esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
In this work, we conduct an extensive measurement campaign in the 4G/LTE mobile network to investigate the performance of existing cellular networks in the low-altitude UAVs' flight territory and develop ML-based prediction models for RSRP and RSRQ. According to the discussed results, it can be concluded that, although the existing LTE infrastructures are designed and optimized for terrestrial communications, the terrestrial BSs in suburban environments still can provide a communication link in a range of \"good\" to \"moderate\" conditions in terms of RSRP and RSRQ. It has been revealed that the reliability of the communication link is a function of the distance and the elevation angle, which depends on the cellular network design, flight height, surrounding environment, and terrain profile. The statistical analysis shows that the existing LTE networks are able to provide a reliable communication link for UAVs at a distance up to 400 m and elevation angle up to 30 degrees, while the RSRP is in the range of \(-\)59 dBm to \(-\)79 dBm and RSRQ is in the range of \(-\)9 dB to \(-\)14 dB. The results of RSRP and RSRQ modeling show that the logarithmic and polynomial models outperform the linear regression models because of the nonlinear curvature of data, in which the nonlinear models can accurately predict the considered metrics in a suburban environment up to a distance of 1 km and an elevation angle of 85 degrees. Finally, it was concluded that the proposed models outperform the existing models, and the selection of an appropriate prediction model, linear or nonlinear, depends on its application.  
Conceptualization, M.B., R.N. and N.F.A.; methodology, M.B., R.N. and N.F.A.; software, M.B.; validation, M.B. and H.A.H.A.; formal analysis, M.B.; investigation, M.B. and M.A.Z.; resources, M.B., R.N. and M.A.Z.; data curation, M.B. and M.A.Z.; writing--original draft preparation, M.B.; writing--review and editing, M.B. and H.A.H.A.; visualization, M.B. and M.A.Z.;  
Figure 16: 3D representation of the proposed logarithmic model for RSRQ.  
supervision, R.N. and N.F.A.; project administration, R.N.; funding acquisition, R.N. All authors have read and agreed to the published version of the manuscript.  
We acknowledge the financial support from Universiti Kebangsaan Malaysia (DIP-2019-014 & DFK-2020-014) and Collaborative Research in Engineering, Science, and Technology (CREST) under grant T23C2-19.  
Not applicable.  
The data presented in this study are publicly available on: [PERSON], 2022, UAVCellularDataset_suburban, GitHub, [[https://github.com/Mehranbit/UAVCellularDataset_Suburban](https://github.com/Mehranbit/UAVCellularDataset_Suburban)]([https://github.com/Mehranbit/UAVCellularDataset_Suburban](https://github.com/Mehranbit/UAVCellularDataset_Suburban)) (accessed on 17 July 2022).  
The authors declare no conflict of interest.
## 3 Control Design for Transition Phase
### Control Law Design
#### 3.2.3 Throttle Control  
To stabilize and control the airspeed of the UAV, the throttle control design adopts the NDI control design. Figure 9 shows its structure.  
Figure 8: Structure of ESO.  
Figure 7: Control structure of pitch angle rate.  
The throttle control design adopts the speed hold mode, and the speed control loop outputs the throttle command \(\delta_{T}\):  
\[\delta_{T}=\frac{m}{T_{\max}}\left[\int k_{i}(v_{\rm c}-v)dt-k_{p}v\right] \tag{17}\]  
where \(m\) is the mass of VtoA7, \(T_{max}\) is the maximum thrust, \(k_{i}\) and \(k_{p}\) are throttle controller gains, \(k_{i}=0.25\), and \(k_{p}=0.8\).
## 4 Promising Neuropeptides Inhibitors for the Development of Effective Cancer Therapy
### Emerging Neuropeptides Inhibitors in Colorectal Cancer
#### 4.1.1 Bombesin  
Bombesin (BB) and its receptors (BBR) have been reported to be overexpressed in many cancers, including CRC. Two commonly known BB-related peptides, including gastrin-releasing-peptide (GRP) and neuromodin-B (NMB) and their respective GPCR receptors, i.e., GRPR (also known as BB2) and NMBR (also known as BB1), are expressed at a higher level in CRC patients [119]. Stimulation of these NPs has been shown to increase tumor differentiation and progression, acting mainly as morphogenetic molecules. Therefore, various therapeutic options have been developed for regulating the expression of BB-related peptides and their receptors in CRC. Researchers in their study highlighted the potential inhibitory effect of various BBR antagonists in affecting the development and progression of tumors in patients with CRC [94]. Various combinational therapies, including the use of a suitable antagonist together with a potent inhibitor, have been reported to have even more inhibitory effects on CRC cancer cells [120]. With the help of experimental analysis, it was observed that the application of RC-3095, a GRPR antagonist, together with several cytotoxic agents, such as 5-FU and irinotecan, results in downregulation and growth inhibition of CRC xenografts. Another study reported the antitumor activity and inhibitory tendency of AN-215, a BB-conjugated analog containing doxorubicin, in CRC [121]. These compounds help target and regulate the growth and proliferation of cancer cells.
###### Abstract  
All-inorganic CsPbI\({}_{3}\) perovskite quantum dots (PeQDs) have sparked widespread research due to their excellent optoelectronic properties and facile synthesis. However, attaining highly stable CsPbI\({}_{3}\) perovskite quantum dots (PeQDs) against heat and polar solvents still remains a challenge and hinders any further practical application. Here, by exploiting (3-aminopropyl) triethoxysilane (APTES) as the sole silica (SiO\({}_{2}\)) precursor, we report a one-step in situ synthesis of single SiO\({}_{2}\)-coated CsPbI\({}_{3}\) (SiO\({}_{2}\)-CsPbI\({}_{3}\)) PeQDs, namely that one SiO\({}_{2}\) particle only contains one CsPbI\({}_{3}\) PeQD particle. The obtained SiO\({}_{2}\)-CsPbI\({}_{3}\) PeQDs are cubic in shape, have a more uniform size distribution, and possess narrow emission, with near unit photoluminescence quantum yields of up to 97.5%. Fourier transform infrared spectroscopy and X-ray photoelectron spectroscopy confirm the formation of SiO\({}_{2}\) through the hydrolysis of APTES on the CsPbI\({}_{3}\) PeQDs surface. Furthermore, they have a significantly improved stability against storage, heat, and ethanol. By combining purple-emission GaN light-emitting diodes, the SiO\({}_{2}\)-CsPbI\({}_{3}\) PeQDs were successfully employed as down-conversion emitters and exhibited considerable enhanced luminous performance and excellent stability, demonstrating their promising future in the practical application of solid-state lighting fields.  
perovskite quantum dots; CsPbI\({}_{3}\); photoluminescence; silica coating; heat and polar stability; instability +
Footnote †: journal: Applied Sciences
# Adaptive Dynamic Search for Multi-Task Learning
## 4 Experiments
### Set-Up  
Each task involved the problem of performing image classification, comprising one of the following standard benchmark datasets: CIFAR-10 and CIFAR-100 [41], Tiny-ImageNet ([[https://www.kaggle.com/competitions/tiny-imagenet/data](https://www.kaggle.com/competitions/tiny-imagenet/data)]([https://www.kaggle.com/competitions/tiny-imagenet/data](https://www.kaggle.com/competitions/tiny-imagenet/data)), accessed on 22 September 2022), STL-10 [42], or Mini-ImageNet [43]. We further employed CIFAR-MTL [17], the multi-task version of CIFAR-100 that contains 20 superclasses, and each superclass has 5 subclasses, for which we treated each superclass as a task. For Mini-ImageNet, we followed the practice in [17], wherein we randomly chose 50 classes and created 10 tasks of 5 different classes from the chosen classes. We generated 10 different sets of 50 classes and reported the averaged results.  
In this work, we consider the four following scenarios:  
* Scenario 1: The first scenario is learning two tasks from two datasets of the same domain, CIFAR-10 and CIFAR-100, where each dataset corresponds to a task.
* Scenario 2: The second scenario is a three-task learning problem comprising three datasets (CIFAR-100, Tiny-ImageNet, and STL-10), where each task characterizes different image scales and the number of classes.
* Scenarios 3 and 4: Following a previous work [17] (model selection for MTL), we use the CIFAR-MTL and Mini-ImageNet datasets as other scenarios which are 20-task and 10-task learning problems, respectively.  
Table 2 shows the summary of the scenarios.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Scenario** & **No. Tasks** & **Datasets** & **Architecture** \\ \hline
1 & 2 & CIFAR-10, CIFAR-100 & WRN-32-4 \\
2 & 3 & CIFAR-100, Tiny-ImageNet, STL-10 & ResNet-42 \\
3 & 20 & CIFAR-MTL & SimpleConvNet \\
4 & 10 & Mini-ImageNet & SimpleConvNet \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary of four experimental scenarios with the corresponding architectures.  
We compare ADM with strong MTL alternatives: the cross-stitch network [11], routing network [17], PackNet [13], NestedNet [8], and VirtualNet [25]. Among them, VirtualNet and ADM are able to perform cost-adaptive predictions which can produce a larger number of solutions for different computational costs than the other compared methods. The results for PackNet, NestedNet, and VirtualNet were taken from [25] for the first two scenarios. Additionally, the results for the cross-stitch network and routing network were taken from [17] for CIFAR-MTL and Mini-ImageNet. For all experiments, we used the same network architectures as the ones used in the compared methods for fair comparisons.
# A Deep Reinforcement Learning Approach for Efficient, Safe and Conformtable Driving +
###### Abstract
The choice of RL is due to its ability to cope with highly dynamic environments and non-linear systems for near-optimal decision-making and control. In RL, an agent learns to map environmental states onto actions and subsequently receives a numerical reward for the adopted action. It aims to learn the optimal state-action mapping policy yielding maximum cumulative numerical reward. With the aim of dealing with real-world operational conditions, we focus on Deep Reinforcement Learning (DRL), which incorporates deep neural networks in the RL approach to effectively represent the optimal policy mapping despite the high dimensionality of the states usually observed in real-world situations. In particular, we use the Deep Deterministic Policy Gradient (DDPG) [11] technique, which is a model-free off-policy DRL algorithm, and we develop a DRL-based ACC application (depicted in Figure 1) that can quickly adapt to the surroundings to ensure safe, comfortable, and efficient driving. Figure 1: Architecture of the proposed DRL framework. The key contributions of this work are summarized as follows:  
1. We design a DRL framework that takes into account and appropriately weights the various environmental factors influencing ACC, including vehicle stability. To the best of our knowledge, we are the first to comprehensively and successfully address all relevant issues, as existing studies focusing on ACC have not focused on such a crucial issue as vehicle stability. 2. We assess the performance of our DRL framework by incorporating it into the CoMoVe framework [12], which offers a realistic representation of traffic mobility, vehicle communication, and dynamics. By utilizing such a fully fledged simulation tool, we derive performance results regarding vehicle stability, comfort, and traffic flow efficiency under diverse traffic conditions and road circumstances. 3. We compare the DRL framework results against traditional ACC and cooperative ACC (CACC) algorithms and demonstrate the benefits of utilizing the information obtained through V2X communications in the learning process of the DRL agent, especially concerning the algorithm convergence time. The remainder of this paper is organized as follows. Section 2 discusses the related literature works and emphasizes our contributions. Section 3 introduces some preliminaries on RL and presents the proposed DRL model and its integration with the CoMoVe framework. Section 4 presents the simulation scenarios and the DRL agent's performance, while Section 5 concludes the paper and outlines future research directions.
## 3 Dataset
### Preparation  
The dataset was constructed from a large corpus of news articles, sourced from over 20,000 searches conducted via Google, Bing, and DuckDuckGo between October and December 2022 across the three themes/topics mentioned in Section 3.1, collectively referred to as FactVer_1.0. These articles were processed to extract key information, including titles, body content, and relevant metadata (e.g., publication date, article ID, and URL), resulting in the intermediate version named FactVer_1.1.  
Following this, three independent annotation teams worked with the processed dataset using a unified instructions document. Annotators followed step-by-step instructions in that document for creating a fact-verification dataset for their assigned topics, generating claims based on the article content and identifying corresponding evidence spans. It is important to note that each annotation team worked on non-overlapping topics (as specified in the instructions), annotating separate subsets of the dataset, and inter-annotation agreement was hence not applicable. Each claim received a unique _Claim ID_, and evidence pieces were labeled with unique _Evidence IDs_ (e.g., E1 to E6) for traceability. Claims were labeled as True (T), False (F), or Not Enough Info (N) based on the evidence provided. Annotators also included a _Reason_ field, offering explanations for the assigned labels, which could either be derived directly from the evidence or be a novel, human-generated explanation.  
This process resulted in three intermediate fact-verification datasets, collectively referred to as FactVer_1.2_X, where X represents the _Annotation_ID_ of each respective team. Further details about the annotation process, including the template provided to annotators and the instructions they followed, are available in Appendix A. Although the annotation guidelines recommended supporting each claim with up to six pieces of evidence, the actual number of evidence pieces per claim in the consolidated fact-verification dataset ranges from 1 to 12, as discussed in Section 3.2 and represented in Figure 1, reflecting the varied interpretations and approaches of the annotation teams.  
Building on these intermediate datasets, their consolidation resulted in a unified dataset created through additional data cleaning, preprocessing, and traceability steps (details of which are also provided in Appendix A). This consolidated dataset, named FactVer_1.3, is designed to facilitate AFV and support XAI research in this domain.
# Editorial Board Members' Collection Series: Nonlinear Photonics
###### Abstract
The study of localized surface plasmons (LSPs) in nanoscale structures is described in reference [10]. Nanostructures are an essential step towards identifying optimal plasmonic modes that can facilitate robust optomechanical coupling and a deeper understanding of light-matter interactions at the nanoscale. The last group of papers [11; 12; 13; 14], two studies and two reviews, are dedicated to some of the most important application fields of nonlinear photonics. They begin with a review [11] focused on plasmonic core-shell nanoparticles in photovoltaic (PV) applications. Core-shell nanoparticles have emerged as a promising new technology with unique structural attributes and widely tunable properties. Among label-free nonlinear optical techniques, stimulated Raman scattering (SRS) microscopy plays an important role in enabling imaging systems and performing label-free imaging with high sensitivity, high spatial and spectral resolution, 3D sectioning, and fast image acquisition. In the second review [12], applications and perspectives of SRS in bio photonics are described and discussed. In reference [13], a diode-pumped solid-state (DPSS) laser, used for intracavity pump-enhanced difference frequency generation (DFG) to create a 3.5-micron laser, is presented and potential future applications in free-space optical communication are also discussed. Finally, in reference [14], microwave photonic (MWP) signal processors are presented and their intrinsic advantages such as low loss, large processing bandwidth, and strong immunity to electromagnetic interference are discussed.
## 1 Introduction
There is the subsequent production of 2-C-Methyl-D-erythritol 2,4-cyclodiphosphate (CMDCP) by 2-C-methyl-D-erythritol 2,4-cyclodiphosphate synthase (ISPF). Being catalyzed by 2-C-methyl-D-erythritol 2,4-cyclodiphosphate synthase (GCPE), CMDCP forms 1-Hydroxy-2-methyl-2-butenyl-4-diphosphate. Finally, this is converted to IPP and DMAPP by IPP/DMAPP synthase (IDS) to generate geranylpyrophosph (GPP), which is used to further generate monoterpenes [25]. At the molecular level, studying the molecular mechanism of volatile terpenoids is an important way to understand the mechanism of fruit aroma formation, which has been extensively studied in many species. [PERSON] and [PERSON]'s experiments with aroma have revealed that aroma may be related to key gene expression [26; 27]. In _Arabidopsis thaliana_, the combined action of gibberell and jasmonic acid can regulate the synthesis of sesquiterpenes [28]. In _Rosa rugosa_ Thumb., Nudix hydrolase RhNDX1 regulates the biosynthesis of monoterpene alcohols, thereby regulating the aroma of _Rosa rugosa_ Thumb. [29]. The terpene synthase (TPS) gene is vital for VOC regulation in plants and can regulate its release under specific space and time conditions [30]. However, in _Z. bungeanum_, the research on aroma mainly focuses on the determination of aroma composition and content. There are many gaps in molecular mechanism research, which need to be urgently addressed. In the present study, two varieties of _Z. bungeanum_: Hanchengdahongpao and Fuguhuajiao were used as experimental materials. We analyzed volatile organic compounds in _Z. bungeanum_ by HS-SPME-GC-MS. We designed primers based on the conserved sequence alignment and investigatedthe expression profiles of candidate genes in the metabolic pathway by RT-qPCR. Subsequently, we combined these results with bioinformatics to analyzethe identified key genes. Based on the above analysis results, we provided a reference for the terpenoid biosynthesis of _Z. bungeanumum_. At the same time, it provides a scientific basis for controlling aroma and improving fruit quality.
# Stepwise Multi-Task Learning Model for Holder Extraction in Aspect-Based Sentiment Analysis
## 1 Introduction
[7] analyzed sentiment words and modified target words using dependency parsing of a sentence, while [PERSON] et al. [8] extracted targets using the relationship between nouns and adjectives and a translation model. Accordingly, sentiment word identification within a text and a relevant target extraction task is used as important clues for holder extraction which is the next step. To achieve high accuracy in ABSA, correctly extracting sentiment expression, relevant target, and holder is critical in a fine-grained SA task [9]. [PERSON] and [PERSON] [10] tested a holder extraction task by combining binary classifiers of sentiment word and holder using a conditional random field (CRF). Performance was further improved in [PERSON] and [PERSON] [11]  
Figure 1: An example of ABSA on a restaurant review. Figure 2: Another example of ABSA on a restaurant review. by employing a logistic classifier which was used as a feature by combining target extraction information in [PERSON] and [PERSON] [10]. [PERSON] et al. [12] simultaneously employed two tasks of target extraction and holder extraction by carrying out multi-task labeling using Bi-LSTM-CRF. The advantage of this study is that Bi-LSTM was used for training the model with enriched contextual information. However, the disadvantages are that fixed GloVe embedding [13], not optimized during the training procedure, was used and the performance was not a too high and they did not use the sentiment word and the target information containing a key clue for the target extraction task and holder extraction task, respectively. Therefore, [PERSON] et al. [12] used Bi-LSTM, but a relatively lower F1 score was measured than [PERSON] and [PERSON] [11]. For overcoming these disadvantages, we use RoBERTa [14] as a language model to deliver more enriched contextual information. In particular, for improving the accuracy of the holder extraction task, three tasks of sentiment word, target, and holder extraction sequentially proceed. Therefore, we adopt a hard parameter sharing model [15], a type of multi-task learning, but the input of the task-specific layer consists of the output of the previous task-specific layer and the output of the shared layer, unlike the traditional models. Hence, the task-specific layer of the target extraction task takes sentiment word information as input, while the task-specific layer of the holder extraction task takes sentiment word and target information for training as input. We call this model a stepwise multi-task learning model in this paper. The remainder of this paper is organized as follows. Section 2 introduces the ABSA, holder extraction, and multi-task learning model configuration. In Section 3, the stepwise multi-task learning model proposed in this paper is described in detail, while the performance of each task is evaluated and analyzed through experiments in Section 4. In Section 5, we draw conclusions from experiments and discuss future works.
## 3 Methods
### Task 3--Fill-Missing of the Voids in the Raster Images  
To handle missing values in the structure created in the previous task, we implemented a fill-missing operation in two directions, one we called horizontal fill-missing, and subsequently the vertical fill-missing. Figure 8 shows an excerpt of the structure we created, composed of one raster image per date, with each pixel representing the Land Surface Temperature (LST) value. The white pixels in the figure represent NaN values, indicating areas where the data are unavailable or undefined.  
Now we explore in more detail what we refer to as horizontal and vertical fill-missing operations.  
In order to understand the horizontal fill-missing operation, assume that the red pixel in Figure 9 has a NaN value.  
To determine its value, we performed a spatial interpolation by calculating the average of the pixels within a 5 \(\times\) 5 window, centered on the aforementioned red pixel. It is, in effect, a spatial interpolation over an area of 5 \(\times\) 5 km\({}^{2}\).  
After the horizontal fill, the raster images still contain some fillable voids: therefore, we applied the vertical fill-missing operation which is, in fact, a temporal interpolation over a time-span of about 3 weeks.  
Vertical fill-missing computes the mean value by considering the column of 3 layers of pixels (one per image) aligned with the red one (NaN value) as shown in Figure 10.  
Figure 8: Excerpt of the stacked structure of the MODIS raster images, the _white_ pixels represent an example of missing values (NaN).  
Figure 10: Example of vertical fill for missing values: after performing the horizontal fill, the selected pixel still has no value. To retrieve its value, we perform an interpolation considering the column of 3 layers of pixels aligned with the red one and computing the mean.  
Figure 9: Example of horizontal fill for missing values. In order to retrieve the value of the selected no-value pixel, we performed an interpolation by computing the mean of the pixels within a \(5\times 5\) window centered on the red pixel.