esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Definition of the Theoretical Process Parameters  
### Lower Heating Value (LHV)  
LHV is calculated experimentally according to Equation (1) where \(LHV_{CO}\), \(LHV_{H_{2}}\), and \(LHV_{CH_{4}}\) are equal to 10,110 kJ/kg, 119,494 kJ/kg, and 49,915 kJ/kg, respectively [35,36]. [%CO, %CH4] and [%H2] are the mole fractions of the syngas components and were determined by means of the portable gas analyzer.  
\[LHV_{gas}=[\%CO]\cdot LHV_{CO}+[\%CH_{4}]\cdot LHV_{CH_{4}}+[\%H_{2}]\cdot LHV _{H_{2}} \tag{1}\]  
Gasifier Efficiency (\(\eta_{com}\)) and Motor-Generator Efficiency (\(\eta_{mod\text{-}gm}\))  
The global performance of the transformation from biomass to electrical power through syngas includes two partial transformations: firstly, the conversion of biomass to syngas, taking place in the gasifier (\(\eta_{com}\)). Secondly, the production of electricity from the syngas by means of an adapted internal combustion motor (ICM) directly fed by syngas and coupled to an electric generator (\(\eta_{\textit{mot-}gen}\)). \(\eta_{\textit{con}}\) is calculated as the ratio of the syngas energy outlet rate and the biomass energy inlet rate, as shown in Equation (2), where \(\dot{V}_{syngas}\) and \(\dot{m}_{\textit{ biomass}}\) are the volume and mass flow rates of syngas and biomass, respectively.  
\[\eta_{con}=\frac{E_{syngas}}{E_{\textit{ biomass}}}=\frac{\dot{V}_{ syngas}\cdot LHV_{syngas}}{\dot{m}_{\textit{ biomass}}\cdot LHV_{\textit{ biomass}}} \tag{2}\]  
With \(E_{\textit{out}}\) being the electric power produced from the syngas combustion, the performance of the block motor-generator (\(\eta_{\textit{mot-}gen}\)) is calculated by means of Equation (3):  
\[\eta_{\textit{mot-}gen}=\frac{E_{\textit{out}}}{\dot{V}_{syngas}\cdot LHV_{ syngas}} \tag{3}\]  
Power generation (\(E_{\textit{out}}\)) is directly related to the quality of syngas, its LHV, composition, and impurities (like tar). A block ICM-generator has been proven to be economically feasible for capacities from 5 kWe and above [37], so its use is justified in this case. This power has been measured by means of a network analyzer.
## 2 System Model and Problem Formulation
### Communication Model  
To shed light on the nuanced details of the frequency-selection problem at hand, we methodically partition the entire available frequency band \(B\) into \(M\) equally-sized sub-bands, each possessing a bandwidth delineated as \(b=\frac{B}{M}\). Consequently, the range of communication bandwidth between the UAV and the receiver is strictly demarcated within the frequency range \([f^{c}_{Ul}-\frac{b}{2},f^{c}_{U}+\frac{b}{2}]\). The UAV's center frequency \(f^{c}_{U}\) is ascertained by utilizing the mathematical formula \(f^{c}_{Ul}=f^{s}_{Ul}+\frac{2u-1}{2}b\), where \(f^{s}_{U}\) designates the initial sensing frequency of the UAV, and \(n\) specifies the index of the \(n\)-th sub-band. Within the specialized context of wireless communication networks, the communication link that exists between the UAV and the receiver is conventionally termed an A2G link. Conversely, the link that connects the jammer to the receiver is categorized as a ground-to-ground (G2G) link. The spatial distance between the UAV and the receiver is denoted by  
\[d_{U,R}=\sqrt{\varsigma^{2}_{z_{Ul}}+\|[c_{x_{Ul}},c_{y_{U}}]-[c^{\prime}_{x_{ R}},c^{\prime}_{y_{R}}]\|^{2}}, \tag{1}\]  
and the distance between the receiver and the jammer is  
\[d_{R,J}=\sqrt{\|[c_{x_{R}},c_{y_{R}}]-[c^{\prime}_{x_{J}},c^{\prime}_{y_{J}}] \|^{2}}, \tag{2}\]  
where \(\|\cdot\|\) represents the Euclidean norm. In the case of Line-of-Sight (LOS) communication, the channel gain \(g_{U}\) between the UAV and the receiver can be expressed as  
\[g_{U}=g_{U0}d^{-\beta}_{U,R}, \tag{3}\]  
and the channel gain \(g_{J}\) between the jammer and the receiver can be computed as  
\[g_{I}=g_{I0}d^{-\beta}_{U,J}, \tag{4}\]  
where \(g_{U0}\) and \(g_{I0}\) denote the reference channel gain at \(d_{U,R}=d_{U,J}=1\)m, respectively, and \(\beta\geq 2\) is the path loss exponent, with the assumption that the channel gain does not change during each time slot. The following is the received signal-to-jamming-plus-noise ratio (SJNR) from the UAV to the receiver:  
\[\rho(f^{c}_{U})=\frac{g_{U}P_{U}}{\int_{f^{c}_{U}-b/2}^{f^{c}_{U}+b/2}[N(f)+g_ {J}I(f-f^{c}_{J})]\mathrm{d}f}. \tag{5}\]  
At the start of each time slot, the UAV determines the distance and quality of the communication channel between itself and the receiver. Simultaneously, the jammer performs the same action between itself and the UAV.
## 6 Conclusions  
The present study considered the operation of a multireservoir system with the aim of increasing hydropower generation based on a new, evolutionary CA. Based on the proposed model, the average generated energy for normal years was 0.94%, 2.09%, and 1.8% more than via the MBF, GA, and PSO, respectively. The average generated energy for wet years based on the CA was 1.3%, 2.6%, and 2.2% more than via the MBF, GA, and PSO, respectively. The computational time based on the CA over three years was less than via the GA, PSO, and MBF. The results indicated that the CA could supply the required hydropower for downstream demand based on high values of the TRI and VRI. For example, the VRI via the CA for the HJA reservoir was 91%, while it was 83%, 76%, and 65% via the MBF, PSO, and GA, respectively. Additionally, the CA decreased the vulnerability index compared to the MBF, PSO, and GA for all reservoirs. The results indicated that the HJD reservoir had an important role in the process of hydropower generation: Therefore, the inflow volume into this reservoir had considerable value when storing flow during critical periods. Additionally, the reservoir with an increase in outflow attempted to transfer the necessary inflow toward downstream reservoirs for hydropower generation. Thus, the CA has high potential for water resource and energy management based on the computed results: Also, future studies can be related to the operation of a multireservoir system under climate change conditions based on the CA to determine the ability of the CA under climate change conditions when managing and planning in water resource and energy fields.  
Conceptualization, M.A.M. and A.N.A.; formal analysis, M.E., H.A.A., A.H.E.-s., and S.H.L.; methodology, M.E.; supervision, A.E.-S; validation, N.S.M.; writing--original draft, S.B.K., H.A.A., M.A.M., and C.C.O.; writing--review and editing, M.S.H. This research was funding by University of Malaya Research Grant (UMRG) coded RP025A-18 SUS, University of Malaya, Malaysia. In addition, this study was partially supported by the iRMC Bold 2025, Universiti Tenaga Nasional, Malaysia. Grant code: RJO 10436494.  
The authors appreciate so much the facilities support by the Civil Engineering Department, Faculty of Engineering, University of Malaya, Malaysia and Department of Water Engineering and Hydraulic Structures, Faculty of Civil Engineering, Semnan University, Iran.  
The authors declare no conflict of interest.
# Study of the Preparation and Dielectric Property of PP/SMA/PVDF Blend Material
## 3 Result and Discussion
### Dielectric Properties
This is attributed to the addition of SMA, which improved the compatibility of the polymer matrix, then made electrons migrate between conductive particles, and thus increased electric field intensity of PVDF, promoted polarization degree, and finally resulted in the increase of the dielectric constant of blends. The dielectric loss of PP/PVDF blends and PP/SMA/PVDF blends with 3% SMA addition were tested under the same condition as Figure 7a, and the results are shown in Figure 7b. It can be found that the dielectric loss of both blends decreased with the increase of PP, while that of the blends with SMA addition was less than that of PP/PVDF blends. Although there was a slight increase when PP content was 50%, the general trend decreased gradually and stayed at around 0.01.
## 2 Results
### Functional Characterization of New TPSs  
Besides previously reported TPSs from Cannabis [28; 29; 30; 31], three new TPSs were identified from the RNA-seq data containing full-length open reading frames (ORFs). They were designated as _CSTPS3 GT_, _CSTPS4 WS_, and _CSTPS5 TH_, to reflect their respective origins in the GT, WS, and TH cultivars. _CSTPS4 WS_ and _CSTPS5 TH_ had very high expressions in the trichomes of all cultivars, whereas _CSTPS3 GT_ showed lower expression in the trichomes of the HB, CD, and WS cultivars compared to the stem (Figure S5). Based  
Figure 4: Biosynthetic profiles of Cannabis cultivars. (**a**) Cultivar-normalized expression heatmap of significantly regulated genes encoding metabolic enzymes involved in cannabinoid and terpen biosynthesis. The heatmap is based on the total expression values of genes that encode enzymes with the same products. The total gene expression values (TMM as units) are also given in each cell. Note that _ALDH_ is associated with both NAD(P)H compensation and the hexanoate pathway. (**b**) Similar heatmap for key TPSs, including those shortlisted for functional validation. For the full names for all enzymes and metabolites, refer to Figures 1 and 2. Refer to Table S8 for the gene IDs involved.  
on amino acid sequence similarity, CsTPS3 GT was 96% similar to CsTPS31 (accession number: QLC36839.1), which was previously characterized as producing an unknown sesquiterpene with an RI of 1916 and a base peak of 93 [29]. Furthermore, CsTPS4 WS was identical to CsTPS29 (accession number: QLC36833.1), which was previously identified as a linalool synthase [29], while CsTPS5 TH was 98.7% similar to CsTPS35 (accession number: QLC36840.1), a linalool/nerolidol synthase [29].  
As expected for TPSs, all three peptides contain the 'DDXXD' and 'NSE/DTE' motifs, which allow them to participate in divalent metal ion-assisted binding of substrates and cofactors [32]. Furthermore, both CsTPS3 GT and CsTPS4 WS contained a tandem arginine/tryptophan motif, 'RR(X8)W', at the N-terminal region (Figure S6), which could be used for monoterpene cyclization [33]. However, the plastid-targeting peptide, which is unique to mono-TPSs, was absent in CsTPS3 GT (Figure S6). Phylogenetic analysis placed CsTPS3 GT and CsTPS4 WS in the TPS-b sub-family, whereas CsTPS5 TH fell under the TPS-g sub-family (Figure 5).  
The intracellular localization of each TPS was investigated in _N. benthamiana_ leaf cells. Upon transient expression of YFP-tagged TPSs in the leaf cells, both CsTPS3 GT and CsTPS5 TH were found to localize to the cytosol (Figure 6), which is consistent with their lack of transit peptide sequences and thus suggests that they are sesqui-TPSs, rather than mono-TPSs (Figure 6). On the other hand, CsTPS4 WS was localized to the chloroplasts, indicating it to be a mono-TPS.  
Figure 5: Phylogenetic analysis of CsTPSs. Neighbour-joining phylogenetic tree of CsTPS3 GT, CsTPS4 WS, and CsTPS5 TH, with other CsTPSs. The scale bar indicates the number of amino acid substitutions per site.
# Aerial Tele-Manipulation with Passive Tool via Parallel Position/Force Control +
## 4 Stability Analysis
### Stability of Teleoperator in Free Flight  
During the free flight, assuming fast rotational dynamics compared to translational dynamics, and gravity compensation, we may express the slave robot and its controller as \(G_{s}(s)=Ms^{2}+K_{D}s\) and \(C_{s}(s)=K_{p}\), the input to \(G_{s}\) is the integrated value of scaled \(x_{m}\), and the force feedback to the master side is the robot velocity. Therefore, \(G_{2}\) can be expressed as (Figure 6):  
\[G_{2}=(\frac{1}{s}I)(G_{s}C_{s}(I+G_{s}C_{s})^{-1})(Is)=G_{s}C_{s}(I+G_{s}C_{s })^{-1}, \tag{43}\]  
which is internally stable by choosing pose controller gains \(k_{p},k_{D}>0\). The master side of the teleoperator is the same as in the contact interaction; therefore, \(G_{1}\) does not change. Considering velocity \(\dot{\mathbf{p}}\) as the output of the system, it can be obtained as:  
\[s\mathbf{p}(s)=G_{2}K_{p}^{f}G_{1}(I+G_{2}K_{p}^{f}G_{1}K_{p}^{b})^{-1}f_{h}. \tag{44}\]  
\(s\mathbf{p}(s)\), that is, velocity in frequency domain, is the system output constituted by the negative feedback interconnection of passive systems \(G_{1}\) and \(G_{2}\). Therefore, the teleoperator in free flight is passive and stable.
# Creating Audio Object-Focused Acoustic Environments for Room-Scale Virtual Reality
## 2 Background
### Spatial Implementation of Music in Room-Scale VR  
The spatial impact and spatial implementation of non-diegetic music in VR are debated [10; 11]. Despite the potential to detract from the spatial audio experience [11], non-diegetic music can guide players through several game spaces, potentially reducing the impact of the event boundary phenomenon [38] and, therefore, improving the spatial cohesion of games [39]. Composers have also sought to soften or exaggerate the spatial disconnect between diegetic sound effects and non-diegetic music, depending on a game's context [40].  
Visualizations of non-diegetic music can be found mainly in music-centered games ([18], p. 166), but tend not to correlate spatially with the music's \"instruments\". For example, _Beat Saber_[41] visualizes various features of non-diegetic music, such as rhythm, tension, or contrasts, but limits spatial audio only to sound effects caused by hand interaction.  
Alternatively, diegetic music would allow the player to approach its source and share its acoustic space with the player. This can be seen in the two music examples in Section 2.1. However, placing the music's instruments across the acoustic environment is challenging in the author's development platform, as FMOD stores audio/music as events and links them to a single position in space when used with a 3D audio spatializer ([42], Chapter 16.2.3). Thus, implementing multi-source diegetic music in FMOD requires an alternative solution.
## 3 Results
### Dispersion Properties  
One main advantage PCFs possess over step-index fibers when used for SC generation is that the dispersion characteristics of the PCF can be tuned by selecting the proper ratio of \(\Lambda\)/d [3], where d is the cladding hole size and \(\Lambda\) is the cladding hole spacing. A near-minimum value of the dispersion at the pump wavelength is desirable when generating SC [24; 25; 26; 27]. Typically, the shorter wavelength  
Figure 3: (**a**) Scanning electron microscope image of the endlessly single-mode fiber (ESF). The simulated (\(\lambda\) = 1060 nm) modes of the ESF are as follows: (**b**) single core-guided mode and (**c–f**) a few higher-order cladding modes (CM), with the first subscript being the angular quantum number and the second subscript being the radial. The effective index of refraction n\({}_{\text{eff}}\) is shown for each mode.  
dispersion is dominated by the material dispersion and the longer wavelength dispersion is dictated by confinement. Under general conditions, if the mode area becomes larger, the region of anomalous dispersion is pushed towards longer wavelengths due to the weakened effects of confinement [28]. In the case of ESF, however, these large area modes of the cladding can still have a relatively short-wavelength zero dispersion because of the additional confinement provided by close air-hole spacing. These types of effects are also seen in supermode fibers, where the overall dispersion properties of the supermode are strongly governed by the properties of each individual core that make up the supermode [29]. This allows the intensity of a supermode to spread out over all of the coupled cores of the fiber while maintaining the dispersion characteristics of the individual fibers.  
The group delay per length of the ESF was experimentally measured (Figure 4) using the interferometric setup with a spatial aperture added to the output of the fiber to filter particular modes. In Figure 4, two of the measured modes are labeled CMij because they are known to be cladding modes, but their exact quantum numbers are unknown. The modes' propagation constants were also calculated using the modified finite-difference algorithm introduced above. As is evident in the group-delay measurements in Figure 4, there are two distinct groups of curves. The lower curve represents the fundamental, core-guided mode, while the upper group of curves represent the dispersion of the cluster of cladding modes. In this case, the zero-dispersion wavelength for all modes falls in the region of 1060 nm, which is ideal for pumping with an ytterbium-doped fiber laser. Calculations showed that there were a very large number of modes associated with this upper group of cladding-mode curves, although it was not possible to experimentally distinguish all of them due to their complex spatial patterns.
## 4 Conclusions  
We successfully synthesized the advanced hypergolic chemicals [TMH]\({}^{+}\)[X]\({}^{-}\) and [EMT]\({}^{+}\)[X]\({}^{-}\) (X = I\({}^{-}\), CN\({}^{-}\), N\({}^{3-}\), NO\({}^{3-}\), NO\({}^{2-}\), ClO\({}^{4-}\), and AlCl\({}^{4-}\)) by ion-exchange reaction following an Sn2 reaction. The physical and chemical properties of the synthesized mixture of hypergolic chemicals with [TMH]\({}^{+}\)[X]\({}^{-}\) and [EMT]\({}^{+}\)[X]\({}^{-}\) were evaluated. We were able to conclude the following:  
Figure 11: FT-IR spectrum of explosion gas of an mixture hypergolic chemicals with [EMT]\({}^{+}\)[CN]\({}^{-}\) and HOCH\({}_{2}\)CH\({}_{2}\)NHNH\({}_{2}\) using H\({}_{2}\)O\({}_{2}\).  
Figure 10: ID time photographs of the mixture hypergolic chemicals with [EMT]\({}^{+}\)[I]\({}^{-}\) and HOCH\({}_{2}\)CH\({}_{2}\)NHNH\({}_{2}\) using H\({}_{2}\)O\({}_{2}\). Figure 11 shows the FT-IR spectrum of the explosion gas of a mixture with [EMT]\({}^{+}\)[CN]\({}^{-}\) and HOCH\({}_{2}\)CH\({}_{2}\)NHNH\({}_{2}\) using H\({}_{2}\)O\({}_{2}\). The characteristic peaks were assigned as shown in Figure 10. However, nitro group peak indicating toxic chemicals were not observed in this experiment.  
1. The synthesized hypergolic chemicals with [TMH]+[X]- and [EMT]+[X]- have good thermal stability.
2. The mixture of hypergolic chemicals with [TMH]+[N3]- have a low ID time of 55.6 ms.
3. The mixture of hypergolic chemicals with [EMT]+[N3]-, [EMT]+[CN]-, and [EMT]+[I]- have very short ID times (18.0, 32.6, and 7.97 ms, respectively).
4. Toxic nitro group-contained chemicals were not detected after detonation between hypergolic chemicals and H2O2 as oxidizer.
5. A synthesized mixture hypergolic chemicals such as [TMH]+[N3]-, [EMT]+[N3]-, [EMT]+[CN]-, and [EMT]+[I]- could be used as rocket propellant liquid fuel.  
This work was supported by a National Research Foundation of Korea Grant, funded by the Korean Government (NRF-2013M1A3A3A02041878).
# Denoising Diffusion Models on Model-Based Latent Space
## 3 Proposed Method
### Latent-Space Diffusion Models  
The lossy compression algorithm defines an encoding function \(\mathcal{E}\) which maps an image \(x\) to a latent representation \(z\in\mathbb{R}^{\frac{n}{\kappa}\times\frac{1}{\kappa}\times\mathcal{E}}\). Ideally, redefining the training objective of the generative model to operate in the new latent space should be straightforward:  
\[\min_{\theta}\ \|\epsilon-\epsilon_{\theta}(z_{t},t)\|_{2}^{2}\ \ \ \ \ \ z_{t}= \mathcal{E}(x_{t}) \tag{17}\]  
This approach has been shown to be valid in the case of a latent space defined by a deep autoencoder [6; 12]. However, when relying on the shallow latent space defined by the proposed compression algorithm, we observed the impossibility of learning anything useful.  
The causes of this limitation can be traced back to the definition of the encoding algorithm using VQ (and its other embodiments), especially in the definition of the codewords using k-means, as defined in Equation (10). Effectively, the encoding of an image patch with the assigned code \(i\in\mathcal{I}\) imposes a bi-univocal correspondence with the set of positive integers, which implies an inherent total ordering of the codewords \(c_{1},\ldots,c_{|\mathcal{I}|}\). The imposed ordering, which by the nature of k-means is fundamentally random, is not reflected in an ordering of the \(\mathbb{R}^{n}\) space where codewords are defined; hence, the addition of Gaussian noise to \(i\) will result in the random assignment of a new codeword \(c_{|\mathcal{I}|+\mathcal{N}(0,1)|}\), while ideally the additive noise should lead to a codeword \(c_{j}\propto c_{i}+\mathcal{N}(0,1)\). The above can be intuitively appreciated by observing the effect of a constant addition on an encoded image in Figure 3b.  
A useful interpretation is to consider the codewords as a finite and discrete set of categorical data, which brings us to the domain of categorical diffusion models, previously introduced in Section 2.2. Recent works on discrete-state diffusion models [24; 25; 26; 27; 28] mainly propose to redefine the forward diffusion process by relying on a transition matrix which defines the probabilities of moving from one state to another. An interesting result of [24] is that we can imitate a continuous-space diffusion model by defining the discrete diffusion process in a way that will transit with higher probability to similar states, introducing an ordinal bias. Along this line, we show that the careful choice of an optimal ordering on \(\mathcal{C}\) can be leveraged to approximate as a continuous process virtually with no additional work.
# A Node Placement Algorithm Utilizing Mobile Nodes in WSN and IoT Networks +
## 2 Related Work  
[PERSON] et al. [11] proposed a data collection algorithm that uses multiple mobile nodes as sink nodes. The main goal is to optimize the path planning procedure. The algorithm starts with a dynamic clustering algorithm that randomly arranges the nodes into clusters. The node with the highest energy is selected to be the cluster head that establishes the data collection cluster. Based on the number of the mobile nodes, the monitoring area is divided into several parts, and each mobile node is assigned to the respective areas to perform the data collection. The authors propose a path-based path equalization algorithm (PEABR) to adjust the path of the mobile nodes in order to optimize the path planning scheme. The proposed algorithm was simulated and analyzed in Matlab and then further analyzed in the laboratory environment to verify its reliability. The results and analysis demonstrated that the algorithm is feasible and effective.  
[PERSON] et al. [12] presented a centralized energy-efficient clustering routing (CECR) protocol for mobile nodes. The main goal is to minimize the energy dissipation, as well as to maximize the packet delivery ratio. The algorithm consists of two steps. In the first step, the cluster head is selected periodically based on the average energy and speed of the nodes. The ability of some nodes to move around the network makes it possible for them to get disconnected from their cluster head; these nodes are called detached nodes. As a result, the second step is about these detached nodes, which will join their optimal clusters based on multiple factors. The proposed algorithm was simulated in NS-2, and it was compared to six clustering-based protocols, LEACH [13], LEACH-C [13], LEACH-Mobile [14], CBR [15], MBR [16], and LEACH-MF [17]. The results suggest that CEECR outperforms the other algorithms in terms of energy consumption and packet delivery ratio.  
To the best of our knowledge, there are no other algorithms in the literature that solve the problem of congestion using an approach as ours. The work in [11; 12] use mobile nodes for routing and data collection and not to resolve any network communication problems. In a recent survey [5], the solutions utilizing mobile nodes follow two approaches. In the first approach, the mobile nodes either refer to mobile sink nodes that collect data from sensor nodes, or they are used for routing purposes. In the second one, mobile nodes are represented as mobile robots that move in the network, replacing and assisting nodes. Our work focuses only on mobile nodes, which take up the role of the sensor nodes within the network; they are not used as mobile sink nodes and are not considered as mobile robots.