esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Radiation dose management of medical staff has become increasingly important. Particularly, based on the statement by the International Commission on Radiological Protection (ICRP) in 2011, a new lower equivalent dose limit for the eye lens is being established in each country. Although many reports have discussed the occupational radiation dose in interventional radiology (IR), few studies have examined the dose during computed tomography (CT) examinations. This study investigated the radiation dose exposure to medical staff present in the CT room during irradiation, with particular focus on the exposure to eye lens. The radiation dose exposure to those who assist patients during head, chest and upper abdomen CT examination was measured in a phantom study. The radiation dose exposure with scattered radiation was never negligible (i.e., high): \(H_{\text{p}}(3)\) was the highest in head CT examination, at 0.44 mSv per examination. Furthermore, the shielding effect of radiation protection glasses was large, and radiation protection glasses are useful tools for the medical staff who are involved in CT examinations. The justification and optimisation should be carefully considered in assistant actions.  
accuational exposure; eye lens; computed tomography; scattered X-ray; patient assistance 22 March 202110 May 20  
Many studies have investigated the occupational radiation dose in interventional radiology (IR), wherein medical staff may be exposed to considerable scattered X-rays. However, few studies have discussed occupational radiation dose in CT examinations [1; 2; 3; 4; 5; 6; 12]. Our literature search did not reveal any report that directly investigated the radiation dose of eye lens as \(H_{\text{p}}\)(3). Moreover, radiation protection glasses (Pb glasses) are useful for reducing the radiation exposure to eye lens; the shielding effect of the light type of Pb glasses (0.07 mmPb) in IR is about 60% [13; 14; 15]. Pb glasses may also need to be worn by the medical assisting staff who are present in the room during CT examination. The tube voltage used in CT examination is generally fixed at 120 kV [16] that is typically higher than that in IR [17]. With an increase in the photon energy, the shielding effect of Pb glasses decreases [18]. Therefore, the protective effect of Pb glasses in CT needs to be determined.  
This phantom study aimed to measure \(H_{\text{p}}\)(3) to the eye with or without Pb glasses and \(H_{\text{p}}\)(10) and \(H_{\text{p}}\)(0.07) to the trunk inside or outside the radiation protection apron for the assisting staff during CT examination.
## 3 Proposed Work
### Network Slice Request Model  
The NSR of user \(i\) at time \(t\) is represented as \(NSR_{i,t}=NSR^{e}\,\cup\,NSR^{m}\,\cup\,NSR^{u}\) with eMBB, mMTC, and uRLLC slice requests denoted by \(NSR^{e}\), \(NSR^{m}\), and \(NSR^{u}\), respectively. For each slice request \(NSR_{i,t}\), the user requests a specific amount of computing capacity \(C_{NSR}\) (number of CPU cores) and link capacity \(B_{NSR}\) (MHz) from the network based on the service or application they use. Additionally, each \(NSR\) includes the number of nodes \(N_{NSR}\), a minimum transmission delay \(D_{NSR}\) (milliseconds), the number of physical links (either wired or wireless) \(L_{NSR}\), and the request lifetime \(T_{NSR}\) (seconds) as request parameters. For example, we denote the uRLLC NSR for a user \(U_{i,t}\) as \(NSR^{u}{}_{i,t}=(C^{u}{}_{NSR},B^{u}{}_{NSR},N^{u}{}_{NSR},D^{u}{}_{NSR},L^{u}{ }_{NSR},\,\,T^{u}{}_{NSR})\). Furthermore, based on this model, the network will be able to identify the resources required by a user for each NSR and handle such requests by allocating the necessary resources through a network slice.  
Figure 1: Proposed Deep Contextual Bandit 5G Network Slicing Architecture.
## 3 Measuring System, Method, and Scope of Research
### Scope of Research  
The tests were carried out for SF12100-CF tapes with silver layers of 4 \(\upmu\)m and 2 \(\upmu\)m. The HTS tapes were subjected to test current impulses (corresponding to the value of the prospective short-circuit current) with a duration of 0.2 s. The research was performed for 8 values of the prospective short-circuit current in the range from 675 A to 1170 A.  
The research included:  
* Determination of the values characterizing the HTS tapes in the process of limiting the prospective short-circuit current (\(I_{0 max}\), \(I_{min}\)) and changes in the critical current (\(I_{C}\)) value occurring as a result of multiple transitions of the tapes from the superconducting state. These values are important from the point of view of cooperation between SFCL and system protection;  
Figure 7: Waveforms of current and voltage for the HTS tape: (**a**) sample in the superconducting state; (**b**) sample in the resistive state.  
Determination of the voltage (\(U_{max}\)) and energy E released in the HTS tapes during the operation of the test current pulse for different values of the prospective short-circuit current;
* Testing changes in quantities that characterize HTS tapes in conditions of multiple exposures to test currents;
* Determination of the safe range of voltage drops per unit length for both tapes for which the degradation of HTS tapes does not occur or is small;
* The above issues are important for determining the required length of the HTS tape in SFCL, operating at a specific rated voltage, so that the voltage drops on the HTS tape do not exceed the permissible values. Additionally:
* Microstructural tests showed changes in the HTS tape due to the current impulses test. The cross-sectional surfaces of the tapes were examined using an atomic force microscope (AFM) and an optical microscope (OM).
## 7 Conclusions  
This paper has proposed a short-block LDPC coding system for underwater acoustic spread-spectrum communication that is highly adaptable to the short-frame data transmission process. Simulation experiments and pool experiments have verified the effectiveness and practicality of the proposed method in this paper. Simulation analysis shows that the proposed direct spread coding system can achieve a coding gain of up to 7.2 dB in BER compared with the direct spread non-coding system. The proposed M-ary spread coding system and CSK spread coding system can achieve coding gains of up to 6.94 dB and 5.64 dB, respectively, compared with their corresponding spread non-coding systems. Pool experiment results also show that the proposed method has a better BER performance compared to conventional coding methods in underwater acoustic communication systems. This method has good application prospects in the field of underwater acoustic communication.  
Conceptualization, Z.Z.; methodology, Z.Z. and Z.S.; software, Z.Z.; validation, Z.Z. and Z.S.; formal analysis, Z.S.; investigation, Z.Z.; resources, Z.Z.; data curation, Z.Z.; writing--original draft preparation, Z.Z. and Z.S.; writing--review and editing, Z.Z. and Z.S.; visualization, Z.S.; supervision, Z.S.; project administration, Z.Z.; funding acquisition, Z.S. All authors have read and agreed to the published version of the manuscript.  
The work was supported in part by the Open Foundation of the Key Laboratory of Underwater Information and Control of Grant no. J2322048.  
The data presented in this paper are available after contacting the corresponding author.  
The authors would like to thank the anonymous Reviewers for their careful reading and valuable comments.  
The authors declare no conflict of interest.
## 1 Introduction  
Real-time strategy (RTS) games like _StarCraft II_ (Blizzard Entertainment, Irvine, CA, USA) represent dynamic and complex environments where players must make quick and precise decisions under uncertainty. These games are widely recognized as ideal platforms for researching advanced artificial intelligence (AI) techniques due to their high dimensionality, partial observability, and the need for long-term strategic planning [1, 2]. However, even though AI for RTS games has come a long way, existing systems often have two major problems: (1) they are difficult to understand, and (2) they cannot generalize to situations that have not been well studied [3].  
Traditional recommendation systems based on deep neural networks often fail to provide transparent reasoning behind their decisions, even though they are powerful in capturing complex patterns. This \"black-box\" nature limits their applicability in strategic environments where understanding the causal relationships between actions and outcomesis crucial [4; 5]. For instance, a neural network might recommend an aggressive strategy based on historical data, but without understanding why this strategy works, it cannot adapt to novel situations or provide actionable insights to players.  
To address these limitations, we propose a hybrid system that integrates causal models with deep neural networks. The causal model, which was made with PyMC v5.7.2, clearly shows how important factors like resources, military units, and strategic decisions are connected [6]. Then, these causal insights are put together with the pattern recognition power of a deep neural network built in PyTorchv2.1.0 to make recommendations that are both correct and easy to understand [7]. Furthermore, we use a Conditional Tabular Generative Adversarial Network (CTGAN) to generate synthetic data, which helps when there is not enough real data in certain strategic scenarios [8].  
This work contributes to the growing body of research that combines causal inference with machine learning to enhance decision-making in dynamic environments [9; 10]. By integrating causal knowledge, our system improves recommendation accuracy, highlighting the potential of hybrid approaches for strategic decision-making in complex scenarios like _StarCraft II_.
# Efficient High-Dimensional Kernel k-Means++ with Random Projection
## 3 Our Method
### Kernel k-Means with Random Projection
2 do
3 for\(i\in 1,\ldots,n\)do
4 for\(j\in 1,\ldots,k\)do
5\(dist_{i,j}=k(\mathbf{x}_{i}^{proj},\mathbf{x}_{i}^{proj})+k(\mathbf{c}_{j}^{ proj},\mathbf{c}_{j}^{proj})-2k(\mathbf{x}_{i}^{proj},\mathbf{c}_{j}^{proj})\)
6 end for
7 Assign \(\mathbf{x}_{i}^{proj}\) to \(C_{j}\), when \(min(dist_{i,j}\in dist_{i,1,k})\)
8 end for
9 for\(i\in 1,\ldots,k\)do
10\(\mathbf{c}_{i}^{proj}=mean(C_{i}^{proj})\)
11 end for
12 whileany of \(\mathbf{c}_{1},\mathbf{c}_{2},\ldots,\mathbf{c}_{k}\) is changed;
```  
**Algorithm 1**Kernel k-means with random projection. Since we propose to use random projection for both the center points initialization and clustering, the same projected matrix in a lower-dimensional space is used as input to both the k-means++ and kernel k-means. However, for kernel methods with random projection, the dot product is approximated like the approximated Euclidean distance using the JL lemma for the preservation of the point-wise Euclidean distance. In [2], an improved upper-bound on the error of the dot product with random projection is provided. Thus, to justify the upper bounded error of random projection for kernel k-means++ using both k-means++ and kernel k-means, the JL lemma and also the improved bound for kernel methods from [2] are considered. In addition, in [26], it has been shown that initial values from k-means++ with the Euclidean distance could also improve the kernel k-means clustering performance measured by within-cluster-sum-of-squares (WCSS) and the time for convergence during training. In [27], it is also shown that the selection of k-means centroids in the instance space provides a good estimate of the kernel k-means centroids with the Gaussian kernel. It is computationally cheaper than the initialization performed in the kernel feature space.
## 3 Test Results and Analyses
### \(d_{m}\)/\(D_{h}\) and \(D_{s}\)/\(D_{h}\) Analyses of Test Results for \(N_{c}\)
The discount equation is shown below in Equation (4):  
\[y=-0.38d_{m}/D_{H}+1.01 \tag{4}\]  
where \(N_{c,max}\) represents the maximum mean value of \(N_{c}\); and y represents the reduction factor of \(N_{c,max}\). Therefore, \(N_{c}\) is calculated as a function of the helical diameter ratio (\(d_{m}\)/\(D_{H}\)), as shown in Equation (5):  
\[N_{c}=N_{c,x}\times(-0.38d_{m}/D_{H}+1.01) \tag{5}\]  
where \(N_{c,x}\) is the formula for calculating the \(N_{c}\) to the helical diameter ratio. Figure 10: Average Nc of the shaft-to-plate diameter ratio test. Figure 9: Curve of the relationship between the uplift bearing capacity coefficient and the depth to diameter ratio. As shown in Figure 9, the interval in which \(N_{c}\) increases with the depth-to-diameter ratio (\(H\)/\(D_{H}\)) between \(0.5\leq H\)/\(D_{H}\leq 4\) is termed the change interval. The convergence of \(N_{c}\) values to a constant in the interval \(H\)/\(D_{H}\geq 4\) is referred to as the stabilization interval. The average slope of the four diameter ratios in the change interval is 1.7. Equation (6) represents the hypothetical variation of \(N_{c}\) in the change interval. \[Y=1.7H/D_{H}+d \tag{6}\]  
where \(Y\) represents the formula for \(N_{c}\) in the change interval, and d represents the intercept of this interval. As shown in Figure 9, the value of intercept \(d\) varies with different diameter ratios, and the relationship between \(d\) and the diameter ratio is provided in Equation (7) below:  
\[d=-4.85D_{S}/D_{H}+4.67 \tag{7}\]  
Therefore, \(N_{c}\) is calculated as a function of the diameter ratio (\(D_{S}\)/\(D_{H}\)), as illustrated in Equation (8) below:  
\[N_{c,m}=\min(N_{c,\max},1.7H/D_{H}-4.85D_{S}/D_{H}+4.67) \tag{8}\]  
where \(N_{c,m}\) represents the formula for calculating \(N_{c}\) as a function of the diameter ratios, and \(N_{c,max}\) represents the maximum value of \(N_{c}\) for the four diameter ratios at the critical depth-to-diameter ratio. In summary, the calculation formula for the combined shaft-to-plate diameter ratio and diameter is presented in Equation (9) below:  
\[N_{c,m}=\min(N_{c,\max},1.7H/D_{H}-4.85D_{S}/D_{H}+4.67)\times(-0.38d_{m}/D_{H} =1.01) \tag{9}\]  
where \(N_{c}\) accounts for the comprehensive consideration of both the shaft-to-plate diameter ratio and the diameter ratio in the calculation formula.
# Data Quality Measures and Efficient Evaluation Algorithms for Large-Scale High-Dimensional Data
## 2 Related Work
### Classifier-Based Approaches  
[PERSON] et al. [15] proposed a classifier-based quality measure called an intrinsic dimension, which is the minimum number of solutions for certain problems. For example, in a neural network, the intrinsic dimension is the minimum number of parameters to reach the desired prediction performance.  
The method has a benefit that it can be applied to many different types of data as long as one has trainable classifiers for the data; however, it often incurs high computation cost since it needs to change the number of classifier parameters iteratively during data quality evaluation.  
Overall, the existing data quality measures are mostly designed for binary classification in low dimension spaces with a small number of data points. Due to their computation complexity, they tend to consume large amount of time when applied to large-scale high-dimensional data. In addition, the existing measures tend to focus only on the inter-class aspects of data quality. In this paper, we propose two new data quality measures suitable for large-scale high-dimensional data resolving the above mentioned issues.
## 3 Background
### The CA Algorithm Used to Handle Dynamic Trustee Profiles  
In this subsection, we present the original version of the CA algorithm, as introduced in [7], including a brief description and the pseudocode, for the sake of completeness and to facilitate comparison with the updated version, presented in Section 4.2 of this work. When a trustor identifies a new task to be executed by a trustee, it broadcasts a request message containing relevant task details (lines 2-3). Upon receiving this request, each potential trustee stores it in a list and establishes a new connection with the requesting trustor if one does not already exist (lines 4-12). Lines 6-11 address the cold start problem, which arises when a trustee lacks prior experience with a specific task and thus cannot assess its own capability to complete it. In the version shown in Algorithm 1, the trustee leverages prior knowledge gained from performing the same task for other trustors. Specifically, line 7 checks whether agent \(i\) has previously interacted with other agents (denoted by \(\sim j\)) for the same task. If such similar connections exist, the agent will use the average weight of those connections to initialize the new one. If no prior experience exists (i.e., the trustee has never performed the task for any trustor), the connection weight is initialized to a default value of 0.5 (line 10), as was performed in the original algorithm proposed in [28]. At each time step, each trustee reviews the tasks stored in that list and attempts to execute the task with the highest connection weight, provided that the task remains available and the weight does not exceed a predefined threshold (lines 13-21). Lines 14-16 are part of the agent's decision-making process before attempting to perform a task. They serve as a sequence of filters that ensure the task is worth pursuing, feasible, and assigned with sufficient trust. The check in line 14--\"task is not visible or not done yet\"--captures scenarios where the agent must move within a range to personally verify the task's completion. In some cases, other agents might provide that information. Line 15 ensures the task is physically accessible and not already being performed by another agent or that the agent has the resources to perform it. Finally, line 16 introduces a trust-based filter. Even if a task is available and the agent is able to approach and undertake it, the agent proceeds only if the trust level with the task requester for the specific task meets
# Moving Towards Body-to-Body Sensor Networks for Ubiquitous Applications: A Survey
## 2 From WBANs to BBNs
### BBN Applications  
BBNs are intended for providing genuine social advantages, for example, remote medicinal services, monitoring of sport teams, accuracy communications of rescue teams in hazardous situations or soldiers on a battlefield, and so on. Indeed, a BBN can be deployed either for medical or non-medical applications. The novel trend of medical systems relies on ubiquitous healthcare services, and could be ensured by BBNs through the remote monitoring of patients relaying each other's physiological data collected by their own body-carried sensors, until the medical central unit. This will make the government's aim of ubiquitous and low-cost healthcare services come true. By embedding tiny sensors into clothes, vehicles, portable devices, glasses or helmets, people no longer need to be in the range of the cellular network to communicate or send data, in addition to the environment and health benefits that could be afforded by such low-power network.  
Figure 3 sorts the different WBAN and BBN applications into medical and non-medical. We just describe some medical BBN scenarios, for example to ensure remote health monitoring out of the 3G/4G coverage area, this could apply to track the vital signs of a rescue team in a disaster area or a group of soldiers in a battlefield. Also, for remote or disabled people, it will be possible to alert his neighbors and broadcast a call for help in an urgent situation. In [48], we proposed a BBN-based epidemic control framework, in mass gathering areas, that differentiates WBANs traffic and ensures real-time quarantine strategies in response to epidemic outbreaks in contaminated areas.
## 1 Introduction
Existing X-band SAR data (i.e., TerraSAR-X) have been used to simulate the NOCTUA signal and to integrate the vegetation biomass and vertical structure in the temporal dimension of different crop cycles. The article is organized as follows: after the introduction of the current remote sensing-based crop monitoring techniques and applications, Section 2 describes the study area and addresses the input data used and the methodology implemented to pre-process and then process optical and SAR data. Section 3 describes the intermediate and final results. In Section 4, the final results are discussed, pointing out the advantages, limitations, and future developments, and finally, Section 5 summarizes the derived conclusions.
# Routing Schemes in FANETs: A Survey
## 2 Background and Motivating the Need for FANETs
### How Do FANETs Differ from Other Kinds of Ad Hoc Networks?
#### 2.1.1 How Do FANETs Differ from MANETs and VANETs in Terms of the Types of Links?  
Table 2 compares the types of links in FANETs with those in MANETs and VANETs [44; 45; 46; 47].  
There are four main types of links among UAVs and different radio access infrastructure (i.e., FANET BS, cellular BS, and satellite) in ad hoc networks [48; 49] as shown in Figure 3:  
* a UAV-UAV link between a UAV and another UAV that supports ad hoc communication. This link is part of a route so that intermediate UAVs can forward packets towards a radio access infrastructure.
* a UAV-BS link (or a direct link) between a UAV and a FANET BS.
* a UAV-cellular link between a UAV and a cellular BS.
* a UAV-satellite link, which is a long-range terrestrial link, between a UAV and a satellite. The UAVs can form a star topology with the satellite. This link is necessary in the absence of other radio access infrastructure (i.e., FANET BS and cellular BS).  
The radio access infrastructure is prone to failure during disaster. Hence, the ad hoc network, which is formed using UAV-UAV links, is more robust and easy to deploy, although routing is necessary to establish routes.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Category** & \multicolumn{2}{c}{**MANETs**} & **VANETs** & **FANETs** \\ \hline \multirow{3}{*}{Types of link} & Ad hoc & Yes & Yes & Yes \\ \cline{2-5}  & Direct link & Yes & Yes & Yes \\ \cline{2-5}  & Satellite & No & No & Yes \\ \cline{2-5}  & Cellular & No & Yes & Yes \\ \hline \multirow{6}{*}{Characteristics} & Mobility degree & Low & Medium & High \\ \cline{2-5}  & Mobility models & Random way & Prediction based & SRCM, Realistic \\ \cline{2-5}  & & point & & \\ \cline{2-5}  & Energy constraint & High & Low & Medium \\ \cline{2-5}  & Radio propagation & NLOS & NLOS & LOS \\ \cline{2-5}  & model & GPS & Assisted-GPS, & Inertia \\ \cline{2-5}  & Localization method & & differential-GPS & measurement \\ \cline{2-5}  & Node density & High & Medium & Low \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of FANETs with MANETs and VANETs.
# 3D Face Profilometry Based on Galvanometer Scanner with Infrared Fringe Projection in High Speed
## 3 Experiments
### Quantitative Evaluation of Heterodyne Method Based on MEMS Projector  
For further quantitative evaluation, a standard ball (model G-BALL25.4, produced by the company of CALIB OPTICS, Shenzhen, China) with an absolute diameter of 25.3988 mm to see the performance of this MEMS projector system using a heterodyne method is measured. The ball is shown in Figure 7a. The ball was placed at ten different positions respectively and then scanned by this proposed system. The placement of the ball in the space is shown in Figure 7b. The distance between the front and back planes is 30 mm. At each position, a 3D shape measurement was performed and the measuring accuracy was analyzed by calculating the absolute error between the fitting diameter of the measured data and its true value. The spherical diameter was fitted using Geomagic Studio(r) 2013, which was made by Geomagic, Inc., Morrisville, NC, USA. Figure 7c shows one of measured 3D shape in the first position. The measurement accuracy of the ball at the ten different positions is shown in Table 2. It can be seen that for the ball the maximum absolute error is 0.172 mm, the minimum absolute error is 0.027 mm.