esa-sceva/satcom-chunk-collection huggingface dataset:
# VLSM-Net: A Fusion Architecture for CT Image Segmentation
## 3 Experiment Design
### Evaluation Metrics  
Several common evaluation metrics are selected to measure the model's advantages and disadvantages, which are _Sensitivity_, Dice similarity coefficient (_DSC_) and accuracy (_Precision_) as the evaluation metrics for segmentation. They are defined as follows:  
\[Sensitivity=\frac{TP}{TP+FN} \tag{6}\]  
\[Precision=\frac{TP}{TP+FP} \tag{7}\]  
Figure 5: The learning curves of the VLSM-Net in LiTS: horizontal axes represents epochs. (**A**) Vertical axis represents training dice. (**B**) Vertical axis represents training loss. (**C**) Vertical axis represents validation dice. (**D**) Vertical axis represents validation loss.  
\[DSC=\frac{2 TP}{2 TP+FP+FN} \tag{8}\]  
where \(TP\) is true positive, \(FP\) is false positive, \(FN\) is false negative.
## 4 Results and Discussion
### AttenFlow System Integration Results  
#### 4.4.1 End-to-End Performance  
When integrated within the complete AttenFlow architecture, RCCF and AM-HDGAT work synergistically to enhance overall document processing performance. Table 5 demonstrates the system's effectiveness across key document management tasks.
## 2 Materials and Methods
### Fuzzification  
The fuzzification process involves transforming the input variables into fuzzy plane values. In other words, each input value is evaluated following the corresponding membership functions, thereby generating a degree of fuzzy set membership. Table 5 presents a comparison of the architectures, demonstrating that the fuzzy inference systems comprising 5 CCI sub-regions and 15 membership functions have the lowest errors, namely, 0.1, 0.289, and 0.250. Moreover, a strong correlation of the variables of 0.98 for maturity and degree is observed in the case of firmness, with a correlation coefficient of 0.97. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Input** & **Number of MF** & **Epochs** & **MSE** \\ \hline  & **ROI of CII** & **Triangle** & & \\ \hline Model 11 & \(3\times 3\) & 3 & 10 & 1.207 \\ \hline Model 12 & \(3\times 3,5\times 5\) & 6 & 10 & 1.021 \\ \hline Model 13 & \(3\times 3,5\times 5,11\times 11\) & 9 & 10 & 0.888 \\ \hline Model 14 & \(3\times 3,5\times 5,11\times 11,21\times 21\) & 12 & 10 & 0.602 \\ \hline Model 15 & \(3\times 3,5\times 5,11\times 11,21\times 21,31\times 31\) & 15 & 10 & 0.381 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Fuzzy models’ architecture is proposed to estimate the firmness of orange Valencia. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Input** & **Number of MF** & **Epochs** & **MSE** \\ \hline  & **ROI of CII** & **Triangle** & & \\ \hline Model 1 & \(3\times 3\) & 3 & 10 & 0.766 \\ \hline Model 2 & \(3\times 3,5\times 5\) & 6 & 10 & 0.705 \\ \hline Model 3 & \(3\times 3,5\times 5,11\times 11\) & 9 & 10 & 0.623 \\ \hline Model 4 & \(3\times 3,5\times 5,11\times 11,21\times 21\) & 12 & 10 & 0.613 \\ \hline Model 5 & \(3\times 3,5\times 5,11\times 11,21\times 21,31\times 31\) & 15 & 10 & 0.172 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Fuzzy models’ architecture is proposed to estimate the maturity of orange fruit. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Input** & **Number of MF** & **Epochs** & **MSE** \\ \hline  & **ROI of CII** & **Triangle** & & \\ \hline Model 6 & \(3\times 3\) & 3 & 10 & 1.738 \\ \hline Model 7 & \(3\times 3,5\times 5\) & 6 & 10 & 1.416 \\ \hline Model 8 & \(3\times 3,5\times 5,11\times 11\) & 9 & 10 & 1.109 \\ \hline Model 9 & \(3\times 3,5\times 5,11\times 11,21\times 21\) & 12 & 10 & 0.582 \\ \hline Model 10 & \(3\Subsequently, an evaluation was conducted to assess the efficacy of different membership functions, including triangular, trapezoidal, bell, Gaussian, and a combination of two Gaussians, as shown in Table 5. Upon analysis of Table 5, it was determined that the Gaussian membership function is optimal for estimating the Brix content of the orange, with a mean squared error (MSE) of 0.289 and a coefficient of determination (R\({}^{2}\)) of 0.98. In contrast, the combination of two Gaussian functions is most suitable for estimating maturity and firmness. In the case of maturity, the error is 0.1, with an R\({}^{2}\) value of 0.98. For firmness, the error was 0.25, with an R\({}^{2}\) value of 0.97.
## 2 Materials and Methods
### Effect of Temperature on Performance  
The sensor's performance was examined under different temperatures (25 \({}^{\circ}\)C, 50 \({}^{\circ}\)C, and 70 \({}^{\circ}\)C). Training aid kits are often stored in locations such as garages or vehicle trunks. Previous research on vehicle cabin temperatures has shown that the hottest temperatures ever reached inside a vehicle were near 70 \({}^{\circ}\)C [30] and, as such, this value was chosen as the highest temperature value in this study. The experimental setup included three mason jars (A, B, and C) inside a Nanuk hard plastic case (Figures S3 and S4). Jar A contained 5.0 mL of 1000 ppm NH\({}_{4}\)OH solution in water. This jar was enclosed in a heat-sealed 8 MIL plastic bag alongside two vapochromic sensors. Jar B contained a blank training aid which was composed of a 2\({}^{\prime\prime}\)\(\times\) 2\({}^{\prime\prime}\) piece of clean gauze inside a 2 MIL plastic bag. Finally, Jar C contained a common, volatile, commercially available explosive training aid, which contained 2-ethyl-1-hexanol (2E1H). The cases were placed inside an oven at the corresponding temperatures and monitored daily for up to three days to check for any change in the sensor color and to monitor for cross-contamination. For this experiment, cross-contamination was determined to have occurred once 2E1H was detected in the headspace of Jar B (the blank training aid).  
The headspace of the blank training aid was analyzed daily using SPME-GC-MS to detect any presence of 2E1H. A gray (DVB/CAR/PDMS) SPME fiber was exposed to the Jar B headspace for 10 min for sample collection. An HP-5 MS UI column (30 m \(\times\) 0.25 mm I.D. \(\times\) 0.25 um, Agilent Technologies) was used with a 1 mL/min flow rate using helium as carrier gas with a splitless injection. The oven temperature parameters had 105 \({}^{\circ}\)C as the initial temperature, increased by 40 \({}^{\circ}\)C/min until 280 \({}^{\circ}\)C. Standard MS parameters were used with the MS source, MS Quad, and instrument transfer line at 230 \({}^{\circ}\)C, 150 \({}^{\circ}\)C, and 280 \({}^{\circ}\)C, respectively. The electron ionization (EI) had an energy of 70 eV and the scan range was \(m/z\) 33-300. Blank testing of the jars and the blank training aid did not show prior presence of 2E1H. Two test kits and one control kit were placed inside the oven for analysis at a time. The control kit contained the same materials as the test kits, except that it did not have any NH4 OH or 2E1H. The control kit was used as a negative control to evaluate the sensor performance in the absence of any targets.
# Feedback Approach for the Relay Channel with Noisy Feedback and Its Security Analysis
###### Abstract  
Relay channels capture the essence of several important communication scenarios such as sensor network and satellite communication. In this paper, first, we propose an efficient coding scheme for an additive white Gaussian noise (AWGN) relay channel in the presence of AWGN feedback, which generalizes the conventional scheme for the AWGN relay channel with noiseless feedback by introducing a lattice-based strategy to eliminate the impact of the feedback channel noise on the performance of the original scheme. Next, we further extend the proposed scheme to the multi-input single-output (MISO) fading relay channel (FRC) with noisy feedback. The key to this extension is to use a pre-coding strategy to transform the MISO channel into a single-input single-output (SISO) channel and applying a two-dimensional lattice coding strategy to deal with the feedback fading channel noise. Finally, we analyze the security performance of our proposed scheme in several cases, and the results of this paper are further illustrated by numerical examples.  
feedback coding; relay channel; noisy feedback; wiretap channel Article
# Optimal Placement of Vibration Sensors for Industrial Robots Based on Bayesian Theory
## 2 Sensor Placement Method
### Derivation of Forward Kinematics of Industrial Robots
For a rotary joint, due to its joint characteristics, it is considered that the definitions of its link length \(a_{i}\), link torsion angle \(\alpha_{i}\) and link offset \(d_{i}\) are unified, and the joint angle \(\theta_{i}\) is a joint variable. Considering the established six-axis industrial robot model, the model parameters need to be calculated according to the specified connecting rod parameter calculation method. For the six-axis industrial robot model shown in Figure 1, the corresponding link D-H parameters are calculated, as shown in Table 2. Through the D-H parameters of the industrial robot in the table, the configuration of the connecting rod of the industrial robot can be described, which can provide calculation support for the subsequent kinematics analysis and provide importable related parameters for the subsequent kinematics and dynamics simulation. Figure 1: Industrial robot connecting rod coordinate system. \({}^{i-1}T_{i}\) is defined as: According to the description method of homogeneous transformation of robotic transformation matrix, \({}^{i-1}T_{i}\) can be obtained according to the principle from left to right:  
\[{}^{i-1}T_{i}= \begin{bmatrix}c\theta_{i}&-s\theta_{i}&0&a_{i-1}\\ s\theta_{i}\alpha_{i-1}&c\theta_{i}\alpha_{i-1}&-s\alpha_{i-1}&-s\alpha_{i-1} d_{i}\\ s\theta_{i}s\alpha_{i-1}&c\theta_{i}s\alpha_{i-1}&c\alpha_{i-1}d_{i}\\ 0&0&0&1\end{bmatrix}  \tag{1}\]  
The parameters corresponding to each joint are brought in, and the motion transfer matrix of each joint is solved as follows:  
\[{}^{0}T_{1}= \begin{bmatrix}c_{1}&-s_{1}&0&0\\ s_{1}&c_{1}&0&0\\ 0&0&1&0\\ 0&0&0&1\end{bmatrix} {}_{1}T_{2}= \begin{bmatrix}c_{2}&-s_{2}&0&0\\ 0&0&1&0\\ -s_{2}&-c_{2}&0&0\\ 0&0&0&1\end{bmatrix} {}_{2}T_{3}= \begin{bmatrix}c_{3}&-s_{3}&0&a_{2}\\ s_{3}&c_{3}&0&0\\ 0&0&1&0\\ 0&0&0&1\end{bmatrix}  \tag{2}\] \[{}^{3}T_{4}= \begin{bmatrix}c_{4}&-s_{4}&0&0\\ 0&0&-1&-d_{4}\\ s_{4}&c_{4}&0&0\\ 0&0&0&1\end{bmatrix} 4T_{5}= \begin{bmatrix}c_{5}&-s_{5}&0&0\\ 0&0&1&0\\ -s_{5}&-c_{5}&0&0\\ 0&0&0&1\end{bmatrix} 5T_{6}= \begin{bmatrix}c_{6}&s_{6}&0&0\\ 0&0&-1&-d_{6}\\ s_{6}&c_{6}&0&0\\ 0&0&0&1\end{bmatrix} \]  
where \(s_{i}=\sin\theta_{i},c_{i}=\cos\theta_{i}\). After obtaining the transformation matrix of the connecting rod coordinate system, the kinematic equation of the industrial robot is derived. The overall transfer matrix is \({}^{0}T_{6}\), and the expression of is:  
\[{}^{0}T_{6}={}^{0}T_{1}{}^{1}T_{2}{}^{2}T_{3}{}^{3}T_{4}{}^{4}T_{5}{}^{5}T_{6}= \begin{bmatrix}n_{x}&o_{x}&a_{x}&p_{x}\\ n_{y}&o_{y}&a_{y}&p_{y}\\ n_{z}&o_{z}&a_{z}&p_{z}\\ 0&0&0&1\end{bmatrix}  \tag{3}\]  
\[
# Covert Communications in Active-RIS-Aided NOMA Systems: Element Grouping or Not?
## 2 System Model
### w/o-EG Scheme  
In this section, we investigate the impacts of the w/o-EG scheme on the covert communication performance of the ARIS-aided NOMA system. It is noted that for the w/o-EG scheme, the amplitude-phase-shift coefficient matrix can be simplified as \(\mathbf{\Theta}=\mathbf{\Theta}_{r}\) with \(\mathbf{M}_{r}=\mathbf{I}_{N}\) regarding the system model of the w-EG scheme. In the w/o-EG scheme, the ARIS selects only one reflection mode for all the elements to reflect the information signal. Similarly to the w-EG scheme, the w/o-EG scheme and the corresponding system model can be described as follows.  
At Bob, the received signal can be expressed as:  
\[\hat{\mathbf{y}}_{b}=\sqrt{P_{a}}x_{a}\mathbf{g}_{ab}+\sqrt{P_{c}}x_{c} \mathbf{g}_{cb}+\mathbf{H}_{rb}\mathbf{\Theta}\mathbf{n}_{r}+\mathbf{n}_{b}. \tag{11}\]  
Then, a beamformer \(\mathbf{w}\) is applied at Bob to obtain \(\hat{y}_{b}=\mathbf{w}^{H}\hat{\mathbf{y}}_{b}\) for further processing. Similarly, we decode \(x_{a}\) at the last stage to guarantee a successful SIC at Bob. The achievable rates of decoding of \(x_{a}\) and \(x_{c}\) are given by \(\hat{R}_{a}=\text{log}_{2}(1+\hat{\gamma}_{a})\) and \(\hat{R}_{c}=\text{log}_{2}(1+\hat{\gamma}_{c})\), respectively, where \(\hat{\gamma}_{a}\) and \(\hat{\gamma}_{c}\) are the received SINRs given by \(\hat{\gamma}_{a}=\frac{P_{a}|\mathbf{w}^{H}\mathbf{g}_{ab}|^{2}}{|\mathbf{w}^{H }\mathbf{H}_{rb}\mathbf{\Theta}|^{2}\sigma_{r}^{2}+\sigma_{b}^{2}}\) and \(\hat{\gamma}_{c}=\frac{P_{c}|\mathbf{w}^{H}\mathbf{g}_{cb}|^{2}}{P_{a}|\mathbf{ w}^{H}\mathbf{g}_{ab}|^{2}+|\mathbf{w}^{H}\mathbf{H}_{rb}\mathbf{\Theta}|^{2} \sigma_{r}^{2}+\sigma_{b}^{2}}\), respectively.  
In the w/o-EG scheme, the received signals at Willie under \(\mathcal{H}_{0}\) and \(\mathcal{H}_{1}\), respectively, can be expressed as:  
\[\mathcal{H}_{0}:\ \hat{y}_{w}=\sqrt{P_{c}}x_{c}g_{cw}+\mathbf{h}_{rw}^{H}\mathbf{ \Theta n}_{r}+n_{w}, \tag{12}\]  
\[\mathcal{H}_{1}:\ \hat{y}_{w}=\sqrt{P_{a}}g_{aw}+\sqrt{P_{c}}g_{cw}+\mathbf{h}_{rw}^{H} \mathbf{\Theta n}_{r}+n_{w}. \tag{13}\]  
The likelihood functions of the received signals under \(\mathcal{H}_{0}\) and \(\mathcal{H}_{1}\) can be written as \(p_{0}(y_{w})\) and \(p_{1}(y_{w})\), respectively, with \(\sigma_{0}^{2}=P_{c}|g_{cw}|^{2}+\|\mathbf{h}_{rw}^{H}\mathbf{\Theta}\|^{2} \sigma_{r}^{2}+\sigma_{w}^{2}\) and \(\sigma_{1}^{2}=P_{a}|g_{aw}|^{2}+P_{c}|g_{cw}|^{2}+\|\mathbf{h}_{rw}^{H} \mathbf{\[PERSON]}\|^{2}\sigma_{r}^{2}+\sigma_{w}^{2}\). Similar to the derivation of (10), the covertness constraint under this setup can be formulated as:  
\[P_{a}|g_{aw}|^{2}+(1-\kappa)(P_{c}|g_{cw}|^{2}+\|\ \mathbf{h}_{rw}^{H} \mathbf{\[PERSON]}\|^{2}\sigma_{r}^{2})\leq(\kappa-1)\sigma_{w}^{2}. \tag{14}\]
## 3 Results
### Structural Repair and Airframe Condition Monitoring by Using RFID and CMB
#### 3.5.2 RAIN RFID Tags  
The more suitable option is to apply RAID RFID tags, which are an advanced version of the basic RFID tags working with the ultra high frequency (UHF) band, which are  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Visual** & \multicolumn{2}{c}{**Evaluation Process**} & \multicolumn{2}{c}{**Release**} \\ \cline{3-5}  & **Inspection** & **Post Processing** & **Verification Process** & **Airworthiness Review** \\ \hline Drone & 5 min & 30 min & 30 min & 120 min \\ Inspection & Mechanic & 30 min & 0 & 60 min & 180 min \\ Inspection & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Time comparison between inspections done by UAV and mechanic, Source: Authors.  
readable from the range of 12 m. RAIN RFID systems comply with the UHF Gen2 standard and use the 860 to 960 MHz band (EU).  
UHF RFID has a faster data transfer rate than lower frequency (LF) or high frequency (HF). UHF RFID is the most sensitive to interference, but many UHF product manufacturers have found ways of designing tags, antennas, and readers to keep performance high even in difficult environments [44]. Such tags could be placed anywhere on the desired component without the need of invasion to its material. It represents a non-invasive method and works as a labeling tool. The limitation is to place them on or beneath the composites, as the signal could only penetrate through composite materials. In our conditions, the application could be on load-bearing structures, in place of conductive connections or laminated parts of the aircraft. These tags can identify also the nut or rivet release if the tag is placed between the airframe surface and the nut. If the conductive connection between the surface and the nut is cut off, the loosening of the nut is indicated. The connection of infrared camera and RFID provides the clear picture of aircraft power line state. As it was mentioned previously, overheating may indicate the possible short circuit caused by corrosion on the electrical contacts or insufficient isolation of connections.
## 6 Results and Discussion
As shown in experiments illustrated for instance in Figures 8 and 9, the improvement provided by Algorithm selection is well observed for Algorithm selectors trained on few images, implying that even with very partial knowledge about the well-behaved Algorithms, the noisy samples from the GAN generator provide enough information to obtain an accuracy \(\mathcal{A}\) at least as good as the best Algorithm. It also shows that for a small size of \(\mathbb{D}_{E}\), the GAN generates images that have enough information to model the sample distribution from the dataset (Figure 8). Similarly, this is reflected by the fact that for few synthetic images, the SAS approach results in highest accuracy \(\mathcal{A}\) (Figure 9). Third, training the _rand_ or the _copy_ Algorithm selector experimentally, shows that it negates the Algorithm selection bias. This can be observed in Figure 11, where not only do the _rand_ and _copy_ SAS improve other instances of Algorithm selection training, but the best Algorithm is actually the _copy_ RAS Algorithm. Note that the sample replication is not a novel method and it is not Algorithm selection specific. The unique position of our approach is that we specifically target synthetic images. In addition, one could ask why we need the GAN generator if we potentially have enough images to train a shallow classifier. As shown in Figures 8 and 9, even if the maximum of 540 (270 \(\times\) 2) is used for training, the resulting Algorithm selection is statistically less accurate than the one that is trained purely on synthetic data. Finally, we also verified the statistical significance of the experimental setup. Interestingly, while the statistical verification demonstrated that the synthetic images are a viable option as a tool to efficiently select Algorithm, it also showed that the improvement is also model dependent. Four out of nine classifiers had constantly improved performance when using the synthetic data, while additional two showed statistical improvement when using the Wilcox test only. This would also imply that this result can be also task specific. Therefore we consider this as a motivation for a direct extension of this work into other domain-specific works.
# Robust 3D Joint Inversion of Gravity and Magnetic Data: A High-Performance Computing Approach
## 2 Methods
### Coding Guidelines
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline  & **Cells** & **RAM Usage (Gb)** & **VRAM Usage (Gb)** & **Processing Time (s)** & **Speeded Up1** \\ \hline CPU (gfortran) & 1440 & 0.30 & 0.00 & 6.24 & 1.00 \\  & 2990 & 0.80 & 0.00 & 57.57 & 1.00 \\  & 4368 & 1.65 & 0.00 & 179.32 & 1.00 \\  & 5568 & 2.00 & 0.00 & 369.85 & 1.00 \\  & 7344 & 4.20 & 0.00 & 848.76 & 1.00 \\  & 8400 & 6.40 & 0.00 & 1274.86 & 1.00 \\ \hline CPU (nvfortran) & 1440 & 0.30 & 0.00 & 1.43 & 4.36 \\  & 2990 & 0.80 & 0.00 & 10.62 & 5.42 \\  & 4368 & 1.65 & 0.00 & 35.49 & 5.05 \\  & 5568 & 2.00 & 0.00 & 74.81 & 4.94 \\  & 7344 & 4.20 & 0.00 & 175.11 & 4.85 \\  & 8400 & 6.40 & 0.00 & 256.38 & 4.97 \\ \hline GPU & 1440 & 0.75 & 0.25 & 0.7 & 8.91 \\  & 2990 & 0.90 & 0.70 & 1.54 & 37.38 \\  & 4368 & 1.20 & 2.00 & 2.87 & 62.48 \\  & 5568 & 2.40 & 3.06 & 4.56 & 81.11 \\  & 7344 & 2.50 & 5.12 & 9.13 & 92.96 \\  & 8400 & 3.20 & 6.62 & 12.72 & 100.22 \\ \hline CPU-GPU & 1440 & 0.70 & 0.35 & 0.79 & 7.90 \\  & 2990 & 1.80 & 0.75 & 1.9 & 30.30 \\  & 4368 & 2.40 & 1.27 & 3.83 & 46.82 \\  & 5568 & 3.20 & 1.92 & 5.94 & 62.26 \\  & 7344 & 4.70 & 3.10 & 11.35 & 74.78 \\  & 8400 & 5.20 & 3.97 & 15.63 & 81.56 \\ \hline \hline \end{tabular} \({}^{1}\times\) times faster than CPU (gfortran) version.  
\end{table} 
Table 1: Table of computational performance of the different processing routines. Figure 2: Radar chart of Table 1 normalized values, highlighting the advantages and disadvantages of each routine as the inversion domain increases.
## 5 Experimental
### Experimental Results and Analysis
#### 5.3.2 Early Risk Prediction
Within the first 20% of the course start time, MTAPSP embodies a high prediction performance, which indicates that the ANN in the MTAPSP model can more accurately map the relationship between the assessment scores characteristics and the students' final performance and map the potential influence of the assessment scores characteristics on the students' final performance. 4. Numerically, MTAPSP's daily model performs better than the weekly model, with performance improvements ranging from 0.1% to 1%, which suggests that the aggregation of student activity data changes important feature values, resulting in MTAPSP's inability to learn better features from this aggregated data. To further demonstrate the early prediction ability of MTAPSP, the prediction performance of the MTAPSP model was compared with that of the TEnc model over three months after the start of the course, and the results are shown in Figures 5 and 6. As can be seen from Figures 5 and 6, in the WF-PD, F-PD, and W-PD tasks, at the beginning of the course, the daily and weekly models of MTAPSP achieve more than 85% in terms of accuracy and recall in predicting students' performance, and gradually increase the indexes over time, outperforming the TEnc model. In the early risk prediction experiment, the MTAPSP model demonstrated excellent early prediction capabilities, enabling instructors to predict final student performance early in the course. Once the model predicts that a student is likely to end up with a \"fail\" performance, the instructor can develop an intervention plan to support these students to adjust their learning before the end of the course to ensure that they can successfully pass the exam. With the predictive results of the MTAPSP model, teachers can identify potential student problems ahead of time and help them overcome their difficulties through effective measures, thus improving overall student performance and learning experience. This early prediction and intervention approach helps to maximize the optimization of the student learning process and improve teaching and learning outcomes. Figure 5: (**a**) Comparison of the accuracy of “daily” models based on the WF-PD task, based on the F-PD task, and based on the W-PD task three months after the start of the course; (**b**) comparison of the F1-scores of “daily” models based on the WF-PD task, based on the F-PD task, and based on the W-PD task three months after the start of the course.
###### Abstract  
The success of all industries relates to attaining the satisfaction to clients with a high level of services and productivity. The success main factor depends on the extent of maintaining their equipment. To date, the Rwandan hospitals that always have a long queue of patients that are waiting for service perform a repair after failure as common maintenance practice that may involve unplanned resources, cost, time, and completely or partially interrupt the remaining hospital activities. Aiming to reduce unplanned equipment downtime and increase their reliability, this paper proposes the Predictive Maintenance (PdM) structure while using Internet of Things (IoT) in order to predict early failure before it happens for mechanical equipment that is used in Rwandan hospitals. Because prediction relies on data, the structure design consists of a simplest developed real time data collector prototype with the purpose of collecting real time data for predictive model construction and equipment health status classification. The real time data in the form of time series have been collected from selected equipment components in King Faisal Hospital and then later used to build a proposed predictive time series model to be employed in proposed structure. The Long Short Term Memory (LSTM) Neural Network model is used to learn data and perform with an accuracy of 90% and 96% to different two selected components.  
Predictive Maintenance (PdM); Internet of Things (IoT); equipment; components; monitoring; reliability; failure +
Footnote †: journal:  
_Future Internet_2020, 12, 224; doi:10.3390/6121200224