esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimization of Heat Pump Systems in Buildings by Minimizing Costs and CO\({}_{2}\) Emissions
## 4 Results
### Performance of Economic and Financial Indicators
The results show that the implementation of the optimal heating system provides significant cost savings in terms of operating expenses for both SH and DHW preparation. The calculations of the annual operating cost savings revealed the potential \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Building**} & \multirow{2}{*}{\begin{tabular}{c} **Initial Capital** \\ **Expenditure** \\ **(ICE) \& **(k EUR)** \\ \end{tabular} } & \multicolumn{2}{c}{**Total Costs**} & \multicolumn{2}{c}{**Levelized**} & \multirow{2}{*}{\begin{tabular}{c} **Developed** \\ **(TC) for** \\ **Heating over 10 Years** \\ **(k EUR)** \\ \end{tabular} } & \multicolumn{2}{c}{**Levelized**} & \multirow{2}{*}{\
\begin{tabular}{c} **Operating Cost** \\ **Heating over 10 Years** \\ **(k EUR)** \\ \end{tabular} } & \multicolumn{2}{c}{**Internal**} & \multirow{2}{*}{
\begin{tabular}{c} **Net Present** \\ **Value (NPV)** \\ **(k EUR)** \\ \end{tabular} } \\ \hline
1. & 39.54 & 146.68 & 186.22 & 0.0866 & 11.63 & 4290 & 2.74 & \(-4.24\) \\
2. & 56.45 & 269.67 & 326.12 & 0.0847 & 9.23 & 7009 & 6.35 & 3.86 \\
3. & 81.02 & 363.61 & 444.63 & 0.0831 & 8.55 & 10,854 & 7.61 & 10.72 \\
4. & 96.81 & 435.67 & 532.49 & 0.0831 & 8.89 & 12,373 & 6.98 & 9.78 \\
5. & 15.90 & 56.15 & 72.05 & 0.0870 & 10.82 & 1824 & 3.80 & \(-0.92\) \\
6. & 15.84 & 39.34 & 55.18 & 0.0956 & 21.73 & 1126 & \(-4.52\) & \(-6.48\) \\
7. & 40.51 & 133.59 & 174.09 & 0.0915 & 13.39 & 3998 & 0.84 & \(-7.80\) \\
8. & 56.26 & 172.05 & 228.31 & 0.0931 & 13.91 & 5415 & 0.36 & \(-12.00\) \\
9. & 61.53 & 192.65 & 254.18 & 0.0961 & 17.86 & 5265 & \(-2.29\) & \(-19.59\) \\
10. & 116.74 & 439.88 & 556.62 & 0.0905 & 11.06 & 13,314 & 3.50 & \(-8.31\) \\
11. & 123.64 & 397.69 & 521.33 & 0.0954 & 16.02 & 11,481 & \(-1.02\) & \(-32.88\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results of economic and financial analysis for analyzed heat systems. for significant financial benefits. The results showed that the cost savings in one year ranged from 1126 EUR to 13,314 EUR, depending on the analyzed building and its heat demand. These results suggest that the implementation of optimal heating systems of HPs and EHs can be a cost-effective alternative to traditional district heating systems, leading to significant operation cost savings and potential long-term financial benefits.
## 3 Numerical Examples and Discussions  
### Cycle Performance Analysis When the HTC of Hot- and Cold-Side HEXs Is Constant  
Determining the relevant parameters according to the Refs. [53; 60; 61; 62]: \(C_{\
u}=0.7165\) kJ\(/(\text{kg}\cdot\text{K})\), \(\dot{m}=1.1165\) kg\(/\)s, \(T_{L}=300K\), \(C_{H}=C_{L}=1.2\), \(k=1.4\), and \(\eta_{e}=0.92\).  
Because the LC lacks an adiabatic compression process, it is a three-branch cycle, missing constraints on cycle pressure ratio, and the basic optimization relationship between \(P\) and \(\eta\) cannot be obtained. When \(U_{H}\) and \(U_{L}\) are given, it can be seen from Equations (4)-(7) and (13)-(15), when the corresponding effectiveness of HEXs and HR inlet temperature are given, the cycle \(P\) and \(\eta\) can be obtained as a certain point. Figure 2 shows the \(P\)-\(\eta\) characteristic of the cycle. It can be seen that when \(E_{H1}\) and \(E_{L1}\) take 0.8 and 0.9, as well as the HR inlet temperature ratio \(\tau\) (\(\tau=T_{Hin}/T_{Lin}\)) are 3.25 and 3.75, respectively, the corresponding \(P\)-\(\eta\) show a \"point\" change. Parameters \(U_{H}\), \(U_{L}\), \(\tau\) and \(\eta_{e}\) have significant effects on \(P\) and \(\eta\). When \(U_{H}\), \(U_{L}\), \(\tau\), and \(\eta_{e}\) increase, \(P\) and \(\eta\) increase. When \(\eta_{e}\) changes from 0.75 to 1, \(P\) and \(\eta\) increase by about 639.3 and 632.2%, respectively.
## 3 Experiments
### Pump-Probe Nonlinear Magneto-Optical Spectroscopy
#### 3.2.2 Time-Resolved Magnetization-Induced Second Harmonic Generation  
The experimental pump-probe set-up is shown in Figure 4a. The output of a Ti:sapphire regenerative amplifier system at 800 nm (1.55 eV) with a pulse width of \(\sim\) 130 fs and a repetition rate of 1 kHz was divided into two beams. One beam was used for resonant pumping of the 4\(f^{\prime}\)-4\(f^{\prime 6}\)5\(d\)\(\left(t_{2g}\right)\) transition at 1.55 eV (Figure 2e). The absorption length of EuO at this photon energy is \(\sim\)100 nm so that the entire film was excited. The other beam was introduced into an optical parametric amplifier and the output was frequency-converted to 954 nm (1.30 eV). This light was used for probing the time evolution of the ferromagnetic order by MSHG at 477 nm (2.60 eV). By tuning the time delay \(\Delta t\) between the pump pulse and the probe pulse, ultrafast spin dynamics caused by the resonant optical pumping can be measured.
## 5 Hardware Detection Circuit Debugging and Result Analysis
### Phase Shifter Module Testing  
A set of harmonic signals with a phase difference of 90\({}^{\circ}\) is required as input to the reference channel. The raw reference signal can be generated using a signal generation module. In this section, we simulate a signal with a phase difference of 90\({}^{\circ}\) by passing the original reference signal through a phase shifter. These two signals, along with the original signal, are utilized as inputs to two multiplier modules. Figure 11 shows the test circuit for the phase shifter. The experiment aims to achieve a 90\({}^{\circ}\) phase lag for a 1 MHz input signal while maintaining the same amplitude and frequency. The input signal has an output amplitude of 100 mV. The use of clear and concise language, along with a logical flow of information, ensures that the reader can easily understand the experiment. The input signal waveform is detected using the left channel (Channel-A) of oscilloscope 3, while the right channel (Channel-B) is used to detect the output signal waveform. The total harmonic distortion rate after bandpass amplification is measured using distortion analyzer 3.  
Figure 11 displays the results from oscilloscope 3. The blue waveform represents the input signal, while the red waveform represents the output signal after phase shifting. The delay response of the output signal is 1.5 us, and the period of the output signal is 998.2 ns. The voltage amplitude is 99.73 mV, and the total harmonic distortion is \(-51.420\) dB. Upon comparing the waveforms of the input and output signals, it is evident that the phase  
Figure 10: Modeling of bandpass filter module. (**a**) Circuit of bandpass filter, (**b**) Input and output waveforms of bandpass filter circuits.  
shifter can shift the phase of the input signal by 90\({}^{\circ}\) while preserving the original waveform of the reference signal. This meets the design requirements of a phase shifter.
## 4 Results and Discussion
### Water Balance Evaluation between 2020 and 2029
#### 4.7.2 Monthly Water Balance without the Consideration of Ecological Water Requirements

Table 25: Monthly water supply and demand balance evaluation without consideration of ecological water requirements between 2020 and 2029.  
condition, the monthly water balance indicates a deficit from December to May. It varies from \(-42.63\) million m\({}^{3}\) in March to \(-5.47\) million m\({}^{3}\) in December. However, the monthly water balance reveals a water surplus from June to November, varying from \(5.22\) million m\({}^{3}\) in November to \(68.77\) million m\({}^{3}\) in October. On the contrary, the monthly water balance shows a deficit from February to April under the wet year scenario in the normal condition. It varies from \(-39.97\) million m\({}^{3}\) in March to \(-21.32\) million m\({}^{3}\) in February. However, the monthly water balance indicates a surplus from May to January, varying from \(3.80\) million m\({}^{3}\) in January to \(252.65\) million m\({}^{3}\) in October. In addition, the monthly water balance indicates a deficit from February to April under the wet year scenario in the new normal condition. It varies from \(-39.52\) million m\({}^{3}\) in March to \(-20.89\) million m\({}^{3}\) in February. However, the monthly water balance shows a surplus from May to January, varying from \(4.26\) million m\({}^{3}\) in January to \(252.97\) million m\({}^{3}\) in October. Furthermore, Figure 15 displays the monthly water balance without the consideration of ecological water requirements in terms of surplus (positive) and deficit (negative) under the dry and wet year scenarios in the normal and new conditions.
## 3 Operational Retrievals of SSM from EO
Using the Famine Early Warning Systems Network (FEWS NET), it was shown that a water requirement satisfaction index could be reformulated to take advantage of a SMAP-like product and provide improved crop yield estimates [79]. At higher spatial resolution (1 km), the experimental BEC L4 SSM product has been shown useful in assessing the impact of dry soil conditions on the decline of different tree species in Catalonia [80], in the determination of the water stress factor used to estimate Gross Primary Production in Mediterranean ecosystems [81] and in predicting the extent and occurrence of wildfires in the Iberian Peninsula [82; 83]. Since the amount of water used for irrigation is largely unknown on a global scale, the capability of EO SSM data to provide irrigation assessment can potentially have a tremendous impact in future applications. In this regard, satellite soil moisture estimates were used to account for irrigation--otherwise not possible through model simulations--in a data assimilation framework for forecasting yields of grain crops in Africa [84]. More recently, [85] showed that AMSR-E soil moisture data could be used for discerning the shifting over time in the irrigation practices in north western India and [86] highlighted that the ESA CCI SM product can be used to detect irrigated areas in eastern China by comparing trends in satellite precipitation and soil moisture.
## Materials and Methods
### Description of the Experiment  
The base metasurface structures developed for the proposed experiment are presented in Figure 1, and their dimensions are shown in Table 1. As was mentioned earlier, all of the structures (CR, JCR, and SRR) must operate under the same conditions. Thus, in the case of all of the base structures, for the reflection coefficient, there was resonance at a frequency of \(f_{\mathrm{r}}=5\) GHz. In order to determine the sensitivity of the analyzed metasurface structures to the changes of their geometric properties, an experiment was conducted in which the following deformations were carried out:  
* slot width \(w\) change in the range of 0.3-1.5 mm,
* resonator to resonator distance \(d\) change in the range of 2-10 mm,
* dielectric laminate thickness \(t\) change in the range of 0.5-2.5 mm,
* mechanical deformation in the \(x\)-direction--constant strain up to 1% (external mechanical force F = 8.2764 kN in case of CR, F = 5.4615 kN in case of JCR, and F = 4.3098 kN in case of SRR),
* mechanical deformation in the \(y\)-direction--constant strain up to 1%.  
The \(s_{11}\) reflection coefficient frequency response was determined for all of the above deformations. On its basis, the impact of a given deformation on the parameters of the resonance curve was examined, namely: the resonance frequency \(f_{\mathrm{r}}\) and the value of the reflection coefficient modulus in the resonance state \(|s_{11}(f_{\mathrm{r}})|\). These parameters were used to assess the selected metasurfaces' sensitivity to planar geometry distortions, because they can be treated as a measure of deviation from the assumed operating conditions (5 GHz resonance). This enabled us to obtain the distributions of \(f_{\mathrm{r}}\) and \(|s_{11}(f_{\mathrm{r}})|\) as a function of the degree of deformation. Such distributions were utilized for the final assessment in the next section.
## 2 Data and Methods
### DMSP Satellite  
The auroral observations used in this study were from the Defense Meteorological Satellite Program (DMSP), consisting of a group of polar, Sun-synchronous satellites flying at \(\sim\)850 km with a period of \(\sim\)100 minutes [36]. The DMSP mission focuses on the observation of the near-Earth space plasma environment. In particular, we used the Special Sensor Ultraviolet Spectrographic Imager (SSUSI) instrument on board the DMSP, which is designed to measure far-ultraviolet emissions via imaging spectrograph (SIS) mapping through 5 spectral bins: 121.6 nm (\(H\)1 Lyman \(\alpha\)), 130.4 nm (\(O\)1), 135.6 nm (\(O\)1), 140-160 nm (\(N_{2}\) LBHS) and 160-180 nm (\(N_{2}\) LBHL) [37]. The image resolution is 16 \(\times\) 156 pixel, while the time resolution (i.e., time to fly above the polar region and acquire an image) is between 20 and 30 min [37].
## 2 Data Preprocessing
### Signal Alignment  
Signal shifting is a potential problem for data series. Chromatography/spectral instrument based sensors are a typical place where this problem often occurs. It is no doubt that shifted signals can result in a failure of sensor analysis. In general, for chromatography data, time alignment begins with locating the peaks that correspond to the same chemicals in a selected template chromatography profile. Then, after getting these peaks aligned, a linear or nonlinear interpolation process is applied for chromatography profile registration. Spline functions are an efficient nonlinear interpolation method [14]. Figure 2 shows a significant improvement on the separation of two groups of gas chromatography mass spectrometry (GC/MS) data before and after time alignment process [14], by employing a clustering tool called principal component analysis. The details of this tool will be discussed in the next section. As an extension of one-dimensional alignment, two dimensional alignment can be applied to the three dimensional spectral data, such as gas chromatography differential mobility spectrometry (GC/DMS) data [15].  
Figure 2: Principal component analysis of total ion chromatograms resulting from GC–MS analysis of headspace above plasma samples from two donors ( Donor A: +, Donor B: o). (a) before and (b) after alignment.
# Borehole Trajectory Optimization Design Based on the SAC Algorithm with a Self-Attention Mechanism
## 2 Materials and Methods
### The Basic Idea of Attention Mechanism  
The attention mechanism was first proposed for machine translation tasks, allowing the model to dynamically focus on different parts of the input sequence. Its core is to give  
Figure 1: SAC algorithm network structure diagram.  
different weights to the input sequence and improve the model's ability to process long sequence data [25; 26; 27; 28; 29].  
For a sequence data:  
\[X=[x_{1},x_{2},\dots,x_{T}],\quad x_{t}\in\mathbb{R}^{d_{x}} \tag{5}\]  
A weight \(\alpha_{t}\) is calculated for each position \(x_{t}\), indicating the importance of the position to the final output:  
\[\alpha_{t}=\text{softmax}(e_{t})=\frac{\exp(e_{t})}{\sum\limits_{j=1}^{T}\exp( e_{j})} \tag{6}\]  
where \(e_{t}\) is the score for the \(t\)th position, calculated as:  
\[e_{t}=\frac{q^{T}x_{t}}{\sqrt{d_{k}}} \tag{7}\]  
where \(q\) is the query vector, \(x_{t}\) is the key/value vector, and \(d_{k}\) is the feature dimension (scaling coefficient, preventing gradient disappearance or explosion).
# Upper Body Pose Estimation Using Deep Learning for a Virtual Reality Avatar
## 7 Discussion
Our proposed pose estimation method is faster with lower latency than the IK method, which yields less interference with the feeling of presence, especially in multiuser VR over a worldwide network. We infer that being visually similar looking and with lower latency are the reasons for user preferences. We aimed for a low-cost and high-performance method to estimate a user's upper body and animate the corresponding avatar in VR. Our method applies to any user with only essential VR equipment and therefore is low-cost compared to VR experiences with onsite motion capture equipment and operational staff. However, our learning method requires motion capture datasets acquired using an expensive capturing system. As such, our method is not low-cost in terms of acquiring datasets. VR developers may use the public human pose datasets or obtain datasets by renting a motion capture studio for a couple of days to minimize the cost of acquiring datasets. As for VR users, our method is low-cost and high-performance. Our pose learning method is stable enough to apply to other datasets, including famous ones. These public human pose datasets mainly cover locomotion such as walking, running, kicking, climbing, etc. Moreover, we can extend our datasets, including the necessary ones, whenever a new VR application requires specific poses that are not typical in general VR applications. We made our pose datasets focus on the dynamic arm gestures in the limited space because most VR users stay in a small room and use their upper body and arms for interaction. We used rhythmic choreographies, but others may use sports action, game action, and other possible actions. Our datasets are a testbed to see that our proposed system works. Our proposed method can extend and replace the datasets if necessary.
###### Abstract  
Flexible resources, including district heat networks (DHN) and battery energy storage systems (BESS), can provide flexible regulation capability for distribution networks (DN), thereby increasing the absorption capacity for renewable energy. In order to improve the operation economy of DN and ensure the information privacy of different operators, a distributed cooperative dispatch method of DN with DHN and BESS considering flexible regulation capability is proposed. First, a distributed cooperative dispatch framework of DN-DHN-BESS is constructed. Then, an optimal dispatch model of DHN under constant flow-variable temperature control strategy is established in order to utilize the heat storage capacity to provide flexible regulation capability for DN. Next, the optimal dispatch models of BESS and DN are established with the objective of minimizing the operation cost, respectively. Finally, a solution method based on the alternating direction multiplier method of distributed cooperative dispatch for DN-DHN-BESS is proposed. Case studies are performed on a system consisting of a 33-node DN and a 44-node DHN, and simulation results demonstrate that the proposed method differs from the centralized dispatch method by only 0.52% in the total system cost, and the proposed method reduces the total system cost by 34.5% compared to that of the independent dispatch method.  
distribution network; district heat network; energy storage; decentralized cooperative dispatch; alternating direction multiplier method +
Footnote †: journal: Applied sciences
## 2 Partial Differential Equations in Image Compression
### Binary Tree Triangular Coding  
In the B-Tree Triangular Coding Scheme (BTTC), the image is decomposed into a large number of triangular areas so that they can be reconstructed to a satisfactory quality using vertex interpolation. The data obtained during triangulation is stored in a binary tree structure.  
This process is shown in Figure 1, where an initial image, Figure 1a, is approximated by its four vertices. The interpolation is performed using the information about the location of the vertices and their grey values. If the original image is presented by \((f_{ij})\) and its approximation is \((u_{ij})\) then an error can be defined \((e_{ij})\) as \(e_{ij}:=\left|u_{i,j}-f_{ij}\right|\). If \((e_{ij})\) satisfies \(e_{ij}\leq\varepsilon\) for every image element \((i,j)\) with given error threshold parameter \(\varepsilon>0\), the representation with triangles is considered to be sufficiently good.  
If the above equality is not satisfied, the triangle containing \((i,j)\) is divided into two similar triangles by reducing its height on the hypotenuse, Figure 1d. This is repeated until the given threshold for triangle formation is reached.  
During this process, two images are generated. One is the mask image containing the coordinates of the vertices, and the other is a sparse image containing the information of the grey values in the same vertices.
## 3 Proposed GaN HEMT MMIC LNA Design  
In the design of the proposed LNA, small-signal models of the GaN HEMT device, which contain measured S-parameters and NF data with given drain voltage and DC current conditions for the frequency range of 20-40 GHz, were used in the simulation. All required electromagnetic simulations were conducted using an advanced design system momentum tool to precisely model the utilized transmission line-based matching net-works and reflect the parasitic components from capacitors, inductors, and bond pads.
###### Abstract  
This research presents a detailed methodology for indoor positioning using visible light communication (VLC) technology, focusing on overcoming the limitations of traditional satellite-based navigation systems. The system is based on an optical positioning framework that integrates trilateration techniques with a semi-spherical array of photodiodes, designed to enhance both positional accuracy and orientation estimation. The system effectively estimates the receiver's position and orientation with high precision by utilizing multiple white-light-emitting diodes (LEDs) as transmitters and leveraging angular diversity. The proposed method achieves an average position error of less than 3 cm and an angular accuracy within 10 degrees, demonstrating its robustness even in environments with obstructed line of sight. These results highlight the system's potential for significant indoor positioning accuracy and reliability improvements.  
visible light communication; visible light positioning; indoor positioning; line of sight +
Footnote †: journal: _electronics_  
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# Scheduling Sparse LEO Satellite Transmissions for Remote Water Level Monitoring
## 2 Materials and Methods
### Online Learning Direct-to-Satellite Packet Scheduling
#### 2.4.3 Modified \(k\)-Armed Bandit  
Regarding the policy for packet scheduling, there is a similarity to the \(k\)-armed bandit problem, whereby an agent repeatedly plays the same one-step episode. In each game, the agent has a selection of options, which may give varying stochastic rewards. The goal is to learn over time which actions give the greatest expected reward [18,19]. A common approach to this problem involves softmax (Boltzmann) exploration, which derives a set of probabilities corresponding to each possible action [20]. The action with the highest expected reward has the highest probability of selection, plus all the choices are guaranteed to sum to 1 by the design.  
Our problem is slightly different from the \(k\)-armed bandit problem in two important ways: (1) the set of actions available to the agent in each episode is different, and (2) expected reward is not only the probability of successful transmission, but its utility in the given application, most notably the timeliness. Regarding the first point, the agent is faced with a different selection of satellite passes each episode, each with their own set of pass characteristics and times at which they occur. This problem is solvable, as the modified Monte Carlo learning methods will allow keeping track of the estimated reward of each action.