esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Characteristics of the Included Studies
& The efficacy of the mHealth strategy (MATADORS) in aiding symptom eHealth and mHealth Strategies: Tackling Psychological Elements in Children and Adolescents with Asthma and Persistent Health Conditions  
In this segment, we will scrutinize articles that delve into the utilization of eHealth and mHealth tools for the treatment, management, or self-monitoring of psychological factors associated with young individuals, ranging from children to adolescents. The inaugural study we examine is the one spearheaded by [PERSON] et al. [32] who have delineated the development and feasibility testing of a mHealth intervention, termed MATADORS, aimed at enhancing symptom self-management among youth (ages 10-17) with asthma and obesity. The intervention amalgamates motivational enhancement and behavioral activation strategies within a family-centric model and an innovative mobile app that encompasses educational content, symptom monitoring, activity tracking, and photo/video/voice diaries. The authors reported the outcomes of a pilot study that randomized 30 youth with asthma and obesity to either MATADORS or a control condition. The intervention group partook in a six-week nurse-guided mHealth program, while the control group received standard care. The feasibility outcomes encompassed recruitment, reach, adherence, satisfaction, and participant burden. The preliminary outcomes included fatigue, pain, self-efficacy, anxiety, sleep, depression, and quality of life, which were measured at baseline, six, and twelve weeks. The authors concluded that MATADORS is a feasible and acceptable intervention with the potential to enhance symptom self-management and quality of life for youth with asthma and obesity. However, they did not report the statistical significance of the preliminary outcomes, rendering it unclear whether MATADORS was effective in terms of reducing these symptoms and improving these outcomes. Subsequently, [PERSON] and colleagues [33] conducted a participatory study to comprehend the experiences, needs, and ideas of young individuals with asthma. The objective was to identify the prerequisites for an asthma app that would be engaging and effective in augmenting their well-being. The study comprised 20 participants aged between 15 and 24 years who completed a workbook and participated in a workshop, culminating in the creation of 102 participant-generated artifacts. The data underscored that psychological aspects such as anxiety and impediments to autonomy, competence, and relatedness were perceived by young individuals with asthma as significant influences on their quality of life. The study proposes that an app designed for young individuals with asthma should provide support for mental health factors associated with lived experience, in conjunction
## 6 Results and Discussion
### Simulation Runs and Sensitivity Analysis  
Simulation runs consist of executing the simulation model to generate the inferred data and to perform a sensitivity analysis. The sensitivity analysis consists of making changes to the model's inputs (using the scenarios designed in the previous section), running those scenarios, inspecting the results by checking the results are compliant with the expectations, and learning from and discussing the results. The time unit selected to run these scenarios in days.
###### Abstract  
The bioavailability of orally administered drugs could be impacted by intestinal and hepatic first-pass metabolism. Testosterone undecanoate (TU), an orally administered ester prodrug of testosterone, is significantly subjected to first-pass metabolism. However, the individual contribution of intestinal and hepatic first-pass metabolism is not well determined. Therefore, the aim of the current study was to predict the metabolic contribution of each site. The hydrolysis-time profiles of TU incubation in human liver microsomes and Caco-2 cell homogenate were used to predict hepatic and intestinal first-pass metabolism, respectively. The in vitro half-life (\(t_{\text{1/2 inv}}\)) for the hydrolysis of TU in microsomal mixtures was \(28.31\pm 3.51\) min. By applying the \"well-stirred\" model, the fraction of TU that could escape hepatic first-pass metabolism (\(F_{\text{H}}\)) was predicted as \(0.915\pm 0.009\). The incubation of TU in Caco-2 cell homogenate yielded \(t_{\text{1/2 inv}}\) of \(109.28\pm 21.42\) min, which was applied in a \"Q gut\" model to estimate the fraction of TU that would escape intestinal first-pass metabolism (\(F_{\text{G}}\)) as \(0.114\pm 0.02\). Accordingly, only \(11\%\) of the absorbed fraction of TU could escape intestinal metabolism, while \(91\%\) can pass through hepatic metabolism. Hence, compared to the liver, the intestinal wall is the main site where TU is significantly metabolised during first-pass effect.  
testosterone undecanoate; first-pass metabolism; bioavailability; human liver microsomes; Caco-2 cells +
Footnote †: journal: Applied Sciences
## 5 Experimental Verification  
To further validate the accuracy of the preceding theoretical analysis and conclusions, a stability experiment of the GCI system was conducted on the RTLAB experimental platform, as depicted in Figure 10. The experimental parameters were consistent with those used in the theoretical analysis. The main circuit, including the DC line, capacitor, inductor, resistor, and power supply, was built on the RTLAB platform, and the control part was completed by the digital control platform. The specific parameters of the system are shown in Table 1.  
Figure 8: Nyquist diagram with \(BW_{\rm v}\) change when voltage feedforward is considered. (**a**) PI control. (**b**) PR control.  
Figure 9: Nyquist diagram with \(BW_{\rm v}\) change without considering voltage feedforward. (**a**) PI control. (**b**) PR control.
## 5 Discussion
### The Challenge of Real-Time Integration and Multi-Platform Fusion  
Yet, with these developments, the most crucial issue remains the adaption and integration of satellite data in real-time across multi-data platforms. This has made real-time data integration the highest priority and will significantly increase the capability of coastal managers' capability to act adequately in the face of sudden environmental changes related to storms, floods, and landslides that may rapidly reshape coastlines. Indeed, different studies, such as [PERSON] et al. [39], showed that Sentinel-1 SAR, in combination with optical Sentinel-2 data, does indeed enable the monitoring of shoreline dynamics under adverse weather conditions on occasions when only optical images may not be sufficient. However,one of the pending challenges is to develop a fully automated system providing real-time data across scales and diverse environmental conditions.  
Integrating remote sensing, GIS, machine learning, and UAV technologies opens new horizons for comprehensively monitoring coastal environments [32]. These are very fitting considering the growing threat of climate change, which accelerates the rate at which coasts erode and places communities at greater risk [44]. The trends in the scalability and efficiency of these technologies will call for further research in the future that considers timescales of real-time monitoring and data integration in view of effective coastal management and disaster preparedness.
## 1 Introduction
### Related Works
#### 1.2.3 Synthesis  
Although many articles have been written about the prospects of A2S integrated maritime communications, there are still many shortcomings in maritime communications. The first issue is that due to the distribution and transmission distance limitations of ground base stations (BSs), A2S integrated communications in the MIoT may be limited by maritime coverage. In areas far from land or offshore, connectivity becomes unstable or unavailable. The second problem is that there are many sources of interference in the marine environment, such as atmospheric influence, surface reflection, and ship structures. These factors lead to signal attenuation and interference, which affect communication quality, and reliability. The third challenge is that offshore equipment usually needs to operate for a long time, but the energy supply is limited. In order to achieve durable A2S integrated communications, the challenges of equipment energy consumption and power supply need to be addressed to ensure the reliable operation of equipment and meet communication needs. The fourth challenge is that DT in the MIoT involves sensitive information, such as ship location and sailing plans. Ensuring the security and privacy of communications is an important challenge that requires effective encryption and authentication mechanisms to prevent data breaches and unauthorized access. Finally, a variety of devices and platforms are involved in the MIoT, such as sensors, communications equipment, data processing systems, and so on. Ensuring interoperability and seamless integration between these devices and platforms is a complex task that requires addressing issues such as standardization and protocol unification. These remaining questions prompted this survey.
# Accelerometers in Monitoring Systems for Rail Vehicle Applications: A Literature Review
## 2 Fundamentals of Accelerometer Data
### Output Interface  
For most accelerometers, the essential connections are power and communication lines. They will communicate over an analogue, digital, or PWM connection interface.  
* The analogue interface shows accelerations through varying voltage levels, as seen in Figure 9. An analogue-to-digital converter on a microcontroller can read this value [18].
* Digital interfaces can communicate using SPI or I2C protocols [19; 20]. These tend to have more functionality and be less susceptible to noise than analogue accelerometers. The resolution of the output data can be between 10 and 16 bits and depends on the output data rate and measurement range.
* Pulse-Width Modulation (PWM) output square waves with a fixed period and a variable duty cycle, as presented by Equation (5).  
\[a=f(t_{1},t_{2}) \tag{8}\]  
In Figure 10, the PWM has a period \(t_{2}\) and a duty cycle proportional to \(t_{1}\); the acceleration is proportional to these two values. The exact expression of (5) depends on each accelerometer.
# Privacy-Preserving Byzantine-Resilient Swarm Learning for E-Healthcare
## 7 Evaluation
### Experimental Results
#### 7.2.1 Experimental Results on Defense Performance  
**Impact of Number of MCs**: The test accuracy was measured by increasing the number of **MCs** involved in collaborative training. The experiments were run with different numbers of **MCs** from \(1\) to \(10\). Figure 4 shows that the more **MCs** participate, the higher the training accuracy.  
**Impact of Different Proportions of Malicious MCs**: To check the robustness of our PBSL scheme, we evaluate the accuracy loss caused by the ratio change in malicious **MCs**. Figures 5 and 6 show the results of PBSL training on two different datasets in the presence of different proportions of malicious **MCs**. The experiment shows that the proposed basic scheme is reasonable and robust.  
Figure 4: The impact of the number of **MCs**.  
**Impact of different attacks:** To prove Byzantine robustness against different typical Byzantine attacks, we performed targeted attacks and untargeted attacks to our PBSL scheme. Table 3 shows the training results on MNIST and FashionMNIST datasets in the presence of different proportions of malicious _M_Cs under targeted and untargeted attacks. The results show that PBSL still keeps the same accuracy as baseline for different typical attacks, achieving the goal of robustness and accuracy.  
Figure 5: Comparison of accuracy with different numbers of malicious **M**Cs under different attacks on different datasets.  
Figure 6: Comparison of accuracy with different epochs under different attacks on different datasets.
## 4 Experimental Results
### Discussion  
The proposed IDP-YOLOV9 technique demonstrates significant improvements in object detection under fog, rain, and snow conditions. However, this method still has some limitations. It may not generalize well to all adverse weather conditions, requiring more data and adjustments to ensure model performance. Moreover, given the intricate nature of deep learning models and parallel processing architecture, the approach may necessitate considerable computational resources and time. Future work should focus on further expanding and enriching object detection datasets under adverse weather to cover more scenarios and situations.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Model** & **MultiRemoval** & **Enhance** & **Detection** & **Fog (\%)** & **Rain (\%)** & **Snow (\%)** \\ \hline Improved YOLOV9 & & & \(\surd\) & 58.1 (\(-\)7.7) & 61.0 (\(-\)6.6) & 60.3 (\(-\)6.1) \\ \hline MultiRemoval & & & \(\surd\) & 60.5 (\(-\)5.3) & 62.1 (\(-\)5.5) & 62.6 (\(-\)3.8) \\ + ImprovedYOLOV9 & & & & & & \\ \hline Enhancement & & & & & & \\ + ImprovedYOLOV9 & & \(\surd\) & \(\surd\) & 59.2 (\(-\)6.6) & 61.4 (\(-\)6.2) & 60.9 (\(-\)5.5) \\ \hline IDP-YOLOV9 & & \(\surd\) & \(\surd\) & 65.8 & 67.6 & 66.4 \\ \hline \end{tabular}
\end{table}
Table 4: Comparison of ablation experiments. \(\surd\) indicates that we used this model for image enhancement, removal, detection operations.
# Integrating PubMed Label Hierarchy Knowledge into a Complex Hierarchical Deep Neural Network +
## 4 Experimental Assessment and Discussion
### Tools and Hardware  
Focusing on the tools used for the implementation and execution of the experimental assessment, the preprocessing phase (tokenization and sentence splitting) was performed using Apache Spark and Stanford CoreNLP wrappers for Spark ([[https://github.com/databrics/spark-corenlp](https://github.com/databrics/spark-corenlp)]([https://github.com/databrics/spark-corenlp](https://github.com/databrics/spark-corenlp)) (accessed on 5 December 2023)). The obtained NLP preprocessed text is the input of the word embedding layers, which converts the words into a vectorial representation. For this purpose, we used the Gensim Python library [36] version 4.2 in the case of static word embeddings, while we adopted the Hugging Face Python library ([[https://huggingface.co/](https://huggingface.co/)]([https://huggingface.co/](https://huggingface.co/)) (accessed on 5 December 2023)) to obtain dynamic embeddings from the BioBERT model, providing a whole sentence as input.  
The proposed HLSE and HDNN algorithms were also implemented in Python. In detail, we exploited Keras ([[https://keras.io](https://keras.io)]([https://keras.io](https://keras.io)) (accessed on 5 December 2023)) version 2.11, with Tensorflow 2 backend for the definition, training, and testing of the HDNN. Moreover, the implementation requires the pyTree library, which provides a list-derived tree structure for Python, which is exploited to represent the label hierarchy. Finally, the Numpy, Pandas, and Pickle libraries were required to run the code. We underline that the code is publicly available on SoBigData research infrastructure (the code is publicly available at [[https://data.d4](https://data.d4) science.net/RgXa]([https://data.d4](https://data.d4) science.net/RgXa) (accessed on 5 December 2023)).  
All the experiments were performed on a deep learning cluster based on an IBM Power9 architecture, where each node was equipped with 2 Power9 CPUs at 3.7 GHz, 512 GB of RAM and 4 Nvidia Tesla V100 GPUs with 16 GB of dedicated VRAM. The operating system is Red Hat Enterprise Linux Server release 7.6. The current implementation of the HDNN runs on a single node and on a single GPU (the parallel implementation is not ready yet).
## 2 Materials and Methods
### Data
#### 2.2.1 Hubei Province Yield Data  
County-level rice yield data for Hubei Province from 2000 to 2019 were obtained from the Hubei Provincial Bureau of Statistics and the China Economic and Social Data Research Platform in tons. The yield data were used as training and validation of the model.
## 4 Materials and Methods
### Transcriptome Sequencing and Analysis  
Total RNA was extracted from chestnut flowers using a TaKaRa MiniBEST Universal RNA Extraction Kit (9769) (TaKaRa, Osaka, Japan). Then, 1 \(\mu\)g of total RNA was used for library preparation. Poly(A) mRNA isolation was performed using Oligo(dT) beads, and mRNA fragmentation was performed using divalent cations. cDNA were synthesized using NEBNext UltraTM RNA Library Prep Kit for Illumina (NEB, Ipswich, MA, USA) following manufacturer's recommendations. The purified double-stranded cDNA was then treated to repair both ends and add a dA tail in a single reaction, followed by a T-A ligation to add adaptors to both ends. Size selection of adaptor-ligated DNA was then performed using DNA Clean Beads. Each sample was then amplified via polymerase chain reaction (PCR) using P5 and P7 primers, and the PCR products were validated. Libraries with different indices were then multiplexed and loaded on an Illumina HiSeq 3000 (Illumina, San Diego, CA, USA) instrument for sequencing using a 2 \(\times\) 150 paired-end (PE) configuration according to the manufacturer's instructions.  
We performed quality control using fastp to ensure meaningful downstream analysis: (1) Discard paired reads with adapter contamination (>10 aligned nucleotides, \(\leq\)10% mismatches). (2) Discard reads with >10% uncertain bases. (3) Discard reads with >50% low-quality bases (Phred < 5). (4) Discard reads <20 nucleotides after trimming [45]. Downstream analyses were conducted on the resulting high-quality data. QC statistics (total reads, raw data, raw depth, error rate, and Q30 percentage) were calculated. Reference genome sequences and gene model annotation files of _C. mollissima_ were downloaded from [[http://www.chestnutgenome.cn/#/download](http://www.chestnutgenome.cn/#/download)]([http://www.chestnutgenome.cn/#/download](http://www.chestnutgenome.cn/#/download)) (accessed on 7 July 2024). Hisat2 (v2.2.1) was used to index reference genome sequence. Clean data were aligned to the reference genome via the Hisat2 (v2.2.1) software. HTSeq (v0.6.1) was used to estimate the raw counts of genes and isoforms and then calculate the FPKM of each gene based on the gene length. DESeq2 (V1.26.0) was used for differential gene analysis, with the following criteria: padj (FDR) \(<0.05\), \(\mid\) fold change \(\mid>1.5\), and \(p\)-value \(<0.005\) for screening.  
Gene Ontology (GO) and KEGG pathways enrichment analysis of differential expressed genes was implemented by the clusterProfiler R package (3.8.1). Terms with corrected \(p\)-values less than 0.05 were considered significantly enriched by differentially expressed genes. In this part, all the annotated genes of _C. mollissima_ were used for the background of the enrichment analysis.
## 4 System Architecture
### Software Development  
The goal of our applications is to repeatedly collect from every node a Cyclops image along with MDA300 sensor readings as frequently as possible. Developing our imaging applications using Tenet involved adding several new software pieces into Tenet. We ported device drivers for the Cyclops camera and the MDA300 data acquisition board into Tenet, added Tenet tasklets and its APIs for accessing these devices and implemented basic image compression algorithms (described in Section 5) to reduce the image transfer latency. All of these were fairly straightforward to implement in Tenet. Finally, two new Tenet applications, one for the pitfall trap monitoring and another for the bird nest monitoring, were written in the Tenet scripting language [2], as we will describe in Sections 6 and 7.
# A Distributed RF Threat Sensing Architecture
## 5 Performance Evaluation
### Drone Detection  
In the current configuration, due to the overall wide bandwidth of the monitored 2.4 GHz and 5.8 GHz bands, the noise floor in urban and suburban areas is higher than the emitted drone signals, and hence they cannot be detected over distances more than 50 m with the proposed sensors. In order to improve drone detection capabilities, the authors propose the utilization of sharper RF filters, or a mixed architecture based on RF down-converting and narrowband RF/IF filtering. Nevertheless, the utilization of a series of sensors as suggested in this work, in a fence-like setup, could provide malicious drone early warning.
# Topical Use of _Camabis sativa_ L. Biochemical
## 5 Cannabinoids  
The cannabinoids, which are synthesized in the glandular trichomes [2] of _Cannabis sativa_ L. are exogenous ligands of the ECS [62]. Therefore, they can interact with endocannabinoid receptors and some of the channels and receptors described below [62]. To date, almost 200 cannabinoids have been identified, and a vast majority of them are from _Cannabis sativa_ L. [63]. They are divided into 11 classes: Delta-9 Tetrahydrocannabinol (A9-THC) type, Cannabigerol (CBG) type, Cannabinol (CBN) type, Cannabichromene (CBC) type, Cannabitriol (CBT) type, Cannabidiol (CBD) type, Delta-8 Tetrahydrocannabiol (A8-THC) type, Cannabielsoin (CBE) type, Cannabicyclo (CBL) type, Cannabinodi (CBND) type, and miscellaneous type. The biosynthesis of all these cannabinoids (see Figure 3) originates from Cannabigerolic Acid (CBGA) products by Geranyl Pyrophosphate (GPP) and Olivetolic Acid (OA) or Divarinic Acid (DA), catalyzed by the Cannabigerolic Acid Synthase (CBGAS) enzyme. Delta-9 Tetrahydrocannabinol (\(\Delta\)9-THC) is the principal cannabinoid from _Cannabis sativa_ L. Cannabidiol (CBD) is the most abundant non-psychoactive cannabinoid derived from _Cannabis sativa_ L. [1]. A9-THC, A9-THCA, A9-THCV, A9-THCVA, CBD, CBDA, CBDV, CBG, CBGA, CBGV, CBC, and CBN have been studied regarding their interaction with the ECS and the secondary cannabinoids targets. The data regarding these interactions is available in Table 2.  
Figure 2: The repartition of CB1 and CB2, the TRPV1-4 channels, the TRPM8 channel, the PPARs transcription factors, and serotonin 5-HT1A, 5-HT2A, 5-HT3 receptors in skin cells, modificated with the permission of Dove Medical Press, Macclesfield, United Kingdom [61].
## 1 Introduction
At present, although certain investigations have been carried out on the atmosphere of the ocean, the understanding of atmospheric optical turbulence, especially high-altitude atmospheric optical turbulence, is still very insufficient. In this paper, through two measurement experiments for turbulence performance, we obtained a large amount of atmospheric optical turbulence data over the ocean. By using measured atmospheric data, the spatiotemporal distribution of optical turbulence over the ocean and corresponding ocean surface was analyzed comprehensively. The influence of various factors on the vertical distribution of turbulence strength are concluded. Furthermore, the value from measurement and the model is used to calculate the coherence length (\(\tau_{0}\)), the seeing (\(\varepsilon\)), the isoplanatic angle (\(\theta_{0}\)), and the wavefront coherence time (\(\tau_{0}\)) over the ocean.
## 4 Results and Discussion
### Comparative Summary  
The best condition (no force, realistic force, or exaggerated force) is selected on the basis of the comparison of six performance parameters for altitude hold, gain, and descent phase (Table 10).  
Figure 14: Mean error analysis at increasing and decreasing altitudes.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Time (s)** & **No Force Feedback** & **Realistic Force Feedback** & **Exaggerated Force Feedback** \\ \hline
35 to 70 & 9 & 18 & 24 \\
70 to 100 & 6 & 8 & 14 \\
100 to 130 & 10 & - & - \\
130 to 160 & 0 & 20 & 25 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Time elapsed in tolerance band at different time intervals.  
The performance parameters include rise time, overshoot control, fall time, undershoot control, settling time, and mean error. We can see that no force condition yields better results only when operators execute the altitude hold command. Exaggerated force turns out to be the best performer for altitude gain/climb phase. Similarly, the altitude descent task is performed better with a realistic force feedback.