esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Power-Saving Transmission Scheme for Multiple-Component-Carrier Cellular Systems
## 4 Proposed Scheme
### Transmission Power Estimation (TPE)  
After the completion of the SAC component, the total transmission power needed is estimated. This is accomplished by estimating the transmission power required by every session of the respective users currently being served in \(\Re_{\text{s}}(s)\), as well as by estimating the transmission power required by every session of the respective users in \(\Re_{\text{w}}(s)\).  
First, \(p_{\text{trans\_s}}(s)\) is defined as the estimated transmission power for the sessions being served in the current round; it is calculated as follows:  
\[p_{\text{trans\_s}}(s)=\alpha\sum\limits_{k\in\Re_{\text{s}}(s)}\sum\limits_{( i,j)\in\Re_{\text{k}}(s)}\frac{N_{0}b_{\text{sub}}}{\tilde{H}_{k}(i,j,s)_{j}}(e \,\frac{\tilde{r}_{k}(i,j,s)}{b_{\text{sub}}}-1) \tag{4}\]  
Notice that in Equation (4), \(\tilde{r}_{k}(i,j,s)\) denotes the estimated version of \(r_{k}(i,j,s)\), \(\tilde{H}_{k}(i,j,s)\) indicates the estimation of \(H_{k}(i,j,s)\), and \(\aleph_{k}(s)\) denotes the set of currently assigned radio resources for user-\(k\) in \(\Re_{\text{s}}(s)\).  
Next, the transmission power, denoted as \(p_{\text{trans\_req}}(s)\), needed to meet the data rate demands of the users in \(\Re_{\text{w}}(s)\) for this round is estimated. To minimize the total amount of power consumed, we simply pre-set the minimum required data rate for each user in \(\Re_{\text{w}}(s)\) for approximate estimations. Thus, \(p_{\text{trans\_req}}(s)\) can be calculated as:  
\[p_{\text{trans\_req}}(s)=\alpha\sum\limits_{k\in\Re_{\text{s}}(s)}\frac{N_{0}b _{\text{sub}}}{\tilde{H}_{k}(s)}(e\,\frac{r_{\text{req},k}}{b_{\text{sub}}}-1) \tag{5}\]  
under the assumption that only a single sub-channel is assigned to an active user session. Notice also that in Equation (5), \(\tilde{H}_{k}(s)\) denotes the estimation of the average channel gain between user-\(k\) and each sub-channel of the active CCs.  
If \(p_{\text{trans\_tot}}(s)\) is used to denote the above estimated total transmission power, then:  
\[p_{\text{trans\_tot}}(s)=p_{\text{trans\_req}}(s)+p_{\text{trans\_s}}(s) \tag{6}\]  
In order to lessen the impact of any undesired fluctuations over a short-term interval, a smoothing exponential filtering version of \(p_{\text{trans\_tot}}(s)\), denoted as \(\hat{p}_{\text{trans\_tot}}(s)\), is utilized. Next, the maximum allowable transmission power of the active CCs in the current configuration \(z\), denoted as \(p_{\text{trans}}^{(z)}\), can be calculated as follows:  
\[p_{\text{trans}}^{(z)}=\sum_{i=1}^{z}p_{\text{trans\_max},i} \tag{7}\]  
with that value being calculated for comparison with \(\hat{p}_{\text{trans\_tot}}(s)\) to determine whether \(\hat{p}_{\text{trans\_tot}}(s)\) can be supported by the current active CCs.
# A Novel Energy-Efficient Reservation System for Edge Computing in 6G Vehicular Ad Hoc Network
## 3 Methodology
### Evaluating Parameters of Simulation
#### 3.16.5 Hop Count  
The hop count is a parameter representing the number of distinct hops between multiple vehicles from the sender to the receiver inside a node radius. This is a measure of distance rather than the hop distance, because the hop distance, according to analysis,refers to the transmission distance for only a hop [55; 56]. The bits, on either side, are passed from one hop to the next, with the computation cost depending on the distance between the transmitter and the receiver. The hop count is divided into two types: one is between the source node and the target node (next-hop node), and the other is between the next-hop node and the destination node. For the first category, the hop count is calculated using the node broadcast range, which is the node diameter. The initial hop count would be the neighbor node with the greatest distance from the source node within the node radius of X meters. The hops' region will be limited to the propagation range of the node alone. The estimated hop count (\(H\)) may be used to acquire the farthest distance inside the node diameter.  
\[H=\frac{D}{l+d} \tag{22}\]  
where  
\(D\) = distance between a source and a target node;  
\(l\) = average Length of a vehicle;  
\(d\) = average distance between vehicles.
## 3 Method
### Models Used  
In this study, we used three popular and publicly available models, namely, ChatGPT, Gemini, and PaLM. These models were chosen because they are currently regarded as state-of-the-art multipurpose LLMs. They have also been trained on diverse datasets and generalize well to tasks--such as question answering, named entity recognition, and text summarization--even in a low-resource language like Yoruba. We considered other models, such as LLaMA-2-7B-Chat, but did not obtain results for our Yoruba prompts and, therefore, decided to stick with those three.  
* ChatGPT is arguably the most popular LLM. Having been trained on a wide range of multilingual datasets that include Yoruba, its strong multilingual capabilities, accessibility, reasoning proficiency, and adaptability to generalize in LRL tasks like text generation and translation [29] make it an ideal choice for this assessment. The model we used was ChatGPT-4-turbo, which was accessed between January and April 2025.
* Gemini was developed by Google DeepMind; it is the second model we chose to assess NLR on Yoruba arithmetic probes. Gemini has advanced multimodal capabilities, a fine NLR architecture, and robust support for LRL [30], including Yoruba. The model we used was Gemini 2.0 Flash, which was accessed between January and April 2025.
* PaLM (Pathways Language Model) [31] has an architecture that supports complex reasoning and understanding of context. Its ability to solve multistep problems and support Yoruba informed our choice of this model for the assessment. The model used was PaLM 2, accessed between January and April 2025.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Iroi** **Ibeère** **(Question Type)** & **Ibeère** **(Question)** \\ \hline
**set 2** & Ti oni baa je ojo aje, ojo wo ni oojo mewa oni yóo je? \\ \hline
**set 2** & Ojo méloo lo wa nínú osu Erelé \\ \hline
**set 3** & Onká wo lo tele Ogünnelügba? \\ \hline
**set 3** & Kinni ogojo ni onka Yoruba \\ \hline \hline \end{tabular}
\end{table}
Table 2: Example of questions in sets two and three.
## 2 Experimental Study
### Test Characterization Method
#### 2.3.4 Interface Performance Tests of Composite Materials  
Currently, the most widely used methods for interface evaluation in micro-composite material experiments are the single fiber pull-out test and the interlaminar shear strength (ILSS) test in macroscopic mechanical testing. The interlaminar shear strength (ILSS) of composite materials can reflect to a certain extent the interfacial bonding strength between the fibers and the resin matrix. In this experiment, an MTS-CMT-4204 electronic universal testing machine was utilized to test the interlaminar shear strength of PPTA/DGEBA composite materials, thereby characterizing the bonding performance of the composite material interfaces.  
The interlaminar shear strength test was conducted in accordance with ASTM D2344 _\"Standard Test Method for Short-Beam Strength of Polymer Matrix Composite Materials and Their Laminates\"_. The specimen dimensions were 12 mm \(\times\) 4 mm \(\times\) 2 mm, with a span-to-depth ratio of 4:1, and the testing speed was set to 1 mm/min. The interlaminar shear strength value was calculated according to Equation (1).  
\[F=0.75\times\frac{P_{m}}{b\times h} \tag{1}\]  
where \(F\) is the interlaminar shear strength (MPa); \(P_{m}\) is the maximum load (N); \(b\) is the specimen width (mm), and \(h\) is the specimen thickness (mm).
# Bioelectrical Signals for the Diagnosis and Therapy of Functional Gastrointestinal Disorders
## 4 Small Intestine
### Therapies  
As mentioned in Section 3.2, the earliest reported study on gastrointestinal electrical stimulation focused on treating post-operative ileus [84]. That study stimulated the stomach and the duodenum and attempted to determine locations where the most effective peristalsis response was obtained. Since that initial study, there have been limited human studies employing electrical stimulation or pacing in the small intestine [114]. However, there have been a number of animal studies that have attempted to either accelerate or decrease intestinal transit by altering the motility patterns. Decreasing transit times can help to increase absorption (for example, to treat short gut syndrome) [115; 116] or to treat obesity by delaying gastric emptying and prolonging satiety [117]. Although SW entrainment and functional changes have been achieved in a number of animal studies, limited success has been observed in one human study reported to date [114].
# Design of a Fixed IF Down-Conversion Double-Balanced Mixer for UHF Band Applications
## 4 Simulation and Experimental Results
### Experimental Validation of Down-Conversion Double-Balanced Mixer Prototype
#### 4.5.3 Linearity Measurement: IIP3 and 1 dB Compression Point
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**References** & [33] & [36] & [39] & [40] & **In This Work** \\ \hline Device & 130 nm & 130 nm & 180 nm & BJT & SiGe BJT \\ \hline \multirow{2}{*}{\(\mathrm{f_{RF}}\)} & \multirow{2}{*}{2 GHz} & \multirow{2}{*}{900 MHz} & \multirow{2}{*}{3.432 GHz} & \multirow{2}{*}{935–960 MHz} & 0.5–2 GHz \\  & & & & & Test freq: 1 GHz \\ \hline \multirow{2}{*}{\(\mathrm{f_{Lo}}\)} & \multirow{2}{*}{N/A} & \multirow{2}{*}{0.850 MHz} & \multirow{2}{*}{N/A} & \multirow{2}{*}{915 MHz} & 0.355–1.855 GHz \\  & & & & & Test freq: 855 MHz \\ \hline \multirow{2}{*}{\(\mathrm{f_{IF}}\)} & \multirow{2}{*}{250 MHz} & \multirow{2}{*}{50 MHz} & \multirow{2}{*}{264 MHz} & \multirow{2}{*}{N/A} & \multirow{2}{*}{145 MHz} \\ \hline \multirow{2}{*}{\begin{tabular}{c} Degeneration \\ Techniques \\ \end{tabular} } & Feedforward & \begin{tabular}{c} Differential \\ linearization \\ \end{tabular} & \begin{tabular}{c} Inductive \\ with multiple \\ feedback \\ \end{tabular} & \multirow{2}{*}{N/A} & \multirow{2}{*}{\begin{tabular}{c} Series inductive and \\ shunt-resistive \\ feedback technique \\ \end{tabular} } \\ \hline \begin{tabular}{c} Conversion \\ Gain \\ \end{tabular} & \multirow{2}{*}{8.5 dB} & \multirow{2}{*}{18.4 dB} & \multirow{2}{*}{11 dB} & \multirow{2}{*}{22.2 dB} & \multirow{2}{*}{12 dB \(\pm\) 1 dB} \\ \hline \
\begin{tabular}{c} Noise Figure \\ (SSB) \\ \end{tabular} & \multirow{2}{*}{18 dB} & \multirow{2}{*}{11.5 dB (DSB)} & \multirow{2}{*}{8 dB} & \multirow{2}{*}{N/A} & \multirow{2}{*}{7 dB (SSB)} \\ \cline{1-1} \cline{5-5} \cline{7-7} \multicolumn{1}{c}{} & \multirow{2}{*}{\(-\)13 dBm} & \multirow{2}{*}{\(-\)18 dBm} & \multirow{2}{*}{\(-\)25.5 dBm} & \multirow{2}{*}{\(-\)8 dBm} \\ \cline{1-1} \cline{5-5} \cline{7-7} \multicolumn{1}{c}{} & \multirow{2}{*}{\(\sim\)9 dBm (1 MHz spacing)} & \multirow{2}{*}{\(\sim\)11 dBm} & \multirow{2}{*}{0.257 dBm} & \multirow{2}{*}{\(-\)11.5 dBm} & \multirow{2}{*}{
\begin{tabular}{c} 4.7 dBm (100 KHz spacing) \\ \end{tabular} } \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of several performance measures of the proposed double-balanced mixer. Figure 30: Comparison between simulated and measured gain and noise figure as a function of RF frequency.
## 5 Conclusions  
As _CsAOS2_ was found to be a specifically upregulated JA synthesis gene in tea flowers exposed to _T. hawahiensis_ attack [16], we provided further evidence that CsAOS2 functions in JA synthesis using _N. benthamiana_ transient expression analysis and subcellular localization analysis. Moreover, in this study, JA in tea flowers showed different functions from tea leaves and other flowers and did not significantly influence the metabolite profiles of tea flowers and the formation of ethylene; this suggests that JA might instead have other functions such as changing stereochemical configurations (Figure 7). The information presented here will help us determine the involvement of phytohormes in interactions between the flowers of horticultural crops such as tea plants and insects.  
**Supplementary Materials:** Supplementary materials can be found at [[http://www.mdpi.com/1422-0067/19/8/2440/s1](http://www.mdpi.com/1422-0067/19/8/2440/s1)]([http://www.mdpi.com/1422-0067/19/8/2440/s1](http://www.mdpi.com/1422-0067/19/8/2440/s1)).  
**Author Contributions:** [PERSON] conceived and designed the experiments; [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] conducted the experiments; [PERSON] and [PERSON] analyzed the results and wrote the manuscript; all authors read and approved the final manuscript.  
**Acknowledgments:** This study was supported by the National Natural Science Foundation for Young Scholars of China (31500244), the Youth Innovation Promotion Association of Chinese Academy of Sciences (Y821131001), the Pearl River Science and Technology New Star Fund of Guangzhou (201806010018), the Guangdong Province Higher Vocational Colleges & Schools Pearl River Scholar, the Guangdong Special Support Plan for Training High-Level Talents (2016 TQ03N617), and the Guangdong Innovation Team of Modern Agricultural Industry Technology System (2017 LM1143).  
**Conflicts of Interest:** The authors declare no conflicts of interest.  
**Abbreviations**  
AOS & Allene oxide synthase  
JA & Jasmonic acid  
LA & Linoleic acid  
LOX & Lipoxygenases  
AOC & Allene oxide cyclase  
AdoMet & _S_-adenosyl-l-methionine  
SAM & _S_-adenosyl-l-methionine synthetase  
ACC & 1-Aminocyclopropane-1-carboxylic acid  
ACS & ACC synthase  
ACO & ACC oxidase  
EIN & Ethylene-insensitive  
13-HPOT & 13(5)-hydroxy linolenic acid  
GC-MS & Gas chromatography-mass spectrometry  
UPLC-QTOF-MS & Ultra-performance liquid chromatography/quadrupole time-of-flight mass spectrometry  
Figure 7: Summary on effect of insect (_Thrips hawaiiensis_ ([PERSON])) attack on pathway of JA and its influence on tea flowers.
## 5 MARL-Based Scheduling Solution
### Action Space  
The action space in the scheduling model corresponds to the number of possible positions available for scheduling data frames, which matches the size of the positions vector defined in the state space. If no available positions exist to schedule frames, another action is determined to reject the service request. To improve the efficiency and stability of the learning process, filters have been applied to the action space. These filters are described as follows:  
1. Non-existent positions filter: All positions that do not exist for the period _prd_ specified in the service request are filtered out to prevent the agent from attempting to schedule frames at invalid positions.
2. Insufficient resources filter: All positions without enough resources to schedule the data flow described by the service request are also filtered out.
# Weighting of Multi-GNSS Observations in Real-Time Precise Point Positioning
## 2 Data and Methods  
### GNSS Data  
We process GPS, GLONASS, Galileo, and BeiDou observations from 12 IGS stations distributed worldwide (Figure 1). Several multi-GNSS receivers and antenna types are installed at selected stations that represent the most popular equipment within the IGS network. Detailed information about hardware configuration and signals used in this study is listed in Table 1.  
We process GNSS data stored in RINEX 3.03 files from two different periods, one week long each. The first period is 6-12 February (day of year DoY 37-43), 2017. The second period is 15-21 July  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Station** & **Receiver Type** & **Antenna Type** & **Pseudoranges** & **Carrier-Phases** \\ \hline MIZU & JAVAD TRE\_G3 TH & JAV\_RINGANT\_G3T & G1W G1C G2X G2W R1P R1C & G1C G2W R1C R2C \\  & DELTA & NONE & R2P R2C E1X E5X C21 C7I & E1X E5X C21 C7I \\ \hline REYK & & & G1C G2W G2S R1C R2P & C1C \\  & LEICA GR25 & LEIAR25.R4 LEIT & E5Q C21 C7I & G1C G2W R1C R2P \\ \hline WROC & & & G1C G2W G2S R1C R2P R2C & E1C E5Q C21 C7I \\ \hline UCAL & & & TRM57971.00 NONE & \\ AREG & & & & \\ CHFG & & & & \\ JFNG & & & & \\ PNGM & & & & \\ KZN2 & & & & \\ NKLG & & & & \\ \hline NNOR & SEPT POLARX4 & SEFCHOKE\_MC NONE & G1W G1C G2W G2L R1C R2P & G1C G2W R1C R2C \\ \hline YEL2 & SEPT POLARX4 TR & LEIAR25.R4 NONE & R2C E1C E5Q C21 C7I & E1C E5Q C21 C7I \\ \hline \hline \end{tabular}
\end{table}
Table 1: Station equipment and observations types used; originally leading C and P letters (denoting pseudorange and carrier-phase observation type, respectively) were replaced by a letter indicating the Global Navigational Satellite Systems (GNSS): G for GPS, R, for GLONASS, E for Galileo, and C for BeiDou (see RINEX 3.03 for signal specification [36]).  
Figure 1: Distribution of test stations.  
(DoY 196-202), 2017. We use real-time corrections and broadcast ephemeris provided by CNES on the mountpoint CLK93 that are stored using BKG Ntrip Client v 2.12 (BNC) in ASCII files. During processing, the mean number of observations for multi-GNSS solutions is about 2-3 times larger than when compared to GPS-only solutions.
# Real-Time and Robust Hydraulic System Fault Detection via Edge Computing
## 7 Robust Fault Detection Using LSTM-AE
### Denoising and Latent Feature Extraction  
The schema of the proposed fault detection method is shown in Figure 5 and is presented as an LSTM-based autoencoder model or LSTM-AE. AEs are used in representation learning with the objective of reconstructing the input data \(\mathbf{x}\) from an encoded representation. The components of the AE include an encoder \(e_{\phi}\) and a decoder \(d_{\theta}\) as expressed in Equation (10). The process of pre-training is described in Algorithm 3 with lines 4-9. The encoder focuses on the task to learn the prominent characteristics and extract the encoded features \(\mathbf{h}\)[39]. The decoder reconstructs the input from the encoded features \(\mathbf{x}^{\prime}\). Because the encoder returns a vector, we require a repeated vector function to convert the vector into sequence data, so that it can input into the decoder. The encoder and decoder feature a symmetric architecture. The reconstruction error measured with the mean squared error is expressed in Equation (11). The model parameter optimization is based on the error via the backpropagation algorithm. An AE can handle noisy data because it can extract latent features from the data if it is clean or noisy [40]. Our LSTM-based autoencoder basically is a sequence-to-sequence model that reconstruct time-series sequences [41]. The LSTM-AE provides the functionality of the  
Figure 4: LSTM illustration.  
feature extractor during the pre-training phase, which provides the initial weight to the model [42]. Therefore, the classifier has an optimized initial weight for training the model:  
\[e_{\phi}(\mathbf{x}) =\mathbf{h} \tag{10}\] \[d_{\theta}(\mathbf{h}) =\mathbf{x}^{\prime}\]  
\[L_{\mathrm{AE}}(\theta,\phi) =\frac{1}{n}\sum_{i=1}^{n}(\mathbf{x}_{i}-d_{\theta}(e_{\phi}( \mathbf{x}_{i})))^{2} \tag{11}\]
## 4 Noble Metal-Based Electrocatalysts for ORR and HER
### Pt-Based Catalysts in ORR and HER
As-prepared ternary NC outperformed commercial Pt-catalysts in both alkaline and acidic mediums [47]. In another study, [PERSON] et al. demonstrated the effect of Pt-metal loading on Cu@Pd core-shell (CuPP) NCs (Figure 6) [48]. The CuPP NCs were synthesized by using a wet chemical reduction method with different Pt loadings of 5 to 14 wt.%. Further, they characterized the CuPP NCs with various X-ray spectroscopic techniques and demonstrated the structural changes with increasing Pt-content. The CuPP NC with 5 wt.% NC produced the optimum mass activity of 639.4 mAmg\({}^{-1}\), which is 9-times higher as compared to that of a commercial Pt-catalyst. The significantly enhanced performance of CuPP NC with 5 wt.% Pt-content is due to the formation of the truncated surface. Meanwhile, when the Pt-content was raised to 9 wt.%, the surface defects are reduced due to the significant amount of Pt loading. In this event, although the initial activity is suppressed because surface defects are the most active sites for ORR, the stability is improved as compared to that of CuPP NC with 5 wt.% NC. Figure 5: (**a**) The linear sweep voltammetry curves and (**b**) corresponding specific activities of nanotube-supported Ni@Pd core-shell structure catalysts (NPP) NCs with different Pt\({}^{+}\) adsorption times. (Reproduced with permission from [45], ACS Publications, 2018)  
In a subsequent study, as prepared Pt-cluster decorated Cu@Pd NCs with 14 wt.% of Pt (Cu@Pd/Pt) were subjected to annealing in an H\({}_{2}\) environment. After annealing, the specific activity is improved by 2-times as compared to that of the pristine NCs. Of special relevance, the stability of Cu@Pd/Pt is remarkably enhanced after annealing. The current density is increased by 26.3% after 36,000 accelerated degradation test (ADT) cycles (Figure 7) [49]. [PERSON] et al. used Au-clusters to decorate Ni@Pt core-shell catalysts with variable thicknesses of Pt-shells and unveiled the structure-performance relationship of ORR [50]. Of upmost importance, [PERSON] et al. used Pt-trimers to decorate Co@Pd core-shell NCs and demonstrated outstanding ORR performance [51]. Figure 6: (**a**) The cyclic voltammetry, (**b**) linear sweep voltammetry curves, (**c**) ORR mass activities, and (**d**) Koutecky–Levich plot of Cu@Pd core-shell (CuPP) NCs with different Pt-loadings. (Reproduced with permission from [48], Royal Society of Chemistry, 2016)
## 2 Theoretical Investigation of Monitoring Models
### Analysis of the Main Component Characteristics and System Performance of the Sensor Structure
#### 2.4.4 The Impact of Current-Carrying Wires on System Performance
Epoxy resin with a curing agent can be used to protect the sensing structure while also preventing corrosion and oxidation of the NdFeB permanent magnets when exposed to air. This approach effectively enhances the durability and applicability of the system at a relatively low cost. In summary, the sensor structure consisting of NdFeB permanent magnets and Hall elements has some limitations under strong magnetic fields and extreme temperature conditions; nevertheless, it can be widely applied in other environments due to its low cost, simple design, and ease of installation. Compared to the vibrating-wire sensors, magnetic sensors, and optical fiber sensors available in the market, this structure has significant advantages in long-term monitoring and large-scale deployment in civil engineering structures.