esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Materials and Methods
### RNA Sequencing of CD163+ Monocytes
#### 4.2.3 Quality Control and Differential Gene Expression Analysis  
The RNA-seq data were analyzed using the Galaxy web platform at _usegalaxy.org_[32]. An initial multi-dimensional scaling (MDS) plot was generated to visualize the degree of separation between D\({}^{+\text{Comps}}\) and D\({}^{-\text{Comps}}\)[33] (Supplementary Figure S2). FASTQ files were generated after a quality control assessment using the 'FastQC' tool [34], and the analysis followed the pipeline shown in Supplementary Figure S3. The raw reads were trimmed using 'Trimmomatic' to remove the adaptors [35], re-assessed using FastQC, and aligned individually to the human reference genome Hg38 using 'HISAT2' (version 2.2.1) [36]. The mapped reads were then quantified using 'feature counts', wherein genes were normalized and those less than 1 count per million were filtered out. DEGs betweenD\({}^{+\text{Comps}}\) and D\({}^{-\text{Comps}}\) were analyzed using Limma-Voom' (version 3.50.1), with the following criteria: log\({}_{2}\) fold change and an adjusted \(p\)-value < 0.05 using the Benjamini-Hochberg method [37; 38]. The list of DEGs was then sorted to identify up- or down-regulated genes using a cut-off value of \(\geq\) 1.5 absolute FC (BHp < 0.05), as described by [PERSON] et al. [39]. The list of GOIs was generated based on the genes with an FC \(\geq\) 1.5 using three different screening procedures, as shown in Figure 1. Firstly, the top 20 most significantly up- and down-regulated genes were selected. Secondly, the Panther 17.0 classification system was used to evaluate the genes with an FC \(\geq\) 1.5 using the over-representation test and functional classification of biological processes (Figure 2) [40; 41]. The genes involved in diabetes, inflammation, immune system functions, or monocyte functions including biological adhesion, immune system processes, localization, and response to stimulus and signaling were included in the GOIs. Finally, the DEGS with an FC \(\geq\) 1.5 were manually searched and added to the GOIs list if their role related to diabetes, monocyte function, inflammation, cytokines, chemokines, or MMPs.  
In order to identify the interactions between individual GOIs, a PPIs network was established using the STRING database and the network was visualized by Cytoscape (version 3.9.1) [42; 43]. Nodes in the PPIs network represent genes, while the edges represent interactions between the genes. In this study, nodes with the greatest number of edges in connection with others were termed 'hub' genes.  
GO and KEGG pathway enrichment analysis were conducted individually on the up- and down-regulated GOIs, as well as the combined GOIs list. The top five most significant enriched 'GO' categories were reported (FDR < 0.05).
## 1 Introduction
On the other hand none of the vertical velocities derived from 8-12 h campaigns agreed with 24 h sessions. [PERSON] and [PERSON] [23] applied a refinement procedure to [PERSON], [PERSON] and [PERSON] [22] to improve the success of the velocities derived from GPS campaigns. They eliminated the noisy time series obtained in the years 1992-1999 from the generation of campaign measurements, chose GPS days from quieter ionospheric conditions with a kappa index <4, used data from three consecutive days, reprocessed Jet Propulsion Laboratory (JPL) orbits and clocks were used in the analysis, and the east component was improved with the new single receiver ambiguity resolution algorithm of GIPSY/OASIS II. They managed to improve the horizontal velocity estimation success on average by up to 50% and 70% for 8 h and 12 h sessions, respectively. The success of the vertical component was only improved by 10% on average. The fractions recommended by the above studies are not significant. [PERSON], et al. [24] showed that GIPSY's PPP-Ambiguity Resolution can further be improved if the satellite clock can be re-estimated as well. [PERSON], [PERSON] and [PERSON] [22] and [PERSON] and [PERSON] [23] used the full 15-year span of the available GPS time series. Furthermore, they used the International GNSS Service (IGS) data to sample the campaign measurements at once per month. Although the above efforts were useful to understand the nature of campaign GPS solutions, they were not indeed applicable for practical situations. Considering this and taking into account weak points, such as observation session length, we set up a different experiment. First of all, we extend the observation session duration of campaign measurements to 24 h. We use GNSS time series as short as four years in which the ground deformation has been found to be detectable [17; 25]. The data is sampled once every four months in a year considering practical needs. We also tested the sampling once per month as done in Akarsu, Sanli and Arslan [22] to quantify what we lose by repeating the surveys every four months. Furthermore, we account for the antenna set-up errors by a scenario based on the studies of [PERSON] [26] and [PERSON] et al. [27]. Monthly samples were generated by decimating the continuous time series of the Jet Propulsion Laboratory (JPL). Data was sampled from a globally distributed network of GNSS stations selected from the archives of the International GNSS service (IGS). For each station of the IGS network, first a velocity was estimated from the generated campaign data and then from the continuous GNSS data. We then tested the significance of the velocity derived from monthly repeated GPS surveys against the velocity obtained from the continuous data.
## 4 Q3: What Are the Methodologies Used to Overcome the Challenges in UIoT?
### Methods to Overcome the Technical Challenges of UIoT
#### 4.1.4 Noise Modeling Methods  
In [71; 72; 73; 74; 75; 76; 77; 78; 79; 80; 81; 82], the existing techniques for solving the environmental noise and ambient noise modeling in UIoT are discussed, and some methods are indicated herewith. In [72], [PERSON] et al. designed a PG mixed noise model based on a single-photon avalanche diode (SPAD) in an underwater visible light communication system by considering the attenuation and turbulence effect. Here, an algorithm for the noise model was also presented. In [76], [PERSON] et al. presented an underwater noise model to identify the noise level of shallow water during different seasons. Finally, in [78], [PERSON] et al. provide the conceptual design and describe the effect of using ships in the UIoT environment by providing various shipping noises for noise modeling underwater.
# An Efficient Optimization of the Monte Carlo Tree Search Algorithm for Amazons
## 5 Experiment and Results
### Quantitative and Qualitative Experiments
#### 5.2.2 Winning Convergence Distance Visualization and Analysis  
In this sub-subsection, we count the number of match point rounds in which the algorithm's winning percentage assessment reaches 90% or more (the threshold for a solid win) in 100 games played by the two algorithms on the SAU platform, as a way to visualize the definition of the distance of convergence of a win, as well as to compare the strengths and weaknesses of the playing abilities of the two algorithms of the Amazons game. Here, we generally consider the outcome of a game to be clear when the algorithm evaluates a winning rate of 90% or more (barring low-level errors by the dominant side); such moments are considered to be match points and we believe that the earlier this occurs, the stronger the algorithms are. The average resulting data for the number of match point rounds for the two algorithms are shown in Table 6. See Appendix C for complete data:  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Algorithm** & **Winning Percentage** \\ \hline MCTS & 74\% \\ MCTS + MG-PEO & 97\% \\ \hline \hline \end{tabular} Red results indicate the best result in the same column.  
\end{table}
Table 5: The winning rate of the two algorithms.  
As can be seen from Table 6, the average number of match point rounds required for the MCTS algorithm to reach the stable winning threshold (evaluation value over 90%) is 42.57 rounds. On the other hand, the MCTS algorithm with the MG-PEO strategy outperforms MCTS in terms of reaching the same stable winning threshold with an average of 39.68 rounds.  
This indicates that the MCTS algorithm with the MG-PEO strategy converges faster than MCTS. It requires fewer average rounds than the traditional MCTS algorithm. The MCTS algorithm with the MG-PEO strategy uses the Move Groups strategy and the Parallel Evaluation strategy to optimize the strategy selection process of the MCTS algorithm so that it can find the best search paths in a shorter period of time and reach a stable winning state with a winning rate of more than 90% in advance.  
In summary, the MCTS algorithm with the MG-PEO strategy has a shorter winning convergence distance than the traditional MCTS algorithm. It requires fewer rounds to achieve a higher winning rate and, thus, demonstrates superior playing ability.
# Outlier Detection for Multivariate Time Series Using Dynamic Bayesian Networks
## 3 Methods
### Scoring
1:procedure
2:for each time slice \(t\)do
3:for each subject \(h\in\mathcal{H}\)do
4:functionScoring(\(D_{t-m:t}^{h},B_{t-m}^{t},t\))
5:for each variable \(X_{i}[t]\)do
6:\(\Pi_{X_{i}[t]}\gets GetParents(X_{i}[t],B_{t-m}^{t})\)
7:\(w_{i}^{h}[t]\gets GetParentsConfig(\Pi_{X_{i}[t]},D_{t-m:t}^{h})\)
8:\(p_{i}^{h}\gets GetProbability(x_{i}^{h}[t],w_{i}^{h}[t],B_{t-m}^{t})\)
9:\(p_{i}^{h}\leftarrow(1-r_{i}\cdot y_{min})p_{i}^{h}+y_{min}\)\(\triangleright\) Probability smoothing
10:\(s_{t-m:t}^{h}\leftarrow\sum_{i=1}^{n}\log p_{i}^{h}\)\(\triangleright\) Transition score
11:if\(s_{t-m:t}^{h}<thr\)then
12:\(outliers\gets outliers.append(D_{t-m:t}^{h})\)
```  
**Algorithm 3** Transition Outlier Detection  
Furthermore, subject outlier detection can be easily computed from Algorithm 3, offering the detection of anomalous entire subject observations. In this case, a subject \(h\) outlierness is measured by the mean of every transition score of that subject. A subject is scored as  
\[s^{h}=\frac{1}{T-m}\sum_{t=m+1}^{T}s_{t-m:t}^{h}, \tag{6}\]  
where \(s_{t-m:t}^{h}\) represents the transition scores of all windows captured from subject \(h\). The algorithm is straightforward, and so we do not present it, but it is available in the current implementation of METEOR. With the computation of all transition/subject scores, a strategy must now discern normal and anomalous ones. The score-analysis phase is discussed next.
# Engineering Resource-Efficient Data Management for Smart Cities with Apache Kafka +
## 5 Evaluation of the Setup
### Dfb  
For DFB, the following high-level performance indicators were considered:  
* **Data Integrity**: to confirm that advanced encryption mechanisms over end-to-end data transfer guarantee data integrity. Metric: Data loss rate.
* **Availability**: to verify that DFB resources are available and discoverable. Metrics: Service availability-failed request, data access restriction.
* **Performance** (for high volume, heterogeneous data streams): to measure different performance metrics under different execution conditions. Metrics: Data transfer latency, data throughput, response time, and number of cluster nodes.  
Table 2 summarises the collected measurements for the specified metrics.
# Fluorescence Spectroscopy of Enantiomeric Amide Compounds Enforced by Chiral Light
## 2 Materials and Methods
### Fluorescence Spectroscopy Setup  
To excite the enantiomers and measure their fluorescence, we used the FDCD setup shown in Figure 3. We used a near-infrared laser (Chameleon Ultra II by Coherent Inc., Santa Clara, CA, USA), with a pulse duration of 140 fs and a repetition rate of 80 MHz. The laser gave maximum power (>3.5 W) at 800 nm, and the spot size had a diameter of 1.2 mm high collimated, M\({}^{2}\) < 1.1. Then, the beam at this wavelength was directed toward a beta barium borate (BBO) crystal through a lens whose focus was 75 mm. In order to prevent any damage, the BBO crystal was positioned out of focus with respect to the lens of 15 mm. The incident angle of the laser on the BBO was set to optimize phase matching in order to produce a linear polarized second harmonic beam at 400 nm. The 400 nm beam, with 0.1 mW of average power, passed through a short pass filter (SPF) filter (<700 nm), which filtered the strong fundamental beam. The light then passed through a linear polarizer (LP) and quarter wave plate (QWP); QWP rotation defines the polarization state of the excitation, which can be LCP, RCP, or linear (Lin). A further SPF (<450 nm) removed any residual of the fundamental beam. We prepared cuvettes with different relative concentrations of \(S\)- and \(R\)-enantiomers in dimethylformamide (DMF) solvent. The cuvette was placed in a holder which was well isolated from the environmental light fluctuations. The laser light at 400 nm impinged on the cuvette with a 2 mm diameter spot on one side of the cuvette, in a way that it was normal to the longer side of the cuvette volume (see Figure 3). From the lateral side of the cuvette, we detected the fluorescence using a Hamamatsu compact spectrometer (200 nm-800 nm range), with a lens whose focus was 20 mm and a UV graded fiber with a 200 micron core. The spectrometer was set to a 5000 ms acquisition time per spectrum in order to acquire a clear luminesce signal. In this way, the detection was always done at the same point (half of the cuvette side) and under the same conditions. Moreover, choosing the shorter side of the cuvette volume for 400 nm light absorption ensured that we obtained a stable fluorescence signal from that part of the excited substance. Before each measurement, the \"dark\" signal from the environment was measured and subsequently subtracted from the signal coming from the fluorescence in the cuvette. During the single spectrum acquisitions, part of the laser light was continuously monitored in time using a photodiode (not represented in the scheme) and used as a reference for the luminescence intensity normalization.  
Figure 2: Absorbance spectra of the pure enantiomeric compounds \(R\)-FA1 and \(S\)-FA1. Insets: photos of the DMF solutions of \(R\)-FA1 and \(S\)-FA1 in daylight (**top**) and under UV light excitation at 365 nm (**bottom**).
## 4 Conclusions  
The possibility of the application of the equivalence approach in the prediction of blast-induced seismic effects is studied in this work. After a thorough analysis of the results, the following conclusions were drawn:  
1. The results have shown that the equivalency approach is a potential tool in the assessment of the blast-induced seismic effect. It is especially useful in the situation when the type of explosive is changed within a single project;
2. It was demonstrated that the best results are obtained when the equivalent weight of the studied explosive is calculated as a ratio of detonation velocities of studied and reference explosives;
3. Analysis of experimental results has shown that the measurement uncertainty is quite high. This may be attributed to the fact that the weight of the explosives used in the tests was too small (15-75 g). To validate the approach, it is necessary to improve the accuracy of experimental measurements, which can be achieved by using larger samples, including the amounts required for mass blasting at open pits;
4. The advantage of the approach is that EMEe is determined using detonation velocity, the parameter that is relatively easy to measure experimentally;
5. Experimental PPV-SD data in this study are analyzed using Blastware 10.74 (Instantel application) and Curve Expert 1.4, however, any other software capable of performing regression analysis may be used.  
Following the Conclusions, point (c) specifically, the plan for further study is to extend the research with larger quantities of explosives, including the amounts required for mass blasting at open pits, and more measurement positions to improve accuracy for application of explosive equivalency in blast-induced seismic effect calculations.  
Conceptualization, S.S. and J.O.; methodology, S.S.; software, M.S.; validation, S.S., LD. and M.S.; formal analysis, LD. and J.O.; investigation, J.O.; data curation, M.S.; writing--original draft preparation, S.S.; writing--review and editing, S.S.; visualization, LD.; supervision, S.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
This work has been supported by the Croatian Science Foundation (HRZZ) under the projects IP-2019-04-1618.  
The authors declare no conflicts of interest.
## 4 Multi-Threaded Control with C/S Architecture Incorporating AES Encryption and TLS Protocol
### Security Analysis
#### 4.3.2 Transport Layer Security Analysis  
Authentication: Assuming that client A wants to communicate securely with server B over TLS, client A and server B first establish a TLS connection. Client B, as a server, provides a digital certificate to Client A. Client A verifies the validity of the digital certificate to ensure that they are communicating with Server B and not a server disguised by an attacker. In this way, security threats such as man-in-the-middle attacks, server forgery and client forgery are prevented by authentication methods, and data security and integrity are protected.
# Collision-Free Motion Planning of a Six-Link Manipulator Used in a Citrus Picking Robot
## 2 Materials and Methods
### Methods  
The establishment of the manipulator's kinematic model is the premise work of motion planning. Common kinematic modeling methods include Denavit-Hartenberg (DH) method [32] which includes the standard DH method [33] and the improved DH method [34], as well as the ETS method [35]. The DH method describes each link of a manipulator by establishing a DH coordinate frame. Because the DH method has only a few indispensable coordinate frames to describe all links of a manipulator, the description of most points on the manipulator instead of DH coordinate frame origins requires complex geometric transformation and mathematical calculation, which cannot meet the requirements of APF in both real-time performance and complete description of a manipulator. Therefore, ETS is used to conduct kinematics modeling of EC63M in this paper, because of the full description of all the points, as shown in Figure 3. The notations used in the ETS model are explained in Table 1. From the ETS model, the Jacobian matrix of every point on the manipulator can be easily obtained, see the paper [35] for more details.  
Figure 3: ETS model of EC63M manipulator.  
Figure 2: Conceptual diagram for motion planning.  
Figure 4 shows that, in this paper, it needs three steps to pick a single citrus fruit. Step 1: the picking manipulator moves from the initial configuration to the picking configuration, and the target citrus fruit is detached by the end-effector. The picking configuration is designed and calculated simultaneously when planning the movement. Step 2: The picking manipulator moves from the picking configuration to the release configuration to put the fruit into the storage box. Step 3: the picking manipulator moves from the release configuration to the initial configuration and waits for the next picking cycle. Both the initial and release configurations are pre-set in the picking experiment to further increase the real-time performance.
# A Self-Powered UHF Passive Tag for Biomedical Temperature Monitoring
## 5 Conclusions  
A self-powered UHF passive tag prototype for biomedical temperature monitoring has been proposed, modeled, and experimentally verified. A bottom-up design methodology was also discussed and addressed. The proposed passive sensor was tested using a 1 W EIRP 860-960 MHz RF-link, which was employed as an energy source and as a data carrier. The whole sensor registered a power consumption of 13.75 uW, which represented a power conversion efficiency of 10.92%, and achieved a read range of 9.5 m. The temperature is sensed through the variation of a Proportional To Absolute Temperature (PTAT) current which is fed into a Flipped-Voltage-Follower (FVF) current-mode ring oscillator, which generated a proportional deviation in the frequency of the ring oscillator with respect to the temperature. The FVF current-mode ring oscillator is used for the first time in the literature. This temperature sensing mechanism allowed that the sensor measured temperature gradients between 0 \({}^{\circ}\)C to 60 \({}^{\circ}\)C with a 823.29 Hz/\({}^{\circ}\)C sensitivity and a 13.67 Hz/\({}^{\circ}\)C standard error over linear regression. A prototype was fabricated using a 0.18 um CMOS standard process, with a silicon area consumption of 1065 um \(\times\) 560 um. The overall size of the implemented self-powered passive tag prototype, including chip and antenna, was 8 cm \(\times\) 2 cm. Measurements and characterizations have shown that the proposed self-powered passive tag prototype could be used to measure biomedical temperature signals.  
Conceptualization, [PERSON]-M., [PERSON]-C., J.M.R.-P., and A.D.-S.; Formal analysis, [PERSON]-M., A.L.H.-M., V.H.C.-G., and U.G.Z.-R.; Data curation, [PERSON] and [PERSON]; Validation, [PERSON], [PERSON], [PERSON], and [PERSON]-[PERSON]; Writing--original draft preparation, [PERSON] and [PERSON]; Writing--review and editing, [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON]R. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors thank SILTERA for providing the design manufacturing service. In addition, the authors express their gratitude to the National Council of Science and Technology (CONACyT-Mexico) for the support to [PERSON] and [PERSON] through the \"Investigadoras e Investigadores por Mexico\" program.  
The authors declare no conflict of interest. The founding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.