esa-sceva/satcom-chunk-collection huggingface dataset:
# Effect of Target Size, Location, and Input Method on Interaction in Immersive Virtual Reality
## 5 Discussion
### Influence of Target Position  
The target position is also a factor that has a significant influence on the task completion time. Figure 8 shows that the time taken to select the targets close to the center was short and that the targets at the edges took much time. These results also seem to have affected the viewing angle. In this experiment, the FOV in which the targets were located was 127\({}^{\circ}\), larger than the viewing angle provided by the HMD (96\({}^{\circ}\)). Therefore, not all targets were visible at once. However, targets located near the center were mostly exposed. It is therefore assumed that the centrally located targets were able to select relatively sooner than the target located at the edge. This is consistent with the results of other studies comparing the interaction between users and devices. Therefore, it is suggested that targets should be placed near the center when the input task requires quick selection in a virtual environment. For computer work using a mouse, because the edge of the screen is a boundary, pointing can be performed without finely adjusting the position of the pointer. However, the utility of the edge is relatively lower because all spaces can be utilized without a VR boundary.  
In this study, it was found that the probability of choosing the wrong target was not related to the target's position. When the user selected the button on the touchscreen by hand, most errors occurred when the button was located at the lower-right, and the fewest errors were found at the upper-left [59]. In the case of controlling the cursor via head tracking, it is expected that the target location may have less influence on the ER than when using the hand.
# Interference Signal Feature Extraction and Pattern Classification Algorithm Based on Deep Learning
## 3 Interference Monitoring Research
### Interference Signal  
Interference signal is a broad concept, which is usually the general name of various signals harmful to useful signals. Generally, interference signals can be simply divided into human interference and non-human interference. Non-human interference is mainly determined by the communication environment. It is relatively stable in a certain area and a certain period of time, and the power is small and easy to suppress, which will not be studied by this paper. Human interference has great randomness, and the interference power is greater than that of non-human interference. Additionally, the interference frequency band is more targeted, which has a serious impact on communication.  
The influence of interference signals on communication is different, and the frequency characteristics and time characteristics of different interference signals may also be different. According to the relative size of the spectrum width of interference and the spectrum width of spread spectrum signal, the interference in satellite communication systems can be divided into narrowband interference and wide band interference. The narrowband interference mentioned here refers to the interference signal whose bandwidth is far less than that of the spread spectrum signal, including continuous wave interference and other possible narrowband digital interference. Broadband interference refers to those interference signals whose bandwidth is comparable to that of spread spectrum signals, including continuous wave interference, linear frequency modulation interference, pulse interference, broadband comb-spectrum interference, frequency hopping interference and other spread spectrum signals [20; 21].
## 4 Challenges and Solutions
### Environmental and Cost Considerations
#### 4.4.3 Conductive Fillers  
The high cost of carbon-based conductive fillers stands out as another primary drawback of multifunctional cementitious composites [131]. Nonetheless, when considering the life cycle cost analysis of these composites, the expense associated with carbon-based conductive fillers can be offset by the low maintenance costs and prolonged service life of infrastructures facilitated by timely and ongoing structural health monitoring. Additionally, carbon-based conductive fillers offer the potential for recycling waste materials [217]. For instance, various coal-based carbon materials can be synthesized using different techniques, as illustrated in Figure 21[218].  
Recycling waste as conductive fillers presents a viable solution to justify the cost of self-sensing cementitious composites and promotes environmental sustainability [219]. To address this, [PERSON] et al. [156] partially replaced CNFs with recycled milled carbon fibers (RMCFs). The study revealed a cost saving of approximately USD 1.99/g by incorporating RMCFs instead of CNFs, signifying notable budget savings and enhanced environmental sustainability. The optimal conductive filler content, resulting in the high piezoresistivity performance of multifunctional cementitious composites, was achieved with 1.5% CF (33.3% RMCFs + 66.7% CNFs).  
[PERSON] et al. [220] demonstrated the effectiveness of conductive rubber fiber in fabricating multifunctional cementitious composites, with a percolation threshold of 0.96-1.6 vol.% for achieving ideal piezoresistivity performance. In another study, [PERSON] et al. [221] integrated shungite, a rock mining waste containing 34.3 wt.% carbon, into cementitious composites as an alternative to carbon-based conductive fillers, aiming to reduce costs and address dispersion challenges. Incorporating 20 vol.% shungite resulted in the stable piezoresistivity performance of cementitious composites.  
Additionally, the price of CNMs varies depending on their type, fabrication technique, purity, and number of layers [222]. For example, MWCNTs are 4-6 times cheaper than single walled carbon nanotubes (SWCNTs), making them widely utilized for enhancing the mechanical and piezoresistivity properties of self-sensing cementitious composites [8]. In addition, the performance of the multifunctional cementitious composite containing 0.1 vol.% CFs and 0.5 vol.% MWCNTs was found to be comparable to that of the cementitious composite with 1 vol.% MWCNTs, while simultaneously reducing the cost by 50% [223]. This finding indicates that the use of hybrid conductive fillers is an effective strategy for achieving stable electrical signals while significantly lowering costs.  
Figure 21: Coal-based conductive materials and their production techniques [218], published by Elsevier (2022).
# Landsat Surface Product Validation Instrumentation:
## 5 Mirror Empirical Line Method (MELM)
### UAS Experiment
The difference in reflectance can be attributed to an inaccurate reflectance estimate of the black felt panel used as background for the mirrors. The background subtraction step is done by averaging the uniform dark background pixel radiance response around each target window (blue and red box in Figure 16) and subtracting that value from each pixel within the window to isolate the two-point calibration reference radiance form the reference targets. Thus, the background signal is simply an additive term to the scene reflectance that can be subtracted out when using the MELM technique [26] to isolate the calibration target signal in the image analysis. A strong BRDF component in the black felt can therefore account for the reflectance bias, though the spectral shapes were consistent. The BRDF bias can be the result because the drone image collection of the red felt panel (producing the MELM-derived spectrum shown in Figure 17, black curve on right) and the ASD spectroradiometer measured \"ground truth\" spectral reflectance factor collection were recorded at different times, so the illumination and view geometries were significantly different. Figure 16: (**Left**) RGB composite image of the targets of interest. (**Right**) Ground photo of the targets. The colored boxes highlight the targets between the aerial and ground images.
# A Dynamic Surface Gateway Placement Scheme for Mobile Underwater Networks +
## 4 PADP Algorithm Description
### Problem Formulation  
This section formulates the gateway deployment problem as a linear optimization problem, which can be solved with the branch-and-cut method. The branch-and-cut method is widely used for solving mixed integer programming (MIP) problems. In underwater sensor networks, the number of transport nodes and water gateway nodes in the first layer is limited, so we use the branch-and-cut method because of its low computational complexity.
# Segment-Based Clustering of Hyperspectral Images Using Tree-Based Data Partitioning Structures
## 2 Methodology
### K-Means Clustering
The set is defined to be a set of center points that can be the nearest neighbor for some pixel within the bounding box of a tree node. It follows that the candidate centers for the root node consist of all \(k\) centers. For a tree node \(P_{R}(i)\), the closest candidate center \(u^{*}\in U\) to the midpoint of the bounding box is found during clustering. The closest centre search involves the computation of the Euclidean distances. The remaining \(u\in U\setminus\{u^{*}\}\) is pruned to the following node down the tree if no part of \(C\) is closer to \(u\) than the closest center \(u^{*}\), since it is not the nearest center to the pixels within \(C\). If \(P_{R}(i)\) is an internal node, its left and right children nodes are examined. The center update occurs if a leaf is reached or only one candidate remains. ```
function Filter (RootTree \(P_{R}\), CandidateSet \(U\)) { \(U\gets P_{R}.U\) if(\(P_{R}\) is a leaf) { \(u^{*}\leftarrow\) the closest point in \(U\) to \(P_{R}.data\_values\) \(u^{*}.wgtCent\gets u^{*}.wgtCent+P_{R}.data\_values\) \(u*.count\gets u^{*}.count+1\) } else{ \(u^{*}\leftarrow\) the closest point in \(U\) to \(C\)'s midpoint for all(\(u\in U\setminus\{u^{*}\}\)) do if\(u.\)isFarther(\(u^{*},C\)) then\(U\gets U\setminus\{u\}\) endfor if(\(|U|=1\)) { \(u^{*}.wgtCent\gets u^{*}.wgtCent+P_{R}.wgtCent\) \(u*.count\gets u^{*}.count+P_{R}.count\) } else{  Filter(\(P_{R}.left\), \(U\))  Filter(\(P_{R}.right\), \(U\)) } }
```  
**Algorithm 1** The filtering algorithm introduced in [30]. The _isFarther_ function in Algorithm 1 checks whether any point of a bounding box \(C\) is closer to \(u\) than to \(u^{*}\). The function returns true if the sum of squares difference between the candidate \(u\) and closest candidate \(u^{*}\) is greater than 2\(\times\) the sum of squares difference between either boundary points (high or low) and the closest candidate center \(u^{*}\). On termination of the filtering algorithm, center \(u\) is moved to the centroid of its associated points, which is, \(u\gets u.wgtCent/u.count\), resulting in the final cluster map.
# CFD Based Non-Dimensional Characterization of Energy Dissipation Due to Verticle Slosh
## 5 Scaling of Violent Sloshing Systems  
To provide a perfect scaled experiment requires complete mechanical similarity with the full scale system. This implies geometric, kinetic and dynamic similarity (identicalforce ratios for all terms in Equation (3)). While it is possible to achieve geometric and kinetic similarity, ensuring complete dynamic similarity is not practically feasible. This is as it requires identical non-dimensional numbers for all flow physics related ratios (\(\bar{\rho}\), \(Re\) etc.) which is impossible using available fluids. As a result, experimental similarity of a single non-dimensional number is then targeted in industry, which in the case of slosh is the Froude number. Scaled slosh experiments are therefore designed by ensuring similar Froude numbers between experimental and real systems.
# An Integrated Safety Monitoring and Pre-Warning System for Fishing Vessels
## 3 Materials and Methods
### Hardware System Structure  
Figure 2 displays the hardware schematic for the intelligent monitoring and early warning system, which was installed on the fishing vessel. The hardware included a sensing terminal (consisting of the GPS module, nine-axis sensor, temperature, and humidity sensors), power supply equipment, a monitoring camera, and four servers. The sensing terminal collects position, temperature, humidity, and vessel attitude (roll, pitch, and yaw angles). The power supply equipment powers the sensing terminal. To provide comprehensive coverage, the surveillance camera should be installed to face the vessel's forward direction. Servers save and process the data collected from the sensing terminal and then transmit the warning information to the vessels' crews.
# Journey Planning Algorithms for Massive Delay-Prone Transit Networks +
## 2 Background
Then journey \(J^{\prime\prime}\) is time-dominated by journey \(J^{\prime}\) if and only if both the following conditions hold:_  
* _the departure time of the first connection of_ \(J^{\prime}\) _is larger than the departure time of the first connection of_ \(J^{\prime\prime}\)_;_
* _the arrival time of the last connection in_ \(J^{\prime}\) _is smaller than the arrival time of the last connection in_ \(J^{\prime\prime}\)_._  
By the above, if we let \(\mathcal{J}\) be the set of all journeys connecting two stops \(s_{i}\) and \(s_{j}\) in a transit network, then trivially a journey \(J\in\mathcal{J}\) is non-time-dominated if and only if either one of the two following conditions hold: (i) the departure time of the first connection of \(J\) is larger than the departure time of the first connection of all other journeys in \(\mathcal{J}\); (ii) the arrival time of the last connection of \(J\) is smaller than the arrival time of the last connection of all other journeys in \(\mathcal{J}\). Hence, we define a profile query \(\textsc{pq}(s_{i},s_{j},\tau,\tau^{\prime})\) as the one that asks for the set of non-time-dominated journeys between stops \(s_{i}\) and \(s_{j}\) in the time range \(\langle\tau,\tau^{\prime}\rangle\), subject to \(\tau<\tau^{\prime}\), that is, the set of journeys connecting stops \(s_{i}\) and \(s_{j}\) that start at any time in \([\tau,\tau^{\prime}]\) and are non-time-dominated journeys. Finally, we define a multi-criteria query \(\textsc{mc-ea}(s_{i},s_{j},\tau)\) as the one asking to compute the set of Pareto-optimal journeys. Informally, such journeys simultaneously optimize more than one criterion (e.g., traveling time and number of vehicle transfers), departing in \(s_{i}\) at some time \(t\geq\tau\geq 0\) and arriving at stop \(s_{j}\). More precisely, given a set of criteria, a journey is in the Pareto-optimal set \(S\) if it is non-dominated by any other journey. A journey \(J_{1}\) dominates a journey \(J_{2}\) if it is better with respect to every criterion, while it is non-dominated otherwise. Note that, most commonly considered optimization criteria are traveling time and number of vehicle transfers, although other optimization can be found in the literature, for example, monetary cost. It is long known that the problem of computing the mentioned Pareto-optimal set is (weakly) NP-hard [25], since such journeys can be exponential in number. However, if some degree of importance of the optimization criteria is imposed then the problem is polynomially solvable, by using a simple multi-criteria modification of the [PERSON]'s algorithm, based on lexicographical optimality [25]. An example of this scenario is when one wants to compute the set of quickest journeys between two stops \(s_{i}\) and \(s_{j}\) and then, among them, to choose the one minimizing the number of transfers between vehicles. In this paper, we focus on this latter realistic variant of the journey planning problem. As a final remark, observe that profile queries are a special case of multi-criteria ones using arrival and departure times as criteria.
# Beyond Prompt Chaining: The TB-CSPN Architecture for
## 5 Implementation Walkthrough
### Agent Hierarchy Implementation
#### 5.3.1 Consultant Agent Architecture  
Consultant agents transform unstructured input into semantically annotated tokens through a multi-stage process, as implemented in our financial news processing system available in the repository:  
* _Input Processing_: CSV-based news ingestion with company-specific grouping for contextual analysis.
* _LLM-based Topic Extraction_: Structured prompting of large language models (GPT-4) to identify financial topics and assign relevance scores based on potential market impact.
* _Topic Aggregation_: Optional LLM-driven consolidation of semantically similar topics to reduce dimensionality while preserving semantic richness.
* _Token Generation_: Creation of structured tokens containing topic distributions, metadata, and traceability information.  
The consultant implementation demonstrates TB-CSPN's ability to leverage modern LLM capabilities while maintaining structured coordination through explicit topic representation rather than implicit prompt chaining.
## 6 Conclusions  
This paper proposed a RAIM satellite selection method for the developed 3D building model based positioning method. This RAIM method is capable of excluding the measurements that are inconsistent with the simulated pseudorange measurements by the 3D building model and ray-tracing technique. These inconsistent pseudorange measurements usually suggest that the received raw pseudorange measurement is a double or multiple reflected NLOS signals or has strong multipath effects, as shown in the experimental results. By excluding the abnormal NLOS signals, the lateral positioning error mean of the proposed 3D map pedestrian positioning method can be reduce from about 3.97 to 2.96 m and 8.78 to 3.85 m in the middle and deep urban canyons, respectively. In addition, the RAIM satellite selection method can also reduce about 8.4% and 36.2% of the positioning solutions with large errors (points that estimated the wrong side of the road) in the middle and deep urban canyon environments, respectively. These results indicate that the proposed RAIM method is more efficient in the deep urban canyon because double reflected NLOS are more frequently observed. Finally, the lateral positioning errors are less than 3 m in around 60% of the solutions of the proposed 3D building model based positioning method in both middle and deep urban canyons.
## 3 Results and Discussion
### Determination of ASNase Isoelectric Point  
The \(pl\) is the pH of a solution at which the net charge of the enzyme becomes zero. Thus, the \(pl\) determination allows verifying the enzyme charge at a given pH value, an important parameter for the enzyme's immobilization. When the solution pH value is equal to \(pl\), the enzyme shows an absence of inter-particle repulsive forces, being the enzyme more compact and hydrophobic and, consequently, less soluble and stable [37]. Thus, the \(pl\) determination by isoelectric precipitation consists in varying the pH of a highly concentrated solution until protein precipitation. The knowledge of ASNase \(pl\) and MWCNTs \(pH_{PZC}\) is fundamental for understanding the type and forces of interaction between the support and the enzyme after immobilization [13].  
The accession number in the UniProt database of the commercial ASNase from _E. coli_ used in this work is P00805, meaning that it has about 348 amino acids and a mass of 139 kDa. The corresponding protein accession number was transferred to the ExPASy ProtPram tool, which estimated that the ASNase theoretical \(pl\) is around 5.66. However, this estimate may have some errors associated with ASNase quaternary structure and the probable methylation, deamidation reported [38] and post-translational modifications not accounted for in the ProtPram model used. Several authors reported an ASNase \(pl\) of 4.9 [39, 40], ranging between 4.6 and 5.1, accounting for different mutations [41], which is somewhat different from the estimated theoretical \(pl\).  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & \multicolumn{2}{c}{\(S_{BET}\) (\(\pm\)5 m\({}^{2}\)/g)} \\ \cline{2-3}  & MWCNT & F-MWCNT \\ \hline
**MWCNT-10** & 350 & 408 \\
**MWCNT-1020** & 104 & 123 \\
**MWCNT-2040** & 67 & 85 \\
**MWCNT-60100** & 30 & 33 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Brunauer-Emmett-Teller specific surface area (\(S_{BET}\)) of pristine and functionalized multiwalled carbon nanotubes.  
In this work, the ASNase \(pl\) was determined using a method based on zeta potential, where the enzyme \(pl\) corresponds to the pH value where the zeta potential equals zero [37]. Figure 1 shows that the ASNase \(pl\) is around 5.2, close to the values reported in the literature [42]. The slight differences can be explained using different methods or ionic environments, such as ionic strength and ion type [37].
# A Survey on Robust Video Watermarking Algorithms for Copyright Protection
## 2 Basic Models of Video Watermarking
### Watermark Generation
#### 2.1.1 Arnold Transform  
Arnold transform [13], also called cat map, can be seen as a combination process of stretching, compressing, folding, and splicing. Through this process, watermark information can be scrambled, which makes the original meaningful watermark become meaningless. Arnold transform has periodicity, that is, if it is continuously carried out on the image, finally the original image can be obtained. The period of transform is related to the size of the image. For watermark images with size of \(N\times N\), the definition of Arnold transform can be expressed as:  
\[\left[\begin{array}{c}x^{\prime}\\ y^{\prime}\end{array}\right]=\left[\begin{array}{cc}1&a\\ b&ab+1\end{array}\right]\left[\begin{array}{c}x\\ y\end{array}\right]\text{mod}(N), \tag{1}\]  
where \(\left[x\,y\right]^{\text{T}}\) represents the pixel coordinate in original image; \(\left[x^{\prime}\,y^{\prime}\right]^{\text{T}}\) denotes the transformed pixel coordinate; and \(a\), \(b\), and \(N\) are all positive integers. In general, the period \(P\) of transform is related to the size of \(N\): \(P\) increases with the increase of \(N\). This transform not only is a reversible transform, but also has good effectiveness and is easy to implement. However, its recovery time is long.
# Factors Affecting Implementation of Radiological Protection Aspects of Imaging in Radiotherapy
###### Abstract  
Dramatic improvements in radiotherapy equipment have allowed radiation fields to be conformed to tumours for more accurate treatment. Successful delivery often requires imaging at every treatment fraction, a method known as image guided radiation therapy (IGRT). But increased X-ray imaging exposes patients to doses that carry risks of inducing second cancers in normal tissues. Therefore, reductions in high-dose treatment margins achieved with IGRT must be balanced against detrimments from greater imaging doses. ICRP Task Group 116 has been set up to prepare guidance on radiological protection aspects of IGRT. Factors affecting the optimization of radiological protection are the modalities used, the frequency of imaging, the image acquisition parameters influencing image quality and radiation dose, and the volume of normal tissue included in the images. The Task Group has undertaken two projects: (1) a snapshot survey of radiotherapy imaging practices across six continents, which has shown that use of kV cone beam CT (CBCT) increases with Human Development Index for the country; and (2) a project looking at ways for measuring CBCT doses that could be applied more widely. The results highlight the need for raising awareness of imaging doses, and development of the dose quantities displayed on imaging equipment used in radiotherapy.  
image guided radiation therapy; cone beam CT; radiotherapy planning; imaging in radiotherapy; imaging dose