esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 The Query Management Scheme
### The Query Response Phase (QRP)  
In this phase, responses are transmitted from the nodes to the NM, which puts a number label to each of the tuples and passes the pairs to the Distributor. When the NM receives a response message, the NM can generate a query timeline (similar to the timelines in the above examples), using the time information elucidated by the response message, the start time for each query set, and each query's temporal information. It can also distinguish the sequence of the response tuples. The NM generates the final response tuple by extracting the requested data fields from the response message and putting the query ID, node ID, and a sequence number label to each of the extractions, and passes the final tuple to the Distributor. The final tuple is organized by the Distributor and sent to each user. Result tuples can be extracted by using the Data Extraction Theorem. The proof of the theorem is obvious, so it is not provided.  
Figure 9: Examples of how single time stamps can be misleading in case of communication failures. **(a)** The response message only contains node time stamp. **(b)** The response message only contains set time stamp.  
**Theorem 3**.: Data Extraction Theorem  
The NM can extract the data requested by query \(q_{i}\) from the result tuple by the following operation:  
\[q_{i}(S)=\Pi_{A_{i}}(T)\]  
where \(A_{i}\) is the data attribute requested by \(q_{i}\) and \(T\) is the result pair relation.  
Figure 10 is an example of message processing at this phase. \(A_{i}\) is the user application that sent the query \(q_{i}\):  
**Figure 10.** An example of message processing at the QRP. Analyzing the disambiguation code, the NM can determine that the queries should be sent to applications A2 and A4.  
In this example, the NM generated a query timeline. When the NM receives a query response message from a node (sensor node 5 in this case), the node can identify which users to send the queries to. In our case, the disambiguation code is 101, which indicates that there are three ambiguous queries and the query results should be transmitted to the user applications for the first and last of the three (A2 and A4). In the final response messages generated for user application A2, the sequence number label would read \"4\" since it is the fourth response to query q2. Similarly, the sequence number label for user A4 would read \"2\". Final response messages are sent to each distributor would look like Figure 11.  
**Figure 11.** Two examples of response messages delivered to the Distributor. **(a)** is delivered to A2 and **(b)** is delivered to A4.
# Reshaping the Digital Twin Construct with Levels of Digital Twinning (LoDT)
## 4 Assessing the Level of Digital Twinning (LoDT)
### UNI-TWIN--The Unified Digital Twinning Assessment Model
\begin{table}
\begin{tabular}{p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt}} \hline \hline
**Level** & **Hierarchy** & **Connection** & **Synchro.** & **Geom. Rep.** & **NGeom. Rep.** & **Intelligence** & **Interface** & **Accessibility** & **Autonomy** \\ \hline
1 & Component & No connection & No synchronization & No synchronization & No representation-geometric representation & No representation of characteristic & Descriptive (what is geometric characteristics & No interface & Single user & No autonomy (decisions and actions are executed exclusively by humans) \\ \hline
2 & Asset & & & & & & & & \\  & & Manual data flow in both ways (from physical to digital and vice versa) & & & & & & & \\  & & & & & & & & \\  & & & & & & & & & \\  & & & & & & & & & \\  & & & & & & & & & \\  & & & & & & & & & \\  & & & & & & & & & \\ \hline
3 & Asset class/group of assets & Semi-automatic in acquisition supported by automatic tools like drones, RFID, or LiDAR & Approximate one way & The necessary are partially defined by acquisition & & & & & \\  & & & & & & & & & \\  & & & & & & & & & \\  & & & & & & & & & \\  & & & & & & & & & \\  & & & & & & & & & \\ \hline
4 & System of assets & Automatic in the way & Proxise & Both the elements are uniformly as 3D object & The necessary and requirements & & & & & High autonomy \\  & & & & & & & & & & \\  & & & & & & & & & \\  & & & & & & & & & \\  & & & & & & & & & \\  & & & & & & & & & \\  & & & & & & & & & \\  & & & & & & & & & \\ \hline
5+ & System of Systems (portfolio) & Automatic in the system of both ways (digital twin) & & As-built (a verified representation of in terms of size, driver) & Cognitive representation of size, shape, location, and quantity, and orientation.) & Smart hybrid in time-to-have are integrated to cause something to & Full human-like intelligence to cause something to happen) & Limited (multiple users across the stress) & & & & \\  & & & & & & & & & \\  & & & & & & & & & \\  & & & & & & & & \\ \hline Ad. from: [82, 83] from: [82, 83] & [45, 63, 74] & [63, 74] & [63, 74] & [63, 84, 86] & [87, 88] & [63, 89, 90] & [74, 76] & [63] & [63, 70, 73, 74] \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Proposed UNI-TWIN model for assessing the LoDT. * Because cyber-physical systems possess all the characteristics of a \"true Digital Twin\" but lack the geometric representation of the physical entity, these are assigned level 1 in \"geometric representation\".
# Parallel-Based Corpus Annotation for Malay Health Documents
## 4 Corpus Malay Health Document
Furthermore, the annotated corpus serves as a starting point for future research in Malay NLP, particularly in the health domain, opening up opportunities for advancements in this field. By enabling the development of more accurate NER models for Malay health documents, the Malay Health Document Annotated Corpus can contribute to the creation of innovative healthcare technologies. These technologies can automate the analysis and interpretation of health information, leading to faster diagnosis, more personalized treatment plans, and improved patient outcomes. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Entity** & **Quantity** & **Examples** \\ \hline Penyakit (Disease) & 1431 & Barah otak, Angin ahmar, Kanser prostat, Batu \\  & & kering, Kencing manis \\ \hline Simptom (Symptom) & 186 & Loya, Sembelit, Pening, Muntah, Kejang otot \\ \hline Rawatan (Treatment) & 639 & Vaksinasi, Terapi laser, Kemoterapi, Senaman \\  & & abdomen, Pembedadhan \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Descriptions and examples for each entity type. Examples are translated from Malay. Figure 2: Example of tagging. Unlike existing NLP resources for the Malay language that focus on general text or news articles, the Malay Health Document Annotated Corpus specifically targets the healthcare domain. This makes it a specialized resource that captures the unique vocabulary, terminology, and entities found in health documents. By focusing on this specific domain, the corpus provides researchers and practitioners with a more accurate and tailored resource for developing healthcare-related NLP technologies.
## 3 Artificial Intelligence as Enabler for Adoption of Nuclear Maritime Ships
### Core Refueling Optimization  
One of the key advantages of nuclear-powered ships is their long operational period before requiring refueling. This timescale is measured in decades, and ideally, a vessel may need only one--or even no--refueling throughout its lifetime. However, for the rare occasions when refueling is necessary, AI can offer solutions to optimize scheduling, thereby reducing both downtime and associated costs.  
Nuclear-powered maritime ships will need to dock at specialized ports equipped with dedicated refueling facilities. Given the high investment costs required to modify existing ports or construct new nuclear docks--including the expenses for necessary security measures--many host countries may only be able to afford a single refueling dock. As a result, these facilities may be located far from a vessel's regular routes, requiring additional travel time. Furthermore, the strict schedules of maritime vessels--many of which will play a crucial role in sustaining global trade and supply chains--necessitate that refueling is conducted as efficiently as possible. Optimizing refueling operations not only minimizes docking time but also enhances nonproliferation safeguards.  
To that end, AI provides a comprehensive set of tools to ensure the fast and secure refueling of nuclear ships. The existing literature on conventional nuclear power plants has explored intelligent methods for determining optimal refueling patterns. Specifically, a variety of AI-based techniques have been proposed, including commonly used methods such as neural networks, fuzzy logic systems, and rule-based systems [55]. Additionally, advanced optimization techniques have been explored, such as Tabu search, simulated annealing, and particle-based optimization methods, including genetic algorithms, particle swarm optimization, ant colony optimization [56], gravitational search algorithms, and harmony search algorithms [57; 58].  
The plethora of algorithms and their demonstrated efficiency--particularly that of particle-based optimization methods--suggest that these approaches can also serve as effective solutions for optimizing the refueling of nuclear maritime ships. Notably, these intelligent algorithms must account for the physical space constraints imposed by docking facilities and the challenges of the aqueous environment.
## 2 Materials and Methods
### Data Analysis  
The Python package SpMap performed all spectral processing [21]. The data processing workflow is shown in Figure 1B. SpMap provides several pre-processing tools, such as dark noise subtraction, wavelength and intensity calibration, cosmic ray correction, and filtering of spectra below an intensity threshold. To suppress detector and probe background noise, a dark spectrum (laser off) and probe background (laser on) without samples were collected. Then, the fingerprint region from 500 to 1800 cm\({}^{-1}\) was selected. An advanced airPLS baseline correction [22] was applied to remove the remaining tissue and microscope system backgrounds. Finally, all spectra were smoothed with a Savitzky-Golay filter [23] and vector normalized.  
After preprocessing, the first step involved the use of k-means cluster analysis (KMC) in the spectral range 600-1800 cm\({}^{-1}\) to identify the main spectral signatures in each sample, such as control, tumor, necrosis, and lipid-rich tissues. KMC was also employed to reduce the size of data to a few clusters and increase the signal-to-noise ratios because each cluster centroid represents the mean of the cluster. These cluster centroids were then labeled based on the sample origin, i.e., whether they originated from a non-tumor or a tumor specimen. Hierarchical cluster analysis (HCA) was then performed in the spectral range 800-1150 cm\({}^{-1}\) to arrange the KMC centroids found in the bladder tissue into the four  
Figure 1: Dispersive Raman instrument with 1064 excitation laser (**A**) with fiber probe, microscope, and motorized stage, and (**B**) hyperspectral processing algorithm provided by SpMap.  
categories, thereby reducing the computational cost. As the distance matrix in HCA can become large and calculation of the dendrogram can become time-consuming for large data sets, similar strategies have been applied before [24].
## 2 Mobility Models in FANETs
### Group Mobility Models
#### 2.2.3 Nomadic Community  
In the Nomadic Community (NC) mobility model, every mobile node moves randomly around a certain reference point [53]. Unlike CLMN, a set of nodes shares the same space determined by a singular reference point. Figure 10 shows an example of two nodes following the path of NC. Furthermore, there are nodes in NC that share similar locations, resulting in UAV collisions. In order to partition the flying areas, in the beginning, additional limitations might be applied to the updated versions of this mobility pattern. This mobility model is easily adaptable to agricultural and multi-UAVs in military battlefield environments [54].  
Figure 8: The trajectory of FANETss using CLMN models.  
Figure 9: The trajectory of FANETs using ECR models.
# The Role of PET and SPECT Imaging in Prostate Cancer Targeted Alpha Therapy: When and How?
## 4 \({}^{223}\)Radium-dichloride (Xofigo)
### Imaging for Patients' Selection and Follow-Up after \({}^{223}\)RaCl2
#### 4.1.4. PSMA PET  
There is little evidence regarding PSMA PET and patients selection and follow-up after \({}^{223}\)Ra-Cl\({}_{2}\) treatment; PSMA PET may be useful in patients' selection for \({}^{223}\)Ra-Cl\({}_{2}\) treatment: in fact, this imaging technique may provide additional information regarding visceral metastasis, leading to a change in the therapeutic management in some cases [59; 60]. Moreover, [PERSON] et al., evaluating 63 patients who underwent \({}^{223}\)Ra-Cl\({}_{2}\) treatment, demonstrated a significant correlation between PSA changes and therapeutic response reported by PSMA-PET, thus highlighting the possible effectiveness of PSMA-PET in the evaluation of response to \({}^{223}\)Ra-Cl\({}_{2}\) treatment [61]. Figure 2 shows the case of a patient affected by mCRPC, whose overall tumor burden before \({}^{223}\)Ra-therapy was evaluated by both \({}^{18}\)F-choline and \({}^{68}\)Ga-PSMA-11 PET/CT.
## 3 Materials and Methods  
In the context of the rapid development of intelligent manufacturing, the data generated on industrial sites present the characteristics of large data volume, multiple types, and strong real-time performance, which puts forward higher requirements for data collection, processing, and transmission capabilities. At present, industrial communication protocols such as OPC UA have been widely used in the data collection of underlying devices; however, there are still challenges in multi-source heterogeneous device access, communication delay, and data integration. The traditional cloud computing model has limitations in real-time and bandwidth utilization. The introduction of edge computing effectively alleviates these problems, realizes local processing and rapid response of data, and improves the stability and intelligence level of the system. However, edge devices still need to be further optimized in terms of computing power, protocol adaptation, and collaboration with the cloud. Therefore, this study designs and implements an edge data collection and transmission framework based on OPC UA and Kafka around the high-concurrency data collection and processing requirements in intelligent manufacturing scenarios to improve the system's data processing capabilities and response efficiency and provide support for the efficient use of industrial big data.  
As shown in Figure 1, the architecture includes a data collection layer, a data processing layer, a data transmission layer, and a data application layer, which is mainly composed of PDC (Production Data Collection) components, edge-computing modules, Kafka clusters, databases, and Web front ends. At the edge node layer, the OPC UA protocol is used to collect equipment production data in a standardized manner. The edge-computing algorithm is used to process and analyze the raw data, and the results are forwarded to the distributed Kafka service cluster for persistent storage. At the storage layer, PostgreSQL is used to manage user and equipment metadata, and the Kafka cluster is used to store a large amount of process data produced by equipment to achieve scalable and reliable storage of large-scale data and to share key data through the cloud.
## 5 Security Analysis
### Heuristic Analysis
#### 5.2.10 Denial of Service (DoS) Attacks  
In the proposed protocol, an adversary might attempt to disable \(S_{j}\) by replaying old messages. However, \(S_{j}\) initially checks if the time gap meets the condition \(|T-t_{u}|>\Delta T\). If this condition is met, \(S_{j}\) immediately ends the session. Furthermore, should the adversary manipulate the timestamp \(t_{u}\) to satisfy \(|T-t_{u}|<\Delta T\), \(S_{j}\) still dismisses the session due to the subsequent failure in validating the \(V_{i}^{*}\) value, which is dependent solely on the original timestamp. Thus, such Denial of Service (DoS) attacks are rendered ineffective.
# XSShield: Defending Against Stored XSS Attacks Using LLM-Based Semantic Understanding
## 2 Background and Motivation
### Motivation  
Existing stored XSS defense mechanisms have inherent limitations, highlighting the urgency with respect to developing innovative solutions. Content Security Policy (CSP), as a declarative security measure, heavily depends on the integrity of server-side configurations for its effectiveness. Once attackers gain server access, they can tamper with or disable CSP directives, rendering the defense ineffective [15; 16; 17]. Additionally, the complexity of CSP implementation and compatibility issues limits its widespread adoption.  
Sandbox mechanisms provide protection by executing potentially malicious code in isolated environments, but their practical application faces numerous limitations. Evolving sandbox bypass techniques and limitations in sandbox defense capabilities make it difficult to provide uniform, reliable protection across different systems and browser versions [18]. Furthermore, sandboxes have limited defense scope and are vulnerable to complex side-channel attacks [19].  
Traditional filtering techniques [20; 21; 22; 23; 24; 25; 26; 27] such as input validation and output encoding are increasingly struggling to cope with modern stored XSS attacks. When servers are compromised, attackers can bypass or disable these protective measures, rendering them ineffective against persistent threats.  
These issues are particularly prominent in environments where server integrity cannot be guaranteed, urgently necessitating the redesign of defense strategies. Our research specifically focuses on detecting stored XSS attack vectors in such scenarios where server trustworthiness is compromised. While most existing XSS defense mechanisms operate under the assumption of trusted server environments, this assumption often fails to hold in real-world attack contexts.
## 2 Model Development
### Model Verification  
In order to verify the reliability of the PEM fuel cell model, the simulated data was compared with the experimental data under the same operating conditions using a single flow field PEM fuel cell, of which the length was 40 mm. The experimental conditions were as follows: the operation temperature was 160 \({}^{\circ}\)C, with no pre-humidification for hydrogen and air, the flow rate of hydrogen and air were 100 mL/min and 200 mL/min, respectively, and the back pressure was 101 kPa [30; 33]. As can be seen in Figure 3, under the high and medium cell voltage conditions, the polarization and power density curves of the simulated results matched very well with the experimental results; when the cell voltage was low, the experimental results were slightly higher than the simulated results. However, on the whole, there was good agreement between the simulation data and the experimental results. Hence, the reliability of the mathematical model was proven. In addition, grid-independency tests were conducted and the results were independent.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Value** & **Units** \\ \hline GDL porosity & 0.4 & – \\ GDL electric conductivity & 222 [31] & S/m \\ Inlet O\({}_{2}\) mass fraction (cathode) & 0.228 [31] & – \\ Inlet H\({}_{2}\)O mass fraction (cathode) & 0.023 [31] & – \\ Anode inlet flow velocity & 0.2 & m/s \\ Cathode inlet flow velocity & 0.5 & m/s \\ Anode viscosity & \(1.19\times 10^{-5}\)[31] & Pa*s \\ Cathode viscosity & \(2.46\times 10^{-5}\)[31] & Pa*s \\ Reference pressure & \(1.01\times 10^{5}\) & Pa \\ Cell temperature & 433.15 & K \\ Hydrogen reference concentration & 40.88 [31] & mol/m\({}^{3}\) \\ Oxygen reference concentration & 40.88 [31] & mol/m\({}^{3}\) \\ Membrane conductivity & 9.825 [32] & S/m \\ \hline \hline \end{tabular}
\end{table}
Table 2: Key physiochemical parameters used in the simulation.
# Object-Tracking Algorithm Combining Motion Direction and Time Series
## 4 Experimental Results and Analysis  
### Datasets  
The proposed algorithm is evaluated on four public datasets.  
LaSOT [32]. LaSOT is a large-scale long-term single-object-tracking dataset that comprises 1400 video sequences spanning 70 categories and a minimum of 1000 frames per  
Figure 3: Tracking result scoring module (TRSM). TRSM consists of three stages: stage 1 focuses on extracting features from both history and tracking results; stage 2 processes time series based on attention mechanism; and stage 3 calculates confidence value by fully connected network (FCN).  
sequence, with an average frame count of 2500. Evaluation of LaSOT is performed using success rate and accuracy rate.  
TrackingNet [33]. TrackingNet is a large-scale short-term object-tracking dataset with over 30,000 video sequences and a test set comprising 511 video sequences that feature a diverse range of target classes. To mitigate the influence of image resolution and target box size, TrackingNet adopts normalized accuracy and success rate for evaluation, with test results requiring official server evaluation.  
GOT-10K [34]. GOT-10K is a comprehensive object-tracking dataset with a collection of 10,000 video sequences featuring 563 categories. Evaluation of GOT-10K is conducted on an official server, with algorithm performance measured using average overlap and success rates.  
VOT2020 [35]. VOT2020 is a dataset specifically designed for the VOT (Visual Object Tracking) Challenge, containing 60 video sequences featuring various challenging scenarios, such as fast motion and occlusion. Expected average overlap (EAO) is employed to measure algorithm performance on the VOT2020 dataset.
# Harnessing Digital Twins for Sustainable Agricultural Water Management: A Systematic Review
## 2 Background
### DT Architecture
#### 2.3.1 Real-Time Monitoring  
The adoption of smart technologies in agriculture is on the rise [46], transforming farms into systems that operate intelligently. These systems use a variety of tools to sense and monitor environmental conditions and manage farming equipment effectively [47]. Agricultural water management can be categorized into monitoring and control strategies. Monitoring strategies include soil moisture, weather, and vegetative parameters, and control strategies include open-loop and closed-loop methods [48]. The open-loop method involves a predetermined schedule or set of rules for irrigation, without real-time feedback from the environment, while the closed-loop method, on the other hand, involves the monitoring and dynamic adjustment of irrigation based on continuous feedback from sensors [49]. Designing DTs is among the most sophisticated approaches for creating an intelligent system for monitoring and control [50]. As discussed above, a DT is a virtual representation of physical objects, so DTs enable real-time interaction between a physical object and its digital counterpart. Modifications in the physical object are automatically reflected in the digital model, and vice versa [51].