esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Simulation Test and Results Analysis  
### Experimental Test of IBOA  
#### 6.1.1 Test of IBOA Population  
In order to evaluate the performance of the IBOA, experiments were carried out, and the results are analyzed in this section. The population distribution and the convergence speed of the IBOA are analyzed.  
Figure 4 reveals, with the help of the circle chaotic map, that the population distribution of the butterfly optimization algorithm is more uniform than a random distribution.
## 3 Results
### GPP Simulations  
Simulated monthly GPP across the four study plots and the twelve model setups is presented in Figure 3. All model setups illustrated a characteristic peak of GPP during the growing season with the highest values in June, following seasonal variation in PAR flux and temperature. In all model setups, simulated annual GPP increased with stand age (Figure 4) until maturity (72 year-old) and then slightly decreased, except for the HPM model setups, where GPP increased continuously with post-fire time. In general, the BL model simulated lower monthly GPP fluxes compared to the SS model, yielding a lower annual GPP across all study plots (Table 5). Compared with the independent GPP dataset predictions, the BL model estimates were within the mean \(\pm\) one SD range 5 out of 24 times and the SS model estimates were within the baseline range 10 out of 24 times. Figure 3: Gross Primary Productivity (GPP) monthly estimations for the examined period by the Big Leaf model with average \(A_{sat}\) and \(K_{nt}\), (BLa)—red line, Big Leaf with variation of \(A_{sat}\) and \(K_{nt}\) (BLv)—green line, Sun/Shade model with average \(A_{sat}\) and \(K_{nt}\), (SSa)—blue line, and Sun/Shade model with variation of \(A_{sat}\) and \(K_{nt}\), (SSv)—purple line for all three methods of LAI estimation, ceptometer method (CMM), hemispherical photography method (HPM) and litterfall method (LFM). In terms of the variation related to the method used to estimate LAI, the LFM model setups yielded the highest GPP, as expected from the higher LAI input values. Under the LFM parameterisation the simulated annual GPP was 28% and 38% higher compared to the CMM and HPM methods, respectively. Compared with the independent GPP dataset predictions, the CMM model setup annual estimates were within the mean \(\pm\) one SD range 4 out of 16 times, the HPM 5 out of 16 and the LFM 6 out of 16. Models that run with the average photosynthetic capacity parameters illustrated a lower variability in simulated monthly GPP compared to the variant model setups (Cva = 42.3% vs. CVv = 52.4%, D_AD = 13.60, \(p<0.001\)). During most of the simulation period the variant model setups simulated higher GPP than the average photosynthetic capacity parameters were obtained. The results of the proposed LFM model setup showed that the proposed LFM model outperforms the CMM model with the highest GPP. Figure 4: Annual GPP estimation with time since last stand-replacing fire based on different model setups. Big Leaf model with average \(A_{sat}\) and \(K_{m}\), (BLA)—red line, Big Leaf with variation of \(A_{sat}\) and \(K_{m}\) (BLv)—green line, Sun/Shade model with average \(A_{sat}\) and \(K_{m}\), (SSA)—blue line, and Sun/Shade model with variation of \(A_{sat}\) and \(K_{m}\) (SSv)—purple line for all three methods of LAI estimation, ceptometer method (CMM), hemispherical photography method (HPM) and litterfall method (LFM). Big dots represent plot-specific simulations, and dotted lines represent a generic trend with age.
## 4 Application
### Non-Clinical Application
#### 4.2.10 Animal Research  
In animal research, camera-based techniques have numerous potential applications such as monitoring vital signs, assessment of motion activity and wound infection [148]. In [3], they extracted the HR and RR of mice, zebrafish and pigs using a CMOS camera and exploiting ICA based on the iPPG technique. [PERSON] et al. [149] used the iPPG technique to extract HR and RR of anesthetized pigs with Acute Respiratory Distress Syndrome (ARDS) using a CCD camera. By means of iPPG, [PERSON] et al. [150] accurately estimated the HR of rhesus monkeys from facial videos captured by Microsoft Kinect and a monochrome near-infrared (NIR) camera.
## 3 Models and Methods
### Methods
#### 3.2.2 Transmitted Energy Profile (TEP) Calculation
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Symbol** & **Unit** & **Values** \\ \hline Optics transmission & \(\tau_{opt}\) & dimensionless & 0.67 \\ Telescope area & \(A_{scope}\) & m\({}^{2}\) & 0.709 \\ Roundtrip atmosphere & \(\tau_{atm}\) & dimensionless & \(\tau_{atm}\) is equal to the value of \\ transmission & \(\tau_{atm}\) & dimensionless & \"d_refLCor_atm”. \\ Range between GLAS & \(D\) & m & \(D=(i\_{RespEndTime}-i\_{T}xW/Start)\cdot C/2\) \\ sensor and ground & & & where \(C\) is the light transmission speed. \\  & & & \(I_{0}\) is calculated from the GLAS transmitted \\ GLAS sensor emitted & \(I_{0}\) & J & waveform recorded in the GLAS product \\ total pulse energy & & & (i.e., r\_{w}\_{wf}), and the calculation details \\  & & & are shown in the Appendix A. \\  & & & \(R_{i}\) is calculated from the GLAS received \\ GLAS sensor received & & & waveform recorded in the GLAS product \\ echo energy at each & \(R_{i}\) & J & (i.e., r\_mg\_wf), and the calculation details \\ recorded layer & & & are shown in the Appendix A. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The instrument parameters of the GLAS lidar.
# GNSS Signal Quality in Forest Stands for Off-Road Vehicle Navigation
## 1 Introduction
Verification of this accuracy is relatively time-consuming, requiring appropriate selection of representative stands, and measurements in different seasons and different meteorological conditions. The mentioned factors can significantly influence the results of research in this area. The aim of this study was to verify the reliability of GNSS navigation of off-road vehicles in a temperate vegetation zone, focusing on a typical monocultural coniferous and deciduous forest.
# Helping the Visually Impaired See via Image Multi-labeling Based on SqueezeNet CNN
## 3 Experimental Results
### Results of the Proposed Deep Solution for Image Multi-Labeling  
First, we present the results for the baseline model (Model 1) shown in Figure 5a based on the SqueezeNet CNN with fixed pre-trained weights. We perform sensitivity analysis for this model by varying the presence threshold \(T_{p}\) in a range from 0.1 to 0.8 with a step size of 0.1 and by varying the hidden layer size as the following values 64, 128, 256, or 512. Figure 11 shows the analysis for the different datasets. The first observation in these figures is that a presence threshold value equal to 0.5 does not provide the most accurate results. In fact, a \(T_{p}=0.4\) gives us the highest AVG score result overall. Secondly, in terms of the optimal hidden layer size, we can see from the figures that a hidden layer size between 64 and 128 is the most appropriate when considering all datasets.  
Next, we present the results of fine-tuning Models 2 and 3 shown in Figure 5b,c, respectively. Figures 12 and 13 gives sensitivity analysis for Models 2 and 3, respectively. As can be seen, Model 3 gives us the best performance in terms of AVG score. Moreover, in general, a presence threshold \(T_{p}=0.3\) is the best choice considering all datasets. The number of filters for the CONV2D layer in Model 3 does not significantly affect the results. Therefore, we selected a number equal to 256 that gives good results for most datasets, but keeps processing time per test image low.  
Figure 11: Effect of the hidden layer size and the presence thresholds \(T_{p}\) on the AVG score for the Model 1 (base model): (**a**) KSU1 dataset, (**b**) KSU2 dataset, (**c**) UTrento1, and (**d**) UTrento2.  
Figure 12: Effect of the hidden layer size and the presence thresholds \(T_{p}\) on the AVG score for Model 2: (**a**) KSU1 dataset, (**b**) KSU2 dataset, (**c**) UTrento1, and (**d**) UTrento2.
## 5 Conclusions  
In this study, we addressed the critical challenge of handover selection in large\(-\)scale LEO satellite\(-\)to\(-\)ground networks involving numerous TIGs. To adapt to dynamic traffic patterns, we incorporated an LSTM\(-\)based traffic prediction model using historical data, enabling proactive and informed decision\(-\)making. The handover problem was formulated as an MDP, and we propose an attention\(-\)enhanced rainbow DQN algorithm that jointly considers the satellite switching frequency, communication quality, and satellite load. The proposed ARTHF features a flexible architecture applicable to various constellation configurations and traffic scenarios, as reflected in the Milan dataset's diverse traffic patterns. Extensive simulations consistently demonstrated ARTHF's significant superiority over the baseline methods, where it achieved substantial improvements in load balancing, improved communication quality, and reduced handover frequency. These compelling results affirm ARTHF's robustness, efficiency, and practical effectiveness in ensuring seamless and optimized connectivity within dynamic satellite network systems.  
Conceptualization, D.F. and J.L.; methodology, S.Z.; software, Z.Y.; validation, D.F., J.L., M.Z. and S.Z.; formal analysis, Z.Y. and M.Z.; investigation, D.F.; resources, D.F.; data curation, J.L.; writing\(-\)original draft preparation, J.L.; writing\(-\)review and editing, J.L.; visualization, J.L.; supervision, J.L.; project administration, M.Z.; funding acquisition, M.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Beijing Natural Science Foundation\(-\)Haidian Original Innovation Joint Fund, grant number L232002.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} LEO & Low Earth orbit \\ NTN & Non\(-\) terrestrial network \\ TN & Terrestrial network \\ TTG & Terrestrial terminal group \\ LSTM & Long short-term memory \\ MDP & Markov Decision Process \\ DQN & Deep Q network \\ D3 QN & Dueling-double-deep-Q-learning network \\ ARTHF & Attention-enhanced rainbow-DQN-based joint traffic prediction and handover design framework \\ \end{tabular}
# High-Frame-Rate 3-D Vector Flow Imaging in the Frequency Domain
## 2 Materials and Methods
### Transmission and Reception Settings  
The proposed method was validated through simulation and experiments. In both cases, the tests were based on a 256-element gridded spiral array probe built by Vermon (Vermon S.A.,Tours, France) according to our design [34; 35], at 3.7 MHz central frequency. The position of the array elements matched that of a spiral pattern of 256 seeds, the density of which was modulated according to a 50% Tukey (tapered cosine) window. Moreover, to test the method at a different frequency (7 MHz), additional simulations were carried out considering the layout of a prototype Capacitive Micromachined Ultrasonic Transducer (CMUT) probe, based on a 256-element spiral array with spatial density tapered by a Blackman window. For both probes, the TX signal was always a 5-cycle sine burst at the central frequency of the probe and weighted by a Hamming window. The PRF was set to 500 Hz. In RX, a dynamic Hamming apodization window with constant f-number = 2 was implemented.
## 3 DC-DC Converters
### Floating Interleaved Boost Converter  
Another topology examined in this paper is a floating interleaved boost converter which is a mixture of two boost converters at the same input and output. This topology is shown in Figure 4.  
This two-stage converter is able to operate in lower current and voltage ripples resulting in an increased rating for the converter. According to the literature [41], the advantages of the floating interleaved boost converter over the basic topology are as follows:  
Figure 4: Circuit diagram of the floating interleaved boost converter.  
Figure 3: Circuit diagram of the interleaved boost converter.  
* Higher efficiency
* Higher voltage ratio for the converter
* Higher input and output frequency--fewer losses
* Improved reliability due to the parallel structure
* Reduced size of the parasitic elements, weight, and volume  
During various modes of operation, the two output capacitors always have a series connection with the voltage source. The relationship between the input and output voltages is as follows [42]:  
\[\frac{V_{out}}{V_{in}}=\frac{1+D}{1-D} \tag{18}\]  
As is evident, to achieve the same output voltage level in the floating boost converter, a lower duty cycle is required compared to the conventional boost converter. This means this topology is able to provide a higher gain than the basic boost.
# Shaping 1,2,4-Triazolium Fluorinated Ionic Liquid Crystals
## 3 Results and Discussion
### Thermal Analysis
In Figure 2, the typical morphological structure of the liquid crystalline phase that can be identified as a columnar mesophase can be observed, different from what was previously observed for a phenyl ring bearing a mono alkyloxy chain in which a smectic A (SmA) mesophase was recognized [23]. The presence of two alkyloxy chains on a phenyl ring likely awarded a trigonal geometry to the molecule instead of the banana-shaped structures observed for mono alkyloxy salts. This trigonal geometry could favour the supramolecular stacking and the formation of columnar mesophases in contrast to the SmA mesophases previously observed for bent cations [23]. Additionally, the three transitions observed at DSC for **[Ph-Tri-(16)2][OTf]** were confirmed by the POM analysis. Figure 3a shows the liquid crystalline columnar mesophase obtained after the first transition from isotropic liquid solution, while Figure 3b can be indicative of a more structured liquid crystalline mesophase that can be recognized as a liquid crystalline columnar hexagonal mesophase. This is different for the appearance of striations across the fans, which is indicative of additional order growing. The presence of an LC phase columnar (Col) and hexagonal columnar (Col\({}_{\text{h}}\)) is in agreement with what was previously observed for ILCs with a rigid core linked to long alkyl chains [33; 34]. The comparison of ILC mesophase ranges sheds light on the presence of a wider mesophase range increasing the length of dialkyloxy chains on the phenyl unit of ILCs. This is in agreement with what was previously observed for the corresponding monoalkyloxy triflate ILCs (Figure 4) [23]. The temperature range in which the presence of mesophases can be observed ranges from 10 \({}^{\circ}\)C for **[Ph-Tri-(10)2][OTf]** to 20 \({}^{\circ}\)C for **[Ph-Tri-(14)2][OTf]** and up to 80 \({}^{\circ}\)C for **[Ph-Tri-(16)2][OTf]**. The only exception to this trend is the shortest mesophase range of 5 \({}^{\circ}\)C, which was observed for **[Ph-Tri-(12)2][OTf]**. On the other hand, the widest mesophase temperature range, from 102 to 22 \({}^{\circ}\)C, was displayed by **[Ph-Tri-(16)2][OTf]**, which was the only one to demonstrate two different forms of liquid crystalline mesophases, LC (Col)-LC (Col\({}_{\text{h}}\)). The mesophase range observed for dialkyloxy triflate ILCs is narrower than the range of the corresponding monoalkyloxy ILCs. Indeed, columnar mesophases can be recognized instead of SmA phases. Figure 4: Plot of transition temperatures of LCs reported in this work and in previously reported data [23]. Abbreviations: Iso—isotropic phase; LC—iquid crystalline phase; Col—columnar, Col\({}_{\text{h}}\)—columnar hexagonal, SmA—smectic A phase, Cr—crystalline solid state.
## 4 Results and Discussion
### Impact Analysis and Ablation Study on the Information Entropy Fusion Module
In contrast, the fixed obfuscation strategy, which lacks dynamic adaptation, leads to either insufficient or excessive obfuscation under concept-drift conditions. Since it does not consider data specific characteristics, the random obfuscation strategy results in suboptimal privacy protection and predictive performance. Consequently, the experimental results confirm the effectiveness of the information entropy fusion module in non-stationary time-series environments and demonstrate its ability to dynamically balance privacy protection and task performance.
# First Evidences of Ionospheric Plasma Depletions Observations Using GNSS-R Data from CYGNSS
## 4 Discussion  
The results presented in this study are consistent with previous observations and studies. As a general starting point, several peaks in \(S_{4}\) are found in many days during the years of operations of the CYGNSS mission.  
One of the main concerns during the study was the need to filter the GNSS reflections only for coherent reflections, as ionospheric scintillation may only be \"visible\" when a coherent wave crosses the ionosphere. In practice, this means that the signal has to be reflected over calm water. That is the reason why in every particular case, the wave height and winds speed maps were checked before a more in-depth analysis was performed to the data. For example, two of the days shown, 24 August 2017 (Figure 10) and 21 November 2017 (Figure 6), exhibit calm sea on the regions of interest where some scintillation was found.
# Toward SDM-Based Submarine Optical Networks: A Review of Their Evolution and Upcoming Trends
## 3 Recently Announced Submarine Cable Systems
### Attainable Capacity of Submarine Cable Systems
SDM systems are divided into two main categories: the SDM systems using a high-count of parallel FPs in the same cable achieving a reduced cost/bit (first generation of submarine SDM by ASN), and SDM systems by using the novel pump farming technique at the repeater's side. SDM cable systems of the first category are furthermore subdivided based on the number of FPs. Cable systems using both techniques (high count of FPs and pump farming) are represented by colorful dots. Figure 13 shows that SDM-based cable systems can achieve higher system capacities. As expected, a higher number of FPs achieve a higher Tb/s of total capacity. Because the most crucial factor is often not the maximum capacity but the distance over which this maximum capacity is achieved, Figure 14 presents the evolution of the product of the submarine cable systems' capacity multiplied by the associated length through time. Again, blue and red dots represent non-SDM and SDM cable systems, respectively. Orange dots represent multiband (C + L) cable systems. SDM cable systems of the first category are furthermore subdivided based on the number of FPs. We observe that recently announced SDM cable systems can achieve significantly higher values of the capacity \(\times\) length product over non-SDM systems. Figure 13: Evolution of submarine cable systems’ capacity through time (pre-SDM era vs. first-generation SDM era).
# Resource-Efficient Multicast URLLC Service in 5G Systems
## 3 Analyses of Possible Solutions and Their Adaptation to mURLLC
### The Maximum Number of Transmission Attempts  
The number of transmission attempts (TXs) affordable for each multicast packet depends on the latency limitation and the TDD configuration. In particular, for very strict latency requirements, i.e., \(D^{QoS}<(k_{dl}+k_{ul})T_{slot}\), HARQ retransmissions cannot be delivered in time. Thus, only a single robust TX is possible. For moderate latency requirements, i.e., \(D^{QoS}\sim(k_{dl}+k_{ul})T_{slot}\), the gNB can obtain UE feedback and make a conditional HARQ retransmission if the initial TX fails. Taking into account typical values of \(D^{QoS}\sim\) 1-10 ms for URLLC and \(T_{slot}\sim\) 0.5-1 ms for numerologies used in frequency bands below 6 GHz, in the paper, we focus on two cases: (i) One TX case when a single TX is possible, and (ii) Two TXs case, i.e., one initial TX and one HARQ retransmission are possible. We assume that the gNB selects this parameter at the beginning of the multicast flow and changes it very rarely (e.g., when the set of served UEs or their channel conditions significantly change).
# The Recent Development of the Surgical Treatment for Hepatocellular Carcinoma
## 4 Surgical Treatment of Highly Advanced HCC Outside Milan Criteria
### Liver Resection for HCC with Portal Vein Tumor Thromus  
The incidence of Portal vein tumor thrombus (PVTT) has been confirmed in more than 40% of HCCs and have an extremely poor prognosis with a median survival of 2.7-4 months without any treatment [26; 27]. In addition, the extent of the disease correlates with prognosis, and the classification of PVTT proposed by the Liver cancer study group of Japan has been widely used worldwide [28; 29]. The classification is as follows: Vp0, no tumor thrombus in the portal vein; Vp1, presence of a tumor thrombus distal to, but not in, the second-order branches of the portal vein; Vp2, presence of a tumor thrombus in the second-order branches of the portal vein; Vp3, presence of a tumor thrombus in the first-order branches of the portal vein; and Vp4, presence of a tumor thrombus in the main trunk of the portal vein or a portal vein branch contralateral to the primarily involved lobe (or both) [9]. Systemic therapy is recommended as standard therapy for patients with PVTT in Western countries, but in Asian countries, liver resection is usually attempted regardless of the extent of PVTT [30; 31; 32]. In 2016, [PERSON] et al. reported the outcomes of 6474 HCC patients with PVTT using Japanese national registry data [28]. Of these patients, 2093 who underwent liver resection (LR) and 4381 who received other treatments (non-LR) were compared. The median survival time (MST) of the LR group was 23.2 months longer than that of the non-LR group (29.6 months vs. 9.7 months) and 12.4 months longer than the non-LR group (28.9 months vs. 16.6 months) in a propensity score-matched cohort. Subgroup analysis based on the degree of PVTT showed that only Vp4 patients did not benefit from resection. Although similar results have been reported from Asian countries, there is an urgent need to develop more appropriate selection criteria and perioperative therapy to control postoperative recurrence [33]. So far, the usefulness of HAIC, radiation therapy, etc., have been reported [34; 35; 36; 37; 38; 39].