esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Cross-Layer Routing Protocol Based on Network Coding for Underwater Sensor Networks
## 5 Performance Evaluation
### Performance Analysis
The results are shown in Figure 13. For the other four protocols, a waiting time before sending procedure is needed to confirm that there are no other nodes sending the same packet. This scheme will result in long end-to-end delay especially when nodes are distributed randomly and most nodes have low priorities for forwarding data. In NCRP, data packets are sent continuously in a block without any waiting time. Moreover, once a block of packets are received successfully, the nodes will send feedback messages to the previous sender and send a new block of packets to its next hop nodes immediately. This scheme can greatly reduce the delay and improve the communication efficiency. The \(EED\) in NCRP is only about 10% and 1% of \(EED\) in VARP and DBR, respectively. Figure 14 shows the \(AET\) performance in different scenarios. As the power for transmitting data is much larger than that of receiving data, the redundant transmissions will increase the power consumption significantly. For the other four protocols, the sending nodes need to broadcast data packets to their neighborhood nodes and the receiving nodes with high priorities will forward the received packets first. The forwarding procedure in low priority nodes would be suppressed if duplicated packets are received. However, repetitive data transmissions still exist and the reasons are listed as follows: (1) The neighborhood nodes may be too far away to hear each other. (2) The packets sent by high priority nodes are not able to reach their neighborhood nodes due to the high PER in UWSNs. (3) Nodes cannot receive the suppressed information in their waiting time as their priorities are too close. Except for repetitive data transmission, these four protocols cannot use the packets received from unstable link effectively. Compared with other protocols, NCRP does not limit the  
Figure 12: PDR performance with varying node numbers. Figure 13: EED performance with varying node numbers. maximum transmission range for one hop. Nodes can jointly decode messages with packets received from different sending nodes. Meanwhile, the transmission power is optimized as we adopt loop filter and feedbacks in our design. Last but not the least, the inefficient nodes in the routes can be replaced timely and this can reduce the unnecessary energy consumption. So NCRP outperforms other protocols significantly in terms of average energy consumption and prolong the network lifetime. In the 50 nodes scenario, the \(AET\) of NCRP is only about 28% and even 12.3% than that of VBF and VARP.
# Improving the Energy Efficiency of an Existing Building by Dynamic Numerical Simulation
## 2 Case Study  
The objectives of the present study are to study by means of dynamical simulation with DesignBuilder tool, an existing residential building, situated in the countryside zone of Romania, district of Prahova, having a \(-15\)\({}^{\circ}\)C conventional temperature for winter time. Our aim is to determine the optimal balance between energy efficient and economical solutions possible for an existing house by means of dynamic simulation. The energy efficiency of existing house envelope is experimentally fully determined.  
The net living space of the house is of 214 m\({}^{2}\), between the ground floor and first floor. The studied house was built in the year 2008, in concordance with the Romanian regulations from that period, making it a real case study house. For indoor heating, the building is equipped with wood fuel type boiler. The hot water supply is represented by an electrical boiler and no cooling system is provided, as the building is located near the mountains, having a mild summer period. The studied house is inhabited by a family of four, a couple of retirees and a couple of young people. Due to the fact that the retired couple stay home all day long, the heating system works 24 h and seven days a week. It should also be mentioned that there are unheated/unoccupied spaces, namely: the garage, the technical room, the attic of the house, and the basement. The domestic hot water demand was calculated according to the Romania legislation [17], considering a 50 L per person and day hot water demand.  
Our goal is to apply as many of the passive house requirements, as the existing building allows in order to reduce the building's energy consumption. As already described, the study was conducted on dynamical simulation level using DesignBuilder tool [18],which allows the design of a perfect match, between the architecture of the existing building and the simulated model, as can be seen in Figure 2.
## 3 Results and Discussion
### Anions  
Chloride (Cl\({}^{-}\)), bicarbonate (HCO\({}_{3}\)\({}^{-}\)), nitrate (NO\({}_{3}\)\({}^{-}\)), and sulfate (SO\({}_{4}\)\({}^{2-}\)) are ubiquitous inorganic anions in natural waters and can potentially influence KMnO\({}_{4}\)-mediated TCE degradation [57; 58]. As shown in Figure 5a, these anions, at a concentration of 0.2 M, did not significantly affect the TCE degradation rate. Similar have been reported for KMnO\({}_{4}\) oxidation of other organic contaminants such as hexachlorophene and benzophenone-3 [58].  
The corresponding \(\varepsilon\) values in the presence of these anions were \(-\)25.6 \(\pm\) 0.6% (Cl\({}^{-}\)), \(-\)25.6 \(\pm\) 0.6% (HCO\({}_{3}\)\({}^{-}\)), \(-\)23.8 \(\pm\) 0.4% (NO\({}_{3}\)\({}^{-}\)), and \(-\)24.2 \(\pm\) 0.5% (SO\({}_{4}\)\({}^{2-}\)), as shown in Figure 5b. Cl\({}^{-}\) and HCO\({}_{3}\)\({}^{-}\) yielded \(\varepsilon\) values similar to the blank (\(-\)25.4 \(\pm\) 0.9%), indicating minimal impact. However, slightly elevated values in NO\({}_{3}\)\({}^{-}\) and SO\({}_{4}\)\({}^{2-}\) systems that, under acidic conditions (final pH = 3.4), NO\({}_{3}\)\({}^{-}\) and SO\({}_{4}\)\({}^{2-}\) may act as competing oxidants, potentially influencing the oxidation pathway of TCE and hence its isotope signature. The stronger oxidizing ability of NO\({}_{3}\)\({}^{-}\) compared to SO\({}_{4}\)\({}^{2-}\) at the same condition may explain the more pronounced deviation in \(\varepsilon\) values.  
Given that Cl\({}^{-}\) is both a ubiquitous ion and a degradation product of TCE, its influence was further evaluated across a concentration range. As shown in Figure 5d, increasing Cl\({}^{-}\) concentration up to 2 M had negligible impact on TCE degradation efficiency, although a slight inhibitory effect was observed. This trend was consistent with previous findings by [PERSON] and [PERSON] [40], who reported a \(\sim\)30% oxidation rate reduction at 0 to 5 M Cl\({}^{-}\). This is likely due to a salting-out effect, which reduces TCE solubility and thereby slows its oxidation. The carbon isotope fractionation shown in Figure 5e supports this, where higher Cl\({}^{-}\) concentration (2 M) led to a change in the carbon isotope effect. Corresponding \(\varepsilon\) values (Figure 5f) decreased from \(-\)25.4 \(\pm\) 0.9% (0 M Cl\({}^{-}\)) to \(-\)23.9 \(\pm\) 0.4% (2 M Cl\({}^{-}\)), matching trends previously reported by [PERSON] and [PERSON] [40].  
Figure 4: (**a**) Effect of pH on the oxidative degradation of TCE in the KMnO\({}_{4}\) reaction. (**b**) The evolution of stable carbon isotope fractionation of TCE with pH. (**c**) The variation of \(\varepsilon\) values of KMnO\({}_{4}\) degradation of TCE at pH 2–12. Experimental boundary conditions: [TCE]\({}_{0}\) = 75 mg/L and [KMnO\({}_{4}\)]\({}_{0}\) = 250 mg/L at initial suspension pH 8.  
Therefore, in practical application, caution should be taken when selecting \(\varepsilon\) value for quantitative TCE degradation using isotope analysis, as background ions composition, including naturally occurring salinity or introduced by pH adjustments, can influence both degradation kinetics and isotope fractionation.
# Impact Analysis of Security Attacks on Mobile Ad Hoc Networks (MANETs)
## 5 Methodology
### Performance Metrics
#### 5.2.1 Packet Delivery Ratio  
This metric measures the percentage of packets successfully delivered from the source to the destination. A lower PDR indicates increased packet loss due to the attacks. The PDR can be calculated by using the following Formula (1).  
\[PDR=\frac{PacketsReceived}{PacketsSent} \tag{1}\]
## 5 Future Directions  
This research aims to develop an automated vision-based damage evaluation system capable of detecting and characterising defects in metallic and composite aircraft specimens by analysing 3D data acquired using both an RGB camera and an infrared thermal camera through photogrammetry.  
The respective scientific objectives of the research are:  
1. To develop a dataset comprising at least 1000 real and 500 synthetic images derived from the 3D model, ensuring it adheres to deep learning standards for accuracy, diversity, and quality.
2. To train and develop a machine learning model capable of detecting and classifying the most common defects for each material, achieving a minimum precision of 80%, a recall of 70%, and an F1 score of at least 0.75.
3. To address the challenges an unbalanced dataset poses and enhance the model's performance by at least 5% by applying advanced imaging techniques.
4. To develop a defect measurement tool capable of estimating the size and depth of the detected defect with an error rate below 10%.  
**Author Contributions:** Conceptualisation, N.P.A.; methodology, K.B., N.P.A. and H.F.; investigation, All Authors; resources, N.P.A. and H.F.; writing--original draft preparation, K.B.; writing--review and editing, All Authors; supervision, N.P.A. and H.F.; funding acquisition, N.P.A. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was supported and funded by the British Engineering and Physical Sciences Research Council (EPSRC), grant number EP/T518104/1.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
**Acknowledgments:** H.F. is grateful for the support provided by the Brazilian National Council for Scientific and Technological Development (CNPq).  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# A Bearing Fault Diagnosis Method Based on Wavelet Denoising and Machine Learning
## 4 Conclusions  
This paper proposes a machine learning fault signal diagnosis and classification model based on wavelet denoising, which can be used for intelligent diagnosis of rolling bearing faults in rotating machinery in practical mechanical applications. Experimental results show that the proposed model is significantly better than traditional diagnostic models on instrument faults. Aiming at weak signal strength and background noise interference, under the quantitative evaluation index, a better noise reduction algorithm is selected for wavelet denoising, which can better restore the true characteristics of the fault signal. Using unsupervised learning and supervised machine learning classification algorithms, the evaluation indicators before and after denoising are compared to make the classification results more accurate and reliable. By designing the fault classification evaluation prediction criteria, the following conclusions are drawn. In equipment fault diagnosis, if the unsupervised machine learning method is used, K-means clustering can achieve good classification results. If the supervised machine learning method is used, the effect of classification by decision tree is the most ideal. This article will help researchers to intelligently diagnose the faults of instruments and equipment.Therefore, the classification model is widely used in industrial production.  
In addition, increasing the types of rolling bearing faults, narrowing the characteristic differences of fault signals and improving the adaptability and flexibility of the model will be the focus of our future work. The dataset used in this paper is limited to one hundred groups of two types of faults under different working conditions. More types of faults and larger datasets can be considered in future research to improve the generalization ability of the model. The wavelet denoising algorithm can be further optimized by selecting different wavelet bases, levels of decomposition and threshold functions according to different types of signals. Machine learning models can further improve their performance by tuning parameters, selecting features, or combining multiple models.  
Conceptualization, Y.W. and S.F.; methodology, S.F.; software, S.F.; validation, S.F. and R.W.; formal analysis, R.W., Y.W. and S.F.; investigation, Y.W.; resources, R.W.; data curation, R.W.; writing--original draft preparation, S.F.; writing--review and editing, M.M.; visualization, Y.W.; supervision, M.M.; project administration, Y.W. and R.W. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The author declares no conflict of interest.
# Monitoring Acute Heart Failure Patients Using Internet-of-Things-Based Smart Monitoring System
## 4 Materials and Methods
### Deep Learning Model  
An expanding area of research in the field of artificial intelligence is deep learning. The modeling of data in deep learning gives promising results. The adoption of an automated process by medical professionals has been shown to be a highly useful and successful tool for disease diagnosis. Deep learning is a common method for processing enormous amounts of data. It eliminates the need for manual feature extraction and is being employed widely in medical data analysis.  
Multilayer Perceptron Neural Network  
When we are talking about training sets that are not large, easy implementation, speed, and the quick-result Multi-Layer Perceptron are the best choice [12]. The internal structure of the MLP comprises three layers, input, output, and hidden layers. The hidden layer is an intermediate layer connecting the input layer with the output layer during neuron  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sr. No.** & **Attributes** & **Description** & **Range** & **Measured in** \\ \hline
1 & Time & Development age & 4–285 & Days \\ \hline
2 & Events & During development period patient died & 0,1 & Boolean \\ \hline
3 & Gender & Man or woman & 0,1 & Binary \\ \hline
4 & Smoking & Smoke addicted patient & 0,1 & Boolean \\ \hline
5 & Diabetics & Diabetic patient & 0,1 & Boolean \\ \hline
6 & BP & Patient has blood pressure & 0,1 & Boolean \\ \hline
7 & Anaemia & Red blood cell deficiency & 0,1 & Boolean \\ \hline
8 & Age & Patient age & 40–49 & Years \\ \hline
9 & Sodium & Sodium level in body & 114–148 & mEq/L \\ \hline
10 & Creatinine & Creatinine level in body & 0.50–9.40 & mg/dL \\ \hline
11 & Platelets & Blood platelets & 25.01–850 & Kiloplatelets/mL \\ \hline \hline \end{tabular}
\end{table}
Table 4: Dataset specification.  
processing. The internal workings of MLP are simply based on the multiplication of input neurons with weights \(w_{ij}\), and the output \(y_{j}\) is the sum. Mathematically, it is computed as  
\[y_{j}=f\big{(}\sum w_{ij}*O_{i}\big{)},\]  
In this equation, the gradient descent algorithm is assigned weights \(w\), and \(O\) represents hidden layers.
## 4 Conclusions  
In this paper, a fast diffraction calculation between plane and sphere is proposed for SCGH based on PC method. After recording the wavefront distribution in WRP from the object plane, the phase difference between WRP and SCGH introduced by the optical path difference is approximately compensated point-to-point to synthesis the ultimate SCGH, during which the SCGH is nonuniformly sampled, and the pixel resolution required is greatly decreased. The feasibility, the approximation error and the acceleration performance of the proposed method are verified and analyzed according to the numerical simulations. Compared with the original PS method, the computing speed is significantly accelerated with good scalability. As the object-plane resolution increases, the speedup between PS and PC method is improved significantly from 954 (64 \(\times\) 64, 90\({}^{\circ}\)) to 102,176 (256 \(\times\) 256, 90\({}^{\circ}\)). Moreover, the sampling points are significantly reduced by 10\({}^{4}\) times compared with the convolution and spherical spectrum method. Furthermore, in spite of the approximation of our proposed PC method, the reconstructed images are of good quality regardless of the increase on the curvature of SCGHs or the resolution of the object planes. Therefore, the calculation speed problem of the propagation model between plane and sphere has been solved firstly as far as we know. Meaningfully, it is possible for the practical applications of SCGH as the visible range of light wavelengths can be adopted to synthesize and reconstruct SCGHs. Moreover, an alternative approach using the proposed  
Figure 11: Optical reconstructed results of the proposed PC method. (**a–f**) and (**g–l**) are results without and with optimization, respectively.  
Figure 10: Schematic of optical experimental set-up.  
PC method to implement optical experiments of SCGH is provided to address the current issue of realizing holographic display with curved surfaces. In the future, it is expected to achieve an omnidirectional spherical holographic display by stitching SCGH segments; all these endow the spherical holography with more practical prospects.  
Conceptualization, R.Y. and J.W.; methodology, R.Y.; software, R.Y. and Y.W.; validation, R.Y. and J.W.; formal analysis, B.L. and Y.L.; investigation, J.W.; resources, R.Y.; data curation, R.Y.; writing--original draft preparation, R.Y.; writing--review and editing, J.W., C.C., B.L., Y.L., N.C. and B.J.J.; visualization, R.Y., C.C. and Y.W.; supervision, J.W.; project administration, J.W.; funding acquisition, J.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (NSFC) (U1933132) and the Chengdu Science and Technology Program (2019-GH02-00070-HZ).  
The authors declare no conflict of interest.
# Data Mining Approaches for Landslide Susceptibility Mapping in Unyeonsan, Seoul, South Korea
## 1 Introduction
In South Korea, according to an analysis of the scale of landslides by the Korea Forest Service (KFS), the average annual area affected by landslides increased markedly from 231 ha in the 1980s, to 349 ha in the 1990s, and 713 ha in 2000 [26]. Typhoons accompanied by intense storms caused by local climatic conditions often cause significant damage; and landslides are concentrated in the rainy season every summer. Because much of South Korea is mountainous, most studies on landslides in the country have focused on mountainous areas [27; 28; 29]; e.g., in Jangheung [23] or Yongjin [6; 30]. Furthermore, due to the frequency of landslides in mountainous areas, most of the studies in Southeast Asia have been performed in non-urban areas [31; 32; 33]. Assessment of landslide susceptibility in urban areas could provide an important contribution to minimizing additional damage from these natural disasters; it could be also used to planning and multi hazard assessment [34; 35; 36; 37]. Seoul, the capital of South Korea, also has urban areas occupying mountainous land. Unlike landslides that occur in sparsely populated mountainous areas, landslides that occur in mountainous areas in cities result in significant casualties/fatalities and structural damage. For example, landslides in Umyeonsan located in central Seoul in July 2011 caused unprecedented damage to the city. Following the 2011 Umyeonsan landslide, the downtown area, which had previously been considered safe, was no longer regarded as a landslide-safe zone. Various aspects of Korea's landslide policy have been re-evaluated and are expected to be revised in the near future. Landslides are more dangerous in urban areas than non-urban mountainous areas. Policies have shifted to preventative and resident-orientedinitiatives, with a focus on minimizing injuries to citizens and restoration of their properties after landslides. Therefore, it is important to analyze potential landslide hazards in urban areas. To apply the model for this type of data mining analysis, a spatial database should first be constructed containing information on the area of landslide damage. Because landslides are difficult to access, the collection of field survey data to build a landslide spatial database is costly and time-consuming. An effective alternative is to build a database using aerial photography and geographic information systems (GISs). Landslides leave traces that last for months or years; hence, aerial imagery can be used to extract data on areas damaged by landslides after the event. Using these data, the susceptibility for landslides can be analyzed by data mining modeling techniques.
## 2 Materials and Methods
### Predictive Functional Control (PFC)  
The principle of the PFC is similar to that of classic predictive control. Both use the same model for predicting the future of the system output. In predictive functional control, the control law structure is considered as a linear combination of a set of base functions. Hence, the weight coefficients of these base functions should be calculated. Selection of base functions depends on the process features and the desired reference input. The control law can be considered as [28]:  
\[u(k+i)=\sum_{n=1}^{N}\mu_{n}u_{bn}(i) \tag{10}\]  
In (10), \(\mu_{n}\) denotes the weight coefficients of the base functions in linear combination, where \(N\) specifies the number of the base functions. \(u_{bn}\) (\(i\)) represents the base functions' values at the moment \(t=k+i\). As stated before, base functions are selected according to the process nature and the reference input. Generally, step, ramp, and parabolic functions are utilized. In most cases, using step and ramp functions together is sufficient [28]:  
\[u(k+i)=\mu_{1}(k)+\mu_{2}(k)\times i \tag{11}\]  
The reference trajectory is calculated by an exponential relationship [28]:  
\[y_{r}(k+i)=w(k+i)-\lambda^{i}[w(k)-y(k)] \tag{12}\]  
where \(i=1,\ldots,H_{i}\) is the number of total coincidence points. \(y_{r}\ (k+i)\) denotes the reference trajectory values at the moment \(t=k+i\). \(w(k)\) represents the set point and \(y(k)\) is the output of the process. \(\lambda^{i}=exp(-T_{s}T_{r})\) in which \(T_{s}\) and \(T_{r}\) are the sampling period and the expected time for a response to the reference trajectory, respectively. Accordingly, by combining (6) and (11), the system future output could be calculated as follows [28]:  
\[y_{m}(k+i)=CA^{i}L(k)+C\Big{[}A^{i-1}+A^{i-2}+\ldots+I\Big{]}B\mu_{1}(k)+C \Big{[}A^{i-2}+2A^{i-3}+\ldots+(i-1)I\Big{]}B\mu_{2}(k) \tag{13}\]  
In (13), \(y_{m}\ (k+i)\) is the model output at the moment \(t=k+i\). \(e(k+i)\) represents the predicted error, which is the difference between the model and the process outputs at the moment \(t=k+i\). \(e(k+i)\) is derived by the following polynomial using filtered observations on the past horizon:  
\[e(k+i)=y(k)-y_{m}(k) \tag{14}\]