esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Discussion
#### Novel QTLs and Candidate Genes
The region defined by these significant SNPs (spanning positions 671,378,841 bp to 953,872,991 bp) harbours the _zt-1_ (_zero tannin-1_) gene, an orthologue of _Mediacq truncatula_'s _TTG-1_ (_transparent testa glabra-1_). _TTG-1_ is already known to regulate tannin biosynthesis in faba bean [30]. Despite detecting a clear and expected signal, no significant SNPs directly flank _zt-1_ within the \(\sim\)268 kb linkage disequilibrium (LD) window, calculated as a genome-wide average [37]. The several significant SNPs detected within this extensive region suggest an extended LD in this area, possibly due to historical selection pressure for low tannin content due to their anti-nutritional properties. This study is the first to map the genetic control of phenolic acids and flavonoids in faba bean. However, a limitation was the relatively small sample size due to missing data from poor NIRS predictions. Although phenolics and flavonoids both belong to the phenyl-propanol pathway, no shared QTLs were identified except for SNP chr4_935741377, associated with both myricetin and quercetin in 2021. The lack of stable QTLs across the years hinders robust marker-assisted selection for these traits. Positively, SNPs chr6_1188716500 and SNP chr6_1188716505, associated with caffe acid in 2022, were located within a _shikimate kinase_ (_SK_) gene. This gene is crucial for producing phenylalanine--a precursor of these phenolic compounds. [PERSON] et al. [58] showed that silencing the _PliSK_ gene altersflavonoid metabolism and reduces anthocyanin content in _Petunia_. We could hypothesize that SK protein alterations may reduce caffeic acid and other polyphenols in crops, but further validation is needed. Our analysis confirmed that members of the bHLH and MYB transcription factors family play a role in phenylpropanolid pathways [59]. SNP chr2_964372547 and chr2_964372565, associated with p-coumaric acid in 2021, were located near a _bHLH137-like_ gene. Transcriptome analyses in carrot and ornamental cabbage revealed that _bHLH137-like_ expression correlates with anthocyanin content and purple pigmentation [60, 61], suggesting it may regulate the phenylpropanolid pathway from which anthocyanin and p-coumaric acid originate. In 2022, SNP chr1S_1040083079 was located near a gene encoding a protein with domains typical of MYB transcription factors, emphasizing the complexity of transcriptional regulation in the phenylpropanolid pathway. For the first time in faba bean, we report the candidate genes involved in the control of volatile lipid-derived products such as hexanal and non-volatile lipid-derived products such as 1-linoleoyl glycerol and 2-hydroxyoleic acid (OHOA). Since these genes are not directly linked to the expected oxidation pathways, their roles need to be verified further.
# Research on Satellite Network Traffic Prediction Based on Improved GRU Neural Network
## 3 Definition and Model of the Satellite Traffic Forecast Problem
By weighing the output of the encoder, the alignment between the input and the output can be realized, and at the same time, more information about the original data can be used. The attention module can automatically learn the weight to capture the correlation between the hidden state of the encoder and the hidden state of the decoder. Attention mechanism, as a milestone in deep learning research, can adjust the original input data according to different needs, and get new input data more in line with the current model. The purpose of this paper is to improve the GRU unit by introducing an attention mechanism, which is to improve the model's acquisition of historical time series information, and integrate the historical time series information into memory cells, so that the whole model can obtain more historical flow information, and extract more helpful information for future prediction for prediction and analysis, thus improving the overall prediction accuracy. The combination of the attention mechanism and GRU can learn the spatial relationship between input variables in the coding stage, and then pay attention to the importance of different input sequences. In the decoding stage, the attention mechanism is introduced to select the hidden state of the encoder, and then the attention weight of the hidden state of the encoder is obtained, and finally, the network traffic prediction value is obtained.
# Generative Adversarial Networks for Zero-Shot Remote Sensing Scene Classification
## 3 Results
### Implementation Details  
We first must extract image features, obtain the corresponding class semantic information, and then use the GAN network for training. We used the ResNet-101 model pre-trained on ImageNet to extract 2048-dimensional features of remote sensing images. We obtained the word vectors of the corresponding class by using Word2 vec, Glove, Fast-text, and Bert. We implemented our model via multilayer neural networks. The generator \(G\) contains a 4096 nodes hidden layer activated by LeakyRelu [33], the output layer has 2048 nodes activated by Relu [34]. The discriminator \(D\) also contains a 4096 nodes hidden layer activated by LeakyRelu, and the output layer has no activation. The regressor \(R\) contains a 2048 nodes hidden layer, the dimensionality of the output layer is the same as the semantic information dimension. We used the adam [35] optimizer to optimize our model, \(lr=0.0001\), \(\beta_{1}=0.5\), and \(\beta_{2}=0.999\). In this study, we set \(\alpha=0.001\), \(\beta=10\), \(\gamma=1\)  
Figure 2: Some remote sensing sample images in UCM21, AID30, and NWPU45 datasets.  
\(\gamma_{1}=0.01\), \(\gamma_{2}=0.001\). The above algorithms were implemented using the PyTorch, and the experiments were completed on four RTX2080s.
## 2 Literature Review
### Topic Modeling  
Topic modeling is a statistical model designed to identify abstract \"themes\" within a collection of documents. Each topic, considered a potential variable, encapsulates the main points of discussion within the documents. Through the process of topic modeling, it is possible to identify various topics, each characterized by a distinct set of related keywords.  
Topic modeling is a prominent task for automatic topic extraction in many applications to uncover the hidden structures within large text collections [27]. This technique facilitates a deeper understanding and interpretation of text data, enabling researchers to systematically analyze and categorize content.  
Latent Dirichlet Allocation (LDA) is the most prevalent method used in topic modeling. It posits that each document is composed of a mixture of multiple topics, where each topic itself emerges from a distinct distribution of words. This approach is extensively employed in large-scale text analysis to unearth underlying topics.  
For instance, researchers have successfully applied the LDA topic modeling technique to analyze user reviews on Google Play, which has led to enhancements in mobile role-playing games based on user feedback [28]. Similarly, LDA topic modeling has been effective in examining Weibo posts and comments, shedding light on public attitudes towards various issues. For example, LDA has been used to scrutinize Weibo posts about ChatGPT to gauge public attitudes towards this emerging technology [29]. Another study employed the LDA approach to investigate Weibo posts concerning plastic waste treatment, revealing public perspectives on this issue [30]. Additionally, research using LDA to analyze Weibo posts has provided insights into public attitudes towards nuclear power [31].  
LDA can extract multiple themes implied in the same post. A post related to common prosperity often involves two or more themes. Therefore, we chose LDA to avoid losing information.
## 4 Discussion  
The comprehensive analysis of daylight autonomy (\(DA_{300}\) and \(DA_{500}\)) and circadian stimulus autonomy (\(CSA1\) and \(CSA2\)) has underscored the critical impact of various parameters--namely window-to-wall ratio (WWR), orientation, location (Seville/Barcelona), and interior reflectance (bright/dark finishes)--on natural daylight penetration and circadian stimulus in hospital cubicles. The goal is to optimize these configurations to enhance patient recovery through improved visual comfort and circadian alignment. The findings suggest specific recommendations for window positioning, WWR, and interior reflectance to achieve optimal performance for both the \(DA\) and \(CSA\) metrics.
# Prediction of Arsenic Removal from Contaminated Water Using Artificial Neural Network Model
## 3 Results and Discussion  
As mentioned earlier, an efficient ANFIS model was developed based on a database comprised of the experimental measurements published in seven independent studies. The computational platform used for the modeling was MATLAB 2020. The input variables used for the modeling were _As_ initial concentration, pH, temperature, contact time, adsorbent dosage, solution volume, agitation speed, inoculum size, and flow rate. The removal percentage of arsenate from the contaminated water was the output parameter. The log method was used for normalizing the data. The prediction capability of the developed model was evaluated using four statistical parameters: MSE, RMSE, R%, and R\({}^{2}\).
## 3 Methodology and Architecture  
This research examined the closing prices of cryptocurrencies--specifically Bitcoin (BTC), Ethereum (ETH), Binance Coin (BNB), and Litecoin (LTC)--utilizing a Random Forest machine learning algorithm. As demonstrated in Figure 1, the proposed methodology for forecasting closing prices adopts a multi-stage approach that integrates data transformation, machine learning, and cryptocurrencies. Historical price data were initially sourced from various platforms, including stock exchanges and financial APIs. Following data collection, this dataset underwent preprocessing to ensure its quality and relevance, involving cleaning, normalizing, and transforming data into an appropriate format for analysis. Additionally, key features such as trading volume and market capitalization were engineered to enhance the accuracy of the predictive model.  
Once the dataset was prepared, the ensemble machine learning algorithm was created to forecast prices. The Random Forest algorithm was chosen for its robustness against overfitting and adeptness at handling non-linear relationships [37]. To determine the model's effectiveness, metrics such as the Mean Absolute Error and Root Mean Squared Error were utilized to evaluate its predictive accuracy.  
The methodology employs an anomaly detection framework to identify normal versus abnormal closing price scenarios. Predicted closing prices were consistently compared to actual closing prices to create a baseline of expected behavior. This involved calculating the standard deviation of the predicted closing prices to set a threshold for categorizing them. Specifically, a threshold was defined as the mean plus or minus one standard deviation, allowing predicted closing prices to be classified as either normal or abnormal. Furthermore, statistical tools like Z-scores were used to further classify predictions into normal (within an acceptable range) and abnormal (outliers) categories. Abnormal cases may signal significant market events or anomalies that should be investigated, while typical cases reflect standard market variations. The acceptable Z-score range was from \(-1\) to \(1\) percent, so if the predicted closing price for the next day lied within this range, it was deemed normal; otherwise, it was classified as abnormal.
## 1 Introduction
However, using this method, the data rate is limited due to the kHz-level WPT frequency used as a carrier for communication. In [15, 16, 17], the modulated communication signal was coupled with the WPT path using extra analog components so that the frequency multiplexing technology could be applied. However, due to the narrow bandwidth of the analog circuit, the number of carriers was, therefore, limited, which resulted in a low data rate. Furthermore, such communication methods using an HF coupler are typically only suitable for a low-power WPT system because the analog components are in the power path of the WPT system. In this work, a communication method with a higher data rate and a wider frequency band for the kW-WPT system is presented. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Reference** & [12] & [15] & [16] & [17] & [13] & [14] & [18] & [19] & **In This Work** \\ \hline \multirow{2}{*}{Datarage Carrier Feq. Delay} & 10 kbps & 20 kbps & 19.2 kbps & 119 kbps & 560 kbps & 230 kbps & n.a. & 40 kbps & 1.32 Mbps \\  & \(\approx\)3 kHz & 1.67 MHz & 1.5 MHz & 850 kHz & 10 MHz & 10 MHz & n.a. & 3 MHz & \(\leq\)15 MHz \\  & \(\approx\) n.a. & n.a. & 9.4 μs & n.a. & 5 μs & 3 μs & 10.24 μs \\ \hline \multirow{2}{*}{Power WPT Freq.} & 426 W & 500 W & 250 W & 100 W & 25 W & 700 W & 40 W & 88 W & 1.2 kW \\  & 300 kHz & 22.4 kHz & 47 kHz & 85 kHz & 91 kHz & 40 kHz & 19–24 kHz & 83 kHz & 85 kHz \\ \hline \hline \end{tabular}  
* Extra delay of 5.12 μs due to field-programmable gate array (FPGA) computation delay is not included for fair comparison with references.  
\end{table} 
Table 1: Performance comparison of the state-of-the-art near-field-based communication system for WPT.
## 1 Introduction
### Paper Organization  
The rest of this paper is organized as follows. In Section 2, the system model and channel model of V2I-VLC are discussed. In Section 3, the performance analysis methods are discussed and closed-form expressions for the performance metrics, such as average path loss, received optical power, channel capacity, and outage probability, are derived. In Section 4, numerical results are presented. In Section 5, some discussions are provided. Finally, summary and conclusions are given in Section 6.
# Augmenting GPS with Geolocated Fiducials to Improve Accuracy for Mobile Robot Applications
## 5 Results and Discussion
### Simulation  
Figure 6a,b show the simulated error present for latitude and longitude for the non-FAGPS and FAGPS simulated data sets respectively. Visually it can be observed that although the datasets have a similar precision, the FAGPS dataset has significantly better accuracy.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Survey Point Name** & **Latitude** & **Longitude** \\ \hline PCM113580231 & \(-\)37.71373333 & 145.02958889 \\ PCM113580230 & \(-\)37.71371667 & 145.02958333 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Locations of survey points.  
Figure 5: Map for mobile experiment.  
Figure 7 compares the 2D RMS error of the FAGPS and non-FAGPS schemes. It can be observed the in the short term the FAGPS performs with significantly lower error, due to the lower contribution from the time dependency in the additive noise. As the time dependent variable increases (above 1500 data points) the FAGPS scheme no-longer has an accuracy advantage. Reacquiring the fiducial marker and recalculating offsets would once again lower this time dependant noise and realise the accuracy advantage in the FAGPS scheme.
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Test** & **tHAS (ms)** & **tHA (ms)** & **tHV (ms)** & **tBAS (ms)** & **tBA (ms)** & **tBV (ms)** \\ \hline RPNH & U & 290.00 & 677.00 & 759.50 & 231.00 & 827.00 & 511.00 \\ vs. & Sig. & 0.000 & 0.007 & 0.040 & 0.000 & 0.134 & 0.000 \\ RPH & r & –0.615 & –0.286 & –0.217 & –0.665 & –0.158 & –0.427 \\ \hline RPNH & U & 22.00 & 643.50 & 673.50 & 161.00 & 911.50 & 539.50 \\ vs. & Sig. & 0.000 & 0.010 & 0.020 & 0.000 & 0.776 & 0.001 \\ RPSM & r & –0.841 & –0.276 & –0.249 & –0.714 & –0.031 & –0.370 \\ \hline RPH & U & 478.50 & 919.00 & 912.00 & 789.50 & 632.00 & 794.50 \\ vs. & Sig. & 0.000 & 0.824 & 0.777 & 0.186 & 0.008 & 0.201 \\ RPSM & r & –0.425 & –0.024 & –0.030 & –0.142 & –0.286 & –0.137 \\ \hline  & & **tBRa (ms)** & **HA (g0)** & **HV (m/s)** & **BA (g0)** & **BV (m/s)** & **BRa (deg)** \\ \hline RPNH & U & 265.00 & 622.00 & 40.00 & 328.00 & 1.00 & 0.00 \\ vs. & Sig. & 0.000 & 0.002 & 0.000 & 0.000 & 0.000 & 0.000 \\ RPH & r & –0.637 & –0.332 & –0.827 & –0.582 & –0.861 & –0.861 \\ \hline RPNH & U & 440.50 & 616.00 & 6.00 & 542.00 & 0.00 & 0.00 \\ vs. & Sig. & 0.000 & 0.005 & 0.000 & 0.001 & 0.000 & 0.000 \\ RPSM & r & –0.461 & –0.300 & –0.855 & –0.367 & –0.861 & –0.861 \\ \hline RPH & U & 754.00 & 830.00 & 652.00 & 494.00 & 482.00 & 646.00 \\ vs. & Sig. & 0.104 & 0.329 & 0.013 & 0.000 & 0.000 & 0.011 \\ RPSM & r & –0.174 & –0.105 & –0.267 & –0.411 & –0.422 & –0.272 \\ \hline  & & & & & & & \\ \end{tabular}
\end{table} 
Table 3: The pairwise comparison and the effect size of the temporal and kinematic parameters of RP in relation to the modality of execution.
# Handover Decisions for Ultra-Dense Networks in Smart Cities: A Survey
## 3 Related Works
### AI-Based Approaches
However, the study's main limitation lies in the lack of real-world testing or large-scale deployment data, relying solely on simulations for performance validation. Further validation in dynamic real network environments would strengthen its practical applicability. In 2024, [PERSON] et al. [52] proposed a deep neural network-based handover mechanism aimed at proactively managing traffic steering in 6G networks. Unlike traditional HO schemes that react after performance degrades, their model predicts and executes HO decisions in advance by learning from performance measurement datasets generated through a custom emulator. The trained DNN achieved high training and validation accuracy (96% and 93%, respectively) with low loss, and optimized handover decisions under constraints of ping-pong rate and failure rate to maximize user throughput. Simulations showed that their approach outperformed the conventional A3 event-based HO in 5G NR, particularly in improving downlink throughput. However, the model's reliance on emulator-generated synthetic data raises questions about generalization to real-world conditions, and the paper does not address computational overhead or inference latency--both critical factors for real-time deployment in ultra-dense 6G environments.
## 2 Materials and Methods
### Melanoma  
Malignant melanoma (MM) is one of the most prevalent cancers in AYA, constituting 14% of all malignancies in this age group. In general, it is associated with favorable prognosis, with 5-year overall survival rates exceeding 85% [131]. However, the biology and clinical course of MM in AYA have not been comprehensively studied. Young patients diagnosed at advanced stages of disease face specific obstacles, including lack of treatment standards and limited accessibility to clinical trials [182].  
The aberrant expressions of Hsps, including Hsp27, Hsp40, Hsp60, Hsp70, Hsp90, and glycoprotein gp96 were reported in skin, conjunctival and uveal melanoma cells [183; 184; 185; 186; 187; 188; 189; 190; 191]. The expressions of Hsps were associated with clinical features of MM: worse prognosis (Hsp40 and Hsp90) [183; 190; 192], more advanced stage (Hsp40, Hsp60 and Hsp90) [186; 190; 191], and better response to treatment with bevacizumab (Hsp27) [193]. Based on these observations, some experimental studies evaluating the efficacy of Hsps inhibitors in MM treatment in vitro and on animal models were performed [194; 195; 196; 197; 183]. Some of them have been translated into clinical studies [198; 199]. Immunotherapies based on tumor-derived Hsp peptide complexes (HspPCs) also reached clinical trials [200; 201; 202; 203; 204]. However, the role of extracellular Hsps released from MM cells in exosomes has not been extensively investigated.  
Several studies detected various Hsps, including Hsp70 and Hsp90, in MM-derived exosomes [89; 137]. The chaperones were more abundant in exosomes in comparison to microvesicles and apoptotic vesicles obtained from the same cell lines [138]. Exosomes containing Hsp70 and Hsp80 were also detected in malignant ascites in patients with solid tumors, including two with advanced MM [102]. The studies evaluating the role of MM-derived exosomes carrying Hsp70 yielded conflicting results. [PERSON] et al. found that Hsp70-overexpressing MM cells release Hsp70-positive exosomes, which in vitro activate mouse natural killer (NK) cells to kill mouse lymphoma and human melanoma cells expressing NK-activating receptors. Therefore, they suggested that Hsp70-carrying exosomes play a role in the inhibition of MM progression and dissemination [90]. Conversely, [PERSON] et al. suggested that exosomal Hsp70 played a role in the inhibition of antitumor immune responses. They reported that MM-derived exosomes presenting Hsp70 on their surfaces might bind to toll-like receptor 2 (TLR2) on the myeloid-derived suppressor cells (MDSC), leading to their activation. In this study, inhibition of the extracellular domain of exosomal Hsp70 with specific peptide A8 led to the blockage of Hsp70/TLR2 association, decreasing the ability of exosomes to activate MDSCs. Consequently, treatment of melanoma-bearing mice with A8 peptide reduced the number of MDSCs in the mice spleen, inhibited MM progression and potentiated the antitumor effect of cisplatin [95].
# Low-Cost Wireless Sensing System for Precision Agriculture Applications in Orchards
## 3 Results and Discussion
### Energy Consumption
After connecting the solar panel, the battery recharge was continuous, with slower rate during the days with low solar radiation. Therefore,  
Figure 4: Energy balance of the Coordinator (always on; receiving and sending to cloud 14 full data packets per hour) starting at 20% battery level charging solely on solar harvesting panels during winter. Figure 3: Energy depletion (red line-nodes powered solely by battery) and charging rate (blue line–nodes’ batteries being charged by solar panels) for the coordinator (always on) and for the End-Nodes based on the data acquisition time intervals (5-, 10-, and 20-min cycle). the 20-min sleep cycle is considered as the minimum safest option ensuring continuous operation all year long.
## 3 The Proposed IDS Framework at the Packet Level  
In the packet-level intrusion detection, messages and headers transported by network packets are primarily extracted for detecting malignant traffic [2; 3]. A packet structure consists of messages generated in the application layer and three headers generated in the network layers. The content of messages generated in the application layer is different for different protocols. For example, HTTP generates particular HTTP request messages. Other headers carry further information. TCP and UDP, with TCP and UDP headers, are the two most common protocols in the transportation layer. IP, with the IP header, is the most common protocol in the network layer. Similarly, an Ethernet header is generated in the Physical/Ethernet layer. The knowledge carried inside these headers is called a field.  
The proposed IDS framework at packet-level is shown in Table 1. The framework consists of two major modules and a number of sub-modules. The Preprocessing module is devised for data transformation and feature engineering. The Reinforcement Learning module is devised for training the IDS with RL approaches. This module further comprises a training module and an interaction module. An additional anomaly detection module is designed in this paper to detect those novel attacks blind to the training module.