum alloy (Al6061) with a size of 150 mm \(\times\) 150 mm \(\times\) 80 mm. Machining operations were performed by full immersion of slot milling, as shown in Figure 4. Each slot was milled in the X-direction under specific cutting parameters at different levels. Considering the effect of chatter on the machining process, the cutting parameters from rough to finish machining were selected within the stable regions that were defined by the machining stability lobes of the milling tool. The axial cutting depth (\(a_{p}\)) was defined in the range of 0.1 and 2.0 mm with an increment of 0.1 mm. The spindle speed (\(n\)) was set to 3000-8000 rpm, increased in increments of 1000 rpm, respectively. The linear feed rate (\(f\)) was set at 600 to 2400 mm/min with increments of 100, which was determined by spindle speed (\(n\)) and feed per tooth of the cutter based on the formula: \(f\) (mm/min) = \(n\) (rpm) \(\times\)\(f_{z}\) (mm/tooth) \(\times\)\(z\) (number of tooth). Herein, feed per tooth of the cutter was specified at two levels, 0.05 and 0.075 mm/tooth, respectively. For a given spindle speed, the linear feed rate in machining was set at lower and higher rate, respectively. During milling operations, a triaxial vibration sensor was mounted on the spindle housing to measure the acceleration signals of the vibration in the orthogonal directions (X, Y, and Z) simultaneously. For each slot machining, the average value taken from the time domain root mean square (RMS) values of the accelerations were calculated and used to compare the vibration extent of milling under different cutting parameters. After machining tests, the surface roughness (\(R_{a}\)) was measured using white light interferometer (Optical Surface Profiler, [PERSON], NewView(tm) 8000 Series (Zyzo Corporation, Middlefield, CT USA). For each machined slot, roughness values were measured at five equally spaced points along the feeding direction, and then, the average of these values was recorded for subsequent analysis. There are a total of 240 machining conditions defined by the different levels of cutting parameters, including 6 spindle speeds, 2 feed rates, and 20 cutting depths.
## 2 Literature Review
### Oversampling Method for Imbalance Dataset
In the case of medical diagnosis or disease classification, the combination of SMOTE with classification algorithms has shown significant results. [PERSON] et al. proposed hybrid algorithm by utilizing well-known classifier, SMOTE, and particle swarm optimization (PSO) to improve the competence of classification for five-year survivability of breast cancer patients from a gigantic dataset with imbalanced property [37]. The findings revealed that the hybrid algorithm surpassed other algorithms. Moreover, applying SMOTE in appropriate searching algorithms, for example, PSO and classifiers, such as C5, can considerably enhance the efficiency of classification for gigantic imbalanced data sets. Furthermore, [PERSON] et al. investigated the performance of machine learning methods for predicting diabetes incidence while using medical records of cardiorespiratory fitness [21]. The dataset consists of 32,555 patients of whom 5099 have developed diabetes after five years. The dataset contained 62 attributes that are classified into four categories: demographic characteristics, disease history, medication use history, and stress test vital signs. The study utilized SMOTE to deal with imbalance dataset. The result showed that, with the help of SMOTE, the performance of the predictive model was enhanced. Furthermore, the study showed that ensembling and SMOTE approaches achieve the highest accuracy for predicting incident diabetes while using cardiorespiratory fitness data. The present literature demonstrates that greater prediction performance is attained by utilizing SMOTE to balance data. Therefore, an HPM that consists of DBSCAN-based outlier detection to identify and remove the outlier and SMOTE to balance the distribution dataset is proposed in our study. The hybrid model is predicted to enhance the classification accuracy, in this way helping people to detect the danger of diabetes and hypertension at the initial stage. Along these lines, a person can evade the most exceedingly bad conditions later on.
## 3 Classification
### Mode of Communication
#### 3.3.3 Hybrid  
_Hybrid_ mode of communication involves vehicles and VRUs communicating with each other _directly_ using ad-hoc communication as well as _indirectly_ through infrastructure. This may be achieved by equipping the devices with multiple communication technologies and designing the system that may leverage the capabilities of these technologies. References [5; 25] are a few examples of the  
Figure 3: Various pre-crash scenarios. (**a**) Pedestrian crossing in front of vehicle; (**b**) Pedestrian moving parallel to vehicle; (**c**) Vehicle turning left into cyclist’s path (**d**) Vehicle turning right into cyclist’s path.  
V2P systems for VRUs that use the hybrid mode of communication. This mode may overcome the limitations of _direct_ and _indirect_ modes i.e., communication range and communication latency respectively. However, this mode increases the system complexity as it requires the precise partitioning of the functionalities of various communication technologies in the system.
# Light-Controlled Microbots in Biomedical Application: A Review
## 2 Prospective Applications
### Cell Manipulation and Diagnosis  
Cell manipulation with light-actuated microbots is an active research field. For biomedical research, it is important to isolate cells of interest for diagnosis, pathogen dynamics, treatment, or to quantify cell characteristics. Sometimes it is essential to separate specific  
Figure 4: **(i)** The polymerized microtool and sample arrangement utilized in cell characterization studies. SEM images of the microtool from: (**a**) side view, (**b**) top view (scale bars: 5 m). It is clear that the tip and trapping spheres are on a separate plane from the rods that connect them. The inset in figure (**b**) depicts the tip of the microtool from the side (scale bar: 1 m), (**c**) 3D model view of the working of the microtool, and (**d**) steps involved in the maneuver of the microtool and indentation process. (**ii**) (**a,b**) Cell membrane indentation and characterization in action, and (**c**) displays the function of the trapping beam position on the tip positions from two cell indentation studies (solid blue and solid red lines). When the red line is aligned to the blue one using an alignment technique, it also displays the outcome of the trace alignment procedure (dashed red). The variations between the red and blue traces during the alignment process are displayed in the inset in (**c**) [86]. Reproduced under the terms of the CC-BY license.  
cells to study them, such as peripheral hematopoietic stem cells [98], endothelial progenitor cells [99] or circulating tumor cells [100] etc., which gives valuable information on the progression of certain diseases and helps to find the cure. In addition to diagnosis, many of these isolated rare cells are cultured for regenerative biomedical applications such as mature stem or progenitor phenotypes for the construction of autologous tissue. Many forms of cell manipulation techniques have been studied to assess the capability of microfluidic devices in the diagnosis and treatment of diseases [101]. Manipulation of biological cells has already been demonstrated with microrobots using different control modalities [102; 103; 104; 105; 106; 107]. Researchers have been using focused laser traps in cell manipulation and sorting since the beginning of the invention of optical traps [108; 109; 110]. [PERSON] et al. used single beam as well as multiple beam configurations to demonstrate manipulation of trapped biological cells within microfluidic systems without causing significant damage to cells with high accuracy [111]. [PERSON] et al. demonstrated translocation of _S. cerevisiae_ (one type of yeast) with a moving speed of \(0.41\pm 0.06\) mm/s using a \(26.8\pm 0.1\) mW laser diode [112]. [PERSON] et al. used a far-field Bessel beam to trap a single blood cell in a crowded environment [113]. They have successfully demonstrated independent manipulation of two lymphocytes with a dual trap laser, which might extend the feasibility of the method to study the killer cells' response to virus-infected cells.
## 5 Consideration of Convergence Time:  
Although the WSO-WOA algorithm required 1.68 to 2.12 times longer to converge to an optimal solution compared to other methods, it is essential to recognize that this extended time is associated with offline optimization. The optimization process occurs before deployment, ensuring that the real-time operation of the system remains unaffected. This aspect emphasizes the algorithm's suitability for offline tasks where accuracy is prioritized over speed.  
Figure 17: The outcomes of the fault scenario are (a) using the DSTATCON and using the FOPI-FOPI: (**a**) the load voltage without enhancement, (**b**) the load voltage with enhancement, and (**c**) the injected voltage and current.  
Real-World Application in Hybrid Microgrids:  
The integration of a PV system and PEMFC to supply power to EV charging stations significantly impacts the overall power quality of the microgrid. The proposed controllers, optimized by the WSO-WOA algorithm, contribute to maintaining robust power quality amid varying operational demands.
## 1 Introduction
### Data Monetization  
Mckinsey analyze incentivization through data monetization, which states it is a differentiator and is still nascent. They also assert that with the available data, it is necessary to engage with other partners to create new business ecosystems, and it is essential to dissolve sectoral borders. This is one of the business models we would approach in our architecture to create an ecosystem of related businesses. Data monetization defined by Gartner as \"using data for quantifiable economic benefit\" can be one of the following strategies as highlighted in [1] along with the adopted organizations:  
* Asset Sale: Sale of Direct Data by Strava, Verizon Wireless.
* Business Process Improvement: Value is extracted from data for optimization of one's business process by Lufthansa, ThyssenKrupp, and Deutsche Bank.
* Product/Service Innovation: Offering new business services or processes based on data by IBM, Rolls Royce.
* Product/Service Optimization: Optimization of existing service based on data by Ford, Zara and Pirelli.
* Data Insight Sale: Selling derived knowledge by analytics, visualization by Olery, Sendify and DealAngel.
* Contextualization: Addition of supplementary over the existing data for economic benefits by Staples and Walmart.
* Individualization: Customer data are used to customize the product offering and preferences, enhancing the value proposition by eBay, Daimler or Netflix.  
In our use case, we adopt data bartering indirectly as the strategy process between the different ecosystem actors. It is a simplified approach, not considering the capabilitiesof Big Data or Artificial Intelligence Services, which can be very well included in future scenarios for enhanced returns. We concentrate on an extensible, generic architecture that can be recast and shaped vertically, pipelining other technological services or products. Some challenges in data monetization are final data usage policy, legal liability, cross-border data trade, security, and privacy challenges as uncovered in [2], which we also analyze in our architecture.
## 2 Materials and Methods
### Doxycycline Photocatalytic Degradation Experiments  
The photocatalytic efficiency of ZnO films was assessed by measuring the degradation of doxycycline (DC, 15 ppm) in an aqueous phase. The reaction was carried out in a 150 mL cylindrical glass reactor equipped with a UV lamp (Sylvania BLB, 18 W, emission range: 315-400 nm), as illustrated in Figure 1. The light power density at the sample position was measured at 0.66 mW/cm\({}^{2}\). The pristine and Pd-functionalized ZnO thin films were mounted on a custom-made borosilicate glass sample holder, which elevated them above the magnetic stirring bar to ensure proper agitation of the reaction mixture. An uncoated glass slide was used as a backing to prevent contact between the reaction medium and the side of the ZnO samples facing away from the light source.  
UV-vis absorbance spectroscopy was used to monitor the time-dependent concentration decay of DC by periodic aliquot sampling. Control experiments in the absence of UV light were conducted to account for adsorption and dark-state reactions. All photocatalytic tests were performed at room temperature (23 \(\pm\) 2 \({}^{\circ}\)C) with constant stirring at 500 rpm. Before irradiation, the samples were equilibrated in the dark for ten minutes.  
The reduction in DC concentration over irradiation time was calculated using the formula:  
\[D\%=\frac{C_{i}-C}{C_{i}}\times 100\%, \tag{1}\]where \(C\) is the DC concentration at a given time and \(C_{i}\) is the initial DC concentration, allowing for an assessment of doxycycline degradation. Experiments were conducted with both distilled and tap water. Natural low-hardness tap water (Na\({}_{+}\) \(<\) 5 mg/L, Ca\({}^{2+}\) \(<\) 11 mg/L, Cl\({}^{-}\) \(<\) 5 mg/L, NO\({}_{3}\)\({}^{-}\)\(<\) 1 mg/L) was sourced from the municipal water supply in Sofia.
# Synthesis of High-Precision Sub-Micron CaCO\({}_{3}\) Anticancer Drug Carriers from Coral Remains
## 2 Materials and Methods
### Exploration of Optimal Parameters for the Synthesis of Vaterite
#### 2.4.1 Adding Order of the Salts  
CaCO\({}_{3}\) was synthesized using the precipitation method, involving CaCl\({}_{2}\) and Na\({}_{2}\)CO\({}_{3}\). We found that the order in which these salts are added plays a critical role, a parameter not extensively discussed in the existing literature. Altering the sequence of adding CaCl\({}_{2}\) and Na\({}_{2}\)CO\({}_{3}\) resulted in significant variations in the crystal forms and particle sizes of the synthesized CaCO\({}_{3}\). Therefore, the impact of the adding order of these salts was emphasized in this study.
## 3 Security and Privacy Challenges
### Classification of Edge Computing Security Threats
\begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt} p{142.3 pt}} \hline \hline
**Type of Threat** & **Description** & **Mitigation Strategies** \\ \hline Distributed Denial of Service (DDoS) Attacks & The continuous transmission of junk data packets toward the target node can exhaust all resources allocated for handling the malicious data packets. This may result in genuine requests being dropped due to the overload of the target node’s resources [56]. Three significant DDoS attacks on edge computing devices are outage attacks, sleep deprivation attacks, and battery-draining attacks. \\ \hline Privacy Leakage & Privacy leakage in EC mainly involves three separate classes of privacy concerns, i.e., data privacy, location privacy, and identity privacy. Attackers might exploit the location awareness of EC nodes to detect and track device status or to gain access to classified data, posing further risks to privacy. \\ \hline Eavesdropping or Sniffing & An intruder listens over communication channels to gain access to private data, like the physical location of specific nodes, access or control information of the EC node, like node identification or node configuration, message identities (IDs), timestamps, usernames, and passwords. \\ \hline Jamming Attacks & The attacker transmits a wide range of signals with a similar frequency, potentially disrupting network security. Also, unintentional interference is thus triggered in wireless networks due to induced noise and collisions. \\ \hline Integrity Attacks Against Machine Learning & ML techniques utilized in EC-assisted Internet of Things (IoT) are susceptible to two different categories of data security attacks. Causative attacks involve manipulating and injecting misleading training datasets to compromise the training process of ML models and Exploratory attacks, where adversaries exploit vulnerabilities. & Researchers propose the use of virtual machines (VMs) with boundaries for running ML processes, hence accelerating the testing and deployment of ML models, and systematic study of attacks in simulated environments or sandboxes [126; 127]. \\ \hline \hline \end{tabular}
\end{table} 
Table 3: _Cont._
# Maintaining Secure Level on Symmetric Encryption under Quantum Attack
## 2 Literature Review
### Avalanche Effect  
This term was first used by [PERSON] [12], and the origin of the concept can be traced back to the dissemination by [PERSON]. The term diffusion refers to the redundancy of plain text in [PERSON] to spread the effects of a single key to as many ciphertexts as possible and avoid the possibility of deciphering the input from the output using brute force methods.  
The avalanche effect is designed to do the same, and is considered an important metric in cryptographic security to ensure that text or keys cannot be corrupted by statistical analysis.  
The Strict Avalanche Criterion [13] was proposed by [PERSON] and [PERSON] as a formalization of the avalanche effect. If the encryption method meets the strict avalanche criteria, when any input bit is inverted, there is a 50% chance that each bit of the output will change.
# Stream: Spatial Trajectory Enhanced Attention Mechanism for Abnormal UAV Trajectory Detection
## 4 Evaluation
### Evaluation of System Performance  
This section assesses the overall performance of our proposed system. It is hypothesized that defects may occur in the process of collecting UAV location data due to external environmental factors and network conditions. To address this, some images from each label were intentionally removed. These images were then recovered through interpolation and classified using the STEAM model. The dataset employed for this evaluation is divided into 12 labels, comprising a total of 240 images, with 20 images per label. For 30% of the images, 30% of their content was removed, 20% had 50% removed, 10% had 70% removed, and the remaining 40% were left intact.  
Table 3 displays the classification results using the STEAM model, including precision, recall, and F1-score for each label. These metrics demonstrate the system's effectiveness in classifying UAV trajectory, even with partial missing data.  
The subsequent Table 4 details the accuracy of STEAM classifiers for each label relative to the extent of data omission.  
While Table 4 indicates a decrease in accuracy for certain labels with increased missing data, the system demonstrated high accuracy for most labels, underscoring its resilience in effectively classifying UAV trajectory, even in scenarios with significant data gaps.
# Knowledge-Graph- and GCN-Based Domain Chinese Long Text Classification Method
## 3 Domain Chinese Long Text Classification Model  
In this section, we present the proposed knowledge-graph- and BERT-based graph convolutional neural network model (KGBGCN). The general framework is shown in Figure 1. It is shown how the model constructs a relational graph and its relation types based on the entity and relation types of the input sentences and the dependent syntactic tree, models the contextual information by the attention graph convolutional neural network model, further allows the model to understand the semantic dependencies by the graph structure mask, and, finally, achieves classification by backpropagation together with the BERT model.  
Specifically, for each input text, an entity-relationship extraction model is first used to extract its entities and inter-entity-relationships to construct a knowledge graph. Entities are used as nodes, entity relations are used as connecting edges between nodes, and the GCN transfer matrix is constructed with the existence of edges and edge types. Similarly, a dependency syntax tool is used to automatically process the text and generate a dependency syntax tree, and then, a graph is constructed on the dependency relationship tree.  
Then, the representation of the text sequence is extracted using BERT as an encoder, and the entity-relationship graph and the dependency graph, which are characterized by the text sequence, are fed into two GCN modules. In the GCN modules, for each word in the sequence, its entity-relationship or dependency type with the related context word is encoded as a contextual feature. Meanwhile, using the attention mechanism to assign different weights to different marked entity relations and dependency relations between any two words, the weights are calculated based on the connection and its relation type, and the attention matrix is constructed by calculating the dot product between words. In addition, to improve the learning ability of the model for semantic dependencies between words, the graph structure mask model is used to randomly mask the edge connection between two tokens, and then, the model is allowed to predict whether there is a connection relationship between two tokens and the type of connection. The two GCN modules and the graph structure mask training are used to obtain entity-relationship representations and dependency representations, respectively, and the two vectors are stitched together to obtain the GCN output. Finally, the fusion output of BERT, GCN, and the graph structure mask is used to predict the category labels of documents. The flow of the method is shown in Figure 2.