esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Result and Discussion
### Performance Comparison  
The diagram demonstrates the effects on presentation of equally comparing the planned protocol with the actual protocol. The procedure of cluster formation in planned research work is depicted below shown in Figure 9.  
The routing protocol's package drop rate is indicated in Figure 10, demonstrating the proposed method's reduced latency when compared to the existing protocols such as PSO, GA and WOA based on network flow.  
Figure 9: Step by step procedure of cluster formation.  
Figure 11 demonstrates the current of nodes in the routing protocol. The graph clearly shows the accuracy of packet delivery compared to the existing ones. Compared to the present techniques such as PSO, GA and WOA, the planned technique provides greater effectiveness in package delivery.  
Figures 12 and 13 show the performance metrics of the proposed OWOA algorithm, which achieved a more efficient outcome than other existing techniques such as PSO, GA and WOA by varying the nodes.  
Figure 11: Node vs. Delivery Ratio.  
Figure 10: Nodes vs. Delay.  
Figure 14 shows the result of throughput obtained for the proposed OWOA and existing PSO, GA and WOA techniques by varying the nodes. The proposed OWOA algorithm achieved a more efficient outcome than the other algorithms.  
Figure 12: Node vs. Energy.  
Figure 13: Node vs. NLT.
## 3 Indoor Positioning Systems
### Systems Based on Computer Vision
#### 3.5.1 Cameras Fixed to the Scene  
[PERSON] et al. introduced an indoor positioning system that uses a set of fixed IP cameras to reduce the rate of location errors [110]. In systems that use computer vision, the position is considered a primary issue to guarantee the confidentiality of the information generated. An application built for the Android platform receives images obtained from several simultaneous transmission links. Experimentally, the authors used a set of four cameras to provide the location of a target, resulting in a 0.10 m error margin.  
[PERSON] et al. built a computer vision project with an active approach (with a fixed camera), to make the environment intelligent in tracking people [111]. Users interact with the system by voice commands, and the system responds with audible instructions. The precision achieved was about 0.10 m in the horizontal plane. The weakness of the model is that it does not identify elements in the vertical plane. This deficiency (horizontal-vertical perception) is corrected with the use of stereo cameras, such as Microsoft Kinect [112].
## 4 Experimental Results and Analysis
### Experimental Designs
RMSE comprehensively considers the error of all samples and can intuitively reflect the average deviation degree between the predicted value and the real value of the model. It complements MAE and can evaluate the accuracy of the model more comprehensively. MAPE is used to measure the average percentage error of the predicted value relative to the true value. \[MAE=\frac{1}{n}{\sum_{i=1}^{n}\frac{|y_{i}-\hat{y}_{i}|}{y_{i}}}\times 100\% \tag{20}\]  
The advantage of MAPE is that it can eliminate the influence of dimension and show the prediction error in the form of a percentage, which is convenient for comparisons between data of different scales or different models. For the prediction results of different lines or different time periods with large differences in passenger flow, it may be difficult to compare MAE and RMSE directly because of the different magnitudes of the data themselves, while MAPE can provide a relatively unified evaluation standard. \[\text{R}-\text{Squared}=1-\frac{\sum_{i=1}^{n}(y_{i}-\hat{y}_{i})^{2}}{\sum_{ i=1}^{n}(y_{i}-\overline{y})^{2}} \tag{21}\]  
where \(\overline{y}\) is the mean value for the true observation. The closer the value of R-Squared to 1, the better the fitting effect of the model on the data; that is, the model can explain most of the variation in the data. On the contrary, the closer it is to 0, the worse the fitting effect of the model, and there may be problems such as an unreasonable model structure and inaccurate parameter estimation. Through the comprehensive analysis of these evaluation indexes, the performance of the airport express passenger flow prediction model based on an FINM can be comprehensively and accurately evaluated so as to provide strong support for the optimization, adjustment, and application of the model in actual operation. In practical application scenarios, for example, airport operation management departments can evaluate the reliability of model predictions based on these indicators and reasonably arrange resources such as train scheduling and staffing to improve the operation efficiency and service quality of airport express lines.
## 5 Influence of Angular Velocity on Hydrodynamic Ram Effect  
Space debris produced by the explosion or disintegration of spacecraft not only has translational velocity, but also angular velocity. When space debris with an angular velocity impacts the satellite tank, the direction and magnitude of angular velocity can affect the hydrodynamic ram effect.
## 1 Introduction  
### Wireless Sensor Network (WSN) and Internet of Things (IoT) Technologies  
In Internet of Things (IoT) systems, each device has the ability to collect and then transfer data between devices, via wired or wireless networks, using an electromagnetic field (EMF) from the radiofrequency band. Depending on the required range and data transfer rate of wireless connectivity, a number of technologies may be applied: Wireless Fidelity (Wi-Fi), Bluetooth, ZigBee, Long Range(LoRa), RadioFrequency IDentification (RFID) and four to five generations of mobile communication systems (4G-5G), as well as Long Term Evolution (LTE) [1].  
A major technology enabling IoT is the Wireless Sensor Network (WSN). A WSN can be defined as a group of spatially deployed sensors and actuators (recognised as nodes) used to monitor or control physical systems or environmental conditions. Each node (from a few to a few thousand) of the WSN is connected to one or more nodes. The nodes communicate with each other using radio signals.  
WSNs can be used for various purposes, such as intelligent transportation systems and vehicular communications; electricity, gas and water monitoring; and smart homes, cities and factories (Industry 4.0) [1; 2; 3; 4]. They are also used to monitor various bodily functions, typically in e-Health solutions but also for firefighters, industry and military workers, to detect firefighter movements or track soldiers and predict the warzone [5; 6; 7; 8; 9; 10; 11; 12; 13; 14].  
The WSN and IoT can also be used to monitor the level of harmful agents in the work environment (such as noise), or to reduce hazards to workers' health and safety related to environmental agents [15]. In such applications, the protected person often has to be equipped with a wearable device, either a sensor or an actuator, which is necessary to assess the monitored parameters of exposure or evaluate the hazards, or is aimed at reducing them. Such a sensor or actuator (usually a small wireless device in various shapes and forms) can be worn, for example, in a way comparable to a watch or some kind of band, or can be built-in or attached to various items of work clothes or personal protective equipment (such as a helmet, life vest or mask). In the further part of the article, two examples of WSNs developed at the Central Institute for Labour Protection--National Research Institute (CIOP-PIB), Poland, aiming at monitoring and reducing hazards in the work environment are presented. They include wearable devices whose construction is based on the ESP32-WROOM-32 radio communication module.
# Effect of Cavity Vacuum Pressure Diminution on Thermal Performance of Triple Vacuum Glazing
## 1 Introduction
The influence of vacuum pressure diminution on the thermal transmittance (U value) of a triple vacuum glazing when the vacuum space (cavity) pressure decreases from 0.001 Pa (high vacuum pressure) to 101.321 kPa (atmospheric pressure) is investigated. This study enables the future prospects of improving the edge sealing of TVG and presenting significant findings on understanding how the diminution of vacuum pressure may affect the heat loss through TVG.
# Efficient Coverage Path Planning for a Drone in an Urban Environment
## 5 SST--An Efficient Heuristic Algorithm for the GPP Problem
### Heuristics and Methods
#### 5.5.2 Euclidean Distance  
In the presence of obstacles, the Euclidean distance is a lower bound for the length of the shortest path between two points \(v_{1},v_{2}\in\pi_{f}\).  
According to their scores, waypoints \(v\in\mathcal{V}\) are added one by one to the subset \(\mathcal{W}^{h}\). Prior to adding a waypoint to the subset \(\mathcal{W}^{h}\), it has to meet two criteria regarding the distance from current members:  
* \(dist\geq min\_dist\)--to avoid redundancy in the data collected during the flight.
* \(dist\leq max\_dist\)--as the trajectory length is restricted due to the time-of-flight constraint.  
Calculating the Euclidean distance is an \(O(1)\) operation, as opposed to finding the shortest path, which requires \(O(n^{2})\), where \(n\) is the number of vertices of the visibility graph \(\mathcal{VG}\) (number of obstacle vertices). Therefore, for better performance, a heuristic based on the Euclidean distance is used to decide whether a waypoint should be further examined or not.
## 7 Additional Fundamental Aspects of Chemometric Modelling: Data Preprocessing and Validation
### Validation  
Having built a chemometric model does not guarantee per se its quality and/or validity, as these two aspects are strictly influenced by user-dependent factors like the chosen analysis technique or the values of certain metaparameters [2]. To assess whether it can lead to solid conclusions, the outcomes/predictions resulting from it are reliable and generalizable and its interpretation is sound and meaningful, such a model needs, therefore, to be _validated_[277]. Operationally, validation consists in evaluating the model's performance on a dataset (called a _test set_) different from the one used in the training or calibration phase (referred to as a _training_ or _calibration set_, as also specified before), but reflecting as closely as possible the distribution of future individuals and accounting for all potential sources of expected variability (e.g., in terms of geographical origin, composition, manufacturingprocess, etc.)--it is fundamental not to use for validation the same data exploited for model building as this could lead to overoptimistic considerations (being data-driven, chemometric models are conceived so as to return the best performance on those data). Unfortunately, in many real-world scenarios, it is not possible to design additional sampling campaigns and assemble completely new test sets. In these circumstances, it is common practice to split all the recorded measurement observations into two different blocks, one for model building and the other for model validation. However, for the validation procedure to be effective, the sample splitting scheme should ensure that both training and test sets span a representative amount of the overall data variability: in this sense, one should avoid random partitioning and, instead, resort to targeted subset selection algorithms like Kennard-Stone [278], Duplex [279, 280] or D-optimal design-based approaches [281].  
An alternative is constituted by internal resampling strategies [282], like cross-validation [70], in which the original data are repeatedly divided into a training and a test set and the procedure is iterated either until each object has been left out at least once from the former or until a maximum number of computational cycles has been reached. Cross-validation is often exploited when small sample sizes are concerned but, due to its underlying principle, usually yields optimistically biased outputs, as training and test sets are never completely independent from one another. Nevertheless, cross-validation can be fruitfully adopted to estimate optimal values for model metaparameters like the number of latent variables in PLS regression or PLSDA classification.
# Neutralization of Toxic Malodorous Gases from Cattle Slurry
## 5 Conclusions  
In this study, three _Bacillus_ strains and three _Pseudomonas_ strains isolated from natural environments were analyzed for NH\({}_{3}\) and H\({}_{2}\)S degradation from cattle slurry. The studies have been conducted on a lab scale. Chemical analysis of CS confirmed high concentrations of ammonia and hydrogen sulfide and compounds responsible for odor development. The results showed that the strains _Bacillus_ and _Pseudomonas_ had a strong ability to remove malodorous gases. The final NH\({}_{3}\) concentration ranged from 0 to 20 ppm, thereby achieving an 86-100% removal efficiency, compared to the initial sample within 10 days. Our results showed that H\({}_{2}\)S was completely removed after 6 days of incubation.  
Among the six bacterial strains, two strains with the highest reduction of ammonia and hydrogen sulfide (_Bacillus subtilis_ KKP 3903 and _Pseudomonas fluorescens_ KKP 3909) were selected and the microbial biopreparation (1:1) was prepared. The data showed that the use of biopreparation reduced the concentration of ammonia from 148 to 2.3 ppm (98% reduction) after 10 days of incubation and hydrogen sulfide from 113 to 0.0 ppm (100% reduction) after 6 days of incubation.  
It was found that the aeration of the slurry inoculated with the bacterial consortium intensified the neutralization of odor compounds. Our studies also showed that the addition of microorganisms to slurry had a positive effect on its chemical composition, increasing the content of macro-elements: nitrogen, phosphorus, and potassium and reducing the amount of heavy metals.  
Conceptualization, K.K. and W.D.; methodology, K.K., W.D. and B.S.; software, K.K. and W.D.; validation, A.S. and M.L.; formal analysis, K.K. and W.D.; investigation, K.K., W.D., A.S. and M.L.; resources, K.K and A.S.; data curation, W.D. and K.K.; writing--original draft preparation, K.K. and W.D.; writing--review, and editing, K.K. and B.S.; visualization, K.K. and W.D.; supervision, K.K.; project administration, K.K.; funding acquisition, K.K. and B.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Ministry of Science and Higher Education, Poland, grant number MNiSW/2020/318/DIR \"Innovation Incubator 4.0\".  
Not applicable.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.
## 2 Split Spectrum Processing
### Filter Bank Design  
Unlike the Gaussian (GS) filters, the raised cosine (RC) filters, which are shown in Figure 2a, have transition bands in the shape of a truncated, raised cosine cycle, and the energy contained in the RC filter is mostly concentrated around the center frequency for the flat top design [23]. It is easy to foresee that the RC filters can show a better split effect compared with that of GS filters, considering the degree of overlap in RC filters is lower than the GS filters, given the constant filter spacing. According to the frequency distributions of GS and RC filters in Figure 2b,c, the filter parameters shared by GS and RC filters are total processing bandwidth \(B\) (\(B=f_{H}-f_{L}\), where \(f_{H}\) and \(f_{L}\) are the high and low cutoff frequencies, respectively). Filter bandwidth \(B_{\text{GGS}}\) or \(B_{\text{FRC}}\) and filter spacing \(\Delta f\) and \(B_{\text{FRC}}\) can be further replaced by the transition bandwidth \(B_{t}\) and the flat top width \(B_{r}\).  
The frequency impulse of RC filter with center frequency \(f_{ci}\) is given by:  
\[RC_{i}(f)=\begin{cases}1/2\{1+\cos[\pi(f-f_{ci}+B_{r}/2)/B_{t}]\}&f_{ci}-B_{r}/ 2-B_{t}\leq f\leq\ f_{ci}-B_{r}/2\\ 1&f_{ci}-B_{r}/2\leq f\leq f_{ci}+B_{r}/2\\ 1/2\{1+\cos[\pi(f-f_{ci}-B_{r}/2)/B_{t}]\}&f_{ci}+B_{r}/2\leq f\leq f_{ci}+B_{r }/2+B_{t}\\ 0&\text{\ \
## 2 Methodology
### Annual Simulations
#### 2.3.1 Glare Simulations
* _Evalglare_ uses the _rpict_ generated fisheye HDR images to evaluate glare. It outputs DGP value along with several other glare indices. Though the workflow looks simple and straight forward, there are several challenges in it. At every timestep, simulations for 64 different combinations of EC glass with a particular sky condition are made. This requires generating 64 octrees, each for every combination and sky file. This is repeated for every hour in a year and then fisheye images are rendered for each viewpoint of the occupant. Therefore a total of 8760 \(\times\) 64 \(\times\) 4 = 2.2 million fisheye HDR images are generated. These images are then evaluated with _Evalglare_ and the obtained DGP values are stored in a database. The simulations are performed using python wrappers on radiance tools with a multiprocessing approach to rendering several HDRs simultaneously. These simulations took about a month on two 64 core XEON E5 2670 servers.
## 3 Results and Discussion  
This section briefly articulates the results of the proposed method in the presence of the pre-processed dataset shown in Section 2.3. A few output samples are shown in the image below (see Figure 5). We encircled each \(\mathcal{H}_{i}\) considering the distance between the two farthest dark pixels of \(\mathcal{H}_{i}\) (say, \(\mathcal{P}_{i,1}=(x_{i,1},y_{i,1})\) and \(\mathcal{P}_{i,n}=(x_{i,n},y_{i,n})\), and \(\mathbb{D}(\mathcal{P}_{i,1},\mathcal{P}_{i,n})\) is the distance between them) or major(\(\mathcal{H}_{i}\)) as the diameter of the circle (see Figure 4b). We chose a yellow circle if \(\mathcal{H}_{i}\) is classified as a pore and a white circle for a fracture (see Figure 5). Here, \(\mathbb{D}(\mathcal{P}_{i,1},\mathcal{P}_{i,n})=\sqrt{\left(x_{i,1}-x_{i,2} \right)^{2}+\left(y_{i,1}-y_{i,2}\right)^{2}}\in\mathbb{R}^{+}\) is the Euclidean distance between \(\mathcal{P}_{i,1}\) and \(\mathcal{P}_{i,n}\) (see Figure 5b).  
To test the effectiveness and applicability of the proposed identification strategy (see Section 2.4), we used the pre-processed data elaborated on in Section 2.3 and confined its execution within the effective region of each image. The subsequently detected pores (encircled yellow) and fractures (encircled white) are shown in Figure 5. Here, the pores and fractures are classified based on the shape and size of \(\mathcal{H}_{i}\) (see Algorithm 2).
## 4 Results of Prediction Accuracy Tests
The prediction values presented in the left column are computed from the RSM-computed surrogate models with G-DLB-4-3, G-DLB-8-3, and O-DLB-6-4 from the top to bottom rows, respectively. The prediction values presented in the right column are identical to the ones plotted in the left column, but these values are from the ANN-computed surrogate models. The dashed line in which the slope is unity and passes through the origin is also presented for each figure to show how accurately each surrogate model predicts the exact outcome values for the 50 test samples. With G-DLB-4-3, the prediction using the RSM-computed surrogate model shows slightly higher accuracy than the surrogate model computed from the ANN, but the ANN-computed models are better in the other two cases. This can be interpreted from the comparative analysis of \(E\), whose values are smaller for the prediction accuracy tests of the ANN-computed surrogate models with G-DLB-8-3 and  
Figure 3: The values of \(\alpha_{4}\) and \(\alpha_{6}\) of the surrogate models computed from the RSM and ANN. O-DLB-6-4 than the RSM-computed ones, allowing us to expect the phenomenon shown in Figure 4. A similar interpretation is possible for the prediction accuracy comparison of the surrogate models with G-DLB-4-3. Figure 4: The accuracy comparison of the surrogate models computed from the RSM and ANN with the 50 test samples. The values for the lift-to-drag ratio of the 50 test samples computed from XFOIL (dealt as the exact value) and surrogate models are presented. The figures on the left and right columns show the values computed from the RSM-based and ANN-based surrogate models, respectively. The surrogate models presented in the first, middle, and last rows are created with G-DLB-4-3, G-DLB-8-3, and O-DLB-6-4, respectively. Note that the lift-to-drag ratios of the 50 test samples for O-DLB-6-4 were spread wider than the other cases because the performance of a wind turbine airfoil is affected by its operating conditions. For example, the lift-to-drag ratios of the three wind turbine airfoils presented in Figure 5 are 57.1, 98.7 and 114.5, respectively, with their own operating conditions. However, these values are respectively changed to 125.1, 121.8 and 119.3 at \(\text{Re}=10^{6}\) and \(\text{AOA}=5^{\circ}\), where the deviation of the three airfoils' lift-to-drag ratios is much smaller than the case when each airfoil has its own operating conditions. When the distributions of pressure coefficients on these three wind turbine airfoils are compared between the two cases (when the airfoils are simulated with their own operating conditions and when the airfoils are simulated at the reference operating conditions), the effect of the operating conditions on the aerodynamic performance of the wind turbine airfoils is also observed (see Figure 6).
# Opportunities for Integrating Underground Railways into Low Carbon Urban Energy Networks: A Review
## 3 Urban Waste Heat Sources
### Underground Railway Tunnels  
Railway tunnels represent a reliable waste heat source in cities where public transportation heavily relies on underground metro systems. The potential to recover secondary energy from underground railway tunnels in the UK has been investigated by the authors of [31; 32], who analyzed the environment of the subway systems of Glasgow and London, respectively. The authors of [31] highlighted how this opportunity can help in achieving the UK targets for decarbonizing the heating sector through the application of heat pumps, which can reach better performance with warmer and stable source temperatures. The authors measured the annual temperatures for a station of the Glasgow subway and showed how they range from 15 and 18 \({}^{\circ}\)C, while the outside average temperatures can range from 4 to 16 \({}^{\circ}\)C, indicating how heat pumps can operate more efficiently using the underground air as a heat source. The potential of exploiting the London Underground's waste heat was investigated by the authors of [32], who showed that average platform temperatures can be as high as 20 \({}^{\circ}\)C on a cold winter's day and reach up to 32 \({}^{\circ}\)C during summer. The authors also analyzed different design options for placing a heat recovery heat exchanger either within stations or in the ventilation system of the network, and highlighted how solutions that hinder the ability to deliver a reliable train service are unlikely to be considered by the Underground operators.  
As cities all over the globe aim to reduce their carbon footprints, electrifying the heating sector so that it incorporates renewable energy sources, particularly in colder climates, is fundamental. Therefore, heat pumps will play an essential role in the path towards decarbonization and using them in the most efficient way, exploiting any available waste heat source, is a priority. Amongst all the sources described, underground railway tunnels are particularly interesting for cities such as London, where buildings have high heat demands, heat supply is very carbon intensive, as described in [11], and a long subway network covers a considerable area of the city. However, many challenges are involved in exploiting waste heat from underground metro systems and it is important to carefully select the most suitable technology to recover waste heat from the tunnels, maximising energy efficiency without posing any risks to the safe and reliable operation of urban mobility systems.