esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
In recent years, compact light sources have been developed in the terahertz (THz) region, and the field of THz wave spectroscopy has made remarkable progress [1, 2, 3, 4, 5, 6]. Because there are many vibration modes characteristic of molecules in the THz region, it is called the fingerprint region of a substance, and THz wave spectroscopic imaging is applied to identify explosives [7], drugs [8], and foreign bodies in dry food [9]. THz waves are nonionizing, meaning that they do not cause any detrimental effects on dividing human keratinocytes used as a promising noninvasive imaging tool [10]. THz waves are strongly sensitive to polar materials such as water molecular clusters [11], if there is a difference in the form of water taken up by each tissue in the body, it is possible to identify the tissue by observing changes in the transmission/absorption rate of THz waves [12]. In previous reports, the THz spectroscopy and imaging were applied to diagnose malignant and benign neoplasms with different nosology's [13, 14]. The THz pulsed imaging method has been used to detect skin cancer [15, 16, 17], colon cancer [18, 19], oral cancer [20], breast cancer [21], burn injuries [22], corneal cancer [23], caries lesion [24], brain glioma [25], and brain injuries [26]. A few imaging studies using THz waves have been conducted on liver tissue. However, previous reports have described that the transmission of the tumor tissue was not stable in the THz region [27; 28].  
To develop an effective technique for THz imaging of samples of formalin fixed human liver in which normal tissue and tumor tissue were mixed, we performed transmission imaging experiments using a beamline at the Laboratory for Electron Beam Research and Application (LEBRA) in Nihon University. This beamline, which is called the infrared free-electron laser radiation (FEL)/the THz coherent synchrotron radiation (CSR) beamline, supplies intense CSR in a frequency range of 0.1-0.3 THz and FELs in a wavelength range of 1-6 \(\upmu\)m. Multimodal imaging (THz CSR and infrared FEL) can be applied to spectroscopic and imaging experiments using the same optical system. In a previous report, THz and visible light (VIS) imaging were applied to study non-melanoma skin cancer [15]. Using the infrared FEL/THz CSR beamline, we can observe both intermolecular vibration and hydroxide stretching vibration for water in a living tissue and can provide useful information for the tissue analysis and differentiation.  
In this article, we first clarified the deviation in THz transmission between the normal liver tissue and the malignant liver tumor. Subsequently, we reported that the factor that caused the deviation was the advanced fibrosis of the tissue. The malignant liver tumor with less fibrosis contained more water molecular clusters, and its transmission in the THz region was lower than that in the normal liver tissue. Finally, THz imaging can distinguish between malignant tumor tissues and normal tissues of the human liver.
## 2 Materials and Methods
### Analytical Procedures  
#### 2.3.1 Determination of Total As Content in Animal Tissues and Experimental Diets  
A 200 mg aliquot of homogenised freeze-dried organ tissue samples were decomposed in a mixture of HNO\({}_{3}\) (Analpure(r), Analytika, Prague, Czech Republic) and H\({}_{2}\)O\({}_{2}\) (Rotipuran(r), [PERSON], Karlsruhe, Germany) in a microwave oven (Discover SP-D, CEM Corp., Charlotte, NC, USA). The concentration of total As was measured by inductively coupled plasma mass spectrometry (ICP-MS; Agilent 7700x, Agilent Technologies Inc., Santa Clara, CA, USA) operated in He mode. For quality assurance of the analytical results, certified reference material, specifically bovine liver (BCR-185R), and procedural blanks were analysed in parallel. In the reference material containing 0.033 \(\pm\) 0.003 mg/kg of As, the value 0.037 \(\pm\) 0.006 mg/kg (n = 3) was determined.  
3.2 Extraction of As Species and As Speciation Using High-Performance Liquid Chromatography in the Liver  
Arsenic species were released into an aqueous solution by applying a slightly modified microwave-assisted extraction procedure developed by [PERSON] et al. [53]. The individual extracts were centrifuged (2680\(\times\)_g_) for 10 min, passed through a syringe filter (0.2 \(\mu\)m, NYLON), combined and homogenised. The extract was subjected to As speciation analysis by employing a high-performance liquid chromatography system (HPLC, Agilent 1260, Agilent Technologies Inc., USA) hyphenated to ICP-MS as an As detector. The HPLC system was equipped with an anion exchange column PRP-X100 (150 \(\times\) 4.6 mm, 10 \(\mu\)m; Hamilton, Reno, NV, USA), which was eluted with a gradient with 4 mmol L\({}^{-1}\) NH\({}_{4}\)NO\({}_{3}\) (A) and 60 mmol L\({}^{-1}\) NH\({}_{4}\)NO\({}_{3}\) (B) at a flow rate of 0.95 mL min\({}^{-1}\) at pH = 8.7, similar to [PERSON] et al. [54]. Standard solutions at 0.1, 0.5, 2.5, 10 and 50 \(\mu\)g L\({}^{-1}\) of each As species to be analysed were prepared using dissolution and dilution of NaAsO\({}_{2}\) (AsIII), Na\({}_{2}\)HAsO\({}_{4}\)\(\times\) 7H\({}_{2}\)O (AsV), and dimethylarsinic acid (DMA), all purchased from Fluka (Buchs, Switzerland), while monomethylarsonate (MMA) and arsenobetaine were synthesised in house. All individual samples were tested in duplicate.
# Real-Time Tunnel Deformation Monitoring Technology Based on Laser and Machine Vision
## 4 Test Results and Analysis
### System Accuracy Test Experiment
#### 4.6.2 Repeatability Accuracy Test Experiment  
The micro motion platform performed a linear reciprocating motion at an interval of 10 mm. Experimental data are shown in Table 4.  
Figure 21: Result of data change caused by thermal deformation.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Position of Micro** & **Laser Spot Center** & **Laser Spot Center** & & & \\
**Motion Platform** & **before Calibration** & **after Calibration** & **\(x\) (mm)** & **lAx\(-\)101 (mm)** \\
**(mm)** & **(pixel)** & **(mm)** & & \\ \hline (70, 0) & (598.27, 317.10) & (86.99, 46.11) & — & — \\ (60, 0) & (529.16, 317.29) & (76.94, 46.13) & 10.05 & 0.05 \\ (50, 0) & (459.92, 317.45) & (66.87, 46.15) & 10.07 & 0.07 \\ (40, 0) & (390.32, 317.60) & (56.75, 46.18) & 10.12 & 0.12 \\ (30, 0) & (322.41, 317.84) & (46.87, 46.21) & 9.88 & 0.12 \\ (20, 0) & (253.86, 318.14) & (36.91, 46.25) & 9.96 & 0.04 \\ (10, 0) & (185.46, 318.42) & (26.97, 46.29) & 9.94 & 0.06 \\ (0, 0) & (116.81, 318.61) & (16.88, 46.32) & 10.09 & 0.09 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Detection accuracy test experimental data.  
According to Table 4, the monitoring system had good stability and repeatability, and the repeatability accuracy was better than 0.11 mm.
## 2 Materials and Methods
### Sampling Methodology
Their distribution within the Borromean Gulf was based on previous knowledge of Hg spatial contamination to include sites  
Figure 2: Overview of treatment procedures undergone by sediments sampled for different purposes in the course of the present study. with both high and low total Hg contents (Figure 1, Tables 1 and 2) [27; 28]. All samples of indigenous organisms were first washed with lake water using a 250 \(\upmu\)m mesh aperture net to allow the collection of even the smallest species. A 10% formalin solution, neutral buffered, was then used to fix samples and inhibit autolysis and putrefaction. Sieving, sorting (under a stereo microscope Leica 80\(\times\)) and taxonomic identification of organisms (through a Zeiss microscope) were performed in the laboratory. The taxonomic identification based on morphological and phenotypic characteristics was carried out for oligochaetes [29], chronomids [30; 31] and the remaining groups of macroinvertebrates [32]. Species level of identification was generally preferred, but sometimes a higher taxonomic rank (genus or family) has been employed because of the presence of unidentifiable juveniles. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Sampling Site** & **Code** & **Coordinates** & **Depth** & **OC (\%)** & **Tax** & **[Hg]\({}_{\text{ind}}\)** & **[Hg]\({}_{\text{Lab}}\)** & **[Hg]\({}_{\text{Lab}}\)** & **[Hg]\({}_{\text{Lab}}\)** & **[x]\({}_{\text{Lab}}\)** \\ \hline Pallanza subtitoral & PAL-SL & 464,649.7 & 5,085,949.2 & 28 & 2.04 & Y & Y & Y & Y \\ Pallanza deep & PAL-P & 463,631.9 & 5,085,064.0 & 115 & 1.10 & Y & Y & Y & Y \\ Baveno sublimtral & BAV-SL & 461,540.2 & 5,084,798.2 & 28 & 1.44 & Y & Y & Y & Y \\ Baveno deep & BAV-P & 461,457.8 & 5,084,955.1 & 54 & 1.12 & Y & Y & Y & Y \\ Carmine subtitoral & CARM-SL & 477,279.26 & 5,097,150.2 & 29 & 1.32 & Y & Y & Y & Y \\ Carmine deep & CARM-P2 & 477,725.2 & 5,096,980.3 & 113 & 1.30 & Y & Y & N & Y \\ Borromean Gulf & PAL-U & 463,950.3 & 5,084,800.0 & 30 & 1.14 & N & Y & Y & N \\ L. Mergezzo subtitoral & LMRE-SL & 459,174.7 & 5,088,792.7 & 25 & 2.97 & N & Y & N & Y \\ Borromean Gulf east side & LM16 & 464,140.3 & 5,086,003.8 & 118 & — & — & — & — & — \\ Borromean Gulf east side & LM17 & 463,707.9 & 5,085,728.5 & 123 & — & — & — & — & — \\ Borromean Gulf west side & LM51 & 462,892.1 & 5,082,985.3 & 59 & — & — & — & — & — \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Information on sampling sites and activities performed in lakes Maggiore and Mergozzo for the present study. Cores LM16, LM16 and LM51 were collected during previous studies [21, 22] and used for data interpretation and comparison purposes. OC (%): organic carbon (in %): Tax: taxonomical identification (Yes/No); [Hg]\({}_{\text{ind}}\): total Hg measurements in indigenous organisms (\(\gamma_{\gamma}\)); [Hg]\({}_{\text{Lab}}\): bioaccumulation tests with Ch (_Chinomus triparis_) and Lv (_Lumricululus variegitats_) (Y/N); —; not analyzed or not performed.
# QiOi: Performance Isolation for Hyperledger Fabric
## 5 Evaluation
### Overhead Measurement  
Finally, we evaluate the overhead of QiOi. Note that the algorithm of QiOi has O(1) time complexity. We measure CPU utilization and Fabric performance when the Fabric components run with QiOi (_w_/_QiO_) and without QiOi (_Alone_) when there are no co-located tasks. Note that we run the smallbank chaincode.  
Table 5 shows that the average CPU utilization of Fabric components decreases by 1.4% (peers) and 1.9% (orderers), respectively, with QiOi. This shows that QiOi does not incur additional overhead in terms of CPU utilization. In addition, Fabric throughput and latency decrease by 0.2% and 27%, respectively. This indicates that QiOi does not decrease Fabric performance.
## 4 Privacy and Ethical Considerations  
Privacy considerations represent another crucial aspect when processing sensitive data from children with autism, particularly in educational settings. On-device processing with minimized data transmission would be essential for addressing these concerns while enabling effective emotion recognition capabilities. Additionally, implementing user-controlled data management protocols would ensure that emotional data are collected and utilized in accordance with ethical guidelines and parental consent parameters.  
Addressing these implementation considerations represents an important direction for future research that would bridge the gap between our current laboratory model and viable real-world applications across educational, clinical, and home environments. By systematically addressing these factors, emotion recognition systems can be effectively tailored to meet the diverse needs of children across the autism spectrum in various real-world contexts.
# Evaluation of the Effects of Hidden Node Problems in IEEE 802.15.7 Uplink Performance
## 5 Simulation Results Analysis
### Performance of Uplink Communications with No Hidden Nodes  
In order to exclude the existence of hidden nodes in the network due to the directivity of the optical transmitters and receivers, the simulated channel model was modified to remove all directivity characteristics in them. Additionally, devices were configured with an optical transmission power of 150 mW to prevent the occurrence of hidden nodes due to the limitation on the optical signal coverage. Using the described channel model, we verified that each and every device in the network detects all transmissions from all other devices in all of the simulation scenarios. Hence, the existence of hidden nodes is discarded. Although the employed channel model does not match any link configuration used in VLC networks, it allows establishing the simulation conditions that guarantee the non-existence of hidden nodes in the network. The simulation results achieved with these conditions will be compared with the results of simulations performed employing the LOS channel model, in order to assess the impact of hidden nodes on network performance.
Effects of 20 Weeks of Endurance and Strength Training on Running Economy, Maximal Aerobic Speed, and Gait Kinematics in Trained Runners  
[PERSON]  
1 Sports Training Laboratory (GIRD), Faculty of Sports Sciences, University of Castilla-La Mancha, 45071 Toledo, Spain; [EMAIL_ADDRESS] (S.R.-B.); [PERSON].M.G.-R.); [EMAIL_ADDRESS] (D.J.-S.-G.); [EMAIL_ADDRESS] (V.M.d.I.C.)  
[PERSON]  
1 Sports Training Laboratory (GIRD), Faculty of Sports Sciences, University of Castilla-La Mancha, 45071 Toledo, Spain; [EMAIL_ADDRESS] (S.R.-B.); [PERSON].M.G.-R.); [EMAIL_ADDRESS] (D.J.-S.-G.); [EMAIL_ADDRESS] (V.M.d.I.C.)  
Benedcite Vanwangeele  
2D  
velocity of VO2 max) [9]. The ventilatory anaerobic threshold is defined as the exercise intensity above which there is a disproportionate increase in ventilation relative to oxygen consumption [5].  
Factors such as training, environment, physiology, biomechanics, and anthropometry influence RE [6]. RE can be altered acutely with footwear modifications [10,11] or chronically through training adaptations (physiological and biomechanical factors) [7,12]. Kinematics [13] and kinetics parameters [7] can also impact RE. Thus, more efficient mechanics lead to less energy wastage [14] by using the muscles' ability to store and release elastic energy. In this way, a lower vertical displacement and higher vertical and leg stiffness (Kleg) have shown significant associations with better RE [15].  
Previous research has suggested that different training strategies may influence RE and running performance [16] due to improved muscle work and optimization of running gait [17]. Similarly, strength training has shown performance improvements related to a better RE [18]. This is due to enhanced intramuscular coordination of the lower limbs and an increase in muscle coactivation and Kleg [19]. Medium-load (60-85% 1 RM) and high-load (>85% 1 RM) strength exercises and pylemetric exercises carried out 2-3 times per week appear to be efficient in enhancing RE [18]. Similarly, endurance training improves the functionality of skeletal muscle mitochondria and hematological changes [6] with mainly two endurance training strategies, interval and continuous training methods [20]. The influence of these two endurance training methods has usually been studied in isolation in the literature [20].  
However, few studies have analyzed the changes in RE following a combined strength and endurance training program. [PERSON] et al. [21] showed an improvement of around 5% in RE in recreational and well-trained runners after 8 weeks of a regular endurance training program without changes in spatiotemporal parameters and Kleg. However, to our knowledge, no studies have analyzed changes in running performance after long-term training. Therefore, this study aimed to evaluate the effects of a 20-week endurance and strength training program on RE and physiological, spatiotemporal, and neuromuscular variables in nationally trained long-distance runners. We hypothesized that runners would improve their RE although without significantly modifying their biomechanics.
# ERCP: Energy-Efficient and Reliable-Aware Clustering Protocol for Wireless Sensor Networks
## 5 Performance Evaluations
### Simulation Results  
For the verification of the feasibility and efficacy of our solution, its network lifetime, end-to-end delay, packet delivery ratio, energy imbalance factor, and time complexity are compared with the HMBCR [10], ECRP [11], and EPSO-CEO [24]. In order to better evaluate the network performance, the sensor nodes deployed in the network are considered homogeneous in some of the experiments and heterogeneous in other experiments. The heterogeneous sensors mean that sensors' battery energies might be different from each other. In a homogeneous network, the sensor nodes have the same initial energy of 200 mJ. In a heterogeneous network, the nodes' initial energy is random between 175 mJ and 200 mJ. In the first three experiments, the typical traffic rate ranges anywhere from 3 to 11 packets per second on average.
# A Simple Technique for Studying Chaos Using Jerk Equation with Discrete Time Sine Map
## 5 Numerical Simulation & Results
### Frequency Spectra  
Frequency spectra is an essential visual tool to study chaos in a system. Many researchers have also used wavelet transform instead of frequency spectra as it can not only show the change in the system dynamics but also can point out the time at which such a change has occurred [42]. However, the size of the window plays a pivotal role in getting better visualization in the wavelet transform method. Frequency spectra method was preferred in this paper as it broadly illustrates the frequency dispersion distribution present in the system dynamics, which is enough for the visualization of chaos. Because of the highly chaotic region (5-5.5) in the bifurcation diagram Figure 5, the Fourier power spectrum was calculated only for four different cases. With every variation in the control parameter \(\beta\), the presence of different attractors with the corresponding Fourier power spectrum is shown in Figure 7.  
Figure 6: \(C_{r}\) vs. \(ln(r)\) plot for calculating the Correlation Dimension.  
Clearly, one can see by visual inspection that there are 1, 2, and 4 periodic peaks in Figure 7b,e,h respectively corresponding to period 1, period 2 and period 4 attractors  
Figure 7: Attractor (**left**), Frequency Spectrum (**center**), & Poincare Section: (**right**) (**a–c**) Period 1 or Limit Cycle Attractor case for \(\beta=9\), (**d–f**) Period 2 Attractor case for \(\beta=6\), (**g–i**) Period 4 Attractor case for \(\beta=5.84\), (**j–l**) Chaotic Attractor case for \(\beta=5.535\).  
shown in Figure 7a,d,g. Also, an aperiodic power spectrum related to the chaotic attractor shown in Figure 7j is given in Figure 7k.
## 4 Discussion
### Limitations and Strengths  
Regarding the limitations of our study, we can highlight that an exhaustive analysis of the current literature on the acute effects of a single-bout HIFT and MIFT training on cognitive levels, anxiety, and stress could not be conducted due to the scarcity of specific studies on the topic. Another limitation of the study was the loss of sample size, as shown in the flowchart, starting with 71 invited subjects and ending with 57 participants. In relation to this, a larger sample size would have allowed us to obtain more representative results. Finally, it should be noted that the sample presents a gender imbalance, with a significantly higher percentage of women (69.86%) compared to men (30.14%), and the age range of the participants is very broad, which could influence the generalization of the results.  
Among the strengths of the study, the execution of a randomized clinical trial stands out. Furthermore, a precise intensity control system, the Uptivo Bridge Cloud heart rate monitor (Uptivo, S.r.l, Carate Brianza, Italy) [36], was utilized, ensuring the reliability of the data obtained regarding exercise intensity. Another strength of the manuscript is that it is a pioneering study, which can serve as a reference for other authors to further explore the topics and methodologies addressed throughout the study. Lastly, we want to highlight the clarity of the procedure and protocol carried out to make it replicable in different studies, allowing it to be conducted in diverse populations, regardless of age, gender, or level of training experience, as the proposed exercises can be adapted to different levels, while considering the principle of individualization.
## 3 The Radiative Transfer Equation
### RTE Analytical Solutions with Simplification
#### 3.3.3 Two-Stream Approximation  
The TSA divides the radiative flux into two components: one representing the downward flux and the other representing the upward flux. It simplifies the RTE by considering only two directions of radiation propagation and is often used in radiative transfer models for homogeneous media. While it is not as detailed as more complex radiative transfer  
Figure 7: SSA with HG- SPF.  
models, the TSA provides a computationally efficient way to estimate the radiation field in various atmospheric and oceanic environments [5; 6; 97; 98].  
\[\mathrm{L}(w,\mathrm{z})=\frac{(1-w).\ S}{2+(1-w_{s})L_{d}} \tag{49}\]  
where \(w\) is the attenuation albedo, \(w_{s}\) is the scattering albedo, \(S\) is the solar constant, and \(L_{d}\) is the diffused flux. Figure 8 shows the prediction of the TSA method over a range of \(w_{s}\) values (lower \(x\)-axis) and the corresponding single-scattering parameter \(b(\lambda)\) values (top \(x\)-axis). The most noticeable feature that the TSA method exposes is that the diffused light component shows a slow increase with \(b(\lambda)\) while the direct flux (i.e., the coherent part of the radiation) exhibits a fast decrease over the same range of \(b(\lambda)\).