esa-sceva/satcom-chunk-collection huggingface dataset:
# Time-Domain Near-Infrared Spectroscopy and Imaging: A Review
## 4 Advanced Theories and Methods for TD-NIRS
### Solving the TD-RTE and TD-DE Numerically  
The TD-DE is a parabolic type of partial differential equation which can be easily solved by commercially available software codes even for geometrically complicated media such as the human head and small animal models using the finite element method (FEM). [PERSON] et al. [69] generally described the numerical method using FEM to solve the TD-DE as a forward calculation, as well as an inverse calculation for time-domain diffuse optical tomography (TD-DOT).  
Solving the TD-RTE is not an easy task even by numerical computation due to the term of the scattering integral in Equation (1). The discrete ordinates method (DOM) is a standard technique to solve the RTE and some good monographs have been published for the method [70]. A review of solving the TD-RTE numerically is beyond the scope of this article, and only a few articles, which were arbitrarily chosen, are reviewed below.  
[PERSON] et al. [71; 72] solved the TD-RTE using the DOM and the calculated TOF distributions were compared with measured TOF distributions by TD experiments using tissue phantoms, rat tissue samples in vitro, and anesthetized rats in vivo.  
[PERSON] et al. [73] solved the TD-RTE using the DOM to understand light propagation in the human neck including the thyroid and trachea for diagnosis of thyroid cancer by DOT. Light propagation inside the trachea cannot be expressed by the TD-DE because the trachea is a perfect void region of air, and reflection and refraction at the tissue-trachea interface should be considered. The results showed an interesting pattern of light propagation inside the trachea.  
Some methods have been proposed to calculate the TD-RTE with smaller computation loads. Only two studies are reviewed briefly for your information. One is to employ a cell-vertex finite volume method for discretization of the space for faster computing of the 2D TD-RTE [74]. The other is to employ a new renormalization approach to calculate the scattering integral for accurate and efficient computation of the 3D TD-RTE [75].
## 3 Theoretical Framework and Hypotheses Development
### Hypotheses Related to Moderating Factors
#### 3.2.1 Hypothesis Related to Age  
There are two aspects to age as a moderating factor. One could argue that older people are less accepting of modern technology, whereas younger generations are. On the other hand, older generations may be more willing to accept YouTube because they value such sources more. Many studies, including [47; 50; 51; 52; 53], used age as a moderating factor. As a result, the following hypothesis is conceived based on the preceding.  
**Hypothesis 9 (H9).**_Age has a significant moderating effect on information adoption (IA) towards YouTube._
## 5 Open-Loop Simulation
### Optimality Performance  
The SCvx algorithm proposed in [25] is regarded as a standard algorithm, and the errors between the DNN-LCvx and the SCvx are analyzed. If the control profiles computed by the two algorithms are similar, and the terminal landing mass and terminal time are close, it can be considered that the DNN-LCvx proposed in this paper has good optimality performance.  
The initial state variables are shown in Table 2. The two algorithms are discretized with uniform time interval, and \(N=100\). Figures 14 and 15 show the control profiles of the two algorithms.  
It can be found that the control profiles computed by the two algorithms are similar. The optimal terminal time solved by SCvx is 32.815 s and the terminal mass is 31,760.5 kg. The optimal terminal time solved by the DNN-LCvx algorithm is 32.719 s and the terminal mass is 31,755.7 kg. The terminal time error is \(-\)0.096 s and the terminal mass error is \(-\)4.8 kg. The errors are at a small level. Therefore, the optimality of the DNN-LCvx algorithm can be verified.  
Next, the impact of the discrete number on optimality is discussed. Generally, the larger the discrete number is, the closer the result is to the optimal solution, but the computation cost will increase significantly. The variation curve of terminal mass with the discrete number is plotted in Figure 16.  
It can be seen from Figure 16 that there is a systematic error in the DNN-LCvx algorithm, which is mainly caused by the linearization of (13) and the error of the network. It is worth noting that when the discrete number is greater than 30, the terminal mass changes very smoothly with \(N\). Therefore, \(N\) > 30 can ensure the optimality of the algorithm.
# Efficacy Improvement of Anomaly Detection by Using Intelligence Sharing Scheme
## 6 Conclusions and Future Work  
The main goal of this research work was to improve the detection efficacy of the existing Cognitive Threat Analytics (CTA) anomaly-based network IDS on malware that uses network traffic encryption to avoid detection. Therefore, we propose to utilize the fact that CTA-IDS is used in a large number of various networks and share threat information between them, since the amount and quality of features that can be extracted from HTTP(S) network traffic was low.  
The global threat intelligence method was created and shared among all the CTA-IDS systems that are monitoring the individual enterprise networks to improve the anomaly detection efficacy on both encrypted and non-encrypted network traffic. Several variations of the method were developed. One such method incorporates a novel method for outlier ensemble normalization in the presence of large numbers of missing values.  
Experimental evaluation performed on a large amount of real-world network traffic showed that the proposed algorithm performs only marginally better than the relevant state-of-the-art methods in the average case, but outperforms all of the compared methods in the worst-case scenario. Additionally, the efficacy improvements were measured on HTTP(S) communication, and the proposed algorithm and detection capability on HTTP network traffic were improved.  
Apart from improving detection accuracy in participating networks, the gathered intelligence can also be used on its own to help better understand global statistics of network threat behavior, and to help network analysts assess threat severity, determine the scale of an attack, or analyze command and control mobility patterns.  
Figure 9: Network AUC score and network traffic size on all input data (HTTP and HTTP(S)): (**a**) network traffic size (number of flows) with respect to “plain”, (**b**) network traffic size (number of flows) with respect to “pairwise”.  
Future work: There are a large number of possibilities to improve the final accuracy and the false positive rate in future research work. One possibility is to employ a greater amount of training and testing samples, and to find other available information in BRO logs describing HTTP(S) traffic in different ways or to find different open source software describing HTTP(S) traffic. Next, more in-depth studies could be conducted to analyze how malware works in HTTP(S) and also to try more machine learning algorithms with more different parameters.  
Conceptualization, M.T., M.L. N.A. and M.A.; Formal analysis, M.T., M.L., N.A. and M.A.; Funding acquisition, M.L.; Methodology, M.T.; Project administration, M.L., M.T., M.A. and N.A.; Software, M.T., M.A. and N.A.; Supervision, M.L.; Validation, M.T.  
This work has been supported by National Natural Science Foundation (NSFC) of P.R. China under grant number 61572095 and 61877007.  
The authors declare no conflict of interest.
## 2 HST-Net Model
### Backbone Network with Transformer-CSP  
#### 2.1.1 Structure of Backbone Network  
As shown in Figure 2, the backbone network of HST-Net is composed of an assortment of modules, namely the convolution layer (Conv), three convolution layers with a shortcut connection (C3), Transformer-CSP, and spatial pyramid pooling--fast (SPPF). In the initial phase, a Conv module with a stride of 2 is employed to halve the input image size, augmenting computational efficiency while concurrently eliciting preliminary features. This is subsequently succeeded by the iterative application of Conv and C3 modules, which execute a gamut of convolutional operations and residual connections, facilitating the extraction of intricate features with depth from the input.
## 6 Simulation Results and Discussion
### UAV Transmit Power (\(P_{i}(t)\)) vs. SNR for OTFS Modulation  
The variation in UAV transmit power (\(P_{i}(t)\)) vs. SNR for OTFS modulation is illustrated in Figure 17 for \(V\) = 1-100. Low SNR and a larger transmission delay lead to a longer flying time and higher energy consumption. The high mobility of vehicles results in lower SNR as velocity increases, increasing the average power consumption. Low SNR results in decreased system throughput and higher power consumption from UAV to maintain the SNR requirement for vehicles. Higher altitude requires high transmission power in order to meet the SNR requirement for vehicles, which includes both the flying and transmission power of the UAV. The BER is compared at different UAV and vehicle velocities. As the flight speed of the UAV increases, the BER increases, indicating that the Doppler shift from high-speed movement affects communication. It is noted that the difference in BER becomes larger as the SNR increases, and OTFS allows the UAV to communicate over longer distances. With increasing packet size, the weighted sum energy consumption increases, as the UAV is limited in resources to process the incoming packets.  
Figure 16: Probability of optimal trajectory prediction for GRU vs. UAV altitude (\(\mathcal{H}\)) for varying number of vehicles (\(V\)) over trials of 1000 episodes.  
Figure 17: UAV transmit power (\(P_{i}(t)\)) vs. SNR in OTFS modulation scheme for varying number of vehicles (\(V\)).
# Systematic Review on Individualized Versus Standardized Parental Nutrition in Preterm Infants
## 2 Materials and Methods  
### Design  
This review was designed to update the 2018 conditional recommendation of the combined working group on pediatric parenteral nutrition of ESPGHAN, ESPEN, ESPR, and CSPEN that standard PN solutions (SPN) should generally be used over individualized PN solutions (IPN) in the majority of preterm infants [3] using standard methods [11] and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) criteria [12; 13]. The level of evidence of eligible studies and the degree of recommendation were assessed following the recent guideline approach [1; 3]. The research questions were defined following the PICO framework [14] (Table 1). Primary outcomes were defined as protein intake, immediate in-hospital complications such as mortality, sepsis incidence, necrotizing enterocolitis (NEC) incidence, duration of PN (days), growth, and neurodevelopmental long-term outcome. The NEC data were defined and extracted as stage \(\geq\)2 NEC [15]. Growth was assessed as weight gain in g/kg/d during the study period, weight standard deviation score (SDS) at discharge, head circumference (HC) SDS at discharge, or weight and occipitofrontal circumference (OFC) SDS change from birth to 36 weeks postmenstrual age.
## 5 Results
### Stability Characteristics  
In the analysis of dynamic characteristics, the first stage is to check and evaluate the handling qualities. Because typically the modes of motion are determined as a response to small disturbances around the equilibrium state, their calculation is preceded by an equilibrium analysis, the results of which are presented in Figure 12, left. The graph shows the angle of attack and elevator deflection calculated from Equation (7) by equating the right side of the equation to zero. In the case of the angle of attack, the differences between the results for different source data are negligible. Some differences occur in the elevator deflection angle. However, these are small quantitative differences.  
Figure 12 shows the time to double of the phugoid against the MIL [43] criteria. For both calculated aerodynamic characteristics, the results are very similar, while the results for the wind tunnel data indicate stronger damping, which is the result of a higher drag coefficient for a low lift coefficient, which relates to a higher flight speed.  
The second longitudinal mode of motion taken under analysis is short-period oscillation, which is crucial in handling qualities evaluation. Figure 13 presents the short-period characteristics against the background of MIL [43] and ESDU [12] criteria. The graph on the left shows the damping factor as a function of the calibrated air  
Figure 11: Vortex development on the side plates.  
Figure 12: Trim conditions—AoA and elevator deflection vs. CAS airspeed (**left**). Phugoid characteristics—time to double vs. CAS (**right**).  
occur for low airspeeds, which is the result of differences in the derivatives of the pitching moment at higher values of the angle of attack. For all three data sets, the characteristics are within the second acceptance level. The characteristics shown in the ESDU figure of merit (Figure 13, right)) show equally small differences.  
The most crucial mode of motion in case of lateral modes is the Dutch roll. Figure 14 presents its characteristic against the background of MIL [43] and EASA CS-23 [44] criteria. The results show good accordance with different source data despite some differences in the lateral derivative \(Cl_{\beta}\).  
Table 1 presented below shows the numerical values of oscillation parameters of periodical modes of motion. The results indicate very good compliance for all data sets, which in turn allows us to conclude that even the low-fidelity methods, i.e., potential methods, are good enough to generate data for dynamic analyses.
# Bi-Directional Point Flow Estimation with Multi-Scale Attention for Deformable Lung CT Registration
## 4 Experiments  
### Dataset  
The experiments utilize the Lung250M dataset, which consists of approximately 250 million points extracted from 248 lung CT scans (124 patients), excluding the full-resolution voxel data (4B) provided in Lung250M-4B [16]. Lung250M integrates scans from Empire10 [3], TCIA-NSCLC [4; 41; 42; 43; 44], TCIA-Ventilation [4; 6], L2R-LungCT [7], TCIA-NLST [4; 45], and DIR-LAB COPDgene [1]. Each dataset features distinct imaging protocols, scanner settings, and pathology distributions, enhancing the diversity and complexity of the lung deformations. The detailed composition of the dataset is summarized in Table 1.
# Overcoming Challenges in Large-Core SI-POF-Based System-Level Modeling and Simulation
###### Abstract  
The application areas for plastic optical fibers such as in-building or aircraft networks usually have tight power budgets and require multiple passive components. In addition, advanced modulation formats are being considered for transmission over plastic optical fibers (POFs) to increase spectral efficiency. In this scenario, there is a clear need for a flexible and dynamic system-level simulation framework for POFs that includes models of light propagation in POFs and the components that are needed to evaluate the entire system performance. Until recently, commercial simulation software either was designed specifically for single-mode glass fibers or modeled individual guided modes in multimode fibers with considerable detail, which is not adequate for large-core POFs where there are millions of propagation modes, strong mode coupling and high variability. These are some of the many challenges involved in the modeling and simulation of POF-based systems. Here, we describe how we are addressing these challenges with models based on an intensity-vs-angle representation of the multimode signal rather than one that attempts to model all the modes in the fiber. Furthermore, we present model approaches for the individual components that comprise the POF-based system and how the models have been incorporated into system-level simulations, including the commercial software packages Simulink(tm) and ModeSYSTM.  
step-index (SI)-POF; computer simulation; fiber modeling; large-core fiber; system-level modeling; diffusion; optical power propagation; multimode fiber +
Footnote †: journal: _Photomics_
## 5 Experimental Results and Discussions
### Evaluation Measures
#### 5.2.3 Throughput  
The total time taken for the amount of data to transmit or receive to the destination node is known as throughput.  
\[T_{p}\;=\;\frac{Amount\;\;of\;data\;to\;transmit\;or\;receive}{ Required\;time} \tag{30}\]
## 3 Results
### Comparison of Experimental and Computation Results
The positive tangential velocity is the velocity in the direction of rotation. It can be confirmed that at all r/R, there is a positive velocity distribution when the jet is not injected. This means that when the propeller rotates, absorbed flow is generated through the slit of the propeller into the inner structure. When injecting a jet, the velocity distribution appears similar to not injecting jet until r/R = 0.6. After r/R = 0.6, the velocity decrease but still shows a positive velocity component. After r/R = 0.82, a negative velocity component appears. The reason why these velocity distributions are displayed can be found in the direction of rotation of the propeller and the shape of the slit. Since the slit of the propeller faces in the opposite direction to the rotation direction, when the propeller rotates, low pressure is generated in the inner structure. Therefore, a flow of relatively high pressure absorbed from the outside to the inside is generated. With a jet, the speed is reduced by offsetting the velocity of suction due to the flow generated in the internal structure. It can be confirmed that these velocity changes are mainly displayed at high r/R due to the centrifugal force generated by the rotation of the propeller. The flow around the blade section was compared in order to analyze the cause of the change in thrust and torque due to jet injection in detail. Figure 11 shows the nondimensionalized tangential velocity divided by the relative velocity at r/R = 0.73, where the changes in thrust and torque are large. Looking at Figure 11a, it can be confirmed that when the jet is not injected, an absorbed velocity distribution is generated in the slit. High thrust and torque appeared in a small area just in front of the slit where water is rapidly drawn in. Figure 9: Torque PUA distribution on the propeller surface with and without jet injection (\(J_{A}=0.4\)). Figure 11: Tangential velocity distribution according to with and without jet injection (r/R = 0.73). Figure 10: Tangential velocity distribution at the center of the slit (\(J_{A}\) = 0.4). to manufacture, so additional experiments are difficult. In addition, when comparing the results of the experiment and computation, an error occurs at low advance coefficient, but the tendency of the decrease in thrust, torque, and efficiency generated during jet inject is similar, so it judged that the results of computations can be trusted. Therefore, the higher injection volume and the changed according to the slit height was performed only by computations.
## 3 Antenna Implementation
Alternative designs include [23], which employed capacitive coupling between two orthogonally positioned resonators to realize second-order bandpass filtering, and [24], which utilized crossed-dipole radiators with nested square-ring structures to create current cancellation for enhanced out-of-band suppression. While all these circularly polarized filtering antennas successfully  
Figure 9: Radiation patterns of the proposed antenna at (**a**) 3.5 GHz and (**b**) 4.35 GHz (solid lines: simulation; dashed lines: measurement). demonstrate bandpass filtering functionality, they share a common limitation of exhibiting relatively narrow axial ratio bandwidths. In contrast, the proposed antenna simultaneously attains a wide impedance bandwidth of 66.4%, an AR bandwidth of 31.9%, high out-of-band suppression, and stable in-band gain. It is worth mentioning that, following the methodology in [26], the roll-off rate of filtering antennas can be quantified using the Gain Suppression Index (GSI) or Radiation Skirt Index (RSI):  
\[GSI=\frac{GBW_{3 dB}}{GBW_{10 dB}}\times\frac{f_{H3}-f_{L3}}{f_{H10}-f_{L10}} \tag{1}\]  
\[RSI=\frac{EBW_{80\%}}{EBW_{16\%}}\times\frac{f_{H80}-f_{L80}}{f_{H16}-f_{L16}} \tag{2}\]  
The proposed antenna exhibits higher GSI/RSI values than most referenced designs, indicating superior filtering performance. However, since many existing CP filtering antennas either omit radiation efficiency data or report efficiencies below 80%, RSI values cannot be calculated for those designs.
## 3 Results and Discussion
### Stability Study
#### 3.2.2 SEM Images  
The morphology of the stored samples was analyzed using SEM to evaluate potential changes in particle structure. SEM images of the spray-dried formulation (F1) demonstrated a near-spherical morphology with a wrinkled surface (Figure 6). Spray-dried powders  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Sample \({}^{*}\)** & **D[0.1] (\(\upmu\)m)** & **D[0.5] (\(\upmu\)m)** & **D[0.9] (\(\upmu\)m)** & **Span** \\ \hline F1\_0m & 1.073 \(\pm\) 0.07 & 2.325 \(\pm\) 0.14 & 13.867 \(\pm\) 2.34 & 5.503 \(\pm\) 0.68 \\ \hline F1\_1m & 1.065 \(\pm\) 0.12 & 2.337 \(\pm\) 0.38 & 143.822 \(\pm\) 5.91 & 61.073 \(\pm\) 3.20 \\ \hline F1\_3m & 1.125 \(\pm\) 0.45 & 2.444 \(\pm\) 0.93 & 297.506 \(\pm\) 10.36 & 121.275 \(\pm\) 11.51 \\ \hline \hline \end{tabular} \({}^{*}\) Sampling was conducted immediately after preparation (F1—0m), as well as after 1 month (F1—1m) and 3 months (F1—3m) of storage.  
\end{table}
Table 1: The particle sizes of the F1 sample as freshly prepared and after storage according to the laser diffraction analysis.  
Figure 5: Laser diffraction results of particle size (D[0.5] (\(\upmu\)m)) and particle size distribution (Span) for sample F1 (freshly prepared; F1—0m, and after one month; F1—1m and 3 months; F1—3m of storage). Results are presented as means \(\pm\) SD, \(n\) = 3, \(\tt{\tt{\tt{\tt{\tt{\tt{\tt{\tt{\tt{\tt{\tt{\tt{\tt{\tt{\tt{\tt{\tt{\tt{    }}}}}}}}}}{}}}}}}}\)\(p\)\(<\) 0.0001 significantly different.  
intended for pulmonary delivery must adhere to specific shape and size criteria to ensure effective deposition in the bronchioles or alveoli. The spherical morphology facilitates a uniform size distribution, while the wrinkled surface improves contact angles with lung tissues, enhancing aerodynamic performance [26]. After storage, ImageJ 1.53e software analysis indicated a slight increase in particle size (Table 2), which likely resulted from moisture absorption under humid conditions. Similar findings were corroborated by laser diffraction measurements. Despite this increase, the particle size remained below 5 \(\upmu\)m, which is ideal for pulmonary delivery [27]. Additionally, SEM images of F1_3m revealed a slightly rougher surface, potentially caused by solid-state change during storage under high humidity [13]. This change may have led to uneven swelling or surface roughening.