esa-sceva/satcom-chunk-collection huggingface dataset:
# Calculation Methods for Construction Material Stocks: A Systematic Review
## 4 Discussion
### Procedure to Calculate Material Stock
#### 4.1.1 Selecting the Region  
The first step in estimating the material stock is to select the project area. The area selected should be based on the research aims and objectives. For example, to estimate future waste generation using the building material stock, the project area selected should have buildings that have completed their lifespan. However, if the research objective is to estimate future material stock requirements, the project area should be developing steadily. Similarly, the necessary data should be available. For instance, in bottom-up methodology, extensive details such as construction drawings, materials used, and methodologies adopted should be known. However, in the case of top-down methodology, extensive statistical data of the projects similar to the project area should be available. Similarly, for demand-driven methodology, socio-economic data should be available.
## 4 Discussion  
### Comparison of Neural Network Prediction Time with SSFM  
We recorded the generation times as shown in Figure 6, which compares the times required for the Split-Step Fourier Method (SSFM) and DeepONet across different fiber lengths. The step size for SSFM was set to 1 mm. The vertical axis represents generation time (in seconds, on a linear scale), while the horizontal axis represents fiber length. The figure demonstrates that the generation time for SSFM increases exponentially with fiber length, whereas the generation time for DeepONet remains significantly lower and nearly constant at approximately \(10^{-3}\) s across different fiber lengths. This indicates that DeepONet is far more efficient than SSFM as the fiber length increases.
# Point Cloud Deep Learning Network Based on Local Domain Multi-Level Feature
## 5 Application of Semantic Segmentation in Outdoor Scenes  
We used the proposed method to conduct semantic segmentation in outdoor scenes, and different objects are displayed in different colors after segmentation, as shown in Figure 13.  
From Figure 13, it is clearly visible that the proposed method can accurately identify the spatial extent of various objects and precisely segment the target objects. For example, the proposed method divides outdoor scenes into man-made terrain, natural terrain, high vegetation, low vegetation, buildings, cars and other objects. The man-made terrain and natural terrain are accurately segmented by the proposed method. However, for some adjacent or structurally similar objects, such as low vegetation placed on buildings, segmentation errors can easily occur. The proposed method may also struggle to accurately extract features and identify objects with blurry boundaries. For example, segmentation errors may occur at the junctions between tall vegetation and landscapes.  
Figure 12: (**a**) Sparse point cloud; (**b**) classification accuracy of different sampling points by different methods.  
Figure 13: The semantic segmentation results in outdoor scenes.
## 3 Performance Analysis
### Ergodic Capacity  
The ergodic capacity, which represents the time average of the maximum information rate of the random signal in all fading states, is another important parameter of the communication system and is expressed as follows:  
\[\overline{C}\overset{\Delta}{=}\mathbb{E}[\log_{2}(1+\tau\gamma)]\, \tag{16}\]  
where \(\mathbb{E}[\cdot]\) is expectation function.  
Substituting (9) into (16) and using Equation (07.34.21.0013.01) in [33], while utilizing the identity of \(\ln(1+\varepsilon\gamma){=}G_{2,2}^{1,2}\biggl{[}\tau\gamma\gamma\bigl{|}_{1,0}^{1,1}\biggr{]}\), the ergodic capacity of drone-aided downlink FSO system under AoA fluctuations with the heterodyne detection and the IM/DD techniques, respectively, as  
\[\overline{C}=\frac{A\xi^{2}C\sigma_{n}^{2}}{2\ln 2\eta P_{t}}\Biggl{(}1-\exp \biggl{(}-\frac{\theta_{F,W}^{2}}{2\sigma_{a}^{2}}\biggr{)}\biggr{)}\sum \limits_{i=1}^{b}b_{i}G_{5,7}^{5,3}\biggl{[}\frac{C\sigma_{n}^{2}}{\eta P_{t} }\biggr{|}\begin{array}{c}-1,0,-1,0,\xi^{2}\\ \xi^{2}-1,a-1,i-1,-1,-1,0,-1\end{array}\biggr{]}, \tag{17}\]\[\overline{C}=\frac{2^{a-\frac{13}{2}}A\xi^{2}C\sigma_{n}}{\pi\ln 2\eta P_{t}} \left(1-\exp\left(-\frac{\theta_{FoV}^{2}}{2\sigma_{a}^{2}}\right)\right)\sum \limits_{i=1}^{b}b_{i}2^{i} \tag{18}\] \[\cdot\]  
**Remark 3**.: _Specifically, the ergodic capacity under the influence of atmospheric attenuation, atmospheric turbulence, and pointing errors also can be converted from (17) and (18) when \(\sigma_{a}\to 0\), and the obtained expressions are consistent with the Equation (28) in [31]._
## 4 Results
### Capacity Fade Forecasting at Different Discharge Conditions

Table 5: Comparative RMSE results between the proposed health indicator \(\Delta V_{RMS}\) and the capacity fade \(\Delta C\) with respect to the measured capacity fade \(\Delta C\) (objective time series) for cell B0034 with a discharge current of 4 A (C-rate of 2). Figure 9: ARIMA predictions at different cycles for cell B0034 with a discharge current of 4 A (C-rate of 2). Behavior of the measured capacity fade \(\Delta C\) (black line, which corresponds to the objective time series), the prediction results with the proposed \(\Delta V_{RMS}\) health indicator in the scale of \(\Delta C\) (blue line) and the prediction of \(\Delta C\) (red line). The results presented in Figure 10 show that the discharge behavior based on the predicted values of \(\Delta V_{RMS,n}\) and \(\Delta C_{n}\) at cycle \(n\) approximates the measured discharging behavior, thus validating the usefulness of the proposed approach. Table 6 shows the RMSE values between the measured data (dashed black lines) and predictions made by the proposed degradation model and the Nernst equation (solid magenta lines). The results presented in Figure 10 and Table 6 exhibit that, at a given cycle \(n\), the discharge behavior of the system can be approximated using the proposed \(\Delta V_{RMS}\) health indicator at different discharge currents.
## 5 Experimental Results
### Evaluation Metrics
#### 5.2.3 Precision  
To evaluate the numerical precision of the predicted ratings, we employ a metric called Precision, which is computed using the Equation (12). In this equation, \(TP\) refers to the number of predicted ratings that agree with the corresponding test ratings, while \(FP\) corresponds to the number of predicted ratings that differ from the test ratings [53].  
\[Precision=\frac{TP}{TP+FP} \tag{12}\]
## 1 Introduction  
To obtain patient consent for an operation, the nature of the procedure must be explained, as well as expected benefits and potential risks or adverse effects, alternative treatment options, and likely outcomes if surgery is declined [1]. Appropriate and well-documented informed consent is mandatory to preclude civil action for negligence or otherwise protect medical staff from legal conflicts [2, 3]. Unfortunately, attempts to convey the fundamentals of intended procedures sometimes fail, depriving patients of proper working knowledge for granting consent [4]. There are reports that visual aids used to supplement verbal descriptions allow better conceptualization and processing of the information delivered [5, 6]. Three-dimensional (3D) models, in particular, may depict the visual aspects of procedures more effectively. Recently, 3D printers have been studied expressly for this purpose [7, 8, 9].  
3D-printed anatomic models are widely used in surgical fields, particularly in orthopedic and maxillofacial specialties, as operative guides or templates for surgical planning, implant design, molding of prosthetics, and patient selection [10]. They have also been assets in patient education [7, 8]. Personalized 3D models that replicate actual organ or tissue conditions may aid in evaluating disease states and help expedite preoperative preparations [11]. However, such models have not yet proven cost-effective, the expense and time invested acknowledged as shortcomings [7, 8, 10].  
The present study was undertaken to preliminarily investigate a mesh-type 3D modeling technique for solid tumors that effectively reduces cost and printing time, without loss of organ-tumor anatomic relations.
###### Abstract  
High-precision Low Earth Orbit (LEO) satellite clocks are essential for LEO-augmented Positioning, Navigation, and Timing (PNT) services. Nowadays, high-precision LEO satellite clocks can be determined in real-time using a Kalman filter either onboard or on the ground, as long as the GNSS observations collected onboard LEO satellites can be transmitted to the ground in real-time. While various real-time and high-precision GNSS products are available nowadays in the latter case, their continuity and latencies in engineering reality are not as perfect as expected and will lead to unignorable impacts on the precision of the real-time LEO satellite clocks. In this study, based on real observations of Sentinel-3B, the impacts of different latencies and continuity of the real-time GNSS products on LEO real-time clocks are determined and discussed for two scenarios, namely the \"epoch estimation\" and \"arc estimation\" scenarios. The former case refers to the traditional filter-based processing epoch-by-epoch, and the latter case connects LEO satellite clocks from different rounds of filter-based processing under a certain arc length. The two scenarios lead to the \"end-loss\" and \"mid-gap\" situations. Latencies of the real-time GNSS products are discussed for the cases of orbit-only latency, clock-only latency, and combined forms, and different handling methods for the missing GNSS satellite clocks are discussed and compared. Results show that the real-time LEO satellite clock precision is very sensitive to the precision of real-time GNSS satellite clocks, and prediction of the latter becomes essential in case of their latencies. For the \"end-loss\" situation, with a latency of 30 to 120 s for the GNSS real-time clocks, the LEO satellite clock precision is reduced from about 0.2 to 0.28-0.57 ns. Waiting for the GNSS products in case of their short latencies and predicting the LEO satellite clocks instead could be a better option. For \"arc-estimation\", when the gap of GNSS real-time products increases from 5 to 60 min, the real-time LEO clock precision decreases from 0.26 to 0.32 ns.  
LEO; real-time; satellite clocks; GNSS products; latency; gap +
Footnote †: journal:
## 4 Conclusions  
In this paper, we propose a novel VLP algorithm for indoor positioning. The image sensor is used as the positioning terminal and the LED is utilized as the transmitting terminal. The essence and innovation point of our work is replacing the repeating and complex VLP positioning algorithm in a clever way which only needed to know the initial position and relative position relationships of the LED and the positioning terminal. The mean shift algorithm is utilized to track the moving LED in the image sensor, solving the problem of the blur effect and ensuring the real-time ability. The unscented Kalman filter improves the highest allowable motion speed of the positioning terminal as well as reduces the running time of our algorithm. Also, thanks to the excellent robustness of the mean shift algorithm under simple backgrounds, even when most of the LED is shielded in the image, the VLP method will not fail. Furthermore, the results of our visible light positioning method are combined with the noise matrix of UKF to get the output when the LED is shielded, which reduces the error of positioning results. Therefore, our algorithm possesses strong robustness and high accuracy.  
As for the experiment, the proposed VLP method can reach a high positioning accuracy up to 0.42 cm and the average processing time per frame is 24.93 ms. Furthermore, even when nearly 50% of the LED is shielded, the positioning accuracy maintains at 1.41 cm, which confirms that our proposed algorithm has strong robustness. All the mentioned results indicate that our proposed VLP method has excellent performance with high positioning accuracy, good real-time ability, and strong robustness.  
**Author Contributions:** conceptualization, W.G. and Z.X.; methodology, W.G.; software, S.C.; validation, J.Z., Z.X. and X.Z.; formal analysis, W.G.; investigation, J.Z.; resources, B.C.; data curation, Z.X.; writing--original draft preparation, Z.X.; writing--review and editing, Z.X.; visualization, X.Z.; supervision, W.G.; project administration, W.G.; funding acquisition, W.G.  
**Funding:** This work was supported in part by the National Undergraduate Innovative and Entrepreneurial Training Program under Grants 201510561003, 201610561065, 201610561068, 201710561006, 201710561054, 201710561057, 201710561058, 201710561199, and 201710561202; in part by the Special Funds for the Cultivation of Guangdong College Students' Scientific and Technological Innovation (\"Climbing Program\" Special Funds) under Grants pdijh2017b0040 and pdijha0028; and in part by the Guangdong Science and Technology Project under Grant 2017B010114001.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
Figure 22: The CDF of positioning error when random areas of the LED was shielded.
## 4 Discussion
### Uncertainties and Further Studies  
This study explores the influence of land cover on GPP estimation by NIRv. Using NIRv as a proxy of GPP is a new method, and the influence of land cover change on NIRv-GPP has not been systematically studied. [PERSON] et al. thought that the classification system was the reason for the differences between theoretical and measured GPP estimation at coarse resolution [28]. [PERSON] et al. produced two sets of GPP with different remote-sensing products derived from different sensors, and the results showed significant differences [54]. Our results showed that the dynamic change in LC had a significant impact on the GPP estimation of the Yellow River basin (Figure 5). However, the influence of LC on NIRv-GPP in areas with different intensities of LCC (such as Qinghai-Tibet Plateau) is still not clear. In addition, due to the lack of spatial observation data, we could not determine which land cover product was most in line with the actual land type distribution in the study area. Our work only cross-contrasted the two land cover products (that is, MCD12 CI V6 and ESACCI), which are widespread products and could be well matched with remote-sensing GPP estimation. However, both of the two products show unreasonable phenomena, such as misclassification or mixed pixels. The ESACCI also introduced uncertainties in the scaling transformation. The defects of land cover products might lead to uncertainties in GPP estimation. More products could be considered in the follow-up study to provide references to accurately estimate GPP by NIRv. Notably, the relationship between NIRv and GPP might be influenced by a series of other factors, such as community succession, anthropogenic management, fertilization, etc., which also need to be further explored. At present, our work just gives a clear signal that the LCC and the differences between different products need to be considered in the GPP estimation. In the future, we will conduct more detailed studies on these aspects to give practical suggestions for GPP estimation.  
Figure 9: Boxplots of GPP distribution obtained from the different models and products. First four blue boxplots are obtained from the NIRv-based GPP with the four schemes (staMOD, dynMOD, staCCI, and dynCCI), respectively. The red boxplots represent the eight different GPP products. For each sub-boxplot, the box plots show the lower whisker (=Q\(1-1.5\)IQR), lower quartile (Q1), median, upper quartile (Q3), and upper whisker (=Q3 + 1.5 IQR), where the IQR = Q3 – Q1, from the bottom to top.
# A Joint Positioning Algorithm in Industrial IoT Environments with mm-Wave Communications
## 4 Design of Joint Positioning Algorithm  
Considering the fact that UL transmission is dominant in IIoT with a limited traffic packet size as around 256 bytes [1], and much more wide bandwidth is available in mm-Wave bands [29; 30], this paper proposes considering UL-based positioning for IIoT. Another consideration is UEs do not have to monitor PRS within a long duration and report the measurement results.  
Then, assuming there is only one transmission antenna for a IIoT device, the received signal can be expressed as  
\[\mathbf{Y} =\left(\mathbf{Y}_{1},\ldots,\mathbf{Y}_{N_{\text{TRP}}}\right) \tag{3}\] \[=\mathbf{H}_{\text{SRS-Pos}}+\mathbf{Z}\in\mathbb{C}^{N_{\text{R} }\times N_{\text{symb}}^{\text{SRS}}\times N_{\text{RE}}^{\text{SRS}}\times N _{\text{TRP}}},\]  
where \(\mathbf{Y}\) denotes the received signal in frequency domain, and \(\mathbf{Y}_{i}\in\mathbb{C}^{N_{\text{R}}\times N_{\text{symb}}^{\text{SRS}} \times N_{\text{RE}}^{\text{SRS}}}\) is the received signal between the device and \(i\)th TRP; \(N_{\text{R}}\) is the number of receiving antenna with the assumption that the receiving antenna number is identical for each TRP; \(\mathbf{Z}\) is independent circularly symmetric complex Gaussian noise as \(\mathbf{Z}\sim\mathcal{CN}\big{(}0,\sigma^{2}\big{)}\); \(\mathbf{H}\) is the channel response between UE and all TRPs, and the \(i\)th element \(\mathbf{H}_{i}\in\mathbb{C}^{N_{\text{R}}\times N_{\text{symb}}^{\text{SRS}} \times N_{\text{RE}}^{\text{SRS}}}\) within \(\mathbf{H}\) represents the SIMO (single input multiple output) channel between UE and \(i\)th TRP. Considering many obstacles in an IIoT environment wherein shadowing and pathloss cannot be ignored, a composite fading channel is assumed, and each element of \(\mathbf{H}_{i}\) can be expressed as  
\[\mathbf{H}_{i}(k,l)=\sqrt{\Omega_{i}\rho_{i}}g, \tag{4}\]  
where \(\mathbf{H}_{i}(k,l)\) represents the channel response within \(\mathbf{H}_{i}\) at \(k\)th RE and OFDM symbol \(l\); \(\Omega_{i}\) represents the shadowing effect, and is modeled by a log-normal distribution as \(10\log\Omega_{i}\sim\mathcal{CN}\big{(}0,\chi_{i}^{2}\big{)}\); \(g\) is the fast fading and is assumed to follow a Rayleigh distribution; \(\rho_{i}\) is the pathloss and modeled as [25]  
\[\rho_{i}=\left(\frac{d_{t}}{d_{i}}\right)^{\beta_{i}} \tag{5}\]  
where \(d_{t}\) is the reference distance, \(d_{i}\) denotes the distance from UE to the \(i\)th TRP, and \(\beta_{i}\) is the path loss exponent.  
Based on the received signal in (3), the signal detection for delay and angle under the joint positioning algorithm can be performed according to the following subsections. The algorithm process is shown in Figure 4.
## 4 A Biased-Randomized Discrete-Event Algorithm
```
1:**BR-STNEH(\(jobsList\), \(\beta\), \(pT\), \(sT\), \(typesPriority\), \(mL\))**
2:\(typeslobs\gets emptyList\)
3:forall\(typelob\in types(jobsList)\)do
4:\(jobstypelob\leftarrow\{job\mid\forall job\in jobsList:type(job)=typelob\}\)
5:\(jobstypelob\leftarrow\) SortingByNEH(\(jobstypelob\), \(pT\))
6:\(typeslobs\leftarrow\) append(\(typeslobs\),\(jobSteplob\))
7:endfor
8:\(typeslobs\leftarrow\) SortingBySetupTimes(\(typeslobs\),\(sT\))
9:forall\(typelob\in typesPriority\)do
10:\(jobsList\leftarrow\) extend(\(jobsList\),\(jobstypelob\))
11:\(typeslobs\leftarrow\) remove(\(typeslobs\),\(jobstypelob\))
12:endfor
13:while\(jobsList\) is not complete do
14:\(typelob\leftarrow\) pickList(\(typeslobs\),\(\beta\))
15:\(lglth\leftarrow\) randomInt(\(1\),\(mL\))
16:while\(lglth>0\)do
17:\(job\leftarrow\) pickJob(\(jobstypelob\),\(\beta\))
18:\(jobsList\leftarrow\) append(\(jobsList\),\(job\))
19:\(jobstypelob\leftarrow\) remove(\(jobtypelob\),\(job\))
20:\(lglth\gets lglth-1\)
21:endwhile
22:if\(jobtypelob=emptylist\)then
23:\(typeslobs\leftarrow\) remove(\(typeslobs\),\(jobstypelob\))
24:endif
25:endwhile
26:return\(jobsList\)
```  
**Algorithm 1** Multi-Start Framework  
In order to compute the makespan, we developed a discrete-event based procedure. The idea of this methodology is to use a discrete-event list to manage complex time dependencies that arise as events occur over time [4]. This event list is constantly sorted, taking into account the chronological order of each event (e.g., the assignment of a job to an available machine or the arrival of a job to a batch queue), and it is iteratively processed until no events are left. Making all decisions in chronological order avoids complex and time-consuming back-rolling issues. Hence, when a new event is scheduled, it is chronologically inserted into a list \(eventList\). The makespan is iteratively computed as this \(eventList\) is processed, and others are scheduled, following a chronological order. Algorithm 3 shows an overview of the proposed discrete-event procedure. The algorithm receives as input parameters the \(jobsList\), which is the complete list of jobs in the specific order that will enter in the system to be processed the machine-dependent processing times of each job (\(pT\)) and the setup times of each job-machine (\(sT\)). The algorithm requires auxiliary data structures, such as: \(availableTime[m]\) (i.e., at which time a machine \(m\) will be available), \(previousTypeMachine[m]\) (which keeps track of the machine that previously processed the job), \(machinesTypes[m]\) (which stands for the type \(typelob\) that machine \(m\) can process),and \(jobsWaiting[m]\) (which stores a set of jobs in the batch \(m\)). In the beginning, all jobs are being processed in machine 1, in concordance with the scheduling \(jobsList\). Therefore, the algorithm is initialized, creating ending events at machine 1 and inserting them into the event list \(eventList\) (lines 9 to 16). In line 17, \(eventList\) is sorted by time \(t\) of occurrence. The main loop iterates until \(eventList\) is empty.
## 3 Results
### Impact of Thrusters on Surface Charging and Discharging Effects in Spacecraft
This capture biases the electron orbits toward the direction attracted by surrounding ions, expanding their diffusion range in the simulation domain. The density distribution of photoelectrons under two operating conditions is shown in Figure 8. Consequently, this phenomenon reduces the number of photoelectrons and secondary electrons per unit area, resulting in decreased current density. As a consequence, the incident currents of photoelectrons and secondary electrons on the spacecraft surface decrease, leading to a reduction in the spacecraft surface potential. In contrast, backscattered electrons with higher energies exhibit greater velocities, enabling them to more easily overcome the gravitational pull of surrounding ions. This occurs under the influence of the electric field, allowing them to maintain straighter trajectories without being captured. Figure 7: Variation of total particle surface currents on the spacecraft under different thruster conditions: (**a**) thruster is turned on; (**b**) thruster is turned off. CEX collisions between fast ions and slow neutral Xe atoms in the beam stream lead to the generation of CEX_Xe+. The low-velocity, low-energy CEX_Xe+ is particularly susceptible to negative potentials on the spacecraft surface, making them more likely to attach to the spacecraft surface. This attachment results in a decrease in the spacecraft surface potential. When the thruster is in operation, the charging effect on the spacecraft is no longer primarily influenced by space plasma but is instead dominated by the return flow of plasma from CEX collisions and its interaction with the spacecraft. This process helps maintain the spacecraft surface potential at a low level, significantly reducing the occurrence of electrostatic discharges between spacecraft surfaces. When the thruster is operational, the density of neutralizing electron currents on the spacecraft surface is negligible, primarily due to the low energy of neutralizing electrons generated by the hollow cathode (\(\sim\)1 eV\(\sim\)-10 eV). These neutralizing electrons are subjected to repulsion by the spacecraft surface potential, and as a result, they hardly return to the spacecraft surface. In essence, their influence on the spacecraft surface potential is minimal.