esa-sceva/satcom-chunk-collection huggingface dataset:
## 8 Results of the Parametric Analyses Carried Out with the Proposed Tool
### Impact of the Line Length  
In this last analysis, a cable with a rated maximum power of 2 GW and a \(V_{rms}\) level of 220 kV was connected to a load with a _cos\(\varphi\)_ equal to 0.9. The line length was varied between 5 km and 100 km to check the main techno-economic trends in this range.  
Figure 17 shows the model's output for the total costs per unit of line length for the values of \(L_{line}\) considered, distinguishing between the overall capital and operating costs. The economic convenience of developing longer HVAC superconducting lines compared with shorter lines appears to be limited though not negligible. Furthermore, the cost reduction was more pronounced than in the case of medium-voltage cables discussed in [35], as the quantitative discounting approach was not considered in the previous publication. Extending the line length from 5 km to 100 km resulted in approximately a 13% reduction in the total costs. The ratio of operating to capital costs consistently remained between 15% and 16%.  
It is reasonable to expect that the cost reduction with an increasing \(L_{line}\) will eventually saturate once certain lengths are achieved, as the quantities of the discounted terms would reach their respective discount limits \(d_{max\%_{xx}}\). However, up to 100 km, this saturation trend was not observed, in contrast to the findings in [35].  
Lastly, Figure 18 shows the trend of the optimized value of \(n_{stat}\) with the cable length as blue bars whose values are reported on the left axis. These varied from 0 units for the shortest line up to 16 units for the longest one. Since the distances on the \(x\) axis of Figure 18 are not proportional, it can be difficult to visualize that the ratio between the number of intermediate cooling stations and the length of the line is not exactly constant. This is better displayed by the red bars in Figure 18 (referenced on the right axis), which represent the distance between adjacent stations or the length of each modular cable segment \(d_{stat}\). This distance varied between 5 km, the minimum value set in Table 5, and 7.1 km.  
Figure 16: Operating costs per kilometer of cable, varying the voltage level and the frequency. The cases considered had \(P_{max}\) = 2 GW, \(cos\varphi\) = 0.9, and \(L_{line}\) = 50 km.
## 7 Mobility Framework
An upper limit of 4 was chosen, considering the Interest retransmission timeout. Another parameter that was made explicit was the Data PDU payload size. As was briefly mentioned in Section 4 regarding the definition of a Part, the Part was considered to be the size of the MTU. Considering the size of the ICN headers and the 3 NA headers, the definition of a payload size of 1024 Bytes was proposed and tested. The final parameter modified was the application start time for the producer and the consumer. It was necessary for the push producer to not push information exactly at the same time that the consumer requested the same information. This discrepancy was needed to see how ICN forwarding strategies dealt with Parts being dispersed in the network and not necessarily on the path to the producer. A low number of 20 s after the push producer began transmitting content was chosen. The producer node is an 3 NA enabled edge pushing content node. This means that the producer has a 3N name and continuously pushes its content to the WAP to which it is connected. The producer does not respond to any requests for content from the network. The use of a 3N name enables us to push content without having to modify ICN's Interest/Data PDU protocol flow as established in [20]. All of the network parameters used in the simulation are summarized in Table 4.  
Figure 6: Graph of the network used for the mobility framework.
## 3 Results
### Essential Oil Components  
A chromatographic analysis of essential oil is represented in Table 3. The study identified a total of 21 different essential oil components and unidentified oil (about 3-4% in total). The major components were eugenol (up to 54%), linalool (from 10% to 20%), eucalypt (10%) and sabinene (up to 5%). It was demonstrated that the additional irradiation of plants with UV did not affect the essential oil total content. At the same time, the essential oil composition changed. Generally, UV-treatments decreased the proportion of sabinene and fenchone and increased the linalool, \(\alpha\)-guaiene, \(\beta\)-cubebene and \(\alpha\)-bulnesene fraction. The concentration of other oil components remained practically unchanged. However, in some cases, the effect of UV-C differed from that of other types of ultraviolet. For example, UV-A and UV-B increased the linalool proportion from 10% to 20-25%, whereas UV-C \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Combination of Light Spectrum**} & \multicolumn{2}{c}{**Average FM/Plant (g)**} & \multicolumn{2}{c}{**Average Dry**} & \multicolumn{2}{c}{**Average Light**} & \multicolumn{2}{c}{**Average Leaf**} \\  & **Stems** & **Leaves** & **(\%)/EM** & **(cm)** & **(cm2)** \\ \hline \multicolumn{6}{c}{30 th day of cultivation} \\ FL (Control) & 0.33 \(\pm\) 0.06 b & 1.47 \(\pm\) 0.07 b & 8.41 \(\pm\) 0.10 b & 6.4 \(\pm\) 0.3 b & 59.9 \(\pm\) 4.9 b \\ FL+(UV-A) & 0.69 \(\pm\) 0.16 a & 2.62 \(\pm\) 0.53 a & 8.77 \(\pm\) 0.17 a & 10.3 \(\pm\) 1.6 a & 102.2 \(\pm\) 19.0 a \\ FL + (UV-A+UV-B) & 0.73 \(\pm\) 0.07 a & 2.91 \(\pm\) 0.37 a & 8.23 \(\pm\) 0.10 b & 10.1 \(\pm\) 0.4 a & 116.0 \(\pm\) 11.1 a \\ FL+(UV-C) & 0.22 \(\pm\) 0.08 b & 0.86 \(\pm\) 0.23 c & 8.56 \(\pm\) 0.14 ab & 5.5 \(\pm\) 1.2 b & 19.2 \(\pm\) 3.5 c \\ \hline \multicolumn{6}{c}{60 th day of cultivation} \\ FL (Control) & 2.33 \(\pm\) 0.26 b & 6.73 \(\pm\) 0.03 b & 8.15 \(\pm\) 0.20 b & 17.0 \(\pm\) 1.0 b & 251.5 \(\pm\) 16.5 b \\ FL+(UV-A) & 5.02 \(\pm\) 0.36 a & 15.92 \(\pm\) 2.56 a & 11.81 \(\pm\) 0.43 c & 32.5 \(\pm\) 0.5 a & 357.0 \(\pm\) 19.6 a \\ FL + (UV-A+UV-B) & 2.93 \(\pm\) 0.77 b & 9.29 \(\pm\) 2.55 ab & 11.01 \(\pm\) 0.09 c & 26.0 \(\pm\) 4.0 a & 298.6 \(\pm\) 14.3 ab \\ FL+(UV-C) & 2.09 \(\pm\) 0.41 b & 5.62 \(\pm\) 0.55 c & 14.76 \(\pm\) 2.76 c & 19.5 \(\pm\) 2.5 ab & 156.7 \(\pm\) 17.3 c \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Morphological parameters of sweet basil “Red Ruby” plants on the 30 th and 60 th days of cultivation. Values represent mean SEM (n = 20). Letters indicate significant differences among treatment and control samples (_p_ \(<\) 0.05). Figure 1: Typical basil plants on the 60 th day from germination. reduced it to 3%. UV-C induced the eugenol methyl ether accumulation (17%) content, which remained unchanged under UV-A and UV-B. Moreover, under UV-C the proportion of fenchone practically did not change. Thus, UV, jointly with main growth light, may be applied to change the essential oil profile.
# Multi-Level Credit Assignment for Cooperative Multi-Agent Reinforcement Learning
## 4 Method
### Multi-Level Credit Assignment  
After the introduction of the hierarchical model and the dilated GRU, in Figure 3 we present the multi-level credit assignment and the whole structure of the proposed method MLCA.  
To achieve multi-level credit assignment for this two-hierarchical model, we propose to use one centralized critic network for each hierarchy. Then, there are two centralized critics, i.e., the manager critic for the high-level manager and the worker critic for the low-level worker. The manager critic takes all high-level plans \(\{\alpha_{i}\}_{i=0}^{N}\) generated by the managers of all agents as input and outputs a total Q-value for the current joint plan. Thus,  
Figure 3: The structure of the MLCA algorithm.  
Figure 2: The traditional GRU versus the dilated GRU: **(a)** The unrolling structure of the traditonal GRU; **(b)** The unrolling structure of the dilated GRU.  
the total Q-value for the manager level can be denoted as \(Q^{m}_{tot}(s,\{\alpha_{i}\}_{i=0}^{N})\), which is what the method wants to maximize. Moreover, in order to obtain a good critic, the object function for the manager critic is  
\[\mathcal{L}^{m}=\mathbb{E}[R_{t}-Q^{m}_{tot}(s,\{\alpha_{i}\}_{i=0}^{N})], \tag{10}\]  
where \(R_{t}\) denotes the discounted accumulated rewards and can be defined as  
\[R_{t}=\sum_{i=t}^{T}\gamma^{i-t}r_{i}. \tag{11}\]  
Similarly, the worker critic takes all low-level primitive policies \(\{\pi_{i}\}_{i=0}^{n}\) as input and outputs a total Q-value for current joint policies. Thus, the total Q-value for the worker level can be denoted as \(Q^{w}_{tot}(s,\{\pi\}_{i=0}^{N})\), which is what the method wants to maximize. The object function for the manager critic is  
\[\mathcal{L}^{w}=\mathbb{E}[R_{t}-Q^{w}_{tot}(s,\{\pi\}_{i=0}^{N})], \tag{12}\]  
To this end, the whole learning process can be represented as Algorithm 1.  
```
1:Randomly initialize the neural networks (i.e., \(\theta\) and \(\
u\)) for agents' individual functions and all centralized critics
2:for number of training iterations do
3: Sample b trajectories \(D_{1},D_{2},\cdots,D_{b}\) by interacting with the environment
4: Calculate accumulated rewards \(\{R_{0,i},\cdots,R_{T,t}\}\) for each trajectory \(D_{i}\)
5:for k iterations do
6: Update both centralized critics by minimizing Equation (13) \[\mathcal{L}=\mathcal{L}^{m}+\mathcal{L}^{w}\] (13)
7: Update decentralized hierarchical model by maximizing Equation (14) \[Q^{m}_{tot}(s,\{\alpha_{i}\}_{i=0}^{N})+Q^{w}_{tot}(s,\{\pi\}_{i=0}^{N})\] (14)
8:endfor
9:endfor
```  
**Algorithm 1**Optimization Process for MLCA
# A Review of Unmanned Aerial Vehicle Based Antenna and Propagation Measurements
## 1 Introduction
UAVs are used to evaluate the performance of existing ATG communication systems and to identify potential sources of interference or signal degradation [16]. Propagation measurements can be conducted by mounting a transmitting antenna and lightweight transmitter with a built-in battery on a UAV and a standard calibrated antenna acting as a receiver connected to a spectrum analyzer at the ground level. A critical issue is airframe shadowing, which obstructs the line-of-sight (LOS) path between the transmitter and receiver caused by the UAV body. In this regard, it is important to verify that the transmitting antenna mounted on the UAV is in direct LOS with the receiver before performing any propagation measurements [17]. This article describes how UAVs equipped with an antenna and an SDR configured as a receiver can be used to measure antenna and propagation characteristics in various practical cases. In contrast, previous studies have emphasized only one specific case each time. The main contributions of this study are in the following topics:* A comprehensive review of the latest advancements in UAV-based antenna and propagation measurements encompassing various techniques and applications. * Implementation of both near-field and far-field measurement techniques, emphasizing the practical advantages, including a discussion on the use of SDRs in UAV-based measurements. * Exploration of a wide range of applications, from the SKA and large-scale biconical antennas to 5G/6G network evaluations. * Illustration of practical implementations and effectiveness of UAV-based measurement systems, presented using real-world test cases, such as measurements of parabolic reflector systems and large-scale propagation channel effects. The remainder of this paper is organized as shown in Figure 1. Section 2 focuses on UAV-based measurements. In this section, we provide design recommendations for UAV-based measurements. By following the design recommendations, we describe how UAV-based measurements can be performed in the far- and near-field regions. For UAV-based antenna measurements in the far-field region, we describe the procedure for measuring base station antennas (BASTAs) and digital television (DTV) stations deployed in the field. To measure antennas operating at lower frequencies, a larger distance between the AUT and UAV is required, owing to the large dimensions of the AUT. To overcome these large separation distances, antenna measurements can be performed in the near-field region. We explain UAV-based near-field measurements for structurally large antennas, such as low-frequency aperture arrays (LFAAs) and biconical antennas. UAV-based propagation measurements are presented in Section 3. In this section, a detailed explanation of the simulation results for large-scale and small-scale propagation measurements is presented. Based on the UAV-based antenna and propagation measurements, we explain the design considerations for selecting the UAV probe, and the conclusions are provided in Section 4. Figure 1: Organization of the paper.
## 4 Experimental Section  
### Test Methodology  
Four types of LED lamps were used to verify the SAC and CDNE methods. Their selection was guided by the level of the electromagnetic disturbance they generated. The parameters of the selected lamps for testing are summarised in Table 4. Lamps 5 W, 9 W, 15 W, and 18 W were selected for testing, respectively. The lamps selected for testing allowed to test three scenarios. The first was when the lamp under test was characterised by emission levels far removed from the limit values specified in the standard [6] for the SAC and CDNE methods. The second was when the emission values of the lamp under test were close to the limit values, and the third was when the lamp under test had emission levels well above the limit values.  
Figure 4: CDN-M2 network voltage division ratio measurement stand. (**a**) Electrical diagram and (**b**) photograph of the stand.  
Figure 5: Frequency characteristics of the voltage division factor for [PERSON]’s CDN-M2 network.  
The following test chronology was adopted in this study: Firstly, the dispersion of the disturbance signal values emitted by LED lamps of the same nominal power and manufacturer was checked. For this purpose, three 18 W lamps were tested sequentially once with the SAC method and then with the CDNE method. Then three 5 W lamps were tested using both methods. These tests showed that there were no significant differences in the achieved values of emitted disturbances between the groups for each method separately. This made it possible to carry out further tests in such a way that each of the four LED lamps from different manufacturers with powers of 5, 9, 15, and 18 W were first tested using the SAC method and then using the CDNE method. For the SAC method, the required frequency range of 30 to 300 MHz was divided into 60 intervals. In each interval, one frequency value corresponding to the maximum emission level was obtained after measurement with the peak detector. Then, for this frequency value, the emission level was measured with a quasipeak QP detector. In this way, 60 QP values were obtained for each of the tested lamps. It should be noted that the frequency values for which QP values were obtained were different for each tested lamp. This is because each LED lamp has its own characteristic spectrum of disturbance emission. Subsequently, each LED lamp was tested using the CDNE method for the frequency values that the individual lamp had previously obtained using the SAC method. Thus, a given lamp was tested first with SAC and then with CDNE for the same frequency values. The recorded QP values obtained using the SAC and CDNE methods for each tested lamp, together with the corresponding limits (Limit) and margins (Margin), were then compared using the statistical tools available in Statistica software v13. An analysis of the results showed different performance scenarios for a given tested LED lamp once with the SAC method and then with the CDNE method. The obtained results and their analysis are presented in the next subsections.
## 5 Conclusions and Way Forward
While temporal changes in \(TV_{AB}(t)\) simply scale linearly with the momentary velocity \(v(t)\),  
\[\frac{d(TV_{AB}(\tau_{AB}))}{dt}\,\sim\,L_{AB}\,\frac{dv(t)}{dt}, \tag{29}\]  
the changes in \(ETV_{AB}(t)\) vary in a more complicated way:  
\[\frac{d(ETV_{AB}(t))}{dt}\,\sim-\frac{L_{AB}}{v(t)^{2}}\,\frac{dv(t)}{dt}. \tag{30}\]  
A common feature of both temporal changes is that determining the time variation of both FOMs requires the measurement of the momentarily acting acceleration \(\overset{\rightarrow}{dv(t)}/dt\) inside a car. Such measurements are easy to perform due to the wide availability of micro-inertial sensors in cars. An example of a micro-inertial sensor chip is sketched in Figure 13.  
Figure 12: (**a**) Transport value \(TV_{AB}\) (blue) and eco-rated transport value \(ETV_{AB}\) (green) drawn as functions of the transport speed \(v_{AB}\) in a ficuous, frictionless “start–stop” scenario with \(\delta=0\); (**b**) same kind of data but for a “start–stop” scenario with air friction all along the transport distance \(L_{AB}\). Black arrows denote increases in both quantities with increasing transport length. Simple, one-dimensional acceleration sensors were first introduced into cars to trigger airbag safety systems (ABS). Later, multi-dimensional sensor systems, like the one shown in Figure 13, were introduced as key elements in ESP systems (ESP = electronic stability program), which sense the momentary motion of cars in three-dimensional space, and which apply brake forces to individual wheels to generate yaw moments that bring the car's motion in line with the driver's intention as signaled by measured steering wheel angles [30]. As the introduction of such systems into cars has significantly reduced the numbers of fatal car accidents, the introduction of such systems into cars has become mandatory via European and national legislations [31]. Modern cars, therefore, are comprehensively equipped with such systems, which in principle can additionally be used to determine momentary and time-integrated values of \(TV_{AB}\) and \(ETV_{AB}\). While online monitoring of both FOMs can give drivers immediate feedback on their performance as drivers, time-integrated values of \(TV_{AB}(\tau_{AB})\) and \(ETV_{AB}(\tau_{AB})\), determined after completing a specific route joining start and endpoints \(A\) and \(B\), can provide valuable statistical data for the economic value and its associated ecological footprint of driving along a specified route. Frequently repeating such journeys along different routes within a specific area, can in the long run provide maps of FOM values, which represent averages over different traffic-, weather- and road conditions, and which can be used as cost factors in traffic and logistics optimization algorithms. This research received no external funding. Not applicable. All relevant data are contained in the above paper. The author is grateful for constructive comments during the review period. The authors declare no conflicts of interest.
# PEiD: Precise and Real-Time LOS/NLOS Path Identification Based on Peak Energy Index Distribution
## 5 Experimental Evaluation
### LOS/NLOS Identification in the Mobile Scenario  
We conduct experiments to test the performance of PEiD in the mobile scenario. Specifically, we asked a volunteer to carry a WiFi receiver and walk out or enter the room at a speed of 0.8 m/s. The transmitter is placed in the room and transmits packets at a rate of 500 Hz. When the volunteer is in the room, no obstacle could block the LOS path between the transceiver, and the model is expected to output LOS accordingly. Once the volunteer steps out of the room, the LOS path will be disturbed by the wall, and the channel state is supposed to be NLOS.  
We collect two sets of data: one for walking out of the room (from LOS to NLOS) and the other for entering the room (from NLOS to LOS). Each lasts 10 s in total and contains 5000 packets. To verify the effectiveness of the selected feature, we calculate the STD of SIFPE during the time the user moves and plot the results in Figure 11. Since the window length is set to 95, we can obtain 4906 time indices for LOS/NLOS identification.  
As shown in Figure 11, we present the concatenated curves of STD obtained from two consecutive experiments. The path status initially begins with NLOS, then transitions to LOS, and eventually returns to NLOS. It can be seen that the STD of NLOS paths is notably higher than that of LOS paths, enabling easy identification of the transition points between them. We can determine that the time point when the user enters the room lies between packet indices 3000 and 3200, while the time point when the user walks out of the room falls within the range of packet indices 6500 and 6700.  
However, a significant challenge arises due to the presence of considerable noise in the calculated STD which can adversely impact the identification accuracy. To mitigate this issue, increasing the window length proves effective in reducing the impact of fluctuation and maintaining output stability. Nevertheless, it compromises the identification latency by extending the length of ambiguous outputs where LOS samples coexist with NLOS ones in the window.  
Figure 11: The change of STD between LOS and NLOS.
## 5 Conclusions  
The use of relatively coarse Ti powder in MIM technology with the potential for biomedical applications has been explored and compared with commercial Ti feedstock PolyMIM Grade 2 in terms of the rheological, melting and sintering behaviours, as well as dimensional shrinkages. Samples made by the current study feedstock with the coarse Ti powder shrank 35% less than samples processed by the commercial feedstock and had a relative density 14% lower than that of the samples processed using commercial feedstock. The average pore diameter for samples processed by the current study feedstock was approximately three-times larger than that of the commercial feedstock, and the pores were less round with a sphericity of about 0.49. Based on the results, it can be concluded that the use of coarse Ti powder could be beneficial in increasing the competitiveness of MIM among other processes for the production of biomedical parts, as it saves cost, reduces shrinkage and also results in more of the intentionally-induced micropores that are desired for cell attachment and bone ingrowth. In addition, plasma oxidation treatment for the microporous samples processed by the coarse Ti powder was carried out successfully, yielding a thick rough coating with interconnected microporous channels and openings resembling the mouth of a volcanic crater. The coating was mainly comprised of a mixture of rutile and anatase titanium dioxide. These coated microporous samples could have huge potential in biomedical applications, as they do not only possess different sizes of micropores that arise from the production process and PEO treatment, but also have a rough biologically-active surface, unlike pure Ti.  
Partial support for this work was provided by the ERC Advanced Grant (#320879 'IMPUNEP') and the Russian Ministry of Education and Science under Increase Competitiveness Program of National University of Science and Technology \"MISiS\" (No. K2-2016-011). One of the authors ([PERSON]) would like to acknowledge a studentship from the Iraqi Ministry of Higher Education and Scientific Research.  
[PERSON] designed and performed the experiments, and analyzed the data. The study was conceived and the paper written by [PERSON] in collaboration with [PERSON] and [PERSON], acting as supervisors.  
The authors declare no conflict of interest.