esa-sceva/satcom-chunk-collection huggingface dataset:
# Numerical Analysis on the Performance of a Heat Pump Powered by Surface Water Freezing
## 2 The Heat Pump System and Its Numerical Model  
Figure 1 shows the direct surface water solidification heat source heat pump (WSHP) heating system, including the surface water circulation system, the refrigerant circulation system, and the user heating circulation system. The heat pump mainly includes a shell-tube solidification heat evaporator, shown in Figure 2, condenser, compressor, and expansion valve and the specific structural parameters of the evaporator are shown in Table 1. Firstly, the filtered low-temperature surface water in the riverbed wells is forced by the submersible pump to flow past the shell side of the evaporator and back to the river. In this process, some ice is formed along the tube's external surface, and so deicing is often needed. Then, the refrigerant is circulated by the compressor along the route 1-2-3-4-5-6-1 shown in Figure 3, which gives a pressure-enthalpy diagram. At point 1 of the evaporator outlet and the compressor inlet, the refrigerant is a superheated gas of low temperature and low pressure; at point 2 of the compressor outlet and the condenser inlet, the refrigerant is also a gas of high temperature and high pressure; at point 5 of the condenser outlet and  
the expansion valve inlet, the refrigerant is a subcooled liquid of low temperature and high pressure; and at point 6 of the expansion valve outlet and the evaporator inlet, the refrigerant is a liquid with a little gas of low temperature and low pressure. Finally, the water circulated by the circulation pump obtains the heat in the heat pump condenser, and by doing so, it provides heating for the user.  
In this section, simulation models of the evaporator, compressor, condenser, and throttle valve in a heat pump are built, respectively, and then four unit models are numerically  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Evaporator Structure Parameters**} \\ \hline
**Number of Round Tubes** & **16** & **Tube Length** & **4** & **Arrangement** & **Crossbar** \\ \hline Pipe spacing & 80 mm & Pipe diameter & 10 mm &
\begin{tabular}{c} Total length of heat \\ exchanger tube \\ \end{tabular} & 110 m \\ \hline \hline \end{tabular}
\end{table}
Table 1: Evaporator structure parameters.  
Figure 3: Pressure–enthalpy diagram of refrigerant cycle.  
Figure 2: Evaporator structure diagram.  
connected to obtain the simulation model of the heat pump. Following this, the dependence of the heat pump system performance on the inlet water velocity, the inlet water temperature, and the evaporation temperature of the evaporator is numerically analyzed.
# A Bayesian Modeling Approach to Situated Design of Personalized Soundscaping Algorithms
## 5 Conclusions
### Soundscaping  
The inference task for soundscaping as described by (5) is simplified here to limit computational complexity and to prevent cyclic conditional dependencies. The mean values of the posterior distributions of the latent constituent signals \(\mathbf{s}_{n}^{k}\), as obtained by the second inference task (3), are extracted, converted to an acoustic signal and amplified/suppressed according to the deterministic gain preferences \(w^{1:K}\). These are set by the user in order to \"soundscape\" the final processed signal and to personalize their acoustic environment. As the extracted signals are in the log-power spectrum, the conversion back to the acoustic signal is not immediate evident. For this purpose we will make use of the generalized Wiener filter. This filter applies a frequency-dependent gain to the frequency coefficients of the acoustic signal. The Wiener filter gain for each of the frequency bins is calculated individually as \(G^{f}=P_{s}^{f}/(P_{s}^{f}+P_{s}^{f})\), where \(G^{f}\) is the Wiener filter gain for frequency bin \(f\) and where \(P_{s}^{f}\) and \(P_{s}^{f}\) represent the corresponding calculated average powers of the signal of interest and all other signals, respectively. From the filtered frequency coefficients the acoustic signal can be reconstructed using overlap-add or overlap-save.
# Progress of Organic/Inorganic Luminescent Materials for Optical Wireless Communication Systems
###### Abstract  
The growing demand for faster data transference and communication allowed the development of faster and more efficient communication network-based technologies, with wider bandwidth capability, high resilience to electromagnetic radiation, and low latency for information travelling. To provide a suitable alternative to satisfy data transmission and consumption demand, wireless systems were established after a decade of studies on this topic. More recently, visible light communication (VLC) processes were incorporated as interesting wireless approaches that make use of a wide frequency communication spectrum to reach higher bandwidth values and accelerate the speed of data/information transmission. For this aim, light converters, such as phosphor materials, are reported to efficiently convert blue light into green, yellow, and red emissions; however, long carrier lifetimes are achieved to enlarge the frequency bandwidth, thereby delaying the data transference rate. In this review, we focused on recent advances using different luminescent materials based on prominent polymers, organic molecules, and semiconductor nanocrystals with improved photophysical properties and favored carrier recombination dynamics, which are suitable to enhance the VLC process. Here, the main features of the above materials are highlighted, providing a perspective on the use of luminescent systems for efficient optical communication applications.  
luminescent materials; visible light communication; optical properties; radiative recombination; light conversion +
Footnote †: journal: _Photonics_  
0
Footnote 0: 0203 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/)]([https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/))).
# Automatic Literature Mapping Selection: Classification of Papers on Industry Productivity
## 2 Methodology
### Category Classification of Scientific Articles
#### 2.5.3 Method Validation Metrics  
A crucial aspect of the development of machine learning systems is the validation of the model. This process allows us to assess how well the model performs and generalizes when faced with new and unseen data. Numerous key elements of model validation are underscored:  
* Recall calculates the ratio of true positives to the total count of positive samples within the testing dataset. This measurement holds significance when the objective is to reduce false negatives, thus preventing the model from erroneously labeling positive samples as negative.
* Precision represents a measurement that gauges the fraction of accurate positive predictions (true positives) to the complete count of positive predictions generated by the model. This metric is valuable when the emphasis diminishes false positives, preventing the model from categorizing negative samples as positive.
* Accuracy serves as a prevalent assessment measure, quantifying the ratio of accurate forecasts produced by the model relative to the predictions rendered. This calculation entails adding the correct forecasts and dividing this sum by the overall count of instances. Accuracy proves advantageous in scenarios where classes are evenly distributed, signifying that they possess comparable instance quantities.
* The F-measure represents a measurement that amalgamates precision and recall, forging a singular gauge. This metric comes in handy when class distribution is uneven, as it considers false positives and negatives alike. The F-measure is computed as the harmonic average of precision and recall, offering a cohesive yardstick to portray the comprehensive prowess of the model's performance.
* The confusion matrix serves as a technique to assess the outcomes of machine learning-based classification. It furnishes a thorough overview of the model's predictions through a juxtaposition with the factual actual values. We can pinpoint instances of false positives and false negatives by conducting tests with labeled test data.  
While validating a machine learning model, it becomes imperative to scrutinize these factors to cherry-pick the most suitable model coupled with finely tuned parameters. This process aims to attain peak performance, gauged by a spectrum of metrics encompassing accuracy, precision, recall, and the F-measure. The choice of which metric to prioritize hinges on the distinctive requirements of the problem and the classes under consideration.
# Research on the Reasonable Width of Coal Pillar Driving along Goal under Thick Hard Roof
## 4 Results and Discussion
### Numerical Simulation Analysis of Coal Pillar Width
#### 4.2.1 Characteristics of the Stress Distribution along the Goaf  
As shown in Figure 9, after the end of the upper working face mining and the balance of the roadway excavation calculation, a 50 m measuring line is arranged along the upper section along goat to obtain the vertical stress of the roadway surrounding rock during the excavation. The peak stress is obtained by measuring the line; then, the stress concentration factor can be calculated, which is calculated as the peak stress divided by the initial stress.  
Figure 9: Vertical stress distribution of the roadway under different widths of coal pillars during excavation. (**a**) Width of coal pillar 7 m. (**b**) Width of coal pillar 8 m. (**c**) Width of coal pillar 9 m. (**d**) Width of coal pillar 10 m. (**e**) Width of coal pillar 11 m.  
(1) When the width of the coal pillar is 7 m, the vertical stress distribution pattern is \"single hump\", and when it is 8 m and above, the vertical stress is \"saddle\" distribution, and it moves along the middle of the coal pillar to both sides. With the increase of the width of the coal pillar, the coal pillar gradually changed from the broken state to the supporting state of the elastic core area with a certain width;  
(2) The peak vertical stress on the side of the coal pillar decreases with the increase of the width of the coal pillar left, under the width of the coal pillar of 8 m, the vertical stress peak value between the left side and the right side has an inflection point, which suggests that the width of the pillar of 8 m can effectively carry the overburden rock layer. This indicates that the width of an 8 m coal pillar can effectively carry the stress of the overlying rock layer.
# A Case Study on the Design and Implementation of a Platform for Hand Rehabilitation
## 6 Discussion
### Benefits and Obstacles for Using Local Production Rehabilitation Aids in Rehabilitation Facilities  
Global health care costs were rated at US$7 trillion in 2015, but in 2020, the estimated global healthcare costs were estimated at US$8.7 trillion [55]. However, this estimation was calculated before COVID-19 appeared in the early months of 2020. The same trends are emerging with rehabilitation costs. The highest absolute growth of physical rehabilitation needs was observed in lower-income countries that usually have deprived rehabilitation services [55]. Finally, a significant reduction in age-standardized needs per capita occur only in high-income countries, which can be related to the wider implementation of physical rehabilitation services in these countries. To reduce these costs further, health care institutions must adapt and seek new opportunities continuously. Technological advances can be of great support. The RehabHand is one such example of an advanced rehabilitation platform that can reduce waiting queues in rehabilitation centers. Therapists do much repetitive work with patients, which could be reduced using modern rehabilitation tools and doing treatments with several patients simultaneously.  
We believe future rehabilitation aid manufacturers should think about functionalities, devices, and unified description of exercises, as discussed in Section 4. We are aware that RehabHand is not a panacea for open issues in rehabilitation institutions, or that rehabilitation institutions' Regulations and Standards would support the use of a rehabilitation aid supplemented with local production. Therefore, we foresee many complications in applying such a platform in modern health care. A potential rehabilitation aid manufacturer needs to comply with regulations, safety, sustainability issues, and so forth to enter rehabilitation facilities. It would be easier for the RehabHand platform to come to life in a community where active participants seek rehabilitation aids that would enable part of the rehabilitation process in the home environment.
## 2 Materials and Methodology  
This section describes the HSI instrumentation used to acquire the dataset, as well as the proposed methodology. An HS camera system (Quest Medical Imaging BV, Mildenmeer, The Netherlands) has been prototyped; the system consists of a snapshot camera, which is intended to acquire both spatial and spectral information with one single exposure on the area detector. Compared to the conventional spectral imaging modalities, the snapshot system used is simple and robust to motion artifacts. The system applies a white light source to provide illumination and collects the skin-reflection images using the snapshot HS camera [26].  
We have developed a computer vision framework to perform skin feature detection and matching for different breathing conditions of a subject. The framework consists of preprocessing the acquired images, detecting the skin features and estimating the feature location with respect to four adhesive optical markers glued in the field of view. The glued markers represent the ground truth in this study. We first apply band selection algorithms and then show how they can outperform band extraction methods. Then, we evaluate two different approaches for skin feature detection, applying a handcrafted local feature algorithm and adapting a deep learned local feature detection algorithm to our case. Afterwards, we evaluate the system on 17 healthy subjects. The error is assessed using adhesive optical markers on the skin of the volunteers to measure the skin displacement. In order to prove and exploit the advantage of sampling throughout a wider spectral range from the visible to the near-infrared light range, the obtained results are compared with the results found when extracting features from RGB color images synthesized and transformed by the HS data cubes.  
An overview of the designed framework is depicted in Figure 1.  
Figure 1: Schematic view of the complete framework. The hyperspectral (HS) cubes are preprocessed and used for feature detection and matching in order to estimate the 2D feature localization. The 2D Euclidean distances for the matched inliers are computed and compared with the Euclidean distances found with the optical markers, which represent the ground truth in this study.
# Adaptive Seasonality Anomaly Detection Algorithm under Intricate KPI Profiles
## 3 Materials and Methods
### eBeats Clustering Algorithm
#### 3.2.2 DBSCAN Clustering Algorithm  
The second step in _ASAD_ is an unsupervised learning technique to cluster the sub-sequences. When the samples are not labeled, it may divide the data into various clusters. The clustering process is based on calculating the distance between the data points. According to previous studies, there are two main clustering algorithms available for _ASAD_. K-means and DBSCAN are two of the most widely used clustering methods. The k-means algorithm is a traditional clustering algorithm that requires the number of groups to be specified. On the contrary, DBSCAN clustering algorithm does not need to determine the number of clustering centers. DBSCAN can cluster KPI time series data automatically based on the density of data points by setting the minimum number of clustering points and the clustering distance radius, rather than the number of clusters.  
The result of DBSCAN clustering algorithm is shown in Figure 9. Five separate KPI time series data sub-sequences are shown in the figure by five different lines with varied patterns. Moreover, they are divided into two groups after being calculated using the DBSCAN clustering technique, which are denoted by red and green respectively. The working day mode is represented by the red KPI time series data sub-sequences, while the rest day mode is represented by the green KPI time series data sub-sequences. According to the experimental results, the similar KPI profiles among three sub-sequences in red belong to one category, while similar profiles between the two sub-sequences in green belong to another category. Meanwhile, it is worth noting that the clustering results corroborate the observations.  
Figure 8: The comparison before and after information extraction. (**a**) Sub-sequences before extracting the principal information. (**b**) Sub-sequences after extracting the principal information.
## 3 Influence of Physical Properties of NS on Communication, Navigation, and TT&C Integration
### Atmospheric Loss in NS
#### 3.3.5 NS Multipath and Doppler
From the simulation results in Figure 9, it can be seen that for Figure 9a, as the number of multipaths increases, the channel deteriorates, and the frequency selectivity of the channel increases, which in turn leads to a significant increase in the BER of the communication, navigation, and TT&C system. That is, under the same signal-to-noise ratio (SNR) condition, the BER caused by the signal path with a larger number of multipaths will increase. In other words, under the same BER condition, if we need to demodulate the original signal, the signal path with a larger number of multipaths requires a higher SNR. For Figure 9b, under the same number of multipaths, the longer the time delay of the signal and the lower the relative power, the worse the quality of the signal path.
# Complex Network Modeling and Analysis of Microfracture Activity in Rock Mechanics
## 4 Results and Discussion
### Limitations and Uncertainties
#### 4.4.1 Limitations  
Specific Rock Type and Testing Conditions: Our experiments were conducted on plagiogorinite samples from the Yebatan hydropower station dam area. The results, therefore, may not be directly applicable to other rock types or geological conditions without further investigation.  
Sample Size and Representativeness: While we have endeavored to ensure representative sampling, the finite number of samples used in this study imposes limitations on the generalizability of our findings. A larger and more diverse sample set could reveal additional nuances in microfracture behavior.  
Experimental Setup: The laboratory environment and the controlled conditions under which the tests were conducted might not fully replicate the complexities of in situ stress states and geological processes. This discrepancy could influence the observed microfracture dynamics.
## 2 System Model
### Communication Model  
We assume a multi-node downlink communication scenario where both the RRHs and UE are equipped with a single antenna. The channel is modeled by distance-dependent  
Figure 1: Illustration of the CRAN system model for different transmission schemes with a typical link and N = 5 interfering RRHs.  
path loss and small-scale fading. We assume the channel undergoes quasi-static Rayleigh fading and the path loss exponent is \(\alpha\). Because of latency constraints, all processing is done centrally at the CRAN. Similar to [40], we assume that full channel state information (CSI) is available at CRAN BBU. We denote the i.i.d squared-envelope fading coefficients of the typical, cooperating and interfering link as \(h_{0}\),\(h_{c}\),\(h_{j}\sim\text{Exp}(1)\), respectively. We consider that each RRH transmits with fixed unit power, and there is a dense network deployment such that the system is interference limited. Therefore, the noise is negligible compared to the sum interference [44].  
Under these assumptions and settings, the Signal-to-Interference Ratio (SIR) at UE for different transmission schemes as outlined in Section 2.2 is given by,  
\[\text{SIR}=\begin{cases}\frac{h_{0}d_{0}^{-\alpha}}{l_{j}},&\text{for silencing}\\ \frac{\max(h_{0}d_{0}^{-\alpha},h_{c}d^{-\alpha}),\varepsilon=1,\cdots,k}{l_{ j}},&\text{for TAS}\\ \frac{h_{0}d_{0}^{-\alpha}+\sum_{c=1}^{k}h_{c}d^{-\alpha}}{l_{j}},&\text{for MRT} \end{cases} \tag{1}\]  
Note that while writing SIR expression in (1) it is assumed that first \(k\) RRHs are cooperating with the transmission of the typical link, the rest of the RRHs are interferers, and \(l_{j}=\sum_{k=1}^{N}h_{j}d^{-\alpha}\) is the sum of interference from the other interferers. We assume that, at a given time, there are other active users being served by non-cooperating RRHs using the same spectrum resources. Hence, the total sum interference \(l_{j}\) takes into account the collective interference from all transmitting streams corrupting the typical link.
# Meniscal Tear Management Associated with ACL Reconstruction
## 2 Therapeutical Options for Meniscal Lesions with Concomitant ACL Reconstruction
### Masterly Neglect (Leaving In Situ) Associated with ACL Reconstruction  
Healing of a meniscus lesion, without performing any type of procedure, is possible in certain conditions [56]. Lateral stable meniscal tears, left in situ (untreated), can heal in a high percentage of cases, avoiding the complications related to meniscectomy or suture but this happens only if the ACL is reconstructed [52]. If the ACL deficient knee is not stabilized, these stable meniscal tears will progress in size over time and become symptomatic [52].  
There are strict indications for leaving the meniscus \"in situ\" or \"masterly neglect\": stable, complete longitudinal ruptures, less than 10 mm long [52]. This type of meniscal rupture, especially if associated with recent trauma, usually doesn't generate mechanical symptoms, and heals spontaneously in the vast majority of cases [57; 58; 59; 60; 1]. Lateral stable meniscus tears seem to heal better than medial tears if left in situ and therefore they are the best candidates for this type of approach [1; 60; 61; 62; 63]. Ramp lesions are a special category and will be treated separately.