esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Data Acquisition and Analyses  
Unipolar ECoG and ECG signals were amplified (4-Channel EEG or ECG Amplifiers, Biovision, Wehrheim, Germany), filtered (time constant was 0.1 s, cut-off frequency was 1000 Hz), fed into a multi-channel recording device (GJB Dattenechnik Bolten & Jannek GbR, Ilmenau, Germany) and stored after A/D conversion continuously on hard disc at 1000 Hz. In order to reveal the electrocorticographic activity, the signals derived from head screw electrodes were bandpass filtered (0.5-45 Hz) and stored. The instantaneous heart rate (HR) was derived from the reciprocal RR interval time series. Therefore, the individual R-waves, with the R-wave peak as the trigger point, were sequentially recognized (ATISApro(r), GJB Dattenechnik GmbH, Langewiesen, Germany). Accurate R-wave peak detection was verified by visual inspection. The distance of consecutive R wave peaks was measured by a precision of 1 ms. The series of R-R intervals (T1, T2, T3... Tn) was stored as a function of the beat number. This series constitutes the RR interval time series (measured in ms). The reciprocal of this series represents the instantaneous HR (in beats per minute) and this was stored simultaneously to the electrocorticographic activity (ECoG, in \(\upmu\)V) [30].  
The types of stimulation were administered in a predetermined order. The times at which the stimuli were applied and their effects on ECoG, ECG and heart rate were recorded and subsequently evaluated semi-quantitatively. Specifically, in each case of stimulation, immediate reactions in the ECoG recordings, as well as in the ECG curve or the instantaneous heart rate (e.g., induced arrhythmias), were checked and, if necessary, the extent and duration of the electrophysiological events were classified (Table 1).  
Figure 3: Schematic illustration of Boron-doped diamond electrode—outside. The anode consisted of a 50 \(\upmu\)m niobium wire coated with 1\(\upmu\)m boron-doped diamond. The cathode consisted of an endodontic needle (28 G, VMK Endoneedle, Vedefar N.V, Dilbeek, Belgium). The anode and cathode were separated by a thin polymeric layer. (**a**) CAD schematic overview (FreeCAD, version 0.18, [[http://www.freecadweb.org/](http://www.freecadweb.org/)]([http://www.freecadweb.org/](http://www.freecadweb.org/)), accessed on 11 December 2023) of the kinked endodontic needle. (**b**) In-depth view of the electrolytically active tip of the BDD electrode type ii probe.  
Figure 2: Schematic illustration of Boron-doped diamond electrode—inside. The anode consisted of a 50 \(\upmu\)m niobium wire coated with 1 \(\upmu\)m boron-doped diamond. The cathode consisted of an endodontic needle (0.3 \(\times\) 25 mm, transcodent, Sulzer GmbH, Munich, Germany). The anode and cathode were separated by a thin polymeric layer. (**a**) CAD schematic overview (FreeCAD, version 0.18, [[http://www.freecadweb.org/](http://www.freecadweb.org/)]([http://www.freecadweb.org/](http://www.freecadweb.org/)), accessed on 11 December 2023) of the irrigation needle. (**b**) In-depth view of the electrolytically active tip of the BDD electrode type ii probe.
## 4 Implementation and Discussion  
### System Implementation  
MultiChain 2.0 (alpha 3) [71; 72], an open source blockchain implementation platform, was used to develop the prototype for assessing the scalability of the PoF consensus algorithm. The study prepared a consortium blockchain scenario with ten Windows 8 computers (Intel i7-6700, 3.40 GHz with 8 GB RAM Samsung Inc. Seoul, South Korea), each representing a participating entity of Table 4. This test trials a similar concept of the running example of Section 3.3 (Figure 13). The reason for choosing MultiChain 2.0 is its global acknowledgement that facilitates off-chain blockchain implementation [73; 74; 75]. Multichain 2.0 uses both blockchain and local storage. Unlike any other blockchain architectures, MultiChain 2.0 solves the problems of mining, privacy, and openness via integrated management of user permissions, thereby three folding the core aims:  
* Individual node writes data in local storage.
* Hash of the data and data size are published to the network.
* After agreement from the network (consensus), the hash of locally stored off-chain data and insensitive information are stored in blockchain as a new block without any associated costs.
* Addition of a new block finishes the blockchain transaction procedure.
* To ensure privacy blockchain's activities are visible to chosen participants only.
* To enable mining to take place securely without proof of work and its associated costs.
* Facilitates privacy preservation and control over data with an easy to configure and deploy package by supporting both UNIX and Windows system.
* Provides a comprehensive JSON-RPC (Remote Procedure Call) application programming interface (API) for easy integration with the systems.
* The maximum block size is under the participant's control, it solves block size monitoring issues.
* As this is a closed (consortium) system, it considers decision only from interested participants.
* In MultiChain 2.0, users are permitted to configure configuration parameters.
* This study uses a round robin scheduling scheme for PoF. P, D, I\({}_{\text{c}}\), and C\({}_{\text{p}}\) must approve or deny a decision during their turn to generate a valid new block. For robustness of the system, mining diversity is set to 0.75 out of 1. For approving a new block, at least 75% of the total nodes or P, D, I\({}_{\text{c}}\), and C\({}_{\text{p}}\) of a block must agree and respond. This mechanism saves the system from freezing up if any set of miners become inactive for a prolonged period (Figure 13).  
Figure 13: PoF communication (transaction) environment tested in the Multichain 2.0 environment.
## 2 Materials and Methods
### Determination of PAHs
#### 2.3.3 GC/MS/MS Settings  
The PAHs analysis was conducted using an Agilent 7890B gas chromatograph coupled with a 7000D Triple Quadrupole (Agilent Technologies, Santa Clara, CA, USA) (GC/MS/MS). Helium (6.0 grade) was used as the carrier gas at a constant flow rate of 1.0 mL/min (22 psi). Separation was achieved on an Rxi-XLB capillary column (30 m \(\times\) 0.25 mm i.d., 0.25 \(\upmu\)m d.f.; Restek, Bellefonte, PA, USA). The GC oven temperature was programmed as follows: initial temperature of 60 \({}^{\circ}\)C for 1 min, ramped to 220 \({}^{\circ}\)C at 40 \({}^{\circ}\)C/min, then to 320 \({}^{\circ}\)C at 10 \({}^{\circ}\)C/min, with a final hold at 320 \({}^{\circ}\)C for 1 min. The total run time was 30 min (plus an additional 5 min post-run).  
The mass spectrometer operated in dynamic multiple reaction monitoring (dMRM). The collision energies for every target compound are depicted in Supplementary Table S2. The transfer line and ion source were heated at 320 \({}^{\circ}\)C. Samples were injected using a PAL RTC autosampler (CTC Analytics AG, Zwingen, Switzerland) in a splitless mode with a 1 min splitless period. The inlet was kept at 310 \({}^{\circ}\)C. MassHunter B.07.06.2704 software (Agilent, Santa Clara, CA, USA) was used for instrument control. Standard control solutions were frequently analyzed to ensure accuracy. Quantification of the target compounds was achieved using calibration curves consisting of eight concentration points, ranging from 0.5 ng/mL to 1000 ng/mL. The limits of detection (LODs) of the individualcompounds (in \(\upmu\)g/kg) were as follows: 0.09 for naphtalene, acenaphylene, acenaphene, fluorene, anthracene, and chrysene; 0.12 for phenentrene; 0.15 for fluoranthene, pyrene, benzo[k] fluoranthene, benzo[a]pyrene, indeno[l,2,3-c,d]pyrene, and benzo[g,h,i] perylene; 0.21 for benz[a] anthracene; 0.27 for dibenz[a,h]anthracene; and 1.20 for benzo[b] fluoranthene. The corresponding limits of quantification (LOQs) were (in \(\upmu\)g/kg) as follows: 0.3 for naphtalene, acenaphylene, acenaphene, fluorene, anthracene, and chrysene; 0.4 for phenentrene; 0.5 for fluoranthene, pyrene, benzo[k] fluoranthene, benzo[a]pyrene, indeno[l,2,3-c,d]pyrene, and benzo[g,h,i] perylene; 0.7 for benz[a] anthracene; 0.9 for dibenz[a,h]anthracene; and 4.0 for benzo[b] fluoranthene.
## 3 Results and Discussion
### CH/CNC and CH/CD Edible Films
#### SEM Analysis  
The SEM images of the CH, CH/CNC and CH/CD final formed edible films depict the distribution of the composing materials on the surface of the films (Figure 2).  
The surface of the CH/CNC films was rougher compared to those with only CH, probably due to the formation of a polyelectrolyte-macroion complex (PMC) between CNC and CH [61; 62]. The 75/25 and 50/50 proportions of the CH/CNC films showed lower heterogeneity and a more dense structure compared to the 25/75 ones, with less CNC agglomerates and/or PMC crystals on the film surface, indicating a better dispersion of the CNC within the CH matrix at the lower CNC concentration [61; 62]. Additionally, the surface of the 25/75 CH/CNC films contained more crystals, apparently due to the increased CNC and/or PMCs concentration. On the other hand, the addition of CD to the CH samples created a heterogeneous structure in which crystals of CD were entrapped in the continuous polymer network. It is obvious that the CD crystals became more intense as the amount of CD was increasing.
# A Method for Identifying the Mood States of Social Network Users Based on Cyber Psychometrics
## 4 Results and Discussion
It is defined as follows:  
\[RMSE=\sqrt{\sum_{i=0}^{n}(y_{i}-\hat{y_{i}})^{2}} \tag{9}\]  
where \(y_{i}\) represents the true value of sample \(i\), \(\hat{y_{i}}\) is the predicted value of sample \(i\) and \(n\) is the number of samples. Figure 3 shows the RMSE of the different regression models on the experimental data set. It could be seen that the regression of total score always reach the largest RMSE in each regression model, since total score is a comprehensive index and may cause more error. The Gaussian process has the largest prediction error on each task, followed by the linear regression and Lasso regression. The prediction error of multitask regression is slightly less than that of the Lasso regression method on each task. For the average RMSE of multiple tasks, the values of the Gaussian process, linear regression,  
Figure 2: Correlation coefficients of sentimental scores within text during different time spans and real mood states. Lasso regression, and multitask regression is 23.919, 12.206, 8.336 and 6.273, respectively, indicating the smallest for the multitask regression model. Table 5 shows the prediction error rate of different algorithms on different tasks. In each task, specifically, for each dimension of mood state, the error rate of multi-task regression is the smallest. Therefore, the accuracy of the multi-task regression method is superior to other algorithms, i.e., the cyber psychometrics produces a good validity and accuracy rate. This demonstrates that multi-task regression could learn the correlation between different dimension of mood state and outperform the single-task regression method [44]. This study has proved the usefulness of this automated identification method and thus, could be applied to identifying other psychological variables or preferences of social network users using the rich UGC data in the social network. Businessmen and policy-makers could also possibly make use of the predictions to support decision-making.
###### Abstract  
Phosphogypsum (PG), a by-product of phosphoric acid production, contains high levels of fluorine and phosphorus impurities, which negatively impact the strength and setting time of PG-based cement materials and pose environmental risks. This study explores a dual approach combining physical adsorption using zeolite powder and chemical modification with quicklime (CaO) to immobilize these impurities. The composition of 90 wt.% PG, 5 wt.% zeolite powder, and 5 wt.% quicklime reduces the soluble phosphorus to below the detection limits and significantly lowers the free water content in the PG. Through SEM, XRD, and FT-IR analyses, it was found that zeolite powder adsorbs fluorine and phosphorus through encapsulation, while quicklime chemically reacts to form insoluble calcium phosphate and calcium fluoride. This transformation decreases the solubility, mitigating potential environmental contamination. The combination of physical adsorption and chemical conversion provides a sustainable strategy to reduce environmental hazards and enhance PG's suitability for cement-based materials. The findings from this research offer a promising pathway for the sustainable utilization of PG, providing a mechanism for its safe incorporation into building materials, while addressing both environmental and material performance concerns.  
Keywords:phosphogypsum; physical adsorption; chemical modification; fluorine/ phosphorus curing; curing mechanism +
Footnote †: journal: Applied sciences
### Case Study No. 2: CODESYS Linked with Node-RED and Microsoft Azure
#### 6.2.2 Communication  
The most important aspect of communication (Figure 18) in this case is the communication between CODESYS and Factory I/O, as it provides the control process. The communication is provided by the modern OPC UA protocol. CODESYS will now behave as OPC UA server and Factory I/O will behave as OPC UA client. Thanks to the existence of OPC UA server, we can view the variables in any OPC UA client. We will use Node-RED again in this case to share data to the cloud, but it is not needed for system control. We will communicate with the cloud via MQTT protocol.  
Figure 17: CODESYS linked with Node-RED and Microsoft Azure.
## Underlying Theories
### IEEE802.15.4e Tsch
The active part of the slotframe is marked in orange, while the idle/sleep section is marked  
Figure 4: Timeslot channel hopping mechanism in the IEEE802.15.4e standard. Figure 3: TSCH slotframe with six timeslots. in light blue. The figure shows one green cell that occupies timeslot 1 and ChOff 2. According to the IEEE802.15.4e standard, the cell is a link, because the standard states that a link is defined as \"the pairwise assignment of a directed communication between devices in a given timeslot on a given channel offset\". The use of the cell/link enables communication between two nodes on the TSCH network. Even if the green cells use the same ChOff and T values, based on Equation (1), the ChOff and T values will produce different frequencies because the ASN values for the two cells are different, i.e., 2 and 14. With different ASN values, the transmit/receive process on the link will use different frequencies, \(\mathrm{f_{1}}\) in cycle 1 and \(\mathrm{f_{2}}\) in cycle 2, which leads to the occurrence of the channel hopping mechanism.
## 1 Introduction
First RS investigations on bladder examined the bladder wall layers [25], followed by approaches to differentiate between tumor and non-tumor bladder tissue [26; 27]. Analysis of the biochemistry in bladder lesions was explored by [PERSON] et al. to understand the molecular changes associated with different bladder cancer grades [28]. [PERSON] et al. demonstrated the feasibility of RS for in vivo bladder cancer detection with an endoscope [29]. Recently, the feasibility of low-resolution RS for biological samples was introduced for the first time by [PERSON] et al. [30] and implemented by [PERSON] et al. in a fiber-optic probe for the diagnosis of bladder cancer [31], strengthening the potential of RS as a diagnostic tool in bladder cancer. The combination of OCT with spontaneous RS provides complementary information on the structural changes and the associated molecular differences in tissue. While OCT provides high speed 3D structural information from large areas and can be used as a red-flag technology, RS can identify the full molecular fingerprint of tissue. The qualitative and quantitative molecular information obtained with RS can be localized with high-speed high-resolution OCT revealing structural information with \(\upmu\)m isotropic resolution. First OCT-RS combinations have been tested in colon [32], retina [33], atherosclerotic plaques, [34] and skin [35; 36]. We report on a multi-modal OCT and RS approach to explore its capabilities to increase the accuracy of the staging and grading of suspicious lesions in the bladder wall ex vivo by the combination of these complementary techniques.
## 2 Materials and Methods
### Data Synchronization  
In this paper, three apparatuses were used, and 10 channels of data were recorded. Because some of these apparatuses provided no hardware synchronization interface, a two-step data synchronization method was developed.  
In the first step, the recording command was sent to the apparatuses simultaneously by the host computer. There was a 5 s waiting period to ensure that all of the apparatus had started up. The data received by the host computer were labeled with global timestamps. There were still inevitable time delays resulting from hardware differences in command receipt and data sending among the different apparatuses. This kind of time delay could be tens of milliseconds, which influences the results and the corresponding explanation. Therefore, in the second step, a cross-correlation (CC) method was implemented to further modify the timestamps.  
The CC function can be described as:  
\[R_{xy}(\tau)=\frac{\frac{1}{T}\int x(t)y(t+t_{lag})dt}{\sqrt{R_{xx}(0)R_{yy}(0)}} \tag{12}\]  
where \(R_{xy}\) is the CC coefficient and \(t_{lag}\) represents the time shift between \(x\) and \(y\). \(R_{xy}\) is at its maximum when \(x(t)\) and \(y(t+t_{lag})\) are maximally related. The \(t_{lag}\) at that maximum point is the time delay. Timestamps are modified by the corresponding \(t_{lag}\).  
As forces are the instantaneous results of interaction and are measured by analogous circuit, we assume that there is no time delay in force data. Consequently, torque data calculated from force plate recordings were used as common signals. Ankle kinematic and kinetics data timestamps were adjusted by the \(t_{lag}\) of linear acceleration and torque data. Because no external assisting force was applied in the experiment, leg linear acceleration changes were caused instantaneously by the change in ankle joint torque. Muscle activation data timestamps (the same with the sEMG timestamps) were adjusted by the \(t_{lag}\) of muscle activation and torque data. According to the results of the currently existing research [32], in order to represent the electromechanical delays, the time from the onset of  
Figure 4: Upright standing push-recovery. Left picture shows a subject standing on the force plate. Pushing force is exerted on his back. Right upper picture shows the sEMG device. Right lower picture shows the IMU sensor.  
EMG activity and ankle joint torque variations, a 20 ms time lag is manually added to torque, angular acceleration, angular velocity and angle data.
# A Review of Deep Learning Applications for Railway Safety
## 8 Discussion and Conclusions
### Performance Optimization  
#### 8.1.1 Dealing with a Lack of Data  
Developing deep learning models for railway safety is challenged by practical limitations of data volume or quality, such as diverse noises in railway environments and insufficient labeled data. Therefore, it is necessary to deal with such data deficiencies when developing artificial intelligence for railway safety. For example, [PERSON] et al. [77] utilized a hierarchy of features for training a deep learning model with a small number of labeled data. Ensemble methods that integrate different machine learning algorithms can help increase the efficiency of model learning. For example, [PERSON] et al. [72] considered an ensemble method that integrates a CNN-based model and RF for bearing fault diagnosis. The ensemble approach can be efficient with a relatively small number of data rather than the end-to-end deep learning approach. In addition, unsupervised models can help deal with a small number of labeled data. [PERSON] et al. [63] expected to improve system performance by analyzing other clustering algorithms or adjusting internal parameters.
# Research on a Collaborative Control Strategy of an Urban Expressway Merging Bottleneck Area
## 5 Discussion and Conclusions
It can be seen that the characteristics of speed mutation at the merging bottleneck are controlled, the running state of traffic flow near the merging bottleneck is no longer separated, the spatial and temporal distribution uniformity of speed is improved, and the conflict risk caused by frequent and large speed changes of traffic flow is controlled. Although the effectiveness of the cooperative control strategy is verified in this paper, how to accurately transmit the control signal to road users is a problem to be studied in the next step. At present, the cooperative control mainly releases the control signal through the variable information board and the ramp signal lamp. In the future, with the development of vehicle networking, automatic driving, 5G communication, vehicle road coordination, and other technologies, it will be possible to exchange information between the control system and the traffic system, and also provide a new channel for data acquisition and control signal release. Conceptualization, C.M. ; methodology, J.W. ; software, S.W. ; investigation, X.L. ; project administration, B.G. 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 under Grant No. 52172338. Funds for Central Universities and Colleges of Chang'an University (No. 300102412101). Not applicable. The authors declare no conflict of interest.
## 2 Experimental Section
### HS-SPME Extraction  
Volatile fractions of honey and flower samples were extracted by HS-SPME with a divinylbenzene/carboxen/polydimethylsiloxane (DVB/CAR/PDMS, 30 \(\upmu\)m) fiber (Supelco Sigma Aldrich). The optimization of HS-SPME parameters was performed using two honey samples (9 and 24) and two flower samples (clementine and asphodel flowers). These samples and subsequent analyses (all honey and flower samples studies) were performed in triplicate to ensure that the coefficient of variation (CV: ratio of standard deviation to the mean) of the major compounds and the sum of the total peak areas were always \(<\)15%. The samples analyzed were placed in a 20 mL vial. The parameter optimization was based on the sum of the total peak areas measured using a gas chromatography-flame ionization detection (GC-FID) system. For each sample (both honeys and flowers): the temperatures (25 \({}^{\circ}\)C, 50 \({}^{\circ}\)C and 70 \({}^{\circ}\)C), the equilibration times (30, 60 and 90 min) and the extraction times (15, 30 and 45 min) were tested in various experiments. The honey concentration in distilled water was optimized after six different experiments (0.5 g/mL, 1 g/mL, 1.5 g/mL and 2 g/mL) with Na\({}_{2}\)SO\({}_{4}\) addition (1 g and 2 g). The maximum sum of the total peak areas was obtained from 4 g of honey sample with 4 mL of water and 2 g of Na\({}_{2}\)SO\({}_{4}\) at a temperature of 70 \({}^{\circ}\)C, an equilibrium time of 90 min, and an extraction time of 30 min. The flower weight was optimized after three different experiments (1 g, 3 g and 5 g). For the Asphodel flowers, the maximum sum of the total peak areas was obtained from 3 g of sample at a temperature of 70 \({}^{\circ}\)C, an equilibrium time of 90 min, and an extraction time of 30 min. Otherwise, the best sampling conditions of Clementine flowers were 1 g of sample at room temperature (25 \({}^{\circ}\)C) with an extraction time of 15 min. Before sampling, the fiber was reconditioned for 5 min in the GC injection port at 280 \({}^{\circ}\)C. After sampling, the SPME fiber was consecutively inserted into the GC-FID and GC-MS injection ports for 5 min for desorption of volatile components, both techniques using the splitless injection mode.