esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Applications of Biosensors in Bacteria Detection
### Resistance to Antibacterials and to Other Antimicrobial Agents
For example, whole-cell biosensors are easy-to-use screening tools for the rapid and sensitive detection of antibiotics [184]. In a recent study by [PERSON] et al., the authors developed a novel whole-cell biosensor in _Bacillus subtilis_, based on the \(\beta\)-lactam-induced regulatory system BlaR1/BlaI from _S. aureus_[184]. This system can be applied for detecting \(\beta\)-lactams on solid media or in liquid for identifying potential \(\beta\)-lactam producers [184]. [PERSON] et al. used the \(\sigma\)M-mediated regulatory system of _B. subtilis_ to create a whole-cell biosensor for the detection of cell envelope-acting antibiotics [185]. As a result, the detection range for polymycin B was between 0.125 and 12 \(\mu\)g/mL [185]. [PERSON] and co-workers tested the microfluidic biosensor (miLab) on the blood, plasma, urine, saliva, and breath samples of pigs who had received piperacillin/tazobactam antibiotics [186]. The result achieved with this device in the plasma of the animals were as accurate as in the standard medical laboratory process [186]. This antibody-free biosensor using penicillin-binding proteins enables a rapid (less than 90 min) and highly sensitive (range of ng/mL) detection of \(\beta\)-lactams [186]. [PERSON] et al. reported the first-in-human study, where a minimally invasive, microneedle \(\beta\)-lactamase biosensors were used for real-time, in-vivo antibiotic drug monitoring [187]. The limit of detection for the microneedles was 0.17 mg/L for phenoxymethylpenicillin [187]. This experiment can be the first step towards the individualized and automated antibacterial dosing closed-loop control system for human patients [187].
# Word Sense Disambiguation Using Clustered Sense Labels
## 4 Experiment and Result  
### Experiment Setting  
We used the affinity propagation program and the \(k\)-means program provided in scikit-learn (2020) [27], and the values of the parameters used in our experiment are shown in Table 1a,b. For the BERT-WSD sequence model, we used the hyper-parameter values shown in Table 1c. The pre-trained BERT-base-cased model [28] was used for English and the KorBERT-morp model [29] for Korean.  
Figure 3: BERT-WSD: BERT-based model for word sense disambiguation.  
Figure 2: Process of sense clustering.  
The proposed model was evaluated on English and Korean data. For the English data, we used the sense definitions in WordNet 3.0 [30] for sense vector generation to compare with SVC using the same thesaurus [18]. For the same reason, we used SemCor 3.0 for training and Senseval and Semeval data for testing. For Korean data, we used the Standard Korean Language Dictionary [31] for sense vector generation and used the Sejong sense annotated corpus [32] for training and testing of the model.  
Table 2 shows the statistics of each sense vocabulary in WordNet 3.0 [30] and in the Standard Korean Language Dictionary. Table 3 shows the statistics of the annotated dataset provided by [33] and that of the Sejong sense annotated corpus. The _Ambiguity_ field represents the average number of senses per annotated word instance. Table 4 shows the compression rate at various threshold points. The lemma in the Standard Korean Language Dictionary does not always match the morpheme in the Sejong sense annotated corpus because of the different morpheme analysis principle. In these cases, we morphologically analyzed the lemma with Sejong morpheme annotation principle and tried to match it to the annotated units in Sejong sense annotated corpus. If the trial failed, we ignored the sentences containing the unmatched annotations in the evaluation. The sentences that exceeded 512 bytes after BERT tokenizing were also not used for the evaluation.
# Optical Flow Odometry with Panoramic Image Based on Spherical Congruence Projection
## 5 Conclusions  
In this paper, we proposed a spherical congruence projection method for panoramic visual odometry. By projecting the panoramic image onto a spherical domain and mapping the uniformly sampled spherical pixels onto a topologically consistent 2D structure, our approach mitigates the issue of spatially varying distortion typically found in planar projections of panoramic images. The proposed framework enhances the consistency of optical flow estimation across the full field of view and demonstrates improved tracking accuracy, particularly in high-speed or large-displacement scenarios. The associated SCP-VO system achieved performance improvements of approximately 10\({}^{\circ}\)20% over representative baseline methods in terms of translational and rotational accuracy while maintaining comparable runtime efficiency. We acknowledge that the current implementation introduces additional preprocessing overhead due to spherical mapping. In future work, we plan to reduce the projection cost through precomputed mappings and GPU parallelization and to improve robustness in complex scenes by exploring multi-frame flow consistency, adaptive sampling strategies, and fusion with other modalities such as LiDAR or event cameras.  
Conceptualization, Y.Y. and Y.X. ([PERSON]); methodology, Y.X. ([PERSON]) and Y.X. ([PERSON]); software, Y.X. ([PERSON]); validation, Y.X. ([PERSON]); formal analysis, Y.X. ([PERSON]); investigation, Y.X. ([PERSON]); resources, J.Z.; data curation, Y.X. ([PERSON]); writing--original draft preparation, Y.Y.; writing--review and editing, Y.X. ([PERSON]); visualization, X.Z. and Y.L.; supervision, Y.X. ([PERSON]); project administration, Y.X. ([PERSON]); funding acquisition, Y.Y. and Y.X. ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (NSFC) (No. 62173220 and No. 62303294).  
The original data presented in the study are openly available in [[https://github.com/DeanXY/SCP_VIO_dataset](https://github.com/DeanXY/SCP_VIO_dataset)]([https://github.com/DeanXY/SCP_VIO_dataset](https://github.com/DeanXY/SCP_VIO_dataset)) (accessed on 10 April 2025 ).  
The authors declare no conflicts of interest.
## 3 Data and Application  
### Data  
In this study, we generate synthetic nonlinear signals to evaluate the effectiveness of various change detection methods. The generation process involves combining sine waves of different frequencies and applying nonlinear transformations to introduce complex behaviors that are representative of real-world biomedical signals.  
To generate the synthetic nonlinear signal, we start with a combination of sine waves, as described below:  
1. Base frequencies: We start by generating three sine waves with distinct frequencies: 5 Hz, 10 Hz, and 20 Hz. These frequencies were chosen to provide a mix of low, medium, and high frequency components, simulating the variety of oscillatory behaviors seen in biomedical signals.
2. Mathematical representation: The synthetic signal \(x\)(\(t\)) is initially defined as: \[x(t)=\sin(2\pi.5.t)+\sin(2\pi.10.t)+\sin(2\pi.20.t)\] To introduce the non-linear behavior, we perform the following steps: 1. Nonlinear transformation: To incorporate nonlinear characteristics, we apply a nonlinear function to one of the sine wave components. In this case, we square the amplitude of the 20 Hz component. 2. Transformed signal: The modified signal \(y(t)\) becomes: \[y(t)=\sin(2\pi.5.t)+\sin(2\pi.10.t)+(\sin(2\pi.20.t))^{2}\] 3. Effect: This squaring operation introduces harmonic distortions and amplitude modulation, which are characteristic of nonlinear systems.  
To generate the anomalous signal and to evaluate the performance of change detection algorithms, we introduce anomalies into the synthetic nonlinear signal. These anomalies simulate deviations from typical patterns, such as those caused by pathological events in biomedical contexts. To introduce the base anomalous signal, we perform the following steps:  
1. Primary pattern: The anomalous signal retains the fundamental sinusoidal pattern but at a consistent frequency of 10 Hz, replicating a basic form of brain electrical activity.
## 3 Results and Discussion
### Molecular Weight Distribution of FBP Hydrolysates  
The molecular weight distribution of FBP, hydrolyzed by Flavourzyme at different times, is shown in Figure 3. The samples were divided into six fractions (>10,000 Da, 10,000-6000 Da, 6000-3000 Da, 3000-1000 Da, 1000-500 Da, and <500 Da), by gel filtration chromatography. Interestingly, there was a difference in the molecular weight distribution, even before enzymatic hydrolysis. Compared to the hydrolysates, the majority of the protein recovered from fish bone ranged above 10,000 Da, with values of 76.44% and 92.49% for the SFBP and HFBP samples, respectively. When Flavourzyme was added to the samples, FBP was constantly decomposed into low-molecular-weight peptides. After 0.5 h of hydrolysis, the proportions of SFBP and HFBP hydrolysates with a molecular weight below 6000 Da, were 80.12% and 51.91%, respectively. The hydrolysates with higher DH showed a higher proportion of low-molecular-weight peptides, as demonstrated by [PERSON] et al. (2014). The content of the components that were below 3000 Da in 3-h hydrolysate from the SFBP sample was 86.53%, which was about 1.38 times higher than that of the HFBP hydrolysates. Under the same hydrolysis conditions, the fractions of small peptides, below 1000 Da, in the SFBP and HFBP hydrolysates were 47.66% and  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Samples** & \(\beta\)**-Sheet/\%** & **Random Coil/\%** & \(\beta\)**-Turn/\%** & \(\alpha\)**-Helix/\%** \\ \hline SFBP & 52.14 & 33.32 & 14.54 & - \\ HFBP & 51.40 & 33.36 & 11.88 & 3.35 \\ SFBP-H & 41.72 & 34.13 & 19.99 & 4.17 \\ HFBP-H & 35.62 & 36.46 & 20.85 & 7.07 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The relative contents of the secondary structure (%) of fish bone protein before and after enzymatic hydrolysis.  
Figure 2: FTIR spectra of SFBP, HFBP, SFBP-H and HFBP-H. SFBP: fish bone protein prepared through steam explosion-assisted extraction; HFBP: fish bone protein prepared through steam explosion-assisted extraction; HFBP: fish bone protein prepared through hot-pressure extraction; SFBP-H: SFBP hydrolysates; HFBP-H: HFBP hydrolysates.  
31.07%, respectively. These differences might be largely due to the structural differences of the substrates for enzymatic hydrolysis, which were mainly caused by the pretreatment methods. The experiments indicated that SE pretreatment might expose more enzyme sites to accelerated peptide release during the hydrolysis reaction.
# StructSim: Meta-Structure-Based Similarity Measure in Heterogeneous Information Networks
## 3 The Proposed Method
### General Formula of Similarity Measure Based on Meta Structure
#### 3.3.2 The Formula of StructSim  
In this section, we clarify the formula definition of StructSim. Let \(x\) and \(y\) be the source and target nodes, respectively. Based on the meta structure \(S\), we can define the general formula of StructSim as follows:  
\[StructSim(x,y|S)=\sum_{s\in S}f(x,y|s), \tag{1}\]  
where \(f(x,y|s)\) denotes the similarity score between the source node \(x\) and target node \(y\) based on an instance \(s\) of the meta structure. Here, \(f(x,y|s)\) has several different definitions, such as the number of subgraphs and the expansion probability. In general, the number of subgraphs can be a special case of expansion probability. Therefore, we define the general formula of \(f(x,y|s)\) as follows:  
\[f(x,y|s)=\prod_{i=1}^{d_{S}-1}h\Big{(}g_{b}(i|S,G),g_{f}(i|S,G)\Big{)}, \tag{2}\]  
where \(d_{S}\) denotes the number of layers of instance \(s\) of the meta structure. \(h(g_{b}(i|S,G),g_{f}(i|S,G))\) is the subgraph expansion probability with bias in the ith layer, which can be obtained by subgraph expansion with bias starting from the source node to the target node along a given meta structure in the whole network. Here, the subgraph expansion with bias is a kind of novel strategy proposed to make full use of the structure information of the network. Equation (2) shows that \(f(x,y|s)\) can be regarded as the product of expansion probability with bias of all layers of instance \(s\) of the meta structure.  
The function \(h\Big{(}g_{b}(i|S,G),g_{f}(i|S,G)\Big{)}\) in Equation (2) is the subgraph expansion probability with bias and it depends on two factors: \(g_{b}(i|S,G)\) and \(g_{f}(i|S,G)\). Specifically, \(g_{b}(i|S,G)\) is the set of expanded subgraphs in the \(i\)th layer, which controls the extent of de pending on their out-neighbor information. \(g_{f}(i|S,G)\) denotes the set of forward subgraphs of the next-hop nodes expanded from the \(i\)th layer, which controls the extent of dependence on their in-neighbor information. Through combining both types of information above, we can determine the bias in subgraph expansion and then obtain the subgraph expansion probability with bias, as shown in Equation (3).  
\[h\Big{(}g_{b}(i|\mathcal{S},G),g_{f}(i|\mathcal{S},G)\Big{)}=\omega \frac{1}{|g_{b}(i|\mathcal{S},G)|}+(1-\omega)\frac{1}{\Big{|}g_{f}(i|\mathcal{S },G)\Big{|}}, \tag{3}\]  
where \(g_{b}(i|\mathcal{S},G)\) is the set of expanded subgraphs in the \(i\)th layer during the process of subgraph expansion. The number of expanded subgraphs is \(|g_{b}(i|\mathcal{S},G)|\). \(g_{f}(i|\mathcal{S},G)\) denotes the set of forward subgraphs of next-hop nodes expanded from the \(i\)th layer; \(\Big{|}g_{f}(i|\mathcal{S},G)\Big{|}\) is the number of forward subgraphs. \(\omega\) means the weight which controls the proportion of out-neighbors and in-neighbors in expansion probability with bias.
# Two-Phase Deep Learning-Based EDoS Detection System
## 5 Experimental Setup
### Dataset Description  
Following the recommendations in [14; 15; 29; 30], we choose the SMD dataset and UNSW-NB15 dataset for ANN and LSTM model training, respectively. The SMD dataset was publicly published in KDD 2019 and was collected from real network traffic statistics of a large internet company. It is also used in many anomaly detection studies. There are 38 features in each SMD dataset sample. By using the correlation-based feature selection process, we chose 10 key features for ANN model. UNSW-NB15 is created by the IXIA PerfectStorm tool in the Cyber Range Lab of the Australian Center for Cyber Security. A raw network packet file (pcap file) of 50.2 GB is provided. We extracted this pcap file to calculate multivariate time series of features for the LSTM model. The shapes of the training set, validation set, and testing set of the ANN model are (28,479,10), (4257,10), and (20,300, 10). For the LSTM model, these values are (30,400, 10), (6010, 10), and (18,379,10), respectively.
# The NEWA Ferry Lidar Experiment: Measuring Mesoscale Winds in the Southern Baltic Sea
###### Abstract  
This article presents the Ferry Lidar Experiment, which is one of the NEWA Experiments, a set of unique flow experiments conducted as part of the New European Wind Atlas (NEWA) project. These experiments have been prepared and conducted to create adequate datasets for mesoscale and microscale model validation. For the Ferry Lidar Experiment a Doppler lidar instrument was placed on a ferry connecting Kiel and Klaipeda in the Southern Baltic Sea from February to June 2017. A comprehensive set of all relevant motions was recorded together with the lidar data and processed in order to obtain and provide corrected wind time series. Due to the existence of the motion effects, the obtained data are essentially different from typical on-site data used for wind resource assessments in the wind industry. First comparisons show that they can be well related to mapped wind trajectories from the output of a numerical weather prediction model showing a reasonable correlation. More detailed validation studies are planned for the future.  
Doppler lidar; NWP model; mesoscale; Floating Lidar System (FLS), wind resource assessment; wind atlas 2018 10.3390/rs1010162018
# Global Power Dynamics in the Contemporary Space System
## 4 System Actors--Space Powers
### Major Space Actors
#### 4.2.4 Republic of India
However, the beginning of the 21 st century marked a notable shift, primarily driven by a shared concern regarding China's expanding capabilities and ambitions in outer space and their potential implications for the interests of both nations [55] (p. 151). India has demonstrated an active role in space cooperation, making bilateral agreements with most space-faring nations (Brazil, Russia, China, and the U.S.), while maintaining an equidistant position between the major space blocs, partnering with the Brazil, Russia, India, China and South Africa Group (BRICS) but also joining the Artemis Accords. This strategic move is aimed at expanding India's global influence and enhancing its status as a major player in the global space sector. In recent years, India has given increased consideration to the military uses of space. For instance, India has launched advanced reconnaissance satellites, established a national system for precision navigation, and set up a military space command. Additionally, the country has engaged in open discussions regarding future kinetic anti-satellite capabilities and laser weapons. In March 2019, India became the fourth nation to successfully launch an anti-satellite (ASAT) missile, when its ASAT missile successfully intercepted a live satellite in lower Earth orbit. In the aftermath of this historic achievement, Prime Minister [PERSON] delivered a nationally televised address, asserting that \"India registered its name as a space power\" [56]. He placed particular emphasis on the role of India's space capabilities in projecting its global influence. As [57] suggest, India had learned its lesson in the 20 th century from its failure to enter to the formal nuclear club, and the quick development and demonstration of ASAT capabilities on time would ensure India its membership in a future formal space club. Also, India initiated a policy shift toward the privatization and commercialization of the local space industry. This strategic decision was made to cultivate a robust and independent domestic space sector, which would support the nation's ambitious projects, stimulate innovation, and enhance investment and global competitiveness. A significant development in this regard was the establishment of N-SPACe (Indian National Space Promotion and Authorisation Centre) in 2020, which was designed to streamline private sector involvement in space-related missions, encompassing satellite launches and exploration initiatives.
## 2 Materials and Methods
### Polymerase Chain Reaction (PCR) and Quantitative Reverse Transcription PCR (RT-qPCR)  
nHEKs were pretreated with _Iris_-exosomes for 4 h, followed by treatment with 200 uM H\({}_{2}\)O\({}_{2}\) for 4 h. The cells were further incubated for 40 h, and total RNAs were extracted using RiboEx reagent (GeneAll Biotechnology, Seoul, Korea). cDNA was synthesized using Moloney murine leukemia virus (M-MLV) reverse transcriptase (Thermo Fisher Scientific, Waltham, MA, USA). RT-qPCR was conducted using a reaction mixture containing each primer and the Evagreen qPCR master mix (Solis BioDyne, Tartu, Estonia). Gene expression was normalized to that of _GAPDH_. The primer sequences for specific genes used in the analysis were as follows: _p16_ forward, 5'-TGCCTTTTCACTGTG-3' and _p16_ reverse 5'-GCCATTGTGTACCAGTGTGA-3'; _p21_ forward, 5'-GAACTTTGACTTGCTACCGAGAC-3' and _p21_ reverse, 5'-TGGAGTGTGTGAGAATCTGTACTGTCTGT-3'; _p53_ forward, 5'-CGGAT CCCGAATGTGTGTACCTGGAAAACAAATG-3' and _p53_ reverse, 5'-GCTCTCTCAGCTACCTCCCTTTGATG-3'; _p63_ forward, 5'-CGGATCCGAAAACATG-3' and _p63_ reverse, 5'-GCTCTCAGCTCACCTCCCTTGTATG-3', _HO-1_ forward, 5'-GCCCTTCAGTCCTCAGTTCC-3' and _HO-1_ reverse, 5'-AGTGTGTGCATGGCCGTGTCAAC-3', _NQO-1_ forward, 5'-GGGACACAGCCTCTTACTGTCC-3' and _NQO-1_ reverse, 5'-AACACCCAGCCGTCAGCTATG-3', _GSS_ forward, 5'-TAGATCCAGCTTGTGT-3' and _GSS_ reverse, 5'-ATCCCATGGAGAAGATCGA-3'; _SOD_ forward, 5'-CCAGTGCAGGCATCATCA-3' and _SOD_reverse, 5'-TTGCCACCGCGTTTCT-3', _GPx1_ forward, 5'-GCAGCTCGTTCATCGGTG-3' and _GPx1_ reverse, 5'-ATGTG3\({}^{\prime}\), GAPDH reverse, 5\({}^{\prime}\)-GCCAGTAGAGGCAGGGATGATGT-3\({}^{\prime}\) and \(\beta\)-actin forward, 5\({}^{\prime}\)-CGCTCGGAGGATCTTCATG-3\({}^{\prime}\), \(\beta\)-actin reverse, 5\({}^{\prime}\)-GGATTCTATGTGGCGACGAGA-3\({}^{\prime}\).
## 2 Materials and Methods  
Multi-walled carbon nanotubes (MWCNT), ferric chloride hexahydrate (FeCl\({}_{3}\)-6H\({}_{2}\)O), iron chloride tetrahydrate (FeCl\({}_{2}\)-4H\({}_{2}\)O), chitosan with medium molecular weight (deacetylated chitin), tetraethylorthosilicate (TEOS), ammonium solution (28.0-30.0% NH\({}_{3}\) basis), N, N-dimethylformamide (DMF), sodium chloride (NaCl), potassium chloride (KCl), sodium phosphate dibasic (Na\({}_{2}\)HPO\({}_{4}\)), ascorbic acid, potassium dihydrogen phosphate (KH\({}_{2}\)PO\({}_{4}\)), hydrogen peroxide solution (30% in H\({}_{2}\)O, contains stabilizer), and Nafion\({}^{\text{\textregistered}}\) 117 solutions (5% in a mixture of lower aliphatic alcohol and water) were purchased from Sigma-Aldrich (St. Louis, MO, USA). Absolute ethanol was purchased from VWR (Radnor, PA, USA). The glassy carbon electrode (GCE), (d = 3 mm) was purchased from the BASi (West Lafayette, IN, USA). The water (18.20 M\(\Omega\) cm) used for solution preparation was first double-distilled by an Aqua Osmotic 02 (Aqua Osmotic, Tisnov, Czech Republic) and then deionized by using a Millipore RG (Milli-Q water, Millipore Corp., Billerica, MA, USA). Autolab electrochemical analyzer model 2.1 software (Metrohm Autolab, Utrecht, The Netherland) was used for running the electrochemical experiments.  
Phosphate-buffered saline (PBS; 0.01 M) was used in the electrochemical measurements. A solution of 0.1 M hydrogen peroxide (H\({}_{2}\)O\({}_{2}\)) was freshly prepared as a stock solution and used for the analysis. Nafion (NF; 1%) was prepared by neutralization of NF using an 8.0% ammonium solution.  
A three-electrode configuration was used for the electrochemical measurements, where a platinum electrode was used as a counter electrode, Ag/AgCl (1.0 M, KCl) as the reference electrode and a modified GCE served as a working electrode.
## 4 Result and Discussion  
In the experiments, data transmission between the UAV and GS was simulated, and the QoS metrics were collected and assessed. The simulation assesses the network performance using bandwidth allocation estimated using the LSGB and BRFFNN ML models. The estimated bandwidth allocations are set to be within the standard ranges given by the 5G specification. It starts by initializing the position of UAVs and GS, setting a maximum simulation time, and defining network's QoS metrics. During each simulation iteration, new positions are assigned to the UAVs. Packets are generated for various applications that use data, voice, and video. The allocated bandwidth is estimated using the ML models based on the type of application. Then, the simulation checks if there is any GS that falls within the UAV coverage, and the packets will be delivered accordingly. During the simulation, the network performance metrics, including the PDR, throughput, and delay, are calculated, and the results are recorded for analysis.  
Despite the promising results achieved using the BRFFNN model in terms of bandwidth prediction and QoS optimization, certain limitations must be acknowledged. First, the model is sensitive to the choice of hyperparameters, including regularization strength, training function, and the number of hidden neurons. Overfitting or less-than-ideal performance can result from improper tuning. Second, compared to more transparent models like gradient boosting trees, Bayesian regularization can limit interpretability by decreasing the transparency of the learned relationships between inputs and outputs, even though it improves generalization. These elements could make the real-time deployment of UAV systems difficult, particularly in environments with limited resources or high levels of dynamicity.