esa-sceva/satcom-chunk-collection huggingface dataset:
# Determination of Optimum Frequency for Electromagnetic-Assisted Nanofluid Core Flooding
## 4 Conclusions  
The EM propagation of a half-wave dipole antenna in a cylindrical reservoir model has been evaluated. The far-field patterns of the geometry are illustrated using 2D and 3D diagrams to depict the propagation at different operating frequencies. The antenna was observed to exhibit double reflection loss peaks in the sandstone containing oil and brine (\(-21.12\) dB at 1 GHz and \(-21.37\) dB at 1.4 GHz), and that containing oil, brine and magnetite nanofluid (\(-18.91\) dB at 800 MHz and \(-20.00\) dB at 1.3 GHz). Evaluation of the radiating far-field pattern reveals that the E-field distribution, isotopic radiated power  
Figure 11: (**a**) The E-field distribution of the antenna in sandstone saturated with oil, brine and nanofluid at 600 MHz and (**b**) the E-field distribution using a double antenna, with the transmitter placed in the injection and production well.  
Figure 10: A two-dimensional polar plot of the far-field pattern showing (**a**) a radiation pattern along the phi component, (**b**) a radiation pattern along the theta component and (**c**) the distribution of the radiated isotropic power around the antenna.  
and phi and theta components become directional with the appearance of side lobes as the frequency increases beyond 700 MHz. Hence, operating the antenna at 600 MHz could improve the efficiency of EM propagation and energy transfer within the medium, as this coincides with the resonant frequency of the reservoir medium.  
Conceptualization, N.Y.; Funding acquisition, N.Y.; Investigation, F.A.W.; Methodology, F.A.W. and A.R.; Software, H.S. and A.R.; Visualization, F.A.W.; Supervision, N.Y. and A.S.; Project Administration, M.G., Writing--original draft, F.A.W.; Formal analysis, F.A.W., Writing--review and editing, N.Y., A.S., H.S., A.R. and M.G.  
This research received no external funding.  
The authors express their appreciation to Universiti Teknologi PETRONAS for providing excellent research facilities and financial support through the graduate assistantship scheme.  
The authors declare no conflict of interest.
## 3 Approach
### Task Formulation
#### 3.1.2 Prompt-Based Fine-Tuning  
When performing standard fine-tuning, previous methods usually train a task-specific head \(f_{c}\) that maximizes the log probability of the correct label through a \(softmax\) function as:  
\[\begin{split} p(y_{i}|x_{i})&=p(\hat{y}=y_{i}\mid x _{i})\\ &=softmax(W_{y}\cdot f_{c}(h_{[CLS]}))\\ &=\frac{exp(W_{y_{i}}\cdot f_{c}(h_{[CLS]}))}{\sum_{y^{\prime}\in Y }exp(W_{y^{\prime}}\cdot f_{c}(h_{[CLS]}))},\end{split} \tag{1}\]  
where \(h_{[CLS]}\) is the hidden vector of the special head token \([CLS]\), and \(W\in\mathbb{R}^{|y|\times d}\) is a set of randomly initialized parameters introduced before fine-tuning.  
Contrarily, when performing prompt-based fine-tuning, language model \(\mathcal{L}\) is directly tasked with \"auto-completing\" natural language prompts. It usually takes two steps to form a prompt, namely, label representing \(\mathcal{M}()\) and template transformation \(\mathcal{T}()\). Specifically, \(\mathcal{M}()\) converts the label space into several individual words in the vocabulary. On the other hand, \(\mathcal{T}()\) is a transformation function that concatenates the input \(x_{i}\) with a predefined template that contains a \([MASK]\) position. The probability of predicting the label \(y_{i}\in Y\) is then transformed to:  
\[\begin{split} p(y_{i}|x_{i})&=p([MASK]=\mathcal{M}(y_{i})\mid\mathcal{T}(x_{i}))\\ &=softmax(W_{\mathcal{M}(y_{i})}\cdot h_{[MASK]})\\ &=\frac{exp(W_{\mathcal{M}(y_{i})}\cdot h_{[MASK]})}{\sum_{y^{ \prime}\in Y}exp(W_{\mathcal{M}(y^{\prime})}\cdot h_{[MASK]})},\end{split} \tag{2}\]  
Figure 2: The workflow of the proposed model.The green rectangle represents the prompt generation module in Section 3.2 and the blue rectangle represents the demonstration candidate filtering in Section 3.3.  
where \(W_{M(y)},h_{[MASK]}\) denote the hidden vector of \([MASK]\) and the label word \(\mathcal{M}(y)\), respectively.Compared with standard fine-tuning, prompt-based fine-tuning re-uses the pretrained weights and does not introduce any new parameters. Moreover, it also reduces the gap between pretraining and fine-tuning, formulating an effective few-shot scenario.  
Note that in our implementation, the label words used for label representation function \(\mathcal{M}()\) are manually-designed. Since the text classification tasks contain a small number of categories and are all easily expressible. Therefore manual design is the easiest and most efficient way of designing label words.
## 2 Materials and Methods
### Eddy Covariance Flux Calculations and Data Quality Control  
The half-hour NEE (net ecosystem exchange of CO\({}_{2}\)) amounts were calculated by Eddypro software (LI-COR Inc.) following standard quality control procedures, including despiking, 2D coordinate rotation, time lag removal, frequency response correction using model spectra and transfer functions [33] and air density correction [34]. Additional outliers still existed under the procedure as stated below, thus they needed to be screened and then removed through several steps using the data quality assurance and control (QA/QC) procedures:  
Figure 1: The schematic diagram of the Heihe River Basin (HRB), selected _Populus euphrataa_ stand and Ejin Meteorological Station, northwest China.  
1 According to the empirical values of the CO\({}_{2}\) fluxes and the statistical analyses of data, the scope of the threshold was defined, which ranged from approximately \(-60\) to \(60\)\(\upmu\) mol m\({}^{-2}\) s\({}^{-1}\). All data outside of the threshold were removed;
2 Because of the complex structures, the eddy covariance instruments are susceptible to instability generated within the circuits, from dust and moisture in the air, precipitation, human operation, etc. Therefore, the spikes in the data appeared occasionally. For this reason, these spikes were filtered using the analysis of variance test [35] and 4 standard deviations were selected;
3 Because \"photosynthesis\" is unlikely to occur during the night, the occasional negative CO\({}_{2}\) fluxes during the night were also filtered; and
4 A friction velocity (\(u^{\star}\)) threshold was used to filter the fluxes at night when the atmospheric turbulence was not well developed. The threshold for \(u^{\star}\) was estimated by the Moving Point Test as described in literature [36]. The flux data were discarded when the corresponding \(u^{\star}\) value was less than the threshold value.
## 5 Discussion
Despite the complex and dispersed distribution of vegetation types in the CAT region, the U-net model, enhanced by data augmentation techniques and hyperparameter optimization, achieved high classification accuracy, particularly for transitional vegetation types like Cerradao. This highlights the potential of deep learning in classifying tropical complex ecosystems [132; 133; 134; 135; 136]. The success of the U-net model can be attributed to its unique encoder-decoder structure, which preserves image details through skip connections while improving gradient propagation. This structure allows the U-net to excel in processing high-resolution remote sensing images by capturing spatial features at multiple scales. Furthermore, data augmentation techniques, such as image rotation, flipping, and cropping, significantly enhanced the diversity of the training samples, improving the model's generalization ability. As a result, the U-net achieved high classification accuracy even with a limited training dataset. Despite improving land cover classification accuracy in the CAT region through data fusion and deep learning techniques, several limitations remain. First, the limited availability of field-based vegetation survey data affects the diversity and representativeness of the training samples. Future studies should aim to increase the number of field samples, particularly detailed surveys of transitional vegetation types such as Cerradao, to further enhance the accuracy of classification models. Additionally, while this study focused on the fusion of optical and SAR data, future research could explore integrating other remote sensing data sources, such as spaceborne LiDAR and L-band SAR, to further improve classification performance and the discrimination of vegetation structures. Furthermore, although our current analysis focused on the year 2023, the proposed methodology shows promising potential for multi-year land cover monitoring and broader spatial applications. PlanetScope NICFI monthly mosaics provide regular high-resolution imagery that enables such temporal extension. However, we observed that in earlier years, image quality may be affected by cloud cover, shadows, and seasonal haze, which could impact classification performance. Future studies should consider these factors when applying the method across time and evaluate preprocessing strategies to improve temporal consistency.
## 4 User Testing Methodology
### Key Metrics Evaluated  
The metrics used to evaluate the VA's performance were meticulously selected to ensure that technical accuracy and user satisfaction were adequately assessed. This careful selection process ensured the validity of the evaluation.  
* The accuracy and hallucination rate were focused on in this metric, emphasizing measuring the correctness of the VA's responses, particularly regarding the presence or absence of hallucinations. Instances where the model generates inaccurate or misleading information that does not align with the given context are hallucinations. The frequency of hallucinations was tracked to identify patterns in the VA's response generation that could lead to inaccurate outputs. The establishment of the VA as a reliable tool in an academic setting, a vital focus of this metric, depended on providing accurate responses.
* The relevance metric was utilized to evaluate the extent to which the responses provided by the VA aligned with the specific queries posed by participants. The responses were scored based on the degree to which the question was addressed and the provision of practical, contextually appropriate information. Each response's grammatical and syntactical correctness was assessed in terms of quality, ensuring that the output was relevant but also clear and professional. These metrics offered a comprehensive view of the VA's ability to deliver polished and pertinent responses.
* The ability of the VA to interpret user queries accurately was measured by this metric, with each response being analyzed for logical coherence and the absence of fallacies or misunderstandings. In a VA setting, the critical nature of comprehension is recognized, as the system needs to provide clear and logically consistent answers that align with the intent of the question posed by users. High scores in comprehension were indicated, suggesting that practical parsing and responding to complex queries by the VA could be achieved. In contrast, lower scores reflected areas where the model struggled with understanding user intent.
* Overall User Experience: Beyond technical performance, a broader assessment of user satisfaction was provided by the overall user experience metric. Participants rated the experience based on the ease of use of the VA, the response time, and the general helpfulness in answering queries. The user's perspective on the VA's functionality was captured through this metric, with the identification of any friction points that could impact user adoption and satisfaction being achieved. The high overall user experience score indicated that the VA effectively provided accurate information, while ease of use and engagement were also noted.  
The metrics provided a robust and holistic evaluation framework, allowing for an in-depth understanding of the VA's technical capabilities and reception among users. Areas of success were highlighted, and specific aspects needing refinement to enhance the VA's functionality and user satisfaction were identified.
# Performance Analysis of Mesh Based Enterprise Network Using RIP, EIGRP and OSPF Routing Protocols +
## 6 Analysis Results  
A simulation duration of four minutes for voice, HTTP, and video data transport is specified for LAN-to-server and server-to-LAN configurations in full and half mesh RIP, OSPF and EIGRP. Figure 5a shows the average voice packet end-to-end latency topology.  
Figure 5b depicts the response time of an HTTP page for a simulated network. Based on distance-vector techniques, the RIP routing protocol showed better performance than other routing protocols. OSPF performs better in video transfer, responds faster to network changes and better utilizes bandwidth, resulting in a minimal delay, as seen in Figure 5c.  
Figure 6 illustrates the average network throughput for three protocols. The OSPF protocols produce better throughput than any of the other protocols evaluated in this test case. The following result is the findings of the analysis based on the experiments.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & & \multicolumn{6}{c}{**Full Mesh**} \\ \hline
**Client PC** & **Client PC** & **RIP** & **EIGRP** & **OSPF** & **Client PC** & **Client PC** & **RIP** & **EIGRP** & **OSPF** \\ \hline PC–1 & PC–2 & 8 ms & 1 ms & 2 ms & PC–1 & PC–7 & 8 ms & 9 ms & 4 ms \\ PC–1 & PC–3 & 6 ms & 7 ms & 2 ms & PC–1 & PC–8 & 9 ms & 9 ms & 9 ms \\ PC–1 & PC–4 & 8 ms & 8 ms & 3 ms & PC–1 & PC–9 & 11 ms & 9 ms & 5 ms \\ PC–1 & PC–5 & 8 ms & 8 ms & 5 ms & PC–1 & PC–10 & 12 ms & 8 ms & 6 ms \\ PC–1 & PC–6 & 7 ms & 8 ms & 6 ms & - & - & - & - & - \\ \hline \hline \end{tabular}
\end{table}
Table 2: Router full mesh RIP, EIGRP and OSPF.  
Figure 5: **(a)** The average voice packet end-to-end latency, (**b**) the average value of HTTP page response time and (**c**) the average video packet end-to-end latency.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & & \multicolumn{6}{c}{**Half Mesh**} \\ \hline
**Client PC** & **Client PC** & **RIP** & **EIGRP** & **OSPF** & **Client PC** & **Client PC** & **RIP** & **EIGRP** & **OSPF** \\ \hline PC–1 & PC–2 & 12 ms & 3 ms & 2 ms & PC–1 & PC–7 & 8 ms & 9.66 ms & 5 ms \\ PC–1 & PC–3 & 7 ms & 2 ms & 2 ms & PC–1 & PC–8 & 9 ms & 9.33 ms & 3 ms \\ PC–1 & PC–4 & 8 ms & 5 ms & 3 ms & PC–1 & PC–9 & 9 ms & 9.66 ms & 3 ms \\ PC–1 & PC–5 & 9 ms & 4 ms & 3 ms & PC–1 & PC–10 & 10 ms & 8.33 ms & 5 ms \\ PC–1 & PC–6 & 8 ms & 8 ms & 4 ms & - & - & - & - & - \\ \hline \hline \end{tabular}
\end{table}
Table 3: Router half mesh RIP, EIGRP and OSPF.
## 2 Results  
### Genome-Wide Identification and Subgroup Classification of TPSs in 115 Angiosperms  
To gain a comprehensive understanding of TPSs in angiosperms, we chose 115 angiosperms with available genomic sequences to construct a phylogenetic tree and used published genomes data to identify TPS sequences (Figure 1). Our genome-wide sequence homology search, focused on two conserved domains (PF01397 and PF03936), identified 3802 TPS genes (Table S1). The number of TPS genes varied significantly among the species studied, ranging from 91 in _Gossypium barbadense_ [PERSON] to just two in _Zostera marina_ L. Notably, five monocot species, including _Oropetium thomeum_ (L.f.) [PERSON] and _Elaesi guinensis_ Jacq., harbored fewer than ten TPS genes each.  
Based on conserved domain motif and phylogenetic analyses, we further divided the 3802 TPSs into five subgroups: TPS-a, TPS-b, TPS-c, TPS-g, and TPS-e/f; no TPS-d and TPS-h subgroup were identified (Table S2). We arranged the subgroups based on the phylogenetic order of the 115 angiosperms, as shown in Figure 1. TPS-a was the largest subgroup, with 1806 members, followed by TPS-b. For most (85 out of 115) angiosperms, the TPS-a subgroup accounts for the largest proportion of TPS families. The length of a branch in a phylogenetic tree has a certain relationship with the evolutionary times of the corresponding species [5; 20; 21]. We observed a significant trend where species positioned at the termini of longer evolutionary branches exhibit an expanded number of members in the TPS-a subgroup, such as the Poaceae branch in monocots (from _Tritcium turgidum_ L. to _Echinochla crus-galli_ (L.[PERSON]), the Solanaceae branch in super-asterids (from _Capsicum chinense_ [PERSON]. to _Petunia axillaris_ ([PERSON].) [PERSON], [PERSON]), and the Rosaceae branch in super-rosids (from _Prunus mume_ ([PERSON] to _Fragaria vesca_ L.) (orange bars in Figure 1). Other TPS subgroups did not exhibit such a pattern, especially TPS-b, which was the second largest subgroup. In the Poales branch, the size of TPS-b subgroup did not exceed four, and eight Poaceae plants lack TPS-b subgroup members (yellow bars in Figure 1). Our data also revealed the absence of some subgroups in various species. For example, the TPS-a, -b, and -f subgroups were not observed in the aquatic plants _Z. marina_ and _Spirodela polynhiza_ ([PERSON] [PERSON]. In addition, five basal-eudicots lack TPS-f subgroup members. The TPS-c and TPS-e/f subgroups contain TPSs involved in ent-kaurene production, which is required for primary metabolism [19]. We found that no angiosperm had lost both TPS-c and TPS-e/f subfamily members. In most angiosperms of our study, the expansion of TPS gene families was due to the expansion of the TPS-a and TPS-b subgroups, whereas the quantities of TPSs in TPS-c, TPS-e/f, and TPS-g subgroups had not fluctuated greatly.
## 2 Results and Discussion
### Distribution of ARR VOCs In Vivo in Rats
#### 2.2.2 Distribution of VOCs in Decoction-Treated Group  
A total of 43 original constituents and six metabolites of ARR were identified in the decoction-treated group. Compared to the powder-treated group, the peak areas of main constituents in the decoction-treated group (2,3,5-TMT, 3,4,5-TMT, kakuol, and 3_4_-methylenedioxypropiophenone) were significantly reduced. For example, the ratio of the peak area of 2,3,5-TMT in the samples between the two groups was 629 for feces, 22 for urine, 59 for blood, 103 for brain, 79 for heart, 49 for lung, 104 for spleen, 73 for liver, 46 for kidney, 64 for stomach, and 251 for intestine. The main absorbed constituents of the powder-treated group (3,5-DMT, safrole, and methyleugenol) were not detected in the blood, brain, heart, or lung in the decoction-treated group. Moreover, 3,5-DMT was not detected in the urine or feces. The peak areas and the ratio between the main absorbed constituents between groups are shown in Table 4.  
Eleven original constituents were identified in the blood: thymquinone, 2,3,5-TMT, 3,4,5-TMT, M97 (2,4,5-trimethoxybenzoic acid), M104 (piperonal), elemicin, M108 (3,4,5-TMT isomer), M116 (3,4-methylenedioxyacetophenone), M122 (3,4-methylenedioxypropiophenone), kakuol, and M148 (2',4'-dimethoxy-3'-methylpropiophenone). Five constituents were detected in the brain: 2,3,5-TMT, 3,4,5-TMT, elemicin, 3,4-methylenedioxypropiophenone and kakuol. Six metabolites were detected in the liver, kidney, and urine: M15 (_cis_-limonene oxide), M36 (_l_-pinocarveol), M47 (\(\alpha\)-cyclodextrinaniol), M88 (dihydroteugenol), M132 (piperonol), and M137 (methoxy eugenol). No metabolite of ARR was found in the blood, feces, or other organs. In general, lower numbers and peak areas of VOCs were detected in the decoction-treated group, which suggests that many VOCs with low polarity and strong volatility were lost during boiling water decoction [14]. Additionally, it is interesting that safrole, which is considered to be a toxic constituent of ARR, was not distributed to the blood, brain, lungs, or heart in the decoction-treated group. It was suggested that after oral administration of ARR decoction, the levels of safrole and/or other potential toxic constituents in vivo are very low, and they are no longer distributed to important organs, thus ARR can be safely used in higher doses after decoction than before or in powder form.
## 6 Instrument description  
A preliminary definition study of a coupled altimeter-radiometer instrument was performed in 2001-2003 with the support of Alcatel Space [29-30]. The following description is based on the results of this study.  
The proposed architecture for the Ka-band altimeter is based on the classical deramp technique for pulse compression. The AltiKa altimeter is based on proven concepts and already developed subsystems, as it inherits a lot from Siral (European Space Agency CRYOSAT mission) and Poseidon-3 to fly on the JASON-2 mission.  
The radiometer is of the total power type and is based on the direct detection principle. The altimeter and the radiometer share the same antenna.
## 5 Social Big Data Analysis Applications
### Topic Discovery from Social Media
#### 5.3.4 Use Cases and Results  
In this section we show the results achieved by exploiting the recommendation abilities of HASHET for identifying the discussion topic of tweets related to COVID-19 pandemic. As a first step, as explained in Section 5.3.3, we extracted a set of discussion topics from the input dataset, which are representative of the online discussion about the health emergency. In particular, we identified discussion groups about antivaccine protests in the USA (_#protests_, _#losangles_), pro-vaccination campaigns (_#covidvocine_, _#healthcare_), smartworking (_#workfromhome_, _#remotejobs_), and COVID-19 prevention rules (_#vearamask_, _#washyourhands_).  
Afterwards, we maintained only single-topic tweets, i.e., those having hashtags belonging to exactly one cluster. Then, we masked the real hashtags and leveraged the HASHET model to identify the topic for each tweet as explained in Section 5.3.3. Finally, discovered topics were compared to the real ones. Specifically, a topic assignment is correct if the recommended hashtags and the real ones belong to the same cluster, i.e., they are related to the same topic; otherwise the assignment may be neutral, ambiguous, or incorrect. This last case occurs when a unique but wrong cluster label can be determined from the recommended hashtags, i.e., the tweet can be assigned to a topic other than the one it actually belongs to. The results achieved by HASHET were compared with that of the main techniques present in the literature, which include unsupervised techniques and neural models based on different implementations of the attention mechanism.  
From the carried out comparison, shown in Table 3, we found what follows. First, the HF-IHU technique achieved the worst results, while LDA-Gibbs and DBSCAN-based models performed better, being able to model the corpus of tweets in an unsupervised manner, identifying an underlying structure through topic modeling and clustering analysis. Deep learning models based on the attention mechanism achieved even better results, as they can fully exploit the semantic information contained in the analyzed tweets, by learning a representative latent representation of them. Finally, HASHET proved to be the best overall model in uncovering the main hashtag-based discussion topics, achieving both the highest percentage of correct and the lowest amount of incorrect and neutral classifications, which confirms the effectiveness of the proposed approach.