esa-sceva/satcom-chunk-collection huggingface dataset:
# Towards a Multi-Perspective Time of Flight Laser Ranging Device Based on Mirrors and Prisms
## 2 Materials and Methods
### Measurement Setup
#### 2.4.1 Measurement Hardware  
Ranging was carried out using a direct ToF laser ranging device, which was thoroughly discussed in the previous sections. The device was validated in advance to achieve sub centimeter accuracy on the range of zero to eighty centimeters for a range of targets with different albedos. The ToF ranging device was mounted on a stand that allowed for some minor height and angle adjustments.  
For ranging through an FOV redirection device (mirrors and prisms), a special jig was devised. The jig comprises a linear actuator, and an acrylic snap-in plate, fixedly mounted on a solid particle board baseplate. They were mounted in a way that their axis intersected at a right angle. The FOV redirection device was mounted on a pedestal at the desired height, angle and position. Mirrors were angled at 45\({}^{\circ}\) to both the linear rail and the optical axis of the ranging device. This was the same with the prism, when observing the hypotenuse. Minor adjustments could be made before fixing the stand in place to ensure the desired orientation. That means that experiments were repeatable so long as the redirection device was not changed. The measurement setup is shown in Figure 6.  
Optics for redirecting FOV were Thorlabs' first surface protected aluminum mirror (ME2S-G01), a generic aluminum back coated mirror from Duratool (S5003), and Knight Optical's right angle prisms made of N-BK7 glass with AR coated legs (PTK4001). It was known from our previous experiments that using an uncoated prism does not work, as too much light is reflected off of the first surface back into the receiver diode; thus, we did not test that configuration. Mirrors and prism were large enough such that the entire laser beam was safely within the bounds of mirrors or the prism. The target was covered with multiple layers of white office paper. During the aligning process, we made sure that the entire transmitted pulse was hitting the target.  
To control for errors rising from variable system parameters, such as temperature and varying voltage levels, all power supplies were left to warm up and then left powered for the entire duration of experiments. The ranging device was left ranging for at least one day before the experimental data were gathered. The walk error compensation curve was recorded on a warmed up system, which was not powered off until all data were collected.  
Figure 5: Walk error vs. pulse width for raw data (yellow), average of eight samples (green) and piecewise linear function fit (blue).
# Precise Point Positioning Using Dual-Frequency GNSS Observations on Smartphone
## 2 Methods
### GPS/Galileo PPP Algorithm
\begin{array}{cccccc}N_{r,1}^{1}&N_{r,1}^{2}&\ldots&N_{r,1}^{m }\end{array} \right]^{T}\) is single-frequency phase ambiguity, \(N_{r,lF}=\left[ \begin{array}{cccccc}N_{r,lF}^{1}&N_{r,lF}^{2}&\ldots&N_{r,lF }^{m}\end{array} \right]^{T}\) is ionosphere-free phase ambiguity, \(m\) is the number of valid satellites in an epoch. The measurement vector \(y_{single}\), \(y_{dual}\) for the single-frequency and dual-frequency are expressed as:  
\[y_{single}=\left( \begin{array}{cccccc}\phi_{1}T&P_{1}T\end{array} \right)^{T} \tag{20}\]  
\[y_{dual}=\left( \begin{array}{cccccc}\phi_{lF}T&P_{lF}T\end{array} \right)^{T} \tag{21}\]  
where:  
\[\phi_{1}=\left(\phi_{r,1}^{1},\phi_{r,1}^{2},\phi_{r,1}^{3},\cdots,\phi_{r,1}^ {m}\right)^{T} \tag{22}\]  
\[P_{1}=\left(P_{r,1}^{1},P_{r,1}^{2},P_{r,1}^{3},\cdots,P_{r,1}^{m}\right)^{T} \tag{23}\]  
\[\phi_{lF}=\left(\phi_{r,lF}^{1},\phi_{r,lF}^{2},\phi_{r,lF}^{3},\cdots,\phi_{r, lF}^{m}\right)^{T} \tag{24}\]  
\[P_{lF}=\left(P_{r,lF}^{1},P_{r,lF}^{2},P_{r,lF}^{3},\cdots,P_{r,lF}^{m}\right)^ {T} \tag{25}\]  
By using EKF, the state vector \(\hat{x}_{k}\) and its covariance matrix \(P_{k}\) at the epoch time \(t_{k}\) can be estimated by:  
\[\hat{x}_{k}(+)=\hat{x}_{k}(-)+K_{k}(y_{k}-h(\hat{x}_{k}(-))) \tag{26}\]  
\[P_{k}(+)=\left(I-K_{k}H(\hat{x}_{k}(-))\right)P_{k}(-) \tag{27}\]  
\[K_{k}=P_{k}(-)H(\hat{x}_{k}(-))\left(H(\hat{x}_{k}(-))P_{k}(-)H(\hat{x}_{k}(- )\right)^{T}+R_{k}\right)^{-1} \tag{28}\]  
where \(h(x)\), \(H(x)\) and \(R_{k}\) are the measurement model vector, the matrix of partial derivatives and the covariance matrix of measurements errors, respectively; \((-)\) and \((+)\) indicate before- and after-measurement update of EKF. For the single-frequency and dual-frequency PPP, the matrix \(H(x)\) can be expressed as:  
\[H(x)=\left[ \begin{array}{cccccc}-DE&\mathbf{1}&DM_{T}&\mathbf{I}\\ -DE&\mathbf{1}&DM_{T}&\mathbf{0}\end{array} \right] \tag{29}\]  
where:  
\[\mathbf{1}=\left[ \begin{array}{cccccc}1\\ 1\\ \vdots\\ 1\end{array} \right] \tag{30}\]\[M_{T}=\left[ \begin{array}{cccc}M_{W}^{1}&M_{W}^{1}\cot E_{r}^{1}\cos Az_{r}^{1}&M _{W}^{1}\cot E_{r}^{1}\sin Az_{r}^{1}\\ M_{W}^{2}&M_{W}^{2}\cot E_{r}^{2}\cos Az_{r}^{2}&M_{W}^{2}\cot E_{r}^{2}\sin Az_{r} ^{2}\\ \vdots&\vdots&\vdots&\vdots\\ M_{W}^{m}&M_{W}^{m}\cot E_{r}^{m}\cos Az_{r}^{m}&M_{W}^{m}\cot E_{r}^{m}\sin Az_{ r}^{m}\end{array} \right] \tag{31}\]  
\[D=\left[ \begin{array}{cccc}1&-1&0&\cdots&0\\ 1&0&-1&\cdots&0\\ \vdots&\vdots&\vdots&\ddots&\vdots\\ 1&0&0&\cdots&-1\end{array} \right] \tag{32}\]  
\[E=\left( \begin{array}{cccc}e_{r}^{1T}&e_{r}^{2T}&\cdots&e_{r}^{mT}\end{array}  \right)^{T} \tag{33}\]  
where \(M_{w}\) is the mapping factor of ZWD (zenith wet delay), \(E\)I and \(Az\) are elevation angle and azimuth angle of the satellite from the receiver, respectively, \(e_{r}^{j}\) is LOS (line-of-sight) vector from receiver to satellite. The EKF state transition matrix is the identity matrix in PPP, and the state vector is expressed as:  
\[\hat{X}_{k+1}(-)=\hat{X}_{k}(+) \tag{34}\]
## 1 Introduction
On the one hand, DR can effectively reduce the peak load of the power system and delay the investments required for new generation units and transmission lines. On the other hand, DR can improve the consumption of renewable energy and reduce the adjustment and start-up/shut-down costs of thermal power units during the off-peak periods [10]. Generally, there are two kinds of DR, such as price-based DR and incentive-based DR [11]. The research content of this paper belongs to price-based DR. Existing research on DR mainly focuses on two aspects: the optimal operation of the power system considering DR, and the optimal strategy for customers to participate in DR according to external signals [10]. In this paper, firstly, the ACVES model and EWHVES model are built by exploiting the heat/cold storage capability of building and the electric water heater, respectively. In a building, due to the insulating properties of the building walls, the heat exchange between the interior and exterior is a slow process, the indoor temperature will not change rapidly relative to the electrical characteristics. Thus, during periods of low electricity prices, it is possible to increase the power consumption of AC to provide more cool/warm air to the building, which can be used in the following time intervals with higher electricity prices and thus reduce the total costs. Thus, the AC can be operated through mimicking the charging and discharging characteristics of a storage system, while meeting the indoor comfort requirements. Similarly, based on the good thermal insulation of the water heater tank, the power consumption of the water heater is increased during the low electricity price period, and the excess hot water is stored in the water tank for usage during the high electricity price period. Based on the above analysis, the ACVES and EWHVES model is built in this paper. Then, the optimal scheduling model considering the VES is established. The objective function is built through considering the system's optimal scheduling, which aims at the lowest daily operating cost of the BIPV microgrid. The comprehensive constraints are also considered in the optimal model, which includes the constraints of each unit device and the operating constraints of the microgrid, such as the charging/discharging power and capacity constraints of the batteries, indoor temperature comfort zone constraints, power balance constraints, etc. Finally, CPLEX is applied to solve the optimal scheduling model. In this paper, in order to improve the operation efficiency of BIPV microgrid, an optimal scheduling strategy of BIPV microgrid considering virtual energy storage (VES) is proposed. The main objectives of this paper are as follows:  
* The potential of VES is exploited to make them play a role similar to the battery storage system. * The differences of charging/discharging characteristics between VES and battery storage system are analyzed and compared. * The differences of AC and EWH that participate in the VES control are analyzed and compared. * The performance of the proposed strategy is verified with different case studies for all the four different seasons.
# Human Pose Detection for Robotic-Assisted and Rehabilitation Environments
## 2 Human Pose Detection and Body Feature Extraction: A State of the Art
We employed the skeleton obtained by two Kinect sensors as our ground truth to measure the performance of the estimation of shoulder and elbow angles using two CNN (convolutional neural network)-based human pose estimation methods in rehabilitation exercises. The selected CNN-based methods were OpenPose [28] and Detectron2 [29]. OpenPose is a multi-person pose detection system, and it can detect a total of 135 body points from a digital image [28; 30]. OpenPose has been trained to produce three distinct pose models. They differ from one another in the number of estimated key points: (a) MPII is the most basic model and can estimate a total of 15 important key points: ankles, knees, hips, shoulders, elbows, wrists, necks, torsos, and head tops. (b) The COCO model is a collection of 18 points including some facial key points. (c) BODY pose provides 25 points consisting of COCO + feet keypoints [30; 31]. Detectron2 was built by Facebook AI Research (FAIR) to support the rapid implementation and evaluation of novel computer vision research. Detectron2 is a ground-up rewrite of the previous Detectron version and it originates from the Mask R-CNN benchmark. Detectron2 includes high-quality implementations of state-of-the-art object detection algorithms, including DensePose [29].
# Numerical Modeling of Engineering-Scale Jointed Coal Mass and Confining Pressure Effect
## 4 Effect of Confining Pressure on Jointed Coal Mass Under Different Joint Distributions  
The state of yielding to the confining pressure is one of the most common force states of coal masses. The compressive strength, residual strength, and fissure evolution characteristics of coal mass change significantly during the transition from uniaxial compression to confining pressure loading [44; 45]. In mine engineering, deep coal masses are subjected to abutment pressures under mining conditions. The change in the confining pressure is a dynamic process, and the physical behavior of the coal mass changes accordingly. Therefore, it is of great significance to study the physical properties of jointed coal masses under different confining pressures.
# Ru(II)-Dppz Derivatives and Their Interactions with DNA: Thirty Years and Counting
## 3 DNA Binding
### Effects of Tuning the Photophysical and Redox Properties
However, it is important to emphasize that, when designing a system capable of targeting DNA in vivo, not only is the efficiency of the luminescence processes to be important, but also the range of emission, which must overlap with the optical therapeutic window, as much as possible. The [Ru(tpy)\({}_{2}\)]\({}^{2+}\) complex (tpy = 2,2':6',2''-terpyridine), for example, is characterized by a short excited state and a low quantum yield, making it, in principle, unsuitable for photobiological applications [147; 148]. However, it is possible to modulate the properties of this family of complexes by operating substitutions on the terpyridine ligands [149]. [PERSON] et al. reported that the [Ru(tpy)(pydppz)]\({}^{2+}\) species, based on a Ru(II)-terpyridine fragment and a tridentate dppz derivative known as pydppz (3-(pyridyl-2'-yl)dipyrido-[3,2-a:2',3'-cl)phenazine), is capable of intercalating into DNA and behaving as a lightswitch [149]. The photophysical properties of this complex, characterized by a weak emission centered at 698 nm in acetonitrile (\(\tau\) = 5.4 ns and \(\varphi\) = 2.1 \(\times\) 10\({}^{-4}\)), although more interesting than the model species [Ru(tpy)\({}_{2}\)]\({}^{2+}\), are not such as to warrant its use for photobiological applications. Starting with these bases, and since it is known that (i) with the same ligands, Os(II) polypyridine complexes exhibit excited states with significantly lower energy than that of the analogous Ru(II) compounds, and that (ii) osmium compounds with tridentate ligands such as terpyridine possess excited states with a longer lifetime, the complex [Os(tpy)(pydppz)]\({}^{2+}\), shown in Figure 7, was synthesized. This new species of Os(II) exhibits an absorption spectrum extending to 800 nm. Its emission characterized in aprotic organic solvent, by relatively high quantum yields and lifetimes in the order of hundreds of ns, is centered at 800 nm, and the complex represents the first example of an [Os(tpy)\({}_{2}\)]-like species capable of intercalating into the DNA [91]. Moreover, it is also interesting to note that, despite the structural and chemical variation, it behaves as a true DNA OFF-ON light switch, like the analogous [Os(phen)\({}_{2}\)(dppz)]\({}^{2+}\)[150; 151; 152; 153; 154]. Figure 6: Molecular structure of Ru-qdppz and Ru-qdpq.
## 3 Federated Learning for Space-Air-Ground Integrated Networks
### Privacy-Preserving Federated Learning
#### 3.5.1 Local Training at Terminal Nodes
The update formula for Q-learning to update the privacy budget allocation strategy is as follows:  
\[x_{i}^{\text{norm}}=\frac{x_{i}-\mu_{i}}{\sigma_{i}} \tag{27}\]5  
where \(\alpha\) is the learning rate, controlling the adaptation speed of the model to new experiences. \(\gamma\) is the discount factor, controlling the influence of future rewards.
## 3 Price Predictions Using Deep Learning Models
### Time-Series Forecasting Models
#### 3.2.5 Neural Basis Expansion Analysis for Interpretable Time-Series Forecasting (N-BEATS)  
N-BEATS is a deep learning architecture that is designed for time-series forecasting. N-BEATS is built upon a stack of fully connected layers, referred to as \"blocks\", which are responsible for capturing different patterns and trends within the time-series data. Eachblock consists of a fully connected feedforward neural network. The architecture allows for flexibility in capturing various temporal patterns, such as trends, seasonality, and irregular fluctuations [43].
# Application of Soft Computing Techniques for Predicting Thermal Conductivity of Rocks
## 4 Results
### Verification and Discussion of the Results  
To evaluate the prediction reliability of the developed models, the corresponding performance indices were compared with those of previously published studies. For the prediction of the TC using the same parameters and datasets as those used in our study, two multivariable equations were developed by [PERSON] [24] (Equation (10)) and [PERSON], [PERSON], [PERSON], and [PERSON] [47] (Equation (11)); hence, these two models were chosen for the verification of the newly developed models. It is noteworthy that there are also some other efforts in the literature to predict TC by computer-aided methods using the same datasets [3,24,70,71].  
\[\lambda=-1.1864+0.006\;\text{UCS}+0.1493\times\rho+0.0134\;\phi+0.0004\;\text{V} _{\text{P}} \tag{10}\]  
\[\lambda=0.00037\;\text{V}_{\text{P}}-0.01653\;\phi-0.00058\;\rho+0.02053\;\text {UCS}-0.06072 \tag{11}\]  
Figure 11: The correlation coefficient of the GEP model for the 35% testing subsets.  
Figure 10: The correlation coefficient of the GEP model for the 65% training subsets.  
These studies used some kinds of methods called black boxes, but these methods did not have the practical potential to be utilized in applications. In the meantime, as is evident from Equations (10) and (11), in the developed mathematical equations, only simple functions are used, but to represent the effect of parameters on TC, complex non-linear functions are needed. To avoid any confusion made by the selection of training and testing subsets, the performance indicators of all models were calculated again by substituting the input variables using all 50 datasets. Thus, the accuracy of the previous model could be compared exactly to the proposed one in this study. The results of the performance indicators of the proposed models for all 50 datasets are listed in Table 7.  
Comparing the performance indices of the proposed models and the previously published model revealed that the GEP and NLMR models produced more accurate predictions than the MVRA model did. The high value of R-square (equal to 0.95) and the low value of RMSE (equal to 0.17) confirmed the higher accuracy of the proposed GEP model. The GEP was more accurate since it used longer terms and a wider variety of mathematical functions than the NLMR one did. The existing MVRAs in the literature [24; 47; 71] were developed using simple non-linear variables that were regulated using commercial software. Based on the low correlation coefficients of these models, we concluded that they could not capture the complexity of the problem and the relationship between TC and the influential parameters. The NLMR model and the GEP model developed in our study were developed not only using non-linear mathematical terms to represent each parameter but also via an extensive trial and error process; thus, a soft computing approach was used to enhance the accuracy of predictions.
## 2 Materials and Methods  
A total of 43 participants were enrolled in this double-blinded pilot study: 21 in the supplementation group and 22 in the placebo group. The inclusion criteria were: (a) obesity or markedly overweight with pre-diabetes and/or dyslipidemia (b) signed consent (c) age over 18 years old. The exclusion criteria were (a) the pharmaceutical treatment of any conditions such as dyslipidemia, hypertension, or diabetes, (b) co-morbidity that impacts metabolic profile, e.g., hormonal disorders, (c) epilepsy, (d) pregnancy (e) an implanted pacemaker or cardioverter-defibrilator, and metal implants, excluding dental implants. The participants were volunteers recruited through the press, radio and TV communications among the citizens of the city of Lublin to provide the most representative study group. The participants underwent an initial interview to assess the inclusion and exclusion criteria. They represented similar lifestyles in terms of dietary patterns and physical activity.  
The participants were divided into two groups: supplementation and placebo. The control group participants were selected based on the \"pair matching\" principle, i.e., according to individual compatibility in setting the control group to the study group regarding matching variables: gender, age, BMI, and lipid profile. The supplementation was based on extracts of whole dwarf kiwi and Chopin apple. The water extractions were carried out at the temperature of 50 \({}^{\circ}\)C, then the extracts were freeze-dried. The supplementation group received 120 hypromellose capsules with microcrystalline cellulose as an excipient. One 1000 mg capsule contained 10% Chopin apple peel extract, 10% whole dwarf kiwi fruit extract, 75% Chopin apple core extract, and 5% rapeseed peptides. The chemical composition of one capsule was: 5.75 mg of gallic acid equivalent for total content of polyphenolic compounds, 2.23 mg of quercetin equivalent for total flavonoid content, 1.79 mg catechin equivalents for total flavanol content, 0.65 mg catechin equivalent for total content of condensed tannins, and 0.08 mg glucoside equivalent for total anthocyanin content. The placebo group received capsules containing maltodextrin. The participants were asked to take two capsules/day for 60 days, and not to modify their previous dietary and physical activity patterns in order to assess the effect of supplementation alone.  
The participants underwent the anthropometrical and metabolic assessment. The body mass, height, and waist circumference were measured by the trained dietitian. Body composition was assessed with SECA mBCA515 analyzer using the bioelectric impedance method. The measurement was carried out using the eight-point method. The metabolic assessment included laboratory parameters: fasting glucose, insulin level, Hb1 AC, lipid profile (TC, LDL, HDL and TG), liver functions assessment (alanine aminotransferase [ALT], aspartate aminotransferase [AST], gamma glutamyl transpeptidase [GGTP], albumin level), renal functions assessment (creatinine, uric acid). The measurements were taken before (day 0) and after the supplementation (day 60).
## 2 Materials and Methods
### Water Uptake (Wul), Ion Exchange Capacity (IEC), and Linear Swelling Ratio (LSR)  
The IEC values of membrane samples were recorded using acid-base titration. First, 0.2 g of membrane was immersed in 1 M HCl for 24 h to make sure all counterions exchanged into H\({}^{+}\). Secondly, the dry membrane after washing with DI water was weighed with an analytical balance. Finally, the membrane was soaked in 1 M NaCl and equilibrated for 24 h. Using phenolphthalein as an indicator, 0.04 M NaOH aqueous solution was used to titrate the soaking solution, and the IEC value was calculated by Equation (1) based on the NaOH consumption in titration (\(V_{\text{NaOH}}\times C_{\text{NaOH}}\)) and the dry membrane weight in H\({}^{+}\) form (\(W_{\text{dry}}\)).  
The dry membranes were cut into pieces of a certain size (10 mm \(\times\) 30 mm). The samples were immersed in DI water for 24 h to reach a saturated water absorption state. WU and LSR were calculated by Equations (2) and (3) based on the weight and length differences between the samples under wet and dry states, respectively.  
\[\text{IEC}= \frac{V(\text{NaOH})\times C(\text{NaOH})}{W_{\text{dry}}} \tag{1}\] \[\text{WU}= \frac{W_{\text{wet}}-W_{\text{dry}}}{W_{\text{dry}}}\times 100\%\] (2) \[\text{LSR}= \frac{L_{\text{wet}}-L_{\text{dry}}}{L_{\text{dry}}}\times 100\% \tag{3}\]  
where \(W_{\text{dry}}\) and \(W_{\text{wet}}\) represent the weights of the samples in the dry and wet states, respectively, and \(L_{\text{dry}}\) and \(L_{\text{wet}}\) represent the lengths of the samples in the dry and wet states, respectively.