esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 System Model  
### Network Model  
Figure 1 presents a high-level overview of the proposed system, which includes both the operational scenario and the internal routing logic within each UAV. In the illustrated mission environment, UAVs perform collaborative tasks over urban and forest areas with diverse mobility patterns. Each UAV is equipped with a routing module enhanced by trajectory knowledge and a Q-learning agent. The example highlights a typical routing void encountered when the GPSR protocol fails due to insufficient neighbor coverage. By contrast, the proposed method anticipates future link availability and dynamically selects more reliable multi-hop paths.  
This paper introduces a UAV swarm that operates autonomously without ground station control, using only intra-swarm communication to execute tasks. The swarm consists of m low-altitude drones, \(\mathcal{U}=\{U_{1},U_{2},\,\ldots,U_{m}\}\), initially distributed randomly in a 3D space. Trajectory information is shared within the swarm to coordinate task planning and execution. To support predictive coordination and efficient routing, each UAV periodically disseminates its future trajectory through extended beacon messages embedded in HELLO packets. Neighboring nodes maintain a time-indexed local trajectory table, which is updated upon reception to support anticipatory coordination and predictive routing decisions.  
Each UAV node \(U_{i}\) determines its three-dimensional coordinates at time \(t\) via GPS, represented as \(P_{i}(t)=(x_{i}(t),y_{i}(t),z_{i}(t))\). We suppose that the 3D misssion area is limited in \((x_{\min}\leq x\leq x_{\max},y_{\min}\leq y\leq y_{\max},z_{\min}\leq z\leq z _{\max})\). The swarm follows a predefined  
Figure 1: Trajectory-aware routing system model for mission-oriented UAV swarms.  
task planning algorithm to determine its collective trajectory, where the future position of each UAV is governed by its current state and planned motion:  
\[\mathbf{p}_{i}(t+\Delta t)=f(\mathbf{p}_{i}(t),\mathbf{v}_{i}(t),\mathbf{a}_{i}( t),\Delta t) \tag{1}\]  
Here, \(f(\cdot)\) represents the motion model function, \(\mathbf{v}_{i}(t)\) denotes the velocity vector, and \(\mathbf{a}_{i}(t)\) represents the acceleration vector. The trajectory planning algorithm ensures collision avoidance as a fundamental constraint, maintaining a minimum safety distance \(r\) between any two UAVs at all times:  
\[\left\|q_{i}(t)-q_{j}(t)\right\|\geq r\quad\forall i\
eq j,t\geq 0. \tag{2}\]  
where \(\left\|\cdot\right\|\) represents the Euclidean norm.  
We assume that UAVs have a fixed communication range, allowing wireless communication between UAVs that are within this radius. Consequently, the network topology can be modeled as a time-dependent graph \(\mathcal{G}(t)=(\mathcal{V}(t),\mathcal{E}(t))\), where the edge set \(\mathcal{E}(t))\) represents the dynamic communication links between UAVs.
## 2 Single-Leg Kinematics and Dynamics Modelling
### Single-Leg Kinetic Modeling  
The simplified diagram of the single-leg structure, including the centroid coordinate systems of each link and the joint coordinate systems, is shown in Figure 2, where \(\theta_{A}\), \(\theta_{B}\), and \(\theta_{C}\) are the rotation angles of motor 1, 2, and 3, respectively.  
According to the properties of the parallelogram mechanism, \(\theta_{B}=-\theta_{D}=\theta_{E}=\theta_{F}=\theta_{G}=-\theta_{H}=-\theta_{I}\). Define the length of the link between joint \(i\) and joint \(j\) as \(l_{ij}\) (e.g., the length of the link between joint \(A\) and joint \(B\) is \(l_{AB}\)). Define the length of the centroid of each link relative to its own rotational joint coordinate system as \(l_{C_{i}}\) (e.g., the coordinate of the centroid \(C_{1}\) of the link between joint \(A\) and joint \(B\) relative to the coordinate system \(\{A\}\) is \((0\quad 0\quad-l_{C_{j}})^{\mathsf{T}}\)).  
Figure 1: Single-leg construction diagram.  
Define \(A\to C\to G\to I\to P\) as the main kinematic chain of the single-leg mechanism. According to the graphical method, the position vector \((p_{x},p_{y},p_{z})\) of the end point \(P\) (foot-end) of the main kinematic chain relative to the base coordinate system is as follows:  
\[\left\{\begin{array}{l}p_{x}=\cos\theta_{A}[-l_{GI}\sin(\theta_{B}+\theta_{C })+\cos\theta_{B}(l_{CG}+l_{IP})]\\ p_{y}=\sin\theta_{A}[-l_{GI}\sin(\theta_{B}+\theta_{C})+\cos\theta_{B}(l_{CG}+ l_{IP})]\\ p_{z}=-l_{GI}\cos(\theta_{B}+\theta_{C})-\sin\theta_{B}(l_{CG}+l_{IP})\\ \end{array}\right. \tag{1}\]
## 4 Experiments  
### Experimental Setup  
We demonstrate the effectiveness of proposed hybrid attention CTC/Attention framework firstly in three different ASR datasets, WSJ [20], AMI [21], and SWBD [22]. All experiments are implemented by ESPnet [17] with the default configurations. In particular, for AMI, ESPnet only provides the individual headset microphone (IHM) data training scripts. To ensure fairness, we just exhibit the AMI-IHM results. Moreover, for SWBD, ESPnet only shows the decoding results without CTC and language model. However, we decode SWBD with both CTC constraint and recurrent neural network language model (RNNLM). In addition, we also experiment decoding without language model for all the above datasets. The detail experimental configuration is exhibited in Table 1.  
Since the above-mentioned experimental datasets mainly focus on ASR in clean environments, we intend to explore whether the proposed method is robust to noise and reverberation. In particular, CHIME-4 [23], an ASR task for public noisy environments, consists of speech recorded using a tablet device with 6-channel microphones in four environments: cafe (CAF), street junction (STR), public transportation (BUS), and pedestrian area (PED). Therefore, we conduct the multichannel distant ASR experiments in CHIME-4. In addition, et05 simu and dt05 simu are the simulated evaluate and development datasets. Meanwhile, et05 real and dt05 real represent the real recorded evaluate and development datasets, respectively.  
In this paper, we follow the unified architecture proposed in [24], which jointly optimizes the multichannel enhancement and the ASR components. The experiment details are exhibited in Section 4.3. \"blstmp\" means that encoder is the projected bidirectional long short-term memory neural network. \"Vggblstmp\" means that an encoder is composed of vaccination guidelines group (VGG) [25] layer and blstmp layer.
# Harnessing Digital Twins for Sustainable Agricultural Water Management: A Systematic Review
## 4 Results
### Key Concept and Applications
#### 4.5.5 Digital Twins in Hydroponics  
[PERSON] et al. [102] explored the implementation of DT technology in agriculture. The study presents a DT architecture for smart farms, detailing its application at both laboratory and field levels. The design of the smart farm is comprehensive, incorporating ICT, IoT, big data analytics, and smart equipment to enhance productivity and efficiency. A notable component is the Secure Multi-Crop Smart Irrigation System (SMCSIS), which, while focused on irrigation, exemplifies the broader applicability of DT in smart farming. The paper also discusses future research directions for expanding DT applications in various agricultural domains.  
[PERSON] et al. [103] presented a comprehensive framework for developing a DT of hydroponic grow beds in intelligent aquaponic systems. The framework integrates IoT technology, databases, centralized control, and a virtual interface, enabling real-time monitoring of key parameters such as pH, electroconductivity, water temperature, relative humidity, air temperature, and light intensity. The DT supports the use of techniques to predict crop growth rate and fresh weight, offering a robust platform for optimizing aquaponic crop yields and reducing labor costs by providing real-time feedback and control.  
To our knowledge, this research study represents the first systematic literature review (SLR) on water management in agriculture using DTs. We identified 48 papers and selected 11 high-quality primary studies for detailed analysis. Most of these studies have been identified through the Web of Science database.
## 2 Experimental Setup
The spectral function of the three-stage filtering system is determined by the product of the transmission spectra of the cavity, the second-stage filter and the first-stage filter, shown by the green solid line in Figure 2b. From the above analysis, we can see that this hierarchical filtering system compresses the broadband SRS noise to a single longitudinal mode output of the narrow-band F-P cavity. The linewidth of the three-stage filtering system is approximately equal to that of the F-P cavity. Under locking state, the transmission of the converted light through the F-P cavity is 0.76. To prevent the residual locking light from entering the detection path of the converted field, a dichroic mirror DM2 (94% transmission) and a band-pass filter BF3 (72% transmission) are placed after the F-P cavity to block it. Accounting for these losses introduced by DM2 and BF3, the efficiency of the third-stage filter is 0.51. The efficiency of the three-stage filtering system is 0.13, which is the product of the first-, second-, and third-stage filters. In addition to the three-stage filtering system, the dichroic mirror DM1 is used behind the PPLN module to filter the pumping field. Therefore, the efficiency of the whole filtering system is 0.11. The converted photons are detected by a fiber-coupled single photon detector SPD (ID230, ID Quantique). The SPD works in free running state, and its detection efficiency and dead time is set to 0.2 and 20 us, respectively. Under this setting, the dark count rate is about 250/s. The output data of the SPD are acquired and analyzed by a field programmable gate array (FPGA).
# Real-Time Communication Aid System for Korean Dysarthrie Speech
## 4 Experiments  
### Experimental Setup  
For the development of a Korean ASR model for dysarthria, we use the dysarthric speech recognition data provided by AI-hub [27], which were collected from hospitals in Korea. This dataset contains over 5000 h of speech data collected from approximately 1200 dysarthric patients, covering various ages, regions, genders, and diseases. The total size of the data is about 430 GB, and it includes the voices of patients with various levels of severity without any classification. The data were collected continuously in a format in which patients with dysarthria spoke for less than 10 s, followed by a period of silence of about 30 s. Voice Activity Detection (VAD) was used to remove the silent period and segment it into single sentences or words. To apply VAD, the average energy of the corresponding audio file is calculated and set as the threshold for classifying silence and speech. After that, if a single voice file has a value lower than the threshold for more than five seconds, it is classified as a silent section based on the set threshold, and the section until the voice is detected again is measured and the section is deleted from the entire voice data. The audio segments that are divided into before and after the deleted segment are saved as separate wav files, and the labels are also classified as txt files and saved with the same name as the wav files. This process is applied to all voice data to perform pre-processing. As a result of pre-processing, 17,205 short sentences with fewer than three words, 35,681 medium-length sentences containing four to nine words, and 6511 long sentences with more than ten words were generated. Training was performed using approximately 100 h of pure dysarthric speech data.  
For the development of the Korean TTS model, we use the KSS dataset [40]. This dataset consists of approximately 12 h of clean speech data from a female announcer and is commonly used for Korean TTS model development. The total size of the data is about 3 GB, and it does not include words or short sentences. It contains a total of 12,853 medium-length sentences, all of which were used for training without any additional pre-processing.  
The hardware environment for the experiments was identical across all tests and consisted of a server computer with an NVIDIA RTX A6000 GPU (manufactured by NVIDIA corporation, produced in China, and supplied in South Korea), a 64-core 2.7 GHz CPU, and 400 GB of RAM. Additionally, both the TTS and ASR model training were conducted using the ESPnet toolkit [29].
# Path Planning of Underground Robots via Improved A* and Dynamic Window Approach
## 2 Improved A* Algorithm
### Improvement of Heuristic Function
#### 2.3.1 Euclidean Distance  
The Euclidean distance is a linear distance between two points. The calculation is simple and intuitive. Its calculation formula is  
\[h(n)=\sqrt{\left(x_{2}-x_{1}\right)^{2}+\left(y_{2}-y_{1}\right)^{2}} \tag{2}\]
# Enhanced Fully Generalized Spatial Modulation for the Internet of Underwater Things
## 5 Simulation Results
### Achievable Data Rates  
The highest achievable data rates in the case of infinite SNR conditions of the suggested EFGSM (6) are estimated following various number of \(N_{T}\) and weighted upon the highest available data rates of SM, GSM, and FGSM given by (1), (2), and (4), respectively. Except when declared differently, all the supposed systems use 4-QAM modulation on their transmitter sides. Additionally, the actively transmitted antenna number \(N_{T}\) in standard GSM is considered to be \(N_{u}=2\). This value of active antenna, \(N_{u}=2\), is maintained as increasing the \(N_{u}\) degrades the current GSM BER performance,where increasing \(N_{u}\) will increase the probability of having an equal antenna index in various antenna subsets [16].  
As shown in Figure 2, the proposed EFGSM outperforms the conventional SM, GSM, and FGSM in the available data rates where the available data rate in the suggested EFGSM increases linearly with \(N_{T}\). In SM, the available data rate increases logarithmically with \(N_{T}\), whereas in GSM, it is increases logarithmically with the \(N_{T}\) and \(Nu\), combination. In the FGSM this increase is linear in regards to the \(N_{T}-1\). For \(N_{T}=16\), the proposed EFGSM achieves 19 (bpcu) compared to 6 (bpcu), 8 (bpcu) and 17 (bpcu) in case of SM, GSM and FGSM respectively. Consequently, the proposed schemes provide the highest SE, compared with low SE values given by the current SMTs.
## 5 Conclusions  
The present study aimed to investigate the chemical composition of onion and garlic via SPME-GC/MS and UPLC/MS and further to use both platforms to assess the drying impact on the composition of metabolites as monitored by each technology platform. Several sulphur degradation products were monitored via SPME-GC/MS likely due the thermolabile nature of _Allium_ thiosulphinates, whereas UPLC-MS provided better coverage of the sulphur compounds and other metabolite classes viz., flavonoids and phenolic acids. Further investigation of the influence of different drying methods in both _Allium_ species revealed that freeze and microwave-drying retained more of the compounds present in garlic. Two glutamyl peptides namely, \(N\)-\(\gamma\)-glutamyl phenylalanine and \(\gamma\)-glutamyl-S-allyl thiocysteine were the most representative components in freeze and microwave-dried garlic samples. Similarly, the flavonoid content of onion was significantly altered post drying, remaining more abundant in freeze-dried compared to air- and microwave-dried samples. Despite the advantage of SPME GC/MS in unravelling a phthalate ester \"diethyl-phthalate\" in commercial garlic preparation that could affect product overall quality, it showed inconsistent modelling results for the drying impact on _Allium_ compared to UPLC-MS. Metabolites analyses using either technique clearly showed that species-based separation predominates over the drying effect as revealed from their respective PCA score plot. Antimicrobial analyses of onion and garlic against _B. subtilis_ showed different inhibition levels, with freeze dried garlic extracts exhibiting higher inhibitory responses towards the _B. subtilis_ compared to that of onion, likely due to its enrichment in sulphur compounds.  
We acknowledge that our selection of _Allium_ resources does not cover all worldwide variations, but our approach is certainly feasible for analysing _Allium_ samples either raw or in different formulations from further sources. The same workflow of sample preparation, measurement and processing can indeed be easily transferred to investigate other processing factors such as storage, harvesting time and/or seasonal variation impact on _Allium_ secondary metabolites composition.  
Supplementary Materials are available online.  
**Acknowledgments:** [PERSON] wishes to thank the [PERSON], Germany for the financial support.  
**Author Contributions:** [PERSON] and [PERSON] conceived and designed the experiments; [PERSON] performed the experiments and the measurements; [PERSON] performed the data; [PERSON] and [PERSON] provided the plant material and performed the drying process. [PERSON], [PERSON], [PERSON] and [PERSON] analysed the data and wrote the paper.  
**Conflicts of Interest:** The authors declare no conflict of interest. The founding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.
# Journey to the Market: The Evolution of Biodegradable Drug Delivery Systems
## 2 The Evolution of Biodegradable Polymeric Drug Delivery Systems  
Over the past 60 years, there have been four eras in the evolution of polymeric drug delivery systems. Apart from the maturing of nanotechnology and synthetic polymer chemistry, the size of the delivery apparatus has become progressively smaller, from macroscale to microscale and nanoscale, as summarized in Figure 1.  
Research on drug incorporation into solid polymers for medical applications began in the mid-1960's [12]. Because the field of nanotechnology was not mature at that time, drug delivery using this approach could only be applied on the macroscale. An example is use of a local drug depot device to achieve spatiotemporal control of drug delivery [10]. In these early applications, only readily accessible natural materials were used and some of them were non-biodegradable polymers. Hence, drug release was mainly driven by diffusion of the drug from the depot due to lack of degradation of the polymer matrix [13], and the implanted device needed to be removed after drug release was complete. Along with the development of synthetic polymer chemistry, more and more biodegradable polymers were utilized to evolve the macroscale devices to new versions to minimize side effects. One successful example approved by the United States (USA) Food and Drug Administration (FDA) in the 1990's was Gliadel(r), an implanted copolymer disk (prolifeprospan 20) with the cytotoxic drug (carmustine) encapsulated for the treatment of glioblastoma multiforme [14,15].
## 2 Materials and Methods
### Calculation of Adsorption and Desorption Rates  
The following equation was derived in Reference [32]:  
\[C_{Ln}=C_{Ln,0}+\frac{\alpha\Big{(}1-e^{-Dt}\Big{)}}{1-\beta e^{-Dt}}. \tag{1}\]  
\begin{table}
\begin{tabular}{c c} \hline
**Radio Frequency Power** & **1400 W** \\ \hline Plasma gas flow & 15 L\(\cdot\)min\({}^{-1}\) \\ Carrier gas flow & 1.2 L\(\cdot\)min\({}^{-1}\) \\ Sampling depth & 6.5 mm \\ Sample uptake rate & 0.5 mL\(\cdot\)min\({}^{-1}\) \\ Measurement point & 3 points/peak \\ Integration time & 1.0 sec/point \\ Measured Isotope & \({}^{139}\)La, \({}^{153}\)Eu \\ \hline \end{tabular}
\end{table}
Table 1: Operating conditions of Inductively Coupled Plasma-Mass Spectrometry (ICP-MS).  
Here, \(C_{Ln}\) is the metal ion concentration at a given time \(t\) in the aqueous solution in mmol L\({}^{-1}\) and \(C_{Ln,0}\) is its initial concentration in mmol L\({}^{-1}\). In Equation (1), we defined  
\[\alpha=\frac{(B-D)M}{2 AV}, \tag{2}\]  
\[\beta=\frac{B-D}{B+D}. \tag{3}\]  
In Equations (2) and (3), \(B\), \(D\), and \(M\)/(\(AV\)) are the constants determined by adsorption constant \(k_{ad}\), desorption constant \(k_{d}\), and the volume of the solution in liters \(V\). By fitting the experimental data on the metal ion concentration, \(C_{Ln}\), versus time, \(t\), by Equation (1), we obtain the constants, \(\alpha\), \(\beta\), and \(D\) because \(C_{Ln}\), \(C_{Ln,0}\), and \(t\) are determined experimentally. Using the constants, \(\alpha\), \(\beta\), and \(D\), obtained in this way, we can get \(B\), \(D\), and \(M\)/(\(AV\)) from Equations (2) and (3),  
In Equation (2), we have  
\[A=k_{d}KM/V, \tag{4}\]  
where \(K\) is the adsorption equilibrium constant of the solute,  
\[K=k_{ad}/k_{d}. \tag{5}\]  
From Equation (4), we immediately find  
\[k_{d}=AV/(MK). \tag{6}\]  
Because we have obtained \(M\)/(\(AV\)) above and \(K\) is known from the experiment, we can obtain \(k_{d}\) from Equation (6). Then, from Equation (5), \(k_{ad}\) is expressed as  
\[k_{ad}=Kk_{d}. \tag{7}\]  
Above, \(k_{d}\) and \(k_{ad}\) are the required adsorption and desorption rates, respectively.  
To fit the aqueous concentration of adsorbates versus time by Equation (1) and to obtain \(k_{d}\) and \(k_{ad}\), we have used a genetic algorithm [33]. The remarkable difference between our work and Ref. [32] is that \(k_{DES,Ln}\) was determined by iterating Equation (1) until the best fit was obtained to the experimental results in the latter case, while the genetic algorithm makes it unnecessary to do such computationally demanding iterations in order to obtain \(k_{d}\) and \(k_{ad}\) in the former case.
## 1 Introduction
[26] considered the issues of thermal management in fuel cell aviation systems and investigated different methods with possible solutions. [PERSON] et al. [27] conducted research on the performance of PEMFC propulsion in small UAVs. The cruise time of the tested UAV was 43 min, demonstrating the viability of PEMFC in UAV propulsion. [PERSON] and [PERSON] [28] reduced the weight and volume of the fuel cell system by removing external humidifiers and using water from the cathode to humidity the fuel cell. [PERSON] et al. [29] explored the best storage solutions for aircraft, considering the weight, cost increment, and emissions from hydrogen storage technologies. It was reported that the most effective way is to use materials with inherent porosity which limit emissions whilst using alternative storage technologies (compressed, liquid, and cryo-compressed hydrogen). The inherent porosity materials showed improved outcomes in weight and cost compared to metal hydrides. [PERSON] [30] conducted a study on storage technologies for unmanned aerial vehicles based on performance. It was concluded that an Ion Tiger UAV employing liquid hydrogen storage technology was able to fly for 48 h due to the higher density of the liquid storage. [PERSON] and [PERSON] [31] conducted a study on aviation storage technologies such as physical and chemical storage. They found that compressed hydrogen storage technology, using a conformal vessel, rather than using a cylindrical one, would allow for 20% more hydrogen to be stored. [PERSON] et al. [32] studied the performance of an optimised SOFC gas turbine hybrid system for power generation in aviation, reporting that hybrid systems are twice as efficient in producing power than conventional gas turbines. [PERSON] et al. [33] reported that integration of a PEMFC and SOFC can perform better for aircraft operation.