esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Histology of the Duodenum
\begin{table}
\begin{tabular}{c c} \hline \hline
**Specification** & **Flaxseed Meal** \\ \hline \multicolumn{3}{c}{Proximate chemical composition (\%)} \\ Dry matter, \% & 91.06 \(\pm\) 0.438 \\ Organic matter, \% & 85.92 \(\pm\) 0.354 \\ Crude protein, \% & 33.29 \(\pm\) 1.570 \\ Gross fat, \% & 13.90 \(\pm\) 0.438 \\ Fiber, \% & 9.62 \(\pm\) 0.028 \\ Ash, \% & 5.15 \(\pm\) 0.092 \\ \multicolumn{3}{c}{Fatty acid profoiles (g \% of total fat)} \\ \multicolumn{3}{c}{Miristic acid, (C14:0)} \\ Pentadecanoic acid, (C15:0) & 0.08 \(\pm\) 0.007 \\ Palmitic acid, (C16:0) & 4.69 \(\pm\) 0.403 \\ Palmitoleic acid, (C16:1) & 0.10 \(\pm\) 0.021 \\ Heptadecanoic acid, (C17:0) & 0.36 \(\pm\) 0.035 \\ Stearic acid, (C18:0) & 2.49 \(\pm\) 0.265 \\ Oleic acid, (C18:1) & 18.29 \(\pm\) 0.537 \\ Linoleic acid, (C18:2n6) & 16.83 \(\pm\) 0.576 \\ Arachidic acid, (C20:0) & 0.14 \(\pm\) 0.028 \\ Linolenic \(\upgamma\) acid, (C 18:3n6) & 0.21 \(\pm\) 0.014 \\ Linolenic acid, (C18:3n3) & 55.93 \(\pm\) 0.539 \\ Ctadecetareno acid, (C18:4n3) & 0.14 \(\pm\) 0.010 \\icositorenico acid, (C20:2n6) & 0.13 \(\pm\) 0.027 \\ Eicosatrienoic acid, (C20:3n6) & 0.06 \(\pm\) 0.021 \\ Eicosatrienoic acid, (C20:3n3) & 0.05 \(\pm\) 0.014 \\ Arachidonic acid, (C20:4n6) & 0.11 \(\pm\) 0.010 \\ Docosadinenoic acid, (C22:2n6) & 0.09 \(\pm\) 0.014 \\ Docosatetraenoic acid, (C22:4n6) & 0.10 \(\pm\) 0.014 \\ Other fatty acids & 0.24 \(\pm\) 0.069 \\ SFAs & 7.92 \(\pm\) 0.120 \\ MUFAs & 18.39 \(\pm\) 0.516 \\ PUFAs & 73.65 \(\pm\) 1.089 \\ n3 & 56.12 \(\pm\) 0.559 \\ n6 & 17.53 \(\pm\) 0.940 \\ n6/n3 & 0.31 \(\pm\) 0.028 \\ \hline \hline \end{tabular}  
* SFAs—sum of saturated fatty acids; PUFA—sum of polyunsaturated fatty acids; n3—sum of omega 3 polyunsaturated fatty acids (C18:3n3 + C18:4n3 + C20:3n3); n6—sum of omega 6 polyunsaturated fatty acids (C18:2n6 + C18:3n6 + C20:2n6 + C20:3n6 + C20:4n6 + C22:2n6 + C22:4n6). All data are reported as mean \(\pm\) standard deviation of three replicate analysis.  
\end{table} 
Table 2: Nutrient contents of the flaxseed meal. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{3}{c}{**Dietary Treatment**} \\
**Parameter** & **Control** & **E** & **\(p\)-Value** \\ \hline Villus height (\(\upmu\)m) & 848 \(\pm\) 33.2 & 1148 \(\pm\) 29.1 *** & 1.01 \(\times\) 10\({}^{-12}\) \\ Crypt depth (\(\upmu\)m) & 145 \(\pm\) 7.4 & 164 \(\pm\) 4.6 *** & 1.90 \(\times\) 10\({}^{-5}\) \\ Villus height/crypt depth & 5.9 \(\pm\) 0.4 & 7.0 \(\pm\) 0.3 *** & 1.18 \(\times\) 10\({}^{-5}\) \\ \hline \hline \end{tabular}  
* \(C\) group: basal diet/control group; E group: flaxseed meal supplementation to the basal diet of broiler chickens. All data are reported as mean values \(\pm\) standard deviation (SD) and statistical significance, where *** \(p\)\(<\) 0.001.  
\end{table} 
Table 4: Measurements of the duodenal villus height and crypt depth for the control and experimental groups.
# Electric Vehicles: Benefits, Challenges, and Potential Solutions for Widespread Adaptation
## 4 Strategies for Overcoming Challenges
### Balancing Auxiliary Loads  
Auxiliary loads greatly impact how much energy electric cars use, which cuts down on how far they can go. First, heavy auxiliary loads drain batteries in city driving circumstances, reducing the EV's range. The driving range decreases by 17.2-37.1% (under simulated settings) when the AC is activated in the summer. Similar to how EVs employ PTC (Positive Temperature Coefficient) heaters, the range spans from 17% to 54% (under simulations) owing to the need for heating in the cold [57; 58]. Second, when electric cars are driven at highway speeds, the effects of auxiliary loads such as air conditioning and heating have not yet been fully investigated. Finally, there are significant differences in the impact of supplementary loads in a lab setting and on actual roadways. Under ideal conditions, such as with little auxiliary loads and the help of a regenerative brake system (RBS), electric vehicle producers may achieve low energy consumption and an extended driving range; nevertheless, this ideal outcome is different when EVs are driven on highways amongst towns [33].  
One way to address the problem of limited range and high energy usage brought on by auxiliary loads is to utilize a heat pump to heat EVs in the winter. This can increase the driving range by 7.6-21.1% thanks to a higher heating coefficient of performance (CoP). The vapor compression cycle of a heat pump oversees both cooling and heating. Additionally, a four-way valve that reverses refrigerant flow is included. Additionally, its coefficient of performance is 1% greater than that of PTC heaters. Additionally, a precise assessment of EVs' heating and cooling demands may significantly reduce the energy used by the AC system. An appropriate energy management technique can also lower the total energy consumption when cooling. Consequently, a suitable energy management strategy may regulate energy use instead of the ON/OFF technique [59].  
Another approach is the system configuration that has been suggested, which uses a traction shaft to clutch the AC compressor motor during braking intervals. This method not only helps the EV to weigh less but also uses less energy [60].
# Reinforcement Learning for Efficient Drone-Assisted Vehicle Routing
## 5 Computational Studies
### Results on the VRPD with Unlimited Flying Range
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & & \multicolumn{3}{c}{\(|\mathcal{K}|=\mathbf{1},|\mathcal{D}|=\mathbf{2}\)} & \multicolumn{3}{c}{\(|\mathcal{K}|=\mathbf{2},|\mathcal{D}|=\mathbf{1}\)} & \multicolumn{3}{c}{\(|\mathcal{K}|=\mathbf{2},|\mathcal{D}|=\mathbf{2}\)} \\ \cline{3-11}  & \multicolumn{1}{c}{**Methods**} & **Costs** & **Gaps, \%** & **Time, s** & **Costs** & **Gaps, \%** & **Time, s** & **Costs** & **Gaps, \%** & **Time, s** \\ \hline \multirow{5}{*}{\(\mathbf{\mathcal{O}}_{\text{test}}\)} & Solver & 324.75 & 16.96 & 567.69 & 260.98 & 12.44 & 601.02 & 247.04 & 8.20 & 600.91 \\  & Solver Lazy & 282.90 & 3.95 & 476.00 & 240.47 & 4.91 & 601.71 & 238.00 & 3.98 & 601.68 \\  & Heuristic & 272.92 & 0.00 & 123.89 & 229.39 & 0.01 & 62.33 & 228.58 & 0.01 & 63.45 \\  & RL greedy & 281.62 & 1.75 & 0.01 & 231.23 & 0.80 & 0.02 & 230.99 & 1.13 & 0.02 \\  & RL sampling & 276.93 & 1.40 & 0.61 & 230.42 & 0.45 & 0.68 & 230.43 & 0.88 & 0.98 \\  & Ensemble RL & 276.32 & 1.28 & 4.47 & 229.53 & 0.08 & 4.79 & 228.84 & 0.12 & 6.80 \\ \hline \multirow{5}{*}{\(\mathbf{\mathcal{O}}_{\text{test}}\)} & Heuristic & 290.08 & 3.28 & 229.68 & 236.83 & 0.00 & 203.88 & 230.41 & 0.34 & 290.28 \\  & RL greedy & 292.83 & 4.32 & 0.03 & 253.66 & 7.13 & 0.02 & 248.32 & 7.81 & 0.04 \\ \cline{1-1}  & RL sampling & 286.13 & 2.01 & 1.39 & 249.18 & 5.28 & 1.36 & 237.76 & 3.83 & 2.94 \\ \cline{1-1}  & Ensemble RL & 283.43 & 0.96 & 9.79 & 242.89 & 2.47 & 9.47 & 235.59 & 2.66 & 15.74 \\ \hline \multirow{5}{*}{\(\mathbf{\mathcal{O}}_{\text{test}}\)} & Heuristic & 469.93 & 33.13 & 152.57 & 309.95 & 6.32 & 139.44 & 296.28 & 11.41 & 159.92 \\ \cline{1-1}  & RL greedy & 376.76 & 6.61 & 0.06 & 306.95 & 5.24 & 0.07 & 279.95 & 5.30 & 0.07 \\ \cline{1-1}  & RL sampling & 355.71 & 0.70 & 6.12 & 294.77 & 1.06 & 6.90 & 266.39 & 0.18 & 7.72 \\ \cline{1-1}  & Ensemble RL & 353.27 & 0.00 & 43.15 & 291.64 & 0.00 & 48.74 & 266.68 & 0.30 & 84.59 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The VRPD results with Limited Flying Range of Drones with \(\beta=0.6\). Averages of 10 problem instances. model adjusts when multiple drones are used by balancing their routes with the trucks. This adjustment shows in the nearly optimal solutions we see in the 8- and 10-node cases, indicating that our RL method can find efficient orders for dispatching without needing to check every possibility. However, we notice that as the problem size grows (for example, with 20 nodes), the quality of the solutions may drop a bit (1-2% higher gap) compared to smaller cases. This happens because tracking the coordination between drones and trucks gets more complicated, highlighting the balance between quick solutions and ensuring the best overall result. In summary, findings show that the RL model can quickly create high-quality routes for different situations, offering clear advantages over traditional solvers in terms of speed. This speed can be crucial in real-world operations where route decisions need to change quickly.
## 1 Introduction
Directly ionizing particles can \"directly\" damage the target cancer cells without relying on the production of secondary electrons. On the other hand, indirectly ionizing radiations, such as photons and neutrons, deposit their energy in the cancer cells through a two-step process. First, the uncharged particle travels through tissue and creates charged particles (e.g., photons release electrons or positrons, and neutrons release protons or heavier ions) from the molecules they interact with within the cell medium. Then, the charged particles move to the DNA to produce damage [19]. Nowadays, the use of megavidtage photon is the most commonly used method when treating cancer tumours with external beam radiation. A photon beam can be produced from radioactive sources such as Cobalt-60 or a treatment delivery system such as a linear accelerator. However, the most notable downside to photon therapy is the unwanted damage to the healthy tissues or critical organs in the patient. Usually, the target tumour is located at a certain depth inside the patient. When a photon beam is being irradiated, it deposits most of its energy at the entrance point (surface of the skin) and continuously loses its energy as the beam travels through the patient towards the tumour. The photon beam finally exits through the other side of the patient. In summary, healthy tissues located in front and behind the tumour inevitably receive some radiation dose, resulting in a certain amount of healthy cell death. In charged particle therapy, such as with protons, the beam characteristic of Bragg peak, where proton beam deposits most of its energy at the end of its path, gives rise to three crucial advantages compared to photon therapy. These advantages are the low entrance dose when the proton beam comes in contact with the patient, a large deposited dose at the tumour location and almost no exit dose [20]. Therefore, there is less damage to the healthy tissue of the patient, though both photon and proton therapy still have unnecessary doses at the healthy cells located along the beam path to the tumour. In this study, we investigated the DNA dosimetry of the cancer cell in the presence of a gold nanoparticle irradiated by proton beams. Monte Carlo (MC) simulation was used to predict the dose enhancement by the addition of a gold nanoparticle in the proton irradiation [21; 22; 23; 24; 25]. The enhancement of DNA damage can be estimated by the dose enhancement ratio (DER), which is defined as the ratio of the dose at the DNA with addition of a gold nanoparticle to the dose at the DNA without the gold nanoparticle [26]. By examining the MC results, we can investigate the dependences of DER on different irradiation variables, namely, the nanoparticle size, distance between the nanoparticle and DNA and proton beam energy.
## 5 Multipath Mitigation Results
Whereas in real urban or indoor environments, the multipath condition can be much harsher. The multipath severity is reflected in two aspects: (1) the number of paths may be far more than three; and (2) reflected signals are very powerful relative to the LOS with a quite small time delay. Generally, the performance of conventional methods may deteriorate dramatically in these severe multipath environments. So we particularly set up the simulation scenario 2 with a three-path channel to test the proposed method in the presence of severe multipath. Besides, BOC modulation type is also considered as an important factor affecting the multipath mitigation performance of different methods. Existing multipath mitigation methods may not be optimal for different BOC modulations, especially for some high-order BOC signals. Therefore in the last scenario, we also evaluated the multipath mitigation capability on BOC(14,2) and BOC(15,2.5) modulations.
# Elevation Angle Characterization for LEO Satellites: First and Second Order Statistics
## 3 Methodology for the Elevation Angle Characterization
### Reducing the Shape and Rate Matrices  
From the grid of Figure 9c, an array as shown in Figure 9d can be created for each simulated orbit altitude. Figure 9d shows the diagonal numbers of a rectangular matrix \(D_{h}\) defined as  
\[D_{h}=\begin{bmatrix}d_{0}&d_{1}&d_{2}&\dots&d_{13}\\ d_{-1}&d_{0}&d_{1}&\ddots&d_{12}\\ d_{-2}&d_{-1}&d_{0}&\ddots&\vdots\\ d_{-3}&d_{-2}&d_{-1}&\ddots&\vdots\\ d_{-4}&d_{-3}&d_{-2}&\ddots&\vdots\\ NaN&d_{-4}&d_{-3}&\ddots&\vdots\\ NaN&NaN&d_{-4}&\ddots&\vdots\\ \vdots&\vdots&\vdots&\ddots&\vdots\\ NaN&NaN&NaN&\dots&d_{-4}\end{bmatrix}, \tag{19}\]  
for each altitude \(h\in h_{s}\), with \(NaN\)'s are placed for cases where \(\phi_{ES}>i_{SAT}\).
# Mobility Prediction and Resource-Aware Client Selection for Federated Learning in IoT
## 3 System Models and Problem Formulation
### Problem Formulation  
The formulation of the client selection problem is as follows:  
\[\mathbf{P0}: \max_{u\in\mathbf{U}}y_{u},\] (7) s.t. \[t_{|\mathbf{Q}|}^{comp}+t_{|\mathbf{Q}|}^{ upload}\leq\tau, \tag{8}\] \[|\mathbf{Q}|=\operatorname*{arg\,max}_{u\in\mathbf{U}}\Bigl{\{}t_{u}^{ comp}q_{u}\Bigr{\}},\] (9) \[f_{u}(t+k),r_{u}(t+k)\text{ are predicted from }\hat{M}_{u}(t+k),\] \[\forall u\in\mathbf{U},y_{u}\in\{0,1\}. \tag{10}\]  
The objective is to maximize the number of selected clients in a global iteration. The first constraint ensures that the total latency (including computational and upload latency) for the last client in \(\mathbf{Q}\) is less than or equal to the predefined deadline, denoted as \(\tau\). The computational capacity threshold in MACS ensures that clients can complete local training within the required time. It is determined based on the number of CPU cycles needed per data sample and the client's available processing power. Clients below this threshold are excluded to avoid delays in training rounds. The threshold varies based on the use case and hardware. In resource-limited IoT environments, it is set lower to allow for wider participation, even for low-power devices. In high-performance edge computing, it is raised to prioritize clients with faster processing, improving training efficiency. Adjusting the threshold dynamically helps maintain a balance between computational capability and timely model updates. The second constraint defines the last client in \(\mathbf{J}\). The predicted computational capacity \(f_{u}(t+k)\) and data rate \(r_{u}(t+k)\) depend on the predicted mobility state \(\hat{M}_{u}(t+k)\). The third constraint implies that \(y_{u}\) is a binary variable, indicating whether a client is selected or not.
## 4 Biomedical Applications
### Wound-Healing Materials  
HA-catechol materials have been investigated as wound-healing materials because reactive oxygen species can be scavenged by catechol groups. The incorporation of anti-inflammatory drugs into HA-catechol hydrogels significantly improves wound closure and healing. For instance, arginine derivatives have been incorporated into HA-catechol hydrogels, as shown in Figure 5a [62]. To confirm the antioxidant properties, 2,2-diphenyl-1-picrylhydrazyl (DPPH) and hydroxyl radical (OH) scavenging experiments are generally used. An arginine/HA-catechol composite hydrogel showed strong antioxidant activity with maximum OH and DPPH free-radical scavenging rates of 41.1% and 67.4%, respectively. In addition, complete wound closure and healing could be achieved using these hydrogels after 21 days (Figure 5b). HA-catechol conjugated with chitosan is also used as a wound-healing material [57]. Wound healing can be improved by increasing the concentrations of H2O2 and HRP. HA-catechol/chitosan composite hydrogels with the addition of H2O2 (0.05%, \(v/v\)) and HRP (0.1 mg/mL) showed complete wound healing nine days post-surgery, which depicts a wound closure effect similar to that obtained using surgical suturing, indicating that these hydrogels could replace surgical sutures to achieve wound closure. In addition, a mild acute inflammatory response was observed for six days after the implantation of HA-catechol/chitosan hydrogels, and inflammation receded on the ninth day after surgery. This is similar to the biocompatibility of surgical sutures and enzymatically (HRP) crosslinked hydrogels.  
Figure 5: (**a**) Schematic illustration of methacrylated HA-catechol (HA-DA) and arginine derivatives (AD) composite hydrogels for wound healing. (**b**) Photographic images of rat skin wounds after treatments of HA-DA/AD hydrogels with various pore sizes (14.18 \(\pm\) 1.54 μm for HA-DA/AD1, 13.09 \(\pm\) 0.79 μm for HA-DA/AD2, 11.08 \(\pm\) 1.16 μm for HA-DA/AD3, and 9.07 \(\pm\) 2.17 μm for HA-DA/AD hydrogels). Treatment with PBS was used as a control. Reproduced with permission from [62]. Copyright © 2019 Elsevier.
## 3 Characteristics of the Waveforms
### Classifying Parameters  
**Automatic gain control (AGC):** An automatic gain control (AGC) method was used to ensure that the height of the plateau was adjusted in order to remain constant during the measurement processes.  
**Pulse peaking (PP):** The pulse peaking was the ratio of the maximum sample value in the return echo to the power value in an individual sampler. The usage of the PP values was first proposed by [PERSON] (1994b) as a sea ice mapping technique. Since that time, many researchers have used the PP values for the sea ice classifications of radar altimeters [23; 24; 25; 26; 27]. The PP values also can be used  
Figure 5: Waveforms of the SMLE and OCOG from the quasi-specular surfaces: (**a**) original waveforms of the two different tracking packets; (**b**) the normalized waveform of the two different tracking packets.  
to extract seawater and sea ice information. In the present study, the parameters were improved by ignoring the noise prone areas of the two measurements in 128 bins as follows:  
\[\text{PP}\ =\ \frac{\textit{max}(\textit{Power})}{\sum_{\textbf{21}}^{\textbf{108}} \textit{Power}(i)}\star\ 88 \tag{1}\]  
where PP denote the values of the pulse peaking, _max(Power)_ is the maximum sample value in the return echo from bin 21 to bin 108; and _Power(i)_ is the power value in an individual sampler.
# Model for the Prediction of Rain Attenuation Affecting Free Space Optical Links
## 4 Multiple Scattering Effects  
The simple scattering theory used to derive the coefficients in (1) assumes that the electromagnetic energy associated with the portion of the wave front intercepted by a particle is lost. However, as a matter of fact, the incident energy is scattered by the particle with an angular pattern that, in the case of raindrops and optical wavelengths, exhibits a narrow peak in the forward direction. As a result, a relevant fraction of the radiation transmitted through a rainy atmospheric layer is received because of particle scattering. If the light beam undergoes several particle interactions before reaching the receiver,  
Figure 8: Mean and RMS value (left and right panel, respectively) of the error figure for different path lengths using all models for \(\mu=1\).  
Figure 7: Assessment of the models’ prediction performance: \(L=3\) km and \(\mu=1\).  
the process is called multiple scattering. Forward and multiple scattering reduce path attenuation especially at high rain rates, which needs to be taken into account when devising a model to predict rain attenuation statistics for FSO links. The corresponding multiple scattering gain, which depends on the path length, is calculated here through a Monte Carlo photon propagation simulator [31].
## 6 Conclusions  
The new digital approach that allows remote services has the potential to improve health access and monitor the health status of vulnerable populations, especially those who have been diagnosed with NCDs. The PHC supports health data management at the community level. The PHC was perceived as an added value for the existing community-based health services that are conducted by the primary health center. This model may potentially be used to support a number of health programs where community health workers are involved. Interoperability with medical records in the primary health centers would be beneficial for the continuation of patient care. The successful implementation of PHC is not only considered from the applications of technology but also non-technical aspects related to human impact, organization, and legality.  
The scope of this research was limited to a small number of primary health centers in Indonesia. The study was also limited to the perceptions of health workers and community health workers appointed for the implementation of a PHC for monitoring and detection of NCDs in the community. The feasibility of the PHC can be further analyzed to support other health programs or other areas with different resource conditions.  
Conceptualization, L.L., N.M., A.M., F.Y., R.I. and N.N.; methodology, L.L., N.M., H.W. and T.S.D.; software, N.M., G.Y.S.; validation, N.M. and H.W.; data analysis, H.W. and T.S.D.; writing--original draft preparation, H.W. and T.S.D.; writing--review and editing, L.L. and G.Y.S.; supervision, L.L.; project administration, H.W.; funding acquisition, F.Y., R.I. and N.N. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Aid for Scientific Research (KAKENHI) Program, The Telecommunications Advancement Foundation (TAF), and Kyushu University Institutes for Asian and Oceanian Studies (Q-AOS): Grant # 21K18113.  
The Medical and Health Research Ethics Committee of the Faculty of Medicine, Public Health and Nursing Universitas Gadjah Mada approved the study in December 2020. The approval reference number is KE/FK/1387/EC/2020.  
Informed Consent Statement: Informed consent was obtained from all respondents involved in the study. The questionnaire for the patients consists of history of diseases and medication, health behavior, mental and cognitive assessment, then health examination. The interview guideline for health workers and community health workers associated with perceived of usefulness, ease of use, privacy, resistance to use, social influence, perceived risk, trust, facilitating conditions, and technology anxiety.  
Not applicable.  
The authors declare no conflict of interest.
# NOMA-Based VLC Systems: A Comprehensive Review
## 1 Introduction
To decrypt the data, the strongest users initially utilize SIC for decoding the data of weak users, remove it from associative receivers, and then decrypt their data, but the weak users decrypt their data immediately and face IUI caused by the stronger users' data superposition. Despite the numerous advantages that VLC provides, it has a number of flaws that keep present technology from meeting the expectations of 6G networks. The first constraint is the minimal interaction range caused by the short wavelengths of visible light signals. It leads to significant propagation losses since the VLC channel strength deteriorates substantially as the space between receiving and transmitting devices grows, besides the fact that emission spectra are easily obstructed by obstructions [12]. Furthermore, unlike its typical RF counterpart, the VLC channel is not isotropic, which means that the geometry of the receiving and transmitting instruments have a substantial impact on channel gains [13]. Consequently, the VLC channel quality varies, and the efficiency of sophisticated multiple access strategies, for instance, NOMA, suffers when integrated with VLC systems. An overview of downlink NOMA-VLC for two users is presented in Figure 1. We have summarized recent works on NOMA in Table 1.